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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020043Unsolved MysteryEcologyEvolutionInsectsHomo (Human)EubacteriaThe Strange Case of the Armored Scale Insect and Its Bacteriome Unsolved MysteryNormark Benjamin B 3 2004 16 3 2004 16 3 2004 2 3 e43Copyright: © 2004 Benjamin B. Normark.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Armored scale insects are unusual in that a part of their bodies is genetically distinct from the rest. This extraordinary phenomenon challenges the notion of identity ==== Body I am a clone. That is, I am a colony of cells that developed from a single fertilized egg cell. Most animals are clones like me. It is a slight oversimplification to say that all of an animal's cells are genetically identical to each other. Some cells have mutations. In mammals, some cells (red blood cells) lack a nuclear genome entirely. Some cells have viruses—and when it's in a cell, a virus is basically a gene—that other cells lack. But a typical animal is a clone in the sense that all its cells arise from that single fertilized egg cell. Not all animals, however, are clones. Sometimes two tiny embryos developing inside their mother will fuse together into a single embryo and continue developing. The resulting animal is not a clone, but a chimera: a conglomeration of two different cell lineages into a single organism. Some species of monkeys (marmosets) typically have chimeric blood, from having shared a blood supply with a twin in utero (Haig 1999), and rare cases of accidental chimerism are known from many animal species (Tremblay and Caltagirone 1973; van Dijk et al. 1996). In marine invertebrates, chimeric individuals often arise from the fusion of individuals later in development (Buss 1987). Here I want to draw attention to a remarkable form of chimerism found in armored scale insects. These insects (Figure 1) always develop not from a single fertilized egg but from two genetically different cells. One of these cells develops into a special organ (the bacteriome, which houses symbiotic bacteria) that has a nuclear genome different from that found in the rest of the body. Figure 1 Armored Scale Insects (A) Lepidosaphes gloverii, adult females. (B) Parlatoria oleae, adult females (circular, with dark spot) and immatures (oblong). (C) Quadraspidiotus juglansregiae, adult female with waxy scale cover removed. (Photographs by Raymond J. Gill, © 2003 California Department of Food and Agriculture, published here under the terms of the Creative Commons Attribution License.) Obligate chimerism—the presence of two genetically distinct cell lineages in every individual at each life stage—is found in a few families of scale insects, but nowhere else in nature. The avid naturalist wants to understand this sort of deep oddity for its own sake, but such understanding might have broader implications as well. For instance, although humans are not usually chimeras, we do have a quasi-chimeric phase in our life cycle: pregnancy. Some diseases of pregnancy are apparently due to conflicts between the genetically nonidentical tissues of mother and fetus (Haig 2002). And the main things that humans eat are also quasi-chimeras: the seeds of flowering plants. In a grain of wheat, for instance, the germ, the endosperm, and the bran have three different nuclear genomes, and the conflicts between them may be similar in some ways to the conflicts seen in human pregnancy (Alleman and Doctor 2000; Santiago and Goodrich 2000). Ultimately, we might learn something about the general principles of conflict and cooperation between maternal and embryonic tissues that govern these cases if we can understand the uniquely stable and intimate chimerism of armored scale insects. Two Different Cell Lineages In all sexual animals and plants, production of an egg cell involves meiosis, the complex cellular process (involving DNA replication, recombination, and two nuclear divisions) whereby one diploid nucleus (with two copies of each chromosome) becomes four genetically different haploid nuclei (each with one copy of each chromosome). Only one of these four haploid nuclei becomes the egg cell (oocyte). In ordinary animals, the other three nuclei (the polar bodies) degenerate—they never divide again and are lost or destroyed—and the oocyte is the single maternal cell that (after fusion with a single paternal cell, the spermatocyte) develops into the embryo. But in armored scales, the polar bodies fuse together into a triploid cell (with three copies of each chromosome), and this triploid cell also winds up in the embryo (Figure 2). The triploid cell derived from the polar bodies fuses with one cell from the embryo to become a pentaploid cell (with five copies of each chromosome). This pentaploid cell then proliferates to form the bacteriome of the embryo (Brown 1965). Each cell in the bacteriome thus contains two copies of the mother's complete genome, in addition to the same haploid paternal genome as the rest of the embryo. In contrast, the rest of the embryo contains just one copy of half of the mother's genome. The apparent function of the bacteriome is to house intracellular bacteria. During embryonic development, bacteria move from the mother's bacteriome into the cells of the embryo's bacteriome. The precise role of the bacteria is not known, but it is thought that they synthesize essential nutrients (Tremblay 1990), as they do in scale insects' close relatives, the aphids (Shigenobu et al. 2000). Figure 2 Schematic Diagram of the Genetic System of a Diaspidid Scale Insect Zygote 1 is the fertilized ovum from which all organismal tissues except the bacteriome develop (black arrows). The bacteriome develops from zygote 2 (green arrows). Each haploid genome is represented by an N. A haploid genome may come from the oocyte (pink), sperm (blue), or polar bodies (black). The blue and pink arrows emphasize that the maternal versus paternal identity of a haploid genome is reset (imprinted) in each generation; thus, a male transmits only his maternal genome, but in his offspring the same chromosomes behave as a paternal genome (schematically, the pink N is converted to blue during spermatogenesis). Now the story gets even stranger. If the individual is a male, then the genetic difference between his bacteriome and the rest of his tissues becomes even greater as he develops. This is because most armored scale insects have an unusual genetic system called embryonic paternal genome elimination (Herrick and Seger 1999). In males, the paternal genome is completely eliminated from most tissues very early in development—but it is never eliminated from the bacteriome. As a result, most of a male armored scale insect's tissues (including his sperm) have one copy of half of the mother's genome (the same genome as the oocyte from which he developed), but his bacteriome has two complete copies of the mother's genome and also has a paternal genome. Thus, 60% (three of five) of the gene copies in the male's bacteriome are absent from the rest of the male (Figure 2). Chimerism and Sibling Rivalry What could possibly be going on here? Why should scale insects, of all creatures, have obligate chimerism involving activated polar bodies? Essentially, we have no idea, largely because no one has even ventured a serious guess. When the phenomena were discovered, early in the 20th century, the theoretical tools for making sense of them were unavailable. One such tool is W. D. Hamilton's (1964a) theory of inclusive fitness, which holds that the degree of cooperation between two organisms (or tissues) must depend upon their degree of genetic relatedness. But the rise of Hamiltonian thinking coincided with the eclipse of classical cytogenetics in favor of the molecular biology of model organisms, and these remarkable little chimeras have languished in undeserved obscurity. Perhaps merely by looking at them with a modern eye, we can turn up some plausible hypotheses. Consider the special theoretical difficulty posed by chimerism between tissues derived from the oocyte and those derived from the polar bodies ejected by it during meiosis. Two siblings will typically exhibit some degree of sibling rivalry—their interests are not identical. If an individual were a chimera comprising full-sibling tissues (identical across approximately half of their genomes), there might be conflict between the two nonidentical cell lineages, as there is between the tissues of a mother mammal and her fetus (also identical across half of their genomes) during pregnancy (Haig 2002). This may explain why obligate sibling chimerism never evolves (except perhaps in the very limited case of blood cells between sibling marmosets). But the problem of cooperation between tissues that derive from the oocyte and those that derive from the polar bodies is, if anything, even greater. The oocyte and the polar bodies are less closely related than two siblings would be, because the polar bodies are enriched for chromosome regions not present in the oocyte. If there were no crossing over between homologous chromosomes during meiosis, then the first meiotic division would consistently separate the chromosomes derived from the mother's mother from the chromosomes derived from the mother's father, producing two cells that are not related to each other at all (or, more precisely, exactly as closely related to each other as the mother's mother was to the mother's father). Crossing over prevents this, creating a mosaic of related and unrelated chromosome regions between the products of the first meiotic division and uncertain relationships between the final four meiotic products. Nonetheless, the consistently depressed relatedness between the oocyte and the polar bodies may help to explain why polar bodies are almost always eliminated—sibling rivalry might be even greater if some siblings were derived from each other's polar bodies. Towards an Explanation So how and why did two families of scale insects tame and domesticate their potentially fractious polar bodies, rather than killing them like normal animals do? There are at least three lines of thinking that seem promising for unraveling this mystery. Histological eusociality and relatedness. There are interesting parallels between, on the one hand, the chimerism seen in armored scale insects and, on the other hand, the eusociality (true sociality) seen in ants and honeybees. In ants and honeybees, sterile individuals (workers) provide nutrition to their potentially fertile siblings. In armored scale insects, a genetically distinct but ultimately sterile cell lineage (the bacteriome) provides nutrition to its potentially fertile “sibling” cell lineage (the rest of the scale insect)—though, of course, polar body-derived cells are “sibling” in a strange special sense. Like ants and bees, armored scale insects are effectively haplodiploid: males transmit only the chromosomes they inherited from their mother, and all of a male's sperm are identical to each other. This “clonality” of sperm boosts the relatedness between sisters, and Hamilton (1964b) pointed out that this high relatedness can explain the high level of cooperation between sisters seen in eusocial ants and honeybees. High levels of cooperation between sisters have since been found in haplodiploid thrips as well (Crespi 1992). It is tempting to speculate that similar explanations can be applied to “histological eusociality” seen in the cooperation between related tissues in scale insects. This temptation increases when we consider another case from the old cytogenetics literature of apparent histological eusociality, though not of true permanent chimerism. This occurs in a few families of parasitoid wasps (Tremblay and Caltagirone 1973; Strand and Grbic 1997), in which cells derived from polar bodies form a membrane around the yolkless egg that is deposited in the wasp's insect host (which is often a scale insect!). Similar to the worker ant and the scale insect bacteriome, this membrane is thought to mediate nutrition of the developing embryo, and, similar to ants, honeybees, and scale insects, these wasps are haplodiploid. But although it is tempting to conclude that haplodiploidy plays the same role in promoting histological eusociality as it does in promoting organismal eusociality, the temptation should probably be resisted. In the case of the parasitoid wasps, the polar body-derived tissue has no paternal genome, so the clonality of sperm cannot boost its relatedness to anything. In the case of armored scale insect chimerism, the bacteriome does have a copy of the paternal genome, and that copy is identical to the paternal genome in the rest of the embryo, so in that sense the two tissues do have a high relatedness similar to the high relatedness of full siblings under haplodiploidy. But the scale insect bacteriome gets its copy of the paternal genome directly from the embryo, so the clonality of sperm (the source of elevated relatedness between haplodiploid sisters) apparently has nothing to do with it. Some other explanation is probably needed. Maternal interests. Note that, whatever the relationship between the polar body-derived tissues and the rest of the insect, the polar bodies contain the mother's complete genome. And while your polar bodies may be less related to you, on average, than your siblings are, they are more related to your siblings (and, of course, to your mother) than they are to you! Perhaps the polar bodies function to somehow enforce some maternal or family interest, nipping in the bud some sibling rivalry that would otherwise suppress the family's fitness. In haplodiploid groups, females are more closely related to their full sisters (with whom they share three-quarters of their gene copies) than to their brothers (with whom they share only one-quarter of their gene copies). So if there is competition between siblings for resources, females are expected to behave more antagonistically towards brothers and more cooperatively towards sisters. In contrast, a female is equally related to a son as she is to a daughter (each carries half of her gene copies). These asymmetries in relatedness lead to struggles in haplodiploid social groups, with a mother seeking to direct more resources towards sons and with sisters seeking to direct more resources away from their brothers and towards each other (Seger and Stubblefield 2002). It is difficult to see how such a struggle might play itself out in scale insects, which are hardly social insects. The only motile stage in a female's life is the first instar (“crawler”), after which she settles in one spot permanently. The male is slightly more mobile, having a motile (usually winged) adult form. Nonetheless, (1) the low motility of females, and the fact that they live mostly on long-lived woody plants, means that maternal kin may interact over long timescales, as in social insects; (2) some scale insects appear to make relatively sophisticated social decisions about where to settle, settling nearer to (and thus possibly competing more closely with) non-kin than kin (Kasuya 2000); (3) although most scale insects use phloem sap, an almost inexhaustible resource, armored scales use parenchyma tissues (Rosen 1990), which may be locally exhausted, and therefore may compete against neighbors for food. Thus, it is conceivable that females may compete against brothers and sisters for resources, that they may make decisions that affect the intensity of that competition, and that such decisions may have different optima from the perspectives of maternal versus paternal genes. Possibly, the presence of a massive contingent of maternal genes (a double dose of the complete maternal genome) in a nutritionally significant tissue like the bacteriome might somehow affect such decisions in ways favorable to maternal interests. Proximal mechanisms might include effects on signals or perceptions related to relatedness, gender, site quality, or satiety. Similar manipulation of intersibling interaction might be going on in the case of the parasitoid wasps that have polar body-derived membranes around their eggs. Sometimes these wasps lay a single unfertilized (male) egg and a single fertilized (female) egg into the same host. Both eggs divide to form embryos, which divide into a clone of many embryos. Some of the embryos become sterile “precocious” larvae that can apparently attack other larvae trying to use the same host—including, potentially, their own siblings (Ode and Hunter 2002). Here is a situation in which the polar body genes (in the membranes surrounding the proliferating embryos) might have very different selective optima for levels of between-sibling aggressiveness—and even for rates of development—than the genes in the embryos they surround, and (because they apparently mediate the nutrition of the embryos) they might be able to influence how the embryos develop. Gender crypsis. The endosymbiotic bacteria that dwell in the scale insects' bacteriomes are maternally inherited. Thus, from the perspective of the bacteria, male insects are deadends. Many maternally inherited bacteria have evolved to manipulate the hosts' genetic system for their own advantage. Some bacterial lineages induce parthenogenesis or feminize males. Bacteria may even evolve to suicidally kill male embryos that they find themselves in, if the death of the male frees up resources that his sisters can use (Majerus 2003). In order to do this, bacteria must respond to some cue that indicates the gender of the individual they are in. Potentially, a host could evolve resistance to such manipulation by maternally inherited bacteria by depriving those bacteria of cues indicating gender. In armored scales, the bacteriome has exactly the same genome (two complete copies of the mother's diploid genome and one complete copy of the father's genome) in all full siblings, whether they are male or female, and the same is usually true in mealybugs. This might explain why the bacteriome is the only tissue in which the father's genome remains present and active in both males and females. Prospects Scale insects and their bacteriomes challenge our notion of what an individual is. Is a scale insect's bacteriome a kind of sibling? Is it half sibling, half self? Is it a sterile slave, under control? Is it an extension of the mother, exerting control? In all other organisms, chimeras are temporary and unstable. How have scale insects suppressed the conflicts that normally tear chimeras apart? To approach such questions, we'll have to revive the empirical study of scale insect bacteriomes, combining approaches from recent studies of aphid bacteriomes (Braendle et al. 2003) and of human pregnancy (Haig 2002). We can better understand the nature of genetic conflicts in scale insects by studies of the genetic structure of scale insect populations, together with studies of sex ratio variation and the proximate mechanisms of sex determination. For simplicity, I have described only the most common of the huge variety of very different scale insect genetic systems and modes of bacteriome development (Tremblay 1977, 1990; Nur 1980). This diversity (greater than for the comparable cases of mammalian placentas and flowering-plant endosperms) means there is a huge scope for comparative ecological and genetic studies that could help elucidate general principles. The study of truly strange creatures can tell us what kinds of things are possible. That's why we will be so interested in any life found on another planet and why, in the meantime, we should take a close look at scale insects. For helpful comments on the manuscript, I thank Michael Majerus. For photos of armored scale insects, I thank Ray Gill. For helpful discussions of all aspects of scale insect biology, I thank Dug Miller, Penny Gullan, all my other scale insect colleagues, and the funding agencies that have enabled me and my students to meet and work with them. This material is based in part upon work supported by the United States' National Science Foundation under grant number 0118718 and by the Massachusetts Agricultural Experiment Station (Hatch MAS00839). Benjamin B. Normark is in the Department of Entomology and Graduate Program in Organismic and Evolutionary Biology at the University of Massachusetts, Amherst, Massachusetts, United States of America. E-mail: bnormark@ent.umass.edu ==== Refs References Alleman M Doctor J Genomic imprinting in plants: Observations and evolutionary implications Plant Mol Biol 2000 43 147 161 10999401 Braendle C Miura T Bickel R Shingleton AW Kambhampati S Developmental origin and evolution of bacteriocytes in the aphid–Buchnera symbiosis PLoS Biol 2003 1 e21 10.1371/journal.pbio.0000021 14551917 Brown SW Chromosomal survey of the armored and palm scale insects (Coccoïdea: Diaspididae and Phoenococcidae) Hilgardia 1965 36 189 294 Buss LW The evolution of individuality 1987 Princeton, New Jersey Princeton University Press 224 Crespi BJ Eusociality in Australian gall thrips Nature 1992 359 724 726 Haig D What is a marmoset? Am J Primatol 1999 49 285 296 10553958 Haig D Genomic imprinting and kinship 2002 New Brunswick, New Jersey Rutgers University Press 240 Hamilton WD The genetical evolution of social behaviour I J Theor Biol 1964a 7 1 16 5875341 Hamilton WD The genetical evolution of social behaviour II J Theor Biol 1964b 7 17 52 5875340 Herrick G Seger J Ohlsson R Imprinting and paternal genome elimination in insects Genomic imprinting: An interdisciplinary approach 1999 New York Springer-Verlag 41 71 Kasuya E Kin-biased dispersal behaviour in the mango shield scale, Milviscutulus mangiferae Anim Behav 2000 59 629 632 10715186 Majerus MEN Sex wars: Genes, bacteria, and biased sex ratios 2003 Princeton, New Jersey Princeton University Press 280 Nur U Blackman RL Evolution of unusual chromosome systems in scale insects (Coccoidea: Homoptera) Insect cytogenetics 1980 Oxford, United Kingdom Blackwell Scientific Publications 97 117 Ode PJ Hunter MS Hardy ICW Sex ratios of parasitic Hymenoptera with unusual life-histories Sex ratios: Concepts and research methods 2002 Cambridge, United Kingdom Cambridge University Press 218 234 Rosen D Armored scale insects: Their biology, natural enemies, and control 1990 Amsterdam, The Netherlands Elsevier Health Sciences 688 Santiago MG Goodrich J Genomic imprinting: Seeds of conflict Curr Biol 2000 10 R71 R74 10722305 Seger J Stubblefield JW Hardy ICW Models of sex ratio evolution Sex ratios: Concepts and research methods 2002 Cambridge, United Kingdom Cambridge University Press 2 25 Shigenobu S Watanabe H Hattori M Sakaki Y Ishikawa H Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp Nature 2000 407 81 86 10993077 Strand MR Grbic M The development and evolution of polyembryonic insects Curr Top Dev Biol 1997 35 121 159 9292269 Tremblay E Advances in endosymbiont studies in Coccoidea VA Polytech Inst State Univ Res Div Bull 1977 127 23 33 Tremblay E Rosen D Endosymbionts Armored scale insects: Their biology, natural enemies, and control 1990 Amsterdam, The Netherlands Elsevier Health Sciences 275 283 Tremblay E Caltagirone LE Fate of polar bodies in insects Ann Rev Entomol 1973 18 421 444 van Dijk BA Boomsma DI de Man AJ Blood group chimerism in human multiple births is not rare Am J Med Genet 1996 61 264 268 8741872

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020050Research ArticleBiophysicsMus (Mouse)A Spontaneous, Recurrent Mutation in Divalent Metal Transporter-1 Exposes a Calcium Entry Pathway G185R DMT1 Is an Ion ChannelXu Haoxing 1 Jin Jie 1 DeFelice Louis J 2 Andrews Nancy C nandrews@enders.tch.harvard.edu 1 Clapham David E dclapham@enders.tch.harvard.edu 1 1Howard Hughes Medical Institute, Children's HospitalHarvard Medical School, Boston, MassachusettsUnited States of America2Department of Pharmacology, Vanderbilt University Medical CenterNashville, TennesseeUnited States of America3 2004 16 3 2004 16 3 2004 2 3 e504 11 2003 16 12 2003 Copyright: ©2004 Xu et al. 2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Single Mutation Transforms an Iron Transporter into an Ion Channel Divalent metal transporter-1 (DMT1/DCT1/Nramp2) is the major Fe2+ transporter mediating cellular iron uptake in mammals. Phenotypic analyses of animals with spontaneous mutations in DMT1 indicate that it functions at two distinct sites, transporting dietary iron across the apical membrane of intestinal absorptive cells, and transporting endosomal iron released from transferrin into the cytoplasm of erythroid precursors. DMT1 also acts as a proton-dependent transporter for other heavy metal ions including Mn2+, Co2+, and Cu2, but not for Mg2+ or Ca2+. A unique mutation in DMT1, G185R, has occurred spontaneously on two occasions in microcytic (mk) mice and once in Belgrade (b) rats. This mutation severely impairs the iron transport capability of DMT1, leading to systemic iron deficiency and anemia. The repeated occurrence of the G185R mutation cannot readily be explained by hypermutability of the gene. Here we show that G185R mutant DMT1 exhibits a new, constitutive Ca2+ permeability, suggesting a gain of function that contributes to remutation and the mk and b phenotypes. David Clapham and colleagues offer new evidence that blurs the line between ion transporters and channels ==== Body Introduction Spontaneous mutations in mice and rats have provided important information about mammalian iron homeostasis (reviewed in Andrews 2000). Interestingly, three independent, autosomal recessive mutants have been shown to have the same amino acid substitution in a key iron transport molecule. Two strains of mutant microcytic (mk) mice (MK/ReJ-mk, SEC/1ReJ-mk) and Belgrade (b) rats have severe iron deficiency attributable to a G185R mutation in divalent metal transporter-1 (DMT1) (Fleming et al. 1997; Andrews 2000). Based on the phenotypes of these animals and the properties of DMT1 detailed below, we and others concluded that DMT1 is essential for intestinal absorption of Fe2+ and for unloading of transferrin-derived iron from transferrin cycle endosomes (Fleming et al. 1997, 1998; Gunshin et al. 1997; Picard et al. 2000). It is intriguing that no other DMT1 mutations have been described in mammals, and no features of the DNA sequence suggest that the G185 codon would be hypermutable in two species. We speculated that a novel characteristic of the G185R DMT1 protein might account for this remarkable pattern of remutation. Trace metal ions including Fe2+, Mn2+, Cu2+, Zn2+, and Co2+ are required cofactors for many essential cellular enzymes. They cannot cross the plasma membrane through simple diffusion, and active uptake requires specific transporters. DMT1 is the only molecule known to mediate cellular iron uptake in higher eukaryotes. It is structurally unrelated to known Zn2+ and Cu2+ transporters, but DMT1 can transport those and other divalent metal ions (Gunshin et al. 1997), and it appears to be the major mammalian Mn2+ transporter (Chua and Morgan 1997). DMT1 is predicted to have 12 transmembrane (TM) segments (Figure 1A). It is expressed on the apical brush border of the proximal duodenum (Canonne-Hergaux et al. 1999) and in transferrin cycle endosomes (Su et al. 1998; Gruenheid et al. 1999). It appears to function by coupling a metal entry pathway to a downhill proton gradient, taking advantage of the acidic pH in both of those sites. An earlier study proposed a 1:1 stoichiometry of metal ion and proton cotransport (Gunshin et al. 1997). Figure 1 Wild-Type DMT1-Expressing Cells Exhibit a Proton Current and a Proton-Dependent Mn2+-Induced Current (A) The G185R mutation is in the fourth of 12 putative TM domains in both mouse (shown) and rat DMT1 proteins. (B) 55Fe2+ uptake was greatly reduced for G185R in comparison to wild-type DMT1, although the protein expression levels were comparable (inset). (C–E) Representative currents induced by protons (pH 4.2) and Mn2+ (100 μM) at +50 mV (open triangles; some of the datapoints have been removed for clarity) and −130 mV (open circles) in a wild-type DMT1-transfected CHO-K1 cell. Whole-cell currents were elicited by repeated voltage ramps (−140 to +60 mV, 1,000 ms), shown in (E), with a 4 s interval between ramps. Holding potential (HP) was +20 mV. Neither control solution (10mM Ca2+/140 mM Na+/[pH7.4]) nor isotonic Ca2+ (105 mM) solution induced significant current. Representative I-V relations are shown in (E). Current responses from a vector (pTracer)-transfected cell are shown in (D). (F) pH-dependence of the Erev of the wild-type DMT1 current in the presence or absence of 300 μM [Mn2+]o. In the absence of Mn2+, the pH dependence of the Erev can be fitted by a line with a slope 58 mV/pH unit. In the presence of 300 μM Mn2+, the relationship was nonlinear, especially at higher pH. EH, H+ equilibrium potential. Note that the currents were not leak-subtracted. Ca2+ is not a measurable substrate for wild-type DMT1 (Gunshin et al. 1997; Tandy et al. 2000), even though it is at least 1,000 times more abundant in plasma than trace metals. Surprisingly, we found that the G185R mutation (Figure 1A) dramatically increases the Ca2+-permeability of DMT1, functionally converting DMT1 into a Ca2+ channel. In light of the important and ubiquitous role of Ca2+ in cell signaling (Berridge et al. 2003), this gain of function offers a likely explanation for the remutation. Interpretations of recent structural data have already suggested that permeation pathways exist within some transporters (Hirai et al. 2002), blurring the distinction between transporters and ion channels (DeFelice and Blakely 1996). Our finding, that a single amino acid substitution in a presumed transporter can expose a channel pathway, strongly supports this notion and provides new insight into what must be viewed as a continuum between transporter and channel activities. Results We studied wild-type DMT1 and the G185R mutant proteins by whole-cell patch–clamp in transiently expressing CHO-K1 and HEK-293T cells and in doxycycline-inducible DMT1-HEK-On and G185R-HEK-On cells. Consistent with previous studies, DMT1 expression significantly increased cellular 55Fe2+ uptake at low pH (Figure 1B). As reported in Xenopus oocytes (Gunshin et al. 1997), reduction of extra-cellular pH in the absence of metal (nominal free [Fe2+]o of approximately 0.05 μM) induced large inward currents in DMT1-expressing cells (Figure 1C and 1D). This current is referred to as a substrate-free “leak” pathway and is representative of “drive-slip” phenomena seen in DMT1 and a related yeast metal transporter, SMF1p (Sacher et al. 2001), as well as many other transporters (Nelson et al. 2002). Because we found that protons also activated an endogenous diisothiocyanostilbene 2,2-disolphonic acid (DIDS)-sensitive anion conductance (unpublished data) that was strongly outwardly rectifying (Figure S1), we used SO4 2– to replace most of the Cl– ([Cl–]o = 5 mM) in low-pH bath solutions. With elimination of the background Cl– current, the proton-evoked current was inwardly rectifying (hyperbolic) (Figure 1E). The large proton-induced current caused significant DMT1-specific intracellular acidification (Gunshin et al. 1997). In whole-cell recordings of DMT1 currents, we routinely observed slow inactivation (or decay) after a proton-induced current reached its peak (see Figure 1C). While the extent of the slow inactivation varied from cell to cell, it usually reached a relative steady state within 100 s. Addition of 100 μM Fe2+ (data not shown) or Mn2+ induced an additional current with less pronounced slow inactivation (Figure 1C). Because Fe2+ is readily oxidized to Fe3+ in the absence of substantial concentrations of reducing agents (e.g., ascorbate), and Fe3+ is not transported by DMT1 (Gunshin et al. 1997; Picard et al. 2000), we have used Mn2+ as an Fe2+ surrogate since both metals induced similar currents (Gunshin et al. 1997; unpublished data). The observed Mn2+ deficiency of b rats in vivo (Chua and Morgan 1997) also supports its use in this role. H+ alone or H+/Mn2+ induce distinct currents in DMT1. No significant voltage- or time-dependent fast inactivation was seen when the DMT1-mediated H+/Mn2+ current (IDMT1) was recorded (Figure S2). The amplitude of additional Mn2+-induced current was dependent on [Mn2+]o, with a measurable response at [Mn2+]o < 1 μM (pH 4.2). In the presence of 100 μM Mn2+ (pH 4.2), the additional Mn2+-induced current was typically half the amplitude of the proton-induced current. Addition of Mn2+ alone (100 μM) at pH 7.4 did not induce any additional current. Since H+ or H+/Mn2+ induced two currents with distinct kinetics in DMT1-expressing cells, the underlying charge-carrying ion species and their relative contributions to the macroscopic currents were investigated. We monitored the reversal potential (Erev) and the current amplitude in ion-substitution experiments. Replacement of Na+ with N-methyl-D-glucamine (NMDG+) did not significantly change the Erev of H+ or H+/Mn2+-induced currents, although the net current amplitude was slightly increased (Figure S3). On the other hand, the current amplitude (data not shown) and Erev of the proton current were strongly affected by [H+]o (see Figure 1F). The slope of Erev versus pH was 58 mV/decade, is consistent with an H+-permeable pore. The large positive displacement in Erev from EH (see Figure 1F) may result in part from leak and capacitance-charging, but the carrier mechanism is not well understood. In contrast, when Mn2+ was introduced, the slope of the curve fitted to Erev versus pH deviated considerably from the theoretical slope for a H+-permeable electrode (see Figure 1F). Replacement of Na+ by NMDG+ did not significantly affect the Mn2+-induced response (see Figure S3). Our interpretation of this deviation is that DMT1 transport stoichiometry is variable (Chen et al. 1999; Sacher et al. 2001; Adams and DeFelice 2002) or has a fixed but very low permeation ratio (PMn/PH) (Hodgkin and Horowicz 1959). PMn/PH can be estimated from the slope of Erev versus pH based on an extended Goldman–Hodgkin–Katz equation (Lewis 1979) with two permeable ions (H+ and Mn2+). At pH 4.2, the slope of Erev versus pH did not differ significantly with or without Mn2+(see Figure 1F). Therefore, we estimate that at pH 4.2 the contribution of H+ to IDMT1 is much larger than that of Fe2+/Mn2+ (PMn/PH < 0.01), in contrast to the 1:1 stoichiometry proposed previously (Gunshin et al. 1997). Importantly, no Ca2+ permeability was observed, even in isotonic (105 mM) Ca2+ solution (see Figure 1C). In G185R-expressing cells, we observed a large inward current in control bath solution (10 mM Ca2+ and 140 mM Na+) at pH 7.4 (Figure 2A), though no significant current was detected with wild-type DMT1 under similar conditions (see Figure 1E). This inward current mediated by G185R mutant DMT1 (IG185R) was stable over minutes with no slow inactivation (see Figure 2A), in contrast to the DMT1-mediated proton current (see Figure 1C). We observed IG185R in more than 85% of enhanced green fluorescent protein (EGFP)-positive cells transfected with the pTracer-G185R construct and in stable, doxycycline-induced G185R-HEK-On cells, but never in cells transfected with wild-type DMT1 (Figure 1C) or with 30 DMT1 mutations at other positions (n > 300 cells; unpublished data). The inwardly rectifying current was cationic, since Ca2+ and Na+ substitution by NMDG+ completely abrogated the current (see Figure 2A and 2B). The current and rectification profiles were not significantly changed when ATP and Mg2+ were omitted from the intracellular solution, or when Na+ or K+ replaced Cs+ as the primary intracellular cation. Figure 2 G185R-Expressing Cells Display a Constitutive [Ca2+]o-Dependent Cationic Current (A–B) Large inward currents were evoked by control solution (10mM Ca2+/140 mM Na+ [pH 7.4]) in G185R-transfected cells. The current was inhibited by lowering the solution pH to 5.8 without altering other ions. Further reducing the pH to 4.2 induced IDMT1-like current (enhanced by adding 100 μM Mn2+). No significant inward current was seen in NMDG+ (Na+-free, Ca2+-free) solution. (C) Time- and voltage-dependent kinetics of IG185R recorded in control solution in response to voltage steps. (D) Current densities (mean ± SEM, n = 15) of IG185R in control solution mea-sured at various voltages and normalized by cell capacitance. (E) Time- and voltage-dependent kinetics of IG185R in the presence of 105 mM Ca2+. (F) Ca2+ is more permeant than Na+ in G185R-expressing cells. We found that low pH strongly inhibited IG185R (by approximately 90% at pH 5.8; Figure 2A), in contrast to both wild-type DMT1 currents, which were activated at low pH. However, further reduction to pH 4.2 revealed a current (Figure 2A and 2B) that was similar to the proton current of wild-type DMT1. Addition of Mn2+ at pH 4.2 enhanced the inward current, as with wild-type DMT1 (Figure 2A and 2B). The proton current and Mn2+-induced response displayed similar patterns of inactivation and further activation as in wild-type DMT1-transfected cells, but both currents were much smaller than their wild-type counterparts. Consistent with this result and our previous uptake studies (Su et al. 1998), we found that G185R cells had much lower Fe2+ uptake (approximately 10% measured at 16 min) compared to wild-type DMT1 at similar protein expression levels (see Figure 1B). IG185R rectified more steeply with voltage than IDMT1, probably due to pronounced time- and voltage-dependent fast inactivation (Figure 2C; see Figure S2 for comparison). Fast inactivation was enhanced when [Ca2+]o was increased to 105 mM (Figure 2E), strengthening the notion that IG185R was fundamentally distinct from the currents mediated by wild-type DMT1. In control bath solution (10 mM Ca2+, 140 mM Na+ [pH 7.4]), IG185R was 64 ± 7 pA/pF at −140 mV (mean ± SEM, n = 15; Figure 2D) compared to less than 2 pA/pF in mock and DMT1-transfected cells. IG185R reversed at approximately +20 mV with very little current above 0 mV (Figure 2D), whereas the Erev of IDMT1 was approximately +50 mV at pH 4.2. The dependence of IG185R on holding potential was also distinct from IDMT1 (see below). We next investigated the cation selectivity of IG185R. The amplitude of IG185R was strongly dependent on [Ca2+]o (Figure 2F). With 10 mM Ca2+ in the bath, replacement of 140 mM NMDG+ by 140 mM Na+ only slightly (by approximately 15%) increased the current, indicating that Ca2+ permeated the plasma membrane of G185R-transfected cells much more readily than Na+. As shown in Figure 3A and 3B, increasing [Ca2+]o not only augmented the current amplitude but also shifted Erev toward depolarized potentials. The slope of this shift (25 mV per decade) was close to the slope of 29 mV per decade predicted by the Nernst equation for a Ca2+-selective electrode (Figure 3C). The relative permeability of various divalent cations was studied under bi-ionic conditions (pipette solution containing Na+ and Glutamate; see Materials and Methods). After adding 10 mM test divalent cations to the NMDG+ solution, we recorded currents using step voltages from two holding potentials (-60 mV and +40 mV). We determined G185R-specific currents by measuring the reversal potentials of the currents subtracted from two holding potentials (see Figure 4A and 4B) and corrected for the junction potential. The permeability sequence was Ca2+ > Sr2+ > Ba2+ as calculated (Equation 2; see Materials and Methods) and illustrated in Figure 3E. For divalent cations, we found that the highest conductance was to Ca2+, followed by Sr2+ and Ba2+ (Figure 3D). While Ca2+, Sr2+, and Ba2+ currents were relatively stable over time, currents mediated by Mn2+ and Mg2+ were transient (Figure 3D), the simplest explanation for this behavior being a block by these two weakly permeant ions. The monovalent permeability was calculated using Equation 1 (see Materials and Methods), yielding a selectivity sequence Li+ > Na+ > K+ > Cs+ (Figure 3E). Under these conditions, PMn was insignificant. The cationic permeability sequence (Figure 3E) of IG185R was similar to L-type voltage-gated Ca2+ channels (VGCCs) (Sather and McCleskey 2003), but IG185R was less Ca2+-selective (PCa/PNa of approximately 10) than VGCCs (PCa/PNa of approximately 1,000). Single i G185R channels were not observed in cell-attached patches. Analysis of membrane current noise at −100 mV predicted a single-channel chord conductance of 0.4 ± 0.1 pS (n = 5; unpublished data), too small to be observed under most patch–clamp conditions. Figure 3 Ca2+ Permeability of IG185R (A) Whole-cell I-V relations in the presence of [Ca2+]o are indicated. (B) Enlarged view of (A) to show the Erev measurement. (C) [Ca2+]o dependence of Erev. The slope was fit by linear regression to 25 mV per decade, close to the 29 mV per decade predicted for a Ca2+-selective electrode (dotted line). (D) Currents through G185R in various isotonic divalent solutions. I-Vs are shown in the inset. Note that currents induced by isotonic Mg2+ and Mn2+ were transient. (E) Relative permeability of various divalent and monovalent cations. The reversal potentials of IG185R in 10 mM test divalent cations were measured under bi-ionic conditions as described in Materials and Methods. The permeability was calculated using Equations 1 and 2. (F) [Ca2+]i changes estimated by Fura-2 fluorescence in response to an elevation of [Ca2+]o from 1 to 30 mM. The results were averaged from five (HEK-On) and seven (G185R) independent experiments (n = 3–13 cells each). To minimize potential endogenous depletion-activated and/or TRP-mediated Ca2+ influx, cells were bathed in the presence of 50 μM SKF96365 and 50 μM 2-APB. The F340/F380 ratio was recorded and converted into estimated [Ca2+]i based on an ionomycin-induced Ca2+ calibration. Figure 4 Voltage Dependence and Pharmacological Properties of IG185R (A) Whole-cell currents recorded in 105 mM [Ca2+]o were dependent on holding potential before the voltage ramps (−140 to −120 mV shown). For clarity, only the first 20 ms of the 4 s-long holding potential is shown. (B) Voltage dependence of IG185R in control solution and 105 mM [Ca2+]o. IDMT1 (dotted line) exhibited no depen-dence on the holding potential. Abbreviations: V1/2 , half activation voltage. κ, slope factor. (C and D) Sensitivity of IG185R to various pharmacological agents and cation channel blockers. IG185R was relatively insensitive to RR, 2-APB, or SKF96365, but was blocked by 1mM La3+ or Cd2+ (D). Using the Ca2+ indicator dye Fura-2, we demonstrated G185R-mediated Ca2+ influx by monitoring intracellular Ca2+ levels in response to an elevation of [Ca2+]o (Figure 3F). To minimize the contributions of endogenous Ca2+-influx and/or store release, we bathed cells in the presence of 50 μM SKF96365 and 50 μM 2-APB. Upon raising [Ca2+]o, [Ca2+]i rose from 105 nM to 240 nM in doxycycline-induced G185R-HEK-On cells, significantly higher than in control HEK-On cells treated with doxycycline. Thus, the permeability of G185R to Ca2+ is capable of increasing [Ca2+]i. IG185R displayed hyperpolarization-induced inhibition (Figure 4A and 4B) (Bakowski and Parekh 2000). The half-maximal activation voltages (V1/2) were −33 mV and −10 mV for control and isotonic Ca2+ solutions, respectively (Figure 4B). The voltage-dependence of IG185R was Ca2+-independent, since the Na+ and Li+ currents in nominal [Ca2+]o also exhibited a similar voltage dependence. By contrast, IDMT1 lacked this voltage dependence (Figure 4B). IG185R was not enhanced under low-divalent conditions (less than 10 nM), nor was it blocked by antagonists of known Ca2+-permeant channels. In particular, the current was not blocked by ruthenium red (RR), Ca2+-release activated Ca2+ channel (CRAC) blockers SKF96365 and 2-APB (Kozak et al. 2002; Prakriya and Lewis 2002) (Figure 4C), or the L-type VGCC blocker nifedepine (10 μM). Divalent cations, including DMT1 substrates (Cd2+, Ni2+, Co2+), inhibited IG185R. La3+ (1 mM; Figure 4C) and Cd2+ (1 mM) blocked IG185R in a similar voltage-dependent manner (Figure 4D). Thus, IG185R is distinct from known Ca2+-permeant channels such as VGCCs, transient receptor potentials (TRPs), and CRAC currents, based on its current–voltage (I-V) relation, kinetics, permeation properties, and pharmacological sensitivity. To investigate whether G185R-induced Ca2+ permeability might play a physiological role in the mutant animals, we recorded from intestinal enterocytes isolated from both wild-type and homozygous mk mice. We studied cells from the proximal 1 cm of the mouse duodenum, where DMT1 expression is highest and iron absorption is maximal (Gunshin et al. 1997; Canonne-Hergaux et al. 1999). Because DMT1 expression is very low in iron-replete, wild-type mice, but induced in iron-deficient mice (Canonne-Hergaux et al. 1999), we isolated enterocytes from mice that had been made iron-deficient by prolonged feeding of an iron-deficient diet, and confirmed DMT1 induction by Western blotting using a DMT1-specific antibody (unpublished data). We were able to record IDMT1-like currents in mature enterocytes that stained positive for alkaline phosphatase (I > 80 pA at −130mV, n = 7 out of 20 cells; representative data shown in Figure 5A and 5B). Figure 5 DMT1-Like and G185R-Like Currents in Enterocytes Isolated from Wild-Type and mk/mk Mice, Respectively (A) Enterocyte currents isolated from an iron-deficient wild-type mouse (−Fe). Reducing bath pH (140 mM NaCl) induced a slowly desensitizing inward current that was further enhanced by addition of Mn2+. (B) Both proton and H+/Mn2+currents were inwardly rectifying. (C and D) An mk enterocyte expressed a large constitutive inward current in control bath solution. Reducing the bath pH (140 mM NaCl) first inhibited and then activated another inward current insensitive to the holding potential. This slowly-desensitizing current displayed a less steeply rectifying I-V as shown in (D). Mice homozygous for the mk mutation express large amounts of G185R DMT1 protein in the duodenum. Although much of it is mislocalized to the cytoplasm (Canonne-Hergaux et al. 2000), we expected that some would be present in the plasma membrane. Accordingly, and in contrast with wild-type enterocytes, we recorded a large, constitutive inward current in most mature mk enterocytes (n= 6 out of 8 cells; Figure 5C and 5D), which displayed the same conductance as seen in G185R-transfected cells. The I-V relationship, step current response, dependence on holding potential, ion selectivity and insensitivity to RR, and SKF96365 or 2-APB were indistinguishable from those of transfected IG185R. Furthermore, H+ inhibited the IG185R-like current in mk enterocytes, and the H+/Mn2+-induced DMT1-like current at pH 4.2 (Figure 5C and 5D) was insensitive to holding potential, as observed in transfected cells. Based on these observations, we conclude that the major current observed in mk enterocytes was IG185R. Although our preparation did not allow us to distinguish apical versus basolateral localization, the large size of the current in mk cells was consistent with plasma membrane localization of G185R protein. Discussion We conclude that expression of G185R in transfected cells and in vivo in mk mice is associated with the appearance of a novel Ca2+ permeation pathway that has the properties of a Ca2+ channel. One interpretation is that a Ca2+ channel pathway through the DMT1 protein is exposed or augmented by the G185R mutation. Another possibility is that Ca2+ conduction occurs through an associated Ca2+-permeable protein. We favor the first possibility because the Ca2+ conductance has been observed in diverse cell lines expressing G185R DMT1 (CHO-K1, HEK293T, and HEK-On cell lines) and in mk enterocytes. A putative associated protein, if present in these different cell types, would have to be activated in a G185R-dependent manner. We did not find evidence of an associated protein when we immunoprecipitated wild-type or G185R DMT1 from transfected CHO-K1 cells (unpublished data). Furthermore, a distinct DMT1 mutant, G185K, also displayed Ca2+ permeability, but this mutant was less selective for Ca2+ over Na+ (unpublished data). G185R mutations have occurred at least three times in rodents, which suggests that G185R not only inactivates DMT1, but may confer an unknown selective advantage. Because it has arisen in inbred colonies, the postulated selective advantage must either make the animals more viable than other DMT1 mutants with impaired iron transport or more likely to be noticed by those managing the animal colonies. In parallel with these studies, we have generated knockout mice homozygous for a null DMT1 allele (Dmt1–/–; H. Gunshin and N. C. Andrews, personal communication). Although detailed phenotypic characterization has not yet been completed, we have noted that Dmt1–/– mice invariably die by the end of the first week of life, in contrast to mk/mk mice, which are poorly viable but can survive for more than a year (H. Gunshin and N. C. Andrews, personal communication). This suggests that the small amount of residual function of G185R DMT1, perhaps in combination with its gain-of-function Ca2+ conductance, contributes to viability. Two previous studies support the notion that the gain-of-function reported here is an advantage. Elevated intracellular [Ca2+] has been reported to increase nontransferrin-bound iron uptake through an undefined transport system that has characteristics distinct from DMT1 (Kaplan et al. 1991). This might ameliorate the iron-transport defect caused by inactivation of DMT1, either in the intestine or in erythroid precursors. The transferrin cycle is essential for iron uptake by erythroid precursor cells (Levy et al. 1999), and DMT1 mediates at least some of the transfer of iron from transferrin cycle endosomes to the cytoplasm (Fleming et al. 1998; Gruenheid et al. 1999; Touret et al. 2003). Elevated [Ca2+]i has been reported to accelerate iron uptake through the transferrin cycle, apparently through activation of protein kinase C (Ci et al. 2003). Thus, the influx of Ca2+ might potentiate the residual DMT1 iron-transport activity. Accordingly, 55Fe uptake by mk/mk reticulocytes has been reported to be approximately 45% of the level observed in wild-type reticulocytes (Canonne-Hergaux et al. 2001), higher than expected for a severe loss-of-function mutation. In summary, we have found that a single point mutation (G185R) in a 12-TM transporter protein conferred new Ca2+-selective permeability. Previous studies have suggested that channels, pumps, and transporters may share some common mechanisms for ion translocation (Gadsby et al. 1993; Fairman et al. 1995; Cammack and Schwartz 1996; for review see references in Lester and Dougherty 1998; Nelson et al. 2002). The “channel mode” has been proposed to explain the “drive-slip” mechanism as part of the transport cycle. In this sense, wild-type DMT1 may simply be a proton channel with limited permeability for certain divalent metal ions. By mutating a single residue, G185R, it becomes an unambiguously Ca2+-permeant ion channel. Our findings may add new insight into mechanisms of Ca2+ entry and transporter function. The notion that the 12-TM proteins can be ion channels may inform the search for candidate Ca2+ and/or cationic channels and facilitate the molecular characterization of many unidentified native conductances. We initiated these studies to investigate why a unique DMT1 mutation, G185R, has occurred independently at least twice in mice and once in rats (Fleming et al. 1997,1998). The multiple occurrences of this spontaneous mutation suggested that it might confer some type of selective advantage. We speculate that the proposed Ca2+ entry gain of function helps to account for this remarkable pattern of remutation. Further investigation of this hypothesis will require direct and detailed comparison of DMT1-null and mk mice. Materials and Methods Molecular biology The DMT1 cDNA used in this study was derived from one of four alternatively-spliced DMT1 gene transcripts. The G185R mutation was generated by using M13 phage and the oligonucleotide GTCCCCCTGTGGGGCCGAGTCCTCATCACCA. Wild-type DMT1 and the G185R mutant were tagged with a C-terminal FLAG epitope and subcloned into pTracer-CMV2 (Invitrogen, Carlsbad, California, United States). CHO-K1 or HEK293T cells transiently transfected with DMT1 and G185R were used for the 55Fe uptake assay and Western blot analysis. To obtain a stable G185R-expressing cell line, the G185R-encoding DMT1 gene was subcloned into pRevTRE (Clontech, Palo Alto, California, United States), a retroviral vector that drives expression from a Tet-responsive element. All constructs were confirmed by sequencing. DMT1 Western blot analyses were performed with an anti-FLAG M2 monoclonal antibody (Sigma, St. Louis, Missouri, United States) and, in some cases, with a goat polyclonal antibody raised against human DMT1 (Santa Cruz Biotechnology, Santa Cruz, California, United States). Mammalian cell electrophysiology Wild-type and G185R mutant DMT1 were subcloned into an EGFP-containing vector (pTracer-CMV2, Invitrogen) for transient expression in CHO-K1 and HEK293T cells. Cells were transfected using Lipofectamine 2000 (Invitrogen). Transfected cells, cultured at 37°C, were plated onto glass coverslips and recorded 24 (DMT1) or 30 (G185R) hrs after transfection. A stable cell line (HEK293 Tet-OnTM, or HEK-On) was generated, and expression was induced by adding 1–10 μg/ml doxycycline into the culture medium. Unless otherwise stated, the pipette solution contained 147 mM cesium, 120 mM methane-sulfonate, 8 mM NaCl, 10 mM EGTA, 2 mM Mg-ATP, 20 mM HEPES (pH 7.4). Bath solution contained 140 mM NaCl, 10 mM CaCl2, 10 mM HEPES, 10 mM MES, 10 mM glucose (pH 7.4). Unless otherwise stated, the low pH solutions contained only nominal free Ca2+ (1–10 μM). Data were collected using an Axopatch 2A patch–clamp amplifier, Digidata 1320, and pClamp 8.0 software (Axon Instruments, Union City, California, United States). Whole-cell currents were digitized at 10 kHz and filtered at 2 kHz. The permeability to monovalent cations (relative to PNa) was estimated according to Equation 1 from the shift in Erev upon replacing [Na+]o in nominally Ca2+-free bath solution (150 mM XCl, 20 mM HEPES, 10 mM glucose [pH 7.4]]), where X+ was Na+, K+, Cs+, or Li+. For the permeability to divalent cations (relative to PNa), bi-ionic conditions were used; Y2+ was Ca2+, Ba2+, or Sr2+ (Equation 2). The internal pipette solution contained 100 mM Na-gluconate, 10 mM NaCl, 10 mM EGTA, 20 mM HEPES-Na (pH 7.4 adjusted with NaOH, [Na+]total = 140). The external solution was 140 mM NMDG-Cl, 10 mM Y2+Cl2, 20 mM HEPES (pH 7.4 adjusted with HCl). The permeability ratios of cations were estimated from the following equations (Lewis 1979): where R, T, F, V, and γ are, respectively, the gas constant, absolute temperature, Faraday constant, Erev, and activity coefficient. The liquid junction potentials were measured and corrected as described by Neher (1992). Uptake assay The assay buffer contained 25 mM Tris, 25 mM MES, 140 mM NaCl, 5.4 mM KCl, 5 mM glucose, 1.8 mM CaCl2, 0.8 mM MgCl2. Ascorbic acid was adjusted to 1 mM and the pH was adjusted to 5.8. Most assays were performed with 20 μM Fe2+ at pH 5.8 unless otherwise indicated. A 50-fold 55Fe stock was made immediately before the assay with 1 mM 55Fe (with a 1:20 molar ratio for 55FeCl3 and FeSO4) and 50 mM nitrilotriacetic acid. About 30 h after transient transfection, CHO-K1 or HEK293T cells were washed and harvested with PBS (for CHO-K1 cells, trypsin treatment was required). Cells were resuspended in glass test tubes at 0.5–1 million/ml in 490 μl assay buffer at 30°C. The reaction was started by adding 10 μl of 55Fe stock and stopped at 4, 8, and 16 min by quickly filtering the reaction mix on a nitrocellulose filter (HAWP02500; Millipore, Billerica, Massachusetts, United States). Filters were washed twice with 2 ml of assay buffer, dried, and radioactivity counted by liquid scintillation spectrometry. Calcium imaging Cells were loaded with 2 μM Fura-2 AM in culture medium at 37°C for 30 min. Low levels of G185R protein were expressed in the absence of doxycycline in G185R HEK-On cells (Western blotting; unpublished data). Therefore, doxycycline-treated HEK-On cells not expressing DMT1 were used as controls in imaging experiments. We recorded Fura-2 ratios (F340/F380) on an UltraVIEW imaging system (Olympus, Tokyo, Japan). A standard curve for Fura-2 ratio versus [Ca2+] was constructed according to Grynkiewicz et al. (1985). Isolation of enterocytes Homozygous mk mice (Fleming et al. 1997) were housed in the barrier facility at Children's Hospital (Boston, Massachusetts, United States). Husbandry and use were according to protocols approved by the Animal Care and Use Committee. Wild-type iron-deficient mice were provided by J.-J. Chen (Massachusetts Institute of Technology, Cambridge, Massachusetts, United States). Mouse enterocytes were isolated using a modified protocol provided by Dr. F. Sepulveda (Monaghan et al. 1997). In brief, 1 cm of the proximal duodenum was excised, rinsed with cold PBS, and soaked for 5 min at 37°C in a solution containing 7 mM K2SO4, 44 mM K2HPO4, 9 mM NaHCO3, 15 mM Na3Citrate, 10 mM HEPES, and 180 mM glucose (pH 7.4). The tissue was then incubated with gentle shaking for 3 min in a similar solution containing 7 mM K2SO4, 44 mM K2HPO4, 9 mM NaHCO3, 10 mM HEPES, 180 mM glucose, 1 mM DTT, and 0.2 mM EDTA (pH 7.4). The mucosal cells were gently squeezed from the duodenum with forceps into 5 ml of ice-cold DMEM/F12 medium, pelleted at 800 × g for 4 min, resuspended in 5 ml of prewarmed DMEM/F12 with 0.5 mg/ml collagenase type 1A, and incubated at 37°C for 10 min. Cells isolated by this procedure have been shown previously to be primarily of villus origin and hence are mature enterocytes. We confirmed this by alkaline phosphatase staining. Diluted cells were filtered through a 40-μm nylon cell mesh (BD Biosciences, Palo Alto, California, United States). The cells were then washed with DMEM/F12, resuspended in 20 ml of ice-cold DMEM and kept at 4°C. They were plated on coverslips coated with Cell-TakTM (BD Biosciences) and maintained on ice before patch–clamp recording at room temperature. Data analysis Group data are presented as mean ± SEM. Statistical comparisons were made using analysis of variance and the t-test with Bonferroni correction. A two-tailed value of p < 0.05 was taken to be statistically significant. Supporting Information Figure S1 CHO-K1 Cells Express an Endogenous Proton-Activated Chloride Channel (A) Anion dependence of pH-induced response in a DMT1-expressing cell. Outward current usually appears later than the inward current. (B) Currents generated in response to a voltage ramp. (C) pH-induced outwardly rectifying current in a nontransfected CHO-K1 cell. A similar current was seen also in HEK293T and HEK-On cells, with properties similar to the cloned ClC-7 channel (Diewald et al. 2002). This current exhibits the same anion depen-dence as in (A) (data not shown). We attributed the outward currents shown in (A) and (B) to this endogenous Cl– current. Therefore, for our recordings on DMT1, SO4 2\– was usually used to replace most of the Cl– ([Cl–]o = 5 mM) for all low-pH bath solutions. (718 KB PDF). Click here for additional data file. Figure S2 Time- and Voltage-Dependent Kinetics of H+/Mn2+ Current of DMT1 Whole-cell currents were generated by voltage steps from −140 to +80 mV in 20 mV steps, 400 ms. The interval between steps was 1,000 ms. (1 MB PDF). Click here for additional data file. Figure S3 Na+-Dependence of DMT1 H+ and H+/Mn2+ Currents Replacement of extracellular Na+ by NMDG+ slightly increased the proton current (approximately 20%) and this was further augmented by adding 300 μM Mn2+. The concentrations used were Na+ and NMDG+, 140 mM, (pH 4.2); Mn2+, 300 μM. (141 KB PDF). Click here for additional data file. Accession Numbers The GenBank (www.ncbi.nlm.nih.gov/GenBank/) accession number for DMT1 is AF029758. This work resulted from a balanced collaboration between the Andrews and Clapham laboratories, supported by the Howard Hughes Medical Institute. NCA is also supported by a research grant from the National Institutes of Health (RO1 DK53813). Mark D. Fleming contributed to the care and dissection of mk mice. We thank Jane-Jane Chen for providing the mice on low-iron diet and Francisco Sepulveda for the protocol used to isolate enterocytes. We are grateful to I. Scott Ramsey, Renee M. Ned, Elena Oancea, and Svetlana Gapon for assistance and to Thomas E. DeCoursey, Jian Yang, Lixia Yue, and Richard Aldrich for comments. We appreciate encouragement and helpful comments from other members of the Clapham and Andrews laboratories. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. HX, JJ, LJD, NCA, and DEC conceived and designed the experiments. HX and JJ performed the experiments. HX and JJ analyzed the data. HX, NCA, and DEC wrote the paper. Academic Editor: Chris Miller, Brandeis University Abbreviations CRACCa2+-release activated Ca2+ channel DIDSdiisothiocyanostilbene 2,2-disolphonic acid DMT1divalent metal transporter-1 EGFPenhanced green fluorescent protein Erevreversal potential IG185Rinward current mediated by the G185R mutant DMT1 I-Vcurrent–voltage mkmicrocytic NMDG N-methyl-D-glucamine RRruthenium red TMtransmembrane TRPtransient receptor potential VGCCvoltage-gated Ca2+ channel ==== Refs References Adams SV DeFelice LJ Flux coupling in the human serotonin transporter Biophys J 2002 83 3268 3282 12496095 Andrews NC Iron homeostasis: Insights from genetics and animal models Nat Rev Genet 2000 1 208 217 11252750 Bakowski D Parekh AB Voltage-dependent conductance changes in the store-operated Ca2+ current ICRAC in rat basophilic leukaemia cells J Physiol 2000 529 295 306 11101641 Berridge MJ Bootman MD Roderick HL Calcium signalling: Dynamics, homeostasis and remodelling Nat Rev Mol Cell Biol 2003 4 517 529 12838335 Cammack JN Schwartz EA Channel behavior in a gamma-aminobutyrate transporter Proc Natl Acad Sci U S A 1996 93 723 727 8570623 Canonne-Hergaux F Gruenheid S Ponka P Gros P Cellular and subcellular localization of the Nramp2 iron transporter in the intestinal brush border and regulation by dietary iron Blood 1999 93 4406 4417 10361139 Canonne-Hergaux F Fleming MD Levy JE Gauthier S Ralph T The Nramp2/DMT1 iron transporter is induced in the duodenum of microcytic anemia mk mice but is not properly targeted to the intestinal brush border Blood 2000 96 3964 3970 11090085 Canonne-Hergaux F Zhang AS Ponka P Gros P Characterization of the iron transporter DMT1 (NRAMP2/DCT1) in red blood cells of normal and anemic mk /mk mice Blood 2001 98 3823 3830 11739192 Chen XZ Peng JB Cohen A Nelson H Nelson N Yeast SMF1 mediates H+ -coupled iron uptake with concomitant uncoupled cation currents J Biol Chem 1999 274 35089 35094 10574989 Chua AC Morgan EH Manganese metabolism is impaired in the Belgrade laboratory rat J Comp Physiol [B] 1997 167 361 369 Ci W Li W Ke Y Qian ZM Shen X Intracellular Ca2+ regulates the cellular iron uptake in K562 cells Cell Calcium 2003 33 257 266 12618146 DeFelice LJ Blakely RD Pore models for transporters? Biophys J 1996 70 579 580 8789077 Diewald L Rupp J Dreger M Hucho F Gillen C Activation by acidic pH of CLC-7 expressed in oocytes from Xenopus laevis Biochem Biophys Res Commun 2002 291 421 424 11846422 Fairman WA Vandenberg RJ Arriza JL Kavanaugh MP Amara SG An excitatory amino-acid transporter with properties of a ligand-gated chloride channel Nature 1995 375 599 603 7791878 Fleming MD Trenor CC Su MA Foernzler D Beier DR Microcytic anaemia mice have a mutation in Nramp2, a candidate iron transporter gene Nat Genet 1997 16 383 386 9241278 Fleming MD Romano MA Su MA Garrick LM Garrick MD Nramp2 is mutated in the anemic Belgrade (b ) rat: Evidence of a role for Nramp2 in endosomal iron transport Proc Natl Acad Sci U S A 1998 95 1148 1153 9448300 Gadsby DC Rakowski RF De Weer P Extracellular access to the Na,K pump: Pathway similar to ion channel Science 1993 260 100 103 7682009 Gruenheid S Canonne-Hergaux F Gauthier S Hackam DJ Grinstein S The iron transport protein NRAMP2 is an integral membrane glycoprotein that colocalizes with transferrin in recycling endosomes J Exp Med 1999 189 831 841 10049947 Grynkiewicz G Poenie M Tsien RY A new generation of Ca2+ indicators with greatly improved fluorescence properties J Biol Chem 1985 260 3440 3450 3838314 Gunshin H Mackenzie B Berger UV Gunshin Y Romero MF Cloning and characterization of a mammalian proton-coupled metal-ion transporter Nature 1997 388 482 488 9242408 Hirai T Heymann JA Shi D Sarker R Maloney PC Three-dimensional structure of a bacterial oxalate transporter Nat Struct Biol 2002 9 597 600 12118242 Hodgkin AL Horowicz P Movements of Na and K in single muscle fibres J Physiol 1959 145 405 432 13642309 Kaplan J Jordan I Sturrock A Regulation of the transferrin-independent iron transport system in cultured cells J Biol Chem 1991 266 2997 3004 1993673 Kozak JA Kerschbaum HH Cahalan MD Distinct properties of CRAC and MIC channels in RBL cells J Gen Physiol 2002 120 221 235 12149283 Lester HA Dougherty DA New views of multi-ion channels J Gen Physiol 1998 111 181 183 9450937 Levy JE Jin O Fujiwara Y Kuo F Andrews NC Transferrin receptor is necessary for development of erythrocytes and the nervous system Nat Genet 1999 21 396 399 10192390 Lewis CA Ion-concentration dependence of the reversal potential and the single channel conductance of ion channels at the frog neuromuscular junction J Physiol 1979 286 417 445 312319 Monaghan AS Mintenig GM Sepulveda FV Outwardly rectifying Cl– channel in guinea pig small intestinal villus enterocytes: Effect of inhibitors Am J Physiol 1997 273 Suppl G1141 G1152 9374713 Neher E Correction for liquid junction potentials in patch clamp experiments Methods Enzymol 1992 207 123 131 1528115 Nelson N Sacher A Nelson H The significance of molecular slips in transport systems Nat Rev Mol Cell Biol 2002 3 876 881 12415305 Picard V Govoni G Jabado N Gros P Nramp2 (DCT1/DMT1) expressed at the plasma membrane transports iron and other divalent cations into a calcein-accessible cytoplasmic pool J Biol Chem 2000 275 35738 35745 10942769 Prakriya M Lewis RS Separation and characterization of currents through store-operated CRAC channels and Mg2+ -inhibited cation (MIC) channels J Gen Physiol 2002 119 487 507 11981025 Sacher A Cohen A Nelson N Properties of the mammalian and yeast metal-ion transporters DCT1 and Smf1p expressed in Xenopus laevis oocytes J Exp Biol 2001 204 1053 1061 11222124 Sather WA McCleskey EW Permeation and selectivity in calcium channels Annu Rev Physiol 2003 65 133 159 12471162 Su MA Trenor CC Fleming JC Fleming MD Andrews NC The G185R mutation disrupts function of the iron transporter Nramp2 Blood 1998 92 2157 2163 9731075 Tandy S Williams M Leggett A Lopez-Jimenez M Dedes M Nramp2 expression is associated with pH-dependent iron uptake across the apical membrane of human intestinal Caco-2 cells J Biol Chem 2000 275 1023 1029 10625641 Touret N Furuya W Forbes J Gros P Grinstein S Dynamic traffic through the recycling compartment couples the metal transporter Nramp2 (DMT1) with the transferrin receptor J Biol Chem 2003 278 25548 25557 12724326

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020055Research ArticleBioinformatics/Computational BiologyEvolutionGenetics/Genomics/Gene TherapyCaenorhabditisSaccharomycesPreferential Duplication of Conserved Proteins in Eukaryotic Genomes Genes That Duplicate Evolve SlowlyDavis Jerel C jerel@stanford.edu 1 Petrov Dmitri A 1 1Department of Biological Sciences, Stanford UniversityStanford, CaliforniaUnited States of America3 2004 16 3 2004 16 3 2004 2 3 e5529 10 2003 18 12 2003 Copyright: © 2004 Davis and Petrov.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Conserved Genes Preferentially Duplicated in Evolution A central goal in genome biology is to understand the origin and maintenance of genic diversity. Over evolutionary time, each gene's contribution to the genic content of an organism depends not only on its probability of long-term survival, but also on its propensity to generate duplicates that are themselves capable of long-term survival. In this study we investigate which types of genes are likely to generate functional and persistent duplicates. We demonstrate that genes that have generated duplicates in the C. elegans and S. cerevisiae genomes were 25%–50% more constrained prior to duplication than the genes that failed to leave duplicates. We further show that conserved genes have been consistently prolific in generating duplicates for hundreds of millions of years in these two species. These findings reveal one way in which gene duplication shapes the content of eukaryotic genomes. Our finding that the set of duplicate genes is biased has important implications for genome-scale studies. Gene duplication is a key process in genome evolution. These authors show that highly conserved genes duplicate more often and are therefore likely to contribute more to the content of eukaryotic genomes ==== Body Introduction Gene duplication is the most important source of new genes and consequently a vital source of genetic novelty (Ohno 1970). Recently, the availability of completely sequenced genomes has sparked renewed attention in this subject at the genome scale. Most genomic studies of gene duplication have focused on the mechanisms responsible for generating duplicate genes, the consequences of gene duplication for genetic redundancy, or the effect that duplication has on the molecular evolution of the genes involved (Seoighe and Wolfe 1999; Lynch and Conery 2000; Dermitzakis and Clark 2001; Van de Peer et al. 2001; Gu et al. 2002, Gu et al. 2003; Kitami and Nadeau 2002; Kondrashov et al. 2002; Nembaware et al. 2002). Comparatively less attention has been devoted to the essential question of whether some genes are more likely to give rise to functional and persistent duplicates than others and thus contribute more to the gene content of eukaryotic genomes (but see Kondrashov et al. 2002; Nembaware et al. 2002). Investigating this aspect of gene duplication will not only help answer questions about gene content—such as why certain proteins duplicate to generate multigene families while others remain in single copy—but will provide insight into the process of duplication itself. Each of the three steps leading to the generation of preserved gene duplicates, including their (1) mutational generation, (2) fixation in a population, and (3) preservation through a period when they may be functionally redundant, may favor some genes over others. For example, gene duplicates that lead to an advantageous increase in gene dosage will be preferentially fixed by positive selection, as has been observed in bacteria and Saccharomyces cerevisiae (Romero and Palacios 1997; Brown et al. 1998; Dunham et al. 2002). For other genes, for which stoichiometry is important, the converse may be true: gene duplication may be strongly deleterious (Gerik et al. 1997), and while such duplications may commonly arise in single individuals, they are unlikely to become fixed in the population. The step of preservation also has a great potential to create a bias in the types of genes that duplicate since the vast majority of duplicate gene copies that arise in a population are rapidly lost to nonfunctionalizing mutations (Lynch and Conery 2000). Theoretical accounts of duplicate gene preservation make various predictions about the types of genes that will be preserved following duplication. Specifically, these models predict that genes with a larger number of cis-regulatory regions, expressed in many tissues (Lynch et al. 2001) or encoding multidomain proteins (Gibson and Spring 1998; Stoltzfus 1999), will be preferentially preserved. By investigating the molecular attributes of the types of genes that duplicate, we may be able to validate these predictions and determine which steps in the process of duplication act as a selective sieve, promoting the duplication of some genes and hindering the duplication of others. Beyond providing information about the mechanisms of duplication, data about the biases in which genes duplicate will serve as an essential baseline for other genome-scale studies in this field. For example, recent work has argued that gene duplication leads to a relaxation of selection and consequently an elevation in the rate of molecular evolution for the duplicated genes (Kondrashov et al. 2002; Nembaware et al. 2002). In support of this argument, these studies compared the evolutionary rate of genes that had duplicated to the rate of genes that were in single copy. A higher rate of evolution for the genes with duplicates was taken to support their hypothesis. One problem with this approach is that it is based on the assumption that the set of genes that generate duplicates is not biased with respect to the genes' rate of evolution. Indeed, if the genes that duplicate had higher rates of evolution prior to duplication, this would invalidate the above conclusions. Similarly, any study that reveals differences between the properties of duplicate genes and those in single copy (Kondrashov et al. 2002; Nembaware et al. 2002; Gu et al. 2003) should hesitate to conclude that these differences are caused by duplication per se without considering the biases in the attributes of the genes that lead to duplicates. In some cases, the authors themselves acknowledge this problem (e.g., Kondrashov et al. 2002; Gu 2003). For these reasons we chose to investigate a bias in the molecular attributes of the genes that duplicate. One very informative gene attribute is the rate of protein evolution defined as the number of nonsynonymous substitutions per nonsynonymous site in a given time (K A). This measure of protein evolution has been shown to be related to several important properties of genes, including dispensability, level of expression, and the number of protein–protein interactions (Hirsh and Fraser 2001; Pal et al. 2001; Fraser et al. 2002). We chose to compare the rates of evolution of the genes that have given rise to observable duplicates in the well-studied genomes of S. cerevisiae and Caenorhabditis elegans with those that have not. Such a comparison is not straightforward since gene duplication itself may affect the rate of molecular evolution (Lynch and Conery 2000; Kondrashov et al. 2002). To avoid this problem, we did not use the rate of evolution of each singleton and duplicate pair in S. cerevisiae and C. elegans (the “study genes”), but instead measured evolutionary rates in two distantly related outgroup species, Drosophila melanogaster and Anopheles gambiae (such a pair of orthologs is referred to as a “representative pair”). Because evolutionary rates for a particular gene are highly correlated in diverse lineages (Bromham and Penny 2003), we reasoned that the nonsynonymous divergence between the members of each representative pair would be a good proxy for the rate of evolution of the study genes in a way that is unaffected by the process of duplication (Figure 1). Our results reveal that the genes that have duplicated in the genomes of S. cerevisiae and C. elegans appear to be a biased set of slowly evolving genes and that slowly evolving genes have been consistently prolific in generating duplicates for hundreds of millions of years in these lineages. Figure 1 The Approach Used to Estimate the Rate of Evolution for Duplicate and Singleton Genes For each duplicate (gray lines) and singleton (black lines) gene/pair in S. cerevisiae and C. elegans, unduplicated orthologs were identified in D. melanogaster and A. gambiae. The K A between this representative pair of orthologs was taken as an estimate of the rate of evolution of duplicate and singleton genes in the study species that is independent of the effects of duplication on molecular evolution. Results Evolutionary Rates of Duplicate and Singleton Genes The number of duplicate pairs and singleton genes identified in the genomes of S. cerevisiae and C. elegans and the number of representative pairs of these genes found in D. melanogaster and A. gambiae are provided in Table 1. Our comparison of the nonsynonymous divergence between orthologs of these two classes of genes revealed that representative pairs of duplicates in both S. cerevisiae and C. elegans have much slower rates of evolution (Mann–Whitney U test, p < 0.001 for both) (Figure 2). The representative pairs of the duplicated genes in S. cerevisiae evolve on average more than 50% slower than the representative pairs of singletons (0.192 versus 0.302), while in C. elegans the difference exceeds 25% (0.230 versus 0.296). Figure 2 A Comparison of the Evolutionary Rates of Duplicate and Singleton Genes The average rate of nonsynonymous evolution (K A) for representative pairs of duplicate and singleton genes in the two study organisms S. cerevisiae (A) and C. elegans (B) is shown. Representative pairs of duplicate genes evolve significantly more slowly in both study organisms (Mann–Whitney U test, p < 0.001). Table 1 Number of Genes/Pairs Identified in the Study Organism and Number of Orthologs of These Genes Found in D. melanogaster and A. gambiae In addition to estimating rates of evolution for representative pairs of the two classes of genes, we also attempted to quantify structural protein evolution by computing the number of gaps per basepair in the alignments of the representative pairs. We reasoned that this measure is likely to be a monotonic proxy for the number of indels that have occurred in the evolution of a protein since the split of A. gambiae and D. melanogaster. Results from this analysis echoed those of the K A comparisons: representative pairs of duplicate genes in both S. cerevisiae and C. elegans are much less likely to have accumulated insertions or deletions than representative pairs of singletons (Mann–Whitney U test, p < 0.0001 for both) (Figure 3). Figure 3 A Comparison of the Rate of Structural Evolution for Duplicate and Singleton Genes For each representative pair, the number of gaps per aligned nucleotide was calculated. For both S. cerevisiae (A) and C. elegans (B), representative pairs of duplicates have significantly fewer insertions per basepair than representative pairs for singletons (Mann–Whitney U test, p < 0.0001 for both). To further validate these conclusions, we wanted to test several potential sources of error in our analysis of both K A and the indel rate. First, some of the orthologs identified in D. melanogaster and A. gambiae have undergone duplication in these lineages. This could both affect their rates of evolution, as discussed above, and also lead to the identification of the slowest evolving paralog in D. melanogaster and A. gambiae for the representative pairs of study genes. The latter effect can lead to an artificially low estimate of the evolutionary rates. To test for this possibility, we repeated our analysis using only representative pairs that have not duplicated in either D. melanogaster or A. gambiae. Although this analysis included substantially fewer genes (see Materials and Methods), the results remained unchanged and strongly statistically significant (Mann–Whitney U test, p < 0.005 for both organisms). Second, we wanted to make sure that the bias is not due to the peculiarly slow evolution of duplicate genes in multigene families. A reanalysis for only those duplicated genes (in the study organisms) with no other paralogs in the genome revealed very similar results (data not shown). Third, it is possible that our conservative definition of “singleton” may have artificially biased the set of singletons towards rapidly evolving genes. This could be true if slowly evolving singleton genes tend to possess anciently conserved, widely shared protein domains. By generating homology to other genes, these domains may make these singletons fall below the conservative E-value cutoff that we used. To test this possibility, we relaxed our criteria for singleton genes to include all those genes with no E-value less than 10–10. The average rate of evolution for this group of singleton genes was no different than for the former set (data not shown). Biased Mutation Cannot Explain the Lower K A of Duplicates The simplest interpretation of these data is that the genes generating preserved duplicates are a biased set of constrained, slowly evolving proteins. An alternative explanation is that representative pairs of singletons are found in genomic regions with a higher mutation rate than are representative pairs of duplicates—although there is no a priori reason why this should be true. One way of testing this possibility is to compare the number of synonymous nucleotide substitutions per synonymous site (K S) for the representative pairs of the two classes of genes. This measure is customarily used as a proxy for mutation rate because substitutions at synonymous sites are generally thought to be selectively neutral. However, in many genes, especially those expressed at high levels, synonymous sites appear to be under selection, as evidenced by codon bias. For such genes, the rate of synonymous evolution will underestimate the rate of mutation (Sharp et al. 1988; Shields et al. 1988; Sharp and Li 1989; Li 1997). Given that previous reports have suggested that duplicate genes are expressed at particularly high levels in S. cerevisiae (Seoighe and Wolfe 1999), their rate of synonymous evolution should be lower than that of singletons even in the absence of mutational differences. To overcome this complication, we computed the partial correlation coefficients between each of the three factors: codon bias (measured by the codon adaptation index [CAI] [Sharp and Li 1987] in D. melanogaster), gene class (whether the representative pair was for a duplicate or singleton), and K S (between representative pairs). Our results, presented in Table 2, reveal that, as expected, representative pairs of S. cerevisiae duplicate genes have a lower K S than representative pairs of singleton genes (Spearman Correlation column), but that this correlation disappears when we control for codon bias (Partial Correlation Coefficient column). Thus, in the case of S. cerevisiae, the higher codon bias of the slowly evolving representative pairs completely accounts for the differences in K S between the two groups. For C. elegans, the K S of the representative pairs for duplicate genes is in fact marginally higher than that for singleton genes, and this slight trend remains when codon bias is taken into account. Thus, mutational differences cannot account for the differences in the rate of protein evolution in either S. cerevisiae or C. elegans. Table 2 Correlation Coefficients and Partial Correlation Coefficients for the Three Factors Gene Class (Duplicate or Singleton), CAI (in D. melanogaster), and K S (of Representative Pairs) Significance was tested for the direct and partial correlation coefficients using the statistics and , respectively, where n is the sample size, m is the number of variables held constant, and r is the rank correlation coefficient (Sokal and Rohlf 1995) aFor this parameter, representative pairs were given a value of either 0 (for a singleton) or 1 (for a duplicate) NS, nonsignificant; *, p = 0.05; **, p = 0.01; ***, p = 0.001 Codon Bias and the Rate of Evolution of Duplicate Genes We can also use the level of codon bias to gain additional insight into the potential reasons for the generation and maintenance of duplicate copies of conserved genes. Codon bias is a proxy for the level of expression (Akashi 2001), while the level of expression is a good predictor of the rate of protein evolution (Pal et al. 2001; Krylov et al. 2003). To determine whether the reason for the slow evolution of duplication-prone genes is their higher level of expression, we performed a partial correlation analysis similar to the analysis of K S above. Table 3 shows Spearman rank and partial rank correlations between pairs of the three variables gene class (singleton or duplicate study gene), CAI (in D. melanogaster), and K A (of the representative pairs). This analysis revealed some important differences in how the duplication bias is generated in S. cerevisiae and C. elegans. Table 3 Correlation Coefficients and Partial Correlation Coefficients for the Three Factors Gene Class, CAI (in D. melanogaster), and K A (of Representative Pairs) Significance was tested for the direct and partial correlation coefficients using the statistics and , respectively, where n is the sample size, m is the number of variables held constant, and r is the rank correlation coefficient (Sokal and Rohlf 1995) aFor this parameter, representative pairs were given a value of either 0 (for a singleton) or 1 (for a duplicate) NS, nonsignificant; *, p = 0.05; **, p = 0.01; ***, p = 0.001 First, both direct and partial correlations for S. cerevisiae show that the CAI of the representative pairs of duplicates is greater than that of the representative pairs of singleton genes. This indicates that the genes leading to preserved duplicates in S. cerevisiae tend to be unusually highly expressed (p < 0.001). In contrast, for C. elegans, duplicate genes do not appear to be biased towards highly expressed genes (p > 0.1). This difference may reflect a disparity in the mutational generation, fixation, or preservation of duplicates in these two organisms. This analysis also reveals that when codon bias is held constant, the relationship between K A and gene class persists in both organisms. In the case of C. elegans, the correlation coefficient between gene class and K A remains nearly identical when CAI is held constant. For S. cerevisiae, the partial correlation coefficient between K A and gene class does decrease when CAI is held constant (but remains highly significant), implying that the slower evolution of representative pairs of the duplicated genes in S. cerevisiae is partly mediated by preferential duplication of highly expressed genes. To validate these conclusions, we repeated the same analysis using CAI values in the study organisms rather than in D. melanogaster. This analysis revealed very similar results (data not shown). Time Uniformity of the Bias To determine whether conserved genes have been preferentially duplicated throughout the history of the S. cerevisiae and C. elegans lineages, we plotted the evolutionary rate of representative pairs and the average CAI (both in D. melanogaster and in the study organisms) for duplicate pairs of different age classes (where age is measured by K S between the duplicate study genes) (Figure 4). While large K S estimates are subject to a large amount of error (such that estimates of K S above 2 are typically unreliable), this analysis captures the uniformity of the bias in these lineages. For both organisms, slowly evolving genes appear to have led to the duplicate genes in all age classes (covering hundreds of millions of years). For C. elegans, both the evolutionary rates of the representative pairs and their CAI values remain virtually constant for duplicated genes of all ages. In addition, the CAI values for the duplicate pairs of different ages in C. elegans are very similar to the CAI values for singletons—the only exception is a slight elevation in the CAI for duplicate pairs in the K S range from 1 to 1.5. By contrast, the plot for S. cerevisiae reveals that young duplicate genes (K S < 2.0) tend to have representative pairs with a lower K A than those of older pairs, and this trend is paralleled by the elevated CAI of these young duplicate pairs. Figure 4 The Codon Bias and Rate of Evolution of Genes Leading to Duplicates over the Evolutionary History of S. cerevisiae and C. elegans For both S. cerevisiae (A) and C. elegans (B), moving averages of nonsynonymous substitutions per site (K A, in dark gray), codon bias in the study organism (measured with CAI, in black), and codon bias of the representative ortholog in D. melanogaster (CAI, in light gray) are plotted against the number of synonymous substitutions per site (K S) between duplicate pairs. The bin size is 15, and standard error bars are shown. Dashed lines represent the average CAI of singleton genes and the average K A of representative pairs of singleton genes. A problem for interpreting this trend in S. cerevisiae is that duplicate pairs with a high codon bias are expected to have a depressed value of K S, as discussed above, and thus will appear younger than they really are. To overcome this problem, we corrected K S estimates for S. cerevisiae genes based on their CAI using a simple approach recently developed for this species (see Materials and Methods) (A. Hirsh, H. Fraser, and D. Wall, personal communication). After correcting K S estimates, the plots of K A and CAI shift slightly (Figure 5), but the trends remain. We can further see that the duplicate pairs with the unusually high CAI and the unusually low K A of the representative pairs have corrected K S less than 2.0. It is intriguing that this age range matches the estimated time of the whole-genome duplication in the S. cerevisiae lineage (K S, approximately1.0; 80 million years ago) (Wolfe and Shields 1997; Pal et al. 2001). If the set of genes preserved after polyploidization in S. cerevisiae was biased towards highly expressed genes, this could explain the heterogeneity in both K A and CAI and could explain why duplicate genes in C. elegans, an organism that has likely not undergone a whole-genome duplication, were not enriched for genes with a high level of expression. With respect to this hypothesis, it is interesting to note that for young duplicate genes (K S < 2), K A estimates for representative pairs of duplicate genes in S. cerevisiae are much lower than for duplicate genes in C. elegans, whereas for older duplicate genes (K S > 2), the K A estimates are roughly equivalent in both S. cerevisiae and C. elegans. Figure 5 Correcting for Synonymous Substitutions Reveals That S. cerevisiae Genes That Have Recently Duplicated Have a Higher Codon Bias and Slower Rate of Evolution Than Those That Duplicated in the Ancient Past For duplicate genes in S. cerevisiae, moving averages of the number of nonsynonymous substitutions per nonsynonymous site of representative pairs (K A, in dark gray), the codon bias in S. cerevisiae (CAI, in black), and the codon bias of representative pairs in D. melanogaster (CAI, in light gray) are plotted against the adjusted number of synonymous substitutions per site (see Materials and Methods) between duplicate pairs. The bin size is 15, and standard error bars are shown. Lines with broad dashes show the respective averages for singleton genes in S. cerevisiae, and the line with short dashes shows the average K A for representative pairs of duplicate genes in C. elegans. Other studies have noted that ribosomal subunit proteins were particularly prolific in generating duplicate pairs via polyploidization in S. cerevisiae (Seoighe and Wolfe 1999). Indeed, these genes account for 49 of the duplicate pairs in our study. To determine whether this group is responsible for the depressed rates of evolution of young duplicate pairs, we plotted CAI and K A versus K S without ribosomal proteins (Figure 6). The plot reveals that without ribosomal proteins, young duplicate genes possess rates of evolution comparable to those of other age classes and more similar to the values found for duplicate genes in C. elegans. Thus, the overrepresentation of duplicate ribosomal proteins following the polyploidization event in S. cerevisiae appears to explain the low rates of evolution of young duplicate genes in this species. Even with these ribosomal genes removed, however, younger genes have much higher CAI values. Figure 6 After Removing Ribosomal Genes, the Magnitude of the Bias towards the Slower Evolution of Duplicate Genes Is Similar in Both S. cerevisiae and C. elegans For nonribosomal duplicate genes in S. cerevisiae, moving averages of the number of nonsynonymous substitutions per nonsynonymous site of representative pairs (K A, in dark gray), the codon bias in S. cerevisiae (CAI, in black), and the codon bias of representative pairs in D. melanogaster (CAI, in light gray) are plotted against the adjusted number of synonymous substitutions per site (see Materials and Methods) between duplicate pairs. The bin size is 15, and standard error bars are shown. Lines with broad dashes show the respective averages for singleton genes in S. cerevisiae, and the line with short dashes shows the average K A for representative pairs of duplicate genes in C. elegans. Discussion Most genome-scale studies of duplicate genes have focused either on the mechanisms of duplication or on the consequences of duplication at the molecular or organismal level. In this study we ask a different type of question: namely, which types of genes are more likely to duplicate than others? The method we use—identifying duplicate genes in one organism and obtaining evolutionary rate measurements from two outgroup species (see Figure 1)—allows us to compare the evolutionary rate of genes that have duplicated to that of those that have not. Importantly, it allows us to do this without confounding the effect the duplication itself has on the rate of molecular evolution (Lynch and Conery 2000; Kondrashov et al. 2002). Our data reveal that genes that have duplicated in the genomes of S. cerevisiae and C. elegans have much slower rates of amino acid substitution, as well as lower rates of insertion and deletion, on average than those that have remained in single copy. To strengthen this conclusion, we tested several potential sources of error in our estimates of rates of evolution for the two classes of genes. We found that none of the potential complications—including the effect of duplication within the lineages of D. melanogaster and A. gambiae, duplications predating the split of the studied lineage and the outgroups, the especially slow evolution of multigene families, the operational definitions of duplicate and singleton genes, or the possibility of mutational differences—appear to affect our estimates of evolutionary rates of the two gene classes. We have also attempted to ascertain whether conserved genes have been generating duplications in a persistent fashion or whether this bias was generated at a particular time in the history of the two studied genomes. Our analysis demonstrates that both lineages have experienced a consistent and very similar level of bias over hundreds of millions of years. In addition, there has been a recent duplication of particularly slowly evolving genes in the yeast genome, coinciding roughly with the time of the postulated genome duplication in this lineage. Importantly, the consistency of the pattern over such long evolutionary periods of time in such diverse lineages suggests that the preferential generation or retention of duplicates of slowly evolving genes might be a general feature of eukaryotic evolution. Why do conserved, slowly evolving genes have a proclivity to generate duplicates? In order to answer this question, it is important to determine which of the three steps of duplication—mutation, fixation, or preservation—are responsible for this trend. As discussed above, both fixation and preservation have the potential to create a bias in the types of genes that duplicate. The step of fixation could generate a bias either because (1) many of the genes that are duplicated in a single individual are deleterious and thus are quickly removed from the population or (2) many of the duplicate genes that reach fixation in a population do so because of positive selection for the duplicate copy, rather than reaching fixation neutrally by genetic drift. For the first mechanism to work, increases in the dosage of slowly evolving genes must be less likely to have deleterious consequences to organismal fitness than increases in the dosage of more rapidly evolving genes. Recent empirical work, however, has shown that the opposite might be true. In particular, data from yeast have shown that less dispensable, slowly evolving genes are more likely to be haploinsufficient than dispensable genes (Papp et al. 2003). This implies that changes in dosage of slowly evolving genes may have greater fitness consequences in general. The second mechanism by which fixation may generate the bias is more tenable. This mechanism requires that many duplicate genes fix by positive selection and that duplicates of slowly evolving genes do so with higher likelihood. Examples from S. cerevisiae and bacteria (Romero and Palacios 1997; Brown et al. 1998; Dunham et al. 2002) support the possibility that duplications of genes can lead to beneficial increases in dosage and can be fixed by positive selection. One set of genes that may be especially likely to lead to beneficial increases in dosage following duplication are genes that are already required at high expression levels. It is interesting in this regard that many highly expressed genes have recently duplicated in S. cerevisiae (see Figure 5) (Seoighe and Wolfe 1999) and that the preferential duplication of genes with a high codon bias accounts partially for the bias that we observe in S. cerevisiae (see Table 3). While the preferential duplication of highly expressed genes is not observed for C. elegans, it is possible that duplications of slowly evolving genes are also likely to lead to beneficial increases in dosage for some other, yet unknown, reason. The step of preservation also has the potential to generate the bias we observe since (1) many of the duplicate gene copies that arise in a population are lost quickly to nonfunctionalizing mutations (Lynch and Conery 2000) and (2) several models of duplicate gene preservation suggest that slowly evolving genes may have an increased likelihood of being preserved. In particular, these models predict the preferential preservation of genes with many cis-regulatory regions, expressed in many tissues (Lynch et al. 2001), or of genes that encode multidomain proteins (Gibson and Spring 1998; Stoltzfus 1999). Because the higher level and the greater breadth of expression, as well as the larger number of protein interactions, correlate with the slower rate of protein evolution (Duret and Mouchiroud 2000; Pal et al. 2001; Fraser et al. 2002), these models predict preferential preservation of slowly evolving genes. If the step of preservation accounts for the slower evolution of duplicate genes, one prediction is that the rates of evolution of newly arisen gene duplicates should be higher than the rates of older gene duplicates and closer to the rates of evolution of singletons. Our data do not reveal any such trend for either S. cerevisiae or C. elegans (see Figure 4). The negative result, however, may simply reflect a lack of statistical power. The higher evolution rates of newly arisen gene duplicates should only be apparent for very young duplicate pairs. Indeed, the average half-life of a duplicate pair may be as short as 5 million years (Lynch and Conery 2000), corresponding to a K S of approximately 0.05. There are very few such pairs in our dataset. It is unclear whether fixation, preservation, or both of these steps together cause the bias towards the preferential duplication of slowly evolving genes. The relative importance of these two steps depends largely on the frequency with which duplicate genes are fixed by positive selection. If the vast majority of duplicate genes are initially redundant and fix by genetic drift, as assumed in many models of gene duplication (Ohno 1970; Force et al. 1999; Lynch and Force 2000; Lynch et al. 2001), fixation cannot explain the bias. If, on the other hand, duplicate genes often fix by positive selection (Kondrashov et al. 2002), the step of fixation may be dominant in generating the bias inthe types of genes that duplicate. The relative frequency with which duplicate genes fix because of positive selection and genetic drift remains to be established. Beyond providing insight into the mechanisms of gene duplication, the bias has important consequences for the content of eukaryotic proteomes. If conserved, slowly evolving genes consistently generate preserved duplicate copies of themselves, proteomes will tend to become enriched for these genes over the course of evolution. This prediction is especially interesting in relationship to recent complementary work (Krylov et al. 2003) that shows that genes with a slow rate of evolution, a low dispensability, and a high level of expression are less likely to be lost over the course of evolution. Taken together, these two studies predict that slowly evolving genes should be the main sources of genes in eukaryotic genomes. It is also noteworthy that the two results are not independent. If slowly evolving genes are more likely to duplicate to form multigene families, then they should be less likely to be lost from a particular lineage, since this would entail the loss of many distinct genetic copies. The extent to which this effect explains the preferential loss of fast evolving genes remains to be determined. The mere existence of this bias is very important for the interpretation of genomic-level studies of gene duplication. For example, some recent studies have argued that two general consequences of gene duplication are (1) an increased rate of evolution for the duplicated genes immediately following duplication (e.g., Kondrashov et al. 2002) and (2) increased functional redundancy at the genetic level (Gu et al. 2003). To make their arguments, both of these studies compare duplicate and singleton genes within a single organism under the assumption that the types of genes that duplicate are unbiased with respect to the molecular attribute of interest (note that a correction for this problem has been attempted before by separating genes into functional classes [e.g., Kondrashov et al. 2002]). The study presented here shows that this assumption is not valid. Duplicate genes are, in fact, a very biased set of genes, at least with respect to their rate of evolution. Interestingly, in the case of the studies just mentioned, the bias that we observed makes the conclusions conservative. Indeed, the bias that we observed may explain why other studies have failed to find the expected higher rate of evolution for genes that have recently undergone duplication (e.g., Kitami and Nadeau 2002). The preferential duplication of conserved genes, combined with the increased rate of evolution following duplication, may lead to no measurable difference in the rate of evolution between singleton and duplicate genes. In general, any genome-scale study that attempts to assess the effects of duplication on molecular evolution should consider the prior distribution of the molecular attributes of the genes that lead to duplicates. Materials and Methods Identification of duplicate and singleton genes and their orthologs The gene and protein sequences of S. cerevisiae, C. elegans, D. melanogaster, and A. gambiae were downloaded from GenBank (Bethesda, Maryland, United States) at http://www.ncbi.nlm.nih.gov/Ftp/index.html. To identify duplicate and singleton genes, a reciprocal protein BLAST (Altschul et al. 1997) was performed on the proteomes of the two study organisms using default parameters and simple sequence filtering. Singleton genes were conservatively defined as those genes without a hit with an E-value of less than 0.1, following previous studies (Gu et al. 2003). Duplicate pairs in these genomes of S. cerevisiae and C. elegans were identified as reciprocal best hits with an E-value of less than 10–10 in both directions that could be aligned over greater than 60% of their lengths. Orthologs were identified as reciprocal best BLAST hits between two organisms using the same criteria: E-values of less than 10–10 and alignable over greater than 60% of the gene lengths. In the case of duplicate pairs, the same criteria were used, except that both duplicates needed to hit the same gene in the outgroup species and the duplicate genes needed to be the top two best hits in the reciprocal blast. To identify representative pairs for each singleton and duplicate gene, we first identified an ortholog in D. melanogaster and then identified the ortholog of this gene in A. gambiae. Obtaining K A and indel measurements for representative pairs To obtain the nucleotide alignments for each representative pair, we obtained the BLASTP alignment of the two orthologs, removed gaps in these alignments by trimming back from both ends of each gap until an anchor pair was found (following the method described in Conery and Lynch [2001]), and then replaced the amino acid alignment with the respective nucleotide sequence. Based on these alignments, we used the PAML software package (Yang 1997) to estimate the number of synonymous and nonsynonymous substitutions per site. The number of gaps per nucleotide length of each alignment was also recorded and used as a proxy for the number of indels that have occurred during the divergence of D. melanogaster and A. gambiae. To test whether including duplicate pairs and singleton genes with representative pairs possessing paralogs in the D. melanogaster and A. gambiae lineages biased our results, we reanalyzed the distributions of nonsynonymous rates of evolution and number of indels after removing these genes. For both C. elegans and S. cerevisiae, we eliminated representative pairs with paralogs with a BLAST E-value less than 10–10 in either of the outgroup genomes (leaving 60 duplicates and 225 singletons and 48 duplicates and 530 singletons, respectively) and eliminated all representative pairs with paralogs with an E-value of less than 0.1 (leaving only 38 duplicates and 114 singletons and 29 duplicates and 318 singletons, respectively). Results from the reanalysis revealed significant trends similar to those found when using all representative pairs. Obtaining CAI values and correcting K S We obtained CAI values for genes in the D. melanogaster, S. cerevisiae, and C. elegans genomes using the program CodonW (available from ftp://molbiol.ox.ac.uk/Win95.codonW.zip; written by John Peden, now at Oxagen [www.oxagen.co.uk], and originally developed in the laboratory of Paul Sharp, Department of Genetics at the University of Nottingham, United Kingdom). The table used to calculate CAI for S. cerevisiae is the standard table included in the package. We obtained the appropriate codon usage tables for C. elegans and D. melanogaster from studies by Duret and Mouchiroud (1999) and Carbone et al. (2003), respectively. For duplicate genes in S. cerevisiae, we used CAI values of each pair to help obtain a better relative estimate of their ages. This was necessary because duplicate pairs with a high codon bias effectively have fewer neutral synonymous sites, resulting in the gross underestimation of their age based on K S alone (Sharp et al. 1988; Shields et al. 1988; Sharp and Li 1989; Li 1997). A recent study has shown that the number of synonymous substitutions expected for genes with a given codon bias in S. cerevisiae is given by K S = rt(1 – c), where r is the rate of synonymous substitution in genes with no codon bias, t is time, and c is codon bias as measured by CAI (A. Hirsh, H. Fraser, and D. Wall, personal communication). Rearranging this equation yields the formula K S′ = rt = K S/(1 – c), which we used to obtain corrected estimates of the age of duplicate pairs in S. cerevisiae. No such correction was made for C. elegans genes because they were not shown to have a significantly higher codon bias than singleton genes and because no simple means of correction is presently known. We would like to acknowledge Aaron Hirsh, Jed Dean, Chana Palmer, Nadia Singh, Hunter Fraser, and Emile Zuckerkandl for their many helpful comments. This work was funded by a National Science Foundation Predoctoral Fellowship to JD and a Terman grant to DP. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JCD and DAP conceived and designed the experiments. JCD performed the experiments, analyzed the data, and contributed reagents/materials/analysis tools. JCD and DAP wrote the paper. Academic Editor: Ken H. Wolfe, University of Dublin Abbreviations CAIcodon adaptation index KAthe number of nonsynonymous substitutions per nonsynonymous site KSthe number of synonymous substitutions per synonymous site ==== Refs References Akashi H Gene expression and molecular evolution Curr Opin Genet Dev 2001 11 660 666 11682310 Altschul SF Madden TL Schaffer AA Zhang J Zhang Z Gapped BLAST and PSI-BLAST: A new generation of protein database search programs Nucleic Acids Res 1997 25 3389 3402 9254694 Bromham L Penny D The modern molecular clock Nat Rev Genet 2003 4 216 224 12610526 Brown CJ Todd KM Rosenzweig RF Multiple duplications of yeast hexose transport genes in response to selection in a glucose-limited environment Mol Biol Evol 1998 15 931 942 9718721 Carbone A Zinovyev A Kepes F Codon adaptaion index as a measure of dominating codon bias Bioinformatics 2003 19 2005 2015 14594704 Conery JS Lynch M Nucleotide substitutions and the evolution of duplicate genes Pac Symp Biocomput 2001 6 167 178 Dermitzakis ET Clark AG Differential selection after duplication in mammalian developmental genes Mol Biol Evol 2001 18 557 562 11264407 Dunham MJ Badrane H Ferea T Adams J Brown PO Characteristic genome rearrangements in experimental evolution of Saccharomyces cerevisiae Proc Natl Acad Sci U S A 2002 99 16144 16149 12446845 Duret L Mouchiroud D Expression pattern and, surprisingly, gene length shape codon usage in Caenorhabditis, Drosophila and Arabidopsis Proc Natl Acad Sci U S A 1999 96 4482 4487 10200288 Duret L Mouchiroud D Determinants of substitution rates in mammalian genes: Expression pattern affects selection intensity but not mutation rate Mol Biol Evol 2000 17 68 74 10666707 Force A Lynch M Pickett FB Amores A Yan YL Preservation of duplicate genes by complementary, degenerative mutations Genetics 1999 151 1531 1545 10101175 Fraser HB Hirsh AE Steinmetz LM Scharfe C Feldman MW Evolutionary rate in the protein interaction network Science 2002 296 750 752 11976460 Gerik KJ Gary SL Burgers PM Overproduction and affinity purification of Saccharomyces cerevisiae replication factor C J Biol Chem 1997 272 1256 1262 8995429 Gibson TJ Spring J Genetic redundancy in vertebrates: Polyploidy and persistence of genes encoding multidomain proteins Trends Genet 1998 14 46 49 9520595 Gu X Evolution of duplicate genes versus genetic robustness against null mutations Trends Genet 2003 19 354 356 12850437 Gu Z Cavalcanti A Chen FC Bouman P Li WH Extent of gene duplication in the genomes of Drosophila nematode, and yeast Mol Biol Evol 2002 19 256 262 11861885 Gu Z Steinmetz LM Gu X Scharfe C Davis RW Role of duplicate genes in genetic robustness against null mutations Nature 2003 421 63 66 12511954 Hirsh AE Fraser HB Protein dispensability and rate of evolution Nature 2001 411 1046 1049 11429604 Kitami T Nadeau JH Biochemical networking contributes more to genetic buffering in human and mouse metabolic pathways than does gene duplication Nat Genet 2002 32 191 194 12161750 Kondrashov FA Rogozin IB Wolf YI Koonin EV Selection in the evolution of gene duplications Genome Biol 2002 3 RESEARCH0008 Krylov DM Wolf YI Rogozin IB Koonin EV Gene loss, protein sequence divergence, gene dispensability, expression level, and interactivity are correlated in eukaryotic evolution Genome Res 2003 13 2229 2235 14525925 Li W-H Molecular evolution 1997 Sunderland, Massachusetts Sinauer Associates, Inc 487 Lynch M Conery JS The evolutionary fate and consequences of duplicate genes Science 2000 290 1151 1155 11073452 Lynch M Force A The probability of duplicate gene preservation by subfunctionalization Genetics 2000 154 459 473 10629003 Lynch M O'Hely M Walsh B Force A The probability of preservation of a newly arisen gene duplicate Genetics 2001 159 1789 1804 11779815 Nembaware V Crum K Kelso J Seoighe C Impact of the presence of paralogs on sequence divergence in a set of mouse–human orthologs Genome Res 2002 12 1370 1376 12213774 Ohno S Evolution by gene duplication 1970 New York Springer-Verlag 160 Pal C Papp B Hurst LD Highly expressed genes in yeast evolve slowly Genetics 2001 158 927 931 11430355 Papp B Pal C Hurst LD Dosage sensitivity and the evolution of gene families in yeast Nature 2003 424 194 197 12853957 Romero D Palacios R Gene amplification and genomic plasticity in prokaryotes Annu Rev Genet 1997 31 91 111 9442891 Seoighe C Wolfe KH Yeast genome evolution in the postgenome era Curr Opin Microbiol 1999 2 548 554 10508730 Sharp PM Li WH The codon adaptation index—A measure of directional synonymous codon usage bias, and its potential applications Nucleic Acids Res 1987 15 1281 1295 3547335 Sharp PM Li WH On the rate of DNA sequence evolution in Drosophila J Mol Evol 1989 28 398 402 2501501 Sharp PM Cowe E Higgins DG Shields DC Wolfe KH Codon usage patterns in Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens : A review of the considerable within-species diversity Nucleic Acids Res 1988 16 8207 8211 3138659 Shields DC Sharp PM Higgins DG Wright F Silent sites in Drosophila genes are not neutral: Evidence of selection among synonymous codons Mol Biol Evol 1988 5 704 716 3146682 Sokal RR Rohlf FJ Biometry: The principles and practice of statistics in biological research, 3rd ed 1995 New York WH Freeman 887 Stoltzfus A On the possibility of constructive neutral evolution J Mol Evol 1999 49 169 181 10441669 Van de Peer Y Taylor JS Braasch I Meyer A The ghost of selection past: Rates of evolution and functional divergence of anciently duplicated genes J Mol Evol 2001 53 436 446 11675603 Wolfe KH Shields DC Molecular evidence for an ancient duplication of the entire yeast genome Nature 1997 387 708 713 9192896 Yang Z PAML: A program package for phylogenetic analysis by maximum likelihood Comput Appl Biosci 1997 13 555 556 9367129

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020057Research ArticleEvolutionMolecular Biology/Structural BiologyPaleontologyHomo (Human)No Evidence of Neandertal mtDNA Contribution to Early Modern Humans No Evidence of Neandertal ContributionSerre David 1 Langaney André 2 3 Chech Mario 2 Teschler-Nicola Maria 4 Paunovic Maja 5 Mennecier Philippe 2 Hofreiter Michael 1 Possnert Göran 6 Pääbo Svante paabo@eva.mpg.de 1 1Max Planck Institute for Evolutionary AnthropologyLeipzigGermany2Laboratoire d'Anthropologie Biologique, Musée de l'HommeParisFrance3Laboratoire de Génétique et Biométrie, Université de GenèveGenèveSwitzerland4Department of Anthropology, Natural History MuseumViennaAustria5Institute of Quaternary Paleontology and Geology, Croatian Academy of Sciences and ArtsZagrebCroatia6Ångström Laboratory, Uppsala UniversityUppsalaSweden3 2004 16 3 2004 16 3 2004 2 3 e572 11 2003 18 12 2003 Copyright: © 2004 Serre et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Neandertals Likely Kept Their Genes to Themselves The retrieval of mitochondrial DNA (mtDNA) sequences from four Neandertal fossils from Germany, Russia, and Croatia has demonstrated that these individuals carried closely related mtDNAs that are not found among current humans. However, these results do not definitively resolve the question of a possible Neandertal contribution to the gene pool of modern humans since such a contribution might have been erased by genetic drift or by the continuous influx of modern human DNA into the Neandertal gene pool. A further concern is that if some Neandertals carried mtDNA sequences similar to contemporaneous humans, such sequences may be erroneously regarded as modern contaminations when retrieved from fossils. Here we address these issues by the analysis of 24 Neandertal and 40 early modern human remains. The biomolecular preservation of four Neandertals and of five early modern humans was good enough to suggest the preservation of DNA. All four Neandertals yielded mtDNA sequences similar to those previously determined from Neandertal individuals, whereas none of the five early modern humans contained such mtDNA sequences. In combination with current mtDNA data, this excludes any large genetic contribution by Neandertals to early modern humans, but does not rule out the possibility of a smaller contribution. Analysis of mitochondrial DNA from four Neandertal fossils and five "modern human" contemporaries excludes any large genetic contribution of Neandertals to the gene pool of modern humans ==== Body Introduction Despite intense research efforts, no consensus has been reached about the genetic relationship between early modern humans and archaic human forms such as the Neandertals. While supporters of “multiregional evolution” argue for genetic exchange or even continuity between archaic and modern humans (Weidenreich 1943; Wolpoff et al. 1984, Wolpoff et al. 2000; Duarte et al. 1999; Hawks and Wolpoff 2001), proponents of a “single African origin” of contemporary humans claim that negligible genetic interaction took place (Cann et al. 1987; Stringer and Andrews 1988; Ingman et al. 2000; Underhill et al. 2000; Stringer 2002). Mitochondrial DNA (mtDNA) sequences from early modern humans would in principle be able to resolve the question of a contribution of Neandertal mtDNA to modern humans. However, human DNA is pervasive in palaeontological and archaeological remains as well as in most laboratory environments (e.g., Krings et al. 2000; Hofreiter et al. 2001b; Wandeler et al. 2003). It is therefore currently impossible to differentiate contaminating modern DNA sequences from endogenous human DNA in human remains. Thus, although mtDNA sequences have been reported from remains of early modern humans (Adcock et al. 2001; Caramelli et al. 2003), it is not possible to determine whether such DNA sequences indeed represent endogenous DNA sequences (Abbott 2003). A related problem is that if a Neandertal fossil yields modern human-like DNA sequences, those might be discarded as putative contaminations (Nordborg 1998; Trinkaus 2001), even if they may be endogenous and represent evidence for a close genetic relationship or interbreeding between the two groups. To explore the genetic relationship between early modern humans and Neandertals in spite of these difficulties, we made use of the fact that the four Neandertal mtDNA sequences determined to date can easily be distinguished from those of modern humans (Krings et al. 1997, Krings et al. 2000; Ovchinnikov et al. 2000; Schmitz et al. 2002; Knight 2003). This allowed us to ask whether all well-preserved Neandertal remains contain Neandertal-like mtDNA and whether all well-preserved early modern human remains fail to contain such DNA sequences. Thus, we did not attempt to determine DNA sequences that are similar to present-day human mtDNA. Instead, we determined whether Neandertal-like mtDNA sequences were present or absent in well-preserved remains of Neandertals and of early modern humans. Results and Discussion The preservation of endogenous DNA in fossils is correlated with the amount, composition, and chemical preservation of amino acids (Poinar et al. 1996). We find that endogenous DNA can be amplified from Pleistocene remains when the amino acid content is more than 30,000 parts per million (ppm), the ratio of glycine to aspartic acid between two and ten, and the aspartic acid racemization (i.e., the stereoisomeric D/L ratio) less than 0.10 (Poinar et al. 1996; Krings et al. 1997, 2000; Schmitz et al. 2002; data not shown). We analyzed the amino acid preservation of 24 Neandertal and 40 early modern human fossils (Table S1). Several important Neandertal fossils, such as La Ferrassie and Krapina, as well as important modern human fossils, such as Veternica, proved to be too poorly preserved to be likely to allow DNA retrieval. Thus, further destructive sampling of these specimens was not considered justified. However, four Neandertal and five early modern human fossils fulfilled the above criteria for amino acid preservation and were thus expected to contain endogenous DNA (Figure 1; Table 1). These samples were geographically well distributed across Europe (Figure 2) and included remains whose morphology is typical of Neandertals (e.g., La Chapelle-aux-Saints) and of modern humans (La Madeleine, Cro-Magnon). They also included samples that have sometimes been considered “transitional” between Neandertals and modern humans, based on their morphological features: Vindija (Smith 1984) and Mladecˇ (Frayer 1986, Frayer 1992; Wolpoff 1999). Figure 1 Amino Acid Analyses of 64 Hominid Remains For each bone, the extent of aspartic acid racemization (D/L) and the amino acid concentration (ppm) is given. The dash lines delimit the area of amino acid preservation compatible with DNA retrieval. Circles and triangles represent early modern humans and Neandertals, respectively. The samples from which DNA extractions were performed are green (see also Table S1). Figure 2 Geographical Origin of Neandertal and Early Modern Human Samples from Which mtDNA Sequences Have Been Analyzed Filled squares and filled circles represent Neandertal and early modern human remains, respectively, analyzed in this study. The four Neandertal remains formerly analyzed are represented by empty squares. Table 1 DNA Retrieved from Late Pleistocene Fossils in This Study aFor each specimen and primer pair, the number of amplifications yielding a specific product is given followed by the total number of amplification attempted bA single amplification using the indicated “Neandertal” primers was attempted. The sequence was confirmed by amplification of larger overlapping fragments (cf. Figure S1) If low amounts of DNA are preserved in a specimen, some extracts will fail to contain DNA molecules by chance (Hofreiter et al. 2001a). Therefore, except in the case of Mladecˇ 2, in which the amount of material available permitted only two extractions, we extracted each of the four Neandertal and the five early modern human samples three times. For each extraction, amplifications were performed using two primer pairs: (i) “hominoid primers” that amplify homologous mtDNA sequences from the previously determined Neandertals and contemporary modern humans, as well as African great apes; (ii) “Neandertal primers” that, under the conditions used, amplify only Neandertal mtDNAs even in the presence of a large excess of modern human DNA (Krings et al. 2000; Schmitz et al. 2002). Since authentic ancient DNA is typically highly degraded, both primer pairs were designed to amplify short mtDNA fragments (72 and 31 bp, respectively, excluding primers). In each of these fragments, two substitutions allow the discrimination of previously determined Neandertal mtDNA sequences from contemporary modern human sequences. The sensitivity of both primer pairs is similar, as shown by the fact that they are both able to amplify single template molecules as judged from nucleotide misincorporation patterns (Hofreiter et al. 2001a). In order to determine the nature of the DNA sequences amplified, each amplification product was cloned and approximately 30 clones were sequenced for each “hominoid product” and ten clones for each “Neandertal product.” When amplified with the hominoid primers, all Neandertal and all early modern human remains yielded modern human DNA sequences (see Table 1). In addition, five cave bear teeth from Vindija, Croatia, and one from Gamssulzen, Austria, extracted in parallel with the hominid samples, all yielded human sequences. This confirms previous results in showing that most, if not all, ancient remains yield human DNA sequences when amplification conditions that allow single DNA molecules to be detected are used (Hofreiter et al. 2001b). For three Neandertal and all five modern human remains, several different mtDNA sequences were retrieved from individual extractions, and in the case of one Neandertal and one modern human, at least two of the sequences were also found in an independent extraction from the same specimen. Additionally, one of the cave bear teeth yielded a human sequence found in two independent extracts. Thus, the fact that a DNA sequence is found in two independent extracts is a necessary, but not sufficient, criterion of authenticity when human remains are analyzed. This implies that in the absence of further technical improvements, it is impossible to produce undisputable human mtDNA sequences from ancient human remains. In addition to DNA sequences identical to those previously amplified from present-day humans, the Neandertal bones Vi-77 and Vi-80 from Vindija yielded four out of 89 and 73 out of 85 mtDNA sequences, respectively, that were identical to previously determined Neandertal sequences. Thus, these two specimens contain a proportion of Neandertal-like mtDNA sequences (i.e., sequences that carry two substitutions that differentiate Neandertal mtDNA sequences from modern human mtDNA sequences as described above) that is high enough to detect using primers that amplify also modern human DNA. When amplified with Neandertal-specific primers, Neandertal-like mtDNA sequences were amplified from two independent extractions from all Neandertal fossils (see Table 1; Figure 3). For one of these, Vi-80 from Vindija, DNA preservation was sufficient to allow the retrieval of longer fragments and thus the reconstruction of 357 bp of the hypervariable region I (see Supporting Information section; Figure S1). This mtDNA sequence was identical to that retrieved from another bone from the same locality (Vi-75; Krings et al. 2000). In contrast to the Neandertal remains, none of the early modern human extracts yielded any amplification products with the Neandertal primers, although these remains are similar in chemical preservation to the Neandertal remains (see Figure 1). Figure 3 Sequences Obtained from the Neandertal Remains Using the “Neandertal Primers” Dots indicate identity to the human reference sequence (Anderson et al. 1981) given above. The four upper DNA sequences were determined in this study. Previously determined DNA sequences are shown below. Thus, all Neandertal remains analyzed yielded mtDNA sequences that are not found in the human mtDNA gene pool today but are similar to those found in four previously published Neandertals (Krings et al. 1997, Krings et al. 2000; Ovchinnikov et al. 2000; Schmitz et al. 2002) (see Figure 3). This is compatible with results suggesting that the extent of Neandertal mtDNA diversity was similar to that of current humans and lower than that of the great apes (Krings et al. 2000; Schmitz et al. 2002). It is noteworthy that this result is not an artifact created by discarding “modern-like” mtDNA sequences amplified from Neandertals (Trinkaus 2001), since all Neandertal remains with good biomolecular preservation yield “Neandertal-like” mtDNA sequence. Furthermore, none of the five early modern humans yields “Neandertal-like” mtDNA sequences in spite of the fact that these remains are as well preserved in terms of amino acids as the Neandertal remains. Thus, we fail to detect any evidence of mtDNA gene flow from Neandertals to early modern humans or from early modern humans to Neandertals. However, a relevant question is what extent of gene flow between Neandertals and early modern humans the current data allow us to exclude. In this regard, it is of relevance that the five early modern humans analyzed lived much closer in time to the Neandertals than do contemporary individuals. The probability that mtDNA sequences potentially contributed to modern humans by Neandertals were lost by drift (Nordborg 1998) or swamped by continuous influx of modern human mtDNAs (Enflo et al. 2001) in the Neandertal gene pool is therefore much smaller than when contemporary humans are analyzed (e.g., Relethford 1999). In fact, the five early modern humans analyzed almost double the amount of information about the Upper Pleistocene mtDNA gene pool since, under a model of constant effective population size, all contemporary humans trace their mtDNA ancestors back to only four to seven mtDNA lineages 20,000 to 30,000 years ago (Figure 4A; Figure S2), while all other mtDNA sequences present in the gene pool at that time have been lost by random genetic drift. Since the probability is very low (p < 0.007) that one or more of the five early modern humans analyzed here are among these few ancestors of current humans, the five Upper Pleistocene individuals can be added to the ancestors of the current mtDNA gene pool to allow us to ask what extent of Neandertal mtDNA contribution to early modern humans can be statistically excluded using the coalescent. Under the model of a constant human effective population size (Tavare 1984; Nordborg 1998) of 10,000 over time (Figure 4A), any contribution of Neandertal mtDNA to modern humans 30,000 years ago larger than 25% can be excluded at the 5% level (Figure S3). A more realistic scenario may be that the spread of modern humans was accompanied by an increase in population size before and during their migration out of Africa and subsequent colonization of western Eurasia (see Figure 4B). In that case, the Neandertal contribution that can be excluded is smaller (i.e., less gene flow could have taken place), but that depends critically on when and how the expansion occurred. Finally, under the unlikely scenario that population size was constant during the migration out of Africa and colonization of Europe and expanded only after a putative merging with Neandertals, the Neandertal contribution could have been larger, but this also depends on the nature of the growth (see Figure 4C). Figure 4 Schematic Model of Putative Contribution of Neandertal mtDNA to the Gene Pool of Modern Humans (A) Under the assumption of a constant effective population size of 10,000 for modern humans, contemporary mtDNAs trace back to approximately five mtDNA lineages 25,000 years ago. The modern human fossils represent five additional samples from around the time of putative admixture (stars). The contemporary and early modern human (EMH) samples reject a Neandertal contribution of 25% or more to modern humans about 30,000 years ago (p ≤ 0.05). (B) Under the more realistic scenario of an expansion of the human population during and after the colonization of Europe, a smaller Neandertal contribution can be excluded because the number of ancestors of the current human gene pool was larger 30,000 years ago. However, the contribution that can be excluded would depend on when and how the expansion occurred. (C) Under the scenario that population size was constant before a putative merging with the Neandertal population and expanded only thereafter, the Neandertal contribution could have been larger, but similarly depends on how the expansion occurred. Concluding Remarks It is noteworthy that under the model of constant population size, about 50 early modern human remains would need to be studied to exclude a Neandertal mtDNA contribution of 10%. To exclude a 5% contribution, one would need to study more early modern human remains than have been discovered to date. Thus, definitive knowledge of the extent of a putative contribution of Neandertals to the modern human gene pool will not be possible, although extensive studies of variation in the current human gene pool may clarify this question (Wall 2000). It is, however, worthwhile to note that samples considered as anatomically “transitional” between modern humans and Neandertals, such as Vindija (Smith 1984; Wolpoff 1999) and Mladecˇ (Frayer 1986, Frayer 1992; Wolpoff 1999), analyzed here, fail to show any evidence of mtDNA admixture between the two groups. Thus, while it cannot be excluded that Neandertals contributed variants at some genetic loci to contemporary humans, no positive evidence of any such contribution has yet been detected. Materials and Methods Amino acid preservation About 10 mg of bone were removed from each specimen and analyzed as in Schmitz et al. (2002) with minor modifications. In brief, proteins are hydrolyzed and amino acids labeled with o-phtaldialdehyde/N-acetyl-L-cysteine and analyzed by high performance liquid chromatography (Shimadzu, Kyoto, Japan) under conditions that separate the different amino acids as well as their stereoisomers. Eight amino acids are analyzed and their respective concentration measured: D- and L-alanine, glycine, D- and L-aspartic acid, serine, glutamic acid, valine, D- and L-leucine, and isoleucine. DNA extraction and amplification DNA extractions were performed in a laboratory dedicated to ancient DNA work. In this laboratory, positive air pressure is maintained with filtered air at all times, and all areas and equipment are treated with UV light when the laboratory is not used. A maximum of six bone or teeth samples were processed together with two blank extractions. Neandertal samples were always processed together with early modern human samples or cave bear samples. For each extraction, the samples were ground and between 30 mg and 120 mg of bone powder was extracted as in Krings et al. (1997). mtDNA sequences were amplified by polymerase chain reaction (PCR) using 5 μl of extract and 60 cycles. In addition, a minimum of four blank PCRs were performed together with each amplification from extracts. The “Neandertal-specific” amplification was carried out using the primers NL16230/NH16262 (Krings et al. 1997) and an annealing temperature of 60°C. We consider it highly unlikely that the Neandertal-specific mtDNA fragments represent contaminations from other Neandertals, given that none of the extracts of modern humans or cave bears processed in parallel with the Neandertal remains yielded such products. The “hominoid” amplification was performed with the primers L16022/H16095 (Krings et al. 1997) and an annealing temperature of 54°C. PCR products were cloned into Escherichia coli using the TOPO TA cloning kit (Invitrogen, Leek, The Netherlands), and ten or 30 clones of each amplification were sequenced on a ABI 3700 (Applied Biosystems, Foster City, California, United States). Estimation of admixture Given that previous analyses of mtDNA sequences have rejected a model of complete panmixia between Neandertals and early modern humans (Nordborg 1998), we focused on the estimation of the level of admixture between Neandertals and early modern humans that can be excluded. For this purpose, we considered a population of early modern humans that merged at Tm with a (genetically different) population of Neandertal individuals (see Figure 4) from which point the fused population was panmictic. The probability of picking K individuals by chance in the merged population that all carry a modern human mtDNA sequence is (1 − c)K, where c represents the Neandertal genetic contribution to the merged population. If none of n mtDNA sequences sampled in the merged population is Neandertal-like, we can exclude (at the 5% level) contributions that give a probability smaller than 0.05 of observing only modern human sequences, i.e., (1 − c)K < 0.05. The number of ancestors of n samples at the time t is represented by a probability distribution, An(t). Thus, the probability of observing only one kind of sequences in n samples becomes: where K vary from 1 to n. For a population of constant size over time, Pr(An(t) = K) has been derived in Tavare (1984). We estimated the number of ancestors of n samples at time t as the expected value of An(t), E(An(t)), according to this model and calculate the probability of observing only human sequences for different values of c. Supporting Information Determination of the mtDNA Sequence of Vi-80 from Vindija, Croatia The entire hypervariable region I sequence was determined from this specimen using amplifications and clones given in Figure S1. Its sequence is identical to the sequence previously determined from individual Vi-75 from Vindija (Krings et al. 2000). We could exclude cross-contamination from the old extract to this bone because different primers were used and some of the fragments of mtDNA amplified from Vi-80 were longer than those used to determine the sequence of Vi-75. Morphological analyses do not exclude that these two fragmentary bones (Vi-75 and Vi-80) may come from a single individual. Carbon-14 accelerator mass spectrometry dating, conducted in the Ångstrom Laboratory (Uppsala University, Sweden), yielded a date for Vi-80 of 38,310 ± 2,130 BP (before present). Since Vi-75 has been previously dated to over 42,000 BP (Krings et al. 2000), the possibility exists that the dates overlap since 42,000 BP is within two standard deviations of the Vi-80 date. Therefore, the bone labeled Vi-80 that yields the new mtDNA sequence could either be (i) a fragment of the same skeleton (individual) that was already successfully extracted, (ii) a bone from another individual maternally related to the first individual amplified, or (iii) another unrelated individual having by chance the same mtDNA sequence, which is not unlikely given the apparently low mtDNA diversity of Neandertals (Krings et al. 2000; Schmitz et al. 2002). Figure S1 The DNA Sequences of the Clones Used to Reconstruct the Sequence of the Mitochondrial Hypervariable Region I from the Bone Vi-80 (30 KB PDF). Click here for additional data file. Figure S2 Expected Number of Ancestors E(An(t)) of n Individuals under a Model of Constant Population Size of Ne = 10,000 The number of ancestors of n individuals (x axis) is estimated at 20,000, 25,000, and 30,000 years ago. For example, 150 humans living today have approximately seven ancestors 20,000 years ago. (56 KB PDF). Click here for additional data file. Figure S3 Probability of Different Levels of Admixture Probability of observing only modern human mtDNA sequences in both five early human remains and the current mtDNA gene pool given different proportion of Neandertal contribution c (x axis) under a model of constant population size (see text; Materials and Methods). For example, the probability of observing only human mtDNA sequences given a Neandertal contribution of 25% or more is smaller than 0.05 (dotted line). (42 KB PDF). Click here for additional data file. Table S1 Results of the Amino Acid Analyses of 40 Human and 24 Neandertal Remains The bones were analyzed by high performance liquid chromatography for their amino acid content (see Materials and Methods). The extent of racemization of aspartic acid (D-/L-Asp), the ratio of glycine to aspartic acid (Gly/Asp), and the total amount of the eight amino acid analyzed (ppm) are given for each specimen. Zero indicates values below detection level. The five human and four Neandertal specimens from which DNA extraction were performed are displayed in green. (54 KB PDF). Click here for additional data file. We are indebted to J.-J. Hublin, M. Nordborg, M. Przeworski, M. Stoneking, and L. Vigilant for helpful discussions and comments; to the many persons and institutions that allowed access to fossils; and to the Max Planck Gesellschaft and the Deutsche Forschungsgemeinschaft for financial support. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. DS and SP conceived and designed the experiments. DS, MH, and GP performed the experiments. DS, GP, and SP analyzed the data. AL, MC, MT-N, MP, and PM contributed reagents/materials/analysis tools. DS, AL, and SP wrote the paper. Academic Editor: David Penny, Massey University Abbreviations BPbefore present mtDNAmitochondrial DNA PCRpolymerase chain reaction ppmparts per million ==== Refs References Abbott A Anthropologists cast doubt on human DNA evidence Nature 2003 423 468 Adcock GJ Dennis ES Easteal S Huttley GA Jermiin LS Mitochondrial DNA sequences in ancient Australians: Implications for modern human origins Proc Natl Acad Sci U S A 2001 98 537 542 11209053 Anderson S Bankier AT Barrell BG de Bruijn MH Coulson AR Sequence and organization of the human mitochondrial genome Nature 1981 290 457 465 7219534 Cann RL Stoneking M Wilson AC Mitochondrial DNA and human evolution Nature 1987 325 31 36 3025745 Caramelli D Lalueza-Fox C Vernesi C Lari M Casoli A Evidence for a genetic discontinuity between Neandertals and 24,000-year-old anatomically modern Europeans Proc Natl Acad Sci U S A 2003 100 6593 6597 12743370 Duarte C Maurício J Pettitt PB Souto P Trinkaus E The early Upper Paleolithic human skeleton from the Abrigo do Lagar Velho (Portugal) and modern human emergence in Iberia Proc Natl Acad Sci U S A 1999 96 7604 7609 10377462 Enflo P Hawks K Wolpoff M A simple reason why Neanderthal ancestry can be consistent with current DNA information Am J Phys Anthropol 2001 114 62 Frayer DW Cranial variation at Mladecˇ and the relationship between Mousterian and Upper Paleolithic hominids Anthropos 1986 23 243 256 Frayer DW Evolution at the European edge: Neanderthal and Upper Paleolithic relationships Prehist Europeenne 1992 2 9 69 Hawks JD Wolpoff MH The accretion model of Neandertal evolution Evol Int J Org Evol 2001 55 1474 1485 Hofreiter M Jaenicke V Serre D von Haeseler A Pääbo S DNA sequences from multiple amplifications reveal artifacts induced by cytosine deamination in ancient DNA Nucleic Acids Res 2001a 29 4793 4799 11726688 Hofreiter M Serre D Poinar HN Kuch M Pääbo S Ancient DNA Nat Rev Genet 2001b 2 353 359 11331901 Ingman M Kaessmann H Pääbo S Gyllensten U Mitochondrial genome variation and the origin of modern humans Nature 2000 408 708 713 11130070 Knight A The phylogenetic relationship of Neandertal and modern human mitochondrial DNAs based on informative nucleotide sites J Hum Evol 2003 44 627 632 12765622 Krings M Stone A Schmitz RW Krainitzki H Stoneking M Neandertal DNA sequences and the origin of modern humans Cell 1997 90 19 30 9230299 Krings M Capelli C Tschentscher F Geisert H Meyer S A view of Neandertal genetic diversity Nat Genet 2000 26 144 146 11017066 Nordborg M On the probability of Neanderthal ancestry Am J Hum Genet 1998 63 1237 1240 9758610 Ovchinnikov IV Gtherstrom A Romanova GP Kharitonov VM Liden K Molecular analysis of Neanderthal DNA from the northern Caucasus Nature 2000 404 490 493 10761915 Poinar HN Hss M Bada JL Pääbo S Amino acid racemization and the preservation of ancient DNA Science 1996 272 864 866 8629020 Relethford JH Models, predictions, and the fossil record of modern human origins Evol Anthropol 1999 8 7 10 Schmitz RW Serre D Bonani G Feine S Hillgruber F The Neandertal type site revisited: Interdisciplinary investigations of skeletal remains from the Neander Valley, Germany Proc Natl Acad Sci U S A 2002 99 13342 13347 12232049 Smith FH Fossil hominids from the Upper Pleistocene of Central Europe and the origin of modern Europeans. In: Spencer F, editor. The origins of modern humans: A world survey of the fossil evidence 1984 New York Alan R. Liss 137 210 Stringer C Modern human origins: Progress and prospects Philos Trans R Soc Lond B Biol Sci 2002 357 563 579 12028792 Stringer CB Andrews P Genetic and fossil evidence for the origin of modern humans Science 1988 239 1263 1268 3125610 Tavare S Line-of-descent and genealogical processes, and their applications in population genetics models Theor Popul Biol 1984 26 119 164 6505980 Trinkaus E The Neandertal paradox. In: Finlayson C, editor. Neanderthals and modern humans in late Pleistocene Eurasia 2001 Gibraltar The Gibraltar Museum 73 74 Underhill PA Shen P Lin AA Jin L Passarino G Y chromosome sequence variation and the history of human populations Nat Genet 2000 26 358 361 11062480 Wall J Detecting ancient admixture in humans using sequence polymorphism data Genetics 2000 154 1271 1279 10757768 Wandeler P Smith S Morin PA Pettifor RA Funk SM Patterns of nuclear DNA degeneration over time: A case study in historic teeth samples Mol Ecol 2003 12 1087 1093 12753226 Weidenreich F The “Neanderthal man” and the ancestors of “Homo sapiens.” Am Anthropologist 1943 45 39 48 Wolpoff MH Paleoanthropology 1999 Boston McGraw–Hill 936 Wolpoff M Wu X Thorne AG Modern Homo sapiens origins: A general theory of hominid evolution involving the fossil evidence from East Asia. In: Spencer F, editor. The origins of modern humans: A world survey of the fossil evidence 1984 New York Alan R. Liss 411 483 Wolpoff MH Hawks J Caspari R Multiregional, not multiple origins Am J Phys Anthropol 2000 112 129 136 10766948

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PLoS Biol. 2004 Mar 16; 2(3):e57

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PLoS Biol

10.1371/journal.pbio.0020057

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020064FeatureMolecular Biology/Structural BiologyNeurosciencePhysiologyMammalsTaste Perception: Cracking the Code FeatureBradbury Jane 3 2004 16 3 2004 16 3 2004 2 3 e64Copyright: © 2004 Jane Bradbury.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Our sense of taste begins with taste buds and ends in the brain. Researchers are beginning to unravel the mechanisms and connections that lie in between ==== Body The ability to taste food is a life-and-death matter. Failure to recognise food with a high enough caloric content could mean a slow death from malnutrition. Failure to detect a poison could result in near-instant expiration. And now, as researchers begin to understand some of the nuts and bolts of taste perception, it seems that the sense of taste may also have more subtle effects on health. The Basics of Taste At the front line of the taste sensory system are the taste buds—onion-shaped structures on the tongue and elsewhere in the mouth (Figure 1). Up to 100 taste receptor cells—epithelial cells with some neuronal properties—are arranged in each taste bud. In the tongue, the taste buds are innervated by the chorda tympani (a branch of the facial nerve) and the glossopharyngeal nerve. These nerves carry the taste messages to the brain. Figure 1 A Taste Bud in a Mouse This taste bud was taken from a transgenic mouse in which the marker green fluorescent protein is being driven by the T1R3 promoter; 20%–30% of the cells in the taste bud are expressing T1R3. (Photograph courtesy of Sami Damak, Mount Sinai School of Medicine, New York, New York, United States.) Taste is the sense by which the chemical qualities of food in the mouth are distinguished by the brain, based on information provided by the taste buds. Quality or ‘basic taste’, explains Bernd Lindemann, now retired but an active taste researcher in Germany for many years, is a psychophysical term. Large numbers of people describe different tastants and then statistical analyses are used to define the important tastes. ‘The number of taste qualities has varied over the years’, says Lindemann. ‘We are now settling at around five, though I would not be surprised if some additional qualities pop up’. The five qualities that Lindemann refers to are salty, sour, bitter, sweet, and umami, the last being the Japanese term for a savoury sensation. Salty and sour detection is needed to control salt and acid balance. Bitter detection warns of foods containing poisons—many of the poisonous compounds produced by plants for defence are bitter. The quality sweet provides a guide to calorie-rich foods. And umami (the taste of the amino acid glutamate) may flag up protein-rich foods. Our sense of taste has a simple goal, explains Lindemann: ‘Food is already in the mouth. We just have to decide whether to swallow or spit it out. It's an extremely important decision, but it can be made based on a few taste qualities’. From Physiology to Molecular Biology Taste has been actively researched for many decades. During the 20th century, electrophysiologists and other researchers worked hard to understand this seemingly simple sense system. Then, in 1991, the first olfactory receptors were described. These proteins, which are exposed on the surface of cells in the nose, bind to volatile chemicals and allow us to detect smells. This landmark discovery, in part, encouraged many established taste researchers to investigate the molecular aspects of taste. The olfaction results also enticed researchers from other disciplines into the taste field, including collaborators Charles Zuker (University of California, San Diego [UCSD], La Jolla, California, United States) and Nick Ryba (National Institute of Dental and Craniofacial Research [NIDCR], Bethesda, Maryland, United States). About six years ago, explains Zuker, who previously worked on other sensory systems in flies, ‘there was a disconnect between our understanding of sensations in the case of photoreception, mechanoreception, touch, and so on and what we knew about taste’. There was evidence, says Ryba, that a class of protein receptors called G-protein-coupled receptors (GPCRs) were involved in sweet and bitter taste, ‘but the receptors weren't known, so we started to look for them …. These molecules are intrinsically interesting, but more importantly, they provide tools with which we can dissect out how taste works’. Bitter, Sweet, and Umami Receptors The bitter receptors fell first to the onslaught of the UCSD–NIDCR team and other molecular biologists. In 1999, the ability to taste propylthiouracil, a bitter tasting compound, had been linked to a locus on human Chromosome 5p15. Reasoning that this variation might be due to alterations in the coding sequence for a bitter receptor, the UCSD–NIDCR researchers used the draft of the human genome to search for sequences that resembled GPCRs on Chromosome 5p15. ‘That was how we found T2R1, the first bitter receptor, and, subsequently, a whole family of T2Rs’, says Zuker. Researchers want to know: how is taste coded? All these receptors, says Zuker, are coexpressed in bitter taste receptor cells, a result that contradicts other research showing that different bitter-responsive cells react to different bitter molecules. ‘To me’, says Zuker, ‘it makes sense that all the bitter receptors would be expressed in each bitter taste cell. We just need to know if something is bitter to avoid death’, not the exact identity of the bitter tastant. The sweet receptor story started in 1999 with the identification of two putative mammalian taste receptors, GPCRs now known as T1R1 and T1R2. In early 2001, four groups reported an association between the mouse Sac locus, which determines the ability of mice to detect saccharin, and T1R3, a third member of the T1R family. The UCSD–NIDCR team subsequently showed that the T1R2 and T1R3 heterodimer (a complex of one T1R2 and one T1R3 molecule) forms a broadly tuned sweet receptor, responsive to natural sugars and artificial sweeteners, and that a homodimer of two T1R3 molecules forms a low-affinity sugar receptor that responds to high concentrations of natural sugars only. All sweet detection, concludes Zuker, is via the T1R2 and T1R3 receptors. And umami? A truncated glutamate receptor was identified as an umami receptor by researchers at the University of Miami (Florida, United States) School of Medicine in 2000. Zuker, however, believes that the one and only umami receptor is a heterodimer of T1R1 and T1R3. In October 2003, Zuker and his coworkers reported that mice in which either T1R1 or T1R3 has been knocked out show no preference for monosodium glutamate (MSG), an umami tastant. However, other researchers reported in August 2003 that T1R3 knockouts retain some preference for MSG. ‘We believe this is due either to the truncated glutamate receptor or another unknown receptor’, says lead author Sami Damak (Mount Sinai School of Medicine, New York, New York, United States). Damak says he does not know why the two sets of T1R3 knockout mice behaved differently, but the UCSD–NIDCR researchers suggest that the residual response to MSG seen by Damak et al. is a response to the sodium content of MSG. Damak is not alone, however, in thinking there may be more than one umami receptor (and additional sweet receptors). Commenting on these recent discoveries, taste expert Linda Bartoshuk (Yale University School of Medicine, New Haven, Connecticut, United States) says that ‘it is lovely to see all these details, especially as they confirm what we already believed conceptually’. For example, she says, it is no surprise that there are many bitter receptors but probably only one sweet receptor. ‘There are so many poisons and it makes perfect sense to have lots of receptors feeding into a common transduction pathway. Sweet is a different problem. In nature, there are many molecules with structures similar to sugar that we must not eat because we cannot metabolise them. So I would have predicted one or at most a few highly specific sweet receptors’. What about Salty and Sour Receptors? The salty and sour receptors may be very different from the GPCRs involved in bitter, sweet, and umami perception, which bind complex molecules on the outside of the cell and transmit a signal into the cell. For salty and sour perception, the taste cell only needs to detect simple ions. One way to do this may be to use ion channels—proteins that form a channel through which specific inorganic ions can diffuse. Changes in cellular ion concentrations could then be detected and transmitted to the nervous system. Physiologist John DeSimone (Virginia Commonwealth University, Richmond, Virginia, United States) says there are at least two ion channel receptors for salt in rodent taste receptor cells. The first of these is the epithelial sodium channel, a widely expressed channel that can be blocked specifically with the drug amiloride. In rats, says DeSimone, only 75% of the nerve response to salt can be blocked by amiloride, so there is probably a second receptor. This, he says, seems to be a generalist salt receptor—the amiloride-sensitive channel only responds to sodium chloride—and may be the more important receptor in people. Sour tastants are acids, often found in spoiled or unripe food. DeSimone's current idea is that strong acids enter taste cells through a proton channel (probably a known channel present on other cell types) while weak acids, like acetic acid (vinegar), enter as neutral molecules and then dissociate to lower intracellular pH. DeSimone believes that he has identified the proton channel involved in sour taste as well as an ion channel that could be the second salt receptor, and he plans to do knockout experiments on both. If these channels are essential elsewhere in the body, as DeSimone suspects, to avoid lethality he will need to construct conditional knockouts in which the channel is lost only in the taste receptor cells. Zuker, meanwhile, is not convinced that the current ion channel candidates for salt and sour perception are correct. And, he says, GPCRs could also be involved in these modalities. ‘There is a precedent for that’, he claims, noting that extracellular calcium is sensed by a GPCR. Taste-Coding With many taste receptors now identified, researchers are turning to a long-standing question in taste perception: how is taste coded? When we eat, our tongue is bombarded with tastants. How is their detection and transduction of information organised so that the appropriate response is elicited? Taste physiologist Sue Kinnamon (Colorado State University, Fort Collins, Colorado, United States) explains the two theories of taste-coding. In the ‘labelled-line’ model, sweet-sensitive cells, for example, are hooked up to sweet-sensitive nerve fibres that go to the brain and code sweet. If you stimulate that pathway, says Kinnamon, ‘you should elicit the appropriate behavioural response without any input from other cell types’. In the ‘cross-fibre’ model, the pattern of activity over many receptors codes taste. This model predicts that taste receptor cells are broadly tuned, responding to many tastants. Support for this theory, says Kinnamon, comes from electrical recordings from receptor cells and from nerves innervating the taste buds that show that one cell can respond to more than one taste quality. Zuker and Ryba's recent work strongly suggests that taste-coding for bitter, sweet, and umami fits the labelled-line model in the periphery of the taste system. Their expression data show that receptors for these qualities are expressed in distinct populations of taste cells. In addition, in early 2003, they reported that, as in other sensory systems, a single signalling pathway involving the ion channel TRPM5 and PLCβ2, a phospholipase that produces a TRPM5 activator, lies downstream of the bitter, sweet, and umami receptors. When the UCSD–NIDCR researchers took PLCβ2 knockout mice, which did not respond to bitter, sweet, or umami, and engineered them so that PLCβ2 was only expressed in bitter receptor-expressing cells, only the ability to respond to bitter tastants was regained. These data, says Zuker, support the labelled-line model. The latest data supporting the labelled-line model came last October when Zuker and colleagues described mice in which a non-taste receptor—a modified κ-opioid receptor that can only be activated by a synthetic ligand—was expressed only in cells expressing T1R2, sweet-responsive cells. The mice were attracted to the synthetic ligand, which they normally ignore, indicating that dedicated pathways mediate attractive behaviours. The researchers plan similar experiments to see whether the same is true for aversive behaviours. Even with all these molecular data, the cross-fibre model of taste-coding still has its supporters—just how many depends on whom one talks to. Both Damak and Kinnamon, for example, believe that there is at least some involvement of cross-fibre patterning even in the taste receptor cells. But, says neurobiologist and olfaction expert Lawrence C. Katz (Duke University, Durham, North Carolina, United States), ‘the onus is now on people who believe otherwise [than the labelled-line model] to provide compelling proof for the cross-fibre theory because now, at least at the periphery, the evidence is compelling for a labelled line for bitter, sweet, and umami’. Bartoshuk also says the debate is decided in favour of the labelled-line model in the periphery. The crossfibre model is an interesting historical footnote, she comments. Whether this putative link between taste perception and health can be confirmed and whether it will be possible to manipulate food preferences to improve health remain to be seen. However, it seems certain that, as in the past five years, the next five years will see large advances in our knowledge of many aspects of taste, a fascinating and important sensory system. What Next—and Why Study Taste Anyway? The periphery of the taste sensory system has yielded many of its secrets, but relatively little is known about the transduction pathways in taste, how taste cells talk to the nervous system, or about events further downstream in the brain. How are signals from taste receptors integrated with those from olfactory receptors to form a representation of complex food flavours, for example? With their expanding molecular toolbox, researchers can now delve deeper into these aspects of taste perception. This may tell us not only about taste but about how the nervous system in general is put together, says Ryba. But understanding taste is not just an academic exercise. It has practical uses too. DeSimone suggests that by understanding salt receptors, it may be possible to design artificial ligands to help people lower their salt intake. As Kinnamon succinctly puts it, ‘Can you imagine eating potato chips and not having the salty component?’ An artificial salt receptor ligand could make salt-free foods a palatable option for people with high blood pressure. Lindemann also sees a great future in artificial ligands for taste receptors. The sense of taste is partly lost in elderly people, he says, so better tastants—effectively ‘chemical spectacles’—might give them back their pleasure of eating and thereby improve their quality of life. Finally, some aspects of taste may be inextricably tied up with general health, says Bartoshuk. Many people who can taste propylthiouracil are also ‘supertasters’—they have more fungiform papillae, structures containing taste buds, on their tongues than non-tasters (Figure 2). Supertasters find vegetables bitter—particularly brassicas, like Brussel sprouts—so they tend to eat fewer vegetables as part of their regular diet than non-tasters. ‘Being a supertaster affects your taste preferences, your diet, and ultimately your health’, claims Bartoshuk. Figure 2 Non-Taster or Supertaster? (A) Top surface of the tongue of a non-taster. (B) Tongue of a supertaster. The small circles are fungiform papillae, each of which contains about six taste buds. Jane Bradbury is a freelance science news writer based in Cambridge, United Kingdom. E-mail: janeb@sciscribe.u-net.com Abbreviations GPCRG-protein-coupled receptor MSGmonosodium glutamate ==== Refs Further Reading Damak S Rong M Yasumatsu K Kokrashvili Z Varadarajan V Detection of sweet and umami taste in the absence of taste receptor T1r3 Science 2003 301 850 853 12869700 Zhang Y Hoon MA Chandrashekar J Mueller KL Cook B Coding of sweet, bitter, and umami tastes: Different receptor cells sharing similar signaling pathways Cell 2003 112 293 301 12581520 Zhao GQ Zhang Y Hoon MA Chandrashekar J Erlenbach I The receptors for mammalian sweet and umami taste Cell 2003 115 255 266 14636554

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PLoS Biol. 2004 Mar 16; 2(3):e64

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PLoS Biol

10.1371/journal.pbio.0020064

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020065Research ArticleBioinformatics/Computational BiologyBiotechnologyCell BiologyMolecular Biology/Structural BiologyPharmacology/Drug DiscoverySystems BiologySaccharomycesVertebratesInteraction Networks in Yeast Define and Enumerate the Signaling Steps of the Vertebrate Aryl Hydrocarbon Receptor The AHR NetworkYao Guang 1 Craven Mark 2 Drinkwater Norman 1 Bradfield Christopher A 1 *1McArdle Laboratory for Cancer Research, University of Wisconsin Medical SchoolMadison, WisconsinUnited States of America2Department of Biostatistics and Medical Informatics, University of Wisconsin Medical SchoolMadison, WisconsinUnited States of America3 2004 16 3 2004 16 3 2004 2 3 e658 9 2003 31 12 2003 Copyright: © 2004 Yao et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Holistic Approach to Evaluating Cellular Communication Pathways The aryl hydrocarbon receptor (AHR) is a vertebrate protein that mediates the toxic and adaptive responses to dioxins and related environmental pollutants. In an effort to better understand the details of this signal transduction pathway, we employed the yeast S. cerevisiae as a model system. Through the use of arrayed yeast strains harboring ordered deletions of open reading frames, we determined that 54 out of the 4,507 yeast genes examined significantly influence AHR signal transduction. In an effort to describe the relationship between these modifying genes, we constructed a network map based upon their known protein and genetic interactions. Monte Carlo simulations demonstrated that this network represented a description of AHR signaling that was distinct from those generated by random chance. The network map was then explored with a number of computational and experimental annotations. These analyses revealed that the AHR signaling pathway is defined by at least five distinct signaling steps that are regulated by functional modules of interacting modifiers. These modules can be described as mediating receptor folding, nuclear translocation, transcriptional activation, receptor level, and a previously undescribed nuclear step related to the receptor's Per–Arnt–Sim domain. 4500 yeast deletion mutants, transformed with a reporter construct that is sensitive to dioxin, were used to create a protein-interactive map that outlines five distinct steps required for dioxin-mediated cell signaling ==== Body Introduction The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor found in a variety of vertebrate species. The AHR is a prototype member of the Per–Arnt–Sim (PAS) superfamily of signaling molecules. Members of this superfamily regulate cellular responses to a variety of environmental stimuli, including pollutants, hypoxia, and external light cues (Gu et al. 2000). Our initial interest in AHR biology arose from its pivotal role in mediating the adaptive metabolic response to both polycyclic aromatic hydrocarbons (PAHs) and the toxic effects of more potent agonists like the halogenated dioxins (Schmidt and Bradfield 1996; Whitlock 1999). More recently, it has been observed that the AHR plays an important role in normal vascular development, suggesting the existence of an endogenous ligand (Lahvis et al. 2000). From the broader perspective, the AHR can be viewed as a prototype of all PAS protein signaling. That is, what we learn about AHR biology will have a direct influence on how we think about PAS-mediated hypoxia, circadian, and developmental pathways. An initial understanding of AHR signal transduction has resulted from the biochemical and molecular studies that have been performed over the past two decades (Schmidt and Bradfield 1996; Whitlock 1999). The resultant model holds that the unliganded AHR resides in the cytoplasm, where it is associated with a dimer of the chaperone protein Hsp90 and cochaperones such as ARA9/XAP2 and p23 (Pongratz et al. 1992; Carver and Bradfield 1997; Ma and Whitlock 1997; Meyer et al. 1998; Kazlauskas et al. 1999). Upon binding ligands, the cytoplasmic AHR translocates to the nucleus, where it dimerizes with another PAS protein known as ARNT. The AHR–ARNT heterodimer then binds to specific dioxin-responsive enhancers (DREs) and transactivates a battery of genes encoding xenobiotic-metabolizing enzymes, most notably CYP1A1, CYP1A2, and CYP1B1 (Schmidt and Bradfield 1996; Whitlock 1999). Transactivation of target genes has been shown to be mediated through a variety of histone acetyltransferases (HATs) and SWI/SNF coactivators, such as SRC, p300/CBP, and BRG-1 (Kobayashi et al. 1997; Beischlag et al. 2002; Wang and Hankinson 2002). Although the initial model of AHR signaling provides a valuable framework, its completeness has not yet been assessed. That is, we have no estimates of the total number of gene products involved in AHR signaling, nor can we be sure we have identified all the important steps. Without these estimates, it is difficult to gauge how much or how little we understand about this pathway. In an effort to address these issues, we employed the comprehensive set of gene deletions available in a yeast model system to systematically identify gene products that influence AHR function. We then employed a protein interaction network (PIN) strategy to provide a framework to describe AHR signaling. By coupling both computational and experimental annotations, we were able to deduce the minimum number of genetic loci and signaling events required for AHR signaling. Results Rationale A number of laboratories have demonstrated that the yeast Saccharomyces cerevisiae is a valuable model system for the study of signaling by mammalian nuclear receptors (Garabedian and Yamamoto 1992; McEwan 2001). Although there is no yeast ortholog of the AHR, it has been also shown that AHR signaling can be recapitulated in yeast and that this system can be used to identify novel players in AHR biology (Carver et al. 1994; Whitelaw et al. 1995). The experimental advantages of S. cerevisiae as a tool to study AHR signaling are related to the yeast's fundamental similarities with mammalian systems, the more thorough characterization of its smaller genome, and the availability of its specific genomic tools, such as arrayed deletions of each individual open reading frame (ORF) and large-scale databases describing protein and genetic interactions (Winzeler et al. 1999; Resnick and Cox 2000; Kennedy 2002; Mewes et al. 2002; Xenarios et al. 2002). These convenient genomic tools allowed us to employ a systematic approach to identify gene products involved in the AHR pathway and to interpret them in the context of a protein interaction network. Owing to a lack of corresponding reagents/databases, such an approach is not yet feasible for the study of AHR signaling in more complex eukaryotic systems such as human or mouse. Identification of AHR Modifiers by a High-Throughput Deletion Array Screen In earlier attempts to identify AHR modifiers in yeast, it was demonstrated that genetic screens can be performed more efficiently by using an AHR construct that is fused to the DNA-binding domain of the bacterial LexA protein (AHR–LexA) (Carver et al. 1994; Whitelaw et al. 1995). This chimeric system removes the requirement for ARNT and allows our screens to be more specific for those mutations/modifiers that directly influence AHR function. Using this system, we set out to identify gene products that play important roles in AHR signaling (Figure 1A). Figure 1 High-Throughput Deletion Array Screen for AHR Modifiers (A) The flow chart of the deletion array screen. Each individual deletion strain was transformed with the AHR–LexA chimera and LacZ reporter constructs using a 96-well microtiter plate transformation approach. The AHR-dependent reporter activity of each deletion strain was examined with a 384-well plate-based fluorescence assay method. A total of 92 deletion strains were identified that displayed AHR signaling significantly different from the wt control. (B) Identification of “AHR-specific” modifiers. The effect of modifier deletions on the AHR pathway was compared with their effect on a Gal4TAD control pathway. It was found that 54 deletions influenced AHR signaling specifically, whereas 38 deletions corresponded to general factors. See text for details. To accomplish this screen, we employed the yeast deletion strains made available by the Saccharomyces Genome Deletion Project (Winzeler et al. 1999). We developed a high-throughput approach to efficiently transform each deletion strain with two plasmids, one harboring the AHR–LexA chimera (pCEN-AHR) and the other, a LexA operator-driven LacZ reporter. Of the 4,695 available deletion strains, 4,507 (96%) were successfully transformed with the complete AHR signaling system (i.e., both plasmids). In the primary screen, we selected transformants that exhibited a 4-fold or greater change in AHR response as compared to the wild-type (wt) BY4742 strain (p < 10–6). To minimize false positives, we selected clones that influenced signaling at no less than two of the six concentrations of agonist tested. In addition, we retested each positive strain in a secondary screen with another AHR system containing the same LacZ reporter and a high-copy AHR–LexA chimera (pAHR) (Carver 1996). By these criteria, 92 deletion strains were identified that reproducibly displayed a significant change in AHR signaling as compared to the wt strain (Table S1). To eliminate those deletions that influenced the AHR pathway in a nonspecific manner, each of the 92 deletion strains was examined with a control plasmid pGal4TAD (see Materials and Methods). This construct harbors the transcriptional activation domain (TAD) of Gal4p fused to the LexA DNA-binding domain and was cotransformed into each deletion strain with the LacZ reporter (Figure 1B). Of the 92 deletions, 38 were observed to also influence pGal4TAD signaling. We concluded that these deletions either represented general players in both pathways or exhibited nonspecific effects through their influence on, e.g., the common LexA domain, plasmid maintenance, or cell growth rate. Therefore, the inclusion of the pGal4TAD control led us to eliminate 38 nonspecific factors and identify 54 deletions that appeared to influence the AHR pathway in a specific manner. Of these “AHR-specific” factors, Hsc82p and Cpr7p were previously described AHR modifiers, and the other 52 were novel (Carver et al. 1994; Whitelaw et al. 1995; Miller 2002) (Table S2). The analysis of the annotated function of these AHR modifiers revealed that they were associated with a great variety of cellular functions (Table S3). For many of these annotations, their direct association with AHR signaling appeared elusive. Therefore, in order to appreciate the function of identified modifiers in the AHR pathway, an information framework was required to put them in context. Portrayal of the AHR–PIN Recent experiments from a number of laboratories have provided data to support the idea that protein interaction network (PIN) can be used to portray the workings of complex biological systems (Schwikowski et al. 2000; Ge et al. 2001; Ideker et al. 2001; Tong et al. 2002). To investigate how identified modifiers and their interactions influence AHR signaling, we constructed a modifier network (AHR–PIN) based on known protein and genetic interactions derived from the DIP and MIPS databases (Mewes et al. 2002; Xenarios et al. 2002). Our AHR–PIN map is comprised of “nodes” and “links.” A “node” is a graphic depiction of a protein or locus, and a “link” is a line between two nodes in the map that depicts the known interaction between them. As yeast protein–protein interactions identified to date are still far from saturating and are heavily biased towards proteins of high abundance, genetic interactions were also included in the network building as a complement (Tong et al. 2002; von Mering et al. 2002). In the AHR–PIN, protein interactions are depicted with black lines, and genetic interactions are labeled in red. In addition, nodes also come in two types, “M-nodes” and “I-nodes.” We refer to the protein or locus that has an identified effect on the AHR pathway as the “M-node,” or modifier node, and refer to the nonmodifier node that is required on a path to connect two M-nodes as the “I-node,” or intervening node. In an effort to determine the most informative PIN, we examined how the structure and complexity of the map was influenced by the choice of the maximally allowed number of links between any two M-nodes (we refer to this value as Dmax). One common feature of AHR–PINs with Dmax values greater than 1 was that the majority of M-nodes were interconnected in a single large network with no breaks (Figure 2A–2C). For convenience, we refer to this single large network simply as the AHR–PIN in following discussions. When Dmax was set at low stringency (Dmax ≥ 3), the representation of M-nodes in AHR–PIN was high. For example, at Dmax = 3, 46 of 54 M-nodes were included. However, AHR–PINs resulting from these inclusive, yet low-stringency conditions exhibited high complexity, which made it impossible to assess the interactions visually (Figure 2A and 2B). When Dmax was set at higher stringency (Dmax = 2), the resultant AHR–PIN now comprised 34 closely interconnected M-nodes and was much easier to visualize (Figure 2C; Table S4). Further simplification of the AHR–PIN with Dmax = 1 was of little utility because it resulted in a large proportion of isolated M-nodes, with the largest cluster containing only three M-nodes (Figure 2D). Figure 2 AHR–PIN versus Random PINs (A–D) AHR–PINs at various Dmax levels. AHR modifiers are highlighted with bigger green nodes. A total of 48, 46, 34, and three AHR modifiers are interconnected in the AHR–PINs with Dmax values of 4, 3, 2, and 1, respectively. (E–H) Distribution of random PINs at various Dmax levels in histogram. Each distribution graph represents 5,000 randomly generated PINs. The density estimation curve (in red) is plotted on top of the histogram where applicable. The number of M-nodes in the AHR–PIN and the average number of M-nodes in random networks are marked in each distribution graph. See text for details. The AHR–PIN Is Distinct from Random PINs To examine the statistical significance of the AHR–PINs, we tested whether they could have been generated by random chance. If the AHR–PIN represents a valid description of the AHR pathway, it should comprise significantly more interconnected M-nodes than would be interconnected by random chance. To test this idea, a Monte Carlo simulation was conducted by generating 5,000 random PINs at each Dmax setting. Each of these test PINs was constructed based on 54 mock M-nodes randomly selected from genes contained in the entire deletion set. To estimate the statistical significance of the AHR–PIN, the random graph was defined as the null distribution, and the p value for the AHR–PIN at each Dmax was calculated from the fraction of trials with a higher number of interconnected M-nodes (Figure 2E–2H). The AHR–PIN at Dmax = 1 was not statistically significant compared to those generated at random chance (p < 0.08; Figure 2H). However, at Dmax = 2, Dmax = 3, and Dmax = 4, the number of interconnected M-nodes in the AHR–PIN was significantly larger than that of random PINs (p < 10–4, 10–3, and 3 × 10–3, respectively; Figure 2E–2G). These observations were consistent with the idea that AHR–PINs at these settings provide a biologically meaningful description of AHR signaling. For further exploration, we chose to focus on the network with the greatest statistical significance, i.e., the PIN generated at Dmax = 2. In this AHR–PIN, 63.0% of the M-nodes (34/54) are interconnected, while in corresponding random PINs with mock M-nodes, this number drops to 18.5% (10/54). Although the AHR–PINs at Dmax = 3 and Dmax = 4 also exhibited statistically significant differences from random PINs, these AHR–PINs were not considered further for two reasons. First, these networks were visually complex and could not be simply annotated in two dimensions. Second, the ratios of interconnected M-nodes in these AHR–PINs to those of random PINs were quite low (1.3 and 1.2 for Dmax = 3 and Dmax = 4, respectively). This observation suggests a much greater potential for displaying false positive interactions at these settings as compared to the AHR–PIN at Dmax = 2, where this ratio was 3.4 (34/10). Modular Organization of AHR–PIN as Revealed by Network Clustering Our next objective was to use the PIN to enumerate and define steps in AHR signaling. It has been suggested that PINs exhibit a modular nature, with each module comprising highly interconnected proteins of related cellular functions (Hartwell et al. 1999; Schwikowski et al. 2000). Our hypothesis was that functional modules in the AHR–PIN would correspond to discrete steps in the mechanism of signaling. To test this idea, we attempted to define the functional modules using a number of computational and experimental annotation approaches. As a strictly computational approach, we attempted to identify the functional modules in the AHR–PIN by a network-clustering method (Rives and Galitski 2003). In brief, an all-pairs-shortest-path distance matrix was generated for every pair of nodes within the AHR–PIN (Dmax = 2). Each distance (d) in the matrix refers to the length of the shortest path between a pair of nodes in the full network space of yeast genomic PIN and was transformed into an “association” value (1/d2). The resultant pairwise association matrix was used to identify network clusters in the AHR–PIN by a hierarchical average-linkage clustering algorithm (Eisen et al. 1998; Rives and Galitski 2003). The cluster boundaries were delimited by using a similar “tree-depth threshold” that was set low enough to separate the largest cluster from others (Figure 3A) (Rives and Galitski 2003). If we define a network cluster to include at least two M-nodes, ten such clusters can be identified (Figure 3A). Consistent with the modular PIN hypothesis, we found that these clusters overlapped with ten local areas (modules) in the AHR–PIN, with each module comprised of two to six M-nodes (Figure 3B). Figure 3 Functional Modules Identified by Network Clustering (A) Network clustering of AHR–PIN. Protein nodes in the AHR–PIN (Dmax = 2) were clustered by a hierarchical clustering algorithm. A tree-depth threshold was set to delimit cluster boundaries (Rives and Galitski 2003). Clusters with at least two M-nodes are shown. See text for details. (B) Overlay of the network clusters on the AHR–PIN. The ten network clusters correspond to ten local areas in the AHR–PIN. Each network cluster (local area) is labeled with its significant functional enrichment as calculated using the FunSpec program (Robinson et al. 2002). Color scheme. Nodes: modifier deletions that incurred down- and up-regulation of AHR signaling are marked in green and red, respectively. For intervening nodes, essential genes are marked in gray and nonessential genes in white. Links: physical interactions are labeled in black and genetic interactions in red. If both interactions are available for a given link, only the physical interaction is shown. This color scheme is also applied to Figures 4–7. In an effort to define the function of these proposed network modules, we asked whether each individual module could be best described by a particular annotation. A module is considered to be enriched for a given annotation if the number of components known to have that function within the module exceeds the number that could be expected from random chance. It has been proposed that the degree of enrichment for a given annotation can be measured by its hypergeometric distribution (Tavazoie et al. 1999). Using this approach, we calculated the annotation enrichment for each of the ten protein modules in the AHR–PIN with the FunSpec program (Table S5) (Robinson et al. 2002). As shown in Figure 3B, it was found that the AHR–PIN is organized by protein modules that perform distinct cellular functions (e.g., protein folding and chromatin modification). Functional Modules as Revealed by Their Influence on Different AHR Domains In an effort to test the predicted modules and define how they influence AHR signaling, we annotated the AHR–PIN using a number of independent functional tests. First, we examined whether functional modules could be identified based upon their influence on different domains of the AHR. To this end, we examined the influence of each modifier on the signaling of a partial-deletion mutant, pAHRΔPASB, which contains the AHR's transcriptionally active domain but is missing those domains responsible for ligand binding and Hsp90 interaction (Figure 4A). Of the 53 modifier deletions successfully transformed with the pAHRΔPASB system, we found that 25 deletions affected both the parent AHR and the deletion mutant. This observation indicated that these 25 modifiers had an influence on the shared C-terminal TAD region and not on the PASB domain (Figure 4A). These modifiers were referred to as the “TAD influence group.” The remaining 28 deletions, which required the PASB domain for their effect, were referred to as the “PASB influence group.” Figure 4 Functional Modules Identified by the “Domain Influence” (A) Identification of domain influencing groups. The effects of modifier deletions on the signaling of AHR and AHRΔPASB were compared in parallel. It was found that 28 modifiers were required for the function of the PASB domain (i.e., their deletions affected the AHR, but not the AHRΔPASB). The other 25 modifiers were found to be required for the shared TAD region (i.e., their deletions affected the signaling of both AHR and AHRΔPASB). (B) Overlay of the “domain influence” layer (blue boundary) and the network-clustering layer (shadowed) on the AHR–PIN. The PASB influence group corresponds to a central region in the AHR–PIN. The TAD influence group corresponds to two peripheral areas. Occasional outlier nodes are marked with their corresponding module names. When the AHR–PIN was annotated according to the domain influence of each modifier, it was found that modifiers from the same domain influence group closely interacted in the map. That is, the PASB influence group resided in a single connected region, whereas the TAD influence group occupied two peripheral regions (Figure 4B). Interestingly, the PASB module was found to overlap with the computationally identified clusters 1, 3, 5, 8, 9, and 10. For the two TAD modules, one overlapped with cluster 6, and the other with clusters 4 and 7. This overlap supported both the computational and experimental annotations. For example, the “chromatin modification cluster,” 6, identified and annotated computationally, was found to be associated with the TAD influence group, defined experimentally. Similarly, the “protein folding cluster,” 5, was associated with the PASB domain influence group. The PASB domain is known to interact with the chaperone protein Hsp90, which plays a significant role in the folding of the mammalian AHR (Pongratz et al. 1992; Carver et al. 1994; Whitelaw et al. 1995). Functional Modules as Revealed by Their Effect on AHR Pharmacology To further annotate the AHR–PIN, each of the 54 modifiers was tested for its influence on AHR signaling (pAHR system) at various agonist concentrations, times, and temperatures, as well as after exposure to two distinct AHR agonists, α-naphthoflavone (αNF) and β-naphthoflavone (βNF). The relationship between each modifier and signaling was then examined using a hierarchical average-linkage clustering algorithm (Eisen et al. 1998) (Figure 5A). It was found that the five major clusters corresponded to five closely intraconnected local areas in the map, designated A, B, C, D, and E (Figure 5B). Among them, modules A and C exhibited significant functional enrichment of protein folding and transcriptional control, respectively (data not shown). When the clustering result was overlaid upon the previous maps, it was found that modules A, D, and E corresponded to the PASB influence module, and modules B and C corresponded to the TAD influence module (Figure 5B). Figure 5 Functional Modules Revealed by Effect on AHR Pharmacology (A) Cluster analysis of the effect of modifier deletion on AHR pharmacology. AHR signaling was examined at various doses, timepoints, and temperatures, and with the two AHR agonists βNF and αNF. The influence of modifier deletion on the dose-response of the AHR was analyzed by a hierarchical clustering algorithm. Rows in the clustering diagram represent modifier deletions. Columns correspond to experimental conditions. Green and red indicate down- and up-regulated AHR signaling, respectively. Color brightness is proportional to fold change. Black indicates wt signaling. Sparse gray boxes represent missing datapoints. (Insert) Diagram of corresponding dose-response curves of the wt strain and the average of cluster C. (B) Overlay of the “pharmacology clustering” layer (shadowed, black boundary) and “domain influence” layer (blue boundary) on the AHR–PIN. The major pharmacology clusters are coincident with five local areas in the AHR–PIN. In addition, clusters A, D, and E correspond to the PASB influence module, and clusters B and C correspond to the TAD influence module. Functional annotations determined by pharmacology clustering are indicated in black, and those derived from domain influencing are indicated in blue. Occasional outlier nodes are marked with their corresponding module designation. See the legend of Figure 3 for the color scheme of the nodes and links. Functional Modules as Revealed by Their Influence on AHR Localization Lastly, we examined each modifier's influence on AHR's subcellular localization. This was accomplished using an AHR–GFP fusion protein (pAHRGFP). When the wt strain was transformed with the plasmid pAHRGFP, it was found that the fusion protein was evenly distributed in the cell in the absence of AHR agonist. In the presence of the agonist βNF, the AHR–GFP protein translocated to the nucleus (Figure 6A). To examine the influence of each modifier on this translocation process, the pAHRGFP construct was transformed into each of the 54 modifier deletion strains and its localization was examined by fluorescence microscopy in the presence of agonist. Four localization phenotypes were identified (Figure 6B). About 50% of the deletion strains exhibited AHR translocation similar to that observed in the wt strain (group I). Approximately 30% of the strains were found to contain a marked reduction in the level of AHR protein in the cell (group II). Approximately 10% of the deletion strains displayed receptor aggregates in the cell (group III). The final 10% of the deletion strains displayed a normal level of AHR protein, but the receptor failed to translocate into the nucleus in the presence of agonist (group IV). When overlaid with the previously determined experimental layers, group I was found to overlap with the modules of C and D, and groups II, III, and IV corresponded to modules B, A, and E, respectively (Figure 6C). According to this overlap, module B can be further described as being associated with the regulation of receptor level in the cell, and module E is associated with the regulation of nuclear translocation of the AHR (Figure 6C). Figure 6 Functional Modules Identified by the “Localization Influence” (A) The AHR–GFP fusion protein translocates to nucleus in the presence of agonist βNF. Nucleus position in the cell was confirmed by DAPI staining (data not shown). Dimethyl sulfoxide (DMSO) is a vehicle control for βNF. (B) Classification of modifier deletion strains according to AHR–GFP phenotype (with βNF). Group I displays wt phenotype. Group II contains decreased level of receptor protein. Group III contains aggregated misfolded receptor. Group IV displays the AHR that is not capable of translocating to the nucleus. (C) Overlay of “localization influence” layer (shadowed, red boundary) and the “pharmacology clustering” layer (black boundary) on the AHR–PIN. Group I corresponds to modules C and D. Groups II, III, and IV overlap with modules of B, A, and E, respectively. Functional annotations determined by localization influence are indicated in red, and those derived from pharmacology clustering and domain influencing studies are indicated in black. Occasional outlier nodes are noted with their corresponding module designation. See the legend of Figure 3 for the color scheme of the nodes and links. Discussion Modifier Identification Our initial objective was to identify the number of loci that are required for AHR signal transduction. In this regard, our high-throughput deletion screen identified 52 novel and two known AHR modifiers. Although this is a surprisingly large number of modifiers for the function of a single protein, it is probably an underestimate since the deletion screen cannot identify modifiers that are encoded by essential genes. Moreover, our criteria of including only strong modifiers (influence of 4-fold compared to control) may have caused us to miss some important modifiers of this pathway. Nevertheless, the number of AHR modifier loci reported here is approximately 10-fold greater than what has been reported using mammalian cell culture and animal models (Schmidt and Bradfield 1996; Whitlock 1999). Once we identified these AHR modifiers in yeast, we sought a way to position and characterize them in the context of the AHR pathway. Given the idea that PINs can be used to portray the cellular workings, we attempted to use our deletion data to generate and annotate an AHR–PIN (Hartwell et al. 1999; Schwikowski et al. 2000; Ge et al. 2001; Ideker et al. 2001; Tong et al. 2002). To construct the AHR–PIN, the yeast genomic PIN was decomposed by extracting those nodes/links relevant to AHR modifiers. To test the utility of the resultant AHR–PIN, a series of Monte Carlo simulations were carried out. It was demonstrated that when Dmax was set at 2, 3, or 4, the resultant AHR–PIN was of a complexity that could not have resulted from random chance. Furthermore, the comparison of various simulations at different Dmax settings guided us to select the linking parameter at Dmax = 2. This setting of intervening links resulted in the highest level of statistical significance, displayed the lowest potential for false positive interactions, and decreased the map's visual complexity to a level that was readily understood in a two-dimensional map. The Modular Structure of AHR–PIN Reveals Five Discrete Steps in Signaling Our analysis of the AHR–PIN revealed an underlying modular structure. That is, there are areas in the AHR–PIN that display high interconnectedness of nodes, and these regions represent functionally related modifiers. The modularity of AHR–PIN was revealed by both computational and functional tests. In our initial computational approach, a total of ten clusters were identified, and the functional enrichment of each cluster was calculated by hypergeometric distribution (Tavazoie et al. 1999; Robinson et al. 2002). Although the computational approaches of module identification and annotation were useful in hypothesis generation, they did not provide a direct description of AHR signaling. Therefore, we set out to annotate the AHR–PIN with a number of functional tests. In our first annotation experiment (“domain influence”), we found that the AHR–PIN could be divided into three discrete functional modules (i.e., one module that influenced the PASB domain and two modules that influenced the C-terminal domain we referred to as TAD). Additionally, each of these modules was found to overlap with one to several network clusters (see Figure 4). This tight overlay of functional data with highly interconnected regions in the AHR–PIN also held true when we applied annotations for pharmacological clustering and subcellular localization studies (see Figures 5 and 6). Given the overlay of these annotations derived from both functional and computational tests, we conclude that the AHR–PIN provides a biologically meaningful representation of the regulatory network of AHR signaling (Figure 7A). Moreover, based upon the combined annotations for each individual module, we propose that AHR signal transduction is regulated at five discrete steps: (1) receptor folding, (2) receptor translocation, (3) receptor transcriptional activation, (4) receptor level, and (5) a previously undescribed signaling event related to the PASB domain (Figure 7B). Figure 7 Regulatory Network of AHR Signaling (A) The summary map of AHR–PIN. Functional modules were determined by the overlapped annotations from three experimental layers (domain influence, pharmacology clustering, and localization influence) as well as from network clustering. For each functional module, the main “stacking pattern” of experimental layers is noted in italics. Modifiers initially left outside the single large cluster of the AHR–PIN were assigned to corresponding functional modules by sharing the similar stacking pattern where applicable. See the legend of Figure 3 for the color scheme of the nodes and links. (B) An expanded model of AHR signaling. The AHR signaling pathway is regulated by at least five functional modules that are involved in the control of receptor folding, nuclear translocation, transcriptional activation, receptor level, and a PASB-related nuclear event. Within each functional module, modifers intially enclosed in the single large cluster of the AHR–PIN are highlighted in bold. Known human homologs of the modifiers are noted at the side with a smaller font (Costanzo et al. 2001) . ARNT is dimmed because modifiers were identified in this study from an “ARNT-free” chimeric AHR system. See text for details. The AHR Folding Module A module that regulates AHR folding was identified by the known activities of its constituents, as well as the appearance of receptor aggregates when these modifiers were absent (see Figure 6B, group III). Given that AHR folding has been well studied over the past 15 years, examination of this module provided insight into the fidelity of our screen and the transference of our observations to the mammalian system. For example, two known modifiers were identified by our high-throughput screen: Hsc82p (homolog of human Hsp90) and Cpr7p (homolog of human Cyp40) (Pongratz et al. 1992; Miller 2002). In addition, we identified a previously unknown player in the AHR folding pathway, the chaperone protein Sti1p (homolog of human p60/HOP). Sti1p/p60 has been shown to be an essential component of the glucocorticoid receptor signaling pathway, where it is required to form an Hsp90 chaperone complex (Chang et al. 1997; Dittmar and Pratt 1997). By analogy, we propose that Sti1p/p60 is involved in the formation of an Hsp90·cochaperone complex that is essential for the proper folding of the AHR. Finally, our analysis of this module suggests that a number of proteins not known to be chaperones are involved in receptor folding. These proteins include Sec28p and possibly Rpl19b. The AHR folding module can also be used to explain the existence of I-nodes within a functional module. Given their “linker” position and the observation that they often share similar annotated function with their neighboring M-nodes (data not shown), it is a logical prediction that I-nodes play a role in AHR signaling that is functionally similar to their modifier neighbors. We propose that I-nodes most commonly arise as the result of their essential gene nature (gray nodes in the figure; nontestable in the deletion screen) or because they represent a redundant gene product (white nodes in the figures). We offer two examples that support this idea. First, one essential gene I-node in the folding module, Cns1p, has recently been reported to be involved in AHR signaling (Miller 2002). Second, the possibility that white nodes may often result from redundancy is supported by what we know about Hsp90. The Hsc82p and Hsp82p proteins are yeast orthologs of human Hsp90, a well-studied chaperone required for proper AHR folding (Pongratz et al. 1992; Carver et al. 1994; Whitelaw et al. 1995). Under normal growth conditions, Hsp82p and Hsc82p account for 7% and 93% of the total “Hsp90 level,” respectively (Borkovich et al. 1989). Thus, it is not surprising that Hsp82p was not identified as a modifier, since its deletion would have had little effect on the total Hsp90 level in the cell (Figure 7A). Finally, white I-nodes can also arise from weak modifiers that influenced AHR signaling by less than 4-fold, e.g., Sba1p (ortholog of human AHR modifier p23) (Kazlauskas et al. 1999). In this regard, although a choice of 4-fold was somewhat arbitrary, we found that lowering the cutoff greatly increased the network complexity without enhancing the statistical significance of the AHR–PIN (as compared with random PINs; data not shown). The AHR Employs a Multistep Transcriptional Mechanism The composition of the transcriptional activation module suggests that the AHR activates target genes via the coordination of histone acetylation, ATP-dependent chromatin remodeling, and direct recruitment of basal RNA polymerase II transcriptional apparatus (see Figure 7). We base this idea on the observation that this functional module is composed of components of the histone acetyltransferase SAGA complex (homolog of the mammalian PCAF complex)—Gcn5p, Spt3p, and Spt8p; components of the SWI/SNF chromatin-remodeling complex—Snf12p and Swi3p; and a subunit of the Srb–mediator complex—Srb2p (Grant et al. 1998; Myers et al. 1998; Peterson et al. 1998). This interdependent requirement of three distinct classes of transcriptionally relevant proteins is consistent with observations from mammalian cells, where the involvement of both HAT and SWI/SNF coactivators in AHR signaling has been reported, as has the direct interaction of the AHR with basal transcriptional factors TBP, TFIIF, and TFIIB (Rowlands et al. 1996; Kobayashi et al. 1997; Swanson and Yang 1998; Beischlag et al. 2002; Wang and Hankinson 2002). These collective data support the idea that AHR transactivation is mediated by a multicomponent, synergistic process. Nuclear Translocation of the AHR Our network analysis has also identified a functional module that regulates the ligand-dependent translocation of the AHR (see Figure 7). This nuclear translocation module appears to be associated with the PASB domain, which is known to play roles in both ligand binding and interaction with chaperones (see Figure 4A). This observation is consistent with the idea that ligand exposure releases the AHR from the cytosolic chaperone anchors (Kazlauskas et al. 2001; Petrulis et al. 2003). Although the mechanism for this translocation event remains unclear, it is interesting to note that the “translocation module” overlaps with a protein degradation cluster, cluster 10 (see Figure 7A). This observation suggests that the underlying control of subcellular localization of the AHR might be related to the selective degradation of certain tethering factors by ubiquitination, possibly mediated by Doa1p and other members in this module (Hochstrasser and Varshavsky 1990). Regulation of AHR Expression A module that regulates the amount of receptor protein was also identified in our AHR–PIN (see Figure 7). This module is associated with the C-terminal domain of the AHR (see Figure 4A). Although we have commonly referred to this region as the TAD domain, these data suggest that other functions are also encoded here. We base this assessment on two observations. First, members of this module are not known to play direct roles in transcription (see Table S4). Second, this module influences receptor level in a manner that is upstream of the AHR's activity as a transcription factor. Our interpretation of this module is that these modifiers are associated with a domain that is proximal to or overlaps with the receptor's TAD and that this domain plays a role in the regulation of receptor level (see Figure 4A). At the present time it is not clear whether this module influences the AHR at its mRNA or protein level. A Novel Step Defined by the PASB Module A novel PASB-dependent step in AHR signaling appears to have been revealed by this network analysis (see Figure 7, PASB-related module). Given that corresponding deletions of this PASB-related module did not impair the receptor's nuclear translocation (see Figure 6, group I), we conclude that this module must influence either a downstream nuclear event or some cytosolic event that is not revealed until the receptor is within the nuclear compartment. On the other hand, this module did not appear to be involved in the final transactivation step, as it was distinct from the transactivation module according to our functional annotations (see Figures 4 and 5). Taken in sum, there must exist a PASB-dependent event that is posttranslocation and pretransactivation. Such an event could be related to the receptor's dimerization, DNA binding, or an as-yet-undefined nuclear event, such as the unfolding of a transcriptionally active domain (Sun et al. 1997; Heid et al. 2000). Interestingly, the existence of this PAS-related signaling is consistent with the previous observation that the DNA binding ability of the AHR can be impaired by a point mutation within its PAS domain (Sun et al. 1997). Lastly, the fact that this PASB-related module overlaps with multiple network clusters (1, 2, 8, 9) suggests a cooperative mechanism that involves more than one cellular function (see Figure 7A). Conclusion We began this study with the objective of defining the AHR signal transduction pathway in a manner that would allow us to quantify the number of loci and enumerate the steps involved in signaling. By integrating our deletion screen with the PIN framework and through subsequent computational and experimental annotations, we were able to identify modifier modules that regulate five distinct AHR signaling steps. In this regard, we found that the integration of multiple annotation approaches is vital for the reconstruction of the final picture by connecting and cross-validating individual information pieces. As interaction datasets become more fully developed and annotated, such a map will steadily improve and provide more accurate description of AHR signaling. Lastly, the systematic strategy that we developed in this work should be readily applicable to the study of most mammalian proteins to reconstruct corresponding modifier networks that regulate their signaling. Materials and Methods Strains and plasmids A set of deletion derivatives of S. cerevisiae strain BY4742 (MATα, his3Δ1, leu2Δ0, lys2Δ0, ura3Δ0) was used in this study. This deletion set was obtained from Research Genetics (now a part of Invitrogen, Carlsbad, California, United States) in a 96-well arrayed format. The plasmid pCEN-AHR (PL1605) was constructed by replacing the TRP1 autotrophic marker of PL883 (Hogenesch 1999) with a HIS3 marker using a “marker swap” method (Cross 1997). This CEN-based plasmid contains the LexA–AHR chimera cDNA (LexA-AHRNΔ166) under the control of an alcohol dehydrogenase I (ADH1) promoter. LexA-AHRNΔ166 is a chimeric AHR, with its amino acid residues 1–166 replaced by residues 1–202 of bacterial repressor LexA, and is referred to in the Results section simply as “AHR” for convenience. The reporter plasmid pSH18–34 (PL623) (Clontech, Palo Alto, California, United States) is a 2μ-based, URA3-selectable vector that contains the bacterial LacZ gene, as a reporter, under the control of eight LexA-binding sites. The plasmid pEG202 (Clontech, Palo Alto, California, United States) is a 2μ-based, HIS3-selectable plasmid containing the LexA1–202 sequence under the control of the ADH1 promoter. The plasmid pAHR (PL700) has been described previously (Carver 1996). This plasmid contains the AHRNΔ166 sequence inserted into the EcoRI site of pEG202. The pGal4TAD control plasmid (PL1573) (Display Systems Biotech, now NeuroSearch A/S, Ballerup, Denmark) contains the transcription activation domain of yeast GAL4 inserted into the EcoRI site of pEG202. The control plasmid pAHRΔPASB (PL1799) is the same as pAHR except for the removal of the C-terminal half of the PAS domain. This pAHRΔPASB plasmid was constructed by subcloning the EcoRI fragment of PL248 (Carver et al. 1998) into the EcoRI site of pEG202. The plasmid pAHRGFP (PL1890) was constructed as follows: the GFPS65T cassette (Heim et al. 1995) was amplified by PCR from pRSETBGFPS65T (PL1803) (a generous gift from Dr. Catherine Fox, University of Wisconsin–Madison) using primers OL4125 (5′-ACAGCTCTGAAATTCCAGGTTCTCAGGCATTCCTAAGCAAGGTGCAGAGTGGTCGGGATCTGTACGACGAT-3′) and OL4126 (5′-TTAGCTTGGCTGCAGGTCGACTCGAGCGGCCGCCATGGTCGACGGATCCCACCAGCTGCAGATCTCGAGCT-3′). The amplicon was cloned into the DraIII-digested pAHR by a gap repair method (Lundblad and Zhou 1997). The resulting plasmid was designated PL1855. The coding sequence for amino acids 1–166 of the AHR was amplified by PCR from PL65 (Dolwick et al. 1993) using primers OL4176 (5′-GCTATACCAAGCATACAATCAACTCCAAGCTTGAATTAATTCCGGGCGGAATGAGCAGCGGCGCCAACAT-3′) and OL4177 (5′-CCTTGTGCAGAGTCTGGGTTTAGAGCCCAGTGAAGCTGGCGCTGGAATTCCGCCCGGTCTTCTGTATGGA-3′). The amplicon was cloned into the PmeI/MluI-digested PL1855 by gap repair. The resultant plasmid was designated pAHRGFP (PL1890). High throughput yeast deletion array transformation A high-throughput protocol was developed for 96-well transformation based on work previously described (Chen et al. 1992). Unless otherwise noted, all steps were performed with a Hydra 96-channel dispenser (Robbins Scientific, Sunnyvale, California, United States) and a vortex mixer with a microwell plate adaptor (#12-812 and #12-812C, Fisher Scientific, Hampton, New Hampshire, United States). Deletion strains were stored in a stack of 96-well plates (–80 °C). For transformation, each stock plate was thawed and cells were gently resuspended by vortexing. About 0.5 μl of each strain culture was transferred to a 96-well round bottom target plate (Costar #3795, Corning Inc., Acton, Massachusetts, United States) containing 96 μl per well of yeast extract–peptone–dextrose (YPD) medium plus G418 (200 mg/l). This transfer was accomplished with a 96-pin disposable replicator (GenomeSystems, now Incyte Genomics, Palo Alto, California, United States). The inoculum was incubated at 30°C without shaking until the OD600 absorbance of individual wells reached 0.2–0.7 (approximately 18 h). The OD600 was measured using a SpectraMax 250 microplate reader (Molecular Devices, Sunnyvale, California, United States). Cells were then subjected to centrifugation at 3,500 rpm for 8 min, and the supernatant was decanted. The 96-well plates were placed upside-down on a stack of paper towels for 10 min to drain residual medium. For transformation, each plate was vortexed at maximal speed for 15 s before dispensing 22 μl of DNA in “OneStep” buffer (V1M LiAc:V50% PEG 3350 = 1:4, with BME added to 0.77% V before use) into each well. To make the DNA in “OneStep” buffer, one volume of DNA (5 μg/μl ssDNA, 0.1 μg/μl each plasmid DNA) was mixed vigorously by vortexing with ten volumes of “OneStep” buffer. After DNA was dispensed, the plate was quickly vortexed again at maximal speed for 10 s to resuspend the cells, followed by incubation at 45°C for 40 min. After this “heat shock” step, 5 μl of the transformation mix from each well was inoculated into a fresh 96-well flat-bottomed plate containing 96 μl per well of dropout medium without Trp, Ura, and His (dropout minus TUH medium) plus G418. The inoculum was gently mixed by vortexing and incubated at 30°C for about 4 d until transformants grew out. The 384-well fluorescence assay for LacZ reporter To perform the LacZ reporter assay, transformants from the 96-well plates were rearrayed into 384-well stock plates containing 30 μl per well of dropout minus TUH medium. The inoculum was incubated at 30°C for 2–3 d to allow cell growth. For the LacZ reporter assay at each agonist concentration, 0.5 μl of cell culture was transferred from the 384-well stock plate (30°C) into a clear-bottomed/black-walled 384-well assay plate (Falcon #353962, Becton Dickinson, Franklin Lakes, New Jersey, United States) using a disposable 384-pin replicator (GenomeSystems/Incyte Genomics). In the 384-well assay plate, each well contained 18 μl of dropout minus TUH medium (diluted 1:4 in water) plus agonist at the tested concentration. The plates were then incubated at 30°C for 48 h to allow all strains to reach stationary phase. Cell growth was monitored by measuring the OD600 of each well using a SpectraMax Plus384 microplate reader (Molecular Devices). To initiate the fluorescence assay, 18 μl of lysis/assay buffer was added to each well. Lysis/assay buffer contained a mixture of CUG substrate (#F-2905, Molecular Probes, Eugene, Oregon, United States), 10% SDS, 1 M NaPO4, and 25× TAE in the ratio of 1:1.4:350:17.5. For assays with pCEN-AHR transformants, no TAE was required. Plates were vortexed at medium speed for 1 min and left at room temperature for 20 min. The reaction was stopped by dispensing 6.5 μl of 25× TAE to each well and vortexing at medium speed for 1 min. The fluorescence emission of each well was detected using a Wallac “VICTOR V” microplate reader (Perkin-Elmer, Boston, Massachusetts, United States). The fluorescence reading was normalized to the corresponding OD600 value to obtain the LacZ reporter activity of each deletion strain. In vivo microscopic analysis of AHR–GFP localization Selected deletion strains were transformed with the plasmid pAHRGFP. Transformants were incubated in a 96-well microtiter plate containing 100 μl per well of dropout minus TH medium at room temperature. Given that we have observed that small temperature shifts can affect AHR's localization, we found it more convenient to both grow and examine cells at the same temperature. For some samples, assays were repeated at 30°C using a heating chamber attached to the microscope. Such results were found to be comparable to those obtained at room temperature. For strains that reached early log phase, 0.5 μl of culture was mounted on a glass slide, and the AHR–GFP subcellular localization was examined using a Zeiss (Oberkochen, Germany) Axiovert 200M microscope (α Plan-FLUAR 100× objective). Images were captured using an AxioCam HR digital microscope camera (Zeiss). To stain the nucleus in living cells, 4,6-diamidino-2-phenylindole (DAPI) was added to the dropout minus TH medium to a final concentration of 5 μg/ml. Modifier identification and network analysis To identify deletions that modify AHR signaling, the LacZ reporter activity of each deletion strain was compared to the average of wt BY4742 strain controls included in the same plate, and the fold change was obtained and log2 transformed. These data-processing steps, as well as subsequent modifier selection, were performed automatically using Perl scripts written “in house.” In brief, for the primary screen involving 4,507 deletion strains with low-copy pCEN-AHR system, a stringent cutoff of 4-fold change over wt control was chosen for selecting a pool of most significant AHR signaling mutants. This cutoff corresponds to a p value of less than 10–6 at all six assessed concentrations (null distribution: wt control). The initial positives were subject to validation and characterization in secondary screens with high-copy pAHR and control systems. The cutoffs for control pathways pGal4TAD and pAHRΔPASB in the secondary screens were chosen at 2-fold change over wt control, which corresponds to p values of 3.3 × 10–2 and 5.6 × 10–4 (null distribution: wt control), respectively. For PIN construction, the main physical interaction table was downloaded from the DIP database (http://dip.doe-mbi.ucla.edu) and the genetic interaction table from the MIPS database (http://mips.gsf.de/proj/yeast/). Perl scripts, written “in house,” were used to search the combined physical and genetic interaction database and identify all valid paths (less than or equal to Dmax) that linked each pair of modifiers. The network graph was rendered using the Graphviz tool kit (http://www.research.att.com/sw/tools/graphviz/) (Ellson et al. 2004). Within experimental annotation layers of the AHR–PIN, the region corresponding to each functional module was outlined by a closed line (boundary) drawn manually on the network map. This boundary was delineated to include the maximal number of modifier nodes that are members of the corresponding functional module and the minimal number of modifier nodes that are nonmembers. This boundary was also defined in such a way that all enclosed modifier nodes were interconnected via paths within the enclosed region or through at most one modifier node outside. When defining functional modules in the summary AHR–PIN, the highest weight was given to the results from the localization influence experiments because these results provided the most direct indication of a modifier's effect on AHR signaling, and the lowest weight was given to the pharmacology clustering result because this result was highly sensitive to the choice of clustering algorithm. Supporting Information Table S1 Significant AHR Modifiers This table contains all of the ORFs whose corresponding deletion strains reproducibly displayed a significant change in AHR signaling compared to wt BY4742 strain. Also shown are their known gene names, products, gene descriptions, and Gene Ontology (GO) annotations (Ashburner et al. 2000; Issel-Tarver et al. 2002). (35 KB XLS). Click here for additional data file. Table S2 AHR-Specific Modifiers This table contains all of the ORFs that were observed to influence the signaling of the AHR but not the pGal4TAD control. Also shown are their known gene names, products, gene descriptions, and GO annotations (Ashburner et al. 2000; Issel-Tarver et al. 2002). (27 KB XLS). Click here for additional data file. Table S3 YPD Annotation of AHR Modifiers This table summarizes the annotation on cellular functions of AHR modifiers. The annotation was derived from the YPD database, as of May 2002 (Costanzo et al. 2001) . (23 KB XLS). Click here for additional data file. Table S4 M-Nodes in the AHR–PIN This table contains all of the AHR modifiers that were interconnected in the AHR–PIN (Dmax = 2). Also shown are their known gene names, products, gene descriptions, and GO annotations (Ashburner et al. 2000; Issel-Tarver et al. 2002). (24 KB XLS). Click here for additional data file. Table S5 Functional Enrichment of Network Clusters This table summarizes the functional enrichment of each network cluster as calculated by the hypergeometric distribution of MIPS and GO annotations. For each cluster, the functional enrichment is determined by using M-nodes alone and both M- and I-nodes, respectively. In each case, the annotation that corresponds to the largest number of nodes in the cluster and the smallest p value is shown (k, number of genes from the query cluster in the given category; f, total number of genes in the given category). (22 KB XLS). Click here for additional data file. We thank Drs. Li Ni, Trey Ideker, Hao Helen Zhang, and Zhaobang Zeng for helpful discussions and Aaron Vollrath for critical reading of the manuscript. This work was supported by grants from the National Institutes of Health (R37ES05703 and P30CA014520). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. GY and CAB conceived and designed the experiments. GY performed the experiments and analyzed the data. GY, MC, and ND contributed reagents/materials/analysis tools. GY and CAB wrote the paper. Academic Editor: Erin K. O'Shea, University of California, San Francisco * To whom correspondence should be addressed. E-mail: Bradfield@oncology.wisc.edu Abbreviations ADH1alcohol dehydrogenase I AHRaryl hydrocarbon receptor DAPI4,6-diamidino-2-phenylindole DREdioxin responsive enhancer GOGene Ontology HAThistone acetyltransferase αNFα-naphthoflavone βNFβ-naphthoflavone ORFopen reading frame PAHpolycyclic aromatic hydrocarbon PASPer–Arnt–Sim PINprotein interaction network TADtranscriptional activation domain TUHTrp,Ura wtwild-type YPDyeast extract–peptone–dextrose ==== Refs References Ashburner M Ball CA Blake JA Botstein D Butler H Gene ontology: Tool for the unification of biology—The Gene Ontology Consortium Nat Genet 2000 25 25 29 10802651 Beischlag TV Wang S Rose DW Torchia J Reisz-Porszasz S Recruitment of the NCoA/SRC-1/p160 family of transcriptional coactivators by the aryl hydrocarbon receptor/aryl hydrocarbon receptor nuclear translocator complex Mol Cell Biol 2002 22 4319 4333 12024042 Borkovich KA Farrelly FW Finkelstein DB Taulien J Lindquist S hsp82 is an essential protein that is required in higher concentrations for growth of cells at higher temperatures Mol Cell Biol 1989 9 3919 3930 2674684 Carver LA Characterization of the aryl hydrocarbon receptor signaling pathway using a yeast expression system [dissertation]. 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020067Book Reviews/Science in the MediaMolecular Biology/Structural BiologyMammalsThe DNA Story, Part III Book Review/Science in the MediaDoty Paul 3 2004 16 3 2004 16 3 2004 2 3 e67Copyright: © 2004 Paul Doty.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Maurice Wilkins's autobiography provides an engaging perspective of the events leading to the discovery of the structure of DNA ==== Body As the year-long celebration of the 50th anniversary of the discovery of the structure of DNA came to an end, the engaging autobiography of one of the participants further enlivened the drama of this event. Maurice Wilkins, now 87, postpones the account of his involvement in the DNA affair until the second half of the book. Recounting his background and interesting life before DNA (34 years) in plain but telling sentences brings to life a character that is almost as much out of the ordinary as those of the more flamboyant James Watson and Francis Crick. Wilkins' first six years in New Zealand (a Garden of Eden) were followed by a long, vividly described trip to England, where the family eventually settled in Birmingham. His boyhood was marked by immersion in astronomy and telescope-making, but saddened by the painful illness of his sister. Success in school physics was the key for getting into Cambridge, where he reveled in the world of Ernest Rutherford, Mark Oliphant, and John Bernal. Given his leftist leanings, it was inevitable that Wilkins would become involved in the pacifist movement in Cambridge, with its close connection to the Communist Party. Perhaps too much involvement led to a low degree grade in 1938 and no hope of remaining at Cambridge. Instead, he returned to Birmingham and joined the Luminescence Lab being established by John Randall, a man with whom he would be closely connected for many decades. The work there contributed to Randall's scheme for making radar practical in air defense—the cavity magnetron that may have turned the course of World War II. Early in 1944, Oliphant, then at Birmingham, left to work on the atomic bomb at Berkeley and took Wilkins along. Life in Berkeley was exciting, but beneath the excitement of bomb work and mixed feelings upon its success at Hiroshima, Wilkins read Erwin Schrodinger's What Is Life? Along with others who were to unravel the secrets of DNA, this planted the seed. When, after three transitional years, Randall became head of Kings College London's physics department and director of a biophysics research unit sponsored by the Medical Research Council (MRC), Wilkins was his deputy. The attack on DNA structure soon began. That X-ray diffraction might play a major role in this search rested on two pillars unique to England. One was the British lead in using X-ray diffraction to determine molecular structures—a crown jewel built on the work of the Braggs (father and son), Bernal, and Dorothy Hodgkin. The other was the pre-World War II work of William Astbury in showing that DNA fibers displayed some crystallinity that, if developed, might be the basis of helping to determine the structure. Wilkins confides that in 1950 he knew little of how such X-ray analysis might be done. But in that year he was presented with an opportunity in the form of samples of carefully prepared calf DNA, given to him by a Swiss chemist, Rudolf Signer. With this DNA, much better fibers could be obtained and much sharper diffraction diagrams emerged. The exploitation of this advance, however, became mired in a colossal error in Randall's management of the group. Without telling Wilkins, he wrote to Rosalind Franklin, who was on her way to join the DNA effort, that Wilkins was withdrawing from DNA work and that she would take over. Unaware of this, Wilkins and Alec Stokes continued their work and reported at a meeting in Cambridge in July 1951 that DNA chains were probably in a helical conformation with a diameter of 20 Å. At the close of the meeting, Franklin assailed Wilkins, saying that he should stop his DNA work (as Randall had written would be the case). Understandably, but regrettably, the two groups continued working in isolation from each other. Matters worsened. In October, Watson arrived at Cambridge and set up DNA structure studies with Crick. They quickly arrived at a three-stranded helical structure. But Franklin and Wilkins soon demolished it. Likewise, a three-stranded model at Kings College had a very short life. As if to trump these failures, Bragg at Cambridge and Randall at Kings agreed that DNA studies at Cambridge should stop and that the work should continue only at Kings. Mismanagement and noncooperation were taking their toll. Franklin was moving toward a two-stranded structure, but away from helices. Indeed, in mid-1952 she initiated a discussion with an announcement about the death of the helix. Mysteriously, she put aside a striking photo of the diffraction pattern of B-DNA (one of the two major structural forms of DNA) that emerged in early 1953 as a perfect signature of the helical form. But 1952 continued downhill. Even Wilkins stopped DNA work that November. Suddenly, in the new year, life returned to the DNA effort. Linus Pauling had just published a structure (three-stranded) that did not long survive, but the entrance of the world's leading structural chemist into the race reawakened everyone to the centrality of DNA structure. In January, Raymond Gosling gave to Wilkins the very well-oriented diffraction photo of B-DNA that he and Franklin had taken in July 1952. Wilkins assumed that it was given to him to do as he wished; a few days later, he showed it to Watson. Though hardly an expert in X-ray diffraction, Watson sensed that it was strong evidence for helices and sketched it for Crick on his return to Cambridge. Later that January, Franklin announced she would be moving from Kings College to Birkbeck College to join Bernal's group. In giving her final seminar, she switched from her earlier insistence that B-DNA was nonhelical, but did not show the photo that gave the strongest evidence for helicity. This shift put Franklin in a position to move forward on the structure of DNA, but without others' resorting to model building, the goal would have remained elusive. Finally, in mid-February, Max Perutz, who was a member of the MRC committee overseeing the Biophysics Unit at Kings College, passed on to Crick his copy of a report from that unit. This report contained Franklin's results that the phosphates were on the outside and that the A-form of DNA had a special crystalline arrangement called the monoclinic C2 space group. From his work with proteins, Crick saw immediately that the chains in the helical structure must be antiparallel and that there were probably two chains entwined. Watson used other data in the report to deduce that there were indeed two chains, not three or four. Erwin Chargaff had recently shown that in the base composition of all DNAs examined, adenines and thymines as well as guanines and cytosines are equal, i.e., A = T and G = C. Now released from the ban on DNA studies, Watson and Crick engaged in a frantic search using model building. They found a unique way to fit the bases in the structure by pairing, and by March 7 they had the double-helix model constructed: it obeyed the Chargaff ratios, it fit the X-ray data for B-DNA, and it provided a rational way to encode and transmit genetic information to subsequent generations. Wilkins was invited to view the model in Cambridge. He found it stunning. Watson asked him to be a coauthor of the paper. Wilkins, true to his character, declined, as he had not been involved in the final monumental stage. Back in London, Franklin had already moved to Birkbeck. She received the news of the discovery with equanimity. But a later examination of her notebooks showed that she had moved to favor helices and a two-chain (or possibly a one-chain) model. With the rather complicated story of the greatest discovery in biology in the century now reasonably complete, what is one to make of it? There are many answers. I will mention only three. The first is the key role played by model building. In fiber diffraction there is not enough information, by orders of magnitude, to locate every atom, as would be possible in diffraction by perfect crystals that give thousands of sharp reflections. Instead, the fiber diagram can only provide cues and some specifics, such as the repeat distance. Model building is a way of bringing into the picture previously determined bond distances and bond angles of components such as the purine and pyrimidine bases and the sugars that are unavailable from the fiber diagram. That this was not seen at Kings College left the researchers there well behind in a field that they had pioneered. A second lesson is the importance of bringing the full knowledge of single crystal analysis to fiber diagram interpretation. That Franklin and Wilkins missed noting that the monoclinic C2 space group meant that the chains in the fiber had to be antiparallel robbed them of an important clue to the structure. And third, the management of the Biophysics Unit at Kings College was a recipe for failure. Riddled by secrecy, diffuse lines of authority, the absence of strategies, and a lack of open congeniality, all so well described by Wilkins, who refers to it as Randall's Circus, this unit is a model of how not to succeed in group research. DNA research continued at Kings College in a gradually improving environment: important details were worked out. But there was no real renewal, such as aiming at how DNA is configured to accommodate proteins in the nucleus. Wilkins enjoyed being included in the subsequent awards—the Lasker and the Nobel prizes. With Crick, he was annoyed by Watson's rendering of events in The Double Helix. The final chapter of his own autobiography addresses the criticism that some have leveled against his cold relation with Franklin, but also his happiness in newfound family life. Research gradually gave way to the pursuit of pacifist goals in a number of organizations and to the popularization of science. His has been a useful life, a part of which contributed to the great revolution in biology. It is good to have the insight that this book presents in a candid and personal way. Paul Doty is Professor Emeritus of Biochemistry and Molecular Biology and of Public Policy at Harvard University in Cambridge, Massachusetts, United States of America. E-mail: pauldoty@fas.harvard.edu Book Reviewed Wilkins M (2003) The third man of the double helix: The autobiography of Maurice Wilkins. Oxford, United Kingdom: Oxford University Press. 274 p. ISBN (hardcover) 0-198-60665-6. US$27.50.

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020068PrimerEcologyEvolutionGenetics/Genomics/Gene TherapyMicrobiologyEubacteriaInsectsEndosymbiosis: Lessons in Conflict Resolution PrimerWernegreen Jennifer J 3 2004 16 3 2004 16 3 2004 2 3 e68Copyright: © 2004 Jennifer J. Wernegreen.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Phylogenomics of the Reproductive Parasite Wolbachia pipientis wMel: A Streamlined Genome Overrun by Mobile Genetic Elements Genome Sequence for the Intracellular Bacterium Wolbachia Endosymbiotic bacteria live within a host species. There are many and diverse examples of such relationships, the study of which provides important lessons for ecology and evolution ==== Body Symbiosis, an interdependent relationship between two species, is an important driver of evolutionary novelty and ecological diversity. Microbial symbionts in particular have been major evolutionary catalysts throughout the 4 billion years of life on earth and have largely shaped the evolution of complex organisms. Endosymbiosis is a specific type of symbiosis in which one—typically microbial—partner lives within its host and represents the most intimate contact between interacting organisms. Mitochondria and chloroplasts, for example, result from endosymbiotic events of lasting significance that extended the range of acceptable habitats for life. The wide distribution of intracellular bacteria across diverse hosts and marine and terrestrial habitats testifies to the continued importance of endosymbiosis in evolution. Among multicellular organisms, insects as a group form exceptionally diverse associations with microbial associates, including bacteria that live exclusively within host cells and undergo maternal transmission to offspring. These microbes have piqued the interest of evolutionary biologists because they represent a wide spectrum of evolutionary strategies, ranging from obligate mutualism to reproductive parasitism (Buchner 1965; Ishikawa 2003) (Box 1; Table 1). In this issue of PLoS Biology, the publication of the full genome sequence of the reproductive parasite Wolbachia allows the first genomic comparisons across this range (Wu et al. 2004). Table 1 Examples of Bacterial Endosymbionts of Insects Lifestyle Extremes in Insect Endosymbionts At one end of the spectrum, beneficial endosymbionts provide essential nutrients to about 10%–15% of insects and provide models for highly specialized, long-term mutualistic associations (Figure 1). These ‘primary’ endosymbionts are required for the survival and reproduction of the host, most of which feed on unbalanced diets such as plant sap, blood, or grain, and occur within specialized host cells called bacteriocytes (or mycetocytes) (Baumann et al. 2000; Moran and Baumann 2000). Molecular phylogenetic analyses demonstrate stability of these obligate mutualists over long evolutionary periods, ranging from tens to hundreds of millions of years. By allowing their hosts to exploit otherwise inadequate food sources and habitats, the acquisition of these mutualists can be viewed as a key innovation in the evolution of the host (Moran and Telang 1998). Owing to their long-term, stable transmission from generation to generation (vertical transmission), these cytoplasmic genomes have been viewed as analogs to organelles. Figure 1 A carpenter ant, Camponotus pennsylvanicus, Hosts the Mutualistic Bacterial Endosymbiont Blochmannia Like all species of the ant genus Camponotus, the wood-nesting C. pennsylvanicus (shown here) possesses an obligate bacterial endosymbiont called Blochmannia. The small genome of Blochmannia retains genes to biosynthesize essential amino acids and other nutrients (Gil et al. 2003), suggesting the bacterium plays a role in ant nutrition. Many Camponotus species are also infected with Wolbachia, an endosymbiont that is widespread across insect groups. (Photo courtesy of Adam B. Lazarus.) By contrast, reproductive parasites of insects, including Wolbachia (O'Neill et al. 1998) and the more recently discovered endosymbiont in the Bacteroidetes group (also called CFB or CLO) (Hunter et al. 2003), propagate in insect lineages by manipulating host reproduction. These maternally inherited bacteria inflict an impressive arsenal of reproductive alterations to increase the frequency of infected female offspring, often at the expense of their hosts. Such mechanisms include cytoplasmic incompatibility, parthenogenesis, and male killing or feminization. As parasites, these bacteria rely on occasional horizontal transmission to infect new populations (Noda et al. 2001) and, by directly altering reproductive patterns, may be a causative agent of host speciation (Bordenstein et al. 2001). Comparative molecular analysis of insect endosymbionts over the past decade has provided new insights into their distribution across hosts, their varying degrees of stability within host lineages (ranging from cospeciation to frequent host-switching), and their impressive genetic diversity that spans several major bacterial groups. More recently, studies in genomics of obligate mutualists—and now Wolbachia—illuminate mechanisms of host–symbiont integration and the distinct consequences of this integration in various symbiotic systems. In addition, since hosts and symbionts often have different evolutionary interests, the diverse features of insect–bacterial associations can be understood as different outcomes in the negotiation of genetic conflicts. Some recent highlights and tantalizing research areas are described below. Endosymbiont Genomes: Spanning the Gamut from Static to Plastic The distinct lifestyle of endosymbionts has clear effects on rates and patterns of molecular evolution. Compared to free-living relatives, endosymbionts are thought to have reduced effective population sizes due to population bottlenecks upon transmission to host offspring and, in the case of obligate mutualists that coevolve with their hosts, limited opportunities for gene exchange. The nearly neutral theory of evolution (Ohta 1973) predicts accelerated fixation of deleterious mutations through random genetic drift in small populations, a phenomenon that has been observed in endosymbionts (Moran 1996; Lambert and Moran 1998). Over time, this lifestyle-associated accumulation of deleterious mutations may negatively affect the fitness of both the host and symbiont. It is increasingly clear the distinct lifestyle of endosymbionts also shapes the architecture and content of their genomes, which include the smallest, most AT-rich bacterial genomes yet characterized (Andersson and Kurland 1998; Moran 2002). A common theme is substantial gene loss, or genome streamlining, which is thought to reflect an underlying deletion bias in bacterial genomes combined with reduced strength or efficacy of selection to maintain genes in the host cellular environment. As a result of gene loss, these bacteria completely rely on the host cell for survival. Because they cannot be easily cultured apart outside of the host for traditional genetic or physiological techniques, analysis of genome sequence offers a valuable tool to infer metabolic functions that endosymbionts have retained and lost and to elucidate the steps in the evolutionary processes of genome reduction. Since 2000, full genome sequences have been published for Buchnera of three aphid host species, Wigglesworthia of tsetse flies, and Blochmannia of ants (Shigenobu et al. 2000; Akman et al. 2002; Tamas et al. 2002; Gil et al. 2003; van Ham et al. 2003). Parallels among these mutualist genomes include their small size (each smaller than 810 kb), yet retention of specific biosynthetic pathways for nutrients required by the host (for example, amino acids or vitamins). However, genomes also show signs of deleterious deletions. Early gene loss in Buchnera involved a few deletions of large contiguous regions of the ancestral genome and often included genes of unrelated functions (Moran and Mira 2001). These ‘large steps’ imply that genome reduction involved some random chance (due to the location of genes in the ancestral chromosome) and selection acting on the combined fitness of large sets of genes, rather than the fitness of individual loci. Such deletions are apparently irreversible in obligate mutualists, which lack recombination functions and genetic elements, such as prophages, transposons, and repetitive DNA that typically mediate gene acquisition. The scarcity of these functions, combined with limited opportunities to recombine with genetically distinct bacteria, may explain the unprecedented genome stability found in Buchnera compared to all other fully sequenced bacteria (Tamas et al. 2002) and a lack of evidence for gene transfer in other mutualist genomes. Stability also extends to the level of gene expression, as obligate mutualists have lost most regulatory functions and have reduced abilities to respond to environmental stimuli (Wilcox et al. 2003). The Wolbachia genome presented in this issue allows the first genome comparisons among bacteria that have adopted divergent evolutionary strategies in their associations with insects (Wu et al. 2004). Like other parasites, but unlike long-term mutualists, Wolbachia may experience strong selection for phenotypic variation, for example, to counter improved host defenses, to compete with distinct Wolbachia strains that coinfect the same host, or to increase its transmission to new host backgrounds. High levels of recombination in Wolbachia (for example, Jiggins et al. 2001) may allow rapid genetic changes in this parasite and may be catalyzed by the exceptionally high levels of repetitive DNA and mobile elements in its genome (Wu et al. 2004). Other bacteria that colonize specialized niches for long periods and lack co-colonizing strains also possess high levels of repetitive chromosomal sequences. For example, among ulcer-causing Helicobacter pylori in primate guts, repetitive DNA mediates intragenomic recombination and may provide an important source of genetic variation for adaptation to dynamic environmental stresses (Aras et al. 2003). The potential contributions of repetitive DNA and phage to intragenomic and intergenomic recombination in Wolbachia are exciting areas of research (Masui et al. 2000). The Wolbachia genome also provides a valuable tool for future research to test whether plasticity extends to gene content variation among Wolbachia strains and labile gene expression patterns. Between these two extremes of obligate mutualism and reproductive parasitism lies a spectrum of secondary symbionts of insects, most of which have not yet been studied in detail. Such ‘guest’ microbes transfer among diverse host species (Sandström et al. 2001), may provide more subtle or occasional benefits (for example, relating to host defense against parasitoids [Oliver et al. 2003]), and could represent an intermediate stage between a free-living lifestyle and obligate endosymbiosis. Genome-level data from these secondary symbionts promise to shed light on the range of lifestyles between obligate mutualism and reproductive parasitism and on the early stages in the transition to each. Microarray-based comparisons of gene content among Escherichia coli, a facultative mutualist of tsetse flies (Sodalis glossinidius), and a relatively young mutualist of weevils (Sitophilus oryzae primary endosymbiont [SOPE]) show that genome streamlining in the endosymbionts may preclude extracellular existence, and highlight modifications in metabolic pathways to complement specific host physiology and ecology (Rio et al. 2003). In addition, these endosymbionts may employ similar mechanisms as intracellular parasites in overcoming the shared challenges of entering host cells, avoiding or counteracting host defense mechanisms, and multiplying within a host cellular environment (Hentschel et al. 2000). The rapidly growing molecular datasets for secondary (or young primary) insect endosymbionts have identified pathways that are considered to be required for pathogenicity, such as Type III secretion (Dale et al. 2001, 2002). Such pathways may therefore have general utility for bacteria associated with host cells and may have evolved in the context of beneficial interactions. Genetic Conflicts and Host–Symbiont Dynamics Given their diverse evolutionary strategies, insect endosymbionts also provide a rich playing field to explore genetic conflicts (Frank 1996a, 1996b), which might involve the mode of symbiont transmission, the number of symbionts transmitted, and the sex of host offspring. Genetic conflicts described between organelle and nuclear genomes of the same organism (Hurst 1995) can provide a context to understand the evolutionary dynamics of insect–bacterial associations and the diverse outcomes of these relationships. For example, the uniparental (maternal) mode of inheritance of both mitochondria and insect endosymbionts may reflect host defense against invasion by foreign microbes with strong deleterious effects, which spread more easily under biparental inheritance (Law and Hutson 1992). Host–symbiont conflicts over offspring sex ratio are quite apparent in reproductive parasites (Vala et al. 2003). While the bacteria favor more female offspring and employ a variety of mechanisms to achieve this, the host typically favors a more balanced sex ratio. This conflict may lead to changes in the host that counter the symbiont's effect on sex ratio. For example, the spread of Wolbachia in a spider mite population caused selection on host nuclear genes that decrease the symbiont-induced sex ratio bias (Noda et al. 2001). Obligate mutualists also experience genetic conflicts with the host regarding transmission mode and number. In general, symbionts generally favor dispersal out of the host to avoid competition with their close relatives, while hosts are expected to restrict symbiont migration and thus reduce the virulent tendencies (Frank 1996b). In obligate mutualisms, there may be little room for negotiation. For example, the highly conserved, host-controlled determination of aphid bacteriocytes (Braendle et al. 2003) and the phylogenetic congruence observed in numerous studies suggest that aphids have won this conflict over symbiont transfer. However, the number of bacteria transmitted may be more flexible and is known to vary among aphid taxa (Mira and Moran 2002). Models indicate that the fixation rate for symbiont-beneficial (selfish) mutations increase with the number of symbionts transmitted, reflecting greater efficacy of selection among bacteria within a given host (Rispe and Moran 2000). Prospects In sum, the past few years have witnessed a surge of new empirical and theoretical approaches to understand the dynamics of bacterial–insect relationships. These tools have shed light on the roles of recombination, selection, and mutation on endosymbiont genome evolution and have highlighted parameters that shape the outcome of genetic conflicts between hosts and symbionts. These data provide a foundation for studying the evolution of mutualism and parasitism and modes of transitions between them. In the near future, we can look forward to full genome sequences that span a broader ecological and phylogenetic diversity of endosymbionts and provide a richer comparative framework to test existing models and develop new ones. Developments in endosymbiosis are important not only to questions in basic research, but may have important practical applications. Blood-feeding insects such as mosquitoes and tsetse flies are vectors for parasites that cause significant global infectious diseases such as malaria, dengue virus, and trypanosomiasis, many of which have frustrated attempts at vaccine development. The same insects that transmit these devastating human parasites often possess a diversity of mutualistic and parasitic bacterial endosymbionts. A very promising and urgent area of endosymbiont research is the manipulation of these bacteria to control vector populations in the field. Current studies already provide evidence that endosymbiont manipulation is a promising strategy to reduce the lifespan of the insect vector or limit its transmission of disease-causing parasites (Aksoy et al. 2001; Brownstein et al. 2003). Each advance in our understanding of endosymbiont genomics and evolutionary dynamics represents one step closer to that goal. Box 1. Glossary Endosymbiont:A symbiont that lives inside of its host, often within host cells (intracellular symbiont). Facultative mutualist: A beneficial symbiont that associates with the host, but can also live apart from it. Examples include Rhizobium spp. that associate with legumes, but also have a free-living stage to their life cycle. Obligate mutualist: A beneficial symbiont that lives exclusively with its host and depends on the host for survival. Examples include many nutritional endosymbionts of insects, which cannot survive outside of the insect host cell. These associations are reciprocally obligate when the host cannot survive without the endosymbiont. Parasite: A symbiont that has a negative effect on host fitness, in contrast to a mutualist, which increases host fitness. Reproductive parasite: A symbiont that manipulates host reproduction to its own benefit, but at the expense of host fitness. Reproductive parasites typically bias offspring toward infected females. Symbiosis: An association between two more species. JJW gratefully acknowledges the support of the National Institutes of Health (R01 GM62626-01), the National Science Foundation (DEB 0089455), the National Aeronautics and Space Administration Astrobiology Institute (NNA04CC04A), and the Josephine Bay Paul and C. Michael Paul Foundation. Jennifer Wernegreen is at the Josephine Bay Paul Center in Comparative Molecular Biology and Evolution at the Marine Biological Laboratory at Woods Hole, Massachusetts, United States of America. E-mail: jwernegreen@mbl.edu Abbreviation SOPE Sitophilus oryzae primary endo-symbiont ==== Refs References Akman L Yamashita A Watanabe H Oshima K Shiba T Genome sequence of the endocellular obligate symbiont of tsetse flies, Wigglesworthia glossinidia Nat Genet 2002 32 402 407 12219091 Aksoy S Maudlin I Dale C Robinson AS O'Neill SL Prospects for control of African trypanosomiasis by tsetse vector manipulation Trends Parasitol 2001 17 29 35 11137738 Andersson SG Kurland CG Reductive evolution of resident genomes Trends Microbiol 1998 6 263 268 9717214 Aras RA Kang J Tschumi AI Harasaki Y Blaser MJ Extensive repetitive DNA facilitates prokaryotic genome plasticity Proc Natl Acad Sci U S A 2003 100 13579 13584 14593200 Baumann P Moran N Baumann L Dworkin M Bacteriocyte-associated endosymbionts of insects The prokaryotes: A handbook on the biology of bacteria—Ecophysiology, isolation, identification, applications 2000 New York Springer-Verlag Available at http://link.springer.de/link/service/books/10125 via the Internet Bordenstein SR O'Hara FP Werren JH Wolbachia -induced incompatibility precedes other hybrid incompatibilities in Nasonia Nature 2001 409 707 710 11217858 Braendle C Miura T Bickel R Shingleton AW Kambhampati S Developmental origin and evolution of bacteriocytes in the aphid–Buchnera symbiosis PLoS Biol 2003 1 e21 10.1371/journal.pbio.0000021 14551917 Brownstein JS Hett E O'Neill SL The potential of virulent Wolbachia to modulate disease transmission by insects J Invertebr Pathol 2003 84 24 29 13678709 Buchner P Endosymbiosis of animals with plant microorganisms 1965 New York Interscience Publishers 909 Dale C Young SA Haydon DT Welburn SC The insect endosymbiont Sodalis glossinidius utilizes a type III secretion system for cell invasion Proc Natl Acad Sci U S A 2001 98 1883 1888 11172045 Dale C Plague GR Wang B Ochman H Moran NA Type III secretion systems and the evolution of mutualistic endosymbiosis Proc Natl Acad Sci U S A 2002 99 12397 12402 12213957 Frank SA Host–symbiont conflict over the mixing of symbiotic lineages Proc R Soc Lond B Biol Sci 1996a 263 339 344 Frank SA Models of parasite virulence Q Rev Biol 1996b 71 37 78 8919665 Gil R Silva FJ Zientz E Delmotte F Gonzalez-Candelas F The genome sequence of Blochmannia floridanus : Comparative analysis of reduced genomes Proc Natl Acad Sci U S A 2003 100 9388 9393 12886019 Hentschel U Steinert M Hacker J Common molecular mechanisms of symbiosis and pathogenesis Trends Microbiol 2000 8 226 231 10785639 Hunter MS Perlman SJ Kelly SE A bacterial symbiont in the Bacteroidetes induces cytoplasmic incompatibility in the parasitoid wasp Encarsia pergandiella Proc R Soc Lond B Biol Sci 2003 270 2185 2190 Hurst LD Selfish genetic elements and their role in evolution: The evolution of sex and some of what that entails Philos Trans R Soc Lond B Biol Sci 1995 349 321 332 8577844 Ishikawa H Miller TA Insect symbiosis: An introduction Insect symbiosis 2003 Boca Raton, Florida CRC Press 1 21 Jiggins FM von Der Schulenburg JH Hurst GD Majerus ME Recombination confounds interpretations of Wolbachia evolution Proc R Soc Lond B Biol Sci 2001 268 1423 1427 Lambert JD Moran NA Deleterious mutations destabilize ribosomal RNA in endosymbiotic bacteria Proc Natl Acad Sci U S A 1998 95 4458 4462 9539759 Law R Hutson V Intracellular symbionts and the evolution of uniparental cytoplasmic inheritance Proc R Soc Lond B Biol Sci 1992 248 69 77 Masui S Kamoda S Sasaki T Ishikawa H Distribution and evolution of bacteriophage WO in Wolbachia , the endosymbiont causing sexual alterations in arthropods J Mol Evol 2000 51 491 497 11080372 Mira A Moran NA Estimating population size and transmission bottlenecks in maternally transmitted endosymbiotic bacteria Microb Ecol 2002 44 137 143 12087426 Moran NA Accelerated evolution and Muller's rachet in endosymbiotic bacteria Proc Natl Acad Sci U S A 1996 93 2873 2878 8610134 Moran NA Microbial minimalism: Genome reduction in bacterial pathogens Cell 2002 108 583 586 11893328 Moran NA Baumann P Bacterial endosymbionts in animals Curr Opin Microbiol 2000 3 270 275 10851160 Moran NA Mira A The process of genome shrinkage in the obligate symbiont Buchnera aphidicola Genome Biol 2001 2 RESEARCH0054 Moran N Telang A Bacteriocyte-associated symbionts of insects Bioscience 1998 48 295 304 Noda H Miyoshi T Zhang Q Watanabe K Deng K Wolbachia infection shared among planthoppers (Homoptera: Delphacidae) and their endoparasite (Strepsiptera: Elenchidae): A probable case of interspecies transmission Mol Ecol 2001 10 2101 2106 11555254 Ohta T Slightly deleterious mutant substitutions in evolution Nature 1973 246 96 98 4585855 Oliver KM Russell JA Moran NA Hunter MS Facultative bacterial symbionts in aphids confer resistance to parasitic wasps Proc Natl Acad Sci U S A 2003 100 1803 1807 12563031 O'Neill S Hoffman A Werren J Influential passengers: Inherited microorganisms and arthropod reproduction 1998 Oxford Oxford University Press 228 Rio RV Lefevre C Heddi A Aksoy S Comparative genomics of insect–symbiotic bacteria: influence of host environment on microbial genome composition Appl Environ Microbiol 2003 69 6825 6832 14602646 Rispe C Moran NA Accumulation of deleterious mutations in endosymbionts: Muller's ratchet with two levels of selection Am Nat 2000 156 425 441 Sandström JP Russell JA White JP Moran NA Independent origins and horizontal transfer of bacterial symbionts of aphids Mol Ecol 2001 10 217 228 11251800 Shigenobu S Watanabe H Hattori M Sakaki Y Ishikawa H Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp APS Nature 2000 407 81 86 10993077 Tamas I Klasson L Canback B Naslund AK Eriksson AS 50 million years of genomic stasis in endosymbiotic bacteria Science 2002 296 2376 2379 12089438 Vala F Van Opijnen T Breeuwer JA Sabelis MW Genetic conflicts over sex ratio: Mite–endosymbiont interactions Am Nat 2003 161 254 266 12675371 van Ham RC Kamerbeek J Palacios C Raussel C Abascal F Reductive genome evolution in Buchnera aphidicola Proc Natl Acad Sci U S A 2003 100 581 586 12522265 Wilcox JL Dunbar HE Wolfinger RD Moran NA Consequences of reductive evolution for gene expression in an obligate endosymbiont Mol Microbiol 2003 48 1491 1500 12791133 Wu M Sun LV Vamathevan J Riegler M Deboy R Phylogenomics of the reproductive parasite Wolbachia pipientis w Mel: A streamlined genome overrun by mobile genetic elements PLOS Biol 2004 2 e69 10.1371/journal.pbio.0020069 15024419

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020069Research ArticleBioinformatics/Computational BiologyEcologyEvolutionGenetics/Genomics/Gene TherapyMicrobiologyEubacteriaDrosophilaPhylogenomics of the Reproductive Parasite Wolbachia pipientis wMel: A Streamlined Genome Overrun by Mobile Genetic Elements Sequence of Wolbachia pipientiswMelWu Martin 1 Sun Ling V 2 Vamathevan Jessica 1 Riegler Markus 3 Deboy Robert 1 Brownlie Jeremy C 3 McGraw Elizabeth A 3 Martin William 4 Esser Christian 4 Ahmadinejad Nahal 4 Wiegand Christian 4 Madupu Ramana 1 Beanan Maureen J 1 Brinkac Lauren M 1 Daugherty Sean C 1 Durkin A. Scott 1 Kolonay James F 1 Nelson William C 1 Mohamoud Yasmin 1 Lee Perris 1 Berry Kristi 1 Young M. Brook 1 Utterback Teresa 1 Weidman Janice 1 Nierman William C 1 Paulsen Ian T 1 Nelson Karen E 1 Tettelin Hervé 1 O'Neill Scott L 2 3 Eisen Jonathan A jeisen@tigr.org 1 1The Institute for Genomic Research, RockvilleMarylandUnited States of America2Department of Epidemiology and Public Health, Yale University School of MedicineNew Haven, ConnecticutUnited States of America3Department of Zoology and Entomology, School of Life SciencesThe University of Queensland, St Lucia, QueenslandAustralia4Institut für Botanik III, Heinrich-Heine UniversitätDüsseldorfGermany3 2004 16 3 2004 16 3 2004 2 3 e6919 11 2003 6 1 2004 Copyright: © 2004 Wu et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Endosymbiosis: Lessons in Conflict Resolution Genome Sequence for the Intracellular Bacterium Wolbachia The complete sequence of the 1,267,782 bp genome of Wolbachia pipientis wMel, an obligate intracellular bacteria of Drosophila melanogaster, has been determined. Wolbachia, which are found in a variety of invertebrate species, are of great interest due to their diverse interactions with different hosts, which range from many forms of reproductive parasitism to mutualistic symbioses. Analysis of the wMel genome, in particular phylogenomic comparisons with other intracellular bacteria, has revealed many insights into the biology and evolution of wMel and Wolbachia in general. For example, the wMel genome is unique among sequenced obligate intracellular species in both being highly streamlined and containing very high levels of repetitive DNA and mobile DNA elements. This observation, coupled with multiple evolutionary reconstructions, suggests that natural selection is somewhat inefficient in wMel, most likely owing to the occurrence of repeated population bottlenecks. Genome analysis predicts many metabolic differences with the closely related Rickettsia species, including the presence of intact glycolysis and purine synthesis, which may compensate for an inability to obtain ATP directly from its host, as Rickettsia can. Other discoveries include the apparent inability of wMel to synthesize lipopolysaccharide and the presence of the most genes encoding proteins with ankyrin repeat domains of any prokaryotic genome yet sequenced. Despite the ability of wMel to infect the germline of its host, we find no evidence for either recent lateral gene transfer between wMel and D. melanogaster or older transfers between Wolbachia and any host. Evolutionary analysis further supports the hypothesis that mitochondria share a common ancestor with the α-Proteobacteria, but shows little support for the grouping of mitochondria with species in the order Rickettsiales. With the availability of the complete genomes of both species and excellent genetic tools for the host, the wMel–D. melanogaster symbiosis is now an ideal system for studying the biology and evolution of Wolbachia infections. The genome sequence of Wolbachia provides insights into the origins of mitochondria, as well as the ecology and evolution of endosymbiosis ==== Body Introduction Wolbachia are intracellular gram-negative bacteria that are found in association with a variety of invertebrate species, including insects, mites, spiders, terrestrial crustaceans, and nematodes. Wolbachia are transovarialy transmitted from females to their offspring and are extremely widespread, having been found to infect 20%–75% of invertebrate species sampled (Jeyaprakash and Hoy 2000; Werren and Windsor 2000). Wolbachia are members of the Rickettsiales order of the α-subdivision of the Proteobacteria phyla and belong to the Anaplasmataceae family, with members of the genera Anaplasma, Ehrlichia, Cowdria, and Neorickettsia (Dumler et al. 2001). Six major clades (A–F) of Wolbachia have been identified to date (Lo et al. 2002): A, B, E, and F have been reported from insects, arachnids, and crustaceans; C and D from filarial nematodes. Wolbachia–host interactions are complex and range from mutualistic to pathogenic, depending on the combination of host and Wolbachia involved. Most striking are the various forms of “reproductive parasitism” that serve to alter host reproduction in order to enhance the transmission of this maternally inherited agent. These include parthenogenesis (infected females reproducing in the absence of mating to produce infected female offspring), feminization (infected males being converted into functional phenotypic females), male-killing (infected male embryos being selectively killed), and cytoplasmic incompatibility (in its simplest form, the developmental arrest of offspring of uninfected females when mated to infected males) (O'Neill et al. 1997a). Wolbachia have been hypothesized to play a role in host speciation through the reproductive isolation they generate in infected hosts (Werren 1998). They also provide an intriguing array of evolutionary solutions to the genetic conflict that arises from their uniparental inheritance. These solutions represent alternatives to classical mutualism and are often of more benefit to the symbiont than the host that is infected (Werren and O'Neill 1997). From an applied perspective, it has been proposed that Wolbachia could be utilized to either suppress pest insect populations or sweep desirable traits into pest populations (e.g., the inability to transmit disease-causing pathogens) (Sinkins and O'Neill 2000). Moreover, they may provide a new approach to the control of human and animal filariasis. Since the nematode worms that cause filariasis have an obligate symbiosis with mutualistic Wolbachia, treatment of filariasis with simple antibiotics that target Wolbachia has been shown to eliminate microfilaria production as well as ultimately killing the adult worm (Taylor et al. 2000; Taylor and Hoerauf 2001). Despite their common occurrence and major effects on host biology, little is currently known about the molecular mechanisms that mediate the interactions between Wolbachia and their invertebrate hosts. This is partly due to the difficulty of working with an obligate intracellular organism that is difficult to culture and hard to obtain in quantity. Here we report the completion and analysis of the genome sequence of Wolbachia pipientis wMel, a strain from the A supergroup that naturally infects Drosophila melanogaster (Zhou et al. 1998). Results/Discussion Genome Properties The wMel genome is determined to be a single circular molecule of 1,267,782 bp with a G+C content of 35.2%. This assembly is very similar to the genetic and physical map of the closely related strain wMelPop (Sun et al., 2003). The genome does not exhibit the GC skew pattern typical of some prokaryotic genomes (Figure 1) that have two major shifts, one near the origin and one near the terminus of replication. Therefore, identification of a putative origin of replication and the assignment of basepair 1 were based on the location of the dnaA gene. Major features of the genome and of the annotation are summarized in Table 1 and Figure 1. Figure 1 Circular Map of the Genome and Genome Features Circles correspond to the following: (1) forward strand genes; (2) reverse strand genes, (3) in red, genes with likely orthologs in both R. conorii and R. prowazekii; in blue, genes with likely orthologs in R. prowazekii, but absent from R. conorii; in green, genes with likely orthologs in R. conorii but absent from R. prowazekii; in yellow, genes without orthologs in either Rickettsia (Table S3); (4) plot is of χ2 analysis of nucleotide composition; phage regions are in pink; (5) plot of GC skew (G–C)/(G+C); (6) repeats over 200 bp in length, colored by category; (7) in green, transfer RNAs; (8) in blue, ribosomal RNAs; in red, structural RNA. Table 1 wMel Genome Features Repetitive and Mobile DNA The most striking feature of the wMel genome is the presence of very large amounts of repetitive DNA and DNA corresponding to mobile genetic elements, which is unique for an intracellular species. In total, 714 repeats of greater than 50 bp in length, which can be divided into 158 distinct families (Table S1), were identified. Most of the repeats are present in only two copies in the genome, although 39 are present in three or more copies, with the most abundant repeat being found in 89 copies. We focused our analysis on the 138 repeats of greater than 200 bp (Table 2). These were divided into 19 families based upon sequence similarity to each other. These repeats were found to make up 14.2 % of the wMel genome. Of these repeat families, 15 correspond to likely mobile elements, including seven types of insertion sequence (IS) elements, four likely retrotransposons, and four families without detectible similarity to known elements but with many hallmarks of mobile elements (flanked by inverted repeats, present in multiple copies) (Table 2). One of these new elements (repeat family 8) is present in 45 copies in the genome. It is likely that many of these elements are not able to autonomously transpose since many of the transposase genes are apparently inactivated by mutations or the insertion of other transposons (Table S2). However, some are apparently recently active since there are transposons inserted into at least nine genes (Table S2), and the copy number of some repeats appears to be variable between Wolbachia strains (M. Riegler et al., personal communication). Thus, many of these repetitive elements may be useful markers for strain discrimination. In addition, the mobile elements likely contribute to generating the diversity of phenotypically distinct Wolbachia strains (e.g., mod− strains [McGraw et al. 2001]) by altering or disrupting gene function (Table S2). Table 2 wMel DNA Repeats of Greater than 200 bp Three prophage elements are present in the genome. One is a small pyocin-like element made up of nine genes (WD00565–WD00575). The other two are closely related to and exhibit extensive gene order conservation with the WO phage described from Wolbachia sp. wKue (Masui et al. 2001) (Figure 2). Thus, we have named them wMel WO-A and WO-B, based upon their location in the genome. wMel WO-B has undergone a major rearrangement and translocation, suggesting it is inactive. Phylogenetic analysis indicates that wMel WO-B is more closely related to the wKue WO than to wMel WO-A (Figure S1). Thus, wMel WO-A likely represents either a separate insertion event in the Wolbachia lineage or a duplication that occurred prior to the separation of the wMel and wKue lineages. Phylogenetic analysis also confirms the proposed mosaic nature of the WO phage (Masui et al. 2001), with one block being closely related to lambdoid phage and another to P2 phage (data not shown). Figure 2 Phage Alignments and Neighboring Genes Conserved gene order between the WO phage in Wolbachia sp. wKue and prophage regions of wMel. Putative proteins in wKue (Masui et al. 2001) were searched using TBLASTN against the wMel genome. Matches with an E-value of less than 1e−15 are linked by connecting lines. CDSs are colored as follows: brown, phage structural or replication genes; light blue, conserved hypotheticals; red, hypotheticals; magenta, transposases or reverse transcriptases; blue, ankyrin repeat genes; light gray, radC; light green, paralogous genes; gold, others. The regions surrounding the phage are shown because they have some unusual features relative to the rest of the genome. For example, WO-A and WO-B are each flanked on one side by clusters of genes in two paralogous families that are distantly related to phage repressors. In each of these clusters, a homolog of the radC gene is found. A third radC homolog (WD1093) in the genome is also flanked by a member of one of these gene families (WD1095). While the connection between radC and the phage is unclear, the multiple copies of the radC gene and the members of these paralogous families may have contributed to the phage rearrangements described above. Genome Structure: Rearrangements, Duplications, and Deletions The irregular pattern of GC skew in wMel is likely due in part to intragenomic rearrangements associated with the many DNA repeat elements. Comparison with a large contig from a Wolbachia species that infects Brugia malayi is consistent with this (Ware et al. 2002) (Figure 3). While only translocations are seen in this plot, genetic comparisons reveal that inversions also occur between strains (Sun et al., 2003), which is consistent with previous studies of prokaryotic genomes that have found that the most common large-scale rearrangements are inversions that are symmetric around the origin of DNA replication (Eisen et al. 2000). The occurrence of frequent rearrangement events during Wolbachia evolution is supported by the absence of any large-scale conserved gene order with Rickettsia genomes. The rearrangements in Wolbachia likely correspond with the introduction and massive expansion of the repeat element families that could serve as sites for intragenomic recombination, as has been shown to occur for some other bacterial species (Parkhill et al. 2003). The rearrangements in wMel may have fitness consequences since several classes of genes often found in clusters are generally scattered throughout the wMel genome (e.g., ABC transporter subunits, Sec secretion genes, rRNA genes, F-type ATPase genes). Figure 3 Alignment of wMel with a 60 kbp Region of the Wolbachia from B. malayi The figure shows BLASTN matches (green) and whole-proteome alignments (red) that were generated using the “promer” option of the MUMmer software (Delcher et al. 1999). The B. malayi region is from a BAC clone (Ware et al. 2002). Note the regions of alignment broken up by many rearrangements and the presence of repetitive sequences at the regions of the breaks. Although the common ancestor of Wolbachia and Rickettsia likely already had a reduced, streamlined genome, wMel has lost additional genes since that time (Table S3). Many of these recent losses are of genes involved in cell envelope biogenesis in other species, including most of the machinery for producing lipopolysaccharide (LPS) components and the alanine racemase that supplies D-alanine for cell wall synthesis. In addition, some other genes that may have once been involved in this process are present in the genome, but defective (e.g., mannose-1-phosphate guanylyltransferase, which is split into two coding sequences [CDSs], WD1224 and WD1227, by an IS5 element) and are likely in the process of being eliminated. The loss of cell envelope biogenesis genes has also occurred during the evolution of the Buchnera endosymbionts of aphids (Shigenobu et al. 2000; Moran and Mira 2001). Thus, wMel and Buchnera have lost some of the same genes separately during their reductive evolution. Such convergence means that attempts to use gene content to infer evolutionary relatedness needs to be interpreted with caution. In addition, since Anaplasma and Ehrlichia also apparently lack genes for LPS production (Lin and Rikihisha 2003), it is likely that the common ancestor of Wolbachia, Ehrlichia, and Anaplasma was unable to synthesize LPS. Thus, the reports that Wolbachia-derived LPS-like compounds is involved in the immunopathology of filarial nematode disease in mammals (Taylor 2002) either indicate that these Wolbachia have acquired genes for LPS synthesis or that the reported LPS-like compounds are not homologous to LPS. Despite evident genome reduction in wMel and in contrast to most small-genomed intracellular species, gene duplication appears to have continued, as over 50 gene families have apparently expanded in the wMel lineage relative to that of all other species (Table S4). Many of the pairs of duplicated genes are encoded next to each other in the genome, suggesting that they arose by tandem duplication events and may simply reflect transient duplications in evolution (deletion is common when there are tandem arrays of genes). Many others are components of mobile genetic elements, indicating that these elements have expanded significantly after entering the Wolbachia evolutionary lineage. Other duplications that could contribute to the unique biological properties of wMel include that of the mismatch repair gene mutL (see below) and that of many hypothetical and conserved hypothetical proteins. One duplication of particular interest is that of wsp, which is a standard gene for strain identification and phylogenetic reconstruction in Wolbachia (Zhou et al. 1998). In addition to the previously described wsp (WD0159), wMel encodes two wsp paralogs (WD0009 and WD0489), which we designate as wspB and wspC, respectively. While these paralogs are highly divergent from wsp (protein identities of 19.7% and 23.5%, respectively) and do not amplify using the standard wsp PCR primers (Braig et al. 1998; Zhou et al. 1998), their presence could lead to some confusion in classification and identification of Wolbachia strains. This has apparently occurred in one study of Wolbachia strain wKueYO, for which the reported wsp gene (gbAB045235) is actually an ortholog of wspB (99.8% sequence identity and located at the end of the virB operon [Masui et al. 2000]) and not an ortholog of the wsp gene. Considering that the wsp gene has been extremely informative for discriminating between strains of Wolbachia, we designed PCR primers to the wMel wspB gene to amplify and then sequence the orthologs from the related wRi and wAlbB Wolbachia strains from Drosophila simulans and Aedes albopictus, respectively, as well as the Wolbachia strain that infects the filarial nematode Dirofilaria immitis to determine the potential utility of this locus for strain discrimination. A comparison of genetic distances between the wsp and wspB genes for these different taxa indicates that overall the wspB gene appears to be evolving at a faster rate than wsp and, as such, may be a useful additional marker for discriminating between closely related Wolbachia strains (Table S5). Inefficiency of Selection in wMel The fraction of the genome that is repetitive DNA and the fraction that corresponds to mobile genetic elements are among the highest for any prokaryotic genome. This is particularly striking compared to the genomes of other obligate intracellular species such as Buchnera, Rickettsia, Chlamydia, and Wigglesworthia, that all have very low levels of repetitive DNA and mobile elements. The recently sequenced genomes of the intracellular pathogen Coxiella burnetti (Seshadri et al. 2003) has both a streamlined genome and moderate amounts of repetitive DNA, although much less than wMel. The paucity of repetitive DNA in these and other intracellular species is thought to be due to a combination of lack of exposure to other species, thereby limiting introduction of mobile elements, and genome streamlining (Mira et al. 2001; Moran and Mira 2001; Frank et al. 2002). We examined the wMel genome to try to understand the origin of the repetitive and mobile DNA and to explain why such repetitive/mobile DNA is present in wMel, but not other streamlined intracellular species. We propose that the mobile DNA in wMel was acquired some time after the separation of the Wolbachia and Rickettsia lineages but before the radiation of the Wolbachia group. The acquisition of these elements after the separation of the Wolbachia and Rickettsia lineages is suggested by the fact that most do not have any obvious homologous sequences in the genomes of other α-Proteobacteria, including the closely related Rickettsia spp. Additional evidence for some acqui-sition of foreign DNA after the Wolbachia–Rickettsia split comes from phylogenetic analysis of those genes present in wMel, but not in the two sequenced rickettsial genomes (see Table S3; unpublished data). The acquisition prior to the radiation of Wolbachia is suggested by two lines of evidence. First, many of the elements are found in the genome of the distantly related Wolbachia of the nematode B. malayi (see Figure 3; unpublished data). In addition, genome analysis reveals that these elements do not have significantly anomalous nucleotide composition or codon usage compared to the rest of the genome. In fact, there are only four regions of the genome with significantly anomalous composition, comprising in total only approximately 17 kbp of DNA (Table 3). The lack of anomalous composition suggests either that any foreign DNA in wMel was acquired long enough ago to allow it to “ameliorate” and become compositionally similar to endogenous Wolbachia DNA (Lawrence and Ochman 1997, 1998) or that any foreign DNA that is present was acquired from organisms with similar composition to endogenous wMel genes. Owing to their potential effects on genome evolution (insertional mutagenesis, catalyzing genome rearrangements), we propose that the acquisition and maintenance of these repetitive and mobile elements by wMel have played a key role in shaping the evolution of Wolbachia. Table 3 Regions of Anomalous Nucleotide Composition in the wMel Genome It is likely that much of the mobile/repetitive DNA was introduced via phage, given that three prophage elements are present; experimental studies have shown active phage in some Wolbachia (Masui et al. 2001) and Wolbachia superinfections occur in many hosts (e.g., Jamnongluk et al. 2002), which would allow phage to move between strains. Whatever the mechanism of introduction, the persistence of the repetitive elements in wMel in the face of apparently strong pressures for streamlining is intriguing. One expla-nation is that wMel may be getting a steady infusion of mobile elements from other Wolbachia strains to counteract the elimination of elements by selection for genome streamlining. This would explain the absence of anomalous nucleotide composition of the elements. However, we believe that a major contributing factor to the presence of all the repetitive/mobile DNA in wMel is that wMel and possibly Wolbachia in general have general inefficiency of natural selection relative to other species. This inefficiency would limit the ability to eliminate repetitive DNA. A general inefficiency of natural selection (especially purifying selection) has been suggested previously for intracellular bacteria, based in part on observations that these bacteria have higher evolutionary rates than free-living bacteria (e.g., Moran 1996). We also find a higher evolutionary rate for wMel than that of the closely related intracellular Rickettsia, which themselves have higher rates than free-living α-Proteobacteria (Figure 4). Additionally, codon bias in wMel appears to be driven more by mutation or drift than selection (Figure S2), as has been reported for Buchnera species and was suggested to be due to inefficient purifying selection (Wernegreen and Moran 1999). Such inefficiencies of natural selection are generally due to an increase in the relative contribution of genetic drift and mutation as compared to natural selection (Eiglmeier et al. 2001; Lawrence 2001; Parkhill et al. 2001). Below we discuss different possible explanations for the inefficiency of selection in wMel, especially in comparison to other intracellular bacteria. Figure 4 Long Evolutionary Branches in wMel Maximum-likelihood phylogenetic tree constructed on concatenated protein sequences of 285 orthologs shared among wMel, R. prowazekii, R. conorii, C. crescentus, and E. coli. The location of the most recent common ancestor of the α-Proteobacteria (Caulobacter, Rickettsia, Wolbachia) is defined by the outgroup E. coli. The unit of branch length is the number of changes per amino acid. Overall, the amino acid substitution rate in the wMel lineage is about 63% higher than that of C. crescentus, a free-living α-Proteobacteria. wMel has evolved at a slightly higher rate than the Rickettssia spp., close relatives that are also obligate intracellular bacteria that have undergone accelerated evolution themselves. This higher rate is likely in part to be due to an increase in the rate of slightly deleterious mutations, although we have not ruled out the possibility of G+C content effects on the branch lengths. Low rates of recombination, such as occur in centromeres and the human Y chromosome, can lead to inefficient selection because of the linkage among genes. This has been suggested to be occurring in Buchnera species because these species do not encode homologs of RecA, which is the key protein in homologous recombination in most species (Shigenobu et al. 2000). The absence of recombination in Buchnera is supported by the lack of genome rearrangements in their recent evolution (Tamas et al. 2002). Additionally, there is apparently little or no gene flow into Buchnera strains. In contrast, wMel encodes the necessary machinery for recombination, including RecA (Table S6), and has experienced both extensive intragenomic homologous recombination and introduction of foreign DNA. Therefore, the unusual genome features of wMel are unlikely to be due to low levels of recombination. Another possible explanation for inefficient selection is high mutation rates. It has been suggested that the higher evolutionary rates in intracellular bacteria are the result of high mutation rates that are in turn due to the loss of genes for DNA repair processes (e.g., Itoh et al. 2002). This is likely not the case in wMel since its genome encodes proteins corresponding to a broad suite of DNA repair pathways including mismatch repair, nucleotide excision repair, base excision repair, and homologous recombination (Table S6). The only noteworthy DNA repair gene absent from wMel and present in the more slowly evolving Rickettsia is mfd, which is involved in targeting DNA repair to the transcribed strand of actively transcribing genes in other species (Selby et al. 1991). However, this absence is unlikely to contribute significantly to the increased evolutionary rate in wMel, since defects in mfd do not lead to large increases in mutation rates in other species (Witkin 1994). The presence of mismatch repair genes (homologs of mutS and mutL) in wMel is particularly relevant since this pathway is one of the key steps in regulating mutation rates in other species. In fact, wMel is the first bacterial species to be found with two mutL homologs. Overall, examination of the predicted DNA repair capabilities of bacteria (Eisen and Hanawalt 1999) suggests that the connection between evolutionary rates in intracellular species and the loss of DNA repair processes is spurious. While many intracellular species have lost DNA repair genes in their recent evolution, different species have lost different genes and some, such as wMel and Buchnera spp., have kept the genes that likely regulate mutation rates. In addition, some free-living species without high evolutionary rates have lost some of the same pathways lost in intracellular species, while many free-living species have lost key pathways resulting in high mutation rates (e.g., Helicobacter pylori has apparently lost mismatch repair [Eisen 1997, Eisen 1998b; Bjorkholm et al. 2001]). Given that intracellular species tend to have small genomes and have lost genes from every type of biological process, it is not surprising that many of them have lost DNA repair genes as well. We believe that the most likely explanations for the inefficiency of selection in wMel involve population-size related factors, such as genetic drift and the occurrence of population bottlenecks. Such factors have also been shown to likely explain the high evolutionary rates in other intracellular species (Moran 1996; Moran and Mira 2001; van Ham et al. 2003). Wolbachia likely experience frequent population bottlenecks both during transovarial transmission (Boyle et al. 1993) and during cytoplasmic incompatibility mediated sweeps through host populations. The extent of these bottlenecks may be greater than in other intracellular bacteria, which would explain why wMel has both more repetitive and mobile DNA than other such species and a higher evolutionary rate than even the related Rickettsia spp. Additional genome sequences from other Wolbachia will reveal whether this is a feature of all Wolbachia or only certain strains. Mitochondrial Evolution There is a general consensus in the evolutionary biology literature that the mitochondria evolved from bacteria in the α-subgroup of the Proteobacteria phyla (e.g., Lang et al. 1999). Analysis of complete mitochondrial and bacterial genomes has very strongly supported this hypothesis (Andersson et al. 1998, 2003; Muller and Martin 1999; Ogata et al. 2001). However, the exact position of the mitochondria within the α-Proteobacteria is still debated. Many studies have placed them in or near the Rickettsiales order (Viale and Arakaki 1994; Gupta 1995; Sicheritz-Ponten et al. 1998; Lang et al. 1999; Bazinet and Rollins 2003). Some studies have further suggested that mitochondria are a sister taxa to the Rickettsia genus within the Rickettsiaceae family and thus more closely related to Rickettsia spp. than to species in the Anaplasmataceae family such as Wolbachia (Karlin and Brocchieri 2000; Emelyanov 2001a, 2001b, 2003a, 2003b). In our analysis of complete genomes, including that of wMel, the first non-Rickettsia member of the Rickettsiales order to have its genome completed, we find support for a grouping of Wolbachia and Rickettsia to the exclusion of the mitochondria, but not for placing the mitochondria within the Rickettsiales order (Figure 5A and 5B; Table S7; Table S8). Specifically, phylogenetic trees of a concatenated alignment of 32 proteins show strong support with all methods (see Table S7) for common branching of: (i) mitochondria, (ii) Rickettsia with Wolbachia, (iii) the free-living α-Proteobacteria, and (iv) mitochondria within α-Proteobacteria. Since amino acid content bias was very severe in these datasets, protein LogDet analyses, which can correct for the bias, were also performed. In LogDet analyses of the concatenated protein alignment, both including and excluding highly biased positions, mitochondria usually branched basal to the Wolbachia–Rickettsia clade, but never specifically with Rickettsia (see Table S7). In addition, in phylogenetic studies of individual genes, there was no consistent phylogenetic position of mitochondrial proteins with any particular species or group within the α-Proteobacteria (see Table S8), although support for a specific branch uniting the two Rickettsia species with Wolbachia was quite strong. Eight of the proteins from mitochondrial genomes (YejW, SecY, Rps8, Rps2, Rps10, RpoA, Rpl15, Rpl32) do not even branch within the α-Proteobacteria, although these genes almost certainly were encoded in the ancestral mitochondrial genome (Lang et al. 1997). Figure 5 Mitochondrial Evolution Using Concatenated Alignments Networks of protein LogDet distances for an alignment of 32 proteins constructed with Neighbor-Net (Bryant and Moulton 2003). The scale bar indicates 0.1 substitutions per site. Enlargements at lower right show the component of shared similarity between mitochondrial-encoded proteins and (i) their homologs from intracellular endosymbionts (red) as well as (ii) their homologs from free-living α-Proteobacteria (blue). (A) Result using 6,776 gap-free sites per genome (heavily biased in amino acid composition). (B) Result using 3,100 sites after exclusion of highly variable positions (data not biased in amino acid composition at p = 0.95). All data and alignments are available upon request. Results of phylogenetic analyses are summa-rized in Table S7. Since amino acid content bias was very severe in these datasets, protein LogDet analyses were also preformed. In neighbor-joining, parsimony, and maximum-likelihood trees generated from alignments both including and excluding highly biased positions (6,776 and 3,100 gap-free amino acid sites per genome, respectively), mitochondria usually branched basal to the Wolbachia–Rickettsia clade, but never specifically with Rickettsia (Table S7). This analysis of mitochondrial and α-Proteobacterial genes reinforces the view that ancient protein phylogenies are inherently prone to error, most likely because current models of phylogenetic inference do not accurately reflect the true evolutionary processes underlying the differences observed in contemporary amino acid sequences (Penny et al. 2001). These conflicting results regarding the precise position of mitochondria within the α-Proteobacteria can be seen in the high amount of networking in the Neighbor-Net graph of the analyses of the concatenated alignment shown in Figure 5. An important complication in studies of mitochondrial evolution lies in identifying “α-Proteobacterial” genes for comparison (Martin 1999). For example, in our analyses, proteins from Magnetococcus branched with other α-Proteobacterial homologs in only 17 of the 49 proteins studied, and in five cases they assumed a position basal to α-, β-, and γ-Proteobacterial homologs. Host–Symbiont Gene Transfers Many genes that were once encoded in mitochondrial genomes have been transferred into the host nuclear genomes. Searching for such genes has been complicated by the fact that many of the transfer events happened early in eukaryotic evolution and that there are frequently extreme amino acid and nucleotide composition biases in mitochondrial genomes (see above). We used the wMel genome to search for additional possible mitochondrial-derived genes in eukaryotic nuclear genomes. Specifically, we constructed phylogenetic trees for wMel genes that are not in either Rickettsia genomes. Five new eukaryotic genes of possible mitochondrial origin were identified: three genes involved in de novo nucleotide biosynthesis (purD, purM, pyrD) and two conserved hypothetical proteins (WD1005, WD0724). The α-Proteobacterial origin of these genes suggests that at least some of the genes of the de novo nucleotide synthesis pathway in eukaryotes might have been laterally acquired from bacteria via the mitochondria. The presence of such genes in other Proteobacteria suggests that their absence from Rickettsia is due to gene loss (Gray et al. 2001). This finding supports the need for additional α-Proteobacterial genomes to identify mitochondrion-derived genes in eukaryotes. While organelle to nuclear gene transfers are generally accepted, there is a great deal of controversy over whether other gene transfers have occurred from bacteria into animals. In particular, claims of transfer from bacteria into the human genome (Lander et al. 2001) were later shown to be false (Roelofs and Van Haastert 2001; Salzberg et al. 2001; Stanhope et al. 2001). Wolbachia are excellent candidates for such transfer events since they live inside the germ cells, which would allow lateral transfers to the host to be transmitted to subsequent host generations. Consistent with this, a recent study has shown some evidence for the presence of Wolbachia-like genes in a beetle genome (Kondo et al. 2002). The symbiosis between wMel and D. melanogaster provides an ideal case to search for such transfers since we have the complete genomes of both the host and symbiont. Using BLASTN searches and MUMmer alignments, we did not find any examples of highly similar stretches of DNA shared between the two species. In addition, protein-level searches and phylogenetic trees did not identify any specific relationships between wMel and D. melanogaster for any genes. Thus, at least for this host–symbiont association, we do not find any likely cases of recent gene exchange, with genes being maintained in both host and symbiont. In addition, in our phylogenetic analyses, we did not find any examples of wMel proteins branching specifically with proteins from any invertebrate to the exclusion of other eukaryotes. Therefore, at least for the genes in wMel, we do not find evidence for transfer of Wolbachia genes into any invertebrate genome. Metabolism and Transport wMel is predicted to have very limited capabilities for membrane transport, for substrate utilization, and for the biosynthesis of metabolic intermediates (Figure S3), similar to what has been seen in other intracellular symbionts and pathogens (Paulsen et al. 2000). Almost all of the identifiable uptake systems for organic nutrients in wMel are for amino acids, including predicted transporters for proline, asparate/glutamate, and alanine. This pattern of transporters, coupled with the presence of pathways for the metabolism of the amino acids cysteine, glutamate, glutamine, proline, serine, and threonine, suggests that wMel may obtain much of its energy from amino acids. These amino acids could also serve as material for the production of other amino acids. In contrast, carbohydrate metabolism in wMel appears to be limited. The only pathways that appear to be complete are the tricarboxylic acid cycle, the nonoxidative pentose phosphate pathway, and glycolysis, starting with fructose-1,6-biphosphate. The limited carbohydrate metabolism is consistent with the presence of only one sugar phosphate transporter. wMel can also apparently transport a range of inorganic ions, although two of these systems, for potassium uptake and sodium ion/proton exchange, are frameshifted. In the latter case, two other sodium ion/proton exchangers may be able to compensate for this defect. Many of the predicted metabolic properties of wMel, such as the focus on amino acid transport and the presence of limited carbohydrate metabolism, are similar to those found in Rickettsia. A major difference with the Rickettsia spp. is the absence of the ADP–ATP exchanger protein in wMel. In Rickettsia this protein is used to import ATP from the host, thus allowing these species to be direct energy scavengers (Andersson et al. 1998). This likely explains the presence of glycolysis in wMel but not Rickettsia. An inability to obtain ATP from its host also helps explain the presence of pathways for the synthesis of the purines AMP, IMP, XMP, and GMP in wMel but not Rickettsia. Other pathways present in wMel but not Rickettsia include threonine degradation (described above), riboflavin biosynthesis, pyrimidine metabolism (i.e., from PRPP to UMP), and chelated iron uptake (using a single ABC transporter). The two Rickettsia species have a relatively large complement of predicted transporters for osmoprotectants, such as proline and glycine betaine, whereas wMel possesses only two of these systems. Regulatory Responses The wMel genome is predicted to encode few proteins for regulatory responses. Three genes encoding two-component system subunits are present: two sensor histidine kinases (WD1216 and WD1284) and one response regulator (WD0221). Only six strong candidates for transcription regulators were identified: a homolog of arginine repressors (WD0453), two members of the TenA family of transcription activator proteins (WD0139 and WD0140), a homolog of ctrA, a transcription regulator for two component systems in other α-Proteobacteria (WD0732), and two σ factors (RpoH/WD1064 and RpoD/WD1298). There are also seven members of one paralogous family of proteins that are distantly related to phage repressors (see above), although if they have any role in transcription, it is likely only for phage genes. Such a limited repertoire of regulatory systems has also been reported in other endosymbionts and has been explained by the apparent highly predictable and stable environment in which these species live (Andersson et al. 1998; Read et al. 2000; Shigenobu et al. 2000; Moran and Mira 2001; Akman et al. 2002; Seshadri et al. 2003). Host–Symbiont Interactions The mechanisms by which Wolbachia infect host cells and by which they cause the diverse phenotypic effects on host reproduction and fitness are poorly understood, and the wMel genome helps identify potential contributing factors. A complete Type IV secretion system, portions of which have been reported in earlier studies, is present. The complete genome sequence shows that in addition to the five vir genes previously described from Wolbachia wKueYO (Masui et al. 2001), an additional four are present in wMel. Of the nine wMel vir ORFs, eight are arranged into two separate operons. Similar to the single operon identified in wTai and wKueYO, the wMel virB8, virB9, virB10, virB11, and virD4 CDSs are adjacent to wspB, forming a 7 kb operon (WD0004–WD0009). The second operon contains virB3, virB4, and virB6 as well as four additional non-vir CDSs, including three putative membrane-spanning proteins, that form part of a 15.7 kb operon (WD0859–WD0853). Examination of the Rickettsia conorii genome shows a similar orga-nization (Figure 6A). The observed conserved gene order for these genes between these two genomes suggests that the putative membrane-spanning proteins could form a novel and, possibly, integral part of a functioning Type IV secretion system within these bacteria. Moreover, reverse transcription (RT)-PCRs have confirmed that wspB and WD0853–WD0856 are each expressed as part of the two vir operons and further indicate that these additional encoded proteins are novel components of the Wolbachia Type IV secretion system (Figure 6B). Figure 6 Genomic Organization and expression of Type IV Secretion Operons in wMel (A) Organization of the nine vir-like CDSs (white arrows) and five adjacent CDSs that encode for either putative membrane-spanning proteins (black arrows) or non-vir CDSs (gray arrows) of wMel, R. conorii, and A. tumefaciens. Solid horizontal lines denote RT experiments that have confirmed that adjacent CDSs are expressed as part of a polycistronic transcript. Results of these RT-PCR experiments are presented in (B). Lane 1, virB3-virB4; lane 2, RT control; lane 3, virB6-WD0856; lane 4, RT control; lane 5, WD0856-WD0855; lane 6, RT control; lane 7, WD0854-WD0853; lane 8, RT control; lane 9, virB8-virB9; lane 10, RT control; lane 11, virB9-virB11; lane 12, RT control; lane 13, virB11-virD4; lane 14, RT control; lane 15, virD4-wspB; lane 16, RT control; lane 17, virB4-virB6; lane 18, RT control; lane 19, WD0855-WD0854; lane 20, RT control. Only PCRs that contain reverse transcriptase amplified the desired products. PCR primer sequences are listed in Table S9. In addition to the two major vir clusters, a paralog of virB8 (WD0817) is also present in the wMel genome. WD0818 is quite divergent from virB8 and, as such, does not appear to have resulted from a recent gene duplication event. RT-PCR experiments have failed to show expression of this CDS in wMel-infected Drosophila (data not shown). PCR primers were designed to all CDSs of the wMel Type IV secretion system and used to successfully amplify orthologs from the divergent Wolbachia strains wRi and wAlbB (data not shown). We were able to detect orthologs to all of the wMel Type IV secretion system components as well as most of the adjacent non-vir CDSs, suggesting that this system is conserved across a range of A- and B-group Wolbachia. An increasing body of evidence has highlighted the importance of Type IV secretion systems for the successful infection, invasion, and persistence of intracellular bacteria within their hosts (Christie 2001; Sexton and Vogel 2002). It is likely that the Type IV system in Wolbachia plays a role in the establishment and maintenance of infection and possibly in the generation of reproductive phenotypes. Genes involved in pathogenicity in bacteria have been found to be frequently associated with regions of anomalous nucleotide composition, possibly owing to transfer from other species or insertion into the genome from plasmids or phage. In the four such regions in wMel (see above; see Table 3), some additional candidates for pathogenicity-related activities are present including a putative penicillin-binding protein (WD0719), genes predicted to be involved in cell wall synthesis (WD0095–WD0098, including D-alanine-D-alanine ligase, a putative FtsQ, and D-alanyl-D-alanine carboxy peptidase) and a multidrug resistance protein (WD0099). In addition, we have identified a cluster of genes in one of the phage regions that may also have some role in host–symbiont interactions. This cluster (WD0611–WD0621) is embedded within the WO-B phage region of the genome (see Figure 2) and contains many genes that encode proteins with putative roles in the synthesis and degradation of surface polysaccharides, including a UDP-glucose 6-dehydrogenase (WD0620). Since this cluster appears to be normal in terms of phylogeny relative to other genes in the genome (i.e., the genes in this region have normal wMel nucleotide composition and branch in phylogenetic trees with genes from other α-Proteobacteria), it is not likely to have been acquired from other species. However, it is possible that these genes can be transferred among Wolbachia strains via the phage, which in turn could lead to some variation in host–symbiont interactions between Wolbachia strains. Of particular interest for host-interaction functions are the large number of genes that encode proteins that contain ankyrin repeats (Table 4). Ankyrin repeats, a tandem motif of around 33 amino acids, are found mainly in eukaryotic proteins, where they are known to mediate protein–protein interactions (Caturegli et al. 2000). While they have been found in bacteria before, they are usually present in only a few copies per species. wMel has 23 ankyrin repeat-containing genes, the most currently described for a prokaryote, with C. burnetti being next with 13. This is particularly striking given wMel's relatively small genome size. The functions of the ankyrin repeat-containing proteins in wMel are difficult to predict since most have no sequence similarity outside the ankyrin domains to any proteins of known function. Many lines of evidence suggest that the wMel ankyrin domain proteins are involved in regulating host cell-cycle or cell division or interacting with the host cytoskeleton: (i) many ankyrin-containing proteins in eukaryotes are thought to be involved in linking membrane proteins to the cytoskeleton (Hryniewicz-Jankowska et al. 2002); (ii) an ankyrin-repeat protein of Ehrlichia phagocytophila binds condensed chromatin of host cells and may be involved in host cell-cycle regulation (Caturegli et al. 2000); (iii) some of the proteins that modify the activity of cell-cycle-regulating proteins in D. melanogaster contain ankyrin repeats (Elfring et al. 1997); and (iv) the Wolbachia strain that infects the wasp Nasonia vitripennis induces cytoplasmic incompatibility, likely by interacting with these same cell-cycle proteins (Tram and Sullivan 2002). Of the ankyrin-containing proteins in wMel, those worth exploring in more detail include the several that are predicted to be surface targeted or secreted (Table 4) and thus could be targeted to the host nucleus. It is also possible that some of the other ankyrin-containing proteins are secreted via the Type IV secretion system in a targeting signal independent pathway. We call particular attention to three of the ankyrin-containing proteins (WD0285, WD0636, and WD0637), which are among the very few genes, other than those encoding components of the translation apparatus, that have significantly biased codon usage relative to what is expected based on GC content, suggesting they may be highly expressed. Table 4. Ankyrin-Domain Containing Proteins Encoded by the wMel Genome Conclusions Analysis of the wMel genome reveals that it is unique among sequenced genomes of intracellular organisms in that it is both streamlined and massively infected with mobile genetic elements. The persistence of these elements in the genome for apparently long periods of time suggests that wMel is inefficient at getting rid of them, likely a result of experiencing severe population bottlenecks during every cycle of transovarial transmission as well as during sweeps through host populations. Integration of evolutionary reconstructions and genome analysis (phylogenomics) has provided insights into the biology of Wolbachia, helped identify genes that likely play roles in the unusual effects Wolbachia have on their host, and revealed many new details about the evolution of Wolbachia and mitochondria. Perhaps most importantly, future studies of Wolbachia will benefit both from this genome sequence and from the ability to study host–symbiont interactions in a host (D. melanogaster) well-suited for experimental studies. Materials and Methods Purification/source of DNA wMel DNA was obtained from D. melanogaster yw 67c23 flies that naturally carry the wMel infection. wMel was purified from young adult flies on pulsed-field gels as described previously (Sun et al. 2001). Plugs were digested with the restriction enzyme AscI (GG^CGCGCC), which cuts the bacterial chromosome twice (Sun et al. 2001), aiding in the entry of the DNA into agarose gels. After electrophoresis, the resulting two bands were recovered from the gel and stored in 0.5 M EDTA (pH 8.0). DNA was extracted from the gel slices by first washing in TE (Tris–HCl and EDTA) buffer six times for 30 min each to dilute EDTA followed by two 1-h washes in β-agarase buffer (New England Biolabs, Beverly, Massachusetts, United States). Buffer was then removed and the blocks melted at 70°C for 7 min. The molten agarose was cooled to 40°C and then incubated in β-agarase (1 U/100 μl of molten agarose) for 1 h. The digest was cooled to 4°C for 1 h and then centrifuged at 4,100 × g max for 30 min at 4°C to remove undigested agarose. The supernatant was concentrated on a Centricon YM-100 microconcentrator (Millipore, Bedford, Massachusetts, United States) after prerinsing with 70% ethanol followed by TE buffer and, after concentration, rinsed with TE. The retentate was incubated with proteinase K at 56°C for 2 h and then stored at 4°C. wMel DNA for gap closure was prepared from approximately 1,000 Drosophila adults using the Holmes–Bonner urea/phenol:chloroform protocol (Holmes and Bonner 1973) to prepare total fly DNA. Library construction/sequencing/closure The complete genome sequence was determined using the whole-genome shotgun method (Venter et al. 1996). For the random shotgun-sequencing phase, libraries of average size 1.5–2.0 kb and 4.0–8.0 kb were used. After assembly using the TIGR Assembler (Sutton et al. 1995), there were 78 contigs greater than 5000 bp, 186 contigs greater than 3000 bp, and 373 contigs greater than 1500 bp. This number of contigs was unusually high for a 1.27 Mb genome. An initial screen using BLASTN searches against the nonredundant database in GenBank and the Berkeley Drosophila Genome Project site (http://www.fruitfly.org/blast/) showed that 3,912 of the 10,642 contigs were likely contaminants from the Drosophila genome. To aid in closure, the assemblies were rerun with all sequences of likely host origin excluded. Closure, which was made very difficult by the presence of a large amount of repetitive DNA (see below), was done using a mix of primer walking, generation, and sequencing of transposon-tagged libraries of large insert clones and multiplex PCR (Tettelin et al. 1999). The final sequence showed little evidence for polymorphism within the population of Wolbachia DNA. In addition, to obtain sequence across the AscI-cut sites, PCR was performed on undigested DNA. It is important to point out that the reason significant host contamination does not significantly affect symbiont genome assembly is that most of the Drosophila contigs were small due to the approximately 100-fold difference in genome sizes between host (approximately 180 Mb) and wMel (1.2 Mb). Since it has been suggested that Wolbachia and their hosts may undergo lateral gene transfer events (Kondo et al. 2002), genome assemblies were rerun using all of the shotgun and closure reads without excluding any sequences that appeared to be of host origin. Only five assemblies were found to match both the D. melanogaster genome and the wMel assembly. Primers were designed to match these assemblies and PCR attempted from total DNA of wMel infected D. melanogaster. In each case, PCR was unsuccessful, and we therefore presume that these assemblies are the result of chimeric cloning artifacts. The complete sequence has been given GenBank accession ID AE017196 and is available at http://www.tigr.org/tdb. Repeats Repeats were identified using RepeatFinder (Volfovsky et al. 2001), which makes use of the REPuter algorithm (Kurtz and Schleiermacher 1999) to find maximal-length repeats. Some manual curation and BLASTN and BLASTX searches were used to divide repeat families into different classes. Annotation Identification of putative protein-encoding genes and annotation of the genome was done as described previously (Eisen et al. 2002). An initial set of ORFs likely to encode proteins (CDS) was identified with GLIMMER (Salzberg et al. 1998). Putative proteins encoded by the CDS were examined to identify frameshifts or premature stop codons compared to other species. The sequence traces for each were reexamined and, for some, new sequences were generated. Those for which the frameshift or premature stops were of high quality were annotated as “authentic” mutations. Functional assignment, identification of membrane-spanning domains, determination of paralogous gene families, and identification of regions of unusual nucleotide composition were performed as described previously (Tettelin et al. 2001). Phylogenomic analysis (Eisen 1998a; Eisen and Fraser 2003) was used to aid in functional predictions. Alignments and phylogenetic trees were generated as described (Salzberg et al. 2001). Comparative genomics All putative wMel proteins were searched using BLASTP against the predicted proteomes of published complete organismal genomes and a set of complete plastid, mitochondrial, plasmid, and viral genomes. The results of these searches were used (i) to analyze the phylogenetic profile (Pellegrini et al. 1999; Eisen and Wu 2002), (ii) to identify putative lineage-specific duplications (those proteins with a top E-value score to another protein from wMel), and (iii) to determine the presence of homologs in different species. Orthologs between the wMel genome and that of the two Rickettsia species were identified by requiring mutual best-hit relationships among all possible pairwise BLASTP comparisons, with some manual correction. Those genes present in both Rickettsia genomes as well as other bacterial species, but not wMel, were considered to have been lost in the wMel branch (see Table S3). Genes present in only one or two of the three species were considered candidates for gene loss or lateral transfer and were also used to identify possible biological differences between these species (see Table S3). For the wMel genes not in the Rickettsia genomes, proteins were searched with BLASTP against the TIGR NRAA database. Protein sequences of their homologs were aligned with CLUSTALW and manually curated. Neighbor-joining trees were constructed using the PHYLIP package. Phylogenetic analysis of mitochondrial proteins For phylogenetic analysis, the set of all 38 proteins encoded in both the Marchantia polymorpha and Reclinomonas americana (Lang et al. 1997) mitochondrial genomes were collected. Acanthamoeba castellanii was excluded due to high divergence and extremely long evolutionary branches. Six genes were excluded from further analysis because they were too poorly conserved for alignment and phylogenetic analysis (nad7, rps10, sdh3, sdh4, tatC, and yejV), leaving 32 genes for investigation: atp6, atp9, atpA, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5, nad6, nad9, rpl16, rpl2, rpl5, rpl6, rps1, rps11, rps12, rps13, rps14, rps19, rps2, rps3, rps4, rps7, rps8, yejR, and yejU. Using FASTA with the mitochondrial proteins as a query, homologs were identified from the genomes of seven α-Proteobacteria: two intracellular symbionts (W. pipientis wMel and Rickettsia prowazekii) and five free-living forms (Sinorhozobium loti, Agrobacterium tumefaciens, Brucella melitensis, Mesorhizobium loti, and Rhodopseudomonas sp.). Escherichia coli and Neisseria meningitidis were used as outgroups. Caulobacter crescentus was excluded from analysis because homologs of some of the 32 genes were not found in the current annotation. In the event that more than one homolog was identified per genome, the one with the greatest sequence identity to the mitochondrial query was retrieved. Proteins were aligned using CLUSTALW (Thompson et al. 1994) and concatenated. To reduce the influence of poorly aligned regions, all sites that contained a gap at any position were excluded from analysis, leaving 6,776 positions per genome for analysis. The data contained extreme amino acid bias: all sequences failed the χ2 test at p = 0.95 for deviation from amino acid frequency distribution assumed under either the JTT or mtREV24 models as determined with PUZZLE (Strimmer and von Haeseler 1996). When the data were iteratively purged of highly variable sites using the method described (Hansmann and Martin 2000), amino acid composition gradually came into better agreement with acid frequency distribution assumed by the model. The longest dataset in which all sequences passed the χ2 test at p = 0.95 consisted of the 3,100 least polymorphic sites. PROTML (Adachi and Hasegawa 1996) analyses of the 3,100-site data using the JTT model detected mitochondria as sisters of the five free-living α-Proteobacteria with low (72%) support, whereas PUZZLE, using the same data, detected mitochondria as sisters of the two intracellular symbionts, also with low (85%) support. This suggested the presence of conflicting signal in the less-biased subset of the data. Therefore, protein log determinants (LogDet) were used to infer distances from the 6,776-site data, since the method can correct for amino acid bias (Lockhart et al. 1994), and Neighbor-Net (Bryant and Moulton 2003) was used to display the resulting matrix, because it can detect and display conflicting signal. The result (see Figure 5A) shows both signals. In no analysis was a sister relationship between Rickettsia and mitochondria detected. For analyses of individual genes, the 63 proteins encoded in the Reclinomonas mitochondrial genome were compared with FASTA to the proteins from 49 sequenced eubacterial genomes, which included the α-Proteobacteria shown in Figure 5, R. conorii, and Magnetococcus MC1, one of the more divergent α-Proteobacteria. Of those proteins, 50 had sufficiently well-conserved homologs to perform phylogenetic analyses. Homologs were aligned and subjected to phylogenetic analysis with PROTML (Adachi and Hasegawa 1996). Analysis of wspB sequences To compare wspB sequences from different Wolbachia strains, PCR was done on total DNA extracted from the following sources: wRi was obtained from infected adult D. simulans, Riverside strain; wAlbB was obtained from the infected Aa23 cell line (O'Neill et al. 1997b), and D. immitis Wolbachia was extracted from adult worm tissue. DNA extraction and PCR were done as previously described (Zhou et al. 1998) with wspB-specific primers (wspB-F, 5′-TTTGCAAGTGAAACAGAAGG and wspB-R, 5′-GCTTTGCTGGCAAAATGG). PCR products were cloned into pGem-T vector (Promega, Madison, Wisconsin, United States) as previously described (Zhou et al. 1998) and sequenced (Genbank accession numbers AJ580921–AJ508923). These sequences were compared to previously sequenced wsp genes for the same Wolbachia strains (Genbank accession numbers AF020070, AF020059, and AJ252062). The four partial wsp sequences were aligned using CLUSTALV (Higgins et al. 1992) based on the amino acid translation of each gene and similarly with the wspB sequences. Genetic distances were calculated using the Kimura 2 parameter method and are reported in Table S5. Type IV secretion system To determine whether the vir-like CDSs, as well as adjacent ORFs, were actively expressed within wMel as two polycistronic operons, RT-PCR was used. Total RNA was isolated from infected D. melanogaster yw 67c23 adults using Trizol reagent (Invitrogen, Carlsbad, California, United States) and cDNA synthesized using SuperScript III RT (Invitrogen) using primers wspBR, WD0817R, WD0853R, and WD0852R. RNA isolation and RT were done according to manufacturer's protocols, with the exception that suggested initial incubation of RNA template and primers at 65°C for 5 min and final heat denaturation of RT-enzyme at 70°C for 15 min were not done. PCR was done using rTaq (Takara, Kyoto, Japan), and several primer sets were used to amplify regions spanning adjacent CDSs for most of the two operons. For operon virB3-WD0853, the following primers were used: (virB3-virB4)F, (virB3-virB4)R, (virB6-WD0856)F, (virB6-WD0856)R, (WD0856-WD0855)F, (WD0856-WD0855)R, (WD0854-WD0853)F, (WD0854-WD0853)R. For operon virB8-wspB, the following primers were used: (virB8-virB9)F, (virB8-virB9)R, (virB9-virB11)F, (virB9-virB11)R, (virB11-virD4)F, (virB11-virD4)R, (virD4-wspB)F, and (virD4-wspB)R. The coexpression of virB4 and virB6, as well as WD0855 and WD0854, was confirmed within the putative virB3-WD0853 operon using nested PCR with the following primers: (virB4-virB6)F1, (virB4-virB6)R1, (virB4-virB6)F2, (virB4-virB6)R2, (WD0855-WD0854)F1, (WD0855-WD0854)R1, (WD0855-WD0854)F2, and (WD0855-WD0854)R2. All ORFs within the putative virB8-wspB operon were shown to be coexpressed and are thus considered to be a genuine operon. All products were amplified only from RT-positive reactions (see Figure 6). Primer sequences are given in Table S9. Supporting Information Figure S1 Phage Trees Phylogenetic tree showing the relationship between WO-A and WO-B phage from wMel with reported phage from wKue and wTai. The tree was generated from a CLUSTALW multiple sequence alignment (Thompson et al. 1994) using the PROTDIST and NEIGHBOR programs of PHYLIP (Felsenstein 1989). (60 KB PDF). Click here for additional data file. Figure S2 Plot of the Effective Number of Codons against GC Content at the Third Codon Position (GC3) Proteins with fewer than 100 residues are excluded from this analysis because their effective number of codon (ENc) values are unreliable. The curve shows the expected ENc values if codon usage bias is caused by GC variation alone. Colors: yellow, hypothetical; purple, mobile element; blue, others. Most of the variation in codon bias can be traced to variation in GC, indicating that the mutation forces dominate the wMel codon usage. Multivariate analysis of codon usage was performed using the CODONW package (available from http://www.molbiol.ox.ac.uk/cu/codonW.html). (289 KB PDF). Click here for additional data file. Figure S3 Predicted Metabolism and Transport in wMel Overview of the predicted metabolism (energy production and organic compounds) and transport in wMel. Transporters are grouped by predicted substrate specificity: inorganic cations (green), inorganic anions (pink), carbohydrates (yellow), and amino acids/peptides/amines/purines and pyrimidines (red). Transporters in the drug-efflux family (labeled as “drugs”) and those of unknown specificity are colored black. Arrows indicate the direction of transport. Energy-coupling mechanisms are also shown: solutes transported by channel proteins (double-headed arrow); secondary transporters (two-arrowed lines, indicating both the solute and the coupling ion); ATP-driven transporters (ATP hydrolysis reaction); unknown energy-coupling mechanism (single arrow). Transporter predictions are based upon a phylogenetic classification of transporter proteins (Paulsen et al. 1998). (167 KB PDF). Click here for additional data file. Table S1 Repeats of Greater Than 50 bp in the wMel Genome (with Coordinates) (649 KB DOC). Click here for additional data file. Table S2 Inactivated Genes in the wMel Genome (147 KB DOC). Click here for additional data file. Table S3 Ortholog Comparison with Rickettsia spp (718 KB XLS). Click here for additional data file. Table S4 Putative Lineage-Specific Gene Duplications in wMel (116 KB DOC). Click here for additional data file. Table S5 Genetic Distances as Calculated for Alignments of wsp and wspB Gene Sequences from the Same Wolbachia Strains (24 KB DOC). Click here for additional data file. Table S6 Putative DNA Repair and Recombination Genes in the wMel Genome (26 KB DOC). Click here for additional data file. Table S7 Phylogenetic Results for Concatenated Data of 32 Mitochondrial Proteins (34 KB DOC). Click here for additional data file. Table S8 Individual Phylogenetic Results for Reclinomonas Mitochondrial DNA-Encoded Proteins (117 KB DOC). Click here for additional data file. Table S9 PCR Primers (47 KB DOC). Click here for additional data file. Accession Numbers The complete sequence for wMel has been given GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) accession ID number AE017196 and is available through the TIGR Comprehensive Microbial Resourceat http://www.tigr.org/tigr-scripts/CMR2/GenomePage3.spl?database=dmg The GenBank accession numbers for other sequences discussed in this paper are AF020059 (Wolbachia sp. wAlbB outer surface protein precursor wsp gene), AF020070 (Wolbachia sp. wRi outer surface protein precursor wsp gene), AJ252062 (Wolbachia endosymbiont of D. immitis sp. gene for surface protein), AJ580921 (Wolbachia endosymbiont of D. immitis partial wspB gene for Wolbachia surface protein B), AJ580922 (Wolbachia endosymbiont of A. albopictus partial wspB gene for Wolbachia surface protein B), and AJ580923 (Wolbachia endosymbiont of D. simulans partial wspB gene for Wolbachia surface protein B). We acknowledge Barton Slatko, Jeremy Foster, New England Biolabs, and Mark Blaxter for helping inspire this project; Rehka Seshadri for help in examining pathogenicity factors and reading the manuscript; Derek Fouts for examination of group II introns; Susan Lo, Michael Heaney, Vadim Sapiro, and Billy Lee for IT support; Maria-Ines Benito, Naomi Ward, Michael Eisen, Howard Ochman, and Vincent Daubin for helpful discussions; Steven Salzberg and Mihai Pop for help in comparing wMel with the D. melanogaster genome; Elodie Ghedin for access to the B. malayi Wolbachia sequence data; Maria Ermolaeva for assistance with analysis of operons; Dan Haft for designing protein family hidden Markov models for annotation; Owen White for general bioinformatics support; four anonymous reviewers for very helpful comments and suggestions; and Claire M. Fraser for continuing support of TIGR's scientific research. This project was supported by grant UO1-AI47409–01 to Scott O'Neill and Jonathan A. Eisen from the National Institutes of Allergy and Infectious Diseases. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. M. Wu contributed ideas and analysis in all aspects of the work. L. Sun performed purification of wMel DNA for initial libraries and closure. J. Vamathevan was the closure team leader, performed sequence assembly and analysis, and screened contigs against the Drosophila genome. M. Riegler performed validation of assembly against the physical map and confirmation of rearrangements by long PCR and analysis of repeat regions. R. Deboy was the annotation leader and managed the annotation, ORF management, and frameshifts. J. C. Brownlie performed analysis of Type IV secretion systems. E. A. McGraw performed validation of assembly against physical map and confirmation of rearrangements by long PCR and analysis of wsp paralogs. W. Martin, C. Esser, N. Ahmadinejad, and C. Wiegand performed the mitochondrial evolution analysis. R. Madupu, M. J. Beanan, L. M. Brinkac, S. C. Daugherty, A. S. Durkin, J. F. Kolonay, and W. C. Nelson performed genome annotation. Y. Mohamoud, P. Lee, and K. Berry performed the closure experiments (closed sequencing gaps, multiplex PCR, resolution of small repeats, coverage reactions, contig editing, resolution of large repeats by transposon and primer walking). M. B. Young was the shotgun sequencing leader. T. Utterback and J. Weidman performed shotgun sequencing and frameshift checking; Utterback also worked on the assembly. W. C. Nierman handled the library construction. I. T. Paulsen performed transporter analysis. K. E. Nelson performed metabolism analysis. H. Tettelin analyzed genome properties, repeats, and membrane proteins. S. L. O'Neill and J. A. Eisen supplied ideas, coordination, and analysis; Eisen is the corresponding author. Academic Editor: Nancy A. 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B Sjolund M Falk PG Berg OG Engstrand L Mutation frequency and biological cost of antibiotic resistance in Helicobacter pylori Proc Natl Acad Sci U S A 2001 98 14607 14612 11717398 Boyle L O'Neill SL Robertson HM Karr TL Interspecific and intraspecific horizontal transfer of Wolbachia in Drosophila Science 1993 260 1796 1799 8511587 Braig HR Zhou W Dobson SL O'Neill SL Cloning and characterization of a gene encoding the major surface protein of the bacterial endosymbiont Wolbachia pipientis J Bacteriol 1998 180 2373 2378 9573188 Bryant D Moulton V Neighbor-Net: An agglomerative method for the construction of phylogenetic networks Mol Biol Evol 2003 20 Dec 5 [Epub ahead of print] Caturegli P Asanovich KM Walls JJ Bakken JS Madigan JE ankA : An Ehrlichia phagocytophila group gene encoding a cytoplasmic protein antigen with ankyrin repeats Infect Immun 2000 68 5277 5283 10948155 Christie PJ Type IV secretion: Intercellular transfer of macromolecules by systems ancestrally related to conjugation machines Mol Microbiol 2001 40 294 305 11309113 Delcher AL Kasif S Fleischmann RD Peterson J White O Alignment of whole genomes Nucleic Acids Res 1999 27 2369 2376 10325427 Dumler SJ Barbet AF Bekker CPJ Dasch GA Palmer GH Reorganization of genera in the families Rickettsiaceae and Anaplasmataceae in the order Rickettsiales: Unification of some species of Ehrlichia with Anaplasma , Cowdria with Ehrlichia and Ehrlichia with Neorickettsia —Descriptions of six new species combinations and designation of Ehrlichiaqui and “HGE agent” as subjective synonyms of Ehrlichia phagocytophila Intl J System Evol Microbiol 2001 51 2145 2165 Eiglmeier K Parkhill J Honore N Garnier T Tekaia F The decaying genome of Mycobacterium leprae Lepr Rev 2001 72 387 398 11826475 Eisen JA Gastrogenomic delights: A movable feast Nat Med 1997 3 1076 1078 9334711 Eisen JA A phylogenomic study of the MutS family of proteins Nucleic Acids Res 1998a 26 4291 4300 9722651 Eisen JA Phylogenomics: Improving functional predictions for uncharacterized genes by evolutionary analysis Genome Res 1998b 8 163 167 9521918 Eisen JA Fraser CM Phylogenomics: Intersection of evolution and genomics Science 2003 300 1706 1707 12805538 Eisen JA Hanawalt PC A phylogenomic study of DNA repair genes, proteins, and processes Mutat Res 1999 435 171 213 10606811 Eisen JA Wu M Phylogenetic analysis and gene functional predictions: Phylogenomics in action Theor Popul Biol 2002 61 481 487 12167367 Eisen JA Heidelberg JF White O Salzberg SL Evidence for symmetric chromosomal inversions around the replication origin in bacteria Genome Biol 2000 1 1 9 RESEARCH0011 11178226 Eisen JA Nelson KE Paulsen IT Heidelberg JF Wu M The complete genome sequence of Chlorobium tepidum TLS, a photosynthetic, anaerobic, green-sulfur bacterium Proc Natl Acad Sci U S A 2002 99 9509 9514 12093901 Elfring LK Axton JM Fenger DD Page AW Carminati JL Drosophila PLUTONIUM protein is a specialized cell cycle regulator required at the onset of embryogenesis Mol Biol Cell 1997 8 583 593 9247640 Emelyanov VV Evolutionary relationship of Rickettsiae and mitochondria FEBS Lett 2001a 501 11 18 11457448 Emelyanov VV Rickettsiaceae, Rickettsia -like endosymbionts, and the origin of mitochondria Biosci Rep 2001b 21 1 17 11508688 Emelyanov VV Mitochondrial connection to the origin of the eukaryotic cell Eur J Biochem 2003a 270 1599 1618 12694174 Emelyanov VV Phylogenetic affinity of a Giardia lamblia cysteine desulfurase conforms to canonical pattern of mitochondrial ancestry FEMS Microbiol Lett 2003b 226 257 266 14553920 Felsenstein J PHYLIP—Phylogeny inference package (version 3.2) Cladistics 1989 5 164 166 Frank AC Amiri H Andersson SG Genome deterioration: Loss of repeated sequences and accumulation of junk DNA Genetica 2002 115 1 12 12188042 Gray MW Burger G Lang BF The origin and early evolution of mitochondria Genome Biol 2001 2 REVIEWS1018 11423013 Gupta RS Evolution of the chaperonin families (Hsp60, Hsp10 and Tcp-1) of proteins and the origin of eukaryotic cells Mol Microbiol 1995 15 1 11 7752884 Hansmann S Martin W Phylogeny of 33 ribosomal and six other proteins encoded in an ancient gene cluster that is conserved across prokaryotic genomes: Influence of excluding poorly alignable sites from analysis Int J Syst Evol Microbiol 2000 50 1655 1663 10939673 Higgins D Bleasby A Fuchs R ClustalV: Improved software for multiple sequence alignment Comput Appl Biosci 1992 8 189 191 1591615 Holmes DS Bonner J Preparation, molecular weight, base composition, and secondary structure of giant nuclear ribonucleic acid Biochemistry 1973 12 2330 2338 4710584 Hryniewicz-Jankowska A Czogalla A Bok E Sikorsk AF Ankyrins, multifunctional proteins involved in many cellular pathways Folia Histochem Cytobiol 2002 40 239 249 12219834 Itoh T Martin W Nei M Acceleration of genomic evolution caused by enhanced mutation rate in endocellular symbionts Proc Natl Acad Sci U S A 2002 99 12944 12948 12235368 Jamnongluk W Kittayapong P Baimai V O'Neill SL Wolbachia infections of tephritid fruit flies: Molecular evidence for five distinct strains in a single host species Curr Microbiol 2002 45 255 260 12192522 Jeyaprakash A Hoy MA Long PCR improves Wolbachia DNA amplification: wsp sequences found in 76% of sixty-three arthropod species Insect Mol Biol 2000 9 393 405 10971717 Karlin S Brocchieri L Heat shock protein 60 sequence comparisons: Duplications, lateral transfer, and mitochondrial evolution Proc Natl Acad Sci U S A 2000 97 11348 11353 11027334 Kondo N Nikoh N Ijichi N Shimada M Fukatsu T Genome fragment of Wolbachia endosymbiont transferred to X chromosome of host insect Proc Natl Acad Sci U S A 2002 99 14280 14285 12386340 Kurtz S Schleiermacher C REPuter: Fast computation of maximal repeats in complete genomes Bioinformatics 1999 15 426 427 10366664 Lander ES Linton LM Birren B Nusbaum C Zody MC Initial sequencing and analysis of the human genome Nature 2001 409 860 921 11237011 Lang BF Burger G O'Kelly CJ Cedergren R Golding GB An ancestral mitochondrial DNA resembling a eubacterial genome in miniature Nature 1997 387 493 497 9168110 Lang BF Seif E Gray MW O'Kelly CJ Burger G A comparative genomics approach to the evolution of eukaryotes and their mitochondria J Eukaryot Microbiol 1999 46 320 326 10461380 Lawrence JG Catalyzing bacterial speciation: Correlating lateral transfer with genetic headroom Syst Biol 2001 50 479 496 12116648 Lawrence JG Ochman H Amelioration of bacterial genomes: Rates of change and exchange J Mol Evol 1997 44 383 397 9089078 Lawrence JG Ochman H Molecular archaeology of the Escherichia coli genome Proc Natl Acad Sci U S A 1998 95 9413 9417 9689094 Lin M Rikihisha Y Ehrlichia chaffeensis and Anaplasma phagocytophilum lack genes for lipid A biosynthesis and incorporate cholesterol for their survival Infect Immun 2003 71 5324 5331 12933880 Lo N Casiraghi M Salati E Bazzocchi C Bandi C How many Wolbachia supergroups exist? Mol Biol Evol 2002 19 341 346 11861893 Lockhart PJ Steel MA Hendy MD Penny D Recovering evolutionary trees under a more realistic evolutionary model Mol Biol Evol 1994 11 605 612 19391266 Martin W Mosaic bacterial chromosomes: A challenge en route to a tree of genomes Bioessays 1999 21 99 104 10193183 Masui S Sasaki T Ishikawa H Genes for the type IV secretion system in an intracellular symbiont, Wolbachia , a causative agent of various sexual alterations in arthropods J Bacteriol 2000 182 22 6529 6531 11053403 Masui S Kuroiwa H Sasaki T Inui M Kuroiwa T Bacteriophage WO and virus-like particles in Wolbachia , an endosymbiont of arthropods Biochem Biophys Res Commun 2001 283 1099 1104 11355885 McGraw EA Merritt DJ Droller JN O'Neill SL Wolbachia -mediated sperm modification is dependent on the host genotype in Drosophila Proc R Soc Lond B Biol Sci 2001 268 2565 2570 Mira A Ochman H Moran NA Deletional bias and the evolution of bacterial genomes Trends Genet 2001 17 589 596 11585665 Moran NA Accelerated evolution and Muller's rachet in endosymbiotic bacteria Proc Natl Acad Sci U S A 1996 93 2873 2878 8610134 Moran NA Mira A The process of genome shrinkage in the obligate symbiont Buchnera aphidicola Genome Biol 2001 2 RESEARCH0054 11790257 Muller M Martin W The genome of Rickettsia prowazekii and some thoughts on the origin of mitochondria and hydrogenosomes Bioessays 1999 21 377 381 10376009 O'Neill SL Hoffmann AA Werren JH Influential passengers: Inherited microorganisms and arthropod reproduction 1997a Oxford Oxford University Press 228 O'Neill SL Pettigrew MM Sinkins SP Braig HR Andreadis TG In vitro cultivation of Wolbachia pipientis in an Aedes albopictus cell line Insect Mol Biol 1997b 6 33 39 9013253 Ogata H Audic S Renesto-Audiffren P Fournier PE Barbe V Mechanisms of evolution in Rickettsia conorii and R. prowazekii Science 2001 293 2093 2098 11557893 Parkhill J Wren BW Thomson NR Titball RW Holden MT Genome sequence of Yersinia pestis , the causative agent of plague Nature 2001 413 523 527 11586360 Parkhill J Sebaihia M Preston A Murphy LD Thomson N Comparative analysis of the genome sequences of Bordetella pertussis , Bordetella parapertussis and Bordetella bronchiseptica Nat Genet 2003 35 32 40 12910271 Paulsen IT Sliwinski MK Saier MH Microbial genome analyses: Global comparisons of transport capabilities based on phylogenies, bioenergetics and substrate specificities J Mol Biol 1998 277 573 592 9533881 Paulsen IT Nguyen L Sliwinski MK Rabus R Saier MH Microbial genome analyses: Comparative transport capabilities in eighteen prokaryotes J Mol Biol 2000 301 75 100 10926494 Pellegrini M Marcotte EM Thompson MJ Eisenberg D Yeates TO Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles Proc Natl Acad Sci U S A 1999 96 4285 4288 10200254 Penny D McComish BJ Charleston MA Hendy MD Mathematical elegance with biochemical realism: The covarion model of molecular evolution J Mol Evol 2001 53 711 723 11677631 Read TD Brunham RC Shen C Gill SR Heidelberg JF Genome sequences of Chlamydia trachomatis MoPn and Chlamydia pneumoniae AR39 Nucleic Acids Res 2000 28 1397 1406 10684935 Roelofs J Van Haastert PJ Genes lost during evolution Nature 2001 411 1013 1014 Salzberg SL Delcher AL Kasif S White O Microbial gene identification using interpolated Markov models Nucleic Acids Res 1998 26 544 548 9421513 Salzberg SL White O Peterson J Eisen JA Microbial genes in the human genome: Lateral transfer or gene loss? Science 2001 292 1903 1906 11358996 Selby CP Witkin EM Sancar A Escherichia coli mfd mutant deficient in “mutation frequency decline” lacks strand-specific repair: In vitro complementation with purified coupling factor Proc Natl Acad Sci U S A 1991 88 11574 11578 1763073 Seshadri R Paulsen IT Eisen JA Read TD Nelson KE Complete genome sequence of the Q-fever pathogen Coxiella burnetii Proc Natl Acad Sci U S A 2003 100 5455 5460 12704232 Sexton JA Vogel JP Type IVB secretion by intracellular pathogens Traffic 2002 3 178 185 11886588 Shigenobu S Watanabe H Hattori M Sakaki Y Ishikawa H Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS Nature 2000 407 81 86 10993077 Sicheritz-Ponten T Kurland CG Andersson SG A phylogenetic analysis of the cytochrome b and cytochrome c oxidase I genes supports an origin of mitochondria from within the Rickettsiaceae Biochim Biophys Acta 1998 1365 545 551 9711305 Sinkins SP O'Neill SL Wolbachia as a vehicle to modify insect populations. In: James AA, editor. Insect transgenesis: Methods and applications 2000 Boca Raton, Florida CRC Press 271 288 Stanhope MJ Lupas A Italia MJ Koretke KK Volker C Phylogenetic analyses do not support horizontal gene transfers from bacteria to vertebrates Nature 2001 411 940 944 11418856 Strimmer K von Haeseler A Quartet puzzling: A quartet maximum-likelihood method for reconstructing tree topologies Mol Biol Evol 1996 13 964 969 Sun LV Foster JM Tzertzinis G Ono M Bandi C Determination of Wolbachia genome size by pulsed-field gel electrophoresis J Bacteriol 2001 183 2219 2225 11244060 Sun LV Riegler M O'Neill SL Development of a physical and genetic map of the virulent Wolbachia strain w MelPop J Bacteriol 2003 185 7077 7084 14645266 Sutton G White O Adams M Kerlavage A TIGR assembler: A new tool for assembling large shotgun sequencing projects Genome Sci Tech 1995 1 9 19 Tamas I Klasson L Canback B Naslund AK Eriksson AS 50 million years of genomic stasis in endosymbiotic bacteria Science 2002 296 2376 2379 12089438 Taylor MJ A new insight into the pathogenesis of filarial disease Curr Mol Med 2002 2 299 302 12041732 Taylor MJ Hoerauf A A new approach to the treatment of filariasis Curr Opin Infect Dis 2001 14 727 731 11964892 Taylor MJ Bandi C Hoerauf AM Lazdins J Wolbachia bacteria of filarial nematodes: A target for control? Parasitol Today 2000 16 179 180 10782070 Tettelin H Radune D Kasif S Khouri H Salzberg SL Optimized multiplex PCR: Efficiently closing a whole-genome shotgun sequencing project Genomics 1999 62 500 507 10644449 Tettelin H Nelson KE Paulsen IT Eisen JA Read TD Complete genome sequence of a virulent isolate of Streptococcus pneumoniae Science 2001 293 498 506 11463916 Thompson JD Higgins DG Gibson TJ ClustalW: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice Nucleic Acids Res 1994 22 4673 4680 7984417 Tram U Sullivan W Role of delayed nuclear envelope breakdown and mitosis in Wolbachia -induced cytoplasmic incompatibility Science 2002 296 1124 1126 12004132 van Ham RC Kamerbeek J Palacios C Rausell C Abascal F Reductive genome evolution in Buchnera aphidicola Proc Natl Acad Sci U S A 2003 100 581 586 12522265 Venter JC Smith HO Hood L A new strategy for genome sequencing Nature 1996 381 364 366 8632789 Viale AM Arakaki AK The chaperone connection to the origins of the eukaryotic organelles FEBS Lett 1994 341 146 151 7907991 Volfovsky N Haas BJ Salzberg SL A clustering method for repeat analysis in DNA sequences Genome Biol 2001 2 RESEARCH0027 11532211 Ware J Moran L Foster J Posfai J Vincze T Sequencing and analysis of a 63 kb bacterial artificial chromosome insert from the Wolbachia endosymbiont of the human filarial parasite Brugia malayi Int J Parasitol 2002 32 159 166 11812492 Wernegreen J Moran NA Evidence for genetic drift in endosymbionts (Buchnera ): Analyses of protein-coding genes Mol. Biol. Evol 1999 16 83 97 10331254 Werren JH Wolbachia and speciation. In: Berlocher SH, editor. Endless forms: Species and speciation 1998 New York Oxford University Press 245 260 Werren JH O'Neill SL The evolution of heritable symbionts. In: O'Neill SL, Hoffmann AA, Werren JH, editors. Influential passengers: Inherited microorganisms and arthropod reproduction 1997 Oxford Oxford University Press 1 41 Werren JH Windsor DM Wolbachia infection frequencies in insects: Evidence of a global equilibrium? Proc R Soc Lond B Biol Sci 2000 267 1277 1285 Witkin EM Mutation frequency decline revisited Bioessays 1994 16 437 444 7521640 Zhou W Rousset F O'Neill SL Phylogeny and PCR-based classification of Wolbachia strains using wsp gene sequences Proc R Soc Lond B Biol Sci 1998 265 509 515

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PLoS Biol. 2004 Mar 16; 2(3):e69

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020070SynopsisBiophysicsMus (Mouse)A Single Mutation Transforms an Iron Transporter into an Ion Channel Synopsis3 2004 16 3 2004 16 3 2004 2 3 e70Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Spontaneous, Recurrent Mutation in Divalent Metal Transporter-1 Exposes a Calcium Entry Pathway ==== Body Trace heavy metals are essential for a number of metabolic reactions in living systems, but cells walk a fine line between feast or famine. While iron, zinc, cobalt, and manganese, for example, contribute to the catabolic activity of enzymes involved in essential pathways from gene regulation to cell signaling, even a mild surplus of these metals can kill cells and cause a variety of diseases. Maintaining the proper concentration, or homeostasis, of cellular metals requires strict policing of what passes through cell membranes and organelles. A single mutation of the amino acid glycine (G) to arginine (R) turns a membrane transporter into a calcium channel One way cells regulate entry is through the hydrophobic lipid (fatty) layer that makes up the cell membrane. While the lipid membrane allows most small fat-soluble or uncharged molecules to simply diffuse through it, nearly all water-soluble molecules, including metal compounds—which typically break down into ions (molecules with positive or negative charge) in solution—rely on either transport or channel proteins to get through. Two types of proteins manage the transport and uptake of iron ions in mammalian cells: the transferrin receptor helps to concentrate iron in discrete intracellular compartments called endosomes, while a protein called divalent metal transporter-1 (DMT1) releases iron into the cytoplasm, where it supports essential metabolic processes. DMT1 also serves to bring dietary iron directly into the intestinal cells involved in iron absorption. DMT1 preferentially carries iron, zinc, copper, and manganese, but not calcium. This selectivity helps strike the right balance of the concentration of these metals in the cell. Recent structural analyses of transporters, however, have raised the possibility that this selectivity may not be as fixed as once thought. Lending support to the notion that the distinctions between transporters and ion channels are blurring, David Clapham, Nancy Andrews, and colleagues report that a mutation causing a single amino acid substitution in the DMT1 metal ion transporter opens a passageway that converts the transporter into a calcium channel. DMT1 is essential for maintaining iron homeostasis and the only molecule known to facilitate transmembrane iron uptake in higher eukaryotes, including humans. It is expressed mainly in epithelial cells of the small intestine, where iron metabolism is monitored, and in endosomes, which release transferrin-imported iron. The Clapham and Andrews groups focused on a mutation in the DMT1 transporter called G185R—which substitutes the arginine (R) amino acid for glycine (G) at a particular location in the protein's amino acid chain, position 185—because the identical mutation has occurred spontaneously in three separate laboratory strains of rodents (two mouse and one rat strain). That a single substitution has arisen independently and persisted in multiple rodent generations suggests it may confer some type of selective advantage. To investigate this idea, the researchers compared the properties of “wild-type” (nonmutant) DMT1 and mutant G185R in laboratory cell lines. They found that cells expressing G185R mutant proteins had much lower levels of iron uptake than cells expressing the nonmutant proteins, but that they also permitted the influx of calcium ions. To see whether the G185R-mediated calcium permeability had a physiological effect on the mice with this mutation, the researchers compared the properties of intestinal epithelial cells taken from the mutant and nonmutant animals. The intestinal cells in the mutant mice showed high levels of the G185R protein and a large current of charged molecules—much as would occur in an ion channel. This current was observed in both the cell lines expressing G185R and the cells extracted from the G185R mutant mice. The G185R mutation, the researchers conclude, appears to either expose or enhance a calcium “permeation pathway” that exhibits the properties of a calcium channel. This transformation appears to offer a selective advantage, since mice engineered without the DMT1 protein die within a week of birth while mice born with the G185R DMT1 mutation can live for over a year. Though the G185R mice exhibit severe iron deficiency, the modest function retained by G185R in combination with the increased influx of calcium may be enough to extend their lifespan. The increased levels of calcium, the researchers propose, may support iron uptake through some other pathway, an advantage that might explain why such a mutation would persist. Whatever mechanism accounts for this advantage, the G185R mutation transforms DMT1 transporter into an “unambiguous” calcium ion channel. Investigating the structural and biochemical properties of this molecular changeling will provide valuable insights into the emerging model of a transporter–channel continuum—which suggests a remarkable adaptability to shifting environmental conditions.

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PLoS Biol. 2004 Mar 16; 2(3):e70

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020071Book Reviews/Science in the MediaDevelopmentEvolutionGenetics/Genomics/Gene TherapyHomo (Human)Evolution for the Next Generation Book Reviews/Science in the MediaKawata Masakado 3 2004 16 3 2004 16 3 2004 2 3 e71Copyright: © 2004 Masakado Kawata.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Do you want to know about evolution? Brian and Deborah Charlesworth provide an excellent and concise account of the core issues for a broad range of readers ==== Body Evolution is a complex phenomenon that requires a broad understanding of many areas of biology for us to appreciate it fully. Moreover, the field has expanded rapidly, especially since the development of molecular techniques in the past two to three decades. Futuyma's classic text on evolution (1998) contains 26 chapters totaling 763 pages. To cover the topic in only eight chapters and 145 pages, as the Charlesworths have done in Evolution: A Very Short Introduction, is no mean feat. Their book is one of a series of short introductions, published by Oxford University Press, covering an eclectic array of subjects that aim to provide an accessible yet stimulating read for anyone wanting a thorough introduction to a topic. In this small volume, the Charlesworths have succeeded on both fronts and provide an excellent account of the core issues for a broad range of readers. One of the reasons for the book's appeal is that the authors draw on a range of carefully chosen human traits to illustrate their points. By contrast, most evolutionary textbooks (other than those purely on human evolution) tend to focus on nonhuman organisms. As with traits in every other organism, many human and human-related characteristics have evolved via genetic drift and natural selection, and they provide an effective means of convincing readers of the reality and relevance of evolution. For example, to explain how mutation can cause the loss of a function, the authors discuss the relatively poor sense of smell in humans, as compared with many other mammals, using an example of a vestigial ‘pseudogene’ of a human olfactory receptor gene. They also discuss tooth decay, enzyme aesthetics, heritable differences, cancer and other diseases, and the ability to taste and so on. Although the topics the Charlesworths choose to focus on are certainly appropriate, they provide only a brief mention of one important process—development. Evolutionary developmental biology is a burgeoning field that can provide interesting and important insights into our understanding of the mechanisms of evolution. For example, the absence of eyes in cavefish, rather than being the result of a degenerative process, might be the result of selection on genes that govern feeding morphology, a selection process that has included suppression of eye development (Pennisi 2002). Such developmental mechanisms and constraints can actually alter the direction of evolution. Although the key forces driving evolution are usually thought of as mutation, genetic drift, natural selection, and divergence, the developmental pathways from genes to phenotypes, along with associated developmental constraints, can also determine the rate and direction of evolution. In Chapter 7, the authors discuss five topics that have traditionally been hard to understand from an evolutionary point of view. These ‘difficult problems’ are ageing, altruism, human consciousness, complex adaptations, and the origin of living cells. Difficult problems can be interpreted in two ways: those that, although hard to solve, have either been explained or will eventually be explained by modern evolutionary theories, and those problems that cannot be fully resolved with our current understanding but leave room for learning about additional mechanisms or factors. The Charlesworths generally consider only those problems of the former type—the explained ones. However, I think that some of the more intractable problems should be described in more detail. For instance, complex adaptation might be fully explained by mutations and natural selection, but additional unknown mechanisms might be essential for the evolution of the complex traits. I realize that opponents of modern evolutionary theory, such as creationists, have often cited these traditional problems to support their conclusion that modern evolutionary theory is wrong; but progress always depends on the consideration of new ideas, and there might be important mechanisms still to be discovered that play a key role in evolution. Describing potentially intractable problems might also spur on young readers who are thinking of studying evolutionary biology with the hope that there are still some theoretical battles to be conquered. Who is the target audience of this book? For many books, the topics chosen and the writing style can perhaps provide clues to the nature of the readers. For instance, The Blind Watchmaker by Richard Dawkins (1990) is a good introductory book for those interested in natural selection because it seems to be written mainly for individuals who either oppose or do not understand the role of natural selection. In the Charlesworths' book, providing evidence for evolution occupies 49 of the 130 pages. They explain how the similarities between living creatures can be understood in terms of evolution (Chapter 3) and subsequently discuss evidence from the geographical distributions of living and fossil species (Chapter 4). My first impression was that this part occupies too large a proportion of the book. However, Chapter 3 serves as a good introduction to the basic background of biology, such as the gene, DNA, and cells. When I read a recent article about a teaching controversy concerning evolution (Scott and Branch 2003), I began to appreciate the importance—at least in the United Kingdom and the United States—of convincing readers of the reality and cogency of evolution and evolution theory by astutely providing them with the evidence to judge for themselves. In Japan, there seem to be few people who deny the facts of evolution, although there are many ideologically motivated books opposing natural selection and Darwinism. To convince creationists of evolution is usually extremely difficult, if not impossible, because they will never doubt their assumption that God created humankind. Education of young and curious people, however, can make a difference. This is where I think the book will be most successful, but this book should not just be limited to young people—I can recommend it to anyone who wants to know about evolution. Moreover, I can recommend it to Japanese students not only as an introduction to evolution, but also as an exercise in reading a well-written and engaging English text. Masakado Kawata is a professor in the Division of Ecology and Evolutionary Biology of the Graduate School of Life Sciences, Tohoku University, Sendai, Japan. E-mail: kawata@mail.tains.tohoku.ac.jp Book Reviewed Charlesworth B, Charlesworth D (2003) Evolution: A very short introduction. Oxford: Oxford University Press. 145 pp. ISBN 0-19-280251-8 (paper). US$9.95 ==== Refs References Dawkins R The blind watchmaker 1990 New York Norton 358 Futuyma DJ Evolutionary biology. 3rd ed 1998 Sunderland, MA Sinauer Associates, Inc 751 Pennisi E Evo-devo enthusiasts get down to details Science 2002 289 953 955 Scott EC Branch G Evolution: What's wrong with teaching the controversy Trends Ecol Evol 2003 18 499 502

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020072Community PageAnimal BehaviorEcologyEvolutionZoologyOtherNCEAS: Promoting Creative Collaborations Community PageReichman O. J 3 2004 16 3 2004 16 3 2004 2 3 e72Copyright: © 2004 O. J. Reichman.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.NCEAS -- an ecological synthesis center -- is changing the way ecological research is conducted by fostering new forms of collaboration and interdisciplinary research ==== Body A substantial portion of research occurs in places where scholars congregate—in campus laboratories, in libraries, or in large specialized facilities, such as oceanographic ships, astronomical observatories, or accelerators. Under any of these circumstances, researchers can interact easily, exchanging ideas and information. Ecological research, especially the large component occurring in the field, takes place in disparate locations all over the world—in the air, on the surface, and below the oceans, lakes, and crust of the planet. Although there are many biological field stations where scientists and students gather, much of the research in ecology takes place in isolation. In addition to being highly dispersed geographically, ecology encompasses a disparate range of disciplines at scales from molecules to the globe, making the exchange of ideas and information even more difficult. Recognizing the benefits of interaction and collaboration, the ecological community began considering a synthesis center where researchers from many fields could meet to address important ecological questions. Several organizations held discussions about the nature of such a center, which culminated in two workshops hosted by the National Science Foundation (NSF) in the early 1990s, to set the scope. In 1994, NSF conducted an open competition for a synthesis center, eventually granting the University of California, Santa Barbara, the award for the National Center for Ecological Analysis and Synthesis (NCEAS). In addition to funding from NSF, the center is supported by the University of California system and its Santa Barbara campus and by several foundations. The center employs various types of research approaches. A primary approach is through Working Groups (Figure 1) where scientists come to NCEAS to concentrate on specific issues requiring synthesis of ideas, in-depth analysis of data, development of models, and preparation of results. The groups generally visit NCEAS two to four times over two years and stay for three to ten days at a time. Working Group topics range from microbial diversity to global change and have included projects in sociology, economics, and computer science. The Center hosts about 100 meetings a year, involving hundreds of participants. Figure 1 A Working Group at NCEAS on Diseases in Natural Populations (Photo used by permission from NCEAS.) NCEAS also supports up to six visiting Center Fellows (sabbatical visitors) each year. These scientists often integrate their own research into a broader context or synthesize what is known about certain areas in ecology. Concurrently, the Center houses 15–18 Postdoctoral Associates for one to three years each. These postdoctoral positions are distinctive in that there are no permanent mentors for the larval scientists—rather, they interact with the other Associates, the resident Fellows, and the hundreds of individuals who annually visit the Center as part of Working Groups. The Center also conducts training activities, including a distributed graduate seminar program. In this approach, graduate students around the world become involved in seminars on specific topics using data from their region (e.g., the relationship between productivity and diversity) and then representatives from each of the seminars are brought to NCEAS for a grand synthesis. As would be expected for a discipline as broad as ecology, the participants at NCEAS are extremely diverse. Over 3,000 individuals have visited NCEAS in just over eight years, representing 43 countries and 49 states in the United States. They come from over 800 institutions, many non-biology departments, and 397 non-academic organizations (e.g., agencies and companies). An interesting measure of their breadth is that participants belong to more than 180 professional societies. Proposals are solicited twice a year and reviewed by a Science Advisory Board. The Board looks for topics that would benefit from synthesis and analysis and that would make significant contributions to our understanding of ecological relationships. While many proposals pertain to core ecological questions, others deal with economics or sociology (e.g., how metaphors affect the way we conduct research). Approximately 40% of the projects have some applied component, many influencing resource management practices and conservation policies. Because the Center is based on the use of existing data, access to highly dispersed and profoundly heterogeneous ecological information is essential, but also very difficult. Recognizing the need for open access to a wide variety of data—versus project-specific data solutions—NCEAS and several collaborators (see http://www.ecoinformatics.org) have embarked on a major research program in developing tools to characterize data and make them available in standardized formats. The initial research effort, called the Knowledge Network for BioComplexity (KNB) is yielding tools to generate metadata (precise information about the data) and to make all the data available. The current research thrust, called Science Environment for Ecological Knowledge, will expand the capabilities of KNB by employing grid technology (in particular, EcoGrid, a network of networks), semantic mediation, knowledge representation, and workflow models for analysis and synthesis. The Center has supported almost 200 projects, the results of which are published in top scientific journals (see project results at http://www.nceas.ucsb.edu). Furthermore, some of the projects have had direct influence on conservation and resource management. For example, scientists at NCEAS developed theories for the design of marine reserves that were soon thereafter applied to the placement of reserves directly off the coast of Santa Barbara. In addition to scientific results, NCEAS is changing the way we conduct ecological research through novel means of encouraging productive collaborations. Sociologists studying the NCEAS model of collaboration have identified several important factors in its success. These include a distant, neutral location facilitating periodic, highly focused opportunities to concentrate on the issues under consideration; logistic support that lowers the activation energy required to develop collaborations; and the proximity of scientists from many disciplines having the opportunity to interact in ways otherwise not possible. Many significant contributions to our understanding of the patterns and processes of the natural world have emerged from NCEAS research activities. In addition, the Center is fostering new forms of collaboration and interdisciplinary research by providing a place where scientists from many disciplines can productively interact and by working to make eclectic ecological data available to many users. This is an extremely simple model for the scientific enterprise—but one not captured in most existing institutional and organizational structures. As recognition of the success of NCEAS spreads, other institutions are attempting to incorporate some of the traits of the Center into their operations, and new centers are being proposed. For example, the NSF is in the midst of a review of proposals for an evolution synthesis center. It is clear that the complexity of ecological systems, as well as the importance of understanding and maintaining them, requires information and knowledge from many disciplines. This is true even at a time when disciplines are becoming more specialized and scientists have less time to concentrate on broader issues. By facilitating interactions among many scholars and practitioners, NCEAS provides both time when and a place where far-reaching topics can be addressed. O. J. Reichman is the director of the National Center for Ecological Analysis and Synthesis at the University of California, Santa Barbara in Santa Barbara, California, United States of America. E-mail: reichman@nceas.ucsb.edu Abbreviations KNBKnowledge Network for BioComplexity NCEASNational Center for Ecological Analysis and Synthesis NSFNational Science Foundation ==== Refs Further Information Knowledge Network for BioComplexity – http://knb.ecoinformatics.org National Center for Ecological Analysis and Synthesis – http://www.nceas.ucsb.edu Science Environment for Ecological Knowledge – http://seek.ecoinformatics.org

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PMC368167

CC BY

2021-01-05 08:26:25

no

PLoS Biol. 2004 Mar 16; 2(3):e72

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PLoS Biol

10.1371/journal.pbio.0020072

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020073Journal ClubBioengineeringMolecular Biology/Structural BiologyProtein Nanomachines Journal ClubStrong Michael 3 2004 16 3 2004 16 3 2004 2 3 e73Copyright: © 2004 Michael Strong.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.At the interface of biology and nanotechnology lies an area of research that aims to construct molecular-scale machines based on protein and nucleic acid ==== Body In 1959 Richard Feynman delivered what many consider the first lecture on nanotechnology. This lecture, presented to the American Physical Society at the California Institute of Technology, prompted intense discussion about the possibilities, or impossibilities, of manipulating materials at the molecular level. Although at the time of his presentation, the manipulation of single molecules and single atoms seemed improbable, if not impossible, Feynman challenged his audience to consider a new field of physics, one in which individual molecules and atoms would be manipulated and controlled at the molecular level (Feynman 1960). As an example of highly successful machines at the “small scale,” Feynman prompted his audience to consider the inherent properties of biological cells. He colorfully noted that although cells are “very tiny,” they are “very active, they manufacture various substances, they walk around, they wiggle, and they do all kinds of wonderful things on a very small scale” (Feynman 1960). Of course, many of these “wonderful things” that he was referring to are a result of the activities of proteins and protein complexes within each cell. The field of nanotechnology has indeed emerged and blossomed since Feynman's 1959 lecture, and scientists from many disciplines are now taking a careful look at the protein “machines” that power biological cells (Drexler 1986). These “machines” are inherently nanoscale, ranging in width from a few nanometers (nm) to over 20 nm, and have been carefully refined by millions of years of evolution. As a graduate student in molecular biology, I have been especially interested in creative approaches to bridging the fields of biology and nanotechnology. Both DNA and protein molecules possess a number of intrinsic characteristics that make them excellent candidates for the assembly of dynamic nanostructures and nanodevices. Properties such as the site-specific molecular recognition among interacting protein molecules, the template-directed self assembly of complementary DNA strands, and the mechanical properties of certain protein complexes have enabled bionanotechnologists to envision a molecular world built “from the bottom up” using biological-based starting materials. In my own research, I have been very interested in investigating protein interactions and protein pathways on a genome-wide scale. In many ways, protein pathways are analogous to nanoscale “assembly lines,” since protein pathways often involve a series of proteins that act in successive order to yield a particular molecular “product” or perform a particular molecular function. While these protein-based “assembly lines” are commonplace within biological cells, they prompt two interesting questions with respect to the field of nanotechnology. First, can we mimic these multicomponent protein-based “assembly lines” on nanofabricated surfaces? And, second, can we tailor these “nanoscale assembly lines” to perform new and unique tasks? Nanomechanical protein complexes, such as the rotary ATP synthase complex, have also generated much interest from a nanotechnology standpoint (Soong et al. 2000). These protein complexes enable highly controlled mechanical motion at the nanoscale and may some day lead to novel rotary machines that function as molecular motors for a variety of nanoscale applications. In order to fully exploit these nanoscale protein machines, it is of prime importance to be able to position individual proteins and protein complexes at the nanoscale. Progress in this area has recently been reported by Yan et al. (2003), who developed a method to construct two-dimensional protein arrays using DNA-directed templates. Building on work pioneered by Nadrian Seeman (Seeman 2003), Yan et al. constructed two-dimensional DNA “nanogrids” by exploiting the pairing that occurs between complementary DNA strands (Figure 1). The two-dimensional DNA nanogrid exhibits a repeating periodic structure (Figure 1B) due to the inherent qualities of the individual DNA tiles that make up the nanogrid (Figure 1A). The distance between adjacent tile centers is approximately 19 nm (approximately 4.5 turns of the DNA double helix plus the diameter of two DNA helices). Figure 1 Self-Assembled DNA Nanostructures (A) DNA “tile” structure consisting of four branched junctions oriented at 90° intervals. These tiles serve as the primary “building block” for the assembly of the DNA nanogrids shown in (B). Each tile consists of nine DNA oligonucleotides as shown. (B) An atomic force microscope image of a self-assembled DNA nanogrid. Individual DNA tiles self-assemble into a highly ordered periodic two-dimensional DNA nanogrid. (Images were kindly provided by Thomas H. LaBean and Hao Yan.) Yan et al. utilized these DNA nanogrids to assemble periodic protein nanoarrays. The DNA nanogrid, in this case, served as a molecular scaffold for the self assembly of protein molecules into ordered arrays. In order to control the location of protein assembly, Yan et al. first tethered a covalently linked biotin moiety to the central region of each DNA tile. The biotin was covalently linked to one of the DNA strands at the position corresponding to the center of the tile. This design resulted in a uniform array of biotinylated tiles, with each biotin moiety separated by about 19 nm. The authors then added streptavidin, a protein that has a strong binding affinity for biotin, to form a periodic streptavidin protein array on top of the biotinylated DNA lattice. The resulting array represents the first periodic, self-assembled DNA lattice in which individual protein molecules are precisely positioned into a periodic array with nanometer dimensions. It is interesting to consider some of the applications of self-assembled protein arrays. Soong et al. (2000) demonstrated that the ATP synthase protein complex could be used to power the rotation of an inorganic nickel “nanopropeller.” ATP synthase is a multisubunit protein complex with a domain that rotates about its membrane-bound axis during the natural hydrolysis of ATP within a cell. Soong et al. attached a nanoscale inorganic “propeller” to the rotary stalk of ATP synthase, creating a “rotary biomolecular motor.” It is intriguing to consider the construction of an ordered array of ATP synthase driven nanomachines, each positioned precisely along a DNA scaffold, similar to that described by Yan et al. Such an assembly, combined with proposed “nanogears” (Han et al. 1997), may one day enable the construction of nanoscale variations of the traditional “gear-train” and “rack-and-pinion” gearing systems. Construction of such systems may facilitate the design of machines that can transmit and transform rotary motion at the nanoscale. In addition to rotary biomolecular motors, proteins that undergo substantial conformational changes in response to external stimuli might also find some interesting uses in nanoarrays. Dubey et al. (2003) are working on methods to exploit the pH dependent conformational changes of the hemagglutinin (HA) viral protein to construct what they term viral protein linear (VPL) motors. Proteins that undergo substantial conformational changes in response to environmental stimuli may facilitate the design of nanoscale machines that produce linear motion (Drexler 1981), as opposed to rotary motion. At neutral pH, the HA2 polypeptide forms a compact structure composed of two α-helices folded back onto each other. At low pH, HA2 undergoes a substantial conformational change, which results in a single “extended” helix. This conformational change results in a linear mechanical motion, with a linear movement of approximately 10 nm (Dubey et al. 2003). It would be interesting to investigate the applications of ordered arrays of dynamic VPL motors, since an array of such “hinge” structures may enable the coordinated linear movement of hundreds of tethered macromolecules in a synchronous manner. The work of Yan et al. (2003) has opened up exciting new avenues in the field of nanotechnology and has provided the molecular framework for the construction of dynamic protein-based assemblies. It is foreseeable that variations of these same DNA scaffolds will eventually be used for the design and construction of more complex protein-based assemblies, such as nanoscale “assembly lines” or periodic arrays of dynamic motor proteins. This work is important to me because it demonstrates not only that it is possible to create uniform arrays of protein biomolecules using biomolecular scaffolds, but the study also emphasizes the important role that molecular biology will undoubtedly play as the field of nanotechnology matures. As the field of nanotechnology continues to evolve, it is likely that we will see many more nanotechnology applications utilizing biological macromolecules. Toward the end of Richard Feynman's 1959 lecture, he quipped, “What are the possibilities of small but movable machines? They may or may not be useful, but they surely would be fun to make.” MS is supported by United States Public Health Service National Research Service award GM07185. Michael Strong is a graduate student in the laboratory of David Eisenberg at the Molecular Biology Institute at the University of California, Los Angeles, in Los Angeles, California, United States of America. E-mail: strong@mbi.ucla.edu Abbreviations HAhaemagglutinin nmnanometer VPLviral protein linear ==== Refs References: Drexler KE Molecular engineering: An approach to the development of general capabilities for molecular manipulation Proc Natl Acad Sci USA 1981 78 5275 5258 16593078 Drexler KE Engines of creation: The coming era of nanotechnology 1986 New York Anchor Press 320 Dubey A Mavroidis C Thornton A Nikitczuk K Yarmush ML Viral protein linear (VPL) nano-actuators Proceedings of the Third IEEE–NANO Conference 2003 1 San Francisco, California 140 143 2003 August 12–14 Available at http://ieeexplore.ieee.org/xpl/RecentCon.jsp?puNumber=8708 via the Internet. Accessed 12 January 2004 Feynman RP There's plenty of room at the bottom: An invitation to enter a new field of physics Engrg Sci 1960 23 22 Available at http://www.zyvex.com/nanotech/feynman.html via the Internet. Accessed 12 January 2004 Han J Globus A Jaffe R Deardorff G Molecular dynamics simulations of carbon nanotube-based gears. (1997) Nanotechnology 1997 8 95 102 Seeman NC Biochemistry and structural DNA nanotechnology: An evolving symbiotic relationship Biochemistry 2003 42 7259 7269 12809482 Soong RK Bachand GD Neves HP Olkhovets AG Craighead HG Powering an inorganic nanodevice with a biomolecular motor Science 2000 290 1555 1558 11090349 Yan H Park SH Finkelstein G Reif JH LaBean TH DNA-templated self-assembly of protein arrays and highly conductive nanowires Science 2003 301 1882 1884 14512621

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PMC368168

CC BY

2021-01-05 08:21:08

no

PLoS Biol. 2004 Mar 16; 2(3):e73

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PLoS Biol

10.1371/journal.pbio.0020073

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020075Research ArticleCell BiologyDevelopmentMus (Mouse)Circulation and Chemotaxis of Fetal Hematopoietic Stem Cells Circulation and Chemotaxis of Fetal HSCsChristensen Julie L jchristensen@cellerant.com 1 ¤Wright Douglas E 1 ¤Wagers Amy J 1 Weissman Irving L 1 1Departments of Pathology and Developmental Biology, Stanford University School of MedicineStanford, CaliforniaUnited States of America3 2004 16 3 2004 16 3 2004 2 3 e7522 8 2003 7 1 2004 Copyright: ©2004 Christensen et al. 2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Tracking Blood-Forming Stem Cells through Development The major site of hematopoiesis transitions from the fetal liver to the spleen and bone marrow late in fetal development. To date, experiments have not been performed to evaluate functionally the migration and seeding of hematopoietic stem cells (HSCs) during this period in ontogeny. It has been proposed that developmentally timed waves of HSCs enter the bloodstream only during distinct windows to seed the newly forming hematopoietic organs. Using competitive reconstitution assays to measure HSC activity, we determined the localization of HSCs in the mid-to-late gestation fetus. We found that multilineage reconstituting HSCs are present at low numbers in the blood at all timepoints measured. Seeding of fetal bone marrow and spleen occurred over several days, possibly while stem cell niches formed. In addition, using dual-chamber migration assays, we determined that like bone marrow HSCs, fetal liver HSCs migrate in response to stromal cell-derived factor-1α (SDF-1α); however, unlike bone marrow HSCs, the migratory response of fetal liver HSCs to SDF-1α is greatly increased in the presence of Steel factor (SLF), suggesting an important role for SLF in HSC homing to and seeding of the fetal hematopoietic tissues. Together, these data demonstrate that seeding of fetal organs by fetal liver HSCs does not require large fluxes of HSCs entering the fetal bloodstream, and that HSCs constitutively circulate at low levels during the gestational period from 12 to 17 days postconception. Newly forming hematopoietic tissues are seeded gradually by HSCs, suggesting initial seeding is occurring as hematopoietic niches in the spleen and bone marrow form and become capable of supporting HSC self-renewal. We demonstrate that fetal and adult HSCs exhibit specific differences in chemotactic behavior. While both migrate in response to SDF-1α, fetal HSCs also respond significantly to the cytokine SLF. In addition, the combination of SDF-1α and SLF results in substantially enhanced migration of fetal HSCs, leading to migration of nearly all fetal HSCs in this assay. This finding indicates the importance of the combined effects of SLF and SDF-1α in the migration of fetal HSCs, and is, to our knowledge, the first demonstration of a synergistic effect of two chemoattractive agents on HSCs. New results on the migratory behavior of blood cell precursors in the early embryo might be relevant to bone marrow transplants and other clinical therapies ==== Body Introduction During fetal development, the primary anatomical concentration of hematopoietic stem cells (HSCs) changes location several times. The migration of blood-borne progenitors is essential for the establishment of hematopoiesis in subsequent hematopoietic tissues (Moore and Metcalf 1970; Johnson and Moore 1975; Weissman et al. 1978; Houssaint 1981; Weissman 2000; Akashi and Weissman 2001). The speculation that this fetal migration process occurs as a series of distinct, timed developmental events, wherein large numbers of fetal HSCs simultaneously enter the bloodstream to seed newly forming hematopoietic organs, arose from observations that a decrease in HSCs and/or hematopoietic progenitor numbers in primary hematopoietic tissues occurs just prior to the seeding of newly forming hematopoietic sites (Morrison et al. 1995; Medvinsky and Dzierzak 1996). Hematopoietic precursor numbers increase in intraembryonic sites such as the aorta–gonad–mesenepheros region (AGM) and yolk sac until 11 days postconception (dpc), then decrease, becoming undetectable by 13 dpc (Moore and Metcalf 1970; Muller et al. 1994; Garcia-Porrero et al. 1995; Sanchez et al. 1996; Godin et al. 1999). This decrease in HSC numbers is hypothesized to result from a wave of multipotent progenitors leaving the AGM (Medvinsky and Dzierzak 1996) or yolk sac (Weissman et al. 1978) to seed the fetal liver on 11 dpc. However this occurs, HSCs increase exponentially in the fetal liver from day 12 until day 15 (Ikuta and Weissman 1992; Morrison et al. 1995) or day 16 (Ema and Nakauchi 2000); then, HSC numbers and activity in the fetal liver decrease, although the fetal liver HSC (FL HSC) population continues to proliferate at an equivalent rate. This decrease in HSC numbers in the fetal liver could result from a mobilization of HSCs from the fetal liver to the spleen and bone marrow (Morrison et al. 1995). While the mechanisms that influence HSC homing and colonization are not completely understood, several experimental models suggest possible regulatory factors. The homing to and colonization of fetal hematopoietic organs by circulating HSCs likely require homing receptor/addressin interactions in the vascular lumen, followed by chemokine/chemokine receptor interactions, integrin/receptor binding, and growth/survival factors. Homing of lymphocytes and leukocytes has been well documented to involve first homing receptor/vascular addressin interactions, resulting in cell tethering and rolling on blood vessel endothelium. The rolling cells respond to a chemoattractant, produced by endothelial or stromal cells within the tissue, by firm adherence to the vessel wall mediated by integrin/receptor interactions. The adhered cells subsequently traverse the vessel wall, migrating toward the increasing gradient of chemoattractant (Butcher and Weissman 1980; Gallatin et al. 1983; Springer 1990; Campbell et al. 1998). A similar cascade of interactions is likely to govern the migration of immature hematopoietic stem/progenitor cells. Mice born with genetic deficiency of the chemokine stromal cell-derived factor-1α (SDF-1α), or its receptor, CXCR4, fail to establish bone marrow hematopoiesis, although fetal liver hematopoiesis is normal (Nagasawa et al. 1996; Zou et al. 1998; Ara et al. 2003). In addition, bone marrow HSCs (BM HSCs) have been shown to migrate selectively in vitro in response to SDF-1α (Wright et al. 2002). SDF-1α may be important both as a chemoattractant (Imai et al. 1999; Peled et al. 1999) and as an activator of adhesion molecules on HSCs (Peled et al. 2000) and may function in the retention and maintenance of fetal HSCs once they reach the hematopoietic niche (Nagasawa et al. 1998; Tachibana et al. 1998; Zou et al. 1998; Kawabata et al. 1999; Ma et al. 1999; Lataillade et al. 2000). Correct localization of HSCs throughout ontogeny may also involve other specific interactions with the hematopoietic microenvironment (Schweitzer et al. 1996). A factor that is well-established to be important to the maintenance, survival and proliferation of HSCs is Steel factor (SLF) (Broxmeyer et al. 1991; Metcalf and Nicola 1991; Ikuta and Weissman 1992; Li and Johnson 1994; Keller et al. 1995; Holyoake et al. 1996; Goff et al. 1998; Domen et al. 2000). Homozygous deficiency mutations of the SLF-encoding gene (Sl), normally expressed in hematopoietic stromal cells, or its receptor gene (W), encoding the c-Kit tyrosine kinase, result in profound but incomplete defects in hematopoiesis (Russell 1979). Functional hematopoietic cells from Steel ligand-deficient mice (Sl/Sld) can be rescued by transplantation to a wild-type host (McCulloch et al. 1965; Fried et al. 1973; Dexter and Moore 1977; Bernstein et al. 1991; Barker 1997). Interestingly, in the lethal Sl/Sl background, FL HSCs double their number daily between 13 and15 dpc (Ikuta and Weissman 1992), indicating that factors other than SLF are responsible for fetal HSC expansion. SLF has also been implicated as a chemotactic factor of human (Kim and Broxmeyer 1998) and mouse (Okumura et al. 1996) hematopoietic progenitor cells. In order to test the hypothesis that fetal HSC migration is a timed developmental event, we collected blood from embryos ranging in age from 12.5 to 17.5 dpc to use in competitive reconstitution assays to measure long-term reconstituting hematopoietic stem cell (LT-HSC) activity. Our results indicate that mouse fetal HSCs are found constitutively rather than episodically in fetal circulation and are present at low numbers throughout mid-to-late fetal development. We also measured the seeding of the fetal spleen and fetal bone marrow during this period. The seeding of these organs is a gradual process occurring over several days and does not appear to involve a large influx of HSC. Finally, we found that FL HSCs migrate in response to the chemokine SDF-1α and that this response is substantially enhanced in the presence of SLF. The enhanced chemotactic response of HSCs to the combination of SLF and SDF-1α is a property of FL HSCs, but not adult BM HSCs. Results HSC Are Found Constitutively Circulating in Fetal Blood To evaluate the presence of rare HSC activity in the fetal circulation, blood was collected from fetuses ranging in age from 12.5 to 17.5 dpc. Blood from a single age group was pooled and assayed by competitive reconstitution. A quantity of fetal cells, measured by fetus equivalents (FEs), was injected into an adult, lethally irradiated congenic recipient, along with a radioprotective dose of host-type bone marrow. A FE was defined as the amount of blood collected from a single fetus of each age group. The amount of fetal blood transplanted ranged from 4 FE to 0.1 FE. The recipient mice were periodically bled and assayed for donor cells of the B, T, and myeloid lineages. Table 1 demonstrates that stem cells capable of long-term multilineage reconstitution (LT-MLR) are found constitutively in the fetal blood throughout the time period assayed, from 12.5 to 17.5 dpc. Figure 1A illustrates the level of donor-derived peripheral blood cells at 20 wk or more. LT-HSC activity is maintained at low but fairly constant levels throughout this time period, with a minor peak apparent at 14.5 dpc (Figure 2). Consistent with these results, cells that display the fetal liver stem cell surface phenotype, c-Kit+ Thy-1.1lo Sca-1+ Lineage– Mac-1lo (Morrison et al. 1995) can be detected by FACS in fetal circulation at both 14.5 and 17.5 dpc (Figure 3). Figure 1 LT-HSC Activity Is Measureable in Fetal Blood, Spleen, and Bone Marrow Cell suspensions of fetal blood (A), spleen (B), and bone marrow (C) were used to competitively reconstitute lethally irradiated recipients. The percentage of donor-derived peripheral blood leukocytes is presented for each dose assayed at 20 wk or more following reconstitution. The bar represents the mean percentage of donor-derived peripheral blood leukocytes in all recipients transplanted with each dose of fetal tissue, blood, spleen, or bone marrow. Fetal tissue from each stage embryo (12.5–17.5 dpc) was transferred in two to three experiments at multiple doses. Positive engraftment was determined by comparison to staining of control mice, which in most cases was less than 0.1%. Figure 2 LT-HSC Activity Is Detectable in Fetal Blood (12.5–17.5 dpc) To illustrate an increase in circulating HSCs at 14.5 dpc, the percentage of recipients with donor multilineage reconstitution from fetal blood are plotted for each timepoint (12.5–17.5 dpc), for 1.0 and 0.5 FE. Figure 3 Phenotypic Analysis of HSC in Fetal Blood, Spleen, and Bone Marrow Cells can be identified in fetal circulation, spleen, and bone marrow with the FL HSC phenotype: Lineage– c-Kit + Sca-1+ Thy-1.1lo Mac-1lo. At 14.5 and 17.5 dpc, fetal tissues were analyzed for Lineage– c-Kit + Sca-1+ Thy-1.1lo and Mac-1lo expression. The leftmost contour plot shows Lineage versus Thy-1.1 staining of live cells. The middle plot shows c-Kit versus Sca-1 staining for gated Lineage– / lo Thy-1.1lo cells. The rightmost histogram shows Mac-1 expression by gated Lineage– / lo Thy-1.1lo Sca-1+ c-Kit + cells. Fetal liver, blood, spleen, and BM HSCs have low-level Mac-1 expression. These data are representative of three independent experiments. Table 1 Fetal Blood Titration Cell suspensions of fetal blood were used to competitively reconstitute lethally irradiated recipients. The fraction of mice exhibiting LT-MLR (B plus T lymphoid and myeloid) for greater than 20 wk, for each dose of fetal blood, is presented Seeding of Fetal Spleen and Bone Marrow by HSCs To measure the kinetics of seeding of the fetal spleen and bone marrow by HSCs, these tissues were similarly assayed by competitive reconstitution for LT-HSC activity. Spleens were collected from 14.5 to 17.5 dpc and fetal bone marrow from 15.5 to 17.5 dpc. For these experiments, a FE was defined as the number of cells collected from a single fetal spleen or bone marrow collected from two femurs plus two tibia. Table 2 illustrates the initiation of long-term multipotent progenitor activity in the fetal spleen and fetal bone marrow. Figures 1B and 1C illustrate the levels of donor-derived peripheral blood cells at 20 wk or more in recipient animals transplanted with fetal spleen or bone marrow cells. Active seeding of the spleen by LT-HSCs occurs at approximately 15 dpc, although infrequent LT-HSCs can be found in 14.5 dpc spleen when multiple embryo equivalents are assayed by transplantation, indicating that very few HSCs initially seed this organ. HSC activity increases daily in the spleen during the period assayed, from 14.5 to 17.5 dpc. While LT-HSC activity is absent from the fetal marrow at the time, it is robustly established in the spleen at 15.5–16.5 dpc. LT-HSC activity can first be detected in the fetal bone marrow at 17.5 dpc. However, when it is established at 17.5 dpc, the initial seeding of the bone marrow is quite robust (see Figure 1C; Table 2), especially given that the amount of bone marrow assayed may only represent approximately 20% of the total bone marrow in the embryo, if the bone marrow is distributed in the fetus as it is in the adult (Smith and Clayton 1970) . The number of spleen HSCs continues to increase as bone marrow colonization proceeds. The appearance of long-term reconstituting activity correlates with visible active erythropoiesis in both tissues (data not shown). HSCs identified by FACS in 17.5 dpc fetal spleen and bone marrow have the FL HSC phenotype, c-Kit+ Thy-1.1lo Sca-1+ Lineage– Mac-1lo (Morrison et al. 1995; see Figure 3). Within the 14.5 dpc fetal spleen and 15.5 dpc fetal bone marrow are mainly hematopoietic cells that give rise to a burst of B lymphopoiesis but do not provide sustained or detectable myelopoiesis upon transplant to adult recipients (Figure 4). This likely indicates a rapid commitment of HSCs and multipotent progenitors to common lymphoid progenitors or prepro-B cells upon seeding these microenvironments, although it could represent early seeding of these sites selectively by committed progenitor cells rather than HSCs. An alternate, though less likely, explanation is that 14.5 dpc splenic HSCs and 15.5–16.5 dpc BM HSCs are unable to seed the adult environment. In both sites, the likely origin of the immigrant cells is the fetal liver, as HSCs are no longer resident in the yolk sac and AGM at this time (Moore and Metcalf 1970; Muller et al. 1994; Garcia-Porrero et al. 1995; Sanchez et al. 1996; Godin et al. 1999). Figure 4 Progenitors Are Found in the Fetal Spleen and Bone Marrow Prior to Colonization by LT-HSC Seeding of the fetal spleen (A) and bone marrow (B) by progenitors unable to provide sustained myelopoiesis precedes colonization of these tissues by HSCs. Reconstituted mice were analyzed for donor contribution in the peripheral blood of B, T, and myeloid lineages at 4 wk post-transplant. Contour plots show gating of donor (Ly-5.2+) cells and analysis of B220 (B cell) versus Mac-1 (myeloid cell) markers on donor cell populations. At 14.5 dpc, progenitors able to give rise only to B cells were detectable from the fetal spleen in transplantation assays; three of eight recipients receiving 1.0 FE 14.5 dpc fetal spleen cell suspension had donor B cell readout; zero of nine recipients receiving 1.0 FE 14.5 dpc fetal spleen cell suspension LT-MLR. B cell progenitors were detectable from the fetal bone marrow at 15.5 dpc (one of five receiving 2.0 FE) and 16.5 dpc (one of eight receiving 1.0 FE), before detectable HSCs were present. Fetal tissue from each stage embryo (12.5–17.5 dpc) was transferred in two to three experiments at multiple doses. Positive engraftment was determined by comparison to staining of control mice, which in most cases was less than 0.1%. Table 2 Fetal Spleen and Bone Marrow Titration Cell suspensions of fetal spleen or bone marrow were used to competitively reconstitute lethally irradiated recipients. The fraction of mice exhibiting LT-MLR for greater than 20 wk is presented for each dose of fetal spleen or marrow Synergistic Effects of SDF-1α and SLF on Chemotaxis of Fetal HSCs To begin to assess whether SDF-1α or SLF may play a direct role in fetal HSC migration, we assayed the ability of FL HSCs to migrate in response to SDF-1α and/or SLF in dual-chamber migration assays. Lineage-depleted fetal liver or adult bone marrow cells were placed in the upper well of a 5-μm transwell chamber, and SDF-1α, SLF, or SDF-1α plus SLF was added to the lower chamber. To evaluate the chemotactic versus chemokinetic effects of SDF-1α and SLF, equal concentrations of factors were added to both the top and bottom chambers. Following a 2 h incubation at 37°C, the cells that had migrated to the lower chamber were collected, stained for HSC markers, and analyzed by FACS to determine the actual number of migrated HSCs. Cell numbers between wells were normalized by the addition of predetermined numbers of fluorescent beads to each well before cell collection (Wright 2002). Like adult BM HSCs, 14.5 dpc FL HSCs migrate in response to a gradient of SDF-1α, albeit at a reduced frequency (Figure 5A). Both FL HSCs and adult BM HSCs displayed an optimal migratory response to SDF-1α at 10 nM. In addition, FL HSCs, but not adult BM HSCs, showed substantial migration, above basal activity, in response to 10 nM SLF. The combination of SLF and SDF-1α had a synergistic affect on the migratory response of FL HSCs, with 70%–90% of the input HSC responding. The increased migration of adult BM HSCs in response to SDF-1α and SLF, above that seen in response to SDF-1α alone, was more modest. Migration in response to SDF-1α of both FL HSCs and adult BM HSCs was largely dependent on the presence of a chemokine gradient (Figure 5B). In contrast, the migration of adult HSCs induced by SDF-1α plus SLF appeared to involve both chemotactic and chemokinetic activity, as inclusion of factors in both the top and bottom of the transwell did not entirely abrogate HSC migration (Figure 5B). Figure 5 Chemotactic Activity of SDF-1α and SLF on Fetal Liver and Adult BM HSCs HSCs were assayed for their ability to chemotax in a transwell assay in response to the chemokine SDF-1α. Migrating cells were labeled with stem cell markers and analyzed by FACS to determine the actual frequency of migrated HSCs. Like adult BM HSCs, FL HSCs migrate in response to SDF-1α (A), although at reduced levels. The optimal concentration of SDF-1α for both fetal liver and adult BM HSCs was determined to be 10 nM. The migratory effect of SLF was also assayed on FL HSCs and adult BM HSCs. FL HSCs migrate equally well to SLF as SDF-1α, while adult BM HSCs showed a lesser response to SLF. SLF and SDF-1α acted synergistically in their chemoattractive effects on FL HSCs (B). To determine whether migration was due to chemokinetic effects of SDF-1α, SLF, or both, equal concentrations of factors were added to both the top and bottom wells (T&B). Data are presented as the percentage of input HSCs that migrate to the bottom chamber for a representative migration assay, each point was performed in triplicate. These data are representative of three independent experiments. The single asterisk shows a significant increase in percent migration over basal migration (p < 0.05). The double asterisk shows a significant increase in percent migration over SDF-1α alone. Discussion As reported previously, HSCs in the fetal liver double in number daily from 12.5 to14.5 dpc, then decrease in number at 15.5 dpc (Ikuta and Weissman 1992; Morrison et al. 1995). At 12.5 dpc, approximately 1,200 HSCs are present, 2,430 at 13.5 dpc, and 5,100 at 14.5 dpc. However, rather than doubling to 10,200, only 4,350 FL HSCs are found at 15.5 dpc (Morrison et al. 1995). This failure of FL HSCs to continue doubling their numbers from day 14.5 to day 15.5 is unexpected, because the same percentage of HSCs remained as actively in cycle on day 15.5 as on day 14.5 (Morrison et al. 1995). Thus, about 5,800 HSCs are “missing” from the 15.5 dpc liver, possibly because they migrated via the blood to seed the spleen or bone marrow. In order to test this hypothesis quantitatively, we assayed fetal blood, spleen, and bone marrow for HSC activity. However, our results indicate that HSCs are found in the fetal blood at low but fairly constant levels during much of late fetal development. Furthermore, we were unable to find evidence of a large influx of HSCs into the fetal spleen and bone marrow at this timepoint. The seeding of the fetal spleen and bone marrow is progressive and does not appear to be the result of a large, developmentally timed migration. Thus, the decrease in FL HSC numbers at 15.5 dpc is more likely explained by their differentiation out of the HSC pool; this may be due to the onset of hepatic differentiation in the fetal liver and an induction of hepatic growth factors (Kinoshita et al. 1999). Initiation of differentiation of HSCs to multipotent and oligopotent progenitors at 15 dpc may account for the increase in colony-forming unit–spleen (CFU-S) activity (a mixture of HSCs, multipotent progenitors, common myeloid progenitors, and megakaryocyte/erythrocyte-restricted progenitors [Na Nakorn et al. 2002]) in the fetal liver at 17 dpc, observed in other studies (Niewisch et al. 1970; Wolf et al. 1995) at this and later timepoints, that occurs despite the decreasing number of HSCs in the liver following 15.5 dpc (Morrison et al. 1995; Ema and Nakauchi 2000). HSCs are found in the blood after the establishment of active circulation at 9 dpc (Toles et al. 1989) and are present in the fetal blood at all times following the onset of circulation. As reported by Kumaravelu et al. (2002), we see an increase in circulating HSCs between 12 and 13 dpc. In adults, the residence time of HSCs in the blood is about 1–3 min (Wright et al. 2001). If this is likewise true in fetuses, then long-term reconstitution is likely an accurate measure of HSC flux during fetal life. Alternatively, if the transit time of blood-borne HSCs decreases at 15 dpc and migrating HSCs lose long-term repopulating potential upon initial seeding of the spleen, we may have been unable to measure such a developmentally timed migration. As measured by reconstitution, a slight peak of circulating embryonic HSCs appears at 14.5 dpc. The slight decrease of circulating HSCs at 15.5 dpc could also be due to HSCs leaving the circulation to seed the spleen and later the bone marrow. The fetal spleen and bone marrow are initially seeded by progenitors unable to provide detectable or sustained myelopoiesis, indicating additional requirements not found in these immature hematopoietic tissues are needed for LT-HSC seeding, maintenance, or both. Our data indicate that fetal liver-derived HSCs (c-Kit+ Thy-1.1lo Lineage– Sca-1+ Mac-1lo) begin to seed the fetal spleen and bone marrow on 14.5–15 dpc and 17.5 dpc, respectively. We propose that fetal HSCs are continuously entering circulation and functionally engraft specialized stem cell niches as they develop (Figure 6). Figure 6 An Updated Model Illustrating the Location and Relative Frequencies of Fetal HSCs in the Embryo HSCs are found constitutively at low numbers in fetal blood following the onset of circulation. Seeding of developing hematopoietic tissues by long-term HSCs is gradual and is not due to a large influx of cells. The large decline in HSC numbers seen in the fetal liver following 14 dpc is most likely the result of differentiation signaled by the developing hepatic environment rather than a timed migration to the fetal spleen and bone marrow. Our results stand in contrast with those previously reported by Delassus and Cumano (1996) and Wolber et al. (2002). Using an in vitro differentiation system, Delassus and Cumano (1996) reported that blood multipotent activity is present in the fetal blood from 10 dpc to 12 dpc and then becomes undetectable. Likewise, we were unable to detect a decrease in circulating HSCs between 13–15 dpc, as reported by Wolber et al (2002), measured by CFU-S. We utilized a competitive reconstitution assay, which is the most stringent and reliable indicator of LT-HSC activity to assay functionally for LT-HSCs in fetal circulation. We were able to detect circulating HSCs throughout the period measured, 12.5–17.5 dpc. Studies indicate that HSC trafficking to and retention in the bone marrow relies on the chemokine SDF-1α and its receptor CXCR4 (Aiuti et al. 1997; Dutt et al. 1998; Kim and Broxmeyer 1998; Kawabata et al. 1999; Ma et al. 1999; Peled et al. 1999; Ara et al. 2003). We have previously shown that BM HSCs migrate only in response to SDF-1α, a large panel of chemokines (Wright et al. 2002). However, a recent study indicated that 11 dpc c-Kit+ fetal liver hematopoietic progenitor cells respond poorly to SDF-1α (Mazo et al. 2002). The discrepancy between our results and those of Mazo et al. may relate to differences in the developmental stage of the cells examined or to differences between HSCs and progenitors. At 11 dpc, the fetal liver is beginning to be seeded, and based on knockout studies, SDF-1α is not required for seeding of the fetal liver (Kawabata et al. 1999). In contrast, our study demonstrates clearly that 14.5 dpc FL HSCs do migrate in response to SDF-1α, although at a reduced frequency as compared to adult BM HSC chemotaxis to SDF-1α. We also found that the migratory response of fetal HSCs to SLF was equal to the response to SDF-1α. The migratory response of fetal HSCs to SDF-1α in combination with SLF was synergistic. This finding indicates the importance of the synergistic effects of SLF and SDF-1α in the migration of fetal HSCs. In contrast, SLF alone or in combination with SDF-1α did not evoke a greatly enhanced migratory response from adult BM HSCs. At best, the combination of SLF and SDF-1α had additive affects on the chemoattractive response of adult BM HSCs. A synergistic effect of two chemoactive agents has not, to our knowledge, been directly demonstrated before for HSCs. The substantial migratory response of 14.5 dpc FL HSCs to the combination of SLF and SDF-1α could explain the increased severity of the Steel mutant phenotype for bone marrow versus fetal liver hematopoiesis (Ikuta and Weissman 1992). The late embryonic lethality seen in the Sl/Sl mutant may be due to the inability of FL HSCs to efficiently seed the bone marrow. The chemotactic response to these factors by 14.5 dpc fetal HSC may also underlie the superior reconstituting ability of FL HSCs (Morrison et al. 1995; Rebel et al. 1996), as SLF is released by bone marrow cells following cytoreductive injury and thus may enhance recruitment of infused FL HSCs over adult BM HSCs, which do not respond to SLF. Our lab has recently described a physiological process in which low numbers of BM HSCs rapidly but constitutively traverse the bloodstream of normal mice to seed unoccupied bone marrow niches (Wright et al. 2001). We now propose that this process, which may serve to survey barren niches in adult bone marrow, also functions to seed newly forming fetal hematopoietic tissues as suitable microenvironments develop. Protocols that induce mobilization of HSCs for clinical hematopoietic cell transplantation may mimic mechanisms, already in place, that allow naturally occurring migration and engraftment of HSCs in the fetus and adult. Further investigation into the mechanisms that regulate these naturally occurring migrations may yield both an improved understanding of the importance of HSC migrations for hematopoietic development and improved protocols for clinical bone marrow transplants. Materials and Methods Mouse strains The C57BL/Ka-Thy-1.1/Ly-5.2 (Thy-1.1, Ly-5.2) donor and C57BL/Ka-Thy-1.2 (Thy-1.2, Ly-5.1) recipient mouse strains were bred and maintained at the Stanford University Laboratory Animal Facility, Stanford, California, United States. All mice were routinely maintained on acidified water (pH 2.5). Irradiated recipient mice were more than 8 wk old at the time of irradiation. All protocols were approved by the Administrative Panel on Laboratory Animal Care at Stanford University School of Medicine. Antibodies A20.1 (anti-Ly-5.1, CD45.2, FITC-conjugated; BD Biosciences [Pharmingen], Palo Alto, California, United States) and AL1-4A2 (anti-Ly-5.2, CD45.1, Texas Red conjugate) were used to analyze donor and host cells following reconstitution. Blood analysis included 6B2 (anti-B220), KT31.1 (anti-CD3), GK1.5 (anti-CD4), 53-6.7 (anti-CD8), 8C5 (anti-Gr-1), and M1/70 (anti-Mac-1). The monoclonal antibodies used in immunofluorescence staining for HSC analysis included 2B8 (anti-c-Kit, APC conjugate), 19XE5 (anti-Thy-1.1, FITC conjugate), E13 (anti-Sca-1, Ly6A/E, Texas Red conjugate), and M1/70 (anti-Mac-1, PE conjugate). Lineage marker antibodies included 6B2 (anti-B220), KT31.1 (anti-CD3), GK1.5 (anti-CD4), 53-7.3 (anti-CD5), 53-6.7 (anti-CD8), Ter119 (anti-erythrocyte-specific antigen), 8C5 (anti-Gr-1), and M1/70 (anti-Mac-1). The antibodies were purified and conjugated within our lab. Each antibody was titrated and used at predetermined optimal concentrations (highest signal with lowest background following staining of control spleen or bone marrow cells). Fetal Tissue Preparation Timed pregnancies of C57BL/Ka-Thy-1.1/Ly-5.2 mice were used to obtain embryos. The day the vaginal plug was observed was designated as 0.5 dpc. The uterus was removed and washed to remove maternal blood. Fetuses were carefully removed to prevent contamination with maternal blood. Fetuses were then decapitated in Hanks' balanced salt solution containing 5 mM EDTA and allowed to bleed out. Fetuses were passaged through several dishes of media until completely pale. Blood was combined and centrifuged. Blood was either prepared for injection by sedimenting RBCs in dextran followed by lysis of erythrocytes in 0.15 M ammonium chloride, 0.01 M potassium bicarbonate solution on ice, or remained unmanipulated and was injected directly into recipients. Results from both preparations were comparable. Spleens were obtained by first removing the spleen and stomach to a dish of Hanks' balanced salt solution containing 2% FBS. The spleens were then peeled from the surface of the stomach and placed in a clean dish of media. Femurs and tibia were removed and cleaned of muscle tissue. Spleens and bone were dissociated using the rubber end of a 1 ml-syringe plunger and filtered through nylon mesh. Blood and tissues were collected from fetuses obtained from at least three pregnant females for each timepoint measured. Fetuses that appeared developmentally advanced or delayed in any age group were discarded. Competitive reconstitution Adult recipient mice were lethally irradiated with a split dose of 950 rad as previously described (Morrison and Weissman 1994). Recipient mice were anesthetized with 3% isoflurane. Fetal cells were transferred by retroorbital injection along with a radioprotective dose of 3 × 105 CD45 congenic (recipient-type) whole bone marrow cells. Recipient mice were periodically bled to analyze peripheral blood for donor B and T lymphocytes and myeloid cells. Recipients were determined to have LT-MLR if all three of these donor subsets were present for greater than 20 wk. Positive engraftment was determined by comparison to control mice, and in most cases the threshold for positivity was less than 0.1%. Chemotaxis assays FL HSCs and adult BM HSCs were prepared as for antibody staining. Cells were stained with a lineage cocktail of the same purified rat IgG monoclonal antibodies used for FACS sorting. The cells were then depleted by magnetic selection using anti-rat IgG beads as per manufacturer's instructions (Dynal Biotech, Oslo, Norway), followed by a 1h incubation in RPMI media (GIBCO-BRL, San Diego, California, United States) containing 10% FBS in a tissue culture flask at 37°C to remove adherent cells. Dual-chamber chemotaxis assays were performed using 24-well plates with 5-μm pore size inserts (Costar/Corning, Corning, New York, United States), as previously described (Wright et al. 2002). SDF-1α-containing medium (PeproTech, Rocky Hill, New Jersey, United States) or SLF-containing medium (R & D Systems, Minneapolis, Minnesota, United States) was added to the lower chamber, and 100 μl of a cell suspension (5 × 106 or 1 × 107 cells/ml) of lineage-depleted cells was placed in the upper chamber. To measure chemokinetic movement, factors were also added to the upper chamber at the same concentration as the lower chamber. Following a 2 h incubation, the upper chamber was removed. A known quantity of fluorescent beads was added to the lower chamber for normalization of migrated HSCs. Migrated cells were removed from the lower chamber and stained with PE-conjugated antirat IgG. After washing, cells were incubated with rat IgG and then labeled with directly conjugated lineage PE, c-Kit APC, Sca-1 Texas Red and Thy-1.1 FITC antibodies. Cells were analyzed by FACS to enumerate migrated HSCs. Statistics Results shown in Figure 5 represent the mean plus the standard deviation. Significant differences were determined using a two-tailed Student's t-test. A p value of <0.05 was considered significant. Supporting Information Accession Numbers The LocusLink (www.ncbi.nlm.nih.gov/LocusLink/) accession numbers of the gene products discussed in this paper are CXCR4 (LocusLink ID 12767), SDF-1α (LocusLink ID 20315), and SLF (LocusLink ID 17311). We thank L. Jerabek for laboratory management, S. Smith for antibody preparation, and L. Hidalgo and D. Escoto for animal care. This work was supported by National Institutes of Health (NIH) grant 5R01 HL-58770 to ILW, NIH Training Grant in Molecular and Cellular Immunobiology 5T32AI07290-16 to JLC, National Institute of Allergy and Infectious Diseases training grant 5T32 AI-07290 to DEW, and American Cancer Society Grant PF-00-017-LBC to AJW. Conflicts of interest. JLC is currently employed by and has stock options in Cellerant Therapeutics, a company seeking to transplant human HSCs. As a former advisory board member of Amgen, ILW owns significant Amgen stock. He also cofounded and consulted for Systemix; cofounded Cellerant Therapeutics, a spin-off from Systemix Novartis to transplant human HSCs; and is a cofounder and a director of Stem Cells, Inc., which is involved in the isolation and study of human central nervous system stem cells, liver-repopulating cells, and pancreatic islet stem/progenitor cells. Author contributions. JLC, DEW, and AJW conceived and designed the experiments. JLC, DEW, and AJW performed the experiments. JLC, DEW, and AJW analyzed the data. JLC wrote the paper. ILW assisted in the analysis of the data and reviewed the manuscript. Academic Editor: Douglas Melton, Harvard University ¤1 Current address: Cellerant Therapeutics, Palo Alto, California, United States of America ¤2 Current address: Massachusetts General Hospital, Department of Medicine, Boston, Massachusetts, United States of America Abbreviations AGMaorta–gonad–mesenepheros region BM HSCbone marrow hematopoietic stem cell CFU-Scolony-forming unit–spleen dpcdays postconception FEfetus equivalent FL HSCfetal liver hematopoietic stem cell HSChematopoietic stem cell LT-HSClong-term reconstituting hematopoietic stem cell LT-MLRlong-term multilineage reconstitution SDF-1αstromal cell-derived factor-1α SLFSteel factor ==== Refs References Ara T Tokayoda K Sugiyama T Egawa T Kawabata K Long-term hematopoietic stem cells require stromal cell-derived factor-1 for colonizing bone marrow during ontogeny Immunity 2003 19 257 267 12932359 Aiuti A Webb IJ Bleul C Springer T Gutierrez-Ramos JC The chemokine SDF-1 is a chemoattractant for human CD34+ hematopoietic progenitor cells and provides a new mechanism to explain the mobilization of CD34+ progenitors to peripheral blood J Exp Med 1997 185 111 120 8996247 Akashi K Weissman IL Stem cells and hematolymphoid development. 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Differentiation of normal and neoplastic hematopoietic cells 1978 Cold Spring Harbor, New York Cold Spring Harbor Laboratory 33 47 Wolber FM Leonard E Michael S Orschell-Traycoff CM Yoder MC Roles of spleen and liver in development of the murine hematopoietic system Exp Hematol 2002 30 1010 1019 12225792 Wolf NS Bertoncello I Jiang D Priestley G Developmental hematopoiesis from prenatal to young-adult life in the mouse model Exp Hematol 1995 23 142 146 7828671 Wright DE Wagers AJ Gulati AP Johnson FL Weissman IL Physiological migration of hematopoietic stem and progenitor cells Science 2001 294 1933 1936 11729320 Wright DE Bowman EP Wagers AJ Butcher EC Weissman IL Hematopoietic stem cells are uniquely selective in their migratory response to chemokines J Exp Med 2002 195 1145 1154 11994419 Zou YR Kottmann AH Kuroda M Taniuchi I Littman DR Function of the chemokine receptor CXCR4 in haematopoiesis and in cerebellar development Nature 1998 393 595 599 9634238

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PLoS Biol. 2004 Mar 16; 2(3):e75

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020076SynopsisBioinformatics/Computational BiologyEvolutionGenetics/Genomics/Gene TherapyMicrobiologyEubacteriaDrosophilaGenome Sequence of the Intracellular Bacterium Wolbachia Synopsis3 2004 16 3 2004 16 3 2004 2 3 e76Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Phylogenomics of the Reproductive Parasite Wolbachia pipientis wMel: A Streamlined Genome Overrun by Mobile Genetic Elements Endosymbiosis: Lessons in Conflict Resolution ==== Body Wolbachia have a thing against males. A member of one of the most diverse groups of bacteria, called Proteobacteria, this parasitic “endosymbiont” lives inside the reproductive cells of a wide variety of the nearly 1 million species of arthropods, including insects, spiders, and crustaceans. It has also been found in worms. Wolbachia's preferred habitat is the cytoplasm of its host's gametes. Since sperm have very little cytoplasm, Wolbachia seek out the company of females, securing its survival by hitching a ride to the next generation in the cytoplasm of the mother's eggs. Wolbachia's effects range from beneficial to pathological, depending on which species infects which invertebrate host, but since most species are not beneficial, Wolbachia infections often turn out badly if the host is male. On the other hand, if female, the host could very well live longer, produce more eggs, and have higher hatching rates than its noninfected cousins—thereby facilitating Wolbachia's transmission from mother to offspring. Wolbachia have evolved an impressive repertoire of “reproductive parasitic” strategies to adapt its host's physiology to its own advantage. One strategy involves inducing “cytoplasmic incompatibility” between sperm and egg, which in effect uses infected males to keep uninfected females from producing viable offspring. Another causes infected females to reproduce asexually, creating a new generation of infected clones. Another turns developing male embryos into females. And, in a pinch, some Wolbachia simply kill developing males. The biochemical mechanisms that trigger different strategies in different hosts are unclear, however, in part because it's so far been impossible to cultivate sufficient quantities of these obligate endosymbionts (that is, intracellular species that cannot survive outside their host). But now that Scott O'Neill, Jonathan Eisen, and colleagues have sequenced the complete genome of one strain of Wolbachia pipientis, scientists investigating the biology and evolution of Wolbachia–host interactions have a valuable new research tool. The strain they sequenced, W. pipientis wMel, lives inside the fruitfly Drosophila melanogaster, the favorite model organism of geneticists for nearly 100 years. This strain causes cytoplasmic incompatibility in its host. Transmission electron micrograph of Wolbachia within an insect cell (Image courtesy of Scott O'Neill) The structure of the wMel genome, the O'Neill and Eisen groups note, is strikingly different from any other obligate intracellular species. While its genome is compact, it nonetheless contains large amounts of repetitive DNA and “mobile” DNA elements. Mobile genetic elements, as the name implies, are DNA sequences that move around the genome and are often acquired from other species. Most of the repetitive and mobile elements in Wolbachia do not appear in other α-Proteobacteria species and were probably introduced some time after Wolbachia split off from its evolutionary ancestors. Wolbachia, unlike other obligate intracellular bacteria, seem quite amenable to incorporating foreign DNA, which the authors speculate was introduced by the bacteria-infecting virus called phage. Analysis of the Wolbachia genome sheds light on the mechanisms that might help the parasite manipulate the host cell's physiology to its own advantage. One likely bacterial weapon for host exploitation is the abundance of predicted genes encoding ankyrin repeat domains, amino acid sequences characteristic of proteins important for protein–protein interactions in eukaryotes (organisms with nuclei, which bacteria lack). In bacteria, ankyrin repeats might regulate host cell-cycle pathways, which one wasp-infecting Wolbachia strain modifies to induce cytoplasmic incompatibility. Other molecular interactions between wMel and its host, the researchers propose, might also rely on proteins with these ankyrin repeats. The Wolbachia genome also provides insight into mitochondrial evolution. It is widely believed that these intracellular energy-metabolizing centers were once free-living bacteria belonging to the α-Proteobacteria group, though it's not clear which branch of the α-Proteobacteria tree they inhabit. Complete genome analysis of various α-Proteobacteria—including wMel, the first non-Rickettsia species sequenced in the Rickettsiales group—provides no evidence that mitochondria are more related to Rickettsia species than to Wolbachia, as was previously thought. In fact, further analysis failed to consistently connect mitochondria to any particular species or group within the α-Proteobacteria. While the information hidden in the Wolbachia genome seems to raise as many issues as it settles, biologists studying a wide range of problems—from the evolution and biology of Wolbachia and endosymbiont–host interactions to the origin of mitochondria—have a valuable new tool to explore their questions. The Wolbachia genome will also provide important molecular guidance for efforts to suppress insect pests and control filariasis, a human disease caused by worms. Since beneficial Wolbachia live in both insect and worm, applying antibiotics to target the Wolbachia will ultimately kill the insect pest and infecting worm, which both depend on the bacteria to survive.

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PLoS Biol. 2004 Mar 16; 2(3):e76

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020077Research ArticleNeurosciencePrimatesPerceptual “Read-Out” of Conjoined Direction and Disparity Maps in Extrastriate Area MT Task Strategy Revealed by MT StimulationDeAngelis Gregory C gregd@cabernet.wustl.edu 1 Newsome William T 2 1Department of Anatomy and Neurobiology, Washington University School of MedicineSt. Louis, MissouriUnited States of America2Howard Hughes Medical Institute and Department of Neurobiology, Stanford University School of MedicineStanford, CaliforniaUnited States of America3 2004 16 3 2004 16 3 2004 2 3 e7721 8 2003 13 1 2004 Copyright: © 2004 DeAngelis and Newsome.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Microstimulation of Neurons Reveals the Influence of Irrelevant Information on Perception Cortical neurons are frequently tuned to several stimulus dimensions, and many cortical areas contain intercalated maps of multiple variables. Relatively little is known about how information is “read out” of these multidimensional maps. For example, how does an organism extract information relevant to the task at hand from neurons that are also tuned to other, irrelevant stimulus dimensions? We addressed this question by employing microstimulation techniques to examine the contribution of disparity-tuned neurons in the middle temporal (MT) visual area to performance on a direction discrimination task. Most MT neurons are tuned to both binocular disparity and the direction of stimulus motion, and MT contains topographic maps of both parameters. We assessed the effect of microstimulation on direction judgments after first characterizing the disparity tuning of each stimulation site. Although the disparity of the stimulus was irrelevant to the required task, we found that microstimulation effects were strongly modulated by the disparity tuning of the stimulated neurons. For two of three monkeys, microstimulation of nondisparity-selective sites produced large biases in direction judgments, whereas stimulation of disparity-selective sites had little or no effect. The binocular disparity was optimized for each stimulation site, and our result could not be explained by variations in direction tuning, response strength, or any other tuning property that we examined. When microstimulation of a disparity-tuned site did affect direction judgments, the effects tended to be stronger at the preferred disparity of a stimulation site than at the nonpreferred disparity, indicating that monkeys can selectively monitor direction columns that are best tuned to an appropriate conjunction of parameters. We conclude that the contribution of neurons to behavior can depend strongly upon tuning to stimulus dimensions that appear to be irrelevant to the current task, and we suggest that these findings are best explained in terms of the strategy used by animals to perform the task. Greg DeAngelis and Bill Newsome use microstimulation to activate small populations of neurons with different combinations of response properties in monkey visual cortex ==== Body Introduction Determining how information is “read out” of sensory maps in the cerebral cortex is of fundamental importance for understanding how neural activity gives rise to cognitive processes such as perception, planning for action, and working memory. A substantial portion of our knowledge about sensory read-out comes from studies of the middle temporal (MT) visual area, an extrastriate area known to play important roles in processing visual motion information (for reviews, see Maunsell and Newsome 1987; Albright 1993; Andersen 1997). The vast majority of MT neurons are directionally selective (Zeki 1974), and they are arranged in an orderly system of direction columns that run perpendicular to the cortical surface (Albright et al. 1984; Malonek et al. 1994). In addition, most MT neurons are also selective for binocular disparity (Maunsell and Van Essen 1983; DeAngelis and Uka 2003), and these neurons are organized in a topographic map of disparity preference. Regions of strong disparity selectivity are intercalated among patches of MT neurons with weak disparity tuning, and these strongly tuned regions contain a set of disparity columns that are interwoven with the direction columns (DeAngelis and Newsome 1999). Understanding how information is read out of cortical structures is complicated by the existence of topographic maps for multiple stimulus dimensions or features within a single area, such as those in MT and many other sensory areas of the cortex (Mountcastle 1997). For example, several studies have shown that electrical microstimulation of direction columns in MT can influence perceptual judgments of visual motion during the performance of a direction discrimination task (Salzman et al. 1990, 1992; Murasugi et al. 1993; Salzman and Newsome 1994; Bisley et al. 2001; Nichols and Newsome 2002), and, similarly, that microstimulation of disparity columns can influence perceptual judgments of depth (DeAngelis et al. 1998). In all of these studies, however, the presence and size of the microstimulation effects were highly variable from experiment to experiment, suggesting that the read-out mechanism is more complex than is presently understood. Notably, each of these studies concentrated on a single physiological property—the one of direct relevance to the task at hand—in selecting MT sites for microstimulation experiments (direction tuning for direction discrimination tasks, and disparity tuning for depth discrimination tasks). Potential effects of tuning to multiple stimulus parameters on the read-out mechanism were largely ignored. We therefore designed the current study to ask two specific questions concerning the interaction of direction and disparity tuning in motion perception. (1) Do MT columns that possess or lack disparity tuning contribute differentially to direction judgments? We used electrical microstimulation to test the hypothesis that neurons in the nondisparity-selective regions of MT contribute to motion perception, whereas those in the disparity-selective regions are mainly involved in depth perception. Our hypothesis was confirmed for two of the three monkeys in this study: microstimulation of nondisparity-selective sites produced strong direction biases, whereas stimulation of disparity-selective sites had little or no effect. For the third monkey, microstimulation biased direction judgments when it was applied at either disparity-selective or nonselective sites. For disparity-tuned sites that did yield effects on direction judgments, we also asked a second question. (2) Does the influence of a disparity-tuned column on direction judgments vary as a function of the actual disparity of the motion display? We found that stimulation effects were stronger when the disparity of the visual stimulus matched the preferred disparity of the stimulated column. We conclude that tuning for task-irrelevant stimulus dimensions can exert dramatic effects on the contribution of cortical neurons to a particular perceptual judgment. In extreme cases, columns tuned for an irrelevant dimension (disparity) fail to contribute at all to perceptual judgments of the task-relevant dimension (direction). In less extreme cases, the contribution of a column is modulated by tuning along the task-irrelevant dimension, so that microstimulation effects are obtained primarily when the visual stimulus possesses the right conjunction of properties (direction and disparity) to excite the column optimally. We discuss our findings in terms of the strategies employed by animals to solve the task. Results Microstimulation experiments were performed at 102 recording sites in area MT of three rhesus monkeys (38 sites in monkey S, 36 sites in monkey T, and 28 sites in monkey R) during the performance of the direction discrimination task illustrated in Figure 1 (see Materials and Methods for details). The results are presented in three sections. First, we examine how the effects of microstimulation depend on the strength of disparity tuning at the stimulation site. Second, we present control analyses to exclude trivial explanations for the dependence of microstimulation effects on disparity-tuning strength. Third, for sites where the multiunit (MU) activity exhibited moderate to strong disparity selectivity, we examine whether the effect of microstimulation on direction judgments depends on the disparity at which the visual stimulus is presented. Figure 1 Behavioral Task Used to Assess the Effects of Microstimulation on Direction Discrimination Performance (A) Schematic depiction of the visual stimulus display, showing the FP, the preferred and null response targets, and a variable-coherence random-dot pattern presented within the MU RF of MT neurons. An adjustable fraction of the dots (signal dots, filled circles) moved in the preferred or null direction of the MT neurons, while the remaining dots (noise dots, open circles) were randomly replotted on each refresh of the display, thus creating a masking motion noise. Signal and noise dots could be presented at a range of binocular disparities. Outside the MU RF, the remainder of the visual display was filled with zero-disparity, stationary dots (not shown). (B) Sequence of trial events in the microstimulation experiment. During each trial, the FP appeared first. Roughly 300–500 ms after the monkey achieved fixation, the random-dot pattern appeared in the MU RF. On half of the trials, selected at random, microstimulation was turned on during the visual stimulus. After a 1-s viewing period, dots and microstimulation were extinguished, and the two small target disks appeared. The animal was rewarded for making a saccade to the target corresponding to the direction of motion of the signal dots. Relationship between Efficacy of Microstimulation and Disparity Selectivity We have previously shown that disparity-selective neurons tend to occur within discrete patches of MT (DeAngelis and Newsome 1999). Given this patchy distribution, we asked whether disparity-selective and nonselective patches of MT contribute equally to performance on the direction discrimination task. In all cases, the disparity of the visual stimulus was chosen to elicit a near-maximal response from MU activity at the stimulation site. Also, because microstimulation was only attempted in portions of electrode penetrations where direction selectivity was consistently near-maximal (see Materials and Methods), all experiments were done at MU recording sites with strong direction tuning. Figure 2 shows data from two illustrative experiments performed on monkey S. Figure 2A shows the disparity tuning of MU activity at a stimulation site with modest disparity selectivity. Based on this tuning curve, we chose a small near disparity of −0.1° for the random-dot stimuli used in the direction discrimination task (arrowhead in Figure 2A). Microstimulation at this weakly tuned site strongly biased the monkey's decisions toward the preferred direction of motion (Figure 2B). The net effect of this bias was a large leftward shift of the psychometric function (equivalent to 38.7% dots; logistic regression, p << 0.001), with no significant change in the slope of the curve (logistic regression, p > 0.5). This effect is qualitatively similar to those obtained previously in our laboratories (e.g., Salzman et al. 1992; Murasugi et al. 1993). Figure 2 Effect of Microstimulation on Direction Judgments at Two Illustrative Stimulation Sites from Monkey S A site with weak disparity tuning (DTI = 0.37) is shown in (A) and (B) and a site with strong disparity tuning (DTI = 0.87) is shown in (C) and (D). (A) Disparity tuning of MU activity at a stimulation site with weak disparity selectivity. Filled circles show the mean response to four stimulus presentations at each disparity, with error bars indicating ±1 SE. The solid curve is a cubic spline interpolation. The letters “L” and “R” are plotted at the response levels obtained when the same stimulus is shown only to the left and right eyes, respectively. The dashed horizontal line gives the spontaneous activity level in the absence of any visual stimulus, and the arrowhead denotes the disparity chosen for the direction discrimination task. (B) Effect of microstimulation on direction judgments for the site with the disparity tuning indicated in (A). The proportion of decisions made by the monkey toward the neurons' preferred direction of motion is plotted against the motion coherence of the random-dot stimulus. Open circles show the behavior obtained in the absence of microstimulation; the dashed curve is the best fit to these data using logistic regression. Filled circles and the solid curve show data from randomly interleaved trials in which microstimulation was applied. Note the large leftward shift of the psychometric function, equivalent to 38.7% dots (logistic regression, p < 0.001). (C) Disparity tuning of MU activity at a stimulation site with strong disparity selectivity. Again, the arrowhead denotes the disparity at which dots were presented in the direction discrimination task. (D) Effect of microstimulation on direction judgments for the site with the disparity tuning indicated in (C). In this case, there was no significant shift of the psychometric function when microstimulation was applied (p > 0.5); the small difference in slope between stimulated and nonstimulated trials is also not significant (p > 0.25). Figure 2C shows MU responses for a stimulation site with strong disparity selectivity. Activity at this site exhibited a clear preference for far disparities, and we chose a disparity of 0.4° for the direction discrimination task. Despite the fact that dots were presented at the preferred disparity and MU activity was strongly direction selective (data not shown), microstimulation had no significant effect on the monkey's judgments (Figure 2D; logistic regression, p > 0.5 for shift, p > 0.25 for slope). Thus, the activity of neurons at this stimulation site did not appear to contribute to direction discrimination. Figure 3A summarizes results from 38 similar experiments performed in monkey S (black symbols) and 36 experiments in monkey T (red symbols). The effect of microstimulation on direction judgments is plotted against the Disparity Tuning Index (DTI) of MU activity at each stimulation site. DTI values near 1.0 indicate very strong disparity selectivity, whereas values near 0.0 denote poor tuning (see Materials and Methods, Equation 2). Filled symbols denote statistically significant shifts of the psychometric function due to microstimulation (logistic regression, p < 0.05), whereas open symbols indicate nonsignificant effects. The filled and open triangles correspond to the examples shown in Figure 2B and 2D, respectively. For both monkeys, the data reveal a strong negative correlation between the magnitude of the stimulation effect and the DTI of MU activity (linear regression, monkey S, r = −0.69, n = 38; monkey T, r = −0.52, n = 36; p << 0.001 for both animals). An analysis of covariance that included monkey identity as a coregressor revealed no significant difference between regression slopes for the two animals (ANCOVA, p > 0.6). Note that microstimulation almost always produced a significant effect on direction judgments in experiments for which the DTI was less than 0.5. In contrast, significant effects of microstimulation occurred much less frequently when the DTI exceeded 0.5. Figure 3 Relationship between the Efficacy of Microstimulation and the Strength of Disparity Tuning Each datum represents one experiment, with filled symbols denoting significant effects of microstimulation (logistic regression, p < 0.05). The vertical axis shows the leftward shift of the psychometric function induced by microstimulation. Thus, positive values correspond to shifts toward the preferred direction of motion. The horizontal axis shows the DTI for MU activity at each stimulation site. (A) Data for monkey S (black symbols, n = 38) and monkey T (red symbols, n = 36). For both animals, there is a highly significant tendency for the effect of microstimulation to decline with increasing disparity selectivity (linear regression, r = −0.69 for monkey S, r = −0.52 for monkey T, p < 0.001 for both). The black, filled triangle denotes the experiment depicted in Figure 2A and 2B; the black, open triangle corresponds to the experiment of Figure 2C and 2D. (B) Data for monkey R (n = 28). In this case, the two variables are uncorrelated (r = −0.025, p > 0.9). The result in Figure 3A is interesting for two main reasons. First, it suggests that a substantial amount of variance in the efficacy of microstimulation may be accounted for by the disparity tuning of neurons at the stimulation site. This may explain why previous microstimulation studies reported a large number of nonsignificant effects (e.g., Salzman et al. 1992; Murasugi et al. 1993). In those studies, the disparity tuning of activity at stimulation sites was not measured, and all stimuli were presented at zero disparity. Second, this result is interesting because it suggests that monkeys S and T may read out activity from MT in a manner that is highly dependent on the functional architecture for binocular disparity. In formulating decisions about motion direction, these animals appeared to rely most heavily on direction-selective columns that were nonselective for disparity. In contrast, columns that were strongly tuned for disparity exerted substantially less influence on the animals' decisions. We shall address possible explanations for this finding in the Discussion. We obtained quite different results in a third animal, monkey R (Figure 3B). For this animal there was no significant correlation between the strength of the microstimulation effect and the DTI (r = −0.025, p > 0.9, n = 28). We often observed significant effects of microstimulation at sites with strong disparity tuning. It is worth emphasizing that all of the data in Figure 3 were collected using a near-optimal stimulus disparity. Thus, monkey R's decisions were usually biased by microstimulation of any direction column that was strongly activated by the visual stimulus. Effects of microstimulation at nonoptimal stimulus disparities will be addressed in a later section. The individual differences between monkeys in the data of Figure 3 may reflect different strategies used by the animals to extract motion information from area MT. Under the conditions of our task, it appears that monkeys S and T relied predominantly on direction columns with poor disparity tuning, whereas monkey R seemed also to utilize motion signals carried by regions of MT with strong disparity selectivity. In principle, this difference in strategy might have allowed monkey R to perform better on the task, as he could pool MT responses over a larger population of neurons. To examine this possibility, we analyzed the monkeys' behavioral data from trials when microstimulation was turned off, and we computed a psychophysical threshold for each stimulus disparity in each experiment (see Britten et al. 1992 for methodological details). Interestingly, we found that the mean psychophysical threshold for monkey R (16.1% ± 1.2% standard error [SE], n = 51) was significantly lower than the mean psychophysical thresholds for monkey S (21.5% ± 0.9% SE, n = 89) and monkey T (22.8% ± 1.0% SE, n = 70) (Student's t-test, p < 0.0005 for both comparisons). In contrast, the average slope of the psychometric functions did not differ between the three animals (ANOVA, p > 0.7). We shall consider these issues further in the Discussion. Functional Segregation of the Perceptual Effects of Microstimulation Monkeys T and R were subjects both in the current set of experiments and in a separate study in which we showed that stimulation of disparity-tuned columns influences perceptual judgments of depth (DeAngelis et al. 1998). For these animals, therefore, we were able to compare directly how the strength of microstimulation effects in these two tasks depended on the disparity selectivity of the stimulation sites. Figure 4 shows, for monkey T, the strength of the microstimulation effects in the direction discrimination task (red symbols, reproduced from Figure 3A) and in the depth discrimination task (blue symbols, r = 0.45, p = 0.01, n = 32) as a function of the DTI. The data reveal a clear inverse relationship between the two effects. Columns with low DTIs produce large effects on direction discrimination performance and little or no effect on depth discrimination. In contrast, columns with large DTIs show the converse pattern. In this monkey, therefore, the functional segregation of MT columns according to the strength of disparity tuning is particularly clear. Figure 4 Effects of Microstimulation on Direction Discrimination and Depth Discrimination for One Animal (Monkey T) That Was Tested in Both Tasks Plotted as a function of DTI, red circles indicate the horizontal shift of the psychometric function induced by microstimulation during the direction discrimination task with stimuli at the preferred disparity for each site (left axis). These data, along with the best linear fit (solid line), are replotted from Figure 3A. Blue circles denote the effects of microstimulation during a depth discrimination task with stimuli at the preferred direction of motion for each site (right axis; data from DeAngelis et al. 1998). The dashed line shows the best linear fit to these data (r = 0.45, p = 0.01, n = 32). It is important to note that the differences between animals seen in Figure 3 cannot be explained by any training experience involving the depth discrimination task. The present experiments were completed before any of the animals were subsequently trained to perform the depth discrimination task. Excluding Alternative Explanations for Dependence of Microstimulation Effects on Disparity Selectivity The striking result in Figure 3A could be explained trivially if disparity-tuned sites provide relatively poor information about motion direction. This situation might occur under at least three possible conditions: (1) sites with strong disparity tuning exhibit weaker or broader direction selectivity than nondisparity-tuned sites, (2) direction preferences are more variable within microstimulation sites that have strong disparity tuning (i.e., direction columns are smaller or less orderly), or (3) neural responses are simply weaker at sites with strong disparity tuning. If disparity-tuned sites indeed provide less-reliable information about the direction of motion, it would be no surprise that the monkey ignored these sites in forming its perceptual decisions. We now describe a battery of analyses to test these possibilities. Unfortunately, we cannot address the first possibility with our current data set since we did not collect quantitative direction-tuning curves in each experiment due to time limitations (see Materials and Methods). We have, however, examined the relationship between disparity tuning and direction tuning in a large number of separate MU recording experiments conducted in monkey S (n = 162) and in three additional monkeys (n = 409). Across this unbiased sample of 571 recordings, we find no significant correlation between Disparity Tuning Index (DTI) and Direction Tuning Index (r = 0.09, p = 0.11; Figure S1). A similar lack of correlation between direction and disparity selectivity was recently reported for a sample of 501 single units recorded in MT (DeAngelis and Uka 2003). We also find no significant correlation (r = 0.07, p = 0.17) between direction-tuning bandwidth and DTI across our sample of 571 MU recordings, indicating that the sharpness of direction tuning also does not covary with disparity selectivity. These observations, combined with the fact that we only performed microstimulation experiments in the portions of MT with the strongest direction tuning (see Materials and Methods), make us quite confident that the findings shown in Figure 3A do not result from any correlation between direction and disparity tuning in MT. The last two concerns described above can be addressed directly from the primary data set described in this paper. To evaluate the possibility that direction preferences are more variable within regions of strong disparity tuning (point 2 above), we computed the standard deviation (SD) of directional preferences within a 400-μm region around each microstimulation site. We find no significant correlation between the strength of microstimulation effects and the SD of preferred directions (r = −0.04, p = 0.68; Figure S2A) and, similarly, no significant correlation between the DTI and the SD of preferred directions (r = −0.05, p = 0.65; Figure S2B). Thus, the findings shown in Figure 3A do not result from variability in directional preferences. This analysis was performed using estimates of preferred directions from our receptive-field (RF) mapping procedure (see Material and Methods). A separate analysis shows that these estimates have sufficient accuracy and precision for our purposes (Figure S3). Systematic variations in responsiveness as a function of disparity tuning (point 3 above) can be excluded as a possible explanation for our findings because there is no correlation between the peak response of MU activity and the DTI (r = −0.09, p = 0.43; data taken from the disparity-tuning curve measured at each stimulation site). Correspondingly, there is no significant correlation between the strength of the microstimulation effects and the peak MU response (r = 0.17, p = 0.08), and all of the microstimulation effects in Figure 3 were obtained using the disparity that elicited the largest MU response. Similar findings were obtained for each monkey analyzed separately. Finally, using a dataset of 409 MU recordings and a multiple regression analysis, we also tested for correlations between DTI and several other response properties, including preferred speed, Speed Tuning Index, RF eccentricity, optimal stimulus size, and percentage of surround inhibition. None of these variables was significantly correlated with DTI (p > 0.1 for all), indicating that variations in these parameters are also unlikely to account for the results shown in Figure 3A. Collectively, the analyses described above indicate that the failure of microstimulation to elicit behavioral biases at disparity-selective sites cannot be explained by any basic response properties of MT neurons. Selectivity of Microstimulation Effects for Binocular Disparity Although significant microstimulation effects were rare at sites with strong disparity tuning in monkeys S and T, significant effects occurred at a good number of sites with moderate disparity tuning (i.e., DTI > 0.4). At these sites, and at many sites in monkey R, we could ask whether the efficacy of microstimulation varied when the random-dot stimulus was presented at different points along the disparity-tuning curve of the stimulated column. The logic of this experiment is illustrated for a disparity-selective site in Figure 5A. We hypothesize that neural activity in an MT column that prefers far disparities (shaded oval in 5A) is used primarily to judge direction of motion for planar stimuli at far disparities. Signals from this column should not influence perceptual decisions when the visual stimulus has a near disparity. Accordingly we predict that microstimulation should bias the monkey's choices when dots are presented at the far disparity (Figure 5A, left) and have little or no effect when dots are presented at the near disparity (Figure 5A, right). “Tuned” microstimulation effects of this nature would indicate that motion signals are read out of MT in a disparity-specific fashion. Alternatively, one could imagine that motion signals are pooled across all disparity columns, in which case we should observe nonselective microstimulation effects that are similar for both far and near disparities. For nondisparity-selective stimulation sites (Figure 5B, the receptive field is elongated in depth with respect to the animal's head), we predict that microstimulation will bias the monkey's choices regardless of the binocular disparity given to the visual stimulus. Figure 5 Schematic Illustration of Experiments Designed to Examine Whether Microstimulation Has Disparity-Dependent Effects on Direction Discrimination Each panel is the top-down view of a subject, whose two eyes are represented by the large, open circles. The plane of fixation is indicated by the long horizontal line, along which dots are plotted to represent the stationary, zero-disparity background of random dots. The shaded oval represents the RF—in width and depth—of a hypothetical cluster of MT neurons. (A) Depiction of a disparity-selective site that prefers far disparities (the RF is located behind the plane of fixation). Here, we expect microstimulation to have a significant effect on direction discrimination when dots are presented at the preferred disparity (left) but not when dots are presented at a nonpreferred disparity (right). (B) Depiction of a nondisparity-selective site. The RF is extended in depth, indicating that it has little disparity selectivity. In this case, the effect of microstimulation should not depend on whether dots are presented at either a far (left) or a near (right) disparity. Figure 6 shows an example of a nicely tuned microstimulation effect. MU activity at this stimulation site exhibited moderate disparity selectivity, with a tuning curve that peaked just to the right of zero disparity (Figure 6A). We performed the microstimulation experiment at two different disparities, denoted by the arrowheads in Figure 6A. In the first block of trials, we presented dots at the preferred disparity (+0.1°), and microstimulation produced a clear leftward shift of the psychometric function that was equivalent to 17% dots (Figure 6B; logistic regression, p < 0.001). In the second block of trials, we presented dots at the nonpreferred disparity (−0.5°), and microstimulation exerted no effect whatsoever on the monkey's choices (Figure 6C; logistic regression, p > 0.5). To be certain that this effect did not result from some nonstationarity in electrode position, cell responsiveness, etc. (Salzman et al. 1992), we collected a third set of data with dots again presented at the preferred disparity. Again, microstimulation produced a leftward shift of the psychometric function equivalent to 17% dots (Figure 6D; p < 0.001). At this stimulation site, therefore, we were able to switch the result from a very substantial effect to no effect and back again simply by manipulating the disparity of the random-dot stimuli. Figure 6 Example of a Disparity-Selective Microstimulation Effect (A) Disparity tuning of MU activity at this stimulation site. Conventions as in Figure 2A. Arrowheads and letters indicate the disparity values used to perform the microstimulation experiments illustrated in (B), (C), and (D). DTI = 0.55. (B) First block of direction discrimination trials, in which dots were presented at the preferred disparity (0.1°). The stimulation psychometric function (filled symbols, solid curve) is shifted well to the left of the nonstimulation function (open symbols, dashed curve) by an amount equivalent to 17% dots (logistic regression, p < 0.001), with no corresponding change in the slope of the curve (p > 0.9). (C) Second block of discrimination trials, in which dots were presented at a nonpreferred disparity (-0.5°). In this case, the two psychometric functions did not differ significantly in horizontal position (p > 0.8) or in slope (p > 0.5). (D) Third block of discrimination trials, with dots again presented at the preferred disparity (repeat of [B]). Again, microstimulation produced a leftward shift equivalent to 17% dots (p < 0.001). The small increase in the slope of the stimulation psychometric function is not significant (p > 0.2). Figure 7 depicts data from experiments performed at a nondisparity-selective site. The MU activity at this site exhibited little selectivity for binocular disparity, although the tuning was marginally significant (Figure 7A; ANOVA, p = 0.025). We chose three different disparities at which to perform the direction discrimination task: 0°, 0.6°, and −0.6°. Figure 7B–7D show the effects of microstimulation on direction judgments at these three different disparities. In each case, microstimulation induced a significant leftward shift of the psychometric function (logistic regression, p < 0.0001), with no corresponding change in slope (p > 0.4). Figure 7 Example of a Nondisparity-Selective Effect of Microstimulation at a Site with Poor Disparity Tuning (A) MU disparity-tuning curve; DTI = 0.27. (B–D) Effects of microstimulation on direction discrimination when dots were presented at disparities of 0°, 0.6°, and −0.6°, respectively. In each case, the leftward shift of the psychometric function is highly significant (logistic regression, p < 0.0001) while the slopes were unchanged (p > 0.4). The individual example sites in Figures 6 and 7 conform well to the predictions of our hypothesis outlined in Figure 5. We observed considerable variation across the population of experiments, however, so we quantified the disparity selectivity of each microstimulation effect in order to evaluate statistical trends in the population. We performed this analysis on 65 out of 102 data sets for which we had applied microstimulation at both the preferred and nonpreferred disparities, and for which the effect of microstimulation was significant (p < 0.05) for at least one of the two disparities. We computed a Microstimulation Selectivity Ratio (MSR) as follows: where EP is the effect of microstimulation when dots are presented at the preferred disparity, and ENP is the effect when dots are presented at the nonpreferred disparity. This index is a standard contrast measure, except that the quantities in the denominator are absolute values. This formulation was necessary to keep the index bounded between −1.0 and 1.0. Figure 8 shows the MSR plotted against the DTI, with different symbols denoting data from the three monkeys. To analyze the relationship between MSR and DTI without confounding possible effects of monkey differences, we performed an analysis of covariance (ANCOVA) with DTI and monkey identity as factors. This analysis reveals a significant correlation between MSR and DTI (ANCOVA, r = 0.37, F(1,61) = 9.9, p < 0.005), with no significant differences between the three monkeys (F(2,61) = 0.14, p > 0.8). Figure 8 Quantitative Summary of the Disparity Selectivity of Microstimulation Effects The ordinate is the MSR, which was computed from the leftward shifts of the psychometric function measured at both the preferred and nonpreferred disparities (Equation 1). The abscissa is the DTI of MU activity at each stimulation site. Data are shown for 65/102 stimulation sites for which a significant effect of microstimulation was observed at either the preferred or nonpreferred disparity. Results from monkeys S, R, and T are shown as black circles, blue squares, and red triangles, respectively. Data points with an MSR equal to1.0 correspond to cases where there was a leftward shift of the psychometric function at the preferred disparity and a rightward (i.e., null-direction) shift, or no shift, at the nonpreferred disparity. The dashed line shows the best linear fit to the data (ANCOVA, r = 0.37, p < 0.005). Thus, as hypothesized (see Figure 5), microstimulation generally exerted selective effects at sites with strong disparity tuning, and nonselective effects at sites with poor tuning. Although this relationship between MSR and DTI was not very strong (as evidenced by the large scatter of points in Figure 8), almost all of the strongly selective microstimulation effects (MSR > 0.5) occurred at sites with moderate to strong disparity tuning (DTI > 0.4). The upper left corner of Figure 8 is notably unpopulated, indicating that selective effects of microstimulation did not occur at poorly disparity-tuned sites. Possible reasons for the variability in Figure 8 will be discussed below. Discussion Using microstimulation to probe the link between neuronal activity and behavior, we have tested whether the contribution of MT neurons to direction discrimination depends on their disparity selectivity. This work addresses the general question of how neurons that are tuned to multiple stimulus dimensions contribute to behavior in situations where one or more of these stimulus dimensions are task-irrelevant. Relatively little is currently known about how the responses of sensory neurons are pooled by decision mechanisms (see Shadlen et al. 1996) and how the demands of a particular task alter the pooling strategies that are used. The present study provides new insights into these issues. Our first main finding is that the strength of tuning for binocular disparity (an irrelevant variable in the direction discrimination task) accounts for a substantial proportion of variance in the strength of microstimulation effects (48% of variance for monkey S, 27% for monkey T). Two of our three monkeys relied mainly on nondisparity-selective sites for performing the direction discrimination task, even though the stimulus was tailored to the disparity preference of all sites. Our second main finding is that the efficacy of microstimulation is reduced when the stimulus disparity is adjusted to be suboptimal for neurons at the stimulation site. Thus, to the limited extent that our monkeys made use of signals from disparity-selective neurons, they did tend to monitor more closely neurons with tuning properties that were matched to the stimulus. This latter finding can be viewed as a generalization to three dimensions of the previous result that microstimulation effects were reduced by moving the visual stimulus out of the RF of the stimulated neurons (Salzman et al. 1992). Effects of Disparity Tuning Strength: Local Circuit Properties, Connectivity, or Task Strategy? How can we explain the finding (see Figure 3A) that regions of MT that are selective for both direction and disparity generally do not contribute to direction discrimination, despite the fact that stimulus parameters were always optimized for the disparity tuning of these neurons? One relatively uninteresting possibility is that unknown cellular or circuit properties specific to disparity-sensitive columns limit the efficacy of microstimulation. For example, disparity-selective regions of MT, which tend to be segregated from nonselective regions (DeAngelis and Newsome 1999), might have different biophysical properties, metabolic properties, local connectivity, or patterns of afferent input. Such factors are unlikely to account for our results, however, given the data illustrated for monkey T in Figure 4. Because columns with large disparity-tuning indices generally fail to yield effects in the direction discrimination task but yield good effects in the disparity discrimination task, we can reject explanations based on factors endogenous to local regions of MT. A second possibility is that the output connections of disparity-selective and nonselective regions of MT have different targets, such that decision mechanisms for motion receive input from nondisparity-selective portions of MT whereas decision mechanisms for depth receive input from disparity-tuned regions. Experiments have not been done to test this hypothesis, so we cannot rule it out. One argument against this idea, however, is that one of the three monkeys (monkey R) did not show a dependence of microstimulation effects on disparity selectivity (see Figure 3B). Thus, for anatomical projections of MT to explain our findings, we would have to assume that both disparity-selective and nonselective regions of MT project to decision mechanisms for motion perception in monkey R, but not in the other two animals. Experiments involving tracer injections into regions of MT chosen for strong versus weak disparity tuning would be valuable for examining this possibility. A third possibility, which we favor, is that our findings reflect the strategy that each monkey adopted for reading out motion signals from MT during the extended period of training on the task. In this scenario, all regions of MT could project to decision mechanisms for both motion and depth, but the relative weights of the connections would vary with the animal's task strategy. This would allow the read-out strategy to be altered rapidly based on the demands of the task. In our experiments, one strategy for performing the task would be to extract motion signals from all MT columns with the appropriate direction selectivity and spatial RF, regardless of their disparity selectivity. This strategy would entail pooling signals from many columns, including those with unfavorable signal-to-noise ratios due to their poor responsiveness to stimuli of nonoptimal disparity. A second strategy, which could yield better performance, would be to monitor primarily columns that are maximally activated by the stimulus, but this would entail pooling responses from columns with different disparity preferences when the stimulus disparity changed. Thus, some sort of complex “switching” would be required to route information to the decision process from the set of columns optimal for each experiment. A third, and perhaps the simplest, strategy would be to monitor motion signals only from the nondisparity-selective portions of MT; these columns would respond well to all stimulus disparities, providing a good signal-to-noise ratio for all stimulus sets on which the monkey was trained. This strategy offers the further advantage that one can monitor the same set of columns for all stimulus conditions in our task. Given that correlated noise among neurons limits the benefits of pooling across large populations of neurons (Britten et al. 1992; Shadlen et al. 1996), this last strategy might yield performance almost as good as that obtained by monitoring all columns that are strongly activated by a particular disparity. If monkeys were to adopt the simple strategy of monitoring only the nondisparity-selective regions of MT, then the microstimulation results shown in Figure 3A (monkeys S and T) would be expected. The very different results seen for monkey R (see Figure 3B) would not be the result of distinct output projections from disparity-selective and nonselective regions of MT, but rather would indicate that synaptic weights were dynamically modulated in monkey R to route information to decision circuits from all columns that were well activated by the stimuli. This conclusion is supported by the data shown in Figures 3B and 8, which together show that monkey R monitors direction signals from disparity-selective columns provided that the stimulus disparity matches the disparity preference of the neurons. Indeed, our finding that monkey R had a significantly lower psychophysical threshold than the other two animals is fully consistent with the task strategy suggested by our microstimulation results. In future experiments, it will be interesting to find ways to alter the monkeys' task strategies while using microstimulation to probe the contributions made by a single column of MT neurons. Disparity Tuning of Microstimulation Effects: Origins of Variability We found a statistically significant, but relatively weak, dependence of microstimulation effects on the difference between the preferred disparity of MT neurons and the stimulus disparity (see Figure 8). What accounts for the relatively large variability in these data? For monkeys S and T, microstimulation effects were usually weak at disparity-selective sites, and this could contribute to the scatter seen in Figure 8. If this were the case, then the correlation in Figure 8 should be stronger for monkey R, given that microstimulation of disparity-selective sites was usually quite effective in this animal. Inspection of Figure 8 reveals that this is not the case, however. In fact, the correlation coefficient between MSR and DTI (see Figure 8) was stronger for monkey S (r = 0.55, p < 0.01) than for monkey R (r = 0.36, p = 0.15). Another possible source of variability in Figure 8 involves the fact that we tested the effects of microstimulation in different blocks of trials for different disparities (see Materials and Methods). Given that microstimulation effects frequently wane as a function of time (Salzman et al. 1992) and are sensitive to small perturbations in electrode position (Murasugi et al. 1993), this block design would be expected to add noise to the population data. Another likely source of variability involves the selection criteria for microstimulation sites. We attempted to center our electrode in the midst of a region of constant direction tuning, but we did not select sites based on the consistency of disparity tuning within the neighborhood of the electrode. Thus, even when MU activity at the stimulation site was strongly disparity tuned, our electrode may have been positioned close to a boundary between a near column and a far column, or simply within a region where disparity tuning was changing rapidly (DeAngelis and Newsome 1999). This may have allowed microstimulation to activate a population of neurons that responded well to both stimulus disparities in some cases. Considering these likely sources of variability, the fact that we see a significant overall effect in Figure 8 provides solid evidence that monkeys do monitor more closely columns of neurons with stimulus preferences that match the prevailing stimulus parameters. It is worth noting that our ability to observe this effect may have been aided by the blocked design that we employed. Because the stimulus disparity was fixed within a block of trials, monkeys could selectively monitor MT columns tuned to that disparity. In contrast, microstimulation effects might be less disparity selective if the stimulus disparity varied from trial to trial, such that the animal was uncertain about which disparity columns to monitor. General Implications Many of the standard experimental approaches in systems neuroscience (e.g., single-unit recording, optical imaging, functional MRI) find their utility in exposing correlations between neuronal activity and external stimuli or behavioral states. Of course, finding signals that are correlated with behavior does not prove that those signals underlie the behavior. The value of electrical microstimulation, reversible inactivation, and lesion techniques is that they can establish causal links between neural activity and behavior. In this study, we only microstimulated at sites in MT that had strong directional selectivity; thus, one might assume that all sites would be equally likely to contribute to performance of the direction discrimination task. The central finding of this study is that the contribution of MT direction columns to task performance is modulated by the tuning of the neurons to a stimulus variable that is irrelevant to completion of the task. Thus, even within a single area of the brain, the causal linkage between neurons and behavior may depend on uncontrolled stimulus dimensions, and may be determined by unexpected factors such as task strategy. This result highlights the importance of causal techniques for studying the neural basis of behavior, and suggests that microstimulation studies may be able to reveal how high-level task strategies modulate the read-out of neuronal signals from topographic maps in the brain. Materials and Methods Our standard procedures for surgical preparation, training, and electrophysiological recording from rhesus monkeys (Macaca mulatta) are described elsewhere (Britten et al. 1992). In addition, extensive details of our microstimulation techniques have been published elsewhere (Salzman et al. 1992). Here, we briefly describe our methods, focusing on aspects that are particularly relevant to the present study. Surgical preparation Three adult macaques were used in this study (two males and one female), all of which had previously been subjects in other studies in the laboratory. Each animal had a scleral search coil implanted in at least one eye (monkey S had coils in both eyes) to allow monitoring of eye position. In addition, each subject was equipped with a head restraint post and a stainless-steel recording chamber that was positioned over the occipital cortex. Electrodes were introduced into the visual cortex through a transdural guide tube that was positioned within a square array of grid holes at 1-mm intervals (Crist et al. 1988). Visual stimuli and tasks All visual stimuli used in this study were dynamic random-dot patterns presented on a standard 21-in. color display (Sony 500PS, Sony Corporation, New York, New York, United States). The display subtended 39° × 29° at the viewing distance of 57 cm and was refreshed at a rate of 100Hz. The visual stimuli were generated by a Cambridge Research Systems VSG2/3 board (Cambridge Research Systems Ltd., Rochester, United Kingdom) that was housed in a dedicated PC. Stereoscopic presentation was achieved through the use of ferroelectric shutters (Displaytech, Inc., Longmont, Colorado, United States) that were switched in antiphase for the two eyes. Left and right half-images were presented on alternate video frames, and the shutters were synchronized to the vertical refresh, thus exposing each eye to the appropriate visual stimulus on alternate frames. With this technique, the quality of stereo separation is limited mainly by phosphor persistence. Thus, random-dot stimuli were always presented using the red gun only, since the red phosphor has a much faster decay than either the green or blue phosphors. We achieved a contrast ratio of approximately 40:1 (“open” eye:“closed” eye) using this approach, and “ghosting” artifacts were barely visible, even under dark-adapted conditions. Monkeys performed two separate tasks in these experiments: a visual fixation task, and a direction discrimination task. In the visual fixation task, a small, yellow fixation point (FP) appeared to begin each trial, and the monkeys were required to maintain fixation within a 2° × 2° or 3° × 3° electronic window, centered on the fixation target, until the fixation target was extinguished. The monkeys received a liquid reward for successful fixation, typically 0.1–0.15 ml of water or juice. If the monkey broke fixation before the end of a trial, the trial was aborted, the data were discarded, and the monkey was not rewarded. During the fixation period, a bipartite random-dot stimulus was presented for 1.5 s. It consisted of a central, circular patch of coherently moving dots that could be presented with variable binocular disparity, and which covered the receptive field of the MT neurons under study. To assist the monkey in maintaining binocular convergence on the FP, we filled the remainder of the visual display with zero-disparity dots that were randomly repositioned every fourth video frame (25 Hz), thus producing a twinkling, zero-disparity background. Each dot was approximately 0.1° in size. Dot density was 32 dots/(deg2-s) for the central patch and 8 dots/(deg2-s) for the background. In the direction discrimination task (see Figure 1), each trial also began with the presentation of a FP. Once the monkey fixated, a bipartite random-dot pattern again appeared. The central, circular patch had variable motion coherence. On each video frame, a fraction of the dots (“signal” dots; filled in Figure 1A) moved coherently in either the preferred or null direction of the MT neurons under study. The remaining dots in this center patch (“noise” dots; unfilled in Figure 1A) were replotted at random positions in each video frame. Thus, the strength of the motion signal (percent coherence) is determined by the percentage of signal dots in the display (see Britten et al. 1992 for additional details). Signal dots moved in the preferred direction on one-half of all trials and in the null direction on the remaining trials (randomly interleaved). Outside of the center patch, the remainder of the video display was filled with stationary zero-disparity dots to serve as a background. The random-dot motion stimulus ran for 1 s, after which both the FP and the dots disappeared. Two disk-shaped targets then appeared, aligned with the axis of stimulus motion, and the monkey indicated its perceived direction of motion by making a saccade to the target toward which the signal dots moved. Again, the monkeys received liquid rewards for correct choices. Incorrect choices resulted in no reward and a brief time-out period between trials. Dot size and density were as described above for the fixation task. Microstimulation On one-half of the direction discrimination trials, selected at random, electrical microstimulation was applied during presentation of the random-dot stimulus. The microstimulation current was delivered through a stimulus isolation unit (Bak Electronics, Inc., Mount Airy, Maryland, United States) operating in constant-current mode. The current was a train of biphasic pulses with a frequency of 200 Hz and an amplitude of 20 μA. Each biphasic event consisted of a 200-μs cathodal pulse followed by a 200-μs anodal pulse, with a 100-μs gap between the two. Microstimulation parameters were chosen to elicit robust perceptual biases but were well below the current and frequency levels at which stimulation has been shown to flatten the slope of the psychometric function (Murasugi et al. 1993). Microstimulation was applied through the same parylene-coated tungsten electrode (MicroProbe, Inc., Carlsbad, California, United States) that was used to record unit activity in MT. Selection of microstimulation sites We searched for candidate microstimulation sites by examining the tuning properties of MU activity at regular intervals of 100 μm along electrode penetrations through MT. At each recording site, we rated the strength of direction selectivity on a scale from 1 to 3 (3 = strongest tuning), and we carefully estimated the preferred direction of motion (see Figure S3 regarding the accuracy and precision of these estimates). We accepted a site for microstimulation when there was a span of at least 300 μm in which direction selectivity was consistently rated a 3 and the preferred direction of motion varied by no more than 45°. Disparity selectivity had no bearing on our selection of stimulation sites in this study; thus, our sample of stimulation sites should be unbiased in terms of disparity tuning. Once a suitable span of direction tuning was identified, we retracted our electrode to approximately the middle of the span and began quantitative testing. Experimental protocol At each identified microstimulation site, we performed the following battery of tests. (1) First, we carefully mapped the MU RF of the MT neurons by dragging a small patch of moving dots (100% coherence) through the RF with a pointing device. Spike densities were plotted on a Cartesian map of visual space during this process to facilitate visual mapping of the RF. In addition, we mapped the direction and speed selectivity of the neurons by moving a cursor throughout a polar direction-speed domain while spike densities were again plotted on the screen. From this procedure, we determined the location and size of the MU RF, as well as the preferred direction and speed of motion. We also estimated the range of disparities over which the neurons were selective, and these parameters were then used in subsequent quantitative tests. (2) We next measured a disparity-tuning curve for MU activity at the identified stimulation site, while the monkey performed a block of fixation trials. Nine evenly spaced disparities were typically tested within the disparity range determined from our initial qualitative probing (e.g., see Figure 2A and 2C). Monocular control conditions were also included, and all trial conditions were block randomized and repeated four to five times. For MU responses in MT, this number of repetitions proved more than adequate to obtain tuning curves with small error bars. The central patch of dots (which varied in disparity) was adjusted to be slightly larger than the MU RF, and all dots within this central patch moved coherently in the neurons' preferred direction of motion (at the preferred speed). Note that in a previous study (DeAngelis and Newsome 1999), we established that these MU measurements of disparity tuning in MT reliably predict the disparity tuning of single units within the neighborhood of the electrode tip. Due to limitations of recording time, we did not measure a quantitative direction-tuning curve at each microstimulation site. (3) We next applied microstimulation during blocks of trials in which the monkey performed the direction discrimination task (see Figure 1) along the preferred-null axis of motion. Motion coherence was varied from trial to trial within a range of values that bracketed the psychophysical threshold of each animal, as determined during training. At each site, we collected at least two blocks of discrimination trials: one at the preferred disparity and one at the nonpreferred disparity. The order of these two blocks was counterbalanced across experiments, and statistical analyses revealed no significant effects of block order on any of our results (ANCOVA, p > 0.3). Whenever possible (e.g., see Figure 6D), we performed a third block of trials at the same disparity tested in the first block. For sites with no clear disparity preference at all (as measured on-line), the choice of disparities for the direction discrimination task was arbitrary. In these cases, we typically performed three blocks of trials with disparities of (approximately) −0.5°, 0°, and 0.5°, although the order in which these disparities were presented was varied from site to site. During training, we attempted to interleave two different disparities within a single block of direction discrimination trials. Although this approach would clearly be superior to a blocked design in some respects, we found that interleaving the disparities resulted in poorer discrimination performance because the monkeys' choices were biased by stimulus disparity when the motion signal was weak. We therefore settled for the block design described above. Data collection Extracellular recordings were made with tungsten microelectrodes (impedance typically 0.5–1.0 MΩ; MicroProbe, Inc.). Neural signals were amplified, filtered (0.5–5.0 kHz), and discriminated using conventional electronics (Bak Electronics, Inc.), and event times were stored on magnetic disk with 1 ms resolution. To record MU activity, we simply set the threshold level of our window discriminator to approximately 1–2 SD above the noise level. Thus, a MU event was defined as any deflection of the analog signal that exceeded this threshold. Since the absolute frequency of the MU response depends heavily upon the event threshold, we attempted to achieve a consistent response magnitude from site to site by adjusting our event threshold such that the spontaneous activity level was in the range from 50 to 100 events/s. This setting typically yielded peak MU responses in the range of 300–500 events/s (mean 378.5 ± 78.3 SD). Horizontal and vertical eye-position signals were low-pass filtered with a cutoff frequency of 250Hz, sampled at 1 kHz, and stored to disk at 250 Hz. Data analysis To construct disparity-tuning curves, we computed the firing rate for each trial during the 1-s stimulus presentation, and we plotted the mean firing rate (± SE) as a function of the horizontal disparity. Smooth curve fits to disparity-tuning curves were achieved using a cubic spline interpolation. To quantify the strength of disparity tuning at each stimulation site, we computed the DTI as follows: where Rmax denotes the response to the preferred disparity, Rmin denotes the response to the antipreferred disparity, and S indicates the spontaneous activity level. Values larger than unity can occur if Rmin is less than S. For the quantification of direction-tuning strength (see Figure S1), a Direction Tuning Index was defined in an identical fashion. We analyzed behavioral data by computing the proportion of preferred decisions that the monkey made for each different combination of motion coherence and direction, where a preferred decision is defined as that in favor of the preferred direction of MU activity at a particular microstimulation site. This proportion was plotted as a function of the signed motion-coherence variable (see Figure 2B), where positive coherences correspond to motion in the preferred direction and negative coherences to motion in the antipreferred direction. The statistical significance of microstimulation effects was determined using a logistic regression analysis, as described by Salzman et al. (1992). Supporting Information Figure S1 Relationship between Strength of Direction Tuning and Strength of Disparity Tuning in MT Data are shown from 571 MU recordings (162 from monkey S, shown in red, and 409 from three additional animals, shown in black) in which we obtained quantitative measurements of both direction tuning and disparity tuning. There is no significant correlation between Direction Tuning Index and Disparity Tuning Index (DTI) across the sample. Note also that the data from monkey S overlap completely with the data from the other animals, indicating that monkey S was not unusual. (358 KB EPS). Click here for additional data file. Figure S2 Analysis of Direction Preference Variability at Microstimulation Sites in Monkey S and Monkey T Monkey S is shown in black; monkey T in red. (A) The strength of the microstimulation effect is plotted against the SD of direction preferences within a 400-μm window centered on each stimulation site (five recording sites, 100 μm apart). There is no significant correlation between these variables, indicating that variability in direction preferences (within the observed range) did not determine the efficacy of microstimulation. Note, however, that all stimulation sites were chosen to have a small range of preferred directions; we did not apply microstimulation at locations in MT where the direction preference changed rapidly over short distances. (B) There is also no significant correlation between the DTI of MU activity at each stimulation site and the SD of direction preferences. This shows that disparity-selective microstimulation sites did not have larger variations in direction preferences. (216 KB PS). Click here for additional data file. Figure S3 Comparison of Direction Preference Estimates Obtained from Post Hoc Gaussian Fits of Direction-Tuning Curves Versus Online Estimates of MT Preferred Directions See Materials and Methods. Data were obtained from 409 single units in MT of three animals that were not part of the present study. For 68% of neurons, the two direction preference estimates differ by less than 20°. By comparison, the mean directional bandwidth (full width at half-maximal height) for this population of neurons was 121° ± 54° SD; hence, the error in hand-mapped estimates of direction preference is quite small relative to the breadth of tuning. (320 KB EPS). Click here for additional data file. We are especially grateful to Bruce Cumming for assisting in these experiments and for many valuable discussions. We are also grateful to Marlene Cohen, Takanori Uka, Jacob Nadler, and Jerry Nguyenkim for helpful comments on the manuscript. We thank Judith Stein and Cynthia Doane for expert technical assistance. This work was supported by the National Eye Institute (EY 05603). GCD was supported by a Career Award in the Biomedical Sciences from the Burroughs-Wellcome Fund and by a National Research Service Award from the National Eye Institute. WTN is an Investigator of the Howard Hughes Medical Institute. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. GCD and WTN conceived and designed the experiments. GCD performed the experiments and analyzed the data. GCD and WTN wrote the paper. Academic Editor: Markus Meister, Harvard University Abbreviations DTIDisparity Tuning Index FPfixation point MSRMicrostimulation Selectivity Ratio MTmiddle temporal MUmultiunit RFreceptive field SEstandard error SDstandard deviation ==== Refs References Albright TD Cortical processing of visual motion Rev Oculomot Res 1993 5 177 201 8420549 Albright TD Desimone R Gross CG Columnar organization of directionally selective cells in visual area MT of the macaque J Neurophysiol 1984 51 16 31 6693933 Andersen RA Neural mechanisms of visual motion perception in primates Neuron 1997 18 865 872 9208854 Bisley JW Zaksas D Pasternak T Microstimulation of cortical area MT affects performance on a visual working memory task J Neurophysiol 2001 85 187 196 11152719 Britten KH Shadlen MN Newsome WT Movshon JA The analysis of visual motion: A comparison of neuronal and psychophysical performance J Neurosci 1992 12 4745 4765 1464765 Crist CF Yamasaki DS Komatsu H Wurtz RH A grid system and a microsyringe for single cell recording J Neurosci Methods 1988 26 117 122 3146006 DeAngelis GC Newsome WT Organization of disparity-selective neurons in macaque area MT J Neurosci 1999 19 1398 1415 9952417 DeAngelis GC Uka T Coding of horizontal disparity and velocity by MT neurons in the alert macaque J Neurophysiol 2003 89 1094 1111 12574483 DeAngelis GC Cumming BG Newsome WT Cortical area MT and the perception of stereoscopic depth Nature 1998 394 677 680 9716130 Malonek D Tootell RB Grinvald A Optical imaging reveals the functional architecture of neurons processing shape and motion in owl monkey area MT Proc R Soc Lond B Biol Sci 1994 258 109 119 Maunsell JH Newsome WT Visual processing in monkey extrastriate cortex Annu Rev Neurosci 1987 10 363 401 3105414 Maunsell JH Van Essen DC Functional properties of neurons in middle temporal visual area of the macaque monkey. II. Binocular interactions and sensitivity to binocular disparity J Neurophysiol 1983 49 1148 1167 6864243 Mountcastle VB The columnar organization of the neocortex Brain 1997 120 701 722 9153131 Murasugi CM Salzman CD Newsome WT Microstimulation in visual area MT: Effects of varying pulse amplitude and frequency J Neurosci 1993 13 1719 1729 8463847 Nichols MJ Newsome WT Middle temporal visual area microstimulation influences veridical judgments of motion direction J Neurosci 2002 22 9530 9540 12417677 Salzman CD Newsome WT Neural mechanisms for forming a perceptual decision Science 1994 264 231 237 8146653 Salzman CD Britten KH Newsome WT Cortical microstimulation influences perceptual judgements of motion direction Nature 1990 346 174 177 2366872 Salzman CD Murasugi CM Britten KH Newsome WT Microstimulation in visual area MT: Effects on direction discrimination performance J Neurosci 1992 12 2331 2355 1607944 Shadlen MN Britten KH Newsome WT Movshon JA A computational analysis of the relationship between neuronal and behavioral responses to visual motion J Neurosci 1996 16 1486 1510 8778300 Zeki SM Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey J Physiol 1974 236 549 573 4207129

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PLoS Biol. 2004 Mar 16; 2(3):e77

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020078Research ArticleBiophysicsNeuroscienceHomo (Human)N-Terminal Phosphorylation of the Dopamine Transporter Is Required for Amphetamine-Induced Efflux Amphetamine-Induced Dopamine EffluxKhoshbouei Habibeh 1 Sen Namita 2 Guptaroy Bipasha 3 Johnson L'Aurelle 3 Lund David 3 Gnegy Margaret E 3 Galli Aurelio Aurelio.Galli@vanderbilt.edu 1 Javitch Jonathan A jaj2@columbia.edu 2 4 1Department of Molecular Physiology and Biophysics and Center for Molecular Neuroscience, Vanderbilt UniversityNashville, TennesseeUnited States of America2Center for Molecular Recognition, Columbia UniversityNew York, New YorkUnited States of America3Department of Pharmacology, University of MichiganAnn Arbor, MichiganUnited States of America4Departments of Psychiatry and Pharmacology, College of Physicians and SurgeonsColumbia University, New York, New YorkUnited States of America3 2004 16 3 2004 16 3 2004 2 3 e7821 10 2003 13 1 2004 Copyright: © 2004 Khoshbouei et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Mechanism of Amphetamine-Induced Dopamine Overload Amphetamine (AMPH) elicits its behavioral effects by acting on the dopamine (DA) transporter (DAT) to induce DA efflux into the synaptic cleft. We previously demonstrated that a human DAT construct in which the first 22 amino acids were truncated was not phosphorylated by activation of protein kinase C, in contrast to wild-type (WT) DAT, which was phosphorylated. Nonetheless, in all functions tested to date, which include uptake, inhibitor binding, oligomerization, and redistribution away from the cell surface in response to protein kinase C activation, the truncated DAT was indistinguishable from the full-length WT DAT. Here, however, we show that in HEK-293 cells stably expressing an N-terminal-truncated DAT (del-22 DAT), AMPH-induced DA efflux is reduced by approximately 80%, whether measured by superfusion of a population of cells or by amperometry combined with the patch-clamp technique in the whole cell configuration. We further demonstrate in a full-length DAT construct that simultaneous mutation of the five N-terminal serine residues to alanine (S/A) produces the same phenotype as del-22—normal uptake but dramatically impaired efflux. In contrast, simultaneous mutation of these same five serines to aspartate (S/D) to simulate phosphorylation results in normal AMPH-induced DA efflux and uptake. In the S/A background, the single mutation to Asp of residue 7 or residue 12 restored a significant fraction of WT efflux, whereas mutation to Asp of residues 2, 4, or 13 was without significant effect on efflux. We propose that phosphorylation of one or more serines in the N-terminus of human DAT, most likely Ser7 or Ser12, is essential for AMPH-induced DAT-mediated DA efflux. Quite surprisingly, N-terminal phosphorylation shifts DAT from a “reluctant” state to a “willing” state for AMPH-induced DA efflux, without affecting inward transport. These data raise the therapeutic possibility of interfering selectively with AMPH-induced DA efflux without altering physiological DA uptake. Using mutated dopamine transporters, the authors find that they can separate the actions of amphetamine from the normal function of the dopamine transporter ==== Body Introduction The dopamine transporter (DAT) plays a critical role in the synaptic clearance of dopamine (DA) by mediating the reuptake of DA released into the presynaptic terminal (Amara and Kuhar 1993; Giros and Caron 1993). It thereby regulates the strength and duration of the dopaminergic response. DAT is also the site of action of several psycho-stimulant drugs, including amphetamine (AMPH) and cocaine (Kuhar et al. 1991). As a substrate, AMPH competitively inhibits DA reuptake, thereby increasing synaptic DA concentration and enhancing the rewarding property of the dopaminergic system. Additionally, AMPH elicits the release of DA through the transporter in the brain (Fischer and Cho 1979; Jones et al. 1998) and in heterologous cells expressing DAT (Eshleman et al. 1994; Wall et al. 1995; Sitte et al. 1998). AMPH-induced DA efflux is thought to be mediated by a facilitated exchange diffusion process, in which inward transport of substrates increases the availability of inward-facing binding sites of the transporter (Fischer and Cho 1979), which leads thereby to increased efflux of cytosolic substrates. Emerging evidence, however, indicates that inward and outward transport of monoamines may differ in more fundamental ways. In particular, it appears that AMPH-induced DA efflux does not rely exclusively on the ability of AMPH to increase the availability of inward-facing DATs (Chen and Justice 2000) but also relates to the ability of AMPH to induce uncoupled currents (Sitte et al. 1998) and to increase intracellular sodium (Khoshbouei et al. 2003) and kinase activity (Kantor and Gnegy 1998). Although AMPH-induced currents have been shown to be of physiological relevance (Ingram et al. 2002), AMPH exerts its primary behavioral effects by inducing DA efflux (Wise and Bozarth 1987; Sulzer and Galli 2003). In addition, enhanced AMPH-induced DA efflux is associated with sensitization to repeated AMPH administration (Robinson and Becker 1986). DAT is thought to comprise 12 transmembrane segments with cytoplasmic N-terminal and C-terminal domains (Giros and Caron 1993). There are numerous putative phosphorylation sites for various protein kinases in the intracellular domains (Giros and Caron 1993; Granas et al. 2003; Lin et al. 2003), and multiple protein kinases have been shown to regulate DAT function (Daniels and Amara 1999; Melikian and Buckley 1999; Granas et al. 2003). Treatment with AMPH also leads to increased intracellular accumulation of DAT (Saunders et al. 2000), and AMPH has been shown to increase striatal particulate PKC activity (Giambalvo 1992) through a calcium dependent pathway (Giambalvo 2003). Importantly, PKC activation leads to N-terminal phosphorylation of DAT in rat striatum (Foster et al. 2002). Consistent with this observation, we recently showed that deletion of the first 22 amino acids from DAT essentially eliminates32P incorporation into DAT in response to PKC activation (Granas et al. 2003). Surprisingly, this truncation did not affect PKC-induced internalization, thereby demonstrating that N-terminal phosphorylation of DAT is not essential for internalization. Since uptake, inhibitor binding, and oligomerization of this truncated DAT were also not significantly different from those of full-length DAT (Hastrup et al. 2001, 2003; Granas et al. 2003), N-terminal phosphorylation has not yet been associated with a functional effect. PKC activation, however, has been shown to stimulate DAT-mediated release of DA (Davis and Patrick 1990; Giambalvo 1992; Kantor and Gnegy 1998). Moreover, AMPH-induced DA efflux is inhibited by the introduction of PKC inhibitors and by downregulation of PKC (Kantor and Gnegy 1998; Cowell et al. 2000; Kantor et al. 2001), whereas DA uptake is unaffected by these manipulations. This suggests that inward and outward transport can be independently regulated and led us to explore the hypothesis that N-terminal phosphorylation of DAT may be involved in AMPH-induced DA efflux. Here we report that deletion of the first 22 amino acids of DAT, as well as mutation of the five N-terminal serines to alanine, greatly decreases AMPH-induced DA efflux without affecting uptake. Mutation of these serines instead to aspartate, thereby mimicking phosphorylation, preserves efflux, suggesting that phosphorylation of one or more of these five N-terminal serines is essential for AMPH-induced DA release. Results/Discussion In our previous studies we created a mutant human DAT construct in which the first 22 amino acids were removed and replaced by tandem FLAG and HA epitope tags (FLAG-HA-DAT) (Hastrup et al. 2001, 2003). This construct was created to tag the protein and to remove Cys6 to facilitate biochemical studies. FLAG-HA-DAT expressed at wild-type (WT) levels in the plasma membrane, and we found it to be functionally normal in terms of uptake, inhibitor binding, DAT oligomerization, and PMA- and receptor-induced internalization FLAG-HA-DAT expressed at wilde-type (WT) levels in the plasma membrane, and we found it to be functionally normal in terms of uptake, inhibitor binding, DAT oligomerization, and PMA- and receptor-induced internalization (Hastrup et al. 2001, 2003; Granas et al. 2003). Since this construct lacks the first five serines in DAT (Ser2, Ser4, Ser7, Ser12, Ser13) and does not appear to be phosphorylated by PKC activation (Granas et al. 2003), we hypothesized that FLAG-HA-DAT might be impaired in AMPH-induced efflux. In accordance with this prediction, we found that AMPH-induced DA efflux was decreased by approximately 80% in the FLAG-HA construct relative to FLAG-tagged full-length DAT (FLAG-DAT) (Figure 1). This resulted from a decrease in the maximal rate of DA efflux and not from a change in the apparent affinity for AMPH in mediating efflux. In contrast, DA uptake by these two constructs was not significantly different (Figure 1, legend). Figure 1 N-Terminal Truncation of DAT Impairs AMPH-Induced DA Efflux Cells were preloaded with 15 μM DA and superfused with AMPH at concentrations ranging from 1 to 100 μM. AMPH-induced DA efflux was defined as the amount of DA released in response to the given concentration of AMPH minus the baseline value. Baseline DA release did not differ between FLAG-HA-DAT and FLAG-DAT (13.2 ± 2.9 and 10.2 ± 1.8, respectively; n = 18). The Vmax of efflux was 31.1 ± 4.6 and 128.3 ± 12.0 pmol/mg protein/fraction (F(2,27) = 52.6, p < 0.0001) with a Km for amphetamine of 7.8 ± 4.1 and 7.6 ± 2.2 μM, for FLAG-HA-DAT and FLAG-DAT, respectively (n = 4). For [3H]DA uptake, the Vmax was 15.4 ± 2.5 and 18.3 ± 2.2 pmol/min/mg protein with a Km of 1.2 ± 0.8 and 1.1 ± 0.4 μM for FLAG-HA-DAT and FLAG-DAT, respectively (F(2,49) = 1.78, p > 0.17). In a cell suspension (or in a population of adherent cells), it is difficult to assess the potential effects on efflux of a change in ionic gradients or membrane potential because the membrane potential and ionic gradients change freely depending on the stimuli. Indeed, AMPH has been shown to induce depolarization through a DAT-mediated uncoupled chloride conductance that can be gated by substrates such as AMPH (Ingram et al. 2002). Therefore, in order to quantify these effects under conditions where we could control the intracellular concentration of the substrates, DA, sodium, and chloride, as well as the membrane potential, we used amperometry in conjunction with the patch-clamp technique in the whole-cell configuration, a method that we have used previously to study the mechanism of efflux (Galli et al. 1998; Khoshbouei et al. 2003). We recorded DAT-mediated currents with the whole-cell pipette by stepping the membrane voltage from a holding potential of −20 mV to +100 mV while simultaneously measuring efflux as assessed by amperometric currents resulting from the release of DA. Consistent with our studies with cell populations, we found that AMPH-induced efflux was decreased at +100 mV by 91% ± 4% (n = 5) in FLAG-HA-DAT relative to FLAG-DAT. Surprisingly, the DAT-mediated whole-cell currents gated by AMPH, which have been shown to be uncoupled from the transport process (Sonders et al. 1997; Khoshbouei et al. 2003), were also reduced to a comparable extent (see below). This reduction in current and efflux resulted from the N-terminal deletion and not from the presence of the HA epitope, since a FLAG-tagged construct lacking the first 22 amino acids of DAT (FLAG-del22-DAT) but without any other added sequence showed a reduction in current and efflux similar to that of FLAG-HA-DAT. Figure 2 shows representative traces for the AMPH-induced current and DA efflux recorded at +100 mV obtained from FLAG-DAT (panels A and B, respectively) and FLAG-del22-DAT (panels C and D, respectively). In panels B and D, the upward (positive) deflections indicate DA oxidation and thus reflect DA efflux. At the onset of the voltage step, the amperometric electrode recorded an oxidative current (positive), which is indicative of DA efflux, and at the termination of the voltage step, the amperometric current relaxed to baseline. At +100 mV, the AMPH-induced whole-cell and oxidative currents recorded from FLAG-del22-DAT cells were much smaller than those recorded from FLAG-DAT cells: in FLAG-del22-DAT cells, the whole-cell currents were 21.8% ± 7.4% whereas the amperometric currents were 23.0% ± 2.5% of the equivalent currents recorded in FLAG-DAT cells (n = 5). Figure 2 N-Terminal Truncation of DAT Reduces AMPH-Induced Currents and DA Efflux Cells were voltage clamped with a whole-cell patch pipette while an amperometric electrode was placed onto the cell membrane. The internal solution of the whole-cell patch pipette contained 2 mM DA. (A) Representative trace of AMPH-induced whole-cell current obtained from FLAG-DAT cells upon AMPH (10 μM) bath application. The membrane potential of the cell was stepped to +100mV from a holding potential of –20 mV. (B) Oxidation current acquired concomitantly to the whole-cell current represented in panel A. (C and D) Representative current traces (whole-cell and amperometric, respectively) obtained from FLAG-del22-DAT cells using the same experimental protocol as in (A) and (B). In marked contrast to this approximately 80% reduction, in the same two sets of stably transfected cells, the Vmax for uptake of the substrate tyramine by FLAG-del22-DAT was 146% that by FLAG-DAT (Table 1). Neither the Km for tyramine uptake (Table 1) nor the apparent Ki for inhibition of tyramine uptake by AMPH (37 ± 4 nM and 63 ± 18 nM, respectively; n = 5) or cocaine (214 ± 34 nM and 281 ± 33 nM, respectively; n = 4) was significantly different in FLAG-DAT and FLAG-del22-DAT. Cell-surface biotinylation studies revealed that the increased Vmax in FLAG-del22-DAT was accounted for by an increased number of DAT molecules at the cell surface (Table 1) and suggested that the truncation had a minimal effect on the turnover rate of the transporter. These results are consistent with our previous studies on the FLAG-HA-DAT deletion construct expressed in EM4 cells, which also showed normal tyramine uptake (Hastrup et al. 2001), as well as with the DA uptake studies described above for FLAG-HA-DAT and FLAG-DAT expressed in HEK-293 cells (see Figure 1, legend). Table 1 Kinetic Properties of [3H]Tyramine Uptake and Cell-Surface Localization of FLAG-DAT, FLAG-del22-DAT, FLAG-S/A-DAT, and FLAG-S/D-DAT aThe surface biotinylation data (mean ± SEM, n = 5) are arbitrary units obtained from the analysis of immunoblots. Since FLAG-DAT and FLAG-del22-DAT were studied in parallel, and FLAG-S/D-DAT and FLAG-S/A-DAT were studied in parallel, the data for each set were normalized to the construct with normal efflux, and therefore these values are shown as 1. Uptake data represent mean ± SEM of 5–6 experiments If the reduction in the AMPH-induced current and efflux resulted from the loss of phosphorylation of one or more of the five N-terminal serine residues, then mutation of the serine(s) that is (are) phosphorylated should lead to an effect similar to that of the truncation. Since it is not known which of the serines are phosphorylated, we simultaneously mutated all five serines to alanine in the full-length FLAG construct (FLAG-S/A-DAT). To obtain further evidence that phosphorylation of one or more of the N-terminal serines is essential for AMPH-induced DA efflux, we also created a construct in which all five of these serines were simultaneously mutated to aspartate (FLAG-S/D-DAT), in an attempt to simulate phosphorylation of the serines. Neither the Km nor the Vmax for tyramine uptake was significantly different in FLAG-S/A-DAT and FLAG-S/D-DAT (see Table 1). The small, nonsignificant reduction in uptake by FLAG-S/A-DAT was accounted for by a similarly decreased number of DAT molecules at the cell surface (see Table 1), suggesting that the turnover rate of the transporter was the same in these two mutants. The apparent Ki's for inhibition of tyramine uptake in FLAG-S/A-DAT and FLAG-S/D-DAT by AMPH (41 ± 13 nM and 48 ± 7 nM, respectively; n = 3) or by cocaine (331 ± 46 nM and 444 ± 47 nM, respectively; n = 4) were not significantly different. Current-voltage and amperometric-voltage relationships were generated for FLAG-DAT, FLAG-del22-DAT, FLAG-S/A-DAT, and FLAG-S/D-DAT by stepping the voltage from a holding potential of −20 mV to voltages between –120 mV and +100 mV in increments of 20 mV (Figure 3). In FLAG-DAT cells, AMPH-induced currents and DA efflux were voltage dependent, with an increase at positive voltages and saturation of DA efflux near +100 mV (Figure 3A and 3B, filled circles). In contrast, in FLAG-del22-DAT cells, the AMPH-induced currents and DA efflux were greatly reduced at all voltages tested (compare Figure 3A and 3B, open circles, with Figure 3A and 3B, filled circles). This phenomenon was not likely a consequence of an alteration of ion gradients or accumulation of intracellular AMPH, because no significant differences were found between the reversal potentials of the current obtained from FLAG-DAT cells (24.5 ± 5.3 mV) and FLAG-del22-DAT cells (32.6 ± 6.3 mV). In FLAG-DAT cells, the amperometric current at +80 mV was 0.305 ± 0.079 pA (mean ± SEM; n = 6) (Figure 3B, filled circles). In contrast, in FLAG-del22-DAT cells the amperometric current recorded at the same potential was significantly reduced (0.077 ± 0.028 pA, mean ± SEM; p < 0.05 by Student's t-test, FLAG-del22-DAT versus FLAG-DAT; n = 5) (Figure 3B, open circles). Figure 3 AMPH-Induced Current-Voltage and Amperometric-Voltage Relationships Obtained from FLAG-DAT, FLAG-del22-DAT, FLAG-S/A-DAT, and FLAG-S/D-DAT (A) Current-voltage relationships of AMPH-induced current obtained from FLAG-DAT (filled circles) and FLAG-del22-DAT (open circles) cells. AMPH (10 μM) was applied to the bath while the membrane potential was stepped from–120 mV to +100 mV from a holding potential of –20 mV in 20 mV increments (n = 5). (B) Amperometric-voltage relationships obtained from FLAG-DAT (filled circles) and FLAG-del22-DAT (open circles) cells acquired concomitantly to the whole-cell current of panel A. (C and D) Current-voltage (C) and amperometric-voltage (D) relationships of whole-cell and oxidative currents obtained from FLAG-S/D-DAT (filled triangles) and FLAG-S/A-DAT (open triangles) cells using the same experimental protocol as above. Similarly, in FLAG-S/D-DAT cells the AMPH-induced currents and DA efflux were much greater than those generated in FLAG-S/A-DAT cells (Figure 3C and 3D, filled triangles and open triangles, respectively). In FLAG-S/D-DAT cells, the amperometric current at +80 mV was 0.202 ± 0.039 pA (mean ± SEM; n = 7) (Figure 3D, filled triangles). In contrast, in FLAG-S/A-DAT cells, the amperometric current recorded at the same potential was significantly reduced (0.014 ± 0.009 pA, mean ± SEM; p < 0.05 by Student's t-test, FLAG-S/D-DAT versus FLAG-S/A-DAT; n = 5). Thus, the ability of AMPH to induce DAT-mediated currents and DA efflux was impaired dramatically, either by N-terminal truncation, or by substitution of the five N-terminal serines to alanine. Remarkably, substituting these five serines to aspartate to mimic phosphorylation restored the ability of AMPH to induce voltage-dependent DA efflux and to produce currents, indicating that negative charges in the DAT N-terminal region are essential for these actions of AMPH. To explore which serine or serines are critical to the effect on efflux, we created five additional mutants in the FLAG-S/A-DAT background in which we mutated each of the five positions, one at time, to aspartate, and we created stable pools of EM4 cells expressing each of these mutants. At +100 mV the amperometric currents in FLAG-S/A-DAT, FLAG-S/A-2D-DAT, FLAG-S/A-4D-DAT, and FLAG-S/A-13D-DAT were 7.4% ± 2.6%, 8.4% ± 5.7%, 11.2% ± 3.1%, and 12.3% ± 7.0%, respectively, of that seen in FLAG-S/D-DAT (n = 3; not significantly different from FLAG-S/A-DAT by One-way ANOVA and Tukey's Multiple Comparison Test). In contrast, amperometric currents in FLAG-S/A-7D-DAT and FLAG-S/A-12D-DAT were 29.8% ± 12.6% and 45.1% ± 9.6%, respectively, of that seen in FLAG-S/D-DAT (n = 3; p < 0.01 compared to FLAG-S/A-DAT by One-way ANOVA and Tukey's Multiple Comparison Test). Thus, negative charge at either position 7 or position 12 restores a substantial fraction of the efflux seen with aspartate at all five positions, and the size of the resulting efflux relative to FLAG-S/D-DAT and FLAG-DAT suggests that both of these serines may be phosphorylated in vivo (see below). The differences in AMPH-induced DA efflux between FLAG-S/A-DAT and FLAG-S/D-DAT could result either from an altered affinity of DAT for intracellular DA or from a change in the Vmax of the transport process. At +80 mV, at what is a saturating concentration of intracellular Na+ for FLAG-DAT (see “Materials and Methods”), the Km for intracellular DA was 1.4 ± 0.4 mM for FLAG-S/A-DAT and 1.3 ± 0.4 mM for FLAG-S/D-DAT. Thus, a change in the Vmax of the AMPH-induced DAT-mediated efflux is likely responsible for the differences between FLAG-S/A-DAT and FLAG-S/D-DAT. Our results suggest that phosphorylation of one or more serines in the N-terminus of the human DAT shifts DAT from a “reluctant” state to a “willing” state for AMPH-induced DA efflux. (A related phenomenon has been proposed for calcium channel regulation [Zhu and Ikeda 1994].) That DAT is significantly phosphorylated under basal conditions and that this phosphorylation can be increased by AMPH (Roxanne Vaughan, pers. comm.) are also consistent with a role for N-terminal phosphorylation in the AMPH-induced efflux mechanism. The structural basis for this regulation of efflux is currently unknown. It may result from a shift in the voltage or sodium dependence of efflux and thus from an increase in the fraction of DAT molecules that reorient to the external milieu empty of DA. Whatever the mechanism, under unclamped, “physiological” conditions, N-terminal phosphorylation does not alter significantly any rate-limiting steps for uptake. Despite our demonstration that the Vmax for uptake is unaltered in the mutants, it is possible that phosphorylation might alter the ionic coupling of DAT. The ratio of whole-cell to amperometric current (Galli et al. 1997) at +100 mV was not different in FLAG-DAT and FLAG-del22-DAT (728 ±193 [n = 8] and 835 ± 300 [n = 5], respectively; p > 0.05 by Student's t test). (Similar results were obtained at +60 and +80 mV [data not shown].) This ratio is a microscopic property of an individual transporter that is inversely proportional to the fraction of charge carried by dopamine (Galli et al. 1997). These data, measured in the presence of saturating intracellular dopamine concentrations in the patch pipette, are consistent, therefore, with a similar ionic coupling in the two mutants. However, given the lack of stoichiometric coupling between substrate flux and charge movement (see below), we cannot absolutely rule out an effect of phosphorylation on the ionic coupling of flux. To rule out such a change, it would be helpful to demonstrate that the WT and mutant transporters can generate similar concentration gradients at equilibrium, even though efflux rates differ. In unclamped cells, however, the persistent presence of substrate might lead to changes in membrane potential, and, therefore, such experiments would best be performed under voltage-clamp conditions with an amperometric electrode inside the cell to measure the accumulation of dopamine (Mosharov et al. 2003). Curiously, AMPH-induced currents, which are largely an uncoupled chloride conductance mediated by DAT that is gated by substrates such as AMPH (Ingram et al. 2002), were reduced in the absence of N-terminal phosphorylation in parallel with DA efflux. Although the underlying mechanisms are unclear, these findings are consistent with the findings of Sitte et al. (1998) that there is a poor correlation between substrate-induced efflux and the uptake of substrates, but a good correlation between the ability of substrates to induce currents and their ability to cause efflux (Khoshbouei et al. 2003). Regardless of the mechanisms, our findings argue that the mechanism of DA efflux is to some extent independent from the inward-transport process. Since truncation of the N-terminus had the same functional effect as neutralization of the N-terminal serines, it is likely that an essential interaction of the phosphorylated N-terminus of DAT must occur to permit efflux, either with another part of DAT or conceivably with an associated protein. These results could lead to the design and synthesis of new therapeutic agents, such as a drug that blocks the effects of AMPH-like psychostimulants without inhibiting DA uptake. Selective enhancement of DA release might be achieved by promoting phosphorylation of the N-terminus of DAT or by modulating critical interactions of the DAT N-terminus. Furthermore, a polymorphism or naturally occurring mutation of the N-terminal portion of DAT could alter efflux in the context of normal uptake, and this might be associated with human psychiatric or neurologic dysfunction, much as a polymorphism of the norepinephrine transporter has been found to be associated with orthostatic intolerance (Robertson et al. 2001). Mutations of Ser7 and Ser12 of DAT were found previously to affect the response to inhibition of PKC and MEK1/2, respectively (Lin et al. 2003). We found that negative charge at either of these positions, but not at the positions of the three other N-terminal serines at positions 2, 4, and 13, restored significant AMPH-induced DA efflux. Nonetheless, the serines that are actually phosphorylated as a result of activation of PKC or by AMPH have not been identified, and the kinase or kinases that directly phosphorylate the N-terminus of DAT are unknown as well. Efforts are underway to identify directly the serines that are phosphorylated in vivo, as well as the responsible kinase, and to further uncover the mechanism by which the phosphorylated N-terminus makes DAT “willing” to efflux DA. Materials and Methods Plasmid construction, transfection, and cell culture The N-terminally FLAG-tagged full-length synthetic human DAT (synDAT) gene in pCIHyg was described previously (Saunders et al. 2000). In the FLAG-HA-DAT construct, an HA tag followed the FLAG tag and the first 22 amino acids (MSKSKCSVGLMSSVVAPAKEPN) of human DAT were deleted (Hastrup et al. 2001). In FLAG-del22-DAT, these 22 amino acids were deleted from the full-length FLAG-DAT, making this construct identical to FLAG-HA-DAT except for the absence of the HA-tag sequence. From the FLAG-DAT background, Ser2, Ser4, Ser7, Ser12, and Ser13 were simultaneously mutated to alanine to create the FLAG-S/A-DAT construct and to aspartate to create the FLAG-S/D-DAT construct. The mutant constructs were generated, confirmed, and expressed stably in human embryonic kidney cells (HEK-293) or EM4 cells, HEK-293 cells stably transfected with macrophage scavenger receptor to promote adherence (Robbins and Horlick 1998), as described previously (Hastrup et al. 2001). Uptake of [3H]tyramine Uptake assays with adherent EM4 cells stably expressing the appropriate DAT construct were performed as described previously (Hastrup et al. 2001). Tyramine was used as a radiolabeled substrate because it is not a substrate for catechol-O-methyl transferase, which is endogenously present in HEK-293 cells and EM4 cells, and therefore is not subject to degradation that might complicate the kinetics of uptake (Hastrup et al. 2001). Nonspecific uptake was determined in the presence of 2 mM tyramine. For determination of Vmax and Km values, increasing concentrations of tyramine from 0.02 to 50 μM were used. Km and Vmax values for [3H]tyramine and [3H]dopamine uptake were determined by nonlinear regression analysis using GraphPad Prism 4. IC50 values were determined using increasing concentrations of AMPH between 0.002 and 2 μM and of cocaine between 0.001 and 10 μM in competition with approximately 60 nM [3H]tyramine. Ki values were calculated from the IC50 values as described by Cheng and Prusoff (1973). Cell-surface biotinylation and immunoblotting EM4 cells stably expressing the DAT constructs were incubated with cleavable sulfo-NHS-S-S-biotin (Pierce Chemical Company, Rockford, Illinois, United States) to label surface-localized transporter, and the biotinylated material was prepared and immunoblotted as described previously (Saunders et al. 2000). AMPH-induced DA efflux Confluent 100-mm plates of HEK-293 cells stably expressing FLAG-DAT or FLAG-HA-DAT were washed twice with KRH (25 mM HEPES [pH 7.4], 125 mM NaCl, 4.8 mM KCl, 1.2 mM KH2PO4, 1.3 mM CaCl2, 1.2 mM MgSO4, and 5.6 mM glucose) and incubated at 37oC with 15 μM DA for 30 min. Following incubation, cells were washed with KRH, harvested, resuspended in 0.20 ml of KRH and superfused in a Brandel superfusion apparatus (Brandel SF-12, Gaithersburg, Maryland, United States) as described by Kantor et al. (2001). The KRH contained 10 μM pargyline, and AMPH was added at concentrations from 1 to 100 μM for 2.5 min only. DA was determined by HPLC with electrochemical detection as described by Kantor et al. (2001). Electrophysiology and amperometry Whole-cell and amperometric currents were recorded as described previously (Khoshbouei et al. 2003). The AMPH-induced whole-cell and amperometric currents were defined as the current recorded in the presence of AMPH, minus the current recorded after the addition of cocaine to the bath with AMPH still present. Previously, we demonstrated that AMPH increases intracellular sodium and that a high concentration of NaCl in the recording pipette maximizes DA efflux (Khoshbouei et al. 2003). Thus, to increase the basal and AMPH-induced DA efflux and to maintain a constant sodium concentration, the whole-cell electrode was filled with internal solution containing 2 mM DA and 90 mM NaCl substituted with KCl to maintain a constant osmolarity of 270 mOsm. The dependence of DA efflux on internal DA was determined by fitting the values of the steady-state amperometric currents, recorded at different intracellular DA concentrations (between 500 μM and 4 mM), to a Hill equation by nonlinear regression. The ratio of whole-cell to amperometric current was calculated by dividing the average whole-cell current during the last 100 ms of the voltage step by the average amperometric current during the same time period (Galli et al. 1998). Supporting Information Accession Numbers The Swiss-Prot (http://ca.expasy.org/cgi-bin/niceprot.pl?Q01959) entry name for the gene discussed in this paper is S6A3_HUMAN, accession number Q01959. This work was supported by National Institutes of Health grants DA12408, DA11495, and MH57324 (JAJ); DA13975 and DA14684 (AG); and DA11697 (MEG). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. HK, NS, BG, LJ, MEG, AG, and JAJ conceived and/or designed the experiments. HK, NS, BG, LJ, and DL performed the experiments. HK, NS, BG, LJ, DL, MEG, AG, and JAJ analyzed the data. MEG, AG, and JAJ wrote the paper. 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Effects of amphetamine in vitro Neuropharmacology 1992 31 1211 1222 1361666 Giambalvo CT Differential effects of amphetamine transport vs. dopamine reverse transport on particulate PKC activity in striatal synaptoneurosomes Synapse 2003 49 125 133 12740868 Giros B Caron MG Molecular characterization of the dopamine transporter Trends Pharmacol Sci 1993 14 43 49 8480373 Granas C Ferrer J Loland CJ Javitch JA Gether U N-terminal truncation of the dopamine transporter abolishes phorbol ester- and substance P receptor-stimulated phosphorylation without impairing transporter internalization J Biol Chem 2003 278 4990 5000 12464618 Hastrup H Karlin A Javitch JA Symmetrical dimer of the human dopamine transporter revealed by cross-linking Cys-306 at the extracellular end of the sixth transmembrane segment Proc Natl Acad Sci U S A 2001 98 10055 10060 11526230 Hastrup H Sen N Javitch JA The human dopamine transporter forms a tetramer in the plasma membrane: Cross-linking of a cysteine in the fourth transmembrane segment is sensitive to cocaine analogs J Biol Chem 2003 278 45045 45048 14519759 Ingram SL Prasad BM Amara SG Dopamine transporter-mediated conductances increase excitability of midbrain dopamine neurons Nat Neurosci 2002 5 971 978 12352983 Jones SR Gainetdinov RR Wightman RM Caron MG Mechanisms of amphetamine action revealed in mice lacking the dopamine transporter J Neurosci 1998 18 1979 1986 9482784 Kantor L Gnegy ME Protein kinase C inhibitors block amphetamine-mediated dopamine release in rat striatal slices J Pharmacol Exp Ther 1998 284 592 598 9454802 Kantor L Hewlett GH Park YH Richardson-Burns SM Mellon MJ Protein kinase C and intracellular calcium are required for amphetamine-mediated dopamine release via the norepinephrine transporter in undifferentiated PC12 cells J Pharmacol Exp Ther 2001 297 1016 1024 11356924 Khoshbouei H Wang H Lechleiter JD Javitch JA Galli A Amphetamine-induced dopamine efflux. 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10.1371/journal.pbio.0020078

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020079Research ArticleCell BiologyMolecular Biology/Structural BiologySystems BiologySaccharomycesExtensive Association of Functionally and Cytotopically Related mRNAs with Puf Family RNA-Binding Proteins in Yeast RNA Targets of Yeast Puf ProteinsGerber André P 1 Herschlag Daniel herschla@cmgm.stanford.edu 1 Brown Patrick O pbrown@cmgm.stanford.edu 1 2 1Department of Biochemistry, Stanford University School of MedicineStanford, CaliforniaUnited States of America2Howard Hughes Medical Institute, Stanford University School of MedicineStanford, CaliforniaUnited States of America3 2004 16 3 2004 16 3 2004 2 3 e793 10 2003 9 1 2004 Copyright: © 2004 Gerber et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. mRNA Targets of RNA-Binding Proteins Suggest an Extensive System for Post-Transcriptional Regulation Genes encoding RNA-binding proteins are diverse and abundant in eukaryotic genomes. Although some have been shown to have roles in post-transcriptional regulation of the expression of specific genes, few of these proteins have been studied systematically. We have used an affinity tag to isolate each of the five members of the Puf family of RNA-binding proteins in Saccharomyces cerevisiae and DNA microarrays to comprehensively identify the associated mRNAs. Distinct groups of 40–220 different mRNAs with striking common themes in the functions and subcellular localization of the proteins they encode are associated with each of the five Puf proteins: Puf3p binds nearly exclusively to cytoplasmic mRNAs that encode mitochondrial proteins; Puf1p and Puf2p interact preferentially with mRNAs encoding membrane-associated proteins; Puf4p preferentially binds mRNAs encoding nucleolar ribosomal RNA-processing factors; and Puf5p is associated with mRNAs encoding chromatin modifiers and components of the spindle pole body. We identified distinct sequence motifs in the 3′-untranslated regions of the mRNAs bound by Puf3p, Puf4p, and Puf5p. Three-hybrid assays confirmed the role of these motifs in specific RNA–protein interactions in vivo. The results suggest that combinatorial tagging of transcripts by specific RNA-binding proteins may be a general mechanism for coordinated control of the localization, translation, and decay of mRNAs and thus an integral part of the global gene expression program. Messenger RNAs that are associated with each of the five Puf RNA-binding proteins can be grouped according to function and localization, suggesting that tagging of transcripts by specific RNA-binding proteins coordinates the control of their localization, translation, and decay ==== Body Introduction The dynamic structure and physiology of a cell depend on coordinated synthesis, assembly, and localization of its macromolecular components (Orphanides and Reinberg 2002). The timing and level of expression of the genes that encode these components are controlled by transcription factors that regulate initiation of transcription in a gene-specific manner by binding to specific DNA sequences proximal to the genes they regulate. The combinatorial binding and activity of specific transcription factors confer a distinctive program of regulation on each individual gene while enabling coherent global responses of large sets of genes in physiological and developmental programs. Much less is known about either the system architecture or molecular mechanisms that underlie regulation of the post-transcriptional steps in the gene expression program. There are approximately 15,000 mRNA molecules in each Saccharomyces cerevisiae cell during exponential growth in rich medium (Hereford and Rosbash 1977) and at least a 10-fold larger number in a typical mammalian cell (Hastie and Bishop 1976). The extent to which the location, activity, and fates of these diverse populations of mRNAs are coordinated and the post-transcriptional mechanisms that might mediate their coordinated regulation remain largely unknown. RNA-binding proteins (RBPs) have been implicated in diverse aspects of post-transcriptional gene regulation, including RNA processing, export, localization, degradation, and translational control (Dreyfuss et al. 2002; Maniatis and Reed 2002; Mazumder et al. 2003). Although there appear to be hundreds of RBPs encoded in eukaryotic genomes (Costanzo et al. 2001; Issel-Tarver et al. 2002), for only a few of these proteins have the RNA targets been systematically identified (Takizawa et al. 2000; Tenenbaum et al. 2000; Brown et al. 2001; Hieronymus and Silver 2003; Li et al. 2003; Shepard et al. 2003; Waggoner and Liebhaber 2003). For example, a recent study in S. cerevisiae found that two nuclear RNA export factors were each associated with large and distinct mRNA populations, and common functional themes were found among the 1,000 or so proteins encoded by each population (Hieronymus and Silver 2003). These observations support a role for RBPs in the coordinated regulation of mRNA subpopulations (Keene and Tenenbaum 2002; Keene 2003). Systematic identification of the mRNA targets of RBPs can be a powerful approach to understanding the cellular roles of RBPs and the mechanisms by which they might regulate the post-transcriptional lives of mRNAs. We have focused first on the Pumilio–Fem-3-binding factor (FBF) (Puf) proteins from S. cerevisiae, which belong to a structurally related family of cytoplasmic RBPs that are implicated in developmental processes in various eukaryotes (Wickens et al. 2002). Puf proteins are defined by the presence of several (typically eight) consecutive repeats of the Pumilio homology domain (Pum-HD), which confers RNA binding activity (Zamore et al. 1997; Wang et al. 2002a). The Puf proteins characterized to date have been reported to bind to 3′-untranslated region (UTR) sequences encompassing a so-called UGUR tetranucleotide motif and thereby to repress gene expression by affecting mRNA translation or stability. Despite the widespread occurrence of Puf family members, only a few mRNA targets have been identified for these RBPs (Wickens et al. 2002). For example, in Drosophila, the PUMILIO protein binds maternal hunchback mRNA and, in concert with NANOS protein, represses translation of the mRNA at the posterior pole during early embryogenesis. The Caenorhabditis elegans Puf homologs, called Fem-3-binding factors (FBFs), regulate the switch from spermatogenesis to oogenesis by repressing fem-3 translation, and they are implicated in the propagation of germline stem cells through binding and inhibition of gld-1 mRNA expression (Zhang et al. 1997; Crittenden et al. 2002). Less is known about the human homologs: PUMILIO-2 protein interacts with DAZ (deleted in azoospermia) protein and is expressed in embryonic stem cells and germ cells, whereas PUMILIO-1 is almost ubiquitously expressed (Moore et al. 2003). In S. cerevisiae, five proteins, termed Puf1p to Puf5p, bear six to eight Puf repeats (Figure 1). Little is known about the physiological function of these proteins. Mutations in either PUF4 or PUF5 result in diminished longevity (Kennedy et al. 1997). PUF1 was isolated as a multicopy suppressor of certain microtubule mutants (Machin et al. 1995), and a PUF2 null mutant displayed increased resistance to cycloheximide and paromomycin (Waskiewicz-Staniorowska et al. 1998). However, S. cerevisiae mutants lacking all five PUF genes are viable (Olivas and Parker 2000). A genome-wide analysis of mRNA expression patterns in yeast mutants lacking all five PUF genes found differential expression of 7%–8% of all mRNAs under steady-state conditions, but no common theme was found among the affected genes (Olivas and Parker 2000). Only two specific mRNA targets have been identified for yeast Puf proteins: Puf3p binds to the COX17 mRNA 3′-UTR in vitro and may regulate its turnover (Olivas and Parker 2000), and Puf5p negatively regulates expression of reporter genes substituting for the HO endonuclease (Tadauchi et al. 2001). Figure 1 Protein Domain Structure of Yeast Puf Proteins Pum-HD repeats (Zamore et al. 1997) are shown as red ovals and classical RNA-binding domains (RBDs) are depicted as blue boxes. Regions of low complexity, such as proline-, serine-, threonine-, and/or methionine-rich domains, are shown in gray boxes; asparagine stretches are striped. The numbers correspond to the length of proteins in amino acids. Using DNA microarrays to identify the specific mRNAs that interact with the five S. cerevisiae Puf proteins, we have found that each Puf protein bound to a large set of distinct and functionally related mRNAs. We identified novel and conserved sequence elements in the mRNAs bound by Puf3p, Puf4p, and Puf5p. The results suggest a system for large-scale coordinated control of cytoplasmic mRNAs and provide insights into the physiological logic of the gene expression program. Results Systematic Identification of mRNAs Associated with Specific RBPs To identify RNAs associated with Puf proteins, tandem-affinity purification (TAP)-tagged proteins were purified from whole-cell extracts of S. cerevisiae (Figure 2). The TAP tag (Rigaut et al. 1999), a sequence encoding two IgG-binding units of protein A, a specific protease recognition site, and a calmodulin-binding domain, was fused in-frame at the C-terminus of the respective open reading frame (ORF) in its original chromosomal location (Ghaemmaghami et al. 2003). This design was intended to preserve normal regulation of the expression of the fusion protein. Cells of the TAP-tagged strains showed growth rates and cell morphologies similar to wild-type cells. Cells were grown to mid-log phase in rich medium, extracts were prepared, and ribonucleoprotein complexes were recovered by affinity selection on IgG beads and subsequent cleavage with tobacco etch virus (TEV) protease (see Materials and Methods). To control for nonspecifically enriched mRNAs, the same procedure was performed with wild-type cells lacking the TAP tag. TEV protease cleavage was superior to direct elution of proteins from beads, as it gave lower contamination from nonspecifically interacting RNAs in the resulting purified fractions (data not shown). RNA was isolated from the purified protein samples and from extracts. We obtained 0.8–2 μg of RNA from the Puf affinity-isolated samples gathered from 1-l cultures, but no detectable RNA (<0.1 μg) was recovered when the same procedure was applied to untagged control cells. The yield of RNA from the Puf affinity isolation procedure was sufficient to perform further labeling steps directly, without amplification of RNA by PCR, as had been required in previous studies (Takizawa et al. 2000; Hieronymus and Silver 2003). Two samples from each cell population, total RNA, and RNA isolated by the Puf affinity procedure were used to prepare cDNA probes labeled with different fluorescent dyes, which were mixed and hybridized to S. cerevisiae DNA microarrays containing all known and putative ORFs, introns, and the mitochondrial genome (see Materials and Methods). The ratio of the fluorescent hybridization signals from the two differentially labeled RNA samples, at the array element representing each specific gene, provided an assay for enrichment of the corresponding mRNA by the Puf-affinity procedure. Figure 2 Strategy for Analyzing Genome-Wide RNA–Protein Interactions Protein A-tagged Puf proteins were captured with IgG–Sepharose and released from the beads by cleavage with TEV protease. RNAs associated with the released proteins were isolated, and cDNA copies were fluorescently labeled and hybridized to yeast DNA microarrays. The Cy5/Cy3 fluorescence ratio for each locus reflects its enrichment by affinity for the cognate protein. Puf3p is the only one of the five S. cerevisiae Puf proteins for which direct in vitro interaction with an mRNA (COX17) has previously been described, thereby providing an internal positive control (Olivas and Parker 2000). COX17 mRNA was substantially and consistently enriched in four independent Puf3p affinity isolations (ratio = 10 ± 1.4; Figure 3A), but not in mock isolations (ratio = 0.8 ± 1.2). In general, after filtering for spots with high background or irregular shapes, enrichment values for the entire set of arrayed sequences were reproducible (median of standard deviations in all arrayed spots = 0.35 on a log2 scale) (see Materials and Methods). To define targets specific to each Puf protein, we first selected all sequences for which enrichment factors in the corresponding affinity isolation procedures were at least two standard deviations above the mean for all arrayed sequences (Figure S1; for samples isolated by the Puf3p-affinity procedure, this corresponded to an enrichment factor of greater than or equal to 2.5). Second, we eliminated from this selected group any sequences that were also consistently enriched in the mock procedure (see Materials and Methods). Although no cutoff can perfectly distinguish the actual physiological targets from false positives, the high reproducibility of the results (see Figure 3B), the occurrence of distinct mRNA populations associated with the different Puf proteins, and the characterization of these targets described in the subsequent sections, including the identification of distinct sequence motifs and in vivo confirmation of the role of these motifs in specific RNA–protein interactions, strongly support the validity of the majority of the targets. Finally, the list of target mRNAs did not change substantially by application of other statistical methods for selection (see Lieb et al. 2001). Figure 3 Defining Puf Target RNAs (A) Distribution of average Cy5/Cy3 fluorescence ratios from four independent microarray hybridizations analyzing Puf3p targets. The arrow depicts enrichment of COX17 mRNA, which is known to bind to Puf3p (Olivas and Parker 2000). The red dashed line indicates the threshold applied for defining 220 target RNAs (a magnification is shown of the enriched region). (B) Cluster of RNA targets for Puf proteins. Rows represent genes (unique cDNA elements) and columns represent individual experimental samples. Each Puf protein and an untagged strain (mock control) were assayed in quadruplicate. The color code indicates enrichments (green–red color scale). The number of mRNAs interacting with each Puf protein is indicated in parentheses. mRNAs clustering with the mock controls were removed as false positives (see Materials and Methods). A large number of arrayed sequences, 818, identified transcripts associated with at least one Puf protein (see Figure 3B; Table S1), with 735 encoding distinct ORFs. This represents approximately 12% of the known and predicted protein-coding sequences in the S. cerevisiae genome. Of these, 90 transcripts interact with more than one Puf protein. The largest overlap was observed between the groups of transcripts associated with Puf1p and Puf2p—which also have the greatest overall similarity in amino acid sequence among the Puf proteins (45% identical); 36 of the 40 Puf1p targets were also associated with Puf2p. Twenty-eight mRNAs were bound by both Puf4p and Puf5p, and 16 were bound both by Puf2p and Puf5p. Seven transcripts were enriched with three different Puf proteins (DHH1 and YOL109w mRNAs with Puf1p, Puf2p, and Puf5p; NOP1 mRNA with Puf1p, Puf4p, and Puf5p; SUR7 and SFL1 mRNAs with Puf2p, Puf4p, and Puf5p; and IFM1 mRNA with Puf3p, Puf4p, and Puf5p). The remaining 645 target mRNAs were each associated with only one of the Puf proteins. Thus, each Puf protein associates with a distinct and highly specific subset of mRNAs (see Tables S3–S7). We estimated the number of Puf proteins per cell by a filter affinity blot analysis using protein A as a standard for calibration (Table S2). We found that Puf1p, Puf2p, Puf3p, and Puf5p were similar in abundance, with 350–400 molecules per cell. Puf4p was approximately twice as abundant (approximately 900 molecules per cell). The relatively low abundance of the Puf proteins is therefore comparable to that of transcription factors, protein kinases, and cell cycle proteins (Futcher et al. 1999). Moreover, our measurements imply that the intracellular concentrations of the Puf proteins range between 20 and 50 nM, approximately one order of magnitude higher than the dissociation constants for binding of their metazoan homologs to the cognate target RNAs. The number of Puf proteins per cell approximates the estimated numbers of cognate Puf target mRNA molecules present in the cell (Holstege et al. 1998; Wang et al. 2002b) (Table S2), consistent with a model in which each Puf protein molecule is associated with one mRNA molecule in the cell. Puf3p Specifically Binds mRNAs Encoding Mitochondrial Proteins As a first step toward identifying functional themes among the mRNAs associated with each Puf protein, we retrieved the Gene Ontology (GO) annotations for process, function, and compartment from the Saccharomyces Genome Database (SGD) (Issel-Tarver et al. 2002). (The target mRNAs for each Puf protein are listed in Tables S3–S7.) We then searched for significant shared GO terms in the lists of Puf mRNA targets (Table S8). Puf3p associated almost exclusively with transcripts of nuclear genes that encode mitochondrial proteins (p < 10−88; see Table S5). In particular, of the 154 Puf3p-associated transcripts for which GO annotation of subcellular localization was available, 135 (87%) were assigned to mitochondria (Figure 4A). Of the Puf3p-associated mitochondrial gene products, 80 (59%) are involved in protein biosynthesis, including structural components of the ribosome (55 genes), tRNA ligases (12 genes), and translational regulators (nine genes). Twenty-two of the Puf3p-bound transcripts are involved in mitochondrial organization and biogenesis, 17 in aerobic respiration, and 12 in mitochondrial translocation. Based on this striking cytotopic (relating to location in the cell) concordance, we suggest that the remaining 66 Puf3p mRNA substrates (30%) for which no GO annotations were available are likely to encode mitochondrial proteins. (While this paper was under review, a genome-wide analysis of protein localization in S. cerevisiae [Huh et al. 2003] reported a mitochondrial localization for 27 additional Puf3p targets, raising the total to 162 of the 220 putative Puf3p mRNA targets encoding mitochondrial proteins.) Figure 4 Classification of mRNAs Interacting with Puf Proteins (A) Column charts showing compartmentalization of characterized gene products encoded by the Puf targets. The same compartments are shown for the entire genome in the columns designed “All” (YPD, May 2003). The number of genes represented in the charts is indicated on the top of columns. An asterisk indicates classes with p values of less than 0.001. (B) Fraction of membrane-associated gene products among the Puf targets. We classified the targets by combining both GO and YPD annotations (May 2003). “Plasma membrane” (light blue) is a subpopulation of the total membrane-associated proteins (blue). Soluble cytoplasmic or nuclear proteins were classified as “non-membrane.” “All” refers to the genome-wide compartmentalization of characterized genes, and respective numbers were retrieved from YPD. “Puf2 Top 40” refers to the 40 highest enriched Puf2p targets and equals the total number of Puf1p targets. Puf1p- and Puf2p-Associated mRNAs Disproportionately Encode Membrane-Associated Proteins Of all the characterized S. cerevisiae genes for which any information about subcellular localization is available, 18% are currently classified as encoding membrane-associated proteins (Yeast Proteome Database [YPD], May 2003; see Costanzo et al. 2001). A much greater fraction of the mRNAs associated with Puf1p and Puf2p encode membrane-associated proteins: 16 of the 28 (57%) known proteins encoded by Puf1p-interacting mRNAs and 55 of 106 (52%) known proteins encoded by Puf2p-interacting mRNAs (see Figure 3B; see Tables S3 and S4). Transcripts encoding proteins associated with the plasma membrane were particularly enriched among the Puf1p- and Puf2p-bound mRNAs. Most of the mRNAs bound by Puf1p were also associated with Puf2p. However, Puf2p bound uniquely to many additional mRNAs (146 Puf2p mRNA targets versus 40 for Puf1p). In terms of cellular processes, many Puf1p- and Puf2p-associated transcripts encode proteins with roles in transmembrane transport and vesicular trafficking of proteins: 9 out of 26 (34%; p < 0.0002) of annotated Puf1p targets and 24 out of 104 (23%; p < 10−5) annotated Puf2p targets (compared to 9% of all characterized genes) (YPD, May 2003). This group includes transporters for spermine (Tpo1, Tpo2, Tpo3), proteins (Nce101, Nce102, Ast1, Vps72, Mas6, Sfk1, Mup3), vesicles (Sso2, Snc2, Yip1, Aps3, Ypr157w), and lipids (Pdr16, Ykl091c, Fps1 [glycerol]). (Tpo2 and Tpo3 may cross-hybridize on arrays because of their high sequence identity [89%], but Tpo1 does not [Shepard et al. 2003]). Puf4p and Puf5p Interact Selectively with mRNAs Encoding Nuclear Components Among the Puf5p targets (see Table S6), we found two common themes. First, a remarkable fraction encodes nuclear proteins that participate in covalent modification of histones, chromatin-remodeling complexes, or transcriptional regulation (64 of the 113 annotated genes [57%; p < 3 × 10−6]). Second, the Puf5p-associated transcripts included a substantial fraction of the mRNAs known to encode components or regulators of the mitotic spindle apparatus in yeast: 14 mRNAs that encode microtubule-based spindle components, including seven of the 25 (28%; p < 4 × 10−5) structural components of the spindle pole body (Kar1, Ccd31, Spc19, Spc42, Bbp1, Cnm67, and Nuf2) (Wigge et al. 1998). Messages encoding nuclear and cytoplasmic proteins that regulate polarized growth (Ame1, Boi2, Bsp1, Bub1, Bud9, Dad2, Elm1, Gic1, Kar9, Rax2, Ste7), some of them known to interact with spindle components, were also Puf5p targets. Transcripts encoding nucleolar proteins were highly enriched among the Puf4p-bound mRNAs: 36 of the 133 (27%) annotated genes in this group encode nucleolar proteins, as compared to 3% of all the annotated genes in the S. cerevisiae genome (p < 10−12). Of these 36, 29 are directly involved in ribosomal RNA (rRNA) synthesis, processing, and ribosome maturation (p < 10−15), major functions of the nucleolus (Fatica and Tollervey 2002; Gerbi et al. 2003) (see Tables S5 and S8). Twenty-eight transcripts were enriched in both the Puf4p and Puf5p affinity isolations, including six transcripts encoding components of the nucleosome (p < 10−11), among them the four core histone proteins (histones 2A and 2B, histone 3, and histone 4; note that histones 2A and 2B are 98% identical and therefore cross-hybridize). Diverse Functional Links among Transcripts Associated with Each Puf Protein In addition to the cytotopic relationships within each group of Puf-associated mRNAs, we were struck by the frequency with which transcripts encoding different components of protein complexes or systems of interacting proteins were bound by the Puf proteins. For example, most of the nuclear transcripts encoding components of the mitochondrial ribosome (55 out of the 77 known genes; Gan et al. 2002) were Puf3p-associated. This observation prompted us to search for other protein complexes and functional systems that shared similarly Puf-associated mRNAs. Other examples of coordinate “tagging” of transcripts encoding subunits of multiprotein complexes include Puf4p association of mRNAs encoding three of the four protein components of the H/ACA core particle (Cbf5p, Gar1p, and Nhp2p), which synthesizes pseudouridine in rRNAs (Henras et al. 1998) (Figure S2; no data were obtained for the fourth component, Nop10p). Puf5p bound mRNAs encoding histone acetylases (Ada2p, Spt8p, and Hfi1p), which are components of the Spt–Ada–Gcn5–acetyltransferase (SAGA) complex, and transcripts encoding at least four of the six members of the RSC (remodels the structure of chromatin) family of DNA-stimulated ATPases with bromodomains (Bdf1p, Bdf2p, Rsc2p, and Rsc4p; no array data were obtained for the two other members, Rsc1p and Spt7p). As mentioned above, the mRNAs encoding at least three of the four core histones were enriched in both Puf4p and Puf5p affinity isolations. We also found numerous cases in which the transcripts encoding multiple members of a functional group of proteins were bound by the same Puf protein. For example, the transcripts encoding the Tpo1, Tpo2, and Tpo3 proteins, the three known spermine transporters in the plasma membrane (Albertsen et al. 2003; see note above about cross-hybridization), and the two known genes implicated in the nonclassical protein export pathway (NCE101, NCE102) (Cleves et al. 1996) were bound by Puf1p and Puf2p and by Puf2p, respectively. Puf5p was associated with all of the histone deacetylases (HDACs) that act on histones located around coding sequences—Sin3p (a class I HDAC), Hda1p (a class II HDAC), and both components of the Set3C complex (Hst1p and Snt1p) (Kurdistani and Grunstein 2003). (Two other HDACs, Hos1p and Hos3p, which deacetylate histones around the ribosomal DNA locus, were not enriched in Puf5p affinity isolations.) Finally, we identified cases in which the mRNAs encoding multiple components of a specific regulatory system were bound by the same Puf protein. For example, Puf2p associates with mRNAs encoding diverse proteins regulating Pma1p, which is an ATP-dependent proton transporter located in the plasma membrane, and with PMA1 mRNA itself (Figure S2). All of the mRNAs encoding nucleolar glycine/arginine-rich (GAR) domain-bearing proteins (Sbp1p, Nsr1p, Nop1p, Gar1p) as well as HMT1 mRNA, encoding a dimethylase that modifies the nucleolar GAR proteins (Xu et al. 2003), were associated with Puf4p, while none of the mRNAs encoding the distinct group of nonnucleolar GAR proteins were bound by Puf4p (Figure S2). Sequence Motifs in the 3′-UTR of mRNA Targets Direct Binding by Puf Proteins The Puf homologs in Drosophila and C. elegans bind to sequences in the 3′-UTR of mRNAs (Wickens et al. 2002). We therefore examined the sets of mRNAs associated with each of the S. cerevisiae Puf proteins for the presence of common sequence motifs in 5′-UTRs and 3′-UTRs, using multiple expectation maximization for motif elicitation (MEME) as a motif discovery tool (Bailey and Elkan 1994). We identified distinct 10- or 11-nucleotide sequence motifs in the 3′-UTR among the mRNAs interacting with Puf3p, Puf4p, and Puf5p (Figure 5A, Tables S9–S11). We have thus far been unable to identify conserved sequence elements among Puf1p and Puf2p targets; these proteins may recognize structural elements in the RNA rather than simple sequence strings, possibly via their classical RNA-binding domains instead of their six-repeat Pumillio domains. Figure 5 Sequence Motifs Interacting with Puf Proteins (A) Consensus motifs detected within 3′-UTR sequences of Puf3p, Puf4p, and Puf5p target mRNAs. Height of the letters specifies the probability of appearing at the position in the motif. Letters with less than 10% appearance were omitted. Fraction of genes bearing a motif in the 3′-UTR sequence is indicated to the right. Y-helicase proteins are nearly identical in sequence and were excluded from this analysis. (B) Scheme of three-hybrid assay for monitoring RNA–protein interactions in vivo (Bernstein et al. 2002). (C) β-Galactosidase activity for three-hybrid assay. Proteins assayed are indicated on top, RNAs to the left. Abbreviations: pum, pum-HD; cons., consensus motif; UGU/AGA, UGU in consensus sequence mutated to AGA. (D) Activation of HIS3 reporter gene and resistance to 3-aminotriazole (3-AT), a competitive inhibitor of the HIS3 gene product, in a three-hybrid assay (Bernstein et al. 2002). The conserved motifs we identified in the Puf3p, Puf4p, and Puf5p targets each include a UGUR tetranucleotide sequence, which is a feature of all previously reported RNA targets of Puf family proteins (Wickens et al. 2002). Furthermore, in each case, the consensus sequence contains a conserved dinucleotide (UA), located two, three, or four nucleotides downstream of the UGUR motif, in the consensus sites for Puf3p, Puf4p, and Puf5p. Remarkably, the Puf3p consensus motif matches a sequence (CYUGUAAAUA) previously identified by computational tools in 3′-UTR sequences of nuclear genes coding for mitochondrial proteins (Jacobs Anderson and Parker 2000). We examined the distribution of the consensus sequence motifs in the entire S. cerevisiae genome (Table 1). Of the genes whose mRNAs were predicted by computational analysis to contain one of these three target sequences in their 3′-UTRs, 42% were identified experimentally as targets in the corresponding affinity isolation procedure (Table 1). The consensus motifs were occasionally found in the coding sequence of an experimentally identified target gene, but were much rarer in the predicted 5′-UTR sequences (Table 1). Moreover, only a few mRNAs had two copies of the motifs: five mRNAs among the Puf3p targets, six among the Puf4p targets, and one among the Puf5p targets (see Tables S5–S7). As our computational method did not detect the cognate consensus sequence elements in all the experimentally identified targets, alternative sequences or structural elements in RNAs might also allow specific interactions with Puf proteins, some mRNAs may be associated indirectly as part of larger complexes, and some of the putative mRNA targets identified by our affinity procedure are likely to be false positives. Table 1 Number of Consensus Motifs Found in the Genome and in Puf Targets aKnown and putative ORFs (6,330 genes) from SGD bThe probability that the motifs are enriched in Puf targets by chance cAverage lengths of predicted UTR sequences (134 bp of 5′-UTR sequences, 237 bp of 3′-UTR sequences; Mignone et al. 2002). Syntax for multiple bases: H = A/C/T, W = A/T, Y = C/T To test the in vivo function of the putative recognition elements identified by the computational analysis, we assayed RNA–protein interactions in vivo using the yeast three-hybrid system (Bernstein et al. 2002) (see Figure 5B). Puf3p, Puf4p, and Puf5p bound specifically to a sequence matching to the cognate consensus sequence, as assayed by activation of the lacZ and HIS3 reporter genes (see Figure 5C and 5D). For Puf3p and Puf4p, the Pum-HD alone was sufficient to confer specific binding (see Figure 5C and 5D), but no interaction could be seen with the Puf5p Pum-HD alone (data not shown). These interactions were specific: mutations in the UGU of the Puf3p consensus sequence disrupted binding, and each Puf protein interacted with its cognate consensus sequence in preference to the closely related consensus sequences recognized by the other Puf proteins. We detected a weak interaction between Puf3p and the Puf4p target sequence, an interaction that was not seen with the Puf3p Pum-HD alone. These results suggest that binding of the Puf proteins to these specific cis-acting elements directs their functions to specific sets of mRNAs. Subcellular Distribution of Puf Proteins We investigated the localization of the TAP-tagged Puf proteins by immunofluorescence with antibodies against the TAP tag (see Materials and Methods). All five Puf proteins were predominantly localized to multiple discrete foci in the cytoplasm (Figure 6). The predominantly cytoplasmic localization is consistent with previous reports for S. cerevisiae Puf3p and Puf5p (Tadauchi et al. 2001) and for the homologous proteins in higher eukaryotes (Lehmann and Nüsslein-Volhard 1991; Zhang et al. 1997). The distribution of the foci of Puf proteins was not obviously related to distinct cellular organelles or structures, with the exception of Puf1p and Puf2p, which localized in foci enriched near the periphery of the cell. Because of the diffuse and pleiomorphic distribution of mitochondria in the cell, we cannot exclude the possibility that Puf3p, which specifically bound transcripts of proteins destined for the mitochondria, is associated with mitochondria. Figure 6 Localization of Puf Proteins TAP-tagged Puf proteins were visualized in fixed cells. DNA was costained with 4′,6-diamidino-2-phenylindole dimethylsulfoxide (DAPI). Altered Levels of Puf3p-Associated mRNAs in a puf3Δ Mutant A previous study compared steady-state mRNAs levels of cells bearing deletions of all five Puf proteins and wild-type cells grown in rich media (Olivas and Parker 2000). Only 12 of the 148 (8%) mRNAs whose abundance changed by more than 2-fold were selectively enriched in our affinity isolations with Puf proteins. The lack of a simple relationship between the mRNA binding specificity we observed and the reported effects of these multiple mutations on global gene expression prompted us to design a more specific experiment to search for a possible connection between specific mRNAs levels and binding to Puf proteins. We focused on Puf3p, as its strong association with mRNA-encoding mitochondrial proteins suggested that we should look for a regulatory function for this protein in mitochondrial physiology. Indeed, we found that puf3Δ cells grew more slowly than isogenic puf3+ cells on minimal media plates with glycerol as the carbon source (Figure S3). We therefore compared mRNA levels in the puf3Δ and puf3+ cells grown under these conditions by DNA microarray hybridization. Although the magnitude of the change was small, the relative expression levels of the 220 Puf3p-associated mRNAs were selectively increased in puf3Δ cells, compared to all other mRNAs analyzed (p < 10−34) (Figure 7). Of the 16 mRNAs whose abundance was increased by more than 2-fold in the puf3Δ mutant, 11 (70%) were among the transcripts identified as Puf3p targets by our co-purification experiments, and all encode mitochondrial proteins. This result could reflect a direct effect of Puf3p on its target mRNAs, for example, by promoting mRNA decay (Olivas and Parker 2000). However, the levels of transcripts involved in respiration and mitochondrial function, including many that did not appear to be bound directly by Puf3p, were increased in the puf3Δ mutant, suggesting the possibility that the elevated abundance of Puf3p target mRNAs could instead be an indirect response to impaired mitochondrial and respiratorial function in puf3Δ cells. Figure 7 Gene Expression Profiling of puf3 Mutants Distribution of average Cy5/Cy3 fluorescence ratios from three independent microarray hybridizations comparing mRNA levels of puf3Δ with wild-type cells grown in minimal media with glycerol. The left frequency axis refers to all genes (black line); the axis to the right refers to Puf3p and Puf4p (control) targets, shown as red and blue lines, respectively. Relative expression levels of the 220 Puf3p mRNA targets in puf3Δ cells were selectively increased compared to all other mRNAs analyzed (p < 10−34), whereas Puf4p targets were not (p > 0.05). Thirty-nine genes involved in aerobic respiration (according to GO annotation and SGD), but not bound by Puf3p, were similarly enriched (p < 5 × 10−5) in the puf3 mutant as random sets of 39 Puf3p targets (p < 10−6). Likewise, 220 randomly selected mRNAs coding for mitochondrial proteins that were not associated with Puf3p in the experiments herein were weakly enriched in the mutant (p < 10−8). Discussion In an analysis of just five of the hundreds of RBPs encoded by the S. cerevisiae genome, we found that more than 700 transcripts appeared to be specifically bound by one or more RBPs, with each of the five Puf family proteins “tagging” a distinct set of mRNAs. These sets encode functionally and cytotopically related proteins. For three of the Puf proteins, we identified distinct short sequences in the associated specific set of mRNAs, typically in the 3′-UTR, which were sufficient for specific binding to the cognate Puf protein in vivo. Many sets of mRNAs encoding proteins localized to the same subcellular compartment, protein complex, or functional system were bound by the same Puf protein. Puf3p, which specifically associated with cytoplasmic mRNAs encoding mitochondrial proteins, generally affected the steady-state levels of its mRNA targets as reflected by their increased abundance in puf3 mutant cells. The selective “tagging” by sequence-specific RBPs of mRNAs that share common physiological roles suggests a general and widespread mechanism for coordinated control of their expression. Previous reports have identified coordinated regulation of small sets of functionally related mRNAs by specific RBPs. For example, mammalian stem–loop binding protein (SLBP) associates with all five classes of histone mRNAs and guides proper 3′-end formation (Dominski and Marzluff 1999). Iron regulatory proteins (IRPs) bind to and regulate translation of five different mRNAs encoding proteins involved in iron metabolism (Eisenstein and Ross 2003), and a cytoplasmic poly(A) polymerase regulates multiple mRNAs in early development (Mendez and Richter 2001). Based on these and other examples (Tenenbaum et al. 2000), Keene and Tenenbaum (2002) have suggested that messenger RBPs could define “post-transcriptional operons.” Our results provide strong support for this general idea of coordination of gene expression via RBPs and suggest that the post-transcriptional control afforded by combinatorial binding of RBPs to mRNAs could allow greater regulatory flexibility than a simple operon (see also Keene and Tenenbaum 2002). Further, we suggest that RBPs may play important roles in subcellular localization and efficient assembly of protein complexes. The RBPs encoded in eukaryotic genomes rival specific transcription factors in their numbers and diversity, raising the intriguing possibility that specific regulation of the localization, translation, and survival of mRNAs might be comparable in their richness and complexity to regulation of transcription itself. Each of the five Puf proteins interacts with a distinct large set of mRNAs, comprising more than 700 different mRNAs in total. Five other RBPs in S. cerevisiae have been subjected to a similar genome-wide survey of their mRNA targets. She2p, which plays a critical role in selective targeting of specific mRNAs to the bud tip (Shepard et al. 2003), Khd1p, which has also been implicated in localizing gene expression to the nascent bud (A. P. Gerber, unpublished data), and Scp160p, an RBP implicated in genome stability (Li et al. 2003), were each found to bind from 20 to hundreds of distinct mRNAs, and two proteins implicated in RNA export from the nucleus, Yra1p and Mex67p, were each associated with more than 1,000 mRNAs (Hieronymus and Silver 2003). Thus, just ten of the 567 S. cerevisiae proteins known or predicted from the genome sequence to have RNA binding activity (Costanzo et al. 2001) have been found to bind, in a functionally specific pattern, a total of approximately 2,500 different transcripts (approximately 40% of the transcriptome). The extent and specificity of the RNA–protein interactions represented by the proteins studied to date, extrapolated to the hundreds of putative RBPs that remain to be investigated, suggest the existence of an extensive network of RNA–protein interactions that coordinate the post-transcriptional fate of large sets of cytotopically and functionally related RNAs through each stage of its “lifecycle.” It further suggests a potential regulatory repertoire comparable in its diversity and richness to that of the DNA-binding transcription factors (Figure 8). Indeed, the combinatorial binding of mRNAs by multiple RBPs could, in principle, define a specific post-transcriptional fate for each individual mRNA (for an example, see Sonoda and Wharton 2001). Figure 8 Specific Proteins Bind Functional Groups of Genes for Regulation At the transcriptional level (top), transcription factors (TFs) regulate initiation of transcription (green arrow) in the nucleus by binding to sequence elements (yellow box) proximal to their target coding regions (boxes). At the post-transcriptional level (middle), RBPs regulate decay, translation, or localization of mRNAs in a coordinated fashion by interaction with sequence/structural elements in the RNA that are often found in 3′-UTR regions (red box). Functional relations at the protein level (bottom) can be reflected at both the transcriptional and post-transcriptional levels: sets of genes that encode functionally related proteins, such as subunits of stochiometric complexes (blue) or components of the same regulatory or metabolic pathway (gray and cross-hatched boxes), may be regulated by common transcription factors and their mRNAs post-transcriptionally coregulated by RBPs (dashed interactions). Many sets of mRNAs bound by the same Puf protein encode proteins that act in the same subcellular location, form stochiometric complexes, or are implicated in the same cellular pathway. This organization is most clearly exemplified by Puf3p, which selectively bound mRNAs encoding mitochondrial proteins, including at least 70% of all mitochondrial ribosomal proteins (see Figure 4). Combinations of RBPs could specify smaller sets of RNAs encoding more precisely defined functional groups of proteins. For example, the mRNAs encoding the core histone proteins were among the small set of mRNAs that were associated with both Puf4p and Puf5p. These results therefore hint that networks of functional and physical interactions among proteins could be reflected in a corresponding network of mRNA–protein interactions that coordinate post-transcriptional control of their expression and fate. For three of the Puf proteins, we found that RNA–protein interactions were directed by compact sequence elements, usually located in the 3′-UTR of the mRNA (see Figure 5). Interactions with 3′-UTR sequences have been described for many cytoplasmic RBPs involved in post-transcriptional regulation (Mazumder et al. 2003). Our analysis has revealed that such recognition elements are probably much more widespread than previously recognized. Sequence and structural elements in mRNAs that are related to the function or cellular localization of the encoded proteins may be a general feature of eukaryotic genes, paralleling the role of the DNA sequences that direct specific transcription factors to promoters and enhancers (Cliften et al. 2003). The multifocal cytoplasmic distribution of Puf proteins raises the possibility that the mRNAs associated with each Puf protein are colocalized (see Figure 6). In mammalian cells, specific mRNA molecules and specific messenger RBPs have also been found to be localized to specific “granular” subcytoplasmic loci, although the generality of this phenomenon has not been established (Andersen and Kedersha 2002; Eystathioy et al. 2002; Farina et al. 2003). One function of the Puf proteins and related proteins that bind specific families of mRNAs could be to localize functionally related mRNAs to specific cytoplasmic loci. Physical clustering of functionally related groups of mRNAs could aid the assembly of complexes and the coordinated control of translation or RNA turnover. In support of this idea, it has recently been suggested that mRNA decay in the cytoplasm of S. cerevisiae occurs in distinct loci (Sheth and Parker 2003) and, further, that mRNAs encoding different subunits of stoichiometric complexes do indeed have concordant decay rates (Wang et al. 2002b). We propose that the location in the cell at which any mRNA is translated or degraded is not left to chance. Instead, every mRNA that leaves the nucleus may be delivered, in a process directed by specific protein–RNA interactions, to one of a limited number of specific foci in the cytoplasm, designated as destinations for a specific functionally related family of mRNAs. These foci could serve to colocalize and coregulate synthesis of proteins that need to assemble or act together, thereby facilitating efficient and rapid assembly and localization of the proteins. The number of distinct families of functionally specialized foci may be quite large. The locations of these foci need not correspond to recognizable cellular features, but may simply be ad hoc sites for localized, coordinated translation of proteins that are to be assembled into a complex or a functional unit. Specific predictions of this hypothesis, such as colocalized translation of the subunits of stoichiometric complexes, should be amenable to direct experimental tests. Combinatorial binding of mRNAs by specific regulatory proteins, linking their post-transcriptional regulation to specific signal transduction pathways, could allow rapid and efficient reprogramming of gene expression during development or in response to changing physiological conditions. Indeed, regulation of specific genes by external signals via RPBs has been described in higher eukaryotes (Lasko 2003). For example, the signal transduction and activation of RNA (STAR) proteins contain RNA-binding motifs combined with protein–protein interaction domains and phosphorylation sites, which could allow integration of stimuli conducted by signal transduction cascades (Lasko 2003). Similarly, the Puf proteins contain numerous putative phosphorylation motifs, as well as domains with characteristics often implicated in protein–protein interactions, such as glutamine/arginine-rich regions (Michelitsch and Weissman 2000) (see Figure 1). Coordination of cellular processes has long been thought to be mediated primarily at the transcriptional and post-translational level. Our results join a growing body of studies (Tenenbaum et al. 2000; Eystathioy et al. 2002; Wang et al. 2002b; Hieronymus and Silver 2003; Shepard et al. 2003; see also Keene and Tenenbaum 2002) that suggest that the localization, translation, and stability of mRNAs are subject to extensive and important regulation and coordination by interaction with a diverse set of RBPs. Systematic mapping of these interactions and deciphering their roles, molecular mechanisms, and coordination will undoubtedly yield important new insights into biological regulation and the gene expression program. Materials and Methods Oligonucleotide primers Restriction sites are in italics: Puf3-F1, 5′-cgggatccATGGAAATGAACATGGATATGGATATGG-3′; Puf3-R1, 5′-ggaattcTCACACCTCCGCATTTTCAACCAATG-3′; Puf3-F6nco, 5′-cCATGgCACTAAAAGACATCTTTGG-3′; Puf4-F2nco, 5′-ccatgGCGGACGCAGTTTTAGACCAATA-3′; Puf4-R1eco, 5′-gaattcgTGAATCTAAATGTAACATTCCG-3′; Puf5-F2nco, 5′-ccATGGTCGAAATCAGCGCACTACC-3′; Puf5-R1xho, 5′-ctcgagcACTTGGAAGTAATTCTTTTGTA-3′; M16-1, 5′-GGGCTCGAGtagggaataccttgtaaatatcctatgaaaGCATG-3′; M16-2, 5′-CtttcataggatatttacaaggtattccctaCTCGAGCCC-3′; M16-1mut, 5′-GGGCTCGAGtagggaatacctacaaaatatcctatgaaaGCATG-3′; M16-2mut, 5′-CtttcataggatattttgtaggtattccctaCTCGAGCCC-3′; Caf-1, 5′-GGGCTCGAGtgggcacgattgtaataatacttcatgataaGCATG-3′; Caf-2, 5′-CttatcatgaagtattattacaatcgtgcccaCTCGAGCCC-3′; Yor-1, 5′-GGGCTCGAGgctttcatcatctgtataatatttatatgtcGCATG-3′; and Yor-2, 5′-CgacatataaatattatacagatgatgaaagcCTCGAGCCC-3′. Strains and plasmid construction The TAP-tagged Puf3p strain (SC1249) was obtained from Cellzome (Heidelberg, Germany) (Gavin et al. 2002). TAP-tagged Puf1p, Puf2p, Puf4p, and Puf5p strains were a gift from Dr. Erin O'Shea (Ghaemmaghami et al. 2003). Correct genomic integration of each tag was verified by PCR and by immunoblot analysis of cell extracts (data not shown). Strain BY4741 was used for mock-control affinity isolations of RNA, and deletions of the PUF3 and PUF4 genes in this strain were obtained from Dr. Ron Davis (Winzeler et al. 1999). The ORF of PUF3 was amplified by PCR with primers Puf3-F1 and Puf3-R1 from S. cerevisiae genomic DNA and cloned into pCR2.1 using the TOPO TA Cloning Kit (Invitrogen, San Diego, California, United States). The PUF3 ORF was sequenced and subcloned into pACTII via NcoI and EcoRI restriction sites, resulting in plasmid pACTII-Puf3. A full-length Puf5p construct pGAD-MPT5 was a gift from Dr. Kenji Irie (Tadauchi et al. 2001). Sequences encoding the Pum-HD domains of Puf3p (amino acids 535–879), Puf4p (amino acids 557–888), and Puf5p (amino acids 202–578) were PCR-amplified from genomic DNA with oligo pairs Puf3-F6nco/Puf3-R1, Puf4-F2nco/Puf4-R1eco, and Puf5-F2nco/Puf5-R1xho, respectively. Products were ligated into pCR2.1-TOPO, sequenced, and further cloned into pACTII via restriction sites present in the oligonucleotides used for amplification. The RNA consensus sequences interacting with Puf proteins plus ten nucleotides of flanking sequences were cloned into the SmaI and SphI sites of the vector pIIIA/MS2-2 (Bernstein et al. 2002) using annealed synthetic oligonucleotides. The PUF3 RNA consensus sequence spanning nucleotides 24–33 in the 3′-UTR of YBL038w/MRPL16 was constructed with oligonucleotides M16-1 and M16-2. In M16mut the conserved UGU motif was changed to ACA. The PUF4 consensus (nucleotides 24–34 in the 3′-UTR of YOR145c) was constructed with oligonucleotides Yor-1 and Yor-2. The PUF5 consensus (nucleotides 105–114 in the 3′-UTR of YNL278w/CAF120) was constructed with oligonucleotides Caf-1 and Caf-2. Isolating RNAs specifically associated with selected RBPs For a detailed protocol, see the Supporting Information on our Web site. In brief, 1 l of cells were cultured in YPAD medium (yeast–peptone–dextrose [YPD] supplemented with 20 mg/ml adenine–sulfate) at 30°C and collected during exponential growth by centrifugation. Cells were washed twice with ice-cold buffer A (20 mM Tris–HCl [pH 8.0], 140 mM KCl, 1.8 mM MgCl2, 0.1% Nonidet P-40 [NP-40], 0.02 mg/ml heparin) and resuspended in 5 ml of buffer B (buffer A plus 0.5 mM dithiothreitol [DTT], 1 mM phenylmethylsulfonylfluoride, 0.5 μg/ml leupeptin, 0.8 μg/ml pepstatin, 20 U/ml DNase I, 100 U/ml RNasin [Promega, Madison, Wisconsin, United States], and 0.2 mg/ml heparin). Cells were broken mechanically with glass beads, and extracts were incubated with 400-μl slurry (50% [v/v]) IgG–agarose beads (Sigma, St. Louis, Missouri, United States) for 2 h at 4°C. The beads were washed four times for 15 min at 4°C with buffer C (20 mM Tris–HCl [pH 8.0], 140 mM KCl, 1.8 mM MgCl2, 0.5 mM DTT, 0.01% NP-40, 10 U/ml RNasin). Puf proteins were released from the beads by incubation with 80 U of TEV protease (Invitrogen) for 2 h at 15°C. RNA was isolated from the TEV eluates, which corresponds to the purified fraction and from extracts (input) by extraction with phenol/chloroform and isopropanol precipitation. Microarray analysis and data selection Equal amounts of a pool of five synthetically prepared Bacillus subtilis RNAs were added to each RNA sample prior to labeling and served as a control for the labeling procedure (Wang et al. 2002b). Total RNA (3 μg) derived from the extract and 300 ng of affinity-isolated RNA (or up to 40% of isolated RNA) were labeled with Cy3 and Cy5 fluorescent dyes, respectively, following cDNA synthesis with amino-allyl dUTP in addition to the four natural dNTPs using a 1:1 mixture of oligo(dT) and random nonamer primers. The Cy3- and Cy5-labeled cDNA samples were mixed and competitively hybridized to DNA microarrays representing all S. cerevisiae ORFs, introns, and the mitochondrial genome (see http://brownlab.stanford.edu/protocols.html). Microarrays were scanned with an Axon Instruments (Foster City, California, United States) Scanner 4000. Scanning parameters were adjusted to give similar fluorescent intensities for B. subtilis spots in both channels. Data were collected with the GENEPIX 3.0 Program (Axon Instruments), and spots with abnormal morphology were excluded from further analysis. Arrays were computer normalized by the Stanford Microarray Database (SMD) (Gollub et al. 2003). Log2 median ratios were retrieved from SMD and exported into Microsoft (Redmond, Washington, United States) Excel after filtering for regression correlation of greater than 0.6 (filters for large variations in the ratios of pixels within each spot), CH1I/CH1B of greater than 1.8 (signal over background in the channel measuring total RNA from extract), and CH2I/CH2B of greater than 1.0 (affinity-isolated RNA signal greater than background) and for data from at least two independent measurements. Average log2 ratios were calculated for each gene across the four independent experiments performed for each Puf protein (microarrays and raw data can be downloaded from our supporting Web sites [http://microarray-pubs.stanford.edu/yeast_puf/ and http://genome-www5.stanford.MicroArray/SMD/]). Genes for which the enrichment ratios were at least two standard deviations above the median across all genes were selected. A total of 923 genes were selected in this way. To eliminate nonspecifically enriched RNAs from this gene list, the results from the affinity enrichments for each of the Puf proteins and the data obtained from four independent mock affinity enrichments were clustered by the Pearson correlation algorithm (Eisen et al. 1998). Transcripts of 84 genes were enriched beyond the two standard deviation threshold in all the Puf affinity isolations as well as in the mock procedure. These were presumed to represent RNAs whose enrichment was unrelated to specific interactions with Puf proteins and therefore were excluded from further analysis. Among the finally selected target mRNAs (see Tables S3–S7), most were represented in the four independent measurements: PUF1, 98%; PUF2, 97%; PUF3, 82%; PUF4, 93%; PUF5, 97%. Gene expression profiling puf3 mutant and wild-type cells were cultured in minimal media supplemented with 3% glycerol and harvested during exponential growth (OD600 = 0.5). Total RNA (8 μg) isolated from wild-type and mutant cells were used to prepare Cy3 and Cy5 fluorescently labeled cDNA as described above, except that only an oligo(dT) primer was used. The two differentially labeled cDNAs were mixed together and hybridized to yeast DNA microarrays. Arrays were scanned and the data were collected, entered into SMD, and computer normalized (Gollub et al. 2003). Log2 median ratios were retrieved from SMD after filtering for regression correlation of greater than 0.6 and signal over background of greater than 1.5. Results from three independent experiments were averaged for this analysis (raw data can be retrieved from our Web site). Motif searches As the exact 5′- and 3′-UTR lengths are unknown for most of the Puf target mRNAs, we used the estimated average lengths from yeast (Mignone et al. 2002). Hence, the coding 237 nucleotides of predicted 3′-UTR and 134 nucleotides of predicted 5′-UTR sequences were retrieved from SGD for the Puf target genes. The sequences were searched for motifs in the sense strand with the program MEME under the proposed default settings (http://meme.sdsc.edu/meme/website/intro.html) (Bailey and Elkan 1994) (see Tables S9–S11). The number and location of consensus motifs in the S. cerevisiae genome was obtained by searching “Pattern Match” in the SGD (Issel-Tarver et al. 2002). Thereby, nucleotides that were at least 19% conserved among the MEME selected sequences were used to compile the Consensus Motif that was searched for. Three-hybrid assays Three-hybrid assays were performed as described elsewhere (Bernstein et al. 2002). Immunofluorescence Immunofluorescence was performed as described at http://www.med.unc.edu/%7Ehdohlman/IF.html. Fixed and permeabilized cells were treated with 5 μg/ml purified rabbit immunoglobulin (Sigma) for 1 h at room temperature. After washing, cells were incubated with Cy3 goat anti-rabbit antibodies (1:400). Images were obtained on a Zeiss (Oberkochen, Germany) Axioplan-2 microscope using an Axiocam HRC camera. Supporting Information Full microarray results and other supporting information can be viewed at http://microarray-pubs.stanford.edu/yeast_puf/ and at http://genome-www5.stanford.MicroArray/SMD/. Figure S1 Distribution of Average Cy5/Cy3 Fluorescence Ratios from Quadruplicate Microarray Hybridizations Analyzing mRNA Targets for Puf1p, Puf2p, Puf4p, and Puf5p See Figure 3A for Puf3p. (167 KB EPS). Click here for additional data file. Figure S2 Examples of Groups of mRNAs Associated with the Same Puf Protein and Encoding Related Proteins (A) Puf2p-bound mRNAs encode diverse proteins involved in regulation of ATP-dependent proton transport. PMA1 and PMA2 encode plasma membrane proteins that comprise the major ATP-dependent proton transporters and regulate cellular pH levels. Pmp1p, Pmp2p, and Pmp3p are small isoproteolipids, which are present in a physical complex with Pma1p and act as regulators of its activity upon stress conditions (Navarre et al. 1994). Hrk1p is a protein histidine kinase, which activates Pma1p in response to glucose (Goossens et al. 2000). Ast1p is implicated in proper delivery of Pma1p to plasma membranes (Bagnat et al. 2001). (B) Puf4p-bound mRNAs encode the nucleolar GAR proteins (blue), members of the H/ACA core complex (boxed), and Hmt1p, a dimethylase acting on GAR proteins. Nop1p performs 2′-O-ribose methylation of pre-rRNA, a process guided by small nucleolar RNAs (snoRNAs) of the box C/D family. Cbf5p catalyzes pseudouridine formation with box H/ACA snoRNAs, and three of the four components of the H/ACA core complex were Puf4p-associated (Cbf5, Gar1, and Nhp2 [Henras et al. 1998]; no data were obtained for the fourth component, Nop10, shown in gray). All transcripts encoding nucleolar proteins of the GAR repeats family (Gar1p, Sbp1p, Nop1p, Nsr1p) were Puf4p-bound. The GAR domain is dimethylated at arginine residues. Remarkably, several mRNAs coding for S-adenosylmethionine-dependent methyltransferases were Puf4p-bound including Hmt1p, the major protein arginine-methyltransferase in yeast (Gary et al. 1996). Hmt1p has recently been shown to dimethylate arginines of the proteins Gar1p, Nop1p, and Nsr1p (Xu et al. 2003). (38 KB EPS). Click here for additional data file. Figure S3 Phenotypic Analysis of puf3Δ Cells Serial dilutions (1:10) of cells were spotted on plates supplemented with the indicated media. Plates were incubated for 3 d at 30°C. Abbreviations: YPD, yeast–peptone–dextrose; YPGE, yeast–peptone–3% glycerol–2% ethanol; SC, synthetic complete. (264 KB PDF). Click here for additional data file. Table S1 Number of mRNA Targets Shared between Puf Proteins (15 KB XLS). Click here for additional data file. Table S2 Protein Copy Number Determination of Puf Proteins Cells were grown to mid-log phase in YPAD medium and the number of cells was counted. Whole-cell extracts were prepared as described previously (Hoffman et al. 2002). In brief, cells were resuspended in 1× SDS-PAGE sample buffer, incubated at 100°C for 10 min, and vortexed for 2 min with glass beads. After a short centrifugation, eight dilutions of cell extracts and protein A (Amersham, Little Chalfont, United Kingdom), which served as a reference standard, were spotted on a nitrocellulose filter. Expression of IgG-binding domains was monitored with rabbit peroxidase–anti-peroxidase soluble complex at 1:5,000 (Sigma). Chemiluminescence was measured with a Typhoon 8600 Imager (Molecular Dynamics, Sunnyvale, California, United States) and quantified with the ImageQuant 5.2 software. Averaged numbers from two independent measurements were used for calculations. The total number of mRNA copies in the pool associated with each Puf protein was estimated as follows: copy numbers for individual mRNAs were retrieved from two independent genome-wide measurements (Holstege et al. 1998; Wang et al. 2002b). For genes with no data, we added the median value for copy numbers of all mRNAs in the respective pool. (30 KB XLS). Click here for additional data file. Table S3 List of Puf1p Target mRNAs Columns indicate the following (from left to right): ORF; gene name; GO annotations; classification of gene products (soluble/membrane-associated); average log2 ratios of enrichment across four independent Puf affinity isolations; standard deviations; association of mRNA with other Puf proteins; mRNA copy numbers. (28 KB XLS). Click here for additional data file. Table S4 List of Puf2p Target mRNAs Notations are as in Table S3. (52 KB XLS). Click here for additional data file. Table S5 List of Puf3p Target RNAs Columns indicate the following (from left to right): ORF; gene name; GO annotations; classification of gene products (soluble/membrane-associated); average log2 ratios of enrichment across four independent Puf affinity isolations; standard deviations; association of mRNA with other Puf proteins; location of consensus motif identified by MEME; mRNA copy numbers. (70 KB XLS). Click here for additional data file. Table S6 List of Puf4p Target mRNAs Notations are as in Table S5. (61 KB XLS). Click here for additional data file. Table S7 List of Puf5p Target mRNAs Notations are as in Table S5. (64 KB XLS). Click here for additional data file. Table S8 Significant Shared GO Annotations among Puf mRNA Targets Only annotations with p values of less than 0.001 are indicated. GO annotations were retrieved from the SGD with GO Finder (http://db.yeastgenome.org/cgi-bin/SGD/GO/goTermFinder) on May 21, 2003. Respective p values are indicated in a column next to the names of the GO term. (30 KB XLS). Click here for additional data file. Table S9 Results of MEME Motif Searches: Motifs among Puf3p mRNA Targets (63 KB XLS). Click here for additional data file. Table S10 Results of MEME Motif Searches: Motifs among Puf4p mRNA Targets (55 KB XLS). Click here for additional data file. Table S11 Results of MEME Motif Searches: Motifs among Puf5p mRNA Targets (34 KB XLS). Click here for additional data file. Accession Numbers All accession numbers for human, Drosophila, or C. elegans proteins are from the SwissProt database (http://www.ebi.ac.uk/swissprot/): CPEB (Q18317), GLD1 (Q17339), DAZL (Q92904), FBF-1 (Q9N5M6), FEM3 (P34691), IRP (P21399), NANOS (P25724), Drosophila PUMILIO (P25822), human PUMILIO-1 (Q14671), human PUMILIO-2 (Q9HAN2), and SLBP (P97330). The accession numbers for S. cerevisiae genes are from SGD (http://genome-www.stanford.edu/Saccharomyces/) (ORF/SGD identification number): ADA2 (YDR448W/S0002856), AME1 (YBR211C/S0000415), APS3 YJL024C/S0003561), AST1 (YBL069W/S0000165), BBP1 (YPL255W/S0006176), BDF1 (YLR399C/S0004391), BDF2 (YDL070W/S0002228), BOI2 (YER114C/S0000916), BSP1 (YPR171W/S0006375), BUB1 (YGR188C/S0003420), BUD9 (YGR041W/S0003273), CBF5 (YLR175W/S0004165), CDC31 (YOR257W/S0005783), CNM67 (YNL225C/S0005169), COX17 (YLL009C/S0003932), DAD2 (YKR083C/S0001791), DHH1 (YDL160C/S0002319), ELM1 (YKL048C/S0001531), FPS1 (YLL043W/S0003966), GAR1 (YHR089C/S0001131), GIC1 (YHR061C/S0001103), HDA1 (YNL021W/S0004966), HFI1 (YPL254W/S0006175), HMT1 (YBR034C/S0000238), HOS1 (YPR068C/S0006272), HOS3 (YPL116W/S0006037), HST1 (YOL068C/S0005429), HTA1 (YDR225W/S0002633), IFM1 (YOL023W/S0005383), KAR1 (YNL188W/S0005132), KAR9 (YPL269W/S0006190), KHD1 (YBL032W/S0000128), MAS6 (YNR017W/S0005300), MEX67 (YPL169C/S0006090), MUP3 (YHL036W/S0001028), NCE101 (YJL205C/S0003742), NCE102 (YPR149W/S0006353), NHP2 (YDL208W/S0002367), NOP1 (YDL014W/S0002172), NSR1 (YGR159C/S0003391), NUF2 (YOL069W/S0005430), PDR16 (YNL231C/S0005175), PMA1 (YGL008C/S0002976), PUF1 (YJR091C/S0003851), PUF2 (YPR042C/S0006246), PUF3 (YLL013C/S0003936), PUF4 (YGL014W/S0002982), PUF5 (YGL178W/S0003146), RAX2 (YLR084C/S0004074), RSC1 (YGR056W/S0003288), RSC2 (YLR357W/S0004349), RSC4 (YKR008W/S0001716), SBP1 (YHL034C/S0001026), SCP160 (YJL080C/S0003616), SFK1 (YKL051W/S0001534), SFL1 (YOR140W/S0005666), SHE2 (YKL130C/S0001613), SIN3 (YOL004W/S0005364), SNC2 (YOR327C/S0005854), SNT1 (YCR033W/S0000629), SPC19 (YDR201W/S0002609), SPC42 (YKL042W/S0001525), SPT7 (YBR081C/S0000285), SPT8 (YLR055C/S0004045), SSO2 (YMR183C/S0004795), STE7 (YDL159W/S0002318), SUR7 (YML052W/S0004516), TPO1 (YLL028W/S0003951), TPO2 (YGR138C/S0003370), TPO3 (YPR156C/S0006360), VPS72 (YDR485C/S0002893), YIP1 (YGR172C/S0003404), YKL091c (YKL091C/S0001574), YPR157w (YPR157W/S0006361), and YRA1 (YDR381W/S0002789). We are grateful to Dr. Erin O'Shea for generously providing TAP-tagged Puf strains in advance of publication, Dr. Marvin Wickens for the yeast three-hybrid assay reagents, and Dr. Kenji Irie for plasmids. We thank Drs. Stefan Luschnig, Yoav Arava, Kevin Travers, and Dan Hogan for critical reading of the manuscript. APG was supported by a long-term fellowship from the Human Frontier Science Organization. This work was supported by the Howard Hughes Medical Institute, by a grant from the National Cancer Institute to POB, and by a grant from the National Institutes of Health (GM49243) to DH. POB is an investigator of the Howard Hughes Medical Institute. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. APG, DH, and POB conceived and designed the experiments. APG performed the experiments. APG, DH, and POB analyzed the data. APG, DH, and POB contributed reagents/materials/analysis tools. APG, DH, and POB wrote the paper. Academic Editor: Sean Eddy, Washington University Abbreviations DAZdeleted in azoospermia DTTdithiothreitol FBFFem-3-binding factor GARglycine/arginine-rich GOGene Ontology HDAChistone deacetylase ORFopen reading frame IRPiron regulatory protein MEMEmultiple expectation maximization for motif elicitation NP-40Nonidet P-40 PufPumilio-FBF Pum-HDPumilio homology domain RBPRNA-binding protein rRNAribosomal RNA SAGASpt–Ada–Gcn5–acetyltransferase SGD Saccharomyces Genome Database SLBPstem–loop binding protein SMDStanford Microarray Database snoRNAsmall nucleolar RNA STARsignal transduction and activation of RNA TAPtandem-affinity purification TEVtobacco etch virus UTRuntranslated region YPDYeast Proteome Database YPDyeast–peptone–dextrose YPGEyeast–peptone–3% glycerol–2% ethanol ==== Refs References Albertsen M Bellahn I Kramer R Waffenschmidt S Localization and function of the yeast multidrug transporter Tpo1p J Biol Chem 2003 278 12820 12825 12562762 Andersen P Kedersha N Stressful initiations J Cell Sci 2002 115 3227 3234 12140254 Bagnat M Chang A Simons K Plasma membrane proton ATPase Pma1p requires raft association for surface delivery in yeast Mol Biol Cell 2001 12 4129 4138 11739806 Bailey TL Elkan C Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology; August 1994 Stanford, California; Menlo Park, California AAAI Press 28 36 Bernstein DS Buter N Stumpf C Wickens M Analyzing mRNA–protein complexes using a yeast three-hybrid system Methods 2002 26 123 141 12054889 Brown V Jin P Ceman S Darnell JC O'Donnell WT Microarray identification of FMRP-associated brain mRNAs and altered mRNA translational profiles in fragile X syndrome Cell 2001 107 477 487 11719188 Cleves AE Cooper DN Barondes SH Kelly RB A new pathway for protein export in Saccharomyces cerevisiae J Cell Biol 1996 133 1017 1026 8655575 Cliften P Sudarsanam P Desikan A Fulton L Fulton B Finding functional features in Saccharomyces genomes by phylogenetic footprinting Science 2003 301 71 76 12775844 Costanzo MC Crawford ME Hirschman JE Kranz JE Olsen P YPD, PombePD and WormPD: Model organism volumes of the BioKnowledge library, an integrated resource for protein information Nucleic Acids Res 2001 29 75 79 11125054 Crittenden SL Bernstein DS Bachorik JL Thompson BE Gallegos M A conserved RNA-binding protein controls germline stem cells in Caenorhabditis elegans Nature 2002 417 660 663 12050669 Dominski Z Marzluff WF Formation of the 3′ end of histone mRNA Gene 1999 239 1 14 10571029 Dreyfuss G Kim NS Kataoka N Messenger-RNA-binding proteins and the messages they carry Nat Rev Mol Cell Biol 2002 3 195 205 11994740 Eisen MB Spellman PT Brown PO Botstein D Cluster analysis and display of genome-wide expression patterns Proc Natl Acad Sci U S A 1998 95 14863 14868 9843981 Eisenstein RS Ross KL Novel roles for iron regulatory proteins in the adaptive response to iron deficiency J Nutr 2003 133 5 Suppl 1 1510S 1516S 12730455 Eystathioy T Chan EK Tenenbaum SA Keene JD Griffith K A phosphorylated cytoplasmic autoantigen, GW182, associates with a unique population of human mRNAs within novel cytoplasmic speckles Mol Biol Cell 2002 13 1338 1351 11950943 Farina KL Huttelmaier S Musunuru K Darnell R Singer RH Two ZBP1 KH domains facilitate beta-actin mRNA localization, granule formation, and cytoskeletal attachment J Cell Biol 2003 160 77 87 12507992 Fatica A Tollervey D Making ribosomes Curr Opin Cell Biol 2002 14 313 318 12067653 Futcher B Latter GI Monardo P McLaughlin CS Garrels JI A sampling of the yeast proteome Mol Cell Biol 1999 19 7357 7368 10523624 Gan X Kitakawa M Yoshino K Oshiro N Yonezawa K Tag-mediated isolation of yeast mitochondrial ribosome and mass spectrometric identification of its new components Eur J Biochem 2002 269 5203 5214 12392552 Gary JD Lin WJ Yang MC Herschman HR Clarke S The predominant protein–arginine methyltransferase from Saccharomyces cerevisiae J Biol Chem 1996 271 12585 12594 8647869 Gavin AC Bosche M Krause R Grandi P Marzioch M Functional organization of the yeast proteome by systematic analysis of protein complexes Nature 2002 415 141 147 11805826 Gerbi SA Borovjagin AV Lange TS The nucleolus: A site of ribonucleoprotein maturation Curr Opin Cell Biol 2003 15 318 325 12787774 Ghaemmaghami S Huh WK Bower K Howson RW Belle A Global analysis of protein expression in yeast Nature 2003 424 727 741 Gollub J Ball CA Binkley G Demeter J Finkelstein DB The Stanford Microarray Database: Data access and quality assessment tools Nucleic Acids Res 2003 31 94 96 12519956 Goossens A de La Fuente N Forment J Serrano R Portillo F Regulation of yeast H(+)-ATPase by protein kinases belonging to a family dedicated to activation of plasma membrane transporters Mol Cell Biol 2000 20 7654 7661 11003661 Hastie ND Bishop JO The expression of three abundance classes of messenger RNA in mouse tissues Cell 1976 9 761 774 1017013 Henras A Henry Y Bousquet-Antonelli C Noaillac-Depeyre J Gelugne JP Nhp2p and Nop10p are essential for the function of H/ACA snoRNPs EMBO J 1998 17 7078 7090 9843512 Hereford LM Rosbash M Number and distribution of polyadenylated RNA sequences in yeast Cell 1977 10 453 462 321129 Hieronymus H Silver PA Genome-wide analysis of RNA–protein interactions illustrates specificity of the mRNA export machinery Nat Genet 2003 33 151 156 Hoffman GA Garrison TR Dohlman HG Analysis of RGS proteins in Saccharomyces cerevisiae Methods Enzymol 2002 344 617 631 11771415 Holstege FC Jennings EG Wyrick JJ Lee TI Hengartner CJ Dissecting the regulatory circuitry of a eukaryotic genome Cell 1998 95 717 728 9845373 Huh WK Falvo JV Gerke LC Carroll AS Howson RW Global analysis of protein localization in budding yeast Nature 2003 425 671 672 14562083 Issel-Tarver L Christie KR Dolinski K Andrada R Balakrishnan R Saccharomyces Genome Database Methods Enzymol 2002 350 329 346 12073322 Jacobs Anderson JS Parker R Computational identification of cis -acting elements affecting post-transcriptional control of gene expression in Saccharomyces cerevisiae Nucleic Acids Res 2000 28 1604 1617 10710427 Keene JD Organizing mRNA export Nat Genet 2003 33 111 112 12560814 Keene JD Tenenbaum SA Eukaryotic mRNPs may represent posttranscriptional operons Mol Cell 2002 9 1161 1167 12086614 Kennedy BK Gotta M Sinclair DA Mills K McNabb DS Redistribution of silencing proteins from telomeres to the nucleolus is associated with extension of life span in S. cerevisiae Cell 1997 89 381 391 9150138 Kurdistani SK Grunstein M Histone acetylation and deacetylation in yeast Nat Rev Mol Cell Biol 2003 4 276 284 12671650 Lasko P Gene regulation at the RNA layer: RNA binding proteins in intercellular signaling networks Sci STKE 2003 179 RE6 Lehmann R Nüsslein-Volhard C The maternal gene nanos has a central role in posterior pattern formation of the Drosophila embryo Development 1991 112 679 691 1935684 Li AM Watson A Fridovich-Keil JL Scp160p associates with specific mRNAs in yeast Nucleic Acids Res 2003 31 1830 1837 12654998 Lieb JD Liu X Botstein D Brown PO Promoter-specific binding of Rap1 revealed by genome-wide maps of protein–DNA association Nat Genet 2001 28 327 334 11455386 Machin NA Lee JM Barnes G Microtubule stability in budding yeast: Characterization and dosage suppression of a benomyl-dependent tubulin mutant Mol Biol Cell 1995 6 1241 1259 8534919 Maniatis T Reed R An extensive network of coupling among gene expression machines Nature 2002 416 499 506 11932736 Mazumder B Seshadri V Fox PL Translational control by the 3′-UTR: The ends specify the means Trends Biochem Sci 2003 28 91 98 12575997 Mendez R Richter JD Translational control by CPEB: A means to the end Nat Rev Mol Cell Biol 2001 2 521 529 11433366 Michelitsch MD Weissman JS A census of glutamine/asparagine-rich regions: Implications for their conserved function and the prediction of novel prions Proc Natl Acad Sci U S A 2000 97 11910 11915 11050225 Mignone F Gissi C Liuni S Pesole G Untranslated regions of mRNAs Genome Biol 2002 3 REVIEWS0004 11897027 Moore FL Jaruzelska J Fox MS Urano J Firpo MT Human Pumilio-2 is expressed in embryonic stem cells and germ cells and interacts with DAZ (Deleted in AZoospermia) and DAZ-like proteins Proc Natl Acad Sci U S A 2003 100 538 543 12511597 Navarre C Catty P Leterme S Dietrich F Goffeau A Two distinct genes encode small isoproteolipids affecting plasma membrane H(+)-ATPase activity of Saccharomyces cerevisiae J Biol Chem 1994 269 21262 21268 8063750 Olivas W Parker R The Puf3 protein is a transcript-specific regulator of mRNA degradation in yeast EMBO J 2000 19 6602 6611 11101532 Orphanides G Reinberg D A unified theory of gene expression Cell 2002 108 439 451 11909516 Rigaut G Shevchenko A Rutz B Wilm M Mann M A generic protein purification method for protein complex characterization and proteome exploration Nat Biotechnol 1999 17 1030 1032 10504710 Shepard KA Gerber AP Jambhekar A Takizawa PA Brown PO Widespread cytoplasmic mRNA transport in yeast: Identification of 22 bud-localized transcripts using DNA microarray analysis Proc Natl Acad Sci USA 2003 100 11429 11434 13679573 Sheth U Parker R Decapping and decay of messenger RNA occur in cytoplasmic processing bodies Science 2003 300 805 808 12730603 Sonoda J Wharton RP Drosophila brain tumor is a translational repressor Genes Dev 2001 15 762 773 11274060 Tadauchi T Matsumoto K Herskowitz I Irie K Post-transcriptional regulation through the HO 3′-UTR by Mpt5, a yeast homolog of Pumilio and FBF EMBO J 2001 20 552 561 11157761 Takizawa PA DeRisi JL Wilhelm JE Vale RD Plasma membrane compartmentalization in yeast by messenger RNA transport and a septin diffusion barrier Science 2000 290 341 344 11030653 Tenenbaum SA Carson CC Lager PJ Keene JD Identifying mRNA subsets in messenger ribonucleoprotein complexes by using cDNA arrays Proc Natl Acad Sci U S A 2000 97 14085 14090 11121017 Waggoner SA Liebhaber SA Identification of mRNAs associated with αCP2-containing RNP complexes Mol Cell Biol 2003 23 7055 7067 12972621 Wang X McLachlan J Zamore PD Hall TM Modular recognition of RNA by a human pumilio-homology domain Cell 2002a 110 501 512 12202039 Wang Y Liu CL Storey JD Tibshirani RJ Herschlag D Precision and functional specificity in mRNA decay Proc Natl Acad Sci U S A 2002b 99 5860 5865 11972065 Waskiewicz-Staniorowska B Skala J Jasinski M Grenson M Goffeau A Functional analysis of three adjacent open reading frames from the right arm of yeast chromosome XVI Yeast 1998 14 1027 1039 9730282 Wickens M Bernstein DS Kimble J Parker R A PUF family portrait: 3′ UTR regulation as a way of life Trends Genet 2002 18 150 157 11858839 Wigge PA Jensen ON Holmes S Soues S Mann M Analysis of the Saccharomyces spindle pole by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry J Cell Biol 1998 141 967 977 9585415 Winzeler EA Shoemaker DD Astromoff A Liang H Anderson K Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis Science 1999 285 901 906 10436161 Xu C Henry PA Setya A Henry MF In vivo analysis of nucleolar proteins modified by the yeast arginine methyltransferase Hmt1/Rmt1p RNA 2003 9 746 759 12756332 Zamore PD Williamson JR Lehmann R The Pumilio protein binds RNA through a conserved domain that defines a new class of RNA-binding proteins RNA 1997 3 1421 1433 9404893 Zhang B Gallegos M Puoti A Durkin E Fields S A conserved RNA-binding protein that regulates sexual fates in the C. elegans hermaphrodite germ line Nature 1997 390 477 484 9393998

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PLoS Biol. 2004 Mar 16; 2(3):e79

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PLoS Biol

10.1371/journal.pbio.0020079

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020080SynopsisEvolutionGenetics/Genomics/Gene TherapyHomo (Human)Neandertals Likely Kept Their Genes to Themselves Synopsis3 2004 16 3 2004 16 3 2004 2 3 e80Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. No Evidence of Neandertal mtDNA Contribution to Early Modern Humans xx ==== Body Scientists searching for clues to our origins have long relied on studying fossils to piece together our evolutionary history. Now, with the tools of molecular genetics, they can reach beyond morphological evidence to retrieve traces of DNA preserved in the remnants of bone. And in these ancient DNA sequences, they're finding bits and pieces of the evolutionary record. Over the course of evolution, changes in DNA sequences accumulate at a predictable rate. These mutations can reveal not only how closely related we are but also when evolutionary lineages diverged. Identifying both a typical range of genetic variation and rate of mutation for a given species or population, for example, can serve as a frame of reference for analyzing DNA sequences from other species or populations. Most molecular anthropologists use DNA found in mitochondria—intracellular structures that convert food into energy—to reconstruct human evolution. Distinct from nuclear DNA, mitochondrial DNA (mtDNA) exists in the cytoplasm of a fertilized egg and is passed on only through the maternal lineage. An ongoing debate about human origins has revolved around the theory that Homo sapiens and Homo Neanderthalensis interbred, since the two species coexisted. Neandertals lived roughly 150,000 to 30,000 years ago, toward the end of the Pleistocene era, and inhabited Europe, parts of Asia, and the Middle East. Modern-day humans arose between 100,000 and 200,000 years ago. Recently, an international multidisciplinary team of scientists led by Svante Pbo of the Max Planck Institute for Evolutionary Anthropology have analyzed the largest sample of Neandertal and early human remains to date and conclude that Neandertals could not have made a significant genetic contribution to early modern humans. Part of the challenge of resolving the human–Neandertal interbreeding issue stems from the fact that so many fossil samples—of both early humans and more archaic humans—are contaminated with the DNA of the contemporary humans who have handled them. So even if a Neandertal sample contained a “real” (or endogenous) DNA sequence resembling early humans—which would indicate intimacy between the two groups—it might be considered contaminated. When Pääbo and colleagues looked for modern DNA, they found it in every sample they examined: in the Neandertal and early human fossils—and even in cave bear teeth. To circumvent this problem, they looked only for Neandertal mtDNA as evidence of interbreeding. Since it is easy to distinguish modern human mtDNA sequences from the four Neandertal mtDNA samples that have been sequenced so far, the researchers decided to determine whether Neandertal-like mtDNA could be found in other Neandertal fossils as well as in early human remains. Neandertal skull from La Chapelle aux Saints As these fossils are precious commodities, Pbo's group applied a technique developed in their lab that uses amino acid content as a measure of extractable endogenous DNA and requires removing just 10 mg of bone from a specimen rather than much larger pieces of bone. Of 24 Neandertal and 40 early modern human fossils analyzed, they found four Neandertal and five early human specimens that passed the amino acid test. These fossils included samples classified as “transitional” between the two groups and represented a wide distribution across Europe, where the two groups would likely have encountered one another. When they analyzed these samples for Neandertal mtDNA, they found mtDNA sequences that are absent in contemporary human mtDNA genes but quite similar to those found in the four previously sequenced Neandertals. They found no Neandertal-like mtDNA in the early human samples. While the authors explain that it's impossible to definitively conclude that no genetic flow occurred between early humans and Neandertals given the limited number of early human fossils available, they point out that even fossil samples considered as anatomically transitional between modern humans and Neandertals failed to show evidence of mtDNA exchange. Thus, Pääbo and colleagues conclude, while it's possible that Neandertals made a small contribution to the genetic makeup of contemporary humans, the evidence cannot support the possibility of a large contribution.

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PLoS Biol. 2004 Mar 16; 2(3):e80

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PLoS Biol

10.1371/journal.pbio.0020080

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020082SynopsisCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyEukaryotesSaccharomycesmRNA Targets of RNA-Binding Proteins Suggest an Extensive System for Post-Transcriptional Regulation Synopsis3 2004 16 3 2004 16 3 2004 2 3 e82Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Extensive Association of Functionally and Cytotopically Related mRNAs with Puf Family RNA-Binding Proteins in Yeast ==== Body The single-celled Saccharomyces cerevisiae, commonly known as baker's yeast, measures just 2 microns—it takes about 4 billion to fill a teaspoon. But as a eukaryote (its cells have nuclei), its genes function in much the same way a human's do. For a gene to function, its DNA sequence must first be transcribed into RNA (called messenger RNA, or mRNA), whose sequence can then be translated into a specific string of amino acids to form the unique protein that the gene encodes. The population of mRNA transcripts in each cell (its “transcriptome”) is dynamic—the genome uses its vocabulary of genes to write an ever-evolving script for the cell as its life unfolds and its environment changes. By binding to specific sequences of DNA, proteins called transcription factors process signals from the cell's sensory and information-processing systems to control which genes are transcribed in each cell, under what conditions, and at what rate. While the steps and regulatory programs that govern gene expression at this level are reasonably well known, much less is known about the orchestration of the later steps in the gene expression program—where in the cell each mRNA molecule goes when it leaves the nucleus, at what rate and under what conditions it is translated into protein, and how long it survives. Cluster of RNA targets for Puf proteins RNA-binding proteins (RBPs) have been implicated in diverse aspects of post-transcriptional gene regulation. Hundreds of RBPs are encoded in the eukaryotic genome, but because few have been studied in detail and few of their mRNA targets are known, the nature and extent of an RBP-mediated post-transcriptional program has been obscure. Now a systemic analysis of a specific family of RBPs and their mRNA targets in yeast by André Gerber, Daniel Herschlag, and Patrick Brown, of Stanford University, suggests that such a program may exert detailed control over the life history of every mRNA. By selectively binding and regulating specific classes of mRNAs, RBPs may provide a mechanism to coordinate the collective fate of these transcripts and serve as an integral part of the global transcriptome. Gerber, Herschlag, and Brown focused on the binding targets of a family of RBPs called Pumilio-Fbf (Puf) proteins, which are defined by the presence and configuration of an amino acid domain that mediates RNA-binding. Little is known about the physiological function of the five yeast Puf proteins the researchers studied here (called Puf1p-Puf5p). After using “affinity tags” to snag each of the five Puf proteins from yeast cells, together with their bound mRNA targets, the researchers identified the associated mRNAs with microarray analysis. They found more than 700 mRNAs bound by at least one Puf protein, with each Puf RBP targeting a distinct group of mRNAs. The group of mRNAs associated with each Puf protein turned out to encode proteins with strikingly similar functions and locations in the cell. Many of the mRNA sets encode proteins that reside in the same cellular location, are part of the same protein complexes, or act in the same signaling pathway. Some Puf proteins target mRNAs that encode membrane proteins while others preferentially bind to mRNAs that encode proteins involved in cell division. The most pronounced bias occurs with Puf3p, which overwhelmingly binds mRNAs that encode proteins destined for the mitochondria, the cell's power generators. This selective tagging of functionally related mRNAs by specific RBPs suggests a mechanism for coordinated global control of gene expression at the post-transcriptional level. Just as transcription factors regulate transcription by binding to specific DNA sequences, RBPs may mediate regulation of the subcellular localization, translation, and degradation of the set of specific mRNAs they target. Noting the striking themes in the subcellular localization of the proteins encoded by the mRNAs bound by each Puf protein, Gerber, Herschlag, and Brown propose that RBPs may play important roles in the subcellular localization and efficient assembly of protein complexes and functional systems by ensuring that the location in the cell at which mRNAs are translated “is not left to chance.” Since the number of RBPs encoded in eukaryotic genomes approaches that of transcription factors, the regulatory program that controls the post-transcriptional fate of mRNAs—their localization, translation, and survival—may prove to be nearly as diverse and complex as the regulation of transcription itself.

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PMC368175

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2021-01-05 08:26:25

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PLoS Biol. 2004 Mar 16; 2(3):e82

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PLoS Biol

10.1371/journal.pbio.0020082

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020083SynopsisCell BiologyDevelopmentMus (Mouse)Tracking Blood-Forming Stem Cells through Development Synopsis3 2004 16 3 2004 16 3 2004 2 3 e83Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Circulation and Chemotaxis of Fetal Hematopoietic Stem Cells ==== Body Of the 200-plus different types of cells that form the mammalian body, most have a finite life span. Like nearly everything in biology, there are exceptions—neurons and muscle cells, for example, can last a lifetime—but the vast majority of cells eventually wear out and must be replaced. Among the most short-lived cells, blood cells are generated continuously, mainly in the bone marrow of an adult, recharging the bloodstream as their depleted predecessors are efficiently dispatched and removed from circulation every 120 days. Some 2.5 million new red blood cells are generated every second from a small pool of stem cells. Fluorescence activated cell sorting is used to study the early development of hematopoietic stem cells Blood cell development, called hematopoiesis, passes through discrete stages in specific tissues in the developing embryo before converging in the bone marrow, where it continues throughout adulthood. Some researchers have proposed that hematopoietic stem cells (HSC) flood the bloodstream during short, precise intervals to build the developing hematopoietic system (which includes the liver, bone marrow, spleen, tonsils, and lymph nodes). Presenting an alternative model for HSC migration, Julie Christensen and her colleagues in Irving Weissman's lab at Stanford University report that HSC in mice gradually leave the fetal liver to colonize the developing spleen and bone marrow as the organs acquire the means to support them. In mouse embryos, HSC precursors develop first in the yolk sac and a region called the aorta-gonad-mesonephros (AGM), then they migrate to the liver, and later to the spleen, before finally settling into the bone marrow just before birth. It was thought that this migration occurs in distinct waves of HSC production because HSC numbers decrease in one region just before increasing in newly forming hematopoietic sites. Analyzing the concentration and activity of HSC, Christensen et al. found the cells in the blood at low but fairly constant levels during much of late fetal development, when they migrate from the liver to the spleen and bone marrow. Although the HSC population decreases in the liver at 15.5 days after conception, the authors propose that this drop occurs primarily because the HSC have differentiated into mature blood cells, not because they've exited the liver en masse to help build the spleen and bone marrow. On the other hand, the slight decrease in circulating HSC, which also occurs around this time, may be attributed to their recruitment from the bloodstream to these developing tissues. Christensen et al. also examined the impact of intercellular signaling proteins called chemokines, which help regulate fundamental developmental processes, on HSC migration. To effectively “seed” developing tissues, HSC must first be recruited from the blood, guided to the appropriate nascent tissue, then corralled and sustained. The chemokine SDF-1 attracts and retains HSC in the bone marrow but was thought to have a lesser effect on fetal liver HSC. Christensen et al. demonstrate not only that liver HSC migrate in response to this chemokine, but that their migratory response increases dramatically when both SDF-1 and a signaling protein called steel factor (SLF) are present. While adult marrow HSC respond to SDF-1, they do not respond to SLF alone and do not show improved migration in the presence of both SLF and SDF-1. Bone marrow transplants have become increasingly common for a number of hematological disorders, including leukemia and aplastic anemia. Since hematopoiesis occurs primarily in the bone marrow in both mice and humans after birth, these findings offer valuable insights into the migratory behavior of these stem cells and suggest how HSC migration might be applied to bone marrow transplants and other clinical therapies.

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2021-01-05 08:21:08

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PLoS Biol. 2004 Mar 16; 2(3):e83

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PLoS Biol

10.1371/journal.pbio.0020083

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020084SynopsisBioinformatics/Computational BiologyEvolutionGenetics/Genomics/Gene TherapyCaenorhabditisSaccharomycesConserved Genes Preferentially Duplicated in Evolution Synopsis3 2004 16 3 2004 16 3 2004 2 3 e84Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Preferential Duplication of Conserved Proteins in Eukaryotic Genomes ==== Body Over the course of evolution, some organisms have gained many genes and become increasingly complex whereas other, simpler, organisms have survived with comparatively fewer genes. (Compare, for example, the 30,000 genes of humans to the 5,500 of brewer's yeast.) But where do these “new” genes come from? Evolutionary biologists have long known that duplication of existing genes is an important source of genetic novelty—it is easier to copy and modify an existing gene than to create a completely new one from scratch. Because gene duplication makes such a major contribution to evolution, researchers have attempted to understand the mechanisms of gene duplication, how genes evolve once they become duplicated, and what functional effect gene duplications have for the organism. Recent genomic studies, for example, appear to show that most duplicated genes go through a period of accelerated evolution and also that the presence of duplicated genes adds robustness to the functioning of genomes. In research published in this issue, however, Jerel Davis and Dmitri Petrov look at gene duplication from a different perspective. Rather than asking how genes are duplicated, they asked which genes tend to be “good” at duplicating over the course of evolution. The answer is important for our understanding of the forces underlying gene duplication and will also help us understand why genomes contain duplicates of some genes and not others. The authors began by identifying duplicated and nonduplicated gene pairs in the yeast Saccharomyces cerevisiae and the worm Caenorhabditis elegans, two model organisms whose genomes have been sequenced. They then looked for the corresponding genes in two distantly related species, the fruitfly and the mosquito, in order to obtain an independent measure of evolutionary rate. This independent measure is vital because of the likelihood that gene duplication itself influences the rate of evolution. After obtaining these rates, the researchers compared the evolutionary rates of duplicated and nonduplicated genes. Stated simply, the authors found that slowly evolving (that is, more conserved) genes are more successful at generating duplicates than faster evolving genes. This is no recent trend—more conserved genes have been better at generating duplicates of themselves consistently over hundreds of million of years. Phylogenetic studies show that slowly evolving genes are more likely to be duplicated than faster evolving genes This research challenges the assumption that genes are duplicated in an unbiased manner. In addition, it provides the essential background for other genomic studies of gene duplication. For example, the acceleration of protein evolution upon duplication is likely to be even more dramatic considering that it is the slowly evolving genes that duplicate preferentially. These findings also open up new questions in the study of gene duplication. The authors convincingly demonstrate the bias toward conserved genes in the process of duplication, but how and why does this happen? For a duplicated gene to be retained in a species, the duplicate must be fixed in the population and then must be preserved by natural selection. The preferential duplication of slowly evolving genes might come from a bias in either of these steps, and the authors outline several models for why this might be the case. Further analysis may enable researchers to test these and other models for gene duplication—especially as more sequence data become available—and learn more about this potent phenomenon in genome evolution.

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PMC368177

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2021-01-05 08:21:09

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PLoS Biol. 2004 Mar 16; 2(3):e84

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PLoS Biol

10.1371/journal.pbio.0020084

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020085SynopsisBioinformatics/Computational BiologyBiotechnologyCell BiologyMolecular Biology/Structural BiologyPharmacology/Drug DiscoverySystems BiologySaccharomycesVertebratesA Holistic Approach to Evaluating Cellular Communication Pathways Synopsis3 2004 16 3 2004 16 3 2004 2 3 e85Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Interaction Networks in Yeast Define and Enumerate the Signaling Steps of the Vertebrate Aryl Hydrocarbon Receptor ==== Body To function properly, cells must keep constant tabs on the environmental conditions around them, such as the presence of growth hormones in the blood or the proximity of neighboring cells. These external cues are relayed into the cell through a cascade of chemical and physical reactions referred to as signal transduction. Signal transduction pathways inform and regulate almost all activity within the cell, from protein production to cell division. Understanding these processes is fundamental to biology, but the sheer number of molecules and interactions in some pathways makes thorough documentation difficult. Taking a holistic approach that combines both computational models and experimental manipulations, scientists have described the web of interactions involved in the aryl hydrocarbon receptor (AHR) signal transduction pathway. AHR belongs to the Per–Arnt–Sim (PAS) superfamily of sensor molecules that regulate functions like development, the sleep-wake cycle, and cellular reaction to oxygen deprivation. Unlike many receptors that are embedded in the cell membrane, AHR floats freely in the main body of the cell, called the cytosol. There it waits for a stimulus or ligand, such as a dioxin molecule, to enter the cell and bind to it. Once bound, AHR undergoes a host of changes, glomming on to additional molecules before it enters the cell nucleus and acts as a transcription factor, initiating the production of enzymes to digest foreign, or xenobiotic, compounds. The AHR pathway is a curiosity; though found in all vertebrates, the natural, or endogenous, ligand remains unknown. Without this knowledge, researchers are limited in the kind of experiments they can perform to evaluate the pathway. Protein-interactive-network for AHR signaling Christopher Bradfield and colleagues used yeast as a model system to elucidate the steps involved in this pathway, which regulates vertebrate cell response to pollutants like dioxins. To first assess the molecules involved in the AHR pathway, the team used 4,507 yeast “deletion” strains, each strain missing one gene from its genome. They then inserted the AHR gene into the strains using small rings of movable DNA called plasmids. Though yeast does not naturally possess AHR, it is an ideal genetic model for studying signaling pathways due to its quick generation time, small, well-characterized genome, and similarity to vertebrate systems. Bradfield's team exposed each strain to a receptor stimulus or agonist and screened them for AHR response. If a deletion strain showed significantly reduced activity, they concluded that the missing gene was a key component to the signal pathway. The researchers identified 54 genes that had a significant influence on AHR response. Only two of these genes, termed modifiers, had been previously identified. Signaling pathways usually boil down to a series of discrete steps. To identify steps of the AHR pathway, the researchers constructed a spider web-like map called a "protein interaction network," or PIN, based on previously known interactions between the proteins encoded by the 54 modifier genes. The resulting map revealed groups of highly connected, related modifiers, which the authors proposed to be steps in the pathway. Though other studies have used the newly developed PIN strategy to investigate cellular processes, Bradfield's team also annotated their PIN through a series of experiments both to support the identity of and to better understand the protein groups, referred to as functional modules. With tests based on discrete receptor signaling events, known active structural regions, reaction to different types and concentrations of agonists, and functional location within the cell, Bradfield's team organized the functional modules into five steps. One group of modifiers is involved in AHR folding, the conformational change that occurs when the receptor binds to a toxin. With the help of other modifiers, the new AHR complex is then translocated into the cell nucleus. Once in the nucleus, a series of modifiers assist the AHR in its role as a transcription factor. The researchers also identified a step in the pathway that controls production of AHR itself and another unknown "step" that takes place inside the nucleus. As AHR is thought to be a prototype PAS receptor, understanding the steps in this pathway will likely guide future research on the entire family, allowing scientists to study in detail individual steps in these complex pathways. The highly integrated method reported here could also be used to study most other mammalian signaling pathways, giving scientists a new tool as they attempt to understand how cells respond to their changing environment.

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PMC368178

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2021-01-05 08:21:09

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PLoS Biol. 2004 Mar 16; 2(3):e85

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PLoS Biol

10.1371/journal.pbio.0020085

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020087SynopsisBiophysicsNeuroscienceHomo (Human)A Mechanism for Amphetamine-Induced Dopamine Overload Synopsis3 2004 16 3 2004 16 3 2004 2 3 e87Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. N-Terminal Phosphorylation of the Dopamine Transporter Is Required for Amphetamine-Induced Efflux xx ==== Body The notion of a neurally encoded “reward system” that reinforces pleasure-seeking behaviors first emerged fifty years ago. Psychologists James Olds and Peter Milner discovered this phenomenon when their “lack of aim” landed an electrode outside their target while studying the behavioral responses of rats given electrical stimulation to a particular brain region. It was known that stimulation of certain brain regions would induce an animal to avoid the behavior that produced the stimulus. But in the rat with the “misplaced” electrode, stimulation of this new region caused the rat to repeat the behavior that caused the stimulus. Stimulation of certain brain regions provides a very strong incentive to restimulate, creating a feedback loop that reinforces both the behavior and the neural response to it. When gentle shocks were delivered to the rat hypothalamus, for example, the animals would “self-stimulate” 2,000 times per hour by pushing a lever. The neurotransmitter dopamine, it was later discovered, plays an important role in the brain's reward system—and in laying the biochemical foundation of drug addiction. Measuring changes in dopamine transport Essential for normal central nervous system function, dopamine signaling mediates physiological functions as diverse as movement and lactation. The dopamine transporter (DAT) is involved in terminating dopamine signaling by removing the dopamine chemical messenger molecules from nerve synapses and returning them into the releasing neurons (a process called reuptake). DAT can also bind amphetamine, cocaine, and other psychostimulants, which inhibit dopamine reuptake, and, in the case of amphetamine, also stimulate the release of dopamine through DAT. It's thought that abnormal concentrations of dopamine in synapses initiate a series of events that cause the behavioral effects of these drugs. The biochemical steps underlying amphetamine-induced dopamine release, however, are not well characterized. Now, a team led by Jonathan Javitch and Aurelio Galli has identified a chemical modification of DAT that is essential for DAT-mediated dopamine release in the presence of amphetamine. Since this modification does not inhibit the ability of DAT to accumulate dopamine, it may suggest a molecular target for treating drug addiction. Embedded in the membrane of nerve cells, the dopamine transporter has a “tail,” called the N-terminal domain, that protrudes into the cell interior and consists of a stretch of about 60 amino acids. Many of these amino acids are potential sites of phosphorylation, a chemical reaction in which a phosphate group is added through the action of enzymes called kinases. Amphetamine has been shown to increase kinase activity and Margaret Gnegy, a coauthor of the current research article, showed previously that inhibiting protein kinase C activity blocks amphetamine's ability to release dopamine. Therefore, Javitch, Galli, and Gnegy hypothesized that N-terminal phosphorylation of DAT might play a critical role in the dopamine overload caused by amphetamine. The researchers found that amphetamine-induced dopamine release was reduced by 80% in cells expressing a mutant dopamine transporter in which the first 22 amino acids of the N-terminal domain had been removed (del-22). Surprisingly, this truncated transporter displayed normal dopamine uptake. In a full-length DAT, mutation of the five N-terminal serine amino acids to alanine amino acids, which prevented phosphorylation, produced an effect similar to removing the 22 amino acids. In contrast, replacing these five serine residues with aspartate residues to mimic phosphorylation led to normal dopamine release as well as normal dopamine uptake. These findings suggest that phosphorylation of one or more of these serine residues is necessary for amphetamine to flood the synapses with dopamine. While phosphorylation is a normal mechanism for regulating protein activity in a cell—and DAT is “significantly phosphorylated” under normal conditions—amphetamine could increase the level of DAT phosphorylation. Elucidating the mechanisms through which phosphorylation of DAT's N-terminus facilitates dopamine overload could lead to the development of drugs that block the “rewarding” effects of amphetamines and other addictive psychostimulants without interfering with normal dopamine clearance.

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PMC368179

CC BY

2021-01-05 08:21:08

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PLoS Biol. 2004 Mar 16; 2(3):e87

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PLoS Biol

10.1371/journal.pbio.0020087

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020088SynopsisNeurosciencePrimatesMicrostimulation of Neurons Distinguishes Neural Contribution to Perception Synopsis3 2004 16 3 2004 16 3 2004 2 3 e88Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Perceptual "Read-Out" of Conjoined Direction and Disparity Maps in Extrastriate Area MT ==== Body The brain is an overwhelmingly complex organ packed with billions of nerve cells, performing a myriad of different functions. To decipher the roles of individual neurons in processing sensation or actions, scientists can measure the neural activity of animals that are shown particular objects or perform simple tasks. In this way, neurons are categorized as having preferences, also known as selective responses. These techniques have been particularly helpful in determining, or mapping, preferences of visual areas in the cerebral cortex. For example, some neurons respond to the color of an object, while others respond to the direction that object is moving. What is less well understood, however, is how the brain integrates information from individual neurons for complex processes such as perception and behavior. That is, how does neural activity affect what we see and do? The neural contribution to mokeys' perception of motion Microstimulation, a technique that activates a cluster of nerve cells by zapping them with a weak electrical current, has helped make causal links between neurons and behavior. For instance, when neurons in an area of the visual cortex that are “tuned” to a particular direction of motion are microstimulated, the way monkeys perceive moving dots on a video screen changes. Microstimulation seems to change what they see. Similar work has also been done for neurons that respond to binocular disparity—the depth-of-field information you gain because each eye has a slightly different view of the world. But many neurons respond, or are tuned, to more than one dimension, leading scientists to wonder how information from these multidimensional neurons contributes to perception—especially when some of that information is irrelevant to a given task. As they report in this issue, Gregory DeAngelis and William Newsome find that neurons tuned to both direction and binocular disparity contribute little to monkeys' perception of motion. The researchers asked three rhesus monkeys to determine the direction a group of dots was moving on a TV screen—a task that can be done regardless of the perceived depth of the dots. The authors had already located two different types of neurons in each of the monkey's brains: sites tuned strongly to direction and multidimensional sites tuned to both direction and binocular disparity. They then determined each site's exact preference: the direction of motion and degree of binocular disparity (if present) that triggered maximum neural activity. The researchers then showed the monkeys several sets of video displays, some with the dots moving in the “preferred” direction and some not. The microstimulation acts somewhat like adding dots in the preferred direction—which confuses monkeys when the real dots are moving against preference and aids them in trials of preferred moving dots. If the behavioral effect of microstimulation—whether it be a help or a hindrance—was significant, it meant that the monkeys were monitoring the activated neurons to perform the task at hand; if there was no change, the stimulated neurons were not being recruited. DeAngelis and Newsome hypothesized that multidimensional neurons (which are also tuned to the irrelevant dimension of binocular disparity) might be ignored during pure motion perception tasks. For two of the three monkeys, this was true. Microstimulation of multidimensional sites had no effect on their behavior, compared to the significant effect of microstimulation of direction-only sites. But for the third monkey, called monkey R, microstimulation of both types of sites had significant effects on his performance. He didn't seem to be ignoring anything. The authors proposed that the monkeys could be using different neural strategies to complete the same task. This conclusion is supported by the fact that monkey R performed better on the task than the other monkeys; he appeared to be recruiting any neuron with applicable information, unlike the others, who seemed to rely on neurons tuned solely to direction of motion. Furthermore, for the few multidimensional sites that affected behavior, their contribution was tempered by how well the depth, or disparity, of the video matched the preference of the stimulated neurons. The results of this paper show that even if neurons carry information that can aid in perceptual decision making, they may not participate, depending on how they are tuned along other (irrelevant) stimulus dimensions. All directional neurons are not created equal—some are more useful than others for a particular task. Whether neurons that respond to a particular stimulus contribute to the task at hand depends on how closely that stimulus hews to the neurons' preference as well as on the subject's learned strategy for performing the task. This neural flexibility, the authors point out, suggests that the brain uses complex, variable strategies to respond to changing environmental stimuli. Techniques like microstimulation will be helpful in drawing the connections between neural activity and behavior.

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PMC368180

CC BY

2021-01-05 08:26:25

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PLoS Biol. 2004 Mar 16; 2(3):e88

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PLoS Biol

10.1371/journal.pbio.0020088

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020089CorrectionCorrections: The Roles of APC and Axin Derived from Experimental and Theoretical Analysis of the Wnt Pathway 3 2004 16 3 2004 16 3 2004 2 3 e89Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. The Roles of APC and Axin Derived from Experimental and Theoretical Analysis of the Wnt Pathway ==== Body In PLoS Biology, volume 1, issue 1: The Roles of APC and Axin Derived from Experimental and Theoretical Analysis of the Wnt Pathway Ethan Lee, Adrian Salic, Roland Krüger, Reinhart Heinrich, Marc W. Kirschner DOI: 10.1371/journal.pbio.0000010 Table 1: In the legend, the words fluxes and flux appeared without the fl. Table 3: In the legend, the word coefficients appeared without the fi. In the table, some numbers in the section “Binding, dissociation” were marked with a ± sign that should have been a ∓. Table 4: In the legend, the word coefficients appeared without the fi. Please see the corrected legends and table below. Table 1. Numeric Values of Input Quantities of the Model for the Reference State The data are grouped into concentrations of pathway components, dissociation constants of protein complexes, concentration ratios, fluxes and flux ratios, and characteristic times of selected processes. Experimental evidence for these data is discussed in the text. From these data, the following rates and rate constants are calculated: v 12 = 0.42 nM · min−1 (rate of β-catenin synthesis), v 14 = 8.2 · 10−5 · nM · min−1 (rate of axin synthesis), k 4 = 0.27 min−1, k 5 = 0.13 min−1, k 6 = 9.1 · 10−2 nM−1 · min−1, k −6 = 0.91 · nM−1 · min−1, k 9 = 210 min−1, k 10 = 210 min−1, k 11 · 0.42 min−1, k 13 = 2.6 · 10−4 min−1, k 15 = 0.17 · min−1. See Table S2, found at http://dx.doi.org/10.1371/journal.pbio.0000010.t002, for more precise numbers used in the calculations. Bold: Measured values, Italics: Estimated values. DOI: 10.1371/journal.pbio.0000010.t001 Table 3 Control Coefficients for the Total Concentrations of β-Catenin and Axin and Parameters Quantifying the Sensitivity and the Robustness of the Wnt/β-Catenin Pathway Table 4. Concentration Control Coefficients for the Total Concentrations of β-Catenin and Axin Relative to Changes in the Concentrations of Pathway Components The control coefficients were obtained by numerical determination of the response to a change of total concentrations by 1%. Coefficients are given for the reference state and for the standard stimulated state. DOI: 10.1371/journal.pbio.0000010.t004 The full text XML and HTML versions of the article have been corrected online. This correction note may be found at DOI: 10.1371/journal.pbio.0020089.

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PMC368181

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2021-01-05 08:26:26

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PLoS Biol. 2004 Mar 16; 2(3):e89

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PLoS Biol

10.1371/journal.pbio.0020089

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020086Research ArticleBiotechnologyCell BiologyInfectious DiseasesMolecular Biology/Structural BiologySaccharomycesDissection and Design of Yeast Prions Dissection and Design of Yeast PrionsOsherovich Lev Z lxoshe@itsa.ucsf.edu 1 2 Cox Brian S 2 Tuite Mick F 2 Weissman Jonathan S 1 1Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of CaliforniaSan Francisco, CaliforniaUnited States of America2Department of Biosciences, University of KentCanterburyUnited Kingdom4 2004 23 3 2004 23 3 2004 2 4 e8611 10 2003 21 1 2004 Copyright: © 2004 Osherovich et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Artificial Prions Created from Portable Control Elements Yeast Prions: Protein Aggregation Is Not Enough Many proteins can misfold into β-sheet-rich, self-seeding polymers (amyloids). Prions are exceptional among such aggregates in that they are also infectious. In fungi, prions are not pathogenic but rather act as epigenetic regulators of cell physiology, providing a powerful model for studying the mechanism of prion replication. We used prion-forming domains from two budding yeast proteins (Sup35p and New1p) to examine the requirements for prion formation and inheritance. In both proteins, a glutamine/asparagine-rich (Q/N-rich) tract mediates sequence-specific aggregation, while an adjacent motif, the oligopeptide repeat, is required for the replication and stable inheritance of these aggregates. Our findings help to explain why although Q/N-rich proteins are relatively common, few form heritable aggregates: prion inheritance requires both an aggregation sequence responsible for self-seeded growth and an element that permits chaperone-dependent replication of the aggregate. Using this knowledge, we have designed novel artificial prions by fusing the replication element of Sup35p to aggregation-prone sequences from other proteins, including pathogenically expanded polyglutamine. Artificial prions - infectious, misfolded proteins - can be created by fusing the replication element of one prion to aggregation sequences from another ==== Body Introduction The aggregation of misfolded proteins underlies a diverse range of human diseases, including sporadic amyloidoses such as Alzheimer's disease and hereditary neuropathies such as Huntington's disease (Dobson 1999). Prions are a special class of protein aggregates that replicate their conformation and spread infectiously (Prusiner 1998). After the discovery that prion aggregates are responsible for the mammalian transmissible spongiform encephalopathies, several epigenetically heritable traits in fungi were also found to depend on a prion mechanism (Wickner 1994; Uptain and Lindquist 2002; Osherovich and Weissman 2004). In Saccharomyces cerevisiae and Podospora anserina, prions are transmitted from cell to cell through mating and cell division, resulting in readily assayed phenotypes with a non-Mendelian pattern of inheritance (Liebman and Derkatch 1999). The yeast non-Mendelian factors [PSI+] (Cox 1965) and [URE3] (Lacroute 1971), which are prion forms of the translation termination factor Sup35p and the transcriptional activator Ure2p, respectively, have served as useful models for the formation and replication of heritable protein aggregates. Prion forms of Sup35p and Ure2p lead to defects in their respective biochemical activities (translation termination and nitrogen catabolite repression). Mutational analysis has shown the glutamine/asparagine-rich (Q/N-rich) amino-terminal (N) domains of these proteins to be critical for prion behavior (Ter-Avanesyan et al. 1993; Masison and Wickner 1995; Patino et al. 1996; Paushkin et al. 1996; DePace et al. 1998). In vitro, these Q/N-rich domains form self-seeding, β-sheet-rich amyloid fibrils similar to those associated with Alzheimer's and Huntington's diseases (Glover et al. 1997; King et al. 1997; Taylor et al. 1999). The autocatalytic aggregation of yeast prion proteins often shows a high specificity for like molecules; for example, Sup35p N domains from different yeast species form prion aggregates that preferentially interact with molecules of their own kind (Santoso et al. 2000; Chernoff et al. 2000; Kushnirov et al. 2000; Zadorskii et al. 2000; Nakayashiki et al. 2001). [PSI+] and [URE3] can be eliminated by transient growth in the presence of guanidine hydrochloride (GuHCl), which “cures” cells of prions by inhibiting Hsp104p, a molecular chaperone needed for prion replication (Chernoff et al. 1995; Jung et al. 2002; Ness et al. 2002). A surprisingly large number of proteins in S. cerevisiae and other eukaryotes have lengthy Q/N-rich tracts ostensibly similar to those found in the prion-forming domains of Sup35p and Ure2p (Michelitsch and Weissman 2000). From among these, we and another group identified two novel proteins, New1p and Rnq1p, with prion-forming domains resembling those of Sup35p and Ure2p (Santoso et al. 2000; Sondheimer and Lindquist 2000). When these Q/N-rich domains were fused to green fluorescent protein (GFP) and overexpressed, they formed visible aggregates resembling those of GFP-labeled Sup35p in [PSI+] cells. Fusion proteins in which these domains were introduced in place of the Sup35p prion domain could support distinct, self-specific prion states that recapitulated the translation termination defect associated with [PSI+]. Rnq1p was later shown to underlie a naturally occurring prion called [PIN+], which promotes the aggregation of Q/N-rich proteins such as Sup35p; overexpressed Sup35p forms aggregates and stimulates the appearance of [PSI+] only in [PIN+] strains (Derkatch et al. 1997; Derkatch et al. 2001). Aggregates of the New1p prion domain, whether resulting from overexpression or from a constitutive prion form (termed [NU+]), also promoted the aggregation of other Q/N-rich proteins, emulating the effect of [PIN+] (Osherovich and Weissman 2001). Many sequences with Q/N content as high as that of Sup35p and Ure2p, including human polyglutamine expansion disease proteins, form visible aggregates when overexpressed in yeast as GFP fusions (Krobitsch and Lindquist 2000; Osherovich and Weissman 2001; Meriin et al. 2002). However, only a limited number of Q/N-rich sequences are bone fide prion domains capable of propagating these aggregates over multiple cell generations even when expressed at low levels (J. Hood and J.S.W, unpublished data). To understand what distinguishes generic Q/N-rich aggregates from heritable prions, we conducted a detailed dissection of the prion-forming regions of Sup35p and New1p. We found that the prion properties of Sup35p and New1p require the presence of two independent and portable sequence elements within their prion domains. One element mediates the growth of prion aggregates by incorporation of soluble monomers. The second promotes the inheritance of aggregates, generating new heritable “seeds” which can be partitioned between mother and daughter cells during cell division. Results Distinct Regions of the New1p Prion Domain Mediate Prion Growth and Division Sup35p can alternate between a biochemically active, soluble form ([psi–]) and an aggregated prion state ([PSI+]) with diminished translation termination activity, which can be monitored by nonsense suppression of the mutant ade1–14 allele (Liebman and Derkatch 1999). Whereas [psi–] strains form red colonies on yeast extract-peptone-dextrose (YEPD) medium and cannot grow in the absence of adenine, [PSI+] strains suppress the premature stop codon in ade1-14, and thus appear pink or white on YEPD medium and grow on adenine-free medium (a phenotype termed adenine prototrophy, Ade+). The N or prion domain of Sup35p (residues 1-112) is required for [PSI+] formation but is dispensable for the translation termination activity of the carboxy-terminal C domain (Ter-Avanesyan et al. 1993). The charged middle domain (M) is not required for prion behavior, but modulates the efficiency of chaperone-dependent prion transmission (Liu et al. 2002; L.Z.O., unpublished data) (Figure 1). Two distinct regions in the N domain have previously been implicated in Sup35p aggregation: a Q/N-rich tract (residues 1–39) (DePace et al. 1998) and an oligopeptide repeat (residues 40–112) that consists of five and a half degenerate repeats of the consensus sequence P/QQGGYQQ/SYN (Liu and Lindquist 1999; Parham et al. 2001; Crist et al. 2003). Figure 1 Schematic Diagram of Sup35p and New1p Prion domains of both proteins are enlarged in the center, highlighting the Q/N-rich tract of Sup35p (blue), the NYN tripeptide repeat of New1p (purple), and the oligopeptide repeat sequences (orange) found in both proteins. The sequence of the NEW1 oligopetide repeat (residues 50–70) is QQQRNWKQGGNYQQGGYQSYN, while that of the adjacent tripeptide repeat region (residues 71–100) is SNYNNYNNYNNYNNYNNYNNYNKYNGQGYQ. We had earlier identified New1p as an uncharacterized protein with a Sup35p-like N-terminal domain; when fused to the M and C domains of Sup35p, the first 153 residues of New1p (New11–153) supported a [PSI+]-like prion state termed [NU+] (Santoso et al. 2000). Sup35p and New1p have regions of clear similarity beyond their high Q/N content (Figure 1). The prion domains of both have Q/N-rich tracts and oligopeptide repeat regions, although their order is reversed. The C-terminal domains of New1p and Sup35p also appear to be related, based on modest homology and the similarity of the translation termination defects in sup35 (Song and Liebman 1985) and new1 mutants (L.Z.O., unpublished data). To understand the sequence requirements for the prion behavior of New1p, we constructed a series of truncated prion domains (Figure 2A) and examined their participation in several critical steps of the prion replication cycle. We first asked whether these truncated prion domains could form visible foci when fused to GFP (aggregation). Next, we examined whether such aggregates could convert New11–153 into a [NU+] prion state (induction). Finally, we fused these constructs to the M and C domains of Sup35p (–M-C), introduced them in place of endogenous SUP35, and assessed whether these proteins could adopt stable prion states (maintenance). Figure 2 Dissection of the New1p Prion Domain Reveals Distinct Regions Responsible for Aggregation and Prion Inheritance (A) Indicated fragments of New1p (left) were expressed as GFP fusions (inducers) in a [nu–] [pin–] strain, examined by microscopy for GFP aggregation, then plated on SD-ade medium to assess induction of [NU+]. The symbol “+” indicates induction frequencies of at least 5%; the symbol “–” indicates no induction. Maintenance was assessed by the ability of an episomal maintainer version of the indicated fragment to support an Ade+ state after overexpression of New11–153-GFP (see Materials and Methods). The aggregation of New1-GFP fusions (second column) has been previously reported (Osherovich and Weissman 2001). (B) The NYN repeat of New1p induces [NU+] and [NU+]mini. New170–100-GFP was overexpressed in [nu–] and [nu–]mini strains ([pin–] and [PIN+] derivatives of each), along with vector only or New11–153-GFP controls. Averages of three independent trials, representing 600–2000 colonies, are shown for most induction experiments; inductions using New170–100-GFP were conducted twice. Error bars show minimal and maximal observed induction efficiencies. (C) Reversibility of [NU+]mini. The [pin–] Ade+ convertants obtained in (B) were colony purified on SD-ade medium and confirmed to have lost the inducer plasmid. A stable [NU+]mini isolate is shown before and after induction, as well as after GuHCl treatment, along with [nu–] and [NU+] reference strains. We found that distinct regions within the New1p prion domain are necessary for the induction and maintenance of [NU+] (Figure 2A). The asparagine-tyrosine-asparagine (NYN) repeat (residues 70–100), which we had earlier shown to be sufficient for aggregation (Osherovich and Weissman 2001), also proved sufficient for induction of [NU+]. As with the full-length New1p prion domain, overexpression of the NYN repeat efficiently stimulated the appearance of Ade+ in [nu–] cells (Figure 2B, left). However, stable prion maintenance required both the NYN repeat and the adjacent oligopeptide repeat. In a strain with this minimized New1p prion domain (residues 50–100), overexpression of the full prion domain or of the NYN repeat alone promoted the appearance of Ade+ colonies (Figure 2B, right). The resulting convertants remained Ade+ after loss of the inducer plasmid but reverted to Ade- after transient GuHCl treatment (Figure 2C). We conclude that the oligopeptide repeat and the NYN repeat of New1p together are sufficient to support a prion state, termed [NU+]mini, which recapitulates the characteristics of [NU+]. Dissection of the Sup35p Prion Domain In light of the similarity between New1p and Sup35p prion domains, we asked whether separate regions of Sup35p were involved in the induction and maintenance of [PSI+] aggregates (Figure 3). We constructed a series of truncated Sup35p N domains and analyzed their behavior in the aggregation, induction, and maintenance assays described above for [NU+]. Additionally, we examined the ability of truncated N domains to decorate preexisting Sup35p aggregates in [PSI+] strains. Figure 3 Dissection of the Sup35p Prion Domain At top are schematic diagrams of these experiments; positive outcomes are shown below the arrows. In some cases, similar experiments have been reported by Parham et al. (2001) (indicated by “a”) and are repeated here as controls. Aggregation: Plasmid-borne M-GFP fusions of the indicated Sup35p N domain fragments (green) were overexpressed in a [psi–] [PIN+] strain and examined for fluorescent focus formation. The symbol “+” indicates that 10% or more of cells displayed aggregates. Sup351–57-M-GFP displayed a lower frequency of aggregation (approximately 1%). Induction: Strains from the aggregation experiment were plated onto SD-ade medium and scored for growth to test whether aggregates of truncated protein (green) convert chromosomally encoded protein (blue) to [PSI+]. The symbol “+” indicates approximately 5–10% conversion frequency. Consistent with the aggregation experiment, Sup351–57-M-GFP displayed a lower frequency of [PSI+] induction (approximately 1%). Decoration: Indicated proteins were expressed as –M-GFP fusions in [PSI+] [PIN+] cells, which were examined to determine whether GFP-labeled truncations (green) decorate preexisting aggregates of full-length Sup35p (blue). Curiously, Sup351–49-M-GFP in [PSI+] cells formed abnormally large “ribbon” aggregates of the kind typically observed during de novo [PSI+] induction; furthermore, approximately 10% of the cells reverted to [psi–] (indicated by “*”). Thus, this truncation was a potent dominant PNM mutant. Maintenance: A SUP35-deleted [PSI+] [PIN+] bearing wild-type SUP35 maintainer (blue) was transformed with maintainer plasmids containing the indicated truncation (purple). The wild-type maintainer was lost by counterselection, and the resulting strain was tested for [PSI+] by color and growth on SD-ade medium. The Sup351–93 mutant displayed an intermediate pink color and grew poorly on SD-ade medium, as previously reported (Parham et al. 2001). Note: King (2001) reports that Sup351–61-GFP fusion could decorate [PSI+] aggregates in certain strains and could induce [PSI+] de novo when overexpressed. We found that the Q/N-rich tract and a small portion of the adjacent oligopeptide repeat are responsible for Sup35p aggregation and de novo [PSI+] induction. Deletions within the Q/N-rich tract or of oligopeptide repeat 1 abolished these properties, whereas a construct containing only the Q/N-rich region and the first two oligopeptide repeats (residues 1–64) aggregated and induced [PSI+] at levels comparable to the full prion domain, in agreement with King (2001). A construct (residues 1–57) with a partial deletion of oligopeptide repeat 2 could still aggregate and induce [PSI+], albeit with decreased efficiency. Although a construct lacking oligopeptide repeat 2 entirely (residues 1–49) did not induce [PSI+] de novo, this GFP fusion could nonetheless decorate preexisting Sup35p aggregates. Thus, while oligopeptide repeat 2 contributes to the aggregation of Sup35p, the primary determinants of prion induction reside in the amino-terminal Q/N-rich region and oligopeptide repeat 1. In contrast, the rest of the oligopeptide repeat region is needed for stable inheritance of [PSI+] aggregates. Constructs that did not form fluorescent foci could not retain [PSI+], suggesting that aggregation is a prerequisite for prion maintenance. However, aggregation is not sufficient for prion inheritance, as Sup35p constructs with deletions spanning oligopeptide repeats 3–5 could not support a prion state despite their ability to form aggregates and efficiently induce [PSI+]. Only the sixth (incomplete) oligopeptide repeat proved dispensable for [PSI+] maintenance, consistent with an earlier report (Parham et al. 2001). The PNM2-1 Mutation in Oligopeptide Repeat 2 Specifically Compromises the Inheritance of [PSI+] Our deletion analysis suggested that oligopeptide repeat 2 participated in both the formation and inheritance of Sup35p aggregates. We made use of a point mutation within oligopeptide repeat 2 known as PNM2-1 (G58D) to distinguish between these two functions. PNM2-1 (PSI No More) shows strong interference with [PSI+] in certain strain backgrounds through a poorly understood mechanism (McCready et al. 1977; Doel et al. 1994; Kochneva-Pervukhova et al. 1998; Derkatch et al. 1999). Using both in vivo and in vitro assays, we established that PNM2-1 does not have a defect in aggregation or [PSI+] induction. Earlier work indicated that PNM2-1 is capable of seeding [PSI+] in vivo (Kochneva-Pervukhova et al. 1998; Derkatch et al. 1999; King 2001). Consistent with these reports, we found that overexpression of a PNM2-1-GFP fusion in [psi–] [PIN+] cells with a wild-type SUP35 locus led to both focus formation and [PSI+] induction (Figure 4A). A previous study of Sup35p polymerization in extracts had suggested that PNM2-1 might interfere with [PSI+] through a defect in seeding (Kochneva-Pervukhova et al. 1998). We tested this by examining the rate of seeded polymerization of recombinant PNM2-1 protein. Like wild-type Sup35p, purified PNM2-1 spontaneously formed amyloid fibrils in vitro; this was accelerated by the addition of preformed Sup35p polymer seeds (data not shown). We measured the initial rates of polymerization of wild-type and PNM2-1 protein seeded by preformed wild-type polymers (Figure 4B) and by PNM2-1 polymers (Figure 4C) using a thioflavin-T–binding assay. We observed that wild-type and PNM2-1 monomers were seeded by wild-type polymers with similar kinetics; likewise, PNM2-1 polymers seeded both wild-type and PNM2-1 monomers equivalently. Thus, the PNM2-1 mutation does not affect polymerization or seeding. Figure 4 PNM2–1 (G58D) Prevents Inheritance But Not Aggregation of Sup35p Prions (A) PNM2-1 protein can seed [PSI+]. A Sup35p inducer containing the PNM2-1 (G58D) mutation was overexpressed in [psi–] [PIN+] cells; shown are cells (inset) with representative fluorescent foci, which were the same in frequency and appearance as cells with a wild-type inducer. Cells overexpressing inducer versions of wild-type Sup35p (SUP), an aggregation-defective N-terminal truncation (Δ1–38), and PNM2-1 were plated and scored for Ade+. Approximately 1000 colonies were counted. (B) PNM2-1 protein polymerization is similar to that of wild-type protein. (C) Preformed PNM2-1 polymers seed wild-type and PNM2-1 monomers with comparable efficiency. Endpoint PNM2-1 polymers were used to seed fresh reactions. (D) PNM2-1 displays a partially dominant, incompletely penetrant defect in [PSI+] maintenance. [psi–] (1) and [PSI+] (2) SUP35::TRP1 pSUP35 controls are shown. [PSI+] [PIN+] SUP35::TRP1 pSUP35 was transformed with a second maintainer expressing PNM2-1 (3). The wild-type maintainer (pSUP35) was then lost through counterselection (4). Red sectors from (4) were isolated, retransformed with the wild-type maintainer, and allowed to lose the PNM2-1 maintainer (5). (E) Mitotic instability of [PSI+] in the PNM2-1 strain. A pink (Ade+) [PSI+] [PIN+] PNM2-1 isolate was grown to log phase in SD-ade liquid then shifted into nonselective (YEPD) medium. At indicated time points, aliquots were plated onto SD-ade and YEPD media to determine the fraction of [PSI+] cells (minimum of 200 colonies counted per time point). Whereas a wild-type control remained [PSI+] through the experiment, the PNM2-1 strain rapidly lost [PSI+] during logarithmic growth; during stationary phase (18 h and beyond), the percentage of [PSI+] PNM2-1 strains remained unchanged (approximately 5%). (F) Propagon count of PNM2-1 vs. wild-type [PSI+] strains. The majority of PNM2-1 cells had no [PSI+] propagons (i.e., were [psi–]). In both strains, a small number of “jackpot” cells contained over 200 propagons; see Cox et al. (2003). Instead, the PNM2-1 strain shows a marked defect in the inheritance of [PSI+]. When the wild-type SUP35 gene of a [PSI+] strain was replaced with PNM2-1, the strain retained the prion on synthetic defined (SD) yeast medium that selected for [PSI+] (SD-ade medium) but reverted to [psi–] at a high frequency in nonselective YEPD medium, resulting in sectored colonies (Figure 4D). We measured the rate of [PSI+] loss in a PNM2-1 strain by growing it in YEPD medium and, at various time points, plating aliquots of the culture onto SD-ade medium to determine the fraction of cells that had retained [PSI+] (Figure 4E). A wild-type strain retained [PSI+] in all of the cells throughout the experiment. By contrast, in the PNM2-1 strain the fraction of [PSI+] cells decreased rapidly while the cells grew logarithmically, but remained at a constant level when the cells entered stationary phase. These findings indicate that PNM2-1 acts to eliminate [PSI+] in dividing cells, consistent with a defect in prion replication. We next used a recently described assay to measure the number of heritable prion seeds (propagons) in a PNM2-1 strain. Here, prion replication is inhibited by GuHCl treatment. As the cells divide, preexisting propagons are diluted but not destroyed. The number of propagons present in a colony arising from a single cell is then evaluated by removing the GuHCl prion replication block after a large number (10 or more) of cell divisions and counting the total number of [PSI+] cells in that colony (Cox et al. 2003). Whereas a wild-type strain had a median of 92 (n = 24) propagons per cell, the PNM2-1 strain had dramatically fewer: 41 of 50 cells had no [PSI+] propagons at all (i.e., were [psi–]), and among the remaining nine [PSI+] cells, the median propagon number was six (Figure 4F). Thus, although a PNM2-1 strain can harbor [PSI+] prions, a defect in propagon replication causes mitotic instability, demonstrating the importance of oligopeptide repeat 2 in prion replication or segregation. Design of Novel Prion Domains Our data suggested that the formation and inheritance of prions involve distinct regions of Sup35p and New1p prion domains. To assess the interchangeability of these prion domain components, we constructed a chimeric prion domain, termed F, in which the aggregation-determining NYN repeat of New1p was fused to the oligopeptide repeats of Sup35p (Figure 5A). While initially soluble and active, a fusion of F and the Sup35p M and C domains (F-M-C) could be converted into an aggregated state, termed [F+], after transient overexpression of F-M-GFP. As with [NU+], [F+] induction did not require [PIN+] (data not shown). [F+] could be eliminated by GuHCl treatment (Figure 5B) and was inherited in a dominant, non-Mendelian manner (Figure 5C). As with Sup35p in a [PSI+] strain, F-M-C protein in [F+] but not in [f –] extracts sedimented entirely to the pellet fraction following high-speed centrifugation (Figure 5D). Thus, [F+] results from a prion state of F-M-C. Figure 5 F, A New1p–Sup35p Chimera, Shows Prion Characteristics of New1p (A) Schematic diagram illustrating the construction of chimera F. (B) Chimera F forms a prion, [F+]. The SUP35 gene in a [psi–] [pin–] strain was replaced with the F-M-C fusion; after transient overexpression of F-M-GFP, approximately 10% of these cells converted from an Ade- ([f –]) to an Ade+ ([F+]) state. Shown are examples of[f –] and [F+] strains, before and after GuHCl treatment, along with [psi–] and [PSI+] controls. (C) Non-Mendelian inheritance of [F+]. A diploid made by mating a [F+] MATa strain against an [f –] MATα displayed a [F+] phenotype and, when sporulated, produced four [F+] meiotic progeny. All 11 tetrads examined showed this 4:0 pattern of inheritance. (D) Sedimentation analysis of F-M-C. Extracts of [f –] and [F+] strains, along with [psi–] and [PSI+] controls, were subjected to 50K × g ultracentrifugation for 15 min. Total, supernatant, and pellet fractions were separated by SDS-PAGE, transferred to nitrocellulose, and probed with anti-SUP35NM serum. As with Sup35p, the prion form of F-M-C sediments primarily to the pellet but remains in the supernatant in [f –]. (E) F-M-GFP overexpression induces [NU+] but not [PSI+]. Indicated inducers and maintainers were used in an induction experiment. The symbol “+” indicates approximately 5–10% conversion to Ade+. F induced [NU+] at a comparable efficiency to New11–153; although New11–153 overexpression promoted the appearance of Ade+ colonies in the F-M-C strain, these were fewer in number (less than 5%) and reverted to Ade- after restreaking. (F) [F+] and [NU+] prion proteins interact with each other but not with [PSI+]. Episomal “second maintainers” were introduced into the indicated strains, along with an empty vector control. Antisuppression (red) indicates that the second maintainer is soluble, while white/pink indicates coaggregation of the endogenous and episomal maintainers. We next explored the specificity of [F+] prion seeding. Overexpression of the Sup35p prion domain did not induce [F+]; conversely, F-M-GFP overexpression did not induce [PSI+] (Figure 5E). However, F-M-GFP readily induced [NU+], indicating that mismatched sequences outside of the aggregating region did not prevent cross-interactions between heterologous proteins. Interestingly, overexpression of New11–53-GFP induced Ade+ colonies in the [f –] strain, but this adenine prototrophy proved unstable. We also examined the ability of preexisting prion aggregates to recruit different prion-forming proteins using an antisuppression assay (Santoso et al. 2000) (Figure 5F). [PSI+], [F+], and [NU+] strains were transformed with Sup35p–, F-M-C– or New11–153-M-C–encoding plasmids; the color of the resulting colonies indicates whether the second maintainer protein is soluble (red) or aggregates as a result of the resident prion (pink/white). Consistent with the induction data, F-M-C and New11–153-M-C were not incorporated into [PSI+] aggregates; likewise, Sup35p did not interact with [F+] or [NU+] aggregates. However, [F+] prions recruited New11–153-M-C and, to a lesser extent, [NU+] recruited F-M-C. Thus, F and New1p prion domains can cross-interact during de novo induction and at normal levels of expression, indicating that the NYN repeat is sufficient to specify homotypic interaction between two otherwise distinct prion domains. Can a simple aggregation-prone sequence such as polyglu-tamine (Zoghbi and Orr 2000) be turned into a heritable prion by fusion to an oligopeptide repeat? We designed artificial prion domains containing short (Q22) and pathogenically expanded (Q62) polyglutamine tracts, either alone or adjacent to the Sup35p oligopeptide repeat (Figure 6A). These domains were fused to –M-GFP and –M-C to create polyglutamine inducers and maintainers, respectively. Q22 inducers did not aggregate upon overexpression, but Q62 inducers (with and without oligopeptide repeats) formed visible foci in [psi–] [PIN+] cells (Figure 6B). Transient overexpression of Q62 inducers had no effect on the Q22 maintainer with the oligopeptide repeat or on the Q62 maintainer lacking the oligopeptide repeat. However, the Q62 maintainer with an oligopeptide repeat supported prion inheritance, converting to a stable Ade+ state following overexpression of the cognate inducer (Figure 6C). Several tests confirmed the prion nature of this state, termed [Q+]. Like [PSI+], [Q+] did not require the presence of the inducer plasmid after transient overexpression, was sensitive to GuHCl treatment (Figure 6D), and displayed a dominant, non-Mendelian pattern of inheritance (Figure 6E). We further tested the specificity of the [Q+] state by introducing a plasmid encoding a noncognate second maintainer into a [Q+] strain (Figure 6F). The Q62 maintainer failed to be incorporated into [PSI+] aggregates, causing antisuppression (red); conversely, Sup35p did not enter [Q+] aggregates. Figure 6 [Q+], a Prion Form of Pathogenically Expanded Polyglutamine (A) Schematic illustrating the construction of polyglutamine-derived prion domains. (Op) indicates the presence of the Sup35p oligopeptide repeats (residues 40–124). (B) Fluorescence micrographs of [psi–] [PIN+] strains expressing indicated polyglutamine inducers. (C) Polyglutamine-based prion inheritance. Strains with indicated inducers and maintainers were plated onto SD-ade and YEPD media to determine the fraction of Ade+ after a standard induction experiment. Interestingly, Q62 inducer forms aggregates but does not promote Ade+ in the Q62(Op) maintainer strain. Note that Q62(Op) shows a high rate of spontaneous appearance of Ade+. (D) GuHCl sensitivity of the [Q+] state. An Ade+ convertant obtained in (C) was restreaked to lose the inducer plasmid, then grown on GuHCl. Shown are plates before and after GuHCl treatment, along with [psi–] and [PSI+] controls. (E) Dominance and non-Mendelian inheritance of [Q+]. See Figure 5C. (F) [Q+] does not interact with Sup35p and vice versa. [Q+] and [PSI+] strains were transformed with indicated maintainers; mismatches between the maintainer and the chromosomally encoded allele result in antisuppression (red). Discussion A number of epigenetic traits in fungi result from the stable inheritance of self-propagating, infectious protein aggregrates (prions) (Uptain and Lindquist 2002). Prion inheritance requires three sequential events that must keep pace with cell division to preserve the number of heritable prion units, or propagons, per cell (Osherovich and Weissman 2004). First, prion aggregates must grow in size by incorporating newly synthesized protein. Next, these enlarged aggregates must be divided into smaller ones through the action of cellular chaperones (Kushnirov and Ter-Avanesyan 1998; Borchsenius et al. 2001; Ness et al. 2002; Kryndushkin et al. 2003). Finally, these regenerated propagons must be distributed to mother and daughter cells (Cox et al. 2003); for small, cytoplasmic aggregates, this distribution may occur passively by diffusion during cytokinesis. In the present study, we have dissected the prion-forming domains of Sup35p and New1p to discover the sequence elements involved in these steps. We have found that these domains consist largely of modular, interchangeable elements that serve distinct functions of prion growth and division or transmission. Aggregation underlies the growth phase of the prion replication cycle (Figure 7A) and occurs through the templated addition of conformationally compatible monomers onto preexisting seeds. Like other amyloids, yeast prions display a high specificity for homotypic aggregation (Santoso et al. 2000; Chernoff et al. 2000; Kushnirov et al. 2000; Zadorskii et al. 2000; Nakayashiki et al. 2001). This discrimination arises from differences in the amino acid sequence and the conformation (Chien and Weissman 2001) of the aggregation-promoting Q/N-rich elements found in each yeast prion protein. Aggregation and specificity are dictated by the NYN repeat (residues 70–100) of New1p and by the Q/N-rich amino terminal region (residues 1–57) of Sup35p. Figure 7 Model for Prion Growth and Division (A) During prion growth, polymers seed the incorporation of monomers through interactions between Q/N-rich aggregation sequences (blue). Proteins with noncognate aggregation sequences (red) are excluded. (B) The division phase of prion replication requires the oligopeptide repeats (orange), which may facilitate the action of chaperones such as Hsp104p (scimitar) in breaking the polymer into smaller, heritable units. In contrast, the conserved oligopeptide repeat sequence mediates the division and/or segregation phase of prion replication (Figure 7B). In New1p, the NYN repeat alone can aggregate and induce [NU+] but requires an adjacent oligopeptide repeat to form a minimal heritable New1p prion, [NU+]mini. Similarly, in Sup35p, the Q/N-rich amino terminal region mediates aggregation whereas most of the oligopeptide repeats are needed for the inheritance of [PSI+] propagons. Oligopeptide repeats 1 and 2 appear to contribute to both growth and inheritance, consistent with earlier reports that expansion and deletion within this region modulate in vitro polymerization of Sup35p and the appearance of [PSI+] in vivo (Liu and Lindquist 1999). However, the two functions can be distinguished by a point mutant in oligopeptide repeat 2 (PNM2-1), which displays a specific defect in [PSI+] inheritance despite normal aggregation. Certain [PSI+] variants are resistant to the dominant negative effect of PNM2-1 (Derkatch et al. 1999; King 2001); this suggests that although oligopeptide repeat 2 is critical for the replication of the [PSI+] variant used in our studies, it may be less important for the replication of other Sup35p prion conformations. Many studies have established that prion inheritance requires the action of cellular chaperones such as Hsp104p and Hsp70 proteins (reviewed in Osherovich and Weissman 2002), although how these proteins contribute is poorly understood. We propose that oligopeptide repeats turn nonheritable aggregates into prions by facilitating chaperone-mediated division. Oligopeptide repeats may allow the division of aggregates by providing direct binding sites for chaperones or by altering the conformation of the amyloid core to allow chaperone access. An earlier study established that deletion of residues 22–69 of Sup35p (which include parts of both the Q/N tract and the oligopeptide repeat) interferes with both [PSI+] induction and chaperone-mediated prion disaggregation (Borchsenius et al. 2001). Unlike the Δ22–69 mutant, the prion replication defect in PNM2-1 could not be corrected by increasing Hsp104p levels (data not shown), arguing that the mitotic instability of PNM2-1 [PSI+] is not due solely to inadequate Hsp104p binding. Our findings help to explain why, among many Q/N-rich proteins in yeast, only a small subset form heritable prions. While many Q/N-rich proteins can aggregate when overexpressed (Sondheimer and Lindquist 2000; Derkatch et al. 2001; Osherovich and Weissman 2001), prion inheritance of such aggregates requires that they be divided and passed on to the next generation. Although the inheritance of Sup35p and New1p prions is mediated by oligopeptide repeats, other sequences could also serve this purpose. Ure2p lacks an oligopeptide repeat; interestingly, many isolates of [URE3] are mitotically unstable in the absence of selection (Schlumpberger et al. 2001). Rnq1p, which underlies [PIN+], also lacks a strict oligopeptide repeat, but a region (residues 218–405) within its prion domain has an amino acid content reminiscent of the oligopeptide repeat sequence (i.e., numerous Q, N, S, Y, and G residues) (Resende et al. 2003). Only two other yeast proteins, YDR210W and YBR016W, have clearly recognizable oligopeptide repeats; both proteins also have Q/N-rich regions. YBR016W forms aggregates when overexpressed (Sondheimer and Lindquist 2000), but it is not known whether either protein can maintain a heritable aggregated state. Although the mammalian prion protein PrP contains a sequence resembling the oligopeptide repeat that can functionally replace one of the Sup35p repeats (Parham et al. 2001), it is unclear whether this sequence is important in the replication of the PrPSc state. The interchangeable nature of prion domain components allowed us to design novel artificial prions. The F chimera, consisting of the aggregation sequence of New1p and the oligopeptide repeat of Sup35p, demonstrates that the growth and specificity of prions is largely determined by the Q/N-rich tract, not by the oligopeptide repeat. Despite a sequence derived primarily from Sup35p, the F chimera behaved like New1p rather than like Sup35p. The [F+] prion cross-interacted with New1p but not Sup35p. Like [NU+], [F+] could be induced in the absence of a prion-promoting (PIN) factor. Finally, [F+] could itself act as a PIN factor, as does [NU+] (data not shown). Notably, the NYN repeat of New1p functions as an aggregation module apparently without regard to its position within a protein; this sequence induced prions when overexpressed by itself or with oligopeptide repeats at its N-terminal (in New11–153 and New150–100) or C-terminal regions (in the F chimera). These results suggest that aggregation sequences are portable and functionally separable from the oligopeptide repeat, perhaps constituting a structurally discrete amyloid core. Indeed, a peptide derived from the amino-terminal region of Sup35p forms a self-seeding amyloid in vitro (Balbirnie et al. 2001). A simple aggregation-prone sequence, pathogenically expanded glutamine, also supports prion inheritance when adjacent to the oligopeptide repeat, suggesting that prion domains can consist of little more than a generic, aggregating core sequence and an inheritance-promoting element. In addition to illuminating the principles of yeast prion domain architecture, artificial prions with distinct specificity may be useful as controllable epigenetic regulators of protein activity. Such prion “switches” can be turned on and off by transient overexpression and genetic repression; for example, the Q prion domain could be fused to other proteins in order to conditionally and reversibly inactivate them independently of [PSI+]. It may also be possible to design additional artificial yeast prion domains whose aggregation is driven by non-Q/N-rich amyloidogenic proteins such as the Aβ peptide that accumulates in Alzheimer's disease (Koo et al. 1999) or the mammalian prion protein PrP (Cohen and Prusiner 1998). Such artificial prions could serve as models for aggregate–chaperone interactions in metazoans and could provide a genetic system for the high-throughput screening of modulators of human aggregation diseases. Materials and Methods Yeast strains and methods Derivatives of W303 (Osherovich and Weissman 2001), with the initial genotypes ade1-14, his3-11,15, leu2-3, trp1-1, and ura3-1, were used throughout unless otherwise noted; all strains were [PIN+]. Strain numbers, with indicated genotypic differences, are as follows: YJW 584 [psi–] MATa, YJW 508 [PSI+] MATα, YJW 716 [nu–] MATα sup35::TRP1 pRS315SpNew11–153-M-C, YJW 717 [NU+] MATα sup35::TRP1 pRS315SpNew11–153-M-C, YJW 844 [f –] MATα sup35::F-M-C C.g. HIS3, YJW 881 [F+] MATa sup35::F-M-C C.g. HIS3, YJW 867 [q–] MATα sup35::Q-M-C C.g. HIS3, YJW 868 [Q+] MATa sup35::Q-M-C C.g. HIS3. Maintainer plasmids used in Figure 3 (see plasmid and gene replacement construction, below) were introduced by plasmid shuffling into YJW 716 or YJW 753 ([PSI+] MATa sup35::TRP1 pRS316SpSUP35), followed by loss of the maintainer spontaneously or through 5-FOA counterselection. The PNM2-1 strain in Figure 4 was generated in this manner and was subsequently restreaked on SD-ade to select for [PSI+]. HIS3-marked oligopeptide repeat truncations and PNM2-1 maintainers were from Parham et al. (2001); all other Sup35p and New1p maintainers were marked with LEU2. The [f–] strain was generated by “gamma” chromosomal integration of pRS306 F-M-C into the SUP35 locus of YJW 584; excision of the wild-type gene was confirmed by PCR of Ade- colonies arising from subsequent growth on 5-FOA. The [q–] strain was made by “omega” chromosomal gene replacement (Kitada et al. 1995) of SUP35 with a C.glabrata HIS3-marked –M-C variant (with or without oligopeptide repeats) into the SUP35 locus of a diploid [PSI+] [PIN+] strain. After sporulation, gene replacement was confirmed by PCR and by loss of [PSI+] in half of the haploid progeny. Yeast culture methods were according to standard procedures (Sherman 1991), but YEPD-medium plates contained 1/4 of the standard amount of yeast extract to accentuate color phenotypes. For prion curing, strains were grown on YEPD medium plus 3 mM GuHCl, then restreaked onto YEPD medium. Plasmid and gene replacement construction The modular SUP35 cloning system described in previous reports was used throughout (Santoso et al. 2000; Osherovich and Weissman 2001). All plasmids are derived from Sikorski and Hieter (1989); sequence files of all constructs are available as a web supplement (Data S1). Maintainer plasmids are low-copy CEN/ARS (pRS31x series) with the native SUP35 promoter (Sp) driving the expression of the indicated prion domain followed by the M and C domains of Sup35p. Inducer plasmids are high-copy 2μ (pRS42x series) with the inducible CUP1 promoter (Cp) driving the expression of the indicated prion domain followed by the Sup35p M domain and GFP. New1p inducers did not include the Sup35p M domain. For polyglutamine constructs, polyglutamine tracts (22 and 62) were amplified out of the MJDtr constructs used in an earlier study (Osherovich and Weissman 2001). To permit amplification, primers contained sequences homologous to several codons adjacent to the 5′ and 3′ ends of the polyglutamine tracts plus an initiator ATG codon. Thus, the polyglutamine sequences read MAYFEK(Q22/62)DLSG. The resulting PCR fragments were cloned into maintainer and inducer plasmids, which were used as templates for gene replacement PCR (see yeast strains and methods, above). In vivo prion assays For aggregation, inducers were overexpressed by growth of cells in selective medium with 50 μM CuSO4 until the culture reached stationary phase; cells were then examined by fluorescent microscopy (Zeiss Axiovert, Zeiss, Oberkochen, Germany; Metamorph imaging software, Universal Imaging Corporation, Downingtown, Pennsylvania, United States). Unless otherwise noted, cultures displaying 10% or more cells with foci were scored as positive. For induction, dilutions of the above cultures were plated onto SD-ade and YEPD media to determine percentage of Ade+. In qualitative assessments, strains were scored as positive if 5% or more of plated cells grew on SD-ade medium after 5 d. In [NU+] maintenance experiments, strains with indicated maintainers were tested for the ability to support an Ade+ state following New11–153-GFP overexpression. In [PSI+] maintenance experiments, strains that began as [PSI+] were tested for Ade+ after plasmid shuffle gene replacement with the indicated maintainer. For decoration, a [PSI+] [PIN+] strain was transformed with the indicated inducers, grown in selective medium with 50 μM CuSO4, and examined by fluorescence microscopy during midlogarithmic phase. Propagon counts were performed as described in Cox et al. (2003). For the antisuppression assay, indicated strains were transformed with a second, differently marked maintainer plasmid, and color phenotypes were assayed on medium selective for both plasmids. In vitro prion assays Centrifugation was performed as described in Ness et al. (2002). Immunoblots were visualized with MT130 anti-Sup35p N-M domain serum.For the polymerization of PNM2-1, the PNM2-1 N and M domains were cloned as 7-histidine fusions into pAED4 and expressed and purified as described in DePace et al. (1998). Thioflavin-T binding was conducted as in Chien et al. (2003). The slope of early (0–6 min) dye binding was obtained from seeded polymerization reactions conducted in triplicate. To correct for a difference in dye binding between wild-type and PNM2-1 protein, these values were normalized to the end point (90 min) maximum signal for each protein. Monomer concentrations were 2.5μM. Supporting Information Data S1 DNA Sequences of Constructs (30 KB ZIP). Click here for additional data file. Accession Numbers The GenBank accession numbers for the proteins discussed in this paper are Hsp104p (NP_013074), New1p (NP_015098), Rnq1p (NP_09902), Sup35p (NP_010457), Ure2p (NC_014170), YDR210W (NP_010496), and YBR016W (NP_010319). We thank Maya Shuldiner, Kim Tipton, Peter Chien, Sean Collins, and other members of the Weissman and Tuite labs for critical comments. Work in the Weissman lab was funded by the Howard Hughes Medical Institute and the Packard Foundation. Work in the Tuite lab was funded by the Wellcome Trust and by the Biotechnology and Biological Sciences Research Council LZO was a Howard Hughes Medical Institute Predoctoral Fellow and thanks Werner Herzog for inspiration. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. LZO, BSC, MFT, and JSW conceived and designed the experiments. LZO performed the experiments. LZO and JSW analyzed the data. BSC, MFT, and JSW contributed reagents/materials/analysis tools. LZO and JSW wrote the paper. Academic Editor: Greg Petsko, Brandeis University Note Added in Proof A recent study of the specificity-determining region of Sup35p prion (Hara et al. 2003) describes complementary results. Hara H, Nakayashiki T, Crist CG, Nakamura Y (2003) Prion domain interaction responsible for species discrimination in yeast [PSI +] transmission. Genes Cells 8:925–939. Abbreviations Ade+adenine prototrophy Ccarboxy-terminal GFPgreen fluorescent protein GuHClguanidine hydrochloride Mmiddle Namino-terminal NYNasparagine-tyrosine-asparagine Q/Nglutamine/asparagine PNMPSI No More SDsynthetic defined (yeast medium with dextrose) YEPDyeast extract-peptone-dextrose ==== Refs References Balbirnie M Grothe R Eisenberg DS An amyloid-forming peptide from the yeast prion Sup35 reveals a dehydrated beta-sheet structure for amyloid Proc Natl Acad Sci U S A 2001 98 2375 2380 11226247 Borchsenius AS Wegrzyn RD Newnam GP Inge-Vechtomov SG Chernoff YO Yeast prion protein derivative defective in aggregate shearing and production of new “seeds.” EMBO J 2001 20 6683 6691 11726504 Chernoff YO Lindquist SL Ono B Inge-Vechtomov SG Liebman SW Role of the chaperone protein Hsp104 in propagation of the yeast prion-like factor [psi+ ] Science 1995 268 880 884 7754373 Chernoff YO Galkin AP Lewitin E Chernova TA Newnam GP Evolutionary conservation of prion-forming abilities of the yeast Sup35 protein Mol Microbiol 2000 35 865 876 10692163 Chien P Weissman JS Conformational diversity in a yeast prion dictates its seeding specificity Nature 2001 410 223 227 11242084 Chien P DePace AH Collins SR Weissman JS Generation of prion transmission barriers by mutational control of amyloid conformations Nature 2003 424 948 951 12931190 Cohen FE Prusiner SB Pathologic conformations of prion proteins Annu Rev Biochem 1998 67 793 819 9759504 Cox BS ψ, a cytoplasmic suppressor of super-suppressor in yeast Heredity 1965 20 505 521 Cox BS Ness F Tuite MF Analysis of the generation and segregation of propagons: Entities that propagate the [PSI+ ] prion in yeast Genetics 2003 165 23 33 14504215 Crist CG Nakayashiki T Kurahashi H Nakamura Y [PHI+ ], a novel Sup35-prion variant propagated with non-Gln/Asn oligopeptide repeats in the absence of the chaperone protein Hsp104 Genes Cells 2003 8 603 618 12839621 DePace AH Santoso A Hillner P Weissman JS A critical role for amino-terminal glutamine/asparagine repeats in the formation and propagation of a yeast prion Cell 1998 93 1241 1252 9657156 Derkatch IL Bradley ME Zhou P Chernoff YO Liebman SW Genetic and environmental factors affecting the de novo appearance of the [PSI+ ] prion in Saccharomyces cerevisiae Genetics 1997 147 507 519 9335589 Derkatch IL Bradley ME Zhou P Liebman SW The PNM2 mutation in the prion protein domain of SUP35 has distinct effects on different variants of the [PSI+ ] prion in yeast Curr Genet 1999 35 59 67 10079323 Derkatch IL Bradley ME Hong JY Liebman SW Prions affect the appearance of other prions: The story of [PIN(+) ] Cell 2001 106 171 182 11511345 Dobson CM Protein misfolding, evolution and disease Trends Biochem Sci 1999 24 329 332 10470028 Doel SM McCready SJ Nierras CR Cox BS The dominant PNM2-mutation which eliminates the psi factor of Saccharomyces cerevisiae is the result of a missense mutation in the SUP35 gene Genetics 1994 137 659 670 8088511 Glover JR Kowal AS Schirmer EC Patino MM Liu JJ Self-seeded fibers formed by Sup35, the protein determinant of [PSI+ ], a heritable prion-like factor of S. cerevisiae Cell 1997 89 811 819 9182769 Jung G Jones G Masison DC Amino acid residue 184 of yeast Hsp104 chaperone is critical for prion-curing by guanidine, prion propagation, and thermotolerance Proc Natl Acad Sci U S A 2002 99 9936 9941 12105276 King CY Supporting the structural basis of prion strains: Induction and identification of [PSI ] variants J Mol Biol 2001 307 1247 1260 11292339 King CY Tittmann P Gross H Gebert R Aebi M Prion-inducing domain 2–114 of yeast Sup35 protein transforms in vitro into amyloid-like filaments Proc Natl Acad Sci U S A 1997 94 6618 6622 9192614 Kitada K Yamaguchi E Arisawa M Cloning of the Candida glabrata TRP1 and HIS3 genes, and construction of their disruptant strains by sequential integrative transformation Gene 1995 165 203 206 8522176 Kochneva-Pervukhova NV Paushkin SV Kushnirov VV Cox BS Tuite MF Mechanism of inhibition of Psi+ prion determinant propagation by a mutation of the N-terminus of the yeast Sup35 protein EMBO J 1998 17 5805 5810 9755180 Koo EH Lansbury PT Kelly JW Amyloid diseases: Abnormal protein aggregation in neurodegeneration Proc Natl Acad Sci U S A 1999 96 9989 9990 10468546 Krobitsch S Lindquist S Aggregation of huntingtin in yeast varies with the length of the polyglutamine expansion and the expression of chaperone proteins Proc Natl Acad Sci U S A 2000 97 1589 1594 10677504 Kryndushkin DS Alexandrov IM Ter-Avanesyan MD Kushnirov VV Yeast [PSI+ ] prion aggregates are formed by small Sup35 polymers fragmented by Hsp104 J Biol Chem 2003 278 49636 49643 14507919 Kushnirov VV Ter-Avanesyan MD Structure and replication of yeast prions Cell 1998 94 13 16 9674422 Kushnirov VV Kochneva-Pervukhova NV Chechenova MB Frolova NS Ter-Avanesyan MD Prion properties of the Sup35 protein of yeast Pichia methanolica EMBO J 2000 19 324 331 10654931 Lacroute F Non-Mendelian mutation allowing ureidosuccinic acid uptake in yeast J Bacteriol 1971 106 519 522 5573734 Liebman SW Derkatch IL The yeast [PSI+ ] prion: Making sense of nonsense J Biol Chem 1999 274 1181 1184 9880481 Liu JJ Lindquist S Oligopeptide-repeat expansions modulate “protein-only” inheritance in yeast Nature 1999 400 573 576 10448860 Liu JJ Sondheimer N Lindquist SL Changes in the middle region of Sup35 profoundly alter the nature of epigenetic inheritance for the yeast prion [PSI+ ] Proc Natl Acad Sci U S A 99 2002 (Suppl 4) 16446 16453 Masison DC Wickner RB Prion-inducing domain of yeast Ure2p and protease resistance of Ure2p in prion-containing cells Science 1995 270 93 95 7569955 McCready SJ Cox BS McLaughlin CS The extrachromosomal control of nonsense suppression in yeast: An analysis of the elimination of [psi+ ] in the presence of a nuclear gene PNM Mol Gen Genet 1977 150 265 270 321935 Meriin AB Zhang X He X Newnam GP Chernoff YO Huntington toxicity in yeast model depends on polyglutamine aggregation mediated by a prion-like protein Rnq1 J Cell Biol 2002 157 997 1004 12058016 Michelitsch MD Weissman JS A census of glutamine/asparagine-rich regions: Implications for their conserved function and the prediction of novel prions Proc Natl Acad Sci U S A 2000 97 11910 11915 11050225 Nakayashiki T Ebihara K Bannai H Nakamura Y Yeast [PSI+ ] “prions” that are crosstransmissible and susceptible beyond a species barrier through a quasi-prion state Mol Cell 2001 7 1121 1130 11430816 Ness F Ferreira P Cox BS Tuite MF Guanidine hydrochloride inhibits the generation of prion “seeds” but not prion protein aggregation in yeast Mol Cell Biol 2002 22 5593 5605 12101251 Osherovich LZ Weissman JS Multiple Gln/Asn-rich prion domains confer susceptibility to induction of the yeast [PSI(+) ] prion Cell 2001 106 183 194 11511346 Osherovich LZ Weissman JS The utility of prions Dev Cell 2002 2 143 151 11832240 Osherovich LZ Weissman JS Buchner J Insights into the structure of yeast prions Protein folding handbook 2004 New York Wiley and Sons Parham SN Resende CG Tuite MF Oligopeptide repeats in the yeast protein Sup35p stabilize intermolecular prion interactions EMBO J 2001 20 2111 2119 11331577 Patino MM Liu JJ Glover JR Lindquist S Support for the prion hypothesis for inheritance of a phenotypic trait in yeast Science 1996 273 622 626 8662547 Paushkin SV Kushnirov VV Smirnov VN Ter-Avanesyan MD Propagation of the yeast prion-like [psi+ ] determinant is mediated by oligomerization of the SUP35-encoded polypeptide chain release factor EMBO J 1996 15 3127 3134 8670813 Prusiner SB Prions Proc Natl Acad Sci U S A 1998 95 13363 13383 9811807 Resende CG Outeiro TF Sands L Lindquist S Tuite MF Prion protein gene polymorphisms in Saccharomyces cerevisiae Mol Microbiol 2003 49 1005 1017 12890024 Santoso A Chien P Osherovich LZ Weissman JS Molecular basis of a yeast prion species barrier Cell 2000 100 277 288 10660050 Schlumpberger M Prusiner SB Herskowitz I Induction of distinct [URE3 ] yeast prion strains Mol Cell Biol 2001 21 7035 7046 11564886 Sherman F Getting started with yeast Meth Enzymol 1991 194 3 21 2005794 Sikorski RS Hieter P A system of shuttle vectors and yeast host strains designed for efficient manipulation of DNA in Saccharomyces cerevisiae Genetics 1989 122 19 27 2659436 Sondheimer N Lindquist S Rnq1: An epigenetic modifier of protein function in yeast Mol Cell 2000 5 163 172 10678178 Song JM Liebman SW Interaction of UAG suppressors and omnipotent suppressors in Saccharomyces cerevisiae J Bacteriol 1985 161 778 780 3881411 Taylor KL Cheng N Williams RW Steven AC Wickner RB Prion domain initiation of amyloid formation in vitro from native Ure2p Science 1999 283 1339 1343 10037606 Ter-Avanesyan MD Kushnirov VV Dagkesamanskaya AR Didichenko SA Chernoff YO Deletion analysis of the SUP35 gene of the yeast Saccharomyces cerevisiae reveals two non-overlapping functional regions in the encoded protein Mol Microbiol 1993 7 683 692 8469113 Uptain SM Lindquist S Prions as protein-based genetic elements Annu Rev Microbiol 2002 56 703 741 12142498 Wickner RB [URE3 ] as an altered URE2 protein: Evidence for a prion analog in Saccharomyces cerevisiae Science 1994 264 566 569 7909170 Zadorskii SP Sopova IV Inge-Vechtomov SG Prionization of the Pichia methanolica SUP35 gene product in the yeast Saccharomyces cerevisiae Genetika 2000 36 1322 1329 11094743 Zoghbi HY Orr HT Glutamine repeats and neurodegeneration Annu Rev Neurosci 2000 23 217 247 10845064

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PLoS Biol. 2004 Apr 23; 2(4):e86

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PLoS Biol

10.1371/journal.pbio.0020086

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020093SynopsisBiotechnologyCell BiologyInfectious DiseasesMolecular Biology/Structural BiologySaccharomycesArtificial Prions Created from Portable Control Elements Synopsis4 2004 23 3 2004 23 3 2004 2 4 e93Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Dissection and Design of Yeast Prions Yeast Prions: Protein Aggregation Is Not Enough ==== Body For decades, scientists accepted that the nucleic acids, DNA and RNA, packed with thousands of protein-coding genes, were the sole purveyors of genetic information; all inherited traits, from eye color to shoe size, must be stored and expressed through nucleic acid mechanisms. But prions are an exception. These misshapen proteins are capable of growing, replicating, and infecting other cells—that is, they are heritable. And all without a scrap of DNA. Most famous as the culprit behind bovine spongiform encephalopathy, or mad cow disease, prions also occur naturally in some organisms and may play important roles in their growth and development. Prion-forming proteins normally exist as benign cellular components, such as enzymes or receptors. But they possess the innate ability to alter their three-dimensional structure, or fold, which changes their function and makes them almost impossible to destroy. Like other misfolded proteins, such as those responsible for Alzheimer's and Huntington's diseases, prions pack together and form aggregates. But what distinguishes prions from simple protein aggregates is their exponential growth and amplification, which allows them to infect new host cells. Prions grow by inducing normal proteins to alter their shape and adhere to an initial aggregate “seed.” These growing masses are then thought to divide with the help of “chaperones,” cellular proteins that aid in protein folding and transport, resulting in smaller prion particles called propagons. The propagons are then distributed to both mother and daughter cells during division, thereby infecting the next generation of cells. Though this theory of the prion life cycle was proposed a few years ago, scientists are still working out the underlying molecular mechanisms As they report in this issue, Lev Osherovich and colleagues dissected yeast prions and found that growth and heritability are controlled by two independent and “portable” sequences. Furthermore, the heritability element seems to be the only thing that keeps slow growing protein aggregates from becoming infectious prions. Previous research showed that one end of the yeast protein, Sup35p, is critical for turning this normal housekeeping enzyme into a prion. The “prion-forming domain” of Sup35p consists of two segments: one stretch rich in the amino acids glutamine and asparagine and another made up of several, small series of amino acids, called oligopeptides. Osherovich and colleagues had earlier found another yeast protein, New1p, which had similar segments, though in reverse order. To study the function of these sequences, the team constructed several strains of yeast, each with a small part of the prion-forming domain missing. By watching the behavior of these modified proteins, each fused to a green fluorescent protein for easy observation, the authors could infer the roles of the deleted segment. For both Sup35p and New1p, the authors found that the area rich in glutamine and asparagine was responsible for the aggregation and growth of prions—acting like a patch of Velcro that locks the misshapen proteins together. While this had been suggested by previous research, the authors also found that this sticky sequence only adheres to proteins that mirror its own pattern of amino acids, thereby explaining why prions from different species don't often interact, a phenomenon called the species barrier. The stretch of oligopeptide repeats in Sup35p and New1p, however, was required for the inheritance of prions—the proper division of prion masses and subsequent distribution of propagons during cell division. The authors suggest that oligopeptide repeats function as a secure binding location for the chaperone proteins, which are necessary for heritability, and thus infectiousness, of prions. Their results also help to explain why stable inheritance of prions is rare; while many proteins have stretches of amino acids similar to the described aggregation sequence, few also contain sequences like oligopeptide repeats that permit inheritance. Though both the aggregation sequence and the oligopeptide repeats are required for prion growth and infection, the segments seemed to function completely separately, allowing the authors to create a synthetic prion-forming domain by combining the aggregation element of New1p with the Sup35p replication/heritability element. This artificial prion acted like New1p, again showing that it is the sticky, aggregation element that specifies which proteins will be added to the growing prion mass. Osherovich and colleagues then went on to create another artificial prion by fusing the oligopeptide repeats to an expanded polyglutamine tract, the type of aggregation sequence responsible for the toxic buildup of brain proteins in Huntington's disease. With this simple addition, the slow growing aggregate was transformed into a heritable, infectious prion. By creating artificial hybrid prions, Osherovich and colleagues showed that the two discrete elements of prion-forming domains are portable and work together regardless of their origins. The authors suggest that other artificial prions could be used as a model system to study different types of aggregation sequences, such as those found in the human prion protein responsible for Creutzfeldt-Jakob's disease or the misshapen plaques of proteins that contribute to Alzheimer's disease.

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PLoS Biol. 2004 Apr 23; 2(4):e93

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PLoS Biol

10.1371/journal.pbio.0020093

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020126Research ArticleEvolutionGenetics/Genomics/Gene TherapyNeurosciencePrimatesHomo (Human)Accelerated Evolution of the ASPM Gene Controlling Brain Size Begins Prior to Human Brain Expansion Evolution of the ASPM GeneKouprina Natalay 1 Pavlicek Adam 2 Mochida Ganeshwaran H 3 Solomon Gregory 4 Gersch William 4 Yoon Young-Ho 1 Collura Randall 5 Ruvolo Maryellen 5 Barrett J. Carl 1 Woods C. Geoffrey 6 Walsh Christopher A 3 Jurka Jerzy jurka@girinst.org 2 Larionov Vladimir larionov@mail.nih.gov 1 1Laboratory of Biosystems and Cancer, National Cancer InstituteBethesda, MarylandUnited States of America2Genetic Information Research Institute, Mountain ViewCaliforniaUnited States of America3Department of Neurology, Howard Hughes Medical Institute and Beth Israel Deaconess Medical CenterBoston, MassachusettsUnited States of America4Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health SciencesResearch Triangle Park, North CarolinaUnited States of America5Harvard University, CambridgeMassachusettsUnited States of America6St. James's University HospitalLeedsUnited Kingdom5 2004 23 3 2004 23 3 2004 2 5 e1268 1 2004 24 2 2004 Copyright: © 2004 Kouprina et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Evolutionary History of a Brain Controlling Brain Size Primary microcephaly (MCPH) is a neurodevelopmental disorder characterized by global reduction in cerebral cortical volume. The microcephalic brain has a volume comparable to that of early hominids, raising the possibility that some MCPH genes may have been evolutionary targets in the expansion of the cerebral cortex in mammals and especially primates. Mutations in ASPM, which encodes the human homologue of a fly protein essential for spindle function, are the most common known cause of MCPH. Here we have isolated large genomic clones containing the complete ASPM gene, including promoter regions and introns, from chimpanzee, gorilla, orangutan, and rhesus macaque by transformation-associated recombination cloning in yeast. We have sequenced these clones and show that whereas much of the sequence of ASPM is substantially conserved among primates, specific segments are subject to high Ka/Ks ratios (nonsynonymous/synonymous DNA changes) consistent with strong positive selection for evolutionary change. The ASPM gene sequence shows accelerated evolution in the African hominoid clade, and this precedes hominid brain expansion by several million years. Gorilla and human lineages show particularly accelerated evolution in the IQ domain of ASPM. Moreover, ASPM regions under positive selection in primates are also the most highly diverged regions between primates and nonprimate mammals. We report the first direct application of TAR cloning technology to the study of human evolution. Our data suggest that evolutionary selection of specific segments of the ASPM sequence strongly relates to differences in cerebral cortical size. Mutation of the ASPM gene is associated with abnormally small brain size. Comparison of the ASPM gene from several primate species suggests it as a target of evolutionary selection for increased brain size ==== Body Introduction The human brain, particularly the cerebral cortex, has undergone a dramatic increase in its volume during the course of primate evolution, but the underlying molecular mechanisms that caused this expansion are not known. One approach shedding light on the molecular mechanisms of brain evolution is the analysis of the gene mutations that lead to defects in brain development. Among the best examples of such defects is the human primary microcephaly syndrome. Primary microcephaly (MCPH) is an autosomal recessive neurodevelopmental disorder in which the brain fails to achieve normal growth. The affected individuals have severe reduction in brain size; however, the gyral pattern is relatively well preserved, with no major abnormality in cortical architecture (McCreary et al. 1996; Mochida and Walsh 2001). Moreover, there are no recognizable abnormalities in the organs other than the central nervous system. The most common cause of MCPH appears to be mutations in the ASPM gene (Roberts et al. 2002). The ASPM gene encodes a 10,434-bp-long coding sequence (CDS) with 28 exons, and spans 65 kb of genomic DNA at 1q31. ASPM contains four distinguishable regions: a putative N-terminal microtubule-binding domain, a calponin-homology domain, an IQ repeat domain containing multiple IQ repeats (calmodulin-binding motifs), and a C-terminal region (Bond et al. 2002). Though the exact function of the human ASPM in the brain needs to be clarified, the homologue in the fruit fly, Drosophila melanogaster, abnormal spindle (asp), is localized in the mitotic centrosome and is known to be essential for both the organization of the microtubules at the spindle poles and the formation of the central mitotic spindle during mitosis and meiosis. Mutations in asp cause dividing neuroblasts to arrest in metaphase, resulting in reduced central nervous system development (Ripoll et al. 1985; do Carmo Avides et al. 2001; Riparbelli et al. 2001). In the mouse (Mus musculus) brain, the Aspm gene is expressed specifically in the sites of active neurogenesis. Expression in the embryonic brain was found to be greatest in the ventricular zone, which is the site of cerebral cortical neurogenesis (Bond et al. 2002). This expression profile suggests a potential role for Aspm in regulating neurogenesis. Interspecies comparisons of ASPM orthologs have shown their overall conservation, but also a consistent correlation of greater protein size with larger brain size (Bond et al. 2002). The increase in protein size across species is due mainly to the increased number of IQ repeats, suggesting that specific changes in ASPM may be critical for evolution of the central nervous system. In an attempt to reconstruct the evolutionary history of the ASPM gene, we isolated large genomic clones containing the entire ASPM gene in several nonhuman primate species. Sequence analysis of these clones revealed a high conservation in both coding and noncoding regions, and showed that evolution of the ASPM gene might have been under positive selection in hominoids. These clones could also provide important reagents for the future study of ASPM gene regulation in its native sequence context. Results Comparison of Genomic Organization of the ASPM Genes in Primates Homologues from chimpanzee (Pan troglodytes), gorilla (Gorilla gorilla), orangutan (Pongo pygmaeus), and rhesus macaque (Macaca mulatta) were isolated by transformation-associated recombination (TAR) cloning in yeast (Saccharomyces cerevisiae), the technique allowing direct isolation of a desirable chromosomal region or gene from a complex genome without constructing its genomic library (Kouprina and Larionov 2003). The method exploits a high level of recombination between homologous DNA sequences during transformation in the yeast. Since up to 15% divergence in DNA sequences does not prevent selective gene isolation by in vivo recombination in yeast (Noskov et al. 2003), for cloning purposes, a TAR vector was designed containing short human ASPM-gene-specific targeting hooks specific to the exon 1 and 3′ noncoding regions (see “Materials and Methods”). The TAR cloning scheme for isolating the ASPM gene homologues from nonhuman primates is shown in Figure 1. The yield of ASPM-positive clones from chimpanzee, gorilla, orangutan, and rhesus macaque was the same as that from the human DNA, suggesting that most homologous regions from nonhuman primates can be efficiently cloned by in vivo recombination in yeast using targeting hooks developed from human sequences. Figure 1 Isolation of the Syntenic Genomic Regions Containing the ASPM Gene from Human, Chimpanzee, Gorilla, Orangutan, and Rhesus Macaque by TAR Cloning The method exploits a high level of recombination between homologous DNA sequences during transformation in the yeast Saccharomyces cerevisiae. For isolation, genomic DNA is transformed into yeast spheroplasts along with a TAR vector that contains targeting hooks homologous to the genomic DNA sequence. CEN corresponds to the yeast Chromosome VI centromere; HIS3 is a yeast selectable marker. Recombination between the vector and the genomic DNA fragment results in cloning of the gene/region of interest as YAC. Chromosomal regions with sizes up to 250 kb can be isolated by TAR cloning. For cloning purposes, TAR vector was designed containing a 5′ hook specific to exon 1 and a 3′ hook specific to the 3′ end of the human ASPM. Transformation experiments were carried out with freshly prepared spheroplasts for each species. To identify ASPM-containing clones, the transformants were combined into pools and examined by PCR for the presence of the unique ASPM sequences not present in the vector. The yield of ASPM-positive clones from primate species was the same as that from the human DNA (3%). Because the TAR procedure produces multiple gene isolates, six independent TAR isolates for each species were checked. The detectable size of the cloned material corresponded to that predicted if the entire ASPM gene had been cloned, i.e., all gene-positive clones contained circular YACs with approximately 65-kb DNA inserts. Alu profiles for each species were determined and found to be identical for each species, suggesting that the isolated YACs contained nonrearranged genomic segments. Finally the YACs were retrofitted into BACs, and their restriction patterns were examined by three restriction endonuclease digestions. No differences between ASPM clones for each species were found. We have compared complete gene sequences from primate species with a 65-kb, full-size human ASPM gene. All the analyzed genes are organized into 28 exons encoding a 3,470–3,479-amino-acid-long protein. ASPM genes start with an approximately 800-bp-long CpG island, that harbors promoter sequences, 5′ untranslated regions, and the first exon (Figure 2). ASPM sequences share a high degree of conservation (Figure 2H), and pairwise DNA identity ranges from 94.5% for macaque and gorilla to 99.3% for the human–chimpanzee comparison (Table 1). Multiple alignment of the genes revealed a low proportion of indels. Only ten insertions/deletions equal to or longer than 50 bp have been found, all of them located within introns (Figure 2B). Seven detected insertions were mainly associated with repetitive DNA: two (AT)n microsatellite expansions, three Alu insertions, including retroposition of AluYi9 in the orangutan–gorilla–chimpanzee–human clade, and retroposition of a new macaque-specific AluY subfamily similar to human AluYd2. Analysis of eight different macaque individuals showed that this particular insertion is polymorphic in the macaque population (data not shown), and thus the insertion appears to be very recent. One macaque-specific 245-bp-long insertion is linked to expansion of a 49-bp-long, minisatellitelike array. The remaining macaque-specific insertion (50 bp) is nonrepetitive. A closer analysis suggests that the insert is not a processed pseudogene of known genes (data not shown). Figure 2 Structure and Evolution of the ASPM Gene in Primates The scale of all plots corresponds to the consensus sequence obtained based on a multiple alignment of five ASPM genes. (A) Schematic representation of the alignment. Promoter regions, exons, and introns are marked in gray, red, and blue, respectively. White segments correspond to gaps. (B) Positions of long (50 bp or longer) insertions/deletions. “O” denotes orangutan, “M” macaque, “OGCH” the orangutan–gorilla–chimpanzee–human clade, and “GCH” the gorilla–chimpanzee–human clade. (C) Positions of polymorphic bases derived from the GenBank single nucleotide polymorphism (SNP) database. (D) Positions of the CpG island. The approximately 800-bp-long CpG island includes promoter, 5′ UTR, first exon, and a small portion of the first intron. (E) Location of an approximately 3-kb-long segmental duplication. (F) Positions of selected motifs associated with genomic rearrangements in the human sequence. Numbers in parentheses reflect number of allowed differences from the consensus motif (zero for short or two ambiguous motifs, two for longer sites). (G) Distribution of repetitive elements. The individual ASPM genes share the same repeats except of indels marked in (B). (H) DNA identity and GC content. Both plots were made using a 1-kb-long sliding window with 100-bp overlaps. The GC profile corresponds to the consensus sequence; the individual sequences have nearly identical profiles. Table 1 Pairwise Identity of Aligned Primate ASPM Genes The pairwise identities were calculated for five complete ASPM genes and therefore include all promoter regions, introns, and exons. The values above the diagonal show DNA identities (in percent) calculated after removing indels. Under the diagonal are values for comparisons with gaps Of the two detected deletions, the macaque-specific 72-bp-long deletion appears to be associated with nonrepetitve DNA. The second one, an 818-bp-long deletion in orangutan, was probably caused by homologous Alu–Alu recombination (see below and Figure S1). The remaining indels are related to expansion/contraction of a short minisatellite array. It was caused either by a 53-bp expansion in the gorilla–chimpanzee–human clade or by two independent deletions/contractions in the macaque and orangutan lineages. An approximately 3-kb-long intronic segment between exons 4 and 5 is present in several copies in the genome (Figure 2E; Figure S2). Closer analysis of the human genome confirmed that copies of this region are homologous to 24 segmental duplications located mainly in telomeric regions of Chromosomes 1–8, 10, 11, 16, 19, 20, and Y. Based on the sequence similarity and the presence of an L1P4 LINE insertion at the 5′ end, the most closely related are three duplications at 7q11–13. The most similar copy is located on Chromosome 7 and shares 93% identity with the ASPM intronic segment. Five duplications are located on Chromosome 1; the closest copy is found 27 Mb away from the ASPM gene. We looked for several common motifs associated with genomic breakpoints in cancers (Abeysinghe et al. 2003). Figure 2F shows the positions of such potentially unstable oligonucleotides. Interestingly, the orangutan-specific deletion (Figure 2B) has its 5′ breakpoint located just 1 bp upstream of a sequence 100% identical to the chi-like consensus motif GCWGGWGG (see Figure S1). The chi motif is recognized by the RecBCD-mediated recombination pathway in prokaryotes and seems to be associated with rearrangements in the human genome (Dewyse and Bradley 1991; Chuzhanova et al. 2003). Both deletion breakpoints in the orangutan deletion are located within 5′ parts of two Alu sequences, suggesting that the deletion was created by homologous Alu–Alu recombination. Similar homologous recombinations with breakpoints located near chi-like motifs in 5′ regions of Alu sequences were described previously (Chen et al. 1989; Rudiger et al. 1995). In summary, despite the presence of a few indels, coding and noncoding regions of ASPM homologues show a marked degree of conservation. Evolution of the ASPM Protein We have analyzed ASPM CDSs from six primate species: human, chimpanzee, gorilla, orangutan, rhesus macaque, and African green monkey (Cercopithecus aethiops). Except for orangutan and rhesus macaque, two or more ASPM CDSs were used for analysis. ASPM proteins showed the same overall length and domain structure (Figure 3A). The IQ repeat domain contains the same number of repeats, suggesting that their expansion occurred in early primate evolution. The CDSs are, as expected, more conserved than the complete gene sequences with promoter and intronic regions (Table 2; Table 3). Only six short indels were identified (Figure 3B). Figure 3 Structure of ASPM CDSs and Evolution in Primates The scale of all plots corresponds to the 3,480-amino-acid-long protein alignment; positions in the CDS were scaled accordingly. (A) Structure of the human ASPM CDS and protein. The first scheme shows positions of major domains in the ASPM protein (Bond et al. 2002). The putative microtubule-binding domain is in gray, the calponin-homology domain in orange, IQ repeats in blue, and the terminal domain in black. Positions of exons in the CDS are drawn in the second block. To separate individual exons, odd numbered exons are colored in black and even numbered ones in white. (B) Positions of insertions/deletions in the protein sequences. Coordinates correspond to the human protein sequence. “O” denotes orangutan, “G” gorilla, “M” macaque, “Gm” African green monkey, and “OGCH” the orangutan–gorilla–chimpanzee–human clade. (C) Substitutions in hominoid CDSs relative to the common ancestor. The expected ancestor CDS was derived using ML codon reconstruction implemented in PAML. African green monkey and rhesus macaque were outgroups. Nonsynonymous/synonymous (ω = Ka/Ks) ratios were free to vary in all branches. Positions marked in green correspond to synonymous changes relative to the ancestral sequence; the red bars indicate nonsynonymous changes. (D) Synonymous (red) and nonsynonymous (green) changes in ancestral lineages leading to human. aOGCH–aGCH is the ancestral lineage from the orangutan divergence to the gorilla divergence; aGCH–aCH represents the lineage from the gorilla divergence to the chimpanzee common ancestor. aCH–human corresponds to the human lineage after the chimpanzee divergence. There are seven synonymous and 19 nonsynonymous human-specific substitutions. Methods and description are the same as in (C). (E) Positions of polymorphic bases for different CDSs of African green monkey, gorilla, chimpanzee, and human. Positions marked in green correspond to synonymous polymorphisms, and the red bars indicate nonsynonymous sites. Numbers of compared sequences are in parentheses; in the case of human we show nine polymorphic positions (four synonymous and five nonsynomous) from the GenBank SNP database. ASPM mutations detected in MCPH patients are shown separately in (F). (F) Positions of 19 mutations reported for MCPH patients (Bond et al. 2002; Bond et al. 2003). All the reported mutations introduce premature stop codons. Mutation sites located within CpG dinucleotides are highlighted in red. (G) Positions of CpG dinucleotides in the human CDS. (H) Comparison of Ka and Ks rates with codon adaptation index (CAI). Ka and Ks values are for all branches (fixed ω ratio); CAI is an average for all five primates (note that CAI differences are very small between the five species). The window was set to 300 bp (100 amino acids) with a 30-bp (10-amino-acid) step. (I) Conservation at the nucleotide and protein level in primates. Y-axis corresponds to proportions of conserved (identical) positions in the CDS and the protein alignment. The plot was obtained using 100-amino-acid-long, overlapping windows, and the step was set to 10 amino acids. In the case of CDS conservation, the window was 300 bp and step 30 bp. Table 2 Pairwise Identity of ASPM CDSs The pairwise identities were calculated for six CDSs. The values above the diagonal show DNA identities (in percent) calculated after removing indels. Under the diagonal are values for comparisons with gaps Table 3 Pairwise Identity of ASPM Proteins The pairwise identities were calculated for six protein sequences. The values above the diagonal show DNA identities (in percent) calculated after removing indels. Under the diagonal are values for comparisons with gaps From the DNA and protein conservation profiles (Figure 3I), ASPM segments evolve differently along the length of the CDS. N- and C-terminal regions and the region corresponding to exons 5–15 are conserved. In contrast, exons 3 and 4 and the complete IQ repeat domain (positions 1,267–3,225) are more variable. Indeed, nonsynonymous substitutions in hominoid primates (Figure 3C) and in ancestral lineages (Figure 3D) and nonsynonymous polymorphism (Figure 3E) are nearly absent in the conserved central (exons 5–15) and C-terminal regions. This pattern indicates different rates of evolution along the ASPM protein, visualized by plots of synonymous Ks and nonsynonymous Ka rates (Figure 3H) and supported by phylogenetic analysis (see below and Figure 4). It is notable that the comparison of the primate and mouse proteins also revealed the same pattern of conservative and nonconservative regions along ASPM protein (Figure S3). Figure 4 Phylogenetic Trees and ω ratio for Complete ASPM and Three Selected Segments Trees and ω (Ka/Ka) ratios were computed using the ML method for codons implemented in PAML. Branch lengths represent ML distances for codons, i.e., using both synonymous and nonsynonymous nucleotide sites, and in all branches the ω ratio was set free to vary. All trees are drawn to the same scale. Branch labels mark the ω ratios for corresponding branches. Values in square brackets show ω for additional cDNA sequences whenever available. Default values and branch lengths were calculated from genomic copies. Selected tested hypotheses are listed. ωH stands for the ω rate in the human lineage, ωC in the chimpanzee lineage, ωCH in the common human–chimpanzee ancestral lineage after the gorilla divergence, ωG in the gorilla lineage, and ω0 in all other branches. Single asterisks indicate p < 0.05, χ2 1 = 3.84; double asterisks indicate p < 0.01, χ2 1 = 6.63. (A) Phylogeny for the complete ASPM CDS. In addition to testing different ω values in the human lineage, we also tested the hypothesis that the complete gorilla–chimpanzee–human clade evolved at a constant rate, different from the rest of the tree (compared to the one-ratio model, boxed). (B) The ASPM phylogeny derived from a conserved segment from exon 5 to the beginning of the IQ domain (amino acids 676–1,266). The branch connecting the human and chimpanzee common ancestor with the gorilla–chimpanzee–human common ancestor had no substitutions, therefore the ω ratio could not be calculated. (C) IQ domain (amino acids 1,267–3,225). We also tested the hypothesis that the gorilla and human lineages evolved at the same ω rate, different from the rest of the tree (compared to the one-ratio model, boxed). (D) Phylogeny of eight primate sequences from a 1,215-amino-acid-long segment of exon 18 (amino acids 1,640–2,855). We also tested the hypothesis that the gorilla and human lineages evolved at the same ω rate, different from the rest of the tree (compared to the one-ratio model, boxed). Analysis of the nonsynonymous/synonymous substitution ratio (ω = Ka/Ks) revealed an elevated value in the human branch (Figure 4A). According to the likelihood ratio test, the human ω rate is significantly different from the rate in the rest of the tree (p < 0.05). Also the model that the complete gorilla–chimpanzee–human clade is evolving at one ω rate different from that in the rest of the tree is well supported (p < 0.01). Because ASPM consists of regions with different degrees of sequence conservation (see Figure 3), we separately analyzed a conserved region (exons 5–15 plus a small part of exon 16) and a variable IQ repeat domain. As can be seen (Figure 4B) the conserved region has all branches shorter, indicating overall a slower rate of evolution. In the human lineage, the ω ratio equals zero; however, the test for whether the human branch has a different (lower) ω rate than the rest did not yield significant values. In contrast, the tree based on the variable IQ repeat domain exhibits ω values greater than one for the human and gorilla branches (Figure 4C). The likelihood ratio test supports the model in which human and gorilla lineages evolved under a significantly higher ω ratio than the rest of the tree. Similar results were obtained for exon 18 with additional sequences from two New World monkeys (Figure 4D). As seen from Figure 4A–4D, different sequences from African green monkey, gorilla, and chimpanzee individuals result in different ω values for their corresponding terminal branches. One chimpanzee sequence also produced an ω ratio greater than one for exon 18 (Figure 4D). It is worth noting that neither codon bias nor selection on third codon positions seemed to influence the synonymous rate Ks strongly (Table S1). Therefore, the high Ka/Ks ratios in human and gorilla are likely to be products of adaptive evolution. Sequencing of two CDSs in African green monkey, three in gorilla, and three in chimpanzee allowed us to look for ASPM polymorphism in those species (see Figure 3E). Human polymorphism data from ASPM mutant haplotypes are not representative of wild-type variation so were not used in these comparisons. For African green monkey, five synonymous and five nonsynonymous changes were found between two sequences. The gorilla and chimpanzee CDSs in particular showed an apparently high degree of replacement polymorphism. Gorilla polymorphism included 35 point mutations (15 silent mutations and 21 replacements). Chimpanzee sequences differed in five synonymous and 11 nonsynonymous sites. In order to interpret this seemingly high level of observed polymorphism, intraspecific diversity was compared to interspecific diversity using the McDonald and Kreitman test (McDonald and Kreitman 1991). In the case of chimpanzee polymorphism compared to divergence with human, we could not reject the null hypothesis that polymorphism and divergence between species were significantly different (William's adjusted G statistic = 0.083, chi-square with 1 d.f., not significant; values based on PAML-generated Ka and Ks values using the free ratio model). Gorilla polymorphism was compared to divergence between the gorilla common ancestor and the human–chimpanzee common ancestor. In this case we can reject the null hypothesis (William's adjusted G statistic = 122.45, chi-square with 1 d.f., p < 0.001) to conclude that the pattern of gorilla polymorphism is therefore different from the divergence pattern. Indeed gorilla polymorphism is less than variation resulting from divergence: within species, the ω ratio is 1.43 for gorillas compared to 2.2 for the divergence between the gorilla common ancestor and the human–chimpanzee common ancestor. Intraspecific variation, although seemingly unusual in showing so many replacement substitutions in both chimpanzee and gorilla, is less than or in line with what we have observed for ASPM divergence between species. Therefore, relaxation of selection cannot explain the high nonsynonymous/synonymous substitution ratios among African hominoids, further supporting the idea that adaptation has occurred in ASPM. Discussion In this study, we applied TAR cloning technology to investigate molecular evolution of the ASPM gene, which is involved in determining the size of the human brain and in which mutations lead to MCPH. The ASPM homologue in the fruit fly is essential for spindle function, suggesting a role for this gene in normal mitotic divisions of embryonic neuroblasts. Complete gene homologues from five primate species were isolated and sequenced. In agreement with the predicted critical role of ASPM in brain development, both coding and noncoding regions of ASPM homologues showed a marked degree of conservation in humans, other hominoids, and Old World monkeys. The differences found in noncoding regions were small insertions/deletions and lineage-specific insertions of evolutionarily young Alu elements into introns. Analysis of nonsynonymous/synonymous substitution ratios indicates different rates of evolution along the ASPM protein: part of ASPM evolved under positive selection while other parts were under negative (purifying) selection in human and African ape lineages. Such “mosaic” selection has been previously described for other proteins (Endo et al. 1996; Crandall et al. 1999; Hughes 1999; Kreitman and Comeron 1999). When our work was completed, the paper by Zhang supporting accelerated evolution of the human ASPM was issued (Zhang 2003). However, because the author did not analyze the gorilla gene homologue, he concluded that accelerated sequence evolution is specific to the hominid lineage. Our finding that selection on ASPM begins well before brain expansion suggests that the molecular evolution of ASPM in hominoids may indeed be an example of a molecular “exaptation” (Gould and Vrba 1982), in that the originally selected function of ASPM was for something other than large brain size. In the case of ASPM, rapidly evolving residues are mainly concentrated in the IQ repeat domain containing multiple IQ motifs, which are calmodulin-binding consensus sequences. While there is no direct evidence yet, it is likely that the function of human ASPM is modulated through calmodulin or calmodulinlike protein(s). Previous interspecies comparisons of ASPM proteins have shown a consistent correlation of greater protein size with larger brain size mainly because of the number of IQ repeats (Bond et al. 2002). For example, the asp homologue of the nematode Caenorhabditis elegans contains two IQ repeats, the fruit fly—24 IQ repeats, and the mouse—61 IQ repeats, and there are 74 IQ repeats in humans (Bond et al. 2002). ASPM homologues in the nonhuman primates examined here contain the same number of IQ repeats as human, supporting the idea that repeat expansion occurred prior to the anthropoid divergence (which gave rise to New World monkeys, Old World monkeys, and hominoids) and possibly even earlier in primate evolution. IQ motifs are seen in a wide variety of proteins, but the ASPM proteins in primates are unique, because they have the largest known number of IQ repeats. Given the proposed role of ASPM in regulating divisions of neuronal progenitors, both the number of repeats and the particular amino acid substitutions in the IQ repeats may be strongly related to brain evolution. Human ASPM gene mutations which lead to MCPH provide a direct link between genotype and phenotype. ASPM is yet another example on the growing list of positively selected genes that show both accelerated evolution along the human lineage and involvement in simple Mendelian disorders (Clark et al 2003). However, ASPM is unique because its distinctive pattern of accelerated protein evolution begins several million years prior to brain expansion in the hominid lineage. Absolute brain size in orangutans (430 g in males; 370 g in females) is barely different from that in gorillas (530 g in males; 460 g in females) and common chimpanzees (400 g in males; 370 g in females) (Tobias 1971), yet accelerated ASPM evolution began in the common ancestor of gorillas, chimpanzees, and humans, approximately 7–8 million years ago. Only much later did brain expansion begin in hominids, starting at 400–450 g roughly 2–2.5 million years ago and growing to its final current size of 1350–1450 g approximately 200,000–400,000 years ago (Wood and Collard 1999). Therefore genotypic changes in ASPM preceded marked phenotypic changes in hominoid brain evolution, at least at the level at which they have currently been studied. The molecular changes in ASPM may predict the existence of differences in early neurogenesis between orangutans, on the one hand, and gorillas, chimpanzees, and humans, on the other, which may manifest as more subtle differences in brain anatomy than gross changes in brain volume. How might evolutionary changes in the ASPM protein affect cerebral cortical size? One potential mechanism might be that changes in ASPM induce changes in the orientation of the mitotic spindle of neuroblasts. Normally, neural precursor cells can have mitotic spindles oriented parallel to the ventricle or perpendicular to the ventricle. Mitoses in which daughter cells are oriented next to one another at the ventricular zone are typically “symmetric” in that a single progenitor cell generates two progenitor cells, causing exponential expansion of the progenitor pool. In contrast, mitoses that generate daughter cells that are vertically arranged are typically “asymmetric” so that one daughter cell becomes a postmitotic neuron, whereas the other daughter cell remains as a progenitor, causing only a linear increase in cell number. Control of this proliferative symmetry can cause dramatic alterations in cerebral cortical size (Chenn and Walsh 2002), and so changes in ASPM could regulate cortical size by making subtle changes in spindle orientation. Alternatively, evolutionary changes in ASPM may not themselves have led to increase in the size of the brain, but instead perhaps ASPM might be essential to insure faithful DNA replication and proper chromosome segregation. In rodents, a surprising number of cerebral cortical neurons are aneuploid (Rehen et al. 2001). Perhaps directed selection of specific domains of ASPM helps insure faithful chromosome segregation to allow a larger number of cerebral cortical neurons to be formed without an unduly high incidence of chromosome aneuploidy. Functional genomics studies are clearly needed to elucidate the exact nature of the molecular mechanisms affected by ASPM gene evolution in hominoids. Here, we have demonstrated the utility of TAR cloning for evolutionary sequence comparisons among humans and other primates. In addition, the ASPM TAR clones isolated in these studies could provide valuable reagents for studying ASPM gene regulation in its natural sequence context. Overall, we anticipate this technology will be extremely useful in studying the evolution of other genes that may be responsible for uniquely human traits. Note The related paper by Evans et al. (2004) was published in Human Molecular Genetics shortly after this paper was submitted. Materials and Methods TAR cloning of the ASPM gene homologues by in vivo recombination in yeast To isolate the full-size ASPM gene from the human (Homo sapiens), chimpanzee (Pan troglodytes), gorilla (Gorilla gorilla), orangutan (Pongo pygmaeus), and rhesus macaque (Macaca mulatta) genomes, a TAR vector containing two unique hooks was constructed. Two targeting sequences were designed, 131 bp 5′ and 151 bp 3′, from the available human genomic sequence of ASPM (positions 155,758–155,888 and 92,922–93,071 in the BAC RP11–32D17 [GI:16972838]). The targeting sequences were PCR amplified from the genomic DNA using two specific primers (Table S2). PCR products were cloned into a polylinker of the basic TAR vector pVC604 as ApaI–SalI and SalI–XbaI fragments. Before transformation experiments, the TAR cloning vector was linearized with SalI to release targeting hooks. Genomic DNA samples were prepared from chimpanzee, gorilla, orangutan, and rhesus macaque fibroblast culture cell lines (Coriell Institute for Medical Research, Camden, New Jersey, United States) in agarose plugs. Spheroplast transformation experiments were carried out as previously described in Kouprina and Larionov (1999). To identify clones positive for ASPM, yeast transformants were examined by PCR using diagnostic primers specific for exon 2 and exon 27 of ASPM (Table S2). Integrity of yeast artificial chromosomes (YACs) and the issue of their stability during propagation in yeast were examined. DNA was isolated from ten subclones carrying the ASPM YACs for each primate, and their size was analyzed by NotI digestion followed by CHEF. Each subclone carried a YAC of similar size, indicating that these clones were stable in yeast. Alu profiles of the clones were checked by TaqI digestion of 1 μg of total yeast DNA isolated from transformants. Samples were run by electrophoresis, transferred to a nylon membrane, and hybridized with an Alu probe. YACs were retrofitted into bacterial artificial chromosomes (BACs) by homologous recombination in yeast using a BAC/NeoR retrofitting vector, BRV1, and then transformed into a recA DH10B E. coli strain (Kouprina and Larionov 1999). Before sequencing, the integrity of inserts in BACs was confirmed by NotI, HindIII, EcoRI, and PstI digestions. The promoter regions of the chimpanzee, gorilla, orangutan, and rhesus macaque (approximately 3.2 kb) and exon 18 of the red-chested mustached tamarin (Saguinus labiatus) and black-handed spider monkey (Ateles geoffroyi) (approximately 4.7 kb) were PCR amplified using a pair of specific primers (Table S2) from primate genomic DNAs (Coriell Institute for Medical Research) and then TA-subcloned for further sequencing. RT-PCR of ASPM coding regions RNAs were extracted from primate cell lines (African green monkey [Cercopithecus aethiops] kidney, COS-7 [American Type Culture Collection, Manassas, Virginia, United States], chimpanzee peripheral lymphoblast, EB176 [JC], and gorilla peripheral lymphoblast, EB [JC] [European Collection of Cell Cultures, Wiltshire, United Kingdom]) using TRIzol reagent (Invitrogen, Carlsbad, California, United States). Reverse transcription and 5′- and 3′-RACE reactions were performed using SMART RACE cDNA Amplification Kit (BD Biosciences, San Jose, California, United States). Sequencing Chimpanzee, gorilla, orangutan, and rhesus macaque TAR clones containing full-size ASPM genes were directly sequenced from BAC DNAs (Polushin et al. 2001). Forward and reverse sequencing of the promoter and exon 18 as well as fragments of coding regions of the ASPM homologues were run on a PE-Applied Biosystem 3100 Automated Capillary DNA Sequencer (Applied Biosystems, Foster City, United States). Primer pairs for cDNA sequencing were designed based on the human ASPM mRNA sequence. Primer sequences are available upon request. All sequences were named and numbered according to the clone/accession identifier. Sequence analysis Genomic sequences were aligned using MAVID (http://baboon.math.berkeley.edu/mavid/) (Bray and Pachter 2004); proteins and protein-coding DNA sequences were aligned by DIALIGN2.1 (http://bibiserv.techfak.uni-bielefeld.de/dialign/) (Morgenstern 1999). Alignments were manually edited in the SEAVIEW editor (http://pbil.univ-lyon1.fr/software/seaview.html) (Galtier et al. 1996). We have used a number of programs from the EMBOSS package (http://www.hgmp.mrc.ac.uk/Software/EMBOSS/) for sequence analysis. Short nucleotide patterns associated with genome rearrangements were searched using FUZZNUC (EMBOSS). We searched for the following recombinogenic motifs: chi-like octamer (GCWGGWGG), immunoglobulin heptamer (GATAGTG), translin (ATGCAGN(0,4)GCCCWSW and GCNCWSCTN(0,2)GCCCWSSW), topoisomerase II (RNYNNCNNGYNGKNYNY), topoisomerase IId (GTNWAYATTNATNNR), topoisomerase IIi (YYCNTASYGGKYYTNNC), and V(D)J recombinase (CACAGTGN(12/23)ACAAAAACC). For short or highly ambiguous patterns (topo-isomerase II), no mismatches were allowed; for longer motifs (translin, V(D)J recombinase) up to two mismatches were permitted. Prediction of CpG islands was performed by CPGPLOT (EMBOSS) with default parameters (length ≥ 200; CpG/GpC ≥ 0.6; GC ≥ 0.5). CENSOR (http://www.girinst.org/Censor_Server-Data_Entry_Forms.html) (Jurka et al. 1996) and REPEATMASKER (http://repeatmasker.genome.washington.edu/cgi-bin/RepeatMasker; developed by A.F.A. Smit and P. Green) were used for identification of repetitive elements. Minisatellites were detected by TANDEM REPEAT FINDER (Benson 1999). ASPM segmental duplications in the human genome were detected by local BLAT searches (http://genome.ucsc.edu/cgi-bin/hgBlat) (Kent 2002). First, we used ASPM genomic sequences with all repeats masked to detect segmental duplications. Full-size duplications were then obtained by BLAT alignment with full (i.e., repeat-containing) ASPM sequence. Primate CDSs were deduced from the ASPM gene alignment with human sequences. Synonymous and nonsynonymous substitutions were detected by SNAP (http://www.hiv.lanl.gov/content/hiv-db/SNAP/WEBSNAP/SNAP.html). Codon maximum likelihood (ML) in CODEML in PAML v. 3.13 (http://abacus.gene.ucl.ac.uk/software/paml.html) (Yang 1997) has been applied for reconstruction of phylogenetic trees, reconstruction of ancestral sequences, and detection of positive selection. Branch lengths and ancestral sequences were reconstructed using a free ω ratio for individual branches. The methodology of likelihood ratio tests is described elsewhere (Yang 1998). For large alignments, the initial input trees for PAML were estimated by ML implemented in PHYLO_WIN (http://pbil.univ-lyon1.fr/software/phylowin.html) (Galtier et al. 1996). Segmental duplications were clustered by a neighbor-joining method implemented in the same program. Distance measurements for examining intraspecific/interspecific diversity were calculated in PAUP (Swofford, D. L. 2003. PAUP v. 4.0b10; Sinauer Associates, Sunderland, Massachusetts, United States; http://paup.csit.fsu.edu/index.html) and corrected for multiple substitutions using the Tamura-Nei algorithm. Supporting Information Commentary Selection operating on codon usage may increase the ω ratio by lowering the rate of synonymous substitutions (Sharp and Li 1987, 1989). Therefore, we tested the correlations between the CAI (Sharp and Li 1987) and the rate of synonymous substitutions (Ks). We found no significant association between the tested variables. Moreover, interspecies comparisons disclosed that CAI is nearly identical for all compared species, and no CAI increase over other species was detected for human or gorilla (data not shown). On the other hand, there was a significant negative correlation between CAI and both protein and DNA identity. A partial correlation analysis revealed that the significant positive linear correlation between Ka and CAI was merely caused by the strong negative correlation of Ka with DNA and protein identity. When we controlled for identity, the correlation between Ka and CAI disappeared (data not shown). These results may indicate that at positively selected sites, protein changes are preferred over optimization of codon usage, and thus mutations causing amino acid replacements do not immediately produce optimal codons. It should be noted that selection on codon usage seems to be generally relaxed in mammals (Duret and Mouchiroud 2000). Mammalian codon usage as well as the rate of nonsynonymous substitutions can be potentially biased by selection favoring a high GC content (or even saturation by G and C) at the third codon positions (GC3) (Bernardi and Bernardi 1985; Aota and Ikemura 1986). However, ASPM is an AT-rich gene (GC content 36.4%–37%) and, as expected (Bernardi and Bernardi 1985; Aota and Ikemura 1986), the third codon positions are also AT-rich (GC3 content, 30.6%–31.4%) and thus far from saturation. In summary, neither the codon bias nor selection on the third codon seems to strongly influence the synonymous rate Ks. Therefore the high Ka/Ks ratio in human and gorilla is likely to be the product of adaptive evolution. Figure S1 Recombination Breakpoints in the Orangutan-Specific 818-bp-Long Deletion Both orangutan breakpoints are located within 5′ portions of two Alu elements. The sequence conservation is marked by different shades of gray. Both Alu elements are compared to their corresponding AluSp and AluSz subfamily consensus sequences. Gorilla, chimpanzee, and human sequences located 1 bp downstream of the 5′ breakpoint share a perfect match with the chi-like octamer consensus sequence GCWGGWGG (first box, positions matching the chi consensus are shown in black). On the other hand, the 3′ breakpoint sequences are diverged from the chi consensus (second box). Both Alu elements in the alignment are shown from the first position and end at the same position, and thus positions in one element correspond to positions in the other Alu copy. As can be seen, the breakpoint position in the first AluSp repeat exactly corresponds to the breakpoint position within the second AluSz element, suggesting homologous recombination between the two repeats. (163 KB PDF). Click here for additional data file. Figure S2 Segmental Duplications of the Fourth Internal Intron From left to right: phylogeny, chromosomal position, band name, identity to ASPM segment (percent same), and a schematic alignment of segmental duplications. The ASPM segment (black) shares similarity with 24 segmental duplications that contain additional sequences and are present on several human chromosomes. The ASPM copy and three duplications on Chromosome 7 share the same L1P4 terminal insertion, which is absent from all other duplications. The tree on the left shows evolutionary relationships among the duplications estimated by the neighbor-joining method. (169 KB PDF). Click here for additional data file. Figure S3 Comparison of Mouse and Human ASPM Proteins The amino acid identity in the conserved regions is 85.44%, 49.39%, and 68.74% for exon 3, exon 4, and the IQ domain, respectively. In addition, while the alignment of conserved regions is completely gap-free, the variable domains exhibit several gaps including a large deletion in the mouse IQ domain (human positions 2655–2943). (97 KB PDF). Click here for additional data file. Table S1 Primers Used in This Work Upper case letters indicate sequences homologous to ASPM and lower case letters indicate cloning sites. (118 KB PDF). Click here for additional data file. Table S2 CDS and Protein Correlations All correlations were obtained for the same 100-amino-acid-/300-nucleotide-long, nonoverlapping windows. The first value shows the correlation coefficient; p-value is in parentheses. The section over the diagonal is calculated using the Pearson (linear) correlation coefficient; under the diagonal are correlations obtained using the Spearman's rank coefficient—nonparametric). Nontrivial or interesting significant correlations are shown in bold and italics. The CAI represents the mean for all species (the CAI values are nearly identical for individual species). The ω ratio, Ka, and Ks (rows/columns 2, 3, and 4) correspond to all branches of the phylogenetic tree. They were obtained using a ML model with one fixed ω ratio for all branches. Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) accession number for the human ASPM mRNA sequence used in this study is NM_018136. The sequence data from chimpanzee, gorilla, orangutan, and rhesus macaque full-length ASPM have been submitted to GenBank under accession numbers AY497016, AY497015, AY497014, and AY497013. The sequence data from chimpanzee, gorilla, and African green monkey ASPM cDNA have been submitted to GenBank under accession numbers AY508452, AY508451, and AY486114. The sequence data from spider monkey and tamarin exon 18 have been submitted to GenBank under accession numbers AY497017 and AY497018. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. NK, JCB, CGW, CAW, and VL conceived and designed the experiments. NK, GHM, GS, WG, and Y-H Y performed the experiments. AP, GHM, RC, MR, CGW, JJ, and VL analyzed the data. AP and GS contributed reagents/materials/analysis tools. NK, AP, MR, JCB, CAW, JJ, and VL wrote the paper. Academic Editor: Joseph Hacia, University of Southern California Abbreviations BACbacterial artificial chromosome CAIcodon adaptation index CDScoding sequence MCPHprimary microcephaly MLmaximum likelihood SNPsingle nucleotide polymorphism TARtransformation-associated recombination YACyeast artificial chromosome ==== Refs References Abeysinghe SS Chuzhanova N Krawczak M Ball EV Cooper DN Translocation and gross deletion breakpoints in human inherited disease and cancer. I: Nucleotide composition and recombination-associated motifs Hum Mutat 2003 22 229 244 12938088 Aota S Ikemura T Diversity in G + C content at the third position of codons in vertebrate genes and its cause Nucleic Acids Res 1986 14 6345 6355 3748815 Benson G Tandem repeats finder: A program to analyze DNA sequences Nucleic Acids Res 1999 27 573 580 9862982 Bernardi G Bernardi G Codon usage and genome composition J Mol Evol 1985 22 363 365 3936938 Bond J Roberts E Mochida GH Hampshire DJ Scott S ASPM is a major determinant of cerebral cortical size Nat Genet 2002 32 316 320 12355089 Bond J Scott S Hampshire DJ Springell K Corry P Protein-truncating mutations in ASPM cause variable reduction in brain size Am J Hum Genet 2003 73 1170 1177 14574646 Bray N Pachter L MAVID: Constrained ancestral alignment of multiple sequences Genome Res 2004 (in press) Chen SJ Chen Z d'Auriol L Le Coniat M Grausz D Ph1+bcr− acute leukemias: Implication of Alu sequences in a chromosomal translocation occurring in the new cluster region within the BCR gene Oncogene 1989 4 195 202 2648256 Chenn A Walsh CA Regulation of cerebral cortical size by control of cell cycle exit in neural precursors Science 2002 297 365 369 12130776 Chuzhanova N Abeysinghe SS Krawczak M Cooper DN Translocation and gross deletion breakpoints in human inherited disease and cancer. II: Potential involvement of repetitive sequence elements in secondary structure formation between DNA ends Hum Mutat 2003 22 245 251 12938089 Clark AG Glanowski S Nielsen R Thomas PD Kejariwal A Inferring nonneutral evolution from human–chimp–mouse orthologous gene trios Science 2003 302 1960 1963 14671302 Crandall KA Kelsey CR Imamichi H Lane HC Salzman NP Parallel evolution of drug resistance in HIV: Failure of nonsynonymous/synonymous substitution rate ratio to detect selection Mol Biol Evol 1999 16 372 382 10331263 Dewyse P Bradley WE A very large spontaneous deletion at aprt locus in CHO cells: Sequence similarities with small aprt deletions Somat Cell Mol Genet 1991 17 57 68 1998142 do Carmo Avides M Tavares A Glover DM Polo kinase and Asp are needed to promote the mitotic organizing activity of centrosomes Nat Cell Biol 2001 3 421 424 11283617 Duret L Mouchiroud D Determinants of substitution rates in mammalian genes: Expression pattern affects selection intensity but not mutation rate Mol Biol Evol 2000 17 68 74 10666707 Endo T Ikeo K Gojobori T Large-scale search for genes on which positive selection may operate Mol Biol Evol 1996 13 685 690 8676743 Evans PD Anderson JR Vallender EJ Gilbert SL Malcom CM Adaptive evolution of ASPM, a major determinant of cerebral cortical size in humans Hum Mol Genet 2004 13 489 494 14722158 Galtier N Gouy M Gautier C C. SEAVIEW and PHYLO_WIN: Two graphic tools for sequence alignment and molecular phylogeny Comput Appl Biosci 1996 12 543 548 9021275 Gould SJ Verba ES Exaptation: A missing term in the science of form Paleobiology 1982 8 4 15 Hughes AL Evolutionary diversification of the mammalian defensins Cell Mol Life Sci 1999 56 94 103 11213266 Jurka J Klonowski P Dagman V Pelton P CENSOR: A program for identification and elimination of repetitive elements from DNA sequences Comput Chem 1996 20 119 121 8867843 Kent WJ BLAT: The BLAST-like alignment tool Genome Res 2002 12 656 664 11932250 Kouprina N Larionov V Selective isolation of mammalian genes by TAR cloning. In: Current protocols in human genetics. Volume 1 1999 New York John Wiley and Sons, Inc pp5.17.1 5.17.21 Kouprina N Larionov V Exploiting the yeast Saccharomyces cerevisiae for the study of the organization of complex genomes FEMS Microbiol Rev 2003 27 629 649 14638416 Kreitman M Comeron JM Coding sequence evolution Curr Opin Genet Dev 1999 9 637 641 10607619 McCreary BD Rossiter JP Roberston DM Recessive (true) microcephaly: A case report with neuropathological observations J Intellect Disabil Res 1996 40 66 70 8930059 McDonald JH Kreitman M Adaptive protein evolution at the Adh locus in Drosophila Nature 1991 351 652 654 1904993 Mochida GH Walsh CA Molecular genetics of human microcephaly Curr Opin Neurol 2001 14 151 156 11262728 Morgenstern B B. DIALIGN 2: Improvement of the segment-to-segment approach to multiple sequence alignment Bioinformatics 1999 15 211 218 10222408 Noskov VN Leem S-H Solomon G Mullokandov M Chae J-Y A novel strategy for analysis of gene homologs and segmental genome duplications J Mol Evol 2003 56 702 710 12911033 Polushin N Malykh A Malykh O Zenkova M Chumakova N 2′-modified oligonucleotides from methoxyoxalamido and succinimido precursors: Synthesis, properties, and applications Nucleosides Nucleotides Nucleic Acids 2001 20 507 514 11563067 Rehen SK McConnell MJ Kaushal D Kingsbury MA Yang AH Chromosomal variation in neurons of the developing and adult mammalian nervous system Proc Natl Acad Sci U S A 2001 98 13361 13366 11698687 Riparbelli MG Callaini G Glaves DM do Carmo Avides M A requirement for the abnormal spindle protein to organise microtubules of the central spindle for cytokinesis in Drosophila J Cell Sci 2001 115 913 917 Ripoll P Pimpinelli S Valdivia MM Avila J A cell division mutant of Drosophila with a functionally abnormal spindle Cell 1985 41 907 912 3924413 Roberts E Hampshire DJ Pattison L Springell K Jafri H Autosomal recessive primary microcephaly: An analysis of locus heterogeneity and phenotypic variation J Med Genet 2002 39 718 721 12362027 Rudiger NS Gregersen N Kielland-Brandt MC One short well conserved region of Alu -sequences is involved in human gene rearrangements and has homology with prokaryotic chi Nucleic Acids Res 1995 23 256 260 7862530 Sharp PM Li WH The rate of synonymous substitution in entero-bacterial genes is inversely related to codon usage bias Mol Biol Evol 1987 4 222 230 3328816 Sharp PM Li WH On the rate of DNA sequence evolution in Drosophila J Mol Evol 1989 28 398 402 2501501 Tobias PV Human skeletal remains from the Cave of Hearths, Makapansgat, Northern Transvaal Am J Phys Anthropol 1971 34 335 367 5120551 Wood B Collard M The human genus Science 1999 284 65 71 10102822 Yang Z PAML: A program package for phylogenetic analysis by maximum likelihood Comput Appl Biosci 1997 13 555 556 9367129 Yang Z Likelihood ratio tests for detecting positive selection and application to primate lysozyme evolution Mol Biol Evol 1998 15 568 573 9580986 Zhang J Evolution of the human ASPM gene, a major determinant of brain size Genetics 2003 165 2063 2070 14704186

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PLoS Biol. 2004 May 23; 2(5):e126

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020131Research ArticleCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologySaccharomycesA Protein Complex Containing the Conserved Swi2/Snf2-Related ATPase Swr1p Deposits Histone Variant H2A.Z into Euchromatin SWR1-Com Deposits H2A.Z into EuchromatinKobor Michael. S 1 Venkatasubrahmanyam Shivkumar 2 Meneghini Marc D 2 Gin Jennifer W 1 Jennings Jennifer L 3 Link Andrew J 3 Madhani Hiten D hiten@biochem.ucsf.edu 2 Rine Jasper jrine@uclink4.berkeley.edu 1 1Department of Molecular and Cell Biology, University of CaliforniaBerkeley, CaliforniaUnited States of America2Department of Biochemistry and Biophysics, University of CaliforniaSan Francisco, CaliforniaUnited States of America3Department of Microbiology and Immunology, Vanderbilt University School of MedicineNashville, TennesseeUnited States of America5 2004 23 3 2004 23 3 2004 2 5 e13117 10 2003 26 2 2004 Copyright: © 2004 Kobor et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Depositing a Histone That Protects Active Chromosomal Regions from Silencing The conserved histone variant H2A.Z functions in euchromatin to antagonize the spread of heterochromatin. The mechanism by which histone H2A is replaced by H2A.Z in the nucleosome is unknown. We identified a complex containing 13 different polypeptides associated with a soluble pool of H2A.Z in Saccharomyces cerevisiae. This complex was designated SWR1-Com in reference to the Swr1p subunit, a Swi2/Snf2-paralog. Swr1p and six other subunits were found only in SWR1-Com, whereas six other subunits were also found in the NuA4 histone acetyltransferase and/or the Ino80 chromatin remodeling complex. H2A.Z and SWR1 were essential for viability of cells lacking the EAF1 component of NuA4, pointing to a close functional connection between these two complexes. Strikingly, chromatin immunoprecipitation analysis of cells lacking Swr1p, the presumed ATPase of the complex, revealed a profound defect in the deposition of H2A.Z at euchromatic regions that flank the silent mating type cassette HMR and at 12 other chromosomal sites tested. Consistent with a specialized role for Swr1p in H2A.Z deposition, the majority of the genome-wide transcriptional defects seen in swr1Δ cells were also found in htz1Δ cells. These studies revealed a novel role for a member of the ATP-dependent chromatin remodeling enzyme family in determining the region-specific histone subunit composition of chromatin in vivo and controlling the epigenetic state of chromatin. Metazoan orthologs of Swr1p (Drosophila Domino; human SRCAP and p400) may have analogous functions. The histone variant H2A.Z localizes specifically to euchromatin and prevents spreading of silent chromatin into adjacent euchromatin. This study shows that the chromatin-remodeling complex SWR1-Com helps deposit H2A.Z onto euchromatin in yeast ==== Body Introduction Histones are the major constituent of chromatin and exert a profound influence on most if not all aspects of chromosome behavior. The functional state of chromatin is regulated, in part, by histone modifying enzymes and ATP-dependent chromatin remodeling enzymes. Members of the latter enzyme class alter the structure of nucleosomes or slide them along DNA in vitro (reviewed in Becker and Horz 2002; Peterson 2002). These enzymes have a catalytic DNA-dependent ATPase subunit, which is similar in sequence to those of the DEAD/DEAH-box class of RNA-dependent ATPases. The prototype for this family is the Saccharomyces cerevisiae Swi2/Snf2 protein, originally identified for its role in promoting transcription. In addition to histone modification and nucleosome remodeling/sliding, there is a third form of chromatin regulation that involves the replacement of canonical histones with histone variants. For example, replacement of histone H3 by a conserved H3 variant (called Cse4p in S. cerevisiae, Cid in Drosophila, or CENP-A in humans) is essential for the assembly of the kinetochore (reviewed in Smith 2002). The other histone variant that is conserved between yeast and humans is H2A.Z, which replaces H2A in about one in ten nucleosomes. By convention, the gene encoding H2A.Z in Saccharomyces is referred to as HTZ1 and mutant forms of the gene are referred to as htz1. We have shown previously that an important function of H2A.Z in S. cerevisiae is to prevent the spreading of silent chromatin, also termed heterochromatin, into adjacent euchromatic regions (Meneghini et al. 2003). Silencing in S. cerevisiae occurs at the HMR and HML silent mating type cassettes, near telomeres, and in the rDNA (reviewed in Rusche et al. 2003). All three types of silencing require the NAD-dependent histone deacetylase Sir2p. Telomeric and HM silencing also require the histone H3/H4 tail binding proteins, Sir3p and Sir4p. In yeast cells lacking H2A.Z, the Sir complex spreads beyond its normal boundaries at HMR and into neighboring euchromatin, resulting in the repression of gene expression (Meneghini et al. 2003). This repression is reversed by a deletion of SIR2 or a deletion of the nucleation sites for silencing at HMR. Likewise, the silencing of genes near telomeres in htz1Δ cells is reversed by a deletion of SIR2. In yeast, H2A.Z itself is enriched in the euchromatic region flanking HMR and is depleted in silent regions. Genetic analysis indicates that H2A.Z acts independently of a characterized chromatin boundary element that occurs on the right border of the HMR silent cassette. Thus, H2A.Z is a euchromatin-specific factor that antagonizes the spread of silencing through a mechanism that is independent of at least one characterized boundary element (Meneghini et al. 2003). However, the creation of a boundary for the spread of silenced chromatin likely involves additional protein factors, such as the double bromodomain protein, Bdf1p, whose function is similar to that of H2A.Z and which binds preferably to acetylated histones that are found in euchromatin outside of silenced regions (Ladurner et al. 2003; Matangkasombut and Buratowski 2003). Despite its critical role in preventing the spread of heterochromatin, the mechanism by which H2A.Z is deposited in euchromatin is unknown. The canonical histones can be deposited by both replication-coupled and replication-independent deposition mechanisms (reviewed in Haushalter and Kadonaga 2003). In human cells, the replication-coupled deposition pathway is essential for progression through S-phase and for cell viability (Hoek and Stillman 2003; Ye et al. 2003). In contrast, in budding yeast, no single deposition pathway is essential for viability (Kaufman et al. 1998; Formosa et al. 2002). For example, the histone H3/H4 chaperones CAF-I and Asf1p function synergistically during replication-coupled histone deposition in vitro and cooperate to form heterochromatin in vivo. However, neither CAF-I nor Asf1p is essential for cell viability in S. cerevisiae, and mutants lacking both proteins are also viable (Tyler et al. 1999). Nap1p, a yeast homolog of a mammalian histone chaperone purified on the basis of a replication-independent assembly assay, is also dispensable for viability in S. cerevisiae (Ishimi and Kikuchi 1991; Kellogg and Murray 1995). Thus, other mechanisms must operate to deposit chromatin in living cells. One candidate is the Drosophila factor ACF and the orthologous human complex RSF, which each contain a ISWI-type Swi2/Snf2 ATPase subunit (reviewed in Haushalter and Kadonaga 2003). These factors promote the ATP-dependent assembly of ordered nucleosome arrays in vitro, but their precise in vivo roles have not been firmly established. Even less is known about the mechanisms of deposition of variant histones. Understanding the mechanism by which euchromatin that contains H2A.Z is formed requires the identification of the machinery that catalyzes its deposition. The results of this study identify a multisubunit protein complex, SWR1-Complex (SWR1-Com), which contains a Swi2/Snf2 paralog and is required for H2A.Z deposition and function in S. cerevisiae. We link this complex structurally and genetically to the NuA4 histone acetyltransferase (HAT) and the Ino80-C chromatin remodeling complex. Results A Protein Complex (SWR1-Com) Associated with the Histone Variant H2A.Z H2A.Z is important for specifying euchromatic regions in the genome of S. cerevisiae (Meneghini et al. 2003). To determine which other proteins contribute to directing H2A.Z to its chromosomal locations, we purified proteins associated with a soluble pool of H2A.Z from whole cell extracts of a yeast strain harboring an allele of HTZ1 that encodes a carboxyl-terminal fusion to the tandem affinity purification (TAP) tag (see Materials and Methods) (Rigaut et al. 1999). These initial purifications were performed under low salt conditions and with limited wash steps to maximize protein complex recovery, with more stringent conditions used subsequently to distinguish strong from weak and potentially artifactual interactions (see below). The protein compositions of the samples were determined using Direct Analysis of Large Protein Complexes methodology, which consists of tryptic digestion of the mixture, multidimensional microcapillary chromatography, tandem mass spectrometry, and genome-assisted analysis of the acquired spectral data (Link et al. 1999; Sanders et al. 2002). A protein was judged to be associated with H2A.Z if the number of corresponding peptides in the H2A.Z-TAP purified material was higher than in the material purified from strains lacking a tagged H2A.Z protein and if the protein passed additional criteria described below. Proteins established to nonspecifically copurify with TAP-tagged proteins were excluded from the analysis (Shevchenko et al. 2002). Using these criteria and additional purifications (described below), we identified 15 proteins associated with H2A.Z (Table 1), of which 13 form a complex designated SWR1-Com (Figure 1 and see below). The largest subunit corresponded to Swr1p (Swi2/Snf2-related), an uncharacterized member of the Swi2/Snf2 family of ATP-dependent chromatin remodeling enzymes (Pollard and Peterson 1998). Figure 1 Subunit Architecture of SWR1-Com and Overlap with NuA4 and Ino80-C Complexes Venn diagram showing proposed subunit compositions of the SWR1, NuA4, Ino80p-C, and Nap1p/Kap114p complexes. Assignments were based on the data shown in Table 1 and Figure 2 and Figure 3. Proteins used in TAP purifications are indicated by “*” and proteins encoded by essential genes are underlined. Table 1 Peptides in TAP Purifications ND, not determined. We named SWR1-Com by convention used for similar complexes. Shown is the number of peptides corresponding to the protein indicated in the left column that were found in the mass spectrometry analysis of purifications from strains with H2A.Z-TAP, Swr1-TAP, Yaf9-TAP, and Swc4-TAP. Previously undescribed subunits of SWR1-Com are referred to as SwcNp (for SWR1-complex) and the corresponding genes as SWCN, where N is an integer assigned in order of decreasing molecular weight. However, an important point of this work is that SWR1-Com shared subunits with other protein complexes. We have retained all the previously published names assigned to these proteins. Protein abundance and subcellular localization data are adapted from recently published data (Ghaemmaghami et al. 2003; Huh et al. 2003) SWR1-Com Shared Subunits with the Essential HAT NuA4 and the Ino80-C Chromatin Remodeling Complex Four SWR1-Com subunits that are also found in the Ino80-C chromatin remodeling complex are Rvb1p, Rvb2p, Arp4, and actin (Shen et al. 2000). Similarly, Yaf9p, as shown below and by others (Le Masson et al. 2003), as well as Arp4p and actin, are also components of the NuA4 HAT (Galarneau et al. 2000). To determine whether the proteins that associated with H2A.Z formed one discrete complex, multiple complexes, or were copurifying contaminants, three of these proteins were themselves tagged with the TAP domain. Complexes from the soluble fraction of whole cell extracts were purified in conditions similar to those used for the H2A.Z-TAP purification, and the composition of the purified material was evaluated by the same procedure used for the H2A.Z-TAP (summarized in Table 1 and Figure 1). With the exception of the histone chaperone Nap1p and the import protein Kap114p, the proteins that copurified with TAP-tagged Swr1p were similar to the set found with H2A.Z, except that two additional proteins, designated here as Swc3p and Swc7p, were identified. Similarly, purifications from strains with TAP-tagged Swc4p and Yaf9p yielded nearly all the proteins associated with Swr1p and H2A.Z and lacked Nap1p and Kap114p. Like the Swr1-TAP material, the Swc4-TAP-associated material contained Swc3p and Swc7p, supporting the assignment of these two proteins to SWR1-Com. TAP-tagged Swc4p and Yaf9p also associated with most of the subunits of the NuA4 complex (including Tra1p, Epl1p, Eaf3p, Yng2p, and the catalytic subunit Esa1p). These data suggested that SWR1-Com and NuA4 shared the Yaf9p, Swc4p, Arp4, and actin subunits. Representative complex purifications under high stringency conditions (see Materials and Methods) from strains with either the Swr1-TAP, Yaf9-TAP, or an untagged control strain are shown in Figure 2A. Proteins that copurified with both Swr1-TAP and Yaf9-TAP represented subunits of SWR1-Com (Figure 2A, arrows), whereas proteins that only copurified with Yaf9-TAP represented specific NuA4 subunits (Figure 2A, stars). A schematic representation of the domain structures of SWR1-Com subunits is presented in Figure 2B. Several of the proteins in the complex contained motifs (SANT, Bromo, YEATS, and HIT) found in proteins associated with chromatin, suggesting that SWR1-Com acts directly on chromatin. Figure 2 SWR1-Com Shared Subunits with NuA4 and Contained Proteins with Motifs Involved in Chromatin Biology (A) Protein complex overlap. Purifications were performed under high stringency conditions (see Materials and Methods) from Swr1-TAP, Yaf9-TAP, and untagged control strains, resolved by SDS-PAGE and stained with silver. Due to the relatively low efficiency of the Swr1-TAP purification, the wt and Swr1-TAP purifications were performed from twice the amount of starting material compared to Yaf9-TAP. Not all proteins identified by mass spectrometry were clearly visible on the gel. Arrows point to proteins that were common to the Swr1-TAP and Yaf9-TAP purifications, whereas stars point to proteins that were found only in the Yaf9-TAP purifications as judged by visual inspection and comparison of protein sizes with the data deduced from mass spectrometry. The vertical bar indicates that proteins in that area of the gel could not be clearly resolved. (B) Domain structure of SWR1-Com. Shown are SMART domain representations of individual proteins assigned to the SWR1-Com taken from the SMART database (http://smart.embl-heidelberg.de/). Domain names are included, green bars indicate coil-coiled regions, and magenta bars indicate regions of low complexity. The amino-terminal part of Swr1p is not to scale. Since the histone chaperone Nap1p and the import factor Kap114p copurified with H2A.Z but not other members of the complex, they were likely to be part of an H2A.Z-containing protein complex distinct from SWR1-Com. Affinity purification of TAP-tagged Rvb2p, an established component of the Ino80-C chromatin remodeling complex, yielded peptides corresponding to the other known subunits of the Ino80-C complex as well as six members of SWR1-Com, three of which (Swc4p, Arp4p, and actin) are also subunits of NuA4 (Table S1). Consistent with the assignment of SWR1-Com subunits, a percentage of the cellular pool of these proteins cosedimented with each other upon glycerol gradient centrifugations of whole cell extracts (Figure S1). The initial purifications suggested that Swc4p and Bdf1p, both of which have domains that are involved in recognition of histone tails, copurified with H2A.Z-TAP and might be part of SWR1-Com. Independent assessment of the composition of the complexes deduced by mass spectrometry was obtained by analytical-scale affinity purifications of Yaf9-TAP, Esa1-TAP, Rvb2-TAP, Swr1-TAP, and Ino80-TAP from cells containing a version of Swc4p that was fused at its carboxyl-terminus to a triple hemagglutinin (HA) tag. These analytical-scale affinity purifications were more stringent than the initial TAP purifications and therefore served to eliminate false-positive results and to provide independent tests of interactions. Anti-HA epitope antibodies and antibodies against Tra1p, the largest subunit of NuA4, were used to analyze the copurified material. Both Yaf9-TAP and Esa1-TAP associated with comparable amounts of Tra1p and Swc4p, supporting the assignment of Yaf9p and Swc4p as new subunits of NuA4. Likewise, Rvb2-TAP and Swr1-TAP copurified with a substantial amount of Swc4-HA, but Ino80-TAP did not. Rvb2-TAP, Swr1-TAP, and Ino80-TAP were not associated with Tra1p (Figure 3A). These data were consistent with Swr1p and Rvb2p being components of SWR1-Com and not of NuA4. Further supporting the assignment of Swc4p as a subunit of NuA4, significant amounts of the NuA4 subunits Tra1p and Esa1p were present in material from Swc4-TAP analytical-scale purifications (Figure 3B). Figure 3 Swc4p and Bdf1p Were Components of SWR1-Com This figure shows immunoblots of analytical-scale TAP purifications. The captured TAP-tagged protein is indicated above the gels, and the protein that was tested for association is indicated at the right side. (A) Association of Swc4p and Tra1p. Swc4-HA was present in purifications from Yaf9-TAP, Esa1-TAP, Rvb2-TAP, and Swr1-TAP but not Ino80-TAP. NuA4 was only present in the Yaf9-TAP and Esa1-TAP material. (B) Reciprocal confirmation of Swc4p being part of NuA4. Swc4-TAP and Yaf9-TAP purified material contained NuA4 components Esa1p and Tra1p. (C) Association of Bdf1p. Bdf1p was present in purifications from Swr1-TAP, Yaf9-TAP, and Swc4-TAP but not Esa1-TAP. The number of peptides corresponding to Bdf1p in the TAP purifications was low, and Bdf1p was found only in the H2A.Z-TAP and the Yaf9-TAP (Table 1). Bdf1p's potential presence was tested further by additional analytical-scale affinity purifications from strains carrying Yaf9-TAP, Swr1-TAP, Swc4-TAP, and Esa1-TAP and immunoblotting with an antibody against Bdf1p. Bdf1p associated with Swr1-TAP, Yaf9-TAP, and Swc4-TAP but not with Esa1-TAP or untagged control material, supporting the assignment of Bdf1p as a subunit of SWR1-Com (Figure 3C). SWR1-Com Selectively Associated with Histone H2A.Z Versus H2A To determine whether subunits of SWR1-com associated specifically with H2A.Z or both H2A.Z and H2A, TAP-tagged versions of H2A.Z and H2A were purified from cells containing HA-tagged versions of the five different SWR1-Com components Swr1p, Swc2p, Swc3p, Swc4p, and Swc7p, and the nuclear import factor Kap114p. The composition of the copurifying material was then evaluated by immunoblotting with antibodies against the HA tag, Bdf1p, and Tra1p. Yaf9-TAP served as a positive control for recovery of SWR1-Com and NuA4. H2A.Z associated with a substantial fraction of the SWR1-Com as judged by the comparable intensity of the signal for SWR1-Com subunits in the material copurified with H2A.Z-TAP and Yaf9-TAP, whereas no NuA4 copurified with H2A.Z-TAP based upon the absence of Tra1p in the H2A.Z-TAP sample (Figure 4A). In contrast, histone H2A copurified with only trace amounts of Swc2-HA, Swc3-HA, Swc4-HA, Swc7-HA, and Bdf1p and with virtually no Swr1-HA (see Figure 3). Kap114-HA associated with both H2A.Z-TAP and H2A-TAP but not Yaf9-TAP, suggesting that it did not discriminate canonical and variant H2A. Hence, based on the apparent relative strength of the interactions, SWR1-Com (in contrast to Kap114-HA) associated primarily with H2A.Z, although weak affinity of SWR1-Com to H2A was possible. Furthermore, these experiments also supported the assignment of Swc3p and Swc7p to SWR1-Com despite peptides for these two proteins being present only in the initial Swr1-TAP and Swc4-TAP purifications. Figure 4 SWR1-Com Associated Selectively with H2A.Z and Contained H2B (A) Analytical-scale TAP purifications from H2A.Z-TAP, Yaf9-TAP, and H2A-TAP were analyzed by immunoblotting for the components indicated on the right. SWR1-Com preferentially associated with H2A.Z-TAP, whereas Kap114-HA associated equally with H2A.Z-TAP and H2A-TAP but not Yaf9-TAP. (B) SWR1-Com was purified from strains with HA-tagged versions of either H2A.Z or H2B and analyzed by immunoblotting for the presence of these histones as well as the SWR1-Com subunit Act1p. Canonical H2A that is not bound to chromatin is usually found in a H2A/H2B dimer (Jackson 1987). The presence of H2B in the SWR1-Com was investigated by purifying SWR1-Com from strains containing H2A.Z-HA and H2B-HA. SWR1-Com contained H2A.Z-HA and also H2B-HA (Figure 4B). These data raised the possibility that this complex used H2A.Z/H2B dimers as a substrate. Similar Gene Expression Profiles of htz1Δ and swr1Δ Cells To determine the extent to which the role of H2A.Z depends upon SWR1-Com function, genome-wide transcription profiles of swr1Δ cells were compared to the profiles of htz1Δ cells (Meneghini et al. 2003). To permit an optimal comparison, experiments were performed under the conditions used previously to analyze htz1Δ cells (see Materials and Methods). Due to the role of H2A.Z in anti-silencing, H2A.Z-dependent genes tend to be located near silenced domains such as telomeres. This theme was echoed in the results from the swr1Δ mutant. Specifically, 42 of the 94 (45%) Swr1p-dependent genes were within 20 kb of a chromosome end, which is less than 3% of the genome (Figure 5A). This enrichment is highly significant, as judged by p-values estimated from the hypergeometric distribution (Figure 5B). Swr1p-dependent genes were underrepresented from regions more than 40 kb from a telomere, suggesting that, as seen earlier for H2A.Z, the telomere-proximal genes were most sensitive to loss of Swr1p function. Figure 5 Chromosomal Distribution of Swr1p-Activated Genes (A) Histogram showing the number of Swr1p-activated genes as a function of their distance to the nearest chromosome end. (B) The statistical significance of the enrichment of Swr1p-activated genes as a function of distance to the nearest telomere, and the significance of the depletion of Swr1p-activated genes in regions greater than 40 kb from a telomere, were determined using the hypergeometric function (Tavazoie et al. 1999). Comparison of the transcript profile across the genome of swr1Δ cells to that of htz1Δ cells also revealed a marked overlap (Figure 6A). Ninety-four genes displayed reduced expression in the swr1Δ mutant compared to wild type. Of these 94 Swr1p-dependent genes, 64 were also reduced in expression in htz1Δ (Figure 6A). This remarkably large overlap is highly significant (p = 2.9 × 10−80, calculated using the hypergeometric distribution) and even more impressive for telomere-proximal genes. These data suggested that Swr1p and H2A.Z shared a common function in regulating gene expression. Figure 6 Relationship of Genes Activated by Swr1p or H2A.Z (A) The Venn diagram of number of genes that exhibited a significant decrease in expression in swr1Δ cells (this work) or htz1Δ cells (Meneghini et al. 2003), revealing a large overlap. Shown on the top is the relationship for the genome overall and on the bottom for genes within 20 kB of a telomere. H2A.Z-dependent genes whose expression could not be determined in swr1Δ cells were omitted. (B) A color representation of all genes that were significantly reduced in expression in swr1Δ cells only, htz1Δ cells only, or both, grouped according to (A). Each column represents data from an independent microarray experiment that compared genome-wide expression in mutant cells of the indicated genotype to wt cells. Each row represents the changes in expression of a single gene across the eight experiments. Change in expression measured as the log2 of the mutant/wt expression ratio is indicated according to the color scale shown. Red cells refer to genes found to have increased expression in either swr1Δ cells or htz1Δ cells that decreased in expression in the other mutant. Excluded from representation are genes that increased expression in both mutants. A substantial number of H2A.Z-dependent genes (116) did not appear to require Swr1p for expression. A color representation of the swr1Δ and htz1Δ datasets grouping the genes described in Figure 6A (Figure 6B) revealed that a subset of these appeared to have mildly reduced expression levels in swr1Δ cells but were not reduced enough to meet the stringent significance cutoff. However, there also were clear examples of genes that required H2A.Z for expression but not Swr1p. Likewise, there were clear examples among the 94 genes that required Swr1p for expression but did not require H2A.Z (Figure 6). Swr1p Was Required for H2A.Z Deposition In Vivo The evidence linking H2A.Z and Swr1p function and the association of both H2A.Z and H2B with SWR1-Com suggested that SWR1-Com was responsible for depositing H2A.Z onto chromatin in vivo, perhaps in the form of an H2A.Z/H2B dimer. (The Swr1p relatives in the ACF and RSF complexes perform related roles in assembling chromatin in vitro (reviewed in Haushalter and Kadonaga 2003). If so, then cells lacking Swr1p should display reduced levels of H2A.Z in chromatin. To test this prediction, we performed chromatin immunoprecipitation (ChIP) experiments comparing wild type to swr1Δ strains expressing a functional amino-terminal triple-HA-tagged version of H2A.Z expressed from the HTZ1 promoter at the normal chromosomal locus (HA3-H2A.Z). Consistent with H2A.Z being in a stable complex with Swr1p that protected it from protein degradation, the level of HA3-H2A.Z in swr1Δ strains was reduced approximately 2- to 3-fold (Figure S2). To normalize the signals from each experimental locus assayed, we measured the levels of DNA derived from a control locus whose expression is H2A.Z independent (the middle of the PRP8 open reading frame [ORF]) in samples derived from each whole cell extract and precipitate (see Materials and Methods). We first examined HA3-H2A.Z levels at two chromosomal regions where H2A.Z prevents the spread of Sir-dependent silencing: one flanking the silent mating locus HMR and another near the telomere on the right arm of chromosome XIV (Figure 7A). In wild type, H2A.Z was present at levels similar to those described previously (Figure 7B); HA3-H2A.Z was depleted from the silenced HMR locus and enriched in the flanking euchromatic regions (Figure 7B). In addition, HA3-H2A.Z was depleted from the most telomere-proximal locus tested, AAD3, presumably because of telomeric silencing of this gene. Likewise, HA3-H2A.Z was highly enriched at the YNR074C gene, a telomere-proximal gene on chromosome XIV strongly protected from silencing by H2A.Z. Figure 7 ChIP Analysis of HA3-H2A.Z Deposition in the HMR Region and Near the Right Telomere of Chromosome XIV (A) Location of PCR primers. (B) ChIP results in wild type (bars indicate relative enrichment versus a probe in the PRP8 ORF; standard errors are shown). The ChIP enrichment signal at HMR relative to PRP8 being less than 1.0 indicated some H2A.Z deposition occurred at the PRP8 control region. (C) ChIP results in swr1Δ cells. In striking contrast, in swr1Δ cells, the enrichment (relative to the PRP8 locus) of HA3-H2A.Z at every locus tested approached a ratio of one (Figure 7C). These data were consistent with Swr1p being essential for the deposition of H2A.Z in the HMR region and near the right telomere of chromosome XIV. However, because the ChIP measurements were normalized to the PRP8 locus, we considered the possibility that a uniform amount of HA3-H2A.Z remained at all chromosomal loci examined in the mutant, for instance if there was a specific increase in the association of HA3-H2A.Z with the PRP8 locus rather than a decrease at all other loci. This possibility was discounted by the approximately 13-fold mean decrease in the ratio of DNA obtained from the pellet versus whole cell fractions in swr1Δ cells relative to wild type, indicating a substantial defect in the absolute chromatin association of HA3-H2A.Z in cells lacking Swr1p. H2A.Z is also deposited at several loci that are not near silenced regions (Meneghini et al. 2003). The function of H2A.Z at these regions is unknown. To determine if Swr1p was also required for H2A.Z deposition at such loci, we examined H2A.Z levels at 12 euchromatic regions on chromosome III that each displayed some level of deposition of HA3-H2A.Z (Figure 8A). These loci were identified in a comprehensive study of H2A.Z deposition on chromosome III (M.D.M., M. Bao, H.D.M., unpublished data). As with the regions examined above, the relative ChIP enrichment of HA3-H2A.Z approached one at each of these loci in the absence of Swr1p (Figure 8B), and the absolute amount of DNA precipitated from these loci showed a large decrease in the swr1Δ mutant (data not shown). Thus, Swr1p was broadly required for the deposition of HA3-H2A.Z, even in regions distant from silenced domains. It is worth noting that for several of the loci examined in the swr1Δ mutant, the ChIP enrichment was significantly less than one, suggesting that there may exist some residual deposition of HA3-H2A.Z at the PRP8 locus in these cells. Figure 8 ChIP Analysis of H2A.Z Deposition at Nontelomeric Euchromatic Sites (A) ChIP results in wild type. (B) ChIP results in swr1Δ cells. We detected a reduced enrichment of H2A.Z at all these loci when we estimated the absolute H2A.Z abundance by dividing the amount of immunoprecipitated DNA by the amount of total input DNA for each locus. NuA4 Function Was Required for SWR1-Com to Support Cell Growth The sharing of four proteins between the SWR1-Com and NuA4 (see Figure 1) raised the possibility that SWR1-Com was functionally linked to NuA4, which is the major HAT for histones H4 and H2A. Initial purifications from Yaf9-TAP and Swc4-TAP strains suggested that the protein encoded by the nonessential gene YDR359C was a subunit of NuA4, consistent with some earlier results from high-throughput studies (Gavin et al. 2002). Initial efforts to fuse a triple HA-tag to the carboxy-terminus of YDR359C were unsuccessful, but we noticed that all proteins encoded by YDR359C orthologs from the Saccharomyces sensu strictu strains had a carboxy-terminal extension of approximately 22 amino acids, suggesting a possible error in the S. cerevisiae sequence. Therefore, we chose to integrate a triple HA-tag at the chromosomal location that corresponded to the second-to-last codon of the sensu strictu strains and found that this version was now successfully tagged. Recently, a revised copy of YDR359C with the stop codon at the location we chose was deposited in GenBank and named EAF1. Therefore, we used this name here rather than a previously assigned name for the shorter version of YDR359C. Eaf1p contained a SANT domain as well as an HSA domain that is associated with SANT domains and found in helicases (Letunic et al. 2002). Analytical-scale affinity purifications showed that Eaf1-HA, similar to Tra1p, copurified with Yaf9-TAP, Swc4-Tap, and Esa1-TAP but not with H2A.Z-TAP and Swr1-TAP (Figure 9A). In addition, global H4 acetylation defects were evident in eaf1Δ but not in htz1Δ, yaf9Δ, or swr1Δ cells (Figure 9B). This was based on the reduced signal in immunoblot experiments obtained with an antibody directed against tetra-acetylated H4. Similar experiments with antibodies directed against individual acetylated residues revealed that the H4 acetylation defect of strains lacking EAF1 was most profound on K8 and K12 of H4, whereas K5 and K16 of H4 were less affected (Figure 9B). Similarly, K9 of H2A did not have a strong acetylation defect in any of the mutants (Figure 9B). The physical association and the H4 acetylation defects provided independent evidence that Eaf1p was a subunit of NuA4. Figure 9 Eaf1p Was a Subunit of the NuA4 HAT (A) Eaf1-HA associated with NuA4 subunits. Immunoblots of analytical-scale TAP purifications are shown. The captured TAP-tagged protein is indicated above the gels, and the protein that was tested for association is indicated at the right. (B) Strains lacking EAF1 have defects in histone H4 acetylation. Whole cell extracts from mutant strains indicated on the top were tested for global histone acetylation using antibodies directed against different forms of acetylated H4 and H2A as indicated on the right. To explore genetic links between SWR1-Com and NuA4, phenotypic and double mutant analyses were performed. SWR1-Com mutants and the strain lacking EAF1 shared sensitivities to genotoxic and stress conditions (Figure 10A). The eaf1Δ strains were also slow growing whereas the other strains were not. All mutant strains tested were sensitive to the DNA replication inhibitor hydroxyurea (HU) and the microtubule poison benomyl and to caffeine and formamide, reagents that elicit a number of cellular responses (Figure 10A). Strains lacking HTZ1 and YAF9 were comparably sensitive to HU and formamide, but htz1Δ strains were more sensitive to benomyl and caffeine. Strains lacking SWR1 were less sensitive than the other strains to HU and formamide, but the sensitivity to caffeine and benomyl was comparable to that of yaf9Δ strains (Figure 10A). Cells lacking the NuA4 subunit Eaf1p were most sensitive to HU and caffeine, and their sensitivity to benomyl and formamide was comparable to that of htz1Δ mutants (Figure 10A). While the severity of the defects varied, the similar phenotypes of mutants in SWR1-Com and NuA4 suggested that the two complexes were broadly required for resistance to DNA damage and genotoxic stress. Figure 10 NuA4 and SWR1-Com Shared Similar Phenotypes and Interacted Genetically (A) SWR1-Com and Eaf1p were required for resistance to DNA damage and genotoxic stress. Ten-fold serial dilutions of strains from a stationary overnight culture with the indicated deletions of SWR1-Com subunits and of EAF1 were plated and incubated at 30 °C for 2–3 d. YPD plates with the following concentrations of chemicals were used: 100 mM HU, 10 μg/ml of benomyl, 2% formamide, or 3 mM caffeine. (B) SWR1-Com and NuA4 interacted genetically. Double mutants, deduced from genetic analysis of the viable spore clones, are circled, with the two mutations of interest in each cross indicated at the side. All double mutants were inviable. To test whether the sensitivity to DNA damage and genotoxic stress was a shared function of SWR1-Com and NuA4, or whether these sensitivities were caused by independent functions, double mutant analysis was performed using the EAF1 gene as an exemplary NuA4 subunit. No viable spores were obtained that had deletions of EAF1 and HTZ1, SWR1, or YAF9 (Figure 10B). Thus, SWR1-Com and NuA4 interacted genetically, and the two complexes shared at least one essential function. Discussion Protein complexes that can substitute canonical histones with variant histones represent a fundamental mechanism for regulating the functional state of chromatin. Previous work has identified large protein complexes that assemble, remodel, and modify chromatin (reviewed in Becker and Horz 2002; Peterson 2002). In contrast, the studies described here identified a novel complex, referred to as SWR1-Com, whose putative ATPase, Swr1p, promoted the deposition of the histone H2A variant, H2A.Z, into chromatin in vivo. SWR1-Com, a Multisubunit Complex, Associated Specifically with H2A.Z SWR1-Com was identified by its specific association with H2A.Z. SWR1-Com consisted of 13 subunits: six were only found in SWR1-Com, four were shared between SWR1-Com and NuA4, and four were shared between SWR1-Com and the Ino80 complex. Two subunits, Arp4 and actin, were in all three complexes (see Figure 1). Several of the subunits of SWR1-Com contained motifs highly suggestive of a role for this complex in affecting chromatin structure. Chief among these was Swr1p, a relative of the ATPase-containing subunit of the Swi2/Snf2 ATP-dependent chromatin remodeling enzyme complex (Pollard and Peterson 1998). The Swc4p subunit contained a SANT domain, suggested in other contexts to mediate association of proteins with histone tails (Boyer et al. 2002; Sterner et al. 2002). Similarly, Bdf1p contained two bromodomains that preferentially bind to acetylated tails of histones H3 and H4 (Ladurner et al. 2003; Matangkasombut and Buratowski 2003). The Swc6p subunit contained a HIT domain found in a human protein that binds to steroid receptors (Lee et al. 1995), and the Yaf9p subunit contained a YEATS domain found in several proteins involved in chromatin modification, such as the SAS-I HAT complex, and several proteins implicated in human leukemias (Xu et al. 1999; Le Masson et al. 2003). The weak interactions between SWR1-Com subunits and H2A relative to those between SWR1-Com subunits and H2A.Z suggested that the role of SWR1-Com was dedicated to those chromatin structures enriched for H2A.Z. This was further supported by the association of H2B-HA along with H2A.Z-HA with highly purified SWR1-Com, suggesting that this histone dimer was the physiological substrate for activity of SWR1-Com. Genome-Wide Expression Profiles and Phenotypic Analysis Identified Functional Links between H2A.Z and SWR1-Com Similarities between the consequences of disruptions of SWR1-Com function and loss of H2A.Z protein implied that SWR1-Com was required for H2A.Z function. These similarities included the striking sensitivities of cells lacking SWR1-Com function or H2A.Z to a variety of cellular and genotoxic stresses. Comparison of the genome-wide expression profiles of swr1Δ and htz1Δ strains also revealed similar responses to loss of either function at many loci. These included silencing of genes near telomeres and the HMR silent mating type locus, which is antagonized by H2A.Z (Meneghini et al. 2003). In addition, there were genes distal to silenced domains that required both H2A.Z and Swr1p for their expression. Because the majority of gene expression defects seen in swr1Δ cells also occurred in htz1Δ cells, the role of Swr1p, and presumably SWR1-Com, was predominantly in promoting the function of H2A.Z. H2A.Z Deposition into Chromatin was Promoted by SWR1-Com SWR1-Com promoted the deposition of H2A.Z into chromatin. At 20 sites flanking the silent HMR locus that were previously identified as enriched or depleted for H2A.Z, the ratio of H2A.Z at these loci relative to the PRP8 ORF as determined by ChIP converged to unity in swr1Δ cells. In addition, a dramatic 13-fold decrease in the absolute enrichment of HA-H2A.Z-associated DNA was observed in swr1Δ cells. A similar picture emerged from the analysis of 12 sites of H2A.Z deposition across chromosome III. Therefore, Swr1p was required for enrichment of H2A.Z at a wide variety of loci, including those distal to silent regions. Several lines of evidence suggested that Swr1p and presumably SWR1-Com play direct roles in H2A.Z deposition into chromatin. Foremost in favor of this view is the tight physical association of H2A.Z with SWR1-Com in whole cell extracts. Additionally, Swr1p, and other members of SWR1-Com, had sequence motifs found in proteins acting in chromatin and were localized in the nucleus. In particular, the Bdf1 protein via its bromodomains might be responsible for the recruitment of SWR1-Com to deposit H2A.Z to euchromatic regions, which are generally characterized by acetylation of the H4 tail. Lastly, the profound defect of swr1Δ cells in H2A.Z deposition, and the established actions of the Swi2/Snf2 family members directly on nucleosomes, provided further support for a direct role of Swr1p and SWR1-Com in H2A.Z deposition. Several observations were consistent with a small amount of H2A.Z deposition in chromatin in cells lacking Swr1p function. First, some genes affected by htz1Δ were not affected by swr1Δ. Second, the enrichment of H2A.Z at some loci relative to the PRP8 ORF was less than unity in the swr1Δ mutant, suggesting residual H2A.Z present at PRP8. Perhaps in the absence of SWR1-Com, some H2A.Z is deposited by the same mechanisms responsible for the bulk deposition of H2A. Nevertheless, the key observation was a pronounced deficiency in H2A.Z deposition in the absence of Swr1p function. The conservation of Swr1p orthologs raises the possibility of SWR1-Com-like complexes dedicated to the deposition of variant histones in other organisms. The Drosophila Domino protein, human SRCAP, and human p400 are orthologs of SWR1, and serve as candidates for the founding members of such complexes. Mutations in Domino affect silencing by Polycomb proteins, although the directness of these effects is unknown (Ruhf et al. 2001). The SRCAP protein is associated with CREB-binding protein, and p400 is recruited by the Adenovirus E1A oncoprotein (Johnston et al. 1999; Fuchs et al. 2001). Although SRCAP and p400 are known primarily as transcription factors, our results suggest possible roles for these proteins in deposition of variant histones. While this work was under review, two groups independently reported on the SWR1-Com and described its role in H2A.Z deposition (Krogan et al. 2003; Mizuguchi et al. 2004). Consistent with our data, purified SWR1-Com has a Swr1p-dependent histone exchange activity (Mizuguchi et al. 2004) and hence presents a third mechanism of chromatin remodeling. SWR1-Com and NuA4 Function Were Linked The SANT-domain-containing proteins Swc4p and Eaf1p were subunits of NuA4, newly described here. Both proteins associated with other NuA4 subunits, and cells lacking EAF1 had defects in global histone H4 acetylation. Similar defects were found in a strain carrying a conditional allele of the essential SWC4 gene (M.S.K., H.Xu, C. Boone, and J.R., unpublished data). Whereas Swc4p was shared with SWR1-Com, Eaf1p was not. However, cells lacking EAF1 were sensitive to DNA-damaging drugs and genotoxic stress conditions, as were cells lacking subunits of SWR1-Com and H2A.Z. While NuA4's involvement in DNA damage survival was known (Bird et al. 2002; Choy and Kron 2002; Boudreault et al. 2003), the data presented here extended this view, suggesting that it might be more broadly required in the maintenance of genomic integrity in concert with SWR1-Com. Genetic interactions between EAF1 and three SWR1-Com subunits uncover a deeper connection. Specifically, the synthetic lethality of eaflΔ in combination with null alleles of SWR1-Com indicated that these complexes were likely to share an essential function. That is, genes encoding subunits of SWR1-Com became essential when NuA4 activity was compromised by deletion of EAF1, and vice versa. While understanding the mechanisms will require further work, these data suggested important functional links between the H2A.Z deposition machinery and the NuA4 HAT. Why Do SWR1, NuA4, and Ino80 Complexes Share Subunits? As discussed above, a third of the subunits of SWR1-Com are shared with the Ino80 complex, the NuA4 HAT, or both. While the sharing of subunits between different protein complexes is not unprecedented, it may reflect highly related functions, rather than vagaries of chance and circumstance in evolution. This was supported by the functional overlap and genetic interactions between SWR1-Com and NuA4. The shared subunits may act as a core scaffold, upon which the unique subunits can be assembled and exchanged during a cycle of chromatin modification. This notion finds some support in the existence of a mini-NuA4 complex, known as piccolo NuA4, which contains only some of those subunits that are unique to NuA4 (Boudreault et al. 2003). Shared subunits of SWR1-Com could coordinate the recruitment of an analogous mini-SWR1-Com to achieve histone subunit replacement, with the replacement of mini-SWR1-Com by piccolo NuA4 to achieve the acetylation of the newly reconstituted nucleosome. This model could explain why two subunits of NuA4 (Tra1 and Epl1p) were detected in the H2A.Z-associated material under low stringency conditions (see Table 1). Alternatively, the acetylation of H2A by NuA4 may facilitate its replacement by H2A.Z. Other orders of action involving the SWR1-Com, NuA4, and Ino80-C complex are also possible, such as acetylation of H2A.Z by NuA4 being a prerequisite for its exchange by SWR1-Com. Other potential roles for the sharing of subunits include targeting complexes to common locations or promoting their biogenesis or assembly. Our data may resolve an interesting paradox concerning the localization of Bdf1p on chromatin. Earlier work showed that Bdf1p is a subunit of TFIID, yet Bdf1p was found in regions where TATA box binding protein, the core subunit of TFIID, was not (Matangkasombut and Buratowski 2003). The discovery that Bdf1p is part of two distinct complexes, SWR1-Com and TFIID, explains the lack of a perfect correspondence between Bdf1p and TATA box binding protein localization. Materials and Methods Yeast techniques Strains are listed in Table S2. Sequences encoding the TAP-tag (Rigaut et al. 1999) or a triple HA-tag (Longtine et al. 1998) were integrated in frame at the 3′ end of genes using homologous recombination and one-step gene integration of PCR-amplified modules. Similarly, complete deletion of genes was achieved by a similar strategy as described before (Longtine et al. 1998). Large-scale affinity purifications Purifications of native protein complexes were performed using extracts from strains with a segment encoding the TAP tag fused in-frame to the 3′ end of the chromosomal gene of interest (Rigaut et al. 1999). In general, purifications were performed from extracts obtained from 2 l cultures that were harvested in late logarithmic phase. Our protocol for the initial purifications presented in Table 1 was modified from published protocols in a way to maximize recovery of intact protein complexes. Briefly, cells were disrupted with a coffee grinder in the presence of dry ice pellets and resuspended in 0.8 volumes/weight of TAP-B1 (50 mM Tris-Cl [pH 7.8], 200 mM NaCl, 1.5 mM MgAc, 1 mM DTT, 10 mM NaPPi, 5 mM EGTA, 5 mM EDTA, 0.1 mM Na3VO4, 5 mM NaF, Complete Protease inhibitor cocktail [Roche, Basel, Switzerland]). Crude extracts were prepared by centrifugation in a SS34 rotor for 20 min at 14,000 rpm. These were then further clarified by ultracentrifugation (Ti70 rotor, 33,500 rpm for 60 min). NP-40 was added to a final concentration of 0.15%, and the extract was incubated with 200 μl of IgG Sepharose beads (Amersham Biosciences, Little Chalfont, United Kingdom) for 90 min at 4 °C. The beads were then washed with 800 μl of TAP-B2 (50 mM Tris-Cl [pH 7.8], 200 mM NaCl, 1.5 mM MgAc, 1 mM DTT, 0.15% NP-40). After washing, the TAP tag was cleaved by adding 10 μl of TEV protease (GIBCO, San Diego, California, United States) in 200 μl of TAP-B2 to the beads and incubating at 16 °C for 90 min. Cleaved protein complexes were eluted with an additional 200 μl of TAP-B3 (50 mM Tris-Cl [pH 7.5], 200 mM NaCl, 1.5 mM MgAc, 1 mM DTT, 4 mM CaCl2, 0.15% NP-40) The material eluted by the TEV protease cleavage from the first affinity matrix was incubated with 200 μl of Calmodulin beads (Stratagene, La Jolla, California, United States) for 60 min at 4 °C. Beads were washed with 400 μl of TAP-B4 (50 mM Tris-Cl [pH 7.8], 200 mM NaCl, 1.5 mM MgAc, 1 mM DTT, 2 mM CaCl2, 0.15% NP-40) followed by 200 μl of TAP-B5 (50 mM Tris-Cl [pH 7.5], 200 mM NaCl, 1.5 mM MgAc, 0.5mM CaCl2). Finally, the proteins were eluted by adding 600 μl of TAP-EB (20 mM Tris-Cl [pH 7.9], 5 mM EGTA) to the beads and incubating for 30 min at room temperature, and were then precipitated with trichloroacetic acid. A similar, but more stringent, procedure was used to purify the complexes shown in Figures 2A and 4B. The main differences were an increase in salt concentration to 350 mM NaCl during extraction, column binding, and washing and the amount of washes applied to the columns, which were increased to 40 column volumes at each step. In addition, 10% glycerol was present in all buffers. Protein identification The protein composition of the final fraction resulting from the TAP procedure was determined using Direct Analysis of Large Protein Complexes technology as described previously (Sanders et al. 2002). Briefly, proteins were precipitated and proteolyzed by trypsin. The peptides resulting from the digestion were separated by multidimensional capillary chromatography and subjected to mass spectrometry. Analytical-scale affinity purifications For coprecipitation assays, we prepared extracts from 150 ml yeast cultures harvested at an OD600 of 1.0. Cells were pelleted, washed with PBS, and resuspended in 0.6 ml of TAP-IPB (50 mM Tris [pH 7.8], 150 mM NaCl, 1.5 mM MgAc, 0.15% NP-40, 1 mM DTT, 10 mM NaPPi, 5 mM EGTA, 5 mM EDTA, 0.1 mM Na3VO4, 5 mM NaF, CompleteTM Protease inhibitor cocktail). Acid-washed glass beads were added, and the cells were disrupted mechanically using a bead beater (BioSpec Products, Bartlesville, Oklahoma, United States) for 5 min. Insoluble material after cell disruption was removed by centrifugation in a microfuge at 14,000 rpm for 20 min. The supernatant was incubated with 25 μl of IgG sepharose beads (Amersham Biosciences) for 90 min at 4 °C. Beads were then pelleted and washed three times with 0.6 ml of TAP-IPB. After washing, the beads were resuspended in SDS sample buffer and subjected to SDS PAGE and immunoblotting with anti-HA-Peroxidase antibody (#2 013 819; Roche) and antibodies against Tra1p (a generous gift from J. Workman), Bdf1p (a generous gift from A. Ladurner), and Act1p (a generous gift from D. Drubin). Microarray expression analysis The strains used for expression analysis were derived from S288c: YM1823 MATα swr1Δ::kanMX4 his3Δ1 leu2Δ0 ura3Δ0 lys2Δ0 (obtained from the MATα yeast deletion collection; Research Genetics, Huntsville, Alabama, United States) and YM1769 MATα his3Δ1 leu2Δ0 ura3Δ0 lys2Δ0. Exponentially growing cultures were diluted to OD600 0.1 in yeast extract-peptone-dextrose medium (YPD) (Qbiogene, Carlsbad, California, United States) supplemented with tryptophan and adenine. Each mutant culture was paired with a wild-type (wt) culture placed in an adjacent slot in a shaker. Four such pairs of cultures were grown at 30 °C to OD600 0.8. Cultures were harvested at identical optical densities by vacuum filtration onto nitrocellulose filters (0.45 μm; Millipore, Billerica, Massachuesetts, United States), and snap-frozen in 15 ml conical tubes in liquid nitrogen. Total RNA was extracted as described (http://www.microarrays.org), and mRNA was prepared using oligo-dT coupled to latex beads, using the manufacturer's protocol (Oligotex mRNA Mini Kit; Qiagen, Valencia, California, United States). mRNA was then reverse-transcribed into cDNA. Microarrays were fabricated as described by DeRisi et al. (1997). Yeast ORFs were amplified using a commercially available primer set (Research Genetics), with yeast genomic DNA as a template. PCR products were verified by gel electrophoresis, precipitated and resuspended in 3X SSC and robotically spotted onto poly-L-lysine-coated glass slides. The exposed poly-L-lysine was then blocked using the succinic anhydride method. Detailed protocols are available at http://www.microarrays.org. After chemical coupling to Cy5 and Cy3 fluorescent dyes, mutant and wt cDNA samples were mixed and hybridized to microarrays at 63 °C for 12–16 h. Two of the four hybridizations were performed with fluor-reversed samples to avoid artifacts arising from differences in coupling efficiency of the two dyes. After washing and drying, the arrays were scanned on a Genepix 4000B scanner (Axon Instruments, Union City, California, United States) and the images analyzed using Genepix 3.0 software to determine the ratio of median fluorescence intensity (above background) for each spot. After flagging poor quality spots, the ratios were normalized for total signal in the two samples. After filtering the data for dim and uneven spots, genes with at least three good measurements were retained for statistical analysis. The swr1Δ/wt mRNA ratios were analyzed using the SAM (Significance Analysis of Microarrays) statistical package (Tusher et al. 2001) to determine significantly induced or repressed genes. Missing values were estimated using the KNN algorithm with ten nearest neighbors. The analysis was performed with a delta value corresponding to a median false-positive rate less than 1% (Tibshirani et al. 2002). The full dataset is available at http://madhanilab.ucsf.edu/public/swr1. Chromatin immunoprecipitation ChIP assays were performed and analyzed exactly as described by Meneghini et al. (2003) with the following modifications. DNA derived from the whole cell and pellet fractions was analyzed by real-time PCR and Syber Green fluorescence on an MJ Research (Waltham, Massachusetts, United States) Opticon instrument using DNA derived from whole cell extracts as a standard. Oligonucleotides used correspond to those described by Meneghini et al. (2003) and those in Table S3. Histone acetylation assays Yeast whole cell extracts were prepared from cells growing in logarithmic phase by glass bead lysis in the presence of trichloroacetic acid. Equal amounts of whole cell extract were subjected to SDS-PAGE and immunoblotting. The antibodies used were directed against tetraacetylated H4 (#05-698; Upstate Biotechnology, Lake Placid, New York, United States), acetylated K5 of H4 (#AHP414; Serotec, Raleigh, North Carolina, United States), acetylated K8 of H4 (Serotec # AHP415), acetylated K12 of H4 (Serotec #AHP416), acetylated K16 of H4 (Serotec #AHP417), and acetylated K9 of H2A (Upstate Biotechnology #07-289). Supporting Information Figure S1 A Fraction of SWR1-Com Subunits Cosedimented Fractions collected from glycerol gradient centrifugations of whole cell extracts containing HA-tagged SWR1-Com subunits (shown on the right) were analyzed by immunoblot with an anti-HA antibody. The gradients were from 10% to 40 % glycerol and 22 0.1-ml fractions were collected in each case, starting at the top (Fraction 1). A percentage of the total cellular pool of all six SWR1-Com subunits that were tested was present in the same fractions, consistent with their association in one complex. (263 KB PDF). Click here for additional data file. Figure S2 H2A.Z Was Protected from Degradation by SWR1-Com Three different dilutions of whole cell extracts from wt or swr1Δ strains were tested for levels of 3HA-H2A.Z using an anti-HA antibody. Equal amounts of total protein extract were present at each dilution, as seen by the immunoblot with the antibody against Vma1p. The level of H2A.Z in the swr1Δ mutant was reduced approximately 2- to 3-fold. This suggested that the SWR1-Com contributed to the stability of H2A.Z, likely by protecting it from protein degradation. (54 KB PDF). Click here for additional data file. Table S1 Peptides in the Rvb2-TAP Purification (54 KB PDF). Click here for additional data file. Table S2 Yeast Strains Used in This Study (95 KB PDF). Click here for additional data file. Table S3 ChIP Oligo Sequences (60 KB PDF). Click here for additional data file. Accession Numbers The Saccharomyces genome database (http://www.yeastgenome.org) accession numbers of the proteins discussed in this paper are actin (SGDID S0001855), Arp4 (SGDID S0003617), Asf1p (SGDID S0003651), Bdf1p (SGDID S0004391), CAF-I (SGDID S0006222), Cse4p (SGDID S0001532), Eaf3p (SGDID S0006227), Epl1p (SGDID S0001870), Esa1p (SGDID S0005770), H2A (SGDID S0002633), H2B (SGDID S0002632), Kap114p (SGDID S0003210), Nap1p (SGDID S0001756), Rvb1p (SGDID S0002598), Rvb2p (SGDID S0003118), Rvb2p (SGDID S0006156), SAS-I HAT (SGDID S0005739), Sir2p (SGDID S0002200), Sir3p (SGDID S0004434), Sir4p (SGDID S0002635), Swc2p (SGDID S0002893), Swc3p (SGDID S0000009), Swc4p (SGDID S0003234), Swc6p (SGDID S0004505), Swc7p (SGDID S0004377), Swi2/Snf2 (SGDID S0005816), Swr1p (SGDID S0002742), Tra1p (SGDID S0001141), Yaf9p (SGDID S0005051), and Yng2p (SGDID S0001132). The Saccharomyces genome database accession numbers of the genes discussed in this paper are AAD3 (SGDID S0000704), HML (SGDID S0029214), HMR (SGDID S0029655), HTZ1 (SGDID S0005372), PRP8 ORF (SGDID S0001208), YDR359C (SGDID S0002767), and YNR074C (SGDID S0005357). The GenBank (http://www.ncbi.nih.gov/Genbank/index.html) accession number of EAF1 is AY464183. We are grateful to C. Wu, P. Nakatani, J. Cote, and J. Greenblatt for sharing unpublished data and to J. Workman, B. Seraphin, D. Drubin, and A. Ladurner for reagents. We also thank J. Babiarz, A.D. Johnson, and P.D. Kaufman for critical reading of the manuscript. This work was supported by long-term fellowships from the Human Frontier Science Program (MSK) and the Damyon Runyon Cancer Foundation (MDM). SV was a predoctoral fellow of the Howard Hughes Medical Institute. JLJ was supported by NIH grants GM64779 and HL68744. AJL was supported by NIH grants GM64779, HL68744, NS43952, ES11993, and CA098131. Work in the laboratory of HDM was supported by the David and Lucille Packard Foundation and the Sandler Foundation, and work in the laboratory of JR was supported by NIH grant GM31105, with core support from an NIEHS Mutagenesis Center Grant. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. MSK, SV, MDM, AJL, HDM, and JR conceived and designed the experiments. MSK, SV, MDM, JWG, JLJ, and AJL performed the experiments. MSK, SV, MDM, JLJ, AJL, HDM, and JR analyzed the data. MSK contributed reagents/materials/analysis tools. MSK, SV, MDM, HDM, and JR wrote the paper. Academic Editor: Peter Becker, University of Münich Abbreviations ChIPchromatin immunoprecipitation HAhemagglutinin HAThistone acetyltransferase HUhydroxyurea ORFopen reading frame SWR1-ComSWR1-Complex TAPtandem affinity purification wtwild-type YPDyeast extract-peptone-dextrose medium ==== Refs References Becker PB Horz W ATP-dependent nucleosome remodeling Annu Rev Biochem 2002 71 247 273 12045097 Bird AW Yu DY Pray-Grant MG Qiu Q Harmon KE Acetylation of histone H4 by Esa1 is required for DNA double-strand break repair Nature 2002 419 411 415 12353039 Boudreault AA Cronier D Selleck W Lacoste N Utley RT Yeast enhancer of polycomb defines global Esa1-dependent acetylation of chromatin Genes Dev 2003 17 1415 1428 12782659 Boyer LA Langer MR Crowley KA Tan S Denu JM Essential role for the SANT domain in the functioning of multiple chromatin remodeling enzymes Mol Cell 2002 10 935 942 12419236 Choy JS Kron SJ NuA4 subunit Yng2 function in intra-S-phase DNA damage response Mol Cell Biol 2002 22 8215 8225 12417725 DeRisi JL Iyer VR Brown PO Exploring the metabolic and genetic control of gene expression on a genomic scale Science 1997 278 680 686 9381177 Formosa T Ruone S Adams MD Olsen AE Eriksson P Defects in SPT16 or POB3 (yFACT) in Saccharomyces cerevisiae cause dependence on the Hir/Hpc pathway: Polymerase passage may degrade chromatin structure Genetics 2002 162 1557 1571 12524332 Fuchs M Gerber J Drapkin R Sif S Ikura T The p400 complex is an essential E1A transformation target Cell 2001 106 297 307 11509179 Galarneau L Nourani A Boudreault AA Zhang Y Heliot L Multiple links between the NuA4 histone acetyltransferase complex and epigenetic control of transcription Mol Cell 2000 5 927 937 10911987 Gavin AC Bosche M Krause R Grandi P Marzioch M Functional organization of the yeast proteome by systematic analysis of protein complexes Nature 2002 415 141 147 11805826 Ghaemmaghami S Huh WK Bower K Howson RW Belle A Global analysis of protein expression in yeast Nature 2003 425 737 741 14562106 Haushalter KA Kadonaga JT Chromatin assembly by DNA-translocating motors Nat Rev Mol Cell Biol 2003 4 613 620 12923523 Hoek M Stillman B Chromatin assembly factor 1 is essential and couples chromatin assembly to DNA replication in vivo Proc Natl Acad Sci U S A 2003 100 12183 12188 14519857 Huh WK Falvo JV Gerke LC Carroll AS Howson RW Global analysis of protein localization in budding yeast Nature 2003 425 686 691 14562095 Ishimi Y Kikuchi A Identification and molecular cloning of yeast homolog of nucleosome assembly protein I which facilitates nucleosome assembly in vitro J Biol Chem 1991 266 7025 7029 2016313 Jackson V Deposition of newly synthesized histones: New histones H2A and H2B do not deposit in the same nucleosome with new histones H3 and H4 Biochemistry 1987 26 2315 2325 3620448 Johnston H Kneer J Chackalaparampil I Yaciuk P Chrivia J Identification of a novel SNF2/SWI2 protein family member, SRCAP, which interacts with CREB-binding protein J Biol Chem 1999 274 16370 16376 10347196 Kaufman PD Cohen JL Osley MA Hir proteins are required for position-dependent gene silencing in Saccharomyces cerevisiae in the absence of chromatin assembly factor I Mol Cell Biol 1998 18 4793 4806 9671489 Kellogg DR Murray AW NAP1 acts with Clb1 to perform mitotic functions and to suppress polar bud growth in budding yeast J Cell Biol 1995 130 675 685 7622567 Krogan NJ Keogh MC Datta N Sawa C Ryan OW A Snf2 family ATPase complex required for recruitment of the histone H2A variant Htz1 Mol Cell 2003 12 1565 1576 14690608 Ladurner AG Inouye C Jain R Tjian R Bromodomains mediate an acetyl-histone encoded antisilencing function at heterochromatin boundaries Mol Cell 2003 11 365 376 12620225 Le Masson I Yu DY Jensen K Chevalier A Courbeyrette R Yaf9, a novel NuA4 histone acetyltransferase subunit, is required for the cellular response to spindle stress in yeast Mol Cell Biol 2003 23 6086 6102 12917332 Lee JW Choi HS Gyuris J Brent R Moore DD Two classes of proteins dependent on either the presence or absence of thyroid hormone for interaction with the thyroid hormone receptor Mol Endocrinol 1995 9 243 254 7776974 Letunic I Goodstadt L Dickens NJ Doerks T Schultz J Recent improvements to the SMART domain-based sequence annotation resource Nucleic Acids Res 2002 30 242 244 11752305 Link AJ Eng J Schieltz DM Carmack E Mize GJ Direct analysis of protein complexes using mass spectrometry Nat Biotechnol 1999 17 676 682 10404161 Longtine MS McKenzie A 3rd Demarini DJ Shah NG Wach A Additional modules for versatile and economical PCR-based gene deletion and modification in Saccharomyces cerevisiae Yeast 1998 14 953 961 9717241 Matangkasombut O Buratowski S Different sensitivities of bromodomain factors 1 and 2 to histone H4 acetylation Mol Cell 2003 11 353 363 12620224 Meneghini MD Wu M Madhani HD Conserved histone variant H2A.Z protects euchromatin from the ectopic spread of silent heterochromatin Cell 2003 112 725 736 12628191 Mizuguchi G Shen X Landry J Wu WH Sen S ATP-driven exchange of histone H2AZ variant catalyzed by SWR1 chromatin remodeling complex Science 2004 303 343 348 14645854 Peterson CL Chromatin remodeling enzymes: Taming the machines. Third in review series on chromatin dynamics EMBO Rep 2002 3 319 322 11943761 Pollard KJ Peterson CL Chromatin remodeling: A marriage between two families? Bioessays 1998 20 771 780 9819566 Rigaut G Shevchenko A Rutz B Wilm M Mann M A generic protein purification method for protein complex characterization and proteome exploration Nat Biotechnol 1999 17 1030 1032 10504710 Ruhf ML Braun A Papoulas O Tamkun JW Randsholt N The domino gene of Drosophila encodes novel members of the SWI2/SNF2 family of DNA-dependent ATPases, which contribute to the silencing of homeotic genes Development 2001 128 1429 1441 11262242 Rusche LN Kirchmaier AL Rine J The establishment, inheritance, and function of silenced chromatin in Saccharomyces cerevisiae Annu Rev Biochem 2003 72 481 516 12676793 Sanders SL Jennings J Canutescu A Link AJ Weil PA Proteomics of the eukaryotic transcription machinery: Identification of proteins associated with components of yeast TFIID by multidimensional mass spectrometry Mol Cell Biol 2002 22 4723 4738 12052880 Shen X Mizuguchi G Hamiche A Wu C A chromatin remodelling complex involved in transcription and DNA processing Nature 2000 406 541 544 10952318 Shevchenko A Schaft D Roguev A Pijnappel WW Stewart AF Deciphering protein complexes and protein interaction networks by tandem affinity purification and mass spectrometry: Analytical perspective Mol Cell Proteomics 2002 1 204 212 12096120 Smith MM Histone variants and nucleosome deposition pathways Mol Cell 2002 9 1158 1160 12086613 Sterner DE Wang X Bloom MH Simon GM Berger SL The SANT domain of Ada2 is required for normal acetylation of histones by the yeast SAGA complex J Biol Chem 2002 277 8178 8186 11777910 Tavazoie S Hughes JD Campbell MJ Cho RJ Church GM Systematic determination of genetic network architecture Nat Genet 1999 22 281 285 10391217 Tibshirani R Hastie T Narasimhan B Chu G Diagnosis of multiple cancer types by shrunken centroids of gene expression Proc Natl Acad Sci U S A 2002 99 6567 6572 12011421 Tusher VG Tibshirani R Chu G Significance analysis of microarrays applied to the ionizing radiation response Proc Natl Acad Sci U S A 2001 98 5116 5121 11309499 Tyler JK Adams CR Chen SR Kobayashi R Kamakaka RT The RCAF complex mediates chromatin assembly during DNA replication and repair Nature 1999 402 555 560 10591219 Xu EY Kim S Replogle K Rine J Rivier DH Identification of SAS4 and SAS5, two genes that regulate silencing in Saccharomyces cerevisiae Genetics 1999 153 13 23 10471696 Ye X Franco AA Santos H Nelson DM Kaufman PD Defective S phase chromatin assembly causes DNA damage, activation of the S phase checkpoint, and S phase arrest Mol Cell 2003 11 341 351 12620223

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PLoS Biol. 2004 May 23; 2(5):e131

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10.1371/journal.pbio.0020131

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020134SynopsisEvolutionGenetics/Genomics/Gene TherapyNeurosciencePrimatesHomo (Human)Evolutionary History of a Gene Controlling Brain Size Synopsis5 2004 23 3 2004 23 3 2004 2 5 e134Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Accelerated Evolution of the ASPM Gene Controlling Brain Size Begins Prior to Human Brain Expansion ==== Body Biologists have long known that the African great apes (including the chimpanzee, bonobo, and gorilla) are our closest relatives, evolutionarily speaking. The recent release of the chimp draft genome sequence confirms this relationship at the nucleotide level, showing that human and chimp DNA is roughly 99% identical. Given the genetic similarity between human and nonhuman primates, the next big challenge is to identify those changes in the human genotype (the genetic complement of an organism) that generated the complex phenotype (the physical manifestation of gene expression) that distinguishes humans from the great apes. For example, modern humans have larger brains and a larger cerebral cortex than both nonhuman primates and their forebears, the early hominids. Elucidating the molecular mechanisms that account for this expansion will provide insight into brain evolution. MRIs of a normal individual (bottom left) and a patient with microcephaly caused by an ASPM mutation (bottom right). Primate skulls provided courtesy of the Museum of Comparative Zoology, Harvard University One way to figure out which genes are involved in a physiological process is to analyze mutations in the genotype that generate an abnormal phenotype. Such efforts are easier in the relatively rare instance that one gene affects a single trait. Mutations in the ASPM gene cause microencephaly, a rare incurable disorder characterized by an abnormally small cerebral cortex. Since the microencephalic brain is about the same size as the early hominid brain, researchers hypothesized that ASPM—whose normal function is unclear—may have been a target of natural selection in the expansion of the primate cerebral cortex. Last year, researchers showed that selective pressure on the ASPM gene correlated with increased human brain size over the past few million years, when humans and chimps diverged from their common ancestor. Now, Vladimir Larionov and colleagues report that the selective pressure began even earlier—as far back as 7–8 million years ago, when gorillas, chimps, and humans shared a common ancestor. The researchers used a newly developed technology (called TAR-cloning) to extract specialized cloning agents in yeast (called yeast artificial chromosomes, or YACs) containing the entire ASPM gene, including promoter and intronic (noncoding) sequences, from chimpanzees, gorillas, orangutans, and rhesus macaques. They sequenced these YACs to determine the complete genomic sequence of the ASPM gene from each species. Next, they characterized sequence changes among these species, based on whether the resulting substitutions in amino acids produced changes in the ASPM protein, to determine how fast the protein was evolving. Larionov and colleagues found that different parts of the protein evolved at different rates, with the rapidly evolving sequences under positive selection (beneficial mutations were selected for, or retained) and the slowly evolving sequences under “purifying” selection (significant disruptions were jettisoned). Positive selection on genes is one important way to drive evolutionary change. By reconstructing the evolutionary history of the ASPM gene, Larionov and colleagues show that the increase in human brain size—which began some 2–2.5 million years ago—happened millions of years after the gene underwent accelerated selective pressure. The ASPM gene, they conclude, likely plays a significant role in brain evolution. The next big challenge will be identifying the forces that preferentially acted on the human genotype to kick-start the process of brain expansion, forces that promise to shed light on what makes us human. New genomic technologies like TAR-cloning will likely accelerate this process.

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PLoS Biol. 2004 May 23; 2(5):e134

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PLoS Biol

10.1371/journal.pbio.0020134

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020136SynopsisCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologySaccharomycesDepositing a Histone That Protects Active Chromosomal Regions from Silencing Synopsis5 2004 23 3 2004 23 3 2004 2 5 e136Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Protein Complex Containing the Conserved Swi2/Snf2-Related ATPase Swr1p Deposits Histone Variant H2A.Z into Euchromatin ==== Body When James Watson and Francis Crick reported the structure of DNA in 1953, the mechanism of inheritance was instantly apparent. The complementary pairing of the DNA bases in the double helix, the pair famously wrote, “immediately suggests a possible copying mechanism for the genetic material.” The structure helped explain one of the central problems of modern biology: how does genetic material get faithfully replicated and then passed on from generation to generation? It was long thought that DNA is the only unit of inheritance. Nucleosome containing H2A.Z Since then, it's become clear that molecules of DNA are packaged into highly organized superstructures that themselves are inherited. These structures play a significant role in the regulation of genes by preventing or facilitating protein–DNA interactions. In the eukaryotic cell (a cell with a nucleus), DNA exists as long threadlike molecules—a typical human cell contains some 6.5 feet (2 meters) of DNA—that associate with a variety of proteins to form a network called chromatin. Genomic DNA wraps around specialized DNA-packing proteins called histones to form nucleosomes, which condense chromatin into chromosomes and thereby influence chromosome behavior. Chromosomes are in turn packaged in increasingly higher levels of organization, with some parts being dispersed and others condensed. The most condensed region is called heterochromatin, or silent chromatin. Gene expression is largely silent in these regions, since the proteins required for transcription can't access DNA to transcribe genes when chromosomes are so tightly packed. Other regions of chromosomes exist in an extended state, called euchromatin. This is the most genetically active state; with genes exposed, transcription can easily occur. As chromatin shifts between these states, it influences gene expression, largely through the interactions of histones and large protein complexes that together assemble, remodel, and modify chromatin. Since proper cell function depends largely on activating the right gene at the right time, mechanisms have evolved that protect active genes from the intrusions of silencing structures like heterochromatin. Both euchromatin and heterochromatin respond to mechanisms that resist encroachments of the opposite state. One mechanism involves replacing “canonical” (that is, archetypal) histones with a histone variant. Previous work on yeast from Hiten Madhani and colleagues had shown that one histone variant, called H2A.Z, is found specifically in euchromatin and prevents silent chromatin from spreading into adjacent euchromatic regions. While researchers have characterized some of the mechanisms that deposit canonical histones onto euchromatin, they knew little about the mechanisms that deposit variant histones. In this issue of PLoS Biology, Jasper Rine, Hiten Madhani, and colleagues identify and characterize the function of a protein complex that helps deposit the variant H2A.Z onto euchromatin in yeast. To investigate which proteins help direct H2A.Z to specific chromosomal locations, the authors isolated H2A.Z, along with whatever proteins were associated with it, from yeast cell extracts. They determined that 15 proteins were true binding partners of H2A.Z and that 13 of them form a complex called SWR1-Com. The largest subunit of this complex, called Swr1p, belongs to a well-known family of adenosine triphosphate (ATP)-dependent chromatin remodeling enzymes (they use the energy of ATP to power remodeling) that provide access to DNA in chromatin. Rine, Madhani, and colleagues show that protein subunits of SWR1-Com associate specifically with the histone variant H2A.Z. By comparing the gene expression profiles of yeast mutants lacking the H2A.Z-encoding gene with mutants lacking the Swr1p-encoding gene, the authors show that H2A.Z depends on the SWR1-Com protein complex to function. Most importantly, they show that SWR1-Com is required in living cells to deposit H2A.Z onto euchromatin. Interestingly, the authors note, SWR1-Com shares subunits with a histone-acetylating enzyme involved in the regulation of transcription (called the NuA4 histone acetyltransferase) and with another chromatin remodeler, which suggests that biochemical modifications of the subunits on histone “tails” may play a role in replacing H2A with H2A.Z. This histone–protein complex, the authors conclude, represents a chromatin remodeling machine with a novel function, revealing a new role for Swr1p-type enzymes and a novel mechanism of genome regulation. By preventing the spread of silent chromatin into transcriptionally active chromosomal regions—the result of the interaction described here—this mechanism allows the cell's gene expression program to operate with precision and on schedule. Since chromosomes can be inherited by daughter cells in this active state, such mechanisms ensure that gene expression programs essential for ongoing fundamental processes like embryogenesis and cellular differentiation proceed without interference.

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2021-01-05 08:26:25

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PLoS Biol. 2004 May 23; 2(5):e136

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PLoS Biol

10.1371/journal.pbio.0020136

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020109Research ArticleEvolutionGenetics/Genomics/Gene TherapyTeleost FishesThe Genetic Architecture of Parallel Armor Plate Reduction in Threespine Sticklebacks Genetics of Parallel Armor EvolutionColosimo Pamela F 1 Peichel Catherine L 2 Nereng Kirsten 1 Blackman Benjamin K 1 Shapiro Michael D 1 Schluter Dolph 3 Kingsley David M kingsley@cmgm.stanford.edu 1 1Department of Developmental Biology and Howard Hughes Medical Institute, Stanford University School of MedicineStanford, CaliforniaUnited States of America2Fred Hutchinson Cancer Research Center, SeattleWashingtonUnited States of America3University of British Columbia, VancouverBritish ColumbiaCanada5 2004 30 3 2004 30 3 2004 2 5 e10919 12 2003 5 2 2004 Copyright: © 2004 Colosimo et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Single Locus Controls Majority of Armor Evolution in Two Populations of Sticklebacks How many genetic changes control the evolution of new traits in natural populations? Are the same genetic changes seen in cases of parallel evolution? Despite long-standing interest in these questions, they have been difficult to address, particularly in vertebrates. We have analyzed the genetic basis of natural variation in three different aspects of the skeletal armor of threespine sticklebacks (Gasterosteus aculeatus): the pattern, number, and size of the bony lateral plates. A few chromosomal regions can account for variation in all three aspects of the lateral plates, with one major locus contributing to most of the variation in lateral plate pattern and number. Genetic mapping and allelic complementation experiments show that the same major locus is responsible for the parallel evolution of armor plate reduction in two widely separated populations. These results suggest that a small number of genetic changes can produce major skeletal alterations in natural populations and that the same major locus is used repeatedly when similar traits evolve in different locations. In stickleback populations separated over 10,000 years ago, dramatic morphological evolution appears to result from a relatively small number of genetic changes ==== Body Introduction The number and type of genetic changes that control morphological and physiological changes during vertebrate evolution are not yet known. The evolutionary history of threespine sticklebacks (Gasterosteus aculeatus) provides an unusual opportunity to directly study the genetic architecture of adaptive divergence in natural populations. At the end of the last ice age, marine sticklebacks colonized newly formed freshwater environments throughout the Northern Hemisphere. Over the last 10,000 to 15,000 years, these fish have adapted to a wide range of new ecological conditions, giving rise to diverse populations with striking differences in morphology, physiology, and behavior (Bell and Foster 1994). Major changes in the bony armor have evolved repeatedly in different locations, and several hypotheses have been proposed to explain this morphological transformation, including response to changes in calcium availability (Giles 1983), stream gradients (Baumgartner and Bell 1984), or temperature, salinity, or other factors that may vary in parallel with climate (Heuts 1947; Hagen and Moodie 1982); or exposure to different types of predators (Hagen and Gilbertson 1973a; Moodie et al. 1973; Reimchen 1992; Reimchen 1995). Three distinctive patterns of body armor, now known as the “lateral plate morphs,” have been recognized as one of the most distinguishing characteristics in sticklebacks since at least the early 1800s (Cuvier and Valenciennes 1829). Most marine sticklebacks have a continuous row of bony plates that covers the lateral side of the body from head to tail (the “complete morph”; see marine fish in Figure 1). In contrast, many freshwater sticklebacks show substantial reductions in total plate number, developing either as “partial morphs,” which lose plates in the middle of the row (not shown), or as “low morphs,” which retain only a few plates at the anterior end (see Paxton benthic and Friant California [lower animal] fish in Figure 1). The anterior plates present in low morphs are the first to form during larval development. In contrast, the middle plates absent in partial morphs are the last to form during normal development (Igarashi 1964; Igarashi 1970; Bell 1981). Thus, the adult plate patterns of low and partial morphs resemble early developmental stages of plate patterns in complete morphs, and paedomorphosis has been proposed as a possible explanation for the repeated evolution of low and partial morphs from completely plated ancestors (Bell 1981). Figure 1 Mapping the Genetic Basis of Lateral Plate Reduction in Different Natural Populations of Threespine Sticklebacks Crossing a completely plated Japanese marine fish with a low-plated fish from Paxton Lake, British Columbia, produced a mixture of complete, partial, and low morph phenotypes in F2 progeny animals (Cross 1). In contrast, crossing a completely plated fish and a low-plated fish from an inland freshwater stream in Friant, California, produced only complete and low-plated progeny (Cross 2). Red dots show the geographic origins of the populations studied. Scale bars equal 1 cm. AA, Aa, and aa refer to genotypes at Gac4174 (a microsatellite marker) near the major plate locus on LG 4. The genotype at Gac4174 is missing in ten of the 360 F2s in Cross 1. All fish were stained with alizarin red to reveal bony structures. This dramatic variation in lateral plate patterning has led to repeated efforts to determine the genetic basis of the major plate morphs. Previous studies have shown that plate morphs are reproducibly inherited in the laboratory and that crosses between different morphs generate relatively simple ratios of the three major phenotypes among the progeny. Based on these qualitative results, at least six different genetic models have been proposed for lateral plate patterns in sticklebacks. The simplest models proposed a single major locus with alternative alleles (A and a) (Munzing 1959; Avise 1976). The A allele was first proposed to be incompletely dominant to the a allele, generating either complete (AA), partial (Aa), or low-plated (aa) fish (Munzing 1959). In other populations, the A allele may be completely dominant to the a allele, producing either complete (AA, Aa) or low-plated (aa) fish, but no partials (Avise 1976). More complicated models have proposed two major loci controlling plate inheritance (with alternative alleles A, a and B, b). In one of these models, both major loci contribute equally to plate phenotype, and the total number of A and B alleles determines whether fish develop as complete (three or more A or B alleles), partial (two A or B alleles), or low-plated fish (one or less A or B allele) (Hagen and Gilbertson 1973b). Additional models have proposed either epistatic interactions between a single major locus and one modifier locus, or the presence of more than two alternative alleles at the major locus to account for variant results in some populations (Ziuganov 1983; Banbura 1994). All of these models were proposed before the development of genomewide genetic markers for sticklebacks (Peichel et al. 2001) and have never been tested by linkage mapping. In this study, we take advantage of these recently developed tools to examine the genetic basis of variation in lateral plate phenotypes in natural populations of sticklebacks. Results To directly analyze the number and location of genetic loci that control plate phenotypes, we crossed a completely plated marine fish with a low-plated benthic fish from Paxton Lake, British Columbia. Three hundred sixty progeny from a single F2 family (Cross 1) were examined in detail for the pattern, number, and size of lateral plates and then genotyped for the inheritance of different alleles at 160 polymorphic loci distributed across all linkage groups. The segregation of plate phenotypes was compared to the segregation of all genetic markers using quantitative trait loci (QTL) analysis (MapQTL; van Ooijen et al. 2002). Significance thresholds for detecting linkage were chosen using conservative criteria for genomewide linkage mapping in noninbred populations (log likelihood ratio [LOD] score ≥ 4.5; van Ooijen 1999). When plate morph was scored as a qualitative trait, a highly significant QTL on linkage group (LG) 4 was detected (LOD = 117; Table 1 and Figure 2). The genotype of the QTL on LG 4 was highly predictive of the major plate morph that developed in a fish. Almost all fish that carried two alleles from the complete morph grandparent in the LG 4 region (hereafter referred to as “AA” animals) showed the complete pattern, whereas fish that carried two alleles from the low morph grandparent in this region (hereafter referred to as “aa” animals) showed the low pattern. In contrast, most fish with one allele from the complete grandparent and one allele from the low grandparent (hereafter referred to as “Aa” animals) developed as either complete or partial fish (see Figure 1). Figure 2 Comparison of QTL Positions for Different Traits LOD scores are shown as a function of genetic distance along different stickleback linkage groups. QTL affecting qualitative plate pattern (red line), total plate number (black lines), or plate size (blue lines) show similar shapes on several linkage groups, suggesting that the same or linked genes control multiple aspects of plate phenotype. Points in LOD plots correspond to the following microsatellite markers from left to right along each linkage group: (A) LG 4: Pitx2 (Stn220), Stn38, Gac62, Stn42, Gac4174, Stn45, Stn183, Stn46, Stn47, Stn184, Stn39; (B) LG 7: Stn70, Stn72, Stn76, Stn71, Stn78, Stn79, Stn75, Stn81, Stn80 Stn82, Pitx1; (C) LG 10: Stn119, Stn120, Stn211, Stn121, Stn124, Stn23, Stn125; (D) LG 25: Stn212, Stn213, Stn214, Stn215, Stn216, Gac1125, Stn217; (E) LG 26: Stn218, Stn219, Bmp6, Stn222, Stn223. Note that markers Stn183 and Stn184 from LG 18 in the Priest Lake cross (Peichel et al. 2001) map together with LG 4 markers in the larger Cross 1. Table 1 Summary of QTL Affecting Lateral Plate Phenotypes in Cross 1 All QTL that exceed the genomewide significance threshold (LOD ≥ 4.5) are shown with their respective LG, maximum LOD score, and PVE at the most closely linked microsatellite marker. Each trait was initially mapped in the large panel of F2 animals. Because plate number is dominated by the phenotypic effect of the major locus on LG 4, we have separately listed the phenotypic effects of the plate number modifier QTL within all major genotypic classes near the major locus (AA, Aa, and aa animals). These results are shown even when they do not exceed the LOD ≥ 4.5 threshold, in order to facilitate comparison of the effects of significant modifiers in different genetic backgrounds. Mean plate number and size measurements were calculated for progeny that inherited either two marine alleles (MM), one marine and one benthic allele (MB), or two benthic alleles (BB) at the microsatellite most closely linked to each QTL. Plate number is the sum of plate counts on both sides of the body. Plate width and plate height were measured in millimeters at the positions indicated in Figure 2, summed for both sides of the body, and standardized by overall body length and body depth, respectively. Statistical analysis was done using one-way ANOVA. *significantly different from MM mean (p < 0.05), **highly significantly different from MM mean (p ≤ 0.0001), #significantly different from MB mean (p < 0.05), ## highly significantly different from MB mean (p ≤ 0.0001), “n/a” indicates “not applicable.” When total plate number was scored, the same major LG 4 chromosome region accounted for more than 75% of the total variance in plate number of F2 fish. Three additional QTL were detected that had significant effects on plate number in Aa animals (Table 1; see Figure 2). Increasing the number of benthic alleles at any of the individual modifiers led to a reduction in mean total plate number, even in the heterozygous state (Table 1). Increasing the number of benthic alleles at the three modifiers considered together caused a more than 2-fold reduction in mean plate number of Aa animals, largely accounting for whether Aa fish developed as either complete, partial, or low morphs (Figure 3A and 3D). Increasing the number of benthic alleles at the same modifier loci also led to a 2-fold reduction in the mean plate number of aa animals but had relatively little effect on the plate number of AA animals (Figure 3B and 3C). Taken together, these results suggest that at least four different loci influence lateral plate phenotypes in this cross. Homozygosity at the major locus largely determines whether fish develop as low (aa) or complete (AA) morphs, while the modifier loci affect the actual number of plates, particularly in Aa and aa animals. Figure 3 Cumulative Effects of Freshwater Alleles on the Number, Pattern, and Size of Lateral Plates in Cross 1 Increasing the total number of Paxton benthic freshwater alleles at modifier QTL on LGs 7, 10, and 26 significantly reduces plate number in animals with one marine (complete morph) and one Paxton benthic (low morph) allele near the major QTL on LG 4 (Aa progeny) (A). The same modifier QTL have little effect on fish with two marine alleles near the major QTL (AA animals) (B) and smaller phenotypic effects on animals with two benthic alleles near the major QTL (aa animals) (C). Increasing the number of benthic alleles also significantly increases the proportion of Aa fish whose overall plate pattern is classified as partial instead of complete (D). (E–F) show plate size effects. Increasing the number of benthic alleles at plate size QTL on LGs 4, 7, and 25 significantly reduces mean plate width of F2 progeny (E). (F) shows the schema of plate size measurements. Lateral plates are shown numbered from anterior to posterior. Error bars in (A–E) represent standard error. The size of individual lateral plates varies significantly between different stickleback populations (Miller and Hubbs 1969; Avise 1976). Although this trait has not been systematically analyzed in previous stickleback crosses, studies of meristic characters in other vertebrates suggest that the size and number of repeating skeletal elements can be controlled separately (Christians et al. 2003). When height and width of specific plates were analyzed, we detected three QTL that accounted for a significant percentage of plate size variability in the cross (Table 1; see Figure 2). Increasing the number of benthic alleles at these loci led to a progressive reduction in plate size (Figure 3E). Two of the three plate size QTL mapped to the same chromosome regions that also affected plate morph or plate number, suggesting that the pattern, number, and size of plates may be controlled by the same or linked genes on LGs 4 and 7 (see Figure 2). In contrast, the QTL affecting lateral plate size mapped to different locations than most QTL controlling the size of dorsal spine and pelvic structures (Peichel et al. 2001; Shapiro et al. 2004), suggesting that the size of different bones are controlled separately in the stickleback skeleton. Some of the differences in previously published models of stickleback plate genetics could be due to different genetic mechanisms operating in different populations. To compare the genetic architecture of armor plate patterning in a separate population located over 1300 km from Paxton Lake, we crossed fish from an unusual stickleback population in Friant, California, which is largely dimorphic for complete and low fish with very few partials. A cross between a Friant complete and a Friant low-plated fish resulted in nearly equal numbers of complete and low progeny (see Figure 1, Cross 2), consistent with previous crosses from this population (Avise 1976). Genotyping studies with microsatellite markers linked to the major and minor QTL defined above showed very tight concordance between lateral plate phenotype and genotype near the same major locus on LG 4 that was seen in Cross 1 (LOD = 11.1). All fish with an inferred Aa genotype at the major locus on LG 4 were completely plated in this cross, suggesting that Aa fish develop more plates in Cross 2 than in Cross 1. This could be due to differences in the dominance relationship of the particular alleles at the LG 4 locus in the Friant population (Avise 1976), or to modification of dominance by the different genetic backgrounds in the two crosses. Although the number of animals in Cross 2 was small, significant differences in the mean total plate count of low fish could also be detected in animals that inherited different alleles at microsatellites linked to two of the modifier QTL detected in Cross 1 (alternative alleles at Stn210 on LG 7: mean total plate counts 14.9 ± 0.31 vs. 14.0 ± 0.23, p = 0.0204; alternative alleles at Stn219 on LG 26: 14.8 ± 0.26 vs. 13.9 ± 0.31 plates, p = 0.0352). Overall, these results suggest that both plate morph and plate number are controlled by similar chromosome regions in different populations. To further test whether the same major locus on LG 4 controls armor plate reduction in both populations, we carried out genetic complementation crosses between two low female fish from Friant and one low male fish from Paxton Lake. All 84 progeny developed as low morphs, suggesting that the low-plated phenotype in both populations is likely to be due to the same major locus on LG 4. Discussion QTL Architecture This study reports the first genomewide linkage mapping of lateral plate phenotypes in crosses between major stickleback plate morphs. Our results confirm previous suggestions that dramatic changes in lateral plate patterning can be controlled by one locus of major effect (Munzing 1959; Avise 1976). This major locus on LG 4 can cause a greater than 5-fold change in total plate number and is sufficient to switch the overall morphology of a fish between the complete, partial, and low-plated states. The dramatic phenotypic effects of this locus likely explain why three types of sticklebacks have long been recognized in natural populations (Cuvier and Valenciennes 1829). Further molecular studies will be required to determine whether there are one or multiple mutations in the LG 4 region that account for the major QTL. Plate number within the complete, partial, and low morphs also varies between fish from different locations. Previous studies suggest that sticklebacks with small changes in plate number show differential survival when exposed to predators, suggesting that selection may fine tune the exact number of plates in different environments (Hagen and Gilbertson 1973a; Moodie et al. 1973; Reimchen 1992). We have identified three modifier QTL that cause changes in plate number within all morphs but are unlinked to the major locus. The individual phenotypic effects of these QTL can be as small as a single plate per side (Table 1), while the combined mean effects of the QTL can be as large as 15 plates per side (see Figure 3A). The number of modifier QTL is larger than predicted in previous models. We suspect that this is because of the general difficulty of predicting genetic architecture from simple phenotypic ratios of progeny in crosses that are segregating more than one or two genes. The magnitude of the phenotypic effects of the modifiers, their linkage relationships, and interactions with the major locus could not be predicted accurately from previous studies, highlighting the value of genomewide linkage mapping for studying the genetic architecture of major morphological variation in natural populations. Postglacial freshwater stickleback populations are thought to be derived from completely plated marine ancestors (Bell and Foster 1994). At all of the plate QTL detected in Cross 1, the net effect of the freshwater alleles from the Paxton benthic grandparent is to cause a progressive reduction in the size or number of armor plates (Table 1). All of the QTL that affect plate morph or plate number also have significant effects in the heterozygous state, showing that plate reduction is likely to evolve through semiadditive genetic changes, rather than through purely recessive or purely dominant mutations. Theoretical studies suggest that semiadditive mutations can be fixed more quickly than purely recessive or dominant mutations when they begin at low frequency, although the overall probability of fixation also depends on whether the mutations arise de novo or are originally present in a founder population (Crow and Kimura 1970; Orr and Betancourt 2001). Strong selection on a small number of chromosome regions that have large, semiadditive effects may help explain how dramatic changes in lateral plate patterns have evolved relatively quickly in postglacial stickleback populations. Parallel Evolution Our mapping and complementation results suggest that the same major locus on LG 4 causes major changes in plate pattern in both the Paxton benthic and Friant populations. Phenotypic reduction of lateral plates almost certainly evolved separately in these different locations, given the geographic distance between them (over 1300 km), the presence of completely plated fish in the marine environment separating the sites, and previous studies showing that sticklebacks in nearby lakes have independent mitochondrial haplotypes (Taylor and McPhail 1999). Additional complementation crosses between low-plated fish from Friant and other California populations (Avise 1976; unpublished data), Paxton benthic fish and pelvic-reduced fish from Iceland (Shapiro et al. 2004), and low-plated populations from British Columbia and Japan (Schluter et al. 2004) also produce low-plated progeny. Thus genetic changes at the same major locus may underlie low-plated phenotypes at numerous locations around the world. The present study provides the first genetic mapping evidence that some of the chromosome regions controlling smaller quantitative variation in plate number may also be used repeatedly in different populations. The QTL on LG 26 in Cross 1 maps to a similar position as a QTL influencing plate number within low morph fish from Priest Lake, British Columbia (Peichel et al. 2001). This QTL is also associated with significant variation in plate number of low morphs of the Friant population (Cross 2), suggesting that this chromosomal region on LG 26 contributes to plate number variation in at least three different populations: Paxton, Priest, and Friant sticklebacks. Recent studies suggest that the same genes are also used repeatedly when pigmentation and larval cuticle phenotypes have evolved in parallel in different fly populations (Gompel and Carroll 2003; Sucena et al. 2003) or when melanism has evolved independently in birds and mammals (reviewed in Majerus and Mundy 2003). Repeated use of particular genes may thus be a common theme in parallel evolution of major morphological changes in natural populations of both invertebrates and vertebrates. Why might some genes be used preferentially when similar phenotypes evolve in parallel in wild populations? Alleles that cause plate reduction may already be present at low frequency in marine populations. In that case, parallel phenotypic evolution could occur by repeated selection for the same preexisting alleles in different freshwater locations. Alternatively, some genes may be particularly susceptible to de novo mutations, either because of the size or structure of coding and regulatory regions, or the presence of hotspots for recombination, insertion, or deletion. Finally, only a limited number of either old or new mutations may actually be capable of producing a specific phenotype without also causing deleterious effects on fitness. Mutations with the largest positive selection coefficients will be fixed most rapidly in evolving populations, and this may lead to parallel selection for mutations in the same genes in different populations. A major goal for future work will be to identify the actual genes and mutations that cause parallel evolution of adaptive traits in wild sticklebacks. This study identifies specific markers that are closely linked to chromosome regions that control the pattern, number, and size of lateral plates. With the recent development of BAC libraries and physical maps of the stickleback genome, it should be possible to use forward genetic approaches to identify the genes responsible for the repeated evolution of major morphological transformations in stickleback armor (Kingsley et al. 2004). Cloning and sequencing of such genes will make it possible to determine the molecular mechanisms that underlie parallel evolution in natural populations and should provide new insight into the nature of genetic, genomic, developmental, and ecological constraints that operate as new characteristics appear during the adaptive evolution of vertebrates. Materials and Methods Fish crosses and husbandry For Cross 1, a wild-caught, completely plated marine female from Onnechikappu stream on the east coast of Hokkaido Island, Japan, was crossed to a wild-caught, low-plated benthic male from Paxton Lake, British Columbia. Both parents showed morphologies typical of the marine and benthic populations at their respective collecting sites. The specific populations were chosen because the large average body size of both parents and the estimated divergence between eastern and western Pacific Ocean fish (Orti et al. 1994) were expected to help maximize the size of the progeny, the number of offspring per clutch, and the informativeness of microsatellites and other markers for genetic mapping. F1 progeny were raised to maturity in 30-gallon aquaria and were mated in pairs. Approximately 2600 F2 progeny were raised to a standard length of greater than 28 mm under the same conditions (30-gallon aquaria in a single 18°C room with 16 hours of light and eight hours of dark per day and twice daily feeding of brine shrimp or frozen blood worms). Although limited phenotypic plasticity has been reported for development of some trophic characters in sticklebacks (Day et al. 1994), previous studies have shown that differences in plate number of wild-caught sticklebacks are stable and reproducible when fish are raised under laboratory conditions (see, for example, Hagen 1967). A total of 360 full siblings from a single F2 family were used for genotypic and phenotypic analysis in this study. For Cross 2, one wild-caught, completely plated female from Friant, California, was crossed to one wild-caught, low-plated male from Friant, California. A total of 58 F1 progeny were raised to a standard length of greater than 28 mm in a ZMOD (Marine Biotech, Beverly, Massachusetts, United States). For the complementation cross, two wild-caught, low-plated females from Friant were crossed to one wild-caught, low-plated benthic male from Paxton Lake, British Columbia. At total of 84 F1 progeny were raised to a standard length of greater than 28 mm in 30-gallon aquaria. Genotyping Genotyping of microsatellite markers was performed and analyzed essentially as described in Peichel et al. (2001). Some PCR products were analyzed on a 48-capillary array on an ABI3730xl with GeneMapper v3.0 software and GeneScan 500 LIZ (Applied Biosystems, Foster City, California, United States) used as an internal size standard. A total of 160 markers were analyzed in Cross 1, including 144 previously described microsatellite markers (Peichel et al. 2001), the genes Pitx1, Pitx2 (Stn220), and Tbx4 (Stn221), (Shapiro et al. 2004) and 13 new markers: Bmp6 gene and 12 additional microsatellites (Stn210–219, 222–223). A polymorphism within the 3′ UTR of the Bmp6 gene was genotyped using single strand conformation polymorphism analysis with MDE Gel Solution (BioWhittaker Molecular Applications, Rockland, Maine, United States). PCR bands were visualized using autoradiography. PCR conditions were the same as for the microsatellite markers except 2.5 mM MgCl2 and 10% DMSO were used. Primers for Bmp6 genotyping are: Bmp6F1: 5′ CCCGGTTTAAATCCTCATCC and Bmp6R1: 5′ AGGAGGTGATTGACAGCTCG. Morphological analysis and QTL mapping Fish were stained with alizarin red to detect skeletal structures as described in Peichel et al. (2001). Lateral plates were counted on both sides of each fish. For QTL mapping, the total plate number of both sides was used. Plate width was measured on the first lateral plate located under the first dorsal spine and above the ascending process of the pelvis. Plate height was measured on the lateral plate posterior to the last plate that is under the second dorsal spine and touching the ascending process. These correspond to plate positions 5 and 8 in previous nomenclature (Reimchen 1983). All measurements were done with Vernier calipers accurate to 0.02 mm and had repeatabilities of 1.1% ± 0.9% (SD)(plate width) and 3.9% ± 2.9% (SD)(plate height). Plate width and height measurements on both sides of the body were summed and standardized by body length and depth, respectively. Similar QTL were detected when residuals from regressions of plate width and height on standard body length and depth were mapped. When raw plate width and height measurements were used, we also detected one additional significant QTL on LG 19 (plate width: LG 19, LOD = 5.42, 7.3 percent variance explained [PVE]; plate height: LG 19, LOD = 7.3, 11.6 PVE). Standard body length itself maps to LG 19 (LOD = 10, 13 PVE). The LG 19 effect on plate size is not significant when plate measurements are normalized by standard body length, suggesting that the LG 19 QTL is a general body size QTL, while the other size QTLs (Table 1; see Figure 2) act on plate size separately from total body size. All morphological traits in Cross 1 were analyzed with MapQTL 4.0 (van Ooijen et al. 2002) using the same parameters as described by Peichel et al. (2001). Microsatellite markers that were closely linked to QTL detected in Cross 1 were genotyped in all Cross 2 animals (Gac4174, Stn40, and Stn47 on LG 4; Stn210, Stn71, and Stn76 on LG 7; Stn211 and Stn121 on LG 10; and Stn218, Stn219, and Stn222 on LG 26). LOD scores between LG 4 markers and the major plate locus in Cross 2 were calculated using Map Manager v2.6.6 (Manly 1993). Mean total plate numbers in low-plated fish that inherited different alleles at microsatellite loci on LGs 4, 7, 10, and 26 were compared using one-way ANOVA (Statview v5.0.1, SAS Institute Inc., Cary, North Carolina, United States). Supporting Information The GenBank accession numbers for the Bmp6 gene is AY547294 and for the 12 additional new microsatellites Stn 210–219, 222–223 are BV102488–BV102499. We thank Seiichi Mori for providing Japanese marine sticklebacks and members of the Kingsley laboratory for useful discussions. This work was supported in part by the National Institutes of Health (1P50 HG02568; DMK), the Ludwig Foundation (DMK), a Helen Hay Whitney Foundation postdoctoral fellowship (MDS), and grants from the Natural Sciences and Engineering Research Council of Canada and the Canada Foundation for Innovation (DS). DS is a Canada Research Chair, and DMK is an Associate Investigator of the Howard Hughes Medical Institute. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. PFC, CLP, DS, and DMK conceived and designed the experiments. PFC, CLP, KN, BJB, MDS, and DS performed the experiments. PFC, CLP, and DMK analyzed the data. DS contributed reagents/materials/analysis tools. PFC, CLP, MDS, and DMK wrote the paper. Academic Editor: Nipam Patel, University of California, Berkeley Abbreviations LGlinkage group LODlog likelihood ratio PVEpercent variance explained QTLquantitative trait loci ==== Refs References Avise JC Genetics of plate morphology in an unusual population of threespine sticklebacks (Gasterosteus aculeatus) Genet Res 1976 27 33 46 Banbura J A new model of lateral plate morph inheritance in the threespine stickleback, Gasterosteus aculeatus Theor Appl Genet 1994 88 871 876 24186191 Baumgartner JV Bell MA Lateral plate morph variation in California populations of the threespine stickleback, Gasterosteus aculeatus Evolution 1984 38 665 674 Bell MA Lateral plate polymorphism and ontogeny of the complete plate morph of threespine sticklebacks (Gasterosteus aculeatus) Evolution 1981 35 67 74 Bell MA Foster SA The evolutionary biology of the threespine stickleback 1994 New York Oxford University Press 571 p Christians JK Bingham VK Oliver FK Heath TT Keightley PD Characterization of a QTL affecting skeletal size in mice Mamm Genome 2003 14 175 183 12647240 Crow JF Kimura M An introduction to population genetics theory 1970 New York Harper and Row 591 p Cuvier GL Valenciennes MA Histoire naturelle des poissons. Tome quatrième 1829 Paris Chez F.G. Levrault 518 p Day T Pritchard J Schluter D Ecology and genetics of phenotypic plasticity: A comparison of two sticklebacks Evolution 1994 48 1723 1734 Giles N The possible role of environmental calcium levels during the evolution of phenotypic diversity in Outer Hebridean populations of the three-spined stickleback, Gasterosteus aculeatus J Zool Lond 1983 199 535 544 Gompel N Carroll SB Genetic mechanisms and constraints governing the evolution of correlated traits in drosophilid flies Nature 2003 424 931 935 12931186 Hagen DW Isolating mechanism in threespine sticklebacks (Gasterosteus) J Fish Res Board Can 1967 24 1637 1692 Hagen DW Gilbertson LG Selective predation and the intensity of selection acting upon the lateral plates of threespine sticklebacks Heredity 1973a 30 273 287 Hagen DW Gilbertson LG The genetics of plate morphs in freshwater threespine sticklebacks Heredity 1973b 31 75 84 Hagen DW Moodie GEE Polymorphism for plate morphs in Gasterosteus aculeatus on the east coast of Canada and an hypothesis for their global distribution Can J Zool 1982 60 1032 1042 Heuts MJ Experimental studies on adaptive evolution in Gasterosteus aculeatus L Evolution 1947 1 89 102 Igarashi K Observation on the development of scutes in landlocked form of three-spined stickleback, Gasterosteus aculeatus aculeatus Linnaeus Bull Japan Soc Sci Fish 1964 30 95 103 Igarashi K Formation of the scutes in the marine form of the three-spined stickleback, Gasterosteus aculeatus aculeatus L Annot Zool Jpn 1970 43 34 42 Kingsley DM Zhu B Osoegawa K de Jong PJ Schein J New genomic tools for molecular studies of evolutionary change in sticklebacks Behaviour 2004 (in press) Majerus ME Mundy NI Mammalian melanism: Natural selection in black and white Trends Genet 2003 19 585 588 14585605 Manly KF A Macintosh program for storage and analysis of experimental genetic mapping data Mamm Genome 1993 4 303 313 8318734 Miller RR Hubbs CL Systematics of Gasterosteus aculeatus with particular reference to intergradation and introgression along Pacific coast of North America: A commentary on a recent contribution Copeia 1969 1969 52 59 Moodie GEE McPhail JD Hagen DW Experimental demonstration of selective predation on Gasterosteus aculeatus Behaviour 1973 47 95 105 Munzing J Biologie, Variabilität und Genetik von Gasterosteus aculeatus L. (Pisces): Untersuchungen im Elbegebiet Int Revue Ges Hydrobiol 1959 44 317 382 Orr HA Betancourt AJ Haldane's sieve and adaptation from the standing genetic variation Genetics 2001 157 875 884 11157004 Orti G Bell MA Reimchen TE Meyer A Global survey of mitochondrial DNA sequences in the threespine stickleback: Evidence for recent migrations Evolution 1994 48 608 622 Peichel CL Nereng KS Ohgi KA Cole BL Colosimo PF The genetic architecture of divergence between threespine stickleback species Nature 2001 414 901 905 11780061 Reimchen TE Structural relationships between spines and lateral plates in threespine stickleback (Gasterosteus aculeatus) Evolution 1983 37 931 946 Reimchen TE Injuries on stickleback from attacks by a toothed predator (Oncorhynchus) and implications for the evolution of lateral plates Evolution 1992 46 1224 1230 Reimchen TE Predator-induced cyclical changes in lateral plate frequencies of Gasterosteus Behaviour 1995 132 1079 1094 Schluter D Clifford EA Nemethy M McKinnon JS Parallel evolution and inheritance of quantitative traits Am Nat 2004 (in press) Shapiro MD Marks ME Peichel CL Blackman BK Nereng KS Genetic and developmental basis of evolutionary pelvic reduction in threespine sticklebacks Nature 2004 (in press) Sucena E Delon I Jones I Payre F Stern DL Regulatory evolution of shavenbaby/ovo underlies multiple cases of morphological parallelism Nature 2003 424 935 938 12931187 Taylor EB McPhail JD Evolutionary history of an adaptive radiation in species pairs of threespine sticklebacks (Gasterosteus) Insights from mitochondrial DNA Biol J Linn Soc 1999 66 271 291 van Ooijen JW LOD significance thresholds for QTL analysis in experimental populations of diploid species Heredity 1999 83 613 624 10620035 van Ooijen JW Boer MP Jansen RC Maliepaard C MapQTL 4.0: Software for the calculation of QTL positions on genetic maps 2002 Wageningen (the Netherlands) Plant Research International Ziuganov VV Genetics of osteal plate polymorphism and microevolution of threespine stickleback (Gasterosteus aculeatus L) Theor Appl Genet 1983 65 239 246 24263421

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2021-01-05 08:21:08

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PLoS Biol. 2004 May 30; 2(5):e109

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PLoS Biol

10.1371/journal.pbio.0020109

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020143SynopsisEvolutionGenetics/Genomics/Gene TherapyTeleost FishesSingle Locus Controls Majority of Armor Evolution in Two Populations of Sticklebacks Synopsis5 2004 11 5 2004 11 5 2004 2 5 e143Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. The Genetic Architecture of Parallel Armor Plate Reduction in Threespine Sticklebacks ==== Body The astounding diversity of life—different body shapes and sizes, physiologies, and behaviors—stems from the accumulation of genetic changes through the process we call evolution. But catching a glimpse into the process of evolution at the gene level is difficult, mostly because significant changes to the plant and animal species of today happened a long time ago. Nevertheless, biologists are keen to understand exactly how evolution progresses. For example, how many genes must be altered before noticeable shifts in appearance can be seen? Is evolution the result of changes in many genes with small additive effects, or of just a few mutations that exert a strong influence? Complete and low armor stickleback morphs, Friant, California To tackle these questions, Pamela Colosimo and colleagues turned to threespine stickleback fish, a longtime favorite model system of evolutionary biologists because of its relatively youthful evolutionary history. At the end of the last ice age 10,000 years ago, when glaciers all over the Northern Hemisphere began to melt, small populations of these originally marine-dwelling fish became trapped in newly formed lakes. There, isolated stickleback colonies adapted to new ecological conditions—different predators, food availability, water chemistry, and temperature—and now look distinctly different from their marine ancestors. One of the most obvious changes in appearance is in their body armor—they come in three distinct types, or “morphs.” Marine sticklebacks are covered from head to tail with rows of tightly packed boney plates (a complete morph), while those found in freshwater lakes have fewer body plates (a partial morph) or almost none at all (a low morph). Colosimo and colleagues found that a single region of the genome is largely responsible for the dramatic changes in plate morph, and that this is true for two widely separated populations of independently evolving freshwater sticklebacks. To uncover the genomic regions that affect armor, Colosimo's team crossed fully armored marine sticklebacks from Japan with deep-water, or benthic, low morph fish from Paxton Lake in British Columbia, Canada. They then “mapped” the full genome of second generation offspring using 160 known genetic markers, or loci, as guideposts for distinct regions of the genome—loci that are inherited along with differences in the overall type of plating, and individual plate number and size. The team found that one such locus explained 75% of the variation in plate morphs. Offspring that carried two alleles—versions of the gene—from their marine grandparents, genotype AA, were almost always fully plated. Those that inherited two copies of the allele from their benthic progenitors, aa, were mostly low morphs with very little plating. And Aa heterozygous fish (with one allele from each population) had mostly full or partial plates. Colosimo and colleagues also found three other regions in the genome that significantly affected the number and size of plates. These modifiers had an additive effect—the more benthic alleles inherited, the fewer and smaller the plates; more marine alleles caused a trend toward greater armor. But is this genetic architecture the same for every independently evolving population of lake-bound sticklebacks in North America? Or did the geographically isolated freshwater groups loose their plates through mutations in different genes? Colosimo and colleagues mapped the genome of a population of sticklebacks from Friant, California, which is 800 miles away from Paxton Lake, and found that the same major locus seemed to be controlling plate morph there as well. Crossing a low morph from Friant with a low morph from Paxton yielded only offspring with very little armor. Further, some of the modifiers uncovered in the Paxton fish were also acting on the Friant sticklebacks. So, though these two populations of fish have been separated for 10,000 years, loss of armor in both groups probably stemmed from changes in the same genetic pathway. Without knowing the precise sequence of these genes, it is impossible to tell exactly how and when the alleles that reduce armor arose. Small numbers of individuals with genes causing less plating could have been present in ancestral populations of marine sticklebacks when they were originally locked in newly formed lakes. Alternatively, reduced armor could have arisen independently in different lakes following isolation if, for example, some genes that control armor are predisposed to mutation, or certain armor-related mutations are more advantageous than others. But however it happened, this study clearly shows that dramatic morphological evolution can result from a small number of genetic changes. Further study of this classic system should provide a detailed picture of the genes involved, and of the molecular events that underlie morphological changes in natural populations evolving in new environments.

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2021-01-05 08:21:09

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PLoS Biol. 2004 May 11; 2(5):e143

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PLoS Biol

10.1371/journal.pbio.0020143

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020081Research ArticleEvolutionGenetics/Genomics/Gene TherapyInfectious DiseasesMicrobiologyEubacteriaSegmentally Variable Genes:A New Perspective on Adaptation Segmentally Variable GenesZheng Yu zhengyu@bu.edu 1 Roberts Richard J 2 Kasif Simon 1 3 1Bioinformatics Graduate Program, Boston UniversityBoston, MassachusettsUnited States of America2New England Biolabs, BeverlyMassachusettsUnited States of America3Department of Biomedical Engineering, Boston UniversityBoston, MassachusettsUnited States of America4 2004 13 4 2004 13 4 2004 2 4 e8115 10 2003 20 1 2004 Copyright: © 2004 Zheng et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. "Mosaic" Genes Highlight Forces of Genome Diversity and Adaptation Genomic sequence variation is the hallmark of life and is key to understanding diversity and adaptation among the numerous microorganisms on earth. Analysis of the sequenced microbial genomes suggests that genes are evolving at many different rates. We have attempted to derive a new classification of genes into three broad categories: lineage-specific genes that evolve rapidly and appear unique to individual species or strains; highly conserved genes that frequently perform housekeeping functions; and partially variable genes that contain highly variable regions, at least 70 amino acids long, interspersed among well-conserved regions. The latter we term segmentally variable genes (SVGs), and we suggest that they are especially interesting targets for biochemical studies. Among these genes are ones necessary to deal with the environment, including genes involved in host–pathogen interactions, defense mechanisms, and intracellular responses to internal and environmental changes. For the most part, the detailed function of these variable regions remains unknown. We propose that they are likely to perform important binding functions responsible for protein–protein, protein–nucleic acid, or protein–small molecule interactions. Discerning their function and identifying their binding partners may offer biologists new insights into the basic mechanisms of adaptation, context-dependent evolution, and the interaction between microbes and their environment. Segmentally variable genes show a mosaic pattern of one or more rapidly evolving, variable regions. Discerning their function may provide new insights into the forces that shape genome diversity and adaptation ==== Body Introduction Microbes occupy almost every habitable niche in the biosphere, highlighting their enormous capability for adaptation and survival. This adaptive ability has been refined during millennia of evolution and has resulted in genes that evolve at very different rates. Some, such as housekeeping genes that code for the essential biochemical functions of the organism, are now evolving rather slowly. Others that have to defend against potentially lethal attack by viruses or toxins and adapt to varying environmental conditions, often evolve more rapidly (Murphy 1993; Moxon and Thaler 1997; Jordan et al. 2002). Pathogenic microbes, for example, face stringent tests of their adaptive potential because of the escalating efficiency of the host-defense mechanisms (Moxon and Thaler 1997). In the arms race between pathogens and their hosts, both sides try to improve their overall fitness by deploying sophisticated strategies to generate genetic variability (Elena and Lenski 2003). Sequence divergence during rapid evolution can take many forms. Some genes change throughout their entire sequences, resulting in apparently lineage-specific genes that lack clear similar sequences in current versions of GenBank. Others show a mosaic pattern of one or more variable regions interspersed within conserved regions. This latter group is the subject of this paper and we refer to them as segmentally variable genes (SVGs). For the purpose of the current analysis, we define such variable regions as having a minimum length of 70 amino acids, which would permit them to fold into independent domains. This distinguishes them from most nonfunctional interdomain segments, which are usually shorter and whose principal function depends on length rather than specific sequence content. An example of an SVG family is provided by the cytosine-5 DNA methyltransferases (Posfai et al. 1989). These enzymes typically form parts of restriction-modification systems, which are key components of an important bacterial defense mechanism to protect against phage attack and other unwanted infiltration of foreign DNA (Cheng 1995). These methyltransferases catalyze the addition of a methyl group from S-adenosylmethionine to the 5-position of cytosine and contain a highly variable region of more than 90 amino acids that is responsible for specific DNA sequence recognition (Figure 1A; Posfai et al. 1989; Cheng 1995; Lange et al. 1996). A detailed examination of the three-dimensional (3D) structure of the variable region suggests that it folds into an independent domain, which has been shown to bind to DNA (Cheng et al. 1993). The flanking sequences are highly conserved because they are responsible for the chemistry of methylation, which is common to all members of the family. Variability in this family has arisen because there is a need for great variation in the DNA sequences being recognized so that the specific pattern of methylation becomes a marker to distinguish innate DNA from foreign DNA. Figure 1 Variability Profile for Typical SVGs Blocks in the lines are conserved subsequences identified using the Pfam, BLOCKS, and PRINTS databases. In the variability profile, the x-axis is the amino acid position and the y-axis is the variability index (see Materials and Methods). Variable domains are marked by the black lines over the graph. (A) Cytosine-specific DNA methyltransferase of 355 amino acid long in H. pylori. Notice the variable domain in the middle and the variable segment in its N-terminal region, which is shorter than 70 amino acids and has no known function. (B) Virulence-associated protein homolog (VacB) of 644 amino acid long in H. pylori. It has two variable domains at the N- and C-termini. To the best of our knowledge, there has been no systematic attempt to identify, catalog, and classify similar SVGs in the sequenced microbial genomes. Nor has any attempt been made to find potentially common functions among genes displaying this property. Since it is known that many genes involved in defense mechanisms, such as the DNA methyltransferases and the antigens exposed on the surface of bacteria, show such variability (Roche et al. 2001), it is tempting to speculate that one might identify host-defense genes based on this property. Thus, the regional variability might reflect the influence of diversifying selection pressure that could come from constant interaction with other fast-evolving molecules in the environment. Could such genes be the predominant members of the SVG families? Or do other genes, such as those involved in basic energy metabolism and synthesis, show similar variability? In this paper we provide an initial systematic analysis. We describe our findings about the distribution of SVGs and the potential function achieved by segmental variability. Results Classification of Genes into Three Broad Groups We carried out a classification of the genes in 43 fully sequenced microbial genomes (see Table S1 for a full name list). A Web site (http://geneva.bu.edu) is also provided with results for several selected genomes, including Escherichia coli, Helicobacter pylori, Neisseria meningitidis, and several others. Each gene is accompanied with schematic diagrams from Pfam (Bateman et al. 2002), BLOCKS (Henikoff et al. 1999), PRINTS (Attwood et al. 2003), and the nongapped BLAST (Altschul et al. 1990) analyses. For each genome, the full proteome is compared with the nonredundant GenBank sequence set using nongapped BLAST (see Materials and Methods for the parameters used). Based on the degree of conservation or divergence among similar genes in different species, we classify them into three broad groups. Lineage-specific genes are defined as genes with no significantly similar hits from other species in the current GenBank (E-value cutoff, 1E-5). SVGs are defined as genes containing at least one highly variable region, containing more than 70 amino acids, interspersed among well-conserved regions. In any single SVG family, the length of the variable region can differ only within a certain range (see Materials and Methods for more details). In this paper, regions are considered to be variable if no sequence similarity can be detected against possible homologous genes, where the overall homology is determined by the conserved portions. The rest of the genes in the genome are considered as fully conserved genes. Naturally, this initial soft classification is somewhat dependent on specific thresholds and will be biased by the current state of GenBank and the quality of the annotation. In Figure 2 we show a scatter plot of the three classes of genes in the H. pylori genome in two-dimensional (2D) space, where the x-axis shows the length of the variable region and the y-axis shows the number of possible homologs of each gene. Lineage-specific genes (filled square in Figure 2) by definition naturally cluster on the x-axis. Most of the genes in this group are still annotated as unknown. A few genes with annotated functions in this group, such as the outer-membrane protein family in H. pylori (Tomb et al. 1997), only appear in this organism and contribute to its unique biology. A second group contains fully conserved genes (filled triangle in Figure 2) with only short variable regions. It is in this class that most “housekeeping” genes fall. Examples include the subunits of ATP synthetase F1 (atpD, atpA, atpG) and ribosomal proteins such as rps4 (Figure 2), etc. The third group contains the SVGs (filled diamond in Figure 2). A few examples in this group are labeled with their names in Figure 2 and will be discussed later. In Table 1 we list the number of genes in each category for a representative set of microbial genomes (see Table S1 for a full list). Figure 2 Classification of Three Groups of Genes from a Single Genome, H. pylori, in 2D Space The x-axis is the length of the variable region and the y-axis is the number of possible homologs a gene has from a BLAST search. The variable region length for a lineage-specific gene is defined as the length of the gene so that they naturally cluster onto the x-axis. Multiple variable regions in one gene are represented separately. Table 1 Classification of Genes into Three Broad Categories for a Representative Set of Microbial Genomes See Table S1 for the entire table SVGs are subdivided into different types depending on whether they have one, two, or more variable regions. The number of genes with a single variable region is much larger than the number of genes with multiple ones. In Figure 1A we show the variation profile of an SVG containing one variable region. The variation profile is displayed together with conserved subsequences identified using the Pfam (Bateman et al. 2002), BLOCKS (Henikoff et al. 1999), and PRINTS (Attwood et al. 2003) databases. This gene is the cytosine-specific DNA methyltransferase, M.HpyAVIB, from H. pylori. The variability lies in its DNA recognition domain (approximately 140 amino acids), which in this case recognizes the DNA sequence CCTC (Lin et al. 2001). In Figure 1B we give an example with two variable regions. It is the virulence-associated protein homolog VacB from H. pylori, which has variable regions at both its N-terminus (approximately 200 amino acids) and C-terminus (approximately 100 amino acids). VacB has been shown to encode a 3′–5′ exoribonuclease and is necessary for expression of virulence (Cheng and Deutscher 2002). The conserved central region (approximately 400 amino acids (Pfam domain: RNB) defines a group of homologs distributed in a number of microbial genomes (Zuo and Deutscher 2001). Note that the C-terminal region is variable, and its E. coli homolog contains RNA-binding motifs (Zuo and Deutscher 2001). Although the detailed physiological roles of VacB remain unknown (Cheng and Deutscher 2002), the variable regions may contribute to the determination of substrate specificity of VacB in the RNA quality-control process that eliminates defective ribosomal RNA (rRNA) molecules in different species. The number of SVGs increases as genome sizes vary, from 0.5 MB(Mycoplasma genitalium) to 8.6 MB(Streptomyces coelicolor) (Table 1). For most microorganisms included, the proportion of SVGs varies in the range of 10%–20%. The number of lineage-specific genes, on the other hand, does not appear to correlate with the genome size. Instead, it is influenced by the content of the database. For instance, a “minimal” genome, M. genitalium, has a relatively high content of SVGs (20%) and a low percentage of lineage-specific genes (0.2%). However, when a closely related species, M. pneumoniae, is excluded from the database, its proportion of lineage-specific genes rises to 14%, while the proportion of SVGs remains unchanged. In general, the genomic proportion of SVGs is less affected by the database content. Case Studies of SVGs and Functional Implication of Variability In the following sections, we have selected several SVG families to demonstrate the functional implication of segmental variability. Outer-membrane signal transduction genes/sensor histidine kinases In prokaryotes, two-component signal-transducing systems are common and consist of a histidine kinase (HK) and a response regulator. Most HKs are membrane-bound, homodimeric proteins with an N-terminal periplasmic sensing domain and a C-terminal cytoplasmic kinase domain. HKs usually possess a highly variable sensing domain (usually over 150 amino acids), while the cytoplasmic kinase domain is quite conserved. By diversifying the sensing domain, microorganisms can develop different two-component modules to respond to different signals and interact with small molecules from the exterior. Figure 3 displays the distance matrix calculated from the sensing domains and the kinase domains from a group of highly similar HK genes. As shown in Figure 3, sensing domains are much more diverse than the kinase domains. Moreover, the two regions show distinct clustering patterns, of which only the one for the conserved kinase domains is close to the phylogenetic relationship inferred from 16S rRNA sequences (data not shown). Significant homologies in the sensing regions can only be found in closely related species (e.g., Ralstonia solanacearum [Rs] and Ralstonia metallidurans [Rm] in Figure 3), suggesting rapid divergence after speciation. Other sensor genes involved in cell motility, e.g., genes encoding methyl-accepting chemotactic protein (MCP) (see tlpA, tlpC in Figure 2), are also highly variable in their N-terminal domains. In several bacteria, e.g., Vibrio cholerae, there is a greater number of segmentally variable MCP genes (approximately 40) than in other genomes (see the gene list of V. cholerae at http://geneva.bu.edu), which must correspond to its expanded ability to detect different chemical signals and find favorable environments. Although a few conserved motifs have been detected in the sensing region (Galperin et al. 2001), the exact sensing signals for most prokaryotic HKs are unknown. Figure 3 2D Representation of the Distance Matrix Computed from the Variable and Conserved Domains in a Group of Similar HKs The upper triangle shows the variable domains, the lower one the conserved domains. Amino acid sequence distances are calculated by the PROTDIST program using the Dayhoff PAM matrix. The sequence from each species is the best match (E-value < 1E-10) in that genome to the query E. coli gene. Abbreviations for organisms: Ec, Escherichia coli K12; Ps, Pseudomonas syringae pv. syringae B728a; Rm, Ralstonia metallidurans; Rs, Ralstonia solanacearum; Li, Listeria innocua; Tm, Thermotoga maritime; Ml, Mycobacterium leprae; Mt, Mycobacterium tuberculosis CDC1551; No, Nostoc sp. PCC 7120; Ef, Enterococcus faecalis; Bs, Bacillus subtilis; Ne, Nitrosomonas europaea; Sy, Synechococcus sp. PCC 7942; At, Agrobacterium tumefaciens. The PROTDIST program is included in the PHYLIP software package version 3.5 (Felsenstein 1989). Transporter genes and outer-membrane proteins The biggest family of SVGs is cell envelope-related, including the ATP-binding cassette transporters (ABC transporters), outer-membrane proteins, and virulence-related gene products. For membrane proteins, since part of their sequences are exposed to the outside of the cell and interact directly with the environment, one might hypothesize that the variable portions have evolved rapidly to deal with the changing environmental conditions. ABC transporters are essential for microorganisms because they import nutrients into the cell and export noxious substances and toxins out of the cell. A typical ABC transporter gene in a prokaryote genome has a conserved ATPase domain (approximately 150 amino acids) and a large (over 300 amino acids) variable integral membrane domain. Two examples from this group are the multidrug-resistance genes hetA and spaB shown in Figure 2. It is known that substrates interact with the specific binding sites inside the membrane domain (Holland and Blight 1999), which suggests that the variability in the membrane domain may have to do with substrate selectivity or with different transport kinetics. Moreover, outer-membrane transporters are binding targets for bacteriophages and bacterial toxins. For example, the vitamin B12 transporter BtuB (614 amino acids) is the receptor for bacteriophage BF23 and E-colicin (Bradbeer et al. 1976; Mohanty et al. 2003). The crystal structure of BtuB in E. coli has been solved (Chimento et al. 2003). The variable region in E. coli BtuB overlaps with the 22-strand β-barrel (position 150–360), while the N-terminal hatch domain (position 6–132) and the extreme C-terminal TonB-box domain (position 550–614) are conserved among many homologs (Figure S1). The extracellular loops between contiguous strands in the β-barrel are displayed outside the cell (Chimento et al. 2003) and possibly serve as receptor sites for bacteriophages and toxins. The variability in these loops may be driven by attempts to defend against bacteriophages and interaction with different bacterial toxins. DNA/RNA-processing enzymes DNA/RNA processing enzymes form another large family of SVGs. Characteristic examples are the restriction and modification enzymes, where the DNA methylases have a variable region designed for DNA sequence recognition (Cheng 1995) and the restriction enzymes are almost completely variable. Here we discuss two other genes: DNA gyrase B (gyrB) and DNA topoisomerase A (topA), whose competing actions control the degree of DNA supercoiling (Tse-Dinh et al. 1997). Schematic alignments anchored by the conserved motifs from the BLOCKS database (Henikoff et al. 1999) for both enzymes are shown in Figure 4. The variable region in GyrB is an additional approximately 160 amino acids long segment that is only present in the gram-negative eubacteria (Figure 4B). Experiments probing the role of this region in E. coli GyrB have demonstrated its involvement in DNA binding, although the detailed function is unknown (Chatterji et al. 2000). We suspect that variability in this inserted domain may determine the specificity of the interaction between GyrB and DNA or suggest interaction with other molecules. It is intriguing to see that other gyrases lacking this region are also functional. Figure 4 Schematic Alignment of TopA and GyrB (A) TopA. (B) GyrB. Each line represents a sequence. Black boxes indicate the conserved blocks from the BLOCKS database and are aligned correspondingly. Red boxes in (A) are the zinc-finger motifs reported by Pfam. Notice that the number of occurrences of this motif varies and that there are several sequences without this motif in the C-terminal. The lines between the boxes are the variable sequences that cannot be aligned. Variable domains are labeled in the figure. For TopA, the N-terminal region of approximately 600 amino acids shows extensive sequence similarity while the C-terminal region (over 100 amino acids) is variable both in sequence content and in length (Figure 4A). The conserved N-terminal region of TopA has the catalytic function of relaxing negatively supercoiled DNA (Feinberg et al. 1999). The variable C-terminus of TopA sometimes contains multiple copies of zinc-binding motifs, although there are a few exceptions, e.g., TopA in Mycobacterium tuberculosis (Figure 4A). Interestingly, there are two copies of TopA in H. pylori 26695; one has three zinc-binding motifs in C-terminal region and the other does not. The zinc-binding motifs in E. coli TopA are shown to be involved in the interaction with the β′ subunit of RNA polymerase (Cheng et al. 2003) and in DNA binding (Ahumada and Tse-Dinh 1998). Since RNA polymerase β′ subunit is a fully conserved gene, the overall sequence variation in the C-terminal region of TopA seems more likely to relate to DNA binding. TopA plays an important role in adaptation to environmental challenges, such as heat shock conditions (Tse-Dinh et al. 1997). Deletion experiments show that in E. coli the C-terminal region is important for the in vivo function of TopA during the osmotic stress response (Cheng et al. 2003). All together, these facts suggest a versatile role that the C-terminal region of TopA might play in those processes. Variable regions are sometimes found in DNA processing enzymes with essential and conserved functions. One example is DNA polymerase I, which has a variable region between the conserved C-terminal 5′–3′ polymerase domain and the N-terminal 5′–3′ exonuclease domain. In some polymerases, this region encodes a 3′–5′ exonuclease activity for proofreading replication errors, and conserved motifs can be observed (Derbyshire et al. 1995). However, other polymerases in the same family that lack such proofreading activity show much sequence divergence in this region (Derbyshire et al. 1995). The exact reason why sequence variability is observed in these polymerases is unknown. Another interesting family is the aminoacyl-tRNA synthetases (AARS) (Ibba and Söll 2000). This family of genes is well known for its precision in substrate selection. The molecules known to interact with AARS include tRNA, amino acids, and ATP. Since the same amino acids and ATP molecules are found in all organisms, variability inside the AARS sequences must relate to the recognition and interaction with the tRNAs. Correspondingly, each AARS usually contains a conserved domain for catalysis and acceptor helix interaction and a nonconserved domain that interacts with the variable distal parts of its substrate tRNA (Schimmel et al. 1993). For instance, in bacterial-type prolyl-tRNA synthetase (ProRS), the N-terminal catalytic domain (approximately 200 amino acids) and the C-terminal anticodon-binding domain (approximately 150 amino acids) are highly conserved, while a less conserved region of about 180 amino acids is inserted between them (Figure S2). This variable domain shows similarity to the YbaK domain, which is thought to be involved in oligonucleotide binding (Zhang et al. 2000). Sporadic conserved residues in this region of E. coli ProRS are known to be involved in the posttransfer editing for mischarged Ala-tRNAPro (Wong et al. 2002). ProRS is also known to possess an inherent ability to mischarge cysteine (Ahel et al. 2002). Partial deletion of this variable region of E. coli ProRS results in a lower rate of proline acylation to cysteine acylation (Ahel et al. 2002), suggesting a possible role of substrate discrimination in this region. Thus, the variability in this inserted domain of ProRS appears to contribute to substrate recognition and the editing function of the enzyme. Intriguingly, ProRS in Methanococcus jannaschii, which does not have this inserted region, also possesses editing abilities (Beuning and Musier-Forsyth 2001). As a result, there is a possibility that this region may have another unknown function, e.g., interaction with other undetected molecules. Carbohydrate active enzymes Variable regions exist in carbohydrate metabolizing enzymes, such as glycosyltransferases (GTs) and glycoside hydrolases (GHs), which respectively catalyze the biosynthesis of diverse glycoconjugates and their selective cleavage (Bourne and Henrissat 2001). Many pathogens express outer-membrane glycosylated oligosaccharides, which closely interact with the host environment (Saxon and Bertozzi 2001). For example, they even mimic host cell surface glycoconjugates to evade immune recognition (Persson et al. 2001). Both GTs and GHs have been classified into subfamilies based on sequence similarity (Bourne and Henrissat 2001). Structural studies on bacterial GTs from different subfamilies always reveal two-domain molecules, such as LgtC (Persson et al. 2001), GtfB (Mulichak et al. 2001), MurG (Hu et al. 2003), and SpsA (Charnock and Davies 1999), with one domain responsible for donor molecule (usually nucleotide-diphospho-sugar) binding and the other domain involved in acceptor sugar molecule binding. These genes exhibit great variability in the acceptor-binding domains and conservation in the donor-binding domains (see Figure S3 for the example of GtfB), which agrees with the relatively limited types of donor species (usually UDP/TDP-sugar) and their conserved binding modes, but a diversity of acceptor molecules (LgtC: lactose; GtfB: vancomycin aglycone; MurG: N-acetyl muramyl pentapeptide; SpsA: unknown). Owing to the lack of homology in the acceptor binding domains, the substrate specificities encoded by these regions for most GTs are still unknown. Transcriptional regulators Prokaryotic transcriptional regulators form another large group of SVGs. Transcription regulators are usually two-domain proteins with one binding to DNA and one binding to ligand. The DNA-binding domains, which usually interact with DNA via helix–turn–helix, zinc-finger, or other modes, are more conserved than ligand-binding domains. Based on the characteristic conserved DNA-binding domains, transcriptional regulators can be classified into many different families (Nguyen and Saier 1995; Rigali et al. 2002). Even within each family, the ligand-binding domains are variable. For instance, the C-terminal regions involved in effector molecule binding and oligomerization (E-b/O) inside the GntR transcriptional regulator family are highly variable both in sequence content and in size (Rigali et al. 2002). The variability in the effector molecule-binding domains enables the transcriptional regulators to sense the presence of diverse ligands and signal the regulation of the downstream genes or operons accordingly. As in most previous cases, these variable regions remain functionally uncharacterized. Hypothetical genes In addition to genes with functional annotations, our method identifies a number of SVGs with unknown or hypothetical annotations in each genome (H. pylori: 17 genes; N. meningitidis: 32 genes; V. cholerae: 69 genes, etc.; see http://geneva.bu.edu for the full list). In contrast to lineage-specific hypothetical genes, these hypothetical genes contain conserved domains, which suggest their functional importance. Although most of the conserved domains in these hypothetical genes have currently unknown function, there are a few exceptions. Among them are the prokaryotic mechanosensitive channel proteins, which respond to external osmotic pressure (Pivetti et al. 2003). Examples include the 343 amino acid long E. coli B1330 and 371 amino acid long Bacillus subtilis YhdY, both of which are currently annotated as “hypothetical.” However, they both have the characteristic domain of mechanosensitive proteins (Pfam domain: MS_channel). The central regions (approximately 150 amino acids) of these genes are conserved while both the N-terminal region (approximately 100 amino acids) and the C-terminal region (approximately 100 amino acids) are variable (see alignment in Figure S4). The conserved central region encodes three transmembrane segments, and the molecules are predicted to have their N-terminus outside and C-terminus inside the cell (Miller et al. 2003). Although the C-terminus is variable, the deletion experiments show that it is indispensable for stability and activity of this protein (Miller et al. 2003). It is tempting to hypothesize that the interacting partners for both N- and C-termini might vary in different organisms. Functional Classification of SVGs We are interested in probing the functional distribution of SVGs within a single genome. Are certain functional categories overrepresented? In Figure 5, we show a functional classification of SVGs in three microorganisms using 18 broad functional categories of the clusters of orthologous group (COG) database (Tatusov et al. 1997). We calculated the percentage (r in Figure 5) of SVGs within each functional class and the p-value of overrepresentation (Figure 5). Several functional categories are overrepresented (p-value < 0.01; see Figure 5 for details): (i) cell envelope biogenesis, outer membrane; (ii) DNA replication, recombination and repair; (iii) secondary metabolite biosynthesis, transport and catabolism; (iv) cell motility and secretion; (v) cell division and chromosome partitioning. Among them, only categories (i) and (ii) are overrepresented in all three genomes. Most functional categories involved in the basic metabolic processes are not significantly overrepresented or even underrepresented. The number of overrepresented categories and the order of significance differ from one genome to another, reflecting differences in genome content and presumably the relative importance of the different specific adaptations. Figure 5 Functional Classification of SVGs in Three Microorganisms M is the total number of genes in a COG broad functional category, and m is the number of SVGs within that category. r ( = m/M) is the proportion of SVGs in that category. The p-value is calculated using a hypergeometric distribution: let N = number of genes in the genome; n = number of SVGs identified; M = number of genes belonging to a particular category; m = number of SVGs belonging to a particular category: The set of lineage-specific genes has been excluded in each genome to avoid the possible skew it brings to the estimation of significance. The significance level is set at 0.01. Cells with p-value less than 0.01 are shaded. In Figure 6 we show the relative abundance of a set of SVG families in several microorganisms based on shared keywords in the annotations. The relative enrichments in several gene families for some microbes seem to correlate with the peculiarities of niche adaptation. In particular, H. pylori has more SVGs involved in cell motility and chemotaxis than two other genomes with a similar genome size (N. meningitidis, Streptococcus pneumoniae). H. pylori is one of the few microbes that can colonize the highly acidic gastric environment (Tomb et al. 1997). The motility of H. pylori is crucial for its infectious capability and there is evidence that poorly motile strains are less able to colonize or survive in the host (O'Toole et al. 2000). S. pneumoniae has more carbohydrate-metabolizing enzymes, especially glycosyltransferases (GTs), which appear to be segmentally variable. The unique pattern of cell surface glycosylation in S. pneumoniae has been under extensive investigation and plays an important role in pathogenesis (Tettelin et al. 2001). The GTs are responsible for making O-linked glycosylations on surface proteins, which coat the surface of the bacterium and interact with the host (Tettelin et al. 2001). Figure 6 Abundance of SVGs in Different Functional Categories in Five Microorganisms The approximate total gene number for each organism is as follows: H. pylori, 1,566 genes; S. pneumoniae, 2,094 genes; N. meningitidis, 2,065 genes; E. coli, 4,289 genes; B. subtilis, 4,100 genes. Gene Duplication and SVGs Duplication followed by diversification is an efficient way of generating functional innovations (Prince and Pickett 2002). Regional sequence divergence has been observed between duplicated gene copies (Gu 1999; Dermitzakis and Clark 2001; Marin et al. 2001). We thus asked the following questions: (1) What is the distribution of paralogous genes in the set of SVGs in a single genome? (2) Is there a significant association between gene duplication and SVGs? In Figure 7A, we show the distribution of paralogous genes among SVGs in several genomes. We consider paralogous genes to be similar genes in the same genome with a BLAST E-value less than 1E-5. As shown in Figure 7A, in H. pylori, N. meningitidis, and S. pneumoniae, the largest group of SVGs is the one with no paralogs. However, in E. coli, the largest group is the one with a single paralog. E. coli obviously has more paralogous genes in the SVG set, probably owing to a larger genome size by duplication. In Figure 7A (inset), we show the percentage of genes with different numbers of paralogs in each class for both segmentally variable and fully conserved genes in E. coli. Interestingly, over half of the fully conserved genes in E. coli do not have paralogs. There is a significant difference between the two distributions (χ2 test, p-value < 1E-5). In Figure 7B, we list the number of genes in a contingency table and test the significance using a χ2 test. For all genomes examined, there is a strong association between gene duplication and SVGs, suggesting an SVG is more likely to have originated from a duplicated gene. Figure 7 Paralogous Genes in SVGs (A) Paralog families in SVGs for four microorganisms. The x-axis shows the number of paralogs for each SVG. The y-axis shows the number of SVGs. The inset figure shows the percentage of genes with different numbers of paralogs for SVGs and fully conserved genes in E. coli genome. The x-axis is the number of paralogs, and the y-axis is the percentage. (B) Contingency tables to examine the dependence between SVG and paralogous gene. χ2 statistics are computed using standard formula. Here we give an interesting example where one paralogous copy of a gene is segmentally variable and the other copy is fully conserved. In H. pylori strain 26695, gene products of HP1299 (253 amino acids) and HP1037 (357 amino acids) both have a conserved domain (approximately 250 amino acids; Pfam: Peptidase_M24) that is characteristic of the methionyl aminopeptidase (map) family (metalloprotease family M24) (Rawlings and Barrett 1995). HP1299 is fully conserved in a number of microbes and is homologous to the E. coli map gene (Figure S5), while the product of HP1037 has an extra N-terminal region (approximately 100 amino acids) that is variable among its similar genes (Figure S6). Additionally, HP1037 is annotated as a conserved hypothetical gene. The five residues found in the E. coli map that are involved in cobalt (Co2+) binding (Asp-97, Asp-108, His-177, Glu-204, Glu-235; Rawlings and Barrett 1995), are conserved in both genes by examining the multiple alignment. These findings suggest that HP1037 might also encode a map activity and that its variable N-terminal region might be involved in additional functional roles, e.g., interactions with other molecules. In Saccharomyces cerevisiae, there are two map genes and both have an extra N-terminal region compared to the E. coli map gene. One copy of the yeast map gene contains zinc-finger motifs in the N-terminal region that are indispensable for in vivo function (Li and Chang 1995). A functional role involving interaction with the ribosome has also been suggested for this N-terminal domain (Vetro and Chang 2002). In most prokaryotes, it has been assumed that there is only one copy of the map gene. The SVG family exemplified by HP1037 may represent another family of map genes in prokaryotes. Discussion A major fraction of bioinformatics research on sequence analysis has focused on the conserved regions in proteins, trying to hypothesize the role of the protein by identifying sequence motifs that have been shown experimentally to correlate with a specific function. Some work has gone into cataloging the groups of lineage-specific proteins that show no similarity to other proteins in GenBank (Galperin and Koonin 1999), but there the route to assigning function usually needs experimental approaches requiring biochemistry or genetics or more rarely by determining the crystal structure of the gene product (Zhang et al. 2000). Unfortunately, current bioinformatics methods are only occasionally helpful in suggesting where to begin such studies. In this paper we have initiated an effort to identify SVGs, which contain both well-conserved regions and highly variable regions. By looking carefully at a few specific examples where functional information is available from experimental data, we find that the variable region often seems to play a key role in mediating interactions with other molecules, both large and small. Sometimes the variable portions are involved in biological processes with a component of interaction between the cell and agents from the external environment. For instance, the DNA methyltransferases are part of a defense system that recognizes and clears invading foreign DNA; membrane-bound sensory HKs and mechanosensitive ion channels, etc., monitor changes of living conditions. Sometimes the variable portions are involved in intracellular processes that appear to have lineage-specific features. Thus, the variable regions inside DNA GyrB and several types of AARSs probably determine the specificity of substrate recognition. The detailed factors that introduce the molecular variability may go well beyond our explanations here and likely vary from case to case. Some variable regions may have diverged a long time ago and are now kept constant, while others may keep changing. In all of these cases, SVGs are exceptionally worthy targets of further experimental investigation, and such investigations can be greatly aided by the presence of the conserved regions that may suggest a preliminary function to be tested. Why might certain genes contain these variable regions? Could they be simply relics left over during evolution and now serve no purpose? Are they just “pseudo-segments” with no function? There are several lines of evidence that support the hypothesis that when variable regions have been retained, they indeed serve a function. First, several studies have shown that deletions are, on average, more frequent than insertions (Halliday and Glickman 1991). As a result, if a region is evolving under weak functional constraints, it tends to get smaller over time (Lipman et al. 2002). Second, in a special case, one can imagine that when a variable region occurs at the C-terminus of a protein and is not being selected, it is likely to suffer random mutations including nonsense mutations or insertions/deletions that cause a shift in reading frame. Thus, we searched GenBank release 136.0 for examples of genes that matched the conserved region of an SVG, but in which the C-terminus was missing or much shorter. The DNA sequences downstream of such hits were examined for similarity to the variable region in the query gene. Of the 83 SVGs with a C-terminal variable region in H. pylori, none of them had hits with a disrupting stop codon in the variable region; 20 of them have hits with genes showing insertions/deletions that cause frame shifts in the variable region. However, the real number is likely to be much fewer, since, based on previous work, many of them may be the results of sequencing errors (Posfai and Roberts 1992). In other cases, we find that some proteins have lost the variable segment in a subset of genomes. For instance, in ProRSs, the variable segment is absent in archaea; in GyrB, the variable segment is absent in the Gram-positive bacteria. Clearly in those cases the organisms can get by without the variable domain, although they may have a compensating function in a different gene. But this again does not imply that the variable region has no function in those genes that have retained it. SVGs are distinct from sequences with shuffled domains (Doolittle 1995) in that the variable region is bounded by the same sets of conserved portions, while domain shuffling usually manifests itself in a different sequential order of conserved domains. We also hypothesize that the variable regions in SVGs are not the result of multiple domain fusion events, each resulting in an insertion of a different sequence into the protein. This hypothesis is supported by the fact that the fused domains are often conserved across multiple organisms (Marcotte et al. 1999). Additionally, our procedure requires that the variable regions are of similar length within a family of proteins, which are also restricted to conserved length distributions. This filter suggests a mutational mechanism that originated from an ancient protein. Indeed, it is possible that originally the variable region was a result of a single or possibly relatively few ancient fusion events, but this paper does not focus on the evolutionary origin of SVGs. Another prediction from our observations is that the variable regions are excellent candidates to bind substrates or partner macromolecules. They may be extremely helpful in discovering the networks of protein–protein or protein–nucleic acid interactions within a cell. Bioinformatics may even be able to help in this endeavor by finding genes that seem to have coevolving variable regions as a result of such interactions. Experimental data from techniques such as the yeast two-hybrid system or microarrays may provide evidence for interactions that can involve two variable regions. Much additional bioinformatics work will be needed to explore fully the potential of this method in hypothesizing function. For instance, the size limits we have arbitrarily imposed on the variable region should be tested systematically. In our relatively simple formulation presented here, the length of the variable region and the number of proteins in the same family that do not have an alignment to the variable region are the primary factors in determining its statistical significance. Methods using other sequence analysis tools, such as multiple alignment and sequence profiles, may provide alternative ways to identify segmental pattern of variability. A fundamental problem is to differentiate random evolutionary drift from positive selection correlated to functional requirements. Although one might expect that the N- and C-termini may be more variable than the regions in the middle, our data suggest that variable regions in SVGs are not preferentially located in either end (data not shown). We have also examined the amino acid composition, codon usage, and GC content in the variable regions and the conserved regions of the same SVG. While there is no significant deviation of amino acid composition and GC content between the two regions in general, codon usage appears to be biased in the variable regions (data not shown). SVGs usually account for 10%–20% of the total genes in a microbial genome. Currently, we think of the class of lineage-specific genes as being the key factor that distinguishes one strain or species from another. The class of SVGs that we have defined in this paper must now be added to this collection of lineage-specific genes by virtue of the unique segments that constitute their variable regions. They also appear to provide functional elements that help to differentiate among strains and species. This point is well illustrated by considering the restriction-modification systems. Here, the DNA methyltransferases, which have a variable region responsible for DNA recognition, are members of the SVG class. With the help of their companion restriction endonucleases, which typically appear as lineage-specific genes, they serve to keep foreign, unmodified DNA sequences from entering the genome. In this case, the synergy of function provided by members of the two classes highlights the key role that both sets of genes must play in defining the individuality of a strain or species. Our analysis to date is limited to prokaryotes and archaea where SVGs are transcribed and translated as contiguous genomic segments. In eukaryotes, alternative RNA splicing introduces substantial additional complexity into the interpretation of gene structure and protein product, thereby rendering impossible the simple analysis we have applied here. It is tempting to consider alternative splicing as a highly evolved control mechanism to introduce the variability we find in the SVGs and thereby achieve the functional diversity necessary for cell survival under different conditions. In eukaryotes, alternatively spliced exons can be introduced in response to the functional demands of different cell types by merely juggling protein coding regions in the genome, thereby creating an SVG structure. If this view is correct, then it reinforces and highlights the importance of these SVGs to the workings of the cell. In this paper we have provided an initial glimpse of SVGs, which appear to provide an important genetic layer in the adaptation of cells to novel environments and hazardous pathogens. We have focused attention on the biological significance of these genes, especially those that have highly diverged segments. We are currently trying to develop a more refined classification of these genes so as to explore the functional significance of the variability. We would like to know whether extreme variability is required for diverse function or whether more modest variation is sufficient. Such questions require that we can first distinguish positive selection acting on these variable regions from neutral evolution leading to gene decay and eventual loss. Since the variable regions we report are often not amenable to current tools available for alignment, we are exploring new methods that will help us to assess whether positive selection is driving the evolution of these genes. In summary, we have identified an extremely useful way of classifying genes that leads to the identification of those with a high priority for both experimental and computational research. Materials and Methods Our method for detecting SVGs includes several steps: (1) identification of similar genes followed by query-anchored multiple alignment using nongapped BLAST (Altschul et al. 1990); (2) taxonomy clustering of similar genes to avoid bias; (3) detection of segmental variability. Identification of similar genes Given a gene, we start by searching for all its similar genes in the nonredundant database (GenBank release 136.0, 15 June 2003) using nongapped BLAST (Altschul et al. 1990). We use the nongapped BLAST because the gapless high scoring pairs (HSPs) reported are rather conservative. The gapped BLAST, however, tends to extend HSPs over variable regions, which has been observed in several examples (e.g., DNA-recognition domain in cytosine-specific methyltransferase; data not shown). Two criteria are used to define close similarity. First, the E-value is less than 1E-10. Here we use a strict E-value threshold to avoid possible functional divergence among the homologs. Accordingly, we use the BLOSUM80 scoring matrix in the BLASTP search, although the result does not change dramatically if BLOSUM62 is used. Second, the overall length of the hit sequence does not differ significantly from the query sequence. We define the gap content (GapC) between two sequences: where L,l are the lengths of the protein sequences of two genes. It is a measure of the smallest percentage of gaps needed to be introduced into the pairwise alignment. Sequences with a high GapC value indicate significantly different domain structures, possibly owing to domain insertions or losses, and thus are excluded from the set of similar genes. In our current implementation, we require that GapC must be less than 0.2. Taxonomy clustering of the similar genes Similar genes reported by BLASTP are not evenly distributed among different species. In many cases, highly similar genes from different strains of the same species or highly similar paralogous genes from a particular strain tend to introduce bias into the dataset. We adopted a simple taxonomy clustering by using the NCBI Taxonomy Database (Wheeler et al. 2003) to reduce this bias. We collapse all the similar genes from the same species into a single group. Then we choose the gene with the best similarity score to the query sequence as the representative of that species for later calculations. The definition of species follows the hierarchical taxonomy used in the NCBI Taxonomy database (superkingdom → phylum → class → subclass → order → family → genus → species → no rank [strain]). By doing taxonomy clustering, we are able to collect a less biased sample of similar genes from different species. Detection of segmental variability Query-anchored multiple alignment after taxonomy clustering is performed by aligning the HSPs reported by nongapped BLAST (see Figure S2 and http://geneva.bu.edu). Two unaligned regions in two sequences are considered as the variable regions if they are bounded by similar HSPs at both ends (or one end, if the unaligned region is at the terminus of the gene). To avoid the possibility of a large segment containing insertions or deletions, we again require that GapC be less than 0.2 between these two unaligned regions. For each amino acid position in the query gene, we can count the number of times (m) it is inside an HSP region and the number of times (n) it is inside a variable region. A high ratio of n over m + n suggests that this position is inside the variable region most of the time. We estimate the statistical significance (p-value) of the variability for each position by a binomial distribution: where q is the probability of an amino acid position being inside a HSP region. We estimate q by averaging the proportion of HSP in each hit sequence among all hits. If the p-value calculated using the above formula is less than the significance level, which we set at 0.05, we then consider this position as a variable position; otherwise, it is a conserved position. A consecutive run of variable positions forms a variable region. The next question is how long the variable region should be to be considered meaningful, as opposed to functionally unimportant regions such as linker regions, which are usually short. From our experience, there is no clear decision boundary between the length of the region and its functional importance. Any choice of cutoffs would have to balance between false positives and false negatives. However, previous studies on the length distribution of protein domains has shown that the most likely length of a protein domain is around 70 amino acids, and regions shorter than this are less likely to form a functional domain (Wheelan et al. 2000). Based on this, we chose 70 amino acids as the length threshold for a variable region to be considered functionally important. In Figure S7, we show the length distribution of the variable regions in all genes of H. pylori. A direct way of visualizing the variability of a protein sequence is by calculating the ratio of n over (m + n) for each position and plotting it. We call such plots variability profiles. Sample variability profiles are shown in Figure 1. In Figure 1A, two obvious peaks are present: one from position 20 to 70, the other from position 160 to 300. The latter (approximately 140 amino acids) forms a separate DNA recognition domain, while the former (approximately 50 amino acids) has no known function. In Figure 1 we also show conserved subsequences from the Pfam (Bateman et al. 2002), BLOCKS (Henikoff et al. 1999), and PRINTS (Attwood et al. 2003) databases. The BLOCKS and PRINTS databases are relatively conservative in defining motifs. However, the Pfam domain seems to include the variable region within the conserved region, as shown in Figure 1A. Supporting Information Data Deposit We provide a static collection of segmentally variable genes at our Web site, http://geneva.bu.edu. SVGs for several representative genomes are listed there. For SVG lists in other genomes, please request more information from Y. Zheng at E-mail: zhengyu@bu.edu. All the case examples mentioned throughout the paper and Supporting Information have been compiled into one Web page, http://geneva.bu.edu/paper03.html, with hyperlinks. Readers can follow each hyperlink to access additional information from Pfam, BLOCKS, PRINTS, COG, and nongapped BLAST for each gene. Figure S1 Multiple Alignment of BtuB and Homologs Conservation score is plotted under the alignment (ClustalX). The conserved portions are as follows: N-terminal domain, extreme C-terminal domain, and a segment between N-terminal and C-terminal domain. The variable domain (between N-terminal and C-terminal) overlaps with the transmembrane 22-strand β-barrel regions. (2.69 MB EPS). Click here for additional data file. Figure S2 Query-Anchored Alignment of ProRS The query protein is H. pylori ProRS. The blue segments are HSPs reported by nongapped BLAST. The yellow segments are the variable region. The gray region is the gap-rich region (GapC > 0.2, deletion in this alignment). See http://geneva.bu.edu/paper03.html for a high-resolution Web figure. (4.71 MB EPS). Click here for additional data file. Figure S3 Multiple Alignment of GtfB and Its Homologs (3.12 MB EPS). Click here for additional data file. Figure S4 Multiple Alignment of B. subtilis Gene yhdY and Its Homologs YhdY is currently annotated as a hypothetical protein and contains a conserved domain for mechanosensitive proteins (the middle region of the alignment) and two variable domains (N- and C-termini). (2.86 MB EPS). Click here for additional data file. Figure S5 Multiple Alignment for H. pylori Gene HP1299 It is the methionine aminopeptidase (type Ia map). This is an example of a fully conserved gene. (1.87 MB EPS). Click here for additional data file. Figure S6 Multiple Alignment for H. pylori Gene HP1037 It is currently annotated as “conserved hypothetical protein.” The N-terminal region is variable. The conserved C-terminal domain is characteristic of methionine aminopeptidase. (2.22 MB EPS). Click here for additional data file. Figure S7 Length Distribution of Variable Regions in the Genome of H. pylori Shown as a histogram. Only variable regions inside fully conserved genes and SVGs are included. Pink line shows the domain size distribution in 3D-structure database (data from Wheelan et al. 2000). (643 KB EPS). Click here for additional data file. Table S1 Classification of Genes into Three Broad Categories (62 KB DOC). Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/GenBank/) accession numbers for the genes discussed in Figure 3 are as follows: atpA (2314285), atpD (2314283), atpG (2314284), dnaX (2313841), flgK (2314271), ftsK (2314237), gyrB (2313611), hetA (2314367), HP1450 (2314626), infB (2314195), M.hpyAVIB (2313124; REBASE [http://rebase.neb.com] ID M2.hpyAVI), mutS (2313742), NQO3 (2314431), NQO8 (2314432), polA (2314647), rps4 (2314460), spaB (2313717), spoT (2313901), tlpA (2313179), and tlpC (2313162). The GenBank accession numbers for the genes discussed in Figure 3 are as follows: Agrobacterium tumefaciens (15890351), B. subtilis (16079962), Enterococcus faecalis (8100675), E. coli K12 (16128553), L. innocua (16801788), Mycobacterium leprae (15826988), M. tuberculosis CDC1551 (15840173), Nitrosomonas europaea (22955201), Nostoc sp. PCC 7120 (17228666), P. syringae pv. syringae B728a (23470301), Ralstonia metallidurans (22980570), R. solanacearum (17548875), Synechococcus sp. PCC 7942 (21954778), and Thermotoga maritime (15644402); in case studies, B. subtilis yhdY (2633299), E. coli b1330 (1787591), H. pylori cytosine-specific DNA methyltransferase (2313124), H. pylori HP1299 (2314463), H. pylori HP1037 (2314181), H. pylori prolyl-tRNA synthetase (2313329), and H. pylori VacB (2314413). We thank the following researchers for encouraging discussion: Drs. A. Maxwell, D. Söll, C. Joyce, Y.-C. Tse-dinh, and Y.-H. Chang. We thank Drs. E. Raleigh, R. Hallick, C. Delisi, D. Lipman, S. Eddy, S. Salzberg, G. Stormo, and J. Posfai for critically reading an early draft of this manuscript and many invaluable suggestions. We thank all the sequencing teams who have made genome sequence data publicly available for analysis. This work is supported by National Science Foundation grants 998088 and 0239435 to SK and YZ and by New England Biolabs (RJR). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. The original idea of SVGs was conceived by RJR as a general phenomenon. RJR also hypothesized the notion of three classes of genes. Originally, the genes studied in the paper were described as mosaic genes; however, owing to a conflict with a previously defined structural class of proteins, the name was changed to segmentally variable genes (SVGs), although the authors still feel that the term mosaic genes captures the concept proposed. SK and YZ proposed the term “the adaptation layer of the microbial proteome.” All the authors participated in the high-level design of the computational analysis software; however, the specific variant described in this paper was primarily designed by YZ. This design is still evolving and will be described in follow-up publications. YZ implemented the computational pipeline that supported the computational analysis described in this paper. All the authors participated in the analysis and the interpretation of the results. The Web server was implemented by YZ. All the authors participated in the write-up and numerous revisions of the text. Academic Editor: Ford Doolittle, Dalhousie University Abbreviations AARSaminoacyl-tRNA synthetase ABC transporterATP-binding cassette transporter COGclusters of orthologous group 2Dtwo dimensional 3Dthree dimensional E-b/Oeffector molecule binding and oligomerization GapCgap content GHglycoside hydrolase GTglycosyltransferase GyrBDNA gyrase B HKhistidine kinase HSPhigh scoring pair mapmethionyl aminopeptidase MCPmethyl-accepting chemotactic protein ProRSprolyl-tRNA synthetase rRNAribosomal RNA SVGsegmentally variable gene TopADNA topoisomerase A ==== Refs References Ahel I Stathopoulos C Ambrogelly A Sauerwald A Toogood H Cysteine activation is an inherent in vitro property of prolyl-tRNA synthetases J Biol Chem 2002 277 34743 34748 12130657 Ahumada A Tse-Dinh YC The Zn(II) binding motifs of E. coli DNA topoisomerase I is part of a high-affinity DNA binding domain Biochem Biophys Res Commun 1998 251 509 514 9792804 Altschul SF Gish W Miller W Myers EW Lipman DJ Basic local alignment 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020090Research ArticleBiotechnologyImmunologyMolecular Biology/Structural BiologyMus (Mouse)Mimotopes for Alloreactive and Conventional T Cells in a Peptide–MHC Display Library Fishing in a Peptide-MHC Display LibraryCrawford Frances 1 2 Huseby Eric 1 2 White Janice 1 Marrack Philippa 1 2 3 Kappler John W kapplerj@njc.org 1 2 4 1Howard Hughes Medical Institute, Integrated Department of ImmunologyNational Jewish Medical and Research Center, Denver, ColoradoUnited States of America2Integrated Department of Immunology, University of Colorado Health Science CenterDenver, ColoradoUnited States of America3Department of Biochemistry and Molecular Genetics, University of Colorado Health Science CenterDenver, ColoradoUnited States of America4Department of Pharmacology and the Program in Biomolecular Structure, University of Colorado Health Science CenterDenver, ColoradoUnited States of America4 2004 13 4 2004 13 4 2004 2 4 e9016 12 2003 21 1 2004 Copyright: © 2004 Crawford et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Phage Display Libraries Identify T Cells The use of peptide libraries for the identification and characterization of T cell antigen peptide epitopes and mimotopes has been hampered by the need to form complexes between the peptides and an appropriate MHC molecule in order to construct a complete T cell ligand. We have developed a baculovirus-based peptide library method in which the sequence encoding the peptide is embedded within the genes for the MHC molecule in the viral DNA, such that insect cells infected with virus encoding a library of different peptides each displays a unique peptide–MHC complex on its surface. We have fished in such a library with two different fluorescent soluble T cell receptors (TCRs), one highly peptide specific and the other broadly allo-MHC specific and hypothesized to be much less focused on the peptide portion of the ligand. A single peptide sequence was selected by the former αβTCR that, not unexpectedly, was highly related to the immunizing peptide. As hypothesized, the other αβTCR selected a large family of peptides, related only by a similarity to the immunizing peptide at the p5 position. These findings have implications for the relative importance of peptide and MHC in TCR ligand recognition. This display method has broad applications in T cell epitope identification and manipulation and should be useful in general in studying interactions between complex proteins. A baculovirus expression library -- encoding peptides in the context of MHC molecules -- has been developed to identify and characterize unique peptide-MHC complexes that bind specific T cell receptors ==== Body Introduction The identification of peptide epitopes associated with particular αβ T cell receptors (αβTCRs) is often still a bottleneck in studying T cells and their antigenic targets in, for example, autoimmunity, hypersensitivity, and cancer. A direct genetic or biochemical attack on this problem can be successful, especially with class I major histocompatibility complex (MHCI)-presented peptides. For example, tumor (Van Der Bruggen et al. 2002) and transplantation (Scott et al. 2000; Simpson et al. 2001; Shastri et al. 2002; Sahara and Shastri 2003) peptide epitopes have been found this way. Identification of the antigenic peptide in a mix of peptides stripped from MHC molecules isolated from antigen-presenting cells (APCs) has sometimes been possible using a combination of a biological assay, peptide fractionation, and peptide sequencing (Guimezanes et al. 2001). However, this method is extremely labor intensive and depends on relatively high peptide frequency in the mix and a very sensitive bioassay. These conditions are not always achievable, especially with peptides presented by MHCII, in which peptide loading of surface MHC may require peptide concentrations orders of magnitude higher than those required for MHCI loading. The reward for the labor involved in identifying peptide epitopes directly can often be the identification of the protein source of the peptide, especially as the sequencing of the genomes of many organisms approaches completion. However, in many situations, rather than identifying this precise peptide epitope, it is sufficient to identify a peptide “mimotope.” Mimotopes can be defined as peptides that are different in sequence from the actual peptide recognized in vivo, but that are nevertheless capable of binding to the appropriate MHC molecule to form a ligand that can be recognized by the αβTCR in question. These peptides can be very useful for studying the T cell in vitro, for altering the immunological state of the T cell in vivo (Hogquist et al. 1994), for vaccine development (Partidos 2000), and potentially in preparing multimeric fluorescent peptide–MHC complexes for tracking T cells in vivo (You et al. 2003). Mimotopes can sometimes be identified in randomized peptide libraries that can be screened for presentation by a particular MHC molecule to the relevant T cell (Gavin et al. 1994; Linnemann et al. 2001; Sung et al. 2002; reviewed in Hiemstra et al. 2000; Liu et al. 2003). Thus far, strategies for screening these types of libraries have involved testing individual pools of peptides from the library and then either deduction of the mimotope sequence from the pattern of responses or sequential reduction in the size of the pool until a single peptide emerges. There are several limitations to this type of approach. Again, a very sensitive T cell bioassay is needed in which the activity of the correct stimulating peptide is not masked by competition with the other peptides in the pool. Also, an APC that expresses the relevant MHC molecule, but not the relevant peptide, must be found or constructed. Finally, because the screen relies on T cell stimulation, only agonist mimotope peptides are identified. In other applications, another powerful library method has been sequential enrichment/expansion of a displayed library of protein–peptide variants by direct ligand–receptor binding, e.g., using bacterial phage or yeast (also reviewed in Liu et al. 2003). These methods have not yet been developed for the routine identification of T cell antigen mimotopes, because of the lack of a suitable system for the display of peptide–MHCs or for screening via αβTCR binding using these organisms. In this paper, we describe such a method using modifications of previously described systems for producing soluble peptide–MHC complexes (Kozono et al. 1994; Crawford et al. 1998; Rees et al. 1999) and αβTCRs (Kappler et al. 1994) from baculovirus-infected insect cells. We constructed a library of peptides displayed on the surface of baculovirus-infected cells bound to the mouse MHCII molecule, IAb. The peptides in the library varied in five peptide amino acids known to be surface exposed and predicted to lie within the footprint of αβTCR interaction. Using fluorescent αβTCRs as probes, we have identified in the library mimotopes for two types of T cells, both originally produced by immunization of mice with the same IAb–peptide combination. One of these T cells was predicted from previous data (Liu et al. 2002) to be very dependent on all of the peptide surface exposed amino acids. Consistent with these predictions, a single peptide mimotope was identified in the library for this T cell. The sequence of this peptide was highly related to the immunizing peptide. In contrast, the other T cell was hypothesized to be very peptide promiscuous (Marrack et al. 2001) based on its broad allo-MHC reactivity. Consistent with this hypothesis, its αβTCR selected a large family of peptide mimotopes from the library. Comparison of these peptides indicated that attention of this αβTCR was focused primarily on a single position in the peptide. Results Characteristics of a Broadly Alloreactive and Conventional T Cell For this study we selected two T cell hybridomas, both prepared from IAb mice immunized with the peptide p3K. This peptide binds well to IAb (Rees et al. 1999), and its crystal structure bound to IAb has been determined (Liu et al. 2002) (Figure 1A). The hybridoma B3K-06 was produced from wild-type C57BL/6 immunized conventionally with the peptide (Rees et al. 1999). Like most T cells resulting from immunization with a foreign peptide, it responds to IAb-expressing APCs in the presence, but not the absence, of p3K (Figure1B). It does not respond to APCs expressing other alleles of the IA MHCII molecule (data not shown). Also, as is commonly seen with conventional T cells, the interaction of the αβTCR of B3K-06 with IAb-p3K is very sensitive to changes in any of the peptide amino acids exposed on the surface of the IAb-p3K complex. Mutation of Q2, K3, K5, N7, or K8 to alanine virtually eliminates recognition of p3K by B3K-06 (Liu et al. 2002; see Figure 1B). Figure 1 Structure of IAb-p3K and Properties of T Cell Hybridomas Reactive to It (A) Ribbon structure of the α1 and β1 domains of IAb with a wire-frame representation of the bound p3K peptide (Liu et al. 2002). Amino acids labeled in red are the five central peptide amino acids available for αβTCR interaction. (B) The figure shows the response of 105 B3K-06 hybridoma cells to various peptides presented by 105 IAb-bearing APCs, LB-15.13. (C) The figure shows the response of the T cell hybridoma YAe-62 to various MHCII molecules. In each case, 105 hybridoma cells were incubated overnight with MHCII presented in various ways. For IAb-p3K, soluble IAb-p3K was immobilized in the culture well before the addition of the hybridoma cells. In other cases, 106 spleen cells were used directly as APCs without additional peptide antigen. For pEα, the spleen cells came from IAb-pEα/ΔIAβ/ΔIi mice (Ignatowicz et al. 1996). For wild-type IAb and allo-MHCII, the spleen cells came from H-2 congenic mice on the C57BL/10 background. Finally, spleen cells from ΔIAβ/ΔIi C57BL/6 mice were used. The hybridoma YAe-62 was chosen as a representative of broadly allo-reactive T cells present in mice carrying transgenes and gene knockouts that lead to expression of MHCII almost completely occupied by a single peptide (Ignatowicz et al. 1996). It was produced from IAb-p3K-immunized mice that express the IAb molecule covalently linked to pEα, a dominant IAb-binding peptide derived from the MHCII IEα chain. Its properties are shown in Figure 1C. YAe-62 responds to APCs bearing IAb-p3K, but not to APCs lacking MHCII nor to IAb-pEα-bearing APCs from the mouse from which the hybridoma was derived. However, YAe-62 has additional reactivities common to many T cells isolated from these mice (Ignatowicz et al. 1996). In the absence of any added peptide, it also responds to all APCs expressing wild-type IAb, including those from mice with a much reduced MHCII peptide repertoire due to lack of the invariant chain. YAe-62 also responds well to APCs from a variety of mice carrying other alleles of IA. We have postulated that these T cells are focused on structural features of the MHCII molecule and are minimally dependent on direct peptide interaction (Marrack et al. 2001). Display of Functional Peptide–MHC on Baculovirus-Infected Insect Cells We previously established methods that used baculovirus-infected insect cells to produce soluble MHC molecules with covalently bound antigenic peptides (Kozono et al. 1994; Crawford et al. 1998; Rees et al. 1999). These constructions were the starting point for developing insect cells displaying functional peptide–MHCIIs. Several modifications were made to constructs that encoded the mouse MHCII molecule, IAb, with various bound peptides. First, to increase the stability of the molecule, an acid–base leucine zipper (O'Shea et al. 1993) was attached to the C-termini of the extracellular portions of the MHC α and β chains, replacing what would normally be the transmembrane regions of these proteins. The basic half of the zipper was attached to the α chain (Figure 2A) and the acidic half to the β chain (Figure 2B). In addition, sequence encoding the transmembrane and cytoplasmic tail of the baculovirus major coat glycoprotein, gp64, was attached to the end of the acid zipper (Figure 2B). Sf9 insect cells infected with virus encoding this construction produced the MHCII molecule at a high level anchored on the cell surface via the gp64 transmembrane (Figure 3A). Also, to make Sf9 cells better APCs (Cai et al. 1996), we established a version transfected with the genes for mouse ICAM and B7.1 (Figure 3B). When we tested the ability of Sf9 cells displaying the IAb-p3K complex to present the antigen to B3K-06 or YAe-62, the presence of ICAM/B7.1 greatly improved IL-2 production (Figure 3C). These results showed that IAb-p3K could be displayed on the surface of insect cells in a form easily recognized by T cells. In all of the experiments described below, infected conventional Sf9 cells were used for flow cytometry and infected ICAM/B7.1-expressing Sf9 cells were used in IL-2 stimulation assays. Figure 2 Constructions Used in These Experiments (A and B) Previously described constructions (Rees et al. 1999) for the coexpression in a single baculovirus of soluble version of the α (A) and β (B) chains of IAb were modified as described in the Materials and Methods to anchor the molecule on the surface of infected insect cells. (C) The construction was further modified as described in the Materials and Methods to disrupt the IAb β chain with sequence encoding enhanced GFP flanked by sites for the enzymes SbfI and CeuI. (D and E) A degenerate DNA fragment was produced by PCR (D) and cloned into the construct replacing the GFP-encoding sequence (E) as described in the Materials and Methods. Figure 3 Functional Display of IAb-p3K on the Surface of Insect Cells (A) Sf9 insect cells were infected with baculovirus encoding a membrane-bound form of IAb-p3K. After 3 d, the surface expression of IAb-p3K was detected with an anti-IAb mAb using flow cytometry. (B) The genes for mouse ICAM (CD54) and B7.1 (CD80) were cloned into an insect cell expression plasmid as described in the Materials and Methods. The plasmids were used to cotransfect Sf9 cells, and a stable transfectant (Sf9-ICAM/B7.1) was cloned expressing both proteins detected with mAbs using flow cytometry. (C) Either Sf9 (open bars) or Sf9-ICAM/B7.1 (closed bars) cells were infected with baculovirus expressing IAb-p3K. After 3 d, the infected insect cells were used as APCs to stimulate IL-2 production from B3K-06 and YAe-62. Uninfected cells were used as negative controls. Detection of Displayed Peptide–MHC with Multimeric αβTCR Next we prepared fluorescent, soluble αβTCR reagents for use in flow cytometry to detect insect cells displaying the appropriate peptide–MHCII combination. Fluorescent multivalent versions of the soluble αβTCRs of B3K-06 and YAe-62 bound to insect cells displaying the IAb-p3K, but not a control peptide–MHCII combination (Figure 4A). Figure 4 Detection of IAb-p3K-Expressing Insect Cells with Polyvalent, Fluorescent αβTCRs (A) Sf9 insect cells were infected with baculovirus encoding IAb bound either to p3K (filled histogram) or a control peptide (FEAPVAAALHAV) (unfilled histogram). After 3 d, the infected insect cells were incubated with polyvalent, fluorescent soluble αβTCRs from B3K-06 or YAe-62. The binding of each αβTCR was assessed by flow cytometry. (B) Cells, prepared as in (A), were simultaneously analyzed with fluorescent αβTCRs and a mAb specific for IAb (17–227) that does not interfere with αβTCR–IAb interaction. (C) The binding of the αβTCRs is shown only for those infected insect cells that bear a high level of surface IAb (dotted region in [B]). Insect cells displaying IAb-p3K bound the αβTCR reagents very heterogeneously (Figure 4A), probably owing to heterogeneous expression of IAb-p3K due to variations in the multiplicity of infection (MOI) and the lack of synchrony in viral infection and expression. To focus on cells bearing a particular level of IAb, we stained the cells simultaneously with the fluorescent αβTCR reagents and with an anti-IAb monoclonal antibody (mAb) that did not interfere with αβTCR binding. In this case, there was a direct correlation between the amount of surface IAb-p3K expressed by an individual insect cell and the amount of αβTCR bound (Figure 4B) with cells bearing a particular level of IAb-p3K, binding the αβTCRs uniformly (Figure 4C). Therefore, comparing the two types of staining gave us a useful tool to evaluate the relation between peptide sequence and the strength of αβTCR binding (see below). Recovering Baculovirus Carrying a Particular Peptide–MHC Combination Our experiments showed that fluorescent αβTCRs could be used with flow cytometry to identify insect cells infected with a baculovirus encoding a specific peptide–MHC combination. We next tested whether this system could be used to enrich baculoviruses encoding a particular peptide–MHC. Insect cells were infected at an MOI of about 1 with a mixture of baculoviruses. Of these viruses, 1% encoded the IAb-p3K molecule and 99% encoded a control molecule (an αβTCR β chain). The infected cells were stained with fluorescent YAe-62 αβTCR and analyzed by flow cytometry. Although a distinct population of brightly fluorescent cells was not seen, the 1% of the cells with the brightest fluorescence were sorted, as were an equal number of cells that were very dully fluorescent (Figure 5A). The recovered infected cells were cultured with fresh insect cells to produce new viral stocks. These stocks were used to infect insect cells that were tested again with the fluorescent αβTCR reagent (Figure 5B). The cells infected with virus from the few fluorescent positive cells in the original population were now nearly all brightly fluorescent, and those infected with the virus from the fluorescently dull cells were nearly all negative for binding of the αβTCR. These results showed that flow cytometry could be used with a fluorescent multimerized αβTCR to find and greatly enrich insect cells infected with a virus encoding a specific peptide–MHC combination. Figure 5 Recovery of IAb-p3K Virus-Infected Cells with Fluorescent αβTCR (A) Sf9 cells were infected with a mixture of virus, 99% of which encoded a control protein (a TCR β chain linked to the gp64 transmembrane/cytoplasmic tail) and 1% of which encoded IAb-p3K. After 3 d, the infected cells were analyzed as in Figure 3A for binding fluorescent αβTCR from YAe-62. The 1% of the infected cells with the brightest fluorescence was sorted (high sort, 15,700 cells). As a control, a similar number of cells that fluoresced as dully as the background fluorescence were also sorted (low sort). (B) The sorted cells were incubated with fresh Sf9 insect cells to allow propagation of the viruses and production of new stocks. The stocks were used to infect new Sf9 cells, and after 3 d the analysis of αβTCR binding was repeated. Construction of a Peptide Library Attached to IAb in Baculovirus The most widely used method for introducing gene constructions into baculovirus involves assembling the construct first in an Escherichia coli transfer plasmid, where it is flanked by sections of baculovirus DNA. The complete construct is then introduced into baculovirus by homologous recombination using any of the commercially available modified baculovirus DNAs that require homologous recombination with the plasmid in order to generate functional circular viral DNA (Kitts and Possee 1993). Based on this procedure, we constructed an IAb–peptide library in two steps. In the original transfer plasmid that encoded the displayed IAb-p3K, we flanked the site encoding the peptide with unique restriction sites, one in the section encoding the β chain leader and the other in the section encoding the linker from the peptide to the N-terminus of the β chain. The DNA between these sites was replaced with DNA encoding enhanced green fluorescent protein (GFP) (Clontech, Palo Alto, California, United States) in-frame with the IAb signal peptide and with a 3′ termination codon (see Figure 2C). Thus, cells infected with baculovirus carrying this construct produced GFP, but not an IAb molecule, because of disruption of the IAb β chain gene. We then designed a peptide library based on the structure of p3K bound to IAb (see Figure 1A) We used oligonucleotides with random nucleotides in codons encoding five peptide amino acids (p2, p3, p5, p7, and p8) corresponding to the central surface-exposed amino acids of p3K bound to IAb. Other positions were kept identical to p3K, including alanines at the four standard anchor residues at p1, p4, p6, and p9. These oligonucleotides were used in a PCR to create a DNA fragment randomized in these five codons and with 5′- and 3′-end restriction enzyme sites compatible with those in the signal peptide and linker (see Figure 2D). This fragment was ligated into the restricted plasmid, replacing the GFP sequence and restoring a functional IAb β chain gene (see Figure 2E). The mixture of plasmids was then used to transform E. coli and a bulk plasmid preparation was made. The plasmids were cotransfected with BaculoGold baculovirus DNA into Sf9 insect cells to produce a mixed viral stock in which each virus carried the genes for IAb with a different peptide bound. Although it is difficult to calculate the efficiency with which recombination yields infectious baculovirus, we estimate the size of this library was between 3 × 104 and 1 × 105 independent viruses. Successive Enrichment of Baculovirus Carrying Peptide–MHC Combinations That Bind a Particular αβTCR A large number of Sf9 insect cells were infected at an MOI of about 1, with baculovirus carrying the IAb–peptide library. After 3–4 d, the cells were analyzed with fluorescent B3K-06- or YAe-62-soluble αβTCR, as described above. Fluorescent cells were sorted and cultured with fresh uninfected Sf9 cells to create new infected cells for analysis and an enriched viral stock. This process was repeated three to four times. In each case, when no clear fluorescent population was apparent, the brightest 1% of the infected cells was sorted. In later rounds the majority of the cells in a clearly distinguishable fluorescent population were sorted. Figure 6 summarizes the successive enrichment of viruses that produced IAb–peptide combinations that could be detected with each of the fluorescent αβTCRs. Infected cells binding the B3K-06 αβTCR were apparent only after two rounds of enrichment, but eventually yielded a population with uniform binding (Figure 6A). Infected cells that bound the YAe-62 αβTCR were detectable even with the initial library of viruses and enriched rapidly to yield a population with more heterogeneous levels of binding to the receptor (Figure 6B). Figure 6 Summary of Successive Screening of the IAb–Peptide Libraries with Fluorescent αβTCRs Sf9 insect cells (1 × 107 to 1.5 × 107) were infected at a MOI of approximately 1 with an aliquot of baculovirus encoding the IAb–peptide library. After 3 d, the infected cells were analyzed for binding the αβTCR of either B3K-06 or YAe-62. Either obviously fluorescent cells or the brightest 1% of the cells were sorted (2 × 104 to 8 × 104 cells) and added to 3 × 106 fresh Sf9 cells to propagate and reexpress the viruses contained in the sorted cells. These infected cells were then reanalyzed and sorted using the fluorescent αβTCRs. This process was repeated until no further enrichment of αβTCR binding was seen. In most cases, the reanalysis was done directly from the cells that were cocultured with the sorted cells. In a few cases, an intermediate viral stock was made and then used to infect additional Sf9 cells. The turn around time per cycle was 4–7 d. The figure shows the reanalysis in a single experiment of the initial viral stocks and all of the various intermediate enriched viral stocks. Sf9 cells were infected at an MOI of less than 1 with the viral stocks and analyzed as in Figure 4 for either B3K-06 (A) or YAe-62 (B) αβTCR binding. Comparison of αβTCR-Selected Peptides from the Library At the time of the final enrichment, single infected cells binding each of αβTCRs were sorted into individual wells of 96-well culture plates containing fresh Sf9 cells in order to prepare clonal viral stocks. These stocks were used to infect fresh Sf9 cells, which were reanalyzed for binding to the appropriate αβTCR as in Figure 4. Viral DNA from the clones that showed homogeneous TCR binding at a particular level of IAb were used as template in a PCR using oligonucleotides that flanked the peptide site in the construct, and a third internal oligonucleotide was used to sequence the PCR fragment. The majority of PCR fragments yielded a single unambiguous peptide sequence. These viruses were used to infect Sf9 cells that expressed mouse ICAM and B7.1. The infected cells were used as APCs for either the B3K-06 or YAe-62 hybridoma, with IL-2 production being a measure of IAb–peptide recognition. Viruses expressing IAb–peptide combinations that produced high levels of surface IAb, but that neither bound to the αβTCR nor stimulated the T cell hybridomas, were used as negative controls, and virus producing IAb-p3K was used as the positive control. Results with a few representative virus clones are shown in Figure 7A and 7B, and a summary of all of the results is shown in Table 1. Figure 7 Analysis of Baculovirus Clones from the αβTCR-Enriched IAb–Peptide Library (A) Sf9 cells were infected with stock from four baculovirus clones (B9, B13, B17, and B23) isolated from the virus pool enriched with the αβTCR of B3K-06. After 3 d, an aliquot of cells from each infection was analyzed as in Figure 4 to assure uniform binding of the fluorescent B3K-06 αβTCR (top). Viral DNAs prepared from other aliquots of the cells were used as templates in a PCR with oligonucleotides that flanked the DNA encoding the IAb-bound peptide. The fragment was sequenced directly with a third internal oligonucleotide (middle). The clone stock was then used to infect Sf9-ICAM/B7.1 cells. After 3 d, the infected cells were used as APCs for B3K-06 production of IL-2 (bottom). Virus encoding IAb-p3K was used as a positive control. Virus encoding pEα was used as the negative control. (B) Same as (A), but using YAe-62 and clones (Y2, Y14, Y28, Y52) derived from the IAb–peptide library using the YAe-62 αβTCR. Table 1 Summary of Peptides Selected by p3K-Reactive αβTCRs aAmino acids homologous to those in p3K are shown in red bDetermined from mean fluorescence as in Figure 4B and 4C cSorted by frequency and then by level of TCR binding Given our previous data indicating that the B3K-06 αβTCR interacted with all five of the p3K amino acids varied in this library (Liu et al. 2002; see also Figure 1B), we expected that mimotopes satisfying this receptor would be infrequent or perhaps even absent in a library of this size. Indeed, only one peptide was recovered from the library with the B3K-06 αβTCR, FEAQRARAARVD. It was found in all 42 clones analyzed with unambiguous αβTCR binding and peptide sequences. The sequence of this peptide was strikingly similar to that of p3K. Like p3K, it had a glutamine at p2. It had arginines at positions p3, p5, and p8, corresponding to the lysines found in these positions in p3K, most likely reflecting the importance of the positive charges at these positions. We do not know the relative importance of lysine versus arginine at these three positions, but given that there are two codons for lysine and six for arginine, there was of course a much higher probability of finding arginine than lysine. The most significant difference between this peptide and p3K was an alanine instead of asparagine found at p7. When bound to IAb on ICAM/ B7.1-expressing Sf9 APCs, FEAQRARAARVD was able to stimulate B3K-06 to produce IL-2, but not nearly as well as did p3K. This loss of stimulating activity was caused by one or more of the lysine-to-arginine substitutions and/or the asparagine-to-alanine substitution at p7. Interestingly, the substitution of alanine for asparagine in p3K eliminated the response of B3K-06 to soluble peptide presented by an IAb-bearing mouse APC (see Figure 1B). Perhaps the very high density of IAb–peptide on the surface of the insect cells allows for responses to peptides that would normally not be stimulatory with peptides presented by conventional APCs, although another possibility is that somehow the arginine (particularly at p8) compensated for the absence of the asparagine sidechain. Consistent with the hypothesis that the αβTCR of YAe-62 would be more peptide promiscuous than that of B3K-06, we found 20 different peptide sequences among the analyzed clones that produced an IAb–peptide combination that bound the YAe-62 αβTCR. It is likely that many more would be identified if more clones were analyzed. Five sequences were found multiple times. Not unexpectedly, these were among those that bound the YAe-62 αβTCR most strongly. There was a 100-fold range in the intensity of αβTCR binding to the different IAb–peptide combinations, ranging from about 4-fold to 400-fold binding above that seen with a negative control peptide. One obvious property of these peptides stands out. There appeared to be a very strong selection for a basic amino acid at position 5. In 16 of 20 of the peptides, a lysine, arginine, or histidine was found at position 5, matching the lysine found in p3K. As a control, we sequenced random clones picked either from the original E. coli construction of the library (17 clones) or from the baculovirus library that expressed IAb–peptide well, but did not bind either αβTCR (11 clones). The frequencies of basic amino acids at p5 in these sequences were only 12% and 9%, respectively (data not shown). There was no strong selection for amino acids homologous to those of p3K at positions p2, p3, p7, or p8. The amino acids at positions p2 and p3 appear nearly random, suggesting little or no essential contact between this part of the peptide–MHC ligand and the receptor, although these positions may contribute to the wide range of apparent αβTCR affinities seen. While not homologous to the asparagine in p3K, leucine was found at p7 in six of 20 (30.0%) of the YAe-62 αβTCR-selected peptides and three of 11 (27.2%) of the IAb-binding peptides that were not bound by the YAe-62 αβTCR, but only two of 17 (11.8%) of the random E. coli plasmids. The amino acid in this position is only partially exposed on the surface and can contribute significantly to peptide–MHC interaction (Liu et al. 2002). After asparagine, leucine is the most common amino acid found at this position in peptides found naturally bound to IAb (Dongre et al. 2001; Liu et al. 2002). Therefore, although more data would be required to test its significance, there may have been some slight enrichment of leucine at p7 in the expressed library prior to αβTCR selection, reflecting the role of p7 in stable peptide binding to IAb. The amino acid at position p8 is predicted to be fully surface exposed. In the selected peptides, rather than an amino acid homologous to the lysine of p3K, there may be an overrepresentation of amino acids with small neutral sidechains (threonine, serine, alanine, glycine) at this position. Perhaps this indicates that, in general, larger sidechains can be inhibitory at this position, but again more data would be required to test this idea. The 12 IAb–peptide combinations that bound the YAe-62 αβTCR most strongly were also the ones that were able to induce IL-2 production from YAe-62. Among these, a number with the very highest apparent affinities stimulated YAe-62 better than did p3K. However, there was not a direct correlation between apparent affinity and the level of IL-2 production; i.e., several peptides that yielded complexes with IAb with about the same apparent affinity for the αβTCR nevertheless stimulated very different levels of IL-2 production from YAe-62. This may be related to the phenomenon of altered peptide ligands (see Discussion). Overall, our results supported our original prediction that for conventional T cells, such as B3K-06, most of the surface-exposed residues of the peptide would be important in peptide–MHC recognition, while for broadly allo-MHC-reactive T cells, such as YAe-62, peptide recognition would be much more promiscuous. Discussion The peptide degeneracy allowed for a given αβTCR–MHC combination has been a subject of study over many years. While minor changes in the exposed amino acids sidechains of the peptide can often destroy αβTCR recognition, usually at least some variation is tolerated within the predicted footprint of the αβTCR on the peptide–MHC ligand (Evavold and Allen 1992; Reay et al. 1994). We can understand this flexibility to some extent from the X-ray structures of αβTCR–MHC–peptide complexes that show poor or even absent interactions between some peptide amino acid sidechains and the complementarity region (CDR) loops of the receptor (reviewed in Garcia et al. 1999). We have reported the properties of mice that have been genetically manipulated to express their MHCII molecules virtually completely occupied by a single peptide (Ignatowicz et al. 1996; Marrack et al. 2001). One of the most unusual features of the repertoire of T cells that develop in these animals is that they show an unusually high frequency of broadly allo-MHC–self-MHC-reactive T cells. These T cells are lost when these animals are repopulated with MHCII wild-type bone marrow cells. We have concluded that cells of this type are commonly positively selected in normal animals, but to a large extent negatively selected by self-MHC occupied by a variety of self-peptides. Their survival in single peptide–MHC mice may reflect the need for many different peptides to expose all MHC amino acids and their various conformers during T cell-negative selection. We have proposed that the αβTCRs of these cells are focused on the common conserved features of peptide–MHC complexes rather than on the specific sidechains of the exposed amino acids of the peptide (Marrack et al. 2001). A consequence of this hypothesis is the prediction that T cells of this sort should be much more peptide promiscuous than conventional T cells. The experiments reported here were designed to test this prediction by comparing the peptide promiscuity of one of these broadly allo-reactive T cells, YAe-62, typical of T cells from these mice, to that of a T cell with the same nominal specificity produced by immunization of conventional mice. The results support the conclusion that the broadly allo-reactive T cell has a much greater peptide promiscuity than does the conventional T cell. This question of T cell promiscuity is an important one in that it addresses the existence of a very large set of TCRs that apparently make it through positive selection, but never see the light of day in normal animals, because they are negatively selected on self-MHC with little input from the MHC-bound peptide. Thus, studying the peripheral fully negatively selected T cell repertoire gives a false impression of the interaction requirements necessary or sufficient for positive selection. These promiscuous T cells may also give us insight into possible evolutionary conserved αβTCR–MHC interactions that have been hard to sort out with conventional T cells. While perhaps much less frequent than in single peptide–MHC mice, peptide-promiscuous T cells have been described in normal individuals (Brock et al. 1996). Consistent with the idea that this property may be linked to allo-MHC reactivity, in a parallel study we have shown that peptide-promiscuous T cells are enriched in normal mice in the population of T cells reactive to foreign MHC alleles and isotypes (Huseby et al. 2003). In order to study the relationship between peptide sequence and αβTCR recognition, we developed a baculovirus-based display method for rapid identification of peptides that form complexes with MHC that bind a particular αβTCR. Display is one of the most powerful library techniques available. Its underlying principle is that the protein or peptide members of the library are expressed on the surface of organisms that harbor the DNA encoding them. A binding assay that isolates all members of the library with the appropriate properties copurifies the organism and the encoding DNA. The DNA is then amplified and reexpressed and the process repeated as many time as necessary to enrich fully the relevant molecules, whose sequence can be deduced from the copurified DNA. The great advantage of display libraries is that all members of the library that satisfy the screening conditions are enriched simultaneously without the need to identify them one by one. In order for peptides to be tested for αβTCR binding, they must be complexed with the relevant MHC molecule on a platform suitable for interaction with the T cell and/or its αβTCR. For display libraries, one aspect of this problem has been solved by the ability to express MHC molecules with sequence for a covalently attached antigenic peptide imbedded in the MHC genes (Kozono et al. 1994; Mottez et al. 1995; Uger and Barber 1998; White et al. 1999). However, the most commonly used bacterial display systems do not yet allow for the assembly and display of complex multichain MHC molecules. There is a recent report of the successful display of a single-chain peptide–MHCI on yeast cells (Brophy et al. 2003), but our own previous attempts with yeast had failed to yield displayed peptide–MHCII in a form capable of recognition by T cell hybridomas (data not shown). Our previous success with producing soluble MHC and αβTCR molecules using a baculovirus expression system and a report of peptide libraries displayed in baculovirus (Ernst et al. 1998) led us to adapt these methods for surface display of peptide–MHCII on insect cells. We constructed a library of peptides attached to the displayed mouse class II molecule, IAb. Using fluorescently labeled multimeric soluble αβTCRs as bait and insect cells infected with the IAb–peptide library as fish, we were able to identify rapidly the members of the library that encoded peptide mimotopes for two αβTCRs. In these studies, the immunizing peptide (epitope) for the αβTCR was already known. However, this method should be useful as well in identifying mimotopes for αβTCRs whose peptide epitope is not known, provided that suitable peptide anchor residues for MHC binding are known. One limitation of this display method as presented here is the size of the peptide library. The bottlenecks caused by the preparation of the library in an E. coli plasmid and then its introduction into baculovirus by homologous recombination resulted in a library with only 3 × 104 to 1 × 105 members. This is far below the size required to have all 3.2 × 106 versions of the peptide present when randomizing five amino acids. Large libraries of this size require more efficient baculovirus-cloning methods, such as incorporation of DNA fragments directly into baculovirus DNA by ligation (Ernst et al. 1994) or in vitro recombinase-mediated recombination (Peakman et al. 1992). In preliminary experiments, we have constructed an IAb–peptide library with over 107 clones by directly ligating (Ernst et al. 1994) a randomized PCR DNA fragment encoding the peptide into linearized baculovirus DNA using unique homing restriction enzyme (SceI–CeuI) sites introduced flanking the peptide-encoding region of the construct (data not shown). Since recircularized baculovirus DNA is directly infectious when introduced into insect cells by transfection, there is no theoretical reason why this method cannot be used to create libraries as large as those reported for yeast and phage. We have developed this method using IAb as the displayed MHCII molecule carrying the peptide library. However, using the same strategy, we have successfully displayed numerous other MHCII molecules, such as murine IEk and human DR4, DR52c, and DP2 (data not shown). While the leucine zippers that we included in this construct are not strictly required for expression of IAb, they have helped considerably in expression of some of these other MHCII molecules. Moreover, we (White et al. 1999) and others (Mottez et al. 1995; Uger and Barber 1998) have shown that peptides can be tethered to MHCI molecules via the N-terminus of either β2m or the heavy chain, making this approach feasible for searching for MHCI-bound peptide mimotopes as well. In preliminary experiments we have successfully displayed on the surface of Sf9 cells the mouse MHCI molecule, Dd, with a β2m-tethered peptide from HIV gp120 (data not shown). Given that baculovirus has been such a successful expression system for many different types of complex eukaryotic proteins that express or assemble poorly in E. coli, this method may have broad applications to other receptor–ligand systems. As opposed to methods that use T cell activation as the peptide-screening method, an advantage of display methods that use flow cytometry for screening and enrichment is that the strength of binding of receptor and ligand can be estimated and manipulated. In the results reported here, by limiting the analysis to insect cells bearing a particular level of peptide–MHC, a uniform level of αβTCR binding was seen for an individual peptide sequence, but the strength of binding varied over two orders of magnitude for different peptides, presumably reflecting the relative affinity of the receptor for different IAb–peptide combinations. Thus, depending on whether one was interested in high- or low-affinity ligands for the αβTCR, one could enrich for peptides with these properties directly during the screening of the library. Such an approach has been used with antibody (Boder and Wittrup 2000) and αβTCR (Shusta et al. 2000) variants displayed on yeast to select directly for receptors with increased affinity. It is worth noting that there was not a direct correlation between the strength of αβTCR binding to a particular peptide–MHC combination and the subsequent level of IL-2 secretion seen from the T cell responding to this combination. While in general the best IL-2 secretion was obtained with complexes with the highest apparent affinities, some IAb–peptide combinations with apparent high affinity stimulated IL-2 production poorly. One interesting possibility is that this observation is related to the phenomenon of altered peptide ligands in which amino acid variants of fully immunogenic peptides only partially activate or even anergize the T cell (Evavold et al. 1993), despite minor differences in affinity. In some cases, this phenomenon has been related to αβTCR binding kinetics, rather than just overall affinity (Lyons et al. 1996). Future experiments using surface plasmon resonance or fluorescence peptide–MHC multimers might help to test this idea. In summary, the very properties that have made baculovirus a very successful expression system for complex eukaryotic proteins also make it suitable for library display methods, with potential application not only in T cell epitope/mimotope discovery, characterization, and manipulation, but also in studying a wide variety of other protein–protein interactions. Materials and Methods Synthetic peptides, oligonucleotides, and DNA sequencing The peptides pEα (FEAQGALANIAVD), p3K (FEAQKAKANKAVD), and various alanine-substituted variants of p3K were synthesized in the Molecular Resource Center of the National Jewish Medical and Research Center (Denver, Colorado, United States), as were all oligonucleotides used in PCR and DNA sequencing. Automated DNA sequencing was also performed in this facility. Cell lines and T cell hybridomas The insect cell lines Sf9 and High Five were obtained from Invitrogen (Carlsbad, California, United States). The IAb-p3K-reactive T cell hybridoma B3K-06 was produced from C57BL/6 mice as previously described (Rees et al. 1999). The IAb-expressing B cell hybridoma LB-15.13 (Kappler et al. 1982) was used to present soluble peptides to B3K-06. The T cell hybridoma YAe-62 (Marrack et al. 2001) was produced from previously described (Ignatowicz et al. 1996) C57BL/6 mice that lacked expression of the endogenous IAb β gene (ΔIAβ) and the invariant chain (ΔIi) and that carried a transgene for the IAb β gene that was modified to insert sequence encoding pEα and a flexible linker between the signal peptide and the N-terminus of the β chain. These mice were immunized intravenously with 3 × 106 dendritic cells from ΔIAβ/ΔIi C57BL/6 mice. These cells had been retrovirally transduced (Mitchell et al. 2001; Schaefer et al. 2001) with the IAb β gene that was modified as above to express with a tethered p3K. T cells from these immunized mice were propagated in vitro and converted to T cell hybridomas, by standard techniques (White et al. 2000). The hybridomas were initially screened for binding of multivalent, fluorescent IAb-p3K (Crawford et al. 1998; Rees et al. 1999) and subsequently for IL-2 production in response to immobilized, soluble IAb-p3K, but not to spleen cells from the host ΔIAβ/ΔIi IAb-pEα transgenic mice. Further characterization of YAe-62 is described in the Results. Soluble αβTCRs cDNA, prepared from B3K-06 and YAe-62, was used as template in a PCR using oligonucleotides that flanked the Vα and Vβ regions and introduced restriction enzyme sites that allowed cloning of the PCR fragments into a previously described baculovirus expression vector for soluble αβTCRs (Kappler et al. 1994). The cloned fragments were sequenced and incorporated into baculovirus and αβTCRs were purified from the supernatants of infected High Five cells. For B3K-06, the α chain was AV0401/AJ27 and the CDR3 sequence was CALVISNTNKVVFGTG. The β chain was BV0801/BJ0103 and the CD3 sequence was CASIDSSGNTLYFGEG. For YAe-62, the α chain was AV0412/AJ11 and the CD3 sequence was CAANSGTYQRFGTG. The β chain was BV0802/JD0204 and the CD3 sequence was CASGDFWGDTLYFGAG. Expression of ICAM and B7.1 in Sf9 cells DNA fragments encoding the baculovirus hr5 enhancer element, IE1 gene promoter, and IEI poly(A) addition region were synthesized by PCR using baculovirus DNA as template. The fragments were used to construct an insect cell expression vector (pTIE1) on a pTZ18R (Pharmacia, Uppsala, Sweden) backbone with the hr5 enhancer at the 5′-end, followed by the IE1 promoter, a large multiple cloning site (Esp3I, MunI, SalI, XhoI, BsrGI, HpaI, SpeI, BstXI, BamHI, BspEI, NotI, SacII, XbaI), and the IE1 poly(A) addition region. The complete sequence of the pTIE1 vector has been deposited in GenBank (see Supporting Information). DNA fragments encoding mouse ICAM and B7.1 were cloned between the XhoI and NotI sites of the multiple cloning site. Sf9 cells were transfected with a combination of the plasmids by the standard calcium phosphate method and cells expressing both molecules on their surfaces were cloned without selection at limiting dilution to establish the line Sf9-ICAM/B7.1. IL-2 assays T cell hybridoma cells (105) were added to microtiter wells containing either (1) saturating immobilized peptide–MHC, (2) 10 μg/ml peptide plus 105 LB-15.13 cells, (3) 5 × 104 Sf9-ICAM/B7.1 insect cells infected 3 d previously with baculovirus encoding a displayed peptide–MHC, (4) 106 spleen cells from IAb-pEα single peptide mice, or (5) 106 spleen cells from various knockout or MHC congenic mice. After overnight incubation the culture supernatants were assayed for IL-2 as previously described (White et al. 2000). mAbs and flow cytometry The following mAbs were used in these studies: 17/227, a mouse IgG2a antibody, specific for IAb (Lemke et al. 1979); ADO-304, an Armenian hamster antibody specific for an epitope on the αβTCR Cα region not accessible on the surface of T cells, but exposed on recombinant αβTCR and on CD3-dissociated, NP-40-solublized natural αβTCR (Liu et al. 1998); 3E2 (PharMingen, San Diego, California, United States), specific for mouse ICAM (CD54); and 16–10A1 (PharMingen), specific for mouse B7.1 (CD80). For flow cytometry, an unlabeled version of 17/227 was used with phycoerythrin-coupled goat anti-mouse IgG2a (Fisher Biotech, Foster City, California, United States). To assemble multivalent fluorescent versions of the soluble αβTCRs, first a biotinylated version of ADO-304 was prepared. In brief, purified ADO-304 at 1–3 mg/ml in 0.1 M NaHCO3 was labeled with Sulfo-NHS-LC-Biotin (Pierce Chemical Company, Rockford, Illinois, United States) at a molar ratio of 2.5:1 (biotin:antibody) for 4 h at room temperature. The reaction was quenched with 0.1 M lysine and the product dialyzed extensively against PBS. The resulting derivative contained about one biotin per molecule of mAb. The biotinylated mAb was complexed in excess with AlexaFlour647–streptavidin (Molecular Probes, Eugene, Oregon, United States). The complex was separated from the free biotin–antibody using Superdex-200 size exclusion chromatography (Pharmacia). In preliminary experiments, the amount of soluble αβTCR required to saturate an aliquot of a large single batch of this reagent was determined. To prepare the multivalent αβTCR, the appropriate amount of soluble αβTCR was mixed with an aliquot of the fluorescent anti-Cα reagent overnight. For staining for flow cytometry, this mix was used without further purification. Each 100 μl sample contained approximately 2 μg of the fluorescent reagent plus 105 Sf9 insect cells. This mixture was incubated at 27°C for 1–2 h. The cells were then washed for analysis. The advantages of this method for preparing fluorescent multimers over using direct enzymatic biotinylation (Schatz 1993) of the αβTCR were that only one fluorescent reagent needed to be prepared for all αβTCRs, the mAb–streptavidin complex was very stable over a long period of time, and no special peptide-tagged version of the soluble αβTCR was required. Analytical flow cytometry was performed with a FacsCaliber flow cytometer (Becton-Dickinson, Palo Alto, California, United States). For sorting, a MoFlo instrument was used (Dako/Cytomation, Glostrup, Denmark). IAb and peptide library constructions For displaying IAb on the surface of baculovirus-infected insect cells, modifications were made as described in Figure 2A and 2B to a previously reported baculovirus construct for producing soluble IAb-p3K (Rees et al. 1999). Other versions of this construction were prepared encoding other IAb-binding peptides. The constructions were incorporated into baculovirus by homologous recombination using the BaculoGold system (PharMingen). As described in Figure 2C, this construction was altered in the E. coli transfer plasmid to replace the portion encoding p3K with sequence encoding enhanced GFP, flanked by sites for the restriction enzymes SbfI and CeuI. A PCR fragment was produced as described in Figure 2D that encoded an IAb-binding peptide randomized at positions p2, p3, p5, p7, and p8, but identical to p3K at other positions. This sequence was flanked by sites for the restriction enzymes PstI and BstXI, such that the cohesive ends generated by these enzymes were compatible with those generated by SbfI and CeuI in the GFP-containing plasmid. Cloning the restricted fragment into this site regenerated a covalent peptide in-frame with the signal peptide and flexible linker of the IAb β chain (see Figure 2E). After ligation of the fragment into this plasmid, a bulk transformation was performed using XL1-Blue E. coli (Stratagene, La Jolla, California, United States). An estimated 3 × 104 to 10 × 104 independent transformants were obtained that were used to make a mixed plasmid preparation. This mixture was incorporated into baculovirus by homologous recombination as above. In order to assure a high efficiency of conversion of plasmids to virus, 1.5 × 107 Sf9 cells were cotransfected with 6 μg of the plasmid mixture and 1.5 μg of BaculoGold DNA. Supporting Information Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) accession numbers for the sequences described in this paper are B7.1 (AJ278965), baculovirus DNA (L22858), ICAM (X52264), and pTIE1 vector (AY522575). This work was supported by United States Public Health Service grants AI-17134, AI-18785, and AI-22295. We thank Amy Marrs and Randy Anselment of the National Jewish Molecular Resource Center for oligonucleotide and peptide syntheses well as automated DNA sequencing. We also thank Shirley Sobus, Josh Loomis, and Bill Townend of the National Jewish Flow Cytometry Facility for help with flow cytometric analysis and sorting. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. FC and JWK conceived and designed the experiments. FC, EH, JW, and JWK performed the experiments. JWK and PM wrote the paper Academic Editor: Marc Jenkins, University of Minnesota Abbreviations APCantigen-presenting cell CDR3third complementarity region GFPgreen fluorescent protein ΔIAβinactivated class II MHC molecule IA β gene ΔIiinactivated class II MHC molecule invariant chain gene mAbmonoclonal antibody MHCmajor histocompatibility complex MHCIclass I MHC molecule MHCIIclass II MHC molecule MOImultiplicity of infection p3Ka peptide containing the core sequence FEAQKAKANKAV pEαa peptide containing the core sequence FEAQGALANIAV αβTCRαβ T cell receptor ==== Refs References Boder ET Wittrup KD Yeast surface display for directed evolution of protein expression, affinity, and stability Methods Enzymol 2000 328 430 444 11075358 Brock R Wiesmuller KH Jung G Walden P Molecular basis for the recognition of two structurally different major histocompatibility complex/peptide complexes by a single T-cell receptor Proc Natl Acad Sci U S A 1996 93 13108 13113 8917552 Brophy SE Holler PD Kranz DM A yeast display system for engineering functional peptide–MHC complexes J Immunol Methods 2003 272 235 246 12505727 Cai Z Brunmark A Jackson MR Loh D Peterson PA Transfected Drosophila cells as a probe for defining the minimal requirements for stimulating unprimed CD8+ T cells Proc Natl Acad Sci U S A 1996 93 14736 14741 8962124 Crawford F Kozono H White J Marrack P Kappler J Detection of antigen-specific T cells with multivalent soluble class II MHC covalent peptide complexes Immunity 1998 8 675 682 9655481 Dongre AR Kovats S deRoos P McCormack AL Nakagawa T In vivo MHC class II presentation of cytosolic proteins revealed by rapid automated tandem mass spectrometry and functional analyses Eur J Immunol 2001 31 1485 1494 11465105 Ernst WJ Grabherr RM Katinger HW Direct cloning into the Autographa californica nuclear polyhedrosis virus for generation of recombinant baculoviruses Nucleic Acids Res 1994 22 2855 2856 8052541 Ernst W Grabherr R Wegner D Borth N Grassauer A Baculovirus surface display: Construction and screening of a eukaryotic epitope library Nucleic Acids Res 1998 26 1718 1723 9512544 Evavold BD Allen PM Dissection of the Hb(64–76) determinant reveals that the T cell receptor may have the capacity to differentially signal Adv Exp Med Biol 1992 323 17 21 1485563 Evavold BD Sloan-Lancaster J Allen PM Tickling the TCR: Selective T-cell functions stimulated by altered peptide ligands Immunol Today 1993 14 602 609 8305133 Garcia KC Teyton L Wilson IA Structural basis of T cell recognition Annu Rev Immunol 1999 17 369 397 10358763 Gavin MA Dere B Grandea AG Hogquist KA Bevan MJ Major histocompatibility complex class I allele-specific peptide libraries: Identification of peptides that mimic an H-Y T cell epitope Eur J Immunol 1994 24 2124 2133 7522161 Guimezanes A Barrett-Wilt GA Gulden-Thompson P Shabanowitz J Engelhard VH Identification of endogenous peptides recognized by in vivo or in vitro generated alloreactive cytotoxic T lymphocytes: Distinct characteristics correlated with CD8 dependence Eur J Immunol 2001 31 421 432 11180106 Hiemstra HS Drijfhout JW Roep BO Antigen arrays in T cell immunology Curr Opin Immunol 2000 12 80 84 10679401 Hogquist KA Jameson SC Heath WR Howard JL Bevan MJ T cell receptor antagonist peptides induce positive selection Cell 1994 76 17 27 8287475 Huseby ES Crawford F White J Kappler J Marrack P Negative selection imparts peptide specificity to the mature T cell repertoire Proc Natl Acad Sci U S A 2003 100 11565 11570 14504410 Ignatowicz L Kappler J Marrack P The repertoire of T cells shaped by a single MHC/peptide ligand Cell 1996 84 521 529 8598039 Kappler J White J Wegmann D Mustain E Marrack P Antigen presentation by Ia+ B cell hybridomas to H-2-restricted T cell hybridomas Proc Natl Acad Sci U S A 1982 79 3604 3607 6980413 Kappler J White J Kozono H Clements J Marrack P Binding of a soluble alpha beta T-cell receptor to superantigen/major histocompatibility complex ligands Proc Natl Acad Sci U S A 1994 91 8462 8466 8078904 Kitts PA Possee RD A method for producing recombinant baculovirus expression vectors at high frequency Biotechniques 1993 14 810 817 8512707 Kozono H White J Clements J Marrack P Kappler J Production of soluble MHC class II proteins with covalently bound single peptides Nature 1994 369 151 154 8177320 Lemke H Hammerling GJ Hammerling U Fine specificity analysis with monoclonal antibodies of antigens controlled by the major histocompatibility complex and by the Qa/TL region in mice Immunol Rev 1979 47 175 206 398326 Linnemann T Tumenjargal S Gellrich S Wiesmuller K Kaltoft K Mimotopes for tumor-specific T lymphocytes in human cancer determined with combinatorial peptide libraries Eur J Immunol 2001 31 156 165 11169449 Liu CP Crawford F Marrack P Kappler J T cell positive selection by a high density, low affinity ligand Proc Natl Acad Sci U S A 1998 95 4522 4526 9539770 Liu R Enstrom AM Lam KS Combinatorial peptide library methods for immunobiology research Exp Hematol 2003 31 11 30 12543103 Liu X Dai S Crawford F Fruge R Marrack P Alternate interactions define the binding of peptides to the MHC molecule IA(b) Proc Natl Acad Sci U S A 2002 99 8820 8825 12084926 Lyons DS Lieberman SA Hampl J Boniface JJ Chien Y A TCR binds to antagonist ligands with lower affinities and faster dissociation rates than to agonists Immunity 1996 5 53 61 8758894 Marrack P Bender J Jordan M Rees W Robertson J Major histocompatibility complex proteins and TCRs: Do they really go together like a horse and carriage? J Immunol 2001 167 617 621 11441061 Mitchell TC Hildeman D Kedl RM Teague TK Schaefer BC Immunological adjuvants promote activated T cell survival via induction of Bcl-3 Nat Immunol 2001 2 397 402 11323692 Mottez E Langlade-Demoyen P Gournier H Martinon F Maryanski J Cells expressing a major histocompatibility complex class I molecule with a single covalently bound peptide are highly immunogenic J Exp Med 1995 181 493 502 7836906 O'Shea EK Lumb KJ Kim PS Peptide ‘Velcro:” Design of a heterodimeric coiled coil Curr Biol 1993 3 658 667 15335856 Partidos CD Peptide mimotopes as candidate vaccines Curr Opin Mol Ther 2000 2 74 79 11249654 Peakman TC Harris RA Gewert DR Highly efficient generation of recombinant baculoviruses by enzymatically medicated site-specific in vitro recombination Nucleic Acids Res 1992 20 495 500 1741284 Reay PA Kantor RM Davis MM Use of global amino acid replacements to define the requirements for MHC binding and T cell recognition of moth cytochrome c (93–103) J Immunol 1994 152 3946 3957 7511662 Rees W Bender J Teague TK Kedl RM Crawford F An inverse relationship between T cell receptor affinity and antigen dose during CD4(+) T cell responses in vivo and in vitro Proc Natl Acad Sci U S A 1999 96 9781 9786 10449771 Sahara H Shastri N Second class minors: molecular identification of the autosomal H46 histocompatibility locus as a peptide presented by major histocompatibility complex class II molecules J Exp Med 2003 197 375 385 12566421 Schaefer BC Mitchell TC Kappler JW Marrack P A novel family of retroviral vectors for the rapid production of complex stable cell lines Anal Biochem 2001 297 86 93 11567531 Schatz PJ Use of peptide libraries to map the substrate specificity of a peptide- modifying enzyme: A 13 residue consensus peptide specifies biotinylation in Escherichia coli Biotechnology (N Y) 1993 11 1138 1143 7764094 Scott D Addey C Ellis P James E Mitchell MJ Dendritic cells permit identification of genes encoding MHC class II-restricted epitopes of transplantation antigens Immunity 2000 12 711 720 10894170 Shastri N Schwab S Serwold T Producing nature's gene-chips: The generation of peptides for display by MHC class I molecules Annu Rev Immunol 2002 20 463 493 11861610 Shusta EV Holler PD Kieke MC Kranz DM Wittrup KD Directed evolution of a stable scaffold for T-cell receptor engineering Nat Biotechnol 2000 18 754 759 10888844 Simpson E Scott D James E Lombardi G Cwynarski K Minor H antigens: Genes and peptides Eur J Immunogenet 2001 28 505 513 11881817 Sung MH Zhao Y Martin R Simon R T-cell epitope prediction with combinatorial peptide libraries J Comput Biol 2002 9 527 539 12162891 Uger RA Barber BH Creating CTL targets with epitope-linked beta 2-microglobulin constructs J Immunol 1998 160 1598 1605 9469415 Van Der Bruggen P Zhang Y Chaux P Stroobant V Panichelli C Tumor-specific shared antigenic peptides recognized by human T cells Immunol Rev 2002 188 51 64 12445281 White J Crawford F Fremont D Marrack P Kappler J Soluble class I MHC with beta2-microglobulin covalently linked peptides: specific binding to a T cell hybridoma J Immunol 1999 162 2671 2676 10072510 White J Kappler J Marrack P Production and characterization of T cell hybridomas Methods Mol Biol 2000 134 185 193 10730258 You S Chen C Lee WH Wu CH Judkowski V Detection and characterization of T cells specific for BDC2.5 T cell-stimulating peptides J Immunol 2003 170 4011 4020 12682229

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PLoS Biol. 2004 Apr 13; 2(4):e90

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020091Research ArticleBiophysicsCell BiologyMolecular Biology/Structural BiologyEukaryotesActivation of Arp2/3 Complex: Addition of the First Subunit of the New Filament by a WASP Protein Triggers Rapid ATP Hydrolysis on Arp2 ATP Hydrolysis by Arp2/3 ComplexDayel Mark J 1 Mullins R. Dyche dyche@mullinslab.ucsf.edu 2 1Graduate Group in Biophysics, University of CaliforniaSan Francisco, San Francisco, CaliforniaUnited States of America2Department of Cellular and Molecular Pharmacology, University of CaliforniaSan Francisco, San Francisco, CaliforniaUnited States of America4 2004 13 4 2004 13 4 2004 2 4 e9122 8 2003 29 1 2004 Copyright: © 2004 Dayel and Mullins.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Mechanism for Adding the First Link in a Nascent Actin Filament Chain In response to activation by WASP-family proteins, the Arp2/3 complex nucleates new actin filaments from the sides of preexisting filaments. The Arp2/3-activating (VCA) region of WASP-family proteins binds both the Arp2/3 complex and an actin monomer and the Arp2 and Arp3 subunits of the Arp2/3 complex bind ATP. We show that Arp2 hydrolyzes ATP rapidly—with no detectable lag—upon nucleation of a new actin filament. Filamentous actin and VCA together do not stimulate ATP hydrolysis on the Arp2/3 complex, nor do monomeric and filamentous actin in the absence of VCA. Actin monomers bound to the marine macrolide Latrunculin B do not polymerize, but in the presence of phalloidin-stabilized actin filaments and VCA, they stimulate rapid ATP hydrolysis on Arp2. These data suggest that ATP hydrolysis on the Arp2/3 complex is stimulated by interaction with a single actin monomer and that the interaction is coordinated by VCA. We show that capping of filament pointed ends by the Arp2/3 complex (which occurs even in the absence of VCA) also stimulates rapid ATP hydrolysis on Arp2, identifying the actin monomer that stimulates ATP hydrolysis as the first monomer at the pointed end of the daughter filament. We conclude that WASP-family VCA domains activate the Arp2/3 complex by driving its interaction with a single conventional actin monomer to form an Arp2–Arp3–actin nucleus. This actin monomer becomes the first monomer of the new daughter filament. This paper provides the biochemical and biophysical basis for actin filament formation, necessary for cell shape and motility ==== Body Introduction The actin cytoskeleton determines the shape, mechanical properties, and motility of most eukaryotic cells. To change shape and to move, cells precisely control the location and timing of actin filament assembly by regulating the number of fast-growing (barbed) filament ends (Pollard et al. 2000). The actin-related protein (Arp) 2/3 complex, a seven-subunit protein complex that contains two actin-related proteins, generates these new barbed ends in response to cellular signals (Welch et al. 1998; Machesky et al. 1999; Rohatgi et al. 1999). In a process called “dendritic nucleation,” the Arp2/3 complex nucleates new actin filaments from the sides of preexisting filaments to produce a rigid and highly crosslinked filament array (Mullins et al. 1998; Machesky et al. 1999; Blanchoin et al. 2000a). Such crosslinked arrays form the core of many motile cellular structures, including the leading edges of amoeboid cells and the actin comet tails that propel endosomes and bacterial pathogens through eukaryotic cytoplasm. To understand the construction, function, and regulation of these structures, it is important to understand the molecular mechanism of Arp2/3 activation. The Arp2/3 complex must be activated by both a member of the Wiskott–Aldrich syndrome protein (WASP) family and a preexisting actin filament before it will nucleate a new actin filament (Machesky et al. 1999; Blanchoin et al. 2001; Zalevsky et al. 2001). The structure and the orientation of the Arp2 and Arp3 subunits within the crystal structure of the complex suggest that these subunits may nucleate a new filament by forming an actin-like heterodimer that mimics the barbed end of an actin filament (Robinson et al. 2001). In the crystal structure of the unactivated complex, however, Arp2 and Arp3 are separated by 40 Å so that formation of an actin-like dimer would require a conformational change (Robinson et al. 2001). Binding of the Arp2/3 complex to both a preformed filament and a WASP-family protein is thought to drive at least part of this conformational change (Blanchoin et al. 2001; Marchand et al. 2001; Panchal et al. 2003). The Arp2/3-activating region of WASP-family proteins, also known as the VCA domain, is composed of three sequences arranged in tandem: (1) an actin-binding verprolin-homology (or V) domain (also known as a WASP-homology 2 [WH2] domain), (2) a conserved “connecting” (or C) region that interacts with both the Arp2/3 complex and monomeric actin (Marchand et al. 2001), and (3) an acidic (or A) region that binds the Arp2/3 complex. This VCA domain is both necessary and sufficient for efficient Arp2/3 activation. We and others have previously suggested that an actin monomer provided by the VCA domain to the Arp2/3 complex may drive the formation of an Arp2–Arp3–actin heterotrimer and form a nucleus for actin polymerization (Dayel et al. 2001; Marchand et al. 2001). Both the Arp2 and Arp3 subunits of the complex bind ATP (Dayel et al. 2001). Hydrolysis of this ATP could be used to perform work, to provide a signal, or, like the guanine triphosphate (GTP) bound to the α subunit of tubulin heterodimers, may simply stabilize a protein fold. On conventional actin, ATP hydrolysis is a timing mechanism that promotes construction of dynamic and polarized filament networks. Actin rapidly hydrolyzes ATP upon polymerization (Blanchoin and Pollard 2002) and releases bound phosphate several hundred seconds later (Melki et al. 1996). ATP hydrolysis and phosphate dissociation do not cause immediate filament disassembly, but enable interaction with depolymerizing factors such as cofilin (Blanchoin and Pollard 1999). ATP hydrolysis by actin thereby determines the overall rate of filament turnover. We show here that the Arp2/3 complex rapidly hydrolyzes ATP on the Arp2 subunit upon filament nucleation. There are several events in the Arp2/3 nucleation reaction that might trigger ATP hydrolysis on Arp2: (1) binding of VCA to the Arp2/3 complex, (2) binding of VCA-Arp2/3 to the side of a preformed filament, (3) binding of a VCA-tethered actin monomer to the Arp2/3 complex, or (4) binding of a second or third actin monomer to form a stable daughter filament. We find that ATPase activity requires the combination of a preformed actin filament, a VCA domain, and an actin monomer, but does not require actin polymerization. This indicates that hydrolysis is triggered relatively early in the nucleation reaction—before completion of a stable daughter filament. Capping the pointed ends of actin filaments also stimulates Arp2 to rapidly hydrolyze ATP in the absence of monomeric actin and VCA and without branch formation. Thus, ATP hydrolysis on Arp2 is stimulated directly by interaction with conventional actin, presented to the complex either as a monomer attached to the VC domain of the WASP-family protein or as one of the subunits making up the pointed end of a preformed filament. To our knowledge this is the first direct evidence that the monomer supplied by the VCA domain is the first monomer of the new daughter filament. From these observations we propose a model for the mechanism of Arp2/3 complex activation by WASP-family proteins. Results γ-32P-AzidoATP Can Be Covalently Crosslinked to Arp2 and Arp3 with Approximately Equal Efficiency Previously we used sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) to show that UV irradiation covalently crosslinks α-32P-8-AzidoATP to the Arp2 and Arp3 subunits of the Arp2/3 complex (Dayel et al. 2001). Here we crosslink γ-32P-AzidoATP instead of α-32P to Arp2 to measure ATPase activity. Using SDS-PAGE, we can separate the subunits and simultaneously monitor cleavage of the labeled γ-phosphate from ATP bound to both Arp2 and Arp3. This technique allows us to measure ATP hydrolysis specifically on the Arp2/3 complex in spite of a 100-fold molar excess of actin, which also binds and hydrolyzes ATP. We crosslinked γ-32P-AzidoATP to the Arp2/3 complex by brief (9 s) exposure to UV light. In the presence of γ-32P-AzidoATP at concentrations above the KD for ATP (Dayel et al. 2001), γ-32P-AzidoATP crosslinks to both Arp2 and Arp3 with approximately equal efficiency (Figure 1A). Addition of large amounts of monomeric actin to the labeled Arp2/3 distorts the shape of the Arp2 band, but the 32P signal from Arp2 remains separately quantifiable, and the magnitude is unaffected (Figure 1A). The efficiency of crosslinking for both Arp2 and Arp3 is approximately 10% (unpublished data); therefore, only 1% of the Arp2/3 complex has γ-32P-AzidoATP crosslinked to both Arp2 and Arp3. For simplicity, we refer to this partially crosslinked Arp2/3 complex as γ-32P-AzidoATP-Arp2/3. Reactions using γ-32P-AzidoATP-Arp2/3 are performed in the presence of 100 μM ATP, to occupy the noncrosslinked sites and ensure 100% of the Arp2/3 complex is active. Figure 1 Arp2 Hydrolyzes ATP Rapidly upon Filament Nucleation (A) Arp2/3 (2 μM) was covalently crosslinked to γ-32P-AzidoATP by exposure to UV light. Both Arp2 and Arp3 crosslink with approximately equal efficiency (lane 1). Addition of 100-fold excess monomeric actin (lane 2) distorts the shape of the Arp2 band, but the Arp2 signal remains separate and quantifiable. (B–E) γ-32P-AzidoATP-Arp2/3 (20 nM) was mixed with 2 μM monomeric actin in polymerization buffer. Samples were taken before and at indicated times after the addition of 750 nM VCA, which initiates rapid actin-filament nucleation by the Arp2/3 complex. (B) Subunits were separated by SDS-PAGE and stained with Coomassie. (C) 32P signal shows remaining uncleaved γ-32P on Arp2 and Arp3 subunits. Arp2 rapidly loses γ-32P after addition of VCA. (D) Cleaved γ-32P was separated from free 32P-ATP and protein-32P-ATP by TLC. (E) Quantitation of (B) to (D): Protein-ATP (closed circle), Cleaved Pi (closed square), Free ATP (closed diamonds), and Arp2-ATP from SDS-PAGE (open circle, normalized separately). (F) Arp2 releases phosphate soon after ATP hydrolysis. Reaction conditions were the same as (B)–(E), but with the addition of 2 mM maltose and 2 U/ml maltose phosphorylase. Timepoints were quenched into formic acid and assayed by TLC. Hydrolyzed 32P-ATP was quantified from the decrease in protein conjugated 32P, and released 32P was quantified from the 32P-glucose phosphate produced. Arp2 Hydrolyzes ATP Rapidly upon Actin Filament Nucleation We mixed 20 nM γ-32P-AzidoATP-Arp2/3 with 2 μM monomeric actin in polymerization buffer and initiated polymerization by adding 750 nM VCA, which activates rapid actin filament nucleation by the Arp2/3 complex (t½ actin polymerization ≈ 20 s; unpublished observations). (Unless otherwise stated, VCA refers to 6-histidine [6His]-N-WASP-VCA [398-502]. Cleavage of the 6His tag did not affect the kinetics of Arp2/3-mediated actin polymerization [unpublished data].) We assayed timepoints both by SDS-PAGE and thin-layer chromatagraphy (TLC) during the same reaction to monitor remaining and cleaved 32P, respectively (Figure 1B–1D; quantified in Figure 1E). ATP is hydrolyzed by the Arp2/3 complex at the earliest timepoints after the addition of VCA (monitored by 32P cleavage) and cleavage has ceased by 90 s (Figure 1D). SDS-PAGE analysis separates the subunits and shows that the γ-32P is cleaved rapidly from Arp2 upon addition of VCA, but not significantly from Arp3 (Figure 1C). The kinetics of ATP hydrolysis assayed by SDS-PAGE match the kinetics of phosphate cleavage by TLC (Figure 1E). Since the nucleation reaction is autocatalytic, the rate increases over time, and therefore it is not possible to derive an exact ATPase rate constant from our data. However, we can define a conservative lower bound: khyd > 0.1 s–1, noting that the true rate constant is probably much higher. Isolated Arp2/3 complex in polymerization buffer shows very slow spontaneous cleavage of γ-32P from both Arp2 and Arp3 (<1 × 10–4 s–1) (unpublished data). As a control, 32P-ATP hydrolysis is only seen when the Azido-ATP is covalently crosslinked to the Arp2/3 complex (Figure 3D, compare open and closed circles) indicating that the signal is due only to hydrolysis of ATP covalently bound to the Arp2/3 complex and not due to ATP hydrolysis by polymerizing actin. This is further confirmed by observations of ATP hydrolysis on the Arp2/3 complex under conditions where no actin polymerization takes place (Figure 3E and 3F; Figure 4). Figure 3 A Single Actin Monomer, in the Presence of Actin Filaments and VCA, Stimulates ATP Hydrolysis on Arp2, without Requiring Actin Polymerization (A–C) Remaining unhydrolyzed γ-32P-AzidoATP on Arp2 (closed circle) and Arp3 (open circle) was quantified to assay ATP hydrolysis (same conditions as Figure 1B–1D). γ-32P-AzidoATP-labeled Arp2/3 (20 nM) was mixed at indicated times with either 750 nM VCA then 2 μM G-actin (A), 2 μM G-actin then 750 nM VCA (B), or 2 μM F-actin then 750 nM VCA (C). (D) Latrunculin B (open square) inhibits the ability of VCA plus monomeric actin (open circle) to stimulate ATP hydrolysis on the Arp2/3 complex in the absence of actin filaments. Also, 32P ATP hydrolysis signal requires covalent crosslinking to Arp2/3. Arp2/3 was mixed with 6 μM γ-32P-AzidoATP and exposed to UV either before (closed circle) or after (open circle) the addition of excess (2 mM) unlabeled ATP. Excess ATP added before the UV exposure prevents crosslinking and abolishes the ATP hydrolysis signal, indicating that all the 32P ATP hydrolysis signals measured are due to ATP hydrolysis on Arp2/3 and not from ATP hydrolysis on actin. (E and F) In the presence of phalloidin-stabilized actin filaments, actin monomers are prevented from polymerizing by Latrunculin B, but still stimulate ATP hydrolysis on the Arp2/3 complex. 20 nM γ-32P-AzidoATP–labeled Arp2/3 was premixed with 1 μM phalloidin-stabilized actin filaments. The reaction was initiated by mixing with 750 nM N-WASP VCA, 1 μM G-actin and 4 μM Latrunculin B as indicated, cleaved γ-32P was assayed by phosphomolybdate extraction (E), and separately, actin polymerization was monitored by pyrene–actin fluorescence (F). Figure 4 Pointed-End Filament Capping Is Sufficient to Stimulate ATP Hydrolysis on Arp2 in the Absence of VCA (A) The Arp2/3 complex prevents actin filament reannealing by capping the pointed ends. The length distribution of 2 μM Alexa-488 phalloidin-stabilized actin filaments is unaffected in the absence (i) or presence (iii) of 20 nM Arp2/3 complex. (ii) 5 min after shearing the filaments, filaments have begun to reanneal in the absence of the Arp2/3 complex, but 20 nM Arp2/3 complex (iv) maintains short filaments, preventing reannealing by capping filament pointed ends. (B) ATP hydrolysis on Arp2 is stimulated by pointed-end capping. Crosslinked γ-32P-AzidoATP-Arp2/3 (20 nM) was mixed with 2 μM phalloidin-stabilized actin filaments. The mixture was split in two and one sample was sheared. Timepoints were taken as shown. (C) Uncleaved 32P on Arp2 (unsheared [closed circle] and sheared [closed square]) and Arp3 (unsheared [open circle] and sheared [open square]) were quantified from (B). Arp2 rapidly hydrolyzes bound ATP upon filament pointed-end capping. Phosphate Release by Arp2 Lags Hydrolysis by Approximately 40 s To investigate the kinetics of phosphate release fromArp2/3 during the polymerization reaction, we added maltose and maltose phosphorylase to the reaction. In the presence of 32P-labeled Arp2/3 complex, maltose phosphorylase conjugates the 32P-orthophosphate released from Arp2 to a hydrolyzed maltose molecule to make 32P-glucose phosphate. The phosphate from adenosine diphosphate-inorganic phosphate (ADP-Pi)-bound Arp2 is inaccessible to the enzyme and remains unconjugated orthophosphate. We quantified hydrolyzed 32P-ATP and released phosphate by TLC (Figure 1F). Phosphate release from Arp2 lags behind ATP hydrolysis by approximately 40 s. The Rate of Filament Nucleation Matches the Rate of ATP Hydrolysis by Arp2 To determine whether ATP hydrolysis on Arp2 is coupled to filament nucleation, we varied the rate of nucleation and looked to see whether the rate of ATP hydrolysis by Arp2 varied accordingly. We varied the nucleation rate by using N-WASP and Scar1 VCA domains, which stimulate different rates of Arp2/3 complex-dependent actin nucleation (Zalevsky et al. 2001). To slow the nucleation reaction and allow more accurate kinetic measurements, we used only 1 μM monomeric actin. We used pyrene–actin polymerization data (Figure 2A) to calculate the concentration of barbed ends produced over time (Figure 2B, open symbols) (see Methods and Materials; Zalevsky et al. 2001). Note that this calculation is model-independent and simply uses the established kinetic parameters for actin polymerization and the change in the amount of monomeric and filamentous actin over time measured from the pyrene–actin curves. The same reagents were used to monitor ATP hydrolysis by Arp2 under the same conditions. We used loss of γ-32P labeling as a probe for ATP hydrolysis and scaled the initial labeling intensity to the Arp2/3 concentration used in the reaction (20 nM) to calibrate the stoichiometry of ATP hydrolyzed by Arp2 (Figure 2B). Using Scar1 VCA instead of N-WASP VCA halves both the rate of nucleation of actin filaments and the rate of ATP hydrolysis on Arp2. Figure 2 ATP Hydrolysis by Arp2 Coincides with Nucleation of New Actin Filaments, and Not Filament Debranching (A, B) The kinetics of nucleation were slowed by using only 1 μM monomeric actin (compared to 2 μM for Figure 1). γ-32P-AzidoATP-Arp2/3 (20 nM) was mixed with either 750 nM N-WASP WWA (closed circle) or Scar1 WA (closed square) and 1 μM 7% pyrene-labeled monomeric actin. (A) Actin polymerization measured by pyrene fluorescence. (B) The concentration of new filament ends (open symbols) was calculated from the polymerization data in a model-independent way (see Methods and Materials), and Arp2-ATP hydrolysis (closed symbols) was measured under the same reaction conditions for both N-WASP WWA (open and closed circles) and Scar1 WA (open and closed squares). (C) ATP hydrolysis on Arp2 does not accompany filament debranching. Using a large excess (100 nM) of γ-32P-AzidoATP-Arp2/3 creates a slow hydrolysis phase that follows the rapid nucleation phase. The slow phase of ATP hydrolysis can be inhibited by excess (1.5 μM) uncrosslinked Arp2/3 added at t = 200 s, showing that the slow phase of ATP hydrolysis is from Arp2/3 being recruited from solution and not from that already incorporated in branches. We note that the total amount of Arp2 that hydrolyzes ATP in the polymerization reaction is 30% less for Scar1 VCA than for N-WASP VCA, which we interpret as 30% fewer filaments produced. Although it is possible to calculate the rate of end production from the pyrene–actin polymerization curve in a model-independent way, it is not possible to calculate the total number of barbed ends produced, since once polymerization reaches equilibrium, the pyrene–actin curve will not change even if new barbed ends continue to be produced. From the ATP hydrolysis data, therefore, the Arp2/3 complex produces filament ends more slowly when activated by Scar1, and under our conditions, the reaction ends when monomeric actin is depleted by incorporation into the new filaments. Fewer total filaments are therefore produced by the less active VCA domain. ATP Hydrolysis on Arp2 Does Not Accompany Filament Debranching A previous study claimed that ATP hydrolysis on Arp2 occurs very slowly (t1/2 ≈ 800 s), coincident with filament debranching (Le Clainche et al. 2003). Le Clainche et al. (2003) used a much higher concentration of Arp2/3 complex (100 nM) in their assays than the 5 nM Arp2/3 complex that they estimate was used up during their polymerization reaction. Using these conditions, we find that Arp2/3 complex hydrolyses ATP in two discrete phases: a fast (nucleation) phase, followed by a slow, approximately linear phase (Figure 2C, open symbols). This slow phase does not plateau within 6000 s and is similar to the data presented in Le Clainche et al. (2003). To demonstrate that this slow ATP hydrolysis is not due to the Arp2/3 complex hydrolyzing ATP upon debranching, we added an excess of unlabeled Arp2/3 complex into solution at t = 200 s, after the polymerization phase is complete. This unlabeled Arp2/3 complex competes for nucleating factors with γ-32P-AzidoATP-Arp2/3 in solution, but it does not compete with γ-32P-AzidoATP-Arp2/3 already incorporated in branches. Addition of excess unlabeled Arp2/3 complex at t = 200 s inhibits the slow phase of ATP hydrolysis (Figure 2C, closed symbols), indicating that the slow phase is due to ATP hydrolysis on Arp2/3 complex being recruited from solution and not due to ATP hydrolysis on Arp2/3 complex already in branches. This slow ATP hydrolysis probably represents a low rate of filament nucleation by the excess unused Arp2/3 complex, the rate of nucleation being limited by the low monomeric actin concentration that remains after most of the actin has polymerized. Both VCA and Monomeric Actin Are Required to Stimulate ATP Hydrolysis by Arp2 during the Polymerization Reaction Although the kinetics of ATP hydrolysis on Arp2 match the kinetics of actin polymerization, these data do not rule out the possibilities that VCA alone or the filamentous actin created during the polymerization reaction stimulates the ATPase activity independent of nucleation. To more specifically determine what stimulates ATP hydrolysis on Arp2, we varied the order of addition of the components that initiate the polymerization reaction. Incubation of the Arp2/3 complex with VCA does not induce ATP hydrolysis by the complex until monomeric actin is added to the reaction (Figure 3A), showing that VCA alone does not stimulate the ATPase activity. Similarly, monomeric actin alone does not stimulate the Arp2/3 complex to hydrolyze ATP until the addition of VCA (Figure 3B). To test whether actin filaments themselves stimulate Arp2/3 ATP hydrolysis, we used phalloidin-stabilized actin filaments to ensure that no monomeric actin would be present and took care not to shear the filaments in order to reduce the number of free pointed ends. ATP hydrolysis is not stimulated on the Arp2/3 complex by filamentous actin, even in presence of VCA (Figure 3C). As controls, we found that neither 5 μM phalloidin nor 20 mM phosphate inhibit the kinetics of ATP hydrolysis by Arp2 during the polymerization reaction (unpublished data). When Arp2/3 concentration is low (20 nM), and nucleation is rapid (using N-WASP VCA), initiation of the polymerization reaction causes striking and near-complete ATP hydrolysis on Arp2 (approximately 80%, i.e., approximately 16 nM; Figure 3B and 3C). We detect a small amount of ATP hydrolysis on Arp3 with similar kinetics but much lower stoichiometry (10%–20%). The decrease is not caused by the dilution effect of adding the second component (approximately 4%), which is already compensated for in the data presented. In the Presence of Both VCA and Actin Filaments, a Nonpolymerizable Actin Monomer Is Sufficient to Trigger Rapid ATP Hydrolysis on Arp2 The timing and stoichiometry of ATP hydrolysis and the combination of factors required to stimulate it suggest that Arp2 hydrolyzes ATP during the filament nucleation reaction. Kinetic and light-microscopy data indicate that most or all Arp2/3-dependent filament nucleation occurs from Arp2/3 complex bound to the sides of filaments produced earlier in the polymerization reaction (Blanchoin et al. 2000a, 2001; Zalevsky et al. 2001). To test whether filament side-binding is necessary for ATP hydrolysis on Arp2, we blocked filament formation with the actin-monomer binding toxin, Latrunculin B. Latrunculin B binds to monomeric actin and prevents it polymerizing, but does not affect its binding to VCA (R. D. Mullins and A. E. Kelly, unpublished data). The combination of VCA and Latrunculin B–actin monomers does not stimulate ATP hydrolysis on Arp2/3 complex (Figure 3D, open squares), nor do preformed, phalloidin-stabilized actin filaments and Latrunculin B–actin monomers without VCA (Figure 3E, filled squares). In the presence of preformed actin filaments and VCA, however, Latrunculin B–actin monomers stimulate rapid ATP hydrolysis on Arp2/3 (Figure 3E, filled circles) without actin polymerization (Figure 3F). Table 1 summarizes the requirements for stimulation of ATP hydrolysis on Arp2. These data indicate that during the nucleation reaction, actin filament side-binding by Arp2/3 complex is a prerequisite for VCA and monomeric actin to stimulate ATP hydrolysis on Arp2. The observation that polymerization of the daughter filament is unnecessary implies that the VCA-mediated interaction of a single actin monomer with the Arp2/3 complex is the trigger for ATP hydrolysis on Arp2. Table 1 Requirements to Stimulate ATP Hydrolysis on the Arp2 Subunit of Arp2/3 Complex Abbreviations: G-actin, monomeric actin; F-actin, actin filaments; LatB, Latrunculin B Pointed-End Capping by the Arp2/3 Complex Stimulates Rapid ATP Hydrolysis by Arp2 in the Absence of Either Branch Formation or a WASP-Family VCA Domain The Arp2/3 complex is known to cap the pointed ends of preformed actin filaments in vitro, inhibiting both polymerization and depolymerization from the pointed ends of gelsolin-capped filaments (Mullins et al. 1998). The Arp2/3 complex does not cap the barbed ends of actin filaments and does not affect the rate of addition of monomers from the barbed ends of spectrin-capped filaments (unpublished data). We speculated that the way the Arp2/3 complex caps a free-filament pointed end in solution might mimic the way the Arp2/3 complex anchors the pointed end of the new daughter filament in a branch. If the actin monomer that triggers ATP hydrolysis during nucleation is the first monomer of the daughter filament, pointed-end capping, like nucleation, should drive interaction with this monomer and trigger ATP hydrolysis on Arp2. To test this, we sheared preformed, phalloidin-stabilized actin filaments in the presence of the Arp2/3 complex. Mechanical shearing fragments long actin filaments into many short filaments, creating many new filament ends that rapidly reanneal to produce long filaments again (Murphy et al. 1988). This reannealing process is blocked by proteins that cap filament ends (Andrianantoandro et al. 2001). Without shearing, the addition of 20 nM Arp2/3 complex does not alter the length distribution of phalloidin-stabilized actin filaments (Figure 4A, compare [i] and [iii]). After shearing in the presence of 20 nM Arp2/3 complex, pointed-end capping by the Arp2/3 complex blocks reannealing and results in significantly shorter filaments (Figure 4A, compare [ii] and [iv]). No branches form within this time—it takes several hours for even a few branches to assemble under these conditions (unpublished data). To assay for ATP hydrolysis by the complex, we incubated γ-32P-AzidoATP-Arp2/3 complex with actin filaments under the same conditions as the microscopy experiment. We split the mixture into two parts, sheared one half, and took timepoints to assay for ATP hydrolysis from both samples (Figure 4B; quantified in Figure 4C). No ATP hydrolysis occurs in the unsheared condition, confirming that binding to the sides of actin filaments is not sufficient to stimulate ATP hydrolysis. ATP hydrolysis occurs rapidly in the sheared condition and occurs only on Arp2 (Figure 4C). Since this occurs well before any branches form, pointed-end capping by the Arp2/3 complex is sufficient to stimulate ATP hydrolysis on Arp2 not only in the absence VCA, but also in the absence of filament side-binding. Discussion Conventional actin and all actin-related proteins share a conserved nucleotide binding pocket. Actin monomers bind ATP but do not hydrolyze it until they are induced to polymerize. Actin polymerization triggers rapid ATP hydrolysis, followed by a slow release of cleaved phosphate from the filament (Blanchoin and Pollard 2002). Arp2 also hydrolyzes its bound ATP, and we find that the conditions that promote ATP hydrolysis and the kinetics of the reaction are remarkably similar to those of conventional actin. In the presence of VCA and actin filaments, monomeric actin stimulates ATP hydrolysis on Arp2 (Table 1). We also find that binding of the Arp2/3 complex to the pointed end of a preformed actin filament is sufficient to trigger Arp2 ATP hydrolysis, even in the absence of VCA. The stimulation of Arp2 ATPase activity by both filament pointed ends and by actin monomers under nucleating conditions suggests that the geometry of the Arp2/3–actin interaction is the same in both cases. Interaction between the Arp2/3 complex and conventional actin can occur in three distinct ways: (1) the Arp2/3 complex binds the sides of preformed actin filaments; (2) the Arp2/3 complex binds to the pointed ends of filaments, either by remaining associated with the daughter filament following nucleation or by capping preformed pointed ends; and (3) the Arp2/3 complex may interact with an actin monomer bound to the VCA domain of a WASP-family protein. There is abundant experimental evidence for filament side- and pointed-end binding by the complex (Mullins et al. 1998; Blanchoin et al. 2000a, 2001; Amann and Pollard 2001a, 2001b). Evidence that a VCA-bound actin monomer interacts with the Arp2/3 complex is more circumstantial and is supported by four observations: (1) VCA domains can simultaneously bind both the Arp2/3 complex and monomeric actin (Marchand et al. 2001; Panchal et al. 2003); (2) removal of the actin monomer-binding WH2 (V) domain from a WASP-family protein severely decreases the efficiency of Arp2/3 activation (Marchand et al. 2001); (3) kinetic modeling suggests that the Arp2/3 complex requires monomeric actin to form a filament nucleus (Zalevsky et al. 2001); and (4) Arp2/3-dependent nucleation is not limited to the end of the mother filament (Amann and Pollard 2001a), indicating that the VCA-bound actin monomer does not incorporate into the mother filament. Two of the three interactions between the Arp2/3 complex and conventional actin—nucleation and pointed-end capping—are thought to be mediated by the actin-related subunits, analogous to actin–actin interactions in a filament. Both interactions stimulate rapid ATP hydrolysis by Arp2. Based on sequence conservation and biochemical similarities, ATP hydrolysis on Arp2 is probably driven by a mechanism similar to that which stimulates ATP hydrolysis on actin. The molecular details of how polymerization activates ATP hydrolysis on conventional actin, however, are not well understood. A leading hypothesis is that a “hydrophobic plug”—a loop between subdomains 3 and 4 of actin (residues 262–274 in yeast; Kuang and Rubenstein 1997)—undocks from the monomer surface and binds to a hydrophobic cleft formed by adjacent monomers in the opposite strand of the two-start filament helix (Lorenz et al. 1993; Kuang and Rubenstein 1997). Our data are consistent with stimulation of ATP hydrolysis by docking of a hydrophobic plug sequence on Arp2 into a hydrophobic cleft created by Arp3 and the first actin monomer of the daughter filament (Figure 5). In the crystal structure of the inactive Arp2/3 complex, Arp2 and Arp3 are oriented like a pair of actin monomers in opposite strands of the two-start filament helix (Robinson et al. 2001), but they are separated by a 40 Å cleft. Our data support a model in which activation of the complex involves closure of the cleft, allowing actin to polymerize from an Arp2–Arp3 heterodimer (Kelleher et al. 1995; Robinson et al. 2001), which then remains attached to the pointed end of the new daughter filament, anchoring it to the branch (Figure 5B [iv]). Based on the geometry of the subunits in the crystal structure and the hydrophobic plug model, we expect that the Arp3–actin contact creates a pocket to bind the hydrophobic plug of Arp2 (residues 265–277 in yeast Arp2). The geometry of the interaction would stimulate the ATPase activity of Arp2, but not Arp3 (Figure 5A). Figure 5 Model for Activation of ATP Hydrolysis on the Arp2/3 Complex and Mechanism by which WASP-Family Proteins Activate the Arp2/3 Complex to Nucleate New Actin Filaments (A) Filament pointed-end capping stimulates ATP hydrolysis on Arp2 without branch formation. (i) Arp2 and Arp3 are separated when the Arp2/3 complex is free in solution. (ii) Upon pointed-end capping, the binding energy of the actin-Arp2/3 interface drives Arp2 and Arp3 together and (iii) a conformational change on Arp2 (shown by the red the subdomain 3/4 loop flipping out) triggers ATP hydrolysis by Arp2 (filament pointed-end capping is probably not a significant function of the Arp2/3 complex in vivo). (b) A VCA-bound actin monomer drives the activation of the Arp2/3 complex and stimulates ATP hydrolysis on Arp2. (i) The Arp2/3 complex must first be bound to the side of an actin filament, and an actin monomer is bound to the VC domain of the WASP-family protein. (ii) The VC domain of the WASP-family protein docks the first monomer of the daughter filament onto the Arp2/3 complex, stabilizing the Arp2–Arp3–actin interaction and promoting the active conformation of the complex. (cf. Aii). (iii) The active conformation of the Arp2–Arp3–actin monomer triggers a conformational change on Arp2 and ATP hydrolysis by the subunit. (iv) Actin polymerizes from the activated Arp2/3 complex. ATP hydrolysis by Arp2 may promote dissociation of the CA domain of the WASP-family protein from the Arp2/3 complex, aided by actin polymerization, which competes its WH2 domain from the first actin monomer. Monomeric actin does not interact directly with the Arp2/3 complex in the absence of VCA, but under conditions that promote nucleation, a single actin monomer triggers VCA-dependent ATP hydrolysis on Arp2. By analogy with capping-induced ATP hydrolysis, the monomer that triggers ATPase activity is therefore the first monomer of the new daughter filament (Figure 5B [i]–[iii]). The hydrophobic pocket formed between Arp2, Arp3, and the actin monomer would therefore promote a similar conformational change in Arp2 and stimulate ATP hydrolysis (Figure 5B [iv]). Interaction of the Arp2/3 complex with the sides of filaments is not sufficient to trigger Arp2 ATPase activity, even in the presence of VCA. Binding of Arp2/3 to the sides of filaments is, however, required for ATP hydrolysis on Arp2 stimulated by VCA and monomeric actin. These data suggest that binding the side of an actin filament induces a conformational change in the Arp2/3 complex that enables it to interact with the actin monomer bound to VCA. The filament side-binding activity of Arp2/3 does not require the presence of the Arp2 or Arp3 subunits and can be reconstituted by a combination of the Arc2 (p34) and Arc4 (p20) subunits (Gournier et al. 2001). The Arc2 and Arc4 subunits contact both Arp2 and Arp3, and therefore filament side-binding might favor association of Arp2 and Arp3. The fact that Arp2-ATP hydrolysis induced by VCA and an actin monomer requires filament side-binding strongly suggests that all Arp2/3-generated actin filaments are born on the side of preformed filaments. Our results disagree with a recent paper that claims that ATP hydrolysis on Arp2 is slow and accompanies filament debranching (Le Clainche et al. 2003). Using experimental conditions similar to the previous study, we observe similar slow ATP hydrolysis kinetics (Figure 2C) and show that this ATP hydrolysis occurs on Arp2/3 complex recruited slowly from solution. The slow hydrolysis does not reflect delayed ATP hydrolysis on Arp2/3 complex that had been rapidly incorporated into branches early in the experiment. ATP hydrolysis on Arp2, therefore, cannot be associated with debranching. Le Clainche et al. (2003) claim that ATP hydrolysis does not occur during nucleation and present data with a lag of several hundred seconds between computer-simulated nucleation kinetics and measured ATP hydrolysis kinetics (Figure 1B in Le Clainche et al. 2003). In this experiment, Le Clainche et al. (2003) initiate polymerization in the absence of free ATP. These conditions would deactivate up to 97% of the Arp2/3 complex (the fraction that is not crosslinked to ATP on both subunits). In our experience, removal of free ATP introduces an artificial lag in polymerization that lasts until tightly bound ATP is released from monomeric actin (1/kATP release = 330 s; Selden et al. 1999) and is free to interact with the Arp2/3 complex (unpublished data). The claim by Le Clainche et al. (2003) that the absence of free ATP does not affect ATP hydrolysis kinetics is contradicted by their observation that the 32P signal is unchanged by the addition of free ATP. The 32P signal is only equivalent to hydrolyzed ATP in the absence of free ATP. The addition of free ATP should cause the excess of uncrosslinked Arp2/3 complex to compete with the small fraction of crosslinked 32P-ATP-Arp2/3 complex and thereby significantly reduce the 32P signal. The observation that the 32P signal is not reduced, rather than confirming that removal of free ATP has no effect, instead confirms that contaminating ATP is present for the latter part of the “ATP-free” condition, presumably released slowly from monomeric actin. The lag in the polymerization created by the initial absence of ATP would be present in the experimental ATP hydrolysis measurement, but may not have been present in the nucleation data presented because this was generated by a model-dependent computer simulation (Le Clainche et al. 2003). We find that ATP hydrolysis and phosphate release from Arp2 (approximately 40 s) are more than an order of magnitude faster than debranching of Arp2/3-generated dendritic networks (approximately 1000 s) (Blanchoin et al. 2000b). The kinetics of phosphate release from Arp2 are also about an order of magnitude faster than phosphate release from actin (1/kPi release = 384 s for skeletal muscle actin; Melki et al. 1996), suggesting that, if phosphate release controls debranching, it is the phosphate release from the daughter actin filament that is important, not the phosphate release from Arp2. This is supported by the observation that phalloidin, which slows phosphate release from actin, slows filament debranching, and cofilin, which accelerates phosphate release from actin, accelerates filament debranching (Blanchoin et al. 2000b). Le Clainche et al. (2003) show that chromium-ATP Arp2/3 debranches more slowly than magnesium-ATP Arp2/3 and claim (but do not demonstrate) that chromium-ATP Arp2/3 releases phosphate more slowly. If chromium does slow the phosphate release from Arp2/3, in light of our data, this suggests that phosphate release from Arp2 may be a prerequisite for filament debranching—but is not a direct cause, since it occurs much too rapidly. We previously showed that the Arp2/3 complex requires hydrolyzable ATP for nucleation activity (Dayel et al. 2001), and the current study adds weight to the hypothesis that ATP hydrolysis has a direct role in nucleation by showing that ATP is hydrolyzed by Arp2 upon nucleation. The separation of the Arps in the crystal structure and the very low nucleation rate of the unactivated complex probably reflect the tendency of Arp2 and Arp3 to remain separated in the absence of all the required nucleation promoting factors. This suggests that there is a large free energy barrier to the formation of an Arp2–Arp3 heterodimer. Our data indicate that there are two ways to overcome this energy barrier, both using the binding energy of actin: one using the combined binding energy of the two actin monomers at the pointed end of an actin filament during pointed-end capping, and the other the combined binding energy of the side of the mother filament, the VCA domain, and a single actin monomer. The surface area of the filament pointed end that would be buried by interaction with an Arp2–Arp3 dimer would be large (approximately 6800 Å2). This is consistent with the fact that in vitro the binding energy of this interface is sufficient to drive the interaction and promote the active conformation of the complex directly, even in the absence of VCA or a mother filament (Mullins et al. 1998). The binding of monomeric actin alone is insufficient to overcome the free-energy barrier, which ensures that the inactive conformation of the Arp2/3 complex is robust despite high cellular concentrations of actin. Because of the free energy of all the binding partners involved in nucleation, however, the energy of ATP hydrolysis may not be needed to stabilize the nucleus. Regardless, it is very likely that ATP hydrolysis on Arp2, like actin, provides a timing signal to the system. ATP hydrolysis on Arp2/3 would promote release of VCA from the complex and allow a new actin branch to move away from the site of its creation (Dayel et al. 2001). ATP hydrolysis may also regulate the timing of the interaction of the Arp2/3 complex with other binding partners such as cortactin and cofilin. Temporal regulation of these interactions is likely to be essential to construction of functional motile structures. The Arp2/3 ATP hydrolysis assay presented here provides a novel assay for activation of the Arp2/3 complex that does not rely, as all previous assays have done, solely on actin polymerization. Pyrene–actin polymerization is only useful over a limited range of actin concentrations because at high concentrations, spontaneous assembly obscures Arp2/3-mediated nucleation. The pyrene–actin assay also has temporal limits since it rapidly uses up one of the factors necessary for Arp2/3 activation–monomeric actin. Our observation that ATP is hydrolyzed by Arp2 rapidly during, or soon after, the nucleation reaction means that we can use ATP hydrolysis on Arp2 as an assay to study the factors required to promote activation of the Arp2/3 complex. The fact that nonpolymerizable actin monomers are competent to stimulate hydrolysis enables us to investigate the conditions for Arp2/3 complex activation under a wider range of conditions. This system will be useful for further studies of the biophysics of Arp2/3-mediated actin assembly. Materials and Methods Purification of proteins We purified Arp2/3 from Acanthamoeba castellini by a combination of conventional and affinity chromatography (Dayel et al. 2001). We flash-froze Arp2/3 complex in aliquots of approximately 40 μM in 10% glycerol, 0.5 μM TCEP, and 2 mM Tris (pH 8.0), and stored them at –80°C for later use. We purified actin from Acanthamoeba by the method of MacLean-Fletcher and Pollard (1980). Actin was stored in fresh G-buffer (0.5 μM TCEP, 0.1 μM CaCl2, 0.2 μM ATP, 2 mM Tris [pH 8.0]) and gel-filtered before use. Rat N-WASP VCA (398–502) and Human Scar1-VCA (489–559) with N-terminal 6His tags and TEV cleavage sites were bacterially expressed and purified by nickel affinity chromatography. We prepared phalloidin-stabilized actin filaments by adding 1/10 volume of 10× KMEI to monomeric actin at room temperature for 20 min to initiate polymerization, then added twice the concentration of phalloidin and incubated for a further hour at room temperature (1× KMEI buffer: 50 mM KCl, 1 mM MgCl2, 1 mM EGTA, 10 mM Imidazole [pH 7.0]). We took care not to unintentionally shear the phalloidin-stabilized actin filaments by using wide-bore pipette tips. Arp2/3 ATPase assay We diluted freshly thawed aliquots of Arp2/3 to 2.0 μM in 1 mM MgCl2, 50 mM KCl, 10 mM Imidazole (pH 7.0) and added 6 μM γ-32P-labeled 8-AzidoATP (Affinity Labeling Technologies, Lexington, Kentucky, United States). After a 2-min incubation to allow nucleotide exchange, we crosslinked for 9 s using a UV hand lamp (312 nm; Fisher Scientific, Hampton, New Hampshire, United States), added 1 mM ATP and 1 mM DTT to quench the reaction and buffer exchanged into 1× KMEI plus 100 μM ATP, 1 mM DTT using a NAP5 column (Amersham Pharmacia Biotech, Little Chalfont, United Kingdom). We used the Arp2/3 for assays within 10 min of crosslinking. The same actin (including 7% pyrene–actin) was used for both ATP hydrolysis assays and correlative pyrene–fluorescence polymerization assays. We took ATPase time points by mixing 400 μl of the reaction mixture with premixed 400 μl of methanol and 100 μl of chloroform. We ran the precipitated protein on SDS-PAGE gel to separate the subunits and quantified 32P-labeling using a phosphoimager (Storm 840; Molecular Dynamics, Sunnyvale, California, United States). For phosphate cleavage assays, we quenched timepoints into 1/10 volume 26 M formic acid, spotted on cellulose TLC plates, and separated the components in 0.4 M KH2PO4 (pH 3.4). We separately ran 32P-ATP and 32P-ATP treated with apyrase as standards to confirm the separation of 32P-ATP and cleaved 32P, respectively (unpublished data). As an alternative method of quantifying cleaved 32P, phosphomolybdate was extracted as in Shacter (1984) and quantified using a scintillation counter. To distinguish the ADP-Pi state of Arp2 from the ADP state, the kinetics of phosphate release were measured by performing the reaction in the presence of 2 mM maltose and 2 U/ml maltose phosphorylase (Sigma-Aldrich, St. Louis, Missouri, United States), which uses only the released Pi to form glucose phosphate. Glucose phosphate was separated from free ATP, protein-ATP, and Pi using TLC. Actin polymerization assays We doped Acanthamoeba actin with 7% pyrene–actin to monitor actin polymerization by fluorescence (λex = 365 nm, λem = 407 nm, 25°C) (Mullins and Machesky 2000). We calculated the number of ends produced over time from [ENDS] = (d[F-actin]/dt)/([free G-actin]*10 μM s–1) (cf. Zalevsky et al. 2001). Polymerization reactions were performed in G-buffer plus 1/10 volume 10× KMEI. The Ca2+ cation on monomeric actin was preexchanged with Mg2+ 30 s before use. Microscopy We prepared filamentous actin as above and stabilized filaments with stoichiometric Alexa-488 phalloidin (Molecular Probes, Eugene, Oregon, United States). We mixed 2 μM Alexa-488 phalloidin–F-actin with 20 nM Arp2/3, passed twice through a 30-gauge needle to shear the filaments, and incubated at room temperature. Timepoints were taken by diluting 500-fold and rapidly applying to poly-L-lysine–coated coverslips for visualization. Filament images were quantified for length distribution and branch frequency by a custom MATLAB (MathWorks Inc., Natick, Massachusetts, United States) routine. We are grateful to members of the Mullins and Vale labs, Jack Taunton, Roger Cooke, and Erik Hom for helpful discussions. This work was supported by grants from the National Institutes of Health (GM61010-01), the Pew Charitable Trust (P0325SC), and the Human Frontiers in Science Program (RG0111/2000-M) to RDM. The work was also supported by grants to RDM from the Sandler Family Supporting Foundation. An early version of this work, including the crosslinked AzidoATP hydrolysis assay, was first publicly presented at the Summer 2002 meeting of the Human Frontier Science Program. Conflict of interest. The authors have declared that no conflicts of interest exist. Author contributions. MJD and RDM conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, and wrote the paper. Academic Editor: Marc W. Kirschner, Harvard University Abbreviations ArpActin-related protein ATPadenosine triphosphate N-WASPneuronal Wiskott–Aldrich syndrome protein Piinorganic phosphate SDS-PAGEsodium dodecyl sulphate polyacrylamide gel electrophoresis TLCthin-layer chromatography VCAverprolin-homology WASPWiskott–Aldrich syndrome protein WH2WASP homology 2 ==== Refs References Amann KJ Pollard TD The Arp2/3 complex nucleates actin filament branches from the sides of pre-existing filaments Nat Cell Biol 2001a 3 306 310 11231582 Amann KJ Pollard TD Direct real-time observation of actin filament branching mediated by Arp2/3 complex using total internal reflection fluorescence microscopy Proc Natl Acad Sci U S A 2001b 98 15009 15013 11742068 Andrianantoandro E Blanchoin L Sept D McCammon JA Pollard TD Kinetic mechanism of end-to-end annealing of actin filaments J Mol Biol 2001 312 721 730 11575927 Blanchoin L Pollard TD Mechanism of interaction of Acanthamoeba actophorin (ADF/cofilin) with actin filaments J Biol Chem 1999 274 15538 15546 10336448 Blanchoin L Pollard TD Hydrolysis of ATP by polymerized actin depends on the bound divalent cation but not profilin Biochemistry 2002 41 597 602 11781099 Blanchoin L Amann KJ Higgs HN Marchand JB Kaiser DA Direct observation of dendritic actin filament networks nucleated by Arp2/3 complex and WASP/Scar proteins Nature 2000a 404 1007 1011 10801131 Blanchoin L Pollard TD Mullins RD Interactions of ADF/cofilin, Arp2/3 complex, capping protein and profilin in remodeling of branched actin filament networks Curr Biol 2000b 10 1273 1282 11069108 Blanchoin L Pollard TD Hitchcock-DeGregori SE Inhibition of the Arp2/3 complex-nucleated actin polymerization and branch formation by tropomyosin Curr Biol 2001 11 1300 1304 11525747 Dayel MJ Holleran EA Mullins RD Arp2/3 complex requires hydrolyzable ATP for nucleation of new actin filaments Proc Natl Acad Sci U S A 2001 98 14871 14876 11752435 Gournier H Goley ED Niederstrasser H Trinh T Welch MD Reconstitution of human Arp2/3 complex reveals critical roles of individual subunits in complex structure and activity Mol Cell 2001 8 1041 1052 11741539 Kelleher JF Atkinson SJ Pollard TD Sequences, structural models, and cellular localization of the actin-related proteins Arp2 and Arp3 from Acanthamoeba J Cell Biol 1995 131 385 397 7593166 Kuang B Rubenstein PA The effects of severely decreased hydrophobicity in a subdomain 3/4 loop on the dynamics and stability of yeast G-actin J Biol Chem 1997 272 4412 4418 9020164 Le Clainche C Pantaloni D Carlier MF ATP hydrolysis on actin-related protein 2/3 complex causes debranching of dendritic actin arrays Proc Natl Acad Sci U S A 2003 100 6337 6342 12743368 Lorenz M Popp D Holmes KC Refinement of the F-actin model against X-ray fiber diffraction data by the use of a directed mutation algorithm J Mol Biol 1993 234 826 836 8254675 Machesky LM Mullins RD Higgs HN Kaiser DA Blanchoin L Scar, a WASp-related protein, activates nucleation of actin filaments by the Arp2/3 complex Proc Natl Acad Sci U S A 1999 96 3739 3744 10097107 MacLean-Fletcher S Pollard TD Mechanism of action of cytochalasin B on actin Cell 1980 20 329 341 6893016 Marchand JB Kaiser DA Pollard TD Higgs HN Interaction of WASP/Scar proteins with actin and vertebrate Arp2/3 complex Nat Cell Biol 2001 3 76 82 11146629 Melki R Fievez S Carlier MF Continuous monitoring of Pi release following nucleotide hydrolysis in actin or tubulin assembly using 2-amino-6-mercapto-7-methylpurine ribonucleoside and purine-nucleoside phosphorylase as an enzyme-linked assay Biochemistry 1996 35 12038 12045 8810908 Mullins RD Heuser JA Pollard TD The interaction of Arp2/3 complex with actin: nucleation, high affinity pointed end capping, and formation of branching networks of filaments Proc Natl Acad Sci U S A 1998 95 6181 6186 9600938 Mullins RD Machesky LM Actin assembly mediated by Arp2/3 complex and WASP family proteins Methods Enzymol 2000 325 214 237 11036606 Murphy DB Gray RO Grasser WA Pollard TD Direct demonstration of actin filament annealing in vitro J Cell Biol 1988 106 1947 1954 3384850 Panchal SC Kaiser DA Torres E Pollard TD Rosen MK A conserved amphipathic helix in WASP/Scar proteins is essential for activation of Arp2/3 complex Nat Struct Biol 2003 10 591 598 12872157 Pollard TD Blanchoin L Mullins RD Molecular mechanisms controlling actin filament dynamics in nonmuscle cells Annu Rev Biophys Biomol Struct 2000 29 545 576 10940259 Robinson RC Turbedsky K Kaiser DA Marchand JB Higgs HN Crystal structure of Arp2/3 complex Science 2001 294 1679 1684 11721045 Rohatgi R Ma L Miki H Lopez M Kirchhausen T The interaction between N-WASP and the Arp2/3 complex links Cdc42-dependent signals to actin assembly Cell 1999 97 221 231 10219243 Selden LA Kinosian HJ Estes JE Gershman LC Impact of profilin on actin-bound nucleotide exchange and actin polymerization dynamics Biochemistry 1999 38 2769 2778 10052948 Shacter E Organic extraction of Pi with isobutanol/toluene Anal Biochem 1984 138 416 420 6742419 Welch MD Rosenblatt J Skoble J Portnoy DA Mitchison TJ Interaction of human Arp2/3 complex and the Listeria monocytogenes ActA protein in actin filament nucleation Science 1998 281 105 108 9651243 Zalevsky J Lempert L Kranitz H Mullins RD Different WASP family proteins stimulate different Arp2/3 complex-dependent actin-nucleating activities Curr Biol 2001 11 1903 1913 11747816

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PLoS Biol. 2004 Apr 13; 2(4):e91

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020094SynopsisEvolutionGenetics/Genomics/Gene TherapyInfectious DiseasesMicrobiologyEubacteria“Mosaic” Genes Highlight Forces of Genome Diversity and Adaptation Synopsis4 2004 13 4 2004 13 4 2004 2 4 e94Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Segmentally Variable Genes: A New Perspective on Adaptation ==== Body Microbes are arguably the most adaptable organisms on Earth, inhabiting nearly every crevice of nearly every corner of the globe. Some invade the cavities of a wide variety of insects and other invertebrates while others colonize the skin, blood, eyes, and internal organs of animals. Still others thrive in such inhospitable places as the hydrothermal vents of the ocean floor and the Dry Valleys of Antarctica. These “simple” single-celled organisms have evolved unique molecules and strategies over some 3.5 billion years that suit life on the edge. With the sequenced genomes of nearly 140 microbial species in hand, scientists are gaining valuable insights into the nature of this adaptive diversity. Segmentally variable genes Adapting to such radically different niches, it appears, has produced genes with diverse functions that evolve at very different rates. Genes that code for molecules essential for fundamental cellular functions like maintaining cell metabolism and structure tend to evolve rather slowly, while genes that make proteins charged with mediating cellular responses to internal or external changes often evolve relatively quickly. Pathogenic microbes in particular rely on a flexible genome to keep a step ahead of their hosts' similarly evolving defenses in the never-ending struggle to gain adaptive advantage. This adaptability underlies the increasing antibiotic resistance of diseases like tuberculosis, as selective pressures favor the expansion of resistant bacterial populations. Combating such problems requires a molecular understanding of bacterial infections, yet function has been ascribed to only a fraction of the genes found in microbial genomes. One approach to improve functional analyses of genome sequences combines bioinformatics with experimental methods. With such collaborations in mind, Yu Zheng, Richard Roberts, and Simon Kasif have developed a computational approach to help filter out the genetic noise and home in on genomic regions likely to contain clues to gene function. Their method relies on a novel way of classifying genes that flags sequences likely to reward biochemical and genetic efforts to analyze gene function. Many comparative genomic studies have focused on looking for sequence “motifs” that correlate with well-characterized protein sequences (that is, the amino acid sequence) and predicting function based on their similarity to the known protein sequences. Zheng, Roberts, and Kasif took a different approach, classifying genes based on their sequence variation. The researchers analyzed 43 fully sequenced microbial genomes and, after determining the degree of conservation or divergence among similar genes in different species, divided the genes into three broad categories: rapidly evolving genes unique to a particular species; highly conserved genes; and “segmentally variable,” or mosaic, genes. Stipulating that the boundaries between the categories are somewhat blurred, Zheng et al. define segmentally variable genes as regions that show a mosaic pattern of one or more rapidly evolving, variable regions interspersed with conserved regions. Based on evidence suggesting that retained variable regions tend to serve a function, the researchers predicted that these mosaic genes, with their highly variable, fast-evolving regions, would shed light on the forces that shape genome diversity and adaptation. For most of the microbes analyzed, mosaic genes accounted for about 8–20% of their genomes. Selecting several large families of mosaic genes, Zheng et al. explored the relationship between genes with known function and the structure of their variable regions. Noting an overabundance of particular functional categories in different species—such as signaling proteins that come into either direct or indirect contact with the cell's environment—the researchers speculate that the variable regions may constitute an adaptive layer for the microbe, as they not only “play a key role in mediating interactions with other molecules” but also support a microbe's ability to adapt to its particular niche. Several bacteria species, for example, contain roughly 40% more mosaic sensor genes involved in cell motility, which the authors attribute to the microbes' “expanded ability to detect different chemical signals and find favorable environments.” This regional variability appears to reflect the influence of selective pressures that fuel diversity through ongoing interactions with other rapidly evolving molecules in the environment, adding another source of genetic adaptability as cells adjust to new environments and outmaneuver pathogenic threats. While many of the mosaic genes identified encode proteins involved in host-pathogen interactions, defense mechanisms, and intracellular responses to external changes, their function is only broadly understood. While Zheng et al. cannot say to what extent variability affects function—Is extreme variability required for diversity or can modest variation suffice?—they are refining their classification of segmentally variable genes to address such questions. Until then, the authors' “mosaic” approach to understanding gene function promises to improve efforts to annotate the volumes of sequenced genomes on hand, offering biologists a much-needed tool to sift through the mountains of genomic datasets and identify promising targets for further study.

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PLoS Biol. 2004 Apr 13; 2(4):e94

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10.1371/journal.pbio.0020094

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020096Research ArticleBiophysicsNeuroscienceRattus (Rat)Calcium Dynamics of Cortical Astrocytic Networks In Vivo Calcium Activity of Glia In VivoHirase Hajime hirase@axon.rutgers.edu 1 Qian Lifen 1 Barthó Peter 1 Buzsáki György 1 1Center for Molecular and Behavioral Neuroscience, Rutgers UniversityNewark, New JerseyUnited States of America4 2004 13 4 2004 13 4 2004 2 4 e9621 7 2003 30 1 2004 Copyright: © 2004 Hirase et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Window into the Brain Demonstrates the Importance of Astrocytes Large and long-lasting cytosolic calcium surges in astrocytes have been described in cultured cells and acute slice preparations. The mechanisms that give rise to these calcium events have been extensively studied in vitro. However, their existence and functions in the intact brain are unknown. We have topically applied Fluo-4 AM on the cerebral cortex of anesthetized rats, and imaged cytosolic calcium fluctuation in astrocyte populations of superficial cortical layers in vivo, using two-photon laser scanning microscopy. Spontaneous [Ca2+]i events in individual astrocytes were similar to those observed in vitro. Coordination of [Ca2+]i events among astrocytes was indicated by the broad cross-correlograms. Increased neuronal discharge was associated with increased astrocytic [Ca2+]i activity in individual cells and a robust coordination of [Ca2+]i signals in neighboring astrocytes. These findings indicate potential neuron–glia communication in the intact brain. Two-photon laser scanning microscopy was used to image calcium concentration changes in astrocytes in the cerebral cortex of anesthetized rats ==== Body Introduction Astrocytes are nonneuronal cells of the brain with some known and hypothesized functions (Kettenmann and Ransom 1995; Fields and Stevens-Graham 2002). Traditionally, astrocytes have been considered to mediate supportive and protective functions in the central nervous system because of their strategic placement relative to the vasculature, and because they lack fast sodium action potentials. It is only recently that this family of glial cells has been implicated in controlling the dynamics of the neuronal networks in the central nervous system (Nedergaard 1994; Parpura et al. 1994; Kang et al. 1998; Parri et al. 2001). Although the membrane potential of unidentified glial cells shows correlated changes with neuronal activity in vivo (Amzica and Steriade 2000; Amzica and Massimini 2002), most of our knowledge on neuron–glia and glia–glia communication comes from studies in vitro. In cultured and acutely prepared astrocytes, free calcium concentration ([Ca2+]i) in the cytosol undergoes large changes spontaneously or in response to various physiological and pharmacological manipulations, such as mechanical stimulation, membrane potential depolarization, and activation of metabotropic glutamate receptors (Cornell-Bell et al. 1990a; Pasti et al. 1997). These slow events are mediated by release of Ca2+ from intracellular stores (Charles et al. 1993; Venance et al. 1997). The [Ca2+]i surges can be evoked by strong neuronal activity (Dani et al. 1992; Porter and McCarthy 1996), suggesting a potential homeostatic role of astrocytes in the regulation of extracellularly accumulating neurotransmitters (Verkhratsky et al. 1998). Conversely, spontaneous [Ca2+]i changes in astrocytes have been shown to influence neuronal excitability (Parpura et al. 1994; Kang et al. 1998; Pasti et al. 2001). The mechanism of activity propagation among astrocytes is controversial. In tissue cultures, [Ca2+]i events can propagate among a network of astrocytes via gap junction or by elevation of adenosine triphosphate level (Cornell-Bell et al. 1990b; Charles et al. 1991; Nedergaard 1994; Reetz et al. 1997; Newman 2001). In the in vitro slice preparation, coordination of [Ca2+]i activity appears independent of gap junctions but may require transmitter activation of N-methyl-D-aspartic acid (NMDA) and/or metabotropic glutamate receptors (Parri et al. 2001; Aguado et al. 2002; Nett et al.2002; Tashiro et al. 2002). Moreover, the extent and magnitude of these network effects vary as a function of the preparation used, and can involve correlated [Ca2+]i changes in no, or only a few, neighboring astrocytes, or the whole population (Porter and McCarthy 1996; Verkhratsky et al. 1998). Whether and how the observations in the various in vitro situations apply to the intact brain have yet to be determined. We have used two-photon laser scanning microscopy (2-PLSM) to monitor cytosolic Ca2+ concentration in astrocytes labeled with Fluo-4 acetoxymethyl (AM) ester in juvenile rats in vivo. We find that [Ca2+]i dynamics in astrocytes is rather quiescent during baseline anesthesia. However, increased population bursting, brought about by attenuating γ-aminobutyric acid (GABAA) receptor-mediated neurotransmission, leads to increased magnitude [Ca2+]i surges, and the [Ca2+]i changes become more strongly coordinated in neighboring astrocytes. Results Loading of Calcium-Sensitive Dye To examine the depth of penetration of the Fluo-4 AM, coronal brain slices (300 μm thick) were acutely prepared after the residual dye was washed off from the craniotomy. A large number of cells below the craniotomy showed fluorescence labeling (Figure 1). On the basis of morphological appearance (see also Videos S1-S4), most brightly labeled cells were astrocytes, in accordance with recent observations using a pressure application of the indicator (Stosiek et al. 2003). The large overlap between Fluo-4 AM-loaded cells and astrocytes identified by S100B immunoreactivity provided confidence that most of the loaded cells were astrocytes (Video S5). In addition to astrocytes, capillary endothelial cells and pericytes, outlining microvessels, were also observed, albeit less regularly. Some processes of astrocytes contacted local vessels. To quantify the dye penetration, mean bulk fluorescence intensity was plotted for different depths from the pial surface. Most intensive labeling occurred between 50–150 μm below the surface (i.e., layers I/II), but labeled cells could be visualized at greater than 300 μm as well (Figure 1C). The decreased fluorescence on the surface is likely due to the diluting effect of the washout procedure in the superficial tissue. Like the histological appearance, in vivo imaging revealed numerous astrocytes (Figure 1E). Although the labeling was dense, the somata and several associated processes, including vessel-contacting end feet, of single astrocytes could be clearly revealed (Figure 2). Figure 1 In Vivo Loading and Imaging of Astrocytes Using Fluo-4 AM (A) Acute slice prepared 1 h after dye loading. Scale bar, 200 μm. (B) Higher magnification reveals cells with typical astrocyte morphology. Scale bar, 20 μm. (C) Average bulk fluorescence as a function of the depth from the pial surface. (D) Schematic drawing of the experimental arrangement. Abbreviations: EKG, electrocardiogram. PMT, photomultiplier. LFP, glass micropipe for local field potential and multiple unit recording. The same pipette was used to deliver bicuculline. (E) Image taken 50–150 μm below pial surface in vivo. Flattened xyz stack. (F) Fluo-4 AM loaded cells (left) were stained for S100B immunoreactivity (right), and the images were merged (center). See Video S3 for large-scale staining. Scale bar, 20 μm. Figure 2 Time-Lapse Imaging of Astrocytes In Vivo Four astrocytes, from which fluorometric Ca2+ imaging (0.5 Hz) was made, are outlined. A blood vessel, outlined by the astrocyte end feet, runs diagonally across the viewed area. White arrows show the end foot connected to the imaged astrocyte. Spontaneous Calcium Events in Astrocytes In our initial experiments, we made a large number of line scans (sampling rate ∼200 Hz) of dye-loaded cells to examine whether some of them were neurons. We never observed short-lasting [Ca2+]i transients (less than 200 ms; Svoboda et al. 1997; Garaschuk et al. 2000), suggesting that the brightly loaded cells were likely to be non-neuronal (Parri et al. 2001; Stosiek et al. 2003). In subsequent experiments (n = 8 rats), cells with astrocytic appearance (n = 185) were selected for long-term (10–20 min) monitoring. For quantitative studies, three states of [Ca2+]i activity were distinguished: (a) quiescent state with very slow (less than 0.025 Hz) oscillations of baseline fluorescence level, (b) [Ca2+]i spikes (greater than or equal to 20% increase in ΔF/F0 between 5–50 s), and (c) [Ca2+]i plateau potentials (greater than or equal to 20% increase in ΔF/F0 for greater than 50 s). [Ca2+]i spikes and [Ca2+]i plateau potentials were automatically detected. In the control (baseline) condition, 11% of astrocytes had at least one spike event, and 52% had at least one plateau event in 10 min. The mean frequency of [Ca2+]i spikes among the cells that had at least one [Ca2+]i spike was 0.121 ± 0.098 per minute (mean width at greater than or equal to 20% ΔF/F0: 25.1 ± 10.31 s) and the mean frequency of [Ca2+]i plateau was 0.118 ± 0.058 per minute (mean duration: 160.4 ± 114.9 s). To investigate whether the baseline values of [Ca2+]i dynamics were affected by increasing neuronal activity, we induced regularly occurring population bursts by local application of bicuculline (Schwartz and Bonhoeffer 2001; n = 7 rats). Large amplitude (0.69 ± 0.26 mV) synchronous field events (approximately 100 ms) occurred at relatively regular frequency (0.15 ± 0.06 Hz), associated with multiple unit discharges. No significant difference was observed in average heartbeat frequency between the control sessions and bicuculline sessions (4.51 ± 0.54 Hz and 4.36 ± 0.74 Hz, respectively; paired t-test, p = 0.13). We used two methods to evaluate the effect of neuronal activity on [Ca2+]i in astrocytes (n = 214 cells). First, the incidence of [Ca2+]i spikes and plateau events was counted in the absence and presence of bicuculline-induced population bursts. Under bicuculline condition significantly more astrocytes had [Ca2+]i spikes (11% versus 24%; p < 0.001; Fisher's exact test), whereas the probability (52% versus 54%) of plateau potentials did not differ significantly. The mean duration of plateau potentials, however, was significantly longer (160.4 ± 114.9 s versus 211.12 ± 152.175 s; t-test, p < 0.001) after bicuculline treatment. Among the cells that exhibited at least one spike or plateau event, there was not a significant difference in frequency of the event occurrences (spike: 0.121 ± 0.098/min versus 0.098 ± 0.068/min; t-test, p = 0.24; plateau 0.118 ± 0.058/min versus 0.112 ± 0.049/min; t-test, p = 0.46). Thus, the major difference between control and bicuculline conditions was the higher proportion of active astrocytes under bicuculline. The second method examined [Ca2+]i changes in the frequency domain. The ΔF/F0 trace was considered as a continuous process, and the power spectrum estimate was calculated with a multi-taper method for each astrocyte and averaged across cells. There was a general increase of power at all frequencies in bicuculline-treated animal. The most consistent significant increase (p < 0.05) of power appeared in the frequency range of 0.10–0.24Hz, reflecting the increased incidence of [Ca2+]i spikes. Short-term cross-correlation of neuronal field bursts and [Ca2+]i signals (± 10 s) did not show a significant time-locked relationship (Figure 3). Figure 3 Frequency Domain Analysis of Population Dynamics of Fluorescence in Astrocytes in Control State and during Bicuculline-Induced Neuronal Hyperactivity Insets show local field potentials in a control animal and regular spiking in a bicuculline treated mouse (scale bar: 2.0 s, 500 μV). Asterisks show significant differences (p < 0.05) between groups at various frequencies. Spatio-Temporal Dynamics of [Ca2+]i Events In individual experiments, propagation of synchronous activity could be observed visually (Figure 4A; Video S6) but the spatio-temporal relationship of [Ca2+]i dynamics among astrocytes varied across experiments. To quantify the magnitude and spatial extent of this population effect, pair-wise cross-correlograms of ΔF/F0 intensity were calculated separately for nearby cell pairs (local: less than or equal to 50 μm) and distant cell pairs (greater than 50 μm). In control conditions, the temporal correlation of [Ca2+]i signals in neighboring pairs was somewhat larger than in distant pairs, but this difference was not significant (n = 374 neighbor pairs and n = 1,138 distant pairs). Nevertheless, [Ca2+]i signals in astrocytes were not completely random, since the cross-correlograms had wide central peaks at the 10–100 s scale (Figure 4B). In contrast to the baseline condition, the temporal correlation of [Ca2+]i changes in local and distant pairs were significantly different after large population bursts were brought about by bicuculline (Figure 4C). Correlation of distant pairs under bicuculline (n = 433 pairs) was similar to those in the control condition. However, synchrony between local pairs (n = 1,282) increased several-fold relative to both distant pairs under the same condition (t-test, p < 0.0001) and to local pairs in the baseline condition (t-test, p < 0.0001). Figure 4 Spatio-Temporal Dynamics of Astrocyte Ca2+ Activity (A) Definition of nearby (less than 50 μm) and distant (greater than 50 μm) cell pairs. (B) Fluorescence changes in two nearby astrocytes. (C) Cross-correlogram of fluorescent intensity. (D) Mean cross-correlation of ΔF/F0 in all nearby (thick line) and distant (thin line) cell pairs in control condition (left) and in the presence of bicuculline (right). Note large increase of ΔF/F0 correlation in nearby cell pairs in the bicuculline condition (error bar: standard error of the mean). (E) Relationship between distance of the two cells and the magnitude of correlation at zero timelag. Note lack of a reliable relationship in the control condition (left). Note also the significant negative correlation between the distance and correlated ΔF/F0 changes in cell pairs in the bicuculline-treated cortex (right). Using a different approach, the magnitude of the zero-timelag correlation coefficient for each cell pair was plotted against distance between the cell pairs. Under control condition, no notable relationship was observed between these variables (Figure 5; n = 1,512 cell pairs, r = 0.019, p = 0.46). In contrast, a significant negative correlation was found between the synchrony of [Ca2+]i signals in the bicuculline condition (n = 1,715; r = −0.281; p < 0.0001). Discussion Astrocytes in superficial cortical layers were successfully loaded using Fluo-4 AM by surface application up to 350 μm from the pial surface in juvenile rats. In agreement with previous literature (Parri et al. 2001; Dallwig and Deitmer 2002; Simard et al. 2003), the majority of the Fluo-4-loaded cells exhibited astrocytic morphology with multipolar branching and bushy microprocesses impinging on local vasculature. 2-PLSM imaging revealed spontaneous [Ca2+]i events in individual astrocytes in vivo. Some coordination of these events was indicated by the broad cross-correlograms in the baseline condition. Increased neuronal discharge was associated with increased astrocytic activity and a robust coordination of [Ca2+]i signals in neighboring astrocytes, providing evidence for neuron–glia communication in the intact brain. The magnitude, frequency and pattern of [Ca2+]i events observed here are qualitatively similar to those described in tissue cultures (Dani et al. 1992; Charles 1998) and acute hippocampal, neocortical, and thalamic slice preparations (Parri et al. 2001; Aguado et al. 2002; Nett et al.2002; Tashiro et al. 2002). It has been reported that the percentage of active astrocytes in brain slices showed a 2- to 3-fold decrease from early postnatal days to juvenile age (Parri et al. 2001; Aguado et al. 2002). In our experiments, a large portion of the imaged astrocytes were active, showing either [Ca2+]i or plateau potentials. It is unlikely that the elevated activity in vivo is due to anesthesia because urethane is known to suppress transmitter release from presynaptic vesicles and attenuate both α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) and NMDA receptors (Hara and Harris 2002). Since blockade of these receptors decreases astrocytic [Ca2+]i activity in vitro (Parri et al. 2001; Aguado et al. 2002), it is expected that in the drug-free animal the percentage of active cells will be even higher. A different explanation for the lower percentage of active astrocytes in the slice, relative to the in vivo situation and tissue culture preparation, is that the trauma of brain slicing attenuates spontaneous [Ca2+]i activity. Reactive astrocytes in a stab wound area show very limited [Ca2+]i activity (Aguado et al. 2002). In addition, the temperature at which the cells are kept may be playing an important role. In the absence of provoking conditions, spontaneous [Ca2+]i activity in individual astrocytes does not spread among astrocytes as an intercellular Ca2+ wave (Nett et al.2002). In baseline condition, the magnitude of correlated activity in nearby and distant astrocytes was quite similar. Nevertheless, the presence of zero-timelag correlation suggests that activity in the astrocytic syncytium in vivo is not random, but is under some coordinated control. Widespread but limited coordination of glial cells can be brought about by common synchronizing inputs in the intact brain, such as vascular and vegetative nervous system control or large-scale slow changes of neuronal excitability. The latter possibility is supported by the observation that ionotropic glutamate receptor antagonists and tetrodotoxin effectively decorrelated the astrocytic network without altering the number of active astrocytes (Aguado et al. 2002). Furthermore, the intact corticothalamic system displays substantial excitability fluctuation at the time scale of the astrocytic [Ca2+]i events (Jando et al. 1995). Although neuronal activity is not needed to generate [Ca2+]i surges in astrocytes (Aguado et al. 2002; Nett et al.2002), neurotransmitters can enhance the frequency of such events. The impact of neuronal activity on the glial network is illustrated by the increased activity and enhanced local correlation of [Ca2+]i signal in astrocytes after regular population bursting of neurons was brought about by the GABAA-receptor blocker bicuculline. These changes shared similarities to those observed in hippocampal and neocortical slices (Aguado et al. 2002; Tashiro et al. 2002). In contrast to the slice situation, we did not find a time-locked triggering of astrocytic events to the neuronal bursts (see also Nett et al.2002). This discrepancy may be explained by the magnitude of the evoked neuronal bursts. Bicuculline in vitro evoked rare (greater than 30 s intervals), but very large bursts or afterdischarges (Tashiro et al. 2002; Aguado et al. 2002). In vivo, synchronous events of moderate size occurred frequently (approximately 0.3 Hz). The enhanced bursts, associated with large field potentials, can be regarded as interictal epileptic spikes (Schwartz and Bonhoeffer 2001), but seizures were never observed. Although the exact mechanisms of neuron–astrocyte signaling remain to be disclosed, our findings indicate that neuronal and glial networks are coupled in the intact brain. Many of the imaged astrocytes had processes (end feet) in close contact with small brain vessels (Peters et al. 1970). It has been shown that surges of [Ca2+]i in astrocytes trigger the release of vasoactive compounds (Bezzi et al. 1998). Furthermore, stimulation of single astrocytes in cortical slices led to delayed (greater than 30 s) and protracted dilation of the contacted arteriole (Zonta et al. 2003). These findings support the view that a cardinal function of astrocytes in the intact brain is to regulate local circulation according to the metabolic needs of neurons. Overall, the approach introduced in this paper will be a potent tool to investigate these issues in vivo. Materials and Methods Subjects and surgery Male and female rats, 12–16 d postnatal (P12– P16), of the Sprague–Dawley strain were used in these experiments. Animals were deeply anesthetized with 1.7 g/kg urethane. An outline of the craniotomy above the primary somatosensory (barrel) cortex was marked with a dental drill. A metal frame, similar to what has been described in Kleinfeld and Denk (2000), was attached to the skull with cyanoacrylic. A craniotomy (3–4 mm diameter), centered at 1.5 mm posterior to bregma and 2.5 mm from midline, was performed and the dura mater was surgically removed. Care was taken to avoid any damage to pial vessels or the cortex. Dye loading Fluo-4 AM (F-14201, 50 μg; Molecular Probes, Eugene, Oregon, United States) was mixed with 2 μl of Pluronic (P-3000, Molecular Probes) and 5 μl of dymethyl sulfoxide (D-8779; Sigma, St. Louis, Missouri, United States) for 15 min. The solution was then diluted in 18 μl of artificial cerebrospinal fluid (ACSF) (125 mM NaCl, 3 mM KCl, 10 mM glucose, 26 mM NaHCO3, 1.1 mM NaH2PO4, 2 mM CaCl2, 1 mM MgSO4; pH adjusted to 7.4) and mixed for a further 15 min. A small volume (up to 12 μl) of the dye-containing solution was applied to the cortical surface by a micropipette. The solution was retained in place by a small piece gelfoam. The unbound dye was removed 45–60 min after the surface application of Fluo-4 AM by irrigating the exposed surface with ACSF for at least 10 min. The craniotomy was then covered with 1% agar dissolved in phosphate-buffered saline (pH 7.4), and a glass coverslip was placed on a metal frame. This arrangement allowed access for a glass recording electrode from the side. Juvenile rats (P13–P15) were used because we found in preliminary experiments that in adult animals, mostly vascular cells were loaded with the current protocol. Electrophysiological recording During the recording session, a heating blanket was placed under the rat to maintain body temperature at approximately 37°C. The electrocardiogram (EKG) was monitored continuously. The R wave of EKG was used to monitor brain pulsation-derived movement of artifacts during imaging. Population bursts of cortical neurons (“interictal” spikes; Schwartz and Bonhoeffer 2001) were induced by inserting a large-tip (20–50 μm tip diameter) glass pipette, containing 2 mM bicuculline in 0.9% (w/v) NaCl, into the deep layers of the somatosensory cortex. This electrode also served to record local field potential and multiple unit activity. Large population bursts were reliably induced 10–30 min after the insertion of the pipette. Imaging A custom-made 2-PLSM was constructed as described earlier (Majewska et al. 2000). In brief, a Ti:S laser (Mira 800F; Coherent, Santa Clara, California, United States) was pumped by a solid state CW laser (Verdi 8; Coherent) to produce a mode-locked beam (840 nm; approximately 100 fs pulse width at 76 MHz repetition rate). The beam was directed to a modified confocal scanhead (Fluoview 300; Olympus, Tokyo, Japan). The fluorescent signal was first filtered with an emission filter (HQ525, passband 525 ± 25 nm; Chroma, Rockingham, Vermont, United States) and detected by an external photo-multiplier tube (R-3896, Hamamatsu Photonics, Hamamatsu City, Japan) with a built-in preamplifier board (F-5 PSU-B; Olympus). Data analysis Fluorescence signal was quantified by measuring the mean pixel value of a manually selected somatic area for each frame of the image stack using ImageJ software. The values were exported to MatLab and the fluorescence change ΔF/F0 was computed, where F0 is the mean of the lowest 20% of the somatic fluorescence signals. Sessions that had visible drifts when image sequences were replayed as animation (the majority of the cells showed correlated activity [|r| > 0.6], or greater than 10% fluorescence change due to the heartbeat when the cell was imaged in line scan [approximately 200 Hz]) were excluded from the analysis. For display purposes, the signal was convolved with a Hanning window of order three to smooth the signal trace. Power spectra of fluorescent signals were computed using the multi-taper method (NW = 4). For the calcium event detection, ΔF/F0 signal was convolved with a Hanning window of order 15. “Spike” events were defined as transient increase of ΔF/F0 signal exceeding 20%, lasting 5–50 s. “Plateau” events were defined as sustained increase of ΔF/F0 (greater than 20%) signal longer than 50 s. Peak amplitudes of both spike and plateau events required an increase of at least 50% ΔF/F0 from the onset of events. Calcium events were automatically detected with the above detection. Cross-correlation between cell pairs was computed by normalizing the ΔF/F0 signals to unity (zero mean, unity standard deviation) so that the computed values represent the correlation coefficient between the two signals at a given timelag. All numbers are indicated as mean ± standard deviation, unless otherwise noted. Immunocytochemisty Since Fluo-4 AM loading was best visible in the somatic region of the putative astrocytes, we chose S100B antibody (A5110; DakoCytomation, Glostrup, Denmark) because this antibody stains the somatic region of astrocytes as well as its processes (Ren et al. 1992). Following Fluo-4 AM loading, acute brain slices (300 μm thickness) were cut coronally around the dye-loaded area using standard procedures. Fluo-4 in cells of the acute brain slices were fixed by incubating the acute brain slices in freshly made saline containing 40 mg/ml 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride (EDAC, E7750; Sigma) for 30 min. Next, the slices were incubated in formalin-based fixative (4% formaldehyde, 0.1 M phosphate buffer, [pH ∼7.1]) for 30 min. Once the fixation procedures were completed, the sections were mounted on a glass slide and imaged with 2-PLSM (z-stack; wavelength, 840 nm). After imaging of calcium-loaded cells, and three subsequent washes in phosphate-buffered saline (PBS) (1.06 mM KH2PO4, 155.17 mM NaCl, 2.96 mM NaHPO4, pH approximately 7.4), the slices were treated with S100B antibody (made in rabbit, 1:50 dilution) in Triton X-PBS (0.5% Triton X in PBS) overnight. The sections were subsequently washed three times in PBS, followed by incubation with the secondary fluorescent antibody (1:1000 dilution, 711-166-152, CY3 Anti-Rabbit IgG [H + L]; Jackson ImmunoResearch Laboratories, West Grove, Pennsylvania, United States) in Triton X-PBS solution for 2 h. Simultaneous viewing of the two image stacks allowed a systematic comparison of the extent of overlap between Fluo-4 loading and S100B immunoreactivity (Video S5). Supporting Information Video S1 Visualization of Loaded Astrocytes (Low Magnification) The primary somatosensory cortex (P15) was stained with Fluo-4 AM in vivo and subsequently imaged in vitro. Acute slices (approximately 300 μm thickness) were cut in cold ACSF after the cells were loaded in vivo. (Z step = 1 μm; scale bar = 50 μm). (49 MB AVI). Click here for additional data file. Video S2 Visualization of Loaded Astrocytes (High Magnification, Layer I) Same slice as shown in Videos S1, but with higher magnification. Z step = 1 μm; scale bar = 20 μm. (48 MB AVI). Click here for additional data file. Video S3 Visualization of Loaded Astrocytes (High Magnification, Layers II/III) Detailed imaging of in vivo-loaded acute slice preparation of the primary somatosensory cortex (P15; approximately 270 μm below the pial surface). Z step = 1 μm; scale bar = 20 μm. (39 MB AVI). Click here for additional data file. Video S4 High-Contrast Image Upper Layers (I to II/III) of the Fluo-4 AM-Loaded Somatosensory Cortex (P15) Empty circles in layers II/III, presumed unloaded neurons (note their absence in layer I). The loaded cells have typical glial morphological appearance. Z step = 1 μm; scale bar = 50 μm. (50 MB AVI). Click here for additional data file. Video S5 Double-Labeling of Fluo-4 AM-Loaded Astrocytes with S100B Antibody Acute slices (300 μm thickness) were prepared from the in vivo Fluo-4 AM-loaded somatosensory cortex. The slices were subsequently incubated in EDAC containing saline followed by formalin fixation. The loaded astrocytes were identified by examination at various depths and numbered (left). Next, the slices were processed for immunocytochemistry with astrocyte marker S100B. Depth scans (1 μm between the frames) were taken again to determine immunoreactivity of cells with S100B (right movie). An overlapping set of the cells was identified to be S100B-immunoreactive, indicating that nearly all Fluo-4 AM-loaded cells were astrocytes. (5 MB AVI). Click here for additional data file. Video S6 Imaging of Fluo-4 AM Fluorescence Activity in Astrocytes In Vivo Movie taken from a P14 rat. Image was taken with 2 Hz sampling rate for 10 min and compressed to 36 s for display purposes. Note spatial- and light-emission-stability of the recorded cells. Note also that at frames approximately 9 s and 15 s, two of the astrocytes in the middle display transient increased fluorescence. Scale bar 50 micro μ. (55 MB AVI). Click here for additional data file. We thank Ian Creese for his continuous support and Karel Svoboda and Raphael Yuste for their comments on an earlier version of this manuscript. This work was supported by National Institutes of Health grant NS043157 (GB) and by the Epilepsy Foundation (HH). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. HH and GB conceived and designed the experiments. HH performed the experiments and analyzed the data. HH, LQ, and PB contributed reagents/materials/analysis tools. HH and GB wrote the paper. Academic Editor: Winfred Denk, Max-Planck-Institut für Medizinische Forschung Abbreviations AMacetoxylmethyl AMPAα-amino-3-hydroxy-5-methylisoxazole-4-propionic acid GABAγ-aminobutyric acid NMDAN-methyl-D-aspartic acid Pdays postnatal 2-PLSMtwo-photon laser scanning microscope ==== Refs References Aguado F Espinosa-Parrilla JF Carmona MA Soriano E Neuronal activity regulates correlated network properties of spontaneous calcium transients in astrocytes in situ J Neurosci 2002 22 9430 9444 12417668 Amzica F Massimini M Glial and neuronal interactions during slow wave and paroxysmal activities in the neocortex Cereb Cortex 2002 12 1101 1113 12217974 Amzica F Steriade M Neuronal and glial membrane potentials during sleep and paroxysmal oscillations in the neocortex J Neurosci 2000 20 6648 6665 10964970 Bezzi P Carmignoto G Pasti L Vesce S Rossi D Prostaglandins stimulate calcium-dependent glutamate release in astrocytes Nature 1998 391 281 285 9440691 Charles A Intercellular calcium waves in glia Glia 1998 24 39 49 9700488 Charles AC Dirksen ER Merrill JE Sanderson MJ Mechanisms of intercellular calcium signaling in glial cells studied with dantrolene and thapsigargin Glia 1993 7 134 145 8094375 Charles AC Merrill JE Dirksen ER Sanderson MJ Intercellular signaling in glial cells: Calcium waves and oscillations in response to mechanical stimulation and glutamate Neuron 1991 6 983 992 1675864 Cornell-Bell AH Thomas PG Smith SJ The excitatory neurotransmitter glutamate causes filopodia formation in cultured hippocampal astrocytes Glia 1990a 3 322 334 1699891 Cornell-Bell AH Finkbeiner SM Cooper MS Smith SJ Glutamate induces calcium waves in cultured astrocytes: Long-range glial signaling Science 1990b 247 470 473 1967852 Dallwig R Deitmer JW Cell-type specific calcium responses in acute rat hippocampal slices J Neurosci Methods 2002 116 77 87 12007985 Dani JW Chernjavsky A Smith SJ Neuronal activity triggers calcium waves in hippocampal astrocyte networks Neuron 1992 8 429 440 1347996 Fields RD Stevens-Graham B New insights into neuron–glia communication Science 2002 298 556 562 12386325 Garaschuk O Linn J Eilers J Konnerth A Large-scale oscillatory calcium waves in the immature cortex Nat Neurosci 2000 3 452 459 10769384 Hara K Harris RA The anesthetic mechanism of urethane: The effects on neurotransmitter-gated ion channels Anesth Analg 2002 94 313 318 11812690 Jando G Carpi D Kandel A Urioste R Horvath Z Spike-and-wave epilepsy in rats: Sex differences and inheritance of physiological traits Neuroscience 1995 64 301 317 7700522 Kang J Jiang L Goldman SA Nedergaard M Astrocyte-mediated potentiation of inhibitory synaptic transmission Nat Neurosci 1998 1 683 692 10196584 Kettenmann H Ransom BR Neuroglia 1995 Oxford Oxford University Press 1104 Kleinfeld D Denk W Two-photon imaging of neocortical microcirculation. In: R Yuste, F Lanni, A Konnerth, editors. Imaging neurons: A laboratory manual 2000 Cold Spring Harbor, New York Cold Spring Harbor Laboratory Press 23.1 23.15 Majewska A Yiu G Yuste R A custom-made two-photon microscope and deconvolution system Pflugers Arch 2000 441 398 408 11211128 Nedergaard M Direct signaling from astrocytes to neurons in cultures of mammalian brain cells Science 1994 263 1768 1771 8134839 Nett WJ Oloff SH McCarthy KD Hippocampal astrocytes in situ exhibit calcium oscillations that occur independent of neuronal activity J Neurophysiol 2002 87 528 537 11784768 Newman EA Propagation of intercellular calcium waves in retinal astrocytes and Muller cells J Neurosci 2001 21 2215 2223 11264297 Parpura V Basarsky TA Liu F Jeftinija K Jeftinija S Glutamate-mediated astrocyte-neuron signalling Nature 1994 369 744 747 7911978 Parri HR Gould TM Crunelli V Spontaneous astrocytic Ca2+ oscillations in situ drive NMDAR-mediated neuronal excitation Nat Neurosci 2001 4 803 812 11477426 Pasti L Volterra A Pozzan T Carmignoto G Intracellular calcium oscillations in astrocytes: A highly plastic, bidirectional form of communication between neurons and astrocytes in situ J Neurosci 1997 17 7817 7830 9315902 Pasti L Zonta M Pozzan T Vicini S Carmignoto G Cytosolic calcium oscillations in astrocytes may regulate exocytotic release of glutamate J Neurosci 2001 21 477 484 11160427 Peters A Palay SL Webster HF The fine structure of the nervous system 1970 New York Harper & Row 406 Porter JT McCarthy KD Hippocampal astrocytes in situ respond to glutamate released from synaptic terminals J Neurosci 1996 16 5073 5081 8756437 Reetz G Wiesinger H Reiser G ATP-induced oscillations of cytosolic Ca2+ activity in cultured astrocytes from rat brain are modulated by medium osmolarity indicating a control of [Ca2+ ]i oscillations by cell volume Neurochem Res 1997 22 621 628 9131642 Ren JQ Aika Y Heizmann CW Kosaka T Quantitative analysis of neurons and glial cells in the rat somatosensory cortex, with special reference to GABAergic neurons and parvalbumin-containing neurons Exp Brain Res 1992 92 1 14 1486945 Schwartz TH Bonhoeffer T In vivo optical mapping of epileptic foci and surround inhibition in ferret cerebral cortex Nat Med 2001 7 1063 1067 11533712 Simard M Arcuino G Takano T Liu QS Nedergaard M Signaling at the gliovascular interface J Neurosci 2003 23 9254 9262 14534260 Stosiek C Garaschuk O Holthoff K Konnerth A In vivo two-photon calcium imaging of neuronal networks Proc Natl Acad Sci U S A 2003 100 7319 7324 12777621 Svoboda K Denk W Kleinfeld D Tank DW In vivo dendritic calcium dynamics in neocortical pyramidal neurons Nature 1997 385 161 165 8990119 Tashiro A Goldberg J Yuste R Calcium oscillations in neocortical astrocytes under epileptiform conditions J Neurobiol 2002 50 45 55 11748632 Venance L Stella N Glowinski J Giaume C Mechanism involved in initiation and propagation of receptor-induced intercellular calcium signaling in cultured rat astrocytes J Neurosci 1997 17 1981 1992 9045727 Verkhratsky A Orkand RK Kettenmann H Glial calcium: Homeostasis and signaling function Physiol Rev 1998 78 99 141 9457170 Zonta M Angulo MC Gobbo S Rosengarten B Hossmann KA Neuron-to-astrocyte signaling is central to the dynamic control of brain microcirculation Nat Neurosci 2003 6 43 50 12469126

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020097Research ArticleNeuroscienceHomo (Human)Neural Activity When People Solve Verbal Problems with Insight Insight in the BrainJung-Beeman Mark mjungbee@northwestern.edu 1 Bowden Edward M 1 Haberman Jason 1 Frymiare Jennifer L 2 Arambel-Liu Stella 1 Greenblatt Richard 3 Reber Paul J 1 Kounios John kounios@drexel.edu 2 1Department of Psychology, Northwestern UniversityEvanston, IllinoisUnited States of America2Department of Psychology, Drexel UniversityPhiladelphia, PennsylvaniaUnited States of America3Source Signal Imaging, IncSan Diego, CaliforniaUnited States of America4 2004 13 4 2004 13 4 2004 2 4 e973 11 2003 30 1 2004 Copyright: © 2004 Jung-Beeman et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Neural Basis of Solving Verbal Problems with Insight People sometimes solve problems with a unique process called insight, accompanied by an “Aha!” experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1) revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2) revealed a sudden burst of high-frequency (gamma-band) neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are used to study neural activity in subjects during a verbal task for which they report solutions achieved by insight ==== Body Introduction According to legend, Archimedes shouted “Eureka!” (“I have found it!”) when he suddenly discovered that water displacement could be used to calculate density. Since then, “Eureka!,” or “Aha!,” has often been used to express the feeling one gets when solving a problem with insight. Insight is pervasive in human (and possibly animal [Epstein et al. 1984]) cognition, occurring in perception, memory retrieval, language comprehension, problem solving, and various forms of practical, artistic, and scientific creativity (Sternberg and Davidson 1995). The Archimedes legend has persisted over two millennia in part because it illustrates some of the key ways in which insight solutions differ from solutions achieved through more straightforward problem solving. We examine the neural bases of these different problem-solving methods. Although many processes are shared by most types of problem solving, insight solutions appear to differ from noninsight solutions in several important ways. The clearest defining characteristic of insight problem solving is the subjective “Aha!” or “Eureka!” experience that follows insight solutions (Schooler et al. 1993). This subjective experience can lead to a strong emotional response—according to legend, Archimedes ran home from the baths shouting “Eureka!” without donning his clothes first. In addition, problem solving with insight is characterized by the following features. (1) Solvers first come to an impasse, no longer progressing toward a solution (Duncker 1945). Archimedes, for example, was stymied by King Hiero's challenge to determine whether his new crown was pure gold without damaging the crown. (2) Solvers usually cannot report the processing that enables them to reinterpret the problem and overcome the impasse (Maier 1931). Insight often occurs when people are not even aware they are thinking of the problem, as reportedly happened to Archimedes while in the baths. (3) Solvers experience their solutions as arising suddenly (Metcalfe and Wiebe 1987; Smith and Kounios 1996) and immediately recognize the correctness of the solution (or solution path). (4) Performance on insight problems is associated with creative thinking and other cognitive abilities different from those associated with performance on noninsight problems (Schooler and Melcher 1997). Some researchers have argued that all these characteristics of insight solutions are essentially epiphenomenal, that insight and noninsight solutions vary only in emotional intensity, and that they are attained with precisely the same cognitive (hence neural) mechanisms (Weisberg and Alba 1981; Weisberg 1986; Perkins 2000). Persistent questions about insight concern whether unconscious processing precedes reinterpretation and solution, whether distinct cognitive and neural mechanisms beyond a common problem-solving network are involved in insight, and whether the apparent suddenness of insight solutions reflects truly sudden changes in cognitive processing and neural activity. Recent work suggests that people are thinking—at an unconscious level—about the solution prior to solving problems with insight. Specifically, while working on a verbal problem they have yet to solve, people presented with a potential solution word read the actual solution word faster than they read an unrelated word (Bowden and Beeman 1998). This “solution priming” effect is greater—and in fact people make solution decisions about presented words more quickly—when words are presented to the left visual hemifield, which projects directly to the right hemisphere (RH), than when words are presented to the right visual hemifield, which projects to the left hemisphere (LH). This suggests that RH semantic processing is more likely than LH semantic processing to produce lexical or semantic information that leads to the solution. These RH advantages occur only when solvers experience insight—the “Aha!” or “Eureka!” feeling that comes with insight solutions (Bowden and Jung-Beeman 2003a). Moreover, when subjects try to solve classic insight problems, they benefit more from hints presented to the left visual field (i.e., the RH) than from hints presented to the right visual field (i.e., the LH) (Fiore and Schooler 1998). Problem solving is a complex behavior that requires a network of cortical areas for all types of solving strategies and solutions, so solving problems with and without insight likely invokes many shared cognitive processes and neural mechanisms. One critical cognitive process distinguishing insight solutions from noninsight solutions is that solving with insight requires solvers to recognize distant or novel semantic (or associative) relations; hence, insight-specific neural activity should reflect that process. The most likely area to contribute to this component of insight problem solving is the anterior superior temporal gyrus (aSTG) of the RH. Language comprehension studies demonstrate that the RH is particularly important for recognizing distant semantic relations (Chiarello et al. 1990; Beeman 1998), and bilateral aSTG is involved in semantic integration. For example, sentences and complex discourse increase neural activity in aSTG bilaterally (Mazoyer et al. 1993; Stowe et al. 1999), and discourse that places particular demands on recognizing or computing distant semantic relations specifically increases neural activity in RH temporal areas (St. George et al. 1999; Mason and Just 2004), especially aSTG (Meyer et al. 2000; Kircher et al. 2001). If this prediction of RH aSTG involvement is confirmed, it will help constrain neurocognitive theories of insight. Other cortical areas, such as prefrontal cortex and the anterior cingulate (AC) may also be differentially involved in producing insight and noninsight solutions. We used functional magnetic resonance imaging (FMRI) in Experiment 1 and electroencephalogram (EEG) measurement in Experiment 2 to test the empirically and theoretically derived hypothesis that solving problems with insight requires engagement of (or increased emphasis on) distinct neural mechanisms, particularly in the RH anterior temporal lobe. Event-related experimental designs compared neural activity when people solved verbal problems with insight to neural activity when they solved problems (from the same problem set) without insight. As in earlier behavioral work, we used a set of compound remote associate problems (Bowden and Jung-Beeman 2003b) adapted from a test of creative cognition (Mednick 1962). Figure 1 illustrates the sequence for each trial. Subjects saw three problem words (pine, crab, sauce) and attempted to produce a single solution word (apple) that can form a familiar compound word or phrase with each of the three problem words (pineapple, crab apple, applesauce). We relied on solvers' reports to sort solutions into insight solutions and noninsight solutions, avoiding the complication that presumed insight problems can sometimes be solved without insight (Davidson 1995) and circumventing the use of different types of problems requiring different cognitive operations. Thus, we made use of the most important defining characteristic of insight problems: the subjective conscious experience—the “Aha!” A similar technique revealed distinct behavioral characteristics when people recognized solutions with insight (Bowden and Jung-Beeman 2003a). Note that this is a very “tight” comparison. In both conditions problems are solved using a network of processes common to both insight and noninsight solutions. If insight ratings reflect some distinct cognitive processes, this contrast will reveal the distinct underlying brain activity. In other words, within the cortical network for problem solving, different components will be engaged or emphasized for insight versus noninsight solutions. FMRI (Experiment 1) should reveal neuroanatomical locations of processes that are unique to insight solutions, and EEG (Experiment 2) should reveal the time course (e.g., whether insight really is sudden) and frequency characteristics of neurophysiological differences. Figure 1 Sequence of Events for Each Trial (A) The “Compound” prompt was presented for 0.5 s, then persisted for a variable amount of additional time (0–2 s) until a cue from the scanner indicated the beginning of a new whole brain acquisition. (B) A three-word problem appeared in the center of the screen and persisted until subjects indicated with a bimanual button press that they had solved the problem, or until the 30-s time limit elapsed. Thus, event timing and condition were completely dependent on subjects' responses. (C) Following the button press or time limit, subjects were prompted to verbalize the solution (or press the buttons and say “Don't know” if the time limit expired prior to solution) then (D) prompted to indicate (with a bimanual button press) whether they felt insight, as described prior to the experiment. (E) Next, subjects performed 9 s of an unrelated filler task (three line-matching trials, 3 s each), allowing BOLD signal to return to baseline (in areas not involved in line matching). Results Experiment 1 Subjects solved 59% of the problems presented, and pressed buttons indicating “insight” for 56% (s.d. = 18.2) of their solutions, “no insight” for 41% (s.d. = 18.9) of their solutions, and “other” for 2% of their solutions. We marked a point about 2 s (rounded to the nearest whole second) prior to each solution button press as the solution event, and examined a time window 4–9 s after this event (i.e., 2–7 s after the button press) to isolate the corresponding hemodynamic response. Solving problems and responding to them required a strict sequence of events (reading of words, solving effort, solving, button press, verbalizing the solution, insight decision), but this sequence was identical whether subjects indicated solving with or without insight, so differences in FMRI signal resulted from the degree to which distinct cognitive processes and neural systems led to insight or noninsight solutions. Figure 2 illustrates the most robust insight effect: as predicted, insight solutions were associated with greater neural activity in the RH aSTG than noninsight solutions. The active area was slightly anterior to primary auditory cortex, posterior to temporal pole, and along the medial aspect of the aSTG, extending down the lateral edge of the descending ramus of the Sylvian fissure to midway through the middle temporal gyrus (MTG). (This site is also close to the superior temporal sulcus, which has been implicated in language). Across all 13 subjects, the peak signal difference at a single voxel within the RH aSTG was 0.25% across the 6-s window, and 0.30% at a single time to repetition (TR), i.e., the time needed to repeat the image of the whole brain. Overall signal in this region was robust, reaching 96.8% of the brainwide average (after removing voxels in other brain areas with signal below a standard criterion). Within the cluster of voxels identified across the group, 12 subjects showed from 0.03% to 0.35% greater signal for insight than for noninsight solutions; one subject showed 0.02% greater signal for the noninsight solutions. It is not likely that RH aSTG is involved only in output or in emotional response following insight solutions, because neural activity in this area also increased when subjects first encountered each problem (Figure 3). Thus, RH aSTG is involved in processing the problem words both initially and at solution. (Of course, event-related FMRI signal occurred in many other cortical regions at problem onset, especially visual cortex). There was no insight effect in response windows immediately preceding or following the defined response window. All indications point to a striking transient event in the RH aSTG near the time when subjects solve problems with insight. Figure 2 FMRI Insight Effect in RH aSTG (A) Voxels showing greater FMRI signal for insight than noninsight solutions, overlaid on the averaged normalized structural image of all subjects. The active area has a volume of 531 mm3 (peak t = 4.89 at 44, −9, −9 in Talairach space). (B) and (C) Group average signal change following the solution event, for insight (red line) and noninsight (blue line) solutions (yellow arrow indicates button press): (B) over entire LH aSTG region; (C) over entire RH aSTG region. (D) Insight solution signal change minus noninsight solution signal change, in RH aSTG (error bars show the standard error of the mean of the difference at each timepoint). Figure 3 FMRI Signal in RH aSTG during Initial Solving Efforts (A) Voxels in right temporal lobe showing baseline-to-peak event-related FMRI signal when subjects first encounter problems, overlaid on the averaged normalized structural image of all subjects. The cluster is in RH aSTG, with a volume of 469 mm3, with peak t value of 4.37 at 41, −6, −12 in Talairach space, clearly overlapping with the cluster showing an insight effect at solution. (B) Group average signal change following problem onset (time = 0), for the cluster defined by signal at the problem onset (green line) and the cluster (illustrated in Figure 2A) showing the insight effect at solution (white line). Error bars show the standard error of the mean of the difference at each time point. The involvement of the RH rather than the LH for this verbal task is not due to greater difficulty in producing insight solutions: subjects produced insight solutions at least as quickly (mean solution time = 10.25 s, s.d. = 3.58 s) as they produced noninsight solutions (mean = 11.28 s, s.d. = 4.13 s) (t < 1.0, p > 0.3). More importantly, the hemodynamic responses to both insight and noninsight solutions in the homologous area of the LH are about equivalent to the response to noninsight solutions in the RH aSTG—it is the strong response to insight solutions in the RH aSTG that stands out. There is no insight effect anywhere within temporal cortex of the LH. At statistical thresholds below significant levels (p < 0.1 uncorrected), there are as many voxels in LH temporal cortex showing a noninsight effect as showing an insight effect. Several other cortical areas showing insight effects that did not meet significance criteria are listed in Table 1 (see also Figure S1). Some of these effects were in frontal cortex, which is notable because various frontal areas have been implicated in problem solving and reasoning. Patients with prefrontal damage have particular difficulty integrating relations in reasoning tasks (Waltz et al. 1999), and when healthy subjects perform the same task, neural activity increases in rostrolateral prefrontal cortext (Christoff et al. 2001). Some problem solving increases activity in dorsolateral prefrontal cortex (Prabhakaran et al. 1997), perhaps because of working memory demands. Solving of poorly structured problems seems particularly impaired following damage to the prefrontal cortex of the RH (Goel and Grafman 2000). Moreover, the inferior frontal gyrus (IFG) is highly active when people engage in directed semantic retrieval (Wagner et al. 2001) or when they select particular semantic concepts over competing ones (Thompson-Schill et al. 1997), e.g., to generate a response (Frith et al. 1991). Usually in these circumstances the IFG activity is stronger in the LH, even when people are reasoning about spatial problems (Goel et al. 1998), but the IFG responds particularly strongly in the RH when subjects select more distant semantic relations because of task demands (Seger et al. 2000) or comprehension goals (Robertson et al. 2000). Because of its putative importance for problem solving, semantic retrieval, and semantic selection, IFG was an a priori region of interest. One question we had hoped to answer was whether the semantic selection of insight solutions would preferentially evoke activity in RH or LH IFG, but the insight effects in both areas were too small (in area and in reliability) to test this question. When a more lenient statistical threshold was adopted, small clusters of signal were observed in both RH and LH IFG (Table 1; Figure S1A). Indeed, within the small region surpassing this weak statistical threshold, signal change in the RH IFG region was moderately strong (peak = 0.21% across the whole window). However, as is often the case, FMRI signal in this region was low (about 72% of the brainwide average) and variability was high, decreasing our confidence in the effect. Table 1 Full FMRI Results of Insight Effect All areas showing an “insight effect”—stronger signal for insight solutions than noninsight solutions (ordered by mean percent signal change). All cluster sizes represent active voxels at t(12) = 3.43, p < 0.005, except bilateral inferior frontal gyrus areas (*), shown at 2.83, p < 0.015, because it was an a priori region of interest. Location of cluster centers and peak t values are shown in Talairach coordinates After RH aSTG, the second largest area showing an insight effect in FMRI signal was the medial frontal gyrus in the LH (Table 1; Figure S1B). Although this area was 85% as large (453 mm3 at p < 0.005 threshold) as RH aSTG, the event-related signal within it was weak and the insight–noninsight difference (peak difference = 0.15%) was relatively small. (The insight effect may be attributable as much to a negative response for noninsight solutions as to a positive response for insight solutions.) There also was an insight effect in small clusters in or near bilateral amygdala or parahippocampal gyrus. Again, regional signal was low (83% of the brainwide average), and the signal difference was small (peak = 0.16%). However, an amygdalar response may be expected, given the emotional sensation of the insight experience (Parsons and Osherson, 2001). Hippocampal or parahippocampal involvement is also plausible, if memory interacts with insight solutions differently from how it interacts with noninsight solutions. For instance, insight problems may encourage distinct memory encoding (Wills et al. 2000) or may require distinct retrieval. Finally, a small cluster in the LH posterior cingulate (PC) also showed an insight effect. There was strong, sustained FMRI signal for both solution types in this region; on the fringe of this responding region, FMRI signal began earlier following insight than noninsight solutions. The lateness of the FMRI signal across LH PC suggests that this effect began later in the response sequence, rather than during solution generation. Finally, as in most FMRI studies, signal was relatively weak in temporal pole and orbitofrontal areas due to magnetic susceptibility artifact, so we cannot rule out undetected effects in those areas. Several cortical areas showed strong solution-related FMRI signal, but approximately equally for insight and noninsight solutions. Some of these areas (e.g., motor cortex) relate to the response sequence rather than solution processes; other areas probably reflect component processes of a problem-solving network common to both insight and noninsight solving, such as retrieving potential solutions. Two areas that may be of interest for future studies are AC and posterior middle/superior temporal gyrus. Both these areas, in the RH only, showed strong, negative solution-related signal, approximately equal in the two solution types. AC is an area that might be predicted to be involved in reorienting attention as solvers overcome impasses, given its role in performance monitoring and cognitive control (MacDonald et al. 2000). RH posterior MTG is active when subjects “get” jokes (Goel and Dolan 2001) and when they attempt to solve problems with deductive reasoning (Parsons and Osherson 2001). However, in our experiment, only the RH aSTG showed a robust insight effect. Experiment 2 A separate group of subjects participated in fundamentally the same paradigm while we continuously recorded EEGs from the scalp. We then compared time-frequency analyses of the EEGs associated with insight solutions versus noninsight solutions. EEG provides temporal resolution greatly superior to that of FMRI and thus can better elucidate the time course and suddenness of the insight effect. Furthermore, complex EEG oscillations can be parsed into constituent frequency components, some of which have been linked to particular types of neural and cognitive processes (Ward 2003). The high temporal resolution of EEG allows us to address one of the fundamental questions raised earlier: does insight really occur suddenly, as subjective experience suggests? For problems typically solved without insight, solvers report gradually increasing closeness to solution. In contrast, for problems typically solved with insight, solvers report little or no progress until shortly before they actually solve the problem (Metcalfe 1986; Metcalfe and Wiebe 1987). Similarly, quantitative analyses of the distributions of response times and accuracies during anagram solving (a task frequently eliciting the experience of insight) reveal that a solution becomes available in a discrete transition from a state of little or no information about the correct response directly to the final state of high accuracy. This contrasts with various language and memory tasks not associated with insight, which yield partial outputs before processing has been completed (Kounios and Smith 1995; Smith and Kounios 1996). We predicted that a sudden change in neural activity associated with insight solutions would produce an EEG correlate. Specifically, we predicted that high-frequency EEG oscillations in the gamma band (i.e., greater than 30 Hz) would reflect this sudden activity, because prior research has associated gamma-band activity with the activation of perceptual, lexical, and semantic representations (Tallon-Baudry and Bertrand 1999; Pulvermüller 2001). Gamma-band electrical activity correlates with the blood oxygenation level–dependent (BOLD) response apparent in FMRI signal; lower-frequency EEG components do not seem to have direct correlates in FMRI signal (Foucher et al. 2003; Laufs et al. 2003). Consequently, based on the language literature discussed earlier and on our FMRI results, we predicted a discrete insight-related increase in gamma-band activity at electrodes over the anterior temporal lobe of the RH. Participants solved 46% (s.d. = 8.2) of the problems correctly within the time limit. Of correctly solved problems, subjects reported more insight solutions (56%, s.d. = 8.4) than noninsight solutions (42%, s.d. = 9.0), (t[18] = 3.47, p=0.003); there was no difference in mean response times (insight solutions = 9.94 s, s.d. = 2.60; noninsight solutions=9.25 s, s.d. = 3.06; t < 1.0). There was a burst of gamma-band activity associated with correct insight solutions (but not noninsight solutions) beginning approximately 0.3 s before the button-press solution response at anterior right temporal electrodes (Figure 4), with no significant difference between insight and noninsight solutions over homologous LH sites. A repeated-measures analysis of variance (ANOVA) performed on log-transformed gamma-band (39 Hz) EEG power at left and right temporal electrode sites (T7 and T8, respectively) for insight and noninsight trials using two time windows (−1.52 to −0.36 s and −0.30 to −0.02 s, measured with respect to the solution response) yielded significant insight × time window (F[1,18] = 6.68, p = 0.019) and insight × time window × Hemisphere (F[1,18] = 8.11, p = 0.011) interactions. The overall interaction occurred because there was an insight × hemisphere interaction from −0.30 to −0.02 s (F[1,18] = 4.61, p = 0.046) but no effect in the −1.52 to −0.36 s time window. Within the −0.30 to −0.02 s interval for these two electrodes, there was a significant insight effect at the right temporal (T8) site (t[18] = 3.48, p = 0.003), but not at the homologous left temporal (T7) site or any other LH temporal electrode. Laplacian mapping of this effect (Figure 4B) is remarkably consistent with the FMRI signal in RH aSTG observed in Experiment 1. (EEG does not have the spatial resolution of FMRI. However, we used the Laplacian transform [i.e., second spatial derivative] to localize observed activity. The Laplacian derivation acts as a high-pass spatial filter that reduces the contribution from activity in distant areas of the brain to the signal at a given electrode, and therefore reflects relatively focal and proximal brain activity. Given our FMRI results and the demonstrated correspondence between high-frequency EEG activity and FMRI signal [Foucher et al. 2003; Laufs et al. 2003], we are confident in the localization of this effect.) Figure 4 Gamma-Band Power for Insight and Noninsight Solutions (A) Grand average time course of EEG power (in v2) at 39 Hz estimated with the Morlet wavelet transform at right superior temporal electrode T8. The x-axis represents time (in seconds) with the yellow arrow and R marking the point in time of the solution button-press response (i.e., 0.0 s). The green horizontal bars above the x-axis represent the time intervals used in the statistical analyses and topographic maps. Note that gamma-band power for insight trials (red line) starts to increase above power on noninsight trials (blue line) by approximately 0.3 s before the button press. (B) Time-frequency plots of the insight minus noninsight difference shown in (A). The y-axis represents frequency (in Hz); the x-axis represents time (in seconds, with respect to the button press, exactly as shown in [A]). Red areas of the plot reflect times and frequencies at which insight EEG power is greater than noninsight EEG power; blue areas reflect times and frequencies at which noninsight EEG power is greater than insight EEG power. Note the sudden emergence of increased gamma power for insight solutions approximately 0.3 s before the button press. (C) Insight minus noninsight gamma-band differences plotted as topographic maps (LH and RH) of scalp current density (in v2/m2) estimated by a spline-based Laplacian transform computed with a realistic FMRI-derived head model. The Laplacian transform acts as a high-pass spatial filter that minimizes the contribution of activity distant from each electrode, thereby manifesting discrete, relatively superficial sources. The maps are thresholded to show foci of current density at the upper and lower 20% of the scale. Note the prominent effect of insight (effect for insight greater than effect for noninsight, in red) at the right superior temporal electrode (T8) and surrounding electrodes present from −0.30 to −0.02 s (measured with respect to the solution response) that is not present in the earlier epoch (−1.52 to −0.36 s). The blue area over left inferior parietal cortex (electrode P7) indicates that noninsight gamma power is nonsignificantly greater than insight power (F[1,19] < 1) over this region. The gamma burst in the right temporal area cannot be attributed to motor processes involved in making the response because (A) motor activity associated with the bimanual button press would have caused a bilateral gamma burst, not a unilateral one; (B) the location of the gamma burst as determined by Laplacian mapping (Figure 4B) is not consistent with hand-related motor cortex activity; and (C) both insight and noninsight solutions required button presses. Other planned statistical tests (ANOVAs) examined possible insight-related frontal theta (5–8 Hz), posterior alpha (8–13 Hz), and fronto-central beta (13–20 Hz) activity. There were no statistically significant theta or beta effects. (Visual inspection and post hoc statistical tests suggested insight-related frontal 4-Hz activity, but this effect cannot be reliably distinguished from possible artifacts due to small vertical eye movements.) There was a significant posterior alpha effect, which is discussed below. Discussion Complex problem solving requires a complex cortical network to encode the problem information, search memory for relevant information, evaluate this information, apply operators, and so forth. The FMRI and EEG results reported here conclusively demonstrate that solving verbal problems with insight requires at least one additional component to this cortical network, involving RH aSTG, that is less important to solving without insight. The insight effect in RH aSTG accords with the literature on integrating distant or novel semantic relations during language comprehension. When people comprehend (read or listen to) sentences or stories, neural activity increases in aSTG or temporal pole bilaterally more than when comprehending single words (Mazoyer et al. 1993; Bottini et al. 1994; Stowe et al. 1999; Humphries et al. 2001; Meyer et al. 2000). Neural activity increases in predominantly RH aSTG during tasks that emphasize integration across sentences to extract themes (St. George et al. 1999) or to form more coherent memories for stories (Mason and Just 2004). RH aSTG is also selectively active when subjects must generate the best ending to a sentence (Kircher et al. 2001) or mentally repair grammatically incorrect sentences (Meyer et al. 2000), both of which likely require intense semantic integration. Like the results in language processing, the current results are predicted by the theory that the RH performs relatively coarse semantic coding (Beeman 1998; similarly, Chiarello et al. 1990). This theory contends that when people encounter words, semantic processing in several LH areas engages in relatively fine semantic coding which produces small semantic fields—i.e., this processing strongly focuses on a few concepts closely related to the input word in the given context. This is very effective for most straightforward language processing. In contrast, the homologous RH areas engage in relatively coarse semantic coding, which produces large and weak semantic fields—i.e., this processing includes many concepts, even concepts distantly related to the input words and context. This process is ineffective for rapid interpretation or selection but increases semantic overlap among multiple semantic fields (Beeman et al.1994), which is useful when drawing together parts of a story or conversation that are only distantly related (Beeman 1993; Beeman et al. 2000). In this view, the coarseness of semantic coding is largely influenced by slight asymmetries in neural microcircuitry that produce more discrete, less redundant input fields in pyramidal neurons of the LH language cortex, and more overlapping input fields in corresponding neurons in the RH (for reviews see Beeman 1998; Hutsler and Galuske 2003). We suggest that semantic integration, generally, is important for connecting various problem elements together and connecting the problem to the solution, and that coarsely coded semantic integration, computed in RH aSTG, is especially critical to insight solutions, at least for verbal problems (or problems that can be solved with verbal or semantic information). People come to an impasse on insight problems because their retrieval efforts are misdirected by ambiguous information in the problem or by their usual method for solving similar problems. Large semantic fields allowing for more overlap among distantly related concepts (or distantly associated lexical items) may help overcome this impasse. Because this semantic processing is weak, it may remain unconscious, perhaps overshadowed by stronger processing of the misdirected information (Schooler et al. 1993; Smith 1995), and solvers remain stuck at impasse. Eventually, solution-related information bursts into awareness “in a sudden flash.” This can happen after misdirected processing decays or is suppressed, after solution-related processing grows, or after environmental cues occur—such as the water overflowing the bathtub when Archimedes got in. Archimedes had semantic and verbal knowledge about how to compute density from weight and volume, but struggled with measuring the volume of an irregularly shaped crown without harming the crown (e.g., melting it). His observation of water displacement allowed him to connect known concepts in new ways. This is the nature of many insights, the recognition of new connections across existing knowledge. A persistent question has been whether the cognitive and neural events that lead to insight are as sudden as the subjective experience. The timing and frequency characteristics of the EEG results shed light on this question. We propose that the gamma-band insight effect in Experiment 2 reflects the sudden transition of solution-related cognitive processing from an unconscious to a conscious state. Recent research associates gamma-band oscillations with the ignition of neural cell assemblies supporting the transient feature binding necessary to activate a representation (Tallon-Baudry and Bertrand 1999; Pulvermüller 2001)—in this case, a phonological, lexical, or semantic representation corresponding to the solution word and its associations to the problem words. According to this hypothesis, greater synchronous gamma-band activity for insight than for noninsight solutions could reflect a more integrated or unitized solution representation. Furthermore, synchronous gamma-band activity has been hypothesized to play a critical role in the accessibility to consciousness of such representations (Engel and Singer 2001). The timing (with respect to the solution button press) of the insight gamma-band effect closely approximates estimates derived from cognitive behavioral studies of the amount of time required to access an available solution and generate a two-alternative, forced-choice button-press response (e.g., Kounios et al. 1987; Meyer et al. 1988; Smith and Kounios 1996). The present experiments had no response choice (i.e., always the same bimanual button press for solutions), so subjects could easily have responded 0.3 s after solving the problems. Thus, we infer that the observed gamma burst reflects the sudden conscious availability of a solution word resulting from an insight. Suddenly recognizing new connections between problem elements is a hallmark of insight, but it is only one component of a large cortical network necessary for solving problems with insight, and recognizing new connections likely contributes to other tasks, such as understanding metaphors (Bottini et al. 1994) and deriving a story theme (St. George et al. 1999). Similar tasks may depend on related cortical networks. For example, appreciating semantic jokes (Goel and Dolan 2001) and engaging in deductive reasoning that sometimes involves insight (Parsons and Osherson 2001) both increase activity in RH posterior MTG. It is striking that the insight effect observed in the RH in our experiments occurred when people solved verbal problems, which traditional views suggest should involve mostly LH processing with little or no contribution from the RH. It is possible that insight solutions to nonverbal problems would require different cortical networks. However, the observed effect cannot be due simply to verbal retrieval, which must occur for both insight and noninsight solutions; it could be due to a type of verbal retrieval specific to insight solutions, but not involved in noninsight solutions. We turn now to another result from the EEG time-frequency analysis, which was not predicted but nevertheless suggests a provocative interpretation. The gamma burst thought to reflect the transition of the insight solution from an unconscious to a conscious state was preceded by insight-specific activity in the alpha band (8–13 Hz). Specifically, there was a burst of alpha power (estimated at 9.8 Hz) associated with insight solutions detected over right posterior parietal cortex from approximately 1.4 s until approximately 0.4 s before the solution response, at which point insight alpha power decreased to the level of noninsight alpha power, or below (Figure 5). An ANOVA was performed on log-transformed alpha-band (9.8 Hz) EEG power at left and right parietal-occipital electrode sites (PO7 and PO8, respectively) for insight and noninsight trials using three time windows: −2.06 to −1.56 s, −1.31 to −0.56 s, and −0.31 to 0.06 s (measured from the solution button press). This analysis yielded a significant insight × time window interaction (F[2,36] = 4.13, p = 0.027, with the Huynh-Feldt correction). Follow-up t-tests in each time window yielded significant effects of insight in the first time window at both electrode sites (PO7: t[18] = 2.32, p = 0.033; PO8: t[18] = 2.42, p = 0.026) and in the second time window only at the RH site (PO8: t[18] = 2.17, p = 0.043), with a reversal of the direction of the effect. The third time window yielded no significant effects. Figure 5 Alpha-Band Power for Insight and Noninsight Solutions (Same conventions as in Figure 4). (A) Time course of EEG power at 9.8 Hz (in v2) at right parietal-occipital electrode (PO8). The x-axis represents time (in seconds), with the green horizontal bars above the x-axis representing the time intervals used in the statistical analyses and topographic maps. The yellow arrow and R (at 0.0 s) signify the time of the button-press response. (B) Time-frequency plots of the insight minus noninsight difference shown in (A). (C) Insight minus noninsight alpha-band differences plotted as topographic maps of scalp current density (in v2/m2). Note that alpha-band power is significantly greater for insight solutions than noninsight solutions during the −1.31 to −0.56 s interval, but not during the preceding (−2.06 to −1.56 s) or subsequent (−0.31 to +0.06 s) intervals. This alpha burst was embedded in a slow decrease in alpha (see [A]), probably reflecting a general increase in cortical activity as effort increases during the course of problem solving. Alpha rhythms are understood to reflect idling or inhibition of cortical areas (Pfurtscheller et al. 1996). Increased alpha power measured over parietal-occipital cortex indicates idling or inhibition of visual cortex. This has been attributed to gating of visual information flowing into the perceptual system in order to protect fragile or resource-intensive processes from interference from bottom-up stimulation (Ray and Cole 1985; Worden et al. 2001; Jensen et al. 2002; Cooper et al. 2003; Ward 2003). This interpretation assumes that brain areas are normally highly interactive, and that allowing one process to proceed relatively independently requires active attenuation of this interaction. For instance, when subjects attend to visual space in the hemifield projecting to one hemisphere, posterior alpha increases over the other hemisphere, which receives inputs from the unattended hemifield (Worden et al. 2001). Analogously, the present results suggest selective gating of visual inputs to the RH during the interval preceding the insight-related right temporal gamma burst (Figure 6). Hypothetically, this allows weaker processing about more distant associations between the problem words and potential solutions to gain strength, by attenuating bottom-up activation or other neural activity not related to solution that would decrease the signal-to-noise ratio for the actual solution. Figure 6 The Time Course of the Insight Effect Alpha power (9.8 Hz at right parietal-occipital electrode PO8) and gamma power (39 Hz at right temporal electrode T8) for the insight effect (i.e., correct insight solutions minus correct noninsight solutions, in v2). The left y-axis shows the magnitude of the alpha insight effect (purple line); the right y-axis applies to the gamma insight effect (green line). The x-axis represents time (in seconds). The yellow arrow and R (at 0.0 s) signify the time of the button-press response. Note the transient enhancement of alpha on insight trials (relative to noninsight trials) prior to the gamma burst. This interpretation of the early insight-specific alpha effect is consistent with previous behavioral research suggesting that, prior to an insight, the solution to a verbal problem can be weakly activated (Bowers et al. 1990), especially in the RH (Bowden and Beeman 1998; Bowden and Jung-Beeman 2003a). Thus insight solutions may be associated with early unconscious solution-related processing, followed by a sudden transition to full awareness of the solution. We suggest that, in Experiment 2, the early posterior alpha insight effect is an indirect correlate of the former, and the right temporal gamma effect is a direct correlate of the latter. In sum, when people solve problems with insight, leading to an “Aha!” experience, their solutions are accompanied by a striking increase in neural activity in RH aSTG. Thus, within the network of cortical areas required for problem solving, different components are engaged or emphasized when solving with versus without insight. We propose that the RH aSTG facilitates integration of information across distant lexical or semantic relations, allowing solvers to see connections that had previously eluded them. In the two millennia since Archimedes shouted “Eureka!,” it has seemed common knowledge that people sometimes solve problems—whether great scientific questions or trivial puzzles—by a seemingly distinct mechanism called insight. This mechanism involves suddenly seeing a problem in a new light, often without awareness of how that new light was switched on. We have demonstrated that insight solutions are indeed associated with a discrete, distinct pattern of neural activity, supporting unique cognitive processes. Materials and Methods Subjects Ten men and eight women were paid to participate in Experiment 1; 19 new subjects (nine men, ten women) were paid to participate in Experiment 2. All were young (18–29) neurologically intact, right-handed, native English speakers; Experiment 1 participants met safety criteria for FMRI scanning. After hearing about all methods and risks and performing practice trials, they consented to participate. In Experiment 1, data from four men and one woman were excluded due to poor FMRI signal or because subjects provided fewer than ten insight or noninsight responses. This research was approved by the University of Pennsylvania Institutional Review Board. Behavioral paradigm Following practice, subjects attempted 124 compound remote associate problems during FMRI scanning. These problems (Bowden and Jung-Beeman 2003b) can be solved quickly and evoke an “Aha!” experience, producing a distinct behavioral signature (Bowden and Jung-Beeman 2003a), roughly half the time they are solved. Figure 1 illustrates the sequence of events for each trial. Each trial began with the task label “Compound” presented on liquid crystal diode goggles for 0.5 to 2.5 s. A gating signal from the scanner triggered the central presentation of three problem words, which persisted until subjects solved the problem or 30 s elapsed. If subjects solved the problem, they made a bimanual button press, after which the word “Solution?” prompted them to verbalize their solution. After 2 s the word “Insight?” prompted subjects to press buttons indicating whether they solved the problem with insight. Prior to the experiment subjects were told the following: “A feeling of insight is a kind of ‘Aha!' characterized by suddenness and obviousness. You may not be sure how you came up with the answer, but are relatively confident that it is correct without having to mentally check it. It is as though the answer came into mind all at once—when you first thought of the word, you simply knew it was the answer. This feeling does not have to be overwhelming, but should resemble what was just described.” The experimenter interacted with subjects until this description was clear. This subjective rating could be used differently across subjects (or even across trials), blurring condition boundaries; yet the distinct neural correlates of insight observed across the group demonstrate that there was some consistency. If subjects failed to solve problems within 30 s, the “Solution?” prompt appeared, and subjects pressed the “no” buttons and verbalized “Don't Know.” Then the “Insight?” prompt appeared, and subjects pressed the “no” buttons again. After the insight rating, subjects performed three line-matching trials (3 s each) to distract them from thinking about the problems, allowing the critical BOLD signal to return to baseline (Binder et al. 1999). The total time from the end of one problem to the onset of the next was 14.5–16.5 s. The condition (e.g., insight or noninsight solution) and time of events was determined by subjects' responses. Image acquisition Imaging was performed at the Hospital of the University of Pennsylvania, on a 1.5 Tesla GE SIGNA scanner with a fast gradient system for echo-planar imaging and a standard head coil. Head motion was restricted with plastic braces and foam padding. Anatomical high-resolution T1-weighted axial and sagittal images were acquired while subjects performed practice trials. Functional images (21 slices, 5 mm thick; 3.75-mm × 3.75-mm in-plane resolution; TR = 2000 ms for 21 slices; time to echo = 40 ms) were acquired in the same axial plane as the anatomical images using gradient-echo echo-planar sequences sensitive to BOLD signal (Kwong et al. 1992; Ogawa et al. 1992). Each functional run was preceded by a 20-s saturation period. Subjects participated in four 15-min runs and a fifth run of varying length, depending on the number of remaining problems. Image analysis Images were coregistered through time with a three-dimensional registration algorithm (Cox 1996). Echo planar imaging volumes were spatially smoothed using a 7.5-mm full-width half-maximum Gaussian kernel. Within each run, voxels were eliminated if the signal magnitude changed more than 10% across successive TRs, or if the mean signal level was below a noise threshold. Functional data were transformed (Collins et al. 1994) to a standard stereotaxic atlas (Talairach and Tournoux 1988) with a voxel size of 2.5 mm3. Data were analyzed using general linear model analysis that extracted average responses to each trial type, correcting for linear drift and removing signal changes correlated with head motion. Each TR was divided into two 1-s images to improve time locking of the solving event and the functional image data (time-course data were temporally smoothed in Figures 2 and 3). Solution-related responses were calculated using the average signal change within the window 4–9 s (to account for hemodynamic delay) after the solving event (beginning about 2 s prior to the button press). Differences between insight and noninsight solution events were estimated for each participant, then combined in a second-stage random effects analysis to identify differences consistent across all subjects. A cluster threshold was set at regions at least 500 mm3 in volume (32 normalized voxels, or 7.1 original-sized voxels) in which each voxel was reliably different across subjects, (t[12] > 3.43, p < 0.005 uncorrected). Monte Carlo simulations with similar datasets reveal low false positive rates with these criteria. RH aSTG was the only cluster to exceed these criteria, and converging evidence and the a priori prediction about RH aSTG strengthen confidence in this result. Experiment 2 Behavioral procedures were similar to those of Experiment 1, except that (A) problem words were presented at smaller visual angles to discourage eye movements, (B) there were 2-s delays between each event in the response sequence, and (C) subjects triggered a new problem directly after responding to the previous problem (i.e., no line task occurred between problems). EEG methods Continuous high-density EEGs were recorded at 250 Hz (bandpass: 0.2–100 Hz) from 128 tin electrodes embedded in an elastic cap (linked mastoid reference with forehead ground) placed according to the extended International 10–20 System. Prior to data analysis, EEG channels with excessive noise were replaced with interpolated data from neighboring channels. Eyeblink artifacts were removed from the EEG with an adaptive filter separately constructed for each subject using EMSE 5.0 (Source Signal Imaging Inc., San Diego, California, United States). Induced oscillations were analyzed by segmenting each subject's continuous EEG into 4-s segments beginning 3 s before each solution response. (An analysis epoch beginning at an earlier point in time would have resulted in the loss of trials associated with response times of less than 3 s.) Time-frequency transforms (performed with EMSE 5.0) were obtained by the application of complex-valued Grossmann-Morlet wavelets, which are Gaussian in both time and frequency. Following Torrence and Campo (1998), the mother wavelet, ω0, in the time domain has the form where ω0 is a nondimensional frequency. In this case, ω0 is chosen to be 5.336, so that ∫ϕ0(t) ≅ 0. The constant π−¼ is a normalization factor such that ∫(ϕ0(t))2 = 1. For the discrete time case, a family of wavelets may be obtained as where δt is the sample period (in seconds), s is the scale (in seconds), and n is an integer that counts the number of samples from the starting time. The Fourier wavelength λ is given by In the frequency domain, the (continuous) Fourier transform of Equation 2 is where One reasonable way to measure the “resolution” of the wavelet transform is to consider the dispersion of the wavelets in both time and frequency. Since the wavelets are Gaussian in both domains, the e-folding time and frequency may serve as quantitative measures of dispersion. Note that these dispersions are a function of the scale, s. For a selected frequency, 𝒻c = 1/λ, or from Equation 3 Then substituting into Equation 2, we find that the e-folding time is for frequency 𝒻c. From Equation 2, the e-folding frequency is . To make this concrete, we find that for a 10-Hz (alpha-band) center frequency, the e-folding time is 0.12 s and the e-folding frequency is 2.6 Hz. For a 40-Hz ( gamma-band) center frequency, the e-folding time is 0.03 s and the e-folding frequency is 10.5 Hz. Note that these e-folding parameters imply that wavelet scaling preserves the joint time-frequency resolution (equal areas in time-frequency space), with higher temporal resolution but broader frequency resolution as the wavelet scale decreases. Segments corresponding to trials for which individual subjects produced the correct response were isolated and averaged separately according to whether or not the subject reported the experience of insight. Planned statistical tests (repeated-measure ANOVAs) were performed in order to detect insight-related effects on frontal midline theta (5–8 Hz), posterior alpha (8–13 Hz), fronto-central beta (13–20 Hz), and left and right temporal gamma (20–50 Hz). Response-locked event-related potentials (ERPs) were also computed using the same analysis epoch. Standard ERP analyses yielded no evidence of statistically significant effects, likely because ERPs reflect phase-locked activity rather than the induced (i.e., nonphase-locked) activity examined in the wavelet analyses; due to the long response times evident in this experiment, phase locking resulting from problem presentation would not be expected. EEG effects were topographically mapped by employing spline-based Laplacian mapping with an FMRI-derived realistic head model and digitized electrode positions. Localization of EEG/ERP signals is a form of probabilistic modelling rather than direct neuroimaging. In contrast to other techniques, source estimation by Laplacian mapping indicates the presence of superficial foci of neuroelectric activity with minimal assumptions. Supporting Information Figure S1 Cortical Regions Showing “Insight Effects” Below Cluster Size Threshold The far left lane shows for each region a single slice best depicting the cluster activated above threshold; middle lane shows time course of signal following insight (red line) and noninsight (blue line) solutions, across the entire active cluster; right panel shows the “insight effect” (insight signal minus noninsight signal, error bars show the standard error of the mean of the difference at each timepoint). (A) depicts bilateral IFG with lowered threshold (t[12] = 2.83, p < 0.015); (B–D) depict clusters of FMRI signal at the same t-threshold used in the main paper (t[12] = 3.43, p < 0.005), but the clusters are too small to surpass cluster criterion. (B) LH medial frontal gyrus; (C) LH PC gyrus; (D) LH amygdala (there was also a small cluster near RH amygdala). Spatial coordinates and other are details listed in Table 1. (914 KB PDF). Click here for additional data file. The authors thank Zoe Clancy, Jamie Hanson, Claudia Maennel, Andrew Schutzbank, and Dan Kimberg for assistance with this project. This work was supported by the National Institute of Deafness and Other Communication Disorders, grants R01 DC-04052 (to MJ-B), and R01 DC-04818 (to JK). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. MJ-B, EMB, and JK conceived and designed the experiments. MJ-B, JH, JLF, SA-L, and JK performed the experiments. MJ-B, JH, JLF, RG, PJR, and JK analyzed the data. MJ-B, EMB, and JK wrote the paper. Academic Editor: Stanislas Dehaene, Service Hospitalier Frederic Joliot Abbreviations ACanterior cingulate ANOVAanalysis of variance aSTGanterior superior temporal gyrus BOLDblood oxygenation level–dependent EEGelectroencephalogram ERPevent-related potential FMRIfunctional magnetic resonance imaging IFGinferior frontal gyrus LHleft hemisphere MTGmiddle temporal gyrus PCposterior cingulate RHright hemisphere TRtime to repetition. ==== Refs References Beeman M Semantic processing in the right hemisphere may contribute to drawing inferences from discourse Brain Lang 1993 44 80 120 8467379 Beeman M Coarse semantic coding and discourse comprehension. In: Beeman M, Chiarello C, editors. Right hemisphere language comprehension: Perspectives from cognitive neuroscience 1998 Mahwah (N.J.) Lawrence Erlbaum Associates 255 284 Beeman MJ Bowden EM Gernsbacher MA Right and left hemipshere cooperation for drawing predictive and coherence inferences during normal story comprehension Brain Lang 2000 71 310 336 10716864 Beeman M Friedman RB Grafman J Perez E Diamond S Summation priming and coarse semantic coding in the right hemisphere J Cogn Neurosci 1994 6 26 45 23962328 Binder JR Frost JA Hammeke TA Bellgowan PSF Rao SM Conceptual processing during the conscious resting state: A functional MRI study J Cogn Neurosci 1999 11 80 93 9950716 Bottini G Corcoran R Sterzi R Paulesu E Schenone P The role of the right hemisphere in the interpretation of figurative aspects of language: A positron emission tomography activation study Brain 1994 117 1241 1253 7820563 Bowden EM Beeman MJ Getting the right idea: Semantic activation in the right hemisphere may help solve insight problems Psychol Sci 1998 6 435 440 Bowden EM Jung-Beeman M Aha! Insight experience correlates with solution activation in the right hemisphere Psychon Bull Rev 2003a 10 730 737 14620371 Bowden EM Jung-Beeman M Normative data for 144 compound remote associate problems Behav Res Meth Instr Comput 2003b 35 634 639 Bowers KS Regehr G Balthazard C Parker K Intuition in the context of discovery Cognit Psychol 1990 22 72 110 Chiarello C Burgess C Richards L Pollock A Semantic and associative priming in the cerebral hemispheres: Some words do, some don't, … sometimes, some places Brain Lang 1990 38 75 104 2302547 Christoff K Prabhakaran V Dorfman J Zhao Z Kroger JK Rostrolateral prefrontal cortex involvement in relation integration during reasoning NeuroImage 2001 14 1136 1149 11697945 Collins DL Neelin P Peters TM Evans AC Automatic 3D inter-subject registration of MR volumetric data in standardized Talairach space Comput Assist Tomogr 1994 18 192 205 Cooper NR Croft RJ Dominey SJJ Burgess AP Gruzelier JH Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses Int J Psychophysiol 2003 47 65 74 12543447 Cox RW AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages Comput Biomed Res 1996 29 162 173 8812068 Davidson JE The suddenness of insight. In: Sternberg RJ, Davidson JE, editors. The nature of insight 1995 Cambridge (Mass.) MIT Press 125 155 Duncker K On problem solving. Psychol Monogr 1945 no 58 Engel AK Singer W Temporal binding and the neural correlates of sensory awareness Trends Cognit Sci 2001 5 16 25 11164732 Epstein R Kirshnit CE Lanza RP Rubin LC ‘Insight' in the pigeon: Antecedents and determinants of an intelligent performance Nature 1984 308 61 62 6700713 Fiore SM Schooler JW Right hemisphere contributions to creative problem solving: Converging evidence for divergent thinking. In Beeman M, Chiarello C, editors. Right hemisphere language comprehension: Perspectives from cognitive neuroscience 1998 Mahwah (N.J.) Lawrence Erlbaum Associates 349 371 Foucher JR Otzenberger H Gounot D The BOLD response and the gamma oscillations respond differently than evoked potentials: An interleaved EEG-fMRI study BMC Neurosci 2003 4 22 14499000 Frith CD Friston KJ Liddle PF Frackowiak RSJ A PET study of word finding Neuropsychologia 1991 29 1137 1148 1791928 Goel V Dolan RJ The functional anatomy of humor: Segregating cognitive and affective components Nat Neurosci 2001 4 3 237 238 11224538 Goel V Grafman J Role of the right prefrontal cortex in ill-structured planning Cognit Neuropsych 2000 17 415 436 Goel V Gold B Kapur S Houle S Neuroanatomical correlates of human reasoning J Cognit Neurosci 1998 10 293 302 9869705 Humphries C Willard K Buchsbaum B Hickok G Role of anterior temporal cortex in auditory sentence comprehension: An fMRI study NeuroReport 2001 12 1749 1752 11409752 Hutsler J Galuske AW Hemispheric asymmetries in cerebral cortical networks Trends Neurosci 2003 26 429 435 12900174 Jensen O Gelfand J Kounios J Lisman JE Posterior 9–12 Hz oscillations increase with memory load during retention in a short-term memory task Cereb Cortex 2002 12 877 882 12122036 Kircher TTJ Brammer M Andreu NT Williams SCR McGuire PK Engagement of right temporal cortex during linguistic context Neuropsychologia 2001 39 798 809 11369403 Kounios J Smith RW Speed-accuracy decomposition yields a sudden insight into all-or-none information processing Acta Psychol 1995 90 229 241 Kounios J Osman AM Meyer DE Structure and process in semantic memory: New evidence based on speed-accuracy decomposition J Exp Psychol Gen 1987 116 3 25 2951481 Kwong KK Belliveau JW Chesler DA Goldberg IE Weisskoff RM Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation Proc Natl Acad Sci U S A 1992 89 5675 5679 1608978 Laufs H Krakow K Sterzer P Eger E Beyerle A Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest Proc Natl Acad Sci U S A 2003 100 11053 11058 12958209 MacDonald AW Cohen JD Stenger VA Carter CS Dissociating the role of the dorsolateral prefrontal cortex and anterior cingulate cortex in cognitive control Science 2000 288 1835 1838 10846167 Maier NRF Reasoning in humans. II: The solution of a problem and its appearance in consciousness J Comp Psychol 1931 12 181 194 Mason R Just M How the brain processes causal inferences in text: A theoretical account of generation and integration component processes utilizing both cerebral hemispheres Psychol Sci 2004 14 1 7 Mazoyer BM Tzourio N Frak V Syrota A Murayama N The cortical representation of speech J Cognit Neurosci 1993 5 467 479 23964919 Mednick SA The associative basis of the creative process Psychol Rev 1962 69 220 232 14472013 Metcalfe J Premonitions of insight predict impending error J Exp Psychol Learn Mem Cogn 1986 12 623 634 Metcalfe J Wiebe D Intuition in insight and noninsight problem solving Mem Cognit 1987 15 238 246 Meyer M Friederici AD von Cramon Y Neurocognition of auditory sentence comprehension: Event related fMRI reveals sensitivity to syntactic violations and task demands Brain Res Cogn Brain Res 2000 9 19 33 10666553 Meyer DE Irwin DE Osman AM Kounios J The dynamics of cognition and action: Mental processes inferred from speed-accuracy decomposition Psychol Rev 1988 95 183 237 3375399 Ogawa S Tank DW Menon R Ellermann J Kim S-G Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging Proc Natl Acad Sci U S A 1992 89 5951 5955 1631079 Parsons LM Osherson D New evidence for distinct right and left brain systems for deductive versus probabilistic reasoning Cereb Cortex 2001 11 954 965 11549618 Perkins D The eureka effect: The art and logic of breakthrough thinking 2000 New York W.W. Norton 292 Pfurtscheller G Stancak A Neuper C Event-related synchronization (ERS) in the alpha band—An electrophysiological correlate of cortical idling: A review Int J Psychophysiol 1996 24 39 46 8978434 Prabhakaran V Smith JA Desmond JE Glover GH Gabrieli JD Neural substrates of fluid reasoning: An fMRI study of neocortical activation during performance of the Raven's Progressive Matrices Test Cognit Psychol 1997 33 43 63 9212721 Pulvermüller F Brain reflections of words and their meaning Trends Cogn Sci 2001 5 517 524 11728909 Ray WJ Cole HW EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes Science 1985 228 750 752 3992243 Robertson DA Gernsbacher MA Guidotti SJ Robertson RWR Irwin W Functional neuroanatomy of the cognitive process of mapping during discourse comprehension Psychol Sci 2000 11 255 260 11273413 St. George M Kutas M Martinez A Sereno MI Semantic integration in reading: Engagement of the right hemisphere during discourse processing Brain 1999 122 1317 1325 10388797 Schooler JW Melcher J The ineffability of insight. 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MIT Press 618 Stowe LA Paans AMJ Wijers AA Zwarts F Mulder G Sentence comprehension and word repetition: A positron emission tomography investigation Psychophysiology 1999 36 786 801 10554592 Talairach J Tournoux P A coplanar stereotaxic atlas of the human brain 1988 New York Thieme 122 Tallon-Baudry C Bertrand O Oscillatory gamma activity in humans and its role in object representation Trends Cogn Sci 1999 3 151 162 10322469 Thompson-Schill SL D'Esposito M Aguirre GK Farah MJ Role of left inferior prefrontal cortex in retrieval of semantic knowledge: A reevaluation Proc Nat Acad Sci U S A 1997 94 14792 14797 Torrence C Campo GP A practical guide to wavelet analysis B Am Meterol Soc 1998 79 61 78 Wagner AD Paré-Blagoev EJ Clark J Poldrack RA Recovering meaning: Left prefrontal cortex guides controlled semantic retrieval Neuron 2001 31 329 338 11502262 Waltz JA Knowlton BJ Holyoak KJ Boone KB Mishkin FS A system for relational reasoning in human prefrontal cortex Psychol Sci 1999 10 119 125 Ward LM Synchronous neural oscillations and cognitive processes Trends Cogn Sci 2003 7 553 558 14643372 Weisberg RW Creativity: Genius and other myths 1986 New York WH Freeman and Company 169 Weisberg RW Alba J An examination of the alleged role of “fixation” in the solution of several “insight” problems J Exp Psychol Gen 1981 110 169 192 Wills TW Soraci SA Chechile RA Taylor HA “Aha” effects in the generation of pictures Mem Cognit 2000 28 939 948 Worden MS Foxe JJ Wang N Simpson GV Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex J Neurosci 2001 20 RC63 61 66

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PLoS Biol. 2004 Apr 13; 2(4):e97

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020099Correspondence and Other CommunicationsOtherAvoiding URL Reference Degradation in Scientific Publications Correspondence and Other CommunicationsKelly Desiree P Hester Eric J Johnson Kathryn R Heilig Lauren F Drake Amanda L Schilling Lisa M Dellavalle Robert P robert.dellavalle@uchsc.edu4 2004 13 4 2004 13 4 2004 2 4 e99Copyright: © 2004 Kelly et al. and Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Arguments are presented concerning the deposit of Internet-based information into the Internet Archive, a digital library of Internet sites and other digital data ==== Body While we applaud PloS' use of Digital Object Identifiers (“The What and Whys of DOIs,” PLoS Biol 1: e57 doi: 10.1371/journal.pbio.0000057), we also note the lack of provisions in your instructions for authors for preserving access to electronic information residing at a cited Internet addresses via Uniform Resource Locators (URLs). Medical and scientific literature increasingly cites information only found on the Internet. However, URLs may become inaccessible shortly after article publication. Please consider requiring PLoS' authors to (1) submit all cited URLs to the Internet Archive (www.archive.org), a nonprofit organization that has been preserving electronic content since 1996, and (2) maintain a printed copy of the electronic information for future communication until the URL becomes available at the Internet Archive (about a six-month lag time). The Internet Archive, the largest digital library of Internet sites and other digital data, stores cited Internet information at no cost to the author, reader, or publisher. By requiring PLoS' authors to submit all cited Internet-based information to the Internet Archive, PLoS will better preserve the integrity of its content for the future. PLoS' Response Ms. Kelly and colleagues raise an important issue about the ephemeral nature of many information sources on the Internet. In the case of online scholarly literature, information is more likely to be archived and able to be found—indeed, an open-access article is one in which, according to the Bethesda Definition, “A complete version of the work and all supplemental materials, including a copy of the permission…, in a suitable standard electronic format is deposited immediately upon initial publication in at least one online repository that is supported by an academic institution, scholarly society, government agency, or other well-established organization that seeks to enable open access, unrestricted distribution, interoperability, and long-term archiving (for the biomedical sciences, PubMed Central is such a repository).” Other types of Internet-based information are more likely to change, move, or be removed. We agree that wherever possible we must find a way to preserve the relevant information from the sources cited in our articles. PLoS has always encouraged authors to submit supporting information for their research articles, including raw datasets, spreadsheets, multimedia, and snapshots of Web-based interactive tools. PLoS makes this supporting information available to everyone for download and use. PLoS also requires authors to deposit all appropriate datasets, images, and information in public databases and to list the relevant accession and version numbers in the article. The question, then, is how PLoS and its authors can preserve access to other Internet-based information, including organizational Web sites, articles in the popular media, or interactive databases. Although submitting cited URLs to the Internet Archive is worthwhile, it is still (unfortunately) far from ideal. The Internet Archive is best at archiving simple HTML and may be the most appropriate place to archive a Web site an author has cited for its static information content. The Internet Archive does not, however, archive content with password restrictions or “crawling” restrictions, and it allows the removal of already archived content at the request of Web administrators; it would therefore not be an effective archive, for example, for popular press articles that have restricted access. In addition, the Internet Archive cannot preserve functions that interact with the originating server, so it is not an appropriate way to archive a Web site an author has cited, for example, for its useful interactive tools. Finally, there is currently no automated way for publishers to redirect links from the original address to the address on the Internet Archive. For the time being, PLoS plans to review all electronic citations on a case-by-case basis and, when appropriate, request that authors submit the cited Web site URL to the Internet Archive and additionally submit a digital copy of the information to PLoS for internal archiving. We would also like to encourage further input on this issue from the scientific and medical community and urge them to support the Internet Archive and other organizations working to preserve the digital record for future generations. University of Colorado Health Sciences Center, Denver, Colorado, United States of America

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PLoS Biol. 2004 Apr 13; 2(4):e99

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020100Journal ClubCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDrosophilaThe Cytoskeleton In Vivo The Cytoskeleton In VivoFernández Beatriz García 4 2004 13 4 2004 13 4 2004 2 4 e100Copyright: © 2004 Beatriz García Fernández.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.A comprehensive understanding of the cytoskeleton can only be achieved by the combination of biochemical, cellular, and whole organism studies ==== Body As a student I always marvelled at the sight of single cells in culture moving over artificial surfaces and exhibiting membrane ruffles and protrusions. However, while I found cultured cells fascinating I always wondered how cells are able to move and regulate their shape in the context of a whole organism where so many space constraints exist and where all cellular processes have to be tightly regulated. Some answers to my questions began to emerge in a paper written by Baum and Perrimon (2001), in which the authors showed the expression and regulation of the actin cytoskeleton and of actin binding proteins in a real epithelium. The cytoskeleton is a meshwork of protein polymers extending throughout the cytoplasm. It not only provides structural support for the cell but also plays a central role in a range of dynamic processes from signalling to endocytosis and intracellular trafficking. A particularly clear example of this is the use of actin cytoskeleton as a “wool” for knitting multiple dynamic structures such as lamellae, filopodia, and stress fibres. These structures determine cell shape and also produce the driving force accompanying many types of cellular movements including muscle contraction and cell division. We know many details about some of the proteins that modulate the dynamics of actin in these structures. However, most of them have been found biochemically and their function has been elucidated primarily using in vitro and cell culture assays of actin assembly. What about these proteins in the context of a developing organism? How do cells generate a spatially and temporally ordered network of actin filaments represented at the tissue level? To answer these questions, we need to move to experimentally accessible multicellular organisms, such as Drosophila, which offers virtually unlimited possibilities as a model system for the genetic and molecular analysis of biological processes. Baum and Perrimon (2001) analyzed the function of a number of proteins involved in actin dynamics within the context of a developing epithelium—the follicle cells that surround the germ line cyst during Drosophila oogenesis. These cells have a simple polarised arrangement of actin filaments, which provides a useful system to study the spatial organisation of the actin cytoskeleton. Taking advantage of the ability to generate clones of cells lacking specific proteins, the authors identified new functional roles for actin regulators such as CAP (a Drosophila homologue of adenylyl cyclase-associated proteins), Enabled (Ena) and Abelson (Abl). These proteins had been well characterized in cell culture and in vitro studies, but little was known about their function in a developing organism. Clones of cells lacking CAP (Figure 1), a protein known to inhibit actin polymerisation, maintained their epithelial polarity but had higher levels of actin and defects in the apical actin organisation. This result indicates that the inhibitory activity of CAP is restricted to one side of the cells, thus demonstrating that actin dynamics can be independently modified at opposite poles of an epithelium. Ena, a member of the Ena/VASP family proteins that catalyse filament formation, and Abl, a protein kinase that binds CAP in mammalian cells, were found to work with CAP in this process. The authors proposed that CAP, Ena, and Abl regulate the level and spatial organization of actin in the follicle cells. Figure 1 CAP Mutant Clones Follicle cells lacking CAP accumulate actin (red) in their apical region. Ena (blue in the bottom panel), also accumulates apically in the mutant cells (looks pink in the clone of cells due to overlap with F-actin in red). The mutant cell clones are identified by the absence of GFP (green in the top panel). Using this technique the cytoskeleton of mutant cells can be analysed in the context of a wild type epithelium. (Image kindly supplied by Buzz Baum.) In contrast to the spatially restricted functions of CAP, Ena, and Abl, profilin and cofilin were shown to regulate actin filament formation throughout the cell cortex, a more global function that matches the results obtained in cell culture experiments. In summary, this study showed how proteins can organise actin in space and began to highlight some of the differences and similarities between cells in culture and in vivo. The functions revealed in the follicular epithelium were consistent with the roles previously shown in mammalian systems, but the experiments on intact tissue began to reveal a spatial and temporal functional dimension that could not have been observed in cell culture. These experiments could be expanded to large-scale screens (St Johnston 2002), but this would be time consuming and could encounter the problem that some genes will be cell lethal, preventing the analysis of their function in actin dynamics. However, two more recent reports (Kiger et al. 2003; Rogers et al. 2003) describe a complementary and exhaustive search for regulators of cytoskeletal dynamics by taking advantage of genomic resources and the powerful RNA interference (RNAi) technique (Hutvágner and Zamore 2002). RNAi allows individual genes to be knocked out in a simple and controlled fashion. Kiger et al. (2003) used RNAi in two different cell lines of Drosophila to screen a number of genes involved in signalling and cytoskeletal dynamics. They targeted 994 genes, of which 160 produced phenotypes in the experiment. The range of phenotypes varied from specific defects in the actin and tubulin cytoskeleton to others affecting cell cycle progression, cytokinesis, and cell shape. They also showed that only about 40% of the genes had similar loss-of-function phenotypes in both cell lines. This alone indicates an important limitation of many tissue culture experiments, since the same protein can have different effects depending on the cell type. Another valuable element of this work is that clustering of genes with similar phenotypes leads to the identification of pathways and networks of genes that are involved in cytoskeletal function. Rogers et al. (2003), using only one Drosophila cell line, studied the effects of proteins involved in the formation of lamellae. The authors looked at the effects of loss of function in 90 genes known to be involved in actin dynamics and the formation and activity of the lamella. As well as confirming the function of many proteins already known to play a role in this process, this analysis allowed them to find interactions between genes and to build genetic pathways. Together these two studies reveal that RNAi screens in tissue culture can be a powerful tool for finding new functions of known and uncharacterized genes, and new relationships between genes. However, this is only the beginning, and the genes identified in this manner will have to be tested in vivo, in systems like that of Baum and Perrimon, where specific functions can be assessed in time and space within the confines of real organisms. The focus must be to understand how all these molecular events and regulation cascades operate in individual cells to contribute to the generation of changes in a whole individual. Increasingly, the attention of developmental biologists is being drawn from genes and their products towards cells (Kaltschmidt and Martinez Arias 2002). The future, it seems to me, lies in the combination of in vitro systems, cell culture, and in vivo studies. I hope to apply this view in my analysis of the process of dorsal closure in Drosophila embryos, as an example of how signalling pathways coordinate and regulate the activity of the cytoskeleton in the generation of shape and morphogenetic movements (Jacinto et al. 2002). Beatriz García Fernández is a PhD student in the laboratory of Antonio Jacinto at the Instituto Gulbenkian de Ciência, Oeiras, Portugal. E-mail: beatrizf@igc.gulbenkian.pt Abbreviations AblAbleson CAP Drosophila homologue of adenylyl cyclase-associated proteins ConAconcavalin A EnaEnabled F-actinfilamentous actin RNAiRNA interference ==== Refs References Baum B Perrimon N Spatial control of the actin cytoskeleton in Drosophila epithelial cells Nat Cell Biol 2001 3 883 890 11584269 Hutvágner G Zamore PD RNAi: Nature abhors a double-strand Curr Opin Genet Dev 2002 12 225 232 11893497 Jacinto A Woolner S Martin P Dynamic analysis of dorsal closure in Drosophila : From genetics to cell biology Dev Cell 2002 3 9 19 12110163 Kaltschmidt JA Martinez Arias A A new dawn for an old connection: Development meets the cell Trends Cell Biol 2002 12 316 320 12185848 Kiger A Baum B Jones S Jones M Coulson A A functional genomic analysis of cell morphology using RNA interference J Biol 2003 2 Epub 2003 Oct 01 Rogers SL Wiedemann U Stuurman N Vale RD Molecular requirements for actin-based lamella formation in Drosophila S2 cells J Cell Biol 2003 162 1079 1088 12975351 St Johnston D The art and design of genetic screens: Drosophila melanogaster Nat Rev Genet 2002 3 176 188 11972155

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PMC387270

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2021-01-05 08:21:09

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PLoS Biol. 2004 Apr 13; 2(4):e100

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PLoS Biol

10.1371/journal.pbio.0020100

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020101PrimerAnimal BehaviorEcologyZoologyMammalsPrimatesHomo (Human)Peace Lessons from an Unlikely Source Peace in Primatesde Waal Frans B. M 4 2004 13 4 2004 13 4 2004 2 4 e101Copyright: © 2004 Frans B. M. de Waal.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Emergence of a Peaceful Culture in Wild Baboons A Pacific Culture among Wild Baboons: Its Emergence and Transmission How much is the aggression we observe in nonhuman primates the result of culture, and will the answer provide insights into our own violent behaviour? ==== Body Upon arrival from Europe, now more than two decades ago, I was taken aback by the level of violence in the American media. I do not just mean the daily news, even though it is hard getting used to multiple murders per day in any large city. No, I mean sitcoms, comedies, drama series, and movies. Staying away from Schwarzenegger and Stallone does not do it; almost any American movie features violence. Inevitably, desensitization sets in. If you say, for example, that Dances with Wolves (the 1990 movie with Kevin Costner) is violent, people look at you as if you are crazy. They see an idyllic, sentimental movie, with beautiful landscapes, showing a rare white man who respects American Indians. The bloody scenes barely register. Comedy is no different. I love, for example, Saturday Night Live for its inside commentary on peculiarly American phenomena, such as cheerleaders, televangelists, and celebrity lawyers. But SNL is incomplete without at least one sketch in which someone's car explodes or head gets blown off. Characters such as Hans and Franz (“We're going to pump you up!”) appeal to me for their names alone (and yes, I do have a brother named Hans), but when their free weights are so heavy that their arms get torn off, I am baffled. The spouting blood gets a big laugh from the audience, but I fail to see the humor. Did I grow up in a land of sissies? Perhaps, but I am not mentioning this to decide whether violence in the media and our ability to grow immune to it—as I also have over the years—is desirable, or not. I simply wish to draw attention to the cultural fissures in how violence is portrayed, how we teach conflict resolution, and whether harmony is valued over competitiveness. This is the problem with the human species. Somewhere in all of this resides a human nature, but it is molded and stretched into so many different directions that it is hard to say if we are naturally competitive or naturally community-builders. In fact, we are both, but each society reaches its own balance between the two. In America, the squeaky wheel gets the grease. In Japan, the nail that stands out gets pounded into the ground. Does this variability mean, as some have argued, that animal studies cannot possibly shed light on human aggression? “Nature, red in tooth and claw” remains the dominant image of the animal world. Animals just fight, and that is it? It is not that simple. First, each species has its own way of handling conflict, with for example the chimpanzee (Pan troglodytes) being far more violent than that equally close relative of ours, the bonobo (P. paniscus) (de Waal 1997). But also within each species we find, just as in humans, variation from group to group. There are “cultures” of violence and “cultures” of peace. The latter are made possible by the universal primate ability to settle disputes and iron out differences. There was a time when no review of human nature would be complete without assertions about our inborn aggressiveness. The first scientist to bring up this issue, not coincidentally after World War II, was Konrad Lorenz (1966). Lorenz's thesis was greeted with accusations about attempts to whitewash human atrocities, all the more so given the Nobel Prize winner's native tongue, which was German. But Lorenz was hardly alone. In the USA, science journalist Robert Ardrey (1961) presented us as “killer apes” unlikely to ever get our nasty side under control. Recent world events have done little to counter this pessimistic outlook. The opposition argued, of course, that aggression, like all human behavior, is subject to powerful cultural influences. They even signed petitions to this effect, such as the controversial Seville Statement on Violence (Adams et al. 1990). In the polarized mind-set of the time, the issue was presented in either-or fashion, as if behavior cannot be both learned and built upon a biological foundation. This rather fruitless nature/nurture debate becomes considerably more complex if we include what is usually left out, which is the ability to keep aggression under control and foster peace. For this ability, too, there exist animal parallels, such as the habit of chimpanzees to reconcile after fights by means of a kiss and embrace. Such reunions are well-documented in a multitude of animals, including nonprimates, such as hyenas and dolphins. They serve to restore social relationships disturbed by aggression, and any animal that depends on cooperation needs such mechanisms of social repair (Aureli and de Waal 2000; de Waal 2000). There are even indications that in animals, too, cultural influences matter in this regard. This may disturb those who write culture with a capital C, and hence view it as uniquely human, but it is a serious possibility nonetheless. Nonhuman culture is currently one of the hottest areas in the study of animal behavior. The idea goes back to the pioneering work of Kinji Imanishi, who in 1952 proposed that if individuals learn from one another, their behavior may over time grow different from that of individuals in other groups of the same species, thus creating a characteristic culture (reviewed by de Waal 2001). Imanishi thus brought the culture concept down to its most basic feature, that is, the social rather than genetic transmission of behavior. Since then, many examples have been documented, mostly concerning subsistence techniques, such as the sweet potato washing of Japanese macaques (Macaca fuscata) and the rich array of tool use by wild chimpanzees, orangutans (Pongo pymaeus), and capuchin monkeys (Cebus spp.) (Whiten et al. 1999; de Waal 2001; Hirata et al. 2001; Perry et al. 2003; van Schaik et al. 2003). However, much less attention has been paid to social culture, which we might define as the transmission of social positions, preferences, habits, and attitudes. Social culture is obviously harder to document than tool use. In human culture, for instance, it is easy to tell if people eat with knife and fork or with chopsticks, but to notice if a culture is egalitarian or hierarchical, warm or distant, collectivistic or individualistic takes time and is difficult to capture in behavioral measures. A well-documented monkey example of social culture is the inheritance of rank positions in macaque and baboon societies. The future position in the hierarchy of a newborn female can be predicted with almost one hundred percent certainty on the basis of her mother's rank. Females with relatives in high places are born with a silver spoon in their mouth, so to speak, whereas those of lowly origin will spend their life at the bottom. Despite its stability, the system depends on learning. Early in life, the young monkey finds out against which opponents it can expect help from her mother and sisters. When sparring with peer A she may utter screams that recruit massive support to defeat A. But against peer B she can scream her lungs out and nothing happens. Consequently, she will come to dominate A but not B. Experiments manipulating the presence of family members have found that when support dwindles dominant females are unable to maintain their positions (Chapais 1988). In other words, the kin-based hierarchy is maintained for generation after generation through social rather than genetic transmission. Returning to the issue of aggressive behavior, here the effects of social culture can be felt as well. Without any drugs or brain lesions, one experiment managed to turn monkeys into pacifists. Juveniles of two different macaque species were placed together, day and night, for five months. Rhesus monkeys (Macaca mulatta), known as quarrelsome and violent, were housed with the more tolerant and easy-going stumptail monkeys (M. arctoides) (Figure 1). Stumptail monkeys easily reconcile with their opponents after fights by holding each others' hips (the so-called “hold-bottom” ritual), whereas reconciliations are rare in rhesus monkeys. Because the mixed-species groups were dominated by the stumptails, physical aggression was rare. The atmosphere was relaxed, and after a while all of the monkeys became friends. Juveniles of the two species played together, groomed together, and slept in large, mixed huddles. Most importantly, the rhesus monkeys developed peacemaking skills on a par with those of their more tolerant group mates. Even when, at the end of the experiment, both species were separated, the rhesus monkeys still showed three times more reconciliation and grooming behaviors after fights than typical of their kind (de Waal and Johanowicz 1993). Primates thus can adopt social behavior under the influence of others, which opens the door to social culture. Figure 1 Stumptail Monkeys Stumptail monkeys (Macaca arctoides) are among the most conciliatory members of the genus Macaca. They are heavily built, yet remarkably friendly and tolerant, such as here: the alpha male is eating attractive food unperturbed by an entire audience around him. When stumptail monkeys were housed with a less tolerant macaque, they modified the latter species' behavior into a more pacific direction. (Photograph by Frans de Waal, used with permission.) Not unlike rhesus monkeys, baboons have a reputation for fierce competition and nasty fights. With the study by Robert Sapolsky and Lisa Share published in this issue of PLoS Biology, we now have the first field evidence that primates can go the flower power route (Sapolsky and Share 2004). Wild baboons developed an exceptionally pacific social tradition that outlasted the individuals who established it. For years, Sapolsky has documented how olive baboons (Papio anubis) on the plains of the Masai Mara, in Kenya, wage wars of nerves, compromising their rivals' immune systems and pushing up the level of their blood cortisol (Sapolsky 1994). An accident of history, however, selectively wiped out all the male bullies of his main study troop. As a result, the number of aggressive incidents dropped dramatically. This by itself was not so surprising. It became more interesting when it was discovered that the behavioral change was maintained for a decade. Baboon males migrate after puberty, hence fresh young males enter troops all the time, resulting in a complete turn-over of males during the intervening decade. Nevertheless, compared with troops around it, the affected troop upheld its reduced aggression, increased friendly behavior, and exceptionally low stress levels. The conclusion from this natural experiment is that, like human societies, each animal society has its own ecological and behavioral history, which determines its prevalent social style. It is somewhat ironic that at a time when researchers on human aggression are increasingly attracted, albeit with a far more sophisticated approach, to the Lorenzian idea of a biological basis of aggression (Enserink 2000), students of animal behavior are beginning to look at its possible cultural basis. There is no reason for animals with a development as slow as a baboon (with adulthood achieved in five or six years) not to be influenced in every way by the environment in which they grow up, including the social environment. How this influence takes place is a point of much debate, and remains unclear in the case of the peaceful male baboons in the Masai Mara. Given their mobility, the males themselves are unlikely transmitters of social traditions within their natal troop. Therefore, Sapolsky and Share look at the females for an answer—female baboons stay all their lives in the same troop. By reacting positively to certain kinds of behavior, for example, females may be able to steer male attitudes in a new direction. This complex problem is hard to unravel with a single study, especially in the absence of experimentation. Yet, the main two points of this discovery are loud and clear: social behavior observed in nature may be a product of culture, and even the fiercest primates do not forever need to stay this way. Let us hope this applies to humanity as well. Frans B. M. de Waal is C. H. Candler Professor of Psychology and Director of Living Links at the Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, United States of America. E-mail: dewaal@emory.edu ==== Refs References Adams D Seville Statement on Violence Am Psychol 1990 45 1167 1168 2252237 Ardrey R African Genesis: A personal investigation into the animal origins and nature of man 1961 New York Simon & Schuster 384 Aureli F de Waal FBM Natural conflict resolution 2000 Berkeley, CA University of California Press 424 Chapais B Rank maintenance in female Japanese macaques: Experimental evidence for social dependency Behaviour 1988 104 41 59 de Waal FBM Bonobo: The forgotten ape 1997 Berkeley, CA University of California Press 235 de Waal FBM Primates: A natural heritage of conflict resolution Science 2000 289 586 590 10915614 de Waal FBM The ape and the sushi master: Cultural reflections of a primatologist 2001 New York Basic Books 448 de Waal FBM Johanowicz DL Modification of reconciliation behavior through social experience: An experiment with two macaque species Child Dev 1993 64 897 908 8339702 Enserink M Searching for the mark of Cain Science 2000 289 575 579 10939970 Hirata S Watanabe K Kawai M Matsuzawa T Sweet-potato washing revisited Primate origins of human cognition and behavior 2001 Tokyo Springer 487 508 Lorenz KZ On aggression 1966 [1963] London Methuen Perry S Baker M Fedigan L Gros-Louis J Jack K Social conventions in wild white-faced capuchin monkeys: Evidence for traditions in a Neotropical primate Curr Anthropol 2003 44 241 268 Sapolsky RM Why zebras don't get ulcers: An updated guide to stress, stress-related diseases, and coping 1994 New York W. H. Freeman & Co 434 Sapolsky RM Share L A pacific culture among wild baboons: Its emergence and transmission PLoS Biol 2004 2 e106 10.1371/journal.pbio.0020106 15094808 van Schaik CP Ancrenaz M Borgen G Galdikas B Knott CD Orangutan cultures and the evolution of material culture Science 2003 299 102 105 12511649 Whiten A Goodall J McGrew WC Nishida T Reynolds V Cultures in chimpanzees Nature 1999 399 682 685 10385119

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PMC387271

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2021-01-05 08:21:08

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PLoS Biol. 2004 Apr 13; 2(4):e101

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PLoS Biol

10.1371/journal.pbio.0020101

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020103SynopsisBiophysicsCell BiologyMolecular Biology/Structural BiologyEukaryotesA Mechanism for Adding the First Link in a Nascent Actin Filament Chain synopsis4 2004 13 4 2004 13 4 2004 2 4 e103Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Activation of Arp2/3 Complex: Addition of the First Subunit of the New Filament by a WASP Protein Triggers Rapid ATP Hydrolysis on Arp2 ==== Body The capacity for self-generated movement is a defining characteristic of animal life. With the molecular components of cellular locomotion conserved in organisms from protozoa to vertebrates, directed cell motility appears to be an ancient cell process, likely dating back a billion years. Most directed motion relies on the assembly, or polymerization, of actin proteins into filaments. Actin is one of the most abundant proteins in cells; about half of the cellular concentration of actin is bound together in filaments at any given time while the other half floats freely as “monomers” in the cytoplasm. The erection and demolition of actin filaments directs the cell motility that lays down the remarkable million miles of nerve cells that form the nervous system and drives a variety of fundamental biological processes, from effective immune response to embryonic development. Mutations in proteins that regulate actin assembly can lead to the abnormal cell migration associated with metastatic cancer. The actin cytoskeleton also provides the structural support for animal cells that the cell wall provides for plants. Actin addition The molecular mechanisms underlying actin assembly and cell motility remained obscure until 1994, when Thomas Pollard and his colleagues discovered the protein complex that initiates actin polymerization. Called actin-related protein 2/3 (Arp2/3) complex, this molecular machine consists of seven subunits, including the two actin-related proteins. Free actin monomers are primed for rapid polymerization, but polymerization must be initiated by the Arp2/3 complex in a process referred to as nucleation. To nucleate a new filament, the Arp2/3 complex must be activated, a job accomplished by a family of proteins called WASP (after Wiskott Aldrich Syndrome, a genetic disease characterized by defects in platelet development and lymphocyte function). WASP proteins bind to both the Arp2/3 complex and an actin monomer. The Arp2/3 complex also binds two molecules of adenosine triphosphate (ATP) on the Arp2 and Arp3 subunits. ATP releases energy in a process called hydrolysis, which drives most energy-dependent processes, from actin polymerization to muscle contraction. The precise mechanisms governing Arp2/3 activation and nucleation are not known. Now Mark Dayel and Dyche Mullins show where hydrolysis occurs during this crucial first step in polymerization and use this finding to investigate the mechanisms that drive nucleation. In previous experiments, Dayel and Mullins found that Arp2/3 appears to require hydrolysable ATP to effect nucleation. To determine when and if ATP hydrolysis occurs on the Arp2/3 complex, Dayel and Mullins developed a technique that allowed them to analyze the Arp2 and Arp3 subunits separately. Dayel and Mullins discovered that hydrolysis occurs only on the Arp2 subunit of the complex and that it happens during the step when WASP initiates the nucleation of a new filament. The researchers then used ATP hydrolysis on Arp2 to dissect the mechanism by which WASP activates the Arp2/3 complex and develop a model of nucleation. (All previous techniques required actin polymerization to monitor the activity of the Arp2/3 complex, but this technique offers a way to decouple activation from polymerization.) They find that WASP proteins activate the Arp2/3 complex by coordinating its interaction with an actin monomer—the first monomer of the new filament. By developing a novel technique to monitor activation of the Arp2/3 complex, the authors contribute a new tool for further investigations of this central part of the cellular motility machinery. And by showing how Arp2/3 is activated, they offer important insights into the workings of a multiprotein cellular machine and the mechanisms that cells enlist to control their shape and motility—which could suggest potential drug targets to inhibit the abnormal cell movement characteristic of cancer and other diseases.

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PMC387272

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2021-01-05 08:26:26

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PLoS Biol. 2004 Apr 13; 2(4):e103

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PLoS Biol

10.1371/journal.pbio.0020103

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020105EditorialScience PolicyWho Pays for Open Access? EditorialDoyle Helen Gass Andy Kennison Rebecca 4 2004 13 4 2004 13 4 2004 2 4 e105Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Publication fees are not borne purely by authors, but are shared by the many organizations whose missions depend on the broadest possible dissemination and communication of scientific discoveries ==== Body In the wake of declarations supporting open access to research literature from international bodies including the Organization for Economic Cooperation and Development (OECD) and the United Nations' World Summit on the Information Society (WSIS), advocates and critics of the movement appear to have agreed that the issue warrants a robust, ongoing dialogue—a development undoubtedly in the interest of the scientific community, regardless of its ultimate outcome. To the extent that listserv messages, editorials, and conference presentations are representative of more widespread reactions to the debate, there appear to be a number of common misconceptions about what open access is and what problems it can or cannot solve. Over the next few months in PLoS Biology, we plan to explore the more pervasive of these misunderstandings, in an effort to expose the real challenges that need to be overcome and to identify some possible solutions. Here we address the first of these—the perception that the publication-charge model puts an unfair burden on authors. Subsequently, we will address concerns about the long-term economic viability of the open-access model, the integrity and quality of work published in open-access journals, and the effect that open access will have on scholarly societies. Publication Charges—Nothing New By charging authors a fee to have their work published in lieu of charging readers to access articles, open-access publishers such as the Public Library of Science (PLoS) and BioMed Central (BMC) have transformed the traditional publishing system. This reliance on a seemingly untested revenue stream has generated skepticism that authors will be both willing and able to pay publication charges. Publication fees are not a phenomenon born of the open-access movement. Many authors regularly pay several thousands of dollars in page charges, color charges, correction costs, reprint costs, and other fees to their publisher, even when such costs are entirely voluntary. In the EMBO Journal, for example, authors are allowed six pages of text free, but are then charged $200 per page beyond that. A review of recent issues shows that almost all authors exceed six pages, voluntarily paying on average over $800 to publish their articles. Furthermore, in addition to paying other publication charges, authors may be willing to pay extra for their articles to be made open access, as several publishers have recently recognized. A recent survey of authors in the Proceedings of National Academy of Science (PNAS) found that although PNAS already makes its content freely available after six months, nearly 50% of PNAS authors expressed a willingness to pay an “open-access surcharge” of $500 or more to make their papers available for free online immediately upon publication—this above and beyond the $1,700 in page charges that the average PNAS author already pays (Cozzarelli et al. 2004). Although we recognize that authors who submit to PLoS Biology may well be a self-selected group of enthusiastic open-access supporters, we have found that nearly 90% of those who submit manuscripts do not request a fee waiver, and the few who do still offer to pay some portion of the fee. The concern about authors' ability to pay publication charges will become less pressing as governments, funding organizations, and institutions increasingly support open-access publication on their researchers' behalf. More funding agencies are joining the Howard Hughes Medical Institute, the Wellcome Trust, and others who have already designated funds for open-access publication. (For more information about these funders' announcements and other international policy statements relevant to open access, see http://www.plos.org/openaccess.) Universities, too, are supporting open access directly by setting aside funds for open-access publication through institutional memberships with BMC and PLoS or through discretionary funds that faculty can tap into to pay publication charges. Such approaches reduce authors' reliance on individual grants to support charges directly and ensure equal access to publishing options that require such payments. The Disenfranchised Even with the steady increase in sources to pay publication fees, detractors claim that open-access publishing may lead to a situation in which some authors are simply unable to publish their work due to lack of funds. The response to this concern is that the ability of authors to pay publication charges must never be a consideration in the decision to publish their papers. To ensure that this happens, PLoS has a firewall in place such that neither the editors nor the reviewers know which authors have indicated whether or not they can pay. Because all work judged worthy of publication by peer review should be published, any open-access business model should be designed to account for fee waivers, just as publishers have always absorbed some authors' inability to pay page and color charges. PLoS grants full or partial publication-charge waivers to any author who requests them, no questions asked. In part, the savings to institutions, hospitals, nongovernmental organizations, and universities provided by open-access publications could help to establish funds for researchers who are less well supported. In the developing world, as free online access to scientific literature is increasingly seen as a political imperative, organizations such as the World Health Organization, the Oxford-based International Network for the Availability of Scientific Publications, and Brazil's SciELO are likely to become more willing to pay open-access publication charges for authors who cannot afford them. The Open Society Institute (OSI) already pays such costs for universities and other organizations in a number of countries in which the foundation is active by way of a PLoS Institutional Membership that grants waived publication charges to authors while providing compensatory revenue for PLoS. Perhaps the real misconception about the unfair burden that open access places on authors resides in the terminology—the term “author charge” is itself misleading. Publication fees are not borne purely by authors, but are shared by the many organizations whose missions depend on the broadest possible dissemination and communication of scientific discoveries. Some of those may provide funding for open-access publication as intermediaries between authors and journals, as OSI does. Others—including many government-financed funding agencies—do so directly through their research grants to scientists. In both cases, funding open access is an effective way to fulfill mandates for public access to and accountability over scientific research and to ensure that all worthy research is published. Helen Doyle is the director of development and strategic alliances, Andy Gass is the outreach coordinator, and Rebecca Kennison is the director of journal production at the Public Library of Science. ==== Refs References Cozzarelli NR Fulton KR Shullenberger DM Results of a PNAS author survey on an open access option for publication Proc Natl Acad Sci U SA 2004 101 1111 Available at http://www.pnas.org/cgi/content/full/101/5/1111 via the Internet. Accessed 28 February 2004 Organization for Economic Cooperation and Development OECD declaration on access to research data from public funding 2004 Available at http://www.oecd.org/document/15/0,2340,en_2649_34487_25998799_1_1_1_1,00.html via the Internet. Accessed 28 February 2004 World Summit on the Information Society Declaration of principles and plan of action 2003 Available at http://www.itu.int/wsis/documents/doc_multi-en-1161|1160.asp via the Internet. Accessed 28 February 2004

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PMC387273

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2021-01-05 08:21:09

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PLoS Biol. 2004 Apr 13; 2(4):e105

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PLoS Biol

10.1371/journal.pbio.0020105

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020106Research ArticleAnimal BehaviorEcologyPhysiologyZoologyPrimatesHomo (Human)A Pacific Culture among Wild Baboons: Its Emergence and Transmission Baboon CultureSapolsky Robert M sapolsky@stanford.edu 1 2 Share Lisa J 1 1Department of Biological Sciences and Department of Neurology and Neurological Sciences, Stanford UniversityStanford, CaliforniaUnited States of America2Institute of Primate Research, National Museums of Kenya KarenNairobiKenya4 2004 13 4 2004 13 4 2004 2 4 e10614 11 2003 18 2 2004 Copyright: © 2004 Sapolsky and Share.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Peace Lessons from an Unlikely Source Emergence of a Peaceful Culture in Wild Baboons Reports exist of transmission of culture in nonhuman primates. We examine this in a troop of savanna baboons studied since 1978. During the mid-1980s, half of the males died from tuberculosis; because of circumstances of the outbreak, it was more aggressive males who died, leaving a cohort of atypically unaggressive survivors. A decade later, these behavioral patterns persisted. Males leave their natal troops at adolescence; by the mid-1990s, no males remained who had resided in the troop a decade before. Thus, critically, the troop's unique culture was being adopted by new males joining the troop. We describe (a) features of this culture in the behavior of males, including high rates of grooming and affiliation with females and a “relaxed” dominance hierarchy; (b) physiological measures suggesting less stress among low-ranking males; (c) models explaining transmission of this culture; and (d) data testing these models, centered around treatment of transfer males by resident females. A unique less-aggressive suite of behaviors that affects the overall structure and social atmosphere of a wild baboon troop potentially represents an intergenerational transfer of social culture ==== Body Introduction A goal of primatology is to understand the enormous variability in primate social behavior. Early investigators examined interspecies differences, e.g., that pair-bonding is more common among arboreal than terrestrial primates (Crook and Gartlan 1966). Attention has also focused on geographical differences in behavior within species (Whiten et al. 1999). Often, such differences reflect environmental factors (e.g., a correlation between quantities of rainfall and foraging time) or, in theory, could reflect genetic drift. However, increasing evidence suggests that group-specific traits can also represent “traditions” or “cultures” (the latter term will be used, commensurate with the near consensus among primatologists that the term can be appropriately applied to nonhuman primates). As traditionally applied to humans, such “culture” can be defined as behaviors shared by a population, but not necessarily other species members, that are independent of genetics or ecological factors and that persist past their originators (Kroeber and Kluckhohn 1966; Cavalli-Sforza 2000; de Waal 2000; de Waal 2001). Thus defined, transmission of culture occurs in apes (McGrew 1998; Whiten et al. 1999; van Schaik et al. 2003), monkeys (Kawai 1965; Cambefort 1981; Perry et al. 2003), cetaceans (Noad et al. 2000; Rendell and Whitehead 2001), and fish and birds (Laland and Reader 1999; Laland and Hoppitt 2003). As particularly striking examples, chimpanzees (Pan troglodytes) across Africa demonstrate variability in 39 behaviors related to tool use, grooming, and courtship (Whiten et al. 1999), and the excavation of near-millenium-old chimpanzee tools has been reported (Mercader et al. 2002). Nearly all such cases of nonhuman culture involve either technology (for example, the use of hammers for nut cracking by chimpanzees), food acquisition, or communication. In this paper, we document the emergence of a unique culture in a troop of olive baboons (Papio anubis) related to the overall structure and social atmosphere of the troop. We also document physiological correlates of this troop atmosphere, the transmission of relevant behaviors past their originators, and possible mechanisms of transmission. Results/Discussion Circumstances Leading to the Emergence of a Unique Culture In the early 1980s, Forest Troop slept in trees 1 km from a tourist lodge. During that period, an open garbage pit was greatly expanded at the lodge. This attracted an adjacent baboon troop, Garbage Dump Troop, which slept near the pit and foraged almost exclusively there. By 1982, many Forest Troop males went to the garbage pit at dawn for food. While such refuse eaters did not differ in age distribution (data not shown) or average dominance rank from non–refuse eaters, they were more aggressive (Table 1); such aggressiveness could be viewed as a prerequisite in order to compete with Garbage Dump males for access to refuse. Refuse eaters were also involved in more dominance interactions within Forest Troop than were non–refuse eaters (note that frequency of dominance interactions is independent of outcome, and thus of rank). Table 1 Characteristics of Forest Troop Males As a Function of Whether They Competed for Refuse with the Garbage Dump Troop Statistical comparisons by unpaired t-test, n = 7 and n = 8 for refuse eaters and remaining males, respectively. Dominance rank based on approach–avoidance criteria (Altmann 1974). Data concerning refuse eaters were derived solely from their time in the troop, rather than including time spent with the Garbage Dump Troop. Rate of male–male aggression consisted of aggression with any other adult or subadult male in the troop. Rate of aggression directed at females included all adult and subadult females. Rates of behaviors are per 100 h of focal observation, except for grooming, which is per 10 h. Data are mean ± standard error of the mean (SEM) In 1983, an outbreak of bovine tuberculosis occurred, originating from infected meat in the dump. From 1983 to 1986, most Garbage Dump animals died, as did all refuse-eating Forest Troop males (46% of adult males); no other Forest Troop animals died (Tarara et al. 1985; Sapolsky and Else 1987). These deaths greatly altered Forest Troop composition, such that there were fewer adult males and more adult females; this more than doubled the female:male ratio (Table 2). By 1986, troop behavior had changed markedly, because only less aggressive males had survived. Table 2 Troop Composition Before and After the Tuberculosis Outbreak Data from annual troop censuses. Census numbers include both “subadult” animals (undergoing the emergence of secondary sexual characteristics) and “fully adult” (fully emerged secondary sexual characteristics) Because of these events, observations of the troop were stopped, and only censusing was done until 1993. Research was begun on Talek Troop, approximately 50 km away. In 1993, informal observation of Forest Troop indicated that the behavioral features seen by 1986 had persisted. Critically, by 1993, no adult males remained from 1983–1986; all current adult males had joined the troop following 1986. Thus, the distinctive behaviors that emerged during the mid-1980s because of the selective deaths were being carried out by the next cohort of adult males that had transferred into the troop. Focal sampling on Forest Troop recommenced in 1993, in order to document this phenomenon. Data from Forest Troop 1993–1996 (henceforth, F93–96) were compared with two other data sets that served as controls: observations from 1993–1998 on the Talek Troop (henceforth T93–98), and observations of Forest Troop itself prior to the deaths (1979, 1980, 1982; henceforth F79–82). These two control data sets did not differ significantly from each other and were combined, henceforth T93–98/F79–82. Atypical Features of the Behavior of Forest Troop Males Male–male dominance interactions Males of F93–96 and T93–98/F79–82 had similar rates of approach–avoidance dominance interactions (data not shown). Moreover, dominance stability did not differ, as measured by the percentage of approach–avoidance interactions which represented a reversal of the direction of dominance within a dyad of males of adjacent rank (16% ± 5% and 20% ± 5% for F93–96 and T93–98/F79–82, respectively, n.s.). There was also no difference in the average tenure length of the highest-ranking male (approximately a year). Despite those similarities, dominance behavior in F93–96 differed from the two control cases in ways that, arguably, made for less stress for low-ranking males. A first example concerns approach–avoidance dominance interactions between males more than two ranks apart in the hierarchy. The overwhelming majority of such interactions were won by the higher-ranking individual. Because a male is rarely seriously threatened by an individual more than two ranks lower in the hierarchy, interactions between individuals that far apart typically represent harassment of or displacement of the subordinate by the higher-ranking male, rather than true competition. In T93–98 and F79–82, approximately 80% of approach–avoidance interactions were between males more than two ranks apart in the hierarchy. In contrast, a significantly smaller percentage of approach–avoidance interactions were soin F93–96 (Figure 1A). Instead, a disproportionate percentage of F93–96 dominance interactions occurred among males of adjacent ranks (with, as noted, no difference in dominance stability)(Figure 1B). Moreover, high-ranking males in F93–96 were more “tolerant” of very low-ranking males, as there was a disproportionately high number of reversals with males more than two steps lower in the hierarchy (Figure 1C). Thus, in F93–96, with a typical level of dominance stability, approach–avoidance dominance interactions were concentrated among closely ranking animals, with low-ranking males being more tolerated and less subject to harassment and/or displacement by high-ranking males. Figure 1 Quality of Male–Male Dominance Interactions (A) Percentage of male approach–avoidance dominance interactions occurring between males more than two ranks apart. (B) Percentage of male approach–avoidance interactions occurring between males of adjacent ranks. (C) Percentage of approach–avoidance interactions representing a reversal of the direction of dominance within a dyad by a male more than two steps lower ranking. Mean ± SEM, ** and *** indicate p < 0.02 and p < 0.01, respectively, by t-test, treating each male/year as a data point. Data were derived from a total of ten different males in F93–96, 31 different males in T93–98, and 19 different males in F79–82. Potentially, the result in (B) could have arisen from different numbers of males in F93–96 versus the other two troops (a smaller group size does not change the number of adjacent animals available to any given subject, but decreases the number of nonadjacent animals available). However, the same results were found if the numbers of males in the three troops were artificially made equal by excluding excess males from either the top or the bottom of the hierarchy (data not shown). Aggression Patterns of aggression also differed between F93–96 and T93–98/F79–82 in a way that suggested a less stressful environment for subordinates in F93–96. The troops had similar overall rates of aggressive interactions (Table 3). However, aggression in F93–96 was more likely than in the control troops to occur between closely ranked animals (i.e., within two rank steps), rather than to reflect high-ranking males directing aggression at extremely low-ranking ones; the latter type of interaction is particularly stressful for a subordinate, because of its typical unpredictability. Moreover, F93–96 males were less likely than T93–98/F79–82 males to direct aggression at females. Table 3 Patterns of Aggression in Forest Troop 1993–1996 versus Talek Troop 1993–1998 and Forest Troop 1979–1982 ** and *** indicate p < 0.025 and p < 0.01 by unpaired t-test, respectively. Observed/expected ratios were derived by comparing observed frequencies of behavior with the frequencies expected with even distribution of aggressive interactions across all dyads; a ratio of 1.0 indicates the behavior occurring at the expected rate. Data from T93–98 and F79–82 did not differ significantly, and thus were pooled. Data were derived from a total of ten different males in F93–96, 31 different males in T93–98, and 19 different males in F79–82 We examined the data for reconciliative behavior (i.e., affiliative behaviors between pairs following aggressive interactions [de Waal and van Roosmalen 1979]) in F93–96 and T93–98/F79–82. However, we saw no male–male reconciliation in any troop, in agreement with prior reports (Cheney et al. 1995). Affiliative behaviors Quantitative data on affiliative behaviors were not available for F79–82. However, F93–96 males socially groomed more often than did control T93–98 males (Figure 2A) this difference was due to more grooming between males and females. F93–96 males were also in close proximity to other animals more often than were T93–98 males (Figure 2B). While males did not differ between troops in the average number of adult male neighbors (i.e., within 3 m), F93–96 males were more likely than T93–98 males to have adult females, infants, adolescents, and juveniles as neighbors. Figure 2 Quality of Affiliative Behaviors (A) Amount of grooming involving adult males in Forest Troop 1993–1996 and Talek Troop 1993–1996. The first pair of columns represents mean time adult males spent grooming adult females; the second pair, mean time adult males were groomed by adult females. (B) Comparison of average number of neighbors (i.e., within 3 m) of adult males in the two troops. Mean ± SEM. *, **, and *** indicate p < 0.05, 0.02, and 0.01, respectively, by unpaired t-test. Data were derived from a total of ten different males and 17 different females in F93–96, 31 different males and 21 different females in T93–98, and 19 different males and 23 different females in F79–82. Sexual behavior Sexual behavior did not differ between F93–96 and T93–98/F79–82. The percentages of nonpregnant, nonlactating females in estrus per day did not differ (27% ± 7% and 30% ± 4%, respectively, n.s.). Moreover, the relationship between male rank and reproductive success did not differ (R2 of correlation between rank and reproductive success: 0.25 ± 0.25 and 0.54 ± 0.10, respectively, n.s.). Physiological Correlates of Behavioral Features of Forest Troop Thus, F93–96 males had high rates of affiliative behaviors, and low-ranking males were subject to low rates of aggressive attack and subordination by high-ranking males. In a stable hierarchy, low-ranking baboon males show physiological indications of being stressed, including elevated basal levels of glucocorticoids (the adrenal hormones secreted in response to stress), hypertension, and decreased levels of high density lipoprotein cholesterol, growth factors, and circulating lymphocytes (Sapolsky 1993; Sapolsky and Share 1994; Sapolsky and Spencer 1997). We tested whether subordinate males in F93–96 were spared the stress-related physiology of subordination seen in other troops. This was the case (Figure 3A). In F79–82, i.e., prior to the tuberculosis outbreak, subordination was associated with elevated basal levels of glucocorticoids, as in other species in which subordination entails extensive stressors and low rates of coping outlets (Sapolsky 2001). While glucocorticoids aid in surviving an acute physical stressor, chronic overexposure increases the risk of glucose intolerance, hypertension, ulcers, and reproductive and immune suppression (Sapolsky et al. 2000). In contrast to this picture in F79–82, in which subordination was associated with a physiology suggesting a chronic state of stress, subordinate F93–96 males did not have elevated basal glucocorticoid levels (levels were unavailable for T93–98). Figure 3 Stress-Related Physiological Profiles (A) Basal glucocorticoid levels (μg/100 ml). Males were split into higher- and lower-ranking 50%, by approach–avoidance criteria. The primate glucocorticoid, cortisol, was measured by radioimmunoassay. (B) Number of anxiety-related behaviors observed 10–20 min after β-carboline-3-carboxylic acid administration (M-156, Research Biochemicals International, Natick, Massachusetts, United States), after subtracting the number observed 10–20 min after vehicle administration (dextrin in 1 ml saline); 0.5 g of the drug in 1ml saline was delivered intramuscularly by dart syringe (Pneu-Dart, Inc., Williamsport, Pennsylvania, United States) fired from a blowgun at 5 m. Mean ± SEM. * and *** indicate p < 0.05 and p < 0.01, respectively, by unpaired t-test. Data were derived from a total of ten different males in F93–96, 31 different males in T93–98, and 18 different males in F79–82. Subordinate F93–96 males were spared another stress-related physiological marker. Experimental anxiety was induced by darting males, intramuscularly, with β-carboline-3-carboxylic acid, a benzodiazepine receptor antagonist which induces behavioral and physiological indices of anxiety in primates (benzodiazepine receptors bind tranquilizers such as valium and librium and mediate their anxiolytic effects)(Ninan et al. 1982). Males were darted on days when they had not had a fight, injury or mating. As a control, they were darted on separate days with vehicle alone (order of dartings randomized). Males were then monitored by an observer unaware of treatment. β-carboline-3-carboxylic acid had no effect on behavior in high-ranking males in T93–98 or F93–96 (Figure 3B).The drug increased anxiety-related behaviors in low-ranking males in T93–98 but not in F93–96 (the recorded anxiety-related behaviors were self-scratching, rhythmic head shaking, assuming a vigilant stance, repeated wiping of nose, and jaw grinding in a solitary male [Ninan et al. 1982; Aureli and van Schaik 1991; Castles et al. 1999]). Thus, in the more typical F79–82 and T93–98 troops, subordination had distinctive stress-related physiological correlates. In contrast, F93–96 males lacked these rank-related differences. Potential Mechanisms Underlying Transmission of This Culture A decade after the deaths of the more aggressive males in the troop, Forest Troop preserved a distinct social milieu accompanied by distinct physiological correlates. Critically, as noted, no adult males in F93–96 had been troop members at the end of the tuberculosis outbreak. Instead, these males had subsequently transferred in as adolescents, adopting the local social style. A number of investigators have emphasized how a tolerant and gregarious social setting facilitates social transmission (e.g., van Schaik et al. 1999), exactly the conditions in F93–96. The present case of social transmission is reminiscent of some prior cases. For example, juvenile rhesus monkeys (Macaca mulatta) housed with stumptail macaques (M. artoides) assume the latter's more conciliatory style (de Waal and Johanowicz 1993). Moreover, anubis baboons (Papio anubis) and hamadryas baboons (P. hamadryas) differ in social structure, and females of either species experimentally transferred into a group of the other adopt the novel social structure within hours (Kummer 1971). Several models have been hypothesized to explain transmission of cultures (Whiten et al. 1999; de Waal 2001; Galef 1990). For clarity, it is useful to first consider their application to an established example of transmission of a “technology” before then applying them to the transmission of the social milieu of F93–96. An example of the former is the nut cracking with stone hammers by West African chimpanzees (Boesch and Boesch 1983; Boesch 2003), a trait transmitted transgenerationally. In “instructional models” of chimpanzee tool use, young are actively taught hammer use. In the case of F93–96, instructional models would involve new transfer males being subject to socially rewarding interactions (e.g., grooming) or aversive ones (e.g., supplantation or attack) contingent upon their assimilating the troop tradition. In such models, a key question is who “instructs.” Much as with the term “culture” being used with respect to animal behavior, the use of the term “instruction” has also generated some controversy, with some preferring the concept of “active behavioral modification” by others bringing about the change. As a striking example of that, when young male cowbirds learn to produce their local song, they initially produce an undifferentiated repertoire of songs, and females react to the production of appropriate dialect with copulation solicitation displays, thus providing positive reinforcement and shaping those behaviors (Smith et al. 2000). In “observational models” applied to chimpanzee tool use, young learn nut cracking by observing and copying adults. As applied to F93–96, transfer males would model behavior upon that of resident males. In “facilitation models” of the chimpanzee example, proximity to adults and their hammers increases the likelihood of the young experimenting with hammers and deriving the skill themselves. As applied to the baboons, male F93–96 behaviors would be an implicit default state where, in the absence of the more typical rates of male aggression (either male–male or male–female), females broadly tend to become more affiliative, and in the context of more affiliative female behavior, transfer males broadly tend to become less aggressive. As perhaps a way of stating the same, the default state may emerge because of the atmosphere of a troop with a high female:male ratio (with less need for male competition for access to estrus females). Finally, a “self-selection model” may apply to the baboons, in which particular kinds of males were more prone to transfer into such a troop (note that the fact that males transferred in from an array of surrounding troops rules out the possibility of an additional model, in which the culture was continued by genetic means). We assessed these models by analyzing cases where adolescent males transferred on known dates and were observed for at least 2–6 mo afterward. Thus, we searched for behavioral patterns involving new transfer males that might differ between F93–96 (five such transfers) and T93–98/F79–82 (12 transfers). Many interactions involving new transfer males did not differ (Table 4). Transfer males in F93–96, T93–98, and F79–82 all attacked and supplanted females from feeding or resting sites at equal rates. Moreover, despite the different dominance structure among resident F93–96 males, resident males in F93–96, T93–98, and F79–82 all treated new transfer males similarly. There were similar latencies until transfer males were first lunged at by residents, and transfer males were involved in dominance and aggressive interactions at similar rates in all three troops (note that because there were half as many resident males in F93–96 as in T93–98 or F79–82, the rate of such interactions within any given resident/transfer male dyad would differ). We examined instances where resident males acted aggressively towards transfer males, determining whether such behaviors were more prevalent during the 20 min after aggressive behavior by the transfer male than at other, randomly selected times (de Waal and Yoshihara 1983; de Waal and Johanowicz 1993). We found no evidence for such contingent behavior (data not shown). Table 4 Behaviors of Newly Transferred Males Subjects consisted of the five transfer males in F93–96 and the 12 in T93–98/F79–82 We then examined affiliative interactions between females and new transfer males, and found striking differences between F93–96 and T93–98/F79–82, in that F93–96 females treated new transfer males in the same affiliative manner that they treated resident males. F93–96 transfer males had a shorter latency until first being groomed by or presented to by a female than did T93–98/F79–82 transfer males (Figure 4A). (The differences between F93–96 and T93–98 did not arise from a single F93–96 female accounting for the much shorter latencies until presentation and grooming: three different females accounted for the first interactions with the five F93–96 transfer males). Moreover, F93–96 transfers sat in closer proximity to and had more grooming bouts with females than did T93–98/F79–82 transfers (Figure 4B). While estrous females are more likely than nonestrous females to interact with transfer males (Smuts 1999), the percentage of females in estrus did not differ among the troops (see above). In addition, F93–96 females did not seem to treat transfer males in a contingent manner (de Waal and Yoshihara 1983; de Waal and Johanowicz 1993). To test for this, we first examined instances where resident females were affiliative towards transfer males, determining whether this was more likely during the 20 min following an affiliative behavior on the part of the transfer male than at other, randomly selected times. Second, we determined whether females were less likely to be affiliative during the 20 min following an aggressive behavior on the part of a transfer male. We found no evidence for either pattern (data not shown). Figure 4 Quality of Interactions between Resident Females and Transfer Males (A) Latency, in days, until a newly transferred male is first groomed by a female (left) or presented to by a female (right). (B) Average number of adult female neighbors per scan (i.e., within 3 m; left) and average number of grooming bouts with females per 100 h of observation (right) for transfer males. Mean ± SEM. * and *** indicate p < 0.05 and p < 0.01, respectively, by unpaired t-test. Latency until first presented to by a female approached significance (p < 0.08). Data were derived from a total of ten different males and 17 different females in F93–96, and 31 different males and 21 different females in T93–98. These data allow some insight as to the mechanisms of social transmission in F93–96 (without remotely allowing an analysis fine-grained enough to see whether these mechanisms were equally relevant to the transmission of all the components of the F93–96 culture, namely the low rates of male aggression, the high rates of female affilitation, and the relaxed dominance structure). The lack of contingency in the treatment of transfer males by residents argues against instruction; commensurate with this, there is relatively little evidence for “instruction” in nonhuman primate cultural transmission (de Waal 2001; for an exception, see Boesch 1991). The similar rates of displacement behaviors by transfer males onto females in all three troops argue against self-selection (i.e., the possibility that F93–96 transfer males already behaved differently than transfer males elsewhere). This is not surprising. While adolescent male baboons may transfer repeatedly before choosing a troop (Pusey and Packer 1986), as well as later in life (Sapolsky 1996), we have seen little evidence among these animals of the systematic sampling of different troops required by a self-selection model. The data instead support either observational or facilitative/default models. Insofar as resident males in all troops interacted with transfer males similarly, transmission in F93–96 could have involved observation only if such observations were of how resident males interacted with females or each other. Some, but not all, studies support observational models of social transmission in other primates (Visalberghi and Fragaszy 1990; Whiten 1998; Boesch 2003; Whiten et al. 2003); there are few data at present from baboons concerning this issue. As shown, F93–96 transfer males were had high rates of affilitative interactions with females. The preponderance of females in F93–96 is a plausible explanation for their unconditional (or, at least, less conditional) increase in tolerance of and affiliation with males (including transfer males), insofar as males in the troop had less numeric means to be aggressive to females. (Note that this skewed sex ratio continues in this troop to the present, for unknown reasons.) Thus, affilative data support a facilitative/default model only if it involves preferential sensitivity to the quality of interactions with females. This analysis raises the possibility that there is no social transmission, but that the F93–96 pattern is merely the emergent outcome of the 2:1 female:male ratio. To test this, we analyzed the five available studies of baboon troops with adult female:male ratios of 2 or more which contained quantitative data comparable to the present data (Seyfarth 1976, 1978; Strum 1982; Bercovitch 1985; Noe 1994). The key question was whether those prior data more closely resembled those of F93–96 or the control troops. Previous data more closely resembled, and did not differ significantly from, data from the control troops for the percentage of time males groomed females (based on Seyfarth 1978), the percentage of time females groomed males (Seyfarth 1978), the rate of intersexual aggression (Seyfarth 1976, 1978), the structure of male–male dominance (Noe 1995), or the structure of of male–male aggression (Strum 1982; Bercovitch 1985). In contrast, no quantitative measures more closely resembled F93–96. This strongly suggests that the F93–96 pattern is unique and is being uniquely maintained, rather than being the social structure that automatically emerges whenever a female-skewed female:male ratio occurs. Thus, insofar as a facilitative/default model is operating in this troop, it cannot be a relative paucity of males which “activates” a default state; instead, it would likely be the paucity of aggressive males. The unconditional (or less conditional) nature of the default model is puzzling, in that it requires that females be relatively affiliative to recent transfer males who, nonetheless, are initially aggressive to them. This seems counter to the long-standing emphasis in primatology on individual relations (i.e., females are unlikely to be unable to distinguish between relatively unaggressive resident males and relatively aggressive newly transferred males). Precedent for this unexpected implication comes from the social epidemiology literature concerning “social capital,” in which health and life expectancy increase in a community as a function of communitywide attributes that transcend the level of the individual or individual social networks (Kawachi et al 1997). In summary, we have observed circumstances that produced a distinctive set of behaviors and physiological correlates in a troop of wild baboons. Moreover, these behaviors were taken on by new troop members; while obviously not conclusive, the data suggest that this most likely occurs through observational or facilitative/default models. Finally, somewhat uniquely in nonhuman primate studies, these findings concern the intergenerational transfer of social, rather than material culture. These findings raise some issues. There appear to be adverse health consequences of the stress-related physiological profile of subordination in typical baboon troops (Sapolsky 1993; Sapolsky and Share 1994; Sapolsky and Spencer 1997). The distinctive rank-related patterns of physiology in F93–96 suggest that subordinate males in that troop may be spared those pathologies. Another issue concerns the consequences of the culture of F93–96 remaining stable over some time. A hallmark of human culture is that it is cumulative (i.e., innovations are built upon each other), and there is only scant evidence, at best, for the same in nonhuman primates (Boesch 2003). It would thus be interesting to see if additional features of the F93–96 social tradition emerge with time. A converse issue concerns circumstances that might destroy the F93–96 culture. The culture might be destroyed if numerous males transfer into the troop simultaneously, or if a male transfers in who, rather than assuming the F93–96 culture, instead takes advantage of it. Game theory suggests that F93–96 would be vulnerable to such “cheating.” Another issue concerns the fate of natal males from F93–96 when they transfer elsewhere. Reciprocal altruism models (Axelrod and Hamilton, 1981) suggest that if one F93–96 male transfers elsewhere and continues his natal behavioral style, he will be at a competitive disadvantage. However, should two F93–96 males simultaneously join another troop and maintain F93–96–typical interactions between them, they might be at a competitive advantage. This might represent a means to transmit this social style between troops. Finally, these findings raise the issue of their applicability to understanding human social behavior and its transmission. Human history is filled with examples of the selective loss of demographic subsets of societies (e.g., the relative paucity of adult men following the American Civil War or the relative paucity of girls in contemporary China due to male-biased reproductive technology practices and female-biased infanticide). The present data suggest that demographic skews may have long-term, even multigenerational consequences, including significant changes in the quality of life in a social group. Materials and Methods Subjects were a troop, Forest Troop, of olive baboons (Papio anubis) living in the Masai Mara Reserve of Kenya. Olive baboons live in multimale troops of 30–150 animals, with polygamy and considerable male–male aggression. Males change troops at puberty and, as adults, achieve ranks in somewhat fluid dominance hierarchies. In contrast, females remain in their natal troop, inheriting a rank one below that of their mother. Subjects were observed each summer from 1978–1986, and continuously since 1993. An additional troop, Talek Troop, was observed continuously since 1984. Behavioral data were collected as 20-min focal samples (Altmann 1974). During years of only summer observation (Forest Troop, 1978–1986), 45 samples were collected per subject per season; otherwise, an average of three samples per subject per week were collected throughout the year. Sampling was distributed throughout the day in the same fashion for each individual. During samples, social behavior, feeding, and grooming were recorded. Rankings were derived from approach–avoidance interactions, which included avoidances, supplants, and presentations, in the absence of aggression. Escalated aggression included open-mouthed lunges, chases, and bites. Nearest neighbor scans were done before and after each sample. Reproductive success was indirectly estimated from frequencies of matings and consortships (maintenance of exclusive mating with and proximity to an estrous female for at least one sample). The value of any given consortship or mating was adjusted by the probability of a fertile mating occurring that day (Hendrickx and Kraemer 1969). Endocrine data were collected under circumstances allowing for measures of basal steroid hormone levels (Sapolsky and Share 1997). Subjects were darted unaware with anesthetic from a blowgun syringe between 7 A.M. and 10 A.M., and only on days on which they were not sick, injured, in a consortship, or had not recently had a fight. Blood samples were collected within 3 min of anesthetization. We thank the Office of the President, Republic of Kenya, for permission to carry out this work, and the John Templeton Foundation and Harry Frank Guggenheim Foundation for generous funding. Field assistance was provided by David Brooks, Denise Costich, Richard Kones, Francis Onchiri, Hudson Oyaro, and Reed Sutherland. Assistance with data analysis was provided by Milena Banjevic, Elle Behrstock, Margaret Crofoot, and Elizabeth Gordon. Helpful discussions were had with Geoffrey Miller and Frank Sulloway, and manuscript assistance was provided by Frans de Waal, Barbara Smuts, and Joan Silk. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. RMS and LJS conceived, designed, and performed the experiments. RMS analyzed the data and contributed reagents/materials/analysis tools. RMS and LJS wrote the paper. Academic Editor: Frans B. 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020108Research ArticleBioinformatics/Computational BiologyCancer BiologyGenetics/Genomics/Gene TherapyHomo (Human)Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data Methods to Predict Patient SurvivalBair Eric ebair@stanford.edu 1 Tibshirani Robert 2 1Department of Statistics, Stanford UniversityPalo Alto, CaliforniaUnited States of America2Department of Heath and Research Policy, Stanford UniversityPalo Alto, CaliforniaUnited States of America4 2004 13 4 2004 13 4 2004 2 4 e10818 7 2003 10 2 2004 Copyright: © 2004 Bair and Tibshirani.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Predicting Cancer Patient Survival with Gene Expression Data An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer. Procedures that utilize both gene expression data and clinical data to identify subtypes of cancer can provide more accurate prognoses ==== Body Introduction Predicting Patient Survival When a patient is diagnosed with cancer, various clinical parameters are used to assess the patient's risk profile. However, patients with a similar prognosis frequently respond very differently to the same treatment. This may occur because two apparently similar tumors are actually completely different diseases at the molecular level (Alizadeh et al. 2000; Sorlie et al. 2001; van de Vijver et al. 2002; van't Veer et al. 2002; Bullinger et al. 2004; Lapointe et al. 2004). The main example discussed in this paper concerns diffuse large B-cell lymphoma (DLBCL). This is the most common type of lymphoma in adults, and it can be treated by chemotherapy in only approximately 40% of patients (NHLCP 1997; Vose 1998; Coiffier 2001). Several recent studies used DNA microarrays to study the gene expression profiles of patients with DLBCL. They found that it is possible to identify subgroups of patients with different survival rates based on gene expression data (Alizadeh et al. 2000; Rosenwald et al. 2002; Shipp et al. 2002). If different subtypes of cancer are known to exist, there are a variety of existing techniques that can be used to identify which subtype is present in a given patient (Golub et al. 1999; Hastie et al. 2001a; Hedenfalk et al. 2001; Khan et al. 2001; Ramaswamy et al. 2001; Nguyen and Rocke 2002a, 2002b; Shipp et al. 2002; Tibshirani et al. 2002; van de Vijver et al. 2002; van't Veer et al. 2002; Nutt et al. 2003). However, most of these techniques are only applicable when the tumor subtypes are known in advance. The question of how to identify such subtypes, however, is still largely unanswered. There are two main approaches in the literature to identify such subtypes. One approach uses unsupervised learning techniques, such as hierarchical clustering, to identify patient subgroups. This type of procedure is called “unsupervised” since it does not use any of the clinical information about the patient. The subgroups are identified using only the gene expression data. (In contrast, “supervised learning” would use the clinical data to build the model.) For an overview of unsupervised learning techniques, see Gordon (1999) or Hastie et al. (2001b). Hierarchical clustering (Eisen et al. 1998) has successfully identified clinically relevant cancer subtypes in several different studies (Alizadeh et al. 2000; Bhattacharjee et al. 2001; Sorlie et al. 2001; Beer et al. 2002; Lapointe et al. 2004). However, one drawback to unsupervised learning procedures is that they may identify cancer subtypes that are unrelated to patient survival. Although several different subtypes of a given cancer may exist, if the prognosis for all patients is the same regardless of which subtype they have, then the utility of this information is limited. Since unsupervised learning procedures by definition do not use the clinical data to identify subtypes, there is no guarantee that the subtypes they identify will be correlated with the clinical outcome. The second approach to identifying subtypes of cancer is based exclusively on the clinical data. For example, patients can be assigned to a “low-risk” or a “high-risk” subgroup based on whether they were still alive or whether their tumor had metastasized after a certain amount of time. This approach has also been used successfully to develop procedures to diagnose patients (Shipp et al. 2002; van de Vijver et al. 2002; van't Veer et al. 2002). However, by dividing the patients into subgroups based on their survival times, the resulting subgroups may not be biologically meaningful. Suppose, for example, that there are two tumor cell types. Suppose further that patients with cell type 2 live slightly longer than patients with cell type 1 but that there is considerable overlap between the two groups (Figure 1). Assume also that the underlying cell types of each patient are unknown. If we were to assign patients to “low-risk” and “high-risk” groups based on their survival times, many patients would be assigned to the wrong group, and any future predictions based on this model would be suspect. We can obtain more accurate predictions by identifying these underlying subtypes and building a model that can determine which subtype is present in future patients. Figure 1 Two Patient Subgroups with Overlapping Survival Times Proposed Semi-Supervised Methods To overcome these difficulties, we propose a novel procedure that combines both the gene expression data and the clinical data to identify cancer subtypes. The crux of the idea is to use the clinical data to identify a list of genes that correlate with the clinical variable of interest and then apply unsupervised clustering techniques to this subset of the genes. For instance, in many studies, the survival times of the patients are known even though no tumor subtypes have been identified (Alizadeh et al. 2000; Bhattacharjee et al. 2001; Sorlie et al. 2001; Beer et al. 2002; Rosenwald et al. 2002; Shipp et al. 2002; van de Vijver et al. 2002; van't Veer et al. 2002; Nutt et al. 2003; Bullinger et al. 2004). We can calculate the Cox score for each gene in the expression data—the Cox score measures the correlation between the gene's expression level and patient survival—and consider only the genes with a Cox score that exceeds a certain threshold. Once such a list of significant genes is compiled, there are several methods we can use to identify clinical subgroups. We can apply clustering techniques to identify subgroups of patients with similar expression profiles. Once such subgroups are identified, we can apply existing supervised learning techniques to classify future patients into the appropriate subgroup. In this study, we will use the “nearest shrunken centroids” procedure of Tibshirani et al. (2002), which is implemented in the package PAM (Tibshirani et al. 2003). For a brief description of the procedure, see “Materials and Methods.” Sometimes, however, a continuous predictor of survival is desired. We also describe a supervised version of principal components analysis that can be used to calculate a continuous risk score for a given patient and identify subtypes of cancer. The resulting predictor performs very well when applied to several published datasets. These two methods will produce satisfactory results in most datasets. However, we will describe some variations of these methods that can sometimes improve their performance. When we cluster a dataset using only a subset of the genes, it is important that we choose the correct subset of genes. Choosing the genes with the largest Cox scores is generally a good strategy, but this procedure sometimes selects some spurious genes. We will show that one can use partial least squares (PLS) to compute a “corrected” Cox score. Selecting the genes with the largest “corrected” Cox scores can produce better clusters than selecting genes with largest raw Cox scores. Additionally, we will describe two other continuous predictors of survival that we will call β˜ and γ^. For some problems, they are better predictors than the continuous predictor based on supervised principal components (Figures S1–S3). These methods are described in Protocol S1. Related Methods in the Literature Ben-Dor et al. (2001) and von Heydebreck et al. (2001) attempt to identify biologically meaningful tumor subtypes from gene expression data by clustering on a subset of the genes. The important distinction between these methods and our semi-supervised clustering method is that our method uses the available clinical data to choose the subset of the genes that is used to perform the clustering. The methods of von Heydebreck et al. (2001) and Ben-Dor et al. (2001) do not use this clinical information. We will show that utilizing the available clinical data can improve the quality of the clustering. There are also related methods for predicting the survival of cancer patients using gene expression data. Nguyen and Rocke (2002a) use a form of PLS to predict survival. Li and Luan (2003) use support vector machines (SVMs). However, a drawback of these methods is the fact that they use a combination of all of the genes to predict survival. Since the vast majority of the genes in a given dataset are unrelated to survival, the result is that many of the inputs to the model are superfluous, which reduces the predictive accuracy of the model. We will show that our semi-supervised methods, which use only a subset of the genes, generally perform better than these methods. Moreover, in many applications, we would like to identify which genes are the best predictors of survival. These genes could be analyzed in the laboratory to attempt to discover how they influence survival. They could also be used to develop a diagnostic test based on immunostaining or reverse transcriptase PCR. For these applications, it is important to have a predictor of survival that is based on a small subset of the genes. This is another important advantage of our methods over existing methods. Beer et al. (2002) utilized an ad hoc method that fit a series of univariate Cox proportional hazards models and took a linear combination of the resulting coefficients. A brief description of their method is given in Protocol S1. This method is similar to our methods in that it selects a relevant subset of genes by choosing the genes with the largest Cox scores. However, this method is a purely supervised procedure. It does not apply any unsupervised methods (such as clustering or principal components analysis) to this subset of genes to identify additional patterns in the data. We will show that our semi-supervised procedures generally perform better than this method. Summary Our goal is to identify subtypes of cancer that are both clinically relevant and biologically meaningful. Suppose that we have 𝓃 patients, and we measure the expression level of p genes for each patient. (Note that 𝓃 ≫ p.) We assume that there are several different types (classes) of cancer, each of which responds differently to treatment, and each of which is distinct at the molecular level. Therefore, given a set of 𝓃 patients with different classes of cancer, we wish to train a classifier that can diagnose which type of cancer a future patient has, given the expression levels of the patient's p genes. We will show that it is possible to identify such subgroups using the semi-supervised learning techniques described in the previous paragraph, and that identification of such subgroups can enable us to predict the clinical outcome of cancer more accurately. Results Fully Unsupervised Clustering As noted in the Introduction, we needed to assign each patient to a subgroup before we could apply nearest shrunken centroids. First, we applied an unsupervised 2-means clustering procedure to the DLBCL data of Rosenwald et al. (2002). This dataset consisted of measurements on 7399 genes from 240 patients. Of these 240 patients, 160 were used for training the model and 80 were reserved for validating the model. A survival time was given for each patient, which ranged between 0 and 21.8 y. We compared the survival times of the two subgroups using a log-rank test. The log-rank test statistic was 0.7, with a corresponding p-value of 0.416. Thus, conventional clustering techniques failed to identify subgroups that differed with respect to their survival times. Subgroups identified using hierarchical clustering also did not differ with respect to survival (data not shown). Using Clinical Data Alone to Generate Class Labels We assigned each patient in the training data to either a “low-risk” or “high-risk” subgroup based on their survival time (see “Materials and Methods” for details.) After applying nearest shrunken centroids with crossvalidation, we selected a model that used 249 genes. We then used this model to assign each patient in the independent test data to the “low-risk” or “high-risk” group. The plots of the two survival curves associated with the two classes generated by the model are shown in Figure 2. The p-value of the log-rank test was 0.03. Figure 2 Comparison of the Survival Curves of the “Low-Risk” and “High-Risk” Groups These were obtained by applying nearest shrunken centroids to the DLBCL test data. Patients in the training data were assigned to either the “low-risk” or “high-risk” group depending on whether or not their survival time was greater than the median survival time of all the patients. Supervised Clustering In order to identify tumor subclasses that were both biologically meaningful and clinically relevant, we applied a novel, supervised clustering procedure to the DLBCL data. We ranked all of the genes based on their univariate Cox proportional hazards scores, and performed clustering using only the “most significant” genes. Recall that when we performed 2-means clustering on the patients in the test data using all 7,399 genes and used a log-rank test to compare the survival times of the patients in the two resulting clusters, the result was not significant. To test our new clustering method, we calculated the Cox scores of all 7,399 genes based on the 160 training observations and ranked the genes from largest to smallest based on their absolute Cox scores. We then clustered the 80 test observations using only the 25 top-scoring genes. This time, the log-rank statistic comparing the survival times of the two clusters was highly significant ( p = 0.0001). A plot of the two resulting survival curves is shown in Figure 3. A plot of the survival curves that we obtained by applying 2-means clustering to all of the genes is also shown for comparison. Figure 3 Comparison of the Survival Curves Resulting from Applying Two Different Clustering Methods to the DLBCL Data Other Clustering Methods Both Ben-Dor et al. (2001) and von Heydebreck et al. (2001) used a subset of the genes to try to cluster a microarray dataset in a biologically meaningful manner. They observed that clustering using a subset of the genes can produce better results than using all of the genes. However, these methods were still fully unsupervised since they used only the gene expression data to perform the clustering. They did not use the clinical data to identify subgroups. Although these methods do a better job of identifying biologically meaningful clusters than clustering based on all of the genes, there is no guarantee that the clusters thus identified are associated with the clinical outcome of interest. Indeed, both Ben-Dor et al. (2001) and von Heydebreck et al. (2001) applied their procedures to a small DLBCL dataset of 40 patients (Alizadeh et al. 2000). The clusters they identified did not (with a few exceptions) differ significantly from one another with respect to survival. We applied the clustering procedure of von Heydebreck et al. (2001) to the larger DLBCL dataset of Rosenwald et al. (2002). Figure 4 shows the survival curves of the two clusters generated using this method. The survival curves generated by clustering on the genes with the largest Cox scores are included for comparison. Note that the two subgroups identified using the clustering procedure of von Heydebreck et al. (2001) do not differ significantly with respect to survival. Figure 4 Comparison of the Survival Curves Resulting from Applying Two Different Clustering Methods to the DLBCL Data Survival Diagnosis We showed that the cancer subgroups identified using this supervised clustering method can be used to predict survival in future patients. The idea is straightforward. First, we identified subgroups of patients using supervised clustering. Then we trained a nearest shrunken centroid classifier to predict the subgroup to which each patient belonged. Details are given in “Material and Methods.” We tested this procedure on the DLBCL data. A clustering based on 343 genes produced the smallest crossvalidation error rate, so we used a classifier based on this clustering to assign each of the 80 test patients to one of the two subgroups. The survival curves of the two predicted subgroups are shown in Figure 5; the p-value of the log-rank test comparing the two survival curves is 0.008. Figure 5 Survival Curves for Clusters Derived from the DLBCL Data Supervised Principal Components We used a form of the principal components of the expression matrix to predict survival. Principal components analysis is an unsupervised learning technique that is used to reduce the dimensionality of a dataset by calculating a series of “principal components.” The hope is that the first few principal components will summarize a large percentage of the variability in the entire dataset. See Hastie et al. (2001b) for a description of principal components analysis. Unfortunately, principal components analysis suffers from the same limitations as purely unsupervised clustering. If we perform principal components analysis using all of the genes in a dataset, there is no guarantee that the resulting principal components will be associated with survival. Thus, we propose a semi-supervised form of principal components analysis that we call “supervised principal components.” Rather than using all of the genes when we perform principal components analysis, we use only a subset of the genes that are correlated with survival. Using the 160 training observations, we computed the Cox scores for each gene. We kept the 17 genes with Cox scores of 2.39 or greater. We calculated the principal components of the training data using only these 17 genes. Then we approximated the principal components of the test data using equation (11) (see “Materials and Methods” for details.) Figure 6 shows that there does appear to be a correlation between the value of the first principal component, υ^I1, and patient survival. To confirm this observation, we fit a Cox proportional hazards model to a linear combination of υ^I1 and υ^I2, the estimated first and second principal components of the test data, respectively. (See “Materials and Methods” for a description of how this linear combination was obtained.) The resulting sum was a significant predictor of survival (R 2 = 0.113, likelihood ratio test statistic = 9.58, 1 d.f., p = 0.00197). This predictor is stronger than the discrete predictor shown in Figure 5 (R 2 = 0.08, likelihood ratio test statistic = 6.7, 1 d.f., p = 0.00966). Figure 6 Plot of Survival Versus the Predictor υ^I for the DLBCL Data A Breast Cancer Example Thus far, all of our examples have been based on the DLBCL data of Rosenwald et al. (2002). We now apply our methodology to a set of breast cancer microarray data. In a recent study, van't Veer et al. (2002) built a model to predict the time to metastasis of breast cancer in patients based on microarray data from 78 patients. They showed that this model could be used to predict the times to metastasis of 20 independent test patients. Later, in a separate study, this same model was applied to a much larger set of 292 patients (van de Vijver et al. 2002). Unfortunately, the expression levels of only 70 genes were available for the 292 patient dataset, making it difficult to test our methodology. However, we were able to apply our supervised principal components method. The expression levels of approximately 25,000 genes were available for the earlier study (consisting of 78 patients). After applying crossvalidation, we selected a model consisting of eight genes, five of which were included among the 70 genes in the larger dataset. Thus, we fit a supervised principal components model using these five genes and applied it to the dataset of 292 patients. The results are shown in Table 1. (To compare the predictive power of the various models, we fit a Cox proportional hazards model to each predictor and computed the R 2 statistic for each model. R 2 measures the percentage of the variation in survival time that is explained by the model. Thus, when comparing models, one would prefer the model with the larger R 2 statistic.) We see that our supervised principal components method produced a stronger predictor of metastasis than the procedure described in van't Veer et al. (2002). Furthermore, our method used only five genes, whereas the predictor of van't Veer et al. (2002) used 70 genes. These results held even though we did not have the expression data for three genes that we would like to have included in our model. Table 1 Supervised Principal Components Applied to Breast Cancer Data Comparison of the values of the R 2 statistic of the Cox proportional hazards model (and the p-value of the associated log-rank statistic) obtained by fitting the times to metastasis to our supervised principal components method and the discrete predictor described in van't Veer et al. (2002) (Of the 78 patients used to build the model in the original study, 61 were included in the larger dataset of 292 patients. Thus, the values of R 2 calculated using all 292 patients are inflated, since part of the dataset used to validate the model was also used to train the model. We include these results merely to demonstrate the greater predictive power of our methodology. Moreover, we repeated these calculations using only the 234 patients that were not included in the earlier study to ensure that our results were still valid.) Comparison With Related Methods in the Literature We compared each of our proposed methods to several previously published methods for predicting survival based on microarray data. In particular, we examined three previously published procedures: a method based on SVMs (Li and Luan 2003), a method based on PLS (Nguyen and Rocke 2002a), and an ad hoc procedure that calculated a “risk index” for each patient by taking an appropriate linear combination of a subset of the genes (Beer et al. 2002). Finally, we considered a naive procedure that split the training data into two groups by finding the bipartition that minimized the p-value of the resulting log-rank statistic. A brief description of each of these procedures is given in Protocol S1; for a full description of these procedures, see the original papers. We compared these methods on four different datasets (See Datasets S1-S13). First, we examined the DLBCL dataset (Rosenwald et al. 2002) that we used in the earlier examples. Recall that there were 7,399 genes, 160 training patients, and 80 test patients. Second, we considered a breast cancer dataset (van't Veer et al. 2002). There were 4,751 genes and 97 patients in this dataset. We partitioned this dataset into a training set of 44 patients and a test set of 53 patients. Third, we examined a lung cancer dataset (Beer et al. 2002). There were 7,129 genes and 86 patients, which we partitioned into a training set of 43 patients and a test set of 43 patients. Finally, we considered a dataset of acute myeloid leukemia patients (Bullinger et al. 2004). It consisted of 6,283 genes and 116 patients. This dataset was partitioned into a training set of 59 patients and a test set of 53 patients. The results are shown in Table 2. Table 2 Comparison of the Different Methods on Four Datasets Comparison of the different methods applied to the DLBCL data of Rosenwald et al. (2002), the breast cancer data of van't Veer et al. (2002), the lung cancer data of Beer et al (2002), and the acute myeloid leukemia (AML) data of Bullinger et al. (2004). The methods are (1) assigning samples to a “low-risk” or “high-risk” group based on their median survival time; (2) using 2-means clustering based on the genes with the largest Cox scores; (3) using the supervised principal components method; (4) using 2-means clustering based on the genes with the largest PLS-corrected Cox scores; (5) using the continuous predictor ; (6) using 2-means clustering to identify two subgroups; (7) partitioning the training data into “low-risk” and “high-risk” subgroups by choosing the split that minimizes the p-value of the log-rank test when applied to the two resulting groups; (8) using SVMs, similar to the method of Li and Luan (2003); (9) using a discretized version of (8); (10) Using partial least squares regression, similar to the method of Nguyen and Rocke (2002a); (11) using a discretized version of (11); (12) using the method of Beer et al. (2002) A Simulation Study We compared each of the methods we proposed above on two simulated datasets. (See Data S1-S4.) The first simulated dataset X had 5,000 genes and 100 samples. All expression values were generated as standard normal random numbers with a few exceptions. Genes 1–50 in samples 1–50 had a mean of 1.0. We randomly selected 40% of the samples to have a mean of 2.0 in genes 51–100, 50% of the samples to have a mean of 1.0 in genes 101–200, and 70% of the samples to have a mean of 0.5 in genes 201–300. We then generated survival times. The survival times of samples 1–50 were generated as normal random numbers with a mean of 10.0 and a standard deviation of 2.0, and the survival times of samples 51–100 were generated as normal random numbers with a mean of 8.0 and a standard deviation of 3.0. For each sample, a censoring time was generated as a normal random number with a mean of 10.0 and a standard deviation of 3.0. If the censoring time turned out to be less than the survival time, the observation was considered to be censored. Finally, we generated another 5000 × 100 matrix of test data X˜I, which was generated the same way X was generated. Survival times for X˜I were also generated in an identical manner. We defined samples 1–50 as belonging to “tumor type 1” and samples 51–100 as belonging to “tumor type 2.” Thus, a successful subgroup discovery procedure should assign samples 1–50 to one subgroup, and samples 51–100 to the other subgroup. We applied the methods discussed above to identify these subgroups (and predict survival) for the simulated dataset. This simulation was repeated ten times. The results are shown in Table 3. The first column of the table shows how many samples were misclassified when the dataset was originally divided into two subgroups. The second column shows the number of crossvalidation errors that occurred when the nearest shrunken centroids model was applied to these putative class labels. The third column shows the number of incorrectly labeled samples when the optimal nearest shrunken centroids model was used to assign labels to the samples in the test data X˜I. The final column is the value of R 2 obtained by fitting a Cox proportional hazards model to the predicted class labels for the test data (or by fitting a Cox model to γ^ in the case of methods 4 and 6). Table 3 Comparison of the Different Methods on Our Simulated Data The methods are (1) assigning samples to a “low-risk” or “high-risk” group based on their median survival time; (2) using 2-means clustering to identify two subgroups; (3) using 2-means clustering based on the genes with the largest Cox scores; (4) using the supervised principal components method; (5) using 2-means clustering based on the genes with the largest PLS-corrected Cox scores; (6) using the continuous predictor . Each entry in the table represents the mean over 10 simulations; the standard error is given in parentheses In the first simulation, we found that the fully supervised and the fully unsupervised methods produced much worse results than the semi-supervised methods. (For each iteration of the “median cut” method, the crossvalidation error was minimized when all of the observations were assigned to the same class. Hence, each such model had no predictive power, and the value of R 2 was zero for each iteration. If we had chosen a smaller value of the tuning parameter Δ, the procedure would have performed better, although not significantly better.) The continuous predictor based on supervised principal components performed nearly as well as the methods based on semi-supervised clustering. Next, we performed a second simulation. The second simulated dataset X had 1000 genes and 100 samples. All expression values were generated as Gaussian random variables with a mean of zero and a variance of 1.5, although again there were a few exceptions. Genes 1–50 had a mean of 0.5 in samples 1–20, a mean of 0.6 in samples 21–40, a mean of 0.7 in samples 41–60, a mean of 0.8 in samples 61–80, and a mean of 0.9 in samples 81–100. And again, we randomly selected 40% of the samples to have a mean of 2.0 in genes 51–100, 50% of the samples to have a mean of 1.0 in genes 101–200, and 70% of the samples to have a mean of 0.5 in genes 201–300. To generate the survival time of each “patient,” we took the sum of the expression levels of the first 50 genes and added a Gaussian noise term with variance 0.01. There was no censoring mechanism for the second simulation. We also generated another 1000 × 100 matrix of test data using an analogous procedure. Under this model, there are actually five “tumor subgroups.” However, we still used 2-means clustering on this simulated dataset in order to evaluate the performance of our methods when the number of clusters is chosen incorrectly. Thus, in this simulation, it does not make sense to talk about the number of “misclassification errors;” we can only compare the methods on the basis of their predictive ability. We applied the six different methods to this new simulated dataset and repeated this simulation ten times; the results are shown in Table 4. The supervised principal component method is the clear winner in the second simulation study. The semi-supervised methods performed poorly because there were a large number of subgroups and there was a considerable overlap between subgroups. This example demonstrates that the supervised principal component method performs well regardless of the number of tumor subclasses and that it seems to perform especially well when survival is an additive function of the expression level of certain genes. Table 4 Comparison of the Different Methods on Our Simulated Data The methods are (1) assigning samples to a “low-risk” or “high-risk” group based on their median survival time; (2) using 2-means clustering to identify two subgroups; (3) using 2-means clustering based on the genes with the largest Cox scores; (4) using the supervised principal components method; (5) using 2-means clustering based on the genes with the largest PLS-corrected Cox scores; (6) using the continuous predictor . Each entry in the table represents the mean over 10 simulations; the standard error is given in parentheses Discussion One important goal of microarray research is to develop more powerful diagnostic tools for cancer and other diseases. Consider a hypothetical cancer that has two subtypes. One subtype is known to spread much more rapidly than the other subtype, and hence must be treated much more aggressively. We would like to be able to diagnose which type of cancer patients have and give them the appropriate treatment. If it is known that two such subtypes of a certain cancer exist, and if we have a training set where it is known which patients have which subtype, then we can use nearest shrunken centroids or other classification methods to build a model to diagnose this cancer in future patients. However, in many cases, we do not know how many subtypes are present, nor do we know which patients belong to which subgroup. Thus, it is important to develop methods to identify such subgroups. Unsupervised methods, such as hierarchical clustering, are popular techniques for identifying such subgroups. However, there is no guarantee that subgroups discovered using unsupervised methods will have clinical significance. An alternative is to generate class labels using clinical data. The simplicity of the approach of dividing the patients into two subclasses based on their survival time is attractive, and there is evidence that this procedure can successfully predict survival. Indeed, this procedure produced a significant predictor of survival in four different datasets, suggesting that this approach has some utility. However, as noted in the Introduction, subgroups identified in this manner may not be biologically meaningful. When we applied this model to the DLBCL data described earlier, the misclassification error rate for the shrunken centroids model was very high (around 40%), so a diagnosis based on this procedure is likely to be inaccurate. Supervised clustering methods can overcome these problems. We have seen that if we selected significant genes prior to clustering the data, we could identify clusters that were clinically relevant. We have also seen how knowledge of these clusters could be used to diagnose future patients. This supervised clustering methodology is a useful prognostic tool. It is also easy to interpret. However, it has certain shortcomings as well. Recall our conceptual model shown in Figure 1. Patients with tumor type 2 live longer than patients with tumor type 1 on average, but there is still significant variability within each tumor type. Even if we can diagnose a patient with the correct tumor type 100% of the time, the prognosis of the patient may be inaccurate if the variability in survival time within each tumor type is large. Thus, it would be desirable to find a continuous predictor of survival that accounts for this within-group variability. One possible such predictor is our supervised principal components procedure. This procedure used the principal components of a subset of the expression matrix X as a predictor of patient survival. The chosen subset contained the genes with the largest Cox scores. This method could also be used to detect cancer subtypes, since the principal components will presumably capture the variation that exists between subtypes. It is also capable of identifying variation within these subtypes, which, as discussed above, cannot be identified using supervised clustering. We showed that this procedure could produce a stronger predictor of survival than the discrete predictor based on supervised clustering. We compared our methods to several previously published methods for predicting survival based on microarray data. In general, our methods performed significantly better than these existing methods. In particular, our supervised principal components method gave the best results on three of the four datasets. (It performed slightly worse than our γ^ method on the DLBCL data, but it still outperformed almost all of the other methods.) Furthermore, each of our proposed methods was a significant predictor of survival (at p = 0.05) for all four datasets, which was not true for any of the other methods. Finally, if we consider only discrete predictors of survival, our semi-supervised clustering methods performed better than the other models on at least three of the four datasets. Another important advantage of our methods is that they select a subset of the genes to use as predictors. The methods of Nguyen and Rocke (2002a) and Li and Luan (2003), by contrast, require the use of all (or a large number) of the genes. If we can identify a small subset of genes that predict the survival of cancer patients, it may be possible to develop a diagnostic test using immunostaining or reverse transcriptase PCR. However, such a test would not be feasible if hundreds or thousands of genes were necessary to make the diagnosis. Throughout this study, we have used survival data to help us identify genes of interest. However, other clinical variables could also be used, such as the stage of the tumor, or whether or not it has metastasized. Rather than ranking genes based on their Cox scores, one would use a different metric to measure the association between a given gene and the clinical variable of interest. For example, suppose we wished to identify a subgroup of cancer that was associated with a high risk of metastasis. For each gene, we could compute a t-statistic comparing the expression levels in the patients whose cancer metastasized to those in the patients with no metastasis. Tusher et al. (2001) described methods for generating such “significant gene lists” for a variety of possible clinical variables. Many of these methods are implemented in the significance analysis of microarrays software package (Chu et al. 2002). Information about the risk of metastasis (and death) for a given patient is essential to treat cancer successfully. If the risk of metastasis is high, the cancer must be treated aggressively; if the risk is low, milder forms of treatment can be used. Using DNA microarrays, researchers have successfully identified subtypes of cancer that can be used to assess a patient's risk profile. Our results show that semi-supervised learning methods can identify these subtypes of cancer and predict patient survival better than existing methods. Thus, we believe they can be a powerful tool for diagnosing and treating cancer and other genetic diseases. Materials and Methods Overview of nearest shrunken centroids The nearest shrunken centroids procedure calculates the mean expression of each gene within each class. Then it shrinks these centroids toward the overall mean for that gene by a fixed quantity, Δ. Diagonal linear discriminant analysis (LDA) is then applied to the genes that survive the thresholding. Details are given in Tibshirani et al. (2002). It has successfully classified tumors based on gene expression data in previous studies. In one experiment, there were a total of 88 patients, each of which had one of four different types of small round blue cell tumors . Nearest shrunken centroids classified 63 training samples and 25 test samples without a single misclassification error (Tibshirani et al. 2002). Generation of “median cut” class labels We created two classes by cutting the survival times at the median survival time (2.8 y). Any patient who lived longer than 2.8 y was considered to be a “low-risk” patient, and any patient that lived less than 2.8 y was considered to be a “high-risk” patient. In this manner, we assigned a class label to each observation in the training data. Unfortunately, many of the patients' survival times were censored, meaning that the individual left the study before the study was completed. When this occurs, we do not know how long the patient survived; we only know how long the patient remained in the study prior to being lost to follow-up. If an observation is censored, we may not know to which class it belongs. For example, suppose that the median survival time is 2.8 y, but that a patient left the study after 1.7 y. If the patient died in the interval between 1.7 y and 2.8 y, then the patient should be assigned to the “high-risk” group. Otherwise, the patient should be assigned to the “low-risk” group. However, there is no way to determine which possibility is correct. Based on the Kaplan-Meier survival curve for all the patients, we can estimate the probability that a censored case survives a specified length of time (Cox and Oakes 1984; Therneau and Grambsch 2000). For example, suppose that the median survival time is 50 months and a patient left the study after 20 months. Let T denote the survival time of this patient. Then, using the Kaplan-Meier curve, we can estimate p(T>50) and p(T>20). Then we can estimate p(T>50|T>20) as follows: and, of course, In this manner, we can estimate the probability that each censored observation belongs to the “low-risk” and “high-risk” classes, respectively. However, it is still unclear how we would train our classifier based on this information. Nearest shrunken centroids is a modified version of LDA. It is described in detail in Hastie et al. (2001b). Like most classification techniques, LDA assumes that the class labels of the training observations are known with complete certainty. The version of LDA described in Hastie et al. (2001b) and most other books cannot handle probabilistic class labels, where there is a certain probability that a training observation belongs to one class, and a certain probability that it belongs to a different class. We will now describe a simple modification of LDA that can be trained based on this type of data. It is similar to a technique described in McLachlan (1992) for training an LDA classifier when some of the training observations are missing. Let {x i} denote the set of input variables, and let {y i} represent the corresponding response variables. Also, let g represent the number of discrete classes to which the y is may belong. (If we are dividing the training data into “low-risk” and “high-risk” patients, then g = 2.) When we perform LDA when all of the y is are known, the problem is to fit the mixture model (Generally, each 𝒻i is a Gaussian density function, and the θis correspond to the mean of the observations in each class. The πis correspond to “prior” probabilities that an observation belongs to class 𝒾.) In this case, we must fit this model on the basis of the classified (uncensored) training data, which we denote by t, and the unclassified (censored) feature vectors x 𝒿 ( 𝒿 = 𝓃+1, …,𝓃+𝓂), which we denote by t 𝓊. (Also, note that Φ = (π′,θ′)′ denotes the vector of all unknown parameters.) We define the latent variables 𝓏ij to be equal to one if the 𝒿th observation belongs to the 𝒾th class, and zero otherwise. Then the complete-data log likelihood is The EM algorithm is applied to this model by treating z j(𝒿 = 𝓃+1,…, 𝓃+𝓂) as missing data. It turns out to be very simple in the case of LDA. The E-step is effected here simply by replacing each unobserved indicator variable 𝓏ij by its expectation conditional on x j. That is, 𝓏ij is replaced by the estimate of the posterior probability that the 𝒿th entity with feature vector x j belongs to G i(𝒾 = 1, …,G, 𝒿 = n + 1, …,n+m) (McLachlan 1992). We take the initial estimates of 𝓏ij to be the earlier estimate that the 𝒾th censored observation belongs to class 𝒿 based on the Kaplan-Meier curve. The estimates of πi and μi in the M-step are equally simple: and where In these expressions, τi(x;Φ) is the posterior probability that the 𝒿th entity with feature vector x j belongs to G i, or, in other words, We continue these imputations until the algorithm converges. In practice, one imputation seems to be sufficient for most problems, since each imputation is computationally intensive, and additional imputations did not seem to change the results significantly. Diagnosing patient survival via supervised clustering We calculated the Cox scores of each gene based on the 160 training observations, and obtained a list of the most significant genes. Then we performed 2-means clustering on these 160 observations using the genes with the largest absolute Cox scores and obtained two subgroups. We repeated this procedure multiple times with different numbers of genes. For each such clustering, we trained a nearest shrunken centroid classifier to assign future patients to one subgroup or the other and examined the crossvalidation error rate. The problem of choosing the number of genes on which to perform the clustering is more complicated than it appears. The obvious way to choose the optimal number of genes on which to cluster is to simply minimize the crossvalidation error rate of the nearest shrunken centroids model based on the clustering. This works up to a certain point. It is possible that the clustering procedure will identify a cluster that is unrelated to survival. (Since we are clustering on the genes with the highest Cox scores, this is unlikely to occur. However, it is still possible, especially if the number of genes on which we are clustering is large.) Thus, we needed to build a safeguard against this possibility into our procedure. After performing clustering based on a given set of high-scoring genes, we performed a log-rank test to determine if the resulting clusters differed with respect to survival. If they did not, the clustering was discarded without further analysis. An outline of the procedure follows: (1) Choose a set G of possible values of Γ. (2) Let p min = 1 and e min = 1. (3) For each Γ in G, do the following: (4) Perform k-means clustering using only those genes with absolute Cox scores greater than Γ. (5) Perform a log-rank test to test the hypothesis that the k clusters have different survival rates. Call the p-value of this test p. (6) If p≥p min, then return to step 3. (7) Fit a nearest shrunken centroids model based on the clusters obtained in step 3. Calculate the minimum crossvalidation error rate across all values of the shrinkage parameter, and call it e. (8) If e<e min, then let Γbest = Γ, and return to step 3. Otherwise return to step 3 without changing the value of Γbest. The optimal value of Γ is taken to be the value of Γbest when this procedure terminates. Several comments about this procedure are in order. First, note that we did not recalculate the Cox scores at each fold of the crossvalidation procedure. We calculated them only once, using all of the patients in the dataset. There are several reasons for doing this. Recalculating the Cox scores at each fold would be extremely expensive computationally. Moreover, we found that the Cox score of a given gene varied depending on the number of patients (and which patients) we included in the model. Thus, if a given value of Γ produced a low crossvalidation error rate, there was no guarantee that a model based on the full dataset using this value of Γ would produce good results, since the model based on the full dataset may use a different list of genes. Other studies have found that using the entire dataset to produce a “significant gene list” prior to performing crossvalidation can produce more accurate predictions (van't Veer et al. 2002). Also, the set G was left unspecified in the procedure. The choice of which (and how many) possible values of Γ to include in G depends on the problem at hand, as well as the computational power available. As a default, we recommend trying 100 evenly spaced values of Γ between the 90th percentile of the Cox scores and the maximum of the Cox scores. However, the optimal Γbest varies greatly from dataset to dataset, so we recommend trying several different forms of G if adequate computing power exists. Furthermore, note that when we calculated the p-value of the log-rank test after performing the original clustering, we insisted not only that the p-value be significant, but also that it be lower than the best p-value obtained thus far. The reasons for this are twofold. First, experience suggests that if a given set of genes produces a good clustering on the training data (“good” defined as having a low p-value from a log-rank test), then it is likely to produce a good clustering on the test data. (We offer no theoretical or biological justification for this statement; it simply represents our experience. However, we have observed this result a sufficient number of times to convince us that it is not coincidental.) Moreover, this speeds up the algorithm substantially. Calculating the nearest shrunken centroids crossvalidation error rate for a given clustering is the slowest part of the procedure; the time required to perform the clustering and calculate the log-rank statistic is insignificant in comparison. Thus, by only considering clusterings which produce a log-rank statistic with a small p-value, we allow the set G to be much larger than would be feasible otherwise. Finally, the number of clusters k was unspecified in the procedure. We have experimented with some algorithms to choose the value of k automatically, but without success. If possible, we recommend that the value of k be chosen based on prior biological knowledge. (Perhaps one could first perform hierarchical clustering, examine a dendogram of the data, and visually search for major subgroups.) If this is not possible, we recommend trying several different small values of k and choosing the one that gives the best results. (Our experience suggests that choosing k = 2 will give good results for almost all datasets.) Supervised principal components As above, let X be the p×n matrix of expression values, for p genes and n patients. Let x ij denote the expression level of the 𝒾th gene in the 𝒿th patient. Assume that each patient has one of two possible underlying tumor types. Without loss of generality, assume that patients 1, …,m have tumor type 1, and that patients m + 1,…,n have tumor type 2. Then assume that the genetic profiles of the two tumor types are distinct from one another, which is equivalent to assuming that the joint distribution of (x 1j, …,x pj) is different for 1 ≤ 𝒿 ≤ 𝓂 than it is for 𝓂 + 1 ≤ 𝒿 ≤ 𝓃. Thus, if we choose constants {a i}pi=1, the distribution of ∑pj=1  ajxij will be different for 1 ≤ 𝒿 ≤ 𝓂 than it is for 𝓂 + 1 ≤ 𝒿 ≤ 𝓃. (Obviously, this is not true for all values of {ai}pi=1. For example, if we let ai = 0 for all 𝒾, then this statement will not hold. However, it will generally be true unless we deliberately choose a pathological set {ai}pi=1.) In particular, consider the singular value decomposition of X: where U is a p × 𝓃 orthogonal matrix, D is an 𝓃 × 𝓃 diagonal matrix, and V is an 𝓃 × 𝓃 orthogonal matrix (Horn and Johnson 1985). Then the matrix V can be written as In other words, for a given column of V, each row of V is a linear combination of the expression values in the corresponding column of X. Thus, by the argument in the preceding paragraph, rows 1 through 𝓂 should have a different distribution than rows 𝓂 + 1 through 𝓃. Hence, we propose that the first few columns of V be used as continuous predictors of survival for each patient. Formally, Moreover, suppose that we have an independent test set X˜I. Then let where U and D are the same as in equation (11) (i.e., derived from the singular value decomposition of the training data). In this case, the first few columns of V^I can be used to estimate survival for the patients in the independent test set. The reason for choosing the first few columns of V is because the matrix U was chosen so that XTu 1 has the largest sample variance amongst all normalized linear combinations of the rows of X (Hastie et al. 2001b). (Here, u 1 represents the first column of U.) Hence, assuming that variation in gene expression accounts for variation in survival, we would expect that XT u1 captures a large percentage of the variation in survival. (Indeed, in some simple models, it can be proven that equation [11] is the best possible predictor of survival; see Protocol S1.) In theory, we could calculate V using the entire dataset X, and the rows of V would have different distributions depending on the tumor type of the corresponding patient. In practice, however, many of the genes in X are unrelated to survival, and if we use the entire dataset X to compute V, the quality of the resulting predictor is poor. We can overcome this difficulty by using only the genes with the largest Cox scores. Formally, we construct a matrix X′ consisting of only those genes whose Cox scores are greater than some threshold Γ, and take the singular value decomposition of X′. To choose the optimal value of Γ, we employ the following procedure: (1) Choose a set G of possible values of Γ. (2) For each Γ in G, split the training data into k random partitions (i.e., perform k-fold crossvalidations). For most problems (and for the rest of this discussion), we can let k = 10. (3) For each crossvalidation fold, take a singular value decomposition of X, leaving out one of the 10 partitions for validation purposes. Use only those genes with absolute Cox scores greater than Γ. (4) Calculate υ^ for the 10% of the data that was withheld, as described above. (5) Fit a Cox proportional hazards model to υ^, and calculate the chi-square statistic for the log-rank test associated with this model. Denote the chi-square statistic for the 𝒾th crossvalidation fold by 𝓌i. (6) Average the 𝓌is over the 10 crossvalidation folds. Call this average 𝓌Γ. (7) If 𝓌Γ is greater than the value of 𝓌Γ∗, then let Γ∗ = Γ and 𝓌Γ∗ = 𝓌Γ. (8) Return to step 2. The set G is left unspecified in the procedure. As a default, we recommend trying 30 evenly spaced values of Γ between the 90th percentile of the Cox scores and the maximum of the Cox scores, although this recommendation is somewhat arbitrary. In some cases, we can improve the predictive power of our model by taking a linear combination of several columns of V (rather than simply taking the first column of V). Suppose we wish to find a predictor based on the first k columns of V. We can perform the following procedure: (1) Let X denote the training data. Take the singular value decomposition of X = UDVT as described above (after selecting an appropriate subset of the genes). (2) Fit a Cox proportional hazards model using the first k columns of V as predictors. (3) Calculate the matrix V^I for the test data using equation (12) above. (4) Take a linear combination of the first k columns of V^I using the Cox regression coefficients obtained in step 2. Use the resulting sum as a continuous predictor of survival. Software and computational details All computations in this study were performed using the R statistical package, which is available on the Internet at http://cran.r-project.org/. R source code for the procedures described in this paper are available from the authors upon request (see also Data S1–Data S4). These methods will also be implemented in a future version of the PAM microarray analysis package (Tibshirani et al. 2003). (The “median cut” method has been implemented in version 1.20, which is now available.) Supporting Information Data S1 Documentation of Our R Functions This file contains a brief description of the functions contained in the semi-super.R file. (1 KB TXT). Click here for additional data file. Data S2 R Source Code This file contains R functions for implementing the procedures we have described in our study. (6 KB TXT). Click here for additional data file. Data S3 Source Code for Simulation Study 1 This file contains the R source code that we used to perform the first simulation study in our paper. (31 KB TXT). Click here for additional data file. Data S4 Source Code for Simulation Study 2 This file contains the R source code that we used to perform the second simulation study in our paper. (39 KB TXT). Click here for additional data file. Dataset S1 Breast Cancer Expression Data: van't Veer et al. (2002) Study The gene expression data for the breast cancer study of van't Veer et al. (2002). We include only the expression levels of 4,751 genes identified in the study whose expression varied (2.9 MB CSV). Click here for additional data file. Dataset S2 Breast Cancer Gene Names: van't Veer et al. (2002) Study The names of each of the 4,751 genes in the study of van't Veer et al. (2002). (74 KB CSV). Click here for additional data file. Dataset S3 Breast Cancer Survival Data: van't Veer et al. (2002) Study The clinical data for the study of van't Veer et al. (2002). The first column represents the time until metastasis (or the time until the patient left the study); the second column is 1 if the tumor metastasized and 0 if it did not. (1 KB CSV). Click here for additional data file. Dataset S4 Breast Cancer Expression Data: van de Vijver et al. (2002) Study The gene expression data for the 70 genes in the breast cancer study of van de Vijver et al. (2002). (141 KB CSV). Click here for additional data file. Dataset S5 Breast Cancer Gene Names: van de Vijver et al. (2002) Study The names of the 70 genes in the study of van de Vijver et al. (2002). (1 KB CSV). Click here for additional data file. Dataset S6 Repeated Breast Cancer Samples A single column that is 1 if the patient was included in the earlier study (that of van't Veer et al. [2002]), and 0 if the patient was not included in the earlier study. (1 KB CSV). Click here for additional data file. Dataset S7 Breast Cancer Survival Data: van de Vijver et al. (2002) Study The clinical data for the study of van de Vijver et al. (2002). The format is the same as the format of the earlier file of clinical data. (5 KB CSV). Click here for additional data file. Dataset S8 DLBCL Expression Data The gene expression data for the DLBCL study of Rosenwald et al. (2002). (24.38 MB CSV). Click here for additional data file. Dataset S9 DLBCL Survival Data The clinical data for the study of Rosenwald et al. (2002). The format is the same as the format of the clinical data above. (2 KB CSV). Click here for additional data file. Dataset S10 Lung Cancer Gene Expression Data This is the gene expression data for the lung cancer dataset of Beer et al. (2002). (5.39 MB TXT). Click here for additional data file. Dataset S11 Lung Cancer Survival Data This is the clinical data for the lung cancer dataset of Beer et al. (2002). The format is the same as in the clinical data above. (1 KB TXT). Click here for additional data file. Dataset S12 AML Gene Expression Data This is the gene expression data for the AML dataset of Bullinger et al. (2004). (9.96 MB TXT). Click here for additional data file. Dataset S13 AML Survival Data This is the clinical data for the AML dataset of Bullinger et al. (2004). It has the same format as the clinical data above. (1 KB TXT). Click here for additional data file. Figure S1 Results of Using PLS-Derived Cox Scores in the Supervised Clustering Procedure (8.26 MB TIFF). Click here for additional data file. Figure S2 Plot of Survival Versus the Least Squares Estimate of β˜ for the DLBCL Data (8.33 MB TIFF). Click here for additional data file. Figure S3 Plot of Survival Versus the Least Squares Estimate of γ˜ for the DLBCL Data (8.42 MB TIFF). Click here for additional data file. Protocol S1 Additional Models and Methods (28 KB TEX). Click here for additional data file. Eric Bair was supported by an NSF graduate research fellowship. Robert Tibshirani was partially supported by National Science Foundation Grant DMS-9971405 and National Institutes of Health Contract N01-HV-28183. We thank the academic editor and three reviewers for their helpful comments. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. EB and RT conceived and designed the experiments. EB performed the experiments and analyzed the data. EB and RT contributed reagents/materials/analysis tools. EB wrote the paper. Academic Editor: Todd Golub, Dana-Farber Cancer Institute Abbreviations DLBCLdiffuse large B-cell lymphoma LDAlinear discriminant analysis PLSpartial least squares SVMsupport vector machine ==== Refs References Alizadeh AA Eisen MB Davis RE Ma C Lossos IS Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Nature 2000 403 503 511 10676951 Beer DG Kardia SL Huang CC Giordano TJ Levin AM Gene-expression profiles predict survival of patients with lung adenocarcinoma Nat Med 2002 8 816 824 12118244 Ben-Dor A Friedman N Yakhini Z Class discovery in gene expression data. 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Accessed 12 July 2003 Tusher VG Tibshirani R Chu G Significance analysis of microarrays applied to the ionizing radiation response Proc Natl Acad Sci U S A 2001 98 5116 5121 11309499 van de Vijver MJ He YD van 't Veer LJ Dai H Hart AA A gene-expression signature as a predictor of survival in breast cancer N Engl J Med 2002 347 1999 2009 12490681 van't Veer LJ Dai H van de Vijver MJ He YD Hart AAM Gene expression profiling predicts clinical outcome of breast cancer Nature 2002 415 530 536 11823860 von Heydebreck A Huber W Poustka A Vingron M Identifying splits with clear separation: A new class discovery method for gene expression data Bioinformatics 2001 17 Suppl 1 S107 S114 11472999 Vose JM Current approaches to the management of non-hodgkin's lymphoma Semin Oncol 1998 25 483 491 9728598

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10.1371/journal.pbio.0020108

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020111SynopsisNeuroscienceHomo (Human)Neural Basis of Solving Problems with Insight Synopsis4 2004 13 4 2004 13 4 2004 2 4 e111Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Neural Activity When People Solve Verbal Problems with Insight ==== Body If you're one of those insufferable people who can finish the Saturday New York Times crossword puzzle, you probably have a gift for insight. The puzzles always have an underlying hint to solving them, but on Saturdays that clue is insanely obtuse. If you had all day, you could try a zillion different combinations and eventually figure it out. But with insight, you'd experience the usual clueless confusion, until—voilà—the fog clears and you get the clue, which suddenly seems obvious. The sudden flash of insight that precedes such “Aha!” moments is characteristic of many types of cognitive processes besides problem-solving, including memory retrieval, language comprehension, and various forms of creativity. Although different problem-solving strategies share many common attributes, insight-derived solutions appear to be unique in several ways. In this issue, researchers from Northwestern and Drexel Universities report on studies revealing a unique neural signature of such insight solutions. Insight lights up the brain Mark Jung-Beeman, John Kounios, Ed Bowden, and their colleagues recount the storied origin of the term Eureka!, which Archimedes reportedly shouted upon realizing that water displacement could be used to compute density. Illustrating the strong emotional response elicited by such a sudden insight, Archimedes is said to have run home from the baths in euphoric glee—without his clothes. Among other characteristics that typically distinguish insight from “noninsight” solutions, people feel stuck before insight strikes; they can't explain how they solved the problem and might say they were not even thinking about it; the solution appears suddenly and is immediately seen as correct. But are the neural processes involved in arriving at a solution through insight actually distinct from those related to more mundane problem-solving? Recent findings suggest that people think about solutions, at an unconscious level, prior to solving insight problems, and that the right cerebral hemisphere (RH) appears to be preferentially involved. Jung-Beeman et al. predicted that a particular region of the RH, called the anterior superior temporal gyrus (aSTG), is likely involved in insight because it seems critical for tasks that require recognizing broad associative semantic relationships—exactly the type of process that could facilitate reinterpretation of problems and lead to insight. To test this hypothesis, Jung-Beeman et al. mapped both the location and electrical signature of neural activity using functional magnetic resonance imaging (fMRI) and the electroencephalogram (EEG). In the first experiment, thirteen people were given three words (pine, crab, sauce) and asked to think of one word that would form a compound word or phrase for each of the words (can you figure it out?). Neural activity was mapped with fMRI while the participants were given 124 similar word problems—which can be solved quickly with or without insight, and evoke a distinct Aha! moment about half the time they're solved. Subjects pressed a button to indicate whether they had solved the problem using insight, which they had been told leads to an Aha! experience characterized by suddenness and obviousness. While several cortical regions showed about the same heightened activity for both insight and noninsight-derived solutions, only the aSTG in the RH showed a robust insight effect. Given that neural activity in this area also increased when subjects first encountered the problem (perhaps reflecting unconscious processing), the authors conclude that the increase does not simply reflect the emotional jolt associated with insight. In a second experiment, 19 new participants engaged in the same type of problem-solving tasks as the first group while their brain waves were measured with an EEG. The researchers then analyzed the EEG recordings to look for differences between insight and noninsight solutions in brain wave activity. The researchers found that 0.3 seconds before the subjects indicated solutions achieved through insight, there was a burst of neural activity of one particular type: high-frequency (gamma band) activity that is often thought to reflect complex cognitive processing. This activity was also mapped to the aSTG of the RH, providing compelling convergence across experiments and methods. Problem-solving involves a complex cortical network to encode, retrieve, and evaluate information, but these results show that solving verbal problems with insight requires at least one additional component. Further, the fact that the effect occurred in RH aSTG suggests what that process may be: integration of distantly related information. Distinct neural processes, the authors conclude, underlie the sudden flash of insight that allows people to “see connections that previously eluded them.”

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2021-01-05 08:21:08

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PLoS Biol. 2004 Apr 13; 2(4):e111

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PLoS Biol

10.1371/journal.pbio.0020111

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020112Book Reviews/Science in the MediaInfectious DiseasesMicrobiologyEubacteriaHomo (Human)When Food Kills Book Reviews/Science in the MediaKrebs John 4 2004 13 4 2004 13 4 2004 2 4 e112Copyright: © 2004 John Krebs.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Food-borne disease kills humans only rarely, although the ramifications and implications of these few deaths for science, regulators, and government are large ==== Body For the estimated 800 million people, living largely in developing countries, without enough food to eat, the main food risk is starvation. But if you ask, ‘When does food actually kill?’ in a country such as the United Kingdom, ‘Not that often’ is the short reply you would give after reading Hugh Pennington's book When Food Kills: BSE, E. coli, and Disaster Science. The two food-borne diseases that occupy much of the book, Escherichia coli 0157 and bovine spongiform encephalopathy (BSE), kill humans very rarely, although the ramifications and implications of these few deaths for science, regulators, and government are large. As Pennington clearly explains, there is still much uncertainty in the science of BSE, and the eventual UK death toll from the human form may be as low as a few hundred, with even the most pessimistic expert assessments putting the upper bound as fewer than 5,000. Food-borne E. coli 0157 kills fewer than a dozen people a year in the UK. Whilst each death is a terrible tragedy and an indescribably harrowing experience for those close to the victim, these figures are small when compared with other ways in which food kills. Epidemiologists estimate that the dietary contributions to cardiovascular disease and cancer between them kill more than 100,000 people a year in Britain. Yet we hear much more about BSE and E. coli as food risks. For instance, a recent study by the King's Fund (http://www.kingsfund.org.uk/pdf/healthinthenewssummary.pdf) reports that the rate of news coverage in the UK of a death from variant Creutzfeldt-Jakob disease, the human form of BSE, is nearly 23,000 times that for a death from obesity. In his characteristically diverting and obscurely erudite way, Pennington describes this discrepancy between public perception and magnitude of risk by referring to an article on railway accidents published in 1859 by one Dionysius Lardner. The systematic and much more revealing analyses of risk perception by psychologists such as Paul Slovic over the past 25 years do not get a mention. In fact, one of the hallmarks of Pennington's style is his enthusiasm for taking his reader down little-known historical byways. Whether it be the drowning (possibly suicide) of King Ludwig II of Bavaria in the Starnberger See or the treatment of James Norris in Bethlehem Lunatic Asylum in 1814, Pennington has an almost endless supply of anecdotes to provide peripheral colour to his main narrative. Indeed, on some occasions his delight in the detail makes it hard to see where the main narrative is leading, although his aim is to show that similar conclusions can be drawn about risk management in food, transport, oil rigs, and other fields. Anyone who has heard Hugh Pennington speak will know that he has a remarkably direct and engaging style, which he translates into the written word with verve. Already on page 2, he gets us into the mood by referring to a sample from a five-year-old girl sent for analysis at the start of the Lanarkshire E. coli outbreak of 1996: ‘It was a stool. The word carries the impression of firmness, even of deliberate effort in its production. Hers was not’. His laconic sense of humour is also reflected in many of the wittily irrelevant or tangential photographs. My personal favourites are ‘Her Majesty in Gloves’ on page 44 and ‘Turds on Campsite Track’ on page 101. The Lanarkshire E. coli 0157 outbreak, which in late 1996 affected 202 people and killed eight, was very much Pennington's show. He chaired the public enquiry that led eventually to a change in the law, requiring all butchers in the UK handling cooked and raw meat to be licensed. The license itself is less important than the training in food safety management principles that precedes it. The butcher John Barr (and his staff), whose shop was the primary source of the outbreak, apparently did not know that you have to keep raw meat and ready-to-eat products separate to avoid cross-contamination with dangerous pathogens, such as E. coli 0157, that can occur in raw meat. Pennington's authoritative and blow-by-blow account shows failings not only in the butcher (who was, incidentally, Scottish Master Butcher of the Year in 1996), but also in the inspectors who had visited his shop eight times in the previous two years. They had not, apparently, picked up that Barr and his staff employed the same knives for cutting up raw and cooked meat, nor that they used a ‘biodegradable’ cleaning fluid, not realising that this is not the same as ‘biocidal’. The second theme, BSE, is given somewhat shorter treatment. Nevertheless, Pennington goes into some detail in assessing the prion theory of transmissible spongiform encephalopathies (he argues that a nucleic acid is not also involved). He also reviews the sequence of events that led the UK government in the early 1990s to conclude that there was not likely to be a risk to human health and to be slow to change its view. This and the concluding part of the book (see below) draw heavily on the Phillips Enquiry into BSE. Although this enquiry focussed on the response of the UK government, its lessons are relevant to other countries where BSE has emerged in recent years, including many European countries, Japan, Canada, and the United States. In his book Mountains of the Mind, Robert Macfarlane writes: ‘[F]or the hunter risk wasn't optional—it came with the job. I sought risk out, however. I courted, in fact paid for it. This is the great shift which has taken place in the history of risk…. [I]t became a commodity’. Pennington reflects a similar shift in attitude to food risk over the past half century or so. Back in 1938, although it was known that over 2,500 people a year in Britain died from drinking raw milk, the risk was not seen as large enough to warrant legislation to make pasteurisation compulsory. We are now used to much higher standards of food safety, and we can, as a society, enjoy the luxury of fear of relatively minor risks. Nevertheless, there are important lessons from past failures for all involved in food safety (and in other areas of risk management), and Pennington discusses some of these in his concluding chapters. He emphasises the need to continually review the evidence underpinning risk assessments, to communicate effectively with the media, to ensure that actions to manage risks are effectively implemented and audited. Notably, he refers to the importance of inclusiveness and openness about risk and uncertainty in decision-making: ‘[I]f [this] becomes the norm, it will be possible to say that good has come out of tragedy’. Sir John Krebs is a Royal Society Research Professor in the Department of Zoology at the University of Oxford in Oxford, United Kingdom. E-mail: John. Krebs@zoology.ox.ac.uk Book Reviewed Pennington TH (2003) When food kills: BSE, E. coli, and disaster science. Oxford: Oxford University Press. 240 pp. ISBN (hardcover) 0-198-52517-6. US$39.50.

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2021-01-05 08:21:09

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PLoS Biol. 2004 Apr 13; 2(4):e112

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PLoS Biol

10.1371/journal.pbio.0020112

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020113FeatureEcologyEvolutionTeleost FishesTroubled Waters: The Future of Global Fisheries FeatureGewin Virginia 4 2004 13 4 2004 13 4 2004 2 4 e113Copyright: © 2004 Virginia Gewin.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Scientists debating how to assess global fisheries are now including studies of long-term ecosystem effects and options for recovery efforts. But is it possible to both conserve and farm the sea? ==== Body It is becoming increasingly apparent that the vast blue expanse of ocean—the last frontier—is not as inexhaustible as it once seemed. While we have yet to fully explore the reaches of the sea, technology has granted humans the ability to harvest its wealth. We can now fish anywhere, at any depth, for any species. Like the American frontier range's bison and wolf populations brought to the brink of extinction swordfish and sharks are the ocean's most pursued prizes. The disadvantages associated with the depth and dimensions of this open range, however, have long obscured the real consequences of fishing. Indeed, scientists have the formidable challenge of assessing the status of species whose home covers over 75% of the earth. Three recent highly publicized papers—a trifecta detailing troubled waters—call attention to overfishing's contributions to the dramatic declines in global fisheries. Delving into the past, Jeremy Jackson and colleagues (2001) combined local historic records with current estimates to detail the ecological impacts of overfishing, Reg Watson and Daniel Pauly (2001) drew attention to distortions of global catches, and Ransom Myers and Boris Worm (2003) highlighted the depletion of the majority of the largest ocean predators. While some have valid criticisms of the assumptions and aggregation of historic data used to assess the global situation, few disagree with the overriding conclusion that humans have drastically altered not only fish biodiversity, but, increasingly, the ocean itself. Recent reports by the United Nation's Food and Agriculture Organization (FAO) which maintains the world's most complete global fisheries database, appear to validate the conclusions of these studies. The most recent FAO report states that 28% of global stocks are significantly depleted or overexploited, and 47% are either fully exploited or meet the target maximum sustainable yield. Only 24% of global stocks are either under- or moderately exploited. As the sea is increasingly harvested, many ecologists wonder how the ecosystem will continue to function (Jackson et al. 2001). Although economic and social considerations often supercede scientific assessments, science will continuously be called upon to deliver management options that will straddle the needs for conservation and production, even in areas where there is only subsistence fishing (Box 1). As scientists debate the details of global fisheries assessment, they are also including studies of the long-term ecosystem effects and options for recovery efforts. Like was done on the open range, shall we conserve or farm the sea—or both? Catches, Collapses, and Controversies The FAO began keeping fisheries records in 1950. Unfortunately, an enormous amount of data comes directly from each country's fishing industry, which is often biased as a result of unreported discarding, illegal fishing, and the misreporting of harvests. For example, mid-level Chinese government officials seeking promotions systematically enhanced China's fisheries numbers in recent years—which inflated and skewed international catch rates. The FAO data show that catches, excluding a recent surge in anchoveta and China's suspect numbers, reached a peak of 80 million metric tons in the late 1980s and have since begun to decline. Regional studies validate these trends. “Most of the line fish around the coast of South Africa are depleted to 5%–15% of pristine levels,” says George Branch, a marine biologist from the University of Cape Town (Cape Town, South Africa). Meryl Williams, Director General of WorldFish in Penang, Malaysia, notes that the Asia-specific database called TrawlBase (www.worldfishcenter.org/trawl/) confirms that the region's commercial species have been depleted to 10%–30% of what they were 30–40 years ago. Obtaining accurate information on highly migratory species is challenging, to say the least. It is not hard to imagine that data quality is the biggest disadvantage to any scientific assessment. Of the 50 managed stocks in the northeast Atlantic Ocean—including invertebrates, sport fishes, and major commercial finfish—data are kept on only one-fifth of the species. There are 250 fish species in the region, but only 55 species are of commercial interest and merit inquiry. “We know next to nothing about noncommercially fished species,” notes Jeff Hutchings, a conservation biologist at Dalhousie University (Halifax, Nova Scotia, Canada). And that is where fisheries have adequate access to current monitoring programs. “With the recent expansion of the Taiwanese and Chinese fleets, we don't have the kind of sampling programs needed for those kinds of fisheries,” says Rick Deriso, a fisheries scientist with the Inter-American Tropical Tuna Commission (IATTC) (La Jolla, California, United States). Couple these inadequacies with previously unknown bycatch rates (i.e., the fish caught in addition to the target catch) and illegal catches, and it is easy to see that the task is formidable. The FAO estimates that roughly one-quarter of the marine commercial catch destined for human consumption—some 18–40 million metric tons of fish—is thrown back in the sea, a harvested catch that is never utilized or counted. It is estimated that the illegal, unreported, and unregulated (IUU) fisheries surpass allowed fishing quotas by 300%. IUU fishers operate in areas where fishing is not permitted, use banned technologies or outlawed net types, or underreport catches. “The IUU fishery for Patagonian toothfish expanded rapidly in the mid-1990s, likely on the order of 20–30 vessels,” says Andrew Constable, an ecological modeler at the Australian Antarctic Division (Kingston, Australia), who also works with the Scientific Committee of the Commission for the Conservation of Antarctic Marine Living Resources (Hobart, Australia). “These rates of IUU fishing could reduce stocks to threshold levels in some areas in two to five years,” he adds. Often overlooked is the inescapable fact that even sustainable harvest rates reduce fish populations quickly. “If the goal is a productive fishery, we're automatically talking about up to a 70% decline in population across the board,” says Deriso. The FAO's Chief of Marine Resource Services, Jorge Csirke, states that “from a stock point of view, there is no way to preserve integrity of wild stocks and exploit them at the same time.” Indeed, the United States' National Marine Fisheries Service (NMFS) considers optimal harvest rates to be between 40%–60% of virgin levels. But once fish populations dip below the 10%–20% mark, declines are of serious concern. Atlantic cod in Canadian waters suffered a total population collapse and are now on Canada's endangered species list (Figure 1). From 2 billion breeding individuals in the 1960s, Atlantic cod populations have declined by almost 90%, according to Hutchings. While advisors called attention to declining cod stocks, Constable notes that by the time a significant declining trend has been detected by traditional catch assessments, stocks are likely to be in poor shape, if not already depleted. Figure 1 Cod in a High Arctic Lake in Canada These cod resemble those of past Atlantic catches. Measuring 47–53 inches (120–135 cm) long and weighing between 44 and 57 pounds (20 and 26 kg), it is easy to see that today's 16–20 inches (40–50 cm) commercially caught cod are less than half this size. (Photo, with permission, by David Hardie, Dalhousie University.) Given the task of compiling data on only the economically important species, fisheries biologists developed a single-species management approach in the 1960s, which assumed that fisheries affect each species in isolation. This approach, although now rife with problems, served the community and the politicians well during the decades of abundant resources. “They brought the approach of single-species management to near-perfection,” says Boris Worm, a marine ecologist at the Institute for Marine Science in Kiel, Germany. A growing discontent with the model, in addition to greater awareness of ecological interactions, however, prompted Worm and his Dalhousie University colleague Ransom Myers to question the sustainability of the single-species approach. Attempting a comprehensive assessment, their widely cited recent paper (Myers and Worm 2003) indicated that the global ocean has lost more than 90% of large predatory fishes, such as marlin, sharks, and rays. However, this new approach to assess fish stocks is not without its critics. Fisheries biologists point out that the nuances of management contained in fisheries data—such as altered fisher behavior, the variable “catchability” of individual species, and altered gear use—were discounted in the Myers and Worm (2003) assessment and led to misinterpretations for some species, notably tropical tunas (Figure 2). A number of tuna biologists have expressed concern that these omissions have left the mistaken impression that all tuna species are among the list of declining predators (Hampton et al 2003). Worm acknowledges that his approach can be improved, but says, “The whole point of our paper was to aggregate species to communities to see what the overall ecosystem is doing.” Figure 2 Pole Fishing for Medium-Sized (40–50 lb or 18.1–22.7 kg) Big-Eye Tuna aboard the Live-Bait, Pole-and-Line Vessel Her Grace (Photo, with permission, by Kurt Schaefer and Dan Fuller, IATTC.) Ecosystem Sustainability Despite the controversy, most agree that the large predators, particularly sharks, skates, rays, and marlin, are in the most dire straits. Unlike other lower-trophic order species, the wholesale removal of top predators has enormous effects on the rest of the ecosystem. One consequence is that overall reproduction rates can potentially suffer. Fish size, gender, and age at maturity have a substantial impact on individual species' reproduction rates. Since larger fish are the most susceptible to fishing, the population's age structure can shift as individuals, particularly females, are fished out. For example, a 23-inch (59-cm) female vermilion rockfish can produce 17 times the young of a 14-inch (36-cm) fish. Given uncertainties with population dynamics, the fact that basic biological data are missing makes the job even harder. While knowledge of these components is still quite spotty, tuna inventories, for example, have started collecting gender data on catches. Daniel Pauly, a fisheries biologist at the University of Vancouver (Vancouver, British Columbia, Canada), has shown that increased fishing has caused the industry to “fish down the food web,” or systematically move to lower trophic levels over time as higher ones were depleted (Pauly et al. 1998). The impact to ecosystems is only beginning to be uncovered. “If you fish out an abundant predator, the species that it was eating or competing with will increase,” says Worm. “The problem is that the ecosystem may change in such a way that recovery is inhibited because a species niche space is taken or altered.” Fisheries science has taken steps to increase the quality of data in recent years. “Traditional fishery models assumed that a fishery was a homogenous thing—like bacteria in a bottle—rather than a spatially diverse system,” says Pierre Kleiber, a fisheries biologist with the Pacific Islands Fisheries Science Center of the NMFS (Honolulu, Hawaii, United States). He adds that recent work accounts for spatial diversity. In addition, fisheries are now dealing with the inherent uncertainty of their work and are factoring that into models and decision-making. “Uncertainty didn't used to be dealt with at all in formulating fishery management advice,” confirms Keith Sainsbury, a marine ecologist with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) (Clayton, South Victoria, Australia), adding that its absence gave rise to an awful lot of troubles. “Traditional models tended to assume perfect data with no holes in it,” says Kleiber. “Now we've tried to craft a model to fit the realities of missing data.” As well as incorporating spatial diversity and uncertainty, researchers are beginning to comprehend the ecological damage caused by different types of fishing gear. Indeed, trawling the bottom of the seafloor for groundfish can destroy a half-acre footprint of habitat (Figure 3). Detailed reports document that, depending on the habitat's stability, bottom trawling can not only remove fish from seafloor habitats, but alter bottom relief such that it compromises the ability of other fish to survive (NRC, 1002). In Australia, for example, lingcod rely on undisturbed bottom relief to lay their eggs, while other groundfish species depend on complex seafloor habitats for the majority of their food. Figure 3 The Effect of Trawling the Seafloor for Groundfish (A) The coral community and seabed on an untrawled seamount. (B) The exposed bedrock of a trawled seamount. Both are 1,000–2,000 meters (1094–2188 yards) below the surface. (Photo, with permission, by CSIRO Marine Research.) “Science is getting more realistic, but it is getting more difficult,” says Branch. Ecological models are far more complex than traditional fisheries models, says Csirke, adding that more model variables make it more difficult to apply to fisheries, an industry whose focus is, understandably, not conservation. Despite its incorporation into national fisheries policies, ecosystem-based management remains a loosely defined term. It is not a well-defined concept because it is not possible to optimize every species, says Deriso. An additional concern to scientists is that of biomass resilience in the face of environmental changes. Francisco Chavez, a biologist with the Monterey Bay Aquarium Research Institute (Moss Landing, California, United States), recently demonstrated that over a 25-year period, warmer and cooler Pacific waters tilt the distribution of anchoveta versus sardines, both open-ocean dwellers (Chavez et al. 2003). Indeed, El Niño influenced the crash of the heavily fished Peruvian anchoveta industry in the late 1970s. These examples illustrate how susceptible fisheries are to environmental fluctuations. When the biomass of a population is reduced, it is much more sensitive to environmental change. We do not know how environmental fluctuations like these will affect the natural production of young fish, says Kleiber, expressing the concern that without a better understanding of climate, fisheries scientists end up trying to estimate moving targets. In the end, many scientists have their doubts about the influence of science on decision-making. “My personal view is that it's naïve to think that modifying and improving models will necessarily lead to improved natural resource management,” says Simon Jennings, a fisheries biologist with the United Kingdom's Centre for Environment, Fisheries and Aquaculture Science in Lowestoft. Indeed, the International Council for the Exploration of the Seas (Copenhagen, Denmark) recently recommended a total ban on North Sea and Irish Sea cod stocks, based on single-species assessment. Although the more intensive ecosystem-based models could not have produced a more stringent recommendation, politicians allowed harvests at roughly half of last year's catch. To Conserve or to Farm? While lowering fisheries' effort seems the most logical approach to the recovery of depleted fisheries, social and economic concerns often stymie political action. Yet demand for seafood continues. Therefore, scientists also are investigating both conservation and alternative production options. Given the social, economic, and political problems associated with that, managers have often used closures to help a hard-hit species recover. In many cases, however, the recovery time for exploited species is longer than once thought (Hutchings 2000). “Based on the available information, it is not unusual for fish populations to show no or little recovery even after 15 years,” says Hutchings. “All else being equal, we predict the earlier the age of maturity, the faster the rate of recovery,” he adds. And that depends on environmental conditions as well. “In the case of Antarctic species, some overexploited populations remain at less than 5% pre-exploitation abundance after 30 years,” says Constable. One management strategy to recover species is to create marine protected areas (MPAs), zones that restrict all removal of marine life (Box 2). A number of marine ecologists are staunch supporters of MPAs for both conservation and fishery's recovery. What looked like sustainability in the past were fisheries out of our reach—naturally protected areas—says Pauly, adding that our increasing ability to harvest fisheries necessitates the creation of MPAs now. In theory, these areas are refugia for fishes to reproduce, spilling over not only healthy adults but also potentially transporting thousands of viable young—seeding surrounding waters. To date, less than 1% of the ocean's area is protected, which hinders the ability to conclusively determine if spillover rates have the predicted impact on fishery's recovery. A review of 89 studies of MPAs by Ben Halpern, a student at the University of California, Santa Barbara (Santa Barbara, California, United States), demonstrated that the average number of fish inside a reserve increases between 60%– and 150% (Halpern 2003). In addition, 59% of the sites had increased diversity. While the numbers inside the reserves look good, the crucial condition of larval spillover has yet to be proven. Most scientists involved in the debate agree that MPAs should be one component in an overall management scheme, but worry that until the crucial element of fishing effort is resolved, MPAs may just displace the vast industrial fleets. In terms of simply producing fish for global food needs, aquaculture (also known as fish farming) is another, increasingly popular, option. In 2001, the European Union produced 17% of total fishery's production via aquaculture. These numbers are projected to steadily increase, but some question whether aquaculture would be sufficient to supply what has been lost by overexploited fisheries. Concentrated in coastal areas, aquaculture has aroused numerous concerns. Indeed, in developed countries, most operations grow carnivorous fish, which necessitates growing fish to feed fish. While the process has become more efficient in recent years, due in part to a growing reliance on vegetarian diets, it still takes about 3 pounds (1.36 kg) of fish to create 2.2 pounds (1 kg) of desirable meat (Aldhous 2004). Yet, the total catch of food fish continues to grow, as do concerns about nutrient runoff and estuary pollution resulting from aquaculture. Increasingly, coastal residents often complain about the aesthetics of such activities, and there is also new research that indicates that farm-raised fish harbor more cancer-causing pollutants than wild species (Hites et al. 2004). To alleviate many of these concerns, open-ocean aquaculture is now being considered. Indeed, the NMFS is set to propose a Code of Conduct for Offshore Aquaculture, which would open up the 200-mile (322-km) United States Exclusive Economic Zone to net pens seaward of coastal state boundaries and authorities. The Sea Grant program in conjunction with interested business, is also currently assessing the carrying capacity of open-water pens as well as their potential environmental impact. Given increased industrial interest and unchanging demand for seafood, many think farming the sea may be around the corner. Undoubtedly, scientific effort will continue to inform both conservationists and industry about fisheries' capacity and potential recovery options. As attitudes towards fisheries continue to change, increased understanding of the ecological underpinnings should help strike a more informed balance between fisheries' conservation and production. “The big mistake is suggesting that you can manage fish stocks,” says Niels Daan, a biologist with the Netherlands Institute for Fisheries Research (IJmuiden, The Netherlands). “In my opinion, we can only manage human activity.” Box 1. Fisheries Management in Developing Countries While industrial-scale fishing is a growing concern to fisheries biologists, the management of subsistence fishing in developing countries is equally complex. Indonesia alone has 1.3 million fishers. Given the lack of alternative economic options for subsistence fishers, it is much more difficult to reduce fishing because it meets immediate food and resource needs. Local scientists, often lacking in resources, have a much more difficult time assessing the effects and offering advice to governmental fisheries regulators, who have limited political influence. Kenyan researcher Tim McClanahan notes that a main problem is a lack of coordination and respect between traditional and national programs of management. Therefore, he focuses on the fishing gear used. By reconciling the impact of certain fishing gear with traditional knowledge, McClanahan has developed a basis for suggested restrictions deemed acceptable to the local community. Box 2. The Establishment of High Profile MPAs While MPAs are heavily touted as one of the best management tools to address both conservation and fisheries management, few have been enacted. In 2001, following a strong mandate by the Australian Minister to the Environment and overwhelming political will, the Great Barrier Reef Marine Park Authority (GBRMPA) in Australia established a network of marine protected, or no-take, areas as an ecosystem-based management approach. In setting up the reserve networks, scientists determined the most effective areas to protect biodiversity with little impact to productivity. “We tried to avoid peak use areas, while protecting at least one-third of each bioregion and minimizing the impact to users of the Great Barrier Reef Park,” says Phil Cadwallader, Director of Fisheries at the GBRMPA. Off the coast of California, the Channel Islands network of marine reserves, established in April 2003, consists of 13 areas designed to protect biodiversity and critical habitat for breeding fish and to maintain biodiversity. The area has suffered serious declines of red snapper, angel sharks, and abalone, once plentiful off the California coast, over the past decade. Scientists designed the network to protect those productive habitats that would help ensure that larval dispersal was maintained between the individual reserves. Totaling 132 nautical square miles (342 nautical square kilometers), 11 of the areas are no-take reserves—allowing no fishing or harvest of any kind. Virginia Gewin is a freelance writer based in Portland, Oregon, United States of America. E-mail: gewin@nasw.org Abbreviations CSIROCommonwealth Scientific and Industrial Research Organisation FAOFood and Agriculture Organization GBRMPAGreat Barrier Reef Marine Park Authority IATTCInter-American Tropical Tuna Commission IUUillegal MPAmarine protected area NMFSNational Marine Fisheries Service ==== Refs Further Reading Aldhous P Fish farms still ravage the sea 2004 Available at http://www.nature.com/nsu/040216/040216-10.html via the Internet. Accessed 18 March 2004 Chavez FP Ryan J Lluch-Cota SE Niquen M From anchovies to sardines and back: Multidecadal change in the pacific ocean Science 2003 299 217 221 12522241 Halpern B The impact of marine reserves: Do reserves work and does reserve size matter? Ecol Appl 2003 13 Suppl S117 S137 Hampton J Sibert JR Kleiber P Effect of longlining on pelagic fish stocks: Tuna scientists reject conclusions of Nature article 2003 Available at http://www.spc.int/OceanFish/Docs/Research/Myers_comments.htm via the Internet. Accessed 1 March 2004 Hites RA Foran JA Carpenter DO Hamilton MC Knuth BA Global assessment of organic contaminants in farmed salmon Science 2004 303 226 229 14716013 Hutchings JA Collapse and recovery of marine fishes Nature 2000 406 882 885 10972288 Jackson J Kirby M Berger W Bjorndal K Botsford L Historical overfishing and the recent collapse of coastal ecosystems Science 2001 297 629 637 Natural Research Council Effects of trawling and dredging on seafloor habitat 2002 Washington, District of Columbia National Academy Press 136 Myers RA Worm B Rapid worldwide depletion of predatory fish communities Nature 2003 423 280 283 12748640 Pauly D Christensen V Dalsgaard J Froese R Torres F Fishing down marine food webs Science 1998 279 860 863 9452385 Watson R Pauly D Systematic distortions in world fisheries catch trends Nature 2001 414 534 536 11734851 WorldFish Center Sustainable management of coastal fish stocks in Asia project information 2004 Available at http://www.worldfishcenter.org/trawl/index.asp via the Internet. Accessed 29 February 2004

15094811

PMC387278

CC BY

2021-01-05 08:21:09

no

PLoS Biol. 2004 Apr 13; 2(4):e113

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PLoS Biol

10.1371/journal.pbio.0020113

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020115SynopsisBiophysicsNeuroscienceRattus (Rat)A Window into the Brain Demonstrates the Importance of Astrocytes Synopsis4 2004 13 4 2004 13 4 2004 2 4 e115Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Calcium Dynamics of Cortical Astrocytic Network In Vivo ==== Body Did you ever wish you could peek inside someone's brain and see what was going on in there? In research reported in this issue of PLoS Biology, Hajime Hirase and his colleagues at Rutgers University have done just that by focusing their microscope on the brains of living rats in order to examine how certain cells called astrocytes function in vivo. Astrocyte in the cerebral cortex In the longstanding quest to understand how the brain works, scientists have focused on neurons. Neurons conduct action potentials, electrical signals that transmit information in the nervous system. But the brain also contains several other types of cells called glia. (Glia is derived from the Latin for “glue”; these cells were thought to “hold it all together.”) One type of glial cell, the astrocyte (named for its starlike shape), is the most populous cell in the brain and forms an intimate association with neurons and their synapses. It was thought that these cells played a supporting role in the brain, ensuring the proper chemical environment for synapses. Recent research, however, has suggested that astrocytes and other glial cells may play a more significant role. When examining astrocytes cultured in the lab, scientists have observed behavior suggesting that astrocytes can communicate with neurons. Though astrocytes cannot propagate electrical signals like neurons do, they can sense the transmission of such signals at the synapse between two neurons. Furthermore, astrocytes are able to propagate a different kind of signal, a chemical signal based on the release of calcium ions. Calcium signaling is a mechanism of chemical signaling that has been observed in many other cell types. The exact properties of neuron–astrocyte communication, however, are not clear because different preparations of these tissues have yielded different results. It has also not been established that this type of communication occurs in the living brain. To explore such questions, Hirase and colleagues have taken the next step by investigating the calcium signaling properties of astrocytes in the brains of living rats. To accomplish this feat, the researchers used a combination of two technologies. They monitored calcium signaling using a fluorescent dye called Fluo-4, which fluoresces in response to calcium ions. Then they used a special type of microscope called a two-photon laser scanning microscope to visualize the dye. Since this type of microscope uses a lower energy laser, it can image the dye in living tissue without causing harm. The researchers applied the dye to the brains of anesthetized rats, washed out the excess dye that had not penetrated into cells, and then imaged the tissue under the microscope. They first confirmed that they indeed were examining astrocytes and noticed that cells displayed a moderate level of baseline calcium signaling activity. They then used a drug called bicuculline to stimulate neurons and observed a significant increase in the calcium signaling activity of the astrocytes. Because bicuculline only affects neurons, this implies that the astrocytes are responding to the activity of the neurons. The researchers also found that neighboring astrocytes often also displayed coordinated calcium signaling activity, suggesting that the communication among astrocytes is facilitated by increased neuronal activity. This research confirms the complexity of astrocyte signaling functions in the living brain and demonstrates that astrocytes play far more than a supporting role in brain function. It also establishes an important experimental system for scientists seeking to understand how these distinct elements of the brain—neurons and astrocytes—work together. Though this research makes it clear that signaling exists both among astrocytes and between neurons and astrocytes, scientists have yet to understand the effect of this signaling. Some possibilities include regulation of synapse formation, modification of synaptic strength, or more complicated roles in information processing resulting from the coordination of neuronal activity. Future research using this and other systems will help reveal these functions.

0

PMC387279

CC BY

2021-01-05 08:21:14

no

PLoS Biol. 2004 Apr 13; 2(4):e115

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PLoS Biol

10.1371/journal.pbio.0020115

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020117SynopsisBiotechnologyImmunologyMolecular Biology/Structural BiologyMus (Mouse)Phage Display Libraries Identify T Cells Synopsis4 2004 13 4 2004 13 4 2004 2 4 e117Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Mimotopes for Alloreactive and Conventional T Cells in a Peptide-MHC Display Library ==== Body Doctors and researchers often look for the rapid proliferation of T cell populations, key defensive players in the immune system, as a telltale sign that the body is working hard to fend off a foreign threat. Every one of these circulating white blood cells carries a T cell receptor (TCR) that binds to a specific protein, or antigen, when displayed on the surface of a cell. A match between TCR and displayed antigen results in the cell's death and the subsequent expansion of T cell clones, all programmed to recognize the original offending protein. Some TCRs bind and expand in response to pathogenic antigens, such as viral or bacterial proteins. But T cells can also react and proliferate inappropriately in response to the body's own proteins, leading to destructive autoimmune diseases such as multiple sclerosis, which is characterized by immune system attacks on nervous tissue. Self-recognizing TCRs, however, can also target and destroy tumors—though full activation of these T cells is inconsistent and poorly understood. Peptide display Identifying the particular antigen behind an exploding population of T cells is invaluable for finding the source of autoimmune diseases and studying immune responses to cancer. But it's a laborious and time-consuming process, as researchers are faced with the prospect of sifting through millions upon millions of possible matches between TCRs and their prospective antigen epitopes—the part of the antigenic molecule to which the receptor binds. Now, as they report in this issue of PLoS Biology, Frances Crawford and colleagues have developed a novel method for rapidly identifying TCR mimotopes—peptide sequences similar or identical to epitopes that also elicit the immune response—which can be used to determine the antigen of a given T cell population. Working backwards, the team started off with two different T cell clones that had been previously selected for with a known antigen—a peptide called p3K. One clone was derived from mice genetically engineered to have broadly reactive T cells; the other, a conventional clone, was much more sensitive to the precise molecular structure of p3K. Crawford and colleagues then created a “peptide library” comprising more than 30,000 baculoviruses (viruses that selectively target insect cells), each one carrying a slightly different version of the p3K gene, varied in regions of the peptide known to be important for TCR binding. These p3K genes were embedded within a major histocompatibility complex (MHC) gene—a type of cell surface protein that holds displayed antigens and is also important for proper TCR recognition. The team then unleashed their virus library onto insect cells that, once infected, began to produce the specific peptide–MHC complexes encoded on the viral DNA. The insect cells then shuttled these proteins to their surfaces, resulting in a vast array of cells that each displayed a unique variant of the p3K–MHC complex. This “display library” was then incubated with fluorescently labeled TCRs from the two different clones. By observing and isolating the insect cells that lit up, the researchers could see which of the thousands of cells displaying peptide–MHC possessed a mimotope capable of binding a TCR. Because the genetic information about the displayed complex was still stored within the virus-infected cell, the researchers could determine the full peptide sequence responsible for the identified mimotopes. Confirming the effectiveness of their method, the results of the fluorescence experiments echoed the authors' original characterizations about the two populations of T cells. The broadly reactive TCR bound to several different uniquely displayed complexes; it had 20 mimotopes. The conventional TCR, however, bound only to one peptide–MHC complex, an almost perfect match to the original p3K peptide. Though this study was based on a known antigen and epitope (which allowed verification of the method), the baculovirus display library technique described here could easily be used on T cell populations with unknown antigens. With such a tool, researchers could, for example, identify the antigens connected with tumor-fighting T cells and, through inoculation, possibly induce the production of similar T cells in cancer patients who lack them.

0

PMC387280

CC BY

2021-01-05 08:21:14

no

PLoS Biol. 2004 Apr 13; 2(4):e117

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PLoS Biol

10.1371/journal.pbio.0020117

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020119SynopsisEcologyInfectious DiseasesZoologyEukaryotesCrustaceansMolluscsEchinoderms (sea urchins, starfish, etc)Teleost FishesEvaluating Disease Trends in Marine Ecosystems Synopsis4 2004 13 4 2004 13 4 2004 2 4 e119Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. The Elusive Baseline of Marine Disease: Are Diseases in Ocean Ecosystems Increasing? ==== Body After the recent mad cow scare in the United States, 61% of Americans said they would start eating more fish, according to a Wall Street Journal Online poll. The respondents may not know that populations of large predatory fish, such as tuna, swordfish, and marlin, have declined 90% over the past 50 years or that less-prized species are increasingly overfished. Or that ever more fish and seafood species show rising levels of mercury contamination, rendering them unfit for human consumption—and contaminating other organisms in the ocean food chain. Humans are also affecting marine life in unexpected ways, as when large numbers of seals in Antarctica in 1955 and in Siberia in 1987 succumbed to canine distemper virus, presumably contracted from domestic dogs. In 2000, more than 10,000 Caspian seals—which also had contact with domestic dogs—died of the same virus. Such human incursions cause even more damage by exacerbating the effects of naturally occurring parasitic and pathogenic diseases that already wreak havoc as they ripple through the food chain. A dead gorgonian sea fan on a wall in Palau (Photograph, with permission, by Drew Harvell) With recent studies suggesting that disease rates have increased over the past 30 years—and are expected to increase even more, thanks to global climate change—prospects for protecting marine ecosystems depend on understanding the causes and nature of these disease outbreaks. While all indicators point to a real increase in disease rates, scientists have no baseline data to measure these increases against and so cannot directly test the hypothesis that marine diseases are increasing. Now Jessica Ward and Kevin Lafferty report a method that uses the recorded incidence of disease as a proxy for baseline data to identify disease trends in major groups of marine organisms. Ward and Lafferty conducted an online search of 5,900 journals published from 1970 to 2001 for reports of disease in nine taxonomic groups: turtles, corals, mammals, urchins, mollusks, seagrasses, decapods (crustaceans), sharks/rays, and fishes. Their approach takes into account three potentially confounding factors in determining trends in this type of search. Fluctuations in publication numbers could skew results, since an increase in the number of scientific reports published in a particular taxonomy might not reflect a true increase in the incidence of disease; a particularly prolific author could bias the search results by turning up more cases of disease in a population than actually occurred; or a single disease event reported multiple times in different papers could create the impression that disease had suddenly increased. To normalize publication rates over time, Ward and Lafferty used a proportion of disease reports from a given population relative to the total number of reports in that group. To determine whether there was an “author effect,” they removed the most prolific author in each taxonomic group and found that an author's abundant contributions did not skew the results. Finally, they confirmed that a single disease didn't bias their results by removing multiple reports of the same disease from the literature before analyzing the trends. When they analyzed the searches without adjusting for the total number of reports published, Ward and Lafferty found that reports of disease increased for all groups. But when they analyzed the normalized results, they found that trends varied. While there was a clear increase in disease among turtles, corals, mammals, urchins, and mollusks, they found no significant trends for seagrasses, decapods, and sharks/rays. And they found that disease reports actually decreased for fishes. (One explanation for this decrease could be that drastic reductions in population density present fewer opportunities for transmitting infection.) Ward and Lafferty tested the soundness of this approach by using a disease (raccoon rabies) for which baseline data exist and showing that normalized reports of raccoon rabies increased since 1970, just as the disease increased from one case reported in Virginia in 1977 to an “epizootic” outbreak, affecting eight mid-Atlantic states and Washington, D.C., by 1992. The pattern of increased reports, the authors propose, confirms scientists' perceptions about the rising distress of threatened populations and thus reflects a real underlying pattern in nature. The fact that disease did not increase in all taxonomic groups suggests that increases in disease are not simply the result of increased study and that certain stressors, such as global climate change, likely impact disease in complex ways. By demonstrating that an actual change in disease over time is accompanied by a corresponding change in published reports by scientists, Ward and Lafferty have created a powerful tool to help evaluate trends in disease in the absence of baseline data. It is only by understanding the dynamics of disease outbreaks that scientists can help develop sound methods to contain them.

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PMC387282

CC BY

2021-01-05 08:21:09

no

PLoS Biol. 2004 Apr 13; 2(4):e119

utf-8

PLoS Biol

10.1371/journal.pbio.0020119

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020120Research ArticleEcologyInfectious DiseasesZoologyEukaryotesCrustaceansMolluscsEchinoderms (sea urchins, starfish, etc)Teleost FishesThe Elusive Baseline of Marine Disease: Are Diseases in Ocean Ecosystems Increasing? The Elusive Baseline of Marine DiseaseWard Jessica R jrw37@cornell.edu 1 Lafferty Kevin D 2 1Department of Ecology and Evolutionary Biology, Cornell UniversityIthaca, New YorkUnited States of America2United States Geological Survey Western Ecological Research Center, Marine Science InstituteUniversity of California, Santa Barbara, Santa Barbara, CaliforniaUnited States of America4 2004 13 4 2004 13 4 2004 2 4 e1203 11 2003 18 2 2004 Copyright: © 2004 Ward and Lafferty.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Evaluating Disease Trends in Marine Ecosystems Disease outbreaks alter the structure and function of marine ecosystems, directly affecting vertebrates (mammals, turtles, fish), invertebrates (corals, crustaceans, echinoderms), and plants (seagrasses). Previous studies suggest a recent increase in marine disease. However, lack of baseline data in most communities prevents a direct test of this hypothesis. We developed a proxy to evaluate a prediction of the increasing disease hypothesis: the proportion of scientific publications reporting disease increased in recent decades. This represents, to our knowledge, the first quantitative use of normalized trends in the literature to investigate an ecological hypothesis. We searched a literature database for reports of parasites and disease (hereafter “disease”) in nine marine taxonomic groups from 1970 to 2001. Reports, normalized for research effort, increased in turtles, corals, mammals, urchins, and molluscs. No significant trends were detected for seagrasses, decapods, or sharks/rays (though disease occurred in these groups). Counter to the prediction, disease reports decreased in fishes. Formulating effective resource management policy requires understanding the basis and timing of marine disease events. Why disease outbreaks increased in some groups but not in others should be a priority for future investigation. The increase in several groups lends urgency to understanding disease dynamics, particularly since few viable options currently exist to mitigate disease in the oceans. Reports of disease in the scientific literature, normalized to overall publication rates, detect important — and some unexpected — trends of disease in major groups of marine plants, invertebrates, and vertebrates ==== Body Introduction Marine organisms serve as hosts for a diversity of parasites and pathogens. Mortalities affect not only the host population, but can cascade through ecosystems. Loss of biologically engineered habitats such as seagrass beds (Lewis 1933; Taylor 1933) and cascading trophic effects due to removal of consumers (Lessios 1988) can alter community structure. Understanding marine disease and the timing of outbreaks is increasingly important given escalating anthropogenic stressors affecting marine ecosystems. Humans directly affect community structure (e.g., overfishing [Jackson et al. 2001; Myers and Worm 2003]) and facilitate introduction of terrestrial pathogens to marine organisms (e.g., canine distemper virus in Antarctic seals [Bengtson and Boveng 1991]). Human-mediated climate change may also affect disease prevalence. A recent review predicts disease in both terrestrial and marine ecosystems could increase with future climate warming (Harvell et al. 2002). Previous literature reviews suggesting a higher rate of disease outbreaks in the last three decades (Epstein et al. 1998; Harvell et al. 1999), coupled with predictions of future increases due to climate change (Harvell et al. 2002), lend new urgency to understanding causes of marine disease outbreaks. Evidence suggests the increase is real (Harvell et al. 1999), yet lack of baseline data for most marine communities precludes a direct test of the hypothesis. We developed a proxy method to test a prediction of the increasing disease hypothesis: that reports of disease in the scientific literature, normalized to overall publication rates, increased since 1970. We searched an online literature database (ISI Web of Science) and quantified reports of disease in natural populations of marine organisms from 1970 to 2001. Nine marine taxonomic groups were searched: turtles, corals, mammals, urchins, molluscs, seagrasses, deca-pods, sharks/rays, and fishes. Previous analyses of ecological literature specifically assessed trends among scientists such as taxonomic bias (Clark and May 2002) and taxonomic chauvinism (Bonnet et al. 2002) in research. Our proxy method is to our knowledge the first quantitative use of normalized trends in the literature to investigate an ecological hypothesis. In the absence of baseline data, the literature proxy method detects important trends of disease in major groups of marine plants, invertebrates, and vertebrates. Results The largest confounding factor when using literature searches to correlate disease events with time is temporal change in the total number of publications (related to disease or not) on the taxonomic group. To control for changes in total publication, data were normalized using a yearly proportion of disease reports from natural populations relative to total literature inputs for each taxonomic group. Total disease reports, not normalized to literature inputs, increased in all groups (Table 1). However, normalized results varied with taxonomic group. Normalized disease reports increased in turtles, corals, mammals, urchins, and molluscs. No significant trends were detected for seagrasses, decapods, and sharks/rays (though disease occurred in these groups). Counter to the hypothesis, disease reports decreased in fishes (Figure 1). Figure 1 Percent of Literature Reporting Disease over Time in Each Taxonomic Group rs is Spearman's ρ. α is controlled for multiple comparisons with Holm's sequential Bonferroni adjustments. (A) Turtle. (B) Coral bleaching and disease (closed square); coral disease including infectious bleaching (open circle); coral bleaching (asterisk). (C) Mammal. (D) Urchin. (E) Mollusc. (F) Seagrass. (G) Decapod. (H) Shark/ray. (I) Fish. Table 1 Spearman's Rank Correlation Analysis The table shows total reports (not corrected for research effort), normalized reports, and normalized reports with most frequent author removed. rs is Spearman's ρ. α is controlled for multiple comparisons with Holm's sequential Bonferroni adjustments. Bold indicates significance The relevance of our approach hinges on the assumption that an actual change in disease over time is accompanied by a corresponding change in publication frequency by scientists. We evaluated this assumption by testing the protocols with a case in which the baseline was known (raccoon rabies [Rupprecht and Smith 1994]). Normalized reports of raccoon rabies increased since 1970 (see Table 1) just as the disease increased from an index case in Virginia in 1977 to an epizootic affecting eight mid-Atlantic states and the District of Columbia by 1992 (Rupprecht and Smith 1994). Despite improvements in search protocols, use of a literature proxy is limited by the inability to distinguish between an event that did not occur and an event that was not reported. We tested whether particular authors contributed disproportionate primary literature inputs that could bias results. Papers by the most prolific author in each taxonomic group were removed to determine whether there was an “author effect,” and none was observed in any taxonomic group (see Table 1). Multiple reports of a single disease event could also bias the data. Multiple reports were removed from the turtle, coral, urchin, mammal, shark/ray, and seagrass literature. Removal of the reports did not alter the significance of the results; thus, multiple reports in the mollusc, decapod, and fish literature were not removed, owing to the large volume of literature in these groups. Discussion We address an ecological hypothesis, that disease of marine organisms increased since 1970, using a quantitative literature proxy method. Although total reports of marine disease increased over time (Epstein et al. 1998; see Table 1), a parallel increase in publication rates confounds interpretation of this pattern. Our approach normalizes data to overall publication within each group to circumvent this problem. While an increase in disease reports was detected in many taxa, our finding that disease did not increase in all taxa has two important implications. First, the increases were not exclusively the result of increased study of disease by marine biologists. Second, factors such as global change may have complex effects on disease. Although some aspects of global change, such as warming and pollution, are predicted to make hosts more susceptible to infection (Scott 1988; Holmes 1996), some stressors may impact parasites more than their hosts (Lafferty 1997). Signs of infection with coldwater disease in salmonids, for example, occur between 4°C and 10°C and disappear as water temperature increases (Holt et al. 1989). In addition, stressors that depress host population density may reduce density-dependent transmission of host-specific infectious disease by reducing contact rates between infected and uninfected individuals (Lafferty and Holt 2003). New or increasing stressors, such as global warming, could increase disease if stressed hosts are more susceptible to infection. Elevated sea surface temperature due to El Niño events is a common explanation for coral bleaching (Williams and Bunkley-Williams 1990; Hoegh-Guldberg 1999) and may increase coral susceptibility to disease (Harvell et al. 2001). Increases in turtle and mollusc disease also appear temperature-related. Green turtle fibropapilloma tumors are hypothesized to grow rapidly in summer and reach a debilitating size by winter, when cold water temperatures further stress turtles, resulting in winter strandings (Herbst 1994). The geographic range of the oyster parasite Perkinsus marinus extended 500 km north owing to an increase in average winter low temperatures (Ford 1996). Pollution is another ubiquitous and increasing stressor. Bioaccumulation of lipophillic toxins in marine mammals affects the immune system and increases susceptibility to disease (Lafferty and Gerber 2002). Disease could also increase if transmission increases with host density. Some sea urchins experienced increased populations due to overfishing of their predators, and these high-density populations are associated with bacterial disease (Lafferty and Kushner 2000). Regulations such as the United States Marine Mammal Protection Act of 1972 fully protect pinniped populations, and several species have increased in abundance to levels where transmission efficiency would be expected to increase. The decline in infectious diseases of wild fishes over time corresponds to documented reductions in fish populations through intense fishing (Jackson et al. 2001; Myers and Worm 2003). Fisheries that reduce the abundance of a fished species should also reduce infectious disease transmission (Dobson and May 1987). This has been documented in experiments (Amundsen and Kristoffersen 1990) and in observations of parasite declines associated with overfishing (Sanders and Lester 1981). Grouping diseases within taxa could obscure important patterns. For example, the trend for increasing coral disease was driven by coral bleaching (rs = 0.87, p < 0.0001), while infectious coral diseases, including infectious bleaching, did not increase over time (rs = 0.13, p = 0.4934; see Figure 1B). The infectious bleaching literature includes several papers since 1996. To ensure the lack of a significant coral disease trend was not due to multiple papers published on this topic at the end of the time range surveyed, an additional analysis was conducted with all infectious bleaching papers excluded; rs and p values did not change (Table 2). Table 2 Normalized Coral Disease Reports Original data include papers on infectious bleaching. rs and p values are the same for both analyses. Italics indicate changes in proportions after removal of infectious bleaching literature While we did not detect an increase in normalized coral disease reports over time, impacts of disease can be high. The recent shift of dominant corals (Acropora to Agaricia) on reefs due to white band disease was unprecedented in the last 3,000 y (Aronson et al. 2002). Future research should take a finer-scale look at disease, particularly disease impacts, within each taxonomic group. Further investigation is also warranted to determine why some groups showed no temporal pattern in disease reports. We examined temporal trends in disease reports since 1970 to identify groups experiencing increased outbreaks. The strong pattern of increased reports in groups such as turtles, mammals, and urchins reflects perceived changes noted by scientists (Harvell et al. 1999). Trends in other groups, such as seagrasses and fishes, suggest that an increase in disease did not occur across all taxa. Although this proxy approach does not directly test hypotheses of temporal changes in disease, a strong signal likely reflects an underlying pattern in nature. In the absence of baseline data, this is a useful approach for detecting quantitative trends in disease occurrence. Understanding disease dynamics, including trends in disease occurrence, is fundamental to conserve ecosystems faced with rising anthropogenic stresses. Materials and Methods We searched the Science Citation Index Expanded (5,900 journals, ISI Web of Science versions 1.1 and 1.2) for papers published from 1970 to 2001 with titles containing specific host taxonomic strings alone and in combination with a disease string (Table 3). We excluded articles clearly about disease in nonnatural settings, such as hatcheries, aquaculture, and mariculture, or about experimental or laboratory infections. Searches for corals were performed twice to quantify reports of bleaching separately from infectious bleaching (e.g., Vibrio shiloi [Israely et al. 2001]) and disease. Only titles were searched, as online abstracts are not available for many articles prior to 1990. Searching the complete citation would bias results after 1990 because more text of each publication would be searched. Table 3 Taxonomic Groups and Search Strings Abstracts (or entire manuscripts, when necessary) were obtained for articles within the turtle, coral, urchin, mammal, shark/ray, and seagrass literature that appeared to report the same disease event (e.g., multiple reports of the Caribbean Diadema urchin mortality). If more than one paper reported an event, only the earliest published report was included in the analysis. Because significance of results was not altered, multiple reports of disease were not removed from mollusc, decapod, and fish literature owing to the large number of publications returned for each group. Often, returned titles contained part of the search string, but were not relevant (e.g. “crab nebula” when searching “crab”). Modifications to search strings excluded most irrelevant articles, and titles were read to determine relevance. If more than 50 titles were returned, titles were randomly sorted and the greater of 20% (maximum of 200) or 50 returned titles were read. Total relevant articles were calculated as the proportion of relevant articles read times the total number returned. Protocols were tested using raccoon rabies, a disease for which baseline data are available (Rupprecht and Smith 1994). Potential biases were considered and tested. Extensive descriptive or taxonomic work early in the study of a group could bias results against a large number of disease reports. If such a bias existed, one would expect both a large number of disease reports and a large number of nondisease publications in the beginning of the literature survey period. Neither prediction is true—the number of both disease reports and nondisease publications either remains relatively constant or increases through time in all groups. Frequent publishing by one author could bias results. Papers by the most published author in a taxonomic group were removed from the analysis to determine their effect. Papers on a particular “hot” topic could also bias results, particularly if that topic is disease and inflates normalized disease reports late in the survey period. For example, a recent mortality event could increase scientists' awareness of disease, resulting in increased publishing without a concomitant increase in the phenomenon. This likely does not affect our results because (a) disease is not the only “hot” topic experiencing increased publication rates and (b) while multiple papers on disease may be published, not all are reports of disease in natural populations. A 3-y running mean was used to reveal trends obscured by clustered reporting (e.g., a symposium volume on a topic) and time lags between observation and publication (approximately 3 y, determined by comparing event and publication dates). Data were analyzed with Spearman's rank correlation (JMP version 5.0) with α controlled for multiple comparisons using Holm's sequential Bonferroni adjustments. This work was conducted as part of the Marine Disease Working Group organized by D. Harvell and supported by the National Center for Ecological Analysis and Synthesis, a center funded by the National Science Foundation (NSF), the University of California, and the Santa Barbara campus. Thank you to M. Torchin for advising on search strings, to A. Shaw for compiling search results, and to A. Kuris for assessing relevant decapod publications. Comments and discussion from working group participants, D. Harvell, and the Harvell lab are gratefully acknowledged. A. Kuris, K. Phillips, and V. McKenzie provided comments on early drafts. JRW was supported by an NSF Graduate Research Fellowship. KDL was supported, in part, by the NSF through the National Institutes of Health/NSF Ecology of Infectious Disease Program and by the United States Environmental Protection Agency's (EPA) Science to Achieve Results Estuarine and Great Lakes Program through funding to the Pacific Estuarine Ecosystem Indicator Research Consortium, EPA Agreement. However, this paper has not been subjected to any EPA review and therefore does not necessarily reflect the views of the EPA, and no official endorsement should be inferred. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JRW performed the experiments, analyzed the data, and wrote the paper. KDL conceived and designed the experiments and wrote the paper. Academic Editor: Larry Crowder, Duke University ==== Refs References Amundsen PA Kristoffersen R Infection of whitefish (Coregonus lavaretus L. s.l.) by Triaenophorus crassus Forel (Cestoda: Pseudophyllidea): A case study in parasite control Can J Zool 1990 68 1187 1192 Aronson RB Macintyre IG Precht WF Murdoch TJT Wapnick CM The expanding scale of species turnover events on coral reefs in Belize Ecol Monogr 2002 72 233 249 Bengtson JL Boveng P Antibodies to canine distemper virus in Antarctic seals Marine Mammal Sci 1991 7 85 87 Bonnet X Shine R Lourdais O Taxonomic chauvinism Trends Ecol Evol 2002 17 1 3 Clark JA May RM Taxonomic bias in conservation research Science 2002 297 191 192 12117005 Dobson AP May RM The effects of parasites on fish populations: Theoretical aspects Int J Parasitol 1987 17 363 370 3294650 Epstein PR Sherman B Spanger-Siegfried E Langston A Prasad S Marine ecosystems: Emerging diseases as indicators of change 1998 Boston, Massachusetts The Center for Health and the Global Environment, Harvard Medical School 85 Ford SE Range extension by the oyster parasite Perkinsus marinus into the Northeastern United States: Response to climate change? J Shellfish Res 1996 15 45 56 Harvell CD Kim K Burkholder JM Colwell RR Epstein PR Emerging marine diseases—Climate links and anthropogenic factors Science 1999 285 1505 1510 10498537 Harvell D Kim K Quirolo C Weir J Smith G Coral bleaching and disease: Contributors to 1998 mass mortality in Briareum asbestinum (Octocorallia, Gorgonacea) Hydrobiologia 2001 460 97 104 Harvell D Mitchell CE Ward JR Altizer S Dobson A Climate warming and disease risks for terrestrial and marine biota Science 2002 296 2158 2162 12077394 Herbst LH Fibropapillomatosis of marine turtles Annu Rev Fish Dis 1994 4 389 425 Hoegh-Guldberg O Climate change, coral bleaching and the future of the world's coral reefs Marine Freshwater Res 1999 50 839 866 Holmes JC Parasites as threats to biodiversity in shrinking ecosystems Biodiversity Conserv 1996 5 975 983 Holt RA Amandi A Rohovec JS Fryer JL Relation of water temperature to bacterial coldwater disease in coho salmon, chinook salmon, and rainbow trout J Aquatic Anim Health 1989 1 94 101 Israely T Banin E Rosenberg E Growth, differentiation and death of Vibrio shiloi in coral tissue as a function of seawater temperature Aquatic Microbial Ecol 2001 24 1 8 Jackson JBC Kirby MX Berger RD Bjornadal KA Botsford LW Historical overfishing and the recent collapse of coastal ecosystems Science 2001 293 629 638 11474098 Lafferty KD Environmental parasitology: What can parasites tell us about human impacts on the environment? Parasitol Today 1997 13 251 255 15275061 Lafferty KD Gerber L Good medicine for conservation biology: The intersection of epidemiology and conservation theory Conserv Biol 2002 16 593 604 Lafferty KD Holt RD How should environmental stress affect the population dynamics of disease? Ecol Lett 2003 6 654 664 Lafferty KD Kushner D Population regulation of the purple sea urchin, Strongylocentrotus purpuratus , at the California Channel Islands. In: Brown DR, Mitchell KL, Chang HW, editors. Fifth California Islands Symposium 2000 Santa Barbara, California Minerals Management Service 379 381 Lessios HA Mass mortality of Diadema antillarum in the Caribbean: What have we learned? Annu Rev Ecol Systemat 1988 19 371 393 Lewis HF Disappearance of Zostera in 1932 Rhodora 1933 35 152 154 Myers RA Worm B Rapid worldwide depletion of predatory fish communities Nature 2003 423 280 283 12748640 Rupprecht CE Smith JS Raccoon rabies: The re-emergence of an epizootic in a densely populated area Semin Virol 1994 5 155 164 Sanders MJ Lester R Further observations on a bucephalid trematode infection in scallops (Pecten alba ) in Port-Phillip Bay, Victoria Aust J Marine Freshwater Res 1981 32 475 478 Scott ME The impact of infection and disease on animal populations: Implications for conservation biology Conserv Biol 1988 2 40 56 Taylor WR Epidemic among Zostera colonies Rhodora 1933 35 186 Williams EH Bunkley-Williams L The world-wide coral reef bleaching cycle and related sources of coral mortality Atoll Res Bull 1990 335 1 71

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PLoS Biol. 2004 Apr 13; 2(4):e120

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PLoS Biol

10.1371/journal.pbio.0020120

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020121FeatureEcologyInfectious DiseasesMammalsChronic Wasting Disease—Prion Disease in the Wild FeatureBunk Steve 4 2004 13 4 2004 13 4 2004 2 4 e121Copyright: © 2004 Steve Bunk.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Chronic wasting disease in deer is the only prion disease that infects both free-ranging and captive animals -- a situation that greatly complicates efforts to control it ==== Body In 1967, mule deer in a research facility near Fort Collins, Colorado, in the United States apparently began to react badly to their captivity. At least, that was the guess of researchers working on the natural history and nutrition of the deer, which became listless and showed signs of depressed mood, hanging their heads and lowering their ears. They lost appetite and weight. Then they died—of emaciation, pneumonia, and other complications—or were euthanized. The scientists dubbed it chronic wasting disease (CWD), and for years they thought it might be caused by stress, nutritional deficiencies, or poisoning. A decade later, CWD was identified as one of the neurodegenerative diseases called spongiform encephalopathies, the most notorious example of which is bovine spongiform encephalopathy (BSE), more commonly known as mad cow disease. Nowadays, CWD is epidemic in the United States. Although no proof has yet emerged that it's transmissible to humans, scientific authorities haven't ruled out the possibility of a public health threat. The media have concentrated on this concern, and politicians have responded with escalated funding over the past two years for fundamental research into the many questions surrounding this mysterious disease. Quite apart from how little is yet known about CWD, media interest is reason enough to step up investigation of it, says Mo Salman, a veterinary epidemiologist at Colorado State University in Fort Collins. He's been scientifically involved with BSE, since it was first discovered among cattle in the United Kingdom in 1986. He recalls predicting that lay interest in BSE would wane after five years. Instead, the disease was found in the mid-1990s to be capable of killing humans who ate tainted beef. “I was wrong, and it really changed my way of thinking, to differentiate between scientific evidence and the public perception,” Salman admits. “Because CWD is similar to BSE, the public perception is that we need to address this disease, to see if it has any link to human health.” CWD is the only spongiform encephalopathy known to naturally infect both free-ranging and captive animals, a situation that greatly complicates efforts to monitor, control, or eradicate it. Increasing Attention In 2001, the United States' Department of Agriculture (USDA) declared an emergency after CWD was first diagnosed in deer east of the Mississippi River, indicating a potential nationwide problem. This year, the USDA is developing a herd certification program to help prevent the movement of infected animals in the game farming industry. This will bolster monitoring already underway in virtually every state, including postmortem examinations of game killed by hunters and by sharpshooters in mass culling operations. By June 2003, brain tissue from more than 111,000 animals had been sampled in North America, and 629 were found to have tested positive for CWD. That's a small epidemic compared to the thousands of BSE cases detected in cattle in the United Kingdom, but CWD is thought to be slow-spreading and perhaps lurking undiscovered elsewhere. So far, the United States and Canada are the only countries in which it has been identified, apart from a few imported cases in the Republic of Korea, but surveillance has not been thorough in North America and is virtually nonexistent in the rest of the world. Considered 100% fatal once clinical signs develop, CWD has struck three species of the cervid family—mule deer, white-tailed deer, and Rocky Mountain elk—which roam wild and are raised on farms for meat and hunting. It's the only spongiform encephalopathy known to naturally infect both free-ranging and captive animals, a situation that greatly complicates efforts to monitor, control, or eradicate it. The economic costs are hard to quantify, but a 2001 survey by the United States Department of Commerce's Bureau of Census shows that big-game hunters nationwide spend more than US$10 billion annually for trips and equipment. By far, their main target is deer. Wildlife watching of large land mammals, principally deer, drew 12.2 million participants in 2001. The North American Deer Farmers Association represents owners of 75,000 cervid livestock raised for their meat and for velvet antler, a health-food supplement made from antlers. These animals are valued at more than US$111 million. Over the past two years, the federal government's emphasis on CWD has been “quite high” compared to other wildlife diseases, says USDA staff veterinarian Dan Goeldner. “In no small part, that's because the disease has cropped up in new places, and those are states that have political clout.” It has now been found in ten more states beyond what became known as the endemic region of Colorado and neighboring Wyoming (Figure 1). Last year, the USDA received US$14.8 million to monitor and manage the disease, and Goeldner says the department expects to get about US$16 million this year. Figure 1 CWD in North America CWD has been detected in both free-ranging and captive animals in Wyoming, Colorado, South Dakota, Wisconsin, and Saskatchewan; only in captive herds in Montana, Nebraska, Kansas, Oklahoma, Minnesota, and Alberta; and only in wild animals in New Mexico, Utah, and Illinois. (Figure courtesy of Gary Wolfe and the CWD Alliance.) The Prion Diseases Those figures don't include scientific research funded by other organizations, such as the US$42.5 million received by the United States' Department of Defense in 2002 to start up a National Prion Research Program. The prion is the protein-like agent that causes transmissible spongiform encephalopathies (TSEs). Its normal function is uncertain, but when it misfolds into an abnormal or “infectious” form, it causes the microscopic holes and globs of toxic, misshapen protein found in the brains of TSE victims. Unlike viruses, prions don't contain nucleic acids—only protein. Without DNA or RNA to issue biochemical commands, abnormal prions shouldn't be able to convert normal prions to the infectious state, but that's exactly what they do (Box 1). Prion diseases occur in many species. In domestic sheep and goats, prion diseases occur as scrapie, which has a virtually worldwide distribution. North America and Europe have also reported rare cases of TSE in ranched mink. Humans get kuru, Creutzfeldt-Jacob disease (CJD), and Gerstmann-Sträussler-Scheinker syndrome—all rare—and BSE itself manifests in people as a variant form of CJD. Since the United Kingdom outbreak, BSE has been discovered in more than 20 countries, most recently in North America. As public fear rose of possible CWD transmission to humans who eat infected venison, the United States' Centers for Disease Control and Prevention (CDC) released a report last year of its investigation into several deaths among venison eaters who might have had a TSE. The report concluded that none of the deaths could be attributed to venison, but it nevertheless cautioned that animals showing evidence of CWD should be excluded from the human and animal feed chains (Box 2). CWD is the least understood of all the prion diseases. Its origins are unknown and may well never be discovered. The question is largely academic, unless one hypothesis is proven true, that it derives from scrapie. In that case, the knowledge might help in efforts to control the two diseases through herd and flock management. Researchers are working to determine the minimum incubation time of CWD before clinical signs appear, now roughly estimated at 15 months in deer and 12—34 months in elk. They're trying to discover whether CWD strains exist that can affect the length of the disease process and different regions of the brain or that can infect different species, including humans. They are also investigating the period during which the prion is passed on, as well as its modes of transmission. They want to know whether disease reservoirs exist in the bodily fluids of hosts, in the environment, or both. They're racing to develop a diagnostic test that can be performed on live animals, enabling identification of the disease before clinical signs appear, which would eliminate the need to kill thousands of apparently healthy animals in areas where CWD is detected. But among the first things they need to clarify are CWD's distribution across North America and its prevalence. “…the disease has cropped up in new places, and those are states that have political clout.” An Initial Step: Improved Surveillance “Before you can start to control CWD, you need to understand where it is and how much of it you have,” says veterinary pathologist Beth Williams of the University of Wyoming in Laramie. “So I think you really need surveillance.” Research on its pathogenesis and transmission will help to develop better diagnostic tools, which will improve surveillance, adds Williams, who first identified the disease as a TSE more than a quarter-century ago. Colorado State's Salman argues that current surveillance is primarily a series of reactions to reported cases, rather than a systematic strategy designed to determine where and at what prevalence the disease exists and where it's absent. The estimated prevalence is about 1% in elk and 2.5% in deer. But Salman says, “We don't have a good idea of areas in which we are saying we haven't found the disease because these areas are not yet, in my estimation, negative for the disease. Scrapie is a wonderful example of systematic surveillance but, to be fair to the decision-makers and technical people involved with CWD, surveillance on wildlife species is very difficult.” The USDA's Goeldner declares, “We have the goal and the hope to eradicate the disease from the farm population.” But Colorado Department of Wildlife veterinarian and CWD expert Mike Miller warns, “Given existing tools, it seems unlikely that CWD can be eradicated from free-ranging populations once established.” The gold standard of diagnosis is based on examination of the brain for spongiform lesions and abnormal prion aggregation. Suspect animals are decapitated and their bodies incinerated. “This is an approach that nobody wants, including the people who have to implement it,” says wildlife ecologist Michael Samuel, principal investigator in the United States Geological Survey–Wisconsin Cooperative Wildlife Research Unit at the University of Wisconsin in Madison. Nevertheless, when three white-tailed deer shot by hunters in the south-central part of that state during the fall of 2001 were diagnosed with CWD, the state government took swift action. By the spring of 2003, almost 40,000 deer had been sacrificed and sampled for the disease, both within and without a 411 square-mile (1065 square-kilometer) region dubbed the eradication zone. There the goal was to remove as many deer as possible, whereas the plan in contiguous outlying areas was to reduce density to about ten deer per square mile. CWD is thought to spread more efficiently in high-density populations, and normal densities in Wisconsin are 50–100 deer per square mile, about five times that of Colorado and Wyoming. The main objectives of the Wisconsin culling were to discover where the disease existed and its prevalence in affected areas. In the eradication zone, it was 6%–7%, although in the outlying region it was only 1%–2%. Samples elsewhere in the state tested negative. In Search of a Live Assay A key to combating the spread of CWD is to put into widespread use a preclinical diagnostic test on live animals. Miller and colleagues recently developed and validated the first such assay, based on a biopsy of lymphoid tissue, where the infectious agent is known to incubate. They showed that tonsillar biopsies taken from live animals can confirm disease at least 20 months prior to death and up to 14 months before the onset of clinical signs. Although the method is a useful screening tool, it requires much time and training. Each deer must be anaesthetized and blindfolded, placed in a restraint, its mouth held open with a gag, the tonsil visualized with a laryngoscope, and the biopsy taken with endoscopic forceps. Lymphoid tissue sampling was first used as a preclinical test in sheep scrapie. “Many attempts have been made to develop and evaluate tests for live animals, but it is fraught with difficulties,” declares TSE specialist Danny Matthews of the United Kingdom Government's Veterinary Laboratories Agency in Weybridge. He says that a live test for BSE in cattle is likely to be evaluated shortly by the European Food Safety Authority, but warns of a major problem: test samples are collected early in the incubation, whereas brain pathology only arises two to three years later. This creates long delays in determining whether a positive preclinical test result is, in fact, accurate: “How can one do an appropriate evaluation?” Matthews notes that blood appears to be a useful medium for testing scrapie in sheep, but current technology cannot deliver a tool applicable across a range of different scrapie genotypes. “Like sheep, elk and mule deer do have a peripheral pathogenesis, which suggests that the blood test route may have some potential, especially if the genotype variability is more restricted than in sheep.” Transmission Mysteries Scrapie can be vertically transmitted from mother to offspring, either in the womb or from the transfer of infected germ plasm. It also can be transmitted horizontally, from any one animal to another. CWD, the only other known contagious TSE, is thought to be transmitted solely by as-yet-undetermined direct or indirect horizontal contact. It probably is not transmitted through infected feed, as is the case for BSE. A number of scientists are currently on the trail of suspected CWD disease reservoirs. Saliva is a leading candidate, because clinical signs of CWD include excessive thirst, drinking, and drooling. Work with lab animals suggests that the infectious agent might be produced in salivary glands and, if so, it could be transmitted through social interactions. Feces is also a possible reservoir because animals nose in the ground for feed, and urine is yet another candidate, because it is involved in the scenting activities of cervids. Soil could be an environmental reservoir, because cervids ingest dirt to supplement their diets with minerals. Bucks also lick soil on which does have urinated to ascertain their mating status. University of Wisconsin soil science professor Joel Pedersen has discovered that abnormally folded prions stick to the surface of some soil types, such as clay, resisting environmental and chemical damage. “Captive elk contracted CWD when introduced into paddocks occupied by infected elk more than 12 months earlier, despite fairly extensive efforts to disinfect the enclosures,” Pedersen notes. He has begun a five-year project to characterize interactions between infectious prions and soil particles and determine the extent to which infectivity is retained. No matter how CWD is transmitted between cervids, the likelihood of human susceptibility seems low. Laboratory evidence has demonstrated a molecular barrier against such cross-species infection, based on the failure of abnormal cervid prions to efficiently convert normal human prions to the infectious state. Likewise, abnormal cervid prions don't easily convert normal cattle prions, suggesting that cattle won't get CWD and pass it on to humans who eat tainted beef. While cattle can contract CWD if inoculated with the infectious agent, long-term studies placing cattle in close proximity to diseased cervids have resulted in no cases of natural transmission. Williams summarizes what all this suggests: “Never say never, but based on the [molecular] work, the CDC's findings, and the epidemiology, we certainly don't have evidence that humans have gotten CWD.” Figure 2 Identifying Animals at Risk from CWD A raccoon family feeds on a deer carcass staked out by researchers at the University of Wisconsin, in a study aimed at determining which species could be at risk of contracting CWD. (Photo courtesy of the Wisconsin Cooperative Wildlife Research Unit, University of Wisconsin-Madison.) Box 1. How Prions Confound Research The relative newness to science of CWD and mad cow disease is one reason they aren't well understood, but sheep scrapie was first identified in Great Britain in 1732—and it still isn't well-characterized. The main problem is the numerous roadblocks to researchers posed by prions, the disease agents of such TSEs. Because normal and abnormal prions have identical amino acid sequences, the immune system neither recognizes an infection nor mounts a prion-specific response. Accordingly, an antibody specific to prions has not yet been identified. Without nucleic acid, prions can't be detected or analyzed using conventional techniques such as polymerase chain reaction. They also are extraordinarily resistant to a range of treatments that typically kill or inactivate infectious agents, such as ultraviolet and ionizing radiation, heating, and most chemical disinfectants. The infectious form is largely resistant to degradation by protease enzymes, and in laboratory animals it can incubate for months to years before clinical disease signs appear. Finally, prion diseases must compete for space in expensive, biohazard-safe labs. It's therefore unsurprising that knowledge of these diseases has not sped forward. Still, scientists hope that the recent upsurge of research into BSE, CWD, and scrapie in the United States and Europe will produce synergistic results for preventing and controlling all TSEs. Box 2. Who Else Might Get CWD? When mad cow disease broke out in the United Kingdom in the 1980s, cattle and humans were far from the only species found to be affected. Among other bovids in zoo and research colonies that contracted spongiform encephalopathy from tainted beef were nyala, gemsbok, eland, Arabian and scimitar-horned oryx, greater kudu, and North American bison. A feline version of the disease was found in domestic cats, cougars, cheetahs, ocelots, and tigers. Among primates, rhesus macaques and lemurs were also infected. Unlike BSE, CWD is not thought to be transmitted through feed. But three species of cervids are naturally susceptible, and the question arises of how many other species might be in danger. To help answer that question, Michael Samuel and colleagues at the University of Wisconsin are staking out deer carcasses to see which scavengers come to feed. With flashlit photography, they've discovered “an amazing cast of characters,” including hawks, owls, crows, dogs, cats, coyotes, raccoons, skunks, mink, foxes, and opossums (Figure 2). Mammalian scavengers in the state's CWD-affected region will later be examined for disease. Steve Bunk is a freelance writer based in Boise, Idaho, United States of America. E-mail: stevebunk@sbcglobal.net Abbreviations BSEbovine spongiform encephalopathy CDCCenters for Disease Control and Prevention CJDCreutzfeldt-Jacob disease CWDchronic wasting disease TSEtransmissible spongiform encephalopathy USDAUnited States Department of Agriculture ==== Refs More Information Chronic Wasting Disease Alliance http://www.cwd-info.org The United States Department of Agriculture's Animal and Plant Health Inspection Service's CWD site http://www.aphis.usda.org/lpa/issues/cwd/cwd.html The United States Geological Survey's National Wildlife Health Center's CWD site http://www.nwhc.usgs.gov/research/chronic_wasting/chronic_wasting.html

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PMC387284

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PLoS Biol. 2004 Apr 13; 2(4):e121

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PLoS Biol

10.1371/journal.pbio.0020121

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020122Community PageOtherA Wee Lesson in Science Communication Community PageKing Emma 4 2004 13 4 2004 13 4 2004 2 4 e122Copyright: © 2004 Emma King.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The University of Edinburgh encourages its Ph.D. students to participate in a broad programme of science communication activities, designed to enhance public engagement in science ==== Body The need for effective communication of research and the promotion of science is more important then ever. Public scepticism of research is high, and the number of students studying science continues to dwindle. In an attempt to combat this, the University of Edinburgh encourages its Ph.D. students to participate in a broad programme of science communication activities, designed to develop transferable skills they can use outside pure research and to enhance public engagement in science (PES). Included as part of the transferable skills programme, the University hosts a science communication module. The course provides an introduction to the different media, educational, ethical, and political issues surrounding the communication of science to a nonspecialist audience. Initially, the course addresses the presentation of science in written and oral forms. However, as the presentation of science to the public is not simply a practical skill, part of the course is dedicated to the tactical communication of science in society and the relationship between scientists and the media. Finally, students are introduced to ongoing PES opportunities. Any involvement requires the approval of supervisors, to ensure no adverse effects on academic performance. The activities available are diverse and flexible, and they enable students to undertake projects that reflect personal interest and availability. What opportunities are available? For enthusiastic students, membership of the Nikon–University of Edinburgh Post-Graduate Science Communication Team (PGSCT) is possible. The PGSCT requires a commitment of 15 days per academic year to paid science communication projects and events, including compulsory support of SCI-FUN, the University's established science and technology roadshow. The team is recruited annually from students of the Science and Engineering, Medicine, and Veterinary Medicine Colleges and takes a leading role in PES activities. Team members are supported by other graduate students who participate on a more casual basis. To ensure that a broad spectrum of activities is available to audiences, the PGSCT members are actively encouraged to design and develop their own ideas. An opportunity to do this is provided through the University's Science Zone, at the Royal Museum of Scotland, for the duration of the Edinburgh International Science Festival. From successful workshops, originally piloted at the science festival, several ex-PGSCT members have gone on to establish their own projects. One example of this is the Natural Environment Science Education scheme, which later received recognition through the Royal Society of Edinburgh/Scottish Executive ‘Science in the Community’ Award for 2003. A primary aim of this scheme was to deliver in isolated and remote communities innovative hands-on earth science- and natural science-based activities beyond city venues. For students, developing and presenting workshops are incredibly rewarding and allow them to experience the enthusiasm of participants and fellow presenters. Some activities are transferred from the Science Zone to the classroom, where students can communicate with children at all stages of their schooling. At primary school age (5–11 years) after-school science clubs, led by graduate students, provide an opportunity for presenters to share with pupils their enthusiasm for science. Alternative activities are directed at secondary school children (12–18 years), with a variety of workshops available across the different scientific disciplines. In particular, The Scottish Institute for Biotechnology Education (SIBE) has been set up within the University to facilitate PES activities such as the popular ‘Green Fingerprinting’ workshop where the principles of DNA fingerprinting are applied to an ecological scenario in the form of a practical activity (Figure 1). The majority of workshops coordinated by SIBE have been funded by the Biotechnology and Biological Sciences Research Council (BBSRC). Figure 1 Students Studying a Stained Agarose Gel at a Green Fingerprinting Workshop (Photograph, with permission, by Douglas Robertson) The presentations may be in person or utilise new technologies such as video conferencing. A BBSRC Dialogue Award has recently been granted to SIBE to design, develop and deliver video-conferences addressing bio-ethical issues surrounding recent advances in biotechnology. Together, the two approaches of in-person and virtual presenting, enhance the schools' curriculum and facilitate dialogue between scientists and the public at a stage where promotion of science can influence the choice of further study and career options. The promotion of biotechnology within schools does not stop with the encouragement of school children's enthusiasm; SIBE also works closely with organisations such as ‘Science and Plants for Schools’ (SAPS) to assist with continuing professional development of biology teachers through the organisation of training days aimed at curriculum enhancement and the practical application of biotechnology in the classroom. Interested graduate students can co-present the workshops, though they are led by permanent staff. The students introduce their own research to highlight the applications of biotechnology and provide a useful technical resource. For students who prefer to communicate through the written word, though no guarantee of publication can be given, the University is in a position to highlight science-writing opportunities, usually as a contribution to publications specifically concerned with science communication or within the scientific press. At the Institute of Cell and Molecular Biology at the University a ‘press-gang’ meets on a monthly basis to discuss research carried out in the Institute and generate press releases for publication in the university press and national newspapers where appropriate. Complementing the University's efforts, many other organisations, such as UK Research Councils and the British Association for the Advancement of Science, endorse graduate students spending time on PES activities. Several schemes have been put in place to facilitate this; Researchers in Residence, the Science and Engineering Ambassadors Scheme and science communication courses. Hosted at the University's science campus—Kings Buildings—‘pgscicom’ provides up-to-date information on opportunities for PES at the University and beyond through regular email communications. As a PhD student and PGSCT member, I believe the approach of the University of Edinburgh to PES is of benefit to all involved. The combination of training and practical experience provides graduate students with new and valuable skills and opportunities to develop them further. The events and activities enthuse and engage audiences with the presentation of science in an informal but informative manner. For their invaluable help in reading over and editing the script, Cath Henderson and Dr. Jan Barfoot. Emma King is a Ph.D. student in the Hardwick Lab at the University of Edinburgh in the United Kingdom. She also works part-time at the Scottish Institute for Biotechnology Education and as a University of Edinburgh Post-Graduate Science Communication Team member. E-mail: emma.king@ed.ac.uk Abbreviations BBSRCBiotechnology and Biological Sciences Research Council PESpublic engagement in science PGSCTPost-Graduate Science Communication Team SIBEScottish Institute for Biotechnology Education ==== Refs Web Resouces Biotechnology and Biological Sciences Research Council http://www.bbsrc.ac.uk British Association for the Advancement of Science http://www.the-ba.net/ Natural Environment Science Education scheme http://www.nescie.co.uk Science and Plants for Schools http://www-saps.plantsci.cam.ac.uk/index.htm University of Edinburgh http://www.ed.ac.uk

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PLoS Biol. 2004 Apr 13; 2(4):e122

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PLoS Biol

10.1371/journal.pbio.0020122

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020125PrimerCell BiologyInfectious DiseasesSaccharomycesYeast Prions: Protein Aggregation Is Not Enough PrimerSherman Michael Y 4 2004 13 4 2004 13 4 2004 2 4 e125Copyright: © 2004 Michael Sherman.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Dissection and Design of Yeast Prions Artificial Prions Created from Portable Control Elements Although many proteins -- both damaged and normal -- have a tendency to aggregate, only some are capable of dividing and propagating. What does it take to turn a protein aggregate into an infectious prion? ==== Body Many damaged and mutant polypeptides, as well as some normal proteins, have a tendency to aggregate in cells. Some protein aggregates are capable of “dividing” and propagating in cells, leading to formation of similar aggregates in daughter cells or even in neighboring cells due to “infection.” These self-propagating protein aggregates are called prions and constitute the basis of prion diseases. The infectious agent in these diseases is an abnormal conformation of the PrP protein (PrPSc), which makes it protease-resistant and initiates its aggregation (Prusiner 1998). The abnormal aggregated species can recruit normal soluble PrP molecules into aggregates, thus inactivating them. The aggregates of PrPSc can proliferate within cells and be transmitted to other cells and tissues, leading to the spread of neurotoxicity. Prion Domains While so far only one prion protein is known in mammals, several prion-like proteins capable of forming self-propagating aggregates have been found in various yeast species. The common structural feature of yeast prion proteins is the so-called prion domain, characterized by the high content of glutamines (Q) and asparagines (N) (DePace et al. 1998; Michelitsch and Weissman 2000), also known as the Q/N-rich domain. The prion domains are the major structural determinants that are solely responsible for the polypeptide aggregation and propagation of the aggregates. Interestingly, the mammalian PrPSc is fundamentally different from yeast prions, since it lacks a Q/N-rich domain, indicating that distinct structural features are responsible for its ability to form self-propagating aggregates. The Q/N-rich domains in yeast prions are transferable in that, when fused to a heterologous polypeptide, they confer prion properties to this polypeptide. With a low probability, soluble proteins with prion domains can change conformation to form self-propagating aggregates, which can be transmitted to daughter cells (Lindquist 1997) (Figure 1). As with PrPSc, yeast prions efficiently recruit soluble molecules of the same species, thus inactivating them (Lindquist 1997; Chernoff 2001; Wickner et al. 2001). Also with low probability, the aggregation-prone conformation of yeast prion proteins can reverse to a soluble functional conformation. Certain yeast prion proteins, when in soluble conformation, function in important pathways; e.g., Sup35 (forming [PSI+] prion) controls termination of translation, and Ure2 (forming [URE3+] prion) controls some membrane transporter systems. Aggregation of these proteins leads to phenotypes (e.g., suppression of nonsense mutations or transport defects) inherited in a non-Mendelian fashion owing to the nonchromosomal basis of the inheritance. Figure 1 Aggregation, Division, and Transfer of Prions in Yeast Inheriting Variations A remarkable feature of yeast prion proteins is their ability to produce distinct inherited “variants” of the prion. For example, [PSI+] prion could exist in several distinct forms that suppress termination of translation to different degrees. These “variants” of yeast prions are analogous to different prion “strains” of PrPSc, which cause versions of the disease with different incubation periods and different patterns of brain pathology. The molecular nature of distinct PrPSc strains is determined by specific stable conformations of PrP. Similarly, “variants” of yeast prions are explained by different stable conformation states of the corresponding prion proteins (Chien et al. 2003). Strict conformation requirements for aggregate formation can also explain interspecies transmission barriers, where prion domains of Sup35 derived from other yeast species cannot cause formation of [PSI+] prion in Saccharomyces cerevisiae, in spite of a high degree of homology. This observation is very intriguing, especially in light of a recent finding that prion conformation of some proteins is required for formation of prions by the other proteins. For example, for de novo formation of [PSI+] prion, a distinct prion [RNQ+] should be present in a cell (Derkatch et al. 2001; Osherovich and Weissman 2001), probably in order to cross-seed Sup35 aggregates. This is in spite of relatively limited homology between the prion domains of these proteins. The apparent contradiction between the interspecies transmission barriers of very homologous prion proteins and possible cross-seeding of aggregates by prion proteins with more limited homology represents an interesting biological problem. On the other hand, this apparent contradiction may indicate that prion formation is a more complicated process than we currently think and that it may involve many cellular factors. What Do Prions Do? Although yeast prions have been studied for almost ten years, very little is known about their biological significance. We do not know the functions of the majority of proteins that can exist as prions. Even if a function of prion proteins, such as with Sup35 or Ure2, is known, we do not understand the biological significance of their “prionization,” i.e., that they aggregate and propagate in the aggregated form. A very intriguing and unexpected finding was that formation of [PSI+] prion causes a wide variety of phenotypic alterations, which depend on the strain background (True and Lindquist 2000). In fact, comparison of yeast strains of different origin, each with and without [PSI+] prion, showed that certain strains with [PSI+] prion have different sensitivity to stresses and antibiotics than their non-prion derivatives, despite their genetic identity. In some strains, cells with [PSI+] prion demonstrated better survival than their non-prion counterparts in the presence of inhibitors of translation or microtubules, heavy metals, low pH, and other deleterious conditions, which of course gives a strong advantage to the [PSI+] cells. It is likely that some genomic mutations could be suppressed and therefore become silent when termination of translation by Sup35 is partially inactivated in [PSI+] prion cells (Lindquist 2000; True and Lindquist 2000). [PSI+] could also reveal previously silent mutations or their combinations. It was hypothesized that switches between prion and non-prion forms of Sup35 enhance survival in fluctuating environments and provide a novel instrument for evolution of new traits. Q/N Does Not Necessarily a Prion Make Searching genomes of various species demonstrated that a relatively large fraction of proteins (between 0.1% and 2%) contain Q/N-rich domains (Michelitsch and Weissman 2000) or polyQ or polyN sequences. These domains are often found in transcription factors, protein kinases, and components of vesicular transport. The Q/N-rich domains usually are not evolutionary conserved and their functional role is largely unknown. Some of the Q/N-rich or polyQ domains facilitate aggregation of polypeptides, especially if expanded owing to mutations. Such expansion of the polyQ domains in certain neuronal proteins could cause neurodegenerative disorders, e.g., Huntington's disease or several forms of ataxia. Importantly, aggregates formed by polypeptides with the Q/N-rich or polyQ domains are not necessarily self-propagating aggregates, i.e., prions. In fact, there are additional structural properties of the polypeptides that provide the self-propagation (see below). Even if a protein with a polyQ domain does not form a prion, its aggregation may depend on certain prions. For example, recent experiments demonstrated that [RNQ+] prion dramatically stimulated aggregation of fragments of recombinant human huntingtin or ataxin-3 with an expanded polyQ domain cloned in yeast (Osherovich and Weissman 2001; Meriin et al. 2002). [RNQ+] facilitated the nucleation phase of the huntingtin fragment aggregation, suggesting that this prion can be directly involved in seeding of the aggregates. The major question now is whether there are analogous prion-like proteins in mammalian cells that are involved in aggregation of huntingtin or ataxin-3 and subsequent neurodegenerative disease. The first indication that mammalian proteins with Q/N-rich domains can form self-propagating prions came from recent work with a regulator of translation cytoplasmic polyadenylation element-binding protein (CPEB) from Aplysia neurons (Si et al. 2003). The neuronal form of this protein has a Q/N-rich domain similar to the prion domains of yeast prions. The Q/N-rich domain from CPEB (CPEBQ), when fused to green fluorescent protein (GFP), conferred upon it prion-like properties. The CPEBQ–GFP fusion polypeptide existed in yeast cells in one of the three distinct states, i.e., soluble, many small aggregates, or few large aggregates. Mother cells almost always gave rise to daughter cells in which the CPEBQ–GFP polypeptide was in the same state, indicating the ability of these aggregates to be inherited, i.e., to self-propagate. Furthermore, full-length Aplysia CPEB protein, when cloned in yeast, can also exist in two distinct states, soluble and aggregated, which is an inherited feature. Very unexpectedly, unlike other prions, the aggregated state of CPEB was more functionally active than the soluble form (Si et al. 2003). These data strongly suggest that metazoan proteins with Q/N-rich domains are potentially capable of forming prions. The challenge now will be to establish whether CPEB can exist as a self-propagating aggregate in Aplysia or mammalian neurons. Mystery of Propagation What makes protein aggregates in yeast propagate? The key cellular element that is critical for this process is molecular chaperone Hsp104 (Chernoff et al. 1995). This factor is specifically required for maintenance of all known prions within generations and probably is not involved in prion formation (i.e., initial protein aggregation). [PSI+] yeast cells have about 60 seeds of this prion (although this number differed in different [PSI+] isolates), and maintenance of about this number of seeds after cell divisions requires functional Hsp104 (Eaglestone et al. 2000). In fact, in the absence of Hsp104, prion aggregates continue to grow without increase in number and are rapidly lost in generations (Wegrzyn et al. 2001). Since this chaperone can directly bind to protein aggregates and promote there disassembly (Glover and Lindquist 1998), it was suggested that the main function of Hsp104 in prion inheritance is to disaggregate large prion aggregates to smaller elements, thus leading to formation of new seeds (Kushnirov and Ter-Avanesyan 1998). Interestingly, although Hsp104 is conserved among bacteria, fungi, and plants, animal cells do not have this chaperone or its close homologs. Therefore, if yeast-type prions with Q/N-rich domains exist in animal cells, there should be alternative factors that disaggregate large prion aggregates into smaller species in order to keep the number of seeds relatively constant and thus maintain the prions. The fact that some proteins with Q/N-rich domains form self-propagating aggregates, while others can aggregate but cannot form prions, suggests that there should be some structural elements either within the Q/N-rich sequence or close to it that confer the ability to propagate. In an article in this issue of PLoS Biology by Osherovich et al. (2004), the authors examined sequence requirements for prion formation and maintenance of two prion proteins, Sup35 and New1. They noted that both prion proteins contain an oligopeptide repeat QGGYQ in close proximity to Q/N-rich sequences and examined the functional significance of the repeats for aggregation and maintenance of the prions. In New1, in contrast to a deletion of the N-rich domain, deletion of the repeat did not affect aggregation of the protein or formation of the prion, but abrogated inheritance of the prion. With Sup35, the situation was somewhat more complicated, since repeats adjacent to Q/N-rich domain affected both protein aggregation and prion maintenance while more distant repeats affected only the prion inheritance. The authors suggested that the oligopeptide repeats facilitate the division of aggregates, either by serving as binding sites for Hsp104 or by altering the conformation of the polypeptides in aggregates to promote access for Hsp104 (Figure 2). Figure 2 Distinct Domains of Sup35 Are Responsible for Aggregation and Division of Aggregates The likely possibility was that the oligopeptide repeats could be interchangeable between different prions, leading to creation of novel chimeric prions. In fact, the authors constructed an F chimera, a fusion protein having the N-rich domain of New1 and the oligopeptide repeat of Sup35. This fusion polypeptide efficiently formed prion [F+]. Furthermore, when the oligopeptide repeat sequence was added to a polyQ sequence, this fusion polypeptide also acquired the ability to form self-propagating aggregates. This work, therefore, clarifies the architecture of prions by defining two structural motifs in prion proteins that have distinct functions in aggregation and propagation. Interestingly, not all yeast prions have similar oligopeptide repeat motifs, indicating that distinct structures could confer prion properties to polypeptides that can aggregate. It would be important to identify these structures in order to understand the mechanisms of aggregate propagation. The work of Osherovich et al. (2004) may help to identify proteins from mammalian cells, plants, and bacteria that can potentially form prions. Finding these novel prions could be of very high significance since they may provide insight into a wide range of currently unexplained epigenetic phenomena. Michael Sherman is in the Department of Biochemistry at the Boston University Medical School in Boston, Massachusetts, United States of America. E-mail: sherman@biochem.bumc.bu.edu Abbreviations CPEBcytoplasmic polyadenylation element-binding protein GFPgreen fluorescent protein ==== Refs References Chernoff YO Mutation processes at the protein level: Is Lamarck back? Mutat Res 2001 488 39 64 11223404 Chernoff YO Lindquist SL Ono B Inge-Vechtomov SG Liebman SW Role of the chaperone protein Hsp104 in propagation of the yeast prion-like factor [psi+ ] Science 1995 268 880 884 7754373 Chien P DePace AH Collins SR Weissman JS Generation of prion transmission barriers by mutational control of amyloid conformations Nature 2003 424 948 951 12931190 DePace AH Santoso A Hillner P Weissman JS A critical role for amino-terminal glutamine/asparagine repeats in the formation and propagation of a yeast prion Cell 1998 93 1241 1252 9657156 Derkatch IL Bradley ME Hong JY Liebman SW Prions affect the appearance of other prions: The story of [PIN+ ] Cell 2001 106 171 182 11511345 Eaglestone SS Ruddock LW Cox BS Tuite MF Guanidine hydrochloride blocks a critical step in the propagation of the prion-like determinant [PSI(+ )] of Saccharomyces cerevisiae Proc Natl Acad Sci U S A 2000 97 240 244 10618402 Glover JR Lindquist S Hsp104, Hsp70, and Hsp40: A novel chaperone system that rescues previously aggregated proteins Cell 1998 94 73 82 9674429 Kushnirov VV Ter-Avanesyan MD Structure and replication of yeast prions Cell 1998 94 13 16 9674422 Lindquist S Mad cows meet psi-chotic yeast: The expansion of the prion hypothesis Cell 1997 89 495 498 9160741 Lindquist S But yeast prion offers clues about evolution Nature 2000 408 17 18 Meriin AB Zhang X He X Newnam GP Chernoff YO Huntington toxicity in yeast model depends on polyglutamine aggregation mediated by a prion-like protein Rnq1 J Cell Biol 2002 157 997 1004 12058016 Michelitsch MD Weissman JS A census of glutamine/asparagine-rich regions: Implications for their conserved function and the prediction of novel prions Proc Natl Acad Sci U S A 2000 97 11910 11915 11050225 Osherovich LZ Weissman JS Multiple Gln/Asn-rich prion domains confer susceptibility to induction of the yeast [PSI(+)] prion Cell 2001 106 183 194 11511346 Osherovich LZ Cox BS Tuite MF Weissman JS Dissection and design of yeast prions PLoS Biol 2004 2 e86 10.1371/journal.pbio.0020086 15045026 Prusiner SB Prions Proc Natl Acad Sci U S A 1998 95 13363 13383 9811807 Si K Lindquist S Kandel ER A neuronal isoform of the Aplysia CPEB has prion-like properties Cell 2003 115 879 891 14697205 True HL Lindquist SL A yeast prion provides a mechanism for genetic variation and phenotypic diversity Nature 2000 407 477 483 11028992 Wegrzyn RD Bapat K Newnam GP Zink AD Chernoff YO Mechanism of prion loss after Hsp104 inactivation in yeast Mol Cell Biol 2001 21 4656 4669 11416143 Wickner RB Taylor KL Edskes HK Maddelein ML Moriyama H Yeast prions act as genes composed of self-propagating protein amyloids Adv Protein Chem 2001 57 313 334 11447695

15094820

PMC387287

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2021-01-05 08:21:09

no

PLoS Biol. 2004 Apr 13; 2(4):e125

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PLoS Biol

10.1371/journal.pbio.0020125

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020118SynopsisCancer BiologyGenetics/Genomics/Gene TherapyHomo (Human)Predicting Cancer Patient Survival with Gene Expression Data Synopsis4 2004 13 4 2004 13 4 2004 2 4 e118Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data ==== Body Cancer specialists often talk about cancer as an umbrella term for over 200 different diseases, each having unique characteristics. But even these categories are too broad, as the same type of cancer can take very different paths in different people. It's not uncommon, for example, for a tumor to grow aggressively in one patient and stabilize or regress in another, even though their tumors are indistinguishable and are treated in the same way. Researchers have traditionally diagnosed and treated cancer based on microscopic analysis of cell size and shape, a method that's especially difficult for very closely related cancers, such as non-Hodgkin's lymphoma, which has 20 subtypes. As scientists learn more about the molecular alterations in cancer, they're beginning to establish cancer subtypes based on the underlying molecular footprint of a tumor. Four years ago, DNA microarray analysis revealed that the most common subtype of non-Hodgkin's lymphoma is in fact two separate diseases. Though the tumor cells of both cancers appear large and diffusely dispersed in a tissue sample under a microscope, each has a distinct genetic profile, possibly explaining why only 40% of patients with this subtype respond to the standard chemotherapy treatment. Selecting expression profiles that can predict cancer outcome Such molecular pathology has led to the discovery of subtypes of several different tumor types and has successfully identified patients with different survival times. But such correlations work best when cancer subtypes based on genetic profiles are already known. If you know that different subtypes exist and which patients belong to which subtype, then you can build a statistical model to diagnose such cancers in future patients. But in most situations, clinicians don't know either of these variables—or even whether such a subtype exists—information that is crucial to developing effective diagnostic and treatment protocols. Statistical methods to identify such subtypes exist, but they can generate classifications that lack clinical relevance. Now Eric Bair and Robert Tibshirani describe a procedure that combines both gene expression data and the patients' clinical history to identify biologically significant cancer subtypes and show that this method is a powerful predictor of patient survival. Their approach uses clinical data to identify a list of genes that correspond to a particular clinical factor—such as survival time, tumor stage, or metastasis—in tandem with statistical analysis to look for additional patterns in the data to identify clinically relevant subsets of genes. In many retrospective studies, patient survival time is known, even though tumor subtypes are not; Bair and Tibshirani used that survival data to guide their analysis of the microarray data. They calculated the correlation of each gene in the microarray data with patient survival to generate a list of “significant” genes and then used these genes to identify tumor subtypes. Creating a list of candidate genes based on clinical data, the authors explain, reduces the chances of including genes unrelated to survival, increasing the probability of identifying gene clusters with clinical and thus predictive significance. Such “indicator gene lists” could identify subgroups of patients with similar gene expression profiles. The lists of subgroups, based on gene expression profiles and clinical outcomes of previous patients, could be used to assign future patients to the appropriate subgroup. An important goal of microarray research is to identify genetic profiles that can predict the risk of tumor metastasis. Being able to distinguish the subtle differences in cancer subtype will help doctors assess a patient's risk profile and to prescribe a course of treatment tailored to that profile. A patient with a particularly aggressive tumor, for example, would be a candidate for aggressive treatment, while a patient whose cancer seems unlikely to metastasize could be spared the debilitating side effects of aggressive anticancer therapies. By providing a method to cull the thousands of genes generated by a microarray to those most likely to have clinical relevance, Bair and Tibshirani have created a powerful tool to identify new cancer subtypes, predict expected patient survival, and, in some cases, help suggest the most appropriate course of treatment.

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PMC387822

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2021-01-05 08:21:09

no

PLoS Biol. 2004 Apr 13; 2(4):e118

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PLoS Biol

10.1371/journal.pbio.0020118

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020124SynopsisAnimal BehaviorEcologyPhysiologyZoologyPrimatesHomo (Human)Emergence of a Peaceful Culture in Wild Baboons synopsis4 2004 13 4 2004 13 4 2004 2 4 e124Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Pacific Culture among Wild Baboons: Its Emergence and Transmission Peace Lessons from an Unlikely Source ==== Body For most animal species, behavioral attributes are largely the product of interactions between genes and environment, with behavioral patterns preserved by natural selection. Birds, for example, know instinctively what type of nest to build for their offspring; salamanders don't need lessons to swim. But when it comes to primates—including humans—a good deal of behavior is learned. Primates exhibit a wide range of behaviors, not just among species but also among populations and even individuals. Yet the nature versus nurture debate still rages, particularly when it comes to understanding the roots of aggression. While bonobos are famous for using sex to resolve disputes, aggression is far more common in most primate species—again humans included. Our closest relative, the chimpanzee, has a reputation for being among the most belligerent, with rhesus monkeys and baboons not far behind. For many of these species, bouts of violence are often followed by gestures of reconciliation, such as grooming or, in the case of chimps, kissing. Since most primates live in social groups, it may be that such conciliatory measures serve to maintain some semblance of social structure, offsetting the disruptive effects of aggression. (To learn more about primate behavior and aggression, see the primer by Frans de Waal in this issue [DOI: 10.1371/journal.pbio.0020101].) In baboons, “grooming” is a socially rewarding behavior. (Photograph, with permission, by Robert Sapolsky) Primatologists characterize these behavioral differences as “cultural” traits, since they arise independent of genetic or environmental factors and are not only shared by a population (though not necessarily a species) but are also passed on to succeeding generations. Such cultural traditions have been documented in African chimp populations, which display over 39 behaviors related to “technology” (such as using stones to crack nuts), grooming, and courtship. While most of these cases involve either tools, foraging, or communication, Robert Sapolsky and Lisa Share report evidence of a higher order cultural tradition in wild baboons in Kenya. Rooted in field observations of a group of olive baboons (called the Forest Troop) since 1978, Sapolsky and Share document the emergence of a unique culture affecting the “overall structure and social atmosphere” of the troop. In his book A Primate's Memoir, Sapolsky studied the activities and lifestyle of the Forest Troop to explore the relationship between stress and disease. In typical baboon fashion, the males behaved badly, angling either to assume or maintain dominance with higher ranking males or engaging in bloody battles with lower ranking males, which often tried to overthrow the top baboon by striking tentative alliances with fellow underlings. Females were often harassed and attacked. Internecine feuds were routine. Through a heartbreaking twist of fate, the most aggressive males in the Forest Troop were wiped out. The males, which had taken to foraging in an open garbage pit adjacent to a tourist lodge, had contracted bovine tuberculosis, and most died between 1983 and 1986. Their deaths drastically changed the gender composition of the troop, more than doubling the ratio of females to males, and by 1986 troop behavior had changed considerably as well; males were significantly less aggressive. After the deaths, Sapolsky stopped observing the Forest Troop until 1993. Surprisingly, even though no adult males from the 1983–1986 period remained in the Forest Troop in 1993 (males migrate after puberty), the new males exhibited the less aggressive behavior of their predecessors. Around this time, Sapolsky and Share also began observing another troop, called the Talek Troop. The Talek Troop, along with the pre-TB Forest Troop, served as controls for comparing the behavior of the post-1993 Forest Troop. The authors found that while in some respects male to male dominance behaviors and patterns of aggression were similar in both the Forest and control troops, there were differences that significantly reduced stress for low ranking males, which were far better tolerated by dominant males than were their counterparts in the control troops. The males in the Forest Troop also displayed more grooming behavior, an activity that's decidedly less stressful than fighting. Analyzing blood samples from the different troops, Sapolsky and Share found that the Forest Troop males lacked the distinctive physiological markers of stress, such as elevated levels of stress-induced hormones, seen in the control troops. In light of these observations, the authors investigated various models that might explain how the Forest Troop preserved this (relatively) peaceful lifestyle, complete with underlying physiological changes. One model suggests that nonhuman primates acquire cultural traits through observation. Young chimps may learn how to crack nuts with stones by watching their elders, for example. In this case, the young baboon transplants might learn that it pays to be nice by watching the interactions of older males in their new troop. Or it could be that proximity to such behavior increases the likelihood that the new males will adopt the behavior. Yet another explanation could be that males in troops with such a high proportion of females become less aggressive because they don't need to fight as much for female attention and are perhaps rewarded for good behavior. But it could be that the females had a more direct impact: new male transfers in the Forest Troop were far better received by resident females than new males in the other troops. Sapolsky and Share conclude that the method of transmission is likely either one or a combination of these models, though teasing out the mechanisms for such complex behaviors will require future study. But if aggressive behavior in baboons does have a cultural rather than a biological foundation, perhaps there's hope for us as well.

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PMC387823

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2021-01-05 08:21:10

no

PLoS Biol. 2004 Apr 13; 2(4):e124

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PLoS Biol

10.1371/journal.pbio.0020124

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020162Research ArticleBioinformatics/Computational BiologyGenetics/Genomics/Gene TherapyHomo (Human)Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones Integrative Annotation of Human GenesImanishi Tadashi 1 Itoh Takeshi 1 2 Suzuki Yutaka 3 68 O'Donovan Claire 4 Fukuchi Satoshi 5 Koyanagi Kanako O 6 Barrero Roberto A 5 Tamura Takuro 7 8 Yamaguchi-Kabata Yumi 1 Tanino Motohiko 1 7 Yura Kei 9 Miyazaki Satoru 5 Ikeo Kazuho 5 Homma Keiichi 5 Kasprzyk Arek 4 Nishikawa Tetsuo 10 11 Hirakawa Mika 12 Thierry-Mieg Jean 13 14 Thierry-Mieg Danielle 13 14 Ashurst Jennifer 15 Jia Libin 16 Nakao Mitsuteru 3 Thomas Michael A 17 Mulder Nicola 4 Karavidopoulou Youla 4 Jin Lihua 5 Kim Sangsoo 18 Yasuda Tomohiro 11 Lenhard Boris 19 Eveno Eric 20 21 Suzuki Yoshiyuki 5 Yamasaki Chisato 1 Takeda Jun-ichi 1 Gough Craig 1 7 Hilton Phillip 1 7 Fujii Yasuyuki 1 7 Sakai Hiroaki 1 7 22 Tanaka Susumu 1 7 Amid Clara 23 Bellgard Matthew 24 Bonaldo Maria de Fatima 25 Bono Hidemasa 26 Bromberg Susan K 27 Brookes Anthony J 19 Bruford Elspeth 28 Carninci Piero 29 Chelala Claude 20 Couillault Christine 20 21 de Souza Sandro J. 30 Debily Marie-Anne 20 Devignes Marie-Dominique 31 Dubchak Inna 32 Endo Toshinori 33 Estreicher Anne 34 Eyras Eduardo 15 Fukami-Kobayashi Kaoru 35 R. Gopinath Gopal 36 Graudens Esther 20 21 Hahn Yoonsoo 18 Han Michael 23 Han Ze-Guang 21 37 Hanada Kousuke 5 Hanaoka Hideki 1 Harada Erimi 1 7 Hashimoto Katsuyuki 38 Hinz Ursula 34 Hirai Momoki 39 Hishiki Teruyoshi 40 Hopkinson Ian 41 42 Imbeaud Sandrine 20 21 Inoko Hidetoshi 1 7 43 Kanapin Alexander 4 Kaneko Yayoi 1 7 Kasukawa Takeya 26 Kelso Janet 44 Kersey Paul 4 Kikuno Reiko 45 Kimura Kouichi 11 Korn Bernhard 46 Kuryshev Vladimir 47 Makalowska Izabela 48 Makino Takashi 5 Mano Shuhei 43 Mariage-Samson Regine 20 Mashima Jun 5 Matsuda Hideo 49 Mewes Hans-Werner 23 Minoshima Shinsei 50 52 Nagai Keiichi 11 Nagasaki Hideki 51 Nagata Naoki 1 Nigam Rajni 27 Ogasawara Osamu 3 Ohara Osamu 45 Ohtsubo Masafumi 52 Okada Norihiro 53 Okido Toshihisa 5 Oota Satoshi 35 Ota Motonori 54 Ota Toshio 22 Otsuki Tetsuji 55 Piatier-Tonneau Dominique 20 Poustka Annemarie 47 Ren Shuang-Xi 21 37 Saitou Naruya 56 Sakai Katsunaga 5 Sakamoto Shigetaka 5 Sakate Ryuichi 39 Schupp Ingo 47 Servant Florence 4 Sherry Stephen 13 Shiba Rie 1 7 Shimizu Nobuyoshi 52 Shimoyama Mary 27 Simpson Andrew J 30 Soares Bento 25 Steward Charles 15 Suwa Makiko 51 Suzuki Mami 5 Takahashi Aiko 1 7 Tamiya Gen 1 7 43 Tanaka Hiroshi 33 Taylor Todd 57 Terwilliger Joseph D 58 Unneberg Per 59 Veeramachaneni Vamsi 48 Watanabe Shinya 3 Wilming Laurens 15 Yasuda Norikazu 1 7 Yoo Hyang-Sook 18 Stodolsky Marvin 60 Makalowski Wojciech 48 Go Mitiko 61 Nakai Kenta 3 Takagi Toshihisa 3 Kanehisa Minoru 12 Sakaki Yoshiyuki 3 57 Quackenbush John 62 Okazaki Yasushi 26 Hayashizaki Yoshihide 26 Hide Winston 44 Chakraborty Ranajit 63 Nishikawa Ken 5 Sugawara Hideaki 5 Tateno Yoshio 5 Chen Zhu 21 37 64 Oishi Michio 45 Tonellato Peter 65 Apweiler Rolf 4 Okubo Kousaku 5 40 Wagner Lukas 13 Wiemann Stefan 47 Strausberg Robert L 16 Isogai Takao 10 66 Auffray Charles 20 21 Nomura Nobuo 40 Gojobori Takashi tgojobor@genes.nig.ac.jp 1 5 67 Sugano Sumio 3 40 68 1Integrated Database Group, Biological Information Research Center, National Institute of Advanced Industrial Science and TechnologyTokyoJapan2Bioinformatics Laboratory, Genome Research Department, National Institute of Agrobiological SciencesIbarakiJapan3Human Genome Center, The Institute of Medical Science, The University of TokyoTokyoJapan4EMBL Outstation—European Bioinformatics Institute, Wellcome Trust Genome CampusCambridgeUnited Kingdom5Center for Information Biology and DNA Data Bank of Japan, National Institute of GeneticsShizuokaJapan6Nara Institute of Science and TechnologyNaraJapan7Integrated Database Group, Japan Biological Information Research Center, Japan Biological Informatics ConsortiumTokyoJapan8BITS CompanyShizuokaJapan9Quantum Bioinformatics Group, Center for Promotion of Computational Science and Engineering, Japan Atomic Energy Research InstituteKyotoJapan10Reverse Proteomics Research InstituteChibaJapan11Central Research Laboratory, HitachiTokyoJapan12Bioinformatics Center, Institute for Chemical Research, Kyoto UniversityKyotoJapan13National Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthBethesda, MarylandUnited States of America14Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique MathematiqueMontpellierFrance15The Wellcome Trust Sanger Institute, Wellcome Trust Genome CampusCambridgeUnited Kingdom16National Cancer Institute, National Institutes of HealthBethesda, MarylandUnited States of America17Department of Biological Sciences, Idaho State UniversityPocatello, IdahoUnited States of America18Korea Research Institute of Bioscience and BiotechnologyTaejeonKorea19Center for Genomics and Bioinformatics, Karolinska InstitutetStockholmSweden20Genexpress—CNRS—Functional Genomics and Systemic Biology for HealthVillejuif CedexFrance21Sino-French Laboratory in Life Sciences and GenomicsShanghaiChina22Tokyo Research Laboratories, Kyowa Hakko Kogyo CompanyTokyoJapan23MIPS—Institute for Bioinformatics, GSF—National Research Center for Environment and HealthNeuherbergGermany24Centre for Bioinformatics and Biological Computing, School of Information Technology, Murdoch UniversityMurdoch, Western AustraliaAustralia25Medical Education and Biomedical Research Facility, University of IowaIowa City, IowaUnited States of America26Genome Exploration Research Group, RIKEN Genomic Sciences Center, RIKEN Yokohama InstituteKanagawaJapan27Medical College of Wisconsin, MilwaukeeWisconsinUnited States of America28HUGO Gene Nomenclature Committee, University College LondonLondonUnited Kingdom29Genome Science Laboratory, RIKENSaitamaJapan30Ludwig Institute of Cancer ResearchSao PauloBrazil31CNRSVandoeuvre les NancyFrance32Lawrence Berkeley National Laboratory, BerkeleyCaliforniaUnited States of America33Department of Bioinformatics, Medical Research Institute, Tokyo Medical and Dental UniversityTokyoJapan34Swiss Institute of BioinformaticsGenevaSwitzerland35Bioresource Information Division, RIKEN BioResource Center, RIKEN Tsukuba InstituteIbarakiJapan36Genome Knowledgebase, Cold Spring Harbor LaboratoryCold Spring Harbor, New YorkUnited States of America37Chinese National Human Genome Center at ShanghaiShanghaiChina38Division of Genetic Resources, National Institute of Infectious DiseasesTokyoJapan39Graduate School of Frontier Sciences, Department of Integrated Biosciences, University of TokyoChibaJapan40Functional Genomics Group, Biological Information Research Center, National Institute of Advanced Industrial Science and TechnologyTokyoJapan41Department of Primary Care and Population Sciences, Royal Free University College Medical School, University College LondonLondonUnited Kingdom42Clinical and Molecular Genetics Unit, The Institute of Child HealthLondonUnited Kingdom43Department of Genetic Information, Division of Molecular Life Science, School of Medicine, Tokai UniversityKanagawaJapan44South African National Bioinformatics Institute, University of the Western CapeBellvilleSouth Africa45Kazusa DNA Research InstituteChibaJapan46RZPD Resource Center for Genome ResearchHeidelbergGermany47Molecular Genome Analysis, German Cancer Research Center-DKFZHeidelbergGermany48Pennsylvania State UniversityUniversity Park, PennsylvaniaUnited States of America49Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka UniversityOsakaJapan50Medical Photobiology Department, Photon Medical Research Center, Hamamatsu University School of MedicineShizuokaJapan51Computational Biology Research Center, National Institute of Advanced Industrial Science and TechnologyTokyoJapan52Department of Molecular Biology, Keio University School of MedicineTokyoJapan53Department of Biological Sciences, Graduate School of Bioscience and Biotechnology, Tokyo Institute of TechnologyKanagawaJapan54Global Scientific Information and Computing Center, Tokyo Institute of TechnologyTokyoJapan55Molecular Biology Laboratory, Medicinal Research Laboratories, Taisho Pharmaceutical CompanySaitamaJapan56Department of Population Genetics, National Institute of GeneticsShizuokaJapan57Human Genome Research Group, Genomic Sciences Center, RIKEN Yokohama InstituteKanagawaJapan58Columbia University and Columbia Genome CenterNew York, New YorkUnited States of America59Department of Biotechnology, Royal Institute of TechnologyStockholmSweden60Biology Division and Genome Task Group, Office of Biological and Environmental Research, United States Department of EnergyWashington, D.CUnited States of America61Faculty of Bio-Science, Nagahama Institute of Bio-Science and TechnologyShigaJapan62Institute for Genomic ResearchRockville, MarylandUnited States of America63Center for Genome Information, Department of Environmental Health, University of CincinnatiCincinnati, OhioUnited States of America64State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui-Jin Hospital, Shanghai Second Medical UniversityShanghaiChina65PointOne SystemsWauwatosa, WisconsinUnited States of America66Graduate School of Life and Environmental Sciences, University of TsukubaIbarakiJapan67Department of Genetics, Graduate University for Advanced StudiesShizuokaJapan68Department of Medical Genome Sciences, Graduate School of Frontier Sciences, University of TokyoTokyoJapan6 2004 20 4 2004 20 4 2004 2 6 e16219 12 2003 1 4 2004 Copyright: © 2004 Imanishi et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Annotation Marathon Validates 21,037 Human Genes The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology. An international team has systematically validated and annotated just over 21,000 human genes using full-length cDNA, thereby providing a valuable new resource for the human genetics community ==== Body Introduction The draft sequences of the human, mouse, and rat genomes are already available (Lander et al. 2001; Marshall 2001; Venter et al. 2001; Waterston et al. 2002). The next challenge comes in the understanding of basic human molecular biology through interpretation of the human genome. To display biological data optimally we must first characterize the genome in terms of not only its structure but also function and diversity. It is of immediate interest to identify factors involved in the developmental process of organisms, non-protein-coding functional RNAs, the regulatory network of gene expression within tissues and its governance over states of health, and protein–gene and protein–protein interactions. In doing so, we must integrate this information in an easily accessible and intuitive format. The human genome may encode only 30,000 to 40,000 genes (Lander et al. 2001; Venter et al. 2001), suggesting that complex interdependent gene regulation mechanisms exist to account for the complex gene networks that differentiate humans from lower-order organisms. In organisms with small genomes, it is relatively straightforward to use direct computational prediction based upon genomic sequence to identify most genes by their long open reading frames (ORFs). However, computational gene prediction from the genomic sequence of organisms with short exons and long introns can be somewhat error-prone (Ashburner 2000; Reese et al. 2000; Lander et al. 2001). Previous efforts to catalogue the human transcriptome were based on expressed sequence tags (ESTs) used for the identification of new genes (Adams et al. 1991; Auffray et al. 1995; Houlgatte et al. 1995), chromosomal assignment of genes (Gieser and Swaroop 1992; Khan et al. 1992; Camargo et al. 2001), prediction of genes (Nomura et al. 1994), and assessment of gene expression (Okubo et al. 1992). Recently, Camargo et al. (2001) generated a large collection of ORF ESTs, and Saha et al. (2002) conducted a large-scale serial analysis of gene expression patterns to identify novel human genes. The availability of human full-length transcripts from many large-scale sequencing projects (Nomura et al. 1994; Nagase et al. 2001; Wiemann et al. 2001; Yudate 2001; Kikuno et al. 2002;Strausberg et al. 2002) has provided a unique opportunity for the comprehensive evaluation of the human transcriptome through the annotation of a variety of RNA transcripts. Protein-coding and non-protein-coding sequences, alternative splicing (AS) variants, and sense–antisense RNA pairs could all be functionally identified. We thus designed an international collaborative project to establish an integrative annotation database of 41,118 human full-length cDNAs (FLcDNAs). These cDNAs were collected from six high-throughput sequencing projects and evaluated at the first international jamboree, entitled the Human Full-length cDNA Annotation Invitational (H-Invitational or H-Inv) (Cyranoski 2002). This event was held in Tokyo, Japan, and took place from August 25 to September 3, 2002. Efforts which have been made in the same area as the H-Inv annotation work include the Functional Annotation of Mouse (FANTOM) project (Kawai et al. 2001; Bono et al. 2002; Okazaki et al. 2002), Flybase (GOC 2001), and the RIKEN Arabidopsis full-length cDNA project (Seki et al. 2002). In our own project, great effort has been taken at all levels, not only in the annotation of the cDNAs but also in the way the data can be viewed and queried. These aspects, along with the applications of our research to disease research, distinguish our project from other similar projects. This manuscript provides the first report by the H-Inv consortium, showing some of the discoveries made so far and introducing our new database of the human transcriptome. It is hoped that this will be the first in a long line of publications announcing discoveries made by the H-Inv consortium. Here we describe results from our integrative annotation in four major areas: mapping the transcriptome onto the human genome, functional annotation, polymorphism in the transcriptome, and evolution of the human transcriptome. We then introduce our new database of the human transcriptome, the H-Invitational Database (H-InvDB; http://www.h-invitational.jp), which stores all annotation results by the consortium. Free and unrestricted access to the H-Inv annotation work is available through the database. Finally, we summarize our most important findings thus far in the H-Inv project in Concluding Remarks. Results/Discussion Mapping the Transcriptome onto the Human Genome Construction of the nonredundant human FLcDNA database We present the first experimentally validated nonredundant transcriptome of human FLcDNAs produced by six high-throughput cDNA sequencing projects (Ota et al. 1997, 2004; Strausberg et al. 1999; Hu et al. 2000; Wiemann et al. 2001; Yudate 2001; Kikuno et al. 2002) as of July 15, 2002. The dataset consists of 41,118 cDNAs (H-Inv cDNAs) that were derived from 184 diverse cell types and tissues (see Dataset S1). The number of clones, the number of libraries, major tissue origins, methods, and URLs of cDNA clones for each cDNA project are summarized in Table 1. H-Inv cDNAs include 8,324 cDNAs recently identified by the Full-Length Long Japan (FLJ) project. The FLJ clones represent about half of the H-Inv cDNAs (Table 1). The policies for library selection and the results of initial analysis of the constituent projects were reported by the participants themselves: the Chinese National Human Genome Center (CHGC) (Hu et al. 2000), the Deutsches Krebsforschungszentrum (DKFZ/MIPS) (Wiemann et al. 2001), the Institute of Medical Science at the University of Tokyo (IMSUT) (Suzuki et al. 1997 ; Ota et al. 2004), the Kazusa cDNA sequence project of the Kazusa DNA Research Institute (KDRI) (Hirosawa et al. 1999; Nagase et al. 1999; Suyama et al. 1999; Kikuno et al. 2002), the Helix Research Institute (HRI) (Yudate et al. 2001), and the Mammalian Gene Collection (MGC) (Strausberg et al. 1999; Moonen et al. 2002), as well as FLJ mentioned earlier (Ota et al. 2004). The variation in tissue origins for library construction among these six groups resulted in rare occurrences of sequence redundancy among the collections. In a recent study, the FLJ project has described the complete sequencing and characterization of 21,243 human cDNAs (Ota et al. 2004). On the other hand, the H-Inv project characterized cDNAs from this project and six high-throughput cDNA producers by using a different suite of computational analysis techniques and an alternative system of functional annotation. Table 1 Summary of cDNA Resources *FLcDNA data were provided for H-Inv project by the FLJ project of NEDO (URL: http://www.nedo.go.jp/bio-e/) and six high-throughput cDNA clone producers Chinese National Human Genome Center (CHGC), the Deutsches Krebsforschungszentrum (DKFZ/MIPS), Helix Research Institute (HRI), the Institute of Medical Science in the University of Tokyo (IMSUT), the Kazusa DNA Research Institute (KDRI), and the Mammalian Gene Collection (MGC/NIH) The 41,118 H-Inv cDNAs were mapped on to the human genome, and 40,140 were considered successfully aligned. The alignment criterion was that a cDNA was only aligned if it had both 95% identity and 90% length coverage against the genome (Figure 1). The mean identity of all the alignments between 40,140 mapped cDNAs and genomic sequences was 99.6 %, and the mean coverage against the genomic sequence was 99.6%. In some cases, terminal exons were aligned with low identity or low coverage. For example, 89% of internal exons have identity of 99.8% or higher, while only 78% and 50% of the first and last exons do, respectively. These alignments with low identity or low coverage seemed to be caused by the unsuccessful alignments of the repetitive sequences found in UTR regions and the misalignments of 3′ terminal poly-A sequences. Although better alignments could be obtained for these sequences by improving the mapping procedure, we concluded that the quality of the FLcDNAs was high overall. Figure 1 Procedure for Mapping and Clustering the H-Inv cDNAs The cDNAs were mapped to the genome and clustered into loci. The remaining unmapped cDNAs were clustered based upon the grouping of significantly similar cDNAs. Due to redundancy and AS within the human transcriptome, these 40,140 cDNAs were clustered to 20,190 loci (H-Inv loci). For the remaining 978 unmapped cDNAs, we conducted cDNA-based clustering, which yielded 847 clusters. The clusters created had an average of 2.0 cDNAs per locus (Table 2). The average was only 1.2 for unmapped clusters, probably because many of these genes are encoded by heterochromatic regions of the human genome and show limited levels of gene expression. The gene density for each chromosome varied from 0.6 to 19.0 genes/Mb, with an average of 6.5 genes/Mb. This distribution of genes over the genome is far from random. This biased gene localization concurs with the gene density on chromosomes found in similar previous reports (Lander et al. 2001; Venter et al. 2001). This indicates that the sampled cDNAs are unbiased with respect to chromosomal location. Most cDNAs were mapped only at a single position on the human genome. However, 1,682 cDNAs could be mapped at multiple positions (with mean values of 98.2% identity and 98.1% coverage). The multiple matching may be caused by either recent gene duplication events or artificial duplication of the human genome caused by misassembled contigs. In our study we have selected only the “best” loci for the cDNAs (see Materials and Methods for details). Table 2 The Clustering Results of Human FLcDNAs onto the Human Genome aUN represents contigs that were not mapped onto any chromosome In total, 21,037 clusters (20,190 mapped and 847 unmapped) were identified and entered into the H-InvDB. We assigned H-Inv cluster IDs (e.g., HIX0000001) to the clusters and H-Inv cDNA IDs (e.g., HIT000000001) to all curated cDNAs. A representative sequence was selected from each cluster and used for further analyses and annotation. Comparison of the mapped H-Inv cDNAs with other annotated datasets In order to evaluate the H-Inv dataset, we compared all of the mapped H-Inv cDNAs with the Reference Sequence Collection (RefSeq) mRNA database (Pruitt and Maglott 2001) (Figure 2). The RefSeq mRNA database consists of two types of datasets. These are the curated mRNAs (accession prefix NM and NR) and the model mRNAs that are provided through automated processing of the genome annotation (accession prefix XM and XR). Figure 2 A Comparison of the Mapped H-Inv FLcDNAs and the RefSeq mRNAs The mapped H-Inv cDNAs, the RefSeq curated mRNAs (accession prefixes NM and NR), and the RefSeq model mRNAs (accession prefixes XM and XR) provided by the genome annotation process were clustered based on the genome position. The numbers of loci that were identified by clustering are shown. From the comparison, we found that 5,155 (26%) of the H-Inv loci had no counterparts and were unique to the H-Inv. All of these 5,155 loci are candidates for new human genes, although non-protein-coding RNAs (ncRNAs) (25%), hypothetical proteins with ORFs less than 150 amino acids (55%), and singletons (91%) were enriched in this category. In fact, 1,340 of these H-Inv-unique loci were questionable and require validation by further experiments because they consist of only single exons, and the 3′ termini of these loci align with genomic poly-A sequences. This feature suggests internal poly-A priming although some occurrences might be bona fide genes. The most reliable set of newly identified human genes in our dataset is composed of 1,054 protein-coding and 179 non-protein-coding genes that have multiple exons. Therefore, at least 6.1% (1,233/20,190) of the H-Inv loci could be used to newly validate loci that the RefSeq datasets do not presently cover. These genes are possibly less expressed since the proportion of singletons (H-Inv loci consisting of a single H-Inv cDNA) was high (84%). On the other hand, 78% (11,974/15,439) of the curated RefSeq mRNAs were covered by the H-Inv cDNAs. These figures suggest that further extensive sequencing of FLcDNA clones will be required in order to cover the entire human gene set. Nonetheless, this effort provides a systematic approach using the H-Inv cDNAs, even though a portion of the cDNAs have already been utilized in the RefSeq datasets. It is noteworthy that H-Inv cDNAs overlapped 3,061 (17%) of RefSeq model mRNAs, supporting this proportion of the hypothetical RefSeq sequences. These newly confirmed 3,061 loci have a mean number of exons greater than RefSeq model mRNAs that were not confirmed, but smaller than RefSeq curated mRNAs. The overlap between H-Inv cDNAs and RefSeq model mRNAs was smaller than that between H-Inv cDNAs and RefSeq curated mRNAs. This suggests that the genes predicted from genome annotation may tend to be less expressed than RefSeq curated genes, or that some may be artifacts. All these results highlight the great importance of comprehensive collections of analyzed FLcDNAs for validating gene prediction from genome sequences. This may be especially true for higher organisms such as humans. Incomplete parts of the human genome sequences The existence of 978 unmapped cDNAs (847 clusters) suggests that the human genome sequence (National Center for Biotechnolgy Information [NCBI] build 34 assembly) is not yet complete. The evidence supporting this statement is twofold. First, most of those unmapped cDNAs could be partially mapped to the human genome. Using BLAST, 906 of the unmapped cDNAs (corresponding to 786 clusters) showed at least one sequence match to the human genome with a bit score higher than 100. Second, most of the cDNAs could be mapped unambiguously to the mouse genome sequences. A total of 907 unmapped cDNAs (779 clusters; 92%) could be mapped to the mouse genome with coverage of 90% or higher. If we adopted less stringent requirements, more cDNAs could be mapped to the mouse genome. The rest might be less conserved genes, genes in unfinished sections of the mouse genome, or genes that were lost in the mouse genome. Based on these observations, we conclude that the human genome sequence is not yet complete, leaving some portions to be sequenced or reassembled. The proportion of the genome that is incomplete is estimated to be 3.7%–4.0%. The figure of 4.0% is based upon the proportion of H-Inv cDNA clusters that could not be mapped to the genome (847/21,037), while the 3.7% estimate is based on both H-Inv cDNAs and RefSeq sequences (only NMs). This statistic indicates that a minimum of one out of every 25–27 clusters appears to be unrepresented in the current human genome dataset, in its full form. Possible reasons for this include unsequenced regions on the human genome and regions where an error may have occurred during sequence assembly. If this is the case, this lends support to the use of cDNA mapping to facilitate the completion of whole genome sequences (Kent and Haussler 2001). For example, we can predict the arrangement of contigs based on the order of mapped exons. In addition we can use the sequences of unmapped exons to search for those clones that contain unsequenced parts of the genome. The mapping results of partially mapped cDNAs are thus quite useful. Primary structure of genes on the human genome Using the H-Inv cDNAs, the precise structures of many human genes could be identified based on the results of our cDNA mapping (Table S1). The median length of last exons (786 bp) was found to be longer than that of other exons, and the median length of first introns (3,152 bp) longer than that of other introns. These observed characteristics of human gene structures concur with the previous work using much smaller datasets (Hawkins 1988; Maroni 1996; Kriventseva and Gelfand 1999). In the human genome, 50% of the sequence is occupied by repetitive elements (Lander et al. 2001). Repetitive elements were previously regarded by many as simply “junk” DNA. However, the contribution of these repetitive stretches to genome evolution has been suggested in recent works (Makalowski 2000; Deininger and Batzer 2002; Sorek et al. 2002; Lorenc and Makalowski 2003). The 21,037 loci of representative cDNAs were searched for repetitive elements using the RepeatMasker program. RepeatMasker indicated that 9,818 (47%) of the H-Inv cDNAs, including 5,442 coding hypothetical proteins, contained repetitive sequences. The existence of Alu repeats in 5% of human cDNAs was reported previously (Yulug et al. 1995). Our results revealed a significant number of repetitive sequences including Alu in the human transcriptome. Among them, 1,866 cDNAs overlapped repetitive sequences in their ORFs. Moreover, 554 of 1,866 cDNAs had repetitive sequences contained completely within their ORFs, including 81 cDNAs that were identical or similar to known proteins. This may indicate the involvement of repetitive elements in human transcriptome evolution, as suggested by the presence of Alu repeats in AS exons (Sorek et al. 2002) and the contribution to protein variability by repetitive elements in protein-coding regions (Makalowski 2000). We detected 2,254 and 5,427 cDNAs containing repetitive sequences in their 5′ UTR and 3′ UTR, respectively. The positioning of the repetitive elements suggests they play a regulatory role in the control of gene expression (Deininger and Batzer 2002) (see Table S1 or the H-InvDB for details). AS transcripts We wished to investigate the extent to which the functional diversity of the human proteome is affected by AS. In order to do this, we searched for potential AS isoforms in 7,874 loci that were supported by at least two H-Inv cDNAs. We examined whether or not these cDNAs represented mutually exclusive AS isoforms, using a combination of computational methods and human curation (see Materials and Methods). All AS isoforms that were supported independently by both methods were defined as the H-Inv AS dataset. Our analysis showed that 3,181 loci (40 % of the 7,874 loci) encoded 8,553 AS isoforms expressing a total of 18,612 AS exons. On average, 2.7 AS isoforms per locus were identified in these AS-containing loci. This figure represents half of the AS isoforms predicted by another group (Lander et al. 2001). Our result highlights the degree to which full-length sequencing of redundant clones is necessary when characterizing the complete human transcriptome. The relative positions of AS exons on the loci varied: 4,383 isoforms comprising 1,538 loci were 5′ terminal AS variants; 5,678 isoforms comprising 1,979 loci were internal AS variants; and 2,524 isoforms comprising 921 loci were 3′ terminal AS variants. The AS isoforms found in the H-Inv AS dataset have strikingly diverse functions. Motifs are found over a wide range of protein sequences. For certain types of subcellular targeting signals, such as signal peptides, position within the entire protein sequence appears crucial. A total of 3,020 (35 %) AS isoforms contained AS exons that overlapped protein-coding sequences. 1,660 out of 3,020 AS isoforms (55%) harbored AS exons that encoded functional motifs. Additionally, 1,475 loci encoded AS isoforms that had different subcellular localization signals, and 680 loci had AS isoforms that had different transmembrane domains. These results suggest marked functional differentiation between the varying isoforms. If this is the case, it would appear that AS contributes significantly to the functional diversity of the human proteome. As the coverage of the human transcriptome by H-Inv cDNAs is incomplete, it would be misleading to conjecture that our dataset comprehensively includes all AS transcripts from every human gene. However, the current collection is a robust characterization of the existing functional diversity of the human proteome, and it represents a valuable resource of full-length clones for the characterization of experimentally determined AS isoforms. In the cases where three-dimensional (3D) structures could be assigned to H-Inv cDNA protein products, we have examined the possible impact of AS rearrangements on the 3D structure. Our analysis was performed using the Genomes TO Protein structures and functions database (GTOP) (Kawabata et al. 2002). We found that some of the sequence regions in which internal exons vary between different isoforms contained regions encoding SCOP domains (Lo Conte et al. 2000). This discovery allowed us to perform a simple analysis of the structural effects of AS. Our analysis of the SCOP domain assignments revealed that the loci displaying AS are much more likely to contain class c (β–α–β units, α/β) SCOP domains than class d (segregated α and β regions, α+β) or class g (small) domains. An example of exon differences between AS isoforms is presented in Figure 3. The structures shown are those of proteins in the Brookhaven Protein Data Bank (PDB) (Berman et al. 2000) to which the amino acid sequences of the corresponding AS isoforms are aligned. Segments of the AS isoform sequences that are not aligned with the corresponding 3D structure are shown in purple. Figure 3 demonstrates that exon differences resulting from AS sometimes give rise to significant alternations in 3D structure. Figure 3 An Example of Different Structures Encoded by AS Variants Exons are presented from the 5′ end, with those shared by AS variants aligned vertically. The AS variants, with accession numbers AK095301 and BC007828, are aligned to the SCOP domain d.136.1.1 and corresponding PDB structure 1byr. Helices and beta sheets are red and yellow, respectively. Green bars indicate regions aligned to the PDB structure, while open rectangles represent gaps in the alignments. AK095301 is aligned to the entire PDB structure shown, while BC007828 is lacking the alignment to the purple segment of the structure. Functional Annotation We predicted the ORFs of 41,118 H-Inv cDNA sequences using a computational approach (see Figure S1), of which 39,091 (95.1%) were protein coding and the remaining 2,027 (4.9%) were non-protein-coding. Since the structures and functions of protein products from AS isoforms are expected to be basically similar, we selected a “representative transcript” from each of the loci (see Figure S2). Then we identified 19,660 protein-coding and 1,377 non-protein-coding loci (Table 3). Human curation suggested that a total of 86 protein-coding transcripts should be deemed questionable transcripts. Once identified as dubious these sequences were excluded from further analysis. The remaining representatives from the 19,574 protein-coding loci were used to define a set of human proteins (H-Inv proteins). The tentative functions of the H-Inv proteins were predicted by computational methods. Following computational predictions was human curation. Table 3 Statistics Obtained from the Functional Annotation Results After determination of the H-Inv proteins, we performed a standardized functional annotation as illustrated in Figure 4, during which we assigned the most suitable data source ID to each H-Inv protein based on the results of similarity search and InterProScan. We classified the 19,574 H-Inv proteins according to the levels of the sequence similarity. Using a system developed for the human cDNA annotation (see Figure S2), we classified the H-Inv proteins into five categories (Table 3). Three categories contain translated gene products that are related to known proteins: 5,074 (25.9%) were defined as identical to a known human protein (Category I proteins); 4,104 (21.0%) were defined as similar to a known protein (Category II proteins); and 2,531 (12.9%) as domain-containing proteins (Category III proteins). In total, we were able to assign biological function to 59.9% of H-Inv proteins by similarity or motif searches. The remaining proteins, for which no biological functional was inferred, were annotated as conserved hypothetical proteins (Category IV proteins; 1,706, 8.7%) if they had a high level of similarity to other hypothetical proteins in other species, or as hypothetical proteins (Category V proteins; 6,159, 31.5%) if they did not. Figure 4 Schematic Diagram of Human Curation for H-Inv Proteins The diagram illustrates the human curation pipeline to classify H-Inv proteins into five similarity categories; Category I , II, III, IV, and V proteins. To predict the functions of hypothetical proteins (Category IV and V proteins), we used 196 sequence patterns of functional importance derived from tertiary structures of protein modules, termed 3D keynotes (Go 1983; Noguti et al. 1993). Application of the 3D keynotes to the H-Inv proteins resulted in the prediction of functions in 350 hypothetical proteins (see Protocol S1). Features of ORFs deduced from human FLcDNAs The mean and median lengths of predicted ORFs were calculated for the 19,574 H-Inv proteins. These were 1,095 bp and 806 bp, respectively (Table 4). The values obtained were smaller than those from other eukaryotes, and are inconsistent with estimates reported previously (Shoemaker et al. 2001). However, as has been seen in the earlier annotation of the fission yeast genome (Das et al. 1997), our dataset might contain stretches which mimic short ORFs. This would lead to a bias in our ORF prediction and result in an erroneous estimate of the average ORF length. We examined the size distributions of ORFs from the five categories, and found that the distribution pattern was quite similar across categories. The exception was Category V, in which short ORFs were unusually abundant (Figure S3). Judging from the length distribution of ORFs in the five categories of H-Inv proteins, the majority of ORFs shorter than 600 bps in Category V seemed questionable. In order to have a protein dataset that contains as many sequences to be further analyzed as possible, we have taken the longest ORFs over 80 amino acids if no significant candidates were detected by the sequence similarity and gene prediction (see Figure S1). The consequence of this is that Category V appears to contain short questionable ORFs, a certain fraction of which may be prediction errors. Nevertheless, these ORFs could be true. It is also possible that those ORFs were in fact translated in vivo when we curated the cDNAs manually. The existence of many functional short proteins in the human proteome is already confirmed, and there are 199 known human proteins that are 80 amino acids or shorter in the current Swiss-Prot database. We think that the H-Inv hypothetical proteins require experimentally verification in the future. Excluding the hypothetical proteins from the analysis, we obtained mean and median lengths for the ORFs of 1,368 bp and 1,130 bp, respectively, which are reasonably close to those for other eukaryotes (Table 4). Table 4 The Features of Predicted ORFs Nonredundant proteome datasets of nonhuman species were obtained from the following URLs: fly (Drosophila melanogaster; http://flybase.bio.indiana.edu/), worm (Caenorhabditis elegans; http://www.wormbase.org/), budding yeast (Saccharomyces cerevisiae; http://www.pasteur.fr/externe), fission yeast (Schizosaccharomyces pombe; http://www.sanger.ac.uk/), plant (Arabidopsis thaliana; http://mips.gsf.de/proj/thal/index.html), and bacteria (Escherichia coli K12; http://www.ncbi.nlm.nih.gov/) Of the 4,104 Category II proteins, 3,948 proteins (96.2%) were similar to the functionally identified proteins of mammals (Figure S4). This implies that the predicted functions in this study were based on the comparative study with closely related species, so that the functional assignment retains a high level of accuracy if we suppose that protein function is more highly conserved in more closely related species. Moreover, the patterns of codon usage and the codon adaptation index (CAI; http://biobase.dk/embossdocs/cai.html) of H-Inv proteins were investigated (Table S2). The results indicated that the ORF prediction scheme worked equally well in the five similarity categories of H-Inv proteins. Each H-Inv protein in the five categories was investigated in relation to the tissue library of origin (Table S3). We found that at least 30% of the clones mainly isolated from dermal connective, muscle, heart, lung, kidney, or bladder tissues could be classified as Category I proteins. Hypothetical proteins (Category V), on the other hand, were abundant in both endocrine and exocrine tissues. This bias may indicate that expression in some tissues may not have been studied in enough detail. If this is the case, then there is likely a significant gap between our current knowledge of the human proteome and its true dimensions. Non-protein-coding genes Over recent years, ncRNAs have been found to play key roles in a variety of biological processes in addition to their well-known function in protein synthesis (Moore and Steitz 2002; Storz 2002). Analysis of the H-Inv cDNA dataset revealed that 6.5% of the transcripts are possibly non-protein-coding, although the number is much smaller than that estimated in mice (Okazaki et al. 2002). We believe that this difference between the two species is mainly due to the larger number of mouse libraries that were used and to a rare-transcript enrichment step that was applied to these collections. To identify ncRNAs, we manually annotated 1,377 representative non-protein-coding transcripts, which were classified into four categories (see Table 3; Figure 5): putative ncRNAs, uncharacterized transcripts (possible 3′ UTR fragments supported by ESTs), unclassifiable transcripts (possible genomic fragments), and hold transcripts (not stringently mapped onto the human genome). Of these, 296 (19.5%) were putative ncRNAs with no neighboring transcripts in the close vicinity (> 5 kb) and supported by ESTs with a poly-A signal or a poly-A tail, indicating that these may represent genuine ncRNA genes. On the other hand, a large fraction of the non-protein-coding transcripts (675; 44.5%) were classified as possible 3′ UTRs of genes that were mapped less than 5 kb upstream. The 5-kb range is an arbitrary distance that we defined as one of our selection criteria for identifying ncRNAs. However, authentic non-protein-coding genes might be located adjacent to other protein-coding genes (as described earlier). Thus, some of the transcripts initially annotated as uncharacterized ESTs may correspond to ncRNAs when these sequences satisfy the other selection criteria. Figure 5 The Manual Annotation Flow Chart of ncRNAs Candidate non-protein-coding genes were compared with the human genome, ESTs, cDNA 3′-end features and the locus genomic environment. The candidates were then classified into four categories: hold (cDNAs improperly mapped onto the human genome); uncharacterized transcripts (transcripts overlapping a sense gene or located within 5 kb of a neighboring gene with EST support); putative ncRNAs (multiexon or single exon transcripts supported by ESTs or 3′-end features); and unclassifiable (possible genomic fragments). We defined a manual annotation strategy (Figure 5) that allowed us to select convincing putative ncRNAs with various lines of supporting evidence. These are the following: absence of a neighboring gene in the close vicinity, overlap with human or mouse ESTs, occurrence in the 3′ end of cDNA sequences, as well as overlap with mouse cDNAs. Out of 296 annotated putative ncRNAs, we identified 47 ncRNAs with conserved RNA secondary structure motifs (Rivas and Eddy 2001), and nearly 60% of these were found expressed in up to eight human tissues (data not shown), indicating that the manual curation strategy employed in this study may facilitate the identification of novel non-protein-coding genes in other species. The functions of human proteins identified through an analysis of domains Proteins in many cases are composed of distinct domains each of which corresponds to a specific function. The identification and classification of functional domains are necessary to obtain an overview of the whole human proteome. In particular, the analysis of functional domains allows us to elucidate the evolution of the novel domain architectures of genes that life forms have acquired in conjunction with environmental changes. The human proteome deduced from the H-Inv cDNAs was subjected to InterProScan, which assigned functional motifs from the PROSITE, PRINTS, SMART, Pfam, and ProDom databases (Mulder et al. 2003). A total of 19,574 H-Inv proteins were examined, and 9,802 of them (50.1%) were assigned at least one InterPro code that was classified into either repeats (a region that is not expected to fold into a globular domain on its own), domains (an independent structural unit that can be found alone or in conjunction with other domains or repeats), and/or families (a group of evolutionarily related proteins that share one or more domains/repeats in common) when compared with those of fly, worm, budding and fission yeasts, Arabidopsis thaliana, and Escherichia coli (Table S4). Moreover, the proteins were classified according to the Gene Ontology (GO) codes that were assigned to InterPro entries (Table S5). Identification of human enzymes and metabolic pathways One of the most important goals of the functional annotation of human cDNAs is to predict and discover new, previously uncharacterized enzymes. In addition, revealing their positions in the metabolic pathways helps us understand the underlying biochemical and physiological roles of these enzymes in the cells. We thus searched for potential enzymes among the H-Inv proteins, and mapped them to a database of known metabolic pathways. We could assign 656 kinds of potential Enzyme Commission (EC) numbers to 1,892 of the 19,574 H-Inv proteins based on matches to the InterPro entries and GO assignments and on the similarity to well-characterized Swiss-Prot proteins (see Dataset S2). The number of characterized human enzymes significantly increased through this analysis. The most abundant enzymes in the H-Inv proteins were protein–tyrosine kinases (EC 2.7.1.112), which is consistent with the large number of kinases found in the InterPro assignments. The other major enzymes were small monomeric GTPase (EC 3.6.1.47), adenosinetriphosphatase (EC 3.6.1.3), phosphoprotein phosphatase (EC 3.1.3.16), ubiquitin thiolesterase (EC 3.1.2.15), and ubiquitin-protein ligase (EC 6.3.2.19). These enzymes are members of large multigene families that are important for the functions of higher organisms. Furthermore, we could assign 726 EC numbers to mouse representative transcripts and proteins (Okazaki et al. 2002), and most of them appeared to be shared between human and mouse (data not shown). The high similarity of the enzyme repertoire between these two species is not surprising if we consider the close evolutionary relatedness between them. It does, however, indicate the usefulness of the mouse as a model organism for studies concerning metabolism. We then mapped all H-Inv proteins on the metabolic pathways of the KEGG database, a large collection of information on enzyme reactions (Kanehisa et al. 2002). In total, we mapped 963 H-Inv proteins on a total of 1,613 KEGG pathways, of which 641 were based on their EC number assignments (Figure S5). Those based on EC number assignments do not necessarily function as they are assigned because they have yet to be verified experimentally. However, if all other enzymes along the same pathway exist in humans, the functional assignment has a high probability of being correct. Using this method, we discovered a total of 32 newly assigned human enzymes from the H-Inv proteins with the support of KEGG pathways (Table S6). For example, we identified (1) pyridoxamine-phosphate oxidase (EC 1.4.3.5; AK001397), an enzyme in the “salvage pathway,” the function of which is the reutilization of the coenzyme pyridoxal-5′-phosphate (its role in epileptogenesis was recently reported [Bahn et al. 2002]), (2) ATP-hydrolysing 5-oxoprolinase (EC 3.5.2.9; AL096750) that cleaves 5-oxo-L-proline to form L-glutamate (whose deficiency is described in the Online Mendelian Inheritance in Man [OMIM] database [ID=260005]), and (3) N-acetylglucosamine-6-phosphate deacetylase (EC 3.5.1.25; BC018734), which catalyzes N-acetylglucosamine at the second step of its catabolism, the activity of which in human erythrocytes was detected by a biochemical study (Weidanz et al. 1996). Many of the newly identified enzymes were supported by currently available experimental and genomic data. An example is a putative urocanase (EC 4.2.1.49; AK055862) that mapped onto the “histidine metabolism” that urocanic acid catabolises. A 14C Histidine tracer study unexpectedly revealed that NEUT2 mice deficient in 10-formyltetrahydrofolate dehydrogenase (FTHFD) excrete urocanic acid in the urine and lack urocanase activity in their hepatic cytosol (Cook 2001). We then found that both the FTHFD and AK055862 genes were located within the same NCBI human contig (NT005588) on Chromosome 3. Moreover, the distance between the two genes was consistent with the genetic deletion of NEUT2 (> 30 kb). We thus assumed that FTHFD and urocanase might be coincidentally defective in mice. This analysis could confirm that the AK055862 protein is a true urocanase. This example demonstrates that this kind of in silico analysis is a powerful method in defining the functions of proteins. Polymorphism in the Transcriptome Sites of potential polymorphism in cDNAs Due to the rapidly increasing accumulation of genetic polymorphism data, it is necessary to classify the polymorphism data with respect to gene structure in order to elucidate potential biological effects (Gaudieri et al. 2000; Sachidanandam et al. 2001; Akey et al. 2002; Bamshad and Wooding 2003). For this purpose, we examined the relationship between publicly available polymorphism data and the structure of our H-Inv cDNA sequences. A total of 4 million single nucleotide polymorphisms (SNPs) and insertion/deletion length variations (indels) with mapping information from the Single Nucleotide Polymorphism Database (dbSNP; http://www.ncbi.nlm.nih.gov/SNP/, build 117) (Sherry et al. 1999) were used for the search. We could identify 72,027 uniquely mapped SNPs and indels in the representative H-Inv cDNAs and observed an average SNP density of 1/689 bp. To classify SNPs and indels with respect to gene structure, the genomic coordinates of SNPs were converted into the corresponding nucleotide positions within the mapped cDNAs. The SNPs and indels were classified into three categories according to their positions: 5′ UTR, ORF, and 3′ UTR (Table 5). The density of indels was higher in 5′ UTRs (1/15,999 bp) and 3′ UTRs (1/12,553 bp) than in ORFs (1/45,490 bp). This is possibly due to different levels of functional constraints. We also examined the length of indels and found a higher frequency of indels in those ORFs that had a length divisible by three and that did not change their reading frames. We observed that the density of SNPs was higher in both the 5′ and 3′ UTRs (1/569 bp and 1/536 bp, respectively) than in ORFs (1/833 bp). Table 5 The Numbers of SNPs and indels Occurring in the Representative cDNAs aThe numbers of SNPs and indels are summarized for representative cDNA sequences which were mapped on the genome. The numbers in parentheses represent the densities of SNPs and indels bSNPs that cause nonsense mutation or extension of polypeptides were classified assuming that the cDNAs represent original alleles cThis figure includes 64 unclassifiable SNPs SNPs located in ORFs were classified as either synonymous, nonsynonymous, or nonsense substitutions (Table 5). We identified 13,215 nonsynonymous SNPs that affect the amino acid sequence of a gene product. At least 4,998 of these nonsynonymous SNPs are “validated” SNPs (as defined by dbSNP). This data can be used to predict SNPs that affect gene function. SNPs that create stop codons can cause polymorphisms that may critically alter gene function. We identified 358 SNPs that caused either a nonsense mutation or an extension of the polypeptide. We classified these 358 SNPs into these two types based on the alleles of the cDNA. Most of these SNPs (315/358) were predicted to cause truncation of the gene products and produce a shorter polypeptide compared with the alleles of H-Inv cDNAs. For example, Reissner's fiber glycoprotein I (AK093431) contains a nonsense SNP that results in the loss of the last 277 amino acids of the protein, and consequently the loss of a thrombospondin type I domain located in its C-terminal end. This SNP is highly polymorphic in the Japanese population, the frequencies of G (normal) and T (termination) being 0.43 and 0.57, respectively. As seen in this example, the identification of SNPs within cDNAs provides important insights into the potential diversity of the human transcriptome. Thus, polymorphism data crossreferenced to a comprehensively annotated human transcriptome might prove to be a valuable tool in the hands of researchers investigating genetic diseases. Sites of microsatellite repeats Among the 19,442 representative protein-coding cDNAs, we identified a total of 2,934 di-, tri-, tetra-, and penta-nucleotide microsatellite repeat motifs (Table 6). Interestingly, 1,090 (37.2%) of these were found in coding regions, the majority of which (86.9%) were tri-nucleotide repeats. Di-, tetra-, and penta-nucleotide repeats made up the greatest proportion of repeats in 5′ UTRs and 3′ UTRs. Coding regions contained mostly tri-nucleotide repeats. This result is consistent with the idea that microsatellites are prone to mutations that cause changes in numbers of repeats. Only tri-nucleotide repeats can conserve original reading frames when extended or shortened by mutations. A previous study showed that many of the microsatellite motifs identified in human genomic sequences, including those in coding regions, are highly polymorphic in human populations (Matsuzaka et al. 2001). We found this to be the case in our study: 36 of the microsatellite repeats we detected were found to be polymorphic in human populations according to dbSNP records (data not shown). We identified 216 microsatellite repeats in 213 genes that showed contradictory numbers of repeats between cDNA and genome sequences (see Dataset S3). This figure includes 25 microsatellites in ORFs that have the potential to alter the protein sequences. Individual cases need to be verified by further experimental studies, but many of these microsatellites may really be polymorphic in human populations and have marked phenotypic effects. Table 6 The Numbers of Microsatellite Repeat Motifs That Occurred in the Representative cDNAs Microsatellites were defined as those sequences having at least ten repeats for di-nucleotide repeats and at least five repeats for tri-, tetra-, and penta-nucleotide repeats. Numbers of polymorphic microsatellites inferred by comparisons of cDNA and genomic sequences are shown in parenthesis. See Table S2 for a list of accession numbers for these cDNAs There were 382 cDNAs that possessed two or more microsatellites in their nucleotide sequences. This is illustrated in RBMS1 (BC018951), a cDNA which encodes an RNA-binding motif. This cDNA has four microsatellites, (GGA)7, (GAG)9, (GAG)6, and (GCC)6, in its 5′ UTR. These microsatellites are all located at least 98 bp upstream of the start codon, but they could still have pronounced regulatory effects on gene expression. Another example is the cDNA that encodes CAGH3 (AB058719). This cDNA has four microsatellites, (CAG)8, (CAG)6, (CAG)8, and (CAG)8, all of which are located within the ORF. These microsatellites all encode stretches of poly-glutamine, which are known to have transcription factor activity (Gerber et al. 1994) and often cause neurodegenerative diseases when the number of repeats exceeds a certain limit. A typical example of a disorder caused by these repeats is Huntington's disease (Andrew et al. 1993; Duyao et al. 1993; Snell et al. 1993). We also searched for repeat motifs containing the same amino acid residue in the encoded protein sequences. We located a total of 3,869 separate positions where the same amino acid was repeated at least five times. The most frequent repetitive amino acids are glutamic acid, proline, serine, alanine, leucine, and glycine. The glutamine repeats of this nature were found in 160 different locations. Evolution of the Human Transcriptome Beyond the study of individual genes, the comparison of numerous complete genome sequences facilitates the elucidation of evolutionary processes of whole gene sets. Moreover, the FLcDNA datasets of humans and mice give us an opportunity to investigate the genome-wide evolution of these two mammals by using the sequences supported by physical clones. Here we compared our human cDNA sequences with all proteins available in the public databases. Focusing on our results, we discuss when and how the human proteome may have been established during evolution. Furthermore, the evolution of UTRs is examined through comparisons with cDNAs from both primates and rodents. Conserved and derived protein-coding genes in humans An advantage of large-scale cDNA sequencing is that it can generate a nearly complete gene set with good evidence for transcription. The human proteome deduced from the FLcDNA sequences gives us an opportunity to decipher the evolution of the entire proteome. Here we compare the representative H-Inv cDNAs with the Swiss-Prot and TrEMBL protein databases using FASTY (Pearson 2000), and we describe the distributions of the homologs among taxonomic groups at two different similarity levels. The number of representative H-Inv cDNAs that have homolog(s) in a given taxon was counted (Figure S6), and the cDNAs were classified into functional categories (Figure 6). These results indicated that homologs of the human proteins were probably conserved much more in the animal kingdom than in the others at both moderate (E <10−10) and weak (E < 10−5) similarity levels (see Figure S6). Moreover, human sequences had as many nonmammalian animal homologs as mammalian homologs, with seemingly little bias to any one function (see Figure 6). This suggests that the genetic background of humans may have already been established in an early stage of animal evolution and that many parts of the whole genetic system have probably been stable throughout animal evolution despite the seemingly drastic morphological differences between various animal species. This result is consistent with our previous observation that the distribution of the functional domains is highly conserved among animal species (see Table S4). The number of homologs may have been inflated by recent gene duplication events within the human lineage. Hence we counted the number of paralog clusters instead of cDNAs that had homologs in the databases, and obtained essentially the same results (Figure S7). Figure 6 The Functional Classification of H-Inv Proteins That Are Homologous to Proteins in Each Taxonomic Group The numbers of representative H-Inv cDNAs with sequence homology to other species' proteins (E < 10−5) were calculated. The cDNAs for which we could not assign any functions were discarded. Mammalian species were excluded from the “animal” group. “Eukaryote” represents eukaryotic species other than those included in the mammal, animal, fungi, and plant groups. See also Table S7. This analysis also revealed a number of potential human-specific proteins, which did not have any homologs in the current sequence databases. In this case the creation of lineage-specific genes through speciation is not completely excluded. However, most ORFs with no similarity to known proteins would not be genuine for the reasons discussed above. Therefore, the number of “true” human-specific proteins is expected to be relatively small. We conducted further BLASTP searches matching entries from the Swiss-Prot database against the H-Inv dataset itself. As a result, 12,813 (45.3%) of 28,263 vertebrate proteins had homologs in nonvertebrates at E < 10–30. Taking into account that the dataset is relatively small (approximately 12,000 sequences) and as a result may be biased, animal species may conceivably share a similar protein-coding gene set. Ohno (1996) proposed that the emergence of a large number of animal phyla in a short period of time would endow them with almost identical genomes. These were collectively referred to as the pananimalia genome. Our data support Ohno's hypothesis from the perspective that the basic gene repertoires of animals are essentially highly similar among diverse species that have evolved separately since the Cambrian explosion. Subsequently, morphological evolution seems to have been brought about mainly by changes in gene regulation. The number of transcription regulator homologs is different between animals and other phyla (Table S7). In this analysis it was not possible to examine the genes recently deleted from the human lineage. However, the similarity of the proteome sets between distantly related mammals such as human and mouse (Waterston et al. 2002) suggests that not many genes have been deleted specifically from humans since humans and mice diverged. A unique feature of the Animalia proteome is, for example, the presence of apoptosis regulator homologs, which are found widely in the animal kingdom, whilst they are rare in the other phyla (Table S7). Since apoptosis plays an important role during the development of multicellular animals, this observation indicates that apoptosis was established independently of both plants and fungi during the early evolution of multicellularization in the kingdom Animalia. Likewise, signal transducers and cell-adhesion proteins are distinctive. In contrast, enzymes, translation regulators, molecular chaperones, etc. were highly conserved among all taxonomic groups. These proteins may have played such essential roles that any alterations were eliminated by strong purifying selection. It is assumed some functions were presumably derived from ancient endocellular symbionts (mitochondria and chloroplasts) (Martin 2002). Evolution of untranslated regions The UTRs of mRNA are known to be involved in the regulation of gene expression at the posttranscriptional level through control of translation efficiency (Kozak 1989; Geballe and Morris 1994; Sonenberg 1994), mRNA stability (Zaidi and Malter 1994; McCarthy and Kollmus 1995), and mRNA localization (Curtis et al. 1995; Lithgow et al. 1997). Only a few studies on very limited datasets have been carried out so far to describe quantitatively either the evolutionary dynamics of mRNA UTRs (Larizza et al. 2002), or their general structural and compositional features (Pesole et al. 1997). The human transcriptome presented here along with the murid data obtained mainly from the FANTOM2 project enables us to stabilize a mammalian genome perspective on the subject (Table S8). A sliding window analysis of UTR sequence identities between humans and mice revealed a positive correlation between the number of indels in an untranslated region and the distance from the coding sequence (Figure 7). Unlike indels, mismatches are distributed equally along whole untranslated regions. In other words, indels seem to be less tolerated in close proximity to a coding sequence, while substitutions are evenly distributed along the untranslated regions of the mRNAs. This seems to be a general pattern observed similarly in other species (data not shown). Indels in UTRs may have been avoided so that the distance between the coding region and a signal sequence for regulation in the UTR could be conserved throughout evolution, while purifying selection against substitutions appeared to be relatively weak. Figure 7 Window Analysis of Similarity between Human and Mouse UTRs Results for 5′ UTRs presented above and for 3′ UTRs below. The whole mRNA sequences were aligned using a semiglobal algorithm as implemented in the map program (Huang 1994) with the following parameters: match 10, mismatch −3, gap opening penalty −50, gap extension penalty −5, and longest penalized gap 10; the terminal gaps are not penalized at all. A window size of 20 bp was used with a step of 10 bp. The analysis window was moved upstream and downstream of start and stop codons, respectively. The normalized score for a given window is calculated as a fraction of an average score for all UTRs in a given window over the maximum score observed in all 5′ or 3′ UTRs, respectively. Untranslated region replacement A replacement of the entire UTR may lead to drastic changes in gene expression, especially if a UTR having a posttranscriptional signal is replaced by another. We compared the evolutionary distances of UTRs between primate and rodent orthologous sequences. We based our analysis on the UTR sequence distances that contradicted the expected phylogenetic tree of relatedness. We could detect 149 UTR replacements distributed among different species. Some of the observed replacements may result from selection of different AS isoforms of a single locus in different species. This is particularly likely if an AS event involves an alternative first or last exon. It seems that UTR replacements are more frequent in rodents than in primates, but the difference is not statistically significant at the 5% significance level (Table S9). We detected a UTR replacement in less than 2% of the analyzed sequences. The evolutionary consequences could be significant because the UTR replacement might result in changes in expression level or the loss of an mRNA localization signal. The H-Invitational Database All the results of the mapping of the FLcDNA sequences onto the human genome, the clustering of FLcDNA sequences, sequence alignments, detection of AS transcripts, sequence similarity searches, functional annotation, protein structure prediction, subcellular localization prediction, SNP mapping, and evolutionary analysis, as well as the basic features of FLcDNA sequences, are stored in the H-InvDB (Figure S8). The H-InvDB is a unique database that integrates annotation of sequences, structure, function, expression, and diversity of human genes into a single entity. It is useful as a platform for conducting in silico data mining. The database has functions such as a keyword search, a sequence similarity search, a cDNA search, and a searchable genome browser. It is hoped that the H-InvDB will become a vital resource in the support of both basic and applied studies in the fields of biology and medicine. We constructed two kinds of specialized subdatabases within the H-InvDB. The first is the Human Anatomic Gene Expression Library (H-Angel), a database of expression patterns that we constructed to obtain a broad outline of the expression patterns of human genes. We collected gene expression data from normal and diseased adult human tissues. The results were generated using three methods on seven different platforms. These included iAFLP (Kawamoto et al. 1999; Sese et al. 2001), DNA arrays (long oligomers, short oligomers [Haverty et al. 2002], cDNA nylon microarrays [Pietu et al. 1999], and cDNA glass slide microarrays [Arrays/IMAGE-Genexpress]), and cDNA sequence tags (SAGE [Velculescu et al. 1995; Boon et al. 2002], EST data [Boguski et al. 1993; Kawamoto et al. 2000], and MPSS [Brenner et al. 2000]). By normalizing levels of gene expression in experiments conducted with different methods, we determined the gene expression patterns of 19,276 H-Inv loci in ten major categories of tissues. This analysis allowed us to clearly distinguish broadly and evenly expressed housekeeping genes from those expressed in a more restricted set of tissues (details will be published elsewhere). The H-Angel database comprises the largest and most comprehensive collection of gene expression patterns currently available. Also provided is a classification of human genes by expression pattern. The second subdatabase of the H-InvDB is DiseaseInfo Viewer. This is a database of known and orphan genetic diseases. We tried to relate H-Inv loci with disease information in two ways. Firstly, 613 H-Inv loci that correspond with known, characterized disease-related genes were identified by creating links to entries in both LocusLink (http://www.ncbi.nlm.nih.gov/LocusLink/) and OMIM (Hamosh et al. 2002). To explore the possibility that cDNAs encoding unknown proteins may be related to “orphan pathologies” (diseases that have been mapped to chromosomal regions, but for which associated genes have not yet been described), we generated a list of H-Inv loci that co-localized with these cytogenetic regions. The nonredundant orphan disease dataset we created consists of 586 diseases identified through OMIM (http://www.ncbi.nlm.nih.gov/Omim/, ver. Jan. 2003), with an additional 108 identified from GenAtlas (http://www.dsi.univ-paris5.fr/genatlas/, ver. Jan. 2003). Using the OMIM and GenAtlas databases in conjunction with the annotation results from the H-InvDB may accelerate the process of identifying candidate genes for human genetic diseases. Concluding Remarks There are a number of established collections of nonhuman cDNAs, such as those of Drosophila melanogaster (Stapleton et al. 2002), Danio rerio (Clark et al. 2001), Arabidopsis thaliana (Seki et al. 2002), Plasmodium falciparum (Watanabe et al. 2002), and Trypansoma cruzi (Urmenyi et al. 1999). The most extensive collection of mammalian cDNAs so far has been that of the RIKEN/FANTOM mouse cDNA project (Kawai et al. 2001; Okazaki et al. 2002). This wealth of information has spurred a wide variety of research in the areas of both gene expression profiling (Miki et al. 2001) and protein–protein interactions (Suzuki et al. 2001). The H-InvDB provides an integrative means of performing many more such analyses based on human cDNAs. The most important findings that have resulted from the cDNA annotation are summarized here. (1) The 41,118 H-Inv cDNAs were found to cluster into 21,037 human gene candidates. Comparison with known and previously predicted human gene sets revealed that these 21,037 hypothesized gene clusters contain 5,155 new gene candidates. (2) The primary structure of 21,037 human gene candidates was precisely described. For the majority of them we observed that both first introns and last exons tended to be longer than the other introns and exons, respectively, implying the possible existence of intriguing mechanisms of transcriptional control in first introns. (3) We discovered the existence of 847 human gene candidates that could not be convincingly mapped to the human genome. This result suggested that up to 3.7%–4.0% of the human genome sequences (NCBI build 34 assembly) may be incomplete, containing either unsequenced regions or regions where sequence assembly has been performed in error. (4) Based on H-Inv cDNAs, we were able to define an experimentally validated AS dataset. The dataset was composed of 3,181 loci that encoded a total of 8,553 AS isoforms. In the 55% of ORFs containing AS isoforms, the pattern of alternative exon usage was found to encode different functional domains at the same loci. (5) A standardized method of human curation for the H-Inv cDNAs was created under the tacit consensus of international collaborations. Using this method, we classified 19,574 H-Inv proteins into five categories based on sequence similarity and structural information. We were able to assign functional definitions to 9,139 proteins, to locate function- or family-defining InterPro domains in 2,503 further proteins, and to identify 7,800 transcripts as good candidates for hypothetical proteins. (6) A total of 1,892 H-Inv proteins were assigned identities as one of 656 different EC-numbered enzymes. This enzyme library includes 32 newly identified human enzymes on known metabolic pathway maps and comprises the largest collection of computationally validated human enzymes. (7) Based on a variety of supporting evidence, 6.5% of H-Inv loci (1,377 loci) do not have a good protein-coding ORF, of which 296 loci are strong candidates for ncRNA genes. (8) We identified and mapped 72,027 SNPs and indels to unique positions on 16,861 loci. Of these, 13,215 nonsynonymous SNPs, 358 nonsense SNPs, and 452 indels were found in coding regions and may alter protein sequences, cause phenotypic effects, or be associated with disease. In addition, we identified 216 polymorphic microsatellite repeats on 213 loci, 25 of which were located in coding regions. (9) During human proteome analysis, it was suggested that the basic gene set of humans might have been established in the early stage of animal evolution. Our analysis of UTRs revealed that insertions or deletions near coding regions were rare when compared with substitutions, though in some cases drastic changes such as UTR replacements occurred. (10) A consequence of the annotation process and our related research was the development of the H-InvDB to contain our annotation work. H-InvDB is a comprehensive database of human FLcDNA annotations that stores all information produced in this project. As a subdivision of H-InvDB, we developed two other specialized subdatabases: H-Angel and DiseaseInfo Viewer. H-Angel is a database of gene expression patterns for 19,276 loci. DiseaseInfo Viewer is a database of known disease-related genes and loci co-localized with 694 orphan pathologies. These pathologies were mapped onto the genome but were not identified experimentally. In the H-Inv project, we collected as many FLcDNAs as possible and conducted extensive analyses concerning the quality of cDNAs, such as detection of frameshift errors, retained introns, and internal poly-A priming, under a unified criterion. Although these analyses are still in an elementary state, we store these results in H-InvDB to share this information with the biological community. We believe that this is an important contribution of our project, because it will provide a reliable way to control the quality of the cDNA clones. In the future, this information will be useful for improving the methods of clone library construction. It has been suggested that the human genome encodes 30,000 to 40,000 genes. In this study we comprehensively evaluated more than 21,000 human gene candidates (up to 70% of the total). Thus, efforts should be continued by the H-Inv consortium and others to “fully” characterize the human transcriptome. For this purpose new technologies should be implemented that are more sensitive in detecting rarely expressed genes and AS transcripts. Nevertheless, there are unavoidable limitations for human cDNA collections, such the identification of embryo-specific genes, for which other approaches should be employed. One alternative is the use of ab initio predictions from genomic sequences, in conjunction with expression profiling studies, to identify rarely expressed genes that share structural similarity to known genes. Additionally, a better characterization of cis-regulatory element units may help to define the boundary of other genes that are undetected by current gene prediction programs. Another area that remains to be explored is the identification of potential hidden RNA gene families that may play vital roles, such as the recently uncovered family of microRNA genes, which is involved in the regulation of expression of other genes (for review see Ambros 2001; Moss 2002). The proteome determination aspects of this project, including the identification of new enzymes and hypothetical proteins, should stimulate more focused biochemical studies. The functional classifications may allow definition of subproteomes that are related to different physiological processes. The H-Inv transcriptome based on the definition of a consensus proteome (the H-Inv proteins) links both the analysis of genomic DNA and direct proteome analysis with the study of expressed mRNA analysis from different tissues, cells, and disease states. It creates a standard for the comparison of disease-related alterations of the human proteome. Moreover, comparison with pathogen proteomes may yield many possible drug target proteins. Also, the annotation of ncRNAs raises the possibility of novel “smart” therapeutics that could either inhibit or mimic the mechanisms of these RNAs. The H-Inv project is the first ever comprehensive compilation of curated and annotated human FLcDNAs. The project may lead to a more complete understanding of the human transcriptome and, as a result, of the human proteome. The preceding examples of the importance of the H-Inv data in understanding human physiology and evolution represent just a small fraction of the research potential of the H-InvDB. In conclusion, the H-InvDB platform constructed to hold the results of the comprehensive annotations performed by our international team of collaborators represents a substantial contribution to resources that are needed for further exploration of both human biology and pathology. Materials and Methods cDNA resources 41,118 H-Inv cDNAs were sequenced by the Human Full-Length cDNA Sequencing Project (Ota et al. 1997; Yudate et al. 2001; Ota et al. 2004) at the Helix Research Institute, the Institute of Medical Science at the University of Tokyo, and the Kazusa DNA Research Institute (20,999 sequences in total); the Kazusa cDNA Sequencing Project (Kikuno et al. 2002) at the Kazusa DNA Research Institute (2,000 sequences); the Mammalian Gene Collection (Strausberg et al. 1999) at the National Institutes of Health in the United States (11,806 sequences); the German Human cDNA Project (Wiemann et al. 2001) coordinated by the Deutsches Krebsforschungszentrum in Heidelberg (5,555 sequences); and the Chinese National Human Genome Center at Shanghai (Hu et al. 2000) (758 sequences). Mapping human cDNAs to the human genome and the comparison of the mapped H-Inv cDNAs with other annotated datasets We have mapped human cDNA sequences to the human genome sequence corresponding to the NCBI build 34 assembly. The datasets we used were a set of 41,118 H-Inv cDNAs and a set of 37,488 human RefSeq sequences available on 15 July 2002 and on the 1 September 2003, respectively. All the revisions for H-Inv cDNA sequences until August 2003 were applied in the datasets. Before performing the mapping procedure, all the repetitive and low-complexity sequences in all the cDNA sequences were masked using RepeatMasker (http://ftp.genome.washington.edu/RM/RepeatMasker.html) and Repbase 7.5. Then we used the cross_match program to mask the remaining vector sequences in each cDNA sequence. Any poly-A tails were also masked by using a custom-made Perl script. In the first step of the mapping procedure, we conducted BLASTN (ver.2.2.6) searches of all the sequences against the human genome sequence and extracted the corresponding genomic regions for each query sequence. Then we used est2genome (EMBOSS package ver.2.7.1) to align each sequence to the genomic region with a threshold of 95% identity and 90% coverage. Coverage of each cDNA sequence was calculated excluding those from the vector and poly-A tails that were masked in the previous step. If the sequences were mapped to multiple positions on the human genome, then we selected their best locus based on the identity, length coverage, and number of exons of those sequences. As a result, 77,315 sequences (including 40,140 cDNAs from the H-Inv project) were successfully mapped onto the human genome and were clustered into 38,587 clusters based on sharing at least 1 bp of an exon on the same chromosome strand. We used all the mapped sequences, including human RefSeq sequences, to compare the clusters that included H-Inv cDNAs with those that consisted of only human RefSeq sequences. 20,190 clusters out of 38,587 consisted of only H-Inv cDNAs or both H-Inv cDNAs and human RefSeq sequences. The rest of the clusters consisted of RefSeq sequences only. All of the mapped cDNAs and the overlap with the RefSeq sequences can be viewed using G-integra in the H-InvDB (http://www.jbirc.aist.go.jp/hinv/g-integra/html/). The mapping procedure for all the unmapped cDNAs against the mouse genome was also performed, using a threshold of 60% identity and 90% coverage. Clustering of unmapped sequences The sequences that were not mapped onto the human genome were clustered by a single linkage clustering method. The similarity search was performed among all the unmapped sequences. The program used was MegaBLAST version 2.2.6 (Zhang et al. 2000). As with to the mapping strategy, some distinctive sequences (repetitive regions, contaminations from cloning vectors and poly-A tails) were excluded from the queries of the similarity search. The similarity was evaluated using the expected value (E-value) between two sequences. Only when the E-value of the two sequences was calculated to be 0, did we assume that a significant level of similarity was detected between the two sequences. Identification of gene structure In order to identify gene structure, we used only the representative H-Inv cDNAs. When detecting repetitive elements in cDNAs, RepeatMasker was conducted in a similar manner to the previous phase. We used curated cDNAs in which frameshift errors and remaining introns were removed. Prediction of ORFs We predicted ORFs in all 41,118 H-Inv cDNAs, as illustrated in Figure S1, based on the alignment of similarity searches by FASTY (Pearson 2000; Mackey et al. 2002) (ver. 3.4t11) and BLASTX (Altschul et al. 1990) (ver. 2.0.11), and gene prediction by GeneMark (McIninch et al. 1996) (http://opal.biology.gatech.edu/GeneMark/) (Table S10). Prior to the prediction of ORFs, we judged if the sequence had any frameshift errors or remaining introns (see Figure S1). During ORF prediction, we corrected the aforementioned sequence irregularities computationally. Procedure of computational and human annotation Prior to the human curation, we performed two computational automated annotation processes to select the representative clone for each locus and to predict function of H-Inv proteins (see Figure S2). We then assigned the most suitable data source ID to each H-Inv protein following a scheme illustrated in Figure S2 and referring to the information using newly developed annotation viewers, named SOUP location viewer, SOUP annotation viewer, and Similarity Motif ORF (SMO) Viewer (Figure S9). Questionable transcripts were determined by human curation based upon evidence such as the following: sequences with no similarity to a known protein or domain, sequences with a very short ORF, cDNAs with only a single exon, and sequences with no EST support. Only 959 (4.9%) of the computationally selected 19,574 representative H-Inv proteins had to be manually corrected. Another 3,142 (16.1%) of the H-Inv proteins had their functional assignment altered by manual curation. Assignment of functional motifs Nonredundant proteome datasets were obtained for fly (http://flybase.bio.indiana.edu/), worm (http://www.wormbase.org/), budding yeast (http://www.pasteur.fr/externe), fission yeast (http://www.sanger.ac.uk/), plant (http://mips.gsf.de/proj/thal/index.html), and a bacteria (ftp://ftp.ncbi.nih.gov/genbank/genomes/Bacteria/Escherichia_coli_K12/). The H-Inv proteins and other nonredundant proteome datasets were assigned InterPro codes by InterProScan ver. 3.1 (Mulder et al. 2003). The codes corresponded to families, domains, and repeats. GO terms were also assigned (see Table S5). Evolutionary relationship of proteomes The top 40 InterPro entries for the human proteome were compared with their equivalents from the fly, worm, yeasts, plant, and bacteria proteomes (see Table S4). Protein domains and low-complexity inserted sequences Folds were assigned by reverse PSI-BLAST (Altschul et al. 1997) searches of the amino acid sequences derived from the H-Inv cDNA against the SCOP database (Lo Conte et al. 2000). Information on protein and gene structures, with the exception of mouse and puffer fish, was obtained from the individual genome projects (Blattner et al. 1997; Kunst et al. 1997; CESC 1998; Adams et al. 2000; AGI 2000; Wood et al. 2002). The data for mouse and puffer fish were obtained from the Ensembl database (Hubbard et al. 2002). Subcellular localization Subcellular localization targeting signals and transmembrane helices of 40,352 H-Inv proteins were predicted using the PSORT II (Nakai and Horton 1999), TargetP (Emanuelsson et al. 2000), TMHMM, and SOSUI (Hirokawa et al. 1998) computer programs. UTR sequences We obtained the UTR sequences from three primates (Pan troglodytes, chimpanzee; Macaca fascicularis, crab-eating macaque; and Macaca mulatta, rhesus monkey) and two rodents (Mus musculus, house mouse; and Rattus norvegicus, Norwegian rat) that corresponded to UTRs from Homo sapiens. In order to do this, we mapped the cDNAs to the human or mouse genome. The corresponding rodent cDNAs were determined by using a human–mouse genome alignment provided by Ensembl. cDNAs of the primates and rodents were retrieved from the DDBJ/EMBL/GenBank databases using the cut off date of 15 July 2002. Additionally, we used the FANTOM2 mouse sequences released on 5 December 2002, and 4,063 5′ ESTs of chimpanzees (Sakate et al. 2003). Corresponding UTRs between human and other species were identified by aligning 5′ and 3′ ends of the human ORFs. To compare evolutionary distances, we analyzed 3,061 and 5,277 orthologous groups that consisted of at least three species' information for the 5′ and 3′ UTR sequences, respectively. Supporting Information Dataset S1 List of Library Origins of H-Inv cDNAs (182 Libraries) The dataset consists of 41,118 H-Inv cDNAs that were cloned from cDNA libraries derived from 182 varieties of cell and tissue. (33 KB XLS). Click here for additional data file. Dataset S2 List of H-Inv Proteins with Potential EC Numbers (1,892 H-Inv Proteins) The allotted EC numbers are based on the corresponding DNA databank records, UniProt/Swiss-Prot and TrEMBL records that show sequence similarity to the proteins, and InterPro records that the proteins hit. (247 KB XLS). Click here for additional data file. Dataset S3 List of Polymorphic Microsatellites Inferred by Comparisons between the H-Inv cDNAs and Genomic Sequences (56 KB XLS). Click here for additional data file. Figure S1 Prediction of ORFs (A) Schematic diagram for the prediction of ORFs. This diagram illustrates the ORF prediction method used on all H-Inv cDNAs. The method was based upon the alignment of similarity searches using FASTY and BLASTX. Gene prediction was carried out using GeneMark. Prior to the prediction of ORFs, we judged if a sequence had any frameshift errors or remaining introns. During ORF prediction, we corrected those sequence irregularities computationally. Details of how sequence irregularities were predicted are described in (B) and (C). (B) Schematic diagram for prediction of unspliced introns. This schematic diagram illustrates the prediction method used for unspliced introns. (C) Schematic diagram for prediction of frameshift errors. Frameshift errors were inferred from cDNA–genome pairwise alignment gaps due to insertion or deletion, exception of multiple of 3 bp, or over 10 bp in either the query cDNA or genome. (D) The statistics for the predicted frameshifts and unspliced introns. (49 KB PDF). Click here for additional data file. Figure S2 Scheme of Prediction for Functional Annotation (A) Schematic diagram for determining a representative transcript for each locus. The procedure of computational autoannotation is illustrated. Prior to the human curation of the representative transcript of each H-Inv cluster, we performed computational autoannotation. (B) Schematic diagram for functional prediction of H-Inv proteins. This schematic diagram illustrates the H-Inv autofunctional annotation pipeline that can determine the most appropriate data source ID, avoiding the following keywords that suggest proteins without experimental verification in the description; (1) hypothetical, (2) similar to, (3) names of cDNA clones (Rik, KIAA, FLJ, DKFZ, HSPC, MGC, CHGC, and IMAGE) and (4) names of InterPro domain frequent hitters. (34 KB PDF). Click here for additional data file. Figure S3 Size Distribution of Predicted ORFs The size distribution of all H-Inv proteins among the five similarity categories. (24 KB PDF). Click here for additional data file. Figure S4 Features of Category II Proteins A total of 4,104 H-Inv proteins were classified as Category II based on sequence similarity to functionally validated proteins. The table and figure show source species of proteins in public databases to which the Category II proteins were similar. (9 KB PDF). Click here for additional data file. Figure S5 H-Inv KEGG Analysis Results (Images of KEGG Pathways) The images illustrate the metabolic pathways of KEGG database based on the EC number assignments to H-Inv proteins. (47 KB PDF). Click here for additional data file. Figure S6 Numbers of Representative H-Inv cDNAs That Are Homologous to Proteins in Each Taxonomic Group Two thresholds (E < 10−5, white bars, and E < 10−10, black bars) were employed. The “animal” group does not include mammalian species. The “eukaryote” group represents eukaryotic species other than animals, fungi, and plants. (9 KB PDF). Click here for additional data file. Figure S7 A Functional Classification of H-Inv Protein Families That Have Homologs in Each Taxonomic Group H-Inv protein families were identified by clustering H-Inv proteins using the single-linkage clustering method. Then, the number of homologs for each H-Inv protein family was calculated. Mammalian species are excluded from the “animal” group. “eukaryote” represents eukaryotic species other than animals, fungi, and plants. Single-linkage clustering. All of the H-Inv proteins were compared with themselves by BLASTP and clustered with the thresholds of E-values of 10−30 and 10−50. The numbers of singleton families detected were 11,890 and 13,938 at the E-value of 10−30 and 10−50, respectively. (49 KB PDF). Click here for additional data file. Figure S8 A Sample View of the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/) A FLcDNA (BC003551) is shown with its detailed annotations, e.g., gene structure, functional annotation, ORF predictions, protein structure prediction by GTOP, etc. The H-InvDB has links to other internal databases (red boxes) such as a genome map viewer (G-integra) and gene expression library (H-Angel). Green boxes show internal viewers for the results of clustering (Clustering Viewer showing results by H-Inv, STACK, TIGR, UniGene, etc.), the prediction of subcellular localization (TOPOViewer showing results of TMHMM, SOSUI, TargetP, and PsortII), and the disease-related information (DiseaseInfo Viewer linking to OMIM and GenAtlas). The H-InvDB also has links to many external public databases (black boxes), including DDBJ/EMBL/GenBank, RefSeq, UniProt/Swiss-Prot and TrEMBL, Genew, InterPro, 3D Keynote, Ensembl, GeneLynx, LocusLink, PubMed, LIFEdb, dbSNP, GO, and GTOP, and to homepages by original data producers of FLcDNA clones and sequences (blue boxes), including the Chinese National Human Genome Center (CHGC), the Deutsches Krebsforschungszentrum (DKFZ/MIPS), Helix Research Institute (HRI), the Institute of Medical Science at the University of Tokyo (IMSUT), the Kazusa DNA Research Institute (KDRI), the Mammalian Gene Collection (MGC/NIH), and the FLJ project. (2,650 KB PDF). Click here for additional data file. Figure S9 H-Inv Annotation Viewers (A) G-integra: A genome mapping viewer. (B) SOUP Locus annotation viewer. (C) SOUP cDNA annotation viewer. (D) SMO Viewer: The similarity, motif, and ORF information viewer. (2,022 KB PDF). Click here for additional data file. Table S1 Gene Structure (A) Gene structure of the cDNAs. (B) The frequencies and varieties of repetitive sequences found in the cDNAs. A list of the 20,899 loci representing cDNAs that RepeatMasker showed contained repetitive elements. (C) The positions (5′ UTR, ORF, and 3′ UTR) of repetitive sequences in the protein-coding cDNAs. A total of 1,863 cDNAs contained repetitive sequences in their ORF, of which 549 had repetitive sequences within their most probable ORF. Repetitive sequences appeared in 2,240 and 5,401 cDNAs in their 5′ UTRs and 3′ UTRs, respectively. (20 KB PDF). Click here for additional data file. Table S2 CAI and Codon Usage (A) CAI was measured for all H-Inv proteins. CAI is a measure of biased patterns for synonymous codon usage (http://biobase.dk/embossdocs/cai.html). (B) Codon usage in predicted ORFs of H-Inv proteins. Total tri-nucleotide frequencies (forward strand) for the sequences of each species are shown. Nonredundant proteome datasets for nonhuman species were obtained from the following sites: fly (Drosophila melanogaster; http://flybase.bio.indiana.edu/), worm (Caenorhabditis elegans; http://www.wormbase.org/), budding yeast (Saccharomyces cerevisiae; http://www.pasteur.fr/externe), fission yeast (Schizosaccharomyces pombe; http://www.sanger.ac.uk/), plant (Arabidopsis thaliana; http://mips.gsf.de/proj/thal/index.html), and bacteria (Escherichia coli K12; ftp://ftp.ncbi.nih.gov/genbank/genomes/Bacteria/Escherichia_coli_K12/). (20 KB PDF). Click here for additional data file. Table S3 Tissue Library Origins of H-Inv Proteins The results of classification into five similarity categories for each of ten tissue classes. (A) Numbers of H-Inv proteins. (B) Histogram. (10 KB PDF). Click here for additional data file. Table S4 The InterPro IDs Identified in H-Inv Proteins The top 40 InterPro IDs identified in H-Inv proteins and proteins from other species are listed for all types (A) and for each type of family, domain, and repeat (B–D). Analyses were conducted by InterPro ver. 3.1. Nonredundant proteome datasets of other species were obtained from the following sites: fly (Drosophila melanogaster; http://flybase.bio.indiana.edu/), worm (Caenorhabditis elegans; http://www.wormbase.org/), budding yeast (Saccharomyces cerevisiae; http://www.pasteur.fr/externe), fission yeast (Schizosaccharomyces pombe; http://www.sanger.ac.uk/), plant (Arabidopsis thaliana; http://mips.gsf.de/proj/thal/index.html), and bacteria (Escherichia coli K12; ftp://ftp.ncbi.nih.gov/genbank/genomes/Bacteria/Escherichia_coli_K12/). (36 KB PDF). Click here for additional data file. Table S5 GO Term Assignment to H-Inv Proteins (A) Molecular function. (B) Cellular component. (C) Biological process. (74 KB PDF). Click here for additional data file. Table S6 List of Newly Assigned Human Enzymes (32 H-Inv Proteins) All these 32 H-Inv proteins were newly assigned enzyme numbers with the support of the KEGG pathway. These enzyme assignments were previously unrepresented in Homo sapiens. (33 KB PDF). Click here for additional data file. Table S7 A Functional Classification of Representative H-Inv cDNAs That Have Homologs in Other Species (See also Figure 6.) (9 KB PDF). Click here for additional data file. Table S8 Basic Statistics for UTR Sequences Analyzed (8 KB PDF). Click here for additional data file. Table S9 UTR Replacements in Primates and Rodents One hundred and forty-seven UTR replacements distributed among different species were detected. (9 KB PDF). Click here for additional data file. Table S10 List of the Databases and Software Used in the H-Inv Project (31 KB PDF). Click here for additional data file. Protocol S1 A Detailed Functional Annotation Based on Protein Modules (25 KB PDF). Click here for additional data file. This paper is dedicated to the late Dr. Yoshimasa Kyogoku, the Director of the Biological Information Research Center, National Institute of Advanced Industrial Science and Technology, who passed away on February 27, 2003. The authors express their most sincere gratitude to Drs. David Lipman, Graham Cameron, Joakim Lundeberg, and Francis Collins for their support, the Research Association for Biotechnology of Japan, the International Human Genome Sequencing Consortium, and the Chromosome 22 Group at the Sanger Institute for providing sequence and annotation data. We are grateful to T. Hasui, T. Habara, K. Yamaguchi, H. Kawashima, F. Todokoro, N. Yamamoto, Y. Makita, R. Aono, Y. Tanada, H. Kubooka, H. Maekawa, Y. Sasayama, T. Yamamoto, S. Okiyama, K. Nakamura, A. Matsuya, Y. Mimiura, R. Matsumoto, K. Takabayashi, Y. Hayakawa, H. Zhang, S. Nurimoto, T. Sugisaki, T. Kawamura, O. Nakano, S. Hosoda, N. Yoshimura, and T. Endo for their technical support. This research is financially supported by the Ministry of Economy, Trade, and Industry of Japan (METI), the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT), the Japan Biological Informatics Consortium (JBIC), the New Energy and Industrial Technology Development Organization (NEDO), the United States Department of Energy, the National Institutes of Health of the United States, the Bundesministerium für Bildung und Forschung (BMBF) of Germany, the European Union through the EURO-IMAGE Consortium (grant BMH4-CT97-2284 coordinated by Charles Auffray), the 863 and 973 Program of the Ministry of Science and Technology of China, and CNRS of France. The work on Module 3D-keynote is supported by Grants-in-Aid for Scientific Research on Priority Areas (C) “Genome Information Science” to Mitiko Go and Kei Yura, and for Scientific Research (B) to MG, from MEXT. KY is also supported by a Grant-in-Aid for Encouragement of Young Scientists from MEXT. The work on subcellular localization is supported by a Grant-in-Aid for Scientific Research on Priority Areas (C) “Genome Information Science” from MEXT and the National Project on Protein Structural and Functional Analyses from the same Ministry. The data were analyzed by T. Imanishi, T. Itoh, Y. Suzuki, C. O'Donovan, S. Fukuchi, K. O. Koyanagi, R. A. Barrero, T. Tamura, Y. Yamaguchi-Kabata, M. Tanino, K. Yura, S. Miyazaki, K. Ikeo, K. Homma, A. Kasprzyk, T. Nishikawa, M. Hirakawa, J. Thierry-Mieg, D. Thierry-Mieg, J. Ashurst, L. Jia, M. Nakao, M. A. Thomas, N. Mulder, Y. Karavidopoulou, L. Jin, S. Kim, T. Yasuda, B. Lenhard, E. Eveno, Y. Suzuki, C. Yamasaki, J.-I. Takeda, C. Gough, P. Hilton, Y. Fujii, H. Sakai, S. Tanaka, C. Amid, M. Bellgard, M. de Fatima Bonaldo, H. Bono, S. K. Bromberg, A. Brookes, E. Bruford, P. Carninci, C. Chelala, C. Couillault, S. J. De Souza, M.-A. Debily, M.-D. Devignes, I. Dubchak, T. Endo, A. Estreicher, E. Eyras, K. Fukami-Kobayashi, G. Gopinathrao, E. Graudens, Y. Hahn, M. Han, Z.-G. Han, K. Hanada, H. Hanaoka, E. Harada, K. Hashimoto, U. Hinz, M. Hirai, T. Hishiki, I. Hopkinson, S. Imbeaud, H. Inoko, A. Kanapin, Y. Kaneko, T. Kasukawa, J. F. Kelso, P. Kersey, R. Kikuno, K. Kimura, B. Korn, V. Kuryshev, I. Makalowska, T. Makino, S. Mano, R. Mariage-Samson, J. Mashima, H. Matsuda, H.-W. Mewes, S. Minoshima, K. Nagai, H. Nagasaki, N. Nagata, R. Nigam, O. Ogasawara, O. Ohara, M. Ohtsubo, N. Okada, T. Okido, S. Oota, M. Ota, T. Ota, T. Otsuki, D. Piatier-Tonneau, A. Poustka, S.-X. Ren, N. Saitou, K. Sakai, S. Sakamoto, R. Sakate, I. Schupp, F. Servant, S. Sherry, R. Shiba, N. Shimizu, M. Shimoyama, A. J. Simpson, B. Soares, C. Steward, M. Suwa, M. Suzuki, A. Takahashi, G. Tamiya, H. Tanaka, T. Taylor, J. D. Terwilliger, P. Unneberg, V. Veeramachanen, S. Watanabe, L. Wilming, N. Yasuda, H.-S. Yoo, W. Makalowski, M. Go, K. Nakai, Y. Okazaki, W. Hide, R. Chakraborty, Z. Chen, P. Tonellato, K. Okubo, L. Wagner, S. Wiemann, T. Isogai, C. Auffray, N. Nomura, T. Gojobori, and S. Sugano. The paper was written by T. Imanishi, T. Itoh, Y. Suzuki, S. Fukuchi, K. O. Koyanagi, R. A. Barrero, T. Tamura, Y. Yamaguchi-Kabata, M. Tanino, K. Yura, K. Homma, M. Hirakawa, L. Jia, M. Nakao, B. Lenhard, C. Yamasaki, C. Gough, P. Hilton, Y. Fujii, S. Tanaka, C. Chelala, M.-D. Devignes, T. Hishiki, I. Hopkinson, W. Makalowski, K. Nakai, W. Hide, P. Tonellato, C. Auffray, N. Nomura, T. Gojobori, and S. Sugano. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. The project was conceived and designed by T. Imanishi, T. Itoh, Y. Suzuki, C. O'Donovan, S. Fukuchi, Y. Yamaguchi-Kabata, S. Miyazaki, K. Ikeo, A. Kasprzyk, T. Nishikawa, M. Stodolsky, W. Makalowski, M. Go, K. Nakai, T. Takagi, M. Kanehisa, Y. Sakaki, J. Quackenbush, Y. Okazaki, Y. Hayashizaki, W. Hide, R. Chakraborty, K. Nishikawa, H. Sugawara, Y. Tateno, Z. Chen, M. Oishi, P. Tonellato, R. Apweiler, K. Okubo, L. Wagner, S. Wiemann, R. L. Strausberg, T. Isogai, C. Auffray, N. Nomura, T. Gojobori, and S. Sugano. Academic Editor: Richard Roberts, New England Biolabs Abbreviations 3Dthree-dimensional ASalternative splicing CAIcodon adaptation index dbSNPSingle Nucleotide Polymorphism Database DDBJDNA Data Bank of Japan ECEnzyme Commission EMBLEuropean Molecular Biology Laboratories ESTexpressed sequence tag FANTOMFunctional Annotation of Mouse FLcDNAfull-length cDNA FLJFull-Length Long Japan FTHFDformyltetrahydrofolate dehydrogenase GOGene Ontology GTOPGenomes TO Protein structures and functions database H-AngelHuman Anatomic Gene Expression Library H-Inv or H-InvitationalHuman Full-Length cDNA Annotation Invitational H-InvDBH-Invitational Database iAFLPintroduced amplified fragment length polymorphism NCBINational Center for Biotechnology Information ncRNAsnon-protein-coding RNAs OMIMOnline Mendelian Inheritance in Man ORFopen reading frame PDBProtein Data Bank RefSeqReference Sequence Collection SMOSimilarity SNPsingle nucleotide polymorphism ==== Refs References Adams MD Kelley JM Gocayne JD 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expansion and phenotypic variation in Huntington's disease Nat Genet 1993 4 393 397 8401588 Sonenberg N mRNA translation: Influence of the 5′ and 3′ untranslated regions Curr Opin Genet Dev 1994 4 310 315 8032210 Sorek R Ast G Graur D Alu-containing exons are alternatively spliced Genome Res 2002 12 1060 1067 12097342 Stapleton M Liao G Brokstein P Hong L Carninci P The Drosophila gene collection: Identification of putative full-length cDNAs for 70% of D. melanogaster genes Genome Res 2002 12 1294 1300 12176937 Storz G An expanding universe of noncoding RNAs Science 2002 296 1260 1263 12016301 Strausberg RL Feingold EA Klausner RD Collins FS The mammalian gene collection Science 1999 286 455 457 10521335 Strausberg RL Feingold E Grouse L Derge J Klausner R Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences Proc Natl Acad Sci U S A 2002 99 16899 16903 12477932 Suyama M Nagase T Ohara O HUGE: A database for human large proteins identified by 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of Alu repeats in cDNAs, as determined by database searching Genomics 1995 27 544 548 7558040 Zaidi SHE Malter JS Amyloid precursor protein mRNA stability is controlled by a 29-base element in the 3′-untranslated region J Biol Chem 1994 269 24007 24013 7929051 Zhang Z Schwartz S Wagner L Miller W A greedy algorithm for aligning DNA sequences J Comput Biol 2000 7 203 214 10890397

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PLoS Biol. 2004 Jun 20; 2(6):e162

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PLoS Biol

10.1371/journal.pbio.0020162

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020166SynopsisBioinformatics/Computational BiologyGenetics/Genomics/Gene TherapyHomo (Human)Annotation Marathon Validates 21,037 Human Genes synopsis6 2004 20 4 2004 20 4 2004 2 6 e166Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones ==== Body The announcement of the human genome sequence three years ago was widely hailed as one of the great scientific achievements in modern history, and with good reason. Determining the structure and nature of the genetic code promises to provide valuable insights into human evolution and the molecular basis of disease. But sequencing the genome is just the first step toward this decidedly worthy goal—the monumental task of ascribing biological meaning to those sequences has just begun. And while researchers know a great deal about some of the 30,000 or so genes in the human genome, they have yet to ascribe function to the majority of them. Takashi Gojobori and a large international team of collaborators have now taken a big step toward narrowing this knowledge gap. Scientists at the annotation “marathon” of 41,118 cDNA clones Deciphering the human genome presents such a daunting challenge in part because it's so huge, making it difficult to distinguish genetic signal from noise. Simpler organisms have much more compact genomes. In the case of brewer's yeast, for example, genes that encode proteins account for about 70% of the genome. In contrast, only about 1% to 2% of the human genome codes for proteins. That translates to about one gene for every 2,000 bases for yeast compared to about one gene for every 150,000 bases for humans. The low density of human genes makes identifying them difficult enough, but this process is further complicated by how genes are organized in the human genome. The functional parts are broken up into smaller segments called exons, which are separated in the genome by intervening sequences called introns. This configuration also occurs in simpler organisms, but since the number and size of introns is relatively small in simpler organisms, it's easier to tell what's a gene and what isn't. In humans, the introns are extremely long, as are the gaps between the genes, and the exons are tiny in comparison; plus, it takes many more of these short, scattered exons to make one gene. One approach to this problem is to use computer algorithms that scan the genome sequence looking for segments of DNA sequences that could potentially encode proteins. Gojobori and colleagues, however, used a different approach. They analyzed the sequences of 41,118 full-length cDNAs available from six sequencing centers around the world. These cDNAs are stretches of DNA that represent genes that have already been expressed and used by the cell for protein production. Since all the exons have been spliced together and the introns removed, these cDNAs correspond to the functional versions of these genes, allowing researchers to work backward, looking for the sequences in the genome. In order to process the 41,118 cDNAs, the researchers used a combination of computer algorithms and expert human analysis. To tackle such an enormous project, 158 genome scientists, representing 67 institutions from 12 countries, gathered in Japan in the summer of 2002. Over the course of a ten-day annotation marathon, the scientists validated, mapped, and annotated the cDNAs. As things stand, the team has been able to assemble the cDNAs into over 20,000 strong candidates for human genes. From just the initial analysis of the data generated by this group, several valuable findings about the human genome have emerged: there are over 5,000 candidates for new genes, including an exciting group of several hundred that do not appear to encode proteins; up to 4% of the genome appears not to be represented in the current human genome sequence; and several thousand DNA sequence variants have been uncovered that will be useful for disease mapping studies. But perhaps most important of all, the data from this study have been collected and assembled into a large searchable database called H-Invitational Database, which is linked to other functional databases around the world. This will be an invaluable resource for geneticists, and will serve as a starting point for further analyses. Future research on the human genome will be aimed at expanding the list of known genes and analyzing the properties of these genes. This study not only moves us closer to a complete functional description of the human genome, it also builds on the traditions of international cooperation and large-scale collaboration that played such an important part in deciphering the sequence itself.

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PLoS Biol. 2004 Jun 20; 2(6):e166

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PLoS Biol

10.1371/journal.pbio.0020166

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020137Research ArticleEvolutionGenetics/Genomics/Gene TherapyYeast and FungiSaccharomycesNoise Minimization in Eukaryotic Gene Expression Noise in Gene ExpressionFraser Hunter B hunter@ocf.berkeley.edu 1 Hirsh Aaron E 2 Giaever Guri 3 Kumm Jochen 3 Eisen Michael B 1 4 1Department of Molecular and Cell Biology, University of CaliforniaBerkeley, CaliforniaUnited States of America2Department of Biological Sciences, Stanford UniversityStanford, CaliforniaUnited States of America3Stanford Genome Technology CenterStanford, CaliforniaUnited States of America4Genome Sciences Department, Genomics DivisionLawrence Berkeley National Laboratory, Berkeley, CaliforniaUnited States of America6 2004 27 4 2004 27 4 2004 2 6 e1378 1 2004 9 3 2004 Copyright:© 2004 Fraser et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Turning Down the Volume: Why Some Genes Need Less Noise All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or “noise.” Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection. Analysis of gene expression data for nearly every gene in yeast provides evidence that random variation in the production rate of proteins could significantly affect the fitness of an organism ==== Body Introduction Stochasticity is a ubiquitous characteristic of life. Such apparent randomness, or “noise,” can be observed in a wide range of organisms, resulting in phenomena ranging from progressive loss of cell-cycle synchronization in an initially synchronized population of microbes to the pattern of hair coloration in female calico cats. An important source of stochasticity in biological systems is the random noise of transcription and translation, which can result in very different rates of synthesis of a specific protein in genetically identical cells in essentially identical environments (Elowitz et al. 2002; Ozbudak et al. 2002; Blake et al. 2003). Understanding how stochasticity contributes to cellular phenotypes is important to developing a more complete picture of how cells work. Accordingly, noise in gene expression and other cellular processes has been a major focus of research for more than a decade. While several cases have been described where stochasticity is advantageous (e.g., phase variation in bacteria [Hallet 2001] and the lysis/lysogeny decision in phage lambda [Arkin et al. 1998]), it is expected that noise is not advantageous in most cellular processes, as precisely controlled levels of gene expression are presumably optimal (c.f. Barkai and Leibler 2000). However, whether noise in expression is of consequence to organismal fitness has not previously been investigated, despite the centrality of this question to our understanding of the role of noise in biological systems. In this study, we investigate whether the differences in noise levels among genes are consistent with the hypothesis that noise in gene expression has been subject to natural selection to reduce its deleterious effects. We propose that random fluctuations in the expression levels of two groups of genes in yeast, essential genes and genes encoding protein complex subunits, should be particularly consequential for organismal fitness. If noise in gene expression is not an important factor to yeast—i.e., if the level of stochasticity experienced by yeast in gene expression is below that which would have negative consequences—then we would expect to see no difference in the randomness of expression in genes for which noisy expression is predicted to be relatively more or less deleterious. However, if stochasticity is an important variable on which natural selection has acted, we would expect to see the strongest signature of such selection in the expression of genes for which yeast are the most sensitive to randomness. Results If deletion of a gene has only a small deleterious effect on the fitness of yeast, then random fluctuations in the amount of protein produced from that gene are likely to have a similarly small, or even smaller, impact. In contrast, the same fluctuations in the level of a protein essential for viability may have a profound effect on fitness; in the extreme, fluctuation to levels below that required for normal cellular function could compromise viability. Considering this predicted difference in the sensitivity of yeast to randomness in expression of essential versus relatively dispensable genes, we reasoned that if noise in gene expression is a biologically important variable, selection for reduction of stochasticity in expression levels would likely be stronger for essential genes than for nonessential ones. A recent study linking noise in protein levels to transcription and translation rates in yeast (Blake et al. 2003) allows us to test this prediction. In the study, noise in the expression of a green fluorescent protein (GFP) reporter gene was measured by flow cytometry; stochasticity was measured as the amount of variation in GFP levels per cell in a population. Thus if all cells in a population had very similar levels of GFP, there was little noise in the production of the GFP. The effect of transcription and translation on noise levels was studied by independently varying these two parameters and measuring the resulting noise levels for a population of cells. This experimental approach, as well as a mathematical model of protein production (Blake et al. 2003), indicates that noise in protein production is maximized at intermediate levels of transcription (at approximately one-third of the maximal transcription rate of a gene, regardless of what that maximum is; see Materials and Methods), as well as at maximal levels of translation per mRNA molecule. To produce a given amount of any particular protein, yeast could adopt one of three qualitatively different strategies (Thattai and van Oudenaarden 2000) (Figure 1): (1) maximize transcription and minimize translation per mRNA, (2) maximize translation per mRNA and minimize transcription, or (3) employ intermediate levels of both transcription and translation per mRNA. Importantly, strategy 1 should result in less stochasticity than strategy 2 or 3. Strategy 2 is noisy due to the high translation, and strategy 3 is noisy due to both intermediate transcription and translation (the data currently available do not allow us to predict whether expression strategy 2 is more or less noisy than strategy 3). In contrast, noise is minimized at both transcription and translation steps for genes that exhibit strategy 1. Thus we predicted that if noise in protein production is an important factor in yeast, then genes that are essential for viability would be biased towards having high transcription rates and a low number of translations per mRNA. Figure 1 Strategies for Expression Three different strategies for achieving a given rate of protein production (four proteins will be produced in each case) and the amount of noise in expected to result from each strategy. Curved lines represent mRNA molecules, with ribosomes translating them; a larger number of mRNA molecules represents higher transcription, and a larger number of ribosomes per mRNA represents higher translation per mRNA. To test this prediction, we estimated protein production rates (proteins/s; see Materials and Methods) for all yeast genes and asked whether essential genes tended to adopt strategy 1 more often than nonessential genes with similar protein production rates. It was critical to control for overall rates of protein production, as there is an overall correlation between a gene's dispensability (defined as the growth defect of a yeast strain missing that gene in rich glucose medium, i.e., an essential gene is indispensable) and its rate of protein production (Figure S1). This correlation between dispensability and the rate of protein synthesis may have nothing to do with stochasticity; most essential proteins may simply be needed in somewhat greater quantity than most nonessential proteins, so their genes must be more highly transcribed and/or translated. Since such a relationship could lead to an association between gene importance and the likelihood of adopting expression strategy 1, we employed two statistical methods to control for this possibility. In the first of these two methods, we binned yeast genes by their protein production rate, so that all genes in each of 15 bins had approximately equal levels of protein production (see Table S1 for details). The genes in each bin could have achieved their similar protein production levels by any of the three strategies listed above; our prediction was that if noise in gene expression is relevant to yeast, then essential genes would be biased towards having the highest transcription and lowest translation per mRNA (strategy 1) in each bin. Indeed, this was confirmed by the data: when the genes within each bin were separated into thirds by their number of translations per mRNA, a larger number of essential genes were in the third with the lowest number of translations (low noise) than in the third with the highest number of translations (high noise) for all but one of 15 bins (Figure 2A). A Fisher's exact test (Sokal and Rohlf 1994) demonstrated that for all of the 14 bins with more essential genes in the low noise third than the high noise third, this difference was significant (p ≤ 0.02). Similar results were found when using different numbers of bins, when using halves or quartiles instead of thirds, or when separating bins by transcription rate instead of by number of translations per mRNA (data not shown). This result cannot be explained by the overall positive correlation between dispensability and rate of protein synthesis. (In the binning analysis, the third of each bin with the lowest translation rate had, on average, a slightly lower overall protein synthesis rate than the third with the highest translation rate [data not shown]; this bias is the opposite of what would be expected from the positive correlation between protein synthesis rate and fitness effect or protein complex membership, and thus it acts against our observed bias to make the results of this analysis conservative estimates of the true bias.) Figure 2 Essential Genes and Protein Complex Subunits Minimize Noise in Expression Binning analysis of (A) essential genes and (B) protein complex subunits. All genes for which transcription and translation rate data were available were separated into 15 bins by their protein production rate. Each bin was then separated into thirds by number of translations per mRNA. The two-thirds in each bin with the most extreme transcription and translation are shown: black bars are the number of each type of gene (essential or complex subunit) in the third of each bin with the lowest number of translations per mRNA and the highest transcription rate, and thus low noise; gray bars are the number of each type of gene in the third with the highest number of translations per mRNA and the lowest transcripton rate, and thus high noise. Bins are ordered by their rate of protein synthesis. The number of asterisks indicates the Fisher's exact test probability of observing the values for each bin under the null model of independence. *, p ≤ 0.02; **, p < 0.005; ***, p < 0.0005. Because binning genes still allows for a small amount of variability in protein production within each bin (see Table S1), we sought to control for protein production rate in another fashion as well. We employed partial correlation, a method that allows one to examine the relationship between two variables when other, possibly confounding, variables are statistically held constant (see Materials and Methods). The stochastic model of gene expression (Blake et al. 2003) led us to the prediction that, when protein production rate is controlled for, fitness effect (f, where f = 0 indicates no effect on growth when a gene is deleted, f = 1 indicates that a gene is essential, and 0 < f < 1 indicates a quantitative growth defect [Hirsh and Fraser 2001]) would correlate positively with transcription rate and negatively with translation rate per mRNA. Indeed, this is what we observed (f versus transcription [txn] rate | protein production rate, Spearman partial r = 0.282, n = 4,746, p = 10−87; f versus translations [tlns] per mRNA | protein production rate, Spearman partial r = −0.258, n = 4,746, p = 10−75). We also expected that the relationship between gene importance and implementation of the expression strategy that minimizes noise could additionally be seen by considering transcription rate and translation rate per mRNA together, as a ratio; a large ratio of transcription rate to translations per mRNA would indicate that transcripts are produced quickly but are translated slowly, corresponding to our expression strategy 1. Confirming this, the correlation between fitness effect and the ratio of transcription rate to translations per mRNA (controlling for protein production rate) is highly significant (f versus txn rate/tlns per mRNA | protein production rate, Spearman partial r = 0.275, n = 4,746, p = 10−86). Partial correlation analysis is thus in accordance with the trend illustrated in Figure 2A: essential genes preferentially use expression strategy 1, which minimizes stochasticity. In addition to essential genes, genes whose protein products participate in stable protein complexes (“complex subunits”) would also be expected to exhibit sensitivity to randomness in expression: producing too little or too much of a single protein complex subunit can compromise the proper assembly of the entire complex and waste the energy invested in the production of the other complex subunits. In support of this, it has been found that both under- and overexpression of complex subunits is more likely to result in a reduced growth rate or inviability of yeast than is misexpression of other genes, and also that complex subunits tend to be more precisely coexpressed with other genes than noncomplex subunits (Papp et al. 2003). Using data from two high-throughput studies that identified proteins involved in stable complexes (Gavin et al. 2002; Ho et al. 2002), we assigned genes to two groups: those whose protein products were members of a stable complex found in either study and those whose protein products were not. (Since the protein complex data do not include all protein complexes, we expect that many protein complex subunits will not be classified as such in our list; this, as well as any false positives in the data, makes our results a conservative estimate the true strength of the effect.) We then performed the same binning analysis as described above, substituting our list of complex subunits for our list of essential genes. Again the prediction was confirmed: in all 15 bins, the third of the bin with the least translation per mRNA (and thus the lowest noise level) contained more complex subunits than the third with the most translation per mRNA (Figure 2B). The association between low translation per mRNA and protein complex membership was significant (Fisher's exact test, p ≤ 0.02) for all but one bin. As in Figure 2A, this result is robust with respect to the number of bins and the size of the divisions within bins (data not shown). Also as in Figure 2A, the bias is the opposite of that expected from the positive correlation between fitness effect and protein production rate; it is also the opposite of what would be the result of highly expressed genes being more likely to appear in the list of protein complex subunits than are poorly expressed genes. (It has been found that highly expressed genes are overrepresented in protein complex data [whether this is an experimental artifact or a true relationship is unclear; von Mering et al. 2002]; this would also act against our observed bias of complex subunits being overrepresented in the third with the lowest overall protein synthesis rate in each bin, thus making our results conservative.) When we repeated the partial correlation analysis for complex subunits (genes were assigned a value of one if they were a complex subunit, zero if not), we found similar results. When total protein synthesis was controlled for with the partial correlation, complex subunits were more likely to have a high transcription rate (complex subunit versus txn rate | protein production rate, Spearman partial r = 0.203, n = 4,900, p = 10−46) and a low number of translations per mRNA (complex subunit versus tlns per mRNA | protein production rate, Spearman partial r = −0.200, n = 4,900, p = 10−46). Using the ratio of transcription rate to translations per mRNA also yielded similar results (complex subunit versus txn rate/tlns per mRNA | protein production rate, Spearman partial r = 0.220, n = 4,900, p = 10−56). Thus, partial correlations confirm the finding illustrated in Figure 2B. Since proteins that participate in many protein–protein interactions are more likely to be essential (Jeong et al. 2001; Fraser et al. 2002), it was not immediately clear whether protein fitness effect and membership in a multiprotein complex are independently associated with the expression strategy that minimizes stochastic fluctuations. To address this question, we calculated the partial correlation between fitness effect and the ratio of transcription rate to translations per mRNA, while controlling for both protein production rate and protein complex membership. Likewise, we calculated the correlation between protein complex membership and the ratio of transcription rate to translation rate per mRNA while controlling for both protein production rate and fitness effect. The two partial correlations were both quite significant (f versus txn rate/tlns per mRNA | protein production rate, complex membership: Spearman partial r = 0.227, n = 4,746, p = 10−57; complex membership versus txn rate/tlns per mRNA | protein production rate, f: Spearman partial r = 0.147, n = 4,746, p = 10−24), suggesting that fitness effect and protein complex membership are independently associated with the expression strategy that minimizes stochastic fluctuation. (However, the relative strengths of the partial correlations cannot be interpreted as their true relative contributions because of the differing quality of fitness effect and protein complex membership data.) Repeating the partial correlations above with either transcription rate or translations per mRNA in place of their ratio gave significant partial correlations with both fitness effect and protein complex membership as well (data not shown). The hypothesis that genes of large fitness effect are under stronger selection to reduce stochastic fluctuation in expression provides an explanation for a previously observed, but as yet unexplained, correlate of protein evolutionary rate. Pal et al. (2001) noted a weak but significant negative correlation (r = −0.11, p = 10−9) between an mRNA's rate of decay and the evolutionary rate of the protein it encodes. This correlation was surprising, as it is precisely the opposite of what one would expect if the relationship between the rates of mRNA decay and protein evolution were mediated by the level of expression: slow decay would result in increased expression, which is known to be associated with slow evolution (Pal et al. 2001). Thus, we would expect a positive correlation between rates of mRNA decay and protein evolution, not the negative one that is observed. However, under the present hypothesis that relatively important genes are under stronger selection to reduce noise, the relationship between mRNA decay and protein evolutionary rate is interpretable. Both genes of large fitness effect and genes that encode protein complex subunits are known to evolve slowly (Hirsh and Fraser 2001; Fraser et al. 2002; Jordan et al. 2002). (While the reason why genes of large fitness effect evolve slowly has been debated [Hirsh and Fraser 2003; Pal et al. 2003], the presence of the correlation has not been disputed, and it can be seen to be much stronger than previously reported when using more accurate fitness effect and evolutionary rate data [data not shown]). Here we have shown that they are also associated with a strategy of expression that maximizes the rate of transcription and minimizes the number of translations per mRNA. Given a desired rate of protein production, one way to maximize transcription rate while minimizing the number of translations per mRNA is to maximize the mRNA decay rate. Thus, we would expect rapid mRNA decay among essential genes and protein complex subunits, both of which evolve slowly, yielding the observed negative correlation between the rates of mRNA decay and protein evolution. In support of this prediction, both essential genes and protein complex subunits have substantially shorter mRNA half-lives than the rest of the genome (e.g., mRNA half-lives of nonessential genes are 32% longer than those of essential genes, and the bias remains when controlling for protein production rate; p i= 10−36 by the Wilcoxon test [Sokal and Rohlf 1994]). Discussion We found that noise in protein production is minimized in genes for which it is likely to be most harmful, specifically essential genes and genes encoding protein complex subunits. This finding supports the hypothesis that noise in gene expression is generally deleterious to yeast. Yeast appear to control the noise in their gene expression at both transcriptional and translational levels preferentially for some genes; however, this noise minimization is not without a cost, as the high transcription and high mRNA decay rates that are needed to minimize noise are energetically expensive and are thus expected to be advantageous only when the benefit of reducing noise in a particular gene's expression outweighs this cost (Thattai and van Oudenaarden 2000). Protein degradation rates may also play a role in controlling noise, but this cannot be tested until genome-wide protein degradation rates have been measured. As is the case with many genome-wide studies, it is possible that a hidden variable could bias our results. For example, it is possible that essential genes and genes encoding protein complex subunits tend to have high transcription and low translation for reasons unrelated to noise minimization. However, until such a reason is identified, the most parsimonious interpretation of our results is that yeast adaptively minimize noise in the expression of certain genes. As genome-wide transcription and translation rate data become available for other organisms, it will be interesting to see if the apparent tendency to minimize noise in the expression of important genes extends to organisms other than yeast. Considering that several anecdotal examples of indispensable genes with unusually low translation rates, and thus low noise in expression, have already been noted in Escherichia coli (Ozbudak et al. 2002), this could well be the case. Materials and Methods Functional genomic data sources Transcription rates were calculated from mRNA abundances and decay rates in log-phase yeast growing in rich glucose medium (Wang et al. 2002) according to the steady-state equation R = −ln(0.5) * A/T, where R is transcription rate, A is mRNA abundance, and T is mRNA half-life. Translation rates per mRNA in rich glucose medium were calculated from ribosome occupancy data by Arava et al. (2003); specifically, ribosome density per mRNA present in the polysome fraction was multiplied by the fraction of each mRNA that was found in the polysome fraction to estimate the average ribosome density for all copies of each mRNA in a cell. This density is equivalent to a relative translation rate, assuming that the speed at which ribosomes produce proteins is constant over different mRNAs. An estimate of the actual translation rate was found by multiplying the relative translation rates by a constant: the speed of translation, which is approximately ten amino acids/s (Arava et al. 2003). Protein production rate (proteins/s) was then calculated by multiplying translation rate per mRNA with mRNA abundance. Note that the protein production rate can also be represented as the product of transcription rate and number of translations per mRNA. It is this latter variable that was used to separate each bin into thirds in Figure 2, since it is thought to be more directly relevant to noise in protein production than related quantities such as translation rate per mRNA (Berg 1978); the variable was calculated by dividing protein production rate by transcription rate for each gene. However, bins could also be separated into thirds by transcription rate, transcript abundance, or translation rate per mRNA, all yielding similar results (data not shown). Fitness effect ranks were calculated from 12 replicate growth experiments for all viable homozygous yeast deletion strains in rich glucose medium; growth experiments were conducted using the method described in Giaever et al. (2004). The logarithms of deletion strain tag fluorescence intensities on high-density oligonucelotide microarrays for each growth time course were fitted to a linear model that accounted for time-course-specific effects and variable initial strain concentrations. The slope of the linear regression was then used as the relative growth rate for each strain. Estimates of percent induction levels Blake et al. (2003) showed for two different promoters in yeast, as well as in their mathematical model, that noise due to transcription peaked at approximately one-third of maximal transcriptional induction. Importantly, one of their promoters (PADH1*) was 10-fold weaker than the other two at full induction, but all three showed very similar relationships between noise strength and percent transcriptional induction. Since we do not have genome-wide data for the percent induction for genes in rich glucose medium (or any other environment), in our analysis we make the assumption that the promoters of more highly transcribed genes tend to be at higher percent induction levels. While this certainly does not hold for all genes, we believe that it is a reasonable approximation for most genes. Partial correlations Partial correlations were calculated as described by Sokal and Rohlf (1994). Briefly, let rXY be the correlation coefficient between variables X and Y. To control for a third variable Z, To assess the significance of the partial correlation, it is transformed to be distributed according to a Student's t distribution, by the equation The two-sided p-value can then be calculated according to where the t-value falls with respect to its expected distribution. Supporting Information Figure S1 The Relationship between Fitness Effect and Protein Production Rate Fitness effect ranks are shown on the y-axis (the large number of points at 519.5 are the essential genes, with fitness effect = 1). Protein production rate (proteins/s) is shown on the x-axis. The Spearman rank correlation coefficient is r = –0.202 (p = 10–49). (316 KB PPT). Click here for additional data file. Table S1 Details of the Protein Production Rates (Protein/s) within Each Bin from Figure 2 (37 KB DOC). Click here for additional data file. We thank Otto Berg, Alan Moses, and Angela DePace for helpful comments. Bryan Zeitler and Jennifer Zeitler created the thumbnail image accompanying the paper. HBF is a NSF predoctoral fellow. MBE is a Pew Scholar in the Biomedical Sciences. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. HBF conceived and performed the analyses. HBF and AEH wrote the paper. GG and JK contributed fitness data. MBE edited the paper and provided a nurturing environment. Academic Editor: Ken Wolfe, University of Dublin Abbreviations GFPgreen fluorescent protein tlntranslation txntranscription ==== Refs References Arava Y Wang Y Storey JD Liu CL Brown PO Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae Proc Natl Acad Sci U S A 2003 100 3889 3894 12660367 Arkin A Ross J McAdams HH Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells Genetics 1998 149 1633 1648 9691025 Barkai N Leibler S Circadian clocks limited by noise Nature 2000 403 267 268 10659837 Berg OG A model for the statistical fluctuations of protein numbers in a microbial population J Theor Biol 1978 71 587 603 96307 Blake WJ Kaern M Cantor CR Collins JJ Noise in eukaryotic gene expression Nature 2003 422 633 637 12687005 Elowitz MB Levine AJ Siggia ED Swain PS Stochastic gene expression in a single cell Science 2002 297 1183 1186 12183631 Fraser HB Hirsh AE Steinmetz LM Scharfe C Feldman MW Evolutionary rate in the protein interaction network Science 2002 296 750 752 11976460 Gavin AC Bosche M Krause R Grandi P Marzioch M Functional organization of the yeast proteome by systematic analysis of protein complexes Nature 2002 415 141 147 11805826 Giaever G Flaherty P Kumm J Proctor M Nislow C Chemogenomic profiling: Identifying the functional interactions of small molecules in yeast Proc Natl Acad Sci U S A 2004 101 793 798 14718668 Hallet B Playing Dr. Jekyll and Mr. Hyde: Combined mechanisms of phase variation in bacteria Curr Opin Microbiol 2001 4 570 581 11587935 Hirsh AE Fraser HB Protein dispensability and rate of evolution Nature 2001 411 1046 1049 11429604 Hirsh AE Fraser HB Genomic function: Rate of evolution and gene dispensability [discussion] Nature 2003 421 497 498 Ho Y Gruhler A Heilbut A Bader GD Moore L Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry Nature 2002 415 180 183 11805837 Jeong H Mason SP Barabasi AL Oltvai ZN Lethality and centrality in protein networks Nature 2001 411 41 42 11333967 Jordan IK Rogozin IB Wolf YI Koonin EV Essential genes are more evolutionarily conserved than are nonessential genes in bacteria Genome Res 2002 12 962 968 12045149 Ozbudak EM Thattai M Kurtser I Grossman AD van Oudenaarden A Regulation of noise in the expression of a single gene Nat Genet 2002 31 69 73 11967532 Pal C Papp B Hurst LD Highly expressed genes in yeast evolve slowly Genetics 2001 158 927 931 11430355 Pal C Papp B Hurst LD Genomic function: Rate of evolution and gene dispensability Nature 2003 421 496 497 Papp B Pal C Hurst LD Dosage sensitivity and the evolution of gene families in yeast Nature 2003 424 194 197 12853957 Sokal RR Rohlf FJ Biometry 1994 New York W. H. Freeman and Company 880 Thattai M van Oudenaarden A Intrinsic noise in gene regulatory networks Proc Natl Acad Sci U S A 2000 98 8614 8619 von Mering C Krause R Snel B Cornell M Oliver SG Comparative assessment of large-scale data sets of protein–protein interactions Nature 2002 417 399 403 12000970 Wang Y Liu CL Storey JD Tibshirani RJ Herschlag D Precision and functional specificity in mRNA decay Proc Natl Acad Sci U S A 2002 99 5860 5865 11972065

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PLoS Biol. 2004 Jun 27; 2(6):e137

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020092Research ArticleDevelopmentVertebratesXenopusChickenMus (Mouse)Ascidians (Sea Squirts)Neural Induction in Xenopus: Requirement for Ectodermal and Endomesodermal Signals via Chordin, Noggin, β-Catenin, and Cerberus Neural Induction in XenopusKuroda Hiroki 1 Wessely Oliver 1 Robertis E. M. De derobert@hhmi.ucla.edu 1 1Department of Biological Chemistry, Howard Hughes Medical InstituteUniversity of California, Los Angeles, CaliforniaUnited States of America5 2004 11 5 2004 11 5 2004 2 5 e9228 11 2003 29 1 2004 Copyright: © 2004 Kuroda et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Organizing the Vertebrate Embryo -- A Balance of Induction and Competence Neural Induction without Mesoderm in Xenopus The origin of the signals that induce the differentiation of the central nervous system (CNS) is a long-standing question in vertebrate embryology. Here we show that Xenopus neural induction starts earlier than previously thought, at the blastula stage, and requires the combined activity of two distinct signaling centers. One is the well-known Nieuwkoop center, located in dorsal-vegetal cells, which expresses Nodal-related endomesodermal inducers. The other is a blastula Chordin- and Noggin-expressing (BCNE) center located in dorsal animal cells that contains both prospective neuroectoderm and Spemann organizer precursor cells. Both centers are downstream of the early β-Catenin signal. Molecular analyses demonstrated that the BCNE center was distinct from the Nieuwkoop center, and that the Nieuwkoop center expressed the secreted protein Cerberus (Cer). We found that explanted blastula dorsal animal cap cells that have not yet contacted a mesodermal substratum can, when cultured in saline solution, express definitive neural markers and differentiate histologically into CNS tissue. Transplantation experiments showed that the BCNE region was required for brain formation, even though it lacked CNS-inducing activity when transplanted ventrally. Cell-lineage studies demonstrated that BCNE cells give rise to a large part of the brain and retina and, in more posterior regions of the embryo, to floor plate and notochord. Loss-of-function experiments with antisense morpholino oligos (MO) showed that the CNS that forms in mesoderm-less Xenopus embryos (generated by injection with Cerberus-Short [CerS] mRNA) required Chordin (Chd), Noggin (Nog), and their upstream regulator β-Catenin. When mesoderm involution was prevented in dorsal marginal-zone explants, the anterior neural tissue formed in ectoderm was derived from BCNE cells and had a complete requirement for Chd. By injecting Chd morpholino oligos (Chd-MO) into prospective neuroectoderm and Cerberus morpholino oligos (Cer-MO) into prospective endomesoderm at the 8-cell stage, we showed that both layers cooperate in CNS formation. The results suggest a model for neural induction in Xenopus in which an early blastula β-Catenin signal predisposes the prospective neuroectoderm to neural induction by endomesodermal signals emanating from Spemann's organizer. New insights into the early events of neural induction which render certain cells competent to respond to signals emanating from Spemann's organizer ==== Body Introduction Vertebrate development results from a series of cell–cell interactions in which groups of cells induce their neighbors to acquire new cell differentiation fates. This process, known as embryonic induction, was first reported for the induction of the lens in surface ectoderm by the optic vesicles originating from the brain (Spemann 1901; Lewis 1904). Subsequent work showed that the surface ectoderm itself also plays an important role (reviewed by Grainger 1992). From the analysis of lens induction, Spemann (1938) proposed that a double assurance mechanism (doppelte Sicherung) could provide a way of explaining the robustness of vertebrate development via reciprocal interactions between two layers of cells. Lens induction is an example of a secondary embryonic induction. Most experimental embryologists concentrated their research on the induction of the neural plate, which is considered the primary embryonic induction (Spemann 1938; Saxén and Toivonen 1962; Harland 2000; Gilbert 2001; Stern 2002). In the classical organizer transplantation experiment, Spemann and Mangold (1924) demonstrated that dorsal lip mesoderm is sufficient to induce the differentiation of a complete central nervous system (CNS) in responding ectoderm. Spemann devoted an entire chapter of his book to the discussion of whether a double assurance mechanism existed in the case of neural plate induction (Chapter 8 in Spemann 1938) and concluded that the evidence supported a role for the underlying mesoderm, but not for the prospective neuroectoderm. A role for the gastrula ectoderm in neural plate formation had been proposed on the basis of experiments in which the mesoderm or the ectoderm had been damaged (Goerttler 1925) and received some subsequent support (Lehmann 1928). However, further consideration of the possible role of ectoderm in neural plate formation was hampered by a highly influential exogastrulation experiment performed in axolotl embryos (Holtfreter 1933), in which endomesoderm involution was prevented and the entire ectoderm differentiated into epidermis. Since there was no trace of CNS tissue in these embryos, this experiment was interpreted as a demonstration that the underlying endomesoderm had the essential role in neural plate induction and that the prospective neuroectoderm had none (Holtfreter 1933; Spemann 1938). The debate concerning whether the ectoderm itself has a role in neural plate formation has continued to this day. In dorsal marginal zone explants (Keller and Danilchik 1988; Keller 1991), CNS differentiation can take place in the absence of underlying mesoderm. It has been proposed that in these Keller explants neural tissue induction results from a “planar” signal that diffuses in the plane of the ectoderm from the mesodermal organizer at gastrula (Ruiz i Altaba 1992, 1993; Doniach et al. 1992; Poznanski and Keller 1997) (see Figure 6A). However, the existence of planar neural induction signals has been disputed, with neural induction in Keller explants proposed to result from “vertical” signals resulting from a brief contact between ectoderm and mesoderm at early gastrula (Nieuwkoop and Koster 1995). Therefore, a central question remains unanswered despite many decades of research in amphibian neural induction: What is the differentiation potential of the presumptive neural plate material in the absence of a mesodermal substratum? This is the problem addressed here. Figure 6 Anterior Neural Induction in Keller Explants Requires Chd (A) Proposed vertical and planar signals in neural induction (following Ruiz i Altaba 1993). (B) Diagram of Keller explant preparation and subsequent elongation of the endomesoderm by convergent extension (Keller 1991). (C) The neural and mesodermal regions of Keller explants contain descendants of BCNE cells (in blue) marked by blastomere injection at the 64-cell stage. (D) Expression of Otx2 and Krox20 in Keller explants (n = 7). (E) Injection of 17 ng Chd-MO completely blocked Otx2 and Krox20 expression in neural regions, while expression of Otx2 in anterior endoderm was not affected (n = 10). (F) The differentiated neuron marker N-tubulin is expressed in Keller explants (n = 8). (G) Partial inhibition of N-tubulin by injection of Chd-MO (n = 7). (H and I) Summary of the effects of Chd-MO in Keller explants. Abbreviations: SC, spinal cord; CG, cement gland; Epi, epidermis. (J) RT-PCR analyses of the effect of Chd-MO in Keller explants; samples injected with (plus) or without (minus) Chd-MO are indicated. Lane 1, whole embryos; lanes 2–7, Keller sandwiches. Note that expression of the neural markers NCAM and N-tubulin in Keller sandwiches was abolished by co-injection with 200 pg of dnFGF receptor 4a (dnFGF4a) mRNA and 17 ng of Chd-MO (lane 5). Injection with 600 pg of CerS mRNA, which eliminates mesoderm but not BCNE formation, does not affect neural induction in this assay (lane 6). Two recent technical advances led us to reinvestigate neural induction in Xenopus. First, it is now possible to completely inhibit mesoderm formation by microinjecting Cerberus-short (CerS) mRNA, a secreted antagonist specific for Nodal-related mesoderm inducers (Agius et al. 2000). Interestingly, Xenopus embryos lacking mesoderm still developed a CNS, including a cyclopic eye (Wessely et al. 2001). This was surprising, because such mesoderm-less embryos did not express multiple Spemann organizer markers such as Chordin (Chd), Noggin (Nog), and Goosecoid in dorsal endomesoderm at the gastrula stage. Second, a technical revolution has taken place with the availability of antisense morpholino oligos (MO) that permit loss-of-function studies in Xenopus (Heasman et al. 2000). It is now possible to combine the tools of amphibian experimental embryology with investigations on the role of individual genes, such as the secreted bone morphogenetic protein (BMP) antagonist Chd (Oelgeschläger et al. 2003) or its upstream regulator β-Catenin (Heasman et al. 2000), in experimentally manipulated embryos. In whole embryos injected with Chd-MO, a CNS, although of reduced size, still develops. However, Spemann organizers depleted for Chd lose all neural-inducing activity when grafted to the ventral side of a host embryo (Oelgeschläger et al. 2003). Surprisingly, when similar Chd-depleted grafts are placed on the dorsal side, ectodermal cells lose the ability to contribute to neural plate (Oelgeschläger et al. 2003). This suggested that a cell-autonomous requirement of Chd for neural plate formation might exist in the ectoderm itself. At the blastula stage, the BMP antagonists Chd and Nog are expressed in the dorsal animal cap and marginal zone, in a region we had originally designated as the “preorganizer center” (Wessely et al. 2001). This group of cells constitutes a blastula Chordin- and Noggin-expressing (BCNE) region that contains both prospective neuroectoderm cells and Spemann organizer precursors. The BCNE region also expresses Xenopus Nodal-related 3(Xnr3), a secreted factor with neural-inducing properties that is expressed at high levels in early Xenopus embryos (Haramoto et al. 2004; Wessely et al. 2004). The early phase of expression of Chd and Nog in BCNE cells is regulated by the dorsal accumulation of β-Catenin, whereas later expression of the same genes in Spemann organizer endomesoderm requires in addition Nodal-related signals that can be blocked by CerS (Wessely et al. 2001). In this study we analyze the mechanism of neural induction in Xenopus by means of embryological cut-and-paste and molecular loss-of-function experiments. We find that the BCNE center contains much of the presumptive anterior CNS. Loss-of-function studies show that gene products expressed at blastula—such as Chd, Nog, and β-Catenin—are required for neural induction in the absence of underlying endomesoderm. Cell-lineage studies show that the BCNE center itself gives rise to brain, notochord, and floor plate. Transplantation experiments show that the BCNE center is required for brain formation in Xenopus embryos. Microinjection experiments at the 8-cell stage, in which Chd-MO was injected into dorsal-animal and Cer-MO into dorsal-vegetal blastomeres, confirmed that secreted signals from both prospective neuroectoderm and underlying endomesoderm are required for anterior CNS development. The results support a double assurance mechanism for brain formation of the type proposed by Spemann (1938) for lens induction. Results The BCNE Center Is Distinct from the Nieuwkoop Center The initial asymmetry in Xenopus development is caused by a cortical rotation triggered by sperm entry, thought to redistribute “dorsal determinants” that in turn stabilize β-Catenin protein on the dorsal side of the embryo (Figure 1A) (reviewed in Gerhart et al. 1991; Harland and Gerhart 1997; De Robertis et al. 2000). At the blastula stage, the Nieuwkoop center located in dorsal-vegetal cells secretes mesoderm-inducing signals such as Xnr1, Xnr2, Xnr4, Xnr5, and Xnr6 that induce formation of the gastrula Spemann organizer in overlying mesoderm cells (Agius et al. 2000; Takahashi et al. 2000). The Nieuwkoop center has also been called the “blastula organizer” in the early literature (Gerhart et al. 1991). The BCNE region develops in the dorsal animal and marginal region. In situ hybridization analyses at the blastula stage (7 h after fertilization) showed that the neural-inducing secreted factors Chd, Nog, and Xnr3 are expressed in the animal cap, in a region that includes about 45o of arc above the floor of the blastocoel, as well as in the dorsal marginal zone (Figure 1B–1D). At the gastrula stage, the same genes are expressed in more vegetal regions, in the Spemann organizer located in the dorsal endomesoderm of the marginal zone (e.g., Figure 2E). Figure 1 Two Signaling Centers Coexist in the Xenopus Blastula (A) Diagram of early events between 1-cell stage and early blastula. (B–D) Expression of Chd, Nog, and Xnr3 transcripts just after midblastula transition (7 h postfertilization). Embryos were hybridized as whole mounts, stored in methanol for 1 mo at room temperature to improve contrast, and sectioned with a razor blade. (E) RT-PCR analysis of gene markers at midblastula, early stage 9. Six samples were prepared by dissections of blastula regions as shown in the diagram. (F) Summary of gene expression at blastula. The BCNE center expresses Chd, Nog, Siamois, and Xnr3, while the Nieuwkoop center expresses Xnr2, Xnr6, and Cer. Figure 2 The BCNE Center Contributes to Forebrain and Midline Structures (A) Method used for lineage tracing of the BCNE center with biotin-dextran amine (BDA) labeled grafts. (B) Sagittal section of a recently grafted BCNE at stage 9. (C) Chd mRNA expression at stage 9. (D) BCNE descendants at stage 10.5. (E) Chd mRNA expression at stage 10.5. (F) BCNE center descendants at stage 11. (G) Dorsal view of BCNE descendants at neural plate stage 14. (H) Double staining of transplanted BCNE region with nuclear lacZ mRNA and epidermal ectoderm of the host with epidermal cytokeratin (epi) probe in light red at stage 14. (I) Transverse section at the level of the trunk at stage 16. Abbreviations: fp, floor plate; no, notochord. (J–L) Transverse sections at stage 40. Abbreviations: fp, floor plate; hb, hindbrain; he, heart; le, lens; mb, midbrain; no, notochord; ov, otic vesicle; re, retina. (M) Dorsal view of 6-d embryo transplanted with a BCNE graft from CMV-GFP transgenic embryos. Abbreviations: br, brain; fp, floor plate; on, optic nerve; op, olfactory placode. (N) Side view at 4 d showing labeled retina and brain. Abbreviation: br, brain. The question arises as to whether two distinct signaling centers coexist in the Xenopus blastula. To address this, early blastulae with strong dorsoventral polarity (Klein 1987) were dissected into six fragments, as shown in Figure 1E. The results showed that, although some overlap existed, the region expressing Chd and Nog included the animal cap, whereas the Nieuwkoop center region that expresses Xnr2 and Xnr6 had a more vegetal location (see Figure 1E). Xnr3 expression was observed in fragments 3 and 4, indicating a higher degree of overlap (see Figure 1E). In addition, the results showed that the homeobox gene Siamois was expressed in the BCNE region, even though its expression has been reported to be more vegetal at later stages of development (Lemaire et al. 1995). We also found that Cerberus (Cer), a gene expressed in anterior endoderm at gastrula (Bouwmeester et al. 1996), was a component of the Nieuwkoop center. We conclude that two distinct signaling centers are present at blastula (see Figure 1F). The more animal BCNE center expresses Chd, Nog, Xnr3, and Siamois, whereas the Nieuwkoop center expresses Xnr2, Xnr6, and Cer. Cell Lineage of the BCNE Region To map the fate of the blastula Chd- and Nog-expressing cells during normal development, we transplanted lineage-labeled BCNE regions isotopically into host blastulae at early stage 9 (Figure 2A). These grafts containing the lineage tracer biotin-dextran amine (BDA) marked the Chd-expressing region at blastula (compare Figure 2B and 2C). A few hours later, at early gastrula (stage 10.5), dorsal animal cap descendants were found both in organizer endomesoderm and in prospective neuroectoderm (Figure 2D). We note that by early gastrula stage Chd mRNA was expressed in organizer endomesoderm, but was no longer detectable in prospective neuroectoderm (Figure 2E). At midgastrula, stage 11, the transplanted tissue elongated in organizer endomesoderm and prospective neuroectoderm, with both layers remaining in close apposition (Figure 2F). At neural plate stages, stage 14, BCNE center descendants were found in a wide region in the anterior neural plate and, more posteriorly, in a narrow stripe in the midline (Figure 2G). Double staining using nuclear lacZ mRNA as lineage tracer in combination with an epidermal cytokeratin marker confirmed that BCNE cells give rise to anterior neural plate (Figure 2H). The midline staining in the trunk region corresponded to floor plate and notochord in histological sections (Figure 2I). At tadpole stage (stage 40), BCNE descendants contributed to a large part of the brain and retina (but not lens and otic vesicles) and to dorsal midline structures of the trunk-tail region (Figure 2J–2L). This lineage could be traced up to feeding tadpole stages (Figure 2M and 2N) using dorsal animal cap grafts from cytomegalovirus–green fluorescent protein transgenic embryos (Marsh-Armstrong et al. 1999). These results indicate that blastula Chd-expressing cells give rise to much of the brain and to the floor plate and notochord in the trunk region of the Xenopus embryo. The Dorsal Animal Cap Is Specified to Form CNS In embryology, the test of whether cells are specified to form a particular tissue is to culture them in isolation from the rest of the embryo. Dorsal animal cap explants from embryos injected with CerS mRNA expressed multiple neural molecular markers at stage 26, whereas animal or ventral explants did not (Figure 3A and 3B). CerS was required to inhibit mesoderm formation; when identical explants were prepared without CerS mRNA injection, mesoderm contamination from the marginal zone was detected (data not shown). Neural differentiation could also be obtained in the absence of CerS mRNA when additional care was taken to avoid mesodermal contamination. As shown in Figure 3C, small explants from the top half of the BCNE region were excised, sandwiched together, and cultured in saline solution for 3 d. The sandwich procedure allows such small explants to survive in culture for long periods of time. Dorsal BCNE explants differentiated into histotypic CNS, including gray and white matter (Figure 3D), whereas similar explants from ventral ectoderm differentiated into epidermis (Figure 3E). These results demonstrate that dorsal animal cap cells are already specified to form CNS at the blastula stage. Figure 3 The Blastula Dorsal Animal Cap Is Specified to Form CNS (A) Experimental diagram showing embryos injected with CerS mRNA from which three regions of the animal cap were dissected at blastula, cultured until stage 26, and processed for RT-PCR. The size of the explants was 0.3 mm by 0.3 mm in these samples. Abbreviations: A, animal pole; D, dorsal region; V, ventral animal cap. (B) RT-PCR analysis of animal cap fragments; note that anterior brain markers were expressed in the dorsal fragments in the absence of mesoderm (α-actin) and endoderm (endodermin, Edd) differentiation. Abbreviations: A, animal pole; D, dorsal region; V, ventral animal cap. (C) Experimental diagram of the small animal cap sandwich experiments; these embryos were not injected with CerS. In this case, the size of the explants was 0.15 mm by 0.15 mm leaving a 0.15-mm gap from the floor of the blastocoel to avoid contamination from mesoderm-forming cells. Fragments from two explants were sandwiched together (explants are too small to heal by curling up) and cultured in 1× Steinberg's solution until stage 40. Abbreviations: VSW, ventral sandwich; DSW, dorsal sandwich. (D) Histological section of dorsal animal cap explant (dorsal sandwich). These sandwiches differentiated into histotypic forebrain tissue including white and gray matter (4/17). Abbrevations: DSW, dorsal sandwich; gm, gray matter; wm, white matter. (E) Histological section of a ventral animal cap sandwich. All sandwiches differentiated into atypical epidermis (n = 20). Abbreviations: ae, atypical epidermis; VSW, ventral sandwich. BCNE Tissue Is Required for Brain Formation To test whether the BCNE center is required for brain formation, we first deleted ventral or dorsal regions of the animal cap. Deletion of the dorsal region, but not of the ventral animal cap, resulted in headless embryos (Figure 4A and 4B). Since Xenopus is one of the best-studied vertebrate embryos, it was surprising that this requirement of a region of the blastula for CNS formation had not been reported previously. To investigate this further, we replaced the deleted fragment with various ectodermal grafts. The brain defects could be rescued by transplantation of dorsal, but not ventral, animal cap grafts (Figure 4C and 4D). Ectoderm from the animal pole was unable to rescue the ablated dorsal animal cap (Figure 4F). However, similar animal poles from lithium chloride (LiCl)–treated embryos, in which β-Catenin is stabilized and transcription of BCNE genes activated, were able to rescue head formation (Figure 4E). Figure 4 The Dorsal Animal Cap Is Required for Brain Formation (A) Ventral animal cap deletion (ΔV) produces a normal embryo. (B–F) Dorsal animal cap deletion (ΔD) results in loss of anterior brain structure. The headless phenotype of dorsal animal cap deletions was rescued by dorsal animal cap grafts (C) and animal pole grafts obtained from LiCl-treated embryos (E), but not by ventral animal cap transplants (D) or animal pole transplants (F). The average dorso-anterior indices (DAI) were 4.89 ([A] n = 28), 3.52 ([B] n = 25), 4.90 ([C] n = 10), 3.63 ([D] n = 19), 4.90 ([E] n = 12), and 3.50 ([F] n = 10). (G) Transplantation of the dorsal animal cap into the ventral animal cap region of a host embryo induced weak secondary axes (65.4%, n = 26). The embryo shown here was one of the strongest axes obtained. (H) Activity of BCNE transplanted ventrally was blocked by Chd-MO (n = 15). Despite this requirement for brain development, blastula dorsal animal caps grafted into the ventral side of a host blastula were only able to form weak secondary axes (Figure 4G). Chd-MO, which blocks the activity of Spemann grafts (Oelgeschläger et al. 2003), also inactivated BCNE grafts (Figure 4H). However, an important difference with the mature Spemann organizer was that BCNE cell transplants self-differentiated into spinal cord and muscle in these weak axes and were unable to induce CNS in neighboring cells as the Spemann organizer does (data not shown). We conclude that the dorsal animal cap BCNE center is required for brain formation. However, when transplanted into ectopic sites, BCNE tissue has only weak effects and does not induce neural tissue. Anterior CNS Formation in the Absence of Mesoderm Requires Chd and Nog We next investigated whether BCNE center signals are required for the anterior CNS that forms in embryos lacking mesoderm and Spemann organizer. CerS mRNA was injected at the 4-cell stage and the BCNE region marked with BDA at the 64-cell stage (Figure 5A; see also Figure S1). These mesoderm-less embryos developed forebrain tissue and prominent cyclopic eyes, which were derived from the lineage-labeled BCNE cells (Figure 5B and 5C). To test whether there was a requirement for Chd in these embryos, we injected Chd-MO at the 2-cell stage. When Chd was knocked down, BCNE cells developed into epidermis instead of CNS (Figure 5D and 5E). Brain and eye formation could be rescued by overexpression of Chd mRNA lacking the region targeted by Chd-MO (Figure 5F and 5G). Figure 5 The CNS of Mesodermless Embryos Derives from BCNE Cells and Requires Chd, Nog, and β-Catenin (A) Experimental design. Embryos in which mesoderm induction was inhibited (by injection of 600 pg of CerS mRNA into the vegetal pole) were sectioned at stage 38 and stained with hematoxylin-eosin or for microinjected BDA lineage tracer marking the BCNE region. (B and C) Embryos injected with CerS mRNA alone (n = 40). Abbreviation: br, brain. (D and E) Embryos injected with 17 ng of Chd-MO in addition to CerS (n = 21). Abbreviation: epi, epidermis. (F and G) Coinjection of 17 ng of Chd-MO and CerS, followed by 100 pg of Chd mRNA together with the lineage tracer (n = 19). Abbreviation: br, brain. (H) Expression of anterior CNS markers in mesodermless embryos requires Chd and Nog. RT-PCR analysis of CerS mRNA–injected embryos at tailbud stage 26. Markers of anterior brain (Otx2), eye (Rx2a), midhindbrain boundary (En2), hindbrain (Krox20), and cement gland (XAG) were inhibited by injection of Chd-MO, Nog-MO, or both. A pan-neural marker (NCAM) and a neuronal marker (N-tubulin) were partially inhibited, and the posterior neural marker HoxB9 was not affected. α-actin serves as a mesoderm marker to show that CerS blocked mesoderm in these embryos and ODC as mRNA loading control. The effects of the Nog-MO described here can be rescued by full-length Nog mRNA lacking the 5′ leader sequence targeted by the antisense morpholino (data not shown). (I and J) β-cat-MO (13.6 ng) together with CerS mRNA (n = 15). Abbreviation: epi, epidermis. (K and L) Rescue of β-cat-MO by 800 pg of β-catenin mRNA. Abbreviation: br, brain. (M and N) Rescue of the β-cat-MO phenotype by 100 pg of Chd mRNA (n = 8). (O) Chd is required for the anterior neural induction caused by β-Catenin. Neural and cement gland markers were induced in animal cap explants by activation of β-Catenin signal by the injection of 600 pg β-catenin mRNA, dnGSK3 mRNA, or LiCl treatment (lanes 3–5). Markers of anterior brain (Six3, Otx2), eye (Rx2a), midhindbrain boundary (En2), hindbrain (Krox20), and cement gland (XAG) were inhibited by Chd-MO (lanes 6–8). Although inhibition was not detected for the posterior neural marker HoxB9 and the pan-neural marker NCAM, the neuronal marker N-tubulin was inhibited. α-actin and α-globin are dorsal and ventral mesoderm markers, respectively, used to show the absence of mesoderm formation, and ODC serves as loading control. Molecular analyses confirmed that mesoderm-less embryos injected with Chd-MO did not express anterior neural tissue markers such as Otx2, Rx2a, En2, and Krox20 (Figure 5H, compare lanes 3 and 4). However, spinal cord (HoxB9) or pan-neural markers (N-tubulin, neural cell adhesion molecule [NCAM]) were still expressed, indicating that only anterior neural differentiation was eliminated by Chd-MO and that posterior neural induction continues to take place. We also generated a Noggin antisense morpholino oligo (Nog-MO) reagent, which, like Chd-MO, inhibited brain markers (Figure 5H, lane 5). Nog-MO was slightly weaker than Chd-MO, but even a combination of both morpholinos did not eliminate posterior neural markers (Figure 5H, lane 6). These results show that the brain tissue formed in embryos lacking mesoderm and Spemann organizer derive from BCNE cells. The formation of anterior CNS in mesoderm-less embryos requires the expression of Chd and Nog in prospective neuroectoderm. Neural Induction by β-Catenin Requires Chd It has recently been discovered that microinjection of β-catenin mRNA is able to induce neural tissue in Xenopus animal caps (Baker et al. 1999). Stabilization of β-Catenin has a dual effect, inhibiting the transcription of BMPs (Baker et al. 1999; Leung et al. 2003) and increasing expression of the BMP antagonists Chd and Nog in the blastula animal cap (Wessely et al. 2001). We next tested the effect of β-Catenin knockdown on CNS differentiation. As shown in Figure 5I and 5J, β-cat-MO oligos (Heasman et al. 2000) blocked formation of histological anterior brain and eye structures in CerS mesoderm-less embryos. Importantly, anterior CNS formation could be restored by overexpression of either β-catenin or Chd mRNA in these embryos (Figure 5K–5N). We conclude that brain formation in the absence of mesoderm requires the early β-Catenin signal. To investigate whether neural induction by β-Catenin in animal cap explants required Chd, the β-Catenin pathway was activated by β-catenin mRNA, dominant negative glycogen synthase kinase-3 (dnGSK3) mRNA, or LiCl. These treatments induced multiple neural markers in animal caps (Figure 5O, lanes 3–5). Microinjection of Chd-MO inhibited the expression of anterior neural markers (Six3, Otx2, Rx2a, En2), but not of posterior or pan-neural ones (HoxB9, NCAM) (Figure 5O, lanes 6–8). The results indicate that neural induction by the β-Catenin signal requires expression of its downstream target gene Chordin. Anterior Neural Induction in Keller Explants Requires Chd Is the expression of Chd in prospective neuroectoderm at blastula responsible for the “planar” neural induction signals (Figure 6A) described by earlier workers? To investigate this, we used Keller sandwiches (Keller and Danilchick 1988; Doniach et al. 1992; Ruiz i Altaba 1992), in which neural tissue develops without contacting underlying mesoderm (Figure 6B). Marking of the BCNE region with lineage tracer indicated that Keller sandwiches contain cells that expressed Chd in prospective neuroectoderm at blastula (Figure 6C). Keller sandwiches expressed anterior CNS gene markers (Figure 6D and 6F). However, explants prepared from embryos injected with Chd-MO failed to express Otx2 or Krox20 in anterior neuroectoderm, while retaining Otx2 expression in endoderm (compare Figure 6D and 6E). N-tubulin expression, which marks differentiated neurons, was inhibited by Chd-MO in the anterior CNS, but persisted in prospective spinal cord (Figure 6F and 6G; results summarized in Figure 6H and 6I). These results show that the anterior CNS formation observed in Keller explants lacking underlying mesoderm requires Chd. Molecular analyses of Keller explants confirmed that brain markers were inhibited by Chd-MO, while pan-neural and spinal cord markers were less affected (Figure 6J, compare lanes 2 and 3). As before, posterior neural induction did not require Chd. The origin of this posterior neural differentiation is due to fibroblast growth factor (FGF) signaling (Hongo et al. 1999; Pera et al. 2003), since it could be blocked in explants injected with dominant negative FGF receptor 4a (dnFGFR4a) mRNA (Figure 6J, lanes 4 and 5). Importantly, anterior CNS markers were still expressed in Keller sandwiches when mesoderm induction was blocked by CerS mRNA (Figure 6J, lane 6) and could be blocked by Chd-MO (Figure 6J, lane 7). Since mesoderm-less CerS Keller explants lack an endomesodermal Spemann organizer, their sole source of Chd is the BCNE center. Taken together, these experiments indicate that the anterior neural induction observed in Keller explants, known as “planar” induction, results from the activity of Chd-expressing cells located in the presumptive neuroectoderm at the blastula stage. Chordin and Cerberus Cooperate in Brain Induction Do vertical signals from endomesoderm cooperate with the BCNE center in brain differentiation? The endomesoderm secretes growth-factor antagonists with head-patterning activity, such as Cer, Frzb-1, Crescent, Dickkopf-1, Chd, and Nog (Harland 2000; De Robertis et al. 2000). Several of these secreted antagonists are expressed in the anterior endoderm, which is homologous to the mouse anterior visceral endoderm (Beddington and Robertson 2000). We chose to study one of these antagonists, the head-inducer Cer, because it is expressed in the anterior endoderm of the Spemann organizer (Bouwmeester et al. 1996) and in the Nieuwkoop center, but not in the BCNE center (see Figure 1E). Two recent studies have described morpholino antisense oligos targeting Cer. In both, Cer did not appear to be required for head development on its own, but cooperated when coinjected with other factors (Hino et al. 2003; Silva et al. 2003). Xenopus laevis genes frequently have pseudoalleles thought to have originated from hybridization between two different Xenopus species in the course of evolution (Kobel and Du Pasquier 1986). Examination of the EST database showed that a second Cer allele existed, and that the published morpholinos had three and four mismatches with it, respectively (Figure 7A) (Silva et al. 2003; Hino et al. 2003). We therefore designed a new morpholino oligo, Cer-MO, targeting both X. laevis pseudoalleles (Figure 7A). Cer-MO inhibited head formation in Xenopus embryos, which could be rescued by Cer mRNA lacking the targeted 5′-leader sequence (data not shown). Figure 7 A Double-Assurance Mechanism in Xenopus Neural Induction That Requires Chordin and Cerberus (A) A new Cer-MO is complementary to both Cer pseudoalleles, while two MOs reported by other authors (Hino et al. 2003; Silva et al. 2003) match only one allele, having three or four mismatches, respectively, with the other allele. The Cer-MO used in the present study inhibits head formation in intact embryos (data not shown), while the other two do not (Hino et al. 2003; Silva et al. 2003). (B) Experimental procedure and cell lineages at 32-cell and early gastrula (stage 10.5) for dorsal-animal (FDA, green) and dorsal-vegetal (TRDA, red) blastomeres microinjected at the 8-cell stage. (C and D) Uninjected embryos. (E and F) Dorsal-animal injection with 8.5 ng of Chd-MO alone partially inhibited head formation; green fluorescence was seen in anterior CNS. (G and H) Dorsal-vegetal injection with 17 ng of Cer-MO also inhibited brain formation partially; red fluorescence may be seen in anterior endomesoderm. (I and J) Injection with 8.5 ng Chd-MO dorsal-animally and 17 ng Cer-MO dorsal-vegetally blocked brain formation, but not spinal cord and somites (histological sections not shown). To test whether Cer and Chd cooperated, we targeted Cer-MO to dorsal endomesoderm and Chd-MO to dorsal neuroectoderm at the 8-cell stage (Figure 7B). At the doses used, microinjection of Chd-MO or Cer-MO alone resulted in partial reductions of the anterior CNS (Figure 7C–7H). However, when neuroectodermal and endomesodermal progenitor cells were injected with Chd-MO and Cer-MO, respectively, brain differentiation did not occur (Figure 7I and 7J). In histological sections, these embryos lacked brain structures but still developed spinal cord, somites, and notochord (data not shown). The results suggest that formation of the brain requires partly overlapping distinct factors in different cell layers. Chd is required in prospective neuroectoderm to predispose cells to form anterior CNS and cooperates with vertical signals from the underlying endomesoderm that include Cer (Figure 8). Figure 8 Double-Assurance Model for Brain Formation by the BCNE and Nieuwkoop Centers Blastula Chd- and Nog-expressing cells are located in the dorsal animal region, while the Nieuwkoop center is found in the dorsal-vegetal region. At gastrula, the anterior endoderm derived from the Nieuwkoop center is found in close apposition to the prospective anterior CNS. See text for discussion. Discussion The results presented here are consistent with the following sequence of events during CNS development in Xenopus. A dorsal β-Catenin signal triggered by the early cortical rotation of the egg (Gerhart et al. 1991; De Robertis et al. 2000) induces the expression of anti-BMP molecules such as Chd and Nog in a group of cells located in the dorsal animal region at the blastula stage (see Figure 8). The dorsal prospective neuroectoderm is already specified to form CNS at blastula (see Figure 3). Remarkably, transplantation studies showed that this BCNE center was required for brain formation in vivo (see Figure 4). The normal fate of these dorsal animal cap cells during development is to give rise to anterior CNS, floor plate, and notochord (see Figure 2). We note that our previous term “preorganizer” (Wessely et al. 2001) was somewhat inadequate. Since BCNE grafts are unable to induce CNS in neighboring cells after transplantation, they lack organizer activity. The Nieuwkoop center arises at the same stage as the BCNE center, but in more vegetal cells (see Figure 8). The Nieuwkoop center expresses secreted factors such as mesoderm-inducing Xnrs and Cer and in later development gives rise to the endoderm that underlies the anterior CNS (see Figure 8). By inhibiting Chd in presumptive neuroectoderm and Cer in the endomesodermal substratum (see Figure 7), we were able to provide evidence that partly overlapping functions of distinct growth-factor antagonists secreted by different germ layers cooperate in CNS formation. Neural Induction Starts at Blastula At the blastula stage, gene expression in the BCNE region causes a neural predisposition in the prospective brain tissue itself. When prospective neuroectoderm is explanted at blastula and cultured in the absence of mesoderm, it can develop into histotypic neural tissue (see Figure 3D). Once the Nieuwkoop center induces a Spemann organizer in dorsal mesoderm, a cocktail of growth factor antagonists is secreted by the endomesoderm. These Spemann organizer molecules require Nodal-related signals in order to be produced at gastrula (Agius et al. 2000; Wessely et al. 2001). These endomesodermal factors include Cer (an inhibitor of Nodal, Wnt, and BMP signals); anti-Wnts such as Frzb-1, Crescent, and Dickkopf, and anti-BMPs such as Follistatin, Chd, and Nog (De Robertis et al. 2000). Some molecules, like Chd and Nog, are expressed both in the dorsal animal cap at blastula and in the Spemann organizer at gastrula, whereas others, like Cer, are expressed only in the Nieuwkoop center and its endomesodermal descendants. In amphibians, the ability of a gastrula dorsal lip to induce a complete CNS when transplanted into a host gastrula had been taken as an indication that neural induction occurs at gastrula. We now show that brief expression of Chd and Nog during blastula stages, triggered by the maternal β-Catenin signal, is required for brain formation. This requirement becomes apparent when additional signals from underlying endomesoderm are blocked by inhibition of Nodal signaling or by preventing involution in Keller dorsal explants. We conclude that Xenopus neural induction starts in presumptive neuroectoderm shortly after midblastula. This is in agreement with other recent work in Xenopus (Baker et al. 1999; Gamse and Sive 2001; Wessely et al. 2001). Redundant Signals in Neural Induction Multiple secreted growth factors antagonists participate in CNS induction (Harland 2000; De Robertis et al. 2000), and their activities can be redundant. In the mouse, Chd and Nog mutants have normal neural plates, but in Chd -/-;Nog -/- embryos, development of the forebrain fails (Bachiller et al. 2000). In Xenopus and zebrafish, loss of Chd in the whole embryo results in animals that still are able to form anterior CNS, although its size is reduced (Schulte-Merker et al. 1997; Oelgeschläger et al. 2003). This contrasts with the strong requirement for Chd revealed here when neural induction is driven by a single signaling center in Xenopus. In mesoderm-less embryos, (CerS-injected) blastula dorsal animal cells are the sole source of Chd, and anterior CNS differentiation can be completely inhibited by Chd-MO (see Figure 5). Chd is also required for the neural inducing activity of Spemann organizer grafts (Oelgeschläger et al. 2003). Multiple neural-inducing molecules, such as the above-mentioned growth factor antagonists, have been identified and may compensate for the loss of Chd in intact embryos. In addition other signals located outside of dorsal signaling centers, such as FGFs and insulin-like growth factors (IGFs) may participate in neural induction (see below). Could redundant signals from prospective neuroectoderm and endomesoderm also function in neural induction in other vertebrates? This seems possible in the case of the chick embryo. Chick Chd is initially expressed in the unincubated egg in epiblast central cells just anterior to Koller's sickle (Streit et al. 1998), a region that may correspond to the Xenopus blastula Chd-expressing region. The progeny of this region of the chick epiblast contributes to the prospective forebrain and moves anteriorly during development. The descendants of the chick early Chd-expressing brain progenitors migrate at all times in front of the organizer, which is located at the tip of the primitive streak and has a caudalizing influence (Foley et al. 2000). In zebrafish, mutant embryos lacking Nodal signaling still form brain tissue in the absence of a mesodermal organizer and express Chd (Gritsman et al. 1999). In the mouse embryo, transplantation experiments at the gastrula stage support a role for different germ layers in brain induction (Tam and Steiner 1999). However, expression of mouse Chd and Nog has only been analyzed from early primitive streak stage on (Bachiller et al. 2000). Studies on the expression of these BMP antagonists in prestreak or peri-implantation mouse embryos, or on the earliest nuclear localization of β-Catenin protein, will be required to determine whether a region homologous to that of the Xenopus blastula Chd-expressing region exists in mammalian embryos. Neural-Inducing Signals in Chordates In amphibians the default model of neural induction proposes that BMPs expressed in ectoderm cause epidermal induction. When animal cap cells are dissociated, they become neuralized (reviewed by Weinstein and Hemmati-Brivanlou 1999). When exogenous BMP is added to dissociated animal cap cells, epidermal differentiation is restored. The present work with morpholinos that inhibit Chd, Nog, and Cer highlights the importance of BMP signaling regulation in Xenopus. The BCNE center appears shortly after midblastula and is required for anterior CNS formation when endomesodermal signals are inhibited. Induction of posterior neural tissue can still take in the absence of Chd and Nog (e.g., Figure 5H). Formation of this posterior neural tissue can be blocked by dominant-negative FGF receptor 4a (see Figure 6J). In Xenopus, FGF and IGF signaling are able to induce neural differentiation in animal caps (Hardcastle et al. 2000; Pera et al. 2001; Richard-Parpaillon et al. 2002), and late canonical Wnt signals are known to inhibit anterior brain formation (Kiecker and Niehrs 2001). In chick and ascidian embryos, current models of neural induction highlight the role of FGF and Wnt in neural induction and de-emphasize a role for BMP regulation (Wilson and Edlund 2001; Stern 2002; Bertrand et al. 2003). We are unable to discuss in depth here the relative importance of the different signaling pathways in various organisms (reviewed in Wilson and Edlund 2001). It is clear, however, that multiple pathways cooperate in neural development. For example, in the chick embryo, Wnt or BMP antagonists applied to cells at the border region between epidermis and CNS expand the neural plate, and FGF signaling represses BMP4 expression in the neuroectoderm. In addition, the anti-neural effects of intermediate levels of an FGF antagonist can be reversed by the addition of chick Chd (Wilson and Edlund 2001). One of the difficulties in comparing neural induction between Xenopus and other chordates concerned the different timing of events. We now find a requirement for critical signals triggered by β-Catenin in the prospective neuroectoderm just after midblastula. Thus, the neural induction process seems to start at blastula in all chordates (Wessely et al. 2001; Wilson and Edlund 2001; Stern 2002; Bertrand et al. 2003). In addition, new molecular mechanisms are being discovered that help explain how disparate signaling pathways—such as those of FGF, IGF, and anti-BMPs—can be integrated during development. Tyrosine kinase receptors such as those for FGF and IGF have recently been found to inhibit the BMP pathway effector protein Smad1 by phosphorylation via mitogen-activated protein kinase (MAPK) (Pera et al. 2003; Sater et al. 2003). Neural induction by the BMP antagonist Chd requires the extra boost in Smad1 inhibition provided by FGF and IGF signaling (Pera et al. 2003). This molecular mechanism exemplifies one way in which signaling pathways hitherto considered entirely independent might be integrated in embryonic cells (Massagué 2003). Primary neural induction in the chordate embryo has been an area of active investigation for many years and we can expect this to continue for the foreseeable future. A Role for Ectoderm in Amphibian Neural Induction The role of the ectoderm in amphibian neural induction has been the subject of much debate (Spemann 1938; Holtfreter and Hamburger 1955; Nieuwkoop and Koster 1995). Gene marker studies in Xenopus had noticed a predisposition of dorsal ectoderm for neural induction by mesoderm (Sharpe et al. 1987; London et al. 1988), but a requirement for any specific genes had not been addressed. In addition, it was known that the dorsal animal cap responds much better to the mesoderm-inducer Activin (Sokol and Melton 1991). These earlier findings can now be reinterpreted as reflecting the effects of the early β-Catenin signal that induces expression of genes such as Chd, Nog, Xnr3, and Siamois. Chd is not only expressed in the organizer region during gastrulation, but also in the dorsal animal cap region during blastula, and this is required for neural specification. In this study we have provided evidence that presumptive neural plate material can differentiate into CNS in the absence of a mesodermal substratum. The BCNE center is required for brain formation in the embryo, but requires the cooperation of endomesodermal signals such as Cerberus. A requirement of gastrula prospective neuroectoderm for neural plate formation had been proposed by earlier workers on the basis of defect experiments (Goerttler 1925; Lehmann 1928). However, these results were disputed (Holtfreter 1933; Spemann 1938; Holtfreter and Hamburger 1955; Hamburger 1988; Nieuwkoop and Koster 1995), and vertical induction by the endomesodermal Spemann organizer was attributed the preeminent role in amphibian neural induction. A possible explanation for why the role of the prospective neuroectoderm remained unrecognized for so many years of research on the experimental embryology of neural induction is that, unlike Spemann's organizer, the BCNE center lacks inducing activity when transplanted to ectopic sites. The availability of new tools to investigate the function of individual genes—such as β-catenin, Chd, and Cer—has now provided evidence that both ectodermal and endomesodermal signals are required for primary embryonic induction in Xenopus. Materials and Methods Embryo manipulations Xenopus embryos obtained by in vitro fertilization were cultured in 0.1× modified Barth's medium (Sive et al. 2000). For BCNE transplantation and deletion experiments, dissections were performed in 1× Steinberg's solution (Sive et al. 2000). BCNE grafts were 0.3 mm squares isolated from the dorsal animal cap just above the floor of the blastocoel. They were excised at early stage 9 (6.75–7.25 h after fertilization at room temperature), before extensive epiboly movements begin, just one division after the large-cell blastula stage (stage 8), and could be monitored by the thickness of the animal cap and cell size. Embryos were cultured in 1× Steinberg's solution until healing (0.5–1 h) and then changed into 0.1× Barth's solution. Embryo stages were according to Nieuwkoop and Faber (1994). Keller sandwiches were prepared at early stage 10. The dorsal sector of the gastrula was excised at an angle of 30° from the dorsal midline, from the dorsal lip up to the animal pole, using stainless steel forceps. Two explants were sandwiched and cultured in 1 x Steinberg solution for 12 h for in situ hybridization, 1 d for RT-PCR analysis, and 2 d for morphological analysis. For RT-PCR analyses, RNA was pooled from five Keller explants, five animal caps, or single embryos. The RT-PCR conditions and primers, as well the protocol for whole-mount in situ hybridization, are described in http://www.hhmi.ucla.edu/derobertis/index.html. Lineage tracing To fate map BCNE descendants, an improved lineage tracing method was developed. Embryos were injected with 1–4 nl of 1% BDA (Molecular Probes, Eugene, Oregon, United States) in H2O and cultured explants or embryos were fixed for at least 1 h in MEMFA (Sive et al. 2000). Subsequently, embryos were placed for 24 h in 70% ethanol, 1 h in 100% ethanol, 1 h in 100% isopropanol, 12–16 h in 100% xylene, and 1 h in paraffin at 65°C before embedding. We found that overnight incubation in xylene improved sectioning of early embryos, which are rich in yolk. Sections were cut at 8–10 μm and dewaxed in 100% xylene, 100% ethanol, and 70% ethanol for 2 min each. Next, sections were washed twice in binding buffer (100 mM Tris–HCl, 150 mM NaCl [pH 7.5]) for 5 min and incubated in binding buffer containing streptavidin-coupled alkaline phospatase (Roche, Basel, Switzerland) at a dilution of 1:5,000 overnight at room temperature. Afterwards, slides were washed twice with binding buffer, once with reaction buffer (100 mM Tris–HCl, 100 mM NaCl, 50 mM MgCl2, pH 9.5) for 5 min and incubated overnight with reaction buffer containing 10% BM purple solution (Roche) in a Coplin jar in the dark at 4°C. Staining was stopped by incubation in Stop solution (100 mM Tris-HCl, 1 mM EDTA, pH 7.4), and sections dehydrated in 100% methanol and completely air-dried before mounting in Vectashield medium (Vector Laboratories Inc., Burlingame, California, United States). In some experiments nuclear lacZ mRNA (kind gift of R. Harland), fluorescein dextran amine or Texas red dextran amine were used as lineage tracers. RNA injections To generate synthetic mRNAs, the plasmids pCS2-CerS, pCS2-Chd, pCS2-β-catenin, and pCS2-dnGSK3 were linearized with NotI and transcribed with SP6 RNA polymerase as described previously (Piccolo et al. 1999). The following amounts of mRNA were used for microinjections: 600 pg (150 pg four times into the vegetal region at 4-cell stage) for CerS, 100 pg (50 pg twice into dorsal-animal region at 4-cell stage) for Chd, 800 pg (400 pg twice into the dorsal-animal region at 8-cell stage) for β-catenin, and 600 pg (300 pg twice into the dorsal-animal region at 8-cell stage) for dnGSK3 mRNA. Morpholino oligos Morpholino oligos were as follows: Chd-MO1 (5′-ACG TTC TGT CTC GTA TAG TGA GCG T-3′) and Chd-MO2 (5′-ACA GCA TTT TTG TGG TTG TCC CGA A-3′) (Oelgeschläger et al. 2003); Nog-MO (5′-TCA CAA GGC ACT GGG AAT GAT CCA T-3′) (this work); β-cat-MO (5′-TTT CAA CCG TTT CCA AAG AAC CAG G-3′) (Heasman et al. 2000); Cer-MO (5′-ACT TGC TGT TCC TGC ACT GTG C-3′) (this work); and a control-MO (5′-CCT CTT ACC TCA GTT ACA ATT TAT A-3′) (Oelgeschläger et al. 2003). The morpholino oligos were resuspended to prepare a 1 mM stock solution (SS) that was then further diluted in sterile water to give a working solution: Chd-MO solution (Chd-MO1-SS:Chd-MO2-SS:H2O = 1:1:6), β-cat-MO solution (β-cat-MO-SS:H2O = 1:4), Nog-MO solution (Nog-MO-SS:H2O = 1:1), Cer-MO solution (Cer-MO-SS:H2O =1:1), control-MO solution (control-MO-SS:H2O = 1:1), and Chd-MO/Nog-MO solution (Chd-MO1-SS:Chd-MO2-SS:Nog-MO-SS:H2O = 1:1:4:10). A total of 8 nl (two times 4 nl or four times 2 nl) morpholino solutions were injected at the 2-cell stage or 4 nl (two times 2 nl) of morpholino solution injected at the 8-cell stage. Supporting Information Figure S1 The BCNE Region Can Be Reliably Marked at the 64-Cell Stage Using an Improved BDA Lineage-Tracing Method (A–H) Microinjection of individual 32-cell blastomeres does not faithfully recapitulate the lineage of BCNE grafts at gastrula (compare with Figure 2B and 2D). Note that the lineage of D1 includes part of the Nieuwkoop center and contributes to anterior endoderm at gastrula. This 32-cell map is in general agreement with previously published fate maps (Dale and Slack 1987; Bauer et al. 1994); the minor differences observed are explained by our choice of batches of regularly cleaving embryos (Klein 1987) with tightly adhering small animal blastomeres. Abbreviation: AE, anterior endoderm. (I–L) Diagram indicating the injection of the lower daughter of B1 and the upper daughter of C1 at the 64-cell stage (I), which reliably identify BCNE descendants at stage 9 (J), stage 11 (K), and stage 36 (L). Arrowheads indicate the blastopore. Abbreviations: fp, floor plate; no, notochord. (4.39 MB TIF). Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/index.html) accession numbers discussed in this paper are for β-catenin (M77013), Cer (U64831), Cer pseudoallele (BG160114), Chd (L35764), Goosecoid (M81481), and Nog (M98807). We thank Dr. D. D. Brown for transgenic X. laevis; Dr. H. Okamoto, Dr. D. Wilkinson, Dr. R. Harland, and Dr. D. Kimelman for DNA constructs; U. Tran and A. Cuellar for technical assistance; and E. Neufeld, L. Zipursky, S. Millard, E. Pera, and C. Coffinier for comments on the manuscript. This work was supported by the National Institutes of Health (HD21502–18) and the Howard Hughes Medical Institute, of which EMDR is an investigator. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. HK and EMDR conceived and designed the experiments. HK and EMDR performed the experiments: HK, OW, and EMDR analyzed the data. HK, OW, and EMDR contributed reagents/materials/analysis tools. HK and EMDR wrote the paper. All the authors collaborated on the work. Academic Editor: Christof Niehrs, Deutsches Krebsforschungszentrum Abbreviations β-cat-MO β-catenin morpholino oligos BCNEblastula Chordin- and Noggin-expressing region BMPbone morphogenetic protein Cer-MO Cerberus morpholino oligos CerS Cerberus-Short Chd-MO Chordin morpholino oligos CNScentral nervous system FGFfibroblast growth factor GSK3glycogen synthase kinase-3 IGFinsulin-like growth factor LiCllithium chloride MOantisense morpholino oligo MAPKmitogen-activated protein kinase NCAMneural cell adhesion molecule Nog-MO Noggin morpholino oligos ODCornithine decarboxylase SSstock solution Xnr Xenopus Nodal-related ==== Refs References Agius E Oelgeschläger M Wessely O Kemp C De Robertis EM Endodermal Nodal-related signals and mesoderm induction in Xenopus Development 2000 127 1173 1183 10683171 Bachiller D Klingensmith J Kemp C Belo JA Anderson RM The organizer factors Chordin and Noggin are required for mouse forebrain development Nature 2000 403 658 661 10688202 Baker JC Beddington RS Harland RM Wnt signaling in Xenopus embryos inhibits bmp4 expression and activates neural development Genes Dev 1999 13 3149 3159 10601040 Bauer DV Huang S Moody SA The cleavage stage origin of Spemann's organizer: Analysis of the movements of blastomere clones before and during gastrulation in Xenopus Development 1994 120 1179 1189 8026328 Beddington RS Robertson EJ Axis development and early asymmetry in mammals Cell 2000 96 195 209 Bertrand V Hudson C Caillol D Popovici C Lemaire P Neural tissue in ascidian embryos is induced by FGF9/16/20, acting via a combination of maternal GATA and Ets transcription factors Cell 2003 115 615 627 14651852 Bouwmeester T Kim S Sasai Y Lu B De Robertis EM Cerberus is a head-inducing secreted factor expressed in the anterior endoderm of Spemann's organizer Nature 1996 382 595 601 8757128 Dale L Slack JM Regional specification within the mesoderm of early embryos of Xenopus laevis Development 1987 100 279 295 3652971 De Robertis EM Larraín J Oelgeschläger M Wessely O The establishment of Spemann's organizer and patterning of the vertebrate embryo Nat Rev Genet 2000 1 171 181 11252746 Doniach T Phillips CR Gerhart JC Planar induction of anteroposterior pattern in the developing central nervous system of Xenopus laevis Science 1992 257 542 545 1636091 Foley AC Skromne I Stern CD Reconciling different models of forebrain induction and patterning: A dual role for the hypoblast Development 2000 127 3839 3854 10934028 Gamse JT Sive H Early anteroposterior division of the presumptive neuroectoderm in Xenopus Mech Dev 2001 104 21 36 11404077 Gerhart J Doniach T Stewart R Organizing the Xenopus organizer. In: Keller RE, editor. Gastrulation: Movements, patterns, and molecules 1991 New York Plenum Press. New York 57 77 Gilbert SF Continuity and change: Paradigm shifts in neural induction Int J Dev Biol 2001 45 155 164 11291843 Goerttler K Die Formbildung der Medullaranlage bei Urodelen Roux's Arch Entw Mech 1925 106 503 541 Grainger RM Embryonic lens induction: Shedding light on vertebrate tissue determination Trends Genet 1992 10 349 355 Gritsman K Zhang J Cheng S Heckscher E Talbot WS The EGF-CFC protein one-eyed pinhead is essential for nodal signaling Cell 1999 97 121 132 10199408 Hamburger V The heritage of experimental embryology. 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Cold Spring Harbor, New York: Cold Spring Harbor Laboratory Press 2000 338 Sokol S Melton DA Pre-existent pattern in Xenopus animal pole cells revealed by induction with activin Nature 1991 351 409 411 2034291 Spemann H Über Korrelationen in der Entwicklung des Auges Verh Anat Ges 1901 15 61 79 Spemann H Embryonic development and induction 1938 New Haven Yale University Press 401 Spemann H Mangold H Induction of embryonic primordia by implantation of organizers from a different species Roux's Arch Entw Mech 1924 100 599 638 Reprinted and translated in Int J Dev Biol (2001) 45: 13–38 Stern CD Induction and initial patterning of the nervous system—the chick embryo enters the scene Curr Opin Genet Dev 2002 12 447 451 12100891 Streit A Lee KJ Woo I Roberts C Jessell TM Chordin regulates primitive streak development and the stability of induced neural cells, but is not sufficient for neural induction in the chick embryo Development 1998 125 507 519 9425145 Takahashi S Yokota C Takano K Tanegashima K Onuma Y Two novel nodal-related genes initiate early inductive events in Xenopus Nieuwkoop center Development 2000 127 5319 5329 11076754 Tam PPL Steiner KA Anterior patterning by synergistic activity of the early gastrula organizer and the anterior germ layer tissues of the mouse embryo Development 1999 126 5171 5179 10529433 Weinstein DC Hemmati-Brivanlou A Neural induction Annu Rev Cell Dev Biol 1999 15 411 433 10611968 Wessely O Agius E Oelgeschläger M Pera EM De Robertis EM Neural induction in the absence of mesoderm: Beta-catenin–dependent expression of secreted BMP antagonists at the blastula stage in Xenopus Dev Biol 2001 234 161 173 11356027 Wessely O Kim JI Geissert D Tran U De Robertis EM Analysis of Spemann organizer formation in Xenopus embryos by cDNA macroarrays Dev Biol 2004 In press Wilson S Edlund T Neural induction: Toward a unifying mechanism Nat Neurosci 2001 4 1161 1168 11687825

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020110Research ArticleCancer BiologyCell BiologyMolecular Biology/Structural BiologyHomo (Human)XenopusMre11 Assembles Linear DNA Fragments into DNA Damage Signaling Complexes Mre11 and DNA Damage Signaling ComplexesCostanzo Vincenzo 1 Paull Tanya 2 Gottesman Max 3 Gautier Jean 1 1Department of Genetics and Development, Columbia UniversityNew York, New YorkUnited States of America2Department of Molecular Genetics and Microbiology, University of TexasAustin, TexasUnited States of America3Institute of Cancer Research, Columbia UniversityNew York, New YorkUnited States of America5 2004 11 5 2004 11 5 2004 2 5 e1108 12 2003 10 2 2004 Copyright: © 2004 Costanzo et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. The Mre11 Protein Is Necessary for DNA Damage Response Mre11/Rad50/Nbs1 complex (MRN) is essential to suppress the generation of double-strand breaks (DSBs) during DNA replication. MRN also plays a role in the response to DSBs created by DNA damage. Hypomorphic mutations in Mre11 (which causes an ataxia-telangiectasia-like disease [ATLD]) and mutations in the ataxia-telangiectasia-mutated (ATM) gene lead to defects in handling damaged DNA and to similar clinical and cellular phenotypes. Using Xenopus egg extracts, we have designed a simple assay to define the biochemistry of Mre11. MRN is required for efficient activation of the DNA damage response induced by DSBs. We isolated a high molecular weight DNA damage signaling complex that includes MRN, damaged DNA molecules, and activated ATM. Complex formation is partially dependent upon Zn2+ and requires an intact Mre11 C-terminal domain that is deleted in some ATLD patients. The ATLD truncation can still perform the role of Mre11 during replication. Our work demonstrates the role of Mre11 in assembling DNA damage signaling centers that are reminiscent of irradiation-induced foci. It also provides a molecular explanation for the similarities between ataxia-telangiectasia (A-T) and ATLD. A defective cellular response to double-strand breaks in DNA is associated with several cancer-predisposition diseases. Specific defects affect the formation of a large protein complex that involves several key proteins ==== Body Introduction Cellular response to DNA damage requires the coordinated activation of cell cycle checkpoints with DNA repair (Zhou and Elledge 2000). Failure to block S-phase entry in response to damaged DNA or to repair the DNA leads to genomic instability, the hallmark of cancer cells. DNA double-strand breaks (DSBs) are particularly harmful to cells; if unrepaired, DSBs generate aneuploidy and chromosomal translocations. DSBs activate a network of signaling pathways that coordinate the sensing and repair of the damage with cell cycle arrest. The major signaling pathway triggered by DSBs involves ataxia-telangiectasia-mutated (ATM) protein kinase (Zhou and Elledge 2000). ATM is a serine-threonine kinase related to the PI3 kinase family. DSBs activate ATM by promoting its autophosphorylation (Bakkenist and Kastan 2003). Activated ATM phosphorylates protein substrates involved in DNA repair, cell cycle arrest, and apoptosis. Phosphorylation of Nbs1 by ATM is critical for S-phase checkpoint (Gatei et al. 2000; Lim et al. 2000; Zhao et al. 2000). Nbs1 forms a trimeric complex with Mre11 and Rad50 (MRN) that is needed for DSB repair by homologous recombination (Haber 1998; D'Amours and Jackson 2002; Symington 2003). The three proteins are also essential for vertebrate embryonic development and cell growth (Luo et al. 1999; Yamaguchi-Iwai et al. 1999; Zhu et al. 2001). MRN prevents the accumulation of DSBs during DNA replication (Costanzo et al. 2001). ATM function is defective in patients carrying the recessive genetic disorder ataxia-telangiectasia (A-T). A-T is characterized by cerebellar degeneration, immunodeficiency, radiation sensitivity, chromosomal instability, and cancer predisposition (Gatti et al. 2001). Hypomorphic mutations in Mre11 and Nbs1 give rise, respectively, to an A-T-like disease (ATLD) and Nijmegen breakage syndrome (NBS) (Digweed et al. 1999; Stewart et al. 1999; Tauchi et al. 2002). The clinical presentations of A-T and ATLD are indistinguishable. NBS patients display the symptoms of A-T and ATLD and, in addition, microcephaly and mental deficiency (Tauchi et al. 2002). All three diseases have similar cellular phenotypes. The mutant cells do not respond appropriately to DSBs and display chromosome abnormalities, hypersensitivity to ionizing radiations, radio-resistant DNA synthesis, and an S-phase checkpoint defect (Shiloh and Kastan 2001). These similarities strongly suggest that ATM and MRN function in a common signaling pathway. However, the molecular connection between these proteins is yet to be determined. Mre11 binds DNA and is both a 3′-to-5′ exonuclease and an endonuclease that cleaves hairpin DNA structures (Paull and Gellert 1999). Rad50 belongs to the structural maintenance of chromosomes (SMC) family of proteins. Rad50 contains C-terminal and N-terminal Walker A and B domains separated by a long coiled-coil domain (de Jager et al. 2001; Hopfner et al. 2002). Intramolecular assembly of the coiled-coil domain brings the Walker A and B motifs together to generate a functional nucleotide-binding module. A zinc-binding motif (CXXC), or “zinc hook,” located at the base of the Rad50 coiled coil, mediates Rad50 dimerization through coordination of a zinc ion by four cysteine residues (Hopfner et al. 2002). Rad50 dimer binds to two Mre11 molecules to form a stable tetrameric complex with enhanced nuclease activities (Trujillo and Sung 2001). hRad50/hMre11 complexes tether linear duplex DNA molecules as demonstrated by scanning force microscopy (de Jager et al. 2001). Based on these observations, a model has been proposed in which the Mre11/Rad50 complex bridges broken DNA ends or sister chromatids (van den Bosch 2003). In yeast and mammalian cells, DSBs provoke the formation of defined nuclear structures called irradiation-induced foci (IRIF). IRIF are believed to originate by chromatin modification, such as H2AX phosphorylation, at the site of the DSB, followed by the recruitment of signaling and repair factors. MRN localizes to DSBs, independently of H2AX phosphorylation, and is critical for the formation of IRIF and the consequent response to DNA damage (Petrini and Stracker 2003). Thus, cells with mutations in Mre11 or Nbs1 form IRIF inefficiently. In ATLD cells, which carry a defective Mre11, ATM activation is inhibited. Furthermore, ATM fails to localize to sites of DSBs in cells lacking functional MRN (Uziel et al. 2003). Taken together, these results suggest that MRN plays an early and essential role in assembly of functional signaling complexes at the sites of DNA damage. Furthermore, they place MRN upstream of ATM in the DNA damage signaling pathway. Cell-free extracts derived from Xenopus eggs recapitulate signaling pathways triggered by DNA damage and have been instrumental in unraveling the functions of ATM and Mre11 (Costanzo et al. 2000, 2001). Using this system, we show below that fragmented DNA assembles with proteins into macromolecular structures enriched in activated ATM and MRN. Their assembly requires MRN but not ATM. A truncated form of Mre11 associated with ATLD does not support DNA–protein complex assembly or DSB-induced activation of ATM. This work provides a direct molecular connection between ATM and MRN that can explain the similarities between A-T and ATLD. Results A Rapid Assay for the Response to DNA DSBs Addition of fragmented DNA to Xenopus egg extracts triggers the ATM-signaling pathway (Costanzo et al. 2000). We previously demonstrated this reaction by measuring an ATM-dependent block to DNA replication in extracts treated with DNA fragments (Costanzo et al. 2000). We now describe a rapid assay to monitor the activation of DSB-responsive protein kinases and to assess the contribution of ATM and related protein kinases. Histone H2AX, a well-characterized substrate for DSB-activated protein kinases, is phosphorylated in vivo at serine 139 by ATM and the ataxia-telangiectasia-related protein (ATR) (Rogakou et al. 1998; Burma et al. 2001; Costanzo et al. 2001; Ward and Chen 2001). We used the C-terminal peptide of mouse H2AX (PAVGKKAS134QAS139QEY) as a reporter substrate to monitor the response to DSBs. This peptide contains two putative SQ phosphorylation sites for ATM or ATR: serines 134 and 139. To test the specificity of the kinase(s) activated by DSBs, we synthesized four peptides: wild-type and alanine substitutions at serine 134 (S134A), serine 139 (S139A), and serines 134 and 139 (S134A/S139A). Incubation of interphase extracts for 30 min with fragmented DNA dramatically enhanced phosphorylation of H2AX peptide (Figure 1A). Phosphorylated H2AX peptide could be detected as early as 5 min after addition of fragmented DNA (data not shown). S134A peptide was phosphorylated to a level equivalent to wild-type peptide, whereas S139A and S134/139A peptides were not modified. Thus, phosphorylation of S139 in cell-free extracts in response to DSBs mimics the in vivo situation (Rogakou et al. 1998; Burma et al. 2001; Costanzo et al. 2001; Ward and Chen 2001). Figure 1 Functional MRN Is Required for the Response to DSBs, and Mre11–ATLD Separates Essential and Nonessential Mre11 Functions (A) The activity of protein kinases responsive to DSBs in Xenopus laevis egg extracts was monitored by incorporation of 32P from γ-32P-ATP into H2AX-derived peptides in the presence (plus DSB) or absence (minus DSB) of fragmented DNA. Labels: Wild-Type, H2AX substrate peptide containing serine 134 and serine 139; S134A, H2AX substrate peptide with a substitution of serine 134 to alanine; S139A, H2AX substrate peptide with a substitution of serine 139 to alanine; S134A/S139A, H2AX substrate peptide with a substitution of both serines to alanine. (B) Extract incubated with linear DNA at 50 ng/μl (equivalent to 4.5 × 1010 breaks/μl) was assayed with H2AX peptide in the presence of buffer (Control), ATM-neutralizing antibodies (ATM Ab), ATR-neutralizing antibodies (ATR Ab), ATM- and ATR-neutralizing antibodies (ATM/ATR Abs), ATM- and ATR-neutralizing antibodies in Ku70-depleted extracts (ATM/ATR Abs; Ku depletion), 5 mM caffeine (Caffeine). (C) DSB-responsive kinase activity was measured in the presence of 0, 5, 10, 25, and 50 ng/μl of linear DNA in control extract (filled diamonds), mock-depleted extract (open diamonds), Mre11-depleted extract (open squares), Mre11-depleted extract supplemented with 500 nM of recombinant MRN (filled squares), or Mre11-depleted extract supplemented with 500 nM MRN-ATLD1/2 (filled triangles). (D) DSB accumulation during DNA replication was monitored by TUNEL assay. Postreplicative nuclei were isolated from a control extract (stripes), Mre11-depleted extract (dots), Mre11-depleted extract supplemented with MRN (diamonds), Mre11-depleted extract supplemented with MRN-ATLD1/2 (gray) or mock-depleted extract (white). We next monitored phosphorylation of H2AX peptide in extracts in which specific DNA damage response signaling pathways were inhibited. X-ATM- and X-ATR-neutralizing antibodies were used to abrogate ATM- and ATR-dependent signaling, respectively. We previously demonstrated that these antibodies completely inhibit ATM- and ATR-dependent checkpoints in extracts (Costanzo et al. 2000, 2003). H2AX peptide phosphorylation was significantly reduced in extracts treated with either X-ATM or X-ATR antibodies. Inhibition of both ATR and ATM further decreased H2AX peptide phosphorylation to 20% of control levels (Figure 1B, column 4). Inhibition of DNA-PK by depletion of Ku70 did not further reduce H2AX peptide phosphorylation in the ATM/ATR-inhibited extract. Finally, caffeine completely abrogated H2AX peptide phosphorylation (Figure 1B, column 6). We conclude that most H2AX phosphorylation induced by DSBs in crude extracts is ATM- and ATR-dependent. Functional MRN Is Required for ATM Activation Experiments using cells carrying hypomorphic mutations in Nbs1 or Mre11 (Carney et al. 1998; Varon et al. 1998; Stewart et al. 1999; Petrini and Stracker 2003) suggested that MRN also plays a role in sensing signals triggered by DSBs. However, because Mre11 and Nbs1 are essential genes (Yamaguchi-Iwai et al. 1999; Zhu et al. 2001; Tauchi et al. 2002), the effect of total Mre11 inactivation on the DNA damage response could not be established. We asked whether MRN was required in our system for DSB-dependent activation of H2AX peptide phosphorylation. We have previously established that Mre11 can be quantitatively depleted from extracts (Costanzo et al. 2001). Figure 1C shows that depletion of extracts for Mre11 abrogated the response to DSB-containing DNA (Figure 1C, open squares). Recombinant human MRN restored the DNA damage response in the Mre11-depleted extract (Figure 1C, filled squares). ATLD, a syndrome characterized by failure of the DNA damage response, is caused by hypomorphic mutations in Mre11 (Stewart et al. 1999). In contrast to wild-type protein, MRN containing a mutant Mre11 that lacks the C-terminal DNA-binding domain (MRN-ATLD1/2) (Stewart et al. 1999; Lee et al. 2003) did not restore activity to the Mre11-depleted extract (Figure 1C, filled triangles). At higher fragmented DNA concentrations (greater than or equal to 100 ng/μl), H2AX peptide phosphorylation became partly independent of ATM and Mre11. This phosphorylation was sensitive to vanillin, a specific inhibitor of DNA-PK (Durant and Karran 2003), and to Ku depletion (data not shown). In contrast to its inactivity in the DSB checkpoint reaction, MRN-ATLD1/2 can fulfill the essential function of MRN in preventing the accumulation of breaks during DNA replication. Figure 1D shows a TUNEL assay to detect DNA ends. As we previously reported (Costanzo et al. 2001), chromosomal DNA replicated in Mre11-depleted extracts accumulated DSBs. Addition of purified recombinant MRN to depleted extracts largely prevented DNA fragmentation. MRN-ATLD1/2 was as efficient as wild-type MRN in supporting normal DNA replication. These results establish that MRN is required to activate the DSB signal pathway and that the C-terminal region of Mre11 plays a critical role in this activation. Linear DNA Fragments Trigger Mre11-Dependent Assembly of Large DNA–Protein Complexes Scanning force microscopy data (de Jager et al. 2001) show that Mre11–Rad50 binds preferentially to broken DNA ends, implying that direct interaction with linear DNA is essential for MRN function. To investigate interactions between Mre11 and damaged DNA, interphase extract was incubated with 32P-labeled, 1 kb linear double-strand DNA molecules and applied to a BioGel A15m column. This large-pore gel filtration resin includes most proteins and small DNA fragments, but excludes protein–DNA complexes larger than 1.5 × 107 kDa (Yuzakhov et al. 1999). When radio-labeled DNA at the concentration of 50 ng/μl (equivalent to 4.5 × 1010 ends/μl) was applied to the column in the absence of extract (Figure 2A) or with extract but prior to incubation (data not shown), all radioactivity was recovered in the included volume. In contrast, when fragmented DNA was incubated with extract prior to chromatography, radio-labeled DNA resolved into two peaks (Figure 2B). Most DNA was still recovered in the included volume (fractions 20–30). However, a separate DNA peak corresponding to 3%–5% of the total DNA loaded appeared in the excluded volume (fractions 9–12). In contrast, labeled double-strand circular plasmid DNA did not assemble into DNA–protein complexes after incubation; all labeled DNA was recovered in the included volume (Figure 2C). Figure 2 Requirements for the Assembly of DNA–Protein Complexes Elution profiles of α-32P-dATP-labeled 1 kb linear DNA from BioGel A15m chromatography columns. After loading, fractions 1–31 were collected and radioactivity was counted in a scintillation counter. (A–E) Complete elution profile. (A) Linear DNA alone. (B) Linear DNA incubated 2 h in extract at 22°C. (C) α-32P-dATP-labeled circular plasmid incubated for 2 h in extract at 22°C. (D) Linear DNA incubated with extract treated with 1 mg/ml proteinase K immediately prior to loading. (E) Linear DNA incubated in Mre11-depleted extract. (F and G) Excluded volume (fractions 6–14). (F) Linear DNA incubated in the following extracts: Mre11-depleted extract (open triangles), Mre11-depleted extract supplemented with 500 nM of MRN (filled triangles), Mre11-depleted extract supplemented with 500 nM of MRN-ATLD1/2 (open squares), or control extract supplemented with MRN (filled squares). (G) Linear DNA incubated in the following extracts: control extract (filled circles), extract treated with 5 mM caffeine (open circles), extract treated with TPEN at 100 μM (open diamonds). The peak in the excluded volume represents large DNA–protein complexes that assembled in the extract, since it was eliminated by treatment of the extract with proteinase K immediately prior to chromatography (Figure 2D). Note that the elution buffer contains detergent, ruling out possible membrane aggregation. To determine whether Mre11 plays a role in assembling the DNA–protein complex, we incubated labeled DNA in an Mre11-depleted extract (Figure 2E). In the absence of Mre11, almost no radioactive label was recovered in the excluded volume. Addition of recombinant human MRN to the depleted extract restored the peak of high molecular weight DNA–protein complex (Figure 2F, filled triangles). We conclude, therefore, that the assembly of DNA–protein complexes requires Mre11. Furthermore, addition of MRN to nondepleted extract increased the amount of DNA in the excluded volume (Figure 2F, filled squares), suggesting that MRN is limiting in these extracts. MRN-ATLD1/2 did not restore DNA–protein complex formation in an Mre11-depleted extract (Figure 2F, open squares), indicating that an intact Mre11 C-terminal domain is required for complex assembly. Rad50 protein forms intramolecular coiled-coil interactions as well as intermolecular interactions via a Zn2+-chelating hinge region coordinated by four cysteine residues, the “zinc-hook” (Hopfner et al. 2002). Addition of TPEN, a chelating agent specific for Zn2+ (Shumaker et al. 1998), partially inhibited formation of DNA–protein complexes (Figure 2G, open circles). Finally, caffeine significantly reduced but did not eliminate the amount of labeled DNA in the excluded peak (Figure 2G, open diamonds). This suggests that assembly of the DNA–protein complex is partially independent of ATM/ATR. MRN Complex Is Part of the DNA–Protein Complexes That Tethers Linear DNA Molecules The previous experiments established that Mre11 is required for assembly of DNA–protein complexes. To demonstrate that Mre11 is an integral component of these complexes, we immunoprecipitated Mre11 from chromatographic fractions 10 and 25 and measured the 32P-DNA content of the precipitate. We found labeled DNA associated with MRN in fraction 10 (Figure 3A) but not in fraction 25, although this fraction contains both Mre11 (see Figure 4A) and 32P-DNA. As expected, immunoprecipitates of excluded fractions following chromatography of Mre11-depleted extracts did not contain labeled DNA. When Mre11 immunoprecipitated from control extracts was added to Mre11-depleted extract, we again found MRN–DNA complexes in fraction 10, but not in fraction 25 (Figure 3A). Figure 3 Mre11 Tethers DSB-Containing DNA (A) Control and treated extracts were incubated with α-32P-dATP-labeled DNA fragments and loaded onto BioGel A15 columns. Fractions 10 and 25 were collected and incubated with polyclonal antibodies against Mre11 or protein A beads alone. Beads were collected and washed, and radioactivity was counted in a scintillation counter. Shown are control extract (stripes), Mre11-depleted extract (dots), or Mre11-depleted extract supplemented with Mre11 that had been immunoprecipitated from the extract (diamonds), and extract incubated with beads alone (black). (B) Biotinylated DNA fragments were mixed with α-32P-dATP-labeled DNA fragments and incubated with various extracts. The extracts were then loaded onto BioGel A15 columns. Fractions 10 and 25 were collected and incubated with streptavidin-magnetic beads. Beads were collected and washed, and radioactivity was counted in a scintillation counter. Shown are control extract (stripes), Mre11-depleted extract (dots), Mre11-depleted extract supplemented with 500 nM MRN (diamonds), and streptavidin beads (black). Figure 4 DNA–Protein Complexes Are Signaling Centers Containing Active Mre11 and ATM (A) Western blot analysis of eluted fractions. Fraction numbers are indicated at bottom. Fractions were collected following chromatography of extracts incubated with fragmented (plus DSBs) or without fragmented DNA (minus DSBs). Samples from fractions were processed for SDS-PAGE and blotted with polyclonal antibodies against Mre11, ATM, and phosphorylated ATM. (B) Activity of ATM and ATR kinases in fractions 10 and 25. Extracts were incubated with DNA fragments and applied to BioGel A15m columns. Fraction 10 and fraction 25 from control extract were assayed for H2AX activity in presence of buffer (light gray), ATM-neutralizing antibodies (checks), ATR-neutralizing antibodies (dark gray), 300 μM vanillin (stripes), or 5 mM caffeine (black). (C) Activity of ATM and ATR kinases in fraction 10 and total extract. Control extracts or extracts supplemented with 500 nM recombinant MRN were incubated with DSBs and loaded onto BioGel A15m columns. Total control extract and fraction 10 were assayed for H2AX activity in the presence of buffer (light gray), ATM-neutralizing antibodies (checks), ATR-neutralizing antibodies (dark gray), or 5 mM caffeine (black). We conclude that MRN is a component of the DNA–protein complex. To demonstrate that linear DNA molecules were linked in the DNA–protein complex, we incubated extract with two populations of fragmented DNA. DNA molecules of identical sequence were either 32P-labeled or biotinylated. As expected, the radioactivity elution profile was identical to that observed with a single species of DNA molecules (data not shown). Fraction 10 from the excluded volume and fraction 25 from the included volume were precipitated with PEG, and biotinylated DNA molecules were affinity-purified using streptavidin-magnetic beads. The results of this assay show clearly that 32P-DNA was associated with biotinylated DNA in fraction 10, but not in fraction 25 (Figure 3B, columns 1 and 5). As expected, this association is Mre11-dependent. It was abolished in Mre11-depleted extracts and restored in Mre11-depleted extracts supplemented with recombinant MRN (Figure 3B, columns 2 and 3). Mre11-Containing DNA–Protein Complexes Are Signaling Centers We next looked for a molecular connection between the DNA–protein complex and protein kinase activation. We monitored the distribution and modification of Mre11 and ATM in the chromatographic fractions described above. Control extracts or extracts incubated with fragmented DNA were chromatographed, and fractions 9–12 from the excluded volume and fractions 25–28 from the included volume were PEG-precipitated and processed for Western blotting. In untreated control extracts, Mre11 and ATM were recovered only in the included volume. In extracts treated with linear DNA, however, Mre11 and ATM were present in both included and excluded fractions (Figure 4A, top four panels). Relative to the total protein content of the fractions, both Mre11 and ATM were enriched 18-fold and 46-fold respectively in the excluded fraction, as determined by image analysis. This confirms that the high molecular weight protein–DNA complexes contain Mre11, and additionally establishes the presence of ATM in the complex. Strikingly, Mre11 in the DNA–protein complex was in the active, phosphorylated form (Costanzo et al. 2001). In contrast, Mre11 in the included fractions was unmodified. Furthermore, using an antibody that recognizes specifically the active form of ATM (phosphorylated on the serine equivalent to serine 1,981 of human ATM; Bakkenist and Kastan 2003), we detected phosphorylated ATM only in the excluded fractions (Figure 4A, third panel). To confirm that the excluded peak was enriched in active ATM kinase, we compared H2AX peptide kinase activity in fractions 10 and 25. We also determined the relative contribution of ATM, ATR and DNA-PK to this activity (Figure 4B). ATM and ATR protein kinase activities were inhibited with the specific neutralizing antibodies described above. DNA-PK activity was inhibited by vanillin, a specific inhibitor of DNA-PK (Durant and Karran 2003). Most H2AX kinase activity in fraction 10 was due to ATM, and to a lesser extent, to ATR (Figure 4B). Vanillin had little effect on kinase activity, indicating that the contribution of DNA-PK was small in this fraction. In contrast, the kinase activity in fraction 25 was sensitive to vanillin, but not to ATM- or ATR-neutralizing antibodies (Figure 4B). To provide further evidence that formation of MRN–DNA complexes directly promotes ATM activation, we supplemented extracts with recombinant MRN and compared H2AX peptide phosphorylation in total extract and in fraction 10. The proportion of H2AX kinase that was inhibited by ATM antibodies was significantly higher in fraction 10 than in total extract (compare columns 1 and 2 with columns 9 and 10 in Figure 4C). Incubation of extract with recombinant MRN complex prior to chromatography increased H2AX kinase activity in fraction 10 by 80% (compare columns 1 and 5 in Figure 4C). The increased kinase activity was entirely abrogated by anti-ATM antibody (Figure 4C, columns 2 and 6). Discussion MRN Complex Is Required for ATM Activation The three components of the MRN complex, Mre11, Rad50, and Nbs1, are essential. Mouse embryos or chicken cells carrying inactivating mutations in any of these proteins are not viable (Luo et al. 1999; Yamaguchi-Iwai et al. 1999; Zhu et al. 2001). This has made studies of MRN and its interacting partners difficult to approach. Although a connection between ATM activation and MRN has long been known (Petrini 2000), the precise mechanism that links these two factors had not, to our knowledge, been elucidated. However, using cell-free Xenopus egg extracts, it has been possible to inactivate biochemically essential gene products. We previously determined that depletion of Mre11 and its associated protein partners lead to DSB formation during DNA replication (Costanzo et al. 2001). We used a similar strategy to relate MRN inactivation and ATM function. We provide several lines of evidence that indicate an MRN requirement for ATM activation. The G1–S checkpoint provoked by DSBs entails the sequential activation of protein kinases, including ATM (Zhou and Elledge 2000). We show that depletion of Mre11 from our extracts abolishes DSB-dependent phosphorylation of H2AX peptide, a readout for this cascade. ATM is the major contributor to H2AX phosphorylation in these extracts. Our data strongly suggest that MRN specifically activates ATM. Fragmented DNA incubated in extracts forms high molecular weight DNA–protein complexes that include MRN and ATM. Of H2AX kinase activity in the complex in fraction 10, 75% is inhibited by antibodies to ATM. Furthermore, addition of recombinant MRN to extracts increases the yield of complex and associated H2AX kinase activity. The enhanced activity is entirely ATM-dependent. ATR also contributes significantly to H2AX phosphorylation in extracts treated with DSB-containing DNA. However, ATM is activated earlier than ATR (data not shown). ATR activation might be triggered by processing of DSBs into single-strand DNA (ssDNA) (Zou and Elledge 2003). We previously showed that ssDNA specifically stimulates ATR (Costanzo et al. 2003). Since Mre11 depletion completely prevents H2AX phosphorylation, we propose that Mre11 regulates both ATM-dependent early signaling from DSBs and, possibly by its DNA exonucleolytic activity, delayed signaling by ATR. Whereas caffeine completely inhibits H2AX kinase, treatment with ATM/ATR antibodies combined inhibits only 80% of H2AX kinase. This could be accounted by an additional kinase such as ATX (Abraham 2001). Alternatively, the neutralizing antibodies against ATM and ATR might not inhibit 100% of the activity of respective kinase towards H2AX. MRN Tethers Linear DNA Molecules and Assembles DNA Damage Signaling Complexes We propose that MRN interacts with linear DNA to form DNA–protein complexes that induce the phosphorylation cascade responsible for the G1–S checkpoint. MRN assembles with linear DNA molecules in vitro (de Jager et al. 2001). We have isolated DNA–protein complexes from extracts incubated with fragmented DNA as an excluded fraction from a sizing column. The complexes require Mre11 for assembly, contain linear DNA, and are highly enriched in Mre11 and ATM. Immunoprecipitation studies with Mre11 antibodies show the presence of tripartite complexes (Mre11–ATM–fragmented DNA) in the excluded but not the void volume (data not shown). We believe that the formation of these complexes is a critical step in the kinase cascade that leads to the G1–S checkpoint. Several lines of evidence support this idea: (1) Mre11-depleted extracts do not form complexes and fail to activate ATM in response to DSBs. (2) Mre11 is concentrated 18-fold in the DNA–protein complexes and is heavily phosphorylated. We previously established that phosphorylation of Mre11 correlates with increased nuclease activity (Costanzo et al. 2001). (3) ATM is enriched 46-fold in the complexes and is phosphorylated on serine 1,981 (Bakkenist and Kastan 2003). Therefore, activated ATM is only detected in the DNA–protein complexes. ATM, and possibly ATR, participates in the assembly of the complexes. Pretreatment of extracts with caffeine, an inhibitor of ATM and ATR, significantly reduces the yield of complex. Some H2AX kinase activity is not associated with the DNA–protein complex. This activity is principally accounted for by DNA-PK. Both MRN components Mre11 and Nbs1 are phosphorylated in response to DSBs. Nbs1 phosphorylation is ATM-dependent (Gatei et al. 2000; Lim et al. 2000; Zhao et al. 2000). Once recruited and activated within the signaling complex, ATM might phosphorylate Nbs1 and Mre11, stabilizing the complex and enhancing signaling activity. How might DNA–MRN complexes initiate the cascade of events leading to ATM activation? One of the critical steps could be to bring ATM in close proximity with “chromatinized” DNA fragments. Indeed, it was shown previously that ATM had affinity for DSBs (Andegeko et al. 2001; Uziel et al 2003). ATM enrichment at sites of DSBs is consistent with the localized phosphorylation of H2AX observed in vivo on chromatin flanking DSBs (van den Bosch et al. 2003). Our previous work showed that at high doses of DNA fragment (100 ng/μl, equivalent to 9 × 1010 breaks/μl), the ATM-dependent checkpoint does not require Mre11 function (Costanzo et al. 2001). We also determined that H2AX phosphorylation at 100 ng/μl of linear DNA is partially Mre11-independent (data not shown). This could be due to ATM activation by mass action at this dose of linear DNA as well as to activation of DNA-PK (data not shown). Molecular Bases for the Similarities between A-T and ATLD A powerful argument for placing MRN and ATM in a common signaling pathway derives from the similarities between the clinical and the cellular phenotypes of A-T, NBS, and ATLD (Digweed et al. 1999; Stewart et al. 1999; Tauchi et al. 2002). Uziel et al. (2003) recently showed that the ATM response to DSBs is impaired in ATLD cells, which carry defective Mre11. After our work was completed, additional studies reached similar conclusions using Mre11- or Nbs1-deficient cells (Carson et al. 2003; Mochan et al. 2003; Theunissen et al. 2003). Our data provide a biochemical framework to explain their observations. The ATLD1/2 mutation, which generates a truncated Mre11 that lacks part of its DNA-binding domain, is compatible with viability. Thus, the mutation cannot abrogate the essential role of Mre11, although the mutant Mre11 is defective in the damaged DNA response. We were able to dissociate the two Mre11 reactions using simple biochemical readouts. MRN-ATLD1/2 cannot activate ATM or form DNA–protein complexes in response to DSBs. It can, however, prevent accumulation of DSBs during chromosomal DNA replication. We speculate that MRN-ATLD1/2 has reduced affinity for damaged DNA, resulting in labile interactions with fragmented DNA and an inability to activate ATM. What differentiates the essential function of Mre11 during DNA replication from its ability to activate ATM? We suggest that MRN association with chromatin during DNA replication and, possibly, during meiotic recombination differs from its association with fragmented DNA. Consistent with this hypothesis, chromatin association of Mre11 was shown, by detergent extraction, to differ between replicative and γ-irradiated chromatin (Mirzoeva and Petrini 2003). We previously demonstrated the association of Mre11 with chromatin during normal DNA replication. One can envisage MRN complexes forming on intact chromatin in a manner similar to other SMC proteins such as cohesins, and involving, perhaps, interactions with cohesins (Kim et al. 2002). These complexes could perform the essential functions of MRN during replication and recombination and would not require an intact Mre11 C-terminal domain. This is consistent with the viability and recombination proficiency of ATLD mutant cells. In contrast, tethering of damaged DNA containing DSBs would require the Mre11 C-terminal DNA-binding domain. Failure to interact with broken DNA would account for the various phenotypes of A-T and ATLD. Alternatively, C-terminal truncation of Mre11 might weaken protein–protein interactions within the MRN complex or between MRN and other proteins. This idea is suggested by the Mre11 crystal structure, which shows that the C-terminal domain in close proximity to a hydrophobic region required for protein–protein interaction (Hopfner et al. 2001). The truncated Mre11 might be unable to form the protein–protein interactions required to stabilize MRN–DNA complexes. MRN–DNA Complexes and IRIF The signaling complexes described above are reminiscent of IRIF observed in mammalian cells (Maser et al. 1997). Indeed, Mre11 is one of the first proteins to localize to IRIF following DNA damage (Petrini and Stracker 2003). Furthermore, cells from ATLD patients fail to establish foci (Stewart et al. 1999), consistent with the inability of MRN-ATLD1/2 to support the formation of DNA–protein complexes in extracts. Recall that the ability to form foci and to activate a DNA damage response in mammalian cells are closely correlated (Stewart et al. 1999, 2003; Goldberg et al. 2003). There are several similarities between the formation of IRIF in vivo and assembly of the signaling structures in extracts. Both require (1) intact Mre11 protein and, presumably, binding of Mre11 to DNA, and (2) that IRIF form independently of (Mirzoeva and Petrini 2001), but are stabilized by, ATM, possibly by phosphorylation of Mre11 and/or Nbs1 (Gatei et al. 2000; Lim et al. 2000; Wu et al. 2000; Zhao et al. 2000; Costanzo et al. 2001; Lukas et al. 2003). As shown in Figure 5, our data suggest that MRN concentrates and localizes DNA fragments and signaling proteins such as ATM in IRIF-like structures. MRN may be rate-limiting for assembly of these structures, even though Mre11 can be recovered apart from DNA–protein complexes. It was recently reported that the ends of broken chromosomes localize with phosphorylated H2AX to discrete spots in the nucleus (Aten et al. 2004). The formation of these structures requires functional MRN. We suggest that these are the in vivo counterparts of the MRN-dependent structures that we observe in vitro. We have shown that DNA–protein complexes are essential for the DNA damage checkpoint. The challenge now is to dissect the assembly pathway and to identify the rate-limiting steps in the organization of these signaling centers. Figure 5 Schematic Representation of the Mre11-Dependent Assembly of DNA Damage Signaling Complexes MRN promotes the assembly of DNA–protein structures containing linear DNA fragments enriched with active ATM molecules. These active signaling complexes resemble IRIF in that they are the morphological and functional unit of the DNA damage response. Materials and Methods Xenopus egg extracts CSF-arrested extracts were freshly prepared according to Costanzo et al. (2001). For kinase assays, extracts were supplemented with 100 mg/μl cycloheximide and released into interphase with 0.4 mM CaCl2. DNA template To prepare DNA fragments containing DSBs, we used pBR322 plasmid digested with restriction endonucleases to yield different types of ends (3′-overhang, 5′-overhang, and blunt). These DNA fragments behaved equivalently in our assay (data not shown). For the experiments shown in Figure 1, we used DNA digested with HaeIII. The 1 kb DNA fragment used for size fractionation experiments was obtained by PCR on M13 ssDNA template using 22 nt primers complementary to positions 5,570 and 6,584 (de Jager et al. 2001). The 32P-labeled fragment was obtained by addition of α-32P-dATP (10 mCi/μl) to the PCR. The biotinylated 1 kb fragment was obtained by PCR on M13 ssDNA template using a 22 nt primer complementary to position 5,570 and a 22 nt primer complementary to position 6,584, biotinylated on three thymidine residues (Sigma-Genosys, The Woodlands, Texas, United States). Kinase assays Interphase egg extracts were incubated with DNA fragments, DNA fragments and ATM-neutralizing Ab, ATR-neutralizing antibodies or 5 mM caffeine for 30 min at 22°C. Extract (2 μl) was mixed with 20 μl of EB kinase buffer (20 mM HEPES [pH 7.5], 50 mM NaCl, 10 mM MgCl2, 1 mM DTT, 1 mM NaF, 1 mM Na3VO4, and 10 mM MnCl2) supplemented with 0.5 mg/ml histone H2AX peptide (Sigma-Genosys), 50 μM ATP, and 1 μl of γ-32P-ATP, 10 mCi/μl (greater than 3,000 Ci/mmol). Samples were incubated at 30°C for 20 min, and reactions were stopped by 20 μl of 50% acetic acid and spotted on p81 phosphocellulose filter paper (Upstate Biotechnology, Lake Placid, New York, United States). Filters were air-dried and washed three times in 10% acetic acid. Radioactivity was quantified in a scintillation counter. For kinase assays of fractionated extracts, 50 ng/μl of 1 kb DNA fragments was incubated in interphase extracts at 22°C for 2 h. Extracts were loaded onto the sizing column, and 250 μl fractions were collected. Fractions were supplemented with 9% PEG-6000, incubated on ice for 15 min, and spun in a microfuge at maximum speed at 4°C for 10 min. Pellets were resuspended in 20 μl of EB buffer, and 2 μl was assayed with histone H2AX peptide substrate, with or without ATM-neutralizing antibodies, ATR-neutralizing antibodies, 300 μM vanillin, or 5 mM caffeine. Egg extract fractionation Interphase egg extracts (200 μl) were incubated with or without 50 ng/μl of 32P-labeled 1 kb DNA fragments for 2 h at 22°C. They were then mixed with one volume of buffer A, loaded onto a 15 × 300 mm column prepacked with BioGel A15m resin (Bio-Rad, Hercules, California, United States) previously equilibrated with buffer A at 4°C. Extracts were mock-depleted, Mre11-depleted, or Mre11-depleted supplemented with 500 nM MRN or with 500 nM MRN-ATLD1/2. Control extracts were treated with 500 nM MRN and 100 μM TPEN or 5 mM caffeine or 1 mg/ml proteinase K at 37°C. After the samples were loaded, 15 ml of buffer A (100 mM KCl, 40 mM HEPES [pH 8.0], 0.05% Tween-20, 10 mM MgCl2, 1 mM ATP, 1 mM DTT, 1 mM NaF, 1 mM Na3VO4 leupeptin, pepstatin, and aprotinin protease inhibitors) were gently applied to the column. We collected 31 fractions of approximately 300 μl, and radioactivity was measured in a scintillation counter. For the elution profile of the circular plasmid in control extracts, a 1.8 kb plasmid derived form pUC19 with the SspI–SapI region deleted (Ristic et al. 2001) was used. Nicked plasmid was isolated and labeled by nick translation in the presence of α-32 P-dCTP, ligated, and incubated in extracts. After the fractions were collected, radioactivity was counted in a scintillation counter. Precipitation of DNA fragments bound to Mre11 We incubated 200 μl of control, Mre11-depleted, or Mre11-depleted extract supplemented with Mre11 precipitated from the extract with 50 ng/μl of 32P-labeled 1 kb DNA fragments. Samples were applied to the BioGel A15m sizing column and fractions were collected. Void volume peak fraction 10 and included volume peak fraction 25 were incubated with 50 μl of specific polyclonal antibodies against Mre11 prebound to protein A–Sepharose beads or beads alone overnight at 4°C . Beads were washed with buffer A, and radioactivity was counted in a scintillation counter. Biotinylated DNA pulldown We incubated 200 μl of control, Mre11-depleted, or Mre11-depleted extract supplemented with 500 nM MRN with 50 ng/μl 32P-labeled 1 kb DNA fragments and 50 ng/μl biotinylated 1 kb fragments for 2 h at 22°C. Samples were applied to the BioGel A15m sizing column, and fractions were collected. Void volume peak fraction 10 and included volume peak fraction 25 were incubated with kilobase-BINDER dynabeads (Dynal Biotech, Oslo, Norway) or mock protein A dynabeads (Dynal Biotech), and DNA fragments were isolated according to the kit protocol. Biotinylated DNA fragments bound to beads were washed with buffer A, and radioactivity was counted in a scintillation counter. Recombinant Mre11/Rad50/Nbs1Proteins Human MRN and MRN-ATLD1/2 were purified from baculovirus-infected cells according to published protocols (Paull and Gellert 1998). The recombinant trimeric complex was used at a concentration of 500 nM, unless otherwise specified. X-Mre11 complex depletion/Ku depletion For X-Mre11 complex depletion, 50 μl of interphase extract was incubated with 25 μl of protein A–Sepharose beads coupled with 50 μl of preimmune serum or with 50 μl of X-Mre11 antiserum for 60 min at 4°C. For Ku70/80 depletion, 50 μl of interphase extract was incubated with 25 μl of protein A–Sepharose beads coupled to 50 μl of Ku antiserum (Covance, Princeton, New Jersey, United States) for 60 min at 4°C. TUNEL assay TUNEL assay was performed according to Costanzo et al. (2001). Western blot We incubated 2 μl samples of interphase egg extracts for 30 min at 22°C with 50 ng/μl DNA fragments, DNA fragments and 5 mM caffeine, or with 50 ng/μl circular plasmid, and 2 μl samples were recovered from the BioGel A15m column and precipitated with 9% PEG (see Figure 3A) were diluted in loading buffer, boiled for 3 min, electrophoresed on 6% or 10% SDS-PAGE, transferred to nitrocellulose, and probed with polyclonal antibodies specific for Xenopus ATM, Xenopus ATR, Xenopus Mre11, phosphoserine 1,981 of human ATM (Rockland Immunochemicals, Gilbertsville, Pennsylvania, United States), and phosphorylated ATM/ATR SQ substrates (New England Biolabs, Beverly, Massachusetts, United States). We would like to thank Dr. K. Cimprich for X-ATR antibodies, Dr. L. Symington for reading the manuscript, and the members of the Gautier lab for helpful suggestions. This work is supported by American Cancer Society grant RSG CCG-103367, National Institutes of Health grants (CA95866 and CA92245), and National Cancer Institute contract N01-CN-25111 to JG. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. VC and JG conceived and designed the experiments. VC performed the experiments. VC, MG, and JG analyzed the data. TP contributed reagents/materials/analysis tools. VC, MG, and JG wrote the paper. Academic Editor: James Haber, Brandeis University Abbreviations A-Tataxia-telangiectasia ATLDataxia-telangiectasia-like disease ATMataxia-telangiectasia mutated ATRataxia-telangiectasia-related DSBdouble-strand break IRIFirradiation-induced foci MRNMre11/Rad50/Nbs1 complex MRN-ATLD1/2Mre11/Rad50/Nbs1 complex containing truncated Mre11 NBSNijmegen breakage syndrome SMCstructural maintenance of chromosomes ssDNAsingle-strand DNA Correction note: Because of a labeling error, the size of the DNA fragment used throughout the experiments was reported incorrectly (reported as 1 kb). The actual size of the DNA fragment was 131 bp. The fragment corresponds to the M13 DNA sequence from nucleotide 5656 to nucleotide 5787. This difference in fragment size does not affect any of the conclusions of the paper. 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nuclease activities in the Saccharomyces cerevisiae Rad50*Mre11 complex J Biol Chem 2001 276 35458 35464 11454871 Uziel T Lerenthal Y Moyal L Andegeko Y Mittelman L Requirement of the MRN complex for ATM activation by DNA damage EMBO J 2003 22 5612 5621 14532133 van den Bosch M Bree RT Lowndes NF The MRN complex: Coordinating and mediating the response to broken chromosomes EMBO Rep 2003 4 844 849 12949583 Varon R Vissinga C Platzer M Cerosaletti KM Chrzanowska KH Nibrin, a novel DNA double-strand break repair protein, is mutated in Nijmegen breakage syndrome Cell 1998 93 467 476 9590180 Ward IM Chen J Histone H2AX is phosphorylated in an ATR-dependent manner in response to replicational stress J Biol Chem 2001 276 47759 47762 11673449 Wu X Ranganathan V Weisman DS Heine WF Ciccone DN ATM phosphorylation of Nijmegen breakage syndrome protein is required in a DNA damage response Nature 2000 405 477 482 10839545 Yamaguchi-Iwai Y Sonoda E Sasaki MS Morrison C Haraguchi T Mre11 is essential for the maintenance of chromosomal DNA in vertebrate cells EMBO J 1999 18 6619 6629 10581236 Yuzhakov A Kelman Z O'Donnell M Trading places on DNA—A three-point switch underlies primer handoff from primase to the replicative DNA polymerase Cell 1999 96 153 163 9989506 Zhao S Weng YC Yuan SS Lin YT Hsu HC Functional link between ataxia-telangiectasia and Nijmegen breakage syndrome gene products Nature 2000 405 473 477 10839544 Zhou BB Elledge SJ The DNA damage response: Putting checkpoints in perspective Nature 2000 408 433 439 11100718 Zou L Elledge SJ Sensing DNA damage through ATRIP recognition of RPA-ssDNA complexes Science 2003 300 1542 1548 12791985 Zhu J Petersen S Tessarollo L Nussenzweig A Targeted disruption of the Nijmegen breakage syndrome gene NBS1 leads to early embryonic lethality in mice Curr Biol 2001 11 105 109 11231126

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PLoS Biol. 2004 May 11; 2(5):e110

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PLoS Biol

10.1371/journal.pbio.0020110

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020127PrimerDevelopmentVertebratesXenopusChickenMus (Mouse)Ascidians (Sea Squirts)Organizing the Vertebrate Embryo—A Balance of Induction and Competence PrimerDawid Igor B 5 2004 11 5 2004 11 5 2004 2 5 e127Copyright: © 2004 Igor B. Dawid.2004This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. Neural Induction in Xenopus: Requirement for Ectodermal and Endomesodermal Signals via Chordin, Noggin, β-Catenin and Cerberus Neural Induction without Mesoderm in Xenopus The current understanding of organizer formation and neural induction in vertebrate embryos is discussed ==== Body In what is usually referred to as the most famous experiment in embryology, Hans Spemann and Hilde Mangold (1924) showed that a specific region in early frog embryos called the blastopore lip can induce a second complete embryonic axis, including the head, when transplanted to a host embryo. Most of the axis, including the nervous system, was derived from the host, whose cells were induced to form an axis by the graft, therefore named the organizer. Induction refers to the change in fate of a group of cells in response to signals from other cells. The signal-receiving cells must be capable of responding, a property termed competence. The Spemann–Mangold organizer. which—as the transplantation experiment shows—is able to turn cells whose original fate would be gut or ventral epidermis into brain or somites, is the prototypical inducing tissue. And neural induction has for a long time been regarded as a process by which organizer signals, in their normal context, redirect ectodermal cells from an epidermal towards a neural fate. The nature of the neural inducer or inducers and the mechanism of neural induction have been and remain hot topics in developmental biology. For half a century after Spemann and Mangold, studies on amphibians monopolized the subject, and even more recently, a large part of the progress in analyzing organizer formation and function and neural induction was based on amphibians, mostly the model species Xenopus laevis. In the past few years, however, work in other vertebrate and nonvertebrate chordate systems has come to play an important role in the field and has shed light on generalities and differences among chordates. If the present primer uses Xenopus to illustrate the process, it is because it accompanies an article in this issue of PLoS Biology dealing with neural development in this species (Kuroda et al. 2004) and, of course, because of the experience of this author. Here I shall outline the understanding of organizer formation and neural induction as it has evolved over recent times and attempt to integrate recent results from different species into a common pattern. Cortical Rotation and Nuclear Localization of β-Catenin The frog egg is radially symmetrical around the animal–vegetal axis that has been established during oogenesis. Fertilization triggers a rotation of the cortex relative to the cytoplasm that is associated with the movement of dorsal determinants from the vegetal pole to the future dorsal region of the embryo (Gerhart et al. 1989). (A brief parenthetical point is in order here. Conventionally, the side of the amphibian and fish embryo where the organizer forms has been called dorsal, with the opposite side labeled as ventral. This axis assignment does not project unambiguously onto the clearly defined dorsal–ventral polarity of the larva, as pointed out forcefully in recent publications [Lane and Smith 1999; Lane and Sheets 2000, 2002]. In these papers, a new proposal is made for polarity assignments in the gastrula that, I believe, has some merit, but also presents some difficulties. As the conventional approach of equating organizer side with dorsal seems to remain in wide use at present, I shall apply this convention, albeit with the reservation above.) While the nature of the dorsal determinants is still in dispute, it is clear that the consequence of their translocation is the nuclear localization of β-catenin in a wide arc at the future organizer side (Figure 1) (Schneider et al. 1996; Schohl and Fagotto 2002). Nuclear localization of β-catenin appears to be the first event that determines dorsal/ventral polarity in the Xenopus and zebrafish embryos (Hibi et al. 2002). No comparable early event appears to be involved in amniote (e.g., chick and mouse) embryos. Figure 1 Early Development in X. laevis After fertilization, dorsal determinants are transported from the vegetal pole to one side of the embryo, where β-catenin will achieve nuclear localization. By 32 cells, the row of cells labeled 1 is specified as dorsal. Movements towards the vegetal pole (arrow) start at early cleavage stages. The organizer forms from C1 and B1 progenitors, the dorsal ectoderm or BCNE mostly from A1 progenitors (see Figure 2). The organizer is indicated in the gastrula embryo. See the text for further explanation. Induction by the Organizer: Antagonizing Bone Morphogenetic Protein As gastrulation starts, the Spemann–Mangold organizer, which includes mostly axial mesodermal precursors, was classically believed to instruct naïve ectoderm to convert to neural tissue. In transplant or explant studies, animal ectoderm that forms epidermis, when undisturbed, is susceptible to neural induction by the organizer. This fact prompted a search for neural inducers that eventually led to the identification of several substances with the expected properties—organizer products that can neuralize ectoderm. Their molecular properties were at first surprising: they proved to be antagonists of other signaling factors, mostly of bone morphogenetic proteins (BMPs) and also of WNT (a secreted protein homologous to the Drosophila Wingless protein) and Nodal factors (Sasai and De Robertis 1997; Hibi et al. 2002). These observations led to the formulation of a “default” model of neural induction (Weinstein and Hemmati-Brivanlou 1997), which states that ectodermal cells will differentiate along a neural pathway unless induced to a different fate. The heuristic simplicity and logical cogency of this model facilitated its wide acceptance, although it did not explain the processes that set the “default.” Some of these processes have been the subject of subsequent studies that were conducted in several different species, and this has led to a more refined (and probably more accurate) picture. The Role of Fibroblast Growth Factor For example, additional signaling pathways are now known to operate. Recent work on neural induction comes to two major conclusions: (i) the fibroblast growth factor (FGF) signaling pathway plays a major role in this process, and (ii) neural specification starts well before gastrulation and thus before the formation and function of the organizer. Studies on the role of FGF in early Xenopus development initially discovered its role in mesoderm induction and the formation of posterior tissues (Kimelman et al. 1992). And while the involvement of FGF in neuralization was observed early in this system (Lamb and Harland 1995; Launay et al. 1996; Hongo et al. 1999; Hardcastle et al. 2000), in view of the impressive effects seen with Chordin and other BMP pathway antagonists, the relevance of FGF in neural specification in amphibians and fish was slow to be recognized. It took elegant studies, mostly in chick embryos (Streit et al. 2000), and their eloquent exposition (Streit and Stern 1999; Wilson and Edlund 2001; Stern 2002) to turn the tide, but there is now no doubt that the FGF signaling pathway plays a major role in the specification and early development of the neural ectoderm in chordates. FGF does not seem to behave as a classical organizer-derived neural inducer, however. Maternal FGF mRNA and protein appear to be widely distributed in the early embryo, and at least one FGF family member is expressed primarily in the animal, pre-ectodermal region during blastula stages (Song and Slack 1996). A detailed study of the regions where different signaling pathways are active during embryogenesis (Schohl and Fagotto 2002) showed that the entire ectoderm is probably exposed to FGF signals at or prior to the time of neural induction, with the more vegetal, mesoderm-proximal region of the ectoderm being exposed to higher levels. Thus, exposure to FGF is required to endow the ectoderm with the competence to respond to additional signals that will act later on its way towards neural specification. Such a process was deduced from experiments in the chick, where an FGF signal must be followed by exposure to organizer signals to sensitize the tissue to BMP antagonists that ultimately stabilize the neural fate (Stern 2002). An exciting recent study shows that exposure of the epiblast (ectoderm) to FGF induces, after a time delay, a transcription factor named Churchill. Churchill expression inhibits cell ingression leading to mesoderm formation; the cells remaining in the epiblast assume a neural fate (Sheng et al. 2003). The time delay in Churchill induction appears to be the key in explaining how one signal, FGF, can be involved in mesodermal and neural development at the same time in cells that are in close proximity. The question how FGF signaling can lead to different outcomes was also addressed in a study on neural specification in ascidians (Bertrand et al. 2003). Here, the FGF signal leads to neural induction through the coordinated activation of two transcription factors, Ets1/2 and GATAa, whereas FGF does not activate GATAa during its function in mesoderm formation. Thus, similar input leads to distinct output as a result of different responses by target tissues, stressing the importance of competence in this inductive process. Molecular Predisposition Not surprisingly, then, attention has turned to the target tissues and to the prepatterns that might already exist. In Xenopus, it was long known that the animal region or pre-ectoderm is not uniform or naïve, in that the dorsal, organizer-proximal region is predisposed towards a neural fate (Sharpe et al. 1987). The paper by Kuroda et al. (2004) adds much information about neural specification before gastrulation in Xenopus and the factors involved in this process. The authors identify a region in the dorsal ectoderm of the blastula that they name the “blastula Chordin- and Nogginexpressing” (or BCNE) region (Figure 2). They show that this region, which I prefer to simply call dorsal ectoderm, expresses siamois, chordin, and Xnr3, another β-catenin target. The dorsal ectoderm or BCNE is fully specified as anterior neural ectoderm, as excision of this region led to headless embryos, and explants differentiated into neural tissue in culture, even when the formation of any mesodermal cells was blocked by interference with nodal signaling (Kuroda et al. 2004). Figure 2 Expression Patterns in Dorsal Ectoderm Expression patterns of selected genes in the late blastula of Xenopus, based on the work of Kuroda et al. (2004). See the text for further explanation. Kuroda et al. (2004) further show that induction of anterior neural tissue initiated by β-catenin requires Chordin, whereas formation of posterior neural tissue does not. This latter point concerns an issue not yet mentioned here, namely anterior–posterior patterning of the neural ectoderm, a process that occurs in concert with neural induction per se. This patterning appears to involve the interaction of various signaling factors, including FGF, BMP, WNT, and retinoic acid, all of which act as posteriorizing factors (Kudoh et al. 2002). Suppression of BMP signaling by expression of its antagonists is the condition that specifies the dorsal ectoderm or BCNE as future anterior neural ectoderm; in contrast, posterior neural ectoderm may form under the influence of FGF even in the presence of BMP signaling. The work by Kuroda et al. (2004) thus shows that initial specification of anterior neural ectoderm in Xenopus, as in other vertebrates, takes place before gastrulation and does not require organizer signals; this is not to say that full differentiation and patterning of the nervous system could be achieved without organizer participation. Induction and Competence The formation of the vertebrate nervous system thus depends on multiple signaling pathways, such as the FGF, BMP, and WNT signaling cascades, that interact in complex ways (e.g., Pera et al. 2003). In contrast to the classical view, neural induction is not exclusively promoted by organizer-derived signals, in that earlier signals and intrinsic processes that determine ectodermal competence are prominently involved. Whether inductive signals or competence of responding tissue is more important in embryology has been debated, much like the nature–nurture controversy in the behavioral arena. Current work has given some boost to the competence side of the argument, but, as in behavior, the truth lies somewhere in between, though not necessarily at the halfway mark. Studies such as those discussed here bring us closer to finding the answer to this question. Igor B. Dawid is the head of the Laboratory of Molecular Genetics at the National Institute of Child Health and Human Development, National Institutes of Health, in Bethesda, Maryland, United States of America. E-mail: idawid@nih.gov Abbreviations BCNE regionblastula Chordin- and Noggin-expressing region BMPbone morphogenetic protein FGFfibroblast growth factor ==== Refs References Bertrand V Hudson C Caillol D Popovici C Lemaire P Neural tissue in ascidian embryos is induced by FGF9/16/20, acting via a combination of maternal GATA and Ets transcription factors Cell 2003 115 615 627 14651852 Gerhart J Danilchik M Doniach T Roberts S Rowning B Cortical rotation of the Xenopus egg: Consequences for the anteroposterior pattern of embryonic dorsal development Development 1989 107 Suppl 37 51 2699856 Hardcastle Z Chalmers AD Papalopulu N FGF-8 stimulates neuronal differentiation through FGFR-4a and interferes with mesoderm induction in Xenopus embryos Curr Biol 2000 10 1511 1514 11114518 Hibi M Hirano T Dawid IB Organizer formation and function Results Probl Cell Differ 2002 40 48 71 12353486 Hongo I Kengaku M Okamoto H FGF signaling and the anterior neural induction in Xenopus Dev Biol 1999 216 561 581 10642793 Kimelman D Christian JL Moon RT Synergistic principles of development: Overlapping patterning systems in Xenopus mesoderm induction Development 1992 116 1 9 1483380 Kudoh T Wilson SW Dawid IB Distinct roles for Fgf, Wnt and retinoic acid in posteriorizing the neural ectoderm Development 2002 129 4335 4346 12183385 Kuroda H Wessely O De Robertis EM Neural induction in Xenopus : Requirement for ectodermal and endomesodermal signals via Chordin, Noggin, β-Catenin, and Cerberus PLoS Biol 2004 2 e92 10.1371/journal.pbio.0020092 15138495 Lamb TM Harland RM Fibroblast growth factor is a direct neural inducer, which combined with noggin generates anterior–posterior neural pattern Development 1995 121 3627 3636 8582276 Lane MC Sheets MD Designation of the anterior/posterior axis in pregastrula Xenopus laevis Dev Biol 2000 225 37 58 10964463 Lane MC Sheets MD Rethinking axial patterning in amphibians Dev Dyn 2002 225 434 447 12454921 Lane MC Smith WC The origins of primitive blood in Xenopus : Implications for axial patterning Development 1999 126 423 434 9876172 Launay C Fromentoux V Shi DL Boucaut JC A truncated FGF receptor blocks neural induction by endogenous Xenopus inducers Development 1996 122 869 880 8631265 Pera EM Ikeda A Eivers E De Robertis EM Integration of IGF, FGF, and anti-BMP signals via Smad1 phosphorylation in neural induction Genes Dev 2003 17 3023 3028 14701872 Sasai Y De Robertis EM Ectodermal patterning in vertebrate embryos Dev Biol 1997 182 5 20 9073437 Schneider S Steinbeisser H Warga RM Hausen P Beta-catenin translocation into nuclei demarcates the dorsalizing centers in frog and fish embryos Mech Dev 1996 57 191 198 8843396 Schohl A Fagotto F Beta-catenin, MAPK and Smad signaling during early Xenopus development Development 2002 129 37 52 11782399 Sharpe CR Fritz A De Robertis EM Gurdon JB A homeobox-containing marker of posterior neural differentiation shows the importance of predetermination in neural induction Cell 1987 50 749 758 2441873 Sheng G dos Reis M Stern CD Churchill, a zinc finger transcriptional activator, regulates the transition between gastrulation and neurulation Cell 2003 115 603 613 14651851 Song J Slack JM XFGF-9: A new fibroblast growth factor from Xenopus embryos Dev Dyn 1996 206 427 436 8853991 Spemann H Mangold H Über Induktion von Embryonalanlagen durch Implantation artfremder Organisatoren Wilh Roux Arch Entwicklungsmech Org 1924 100 599 638 Stern CD Induction and initial patterning of the nervous system—The chick embryo enters the scene Curr Opin Genet Dev 2002 12 447 451 12100891 Streit A Stern CD Neural induction: A bird's eye view Trends Genet 1999 15 20 24 10087929 Streit A Berliner AJ Papanayotou C Sirulnik A Stern CD Initiation of neural induction by FGF signalling before gastrulation Nature 2000 406 74 78 10894544 Weinstein DC Hemmati-Brivanlou A Neural induction in Xenopus laevis : Evidence for the default model Curr Opin Neurobiol 1997 7 7 12 9039789 Wilson SI Edlund T Neural induction: Toward a unifying mechanism Nat Neurosci 2001 4 Suppl: 1161 1168 11687825

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PLoS Biol. 2004 May 11; 2(5):e127

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PLoS Biol

10.1371/journal.pbio.0020127

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020128Research ArticleCell BiologyMolecular Biology/Structural BiologySystems BiologySaccharomycesRas and Gpa2 Mediate One Branch of a Redundant Glucose Signaling Pathway in Yeast Ras- and Glucose-Regulated TranscriptionWang Ying 1 Pierce Michael 1 ¤Schneper Lisa 1 Güldal C. Gökçe 1 Zhang Xiuying 1 Tavazoie Saeed 1 Broach James R jbroach@molbio.princeton.edu 1 1Department of Molecular Biology, Princeton UniversityPrinceton, New JerseyUnited States of America5 2004 11 5 2004 11 5 2004 2 5 e1281 8 2003 25 2 2004 Copyright: © 2004 Wang et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Dissecting the Complexities of Glucose Signaling in Yeast Addition of glucose to starved yeast cells elicits a dramatic restructuring of the transcriptional and metabolic state of the cell. While many components of the signaling network responsible for this response have been identified, a comprehensive view of this network is lacking. We have used global analysis of gene expression to assess the roles of the small GTP-binding proteins, Ras2 and Gpa2, in mediating the transcriptional response to glucose. We find that 90% of the transcriptional changes in the cell attendant on glucose addition are recapitulated by activation of Ras2 or Gpa2. In addition, we find that protein kinase A (PKA) mediates all of the Ras2 and Gpa2 transcriptional effects. However, we also find that most of the transcriptional effects of glucose addition to wild-type cells are retained in strains containing a PKA unresponsive to changes in cAMP levels. Thus, most glucose-responsive genes are regulated redundantly by a Ras/PKA-dependent pathway and by one or more PKA-independent pathways. Computational analysis extracted RRPE/PAC as the major response element for Ras and glucose regulation and revealed additional response elements mediating glucose and Ras regulation. These studies provide a paradigm for extracting the topology of signal transduction pathways from expression data. Inducing expression of most glucose responsive genes in yeast can be mimicked by activation of the small GTP-binding proteins Ras2 or Gpa2 even in the absence of a physiologic signal ==== Body Introduction Complex intracellular networks inform a cell's developmental and growth decisions in response to external nutrients or signaling molecules. Defining the topology of such networks has generally relied on combinations of genetic epistasis and biochemical techniques to establish the linear order of components that convey information on the presence of a particular stimulus. Generally, only one or a few endpoints, such as enhanced transcription of a responsive gene, are monitored in gauging the output of a pathway. More recently, global transcriptional analysis has allowed reseachers to capture the entire transcriptional output of a signaling process and assess the consequence of eliminating individual components of the signaling network on the entire response (Fambrough et al. 1999; Roberts et al. 2000). This approach has the potential to extract a complete description of a network from a relatively limited set of experimental perturbations. We have used global transcriptional analysis to dissect the signaling network activated by glucose addition to yeast cells, with an emphasis on the role of the small GTP-binding proteins, Ras2 and Gpa2, in that signaling process. Addition of glucose to yeast cells growing on a nonfermentable carbon source induces a dramatic restructuring of the metabolic and transcriptional state of the cell (Johnston and Carlson 1992). At the metabolic level, the cell becomes reprogrammed for fermentative rather than oxidative growth. This involves the inactivation and repression of gluconeogenic enzymes and mitochondrially based oxidative phosphorylation processes and the induction of glycolytic enzymes. In addition, since yeast cells extract energy more efficiently from fermentable carbon sources, they are able to grow more rapidly and thus require an increase in the capacity for mass accumulation. This translates primarily into a need for increased protein synthetic capacity with an attendant increased production of ribosome components and other elements of the translational apparatus. The dramatic change in the metabolic activity and protein synthesis capacity attendant on glucose addition to starved cells is accompanied, and driven in part, by a reprogramming of the transcriptional state of the cell (Johnston and Carlson 1992; DeRisi et al. 1997; Johnston 1999). Cells respond to glucose addition by repressing genes involved in the use of alternative carbon sources and in oxidative phosphorylation and by upregulating glucose-specific transport systems and glycolytic enzymes. Substantial work on glucose regulation of genes required for metabolism of alternate carbon sources, sometimes referred to as carbon catabolite repression, has identified a number of components of the network responsible for this repression and defined their interconnections (Gancedo 1998). For instance, an AMP-stimulated kinase, Snf1/Snf4, inactivates a repressor, Mig1, thereby allowing transcription of genes normally repressed in the presence of readily fermentable carbon sources, and upregulates Cat8, an activator of gluconeogenic genes (Carlson 1999). In addition, a number of transcriptional activators, such as Hap2/3/4, Adr1, etc., required for transcription of glucose-repressible genes, are inactivated by growth on fermentable carbon sources. Transcriptional upregulation of hexose transporters occurs by a glucose-induced degradation of Rgt1, a repressor of a number of glucose-induced genes (Johnston 1999). The mechanism by which glucose regulates genes needed for increased translational capacity is less clear, although Rap1 and, more recently, Sfp1 and Fhl1 have been implicated as activators responsible for increased expression of growth-related genes in response to glucose (Warner 1999; Jorgensen et al. 2002; Lee et al. 2002; Fingerman et al. 2003). However, it is not well defined whether the signal for such upregulation is the increased energy output or the presence of glucose per se. The small GTP-binding proteins, Ras1 and Ras2, play a role in the cell's adaptation to glucose by coupling cyclic AMP (cAMP) production to the presence of glucose in the medium (Broach and Deschenes 1990; Tatchell 1993; Thevelein 1994). As in other organisms, yeast Ras proteins can transmit a regulatory signal by shuttling between an inactive GDP-bound form and an active GTP-bound form. In yeast, the GTP-bound Ras proteins stimulate adenylyl cyclase, encoded by CYR1, to yield an increase in intracellular cAMP levels (Toda et al. 1985). Addition of glucose to starved cells or cells growing on a nonfermentable carbon source yields within minutes a significant increase in intracellular cAMP concentrations, which rapidly decline to a level somewhat higher than that in prestimulated cells. This cAMP response to glucose is dependent on Ras. cAMP functions in yeast to liberate the yeast cAMP-dependent protein kinase A (PKA) catalytic subunit, encoded redundantly by TPK1, TPK2, and TPK3, from inhibition by the regulatory subunit encoded by BCY1 (Toda et al. 1987). Active PKA can phosphorylate a number of proteins involved in transcription, energy metabolism, cell cycle progression, and accumulation of glycogen and trehalose (Broach and Deschenes 1990; Tatchell 1993; Thevelein 1994; Boy-Marcotte et al. 1998; Smith et al. 1998; Stanhill et al. 1999). Several epistasis experiments have suggested that in some cases Ras may also function upstream of a MAP kinase cascade in yeast, primarily to direct pseudohyphal growth under conditions of nutrient limitation (Mosch et al. 1996, 1999). GPA2, a member of the Gα family of heterotrimeric G proteins, also regulates cAMP levels through a pathway parallel to Ras (Nakafuku et al. 1988). Gpa2 associates with a protein, encoded by GPR1, which is structurally related to seven-transmembrane, G-protein–coupled receptors and whose ligand may be fermentable sugars (Yun et al. 1997; Xue et al. 1998; Lorenz et al. 2000). Several lines of evidence suggest that Gpa2 activates adenylyl cyclase in a Ras-independent fashion. Overexpression of Gpa2 yields increased cAMP levels in the cell and an activated allele of Gpa2, even in a ras2 background, induces phenotypes associated with activated PKA, such as heat-shock sensitivity, repression of Msn2/4-dependent transcription, induction of pseudohyphal development, and loss of cellular stores of glycogen and trehalose (Nakafuku et al. 1988; Lorenz and Heitman 1997). Reciprocally, gpa2 is synthetically lethal with ras2, a phenotype that is reversed by inactivation of PDE2, the major cAMP phosphodiesterase in the cell (Kubler et al. 1997; Xue et al. 1998). Whether Gpa2 functions solely to modulate PKA or has other signaling functions has not been resolved. To address the role of Ras and Gpa2 in reconfiguring the yeast cell's transcriptional framework in response to glucose and to define the signaling network associated with glucose signaling, we examined the global transcriptional response of cells to glucose and compared the response to that of cells following induction of activated alleles of these two G proteins. The results of this analysis indicate that the vast majority of the transcriptional remodeling the cell undergoes in response to glucose addition can be recapitulated by induction of Ras2 or Gpa2. However, much of this change can also be accomplished in the absence of signaling through cAMP. This indicates that glucose signaling of transcriptional reorganization proceeds through redundant, overlapping pathways, only one of which is regulated by Ras2 or Gpa2. Results Activation of Ras2 or Gpa2 Recapitulates Most Glucose-Induced Transcriptional Changes In order to examine the role of Ras2 and Gpa2 in effecting transcriptional changes in the cell in response to glucose, we measured the global transcriptional response of yeast cells immediately following induction of an activated allele of RAS2 or GPA2 (designated RAS2* and GPA2* in the figures) and compared that to the changes following glucose addition to glycerol-grown cells. To focus on signaling events, rather than the transcriptional consequences of metabolic changes in the cell, we examined the transcriptional response as it changed immediately following addition of glucose. Similarly, to examine the effects of Ras2 or Gpa2 activation, we constructed gal1 strains that carried an activated form of RAS2 or GPA2 under control of the galactose-inducible GAL10 promoter. Since gal1 strains cannot metabolize galactose, addition of galactose resulted in induction of the activated RAS2 or GPA2 allele and a small number of other galactose-inducible genes, but resulted in no changes in the metabolic state of the cell. In a parallel set of experiments, we examined the transcriptional changes in response to glucose addition of yeast cells containing a PKA that is unresponsive to intracellular cAMP levels. The mutant PKA, referred to as tpk-w, lacks the regulatory subunit and two of the redundant catalytic subunits, with the third catalytic subunit crippled in its activity (Cameron et al. 1988). As a consequence, such strains possess constitutive, low-level PKA activity that is unresponsive to changes in cAMP levels in the cell. Thus, changes in cellular behavior dependent on modulation of PKA activity should be abrogated in this strain. The results of both sets of experiments are available in Table S1. The results of comparing Ras2 activation to glucose addition, provided in Figure 1, indicate that most of the transcriptional changes in the cell immediately following addition of glucose to glycerol-grown cells are recapitulated by activation of Ras2. Prior to initiation of the experiment (during growth on glycerol), the expression pattern of all genes in the wild-type strain (W303 gal1) closely resembled that of the strain carrying the inducible activated Ras allele (W303 gal1 GAL10p-RAS2 G19V), with only 0.4% of the genes exhibiting greater than 3-fold differences in absolute expression levels (Figure 1A; r = 0.96). This reflects the isogenicity of the strains and indicates that the inducible RAS2 G19V allele is not expressed under these conditions. Figure 1 Glucose Stimulation and Ras2 or Gpa2 Activation Yield Similar Transcriptional Responses (A–E) Expression levels (represented as absolute intensity values from Affymetrix hybridization scans) of individual yeast genes (points) plotted for two different strains and conditions. Dotted red lines indicate 2-fold difference boundary. (A) Strain Y2864 (Wt) prior to glucose addition versus Y2866 (GAL-RAS2*) prior to galactose addition. (B) Strain Y2864 prior to glucose addition versus Y2876 (GAL-GPA2*) prior to galactose addition. (C) Strain Y2864 20 min after glucose addition versus 0 min after addition. (D) Strain Y2866 60 min after galactose addition versus Y2864 20 min after glucose addition. (E) Strain Y2876 60 min after galactose addition versus Y2864 20 min after glucose addition. Values are in log10. (F and G) Induction ratios (mRNA level at 60 min/mRNA level at 0 min) of genes in Y2866 (F) and Y2876 (G) versus induction ratios (mRNA level at 20 min/mRNA level at 0 min) for the same genes in Y2864. Values are in log2. Addition of glucose to wild-type cells yields a substantial and rapid change in the transcriptional profile of the cell. By 20 min postaddition, 22% of all genes changed expression by greater than 3-fold and 41% changed expression by 2-fold, with essentially the same number of genes increasing as decreasing (Figure 1C). This dramatic change in the transcriptional profile was substantially recapitulated by activation of Ras. By 60 min postinduction, the profile of gene expression in the activated strain closely resembled that of the wild-type strain stimulated with glucose (Figure 1D; r = 0.94). Of those genes exhibiting a change in expression levels of at least 3-fold following addition of glucose, greater than 92% of those showed at least a 2-fold change in the same direction following activation of Ras2 (Figure 1F). Thus, since glucose yields activation of Ras2 and since Ras2 activation yields changes in transcription that are substantially similar to those observed following addition of glucose, we conclude that a major portion of the glucose signaling pathway regulating transcription can proceed through cAMP via Ras2. Similar results emerge from analysis of expression changes following activation of Gpa2. Only 0.8% of all genes showed a greater than 3-fold difference in absolute expression levels between the wild-type strain and the strain carrying the inducible activated allele of Gpa2 during growth on glycerol (Figure 1B; r = 0.97). The pattern of expression at 1 h following activation of Gpa2 strongly resembles that at 20 min following addition of glucose to wild-type cells (Figure 1F; r = 0.93). However, the response following activation of Gpa2 under these conditions is not as robust as that following activation of Ras2 or addition of glucose. While the overall magnitude of the Ras2-induced response is essentially equivalent to that obtained by glucose addition, the overall magnitude of the Gpa2-induced response is only half that of the glucose-induced changes (Figure 1F and 1G). Nonetheless, although somewhat muted, the pattern of transcriptional change induced by Gpa2 closely resembles that induced by glucose. These results are consistent with the hypothesis that the major role of Gpa2 in the cell is modulation of cAMP in response to the presence of a fermentable carbon source. Redundant Signaling Pathways Control Glucose- Regulated Genes To analyze the pattern of transcriptional response to glucose addition and cAMP induction, we used a partitional clustering algorithm to group genes on the basis of their behavior over all 32 samples analyzed (Heyer et al. 1999). Prior to clustering, the expression levels of each gene over the 32 samples were normalized by subtracting from each value the average expression of that gene over all experiments and dividing by the standard deviation of the expression values. This procedure emphasizes the pattern of response of each gene over the experiments, rather than the absolute levels of response. This process yielded 144 clusters ranging in size from seven to 506 members each. By hierarchical clustering (Eisen et al. 1998), these clusters were further organized into groups on the basis of the similarity of their patterns, yielding eight major classes exhibiting significant change in some respect over the course of the experiments. These classes, encompassing approximately 50% of all genes, are summarized in Table 1, and the corresponding pattern of expression is shown in Figure 2. The list of genes in each class is provided in Table S3. Figure 2 Expression Patterns of Clustered Genes Diagrams show the patterns of expression of genes in the classes (Roman numerals) listed in Table 1, which were clustered as described in Materials and Methods. Each line represents the average expression level of all genes in that cluster during the time course (20-min intervals over 1 h) in the strain and condition indicated. Absolute intensity values were normalized for each gene over all 32 conditions examined by subtracting the average expression level for that gene over the all conditions and dividing by the standard deviation for that gene. Thus, expression values (y-axis units) are represented as the standard deviations of each time point from the average expression value for each gene over the entire set of experiments. Error bars indicate the standard deviation in expression values of all genes in the cluster at the indicated timepoint. Abbreviations: Wt + Glu, glucose addition to strain Y2864; Wt + Gal, galactose addition to strain Y2864; RAS2* + Gal, galactose addition to strain Y2866; GPA2* + Gal, galactose addition to strain Y2876; tpk-w + Glu, glucose addition to strain Y2872. Table 1 Functional Enrichment among Genes Clustered by Response to Glucose and Ras Activation Gene expression data from 32 experiments representing time course data with five strains were clustered as described in Materials and Methods. The number of genes in each supercluster (Class) is indicated, and the pattern of expression of the members of the group in strains Y2864 (wild type [WT]), Y2866 (Ras*), Y2876 (Gpa2*), and Y2872 (tpk-w) following addition of glucose (Glu) or galactose (Gal) is indicated (I, induced; R, repressed; –, no change). Genes involved in the indicated processes under Functional Association were enriched in the indicated class. The numbers of genes in the functional category present in the class and in the genome are indicated, along with the −log10P that the enrichment is random. Functional enrichment and logP values were determined using the Gene Ontology (GO) term finder in the SGD Web site (http://www.yeastgenome.org/) In general, glucose addition yielded a rapid change in expression of genes, which remained unchanged or tended back to starting conditions at later times. We interpret this behavior to indicate that the initial response, seen at the 20 min timepoint, generally represents the response of genes to the signal initiated by addition of glucose. The later deviation from that initial response represents either adaptation of the signaling process or readjustment of expression as a consequence of the change in metabolism of the cell. In contrast, gene expression in response to activation of Ras2 or Gpa2 generally showed a lag of 20 min, followed by a monotonic change in expression over the remainder of the experiment. This is consistent with the expectation that the effects of induction of Ras2 or Gpa2 can be seen only after the new activated protein is transcribed and translated. Further, since under these conditions no significant changes in metabolism occur, the change in expression is due solely to activation of the signaling pathway. This reinforces the notion that the initial response of the cell to glucose is a signaling response, since the pattern of this monotonic change at later times, following activation of Ras2 or Gpa2, generally matches the initial response of those genes to glucose addition. If those genes induced by glucose and by activation of Ras2 are regulated by glucose solely through the Ras2–Gpa2–cAMP pathway, then we would anticipate that glucose-induced transcriptional alteration would be abrogated in a tpk-w strain. This is the case for a subset of glucose-affected genes (classes II and VI), indicating the existence of a glucose signaling pathway that relies solely on the Ras signaling pathway. Inversely, a subset of genes is activated (or repressed) by glucose in both the wild-type and tpk-w strains but is unaffected by activation of Ras2 or Gpa2, indicating the existence of a Ras2-independent glucose signaling pathway (class III). However, the vast majority of genes that respond to glucose are affected by Ras2 activation and also respond in the tpk-w background (classes I and V). This suggests that the majority of glucose-responsive genes are regulated by redundant pathways, one of which requires Ras2 and the other one(s) of which is Ras2 independent. Thus, the major transcriptional response of glucose addition diverges prior to activation of Ras2, but converges before gene activation. This is elaborated further in the Discussion. Ras and Gpa2 Signal Exclusively through PKA To assess the extent to which the effects on transcription of Ras2 activation are mediated by PKA, we examined the pattern of expression following activation of Ras2 in tpk-w cells compared to that in Tpk+ cells. For those genes whose induction or repression by Ras2 is exerted through PKA, the tpk-w mutations would be expected to abrogate that response. In Figure 3 we plot the change in expression of each gene 60 min after galactose addition to the GAL10p-RAS2 V19 tpk-w strain versus the change in expression of each gene 60 min after galactose addition to the GAL10p-RAS2 V19 strain. As evident, almost all genes fail to respond to Ras2 activation in the tpk-w background. Of the 789 genes (out of 4,037 analyzed) in this experiment whose expression increased by more than 2-fold at 60 min following addition of galactose to the GAL10p-RAS2 V19 strain, only 16 (2%) also showed increased expression through activation of Ras2 in the tpk-w background. Similarly, of the 1,121 genes whose expression decreased by more than 2-fold following activation of Ras2 in a wild-type background, only five (0.5%) also showed decreased expression in the tpk-w background. Repetition of these experiments using cDNA microarrays and direct Northern blot analysis of candidate genes failed to confirm that expression of any gene was altered by Ras induction in a tpk-w background (data not shown). Thus, we conclude that the entirety of the transcriptional response to Ras2 activation is mediated through PKA. Figure 3 Ras and Gpa2 Affect Transcription Exclusively through PKA (Top) Induction ratios (mRNA level at 60 min/mRNA level at 0 min) of genes in strain Y2873 (y-axis) versus induction ratios (mRNA level at 60 min/mRNA level at 0 min) for the same genes in strain Y2866. Values are in log2. (Bottom) Similar analysis for strain Y2897 (y-axis) versus strain Y2876. The results are similar for Gpa2 activation. As noted above, the response to Gpa2 activation is not as robust as that to Ras2 activation, and, as noted in Figure 3, the attenuation of the response to Gpa2 induction in a tpk-w strain is not as obvious as that seen with Ras2. Of the 444 genes in this experiment whose expression increased 2-fold or more in response to Gpa2 activation in a wild-type background, 75 (17%) also showed increased expression in the tpk-w background. Similarly, of the 831 genes whose expression decreased by 2-fold or more, 24 (3%) also showed decreased expression in the tpk-w background. However, multiple replicates of this experiment using cDNA microarrays failed to identify any gene consistently altered in transcription by Gpa2 in a tpk-w background. Thus, as with Ras, the vast majority, if not all, of Gpa2-responsive genes are regulated exclusively through PKA. Gpr1 Is Required for Efficient Glucose Response GPR1 encodes a protein structurally related to seven-transmembrane, G-protein–coupled receptors, and both biochemical and genetic evidence suggests it regulates Gpa2 activity in response to glucose (Xue et al. 1998; Kraakman et al. 1999; Lorenz et al. 2000). Accordingly, to assess the role of Gpr1 in the cell's transcriptional response to glucose, we examined the global transcriptional pattern of isogenic GPR1 and gpr1 strains at 20-min intervals following glucose addition to glycerol-grown cells. Further, to assess the extent to which Gpr1-mediated signaling was processed through PKA, we performed a similar time course experiment with isogenic GPR1 tpk-w and gpr1 tpk-w strains. The full set of data is available in Table S2. In both experiments we found that the overall transcriptional response (both induction and repression) was attenuated, although not eliminated, in the gpr1 strain relative to the GPR1 strain. For instance, for those genes whose expression changed by more than 50% following glucose addition to the GPR1 TPK strain, the average induction or repression ratio in the gpr1 strain was approximately half that in the GPR1 strain. K-means clustering of normalized data confirmed this general view (Figure 4). For instance, cluster 1, which included 470 genes highly enriched in those involved in ribosome biosynthesis, exhibited on average induced expression in the GPR1 TPK strain following glucose addition, but no induction in the gpr1 TPK1 strain. Similar results were observed for genes in cluster 8, and induction of genes in clusters 4 and 6 was attenuated in the gpr1 strain compared to that in the GPR1 strain. Thus, these results are consistent with the hypothesis that Gpr1 participates in glucose signaling, but is not the sole mediator of that signaling. Figure 4 Loss of Gpr1 Diminishes the Glucose Response Diagrams show the patterns of expression of genes in clusters based on time course changes (20-min intervals over 1 h) in gene expression following glucose addition to the indicated strains (GPR1, Y2092; gpr1, Y3159; GPR1 tpk-w, Y2857; gpr1 tpk-w, Y3077). For clustering, absolute intensity values were normalized for each gene over all 12 conditions examined by subtracting the average expression level for that gene over all conditions and dividing by the standard deviation for that gene, but the plotted expression values (y-axis units) represent the average of the absolute intensity of expression (converted to log2) of all the genes in the cluster at the indicated timepoint. Error bars indicate the standard deviation in expression values of all genes in the cluster at the indicated timepoint. The number of genes in each cluster and any highly enriched function group (including the p value) are indicated in each graph. The time course data from the tpk-w strain suggest that Gpr1 might affect multiple glucose signaling pathways. If a Gpr1-initiated signal were transmitted solely through PKA, then the pattern of gene expression following glucose addition to the gpr1 tpk-w strain would be essentially identical to that observed in the GPR1 tpk-w strain. While the correlation between the expression patterns of gpr1 tpk-w and GPR1 tpk-w (r = 0.73) is higher than that between gpr1 TPK and GPR1 TPK (r = 0.65), the patterns of expression of gpr1 tpk-w and GPR1 tpk-w, as highlighted by the cluster analysis, are similar but notably distinct (particularly in clusters 2, 4, 6, and 7). Thus, these results could suggest that Gpr1 impinges on both PKA-dependent and PKA-indepen-dent signaling pathways. Alternatively, the steady-state differences between gpr1 and GPR1 strains at the onset of the experiment could render the strains differentially responsive to glucose. This issue could be resolved by appropriate conditional alleles in GPR1 and TPK. Ras, Gpa2, and Glucose Induce Genes in Mass Accumulation and Repress Genes in Respiration and Mitochondrial Function We have addressed the nature of the genes regulated by glucose and Ras2 in two different but related ways. First, we asked how those genes that have been annotated as performing related functions behave on average over the set of experiments. Second, we have determined whether genes performing a common function are significantly overrepresented in any cluster of coexpressed genes. Both approaches give essentially the same results. In Figure 5, we present the average level of expression of all the genes associated with the indicated function (as annotated by the Munich Information Center for Protein Sequences [MIPS] program) relative to that at time 0 in the wild-type strain. As evident, genes required for translation are upregulated by glucose and activation of Ras2 or Gpa2. This includes genes for RNA polymerase I and III subunits, cytoplasmic tRNA synthetases, rRNA and tRNA processing enzymes, translation initiation factors, and, to a slightly lesser degree, ribosomal proteins. Similarly, genes for these functional categories are highly enriched in those clusters in which expression increases following addition of glucose to wild-type or tpk-w cells or following activation of Ras2 or Gpa2 (see Table 1). Thus, a major portion of the transcriptional restructuring following glucose addition is directed toward enhancement of the translational machinery. Somewhat surprisingly, though, this is induced not solely by increased metabolism, but at least in part by a direct response to a signaling circuit, which is mediated at least in part by Ras2. Figure 5 Functional Analysis of Glucose- and Ras-Induced Expression Changes The average expression levels of genes grouped by the functional category listed on the right in the indicated strains over the 1-h time course are indicated by color (red, induced; green, repressed; yellow, unchanged). Values are relative to the expression level in strain Y2864 prior to glucose addition. The Functional Classification Catalog was obtained from MIPS at http://mips.gsf.de/proj/yeast/CYGD/db/index.html. Functional group analysis was performed using the ratio of vector magnitudes (Kuruvilla et al. 2002). The computer source code was derived from http://www-schreiber.chem.harvard.edu. Strains: Y2864 (WT), Y2872 (tpk-w), Y2866 (RAS2*), Y2873 (RAS2* tpk-w), Y2876 (GPA2*), Y2897 (GPA2* tpk-w). On the other side of the coin, genes involved in oxidative respiration, including components of the TCA cycle, oxidative phosphorylation apparatus, and ubiquinone (CoQ) synthesis, and all the genes required solely for gluconeogenesis are significantly downregulated both by glucose addition and by activation of Ras or Gpa2. These functional categories of genes are significantly overrepresented in that class of coexpressed genes that are downregulated in all conditions tested (class V). Thus, Ras2-dependent and Ras2-independent repression pathways redundantly regulate the restructuring associated with conversion from respiration to fermentation. Several groups of genes appear to be regulated by glucose exclusively through a PKA-dependent pathway. These are genes repressed by Ras2 or Gpa2 and by glucose in the wild-type strain, but not in the tpk-w strain (class VI), and include those involved in carbohydrate storage (trehalose and glycogen) and, to a large extent, in ubiquinone synthesis. A number of genes exhibit induction by glucose in an exclusively Ras2-dependent fashion and include genes involved in ribosome biogenesis. Reciprocally, a number of genes exhibit induction by glucose in a completely Ras-independent fashion. As noted in Figure 2, expression of members of class III increases monotonically following glucose addition, in contrast to the pattern seen with genes in other induction classes, in which an initial rapid increase in expression following glucose addition is followed by an immediate stabilization or downshift. This may indicate that these genes are upregulated as a consequence of the metabolic changes or growth acceleration attendant on glucose addition. The enrichment of genes involved in DNA replication in this category is consistent with this hypothesis. Identification of Potential Transcription Factors Mediating the Response to Ras2 Activation We have used a number of computational approaches to identify potential regulatory sequences and regulatory factors responsible for changes in gene expression in response to glucose and/or Ras2 activation. All of these approaches are based on the assumption that genes exhibiting a common expression pattern over all the experiments are more likely to share a common regulatory sequence or respond to a common transcription factor (see Supporting Information). Several motifs (RRPE, PAC) and transcription factor-binding sites (Sfp1, Rap1, Fhl1) are associated with the class of genes induced by glucose through both a Ras-dependent and a Ras-independent pathway. Rap1- and Fhl1-binding sites have previously been associated with ribosomal protein genes (Lieb et al. 2001; Lee et al. 2002), and the enrichment of these sites in this class represents the high proportion of ribosomal protein genes in the clusters comprising this class. Similarly, the RRPE and PAC motifs have been associated with genes encoding elements of the translational machinery and with genes that are upregulated following overexpression of Sfp1 (Hughes et al. 2000; Wade et al. 2001; Jorgensen et al. 2002). Thus, these three transcription factors and their associated motifs are potential loci through which glucose and/or Ras activates transcription of translation-related genes. To evaluate whether the predicted motifs mediate Ras-activated transcription, we inserted each motif upstream of a reporter gene lacking any other upstream activation sequence (UAS) and then introduced the individual constructs into strains containing the inducible RAS2* or GPA2* alleles. As a positive control, we examined expression of the RPS18B promoter/enhancer region when it was fused to the reporter construct. As evident in Table 2, activation of Ras2 or Gpa2 resulted in a 3-fold increase in expression of the reporter construct, consistent with the observation that expression of this gene increased following induction of either RAS2* or GPA2* in our genome-wide expression analysis. Having confirmed the ability of this system to detect Ras-responsive promoters, we examined the ability of the Rap1-binding site or the RRPE or PAC element to enhance transcription in response to activation of the Ras pathway. As noted in Tables 2, 3, and 4, both the Rap1-binding site and the RRPE element yielded strong enhancer activity, especially when present in multiple copies. In contrast, the PAC element exhibited no enhancer activity. Further, the Rap1 enhancer activity increased modestly but consistently in glucose versus glycerol medium and following activation of Ras2 or Gpa2. Activation of Ras2 or Gpa2 also consistently yielded increased expression driven by the RRPE element. Finally, an MCB element provided modest enhancer activity that was further stimulated by growth on glucose but not by activation of Ras2 or Gpa2. This is consistent with the expression pattern of genes in the cluster in which the MCB motif is enriched. Table 2 Functional Analysis of Motifs: Potential Activator Elements Strains Y2864 (wild type [WT]), Y2866 (GAL-RAS*), and Y2876 (GAL-GPA2*) were transformed with plasmid TBA23 (Vector), RPS18B (fusion of the promoter of RPS18B to lacZ), or TBA23 DNA into which a 20-bp sequence spanning the indicated motif was inserted (2× and 3× indicate that two or three copies, respectively, of the motif oligonucleotide were present in the vector) and grown in SC media with either 5% glycerol (Gly), 2% galactose (Gal), or 2% glucose (Glu) as a carbon source. β-Galactosidase assays were performed on samples from three separate transformants, and the average specific activities (Miller units/OD600) of the three samples are presented. Individual values differed from the mean by less than 10% for all measurements. NA, not available. Test sequences, derived from the indicated promoters, were TATGTGGTGTACGGATATGA (RAP1), TTCCGAAAATTTTCATTGGC (RRPE), GGGATGAGATGAGATGAGAT (PAC), and ACAAAAGACGCGTGAACTAA (MCB) Table 3 Functional Analysis of Motifs: Potential Repressor Elements A 20-bp sequence corresponding to the indicated motif from the indicated gene was inserted into plasmids consisting of TBA30, which were then transformed into strains and grown as described in Table 2. Assays were performed in triplicate and all values differed from the mean by less than 10%. Values are β-galactosidase specific activities, with fold repression relative to the vector grown under similar conditions indicated in parentheses. Sequences used were GAACCTCGGCGGCAAAAATA (CAT8), GAAATATCCCTTAAAACTTC (SSE2), TTGTTACAGCCGCCCGTGGC (PDR10), and GAGGCAGCTTCCCTTCTGAT (FOX2). See Table 2 caption for abbreviations Table 4 Functional Analysis of Motifs: PDR10 Element Is Not Ume6 Dependent Plasmids were transformed into the indicated ResGen (Invitrogen) wild-type (WT) and deletion strains, and transformants were assayed for β-galactosidase activity after growth on SC medium plus glucose. Fold repression is indicated in parentheses Several motifs were identified as correlated with repression by glucose and by Ras2 or Gpa2. These included binding sites for Rpn4, Ume6, Hap2/3/4, and Msn2/4 as well as several sequences of unknown association. We tested several of these motifs for their ability to mediate glucose- or Ras-induced transcriptional repression by inserting them between the CYC1 UAS and the promoter of a CYC1-lacZ reporter construct and examining expression under different growth conditions. Most of the known elements manifested modest repression activity that was not enhanced by growth on glucose or by Ras or Gpa2 activation. However, multiple copies of an Ume6-like element from PDR10 elicited strong glucose-enhanced repression activity. As evident from Table 3, the element caused 5- to 10-fold repression when cells were grown in glycerol and 500-fold repression when cells were grown in glucose. While this element exhibits some similarity to a Ume6-binding site, it does not mediate repression by Ume6. As noted in Table 4, deletion of UME6 (or RPN4, MIG1, MIG2, MSN4, PHD1, RGM1, STD1, RIM101, SFL1, or NRG1; data not shown) did not alleviate the repressive effects of this element, although this deletion eliminated repression effected by a known Ume6-binding site from CAT8. Repression by the PDR10 site was alleviated by deletion of TUP1 or SSN6. Thus, this element likely functions by recruiting the Tup1/Ssn6 repressor complex to the promoter through a specific DNA binding factor intermediate. Given the lack of correspondence between the sequence of the element and known regulatory motifs, the element likely represents a novel glucose repression mechanism. Discussion Defining the Glucose Signal Transduction Pathway Transcriptional regulation by glucose has been examined extensively by genetic and biochemical analyses of specific glucose-repressible and glucose-inducible genes as well as by global transcriptional analysis (DeRisi et al. 1997; Lutfiyya et al. 1998; Hughes et al. 2000; Wade et al. 2001; Jorgensen et al. 2002). These studies have highlighted pathways involved in connecting the presence of glucose with changes in the transcription state of the cell, particularly those pathways mediated by the Snf1/4 kinase and the Grr1 ubiquitin ligase (Carlson 1999; Johnston 1999). Similarly, previous studies have demonstrated that the Ras/PKA pathway responds to glucose addition and affects gene expression, implicating Ras/PKA as a mediator of the cell's response to glucose. However, the overall topology of the glucose signaling network in yeast and the extent to which these different branches contribute and interconnect have not been previously addressed. The approach described in this report, following an earlier conceptual framework (Roberts et al. 2000), provides a means of developing systematically a comprehensive topological map of the glucose signal network. Thus, this report is a first step in defining such a network. In this study, we have shown that most of the changes in transcription attendant on glucose addition can be recapitulated by activation of Ras2 or Gpa2. Thus, most of the glucose-induced changes in gene expression can be mediated by Ras2 and Gpa2. This is surprising since most transcriptional responses to glucose, particularly glucose repression, have been associated with Ras-independent mechanisms (Gancedo 1998). In fact, though, since most of the glucose-induced transcriptional changes are also observed in a strain lacking a cAMP-responsive PKA, most of the glucose effects can also be mediated by a Ras/PKA-independent pathway. Thus, a minimal topology for the signaling pathway for modifying transcription in response to glucose comprises (1) redundant signaling pathways for repression and induction of the majority of genes, (2) a Ras/PKA-independent branch, and (3) a branch that is solely mediated by Ras/PKA (Figure 6). Whether the redundant pathways reconverge at specific transcription factors or at the promoters of genes themselves remains to be determined. In addition, the relative contributions of known glucose regulatory circuits to the Ras-independent pathways, such as those mediated by Snf1/4 and Grr1, have not been determined. Studies similar to those described here for Ras are currently in progress with other contributing pathways. Figure 6 The Role of Ras and Gpa2 in Glucose Regulation of Transcription Diagram of information flow in glucose signal of transcription as deduced from global analysis of expression of genes in the strains used in this study. The number of genes regulated by each branch of the pathway, the nature of the regulation (red, induction; green, repression), and some of the functional categories of genes enriched in each branch are indicated. A redundant pathway for glucose signaling is consistent with previous observations suggesting that while activation of Ras/PKA elicits substantial changes in growth and carbohydrate metabolism in the cell, most of those changes can be effected even in the absence of an active Ras/PKA pathway. Cameron et al. (1988) constructed and analyzed tpk-w strains of yeast, which, as noted above, contain a PKA that is unresponsive to changes in cAMP levels. The authors found that tpk-w strains not only reverse all the phenotypes of bcy1 strains, but also regain the ability to respond to glucose depletion and readdition (glycogen accumulation, sporulation, etc.) in a timely and appropriate manner. Thus, the authors concluded that, while Ras/PKA could affect the cell's growth response to nutrients, one or more cAMP-indepen-dent pathways regulate the cell's response to nutrient availability. Under circumstances in which the cAMP signaling pathway is maintained at a moderate but constant level, this additional pathway(s) is sufficient for normal nutrient regulation. The presence of redundant glucose signaling in yeast could explain these earlier results. Most of the changes in transcription measured in these experiments likely result from the activity of a signal transduction pathway responsive to glucose, rather than from indirect effects due to changes in growth rate or metabolism. We saw the same global response whether the induction protocol was galactose addition in a gal1 background or addition of the gratuitous inducer β-estridiol to a strain with Ras2 or Gpa2 activation driven by a lexA-ER-VP16 chimeric transcription factor (Louvion et al. 1993). Thus, the method of induction does not influence the results, ruling out any metabolic influences on the response. In addition, the glucose-induced transcription effects are observed early, likely prior to substantial reprogramming of the metabolic machinery of the cell. Transcriptional responses to glucose addition at later timepoints are often in opposite polarity to those at early timepoints, which suggests that the cell adapts its transcriptional response to the new conditions and emphasizes the importance of kinetic analysis in order to capture the structure of the signaling network under initial conditions. Several patterns of expression are not explained in a straightforward manner by the network depicted in Figure 6. For instance, genes in class IV are induced by activation of either Ras2 or Gpa2 and by glucose addition to wild-type cells, but are repressed by glucose addition to tpk-w cells. Genes of class VII show the inverse behavior. One possible explanation is that the Ras-dependent and Ras-independent pathways have opposite effects on expression of these sets of genes. Alternatively, the physiology of the tpk-w cells may be significantly different than that of wild-type cells under initial conditions, such that the baseline expression of some genes at time 0 is significantly different in the two strains. In fact, the transcriptional profile of the tpk-w strain at time 0 is significantly different from that of the isogenic wild-type strain. This latter explanation may account for the behavior of genes in class VIII, which exhibit repression only by glucose addition to tpk-w cells. The behavior of class VIII genes may also suggest that some transcription factor activity or promoter activity is saturable, an hypothesis explored in more depth elsewhere (Lin et al. 2003). Ras and Gpa Signal Exclusively through PKA We used epistasis analysis to define the functional topology of the Ras2 and Gpa2 branch of the glucose signaling pathway. That is, we examined the transcriptional consequences of activating Ras2 or Gpa2 in a background lacking a cAMP-responsive PKA. Since the readout of this experiment is the entire transcriptome of the cell, we can determine whether any gene is regulated by Ras in a PKA-independent fashion without knowing a priori what that gene might be. Our results demonstrate that all transcriptional effects of Ras2 and of Gpa2 are mediated by PKA. Previous studies have suggested that in certain strains Ras2 can activate the filamentous growth MAP kinase pathway (Mosch et al. 1996, 1999). Our results clearly indicate that in the strain examined grown under the conditions described, no such connection between Ras and the MAP kinase pathway exists. Further, identical epistasis experiments performed with diploid Σ1278 strains yielded the same result (data not shown). Thus, while Ras exerts PKA-independent effects on the yeast cell, all the transcriptional effects of Ras proceed through PKA. Substantial information has accumulated to suggest that, like Ras2, activated Gpa2 stimulates adenylyl cyclase, leading to an increase in cellular cAMP levels (Kubler et al. 1997; Lorenz and Heitman 1997), although recent evidence suggests that Gpa2 might activate PKA directly (J. P. Hirsch, personal communication). Genetic epistasis data to date indicate that to activate PKA, Ras2 and Gpa2 proteins act in redundant parallel pathways, rather than in sequential steps in the same pathway (Xue et al. 1998). However, whether activation of PKA is the sole activity of Gpa2 is not known. Our results indicate that, like Ras2, all of the transcriptional effects of Gpa2 are mediated by PKA. Consistent with that conclusion, we do not detect any group of genes whose expression is altered by activation of Gpa2 and is not also similarly altered by activation of Ras2. We do note that the intensity of transcriptional response following activation of Gpa2 is approximately half that seen following activation of Ras, suggesting that while both proteins function in similar roles, they have quantitatively different effects. Potential Transcriptional Network Various computational approaches identified a number of sequence motifs and transcription factors through which glucose and Ras2 or Gpa2 might be modulating transcription. The presence of the previously identified RRPE and PAC motifs is strongly correlated with genes induced following Ras2 activation. The pattern of genes induced by Ras2 closely resembles that of genes induced by increased expression of the Sfp1 transcription factor (Jorgensen et al. 2002). We find that RRPE acts as a strong enhancer element in reporter gene constructs and that its enhancer activity is increased following activation of Ras2. Sfp1 contains several PKA consensus phosphorylation sites. However, evidence that Sfp1 acts directly through PAC/RRPE or that Sfp1 is the locus of PKA-induced activation is not yet available. Our studies also returned a strong correlation between genes induced by Ras2 and those containing Rap1-binding sites in their promoters, confirming the previously identified role of Rap1 in mediating PKA regulation of ribosomal protein gene expression (Klein and Struhl 1994; Neuman-Silberberg et al. 1995). Recent results suggest that Rap1 binding to promoter sites serves to recruit the histone acetyl transferase Esa1 and that Rap1 binding is constitutive, but Esa1 recruitment is modulated by growth conditions (Reid et al. 2000; Rohde and Cardenas 2003). Thus, PKA may affect the interaction of Rap1 and Esa1, an hypothesis currently under investigation. Our computational studies confirmed the presence of a number of motifs associated with glucose regulation and PKA, including the STRE element as well as binding sites for Hap2/3/4, Ume6, and Rpn4. Recent data have shown that PKA directly affects the nuclear localization of Msn2, one of the transcription factors that acts through STRE, but that PKA does so through a mechanism independent of the one responsive to environmental stress (Gorner et al. 2002). Thus, the convergence of the glucose signal and the stress response signal on this transcription factor could account in part for the overlap of transcriptional response of the cell to glucose depletion and other forms of environmental stress (Gasch et al. 2000; Causton et al. 2001). We also identified a motif associated with genes repressed by glucose through Ras-dependent and Ras-independent pathways. This motif provokes repression in reporter constructs that is substantially enhanced in growth on high levels of glucose, although the repression does not appear to be altered by PKA activation. While the motif bears resemblance to both Ume6 and Rpn4, it does not mediate repression by either factor, since deletion of either gene does not alleviate glucose-dependent repression by the motif. Thus, we have identified a novel glucose regulatory motif through these computational approaches. Further analysis of the many other motifs identified in this study could yield additional novel regulatory elements. Materials and Methods Strains All strains used in this study were derived from W303–1B and are listed in Table 5. tpk-w alleles were isolated as described by Cameron et al. (1988) and confirmed by sequencing and retransformation of the mutant tpk2 allele. Construction of the galactose-inducible RAS2 G19V allele has been described by Fedor-Chaiken et al. (1990). The activated allele of GPA2 (GPA2 Q300L) was placed under the control of the GAL10 promoter (plasmid B2364), digested with ClaI, and integrated into the LEU2 locus of Y2864 and Y2895 to obtain strains Y2876 and Y2897. Yeast Consortium Deletion Strains created in the BY4742 background (MATα his3Δ leu2Δ lys2Δ ura3Δ) were obtained from Research Genetics (Invitrogen, Carlsbad, California, United States). The hoΔ strain was used as a wild-type control. Table 5 Strains Used in This Study aAll strains used in this study were derived from Y2092 (W303-1B) Cell Growth Cells were streaked on YEPD plates and grown for 2–3 d at 30°C. Fresh colonies were inoculated into synthetic complete (SC) medium supplemented with 3% glycerol as the only carbon source. Cells were grown at 30°C and shaken at 200 rpm to an OD600 of 0.25 (budding index, approximately 20%), at which time an aliquot of cells was removed as the time-0 control. Glucose or galactose was then added to 2% in the remaining culture and aliquots (40 ml) of cells were collected at 20, 40, and 60 min following sugar addition. Cells were mixed with 100 ml of prechilled water and quickly spun down by centrifugation at 2,500 rpm for 3 min at 4°C. RNA Isolation, Labeling, and Hybridization Cell pellets were lysed in TRI reagent (Molecular Research Center, Cincinnati, Ohio, United States) by vortexing with glass beads for 3 min. After a 5-min incubation at room temperature, 0.2 ml of chloroform per 1 ml of TRI reagent was added and mixed well with the homogenate. After centrifugation at 14,000 rpm for 15 min at 4°C, the upper aqueous phase was removed and precipitated with equal volume of isopropanol. RNA pellets were washed with 75% ethanol, air-dried, and dissolved in water. mRNA was purified from the total RNA with oligotex (Qiagen, Valencia, California, United States). First-strand cDNA was synthesized from mRNA using HPLC-purified T7-(dT)24 primer (Genset, San Diego, California, United States) and SuperScript II RT (Invitrogen). Second-strand cDNA was synthesized using DNA ligase (10 U), DNA polymerase I (40 U), and RNase H (2U) from Invitrogen. Biotin-labeled cRNA was made with a BioArray HighYield RNA transcript labeling kit (Enzo Diagnostics, Farmingdale, New York, United States) and purified using an RNeasy mini-kit (Qiagen). The cRNA was fragmented, mixed with control cRNA cocktail, and hybridized to yeast genome S98 array (Affymetrix, Santa Clara, California, United States) for 16 h in a 45°C oven rotating at 60 rpm. The probe arrays were washed and stained using the GeneChip Fluidics station 400 (Affymetrix) and scanned at 570 nm with the Agilent GeneArray scanner (Affymetrix). For each experiment, we examined multiple timepoints, and for samples of significant interest we performed the experiment in triplicate. For initial analysis, we used MicroArray Suite 5.0 software (Affymetrix) to determine whether the hybridization signal for a gene was reliable and incorporated in our analysis only those measurements that were judged present, which generally included greater than 90% of the gene measurements in any one sample, with greater than 75% of all genes yielding reliable values over all the experiments. We also eliminated from our initial analysis those genes that were induced more than 3-fold in the gal1 strains by addition of galactose (25–30 genes, depending on the experiment). All experiments were normalized to the same total signal intensity. Data for all experiments can be obtained from Tables S1, S2, and S3 or at http://www.molbio.princeton.edu/labs/broach/microarray.htm. Computational Methods Expression clustering and motif discovery Partitional clustering of gene expression data was performed using the Qtclust algorithm (Heyer et al. 1999), which creates a partitioning of genes into nonoverlapping clusters. Not all genes are assigned to clusters, as the members of each cluster are guaranteed to have a minimal intergene Pearson correlation (in our case, 0.75). In order to identify putative transcription factor-binding sites, the members of each cluster were used to search for common DNA sequence motifs in their 5′ upstream region using the AlignACE algorithm (Tavazoie et al. 1999). For each cluster, three independent motif searches were performed. The resulting pool of approximately 5,000 motifs contained significant redundancy, as many known binding sites were identified multiple times. Using a motif similarity measure in the CompareACE algorithm (Hughes et al. 2000), we clustered all the motifs into a largely nonredundant set of 251 members. In order to obtain a more “coarse-grained” view of genome-wide expression patterns, the original 144 clusters were combined by hierarchical clustering (Eisen et al. 1998) of their mean expression profiles, yielding the eight classes discussed in the paper. Known transcription factor binding sites We assembled a set of weight matrices corresponding to 45 well-characterized Saccharomyces cerevisiae transcription factors. These matrices were constructed from a mix of experimentally determined binding sites, augmented with extensive expression and chromatin IP-derived data (Lee et al. 2002). To this list, we added three weight matrices (PAC, RRPE, A/T_repeat), which had strong computational evidence for being real transcription factor-binding sites. Statistical analysis To determine the statistical significance of functional enrichments in expression clusters, we used the hypergeometric distribution to quantify the chance probability of obtaining the observed overlap between an expression cluster and any of the 200 functional categories defined in the MIPS database (Tavazoie et al. 1999; Mewes et al. 2002). The hypergeometric distribution was also used to quantify the probability of obtaining the observed overlap between expression clusters and the set of 300 genes with the highest-scoring occurrences of a motif in their 5′ upstream region. Reporter Gene Analysis Oligonucleotides containing motif sequences from selected promoters were cloned into the XhoI site of the CYC1-lacZ reporter vectors pTBA23 and pTBA30, as described previously (Mead et al. 1996). The former vector contains the CYC1 promoter and UAS, with the XhoI site residing between them, and the latter vector contains only the CYC1 promoter. Assays were performed on three separate transformants for each construct, grown as indicated. Results of β-galactosidase assays differed by less than 10% for triplicate measurements (Gailus-Durner et al. 1997). Supporting Information Table S1 Gene Expression Patterns Following Glucose Addition and Ras2 and Gpa2 Activation Strains were as follows: Wild-type = Y2864 (MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1 gal1::HIS3); tpk-w = Y2872 (MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1 gal1::HIS3 tpk1::URA3 tpk2V218G tpk3::KAN bcy1::LEU2); RAS* = Y2866 (MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1 gal1::HIS3 TRP1-GAL10-RAS2V19); RAS tpk-w = Y2873 (MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1 gal1::HIS3 TRP1-GAL10-RAS2V19 tpk1::URA3 tpk2V218G tpk3::KAN bcy1::LEU2); GPA2* = Y2876 (MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1 gal1::HIS3 LEU2-GAL10-GPA2Q300L) GPA tpk-w = Y2897 (MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1 gal1::HIS3 LEU2-GAL10-GPA2Q300L tpk1::HIS3 tpk2V218G tpk3::TRP1 bcy1::hisG) . Experimental conditions were as follows. Cells were grown in SC medium plus 3% glycerol to A600 = 0.3. Glucose or galactose was added to 2%, and samples were removed at 0, 20, 40 and 60 min following the addition. Microarrays were performed as follows. RNA was isolated and labeled as described in Materials and Methods and hybridized to Affymetrix yeast genome S98 arrays. Data presentation is as follows. The first five columns provide the gene name (if known), the Saccharomyces Genome Database (SGD) gene designation, the MIPS functional category, and the function of the gene product, and the Affymetrix probe was set for that gene. For each time sample, the first column provides the normalized intensity values and the second column provides the determination from the MicroArray Suite 5.0 software as to whether the value was significant (P), insignificant (A), or indeterminate (M). The table is in a tab-delimited text format. (1.99 MB TXT). Click here for additional data file. Table S2 Gene Expression Patterns Following Glucose Addition to gpr1 and gpr1 tpkw Strains Strains were as follows: Y2092 = MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1; Y3159 = MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1 gpr1::hphMX; Y2857 = MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1tpk1::HIS3 tpk2V218G tpk3::TRP1 bcy1::LEU2; Y3077 = MATα ade2-1 can1-100 his3-11,15 leu2-3,112 trp1-1 ura3-1tpk1::HIS3 tpk2V218G tpk3::TRP1 bcy1::LEU2 gpr1::hphMX. Experimental conditions were as follows. Cells were grown in SC medium plus 3% glycerol to A600 = 0.3. Glucose was added to 2%, and samples were removed at 0, 20, 40 and 60 min following the addition. Microarrays were performed as follows. Reference samples (RNA from 0 timepoint in each experiment) were labeled with Cy3, and each test sample (RNA from subsequent timepoints) was labeled with Cy5, mixed with the corresponding reference sample, and hybridized to cDNA microarrays printed in-house. Data presentation is as follows. Values are ratios of RNA levels for each gene at the indicated timepoint relative to the level for that gene at time 0 in that particular experiment. (477 KB TXT). Click here for additional data file. Table S3 Members of Gene Expression Classes The set of genes, specified by their SGD designation, comprising each of the gene expression classes listed in Table 1 and diagrammed in Figure 2, is listed for each class. The table is a tab-delimited text file. (29 KB TXT). Click here for additional data file. We thank Dr. Katrin Düvel for her thoughtful comments on the manuscript and Dr. Kara Dolinski for assistance with the GO term finder. YW gratefully acknowledges postdoctoral fellowship support from the New Jersey Cancer Commission. This work by supported by National Institutes of Health grant CA41086 to JRB and by National Science Foundation CAREER award MCB-0133750 and Defense Advanced Research Projects Agency grant N66001-02-1-8929 to ST. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JRB conceived and designed the experiments. YW, MP, LS, CGG, and XZ performed the experiments. YW, ST, and JRB analyzed the data. LS, CGG, and XZ contributed reagents/materials/analysis tools. YW and JRB wrote the paper. Academic Editor: Paul Spellman, Lawrence Berkeley Laboratory ¤ Current address: Microarray Solutions, Warren, New Jersey, United States of America Abbreviations cAMPcyclic AMP GOGene Ontology MIPSMunich Information Center for Protein Sequences PKAcAMP-dependent protein kinase A SCsynthetic complete SGD Saccharomyces Genome Database UASupstream activation sequence ==== Refs References Boy-Marcotte E Perrot M Bussereau F Boucherie H Jacquet M Msn2p and Msn4p control a large number of genes induced at the diauxic transition which are repressed by cyclic AMP in Saccharomyces cerevisiae J Bacteriol 1998 180 1044 1052 9495741 Broach JR Deschenes RJ The function of ras genes in Saccharomyces cerevisiae Adv Cancer Res 1990 54 79 139 2153328 Cameron S Levin L Zoller M Wigler M cAMP-independent control of sporulation, glycogen metabolism, and heat shock resistance in S. cerevisiae Cell 1988 53 555 566 2836063 Carlson M Glucose repression in yeast Curr Opin Microbiol 1999 2 202 207 10322167 Causton HC Ren B Koh SS Harbison CT Kanin E Remodeling of yeast genome expression in response to environmental changes Mol Biol Cell 2001 12 323 337 11179418 DeRisi JL Iyer VR Brown PO Exploring the metabolic and genetic control of gene expression on a genomic scale Science 1997 278 680 686 9381177 Eisen MB Spellman PT Brown PO Botstein D Cluster analysis and display of genome-wide expression patterns Proc Natl Acad Sci U S A 1998 95 14863 14868 9843981 Fambrough D McClure K Kazlauskas A Lander ES Diverse signaling pathways activated by growth factor receptors induce broadly overlapping, rather than independent, sets of genes [see comments] Cell 1999 97 727 741 10380925 Fedor-Chaiken M Deschenes RJ Broach JR SRV2 , a gene required for RAS activation of adenylate cyclase in yeast Cell 1990 61 329 340 2158860 Fingerman I Nagaraj V Norris D Vershon AK Sfp1 plays a key role in yeast ribosome biogenesis Eukaryot Cell 2003 2 1061 1068 14555489 Gailus-Durner V Chintamaneni C Wilson R Brill SJ Vershon AK Analysis of a meiosis-specific URS1 site: Sequence requirements and involvement of replication protein A Mol Cell Biol 1997 17 3536 3546 9199289 Gancedo JM Yeast carbon catabolite repression Microbiol Mol Biol Rev 1998 62 334 361 9618445 Gasch AP Spellman PT Kao CM Carmel-Harel O Eisen MB Genomic expression programs in the response of yeast cells to environmental changes Mol Biol Cell 2000 11 4241 4257 11102521 Gorner W Durchschlag E Wolf J Brown EL Ammerer G Acute glucose starvation activates the nuclear localization signal of a stress-specific yeast transcription factor EMBO J 2002 21 135 144 11782433 Heyer LJ Kruglyak S Yooseph S Exploring expression data: Identification and analysis of coexpressed genes Genome Res 1999 9 1106 1115 10568750 Hughes JD Estep PW Tavazoie S Church GM Computational identification of cis -regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae J Mol Biol 2000 296 1205 1214 10698627 Johnston M Feasting, fasting and fermenting: Glucose sensing in yeast and other cells Trends Genet 1999 15 29 33 10087931 Johnston M Carlson M Regulation of carbon and phosphate utilization. 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The molecular and cellular biology of the yeast Saccharomyces 1992 Cold Spring Harbor, New York Cold Spring Harbor Laboratory Press 193 282 Jorgensen P Nishikawa JL Breitkreutz BJ Tyers M Systematic identification of pathways that couple cell growth and division in yeast Science 2002 297 395 400 12089449 Klein C Struhl K Protein kinase A mediates growth-regulated expression of yeast ribosomal protein genes by modulating RAP1 transcriptional activity Mol Cell Biol 1994 14 1920 1928 8114723 Kraakman L Lemaire K Ma P Teunissen AW Donaton MC A Saccharomyces cerevisiae G-protein coupled receptor, Gpr1, is specifically required for glucose activation of the cAMP pathway during the transition to growth on glucose Mol Microbiol 1999 32 1002 1012 10361302 Kubler E Mosch HU Rupp S Lisanti MP Gpa2p, a G-protein alpha-subunit, regulates growth and pseudohyphal development in Saccharomyces cerevisiae via a cAMP-dependent mechanism J Biol Chem 1997 272 20321 20323 9252333 Kuruvilla FG Park PJ Schreiber SL Vector algebra in the analysis of genome-wide expression data Genome Biol 2002 3 RESEARCH0011 11897023 Lee TI Rinaldi NJ Robert F Odom DT Bar-Joseph Z Transcriptional regulatory networks in Saccharomyces cerevisiae Science 2002 298 799 804 12399584 Lieb JD Liu X Botstein D Brown PO Promoter-specific binding of Rap1 revealed by genome-wide maps of protein–DNA association Nat Genet 2001 28 327 334 11455386 Lin X Floudas C Wang Y Broach JR Theoretical and computational studies of the glucose signaling pathways using global gene expression data Biotechnol Bioeng 2003 84 864 886 14708127 Lorenz MC Heitman J Yeast pseudohyphal growth is regulated by GPA2, a G protein alpha homolog EMBO J 1997 16 7008 7018 9384580 Lorenz MC Pan X Harashima T Cardenas ME Xue Y The G protein-coupled receptor Gpr1 is a nutrient sensor that regulates pseudohyphal differentiation in Saccharomyces cerevisiae Genetics 2000 154 609 622 10655215 Louvion JF Havaux-Copf B Picard D Fusion of GAL4-VP16 to a steroid-binding domain provides a tool for gratuitous induction of galactose-responsive genes in yeast Gene 1993 131 129 134 8370533 Lutfiyya LL Iyer VR DeRisi J DeVit MJ Brown PO Characterization of three related glucose repressors and genes they regulate in Saccharomyces cerevisiae Genetics 1998 150 1377 1391 9832517 Mead J Zhong H Acton TB Vershon AK The yeast alpha2 and Mcm1 proteins interact through a region similar to a motif found in homeodomain proteins of higher eukaryotes Mol Cell Biol 1996 16 2135 2143 8628280 Mewes HW Frishman D Guldener U Mannhaupt G Mayer K MIPS: A database for genomes and protein sequences Nucleic Acids Res 2002 30 31 34 11752246 Mosch HU Kubler E Krappmann S Fink GR Braus GH Crosstalk between the Ras2p-controlled mitogen-activated protein kinase and cAMP pathways during invasive growth of Saccharomyces cerevisiae Mol Biol Cell 1999 10 1325 1335 10233147 Mosch HU Roberts RL Fink GR Ras2 signals via the Cdc42/Ste20/mitogen-activated protein kinase module to induce filamentous growth in Saccharomyces cerevisiae Proc Natl Acad Sci U S A 1996 93 5352 5356 8643578 Nakafuku M Obara T Kaibuchi K Miyajima I Miyajima A Isolation of a second yeast Saccharomyces cerevisiae gene (GPA2 ) coding for guanine nucleotide-binding regulatory protein: Studies on its structure and possible functions Proc Natl Acad Sci U S A 1988 85 1374 1378 2830616 Neuman-Silberberg FS Bhattacharya S Broach JR Nutrient availability and the RAS/cyclic AMP pathway both induce expression of ribosomal protein genes in Saccharomyces cerevisiae but by different mechanisms Mol Cell Biol 1995 15 3187 3196 7760815 Reid JL Iyer VR Brown PO Struhl K Coordinate regulation of yeast ribosomal protein genes is associated with targeted recruitment of Esa1 histone acetylase Mol Cell 2000 6 1297 1307 11163204 Roberts CJ Nelson B Marton MJ Stoughton R Meyer MR Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles Science 2000 287 873 880 10657304 Rohde JR Cardenas ME The Tor pathway regulates gene expression by linking nutrient sensing to histone acetylation Mol Cell Biol 2003 23 629 635 12509460 Smith A Ward MP Garrett S Yeast PKA represses Msn2p/Msn4p-dependent gene expression to regulate growth, stress response and glycogen accumulation EMBO J 1998 17 3556 3564 9649426 Stanhill A Schick N Engelberg D The yeast Ras/cyclic AMP pathway induces invasive growth by suppressing the cellular stress response Mol Cell Biol 1999 19 7529 7538 10523641 Tatchell K RAS genes in the budding yeast Saccharomyces cerevisiae . 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Signal transduction: Prokaryotic and simple eukaryotic systems 1993 San Diego Academic Press 147 188 Tavazoie S Hughes JD Campbell MJ Cho RJ Church GM Systematic determination of genetic network architecture Nat Genet 1999 22 281 285 10391217 Thevelein JM Signal transduction in yeast Yeast 1994 10 1753 1790 7747517 Toda T Cameron S Sass P Zoller M Wigler M Three different genes in S. cerevisiae encode the catalytic subunits of the cAMP-dependent protein kinase Cell 1987 50 277 287 3036373 Toda T Uno I Ishikawa T Powers S Kataoka T In yeast, RAS proteins are controlling elements of adenylate cyclase Cell 1985 40 27 36 2981630 Wade C Shea KA Jensen RV McAlear MA EBP2 is a member of the yeast RRB regulon, a transcriptionally coregulated set of genes that are required for ribosome and rRNA biosynthesis Mol Cell Biol 2001 21 8638 8650 11713296 Warner JR The economics of ribosome biosynthesis in yeast Trends Biochem Sci 1999 24 437 440 10542411 Xue Y Batlle M Hirsch JP GPR1 encodes a putative G protein-coupled receptor that associates with the Gpa2p Gα subunit and functions in a Ras-independent pathway EMBO J 1998 17 1996 2007 9524122 Yun CW Tamaki H Nakayama R Yamamoto K Kumagai H G-protein coupled receptor from yeast Saccharomyces cerevisiae Biochem Biophys Res Commun 1997 240 287 292 9388468

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PLoS Biol. 2004 May 11; 2(5):e128

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020129Research ArticleDevelopmentGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyDanio (Zebrafish)Early Myocardial Function Affects Endocardial Cushion Development in Zebrafish Myocardial Function and Cushion DevelopmentBartman Thomas 1 2 3 4 Walsh Emily C 1 5 Wen Kuo-Kuang 6 McKane Melissa 6 Ren Jihui 6 Alexander Jonathan 1 7 Rubenstein Peter A 6 Stainier Didier Y. R didier_stainier@biochem.ucsf.edu 1 1Department of Biochemistry and Biophysics, University of CaliforniaSan Francisco, San Francisco, CaliforniaUnited States of America2Department of Pediatrics, University of CaliforniaSan Francisco, San Francisco, CaliforniaUnited States of America3Department of Pediatrics, University of Cincinnati College of MedicineCincinnati, OhioUnited States of America4Divisions of Neonatology, Pulmonary Biologyand Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OhioUnited States of America5Whitehead Institute for Biomedical Research, CambridgeMassachusettsUnited States of America6Department of Biochemistry, University of Iowa College of MedicineIowa City, IowaUnited States of America7Division of Pulmonary and Critical Care Medicine, University of CaliforniaSan Francisco, San Francisco, CaliforniaUnited States of America5 2004 11 5 2004 11 5 2004 2 5 e1293 11 2003 25 2 2004 Copyright: ©2004 Bartman et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Role for Early Cardiac Function in Cardiac Morphogenesis Function of the heart begins long before its formation is complete. Analyses in mouse and zebrafish have shown that myocardial function is not required for early steps of organogenesis, such as formation of the heart tube or chamber specification. However, whether myocardial function is required for later steps of cardiac development, such as endocardial cushion (EC) formation, has not been established. Recent technical advances and approaches have provided novel inroads toward the study of organogenesis, allowing us to examine the effects of both genetic and pharmacological perturbations of myocardial function on EC formation in zebrafish. To address whether myocardial function is required for EC formation, we examined silent heart (sih−/−) embryos, which lack a heartbeat due to mutation of cardiac troponin T (tnnt2), and observed that atrioventricular (AV) ECs do not form. Likewise, we determined that cushion formation is blocked in cardiofunk (cfk−/−) embryos, which exhibit cardiac dilation and no early blood flow. In order to further analyze the heart defects in cfk−/− embryos, we positionally cloned cfk and show that it encodes a novel sarcomeric actin expressed in the embryonic myocardium. The Cfks11 variant exhibits a change in a universally conserved residue (R177H). We show that in yeast this mutation negatively affects actin polymerization. Because the lack of cushion formation in sih- and cfk-mutant embryos could be due to reduced myocardial function and/or lack of blood flow, we approached this question pharmacologically and provide evidence that reduction in myocardial function is primarily responsible for the defect in cushion development. Our data demonstrate that early myocardial function is required for later steps of organogenesis and suggest that myocardial function, not endothelial shear stress, is the major epigenetic factor controlling late heart development. Based on these observations, we postulate that defects in cardiac morphogenesis may be secondary to mutations affecting early myocardial function, and that, in humans, mutations affecting embryonic myocardial function may be responsible for structural congenital heart disease. Cardiac anomolies can result from very early defects in heart development. In zebrafish, such defects have been shown to be caused by a new gene called cardiofunk ==== Body Introduction The genetic programs and developmental processes that lead to organ formation are still poorly understood. We are currently witnessing an expansion in research that aims to identify the genes responsible for the structural development of organs and their later function. Among the organs of the body, the heart is unique because it begins to function mechanically before structural development is complete, begging the important question of whether myocardial function is required for the morphogenetic events that occur after the heart begins beating. One of the late steps of heart development is the formation of the endocardial cushions (ECs), which are tissue swellings that develop in characteristic locations along the anterior–posterior (AP) extent of the heart tube and contribute to valves and, in four-chambered hearts, to septae. Because of the clinical significance and prevalence of EC defects in humans (Hoffman 1995), an understanding of the genetic and epigenetic factors controlling cushion and valve formation is critical. During the process of EC and valve development, specific endocardial cells undergo multiple poorly understood specification, differentiation, and migration events en route to becoming functional heart valves. The genes involved in one substep of this process, epithelial–mesenchymal transformation (EMT), are gradually being identified. Analysis of EMT during cardiac-cushion development has implicated molecules such as Fibronectin, Transferrin, ES-130, hLAMP-1, TGF-β2, TGF-β3, BMP-2 (reviewed in Nakajima et al. 2000), Alk-3 (Gaussin et al. 2002), and hyaluronic acid (Camenisch et al. 2000) as being required for this process. However, many other molecules are likely to be involved in the complex process of EMT, and little is known about the events leading to heart-valve formation that precede or follow EMT, prompting us to take a forward genetic approach to this question (Stainier et al. 2002). To examine the role of epigenetic factors involved in EC formation, Hove et al. (2003) surgically manipulated zebrafish embryos and put forth the hypothesis that shear stress on endocardial cells is required for EC development. In our study, we use both genetic and pharmacological approaches to test the importance of myocardial function in EC development. Results sih −/− Embryos Lack ECs In zebrafish, an initial step of EC development is the formation of the endocardial ring, a structure generated by the clustering of endocardial cells at the atrioventricular (AV) boundary. This process is easily visualized at 48 h postfer-tilization (hpf) by examining the endocardial cells expressing green fluorescent protein (GFP) under the control of the mouse tie2 promoter (Motoike et al. 2000; Walsh and Stainier 2001) (Figure 1A). We have previously reported the early cardiac phenotype of silent heart (sih−/−) embryos, which establish neither a heartbeat nor blood flow due to mutation of cardiac troponin T (tnnt2), but do undergo looping morphogenesis (Sehnert et al. 2002). We crossed the sih mutation into the tie2::GFP line and found that although sih−/− embryos are otherwise morphologically normal, they fail to form an endocardial ring at the AV boundary (Figure 1B). Because sih−/− embryos do not have a heartbeat and therefore no blood flow, it is unclear from this observation whether the defect in EC formation is due to lack of myocardial function or lack of shear stress on endocardial cells. Figure 1 Embryos with Defective Myocardial Function Do Not Form AV ECs (A–C) Fluorescence micrographs of embryos carrying a tie2::GFP transgene, visualized at 48 hpf. In (A), the endocardial ring is visible as a collection of GFP-positive cells at the AV boundary in wild-type (wt) embryos (red arrow). In (B), sih−/− embryos fail to form an AV ring at 48 hpf. In (C), cfk−/− embryos fail to form an AV ring at 48 hpf. (D and E) Cushion development remains defective in cfk−/− embryos. In (D), a 5 μm hematoxylin and eosin-stained plastic section shows the initial stages of cushion development at the AV boundary (red arrows) in a 72 hpf wild-type embryo, with the ECs being two to three cell layers thick at this stage. In (E), a cfk −/− embryo at 72 hpf shows dilation of both chambers of a blood-filled heart with no evidence of cushion formation at the AV boundary (red arrows). (F and G) cfk−/− embryos fail to form ECs at late stages. Embryos were visualized at identical magnification after counter-staining with rhodamine phalloidin. Red blood cells (RBCs) are seen in the atria of the hearts. In (F), confocal microscopy of a 96 hpf wild-type heart from a tie2::GFP line shows triangular ECs at the AV boundary (blue arrows). In (G), cfk−/− embryos at 96 hpf lack cushion formation and clustering of GFP-positive cells at the AV boundary (blue arrows). (H) At 72 hpf, wild-type embryos have narrow hearts with forward blood flow through the embryo. (I) At 72 hpf, cfk−/− embryos have dilated hearts filled with blood that regurgitates freely from the ventricle to the atrium. (J and K) The initial phenotype in cfk−/− embryos is cardiac dilation at 36 hpf. In (J), wild-type embryos have a narrow ventricle and generate pulsatile flow at 36 hpf. In (K), cfk−/− embryos have an increased end-diastolic diameter (on average 1.18× wild-type, p < 0.01) and do not generate blood flow at 36 hpf. (L and M) Increased bmp-4 expression at the AV boundary (red arrow) is observed in wild-type (L) and cfk−/− (M) embryos at 42 hpf in anticipation of endocardial ring formation. (N) Orientation of the embryos shown in (L) and (M). cfk −/− Embryos Lack ECs Subsequent to Impaired Function Through genetic screens (Alexander et al. 1998; Stainier et al. 2002) we have identified a new mutant, cardiofunk (cfk), that fails to accumulate tie2::GFP-positive cells at the AV boundary by 48 hpf (Figure 1C). We further examined EC development in cfk−/− embryos histologically. By 72 hpf, wild-type embryos have developed AV ECs that are more than one cell-layer thick (Figure 1D), while cfk−/− embryos show no evidence of cushion formation (Figure 1E). Examination by confocal microscopy further shows that wild-type embryos have well-developed cushions by 96 hpf (Figure 1F), whereas cfk−/− embryos still show no evidence of an endocardial ring or cushion formation (Figure 1G). Thus, EC formation appears to be defective from an early stage, and not simply delayed, in cfk−/− embryos. The lack of EC development in these mutants leads to toggling of the blood between the atrium and ventricle and its accumulation inside the heart by 48 hpf (Figure 1H and Figure 1I; see also Video S1). Interestingly, the earliest observable phenotype in cfk−/− embryos is cardiac dilation, as evidenced by an increase in ventricular end-diastolic diameter at 36 hpf (Figure 1J and Figure 1K), at which time cfk−/− embryos have failed to establish a circulation (Video S2). The cardiac dilation and lack of circulation in cfk−/− embryos are nearly fully penetrant phenotypes, while the failure of EC development occurs in about 50% of cfk−/− embryos. This observation raises the possibility that the lack of EC formation may result secondarily from some other defect, such as changes in myocardial function or shear stress on endocardial cells. AV Boundary Specification Is Not Affected in sih −/− and cfk −/− Embryos To determine more precisely which step of EC formation is affected in sih−/− and cfk −/− embryos, we examined the expression of bmp-4, a gene implicated in EC morphogenesis (Eisenberg and Markwald 1995). In wild-type embryos, bmp-4 is initially expressed throughout the anterior–posterior (AP) extent of the heart tube before becoming localized to the AV boundary at 42 hpf (Walsh and Stainier 2001) (Figure 1L). In sih−/− and cfk−/− embryos, bmp-4 expression similarly becomes restricted to the AV boundary by 42 hpf (Figure 1M) (data not shown). Therefore, the lack of endocardial ring formation in sih−/− and cfk−/− embryos does not appear to be due to a defect in specification of the AV boundary, as assessed by bmp-4 expression, nor general arrest of cardiac development. The wild-typelike restriction of bmp-4 expression in these mutant embryos contrasts with the situation in jekyll−/− embryos, which also lack ECs, but in which bmp-4 expression does not become restricted to the AV boundary (Walsh and Stainier 2001). cfk Encodes a Sarcomeric Actin To understand better the molecular basis for the cfk phenotypes, we isolated cfk by positional cloning. cfk was initially localized to a region of LG13 by bulk-segregant analysis; fine-mapping placed cfk between Z9289 and Z10582 (Shimoda et al. 1999). Analysis of 2,034 meioses allowed us to perform a chromosomal walk that restricted cfk to bacterial artificial chromosome (BAC) zC202M22 (Figure 2A). Following the sequencing of zC202M22, a portion of the BAC insert was assembled as a 52 kb contiguous sequence, and cfk was genetically localized to this region. Using GenScan (Burge and Karlin 1997) and BLAST analysis, we identified four genes in this 52 kb region: abc-b10, rab4a, actin (zeh0631), and fv49b10 (Figure 2B). Comparison of this region of the BAC to fugu and human sequences identified regions of conserved synteny on fugu scaffold 2777 (Figure 2C) and human Chromosome 1q42.13 (Figure 2D) and confirmed our GenScan analysis. The four zebrafish genes in the critical interval were analyzed by sequencing from cfk−/− mutants and reverse genetic techniques using morpholino antisense oligonucleotides. Injections of morpholinos against each of the four coding sequences showed no obvious cardiac phenotype, indicating that we might not be working with a loss-of-function mutation or that cfk has overlapping function with another gene. Subsequently, sequencing of the actin gene (zeh0631) showed a change of arginine 177 to histidine. The three embryos recombinant at either end of the 52 kb region were homozygous at the site of the R177H lesion, suggesting that we had isolated cfk. Figure 2 Positional Cloning of cfk (A) The locus on zebrafish LG13 containing cfk is shown, with the number of recombinants indicated below each marker. The one recombinant at the Sp6 end of BAC zC202M22 and the two recombinants at the T7 end of the same BAC define the critical region. (B) The first 52 kb of zebrafish BAC zC202M22 is shown, including the Sp6 end. One of the two recombinants from the T7 end of the BAC is still present at a marker at 52 kb, narrowing the critical region to this span. GenScan and BLAST analyses identified four coding sequences in this span, including abc-b10 (green), actin (red), a novel EST fv49b10 (black), and rab4a (blue). (C) Each of the three known genes identified on BAC zC202M22 has a homologue on scaffold 2777 of Fugu rubripes, in the same orientation (green, abc-b10; red, actin; blue, rab4a). (D) The same three genes lie in proximity to each other and in the same order on human Chromosome 1q42.13 (green, abc-b10; red, actin; blue, rab4a). Units in black are genes, predicted genes, or ESTs, which are unique in this region to that particular organism. The cardiac dilation caused by the cfk mutation supported our hypothesis that cfk corresponded to an actin gene. Analyses of Cfk indicate that it is a sarcomeric actin by virtue of its homology to zebrafish α-cardiac and α-skeletal actins and lack of homology to zebrafish cytoplasmic actin, as well as its conservation of synteny with the human skeletal actin. Further evidence that cfk encodes a sarcomeric actin and not a cytoplasmic actin includes the presence of an extra residue at the N-terminus of Cfk, which is seen in all sarcomeric actins but no cytoplasmic actins, as well as the presence of residues that are stereotypic for sarcomeric actins at all 20 locations where sarcomeric and cytoplasmic actins have unique amino acids (Khaitlina 2001) (Figure 3A). Searches of GenBank databases identified both zebrafish α-cardiac– and α-skeletal–actin genes that are well represented in the zebrafish expressed sequence tag (EST) collection and are related to, but clearly distinct from, cfk (see Figure 3A). Analysis of multiple actins from several species revealed that the R177 residue, which is mutated to histidine in the cfks11 allele, is universally conserved (Figure 3B). In vitro mutations at the R177 residue usually affect actin polymerization or function, although the biochemical effect of an R117H transition has not been tested. Figure 3 Sequence and Expression Analysis of cfk (A) cfk encodes a sarcomeric actin highly homologous to zebrafish α-cardiac and α-skeletal actins. Cfk differs from zebrafish α-cardiac actin at six residues and from zebrafish α-skeletal actin at four residues, but from zebrafish β-actin at 28 residues. Residues in red are those that differ from Cfk. Dots above the sequence indicate residues that universally distinguish sarcomeric from cytoplasmic actins. The arrow at R177 indicates the location of histidine in Cfks11. (B) The arginine at position 177 is universally conserved in all actin proteins examined. (C and D) cfk is expressed in the myocardium during development. In (C), whole-mount in situ hybridization on a cmlc2::GFP embryo at 36 hpf shows that cfk is expressed throughout the AP extent of the heart tube. Blue staining indicates areas of cfk expression; green is the region of cmlc2 expression. The red arrow indicates the heart tube. In (D), a plastic section of stained embryo shows cfk expression in the myocardium of the heart (blue arrow), but not in the endocardial cells (red arrow). From the onset of its expression in the heart region (around the 16-somite stage), cfk does not appear to be expressed in endothelial and endocardial cells. Weak cfk expression is also seen in the somites (data not shown). To determine the expression pattern of cfk, we performed in situ hybridization with a probe corresponding to its 3′ untranslated region (UTR), which is distinct from the 3′ UTR sequences of zebrafish α-cardiac and α-skeletal actins. These data showed that cfk is expressed in the myocardial but not endocardial cells of zebrafish embryos from 24 to 48 hpf (Figure 3C and Figure 3D), suggesting a nonautonomous role in EC formation. Taken together, the tight linkage between cfk and this actin gene, the presence of a significant genetic lesion in the actin gene, and the expression profile of the actin gene indicate that cfk corresponds to the actin gene previously identified by the EST zeh0631. The R177H Mutation Alters Actin Polymerization Previous biochemical studies have revealed a critical role for R177 in actin polymerization. An R177A yeast variant is heat sensitive and does not grow in 0.9 M NaCl (Wertman et al. 1992; Drubin et al. 1993), while an R177D mutation in chick β-actin leads to severely altered polymerization properties in vitro (Schuler et al. 2000). These results suggest that a positive charge at position 177 is important for actin function in vivo (Wriggers and Schulten 1999). The R177H cfks11 mutation represents a less severe change in charge than the previously mentioned yeast or chick mutations. To assess the effect of the R177H mutation on actin function, we used site-directed mutagenesis to generate a haploid yeast strain in which the R177H mutant actin was the only actin expressed in the cell. These cells were readily obtained and showed no significant altered morphology or growth characteristics on normosmolar complete medium at 30°C. However, the R177H mutation produced a severe growth defect, similar to the R177A mutation, when the cells were incubated in hyperosmolar complete medium containing 0.9 M NaCl (Figure 4C). Figure 4 An Arginine to Histidine Change at Position 177 of Cfk Alters the Location of Positive Charges in the Nucleotide Binding Cleft of Actin (A) The position of R177 (red) in the structure of the yeast-actin monomer. The bound ATP is shown in green. The structure shown is based on a crystal structure of actin (PDB:1YAG). (B) Magnification of the cleft region showing the hydrogen bonding (green dashed lines) involving R177, which will be disrupted in H177. (C) The R177H mutation restricts yeast growth under hyperosmolar stress. Wild-type (wt) and mutant cells were grown to a density of 3 × 106 per milliliter, and aliquots were plated either on YPD or YPD plus 0.9 M NaCl at different dilutions of the culture. The cells were then incubated at 30°C for 72 h. The normosmolar and hyperosmolar experiments were done at different times. (D) Purified R177H actin has a higher critical concentration of polymerization and a delayed nucleation phase. Wild-type or mutant actin was purified from yeast cultures and polymerization mea-sured by light diffraction. Symbols and abbreviations: circles, 5 μM wild-type actin; triangles, 5 μM R177H actin; diamonds, 7.5 μM R177H actin; squares, 10 μM R177H actin; ATP, adenine triphosphate; wt, wild-type. To determine whether the R177H mutation affected actin polymerization, we purified the actin from R177H cells (Cook et al. 1991) and assessed the extent of polymerization by the increase in light scattering as a function of time. The mutant actin exhibited two distinct differences in comparison to a similarly prepared sample of wild-type yeast actin (Figure 4D). First, the extent of polymerization of a quantity of actin equal to that of the wild-type sample was significantly decreased as judged by the final plateau. Second, there was a prolonged apparent nucleation phase not seen with wild-type actin, even at concentrations of mutant actin that produced more F-actin than the wild-type sample. Samples of the polymerization solution were negatively stained with uranyl acetate and examined by electron microscopy to confirm that the increase in light scattering was caused by F-actin formation and not merely aggregation (data not shown). To define better the apparent difference between R177H and wild-type actins in regard to the critical concentration necessary for polymerization, we assessed the extent of polymerization of different amounts of actin relative to the amount of total actin using a light-scattering assay. Based on determinations with two independent preparations of actin, the critical concentration is 0.3 μM for wild-type actin and 1.9 μM for the mutant actin, confirming that this apparently mild mutation exerts a drastic effect on actin-filament stability. During our work with cfks11, we observed that some embryos showing the cfk phenotype were cfk+/− (Figure 5A), suggesting that the R177H mutation can exert a partially dominant effect. Indeed, subsequent experiments (Wen and Rubenstein 2003) demonstrate that the presence of the mutant actin in a solution of wild-type actin exerts a partially dominant effect on actin polymerization. The partial dominance of cfks11 and the yeast data support a model whereby substantial copolymerization of Cfks11 actin monomers with wild-type monomers (either Cfk or α-cardiac actin) leads to unstable filaments, occasionally giving rise to a phenotype in heterozygous embryos. Figure 5 Lack of EC Formation Is Secondary to Defective Myocardial Function (A) Embryos were observed for defective myocardial function and lack of blood flow at 36 hpf and lack of endocardial ring formation at 48 hpf. Approximately half of the embryos with defective myocardial function at 36 hpf did not develop endocardial rings. All embryos with a lack of endocardial rings previously demonstrated a myocardial function phenotype at 36 hpf. Further analyses including the genotyping of a subset of embryos showed that a small percentage of cfk−/− embryos were unaffected at 36 hpf and that they subsequently developed ECs. (B) Embryos treated from 24 to 48 hpf with 2,3-BDM to decrease myocardial force failed to develop endocardial rings in a dose-dependent manner. Loss of ring formation was not linked to loss of blood flow—14% of embryos treated at 4 mM 2,3-BDM did not form rings despite the presence of blood flow, and 58% of embryos treated at 6 mM did form rings despite the absence of blood flow. (C) Example of an embryo treated with 10 mM 2,3-BDM that failed to develop an endocardial ring at the AV boundary (red arrow). cfk Affects EC Development through Its Effect on Myocardial Function Because most cfk−/− embryos have a dilated heart and lack blood flow and because subsequently approximately 50% of these embryos fail to form ECs, we wondered whether these two phenotypes were causally related or whether lack of EC formation could occur independently of poor early myocardial function. To address this question, 379 embryos from multiple cfk clutches were assayed for cardiac dilation and lack of blood flow at 36 hpf and for lack of endocardial ring formation at 48 hpf. Embryos that were phenotypically wild-type at 36 hpf invariably developed endocardial rings over the next 12 h, whereas all embryos that failed to form endocardial rings had previously shown a functional phenotype at 36 hpf (see Figure 5A). Importantly, the few cfk−/− embryos that were phenotypically wild-type at 36 hpf remained so at 48 hpf. These data strongly suggest that lack of EC formation occurs secondarily to poor myocardial function or lack of blood flow at 36 hpf. Myocardial Function, Not Shear Stress, Is Likely Required for EC Formation Because cfk−/− and sih−/− embryos each exhibit both a myocardial phenotype (dilation and silence, respectively) and fail to generate blood flow, it is impossible to conclusively state whether EC formation is affected in these embryos directly as a result of poor myocardial function or indirectly as a result of the perturbation in blood flow and shear stress caused by poor myocardial function. Hove et al. (2003) attempted to address a similar question by inhibiting blood flow mechanically without affecting myocardial function. However, other aspects of cardiogenesis were disturbed in their experiments, leaving open the question of which effects of their manipulations were primary and which were secondary. We chose an alternate approach to analyze the respective roles of myocardial function and blood flow in EC development by finding doses of an inhibitor of myofibril function that would or would not affect blood flow. We treated tie2::GFP embryos with various concentrations of 2,3-butanedione monoxime (2,3-BDM), which blocks myofibrillar ATPase in a dose-dependent manner (Herrmann et al. 1992) and decreases myocardial force. As the treatment concentration of 2,3-BDM increased, the percentage of embryos that formed endocardial rings at 48 hpf decreased (Figure 5B). Importantly, blood flow was abolished in all embryos treated with 2,3-BDM at 6 mM or higher, yet 58% of them (n = 74) formed an endocardial ring, indicating that blood flow is not required for the initial steps of cushion formation. When myofibril function was further decreased by treatment with 10 mM 2,3-BDM, the percentage of embryos with an endocardial ring decreased to 13% (n = 68). Studies with the anesthetic tricaine confirmed the observation that ECs may form in the absence of blood flow and that the likelihood of forming cushions is inversely proportional to the concentration of tricaine (data not shown). In summary, the results from the 2,3-BDM and tricaine treatments suggest that it is poor myocardial function, and not lack of blood flow, which is primarily responsible for the loss of EC formation in cfk−/− and sih−/− embryos. Discussion The hearts of sih−/− embryos fail to beat yet undergo looping morphogenesis and AV boundary specification. The specific absence of EC formation in these embryos clearly demonstrates that myocardial function is required for EC formation. Precisely how myocardial function is required for EC formation is unclear. Prior work has demonstrated a requirement for AV boundary myocardium in the formation of ECs, and several signaling molecules emanating from the myocardium at the AV boundary have been identified (reviewed in Eisenberg and Markwald 1995). Although we have shown that the expression of bmp-4 is not affected by myocardial function defects, it is possible that myocardial function is somehow required for another aspect of this signaling event. Hove et al. (2003) recently argued for the importance of intracardiac hemodynamics as a key epigenetic factor affecting embryonic cardiogenesis. In their experiments, blood flow into the heart was eliminated by surgical placement of a bead at the inflow tract. One of the resulting phenotypes in these embryos was lack of EC formation, leading them to hypothesize that a reduction in shear stress on endocardial cells caused this phenotype. However, it is possible that the lack of EC formation observed by Hove et al. (2003) in surgically manipulated embryos was secondary to either the lack of looping observed in these same embryos, an effect on myocardial function by the surgical manipulations, or a more general arrest of cardiac development. In contrast, sih −/− embryos undergo looping morphogenesis, making them perhaps a more appropriate model to analyze, and these embryos also lack ECs. Therefore, myocardial function and/or blood flow appear to play a role in EC development. Data from 2,3-BDM and tricaine treatments, in which many embryos without blood flow still formed ECs, suggest that endocardial shear stress may not be key to EC morphogenesis. In mouse, a number of studies indicate that mutation of a single gene, including tbx5 (Bruneau et al. 2001), nkx2.5 (Schott et al. 1998; Biben et al. 2000), and has2 (Camenisch et al. 2000), affects both myocardial function and cardiac morphogenesis. These mutations have been thought to affect cardiac structural development and function in parallel pathways or in a causal pathway with the abnormal structure of the hearts leading to abnormal function. Based on our analyses of cfk, we would like to propose an alternative interpretation, namely that some of these mutations may primarily perturb early myocardial function, which then disrupts subsequent steps of heart morphogenesis. This model is supported by studies of the ncx1 null mouse, in which the heartbeat is eliminated and the EMT associated with EC development appears to be defective (Koushik et al. 2001). The ncx1studies, together with ours, indicates that EC development may be particularly sensitive to perturbations in myocardial function. However, because of the difficulty in completely unlinking heart function and blood flow, further studies will be required to elucidate the exact contributions of myocardial function and endothelial shear stress on cushion and valve morphogenesis. Our data show that mutations in two different sarcomeric genes lead to EC defects in zebrafish. Similar mutations affecting myocardial function may thus cause EC defects in humans. While most infants born with EC defects exhibit relatively normal myocardial function, actin-gene expression is known to undergo significant changes during development (Cox and Buckingham 1992). Thus, a mutation in an actin gene expressed in the heart during embryogenesis, when control of actin-gene expression appears to be less specific than in the mature animal, could lead to an EC phenotype without apparent effects on myocardial function at birth. As shown by the cfk mutant, it is possible that genetic lesions in actin genes expressed only transiently in myocardial cells could cause embryonic cardiovascular phenotypes, particularly if those lesions act in a dominant manner. Therefore, as we seek to identify the genes responsible for human cardiac malformations, those primarily involved in myocardial function should also be considered. Materials and Methods Zebrafish Zebrafish were maintained and staged as described (Westerfield 2000). We used the sihtc300b allele (Chen et al. 1996) and the cfks11 allele identified in a screen in our laboratory (Alexander et al. 1998). Animal protocols were approved by the Committee on Animal Research of the University of California, San Francisco (San Francisco, California, United States). In situ hybridization We carried out whole-mount in situ hybridization as described elsewhere (Alexander et al. 1998). We used antisense RNA probes to bmp-4 (Nikaido et al. 1997) and the 3′ UTR of cfk. Visualization of GFP-positive cells after in situ hybridization was performed by treating embryos with a rabbit anti-GFP-IgG followed by a fluoresceinated mouse anti-rabbit-IgG. Morpholino antisense “knock-down.” We designed morpholino oligonucleotides (Gene Tools, Philomath, Oregon, United States) to bind to the initiation codon and flanking sequences of the following zebrafish genes: sih (Sehnert et al. 2002), rab4a (5′-GTCTCTGACATGACTGACGCTGCGT-3′), abc-b10 (5′-TCATTCGCAACATTGTCCCATACAT-3′), fv49b10.y1 (5′-CCGACCTAATTCGCTTGGT-CACCAT-3′), and cfk (5′-CATCTTGATGTATTCTTTCTCTGCT-3′). The morpholinos were injected at 4 ng and 8 ng into tie2::GFP embryos at the one-cell stage and examined at 24 to 72 hpf for myocardial function and EC phenotypes. Morpholinos against abc-b10, rab4a, and fv49b10.y1 did not affect myocardial function or EC formation. The cfk morpholino caused no phenotype, an expected result given the ability of actin genes to compensate for each other when down-regulated (Kumar et al. 1997; Crawford et al. 2002) and the redundancy of cfk with α-cardiac actin. Genetic mapping We genotyped diploid mutant embryos from a cfks11-AB/SJD hybrid strain using SSLP with various CA repeat markers and SSCP with various ESTs. SSCP was performed by denaturing PCR products at 95°C for 10 min in 0.7 mM EDTA and 36 mM NaOH and placing on ice before loading on nondenaturing acrylamide gels. BAC library filters for the CHORI-211 (zC) library were obtained from P. de Jong at BACPAC (Oakland, California, United States). Linkage of the R177H mutation to the cfks11 allele (through genotyping of mutant embryos) was accomplished by performing PCR across the mutation (forward primer = 5′-ATCGTGCTGGACTCTGGTG-3′ and reverse primer = 5′-GAAAGAATAACCGCGCTCAG-3′) and digesting with NsiI, which cuts only the H177 allele. Synteny analysis was performed through http://genome.jgi-psf.org/fugu3/fugu3.home.html (fugu) and http://genome.ucsc.edu (human). Mutation detection We used a pool of 25 cfks11 mutant embryos to extract mRNA (Trizol, GIBCO–BRL, Gaithersburg, Maryland, United States) and synthesize cDNA (SuperScript First Strand, Invitrogen, Carlsbad, California, United States). PCR was then performed from the 5′ UTR to 3′ UTR and multiple clones sequenced. To confirm that the amino acid change we saw was a mutation and not a polymorphism in the strain used for mutagenesis, we examined the DNA of four F1 females from the screen (Alexander et al. 1998) in which cfk was identified, one of which led to the cfk line and thus should be mosaic for the mutation. This female, but not her three sisters, was indeed mosaic for the mutation (data not shown). Pharmacological treatment of embryos Clutches of dechorionated embryos were placed in 2,3-BDM (Sigma B-0753, Sigma–Aldrich, St. Louis, Missouri, United States) at 24 hpf at concentrations between 2 and 20 mM. This concentration range has been shown to affect myofibrillar ATPase in a dose-dependent manner, with 25% of activity remaining at 10 mM (Herrmann et al. 1992). 2,3-BDM had a rapid onset of action—at 2 and 4 mM, the embryos had visibly weakened heartbeats within minutes, and at 6 mM or greater the myocardial force was weakened enough to eliminate blood flow almost immediately. During the next 24 h, the embryos were examined periodically to ensure that the pharmacological effect remained constant over time. At 48 hpf the presence or absence of the endocardial ring was assayed. For tricaine treatment, dechorionated embryos were placed in a solution of 0.4 mg/ml ethyl 3-aminobenzoate methanesulfonate salt (Sigma A-5040, MS-222, 886–86-2) from 24 to 48 hpf and assayed for myocardial function during that time and for EC formation at 48 to 54 hpf. Yeast-actin biochemistry Site-directed mutagenesis of the yeast-actin coding sequence in a centromeric plasmid and construction of haploid cells producing only the mutant actin were carried out as described previously (Cook et al. 1992). Wild-type and mutant actins were purified using a DNase I agarose/DEAE-cellulose-based procedure as described previously (Cook et al. 1991). G-actin was stored at 4°C in G-buffer (10 mM Tris–HCl [pH 7.5], containing 0.2 mM ATP, 0.2 mM CaCl2, and 0.1 mM DTT) and used within 2 d. Actin polymerization was induced by the addition of MgCl2 and KCl to final concentrations of 2 mM and 50 mM, respectively, and polymerization was assessed by the increase in light scattering as a function of time at 25°C in a 120 μl volume in a thermostatted cuvette using a SPEX Fluorolog 3 fluorimeter with excitation and emission wavelengths set at 360 nm. Supporting Information Video S1 Phenotype of Wild-Type and cfk−/− Embryos at 72 hpf Wild-type hearts propel blood through the vasculature, whereas cfk−/− hearts have complete regurgitation of blood from the ventricle to the atrium and therefore no forward blood flow. (2.91 MB MOV). Click here for additional data file. Video S2 Phenotype of Wild-Type and cfk−/− Embryos at 36 hpf Wild-type embryos have a narrow, strongly contracting heart and generate effective blood flow. cfk−/− embryos (two different embryos shown) have dilated hearts with weak contractions and are not capable of generating circulation. (2.9 MB MOV). Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/index.html) accession numbers discussed in this paper are for cfk (AY222742), zebrafish α-cardiac actin (AF116824), zebrafish α-skeletal actin (AF180887), and zebrafish cytoplasmic actin (AF057040). We thank Sally Horne, Ian Scott, Benno Jungblut, Neil Chi, and Dimitris Beis for comments on the manuscript and Steve Waldron for outstanding technical assistance. T. B. was supported by a Postdoctoral Research Fellowship for Physicians from the Howard Hughes Medical Institute. E. W. was supported by a Predoctoral Fellowship in the Biological Sciences from the Howard Hughes Medical Institute. This work was also supported by grants from the National Institutes of Health to P. R. (GM33689) and D. Y. R. S. (HL54737) and the Packard Foundation to D. Y. R. S. Conflict of interest. The authors have declared that no conflicts of interest exist. Author contributions. TB, PAR, and DYRS conceived and designed the experiments. TB, K-KW, MM, and JR performed the experiments. TB, ECW, JA, PAR, and DYRS analyzed the data. ECW and JA contributed reagents/materials/analysis tools. TB, PAR, and DYRS wrote the paper. Academic Editor: Brigid Hogan, Duke University Medical Center Abbreviations APanterior–posterior AVatrioventricular BACbacterial artificial chromosome 2,3-BDM2,3-butanedione monoxime cfk cardiofunk; EC EMTepithelial–mesenchymal transformation ESTexpressed sequence tag GFPgreen fluorescent protein hpfhours postfertilization LGlinkage group sihsilent heart tnnt2 cardiac troponin T; UTR ==== Refs References Alexander J Stainier DY Yelon D Screening mosaic F1 females for mutations affecting zebrafish heart induction and patterning Dev Genet 1998 22 288 299 9621435 Biben C Weber R Kesteven S Stanley E McDonald L Cardiac septal and valvular dysmorphogenesis in mice heterozygous for mutations in the homeobox gene Nkx2-5 Circ Res 2000 87 888 895 11073884 Bruneau BG Nemer G Schmitt JP Charron F Robitaille L A murine model of Holt–Oram syndrome defines roles of the T-box transcription factor Tbx5 in cardiogenesis and disease Cell 2001 106 709 721 11572777 Burge C Karlin S Prediction of complete gene structures in human genomic DNA J Mol Biol 1997 268 78 94 9149143 Camenisch TD Spicer AP Brehm-Gibson T Biesterfeldt J Augustine ML Disruption of hyaluronan synthase-2 abrogates normal cardiac morphogenesis and hyaluronan-mediated transformation of epithelium to mesenchyme J Clin Invest 2000 106 349 360 10930438 Chen JN Haffter P Odenthal J Vogelsang E Brand M Mutations affecting the cardiovascular system and other internal organs in zebrafish Development 1996 123 293 302 9007249 Cook RK Sheff DR Rubenstein PA Unusual metabolism of the yeast actin amino terminus J Biol Chem 1991 266 16825 16833 1885608 Cook RK Blake WT Rubenstein PA Removal of the amino-terminal acidic residues of yeast actin: Studies in vitro and in vivo J Biol Chem 1992 267 9430 9436 1349604 Cox RD Buckingham ME Actin and myosin genes are transcriptionally regulated during mouse skeletal muscle development Dev Biol 1992 149 228 234 1728592 Crawford K Flick R Close L Shelly D Paul R Mice lacking skeletal muscle actin show reduced muscle strength and growth deficits and die during the neonatal period Mol Cell Biol 2002 22 5887 5896 12138199 Drubin DG Jones HD Wertman KF Actin structure and function: Roles in mitochondrial organization and morphogenesis in budding yeast and identification of the phalloidin-binding site Mol Biol Cell 1993 4 1277 1294 8167410 Eisenberg LM Markwald RR Molecular regulation of atrioventricular valvuloseptal morphogenesis Circ Res 1995 77 1 6 7788867 Gaussin V Van De Putte T Mishina Y Hanks MC Zwijsen A Endocardial cushion and myocardial defects after cardiac myocyte-specific conditional deletion of the bone morphogenetic protein receptor ALK3 Proc Natl Acad Sci U S A 2002 99 2878 2883 11854453 Herrmann C Wray J Travers F Barman T Effect of 2,3-butanedione monoxime on myosin and myofibrillar ATPases: An example of an uncompetitive inhibitor Biochemistry 1992 31 12227 12232 1457420 Hoffman JI Incidence of congenital heart disease. 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10.1371/journal.pbio.0020129

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020130Research ArticleCell BiologyDevelopmentGenetics/Genomics/Gene TherapyMus (Mouse)Pax7 Is Necessary and Sufficient for the Myogenic Specification of CD45+:Sca1+ Stem Cells from Injured Muscle Pax7 Induces Myogenesis in Adult Stem CellsSeale Patrick 1 2 Ishibashi Jeff 2 Scimè Anthony 2 Rudnicki Michael A mrudnicki@ohri.ca 1 2 1Department of Biology, McMaster UniversityHamilton, OntarioCanada2Ottawa Health Research Institute, Molecular Medicine ProgramOttawa, OntarioCanada5 2004 11 5 2004 11 5 2004 2 5 e13012 1 2004 25 2 2004 Copyright: © 2004 Seale et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Gene That Directs the Regeneration of Injured Muscle from Adult Stem Cells CD45+:Sca1+ adult stem cells isolated from uninjured muscle do not display any myogenic potential, whereas those isolated from regenerating muscle give rise to myoblasts expressing the paired-box transcription factor Pax7 and the bHLH factors Myf5 and MyoD. By contrast, CD45+:Sca1+ isolated from injured Pax7  −/− muscle were incapable of forming myoblasts. Infection of CD45+:Sca1+ cells from uninjured muscle with retrovirus expressing Pax7 efficiently activated the myogenic program. The resulting myoblasts expressed Myf5 and MyoD and differentiated into myotubes that expressed myogenin and myosin heavy chain. Infection of CD45−:Sca1− cells from Pax7  −/− muscle similarly gave rise to myoblasts. Notably, infection of Pax7-deficient muscle with adenoviral Pax7 resulted in the de novo formation of regenerated myofibers. Taken together, these results indicate that Pax7 is necessary and sufficient to induce the myogenic specification of CD45+ stem cells resident in adult skeletal muscle. Moreover, these experiments suggest that viral transduction of Pax7 is a potential therapeutic approach for the treatment of neuromuscular degenerative diseases. The gene Pax7 is shown to be a key regulator of muscle cell differentiation in specific populations of adult stem cells during muscle tissue regeneration ==== Body Introduction Skeletal muscle regeneration has long been considered to be mediated solely by monopotential skeletal muscle stem cells known as satellite cells (Bischoff 1994; Charge and Rudnicki 2004). However, recent studies have identified novel populations of adult stem cells in skeletal muscle. For example, “side-population” (SP) cells isolated from muscle tissue participate in the regeneration of skeletal muscle and give rise to satellite cells (Gussoni et al. 1999; Asakura et al. 2002). In vitro, muscle SP cells readily form hematopoietic colonies, but do not spontaneously differentiate into muscle cells unless cocultured with satellite-cell-derived myoblasts (Asakura et al. 2002). Various cell surface markers have been employed to purify adult stem cell populations from skeletal muscle, including c-kit, Sca1, CD34, and CD45 (reviewed by Charge and Rudnicki 2004). Almost all muscle-derived hematopoietic progenitor and blood reconstitution activity is derived from CD45+ cells (Asakura et al. 2002; McKinney-Freeman et al. 2002). Muscle-derived CD45+ cells purified from uninjured muscle are uniformly nonmyogenic in vitro and do not form muscle in vivo (Asakura et al. 2002; McKinney-Freeman et al. 2002). However, coculture and in vivo injection experiments indicate that CD45+ SP, as well as CD45− SP, cells possess myogenic potential (Asakura et al. 2002; McKinney-Freeman et al. 2002). Recent experiments have established that CD45+ adult stem cells have a normal physiological role in tissue regeneration (Polesskaya et al. 2003). CD45+:Sca1+ cells display a 30-fold expansion in number following cardiotoxin-induced (ctx-induced) injury. Importantly, a large proportion of CD45+:Sca1+ cells isolated from regenerating muscle acquire myogenic potential and appear to represent a significant source of myogenic progenitors during regenerative myogenesis (Polesskaya et al. 2003). Moreover, the myogenic specification of these adult stem cells during regeneration occurs by a Wnt-signaling-dependent mechanism (Polesskaya et al. 2003). The paired-box transcription factor Pax7 is specifically expressed in satellite cells and is required for the specification of the satellite cell lineage (Seale et al. 2000). Following Wnt treatment of isolated CD45+ adult stem cells, Pax7 is rapidly induced as an early marker of satellite cell myogenic specification (Polesskaya et al. 2003). Together, these data suggest the hypothesis that Pax7 represents the target of Wnt signaling that directs the myogenic specification of adult stem cells resident in muscle. To investigate this hypothesis, we examined the myogenic potential of adult stem cells from Pax7  −/− muscle, and employed viral vectors to transduce Pax7 into cells in vivo and in vitro. Our experiments demonstrate that Pax7 induces the myogenic program in specific populations of adult stem cells within muscle tissue and support the conclusion that Pax7 regulates myogenic determination during regenerative myogenesis. Results Pax7 Is Required for the Myogenic Commitment of CD45+:Sca1+ Cells To determine whether Pax7 is required for myogenesis in muscle-derived CD45+ cells, we analyzed the myogenic differentiation capacity of CD45+:Sca1+ cells from Pax7  −/− muscle undergoing ctx-induced regeneration. Flow cytometry analysis revealed a higher average proportion of CD45-expressing cells in Pax7  −/− muscle relative to wild-type (Figure 1A). Specifically, in muscle suspensions from Pax7  −/− and wild-type littermates, 39% ± 4% versus 31% ± 9% of cells were CD45+:Sca1− and 9% ± 2% versus 5% ± 2% of cells were CD45+:Sca1+, respectively (n ≥ 6). Four days following ctx injury, a significantly higher proportion of CD45+:Sca1+ cells (26% ± 3% compared to 19% ± 4% for Pax7 −/− and wild-type, respectively, p < 0.05) and a reduced proportion of CD45−:Sca1+ cells were observed in Pax7  −/− muscle (19% ± 4% compared to 25% ± 6% for Pax7  −/− and wild-type, respectively, p = 0.07, n = 3) (Figure 1A). Figure 1 Pax7 Is Required for the Myogenic Specification of CD45+:Sca1+ Cells (A) Flow cytometric analysis of cell suspensions derived from uninjured and regenerating wild-type and Pax7  −/− muscle (4 d after ctx injection) showed an increased proportion of CD45+ cells in Pax7  −/− samples. (B and C) Pax7 protein was expressed in approximately 6%–10% of CD45+:Sca1+ cells purified from regenerating Pax7 +/− muscle. (D–K) MyoD (D and E) and Desmin (F and G) were induced in CD45+:Sca1+ cells from regenerating Pax7 +/− but were not expressed in CD45+:Sca1+ cells from regenerating Pax7  −/− muscle (H–K). By immunohistochemical analysis, Pax7 protein was upregulated in 6%–10% of CD45+:Sca1+ cells from wild-type muscle 4 d after ctx injury (Figure 1B and 1C). Importantly, Pax7 expression was not detected in CD45+:Sca1+ cells purified from uninjured muscles (Polesskaya et al. 2003). Furthermore, MyoD− (Figure 1D and 1E) and Desmin− immunoreactive cells (Figure 1F and 1G) were readily detected in cultured (18 h in growth medium) CD45+:Sca1+ cells purified from regenerating Pax7 +/ − muscle (4 d post-ctx). By contrast, Pax7  −/− CD45+:Sca1+ cells from regenerating muscle did not give rise to any MyoD-expressing (Figure 1H and 1I) or Desmin-expressing (Figure 1J and 1K) myogenic cells (n = 3 independent isolations with three mice per experiment). Taken together, these results support a central role for Pax7 in the myogenic specification of CD45+:Sca1+ cells in response to acute muscle damage. Pax7 Is Sufficient to Induce Myogenesis in CD45+:Sca1+ Cells Adenoviral and retroviral expression systems were developed to ectopically introduce the Pax7 gene into putative adult stem cell populations. Pax7 was efficiently expressed from retrovirus (HAN-Pax7) in C3H10T1/2 fibroblasts and other cell cultures (Figure 2A). Stable expression of Pax7 did not induce MyoD (data not shown) or Myogenin protein expression (Figure 2B and 2C) in C3H10T1/2 cells. MyoD, as expected, readily converted C3H10T1/2 cells into skeletal myocytes (Figure 2D and 2E). These results show that Pax7 is not sufficient to induce myogenic determination in an established multipotent mesenchymal cell. Figure 2 Pax7 Induces Myogenic Commitment in CD45+:Sca1+ Cells (A) Western blot analysis with anti-Pax7 antibody confirmed high levels of ectopic Pax7 in C3H10T1/2 cells infected with retrovirus-Pax7 (HAN-Pax7) but not with control virus expressing a puromycin-resistance marker (HAN-puro). (B and C) HAN-Pax7 did not induce expression of myogenin in C3H10T1/2 cells. (D and E) By contrast, MyoD virus (HAN-MyoD) efficiently converted C3H10T1/2 cells to myogenin-expressing myocytes (green) . (F–I) HAN-Pax7 (F and G) but not HAN-puro (H and I) activated expression of MyoD (red) in CD45+:Sca1+ cells from uninjured muscle. (J–M) HAN-Pax7 (J) but not HAN-puro (K) also induced Myf5nLacZ expression in CD45+:Sca1+ cells. Furthermore, HAN-Pax7-infected CD45+:Sca1+ cultures differentiated into MyHC-expressing myocytes (green) under differentiation conditions (L), whereas HAN-puro-infected cells did not undergo myogenic differentiation (M). DAPI staining (blue) was used to visualize all nuclei. To determine whether Pax7 expression was sufficient to activate myogenesis in adult CD45+ progenitors, cells were fractionated from uninjured muscle and infected with Pax7-expressing retrovirus. Strikingly, CD45+:Sca1+ cells expressed Myf5 (data not shown) and MyoD (Figure 2F–2I) protein after infection only with Pax7 (HAN-Pax7), and not with puromycin-alone control virus (HAN-puro), indicating that these cells undergo myogenesis in response to Pax7 Infection of CD45+:Sca1+ cells from Myf5nLacZ reporter mice with HAN-Pax7 retrovirus specifically induced Myf5nLacZ expression and myogenesis in about 50% of infected cells (Figure 2J). The Myf5nLacZ allele faithfully recapitulates the expression pattern of the endogenous Myf5 gene and is rapidly induced following myogenic commitment (Tajbakhsh et al. 1996; Tajbakhsh et al. 1997). Importantly, infection of CD45+:Sca1+ cells with control retrovirus expressing the puromycin-resistance gene (HAN-puro) did not activate Myf5nLacZ expression (Figure 2K). Moreover, exposure of these cultures to differentiation conditions caused Pax7-expressing cells to differentiate into myotubes expressing myosin heavy chain (MyHC) (Figure 2L and 2M). Ectopic expression of Pax7 in CD45−:Sca1+ or CD45+:Sca1− cells did not result in the generation of myogenic cells. Taken together, these results demonstrate that Pax7 induces the myogenic program selectively in CD45+:Sca1+ adult stem cells from skeletal muscle. Expression of Pax7 Converted CD45+:Sca1+ Cells into Myogenic Progenitors CD45+:Sca1+ cells expressing retroviral Pax7 were stably selected using puromycin, hereafter called CDSC-Pax7 cells (n = 4 independent isolates analyzed). CDSC-Pax7 cells displayed a stellate, fibroblastic morphology that was distinct from the round, refractile appearance of primary satellite-cell-derived myoblasts. Proliferating CDSC-Pax7 cells expressed the myogenic determination bHLH factors, Myf5 (Figure 3A–3C), and MyoD (Figure 3D–3F). CDSC-Pax7 cells cycled approximately three times faster than satellite-cell-derived myoblasts isolated simultaneously (data not shown) and maintained their myogenic identity as primary cultures in excess of three months. CDSC-Pax7 cultures also differentiated efficiently into multinucleated myotubes expressing the terminal differentiation markers MyHC (Figure 3G–3I) and myogenin (Figure 3J–3L). These results demonstrate that the constitutive expression of Pax7 (Figure 3M–3O), which is normally downregulated during differentiation (Seale et al. 2000), did not interfere with cell-cycle arrest and normal myotube formation. By contrast, overexpression of Pax7 in C2C12 myoblasts prevented their differentiation into MyHC-positive myocytes (data not shown). These experiments therefore demonstrate that myoblasts derived from Pax7-infected CD45+:Sca1+ stem cells are amenable to ex vivo expansion and subsequent terminal muscle differentiation. Figure 3 CDSC-Pax7 Cells Become Myogenic Progenitors Myf5 (A–C) and MyoD (D–F) protein (green) are expressed in proliferating CDSC-Pax7 cells. Exposure of CD45+:Sca1+ cultures to low mitogen medium induced the formation of multinucleated myotubes and expression of myogenic differentiation markers including MyHC (red) (G–I) and myogenin (red) (J–L). Sustained expression of Pax7 (red) (M–O) in these cultures did not interfere with their differentiation. DAPI staining (blue) was used to visualize all nuclei. CDSC-Pax7 Cells Express High Levels of Myf5 and Sca1 The expression pattern of myogenic factors in proliferating and differentiating CDSC-Pax7 cell lines was analyzed by Western blot (n = 2). These experiments indicated that Myf5 was expressed at high levels in proliferating CDSC-Pax7 cells (Figure 4A; day 0). Moreover, CDSC-Pax7 cells continued to express Myf5 protein during their differentiation. CDSC-Pax7 cells also expressed MyoD but at low levels relative to primary myoblasts. MyoD was transiently upregulated in CDSC-Pax7 cells as they entered their differentiation program (Figure 4A; days 1 and 2). Figure 4 CDSC-Pax7 Cells Express High Levels of Myf5 and Sca1 (A) Western blot analysis of CDSC-Pax7 cells in proliferation conditions (day 0) and during differentiation (days 1–4) revealed high levels of Myf5 expression and low levels of MyoD expression. By contrast, satellite-cell-derived myoblasts (Wt-Mb) displayed the opposite profile of Myf5 and MyoD expression. Myogenin (Myg) was upregulated during the differentiation of CDSC-Pax7 and satellite-cell-derived myoblasts (Wt-diff). Note the sustained expression of Pax7 during the differentiation of CDSC-Pax7 cells. C3H10T1/2 (10T) lysate was used as a negative control. (B) RT-PCR analysis indicated that CDSC-Pax7 cells (two different lines) upregulated the endogenous Pax7 mRNA. Satellite-cell-derived myoblasts (Wt-Mb) and Jurkat cells were used as positive and negative controls, respectively. (C) Flow cytometry indicated that CDSC-Pax7 cells lost expression of CD45 but retained high levels of Sca1. About 24% of satellite-cell-derived myoblasts (wt-myoblasts) expressed low levels of Sca1. (Black graph depicts staining with IgG-PE control antibody; red graph shows target staining using Sca1-PE or CD45-PE.) The primary myogenic regulatory factor (MRF) expression profile in CDSC-Pax7 cells contrasted with the pattern observed in satellite-cell-derived primary myoblasts (Figure 4; Wt-Mb). Primary myoblasts expressed higher levels of MyoD and lower levels of Myf5 and downregulated Myf5 immediately upon differentiation (Wt-diff). Myogenin (Myg) was upregulated during the differentiation of CDSC-Pax7 cells, albeit at lower levels compared with differentiating satellite-cell-derived myoblasts (Wt-diff). Interestingly, CDSC-Pax7 cells also expressed endogenous Pax7 mRNA as demonstrated by reverse transcriptase PCR (RT-PCR) using primers that amplify a sequence within the Pax7 3′ UTR that is not present in the viral-Pax7 vector (Figure 4B). This result suggests that autoregulatory mechanisms may control Pax7 gene expression. Taken together, these analyses demonstrate that CDSC-Pax7 cells and primary satellite-cell-derived myoblasts express different levels of MyoD and Myf5 but are similar in their ability to undergo terminal differentiation. CDSC-Pax7 cells were originally derived from cells expressing cell surface CD45 and Sca1 proteins. Flow cytometry was employed to determine whether expression of these markers was maintained in vitro. Notably, CDSC-Pax7 cells continued to express high levels of Sca1 (approximately 90% of cells showed intense staining), but CD45 expression was extinguished (Figure 4C). Interestingly, approximately 24% of primary satellite-cell-derived myoblasts displayed low levels of Sca1 staining. Sca1 levels were not increased in satellite-cell-derived myoblasts overexpressing Pax7, demonstrating that CDSC-Pax7 cells did not arise from a small number of committed myoblasts fractionated with CD45+:Sca1+ cells (data not shown). CDSC-Pax7 Cells Differentiate In Vivo To establish whether CDSC-Pax7 cells were capable of integrating and differentiating as myofibers in vivo, intramuscular transplantation studies were performed in dystrophic (dystrophin-deficient) muscle. Specifically, 1 × 105 CDSC-Pax7 cells were injected into the tibialis anterior (TA) muscle of 4- to 6-week-old mdx:nude mice. Mdx mice carry a point mutation in the dystophin gene and are a mouse model of Duchenne muscular dystrophy (Bulfield et al. 1984; Sicinski et al. 1989; Blaveri et al. 1999). As expected, dystrophin was localized at the myofiber sarcolemma in wild-type muscle (Figure 5A) and was absent in mdx:nude skeletal muscle (Figure 5B). Two months after transplantation, TA muscles were processed for immunohistochemical detection of dystrophin and Pax7. These experiments revealed that CDSC-Pax7 cells differentiated in vivo, readily forming dystrophin-expressing myofibers in the dystrophin-deficient recipient muscle (Figure 5C and 5D). Endogenous Pax7 protein expression was not observed in central nuclei within differentiated wild-type myofibers (data not shown). Therefore, the expression of Pax7 protein (from retrovirus) in central nuclei within dystrophin+ fibers established the contribution of CDSC-Pax7 donor cells to recipient muscles (Figure 5E and 5F). These results thus document the capacity for CDSC-Pax7 cells to differentiate in vivo and contribute to the repair of dystrophic muscle. Figure 5 CDSC-Pax7 Cells Efficiently Contribute to the Repair of Dystrophic Muscle (A) Wild-type muscle expressed dystrophin at the plasmalemma of all myofibers. (B) Dystrophin protein was not detected in muscle sections from dystrophin-deficient mdx:nude mice (mdx:nu). (C–F) CDSC-Pax7 cells differentiated in vivo after transplantation, readily forming large numbers of dystrophin-expressing myofibers (green) in mdx:nude muscle (C and D). Serial cross sections showing the viral expression of Pax7 protein in central nuclei of regenerated fibers (red staining in [E]) confirmed the donor origin of dystrophin-positive myofibers (red staining in [F]). Pax7 Does Not Induce Myogenesis in CD45+:Sca1+ Cells from Pax7  −/− Muscle The myogenic differentiation of wild-type CD45+:Sca1+ muscle cells suggested that ectopic Pax7 would induce myogenesis in this cell population from Pax7  −/− muscle. Infection of Pax7  −/− CD45+:Sca1+ cells with Pax7 retrovirus resulted in high levels of retroviral Pax7 transcript but no expression of Myf5 mRNA by Northern blot hybridization (Figure 6A) or RT-PCR (data not shown). The absence of Myf5 (Figure 6B–6D) or MyoD (data not shown) expression, determined by immunochemical staining of Pax7-transduced cells, ruled out the possibility that a minor subpopulation of CD45+:Sca1+ cells underwent myogenesis. These experiments illustrate that Pax7  −/− CD45+:Sca1+ cells do not enter the myogenic lineage in response to Pax7, suggesting that intrinsic differences exist between wild-type and Pax7-deficient populations of CD45+:Sca1+ cells. Figure 6 Pax7 Does Not Induce Myogenesis in CD45+:Sca1+ Cells from Pax7  −/− Muscle (A) Northern analysis shows that MyoD −/− satellite-cell-derived myoblasts (MD −/− M) and differentiating cells (MD −/− D) express endogenous Pax7 (upper arrow, Pax7 blot) and Myf5 transcripts. Pax7  −/− CD45+:Sca1+ cells (CDSC) transduced with HAN-Pax7 (+Pax7) or HAN-puro (+puro) did not initiate expression of Myf5 mRNA. The retroviral transcript producing Pax7 (lower arrow) is smaller than the endogenous Pax7 mRNA (e.g., lower arrow). (B–D) Ectopic expression of Pax7 (red) (B) in Pax7  −/− CDSC cells did not induce Myf5 protein expression (C). DAPI staining (blue) was used to visualize nuclei (D). Pax7 Promotes Myogenic Commitment in Pax7-Deficient CD45−-:Sca1− Cells In cell suspensions from uninjured muscle, satellite cells and their daughter myogenic precursors are uniformly CD45− and Sca1− (Polesskaya et al. 2003). In Pax7  −/− mice, the extremely rare myogenic cells in muscle tissue do not express CD45 or Sca1, and do not survive or expand in a variety of culture conditions (S.B.P. Chargé, P. Seale, and M.A. Rudnicki, unpublished data). Interestingly, ectopic expression of Pax7 in CD45−:Sca1− cells isolated from Pax7  −/− muscle resulted in the expression of Myf5 protein in more than 50% of infected cells (n = 3) (Figure 7A–7C). Analysis of HAN-puro-infected control cultures did not reveal any myogenic cells (Figure 7D–7F). Importantly, all Myf5-expressing myoblasts (Figure 7G–7I) and MyHC-expressing differentiated myotubes (Figure 7J–7L) in Pax7-infected CD45−:Sca1− cultures expressed viral Pax7. Figure 7 Pax7 Promotes Myogenesis in CD45−:Sca1− Cells from Pax7  −/− Muscle (A–C) Ectopic expression of Pax7 (HAN-Pax7) induced Myf5 expression (green) and myogenic commitment in CD45−:Sca1− cells from Pax7  −/− muscle. (D–F) By contrast, Myf5-expressing cells were completely absent from HAN-puro-infected cultures after selection. (G–L) CD45−:Sca1− cells from Pax7  −/− muscle expressed Myf5 (red) (H) and MyHC (red) (K) only in cells that also coexpressed high levels of Pax7 protein (G and J). Arrowheads indicate cells coexpressing Pax7 and Myf5/MyHC. Arrow in (G) and (I) depicts a Pax7+, Myf5− cell. In these experiments we cannot formally exclude the possibility that Pax7 promoted the survival and proliferation of committed myoblasts. However, given the extremely low number of myogenic cells recovered in culture (less than 0.7%), the low efficiency of primary myoblast infection (approximately 5%–10%), and the absence of any Myf5- or MyoD-expressing cells in control HAN-puro cultures, our results strongly suggest that Pax7 induces myogenic specification in a nonmyoblast, CD45- and Sca1-negative cell. Adenoviral Expression of Pax7 Enhances Regeneration in Pax7-Deficient Muscle To investigate whether Pax7 was sufficient to stimulate myogenesis in vivo, adenovirus was used to ectopically express Pax7 in damaged Pax7  −/− muscle. Adenoviral particles (1 × 108) expressing either Pax7 (Ad-Pax7) or the bacterial β-galactosidase gene (LacZ) (Ad-LacZ) were injected directly into injured TA muscles of 4- to 6-week-old Pax7  −/− animals 2 d after administration of ctx (n = 3). Immunohistochemistry for Pax7 in adenovirus-infected muscles demonstrated widespread Pax7 expression primarily in mononuclear cells within the damaged tissue (data not shown). To assess the effect of Pax7 expression in damaged tissue, TA muscles were analyzed and scored for regeneration 12 d after infection by enumerating the number of regenerated fibers with centrally located nuclei. The newly regenerated status of centrally nucleated fibers was confirmed by Desmin and embryonic MyHC immunoreactivity. Ad-Pax7 induced a markedly enhanced regenerative response relative to Ad-LacZ in Pax7  −/− muscle as evidenced by the increased number of Desmin-positive (Figure 8A and 8B) and centrally nucleated fibers (Figure 8C and 8D). Figure 8 Adenovirus-Pax7 Significantly Improves Regeneration In Vivo (A and B) Infection of ctx-damaged Pax7  −/− muscles with Ad-Pax7 resulted in markedly improved muscle integrity and a significantly increased number of Desmin immunoreactive (green) regenerated fibers (B) relative to muscles treated with Ad-LacZ (A). (C and D) Hematoxylin and Eosin staining similarly showed an increased number of centrally nucleated fibers in Ad-Pax7-treated Pax7  −/− muscles. (E) In three separate experimental trials, the number of regenerated fibers was markedly increased in Ad-Pax7-treated muscles relative to Ad-LacZ; however, the response was biologically variable between groups. On average, Ad-Pax7 infection resulted in a 4.1 ± 0.72–fold increase in regenerated Pax7  −/− myofibers (F). Wild-type TA muscles typically contained in excess of 700 regenerated fibers 14 d after injury (data not shown). In three independent experiments, ctx-damaged TA muscle from Pax7  −/− mice typically contained an average of 46 surviving or regenerated fibers following regeneration (Figure 8E). By contrast, infection of regenerating Pax7  −/− TA with Ad-Pax7 resulted in the generation of an average of 192 myofibers (Figure 8E). Therefore, Pax7-infected tissue contained a 4.1 ± 0.72–fold increase in the number of regenerated fibers (Figure 8F). Together, these results demonstrate the ability of virally transduced Pax7 to direct the de novo generation of myogenic progenitors capable of forming new myofibers and participating in regenerative myogenesis. Discussion In this article, we demonstrate that expression of Pax7 induces the myogenic specification of CD45+ muscle-derived adult stem cells. First, CD45+:Sca1+ cells isolated from regenerating Pax7  −/− muscle were incapable of undergoing myogenic specification (see Figure 1). Second, expression of Pax7 with viral vectors in CD45+:Sca1+ cells purified from uninjured muscle promoted the formation of highly proliferative myoblasts that readily differentiated as multinucleated myotubes (see Figures 2 and 3). CD45+:Sca1+ cells engineered to express Pax7 (CDSC-Pax7) also differentiated in vivo, readily contributing to the regeneration of dystrophic muscle (see Figure 5). Lastly, Ad-Pax7 gene delivery into chemically damaged Pax7  −/− muscle resulted in the efficient de novo generation of myofibers in the absence of endogenous satellite cells. Taken together, these data unequivocally establish that Pax7 plays a key regulatory role for directing myogenic specification in some populations of adult stem cells during regenerative myogenesis. Moreover, these results emphasize the possibility of designing strategies to upregulate or ectopically express Pax7 in stem cells for the treatment of muscle degenerative diseases. The presence of adult stem cell populations distinct from satellite cells resident in skeletal muscle tissue has been well documented (Gussoni et al. 1999; Jackson et al. 1999; Torrente et al. 2001; Asakura et al. 2002; McKinney-Freeman et al. 2002; Qu-Petersen et al. 2002; Cao et al. 2003). An understanding of the developmental origin of these various cell populations and their physiological relevance in the maintenance of tissue integrity is beginning to emerge. Several lines of evidence argue that skeletal muscle regeneration is normally mediated entirely by stem cells resident in muscle tissue. First, destruction of stem cells resident in muscle with high-dose local irradiation of limbs results in a long-term deficit in muscle growth and regeneration (Wakeford et al. 1991; Pagel and Partridge 1999; Heslop et al. 2000). Second, transplanted muscles do not incorporate host nuclei after injury and regeneration (Schultz et al. 1986). Together, those experiments argue that CD45+ stem cells from marrow do not normally transit in significant numbers through the circulation to sites of muscle damage. Our experiments, however, suggest that a population of specialized CD45+ cells resides in muscle and can efficiently form myogenic progenitors in response to Wnt signaling (Polesskaya et al. 2003). In the current work we demonstrate that induction of Pax7 is required for the myogenic specification of CD45+ stem cells and that retroviral transduction can dominantly induce the myogenic specification of these cells. These observations therefore provide compelling evidence that some adult stem cells participate in regenerative myogenesis by forming myogenic progenitors following Pax7 induction in response to Wnt signaling. These data additionally suggest the hypothesis that Pax7 is a transcriptional target of the β-Catenin complex in Wnt-stimulated adult stem cells. Interesting parallels exist between the inductive mechanisms and transcriptional networks in embryonic and regenerative myogenesis (Parker et al. 2003). For example, the Pax7-dependent myogenic specification of CD45+ adult stem cells appears analogous to the Pax3-dependent induction of muscle precursors during somitogenesis. In the early embryo, Pax3 is expressed in the presomitic mesoderm and immature epithelial somites prior to the onset of muscle-specific gene expression (Goulding et al. 1994; Williams and Ordahl 1994). Moreover, Pax3 functions upstream of MyoD in the formation of trunk and body-wall muscle (Tajbakhsh et al. 1997). Consistent with a direct role for Pax3 in myogenic induction, ectopic Pax3 activates MyoD expression in embryonic tissues (Maroto et al. 1997; Bendall et al. 1999; Heanue et al. 1999). However, Pax3 also regulates cell survival in the presomitic mesoderm in areas that do not express Pax7, suggesting an indirect mechanism by which Pax3 may act genetically upstream of MyoD (Borycki et al. 1999). Our experiments do not rule out the possibility that Pax7 promotes the survival of CD45+ progenitors that are already competent to give rise to myogenic cells. Characterization of the downstream targets of Pax7 in CD45+ cells will be required to directly address this issue. In explanted embryonic tissues, signals from the floor plate and neural tube are required for induction of the MRFs (Munsterberg and Lassar 1995; Pourquie et al. 1995, 1996; Cossu et al. 1996). In particular, Wnt7a activates expression of MyoD in explanted paraxial mesoderm from 10.5-d-old mouse embryos (Tajbakhsh et al. 1998). The requirement for Pax3 expression in somitic precursors prior to the onset of MyoD expression suggests that Wnt signals may activate Pax3 and indirectly promote MRF expression (Borycki et al. 1999). An analogous requirement for Pax7 in the myogenic commitment of adult CD45+ progenitors suggests a conserved hierarchy whereby Wnt signaling activates Pax3 or Pax7 expression upstream of the MRFs in somitic and adult muscle stem cells, respectively. This notion is supported by the observed loss of Pax3 expression in P19 mesodermal precursors engineered to express a dominant negative form of the Wnt effector protein, β-Catenin (Petropoulos and Skerjanc 2002). A confounding result from our study was the inability of Pax7 to induce myogenesis in CD45+:Sca1+ cells recovered from Pax7  −/− muscle (see Figure 6). Several possible explanations may account for this observation. First, CD45+:Sca1+ muscle cells represent a heterogeneous cell population, as evidenced by their nonuniform response to stimuli such as myoblast coculture, Wnt proteins, and ectopic expression of Pax7 (results herein and Polesskaya et al. 2003). Analysis of muscle suspensions from young Pax7 −/− mice revealed a significantly increased number of hematopoietic progenitors and adipogenic cells (Seale et al. 2000). We also observed altered proportions of CD45- and Sca1-expressing cells in uninjured and regenerating muscle (see Figure 1A). The putative stem cell subfraction coexpressing CD45 and Sca1 may have been exhausted prematurely during postnatal Pax7  −/− muscle development. It is also conceivable that a reduced proportion of stem cells in the Pax7  −/− CD45+:Sca1+ muscle fractions was not detected in our assay due to a low efficiency of retroviral transduction (approximately 10% of surviving CD45+:Sca1+ cells with GFP virus). The identification of additional markers expressed by adult muscle-derived stem cells is required to more thoroughly explore these issues. Alternatively, adult stem cells may require additional signals to undergo myogenesis in response to Pax7. The profound growth deficit in Pax7  −/− muscles is likely to invoke nonspecific and indirect changes to the muscle microenvironment (Seale et al. 2000). Specific cues required to “prime” or activate adult stem cells may thus be absent or ineffective in Pax7  −/− muscle. Finally, our experiments also revealed that the endogenous Pax7 gene is upregulated during the myogenic specification of CD45+:Sca1+ cells (Figure 4B). Therefore, endogenous gene activity, possibly through the regulated expression of different isoforms (Kay et al. 1995; Ziman et al. 1997), may be essential to the stability of myogenic commitment. Future experiments addressing the functional differences between CD45+:Sca1+ cells in wild-type and Pax7-deficient muscle will provide a unique opportunity to gain a more complete understanding of the role of these cells during postnatal muscle development. Although CD45+ cells from Pax7  −/− muscle were apparently unable to undergo myogenesis, ectopic Pax7 induced expression of Myf5 and myogenic specification in Pax7-deficient CD45−:Sca1− cells (see Figure 7). Moreover, Ad-Pax7 significantly increased the in vivo regenerative capacity of Pax7  −/− muscle (see Figure 8). Skeletal muscle in adult Pax7  −/− mice displays a profound regeneration deficit with only occasional regenerated fibers observed at the site of injury 30 d after ctx injection (S.B.P. Chargé, P. Seale, and M.A. Rudnicki, unpublished data). Taken together, these results imply the presence of Pax7  −/− muscle progenitors that require the activity of Pax7 to generate sufficient numbers of myoblasts for effective regeneration. Further studies will be required to molecularly characterize the responsive cells and their developmental relationship to other muscle stem cell populations. The dominant expression of Myf5 in Pax7-infected CD45+:Sca1+ cells (CDSC-Pax7) (see Figure 4A) suggests a paradigm wherein Pax7 preferentially activates Myf5 compared to MyoD. Interestingly, Pax3 has been implicated in myogenesis specifically upstream of MyoD (Tajbakhsh et al. 1997). Taken together, these observations suggest the hypothesis that Pax3 and Pax7 specify distinct myogenic lineages through the preferential activation of MyoD and Myf5, respectively. Several experimental observations have noted a role for Myf5 in promoting myoblast proliferation. For example homozygous Myf5nLacZ, (e.g., Myf5-deficient) embryos display significantly reduced numbers of LacZ-expressing myogenic progenitors (Tajbakhsh et al. 1996). In avian embryos, Myf5 is preferentially expressed in proliferating myoblasts, whereas MyoD appears to be upregulated in differentiating cells (Delfini et al. 2000). Furthermore, Myf5 −/− satellite-cell-derived myoblasts display a profound proliferation deficit (Montarras et al. 2000). The increased growth rate of CDSC-Pax7 cells is reminiscent of MyoD−/− myoblasts that also express elevated levels of Myf5 (Sabourin et al. 1999). These observations raise the possibility that Pax7 activates expression of Myf5 to promote adult myoblast expansion whereas Pax3 preferentially induces MyoD and differentiation. The requirement for Pax7 in the specification of muscle satellite cells (Seale et al. 2000) and its induction during the myogenic recruitment of CD45+ adult stem cells provide further evidence for a developmental relationship between CD45+ adult muscle stem cells and satellite cells. Together, our experiments suggest the hypothesis that CD45+:Sca1+ cells give rise to satellite cells by a Pax7-dependent mechanism in response to Wnt signals. In conclusion, our work establishes that Pax7 is necessary and sufficient for the myogenic specification of specific populations of adult stem cells resident in muscle tissue. The proliferative myogenic character of CDSC-Pax7 cells and their efficient engraftment into dystrophic muscle further argue that methods to deliver Pax7 or upregulate its expression in stem cells may be useful in treating degenerative muscle disease. Materials and Methods Mice Mice carrying a targeted null mutation in Pax7 (hereafter referred to as Pax7 −/−) were generously provided by Drs. A. Mansouri and P. Gruss (Mansouri et al. 1996) and outbred into the SV129 background to increase survival. Myf5nLacZ mice were provided by Dr. S. Tajbakhsh (Tajbakhsh et al. 1996). Mdx mice were obtained from Jackson Laboratory (Bar Harbor, Maine, United States). Mdx:nu mice were provided by Dr. T.A. Partridge (see Blaveri et al. 1999). Cell sorting Mononuclear cells were recovered from uninjured hindlimb muscles or from ctx-damaged TA muscles of Pax7 +/+,Pax7 +/−, and Pax7  −/− mice as described previously (Megeney et al. 1996). Cells were washed twice with ice-cold DMEM supplemented with 5% FBS, passed through 30-μm filters (Miltenyi Biotec, Bergisch Gladbach, Germany) and suspended at a concentration of 2–3 × 106 cells/ml. Staining was performed for 30 min on ice using the antibodies CD45-APC (30-F11), CD45.2-FITC (104), Sca1-PE, or FITC (D7), all from BD Biosciences Pharmingen (San Diego, California, United States) and CD45-TC (30-F11) from Caltag Laboratories (Burlingame, California, United States). Primary antibodies were diluted in cell suspensions at 1:200. After two washes with cold PBS supplemented with 2% FBS, cells were separated on a MoFlo cytometer (DakoCytomation, Glostrup, Denmark). Sort gates were strictly defined based on isotype control stained cells and single antibody staining. Dead cells and debris were excluded by gating on forward and side scatter profiles. Sorting was performed using single cell mode to achieve the highest possible purity. The purity of sorted populations was routinely greater than 98%. Retroviral and adenoviral gene expression Retrovirus was produced according to the 3-plasmid HIT system with plasmids pHIT60, pHIT456, and pHAN-puro as described elsewhere (Soneoka et al. 1995). pHIT60 encodes the MLV retroviral gag-pol, pHIT456 expresses an amphotrophic envelope protein, and pHAN-puro is an expression vector with a hybrid CMV-5′ LTR promoter driving production of the retroviral transcript. Pax7 expression vectors were generated using the mouse Pax7d isoform containing a single Ala→Thr substitution at the seventh amino acid (the Thr residue is conserved in human, chicken, and zebrafish Pax7 proteins). Pax7d or mouse MyoD are translated from the full retroviral transcript, whereas the puromycin-resistance marker is expressed following integration from a shorter transcript produced by the SV40 early promoter located 3′ to the multiple cloning site. Transient cotransfection of all three plasmids into 293FT cells (Invitrogen, Carlsbad, California, United States) by the calcium phosphate method (Graham and van der Eb 1973) routinely produced viral titres between 106 and 107 cfu per ml. pHAN-puro was used to produce puromycin-resistant virus for controls. Purified CD45+:Sca1+ or CD45−:Sca1− cells were spun down, counted, and 20,00–50,000 cells were then cultured overnight on collagen-coated 4-well chamber slides in HAM's F10 medium (Invitrogen) supplemented with 20% FBS, antibiotics, and 10 ng/ml Stem Cell Factor (R & D Systems, Minneapolis, Minnesota, United States). The following day, cells were incubated for 6 h with retrovirus at a 1:1 ratio (complete medium: retrovirus supernatant) with 8 μg/ml polybrene (hexadimethrine bromide; Sigma, St. Louis, Missouri, United States). After infection, cells were rinsed twice with PBS, and all cells were replated in myoblast growth medium. After 48 h, infected pools were selected in 1 μg/ml puromycin (Sigma) to establish stable CDSC-Pax7 lines. C3H10T1/2 cells were incubated overnight with MyoD, Pax7, or puro virus and 8 μg/ml polybrene. Adenovirus (type V) was prepared using the Ad-Max adenovirus creation kit (Microbix Biosystems, Toronto, Ontario, Canada). Ad-Pax7d (cDNA as described above) and Ad-LacZ were expressed from the murine CMV promoter. Adenovirus was purified in CsCl gradients by centrifugation, dialyzed against sterile PBS, and frozen down in 15% glycerol at −80 °C. Titres of purified adenovirus were determined by plaque assays on 293 cells and were always above 1010 pfu/ml. Western blot analyses Cell cultures were lysed in RIPA extraction buffer (50mM Tris-HCl [pH 7.4], 1% Nonidet P-40, 0.5% NaDeoxycholate, 0.1% Sodium-dodecyl-sulphate, 5 mM EDTA, 150 mM NaCl, 50 mM NaF) supplemented with protease inhibitors (Complete; Roche, Basel, Switzerland). The extracts were normalized for protein content using Bio-Rad dye (Hercules, California, United States). Forty micrograms of lysate was separated by sodium-dodecyl-sulfate-polyacrylamide gel electrophoresis and transferred to PVDF filters (ImmobilonP; Millipore, Billerica, Massachusetts, United States). Filters were probed with antibodies to Pax7 (Developmental Studies Hybridoma Bank [DSHB], Iowa City, Iowa, United States); Myf5, 1:1000 (C-20, Santa Cruz Biotechnology, Santa Cruz, California, United States); MyoD, 1:1000 (C-20, Santa Cruz Biotechnology); myogenin (F5D, DSHB); and α-tubulin, 1:2000 (T 9026, Sigma). Secondary detection was performed with horseradish peroxidase-conjugated antibodies (Bio-Rad). Protein expression was visualized using the ECL Plus kit (Amersham Biosciences, Little Chalfont, United Kingdom). Ctx-induced regeneration and in vivo adenovirus infections Four- to six-week-old Pax7  −/− and wild-type littermates were anesthetized with Halothane gas. Twenty-five microliters of 10 μM ctx (Latoxan, Valence, France) was injected into the midbelly of the TA muscle, using a 29½ G insulin syringe. Mice were sacrificed at 4 d or 2 wk after ctx injection. For adenovirus infections, 25 μl of sterile PBS containing 108 particles of purified Ad-Pax7 or Ad-LacZ was injected 2 d after ctx injection into damaged TA muscles with a 29½ G insulin syringe. Cell transplantation Primary CDSC-Pax7 cells cultured in myoblast conditions were trypsinized, washed twice with PBS, and suspended at 5 × 105 cells/25-μl in sterile PBS for cell transplantation. Cells were injected directly into the TA midbelly of 4- to 6-wk-old mdx:nude mice. Mice were sacrificed 2 mo after cell injections to analyze the myogenic contribution of transplanted cells. Cell cultures Primary satellite-cell-derived myoblasts were established from purified CD45−:Sca1− fractions of hindlimb muscle of 4- to 6-wk-old Pax7 +/+ or Pax7 +/− mice. Myoblasts and CDSC-Pax7 cells were maintained in HAM's F-10 medium (Invitrogen) supplemented with 20% FBS and 2.5 ng/ml bFGF (Invitrogen) on collagen-coated dishes. CDSC-Pax7 cells and primary satellite-cell-derived myoblasts were differentiated for 1–3 d in DMEM supplemented with 5% horse serum. C3H10T1/2 and HEK 293 cells were obtained from the ATCC (Manassas, Virginia, United States) and maintained in DMEM supplemented with 10% FBS. Histology and immunocytochemistry For analysis of regeneration and enumeration of regenerated myofibers, TA muscles were isolated, embedded in OCT (Tissue-Tek; Sakura Finetek, Torrance, California, United States)/20% sucrose and immediately frozen in liquid nitrogen. Ten-micrometer cryosections (cross sections) from the TA midbelly at the site of ctx injection were stained with Hematoxylin and Eosin. Central myonuclei in regenerating muscles were counted on at least two independent cross sections of the entire TA muscle per mouse analyzed. Fibers were further identified by immunostaining with antibodies specific to Desmin, 1:200 (D33, DakoCytomaton); dystrophin, 1:500 (Sigma); Pax7 (DSHB); or embryonic fast MyHC (F1.652, DSHB) followed by secondary detection with anti-mouse FITC conjugated antibody, 1:200 (Chemicon, Temecula, California, United States). Sections were analyzed on a Zeiss (Oberkochen, Germany) Axioplan 2 microscope. Cultured cells were fixed with 4% paraformaldehyde, nonspecific antigens were blocked in 5% horse serum/PBS, and cells were reacted with primary antibodies as follows: Desmin, 1:200 (DakoCytomaton); MyoD, 1:200 (5.8A, BD Biosciences Pharmingen); all MyHC (MF-20, DSHB); Myf5, 1:1000 (C-20, Santa Cruz Biotechnology); Pax7 (DSHB); and myogenin (F5D, DSHB). Secondary detection was performed using fluorescein- or rhodamine-conjugated antibodies, 1:200 (Chemicon). Myf5nLacZ expression was detected by X-Gal reaction as described previously (Polesskaya et al. 2003). RT-PCR and Northern blot analysis Total RNA was extracted using RNeasy kits (Qiagen, Valencia, California, United States), according to manufacturer's instructions. RT-PCR analysis for endogenous Pax7 mRNA was performed using the GeneAmp PCR Core kit (PerkinElmer, Wellesley, Massachusetts, United States). RT-PCR using 1 μg of total RNA was conducted as per manufacturer's instructions with the following modifications. cDNA synthesis was extended for 1 h at 42 °C, and 5 μl of the first-strand RT product was used for PCR amplification. PCR conditions for endogenous Pax7 were 94 °C for 5 min; 35 cycles of 94 °C for 45 s, 56 °C for 45 s, 72 °C for 45 s; and finally 72 °C for 7 min. The PCR primers span intron 8 of the Pax7 gene (Pax7-exon8-fwd 5′ gct acc agt aca gcc agt atg 3′ and Pax7-exon9-rev 5′ gtc act aag cat ggg tag atg 3′) and amplify sequence in the 3′-UTR of the gene that is not contained in the viral Pax7 expression cassette. RT-PCR products were analyzed by electrophoresis through a TAE-ethidium-agarose gel. Northern blot studies were performed according to standard techniques using random-primed 32P-dCTP radiolabeled cDNA fragments as probes (Redi-prime, Amersham Biosciences)(Sabourin et al. 1999). Fifteen micrograms of total RNA from various cell cultures was electrophoresed in denaturing formaldehyde gels and transferred to Hybond-N filters (Amersham Biosciences). Supporting Information Accession Numbers The accession numbers for the proteins discussed in this paper are Desmin (LocusLink ID 13346), mouse MyoD (GenBank NM_010866), MyoD (LocusLink ID 17927), Myogenin (LocusLink ID 17928), Pax7 (LocusLink ID 18509), and Pax7d isoform (GenBank AF_254422). The authors would like to thank Dr. Ruedi Braun, Sylvain Gimmig, and Caroline Vergette for flow cytometry, Dr. Robin Parks for assistance with purification of adenovirus, Dr. Terry Partridge for providing mdx:nude mice; and Dr. V. Sartorelli for retroviral expression plasmids. PS is supported by a Doctoral Research Award from the Canadian Institutes of Health Research. MAR holds the Canada Research Chair in Molecular Genetics and is a Howard Hughes Medical Institute International Scholar. This work was supported by grants to MAR from the Muscular Dystrophy Association, the National Institutes of Health, the Canadian Institutes of Health Research, the Howard Hughes Medical Institute, and the Canada Research Chair Program. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. PS and MAR conceived and designed the experiments. PS performed the experiments, with the exception of Figure 4B performed by JI. PS and MAR analyzed the data. PS, JI, and AS contributed reagents/materials/analysis tools. PS and MAR wrote the paper. Academic Editor: Jon Epstein, University of Pennsylvania Abbreviations Ad-Pax7adenovirus-Pax7 Ad-LacZadenovirus-β-galactosidase; CDSC-Pax7 ctxcardiotoxin DSHBDevelopmental Studies Hybridoma Bank HAN-puroretroviral HAN-puromycin MRFmyogenic regulatory factor MyHCmyosin heavy chain RT-PCRreverse transcriptase PCR SPside population TAtibialis anterior ==== Refs References Asakura A Seale P Girgis-Gabardo A Rudnicki MA Myogenic specification of side population cells in skeletal muscle J Cell Biol 2002 159 123 134 12379804 Bendall AJ Ding J Hu G Shen MM Abate-Shen C Msx1 antagonizes the myogenic activity of Pax3 in migrating limb muscle precursors Development 1999 126 4965 4976 10529415 Bischoff R The satellite cell and muscle regeneration. In: Myogenesis, AG Engel, C Franszini-Armstrong, editors 1994 New York McGraw-Hill 97 118 Blaveri K Heslop L Yu DS Rosenblatt JD Gross JG Patterns of repair of dystrophic mouse muscle: Studies on isolated fibers Dev Dyn 1999 216 244 256 10590476 Borycki AG Li J Jin F Emerson CP Epstein JA Pax3 functions in cell survival and in pax7 regulation Development 1999 126 1665 1674 10079229 Bulfield G Siller WG Wight PA Moore KJ X chromosome–linked muscular dystrophy (mdx) in the mouse Proc Natl Acad Sci U S A 1984 81 1189 1192 6583703 Cao B Zheng B Jankowski RJ Kimura S Ikezawa M Muscle stem cells differentiate into haematopoietic lineages but retain myogenic potential Nat Cell Biol 2003 5 640 646 12792651 Charge SBP Rudnicki MA Cellular and molecular regulation of muscle regeneration Physiol Rev 2004 84 209 238 14715915 Cossu G Kelly R Tajbakhsh S Di Donna S Vivarelli E Activation of different myogenic pathways: Myf-5 is induced by the neural tube and MyoD by the dorsal ectoderm in mouse 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dystrophic muscle J Cell Sci 2000 113 2299 2308 10825301 Jackson KA Mi T Goodell MA Hematopoietic potential of stem cells isolated from murine skeletal muscle Proc Natl Acad Sci U S A 1999 96 14482 14486 10588731 Kay PH Mitchell CA Akkari A Papadimitriou JM Association of an unusual form of a Pax7-like gene with increased efficiency of skeletal muscle regeneration Gene 1995 163 171 177 7590262 Mansouri A Stoykova A Torres M Gruss P Dysgenesis of cephalic neural crest derivatives in Pax7  −/− mutant mice Development 1996 122 831 838 8631261 Maroto M Reshef R Munsterberg AE Koester S Goulding M Ectopic Pax-3 activates MyoD and Myf-5 expression in embryonic mesoderm and neural tissue Cell 1997 89 139 148 9094722 McKinney-Freeman SL Jackson KA Camargo FD Ferrari G Mavilio F Muscle-derived hematopoietic stem cells are hematopoietic in origin Proc Natl Acad Sci U S A 2002 99 1341 1346 11830662 Megeney LA Kablar B Garrett K Anderson JE Rudnicki MA MyoD is required for myogenic stem cell function in adult skeletal muscle Genes Dev 1996 10 1173 1183 8675005 Montarras D Lindon C Pinset C Domeyne P Cultured myf5 null and myoD null muscle precursor cells display distinct growth defects Biol Cell 2000 92 565 572 11374435 Munsterberg AE Lassar AB Combinatorial signals from the neural tube, floor plate and notochord induce myogenic bHLH gene expression in the somite Development 1995 121 651 660 7720573 Pagel CN Partridge TA Covert persistence of mdx mouse myopathy is revealed by acute and chronic effects of irradiation J Neurol Sci 1999 164 103 116 10402020 Parker MH Seale P Rudnicki MA Looking back to the embryo: Defining transcriptional networks in adult myogenesis Nat Rev Genet 2003 4 497 507 12838342 Petropoulos H Skerjanc IS Beta-catenin is essential and sufficient for skeletal myogenesis in p19 cells J Biol Chem 2002 277 15393 15399 11856745 Polesskaya A Seale P Rudnicki MA Wnt Signaling Induces the Myogenic Specification of Resident CD45+ Adult Stem Cells during Muscle Regeneration Cell 2003 113 841 852 12837243 Pourquie O Coltey M Breant C Le Douarin NM Control of somite patterning by signals from the lateral plate Proc Natl Acad Sci U S A 1995 92 3219 3223 7724542 Pourquie O Fan CM Coltey M Hirsinger E Watanabe Y Lateral and axial signals involved in avian somite patterning: A role for BMP4 Cell 1996 84 461 471 8608600 Qu-Petersen Z Deasy B Jankowski R Ikezawa M Cummins J Identification of a novel population of muscle stem cells in mice: Potential for muscle regeneration J Cell Biol 2002 157 851 864 12021255 Sabourin LA Girgis-Gabardo A Seale P Asakura A Rudnicki MA Reduced differentiation potential of primary MyoD−/− myogenic cells derived from adult skeletal muscle J Cell Biol 1999 144 631 643 10037786 Schultz E Jaryszak DL Gibson MC Albright DJ Absence of exogenous satellite cell contribution to regeneration of frozen skeletal muscle J Muscle Res Cell Motil 1986 7 361 367 3760154 Seale P Sabourin LA Girgis-Gabardo A Mansouri A Gruss P Pax7 is required for the specification of myogenic satellite cells Cell 2000 102 777 786 11030621 Sicinski P Geng Y Ryder-Cook AS Barnard EA Darlison MG The molecular basis of muscular dystrophy in the mdx mouse: A point mutation Science 1989 244 1578 1580 2662404 Soneoka Y Cannon PM Ramsdale EE Griffiths JC Romano G A transient three-plasmid expression system for the production of high titer retroviral vectors Nucleic Acids Res 1995 23 628 633 7899083 Tajbakhsh S Bober E Babinet C Pournin S Arnold H Gene targeting the myf-5 locus with nlacZ reveals expression of this myogenic factor in mature skeletal muscle fibres as well as early embryonic muscle Dev Dyn 1996 206 291 300 8896984 Tajbakhsh S Rocancourt D Cossu G Buckingham M Redefining the genetic hierarchies controlling skeletal myogenesis: Pax- 3 and Myf-5 act upstream of MyoD Cell 1997 89 127 138 9094721 Tajbakhsh S Borello U Vivarelli E Kelly R Papkoff J Differential activation of Myf5 and MyoD by different Wnts in explants of mouse paraxial mesoderm and the later activation of myogenesis in the absence of Myf5 Development 1998 125 4155 4162 9753670 Torrente Y Tremblay JP Pisati F Belicchi M Rossi B Intraarterial injection of muscle-derived CD34(+)Sca-1(+) stem cells restores dystrophin in mdx mice J Cell Biol 2001 152 335 348 11266450 Wakeford S Watt DJ Partridge TA X-irradiation improves mdx mouse muscle as a model of myofiber loss in DMD Muscle Nerve 1991 14 42 50 1992296 Williams BA Ordahl CP Pax-3 expression in segmental mesoderm marks early stages in myogenic cell specification Development 1994 120 785 796 7600957 Ziman MR Fletcher S Kay PH Alternate Pax7 transcripts are expressed specifically in skeletal muscle, brain and other organs of adult mice Int J Biochem Cell Biol 1997 29 1029 1036 9375383

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020132Research ArticleEvolutionGenetics/Genomics/Gene TherapyPrimatesHomo (Human)Mus (Mouse)A Neutral Model of Transcriptome Evolution Neutral Transcriptome EvolutionKhaitovich Philipp 1 Weiss Gunter 1 2 Lachmann Michael 1 Hellmann Ines 1 Enard Wolfgang 1 Muetzel Bjoern 1 Wirkner Ute 3 Ansorge Wilhelm 3 Pääbo Svante paabo@eva.mpg.de 1 1Max-Planck-Institute for Evolutionary AnthropologyLeipzigGermany2WE Informatik, BioinformatikUniversity of Düsseldorf, DüsseldorfGermany3European Molecular Biology LaboratoryHeidelbergGermany5 2004 11 5 2004 11 5 2004 2 5 e13221 11 2003 2 3 2004 Copyright: © 2004 Khaitovich et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Random Processes Underlie Most Evolutionary Changes in Gene Expression Microarray technologies allow the identification of large numbers of expression differences within and between species. Although environmental and physiological stimuli are clearly responsible for changes in the expression levels of many genes, it is not known whether the majority of changes of gene expression fixed during evolution between species and between various tissues within a species are caused by Darwinian selection or by stochastic processes. We find the following: (1) expression differences between species accumulate approximately linearly with time; (2) gene expression variation among individuals within a species correlates positively with expression divergence between species; (3) rates of expression divergence between species do not differ significantly between intact genes and expressed pseudogenes; (4) expression differences between brain regions within a species have accumulated approximately linearly with time since these regions emerged during evolution. These results suggest that the majority of expression differences observed between species are selectively neutral or nearly neutral and likely to be of little or no functional significance. Therefore, the identification of gene expression differences between species fixed by selection should be based on null hypotheses assuming functional neutrality. Furthermore, it may be possible to apply a molecular clock based on expression differences to infer the evolutionary history of tissues. Analysis of differences in gene expression between primate species suggests that the majority of them are selectively neutral and likely to have little or no functional consequences ==== Body Introduction Advances in microarray technology have made the systematic study of expression levels of thousands of transcripts possible. This has been heralded as a major step forward in understanding the function of genomes, since transcript expression levels are expected to correlate with biological functions. Although this is clearly the case for many genes that change their expression in response to environmental stimuli (e.g., Spellman et al. 1998; Hughes et al. 2000; Miki et al. 2001), it is not known whether evolutionary changes in gene expression are determined primarily by Darwinian selection or by stochastic processes. Indeed, the extent to which natural selection has shaped the properties of organisms has been hotly debated ever since Charles Darwin proposed that organisms are adapted to their environment as a result of natural selection. At the molecular level, the view that most changes are due to Darwinian selection was challenged by Kimura's neutral theory of molecular evolution (Kimura 1983). This theory states that the vast majority of differences seen in nucleotide and amino acid sequences within and between species have no or only minor selective effects. Consequently, their occurrence within a species and the fixation of differences between species are primarily the result of stochastic processes. Thus, it is believed today that the evolution of the overwhelming majority of synonymous nucleotide changes within protein-coding exons, as well as changes in noncoding parts of genomes, are determined by mutational processes and random genetic drift (Li 1997). In fact, even at the level of morphology, it has been argued that many features are not adaptive, but instead result from physical constraints or historical accidents (Gould and Lewontin 1979). However, since selection acts at the level of the phenotype while variation is generated at the level of the genotype, the proportion of changes caused by selection can be expected to be largest at the phenotypic level and smallest at the DNA sequence level. As a corollary, we may expect the proportion of selected changes to gradually decrease at the proteome and the transcriptome levels, since these are located progressively further from the phenotype. Consequently, a large proportion of transcriptome changes might be explained by historical accidents rather than by selective events. To test whether this may be the case, we have investigated whether a neutral model can describe transcriptome differences observed among primate and mouse species as well as among various brain regions within a species. Results/Discussion Transcriptome Evolution among Species If the majority of evolutionary changes are caused by historical accidents rather than by natural selection, they will accumulate mainly as a function of time rather than as a function of morphological or behavioral change of organisms. Applied to transcriptome evolution, a neutral model therefore implies that the rate of transcriptome change is proportional to time. In particular, if we assume that mutations cause changes in the relative amounts of transcripts independently of the absolute expression level of the gene, then the squared difference of the logarithm of the expression level is expected to increase linearly with divergence time (Lande 1976; Felsenstein 2004). To investigate whether this is the case, we have studied differences in the gene expression levels of around 12,000 genes in the prefrontal cortex of six humans, three chimpanzees (Pan trogodytes), one orangutan (Pongo pygmaea), and one rhesus macaque (Macaca mulatta) using oligonucleotide microarrays. To exclude the influence of DNA sequence differences on the hybridization results, at least between humans and chimpanzees, only oligonucleotide probes that matched perfectly to the chimpanzee DNA sequences were used in the analysis (see Materials and Methods). In Figure 1A, we plot species divergence times against the average squared difference between the logarithm of the expression levels of 1,998 genes that had expression levels large enough to be detected in all primate samples. Although comparisons involving orangutan and rhesus were complicated by nucleotide sequence differences to array probes, the result shows that the squared differences represent an approximately linear function of time over at least 20 million years. When we apply the same analysis to published gene expression data for the livers of three humans, three chimpanzees, and one orangutan (Enard et al. 2002), we again observe a linear relationship between gene expression differences and species divergence times (Figure 1B). Figure 1 Brain and Liver Transcriptome Change among Primates as a Function of Time Average expression differences within and between primates in brains (A), in liver (B), and for genes in brain for genes with high (red) and low (blue) variation among six humans (C). Colors: red, comparisons between and with humans; blue, comparisons between and with chimpanzees; purple, comparisons between humans and chimpanzees; orange, comparisons between orangutan and rhesus macaque; black, comparisons between experimental duplicates. Vertical error bars for expression indicate 95% confidence intervals calculated by 10,000 bootstraps over genes. Divergence times are according to Glazko and Nei (2003). Since oligonucleotide-based microarrays are sensitive to DNA sequence differences and the orangutan and rhesus macaque genome sequences are not yet known—so that we cannot delete oligonucleotides carrying mismatches between the species—we used arrays containing around 28,000 cDNAs ranging in length from 500 to 1,500 nucleotides to assay gene expression patterns in the prefrontal cortex of six humans, five chimpanzees, five rhesus macaques, and five crab-eating macaques (Macaca fascicularis). Due to the greater probe length, these arrays are much less sensitive to DNA sequence differences and therefore can be used to compare gene expression in humans and macaques (Ranz et al. 2003). When we plot the extent of gene expression divergence for 5,829 genes whose expression was detected in all samples against species divergence time, we again observe that expression differences accumulate approximately linearly with time (Figure 2). Figure 2 Brain Transcriptome Change as Measured by cDNA Arrays Colors and symbols as in Figure 1 except orange, which indicates comparisons between chimpanzee and both macaque species, and blue, which indicates comparisons between rhesus macaque and crab-eating macaque. Divergence times are according to Hayasaka et al. (1996) and Glazko and Nei (2003). In a recent study of gene expression in the brains of humans, chimpanzees, and orangutans, we found that the rate of expression change on the human lineage has been larger than on the chimpanzee lineage (Enard et al. 2002). This is in apparent contradiction to the linearity observed here. However, the analysis of Enard et al. (2002) was based on less than 5% of all genes expressed in the brain because it was confined to genes that differed significantly in expression between humans and chimpanzees. In contrast, here we perform a transcriptome-wide analysis of all genes with detectable expression in several primate species. However, the slightly higher divergence of humans than chimpanzees from the two macaque species may reflect the previously reported higher rate of gene expression divergence on the human evolutionary lineage (Enard et al. 2002; Caceres et al. 2003; Gu and Gu 2003). However, additional experiments are necessary to exclude the possibility that this is caused by experimental artifacts. The clocklike accumulation of expression differences between species observed for primates is in agreement with the recent observation that differences in gene expression are consistent with phylogenetic relationships among Drosophila species (Rifkin et al. 2003), and both these observations are compatible with the predictions of the neutral model. However, under certain selection scenarios, positively selected changes would also accumulate linearly with time (Felsenstein 2004). Therefore, linear accumulation of expression differences alone does not rule out selection. In addition to the clocklike accumulation of evolutionary changes, the neutral theory states that the same forces determine the rate of evolution both within and between species (Kimura 1983). Thus, a neutral prediction with respect to transcriptome evolution is that genes that vary more within species should be more likely to change between species as well. In order to test this, we ranked 2,926 genes with detectable expression levels in six humans and three chimpanzees according to their variation within humans and calculated the species divergences for the 25% of genes that had the largest and the smallest human variation, respectively. Figure 1C shows that the genes with high variation among humans changed significantly faster between species than the genes with low variation. The magnitude of observed expression differences may be influenced by DNA sequence mismatches affecting hybridization between orangutan and rhesus samples and array probes. However, the difference in divergence rates between genes with high and low expression variation within species is unlikely to be explained by hybridization artifacts, since this would require a difference in sequence divergence between the two groups of genes. We further considered the correlation between the average diversity within humans and chimpanzees and the divergence between the species for the 2,926 genes. This correlation is highly significant (p < 0.001) as gauged by a permutation test (see Materials and Methods). Since all array probes that carried sequence differences between humans and chimpanzees were removed prior to analysis, this correlation is not affected by hybridization artifacts. The strength of the correlation (τ = 0.24) is of a similar magnitude as the one obtained for the correlation of diversity and divergence of random genomic DNA sequences in humans and chimpanzees (τ = 0.179, p = 0.028, n = 76), the vast majority of which are noncoding (Hellmann et al. 2003). Thus, although the two measures are not directly comparable, the degree of correlation between intraspecific diversity and interspecific divergence is similar for brain transcriptomes and random genomic DNA sequences in humans and chimpanzees. To investigate whether gene expression differences accumulate as a function of time also in another group of mammals, we analyzed three mouse species. An advantage in this case is that post mortem artifacts are less likely to influence the results than in the case of autopsy material of humans and great apes. We determined differences in gene expression levels for around 9,000 genes in the frontal cortex of six outbred Mus musculus, three outbred M. spretus, and one M. caroli. As shown in Figure 3A, the squared transcriptome differences accumulated linearly with time among the mouse species. To test if divergence rates differ for the genes with high and low variation within species, we investigated the 25% of the 2,742 genes detected in all samples with the highest and the lowest variation within M. musculus, respectively, as was done in the primates. Figure 3B shows that genes that vary more within M. musculus diverged faster among mouse species than genes that vary less. As in the case of primate species, imperfect matches of M. spretus and M. caroli mRNAs to the array oligonucleotides may partly influence the observed expression differences between species. Nonetheless, as for primates, the difference in divergence rates between genes with high and low expression variation within species is unlikely to be explained by hybridization differences since there is no indication that genes that vary more in expression within species diverge faster between species with respect to their DNA sequence. The correlation between diversity and divergence for M. musculus and M. spretus for genes detected in both species is highly significant (τ = 0.29, p < 0.001, n = 3,139), although in this case we cannot correct for DNA sequence differences. A correlation between gene expression differences within and between species was recently demonstrated also in teleost fish (Oleksiak et al. 2002). Thus, in agreement with the neutral model, genes that vary more within species tend to vary more between species in three vertebrate groups. Figure 3 Brain Transcriptome Change among Mice as a Function of Time Average expression differences within and between the mouse species (A) and for genes with high (red) and low (blue) variation among M. musculus individuals (B). Colors: red, comparisons between and with M. musculus; blue, between and with M. spretus; purple, between M. musculus and M. spretus. Vertical error bars for expression indicate 95% confidence intervals calculated by 10,000 bootstraps over genes. Divergence times are according to She et al. (1990). A Test for Neutrality One way to test whether gene expression differences between species accumulate at a rate consistent with neutral expectation is to compare them to the expression differences observed for a class of genes that can reasonably be expected to not be the direct targets of positive or negative selection. Since expressed pseudogenes do not produce any functional gene products, they can be viewed as such a class of genes. Thus, if a substantial proportion of intact genes accumulate expression differences faster than pseudogenes, this would indicate that they are positively selected. Such an observation would falsify a neutral model. To test this, we considered the expression patterns in four regions of the brain in three humans and three chimpanzees using the Affymetrix U95 array set interrogating approximately 40,000 genes (Philipp Khaitovich, unpublished data). In order to identify all probe sets on these arrays that interrogate expressed pseudogenes, we aligned the probe sequences, as well as published lists of human pseudogenes, to the human genome (see Materials and Methods). In total, 889 probe sets that overlap with pseudogenes were identified. Thirty-three of these were detected (detection p-value < 0.05) in at least one of four brain regions in either the chimpanzees or the humans after masking all probes carrying DNA sequence differences between the species. Of these, 28 contained at least one mutation that leads to a loss of function in both humans and chimpanzees. We therefore assumed that these pseudogenes were nonfunctional in the common ancestor of humans and chimpanzees. Finally, we checked whether these probe sets may crosshybridize with any intact genes by aligning them to the human genome. This left us with 23 expressed pseudogenes. We compared the distributions of the squared differences between the mean expression levels of each gene in humans and in chimpanzees for the 23 pseudogenes and 12,647 intact genes for each of the four brain regions. In each case, only the genes detected in a given brain region were used for the calculation. In all four brain regions the distribution of expression distances among intact genes did not differ significantly from that among pseudogenes in either a Kolmogorov-Smirnov test or a Wilcoxon rank sum test. These tests would have been significant if more than 5% (1/23) of the genes had a distribution radically different from that of the pseudogenes. When the data for four brain regions were combined, no visual difference between the two distributions was apparent (p = 0.16 and p = 0.69, respectively) (Figure 4A). Figure 4 Comparison between Intact Genes and Pseudogenes (A) shows the distributions of expression divergence between humans and chimpanzees for intact genes and pseudogenes. (B) shows the distributions of the ratio of expression divergence between humans and chimpanzees and expression diversity within humans for intact genes and pseudogenes. Thus, we failed to detect any significant excess of intact genes that diverged faster in expression than pseudogenes. This indicates that the fraction of gene expression differences between the species that are fixed by positive selection is small. Interestingly, there was also no detectable excess of intact genes that diverged slower than pseudogenes. This may seem unexpected, since the expression of many intact genes might be thought to be stabilized by negative selection and therefore to change more slowly than pseudogenes. This may indicate that purifying selection as well is a weak force affecting gene expression. However, it should be noted that the small number of expressed pseudogenes analyzed limits the power to detect positive and negative selection. A targeted effort to study expressed pseudogenes in closely related species would be a worthwhile undertaking. A Test for Positive Selection The fact that the overall accumulation of expression differences conforms to a selectively neutral model does not mean, of course, that all expression differences between species are selectively neutral. As for nucleotide changes, some changes in gene expression will have had phenotypic consequences and some of these will have become fixed due to positive selection. To identify such gene expression differences, we propose to use the ratio of divergence between species to diversity within species, akin to the tests suggested for quantitative genetic traits (Charlesworth 1984; Lynch and Hill 1986; Turelli et al. 1988) and in agreement with recent suggestions by Rifkin et al. (2003) or Hsieh et al. (2003). However, to do this it is necessary for each gene considered to distinguish the gene expression diversity caused by genetic differences between individuals from the diversity caused by environmental factors. This is crucial since the environmental component is likely to be much larger than the genetic component. For example, under strict neutrality and no environmental influence, we expect a divergence to diversity ratio that is equal to the ratio of time of divergence of the species to the average time to the common ancestors of the individuals sampled within a species. This would be about 1:10 for humans and chimpanzees (Chen and Li 2001; Lander et al. 2001). However, the observed ratio is approximately 1:3, suggesting that the environmental component is on the order of three times bigger than the genetic component. Studies of gene expression differences among individuals with different genetic relatedness will eventually allow an estimation of the genetic component of expression variation. Since we are unable to tease apart genetic and environmental contributions to expression diversity, we instead used pseudogenes to estimate the distribution of divergence to diversity ratios observed in the absence of selection and compared these ratios to intact genes. No significant difference was found (Kolmogorov-Smirnov test, p = 0.388; Wilcoxon rank sum test, p = 0.134), and both distributions appeared to center around roughly the same values (Figure 4B). Note that this observation has to be taken cautiously since it is based on a small number of pseudogenes and the gene expression diversity is calculated from only three human individuals. Nevertheless, this result indicates that there is no drastic difference between the expression patterns of intact genes and expressed pseudogenes, since our tests would have been significant if 5% or more of the genes had had a radically different divergence to diversity ratio than that observed among the pseudogenes. Transcriptome Evolution among Brain Regions Different anatomical brain structures appeared at different times during vertebrate evolution. These time points can be viewed as divergence times between brain regions extending millions of years back in the past (Figure 5A). If gene expression changes between different brain regions have a large random component, gene expression differences between brain regions within species could potentially be used as a molecular clock to time the divergences of tissues. To investigate whether this may be the case, we compared expression patterns for Brodmann's area 44, the prefrontal cortex, the anterior cingulate cortex, the primary visual cortex, the caudate nucleus, and the cerebellum in three adult human and three adult chimpanzee males (Philipp Khaitovich, unpublished data). All comparisons were performed between brain regions within the same individual. This has two advantages. First, such comparisons are unaffected by nucleotide sequence variation between and within species. Second, environmental differences and post mortem changes have little effect when expression differences within one individual are studied. In Figure 5B, we plot the average squared distances between the six brain regions in humans and chimpanzees against the time when these brain regions emerged during vertebrate evolution (Butler and Hodos 1996; Nieuwenhuys et al. 1998) for 2,297 and 2,525 genes detected in all human and all chimpanzee samples, respectively. It can be seen that the expression differences increase approximately linearly with time over more than half a billion years. To investigate if this finding holds also in another mammalian species, we used published expression data for 1,346 genes with detectable expression in eight brain regions in the mouse (Su et al. 2002). In this case as well there is an approximately linear relationship between transcriptome differences and evolutionary divergence times (Figure 5C). Figure 5 Transcriptome Change among Brain Regions as a Function of Evolutionary Time (A) Schematic evolutionary tree for six human brain regions: B.44, Brodmann's area 44; PFC, prefrontal cortex; ACC, anterior cingulate cortex; PVC, primary visual cortex; CN, caudate nucleus; and CB, cerebellum. Numbers indicate approximate divergence time in millions of years (Butler and Hodos 1996; Nieuwenhuys et al. 1998). (B) Average expression differences among brain regions in humans (red) and in chimpanzees (blue). (C) Average expression differences among brain regions in M. musculus. Error bars for expression indicate 95% confidence intervals calculated from 10,000 bootstrap replications over genes. If gene expression differences between the brain regions were largely adaptive, one would expect them to correlate with tissue function and not with evolutionary divergence time. Our data show that tissues that diverged recently have very similar gene expression profiles irrespective of the differences in function. For instance, the transcriptome of Brodmann's area 44 in the left hemisphere (Broca's area) is very similar to that of the prefrontal cortex in both humans and chimpanzees, although it is known to be involved in speech processing in humans while it must have another function in chimpanzees (Kandel et al. 2000). This is what we would expect if the time since divergence rather than the extent of functional differences determined the magnitude of transcriptome change. Thus, although a number of expression differences between brain regions surely correspond to functional differences, our findings suggest that a sizeable proportion of the differences are functionally neutral. A noteworthy finding is that the accumulation of expression differences between brain regions within a species is much slower than the accumulation of expression differences within a brain region between species. In fact, the expression differences that have accumulated among the primate species over 20 million years (see Figure 1A) are approximately as extensive as those that have accumulated among brain regions over 500 million years (see Figure 5B). This is likely to result from the fact that all expression differences seen between brain regions within an individual are caused by changes in regulatory networks established during development by cells that carry the same genome. In addition, expression differences between brain regions reflect the different cell-type compositions of these regions. In contrast, transcriptome differences between species are the result of changes in regulatory networks and cellular composition of tissues, as well as nucleotide sequence differences between species that affect promoters and other genomic elements that determine transcript levels. Our results show that the latter type of changes are much more common than the former. A possible alternative explanation for the correlation between differences in gene expression and evolutionary divergence time among brain regions could be that differences in gene expression do not correlate with evolutionary divergence time, but instead with divergence time during fetal development. Our observations would then result from the fact that both developmental divergence times and expression differences correlate with evolutionary divergence. A correlation between developmental and evolutionary divergence times has been hypothesized before (for a review, see Gould 1977). In fact, gene expression analyses now provide a quantitative approach to address this question and may also provide a tool to date the evolutionary emergence of brain regions that cannot be discerned in the fossil record. Conclusions We show that a neutral model of evolution can predict the main features of transcriptome evolution in the brains of primates and mice. A neutral model is also in agreement with published observations in Drosophila (Rifkin et al. 2003) and fish (Oleksiak et al. 2002). Although selective scenarios that explain some or even most of these observations can be found, the combined evidence presented leads us to conclude that a neutral model is the most adequate null model for transcriptome evolution. This suggests that the majority of gene expression differences within and between species are not functional adaptations, but selectively neutral or nearly neutral. The main challenge now is to develop a mathematical model of transcriptome evolution that allows quantitative predictions of transcriptome changes. Such a model, combined with experimental data estimating the normal variation of gene expression within a species and the relative contributions of genetic and environmental factors to this variation, should allow adaptive gene expression changes to be identified. Further work is also needed to reveal whether proteome evolution is also dominated by changes that are largely selectively neutral. Finally, the finding that gene expression differences can be used as a molecular clock to date tissue divergences opens the prospect of reconstructing the evolutionary history of organs and tissues based on gene expression measurements in a single species. Materials and Methods Tissue samples and microarray data collection For the primate samples, approximately 200 mg of gray matter was collected from post mortem brain samples from prefrontal cortex region corresponding to Brodmann's area 9 in the left hemisphere from six male humans who were 45, 45, 63, 65, 70, and 70 years old; five male chimpanzees that were 7, 12, 12, 12, and approximately 40 years old; one 16-year-old male orangutan; five approximately 10-year-old male rhesus macaques; and five approximately 15-year-old male crab-eating macaques. All individuals had no history of brain-related diseases and suffered sudden deaths without associated brain damage. For the mouse samples, approximately 50 mg of gray matter was collected from the frontal cortex regions of six M. musculus (three of which are previously described in Enard et al. 2002), three M. spretus, and one M. caroli individuals. All mice were outbred, older than 14 weeks, and healthy. Total RNA was isolated using the TRIzol reagent (GIBCO, San Diego, California, United States) according to manufacturer's instructions and purified with Quiagen RNeasy kit (Quiagen, Valencia, California, United States) following the “RNA cleanup” protocol. RNAs were of high and comparable quality as gauged by the ratio of 28S to 18S ribosomal RNAs visualized on agarose gels and by the signal ratios between the probes for the 3′ and 5′ ends of the mRNAs of GAPDH and β-actin genes used as quality controls on Affymetrix microarrays (Affymetrix, Santa Clara, California, United States). For Affymetrix microarrays, labeling of 5 μg of the RNA, hybridization, staining, washing steps, and array scanning were carried out following Affymetrix protocols. Expression data were collected using Affymetrix HG U95Av2 arrays for the primate samples and Affymetrix MG U74Av2 arrays for the mice samples. The Affymetrix CEL files containing expression data for the different regions of the mouse brain, including amygdala, cerebral cortex, hippocampus, hypothalamus, cerebellum, olfactory bulb, and two regions of spinal cord were provided by John Hogenesch. Arrays containing 51,000 cDNAs corresponding to approximately 40,000 UniGene clusters were manufactured in the laboratory of W.A. as described elsewhere (Anonymous 2003). Labeling, hybridization, staining, washing, and array scanning were carried out as described by Cortes-Canteli et al. (2004) with slight modifications. All samples were hybridized twice with dye reversal, using a mixture of all samples as a common reference. All primary expression data were submitted to the Array Express database (http://www.ebi.ac.uk/arrayexpress/). Masking of sequence differences between humans and chimpanzees In order to exclude all oligonucleotide probes that did not match perfectly between humans and chimpanzees, we aligned all Affymetrix target sequences (http://www.affymetrix.com/analysis/index.affx) first to the human genome (build 33) and then to a draft version of the chimpanzee genome (the assembly was given courtesy of David Jaffe in June 2003). Using BLAT (Kent 2002), we matched chimpanzee sequences with Affymetrix target sequences containing the 16 oligonucleotide probes and determined the best hit using a scoring function. The chimpanzee sequence was then aligned to the human genome to determine whether the best match coincided with the match obtained from alignment of Affymetrix target sequences with the human genome. To identify insertion and deletions (indels), we compared the alignment of the Affymetrix target sequence to the human genome and to the chimpanzee genome, and differences in the indel structure relative to the target sequence were identified as indels. We then identified all oligonucleotide probes within target sequences that matched the chimpanzee sequence perfectly. These probes were used for the analysis while the rest of the probes were masked. Microarray data analysis Affymetrix microarray image data were analyzed with Affymetrix Microarray Suite v5.0 using default parameters. Arrays were scaled to the same average intensity using all probes on the array. Detected genes were defined as those with a detection p-value less than or equal to 0.05. For calculation of the expression values, data were processed with the Bioconductor “affy” software package (Ihaka and Gentleman 1996) using the quantile normalization procedure (Bolstad et al. 2003). cDNA arrays were analyzed using the TM4 software package (Saeed et al. 2003). Detected genes were defined as those with a spot intensity exceeding the background intensity by more than 2-fold. All slides were normalized to the common reference using the LOWESS normalization algorithm. For calculation of diversity and divergence, signal to reference ratio measurements were transformed into standardized signal intensities by multiplying them by the average reference intensity for each gene. Divergence was defined as the squared difference between the mean expression of two groups of samples averaged over (all detected) genes. Diversity was defined as the expression variance within a group of samples. Correlation significance test We measured the divergence between human and chimpanzee by looking at the squared difference between the mean expression values in humans and chimpanzees. This estimate of divergence includes the errors in our estimates of the two means, which is proportional to the variance in each of the species, and thus to the diversity in each species. Therefore, even if no correlation between divergence and diversity existed, our measured divergence and diversity estimates would correlate, and the smaller the divergence is relative to diversity, the stronger the correlation would be. To estimate if the observed correlation is larger than that expected from this effect alone, we performed a randomization test, in which we computed how much correlation between diversity and divergence would be generated from the above effect even if no correlation between diversity and divergence exists. To be conservative, we first generated a distribution that deliberately underestimated the real divergence between humans and chimpanzees. This was done by first generating a distribution of the expected observed differences (X) in gene expression between humans and chimpanzees if the real divergence is zero. Then using this distribution and the observed distribution of differences (Z), we generated a distribution (Y) that-added to values from X-would give Z. In order to underestimate the divergence, we generated Y assuming that the correlation of X and Y is one. We then generated random samples in the following way: For each gene (g), we chose a random difference of expression (d) from our generated distribution. We then drew six samples from a normal distribution whose mean is zero and whose variance is the diversity in humans for gene g, and three samples from a normal distribution whose mean is d and whose variance is the diversity of chimps for gene g. For these expression values we then calculated the correlation between diversity and divergence. We repeated the whole procedure 1,000 times. None of these randomizations generated a correlation that is as strong as the observed one. To make sure that the whole test is conservative, we generated 100 datasets of three types, all of which had a similar diversity, but had a “real” divergence distribution of (1) zero, (2) the underestimated divergence, or (3) the measured divergence, and had uncorrelated diversity and divergence. We then performed the whole test described above, doing just one randomization test. If the test was not conservative, one would expect the correlation in the dataset to be higher than the correlation after randomization in 50% of the cases. Instead, the correlation after randomization was higher in 98, 98, and 99 cases respectively-showing that our test is indeed conservative. Expressed pseudogenes We retrieved sequences of all pseudogenes as determined by Torrents et al. (2003), Zhang et al. (2003), and the VEGA project (http://vega.sanger.ac.uk). These sequences, as well as the Affymetrix target sequences, were mapped to the human genome (build 34) using BLAT (Kent 2002), and the best hit was determined using the following parameters: match, +1; mismatch, −3; gap-opening penalty only for gaps ≤ 20, −5; and gap extension, −1. Next, using BLAT, we determined the Affymetrix target sequences where the best-matching sequence did not overlap with the genomic region of a known gene (http://genome.ucsc.edu). Thus, we identified 889 probe sets that overlapped with a pseudogene, but not with a known gene. Combined with gene expression data collected in four brain regions (anterior cingulate cortex, Broca'a area, caudate nucleus, cerebellum; Philipp Khaitovich, unpublished data) in three humans and three chimpanzees, 33 of these probe sets had detectable expression levels in at least one brain region in either three chimpanzees or three humans. For these probe sets, we checked whether at least one of the identified interruptions of the human pseudogene was also present in the chimpanzee, indicating that the pseudogene was already nonfunctional at the time of the chimpanzee–human divergence. This left us with 28 probe sets that were checked for crosshybridization with other genes by aligning oligonucleotide probes from these probe sets to the human genome. Finally, we were left with 23 expressed pseudogenes that did not match perfectly to any other gene by more than seven out of 16 probes in the probe set. We thank M. Przeworski for helpful discussions and critical reading of the manuscript, M. Leinweber for help with microarray data analysis and Jun Li and M. Donaldson for providing critical insights into the permutation test. This work was supported by the Bundesministerium für Bildung und Forschung and the Max Planck Society. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. PK, GW, ML, WE, and SP conceived and designed the experiments. PK performed the experiments. PK, GW, ML, IH, and BM analyzed the data. IH, UW, WA, and SP contributed reagents/materials/analysis tools. PK, ML, and SP wrote the paper. Academic Editor: David Botstein, Princeton University ==== Refs References [Anonymous] Human Unigeneset-RZPD3 microarray—A microarray representing 51K human unigene clusters. Available: http://embl-h3r.embl.de via the Internet 2003 Accessed 11 February 2004 Bolstad BM Irizarry RA Astrand M Speed TP A comparison of normalization methods for high density oligonucleotide array data based on variance and bias Bioinformatics 2003 19 185 193 12538238 Butler AB Hodos W Comparative vertebrate neuroanatomy: Evolution and adaptation 1996 New York John Wiley and Sons 514 Caceres M Lachuer J Zapala MA Redmond JC Kudo L Elevated gene expression levels distinguish human from non-human primate brains Proc Natl Acad Sci U S A 2003 100 13030 13035 14557539 Charlesworth B Some quantitative methods for studying evolutionary patterns in single characters Paleobiology 1984 10 308 318 Chen FC Li WH Genomic divergences between humans and other hominoids and the effective population size of the common ancestor of humans and chimpanzees Am J Hum Genet 2001 68 444 456 11170892 Cortes-Canteli M Wagner M Ansorge W Perez-Castillo A Microarray analysis supports a role for CCAAT/enhancer binding protein beta in brain injury J Biol Chem 2004 (in press) Enard W Khaitovich P Klose J Zollner S Heissig F Intra- and interspecific variation in primate gene expression patterns Science 2002 296 340 343 11951044 Felsenstein J Inferring phylogenies 2004 Sunderland (Massachusetts) Sinauer Associates 664 Glazko GV Nei M Estimation of divergence times for major lineages of primate species Mol Biol Evol 2003 20 424 434 12644563 Gould SJ Ontogeny and phylogeny 1977 Cambridge (Massachusetts) Belknap Press 501 Gould SJ Lewontin RC The spandrels of San Marco and the Panglossian paradigm: A critique of the adaptationist programme Proc R Soc Lond B Biol Sci 1979 205 581 598 42062 Gu J Gu X Induced gene expression in human brain after the split from chimpanzee Trends Genet 2003 19 63 65 12547510 Hayasaka K Fujii K Horai S Molecular phylogeny of macaques: Implications of nucleotide sequences from an 896-base pair region of mitochondrial DNA Mol Biol Evol 1996 13 1044 1053 8752012 Hellmann I Ebersberger I Ptak SE Paabo S Przeworski M A neutral explanation for the correlation of diversity with recombination rates in humans Am J Hum Genet 2003 72 1527 1535 12740762 Hsieh WP Chu TM Wolfinger RD Gibson G Mixed-model reanalysis of primate data suggests tissue and species biases in oligonucleotide-based gene expression profiles Genetics 2003 165 747 757 14573485 Hughes TR Marton MJ Jones AR Roberts CJ Stoughton R Functional discovery via a compendium of expression profiles Cell 2000 102 109 126 10929718 Ihaka R Gentleman R R: A language for data analysis and graphics J Comput Graph Stat 1996 5 299 314 Kandel ER Schwartz JH Jessell TM Principles of neural science, 4th ed 2000 New York McGraw-Hill 1414 Kent WJ BLAT—the BLAST-like alignment tool Genome Res 2002 12 656 664 11932250 Kimura M The neutral theory of molecular evolution 1983 Cambridge (United Kingdom) Cambridge University Press 367 Lande R Natural selection and random genetic drift in phenotypic evolution Evolution 1976 30 314 334 Lander ES Linton LM Birren B Nusbaum C Zody MC Initial sequencing and analysis of the human genome Nature 2001 409 860 921 11237011 Li WH Molecular evolution 1997 Sunderland (Massachusetts) Sinauer Associates 487 Lynch M Hill WG Phenotypic evolution by neutral mutation Evolution 1986 40 915 935 Miki R Kadota K Bono H Mizuno Y Tomaru Y Delineating developmental and metabolic pathways in vivo by expression profiling using the RIKEN set of 18,816 full-length enriched mouse cDNA arrays Proc Natl Acad Sci U S A 2001 98 2199 2204 11226216 Nieuwenhuys R Donkelaar HJT Nicholson C The central nervous system of vertebrates 1998 New York Springer Verlag 2219 Oleksiak MF Churchill GA Crawford DL Variation in gene expression within and among natural populations Nat Genet 2002 32 261 266 12219088 Ranz JM Castillo-Davis CI Meiklejohn CD Hartl DL Sex-dependent gene expression and evolution of the Drosophila transcriptome Science 2003 300 1742 1745 12805547 Rifkin SA Kim J White KP Evolution of gene expression in the Drosophila melanogaster subgroup Nat Genet 2003 33 138 144 12548287 Saeed AI Sharov V White J Li J Liang W TM4: A free, open-source system for microarray data management and analysis Biotechniques 2003 34 374 378 12613259 She JX Bonhomme F Boursot P Thaler L Catzeflis F Molecular phylogenies in the genus Mus Comparative analysis of electrophoretic, scnDNA hybridization, and mtDNA RFLP data Biol J Linn Soc 1990 41 83 103 Spellman PT Sherlock G Zhang MQ Iyer VR Anders K Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization Mol Biol Cell 1998 9 3273 3297 9843569 Su AI Cooke MP Ching KA Hakak Y Walker JR Large-scale analysis of the human and mouse transcriptomes Proc Natl Acad Sci U S A 2002 99 4465 4470 11904358 Torrents D Suyama M Zdobnov E Bork P A genome-wide survey of human pseudogenes Genome Res 2003 13 2559 2567 14656963 Turelli M Gillespie JH Lande R Rate tests for selection on 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PLoS Biol. 2004 May 11; 2(5):e132

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020133PrimerCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyPlant SciencePlantsPlanting the Seeds of a New Paradigm PrimerMatzke Marjori A Matzke Antonius J. M 5 2004 11 5 2004 11 5 2004 2 5 e133Copyright: © 2004 Matzke and Matzke.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. RNAi Therapeutics: How Likely, How Soon? Small RNA Pathways in Plants Genetic and Functional Diversification of Small RNA Pathways in Plants RNA-mediated gene silencing has emerged in recent years as an important mechanism for regulating gene expression. Some of the key discoveries have been made in plants ==== Body Although the word ‘revolution’ should not be used lightly in science, there is no other way to describe the recent explosion in our awareness and understanding of RNA-mediated gene silencing pathways. The central player in RNA-mediated gene silencing is a double-stranded RNA (dsRNA) that is chopped into tiny RNAs by the enzyme Dicer. The tiny RNAs associate with various silencing effector complexes and attach to homologous target sequences (RNA or DNA) by basepairing. Depending on the protein composition of the effector complex and the nature of the target sequence, the outcome can be either mRNA degradation, translational repression, or genome modification, all of which silence gene expression (Figure 1). Present in plants, animals, and many fungi, RNA-mediated gene silencing pathways have essential roles in development, chromosome structure, and virus resistance. Although the mechanistic details are still under investigation, RNA-mediated silencing has already provided a powerful tool for studying gene function and spawned a fledgling industry that aims to develop novel RNA-based therapeutics to treat human diseases (Robinson 2004). Figure 1 RNA-Mediated Silencing Short RNAs derived from Dicer cleavage of dsRNA are incorporated into multiprotein effector complexes, such as RISC and RITS (RNA-induced initiation of TGS) (Verdel et al. 2004) to target mRNA degradation (RNAi/PTGS), translation inhibition, or TGS and genome modifications. ARGONAUTE (AGO) proteins (the name comes from a plant mutant [Bohmert et al. 1998]) bind short RNAs and ‘shepherd’ them to appropriate effector complexes (Carmell et al. 2002). siRNAs originate from perfect RNA duplexes, which can be produced by RDR activity on ssRNA templates; miRNAs originate from imperfect RNA hairpins that are encoded in intergenic regions of plant and animal genomes. Functions are shown at the bottom. In addition to roles in transgene silencing, both TGS and RNAi/PTGS control genome parasites called transposons (Flavell 1994; Plasterk 2002). Genome modifications (DNA and histone methylation) can potentially be targeted by short RNAs that basepair to DNA or to nascent RNA synthesized from the target gene (Grewal and Moazed 2003). Target nucleic acids are shown in blue, short RNAs in red, proteins and enzyme complexes as ovals. Many biologists first learned of RNA-mediated gene silencing in 1998 following the discovery, in the nematode worm Caenorhabditis elegans (Fire et al. 1998), of a process called RNA interference (RNAi), in which dsRNA triggers sequence-specific mRNA degradation. The roots of RNA-mediated silencing, however, can be traced back 15 years, when a handful of botanical labs stumbled across strange cases of gene silencing in transgenic plants. To highlight the many seminal contributions of plant scientists to the field, we offer here a personal perspective on the origins and history of RNA-mediated gene silencing in plants. Early Silencing Phenomena Starting in the late 1980s, biologists working with transgenic plants found themselves confronted with a ‘bewildering array’ of unanticipated gene silencing phenomena (Martienssen and Richards 1995). Most intriguing were cases in which silencing seemed to be triggered by DNA or RNA sequence interactions, which could occur between two separate transgenes that shared sequence homology or between a transgene and homologous plant gene. Several early examples supplied the prototypes for two types of RNA-mediated gene silencing that are recognized today. In one type, silencing results from a block in mRNA synthesis (transcriptional gene silencing [TGS]); in the second type, silencing results from mRNA degradation (posttranscriptional gene silencing [PTGS]) (Figure 1). TGS was revealed when two different transgene complexes were introduced in sequential steps into the tobacco genome. Each complex encoded different proteins, but contained identical gene regulatory regions (promoters). Unexpectedly, the first transgene complex, which was stably active on its own, often became silenced in the presence of the second (Figure 2). The promoters of the silenced transgenes acquired DNA methylation, a genome modification frequently associated with silencing. Silencing and methylation were reversed when the transgene complexes segregated from each other in progeny, suggesting that interactions between the common promoter regions triggered silencing and methylation (Matzke et al. 1989; Park et al. 1996). Figure 2 Early Examples of Gene Silencing in Transgenic Plants TGS: Normally when two plants harboring separate transgenes encoding resistance to kanamycin (kan) or hygromycin (hyg), respectively, are crossed, 50% of the progeny are resistant to the individual antibiotics and 25% are resistant to a combination of both (top). In cases of silencing, expression of the KAN marker is extinguished in the presence of the HYG marker, as indicated by only 25% kan resistance and no double resistance (middle). PTGS: Transformation of wild-type petunia (bottom left) with a transgene encoding a pigment protein can lead to loss of pigment (white areas) owing to cosuppression of the transgene and homologous endogenous plant gene. (Photos on the left and in the middle were provided by Jan Kooter and on the right were provided by Natalie Doetsch and Rich Jorgensen.) PTGS was discovered in two ways. One involved experiments to evaluate antisense suppression, a promising approach at the time for selectively silencing plant gene expression. In theory, antisense RNA encoded by a transgene should basepair to the complementary mRNA of a plant gene, preventing its translation into protein. Although the control ‘sense’ transgene RNAs are unable to basepair to mRNA and hence should not induce silencing, they often inexplicably did (Smith et al. 1990). In another type of experiment, efforts to enhance floral coloration in petunia by overexpressing a transgene encoding a protein involved in pigment synthesis led paradoxically to partial or complete loss of color (Figure 2). This resulted from coordinate silencing (‘cosuppression’) of both the transgene and the homologous plant gene (Napoli et al. 1990; Van der Krol et al. 1990), later shown to occur at the posttranscriptional level (De Carvalho et al. 1992; Van Blokland et al. 1994) A related phenomenon, called quelling, was observed in the filamentous fungus Neurospora crassa (Romano and Macino 1992). Similarly to TGS, PTGS was often associated with DNA methylation of transgene sequences (Ingelbrecht et al. 1994). Two influential papers appeared in the early 1990s. One reported the discovery of RNA-directed DNA methylation in transgenic tobacco plants (Wassenegger et al. 1994). This was the earliest demonstration of RNA-induced modification of DNA, a process that we return to below. A second study showed that plant RNA viruses could be both initiators and targets of PTGS. Plants expressing a transgene encoding a truncated viral coat protein became resistant to the corresponding virus, a state achieved by mutual degradation of viral RNA and transgene mRNA (Lindbo et al. 1993). In addition to forging a link between RNA virus resistance and PTGS, this study included a remarkably prescient model for PTGS that featured an RNA-dependent RNA polymerase (RDR), small RNAs, and dsRNA, all of which were later found to be important for the RNAi. PTGS was subsequently shown in 1997 to protect plants naturally from virus infection (Covey et al. 1997; Ratcliff et al. 1997). Transgene PTGS thus tapped into a preexisting natural mechanism for combating viruses. To recap: by 1998—the year in which RNAi was reported—plant scientists had documented sequence-specific RNA degradation (PTGS), sequence-specific DNA methylation that triggered TGS, and RNA-directed DNA methylation. They had also proposed models for PTGS involving dsRNA (Lindbo et al. 1993; Metzlaff et al. 1997), small RNAs, and RDR (Lindbo et al. 1993). RNAi RNAi was discovered in experiments designed to compare the silencing activity of single-stranded RNAs (ssRNAs) (antisense or sense) with their dsRNA hybrid. While only marginal silencing of a target gene was achieved after injecting worms with the individual strands, injection of a sense–antisense mixture resulted in potent and specific silencing (Fire et al. 1998). This unequivocally fingered dsRNA as the trigger of silencing. Shortly thereafter, dsRNA was shown to provoke gene silencing in other organisms, including plants (Waterhouse et al. 1998). Indeed, the relatedness of RNAi, PTGS, and quelling was confirmed when genetic analyses in worms, plants, and Neurospora identified common components in the respective silencing pathways (Denli and Hannon 2003). This included the aforementioned RDR, which can synthesize dsRNA from ssRNA templates (see Figure 1). PTGS is now accepted as the plant equivalent of RNAi. The discovery of RNAi established a requirement for dsRNA in silencing, but details of the mechanism remained unclear. In 1999, plant scientists studying PTGS provided a crucial clue when they detected small (approximately 25 nucleotide-long) RNAs corresponding to silenced target genes in transgenic plants (Hamilton and Baulcombe 1999). They proposed that the small RNAs provided the all-important specificity determinant for silencing. Consistent with this, a rapid succession of studies in Drosophila systems demonstrated that 21–23 nucleotide ‘short interfering'RNAs (siRNAs), derived from cutting longer dsRNA, can guide mRNA cleavage (Zamore et al. 2000; Elbashir et al. 2001); identified RISC (RNA-induced silencing complex), a nuclease that associates with small RNAs and executes target mRNA cleavage (Hammond et al. 2000); and identified Dicer, the enzyme that chops dsRNA into short RNAs (Bernstein et al. 2001) (see Figure 1). RNAi/PTGS was detected originally in experiments involving transgenes, injected RNAs, or viruses. Did the RNAi machinery also generate small RNAs for host gene regulation? Strikingly, the newly discovered siRNAs were the same size as several ‘small temporal’ RNAs, first identified in 1993 as important regulators of developmental timing in worms (Lee et al. 1993; Reinhart et al. 2000). Everything came together in 2001 when heroic cloning efforts unearthed dozens of natural small RNAs 21–25 nucleotides in length, first from worms and flies and later from plants and mammals (Lai 2003; Bartel 2004). Similar to siRNAs, the natural small RNAs, dubbed microRNAs (miRNAs), arise from Dicer processing of dsRNA precursors and are incorporated into RISC (Denli and Hannon 2003). In many cases, miRNAs effect silencing by basepairing to the 3′ ends of target mRNAs and repressing translation (see Figure 1). miRNAs are now recognized as key regulators of plant and animal development. Identifying their target genes and full range of action are areas of intense research (Lai 2003; Bartel 2004). Up until 2002, RNAi/PTGS and miRNAs were the most avidly studied aspects of RNA-mediated gene silencing. The next major advance, however, abruptly turned attention back to RNA-guided modifications of the genome. By 2001, plant scientists working on RNA-directed DNA methylation and TGS had demonstrated a requirement for dsRNAs that are processed to short RNAs, reinforcing a mechanistic link to PTGS (Mette et al. 2000; Sijen et al. 2001). This established the principle of RNA-guided genome modifications, but the generality of this process was uncertain because not all organisms methylate their DNA. Widespread acceptance came with the discovery in 2002 of RNAimediated heterchromatin assembly in fission yeast (Hall et al. 2002; Volpe et al. 2002). This silencing pathway uses short RNAs produced by Dicer and other RNAi components to direct methylation of DNA-associated proteins (histones), thus generating condensed, transcriptionally silent chromosome regions (heterochromatin) (see Figure 1). Targets of this pathway include centromeres, which are essential for normal chromosome segregation. The RNAi-dependent heterochromatin pathway has been found in plants (Zilberman et al. 2003) and Drosophila (Pal-Bhadra et al. 2004) and likely represents a general means for creating condensed, silent chromosome domains. More Lessons from Plants Plant scientists can chalk up other ‘firsts’ in RNA-mediated gene silencing. Systemic silencing, in which a silencing signal (short RNA or dsRNA) moves from cell to cell and through the vascular system to induce silencing at distant sites, was initially detected in plants in 1997 (Palauqui et al. 1997; Voinnet and Baulcombe 1997) and later in worms (Fire et al. 1998), although not yet in Drosophila or mammals. Viral proteins that suppress silencing by disarming the PTGS-based antiviral defense mechanism were discovered by plant virologists in 1998 (Anandalakshmi et al. 1998; Béclin et al. 1998; Brigneti et al. 1998; Kasschau and Carrington 1998). One of these, the p19 protein of tombusviruses, acts as a size-selective caliper to sequester short RNAs from the silencing machinery (Vargason et al. 2003). A recent study suggests that animal viruses encode suppressors of RNA-mediated silencing (Li et al. 2004). Although RNA-mediated gene silencing pathways are evolutionarily conserved, there are various elaborations in different organisms. For example, the plant Arabidopsis has four Dicer-like (DCL) proteins, in contrast to mammals and worms, whose genomes encode only one Dicer protein (Schauer et al. 2002). The RDR family has also expanded in Arabidopsis to include at least three active members. An important goal has been to determine the functions of individual family members. Previous studies in Arabidopsis have shown that DCL1 is needed for processing miRNA precursors important for plant development (Park et al. 2002; Reinhart et al. 2002), but not for siRNAs active in RNAi (Finnegan et al. 2003). The paper by Xie et al. (2004) in this issue of PLoS Biology delineates distinct functions for DCL2, DCL3, and RDR2. Nuclear-localized DCL3 acts with RDR2 to generate short RNAs that elicit DNA and histone modifications; DCL2 produces short RNAs active in antiviral defense in the cytoplasm of cells. This study illustrates nicely how RNA silencing components have diversified in plants to carry out specialized functions. By identifying small RNAs as agents of gene silencing that act at multiple levels throughout the cell, molecular biologists have created a new paradigm for eukaryotic gene regulation. Plant scientists have figured prominently in RNA-mediated silencing research. Instrumental to their success was the early ability to produce large numbers of transgenic plants, which displayed a rich variety of gene silencing phenomena that were amenable to analysis. The agricultural biotechnology industry provided incentives to find ways to stabilize transgene expression and use transgenic approaches to modulate plant gene expression and to genetically engineer virus resistance. As exemplified by the petunia cosuppression experiments, nonessential plant pigments provide conspicuous visual markers that vividly reveal gene silencing. The history of gene silencing research shows once again that plants offer outstanding experimental systems for elucidating general biological principles. We thank Werner Aufsatz for comments on the manuscript. Work on RNA-mediated gene silencing in our lab is supported by the Austrian Fonds zur Förderung der wissenschaftlichen Forschung (grant P-15611-B07) and by the European Union (contract HPRN-CT-2002-00257). Marjori A. Matzke and Antonius J. M. Matzke are at the Gregor Mendel Institute of Molecular Plant Biology of the Austrian Academy of Sciences in Vienna, Austria. E-mail: marjori.matzke@gmi.oeaw.ac.at (MAM) Abbreviations AGOARGONAUTE DCLDicer-like dsRNAdouble-stranded RNA miRNAmicroRNA PTGSposttranscriptional gene silencing RDRRNAdependent RNA polymerase RISCRNA-induced silencing complex RITSRNA-induced initiation of transcriptional gene silencing RNAiRNA interference siRNAshort interfering RNA ssRNAsingle-stranded RNA TGStranscriptional gene silencing ==== Refs References Anandalakshmi R Pruss G Ge X Marathe R Mallory A A viral suppressor of gene silencing in plants Proc Natl Acad Sci U S A 1998 95 13079 13084 9789044 Bartel DP MicroRNAs: Genomics, biogenesis, mechanism and function Cell 2004 116 281 297 14744438 Béclin C Berthomé R Palauqui J Tepfer M Vaucheret H Infection of tobacco or Arabidopsis plants by CMV counteracts systemic post-transcriptional silencing of nonviral (trans)genes Virology 1998 252 313 317 9878609 Bernstein E Caudy A Hammond S Hannon G Role for a bidentate ribonuclease in the initiation step of RNA interference Nature 2001 409 363 366 11201747 Bohmert K Camus I Bellini C Bouchez D Caboche M AGO1 defines a novel locus of Arabidopsis controlling leaf development EMBO J 1998 7 170 180 Brigneti G Voinnet O Li W-X Ji L-H Ding S-W Viral pathogenicity determinants of transgene silencing in Nicotiana benthamiana EMBO J 1998 17 6739 6746 9822616 Carmell M Xuan Z Zhang M Hannon G The Argonaute family: Tentacles that reach into RNAi, developmental control, stem cell maintenance, and tumorigenesis Genes Dev 2002 16 2733 3742 12414724 Covey S Al-Kaff N Lángara A Turner D Plants combat infection by gene silencing Nature 1997 385 781 782 De Carvalho F Gheyson G Kushnir S van Montagu M Inzé D Suppression of β-1,3-glucanase transgene expression in homozygous plants EMBO J 1992 11 2595 2602 1378394 Denli A Hannon G RNAi: An ever-growing puzzle Trends Biochem Sci 2003 28 196 201 12713903 Elbashir S Lendeckel W Tuschl T RNA interference is mediated by 21- and 22-nucleotide RNAs Genes Dev 2001 15 188 200 11157775 Finnegan EJ Margis R Waterhouse P Posttranscriptional gene silencing is not compromised in the Arabidopsis CARPEL FACTORY (DICER-LIKE1) mutant, a homolog of Dicer-1 from Drosophila Curr Biol 2003 13 236 240 12573220 Fire A Xu S Montgomery M Kostas S Driver S Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans Nature 1998 391 806 811 9486653 Flavell RB Inactivation of gene expression in plants as a consequence of specific sequence duplication Proc Natl Acad Sci U S A 1994 91 3490 3496 8170935 Grewal SIS Moazed D Heterochromatin and epigenetic control of gene expression Science 2003 301 798 802 12907790 Hall I Shankaranarayana G Noma K-I Ayoub N Cohen A Establishment and maintenance of a heterochromatic domain Science 2002 297 2232 2237 12215653 Hamilton AJ Baulcombe DC A species of small antisense RNA in posttranscriptional gene silencing in plants Science 1999 286 950 952 10542148 Hammond S Bernstein E Beach D Hannon G An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells Nature 2000 404 293 296 10749213 Ingelbrecht I van Houdt H Montagu M Depicker A Posttranscriptional silencing of reporter transgenes in tobacco correlates with DNA methylation Proc Natl Acad Sci U S A 1994 91 10502 10506 7937983 Kasschau K Carrington J A counterdefensive strategy of plant viruses: Suppression of posttranscriptional gene silencing Cell 1998 95 461 470 9827799 Lai EC microRNAs: Runts of the genome assert themselves Curr Biol 2003 13 Suppl R925 R936 14654021 Lee R Feinbaum R Ambros V The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 Cell 1993 75 843 854 8252621 Li W Li H Lu R Li F Dus M Interferon antagonist proteins of influenza and vaccinia viruses are suppressors of RNA silencing Proc Natl Acad Sci U S A 2004 101 1350 1355 14745017 Lindbo J Silva-Rosales L Proebsting W Dougherty W Induction of a highly specific antiviral state in transgenic plants: Implications for regulation of gene expression and virus resistance Plant Cell 1993 5 1749 1759 12271055 Martienssen RA Richards EJ DNA methylation in eukaryotes Curr Opin Genet Dev 1995 5 234 242 7613094 Matzke M Primig M Trnovsky J Matzke A Reversible methylation and inactivation of marker genes in sequentially transformed plants EMBO J 1989 8 643 649 16453872 Mette MF Aufsatz W van der Winden J Matzke M Matzke A Transcriptional gene silencing and promoter methylation triggered by double stranded RNA EMBO J 2000 19 5194 5201 11013221 Metzlaff M O'Dell M Cluster P Flavell R RNA-mediated RNA degradation and chalcone synthase A silencing in petunia Cell 1997 88 845 854 9118227 Napoli C Lemieux C Jorgensen R Introduction of a chimeric chalcone synthase gene into petunia results in reversible cosuppression of homologous genes in trans Plant Cell 1990 2 279 289 12354959 Palauqui J Elmayan T Pollien J Vaucheret H Systemic acquired silencing: Transgene-specific post-transcriptional silencing is transmitted by grafting from silenced stocks to non-silenced scions EMBO J 1997 16 4738 4745 9303318 Pal-Bhadra M Leibovitch B Gandhi S Rao M Bhadra U Heterochromatic silencing and HP1 localization in Drosophila are dependent on the RNAi machinery Science 2004 303 669 672 14752161 Park W Li J Song R Messing J Chen X CARPEL FACTORY, a Dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana Curr Biol 2002 12 1484 1495 12225663 Park Y-D Papp I Moscone E Iglesias V Vaucheret H Gene silencing mediated by promoter homology occurs at the level of transcription and results in meiotically heritable alterations in methylation and gene activity Plant J 1996 9 183 194 8820605 Plasterk R RNA silencing: The genome's immune system Science 2002 296 1263 1265 12016302 Ratcliff F Harrison B Baulcombe D A similarity between viral defense and gene silencing in plants Science 1997 276 1558 1560 18610513 Reinhart B Slack F Basson M Pasquinelli A Bettinger J The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans Nature 2000 403 901 906 10706289 Reinhart B Weinstein E Rhoades M Bartel B Bartel D MicroRNAs in plants Genes Dev 2002 16 1616 1626 12101121 Robinson R RNAi therapeutics: How likely, how soon? PLoS Biol 2004 2 e28 10.1371/journal.pbio.0020028 14737201 Romano N Macino G Quelling: Transient inactivation of gene expression in Neurospora crassa by transformation with homologous sequences Mol Microbiol 1992 6 3343 3353 1484489 Schauer S Jacobsen S Meinke D Ray A DICER-LIKE1: Blind men and elephants in Arabidopsis development Trends Plant Sci 2002 7 487 491 12417148 Sijen T Vijn I Rebocho A van Blokland R Roelofs D Transcriptional and posttranscriptional gene silencing are mechanistically related Curr Biol 2001 11 436 440 11301254 Smith C Watson C Bird C Ray J Schuch W Expression of a truncated tomato polygalacturonase gene inhibits expression of the endogenous gene in transgenic plants Mol Gen Genet 1990 224 477 481 2266949 Van Blokland R van der Geest N Mol J Kooter J Transgene-mediated suppression of chalcone synthase expression in Petunia hybrida results from an increase in RNA turnover Plant J 1994 6 861 877 Van der Krol A Mur L Beld M Mol JNM Stuitje AR Flavonoid genes in petunia: Addition of a limited number of gene copies may lead to a suppression of gene expression Plant Cell 1990 2 291 299 2152117 Vargason J Szittya G Burgyán J Tanaka Hall T Size selective recognition of siRNA by an RNA silencing suppressor Cell 2003 115 799 811 14697199 Verdel A Jia S Gerber S Sugiyama T Gygi S RNAi-mediated targeting of heterochromatin by the RITS complex Science 2004 303 672 676 14704433 Voinnet O Baulcombe D Systemic silencing in gene silencing Nature 1997 389 553 9335491 Volpe T Kidner C Hall I Teng G Grewal S Regulation of heterochromatic silencing and histone H3 lysine-9 methylation by RNAi Science 2002 297 1833 1837 12193640 Wassenegger M Heimes S Riedel L Sänger H RNA-directed de novo methylation of genomic sequences in plants Cell 1994 76 567 576 8313476 Waterhouse P Graham M Wang MB Virus resistance and gene silencing in plants can be induced by simultaneous expression of sense and antisense RNA Proc Natl Acad Sci U S A 1998 95 13959 13964 9811908 Xie Z Johansen L Gustafson A Kasschau K Lellis A Genetic and functional diversification of small RNA pathways in plants PloS Biol 2004 2 e104 10.1371/journal.pbio.0020104 15024409 Zamore P Tuschl T Sharp P Bartel D RNAi: Double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals Cell 2000 101 25 33 10778853 Zilberman D Cao X Jacobsen S ARGONAUTE4 control of locus-specific siRNA accumulation and DNA and histone methylation Science 2003 299 716 719 12522258

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PLoS Biol. 2004 May 11; 2(5):e133

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PLoS Biol

10.1371/journal.pbio.0020133

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020135SynopsisCell BiologyDevelopmentGenetics/Genomics/Gene TherapyMus (Mouse)A Gene That Directs the Regeneration of Injured Muscle from Adult Stem Cells Synopsis5 2004 11 5 2004 11 5 2004 2 5 e135Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Pax7 Is Necessary and Sufficient for the Myogenic Specification of CD45+:Sca1+ Stem Cells from Injured Muscle ==== Body If the United States' Human Cloning Prohibition Act of 2003 (H.R. 534) becomes law, American researchers practicing any form of cloning could face up to ten years in prison and a minimum $1 million fine. The bill criminalizes a research procedure, called somatic cell nuclear transfer, that involves removing the DNA from a fertilized egg and replacing it with the DNA of a body (soma) cell. While the procedure could theoretically be used to clone a human being, used therapeutically its great promise lies in yielding a renewable source of stem cells to repair and regenerate tissue damaged by disease or injury. Embryonic stem cells appear most suited to this task, but some researchers are finding that adult stem cells could perform similar duties in certain tissues. And adult stem cells, it appears, are responsive to genetic manipulation. H.R. 534 does not threaten researchers working with adult stem cells. Pax7-infected stem cells rescue dystrophin expression The precise origin of adult stem cells is unclear, though some propose that they are “set aside” during embryonic development and sequestered in mature tissue. These cells, which can make identical copies of themselves or give rise to specialized cells, serve primarily to replace damaged or injured cells. Skeletal muscle has a remarkable capacity to regenerate following exercise or injury and harbors two different types of adult stem cells to accomplish the job: satellite cells and adult stem cells that can be isolated as side population (SP) cells. Like embryonic stem cells, the adult cells commit to a certain fate once particular genes are activated. It was thought that only satellite cells could mediate skeletal muscle regeneration until recently, when scientists found that adult stem cells not only participate in muscle tissue regeneration but also spawn satellite cells. A certain population of these stem cells, which are recognized by the cell surface proteins CD45 and Sca1 (stem cell antigen-1), is involved in normal muscle tissue repair, but is only triggered into the muscle cell development pathway by injury. The question then arises: what molecular factors turn these adult stem cells into muscle cells? Now Michael Rudnicki and colleagues have shown that one gene, Pax7, plays a crucial role in directing the differentiation of these adult stem cells into skeletal muscle cells. In previous studies, Rudnicki's group demonstrated that Pax7 is required to turn adult stem cells into myogenic cells during regeneration. Here, the researchers worked with mouse models and in vitro experiments to investigate which cell populations Pax7 targets and how the gene initiates muscle cell formation in injured tissue. They show that CD45:Sca1 cells taken from regenerating muscle in mice lacking the Pax7 gene could not become muscle cells. And they show that by putting Pax7 back into CD45:Sca1 cells taken from uninjured muscle, they can generate a population of proliferating myoblasts that readily differentiate into muscle cells. When CD45:Sca1 cells engineered to express Pax7 proteins were injected into the muscles of mice lacking dystrophin (the protein defective in muscular dystrophy), the cells differentiated, forming dystrophin-expressing muscle cells in the defective muscle. This shows that engineered “donor cells” can differentiate in living tissue and help repair dystrophic muscle. When the researchers injected Pax7 (using a gene therapy virus) into the damaged muscle of mice lacking Pax7, they observed the production of muscle-forming cells that not only gave rise to differentiated muscle cells, but also aided in tissue repair. The researchers argue that these results “unequivocally establish” Pax7 as a key regulator of muscle cell differentiation in specific populations of adult stem cells during muscle tissue regeneration. If therapeutic strategies that activate Pax7 in adult stem cells can turn them into muscle cells, effectively replenishing injured or diseased muscle tissue, there's hope of reversing the debilitating effects of progressive muscle-wasting diseases. Though the clinical efficacy of such an approach will require intensive investigation, the results on these adult stem cells are encouraging—especially in this political climate.

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PLoS Biol. 2004 May 11; 2(5):e135

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PLoS Biol

10.1371/journal.pbio.0020135

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020138SynopsisDevelopmentGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyDanio (Zebrafish)A Role for Early Cardiac Function in Cardiac Morphogenesis synopsis5 2004 11 5 2004 11 5 2004 2 5 e138Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Early Myocardial Function Affects Endocardial Cushion Development in Zebrafish xx ==== Body The heart starts beating and pumps blood through the body long before it has achieved its mature architecture. In theory, this provides a chance for cardiac function to sculpt cardiac structure, an intriguing possibility for developmental biologists, and one of potentially great clinical import for cardiologists seeking to identify the causes of (often fatal) cardiac anomalies. In this issue of PLoS Biology, Thomas Bartman et al. use the powerful tools afforded by zebrafish genetics to dissect the early steps of heart valve formation. In the process, they provide evidence for a causal relationship between the early function of the heart and its final structure. tie2::GFP+ cells form the endocardial cushions At the time of its first beat, the vertebrate heart is little more than a tube, lined on its outside by a myocardial cell layer whose contractions (the heartbeats) power blood flow, and on its inside by an endocardial cell layer, an extension of the inner wall of the connecting blood vessels. What it lacks still are valves and septae, the fibrous gates that subdivide the mature heart into atrial and ventricular chambers, and control the directionality of blood flow. These structures derive from the endocardium in a process that begins—shortly after the establishment of blood flow—with the local accumulation of endocardial cells into what are known as endocardial cushions (ECs). The zebrafish lends itself well to large-scale genetic screens, and powerful genomic tools are now available to efficiently identify the gene affected by any mutation. The authors have used genetic screens to identify several mutations that affect early cardiac function or morphology. Heart anomalies are easy to detect in zebrafish, and can be examined in real time and in live specimens because the embryos develop outside the mother and are fully transparent. Using a fluorescent molecular marker highly expressed in the ECs, the authors narrowed in on mutations that result in valve defects, and identified a mutant they named cardiofunk (cfk), which was devoid of ECs. Genetic mapping of the cfk mutation revealed a single sequence change in a gene encoding a novel actin molecule that is most closely related to the sarcomeric actins found in sarcomeres, the contracting organelles of muscle cells. The result was surprising because contractions are not a property of endocardial cells. Using RNA detection assays, the authors show that the cfk gene is in fact expressed in the myocardium, rather than in the endocardium. It therefore appears that the inability to form ECs in cfk mutants does not reside in the endocardium per se, but is an indirect consequence of a myocardial anomaly. The cfk mutation introduces a single amino acid change in the actin protein, and through detailed biochemical analyses, the authors show that the mutant actin is impaired in its ability to assemble into fibers in vitro. What might be the consequence in vivo? The authors note that cfk mutants display abnormal heart contractions prior to the development of their EC defect. Support for the notion that myocardial contractions are required for EC formation comes from the examination of silent-heart (sih) mutants. sih mutants, which lack a heatbeat, have been shown to harbor a mutation in troponin T, one of the motors of actin contractions; the authors find that sih mutants also fail to develop ECs. The mechanisms linking myocardial contractions and cushion formation remain unclear. Blood flow may be a trigger, though the authors find that ECs can develop even in the presence of pharmacological compounds that abolish it. The characterization of additional mutants should help answer this question. Valve or septal defects represent 40% of cardiac anomalies in humans. Bartman and colleagues suggest that, by analogy with zebrafish, some may result from congenital defects affecting very early myocardial function. Their work thus opens new avenues for the early detection of human cardiac malfunctions and malformations.

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PLoS Biol. 2004 May 11; 2(5):e138

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PLoS Biol

10.1371/journal.pbio.0020138

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020139Research ArticleCancer BiologyGenetics/Genomics/Gene TherapyDanio (Zebrafish)Many Ribosomal Protein Genes Are Cancer Genes in Zebrafish Tumor Suppressor Genes in ZebrafishAmsterdam Adam 1 Sadler Kirsten C 1 Lai Kevin 1 Farrington Sarah 1 Bronson Roderick T 2 Lees Jacqueline A 1 Hopkins Nancy nhopkins@mit.edu 1 1Center for Cancer Research, Massachusetts Institute of TechnologyCambridge, MassachusettsUnited States of America2Department of Pathology, Tufts University School of Veterinary MedicineBoston, MassachusettsUnited States of America5 2004 11 5 2004 11 5 2004 2 5 e13926 11 2003 10 3 2004 Copyright: © 2004 Amsterdam et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Defects in Ribosomal Protein Genes Cause Cancer in Zebrafish We have generated several hundred lines of zebrafish (Danio rerio), each heterozygous for a recessive embryonic lethal mutation. Since many tumor suppressor genes are recessive lethals, we screened our colony for lines that display early mortality and/or gross evidence of tumors. We identified 12 lines with elevated cancer incidence. Fish from these lines develop malignant peripheral nerve sheath tumors, and in some cases also other tumor types, with moderate to very high frequencies. Surprisingly, 11 of the 12 lines were each heterozygous for a mutation in a different ribosomal protein (RP) gene, while one line was heterozygous for a mutation in a zebrafish paralog of the human and mouse tumor suppressor gene, neurofibromatosis type 2. Our findings suggest that many RP genes may act as haploinsufficient tumor suppressors in fish. Many RP genes might also be cancer genes in humans, where their role in tumorigenesis could easily have escaped detection up to now. A screen for tumour suppressor genes in zebrafish has identified several genes encoding ribosomal proteins, indicating that these genes might be an unappreciated class of cancer genes ==== Body Introduction The zebrafish (Danio rerio) has long been used as a model organism for the identification of genes required for early vertebrate development (Kimmel 1989). There is reason to believe that the zebrafish can also be used in genetic screens to identify cancer genes. Zebrafish can live for 4–5 y (Gerhard et al. 2002), and like other fish species (Schmale et al. 1986; Wittbrodt et al. 1989), they develop tumors in a variety of tissues (Amatruda and Zon 2002; Smolowitz et al. 2002). They are also susceptible to chemical carcinogens and to well-known oncogenes, in a manner similar to the conventional mouse models (Beckwith et al. 2000; Spitsbergen et al. 2000a, 2000b; Langenau et al. 2003). Many of the spontaneous and chemically- or oncogene-induced tumor types are histologically similar to their mammalian counterparts (Amatruda and Zon 2002; Langenau et al. 2003). The normal functions of many mammalian tumor suppressor genes are required for normal development (Jacks 1996). In fact, nonessential tumor suppressors, such as p53 (Donehower et al. 1992), appear to be the exception rather than the rule. These findings raised the possibility that one could discover genes with a role in tumorigenesis among zebrafish genes identified initially for having essential roles during embryonic development. We have used retroviral vectors as a mutagen in a large-scale insertional mutagenesis screen and have isolated many zebrafish mutants with lesions in genes essential for embryogenesis (Amsterdam et al. 1999; Golling et al. 2002). We are maintaining approximately 500 lines, in most of which an embryonic lethal mutation is linked to a single proviral insert. We have identified the mutated genes in over 400 of the lines, and these include mutations in 300 distinct zebrafish genes. To maintain the lines, we identify approximately 15 heterozygous carriers and outcross these at 15–20 mo of age to produce the subsequent generation. The maintenance of these mutations in adults provides a unique opportunity to ask whether heterozygosity in genes required for embryonic development predisposes the animals to cancer. Here we describe how such an analysis has identified genes that encode ribosomal proteins (RPs) as cancer genes in zebrafish. Results Mutations in Many RP Genes Predispose Zebrafish to Malignant Peripheral Nerve Sheath Tumors and Other Cancers In the course of establishing and maintaining heterozygous mutant lines of fish, we noticed several lines that displayed early mortality by 2 y of age, and this phenotype was seen in successive generations. Typically, only about 10% to 15% of the fish in a tank are lost by 2 y of age, but in these apparently high-mortality lines losses sometimes exceeded 50%. Furthermore, fish from these lines were often found to have gross lumps (Figure 1A and 1B). Histological analysis of step sections showed that the growths were predominantly large, malignant spindle cell tumors that were highly invasive, had a high mitotic index, and often exhibited focal necrosis (Figure 1C–1H). The tumor cells were aligned into stacks and fascicles to form a whirling, storiform pattern (Figure 1E–1H) that resembled malignant peripheral nerve sheath tumors (MPNSTs) seen in other species of fish (Schmale et al. 1983; Roberts 2001) and in mammals (Cichowski et al. 1999; Jimenez-Heffernan et al. 1999; Woodruff 1999). In keeping with the published work on fish tumors, while adhering to the caution suggested by the National Neurofibromatosis Foundation regarding animal models of MPNST (M. McLaughlin, personal communication), we have designated these tumors zMPNSTs (zebrafish MPNSTs). Figure 1 Spindle Cell Tumors Resembling MPNSTs in Zebrafish Heterozygous for Mutations in RP Genes (A and B) Fish with apparent masses, as indicated by the arrows, or other evident pathology were selected for histological analysis: (A) a hi2582 fish, (B) a hi1034B fish. (C–H) Histopathology of representative tumors stained with hematoxylin and eosin reveals patterns consistent with the diagnosis of MPNST in hi10 fish (C and D), hi1974 fish (E–G), and hi1807 fish (H). (C) Tumors typically filled the entire abdomen (sb, swim bladder; br, brain) (80×). (D) A large tumor with central necrosis is seen emanating from the optic nerve (n) (e, eye) (20×). (E) Tumors consist of spindle cells that stack into short fascicles, typically organizing into whorls (400×). (F) Tumor is aggressively invading muscle (m) and gill (g) (br, brain) (100×). (G) Mitotic figures (arrows) are evident (1000×). (H) Areas of focal necrosis (arrows) are frequently seen (200×). Although we had occasionally observed individual fish with lumps in our colony, it was unusual to find so many within a single line. Thus, we reasoned that the lines with early mortality that also frequently displayed gross lumps by 2 y of age might be lines with elevated rates of lethal cancer. Surprisingly, we found that the several potentially high-tumor lines were all heterozygous for mutations in genes that encode different RPs. This unexpected observation, combined with the knowledge that many tumor suppressors are recessive embryonic lethal genes, prompted us to survey our colony systematically to determine the incidence and spectrum of tumors arising in the colony as a whole, and to ask specifically whether genes that encode many different RPs predispose to cancer. To determine cancer incidence in the colony as a whole, we sectioned 152 “control” fish that were 20–26 mo of age. Forty-nine of the control fish were nontransgenic, while 103 were selected at random from 54 lines heterozygous for mutations in genes other than RP genes. The latter fish had been generated and maintained in a comparable manner to our RP mutant lines and thus were appropriate controls. The incidence of tumors detected by step sectioning in this control population was 11% (Table 1D). Although we observed a variety of different tumor types (most frequently seminomas and pancreatic islet cell adenomas), most of the tumors (15/17) were benign neoplasias and none were zMPNSTs. There was neither a significant difference in spectrum nor an increase in incidence of tumors in the non-RP heterozygous mutant fish relative to the wild-type fish, indicating that the presence of viral insertions, per se, does not have an obvious effect on tumorigenicity. Table 1 Tumor Incidence in Zebrafish RP-Heterozygous Lines and in the Colony aOne individual had two tumors, each of which was malignant. RP animals were collected as tumors became apparent, or as healthy animals at the maximum age specified (age range). Number lost indicates those that either died before the appearance of external symptoms or were lost from their tanks. Control animals from the colony were selected without regard to gross appearance. Incidence rates are based on the number of fish examined histologically (that is, excluding lost fish) To compare the frequency and types of tumors arising in RP mutant lines with those of the control population, we established the fate of all heterozygotes in a single generation of each of 16 RP mutant lines. In each family, some fish were lost prior to any observation of external symptoms, precluding determination of the cause of death. The rest were sacrificed either when they developed visible masses or when they reached 18 or 22 mo of age, and step sections were examined (Table 1). The 16 RP families fell into three groups with respect to tumor incidence. Six lines had high mortality (including both lost fish and those with external growths) and a high tumor incidence (60% or more, including both fish with gross tumors and tumors detected only upon sectioning). Nearly all of the tumors observed by 22 mo in these lines were zMPNSTs (Table 1A). These lines included those with mutations in RP genes S8, S15a, L7, L35, L36, and L36a. Five RP mutant lines made up a second group. These lines had either a moderate incidence of cancer, or had a low incidence but were unusual in having an apparently elevated incidence of zMPNSTs. This group included lines with mutations in L13, L23a, S7, S18, and S29. As in the high-cancer lines, in most lines with moderate cancer incidence, most tumors observed in fish by 22–24 mo of age were zMPNSTs (Table 1B). In one line (hi1026, with a mutation in S18), however, other tumor types predominated, suggesting that RP mutations can increase the frequency of tumor types besides zMPNSTs. The third group of RP mutant lines included five lines that were not tumor prone. These lines, with mutations in L3, L24, LP1, S12, and S15, were indistinguishable from controls in tumor incidence and spectrum (Table 1C versus 1D). In summary, 11 of 16 RP mutant lines had an elevated incidence of cancer, and most of these 11 lines are predisposed to develop zMPNSTs. Together these findings suggest that zMPNSTs are rare in our colony except in RP mutant lines. However, because the cancer incidence was low in the control fish, we observed only 17 tumors in this group of fish in the experiment described above. Furthermore, only four of these 17 tumors were grossly visible, with 13 being detected only after sectioning. To obtain more data on tumor spectrum in our colony, including the types of tumors that present as externally visible growths in non-RP mutant lines and wild type, we sought out fish with externally visible tumors from throughout our colony, coded them to avoid bias, and identified the tumor types by histological analysis of step sections. In total, we analyzed gross tumors from 41 control fish (wild type or non-RP mutant lines, including the four tumors found above). We also analyzed a total of 65 RP heterozygotes with grossly visible tumors (including the fish represented in Table 1A–1C). Figure 2 shows a comparison of the types of tumors in control versus RP mutant lines that presented as externally visible growths. In the control fish, seminomas accounted for 57% of these tumors, while a wide variety of other tumor types, including ultimobranchial gland tumors, neuroblastomas, islet cell adenomas, and lymphomas, each arose at low frequency. Overall, 69% of the grossly visible tumors observed in non-RP fish were benign. Only 10% of these externally visible tumors were zMPNSTs (see below). In contrast to the control fish, and as is apparent from the data in Table 1, the majority of grossly visible tumors in the RP mutants were zMPNSTs (81%), greatly exceeding the number of seminomas (4%) or other (15%) tumor types (Figure 2). Since fish with external growths were found far more often within RP families than in the colony at large, the dramatic shift in the spectrum of tumors in RP relative to non-RP mutant lines reflects the profound increase in incidence of zMPNSTs rather than any obvious reduction in the incidence of seminomas and other tumor types. Figure 2 The Tumor Spectrum in Fish Heterozygous for Mutations in RP Genes Shows an Increased Proportion of zMPNSTs Fish with apparent masses were selected and processed for histological analysis. Numbers are shown as percent of the total number of diagnosed tumors from either population. The control group includes 42 tumors from 41 fish, including both wild-type and non-RP family transgenics. The RP group includes 68 tumors from 65 RP heterozygotes from 18 different lines representing mutations in 16 different genes. The “other” tumor category includes pancreatic islet adenomas, ultimobranchial gland tumors, neuroblastomas, retinoblastomas, lymphomas, ganglioneuromas, ductal carcinomas, gastrointestinal adenocarcinomas, hepatocellular carcinomas, leukemias, meningiomas, and histiocytic sarcomas. As noted above, we detected zMPNSTs in only four of 41 control fish with grossly visible tumors. Two of these fish, aged 15 and 24.5 mo, were from the hi3332 line, the only non-RP line in which more than a single zMPNST has been observed to date. Significantly, the viral insertion that is linked to the embryonic lethal phenotype of this line lies within one (NF2a) of two distinct zebrafish genes that are highly homologous to the mammalian neurofibromatosis type 2 gene (NF2). The insertion abrogates expression of this gene in homozygous mutant embryos (Figure S1 and data not shown). NF2 was originally identified as a tumor suppressor gene that predisposes individuals to develop tumors of the nervous system (Trofatter et al. 1993; Ruttledge et al. 1994). Given this finding, we screened the remaining 53 fish in this family for tumors between 17.5 and 23 mo of age by sectioning. Seven of these 53 fish had small spindle cell tumors. These tumors were not identical to typical zMPNSTs found in RP families, but shared some key characteristics (data not shown). Given the elevated incidence of rare tumor types including zMPNSTs, we conclude that NF2a acts as a tumor suppressor gene in fish, as it does in mammals. Early Mortality in an RP Mutant Line Results from Multiple Types of Cancer The experiment described above identified six RP mutant lines with high mortality. While some of the mortality could be accounted for by fish that displayed gross tumors and therefore were removed from the tanks before they died, many fish simply disappeared or were found dead and were too deteriorated to be analyzed histologically. To determine whether early mortality in these lines was entirely due to lethal cancers, and if so, whether it was due to zMPNSTs or to other tumor types, we performed two experiments using fish from the early-mortality, high-tumor hi10 line. In one experiment we screened hi10 heterozygotes and their wild-type siblings weekly for evidence of ill health or externally visible growths in an effort to catch all sick fish before they died or were lost. Sickly fish were sacrificed and subjected to histological examination, as were all of the fish that still appeared healthy at 22 mo of age. The results are shown in Figure 3. Only the RP heterozygous carrier fish displayed early mortality, and, as anticipated, this was due to cancers. Strikingly, among tumors found by 15 mo of age, while two were zMPNSTs, one was a retinoblastoma and three were lymphomas, tumor types that, like zMPNSTs, arise infrequently in our control populations. The tumors detected in the older fish were predominantly zMPNSTs. By the endpoint of the experiment (22 mo) all of the noncarrier sibling controls appeared healthy, and step sectioning detected only one tumor-bearing fish among 13, a frequency comparable to the control population. These results support the conclusion that the early mortality observed in the hi10 line is the result of lethal tumors, and reveal that these include zMPNSTs but also other tumor types. Figure 3 Rate of Tumor Appearance in hi10 Heterozygotes A cohort of 28 hi10 fish and 13 of their noncarrier siblings were observed over 22 mo for the appearance of ill health or externally visible tumors. Symptomatic individuals were sacrificed, fixed, and sectioned for histological analysis. The graph represents the percentage of fish remaining over time, with the diagnosis of each removed fish. Three fish labeled “dead” died before fixation and had too much tissue damage to establish a diagnosis. Also, seven of the carrier fish (though none of the noncarriers) were lost to unknown causes over the course of the experiment; while they most likely died, to be conservative these were removed from the total number of fish charted. At 22 mo, the remaining externally healthy fish (4/21 carriers, 13/13 noncarriers) were also histologically examined, and the status of these fish is indicated. Further evidence that fish from the hi10 line are predisposed to multiple tumor types was obtained in the second experiment, in which we sectioned hi10 heterozygotes and their noncarrier sibling controls (specifically including any sick or growth-bearing fish along with apparently healthy fish) at approximately six-week intervals between eight and 14 mo of age. As shown in Table 2, we found both grossly visible and occult zMPNSTs and other tumor types in the hi10 carrier fish. Thus, the hi10 line (and presumably other high-mortality RP lines) is predisposed to multiple tumor types, though particularly strongly predisposed to develop zMPNSTs, especially at later time points. Table 2 Onset of Tumor Development in hi10 Fish and Noncarrier Siblings aOne fish at each of these time points had two tumors Carriers: starting population was 70 fish; seven fish were lost over the course of the experiment. Noncarriers: starting population was 92 fish; three fish were lost over the course of the experiment; 32 externally healthy fish at the end of the study (14 mo) were not histologically examined RP Genes May Be Haploinsufficient Tumor Suppressors Dominant mutations that predispose vertebrates to cancer can be activated oncogenes, recessive tumor suppressors, or haploinsufficient tumor suppressors (Largaespada 2001). Several lines of evidence suggest that RP mutant genes may be acting as haploinsufficient tumor suppressors in zebrafish. The mutagenic inserts in all of our RP mutant lines reduced or eliminated expression of the RP gene, as determined by RT-PCR and, in some cases, Northern blotting (Figure 4A and data not shown). Thus, most if not all of these viral insertions appear to be loss-of-function mutations. This suggests that the RP genes are not mutated to form activated oncogenes, but rather may act as tumor suppressors. In mammals, the most frequent mechanism of inactivation of recessive tumor suppressor genes is the acquisition of a mutation (either germline or somatic) in one allele and subsequent loss of the wild-type allele through loss of heterozygosity (LOH) (Haber and Harlow 1997). Thus, we investigated whether the wild-type RP gene had been lost in the zebrafish tumors. We isolated both normal and tumor tissue from three RP heterozygous mutant lines, hi10, hi258, and hi1974, each of which showed a reduction in expression of its respective RP mutant gene of 10-fold or more (Figure 4A) and examined DNA from these samples for the presence of the mutant and wild-type RP alleles by PCR (Figure 4B). In every case, we detected the wild-type allele, arguing against LOH in these tumors. A concern is that tissue contamination can yield misleading LOH results, particularly because the red blood cells of fish are nucleated. Thus control PCR experiments were performed in which DNA samples from heterozygous and homozygous embryos were mixed at different ratios. The results show that our assay was sensitive to a decrease as small as 3-fold in the relative amount of the wild-type allele (data not shown). Thus, unless the tumor samples contained more than 33% nontumor cells, we can conclude that the wild-type RP alleles were not lost in these tumors and thus the RP genes are probably not recessive tumor suppressors. In one of the tumor samples shown in Figure 4, tumor hi10–1, the wild-type allele appears not only to be present but possibly at higher concentration than the mutant allele, and Southern analysis of this same DNA sample supported this observation (data not shown). Thus, in this particular tumor the mutant allele may have been lost and only the wild-type allele retained. Figure 4 RP Genes Appear to Be Haploinsufficient Tumor Suppressors (A) RP mutations decrease the amount of RP gene expression. RNA was prepared from 3-d-old homozygous mutant embryos and their wild-type siblings, and serial dilutions of first strand cDNA were used as templates for PCR. The decrease in expression in the mutants can be determined by the difference in the dilution between wild type and mutant where the PCR product amount diminishes. The actin control shows that the total amount of mRNA was the same between samples. (B) LOH is not observed in RP mutant tumors. DNA was prepared from tumors (T) and normal tissue (N) from the same fish, and PCR was conducted with three primers that show the presence or absence of both the insert-bearing (mutant) and wild-type chromosomes. In each case, the upper band is the wild-type chromosome and the lower band is the insert-bearing one. hi10 fish #1 normal (lane 1), tumor (lane 2); hi10 fish #2 normal (lane 3), tumor (lane 4); hi258 fish normal (lane 5), tumor (lane 6); hi1974 fish normal (lane 7), tumor (lane 8). In mice, a tumor cell line has been described in which one copy of an RP gene is deleted and the other copy has suffered a mutation that may contribute to tumorigenesis (Beck-Engeser et al. 2001). To rule out the acquisition of a point mutation in the wild-type allele in RP mutant tumors, all of the coding exons of the appropriate RP gene and at least 50 bp of intronic sequence flanking them were sequenced from each normal and tumor DNA sample. There was no indication of any point mutations in any of the tumors. The apparent retention of the wild-type allele in the tumor cells in these samples, and the fact that no point mutations were observed in the wild-type RP genes in the tumor cell DNA, suggests that it is not a second hit in these loci that leads to tumorigenesis. Rather, the data obtained suggest that these genes function as haploinsufficient tumor suppressors in zebrafish. RP Mutations Alter the Relative Amounts of 18S and 28S rRNAs In yeast, a decrease in the amount of at least some RP genes results in a reduction in the amount of the corresponding ribosomal subunit and a reduction in the number of assembled ribosomes (Moritz et al. 1990). To determine if this is also true in fish, we examined the relative amounts of 18S and 28S rRNA in homozygous mutant embryos compared to sibling controls. Embryos from heterozygote crosses of lines hi10, hi1974, and hi2649 were sorted by phenotype at 3 d post-fertilization, and total RNA was prepared from pools of mutant or phenotypically wild-type sibling embryos. Electrophoresis and ethidium bromide staining were used to determine the amounts of 18S and 28S RNA, which we assume reflect the amounts of 40S and 60S ribosomal subunits, respectively (Figure 5). As a loading control, the same RNA samples were subjected to Northern analysis and probed for beta actin (Figure 5). In each case we observed a decrease in the overall amount of rRNA, and, significantly, a preferential loss of the rRNA found in the ribosomal subunit with which the mutated RP was associated. Thus in hi10, in which a component of the large ribosomal subunit was mutated, while both 18S and 28S RNA levels were decreased, the level of 28S RNA was affected more than that of 18S. Conversely, in hi1974 and hi2649, in which components of the small ribosomal subunit were mutated, the 28S RNA levels were mildly reduced, but 18S RNA was sharply decreased. In none of these cases was the actin level reduced, so the effect was not simply a result of a reduction of cell number, RNA degradation, or cell death. Thus, as in yeast, RP mutations in fish that result in reduced gene expression lead to a relative decrease in the amount of the subunit to which they belong as measured by a decrease in rRNA. Figure 5 Ribosomal RNA Levels Are Reduced in RP Mutants RNA was prepared from 3-d-old homozygous mutant embryos or their wild-type siblings from lines hi10 (L36a), hi1974 (S8), and hi2649 (S15a), and RNA content was visualized by electrophoresis and ethidium bromide staining. The ratio of 28S/18S as determined by densitometry is shown below each lane. Note that L36a mutants show a preferential loss of the 28S band by 1.5-fold, while S8 and S15a mutants show a preferential loss of the 18S band by 1.9- and 1.8-fold, respectively. These RNAs were also northern blotted and probed for beta actin as an mRNA content control. Discussion In this study, we have found that heterozygous mutations in 11 different ribosomal protein genes predispose zebrafish to cancer, predominantly to zMPNSTs, but also to other rare tumor types. All of these mutations reduce RP gene expression, indicating that these 11 genes are not oncogenes. Moreover, in the tumors we examined, the wild-type allele appeared to be present and did not contain point mutations; thus these genes are not recessive tumor suppressors. Rather, our findings suggest that these 11 genes are haploinsufficient tumor suppressor genes; that is, reducing their activities by about a factor of two increases the likelihood of cancer. These findings raise two important, unanswered questions: first, how do these mutations lead to cancer, and second, do similar mutations cause cancer in humans? How Do These Mutations Cause Cancer? The finding that mutations in so many different RP genes, including S7, S8, S15a, S18, S29, L7, L13, L23a, L35, L36, and L36a, predispose to cancer suggests that a function shared by RPs underlies their role in this phenotype. However, not all RP genes were cancer genes: S12, S15, L3, L24, and LP1 heterozygotes appeared normal. This raises the possibility that the oncogenic RP genes could conceivably share some novel biological function independent of their role in the ribosome and that inhibition of this function leads to tumor formation. Individual RPs have been implicated in a wide variety of biological functions, including cell cycle progression, apoptosis, and DNA damage responses (Ben-Ishai et al. 1990; Sonenberg 1993; Chen et al. 1998; Chen and Ioannou 1999; Hershey and Miyamoto 2000; Volarevic et al. 2000; Volarevic and Thomas 2001; Lohrum et al. 2003), and it has been suggested that their role in these processes may arise independently of their role in the ribosome itself (Wool 1996; Wool et al. 1996; Soulet et al. 2001). However, it seems somewhat unlikely to us that there could be such an important, yet still undetected function involving so many different RPs. Thus, we favor the possibility that it is a shared, ribosome-associated function that allows them to be tumor suppressors. If so, then why were not all RP genes cancer genes in this study? At present we can only speculate. We have not found any correlation that distinguishes the RP genes that predispose to cancer from those that do not. Both can belong to either the large or the small ribosomal subunit, and all the mutants show reduced gene expression. Possibly some RP genes are normally expressed at higher levels than others, so that a 50% reduction in their expression does not reduce their protein level below some critical, hypothetical threshold required for tumor suppression. The best-known function shared by RPs is their role in the assembly of ribosomal subunits, and as a result, their role in translation. In homozygous mutant fish embryos, the RP mutations reduce the amount of the rRNA of the subunit to which they belong, and hence almost certainly reduce the amount of the corresponding ribosomal subunit relative to the remaining subunit. In yeast this is known to reduce the number of ribosomes, and thus also to reduce the amount of protein synthesis. How might this predispose to cancer? In truth, we do not know, and suspect that understanding the mechanism that explains these findings will lead to new insights into growth control. At present we can only list our speculations and several relevant observations. Reduced protein synthesis could lead to a reduction in the level of a critical tumor suppressor protein, or of a positive regulator of apoptosis or differentiation, either of which could favor growth. A reduction in ribosome number might signal the cell to try to overcome the deficit by making more of the components required for ribosome biogenesis, and this in turn might promote cell growth. Alternatively, a reduction in the number of ribosomes might alter the identity of the messages recruited to ribosomes—similar to the way that modulation of the translational capacity of mammalian cells by oncogenes such as Ras or Akt is known to alter the identity of mRNAs recruited to polysomes—changing the translation rate of growth-promoting genes (Rajasekhar et al. 2003). Finally, and most speculative of these possibilities, reduced ability of a ribosomal subunit to assemble properly might generate a signal that cells interpret as growth-promoting. For example, degradation of excess rRNA, a molecule with many hairpins, might generate such a signal in the form of RNAi. Are RP Genes Cancer Genes in Other Vertebrates? Given that so many different RP genes can be cancer genes in fish, it seems surprising that they are not already a well-known class of cancer genes in vertebrates. Only two examples are known that suggest a role for RP mutations in mammalian tumor susceptibility, one in mice and one in humans. In the mouse study, two independent murine tumor cell lines were found to express tumor antigens that were mutated RPs (Beck-Engeser et al. 2001). In both cases, the tumors were found to become more aggressive upon either loss or mutation of the wild-type allele of the RP gene. It was postulated that the mutant RPs might have an oncogenic activity that was suppressed by the wild-type protein. Such a mechanism does not seem to be involved in the tumors that develop in the RP mutant fish described here, since we failed to detect evidence of oncogenic activation of RP genes. In humans, there is a possible association of mutations in one particular RP gene with cancer: approximately 25% of both sporadic and familial cases of Diamond-Blackfan anemia (DBA) are associated with a mutation of rpS19 (Draptchinskaia et al. 1999), and this syndrome includes an increased risk of developing leukemia (Wasser et al. 1978). It has been demonstrated that the anemia is likely due to a block in erythroid differentiation (Hamaguchi et al. 2002), but it is currently unclear whether the leukemia is an indirect result of the anemia, caused by a stimulation in the production of hematopoietic precursors, or whether the rpS19 gene dosage plays a direct role in tumorigenesis. It is important to note that DBA is a multigenic disease with very heterogeneous clinical presentation. While DBA patients in general have an increased predisposition to certain cancers, it is not yet clear whether this is true of the subset whose DBA is caused by rpS19 mutation. While these examples from mouse and human are consistent with the idea that mutations in individual RP genes might contribute to tumorigenesis in mammals, they have seemed to be unusual examples, rather than suggesting that RP genes in general might be potential cancer genes. Our study suggests for the first time, we believe, that this is a general property of many RP genes. The possibility that a reduction in ribosome levels might be oncogenic in mammals is further supported by the fact that mutations in DKC1, a pseudouridine synthase that is required for rRNA processing and for properly functioning ribosomes, cause dyskeratosis congenita, a disease characterized by both premature aging and increased tumor susceptibility (Ruggero et al 2003). If RP genes frequently cause human cancers, it is not at all certain that their role would have been detected. Even a deliberate search for their involvement in human cancers would be difficult because there are so many (80) RP genes. This plethora of genes, the fact that it is hard to know which tumor type(s) to examine for RP mutations, and the fact that the mutations might lie in regulatory elements rather than protein-coding regions of the genes would make such a search difficult. Nonetheless, given the high degree of conservation of biological mechanisms among vertebrates, it seems likely that rp mutations will prove to increase the incidence of tumors in humans as they do in zebrafish. If so, it may be advantageous to devise diagnostic strategies based on ribosomal protein levels or on a function that these proteins share, for example, in translation, rather than on the analysis of such a large number of individual genes. In summary, by examining aging populations of mutant lines of fish with defects in embryonic essential genes, we identified a novel group of cancer genes. The ability to identify cancer genes by screening populations of fish heterozyogous for recessive embryonic mutations and the reassuring finding that NF2a is a tumor suppressor gene in this system demonstrate the power of large-scale, forward-genetic screens in the zebrafish to identify new disease susceptibility genes. Materials and Methods Mutagenesis and maintenance of mutant lines The insertional mutagenesis screen was carried out as previously described (Amsterdam et al. 1999). Stocks of all lines were maintained by outcrossing heterozygotes to nontransgenic fish, preparing DNA from tail fin biopsies of 8- to 18-wk-old fish, and performing PCR with insert-specific primers for each line to identify heterozygotes. Fixation and histology Adult fish were euthanized in ice water and fixed within 30 min in Bouin's solution, embedded in paraffin, and sectioned as previously described (Moore et al. 2002). LOH analysis DNA was prepared from tumor tissue or tail tissue isolated from fish prior to fixation for histology. PCR was conducted with one primer complementary to proviral sequence and two primers complementary to sequences on either side of the insertion for the appropriate mutation. Primer sequences were as follows: hi10: 10gen5 (5′-CAGCACAGATTCTTGAAAGCGCC-3′), 10gen3 (5′- GCATATGTAGCATCTCGAAGGTCC-3′), and NU3X (5′- TGATCTCGAGCCAAACCTACAGGTGGGGTC-3′); hi258: 258A5a (5′-GGTACGTCTGTGCTTATGTTTGTGTC-3′), 258A3a (5′-TCTCAAGACTTCATCCATTCATAATTCTGC-3′), and NU3X; hi1974: 1974c1 (5′-CTACACCACAGGTATCTCAAGGG-3′), 1974c1est3 (5′-CCACCACGGACTCTTATTGTGTG-3′), and IPL3 (5′-TGATCTCGAGTTCCTTGGGAGGGTCTCCTC-3′). RNA analysis RNA was prepared from mutant and wild-type embryos using Trizol reagent (Invitrogen, Carlsbad, California, United States). For RT-PCR, serial dilutions of first strand cDNA were amplified for 30 (hi1974) or 35 (hi10 and hi258) cycles using the following primers for the genes indicated: rpL36a: 10rt5 (5′-CAACCATGGTAAACGTACCGAAG-3′) and 10RTR (5′-CACAAAAGAAGCACTTGGCCCAGC-3′); rpL35: 258RTF2 (5′-GCTGCTTCCAAGCTCTCAAAAATCC-3′) and 258RTR (5′-TGCCTTGACGGCGAACTTGCGAATG-3′); rpS8: 1974RTF1 (5′-TCTCAAGGGATAACTGGCACA-3′) and 1974RTR1 (5′-GAACTCCAGTTCTTTGCCCTC-3′); actin: actinF (5′-CATCAGCATGGCTTCTGCTCTGTATGG-3′) and actinR (5′-GACTTGTCAGTGTACAGAGACACCCT-3′). For visualization of 18S and 28S RNA, two embryo equivalents of RNA were electrophoresed through a nondenaturing agarose gel containing 0.5 μg/ml ethidium bromide. For detection of beta actin RNA, four embryo equivalents of RNA were electrophoresed through a 7.5% formaldehyde/MOPS-buffered agarose gel, blotted to Hybond N+ (Amersham Biosciences, Little Chalfont, United Kingdom), and hybridized with a random primed beta actin probe. Supporting Information Figure S1 Position of Mutagenic Insertions The genomic sequence of part of each of these genes is represented as exonic (boxed) and promoter or intronic (line). White boxes represent 5′ UTR while shaded boxes represent coding exons. Where no white boxes are shown, the location of the 5′ UTR and beginning of the coding region has not been determined relative to the part of the locus shown here. In all cases, at least one coding exon (and all of the 3′ UTR) is downstream of the region of the gene represented here. The position and orientation of the proviruses are shown above each genomic sequence. All drawings are to the scale of the top scale bar, except the rpl36 locus, which has its own scale bar. (62 KB PDF). Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) accession numbers for the genes discussed in this paper are L13 (AY561516), L23a (AY561517), L24 (AY099532), L3 (AY561514), L35 (AF506205), L36 (AY561518), L36a (AY099511), L7 (AY561515), LP1 (AY561519), NF2a (AY561520), S12 (AY561510), S15 (AY561511), S15a (AY561512), S18 (AY099517), S29 (AY561513), S7 (AY561508), and S8 (AY561509). We thank Meg Cunningham and Kate Anderson for maintenance of the mutant lines of fish and Tom Such, Sam Farrington, Chris Doller, and Tim Angelini for maintenance of the zebrafish colony. We thank the Center for Cancer Research Histology Facility, especially Alicia Caron, for sample processing and sectioning, and Jan Spitsbergen, Michael Schmale, and Margaret McLaughlin for their advice on the analysis of the histology. We thank Tyler Jacks for valuable comments on the manuscript and Keith Cheng and Philip Sharp for useful discussions. We thank Joan Ruderman for her support of K.C.S. This work was supported by grants from the National Center for Research Resources of the National Institutes of Health (NIH) (to N.H.), Amgen (to N.H.), the David Koch Research Fund (to J.A.L. and N.H.), and the NIH (to the Center for Cancer Research at the Massachusetts Institute of Technology). A.A. was supported by a fellowship from the Ford Foundation, K.C.S. was supported by a fellowship from the NIH, and K.L. was supported by a predoctoral training grant from the NIH. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. AA, KCS, SF, KL, JAL, and NH conceived and designed the experiments. AA, KCS, SF, and KL performed the experiments. AA, KCS, SF, KL, RTB, JAL, and NH analyzed the data. AA, KCS, JAL, and NH wrote the paper. Academic Editor: Derek Stemple, Sanger Center Abbreviations DBADiamond-Blackfan anemia LOHloss of heterozygosity MPNSTmalignant peripheral nerve sheath tumor NF2 neurofibromatosis type 2 RPribosomal protein zMPNSTzebrafish malignant peripheral nerve sheath tumor ==== Refs References Amatruda JF Zon LI Zebrafish as a cancer model system Cancer Cell 2002 1 229 231 12086858 Amsterdam A Burgess SB Golling G Chen W Sun Z A large-scale insertional mutagenesis screen in zebrafish Genes Dev 1999 13 2713 2724 10541557 Beck-Engeser GB Monach PA Mumberg D Yang F Wanderling S Point mutation in essential genes with loss or mutation of the second allele: Relevance to the retention of tumor-specific antigens J Exp Med 2001 194 285 300 11489948 Beckwith LG Moore JL Tsao-Wu GS Harshbarger JC Cheng KC Ethylnitrosourea induces neoplasia in zebrafish (Danio rerio) Lab Invest 2000 80 379 385 10744073 Ben-Ishai R Scharf R Sharon R Kapten I A human cellular sequence implicated in trk oncogene activation is DNA damage inducible Proc Natl Acad Sci 1990 87 6039 6043 1696715 Chen FW Ioannou YA Ribosomal proteins in cell proliferation and apoptosis Int Rev Immunol 1999 18 429 448 10672495 Chen FW Davies JP Ioannou YA Differential gene expression in apoptosis: Identification of ribosomal protein 23K, a cell proliferation inhibitor Mol Genet Metab 1998 64 271 282 9758718 Cichowski K Shih TS Schmitt E Santiago S Reilly K Mouse models of tumor development in neurofibromatosis type 1 Science 1999 286 2172 2176 10591652 Donehower LA Harvey M Slagle BL McArthur MJ Montgomery CA Mice deficient for p53 are developmentally normal but susceptible to spontaneous tumors Nature 1992 356 215 221 1552940 Draptchinskaia N Gustavsson P Andersson B Pettersson M Willig TN The gene encoding ribosomal protein S19 is mutated in Diamond-Blackfan anemia Nat Genet 1999 21 169 175 9988267 Gerhard GS Kauffman EJ Wang X Stewart R Moore JL Life spans and senescent phenotypes in two strains of Zebrafish (Danio rerio) Exp Gerontol 2002 37 1055 1068 12213556 Golling G Amsterdam A Sun Z Antonelli M Maldanado E Insertional mutagenesis in zebrafish rapidly identifies genes essential for early vertebrate development Nat Genet 2002 31 135 140 12006978 Haber D Harlow E Tumor-suppressor genes: Evolving definitions in the genomic age Nat Genet 1997 16 320 322 9241260 Hamaguchi I Ooka A Brun A Richter J Dahl N Gene transfer improves erythroid development in ribosomal protein S19-deficient Diamond-Blackfan anemia Blood 2002 100 2724 2731 12351378 Hershey JWB Miyamoto S Translational control and cancer. In: Sonenberg N, Hershey JWB, Mathews MB, editors. Translational control of gene expression, 2nd ed 2000 Cold Spring Harbor (New York) Cold Spring Harbor Laboratory Press 637 654 Jacks T Tumor suppressor gene mutations in mice Annu Rev Genet 1996 30 603 636 8982467 Jimenez-Heffernan JA Lopez-Ferrer P Vicandi B Hardisson D Gamallo C Cytologic features of malignant peripheral nerve sheath tumor Acta Cytol 1999 43 175 183 10097706 Kimmel CB Genetics and early development of zebrafish Trends Genet 1989 5 283 288 2686119 Langenau DM Traver D Ferrando AA Kutok JL Aster JC Myc-induced T cell leukemia in transgenic zebrafish Science 2003 299 887 890 12574629 Largaespada DA Haploinsufficiency for tumor suppression: The hazards of being single and living a long time J Exp Med 2001 193 F15 F18 11181707 Lohrum MAE Ludwig RL Kubbutat MHG Hanlon M Vousden KH Regulation of HDM2 activity by the ribosomal protein L11 Cancer Cell 2003 3 577 587 12842086 Moore JL Aros M Steudel KG Cheng KC Fixation and decalcification of adult zebrafish for histological, immunocytochemical, and genotypic analysis Biotechniques 2002 32 296 298 11848405 Moritz M Paulovich AG Tsay YF Woolford JL Depletion of yeast ribosomal proteins L16 or rp59 disrupts ribosome assembly J Cell Biol 1990 111 2261 2274 2277060 Rajasekhar VK Viale A Socci ND Wiedmann M Hu X Oncogenic Ras and Akt signaling contribute to glioblastoma formation by differential recruitment of existing mRNAs to polysomes Mol Cell 2003 12 889 901 14580340 Roberts RJ Neoplasia of teleosts. In: Roberts RJ, editor 2001 Fish pathology. London W. B. Saunders 151 168 Ruggero D Grisendi S Piazza F Rego E Mari F Dyskeratosis congenita and cancer in mice deficient in ribosomal RNA modification Science 2003 299 259 262 12522253 Ruttledge MH Sarrazin J Rangarantnam S Phelan CM Twist E Evidence for the complete inactivation of the NF2 gene in the majority of sporadic meningiomas Nat Genet 1994 6 180 184 8162072 Schmale MC Hensley G Udey LR Neurofibromatosis, von Recklinghausen's disease, multiple schwannomas, malignant schwannomas. Multiple schwannomas in the bicolor damselfish, Pomacentrus partitus (Pisces, Pomacentridae) Am J Pathol 1983 112 238 241 6410922 Schmale MC Hensley GT Udey LR Neurofibromatosis in the bicolor damselfish (Pomacentrus partitus) as a model of von Recklinghausen neurofibromatosis Ann N Y Acad Sci 1986 486 386 402 3105403 Smolowitz R Hanley J Richmond H A three-year retrospective study of abdominal tumors in zebrafish maintained in an aquatic laboratory animal facility Biol Bull 2002 203 265 266 12414614 Sonenberg N Translation factors as effectors of cell growth and tumorigenesis Curr Opin Cell Biol 1993 5 955 960 7907492 Soulet F Al Saati T Roga S Amalric F Bouche G Fibroblast growth factor-2 interacts with free ribosomal protein S19 Biochem Biophys Res Commun 2001 289 591 596 11716516 Spitsbergen JM Tsai HW Reddy A Miller T Arbogast D Neoplasia in zebrafish (Danio rerio) treated with 7,12-dimethylbenz[a]anthracene by two exposure routes at different developmental stages Toxicol Pathol 2000a 28 705 715 11026607 Spitsbergen JM Tsai HW Reddy A Miller T Arbogast D Neoplasia in zebrafish (Danio rerio) treated with N-methyl-N′-nitro-N-nitrosoguanidine by three exposure routes at different developmental stages Toxicol Pathol 2000b 28 716 725 11026608 Trofatter JA MacCollin MM Rutter JL Murrell JR Duyao MP A novel moesin-, ezrin-, radixin-like gene is a candidate for the neurofibromatosis 2 tumor suppressor Cell 1993 72 791 800 8453669 Volarevic S Thomas G Role of S6 phosphorylation and S6 kinase in cell growth Prog Nucleic Acid Res Mol Biol 2001 65 101 127 11008486 Volarevic S Stewart MJ Ledermann B Zilberman F Terracciano L Proliferation, but not growth, blocked by conditional deletion of 40S ribosomal protein S6 Science 2000 288 2045 2047 10856218 Wasser JS Yolken R Miller DR Diamond L Congenital hypoplastic anemia (Diamond-Blackfan syndrome) terminating in acute myelogenous leukemia Blood 1978 51 991 995 273451 Wittbrodt J Adam D Malitschek B Maueler W Raulf F Novel putative receptor tyrosine kinase encoded by the melanoma-inducing Tu locus in Xiphophorus Nature 1989 341 415 421 2797166 Woodruff JM Pathology of tumors of the peripheral nerve sheath in type 1 neurofibromatosis Am J Med Genet 1999 89 23 30 10469433 Wool IG Extraribosomal functions of ribosomal proteins Trends Biochem Sci 1996 21 164 165 8871397 Wool IG Chan Y-L Gluck A Mammalian ribosomes: The structure and the evolution of the proteins. In: Hershey JWB, Mathews MB, Sonenberg N, editors. Translational control 1996 Cold Spring Harbor (New York) Cold Spring Harbor Laboratory Press 685 732

15138505

PMC406397

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2021-01-05 08:26:26

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PLoS Biol. 2004 May 11; 2(5):e139

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PLoS Biol

10.1371/journal.pbio.0020139

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020140Journal ClubAnimal BehaviorNeurosciencePrimatesHomo (Human)Learning to Change Journal ClubKringelbach Morten L 5 2004 11 5 2004 11 5 2004 2 5 e140Copyright: © 2004 Morten L. Kringelbach.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.A paper published over 20 years ago by Susan Iversen and Mortimer Mishkin on reversal learning continues to inform cognitive neuroscience today ==== Body One of the hallmarks of human nature is our remarkably flexible behaviour, especially in the social domain, which is perhaps also a major reason for our relative evolutionary success. Our social skills are already being honed in childhood and early adolescence, when we quickly become very adept at forming and breaking alliances within and between groups and spend much of our time engaged in complex social interactions. At best, these interactions enrich our society; at worst, they become ‘Machiavellian’ and exploitative. While science might appear removed from such politics, many scientists would probably agree that science is in fact a social enterprise, sharing many characteristics with other human pursuits, and that any claim to greater scientific truth can only be accorded over decades, even centuries. I have always been fascinated by social intelligence, particularly of the ‘Machiavellian’ kind, and found myself wondering at the start of my doctoral research how one might use neuroimaging to study social intelligence in the human brain. I was also interested by the fact that some of this flexible behaviour is shared with other primates such as chimps, bonobos, and even monkeys, who also spend inordinate amounts of time in social interactions, working out social hierarchies. However, it was not immediately obvious how one might go about designing experiments that would address these somewhat intangible issues of social behaviour. Trawling the scientific literature, I came across the concept of reversal learning. While it is obviously important that we can learn arbitrary associations between stimuli and actions, it is also extremely important that we can relatively easily break these associations and learn others. If we learn that choosing a certain object leads to a reward, it would be rather maladaptive to keep choosing this object when it was no longer associated with a reward but, say, a punishment instead. In order to accommodate complex behaviour, we need to be able to adapt or reverse the learning patterns when things change. For a long time, it was thought that complex behaviour depended crucially on the prefrontal cortex of the brain, but it was not clear which parts were important for reversal learning. This was investigated in a classic paper by the eminent neuroscientists Susan Iversen and Mortimer Mishkin (1970), who studied lesions in monkeys, with elegant and important results. The authors lesioned discrete parts of the prefrontal cortex in different monkeys and showed convincingly that these lesions had differential effects on the animals' ability to reverse rewarding associations in an object reversal task. When the inferior prefrontal convexity and parts of the lateral orbitofrontal cortex (which is the ventral part of the prefrontal cortex over the orbits) (see Figure 1) were lesioned, the monkeys became significantly impaired with respect to object reversal learning. Specifically, they continued to respond much longer than controls to an object that was no longer rewarded on the first reversal trial. Figure 1 Reversal Learning and the Orbitofrontal Cortex (A) Lateral and ventral views of the surface reconstructions of the lateral and medial orbitofrontal cortex lesions in monkeys (adapted from Iversen and Mishkin 1970), with the former monkeys having difficulty with the reversal task. (B) A ventral view of the human brain, with the cerebellum removed. Red activations in the lateral orbitofrontal cortex indicate the maximal activation for reversal compared to stable acquisition events. Blue activations indicate the main effects of facial expression (adapted from Kringelbach and Rolls 2003). This was not the case for monkeys who had had the medial parts of the orbitofrontal cortex lesioned. These monkeys were not completely unaffected by the lesion, but showed moderate impairment on all but the first of the object discrimination reversals. Furthermore, they had moderate difficulty withholding response between trials on an auditory differentiation task. These results strongly suggested a differential role for the lateral and medial parts of the orbitofrontal cortex. Although the paper was not published in a high-profile journal, this elegant and very significant result has had a huge influence on subsequent research. The paper, like many other great papers, was ahead of its time, and it took almost a decade before the citations started to pick up (at last online count, on February 1, 2004, of the ISI database, the paper had generated 229 citations since 1981). Iversen and Mishkin (1970) persuasively demonstrated the importance of the orbitofrontal cortex in reversal learning, and other studies have since extended this result in nonhuman primates. One study demonstrated that single neurons in the macaque orbitofrontal cortex change their responses to a visual cue after a single trial in which the reward association of the visual cue is reversed (Thorpe et al. 1983). Another lesion study in marmosets by Dias et al. (1996) found that the orbitofrontal cortex is essential for the performance of emotion-related reversal learning tasks. There was also some evidence that humans with lesions to the orbitofrontal cortex have problems with reversal learning, but the lesions, caused by neurological insult, were not very clean or focal (Rolls et al. 1994). In addition, it had also become clear that lesions to the orbitofrontal cortex were associated with impairments in emotional and social behaviour, characterised by disinhibition, social inappropriateness, and irresponsibility (Anderson et al. 1999). These interesting but nonconclusive results in humans spurred us on to use neuroimaging on a modified version of a probabilistic reversal learning task designed by Julia Hornak and John O'Doherty (Hornak et al. 2004), whose preliminary data suggested that patients with surgical lesions to the orbitofrontal cortex were impaired. The subjects' task was to determine, by trial and error, which of two stimuli was the more profitable to choose and to keep track of this, reversing their choice when a reversal occurred. By design, the actual reversal event was not easy to determine, since ‘money’ could be won or lost on both stimuli, but a choice of the rewarding stimulus would in general give larger rewards and smaller punishments. The converse was true of the punishing stimulus; losing a large amount of money would often (but not always) signal that a reversal had occurred. We used functional magnetic resonance imaging to show that dissociable activity in the medial orbitofrontal cortex was correlated with the magnitude of the monetary gains received, while activity in the lateral orbitofrontal cortex was correlated with the monetary losses incurred (O'Doherty et al. 2001). This dissociation between the functions of medial and lateral orbitofrontal cortex seemed to mirror Iversen and Mishkin's initial dissociation in monkeys, in which the lateral orbitofrontal cortex was linked, in both cases, to the reversal trials. However, owing to the probabilistic nature of the task, in which receiving a monetary punishment did not always signal reversal, our imaging study did not reveal the cortical localisation of reversal trials. In addition, our task used money as the secondary reinforcer, which might be a powerful influence on humans but has little biological relevance for other animals, and certainly none in the social domain that I was interested in. One way to solve these problems was to use facial expressions rather than money as the reinforcing stimuli. This made sense, given that the key to social intelligence is the ability to detect subtle changes in communication and act upon these changes rapidly as they occur. Such changes in social behaviour are often based on facial expression and come so naturally to humans (and are in place so early in child development) that some might argue that this functionality is essentially innate. However, our human social behaviour is sufficiently flexible that we can easily learn to adapt our behaviour to most facial expressions. For example, other people's neutral expressions do not normally indicate that our behaviour should change, but it is easy to think of social contexts in which a neutral expression does indeed imply that our current behaviour is inappropriate and should change. I designed a reversal task in which the subject's overall goal was to keep track of the mood of two people presented in a pair and, as much as possible, to select the ‘happy’ person, who would then smile. Over time, the person with the ‘happy’ mood (who would smile when selected), changed his/her mood to ‘angry’. This person thus no longer smiled when selected, but instead changed to a facial expression that signalled that he/she should no longer be selected. In the main reversal task, the facial expression used to cue reversal was an angry expression (the most natural facial expression to cue reversal), while in the second, control, version of the reversal task, a neutral expression was used. By using two different reversal tasks in which different facial expressions signalled that behaviour must change, we were able to determine which brain areas were specific to general reversal learning, rather than just to reversal following a particular expression, such as anger. We used functional magnetic resonance imaging to show that the ability to change behaviour based on facial expression is not reflected in the activity of the fusiform face area (which invariably appears to reflect only identity and not valence), but that general reversal learning is specifically correlated with activity in the lateral orbitofrontal and anterior cingulate/paracingulate cortices (as well as other brain areas, including the ventral striatum and the inferior precentral sulcus) (Kringelbach and Rolls 2003). This result confirmed and extended the results from Iversen and Mishkin's original paper. Further confirmation came from the neuropsychological testing, carried out by Julia Hornak on human patients with surgical lesions to the orbitofrontal cortex, which showed that bilateral (but not unilateral) lesions to the lateral orbitofrontal cortex produce significant impairments in reversal learning (Hornak et al. 2004). Yet, as always, these results are not conclusive and raise many new issues. It is, for instance, not presently clear what other areas of the brain are necessary and sufficient for reversal learning. Among the other brain areas we found relating to general reversal learning in our study, the ventral striatum is, for instance, an obvious candidate (Cools et al. 2002). In addition, functional magnetic resonance imaging is essentially a correlative technique, with poor temporal information, which makes it very difficult to infer causal relations between brain regions. Thus, further investigations, e.g., with magnetoencephalography, will still be required to gain temporal information on the milliseconds scale. I take heart from a friend, a very distinguished scientist, who states that the price for having spent a lifetime in cutting-edge research is that 99% of his (and other scientists') research is wrong—perhaps not completely wrong, but certainly wrong in the details. I would like to think that the original result from the Iversen and Mishkin paper is among the rare 1%, but the trouble with such foresight is that it lacks the vantage point of true hindsight. In his masterpiece, The Prince, Niccolò Machiavelli offers a rather pessimistic view on human nature, in which ‘love is held by a chain of obligation which, since men are bad, is broken at every opportunity for personal gain’. It may be that our capacity for rapid reversal learning is sometimes used for less than noble pursuits, both in science and in interpersonal relations in general, but we would be in real trouble if we couldn't learn to change. Morten L. Kringelbach is a postdoctoral researcher working with Peter Hansen in the University Laboratory of Physiology, Parks Road, Oxford OX1 3PT, United Kingdom. email: morten.kringelbach [at] physiol.ox.ac.uk ==== Refs References Anderson SW Bechara A Damasio H Tranel D Damasio AR Impairment of social and moral behavior related to early damage in human prefrontal cortex Nat Neurosci 1999 2 1032 1037 10526345 Cools R Clark L Owen AM Robbins TW Defining the neural mechanisms of probabilistic reversal learning using event-related functional magnetic resonance imaging J Neurosci 2002 22 4563 4567 12040063 Dias R Robbins T Roberts A Dissociation in prefrontal cortex of affective and attentional shifts Nature 1996 380 69 72 8598908 Hornak J O'Doherty J Bramham J Rolls ET Morris RG Reward-related reversal learning after surgical excisions in orbitofrontal and dorsolateral prefrontal cortex in humans J Cogn Neurosci 2004 In press Iversen SD Mishkin M Perseverative interference in monkeys following selective lesions of the inferior prefrontal convexity Exp Brain Res 1970 11 376 386 4993199 Kringelbach ML Rolls ET Neural correlates of rapid context-dependent reversal learning in a simple model of human social interaction NeuroImage 2003 20 1371 1383 14568506 O'Doherty J Kringelbach ML Rolls ET Hornak J Andrews C Abstract reward and punishment representations in the human orbitalfrontal cortex Nat Neurosci 2001 4 95 102 11135651 Rolls ET Hornak J Wade D McGrath J Emotion-related learning in patients with social and emotional changes associated with frontal lobe damage J Neurol Neurosurg Psychiatry 1994 57 1518 1524 7798983 Thorpe SJ Rolls ET Maddison S The orbitofrontal cortex: Neuronal activity in the behaving monkey Exp Brain Res 1983 49 93 115 6861938

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PMC406398

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2021-01-05 08:21:10

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PLoS Biol. 2004 May 11; 2(5):e140

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PLoS Biol

10.1371/journal.pbio.0020140

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020144SynopsisCancer BiologyCell BiologyMolecular Biology/Structural BiologyHomo (Human)XenopusThe Mre11 Protein Is Necessary for DNA Damage Response Synopsis5 2004 11 5 2004 11 5 2004 2 5 e144Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Mre11 Assembles Linear DNA Fragments into DNA Damage Signaling Complexes xx ==== Body With billions of cells in the adult human body, all replicating and dividing in an environment laden with toxins, radiation, and free radicals, a certain amount of DNA damage is guaranteed to occur. Fortunately, all organisms have built-in checkpoints throughout the cell cycle that prevent such mistakes from propagating. At the G1 checkpoint during cell division, for example, molecules survey nuclear DNA for errors and breaks before the cell is deemed fit to undergo S phase, the DNA replication stage. If damage is found, enzymes either work to repair it or, in some cases, trigger programmed cell death, or apoptosis. But when checkpoints fail, and DNA damage is left unrepaired, disease such as cancer can result. A better understanding of these events, as provided, for example, by Vincenzo Costanzo and colleagues in this issue, will consequently lead to a better understanding of the mechanisms that give rise to cancer. Model for Mre11 complex bridging DNA molecules A serious form of DNA damage, called a double strand break (DSB), cuts the helix clean through—a far worse scenario than if just one strand slips free. In response to a DSB, the cell recruits a signaling protein called ATM and a three-protein complex called MRN, whose components selectively bind to broken DNA ends. A malfunction of this signal and repair pathway is dire. People who suffer from the genetic disease ataxia-telangiectasia (A-T) lack a functioning ATM molecule and therefore cannot properly handle DSBs or successfully navigate the G1 checkpoint. This condition leads to a host of problems, including abnormal chromosomes, deficient immune function, and a predisposition to cancer. A-T-like disease (ATLD), another rare genetic condition, has very similar symptoms. The only difference is that the protein missing is Mre11, a subunit of MRN. While recent work on the cellular level has indicated that MRN activates ATM, the biochemical relationship between these proteins has yet to be fully understood. Studying these two molecules using traditional biochemical assays is difficult because knocking out the activity of these proteins is lethal to many cells. Costanzo and colleagues used a novel test system of cell-free frog extracts and found that Mre11 is necessary for both ATM activation and for the formation of large protein–DNA complexes apparently responsible for triggering the cascade of signaling molecules underlying the DNA damage response at the G1 checkpoint. The frog extract system allowed the team to manipulate the presence or absence of Mre11 and accurately measure the response triggered by the addition of fragmented bits of DNA (simulating naturally occurring DSBs). As predicted, without a functional Mre11 protein, ATM was not activated and there was no response. By simply adding the protein Mre11 back to the mixture, the damage response was restored. But when the researchers added a mutant form of Mre11, still capable of performing its essential tasks in another stage of the cell cycle—DNA replication—the G1 damage response remained suppressed. This mutant form of the Mre11 protein lacks the C-terminal, or DNA-binding, end. Costanzo and colleagues also found that this DNA-binding end is required for the assembly of DNA–ATM–MRN complexes in the presence of fragmented DNA and seems to direct the entire damage response. This work helps to explain the similarity between patients with A-T and those with ATLD, and hints at the formation of a large “signaling” complex that helps to orchestrate the crucial response to DBSs in DNA.

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PMC406399

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2021-01-05 08:21:15

no

PLoS Biol. 2004 May 11; 2(5):e144

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PLoS Biol

10.1371/journal.pbio.0020144

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020145EssayOtherHomo (Human)Open Access: A PLoS for Education EssayCampbell A. Malcolm 5 2004 11 5 2004 11 5 2004 2 5 e145Copyright: © 2004 A. Malcolm Campbell.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Genomics Research and Malaria Control: Great Expectations The Transcriptome of the Intraerythrocytic Developmental Cycle of Plasmodium falciparum Microarray Analysis: Genome-Scale Hypothesis Scanning The Transcriptome of the Intraerythrocytic Development Cycle of Plasmodium falciparum Teaching -- and testing -- students creatively is a challenge, but new public databases and more accessible literature are now helping to develop the critical thinking skills of students ==== Body The next generation of life scientists are currently undergraduates—and the success of this generation depends upon the quality of the education they receive. It is clear the expectations for undergraduate education are changing (Collins et al. 2003). When the National Research Council published its recommendations for changing the undergraduate training of future life scientists, the BIO2010 report, access to student-based research was a primary recommendation: “Colleges and universities should provide all students with opportunities to become engaged in research …” (National Research Council 2003). As every investigator knows, research begins in the literature, not in the laboratory. Therefore, an unstated assumption of the BIO2010 report was that students need to have unencumbered access to the research literature in order to engage in research and become scientific leaders in the 21st century. Early in my teaching career, I discussed graduate student preparation with a colleague at MIT. He said new graduate students knew about the different methods, they could even recite fine definitions—but if you asked them which method would be best to answer a particular question, they were uncertain. This reinforced my attitude towards teaching and testing. I realized that teaching science to students should be modeled on the way all scientists learn new information: in the context of an interesting question and on a need-to-know basis. This new style of teaching, “applied education,” would require me to reorganize reading materials for students, since most textbooks are written by someone who already knows all the information and has organized it accordingly. For example, describing membrane structure, protein structure, and signal transduction in Chapters 5, 12, and 15, respectively (spanning 227 pages) is not helpful for most students. It makes more sense to cover these three topics in close succession. Gradually, I converted all my courses over to this “applied education” format in which students were learning new information the same way all other scientists do. I began by asking questions that could be answered by learning the information provided by textbooks or the literature. With time, I realized that published research papers are ideal teaching tools because they cover information in the context of an interesting question and new material is presented as needed. This led me to collect series of related papers to create my own course materials (see www.bio.davidson.edu/courses/Molbio/Publicschedule.html#anchor99574051). So, for example, in my classes students first read the elegant paper by Munro and Pelham (1987) that uncovered the tetrapeptide lysine–aspartic acid–glutamic acid–leucine (KDEL) retention signal for proteins destined to remain in the endoplasmic reticulum lumen. Then, students read four additional papers, one of which is composed of weak data and overinterpreted analysis. Through this series of papers, students learn to trust their own assessment of the data rather than the authors': this is a very substantial improvement in student thinking and in their attitude towards the literature. I do not emphasize the particular details of these paper, but I do want the students to gain higher-order thinking skills. Therefore, my tests consist of figures from research papers that the students have never seen before. They are asked to interpret the figures as they appear in the papers and/or to design new experiments to answer a new question, given what they have learned from the published figure. Testing them in this way, students very quickly understand that memorizing details is not productive, but learning how to read scientific literature and design well-controlled experiments is much more rewarding (see www.bio.davidson.edu/courses/Molbio/molecular.html#2003exams). Based on this success, I have designed my genomics course on the “applied education” principle (see below; see also www.bio.davidson.edu/genomics). Access to Information Changes Education When I was a graduate student (in the late 1980s and early 1990s), PubMed was restricted to those institutions that could afford the subscription fee; now PubMed is freely available to all who have Internet access. This change in access to PubMed has significantly improved undergraduate training by providing students with the opportunities to do literature searches for their lab reports, papers, seminars, and of course original research. Free access to information in the life sciences has continued to evolve with the newest phenomenon in publishing—open-access journals. PubMed Central (http://www.pubmedcentral.nih.gov/) is a rich repository of and portal to open access articles, BioMed Central (http://www.biomedcentral.com/) publishes a growing number of open-access journals, and there are a few new open-access education journals such as Cell Biology Education (http://www.cellbioed.org) and the Journal of Undergraduate Neuroscience Education (http://www.funjournal.org). As the newest player in the open-access arena, PLoS Biology has further enriched the growing espritdes-corps of publishing and has already improved undergraduate education. My students now have equal access to a growing portion of the literature that Nobel laureates and investigators at wealthy institutions enjoy. Interestingly, the push towards open access has led many subscription-based journals to permit “free access” two to 12 months after publication. These time-delayed free-access journals are helpful for course adjustments in the subsequent academic year, but not the current semester. Unfortunately, owing to the high cost of subscriptions for many journals, the library at my institution (like many other libraries) is forced to make difficult choices about which journals we can afford. The number of journal subscriptions goes down in proportion to the rise of subscription costs, but fortunately this loss is being offset by the creation of new open-access journals. The Promise of the Internet I have been teaching undergraduates since 1993 and have noticed a trend in the way I teach—increasingly, I have provided research papers to my students so they can learn to read those papers and improve their critical thinking skills. One reason for my increased use of research papers is the development of PDFs. When I first started using journal articles in my molecular biology course, the class had to meet in the library so we could pass around the bulky bound volumes to detect the important subtleties often lost in photocopied versions of figures. Later, I learned how to scan the figures and generate Web pages so that I could project the images in class and so that students could print laser-quality versions of papers (see http://www.bio.davidson.edu/molecular). Now I use PDF files for students to print and for me to display in class with no loss of information due to reformatting or resolution problems (Figure 1). Figure 1 Comparison of Published and Photocopied Figures Example of an image that, when seen in color (A), is rich with information; much of this information is lost when it is photocopied by students (B), as when the original is held on reserve in the library, as is required for subscription-based journals, or is provided via interlibrary loan. This image of a developing fly embryo was labeled to reveal bands of differentially expressed proteins, with HAIRY in red, KRüPPEL in green, and GIANT in blue. (Image courtesy of Stephen W. Paddock, Jim Langeland, and Sean Carroll at the University of Wisconsin–Madison.) With my increased confidence from using research papers in my molecular biology class, I began experimenting with research papers for my introductory students. First-year students are not ready to critically evaluate complex data, but they are beginning their first forays into reading review articles and occasionally original research papers. When introductory students make presentations of their findings in laboratory courses, increasing numbers are utilizing PubMed and PDF reprints when they are available. Students have been reading primary research papers since well before PDF files became available, but the increased access to papers online and the improved quality of the format has significantly enhanced the use of research and review papers in the undergraduate curriculum. It is common for students in upper-level lecture and lab courses to read papers (DebBurman 2002; Hall and Harrington 2003; Kitchen et al. 2003; Mulnix 2003), and seminar courses are usually dominated by student presentations of literature (Wright and Boggs 2002; Hales 2003; Lom 2003). It is worth noting that most colleges and universities are being told to reduce expenditures, and one frequent target of money-saving measures is the ever-increasing costs of library journal subscriptions. This fiscal reality will erode the pedagogical gains made by faculty who are already meeting one of the goals of the BIO2010 report by immersing students in the research literature. However, open-access journals are proving to be virtual oases in a desert of pay-per-view journals that are available on a sliding scale that favors the richest and biggest institutions. Using Open-Access Resources for Creative Teaching … During the past three years, I have taught an undergraduate course in genomics (www.bio.davidson.edu/genomics) in which I capitalize on a confluence of two trends in the field: public domain databases and open-access journals (Campbell 2003). In my genomics class, students have three assignments for which they are required to mine databases for sequence, transcriptome, and proteome information (see www.bio.davidson.edu/courses/genomics/2003/cain/home.html). But genomics courses are not the only beneficiaries, since other classes at many institutions (e.g., introductory biology, biochemistry, cell development, genetics, microbiology, molecular biology [see http://www.bio.davidson.edu/courses/Molbio/standardsHP.html#anchor78181983], and neuroscience) require students to mine public domain databases (Dyer and LeBlanc 2002; Honts 2003). This year, we introduced genome database searching to our introductory biology students (see www.bio.davidson.edu/people/macampbell/Hope/DQ/DQ9.html and www.bio.davidson.edu/people/macampbell/Hope/DQ/DQ10.html). First-year students use Genome Browser and BLAST to determine the molecular causes of cystic fibrosis and Huntington disease, respectively. The benefit of public databases and open-access literature to educators is obvious and immediate. Images can be used in lectures, and papers can be distributed easily and on short notice for class use. There is no need to worry about limited access due to subscription costs nor an obligation to obtain copyright permission from publishers, which is a bothersome and sometimes expensive process for busy faculty members. By reducing nonproductive busy work for faculty, open-access journals have already created an environment that is improving undergraduate education today with long-term benefits in creating research-ready graduate students. Students who are exposed to publicly available literature through their coursework often develop an expectation that all research papers will be freely available to them from any computer and become frustrated if they do not have access to all the journal articles they want and need to read. Increasingly, I have students sending me PDF files of open-access journal articles they have read and want to share with me. Who would have guessed that free access to journals would result in students mining the literature for relevant papers and sending them to their instructors for consideration? In addition to papers related to their own classes and research, students also enjoy learning about “hot topics” from scientific publications and those stories that quickly reach the popular press. Examples include the use of DNA microarrays and sequencing to identify the causative agent for SARS (Wang et al. 2003) and a good review article of small inhibitory RNA (Dillin 2003). Two common educational goals are to encourage students to become skeptical of unsubstantiated claims and to enable students to evaluate data critically. One way to accomplish these goals is to capitalize on the natural curiosity of students and ask them to compare topics in the popular press to that in the scientific literature (see http://www.bio.davidson.edu/courses/genomics/2003/poulton/p21.html). Open-access journals make these two educational goals much more feasible because students can utilize current findings immediately without having to wait for interlibrary loans, which can take up to two weeks, can cost up to $20 per article, and can result in poor-quality black-and-white photocopies. … and for Thought-Provoking Testing If we want students to achieve higher levels of thinking (Bloom et al. 1956), we need to model our courses so students can learn by examples and are rewarded for learning to critically evaluate data and for inspecting evidence before believing claims made by authors (Brill and Yarden 2003). Students quickly figure out what intellectual behaviors are rewarded in exams. If exam questions simply require students to regurgitate factoids, then higher levels of thinking are unlikely to be demonstrated by students. It is difficult to create good exam questions that cover the course material and reward students who have learned to read critically and to interpret data. Over the last few years, increasingly I have turned to current literature to find raw data for my exam questions. For example, for my genomics class in Fall 2003, I used a paper published in PLoS Biology that utilized DNA microarrays to analyze the life cycle of malaria-causing Plasmodium (Bozdech et al. 2003). I asked students to interpret several figures, using their own words (Figure 2). Owing to my choosing to use an open-access journal, my students also had full access to the supporting information, which two students utilized to enhance their answers. For this question, these two produced answers that were better than mine. Another exam question required students to mine a database associated with the Bozdech paper (see http://malaria.ucsf.edu/index.php). Students were asked to combine what they learned from the paper and the course and choose new proteins (in addition to the ones described in Bozdech et al. [2003]) that would make good candidates for vaccines based on the timing of gene transcription. In order to answer this question, students performed the first steps in real research, which rewards students for learning higher-order thinking skills. Figure 2 Example of Student's Data Mining for Exam Question Figure 2 from Bozdech et al. (2003) showing the gene expression profiles for 12 groups of genes expressed at different stages of Plasmodium life cycle inside red blood cells. Genomics students were asked to summarize this figure as a part of a take-home exam. At the end of their exam, students were given an opportunity for extra credit points (a maximum of three points out of 100 available on the exam) if they provided constructive criticism directly to the database curators. About 70% of the students sent comments, including this one: “In recently using your database, I found it difficult to search the Plasmodium gene expression data with multiple constraints. For example, it would be helpful if there were a way to identify all the genes within a certain functional group that fell within certain time or amplitude constraints. Is this possible in this database?” The curators very professionally responded to the students' suggestions, which resulted in three new search capacities being added to the database, as can be seen on the left side of the main page (see http://malaria.ucsf.edu/index.php). As a result of these professional interactions, students became participants in a community of scholars, interacting with investigators at the University of California, San Francisco, while taking their exams. The use of open-access journals for teaching and testing has already improved my courses. I can provide exam questions that are more interesting, more educational, and more current. Furthermore, I accomplish two tasks simultaneously: I keep abreast of new developments in my field and I write exam questions. But what do students think? While I have not formally assessed student attitudes, I have collected information from end-of-semester course evaluations, including the following comments: “One of the best parts of the entire course for me were the exams. The exams really gave me an opportunity to show how I could work through real problems. This class definitely increased my critical thinking skills. Each test presented me with new ideas and problems to work through. I enjoyed the idea that each exam would be a learning experience.” The Future Teaching is a lot like raising children. Like parents, teachers provide learning opportunities in part by modeling the behavior we want our students to learn. By choosing the most current literature as testing material, my students realize that I read the literature to stay current in my field and that there are always new opportunities to learn, analyze, and design experiments, etc. By my choosing open-access papers such as those published in PLoS Biology, my students benefit from free access to published research results. Free access to research literature enhances student learning and helps produce the next generation of graduate students, who are then better trained. Open-access publishing provides the right mix of benefits for educators and students alike. I would like to thank my students for their willingness to experiment with the boundaries of the teaching/learning relationship. Also, I would like to thank Barbara Lom for critical reading of this manuscript and her helpful suggestions. A. Malcolm Campbell is an associate professor of biology at Davidson College in Davidson, North Carolina, United States of America, and the founding director of the Genome Consortium for Active Teaching. E-mail: macampbell@davidson.edu ==== Refs References Bloom BS Englehart MD Furst EJ Hill WH Krathwohl DR A taxonomy of educational objectives. Handbook 1, Cognitive domain 1956 New York McKay 207 Bozdech Z Llinás M Pulliam BL Wong ED Zhu J The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum PLoS Biol 2003 1 e5 10.1371/journal.pbio.0000005 12929205 Brill G Yarden A Learning biology through research papers: A stimulus for question-asking by high-school students Cell Biol Educ 2003 2 266 274 14673492 Campbell AM Public access for teaching genomics, proteomics, and bioinformatics Cell Biol Educ 2003 2 98 111 12888845 Collins FS Green ED Guttmacher AE Guyer MS A vision for the future of genomics research Nature 2003 422 835 847 12695777 DebBurman SK Learning how scientists work: Experiential research projects to promote cell biology learning and scientific process skills Cell Biol Educ 2002 1 154 172 12669101 Dillin A The specifics of small interfering RNA specificity Proc Natl Acad Sci U S A 2003 100 6289 6291 12754379 Dyer BD LeBlanc MD Meeting report: Incorporating genomics research into undergraduate curricula Cell Biol Educ 2002 1 101 104 12669103 Hales K Biology 2003 362 Human genetics. Available at www.bio.davidson.edu/people/kahales/Bio362HumanGenetics.html via the Internet. Accessed 9 January 2004 Hall AC Harrington ME Experimental methods in neuroscience: An undergraduate neuroscience laboratory course for teaching ethical issues, laboratory techniques, experimental design, and analysis J Undergrad Neurosci Educ 2003 2 Suppl A1 A7 23493933 Honts JE Evolving strategies for the incorporation of bioinformatics within the undergraduate cell biology curriculum Cell Biol Educ 2003 2 233 245 14673489 Kitchen E Bell JD Reeve S Sudweeks RR Bradshaw WS Teaching cell biology in the large-enrollment classroom: Methods to promote analytical thinking and assessment of their effectiveness Cell Biol Educ 2003 2 180 194 14506506 Lom B Biology 2003 351 Group investigation in developmental neurobiology. Available at www.bio.davidson.edu/people/balom/351syll.html via the Internet. Accessed 9 January 2004 Mulnix AB Investigations of protein structure and function using the scientific literature: An assignment for an undergraduate cell physiology course Cell Biol Educ 2003 2 248 255 14673490 Munro S Pelham HRB A C-terminal signal prevents secretion of luminal ER proteins Cell 1987 48 899 907 3545499 National Research Council BIO2010: Transforming undergraduate education for future research biologists 2003 Washington, District of Columbia The National Academies Press 191 Wang D Urisman A Liu Y-T Springer M Ksiazek TG Viral discovery and sequence recovery using DNA microarrays PLoS Biol 2003 1 e2 10.1371/journal.pbio.0000002 14624234 Wright R Boggs J Learning cell biology as a team: A project-based approach to upper-division cell biology Cell Biol Educ 2002 1 145 153 12669105

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020146Unsolved MysteryNeuroscienceHomo (Human)Mus (Mouse)The Human Sense of Smell: Are We Better Than We Think? Human Sense of SmellShepherd Gordon M 5 2004 11 5 2004 11 5 2004 2 5 e146Copyright: © 2004 Gordon M. Shepherd.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Gordon Shepherd challenges the notion - based on genetic evidence - that olfaction is less well developed in humans as compared to other mammals ==== Body “… a complete, comprehensive understanding of odor … may not seem a profound enough problem to dominate all the life sciences, but it contains, piece by piece, all the mysteries.” — Lewis Thomas One of the oldest beliefs about human perception is that we have a poor sense of smell. Not only is this a general belief among the public, but it appears to have a scientific basis. Recent genetic studies show a decline in the number of functional olfactory receptor genes through primate evolution to humans. Human evolution was characterized by the gradual ascendance of vision and reduction of smell, evidenced in the anthropological record by the progressive diminution of the snout as the eyes moved to the middle of the face to subserve depth vision (Jones et al. 1992). Concurrently, the use of an arboreal habitat and the adoption of an erect posture moved the nose away from the ground, with its rich varieties of odors. However, some recent behavioral studies suggest that primates, including humans, have relatively good senses of smell. Resolution of this paradox may come from a larger perspective on the biology of smell. Here we begin by reassessing several overlooked factors: the structure of the nasal cavity, retronasal smell, olfactory brain areas, and language. In these arenas, humans may have advantages which outweigh their lower numbers of receptors. It appears that in the olfactory system, olfactory receptor genes do not map directly onto behavior; rather, behavior is the outcome of multiple factors. If human smell perception is better than we thought, it may have played a more important role in human evolution than is usually acknowledged. Gene Studies From rodents through the primate series to humans there is a progressive reduction in the proportion of functional olfactory receptor genes (Rouquier et al. 2000; Gilad et al. 2004). Mice have approximately 1,300 olfactory receptor genes, of which some 1,100 are functional (Young et al. 2002; Zhang and Firestein 2002), whereas humans have only some 350 functional genes of approximately 1,000 (Glusman et al. 2001; Zozulya et al. 2001). The conclusion seems obvious: the low number of functional olfactory receptor genes in humans compared with rodents—and presumably most other mammals—is directly correlated with the evolutionary decline in the human sense of smell. Behavioral Studies Although these conclusions seem incontrovertible, they are challenged by some recent behavioral studies. One type of study shows that much of the olfactory system can be removed with no effect on smell perception. The olfactory receptor genes map topographically onto the first relay station, a sheet of modules called glomeruli in the olfactory bulb. Up to 80% of the glomerular layer in the rat can be removed without significant effect on olfactory detection and discrimination (Bisulco and Slotnick 2003). If the remaining 20% of the glomeruli—and the olfactory receptor genes they represent—can subserve the functions of 1,100 genes, it implies that 350 genes in the human are more than enough to smell as well as a mouse. Another type of study has tested smell perception in primates, and has shown that, despite their reduced olfactory receptor gene repertoire, primates, including humans, have surprisingly good senses of smell (Laska et al. 2000). Comparing the data on smell detection thresholds shows that humans not only perform as well or better than other primates, they also perform as well or better than other mammals. When tested for thresholds to the odors of a series of straight-chain (aliphatic) aldehydes, dogs do better on the short chain compounds, but humans perform as well or slightly better than dogs on the longer chain compounds, and humans perform significantly better than rats (Laska et al. 2000). Similar results have been obtained with other types of odors. A third type of study demonstrating human olfactory abilities shows that in tests of odor detection, humans outperform the most sensitive measuring instruments such as the gas chromatograph. These results indicate that humans are not poor smellers (a condition technically called microsmats), but rather are relatively good, perhaps even excellent, smellers (macrosmats) (Laska et al. 2000). This may come as a surprise to many people, though not to those who make their living by their noses, such as oenologists, perfumers, and food scientists. Anyone who has taken part in a wine tasting, or observed professional testing of food flavors or perfumes, knows that the human sense of smell has extraordinary capacities for discrimination. The Mystery Here, then, is the mystery: how can one reconcile a relatively high sensitivity to smell with a relatively low number of olfactory receptors in the nose? To answer this question, I think we need to look beyond the olfactory receptor genes and consider olfaction in its full behavioral context. This requires considering several overlooked aspects of the olfactory system: the nasal cavity, the oropharyngeal cavity, the olfactory brain, and the role of language. In this article I focus on behaviors related to conscious perception of ordinary smells. Pheromones, and the rich world of unconscious effects of odors and pheromones, are beyond the present scope (cf. Jacob et al. 2004), though they undoubtedly will add to the general conclusions. The Filtering Apparatus of the Nasal Cavity A marked difference between the noses of primates and other mammals is that in nearly all nonprimate mammals, the nasal cavities contain at the front a much-convoluted filtering apparatus (formed by the ethmo- and maxillo-turbinals) covered with respiratory membrane. This filtering apparatus is a biological air conditioner (Negus 1958) with three key functions: cleaning, warming, and humidifying the inspired air. An important function of the filtering apparatus is presumably to protect the nasal cavity from infections. In many mammals, air drawn into the nose is often highly contaminated with bacteria from fecal material, decaying animal and plant material, and noxious fumes from the environment, all of which attack the olfactory epithelium. Rodents are susceptible to chronic rhinitis, which causes substantial loss of functioning olfactory receptor cells (Hinds et al. 1984). This filtering, however, might have negative consequences for odor detection. Warming and humidification presumably enhance the odor-stimulating capacity of the inhaled air, but cleaning would remove odor molecules by absorbing them into the lining of the epithelium, an effect which could be large depending on the size of the filtering apparatus. If so, mammals with large snouts might have a large inventory of olfactory receptors at least in part to offset the loss of odor molecules absorbed by the filtering apparatus. How do these considerations relate to humans? The evolution of humans involved lifting the nose away from the noxious ground environment as they adopted a bipedal posture (Aiello and Dean 1990). This would have reduced the need for the filtering apparatus and with it the losses of absorbed odor molecules. The large numbers of olfactory receptors and receptor cells would have come under reduced adaptive pressure and could accordingly be reduced in proportion. By this hypothesis, during human evolution the snout could be reduced in dimensions and complexity without compromising the ultimate amounts of odorized air reaching the olfactory epithelium. The reduced snout allowed the eyes to come forward and lie closer together to promote more effective stereoscopic vision. Thus, vision could become more dominant in humans without sacrificing unduly the sense of smell. Tests of this hypothesis are needed, including calculations of air flows and odor losses through the filtering apparatus in mammals with extensive filtering apparatuses compared with the simpler nasal cavities of primates. Humans Receive Richer Retronasal Smells Being carried in with inhaled air (the orthonasal route) is not the only way for odor molecules to reach the olfactory receptor cells. Odor molecules also reach the olfactory receptor cells via the retronasal route, from the back of the oral cavity through the nasopharynx into the back of the nasal cavity. Although the orthonasal route is the one usually used to test for smell perception, the retronasal route is the main source of the smells we perceive from foods and liquids within our mouths. These are the smells that primarily determine the hedonic (i.e., pleasurable or aversive) qualities of foods, and that, combined with taste and somatosensation, form the complex sensation of flavor. It is likely, for several reasons, that this is an important route for smell in humans. First, with the adoption of bipedalism, humans became increasingly wide ranging, with concomitant diversification of diet and retronasal smells. Second, the advent of fire, perhaps as early as 2 million years ago (Wrangham and Conklin-Brittain 2003), made the human diet more odorous and tasty. From this time also one can begin to speak of human cuisines of prepared foods, with all their diversity of smells. Wrangham and Conklin-Brittain (2003) support the view that prepared cuisines based on cooked foods are one of the defining characteristics of humans. Third, added to the cooked cuisines were fermented foods and liquids, with their own strong flavors. These developments occurred among the early hunter-gatherer human cultures and continued through the last ice age. With the transition to agricultural and urban cultures 10,000 years ago, human cuisines changed by the advent of animal domestication, plant cultivation, use of spices, and of complex procedures, such as those for producing cheeses and wines, all of which produced foodstuffs that especially stimulate the smell receptors in the nose through the retronasal route and contribute to complex flavors. These considerations suggest the hypothesis that the retronasal route for smells has delivered a richer repertoire of smells in humans than in nonhuman primates and other mammals (see Figure 1). Research on retronasal olfaction is being actively pursued (reviewed in Deibler and Delwiche 2004). Studies are needed of the evolutionary pressures on this route in addition to the pressures on the evolution of the snout. Figure 1 Hypothetical “Odor Wheel” Representing and Comparing the Odor Worlds of Mouse and Human The inner part represents the different categories of odors for the mouse; the relative importance of each category for mouse smell-dependent behavior is indicated by the area of each wedge. The outer part represents the same categories for the human; the importance of each category for human smell-dependent behavior compared with the mouse is indicated by the area of each wedge. Note the greater importance of food odors for the human, reflecting the factors discussed in the text. Note also the retention of some sensitivity in humans to social odors and other odors prominent in rodents, though in many cases to still undetermined degrees. Based on numerous sources and the hypotheses discussed in the text. Humans Smell with Bigger and Better Brains Comparisons of the decreasing size of the olfactory system relative to expansion of the visual, auditory, and somatosensory systems usually focus on the olfactory bulb and lateral olfactory tract, which are relatively small. However, what matters more are the central olfactory brain regions that process the olfactory input as the basis for smell perception. These regions are more extensive in humans than is usually realized. The dedicated olfactory regions include the olfactory cortex, the olfactory tubercle, the entorhinal cortex, parts of the amygdala, parts of the hypothalamus, the mediodorsal thalamus, the medial and lateral orbitofrontal cortex, and parts of the insula (Neville and Haberly 2004). These regions are involved in immediate processing of odor input and probably subserve the specific tasks of smell detection and simple smell discrimination. For more complex tasks, memory becomes important in comparing smells, thus involving the temporal and frontal lobes (e.g., Buchanan et al. 2003) and the specifically human higher association areas. It may be hypothesized that these regions enable humans to bring far more cognitive power to bear on odor discrimination than is possible in the rodent and other mammals. The reduced repertoire of olfactory receptor genes in the human is thus offset by the expanded repertoire of higher brain mechanisms. Rather than being restricted to a tiny part of the brain, olfactory processing of complex smells, such as those produced by human cuisines, draws on the enlarged processing capacity of the human brain. Language Is Necessary for Human Smell In the enlarged processing capacity for perceiving and discriminating odors, language plays a critical role. This seems paradoxical, for we have great difficulty describing a smell in words. Insight into this difficulty comes from the finding that different smells are represented in the olfactory bulb by different patterns of olfactory glomerular activity. These patterns function as virtual “odor images” (Xu et al. 2003). It has been hypothesized that these odor images provide the basis for discrimination between odors, analogous to the way that retinal images are the basis for discrimination of visual pattern stimuli. The complex patterns constituting odor images may be considered as analogous to the complex patterns constituting visual images of faces. And just as we are very good at recognizing a human face, yet have difficulty describing it in words, we have a hard time describing and verbally comparing odor images. Because of this difficulty, describing a smell or a taste in words is very demanding. A professional wine tasting, for example, requires many steps: analysing both orthonasal and retronasal perception, comparing the two in memory with each other and with all other wines to be compared, identifying the constituent properties separate from the hedonic qualities, and finding the words to describe the process as it unfolds, leading to the final formulation to characterize the quality of the wine and identify it as distinct from all others. It may be characterized as hard cognitive work that only a human, among all the animals with olfactory organs, can do. It may be argued that this is what humans are adapted to do (Wrangham and Conklin-Brittain 2003). This cognitive work is largely independent of the numbers of peripheral receptor cells and their genes. A good analogy is with language. There are some 17,000–20,000 auditory nerve fibers in the rat and cat and some 25,000–30,000 in the human (cf. Hall and Masengill 1997). This modest increase in the input from the peripheral auditory receptors provides little basis for the development of human speech and language, which had much more to do with the increase in the central brain mechanisms that elaborate the input. It may be hypothesized that a similar conclusion applies to human olfaction. Implications for Systems Biology A general result from these considerations is that there appears not to be a one-to-one relation between the number of olfactory receptor genes and the detection and discrimination of odors. This implies that we are dealing with a fundamental problem in relating genes to systems behavior: a given set of genes may not map directly onto a given behavior. In this respect the mystery being addressed here is a caution for the new era of “systems biology” and against any belief that behavior can be related directly to genomes, proteomes, or any other type of “-ome.” We are reminded instead that the functional ecology of the body is dependent on many factors. Conclusions Much about the sense of smell seems enigmatic and conflicting. This is partly because of the inherent difficulties in presenting smell stimuli, and partly because there is not yet a recognition of all the relevant mechanisms that are involved. It may be hoped that the hypotheses and mechanisms discussed here can help to address and resolve the mystery of the apparent noncorrelation of olfactory receptor gene numbers with smell acuity, and in doing so stimulate a major reassessment of human smell perception. Such an effort cuts across many academic disciplines. Molecular biologists need to continue their efforts to characterize the olfactory genomes of humans and nonhuman mammals more closely, to compare how different organisms sample odor space. Physiologists need to devise high-throughput systems to test these odor spaces. Behavioral neuroscientists need to develop increasingly accurate tests of olfactory function that enable comparisons across different species. Psychologists need to explore even more vigorously the subtle ways that smells can influence human behavior. Anthropologists and paleontologists need to study the olfactory parts of the cranium and face from this new perspective, to reassess the role that both orthonasal and retronasal smell may have played in primate and human evolution. The factors reviewed here suggest that the sense of smell is more important in humans than is generally realized, which in turn suggests that it may have played a bigger role in the evolution of human diet, habitat, and social behavior than has been appreciated. All of these considerations should stimulate a greater interest in this neglected sense. I am grateful to C. Greer, L. Bartoshuk, and D. Waddle-Stock for valuable discussions. The work of my laboratory has been supported by the National Institute for Deafness and other Communicative Disorders, the Human Brain Project (National Institute for Deafness and other Communicative Disorders, National Institute for Neurological Diseases and Stroke, National Institute for Mental Health, National Institute for Aging, and National Institute for Alcoholism and Drug Abuse), and a Multi-university Research Initiative grant from the Department of Defense. Gordon M. Shepherd is in the Department of Neurobiology at the Yale University School of Medicine, New Haven, Connecticut, United States. E-mail: gordon.shepherd@yale.edu ==== Refs References Aiello L Dean C An introduction to human evolutionary anatomy 1990 New York Academic Press 608 Bisulco S Slotnick B Olfactory discrimination of short chain fatty acids in rats with large bilateral lesions of the olfactory bulbs Chem Senses 2003 28 361 370 12826532 Buchanan TW Tranel D Adolphs R A specific role for the human amygdala in olfactory memory Learn Mem 2003 10 319 325 14557604 Deibler KD Delwiche J Handbook of flavor characterization: Sensory analysis, chemistry, and physiology 2004 New York Marcel Dekker 493 Gilad Y Wiebe V Przeworski M Lancet D Pääbo S Loss of olfactory receptor genes coincides with the acquisition of full trichromatic vision in primates PLoS Biol 2004 2 e5 10.1371/journal.pbio.0020005 14737185 Glusman G Yanai I Rubin I Lancet D The complete human olfactory subgenome Genome Res 2001 11 685 702 11337468 Hall RD Massengill JL The number of primary auditory afferents in the rat Hear Res 1997 103 75 84 9007576 Hinds JW Hinds PL McNelly NA An autoradiographic study of the mouse olfactory epithelium: Evidence for long-lived receptors Anat Rec 1984 210 375 383 6542328 Jacob S Spencer NA Bullivant SB Sellergren SA Mennella JA Effects of breastfeeding chemosignals on the human menstrual cycle Hum Reprod 2004 19 422 429 14747191 Jones S Martin R Pilbeam D The Cambridge encyclopedia of human evolution 1992 New York Cambridge University Press 520 Laska M Seibt A Weber A “Microsmatic” primates revisited: Olfactory sensitivity in the squirrel monkey Chem Senses 2000 25 47 53 10667993 Negus V The comparative anatomy and physiology of the nose and paranasal sinuses 1958 London E & S Livingstone 402 Neville KR Haberly LB Shepherd GM Olfactory cortex The synaptic organization of the brain, 5th ed 2004 New York Oxford 415 454 Rouquier S Blancher A Giorgi D The olfactory receptor gene repertoire in primates and mouse: Evidence for reduction of the functional fraction in primates Proc Natl Acad Sci U S A 2000 97 2870 2874 10706615 Wrangham R Conklin-Brittain N Cooking as a biological trait Comp Biochem Physiol A Mol Integr Physiol 2003 136 35 46 14527628 Xu F Liu N Kida I Rothman DL Hyder F Odor maps of aldehydes and esters revealed by functional MRI in the glomerular layer of the mouse olfactory bulb Proc Natl Acad Sci USA 2003 100 11029 11034 12963819 Young JM Friedman C Williams EM Ross JA Tonnes-Priddy L Different evolutionary processes shaped the mouse and human olfactory receptor gene families Hum Mol Genet 2002 11 535 546 11875048 Zhang X Firestein S The olfactory receptor gene superfamily of the mouse Nat Neurosci 2002 5 124 133 11802173 Zozulya S Echeverri F Nguyen T The human olfactory receptor repertoire Genome Biol 2001 2 18 RESEARCH00

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PLoS Biol. 2004 May 11; 2(5):e146

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PLoS Biol

10.1371/journal.pbio.0020146

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020147Book Reviews/Science in the MediaEcologyEvolutionZoologyNiche Markets Niche MarketsManning Peter Godfray H. Charles J 5 2004 11 5 2004 11 5 2004 2 5 e147Copyright: © 2004 Manning and Godfray.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Is evolutionary theory is incomplete and are we failing to understand phenomena as disparate as ecosystem development and the interplay of genes and culture in shaping human evolution? ==== Body This book, the latest in the excellent Monographs in Population Biology series from Princeton University Press, is a work of advocacy in which the authors argue that evolutionary theory is incomplete and that, in consequence, we are failing fully to understand phenomena as disparate as ecosystem development and the interplay of genes and culture in shaping human evolution. What we are missing, they argue, is an appreciation of niche construction, the process by which an organism modifies the abiotic and biotic environment in which it is subject to natural selection. The authors' major assertion is that the importance of niche construction is so great that it should be regarded “after natural selection, as a second major participant in evolution” and that it is “not just an important addition to evolutionary theory” but “requires a reformulation of evolutionary theory”. Bold claims indeed. After introducing the conceptual framework Odling-Smee et al. set out a series of arguments to support this position, the first of these being the empirical case for the existence of niche construction. Niche construction, as broadly interpreted here, is everywhere. Animals build nests, burrows, and protective cases and so alter the environment they experience in a way that may select for further adaptations. The changes caused by some animal species, such as beavers and earthworms, are of a sufficient magnitude that the environment experienced by a host of other species is affected. Many plant species also modify the environment they experience by generating organic litter; influencing hydrological and biogeochemical cycles; affecting temperature, humidity, and light regimes; and, over the longer term, determining the make up of the atmosphere. Decomposer and chemoautotrophic microorganisms similarly influence biogeochemical transformations, while parasitic species can manipulate the behaviour and internal environment of the hosts they infect. Perhaps less obvious examples of niche construction are the many types of migration and cultural evolution that, like physical transformations, cause the organism's descendants to experience a different selective environment. After this broad, accessible survey, the authors change key rather abruptly and explore two-locus, frequency-dependent population genetics. The novelty here is that selection on one locus depends on the history of gene frequencies at the other, “niche construction”, locus. In an extension, gene frequencies at one locus affect an environmental variable with its own dynamics that in turn influences the second locus. As one would guess, the models display a range of potentially interesting dynamics, though generalisations and broad conclusions are sparse. We guess the aim of the chapter is to illustrate that environmental feedbacks can be potent and general agents of evolutionary change, but the restriction of the theory to such a narrow model, with very technical explanation, risks losing the few readers who we suspect will stay the course (did we really need a rederivation for haplodiploids?). Perhaps aware of the dangers of getting bogged down in detail, the argument then moves to proving a case for the universality of niche construction. Invoking the second law of thermodynamics and Maxwell's Demon, the authors lead us through a challenging thesis that concludes that the persistence of life on earth requires both natural selection and niche construction, thereby justifying some of the bold claims for their new theory. We think they are technically correct, but we are concerned that the demonstration of the inescapability of niche construction, as defined here, does not guarantee that it will actually tell us new and important things about the world, as the theory of natural selection has. The remainder of the book explores the implications of niche-construction thinking for evolutionary biology, ecology, and the human sciences, and in our view is the most successful part. Though it is rare for the authors to offer new analysis and insight, their sideways look at many issues from the niche-construction viewpoint often offers interesting new angles on old problems, and suggests new avenues of enquiry that may be the book's greatest legacy. A good example of this is their convincing and timely argument that a more explicit recognition of evolution's role in environmental feedbacks will help to unify population/community ecology and ecosystem science. The chief argument for the prosecution is that niche construction is common but not pervasive, and that wherever ecologists and evolutionists have found interesting examples of it, they have developed appropriate theory and concepts to understand its ramifications. For example, some of the clearest examples of niche construction occur in plant succession where, as F.E. Clements realised nearly a hundred years ago, early-succession plants frequently modify their environment in ways that allow other species to replace the pioneers. Interestingly, the strict Clementsian theory of facilitation, niche construction avant la lettre, has given way to a more pluralistic theory of succession. It is a great pity that the authors give so little space to plants and plant ecology, as it here that some of the finest examples of niche construction are found, as well as the best-developed conceptual framework for studying the roles of environmental feedback. Other areas where biologists have well-developed theories of the influences and impacts of niche construction include co-evolutionary theory, where the environmental feedbacks are largely biotic, and Dawkins' theory of the extended phenotype. Very close to some of the arguments discussed here, and generously acknowledged, is the idea developed by Jones, Lawton, and colleagues of ecosystem engineers, species that have a major impact on the abiotic environment experienced by a large number of species. A major strength of the book is that it reveals common processes and patterns underlying disparate biology in consistently interesting ways. Its chief contribution is thus not to tell us new things about how nature works but to link together many different aspects of ecology under an umbrella of theory that may in the future lead to new insights. Do they deliver on their grand claims? Time will tell, but our view is that they don't. They engagingly admit that for their project to succeed the new theory must earn its keep by producing significant new biology—something which has yet to occur. However, the great need for the biological and human sciences to integrate across subdisciplines, as the authors bravely attempt here, makes this a hugely worthwhile book. Its breadth of scope and its boldness in creating syntheses have resulted in a stimulating and challenging read. Peter Manning and Charles Godfray are at the Natural Environment Resource Council Centre for Population Biology, Imperial College London, Ascot, United Kingdom. E-mail: c.godfray@imperial.ac.uk Book Reviewed Odling-Smee FJ, Laland KN, Feldman MW (2003) Niche construction: The neglected process in evolution. Princeton (New Jersey): Princeton University Press. 468 p. ISBN (hardcover) 0-691-04438-4. US$75.00.

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PLoS Biol. 2004 May 11; 2(5):e147

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PLoS Biol

10.1371/journal.pbio.0020147

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020148FeatureDevelopmentGenetics/Genomics/Gene TherapyPharmacology/Drug DiscoveryDanio (Zebrafish)Small Fish, Big Science Small Fish, Big ScienceBradbury Jane 5 2004 11 5 2004 11 5 2004 2 5 e148Copyright: © 2004 Jane Bradbury.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The European Union recently awarded 12 million Euros to the ZF-MODELS research consortium to study zebrafish models for human development and disease ==== Body Francis Hamilton, the Briton who first described zebrafish (Danio rerio) in 1822, would be astounded to see the scientific attention now afforded to this two-inch-long native of Indian rivers. A fish with no economic worth was how he described this little creature. Yet recently, the European Union awarded 12 million Euros to the ZF-MODELS research consortium to study zebrafish models for human development and disease. When and why did zebrafish swim from home aquaria into research labs, and what can we learn about our biology from this surprising source? The Early Days It was the late 1960s when phage geneticist George Streisinger began to look for a model system in which to study the genetic basis of vertebrate neural development. His passion for tropical fish led him to the humble zebrafish. He was a ‘visionary’, remembers neurobiologist Judith Eisen (University of Oregon, Eugene, Oregon, United States), ‘who laid the groundwork for the use of zebrafish as a developmental model’. Eisen, who now heads her own research group, went to Oregon in 1983 to work on Xenopus neural development but soon became attracted to zebrafish as a model organism. By the early 1980s, she explains, Streisinger had worked out many of the genetic tricks needed to tackle zebrafish development. What's more, the fish had ‘wonderful embryology’. The embryo, which develops outside its mother, is transparent. ‘You can see different cell types, watch individual cells develop, do transplantation experiments’, Eisen enthuses, ‘and development is quick but not too quick’. Being able to watch individual neurons developing in real time opened up whole new avenues of research for Eisen and other neurobiologists. Fast Forward to the Big Screen The properties of zebrafish that attracted Eisen soon attracted people interested in other aspects of vertebrate development to the stripy tiddler (Figure 1). As Eisen comments: ‘No other developmental model has risen to prominence so quickly’. These days more than 3,000 researchers are listed on ZFIN, a United States–based information resource for the zebrafish research community. Figure 1 Adult Zebrafish (Image courtesy of Lukas Roth, University College London, London, United Kingdom.) The speedy expansion was driven in great part by two genetic screens initiated in 1992–1993 by Christiane Nüsslein-Volhard in Tübingen, Germany, and Wolfgang Driever and Mark Fishman in Boston, Massachusetts. The aim of both screens was to identify genes with unique and essential functions in zebrafish development, and in 1996 an issue of the journal Development was dedicated to the mutants that had been isolated and characterised. These screens, says Ralf Dahm (Max Planck Institute for Developmental Biology, Tübingen, Germany), project manager of the ZF-MODELS consortium, ‘were the first major zebrafish projects, and they showed that zebrafish was a model organism to be reckoned with’. ‘That was a fantastic time’, says Derek Stemple, then a postdoc with Driever but now a group leader at the Wellcome Trust Sanger Institute (Cambridge, United Kingdom) and a ZF-MODELS participant. ‘From Wolfgang's lab, I was able to take the mutations that affected notochord development, and have been studying them ever since’. The notochord is an embryonic structure that forms the primitive axial skeleton of the developing embryo, and because mutations affecting notochord development result in shortened embryos, seven of the affected genes have been named after the dwarves in Snow White—zebrafish, like some other developmental models, have many imaginatively named mutants. Stemple now knows the identity of six of these mutated genes, all of which lead to disruption of basement membrane around the notochord. Many mutants from those first two screens are still used by developmental biologists, but another set of mutants has recently been isolated by Nancy Hopkins, Amgen Professor of Biology at the Massachusetts Institute of Technology (Cambridge, Massachusetts, United States). About ten years ago, Hopkins started to develop insertional mutagenesis in zebrafish (Figure 2). In this approach, mutations are caused by the random insertion of viral DNA throughout the fish genome. The inserted DNA acts as a tag, making cloning of mutated genes very straightforward, although the efficiency of the initial insertional mutagenesis is much lower than that of the chemical mutagenesis used in the 1992–1993 screens. Hopkins has isolated 550 mutants in her screen, representing around 400 different genes, and has cloned more than 300 of these genes to date. Some of the fruits of this project are published in this issue of PLoS Biology. Hopkins's group is now collaborating with 25 external laboratories on the annotation of the mutant collection with funding from the National Center for Research Resources, part of the United States National Institutes of Health. Figure 2 A Zebrafish Pigment Mutant The mutant called bleached blond was produced by insertional mutagenesis. The embryos in the picture are four days old. At the top is a wild-type embryo, below is the mutant. The mutant lacks black pigment in the melanocytes because it fails to synthesise melanin properly. (Image courtesy of Adam Amsterdam, Massachusetts Institute of Technology, Boston, Massachusetts, United States.) The Tübingen researchers did another chemical mutagenesis screen between 2000 and 2001, and are now starting a third screen of 6,000 genomes as part of the ZF-MODELS project. ‘Each of our screens has built on the previous one by including more specific assays’, explains Dahm. Mutagenesis for the third screen is underway, but the assays, which include looking for defects that specifically affect adults, are still at the pilot stage; this autumn, the project's executive committee, which is headed by Nüsslein-Volhard, will decide which assays to use in the full-scale screen. ‘Just over half the 17 partners in the consortium will come to Tübingen to do screens’, predicts Dahm. ‘By bringing in expertise in different systems in this way we should greatly increase the efficiency of the screen’. What Else Will ZF-MODELS Do? The ZF-MODELS consortium, which is funded under the European Union's Sixth Framework Programme, aims to establish zebrafish models for human diseases, discover genes that will lead to the identification of new drug targets, and gain fundamental insights into human development. ‘We will mainly focus on using advanced technologies that have recently become available’, says scientific coordinator Robert Geisler (Max Planck Institute for Developmental Biology). For example, Geisler's lab will use DNA chip technology to investigate gene expression patterns in zebrafish mutants and so provide increased knowledge of the regulatory pathways that act in zebrafish development. Consortium members will also use ‘reverse genetics’ to investigate these pathways. In reverse genetics, researchers start with a gene of interest and investigate the phenotypic effect of altering its activity; by contrast, in ‘forward genetics’ the starting point is to look for a particular phenotype and then hunt out the altered gene that is causing it. Two reverse genetics approaches will be used by the consortium. Gene expression will be transiently knocked down with morpholinos, short segments of the gene that block its function. In addition, a recently developed technique known as TILLING (targeting induced local lesions in genomes) will be used to knock out gene activity permanently. The first step in TILLING is to mutagenise male zebrafish and mate them with untreated female fish, explains Stemple, whose group is one of three ZF-MODELS partners who will use this approach. Offspring are raised to adulthood, and the DNA of each individual is then genotyped for the exon of interest. The consortium already has a collection of 6,000 such individuals, and once a fish carrying a mutation in the gene of interest has been identified, it will be outcrossed to produce offspring, half of which will carry the desired mutation on one of their chromosomes. ‘It is then a matter of identifying these heterozygote fish and incrossing them to get homozygous fish in which you can see the phenotype that correlates with that mutation’, says Stemple. As well as helping to produce knock-outs for other researchers, Stemple is also using the TILLING technique to develop zebrafish models for muscular dystrophy. Among the genes that are important in notochord development are those that encode laminins. This led Stemple into studying muscular dystrophy because laminins are involved in the human disease. ‘When we used morpholinos to disrupt [the production of] dystroglycan, a laminin receptor, we got a good model for muscular dystrophy’, he explains. Now, he plans to use TILLING to disrupt up to 30 other genes known to be involved in human muscular dystrophy. ‘In particular, we will look for hypomorphic mutants, fish that are viable but on the edge of falling apart’. These mutants can be used to identify small molecules that push the fish into muscular dystrophy. Finding molecules that can cause a disease in this way ‘might give us a handle on something to fix the disease’, says Stemple. In another strand of the ZF-MODELS project, zebrafish expressing green fluorescent protein (GFP) in specific cells or tissues will be generated and characterised (Figure 3). In such fish, developing structures can be easily imaged over time in the living embryo. One researcher working on this aspect of the project is Stephen Wilson, Professor of Developmental Genetics at University College London (United Kingdom). GFP lines can be made either by attaching to the GFP gene regions of DNA that control, or ‘drive’, GFP expression in selected cell types or by allowing the GFP gene to insert randomly in the genome and looking for fish with specific expression patterns. ‘There are now many lines of fish available with different GFP expression patterns’, says Wilson, ‘and it is important to catalogue their expression so that people can use the most appropriate lines for their research’. Figure 3 Zebrafish Hindbrain (Left) Dorsal view of GFP-expressing neurons in the hindbrain of a one-day-old zebrafish embryo. (Right) Antibody-labelled axons. (Image courtesy of Dave Lyons, University College London.) Wilson's own interest is in neuroanatomy. Together with Jon Clarke, another developmental neurobiology group leader at University College London, he plans to analyse GFP lines in which small groups of neurons or particular parts of neurons are labelled, and in this way start to build a detailed reconstruction of early brain neuroanatomy. This, in combination with other work on zebrafish carrying mutations affecting neural development, will give the team ‘a better picture of how a vertebrate brain is built’. A final, important aspect of the ZF-MODELS project, adds Dahm, is database construction. ‘We will be developing a set of databases that will integrate all of the project data’, he explains. ‘In addition, we hope to integrate our data with that of ZFIN in the United States to make one central zebrafish resource’. But Fish Aren't People The researchers of the ZF-MODELS consortium are understandably excited about participating in what will, says Geisler, bring an already strong European zebrafish community closer together. But zebrafish researchers in the United States are also excited by the ZF-MODELS project. ‘We need big lab models like ZF-MODELS in developmental biology’, says Hopkins, noting that the days of small groups working in isolation are long gone. This consortium, adds Howard Hughes Medical Institute Investigator Leonard Zon (Harvard Medical School, Boston, Massachusetts, United States), ‘will not only have an effect on European zebrafish science but also on how it is done in the United States’. But how much can zebrafish tell us about human development and disease? A lot, say zebrafish researchers. ‘Fish really are just little people with fins’, says Hopkins. ‘Of course, there are developmental differences between people and fish, and no one pretends that we can answer every question about human development in zebrafish’. Nevertheless, zebrafish studies can provide valuable clues to the genes involved in human diseases and to potential targets for therapeutic interventions. Hopkins provides the following illustration: ‘We have been doing “shelf screens”, in which we go back to our collection of mutants to find all those that affect the development of a single organ. When Zhaoxia Sun, a postdoc in my lab who now has an independent position at Yale Medical School, screened three-day-old fish for cystic kidney disease, she found 12 different genes. Two were known to cause human cystic kidney disease, so we knew we were in the human disease pathway somewhere, but we had no idea what the other genes were’. Hopkins and Sun have since identified the remaining genes, and these point to a single pathway being involved in the human disease. Developmental geneticist Didier Stainier (University of California, San Francisco, California, United States) is also using zebrafish to study organ development, in particular, heart development. The zebrafish heart is like the early human heart—a tube with an atrium, ventricle, and valves. ‘Everything we have found in the fish is relevant to the human heart’, says Stainier. ‘Obviously, there are additional processes involved in humans, but the basic outline of heart development in fish and people is largely similar’. Stainier has a collection of zebrafish in which valve formation is faulty. ‘Some of the genes we have found will be involved in human congenital valve defects’, he predicts. Knowing the identity of these genes will be useful diagnostically, but, in addition, zebrafish studies can reveal exactly what has gone wrong at a cellular level. The ability to follow individual cells as organs develop is key to this, says Stainier, who reported in March that fibronectin is required for heart development because, by regulating the polarisation of epithelial cells, fibronectin ensures the correct migration of myocardial cells. And in this issue of PLoS Biology, Stainier's lab have identified another zebrafish gene that is involved in heart development—cardiofunk, which encodes a special type of muscle protein. A Proliferation of Zebrafish Models of Human Disease Many researchers are now recognising the value of zebrafish models of human disease. Over the past three to four years, says Zon, this area of research has become a growth industry. The interest in disease models has grown hand-in-hand with the development of morpholinos to knock out specific genes, and the advent of TILLING, says Zon, ‘has set off a whole new fury. There are now large numbers of investigators who will try to knock out their favourite gene and come up with a model’. Zon has worked on disease models for blood (Figure 4), blood vessel, and heart disorders but is currently studying zebrafish models of cancer. ‘We started by doing chemical mutagenesis and screened for cell-cycle mutants. These were embryonic lethals, but when we looked at heterozygote carriers of these mutations, some developed cancer at a high rate as adults’. Now Zon and his colleagues have returned to the cell-cycle mutant that yielded this cancer-susceptible heterozygote and are using embryos in high-throughput screening assays to look for small molecules that can suppress the cell-cycle phenotype. These molecules, reasons Zon, may have potential as anticancer drugs. Figure 4 Zebrafish kugelig Mutants The image shows live one-week-old zebrafish embryos. The embryos around the outside are wild-type fish. Those in the middle are a mutant called kugelig and have a homozygous mutation in a gene called cdx4. Loss of the proper functioning of this gene causes the obvious trunk and tail defects but also causes a reduction in the number of haematopoietic stem cells in the embryos, which therefore become severely anaemic. Studies on this mutant might lead to the discovery of molecules that can drive stem cell differentiation, for example, or could help improve understanding of human haematological malignancies. (Image courtesy of Alan Davidson, Harvard Medical School, Boston, Massachusetts, United States.) And the Future of Zebrafish Research? Bigger and bigger seems to be the consensus. Chemical screens like Zon's for anticancer drugs can be set up for other human diseases such as muscular dystrophy. Work like Stainier's on organ development may have applications in tissue engineering. ‘If we can find out what drives differentiation in zebrafish’, he suggests, ‘we might be able to do the same for human cells’, making human tissue replacement therapy a practical possibility. And while many zebrafish researchers will continue to study development, others are now moving into the realms of physiology and behavioural studies. Geisler sums up zebrafish developmental research past, present, and future as follows: ‘No other [vertebrate] organism offers the same combination of transparent and accessible embryos, cost-effective mutagenesis screening, and, more recently, a sequenced genome, [DNA] chip, GFP, and knockout technology’. Add to that the potential of zebrafish embryos as a screening platform for small molecule libraries and the new technologies that allow forward and reverse genetics, and it is clear that zebrafish are not about to revert to being pretty pets swimming in small tanks in the corner of the living room. Where to Find Out More ZF-MODELS More details of the work included in this European Union Integrated Project can be found at http://www.zf-models.org ZFIN The ZFIN Web site, at http://z.n.org/ZFIN, provides an extensive database for the zebrafish community including genetic, genomic, and developmental information; search engines for zebrafish researchers and laboratories; listings of meetings; and links to many other zebrafish sites, including sites with movies of zebrafish development. The special issue of Development (Dec 1; 1996; 123: 1–461) on the first two mutagenesis screens contains 37 research articles and can be freely accessed at http://dev.biologists.org/content/vol123/issue1/index.shtml Jane Bradbury is a freelance science news writer based in Cambridge, United Kingdom. E-mail: janeb@sciscribe.u-net.com Abbreviations GFPgreen fluorescent protein TILLINGtargeting induced local lesions in genomes ==== Refs Further Reading Amatruda JF Shepard JL Stern HM Zon LI Zebrafish as a cancer model system Cancer Cell 2002 1 1 3 Dooley K Zon LI Zebrafish: A model system for the study of human disease Curr Opin Genet Dev 2000 10 252 256 10826982 Golling G Amsterdam A Sun Z Antonelli M Maldonado E Insertional mutagenesis in zebrafish rapidly identifies genes essential for early vertebrate development Nat Genet 2002 31 135 140 12006978 Grunwald DJ Eisen JS Headwaters of the zebrafish—Emergence of a new model vertebrate Nat Rev Genet 2002 3 717 724 12209146 Nüsslein-Volhard C Dahm R Zebrafish, a practical approach. Volume 261, The practical approach series 2002 Oxford Oxford University Press 352 Stemple DL TILLING—A high-throughput harvest for functional genomics Nat Rev Genet 2004 5 145 150 14726927 Trinh LA Stainier DYR Fibronectin regulates epithelial organization during myocardial migration in zebrafish Dev Cell 2004 6 371 382 15030760

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PLoS Biol. 2004 May 11; 2(5):e148

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PLoS Biol

10.1371/journal.pbio.0020148

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020149SynopsisDevelopmentXenopusNeural Induction without Mesoderm in Xenopus Synopsis5 2004 11 5 2004 11 5 2004 2 5 e149Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Neural Induction in Xenopus: Requirement for Ectodermal and Endomesodermal Signals via Chordin, Noggin, β-Catenin and Cerberus Organizing the Vertebrate Embryo -- A Balance of Induction and Competence xx ==== Body Formation of the central nervous system has long been thought to result from an induction process, whereby signals emanating from a portion of the dorsal endomesoderm (the inner middle layer of the developing embryo), known as the Spemann–Mangold organizer, instruct cells of the overlying dorsal ectoderm (outer layer) to become neural instead of epidermal. The Spemann–Mangold organizer was itself defined in Spemann and Mangold's seminal 1924 publication as a portion of the dorsal “vegetal” half (also known as the endodermal, or inner, layer) of a gastrulating Xenopus frog embryo that could induce the differentiation of a whole new axis, including a new central nervous system, when grafted into an abnormal location. (Gastrulation is the process that establishes the basic body plan of the organism as cells arrange themselves into three embryonic germ layers: the endoderm, mesoderm, and ectoderm.) From these and later experiments, the notion emerged that neural induction in Xenopus takes place at gastrulation and requires signals from the mesoderm. (The Spemann–Mangold organizer is itself derived from the endomesoderm.) Blastula cells that give rise to the brain Now Hiroki Kuroda, Oliver Wessely, and Edward De Robertis challenge this model by demonstrating that a group of cells in the dorsal region of the prospective ectoderm is fated to become neuronal as early as the blastula stage (which precedes gastrulation) and that these cells can express their neural character in the absence of any mesodermal influence. The authors call this group of cells the BCNE (blastula Chordin- and Noggin-expressing) center, based on their previous observation that this center expresses the proteins Chordin and Noggin at the blastula stage. Chordin and Noggin are also expressed later in the Spemann–Mangold organizer and are among the key signals that mediate neural induction by the organizer. The presence of the neural inducers in blastula ectodermal precursor cells prompted the authors to test these cells' neural potential. They first demonstrated that BCNE cells normally give rise to the anterior portion of the brain, which confirms these cells' neural fate. Moreover, when cultured in vitro, BCNE cells taken from tissue begin to express neural protein markers, even when extra care is taken to prevent any contact with mesodermal precursors. It therefore appears that BCNE cells are already specified to become neural by the blastula stage, before the Spemann–Mangold organizer forms. To further demonstrate BCNE cells' independence from mesodermal signals, the authors generate embryos without a mesoderm. Having previously observed that such embryos do develop a central nervous system, Kuroda et al. now demonstrate that this intrinsic neuronal potential depends on Chordin and Noggin expression in BCNE cells. The model that emerges from these experiments suggests that neural induction begins at the blastula stage, with Chordin and Noggin signaling within the BCNE center and may later be consolidated or modulated by signals emanating from the organizer. What of the endodermal portion of the Spemann–Mangold organizer? It expresses a secreted protein called Cerberus that is involved in development of the head. The authors show that abolishing Cerberus function in the prospective endoderm results in headless embryos. Complete brain removal can also be achieved by partially inhibiting Cerberus function, so long as Chordin is simultaneously inhibited in the dorsal ectoderm. It is therefore likely that while BCNE cells harbor an intrinsic neural potential, neural induction in a living organism occurs via cooperation between the germ layers.

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PLoS Biol. 2004 May 11; 2(5):e149

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PLoS Biol

10.1371/journal.pbio.0020149

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020151SynopsisCell BiologyMolecular Biology/Structural BiologySystems BiologySaccharomycesDissecting the Complexities of Glucose Signaling in Yeast Synopsis5 2004 11 5 2004 11 5 2004 2 5 e151Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Ras and Gpa2 Mediate One Branch of a Redundant Glucose Signaling Pathway in Yeast xx ==== Body An organism's survival depends on developing effective strategies for identifying and adapting to available sources of food. Even organisms as small as the budding yeast Saccharomyces cerevisiae can respond in a complex way to the presence of different energy sources. While yeast can metabolize many different sugars, glucose provides the highest energy yield. To achieve this energy efficiency, yeast cells rely on specialized enzymes and metabolic states that mediate glucose metabolism by sensing and responding to this sugar. The presence of glucose triggers a rapid and dramatic change in the expression of about a quarter of S. cerevisiae's 5,500 genes. Ras at the plasma membrane of S. cerevisiae Many of the cellular participants in the glucose “sense-and-response” pathway have been identified, but their exact relationships remain unknown. The gene expression response to glucose has been previously characterized through the use of a cDNA microarray, which allows simultaneous assessment of the transcriptional state of every gene. In research reported in this issue, James Broach and his colleagues at Princeton University have attempted to connect the individual components in this complex pathway by performing microarray analysis on a series of mutants (yeast strains with defects in specific proteins). They link certain portions of the response to known proteins and begin to understand how the pieces of this pathway fit together. The researchers initially focused on two proteins, called Ras2 and Gpa2. These proteins have been previously implicated in the transcriptional response to glucose and are members of a well-established family of signaling proteins. To investigate the roles of these proteins in the response, the researchers used mutants of Ras2 and Gpa2 that could be activated on demand and then performed microarray analysis to see which genes responded to activation of these proteins. They found that even in the absence of glucose, activation of either protein induced a transcriptional profile almost identical to the profile generated in yeast exposed to glucose. This shows that the complex and dramatic transcriptional response to glucose can be recapitulated by the activation of a single protein. The Ras2/Gpa2 pathway, however, is not the whole story. The group then went on to show that not all glucose-responsive genes are regulated in the same manner. They found that another pathway, independent of Ras2 and Gpa2, is able to elicit a portion of the transcriptional response to glucose. The partial redundancy of these pathways is a curious phenomenon and bears further investigation. The authors have achieved an initial step in mapping the topology of the intricate signaling pathway (or pathways) involved in the response to glucose. Furthermore, the approach—that of using microarray analysis of mutants in a pathway to deduce the mechanisms of regulation—will be useful in efforts to map other complex responses in yeast as well as in higher organisms.

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PMC406405

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PLoS Biol. 2004 May 11; 2(5):e151

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PLoS Biol

10.1371/journal.pbio.0020151

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020154SynopsisEvolutionGenetics/Genomics/Gene TherapyPrimatesHomo (Human)Mus (Mouse)Random Processes Underlie Most Evolutionary Changes in Gene Expression Synopsis5 2004 11 5 2004 11 5 2004 2 5 e154Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Neutral Model of Transcriptome Evolution xx ==== Body Are evolutionary changes in gene expression determined mostly by natural selection or by random forces? It's been some 150 years since Charles Darwin proposed that organisms adapt to their environment through the process of natural selection, yet the debate still rages, particularly at the molecular level. Darwinian selection was challenged in 1983 by the Kimura neutral theory of molecular evolution, which argues that the majority of differences in DNA (nucleotide) and protein (amino acid) sequences within and between species have only minor or no selective effect and that these differences arise through mostly random processes. Mutations at the nucleotide level occur randomly and regularly. Some of them survive through generations, resulting in “fixed” evolutionary changes between species. Two potential mechanisms can lead to the fixation of a particular change: natural selection, which favors changes that convey a selective advantage, and stochastic (random) events, such as genetic drift (the random fluctuations in genotype frequencies that occur from generation to generation in small populations). DNA mutations can lead to changes in gene expression levels, some of which may convey a selective advantage to an organism and therefore become fixed via natural selection. But since variation is produced at the genotype level, while selection is thought to operate largely at the phenotype level (that is, the physical manifestation of the genotype), it is reasonable to expect selection to be less apparent at the level of DNA sequence, and by extension, at the level of gene expression. Microarray technology has made it possible to systematically study expression levels of thousands of transcripts (the RNA copies of DNA that are translated into amino acid sequences) and to ask whether most changes of gene expression fixed during evolution between species result from selective or stochastic processes. To investigate this question, Philipp Khaitovich and colleagues analyzed the observed transcriptome differences among primate and mouse species as well as among various brain regions within a species. The team started out by analyzing the expression levels of some 12,000 genes in the prefrontal cortex of various primates, including humans. If evolutionary changes are caused by chance and not by natural selection, they will accumulate as a function of time rather than as a function of physical or behavioral changes in the organism. And that's what the authors found: the changes in gene expression among the species progressed linearly with time, suggesting that gene expression in primate brains evolved in large part from random processes introducing selectively neutral, or biologically insignificant, changes. According to neutral evolution theory, the same forces determine the rate of evolution both within and between species because similar random processes are at work on both levels. Consequently, genes that vary more within species should be more likely to vary between species. Comparing the expression levels of genes according to their variation within humans, the authors showed that genes with high variation among humans changed significantly faster between species than genes with low variation among humans. The authors also compared changes observed in genes to changes observed in pseudogenes (genes that over evolutionary time acquire a mutation that renders them nonfunctional) and found no significant difference between the two, suggesting again that most expression changes have no functional significance. While their analysis cannot exclude a role for natural selection, all the results are consistent with a neutral model of transcriptome evolution. This means that the majority of gene expression differences within and between species are not functional adaptations but selectively neutral and that we won't be able to explain species differences based on variation in gene expression in general. In addition to examining differences in gene expression in a particular tissue between species, the authors also discuss the evolution of different tissues within a species. The human brain is composed of regions that differ in function and histology (microscopic structure). Each of these regions acquired a functional or histological difference that separated it from its sister regions at some point in our evolutionary past. The authors show that the amount of change between regions correlates with tissue-divergence times estimated by other methods. If this finding applies for other tissues within and outside the brain, it could provide a method to reconstruct the evolution of tissues within a species.

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PLoS Biol. 2004 May 11; 2(5):e154

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PLoS Biol

10.1371/journal.pbio.0020154

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020156EditorialOtherOpen Access and Scientific Societies EditorialDoyle Helen Gass Andy Kennison Rebecca 5 2004 11 5 2004 11 5 2004 2 5 e156Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Societies are encouraged to consider their own open-access experiments within the context of the communities they serve ==== Body This is the second in a series of three editorials that aim to address recurring concerns about the benefits and risks associated with open-access publishing in medicine and the biological sciences. Scientific societies serve their members, their broader scholarly communities, and the different components of their missions in many important ways. Making peer-reviewed literature immediately accessible, searchable, and reusable to anyone in the world with an Internet connection is a uniquely direct means of achieving a number of goals that are common to most scholarly associations and of advancing the diverse interests of their constituencies. Setting aside for the moment the question of how feasible it is for societies to alter their journals' access policies, there is by now a broad consensus that widespread open access to scientific publications is good for scientists and good for science. Society members want to maximize the impact of their work—and articles that are freely available online are cited more frequently than those that are not (Lawrence 2001). Most societies are committed to catalyzing innovations within and across scientific disciplines—and open-access archives of full-text literature provide a valuable tool for sharing information globally in order to accelerate the rate of scientific progress. Many societies articulate in their mission statements the goal of communicating the benefits of their members' discoveries with the public—and open-access publishing is a direct means to accomplish this goal. In addition to an interest in exploring new ways to serve their members and their missions, societies have another compelling reason to investigate open access for their journals: the rapidly changing landscape of scholarly publishing. From 1990 to 2000, the average price of an academic journal subscription increased 10% per year (Create Change 2000). While society-run and nonprofit journals may not be the major contributors to those spiraling costs, societies that rely on revenues from subscriptions and site licenses may bear a disproportionate share of the negative consequences of skyrocketing serials prices. As libraries are forced for a variety of reasons (including decreased budgets and the increasing prevalence of “big deals” and journal bundling) to eliminate subscriptions, society journals may be among the hardest hit. Journals that appeal to a relatively specialized readership and those that are not part of larger publishing groups are particularly vulnerable to the contraction of serials collections that has already begun and will likely accelerate (Create Change 2000). A Society Is More Than a Journal The confluence of forces in favor of open access says nothing about its fiscal implications for scientific societies. As any systemic change in research or publishing would, the movement toward open access has generated concern about its ramifications for the scholarly associations that often serve as the backbones of scientific communities. However, the strength of those societies and their essential role in the communities they serve are precisely what should allay fears about the revenue-eroding effect that some argue would plague societies if they converted their traditional subscription-based journals to open access. Scientific societies perform an array of tremendously valuable functions for their constituents and disciplines. Researchers, educators, and others join societies for the many benefits of membership beyond simply discounted or “free” subscriptions to journals, so the concern that open-access publications would be the death knell of voluntary academic associations is misguided. As Elizabeth Marincola, executive director of the American Society for Cell Biology, recently noted, her society “offers a diverse range of products so that if publications were at risk financially, we wouldn't lose our membership base because there are lots of other reasons why people are members” (Anonymous 2003). While open-access publication can, in fact, be paid for in a number of different ways, there is no question that a transition toward the elimination of online access barriers requires most societies to restructure the business models for their journals. If journal subscriptions generate surplus revenue that supports other society activities, then the business model of the society as a whole may need to be examined. This is not to say that open-access journals cannot generate a surplus or profit—simply that they do not do so by restricting access to their primary research content. Testing the Open-Access Waters There are a number of societies that have already begun to take transitional steps to wean themselves from subscription revenues. One of the earliest societies to commit to open-access publication, the American Society for Clinical Investigation (ASCI) has since 1996 provided the Journal of Clinical Investigation (JCI) freely online and recently reaffirmed its commitment to open access: “The financing having been resolved, through author charges and other means,” John Hawley, the executive director of the ASCI writes, “the JCI hopefully can bring the greatest benefit to its authors and readers, regardless of who they might be. It is in this spirit that the JCI has always been free online, and will remain so” (Hawley 2003). In order to experiment cautiously with new access policies, several societies have implemented hybrid models of access-restriction for their publications. The American Physiological Society, for example, offers authors in Physiological Genomics the option to pay a surcharge for their articles to be made freely available online immediately upon publication. A recent survey by the Joint Information Systems Committee (JISC) in the United Kingdom suggests that many authors would use such an option if it were more widely available: 48% of authors who had never published in an open-access journal and 60% of authors who had done so indicated that they would be willing to “pay a publisher of a journal sold according to the traditional subscription model an additional fee for them to make [the author's] particular paper ‘open access’” (JISC 2004). JISC is also directly encouraging society and nonprofit publishers to implement hybrid models and other open-access experiments and to launch new open-access journals by providing grants to offset the publication charges for authors during this transitional phase. In the long run, of course, open access will prove sustainable when more funders of research, in addition to interested third parties, designate funds specifically for the costs of publishing articles to be made freely available, searchable, and reusable online. Starting the Dialogue Reaching a “steady-state” system of open-access publishing by scientific societies will require three critical components: recognition that open access serves societies' members and missions; diversified revenue streams not solely dependent on subscription or site-license fees; and society publishers' making use of recent innovations in journal production and dissemination, which can dramatically reduce the costs of publishing. It is, after all, the increased efficiencies born of new technologies—from the Internet itself to electronic journal management systems—that have made the idea of open access possible. And while proponents of open access are confident that publication charges of around $1,500 per article will be sufficient to cover the costs of publishing an efficiently operated society journal, there is no question that many existing journals may need to update their infrastructure in order to make open access financially viable (PLoS 2004). There is also no question that many societies do not, at present, have a wealth of revenue streams beyond the proceeds from their journals, which they often use to fund valuable activities from education initiatives to annual meetings. As open-access journals become more established, however, and as the benefits of open access to scientific and medical literature become more apparent to society members, the demand for the broadest possible dissemination of research is only likely to grow. Those societies that embrace the developments taking place in scholarly publishing may well see their membership and publications thrive more than societies that cling to the potentially unstable status quo. In any case, a constructive discussion about the pitfalls to be avoided and the benefits to be gained through a transition to open-access publishing would be a worthy first step for any scientific society to take—and PLoS welcomes the questions, comments, and feedback of those who are intrigued by the potential that open access affords and want to learn more. Helen Doyle is the director of development and strategic alliances, Andy Gass is the outreach coordinator, and Rebecca Kennison is the director of journal production at the Public Library of Science (PLoS). ==== Refs References [Anonymous] Interview: The society lady 2003 Open Access Now: 6 October. Available at http://www.biomedcentral.com/openaccess/archive/?page=features&issue=6 via the Internet. Accessed 17 March 2004 Create Change Scholars under siege 2000 Available at http://www.createchange.org/librarians/issues/silent.html via the Internet. Accessed 17 March 2004 Hawley JB The JCI' s commitment to excellence—and free access J Clin Invest 2003 112 968 969 Available at http://www.pubmedcentral.nih.gov/articlerenderfcgi?artid=198538 via the Internet. Accessed 17 March 2004 [JISC] Joint Information Systems Committee Journal authors survey report 2004 Available at http://www.jisc.ac.uk/uploaded_documents/JISCOAreport1.pdf via the Internet. Accessed 17 March 2004 Lawrence S Free online availability substantially increases a paper's impact 2001 Nature Web Debates. Available at http://www.nature.com/nature/debates/e-access/Articles/lawrence.html via the Internet. Accessed 17 March 2004 [PLoS] Public Library of Science Publishing open-access journals 2004 Available at http://www.plos.org/downloads/oa_whitepaper.pdf via the Internet. Accessed 22 March 2004

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PMC406407

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PLoS Biol. 2004 May 11; 2(5):e156

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PLoS Biol

10.1371/journal.pbio.0020156

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020159SynopsisCancer BiologyGenetics/Genomics/Gene TherapyDanio (Zebrafish)Defects in Ribosomal Protein Genes Cause Cancer in Zebrafish Synopsis5 2004 11 5 2004 11 5 2004 2 5 e159Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Many Ribosomal Protein Genes Are Cancer Genes in Zebrafish xx ==== Body To investigate the genetic underpinnings of a particular biological process, geneticists screen large collections of mutant organisms to characterize their physical defects. By comparing the genetic makeup of nonmutant (called wild-type) organisms to mutants, it's possible to tease out the genes responsible for a defective appearance, or phenotype. In a classic study in the fruitfly, Christiane Nüsslein-Volhard and Eric Weischaus bred many lines of flies with mutations that were lethal: the fly embryos died, but not before displaying a wide range of developmental defects. Since it was known that the fruitfly needed only a single wild-type copy of these genes to survive, the mutations in these “embryonic lethals” had to be recessive, meaning that both copies, or alleles, of the gene had to be mutated for the lethal defect to appear. Nüsslein-Volhard and Weischaus's work revealed many such recessive genes crucial to early development and earned them a Nobel Prize. Zebrafish tumors caused by mutation of a ribosomal protein gene Among the model systems for studying development, the zebrafish has become prized because its transparent embryo develops outside the mother's body. The zebrafish has helped biologists identify many genes involved in embryogenesis and, because it's a vertebrate animal, has become a valuable resource for identifying genes involved in human disease. Now, a team led by Nancy Hopkins of the Massachusetts Institute of Technology, has created over 500 lines of zebrafish with lesions in key embryogenic genes and used them to identify a group of genes that predispose the fish to cancer, with some surprising results. All of the 500 lines created by the researchers carried a recessive embryonic lethal mutation; for about 400 of the lines, mutations in 300 distinct genes were identified as the cause of the embryonic phenotype. During the process of cultivating some of these mutant lines, the Hopkins team noticed that an abnormally large percentage of fish experienced early mortality (in some cases, over 50% compared to the 10%–15% seen in nonmutant fish), while the surviving fish in these lines developed large, highly invasive malignant tumors; both phenotypes persisted over successive generations. The tumors resembled malignant peripheral nerve sheath tumors (MPNSTs) that have been found in other fish species as well as in mammals. Suspecting that these mutant lines had elevated rates of cancer, the researchers investigated the genetic makeup of the fish and discovered to their surprise that each line was heterozygous for a mutation in a different ribosomal protein gene (rp)—that is, each line carried one healthy version and one defective version of a different rp gene. These proteins are components of ribosomes—the massive molecular complexes within cells that mediate protein synthesis—and are essential for embryonic development. All of the rp mutations, the researchers report, either reduced or eliminated expression of the corresponding rp gene. In the case of “classic” tumor suppressor genes, the wild-type allele must be lost for the defective allele to set the stage for cancer. Here, the wild-type allele appeared to remain intact in the tumor cells, implicating the proteins as “haploinsufficient” tumor suppressors—a reduction from two gene copies to one functional copy seems to be enough to increase the risk of cancer. Apart from the mutations in rp genes, the authors also found a loss-of-function mutation in a gene (called NF2) that acts as a tumor suppressor in mammals—establishing the soundness of this approach for identifying mammalian cancer genes. While these experiments do not explore how these mutations lead to cancer, the results suggest that some shared, ribosome-associated function allows these genes to act as tumor suppressors and that disrupting this function somehow leads to tumor formation. Though it's not clear what distinguishes the 11 rp genes whose mutations caused cancer from the five other rp genes whose mutations did not, the authors raise a number of possibilities for future study. And given the high degree of conservation of genes and pathways among vertebrates, it's likely that rp mutations also raise cancer risk in humans. Together, these results demonstrate that the tiny freshwater workhorse of developmental biology has a promising future as a model system for human cancer.

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PLoS Biol. 2004 May 11; 2(5):e159

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PLoS Biol

10.1371/journal.pbio.0020159

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020161FeatureScience PolicyThe European Research Council—A European Renaissance The European Research CouncilO'Neill Bill 5 2004 11 5 2004 11 5 2004 2 5 e161Copyright: © 2004 Bill O'Neill.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.European scientists are pressing for the creation of an independent body to fund European research - driven by the pursuit of scientific excellence ==== Body Science looks set for a fundamentalist revival within the European Union. Its leading proponents are taking advantage of unprecedented political upheaval—as ten new Member States accede to the Union—to press their case for funding of basic research that is driven solely and independently by investigators themselves in the pursuit of excellence. Bob May, professor of mathematical biology at the University of Oxford, president of the Royal Society, and former UK Chief Scientist The broad thrust of their appeal calls for the setting up of a new agency, most commonly referred to as a ‘European Research Council’. The ERC could be an entirely new organisation or a new division within an established body, run by a small staff able to draw on the best expertise available. It would administer a new fund from EU coffers, tagged the European Fund for Research Excellence, that would be valued modestly, initially at least, at much less than half of the EU's existing budget for research. Most importantly, dispersal of that fund would reflect the wishes of eminent peer reviewers, assessing competitive bids in search of the best science, rather than the judgements of Eurocrats, looking for the most politically and economically expedient solutions and operating on a lead time of two years or more. Although the modus operandi of the proposed ERC has still to be worked out, European scientists have been looking to the United States and at the way that the National Science Foundation and the National Institutes of Health operate, as well as to private institutions such as the Howard Hughes Medical Institute in the United States and the Wellcome Trust in the United Kingdom. In particular, they seek the independence and excellence achieved outside of the EU framework. More to the point, they are weary of the bureaucratic formulations that determine how the EU's research budget, currently known as the Sixth Framework Programme (2002–2006) and worth around €4.4 billion/year (or just over 5% of all public spending on nonmilitary research in the region), is spent and distributed. The EU's guiding principle is often one of juste retour, or fair reward, in which Member States traditionally seek to recover grants at least equal to their contributions to the EU pot (see Box 1). Bernard Larrouturou, director general of France's National Centre of Scientific Research (CNRS) in Paris ‘Most of the Anglo-Saxon countries in Europe—the Scandinavian countries, the United Kingdom, the Netherlands—operate a peer review process and a research funding council process that's very similar to best practice in North America,’ says Michael Morgan, a consultant to the Wellcome Trust on European issues and former chief executive of the Trust's Genome Campus at Hinxton, near Cambridge, United Kingdom. ‘The French and Germans and others have elements of that but they also have what you might call more “state-funded science”, scientists as civil servants, and there is obviously much greater possibility of science being funded for less than the best scientific reasons,’ notes Morgan, referring to the opportunities for greater political influence on decision-making. ‘I'm not suggesting that that is the case, but it is the possibility,’ he adds. ‘What we need in Europe is something that should strictly adhere to the international standards of research funding and be evaluated by peer review,’ says Peter Gruss, professor of molecular cell biology at the University of Göttingen and president of the Max Planck Society in Munich, Germany. ‘The sole criterion has to be quality, not geographical distribution, not management capacity,’ he adds, alluding to the EU practice of juste retour. ‘We want to encourage excellence in Europe. We want to have as a benchmark a European standard that should be as high as the standard is in the US.’ Kai Simons, president of the ELSO and director of the Max Planck Institute for Cell Biology and Genetics Gruss acknowledges the tensions that the ERC proposal has generated among Member States: ‘I'm not saying that there aren't countries that have this standard—like the UK, parts of Germany, Sweden, and some other Nordic countries—but of course this is not the general European standard, and in order to get one and the same, the common standard, we need a common structure.’ A Fund for Excellence The European Commission now appears ready to accept the need for a common structure that would have, as the Commission puts it, ‘more open and less binding’ programmes of basic research, in contrast to the Framework Programme, whose emphasis is on applied research with commercial objectives. The Commission expects to publish its endorsement of the ERC proposal this month, so that approval by the Council of the EU should follow later this year. On this timetable, setting up of the ERC could begin in 2006 when the next five-year Framework Programme, FP7, gets underway. Over the ERC's first five years, its grant is expected to grow from around €500 million/year to €2 billion/year, and to derive from a reallocation of funds within the EU's budget rather than from any top-up contributions from Member States. Furthermore, Gruss released a legal opinion in March that advised how an ERC need not be an executive agency of the Commission, as many scientists had feared it would have to be under the terms of the EU Treaty, but could be established as an independent and autonomous body. The opinion is a real coup for the ERC lobbyists. Origins of the ERC Moves to establish an ERC are founded in a ‘new strategic goal’ for the EU that the leaders of its 15 Member States set during their European Council in Lisbon in March 2000. Over the first decade of the new millennium, they urged the EU ‘to become the most competitive and dynamic knowledge-based economy in the world’. They enthusiastically endorsed a notion, floated by the European Commission, of a European Research Area (ERA). ‘Research activities at national and [European] Union level must be better integrated and co-ordinated to make them as efficient and innovative as possible, and to ensure that Europe offers attractive prospects to its best brains,’ concluded the EU leaders, eager to reverse the flow of trained talent abroad, notably to North America. All appropriate means, they added, ‘must be fully exploited to achieve this objective in a flexible, decentralised and non-bureaucratic manner’. Two years later, at the European Council in Barcelona, the EU leaders went one step further by defining the target more precisely. ‘In order to close the gap between the EU and its major competitors,’ they said, ‘overall spending on R & D and innovation in the Union should be increased with the aim of approaching 3% of GDP by 2010. Two-thirds of this new investment should come from the private sector.’ The scale of the challenge is illustrated by the latest figures for R & D expenditure, published in February by the Statistical Office of the European Communities (Eurostat). The EU's estimated R & D spending in 2002 was 1.99% of GDP, still far short of the US (2.80%) and Japan (2.98% in 2000), and a long way from the target of 3%. Emphasising the UK's uneasiness about the EU's escalating enthusiasm for a regional science base, the Royal Society (the UK national academy of science) poured scorn on the ‘ambitious’ GDP target by noting how the UK alone would have needed an extra £11 billion in 2000, or more than 60% of total spending on R & D, to lift its ratio of 1.85% to the 3% target. The Royal Society also noted how public funding of R & D in the EU matches that in the US and Japan, with the disparity among GDP ratios reflecting the differentials in private investment in R & D, over which the EU has little control. Nevertheless, the challenge could not be ignored. According to Bob May, professor of mathematical biology at the University of Oxford, president of the Royal Society, and former UK Chief Scientist, such initiatives might be ‘driven more by political expediency than common sense, but the moment you see that train beginning to roll, there's a chance to do something useful with it’. Among the leading proponents of an ERC is Bernard Larrouturou, director general of the National Centre of Scientific Research (CNRS) in Paris, France. For Larrouturou, a biomathematician currently engaged in streamlining the organisation, the changes at the European level are a breath of fresh air. However, he is not convinced that funded investigators should expect to exclude Commission strategists entirely from their lives. The scientific community should lead an ERC, says Larrouturou, ‘but I do not like the idea that this should be completely under the guidance and wisdom of the scientific community with no strategy guidance. You cannot ask for 1 or 2 billion Euros every year and say there will not be any strategy and [that it will be done solely] on this basis of excellence.’ And Larrouturou distances himself from the idea that basic and applied research can be treated separately because this suggests, wrongly he says, a conflict between the two. On these issues, Larrouturou moves onto some common ground with John Taylor, former director general of Research Councils UK and now chairman of Roke Manor Research, a UK subsidiary of Siemens, the German electronics group. Research Councils UK oversees spending of Britain's national research councils (currently, just over £2 billion from its 2004–2005 Science Budget of nearly £2.7 billion). Interactions across disciplines and between scientists and technologists ‘are not helped by making artificial distinctions between this kind of research and that kind of research,’ says Taylor. ‘The distinctions I make are much more between top-down and bottom-up.’ While Taylor is a joint architect of one proposal to create an ERC, he remains unconvinced that the research funding system is broken, especially from the UK's perspective, and needs to be fixed. Nor is he convinced that EU funds for an ERC will not affect national R & D budgets. ‘I'm middle of the road,’ he says. ‘Much greater collaboration is good. It has to be a slow process, with all the different cultures involved. Collaboration on various areas of science is an excellent way to go, provided you don't try to organise it from the top and legislate for it all to happen in a particular way and to a particular timescale. Excellence is key.’ Taylor's cautions reflect his experience of the EU's Framework Programme and his reservations that any initiative from Brussels can be free of red tape. ‘If you want to do research, then you can't lay out beforehand all the answers you're going to get,’ he says. ‘And if you try to get people to stick rigorously to a plan, then you get a lot of silly things going on. If you try to form very complex bureaucratic organisations to do the research, you get a lot of delays and so on, so things are not very timely.’ But the Framework Programme's failures need not spell disaster for the fledgling funding council, insists Lennart Philipson, former director general of the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany, and now an emeritus professor at the Karolinska Institute in Stockholm, Sweden. Drawing on his 11 years as head of EMBL, until 1993, Philipson recalls how ‘pan-European peer review was the best method for distributing the funds of EMBL and EMBO [European Molecular Biology Organization]’. The continuing high status of the two organisations, he says, is testimony that the system works. In fact, EMBO is mentioned as a possible incubator for an ERC, in spite of its specialisation. Other proponents of the proposed research changes in the EU include 45 Nobel Laureates from Europe or of European origin, who headed a petition organised by EMBO. The European Life Scientists Organization (ELSO) organised another. Its president, Kai Simons, also the Director of the Max Planck Institute for Cell Biology and Genetics in Dresden, Germany, says research funding in Europe is just not working. ‘It's not geared for basic research—it has other aims,’ he notes. EU funds are ‘not grants, they are contracts with in-built milestones that have nothing to do [with basic research]. Basic research doesn't work like that.’ The evaluation and peer review system is falling apart, continues Simons. He says that the best people are not interested in peer reviewing a system that doesn't work: ‘You're not attracting the peer reviewers that you need to maintain quality.’ But at last, acknowledges Simons, someone in Brussels is listening. ‘In the past two years there has been enormous progress.’ Many Questions Remain Within a month of the Barcelona Council in 2002, the European Science Foundation (ESF), which brings together the funding agencies of 29 countries and acts as a bridge to Brussels, had formed a High Level Working Group to review the case for an ERC and how it might operate. The group, chaired by Sir Richard Sykes, Rector of Imperial College, London, United Kingdom, reported a year later, in April 2003. It endorsed the creation of an ERC as ‘the cornerstone for the ERA and the key approach to developing a locus for…long-term fundamental curiosity-driven research judged on the basis of excellence and merit’. The Sykes group also proposed, controversially, an enhanced ESF as the most effective medium for establishing an ERC swiftly. ‘Some people say that the ESF has no experience in funding large amounts… for research,’ acknowledges Enric Banda, director general of the Catalan Research Foundation in Barcelona, Spain, who finished a five-year term as the ESF's chief executive at the end of 2003 and is credited with ‘waking up’ the foundation. ‘But certainly if you create a new [organisation], that's the same thing. So the ESF is in a good position because its member organisations are the funding agencies.’ Bertil Andersson, who was a member of the Sykes Group before taking over from Banda at the ESF in January, also stakes the ESF's claim to nurture a fledgling ERC. But he accepts that any one of the respected national funding agencies, such as the German Research Foundation (DFG), or even a specialist body, such as EMBO, could do the job. ‘We don't need a new skyscraper in Brussels, but a lot of… peer review and running of the ERC could be done by existing bodies. ‘Compared to soccer, we have only the national leagues—we don't have the Champions League [the league of Europe's best teams],’ says Andersson. There is no competition for basic research grants across national boundaries in Europe, he insists. ‘The Swedish league is exciting, but the Champions League is more exciting.’ In the meantime, while the Sykes group was still deliberating, the Council of the EU appointed another group of experts to evaluate the case for an ERC. Chaired by Federico Mayor, former director general of the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the ERC Expert Group also delivered its verdict—a resounding endorsement—within 12 months. ‘The first and main task for the ERC should be to support investigator-driven research of the highest quality selected through European competition,’ concluded the Mayor report, published in December 2003. ‘In doing so, the ERC should create and support nodes of excellence in European universities and research institutions, strengthening the knowledge-base that underpins economic, industrial, cultural and societal development, and thereby stimulating European competitiveness and innovative capacity at all levels.’ While few disagreed with the Mayor report's sentiments, the absence of a detailed analysis exposed underlying tensions over the rationale for an ERC. In the UK, in particular, some scientists seemed concerned that their mature and respected system for funding research risked dilution. ‘The British have always had doubts about what goes on in Europe,’ notes Kai Simons. ‘They always think that they can do it better. But the big problem for the British is that they are also too small to fund a new innovative area,’ he says. ‘Of course, we can do it without Britain, but they are an important part of Europe and it would be sad if they're not part of it.’ The agnostic John Taylor, who was a member of the Mayor group, recalls his early reservations when the group convened. ‘I'm way beyond the euphoria; I'm into practical pragmatics,’ he notes. ‘My major input into the whole thing has been to get them to “get real” instead of just philosophising. They've been using the sort of, dare I say it, Gallic approach… of thinking about the reasons why, and the philosophy, and not thinking about what you would actually do.’ Taylor dismisses the notion that wariness of the ERC is representative of a general antipathy in Britain towards European integration. ‘What we're saying is that science in the UK is not yet well-funded enough to say we would rather do this [the ERC] instead of the things that we're already trying to get done in the UK scene.’ Anticipating the Mayor report's publication, the Royal Society quickly pulled together a detailed background paper late last year that identified ‘a number of problems that need resolution, although not necessarily through the establishment of any major new institutions within Europe’. An addendum followed in March, in direct response to the Mayor report. That addendum highlighted what it saw as the paucity of solid evidence in the Mayor report and, in some cases, the confusing data in the report's case for an ERC. On balance it looked as though the Royal Society, and as such the British science establishment as a whole, had weighed the disadvantages of an ERC as greater than its advantages, but Bob May is quick to refute this charge. ‘My vision and the Royal Society's vision of the ERC is that it will fund the very best science,’ he insists. ‘The Mayor committee itself was really good people who'd produced basically a good report…. I'm basically in favour of this European Research Council… provided it can be set up properly, which is by no means certain.’ For May, and other scientists on the continent, the ERC offers a real chance to redress the balance of fortune in favour of young scientists. ‘The way to encourage science is to get the best people and set them free to express their creativity while they are young, which means bring them into the best laboratories—don't let them get entrained in hierarchies of deference to second-rate people,’ says May. ‘The most important single thing to create one Europe in science is a flexible postdoctoral programme that gets the best young people wherever they are and lets them go to the best places,’ enthuses May. An ERC will then foster those collaborations, he forecasts. ‘It won't ask whether they're juste retour, whether they're serving some industrial purpose, it will just try to fund the best science. But I hope increasingly the best projects will involve collaborations, as they do in Britain, collaborations among institutions within Europe.’ Box 1. Glossary of Europe Council of the European Union – Ruling organisation (along with European Parliament), and not to be confused with the European Council (see below). It comprises ministers from governments of the Member States, which have varying voting powers led by France, Germany, Italy, and the UK. Euro (€) – Common European currency launched on 1 January 2002 in 12 participating Member States (the UK, Sweden, and Denmark chose to postpone adoption of the currency indefinitely). European Commission – Executive organisation, mainly based in Brussels, run by 20 Commissioners and around 24,000 civil servants. European Council – Body that brings together leaders of Member States to define broad policy objectives for the EU's six main institutions (Parliament, Council, Commission, Court of Justice, Court of Auditors, and Ombudsman). Meets twice a year in the Member State holding the Council's presidency, which changes every six months. European Parliament – Elected organisation, based in Strasbourg, France, that rules the EU (jointly with the Council of the EU, see top) and will have 732 Members after the accession of the ten new Member States in May 2004. European Research Area – Commissioner Philippe Busquin's vision for the future of research in Europe, and the main focus of the 6th Framework Programme. It aims to achieve ‘scientific excellence, improved competitiveness and innovation through the promotion of increased co-operation, greater complementarity and improved co-ordination between relevant actors, at all levels’. European Union – Evolving political, social, and economic union of an increasing number of European countries, or Member States. First proposed in 1950 during rehabilitation after the Second World War and formally created by the Maastricht Treaty in 1992. Grew from six nations in 1951 (Belgium, France, Germany [then West Germany], Italy, Luxembourg, and the Netherlands) to nine in 1973 (addition of Denmark, Ireland, and the UK), to ten in 1981 (addition of Greece), to 12 in 1986 (addition of Spain and Portugal), to 15 in 1995 (addition of Austria, Finland, and Sweden), with a total population of 380 million people (cf. 290 million for US; 130 million for Japan). Ten more countries (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia) join in May 2004, which will lift the EU's population to 450 million people. Bulgaria and Romania are due to join in 2007, which will add another 50 million people. Framework Programme – The EU's principal mechanism for funding research in Member States, proposed by the Commissioner for Research (Philippe Busquin) and adopted by the Council and Parliament. Framework Programmes have four-year budgets but cover five-year periods, so consecutive programmes overlap, and are prescribed two years before they begin. The 6th programme (FP6) is worth €17.5 billion (or about 4% of the EU's total budget and 5.4% of all public, nonmilitary research spending in Europe) and runs from the beginning of 2003 to the end of 2006. Juste retour (fair reward) – Claim made by Member States for rewards at least equal to their share of the cost of any programme or initiative; critics say it promotes bureaucracy and uncompetitiveness. Bill O'Neill is a freelance journalist from London, United Kingdom. E-mail: billoneill@cix.co.uk URLs European Council: Presidency Conclusions - http://ue.eu.int/presid/conclusions.htm Europa: Gateway to the European Union - http://europa.eu.int Activities of the European Union: Research and Innovation - http://europa.eu.int/pol/rd/index_en.htm Sixth Framework Programme - 2002–2006 - http://europa.eu.int/scadplus/leg/en/lvb/i23012 Articles about the European Research Area - http://europa.eu.int/scadplu/leg/en/lvb/i23010.htm; http://ueropa.eu.int/comm/research/era/index_en.html Abbreviations ELSOEuropean Life Scientists Organization EMBLEuropean Molecular Biology Laboratory EMBOEuropean Molecular Biology Organization ERAEuropean Research Area ERCEuropean Research Council ESFEuropean Science Foundation ==== Refs Further Reading [Anonymous] Commission calls for boost in basic research 2004 Europa: Gateway to the European Union. Available: http://europa.eu.int/comm/research/press/2004/pr1501en.html via the Internet. Accessed 25 March 2004 [Anonymous] European Research Council must be independent: The Max Planck Society calls for decision on the establishment of a European Research Council before the end of 2004 2004 Max Planck Society. Available: http://www.mpg.de/english/illustrationsDocumentation/documentation/pressReleases/2004/pressRelease20040308/index.html via the Internet. Accessed 25 March 2004 Commission of the European Communities Communication from the Commission: Europe and basic research 2004 Available: http://europa.eu.int/comm/research/press/2004/pdf/acte_en_version_final_15janv_04.pdf via the Internet. Accessed 25 March 2004 Expert Group chaired by Federico Mayor The European Research Council: A cornerstone in the European Research Area 2003 Denmark's Ministry of Science, Technology, and Innovation. Available: http://www.ercexpertgroup.org/finalreport.asp via the Internet. Accessed 25 March 2004 Gruss P Conclusions of the conference ‘Changes and Challenges for European Research Structures and Politics’ on March 1st, 2004 in Berlin 2004 Available: http://www.mpg.de/pdf/statementGruss.pdf via the Internet. Accessed 25 March 2004 High Level Working Group chaired by Richard Sykes An ESF position paper: New structures for the support of high-quality research in Europe 2003 European Science Foundation. Available: http://www.esf.org/newsrelease/63/ERC.pdf via the Internet. Accessed 25 March 2004 The Royal Society The future funding of the European science base: A Royal Society background working paper, V1.0 2004 Available: http://www.royalsoc.ac.uk/templates/statements/StatementDetails.cfm?statementid=243 via the Internet. Accessed 25 March 2004 The Royal Society The place of fundamental research in the European Research Area: The Royal Society response to the Mayor report 2004 Available: http://www.royalsoc.ac.uk/policy via the Internet. Accessed 25 March 2004

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PLoS Biol. 2004 May 11; 2(5):e161

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PLoS Biol

10.1371/journal.pbio.0020161

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020161FeatureScience PolicyThe European Research Council—A European Renaissance The European Research CouncilO'Neill Bill 5 2004 11 5 2004 11 5 2004 2 5 e161Copyright: © 2004 Bill O'Neill.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.European scientists are pressing for the creation of an independent body to fund European research - driven by the pursuit of scientific excellence ==== Body Science looks set for a fundamentalist revival within the European Union. Its leading proponents are taking advantage of unprecedented political upheaval—as ten new Member States accede to the Union—to press their case for funding of basic research that is driven solely and independently by investigators themselves in the pursuit of excellence. Bob May, professor of mathematical biology at the University of Oxford, president of the Royal Society, and former UK Chief Scientist The broad thrust of their appeal calls for the setting up of a new agency, most commonly referred to as a ‘European Research Council’. The ERC could be an entirely new organisation or a new division within an established body, run by a small staff able to draw on the best expertise available. It would administer a new fund from EU coffers, tagged the European Fund for Research Excellence, that would be valued modestly, initially at least, at much less than half of the EU's existing budget for research. Most importantly, dispersal of that fund would reflect the wishes of eminent peer reviewers, assessing competitive bids in search of the best science, rather than the judgements of Eurocrats, looking for the most politically and economically expedient solutions and operating on a lead time of two years or more. Although the modus operandi of the proposed ERC has still to be worked out, European scientists have been looking to the United States and at the way that the National Science Foundation and the National Institutes of Health operate, as well as to private institutions such as the Howard Hughes Medical Institute in the United States and the Wellcome Trust in the United Kingdom. In particular, they seek the independence and excellence achieved outside of the EU framework. More to the point, they are weary of the bureaucratic formulations that determine how the EU's research budget, currently known as the Sixth Framework Programme (2002–2006) and worth around €4.4 billion/year (or just over 5% of all public spending on nonmilitary research in the region), is spent and distributed. The EU's guiding principle is often one of juste retour, or fair reward, in which Member States traditionally seek to recover grants at least equal to their contributions to the EU pot (see Box 1). Bernard Larrouturou, director general of France's National Centre of Scientific Research (CNRS) in Paris ‘Most of the Anglo-Saxon countries in Europe—the Scandinavian countries, the United Kingdom, the Netherlands—operate a peer review process and a research funding council process that's very similar to best practice in North America,’ says Michael Morgan, a consultant to the Wellcome Trust on European issues and former chief executive of the Trust's Genome Campus at Hinxton, near Cambridge, United Kingdom. ‘The French and Germans and others have elements of that but they also have what you might call more “state-funded science”, scientists as civil servants, and there is obviously much greater possibility of science being funded for less than the best scientific reasons,’ notes Morgan, referring to the opportunities for greater political influence on decision-making. ‘I'm not suggesting that that is the case, but it is the possibility,’ he adds. ‘What we need in Europe is something that should strictly adhere to the international standards of research funding and be evaluated by peer review,’ says Peter Gruss, professor of molecular cell biology at the University of Göttingen and president of the Max Planck Society in Munich, Germany. ‘The sole criterion has to be quality, not geographical distribution, not management capacity,’ he adds, alluding to the EU practice of juste retour. ‘We want to encourage excellence in Europe. We want to have as a benchmark a European standard that should be as high as the standard is in the US.’ Kai Simons, president of the ELSO and director of the Max Planck Institute for Cell Biology and Genetics Gruss acknowledges the tensions that the ERC proposal has generated among Member States: ‘I'm not saying that there aren't countries that have this standard—like the UK, parts of Germany, Sweden, and some other Nordic countries—but of course this is not the general European standard, and in order to get one and the same, the common standard, we need a common structure.’ A Fund for Excellence The European Commission now appears ready to accept the need for a common structure that would have, as the Commission puts it, ‘more open and less binding’ programmes of basic research, in contrast to the Framework Programme, whose emphasis is on applied research with commercial objectives. The Commission expects to publish its endorsement of the ERC proposal this month, so that approval by the Council of the EU should follow later this year. On this timetable, setting up of the ERC could begin in 2006 when the next five-year Framework Programme, FP7, gets underway. Over the ERC's first five years, its grant is expected to grow from around €500 million/year to €2 billion/year, and to derive from a reallocation of funds within the EU's budget rather than from any top-up contributions from Member States. Furthermore, Gruss released a legal opinion in March that advised how an ERC need not be an executive agency of the Commission, as many scientists had feared it would have to be under the terms of the EU Treaty, but could be established as an independent and autonomous body. The opinion is a real coup for the ERC lobbyists. Origins of the ERC Moves to establish an ERC are founded in a ‘new strategic goal’ for the EU that the leaders of its 15 Member States set during their European Council in Lisbon in March 2000. Over the first decade of the new millennium, they urged the EU ‘to become the most competitive and dynamic knowledge-based economy in the world’. They enthusiastically endorsed a notion, floated by the European Commission, of a European Research Area (ERA). ‘Research activities at national and [European] Union level must be better integrated and co-ordinated to make them as efficient and innovative as possible, and to ensure that Europe offers attractive prospects to its best brains,’ concluded the EU leaders, eager to reverse the flow of trained talent abroad, notably to North America. All appropriate means, they added, ‘must be fully exploited to achieve this objective in a flexible, decentralised and non-bureaucratic manner’. Two years later, at the European Council in Barcelona, the EU leaders went one step further by defining the target more precisely. ‘In order to close the gap between the EU and its major competitors,’ they said, ‘overall spending on R & D and innovation in the Union should be increased with the aim of approaching 3% of GDP by 2010. Two-thirds of this new investment should come from the private sector.’ The scale of the challenge is illustrated by the latest figures for R & D expenditure, published in February by the Statistical Office of the European Communities (Eurostat). The EU's estimated R & D spending in 2002 was 1.99% of GDP, still far short of the US (2.80%) and Japan (2.98% in 2000), and a long way from the target of 3%. Emphasising the UK's uneasiness about the EU's escalating enthusiasm for a regional science base, the Royal Society (the UK national academy of science) poured scorn on the ‘ambitious’ GDP target by noting how the UK alone would have needed an extra £11 billion in 2000, or more than 60% of total spending on R & D, to lift its ratio of 1.85% to the 3% target. The Royal Society also noted how public funding of R & D in the EU matches that in the US and Japan, with the disparity among GDP ratios reflecting the differentials in private investment in R & D, over which the EU has little control. Nevertheless, the challenge could not be ignored. According to Bob May, professor of mathematical biology at the University of Oxford, president of the Royal Society, and former UK Chief Scientist, such initiatives might be ‘driven more by political expediency than common sense, but the moment you see that train beginning to roll, there's a chance to do something useful with it’. Among the leading proponents of an ERC is Bernard Larrouturou, director general of the National Centre of Scientific Research (CNRS) in Paris, France. For Larrouturou, a biomathematician currently engaged in streamlining the organisation, the changes at the European level are a breath of fresh air. However, he is not convinced that funded investigators should expect to exclude Commission strategists entirely from their lives. The scientific community should lead an ERC, says Larrouturou, ‘but I do not like the idea that this should be completely under the guidance and wisdom of the scientific community with no strategy guidance. You cannot ask for 1 or 2 billion Euros every year and say there will not be any strategy and [that it will be done solely] on this basis of excellence.’ And Larrouturou distances himself from the idea that basic and applied research can be treated separately because this suggests, wrongly he says, a conflict between the two. On these issues, Larrouturou moves onto some common ground with John Taylor, former director general of Research Councils UK and now chairman of Roke Manor Research, a UK subsidiary of Siemens, the German electronics group. Research Councils UK oversees spending of Britain's national research councils (currently, just over £2 billion from its 2004–2005 Science Budget of nearly £2.7 billion). Interactions across disciplines and between scientists and technologists ‘are not helped by making artificial distinctions between this kind of research and that kind of research,’ says Taylor. ‘The distinctions I make are much more between top-down and bottom-up.’ While Taylor is a joint architect of one proposal to create an ERC, he remains unconvinced that the research funding system is broken, especially from the UK's perspective, and needs to be fixed. Nor is he convinced that EU funds for an ERC will not affect national R & D budgets. ‘I'm middle of the road,’ he says. ‘Much greater collaboration is good. It has to be a slow process, with all the different cultures involved. Collaboration on various areas of science is an excellent way to go, provided you don't try to organise it from the top and legislate for it all to happen in a particular way and to a particular timescale. Excellence is key.’ Taylor's cautions reflect his experience of the EU's Framework Programme and his reservations that any initiative from Brussels can be free of red tape. ‘If you want to do research, then you can't lay out beforehand all the answers you're going to get,’ he says. ‘And if you try to get people to stick rigorously to a plan, then you get a lot of silly things going on. If you try to form very complex bureaucratic organisations to do the research, you get a lot of delays and so on, so things are not very timely.’ But the Framework Programme's failures need not spell disaster for the fledgling funding council, insists Lennart Philipson, former director general of the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany, and now an emeritus professor at the Karolinska Institute in Stockholm, Sweden. Drawing on his 11 years as head of EMBL, until 1993, Philipson recalls how ‘pan-European peer review was the best method for distributing the funds of EMBL and EMBO [European Molecular Biology Organization]’. The continuing high status of the two organisations, he says, is testimony that the system works. In fact, EMBO is mentioned as a possible incubator for an ERC, in spite of its specialisation. Other proponents of the proposed research changes in the EU include 45 Nobel Laureates from Europe or of European origin, who headed a petition organised by EMBO. The European Life Scientists Organization (ELSO) organised another. Its president, Kai Simons, also the Director of the Max Planck Institute for Cell Biology and Genetics in Dresden, Germany, says research funding in Europe is just not working. ‘It's not geared for basic research—it has other aims,’ he notes. EU funds are ‘not grants, they are contracts with in-built milestones that have nothing to do [with basic research]. Basic research doesn't work like that.’ The evaluation and peer review system is falling apart, continues Simons. He says that the best people are not interested in peer reviewing a system that doesn't work: ‘You're not attracting the peer reviewers that you need to maintain quality.’ But at last, acknowledges Simons, someone in Brussels is listening. ‘In the past two years there has been enormous progress.’ Many Questions Remain Within a month of the Barcelona Council in 2002, the European Science Foundation (ESF), which brings together the funding agencies of 29 countries and acts as a bridge to Brussels, had formed a High Level Working Group to review the case for an ERC and how it might operate. The group, chaired by Sir Richard Sykes, Rector of Imperial College, London, United Kingdom, reported a year later, in April 2003. It endorsed the creation of an ERC as ‘the cornerstone for the ERA and the key approach to developing a locus for…long-term fundamental curiosity-driven research judged on the basis of excellence and merit’. The Sykes group also proposed, controversially, an enhanced ESF as the most effective medium for establishing an ERC swiftly. ‘Some people say that the ESF has no experience in funding large amounts… for research,’ acknowledges Enric Banda, director general of the Catalan Research Foundation in Barcelona, Spain, who finished a five-year term as the ESF's chief executive at the end of 2003 and is credited with ‘waking up’ the foundation. ‘But certainly if you create a new [organisation], that's the same thing. So the ESF is in a good position because its member organisations are the funding agencies.’ Bertil Andersson, who was a member of the Sykes Group before taking over from Banda at the ESF in January, also stakes the ESF's claim to nurture a fledgling ERC. But he accepts that any one of the respected national funding agencies, such as the German Research Foundation (DFG), or even a specialist body, such as EMBO, could do the job. ‘We don't need a new skyscraper in Brussels, but a lot of… peer review and running of the ERC could be done by existing bodies. ‘Compared to soccer, we have only the national leagues—we don't have the Champions League [the league of Europe's best teams],’ says Andersson. There is no competition for basic research grants across national boundaries in Europe, he insists. ‘The Swedish league is exciting, but the Champions League is more exciting.’ In the meantime, while the Sykes group was still deliberating, the Council of the EU appointed another group of experts to evaluate the case for an ERC. Chaired by Federico Mayor, former director general of the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the ERC Expert Group also delivered its verdict—a resounding endorsement—within 12 months. ‘The first and main task for the ERC should be to support investigator-driven research of the highest quality selected through European competition,’ concluded the Mayor report, published in December 2003. ‘In doing so, the ERC should create and support nodes of excellence in European universities and research institutions, strengthening the knowledge-base that underpins economic, industrial, cultural and societal development, and thereby stimulating European competitiveness and innovative capacity at all levels.’ While few disagreed with the Mayor report's sentiments, the absence of a detailed analysis exposed underlying tensions over the rationale for an ERC. In the UK, in particular, some scientists seemed concerned that their mature and respected system for funding research risked dilution. ‘The British have always had doubts about what goes on in Europe,’ notes Kai Simons. ‘They always think that they can do it better. But the big problem for the British is that they are also too small to fund a new innovative area,’ he says. ‘Of course, we can do it without Britain, but they are an important part of Europe and it would be sad if they're not part of it.’ The agnostic John Taylor, who was a member of the Mayor group, recalls his early reservations when the group convened. ‘I'm way beyond the euphoria; I'm into practical pragmatics,’ he notes. ‘My major input into the whole thing has been to get them to “get real” instead of just philosophising. They've been using the sort of, dare I say it, Gallic approach… of thinking about the reasons why, and the philosophy, and not thinking about what you would actually do.’ Taylor dismisses the notion that wariness of the ERC is representative of a general antipathy in Britain towards European integration. ‘What we're saying is that science in the UK is not yet well-funded enough to say we would rather do this [the ERC] instead of the things that we're already trying to get done in the UK scene.’ Anticipating the Mayor report's publication, the Royal Society quickly pulled together a detailed background paper late last year that identified ‘a number of problems that need resolution, although not necessarily through the establishment of any major new institutions within Europe’. An addendum followed in March, in direct response to the Mayor report. That addendum highlighted what it saw as the paucity of solid evidence in the Mayor report and, in some cases, the confusing data in the report's case for an ERC. On balance it looked as though the Royal Society, and as such the British science establishment as a whole, had weighed the disadvantages of an ERC as greater than its advantages, but Bob May is quick to refute this charge. ‘My vision and the Royal Society's vision of the ERC is that it will fund the very best science,’ he insists. ‘The Mayor committee itself was really good people who'd produced basically a good report…. I'm basically in favour of this European Research Council… provided it can be set up properly, which is by no means certain.’ For May, and other scientists on the continent, the ERC offers a real chance to redress the balance of fortune in favour of young scientists. ‘The way to encourage science is to get the best people and set them free to express their creativity while they are young, which means bring them into the best laboratories—don't let them get entrained in hierarchies of deference to second-rate people,’ says May. ‘The most important single thing to create one Europe in science is a flexible postdoctoral programme that gets the best young people wherever they are and lets them go to the best places,’ enthuses May. An ERC will then foster those collaborations, he forecasts. ‘It won't ask whether they're juste retour, whether they're serving some industrial purpose, it will just try to fund the best science. But I hope increasingly the best projects will involve collaborations, as they do in Britain, collaborations among institutions within Europe.’ Box 1. Glossary of Europe Council of the European Union – Ruling organisation (along with European Parliament), and not to be confused with the European Council (see below). It comprises ministers from governments of the Member States, which have varying voting powers led by France, Germany, Italy, and the UK. Euro (€) – Common European currency launched on 1 January 2002 in 12 participating Member States (the UK, Sweden, and Denmark chose to postpone adoption of the currency indefinitely). European Commission – Executive organisation, mainly based in Brussels, run by 20 Commissioners and around 24,000 civil servants. European Council – Body that brings together leaders of Member States to define broad policy objectives for the EU's six main institutions (Parliament, Council, Commission, Court of Justice, Court of Auditors, and Ombudsman). Meets twice a year in the Member State holding the Council's presidency, which changes every six months. European Parliament – Elected organisation, based in Strasbourg, France, that rules the EU (jointly with the Council of the EU, see top) and will have 732 Members after the accession of the ten new Member States in May 2004. European Research Area – Commissioner Philippe Busquin's vision for the future of research in Europe, and the main focus of the 6th Framework Programme. It aims to achieve ‘scientific excellence, improved competitiveness and innovation through the promotion of increased co-operation, greater complementarity and improved co-ordination between relevant actors, at all levels’. European Union – Evolving political, social, and economic union of an increasing number of European countries, or Member States. First proposed in 1950 during rehabilitation after the Second World War and formally created by the Maastricht Treaty in 1992. Grew from six nations in 1951 (Belgium, France, Germany [then West Germany], Italy, Luxembourg, and the Netherlands) to nine in 1973 (addition of Denmark, Ireland, and the UK), to ten in 1981 (addition of Greece), to 12 in 1986 (addition of Spain and Portugal), to 15 in 1995 (addition of Austria, Finland, and Sweden), with a total population of 380 million people (cf. 290 million for US; 130 million for Japan). Ten more countries (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia) join in May 2004, which will lift the EU's population to 450 million people. Bulgaria and Romania are due to join in 2007, which will add another 50 million people. Framework Programme – The EU's principal mechanism for funding research in Member States, proposed by the Commissioner for Research (Philippe Busquin) and adopted by the Council and Parliament. Framework Programmes have four-year budgets but cover five-year periods, so consecutive programmes overlap, and are prescribed two years before they begin. The 6th programme (FP6) is worth €17.5 billion (or about 4% of the EU's total budget and 5.4% of all public, nonmilitary research spending in Europe) and runs from the beginning of 2003 to the end of 2006. Juste retour (fair reward) – Claim made by Member States for rewards at least equal to their share of the cost of any programme or initiative; critics say it promotes bureaucracy and uncompetitiveness. Bill O'Neill is a freelance journalist from London, United Kingdom. E-mail: billoneill@cix.co.uk URLs European Council: Presidency Conclusions - http://ue.eu.int/presid/conclusions.htm Europa: Gateway to the European Union - http://europa.eu.int Activities of the European Union: Research and Innovation - http://europa.eu.int/pol/rd/index_en.htm Sixth Framework Programme - 2002–2006 - http://europa.eu.int/scadplus/leg/en/lvb/i23012 Articles about the European Research Area - http://europa.eu.int/scadplu/leg/en/lvb/i23010.htm; http://ueropa.eu.int/comm/research/era/index_en.html Abbreviations ELSOEuropean Life Scientists Organization EMBLEuropean Molecular Biology Laboratory EMBOEuropean Molecular Biology Organization ERAEuropean Research Area ERCEuropean Research Council ESFEuropean Science Foundation ==== Refs Further Reading [Anonymous] Commission calls for boost in basic research 2004 Europa: Gateway to the European Union. Available: http://europa.eu.int/comm/research/press/2004/pr1501en.html via the Internet. Accessed 25 March 2004 [Anonymous] European Research Council must be independent: The Max Planck Society calls for decision on the establishment of a European Research Council before the end of 2004 2004 Max Planck Society. Available: http://www.mpg.de/english/illustrationsDocumentation/documentation/pressReleases/2004/pressRelease20040308/index.html via the Internet. Accessed 25 March 2004 Commission of the European Communities Communication from the Commission: Europe and basic research 2004 Available: http://europa.eu.int/comm/research/press/2004/pdf/acte_en_version_final_15janv_04.pdf via the Internet. Accessed 25 March 2004 Expert Group chaired by Federico Mayor The European Research Council: A cornerstone in the European Research Area 2003 Denmark's Ministry of Science, Technology, and Innovation. Available: http://www.ercexpertgroup.org/finalreport.asp via the Internet. Accessed 25 March 2004 Gruss P Conclusions of the conference ‘Changes and Challenges for European Research Structures and Politics’ on March 1st, 2004 in Berlin 2004 Available: http://www.mpg.de/pdf/statementGruss.pdf via the Internet. Accessed 25 March 2004 High Level Working Group chaired by Richard Sykes An ESF position paper: New structures for the support of high-quality research in Europe 2003 European Science Foundation. Available: http://www.esf.org/newsrelease/63/ERC.pdf via the Internet. Accessed 25 March 2004 The Royal Society The future funding of the European science base: A Royal Society background working paper, V1.0 2004 Available: http://www.royalsoc.ac.uk/templates/statements/StatementDetails.cfm?statementid=243 via the Internet. Accessed 25 March 2004 The Royal Society The place of fundamental research in the European Research Area: The Royal Society response to the Mayor report 2004 Available: http://www.royalsoc.ac.uk/policy via the Internet. Accessed 25 March 2004

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PMC406410

CC BY

2021-01-05 08:21:10

no

PLoS Biol. 2004 May 11; 2(5):e201

latin-1

PLoS Biol

10.1371/journal.pbio.0020201

oa_comm

==== Front BMC EcolBMC Ecology1472-6785BioMed Central London 1472-6785-4-31506848610.1186/1472-6785-4-3Research ArticleEffects of fire and fire intensity on the germination and establishment of Acacia karroo, Acacia nilotica, Acacia luederitzii and Dichrostachys cinerea in the field Walters Michele 1michelewalters@yahoo.comMidgley Jeremy J 2midgleyj@botzoo.uct.ac.zaSomers Michael J 3michaelsomers@yahoo.com1 Conservation Ecology Department, University of Stellenbosch, Private Bag X1, Matieland, 7602, South Africa2 Department of Botany, University of Cape Town, Private Bag, Rondebosch, South Africa3 Applied Behaviour and Ecology Lab, Department of Zoology, University of Transkei, Private Bag X1, Umtata, 5117, South Africa2004 7 4 2004 4 3 3 10 10 2003 7 4 2004 Copyright © 2004 Walters et al; licensee BioMed Central Ltd.2004Walters et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background While fire has been used in some instances to control the increase of woody plants, it has also been reported that fire may cause an increase in certain fire-tolerant Acacia tree species. This study investigated germination of Acacia karroo, A. luederitzii and Dichrostachys cinerea, thought to be increasing in density, as well as the historically successful encroaching woody species, A. nilotica, in savanna grassland, Hluhluwe-iMfolozi Park, South Africa. A. karroo is thought to be replacing A. nilotica as the dominant microphyllous species in the park. We tested the hypothesis that observed increases in certain woody plants in a savanna were related to seed germination and seedling establishment. Germination is compared among species for burnt and unburnt seeds on burnt and unburnt plots at three different locations for both hot and cool fires. Results Acacia karroo showed higher germination (A. karroo 5.1%, A. nilotica 1.5% and A. luederitzii 5.0%) levels and better establishment (A. karroo 4.9%, A. nilotica 0.4% and A. luederitzii 0.4%). Seeds of the shrub Dichrostachys cinerea did not germinate in the field after fire and it is thought that some other germination cue is needed. On average, burning of A. karroo, A. nilotica and A. luederitzii seeds did not affect germination. There was a significant difference in the germination of burnt seeds on burnt sites (4.5%) and burnt seeds on unburnt plots (2.5%). Similarly, unburnt seeds on unburnt sites germinated better (4.9%) than unburnt seeds on burnt sites (2.8%). Conclusion We conclude that a combination of factors may be responsible for the success of A. karroo and that fires may not be hot enough or may occur at the wrong time of year to control A. karroo establishment in HiP. Although germination and establishment of A. karroo was higher than for A. nilotica a competitive advantage after fire could not be shown. ==== Body Background The increasing density in the woody component of savannas has been widely reported [1-5] with special mention being made of Acacia karroo Hayne [6,7] and A. nilotica (L.) Willd. Ex Del. subsp. kraussiana (Benth.) Brenan, [8,9]. in some areas, as major contributors to the phenomenon. In Hluhluwe-iMfolozi Park Dichrostachys cinerea (L.) Wight & Arn. and A. luederitzii Engl. var. retinens (Sim) Ross & Brenan are also thought to contribute to this phenomenon. In hard seeded legumes dormancy is broken by rupturing part of the seed coat. The rupturing of the seed coat may be induced by heat from fire [10] enabling water to enter the seed and start the process of germination. Many studies have confirmed a release of legume seeds from dormancy after fire [10-17]. Fire temperature or intensity also has an effect on the germination of seeds [17,18] and low intensity fires may not be enough to break dormancy of hard-seeded legumes [19]. In other cases lower fire temperatures are preferable for germination with an increase in fire temperature causing seed mortality [18]. While some studies report that a decrease in grass cover favours the establishment of woody seedlings due to reduced competition [20,21], others [6,22] challenge these findings. These differences may however, be a result of species reacting differently to fire or competition. Some Acacia species are shade intolerant resulting in decreased seedling establishment in shady areas [20,23,24]. Other Acacia species have been found to be tolerant of low light conditions and may even experience increased seedling survival [6]. The frequency of fires may affect the direction of change in woody plant density [5]. While it has been suggested that fire may increase Acacia densities [10], it is also used to clear acacias from grassland [25]. This contradictory situation in the literature concerning the effect of fire necessitates further research, as it is clear that continuous use of incorrect burning practices may have disastrous consequences. This study investigated the direct (heat) and indirect (grass removal) effects of fire on seed germination and seedling establishment of A. nilotica, A. karroo, A. luederitzii and Dichrostachys cinerea in Hluhluwe-iMfolozi Park (HiP), where an increase in woody plant density over the past 40 years has been reported [26-28]. It has also been reported that A. karroo is apparently replacing A. nilotica as the dominant microphyllous element [27,28]. This study reports on the effects of burning, fire intensity and burning of sites on germination; burning, fire intensity, burning of sites and grass length (shade) on seedling establishment and specific species responses to treatments (treatment species interactions). Results Germination None of the seeds of D. cinerea germinated in the field and it was therefore excluded from the model for the field experiment. Testing for differences among treatments was based on the maximum number of seedlings for each species at each location over the 31-week period (Figure 2). A description of the factors used in both the germination and establishment models is given in Table 1. Figure 2 Mean number of germinated seeds recorded over a 31-week period at three different locations in HiP for a) Acacia karroo, b) Acacia luederitzii and c) Acacia nilotica. Table 1 Descriptions of factors used in the models and number of seeds used for each factor. Germination Establishment Factor/Description Total number of seeds Number not germinated Number germinated Percent germinated Total number of seeds Number not established Number established Percent established Total 4073 3923 150 3.68 4062 3966 96 2.36 Location Seme 1348 1287 61 4.53 1337 1302 35 2.62 Nombali 1364 1300 64 4.69 1364 1316 48 3.52 Le Dube 1361 1336 25 1.84 1361 1348 13 0.96 Species A. karroo 1786 1695 91 5.10 1788 1701 87 4.87 A. luederitzii 720 684 36 5.00 707 704 3 0.42 A. nilotica 1567 1544 23 1.47 1567 1561 6 0.38 Burnt or unburnt burnt 2021 1950 71 3.51 2030 1985 45 2.22 unburnt 2052 1973 79 3.85 2032 1981 51 2.51 Tall or short grass tall (>0.1 m) 2039 1961 78 3.83 2041 1993 48 2.35 short 2034 1962 72 3.54 2021 1973 48 2.38 Site burnt or unburnt burnt 2052 1977 75 3.65 2052 2003 49 2.39 unburnt 2021 1946 75 3.71 2010 1963 47 2.34 The ratio of the model deviance to the degrees of freedom was small (0.29) indicating that the model was a good fit. Location and species were the only main effects significantly affecting germination (Table 2). Acacia karroo had the highest germination of all species (Table 1). Table 2 Statistics indicating significance of the factors and interactions on germination. Significant factors are in bold. Factor df Log-likelihood Chi-Square Wald Stat. P Location 2 -587.555 13.915 11.547 0.003 Species 2 -597.790 34.386 25.394 0.000 Burnt status 1 -582.073 2.951 2.822 0.093 Grass length 1 -580.622 0.050 0.050 0.822 Site burn status 1 -580.608 0.021 0.021 0.885 Location*species 4 -582.584 3.974 3.827 0.430 Location*burn status 2 -582.929 4.664 4.373 0.112 Location*grass length 2 -586.296 11.397 10.812 0.004 Location*site burn status 2 -580.703 0.212 0.211 0.900 Species*burn status 2 -581.173 1.151 1.145 0.564 Species*grass length 2 -581.019 0.843 0.837 0.658 Species*site burn status 2 -583.309 5.424 5.166 0.076 Burn status*grass length 1 -580.767 0.340 0.341 0.559 Burn status*site burn status 1 -585.060 8.926 8.656 0.003 Grass length*site burn status 1 -587.530 13.866 13.082 0.000 Interaction terms that had a significant effect on germination were, location × grass length, burn status × site burn status and grass length × site burn status (Table 2). Germination of burnt seeds in burnt sites (4.5%) was significantly higher than that of burnt seeds in unburnt sites (2.5%). Similarly, unburnt seeds in unburnt sites had a higher germination percentage (4.9%) than unburnt seeds in burnt sites (2.8%). The estimated odds of germination and their associated probabilities for the factors and their interactions are given in Additional file 1. The odds ratios for significant effects were calculated. Thus a comparison between A. karroo and A. nilotica with regards to seeds germinating was made, where Thus the odds of germinating are four times more for A. karroo than for A. nilotica. Similarly A. nilotica was four times less likely to germinate than A. luederitzii while A. karroo and A. luederitzii had the same odds of germinating. Differences in germination among species for the various treatments are given in Table 3. Table 3 A comparison of germination among species for the different levels of the main factors. A. karroo A. luederitzii A. nilotica Factor/description n Total count Not germ germ % erm Total count Not germ germ %germ Total count Not germ germ % germ Location*Species Seme 48 591 558 33 5.91 240 224 16 7.14 517 505 12 2.38 Nombali 48 596 551 45 8.17 240 227 13 5.73 528 522 6 1.15 Le Dube 48 599 586 13 2.22 240 233 7 3 522 517 5 0.97 Burnt or unburnt*Species burnt 72 886 839 47 5.6 360 344 16 4.65 775 767 8 1.04 unburnt 72 900 856 44 5.14 360 340 20 5.88 792 777 15 1.93 Tall or short grass*Species tall 72 895 851 44 5.17 360 340 20 5.88 784 770 14 1.82 short 72 891 844 47 5.57 360 344 16 4.65 783 774 9 1.16 Site burnt or unburnt*Species yes 72 900 854 46 5.39 360 338 22 6.51 792 785 7 0.89 no 72 886 841 45 5.35 360 346 14 4.05 775 759 16 2.11 There was 2.3 times less germination at Le Dube than at Nombali and 2.6 times less at Le Dube than at Seme. Germinations were 1.2 times more likely at Seme than at Nombali. Seedling establishment The ratio of the model deviance to the degrees of freedom was small (0.17) indicating that the model fitted the data well. Location and species were the only main effects significantly affecting establishment in the field (Table 4 & Figure 3). Acacia karroo showed significantly higher percentage establishment than any of the other species (Additional file 2, Table 5 & Figure 3). Table 4 Statistics indicating significance of factors and interactions on establishment. Significant factors are indicated in bold. Factor df Log-likelihood Chi-Square p Location 2 -443.238 22.292 <0.001 Species 2 -395.199 96.079 <0.001 Burnt status 1 -395.050 0.297 0.586 Grass length 1 -395.049 0.002 0.962 Site burn status 1 -395.040 0.018 0.894 Location*species 4 -391.756 6.568 0.161 Location*burn status 1 -380.850 21.812 <0.001 Location*grass length 2 -373.542 14.617 <0.001 Location*site burn status 2 -367.865 11.353 0.003 Species*burn status 2 -367.468 0.795 0.672 Species*grass length 2 -367.344 0.248 0.884 Species*site burn status 2 -367.180 0.329 0.848 Burn status*grass length 1 -366.723 0.913 0.339 Burn status*site burn status 1 -360.267 12.913 <0.001 Grass length*site burn status 1 -351.784 16.965 <0.001 Figure 3 Predicted mean establishment for the significant main effects of a) species and b) location. Vertical error bars show 95% confidence limits. Table 5 A comparison of establishment among species for the different levels of the main factors A. karroo A. luederitzii A. nilotica Factor/Description n Total count Not estab estab % estab Total count Not estab estab % estab Total count Not estab estab %estab Location*Species Le Dube 48 599 590 9 1.53 240 239 1 0.42 522 519 3 0.58 Nombali 48 598 553 45 8.14 238 237 1 0.42 528 526 2 0.38 Seme 48 591 558 33 5.91 229 228 1 0.44 517 516 1 0.19 Burnt or unburnt*Species Burnt 72 886 843 43 5.1 347 346 1 0.29 797 796 1 0.13 Unburnt 72 902 858 44 5.13 360 358 2 0.56 770 765 5 0.65 Tall or short grass*Species Tall 72 897 854 43 5.04 360 358 2 0.56 784 781 3 0.38 Short 72 891 847 44 5.19 347 346 1 0.29 783 780 3 0.38 Site burnt or unburnt*Species Yes 72 900 855 45 5.26 360 359 1 0.28 792 789 3 0.38 No 72 888 846 42 4.96 347 345 2 0.58 775 772 3 0.39 Interaction terms, location × burn status, location × grass length, location × site burn status, burn status × site burn status and grass length × site burn status had a significant effect on establishment (Table 4) (Figure 4). Figure 4 Predicted mean establishment for significant interactions of site burn status and a) location, b) seed burn status and c) grass length. The solid line represents unburnt sites and the dotted line burnt sites. Vertical error bars show 95% confidence limits. Additional file 2 gives the estimated odds of non-establishment and their associated probabilities for the factors and their interactions. The odds ratios for significant effects were calculated and are given (see Additional file 3). Acacia karroo was 16.2 times more likely to establish than A. nilotica. Similarly A. luederitzii was 1.4 times more likely to establish than A. nilotica while A. karroo had 11.2 times more chance of establishing than A. luederitzii. Species differences in establishment for the various treatments are given in Table 5. The odds of establishment were 8046.2 times less at Le Dube than at Nombali and 5850.5 times less at Le Dube than at Seme. 1.4 times more seedlings were likely to establish at Nombali than at Seme. Discussion The lack of germination of D. cinerea in the field suggests that some disturbance other than fire is needed to cause a release from dormancy and commence germination. Germination of all species in the field was low. As the seeds relied on cotyledons for food, soil moisture may have been a limiting factor. As rainfall was not recorded, this should be kept in mind when interpreting the results. Five point one percent of A. karroo seeds germinated, which was higher than the other two species. Story [29] found similar levels of germination for A. karroo, with 6.6% of seeds germinating under natural conditions in the field. He also found that A. karroo germination was erratic, with germinations still being recorded after 423 days. This was similar to what was found in this study, with the number of A. karroo seedlings still increasing until the end of the experiment. Acacia nilotica also showed dormancy with sporadic germination events over the 31-week period. Acacia luederitzii did not show dormancy with most germinations taking place in the first 3 weeks of the experiment. Acacia nilotica has a thick seed coat, which could account for it's poor level of germination. One would predict increased germination of burnt seeds due to a breaking of dormancy [18], but this was not the case. A possible explanation is that the temperature of the fires in this study, though not measured, might not have been sufficient to break dormancy in this species. Some Acacia species are temperature specific, suggesting a temperature threshold for germination [18,20]. This is unlikely in this case as Radford et al. [30] found A. nilotica seeds to be highly vulnerable to fire with a 80% mortality of seeds on the soil surface. The current study, however, found no difference in germination between burnt and unburnt seed or seeds burnt at different temperatures. This finding is inconsistent with the recent study by Kanz [20] who found increased seed germination in low fires compared to the control as well as that of Okello and Young [31] who found increased germination of unburnt seeds. Auld & O'Connell [18] had similar results to that of Kanz [20] with strong germination responses to heat. Location had a significant effect on germination with Le Dube having very low germination overall and Seme having the most germinations. Germination at Nombali and Seme were similar. Site-specific effects may be attributed to various factors such as microclimate or soil type. Sites may also have different water infiltration rates and runoff, which may result in differences in germination levels. Okello and Young [31], however, found that soil type did not affect germination or establishment of Acacia drepanolobium in Kenya. The current study did not find a difference in the number of seedlings in burnt and unburnt patches. While neither burning of seeds nor burning of sites had any effect on germination, the interaction factor proved significant with unburnt seeds showing increased germination in unburnt sites as did burnt seeds in burnt sites. Kanz [20] also found greater seedling emergence of unburnt seeds in unburnt areas. This might be a result of burnt seeds imbibing faster than unburnt seeds, possibly making them more susceptible to rot. Burnt seeds would therefore show poorer germination in unburnt areas due to increased moisture retention. Similarly, unburnt seeds would require more moisture to imbibe, resulting in decreased germination in burnt areas due to decreased moisture in these open areas. Whilst more seeds germinated in short grass at both Le Dube and Nombali, those at the short-grass site (Seme) had higher levels of germination in tall grass sites. The short grass site at Seme is a white rhinoceros (Ceratotherium simum) grazing lawn with very short grass, which may lead to seeds losing moisture through more direct sunlight. This suggests a similar pattern to the seed burn × site burn interaction. The tall grass site at Seme had higher germination than any of the other tall or short grass sites. This may be due to possible site-specific effects mentioned earlier. There was also an interaction between grass length and site burn with seeds in burnt, short grass showing higher germination than those in burnt, tall grass and unburnt sites showing higher germination in tall grass. As half of the seeds on a burnt or unburnt site were burnt themselves, it is possible that this interaction is due to temperature sensitivity in seeds. Burning in tall grass (hotter fires) may be detrimental to the germination of seeds [18] while cooler fires may be sufficient to break dormancy and cause germination. Higher germinations in unburnt tall grass areas suggest a shade effect. This is not certain, as the effects of shade and grass competition were not separated in this study. Acacia karroo has however been reported as having an increased ability to survive in shade with recruitment of seedlings being dependent on moisture availability [6]. Tall grass species may retain more moisture than short grass species, affording seeds a better opportunity for germination. No species factor interactions were observed suggesting that though species had different germination levels, they did not respond differently to the treatments. The same factors and interactions found to be significant influences on germination were found to influence establishment. This was expected as increased germination for these treatments would result in better establishment. The interaction patterns for most of the treatments, however, were different to those of the germination model. Owing to the low levels of germination, interspecific and intraspecific competition was thought to play a minor role in seedling establishment. Le Dube again had the least seedlings at 31 weeks while Nombali had the best establishment. Seme, which had the highest level of germination, had establishment levels somewhere between that of the other two sites. It is again suggested that this may be due to soil or rainfall factors. Forty-five out of forty-eight seedlings established at Nombali and thirty-three out of thirty-five at Seme were A. karroo seedlings. This species is known to be dependent on moisture availability for survival [6] and these two sites might have better water retaining ability than Le Dube. At week 31, 87 A. karroo seedlings had established as opposed to six of A. nilotica and three of A. luederitzii. The high germination, but poor survival of A. luederitzii suggests that the absence of this species in the Hluhluwe section of HiP is not due to seed limitation or germinability, but possibly due to environmental factors decreasing its ability to establish. The differences in seedling survival between species are consistent with those reported by Kanz [20] who found higher seedling survival for A. karroo than A. nilotica. The location × grass length interaction revealed the same patterns as for germination with regards to Nombali and Seme with Seme showing better establishment in tall grass and Nombali showing better establishment in short grass. There was no difference between establishment on tall and short grass at Le Dube. The short grass site at Nombali had the highest number of seedlings surviving at week 31. The grass length × site burn interaction displayed the same patterns as for the germination model, but this was not the case for the seed burn status × site burn status interaction. While unburnt seeds still did well on unburnt sites, burnt and unburnt seeds showed decreased establishment on burnt sites suggesting that, as a result of increased irradiance, burnt (open) sites may not hold sufficient moisture for seedlings to survive. The interaction effects found to be significant for establishment only, both suggest the importance of fire temperature. Location × seed burn status and location × site burn status could both relate to the different grass lengths, and thus specific fire temperatures, at the three sites. Temperature sensitivity in Acacia species have been reported elsewhere [11,14,17,20]. Kanz [20] found increased survival and growth in burnt areas. In this study, Nombali was the only location to have higher establishment on burnt sites, while Seme had increased establishment on unburnt sites and Le Dube very little establishment overall. In general, however, this study found no difference in establishment in burnt and unburnt areas. Chirara, Frost & Gwarazimba [7] found that intensity of grass defoliation does not affect seedling establishment of A. karroo during the first year. Similarly, there was no difference in establishment of A. karroo in burnt or unburnt and tall or short grass sites. Smith & Goodman [32] reported that A. nilotica seedlings, however, almost exclusively occurred away from canopy cover, suggesting an inability to establish in shaded environments. Acacia tortilis also showed a greater proportion of established seedlings in open than shaded areas [23]. We did not find a difference in establishment of A. nilotica in tall and short grass, but its establishment was so low that no real prediction can be made. Conclusions Seedling establishment of A. karroo is strongly moisture dependent [6] and one would expect that A. karroo is more likely to invade moist rather than semi-arid grassland. This suggests that Hluhluwe Game Reserve, being an area with moist grassland, would be more prone to invasion by A. karroo. It has also been reported that A. karroo has the ability to withstand fire [17]. A combination of these factors may contribute to the success of A. karroo in the field and may be the reason for A. karroo's success over A. nilotica as the most important encroaching Acacia species in HiP at present. The literature does, however, suggest that high intensity fires may result in seed mortality [18,20]. It has, however, been reported that A. karroo seedlings survive fires from as little as 12 months of age [29]. Therefore, if fires are not hot enough to kill the seeds allowing them to germinate and seedlings to establish, management burns in the following year may not be useful in its attempt to control the establishment of this species. Back fires have higher fire intensities than head fires [20]. We therefore suggest that backfires be used during management burns and that fire frequency be increased in suitable areas in an attempt to slow down the rate of encroachment by A. karroo. It has been reported that spring burns are the most effective ([33] in [29]) and this should be taken into account. Methods Study site The study was done in HiP, KwaZulu-Natal, South Africa (28°00' – 28°26' S, 31°43' – 32°09'E). HiP is a 960 km2 fenced protected area comprising the former Hluhluwe and iMfolozi Game Reserves, and the corridor of land that links the areas. The park has a moderate coastal climate, ranges in altitude from 60 – 750 m above sea level [34] and has a summer rainfall ranging between 760 and 1250 mm per annum. Hluhluwe Game Reserve has a mean annual rainfall of 990 mm, while iMfolozi Game Reserve has a mean annual rainfall of 720 mm [34]. Periodic fluctuations in above or below average annual rainfall occur, resulting in wet and dry spells of approximately nine years [35]. The range in average monthly temperature is between 13 and 33°C [36]. Most of Hluhluwe Game Reserve is found on rocks of the Ecca and Beaufort series with some basalt in the east [37]. King [37] identified seven geological formations: (1) the Granite-Gneiss base, (2) the Table Mountain sandstone, (3) the Dwyka tillite, (4) The Ecca and Beaufort series, (5) the Stormberg series, (6) fault breccias and (7) recent deposits. The main soils types associated with the Ecca and Beaufort series are Swartland and Sterkspruit, while areas of Shortlands, Milkwood and Bonheim series are found in association with the dolerite regions [34]. They also report that shallow Mispah soils occur extensively in the reserve. The vegetation in the park has been described as bushveld – savannah comprising five broad vegetation types [38]. The thickets are wooded groups of similar-sized, small (usually less than three metres) trees of mainly one species that grows densely to the exclusion of other species. The thornveld consists of scattered thorn trees on grassland with deciduous, broad-leaved trees standing out above the thorn trees while the woodlands are densely wooded areas of tall trees that may contain many different, mainly broadleaved species. The well drained, shallow soils of the rocky outcrops support scattered trees of various sizes, while the termite mounds are nutrient rich patches sustaining dense clumps of trees that form small, wooded islands [38]. Locally the reserve is described as Natal Lowveld Bushveld and falls within the savanna biome [39]. The field experiment took place in the Hluhluwe and Corridor sections of the HiP. Acacia luederitzii occurs in large numbers in certain areas of the iMfolozi part of the reserve but is mostly absent from the Hluhluwe and Corridor sections. Acacia nilotica, A. karroo and D. cinerea are found throughout the park. As opposed to the scattered trees found in iMfolozi, A. nilotica covers extensive areas of Hluhluwe and the Corridor and is usually found below the 300 m contour [34]. Whateley & Porter [34] described an A. karroo – D. cinerea induced thicket throughout the area, but particularly in the Corridor and Hluhluwe Reserves. Acacia luederitzii seeds used in this study were therefore collected in iMfolozi Game Reserve while those of the other species were collected in Hluhluwe. Germination The effect of fire, fire intensity and burning of sites on the germination of seeds of A. nilotica, A. karroo, A. leuderitzii and D. cinerea was tested in a field experiment. Seeds of all species were collected between May and August 2000. Parasitized seeds were extracted. Prior to planned management burns, six groups of seeds were placed in tall grass (taller than 0.10 m) and six in short grass (shorter than 0.10 m) at three locations (Nombali, Seme and Le Dube). Tall grass produces hotter fires than short grass due to increased fuel load, which increases available heat energy [40]. Sites were cleared of existing pods/ seeds prior to the experiment and as podding season was over, no uncontrolled additions are expected to have occurred. Dichrostachys cinerea seeds were only put out at Seme and Nombali. Each group contained 22 A. nilotica, 25 A. karroo, 10 A. leuderitzii and 10 D. cinerea seeds. Seeds were placed on the soil surface a day before each of the burns (Nombali two days before). This is considered the natural situation for the seeds with soil stored seed banks being virtually non-existent [41]. Seme and Le Dube were burnt on 2 October and Nombali on 30 September 2000 shortly before the start of spring rains and natural seed release. After the burns, three of the groups of burnt seeds were removed from the tall and short grass and placed on unburnt tall and short grass sites at the same location respectively. Three groups of unburnt seeds were then added to each of the tall and short grass sites. A 13 mm mesh cage with 18 cm × 18 cm × 18 cm sides was used to protect each group of seeds and any germinated seedlings from rodent and herbivore predation. Cages were placed at half metre intervals and seeds placed on the soil surface in a group in the middle of each cage Seeds were considered to be germinating when a root started showing. A diagrammatical representation of the experiment is given in Figure 1. Germination was recorded at 1, 3, 5, 7, 9, 11, 14, 17, 20, 23, 27 and 31 weeks. The experiment ended in May 2001. Figure 1 Diagrammatical representation of the experimental design used to test the effect of fire on seed germination and establishment. Arrows indicate movement of seeds between burnt/unburnt tall/short grass plots. We thus applied 96 possible seed treatment combinations for investigating factors affecting germination in the field (4 species × 2 burn treatments × 3 locations × 2 location burn treatments × 2 fire intensities). Seedling establishment To test the effect of fire, fire intensity, burning of sites and grass length (shade) on seedling establishment of A. nilotica, A. karroo, A. leuderitzii and D. cinerea, data as on week 31 of the field experiment described above were used. Seedlings were considered to be established when they were rooted in the ground and the cotyledons replaced with leaves. Establishment was based on the total number of seeds. Data analysis The "STATISTICA®" [42] Generalized Linear Model (GLZ) module was used to construct linear logistic models for germination and establishment proportions as response variables for the field experiment. As data were recorded as presence (1) or absence (0) of seedlings, a binomial distribution was assumed [43]. In both cases, main effects and second order interactions were included in the model. The logit model may therefore be written as follows: where = the log of variable 1 and 2 at different levels of the factors as given below λ' = the overall mean effect of the categories = the effect of the jth species (j = A. karroo, A. luederitzii, A. nilotica, D. cinerea) = the effect of the kth location (k = Le Dube, Nombali, Seme) = the effect of the lth seed burn status (l = burnt, unburnt) = the effect of the mth grass length (m = short, tall) = the effect of the nth site burn status (n = burnt, unburnt) = the interaction effect between the jth species and the kth location = the interaction effect between the mth grass length and the nth site burn status. The logit model may be written as a generalized linear model as follows: where , , , , , , , , , and are parameters to be estimated from the data and B, C, D, E and F refer to the explanatory variables species, location, burn status, grass length and site burnt status respectively. The estimated parameters for the GLZ were used to obtain the estimated parameters for the logit model. The estimated parameters of the odds were calculated for each factor or combination of factors (including the intercept) as the exponent of the estimated parameters of the logit model. The estimated odds of germination under any condition were then calculated as the product of the estimated parameter of the odds of the intercept (estimated geometric mean odds) and the factor or combination of factors in question. The odds of germination for significant treatment combinations were compared. The predicted number of seeds germinating and seedlings establishing as calculated with the model based on presence/absence data, were seen as being appropriate for interpretation as summaries of the data. Thus, differences in the predicted mean number of seeds germinating and seedlings establishing (given as a fraction of the total number of seeds) were illustrated graphically for each significant treatment combination. Authors' contributions MW designed the experiment, participated in fieldwork, performed the statistical analysis and drafted the document. MJS participated in fieldwork, the coordination of the study and drafting of the document. JJM supervised the work and assisted in the drafting of the document. All authors read and approved the final manuscript. Supplementary Material Additional File 1 The parameters of the logit model and odds, estimated odds of germination and the ratio of germination to non-germination for the factors included in the model for germination of certain Acacia seeds in HiP. Gives parameters of the logit model and estimated odds of germination for the various levels of the factors used. Click here for file Additional File 2 The parameters of the logit model and odds, estimated odds of establishment and the ratio of establishment to non-establishment for the factors included in the model for establishment of certain Acacia species in HiP. Gives parameters of the logit model and estimated odds of establishment for the various levels of the factors used. Click here for file Additional File 3 Odds ratios for all significant interactions of the establishment model. Compares the odds of establishment between different levels of the factors used. Click here for file Acknowledgements The University of Stellenbosch (Hofmeyer Fund) and the University of Cape Town (Andrew Mellon Foundation) provided financial support for this study. The KwaZulu-Natal Nature Conservation Service is thanked for permission to work in the park. We wish to thank the staff of the Hluhluwe Research Centre for their input and support in managing the logistics of the project and for assistance in the field. Thanks go to Charlie Boucher, Sue Milton, Tim O'Connor and Nico Smit for their comments on earlier drafts of this document. ==== Refs West O Thorn bush encroachment in relation to the management of veld grazing Rhod Agric J 1947 44 488 497 Scott JD Bush encroachment in South Africa S Afr J Sci 1967 63 311 314 Archer S Have southern Texas savannas been converted to woodlands in recent history? Am Nat 1989 134 545 561 10.1086/284996 Grossman D Gandar MV Land transformation in South African savanna regions S Afr Geogr J 1989 71 38 45 Roques KG O'Connor TG Watkinson AR Dynamics of shrub encroachment in an African savanna: relative influences of fire, herbivory, rainfall and density dependence J Appl Ecol 2001 38 268 280 10.1046/j.1365-2664.2001.00567.x O'Connor TG Acacia karroo invasion of grassland: environmental and biotic effects influencing seedling emergence and establishment Oecologia 1995 103 214 223 10.1007/BF00329083 Chirara C Frost PGH Gwarazimba VEE Grass defoliation affecting survival and growth of seedlings of Acacia karroo, an encroaching species in southwestern Zimbabwe Afr J Range For Sci 1998 15 41 47 Mackey AP The biology of Australian weeds 29. Acacia nilotica ssp. indica (Benth.) Brenan Plant Protection Quart 1997 12 7 17 Kriticos D Brown J Radford I Nicholas M Plant population control and biological control: Acacia nilotica as a case study Biol Control 1999 16 230 239 10.1006/bcon.1999.0746 Sabiiti EN Wein RW Fire and Acacia seeds: a hypothesis of colonization success J Ecol 1987 74 937 946 Pieterse PJ Cairns ALP The effect of fire on an Acacia longifolia seed bank in the south-western Cape S Afr J Bot 1986 52 233 236 Auld TD O'Connell MA Changes in predispersal seed predation levels after fire for two Australian legumes, Acacia elongata and Sphaerolobium vimineum. Oikos 1989 54 55 59 Auld TD Tozer M Patterns in emergence of Acacia and Grevillea seedlings after fire Proc Linn Soc N S W 1995 115 5 15 Bradstock RA Auld TD Soil temperatures during experimental bushfires in relation to fire intensity: consequences for legume germination and fire management in south-eastern Australia J Appl Ecol 1995 32 76 84 Mucunguzi P Oryem-Origa H Effects of heat and fire on the germination of Acacia sieberiana D.C. and Acacia gerrardii Benth. in Uganda J Trop Ecol 1996 12 1 10 Teketay D Germination ecology of twelve indigenous and eight exotic multipurpose leguminous species from Ethiopia For Ecol Manag 1996 80 209 223 Mbalo BA Witkowski ETF Tolerance to soil temperatures experienced during and after the passage of fire in seeds of Acacia karroo, A. nilotica and Chromolaena odorata: a laboratory study S Afr J Bot 1997 63 421 425 Auld TD O'Connell MA Predicting patterns of post-fire germination in 35 eastern Australian Fabaceae Austr J Ecol 1991 16 53 70 Saharjo BH Watanabe H The effects of fire on the germination of Acacia mangium in a plantation in South Sumatra, Indonesia Commonwealth Forestry Rev 1997 76 128 131 Kanz WA Seed and seedling dynamics of certain Acacia species as affected by herbivory, grass competition, fire, and grazing system MSc thesis University of Natal; Grassland Science Department 2001 Schultz AM Lauenbach JL Biswell HH Relationship between grass density and brush seedling survival Ecology 1955 36 226 238 Brown JR Archer S Shrub invasion of grassland: recruitment is continuous and not regulated by herbaceous biomass or density Ecology 1999 80 2385 2396 Smith TM Shackleton SE The effects of shading on the establishment and growth of Acacia tortilis seedlings S Afr J Bot 1988 54 375 379 Belsky AJ Influences of trees on savanna productivity: tests of shade, nutrients, and tree-grass competition Ecology 1994 75 922 932 Thomas DB Pratt DJ Bush control in the drier areas of Kenya. IV. Effects of controlled burning on secondary thicket in upland Acacia woodland J Ecol 1967 55 325 335 Watson HK Macdonald IAW Vegetation changes in the Hluhluwe-Umfolozi Game Reserve Complex from 1937 to 1975 Bothalia 1983 14 265 269 Bond WJ Smythe KA Balfour D Acacia species turnover in space and time in an African savanna J Biogeog 2001 28 117 128 10.1046/j.1365-2699.2001.00506.x Skowno AL Midgley JJ Bond WJ Balfour D Secondary succession in Acacia nilotica (L.) savanna in the Hluhluwe Game Reserve, South Africa Plant Ecol 1999 145 1 9 10.1023/A:1009843124991 Story R A botanical survey of the Keiskammahoek district Mem Bot Surv S Afr 1952 27 1 Radford IJ Kriticos D Nicholas M Brown JR Eldridge D, Freudenberger D Towards an integrated approach to the management of Acacia nilotica in northern Australia In Proceedings of the VIth International Rangeland Congress, Townsville 2000 2 VI International Rangeland Congress, Inc., Aitkenvale 585 586 19–23 July 1999 Okello BD Young TP Effects of fire, bruchid beetles and soil type on germination and seedling establishment of Acacia drepanolobium. Afr J Range and For Sci 2000 17 46 51 Smith TM Goodman PS The effect of competition on the structure and dynamics of Acacia savannah in southern Africa Afr J Ecol 1986 74 1031 1041 Scott JD A contribution to the study of the Drakensberg conservation area DSc thesis University of the Witwatersrand 1949 Whateley A Porter RN The woody vegetation communities of the Hluhluwe-Corridor-Umfolozi Game Reserve Complex Bothalia 1983 14 745 758 Preston-Whyte RA Tyson PD The atmosphere and weather of Southern Africa 1988 Cape Town: Oxford University Press Grobler JH Greyling T, Huntley BJ Natal Parks, Game and Fish Preservation Board Pretoria: Foundation for Research Development 1984 King L The geology of the Hluhluwe Game Reserve Petros 1970 2 16 19 Grant R Thomas V Sappi tree spotting, KwaZulu-Natal, Coast and Midlands Johannesburg: Jacana 1998 Low AB Rebelo TG Vegetation of South Africa, Lesotho and Swaziland Pretoria: Department of Environmental Affairs and Tourism 1996 Trollope WSW Booysen PdV, Tainton NM Fire behaviour In Ecological effects of fire in South African systems 1984 New York: Springer-Verlag 199 217 Walters M Milton SJ The production, storage and viability of seeds of Acacia karroo and A. nilotica in a grassy savanna in KwaZulu-Natal, South Africa Afr J Ecol 2003 41 211 217 10.1046/j.1365-2028.2003.00433.x StatSoft Inc STATISTICA for windows Tulsa: StatSoft, Inc 2000 Bustamante J Predictive models for lesser kestrel Falco naumanni distribution, abundance and extinction in southern Spain Biol Cons 1997 80 153 160 10.1016/S0006-3207(96)00136-X

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BMC Ecol. 2004 Apr 7; 4:3

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BMC Ecol

10.1186/1472-6785-4-3

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020191SynopsisEvolutionGenetics/Genomics/Gene TherapyYeast and FungiSaccharomycesTurning Down the Volume: Why Some Genes Tolerate Less Noise Synopsis6 2004 27 4 2004 27 4 2004 2 6 e191Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Noise Minimization in Eukaryotic Gene Expression ==== Body All organisms have evolved complex mechanisms designed to exquisitely regulate the expression of appropriate genes at their correct levels. Natural random variation in the processes of regulation and expression, however, limits the precision with which protein production can be controlled. This subtle variation, or “noise,” in the expression of genes has been studied with increasing interest. Though much progress has been made in understanding the amount of noise that exists and the cellular processes that underlie it, the physiological impact of noise, and whether it is biologically relevant or can just be ignored, has been less clear. “Shhhhhhhhhhhhhh!” (Photo by Bryan Zeitler and Jennifer Zeitler) To answer this question, Hunter Fraser et al. asked whether noise in gene expression exerts an equal effect on all genes in the genome. Is noise in gene expression irrelevant to the fitness and well-being of cells, or do cells need to minimize noise in the expression of some or all genes? If noise in gene expression has a negative impact on cells, they reasoned, that impact should vary from gene to gene depending on the gene's function. There should be selection to minimize noise for those genes most crucial to cell survival and function. Thus, a genome-wide analysis of noise in gene expression, they predicted, would show that genes for which “noisy” expression would be most harmful would display less of it. The researchers examined this question in the budding yeast Saccharomyces cerevisiae because of the vast quantity and variety of genomic data available for this organism. Previous research has shown that the noise that exists in the expression of a gene is directly related to the rates of transcription and translation. Using data available from previous genome-wide studies, the authors were able to estimate these rates, and therefore the noise, for nearly every gene in the yeast genome. After estimating the amount of noise in the expression level for nearly every gene, the authors examined two subsets of genes that they hypothesized would be particularly affected by noise. First they looked at “essential” genes, reasoning that since total lack of expression of these genes results in death, even small variations in expression resulting from noise would often exert a negative impact. Previous research had identified all the essential genes in yeast by deleting each gene individually and assessing the fitness of the resulting mutant. Here, the authors compared the levels of noise in this pool of essential genes to that of nonessential genes. They found that essential genes usually display less noise than nonessential genes, lending support to their hypothesis. They similarly examined genes encoding proteins involved in forming multiprotein complexes. Because these complexes are built of proteins in specific ratios, over- or under expression of one component will hinder the accurate assembly of productive complexes. So a high degree of noise could interfere with the coordinated expression necessary for proteins involved in these complexes. The authors again used data from previous research to choose members of this group: they relied on two studies which had identified a large number of multiprotein complexes in yeast. Using this group of genes for comparison, the authors found that, like essential genes, genes encoding proteins involved in multiprotein complexes generally display less noise than other genes. This study draws a simple but fundamental conclusion about noise in eukaryotic gene expression—noise has physiological consequences. Importantly, the fact that noise is minimized in those gene groups for which noisy expression would be most harmful suggests that factors contributing to noise are subject to natural selection. This study also demonstrates the power of using the growing number of genomescale datasets in this type of analysis. Researchers will undoubtedly continue to mine the available data to draw biological conclusions not anticipated by the original authors.

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PLoS Biol. 2004 Jun 27; 2(6):e191

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10.1371/journal.pbio.0020191

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020123Research ArticleBioinformatics/Computational BiologyDevelopmentDrosophilaTopology and Robustness in the Drosophila Segment Polarity Network Robustness in Drosophila Segment PolarityIngolia Nicholas T 1 1Department of Molecular and Cellular Biology, Harvard UniversityCambridge, MassachusettsUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e12311 7 2003 20 2 2004 Copyright: © 2004 Nicholas T. Ingolia.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Calculus of Purpose Computation Approach Shows Robustness of the Striped Pattern of Fruitfly Embryos A complex hierarchy of genetic interactions converts a single-celled Drosophila melanogaster egg into a multicellular embryo with 14 segments. Previously, von Dassow et al. reported that a mathematical model of the genetic interactions that defined the polarity of segments (the segment polarity network) was robust (von Dassow et al. 2000). As quantitative information about the system was unavailable, parameters were sampled randomly. A surprisingly large fraction of these parameter sets allowed the model to maintain and elaborate on the segment polarity pattern. This robustness is due to the positive feedback of gene products on their own expression, which induces individual cells in a model segment to adopt different stable expression states (bistability) corresponding to different cell types in the segment polarity pattern. A positive feedback loop will only yield multiple stable states when the parameters that describe it satisfy a particular inequality. By testing which random parameter sets satisfy these inequalities, I show that bistability is necessary to form the segment polarity pattern and serves as a strong predictor of which parameter sets will succeed in forming the pattern. Although the original model was robust to parameter variation, it could not reproduce the observed effects of cell division on the pattern of gene expression. I present a modified version that incorporates recent experimental evidence and does successfully mimic the consequences of cell division. The behavior of this modified model can also be understood in terms of bistability in positive feedback of gene expression. I discuss how this topological property of networks provides robust pattern formation and how large changes in parameters can change the specific pattern produced by a network. The striped segmentation pattern of the Drosophila embryo is remarkably insensitive to variation (robust). Ingolia uses computational methods to show that this robustness results from specific positive feedback loops ==== Body Introduction The network responsible for segment polarity in the Drosophila melanogaster embryo has been extensively studied. The segment polarity pattern emerges from a sequence of developmental events that each refine the pattern produced by the previous event. During the early cell cycles of the embryo, cell division is suppressed and maternal morphogens induce a transcriptional cascade of genes (the gap and pair-rule genes). These in turn create a prepattern of local expression of the segment polarity genes, genes that encode a collection of signaling molecules and transcription factors whose expression specifies the location and polarity of parasegment boundaries in the embryo. After cellularization, interactions amongst the segment polarity genes maintain narrow boundaries between parasegments as the embryo grows through cell division (Figure 1A shows how the structure of the parasegment is related to that of the morphologically defined segment). Diffusible signals from the boundaries also influence cell fates across the parasegment. Figure 1 The Segment Polarity Pattern and the Behavior of Different Cells (A) Parasegments in the segment polarity pattern. The prepattern, with stripes of wg and en expression, and the final segment polarity pattern are shown. The parasegment is the basic developmental unit in the segment polarity pattern, but segment boundaries within the adult insect are offset from the parasegment boundary. (B) A simple set of rules sufficient to achieve segment polarity patterning. Cells expressing wg must continue to express wg, en-expressing cells must continue to express en and begin expressing hh, and cells expressing neither wg nor en cannot begin expressing either. (C) The behavior of isolated cells for parameter sets that form the segment polarity pattern. These are like the simple rules in (B), but en expression depends on a wg-expressing neighbor. Many of the qualitative interactions between the components of the segment polarity network are known, but there is little quantitative information about the abundance of the components or the parameters that govern the reactions amongst them (DiNardo et al. 1994; Gilbert 1997; Hatini and DiNardo 2001; Sanson 2001). The existing, qualitative knowledge has been used to develop a variety of mathematical models. Some have employed Boolean idealizations (Albert and Othmer 2003), while others, including von Dassow et al., have used systems of ordinary differential equations to simulate concentrations of proteins and mRNAs (von Dassow et al. 2000; von Dassow and Odell 2002). The model requires 50 quantitative parameters such as rate constants and affinities. The equations and parameters, together with the initial conditions, specify how the protein and mRNA concentrations change over time. Von Dassow et al. tested pattern formation by picking thousands of randomly chosen parameter sets and following the evolution of the pattern from a fixed set of initial conditions. Given the large number of variables, they found that a remarkable fraction (0.5%) of parameter sets converted the prepattern into the correct, stable segment polarity pattern and concluded that the network was surprisingly robust. I asked what general features of the model yield this robustness. As defined by von Dassow et al., the task of forming the segment polarity pattern is simple. Embryos in the model begin with a prepattern composed of a repeating unit of three stripes that encompasses four rows of cells. The first stripe expresses wingless (wg), the second stripe expresses engrained (en), and the third stripe, which is two cells wide, expresses neither. The prepattern is produced by the transient expression of gap and pair-rule genes, but maintaining and elaborating this pattern depends on the activity of wg and en and of genes that interact with them (Hatini and DiNardo 2001; Sanson 2001). For example, the en-expressing stripe must start to express hedgehog (hh), as shown in Figure 1A There is no initial hh expression, but the target pattern as defined by von Dassow et al. requires it to be expressed in the en stripe. Because EN protein induces hh expression, simply maintaining the initial pattern of wg and en expression suffices to produce the desired final pattern (Figure 1B) (Tabata et al. 1992). Thus, stable maintenance of wg and en expression levels within each individual cell will produce the segment polarity pattern. Systems in which genes induce their own expression can display multiple stable expression states, a phenomenon known as bistability, though they only do so under certain conditions (Novick and Weiner 1957; Glass and Kauffman 1973; Keller 1994; Hasty et al. 2000; Thomas and Kaufman 2001). To produce mathematical models that succeeded in converting the prepattern into the final pattern, von Dassow et al. added two interactions to their initial model of the segment polarity network. As they later noted, these created two positive feedback loops, one including en and the other including wg (Figure 2A) (von Dassow and Odell 2002). I asked whether parameter sets that can generate the segment polarity pattern are the ones that produce bistability. Figure 2 The Regulatory Networks in the Segment Polarity Models (A) The regulatory network used in the von Dassow et al. (2000) model. Dashed lines indicate interactions added by the original authors in order to achieve proper patterning, while solid lines indicate interactions based on experimental observations. The positive feedback system including wg is in blue, while the one involving en is green and red. The en feedback involves mutual inhibition of en and ci, so one side of the mutual inhibition scheme is drawn in green while the other is drawn in red. When the green species are active, they will repress the red ones, and vice versa. Adapted from von Dassow et al. (2000). (B) The regulatory network of the model developed here. The positive feedback systems are colored as in (A). The en feedback involves mutual inhibition of slp, however, and ci does not play a role in the en feedback system. To address this question, I asked two questions: could modeling the behavior of individual cells reproduce the overall behavior observed by von Dassow et al., and could I produce simple rules that predicted how the individual cells would behave. When I simulated the behavior of individual cells using the von Dassow et al. model, I found that individual cells in their model can adopt three different stable states of wg and en expression. The overall pattern, and its robustness, can be simply explained as a consequence of single cells maintaining one of these expression stable states, which correspond to the three stripes of gene expression. I also devised tests that determine whether a given parameter set allows positive feedback to stably produce the desired pattern of gene expression in these cells. These allowed us to show that parameter sets that do not produce bistability almost never yield the correct pattern, whereas those that do are much more likely to produce the right segment polarity pattern. I also investigated the role of the prepattern and found that more biologically reasonable initial conditions can dramatically reduce the fraction of parameter sets that obey the bistability rules but fail to form the segment polarity pattern. Finally, I noted that the interactions of these loops do not maintain the observed segment polarity pattern after cell proliferation (Figure 3A). I modified the von Dassow scheme to incorporate recent experimental evidence and produced a model that both forms the segment polarity pattern and maintains it during cell proliferation with many random parameter sets. Figure 3 The Segment Polarity Pattern After Cell Proliferation (A) Parasegments in the segment polarity pattern during cell proliferation. During cell proliferation, each cell duplicates into two cells that initially have identical gene expression. This yields wide stripes of wg and en expression at parasegment boundaries immediately after cell proliferation. Subsequently, differences in intercellular signaling cause the stripes of wg and en narrow. (B) A simple set of rules sufficient to maintain narrow boundaries after cell proliferation. These are like the simple rules in Figure 1C, but wg expression also depends on a hh-expressing neighbor. Results I began by asking if the von Dassow et al. model could be decomposed into the properties of individual cells. The simplest hypothesis is that parameters that allow individual cells to maintain their initial state of wg and en expression will maintain the overall pattern. At the level of the cell, the parameters must allow all three types of cells to evolve from the initial conditions to the final state, and the final state must be stable. The isolated cell rules are: (1) cells that initially express wg must continue to do so, (2) cells that initially express en must continue to do so, and (3) cells that express neither wg nor en must not turn on either gene. I began by studying the properties of wg-expressing cells, as WG protein is modeled as controlling en expression, but not vice versa (data not shown). I used the equations of von Dassow et al. to model the dynamic behavior of an individual cell, starting from the standard prepattern (von Dassow et al. 2000). I tested the isolated cell rules by simulating an individual cell in the context of signals that it would receive from its neighbors in the actual segment polarity pattern, computed assuming constant expression levels of segment polarity genes in those cells. Each parameter set that produces the overall pattern gives two behaviors that depend on the initial state of the cell; cells that are initially wg-expressing remain so, whereas cells that lack wg expression never acquire it. Thus, the wg-expressing stripe could retain wg expression while other cells in the field would not begin expressing wg. The precise expression levels in these two states were generally unaffected by the signals from their neighbors; in particular, HH signaling generally had no effect on wg expression in nearby cells (data not shown). In the segment polarity pattern, cells on the posterior side of the wg stripe maintain en expression while cells on the anterior side of the stripe do not begin expressing en despite experiencing the same level of WG signaling as their neighbors on the other side of the stripe (see Figure 1A). This asymmetry requires bistability in en expression, at least in the context of a neighboring stripe of wg expression. I found that such bistability existed in working parameter sets, as long as extracellular WG exceeded a threshold concentration. Above this threshold, cells expressing en continue to do so, but cells that lack en expression do not start to express en. This threshold was always less than the amount of extracellular WG signal received from a neighboring stripe of high wg expression, which presents two wg-expressing cells. In a very small fraction of parameter sets, additional WG signal above the threshold could switch cells from not expressing to expressing en. However, when this switch was present in working models, it required WG signal from at least three wg-expressing neighbors. Such a switch is not seen in life, however, nor is it seen in most working parameter sets. Behaviors of isolated cells are summarized in Figure 1C. To determine how well the isolated cell rules captured the requirements for patterning, I generated random parameter sets and tested them against the single-cell behavior rules, as well as determining whether they formed the segment polarity pattern, to see how well these correlated. Around half of randomly generated parameter sets that conform to the rules actually achieve the desired segment polarity pattern (Table 1), and parameter sets that do not satisfy these rules cannot generate the desired final pattern (with a single exception in 10,000 trials). Since the rules require cells to reach the states they exhibit in the final segment polarity pattern, it is not surprising that they are necessary. However, the strong agreement between predictions based on individual cell behavior and the observed performance of the whole system argues that the model functions because individual cells adopt one of three stable expression states to form the segment polarity pattern rather than because of the complex, collective behaviors of groups of cells. Table 1 Pattern Formation and Predictive Rules in the von Dassow et al. Model Random parameter sets were generated and tested for segment polarity patterning using the stripe threshold scoring scheme as described in von Dassow et al. (2000). Isolated cell rules: Isolated cell behavior was tested for 10,000 parameter sets. The dynamics of expression in a single cell was simulated using different prepatterns found in the segment polarity network. First, low or high initial wg expression was used to test the stability of a wg-expressing state and a wg-repressed state. Second, the level of extracellular WG signal to a cell adjacent to two wg-expressing cells was used to test the stability of an en-expressing state and an en-repressed state. A cell complying with both of these tests was accepted as obeying isolated cell rules. Bistability rules: Parameter sets were tested for agreement with four parameter rules that should predict bistability (n = 25,000). Pattern formation was also assessed from a modified initial condition. The steady-state levels of CI, CN, PTC, ci, and ptc were computed for a particular parameter set, and these were used as initial levels for these components outside the stripe of en expression aThe predictive value of one of our rules is the fraction of its predictions that are correct. The positive predictive value is the fraction of parameter sets that satisfy our rules which actually form the segment polarity pattern. Similarly, the negative predictive value is the fraction of parameter sets that do not satisfy the rules which do not form the segment polarity pattern Asking whether mathematical expressions can predict the behavior of single cells and the parasegment as a whole is a more stringent test of the idea that the bistability of positive feedback loops explains these stable expression states. Whether a positive feedback loop shows bistability depends on the quantitative values of its parameters. Thus, if I can predict which sets of parameters produce bistable expression of wg and en, I can ask whether bistability in the two feedback loops is both necessary and sufficient to maintain the segment polarity pattern. The parameter sets must meet certain conditions: positive feedback must be sufficient to maintain the high-expression state, while basal or external activation must not overwhelm the low-expression state (see Protocol S1 for details). These conditions can be expressed analytically, and I devised tests to determine whether a parameter set would yield the desired bistability in both the en and the wg positive feedback loops. For instance, the amount of WG present in a cell in the high-wg-expression steady state was compared to KWG→wg, a parameter indicating the amount of intracellular WG needed for half-maximal activation of wg expression. I selected subnetworks within a single cell that could be largely isolated from other parts of the model (for example, see Figure 4A). I solved for approximate steady-state concentrations of components in subnetworks and compared these levels of signaling molecules to those needed to induce or repress target genes. Our derived constraints were the following. Figure 4 Inequalities Necessary for Bistability Are Satisfied by Working Parameter Sets (A) Subnetwork responsible for wg expression bistability. Levels of intercellular WG in a cell with full wg expression and in an adjacent cell can be computed from the transfer rates EndoWG, ExoWG, LMxferWG, and MxferWG; and the decay rates HEWG and HIWG, using the linearity of WG transport processes. (B and C) Intercellular WG levels in a cell expressing wg (green) and in an adjacent cell (red) were plotted against KWG→wg, the threshold level of intercellular WG protein needed for wg autoactivation. In (B), parameter sets that maintain the segment polarity pattern were used, while in (C) random parameter sets were used. (D) Levels of extracellular WG signalling to a cell adjacent to two with full wg expression were computed as described above. These were plotted against KEWG→en, the threshold level of extracellular WG signal needed to activate en expression. (E) Steady-state levels of CN in the absence of en expression plotted against KCN┤en, the threshold level needed to repress en expression. (1) For high wg expression, the net level of intracellular WG must be above KWG→wg, the amount needed for half-maximal activation of wg. To maintain the pattern, expression of wg must be bistable, such that cells beginning with high levels of wg expression maintain this “on” state while those with low levels of wg expression remain “off.” Expression of wg is regulated principally by intracellular WG protein. To achieve bistability, the level of WG protein in a cell with high wg expression must be sufficient to activate wg expression. After being produced, WG protein is lost from the intracellular compartment by transport and decay processes (Figure 4A). The production and loss rates balance at a steady state, whose intercellular WG concentration I compared to KWG→wg (Figure 4B). (2) Transport of WG from a neighbor with high levels of wg expression cannot raise the concentration of intracellular WG above KWG→wg. Similarly, levels of WG protein that accumulate in cells with low wg expression by transport processes and basal transcription must not be high enough to activate wg expression in these cells. In particular, the steady-state concentration in a cell producing wg must exceed KWG→wg, but the concentrations in its non-wg-expressing neighbors must be below this value. Parameter sets achieving the segment polarity pattern satisfy these inequalities, as shown in Figure 4B, while randomly-generated parameter sets do not (Figure 4C). (3) Extracellular WG from two neighbors with high levels of wg expression must be greater than KEWG→en, the amount of extracellular WG signal needed for half-maximal induction of en expression. Extracellular WG signaling must be sufficient to activate en in the absence of Cubitus Interruptus (CI) repression. The parameter KEWG→en indicates the amount of extracellular WG signal needed for half-maximal en activation. The WG signal produced by cells in the high-wg-expression steady state must be strong enough to activate en and thus greater than KEWG→en. Working parameter sets satisfied this constraint (Figure 4D), while random parameter sets typically did not (data not shown). (4) The steady-state level of the CI amino-terminal fragment (CN) must be greater than KCN┤en, the amount of CN needed for half-maximal repression of en expression. Repressive CI must also be sufficient to block en expression in cells that are near the WG stripe, but which lack en expression. In the absence of en expression, levels of CN are governed by transcriptional regulation of Patched (PTC). The equations in the model give a single steady-state level of CN. This must be greater than KCN┤en, the amount of CN needed for half-maximal repression of en. As shown in Figure 4E, this inequality holds for all parameter sets that form the segment polarity pattern. The interpretation of this constraint is more complicated because interactions of CI and PTC can cause persistent limit-cycle oscillations of CN about its steady-state level according to both simulations and analysis. However, this does not seem to affect our results, probably because the average level of CN across the oscillations is typically close to the steady-state level. Mutual inhibition provides two stable states, one in which en is expressed and represses ci, and one in which ci is expressed and maintains en repression. Through such comparisons, I found that of the 0.61% of parameter sets that produced the segment polarity pattern, more than 90% were predicted to produce bistable behavior in both the wg and en positive feedback loops (see Table 1). From another perspective, the fraction of parameter sets that maintain segment polarity is enriched more than 10-fold amongst those obeying the bistability rules: 0.61% of all parameter sets form the desired pattern, but 6.8% of the parameter sets that obey the bistability rules do so. Most likely, the small fraction (0.05%) of parameter sets that form the pattern but do not obey our bistability criteria fail to do so because of approximations used in these tests. In all 12 cases, they violate only a single rule, whereas the median parameter set that does not form the segment polarity pattern violates three of the four constraints. While 8.2% of all random parameter sets are consistent with the above restrictions, only 0.56% actually form the segment polarity pattern (see Table 1) whereas 7.6% do not. These parameter sets should maintain the segment polarity pattern, but cannot form it from the prepattern. Though the prepattern does have en and wg stripes, it lacks any expression of three regulators (hh, ptc, or ci) that are expressed in the final segment polarity pattern. Because the initial conditions are substantially different from the stable segment polarity pattern, there are initially large, rapid changes in the concentrations of the components that can drive the collection of cells towards a different final pattern. These early dynamics are complicated, and I could not determine simple rules that predicted which of the parameter sets that satisfied our bistability criteria would generate the segment polarity pattern starting from the initial conditions used by von Dassow et al. The predictive value of the bistability rules is in marked contrast to the performance of the isolated cell rules, for which half the parameter sets that satisfied the rules produced the correct segment polarity pattern. I believe that the methods differ because the isolated cell rules address the dynamics by using simulations in which early expression dynamics actually occurred, at least in individual cells, rather than the steady-state comparisons of parameters used in the bistability rules. To test this possibility, I asked if I could improve the predictive value of the bistability rules by choosing different initial conditions. I focused on initial levels of CI, PTC, and CN outside the stripe of en expression. Increasing the initial concentrations of CI and PTC is biologically reasonable, as ci and ptc are both expressed before en induction (Motzny and Holmgren 1995). These two regulators constituted one of the isolated subnetworks used above. I solved for the steady-state expression levels of ci and ptc in each parameter set and used this in the initial condition for dynamic simulations with this parameter set. This change brought the prepattern in the model into better agreement with experimental results. The new initial conditions yielded a 6-fold increase in the number of parameter sets achieving the segment polarity pattern. This meant that 41% of the parameter sets meeting the bistability parameter rules actually formed the pattern from the modified prepattern (see Table 1), supporting the idea that many parameter sets obeying the bistability rules are able to form the segment polarity pattern but fail to do so from the initial pattern used by von Dassow et al. This suggests that the expression pattern of ci and ptc established by the pair-rule genes is biologically significant and plays a role in the robust formation of the final segment polarity pattern. This early expression of ci and ptc generates a prepattern that is more similar to the desired stable state. Maintaining the narrow parasegment boundary after cell division is an important role of the segment polarity network. Even at the level of the isolated cell rules, there is a discrepancy between the behavior of the model and experimental results. Experimentally, the maintenance of wg expression depends on HH signal from a neighboring stripe of en expression, but the wg “on” state is unconditionally stable in the von Dassow et al. model (compare Figure 1C and Figure 3B) (Hatini and DiNardo 2001). This difficulty manifested itself when I incorporated cell division into the model. The stripe of wg expression should remain one cell wide as the segment widens by cell division. The daughters of cells in the wg stripe further from the en stripe will not be exposed to HH signaling and will therefore lose wg expression, leaving only one cell in the wg “on” state after each division. In the von Dassow et al. model, the independence of wg expression from HH, and thus en expression, allows both daughters of a cell in the stripe of wg expression to retain the wg “on” state. Thus, the stripe grows wider over repeated rounds of cell division rather than maintaining a narrow border at the segment boundary. Indeed, I found no parameter sets which maintained the physiological segment polarity pattern after cell division. I wanted to modify the model so that it succeeded at this patterning task as well. Principally, I needed to make wg expression dependent on HH signaling (see Figure 2B). All effects of HH signaling are believed to be mediated by CI in its activating or repressive forms (Methot and Basler 2001). These regulate wg in the von Dassow et al. model, but CI plays another role in the en positive feedback loop. Constraints imposed by this second role may limit its effectiveness in regulating wg in response to HH signaling. Recent evidence suggests that, while EN does repress ci expression, sloppy-paired (slp) is the second factor involved in a mutual inhibition loop with en. I therefore removed the repression of en by CN and introduced mutual inhibition of slp and en, with slp mediating the positive effect of EN on hh expression (Alexandre and Vincent 2003). As all other signal transduction systems had been removed in the original model, I also removed ptc and allowed HH to directly inhibit the conversion of CI into CN. The interactions in this model are shown in Figure 2B. The specific equations were similar to those used by von Dassow et al., but some details were modified; for example, the exact form of the effect of CI and CN on wg expression was changed to account for the fact that they compete for binding to the same DNA sites (Muller and Basler 2000). I also simplified the transport processes for the intercellular signaling molecules WG and HH, which I showed play only a minor role in the original model. This modified model can robustly form the segment polarity pattern. Taking the same approach of testing random parameter sets, I found that 9.6% could generate the segment polarity pattern. This is an 8-fold higher fraction of successful parameter sets than that seen for the von Dassow et al. model or any subsequent variants (von Dassow and Odell 2002). In order to test whether this was a result of bistability in wg and en expression, I developed bistability rules for the modified model. These rules require the following: (1) the amount of intercellular WG in a cell with high wg expression must be enough to activate wg expression; (2) the amount of intracellular WG in a cell with low wg expression, but receiving strong HH signaling, must not be high enough to activate wg expression; (3) the amount of EN in a cell with low slp expression and high WG signaling from neighbors must be enough to repress slp expression; and (4) the amount of EN in a cell with high slp expression, but high WG signaling from neighbors, must not be sufficient to repress slp. Nearly all working parameter sets obeyed these rules, as I found for the von Dassow et al. model (see Figure 5A). They were even better predictors of working parameter sets than the parameter rules in the original model; nearly half of random parameter sets that are consistent with these rules form the proper pattern (Table 2). Figure 5 Inequalities Necessary for Bistability in the Modified Segment Polarity Model (A) Intercellular WG levels in a cell that expresses wg (green) and that does not express wg (red) were plotted against KWG→wgfor each parameter set that forms the segment polarity pattern, as in Figure 4B. In both cases, cells are receiving maximal HH signal from two neighbors. (B) Intercellular WG levels in a cell that is expressing wg but is no longer receiving HH signal from any neighbors were plotted against KWG→wg. Parameter sets that can produce the proper pattern after proliferation, including narrow stripes of wg expression, are shown in green while those that fail to do so are shown in red. Table 2 Pattern Formation and Predictive Rules in the Modified Model Random parameter sets (n = 100,000) were generated and tested for segment polarity patterning in the new model, using the stripe threshold scoring scheme as described in von Dassow et al. (2000). Frequency: The fraction of random parameter sets that display the indicated behavior. Parameter sets that display proper four-cell patterning are subdivided based on whether they properly form narrow expression stripes after cell proliferation. Four-cell Bistability: Parameter sets were tested for agreement with four parameter rules that should predict bistability necessary for patterning the four-cell-wide segment. Eight-cell Maintenance: Parameter sets were tested for their ability to maintain the postproliferation expression pattern. The initial conditions were an eight-cell-wide segment with adjacent one-cell-wide stripes of wg and en expression. Gene expression dynamics were simulated, and the final expression pattern was tested with the stripe scoring scheme as modified to test patterning after cell proliferation. Postproliferation Bistability: Parameter sets were tested for agreement with two parameter rules that should predict when removal of WG or HH signaling abolishes bistability in en or wg expression, respectively. Positive predictive value and negative predictive value: As in Table 1. The four-cell bistability rules were tested for their prediction of four-cell patterning. The eight-cell maintenance and postproliferation bistability tests were checked for their prediction of postproliferation patterning amongst cells that formed the proper four-cell pattern. Predictive values are reported as fractions Rules 1 and 2 are exactly analogous to bistability rules for the original model that ensure bistability in wg expression. The only change is the inclusion of HH signaling, which regulates wg expression in the modified model. Because ci is responsible for transducing the HH signal, I needed to find a different mutual inhibition partner for en; recent experiments implicated slp in this process. Rules 3 and 4 are similar to the bistability rules that ensure mutual repression of en and ci expression in the original model. They require high en expression to be strong enough to repress slp and vice versa. This ensures that either of the two states of the mutual inhibition switch is stable, and so en expression is bistable. In addition to maintaining the initial segment polarity pattern, the modified model is also capable of producing the proper pattern after cell division. Fully 1.7% of random parameter sets yielded the desired narrow stripes of gene expression after division, showing that this feature of the modified model is also robust (see Table 2). I investigated whether bistability of wg and en expression also explained which parameter sets could produce the proper pattern after cell proliferation. Achieving this pattern requires two steps: as cells proliferate, half of the daughters of wg- or en-expressing cells must turn off these genes, and the resulting pattern must be stable over time. The criteria for the stability of the pattern after proliferation are quite similar to those for the original pattern. In fact, nearly all parameter sets that form the original pattern can also maintain the eight-cell-wide segment pattern with one-cell-wide stripes if this pattern is used as an initial condition (see Table 2). Thus, parameter sets that fail to generate this pattern after cell proliferation must have difficulty reaching the proper pattern rather than maintaining it once produced. The more important constraint, as discussed above, is that wg and en expression must fade as cells move away from the mutual reinforcement at the boundary. I devised two additional rules based on this mutual dependence: (5) the amount of intracellular WG in a cell with high wg expression, but receiving no HH signaling, must not be sufficient to maintain wg expression (Figure 5B); and (6) the amount of SLP in a cell with initially high en expression that stops receiving strong WG signaling must be enough to repress en expression. Only one row of cells on either side of the parasegment border will be receiving WG or HH signals across the boundary. Rules 5 and 6 ensure that this signaling is necessary to maintain en and wg expression, so daughter cells born away from the boundary lose en or wg expression. This dependence is the mechanism by which the modified model maintains narrow stripes of segment polarity gene expression after cell division. These rules are reasonable predictors for proper behavior during cell proliferation. However, there are a substantial fraction of parameter sets that work despite breaking one or both rules, as well as many which obey them yet cannot produce the proper pattern after one round of cell division. Some of these difficulties probably result from the dynamic nature of the underlying process. As discussed above, bistability rules such as these can determine when a particular expression state is stable, but it is much harder to determine which stable state will be reached for a given initial condition. Thus, it is possible to predict when a parameter set will be able to maintain the final postproliferation pattern, but it is much harder to determine when it will reach this pattern from the expression state immediately following proliferation. This does not explain why there are parameter sets that do not obey rules 5 and 6 but nonetheless give narrow stripes of gene expression after cell division. Those parameter sets expose limits in the approximations used to develop the cell bistability rules. There may be small but important interactions between different feedback loops within a single cell, or perhaps some aspect of intercellular signaling is more complicated than the simple binary model employed in the bistability rules. Discussion I have shown that individual cells in the segment polarity model can adopt three distinct expression states, influenced by signals from their neighbors. I have also presented evidence that positive feedback in the model produces these states. The importance of autoregulation in establishing distinct expression states has been recognized in this system before (Heemskerk et al. 1991). In general, positive feedback can produce discrete stable expression states which are insensitive to small changes in parameters or initial conditions (Thomas and Kaufman 2001). This explains the robustness of the segment polarity patterns in the models. The ways in which intercellular signals impinge on autoregulatory loops will determine which expression patterns are possible. In a field of cells with bistable expression states, the overall pattern is just a specification of a particular expression state for each cell in the field. When signals produced by cells in the pattern are consistent with the states of neighbors receiving them, then this pattern will be a stable steady state. In the segment polarity pattern, there is a stripe of high en expression posterior to the stripe of wg expression, but one with low en expression anterior to it. The stripe of wg expression produces a signal that is strong enough to maintain the high en expression state, but does not induce en expression in cells that do not initially express it. Thus, the states of the cells neighboring the stripe of wg expression are consistent with the signals it produces. Our modifications to the model changed the effect of HH on the wg autoregulatory loop. This destabilized the pattern of wide stripes of wg expression resulting from cell proliferation, retaining the desired pattern with narrow wg expression as a stable pattern. Because the wide-stripe pattern was no longer stable, the model did not become trapped in this state following cell division. Many parameter sets instead progressed to the narrow-stripe pattern. The approach I have taken can be generally applied to models of complicated genetic or biochemical networks. I isolated small subnetworks, chosen to be maximally insulated from the rest of the system, and studied their behavior in isolation. This let us understand the principles that allow the entire network to function. I verified this understanding by creating tests for the behavior of the subnetworks and showing that these were powerful predictive tools for the performance of the entire network. This sort of decomposition is also useful in combination with quantitative phenomenological descriptions of subnetwork behaviors. Recent experimental studies provide such descriptions for a number of biological systems, including vertebrate homologues of the wg signal transduction system (Bagowski and Ferrell 2001; Bhalla et al. 2002; Lee et al. 2003). These could replace subnetworks in a larger model, tying the model more closely to biological evidence and showing how the subnetwork affects the larger system in which it functions. The robustness of the segment polarity network is a result of the fact that the desired pattern is a stable steady state. In a system of ordinary differential equations, such as the models described here, such states correspond to stable fixed points. These are generic features of such systems; small changes in parameters or initial conditions will not change them qualitatively. This can be seen in the bistability rules I developed. They are inequality constraints, so they carve out a volume of parameter space in which parameter sets can maintain the segment polarity pattern. In our analysis, I focused on robustness against changing parameters, which correspond to genetic alterations that change quantitative values of reaction parameters. In the real world, stochastic and environmental perturbations in the system may play at least as large a role. One important question is the extent to which the behavior of a network is determined by its topology, as opposed to quantitative details. The network topology is just the set of interactions in the network, along with their signs. This information is accessible to standard, qualitative biological experiments. Topology limits the possible behaviors of a regulatory network. Positive feedback, which is a topological property, is necessary for multiple stable states (Thomas and Kaufman 2001). Without such autoregulatory loops, all cells would eventually return to the same state after inducing signals are removed. Thus, positive feedback is particularly important in development and differentiation, when many different cell fates are permanently specified. However, quantitative details still have a large influence on network behavior. I held network topology constant while testing random parameter sets, which corresponds to changing quantitative details. Most random parameter sets did not form the segment polarity pattern because they did not display the proper stable states, despite having a topology that was capable of forming the segment polarity pattern. Quantitative details select a particular behavior from the repertoire of behaviors that are accessible from a given network topology. This same phenomenon has been shown experimentally in synthetic genetic networks, where a single topology can give rise to different behaviors when transcription factors and their binding sites are varied (Guet et al. 2002). These examples show how changes in the quantitative details of a regulatory network can result in qualitatively different behaviors. This could explain how pattern formation can be evolvable; mutations which cause large shifts in a critical parameter could cause a network to form a different pattern corresponding to a new stable state. The altered pattern would still correspond to a stable fixed point, so it would also be robust against various kinds of perturbations. This offers a mechanism that could produce new patterns without nonfunctional intermediates and without events such as the creation of a new protein–protein interaction. Materials and Methods The model employed a system of differential equations described by von Dassow et al. (2000). The correspondence between variables and parameters in our model and theirs is in Table 3. Simulations and numerical approximations were performed using the GNU Scientific Library (Galassi et al. 2002). Table 3 Variables in This Work and von Dassow et al. (2000) Isolated cell rule simulations Isolated cell rules were tested by simulations in which the dynamics of an individual cell were modeled using the same equations that govern each cell in the segment for the full segment polarity network. Since WG protein diffused between cells as well as moving into and out of a given cell, it was important to account for the diffusion of WG even in isolated cell simulations. The level of wg mRNA in a cell is represented by wi. Once translated from wg mRNA, WG protein diffuses between the intracellular pool, represented by Ii, and extracellular pools on each face j of the cell, E i,j. Extracellular WG can exchange between faces of the same cell and between opposing faces of adjacent cells. The parameters HIWG and HEWG are the half-lives of WG in the intracellular and extracellular pools, respectively. The diffusion parameters Kout,WG, Kacross,WG, Karound,WG, and Kin,WG are the rate constants for the first order of exchange of WG between the intracellular and extracellular pools and between different cell faces. These are linear equations, so it is possible to solve for the steady-state levels of Ii and Ei,j as a function of the wis, which control WG production, by inverting a matrix of transport and decay rates. In the segment polarity pattern, particularly, there is just one wg-expressing cell in the periodic pattern of four cells. So, I take won for one cell and woff for the other three in the periodic unit. All WG protein is initially intracellular, but it moves to extracellular faces by a roughly first-order process with time constant k = Kin,WG+H−1EWG. Therefore, I used Ei,j(t) = (1 −ekt) · E˜i,j as the amount of WG protein on neighboring cells for the isolated cell simulations. To verify bistability of wg expression, I simulated a single cell with no HH signaling from its neighbors. I calculated the amount of WG protein expected to be present on neighbors by an iterative process. Starting with won,0 = 1 and woff,0 = 0, I computed the steady-state extracellular WG protein E˜i,j(won,woff) presented by the neighbors of the cell expressing wg and used these in simulating a cell with initial w = 1. Similarly, I computed the amount of WG protein on neighbors of a cell next to the stripe of wg expression and used this in simulating a cell with initial w = 0. The final values of w in those two cells were used as w on,i+1 and w off,i+1 to compute the levels of extracellular WG protein for the next iteration. This process quickly converged, and I took the resulting w values as won and woff. I verified that won was above 0.1, the expression level threshold used in scoring pattern formation, and that woff was below 0.1. I then used the same levels of extracellular WG protein, computed from won and woff, to simulate a cell next to a stripe of wg expression. I used initial en mRNA and protein levels of 1 or 0 and ensured that, at the end of the simulation period, the former cell had en expression levels over the threshold but the latter did not. Finally, I verified that a cell with high initial wg mRNA but low initial en mRNA, receiving signals as if it were in the stripe of wg expression, still had low en expression at the end of the simulation. Bistability parameter rules These equations make repeated use of a particular equation form representing saturable and cooperative action of a protein, for instance as a transcriptional activator. In general, the amount of activation, Φ, as a function of the concentration of activator, x, is Here, K indicates the concentration of activator needed for half-maximal activation; it is essentially an affinity of the activator for its target. The parameter ν controls the degree of cooperativity in activator function, with large values of ν giving stronger cooperativity. The function produces sigmoidal curves which asymptotically approach 1 when x is large relative to K. In the model, there is a different Φ for each instance of transcriptional regulation controlled by an affinity parameter K and a cooperativity parameter ν for that interaction. For instance, the activation of en by extracellular WG is controlled by KEWG→en, which indicates the amount of extracellular WG needed for half-maximal activation, and by νEWG→en, which determines how cooperative the activation is. ci and ptc subnetwork I designed parameter rules for bistability by analyzing different subnetworks in the model and solving for steady states consistent with bistability from positive feedback. I solved for the stationary state of the ptc and ci subnetwork in the absence of en expression. The concentrations of ptc and ci mRNAs are p and c, and the concentrations of PTC protein, activating CI protein, and repressive CN are P, Cact, and Crep. The equations governing this system, entirely contained within a single cell, are The affinity and cooperativity parameters for each Φ have been suppressed for clarity. The parameters Hci and Hptc are the half-lives of ci and ptc mRNAs, and similarly the parameters HPtc, HCi, and HCN are the half-lives for the protein species. The level of Bicoid, a constitutive activator of ci expression, is indicated by the parameter B. Finally, C 0 is an affinity parameter for the cleavage of CI by PTC. To find the stationary state, I solve for the simultaneous zero of all five equations. Two variables, c and P, can be trivially eliminated. The remaining three equations in three variables always yielded a unique stationary state. The level of CN at this state, C˜rep, was compared to KCN┤en, the amount needed for half-maximal repression of en expression (parameter rule 4). The levels of CI and CN were also used to compute their influence on wg expression. The strength of this activation was indicated by β, a single term encompassing activation by CI and repression by CN. The only parameters in this expression are the affinity and cooperativity parameters for each Φ. WG and its effect on en Levels of wg mRNA in the ith cell, wi, are governed by β, which indicates the influence of CI and CN on wg expression, and by Ii, the amount of intracellular WG in the cell. In addition to affinity and cooperativity parameters for each Φ, and Hwg, the half-life of wg mRNA, there are scalars αCI→wg and αWG→wg, which determine the relative strengths of CI/CN and WG influences on wg expression. When Ii>K WG→wg, then Φ(Ii) will be large and wg expression high. I computed steady-state intracellular and extracellular WG protein levels as a function of wg expression as described above for the isolated cell rules. Bistability requires that intracellular WG levels in a wg-expressing cell remain high enough to maintain wg expression. I computed successive approximations to steady-state levels of wg mRNA and protein. I found I˜ w=1=Ii(wi=1) by setting and then found w˜ on=wi(Ii=I˜ w=1) by setting . I then required that I˜ on=Ii(wi=w˜ on)>K WG→wg, meaning that the level of intracellular WG is sufficient to maintain wg expression (parameter rule 1). I found no cases in which this much faster test gave different results than actually solving the self-consistent equations for . Bistability also requires that a cell not initially expressing wg must not be activated by WG from a neighboring cell. I used w˜ on to compute the amount of intracellular WG in a cell next to the wg stripe but not itself expressing wg, I˜ nbr=I i+1(wi=w˜ on,w i+1=0), and found w˜ off=w i+1(I i+1=I˜ nbr) and I˜ off=I i+1(w i+1=w˜ off). I then verified that I˜ nbr+I˜ off<K WG→wg, meaning that the sum of intracellular WG transported into a wg “off” neighbor and the intracellular WG produced by the wg “off” neighbor is not enough to activate wg expression (parameter rule 2). Finally, I find levels of extracellular WG signaling E˜ on,j and E˜ off,j in the same manner as I˜ on and I˜ off, respectively. These are used to ensure that the level of extracellular WG signal received by a cell in the en stripe is ΣE˜>K EWG→en (parameter rule 3). Modified initial conditions The modified initial conditions were generated by solving for the steady state of the CI and PTC subnetwork as described above. This yielded steady-state values c˜, C˜ rep, C˜ act, p˜, and P˜, which were used for the initial conditions in the stripe of wg expression and in the stripe expressing neither wg nor en. Initial conditions for components of the CI and PTC subnetwork in the stripe of en expression were kept at 0. The modified initial conditions also used steady-state levels of intracellular and extracellular WG protein. The steady-state I˜ i and E˜ i,j values were computed as described above under the assumption of a single column of cells with maximal wg expression and three columns with no wg expression. This latter change had a very modest impact on the fraction of parameter sets which formed the segment polarity pattern, and I did not pursue it further. Modified model The equations governing the modified model were similar in form to those in the original model. In addition to using the functional form Φ(x), I employed a related functional form Ψ(xr,xa) that represents the effects of an activator and a repressor that compete with equal affinity for a common binding site. Again, K is essentially an affinity parameter and ν controls the cooperativity of the process. The a0 term indicates the basal expression level, seen when neither activator nor repressor is acting. This functional form is used to express the effect of repressive CN and activating CI on wg expression. I also used it to represent the effect of intracellular WG activator with basal wg transcription, setting the repressor term xr=0. In addition to the dynamic variables described above, levels of en mRNA and EN protein are given by n and N, and levels of slp mRNA and SLP protein are given by s and S, respectively. The affinity, cooperativity, and basal transcription parameters are suppressed throughout for clarity. As nearly all dynamic variables are in the same cell, subscripts that index concentrations within a given cell are also omitted. In the two equations that involve intercellular signaling, a term E¯ Nbr or H¯ Nbr indicates the sum of extracellular WG or HH on neighboring cells, respectively; this is equivalent to the average without a normalization for the number of cells. Initial conditions were n=N=1 in the stripe of en expression, w=I=1 in the stripe of wg expression, and s=S=1 in the two-cell-wide stripe expressing neither en nor wg. As in the original model, cell proliferation was accomplished by doubling the grid size and copying the dynamic variables from each cell into two adjacent cells in the new grid. Bistability parameter rules Steady-state levels I˜ on and I˜ off were computed similarly to the way described for the original model. I assumed maximal ci expression, c=1, and maximal HH signal from two neighbors, H¯ Nbr=2, in computing the steady-state levels C˜ act and C˜ rep. As there was no intercellular transport of WG in the modified model, I needed to worry only about basal and activated wg expression in a single cell and did not need to consider intercellular transport. To check parameter rules 1 and 2, I simply compared the two steady-state levels I˜ on and I˜ off to KWG→wg. I computed E˜ w=1 for c=1 and H¯ Nbr=2 to account for WG signaling in en expression. I then found N˜ S=0 using E¯ Nbr=2E˜ w=1 to represent maximal WG signaling from two neighbors and S=0, no slp expression, in the steady-state equation n˜=N˜=Φ(E¯ Nbr)·(1−Φ(S)). I used this to compute S˜ off using the steady-state equation s˜=S˜=(1−Φ(N)). Finally, I used S˜ off and E˜ w=1 to find N˜ on in the en steady-state equation. I compared N˜>K EN┤slp to ensure that steady-state levels of EN were sufficient to repress slp expression. Similarly, I found N˜ S=1 using the steady-state en equation and used this to find S˜ on using the steady-state slp equation. The S˜ on was then used to find N˜ off, and I required that N˜ off<K EN┤slp. This ensured that repressed levels of en expression were not sufficient to repress slp expression. To test that wg expression was dependent on HH signaling, I first found I˜ on as described before. I also computed C˜ act and C˜ rep using c=1 but H¯ Nbr=0, representing a loss of HH signaling. I then used I˜ on and the new C˜ act and C˜ rep to find w˜ H=0 and I˜ H=0 with the steady state wg equation. I then found w˜ on→off and I˜ on→off using the steady state wg equation, the new H=0 values for C˜ act and C˜ rep, and I˜ H=0. Finally, I verified that I˜ on→off<K WG→wg, which ensure that wg autoactivation is not sufficient to maintain its expression after HH signaling is removed. To check whether en expression was dependent on WG signaling, I started with N˜ on and S˜ off as described above. I found E˜ off in the same way in which I found I˜ off and used E¯ Nbr,off=6E˜ off. I used this new level of WG signaling to find N˜ on→off with the steady state en equation, and then used this value to find S˜ off→on with the steady-state slp equation. To verify parameter rule 6, I checked that S˜ off→on>K SLP┤en, ensuring that the unrepressed level of slp expression can block en expression. Supporting Information Protocol S1 Bistability in wg Expression Additional background and explanation of bistability in gene expression. (109 KB PDF). Click here for additional data file. Accession Numbers The FlyBase (http://flybase.bio.indiana.edu/) accession numbers for the genes discussed in this paper are ci (FBgn0004859), en (FBgn0000577), hh (FBgn0004644), ptc (FBgn0003892), slp (FBgn0003430 and FBgn0004567), and wg (FBgn0004009). I thank Andrew Murray for advice and helpful discussions. I also appreciate critical comments on the manuscript from Daniel Fisher, Steve Altschuler, Lani Wu, Scott Schuyler, and all the members of the Murray laboratory as well as the anonymous reviewers. This work was supported by a predoctoral fellowship from the Howard Hughes Medical Institute. It was conducted in the laboratory of Andrew Murray, who is supported by the National Institutes Health. Conflicts of interest. The author has declared that no conflicts of interest exist. Author contributions. NTI conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, and wrote the paper. Academic Editor: Arthur Lander, University of California at Irvine Abbreviations CiCubitus Interruptus CNCubitus Interruptus amino-terminal fragment EnEngrailed HhHedgehog PtcPatched SlpSloppy-Paired Wgwingless. ==== Refs References Albert R Othmer HG The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster J Theor Biol 2003 223 1 18 12782112 Alexandre C Vincent JP Requirements for transcriptional repression and activation by Engrailed in Drosophila embryos Development 2003 130 729 739 12506003 Bagowski CP Ferrell JE Bistability in the JNK Cascade Curr Biol 2001 11 1176 1182 11516948 Bhalla US Ram PT Iyengar R MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network Science 2002 297 1018 1023 12169734 DiNardo S Heemskerk J Dougan S O'Farrell PH The making of a maggot: Patterning the Drosophila embryonic epidermis Curr Opin Genet Dev 1994 4 529 532 7950320 Galassi M Davies J Theiler J Gough B Jungman G GNU scientific library reference manual, 2nd ed 2002 Bristol (United Kingdom) Network Theory Limited 600 Gilbert S Developmental biology, 5th ed 1997 Sunderland (Massachusetts) Sinauer Associates 918 Glass L Kauffman SA The logical analysis of continuous, non-linear biochemical control networks J Theor Biol 1973 39 103 129 4741704 Guet C Elowitz M Hsing W Leibler S Combinatorial synthesis of genetic networks Science 2002 296 1466 1470 12029133 Hasty J Pradines J Dolnik M Collins JJ Noise-based switches and amplifiers for gene expression Proc Natl Acad Sci U S A 2000 97 2075 2080 10681449 Hatini V DiNardo S Divide and conquer: Pattern formation in Drosophila embryonic epidermis Trends Genet 2001 17 574 579 11585663 Heemskerk J DiNardo S Kostriken R O'Farrell PH Multiple modes of engrailed regulation in the progression towards cell fate determination Nature 1991 352 404 410 1861720 Keller AD Specifying epigenetic states with autoregulatory transcription factors J Theor Biol 1994 170 175 181 7967639 Lee E Salic A Kruger R Heinrich R Kirschner MW The roles of APC and axin derived from experimental and theoretical analysis of the Wnt pathway PLoS Biol 2003 1 10.1371/journal.pbio.0000010 Methot M Basler K An absolute requirement for Cubitus interruptus in Hedgehog signaling Development 2001 128 733 742 11171398 Motzny CK Holmgren R The Drosophila cubitus interruptus protein and its role in the wingless and hedgehog signal transduction pathways Mech Dev 1995 52 137 150 7577671 Muller B Basler K The repressor and activator forms of Cubitus interruptus control Hedgehog target genes through common generic gli-binding sites Development 2000 127 2999 3007 10862738 Novick A Weiner M Enzyme induction as an all-or-none phenomenon Proc Natl Acad Sci U S A 1957 43 553 566 16590055 Sanson B Generating patterns from fields of cells EMBO Rep 2001 2 1083 1088 11743020 Tabata T Eaton S Kornberg TB The Drosophila hedgehog gene is expressed specifically in posterior compartment cells and is a target of engrailed regulation Genes Dev 1992 6 2635 2645 1340474 Thomas R Kaufman M Multistationarity, the basis of cell differentiation and memory: I. Structural conditions of multistationarity and other nontrivial behavior Chaos 2001 11 170 179 12779451 von Dassow G Odell GM Design and constraints of the Drosophila segment polarity module: Robust spatial patterning emerges from intertwined cell state switches J Exp Zool Mol Dev Evol 2002 294 179 215 von Dassow G Meir E Munro EM Odell GM The segment polarity network is a robust developmental module Nature 2000 406 188 192 10910359

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020141Research ArticleEcologyEvolutionInfectious DiseasesMicrobiologyVirologyVirusesEubacteriaHomo (human)PlasmodiumYeast and FungiEcology Drives the Worldwide Distribution of Human Diseases Ecology Drives Disease DistributionGuernier Vanina guernier@mpl.ird.fr 1 2 Hochberg Michael E 3 4 Guégan Jean-François 1 1Génétique et Évolution des Maladies InfectieusesMontpellierFrance2Unité Expertise et Spatialisation des Connaissances en EnvironnementMontpellierFrance3Équipe Génétique et Environnement, Institut des Sciences de l'Évolution de MontpellierUniversité Montpellier II, MontpellierFrance4National Center for Ecological Analysis and Synthesis, University of CaliforniaSanta Barbara, CaliforniaUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e14117 10 2003 11 3 2004 Copyright: © 2004 Guernier et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Ecology Drives the Global Distribution of Human Diseases Identifying the factors underlying the origin and maintenance of the latitudinal diversity gradient is a central problem in ecology, but no consensus has emerged on which processes might generate this broad pattern. Interestingly, the vast majority of studies exploring the gradient have focused on free-living organisms, ignoring parasitic and infectious disease (PID) species. Here, we address the influence of environmental factors on the biological diversity of human pathogens and their global spatial organization. Using generalized linear multivariate models and Monte Carlo simulations, we conducted a series of comparative analyses to test the hypothesis that human PIDs exhibit the same global patterns of distribution as other taxonomic groups. We found a significant negative relationship between latitude and PID species richness, and a nested spatial organization, i.e., the accumulation of PID species with latitude, over large spatial scales. Additionally, our results show that climatic factors are of primary importance in explaining the link between latitude and the spatial pattern of human pathogens. Based on our findings, we propose that the global latitudinal species diversity gradient might be generated in large part by biotic interactions, providing strong support for the idea that current estimates of species diversity are substantially underestimated. When parasites and pathogens are included, estimates of total species diversity may increase by more than an order of magnitude. Comparative analyses reveal that human pathogens increase towards the equator and that the relationship is linked to climate - this has important implications for global biodiversity, public health and environmental epidemiology ==== Body Introduction Generally, the number of plant and animal species declines as one moves away from the equator (Pianka 1966; Stevens 1989, 1992; Rohde 1992; Brown 1995; Kaufman 1995; Rosenzweig 1995; Roy et al. 1998; Huston 1999; Chown and Gaston 2000; Hawkins and Porter 2001). This pattern, known as the latitudinal species diversity gradient, has been documented for many contemporary taxonomic groups (see Brown 1995; Rosenzweig 1995; Gaston and Blackburn 2000; Allen et al. 2002; Stevens et al. 2003). Over 30 hypotheses have been proposed to explain it (Rohde 1992), and it is only over the past several years that the most credible candidates have been identified; these are hypotheses related to area, energy, and time (Gaston and Blackburn 2000; Rahbek and Graves 2001) and to habitat heterogeneity and geometric constraints (Rahbek and Graves 2001). The vast majority of studies exploring the latitudinal species diversity gradient have focused on free-living organisms, such as herbivores, mammals, and angiosperms, and with rare exception (Hillebrand et al. 2001; Curtis et al. 2002; Nee 2003), none has examined large-scale latitudinal species diversity patterns of pathogenic microorganisms. Biotic interactions such as parasitism, predation, and symbiosis have been often invoked as a causal mechanism for the gradient (see Rohde 1992), but no serious attempts have been made to quantify its importance to biodiversity. Parasitic and infectious diseases (PIDs), in particular, could prove to be key in understanding large-scale patterns of species diversity on Earth since they comprise a major part of total biological diversity (Combes 1995; Poulin 1998). Moreover, our understanding of human diseases and the existence of complete data sets provide an incomparable opportunity to explore the existence of a relationship between PID species richness and latitude, and to identify the determining factors of this latitudinal gradient. In recent years, research into nonrandom organization in parasite communities has turned, e.g., to the possible existence of nestedness. Nested structure is a hierarchical organization of species composition in which assemblages with successively lower species richness tend to be nonrandom subsets of richer assemblages (Hanski 1982; Patterson and Atmar 1986; Patterson and Brown 1991; Poulin and Guégan 2000). Some species are widely distributed and occur in many communities, whereas other species have more restricted distributions and occur only in a subset of the richest samples (Figure 1). When analysing the most important mechanisms responsible for generating nestedness, Wright et al. (1998) cited four candidate factors: random sampling, area, isolation, and habitat type. In the present study we seek an answer to the following question: To what extent is the global distribution of human pathogens specified by the properties of the physical environment or the organism itself, and to what extent does it depend on chance events? Figure 1 The Spatial Organization of Species Letters represent different PID species. Numbered rectangles represent different countries or areas. (A) Nested organization of species. Applying Diamond's theory, we here distinguish (1) “high-S” species, like species E, which are exclusively confined to the most species-rich communities; and (2) “tramps,” like species A, which occur mostly in richer communities but also in species-poor communities (e.g., measles, which is found in virtually every country). Thus, this nested pattern implies that some pathogens are restricted to the tropics, while others, more ubiquitous species, are widely and regularly distributed all over the world. (B) Random distribution of species, where no spatial organization occurs (see also Materials and Methods). We examine the global spatial distribution of species richness for human PIDs, and test the hypothesis that human diseases follow a latitudinal species richness gradient, with low latitudes being the richest zones in pathogen species diversity. We then test two additional propositions: (i) PID assemblages show nested species patterns along latitudinal gradients, i.e., PIDs present at northern latitudes are also present in larger PID assemblages of equatorial zones, and (ii) PID assemblages may be strongly influenced by environmental climatic forces. Results The Latitudinal Gradient of Species Richness for Pathogens After correcting for cofactors (i.e., area and socio-demographic, physical, and environmental parameters) that could influence the relationships between latitude and PID species richness, we still found that species richness in human pathogens is strongly correlated with latitude (Table 1). On average (seven times out of ten), tropical areas harbor higher pathogen species diversities compared to more temperate areas. Figure 2A illustrates the change in PID species diversity with latitude across the two hemispheres. Figure 2 The Latitudinal Gradients of PID Species (A) Relationship between PID species richness and latitude across the two hemispheres. Linear relationships between PID species richness and latitude (dotted lines) are highly significant (F = 12.29, df = 29, p = 0.0015 and F = 18.01, df = 130, p < 0.0001 for Southern and Northern hemispheres, respectively). No difference in disease species richness with latitude across the two hemispheres was observed (interaction: F = 2.68, df = 159, p = 0.1036). Residuals of PID species richness on the y axis were extracted from minimal models controlling for the effects of confounding factors on PID species diversity estimates (see Materials and Methods). Locally weighted regression (tension 0.5) did not change the general linear shape. Latitude is expressed in minute degrees. (B) Presence/absence matrix for the 229 distinct PID species across the hemispheres. The figure was generated by the Nestedness Temperature Calculator (see Atmar and Patterson 1995). The distribution is nonsymetrical because of the 224 studied countries, 172 countries are found in the Northern hemisphere versus only 52 in the Southern one. (B) indicates that PID species diversity decreases as one moves northwards or southwards from the equator. The black exponential curves are the occurrence boundary lines (see Materials and Methods). The color scale indicates the nonuniform probability of state occupancy among all of the cells of the matrix, i.e., the probability of encountering a species as function of its position in the matrix. Black cells are highly predictable presences, whereas red cells are unexpected presences. (C) Monte Carlo–derived histogram after 1,000 permutations. The histogram represents the 1,000 values obtained after Monte Carlo permutations. The average theoretical value under the null hypothesis is compared to our real value, to assess the likelihood that the parent matrix was nonrandomly generated. The probability is highly significant (p < 0.0001), confirming that the spatial organization of PID species richness on the largest scale matches the nested species subset hierarchy illustrated in Figure 1A. The symmetrical Gaussian distribution indicates that 1,000 permutations are enough to obtain reliable variance estimates for probability calculations. Table 1 Minimal Models for Latitude Explaining PID Species Richness of Etiological Groups Of all factors included as potential predictors of PID species richness (see Materials and Methods), Table 1 focuses on the emergence of latitude as a possible explanatory variable in minimal models. When significant, the probability value (p), the degrees of freedom (df), and the sign of slope (+/−) are given The Nested Organization of Pathogen Species over Large Scales Monte Carlo analyses confirmed an overall nested species pattern of global distribution in PID species richness (Ns = 2,481.4, R0 and R1 procedures, p < 0.0001) and showed diversity to be strongly nested, with some anecdotal differences across the different groups of etiological agents (all groups, p < 0.0001, except for vector-borne viruses, with the R1 procedure [Ns = 1,787, p = 0.0015]). When considering the Northern and Southern hemispheres separately, both were highly nested (R0 and R1 procedures, Ns = 6,602, p < 0.0001 and Ns = 1,230, p < 0.0001, respectively). This was confirmed by the R00 procedure used by the Nestedness Temperature Calculator program (Atmar and Patterson 1995), which provides a useful graphic representation of the results (Figure 2B), showing that PID species diversity decreases as one moves northwards or southwards from the equator (F = 28.2307, df = 161, p < 0.0001). The occurrence boundary lines (black exponential curves) were fitted by nonlinear regression (y = 1.51 + 20.01e-0.29x and y = 1.65 + 35.87e-0.36x for Northern and Southern hemispheres, respectively). Results from Monte Carlo simulations confirmed that our nested matrix was nonrandomly generated (p < 0.0001) (Figure 2C). The spatial organization of PID species richness on the largest scale matches the nested species subset hierarchy illustrated in Figure 1A. Thus, pathogen species that compose a depauperate community in temperate conditions statistically constitute a proper subset of those occurring in warmer conditions, and evidence of pathogen species occurring in temperate areas but not in tropical ones was rare or anecdotal. It should be noted that, at this large spatial scale, our study demonstrates a nested pattern in PIDs, with a progression of species richness from polar regions to the equator, indicating that nestedness is strongly associated with latitude (see Figure 2B). But this does not contradict the fact that some pathogens may be strict endemics of more temperate areas (e.g., Lyme disease). The Effect of Climatic Variables on Biodiversity Latitude is a proxy variable for a wide range of covarying bio-climatic factors and in itself has no meaning regarding factors potentially affecting species diversity. We therefore investigated the relationship between pathogen diversity and individual climatic variables reflected in the composite variable “latitude” (Table 2). Results show significant positive correlations between pathogen species richness and the maximum range of precipitation after Bonferroni multiple corrections for all six of the PID taxa considered: bacteria (r = 0.3545, df = 213, p < 0.0001), viruses directly transmitted from person to person (r = 0.2350, df = 215, p < 0.0001), viruses indirectly transmitted via a vector (r = 0.3575, df = 215, p < 0.0001), fungi (r = 0.3554, df = 216, p < 0.0001), protozoa (r = 0.3744, df = 216, p < 0.0001), and helminths (r = 0.4270, df = 215, p < 0.0001). On the other hand, the relationship between PID species richness and monthly temperature range was only significant for three groups of pathogens: bacteria (r = 0.3016, df = 213, p < 0.0001), directly transmitted viruses (r = 0.2142, df = 214, p = 0.0015), and helminths (r = 0.2590, df = 213, p = 0.0001). In contrast to previous results (Allen et al. 2002), we found no significant relationship between PID species richness and mean annual temperature. Finally, only the relationship between bacteria species richness and mean annual precipitation was significant (r = 0.1987, df = 213, p = 0.0034). Very little difference was observed among hemispheres concerning these relationships (data not shown). Table 2 Relationship Between PID Species Richness by Etiological Group and Four Bio-Climatic Factors Pearson's correlation (r), sign of slope (+/−) and significance levels (p) are given. * indicates significance levels which become nonsignificant after the Bonferroni correction (k = 6 multiple comparisons) Taken together, these findings indicate that the species richness of human pathogens, their spatial distribution and organization on a large scale, the maximum range of precipitation, and, to a lesser extent, monthly temperature might be intimately connected in generating the observed pattern of disease diversity. Discussion To our knowledge, this is the most comprehensive report of how PID species richness varies with latitude and the ecological factors behind observed trends. Our results support previous studies in showing that species diversity increases as one proceeds from the poles to the equator (Pianka 1966; Stevens 1989; Rohde 1992; Brown 1995; Rosenzweig 1995; Chown and Gaston 2000). This similarity in the patterns of PID species and free-living organisms suggests that common mechanisms are at work. Regardless of whether PID richness simply tracks host diversity or, rather, is determined to a greater extent by exogenous factors, our analyses indicate that the most likely explanation for these patterns is the climatically-based energy hypothesis, i.e., that energy availability generates and maintains species richness gradients (Rohde 1992; Gaston and Blackburn 2000; Allen et al. 2002; Hawkins et al. 2003). Many studies have identified correlations between gradients in species diversity and variation in climate (Hill et al. 1999). Climate, in turn, largely determines the species of plants and animals that live in those areas. According to our results—and in contrast to the results of Allen and colleagues (2002), who showed that environmental temperature was the best predictor of species diversity for terrestrial, freshwater, and marine ectotherm taxa—the maximum range of precipitation is highly correlated with the latitudinal gradient of pathogen species, with diversity significantly increasing with this climate-based factor. Interestingly, the annual variation of precipitation around the mean (and not the mean itself) was the best predictor overall of pathogen species distribution. This suggests that pathogen species, their vectors, or their hosts tend to be adapted to regions having more contrasted wetness and dryness conditions through the year (i.e., in tropical regions). Many parasites obviously require water or humid conditions to complete their life cycle, e.g., vector-borne diseases. So, the physical factor of precipitation variation may affect parasitic and infectious microorganism diversity, if the biological cyclicity of a variety of parasitic and infectious stages have adapted to the variability of precipitation. This might be why “latitude” does not appear in the minimal generalized linear models (GLIMs) for explaining the richnesses of bacteria, directly transmitted viruses, and fungi, these taxa being “internal” to the host, so less directly affected by environmental variability. Moreover, these taxa may more readily spread over longer distances via their hosts, and this should minimize the impact of environmental conditions. In contrast, taxa with “external” stages, like helminths or vector-transmitted pathogens, are more influenced by their environment. Nevertheless, other causes might explain why certain taxa do not conform to the general pattern, notably (1) the absence of possible explanatory variables in the GLIMs, (2) missing or imprecise information due to the large scale of our study, or (3) the real absence of correlations between the spatial distributions of certain taxonomic groups and the variables considered here. All three nestedness models (see Materials and Methods) explained some of the variation in pathogen species across latitudes. Distance and isolation from pathogen species–rich regions in the tropics may sort PID species by their extinction–colonization dynamics (Lomolino 1996). In addition, the availability of new hosts and reservoirs, passive sampling, and probabilistic filters screening species with particular characteristics (local habitat suitabilities, differential colonization capacities of species, and sustainability of viable populations within their environment) may further limit PID species (Wright et al. 1998) and thus strongly affect the spatial organization of PID species. Nestedness might in fact be an inevitable second-order consequence of the same factors that cause variation in species richness and range size (Gaston and Blackburn 2000). In addition, our results suggest that total species diversity on the planet might be substantially underestimated, especially because inventories generally focus attention on the most charismatic groups (Shaw and Hochberg 2001), and little is known about the biodiversity of microorganisms associated with each considered group of organisms, i.e., hosts (Ashford and Crewe 1998; Ashford 2000; Nee 2003). Based on a single host species, humans, we estimate that true tropical pathogen species diversity is greater than current estimates by a factor of about 22 in the Northern Hemisphere and about 37 in the Southern Hemisphere. If our work is representative of other (host) species, diversity may be currently underestimated by more than an order of magnitude, and based on our findings, this differential should increase as one goes from temperate to tropical latitudes. Our work quantitatively demonstrates that parasitic and pathogenic organisms, as representatives of biotic interactions, strongly amplify the general latitudinal gradient in species richness. The smallest organisms that have been neglected by science could very well be the biggest in generating the observed diversity pattern. The demonstration that parasitic and infectious organisms in humans do not constitute random assemblages at large spatial scales, but rather that many types of microorganisms show a predictable geographical distribution over the planet, could have important implications for public health policies. Our results show that climatic factors are of primary importance in explaining the occurrence and diversity of human pathogens, suggesting that global climate change might have cascading effects regarding the risks of PIDs. For instance, if specific temperate areas were to become more tropical, our results suggest that PID species and their associated vectors/reservoirs would be likely to colonize these changed areas. This would imply a progressive dissolution of the latitudinal effect and of the nested hierarchical structure as observed in the present study as pathogen species became more globally distributed. There is some recent evidence for this hypothesis (see Lindgren and Gustafson 2001). Other variables are indeed important in explaining global-scale patterns of human pathogens (e.g., modernization, urbanization, and pauperization, especially in developing countries). Thus, we do not mean to imply that latitude and surrogate variables are the only ones affecting PID species richness. Nevertheless, our results challenge the conventional wisdom that socio-economic conditions are of preponderant importance in controlling or eradicating diseases. These considerations indicate that a better understanding of PID species diversity and community dynamics in a changing world will be one of the major challenges in environmental epidemiology in the future. Materials and Methods Presence/absence matrix We compiled data on PID occurrence for a total of 332 different human pathogens, including bacteria, viruses, fungi, protozoa, and helminths distributed across 224 nations. Epidemiological data on PID species were extracted from the Global Infectious Diseases and Epidemiology Network database (http://www.cyinfo.com).The presence/absence matrix for the 229 distinct PIDs (after elimination of 103 unavailable values) across the Northern and Southern hemispheres was organised employing the Nestedness Temperature Calculator (Atmar and Patterson 1995). One hundred seven ubiquitous pathogen species were eliminated from the database because the information they contained was entirely redundant with that of the most ubiquitous species already present in the matrix. The matrix of species presence/absence provides distributional information about which species occurs in which countries. GLIMs We employed GLIMs (Crawley 1993; Venables and Ripley 1999) from the S-Plus statistical package (Venables and Ripley 1999) to identify and characterize the effects of potential independent parameters and their interaction terms on PID species richness, which is the total number of human diseases known within the boundary limits of each country. It has been argued that species richness increases with increasing area sampled (Hawkins and Porter 2001; but see Rohde 1997). Therefore, we included total surface area per country (in square kilometers) in our analyses, in order to control for its effect in the multivariate analysis. Similarly, we considered human population size and human population density per country (in persons per square kilometer), both highly colinear with surface area, as possible explanatory factors, since the number and density of human hosts may also influence parasite species richness (Anderson and May 1991; Guégan et al. 2001). In addition, we considered a variety of environmental, demographic, and economical factors. Variables selected as environmental factors for each country were (1) continent, (2) hemisphere, (3) whether the country was insular or continental, (4) percentages of arable land, permanent pastures, permanent crops, irrigated lands, forest woodlands, and “other,” (5) mean latitude coordinate, centered at the country barycenter (in minute degrees), and (6) mean longitude (in minute degrees) from the Greenwich Meridian. Variables selected as demographic factors were (1) human population size, (2) human population density (persons per square kilometer), (3) human birth rate (births/1,000 people/year), (4) human death rate (deaths/1,000 people/year), and (5) annual population growth rate (average annual percent change in the population, resulting from a surplus or deficit of births over deaths and the balance of migrants entering and leaving a country). We employed the gross national product (per capita in United States dollars) as the economic factor, which is the value of all final goods and services produced within a nation in a given year, plus income earned by its citizens abroad, minus income earned by foreigners from domestic production. We also selected a few other variables linked to particular landscape practices (percentages of arable land, permanent pastures, permanent crops, irrigated lands, forest woodlands, and “other”), which were supposed to interact with the production of the nation. Data were collected from The World Factbook 2001 on the Internet (http://www.cia.gov/cia/publications/factbook) and from the appendix of Scott and Duncan (1998). To relate richness to environmental factors, we employed a GLIM with a Poisson error and a log link function (see Wilson and Grenfell 1997). Factors and their interaction terms were selected by a backward stepwise elimination procedure from the general model according to the Akaike criterion (Crawley 1993; Burnham and Anderson 2002). Deviances were compared using χ2 statistics. Spatial autocorrelation analysis When data suggested nonlinear trends, explanatory variables were transformed and fitted again to improve their contribution to the models. Since close geographical neighbors (i.e., two countries sharing a boundary) probably also share common PID species, simple cross-country comparisons could include spatial autocorrelation artefacts (Manly 1991). To test whether this influenced our regressions, we employed Monte Carlo simulations to calculate Moran's index (I) between the matrix of PID species richness and the matrix of distances across the 224 countries (Manly 1991; Guégan and Hugueny 1994). The I value is bound between −1 and +1, with 0 indicating no spatial autocorrelation, and +1/−1 indicating a strong positive/negative autocorrelation, respectively. We first computed the correlation coefficient based on all pairs of neighboring countries, and we randomly estimated 99 coefficients each time, permuting the matching countries. The decision rule, ensuring significance at α = 0.01, consisted in rejecting the null hypothesis of the absence of spatial correlation if the correlation coefficient obtained for nonpermuted data was maximum among all 100 coefficients. The calculation of I using Monte Carlo simulations indicated no strong spatial autocorrelation (I 0 = 0.08 equals I s = 0.11 at α = 0.01), suggesting that the close similarities between PID species richness and composition observed between neighboring countries conforms to the latitudinal diversity gradient. Nestedness analysis We also employed Monte Carlo simulations (Manly 1991; Guégan and Hugueny 1994) to evaluate PID spatial organization at the largest scale. We used the data matrix of presence/absence values for 229 different pathogen species of the total dataset comprising 224 countries. We assessed the degree of nestedness of the system using two different, but complementary, analysis programs: (1) Nestedness (Guégan and Hugueny 1994) and (2) Nestedness Temperature Calculator (Atmar and Patterson 1995). Nested diversity patterns are identified when species found in depauperate communities represent nonrandom subsets of progressively richer communities (Gaston and Blackburn 2000; Poulin and Guégan 2000). In procedure 1, pathogen species were either selected with uniform probability (null model R0) or with a probability proportional to their incidence (R1) (Guégan and Hugueny 1994), whereas in procedure 2 we tested the null model R00 (Poulin and Guégan 2000; see also Cook and Quinn 1998; Wright et al. 1998; Gaston and Blackburn 2000). Nestedness Temperature Calculator generates simulated null matrices without either row or column constraints (hence “00”); only the total number of presences is fixed at the observed value. All three null hypotheses assume that sites are independent of one another (Wright et al. 1998). According to the procedure adopted by the Nestedness Temperature Calculator (see Atmar and Patterson 1995), the matrix is first “packed” into a state of maximum nestedness, reordering rows and columns. By convention, the most species-rich country is placed along the top row, and the most widely distributed species is placed in the leftmost column, so as to concentrate presences in a corner of the matrix, and to minimize unexpected species absences and presences as in theoretical Figure 1A. This will make differences in PID species distribution across countries readily perceivable. Moreover, not all unexpected species presences and absences are of equal informational value, and this must be taken into account. As we move away from the corner, where cells are most likely to be occupied, unexpected absences and presences begin to appear. The occurrence boundary lines (black exponential curves in Figure 2B) are based on the distribution of unexpected species' presences and absences within the matrix. These curves determine the hypothetical boundary between the occupied area of the matrix and the unoccupied area. A color scale indicates the probability of a cell's occupancy. Nestedness Temperature Calculator also includes a Monte Carlo component to assess the statistical assurance that the parent data matrix was not randomly generated. To assess that probability, 1,000 randomized permutations were drawn to determine a baseline expectation. The result is a histogram representing the 1,000 “temperature” values obtained after permutations (Figure 2C). A black arrow indicates the “temperature” value observed with our master matrix. Lastly, the probability of obtaining this value by random is calculated. We thank A. T. Teriokhin, Paul Epstein, Brad Hawkins, David Currie, Marc Choisy and Serge Morand for useful criticisms on a previous version of this paper and for useful conversations on the work presented here, and the French Ministry of Higher Education and Research for providing a junior fellowship to VG. This study was partially supported by the National Center for Ecological Analysis and Synthesis (University of California, Santa Barbara) for JFG. We thank the Institut de Recherche pour le Développement and the Centre National de la Recherche Scientifique for financial support. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JFG conceived and designed the experiments. VG performed the experiments. VG, MEH, and JFG analyzed the data and wrote the paper. Academic Editor: Paul Harvey, University of Oxford Abbreviations GLIMgeneralized linear model PIDparasitic and infectious disease ==== Refs References Allen AP Brown JH Gillooly JF Global biodiversity, biochemical kinetics, and the energetic-equivalence rule Science 2002 297 1545 1548 12202828 Anderson RM May RM Infectious diseases of humans: Dynamics and control 1991 Oxford Oxford University Press 757 Ashford RW Parasites as indicators of human biology and evolution J Med Microbiol 2000 49 771 772 10966223 Ashford RW Crewe W The parasites of Homo sapiens An annotated checklist of the protozoa helminths and arthropods for which we are home 1998 Liverpool Liverpool School of Tropical Medicine 128 Atmar W Patterson BD The nestedness temperature calculator: A visual basic program, including 294 presence-absence matrices. Chicago: AICS Research. Available: http://aics-research.com/nestedness/tempcalc.html via the Internet 1995 Accessed 30 March 2004 Brown JH Macroecology 1995 Chicago University of Chicago Press 269 Burnham KP Anderson DR Model selection and multimodel inference: A practical information-theoretic approach 2002 New York Springer-Verlag 488 Chown SL Gaston KJ Areas, cradles and museums: The latitudinal gradient in species richness Trends Ecol Evol 2000 15 311 315 10884694 Combes C Interactions durables: Écologie et évolution du parasitisme 1995 Paris Masson 524 Cook RR Quinn JF An evaluation of randomization models for nested species subsets analysis Oecologia 1998 113 584 592 Crawley MJ GLIM for ecologists 1993 Oxford Blackwell Scientific Publications 379 Curtis TP Sloan WT Scannell JW Estimating prokaryotic diversity and its limits Proc Natl Acad Sci U S A 2002 99 10494 10499 12097644 Gaston KJ Blackburn TM Pattern and process in macroecology 2000 Oxford Blackwell Science Publications 377 Guégan JF Hugueny B A nested parasite species subset pattern in tropical fish: Host as major determinant of parasite infracommunity structure Oecologia 1994 100 184 189 Guégan JF Thomas F Hochberg ME de Meeûs T Renaud F Disease diversity and human fertility Evolution 2001 55 1308 1314 11525455 Hanski I Dynamics of regional distribution: The core and satellite species hypothesis Oikos 1982 38 210 221 Hawkins BA Porter ER Area and the latitudinal diversity gradient for terrestrial birds Ecol Lett 2001 4 595 601 Hawkins BA Field R Cornell HV Currie DJ Guegan J-F Energy, water, and broad-scale geographic patterns of species richness Ecology 2003 84 3105 3117 Hill JK Thomas CD Huntley B Climate and habitat availability determine 20th century changes in a butterfly's range margin Proc R Soc Ser B-Bio 1999 266 1197 1206 Hillebrand H Watermann F Karez R Berninger UG Differences in species richness patterns between unicellular and multicellular organisms Oecologia 2001 126 114 124 Huston MA Local processes and regional patterns: Appropriate scales for understanding variation in the diversity of plants and animals Oikos 1999 86 393 401 Kaufman DM Diversity of New World mammals: Universality of the latitudinal gradients of species and bauplans J Mammal 1995 76 322 334 Lindgren E Gustafson R Tick-borne encephalitis in Sweden and climate change Lancet 2001 358 16 18 11454371 Lomolino MV Investigating causality of nestedness of animal communities: Selective immigrations or extinctions? J Biogeogr 1996 23 699 713 Manly BFJ Randomization, bootstrap and Monte Carlo methods in biology 1991 London Chapman and Hall 399 Nee S Unveiling prokaryotic diversity Trends Ecol Evol 2003 18 62 63 Patterson BD Atmar W Nested subsets and the structure of insular mammalian faunas and archipelagos Biol J Linn Soc 1986 28 65 82 Patterson BD Brown JH Regionally nested patterns of species composition in granivorous rodent assemblages J Biogeogr 1991 18 395 402 Pianka ER Latitudinal gradients in species diversity: A review of concepts Am Nat 1966 100 33 46 Poulin R Evolutionary ecology of parasites: From individuals to communities 1998 London Chapman and Hall 212 Poulin R Guégan JF Nestedness, anti-nestedness, and the relationship between prevalence and intensity in ectoparasite assemblages of marine fish: A spatial model of species coexistence Int J Parasitol 2000 30 1147 1152 11027779 Rahbek C Graves GR Multiscale assessment of patterns of avian species richness Proc Natl Acad Sci U S A 2001 98 4534 4539 11296292 Rohde K Latitudinal gradients in species diversity: The search for the primary cause Oikos 1992 65 514 527 Rohde K The larger area of the tropics does not explain latitudinal gradients in species diversity Oikos 1997 79 169 172 Rosenzweig ML Species diversity in space and time 1995 Cambridge Cambridge University Press 436 Roy K Jablonski D Valentine JW Rosenberg G Marine latitudinal diversity gradients: Tests of causal hypotheses Proc Natl Acad Sci U S A 1998 95 3699 3702 9520429 Scott S Duncan CJ Human demography and disease 1998 Cambridge Cambridge University Press 354 Shaw MR Hochberg ME The neglect of parasitic Hymenoptera in insect conservation strategies: The British fauna as a prime example J Insect Conserv 2001 5 253 263 Stevens GC The latitudinal gradient in geographic range: How so many species coexist in the tropics Am Nat 1989 133 240 256 Stevens GC The elevation gradients in altitudinal range: An extension of Rapoport's latitudinal rule to altitude Am Nat 1992 140 893 911 19426029 Stevens RD Cox SB Strauss RE Willig MR Patterns of functional diversity across an extensive environmental gradient: Vertebrate consumers, hidden treatments and latitudinal trends Ecol Lett 2003 6 1099 1108 Venables WN Ripley BD Modern applied statistics with S-PLUS, 3rd ed 1999 New York Springer-Verlag 501 Wilson K Grenfell BT Generalized linear modelling for parasitologists Parasitol Today 1997 13 33 38 15275165 Wright DH Patterson BD Mikkelson GM Cutler A Atmar W A comparative analysis of nested subset patterns of species composition Oecologia 1998 113 1 20

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020142Research ArticleEcologyGenetics/Genomics/Gene TherapyDrosophilaFunctional Divergence Caused by Ancient Positive Selection of a Drosophila Hybrid Incompatibility Locus Drosophila Hybrid Incompatibility GeneBarbash Daniel A dabarbash@ucdavis.edu 1 Awadalla Philip pawadalla@ncsu.edu 1 2 ¤1Tarone Aaron M 1 ¤21Section of Evolution and Ecology, University of CaliforniaDavis, CaliforniaUnited States of America2Department of Genetics, North Carolina State UniversityRaleigh, North CarolinaUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e14223 12 2003 11 3 2004 Copyright: © 2004 Barbash et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Gene Responsible for Hybrid Incompatibility in Drosophila Interspecific hybrid lethality and sterility are a consequence of divergent evolution between species and serve to maintain the discrete identities of species. The evolution of hybrid incompatibilities has been described in widely accepted models by Dobzhansky and Muller where lineage-specific functional divergence is the essential characteristic of hybrid incompatibility genes. Experimentally tractable models are required to identify and test candidate hybrid incompatibility genes. Several Drosophila melanogaster genes involved in hybrid incompatibility have been identified but none has yet been shown to have functionally diverged in accordance with the Dobzhansky-Muller model. By introducing transgenic copies of the X-linked Hybrid male rescue (Hmr) gene into D. melanogaster from its sibling species D. simulans and D. mauritiana, we demonstrate that Hmr has functionally diverged to cause F1 hybrid incompatibility between these species. Consistent with the Dobzhansky-Muller model, we find that Hmr has diverged extensively in the D. melanogaster lineage, but we also find extensive divergence in the sibling-species lineage. Together, these findings implicate over 13% of the amino acids encoded by Hmr as candidates for causing hybrid incompatibility. The exceptional level of divergence at Hmr cannot be explained by neutral processes because we use phylogenetic methods and population genetic analyses to show that the elevated amino-acid divergence in both lineages is due to positive selection in the distant past—at least one million generations ago. Our findings suggest that multiple substitutions driven by natural selection may be a general phenomenon required to generate hybrid incompatibility alleles. Transgenic experiments show that the HMR gene has functionally diverged in Drosophila melanogaster and its sibling species and causes the death of hybrid offspring in interspecific crosses ==== Body Introduction Reproductive isolation is the most commonly used criterion to define species. Hybrid incompatibilities (HIs) such as hybrid sterility and lethality are widely observed examples of reproductive isolation. The Dobzhansky-Muller (D-M) model explains how the genes causing deleterious phenotypes in hybrids can evolve (Dobzhansky 1937; Muller 1942; Turelli and Orr 2000) (Figure 1A). The model holds that HIs arise from the interaction between two or more genes that have evolved independently in two isolated populations; the deleterious phenotypes caused by these genes are a by-product of intraspecific divergence and will occur only when the genes interact in the interspecific hybrid. The essential criterion for defining HI genes, therefore, is that the alleles from the two species have distinct phenotypic properties in hybrids: for example, in Figure 1A the derived allele A from one species causes the incompatibility while the ancestral allele a from the other species does not. Figure 1 Models of Hybrid Incompatibility (A) D-M model of HI evolution. We diagram here an X–autosome incompatibility; for simplicity only haploid genotypes are shown. This model can be easily extended to include more complex multilocus interactions. (1) The ancestral species is fixed for the X-linked allele x and the autosomal allele a. (2) As the two species independently diverge, one becomes fixed for allele X at the first locus and the other for allele A at the second locus. (3) HI is caused by the interaction between these derived alleles, X and A. (4) This interaction may cause misregulation of downstream effector genes (d1, d2, and d3), which in turn causes the HI phenotype. (B) Hmr is an HI gene. Hmrmel has evolved in the D. melanogaster lineage and interacts to cause HI with Asib, an allele of a hypothesized autosomal gene that has evolved in the sibling-species lineage. Mutations in Hmrmel allow hybrid viability by eliminating the activity of this incompatibility allele. (C) Hmr is a downstream effector gene. Here, two unknown genes cause HI by misregulating Hmr. Mutations in Hmr allow hybrid viability by acting as downstream suppressors of the HI alleles. (D) Model with Hmr and gene A extensively diverging (see Discussion). Both Hmr and gene A coevolve with many changes along both lineages. HI could be caused by interactions between derived alleles or between a derived and an ancestral allele. All models to identify the codons in Hmr responsible for functional divergence have two constraints: first, that Hmr and gene A must be fully compatible with each other in each lineage, and second, that candidate codons must differ between Hmrsib from all three sibling species and Hmrmel. This model makes clear and testable genetic predictions, namely, that experimental manipulation of allele A (or X) but not allele a (or x) will affect the HI phenotype. For example, increasing the dosage or activity of allele A should decrease hybrid fitness, while identical manipulations of allele a should not. In contrast, downstream effector genes such as d1 in Figure 1A contribute to the phenotype of HI but are not expected to have functionally diverged alleles in the two hybridizing species; in other words, experimental manipulation of downstream effector alleles from either species will have equivalent effects on the HI phenotype. These alternative possibilities can only be addressed by genetically manipulating each species allele in a controlled hybrid background, but this has yet to be achieved in model organisms such as Drosophila melanogaster. If the genetic changes that cause HI are rare, then genes that have undergone extensive divergence may be more likely to cause HI simply because there is more chance that they have experienced rare HI-causing mutations. But the D-M model itself offers no suggestions about the mode of evolution that leads to this divergence. One possibility is that HI genes accumulate genetic changes over time as they evolve neutrally. Alternatively, many models have suggested that speciation may be driven by molecules that are undergoing natural selection, for example, under the pressure of ecological divergence (Schluter 2001) or sexual selection (Lande 1981; Parker and Partridge 1998). If genes associated with HI evolve under positive selection, one consequence is that they may be disproportionately X-linked, as positively selected, recessive X-chromosome alleles may go to fixation more quickly and therefore cause X-linked loci to evolve more rapidly relative to autosomal loci (Charlesworth et al. 1987). This preferential X-linkage may contribute to Haldane's rule (Haldane 1922), the observation that heterogametic (e.g., XY) hybrids suffer more incompatibilities than homogametic (XX) hybrids. A second consequence of selection is that HI genes may have unique phylogenetic and population genetic signatures (Wang et al. 1997; Ting et al. 2000). Molecular evolutionary analyses of speciation genes are essential to address the role of selection in reproductive isolation and speciation. D. melanogaster can hybridize with three closely related species that we refer to collectively as its sibling species: D. simulans, D. mauritiana, and D. sechellia. Crosses between D. melanogaster females and sibling-species males result in invariantly lethal hybrid sons and temperature-dependent lethal hybrid daughters (Sturtevant 1920; Lachaise et al. 1986; Barbash et al. 2000). The Hybrid male rescue (Hmr) gene has a major effect on the fitness of hybrids from this cross. This point is most strikingly demonstrated by the fact that D. melanogaster Hmr loss-of-function mutations such as Hmr1 suppress the lethality of both hybrid males and females (Hutter and Ashburner 1987; Barbash et al. 2000), as does the X-linked In(1)AB rescue mutation (Hutter et al. 1990). Increasing the dosage of the wild-type gene Hmr+ has the reciprocal property of decreasing hybrid viability (Barbash et al. 2000; Orr and Irving 2000). These studies led to the proposal that HI is caused by an interaction between the X-linked D. melanogaster Hmr+ gene and an unknown autosomal gene(s) from the sibling species. Barbash and colleagues (2003) recently cloned the Hmr gene, which encodes a predicted DNA-binding protein similar to the ADF and MYB family of transcriptional regulators, and proposed that D. melanogaster HI may be caused by transcriptional misregulation. A limitation of previous genetic analyses of Hmr is that all genetic manipulations were done only with the D. melanogaster allele. These studies therefore cannot determine whether Hmr is an HI gene as modeled by Dobzhansky and Muller (Figure 1B) or is instead a downstream effector gene that suppresses hybrid lethality because it interacts with or is regulated by the actual HI genes (Figure 1C). Similar uncertainties apply to the D. melanogaster Zhr and Nup96 genes (Sawamura et al. 1993; Presgraves et al. 2003), which also affect F1 hybrid viability. A preliminary analysis suggested that Hmr is highly diverged between D. melanogaster and the sibling species, with almost 8% divergence at nonsynonymous (amino-acid replacement) sites, a remarkable finding considering that Hmr encodes a predicted protein over 1,400 amino acids in length. Two other Drosophila genes that are involved in HI show elevated divergence that appears to have evolved under positive selection, but the divergence is confined to a small region of each gene (Ting et al. 1998; Presgraves et al. 2003) and Hmr is two times more diverged than either locus. These differences raise the question of whether the extensive divergence of Hmr may instead have evolved neutrally, or whether there are regional differences in selection and divergence at Hmr. In this study we use transgenic assays to test whether Hmr has functionally diverged between D. melanogaster and its sibling species and thus fits the D-M model of HI evolution. We examine the functional consequences of this divergence and discuss models to determine which lineages and which codons have undergone functional divergence. We also test whether Hmr has evolved by positive selection and determine the number and locations of regions and codons that show particularly strong signals of adaptive evolution, asking whether the patterns of polymorphism at Hmr are consistent with ancient selection events. Results Testing Hmr for Functional Divergence In the absence of available Hmr mutations in the sibling species, we chose an alternative approach to test whether Hmr has functionally diverged by introducing cloned copies of D. simulans and D. mauritiana Hmr+ into D. melanogaster (Figure 2). It has been shown previously that increasing Hmr+ activity using P-element transgenes containing D. melanogaster Hmr+ suppresses the hybrid-rescuing activity of Hmr1 and In(1)AB mutations (Barbash et al. 2003). Those experiments can be interpreted as showing that the wild-type D. melanogaster Hmr+ kills hybrids, with the Hmr1 and In(1)AB mutations rescuing hybrids by reducing or eliminating Hmr+ activity. The transgenic copies of D. melanogaster Hmr+ thus suppress rescue by increasing Hmr+ activity in hybrids. We therefore asked whether or not sibling-species Hmr+ has the same activity. We reasoned that if Hmr is functionally diverged between the species, as in Figure 1B, then transgenes containing sibling-species Hmr+ would not have the property of suppressing hybrid rescue. On the other hand, if Hmr is not functionally diverged, as in Figure 1C, then these sibling-species constructs would have phenotypic properties similar to the D. melanogaster Hmr+ transgenes and suppress hybrid rescue. Figure 2 Structure and Expression of Sibling-Species Hmr+ Transgenes (A) Diagram of Hmr+ transgenic constructs. The Hmr gene structure is shown, with the rightward arrow indicating the predicted translation start site. Sibling-species constructs used in this study are shown, together with D. melanogaster constructs previously shown (Barbash et al. 2003) to be Hmr+. (B) Restriction map of RT-PCR fragments spanning part of exons 3 and 4, showing diagnostic restriction site polymorphisms found in the transgenic alleles and the stocks used to assay them. The D. melanogaster map corresponds to both the Hmr1 and In(1)AB rescue alleles, as well as all D. melanogaster alleles from our population sample. (C) RT-PCR products from interspecific hybrids. Hybrids were from the crosses described in Table 1. RNA was collected from 48- to 72-h-old larvae and 2- to 4-d-old adult males. Note that larval samples contain RNA from males and females, half of whom carry the sibling-species Hmr+ transgene. The portion of the PCR product derived from the transgenes is that digested by XbaI or HpaI for the D. simulans and D. mauritiana transgenes, respectively. M, 100-bp ladder marker; G, undigested PCR from an Hmr genomic clone (this product contains a 59-bp intron); cD, undigested PCR from an Hmr cDNA clone. The following are all RT-PCR products: U, undigested; C, ClaI-digested; H, HindIII-digested; X, XbaI-digested; C/X, ClaI- and XbaI-digested; Hp, HpaI-digested; C/Hp, ClaI- and HpaI-digested; –, control containing no reverse transcriptase. Note that undigested lanes (U) contain half the amount of DNA as digested samples. Table 1 Viability of Hybrid Males Carrying D. simulans or D. mauritiana Hmr+ Transgenes D. melanogaster/D. simulans hybrids were from crosses at 25 °C of In(1)AB,w/FM6,w; P{Hmr +}/+ females to D. simulans w/Y males, except for In(1)AB,w/FM6,w; P{Dsim\Hmr+8.6}2-4/+ females, which were crossed to D. simulans w m/Y males. D. melanogaster/D. mauritiana hybrids were from crosses at 22 to 23 °C of w Hmr1 v/FM6,w; P{Hmr+}/+ females to D. mauritiana iso-207-males. The transgenes carried a copy of the w+ gene so that P[Hmr+]/+ hybrids were distinguished from +/+ hybrids by their w+ eye color To maximize the chances that the sibling-species transgenic lines would function normally, we made constructs that are similar to the largest D. melanogaster Hmr+ transgenic construct and that exceed the minimal Hmr+ region previously defined (see Figure 2A). We assayed two independent transformants each of D. simulans Hmr+ and D. mauritiana Hmr+ for suppression of hybrid male rescue by Hmr1 in D. melanogaster/D. mauritiana hybrids and by In(1)AB in D. melanogaster/D. simulans hybrids (Table 1). In all cases hybrid males heterozygous for a sibling-species transgene were at least as viable as their brothers without the transgene. Some crosses showed an excess of transgene-carrying hybrids, which might suggest that the transgenes actually increase the effectiveness of hybrid rescue, perhaps by interfering with the pathway of Hmr+-dependent lethality. This possibility requires further investigation, but we note that such a hypothetical effect must be minor because the sibling-species Hmr+ transgenes by themselves do not rescue hybrid males. We also assayed the D. simulans Hmr+ transformants for suppression of In(1)AB-dependent rescue of hybrid female sterility (Barbash and Ashburner 2003). In contrast to the complete suppression associated with D. melanogaster Hmr+ transgenes (Barbash et al. 2003), we found that our D. simulans Hmr+ transgenes had little or no effect on egg counts in D. melanogaster/D. simulans hybrids. In(1)AB,w/Xsim,w m females heterozygous for the insertion P{Dsim\Hmr+t8.6}2-4 averaged 10.0 ± 8.9 eggs (n = 41) while their non-transgene-carrying sisters averaged 8.0 ± 12.4 eggs (n = 28). Using a second, independent transgenic line, we found that In(1)AB,w/Xsim,w females heterozygous for the insertion P{Dsim\Hmr+t8.6}4-1 had 12.2 ± 13.7 eggs (n = 24) while their non-transgene-carrying sisters had 17.7 ± 17.3 eggs (n = 31). RT-PCR analysis demonstrated that the Hmr+ transgenes are expressed (Figure 2C). These results show that the sibling-species alleles of Hmr+ have no phenotypic effect in species hybrids and strongly support the conclusion that Hmr has functionally diverged between the D. melanogaster and sibling-species lineages, demonstrating that Hmr meets the criteria for being a D-M HI gene as diagrammed in Figure 1B. Hmr Divergence among Drosophila Lineages It has been shown previously that Hmr has a high level of average divergence per nonsynonymous (amino-acid replacement) site (DN) between D. melanogaster and its three sibling species (Barbash et al. 2003). To understand this divergence in the context of genome-wide evolution, we calculated divergence of Hmr between D. melanogaster and D. simulans and compared it to compiled datasets containing over 250 genes from these two species (Begun 2002; Betancourt and Presgraves 2002). Pairwise comparisons revealed that Hmr has one of the highest levels of nonsynonymous divergence (0.089); only four other non–Accessory gland protein (Acp) genes have a higher nonsynonymous divergence, and two of these include expressed sequence tag comparisons less than 350 bases in length. The remaining two loci, both X-linked, are mei-218 and Odysseus. In contrast, the average divergence per synonymous site (Ds) of 0.110 for Hmr is lower than the mean value of 0.125 for this dataset (again excluding Accessory gland protein genes). The exceptional divergence of Hmr raises a number of important questions: (1) Is divergence high on the lineage of either D. melanogaster or its sibling species, or both? (2) Was the divergence on either or both lineages caused by positive selection consistent with the time scale of speciation? (3) Can we identify specific regions and codons subject to positive selection on these lineages, and how does this compare to the few other known candidate speciation genes? (4) Is divergence on one or both lineages potentially responsible for causing the HI phenotype of Hmr? We first addressed whether Hmr began to diverge rapidly after the D. melanogaster–sibling-species divergence by isolating and assembling an outgroup Hmr orthologous sequence from the D. melanogaster subgroup species D. erecta, which is estimated to have diverged from D. melanogaster between 6 and 15 million years ago (Powell 1997). The maximum-likelihood estimate of DN between D. melanogaster and D. erecta Hmr was 0.166, higher than the mean value of 0.057 in a survey of 53 D. erecta genes (Bergman et al. 2002). However, the DN/DS ratio was 0.556, consistent with a more selective constraint on nonsynonymous sites than on synonymous sites. In contrast, we found that the D. melanogaster lineage and the lineage leading to the sibling species both exhibited elevated levels of nonsynonymous divergence relative to synonymous divergence (Figure 3). This observation suggests that the rate of amino-acid evolution at Hmr accelerated after the divergence of D. melanogaster from its sibling species. We also detected accelerated divergence along the sibling-species lineages, but because of the trichotomy for D. simulans, D. sechellia, and D. mauritiana, these branch lengths have little confidence. Furthermore, this divergence appears to be irrelevant with respect to the HI phenotype because we showed above that transgenic copies of Hmr+ from both D. simulans and D. mauritiana have no effect on hybrid viability (Table 1). Figure 3 Maximum-Likelihood Estimates of Hmr Divergence among Drosophila Lineages Estimates of the number of changes per nonsynonymous site (DN) are shown above each lineage, and the number of changes per synonymous site (DS) are shown below each lineage, calculated separately for each branch. The coding region of Hmr is 1,390 to 1,427 amino acids long in the five species. DN/DS ratios differ significantly among branches as tested by the methods of Nielsen and Yang (1998). A model where all DN/DS ratios were free to vary along all branches (Model 2 [M2]) fit the data better than a model with a fixed DN/DS ratio for all branches (M1) (2Δ| = 308, p < 0.0001, chi-square distribution), as did a model where DN/DS ratios for the D. melanogaster lineage and the lineage leading to the sibling species differed from the rest of the tree (local clock, 2Δ| = 292, p < 0.0001). This suggests that most of the heterogeneity in the DN/DS ratio among branches of the phylogeny is due to an elevated ratio for the lineages leading from the ancestor of D. melanogaster and the sibling species. The tree is unrooted and we assume a trifurcation among the sibling species. Positive Selection at Hmr We next asked whether Hmr nonsynonymous substitution rates are elevated along the D. melanogaster and sibling-species lineages because of relaxed selective constraints (e.g., McAllister and McVean 2000) or positive selection. Polymorphism data from populations in concert with interspecific divergence data allow one to detect departures from neutrality and to estimate the time in the past when selection events occurred. If the divergence of Hmr was due solely to a relaxed selective constraint (neutral processes), then one would expect that the ratio of nonsynonymous to synonymous divergence between species would be similar to the ratio of nonsynonymous to synonymous polymorphisms found within these species (McDonald and Kreitman 1991). To test this we collected polymorphism data from 14 D. melanogaster and seven D. simulans Hmr alleles. The average nonsynonymous (πN) and synonymous (πS) polymorphism per site within species was moderately low in both D. melanogaster (πN = 0.0017; πS = 0.0052) and D. simulans (πN = 0.0060; πS = 0.0123) relative to other loci (Andolfatto 2001). However, in contrast to the neutral expectation, the ratio of nonsynonymous to synonymous substitutions was found to be in significant excess of the ratio of nonsynonymous to synonymous polymorphisms (Table 2). When mutations were polarized along both lineages with respect to D. erecta, excess nonsynonymous changes were observed along both the D. melanogaster lineage and the D. simulans lineage. These results are highly indicative of positive selection acting in both the D. melanogaster and D. simulans lineages at coding positions in Hmr. The same tests performed between D. melanogaster or D. simulans and D. erecta were not significant, reinforcing our inference that selection occurred along the D. melanogaster and sibling-species lineages and not along the D. erecta lineage. Table 2 MK Tests for Deviations from Neutrality at Hmr Only the coding region of Hmr was used here. Results for the D. melanogaster and D. simulans lineages remain significant if both coding and noncoding regions are included in counting synonymous polymorphisms and divergence. Row 1 contains polymorphism data from both species. Lineage analyses (rows 2 and 3) include polymorphism data for the indicated species and polarized mutations for that species lineage. Comparisons with D. erecta contain polymorphism data from D. melanogaster only (row 4) or D. simulans only (row 5). All p-values are exact and two-tailed. Comparisons between D. melanogaster and each sibling species were also significant but are not phylogenetically independent and are not shown Significant Regional Variation in Polymorphism and Divergence across Hmr Polymorphism and divergence vary substantially for the separate exons and the DNA binding domains. In contrast to the neutral expectation, we found that there is highly significant heterogeneity in the ratio of polymorphism to divergence across the five exons of Hmr (Hudson-Kreitman-Aguade test [HKA test; Hudson et al. 1987]; p = 0.025; 10,000 simulations performed). This significant variation across the locus can be further refined by examining the ratio of nonsynonymous to synonymous variation across the locus and is visually displayed for D. melanogaster and D. simulans using sliding window analyses in Figure 4. These plots reveal multiple regions that have very high DN/DS ratios and low πN/πS ratios, in other words, much more amino-acid divergence than polymorphism. These contrasting ratios do not suggest that regional variation in substitution rates among the two classes of sites is due solely to mutation (and drift) but rather suggest that selection is contributing to the divergence pattern across the gene. Although this sliding window plot is highly suggestive of selection, we wished to obtain independent and statistically supported evidence for regional selection. We therefore tested rates of divergence relative to polymorphism for each exon in a manner similar to the McDonald-Kreitman test (MK test) described in the previous section. The data were partitioned a priori using each exon as a unit (rather than picking an arbitrary window size). Given the size of the sampled region, there is considerable power to address regional variation. These MK tests for each exon revealed that the fourth and fifth exons of Hmr appear to contribute the most to the overall MK test (Fisher's Exact Test [FET] and Bonferroni correction; exon 4, p = 0.0004; exon 5, p = 0.013). It is interesting to note that these regions are not homologous to any other proteins known in D. melanogaster (or in any other species), including other MADF domain–containing proteins. We conclude that multiple regions of Hmr show strong evidence for positive selection. Figure 4 Sliding Window Analysis of Hmr Divergence and Polymorphism Calculations were made with a window size of 150 nucleotides and a step size of 50 nucleotides. Nucleotide position 1 on the x-axis is the start of the coding sequence, and the last position is the stop codon. The dashed line indicates where the ratio is one. Arrows at the top indicate the positions of codons identified as being under positive selection in Figure 5. Exon boundaries are indicated below the x-axis with horizontal bars. A repeatability analysis (Smith and Hurst 1998) revealed that polymorphism ratios for each window were not correlated (p = 0.43) with divergence ratios between D. melanogaster and D. simulans. We also observed that the flanking regions of the Hmr locus appear to have evolved rapidly since D. melanogaster and the sibling species diverged, relative to both synonymous coding sites and introns. The ratios of divergence to polymorphism between D. melanogaster and D. simulans for the 5′ and 3′ flanking regions combined (including the UTRs) are significantly different from those of the coding and intron regions (3 × 2 FET; p < 0.0001) (Table 3), but this significant difference is not observed between D. simulans and D. mauritiana or D. sechellia (FET; p = 0.543 and p = 0.783, respectively). These observations suggest that adaptive fixations have occurred at a number of noncoding sites. Table 3 Observed Number of Replacement Substitutions Relative to Polymorphisms in Hmr for the Three Categories of Silent Sites Polymorphims and substitutions are from D. melanogaster and D. simulans. Flanking regions include both 5′ and 3′ UTRs and nontranscribed regions. Heterogeneity among the three classes of sites in the relative numbers of polymorphic sites and substitutions was tested by FET (3 × 2; p < 0.0001) We next asked whether we could refine the targets of selection to the level of individual codons and correlate such data with different models of HI evolution (Figure 5). Phylogenetic approaches similar to those used in Figure 3 have been designed to detect recurrent positive selection at individual codons among species (Yang and Nielsen 2002). We reasoned that the large amount of divergence at Hmr might offer a unique opportunity to apply these approaches to identify codons that exhibit positive selection and that could be contributing to HI. We identified 25 amino-acid positions that may have diverged due to selection, 20 of which fit possible models for the evolution of the D. melanogaster–sibling-species incompatibility (Figure 5). Many of these codons map to regions with peak DN/DS values (see Figure 4). We conclude that multiple regions show evidence of positive selection and may have contributed to the functional divergence of Hmr between D. melanogaster and its sibling species. Figure 5 Phylogenetic Analysis of Positive Selection at Individual Codons Site-specific codon Model 8 (M8) in PAML was used to identify codons under selection. This model, which considers a discrete distribution of DN/Ds values plus a “selection category”—DN/DS values greater than one—fit the data better than a neutral model (M1) (2Δ| = 25.43, p < 0.001). Codons listed are those with p-values from posterior distributions greater than 0.5. The positions of these codons are also shown in Figure 4. Because D. melanogaster is incompatible with all three of its sibling species, we expect that Hmr codons involved in HI must be different between D. melanogaster Hmr and all three sibling-species alleles. Codons that fit a model of incompatibility between Hmrmel and Asib are shaded blue, and those that fit a model of incompatibility between Hmranc and Asib are shaded yellow (see Discussion). The five remaining codons (unshaded) are identical between D. melanogaster and at least one of the sibling alleles and are thus excluded from both models. Selection Events Are Ancient Loci directly involved in reproductive isolation may reflect the true species history of divergence more accurately than a “random” locus sampled from the genome, because these loci cease exchanging alleles among species earlier than other loci (Wang et al. 1997; Ting et al. 2000). Both reduced gene flow and adaptive fixations can remove shared ancient polymorphisms. D. melanogaster and D. simulans separated approximately 2 to 3 million years ago (Powell 1997), and many shared polymorphisms have been lost due to drift (Clark 1997). However, shared polymorphisms are still observed in these genomes: among 15 loci (Andolfatto and Przeworski 2000), we found that 5.2% and 3.5% of segregating sites in D. melanogaster and D. simulans, respectively, are segregating at the same positions (among all classes of sites). Based on these values we expected to find approximately three to four shared polymorphic sites for Hmr among the 54 segregating sites in D. melanogaster and 94 segregating sites in D. simulans, but instead found zero. Although the expectation of three to four shared polymorphisms is not a strong test, these findings are consistent with Hmr alleles not having been exchanged between these taxa since at or near the time of species divergence and/or with selection events having occurred throughout the Hmr gene and having removed recurrent mutations. We have shown by several methods that the elevated rate of amino-acid divergence at Hmr is due to positive selection. These findings raise the critical question of whether selection events occurred recently or at the time of speciation. The number of years separating D. melanogaster and the sibling-species lineage makes this a challenging task. We therefore concentrated on the more tractable problem of looking for evidence to exclude the possibility that Hmr divergence reflects the action of contemporary or recent selection. In other words, while it may not be possible to prove that selection occurred at Hmr at the time of speciation, it is nevertheless important to demonstrate that available data are, at the least, not inconsistent with more ancient selective events. Examination of the frequency spectrum of polymorphism within both D. melanogaster and D. simulans showed no evidence of recent sweeps, as the distribution of per-site heterozygosities did not differ from expectations of neutrality (Tajima's D is −0.61 and 0.75, respectively, which is not significant). However, the power for detecting a selective sweep with this method is not high (75%) even if the event occurred in the very recent past, and diminishes to 25% as early as 0.8N generations ago (where N= population size) (Przeworski 2002). Because we have prior knowledge from the analyses above that selection events occurred at Hmr, we reasoned that we could apply more powerful Bayesian approaches that use not only frequency spectrum information but other summary statistics of polymorphism and recombination to estimate the time at which these events happened (Przeworski 2003). To estimate simultaneously the time and strength of selection events we conditioned on three summary statistics of the data: the polymorphism frequency spectrum (Tajima's D), the number of segregating sites, and the number of haplotypes. Prior distributions were chosen based on estimates of recombination, mutation rates, and effective population size. The most recent common ancestor for an X-linked locus is on average 3N generations ago. We determined that in D. melanogaster, a recent selective sweep (within the last 0.25N generations) is clearly not consistent with the polymorphism data, whereas adaptive mutations occurring more than N generations ago are consistent with the polymorphism data, and selective sweeps more than 1.5N generations ago are possible (Figure 6). Similarly, the marginal distribution of selection coefficients suggests that the strength of selection is inconsistent with small selection coefficients and recent selective sweeps (data not shown). Together, these results show that given our knowledge that selection has occurred at a large number of codons at Hmr, the last selection event occurred in the distant past. These procedures have been applied to infer selection events associated with human evolution (Przeworski 2003) but have not previously been applied to HI loci. Estimating the time of fixation events is integral to determining whether selection events that are found at HI loci are consistent with the known time frame of speciation or might merely reflect ongoing contemporary selection. Figure 6 Posterior Distribution of Time (in Generations) Since the Most Recent Selective Sweep at Hmr for D. melanogaster Population size (N) is assumed to be 1 × 106 for D. melanogaster. Samples were generated from the joint posterior distribution of five parameters of a selective sweep model assuming a selection event occurred sometime in the past at the Hmr locus, and from three summaries of polymorphism, including the number of segregating sites (54), Tajima's D (−0.61), the population recombination rate (4Nr = 43, where N = population size and r = per gene recombination rate; McVean et al. 2002) and the number of haplotypes (11). The data is least consistent with a selective sweep in the recent past and is most consistent with selective sweeps occurring more than N generations ago. If there are ten generations per year, this suggests that the last selective sweep occurred at least 100,000 years ago. Data for D. simulans are not shown, as the population structure for the D. simulans Wolfskill populations we sampled would inflate estimates through increasing marginal frequencies of segregating sites (Wall et al. 2002). Discussion We have shown here that transgenes carrying D. simulans or D. mauritiana Hmr+ have no effect on hybrid fitness, in contrast to the strong deleterious effects previously observed with D. melanogaster Hmr+. These transgenic experiments demonstrate that Hmr has functionally diverged between D. melanogaster and its sibling species and thus meets the experimental criteria defined by the D-M model. To our knowledge this is the first demonstration of functional divergence for a D. melanogaster HI gene. Although transgenic technologies have limitations—for example, genes with large and complex regulatory regions may not function correctly when transformed—they have the clear advantage of providing unambiguous analyses of single genes. Transgenics was previously used (Winkler et al. 1994) to show that the Xmrk-2 gene from the platyfish Xiphophorus maculatus causes HI (although for technical reasons it was assayed in the related fish Oryzias latipes rather than the actual hybridizing species X. helleri [Weis and Schartl 1998]). Functional divergence is inferred in this case because Xmrk-2 (also called ONC-Xmrk) is a gene duplication that is present in X. maculatus but appears to be absent from X. helleri). Functional divergence can also be inferred in other HI systems where multigene regions are transferred between species by repeated backcrossing. This approach has been used to show that Odysseus (Ods) has functionally diverged: X-chromosome introgressions from D. mauritiana into D. simulans that contain the D. mauritiana Ods region are sterile, while related introgressions that lack D. mauritiana Ods remain fertile (Ting et al. 1998). Both Hmr and Ods, as well as Nup96, which has recently been implicated in causing lethality in D. melanogaster/D. simulans male hybrids (Presgraves et al. 2003), show high rates of divergence for part (Ods and Nup96) or most (Hmr) of their coding regions. If generally true, this finding may reflect the fact that a high rate of substitution is required in order to generate a rare HI-causing mutation. Alternatively, it may be an indication that the combined effects of multiple substitutions are required to generate an HI allele. Our analysis of Hmr suggests two additional characteristics of HI genes. One is that while the D-M model requires divergence along only one lineage in order to generate HI, we have strong statistical support for accelerated divergence of Hmr along both lineages since D. melanogaster and the sibling species split. One consequence of this extensive divergence is that it becomes unexpectedly complicated to identify the divergent codons in Hmr that are candidates for causing the incompatibility. The simple model shown in Figure 1B, where HI is caused by the interaction of Hmrmel and Asib, implies that any codons in Hmrmel that have diverged from the ancestral allele could potentially be causing the incompatibility; these codons could also diverge in the sibling-species alleles as long as they remain different from the D. melanogaster allele. There are 137 amino acids in Hmr (plus two sites of D. melanogaster–specific insertions) that fit these criteria. However, considering the extensive divergence we have detected at Hmr along both lineages, a more realistic model is shown in Figure 1D, where both Hmr and gene A go through multiple changes in both D. melanogaster and the sibling species. This model raises a possibility described by Muller (1942), namely, that a derived allele may become incompatible with an ancestral allele. For example, Asib may become incompatible with Hmranc, which means that Hmrmel will also be incompatible if it retains the interacting residues present in Hmranc. Since Hmrsib must be compatible with Asib, the candidate codons are those where Hmrsib differs from Hmranc and Hmrmel, and Hmrmel remains identical to Hmranc. There are 49 amino acids that fit this model, all of which are different from those identified in the previous model. Distinguishing between these models may be possible by using our transgene assays on Hmr+ constructs containing site-directed changes at candidate amino acids. Divergence due to positive selection is a second striking characteristic of Hmr as well as Ods and Nup96. Adaptive fixations and reproductive isolation at Hmr have clearly shaped the pattern of polymorphism relative to divergence and swept away any shared polymorphisms that may have been present. This characteristic raises the general question of what forms of selection are responsible and whether genes involved in certain traits or phenotypes that are under strong directional selection may preferentially contribute to HI. In allopatric models, where speciation occurs between two populations in geographical isolation, HI between species is strictly a secondary consequence of divergence of HI genes that has occurred within each species. The target of selection must therefore be sought by looking at gene function within species. We do not yet know the function of Hmr; Hmr1 mutants are viable and fertile, but this allele is clearly hypomorphic (Barbash et al. 2003) and the null phenotype remains unknown. Because Hmr is expressed and causes HI in both sexes, however, it appears unlikely that it diverged under sexual selection. We do know that the pattern of variation in Hmr is not consistent with only weak selection events that have occurred recently. Rather, Hmr polymorphism is consistent with selective events occurring more than 0.5N generations ago and with a potentially large range of selection coefficients. Also unique to Hmr is that we have shown that it is likely to have been subject to multiple selective sweeps in both the D. melanogaster and sibling-species lineages and that the signal of positive selection comes from multiple sites and regions. We have argued here that experimental demonstrations of functional divergence are required to prove that a gene is a bona fide HI locus. While man has undoubtedly been aware of the phenomenon of plant and animal HI for thousands of years, and hundreds of examples have been described in the scientific literature, identifying the genes involved has progressed rather slowly, with candidates generally being discovered by either genetic mapping or suppressor screens. As genomic sequences become available for species closely related to model organisms such as D. melanogaster, we suggest that the characteristics of Hmr, including high levels of divergence due to positive selection, may provide an alternative means of identification, with comparative genomics being used to identify candidate HI genes. Materials and Methods Nomenclature The subscripts mel, sib, and anc are used to designate genes from D. melanogaster, its three sibling species, and their (hypothetical) ancestor, respectively. RT-PCR RT-PCR was performed from total RNA as described in Barbash et al. (2003). Thirty-five or 40 cycles of PCR were performed in a 50-μl volume with the oligos 5′-AAATCGAATCGCTTGTTTGG-3′ and 5′-CTCGAGCGGATGGTAGCGCAC-3′ at an annealing temperature of 61 °C. Two reactions per template were processed with QIAquick PCR Purification (Qiagen, Valencia, California, United States), eluted in 50 μl of 10mM Tris-HCl (pH 8.0), ethanol precipitated in the presence of 10 μg of glycogen, and resuspended in 10 μl of 10mM Tris-HCl (pH 8.0). Two microliters of DNA was digested with the appropriate restriction enzyme and run on a 2% agarose/TAE gel. Clones and constructs Sibling-species Hmr constructs were derived from D. simulans and D. mauritiana lambda genomic libraries (Ting et al. 1998). A D. simulans phage clone was isolated using the D. melanogaster Hmr cDNA clone RE54143 (Barbash et al. 2003) as a probe. An approximately 3.8-kb BamHI/NotI fragment of phage DNA was cloned into pBSII KS(+) (Stratagene, La Jolla, California, United States) to create p88. The adjacent approximately 5.8-kb BamHI phage DNA fragment was then cloned into the BamHI site of p88 to create p89; the correct orientation was determined by PCR across the BamHI junction. Using an XbaI site near the end of the p89 insert, p92 was made by cloning the approximately 8.6-kb NotI/XbaI fragment from p89 into pCasper4. End sequencing of this construct demonstrated that it extends from approximately 2.8 kb 5′ of Hmr to approximately 1.05 kb 3′ of Hmr; this construct also contains the complete predicted coding region of CG2124. The formal designation of this construct is P{w+mc Dsim\CG2124+t8.6 Dsim\Hmr+t8.6= Dsim\Hmr+t8.6}. A D. mauritiana phage clone was isolated using a D. mauritiana PCR product from exon 2 of Hmr as a probe. The entire phage insert was cloned into the NotI site of pBSII KS(+) to make the p94 plasmid. An approximately 9.4-kb NotI/XbaI fragment of the p94 insert was cloned into the corresponding sites of pCasper4. End sequencing of the insert demonstrated that this construct extends from approximately 2.8 kb 5′ of Hmr to approximately 1.8 kb 3′ of Hmr. This construct also contains the complete predicted coding regions of Rab9D and CG2124. The formal designation of this construct is P{w+mc Dmau\Rab9D+t9.4 Dmau\Hmr+t9.4 Dmau\CG2124+t9.4=Dmau\Hmr+t9.4}. D. erecta Hmr was isolated by screening a gridded fosmid library (BACPAC Resource Center, Oakland, California, United States) with D. melanogaster Hmr cDNA clone RE54143 as a probe. An approximately 8-kb XbaI fragment was subcloned from fosmid 11D-19 into pBSII KS(+) and sequenced using a GPS-1 Genome Priming System (New England Biolabs, Beverly, Massachusetts, United States) and custom sequencing primers. We also identified an Hmr ortholog from the more distant D. pseudoobscura, but an unalignable repetitive region in the second exon made unambiguous calculations of amino-acid and silent divergence impossible. As is found in D. melanogaster, the ortholog of CG2124 is located 5′ of Hmr in opposite orientation in both D. erecta and D. pseudoobscura, demonstrating that we have correctly identified the ortholog of Hmr in both of these species. Population samples Ten D. melanogaster Hmr alleles were sequenced from a collection of iso-X-chromosome stocks derived from isofemale lines collected in Zimbabwe (Begun and Aquadro 1993). Five D. simulans Hmr alleles were sequenced from a collection of highly inbred isofemale lines collected in Wolfskill, California, United States (Begun and Whitley 2000). Hmr alleles were amplified by PCR in five overlapping segments of approximately 1 to 1.5 kb and sequenced directly using Big Dye chemistry (version 3; Applied Biosystems, Foster City, California, United States). We also included in our analyses four D. melanogaster alleles and one each from the three sibling species reported previously (Barbash et al. 2003), as well as the D. simulans p92 clone described above. Our population data set contains the complete coding region of Hmr as well as 447 bases upstream and 326 bases downstream of the coding region. Sequence analysis Sequences were aligned using ClustalX. Phylogenetic analyses were performed with PAML (Yang 1997). Population genetic analyses were performed using C++ libraries from K. Thornton. Estimates of the population recombination rate were calculated using Ldhat (McVean et al. 2002). Exact p-values for FETs were derived using the R statistical package. The HKA test was performed using the HKA program by Jody Hey. Estimates of the posterior distribution for time in generations since the most recent selective sweep were estimated using the method of Przeworski (2003). Supporting Information Accession Numbers The GenBank accession numbers of the genes discussed in this paper are Hmr coding region from D. erecta (AY568390), Hmr coding region from D. mauritiana contained in plasmid p94 (AY573924), Hmr coding region from D. simulans contained in plasmid p92 (AY568391), D. melanogaster Hmr alleles sequenced from iso-X-chromosome stocks derived from isofemale lines collected in Zimbabwe (AY568380–AY568389), and D. simulans Hmr alleles sequenced from highly inbred isofemale lines collected in Wolfskill, California, United States (AY568392–AY568396). We thank Charles Langley, in whose laboratory this research was done, for many helpful discussions. We also thank Richard Hudson, Kevin Thornton, and Molly Przeworski for advice and Brian Charlesworth, Matthew Hahn, Corbin Jones, and Gilean McVean for comments on the manuscript. Genomic libraries were kindly provided by Chung-I Wu. This research was supported by grants from the National Science Foundation to DAB and C. H. Langley and from the Welcome Trust to PA. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. DAB and PA conceived and designed the experiments. DAB and AMT performed the experiments. DAB and PA analyzed the data. PA contributed reagents/materials/analysis tools. DAB and PA wrote the paper. Academic Editor: Mohamed Noor, Louisiana State University ¤1Current address: Department of Genetics, North Carolina State University, Raleigh, North Carolina, United States of America ¤2Current address: Department of Zoology, Michigan State University, East Lansing, Michigan, United States of America Abbreviations D-MDobzhansky-Muller DNaverage per-site nonsynonymous nucleotide divergence DSaverage per-site synonymous nucleotide divergence FETFisher's Exact Test HIhybrid incompatibility Hmr Hybrid male rescue; HKA test MK testMcDonald-Kreitman test πNaverage per-site nonsynonymous nucleotide polymorphism πSaverage per-site synonymous nucleotide polymorphism ==== Refs References Andolfatto P Contrasting patterns of X-linked and autosomal nucleotide variation in Drosophila melanogaster and Drosophila simulans Mol Biol Evol 2001 18 279 290 11230529 Andolfatto P Przeworski M A genome-wide departure from the standard neutral model in natural populations of Drosophila Genetics 2000 156 257 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Hybrids with Drosophila melanogaster Genetics 1920 5 488 500 17245951 Ting C-T Tsaur S-C Wu M-L Wu C-I A rapidly evolving homeobox at the site of a hybrid sterility gene Science 1998 282 1501 1504 9822383 Ting C-T Tsaur S-C Wu C-I The phylogeny of closely related species as revealed by the genealogy of a speciation gene, Odysseus Proc Natl Acad Sci U S A 2000 97 5313 5316 10779562 Turelli M Orr HA Dominance, epistasis and the genetics of postzygotic isolation Genetics 2000 154 1663 1679 10747061 Wall JD Andolfatto P Przeworski M Testing models of selection and demography in Drosophila simulans Genetics 2002 162 203 216 12242234 Wang RL Wakeley J Hey J Gene flow and natural selection in the origin of Drosophila pseudoobscura and close relatives Genetics 1997 147 1091 1106 9383055 Weis S Schartl M The macromelanophore locus and the melanoma oncogene Xmrk are separate genetic entities in the genome of Xiphophorus Genetics 1998 149 1909 1920 9691046 Winkler C Wittbrodt J Lammers R Ullrich A Schartl M Ligand-dependent tumor induction in medakafish embryos by a Xmrk receptor tyrosine kinase transgene Oncogene 1994 9 1517 1525 8183545 Yang Z PAML: A program package for phylogenetic analysis by maximum likelihood Comput Appl Biosci 1997 13 555 556 9367129 Yang Z Nielsen R Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages Mol Biol Evol 2002 19 908 917 12032247

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020150FeatureBioethicsNeuroscienceScience PolicyHomo (Human)fMRI Beyond the Clinic: Will It Ever Be Ready for Prime Time? Functional NeuroimagingRobinson Richard 6 2004 15 6 2004 15 6 2004 2 6 e150Copyright: © 2004 Richard Robinson.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Functional magnetic resonance imaging offers the promise of peeking into the human mind. What signals in the human brain can we really detect and how should the technology be used? ==== Body Functional magnetic resonance imaging—fMRI—opens a window onto the brain at work. By tracking changes in cerebral blood flow as a subject performs a mental task, fMRI shows which brain regions “light up” when making a movement, thinking of a loved one, or telling a lie. Its ability to reveal function, not merely structure, distinguishes fMRI from static neuroimaging techniques such as CT scanning, and its capacity to highlight the neural substrates of decisions, emotions, and deceptions has propelled fMRI into the popular consciousness. Discussions of the future of fMRI have conjured visions of mind-reading devices used everywhere from the front door at the airport terminal to the back room of the corporate personnel office. At least one “neuromarketing” research firm is already trying to use fMRI to probe what consumers “really” think about their clients' products. But will fMRI's utility in the real world ever match the power we currently imagine for it? Is fMRI likely to leave the clinic for widespread use in the courtroom or the boardroom? Are there neuroethical nightmares just around the corner? Or are all these vivid specters really just idle speculations that will never come to pass? 150,000 Grains of Rice To understand the potential, and the limitations, of fMRI, it's helpful to know how the technique works. The heart of the apparatus is a large donut-shaped magnet that senses changes in the electromagnetic field of any material placed in its center, in particular—when a head is scanned—the blood as it flows through the brain. When a region of the brain is activated, it receives an increased flow of oxygenated blood (the extremely rapid redirection of blood within the active brain is one of the underappreciated wonders supporting neural activity). This influx of oxygenated blood alters the strength of the local magnetic field in proportion to the increase in flow, which is detected and recorded by the imaging machinery. The resolution of the best fMRI machines—the smallest “volume picture element,” or voxel, they can distinguish and make an image of—is currently about 1.5 mm ×1.5 mm × 4 mm, the size of a grain of rice. There are approximately 150,000 of these little volumes in the typical brain, and the immense computers hooked up to the scanners record and integrate signals from all of them. In a typical experiment, a subject, lying still with his head surrounded by the magnet, does nothing for thirty seconds, then performs some task for thirty seconds, then lies still for thirty seconds. For each voxel, the signal during the task is compared to the signal at rest; those areas of the brain with stronger signals during the task are presumed to be processing the information that underlies the performance of the task (Figure 1). According to Joy Hirsch, Director of the Functional Magnetic Resonance Imaging Research Center at Columbia University, fMRI represents a “quantum leap” over any previous technology for imaging the brain. “It enables us for the first time to probe the workings of a normal human brain,” she says. “It's really opening the black box.” Figure 1 The Basics of fMRI Blood oxygen level–dependent signals are measured and compared between test and resting conditions. (Image courtesy of Joy Hirsch, Columbia University.) The first caveat about fMRI's imaging power, though, and one that every neuroimager stresses, is that a voxel is a long way from a neuron. There are an estimated 100 billion neurons, so at best, an fMRI is signaling blood flow changes associated with the increased activity of tens of thousands of neurons. As a result, says Hirsch, fMRI “falls short when we want to ask about more detailed brain processes. We're not learning that much about how neurons are doing local computing.” While resolution will improve over time, it seems unlikely that fMRI will ever detect the activity of individual neurons, and so its ability to dissect the “fine structure” of thought is inherently limited. (Even should it become possible to detect and integrate the workings of every neuron in the brain, it would still be far from clear how neuronal firing patterns translate into coherent, perceived thoughts, and this gap is unlikely to be bridged by any advance in imaging technology alone.) These limitations have not prevented fMRI researchers from making some major discoveries about brain function, however. Hirsch, for instance, showed in one study that minimally conscious individuals still process human speech, and in another, that those who become bilingual as young children employ overlapping language areas in the cerebral cortex, while those who learn a second language later in life use a different area for the second language. The key strength of fMRI, she says, is that it provides the ability to test these kinds of hypotheses about structure–function relationships in the normal brain. All Sizes Do Not Fit One But the hypotheses that can be tested and the conclusions that can be drawn are still largely about group averages, not about the functionings of individual brains, and therein lies a second major caveat about the use of fMRI beyond the clinic. John Gabrieli, Associate Professor of Psychology at Stanford University, has shown that distinct activation patterns in the brains of dyslexic children normalize as they improve their reading skills (Figure 2). It seems like a small leap from there to including an fMRI as part of the workup for a schoolchild struggling in the classroom. But, Gabrieli cautions, that small leap in fact traverses a huge chasm, on one side of which is the group data from which conclusions are drawn in studies, and on the other side, the application of these conclusions to the individual child. “At the moment, fMRI would be among the most useless things to do. We would love to get it to the point that it would be useful [on an individual basis],” he says, but it's not there yet. “There is no single-subject reliability,” says Gabrieli. “Where we are now, I'm not aware of any applications for which it would be responsible to interpret an individual scan [based on group data].” Figure 2 Different Activation Patterns in the Brains of Dyslexics As Compared to Normal Subjects in a Rhyming Task (Images courtesy of John Gabrieli, Stanford University.) There are similar limitations to most other applications of fMRI—while conclusions can be made about aggregated data, individual scans are for the most part too hard to interpret. There is not yet any real understanding of how brain patterns change over time in an individual, or how interindividual differences should be interpreted in relation to the conclusions that are valid for groups. This makes fMRI an unlikely tool for job screening, for instance. While one study has shown a brain signature in a group of white people that is associated with racial bias, denying a particular individual a job on the basis of such a scan would likely lead straight to a lawsuit, with experts debating whether this scan in this individual on this day does or doesn't reflect his underlying racial attitudes. On the other hand, Hirsch has used individual scans to help locate a patient's language structures that must be spared during neurosurgery. “If you are a neurosurgeon planning a resection, you don't want an average brain at all. Millimeters matter.” But her success is precisely because she is not using group data to make inferences about the individual—she is not leaping over the chasm, but instead is toiling entirely on the other side of it. “The goal is personalized medicine,” she says. A Little Guilty Knowledge Is a Dangerous Thing Even this kind of personalized approach with fMRI is fraught with problems when researchers attempt to apply it outside the clinic, because of limitations in the technology itself. One researcher with firsthand knowledge of these problems is Daniel Langleben, Assistant Professor of Psychiatry at the University of Pennsylvania School of Medicine. In 2002, Langleben showed that when subjects were hiding information in an attempt to deceive (so-called guilty knowledge), they had intense activity in five distinct brain areas not seen when they were telling the truth. In effect, Langleben used the fMRI as a lie detector. It is potentially even more powerful than a standard polygraph test, he says, because there are thousands of brain regions which can be scanned for deception-triggered variation, versus only three variables—skin conductance, respiration, and blood pressure—used in the standard polygraph. Not surprisingly, Langleben got a lot of press after he announced his results, and his experiment led directly to speculation that we might eventually see fMRIs installed at airports, scanning the brains of would-be terrorists trying to deceive security screeners, or in courtrooms, catching perjurers red-handed (or perhaps red–anterior-cingulate-gyrused?). Langleben is enthusiastic about the potential for an fMRI-based lie detector, and has even applied to the Department of Justice for a grant to develop the technology (they turned him down, saying it was too expensive). But he is also clear about how difficult it will be to get one that really works outside the highly structured confines of the research lab. “We are a long way from making a working polygraph,” he says. Even with a “Manhattan Project” type effort, he speculates it would take at least ten years. “There are still essential discoveries to make along the way,” he says, “and there's a good chance it would end in total failure.” It's not just a matter of developing the imaging technology, he stresses—“we'll need fundamental developments in semantics, too.” This is because “a lot still depends on how you ask the question”—the subtlest of differences can dramatically shift which areas of the brain respond. Given the sensitivity of the fMRI result to such seemingly minor perturbations, it's hard to imagine it could be reliably adapted to the hurly-burly of an airport security checkpoint. Even well-performed scans done in topnotch clinics may not easily find their way into the courtroom. Perhaps the least likely use of fMRI is in determining if a defendant is telling the truth, according to Hank Greely, Professor of Law at Stanford Law School, since compelling someone on trial to submit to an fMRI could be seen as a violation of the Fifth Amendment right against self incrimination, just as giving spoken testimony against oneself is. On the other hand, says Greely, DNA samples and fingerprints can be compelled—whether a brain scan is more like testifying or more like submitting to a blood test is an open question. Still, for the moment, scanning under duress simply isn't feasible, since all you have to do to ruin a good scan is move your head. Motion-correcting algorithms can be used, but they are nowhere near advanced enough to correct for large-scale movements by an unwilling subject. It's much more likely that an fMRI of a willing defendant would be introduced to convince the jury he is telling the truth, or performed before trial to rule out an innocent suspect. While to Greely's knowledge fMRI evidence hasn't yet been used in court, “it's certain to be tried,” and the barrier to its admission will fall as both the reliability and the ease of administration increase. “The easier, the cheaper, the more pleasant a technique is, the more likely it is to be used in the legal system.” Other forensic uses of fMRI are likely to arrive sooner rather than later. Could scans showing diminished impulse control—a function controlled by several regions of the brain, including the striatum and the ventromedial prefrontal cortex—be used to support more lenient sentencing, or even acquit a defendant, because he couldn't control his violent impulses? Or alternatively, will those same scans be used to argue for harsher sentences, since the defendant is clearly “hardwired” to commit similar crimes again? Courts already consider other factors, such as a history of child abuse, in an attempt to more fully understand the psychological state of the defendant. Will brain scans be seen as the ultimate “objective” look into the mind of the person on trial? Deciding all these issues of admissibility will be judges who will need to weigh competing claims from lawyers with competing interests, backed up by expert witnesses with competing theories. Here, the desire to apply the science may rush ahead of its demonstrated validity. Langleben, for one, doesn't think fMRI will be legitimately ready for the courtroom for a long time. On the other hand, he says, “if you want to abuse this technique and claim that it works, you can create tests that will produce results—I can see how it could be done. We know enough to rig it.” But still, he says, “we have all the tools we need to prevent this—there are enough people who are sufficiently honest [who would counter the premature use of fMRI in these contexts].” For now, at least, given the problems inherent in current fMRI technology, the neuroethical nightmare scenarios of widespread brain scanning seem unlikely to come to pass, at least until radical advances make it far cheaper, much less invasive, far less sensitive to subtle perturbations, and with a much more robust ability to legitimately extrapolate from a finding about a group to a prediction about an individual. Where fMRI is concerned, “a penny for your thoughts” is currently more like “a million pennies for a group-averaged hemodynamic response to highly constrained stimuli under entirely artificial conditions.” In light of this, bioethical concerns about fMRI applications should perhaps be viewed not as predictions of a certain future but rather as worstcase scenarios, a reminder of what we want to avoid. “It's a funny thing about the bioethics field,” says Greely. “The general approach is to look for bad news.” While many of these “worst cases” seem highly unlikely to come to pass, Judy Illes, of the Stanford Center for Biomedical Ethics, thinks some action is warranted now, if only to generate a better understanding of the ethical dimensions of fMRI research. She notes that “bioethicists are often viewed as the ethics police,” but she doesn't see regulations as the right path to shape the future uses of fMRI. Instead, she thinks a coalition of involved parties—scientists, lawyers, ethicists, politicians—should work together to develop guidelines that all will find acceptable. “I'm not in the business of stopping anything.” What everyone apparently already agrees on is the need for carefully designed experiments and cautious interpretation of the data. “A huge message in imaging is that you really have to look at the experimental setup at the common-sense level,” says Gabrieli, and avoid the tendency to “pick the most dramatic interpretation.” “The public needs to be reminded of the limitations of these findings,” agrees Hirsch. And as Langleben puts it, expressing his skepticism that there will ever be a one-size-fits-all, foolproof fMRI mind reader: “I don't think we'll ever be able to be stupid about it.” Richard Robinson is a freelance science writer from Sherborn, Massachusetts, United States of America. E-mail: rrobinson@nasw.org Abbreviation fMRIfunctional magnetic resonance imaging ==== Refs Further Reading Langleben DD Schroeder L Maldjian JA Gur RC McDonald S Brain activity during simulated deception: An event-related functional magnetic resonance study Neuroimage 2002 15 727 732 11848716 Hirsch J Ruge MI Kim KH Correa DD Victor JD An integrated functional magnetic resonance imaging procedure for preoperative mapping of cortical areas associated with tactile, motor, language, and visual functions Neurosurgery 2000 47 711 721 10981759 Kim KH Relkin NR Lee KM Hirsch J Distinct cortical areas associated with native and second languages Nature 1997 388 171 174 9217156 Temple E Deutsch GK Poldrack RA Miller SL Tallal P Neural deficits in children with dyslexia ameliorated by behavioral remediation: Evidence from functional MRI Proc Natl Acad Sci U S A 2003 100 2860 2865 12604786 Illes J Kirschen MP Gabrieli JD From neuroimaging to neuroethics Nat Neurosci 2003 6 205 12601375

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PLoS Biol. 2004 Jun 15; 2(6):e150

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020152Research ArticleGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyVirologyVirusesHomo (Human)Plasticity of DNA Replication Initiation in Epstein-Barr Virus Episomes Plasticity of DNA Replication InitiationNorio Paolo norio@aecom.yu.edu 1 Schildkraut Carl L schildkr@aecom.yu.edu 1 1Department of Cell Biology, Albert Einstein College of MedicineBronx, New YorkUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e1528 12 2003 18 3 2004 Copyright: © 2004 Norio and Schildkraut.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Initiation of DNA Replication: The Genomic Context In mammalian cells, the activity of the sites of initiation of DNA replication appears to be influenced epigenetically, but this regulation is not fully understood. Most studies of DNA replication have focused on the activity of individual initiation sites, making it difficult to evaluate the impact of changes in initiation activity on the replication of entire genomic loci. Here, we used single molecule analysis of replicated DNA (SMARD) to study the latent duplication of Epstein-Barr virus (EBV) episomes in human cell lines. We found that initiation sites are present throughout the EBV genome and that their utilization is not conserved in different EBV strains. In addition, SMARD shows that modifications in the utilization of multiple initiation sites occur across large genomic regions (tens of kilobases in size). These observations indicate that individual initiation sites play a limited role in determining the replication dynamics of the EBV genome. Long-range mechanisms and the genomic context appear to play much more important roles, affecting the frequency of utilization and the order of activation of multiple initiation sites. Finally, these results confirm that initiation sites are extremely redundant elements of the EBV genome. We propose that these conclusions also apply to mammalian chromosomes. Despite overall similarities between genomes, initiation of DNA replication and speed of duplication in different parts of the genome differs amongst EBV strains ==== Body Introduction Biochemical studies performed in higher eukaryotes have shown that DNA replication initiates at specific sites, or within initiation zones, suggesting the involvement of particular DNA sequences called replicators (reviewed by DePamphilis 1999). In contrast, functional studies, as well as studies of DNA replication performed in early embryos of various vertebrates and invertebrates, have suggested that initiation of DNA replication can take place with limited sequence specificity (reviewed in Gilbert 2001). The presence of specific initiation sites and of initiation zones has also been proposed to explain the latent replication of the Epstein-Barr virus (EBV) genome in human cell lines. During latent replication, the EBV genome is maintained as a circular episome (∼175 kb in size), and the host cell provides both the replication machinery and the licensing apparatus that limit the genome's duplication to once per cell cycle (reviewed in Kieff 1996; Yates 1996). Initiation site oriP was the first initiation site identified in the EBV genome. In the presence of the viral protein EBNA1, this DNA sequence confers autonomous replication to plasmids transfected into human cell lines (Yates et al. 1984). In addition, initiation of DNA replication at oriP was recently shown to be regulated by geminin, and to correlate with the binding of various cellular components of the replication complex (Orc1, Orc2, Orc3, Orc4, Orc6, Mcm2, Mcm3, and Mcm7) (Chaudhuri et al. 2001; Dhar et al. 2001; Schepers et al. 2001; Ritzi et al. 2003). These and other reports have been interpreted as evidence that oriP contains a replicator (e.g., Koons et al. 2001). However, other initiation sites have also been described (Kirchmaier and Sugden 1998), and a study performed by two-dimensional (2D) gel electrophoresis at neutral pH has suggested the presence of a large initiation zone (Little and Schildkraut 1995). In addition, reports from different laboratories have shown that various portions of the EBV genome, including oriP, can be deleted without affecting the maintenance of the episomes in replicating cells (see Discussion and references therein). Therefore, the presence of specific replicator sequences and their relationship with the sites of initiation of DNA replication also remain to be demonstrated in this system. We recently began to study the replication of individual EBV episomes using fluorescence microscopy (Norio and Schildkraut 2001). In a previous study, we collected various images of the Raji EBV genome (Norio and Schildkraut 2001). The analysis of those molecules demonstrated that the duplication of different EBV episomes begins at different initiation sites located within the initiation zone identified by 2D gel electrophoresis. However, the number of molecules analyzed was not sufficient to infer the precise dynamics of activation of the initiation sites (i.e., to detect events having a short life or occurring infrequently during the duplication of the episomes). In the present study, we performed an extensive analysis of the replication dynamics of the EBV genome in two human Burkitt's lymphoma cell lines (Raji and Mutu I). By utilizing a different procedure to stretch DNA molecules we were able to collect a large number of images of the EBV genome representative of different stages of duplication. This allowed us to determine how DNA replication initiates, progresses, and terminates throughout the EBV genome and to precisely measure the duplication time of specific portions of the EBV genome. These improvements allowed us to obtain important new results as well as to extend previous observations. Here we show that initiation events are not limited to a specific portion of the EBV genome (namely the initiation zone detected by 2D gel electrophoresis), but, unexpectedly, take place throughout the EBV genome. Multiple initiation events were also detected in individual EBV episomes. Hence, if the initiation sites do correspond to replicators, the latter must necessarily be highly redundant (present at a frequency of one or more every 20 kb). Our new results also indicate that, in these two EBV strains, both the frequency and the order of activation of the initiation sites vary considerably throughout the viral genome. This variation involves initiation sites such as oriP, the sequence of which is highly conserved in the two EBV strains (Salamon et al. 2000). Hence, the utilization of the initiation sites is largely independent of their DNA sequence, and it is affected by the genomic context (i.e., the presence/absence of initiation sites activated earlier or the presence of transcription). Finally, we noticed that the initiation sites that tend to be activated earlier during the duplication of each episome are located in clusters, each of which spans several kilobases. The locations of these clusters are different in the Raji and Mutu I strains. Therefore, the utilization of the initiation sites (particularly their order of activation) appears to be regulated at the level of genomic regions rather than at the level of individual initiation sites. Results Fluorescent Hybridization Immunostaining of Individual EBV Episomes Stretched on Microscope Slides In order to study DNA replication, we used a procedure that we call single molecule analysis of replicated DNA (SMARD). This procedure labels the replicating DNA in a way that allows us to determine the position, the direction, and the density of the replication forks in a steady-state population of replicating molecules (in this case, EBV episomes). This in turn allows us to determine how DNA replication initiates, progresses, and terminates throughout the genomic region analyzed. SMARD presents several advantages over procedures that utilize different labeling schemes and allows us to overcome most of the limiting factors that have traditionally affected studies of replication performed on DNA fibers (see Materials and Methods). In our procedure, an asynchronous population of cells is sequentially labeled with 5′-iodo-2′-deoxyuridine (IdU) and 5′-chloro-2′-deoxyuridine (CldU) (Norio and Schildkraut 2001). The length of each labeling period is longer than the time required to completely replicate the EBV genome (3.5–4 h; see Materials and Methods). This allows some of the replicating EBV episomes to become substituted with the halogenated nucleotides along their entire length (Norio and Schildkraut 2001). The incorporation of these nucleotide analogs is later detected by immunofluorescence of individual DNA molecules stretched on microscope slides. In these molecules, the transitions from IdU to CldU mark the positions of the replication forks at the time of the switch from the first to the second labeling period (see below). Hence, the results of this analysis are presented as a series of images of EBV episomes representative of the different stages of duplication that were present at the end of the first labeling period. In addition, the use of long labeling periods makes the data collected by SMARD suitable for quantitative analysis, allowing us to calculate the duplication time of different genomic regions. In the experiments described in this study, agarose-embedded total DNA was prepared from cells labeled with halogenated nucleotides. The circular EBV episomes were converted to linear molecules by digestion with a restriction endonuclease (PacI or SwaI). After pulsed field gel electrophoresis, the EBV DNA was recovered by agarase treatment (Norio and Schildkraut 2001) and stretched on microscope slides by capillary action (see Materials and Methods). Using this procedure we obtained relatively high numbers of stretched molecules even when the starting amount of purified DNA was very small. The hybridization of specific biotinylated probes (visualized with Alexa Fluor 350-conjugated avidin; shown in blue in the figures throughout the manuscript) was used to identify the EBV molecules and their orientation (Norio and Schildkraut 2001). In addition, the halogenated nucleotides were visualized using specific monoclonal antibodies and secondary antibodies conjugated with Alexa Fluor 568 (shown in red in the figures throughout the manuscript; IdU) and Alexa Fluor 488 (shown in green in the figures throughout the manuscript; CldU). The detection procedure and the analysis of the images are described in Materials and Methods and in Figures 1 and 2. The use of long labeling periods, and the analysis of molecules substituted with the halogenated nucleotides along their entire length, present several advantages. In particular this procedure provides multiple internal controls that could not have been performed if short labeling times had been used (see Materials and Methods). Figure 1 Fluorescent Hybridization Immunostaining of Individual EBV Episomes Image of two stretched DNA molecules in the same optical field. The hybridization signals (p107.5 and pSalF) and the immunostaining to detect the halogenated nucleotides are shown in different pseudocolors (red = IdU, green = CldU, and blue = hybridization probes). The top panel shows the merged image. The different color channels are shown separately in the lower panels. One of the two stretched molecules is a PacI-linearized EBV episome (molecule above) and can be recognized by the presence of the hybridization signals. The molecule below is a piece of cellular genomic DNA of similar size (no hybridization signals). The presence of the hybridization signals decreases the intensity of the immunostaining along the same portion of the EBV episome. This confirms that both halogenated nucleotides and hybridization probes are located on the same DNA molecule. The blue dots visible in the bottom panel represent hybridization background (this background was digitally removed from Figures 3B, 4B, and 6B). The EBV episome is substituted along its entire length with both IdU (red regions) and CldU (green regions). Yellow arrowheads indicate the approximate position of the replication forks at the time of the switch from the first to the second labeling period. The background visible in the red and green channels is mainly other DNA molecules containing halogenated nucleotides (white horizontal arrowheads). Some of these molecules attached to the glass before becoming fully extended and appear thick, displaying a brighter immunostaining. Small dots are also visible (magenta vertical arrowheads), sometimes overlapping with the DNA molecules (white asterisks); however, they were not considered in our analysis because they are too short to be unequivocally ascribed to DNA replication. Figure 2 Stretching DNA Molecules by Capillary Action (A) Lengths of 219 unbroken Raji EBV episomes with a recognizable hybridization pattern. These molecules were stretched by the movement of a DNA solution between a silanized microscope slide and a nonsilanized coverslip. About 94% of these molecules have a size of 70 μm (±15 μm), corresponding to about 2.4 kb/μm. (B) Schematic of the PacI-linearized Raji EBV genome with the positions of various genetic elements shown to scale. The initiation site oriP is shown in green with the FR element and the DS element indicated by green boxes. The positions of EBER genes (black box), terminal repeats (smaller red box), internal repeats 1 (larger red box), and the restriction sites utilized in this study (PacI and SwaI) are also indicated. (C) Images of 6 PacI-linearized Raji EBV episomes aligned with the EBV map after hybridization and immunostaining of the DNA molecules and digital adjustment of length. The hybridization signals are shown in blue. Immunostaining to detect the halogenated nucleotides is shown by red and green. Vertical light blue lines indicate the positions of the ends of the hybridization probes and yellow lines, the position of the PacI site used to linearize the EBV episomes. All the molecules shown in this figure represent EBV episomes duplicated during either the first labeling period (red) or the second labeling period (green). The quality of the alignment of the images with the EBV map is comparable to the alignment previously obtained with combed EBV episomes (Norio and Schildkraut 2001). The resolution of analysis is limited to about 5 kb primarily because of discontinuities in the fluorescent signals, as previously reported for similar assays (Parra and Windle 1993; Jackson and Pombo 1998; van de Rijke et al. 2000). The Raji EBV Genome Contains a Region That Usually Replicates First and a Region That Usually Replicates Last In order to define precisely how the Raji EBV genome replicates, we recovered the images of 245 PacI-linearized EBV episomes that incorporated halogenated nucleotides along their entire length (112 fully stained in red, 84 fully stained in green, and 49 stained in both red and green). The results of this experiment are shown in Figure 3. In the episomes that incorporated both kinds of halogenated nucleotides, the red to green transitions (arrows in Figure 3B) define the position of the replication forks at the time of the switch from the first to the second labeling period. The red portions of these molecules are nested around the ends of the PacI-linearized episomes (Figure 3B). This indicates that DNA replication proceeded in a similar manner in most of episomes. Figure 3 SMARD Performed on PacI-Linearized EBV Episomes Replicated in Raji Cells (A) Map of the PacI-linearized EBV genome with the positions of various genetic elements shown to scale. Below the EBV map, light blue bars indicate the positions of the hybridization probes (p107.5 and pSalF) utilized during SMARD to identify the molecules of interest and their orientation. Gray bars (a–h), and black bars (1–10), indicate the positions of the restriction fragments analyzed by 2D gel electrophoresis. (B) PacI-linearized Raji EBV episomes after hybridization and immunostaining of the DNA molecules (aligned with the map). These molecules incorporated both halogenated nucleotides, and the images are ordered (from 1 to 48) by increasing content of DNA labeled during the first labeling period (red). One additional molecule was unsuitable for precise measurements and is not shown. Vertical light blue lines indicate the positions of the ends of the hybridization probes and yellow lines, the position of the PacI site. Arrowheads mark the approximate position of the red-to-green transitions. Asterisks indicate the position of short colored patches not necessarily related to DNA replication. (C) Replication profile of the Raji EBV episomes. This profile was obtained using both the images shown in (B) and the images collected in a previous SMARD experiment (Norio and Schildkraut 2001), for a total of 69 episomes. Starting from the PacI site, genomic intervals of 5 kb are indicated on the horizontal axis by numbers from 1 to 35. The vertical axis indicates the percentage of molecules stained red within each 5-kb interval. (D) Profile of replication fork abundance and direction throughout the EBV genome. Genomic intervals of 5 kb are indicated on the horizontal axis as for (C). The vertical axis indicates the percentage of molecules (out of a population of 69 episomes) containing replication forks (red-to-green transitions) within each 5-kb interval. The forks moving from left to right are depicted in orange. The forks moving from right to left are depicted in yellow. (E) Map of the EBV genome aligned with the horizontal axes of histograms (C) and (D), and with the restriction fragments analyzed by 2D gel electrophoresis (black and gray bars below the map). Green Is indicate the presence of replication bubbles. Red Ts indicate the presence of replication intermediates produced by random termination events. Replication bubbles were detected by 2D gel electrophoresis across the region marked by the red dashed line (approximately corresponding to the RRF). (F) Transcription of the Raji EBV genome. Red arrows mark the positions of regions that can be transcribed during latency. The level of transcription derived by nuclear run-on according to Kirchner et al. (1991) is shown as gray scale (black = highest level; white = lowest level or not transcribed). The EBER genes represent the most intensively transcribed portion of the EBV genome. Intermediate levels of transcription were detected across and downstream from the long transcription unit of the EBNA genes. According to Sample and Kieff (1990), the level of transcription along the EBNA genes region decreases from left to right (I–III). Intermediate levels of transcription were also reported for the two hatched regions. However, these regions contain either repeated sequences (the terminal repeats) or cross-hybridize with other transcribed regions (oriLytR and oriLytL); therefore, their actual level of transcription could be lower. However, the progression of DNA replication throughout the EBV genome is better described by the replication profile of the molecules analyzed (Figure 3C). This profile was obtained by dividing the map of the episomes into intervals of 5 kb (horizontal axis) and then indicating the percentage of molecules stained in red within each interval (vertical axis). From this profile we can easily identify a genomic region that usually replicates first (RRF; more frequently stained in red), a genomic region that usually replicates last (RRL; less frequently stained in red), and two transition regions. The RRF contains the initiation sites most frequently utilized to begin the duplication of the Raji EBV episomes. More than 80% of the molecules analyzed were stained in red throughout intervals 1–7 and 31–35 (horizontal axis; Figure 3C). In the molecules representing early stages of episomal duplication (upper portion of Figure 3B), initiation events took place either within the RRF (molecules 2–21) or in adjacent portions of the EBV genome (i.e., molecule 1). Interestingly, low levels of replication bubbles had been previously detected by 2D gel electrophoresis within various restriction fragments located in the same portions of the EBV genome (black bars 1–3 and 5–7 in Figure 3A). Therefore, the initiation sites activated earlier during the duplication of each episome are located within what appears to be an initiation zone that spans several tens of kilobases (encompassing intervals 1–7 and 27–35 in Figure 3C). The RRL appears in the replication profile of the Raji EBV episomes as a large valley (Figure 3C). The bottom of the valley spans about 40 kb (intervals 11–18), and its flat appearance indicates that throughout this region the episomes terminate their duplication with similar probability. Note, however, that termination events can also occur within the transition regions (green in molecules 43 and 48; Figure 3B). Interestingly, the level of transcription across the RRL is higher than in the rest of the Raji EBV genome, while across the RRF it is either very low or absent (Figure 3F) (Sample and Kieff 1990; Kirchner et al. 1991). The presence of RRF and RRL was confirmed by a second SMARD experiment in which we digested the EBV episomes with SwaI. This enzyme cleaves twice in the viral genome, producing fragments of 105 and 70 kb. The larger fragment was expected to contain most of the RRF (now located near the center of DNA molecules), and a small portion of the RRL. We recovered 209 fully substituted 105-kb fragments (94 red, 79 green, and 36 red and green). These molecules were analyzed as described for the PacI-linearized EBV episomes (Figure 4). We found that both the RRF (intervals 1–13), and the RRL (intervals 17–21) encompass the same genomic sequences occupied in the previous SMARD experiment (compare Figures 3C and 4C). Initiation events located within the RRF are visible in molecules 1–4 (Figure 4B). We conclude that the results obtained by SMARD are reproducible and do not depend on the particular restriction enzyme used for digesting the DNA molecules. Figure 4 SMARD Performed on SwaI-Digested EBV Episomes Replicated in Raji Cells (A) Map of the approximately 105-kb fragment obtained by digesting EBV episomes with the restriction endonuclease SwaI. (B) Images of 36 EBV molecules ordered and marked as in Figure 3B. Molecules 18 and 19 are distorted, but the positions of the red-to-green transitions are clear. (C) Replication profile of the SwaI-digested EBV episomes shown in (B). Starting from the SwaI site, intervals of 5 kb are indicated on the horizontal axis by numbers from 1 to 21. The vertical axis indicates the percentage of molecules stained red within each 5-kb interval. (D) Profile of replication fork abundance and direction. Intervals of 5 kb are indicated on the horizontal axis as for (C). The vertical axis indicates the percentage of molecules containing replication forks in each 5-kb interval. The partitioning of the EBV genome is different from Figure 3D. Hence, the four pausing sites that in Figure 3D were located within interval 8 are here located between interval 13 and interval 14. As a consequence, the peak visible in Figure 3D is here split into two smaller adjacent peaks. (E) Map of the approximately 105-kb SwaI fragment aligned with the horizontal axes of histograms (C) and (D). Replication Forks Move Without Significant Pausing throughout the Raji EBV Genome with the Exception of the Genomic Region near oriP The movement of the replication forks throughout the EBV genome is described by the profiles of replication fork abundance (see Figures 3D and 4D). These profiles were obtained by dividing the map of the EBV genome into intervals of 5 kb (horizontal axis) and then indicating the percentage of molecules containing red-to-green transitions within each interval (vertical axis). As seen earlier, these transitions indicate the position, and the direction, of the replication forks at the time of the switch from the first to the second labeling period. The significant accumulation of red-to-green transitions visible within interval 8 (Figure 3D) indicates that replication forks were not moving freely across this portion of the EBV genome. A similar accumulation is visible for the SwaI-digested molecules in the same portion of the EBV genome (Figure 4D). This result was expected since four different pausing sites had been previously described within and near oriP (Little and Schildkraut 1995; Norio and Schildkraut 2001). However, no other major accumulation of forks is visible. Therefore, replication forks move mostly unimpeded across the Raji EBV genome. From the profiles of replication fork abundance we can also determine the prevalent direction of the replication forks throughout specific portions of the EBV genome. For example, throughout most of the RRL, replication forks move in both directions at similar frequencies (yellow and orange bars within intervals 11, 13–15, and 17 in Figure 3D and within intervals 18 and 19 in Figure 4D). The bidirectional movement of the replication forks also characterizes the central portion of the RRF (intervals 4–7; Figure 4D). However, this was not evident from the profile of replication fork abundance of the PacI-linearized EBV episomes (Figure 3D) because the extremities of the molecules can be distorted or not fully stretched (such as in molecule 5 in Figure 3). As a consequence, in the PacI experiment, the position of the replication forks could not always be observed properly within the RRF. From this we conclude that within the central portions of RRF and RRL, replication forks move in both directions with a similar probability. Within the rest of the EBV genome, however, the movement of the replication forks is mostly unidirectional (Figures 3D and 4D). For example, replication forks move mainly rightward from interval 11 throughout oriP and beyond (Figure 4D; forks infrequently moving in the opposite direction may not appear in this kind of histogram unless extremely large numbers of molecules are imaged). This direction bias is compatible with a previous 2D gel analysis of the oriP region in Raji cells (Little and Schildkraut 1995) and is not affected by the pausing of the replication fork. We conclude that the direction of movement of the replication forks is mainly a consequence of the dynamics of initiation of DNA replication throughout the viral genome. Active Initiation Sites Are Not Limited to the RRF Early studies performed by electron microscopy identified Raji EBV episomes with multiple replication bubbles but could not identify the position of these initiation events (Gussander and Adams 1984). In order to detect the presence of these events and to determine their location we analyzed the immunostaining patterns of the DNA molecules described above. Multiple initiation events should produce molecules containing multiple red patches, each surrounded by green. The qualitative examination of the replication patterns shown in Figure 3B revealed some of these immunostaining patterns. In molecules 17 and 43, for example, an early initiation event apparently took place within the RRF (large region stained in red). However, shorter red regions are also present, indicating the occurrence of initiation events at later times. Throughout this manuscript, when we refer to multiple initiation events, we will mean that they occur on the same DNA molecule. In addition, if the activation of the initiation sites is not synchronous (as in the molecules described above), we will refer to the initiation events used to begin the duplication of the EBV genome as primary and any subsequent initiation event as secondary. The secondary events visible in molecules 17 and 43 are both located within the long transcription unit of the Epstein Barr nuclear antigen [EBNA] genes (see Figure 3F). In particular, molecule 43 shows a secondary initiation event that occurred when the duplication of the EBV episome was almost complete (red patch near the pSalF hybridization signal). Therefore, initiation events are not limited to the RRF of the Raji episomes. Initiation events located throughout the EBV genome, as well as multiple initiation events, were also identified in a much larger fraction of Mutu I EBV episomes (see below). We conclude that the entire EBV genome constitutes a large initiation zone, although the frequency of the initiation events is reduced throughout RRL (see below). DNA Replication Proceeds at Different Speeds throughout Different Portions of the Raji EBV Genome In the previous sections we showed that different portions of the EBV genome are not equivalent with respect to when and where DNA replication begins and how DNA replication progresses. Here we wanted to determine the quantitative aspects of DNA replication in different portions of the EBV genome. The data obtained by SMARD can be analyzed quantitatively and used to determine the average time required to duplicate any portion of the EBV genome (Td; Figure 5A). By knowing Td and the length of the segment analyzed, the corresponding duplication speed (Sd) can also be calculated (Figure 5A). Importantly, these measurements are based on all the images collected during each SMARD experiment, including the molecules entirely stained in red or in green (several hundred). Therefore, the conclusions reached by this analysis are not solely dependent on the appearance of the immunostaining patterns in a small fraction of the DNA molecules. In addition, the quantitative analysis is performed on relatively large genomic segments; therefore, it is not significantly affected by the resolution at which the positions of the replication forks are determined. Figure 5 Duplication Speed of Various Segments of the Raji EBV Genome (A) A detailed procedure to calculate Td using SMARD was published elsewhere (Norio and Schildkraut 2001). Here we describe how to calculate Tdab for a generic genomic region (a)–(b) (i.e., any portion of the EBV genome) using information derived from the replication patterns of a longer region (A)–(B) (i.e., the whole EBV genome). Depicted are the hypothetical staining patterns for 32 DNA molecules representing the genomic region (A)–(B) after double-labeling with two halogenated nucleotides (red and green). The molecules that started and completed their duplication during the first labeling period are fully red (R). The molecules that started their duplication during the first labeling period, completing it during the second labeling period are stained in both red and green (RG). In the total population of molecules, the fraction of R molecules increases when the length of the first labeling period (Tp1) increases. The fraction of RG molecules is proportional to the time required to duplicate the genomic region (A)–(B). Some of these molecules (marked rg) are stained in red and green also within the region (a)–(b). The fraction of rg molecules is proportional to the time required to duplicate the genomic region (a)–(b). This relationship is expressed by the equation reported at the bottom of the figure and allows us to calculate Tdab using parameters that can be easily measured on individual DNA molecules (NR, the number of R molecules; NRG, the number of RG molecules; Nrg, the number of rg molecules). Finally, the ratio between the size of the genomic segments analyzed (Lab) and Tdab represents the duplication speed of the segment (Sdab). The results obtained from the PacI and the SwaI experiments are reported in (B) and (C). Double-headed arrows indicate the genomic segments analyzed quantitatively. Segments marked with the same letter in the two maps correspond to identical portions of the Raji EBV genome. Above each arrow is indicated the corresponding Sd value in kilobases per minute. The sizes of these segments are as follows: A–G, 25 kb; H, 20 kb; I, 35 kb; K, 10 kb; L, 75 kb; and J, 10 kb. A comparison of the values obtained from the two experiments shows remarkable similarities. The largest variation was found for segment G. However, in both experiments this segment was located at the end of the DNA molecules. Therefore, the variability in stretching in this portion of the molecules may have affected the collection of the data. The red dashed line below the map indicates the position of the RRF. We calculated the value of Sd for each portion of the Raji EBV genome, depicted with double-headed arrows in Figure 5 (segments A–K; Td is reported in Table 1). The results obtained from the PacI and the SwaI experiments were analyzed independently but show remarkable similarities (compare the values reported above segments marked with the same letter in Figures 5B and 5C). Therefore, the quantitative analysis is highly reproducible. Table 1 Quantitative Analysis of Different SMARD Experiments The segments of the EBV genome (left column) are shown as double-headed arrows in Figures 5 and 6F. For each segment the table reports the number of EBV genomes completely stained in red (NR), the number stained in both red and green (NRG), the number stained in both red and green within the genomic segment analyzed (Nrg), and Td (calculated using the equation shown in Figure 5A). Note that the values of NR, NRG, and Nrg reported for the PacI-linearized Raji EBV episomes include data from a previously published SMARD experiment (Norio and Schildkraut 2001). Tp1 was 240 min for the Raji experiments and 210 min for the Mutu I experiment We found that different portions of the EBV genome replicate at different speeds, with values that range from a minimum of 0.3 kb/min (segment K; Figures 5B and 5C) to a maximum of 3.5–4.7 kb/min (segment I). More details are provided later in the text. However, it is important to note that the highest Sd values were detected within the central portion of the Raji RRF (segments A, I, and G). This result can be explained in two ways. First, replication forks may move faster throughout the RRF. Alternatively, the RRF could contain a significant level of multiple initiation events. For the reasons mentioned below we favor the second possibility. Multiple Initiation Events Can Take Place on the Same Raji EBV Episome within the RRF Three lines of evidence indicate that multiple initiation events take place within the Raji RRF. Two lines of evidence are discussed in this section (the presence of multiple red patches in the immunostaining patterns of some EBV episomes and the detection of termination events by 2D gel analysis); the third is discussed in the last section of Results (differences in duplication speed across segments of the RRF of different sizes). The first line of evidence is provided by the immunostaining pattern of the EBV molecules. Although discontinuities in the immunostaining make it difficult to detect multiple initiation events when the distance between converging forks is 5 kb or less, the replication patterns of some of the molecules are compatible with the presence of multiple initiation events within RRF (molecule 8 in Figure 3B and molecules 5 and 9 in Figure 4B). For example, in molecule 9 three regions stained in red (divergent arrows) are separated by two regions stained in green about 4 kb in size (convergent arrows); shorter patches are also visible (asterisks) and might indicate the presence of additional initiation events. In these molecules, the genomic regions stained in red are very close to each other and approach the resolution limits of SMARD. If these signals were produced by multiple initiation events, we should conclude that they took place at about the same time and with a short interorigin distance. Alternatively, secondary initiation events might have taken place in proximity to an incoming replication fork (generated by a primary initiation event). In both cases, the different replication bubbles would rapidly fuse into a larger bubble, making the detection of these events extremely difficult. Since these patterns are too short to be unequivocally ascribed to DNA replication, the presence of multiple initiation events within the RRF was confirmed using a replication mapping approach independent of SMARD. A second line of evidence is provided by the structure of the replication intermediates examined by 2D gel electrophoresis in exponentially growing Raji cells (see Materials and Methods). We analyzed nine restriction fragments, indicated in Figure 3A as gray bars (a–i). We also reexamined the hybridization patterns of ten different fragments analyzed in a previous study (black bars 1–10 in Figure 3A) (Little and Schildkraut 1995). In total, we considered 19 restriction fragments spanning approximately 65% of the Raji EBV genome. The summary of these 2D gel analyses is shown in Figure 3E. Replication intermediates indicative of initiation events were found in several restriction fragments (marked by a green I above the corresponding fragment in Figure 3E). The restriction fragments containing replication bubbles are contiguous and span the genomic regions underlined by the red dashed line at the bottom of Figure 3E (approximately corresponding to the RRF). As expected, termination events (marked by a red T in Figure 3E) were detected in many of the fragments located within the RRL. Importantly, random termination events were also detected in most of the fragments in which we detected bubble arcs. However, the source of these events could not be identified in our previous studies. As discussed earlier, SMARD shows unequivocally that the RRF completes its duplication before forks originating elsewhere have the time to reach its central portion. We conclude that the termination events detected by 2D gel electrophoresis derive from the collision of replication forks generated by multiple initiation events taking place within the RRF. An estimate of the frequency of these multiple initiation events is reported later in the text. In Mutu I Cells, the Order of Activation of the Initiation Sites Varies Throughout the Viral Genome Previous observations have suggested that some initiation sites (such as oriP) are used at a different frequency in different EBV strains (Little and Schildkraut 1995). However, it was not known whether these changes were the result of modifications in the activity of individual initiation sites or involved multiple initiation sites throughout the EBV genome. In order to determine the extent of these differences, we performed SMARD in another Burkitt's lymphoma cell line called Mutu I (Gregory et al. 1990). A brief description of this cell line and of the conditions used for SMARD is reported in Materials and Methods. From this experiment we recovered the images of 271 PacI-linearized EBV episomes substituted along their entire length with halogenated nucleotides (122 red, 107 green, 42 red and green). The results of this analysis are shown in Figure 6. Figure 6 SMARD Performed on PacI-Linearized EBV Episomes Replicated in Mutu I Cells (A) Map of the PacI-linearized EBV genome with the positions of various genetic elements shown to scale. Below the EBV map, light blue bars show the positions of the hybridization probes (pWW and pSalF) utilized to identify the EBV molecules and their orientation. Black bars indicate the positions of two short deletions present in the Raji EBV genome (Polack et al. 1984). (B) Images of 40 PacI-linearized EBV episomes ordered and marked as in Figure 3B (from a population of 42 molecules collected in this experiment). Some molecules are distorted but the positions of the red-to-green transitions are clear. Two additional molecules were unsuitable for precise measurements and are not shown. (C) Replication profile of the PacI-linearized EBV episomes shown in (B). Starting from the PacI site, intervals of 5 kb are indicated on the horizontal axis by numbers from 1 to 35. The vertical axis indicates the percentage of molecules stained red within each 5-kb interval. (D) Profile of replication fork abundance and direction. Intervals of 5 kb are indicated on the horizontal axis as for (C). The vertical axis indicates the percentage of molecules containing replication forks in each 5-kb interval. Three different pausing sites contribute to the significant accumulation of replication forks within interval 8 (the two EBER genes and the FR element). A fourth pausing site (the DS element) is located within interval 9, producing a minor accumulation of replication forks. (E) Map of the EBV genome (to scale) aligned with the horizontal axes of histograms (C) and (D). Red arrows mark the positions of regions transcribed during the type I latency that characterize the Mutu I EBV episomes. The red dashed line above the map indicates the position of the RRF. (F) Duplication speed for various segments of the Mutu I EBV genome. Double-headed arrows indicate the genomic segments analyzed quantitatively. Above each arrow is indicated the corresponding Sd value in kilobases per minute. Segments A′–G′ divide the whole EBV genome into seven parts of identical size, corresponding, respectively, to the intervals 1–5, 6–10, 11–15, 16–20, 21–25, 26–30, and 31–35 on the horizontal axes of Figures 3C and 4C. The sizes of these segments are as follows: A′–G′, 25 kb; and K′, L′, and M′, 10 kb. Due to the presence of small differences in the DNA sequence (see text), segments A′–G′ are similar but not identical to segments A–G in Figure 5. The most striking differences in the replication of the two EBV strains were found in the order of activation of the initiation sites. In Raji episomes, primary initiation events are restricted to an 80-kb region approximately corresponding to the RRF (this study and also Norio and Schildkraut 2001). In contrast, in Mutu I episomes primary initiation events occur at many locations throughout the viral genome (molecules 1, 4, 5, and 8; Figure 6B). Multiple initiation events (mostly largely spaced), firing either synchronously (molecules 2 and 5) or asynchronously (molecules 11, 15, 31 and 34), are also frequent in Mutu I. This explains the heterogeneity detected in the positions of the termination events (green patches in molecules 32–40), as well as in the replication patterns of the episomes at intermediate stages of duplication. Hence, DNA replication initiates, progresses, and terminates differently in different Mutu I episomes. Even if the order of activation of the initiation sites varies from molecule to molecule, more than 80% of the Mutu I episomes are stained in red between intervals 6 and 9 (a 20-kb region that includes oriP; Figure 6C). This indicates that this EBV strain also contains an RRF, although its genomic location differs from that found in Raji (compare Figure 3C with Figure 6C). In summary, these results confirm that initiation sites are not confined to a specific portion of the EBV genome and allow us to conclude that their utilization in different viral strains can change throughout the EBV genome. The RRFs Are Produced by Clusters of Initiation Sites Frequently Activated at the Beginning of the Duplication of the Episomes Modifications in the activity of individual initiation sites (such as oriP) could potentially explain the different location of the RRF in Raji and Mutu I episomes. However, the following considerations indicate that this is not the case. Initiation events occurring at oriP take place in the vicinity of the dyad symmetry (DS) element and produce replication forks that stall in the leftward orientation at the family of repeats (FR) element (Gahn and Schildkraut 1989). Initiation events taking place to the left of oriP (such as in Raji episomes) instead produce replication forks that pause in the rightward orientation (interval 8 in Figure 3D). In the Mutu I genome, oriP is at the center of the RRF, but replication forks accumulate in both orientations within interval 8 (see Figure 6D). This indicates that primary initiation events occur on both sides of oriP. Examples of initiation events that took place near the DS element, or to the left of oriP, are visible, respectively, in molecule 5 and in molecules 1 and 3 (Figure 6B). The presence of initiation events on both sides of oriP is also supported by the replication pattern of molecule 2, in which two initiation events spaced approximately 10 kb apart are visible on the same episome. Therefore, in the Mutu I EBV genome, the RRF (∼20 kb in size) contains multiple active initiation sites that have a shared tendency to be activated at the beginning of the duplication of each episome. Similar conclusions apply to the RRF of the Raji EBV genome (∼80 kb in size), in which primary initiation events were detected at various locations (this study and also Norio and Schildkraut 2001). This could explain why only weak bubble arcs were detected by 2D gel electrophoresis throughout the RRF, even though the duplication of the episomes usually begins within this genomic region (this study and also Little and Schildkraut 1995). We conclude that the RRFs in the Raji and Mutu I EBV genomes are similar in that they contain various initiation sites that have a shared tendency to be activated at the beginning of the duplication of each episome. The Duplication Speed of Various Portions of the EBV Genome Is Different in Raji and Mutu I EBV Episomes In the previous sections we have shown that the order of activation of the initiation sites in Raji and Mutu I EBV episomes is not conserved. Here, we wanted to determine whether the quantitative aspects of DNA replication were also different. SMARD was used to calculate Sd for each portion of the Mutu I EBV genome, depicted as double-headed arrows in Figure 6F (segments A′–M′; see also Table 1). We found that DNA replication proceeds at different speeds throughout different portions of the Mutu I EBV genome (from a minimum of 0.5 kb/min in segment K′, to a maximum of 3.8 kb/min in segment G′). This is very similar to the range of speeds found in the Raji episomes (0.3–4.7 kb/min). Therefore, DNA replication appears to progress with similar kinetics in the two EBV strains. We also noticed that similar portions of the EBV genome have different Sd values in the two viral strains. Segments A′–G′ divide the Mutu I EBV genome in seven parts of identical size (∼25 kb; Figure 6F). These segments encompass portions of the EBV genome similar to segments A–G in the Raji genome (see Figure 5). However, the values of Sd differ significantly in almost every segment. Interestingly, in Mutu I episomes, the highest Sd values were not detected within the RRF (segment G′). Instead, the RRF contained some of the lowest Sd values (segment B′). This is probably due to the presence of strong pausing sites within this portion of the Mutu I EBV genome. Nevertheless, segment B′ replicates faster than the corresponding portion of the Raji EBV genome (segment B in Figure 5), a phenomenon that could be explained by the presence of multiple initiation events on both sides of oriP in a fraction of the EBV episomes. In any case, these results indicate that there is no simple correlation between the Sd of a genomic segment and its location within the RRF or the RRL. Replication Forks Progress at Similar Rates Across Different Portions of the EBV Genome and in Different EBV Strains Previous observations have suggested that in mammalian cells the speed of the replication forks can vary (Housman and Huberman 1975). Here, we wanted to determine if some of the differences detected in the replication of Raji and Mutu I episomes could be ascribed to modifications in the rate of progression of the replication forks as proposed for other systems (Anglana et al. 2003). For a genomic segment replicated by forks moving in a single direction, Sd corresponds to the average speed of the replication forks (provided pausing sites are absent). This allowed us to measure the average speed of the replication forks in various portions of the EBV genomes in which these conditions are satisfied. In Raji episomes, we found that the average speed of the replication forks was about 1.0 kb/min throughout both segment H and segment J (see Figure 5); these segments are replicated by forks moving predominantly in one direction (corresponding, respectively, to intervals 22–25 and 9–10 in Figure 3D). Interestingly, a similar value (1.1 kb/min) was found for two different portions of the Mutu I EBV genome in which replication forks also move predominantly in one direction (segments M′ and L′ in Figure 6F, corresponding, respectively, to intervals 3–6 and 9–11 in Figure 6D). Therefore, in both EBV strains, we found that the average speed of the replication forks is approximately 1.0 kb/min within every segment that could be analyzed. Studies performed in different systems have suggested that transcription could interfere with the progression of the replication forks (Brewer 1988; Liu and Alberts 1995). In the Raji EBV genome, segments J and H (see Figure 5) are located within the long transcription unit of the EBNA genes (see Figure 3F). Throughout segment J, replication forks progress in the same direction of transcription, whereas in segment H their orientation is reversed (see Figure 3D). Nevertheless, as demonstrated above, replication forks move at the same speed in both segments. Replication forks also move at a similar speed across two nontranscribed regions in the Mutu I EBV genome (segments M′ and L′; see Figure 6). We conclude that in our systems the progression of the replication forks is not significantly influenced by transcription. This could be so either because the level of transcription is not sufficiently high or because, as suggested by others, transcription and DNA replication do not occur at the same time in mammalian cells (Wei et al. 1998). The Duplication Speed of a Genomic Segment Reflects the Average Number of Replication Forks Involved in Its Replication Variation in the utilization of the initiation sites and similarity in the speed of the replication forks suggest that the former should have a stronger influence on the duplication speed of a genomic segment. If we assume that the speed of the replication forks is constant throughout the EBV genome (except for the regions containing pausing sites), Sd becomes a function of the number of replication forks actively synthesizing DNA. High Sd values would indicate that a large number of replication forks participate in the replication of a genomic segment. If we apply this assumption to the central portion of the Raji RRF, we can see that the values of Sd for segments A and G (see Figure 5) are compatible with the presence 2–3 active forks/segment (corresponding to about one initiation event per duplication cycle within each of these 25-kb segments). Importantly, a larger segment spanning the same portion of the EBV genome (segment I; 35 kb in size) replicates even faster (3.5–4.7 kb/min; see Figure 5). This increase could not be explained if the changes in duplication speed were caused by modifications in the speed of the replication forks. However, it is precisely what would be expected if an average of two initiation events take place within the 35-kb segment of the RRF (as suggested by the immunostaining pattern of the episomes and supported by the 2D gel analysis). Therefore, the observed duplication speeds support a model in which, within the RRF, initiation sites spaced 25 kb apart or less can become licensed on the same EBV episome. We also noticed that the EBV genome duplicates faster in Mutu I than in Raji cells (Table 1). This is in agreement with the higher level of widely spaced multiple initiation events detected in Mutu I (compare Figures 3B and 6B). We conclude that the differences in Sd across the EBV genome and between the two EBV strains reflect different frequencies of initiation and termination events. Discussion Conserved and Nonconserved Features in the Latent Replication of Different EBV Strains In this study, we determined how DNA replication initiates and progresses in EBV episomes latently replicating in two human Burkitt's lymphoma cell lines (Raji and Mutu I). Previous experiments had suggested that some variability in the utilization of oriP might exist among different EBV strains (Little and Schildkraut 1995). Here, however, we found that the replication dynamics vary across the entire EBV genome to an extent that could have not been predicted from previous studies. As exemplified by the replication profiles, the immunostaining patterns of the episomes is strikingly different in the Raji and Mutu I strains (compare Figures 3 and 6). This indicates that the order of activation of the initiation sites is not conserved. Differences were also found in the direction of movement of the replication forks (see Figures 3D and 6D) and in the duplication speed of different portions of the EBV genome (see Figures 5 and 6F). The last, in particular, indicates that the frequency of initiation and termination events varies across the EBV genome and between the two viral strains. We did not find a simple correlation between the Sd of a genomic segment and its location within the RRFs. For example, the high Sd value for segment G′ indicates the presence of active initiation sites outside the Mutu I RRF. Therefore the factors that influence the order of activation of the initiation sites are at least partially distinct from the factors that control their frequency of activation. The EBV episomes replicating in these two cell lines have a similar size and genomic organization. However, the number of internal repeats 1 (also called Bam HI W) is reduced by one unit in the Mutu I strain (not shown), while the Raji EBV genome contains two short deletions (see Figure 6A) (Polack et al. 1984). In principle, these differences could affect some initiation sites. On the other hand, initiation events were detected throughout the EBV genomes. It is unlikely that localized modifications of the DNA sequence (affecting one or few initiation sites) could account for all the differences in the replication of Raji and Mutu I episomes. Primary or secondary events were detected within almost every 25-kb segment of the Mutu I EBV genome (see Figure 6F), such as segment A′ (molecule 11; see Figure 6B), segment B′ (molecules 1, 2, 3, and 5), segment C′ (molecule 34), segment D′ (molecule 8), segment F′ (molecules 5 and 15), and segment G′ (molecules 4 and 31). Similarly, in Raji episomes, replication bubbles were detected within every restriction fragment analyzed by 2D gel electrophoresis throughout a region of about 80 kb (the sizes of these fragments ranged from 3 to 12 kb; Little and Schildkraut [1995] and this study). Using SMARD, low frequencies of secondary initiation events were also detected in the remaining portion of the Raji EBV genome. Therefore, even if SMARD and the 2D gel analysis do not have the resolution to pinpoint the locations of the initiation events at the nucleotide level, our results clearly indicate that the average distance between the initiation sites is below 25 kb. This study also revealed modifications in the pausing of the replication forks in the oriP region. Accumulation of replication forks is clearly present in both EBV strains within this genomic region. However, only 25% of replicating Mutu I episomes contain replication forks at this location (see Figure 6D), compared with 43% of Raji episomes (see Figure 3D). Quantitative estimates of the average pausing of the replication forks suggested values of about 30 min in Raji episomes and 10 min in Mutu I episomes (data not shown). The decreased pausing could reflect the presence of active initiation sites on both sides of oriP in the Mutu I strain (as suggested by the immunostaining pattern of the episomes). Alternatively, a decreased efficiency of the pausing sites could also contribute to the significant reduction in pausing detected in Mutu I. We also found that some features of the episomal duplication do not vary. In both Raji and Mutu I cells, replication forks move freely throughout the EBV genome (except near oriP; see Figures 3D and 6D), and their progression rate appears to be constant. This indicates that modifications in the speed of the replication forks do not contribute significantly to the differences in DNA replication described above. This contrasts with results obtained by another laboratory for an amplified genomic locus (Anglana et al. 2003), in which the slower progression of the replication forks—caused by a reduction in nucleotide pools—was presented as the cause for a more frequent activation of initiation sites. Instead, the changes in DNA replication detected in our experiments appear to be caused by differences in the order and frequency of activation of groups of initiation sites encompassing large genomic regions (see next section). Another common feature between Raji and Mutu I cells is the presence of a genomic region that usually replicates first during the duplication of each episome. The position of this RRF differs in the two EBV strains. However, the direction of movement of the replication forks throughout the RRFs is similar. For example, within the central portion of each RRF, replication forks move in both directions, while along its distal portions, replication forks move predominantly outward (see Figures 3D, 4D, and 6D). We conclude that the direction of movement of the replication forks throughout the EBV genome is mainly a consequence of the dynamics of the initiation of DNA replication. Utilization of Initiation Sites is Regulated at the Level of Genomic Regions Rather Than at the Level of Individual Initiation Sites Even if the EBV episomes utilize the same replication machinery (provided by the host cell), several aspects of their duplication are not conserved between Raji and Mutu I. In mammalian cells, prereplication complexes are believed to form at the end of mitosis, when general transcription is shut off (Okuno et al. 2001; Dimitrova et al. 2002; Mendez and Stillman 2002). However, the selection of specific initiation sites occurs only later in G1, at the origin decision point (Wu and Gilbert 1996). This suggests that there must be a mechanism that, during G1, restricts the utilization of initiation sites to specific regions of the mammalian genomes. In this study, we have shown that initiation sites are present throughout the EBV genome and that their utilization differs dramatically in different EBV strains. It is therefore reasonable to assume that the utilization of the initiation sites in the EBV episomes is restricted by the same mechanisms responsible for the selection of the initiation sites in mammalian chromosomes. One of the questions we tried to answer is whether initiation of DNA replication is regulated at the level of individual initiation sites. Clues to a possible regulatory mechanism can be found in the replication profiles of the EBV episomes. The RRFs are localized in specific portions of the EBV genome that differ in the two EBV strains. These regions are tens of kilobases in size (about 80 kb in Raji episomes and 20 kb in Mutu I episomes) and encompass multiple initiation sites. The early activation of an individual initiation site could be sufficient to generate a RRF. However, our results have demonstrated that within each RRF various initiation sites have a similar tendency to be activated at the beginning of the duplication of each episome. Therefore, the order of activation of the initiation sites varies at the level of genomic regions rather than at the level of individual initiation sites and might reflect the presence of a particular chromatin organization. Recent findings have shown that histone acetylation can influence the timing of replication origin firing in yeast (Pasero et al. 2002; Vogelauer et al. 2002). In this study we found that even if initiation events were detected at many locations within the MutuI episomes, primary initiation events occurred predominantly within the RRFs. Modifications in chromatin structure could be used in mammalian cells to regulate the order of activation of the initiation sites across genomic regions that encompass multiple initiation sites. The early activation of the initiation sites located in these regions would increase the chance of passively replicating the neighboring initiation sites contributing, at least in part, to the process of selection of the initiation sites. In addition to changes in the order of activation of the initiation sites, other mechanisms could influence their utilization by affecting their frequency of activation. We noticed that in Raji episomes the frequency of initiation events across the RRL appears to be reduced compared to that of the RRF. This difference is reflected in the higher levels of Sd detected within the RRF compared to the RRL (Figure 5) and in the absence of bubble arcs outside the RRF (Figure 3E). The replication profile of the EBV episomes also indicates that the RRL (the genomic region stained in red in less than 40% of the EBV episomes) is larger in Raji (intervals 9–22 in Figure 3C) than in Mutu I (intervals 17–25 in Figure 6C) and that it mirrors the positions of the longest transcription units active in each strain (see Figures 3E and 6E). This suggests that the presence of a long transcription unit could delay the duplication of the corresponding genomic region. This delay is unlikely to be caused by an impaired progression of the replications forks, since we have shown that the average speed of the replication forks is not significantly influenced by transcription (see Figures 5B, 5C, and 6F). An alternative possibility could be that transcription decreases the frequency of initiation events across the genomic regions traversed by RNA polymerases, as previously suggested by others (Kalejta et al. 1998; Snyder et al. 1988; Haase et al. 1994; Tanaka et al. 1994). Perhaps the passage of RNA polymerase removes, or inactivates, prereplication complexes deposited on the DNA at the end of mitosis (see next section). The observation that initiation events appear more diffusely across the Mutu I EBV genome than in Raji is consistent with this hypothesis and might reflect the presence of larger nontranscribed regions in Mutu I (Gregory et al. 1990). Further experiments will be required to shed light on this phenomenon. However, the detection of some initiation events within the transcribed regions (i.e., molecules 17 and 43 in Figure 3B and molecule 8 in Figure 6B) suggests that the relationship between transcription and replication could be more complex. Initiation Sites Are Redundant Elements of the EBV Genome In this study, we have shown that initiation events are not confined to a specific portion of the episomes, suggesting that DNA sequences capable of functioning as initiation sites must be rather common. This can explain why, under various experimental conditions, individual initiation sites do not appear to play an essential role in the replication of EBV episomes. For example, a hundred-nucleotide deletion encompassing the DS element of oriP is sufficient to abrogate both initiation of DNA replication (Norio et al. 2000) and the binding of ORC and MCM proteins at this genomic location (Chaudhuri et al. 2001; Schepers et al. 2001). This deletion, however, has no apparent effect on the stable replication of the episomes in established cell lines (Norio et al. 2000; Kanda et al. 2001). Therefore, other efficiently licensed initiation sites are present in different portions of the EBV genome. Large deletions are also well tolerated (deletions I, II, III, and IV in Figure 7), even when they encompass portions of the EBV genome known to contain multiple initiation sites (such as the Raji RRF). One of the deleted EBV genomes shown in Figure 7 (genome IV; Kempkes et al. 1995a; Kempkes et al. 1995b) was recently analyzed to detect binding sites for ORC and MCM proteins. Significant binding of these proteins was detected only at oriP (Schepers et al. 2001). However, we found that in this mini-EBV genome, oriP is used at a frequency that approaches 100% (B. Chaudhuri and C. L. Schildkraut, unpublished data). Therefore, the absence/reduction of replication complexes at other locations correlates with an increased usage of the licensed initiation site oriP. Interestingly, these short versions of the EBV genome were specifically engineered to preserve the latency genes by removing most of the untranscribed regions. Therefore, a possible effect of transcription could be to reduce the number of replication complexes present throughout the EBV genome. Reductions in initiation events throughout transcribed regions could be relevant in the maintenance of genomic stability. In fact, it has been reported that at least three extremely large transcription units (FHIT, WWOX, and Parkin; each ∼1 Mb in size) encompass known common fragile sites in mammalian genomes (Ohta et al. 1996; Ried et al. 2000; Krummel et al. 2002; Denison et al. 2003). We conclude that initiation sites are redundant elements of the EBV genome and that the deletion of some of them can be compensated for by an increased usage of the remaining sites. Figure 7 EBV Strains Carrying Large Deletions Map of a generic EBV genome linearized with the restriction endonuclease PacI. The number of terminal and internal repeats 1 can vary in different EBV strains; therefore, broken lines were inserted in the map at these positions. The deletions present in four EBV strains are shown as black bars below the map. Deletion I is 12 kb in size and is present in episomes of cell lines obtained by transformation with B95–8 EBV isolates (Raab-Traub et al. 1980; Parker et al. 1990). This deletion encompasses a portion of the EBV genome corresponding to the central portion of the Raji RRF. The remaining deletions were artificially engineered in the EBV genome. Deletion II is described in Robertson et al. (1994). Deletion III is described in Robertson and Kieff (1995). Deletion IV is described in both Kempkes et al. (1995a) and Kempkes et al. (1995b). Role of oriP in the Replication of EBV Episomes Initiation site oriP is the best characterized initiation site of the EBV genome. Initiation of DNA replication has been detected at this site in every EBV strain analyzed to date by 2D gel electrophoresis. However, the frequency of the initiation events at oriP varies in different EBV strains, and it is particularly low in Raji (Little and Schildkraut 1995). The infrequent use of this initiation site in Raji does not appear to be caused by an inability to assemble a prereplication complex. In fact, in this cell line, both ORC and MCM proteins efficiently bind oriP (Chaudhuri et al. 2001; Ritzi et al. 2003). Changes in the DNA sequence of oriP are also an unlikely cause for this difference since only single nucleotide polymorphisms between the Raji and Mutu I strains have been described at this location (Salamon et al. 2000). In our current study we show that primary initiation events are frequently detected by SMARD near oriP in Mutu I but not in Raji episomes. This could be explained, in part, by the decreased frequency of utilization of this site in the Raji strains. However, this is unlikely to be the only reason. Even infrequent primary initiation events occurring at oriP would produce replication forks that pause in the leftward orientation within interval 8 (as seen in Mutu I), but none of the Raji EBV episomes showed forks paused in this orientation (compare Figure 6D with Figure 3D). Therefore, an alternative explanation is that in Raji episomes the activation of oriP is delayed compared to initiation sites located in the RRF. This would cause an increase in the passive replication of this genomic region, explaining the reduced frequency of activation detected by 2D gel electrophoresis. In this context, the residual activity of oriP in Raji could represent secondary initiation events that take place in proximity to replication forks that originated in the RRF and paused near oriP. These events would produce small red patches, such as the one marked by an asterisk in molecule 35 of Figure 3, that are too close to the paused forks to be unequivocally identified by SMARD as separate initiation events. We conclude that oriP is one of the initiation sites preferentially utilized to begin the duplication of the Mutu I episomes, while in Raji only secondary initiation events usually occur at this site. Various groups have suggested that different cellular proteins could participate in regulating the activity of oriP (Dhar et al. 2001; Shirakata et al. 2001; Deng et al. 2002). However, it is currently not clear why, in Raji episomes, oriP is not among the preferred initiation sites. Interestingly, it has been reported that oriP is more extensively methylated in Raji than in other EBV strains (Salamon et al. 2000). It is therefore tempting to speculate that epigenetic modifications of the DNA template, or modifications of the chromatin structure, could be responsible for the differences in the order of activation detected in these EBV strains. The epigenetic regulation of oriP activity could be particularly important during the establishment of latent replication, since it has been demonstrated that an epigenetic event is required for the establishment of oriP-dependent replication (Leight and Sugden 2001). Conclusions—Flexible Utilization of Initiation Sites in Higher Eukaryotes In this study we have shown that, while the basic features of DNA replication are conserved (i.e., the progression of the replication forks), the activity of the initiation sites (order and frequency of activation) varies significantly in different EBV strains and across different portions of the EBV genome. Importantly, using SMARD we are now beginning to detect similar modifications in the utilization of initiation sites across transcriptionally active chromosomal loci of the mouse genome (data not shown). Additional mechanisms could regulate DNA replication at transcriptionally silent loci, as suggested by the complete absence of initiation events throughout an approximately 450-kb portion of the mouse IgH locus in non–B cell lines (Zhou et al. 2002; data not shown). These results are compatible with the flexible utilization of initiation sites also suggested by other laboratories (Kalejta et al. 1998; Lunyak et al. 2002). It is therefore likely that the large redundancy in initiation site usage and the regulation of initiation site activity at the level of genomic regions represent common features of DNA replication in mammalian cells. In particular, our results suggest that long-range changes in chromatin structure or chromosomal organization could be far more important than local modifications at individual initiation sites in regulating DNA replication. This could represent an efficient way for eukaryotic cells to control the replication of their very large genomes, and could have broad implications for the maintenance of genomic stability. By using SMARD on primary cells, we will soon be able to determine if similar dynamics are also present in nontransformed mammalian cells. Methods Cell cultures, EBV strains, and double-labeling of replicating DNA Raji cells were grown in exponential phase (as described in Little and Schildkraut 1995), keeping the cell density between 3 × 105 and 8 × 105 cells/ml. The experiments presented in this manuscript were performed at approximately 5 × 105 Raji cells/ml, using two labeling periods (240 min each) with 25 μM IdU (first label) and 25 μM CldU (second label). IdU was added directly to the growing culture, followed by low-speed centrifugation of the cells at the end of the first labeling period and resuspension in warm medium containing CldU. Early passages of the Mutu I cells (clone c179 p44; Gregory et al. 1990) were provided by Alan B. Rickinson and grown for only seven additional passages (keeping the cell density between 4 × 105 and 8 × 105 cells/ml) before the replicating DNA was labeled. The conditions used for growth and labeling were the same as those used for Raji cells, with the exception that the labeling periods were only 210 min each. This Mutu I cell line is characterized by the presence of a small fraction of cells in which EBV replicates lytically, producing molecules linearized at the terminal repeats. However, this did not affect our analysis of the latently replicating episomes because only the DNA molecules that were circular before the digestion with PacI were recovered from the agarose gels and analyzed by SMARD. Only ten EBV genes (out of about 100) can be expressed during latency (six EBNAs; two latent membrane proteins [LMPs]; two nontranslated Epstein Barr encoded RNAs [EBERs]; reviewed in Kieff 1996). In EBV-associated diseases, where the viral genome is maintained as a circular episome, the phenotype of the infected cell influences the viral patterns of expression (Babcock et al. 2000). Three different latent transcription patterns have been described (Kerr et al. 1992): type I (only EBNA1 and EBERs expressed), type II (only EBNA1, the LMPs, and the EBERs expressed), and type III (all the EBNAs, the LMPs, and the EBERs expressed). Although both Raji and Mutu I are human Burkitt's lymphoma cell lines, their transcription profiles are different. The Mutu I cell line used in this study was an early passage of a type I clone isolated in the Alan B. Rickinson laboratory. Raji cells, instead, have a type III-like transcription pattern and they also carry a deletion of the EBNA3C gene (Polack et al. 1984). Improved method to stretch a large number of EBV molecules on individual slides In order to collect a sufficient number of images of the EBV genome, the population of replicated episomes needed to be enriched by a partial purification using pulsed field gel electrophoresis. However, starting from the limited amount of DNA that can be purified from a pulsed field gel, we could not stretch a sufficient number of DNA molecules on microscope slides by molecular combing (Bensimon et al. 1994). As a consequence, the collection of several hundred images of EBV episomes would have required the analysis of a very large number of microscope slides and the use of large amounts of pulsed field–purified DNA. In order to solve this problem, we decided to stretch the DNA molecules using a modification of the method originally introduced for the optical mapping of restriction sites on individual DNA molecules (Aston et al. 1999 and references therein) as well as for other applications (Henegariu et al. 2001). The stretching was achieved by the movement of a DNA solution (a few microliters) gently deposited at the interface between a silanized microscope slide and a nonsilanized coverslip. In this way it was possible to complete our analysis using just few microscope slides and a fraction of the pulsed field–purified DNA derived from the digestion of 106 cells. The molecules stretched by capillary action vary in their orientation (see Figure 1) and in their size (see Figure 2A). Nevertheless, the EBV molecules were clearly identified by the two hybridization signals. These images were aligned with the map of the EBV genome by computer adjustment of the image size for the entire DNA molecule (see Figures 2B and 2C), as we did previously when EBV episomes were stretched by molecular combing (Norio and Schildkraut 2001). Hybridization, probe detection, and immunostaining of the individual DNA molecules stretched on microscope slides Hybridization was performed as previously described (Parra and Windle 1993) using probes prepared by nick translation in the presence of biotin-16-dUTP (Roche, Basel, Switzerland). The probes used in this study, pSalF, p107.5, and pWW (provided by John L. Yates), were detected using a modification of the DIRVISH procedure (Heng et al. 1995). Briefly, five layers of Alexa Fluor 350 conjugated NeutrAvidin (Molecular Probes, Eugene, Oregon, United States) and biotinylated anti-avidin antibodies (Vector Laboratories, Burlingame, California, United States) were deposited on the microscope slide, washing with PBS, 0.03% Igepal CA-630 (Sigma, St. Louis, Missouri, United States) after each step. The purpose of the hybridization signals is to identify and orient the EBV episomes. Since the DNA molecules studied by SMARD are substituted along their entire length with halogenated nucleotides, they are very easy to detect even in presence of substantial hybridization background (i.e., blue dots in the lowest panel of Figure 1). This hybridization background does not affect SMARD, therefore it was digitally removed from the images of the molecules shown in Figures 3B, 4B, and 6B (as described in other studies performed on stretched DNA molecules; Pasero et al. 2002). Immunostaining to detect IdU and CldU was performed simultaneously with the detection of the biotinylated DNA probes. Mouse anti-IdU (Becton-Dickinson, Palo Alto, California, United States) and rat anti-CldU (Accurate Chemical, Westbury, New York, United States) were used as primary antibodies (monoclonal), while Alexa Fluor 568-conjugated goat anti-mouse (Molecular Probes) and Alexa Fluor 488-conjugated goat anti-rat (Molecular Probes) were used as secondary antibodies. The immunostaining has almost no background. As described previously (Norio and Schildkraut 2001), the specificity of the immunostaining was tested on DNA fully substituted with IdU or with CldU. No cross-reaction of the antibodies was detected using the detection procedure utilized in this study, and both antibodies were unable to recognize the unlabeled DNA. In practice, the background visible in the red and green channels is mainly represented by other DNA molecules containing halogenated nucleotides (white horizontal arrowheads in the central panels of Figure 1). These molecules can be fully or partially stretched (sometimes collapsed or broken in pieces), but are usually clearly distinguishable from the unbroken, fully substituted EBV molecules. By using appropriate dilutions of the DNA sample during stretching, we minimized the overlap of different molecules. Advantages in the labeling scheme utilized for SMARD and internal controls Studies performed by fiber autoradiography have previously shown that the results obtained using DNA fibers (such as the average size of the replicons) are significantly affected by the length of the labeling period utilized to label the replicating DNA (reviewed in Berezney et al. 2000). In these studies bias could also be introduced during the collection of the data as a result of the criteria utilized by the experimenter in the choice of the images analyzed. In addition, if synchronized cells are considered, the length of the labeling period also defines the potential resolution at which initiation sites can be mapped, and the estimate of the replication fork speed. Replacing the radioactive detection of the labels with fluorescence microscopy does not solve any of these problems, nor does the statistical analysis of the data. These problems are completely eliminated by the labeling scheme that characterizes SMARD (Norio and Schildkraut 2001). For our experiments we utilized exponentially growing cells and labeling periods that are longer than the time required to fully replicate the genomic region of interest. In practice, since the replication of a specific genomic region can proceed differently in various DNA molecules, we utilize labeling periods that are sufficiently long to insure the duplication of even the slowest replicating molecules. In addition, only the molecules completely replicated during these labeling periods are examined. By studying this particular population of molecules, we introduce an objective criterion in the collection of the data, eliminating possible biases. Therefore, the molecules replicated during these labeling periods will faithfully represent the distribution of the replication forks in the steady-state population of replicating molecules (Norio and Schildkraut 2001). Using long labeling periods, and limiting our analysis to the molecules entirely substituted with the halogenated nucleotides, also provides multiple internal controls. These controls cannot be performed when short labeling periods are used. Since the molecules are immunostained throughout their length, their images can be easily aligned to the map of the genomic region analyzed. This allows us to detect the presence of unevenly stretched molecules that can therefore be discarded. In addition, since the immunostaining is visible along the entire length of the molecules, the loss of signal caused by the breakage of some molecules is immediately revealed. The complete substitution of the DNA molecule with halogenated nucleotides also allowed us to easily detect overlaps between different DNA molecules. These overlaps can occur relatively frequently during the stretching of the molecules and their frequency increases as the density and the size of the DNA molecules increases. It is also worth noting that the presence of hybridization probes decreases the intensity of the immunostaining along the corresponding portion of the DNA molecules (see Figure 1). This causes a significant loss of information along the hybridized regions, but it also represents an additional control indicating that the immunostaining is indeed present on the DNA molecules that we intend to study (rather than on adventitiously overlapping molecules). Finally, our labeling scheme allows us to insure that the replication proceeded normally during the labeling of the replicated DNA and that no bias was introduced during the collection of the images. In fact, when these conditions are satisfied, the number of molecules fully substituted with IdU is expected to be very similar to the number of molecules fully substituted with CldU. These controls represent a strong proof that the images of the molecules are representative of a steady-state population of replicating molecules. Analysis of the replication intermediates by 2D gel electrophoresis at neutral pH The procedures for the enrichment of replication intermediates, 2D gel electrophoresis, and Southern analysis were essentially as described previously (Little and Schildkraut 1994; Norio et al. 2000). Preparations of replication intermediates from Raji cells were digested with different restriction enzymes depending on the fragment analyzed: EcoRI/DraI for fragments a–e, EcoRI/EcoRV for fragments f and I, EcoRI/HindIII for fragment g, and EcoRI/XbaI for fragment h. The positions (EBV strain B95–8 coordinates) of the restriction fragments analyzed by 2D gel electrophoresis were as follows: fragment a, DraI (79202)–EcoRI (82920); fragment b, EcoRI (82920)–DraI (88865); fragment c, DraI (88865)–EcoRI (91421); fragment d, EcoRI (93162)–EcoRI (95239); fragment e, EcoRI (95239)–DraI (103226); fragment f, EcoRV (100583)–EcoRV (116863); fragment g, HindIII (110942)–EcoRI (125316); fragment h, XbaI (161383)–EcoRI (1); and fragment I, EcoRV (126415)–EcoRI (137221). The probes used to detect the restriction fragments were as follows: pHindLHI for fragments a–c, pHindE for fragments d–e, pSalF for fragment f, the pHindC fragment XbaI (121146)–BglII (120341) for fragment g, the p107.5 fragment XhoI (169423)–XhoI (167487) for fragment h, and the pHindC fragment HpaI (131959)–XbaI (133151) for fragment i. The plasmids pHindLHI, pHindE, pSalF, pHindC, and p107.5 were kindly provided by John L. Yates. Two different preparations of replication intermediates were used to study the replication patterns of fragments a–c. We thank: Alan B. Rickinson for providing early passages of the Mutu I cell clone. We also thank S.M. Shenoy and the AECOM analytical imaging facility for technical support, and Sharon Thomas for critical reading of the manuscript. We thank Joel Huberman and David Gilbert for very helpful suggestions. This work was supported by National Institutes of Health grant GM45751. Support was also provided by Cancer Center Support Grant NIH/NCI P30CA13330. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. PN and CLS conceived and designed the experiments. PN performed the experiments, analyzed the data, and designed the analysis. PN and CLS wrote the paper. Academic Editor: Bill Sugden, University of Wisconsin Abbreviations 2Dtwo-dimensional CldU5′-chloro-2′-deoxyuridine DSdyad symmetry EBEREpstein Barr encoded RNA EBNAEpstein Barr nuclear antigen EBVEpstein-Barr virus FRfamily of repeats IdU5′-iodo-2′-deoxyuridine LMPlatent membrane protein RRFregion that usually replicates first RRLregion that usually replicates last Sdduplication speed SMARDsingle molecule analysis of replicated DNA Tdduplication time ==== Refs References Anglana M Apiou F Bensimon A Debatisse M Dynamics of DNA replication in mammalian somatic cells: Nucleotide pool modulates origin choice and interorigin spacing Cell 2003 114 385 394 12914702 Aston C Hiort C Schwartz DC Optical mapping: An approach for fine mapping Method Enzymol 1999 303 55 73 Babcock GJ Hochberg D Thorley-Lawson DA The expression pattern of Epstein-Barr virus latent genes in vivo is dependent upon the differentiation stage of the infected B cell Immunity 2000 13 497 506 11070168 Bensimon A Simon A Chiffaudel A Croquette V Heslot F Alignment and sensitive detection of DNA by a moving interface Science 1994 265 2096 2098 7522347 Berezney R Dubey DD Huberman JA Heterogeneity of eukaryotic replicons, replicon clusters, and replication foci Chromosoma 2000 108 471 484 10794569 Brewer BJ When polymerases collide: Replication and the transcriptional organization of the E. coli chromosome Cell 1988 53 679 686 3286014 Chaudhuri B Xu H Todorov I Dutta A Yates JL Human DNA replication initiation factors, ORC and MCM, associate with oriP of Epstein-Barr virus Proc Natl Acad Sci U S A 2001 98 10085 11009 11517328 Deng Z Lezina L Chen CJ Shtivelb S So W Telomeric proteins regulate episomal maintenance of Epstein-Barr virus origin of plasmid replication Mol Cell 2002 9 493 503 11931758 Denison SR Wang F Becker NA Schule B Kock N Alterations in the common fragile site gene Parkin in ovarian and other cancers Oncogene 2003 22 8370 8378 14614460 DePamphilis ML Replication origins in metazoan chromosomes: Fact or fiction? 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ML Epstein-Barr virus DNA replication DNA replication in eukaryotic cells 1996 Plainview (New York) Cold Spring Harbor Laboratory Press 751 773 Yates J Warren N Reisman D Sugden B A cis-acting element from the Epstein-Barr viral genome that permits stable replication of recombinant plasmids in latently infected cells Proc Natl Acad Sci U S A 1984 81 3806 3810 6328526 Zhou J Ashouian N Delepine M Matsuda F Chevillard C The origin of a developmentally regulated Igh replicon is located near the border of regulatory domains for Igh replication and expression Proc Natl Acad Sci U S A 2002 99 13693 13698 12370427

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10.1371/journal.pbio.0020152

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020153Research ArticleCell BiologyImmunologyMolecular Biology/Structural BiologyAnimalsA Specific Interface between Integrin Transmembrane Helices and Affinity for Ligand Integrin Transmembrane Domain InterfaceLuo Bing-Hao 1 Springer Timothy A springeroffice@cbr.med.harvard.edu 1 Takagi Junichi 1 ¤11Center for Blood Research (CBR) Institute for Biomedical Research and Department of Pathology, Harvard Medical SchoolBoston, MassachusettsUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e1536 1 2004 17 3 2004 Copyright: © 2004 Luo et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Information Transport across a Membrane Integrin Bidirectional Signaling: A Molecular View Conformational communication across the plasma membrane between the extracellular and intracellular domains of integrins is beginning to be defined by structural work on both domains. However, the role of the α and β subunit transmembrane domains and the nature of signal transmission through these domains have been elusive. Disulfide bond scanning of the exofacial portions of the integrin αIIβ and β3 transmembrane domains reveals a specific heterodimerization interface in the resting receptor. This interface is lost rather than rearranged upon activation of the receptor by cytoplasmic mutations of the α subunit that mimic physiologic inside-out activation, demonstrating a link between activation of the extracellular domain and lateral separation of transmembrane helices. Introduction of disulfide bridges to prevent or reverse separation abolishes the activating effect of cytoplasmic mutations, confirming transmembrane domain separation but not hinging or piston-like motions as the mechanism of transmembrane signaling by integrins. Integrin receptors mediate cell-matrix interactions by altering the conformation of their intra- and extra- cellular domains, a process mediated by lateral separation of the transmembrane helices ==== Body Introduction Integrins are major metazoan cell adhesion receptors that have the distinctive property of transducing signals across the plasma membrane in both directions. Intracellular binding of cytoskeletal components to integrin cytoplasmic domains activates the ligand binding competency of the extracellular domain (inside-out signaling). Furthermore, ligand binding to integrin extracellular domains is coupled to alterations in cytoplasmic domains that are linked to downstream signaling (outside-in signaling). The three-dimensional architecture of integrin extracellular domains as well as their rearrangement in activation have been revealed by crystal, nuclear magnetic resonance (NMR), and electron microscopic methods (Xiong et al. 2001, 2002; Adair and Yeager 2002; Beglova et al. 2002; Takagi et al. 2002, 2003). NMR structures of integrin α and β subunit cytoplasmic tails (Vinogradova et al. 2000, 2002; Ulmer et al. 2001; Weljie et al. 2002) and a crystal structure of the β subunit tail in complex with the cytoskeletal protein talin (Garcia-Alvarez et al. 2003) yield structural insights. It is generally accepted that an intersubunit association at the cytoplasmic domain maintains integrins in the low-affinity state (Hughes et al. 1996); however, specific heterodimeric interaction between the isolated cytoplasmic domains in solution is sometimes not observed (R. Li et al. 2001; Ulmer et al. 2001), and when observed the reported structures differ (Vinogradova et al. 2002; Weljie et al. 2002). The dynamic nature of cytoplasmic intersubunit association was revealed using live cell imaging (Kim et al. 2003), which demonstrated upon integrin activation a decrease in fluorescent resonance energy transfer between yellow fluorescent protein and cyan fluorescent protein tags fused to the C-termini of the integrin α and β subunit cytoplasmic domains. This finding demonstrated separation of the cytoplasmic domains; however, whether signal transmission through integrin transmembrane (TM) domains involves hinging or pistoning motions or lateral separation in the plane of the membrane has yet to be definitively established (Hughes et al. 1996; Lu et al. 2001; Takagi et al. 2001, 2002; Gottschalk et al. 2002). Thus far, there are no experimental data on how the two integrin TM segments associate. NMR chemical shift data on the integrin β3 subunit TM-cytoplasmic domain fragment in dodecylphosphocholine micelles predict that the TM segment comprising residues Ile693 to Ile720 is largely α-helical (R. Li et al. 2002). Close apposition of the C-termini of the αV and β3 extracellular domains in the crystal structure (Xiong et al. 2001) as well as specific interactions between α and β cytoplasmic tails (Vinogradova et al. 2002; Weljie et al. 2002) and cryoelectron microscopy of intact integrin αIIbβ3 (Adair and Yeager 2002) suggest that the two TM segments are associated with each other as two interacting α helices, at least in the low-affinity state to which the crystal structure has been shown to correspond (Takagi et al. 2002). However, heterodimeric association between integrin α and β subunit fragments containing the TM and cytoplasmic domains has thus far not been detected in either detergent micelles (R. Li et al. 2001) or lipid bilayers, and association between the TM domains has never been demonstrated in intact cells. Since glycophorin A TM domains dimerize in lipid and detergent micelles (Lemmon et al. 1992) under conditions similar to those under which integrin TM domains fail to heterodimerize, it has been proposed that the interaction between the integrin TM domains is less stable (Gottschalk et al. 2002). Recently, R. Li et al. (2003) reported that both the integrin α and β subunits' TM helices have the potential to undergo homomeric rather than heteromeric interactions, and that stabilization of homooligomerization of integrin TM segments results in integrin activation. Li et al. hypothesize that the homomeric associations between TM segments provide a driving force for integrin activation. Experimental data on the association between integrin TM domains in intact cells are clearly required to decide between the many different models for how conformational signals are transmitted through the membrane in integrins. Here we present extensive experimental evidence using cysteine mutagenesis and disulfide bond formation that integrin α and β TM segments associate with each other with a specific spatial orientation in the resting state. Mutations in the α subunit cytoplasmic tail known to universally activate integrins disrupt the heterodimeric TM domain interaction, but do not result in homomeric interaction. The effects of activating mutations are reversed by disulfide bond formation between α and β subunit TM domains. The results suggest that lateral separation of TM segments is responsible for the initial conversion to the high-affinity receptor. Results Structure of the TM Domain of Integrin αIIbβ3 in the Resting State Cysteine scanning of integrin TM domains Inspection of the primary sequences of integrin subunits readily identifies putative TM segments of approximately 23 hydrophobic amino acids, as widely reported in the literature and as confirmed using a hidden Markov model approach (TMHMM version 2.0) (Krogh et al. 2001) (Figure 1). However, Armulik et al. (1999) experimentally determined the C-terminal boundary of both TM domains in microsomal membranes by introducing N-glycosylation sites at varying positions relative to the membrane, and found that the TM domains extend five or six residues more C-terminally and include a five-residue Lys-Val-Gly-Phe-Phe (KVGFF) sequence in α and a six-residue Lys-Leu-Leu-Ile-Thr-Ile (KLLITI) sequence in β (Figure 1). Figure 1 Sequences of the αIIb and β3 TM Regions Segments predicted as TM by TMHMM version 2.0 (Krogh et al. 2001) are boxed. The more C-terminal KVGFF sequence in αIIb and KLLITI sequence in β3 are additionally predicted to be in the membrane by Armulik et al. (1999). Charged residues involved in intersub-unit salt bridges (dotted lines) in the NMR cytoplasmic domain structure (Vinogradova et al. 2002) are marked with ovals. Residues used for cysteine scanning in this study are indicated by heavy dots. Arrows show the boundary between residues forming disulfide bonds constitutively and after oxidation. The se-quences of the αIIb* GFFKR truncation and αIIb" GFFKR/GAAKR mutants are also shown. In order to deduce the three-dimensional organization of the integrin TM domains, we utilized cysteine-scanning mutagenesis (Lee et al. 1995). Cysteine mutations were sequentially introduced at Pro965 to Leu974 of αIIb and Pro691 to Gly702 of β3 (Figure 1) to give ten different αIIb and 12 different β3 mutants, each containing a single cysteine residue. Mutant αIIb and β3 chains were then cotransfected into 293T cells, biosynthetically labeled with [35S]-methionine and cysteine, and chased for 17 h with medium containing 500 μg/ml of cysteine and 100 μg/ml of methionine. Detergent cell extracts were immunoprecipitated with a monoclonal antibody (mAb) specific to the αIIbβ3 complex and subjected to sodium dodecyl sulphate polyacrylamide gel elecrophoresis (SDS-PAGE). Because of the extensive chase, only mature, cell-surface αIIbβ3 with complex N-linked glycan was isolated, which can readily be distinguished from the lower Mr αIIb and β3 precursors with high mannose N-linked glycans (data not shown). When the two cysteines on the α and β subunits are spatially close and are oxidized during biosynthesis, they form a disulfide bridge that can be detected by the appearance of a covalently attached αβ heterodimer band in nonreducing SDS-PAGE with a concomitant decrease in the intensity of α and β monomer bands (e.g., Figure 2A, lane 5 compared to 1). Cysteines located near one another in the extracellular environment or in the membrane near the extracellular surface form disulfide bonds during the normal course of protein biosynthesis and processing. However, cysteines located more deeply in the membrane form disulfides much more efficiently when cells are treated with an oxidation catalyst such as Cu(II)-(o-phenanthroline)3 (Cu-phenanthroline) (e.g., Figure 2A, lane 8 compared to 7). Wild-type αIIb and β3 subunits do not contain any cysteine residues in their TM domains and appear as 135- and 105-kDa bands, respectively, even after oxidation with Cu-phenanthroline (Figure 2A, lanes 1 and 2). Figure 2 Formation of Intersubunit Disulfide Bonds in the TM Domain of Resting αIIbβ3 (A) 293T cells were transiently transfected with the indicated integrin constructs and metabolically labeled, and were untreated (–) or oxidized with Cu-phenanthroline on ice for 10 min (+), and then lysates were immunoprecipitated with mouse mAb 10E5 against αIIbβ3, followed by SDS-7.5% PAGE under nonreducing conditions and fluorography. Positions of molecular size markers are shown on the left. (B) Disulfide bond formation efficiency. For each residue pair, the radioactivity of the αβ heterodimer band divided by the total radioactivity (sum of α, β, and αβ bands) was used to calculate the disulfide bond formation efficiency and is depicted by a gray scale (white for 0% to black for 100% efficiency). The upper and lower halves of the circle indicate the efficiency before (constitutive) and after (oxidized) Cu-phenanthroline treatment at 0 °C, respectively. Residue pairs that form inducible disulfides (i.e., efficiency increases more than 10% after oxidation) are denoted by asterisks. Results are the mean of at least two independent experiments. Solid line shows the predicted TM boundary; dotted line indicates boundary between residues that form constitutive and inducible disulfide bonds. (C) Relative orientation of the αIIb and β3 TM helices near their N-terminal ends. The TM domains are depicted schematically as α helices, and experimental results from cysteine scanning were used to deduce their relative orientation. The resultant schematic model is shown in both top and side views. Residue pairs that form disulfide bonds at greater than 50% efficiency are connected by solid (constitutive disulfides) or dotted (inducible disulfides) red lines. The gray dotted line represents the boundary between residues that form constitutive and inducible disulfide bonds. Residues are color coded based on the number of constitutive or inducible disulfide bonds formed at greater than 50% efficiency: multiple bonds (interacting residues, red), only one bond (peripheral residues, pink), and no bonds (outside residues, blue). (D) Homodimer formation by the W967C mutant of αIIb. Transfection, radiolabeling, and immunoprecipitation was performed as in (A). Full-length αIIb with the W967C mutation (α-W967C) but not the truncated active mutant αIIb with W697C (α*-W967C) produced a homodimer band (α–α) larger than the heterodimer band (α–β). The α972C/βL697C combination that produces efficient inducible heterodimer is shown as a standard (lanes 1 and 2). We tested all possible combinations between the ten αIIb cysteine mutants (965C to 974C) and the 12 β3 cysteine mutants (691C to 702C), i.e., a total of 120 different cysteine pairs. Transient transfection in 293T cells and CHO cells gave similar results. The disulfide bonding efficiency of all of these pairs is graphically summarized in Figure 2B. All can be classified into three groups based on their ability to form disulfide-linked αβ heterodimers. The first group includes 17 pairs that constitutively formed disulfides with moderate (20%) to high (100%) efficiency and showed no increase in disulfide-bonded heterodimers upon Cu-phenanthroline treatment (Figure 2B, light to dark gray in both the upper and lower halves of individual circles). For example, cysteine pair α-P965C/β-I693C formed a disulfide-bonded dimer with an apparent molecular weight of 200 kDa at greater than 95% efficiency even without Cu-phenanthroline treatment (Figure 2A, lanes 5 and 6), suggesting that these two residues are in close proximity to each other near the exofacial membrane surface. The second group includes 13 pairs (Figure 2B, asterisk) that formed disulfides that increased in efficiency by 10% or more upon treatment with oxidant. For example, the efficiency of disulfide formation by the α-V971C/β-L697C pair was about 5% in the absence of oxidant (Figure 2A, lane 7) and about 70% after treatment of cells with Cu-phenanthroline at 0 °C for 10 min (Figure 2A, lane 8). The residues that formed disulfide bonds with increased efficiency after Cu-phenanthroline treatment are located deeper in the plasma membrane. The boundary between positions where disulfide bonds were constitutive and where they were increased by oxidants was between Trp967 and Trp968 in αIIb and Leu694 and Val695 in β3 (dashed line in Figure 2B and solid arrows in Figure 1). The same results were obtained using oxidation with molecular iodine (I2), except that the efficiency of disulfide induction was slightly lower (data not shown). The third group, corresponding to the remaining 90 pairs, showed little or no intersubunit disulfide bond formation even after treatment with oxidant (Figure 2B, white in both upper and lower semicircles, and 2A, lanes 3 and 4). Helical conformation of the TM domain and the interface between two interacting helices The helical portions of the integrin TM domains are predicted to begin with residues αIIb-Ile966 and β3-Ile693 (Krogh et al. 2001), and the latter boundary is also suggested by NMR chemical shift data (R. Li et al. 2002) (solid lines in Figure 2B and the lower portion of 2C). A helical structure for the integrin α and β subunit TM domains was confirmed by formation of disulfide bonds with a helical periodicity in the entire portions of these segments scanned, corresponding to residues 966–974 in αIIb and 693–702 in β3, i.e., approximately three α-helical turns in each (Figure 2B). Thus, αIIb residue 965 constitutively formed disulfides while residue 967 did not, residues 968 and 969 formed constitutive and induced disulfides while 970 did not, and residues 971 and 972 formed induced disulfide bonds. A similar pattern was seen in β3, with the minima in disulfide formation efficiency occurring at residues 695, 698/699, and 701/702. This periodicity and the disulfide bonding pattern shown below demonstrate a helical structure. To determine the approximate orientation between the αIIb and β3 TM helices, the data on disulfide formation were mapped onto a helical wheel representation (Figure 2C, upper portion) and an orthogonal view with the axes of the helices in the plane of the page (Figure 2C, lower portion). Both the overall disulfide-bond-forming efficiency of individual residues and the pattern of disulfide bond formation are consistent with a unique orientation between the two helices in terms of both the faces of the two helices that are apposed (Figure 2C, upper portion) and the relation between the two helices in their axial directions (Figure 2C, lower portion). Furthermore, the axial relationship deduced from this pattern is identical to that obtained by assuming that the boundary between residues that form constitutive and inducible disulfide bonds should be at the same depth in the membrane for both helices (gray dashed line in the lower portion of Figure 2C). Single and double cysteine mutants are in the low-affinity state On both CHO-K1 (Kashiwagi et al. 1999) and 293T transfectants, αIIbβ3 has low affinity for soluble ligand. As shown below, none of the double mutants that formed disulfide bonds bound ligand spontaneously. Furthermore, none of the ten β3 or 12 αIIb single-cysteine mutants studied here showed elevated ligand binding activity (data not shown). Consistent with this, studies on dimerization of the glycophorin A TM domains have shown that cysteine substitutions are on average less disrupting than substitutions with any other hydrophobic residue (Lemmon et al. 1992). We conclude that the α/β TM domain association depicted in Figure 2C is that of the resting (low-affinity) integrin conformation. Formation of tetrameric receptors with the α-W967C mutant When cysteine mutant α-W967C was used, a high-molecular-weight species that migrated more slowly than the heterodimer appeared in nonreducing gel electrophoresis, accompanied by a decrease in the intensity of the αIIb band but not of the β3 band (Figure 2D, lanes 3 and 5 compared to lane 9). Treatment with Cu-phenanthroline did not further increase the intensity of the new band (Figure 2D, lanes 4 and 6). In reducing SDS-PAGE, the high-molecular-weight band disappeared and was converted into monomeric αIIb (data not shown). Furthermore, the same high-molecular-weight band was observed when α-W967C was cotransfected with any of the β3 cysteine mutants (β-V695C and β-L698C are shown as examples in lanes 3–6 in Figure 2D) as well as with wild-type β3 (Figure 2D, lanes 7 and 8) at a similar efficiency of about 80%, confirming that it was an α–α dimer. Furthermore, α–α cross-linking did not affect α–β association, because a stoichiometric amount of β3 was immunoprecipitated (Figure 2D), and the amount of immunoprecipitation by the αβ complex-specific mAb 10E5 was unaffected. Therefore, disulfide linkage through α-W967C results in the formation of a tetramer in which two αIIbβ3 heterodimers are covalently linked through a Cys967–Cys967 disulfide bond to form a (αIIbβ3)2 tetramer. Notably, among the ten α and 12 β cysteine mutants used in this study, only α-W967C formed a homodimeric disulfide bond. This is consistent with the model of α–β TM domain association deduced here (Figure 2C), because residue Trp967 faces outward, away from the interface with β3. Furthermore, constitutive formation of the Cys967–Cys967 disulfide bond is consistent with the location of Trp967 in the exofacial portion of the αIIb TM α helix, where disulfide bonds form constitutively (Figure 2C, lower portion). Disulfide-bonded Receptor Can Be Activated from Outside by Mn2+ and mAb Previous work has shown that substitution of integrin αL and β2 subunit cytoplasmic domains for α helices that form a noncovalently associated α-helical coiled-coil heterodimer stabilizes the low-affinity state and is dominant over intracellular signaling pathways that activate integrins; nonetheless, such constructs can be activated from outside the cell by activating mAb or Mn2+ (Lu et al. 2001). Consistent with this finding, activation of integrin αLβ2 with Mn2+ does not result in separation of the native cytoplasmic domains tagged with fluorescent proteins (Kim et al. 2003). To test whether the covalent disulfide linkage of the integrin α and β subunit TM domains prevents αIIbβ3 from being activated from the outside by mAb and Mn2+, soluble ligand binding was measured. The 293T cell transfectants expressing wild-type αIIbβ3 did not bind soluble fibrinogen in a physiological buffer containing Ca2+ and Mg2+, but high-affinity binding was observed in the presence of Mn2+ and the activating mAb PT25–2 (Figure 3). Two cysteine mutants with approximately 100% constitutive disulfide bond formation, α-P965C/β-I693C and α-W968C/β-I693C (see Figure 2A and 2B), were tested in parallel. Fibrinogen binding by these disulfide-bonded mutants was activated by Mn2+ and PT25–2 mAb indistinguishably from wild type (Figure 3). Similar results were obtained after Cu-phenanthroline–induced disulfide bond formation in mutants with cysteine substitutions deeper in the membrane, α-V971C/β-L697C and α-G972C/β-L697C (data not shown). These data demonstrate that even a covalent clasp at the TM domain cannot maintain integrins in the inactive state if they are activated from outside the cell by mAb and Mn2+. Figure 3 Disulfide-bonded Receptors Can Be Activated from Outside the Cell Transiently transfected 293T cells expressing wild-type (αwt/βwt) or mutant αIIbβ3 heterodimers that form constitutive disulfide bonds (α965C/β693C and α968C/β693C) or are reported elsewhere to be activated (αwt/βG708N) (R. Li et al. 2003) were incubated with FITC-fibrinogen in a physiological buffer (control, white bars) or in the presence of 1 mM Mn2+ and the activating mAb PT25–2 (+Mn/PT25–2, black bars). Binding of FITC-fibrinogen was determined by flow cytometry as the mean fluorescence intensity and normalized by dividing by the mean fluorescence intensity with Cy3-labeled anti-β3 mAb AP3 and multiplying by 100. Separation of TM Helices Upon Integrin Activation from Inside the Cell Is the specific TM helix association defined here disrupted in response to activation from inside the cell? We mimicked physiological inside-out integrin activation by using αIIbβ3 containing a truncation before the Gly-Phe-Phe-Lys-Arg (GFFKR) motif in the αIIb subunit (O'Toole et al. 1994), or a Gly-Ala-Ala-Lys-Arg (GAAKR) sequence in place of the GFFKR sequence (Lu and Springer 1997; Kim et al. 2003) (see Figure 1). When cotransfected with the wild-type β3 subunit, αIIb truncated at Gly991 (denoted α*) formed a heterodimer on the cell membrane and appeared as an approximately 130-kDa band, slightly smaller than wild-type αIIb in nonreducing SDS-PAGE (Figure 4A, lane 2). Transfectants expressing the mutant α*/β receptor bound soluble fibrinogen in the absence of any activation, confirming the activating effect of C-terminal truncation (Figure 4B). Furthermore, the α*/β heterodimers constitutively expressed three independent activation-dependent epitopes called ligand-induced binding sites (LIBSs) in the absence of ligand (Figure 4C, α*/βwt), demonstrating conversion of the extra-cellular domain to the extended conformation (Takagi et al. 2002). Figure 4 Formation of Intersubunit Disulfide Bonds in the TM Domain of αIIb*β3 and Effect on Ligand Binding and LIBS Epitopes (A) Immunoprecipitation. Immunoprecipitation of [35S]-labeled receptors and nonreducing SDS-PAGE and fluorography was as described in Figure 2. (B) FITC-fibrinogen binding. Binding was determined by immunofluorescence as described in Figure 3. (C) LIBS exposure. Three different anti-LIBS mAbs (LIBS6, D3, and AP5) were used to probe the conformational state. mAb binding is expressed as the mean fluorescence intensity in the absence (control, open bars) or presence (+Mn/RGD, black bars) of Mn2+ and RGD peptide. (D) Disulfide bond formation efficiency. Disulfide bond formation in αIIb*β3 heterodimers with the indicated residues mutated to cysteine was determined as described in Figure 2B. Using this active α* mutant, cysteine scanning was performed. As shown in Figure 4D, the results were very different from those obtained with full-length αIIbβ3 in two important respects. (1) No periodicity in disulfide formation was observed (Figure 4D). The only pattern was that the more N-terminal exofacial residues preferentially bonded to more exofacial residues in the other subunit, whereas more buried residues preferentially bonded to more buried residues in the other subunit. The lack of periodicity is highly unlikely to result from a loss of helical secondary structure in such a large portion of the TM domains (S. C. Li and Deber 1993). Furthermore, even in a dodecylphosphocholine detergent environment and in the absence of association with αIIb, this portion of the β3 TM domain retains an α-helical structure as shown by NMR experiments (R. Li et al. 2002). Therefore, the loss of periodicity in disulfide formation suggests that there is no longer a preferred orientation between the α and β subunit TM helices. (2) Oxidant-induced disulfide bond formation at 0 °C was not observed (Figure 4D). As shown below, this is because in the absence of constitutive disulfide bond formation, the TM domains of the α*/β heterodimers are not, or are only transiently, associated with one another in the membrane. We thought it important to confirm these results with an activated integrin that was not truncated and therefore used αIIb with the GFFKR sequence mutated to GAAKR, designated αIIb". A smaller number of cysteine-scanning substitutions were introduced into αIIb", and tested together with the β3 cysteine mutants (Figure 5A). The same two major trends were found as with αIIb*/β3. (1) Just as in αIIb*/β3, in αIIb"/β3, the helical periodicity of disulfide bonding was lost, as evidenced by the results with the β3 scanning mutants β-I693 to β-V700 (Figure 5A). (2) As found with αIIb*/β3 and not with αIIb/β3, none of the αIIb"/β3 mutants showed increased disulfide bond formation when treated with Cu-phenanthroline at 0 °C (Figure 5A and 5B). Figure 5 Formation of Intersubunit TM Disulfide Bonds in GFFKR/GAAKR Mutant αIIb′′β3 Receptors and Effect on Ligand Binding (A) Disulfide bond formation efficiency in αIIb"β3. Disulfide bond formation in αIIb"β3 heterodimers with the indicated residues mutated to cysteine was determined as in Figure 2B. Boxed residue pairs were also subjected to Cu-phenanthroline oxidation at 37 °C in (B and C). (B) Radiolabeled 293T cells expressing the indicated mutant integrins were treated with Cu-phenanthroline at 0 °C or 37 °C, followed by immunoprecipitation with anti-αIIbβ3, SDS-PAGE, and fluorography to probe disulfide bond formation. (C) Efficiency of intramembranous disulfide bond formation in the context of the αIIb"β3 mutant receptor was assessed after Cu-phenanthroline oxidation at 0 °C or 37 °C and expressed as in Figure 2B. (D) FITC-fibrinogen binding. Binding was determined before (–) and after (+) Cu-phenanthroline oxidation at 37 °C and expressed as in Figure 3. It is significant that a number of α*/β and α"/β cysteine-scanning mutants could form disulfide-bonded heterodimers during biosynthesis, but in contrast to α/β, none showed increased disulfide formation after oxidation at 0 °C. During biosynthesis at 37 °C, the membrane is fluid. Disulfide bond formation is catalyzed in the endoplasmic reticulum by disulfide isomerases, and because the redox balance is oxidizing in the endoplasmic reticulum, disulfide bond formation can covalently trap protein complexes that form only transiently. Therefore, a complex that would not be stable energetically by noncovalent interactions alone may nonetheless be stabilized by a covalent disulfide bond. This may particularly be the case for interactions between integrin TM domains, because the noncovalent association between the α and β subunits in the headpiece in the extracellular domain increases the probability of collision between the α and β subunit TM domains. If disulfide formation is the result of a stable noncovalent interaction between TM domains, it should occur at 0 °C when membranes are in a gel phase and proteins do not diffuse, as well as at 37 °C when membranes are liquid-crystalline and proteins diffuse. On the other hand, if disulfide formation is the result of transient interactions that are energetically unfavored, it should occur at 37 °C but not at 0 °C. To confirm the hypothesis that in α*/β and α"/β transient collision between TM helices can result in disulfide formation, Cu-phenanthroline oxidation was performed both at 0 °C and 37 °C. As described above, the α-G972C/β-L697C pair in the context of the wild-type receptor shows greatly increased disulfide bond formation upon oxidation by Cu-phenanthroline at 0 °C (Figure 6A, lane 3 compared to 1). In contrast, the same residue pair in the context of the truncated active mutant, α*-G972C/β-L697C, did not show increased disulfide bond formation after oxidation at 0 °C (Figure 6A, lane 6 compared to 4). When oxidation was performed at 37 °C, however, this intramembranous disulfide bond formed in the context of the truncated α*/β mutant (Figure 6A, lane 5). This strongly supports the hypothesis that association of the TM segments in the α*/β receptor is not energetically favored—and is thus present only in an undetectably small subpopulation of molecules at any one moment—but is a kinetically accessible state in a fluid membrane at 37 °C that can be trapped by disulfide formation. Increased disulfide bond formation by α*/β mutants by oxidation at 37 °C was not due to increased catalysis by Cu-phenanthroline or other nonspecific factors, because in full-length α/β, disulfide linkage induced by Cu-phenanthroline was the same at 37 °C (data not shown) as at 0 °C (see Figure 2B). Oxidation-induced cross-linking at both 0 °C and 37 °C was extended to all other cysteine pairs in the context of the α*/β mutant (Figure 6B). Nine of them showed significant enhancement in cross-linking at 37 °C compared to 0 °C (Figure 6B), whereas none of the same pairs in full-length αβ showed enhanced cross-linking at 37 °C compared to 0 °C (Figure 6A, lanes 1–3, and data not shown). Figure 6 Formation of an Intersubunit Disulfide Bridge within the Membrane Reverses the Active Phenotype of the αIIb*β3 Receptor (A) Radiolabeled 293T cells expressing the indicated mutant integrins were treated with Cu-phenanthroline at 0 °C or 37 °C, followed by immunoprecipitation with anti-αIIbβ3, SDS-PAGE, and fluorography to probe disulfide bond formation. (B) Efficiency of intramembranous disulfide bond formation in the context of the truncated αIIb*β3 receptor was assessed after Cu-phenanthroline oxidation at 0 °C or 37 °C and expressed as in Figure 2B. (C) Ligand binding by wild-type or mutant αIIb*β3 expressed on 293T cells was determined before (–) and after (+) Cu-phenanthroline oxidation at 37 °C and expressed as in Figure 3. The above results were confirmed with the full-length α"/β receptor containing the Phe-Phe/Ala-Ala substitution (see Figure 5B and 5C). Thus, Cu-phenanthroline did not increase disulfide bond formation between buried residues at 0 °C (Figure 5A and 5B), but it markedly increased disulfide bonding at 37 °C (Figure 5B and 5C). Taken together, the above results demonstrate that (1) integrin α and β subunit TM helices separate from one another upon activation from inside the cell, (2) transient association between TM helices in activated receptors can be trapped either by disulfide bond formation during biosynthesis or by Cu-phenanthroline oxidation at 37 °C, and (3) in activated receptors the specific pattern of association between the TM helices seen in the resting state is not present. A further important finding was that none of the cysteine mutants, including the W967C mutant of αIIb which mediated α–α homodimerization in the wild-type receptor, underwent α–α homodimerization in the context of the activated α*/β receptor (see Figure 2D, lanes 11–14). In contrast, the same cysteine combinations formed α–α homodimers in the context of the full-length αβ receptor without the activating mutation (Figure 2D, lanes 3–8). This result is inconsistent with the notion that homooligomerization of TM domains occurs concomitantly with separation of the α and β subunit TM domains and represents the major mechanism for inside-out activation of integrins. TM Helix Separation Is Responsible for Activation of Integrins from within the Cell As described above, integrins with disulfide-linked TM domains can be activated from the outside by Mn2+ and mAb; however, we now demonstrate that such a linkage prevents activation from the inside. We first examined the activation state of receptors with activating α* or α" mutations that constitutively form disulfide bonds during biosynthesis. When α*-W968C was coexpressed with β-I693C, nearly 100% formation of the intersubunit disulfide linkage was observed (see Figure 4A, lane 4). This cross-linked receptor showed low basal ligand binding activity, like the wild-type receptor (Figure 4B). By contrast, α*-W968C/βwt (Figure 4B) and α*/β-I693C (data not shown), which did not form cross-links, were basally active (Figure 4B). The disulfide cross-link had no deleterious effect on ligand binding itself because, as already mentioned above, α*-W968C/β-I693C bound ligand upon activation by Mn2+ and activating mAb (Figure 4B). Furthermore, in α*-W968C/β-I693C but not in α*-W968C/βwt or α*/β-I693C (data not shown), the exposure of activation-dependent epitopes was reduced to the level of the wild-type receptor (Figure 4C). This result suggests that the shift from the bent to the extended conformation induced by the activating α* mutation was reversed by the TM disulfide bond. The same reversal of basal ligand binding, but not Mn2+/PT25–2-activated ligand binding, was found for all constitutively disulfide-bonded α*/β pairs that we examined. These included α*-I966C/β-I693C, α*-I966C/β-L694C, α*-I966C/β-V965C, α*-W968C/β-L694C, α*-W968C/β-V695C, α*-W968C/β-V696C, and α*-W968C/β-L697C (data not shown). The same reversal of basal ligand binding was also found for all constitutively disulfide-bonded α"/β pairs examined, including α"-I966C paired with either β-I693C, β-L694C, β-V695C, or β-V696C (see Figure 5D). Therefore, a wide range of distinct intersubunit cross-links in the outer leaflet of the membrane reverse, and are hence dominant over, activating mutations in the α subunit at the boundary between the membrane and the cytoplasm. Could a receptor that was already present at the cell surface and active in ligand binding be converted to an inactive receptor by introduction of a disulfide bond between the α and β subunit TM domains? We were able to answer this question by using the more buried pairs of cysteine residues that formed disulfide bonds upon oxidation catalyzed by Cu-phenanthroline. In α*/β we studied the α*-G972C/β-L697C pair, which shows greatly enhanced disulfide bond formation after treatment with Cu-phenanthroline at 37 °C (see Figure 6A and 6B). Under basal conditions, the α*-G972/β-L697C mutant actively binds fibrinogen (Figure 6C). However, after Cu-phenanthroline treatment at 37 °C, basal ligand binding was almost completely lost, but ligand binding activatable by Mn2+/PT25–2 mAb was still present (Figure 6C). Cu-phenanthroline treatment at 37 °C was not toxic for basal ligand binding, because the same treatment did not reverse basal ligand binding by α*-G972C/βwt (Figure 6C) or α*/βwt (data not shown). These results were extended to the α"/β mutant using a different pair of cysteines in the α"-V971C/β-L697C mutant that shows Cu-phenanthroline-induced disulfide bond formation at 37 °C (see Figure 5B and 5C). Cu-phenanthroline treatment at 37 °C almost completely reversed the elevated basal ligand binding by α"-V971C/β-L697C, but had no effect on α"-V971C/βwt (Figure 5D). We conclude (1) that at 37 °C, the α*-G972C/β-L697C and α"-V971C/β-L697C heterodimers are predominantly in an active conformation with separated TM domains but equilibrate with a conformation in which the TM domains are transiently associated, and (2) that when association of the TM domains is trapped by disulfide bond formation, the ligand binding site in the extracellular domain returns to the low-affinity state. Discussion We have obtained for the first time structural information regarding the helix–helix interface between integrin α and β subunit TM domains in the membrane bilayer, and demonstrate that dissociation at this interface occurs upon changes at the cytoplasmic face of the plasma membrane bilayer that activate integrins. Extensive mutagenic cysteine cross-linking experiments revealed the presence of a specific α/β TM helix contact in a resting integrin heterodimer, which is lost upon receptor activation from inside the cell. The data establish the approximate orientation between the integrin TM α helices in the outer leaflet of the membrane bilayer in the resting, low-affinity integrin conformation (see Figure 2C). The mode of association experimentally determined here may be compared to that suggested by computational models (Gottschalk et al. 2002). For comparisons, we used our cross-linking data to construct a model by selecting an alignment to the glycophorin A TM homodimer NMR structure (Mac-Kenzie et al. 1997) that minimized the distances between residues with more than 80% cross-linking efficiency (see Materials and Methods). The overall orientation in our model is not too dissimilar from that of a model for the resting conformation of the αIIbβ3 TM domains (Gottschalk et al. 2002), but our model fits the data better, with a root mean square distance for three Cβ–Cβ and two Cβ–Gly Cα atom distances of 4.8 Å, compared to 8.9 Å for the computational model. Furthermore, our cross-linking data on the activated receptor are completely incompatible with a model for the activated TM domain interface (Gottschalk et al. 2002) because the cross-linked regions in αIIb and β3 are close together in this model, yet a specific pattern of cross-linking predicted by the model was not observed. Integrin TM domain homodimerization and heterodimerization has been assayed using a qualitative assay of induction of β-galactosidase in Escherichia coli (Schneider and Engelman 2003). However, the chimeras that were assayed contain truncated integrin TM domains with only 17 residues of the α and β subunit TM domains, lack the GFFKR motif demonstrated here to be required for physiologic TM domain association, and insert as type II rather than type I membrane proteins. These assays were designed to test the hypothesis that the Gly-Val-Met-Ala-Gly (GVMAG) homodimerization motif in glycophorin is equivalent to G972/VLGG in αIIb and S699/VMGA in β3 (Schneider and Engelman 2003). However, the use of the glycophorin template (MacKenzie et al. 1997) to fit our experimental data demonstrates that the GVMAG dimerization interface is more equivalent to αIIb-W968/VLVG and β3-V696/LLSV (see Materials and Methods). Our cysteine cross-linking data not only define the nature of the interface between the α and β subunit TM domains within integrin heterodimers but also provide information about the spatial relationship between neighboring integrin heterodimers on the cell surface. The formation of a cross-link between the αIIb subunits of two neighboring integrin molecules by the αIIb-W967C mutant demonstrates the lateral accessibility of this site in the resting state. Consistent with this finding, our data demonstrate that the αIIb-W967 residue points away from the TM interface with the β3 subunit (see Figure 2C). It further should be noted that in the bent, low-affinity integrin conformation present on the cell surface (Takagi et al. 2002), the headpiece is folded such that the juxtamembrane portion of the αIIb subunit, including Trp967, is exposed, whereas the juxtamembrane segment of β is occluded (Figure 7). This is consistent with the absence of homodimer cross-linking through β3. Figure 7 Model for Integrin Activation The α and β subunits are red and blue, respectively. The membrane is shown as a solid gray line in (A–H) and as two dashed lines in (I and J). (A–H) Cartoon models. The ligand-binding α subunit β-propeller and β subunit I-like domains are symbolized as a semicircle with a shallow (low-affinity) or deep (high-affinity) ligand binding site. The headpiece additionally contains the α subunit thigh domain (red straight line) and the β subunit hybrid domain (blue straight line); the swing out of the latter is linked to ligand binding affinity. (A–D) Activation from within the cell initiated by TM domain separation. (E–H) Activation from outside the cell initiated by integrin extension, followed by ligand binding and finally TM domain separation. (I and J) Ribbon models. (I) Bent, low-affinity conformation corresponding to (A and E). (J) Extended, high-affinity conformation with the open headpiece corresponding to (D and H). Models are based on the TM domain association results described here, and negative stain electron microscopy (Takagi et al. 2002, 2003), crystallography (Xiong et al. 2001), NMR (Beglova et al. 2002; Vinogradova et al. 2002), and fluorescent resonance energy transfer (Kim et al. 2003). The TM and cytoplasmic domains are schematic, and show the proposed salt bridge (– and +). It is most interesting that we observed no homodimerization with constitutively active mutant receptors. The α and β residues mutated to cysteine in active receptors spanned two and three α-helical turns in the αIIb and β3 TM domains, respectively. The same mutations in resting receptors robustly disclosed heterodimeric interactions. Therefore, if homodimeric interactions between the TM domains were present, they should have been detected. Why were homodimer interactions observed in the resting state, albeit only through cross-linking of one residue, and not in the active state? A full answer to this question would require more knowledge about the dynamics of integrins on cell surfaces; however, based on observations on the heterogeneity of integrin structure from quantitative negative stain electron microscopy of soluble integrins (Takagi et al. 2002), a preliminary answer can be proposed. These studies reveal that the integrin adopts a single homogenous, bent conformation in the resting state. By contrast, in the extended conformation, there are two discrete angles between the β subunit I-like and hybrid domains. Furthermore, the region between the β subunit hybrid domain and the TM domain, which contains four I-EGF domains and the β-tail domain, is quite flexible. Therefore, motion of the headpiece may sweep out a large area and prevent neighboring integrins from coming close. Moreover, motions of the membrane proximal α subunit calf-2 domain relative to the α TM domain and of the β-tail domain relative to the β TM domain would also be much greater after TM domain and tailpiece separation, and would also hinder the close approach of other TM domains. What about observations that integrin fragments consisting of the TM and cytoplasmic domains form dimers (αIIb) and trimers (β3) in detergent micelles (R. Li et al. 2001)? We think that these findings should be interpreted with caution. It is important to point out that the physiological, heterodimeric interaction between the αIIb and β3 TM domains cannot be reconstituted in the same detergents, i.e., in sodium dodecyl sulfate or dodecylphosphocholine (R. Li et al. 2001). There are many important differences between dodecyl detergent micelles and lipid bilayers, including a shorter hydrocarbon chain (12 versus 16 or 18), one (as opposed to two) fatty acyl chains per headgroup, and a spherical (as opposed to a bilayer) shape. The same characteristics that prevent physiological heterodimeric integrin TM interactions in dodecyl detergent micelles may conspire to cause nonphysiologic homomeric interactions. A β3-G708N mutation increases trimerization in detergent by more than 10-fold and is also reported to activate ligand binding in transfectants (R. Li et al. 2003); however, β3 trimerization in membrane bilayers or intact cells has yet to be demonstrated. In 293T transfectants the β3-G708N mutation fails to detectably activate soluble ligand binding by αIIbβ3 (see Figure 3). We could confirm that the G708N mutation in CHO cells increased ligand binding, but to a level only 17% of that of the maximally activated receptor, whereas the G708L mutation is maximally activating (data not shown). Gly708 is in the TM heterodimer interface defined here, and we have additional unpublished data suggesting that the weak activating effect of the G708N mutation is a consequence of the disruption of this interface. The lack of homomeric disulfide cross-linking of integrin α and β subunit TM domains found here with activated αIIbβ3 in intact cells strongly suggests that integrin activation from inside the cell is not sufficient to drive homomeric interactions. Studies with fluorescent resonance energy transfer probes attached to integrin cytoplasmic domains also fail to find homomeric interactions when integrins are activated from within the cell or bind to monomeric ligand outside the cell (Kim et al. 2003; M. Kim, C. Carman, and T. Springer, unpublished data). However, we should point out that binding to multimeric ligands induces integrin clustering (Buensuceso et al. 2003) and that we have not examined homomeric interactions under these conditions. In conclusion, our results suggest that lateral separation of the TM segments of the α and β chains leads to affinity upregulation within a single receptor molecule without facilitating α–α or β–β interactions. Therefore, if the tendency of integrin TM domains to undergo homomeric interactions in detergent micelles also holds for lipid bilayers, it may strengthen adhesion and contribute to outside-in signaling after the initial engagement of multimeric physiological ligands. Our results show that the αIIb and β3 TM domains are associated in a specific manner in the outer leaflet of the membrane bilayer in the resting state and are unassociated in the active state. Upon activation, association between the α and β subunits is also broken at the interface between the TM and cytoplasmic domains (Hughes et al. 1996; Vinogradova et al. 2002), and furthermore, the cytoplasmic domains also separate (Kim et al. 2003). The simplest explanation for separation at all three of these locations is separation of the TM domains in the plane of the membrane. Perhaps a counterargument could be made that a hinge-like motion of the TM domains relative to one another about a pivot point near the middle of the bilayer would also give rise to separation at each of these three positions. We point out that only one specific TM hinge model has been proposed, that it does not give rise to separation in the TM regions scanned in this study (Gottschalk et al. 2002), that our data rule it out, and that much more extreme hinging is unprecedented and is unlikely, because the size of the TM interface would be markedly decreased and hence less likely to stabilize association. Bidirectional signal transmission by integrins across the plasma membrane is not necessarily symmetric (Figure 7A–7D compared to 7E–7H). We show that separation of the TM domains is sufficient to prime the extracellular domain for ligand binding and exposes activation epitopes that report the switchblade-like extension of the extracellular domain (Figure 7A–7D). Furthermore, prevention or reversal of TM domain separation abolishes priming and extension signaled from the inside. The same is not true in the opposite direction (Figure 7E–7H); thus, addition of Mn2+ and an activating mAb to the extracellular environment could prime ligand binding in the absence of TM domain separation. The implication is that with the wild-type receptor, under conditions in which high concentrations of ligand drive the equilibrium toward ligand binding, ligand could bind in the absence of TM domain separation (Figure 7G) and subsequently drive TM domain separation (Figure 7H). Similarly, when separation of fluorescent resonance energy transfer tags fused to the C-termini of the cytoplasmic domains of integrin αLβ2 is measured, priming from inside the cell results in TM domain separation (Figure 7B–7D), priming from outside the cell by Mn2+ does not result in separation (Figure 7F and 7G), and priming with Mn2+ combined with binding to ligand results in separation (Figure 7H) (Kim et al. 2003). Therefore, in Mn2+, integrins on the cell surface appear to adopt an intermediate conformation, with the headpiece extended and the TM domains associated (Figure 7F and 7G). The above results are consistent with the existence of multiple conformational states visualized for integrin extra-cellular domains by electron microscopy, and linked equilibria relating these states (Takagi et al. 2002). Furthermore, extended conformations with both closed and open headpieces are present in Mn2+ (Figure 7F and 7G), whereas only the extended conformation with the open headpiece is present in high concentration of ligand (Figure 7H) (Takagi et al. 2002, 2003). How does TM domain separation trigger integrin extension? In the bent αVβ3 crystal structure (Xiong et al. 2001), the last residue visualized in β3 is Gly690, immediately before the first TM domain residue mutated to cysteine here. In the α subunit, only four to six residues intervene between the last crystal structure residue and the first residue mutated to cysteine. This very tight linkage between the C-terminal extracellular domains and the TM domains (Figure 7I) implies that separation of the α and β TM domains would also lead to separation of the membrane proximal α calf-2 and β-tail domains in the integrin tailpiece. In turn, this separation in the tailpiece would destabilize the extensive interface with the headpiece and lead to switchblade opening (Figure 7J) (Takagi et al. 2002). Separation of TM domains in the plane of the membrane is a novel mechanism for activation of a cell surface receptor. One of the best-known mechanisms for receptor activation, exemplified by receptor tyrosine kinases (Schlessinger 2000), works in almost the opposite manner, in which distinct or identical receptor subunits are brought together in a specific orientation in the plane of the membrane by ligand binding. In the neu (ErbB-2) member of the epidermal growth factor receptor family, enforced dimerization along a series of α-helical TM dimer interfaces gives rise to periodicity in activation, such that dimerization only in certain orientations is activating (Burke and Stern 1998; Bell et al. 2000). In our study, the α*β and α"β receptors with activating mutations were captured with disulfide bonds in many different rotational orientations between the α and β subunit TM α helices. Similarly, disulfide bonding between cysteines located at different depths in the membrane would be expected to give rise to some piston-like motion of one helix relative to the other. It is notable that none of the enforced orientations between disulfide-bonded α and β integrin TM domains were activating. These results argue against hinging, rotation, or piston models in which a relative change in orientation between the two TM domains is activating, and are in agreement with the model that separation of the α and β subunit TM domains in the plane of the membrane is the activation mechanism. Integrins in the extended conformation have their ligand binding site far above the plasma membrane, as appropriate for binding to ligands in the extracellular matrix and on opposing cell surfaces. However, transmission of conformational information over such distances is inefficient, because it is attenuated by interdomain flexibility. Integrins solve the problem of long distance communication by equilibrating between an extended conformation and a bent conformation, and by altering the equilibrium between these conformations by the novel mechanism of separation of the α and β subunit TM domains. Materials and Methods Plasmid construction and transient transfection Plasmids coding for full-length human αIIb and β3 were subcloned into pEF/V5-HisA and pcDNA3.1/Myc-His(+), respectively, as described by Takagi et al. (2002). To mimic inside-out signaling, αIIb cytoplasmic domain mutant receptors were made by introducing a stop codon at residue Gly991 to obtain αIIb1–990 (denoted α*), or by mutating G991/FFKR to GAAKR (denoted α"). Single amino acid substitutions to cysteine were made in αIIb, αIIb*, αIIb", and β3 in the positions indicated in the text. All mutants were made using site-directed mutagenesis with the QuikChange kit (Stratagene, La Jolla, California, United States), and DNA sequences were confirmed before transfection of 293T cells using calcium phosphate precipitates, or CHO-K1 cells using Fugene transfection kit (Roche Diagnostics, Indianapolis, Indiana, United States). Cross-linking and immunoprecipitation Twenty-four hours after transfection, 293T cells were metabolically labeled with [35S]cysteine/methionine for 1.5 h before adding chase medium containing 500 μg/ml of cysteine and 100 μg/ml of methionine, and cells were cultured 17 h overnight (Lu et al. 2001). Then cells were detached and suspended in Tris-buffered saline (TBS) containing 1 mM Ca2+/1 mM Mg2+ (106 cells in 100 μl). After chilling on ice for 5 min, 20 μM CuSO4/100 μM o-phenanthroline was added by 10-fold dilution from stock solutions, and cells were incubated on ice for another 10 min. Oxidation at 37 °C was similar, except cells were suspended at room temperature and after Cu-phenanthroline addition were incubated at 37 °C for 10 min. Oxidation was stopped by adding an equal volume of TBS containing Ca2+/Mg2+ and 5 mM N-ethyl maleimide. Cells were centrifuged and resuspended in 100 μl of TBS containing 1 mM Mg2+, 1 mM Ca2+, and 5 mM N-ethylmaleimide, and lysed by addition of an equal volume of 2% Triton X-100 and 0.1% NP-40 in the same buffer for 10 min on ice. Cell lysate was immunoprecipitated with 10E5 (anti-αIIbβ3-complex-specific mAb) and protein G Sepharose at 4 °C for 1.5 h. After three washes with lysis buffer, precipitated integrin was dissolved into 0.5% SDS sample buffer and subjected to nonreducing 7.5% SDS-PAGE and fluorography (Huang and Springer 1997). The efficiency of disulfide bond formation was quantitated using a Storm PhosphorImager after 1 to 3 h of exposure of storage phosphor screens (Molecular Dynamics, Sunnyvale, California, United States). Efficiency was defined as the ratio of the intensity of the disulfide-bonded heterodimer band to the sum of the intensity of all bands including αIIb, β3, and heterodimer. Two-color ligand binding and flow cytometry Binding of fluorescein-labeled human fibrinogen was performed as previously described (Pampori et al. 1999; Takagi et al. 2002). To determine the effect of inducible disulfide bond on the ligand binding, oxidation by Cu-phenanthroline was carried out at either 0 °C or 37 °C for 10 min, followed by washing with TBS containing 1 mM Ca2+/Mg2+ and 5 mM N-ethyl maleimide. Cells were suspended in 20 mM Hepes (pH 7.4), 150 mM NaCl, 5.5 mM glucose, and 1% bovine serum albumin, and incubated with 1 mM Ca2+/Mg2+ or a combination of 1 mM Mn2+ and 10 μg/ml of activating mAb PT25–2. Then cells were incubated with FITC-conjugated fibrinogen with a final concentration of 60 μg/ml at room temperature for 30 min, Cy3-conjugated AP3 was added to a final concentration of 10 μg/ml, and cells were incubated on ice for another 30 min before subjected to flow cytometry. Binding of soluble fibrinogen was determined and expressed as the percentage of mean fluorescence intensity relative to immunofluorescent staining with Cy3-labeled AP3 mAb. LIBS epitope expression Anti-LIBS mAbs AP5 was from the Fifth International Leukocyte Workshop (Lanza et al. 1994), LIBS-6 was from M. H. Ginsberg, and D3 was from Lisa K. Jennings (Jennings and White 1998). LIBS epitope expression was determined as described previously (Luo et al. 2003). In brief, transiently transfected 293T cells were incubated with either 5 mM Ca2+ or 1 mM Mn2+ and 100 μM GRGDSP peptide at room temperature for 30 min. Anti-LIBS mAbs (AP5, D3, and LIBS6) was added to a final concentration of 10 μg/ml, and cells were incubated on ice for 30 min before staining with FITC-conjugated antimouse IgG and flow cytometry. LIBS epitope expression was determined and expressed as the percentage of mean fluorescence intensity of anti-LIBS mAbs relative to the conformation-independent mAb AP3 (Luo et al. 2003). Structural model of integrin TM domain at resting state Model building was performed using the NMR structure of glycophorin A TM dimer (PDB code: 1AFO, model 1) as a template. The entire TM domains of αIIb and β3 were aligned, with no gaps. Eighteen different alignments roughly compatible with the observed α–β interface orientation were submitted to the SWISS-MODEL server (Peitsch 1996). For each model, the average Cβ–Cβ or Cβ–Gly Cα atom distance between residues that formed disulfides at greater than 80% efficiency (Trp968–Val696, Val969–Val696, Val971–Leu697, Gly972–Leu697, and Gly972–Val700) was calculated. The alignment where αIIb sequence W968/VLVG and β3 sequence V696/LLSV were aligned with glycophorin A sequence G79/VMAG in each monomer gave the lowest root mean square distance (4.8 Å) and thus was chosen as the final model. Models for clusters 11 and 12 were kindly provided by the authors of Gottschalk et al. (2002) and subjected to the same analysis for Cβ–Cβ and Cβ–Gly Cα atom distances. The present work was supported by National Institutes of Health grant HL48675 (to TAS and JT). We thank Daniel P. DeBottis and N'Goundo Magassa for technical assistance, Aideen Mulligan for lab management assistance, Jessica Martin for secretary assistance, and Drs. Motomu Shimaoka, Christopher Carman, Minsoo Kim, Nathan Astrof, and Tsan Xiao for helpful discussion. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JT and TAS conceived and designed the experiments. B-HL and JT performed the experiments. B-HL, JT, and TAS analyzed the data. B-HL, JT, and TAS wrote the paper. Academic Editor: John Kappler, National Jewish Research and Medical Center ¤1 Current address: Institute for Protein Research, Laboratory of Protein Synthesis and Expression, Osaka University, Osaka, Japan Abbreviations Cu-phenanthrolineCu(II)-(o-phenanthroline)3 LIBSligand-induced binding site mAbmonoclonal antibody NMRnuclear magnetic resonance SDS-PAGEsodium dodecyl sulphate polyacrylamide gel elecrophoresis TBSTris-buffered saline TMtransmembrane ==== Refs References Adair BD Yeager M Three-dimensional model of the human platelet integrin αIIbβ3 based on electron cryomicroscopy and x-ray crystallography Proc Natl Acad Sci U S A 2002 99 14059 14064 12388784 Armulik A Nilsson I von Heijne G Johansson S Determination of the border between the transmembrane and cytoplasmic domains of human integrin subunits J Biol Chem 1999 274 37030 37034 10601259 Beglova N Blacklow SC Takagi J Springer TA Cysteine-rich module structure reveals a fulcrum for integrin rearrangement upon activation Nat Struct Biol 2002 9 282 287 11896403 Bell CA Tynan JA Hart KC Meyer AN Robertson SC Rotational coupling of the transmembrane and kinase domains of the Neu receptor tyrosine kinase Mol Biol Cell 2000 11 3589 3599 11029057 Buensuceso C De Virgilio M Shattil SJ Detection of integrin αIIbβ3 clustering in living cells J Biol Chem 2003 278 15217 15224 12595537 Burke CL Stern DF Activation of Neu (ErbB-2) mediated by disulfide bond-induced dimerization reveals a receptor tyrosine kinase dimer interface Mol Cell Biol 1998 18 5371 5379 9710621 Garcia-Alvarez B de Pereda JM Calderwood DA Ulmer TS Critchley D Structural determinants of integrin recognition by talin Mol Cell 2003 11 49 58 12535520 Gottschalk KE Adams PD Brunger AT Kessler H Transmembrane signal transduction of the α (IIb) β (3) integrin Protein Sci 2002 11 1800 1812 12070332 Huang C Springer TA Folding of the β-propeller domain of the integrin αL subunit is independent of the I domain and dependent on the β2 subunit Proc Natl Acad Sci U S A 1997 94 3162 3167 9096363 Hughes PE Diaz-Gonzalez F Leong L Wu C McDonald JA Breaking the integrin hinge J Biol Chem 1996 271 6571 6574 8636068 Jennings LK White MM Expression of ligand-induced binding sites on glycoprotein IIb/IIIa complexes and the effect of various inhibitors Am Heart J 1998 135 S179 S183 9588397 Kashiwagi H Tomiyama Y Tadokoro S Honda S Shiraga M A mutation in the extracellular cysteine-rich repeat region of the β3 subunit activates integrins αIIbβ3 and αVβ3 Blood 1999 93 2559 2568 10194435 Kim M Carman CV Springer TA Bidirectional transmembrane signaling by cytoplasmic domain separation in integrins Science 2003 301 1720 1725 14500982 Krogh A Larsson B von Heijne G Sonnhammer EL Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes J Mol Biol 2001 305 567 580 11152613 Lanza F Moretti S Papa S Malavasi F Castoldi G Report on the fifth international workshop on human leukocyte differentiation antigens, Boston, November 3–7, 1993 Haematologica 1994 79 374 386 7806095 Lee GF Dutton DP Hazelbauer GL Identification of functionally important helical faces in transmembrane segments by scanning mutagenesis Proc Natl Acad Sci U S A 1995 92 5416 5420 7777522 Lemmon MA Flanagan JM Treutlein HR Zhang J Engelman DM Sequence specificity in the dimerization of transmembrane α-helices Biochemistry 1992 31 12719 12725 1463743 Li R Babu CR Lear JD Wand AJ Bennett JS Oligomerization of the integrin αIIbβ3: Roles of the transmembrane and cytoplasmic domains Proc Natl Acad Sci U S A 2001 98 12462 12467 11606749 Li R Babu CR Valentine K Lear JD Wand AJ Characterization of the monomeric form of the transmembrane and cytoplasmic domains of the integrin β3 subunit by NMR spectroscopy Biochemistry 2002 41 15618 15624 12501190 Li R Mitra N Gratkowski H Vilaire G Litvinov SV Activation of integrin αIIbβ3 by modulation of transmembrane helix associations Science 2003 300 795 798 12730600 Li SC Deber CM Peptide environment specifies conformation: Helicity of hydrophobic segments compared in aqueous, organic, and membrane environments J Biol Chem 1993 268 22975 22978 8226811 Lu C Springer TA The α subunit cytoplasmic domain regulates the assembly and adhesiveness of integrin lymphocyte function-associated antigen-1 (LFA-1) J Immunol 1997 159 268 278 9200463 Lu C Takagi J Springer TA Association of the membrane-proximal regions of the α and β subunit cytoplasmic domains constrains an integrin in the inactive state J Biol Chem 2001 276 14642 14648 11279101 Luo B-H Springer TA Takagi J Stabilizing the open conformation of the integrin headpiece with a glycan wedge increases affinity for ligand Proc Natl Acad Sci U S A 2003 100 2403 2408 12604783 MacKenzie KR Prestegard JH Engelman DM A transmembrane helix dimer: Structure and implications Science 1997 276 131 133 9082985 O'Toole TE Katagiri Y Faull RJ Peter K Tamura R Integrin cytoplasmic domains mediate inside-out signal transduction J Cell Biol 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integrin α5β1 in complex with fibronectin EMBO J 2003 22 4607 4615 12970173 Ulmer TS Yaspan B Ginsberg MH Campbell ID NMR analysis of structure and dynamics of the cytosolic tails of integrin alpha IIb beta 3 in aqueous solution Biochemistry 2001 40 7498 7508 11412103 Vinogradova O Haas T Plow EF Qin J A structural basis for integrin activation by the cytoplasmic tail of the αIIb-subunit Proc Natl Acad Sci U S A 2000 97 1450 1455 10677482 Vinogradova O Velyvis A Velyviene A Hu B Haas TA A structural mechanism of integrin αIIb β3 “inside-out” activation as regulated by its cytoplasmic face Cell 2002 110 587 597 12230976 Weljie AM Hwang PM Vogel HJ Solution structures of the cytoplasmic tail complex from platelet α IIb- and β 3-subunits Proc Natl Acad Sci U S A 2002 99 5878 5883 11983888 Xiong J-P Stehle T Diefenbach B Zhang R Dunker R Crystal structure of the extracellular segment of integrin αVβ3 Science 2001 294 339 345 11546839 Xiong JP Stehle T Zhang R Joachimiak A Frech M Crystal structure of the extracellular segment of integrin αVβ3 in complex with an Arg-Gly-Asp ligand Science 2002 296 151 155 11884718

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020155Research ArticleEvolutionGenetics/Genomics/Gene TherapyPrimatesHomo (Human)Absence of the TAP2 Human Recombination Hotspot in Chimpanzees Human Hotspot Absent in ChimpanzeesPtak Susan E 1 Roeder Amy D 1 ¤1Stephens Matthew 2 Gilad Yoav 1 ¤2Pääbo Svante 1 Przeworski Molly Molly_Przeworski@ Brown.edu 1 ¤31Max Planck Institute for Evolutionary AnthropologyLeipzigGermany2Department of Statistics, University of WashingtonSeattle, WashingtonUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e15518 12 2003 21 3 2004 Copyright: © 2004 Ptak et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. What's So Hot about Recombination Hotspots? A DNA Recombination "Hotspot" in Humans Is Missing in Chimps Recent experiments using sperm typing have demonstrated that, in several regions of the human genome, recombination does not occur uniformly but instead is concentrated in “hotspots” of 1–2 kb. Moreover, the crossover asymmetry observed in a subset of these has led to the suggestion that hotspots may be short-lived on an evolutionary time scale. To test this possibility, we focused on a region known to contain a recombination hotspot in humans, TAP2, and asked whether chimpanzees, the closest living evolutionary relatives of humans, harbor a hotspot in a similar location. Specifically, we used a new statistical approach to estimate recombination rate variation from patterns of linkage disequilibrium in a sample of 24 western chimpanzees (Pan troglodytes verus). This method has been shown to produce reliable results on simulated data and on human data from the TAP2 region. Strikingly, however, it finds very little support for recombination rate variation at TAP2 in the western chimpanzee data. Moreover, simulations suggest that there should be stronger support if there were a hotspot similar to the one characterized in humans. Thus, it appears that the human TAP2 recombination hotspot is not shared by western chimpanzees. These findings demonstrate that fine-scale recombination rates can change between very closely related species and raise the possibility that rates differ among human populations, with important implications for linkage-disequilibrium based association studies. The human TAP2 recombination hotspot is absent from the homologous region in western chimpanzees, with important implications for association studies, the HapMap project and understanding fine-scale variation in recombination rates ==== Body Introduction Recombination is a fundamental biological feature about which we still know remarkably little, especially in mammals. Understanding recombination is also of practical importance for evolutionary inference and human genetics (Nachman 2002; Arnheim et al. 2003). Unfortunately, the process is difficult to study, because recombination events occur extremely rarely (e.g., with a probability of ∼10−8 per bp per generation in a typical region of the human or Drosophila melanogaster genome; Ashburner 1989; Kong et al. 2002). Thus, direct measurements for closely linked sites often require the examination of a prohibitive number of individuals. As a result, our knowledge of recombination rates stems primarily from estimates for markers that are megabases apart, obtained from crosses or, for humans, obtained from pedigrees (e.g., Kong et al. 2002). One way to learn about finer-scale recombination rates in males is sperm typing (Li et al. 1988; Hubert et al. 1994; Jeffreys et al. 2001). In this approach (reviewed by Arnheim et al. 2003), genetic markers are amplified and typed from a large number of sperm in order to estimate the fraction of recombinant sperm and hence the recombination rate. Fine-scale rates can also be measured indirectly from patterns of allelic associations, or linkage disequilibrium (LD), observed in samples from natural populations (Hudson 1987; Pritchard and Przeworski 2001). In humans, both direct estimates of the recombination rate using sperm typing techniques and indirect approaches based on analyses of LD have suggested the existence of substantial heterogeneity in rates of recombination at small scales (Daly et al. 2001; Jeffreys et al. 2001; Gabriel et al. 2002; Schneider et al. 2002; Wall and Pritchard 2003). In particular, sperm typing experiments have demonstrated that, in several regions of the human genome, crossover resolutions are not uniformly distributed but instead tend to cluster within narrow regions of 1–2 kb termed “recombination hotspots” (de Massy 2003 and references therein). While there has been recent progress characterizing the extent of spatial variation in recombination rates, the time scale over which recombination rates change remains an open question. It has been known for decades that natural populations harbor genetic variation for recombination rates (Brooks 1988 and references therein). In humans, in particular, there are significant differences in recombination rates among females (Kong et al. 2002) as well as among males (Cullen et al. 2002). Thus, there is a clear potential for the evolution of recombination rates. However, there are only a couple of demonstrated cases that help to delimit the time scale on which this might occur: at the megabase scale, the best example is probably D. melanogaster and D. simulans, two sibling species that differ in their recombination landscape (True et al. 1996). Among primates, the genetic map of humans is approximately 28% longer than that of an Old World monkey, the baboon (Papio hamadryas; Rogers et al. 2000), suggesting that—if physical maps are roughly similar—recombination rates in humans may be higher overall. These instances demonstrate that large-scale recombination rates can change between species that differ on average at roughly 6% to 10% of nucleotide positions (Betancourt and Presgraves 2002; Thomas et al. 2003). At a finer scale, the only evidence stems from a recent study of the β-globin gene, where a hotspot had been characterized by sperm typing in humans. Wall et al. (2003) found no evidence of rate variation in LD data collected from the rhesus macaque (Macaca mulatta), another Old World monkey. For more closely related species, nothing is known. However, observations in yeast (e.g., Petes 2001; Steiner et al. 2002) and mammals (Jeffreys and Neumann 2002; Yauk et al. 2003) raise the possibility that local recombination rates could change rapidly. Indeed, at the MS32 and DNA2 hotspots in humans (Jeffreys et al. 1998; Jeffreys and Neumann 2002) as well as at the Eβ hotspot in mice (Mus sp.; Yauk et al. 2003), some haplotypes were found to lead to higher rates of initiation of crossover events. Such haplotypes tended to be undertransmitted in crossover products (Jeffreys and Neumann 2002), an asymmetry that favors the loss of recombination hotspots (Boulton et al. 1997). If this is a common phenomenon, it may lead hotspots to be short-lived on an evolutionary time scale (Jeffreys and Neumann 2002). To evaluate whether fine-scale recombination rates can change rapidly, we were interested in comparing rates in humans with those in their closest evolutionary relative, the chimpanzee (Pan troglodytes). The two species are thought to have had a common ancestor five to six million years ago and differ at approximately 1.2% of base pairs on average (Ebersberger et al. 2002). Since it is difficult to use sperm typing techniques in chimpanzees, not least of all because of the need for chimpanzee sperm, we took an indirect approach and estimated the extent of recombination rate variation from patterns of LD in a population sample. To do so, we modified a recently developed statistical approach (Li and Stephens 2003). The method estimates recombination rates by exploiting the fact that patterns of LD reflect the rate and distribution of recombination events in the ancestors of the sample (see Materials and Methods for more details). Although it is based on simplistic assumptions about population demography, it has been shown to produce reliable estimates of recombination rates for data sets simulated under a range of demographic assumptions (Li and Stephens 2003; D. C. Crawford, T. Bhangale, N. Li, G. Hellenthal, M. J. Rieder, et al., unpublished data). We focused on the TAP2 genic region, where a sperm typing study of humans characterized a ∼1.2 kb recombination hotspot in one of the introns (Jeffreys et al. 2000). Application of the statistical method to polymorphism data collected for this region (Jeffreys et al. 2000) led to estimates similar to those obtained by sperm typing, providing further evidence for its reliability (Li and Stephens 2003). Samples that include individuals from diverged populations are expected to harbor high levels of LD that may lead to incorrect estimates of recombination rate variation (Pritchard and Przeworski 2001). This is of particular concern in chimpanzees, for which previous studies have reported high levels of genetic differentiation between subspecies (Morin et al. 1994; Stone et al. 2002; Fischer et al. 2004). In addition, there appears to be a high proportion of less informative, rare alleles in samples from central (P. t. troglodytes) but not western (P. t. verus) chimpanzees (Gilad et al. 2003; Fischer et al. 2004). We therefore collected polymorphism data from a sample of 24 chimpanzees that were all known to be from the western subspecies. Strikingly, we found no evidence for recombination rate variation at TAP2 in these data. Results In humans, LD data for the TAP2 region were previously collected by Jeffreys et al. (2000), who resequenced ∼9.7 kb in a sample of eight individuals from the United Kingdom (UK) and found 46 single nucleotide polymorphisms (SNPs), excluding insertion-deletions. The SNPs were then typed in a sample of 30 individuals from the UK, in whom haplotypes were determined experimentally (by allele-specific PCR). We collected genotype data for the same region in western chimpanzees by resequencing 24 individuals (see Materials and Methods for details). This led to the discovery of 57 SNPs. When differences in study design are taken into account, diversity levels in western chimpanzees are very similar to those observed in samples of humans from the UK (θ W = 0.145% versus θ W = 0.144% per bp, respectively), consistent with previous findings (e.g., Gilad et al. 2003; Fischer et al. 2004). The LD data are summarized in Figure 1; overall, there is much less LD in humans than in chimpanzees. In particular, in humans, strong allelic associations are only seen between pairs of sites in close physical proximity, while in chimpanzees, such associations are also found among more distant pairs. Whether this reflects differences in the underlying recombination landscape or chance variation is unclear from visual inspection of these plots alone. We therefore used a statistical approach to assess the evidence for recombination rate variation in the two species. Specifically, we assumed that there is (at most) one hotspot in the region and, as a first step, specified its location according to the results of the sperm typing study in humans. We then applied our modification of the method of Li and Stephens (2003) to estimate a background population recombination rate, ρ, and the relative intensity of recombination in the hotspot segment, λ (see Materials and Methods). Within this model, a λ value of 1 corresponds to an absence of recombination rate variation, while values of λ greater than 1 indicate a hotspot. The approach taken here is Bayesian (see Materials and Methods) so, as a measure of support for a hotspot in the LD data, we report estimates for the probabilities Pr(λ > 1) and Pr(λ > 10); these are the posterior probabilities of a hotspot of any kind and of a hotspot of intensity at least ten times the background rate, respectively. Figure 1 Patterns of Pairwise LD in Humans and Chimpanzees Only SNPs with minor allele frequencies above 0.1 are included. The rows correspond to the consecutive SNPs in the region, as do the columns. Each cell indicates the extent of LD between a pair of sites, as measured by |D′| (estimated using the Expectation Maximization algorithm, as implemented by Arlequin: http://lgb.unige.ch/arlequin/). Application of this method to the human haplotype data led to extremely strong support for rate variation: we estimated Pr(λ > 1) = 1 and Pr(λ > 10) = 0.982. When the same method was applied to the human genotype data (i.e., ignoring the information about the phase of multiple heterozygotes), we estimated Pr(λ > 1) = 1 and Pr(λ > 10) = 0.992. The results are almost identical, suggesting minimal loss of information with the use of genotypes. Interestingly, the point estimate of λ using either haplotypes (28.4) or genotypes (32.1) is higher than the corresponding estimate from sperm typing (11). This difference may reflect error in the estimates; alternatively, it may point to a more intense hotspot in females than in males (Jeffreys et al. 2000). Next, we applied the same method to the genotype data collected from western chimpanzees. The estimate of the background rate of recombination, ρ^, is 5.0 × 10−4 per base pair, which is very similar to the estimate from the human genotype data (Figure 2). However, in contrast to what is found in humans, there is no evidence for recombination rate variation: our estimate of λ is 1, suggesting a uniform rate of recombination throughout the region, and our estimates of Pr(λ > 1) = 0.200 and Pr(λ > 10) = 0.006, reflecting tepid support for a hotspot of any kind and almost no support for a hotspot similar to the one observed in humans. Indeed, the latter figure represents very strong evidence against a hotspot of moderate intensity and rules out the possibility that the chimpanzee polymorphism data are simply uninformative, because of, for example, insufficient sample size or diversity. Figure 2 Estimates of the Recombination Hotspot Intensity, λ, Based on Genotype Data We assumed that, if the hotspot is present, it is in the same location as estimated by sperm typing in humans (see Materials and Methods). A λ value of one corresponds to the absence of recombination rate variation, while values of λ greater than one indicate a hotspot. The estimates for humans from the UK are shown in blue and those for western chimpanzees in orange. To assess how likely we would be to obtain such weak support if there were in fact a hotspot in western chimpanzees similar to the one in humans, we generated 200 simulated genotype data sets under a model with a hotspot of intensity λ = 11 and then tabulated the proportion with posterior probability estimates as low or lower than that observed (see Materials and Methods). We took the λ value estimated from sperm typing because it is the lowest of the various estimates for humans and hence its use was conservative for our purposes. With the ρ value estimated from the data (5.0 × 10−4 per bp), the probability of obtaining Pr(λ > 1) ≤ 0.200 is p = 0.010 and the probability of obtaining Pr(λ > 10) ≤ 0.006 is p = 0.005. With a lower ρ value (2.7 × 10−4 per bp; see Materials and Methods), the probability of obtaining Pr(λ > 1) ≤ 0.200 is p = 0.020. In other words, we can reject the null hypothesis that there is a hotspot in western chimpanzees similar to the one in humans, because we would expect to see more support for a hotspot in these data if one were there. It appears that western chimpanzees do not harbor a hotspot in the same location as humans. The possibility remains, however, that there is a hotspot in a slightly different position in chimpanzees. To evaluate this, we used a more general model in which there is at most one hotspot in the region, but where the location is unknown and estimated together with ρ and λ (see Materials and Methods). Again, we found very little evidence for recombination rate variation: across all pairs of consecutive segregating sites, the largest posterior probability of elevated recombination is estimated to be < 0.060 (Figure 3). Thus, the hotspot appears to be entirely absent from the ∼9.4 kb surveyed in western chimpanzees. Figure 3 Estimates of Recombination Rate Variation in Humans and Western Chimpanzees In this model, there is at most one hotspot in the region, the location and width of which are unknown and estimated along with λ and ρ. On the y-axis is an estimate of the posterior probability of elevated recombination, Pr(λ > 1), between each pair of consecutive SNPs (plotted at the midpoint position). Discussion These estimates of recombination rate parameters are based on assumptions of neutrality, constant population size, and random mating, raising the concern that the hotspot is not absent but instead masked by departures from model assumptions. However, we chose to focus on western chimpanzees precisely because previous studies reported allele frequencies in rough accordance with the assumptions of our model. Consistent with these studies (Gilad et al. 2003; Fischer et al. 2004), the allele frequencies at TAP2 are not significantly different from the expectations of the standard neutral model (as assessed by Tajima's D = 0.848, p = 0.237; see Materials and Methods). Moreover, simulations suggest that the power to detect a hotspot is not strongly affected by population history (Li and Stephens 2003). To some extent, this is expected, as population history tends to affect LD in the entire region, not only in the hotspot, so that estimates of the relative rates of recombination are unlikely to be substantially altered. In summary, there is no evidence for a marked departure from model assumptions in the allele frequencies, and the method is expected to be robust to small departures. Consistent with this, in humans, the approach yields similar results to sperm typing experiments that do not rely on the same assumptions. On this basis, it seems that the hotspot is truly absent from the homologous region in western chimpanzees. This finding implies that the hotspot was lost in chimpanzees or gained in humans, or that it moved in one of the species (over a larger distance than we surveyed). This in turn raises a number of more general questions. Are hotspots frequently born de novo or do they tend to migrate within circumscribed regions of the genome? Are particular sequence motifs sufficient to produce recombination hotspots, or are larger-scale requirements, such as chromatin accessibility, required for their formation (Petes 2001)? The systematic comparison between closely related species with different recombination landscapes may be helpful in addressing these problems. As an illustration, in these data, we found two motifs that were previously implicated in the formation of recombination hotspots (Smith et al. 1998; Badge et al. 2000 and references therein) and that varied between the two species: a Pur binding motif that is present in humans but absent in chimpanzees (because of a single base pair difference) and two scaffold attachment sites that are in different positions in the two species. The significance of these differences cannot be determined on the basis of a single example; however, once a larger sample of hotspot regions has been surveyed, one can begin to test for an association between particular sequence motifs or features and the presence of hotspots. Comparative studies of hotspot regions will also increase our understanding of the determinants of mutation rates. As noted by Jeffreys et al. (2000), there is a significant excess of diversity within the hotspot region in humans from the UK (Figure 4): when the hotspot region is compared to the 8,735 other windows of the same size, only 0.3% have as many or more SNPs. In contrast, in western chimpanzees, levels of diversity are not higher than elsewhere in the region (Figure 4): 17.0% of comparable windows harbor at least as many SNPs as the hotspot. Nor are levels of human–chimpanzee divergence unusual in the hotspot region: 67.3% of windows show the same or higher numbers of fixed differences between species (Figure 4). Given the evidence for a recombination hotspot in humans but not in chimpanzees, these observations are consistent with an association between recombination and mutation in primates (Hellmann et al. 2003) and, in particular, with a mutagenic effect of recombination (Rattray et al. 2002). If indeed recombination events introduce mutations, the lack of a peak of human–chimpanzee divergence in the hotspot region (Jeffreys et al. 2000; Figure 4) would suggest that the hotspot arose fairly recently in human evolution. Figure 4 Distribution of Variable Sites in the Genomic Region The positions of sites that differ between humans and chimpanzees are shown on the first line, while the positions of sites polymorphic in humans from the UK or in western chimpanzees are shown on the next two lines. The human hotspot region is underlined. The dashed lines indicate regions not surveyed for variation in western chimpanzees (see Materials and Methods). In conclusion, these analyses demonstrate that fine-scale recombination rates can change between closely related species. Together with the observations that crossover frequencies can depend on specific haplotypes (Jeffreys and Neumann 2002) and that large-scale recombination rates differ among individuals (Cullen et al. 2002; Kong et al. 2002), this finding raises the possibility that local rates can vary among human groups that differ in their allele frequencies. Unfortunately, demonstrating compelling evidence for variation among human populations on the basis of LD data alone promises to be substantially harder than demonstrating such differences between chimpanzees and humans. In particular, human populations share most of their evolutionary history, making differences between extant populations, if they exist, more difficult to detect. Nevertheless, LD studies should be helpful in identifying interesting regions for further study via sperm typing. The extent to which local recombination rates vary among human populations influences the degree of similarity of LD patterns among them, with important consequences for the design of efficient LD-based association studies (including, for example, the choice of appropriate “haplotype tagging SNPs” [Johnson et al. 2001] in different human populations) and for the relevance of data generated by the current human HapMap project to populations not currently represented in that study (International Hapmap Consortium 2003). Perhaps most importantly, if local recombination rates do vary among groups, then the study of regions with the most pronounced differences should lead to further insights into the underlying biological processes that cause fine-scale variation in recombination rates. Materials and Methods Samples We used DNA from 24 western chimpanzees (Pan troglodytes verus) that were wild caught or known to be unrelated based on recent pedigrees. Twelve samples (Annaclara, Frits, Hilko, Liesbeth, Louise, Marco, Oscar, Regina, Socrates, Sonja, Yoran, and Yvonne) are from the collection stored at the Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, while 12 other samples (NDH0311G1, NDH0312G1, NDH0313G1, NDH0314G1, NDH0317G1, NDH0320G1, NDH0321G1, NDH0322G1, NDH0325G1, NDH0326G1, NDH0328G1, and NDH0329G1) were kindly provided by P. Morin and the Primate Foundation of Arizona. Primer design We amplified 9,491 bp from the TAP2 region, corresponding to base pairs 113102–122585 of the sequence from Beck et al. (1996) (see Supporting Information); the slight discrepancy in the number of base pairs is due to indels. To minimize the chance of allelic dropout, we designed the PCR primers such that most of the sequence would be amplified by two independent sets of primers. The 20 overlapping primer sequences are listed in Protocol S1. PCR and DNA sequencing DNA amplification reactions contained 250 μM of each dNTP, 1–2 mM MgCl2, PCR buffer (10 mM Tris-HCl, 50 mM KCl; pH 8.3), 0.5 U of Taq DNA polymerase (all reagents from Roche, Basel, Switzerland), and 10 pmol of each primer. We used 50–100 ng of DNA in each 30 μl PCR. Amplification conditions for all regions were the following: incubation for 3 min at 94 °C, 35 cycles (45 s at 94 °C, 1 min at 45–62 °C, and 1 min at 72 °C) and a final elongation of 5 min at 72 °C. A nested PCR was performed to obtain regions 6 and 7 by using the product of the primers Tap2 5 5′ and Tap2 8 3′ as a template. PCR products were separated from primers and unincorporated dNTPs by treatment with a solution of 10% PEG 8000/1.25 M NaCl followed by centrifugation. PCR products were then air dried and resuspended in 10–15 μl of H2O. Sequencing reactions consisted of 1 μl of ABI Prism BigDyeTM Terminators version 2.0 (Perkin Elmer Biosystems, Torrance, California, United States), 8–10 ng of purified PCR product, and 1 μl of 2.5 μM primer (the same primers used for PCR) in a volume of 7 μl. Cycling conditions were 96 °C for 2 min and then 35 cycles of 96 °C (20 s), annealing temperature (30 s), and 60 °C (4 min). Isopropanol-precipitated cycle sequencing products were run on an ABI 3730 DNA analyzer. Base calling was done with ABI Prism DNA Sequencing Analysis version 5.0 and ABI Basecaller. BioEdit version 5.0.6 was used for sequence analysis and alignment. In total, 2-fold coverage of a 9,370 bp sequence was obtained for each individual; these are available from GenBank (see Supporting Information). Most of the region was sequenced from both DNA strands. However, due to the presence of insertions, deletions, and T or A stretches, this was not possible for a subset of segments; for these, 2-fold coverage was achieved by sequencing the same strand. For segment 6, we did not obtain reliable sequence data for all individuals (due to suspected allelic dropout); we therefore excluded this region of 487 bp. Otherwise, there are no missing data. SNPs were identified by visualization of the chromatograms using BioEdit version 5.0.6. The polymorphism data used for the analyses are available in Protocol S1. Data analysis We estimated the population mutation rate, θ = 4N e μ (N e is the diploid effective population size and μ is the mutation rate per generation), using Watterson's estimator, θ W (Watterson 1975), based on the number of segregating sites in the sample. We also calculated a commonly used summary of the allele frequency spectrum, Tajima's D (Tajima 1989); both D and θ W were calculated with DNAsp (Rozas and Rozas 1999). We used the D statistic to test the fit of the standard neutral model (of a random mating population of constant size) to allele frequencies in western chimpanzees. Specifically, we ran 104 coalescent simulations of the standard neutral model with the same number of chromosomes and base pairs as in the actual data, with θ equal to θ W, and with the population recombination rate equal to the estimated value (see below). We then tabulated the proportion of simulated runs with a Tajima's D value as or more extreme than that observed. We calculated the GC content of the region and searched for sequence motifs previously associated with recombination hotspots (Badge et al. 2000; Petes 2001; Wall et al. 2003) using the program “scan_for_matches” available from http://bioweb.pasteur.fr/seqanal/interfaces/scan_for_matches.html. The list of motifs found in the human and chimpanzee sequences is given in Protocol S1. Analyses of LD To assess the support in the polymorphism data for a recombination hotspot, we used the Product of Approximate Conditionals (PAC) model of Li and Stephens (2003). Assuming haplotypes are known, the method considers each one in turn and attempts to represent it as a mosaic of the previously considered haplotypes. Qualitatively, the larger the regions over which haplotypes tend to resemble one another, the fewer the pieces required in each mosaic, and the lower the estimates of the recombination rates. The method uses simplistic assumptions about population demography to quantify this qualitative relationship and hence to estimate recombination rates across the region. More formally, the model of Li and Stephens (2003) defines the probability of observing haplotypes H given the underlying recombination parameters α (which in our case may include the background recombination rate and the hotspot location and intensity; see below). This can be used directly to estimate α from H in situations where haplotypes have been experimentally determined (e.g., Li and Stephens 2003). However, in our case the chimpanzee haplotypes are not known. Rather, we have genotype data G and we wish to estimate α from G. A simple approximate solution to this would be first to use a statistical method (e.g., that of Stephens et al. 2001) to obtain an estimate H^ for the haplotypes H from the genotypes G, and then to estimate α from H^. However, a risk of this approach is that overconfident conclusions will be drawn by ignoring uncertainty in the estimated haplotypes. A better solution, and the approach we take here, is to jointly estimate H and α from G, or, more specifically, to obtain a sample from the joint posterior distribution, Pr(H, α | G). To do so, we start with an initial guess for the haplotypes, and iterate the following steps: (i) estimate a new value for α, using the current estimate of H and (ii) estimate a new value for H, using the genotypes G and the current value for α. Step (i) is performed using the PAC-B model of Li and Stephens (2003) and the priors on α described below. Step (ii) is performed by using the method for haplotype inference described in Stephens and Donnelly (2003), but replacing the conditional distribution that they use (which ignores recombination) with the conditional distribution of Fearnhead and Donnelly (2001) (which takes into account recombination) computed using two quadrature points. (Actually, we modified the Fearnhead and Donnelly conditional distribution slightly, replacing the equation qi = ziρ/(j+ ziρ) in their Appendix A with qi =1−exp(−ziρ/j).) Both the PAC-B model and the Fearnhead and Donnelly conditional require the specification of a mutation parameter, θ, and a mutation process. In each case, we used the value of θ given in Li and Stephens (2003) and a mutation process whereby each mutation event at a biallelic site results in a change from one allele to the other. This iterative scheme defines a Markov chain whose stationary distribution is the distribution Pr(H, α | G) from which we wish to sample. Provided that the algorithm is run for sufficiently long, the estimates of α obtained each iteration provide a sample from the distribution Pr(α | G), and thus allow α (i.e., the underlying recombination process) to be estimated directly from G, taking full account of the fact that the actual underlying haplotypes are not known. The algorithm is implemented within the software package PHASE version 2.1, which is available online at http://www.stat.washington.edu/stephens/software.html. We considered two versions of the simple hotspot model of Li and Stephens (2003). In this model, there is a single hotspot of constant intensity λ. Crossovers occur as a Poisson process (i.e., there is no interference) of constant rate r (per base pair) outside the hotspot and of constant rate λr inside the hotspot; gene conversion is not explicitly modeled. In the first version, we assumed that, if present, the hotspot is at the same location as estimated by sperm typing in humans (4180–5417). (This location is not precisely the same as the one used by Li and Stephens [2003], which is why our estimates differ from theirs.) There are two parameters to be estimated: the background population recombination parameter ρ (= 4N e r, where N e is the effective population size) and λ. We assumed a priori that a hotspot exists with probability 0.5 and that, if the hotspot exists, λ is between one and 100. Specifically, we assumed that λ = 1 with probability 0.5 and otherwise that log10 (λ) is uniformly distributed on (0, 2). The prior on ρ is uniform on log10 (ρ) in the range (−8, 3), which covers all plausible values. In the second version, we assumed that the location and width of the hotspot are unknown and to be estimated along with λ and ρ. In this case, we assumed a priori that the hotspot exists with probability 0.18 (corresponding to an assumption that a hotspot occurs roughly once per 50 kb of sequence), that the center of the hotspot is equally likely to be anywhere along the length of the sequence, and that the width of the hotspot is between 200 and ∼4,000 bp (specifically, we assumed that the width had a normal distribution, with a mean of 0 bp and a standard deviation of 2,000 bp, truncated to lie above 200 bp). Priors on ρ and on λ (conditional on there being a hotspot) are as in the first version. To allow for potential problems with convergence of this Markov chain Monte Carlo algorithm, we ran the algorithm ten times for each analysis, using different seeds for the pseudorandom number generator. For each run, we obtained a point estimate of the parameters (using sample posterior medians) and posterior probabilities. The reported estimates are the median of the estimates obtained from the ten runs. To test how likely we would be to obtain such weak support for a hotspot in the LD data if there were in fact a hotspot similar to the one in humans, we ran 200 coalescent simulations of the standard neutral model (Hudson 1990) with the same number of base pairs and sample size as the actual data (48 chromosomes), a hotspot of intensity λ = 11, and θ = θ W. Haplotypes were randomly paired to form genotypes and phase information was ignored. The data were masked to mimic the actual data structure, i.e., they included a gap of 487 bp in the same position. We then counted the proportion of simulated data sets for which our estimate of the posterior probability was as low as observed or lower (using the first version of the Li and Stephens [2003] model). Since we obtained estimates for the simulated data in the same way as for the actual data, significance values obtained from this analysis are valid independent of the convergence, or even the correctness, of the Markov chain Monte Carlo scheme. In the first set of 200 simulations, we used ρ = ρ^, the background rate that we estimated from the western chimpanzee data. In the second set of simulations, we used ρ = 4N^er^ = 2.7 × 10−4 per bp, where N^e = 17,100 is an estimate of the effective population size of western chimpanzees (based on Fischer et al. 2004) and r^ = 0.4 cM/Mb is the rough estimate of the background recombination rate reported in Jeffreys et al. (2000). Supporting Information Protocol S1 Supplementary Materials (91 KB DOC). Click here for additional data file. Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/) accession number for the human TAP2 region of Beck et al. (1996) is X87344. The numbers for the 9,370-bp sequences obtained from the 24 western chimpanzees are AY559252–AY559299. We are grateful to Phil Morin and the Arizona primate facility for providing DNA samples from 12 chimpanzees, to Alec Jeffreys and Rita Neumann for sending us a list of TAP2 primers and conditions, to Jeff Wall for providing us with a computer program, to Anna Di Rienzo and Jeff Wall for helpful discussions, and to the Max Planck sequencing unit. Support for this work was provided by the Max Planck Society and Deutsche Forschungsgemeinschaft grant BIZ6-1/1. MS was supported by National Institutes of Health grant number 1R01HG/LM02585-01. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. MP conceived and designed the experiments. ADR and YG performed the experiments. SEP, MS, and MP analyzed the data. MS, SP, and MP contributed reagents/materials/analysis tools. All authors contributed to the writing of the paper. Academic Editor: Andy Clark, Cornell University ¤1Current address: Biodiversity and Ecological Processes Group, Cardiff School of Biosciences, Cardiff, United Kingdom ¤2Current address: Department of Genetics, Yale University, New Haven, Connecticut, United States of America ¤3Current address: Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America Abbreviations LDlinkage disequilibrium PAC modelProduct of Approximate Conditionals model SNPsingle nucleotide polymorphism. ==== Refs References Arnheim N Calabrese P Nordborg M Hot and cold spots of recombination in the human genome: The reason we should find them and how this can be achieved Am J Hum Genet 2003 73 5 16 12772086 Ashburner M Drosophila: A laboratory handbook 1989 Cold Spring Harbor (New York) Cold Spring Harbor Laboratory Press 1331 Badge RM Yardley J Jeffreys AJ Armour JA Crossover breakpoint mapping identifies a subtelomeric hotspot for male meiotic recombination Hum Mol Genet 2000 9 1239 1244 10767349 Beck S Abdulla S Alderton RP Glynne RJ Gut IG Evolutionary dynamics of non-coding sequences within the class II region of the human MHC J Mol Biol 1996 255 1 13 8568858 Betancourt AJ Presgraves DC Linkage limits the power of natural selection in Drosophila Proc Natl Acad Sci U S A 2002 99 13616 13620 12370444 Boulton A Myers RS Redfield RJ The hotspot conversion paradox and the evolution of meiotic recombination Proc Natl Acad Sci U S A 1997 94 8058 8063 9223314 Brooks LD The evolution of recombination rates. In: Michod RE, Levin BR, editors. The evolution of sex: An examination of current ideas 1988 Sutherland, Massachusetts Sinauer Associates 87 105 Cullen M Perfetto SP Klitz W Nelson G Carrington M High-resolution patterns of meiotic recombination across the human major histocompatibility complex Am J Hum Genet 2002 71 759 776 12297984 Daly MJ Rioux JD Schaffner SF Hudson TJ Lander ES High-resolution haplotype structure in the human genome Nat Genet 2001 29 229 232 11586305 de Massy B Distribution of meiotic recombination sites Trends Genet 2003 19 514 522 12957545 Ebersberger I Metzler D Schwarz C Paabo S Genomewide comparison of DNA sequences between humans and chimpanzees Am J Hum Genet 2002 70 1490 1497 11992255 Fearnhead P Donnelly P Estimating recombination rates from population genetic data Genetics 2001 159 1299 1318 11729171 Fischer A Wiebe V Paabo S Przeworski M Evidence for a complex demographic history of chimpanzees Mol Biol Evol 2004 In press Gabriel SB Schaffner SF Nguyen H Moore JM Roy J The structure of haplotype blocks in the human genome Science 2002 296 2225 2229 12029063 Gilad Y Bustamante C Lancet D Paabo S Natural selection on the olfactory receptor gene family in humans and chimpanzees Am J Hum Genet 2003 73 489 501 12908129 Hellmann I Ebersberger I Ptak S Paabo S Przeworski M A neutral explanation for the correlation of diversity with recombination in humans Am J Hum Genet 2003 72 1527 1535 12740762 Hubert R MacDonald M Gusella J Arnheim N High resolution localization of recombination hot spots using sperm typing Nat Genet 1994 7 420 424 7920662 Hudson RR Estimating the recombination parameter of a finite population model without selection Genet Res 1987 50 245 250 3443297 Hudson RR Gene genealogies and the coalescent process. In: Futuyma D, Antonovics J, editors. Oxford surveys in evolutionary biology, Volume 1 1990 Oxford Oxford University Press 1 44 International Hapmap Consortium The International Hapmap project Nature 2003 426 789 796 14685227 Jeffreys AJ Neumann R Reciprocal crossover asymmetry and meiotic drive in a human recombination hot spot Nat Genet 2002 31 267 271 12089523 Jeffreys AJ Murray J Neumann R High-resolution mapping of crossovers in human sperm defines a minisatellite-associated recombination hotspot Mol Cell 1998 2 267 273 9734365 Jeffreys AJ Ritchie A Neumann R High resolution analysis of haplotype diversity and meiotic crossover in the human TAP2 recombination hotspot Hum Mol Genet 2000 9 725 733 10749979 Jeffreys AJ Kauppi L Neumann R Intensely punctate meiotic recombination in the class II region of the major histocompatibility complex Nat Genet 2001 29 217 222 11586303 Johnson GC Esposito L Barratt BJ Smith AN Heward J Haplotype tagging for the identification of common disease genes Nat Genet 2001 29 233 237 11586306 Kong A Gudbjartsson DF Sainz J Jonsdottir GM Gudjonsson SA A high-resolution recombination map of the human genome Nat Genet 2002 31 241 247 12053178 Li HH Gyllensten UB Cui XF Saiki RK Erlich HA Amplification and analysis of DNA sequences in single human sperm and diploid cells Nature 1988 335 414 417 3419517 Li N Stephens M Modelling linkage disequilibrium, and identifying recombination hotspots using SNP data Genetics 2003 165 2213 2233 14704198 Morin PA Moore JJ Chakraborty R Jin L Goodall J Kin selection, social structure, gene flow, and the evolution of chimpanzees Science 1994 265 1193 1201 7915048 Nachman MW Variation in recombination rate across the genome: Evidence and implications Curr Opin Genet Dev 2002 12 657 663 12433578 Petes TD Meiotic recombination hot spots and cold spots Nat Rev Genet 2001 2 360 369 11331902 Pritchard JK Przeworski M Linkage disequilibrium in humans: Models and data Am J Hum Genet 2001 69 1 14 11410837 Rattray AJ Shafer BK McGill CB Strathern JN The roles of Rev3 and Rad57 in double-strand break repair induced mutagenesis in Saccharomyces cerevisiae Genetics 2002 162 1063 1077 12454056 Rogers J Mahaney MC Witte SM Nair S Newman D A genetic linkage map of the baboon (Papio hamadryas) genome based on human microsatellite polymorphisms Genomics 2000 67 237 247 10936045 Rozas J Rozas R DnaSP version 3: An integrated program for molecular population genetics and molecular evolution analysis Bioinformatics 1999 15 174 175 10089204 Schneider JA Peto TEA Boone RA Boyce AJ Clegg JB Direct measurement of the male recombination fraction in the human β-globin hot spot Hum Mol Genet 2002 11 207 215 11823440 Smith RA Joy Ho P Clegg JB Kidd JR Thein SL Recombination breakpoints in the human β-globin gene cluster Blood 1998 11 4415 4421 Steiner WW Schreckhise RW Smith GR Meiotic DNA breaks at the S. pombe recombination hot spot M26 Mol Cell 2002 9 847 855 11983175 Stephens M Smith NJ Donnelly P A new statistical method for haplotype reconstruction from population data Am J Hum Genet 2001 68 978 989 11254454 Stephens M Donnelly P A comparison of bayesian methods for haplotype reconstruction from population genotype data Am J Hum Genet 2003 73 1162 1169 14574645 Stone AC Griffiths RC Zegura SL Hammer MF High levels of Y-chromosome nucleotide diversity in the genus Pan Proc Natl Acad Sci U S A 2002 99 43 48 11756656 Tajima F Statistical method for testing the neutral mutation hypothesis by DNA polymorphism Genetics 1989 123 585 595 2513255 Thomas JW Touchman JW Blakesley RW Bouffard GG Beckstrom-Sternberg SM Comparative analyses of multi-species sequences from targeted genomic regions Nature 2003 424 788 793 12917688 True JR Mercer JM Laurie CC Differences in crossover frequency and distribution among three sibling species of Drosophila Genetics 1996 142 507 523 8852849 Wall JD Pritchard JK Haplotype blocks and the structure of linkage disequilibrium in the human genome Nat Rev Genet 2003 4 587 597 12897771 Wall JD Frisse LA Hudson RR Rienzo AD Comparative linkage disequilibrium analysis of the β-globin hotspot in primates Am J Hum Genet 2003 73 1330 1340 14628290 Watterson GA On the number of segregating sites in genetic models without recombination Theor Popul Biol 1975 7 256 276 1145509 Yauk CL Bois PR Jeffreys AJ High-resolution sperm typing of meiotic recombination in the mouse MHC Eβ gene EMBO J 2003 22 1389 1397 12628931

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PLoS Biol. 2004 Jun 15; 2(6):e155

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PLoS Biol

10.1371/journal.pbio.0020155

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020157Community PagePhysiologyHomo (Human)Virtual Labs: E-Learning for Tomorrow Virtual LabsHuang Camillan 6 2004 15 6 2004 15 6 2004 2 6 e157Copyright: © 2004 Camillan Huang.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.At Stanford University, the Virtual Labs project takes full advantage of information technology to provide innovative resources for learning and teaching ==== Body Because of the explosive growth in our scientific understanding, today's students are required to learn and maintain a rapidly expanding knowledge base. Students are also expected to understand and follow the crossover of information between different disciplines. As a result, they often have to understand the fundamentals of several disciplines, and be able to integrate that knowledge. Students of every discipline are facing these new challenges, and it is clear that today's students are markedly different from those of the past. Influenced by a lifetime surrounded by media, computers, and the Internet, they bring with them different expectations. As educators, we need to meet these expectations in order to motivate students to move forward. And it's not just the student population that is driving change. The National Institutes of Health, which sponsors many biological and medical advances in the United States, has a new initiative called “Digital Biology: The Emerging Paradigm,” whose goal is to merge biomedical computation with biology and medicine over the next ten years. One way to facilitate this movement is to use information technology (IT) as a teaching tool, so that students, in turn, learn how to use IT most effectively. Using IT to Teach IT presents educators and teachers with a unique opportunity to devise innovative methods of teaching. Students today are more likely than ever to use new tools and technologies to advance their understanding of the sciences. Currently, this usage is mainly limited to searching the Web for information. However, computers and the Web can be used for much more—with computers, you can create learning scenarios like virtual patient simulations, and with the Web, these learning resources can be disseminated to the global community. Educators must harness the power of these enabling technologies, which students have already adopted, to create new and more powerful methods of teaching that will better prepare the students for the next phase of their lives. The Virtual Labs Project at SUMMIT (Stanford University Medical Media and Information Technologies) in the Stanford University School of Medicine has been funded by the Howard Hughes Medical Institute (Chevy Chase, Maryland, United States) since 1998. It is an initiative to augment the Department of Biological Sciences and the Program in Human Biology at Stanford University by developing technology-enhanced materials for these curricula. A major goal of the Virtual Labs Project is to increase scientific literacy by using interactive multimedia to teach the fundamental concepts of biology, and to share those resources via the Internet. The Virtual Labs material is currently hosted on a password-protected site and is freely available to interested parties for educational use. A wise individual once said that a picture is worth a thousand words; with Virtual Labs we use not just pictures, but also animations and interactive simulations. Students are able to visualize and interact with dynamic processes in the body. We have developed learning modules in cardiovascular, gastrointestinal, respiratory, renal (Video 1), visual (Video 2), and neurophysiological systems. The concepts in these modules lay the foundation for medicine and for an increasing number of interdisciplinary programs, such as biomedicine, medical informatics, and bioengineering. For example, a medical student learns how the kidney filters blood in order to understand kidney failure in diabetic patients. A bioengineer could apply the same knowledge to build an artificial kidney. The modules are flexible, and the content can be woven together to highlight the intersections of different disciplines. Virtual Labs also strives to make learning science fun. The more engaged the user is, the more likely the learning experience is to be positive. For example, after learning about how the kidney filters blood (see the online link for Figure 1) and controls water levels, students apply their new knowledge by playing a simulation game. The goal of the game is to maintain water balance in order to survive on a deserted island, which helps to reinforce conceptual understanding and to ensure that students understand how those concepts fit together. Responses from students have shown that these goals are being met. Over the past four years, our undergraduate and medical students have reported that Virtual Labs was fun, engaging, and that it helped them learn: “Virtual Labs was an excellent resource for the class! [It was] a lot of fun to use and the graphics are awesome.” “It was a great interactive way to reinforce what I already had learned from the book and lectures and I think it really helped me better understand.” Similarly, faculty who have used our material during lectures have found it useful to illustrate concepts with animations. Reaching Beyond the Local Community Local schools and other universities are looking for opportunities to bring IT into their classrooms. Access to resources like Virtual Labs, and expertise on how to develop and integrate multimedia content into curricula, are on the rise. In 2003, the Virtual Labs Project began building a network of collaborators in the community and abroad. Together with H.E.L.P for Kids (http://www.stanford.edu/group/help,) we are designing content for the education of local schoolchildren. Abroad, we are working with global partners in Sweden via the Wallenberg Global Learning Network (http://www.wgln.org). We have also partnered with the MedFarmDoIT group at Uppsala University in Uppsala, Sweden (http://doit.medfarm.uu.se/multimedia.html) to help them with multimedia development and IT integration in the classroom. The MedFarmDoIT group shares our vision of bringing IT into the classroom, and together, we are designing content for their medicine/pharmacy program. The Virtual Labs Project is dedicated to supporting its partners by distributing customized Virtual Labs content and offering consultation or workshops to train teachers and developers. As integrated partners, we can bridge the gap between the physical and information sciences, and in doing so, can improve the learning process of students for years to come. Video 1 The “Big Picture” of the Blood Flow through the Vasculature in the Kidney The rich visuals and moving media of this Virtual Labs animation capture the attention of the students. (The animation can be accessed online on computers with Shockwave by clicking and dragging the file into the browser window. A free version of Shockwave can be downloaded from http://sdc.shockwave.com/shockwave/download.) Video 2 Understanding Center–Surround Receptive Fields in Retinal Neurons The virtual experiment in this Virtual Labs interactive program is similar in design to the receptive field experiments from Hubel and Wiesel in the 1960s. The user places an electrode in the retina to take a recording from a neuron. The user moves a spot of light on the screen and then maps correlating changes in the activity level (using the symbols: + − 0 to indicate the strength of the response). The map reveals a center–surround organization. Each time the user moves the electrode, the size, shape, location, and type of receptive field changes (on-center or off-center), as they would during a real experiment. Supporting questions adapt dynamically to each experimental condition and further encourage the student to answer more conceptual questions. (The interactive program can be accessed online on computers with Shockwave by clicking and dragging the file into the browser window. A free version of Shockwave can be downloaded from http://sdc.shockwave.com/shockwave/download.) I would like to thank the Howard Hughes Medical Institute and the Wallenberg Global Learning Network for sponsoring the Virtual Labs Project. Many thanks to SUMMIT for their support. Camillan Huang PhD is the Virtual Labs Project Director at SUMMIT in Stanford, California, United States of America. E-mail: cammy@summit.stanford.edu Abbreviations ITinformation technology SUMMITStanford University Medical Media and Information Technologies ==== Refs Further Reading The Virtual Learning Lab — Available at http://virtuallearninglab.org via the Internet. Accessed 25 March 2004 The Virtual Learning Lab demos — Available at http://chococat.stanford.edu/test/index.html via the Internet. Accessed 25 March 2004

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PLoS Biol. 2004 Jun 15; 2(6):e157

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PLoS Biol

10.1371/journal.pbio.0020157

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020160Research ArticleBioinformatics/Computational BiologyCell BiologyGenetics/Genomics/Gene TherapySaccharomycesIntegrative Analysis of the Mitochondrial Proteome in Yeast Mitochondrial Proteome IntegrationProkisch Holger 1 2 Scharfe Curt 3 Camp David G II 4 Xiao Wenzhong 3 David Lior 3 Andreoli Christophe 1 Monroe Matthew E 4 Moore Ronald J 4 Gritsenko Marina A 4 Kozany Christian 1 Hixson Kim K 4 Mottaz Heather M 4 Zischka Hans 1 Ueffing Marius 1 Herman Zelek S 3 Davis Ronald W 3 Meitinger Thomas 1 2 Oefner Peter J 3 Smith Richard D 4 Steinmetz Lars M lars.steinmetz@embl.de 3 ¤11Institute of Human Genetics, GSF National Research Center for Environment and HealthNeuherbergGermany2Institute of Human Genetics, Technical University of MunichMunichGermany3Stanford Genome Technology Center and Department of Biochemistry, Stanford UniversityStanford, CaliforniaUnited States of America4Environmental Molecular Sciences Laboratory, Pacific Northwest National LaboratoryRichland, WashingtonUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e16015 10 2003 24 3 2004 Copyright: © 2004 Prokisch et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Combining Measures to Characterize Subcellular Machinery In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans. Although individual approaches fall short, integrating multiple common genetic and biochemical approaches yields a description of mitochondrial proteins that is more than the sum of its parts ==== Body Introduction About half of the expected mitochondrial proteins in humans are known to date, and already a fifth of these known proteins are associated with human Mendelian disorders (Online Mendelian Inheritance in Man [http://www.ncbi.nlm.nih.gov/Omim/]; DiMauro and Schon 1998; Andreoli et al. 2004). Mitochondrial core functions such as oxidative phosphorylation, amino acid metabolism, fatty acid oxidation, and iron-sulfur cluster assembly have been highly conserved during evolution, suggesting that a systematic identification of mitochondrial proteins in model organisms will accelerate the search for new human mitochondrial disease genes (Steinmetz et al. 2002). In yeast, 477 proteins (469 encoded by the nuclear genome) show conclusive evidence of mitochondrial localization (this study and those listed in the Mitochondrial Proteome 2 [MitoP2] database [http://ihg.gsf.de/mitop]). About 30% of these proteins have evidence of orthologs in humans (MitoP2 database). Identification of the yeast mitochondrial proteome is far from complete. Thirty to forty percent of the predicted complement of proteins that make up the organelle are still considered unknown although many genome-wide and functional systematic studies have been applied (Westermann and Neupert 2003). These include systematic identification of mitochondrial proteins by mRNA expression analysis under various conditions (DeRisi et al. 1997; Lascaris et al. 2003), DNA microarray analysis of mRNA populations associated with mitochondrion-bound polysomes (Marc et al. 2002), deletion phenotype screening (Dimmer et al. 2002; Steinmetz et al. 2002), large-scale localization studies (Kumar et al. 2002), protein–protein interaction studies (Uetz et al. 2000; Ito et al. 2001; Gavin et al. 2002; Ho et al. 2002), mass spectrometry (MS) of mitochondria (Pflieger et al. 2002; Ohlmeier et al. 2003), and various computational predictions of mitochondrial proteins (Nakai and Horton 1999; Drawid and Gerstein 2000; Small et al. 2004). In addition, two recent studies reduced the gap of missing mitochondrial localized proteins: a comprehensive proteomic study of mitochondria claimed to reduce the gap to 10% and identified 749 proteins (Sickmann et al. 2003), and a protein localization study identified 527 mitochondrial localized proteins by green fluorescent protein (GFP) tagging (Huh et al. 2003). Here we generated a component list of the mitochondrial organelle by first identifying mitochondrial proteins using MS and then integrating 22 datasets relevant to the study of mitochondria, including our proteomic data. The integration generated a comprehensive definition of the proteins involved in mitochondrial function and biogenesis and allowed for a comparison of genomic approaches, with implications beyond mitochondria. Results/Discussion Proteomics We identified mitochondrial proteins by combining different methods for purification of whole mitochondrial organelles from yeast cell cultures and directly measured the proteins present in these fractions using MS. Mitochondria from yeast cells grown under four different conditions, including fermentable (glucose) and nonfermentable (lactate) substrates for both natural and synthetic culture media, were purified by either density gradient or free-flow electrophoresis. Preparations were separated into mitochondrial membrane and matrix fractions and analyzed separately for protein content. In total, 20 fractions were digested with trypsin and analyzed by reversed phase high resolution liquid chromatography/tandem MS (LC/MS/MS) (Ferguson and Smith 2003; Washburn et al. 2003). In addition, eight of the fractions were further analyzed by liquid chromatography/Fourier transform-ion cyclotron resonance MS (LC/FTICR) (Lipton et al. 2002; Smith et al. 2002). Altogether, 28 experimental datasets were generated (Table S1), which in combination identified 546 proteins (Table S2); listed also in the Yeast Deletion and Proteomics of Mitochondria [YDPM] database and the MitoP2 database). The performance of our proteomic and other systematic approaches in identifying mitochondrial proteins was evaluated against a reference set of 477 proteins classified as mitochondrial localized based on single gene studies. Of the 546 proteins identified by our proteomic approach, 47% were known mitochondrial, covering 54% of the reference set (256/477). Sorting the 546 candidates by the number of experiments in which they were found demonstrated that the probability of identifying a mitochondrial protein correlated with its detection frequency and with the confidence associated with its identification based on the number of peptide tags identified (Figure 1A). A separate analysis of membrane and matrix preparations showed that membrane and matrix proteins were more likely to be identified in membrane and matrix preparations, respectively. In addition, similar proportions of known mitochondrial proteins were identified from both fractions, indicating no significant bias towards the identification of either primarily soluble or primarily membrane-associated proteins (Figure 1B). Figure 1 Enrichment for Mitochondrial Proteins by MS (A) shows the 546 proteins (in rows) identified from 28 datasets (columns). The proteins are sorted in decreasing order down rows by the number of experiments in which peptide tags were identified by MS and binned into three classes of detection frequency. The number at the bottom of each class indicates the total number of proteins in the class. Proteins that are part of the reference set, and thus are previously known mitochondrial proteins (M), are marked to the left. The experiments are divided according to fermentable (F) and nonfermentable (NF) mitochondrial preparations. (B) Proportions of proteins identified in membrane and matrix fractions. Whether a protein was detected predominantly in either the membrane or matrix fraction, or equal in both fractions, was determined based on where it was detected with an average higher tag number. Shown are the proportions for all 546 proteins, for known matrix proteins (i.e., matrix and intermembrane space, n = 109), for known membrane proteins (i.e., inner and outer membrane, n = 101), and for detected proteins not previously known to be mitochondrial (n = 290). (C) Distribution of proteins identified under fermentable and nonfermentable conditions by proteomics, and overlap with previously known mitochondrial proteins. Total numbers are given in parentheses. (D) Breakdown by localization of the 546 proteins identified. For mitochondrial localization the reference set was chosen; for localization outside mitochondria the GFP fusion protein data were used (Huh et al. 2003). The inner circle represents the distribution for all proteins in yeast. (E) Distribution of median mRNA expression under fermentable and nonfermentable conditions, protein abundance under fermentable conditions (Ghaemmaghami et al. 2003), and protein length across bins of confidence of identification (maximum number of tags identified in any of the 28 datasets). The bars indicate fold differences from the median for the known mitochondrial proteins that were not detected by MS (“M not det.”). A comparison between fermentable and nonfermentable growth conditions revealed that more proteins were detected under respiration (448) than fermentation (378) conditions (Figure 1C), consistent with the known activation of oxidative phosphorylation during aerobic growth. Notably, of the 477 known mitochondrial proteins, 183 were identified under both growth conditions, suggesting that at least 38% of the mitochondrial machinery is present at moderate to high abundance even under fermentable growth conditions. This finding indicates the presence of a core mitochondrial protein set that exists under multiple growth conditions, which is consistent with previous observations (Ohlmeier et al. 2003). Of the 546 proteins identified by proteomics, 182 proteins are known to localize outside mitochondria, mainly to the cytoplasm, nucleus, endoplasmic reticulum, and plasma membrane (Figure 1D). In addition to contaminants copurified with the fractions, identification of these proteins lends further support to the physical interaction of mitochondria with other cellular compartments and the existence of proteins with multiple localizations (Achleitner et al. 1999). In the analysis of complex protein mixtures by MS, low abundance of proteins can preclude their identification (Patterson and Aebersold 2003). This might explain why 46% of the mitochondrial reference set escaped detection (221 proteins). To assess the correlation between protein detection and expression level systematically, we performed genome-wide mRNA expression analysis by means of high-density oligonucleotide arrays under the same fermentable and nonfermentable growth conditions. This analysis showed that absolute mRNA expression levels increased with the known index for establishing confidence of protein identification (tag number; Figure 1E): while genes identified by proteomics had median expression levels 1.2- to 7.1-fold higher than their unidentified mitochondrial counterparts, they did not differ in protein length, supporting a bias of current proteomic approaches primarily towards the detection of more abundant proteins. We also extended our comparison to the analysis of protein abundance, which was recently determined for about two-thirds of the yeast proteome under fermentation (Ghaemmaghami et al. 2003). To visualize the distribution of identified proteins by their copy number per cell, we divided the 3558 proteins from that study into ten abundance classes, each consisting of an equal number of proteins. We then analyzed the distribution of known mitochondrial proteins across the classes. Figure 2A shows that we were able to detect known mitochondrial proteins over the whole range of expression levels, from 195 to 519,000 copies per cell. However, there is a clear bias towards the detection of more abundant proteins (i.e., in the highest abundancy class, 82% of the reference-set proteins were identified). A recently published study using multidimensional chromatography, Sickmann et al. (2003), achieved a higher coverage of known mitochondrial proteins, but the distribution of their identified proteins is also characterized by a bias against proteins of very low abundance (Figure 2A). Interestingly, even among the most abundant mitochondrial reference proteins, several remained undetected by either proteomic approach. Some of these proteins have a dual localization for which only a minor amount localizes to mitochondria (i.e., tRNA nucleotidyltransferase or synthases), further supporting the failure of proteomics to detect rare proteins in the samples. Figure 2 Evaluation of Proteomic Data for Protein Abundance and Mitochondrial Localization (A) Coverage of known mitochondrial proteins (Mref) by two MS proteome studies (this study and Sickmann et al. [2003]). We evaluated the 340 proteins of the mitochondrial reference set for which protein abundance data existed (Ghaemmaghami et al. 2003). The x-axis represents the median protein abundance of ten consecutive, equally sized bins of proteins. (B) Distribution and overlap of proteins identified by the two MS studies and known mitochondrial proteins. The total number of entries for each dataset is indicated in parentheses outside each circle. The number inside each circle indicates the number of proteins in each of the categories. In addition, the percentage of proteins that were localized to mitochondria by GFP tagging (Huh et al. 2003) is given in parentheses for each category. Analysis of the overlap between both proteomic datasets (Figure 2B) shows that 337 proteins, corresponding to 62% of our study, were identified by both proteomic approaches, while 209 and 412 proteins, respectively, were present in only one or the other dataset. The majority of the proteins identified by both approaches were already known mitochondrial proteins (71%) or were localized to mitochondria by GFP-fusion proteins (an additional 13%; Huh et al. 2003). This high coverage stands in contrast to the much lower number found for proteins detected by only one dataset. Only 23% of the proteins identified by only one method were known mitochondrial proteins. In addition, while 52% of the new candidates (not previously known mitochondrial) identified by both proteomic studies were confirmed by GFP localization to mitochondria (Huh et al. 2003), only 8% and 15% of the candidates identified by only one or the other study were confirmed by the GFP-localization dataset. This analysis suggests that most of the proteins not found by both studies may be nonmitochondrial contaminants. Further indicative of this conclusion is the observation that proteins identified from localization categories outside mitochondria (see Figure 1D) also were among the high-abundancy proteins in those classes (data not shown). Since mitochondria were purified with different methods in the two proteomic studies, these observations suggest the importance of an integration of approaches. Integration Are there classes of proteins that were not captured by a proteomic analysis, whether integrative or not, but that could be found using different approaches, and vice versa? To address this question, we performed a comparative analysis of functional categories identified by our proteomic dataset in comparison to functional approaches of gene expression analysis and quantitative deletion phenotype screening—datasets which were generated in this study and by Steinmetz et al. (2002), respectively. In the proteomic dataset, proteins annotated as localized outside mitochondria were not significantly enriched for any of the known functional classes (Mewes et al. 2002; Huh et al. 2003). In contrast, known mitochondrial proteins were primarily enriched for known mitochondrial functions such as energy production, transport and sensing, protein fate, and amino acid metabolism (Figure 3). Deletion phenotype screening enriched mainly for proteins involved in genome maintenance, transcription, and translation. Very low enrichment of mitochondrial proteins was achieved by mRNA expression, which predominantly detects proteins involved in energy production, the majority of which seem to localize outside the mitochondrial organelle. Figure 3 Functional Categories and Cellular Localization of Our Proteomic, Deletion, and Expression Datasets Each field shows the proportion of proteins found by the experiment out of the total number of proteins known with a given combination. Fields are color coded by the level of coverage gained by the experiment (color scale upper right). Localization outside mitochondria was based on the GFP fusion protein data (Huh et al. 2003). Fields with less than three identified proteins were not evaluated and left blank. In the upper left corner is the distribution and overlap of proteins identified by each experiment. In parentheses are the known mitochondrial proteins based on the reference set. A comparison of the distribution of protein enrichments shows that different functional categories are targeted by different approaches. Overall, the proteomic approach and the deletion approach identified about equal numbers of previously known mitochondrial proteins; however, they overlapped for less than 30% of the proteins. These observations suggest that combining complementary approaches and an integrative data analysis could be advantageous for predicting new mitochondrial proteins. To improve our comparison of methods and to generate a high confidence list of mitochondrial proteins, we expanded our comparison to a total of 22 datasets relevant to the study of the mitochondrial proteome that had been collected to date. These included the experimental and computational approaches listed in the introduction, including a very recent proteome analysis (Sickmann et al. 2003) and GFP-tag localization study (Huh et al. 2003). For most approaches the mitochondrial candidate genes were taken directly from the publication. From the mRNA expression analyses three datasets were generated. Genes were considered predictive of mitochondrial function if they were differentially expressed between fermentable and nonfermentable growth conditions (our study), differentially regulated in response to the diauxic shift (DeRisi et al. 1997), or differentially expressed in response to Hap4p overexpression (Hap4p is a transcription factor of mitochondrial proteins; Lascaris et al. 2003). The protein interaction datasets were screened for genes that interacted with known mitochondrial proteins. Most of the computational predictions searched for signal peptides indicative of mitochondrial targeting sequences. The homology studies searched for proteins similar to, for example, Rickettsia prowazekii, believed to be closest to a common ancestor with mitochondria. The details for each dataset are given in the MitoP2 database (a flatfile with the datasets is also available as Dataset S1). We first assessed the performance of each method. For this purpose, the sensitivity and specificity of the different approaches were calculated by comparing each dataset with the reference set of known mitochondrial proteins (Figure 4). This comparison showed that the multidimensional proteomic data (Sickmann et al. 2003) covered 76% of the reference set (sensitivity of 76%), followed by the GFP fusion protein data (69%; Huh et al. 2003) and our proteomic dataset (54%). Among the experimental approaches that yielded sensitivity and specificity values of 45% or more were the same three datasets as above, in addition to the deletion phenotype screen (Steinmetz et al. 2002) and another localization study (Kumar et al. 2002). Fifty-three proteins were detected by all five methods, all of which were known mitochondrial proteins. In addition, only 51 proteins of the 477 mitochondrial reference-set proteins were not detected by any of these five methods. In comparison, a comprehensive dataset (union) of all 22 approaches covered 6,324 annotated open reading frames (ORFs) in which all 477 known mitochondrial proteins were included. Figure 4 Specificity and Sensitivity of Systematic Approaches with Regard to Mitochondria Various datasets were benchmarked against the mitochondrial reference set. Each dot in the graph represents an entire dataset: PSORT (Nakai and Harton 1999), hap4 expression (Lascaris et al. 2003), deletion phenotype screen (Steinmetz et al. 2002), tag localization (Kumar et al. 2002), GFP localization (Huh et al. 2003), MitoProt greater than 90 (Scharfe et al. 2000), Bayesian prediction (Drawid and Gerstein 2000), pet phenotypes (Dimmer et al. 2002), three MS proteome studies (Pflieger et al. 2002; Sickmann et al. 2003; Ohlmeier et al. 2003), mitochondria localized ribosomes (Marc et al. 2002), Predotar (Small et al. 2004), and yeast proteins with known human mitochondrial orthologs (MitoP2 database). High-throughput protein–protein interaction datasets (PPI) were combined and divided into confidence classes (von Mering et al. 2002). Medium and high confidence PPI datasets were defined by interactions with known mitochondrial proteins (MitoP2 database). The predictive score for a mitochondrial protein (MitoP2) was based on the integration of 22 datasets, most of which are shown, and was calculated for different thresholds. Specificity and sensitivity are current best estimates owing to the incompleteness of the reference set. We next set out to determine whether the information supplied by each one of the different methods could be combined to achieve a predictive power that exceeded that of any single approach. We assessed the overlap among different combinations of the 22 datasets and defined a metric for attaching a numerical value to the likelihood of a protein being mitochondrial. A predictive score (MitoP2 score) was estimated based on the specificity of the best combination of approaches: we calculated for each approach as well as for all possible combinations of approaches, the percentage (R) of observed proteins present in the mitochondrial reference set relative to the total number of proteins detected. Most proteins belonged to more than one combination, and for these proteins multiple R values were calculated. The MitoP2 value was chosen to represent the highest R value calculated for a protein, representing the specificity of the best combination of methods. Figure 4 shows that the list of proteins selected with the MitoP2 score yields a sensitivity and specificity higher than those achieved by any single approach. Among 435 proteins with a MitoP2 value greater than 96, 353 proteins were known mitochondrial. Using a MitoP2 value of 90 as a threshold, 691 yeast proteins were found of which 399 were known mitochondrial localized and 292 were new candidates. These data indicate that the power of defining mitochondrial proteins through combining various genome-wide datasets is significantly greater than that of any single method alone, including proteomics and GFP fusion protein localization. Three lines of evidence further support the success of this integrative analysis for defining the yeast mitochondrial proteome. First, the enrichment level for known mitochondrial proteins correlated with the level of the MitoP2 score and the number of experiments in which candidates were identified by proteomics: for the high, medium, and low classes (see Figure 1A) the median MitoP2 scores were 98, 94, and 82, respectively. Second, MitoP2 prediction was confirmed by import experiments. Ten out of 15 tested candidates with MitoP2 scores greater than 90 were imported into isolated yeast mitochondria, and seven of these were supported with signal sequence cleavage (Figure 5). This ratio (10/15) predicts that 67% of the 292 new candidates could be imported into mitochondria, indicating that 594 of the 691 proteins (with MitoP2 scores greater than 90) may thus be localized to mitochondria (399 plus 195). Third, an investigation of known subunits in mitochondria revealed that most of the components of known complexes were assigned a high MitoP2 score (Figure 6). Comparison with our proteomic dataset showed that while some of the assembly factors of respiratory chain complexes IV and V and subunits of the TIM22 complex were not detected by proteomics, the integrative analysis defined them correctly as mitochondrial proteins. This observation provides further support of the advantage gained by an integrative approach that combines various datasets. Figure 5 Verification of Proteomic Candidates by Mitochondrial Import Samples were incubated in the presence or absence of a membrane potential (MP) and of proteinase K (PK). Cases where import was accompanied by removal of the signal peptide (SP) are marked as “SP-processing” (+). Su9(1–69)DHFR and AAC serve as positive controls for a processed matrix protein and a nonprocessed inner membrane protein, respectively. The bar graphs indicate if a protein was more likely to be found in either the membrane or the matrix fractions of our proteomic data. The height of the bar corresponds to the number of samples in which a protein was identified with higher tag number—in the mitochondrial membrane or mitochondrial matrix fractions, respectively. Figure 6 Verification of Prediction in Selected Mitochondrial Protein Complexes The assignment of complexes to mitochondrial compartments is based on known localizations of the protein subunits. Complexes are shown as clusters of circles, where each circle represents one protein. Red denotes a protein that was detected under fermentable and green under nonfermentable growth conditions by our proteomic dataset; white indicates proteins that were not detected. The numbers indicate the MitoP2 predictive score. For proteins without a number, no predictive score was assigned by the integrative analysis. Ac, acetyl; CoA, coenzyme A; α-KG, α-ketoglutarate; GDC, glycine decarboxylase; NDH, NADH-oxidoreductase; OAA, oxaloacetate; PDH, pyruvate dehydrogenase; RCC, respiratory chain complex; TIM, transport across inner membrane; TOM, transport across outer membrane; MOM and MIM, mitochondrial outer and inner membrane, respectively. A list of the genes for the plotted complexes is available in Table S4. Implications Our use of mitochondria as a model system for an integrative analysis of a subcellular proteome was aided by the large set of reference proteins known and previous experiments performed. All individual systematic approaches were biased to some extent and incomplete. An integration of data sources is therefore essential to go beyond the limitations of any single method and to achieve a more comprehensive view of the mitochondrial organelle. In similar approaches for other organelles and pathways, the use of reference sets to integrate functional genomic approaches and to define parts lists may prove useful. Most of the mitochondrial reference proteins (399 of 477; 84%) had MitoP2 scores greater than 90, and since we have no evidence for a bias in the current reference set, the mitochondrial proteome as defined by the integration of 22 datasets is nearing saturation. In fact, our integration can be used to obtain an estimate of the number of mitochondrial proteins in yeast. Since outer membrane proteins are often not protease protected, the import analysis is conservative and allows us to estimate a lower boundary for the number of mitochondrial localized proteins. Considering that 84% of the reference proteins had MitoP2 scores greater than 90, we can predict a lower bound estimate of approximately 700 mitochondrial localized proteins in yeast (594 predicted true positives/0.84). This number is at the lower level of previous estimates and indicates that the mitochondrial organelle may consist of fewer proteins than the 800 anticipated (Westermann and Neupert 2003). In order to make a prediction as to which combination of methods may be best applied to study a new system where no prior datasets exist, we performed an analysis of all pairwise combinations of methods. Among the comparisons, the union of proteomics (Sickmann et al. 2003) and subcellular localizations via GFP fusion proteins (Huh et al. 2003) achieved the highest coverage of previously known mitochondrial proteins (sensitivity 87%; specificity 45%). Higher specificity can be achieved by considering the overlap between the two datasets; however, coverage is then severely reduced due to a drastic reduction in gene number (sensitivity 58%; specificity 78%). Union of the two most complementary studies, our proteomics and deletion phenotype datasets—even though they are significantly less exhaustive—also achieved high values (sensitivity 76%; specificity 42%). If we concentrate on datasets that can be generated without massive genetic manipulations, as is required for gene tagging and deletion phenotype approaches, we can achieve a similar sensitivity of 78% with a specificity of 35% through combining in silico predictions (Predotar analysis; Small et al. 2004), expression profiling of a transcription factor mutant (Lascaris et al. 2003), and our proteomic data. These data argue that a combination of even a few complementary datasets may identify the majority of expected proteins. A better balance between sensitivity and specificity, however, can be achieved by an integrative analysis of as many complementary approaches as possible. The advantage of integrative analysis combining structural and functional approaches is the high coverage of various mitochondrial components and functions. With this approach we were able to detect with high confidence proteins that had dual localization. For example, Met7p, which was assigned a MitoP2 score of 96, has a cytoplasmic and mitochondrial dual localization (DeSouza et al. 2000). Met7p was not detected as localized to mitochondria in any structural approach, but was identified by the deletion phenotype screen (Steinmetz et al. 2002). Altogether, 40 known mitochondrial reference proteins were not detected by proteomics or by subcellular localization studies. Through the inclusion of functional datasets in the calculations and the use of a localization list as a reference, our candidate list is strongly enriched for mitochondrial localized proteins, but is not limited to those. Consequently, because the MitoP2 calculation is based on both structural and functional datasets, the score not only predicts mitochondrial localized proteins but also reflects proteins that may localize outside mitochondria but affect mitochondrial function and biogenesis from there. It is clear that the current list of mitochondrial proteins is not complete. The addition of further datasets will improve the prediction, as evidenced by the fact that less than 8% of known mitochondrial proteins have a MitoP2 score less than 70. These proteins thus remain rather undefined by the current integration, and further experimentation is needed to capture this class of mitochondrial proteins, consisting in part of three carrier proteins, 12 dual localized proteins, a few small proteins, and 11 mtDNA-encoded proteins (MitoP2 database). Our method of integration serves as one example; other ways of analyzing and integrating the datasets are possible and may reveal more proteins involved in other aspects of the mitochondrial system. Finally, our study has implications for human diseases (Foury 1997). To date, 129 mitochondrial proteins have been implicated in human disorders (MitoP2 database; DiMauro and Schon 1998; Wallace 1999). The integration in yeast identified 143 new human orthologs of the 292 new yeast mitochondrial candidates defined by a MitoP2 score greater than 90 (Table S3 and MitoP2 database). This set of 143 proteins provides new candidates for putative human mitochondrial disorders where intervals have been mapped but no responsible gene has been identified to date (Steinmetz et al. 2002). Materials and Methods Purification of mitochondria Saccharomyces cerevisiae strains were grown aerobically at 30 °C in SC or YP medium, and cells were harvested in logarithmic growth phase (OD600 < 1.3). Mitochondria were isolated by one of two different methods. One method involved differential centrifugation followed by a Nycodenz density gradient (Glick and Pon 1995), where the progress of mitochondrial purification was controlled by Western blot analysis using organelle-specific marker protein antibodies. In the other method, isolated mitochondria were purified by zone electrophoresis using a ProTeam FFE Free-Flow Electrophoresis apparatus (Tecan, Grödig, Austria) (Zischka et al. 2003). The anodic and cathodic circuit electrolytes consisted of 100 mM acetic acid and 100 mM triethanolamine acetate (pH 7.4). The electrolyte stabilizer was 280 mM sucrose, 100 mM acetic acid, and 100 mM triethanolamine (pH 7.4). The separation medium was 280 mM sucrose, 10 mM acetic acid, and 10 mM triethanolamine (pH 7.4). The counterflow medium was 280 mM sucrose. Table S1 lists the strains, growth conditions, and purification methods used for each dataset. Prior to FFE fractionation, the mitochondria sample was equilibrated with separation medium and adjusted to a final protein concentration of 1–2 mg/mL. Electrophoresis was performed in horizontal mode at 5 °C with a total flow rate of 280 mL/h within the separation chamber at a voltage of 750 V. The samples were applied to the separation chamber with a flow rate of 1–2 mL/h via the cathodic inlet. Fractions were collected in 96-well plates, and the distribution of separated particles was monitored at a wavelength of 260 nm with a SynergyHT reader (Bio-Tek, Winooski, Vermont, United States). The peak fraction was isolated, shock-frozen in liquid nitrogen, and used for electron microscopy. To assess purity, the preparations were analyzed by electron microscopy. The mitochondrial preparations were fixed with 4% formaldehyde, 2% glutaraldehyde, 4% sucrose, 2 mM calcium acetate, and 50 mM sodium cacodylate (pH 7.2) at 4 °C. The fixed samples were dissected with a scalpel, washed for 1 h in cacodylate buffer with 1% osmium tetroxide, and dehydrated with alcohol in increasing concentrations. After embedding in Araldite, the preparations were cut into 50-nm slices by means of an ultramicrotome (LKB-Produkter, Bromma, Sweden) and then analyzed on a Zeiss (Oberkochen, Germany) EM 10 electron microscope. Fractionation of matrix and membrane proteins Reagents used for the preparation of peptide samples were purchased from the indicated suppliers. Ammonium bicarbonate and methanol were from Fisher Scientific (Fair Lawn, New Jersey, United States). Sodium carbonate, urea, dithiothreitol, and calcium chloride were obtained from Sigma-Aldrich (St. Louis, Missouri, United States). Thiourea, trifluoroacetic acid, and acetonitrile were from Aldrich Chemical Company (Milwaukee, Wisconsin, United States). Sequencing-grade, modified porcine trypsin was obtained from Promega (Madison, Wisconsin, United States). Ammonium formate was obtained from Fluka (St. Louis, Missouri, United States). CHAPS and bicinchoninic acid (BCA) assay reagents and standards were from Pierce (Rockford, Illinois, United States). Purified water was generated using a Barnstead Nanopure Infinity water purification system (Dubuque, Iowa, United States). Purified mitochondrial samples were disrupted using a Mini Beadbeater-8 (Biospec Products, Bartlesville, Oklahoma, United States) for 3 min at 4,500 rpm with 0.1 mm zirconia/silica beads (Biospec Products) in a 0.5-mL, sterile siliconized microcentrifuge tube. The lysed mitochondria, containing membrane and matrix proteins, were removed from the beads through a puncture at the bottom of the microcentrifuge tube, by centrifugation at 16,000 xg for 2 min at 4 °C, and the flow-through was collected in a second microcentrifuge tube. The collected lysate was then centrifuged at 356,000 xg for 10 min at 4 °C to pellet the mitochondrial membranes. The soluble supernatant was used for the study of mitochondrial matrix proteins, and the pellet was retained for identifying mitochondrial membrane proteins. Mitochondrial membrane protein preparation Using a sonication bath (Branson 1510, Danbury, Connecticut, United States), the membrane pellet was resuspended in 50 mM ammonium bicarbonate (pH 7.8) in an ice bath. The resuspended sample was diluted with ice-cold 100 mM sodium carbonate (pH 11.0) and incubated on ice for 10 min. The membranes were then pelleted by ultracentrifugation at 356,000 xg for 10 min at 4 °C. The pelleted membranes were washed using two aliquots of ice-cold water and pelleted again by centrifugation. The BCA protein assay was performed to determine protein concentration. The membrane pellet was resuspended in 7 M urea, 2 M thiourea, 1% CHAPS in 50 mM ammonium bicarbonate (pH 7.8), using vortexing and sonication in an ice bath. Dithiothreitol was added to a final concentration of 9.7 mM in the resuspended sample, and the proteins were then treated with thermal denaturation for 45 min at 60 °C. The denatured and reduced protein sample was then diluted 10-fold with 50 mM ammonium bicarbonate (pH 7.8), and calcium chloride was added to a final sample concentration of 1 mM. Tryptic digestion was performed for 5 h at 37 °C using a 1:50 (w/w) trypsin-to-protein ratio. Snap-freezing the sample in liquid nitrogen quenched the digestion. The tryptic peptides were cleaned using a 1-mL strong cation exchange column (Discovery DSC-SCX , Supelco, Bellefonte, Pennsylvania, United States) per the manufacturer's instructions. The eluted peptide sample was concentrated by lyophilization and a BCA assay was performed to determine final peptide concentration. The peptide sample was stored at −80 °C until time for LC/MS/MS analysis. Mitochondrial matrix protein preparation The BCA protein assay was performed on the soluble matrix supernatant. The proteins were thermally denatured and reduced using 7 M urea, 2 M thiourea, and 5 mM dithiothreitol and incubating at 60°C for 30 min. The denatured and reduced protein sample was diluted 10-fold with 50 mM ammonium bicarbonate (pH 7.8), and the concentration of calcium chloride was adjusted to a final concentration of 1 mM. The tryptic digestion of the protein sample was performed in the same manner as described above for the membrane protein sample. The tryptic peptides were cleaned using a 1-mL LC-18 SPE column (Reversed Phase Supelclean LC-18 SPE, Supelco) per the manufacturer's instructions. The eluted peptide sample was concentrated by lyophilization, a BCA protein assay was performed, and the sample was stored at −80 °C until time for LC/MS/MS analysis. Identification of potential mass and time tags by LC/MS/MS The LC/MS/MS analysis of the tryptically digested peptides was performed as previously reported (Shen et al. 2001). In brief, the high-resolution reversed phase capillary liquid chromatography (LC) system was composed of a column assembled in-house using a 150-μm id × 360-μm od × 65-cm capillary (Polymicro Technologies, Phoenix, Arizona, United States) fixed with a 2-μm retaining mesh and packed with 3-μm Jupiter C18 stationary phase (Phenomenex, Torrence, California, United States). The column was equilibrated with 100% mobile phase A (0.05% trifluoroacetic acid in water) at 5,000 psi. Ten minutes after injecting a 10-μL sample (∼0.5 μg/μL), the exponential gradient began mixing mobile phase A with mobile phase B (0.1% trifluoroacetic acid:90% acetonitrile:9.9% water [vol/vol/vol]) while maintaining constant pressure. Using an in-house-manufactured electrospray ionization source, the capillary LC was interfaced with an LCQ ion trap mass spectrometer (ThermoFinnigan, San Jose, California, United States) with settings of 2.2 kV and 200 oC for the ESI voltage and heated capillary, respectively. The data-dependent tandem MS analysis was conducted using a series of segmented mass/charge (m/z) ranges. A collision energy setting of 45% was employed for the collision-induced dissociation of the three most abundant ions detected in each MS scan. Dynamic exclusion was used to discriminate against previously analyzed ions. Peptides were identified by searching the tandem MS spectra against the complete annotated S. cerevisiae genome database (available at http://www.yeastgenome.org/) using SEQUEST (ThermoFinnigan) (Eng et al. 1994). “MudPIT” filtering rules were adopted as the acceptance criteria for peptides generated from the SEQUEST results (Washburn et al. 2001). Fully tryptic peptides with a 1+ charge state that had a cross-correlation (Xcorr) factor of 1.9 or greater were accepted. Fully or partially tryptic peptides with a 2+ charge state that had an Xcorr of 2.2 or greater were accepted as well. Peptides with a 2+ charge state that had an Xcorr of 3.0 or greater were accepted. Finally, fully or partially tryptic peptides with a 3+ charge state were accepted if an Xcorr of 3.75 or greater was obtained. Identification of accurate mass and time tags by LC/FTICR Some of the samples analyzed by LC/MS/MS were further analyzed by LC/FTICR. In LC/FTICR, tryptic peptides are analyzed using the same high-resolution reversed phase capillary LC described in the previous section, coupled to an electrospray ionization interface with a Fourier transform-ion cyclotron resonance mass spectrometer (Smith et al. 2002). We used both a custom-made 11.5 Tesla FTICR instrument, designed and constructed in house at Pacific Northwest National Laboratory, and a commercial 9.4 Tesla Bruker Apex III FTICR instrument (Bruker Daltonics, Billerica, Massachusetts, United States). The acquired FTICR spectra (105 resolution) were processed and deconvoluted using ICR-2LS (software written in-house at Pacific Northwest National Laboratory) to obtain peak lists containing the monoisotopic mass, observed charge, and intensity of the major ions in each spectrum. The masses were calibrated using the masses of internal calibrant peaks infused at the beginning and end of each LC/FTICR analysis. The peak lists for each analysis were then matched against the potential mass and time (PMT) tags defined previously (see above; by LC/MS/MS analyses among any of the previous samples) using VIPER (software written in-house at Pacific Northwest National Laboratory). The matching involved finding the groups of ions in the data, computing a median monoisotopic mass for each group, and then comparing the mass and elution time of the group with the mass and normalized elution time of each peptide in the PMT tag database (match tolerance of ± 8 ppm and ± 0.05 normalized elution time), resulting in the generation of an accurate mass and time (AMT) tag. Because the PMT tag database consisted only of the peptide tags produced via the previous LC/MS/MS analyses (a PMT tag database for the whole genome does not exist to date), the LC/FTICR analysis could identify only AMT tags which corresponded to previously identified PMT tags from one of the LC/MS/MS runs. Identification of proteins For the purpose of deriving a final list of proteins identified by MS, we included only proteins that had been detected by at least two tags in any single experimental dataset. As such we adapted the rules that are standard for minimizing false positives from MS and defining the detected proteins (Wu et al. 2003). Gene expression profiling Each sample was done in duplicate. Log phase cultures were grown overnight to an O.D. of 1 in 100 mL of YPD, YPL, SCD, or SCL medium. Total RNA was isolated using a hot phenol glass beads protocol. PolyA+ mRNA was purified using Qiagen's Oligotex kit (Qiagen, Valencia, California, United States). Then 4.5 μg of polyA+ mRNA were reverse transcribed to generate single stranded cDNA. Product was fragmented to approximately 50 bp using DNase digestion, biotin end labeled, and hybridized to Affymetrix S98 arrays as described in the Affymetrix user handbook (Affymetrix, Santa Clara, California, United States). Hybridizations were normalized and duplicate samples integrated to arrive at an estimate of absolute transcript abundance using the dChip computational package (Wong Lab, Harvard University). For genes with multiple probe sets on the array, only the probe set with the highest signal was used. For every gene, we calculated the fold difference between fermentable and nonfermentable growth conditions and considered significant only genes with a 1.2-fold or greater difference (either increased or decreased expression). In the final list we included only genes that showed a consistent direction of expression difference (increase or decrease) in both rich and synthetic media conditions. Comparative genomic analysis between yeast and other organisms All-against-all comparison of genes belonging to human, yeast, R. prowazekii, and Encephalitozoon cuniculi genomes has been conducted using the PSI-BLAST algorithm (Altschul et al. 1997). For each PSI-BLAST match, the following information has been stored in the MitoP2 database: the identification numbers of two matching proteins, the BLAST E-value of the match, the coverage of the BLAST alignment (defined as the fraction of amino acids of the shorter protein covered by the alignment), and whether the match is a bidirectional best hit (ortholog). A compendium of the yeast–human bidirectional blast hits for all yeast proteins with a MitoP2 score greater than 90 is given in Table S3. Prediction of mitochondrial targeting sequences Psort was downloaded locally as a perl5 script (from E-mail: nakai@imcb.osaka-u.ac.jp). MitoProt was run in the same way as in Scharfe et al. (2000). Predotar analysis was performed as described by Small et al. (2004). The protein lists are available in the MitoP2 database. Integration of published datasets and calculation of MitoP2 score To calculate the MitoP2 score, the percentage R of known mitochondrial proteins (reference set of 477 proteins) identified in each single genome-wide experiment (specificity) or in the overlap of all possible combinations of datasets (specificity of the combination of several methods) was calculated. Most proteins belonged to more than one combination, and for those proteins multiple R values were calculated. For example, proteins identified by two approaches received three R values: the specificity of the first approach alone, the specificity of the second approach alone, and the specificity of the overlap of both approaches. The MitoP2 value represented the highest R value calculated for a protein. The relevancy was checked according to the binomial law. The value gives a lower limit of the specificity of a defined combination because the mitochondrial reference dataset is not complete. For more detailed description, please see the MitoP2 database. Protein import into isolated mitochondria For T7 polymerase–driven synthesis of preproteins in vitro, the ORFs were amplified from ATG to STOP-codon by PCR, including the T7 RNA polymerase promoter and transcription initiation site within the 5′ primer. Using reticulocyte lysate (Promega), the resulting PCR products were utilized for coupled in vitro transcription/translation reactions to synthesize preproteins in the presence of 35S-radiolabeled methionine. Mitochondria were isolated by differential centrifugation from yeast strain W334 grown on lactate medium and resuspended at 25 °C in import buffer (0.3 mg/mL fatty-acid-free BSA, 0.6 M sorbitol, 80 mM KCl, 10 mM magnesium acetate, 2 mM KH2PO4, 2.5 mM EDTA, 2.5 mM MnCl2, 2 mM ATP, 5 mM NADH, and 50 mM HEPES/KOH [pH 7.2]). Import was initiated by adding 1% to 4% (vol/vol) of reticulocyte lysate containing radiolabelled preprotein. After 15 min, samples were placed on ice and subsequently treated with proteinase K (50 μg/mL) or not for 15 min to remove nonimported proteins. Protease was inhibited by the addition of 2 mM PMSF. Mitochondria were reisolated and analyzed by SDS-PAGE and autoradiography. Control experiments were performed in the absence of membrane potential in the presence of 1 μM valinomycin and 20 μM oligomycin. Supporting Information Dataset S1 Flatfile with the Integrated Datasets (358 KB TXT). Click here for additional data file. Table S1 Sample Details for Each Proteomic Experiment (34 KB DOC). Click here for additional data file. Table S2 Proteins Identified by MS (544 KB DOC). Click here for additional data file. Table S3 Human Orthologs of Yeast Mitochondria-Related Proteins (828 KB DOC). Click here for additional data file. Table S4 Members of Selected Mitochondrial Protein Complexes (224 KB DOC). Click here for additional data file. URLs The YDPM database is the supporting online database for the proteomic, expression, and deletion datasets discussed in this paper, providing access to data analysis files, candidate lists, and a search function for individual ORFs. Available at http://www-deletion.stanford.edu/YDPM/YDPM_index.html. The MitoP2 database is a mitochondrial proteome database for yeast and human that integrates published datasets and is available at http://ihg.gsf.de/mitop. The database provides annotated ORF information and the MitoP2 scores for the predicted mitochondrial proteins. We thank M. Mindrinos for helpful advice, M. Trebo for help with preparing the supplemental materials, J. McCusker for providing yeast strains, and E. Botz for technical assistance. Environmental Molecular Sciences Laboratory is sponsored by the United States Department of Energy's Office of Biological and Environmental Research. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute for the United States Department of Energy under Contract Number DE-AC06–76RLO 1830. Additional support was provided by National Institutes of Health Grants GM63883 (PJO) and GM62119 (RWD), and by the Bundesministerium für Bildung und Forschung through the German Human Genome Project (HP and TM), and the network Bioinformatics for the Functional Analysis of Mammalian Genomes (TM). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. HP, CS, DGC, WX, LD, TM, RDS, and LMS conceived and designed the experiments. HP, LD, RJM, MAG, CK, KKH, HMM, HZ, and LMS performed the experiments. HP, CS, DGC, LD, CA, MEM, MAG, KKH, HMM, RDS, and LMS analyzed the data. HP, CS, CA, MEM, RJM, MAG, KKH, HMM, MU, ZSH, RWD, TM, PJO, and LMS contributed reagents/materials/analysis tools. HP, CS, DGC, LD, and LMS wrote the paper. Academic Editor: Erin O'Shea, University of California at San Francisco ¤1Current address: European Molecular Biology Laboratory, Heidelberg, Germany Abbreviations AMTaccurate mass and time BCAbicinchoninic acid GFPgreen fluorescent protein LCliquid chromatography LC/FTICRliquid chromatography/Fourier transform-ion cyclotron resonance mass spectrometry LC/MS/MSliquid chromatography/tandem mass spectrometry MitoP2Mitochondrial Proteome 2 MSmass spectrometry ORFopen reading frame PMTpotential mass and time Xcorrcross-correlation YDPMYeast Deletion and Proteomics of Mitochondria ==== Refs References Achleitner G Gaigg B Krasser A Kainersdorfer E Kohlwein SD Association between the endoplasmic reticulum and mitochondria of yeast facilitates interorganelle transport of phospholipids through membrane contact Eur J Biochem 1999 264 545 553 10491102 Altschul SF Madden TL Schaffer AA Zhang J Zhang Z Gapped BLAST and PSI-BLAST: A new generation of protein database search programs Nucleic Acids 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Comparative assessment of large-scale data sets of protein–protein interactions Nature 2002 417 399 403 12000970 Wallace DC Mitochondrial diseases in man and mouse Science 1999 283 1482 1488 10066162 Washburn MP Wolters D Yates JR Large-scale analysis of the yeast proteome by multidimensional protein identification technology Nat Biotechnol 2001 19 242 247 11231557 Washburn MP Koller A Oshiro G Ulaszek RR Plouffe D Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae Proc Natl Acad Sci U S A 2003 100 3107 3112 12626741 Westermann B Neupert W ‘Omics' of the mitochondrion Nat Biotechnol 2003 21 239 240 12610566 Wu CC MacCoss MJ Howell KE Yates JR A method for the comprehensive proteomic analysis of membrane proteins Nat Biotechnol 2003 21 532 538 12692561 Zischka H Weber G Weber PJ Posch A Braun RJ Improved proteome analysis of Saccharomyces cerevisiae mitochondria by free-flow electrophoresis Proteomics 2003 3 906 916 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020163Research ArticleBiophysicsGenetics/Genomics/Gene TherapyNeuroscienceMus (Mouse)Functional Fluorescent Ca2+ Indicator Proteins in Transgenic Mice under TET Control TET Control of Ca2+ Indicator ProteinsHasan Mazahir T mhasan@mpimf-heidelberg.mpg.de 1 Friedrich Rainer W 1 Euler Thomas 1 Larkum Matthew E 1 ¤1Giese Günter 1 Both Matthias 1 Duebel Jens 1 Waters Jack 1 Bujard Hermann 2 Griesbeck Oliver 3 ¤2Tsien Roger Y 3 Nagai Takeharu 4 Miyawaki Atsushi 4 Denk Winfried 1 1Max Planck Institute for Medical ResearchHeidelbergGermany2Universität HeidelbergZentrum für Molekulare Biologie HeidelbergGermany3Department of Pharmacology and Howard Hughes Medical Institute, University of CaliforniaSan Diego, CaliforniaUnited States of America4Brain Research Institute (RIKEN)SaitamaJapan6 2004 15 6 2004 15 6 2004 2 6 e16316 9 2003 6 4 2004 Copyright: © 2004 Hasan et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Method for Studying Calcium Dynamics in Transgenic Mice Genetically encoded fluorescent calcium indicator proteins (FCIPs) are promising tools to study calcium dynamics in many activity-dependent molecular and cellular processes. Great hopes—for the measurement of population activity, in particular—have therefore been placed on calcium indicators derived from the green fluorescent protein and their expression in (selected) neuronal populations. Calcium transients can rise within milliseconds, making them suitable as reporters of fast neuronal activity. We here report the production of stable transgenic mouse lines with two different functional calcium indicators, inverse pericam and camgaroo-2, under the control of the tetracycline-inducible promoter. Using a variety of in vitro and in vivo assays, we find that stimuli known to increase intracellular calcium concentration (somatically triggered action potentials (APs) and synaptic and sensory stimulation) can cause substantial and rapid changes in FCIP fluorescence of inverse pericam and camgaroo-2. Winfred Denk and colleagues succeed in generating transgenic mice that express one of two calcium indicators in their cells, creating a valuable tool to study neuronal activity ==== Body Introduction Central to the study of neuronal networks is the simultaneous measurement of activity at many locations. While important results have been obtained using multiple patch recordings (Stuart et al. 1993; Markram 1997; Markram et al. 1997) and microelectrode arrays (Meister et al. 1994), patch recordings are limited to a few points and electrode arrays can only record spiking activity or compound field potentials. Furthermore, electrical recordings cannot resolve activity in fine branches of individual neurons and are blind to biochemical signals. Optophysiological approaches have, therefore, become strong competitors and complementors of valuable electrophysiological methods for studying neural activity. First attempts used intrinsic optical signals (Cohen et al. 1968) followed by specific chromophores for sensing membrane voltage by absorption (George et al. 1988) or fluorescence changes (for a review see Cohen et al. 1978). Other dyes were found (Gorman and Thomas 1978) and later specifically designed that respond to changes in intracellular calcium (Ca2+) concentration (for a review see Tsien 1992). Although changes in membrane potential are the most direct measurement of neuronal activity, the large fractional changes achievable with Ca2+-dependent fluorophores led to a rapid adoption of Ca2+ measurements (Tank et al. 1988; Ross et al. 1990; Sugimori and Llinas 1990), which acquired additional importance with the discovery that the induction of synaptic plasticity in many cases requires a substantial rise in local [Ca2+] (Malenka et al. 1988; Yang et al. 1999). Ca2+ furthermore plays a role in morphological changes of neurites (Yuste and Bonhoeffer 2001) and in gene regulation (Morgan and Curran 1986). Loading a population of cells with Ca2+ indicators has proven difficult in adult neural tissues. While there has been a recent advance (Stosiek et al. 2003), it is unclear how cell-type specificity could ever be achieved by techniques of bulk loading synthetic indicators. Great excitement, therefore, greeted the molecular engineering, several years ago (Miyawaki et al. 1997; Persechini et al. 1997), of GFP variants that are Ca2+-sensitive (fluorescent calcium indicator proteins [FCIPs]). Two classes of genetic Ca2+ indicators have been designed that use different mechanisms of action. The first class, called “cameleons” (Miyawaki et al. 1997; Miyawaki et al. 1999; Nagai et al. 2002), depends on changes in the efficiency of fluorescence resonance energy transfer between two spectral variants of green fluorescent protein (GFP) that are connected by a Ca2+-sensitive linker. The second class uses a single GFP fluorophore that contains a Ca2+-dependent protein as a sequence insert (Baird et al. 1999; Griesbeck et al. 2001; Nagai et al. 2001). In addition to solving the loading problem, major advantages of genetic indicators are the prospect of targeting specific cell types by using appropriate promoters and the possibility of combining long-term studies of neuronal activity and morphology (Grutzendler et al. 2002; Trachtenberg et al. 2002). The ability to express FCIPs in intact animals has in recent years allowed the measurement of [Ca2+] transients in worm (Kerr et al. 2000; Suzuki et al. 2003), fruitfly (Fiala et al. 2002; Reiff et al. 2002; Liu et al. 2003; Wang et al. 2003; Yu et al. 2003), zebrafish (Higashijima et al. 2003), and, more recently, mouse (Ji et al. 2004). Thus far, there are still no reports of transgenic mice that express functional FCIPs in the brain. Clearly, expression of a functional indicator in the mammalian brain would enable the measurement of neuronal population activity with much higher spatial and temporal resolution than are offered by currently used noninvasive methods, such as functional magnetic resonance imaging, positron emission tomography, and intrinsic signal reflectance imaging. Moreover, in combination with two-photon imaging (Denk et al. 1990; Denk and Svoboda 1997), transgenic indicators would allow the simultaneous recording of Ca2+ signals in neurons and neuronal compartments from multiple sites in vitro and in vivo. In this paper we demonstrate that FCIPs can be transgenetically introduced into mice under the control of the tetracycline (TET) regulation system (for a review see Gossen and Bujard 2002) and are expressed and function widely throughout the nervous system. Results Construction of Transgenic Mice To select indicators for the generation of transgenic mice, we first screened a number of FCIPs in HeLa cells (see Materials and Methods) for brightness, large [Ca2+]-depen-dent fluorescence changes, and inducibility. The FCIPs were flash pericam, inverse pericam (IP), G-CaMP, camgaroo-2 (Cg2), and the cameleons YC 2.12 and YC 3.12 (Griesbeck et al. 2001; Nagai et al. 2001, 2002; Nakai et al. 2001). Figure 1A depicts the genetic design of FCIPs. We found relative fluorescence changes (ΔF/F) of approximately +170% and −40% for Cg2 (n = 5 cells) and IP (n = 2 cells), respectively (Figure 2A), and therefore selected Cg2 and IP for the generation of transgenic animals. In addition, we chose YC3.12, which showed inconclusive results in the screening but is optimized for expression at 37 °C (see Figure 1A). In our rough screen we did not find detectable responses for any of the other indicators. Figure 1 Genetic Designs of the FCIPs and the TET System (A) Genetic design of fluorescence Ca2+ indicator proteins: (i) yellow fluorescent protein, (ii) Cg2, (iii) IP, and (iv) came-leon YC3.12. (B) Operating principles of the TET regulatory system (for details see Gossen and Bujard 2002). “L” indicates short linker sequence. Figure 2 Expression and Functional Tests in Cell Culture (A) HeLa cells expressing tTA and Ptetbi-luciferase/Cg2 or Ptetbi-luiferase/IP and imaged by confocal microscopy. Top row: low [Ca2+] (0.1 μM), bottom row: high [Ca2+] (25 mM) and ionomycin. Relative fluorescence changes are indicated as %ΔF/F. (B) Ratios (rlu-FL/rlu-RL) of FL to RL activity measured in mouse ear fibroblast cell cultures from all DNA-positive founders in the absence (red) and presence (green) of Dox (see Materials and Methods). Circles (solid and open) indicate the lines that were selected for crossing to the transactivator lines. Solid circles indicate lines that showed smooth fluorescence. The TET system (Figure 1B; for a review see Gossen and Bujard 2002) was chosen because it allows combinatorial targeting of different neural cell populations using genetic crosses (Mayford et al. 1996). In addition, we wanted to have temporal control over expression in order to test whether the indicator protein is inactivated by constitutive expression throughout development. The three selected FCIPs (Cg2, IP, and YC3.12), were placed under the control of the bidirectional TET promoter (Ptetbi) (Baron et al. 1995). The opposite side of the Ptetbi contained the firefly luciferase (FL) gene (Baron et al. 1995; Hasan et al. 2001). This allows, using the ear fibroblast method, the screening of founders for the presence of the functional gene without the need for a second generation of crosses into activator lines (Schoenig and Bujard 2003). Of a total of 46 candidate founder animals (Figure 2B), the best four to six founders for each construct (judged by FL/renilla luciferase [RL] luminescence in the fibroblast assay, see Materials and Methods; Figure 2B) were selected for mating with mice expressing the TET-dependent transactivator (tTA) under control of the alpha-calmodulin/calcium-dependent kinase II (αCaMKII) promoter (Mayford et al. 1995, 1996). All selected founders showed, in addition to strong expression, efficient regulation of luciferase activity by doxycycline (Dox) (200- to 20,000-fold increase in luciferase activity with Dox; Figure 2B). All of the following experiments were conducted in the absence of TET derivatives, leaving the controlled genes active. Dox Inducibility and Expression Patterns Brain slices from double-positive animals (i.e., harboring both Ptetbi-FCIP/luc and αCamKII-tTA genes) were used for analysis of expression patterns by immunohistochemistry (Figure 3A–3F) and two-photon microscopy (acute brain slices: Figure 3H–3J; whole-mount retina: Figure 3K). Expression of FCIPs was apparent only in double-positive animals and in the absence of Dox (strong to moderate levels in several lines; Figure 3A–3E), and Dox strongly suppressed the expression of FCIPs (Figure 3A). Expression levels varied by line: high in MTH-YC3.12-7, MTH-YC3.12-8, MTH-Cg2-7, MTH-IP-12, and MTH-Cg2-19; moderate in MTH-Cg2-14 and MTH-IP-1; and low in the remaining lines (e.g., Figure 3E; Figure 2B, open circles). FCIP-positive cells included hippocampal and neocortical pyramidal cells and vomeronasal and main olfactory receptor neurons (see Figure 3G for axon fiber projections in the accessory and main olfactory bulb), as well as granule cells and a few mitral cells in the olfactory bulb (Figure 3F and data not shown). Expression of FCIPs was robust in hippocampal areas CA1 and CA3 and in the mossy fiber area of the dentate gyrus (data not shown); cortical and retinal ganglion cell dendrites were clearly identifiable (Figure 3F, 3J, and 3K). The pattern of expression appeared to be a mosaic subset of that of αCamKII. There was little obvious variation between lines in gene-expression patterns in most areas except in hippocampal areas CA1 and CA3 and in the dentate gyrus (data not shown). In high- and moderate-expression lines (MTH-Cg2-7, MTH-Cg2-14, MTH-Cg2-19, MTH-IP-1, MTH-IP-12, MTH-YC3.12-7, and MTH-YC3.12-8; closed circles in Figure 2B), consistent with their luciferase activities, cytosolic fluorescence (Figure 3H, 3J, and 3K) was smooth with occasional bright spots, with nuclei usually less fluorescent. Sometimes unusually bright neurons were seen, usually located near the slice surface and presumably damaged. In these cells the nuclei were as bright as or brighter than the cytosol (Figure 3H, arrow). In low-expressing lines (the majority: MTH-Cg2-[3, 6, 15, 17], MTH-IP-[5, 6, 15], and MTH-YC3.12-[3, 4, 5, 6]), fluorescence was punctate (Figure 3I). Figure 3 Doxycycline and tTA-Dependent FCIP Expression Immunohistochemical assay (A–F) using rabbit polyclonal GFP antibodies/peroxidase-DAB system: (A) YC3.12, single-positive (MTH-YC3.12-7), double-positive (MTH-YC3.12-7, αCamKII-tTA), and Dox-treated double-positive (MTH-YC3.12-7, αCamKII-tTA). (B) Cg2, single-positive (MTH-Cg2-7) and doubles-positive (MTH-Cg2-7, αCamKII-tTA). (C) IP, single-positive (MTH-IP-12) and double-positive (MTH-IP-12, αCamKII-tTA). (D) Moderate-expression line of Cg2 (MTH-Cg2-14, αCamKII-tTA). (E) Low-expression line (MTH-Cg2-15, αCamKII-tTA). (F) FCIP distribution in various brain areas. (G) Fluorescence in fixed brain slices from the accessory and the main olfactory bulb. (H–K) Two-photon images of acute, living brain slices. (H) Neurons in both CA1 and striatum usually show nuclear exclusion. (I) punctate expression in low-expressing lines (also see Figure 2B, open circles); example from CA1 and cortex. Maximum intensity projection of two-photon 3D stacks taken from a brain slice (J) and a whole-mount retina (K). We compared the spectral properties of smooth and punctate fluorescence in one high-expression line (MTH-Cg2-7) and one moderate-expression line (MTH-Cg2-14), using confocal imaging spectrometry with excitation at 488 nm. The emission spectra of smooth and punctate fluorescence were similar to each other and to Cg2-expressing HEK cells (Figure 4A and 4B; a two-photon image of punctate fluorescence is shown in Figure 4B, right, arrowheads). Punctate fluorescence was occasionally observed in wild-type and double-negative (−/− Pbitet-FCIPs and −/− αCamKII-tTA) mice, but there it had very different, much broader emission spectra (Figure 4C). In three of the seven double-positive mice examined, an additional distinct peak was seen at 600 nm, which was never seen in either single-positive or C57/BL6 wild-type mice (Figure 4D and data not shown). In the further analysis we concentrated mostly on the more promising smooth-fluorescence lines. Figure 4 Fluorescence Spectra and FCIP Mobility (A) Fluorescence distribution and emission spectra of Cg2 in cultured HEK cells and in neurons (dendrites and soma) in an acute brain slice (MTH-Cg2-7). (B) Punctate fluorescence and corresponding emission spectra (MTH-Cg2-7). “*” denotes emission spectrum of a punctate fluorescence in a different brain slice (not shown). Note smooth and punctate fluorescence also in the two-photon image on the right (MTH-Cg2-14). (C) Punctate fluorescence in a double-negative littermate of MTH-Cg2-7. (D) Image (left) and emission spectrum (right) of two-photon-excited fluorescence in an acute brain slice (MTH-Cg2-7). (E) Indicator mobility by two-photon fluorescence photobleaching recovery (IP, MTH-IP-12). To determine what proportion of fluorescent protein is bound or sequestered, and hence immobile, we performed two-photon fluorescence-recovery-after-photobleaching experiments (Svoboda et al. 1996) on both somata and neurites of a high-expressing line (IP, MTH-IP-12) and found that roughly half of the indicator protein is mobile (Figure 4E and data not shown). Punctate fluorescence and the immobile fraction found in the bleach−recovery experiments suggest that a significant fraction of transgenetically expressed FCIPs interact with other cellular components, possibly via binding of the M13 or calmodulin sequences in FCIPs to their normal cellular targets. In Vivo Two-Photon Imaging To evaluate the achievable signal levels in intact animals, we performed in vivo two-photon imaging through the thinned skull in adult anesthetized mice (MTH-YC3.12-8, MTH-YC3.12-7, MTH-IP-1, and MTH-Cg2-7). Imaging up to and occasionally beyond a depth of 500 μm was possible (Figure 5). Densely packed neurites were clearly visible, consistent with the staining patterns seen in acute slices (see Figure 3J) and in histochemical preparations (see Figure 3F). These results show that FCIP fluorescence is sufficiently strong for high resolution in vivo morphological imaging. Figure 5 In Vivo Two-Photon Imaging Through the Thinned Skull Yellow cameleon 3.12 at different depths (MTH-YC3.12-8) (A) and with high resolution (MTH-YC3.12-7) (B). (C) IP at different depths (MTH-IP-1). Functional Responses Next we tested the Ca2+ response properties of FCIPs using a variety of different preparations and stimulation methods. Somatic recordings in slices Mainly to test temporal response characteristics, we performed a series of somatic electrical recording and synaptic stimulation experiments on pyramidal cells in brain slices. Targeted whole-cell tight-seal recordings of FCIP-expressing layer-2/3 cortical cells (the cell identity was confirmed by the overlap of fluorescence from the FCIP and that from Alexa 568, which was contained in the pipette) showed that FCIP-expressing cells have normal electrophysiological properties (Figure 6A). Somatic FCIP fluorescence (recorded with a CCD camera; Figure 6A, left) showed small changes (ΔF/F ∼4%, 10 trials, MTH-Cg2-14; Figure 6A, lower right) in response to trains of current-injection-triggered APs (Figure 6A, upper right). In hippocampal pyramidal cells in area CA1, recorded electrically with sharp high-resistance microelectrodes, two-photon scans of the somatic fluorescence showed larger changes (ΔF/F ∼10%, MTH-Cg2-19; Figure 6B) with a smaller number of APs. The difference in response size may have been due to washout of FCIP into the patch pipette and to the lack of optical sectioning in the CCD measurements, which contributes to an unknown extent to the resting fluorescence from outside the recorded cell. For sharp-electrode recordings, transient fluorescence increases of up to 100% were usually seen during the break in (Cg2, MTH-Cg2-14, and MTH-Cg2-19; data not shown). Figure 6 FCIP Responses to Direct and to Synaptic Stimulation in Acute Brain Slices (A) Whole-field imaged responses of Cg2-positive cells in cortex to bursts of APs evoked by somatic current injection (whole-cell recording electrode indicated schematically); responses in the recorded (red) and in a nonrecorded (green) soma and in a region with no cell body (blue). (B) Two-photon line scan (lower trace) through the soma of a hippocampal CA1 pyramidal neuron during a burst of APs evoked by somatic current injection through a high-resistance microelectrode. (C) Whole-field-imaged responses to synaptic stimulation in cortex (five pulses at 100 Hz, 10 μA); ΔF/F image is shown below. Fluorescence and voltage responses with and without pharmacological block of glutamate channels (note suppression of APs and unmasking of inhibitory synaptic potentials). Synaptic stimulation First, cortical slices from a Cg2 animal (MTH-Cg2-14) were imaged using a CCD camera. Stimulation effectiveness was monitored in a distantly located soma (≈200 μm) by whole-cell tight-seal recording (Figure 6C). Short trains of stimuli (five pulses, 0.1 ms long, at 100 Hz, 10 μA; Figure 6C, lower right) elicited fluorescence increases localized to an area near the stimulating electrode (Figure 6C, upper right). Peak ΔF/F ranged from 3%–8% (15 trials, in three slices). The fluorescence increase began in the frame following the stimulus onset (Figure 6C, right) and was as fast as responses seen in similar experiments with synthetic indicators (Larkum et al. 2003). Smaller fluorescence changes were observed in the soma of the recorded neuron (Figure 6C, upper right, solid green trace). Small changes could also be seen in the neuropil as far as 150 μm from the stimulation site (data not shown). Fluorescence changes were largely abolished by glutamate-receptor blockers 6-cyano-7-nitroquinoxaline-2,3-dione (40 μM) and 2-amino-5-phosphovaleric acid (100 μM) (Figure 6C, upper right, dotted traces), indicating that they were mediated by synaptic activation. Similar experiments were performed using two-photon imaging, which allows optical sectioning and hence better spatial localization and signal-to-noise ratio. Synaptic stimulation again led to reproducible and rapid fluorescence changes. Changes were now much larger for both Cg2 and IP (ΔF/F ∼20%–100%, MTH-Cg2-19, 42 trials from three slices [Figure 6D]; ΔF/F approximately 15%–40%, MTH-Cg2-7, 35 trials from two slices [Figure 6E]; ΔF/F approximately −30%, MTH-IP-12, five trials from one slice [Figure 6F]). Changes were spatially inhomogeneous, showing “hotspot” structures possibly due to the activation of individual synaptic sites (see Figure 6D, lower panel, trace 7; MTH-Cg2-19). Similar results were obtained in dentate gyrus mossy fibers (Figure 6E, note black and blue regions of interest in trace 3). In some experiments response amplitudes started to decrease after a few trials, presumably because of bleaching or photo damage (data not shown). Figure 6 Continued (D–F) Two-photon-imaged responses to synaptic stimulation in the hippocampus. (D) CA1 region with Schaffer collateral stimulation (eight individual response traces and the averaged trace are shown, region of interest indicated in the “response” image). Averaged images (five frames) during rest and response, and their difference, respectively. In localized hot spots, responses reach 100% (panels and traces shown below). (E) Similar response amplitudes and kinetics are seen in the dentate gyrus with mossy fiber stimulation (note that the number of stimuli was only 20). (F) IP responses recorded by a two-photon line scan through a CA1 soma; stimulation (20 pulses at 200 Hz) of neurites (approximately 50 μm away from the somata). Light-evoked responses in retinal whole mount In several mouse lines with YC3.12 (for example MTH-YC3.12-8), a subset of ganglion cells was strongly labeled (see Figure 3K) but no light-induced Ca2+ responses were seen (eight cells in two retinas tested), consistent with YC3.12 results in other tissues. In lines expressing Cg2 (MTH-Cg2-14), fluorescence became too weak and bleached too quickly for optophysiological measurements (eight cells in two retinas tested; data not shown). In one of two IP-expressing mice tested (Figure 7A and 7B; MTH-IP-12), the fluorescence levels were high enough to follow axons and primary dendrites. After the onset of laser excitation, the fluorescence in the cells decreased and then stabilized at a slightly lower level (Figure 7C, asterisk). This effect was more pronounced at higher laser power (data not shown) and probably reflects the development of a steady state between photobleaching and diffusional replenishment from outside the excitation volume. Stimulation with spots of visible light evoked transient decreases in fluorescence, i.e., increases in intracellular [Ca2+], in both soma and primary dendrites (Figure 7C and 7D). Seven of 12 cells tested in the two IP mice displayed obvious light-evoked somatic Ca2+ responses. The variation in response amplitude between cells may in part be due to heterogeneity of the labeled cell population. Figure 7 Light-Evoked Ca2+ Responses in Retinal Ganglion Cells (A) Intact, light-sensitive retinal whole mount with Sulforhodamine 101 (red) in the extracellular space. Blood vessels are red; IP-positive (MTH-IP-12) retinal ganglion cells are green; and unstained ganglion cells are dark. (Scale bar: 50 μm). (B) Projection of an image stack reveals the IP-labeled primary dendrites of the retinal ganglion cells. (C) Time course of Ca2+ response measured by high repetition rate image scan (62.5 Hz) of a soma: The cell responds with a decrease in fluorescence to the onset of the laser (asterisk) and to the repeated light stimulation (arrows). (D) Averaged (four repetitions) light-stimulus-evoked Ca2+ response (black trace; gray traces are single trials) measured in the soma (above) and in the primary dendrite (below) of a retinal ganglion cell. In vivo imaging of odor responses in the olfactory bulb FCIPs are expressed in the olfactory bulb in afferent sensory axons and granule cells, with relative expression levels varying somewhat with FCIP type and across lines (data not shown). Substantial changes in fluorescence were observed for both IP and Cg2 (MTH-IP-12 and MTH-Cg2-19) in response to odor stimulation (Figure 8). We do not know, however, the exact fraction of non-FCIP autofluorescence contained in the resting signal. Figure 8 In Vivo Imaging of Odor-Evoked Ca2+ Signals with Transgenic Indicators in the Olfactory Bulb (A–C) IP (MTH-IP-12). (A) Raw fluorescence image. (B) Time course of fluorescence signal in the corresponding regions outlined in (C) (matching line colors). The black trace shows respiratory activity. (C) Color-coded map showing the relative change in fluorescence evoked by different odors in each pixel during the first second of the odor response. (D–F) Cg2 (MTH-Cg2-19). (D) Raw fluorescence image. (E) Time course of fluorescence signal in the corresponding regions outlined in (F) (matching line colors). (F) Color-coded maps showing the relative change in fluorescence evoked by different odors in each pixel during the first second of the odor response. Odor-evoked overall fluorescence changes were, as expected, negative in IP animals (MTH-IP-12; Figure 8B, responses seen in 41 of 41 trials) and positive in Cg2 animals (MTH-Cg2-19; Figure 8E, responses seen in 54 of 54 trials). The signals consisted of a sustained component and a periodic component, which was phase-locked to the animal's respiration (Figure 8B and 8E). During some of the late sustained component, the well-known negative intrinsic response (Spors and Grinvald 2002) is likely to be superimposed. Signals were significant even for low odor concentrations (e.g., 0.1% 2-Hexanone; Figure 8C), and they increased with concentration. The largest fluorescence changes seen were −8% for IP and +3% for Cg2. The time course of the signals was consistent with the [Ca2+] dynamics in sensory afferents (Wachowiak and Cohen 2001). Maps of odor-evoked fluorescence changes were constructed during early response times, thereby minimizing the contribution of the slow intrinsic signal. Odor-evoked spatial patterns of Ca2+ signals were widespread in MTH-IP-12 (Figure 8C) and more localized in MTH-Cg2-19 (Figure 8F) but in both cases were more diffuse than maps of afferent glomerular activity measured with a synthetic indicator in sensory axon terminals (Wachowiak and Cohen 2001). This is presumably because FCIP is also expressed in granule cells. These receive input from secondary dendrites of mitral cells, which project for several hundreds of micrometers around each glomerulus. Nevertheless, each odor evoked a unique activity map, and odors known to evoke similar maps of glomerular afferent activity (e.g., methyl benzoate and benzaldehyde) evoked similar activity patterns in transgenic animals (Figure 8C and 8F). The more localized signals seen with Cg2 may be due to its lower affinity for Ca2+, reporting only high [Ca2+] in the vicinity of activated glomeruli. Alternatively, Cg2 might be expressed more strongly in olfactory receptor axons. Discussion We have demonstrated that genetically encoded FCIPs can be stably expressed in mice and are functional. Transgenically expressed FCIPs showed changes of up to 100% in response to synaptic stimulation (see Figure 6D–6F). These changes are smaller than those seen in cell culture or protein extracts (Griesbeck et al. 2001; Nagai et al. 2001), suggesting that a fraction of protein, possibly immobile and sequestered, is nonresponsive. The size of the immobile fraction seen in bleach-recovery experiments (see Figure 4E) fluctuates strongly around a mean value of roughly 50% of the total FCIP fluorescence. Fluorescence changes evoked by electrical stimulation in the neuropil show that FCIPs respond quickly to Ca2+ influx. The relative fluorescence changes recorded in slices in response to synaptic stimulation are large when measured by two-photon microscopy (see Figure 6D–6F) but are substantially smaller with wide-field microscopy (see Figure 6C), presumably because signals from activated and nonactivated cells inevitably mix because of a lack of optical sectioning in the wide-field case. In whole-cell tight-seal recordings somatic signals may fade additionally due to washout of responsive protein. Since camgaroos and pericams are intrinsically pH-sensitive (Baird et al. 1999; Griesbeck et al. 2001; Nagai et al. 2001) it is possible that the fluorescence changes contain a component due to changes in [H+] (pH) rather than [Ca2+]. It is, however, unlikely that the changes we saw are dominated by pH effects for the following reasons. In the case of Cg2, stimulation-induced pH changes (Yu et al. 2003) should lead to a decrease in fluorescence while we see an increase (see Figure 6D–6F). For IP the change due to pH would be in the same direction, but, in particular, the size of the changes seen during two-photon measurements (see Figure 6F; ∼30%) are almost an order of magnitude larger than what one might expect from pH changes that occur with high [K+] stimulation (Yu et al. 2003), but see Wang et al. (1994), who found much larger pH changes, albeit with massive glutamate application. Furthermore, changes of pH are much slower than those seen in our synaptic stimulation experiments. The robust and fast FCIP signals detected in response to sensory stimulation in vivo confirm that FCIPs are suitable for their main intended use, the imaging of activity from populations of neurons in living animals. Crucial for addressing functional questions in neuronal networks will also be the cell-type specificity of expression, which we have demonstrated here for the population of αCamKII-positive neurons. Our success rate in generating functional transgenic mouse lines for Cg2 and IP was moderate (five of 36 animals that were transgenic, according to DNA typing; see Figure 2B). YC3.12 was a disappointment and did not yield functional lines. This is consistent with a previous attempt to generate transgenic mice expressing YC3.0 under the control of the β-actin promoter (Tsai et al. 2003). There, animals were produced that also showed mosaic expression patterns and had only very small functional signals (ΔR/R ∼1%–2%) when tested by wide-field imaging of cerebellar slices undergoing synaptic stimulation (A. Miyawaki, V. Lev-Ram, and R.Y. Tsien, unpublished data). An early suggestion that indicator proteins become nonfunctional after expression for an extended period of time (O. Griesbeck, personal communication) certainly does not apply to IP and Cg2 since we found large responses even in mice aged 8–12 wks, which had been expressing indicators since the onset of αCamKII expression before birth. However, even in strongly expressing smooth-fluorescence lines, a substantial fraction of the indicator protein was found to be immobile and potentially nonfunctional at various ages. It is surprising that punctate fluorescence occurs predominantly in weak lines since precipitation typically occurs at high concentrations. It could, however, be that a limited number of binding and sequestration sites for FCIPs exist in the cell and that only after these sites are saturated does the accumulation of mobile (see Figure 3J), cytosolic, and responsive FCIP begin. The accumulation of any functional indicator protein might, therefore, require expression levels above a (rather high) threshold. Such a threshold might explain why in the majority of lines we find weak, nonresponsive, and punctate fluorescence even when there is significant luciferase activity (open circles in Figure 2B). Binding and/or sequestration of FCIP does, of course, raise the specter of interference of the indicator with biochemical processes inside labeled cells. While subtle effects cannot be ruled out at this point, we did not see any obvious abnormalities either at the whole-animal level or at the level of cellular morphology. The labeled neurons, furthermore, are connected synaptically (see Figures 6C, 7, and 8). It will, however, be important to understand and if possible remedy the reasons for the formation of precipitates. Perhaps the use of Ca2+-sensing motifs (Heim and Griesbeck 2004) that lack affinity for proteins normally expressed in neurons will eliminate puncta and the immobile fraction. It is unclear why high expression levels appear to be achievable in brain using the TET promoter system but not other, cellular promoters. One explanation for weakness or outright lack of expression might be that sequences within the Ca2+-indicator genes silence cellular promoters (Robertson et al. 1995; Clark et al. 1997) but not the tTA-responsive promoters (including Ptetbi). The reason for this, in turn, might be that cellular promoters (unlike the TET promoter) contain a substantial number of transcriptional control elements (upstream activator sequences), any of which might be sensitive to chromatin-induced silencing by the FCIP gene (Lemon and Tjian 2000). This is supported by the observation that attempts to create transgenic mice expressing IP under the control of a 3.5-kb gonadotrophic-releasing-hormone promoter fragment resulted in many lines that had the gene inserted but showed at best weak and punctate expression (D.J. Spergel and P.H. Seeburg, personal communuication), similar to our low-expression lines, while the same promoter fragment drove hGFP2 (an EGFP variant) to high levels (Spergel et al. 1999). Similarly, only weak expression was observed when YC3.1 was placed under control of the neuron-specific enolase promoter (Futatsugi et al. 2002; A. Miyawaki, personal communication). One of the remaining problems hampering imaging of population activity with FCIPs is that expression, while cell-type specific, is not complete, i.e., not in every cell of one type, even in lines that express FCIPs at high levels. One possible explanation is position effect variegation (PEV), which occurs when a transgene integrates adjacent to a heterochromatin domain in the genome. In such a situation, expression of the locus variegates, being active in some cells and silent in others (Saveliev et al. 2003; Schotta et al. 2003). If this is the case, it might be possible to avoid mosaicism by generating lines free from PEV by cloning FCIP genes into a bacterial artificial chromosome (Shizuya et al. 1992) derived from a TET responder line that is not prone to PEV (Hasan et al. 2001). In any case, it appears that the use of the TET system allows the expression of genetically encoded Ca2+ indicators in mice, albeit for an unexpected reason. Unlike other promoter systems, such as the CMV promoter, which also appears to resist gene silencing, the TET system allows cell specificity via the expression of the transactivator, which appears to be controllable by cellular promoters without gene silencing. The creation of transgenic mouse lines expressing functional Ca2+ indicators opens the way for the measurement of neural activity patterns in mammals in vivo and in vitro. While the sensitivity of genetic indicators does not (yet) quite reach those of synthetic compounds, it is sufficient for single-trial measurements at least in some applications (see Figure 8). Perhaps the greatest advantage is that the genetically encoded indicators alleviate the labeling problem in general and allow the observation of activity in targeted cell populations without the need to load cell or tissue preparations with synthetic indicators using potentially harmful procedures. In vivo imaging of FCIPs will permit analysis of population activity using minimally invasive procedures such as imaging through the intact skull. Applications for FCIPs are similar to those for intrinsic signal imaging, but FCIPs provide substantially higher spatiotemporal resolution and signal-to-noise ratio. Compared to voltage-sensitive dyes, transgenic Ca2+ indicators yield substantially larger signals and obviate surgical procedures for dye loading. Another important advantage of transgenic Ca2+ indicators is that the optical signal can be interpreted more specifically because of its defined cellular origin. In combination with two-photon microscopy, neuronal activity can be mapped at high resolution, down to the level of individual dendritic branchlets and maybe spines, possibly even in awake, behaving animals (Helmchen et al. 2001). In addition, FCIP lines may be crossed with mouse lines in which the expression of genes of interest has been manipulated. For example, the combination of FCIP mice with lines harboring modifications of plasticity-driven genes (Nakazawa et al. 2002) or genes that cause neurodegenerative diseases (Wong et al. 2002) might help us to understand how specific genes are involved in the construction and experience-dependent modification of brain circuitry. Materials and Methods Screening of indicators and generation of transgenic mice Genes encoding six different Ca2+ indicators (flash pericam, IP, CaMP, Cg2, and cameleons YC2.12 and YC3.12) and FL were cloned into a Ptetbi vector (Clontech, Palo Alto, California, United States). The resulting plasmids (Ptetbi-FL/FP, Ptetbi-FL/IP, Ptetbi-FL/CaMP, Pbi-FL/Cg2, Ptetbi-FL/YC2.12, and Ptetbi-FL/YC3.12) were sequenced and transfected into HeLa cells that stably express tTA (Gossen and Bujard 1992). Cells were then tested for [Ca2+]-dependent fluorescence changes to establish functioning of the indicators (see Figure 1 and Figure 2A). The transgene insert, devoid of vector sequences, was purified by sucrose gradient (Mann and McMahon 1993) and used for the generation of transgenic animals, using the DNA-microinjection method (Gordon and Ruddle 1982) in the facility of the Zentrum für Molekulare Biologie at the University of Heidelberg. All procedures were performed in accordance with German federal guidelines for animal experiments. Screening of founders using cultured ear fibroblasts Ear fibroblast cultures were prepared for every DNA-positive founder animals using the procedure described by Schoenig and Bujard (2003). Cells were trypsinized after reaching confluency and plated into 6-well plates divided into sets with and without Dox (4-[Dimethylamino]-1,4,4a,5,5a,6,11,12a-octahydro-3,5,10,12,12a-pentahydroxy-6-methyl-1,11-dioxo-2-naphthacenecarboxamide; Sigma-Aldrich, St. Louis, Missouri, United States). When cells reached 50% confluency, both the Dox-plus and the Dox-minus cultures were transfected with 0.5 μg of synthetic reverse tTA (rtTA-M2s) (Gossen et al. 1995; Urlinger et al. 2000) and 0.5 μg of RL plasmids (Promega, Mannheim, Germany) using lipofectamine-2000 DNA-transfection reagents, as recommended by the vendor (Invitrogen Life Technologies, Carlsbad, California, United States). After 48 h, cells were washed once with PBS and incubated in 0.5 ml of lysis buffer on ice (Promega). 50-μl aliquots from each lysate were tested for FL activity and RL activity (Lumat LB9501; Berthold Technologies, Wildbad, Germany). The ratio of FL to RL activity was used to correct for DNA transfection efficiency. Individual transfections and measurements were done in duplicate, usually resulting in normalized activity values that agreed within 5%. Visualizing GFP in fixed brain slices Brains from double-positive animals (identified by PCR of tail DNA) were fixed in 4% paraformaldehyde in PBS for 4 h and washed twice with PBS. Brain slices were cut to a thickness of approximately 70 μm using a vibratome (VT 1000S; Leica Instruments, Wetzlar, Germany). Distribution of Ca2+ indicator protein was determined by staining with GFP-specific polyclonal rabbit antibodies (Clontech) (Krestel et al. 2001) and the DAB peroxidase system (Vectastain ABC Kit; Vector Laboratories, Burlingame, California, United States) or by direct observation of fluorescence with an upright microscope (Zeiss, Oberkochen, Germany) equipped with GFP filters. Two-photon imaging All two-photon measurements described in the following sections were done using custom-built two-photon microscopes. Fluorescence was two-photon-excited by a mode-locked Ti-sapphire laser (Coherent Mira F900, 930 nm, 100 fs, 78 MHz) coupled into a custom-built imaging system. The objective used was a 40X/0.8 NA water immersion lens (Nikon, Tokyo, Japan). A photomultiplier-based whole-field detector (Denk et al. 1995) detected emitted light in the range around 535 nm, optimized for yellow fluorescent protein signals. Scanning and image acquisition were controlled using custom software (developed by R. Stepnoski, Lucent Technologies, Murray Hill, New Jersey, United States, and M. Muller, Max-Planck Institute for Medical Research, Heidelberg, Germany). Data analysis was performed with ImageJ (http://rsb.info.nih.gov/ij/) and IgorPro (Wavemetrics, Lake Oswego, Oregon, United States). Fluorescence recovery after photobleaching and spectral analyses Regions rich in neurites were repeatedly scanned in the two-photon microscope. After a first bleaching run (see Figure 4E, red trace, first and last of ten images shown), scanning was interrupted for 15 s. A second bleach run was performed, then scanning was interrupted by 93 s before the final bleach run (see Figure 4E, blue and green traces, pictures 3/4 and 5/6, respectively). Spectral recordings were performed with a confocal microscope (TCS SP2 AOBS; Leica) using an excitation wavelength of 488 nm. Fluorescence emission was measured by recording image sequences with overlapping shifted spectral windows (10 or 20 nm wide) covering the range of 500–650 nm. Spectra were then calculated for different regions of interest and analyzed using the Leica LCS software, Microsoft Excel, and ImageJ. Preparation of living brain slices Parasagittal and transverse brain slices (300 μm in thickness) from mice (between postnatal days 18 and 60) were prepared according to published procedures for hippocampal experiments (Hoffman et al. 2002) and for cortical experiments (Waters et al. 2003) using a custom-built vibratome (Max Planck Institute, Heidelberg). Mice were deeply anesthetized with halothane. After decapitation the brain was quickly removed and placed into ice-cold, oxygenated artifical cerebrospinal fluid (ACSF; Biometra Biomedizinische Analytik, Gottingen, Germany) containing 125 mM NaCl, 25 mM NaHCO3, 2.5 mM KCl, 1.25 mM NaH2PO4, 1 mM MgCl2, 25 mM glucose, and 2 mM CaCl2 (pH 7.4). For the two-photon imaging experiments, hippocampus slices were incubated at 37 °C for 30 min and then allowed to reach room temperature gradually before being used for experiments over a period of several hours. For the cortical imaging experiments, slices were kept at 34 °C for the duration of the experiment. In all cases the slice chamber was continuously perfused with ACSF. Whole-cell recording and synaptic stimulation in brain slices Acute brain slices (see above) were maintained in ACSF. Whole-cell tight-seal recordings were made using pipettes made from borosilicate glass (5–10 MΩ) containing 135 mM K gluconate, 4 mM KCl, 10 mM HEPES, 10 mM Na2-phosphocreatine, 4 mM Mg-ATP, and 0.3 mM Na-GTP. Recording pipettes also contained 0.2% biocytin and 1–5 μM Alexa 568, which is spectrally distinct from the FCIPs and can be used to unambiguously identify the recorded cell. For synaptic stimulation we used saline-filled glass electrodes or tungsten microelectrodes (1 MΩ) (World Precision Instruments, Berlin, Germany). Synaptic transmission was blocked in some experiments by the addition of 40 μM 6-cyano-7-nitroquinoxaline-2,3-dione and 100 μM 2-amino-5-phosphovaleric acid. Imaging and analysis in cortical and hippocampal slices Wide-field images were taken with a MicroMax, 512 × 512 back-illuminated CCD camera (Roper Scientific, Tucson, Arizona, United States) binned 5 by 5. For experiments involving extracellular stimuli we calculated the change in fluorescence relative to the prestimulus period (ΔF/F) for each binned pixel frame by frame. The prestimulus period of regions of interest was fitted with an exponential curve, which was then subtracted from the entire fluorescence time course to correct for bleaching. No corrections were made for autofluorescence, so that the relative FCIP fluorescence changes are likely to be larger. When analyzing action-potential-induced signals from individual neurons, we calculated the fluorescence changes by subtracting the average of two nearby areas from the total fluorescence to account for background fluorescence (the intracellular FCIP concentration may, however, have been reduced by dialysis into the recording pipette—see Discussion). Two-photon image sequences were collected approximately 50 μm away from the stimulating electrode. For synaptic stimulation five prestimulus frames were collected (64 mm × 64 mm, 128 ms/frame) to record baseline fluorescence (100 pulses at 100 Hz or 20 pulses at 100 Hz). Image sequences were 6 or 12 s long. The background level (average intensity with the laser off) was subtracted from every frame in the image sequence. The average of the five prestimulus (rest) images was subtracted from the average of five “response” images (response minus rest). The built-in smoothing function of ImageJ was used to reduce the noise further. The brightness of the difference image was enhanced for better display. Localized small fluorescent structures or hot spots in neurites were identified visually. For IP, traces with high time resolution were acquired using 64-pixel line scans at 500 Hz. Fluorescence was averaged over the width of the soma. The fluorescence from a neighboring region was subtracted to account for background. Fluorescence changes (percent ΔF/F) were calculated relative to the resting fluorescence. Tissue preparation for light-evoked responses in an intact retinal whole mount Mice were dark-adapted for several hours before the experiments and all subsequent procedures were carried out under dim red illumination to minimize photobleaching. Animals were anesthetized with halothane and subsequently killed by cerebral dislocation or by intraperitoneal injection of pentobarbital. Immediately afterward, both eyes were removed and dissected free in Ames medium (Sigma-Aldrich). A piece was cut from a retina and placed photoreceptor side down into the recording chamber, and maintained at 35 °C in Ames medium continuously perfused with oxygen. The remaining retina was kept for further use. Ca2+ imaging and visual stimulation The stimulation and imaging procedures were as described elsewhere (Euler et al. 2002). In brief, simple light stimuli (bright spots, 300 μm in diameter, on dark background) were projected repetitively onto the receptive field of a labeled retinal ganglion cell (light spot centered on the cell body) while monitoring Ca2+-mediated fluorescence (emission 520 BP 30 nm) changes in retinal ganglion cells using a custom-built two-photon microscope. The laser (Coherent Mira 900F) was tuned to 925 nm (for YC3.12, the laser was tuned to 870 nm, see Results) to keep direct photoreceptor excitation at a minimum and prevent bleaching. To visualize the retinal tissue, Sulforhodamine 101 (2 mg/l; Sigma-Aldrich) was added to the extracellular medium. In vivo imaging in the olfactory bulb Mice were anesthetized and dissected as previously described (Wachowiak and Cohen 2001). Odors were delivered through a custom-built flow dilution olfactometer. Dilutions are given relative to the stable vapor in the olfactometer's reservoir. Series of images were collected at rates of 5–15 Hz with a cooled CCD camera (CoolSnapHQ; Photometrics, Huntington Beach, California, United States) mounted on a custom-built upright fluorescence microscope that was equipped with a 20×, 0.95 NA water immersion objective (Olympus, Tokyo, Japan) and the following filter sets (Chroma Technology, Rockingham, Vermont, United States): HQ495/30, Q520LP, and HQ545/50 for IP and Cg2, and D436/20, 455DCLP, and D535/30 for YC3.12. For each pixel and frame, the change in fluorescence relative to the pre-odor period (ΔF/F) was calculated. Trials without odor stimulation were subtracted to correct for bleaching. For the display of activity maps, ΔF/F images taken during the first second of odor stimulation were averaged and low-pass spatially filtered. Later times were not included in activity maps to avoid the contribution of intrinsic signals (Spors and Grinvald 2002). Respiratory activity was measured with a piezoelectric strap wrapped around the animal's thorax. In vivo two-photon imaging Mice were anesthesized with urethane (1.5 mg/g) and body temperature was maintained at 37 °C. For two-photon imaging, a custom-built headplate with an imaging window (4 mm × 3 mm) was glued to the top of the skull using cyano-acrylate (UHU, Buhl-Baden, Germany) and attached to a fixed metal bar before thinning of the skull. The combination of rigid headplate and thinned-skull reduces respiration and cardiac-pulsation-induced brain motion. The microscope objective was positioned at an angle so that the optical axis was perpendicular to the surface of the cortex. Table 1 Summary of Functionality Tests Recorded by Either Wide-Field or Two-Photon Imaging WF, wide-field; 2P, two-photon; −, decrease; IR, inconclusive results; NT, not tested We thank Eric Kandel for the αCamKII-tTA mouse line (line B), Rolf Sprengel for advice and discussions, Volker Mack, Verena Pawlak and Patrick Theer for valuable technical help, Sascha Dlugosz for the production of transgenic mice and Evelin Wachter and Mei-Huei Yeh for animal care. This work was supported by the Max-Planck Society and NIH grant NS27177 support to RYT. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. MTH, RWF, TE, MEL, GG, MB, JD, JW, and WD conceived and designed the experiments. MTH, RWF, TE, MEL, GG, MB, JD, JW, and WD performed the experiments. MTH, RWF, TE, MEL, MB, JD, JW, and WD analyzed the data. MTH, RWF, TE, MEL, GG, MB, JD, JW, HB, OG, RYT, TN, AM, and WD contributed reagents/materials/analysis tools. MTH and WD wrote the paper. Academic Editor: Lawrence Katz, Duke University ¤1 Current Address: Institute of Physiology, Bern, Switzerland ¤2 Current Address: Max-Planck-Institut für Neurobiologie, Abteilung Neuronale Informationsverarbeitung, Martinsried, Germany Abbreviations αCamKIIalpha-calmodulin/calcium-dependent kinase II ΔF/Frelative fluorescence change APaction potential Ca2+calcium Cg2camgaroo-2 Doxdoxycycline FCIPfluorescent calcium indicator protein FLfirefly luciferase GFPgreen fluorescent protein IPinverse pericam PEVposition effect variegation Ptetbibidirectional TET promoter RLrenilla luciferase TETtetracycline tTAtetracycline-dependent transactivator ==== Refs References Baird GS Zacharias DA Tsien RY Circular permutation and receptor insertion within green fluorescent proteins Proc Natl Acad Sci U S A 1999 96 11241 11246 10500161 Baron U Freundlieb S Gossen M Bujard H Co-regulation of two gene activities by tetracycline via a bidirectional promoter Nucleic Acids Res 1995 23 3605 3606 7567477 Clark AJ Harold G Yull FE Mammalian cDNA and prokaryotic reporter sequences silence adjacent transgenes in transgenic mice Nucleic Acids Res 1997 25 1009 1014 9023112 Cohen LB Keynes RD Hille B Light scattering and birefringence changes during nerve activity Nature 1968 218 438 441 5649693 Cohen LB Salzberg BM Grinvald A Optical methods for monitoring neuron activity Annu Rev Neurosci 1978 1 171 182 386900 Denk W Svoboda K Photon upmanship: Why multiphoton imaging is more than a gimmick Neuron 1997 18 351 357 9115730 Denk W Strickler JH Webb WW Two-photon laser scanning fluorescence microscopy Science 1990 248 73 76 2321027 Denk W Piston DW Webb WW Two-photon molecular excitation in laser scanning microscopy. 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2002 420 788 794 12490942 Tsai PS Friedman B Ifarraguerri AI Thompson BD Lev-Ram V All-optical histology using ultrashort laser pulses Neuron 2003 39 27 41 12848930 Tsien RY Intracellular signal transduction in four dimensions: From molecular design to physiology Am J Physiol 1992 263 C723 C728 1329539 Urlinger S Baron U Thellmann M Hasan MT Bujard H Exploring the sequence space for tetracycline-dependent transcriptional activators: Novel mutations yield expanded range and sensitivity Proc Natl Acad Sci U S A 2000 97 7963 7968 10859354 Wachowiak M Cohen LB Representation of odorants by receptor neuron input to the mouse olfactory bulb Neuron 2001 32 723 735 11719211 Wang GJ Randall RD Thayer SA Glutamate-induced intracellular acidification of cultured hippocampal neurons demonstrates altered energy metabolism resulting from Ca2+ loads J Neurophysiol 1994 72 2563 2569 7897473 Wang JW Wong AM Flores J Vosshall LB Axel R Two-photon calcium imaging reveals an odor-evoked map of activity in the fly brain Cell 2003 112 271 282 12553914 Waters J Larkum M Sakmann B Helmchen F Supralinear Ca2+ influx into dendritic tufts of layer 2/3 neocortical pyramidal neurons in vitro and in vivo J Neurosci 2003 23 8558 8567 13679425 Wong PC Cai H Borchelt DR Price DL Genetically engineered mouse models of neurodegenerative diseases Nat Neurosci 2002 5 633 639 12085093 Yang SN Tang YG Zucker RS Selective induction of LTP and LTD by postsynaptic [Ca2+]i elevation J Neurophysiol 1999 81 781 787 10036277 Yu D Baird GS Tsien RY Davis RL Detection of calcium transients in Drosophila mushroom body neurons with camgaroo reporters J Neurosci 2003 23 64 72 12514202 Yuste R Bonhoeffer T Morphological changes in dendritic spines associated with long-term synaptic plasticity Annu Rev Neurosci 2001 24 1071 1089 11520928

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PLoS Biol. 2004 Jun 15; 2(6):e163

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PLoS Biol

10.1371/journal.pbio.0020163

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020164EssayBioinformatics/Computational BiologyDrosophilaA Calculus of Purpose A Calculus of PurposeLander Arthur D 6 2004 15 6 2004 15 6 2004 2 6 e164Copyright: © 2004 Arthur D. Lander.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Topology and Robustness in the Drosophila Segment Polarity Network Computation Approach Shows Robustness of the Striped Pattern of Fruitfly Embryos Biological systems are so complex that we must ask: "What purpose does all this complexity serve?" Lander argues that computational biology may help provide answers ==== Body Why is the sky blue? Any scientist will answer this question with a statement of mechanism: Atmospheric gas scatters some wavelengths of light more than others. To answer with a statement of purpose—e.g., to say the sky is blue in order to make people happy—would not cross the scientific mind. Yet in biology we often pose “why” questions in which it is purpose, not mechanism, that interests us. The question “Why does the eye have a lens?” most often calls for the answer that the lens is there to focus light rays, and only rarely for the answer that the lens is there because lens cells are induced by the retina from overlying ectoderm. It is a legacy of evolution that teleology—the tendency to explain natural phenomena in terms of purposes—is deeply ingrained in biology, and not in other fields (Ayala 1999). Natural selection has so molded biological entities that nearly everything one looks at, from molecules to cells, from organ systems to ecosystems, has (at one time at least) been retained because it carries out a function that enhances fitness. It is natural to equate such functions with purposes. Even if we can't actually know why something evolved, we care about the useful things it does that could account for its evolution. As a group, molecular biologists shy away from teleological matters, perhaps because early attitudes in molecular biology were shaped by physicists and chemists. Even geneticists rigorously define function not in terms of the useful things a gene does, but by what happens when the gene is altered. Molecular biology and molecular genetics might continue to dodge teleological issues were it not for their fields' remarkable recent successes. Mechanistic information about how a multitude of genes and gene products act and interact is now being gathered so rapidly that our inability to synthesize such information into a coherent whole is becoming more and more frustrating. Gene regulation, intracellular signaling pathways, metabolic networks, developmental programs—the current information deluge is revealing these systems to be so complex that molecular biologists are forced to wrestle with an overtly teleological question: What purpose does all this complexity serve? In response to this situation, two strains have emerged in molecular biology, both of which are sometimes lumped under the heading “systems biology.” One strain, bioinformatics, champions the gathering of even larger amounts of new data, both descriptive and mechanistic, followed by computerbased data “mining” to identify correlations from which insightful hypotheses are likely to emerge. The other strain, computational biology, begins with the complex interactions we already know about, and uses computer-aided mathematics to explore the consequences of those interactions. Of course, bioinformatics and computational biology are not entirely separable entities; they represent ends of a spectrum, differing in the degree of emphasis placed on large versus small data sets, and statistical versus deterministic analyses. Computational biology, in the sense used above, arouses some skepticism among scientists. To some, it recalls the “mathematical biology” that, starting from its heyday in the 1960s, provided some interesting insights, but also succeeded in elevating the term “modeling” to near-pejorative status among many biologists. For the most part, mathematical biologists sought to fit biological data to relatively simple mathematical models, with the hope that fundamental laws might be recognized (Fox Keller 2002). This strategy works well in physics and chemistry, but in biology it is stymied by two problems. First, biological data are usually incomplete and extremely imprecise. As new measurements are made, today's models rapidly join tomorrow's trash heaps. Second, because biological phenomena are generated by large, complex networks of elements, there is little reason to expect to discern fundamental laws in them. To do so would be like expecting to discern the fundamental laws of electromagnetism in the output of a personal computer. Nowadays, many computational biologists avoid modeling-as-data-fitting, opting instead to create models in which networks are specified in terms of elements and interactions (the network “topology”), but the numerical values that quantify those interactions (the parameters) are deliberately varied over wide ranges. As a result, the study of such networks focuses not on the exact values of outputs, but rather on qualitative behavior, e.g., whether the network acts as a “switch,” “filter,” “oscillator,” “dynamic range adjuster,” “producer of stripes,” etc. By investigating how such behaviors change for different parameter sets— an exercise referred to as “exploring the parameter space”—one starts to assemble a comprehensive picture of all the kinds of behaviors a network can produce. If one such behavior seems useful (to the organism), it becomes a candidate for explaining why the network itself was selected, i.e., it is seen as a potential purpose for the network. If experiments subsequently support assignments of actual parameter values to the range of parameter space that produces such behavior, then the potential purpose becomes a likely one. For very simple networks (e.g., linear pathways with no delays or feedback and with constant inputs), possible global behaviors are usually limited, and computation rarely reveals more than one could have gleaned through intuition alone. In contrast, when networks become even slightly complex, intuition often fails, sometimes spectacularly so, and computation becomes essential. For example, intuitive thinking about MAP kinase pathways led to the long-held view that the obligatory cascade of three sequential kinases serves to provide signal amplification. In contrast, computational studies have suggested that the purpose of such a network is to achieve extreme positive cooperativity, so that the pathway behaves in a switch-like, rather than a graded, fashion (Huang and Ferrell 1996). Another example comes from the study of morphogen gradient formation in animal development. Whereas intuitive interpretations of experiments led to the conclusion that simple diffusion is not adequate to transport most morphogens, computational analysis of the same experimental data yields the opposite conclusion (Lander et al. 2002). As the power of computation to identify possible functions of complex biological networks is increasingly recognized, purely (or largely) computational studies are becoming more common in biological journals. This raises an interesting question for the biology community: In a field in which scientific contributions have long been judged in terms of the amount of new experimental data they contain, how does one judge work that is primarily focused on interpreting (albeit with great effort and sophistication) the experimental data of others? At the simplest level, this question poses a conundrum for journal editors. At a deeper level, it calls attention to the biology community's difficulty in defining what, exactly, constitutes “insight” (Fox Keller 2002). In yesterday's mathematical biology, a model's utility could always be equated with its ability to generate testable predictions about new experimental outcomes. This approach works fine when one's ambition is to build models that faithfully mimic particular biological phenomena. But when the goal is to identify all possible classes of biological phenomena that could arise from a given network topology, the connection to experimental verification becomes blurred. This does not mean that computational studies of biological networks are disconnected from experimental reality, but rather that they tend, nowadays, to address questions of a higher level than simply whether a particular model fits particular data. The problem this creates for those of us who read computational biology papers is knowing how to judge when a study has made a contribution that is deep, comprehensive, or enduring enough to be worth our attention. We can observe the field trying to sort out this issue in the recent literature. A good example can be found in an article by Nicholas Ingolia in this issue of PLoS Biology (Ingolia 2004), and an earlier study from Garrett Odell's group, upon which Ingolia draws heavily (von Dassow et al. 2000). Both articles deal with a classical problem in developmental biology, namely, how repeating patterns (such as stripes and segments) are laid down. In the early fruit fly embryo, it is known that a network involving cell-to-cell signaling via the Wingless (Wg) and Hedgehog (Hh) pathways specifies the formation and maintenance of alternating stripes of gene expression and cell identity. This network is clearly complex, in that Wg and Hh signals affect not only downstream genes, but also the expression and/or activity of the components of each other's signaling machinery. Von Dassow et al. (2000) calculated the behaviors of various embodiments of this network over a wide range of parameter values and starting conditions. This was done by expressing the network in terms of coupled differential equations, picking parameters at random from within prespecified ranges, solving the equation set numerically, then picking another random set of parameters and obtaining a new numerical solution, and so forth, until 240,000 cases were tried. The solutions were then sorted into groups based on the predicted output—in this case, spatial patterns of gene expression. When they used a network topology based only upon molecular and generegulatory interactions that were firmly known to take place in the embryo, they were unable to produce the necessary output (stable stripes), but upon inclusion of two molecular events that were strongly suspected of taking place in the embryo, they produced the desired pattern easily. In fact, they produced it much more easily than expected. It appeared that a remarkably large fraction of random parameter values produced the very same stable stripes. This implied that the output of the network is extraordinarily robust, where robustness is meant in the engineering sense of the word, namely, a relative insensitivity of output to variations in parameter values. Because real organisms face changing parameter values constantly—whether as a result of unstable environmental conditions, or mutations leading to the inactivation of a single allele of a gene—robustness is an extremely valuable feature of biological networks, so much so that some have elevated it to a sort of sine qua non (Morohashi et al. 2002). Indeed, the major message of the von Dassow article was that the authors had uncovered a “robust developmental module,” which could ensure the formation of an appropriate pattern even across distantly related insect species whose earliest steps of embryogenesis are quite different from one another (von Dassow et al. 2000). There is little doubt that von Dassow's computational study extracted an extremely valuable insight from what might otherwise seem like a messy and ill-specified system. But Ingolia now argues that something further is needed. He proposes that it is not enough to show that a network performs in a certain way; one should also find out why it does so. Ingolia throws down the gauntlet with a simple hypothesis about why the von Dassow network is so robust. He argues that it can be ascribed entirely to the ability of two positive feedback loops within the system to make the network bistable. Bistability is the tendency for a system's output to be drawn toward either one or the other of two stable states. For example, in excitable cells such as neurons, depolarization elicits sodium entry, which in turn elicits depolarization—a positive feedback loop. As a result, large depolarizations drive neurons to fully discharge their membrane potential, whereas small depolarizations decay back to a resting state. Thus, the neuron tends strongly toward one or the other of these two states. The stability of each state brings with it a sort of intrinsic robustness— i.e., once a cell is in one state, it takes a fairly large disturbance to move it into the other. This is the same principle that makes electronic equipment based on digital (i.e., binary) signals so much more resistant to noise than equipment based on analog circuitry. Ingolia not only argues that robustness in the von Dassow model arises because positive feedback leads to network bistability, he further claims that such network bistability is a consequence of bistability at the single cell level. He strongly supports these claims through computational explorations of parameter space that are similar to those done by von Dassow et al., but which also use strippeddown network topologies (to focus on individual cell behaviors), test specifically for bistability, correlate results with the patterns formed, and ultimately generate a set of mathematical rules that strongly predict those cases that succeed or fail at producing an appropriate pattern. At first glance, such a contribution might seem no more than a footnote to von Dassow's paper, but a closer look shows that this is not the case. Without mechanistic information about why the von Dassow network does what it does, it is difficult to relate it to other work, or to modify it to accommodate new information or new demands. Ingolia demonstrates this by deftly improving on the network topology. He inserts some new data from the literature about the product of an additional gene, sloppy-paired, in Hh signaling, removes some of the more tenuous connections, and promptly recovers a biologically essential behavior that the original von Dassow network lacked: the ability to maintain a fixed pattern of gene expression even in the face of cell division and growth. Taken as a pair, the von Dassow and Ingolia papers illustrate the value of complementary approaches in the analysis of complex biological systems. Whereas one emphasizes simulation (as embodied in the numerical solution of differential equations), the other emphasizes analysis (the mathematical analysis of the behavior of a set of equations). Whereas one emphasizes exploration (exploring a parameter space), the other emphasizes the testing of hypotheses (about the origins of robustness). The same themes can be seen in sets of papers on other topics. For example, in their analysis of bacterial chemotaxis, Leibler and colleagues (Barkai and Leibler 1997) found a particular model to be extremely robust in the production of an important behavior (exact signal adaptation), and subsequently showed that bacteria do indeed exhibit such robust adaptation (Alon et al. 1999). Although Leibler and colleagues took significant steps toward identifying and explaining how such robustness came about, it took a subsequent group (Yi et al. 2000) to show that robustness emerged as a consequence of a simple engineering design principle known as “integral feedback control.” That group also showed, through mathematical analysis, that integral feedback control is the only feedback strategy capable of achieving the requisite degree of robustness. From these and many other examples in the literature, one can begin to discern several of the elements that, when present together, elevate investigations in computational biology to a level at which ordinary biologists take serious notice. Such elements include network topologies anchored in experimental data, fine-grained explorations of large parameter spaces, identification of “useful” network behaviors, and hypothesisdriven analyses of the mathematical or statistical bases for such behaviors. These elements can be seen as the foundations of a new calculus of purpose, enabling biologists to take on the much-neglected teleological side of molecular biology. “What purpose does all this complexity serve?” may soon go from a question few biologists dare to pose, to one on everyone's lips. The author is grateful to Taumu Yi for helpful discussions, and for grant support from the National Institutes of Health (P20 GM66051). Arthur D. Lander is Chair of the Department of Developmental and Cell Biology and Director of the Center for Complex Biological Systems at the University of California at Irvine (Irvine, California, United States of America). E-mail: adlander@uci.edu ==== Refs References Alon U Surette MG Barkai N Leibler S Robustness in bacterial chemotaxis Nature 1999 397 168 171 9923680 Ayala FJ Adaptation and novelty: Teleological explanations in evolutionary biology Hist Philos Life Sci 1999 21 3 33 10865876 Barkai N Leibler S Robustness in simple biochemical networks Nature 1997 387 913 917 9202124 Fox Keller E Making sense of life: Explaining biological development with models, metaphors and machines 2002 Cambridge Harvard University Press 352 Huang CY Ferrell JE Ultrasensitivity in the mitogen-activated protein kinase cascade Proc Natl Acad Sci U S A 1996 93 10078 10083 8816754 Ingolia NT Topology and robustness in the Drosophila segment polarity network PLoS Biol 2004 2 e123 10.1371/journal.pbio.0020123 15208707 Lander AD Nie Q Wan FY Do morphogen gradients arise by diffusion? Dev Cell 2002 2 785 796 12062090 Morohashi M Winn AE Borisuk MT Bolouri H Doyle J Robustness as a measure of plausibility in models of biochemical networks J Theor Biol 2002 216 19 30 12076125 von Dassow G Meir E Munro EM Odell GM The segment polarity network is a robust developmental module Nature 2000 406 188 192 10910359 Yi TM Huang Y Simon MI Doyle J Robust perfect adaptation in bacterial chemotaxis through integral feedback control Proc Natl Acad Sci U S A 2000 97 4649 4653 10781070

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PLoS Biol. 2004 Jun 15; 2(6):e164

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PLoS Biol

10.1371/journal.pbio.0020164

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020165SynopsisEcologyGenetics/Genomics/Gene TherapyDrosophilaA Gene Responsible for Hybrid Incompatibility in Drosophila Synopsis6 2004 15 6 2004 15 6 2004 2 6 e165Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Functional Divergence Caused by Ancient Positive Selection of a Drosophila Hybrid Incompatibility Locus ==== Body Nearly 150 years after Darwin published On the Origin of Species, biologists are still debating how new species emerge from old—and even the definition of species itself. Darwin demurred from offering a hard and fast definition, suggesting that such a thing was “undiscoverable.” One of the more enduring definitions characterizes organisms as distinct reproductive units and species as groups of individuals that can interbreed and produce viable, fertile offspring. The lack of genetic exchange between species, called reproductive isolation, lies at the heart of this definition. Environmental changes can create physical barriers between populations that preclude mating between the populations. Reproductive isolation can also involve changes at the genetic level, when molecular barriers prevent two recently diverged populations from producing viable or fertile offspring. Such factors limit gene flow between diverging species and allow the emergence of genetically novel yet sound populations—that is, new species. Multiple regions of Hmr show evidence for divergence driven by positive selection At the heart of reproductive isolation is a phenomenon called hybrid incompatibility, in which closely related species are capable of mating but produce inviable or sterile offspring. The classic example of hybrid incompatibility is the male donkey–female horse cross, which yields a sterile mule, but many other cases have been documented among mammals, and thousands of plant crosses produce infertile offspring. Much has been learned about the genetic architecture of hybrid incompatibility by studying the offspring of closely related, or “sibling,” fruitfly species in the lab. Sibling species are morphologically very similar, or even indistinguishable, but typically do not interbreed in nature. In the lab, their offspring are either sterile or inviable, a fate that varies depending on the gender of the offspring and species of the parents. To elucidate the molecular mechanisms of reproductive isolation, biologists must first identify candidate hybrid incompatibility genes. Species- or lineage-specific functional divergence is an essential trait of these genes. (That is, the genes evolve different functions after the species diverge from their common ancestor.) While several such candidate genes have been identified in the fruitfly Drosophila melanogaster, none has been shown to display this functional divergence. Now, working with D. melanogaster and its sibling species D. simulans and D. mauritiana, Daniel Barbash, Philip Awadalla, and Aaron Tarone establish the functional divergence of a candidate hybrid compatibility gene and confirm its status as a true speciation gene. Since the 1930s, investigations of reproductive isolation have been guided by the Dobzhansky-Muller model, which attributes hybrid incompatibility to the interactions between two or more genes that have evolved independently in two isolated populations. These independently evolving genes diverge functionally, and the interactions of these functionally divergent genes in a hybrid individual are responsible for the defective phenotypes observed (either inviability or sterility). If this is the case, the alleles, or versions, of the gene causing hybrid incompatibility should have distinct phenotypes in the two species. A corollary of the model says that the diverged allele (A) and not the ancestral allele (a) causes the incompatibility phenotype, which means that experimental manipulations of A but not a should affect the hybrid incompatibility phenotype. Barbash et al. tested the model's predictions by genetically manipulating the alleles of the Hybrid male rescue (Hmr) gene from each sibling species and observing the mutations' effects on the flies' hybrid offspring. In previous experiments the researchers had shown that loss-of-function mutations in the D. melanogaster Hmr gene “rescue” hybrid individuals from the hybrid incompatibility phenotype (male inviability) typically observed in the offspring of crosses between D. melanogaster and its sibling species, and that increased Hmr activity suppresses rescue and kills hybrids. If D. melanogaster Hmr has functionally diverged between the species, then transgenes containing Hmr from sibling species should not cause the hybrid incompatibility phenotype caused by the D. melanogaster Hmr. The researchers tested this hypothesis by introducing transgenic Hmr genes from sibling species into D. melanogaster. In all cases, the hybrid male offspring of D. melanogaster/D. mauritiana and D. melanogaster/D. simulans crosses “were at least as viable as their brothers without the transgene.” To examine this divergence at the genomic level, Barbash et al. compared the divergence of 250 genes in D. melanogaster and D. simulans and found that the Hmr gene was among the most rapidly evolving genes. By examining the frequency of mutations that have accumulated between D. melanogaster and sibling species relative to the number of mutations accumulated within species, the authors show that the mutations between species were by and large not neutral and that they occurred after D. melanogaster diverged from its sibling species, indicating that the gene has been under positive natural selection. Barbash et al. have not only identified a bona fide speciation gene by demonstrating its functional divergence, they've also created a platform for investigating the mechanisms through which such genes cause hybrid incompatibility and lay the groundwork for speciation.

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PMC423140

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2021-01-05 08:21:10

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PLoS Biol. 2004 Jun 15; 2(6):e165

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PLoS Biol

10.1371/journal.pbio.0020165

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