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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0000005Research ArticleGenetics/Genomics/Gene TherapyInfectious DiseasesMicrobiologyPlasmodiumThe Transcriptome of the Intraerythrocytic Developmental Cycle of Plasmodium falciparum
P. falciparum IDC TranscriptomeBozdech Zbynek
1
Llinás Manuel
1
Pulliam Brian Lee
1
Wong Edith D
1
Zhu Jingchun
2
DeRisi Joseph L joe@derisilab.ucsf.edu
1
1Department of Biochemistry and Biophysics, University of California, San FranciscoSan Francisco, CaliforniaUnited States of America2Department of Biological and Medical Informatics, University of California, San FranciscoSan Francisco, CaliforniaUnited States of America10 2003 18 8 2003 18 8 2003 1 1 e512 6 2003 25 7 2003 Copyright: ©2003 Bozdech et al.2003This 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.
Microarray Analysis: Genome-Scale Hypothesis Scanning
Monitoring Malaria: Genomic Activity of the Parasite in Human Blood Cells
Plasmodium falciparum is the causative agent of the most burdensome form of human malaria, affecting 200–300 million individuals per year worldwide. The recently sequenced genome of P. falciparum revealed over 5,400 genes, of which 60% encode proteins of unknown function. Insights into the biochemical function and regulation of these genes will provide the foundation for future drug and vaccine development efforts toward eradication of this disease. By analyzing the complete asexual intraerythrocytic developmental cycle (IDC) transcriptome of the HB3 strain of P. falciparum, we demonstrate that at least 60% of the genome is transcriptionally active during this stage. Our data demonstrate that this parasite has evolved an extremely specialized mode of transcriptional regulation that produces a continuous cascade of gene expression, beginning with genes corresponding to general cellular processes, such as protein synthesis, and ending with Plasmodium-specific functionalities, such as genes involved in erythrocyte invasion. The data reveal that genes contiguous along the chromosomes are rarely coregulated, while transcription from the plastid genome is highly coregulated and likely polycistronic. Comparative genomic hybridization between HB3 and the reference genome strain (3D7) was used to distinguish between genes not expressed during the IDC and genes not detected because of possible sequence variations. Genomic differences between these strains were found almost exclusively in the highly antigenic subtelomeric regions of chromosomes. The simple cascade of gene regulation that directs the asexual development of P. falciparum is unprecedented in eukaryotic biology. The transcriptome of the IDC resembles a “just-in-time” manufacturing process whereby induction of any given gene occurs once per cycle and only at a time when it is required. These data provide to our knowledge the first comprehensive view of the timing of transcription throughout the intraerythrocytic development of P. falciparum and provide a resource for the identification of new chemotherapeutic and vaccine candidates.
A tight cascade of gene regulation during the lifecycle of the malaria parasite in human blood cells suggests new functions for many Plasmodium genes
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Introduction
Human malaria is caused by four species of the parasitic protozoan genus Plasmodium. Of these four species, Plasmodium falciparum is responsible for the vast majority of the 300–500 million episodes of malaria worldwide and accounts for 0.7–2.7 million annual deaths. In many endemic countries, malaria is responsible for economic stagnation, lowering the annual economic growth in some regions by up to 1.5% (Sachs and Malaney 2002). While isolated efforts to curb malaria with combinations of vector control, education, and drugs have proven successful, a global solution has not been reached. Currently, there are few antimalarial chemotherapeutics available that serve as both prophylaxis and treatment. Compounding this paucity of drugs is a worldwide increase in P. falciparum strains resistant to the mainstays of antimalarial treatment (Ridley 2002). In addition, the search for a malaria vaccine has thus far been unsuccessful. Given the genetic flexibility and the immunogenic complexity of P. falciparum, a comprehensive understanding of Plasmodium molecular biology will be essential for the development of new chemotherapeutic and vaccine strategies.
The 22.8 Mb genome of P. falciparum is comprised of 14 linear chromosomes, a circular plastid-like genome, and a linear mitochondrial genome. The malaria genome sequencing consortium estimates that more than 60% of the 5,409 predicted open reading frames (ORFs) lack sequence similarity to genes from any other known organism (Gardner et al. 2002). Although ascribing putative roles for these ORFs in the absence of sequence similarity remains challenging, their unique nature may be key to identifying Plasmodium-specific pathways as candidates for antimalarial strategies.
The complete P. falciparum lifecycle encompasses three major developmental stages: the mosquito, liver, and blood stages. It has long been a goal to understand the regulation of gene expression throughout each developmental stage. Previous attempts to apply functional genomics methods to address these questions used various approaches, including DNA microarrays (Hayward et al. 2000; Ben Mamoun et al. 2001; Le Roch et al. 2002), serial analysis of gene expression (Patankar et al. 2001), and mass spectrometry (Florens et al. 2002; Lasonder et al. 2002) on a limited number of samples from different developmental stages. While all of these approaches have provided insight into the biology of this organism, there have been no comprehensive analyses of any single developmental stage. Here we present an examination of the full transcriptome of one of these stages, the asexual intraerythrocytic developmental cycle (IDC), at a 1-h timescale resolution.
The 48-h P. falciparum IDC (Figure 1A) initiates with merozoite invasion of red blood cells (RBCs) and is followed by the formation of the parasitophorous vacuole (PV) during the ring stage. The parasite then enters a highly metabolic maturation phase, the trophozoite stage, prior to parasite replication. In the schizont stage, the cell prepares for reinvasion of new RBCs by replicating and dividing to form up to 32 new merozoites. The IDC represents all of the stages in the development of P. falciparum responsible for the symptoms of malaria and is also the target for the vast majority of antimalarial drugs and vaccine strategies.
Figure 1 Parasite Culturing and Data Characteristics of the P. falciparum IDC Transcriptome Analysis
(A) Giemsa stains of the major morphological stages throughout the IDC are shown with the percent representation of ring-, trophozoite-, or schizont-stage parasites at every timepoint. The 2-h invasion window during the initiation of the bioreactor culture is indicated (gray area).
(B–D) Example expression profiles for three genes, encoding EBA175, DHFR-TS, and ASL, are shown with a loess fit of the data (red line).
(E) MAL6P1.147, the largest predicted ORF in the Plasmodium genome, is represented by 14 unique DNA oligonucleotide elements. The location of each of the oligonucleotide elements within the predicted ORF and the corresponding individual expression profiles are indicated (oligo 1–14). A red/green colorimetric representation of the gene expression ratios for each oligonucleotide is shown below the graph. The pairwise Pearson correlation for these expression profiles is 0.98 ± 0.02.
(F) The percentage of the power in the maximum frequency of the FFT power spectrum was used as an indicator of periodicity. A histogram of these values reveals a strong bias toward single-frequency expression profiles, indicating that the majority of P. falciparum genes are regulated in a simple periodic manner. This bias is eliminated when the percent power was recalculated using random permutations of the same dataset (inset). For reference, the locations of EBA175 (peak B), DHFR-TS (peak C), and ASL (peak D) are shown.
Our laboratory has developed a P. falciparum–specific DNA microarray using long (70 nt) oligonucleotides as representative elements for predicted ORFs in the sequenced genome (strain 3D7) (Bozdech et al. 2003). Using this DNA microarray, we have examined expression profiles across 48 individual 1-h timepoints from the IDC of P. falciparum. Our data suggest that not only does P. falciparum express the vast majority of its genes during this lifecycle stage, but also that greater than 75% of these genes are activated only once during the IDC. For genes of known function, we note that this activation correlates well with the timing for the respective protein's biological function, thus illustrating an intimate relationship between transcriptional regulation and the developmental progression of this highly specialized parasitic organism. We also demonstrate the potential of this analysis to elucidate the function of the many unknown gene products as well as for further understanding the general biology of this parasitic organism.
Results
Expression Profiling of the IDC
The genome-wide transcriptome of the P. falciparum IDC was generated by measuring relative mRNA abundance levels in samples collected from a highly synchronized in vitro culture of parasites. The strain used was the well-characterized Honduran chloroquine-sensitive HB3 strain, which was used in the only two experimental crosses carried out thus far with P. falciparum (Walliker et al. 1987; Wellems et al. 1990). To obtain sufficient quantities of parasitized RBCs and to ensure the homogeneity of the samples, a large-scale culturing technique was developed using a 4.5 l bioreactor (see Materials and Methods). Samples were collected for a 48-h period beginning 1 h postinvasion (hpi). Culture synchronization was monitored every hour by Giemsa staining. We observed only the asexual form of the parasite in these stains. The culture was synchronous, with greater than 80% of the parasites invading fresh RBCs within 2 h prior to the harvesting of the first timepoint. Maintenance of synchrony throughout the IDC was demonstrated by sharp transitions between the ring-to-trophozoite and trophozoite-to-schizont stages at the 17- and 29-h timepoints, respectively (Figure 1A).
The DNA microarray used in this study consists of 7,462 individual 70mer oligonucleotides representing 4,488 of the 5,409 ORFs manually annotated by the malaria genome sequencing consortium (Bozdech et al. 2003). Of the 4,488 ORFs, 990 are represented by more than one oligonucleotide. Since our oligonucleotide design was based on partially assembled sequences periodically released by the sequencing consortium over the past several years, our set includes additional features representing 1,315 putative ORFs not part of the manually annotated collection. In this group, 394 oligonucleotides are no longer represented in the current assembled sequence. These latter ORFs likely fall into the gaps present in the published assembly available through the Plasmodium genome resource PlasmoDB.org (Gardner et al. 2002; Kissinger et al. 2002; Bahl et al. 2003).
To measure the relative abundance of mRNAs throughout the IDC, total RNA from each timepoint was compared to an arbitrary reference pool of total RNA from all timepoints in a standard two-color competitive hybridization (Eisen and Brown 1999). The transcriptional profile of each ORF is represented by the mean-centered series of ratio measurements for the corresponding oligonucleotide(s) (Figure 1B–1E). Inspection of the entire dataset revealed a striking nonstochastic periodicity in the majority of expression profiles. The relative abundance of these mRNAs continuously varies throughout the IDC and is marked by a single maximum and a single minimum, as observed for the representative schizont-specific gene, erythrocyte-binding antigen 175 (eba175), and the trophozoite-specific gene, dihydrofolate reductase–thymidylate synthetase (dhfr-ts) (Figure 1B and 1C). However, there is diversity in both the absolute magnitude of relative expression and in the timing of maximal expression (phase). In addition, a minority of genes, such as adenylosuccinate lyase (asl) (Figure 1D), displayed a relatively constant expression profile. The accuracy of measurements from individual oligonucleotides was further verified by the ORFs that are represented by more than one oligonucleotide feature on the microarray. The calculated average pairwise Pearson correlation (r) is greater than 0.90 for 68% (0.75 for 86%) of the transcripts represented by multiple oligonucleotides with detectable expression during the IDC (Table S1). Cases in which data from multiple oligonucleotides representing a single putative ORF disagree may represent incorrect annotation. The internal consistency of expression profile measurements for ORFs represented by more than one oligonucleotide sequence is graphically shown in Figure 1E for the hypothetical protein MAL6P1.147, the largest predicted ORF in the genome (31 kb), which is represented by 14 oligonucleotide elements spanning the entire length of the coding sequence. The average pairwise correlation (r) for these features is 0.98±0.02.
Periodicity in genome-wide gene expression datasets has been used to identify cell-cycle-regulated genes in both yeast and human cells (Spellman et al. 1998; Whitfield et al. 2002). Owing to the cyclical nature of the P. falciparum IDC dataset, a similar computational approach was taken. We performed simple Fourier analysis, which allowed us to calculate both the apparent phase and frequency of expression for each gene during the IDC (see Materials and Methods). The fast Fourier transform (FFT) maps a function in a time domain (the expression profile) into a frequency domain such that when the mapped function is plotted (the power spectra), sharp peaks appear at frequencies where there is intrinsic periodicity. The calculated power spectra for each expression profile confirmed the observation that the data are highly periodic. The majority of profiles exhibited an overall expression period of 0.75–1.5 cycles per 48 h.
We have used the FFT data for the purpose of filtering the expression profiles that are inherently noisy (i.e., that have low signal) or that lack differential expression throughout the IDC. Since the majority of the profiles display a single low-frequency peak in the power spectrum, we have taken advantage of this feature to classify profiles, similar to the application of a low-pass filter in signal processing. By measuring the power present in the peak frequency window (the main component plus two adjacent peaks) relative to the power present at all frequencies of the power spectrum, we were able to define a score (percent power) that we have used to stratify the dataset. The resulting distribution of expression profiles, scored in this way, is shown in Figure 1F for all oligonucleotides. For reference, the positions of profiles corresponding to eba175 (peak B), dhfr-ts (peak C), and asl (peak D) are indicated. It is striking that 79.5% of the expression profiles have a very high score (greater than 70%). For comparison, we applied our FFT analysis to the Saccharomyces cerevisiae cell cycle data, yielding only 194 profiles (3.8%) above a 70% score (Figure S1). In addition, we randomly permuted the columns of the complete dataset 1,000 times, each time recalculating the FFT, for a total of 5 million profiles (see inset in Figure 1F). The randomized set exhibits essentially no periodicity: the probability of any random profile scoring above 70% is 1.3 × 10−5.
P. falciparum Transcriptome Overview
To provide an overview of the IDC transcriptome, we selected all 3,719 microarray elements whose profiles exhibited greater than 70% of the power in the maximum frequency window and that were also in the top 75% of the maximum frequency magnitudes. Although hierarchical clustering is extremely useful for comparing any set of expression data, regardless of the experimental variables, we sought to specifically address temporal order within the dataset. To accomplish this, the FFT phase was used to order the expression profiles to create a phaseogram of the IDC transcriptome of P. falciparum (Figure 2A). The overview set represents 2,714 unique ORFs (3,395 oligonucleotides). An additional 324 oligonucleotides represent ORFs that are not currently part of the manually annotated collection.
Figure 2 Overview of the P. falciparum IDC Transcriptome
(A) A phaseogram of the IDC transcriptome was created by ordering the transcriptional profiles for 2,712 genes by phase of expression along the y-axis. The characteristic stages of intraerythrocytic parasite morphology are shown on the left, aligned with the corresponding phase of peak gene expression.
(B–M) The temporal ordering of biochemical processes and functions is shown on the right. Each graph corresponds to the average expression profile for the genes in each set and the mean peak-to-trough amplitude is shown in parentheses.
The IDC phaseogram depicts a cascade of continuous expression lacking clear boundaries or sharp transitions. During the first half of the IDC, a large number of genes involved in general eukaryotic cellular functions are induced with broad expression profiles. This gradual continuum includes the transition from the ring to the early trophozoite stage and the trophozoite to the early schizont stage, encompassing approximately 950 and 1,050 genes, respectively. Next, the mid- and late-schizont stages are marked by a rapid, large amplitude induction of approximately 550 genes, many of which appear to be continually expressed into the early-ring stage. However, owing to the level of synchrony in the culture, the ring-stage signal may be partially attributed to cross-contamination from residual schizonts. In the final hours of the IDC, approximately 300 genes corresponding to the early-ring stage are induced, indicating that reinvasion occurs without obvious interruptions to initiate the next cycle. The expression profiles for developmentally regulated genes in the P. falciparum IDC transcriptome reveal an orderly timing of key cellular functions. As indicated in Figure 2B–2M, groups of functionally related genes share common expression profiles and demonstrate a programmed cascade of cellular processes that ensure the completion of the P. falciparum IDC.
Ring and Early-Trophozoite Stage
In the following text, we have grouped the genes according to temporal expression phases based on their association with the common P. falciparum cytological stages.
Following invasion, approximately 950 ORFs are induced during the ring and early trophozoite stage, including genes associated with the cytoplasmic transcriptional and translational machinery, glycolysis and ribonucleotide biosynthesis (Figure 2B–2E). Represented in this group are 23 ORFs involved in transcription, including the four subunits of RNA polymerase I, nine subunits of RNA polymerase II, three subunits of RNA polymerase III, and four transcription factors. The average expression profile for this group is shown in Figure 2B. (See Table S2 for all functional group details.) Also in this set are three previously identified P. falciparum RNA polymerase genes: the large subunits of P. falciparum RNA polymerase I (Fox et al. 1993) and RNA polymerase II (Li et al. 1989) and RNA polymerase III (Li et al. 1991). The cytoplasmic translation gene group (Figure 2C) consists of 135 ORFs including homologues for 34 small and 40 large ribosomal subunits, 15 translation initiation factors, five translation elongation factors, 18 aminoacyl-tRNA synthetases, and 23 RNA helicases. In addition to the manually annotated ORFs, the translation gene group contains three ORFs predicted only by automated annotation including two ribosomal proteins (chr5.glm_215, chr5.glm_185) and a homologue of eIF-1A (chr11.glm_489) (PlasmoDB.org). In one case, chr5.glm_185 overlaps with the manually annotated ORF PFE0850w, which is found on the opposite strand. Oligonucleotide elements for both of these ORFs are present on the array. The oligonucleotide corresponding to the automated prediction yielded a robust FFT score and a phase consistent with the translation machinery, yet no PFE0850w expression was detected. These results suggest that the automated prediction for chr5.glm_185 most likely represents the correct gene model for this genomic locus and illustrates the use of the IDC expression data for further verification of the P. falciparum genome annotations.
Another set of 33 ORFs with homology to components of the translational machinery displayed an entirely distinct expression pattern, being induced during the late-trophozoite and early-schizont stage. This group includes 11 homologues of chloroplast ribosomal proteins, four mitochondrial/chloroplast elongation factors, and six amino acid tRNA synthetases (Table S2). These ORFs also share a common pattern of expression, suggesting that these factors are components of the mitochondrial and/or the plastid translation machinery. This observation is supported by the presence of predicted apicoplast-targeting signals in 18 of these proteins (PlasmoDB.org). In addition, one of these factors, ribosomal protein S9, has been experimentally immunolocalized within the plastid (Waller et al. 1998). These data suggest that the peak of expression for the cytoplasmic translation machinery occurs in the first half of the IDC, whereas plastid and mitochondrial protein synthesis is synchronized with the maturation of these organelles during the second half of the IDC.
In addition to transcription and translation, genes involved in several basic metabolic pathways were also induced during the ring and early-trophozoite stage, including glycolysis and ribonucleotide biosynthesis (Figure 2D and 2E). Unlike the majority of P. falciparum biochemical processes, most of the enzymes involved in nucleotide metabolism and glycolysis have been identified (Reyes et al. 1982; Sherman 1998). The glycolysis group (Figure 2D) is tightly coregulated throughout the IDC and contains all of the 12 known enzymes. Expression initiates after reinvasion and continues to increase toward maximal expression during the trophozoite stage, when metabolism is at its peak. The glycolytic pathway is very well preserved in P. falciparum and exemplifies how data from this study can complement the homology-based interpretation of the genome. First, the genome contains two putative copies of pyruvate kinase on chromosomes 6 and 10, MAL6P1.160 and PF10_0363, respectively (Gardner et al. 2002). However, only one of these genes, MAL6P1.160, has a similar expression profile to the other known glycolytic enzymes, suggesting that this enzyme is the main factor of this step in the glycolytic pathway. Interestingly, PF10_0363 contains a putative apicoplast-targeting signal (PlasmoDB.org). In another case, the malaria genome sequencing consortium has predicted two homologues of triose phosphate isomerase, PF14_0378 and PFC0381w. The latter is not detected by our analysis, suggesting that this gene is utilized in another developmental stage or may be a nonfunctional, redundant homologue.
P. falciparum parasites generate pyrimidines through a de novo synthesis pathway while purines must be acquired by the organism through a salvage pathway (Gero and O'Sullivan 1990). The mRNA levels of 16 enzymes corresponding to members of the pyrimidine ribonucleotide synthesis pathway, beginning with carbamoyl phosphate synthetase and ending with CTP synthetase, were uniformly induced immediately after invasion (Figure 2E). The relative abundance of these transcripts peaked at approximately 18–22 hpi and then rapidly declined. Similar expression characteristics were detected for the enzymes of the purine salvage pathway, including the nucleoside conversion enzymes, hypoxanthine–guanine–xanthine phosphoribosyltransferase, and both guanylate and adenylate kinases (Figure 2E; Table S2).
Trophozoite and Early-Schizont Stage
The mRNA expression data indicate that ribonucleotide and deoxyribonucleotide production is clearly bifurcated into two distinct temporal classes. While ribonucleotide synthesis is required in the early stages of the IDC, deoxyribonucleotide metabolism is a trophozoite/early-schizont function. mRNA transcripts for enzymes that convert ribonucleotides into deoxyribonucleotides, including DHFR-TS and both subunits of ribonucleotide reductase, were induced approximately at 10 hpi, peaking at approximately 32 hpi (Figure 2F). This represents a temporal shift from the induction of ribonucleotide synthesis of approximately 8–10 h. The expression of the deoxyribonucleotide biosynthesis is concomitant with the induction of DNA replication machinery transcripts, reflecting a tight relationship between DNA synthesis and production of precursors for this process.
Thirty-two ORFs with homologies to various eukaryotic DNA replication machinery components are transcribed during the late-trophozoite and early-schizont stage. The timing of their transcription presages cell division. This functional gene group (Figure 2G), with peak expression around 32 hpi, contains the previously characterized P. falciparum DNA Polα, DNA Polδ, and proliferating cell nuclear antigen, as well as the vast majority of the DNA replication components predicted by the malaria genome sequencing consortium (Gardner et al. 2002). These additional components include eight predicted DNA polymerase subunits, two putative origin recognition complex subunits, six minichromosome maintenance proteins, seven endo- and exonucleases, seven replication factor subunits, and two topoiosomerases. Interestingly, a number of proteins typically required for eukaryotic DNA replication, including the majority of the subunits of the origin recognition complex, have not yet been identified by conventional sequence similarity searches of the P. falciparum genome.
All genes necessary for the completion of the tricarboxylic acid (TCA) cycle were detected in the Plasmodium genome (Gardner et al. 2002), although earlier studies indicate an unconventional function for this metabolic cycle. These studies suggest that the TCA cycle does not play a major role in the oxidation of glycolytic products. Instead, it is essential for the production of several metabolic intermediates, such as succinyl-CoA, a precursor of porphyrin biosynthesis (Sherman 1998). The peak of expression for all TCA factors was detected during the late-trophozoite and early-schizont stage (Figure 2H). Consistent with the model suggesting a disconnection of the TCA cycle from glycolysis during the IDC, no expression was detected for the subunits of the pyruvate dehydrogenase complex, including the α and β chains of pyruvate dehydrogenase and dihydrolipoamide S-acetyl transferase, the typical links between glycolysis and the TCA cycle. On the other hand, expression of TCA cycle genes is well synchronized with the expression of a large number of mitochondrial genes, including the three ORFs of the mitochondrial genome (Feagin et al. 1991), and several factors of electron transport (Table S2). Although some of the TCA cycle proteins have been localized to the cytoplasm (Lang-Unnasch 1992), the expression data suggest an association of this biochemical process with mitochondrial development and possibly with the abbreviated electron transport pathway detected in this organelle.
Schizont Stage
A transition from early to mid-schizont is marked by the maximal induction of 29 ORFs predicted to encode various subunits of the proteasome (Figure 2I). Seven α and six β subunits of the 20S particle and 16 ORFs of the 19S regulatory particle were identified in this gene group. The common expression profile for the subunits of both of the 26S particle complexes suggests the involvement of ubiquitin-dependent protein degradation in the developmental progression of the parasite. The peak of proteasome expression coincides with a transition in the IDC transcriptome from metabolic and generic cellular machinery to specialized parasitic functions in the mid-schizont stage. This suggests an association between transcriptional regulation and protein turnover during this and possibly other transitions during the progression of the P. falciparum IDC.
In the schizont stage, one of the first specialized processes induced was expression from the plastid genome (Figure 2J). The essential extrachromosomal plastid (or apicoplast) genome contains 60 potentially expressed sequences, including ribosomal proteins, RNA polymerase subunits, ribosomal RNAs, tRNAs, and nine putative ORFs, including a ClpC homologue (Wilson et al. 1996). Very little is known about the regulation of gene expression in the plastid, but it is thought to be polycistronic (Wilson et al. 1996). In support of this observation, we find that 27 of the 41 plastid-specific elements present on our microarray displayed an identical expression pattern (Figure 3C). The remaining elements correspond mainly to tRNAs and failed to detect appreciable signal. The highly coordinated expression of the plastid genome, whose gene products are maximally expressed in the late-schizont stage, is concomitant with the replicative stage of the plastid (Williamson et al. 2002). Note that not all plastid ORFs are represented on the microarray used in this study, and thus it is a formal possibility that the expression of the missing genes may differ from those shown in Figure 3C.
Figure 3 Coregulation of Gene Expression along the Chromosomes of P. falciparum Is Rare, While Plastid Gene Expression Is Highly Coordinated
Expression profiles for oligonucleotides are shown as a function of location for Chromosome 2 ([A], Oligo Map). With the exception of the SERA locus (B), coregulated clusters of adjacent ORFs are seldom observed, indicating that expression phase is largely independent of chromosomal position. (C) In contrast to the nuclear chromosomes, the polycistronic expression of the circular plastid genome is reflected in the tight coregulation of gene expression. This is an expanded view of the plastid-encoded genes from Figure 2J. Genomic differences between strain 3D7, from which the complete genome was sequenced, and strain HB3 were measured by CGH. The relative hybridization between the gDNA derived from these two strains is shown as a percent reduction of the signal intensity for 3D7 ([A], CGH Data). Differences between the two strains are predominately located in the subtelomeric regions that contain the highly polymorphic var, rifin, and stevor gene families. Intrachromosomal variations, as observed for the msp2 gene, were rare.
Offset from the plastid by approximately 6 h, a set of approximately 500 ORFs exhibited peak expression during the late-schizont stage. Merozoite invasion of a new host cell is a complex process during which the parasite must recognize and dock onto the surface of the target erythrocyte, reorient with its apical tip toward the host cell, and internalize itself through invagination of the erythrocytic plasma membrane. The entire sequence of invasion events is facilitated by multiple receptor–ligand interactions with highly specialized plasmodial antigens (Cowman et al. 2000). The merozoite invasion group contains 58 ORFs, including 26 ORFs encoding antigens previously demonstrated to be important for the invasion process (see Figure 2K). These include integral membrane proteins delivered to the merozoite surface from the micronemes (AMA1 and EBA175), GPI-anchored proteins of the merozoite membrane (MSP1, MSP4, and MSP5), proteins extrinsically associated with the merozoite surface during their maturation in the PV (MSP3 and MSP6), and soluble proteins secreted to the parasite–host cell interface (RAP1, RAP2, and RAP3). In addition, late-schizont-specific expression was observed for several antigens whose functions are not completely understood, but which have been associated with the invasion process. These ORFs include the merozoite-capping protein (MCP1), erythrocyte-binding-like protein 1 (EBL1), reticulocyte-binding proteins (RBP1 and RBP2), acid basic repeat antigen (ABRA), MSP7, and a homologue of the Plasmodium yoelii merozoite antigen 1. As expected, peak expression of these antigens coincides with the maturation of merozoites and development of several apical organelles, including rhoptries, micronemes, and dense granules. Many of these proteins have been considered as vaccine candidates since antibodies against these antigens were readily detected in the immune sera of both convalescent patients as well as individuals with naturally acquired immunity (Preiser et al. 2000).
The sensitivity of invasion to protease and kinase inhibitors indicates an essential role for these activities in merozoite release as well as in the reinvasion process (Dluzewski and Garcia 1996; Blackman 2000; Greenbaum et al. 2002). The merozoite invasion gene group contains three serine proteases, including PfSUB1, PfSUB2, and an additional homologue to plasmodial subtilases (PFE0355c), and two aspartyl proteases, plasmepsin (PM) IX and X. Peak expression during the mid-schizont stage was also observed for seven members of the serine repeat antigen (SERA) family, all of which contain putative cysteine protease domains. In addition to the proteases, expression of 12 serine/threonine protein kinases and three phophorylases was tightly synchronized with the genes of the invasion pathway, including six homologues of protein kinase C, three Ca+-dependent and two cAMP-dependent kinases, phosphatases 2A and 2B, and protein phosphatase J.
Another functionally related gene group whose expression is sharply induced during the late-schizont stage includes components of actin–myosin motors (see Figure 2L) (Pinder et al. 2000). As in other apicomplexa, actin and myosin have been implicated in host cell invasion (Opitz and Soldati 2002). Schizont-specific expression was observed for three previously described class XIV myosin genes, one associated light chain, two actin homologues, and three additional actin cytoskeletal proteins, including actin-depolymerizing factor/cofilin (two isoforms) and coronin (one isoform). Although the molecular details of plasmodial actin–myosin invasion are not completely understood, the tight transcriptional coregulation of the identified factors indicates that the examination of schizont-specific expression may help to identify additional, possibly unique elements of this pathway.
Early-Ring Stage
The expression data are continuous throughout the invasion process, with no observable abrupt change in the expression program upon successful reinvasion. However, a set of approximately 300 ORFs whose expression is initiated in the late-schizont stage persists throughout the invasion process and peaks during the early-ring stages (see Figure 2M). It was previously determined that immediately after invasion, a second round of exocytosis is triggered, ensuring successful establishment of the parasite within the host cell (Foley et al. 1991). One of the main P. falciparum virulence factors associated with this process is ring-infected surface antigen 1 (RESA1). RESA1 is secreted into the host cell cytoplasm at the final stages of the invasion process, where it binds to erythrocytic spectrin, possibly via its DnaJ-like chaperone domain (Foley et al. 1991). The early stages of the IDC contain a variety of putative molecular chaperones in addition to RESA1, including RESA2 and RESAH3, plus five additional proteins carrying DnaJ-like domains. However, the functional roles of these chaperones remain unclear. Despite the cytoplasmic role of RESA1, abundant antibodies specific for RESA1 are present in individuals infected with P. falciparum, indicating that RESA1 is also presented to the host immune system (Troye-Blomberg et al. 1989). Several genes encoding additional antigenic factors are found among the early ring gene group, including frequently interspersed repeat antigen (FIRA), octapeptide antigen, MSP8, and sporozoite threonine- and asparagine-rich protein (STARP). Like RESA1, antibodies against these antigens are also found in the sera of infected individuals, suggesting that the final stages of invasion might be a target of the immune response.
Overall, the genes expressed during the mid- to late-schizont and early-ring stage encode proteins predominantly involved in highly parasite-specific functions facilitating various steps of host cell invasion. The expression profiles of these genes are unique in the IDC because of the large amplitudes and narrow peak widths observed. The sharp induction of a number of parasite-specific functions implies that they are crucial for parasite survival in the mammalian host and hence should serve as excellent targets for both chemotherapeutic and vaccine-based antimalarial strategies.
IDC Transcriptional Regulation and Chromosomal Structure
Transcriptional regulation of chromosomal gene expression in P. falciparum is thought to be monocistronic, with transcriptional control of gene expression occurring through regulatory sequence elements upstream and downstream of the coding sequence (Horrocks et al. 1998). This is in contrast to several other parasites, such as Leishmania sp., in which polycistronic mRNA is synthesized from large arrays of coding sequences positioned unidirectionally along the arms of relatively short chromosomes (Myler et al. 2001). Recent proteomic analyses failed to detect any continuous chromosomal regions with common stage-specific gene expression in several stages of the P. falciparum lifecycle (Florens et al. 2002). However, transcriptional domains have previously been suggested for Chromosome 2 (Le Roch et al. 2002). The availability of the complete P. falciparum genome coupled with the IDC transcriptome allows us to investigate the possibility of chromosomal clustering of gene expression (see Figure 3A). To systematically explore the possibility of coregulated expression as a function of chromosomal location, we applied a Pearson correlation to identify similarities in expression profiles among adjacent ORFs. The pairwise Pearson correlation was calculated for every ORF pair within each chromosome (Figure S2). Gene groups in which the correlation of 70% of the possible pairs was greater than r = 0.75 were classified as putative transcriptionally coregulated groups. Using these criteria, we identified only 14 coregulation groups consisting of greater than three genes, with the total number of genes being 60 (1.4% of all represented genes) (Table S3). In eight of the 14 groups, the coregulation of a pair of genes may be explained by the fact that they are divergently transcribed from the same promoter. A set of 1,000 randomized permutations of the dataset yielded 2.25 gene groups. Contrary to the nuclear chromosomes, there was a high correlation of gene expression along the plastid DNA element, consistent with polycistronic transcription (see Figure 3C). The average pairwise Pearson correlation for a sliding window of seven ORFs along the plastid genome is 0.92±0.03.
The largest group demonstrating coregulation on the nuclear chromosomes corresponds to seven genes of the SERA family found on Chromosome 2 (see Figure 3B) (Miller et al. 2002). Besides the SERA gene cluster and a group containing three ribosomal protein genes, no additional functional relationship was found among the other chromosomally adjacent, transcriptionally coregulated gene groups. The limited grouping of regional chromosomal expression was independent of strand specificity and, with the exception of the SERA group, did not overlap with the groups of “recently duplicated genes” proposed by the malaria genome sequencing consortium (Gardner et al. 2002).
Three major surface antigens, the var, rifin, and stevor families, have a high degree of genomic variability and are highly polymorphic between strains and even within a single strain (Cheng et al. 1998; Afonso Nogueira et al. 2002; Gardner et al. 2002). Expression profiles for only a small subset of these genes were detected in the IDC transcriptome and were typically characterized by low-amplitude profiles. This could be due to two nonmutually exclusive possibilities: first, the HB3 DNA sequence for these genes may be substantially rearranged or completely deleted relative to the reference strain, 3D7; second, only a few of these genes may be selectively expressed, as has been proposed (Deitsch et al. 2001). To identify regions of genomic variability between 3D7 and HB3, we performed microarray-based comparative genomic hybridization (CGH) analysis. Array-based CGH has been performed with human cDNA and bacterial artificial chromosome-based microarrays to characterize DNA copy-number changes associated with tumorigenesis (Gray and Collins 2000; Pollack et al. 2002). Using a similar protocol, CGH analysis revealed that the majority of genetic variation between HB3 and 3D7 is confined to the subtelomeric chromosomal regions containing the aforementioned gene families (Figure 3A; Figure S3). Only 28.3% of rifin, 47.1% of var, and 51.0% of stevor genes predicted for the 3D7 strain were detected for the HB3 genomic DNA (gDNA) when hybridized to the 3D7-based microarray. Thus, the underrepresentation of these gene families in the HB3 IDC transcriptome is likely due to the high degree of sequence variation present in these genes. Excluding the three surface antigen families in the subtelomeric regions, 97% of the remaining oligonucleotide microarray elements exhibit an equivalent signal in the CGH analysis. However, 144 of the differences detected by CGH reside in internal chromosomal regions and include several previously identified plasmodial antigens: MSP1, MSP2 (Figure 3A), S antigen, EBL1, cytoadherence-linked asexual gene 3.1 (CLAG3.1), glutamine-rich protein (GLURP), erythrocyte membrane protein 3 (PfEMP3), knob-associated histidine-rich protein (KAHRP), and gametocyte-specific antigen Pfg377 (Table S4). These results demonstrate a high degree of genetic variation within the genes considered to be crucial for antigenic variation between these two commonly used laboratory strains of P. falciparum.
Implications for Drug Discovery
The majority of the nuclear-encoded proteins targeted to the plastid are of prokaryotic origin, making them excellent drug targets (McFadden and Roos 1999). Moreover, inhibitors of plastid-associated isoprenoid biosynthesis, DNA replication, and translation have been shown to kill the P. falciparum parasite, demonstrating that the plastid is an essential organelle (Fichera and Roos 1997; Jomaa et al. 1999). The plastid has been implicated in various metabolic functions, including fatty acid metabolism, heme biosynthesis, isoprenoid biosynthesis, and iron–sulfur cluster formation (Wilson 2002). It is clear that, within the plastid, functional ribosomes are assembled to express the ORFs encoded by the plastid genome (Roy et al. 1999). However, nuclear-encoded components are required to complete the translational machinery as well as for all other plastid metabolic functions. A bipartite signal sequence is required for efficient transport of these nuclear proteins from the cytoplasm to the plastid via the endoplasmic reticulum (Waller et al. 2000). Computational predictions suggest that the P. falciparum genome may contain over 550 nuclear-encoded proteins with putative transit peptides (Zuegge et al. 2001; Foth et al. 2003).
Given that over 10% of the ORFs in the P. falciparum genome are predicted to contain an apicoplast-targeting sequence, we sought to use the IDC transcriptome as a means to narrow the search space for candidate apicoplast-targeted genes. As mentioned above, the expression profiles for genes encoded on the plastid genome are tightly coordinated (see Figure 3C). We reasoned that genes targeted to the plastid would be expressed slightly before or coincidentally with the plastid genome. Therefore, we utilized the FFT phase information to identify ORFs in phase with expression of the plastid genome (see Materials and Methods) (Table S5). Because the genes of the plastid genome are maximally expressed between 33 and 36 hpi, we searched for all genes in the dataset with an FFT phase in this time window and then cross-referenced the list of predicted apicoplast-targeted sequences (PlasmoDB.org), resulting in a list of 124 in-phase apicoplast genes (Figure 4A). Within this list are two ORFs that have been directly visualized in the apicoplast, acyl carrier protein and the ribosomal subunit S9 (Waller et al. 1998), as well as many ORFs associated with the putative plastid ribosomal machinery, enzymes involved in the nonmevalonate pathway, additional caseineolytic proteases (Clps), the reductant ferredoxin, and replication/transcriptional machinery components. However, this list contains only 14 of the 43 proteins categorized in the Gene Ontology (GO) assignments at PlasmoDB.org as apicoplast proteins by inference from direct assay (IDA). In addition, 30% of the nuclear-encoded translational genes that are not coexpressed with the known cytoplasmic machinery are found within this small group of genes. More importantly, 76 ORFs (62%) are of unknown function, with little or no homology to other genes. This limited subgroup of putative plastid-targeted ORFs are likely excellent candidates for further studies in the ongoing search for malaria-specific functions as putative drug targets.
Figure 4 Temporal Distribution of the Apicoplast-Targeted Proteins and P. falciparum Proteases, Potential Antimalarial Drug Candidates
(A) The expression profiles of all putative plastid-targeted genes represented on our microarray are shown. The yellow box encompasses a highly synchronized group of genes, which are in-phase with plastid genome expression. The average expression profile for this in-phase group of genes is shown and includes most of the known apicoplast-targeted genes as well as many hypothetical genes. For reference, the average expression profile for the plastid genome is shown (dashed gray line).
(B) Proteases represent an attractive target for chemotherapeutic development. The broad range of temporal expression for various classes of proteases and their putative functions are displayed.
Abbreviations: HAP, histo-aspartyl protease (PM III); Clp, caseineolytic protease; sub1, 2, subtilisin-like protease 1 and 2.
Similarly, P. falciparum proteases have received much attention, since they are candidates as drug targets and have been shown to play important roles in regulation as well as metabolism throughout the IDC (Rosenthal 2002). A temporal ordering of expression profiles for several well-characterized P. falciparum proteases is shown in Figure 4B, demonstrating the broad significance of these enzymes throughout the IDC. One of the principal proteolytic functions is considered to be the degradation of host cell hemoglobin in the food vacuole (FV) to produce amino acids essential for protein synthesis. This elaborate process is carried out by a series of aspartyl proteases, cysteine proteases, metalloproteases, and aminopeptidases (Francis et al. 1997).
A family of ten aspartyl proteases, the plasmepsins (PMs), has been identified in the P. falciparum genome, four of which have been characterized as bona fide hemoglobinases: PM I, II, III (a histo-aspartic protease [HAP]), and IV (Coombs et al. 2001). Our data reveal that the PMs are expressed at different times throughout the lifecycle, suggesting that they are involved in different processes throughout the IDC. PM I, II, HAP, and PM IV are adjacent to one another on Chromosome 14 and have been localized to the FV. While HAP and PM II are expressed in the mid-trophozoite stage, during peak hemoglobin catabolism, PMI and IV are maximally expressed in the ring stage along with the cysteine protease falcipain-1 (FP-1). FP-1 has recently been implicated in merozoite invasion and has been localized to the interior of the PV (Greenbaum et al. 2002). The coincident expression of these proteases implies that the development of the PV and the FV occurs during the very early-ring stage. This observation is corroborated by similar expression profiles for the PV-associated protein RESA1 and the FV protein PGH1. Subsequently, a second group of hemoglobinases, including the m1-family aminopeptidase, FP-2, and falcilysin, is expressed simultaneously with HAP and PM II during the trophozoite stage of the IDC. The expression of PM V and the newly identified FP- 2 homologue during this stage suggests they are also important in the trophozoite stage. The other known falcipain, FP-3, does not show a marked induction in expression throughout the IDC. We fail to detect any transcripts for PM VI, VII, and VIII during the IDC. These genes may have roles in any of the other sexual, liver, or mosquito stages of development.
In addition to the hemoglobinases, P. falciparum contains a variety of proteases involved in cellular processing, including a group of Clps and signal peptidases that are all expressed maximally at the late-trophozoite stage (Figure 4B). The timing of these genes may play a key role in protein maturation during trafficking to various compartments, including the plastid. The three Clps contain putative leader peptides and may actually function within the plastid. Finally, a group of proteases are expressed in the schizont stage and include the P. falciparum subtilisin-like proteases PfSUB1 and PfSUB2 as well as PMs IX and X. PfSUB1 and PfSUB2 are believed to be involved in merozoite invasion and have been localized apically in the dense granules. Interestingly, there are two PfSUB1 protease homologues (PFE0355c and PFE0370c); PM X parallels the expression of PfSUB1 (PFE0370c), suggesting that aspartyl proteases may also be involved in merozoite invasion. In addition, the phase of the PfSUB1 homologue suggests a concomitant role, with PM IX slightly preceding merozoite invasion. In total, we have detected gene expression for over 80 putative proteases throughout the entire IDC (Table S6). This set includes over 65 proteases from a group of recently predicted proteases (Wu et al. 2003). The differing temporal expression of these proteases may allow for a multifaceted approach toward identifying protease inhibitors with efficacy at all stages of the IDC.
Implications for New Vaccine Therapies
Merozoite invasion is one of the most promising target areas for antimalarial vaccine development (Good 2001). Many vaccine efforts thus far have focused primarily on a set of plasmodial antigens that facilitate receptor–ligand interaction between the parasite and the host cell during the invasion process (Preiser et al. 2000) (see Figure 2K and 2M). Merozoite invasion antigens are contributing factors to naturally acquired immunity, triggering both humoral and antibody-independent cell-mediated responses (Good and Doolan 1999). Antibodies against these antigens have been demonstrated to effectively block the merozoite invasion process in vitro and in animal models (Ramasamy et al. 2001). Owing to the highly unique character of merozoite surface antigens, homology-based searches have yielded only a limited set of additional invasion factors.
We utilized the IDC transcriptome to predict a set of likely invasion proteins by identifying expression profiles with characteristics similar to previously studied merozoite invasion proteins. The expression profiles for all known invasion factors undergo a sharp induction during the mid- to late-schizont stage and are characterized by large expression amplitudes (see Figure 2A). Among these proteins are seven of the best-known malaria vaccine candidates, including AMA1, MSP1, MSP3, MSP5, EBA175, RAP1, and RESA1. To identify ORFs with a possible involvement in the merozoite invasion process, we have calculated the similarity, by Euclidian distance, between the expression profiles of these seven vaccine candidates and the rest of the IDC transcriptome. A histogram of the distance values reveals a bimodal distribution with 262 ORFs in the first peak of the distribution (Figure S4). This represents the top 5% of expression profiles when ranked by increasing Euclidian distance (Table S7). In addition to the seven vaccine candidate genes used for the search, essentially all predicted P. falciparum merozoite-associated antigens were identified in this gene set (Figure 5). These include the GPI-anchored MSP4; several integral merozoite membrane proteins, such as EBA140 and EBL1; three RBPs (RBP1, RBP2a, RBP2b); and a previously unknown RBP homologue. In addition, components of two proteins secreted from the rhoptries to the host cell membranes, RhopH1 and RhopH3, or to the PVs RAP1, RAP2, and RAP3 were found in the selected set. Surprisingly, CLAG2 and CLAG9 were also classified into the merozoite invasion group. Although the biological function of these genes is believed to be associated with cytoadherence of the infected erythrocyte to the vascular endothelium, a highly related homologue, CLAG3.1 (RhopH1), was recently detected in the rhoptries, suggesting a possible secondary role for these genes in merozoites (Kaneko et al. 2001).
Figure 5 Phaseogram of Putative Vaccine Targets
The similarity of all expression profiles to seven known vaccine candidates (boxed) was calculated. The top 5% of similar profiles correspond to 262 ORFs, 28 of which have been previously associated with plasmodial antigenicity and the process of merozoite invasion.
A number of antigens are presently in various stages of clinical trials and are yielding encouraging results (Good et al. 1998). However, many single-antigen vaccine studies indicate that the most promising approach will require a combination of antigenic determinants from multiple stages of the complex plasmodial lifecycle (Kumar et al. 2002). Searches for new target antigens in the P. falciparum genome are thus vital to the development of future vaccines, since no fully protective vaccine has been assembled thus far. Of the 262 ORFs whose expression profiles were closest to the profiles of the seven major vaccine candidates, 189 are of unknown function. These ORFs represent a candidate list for new vaccine targets.
Discussion
The transcriptome of the IDC of P. falciparum constitutes an essential tool and baseline foundation for the analysis of all future gene expression studies in this organism, including response to drugs, growth conditions, environmental perturbations, and genetic alterations. Essentially all experiments involving asexual intraerythrocytic-stage parasites must be interpreted within the context of the ongoing cascade of IDC-regulated genes.
In our global analysis of the P. falciparum transcriptome, over 80% of the ORFs revealed changes in transcript abundance during the maturation of the parasite within RBCs. The P. falciparum IDC significantly differs from the cell cycles of the yeast S. cerevisiae (Spellman et al. 1998) and human HeLa (Whitfield et al. 2002) cells, during which only 15% of the total genome is periodically regulated. Instead, the P. falciparum IDC resembles the transcriptome of the early stages of Drosophila melanogaster development, which incorporates the expression of over 80% of its genome as well (Arbeitman et al. 2002). Unlike the development of multicellular eukaryotes, there is no terminal differentiation and, with the exception of gametocytogenesis, the parasite is locked into a repeating cycle. In this respect, the P. falciparum IDC mirrors a viral-like lifecycle, in which a relatively rigid program of transcriptional regulation governs the progress of the course of infection.
The lack of continuous chromosomal domains with common expression characteristics suggests that the genes are regulated individually, presumably via distinct sets of cis- and trans-acting elements. However, the extent and the simple mechanical character of transcriptional control observed in the IDC suggest a fundamentally different mode of regulation than what has been observed in other eukaryotes. It is plausible that a comparatively small number of transcription factors with overlapping binding site specificities could account for the entire cascade. While further experiments are ongoing, it may be the case that P. falciparum gene regulation is streamlined to the extent that it has lost the degree of dynamic flexibility observed in other unicellular organisms, from Escherichia coli to yeast. This observation also implies that disruption of a key transcriptional regulator, as opposed to a metabolic process, may have profound inhibitory properties. While a few putative transcription factors have been identified in the P. falciparum genome, no specific regulatory elements have been defined in basepair-level detail. A further analysis of the upstream regions of genes with similar phases should facilitate the elucidation of regulatory regions and their corresponding regulatory proteins.
In general, the timing of mRNA expression for a given gene during the IDC correlates well with the function of the resultant protein. For example, replication of the genome occurs in the early-schizont stage and correlates well with the peak expression of all factors of DNA replication and DNA synthesis. Also, organellar biogenesis of several intracellular compartments such as mitochondria, the plastid, or the apical invasion organelles is concomitant with the maximal induction of mRNAs encoding proteins specific to these organelles. In addition, our data are generally in good agreement with proteomic analyses that have detected intraerythrocytic-stage proteins from the merozoite, trophozoite, and schizont stages. More than 85% of the 1,588 proteins detected in these studies were also expressed in our analysis (Florens et al. 2002; Lasonder et al. 2002). However, a more detailed proteomic analysis at different stages of the IDC will be needed to ascertain the temporal changes of these proteins.
We initially expected that a high percentage of the genome would be specialized for each lifecycle stage (mosquito, liver, blood), yet this was not observed; the mRNA transcripts for 75% of proteins determined to be gamete-, gametocyte-, or sporozoite-specific by mass spectrometry are also transcribed in the plasmodial IDC. These findings confirm previous studies demonstrating that not only genes used for generic cellular processes are present in multiple developmental stages, but also factors of highly specialized Plasmodium functions (Gruner et al. 2001). This may indicate that only a small portion of the genome may actually be truly specific to a particular developmental stage and that the majority of the genome is utilized throughout the full lifecycle of this parasite. It is also feasible to speculate that a multilayer regulatory network is employed in the progression of the entire P. falciparum lifecycle. In this model, the same cis- and trans-acting regulatory elements driving the actual mRNA production in IDC are utilized in other developmental stages. These elements are then controlled by an alternate subset of factors determining the status of the lifecycle progression.
These findings also outline two contrasting properties of the P. falciparum genome. The Plasmodium parasite devotes 3.9% of its genome to a complex system of antigenic determinants essential for host immune evasion during a single developmental stage (Gardner et al. 2002). On the other hand, large portions of the genome encode proteins used in multiple stages of the entire lifecycle. Such broad-scope proteins might be excellent targets for both vaccine and chemotherapeutic antimalarial strategies, since they would target several developmental stages simultaneously. While there are certainly proteins specific to these nonerythrocytic stages, a complementary analysis of both proteomic and genomic datasets will facilitate the search.
With malaria continuing to be a major worldwide disease, advances toward understanding the basic biology of P. falciparum remain essential. Our analysis of the IDC transcriptome provides a first step toward a comprehensive functional analysis of the genome of P. falciparum. The genome-wide transcriptome will be useful not only for the further annotation of many uncharacterized genes, but also for defining the biological processes utilized by this highly specialized parasitic organism. Importantly, candidate groups of genes can be identified that are both functionally and transcriptionally related and thus provide focused starting points for the further elucidation of genetic and mechanistic aspects of P. falciparum. Such biological characterizations are presently a major objective in the search for novel antimalarial strategies. The public availability of the dataset presented in this study is intended to provide a resource for the entire research community to extend the exploration of P. falciparum beyond the scope of this publication. All data will be freely accessible at two sites: http://plasmodb.org and http://malaria.ucsf.edu.
Materials and Methods
Cell culture.
A large-scale culture of P. falciparum (HB3 strain) was grown in a standard 4.5 l microbial bioreactor (Aplikon, Brauwweg, Netherlands) equipped with a Bio Controller unit ADI 1030 (Aplikon, Brauwweg, Netherlands). Cells were initially grown in a 2% suspension of purified human RBCs and RPMI 1640 media supplemented with 0.25% Albumax II (GIBCO, Life Technologies, San Diego, California, United States), 2 g/l sodium bicarbonate, 0.1 mM hypoxanthine, 25 mM HEPES (pH 7.4), and 50 μg/l gentamycin, at 37°C, 5% O2, and 6% CO2. Cells were synchronized by two consecutive sorbitol treatments for three generations, for a total of six treatments. Large-scale cultures contained 32.5 mM HEPES (pH 7.4). The bioreactor culture was initiated by mixing 25.0 ml of parasitized RBCs (20% late schizonts, approximately 45 hpi) with an additional 115.0 ml of purified RBC in a total of 1.0 l of media (14% hematocrit). Invasion of fresh RBCs occurred during the next 2 h, raising the total parasitemia from an initial 5% to 16%. After this period, the volume of the culture was adjusted to 4.5 l, bringing the final RBC concentration to approximately 3.3% to reduce the invasion of remaining cells. Immediately after the invasion period, greater than 80% of the parasites were in the ring stage. Temperature and gas conditions were managed by the Bio Controller unit. Over the course of 48 h, 3–4 ml of parasitized RBCs was collected every hour, washed with prewarmed PBS, and flash-frozen in liquid nitrogen.
RNA preparation and reference pool.
P. falciparum RNA sample isolation, cDNA synthesis, labeling, and DNA microarray hybridizations were performed as described by Bozdech et al. (2003). Samples for individual timepoints (coupled to Cy5) were hybridized against a reference pool (coupled to Cy3). The reference pool was comprised of RNA samples representing all developmental stages of the parasite. From this pool, sufficient cDNA synthesis reactions, using 12 μg of pooled reference RNA, were performed for all hybridizations. After completing cDNA synthesis, all reference pool cDNAs were combined into one large pool and then split into individual aliquots for subsequent labeling and hybridization. Microarray hybridizations were incubated for 14–18 h.
DNA microarray hybridizations and quality control.
In total, 55 DNA microarray hybridizations covering 46 timepoints were performed. Timepoints 1, 7, 11, 14, 18, 20, 27, and 31 were represented by more than one array hybridization. Data were acquired and analyzed by GenePix Pro 3 (Axon Instruments, Union City, California, United States). Array data were stored and normalized using the NOMAD microarray database system (http://ucsf-nomad.sourceforge.net/). In brief, a scalar normalization factor was calculated for each array using unflagged features with median intensities greater than zero for each channel and a pixel regression correlation coefficient greater than or equal to 0.75. Quality spots were retained based on the following criteria. The log2(Cy5/Cy3) ratio for array features that were unflagged and had a sum of median intensities greater than the local background plus two times the standard deviation of the background were extracted from the database for further analysis. Subsequently, expression profiles consisting of 43 of 46 timepoints (approximately 95%) were selected. For those timepoints that were represented by multiple arrays, the ratio values were averaged.
FFT analysis of the expression profiles.
Fourier analysis was performed on each profile in the quality-controlled set (5,081 oligonucleotides). Profiles were smoothed with missing values imputed using a locally weighted regression algorithm with local weighting restricted to 12% using R (http://www.R-project.org). Fourier analysis was performed on each profile using the fft() function of R, padded with zeros to 64 measurements. The power spectrum was calculated using the spectrum() function of R. The power at each frequency (Power()), the total power (Ptot), and the frequency of maximum power (Fmax) were determined. The periodicity score was defined as Power[(Fmax−1) + (Fmax) + (Fmax+1)]/Ptot. The most frequent value of Fmax across all profiles was deemed the major frequency (m) and used in determining phase information. The phase of each profile was calculated as atan2\[−(I (m)],R (m)\, where atan2 is R's arctangent function and I and R are the imaginary and real parts of the FFT. Profiles were then ordered in increasing phase from −π to π. The loess smooth profiles were drawn through the raw expression data using the loess() function found in the modern regression library of R (version 1.5.1). The default parameters were used, with the exception that local weighting was reduced to 30%. For the averaged profiles of the functional groups (see Figure 2B–2M), the loess smooth profiles were calculated for each expression profile individually and subsequently averaged to create the representative profile. These same methods were applied to both the randomized set (see the inset to Figure 1F) and the yeast cell cycle dataset (see Figure S1).
The raw results files (Dataset S1), the fully assembled raw dataset (Dataset S2, the overview dataset (Dataset S3, and the quality control dataset (Dataset S4) are available as downloads.
Evaluation of coexpression along chromosomes.
The evaluation of coexpression of genes along chromosomes was carried out as follows. The Pearson correlation coefficient was calculated for each pair of profiles. For ORFs with multiple oligonucleotides, the average profile was calculated. The neighborhood of each ORF profile was defined as a window of between one and ten adjacent ORF profiles. If any window in an ORF profile's neighborhood displayed more than 70% pairwise correlation of greater than 0.75, it was flagged as enriched. The length of the window was then recorded as a region of coexpression. This process was repeated without strand separation of ORFs and with randomly permuted datasets.
Comparative genomic hybridization.
P. falciparum strains 3D7 and HB3 were cultured as previously described at a concentration of 10% parasitaemia. Genomic DNA (gDNA) was isolated from a minimum of 500 ml of total culture for each P. falciparum strain, as previously described (Wang et al. 2002). Isolated gDNA from each strain was sheared by sonication to an average fragment size of approximately 1–1.5 kb and then was purified and concentrated using a DNA Clean and Concentrator kit (Zymo Research, Orange, California, United States). Amino-allyl-dUTP first was incorporated into the gDNA fragments with a Klenow reaction at 37°C for 6–8 h with random nonamer primers and 3 μg of sheared gDNA. After purification and concentration of the DNA from the Klenow reaction, CyScribe Cy3 and Cy5 dyes (Amersham Biosciences, Buckinghamshire, United Kingdom) were coupled to HB3 DNA and 3D7 DNA, respectively, as previously described (Pollack et al. 1999). Uncoupled fluorescent dye was removed using a DNA Clean and Concentrator kit. Labeled DNA fragments were hybridized to the oligonucleotide-based DNA microarrays. Fluorescence was detected and analyzed using an Axon Instruments scanner and GenePix Pro 3.0 software. Only features that had median intensities greater than the local background plus two times the standard deviation of the background in each channel were considered for further analysis. For each feature, the percent of the total intensity was determined using the signal in the 3D7 channel as the total amount of intensity for each oligonucleotide; intensity differences less than 50% were considered to be significant for subsequence analysis.
Calculation for in-phase plastid-targeted genes.
The range of FFT-based phases for the expression profiles of the plastid genome is between 0.32 and 1.05 (or roughly π/9 −π/3). Using the list of 551 apicoplast-targeted genes available at PlasmoDB.org, we first ordered these genes by phase and then grouped all genes with a phase range between 0.00 and 1.40 (0–4π/9), resulting in 124 genes represented by 128 oligonucleotides on the microarray. This select group represents the in-phase plastid targeted genes (see Table S6).
Calculation for vaccine targets.
To select the expression profiles most related to the AMA1, MSP1, MSP3, MSP5, EBA175, RAP1, and RESA1 vaccine candidates, we calculated the similarity of all expression profiles in the dataset to those of these antigens by Euclidian distance. The minimum Euclidian distance calculated for every profile was then binned into 60 bins and plotted as a histogram. A natural break in the histogram was seen that included the set of 262 ORFs (see Figure S2).
Supporting Information
Dataset S1 Raw GenePix Results
(29.5 MB ZIP).
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Dataset S2 Complete Dataset
(3.7 MB TXT).
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Dataset S3 Overview Dataset
(2.4 MB TXT).
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Dataset S4 Quality Control Set
(3.1 MB TXT).
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Figure S1 Histogram of the Percent Power at Peak Frequencies for the Yeast Cell Cycle Data
The percent of power in the maximum frequency of the FFT power spectrum was used to determine periodicity of the yeast cell cycle data from Spellman et al. (1998). The histogram reveals periodic regulation of gene expression for only a small subset of genes (% power >70%).
(223 KB EPS).
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Figure S2 Pearson Correlation Maps for the P. falciparum Chromosomes
A matrix of the pairwise Pearson correlations was calculated for every expression profile along the chromosomes. The analysis included all annotated ORFs. The gray areas correspond to a Pearson correlation d(x, y) = 0 and indicate ORFs with no detectable IDC expression or ORFs not represented on the microarray. The starting point (left) and the end point (right) of the chromosomes and the ORF order along the chromosomes are identical to the order in PlasmoDB.org.
(30.9 MB EPS).
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Figure S3 CGH of 3D7 versus HB3 for All Chromosomes
Genomic differences between strain 3D7 and strain HB3 were measured by CGH. The relative hybridization between the gDNA derived from these two strains is shown as a percent reduction of the signal intensity for 3D7 along individual chromosomes. (1.7 MB ZIP).
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Figure S4 Distribution of Euclidian Distances between Expression Profiles of the IDC Genes and Seven Vaccine Candidates
The similarity between each IDC expression profile and the profiles of the seven selected vaccine candidate genes was evaluated by Euclidian distance calculations, d(x,y) = Σ(xi − yi)2. The Euclidian distance value to the closest vaccine homologue was selected for each IDC profile and used to generate this plot. Genes with d(x,y) < 20 were selected for the phaseogram of putative vaccine targets (see Figure 5).
(494.02 KB EPS).
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Table S1 Pearson Correlation for ORFs Represented by Multiple Oligonucleotides
This table contains all of the ORFs in the analyzed dataset that are represented by multiple oligonucleotides on the DNA microarray. The average Pearson correlation value has been calculated for the expression profiles of all oligonucleotides for each given ORF.
(44 KB TXT).
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Table S2
P. falciparum Functional Gene Groups
This table contains all of the P. falciparum groups discussed. The groups include the following: transcription machinery, cytoplasmic translation machinery, the glycolytic pathway, ribonucleotide synthesis, deoxyribonucleotide synthesis, DNA replication machinery, the TCA cycle, the proteaseome, the plastid genome, merozoite invasion, actin–myosin motility, early-ring transcripts, mitochondrial genes, and the organellar translational group.
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Click here for additional data file.
Table S3 Coregulation along the Chromosomes of P. falciparum
This table contains the regions of coregulation found in the chromosomes of P. falciparum determined by calculating the Pearson correlation between expression profiles for contiguous ORFs. The cutoff was 70% pairwise correlation of greater than 0.75 for each group. Only groups of two ORFs or more are listed.
(6 KB TXT).
Click here for additional data file.
Table S4 3D7 versus HB3 CGH Data
This table contains all of the intensity data from CGH of gDNA derived from the 3D7 and HB3 strains of P. falciparum. The averaged intensities from three microarray hybridization experiments are listed.
(414 KB TXT).
Click here for additional data file.
Table S5 Putative Apicoplast-Targeted Genes and Expression Profiles
This table contains all of the predicted apicoplast-targeted ORFs from PlasmoDB.org. The presence of each ORF on the DNA microarray is tabulated, as well as whether each ORF is present in the overview set. Finally, the plastid ORFs in-phase with plastid genome expression are listed, as well as the corresponding oligonucleotide identifiers.
(147 KB TXT).
Click here for additional data file.
Table S6 Putative P. falciparum Proteases and Their Expression Data
The table was constructed by searching the database for any putative protease annotations and contains all of the 92 proteases identified by Wu et al. (2003).
(59 KB TXT).
Click here for additional data file.
Table S7 Vaccine Candidate Correlation Table
The similarity of all expression profiles to seven known vaccine candidates was evaluated by a Euclidian distance calculation to all expression profiles measured. These 262 ORFs constitute the top 5% of genes in the IDC with minimum distance to these seven ORFs. The seven candidates used are AMA1, MSP1, MSP3, MSP5, EBA175, RAP1, and RESA1.
(204 KB TXT).
Click here for additional data file.
We would like to thank Ashwini Jambhekar, Pradip K. Rathod, David S. Roos, Phil J. Rosenthal, Anita Sil, Akhil Vaidya, and Dave Wang for critical comments. For technical assistance, we thank Takeshi Irie, Terry Minn, and Samara L. Reck-Peterson. This work was supported by the Burroughs-Wellcome Fund, the Kinship Foundation, a Sandler Opportunity Grant, and National Institute of Allergy and Infectious Diseases grant AI53862.
Conflicts of Interest. The authors have declared that no conflicts of interest exist.
Author Contributions. ZB, ML, and JLD conceived and designed the experiments. ZB, ML, and EDW performed the experiments. ZB, ML, BLP, EDW, JZ, and JLD analyzed the data. BLP and JZ contributed reagents/materials/analysis tools. ZB, ML, BLP, EDW, and JLD wrote the paper.
Academic Editor: Gary Ward, University of Vermont.
Abbreviations
ASLadenylosuccinate lyase
CGHcomparative genomic hybridization
CLAGcytoadherence-linked asexual gene
Clpcaseineolytic protease
DHFR-TSdihydrofolate reductase–thymidylate synthetase
EBAerythrocyte-binding antigen
EBLerythrocyte-binding-like protein
FFTfast Fourier transform
FPfalcipain
FVfood vacuole
gDNAgenomic DNA
HAPhisto-aspartyl protease
hpihours postinvasion
IDCintraerythrocytic developmental cycle
MSPmerozoite surface protein
ORFopen reading frame
PMplasmepsin
PVparasitophorous vacuole
RBCred blood cell
RBPreticulocyte-binding protein
RESAring-infected surface antigen
SERAserine repeat antigen
TCAtricarboxylic acid.
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| 12929205 | PMC176545 | CC BY | 2021-01-05 08:21:03 | no | PLoS Biol. 2003 Oct 18; 1(1):e5 | utf-8 | PLoS Biol | 2,003 | 10.1371/journal.pbio.0000005 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0000006Research ArticleEcologyEvolutionGenetics/Genomics/Gene TherapyZoologyMammalsDNA Analysis Indicates That Asian Elephants Are Native to Borneo and Are Therefore a High Priority for Conservation Borneo Elephant OriginFernando Prithiviraj pf133@columbia.edu
1
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Vidya T. N. C
3
Payne John
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Stuewe Michael
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Davison Geoffrey
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Alfred Raymond J
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Andau Patrick
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Bosi Edwin
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Kilbourn Annelisa
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ΔMelnick Don J
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1Center for Environmental Research and Conservation, Columbia UniversityNew York, New YorkUnited States of America2Department of Ecology, Evolution, and Environmental Biology, Columbia UniversityNew York, New YorkUnited States of America3Center for Ecological Sciences, Indian Institute of ScienceBangaloreIndia4World Wide Fund for Nature–MalaysiaKota Kinabalu, SabahMalaysia5Asian Rhino and Elephant Action Strategy Programme, World Wildlife FundWashington, District of ColumbiaUnited States of America6Sabah Wildlife DepartmentKota Kinabalu, SabahMalaysia7Field Veterinary Program, Wildlife Conservation SocietyBronx, New YorkUnited States of America10 2003 18 8 2003 18 8 2003 1 1 e63 6 2003 29 7 2003 Copyright: ©2003 Fernando et al.2003This 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.
Borneo Elephants: A High Priority for Conservation
The origin of Borneo's elephants is controversial. Two competing hypotheses argue that they are either indigenous, tracing back to the Pleistocene, or were introduced, descending from elephants imported in the 16th–18th centuries. Taxonomically, they have either been classified as a unique subspecies or placed under the Indian or Sumatran subspecies. If shown to be a unique indigenous population, this would extend the natural species range of the Asian elephant by 1300 km, and therefore Borneo elephants would have much greater conservation importance than if they were a feral population. We compared DNA of Borneo elephants to that of elephants from across the range of the Asian elephant, using a fragment of mitochondrial DNA, including part of the hypervariable d-loop, and five autosomal microsatellite loci. We find that Borneo's elephants are genetically distinct, with molecular divergence indicative of a Pleistocene colonisation of Borneo and subsequent isolation. We reject the hypothesis that Borneo's elephants were introduced. The genetic divergence of Borneo elephants warrants their recognition as a separate evolutionary significant unit. Thus, interbreeding Borneo elephants with those from other populations would be contraindicated in ex situ conservation, and their genetic distinctiveness makes them one of the highest priority populations for Asian elephant conservation.
Comparison between DNA sequences of Borneo elephants with those of other Asian elephants settles a longstanding dispute about the origins of these endangered animals
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Introduction
Elephants have a very limited distribution in Borneo, being restricted to approximately 5% of the island in the extreme northeast (Figure 1). There are no historical records of elephants outside of this range. Fossil evidence for the prehistoric presence of elephants on Borneo is limited to a single specimen of a tooth from a cave in Brunei (Hooijer 1972).
Figure 1 Asian Elephant Range and Sampling Locations in Borneo
Solid lines demarcate country borders and the dotted line the boundary between the Malaysian states of Sabah and Sarawak. Black dots indicate areas of sample collection.
Popular belief holds that elephants presented to the Sultan of Sulu in 1750 by the East India Trading Company and subsequently transported to Borneo founded the current population (Harrisson and Harrisson 1971; Medway 1977). These animals presumably originated in India (Shoshani and Eisenberg 1982), where company operations and trade in domesticated elephants were centred. Alternatively, considering the geographic proximity to Borneo, the elephant trade that flourished in Sumatra and peninsular Malaysia during the 16th–18th centuries (Andaya 1979; Marsden 1986[1811]) may have been the source. Thus, if elephants were introduced to Borneo, the source population could have been India, Sumatra, or peninsular Malaysia, and as a feral population, Borneo's elephants would have low conservation importance.
Conversely, if elephants occurred naturally on Borneo, they would have colonised the island during Pleistocene glaciations, when much of the Sunda shelf was exposed (Figure 2) and the western Indo-Malayan archipelago formed a single landmass designated as Sundaland (MacKinnon et al. 1996). Thus, the isolation of Borneo's elephants from other conspecific populations would minimally date from the last glacial maximum, 18,000 years ago, when land bridges last linked the Sunda Islands and the mainland (MacKinnon et al. 1996). If Borneo's elephants are of indigenous origin, this would push the natural range of Asian elephants 1300 km to the east, and as a unique population at an extreme of the species' range, Borneo elephants' in situ conservation would be a priority and ex situ cross-breeding with other populations would be contraindicated.
Figure 2 Asian Elephant Range and Sampling Locations
Central sampling locations denote the countries sampled and represent a number of actual sampling locations within each country. 1. Sri Lanka, 2. India, 3. Bhutan, 4. Bangladesh, 5. Thailand, 6. Laos, 7. Vietnam, 8. Cambodia, 9. Peninsular Malaysia, 10. Sumatra (Indonesia) 11. Borneo (Sabah–Malaysia).
Initially, Borneo elephants were classified as a unique subspecies (Elephas maximus borneensis) based on morphological differences from other populations (Deraniyagala 1950, 1955). Subsequently, they were subsumed under the Indian Elephas maximus indicus (Shoshani and Eisenberg 1982) or the Sumatran Elephas maximus sumatrensis (Medway 1977) subspecies, based on an assumption of their introduction to the region or on the reasoning that morphological divergence was insufficient to warrant separate status. While unique subspecific status would highlight their conservation importance, evaluation of their status in terms of evolutionary significant units (ESUs) and management units (MUs) (Ryder 1986; Moritz 1994) would be more relevant to conservation management.
Results
We PCR-amplified and sequenced a 630 bp fragment of mitochondrial DNA (mtDNA), including the hypervariable left domain of the d-loop (Fernando et al. 2000), from 20 Borneo elephants and compared them with 317 sequences we generated for elephants across ten of the 13 Asian elephant range states (Figure 2). Asian elephant haplotypes segregated into two distinct clades, α and β (Fernando et al. 2000). All ‘Sundaland’ (peninsular Malaysia, Sumatra, and Borneo) haplotypes fell in clade β, while α and β clades were observed in Sri Lanka and mainland populations (Figures 3 and 4). The Borneo population was fixed for the unique β-haplotype BD. Similar tree topologies were obtained by maximum parsimony, neighbour joining, and maximum-likelihood methods of phylogenetic analyses, with some minor rearrangements of the terminal branches. In all trees, Bornean and other haplotypes unique to ‘Sundaland' (Borneo: BD; peninsular Malaysia: BQ, BV; Sumatra: BS, BU, BT, BR) occupied basal positions in the β-clade phylogeny (Figure 3) and were derived from internal nodes in a parsimony network of haplotypes (Figure 4). Uncorrected p distances between the Borneo haplotype and other β-haplotypes ranged from 0.012 (haplotypes BQ, BP, BO, BS, BU) to 0.020 (haplotype BE), with a mean of 0.014. Assuming a nucleotide substitution rate of 3.5% per million years for the elephant mtDNA d-loop (Fleischer et al. 2001), the observed genetic distance indicates divergence of the Borneo haplotype BD and its closest relative from a common ancestor approximately 300,000 years ago. Owing to stochastic coalescent processes, the use of a single gene to infer population parameters is prone to error. Despite any such error, the magnitude of the genetic difference between Borneo and other Asian elephant haplotypes is such that it indisputably excludes divergence since introduction; the observed divergence is so great that even if there was some error it would not have any influence on the conclusion that places the Borneo haplotype in a timeframe supporting a Pleistocene colonisation rather than introduction by humans.
Figure 3 A Neighbour-Joining Phylogram of Asian Elephant Haplotypes Rooted with an African Elephant Out-Group
Sunda Region haplotypes are in bold.
Figure 4 Network of Asian Elephant Haplotypes Based on Statistical Parsimony
Grey circles with letters denote haplotypes unique to the Sunda region (BD: Borneo; BQ, BV: peninsular Malaysia; BR, BS, BT, BU: Sumatra). White circles with letters denote haplotypes found in mainland Asia (excluding peninsular Malaysia) and Sri Lanka. The small open circles denote hypothetical haplotypes. Haplotypes beginning with the letters A and B belong to the two clades α and β, respectively.
We also genotyped 15 Borneo elephants for five polymorphic autosomal microsatellite loci (Nyakaana and Arctander 1998; Fernando et al. 2001) and compared them to 136 five-locus genotypes we generated for Asian elephants from nine range states. Tests of Hardy–Weinberg equilibrium and linkage disequilibrium in all populations indicated simple Mendelian inheritance of five unlinked, selectively neutral loci. The total number of alleles per locus across populations in the Asian elephant ranged from 2.0 (EMX-2) to 11.0 (LafMS03) (x¯, SE = 4.60, 1.51); the average number of alleles across loci, per population (excluding Borneo), from 2.0 (Sumatra) to 3.6 (Sri Lanka) (x¯, SE = 2.93, 0.155); the observed heterozygosity H0 across all populations (excluding Borneo) from 0.38 (EMX-4) to 0.63 (LafMS03) (x¯, SE = 0.44, 0.041); and gene diversity from 0.39 (EMX-4) to 0.69 (LafMS03) (x¯, SE = 0.47, 0.050). Comparatively, all indices demonstrated very low genetic diversity in the Borneo population: proportion of polymorphic loci, 0.4; number of alleles per locus, 1–2 (x¯, SE = 1.40, 0.219); gene diversity, 0–0.13 (x¯, SE = 0.04, 0.024); heterozygosity H0 = 0–0.07 (x¯, SE = 0.01, 0.013). The number of alleles, observed heterozygosity, and gene diversity, averaged across Asian elephant populations, were all higher than those in Borneo, at all loci (Table 1). Similarly, in all populations, the number of alleles and observed heterozygosity, averaged across loci, were higher than in Borneo (Table 2). Five unique genotypes were identified in the 15 Borneo elephants sampled. In tests of population subdivision, all pairwise comparisons between Borneo and other populations demonstrated highly significant differentiation, FST 0.32–0.63 (x¯, SE = 0.44, 0.034) (Table 3). In tests of a recent bottleneck, no heterozygote excess (Maruyama and Fuerst 1985) or mode-shift distortion of allele frequency distributions (Luikart et al. 1998a), characteristic of a recent bottleneck, was observed in the Borneo population. In assignment tests indicating the distinctness of a population's genotypes, all five Borneo genotypes were assigned with maximum likelihood to Borneo (likelihoods ranging from 0.004 to 0.80, x¯, SE = 0.51, 0.175), and maximum-likelihood ratios of the most-likely (Borneo) to the next-most-likely population ranged from 2.97 to 48.20 (x¯, SE = 25.02, 8.795). Borneo was significantly more likely to be the source than any other population for all five genotypes, since each of the assignment likelihoods to Borneo fell outside the upper end of the corresponding distribution of assignment likelihoods to the other populations. Assignment likelihoods to the putative Indian, Sumatran, and peninsular Malaysian source populations were very small (India: 0–0.0004, x¯, SE = 0.000126, 0.000065; Sumatra: 0–0.0355, x¯, SE = 0.007146, 0.006336; peninsular Malaysia: 0.0003–0.1195, x¯, SE = 0.0301, 0.0201), indicating that Borneo's genotypes were highly unlikely to have originated from any of these populations.
Table 1 Comparison of Measures of Genetic Variation at Individual Loci in Borneo with Those of the Other Populations
Table 2 Measures of Genetic Variation Using Five Loci, in Asian Elephant Populations from across the Range
Table 3 FST Values in Pairwise Comparison of Borneo with Other Populations
Discussion
mtDNA evidence supports an indigenous hypothesis in three ways. First, this hypothesis assumes an ancient, independent evolution of Borneo's elephants, resulting in the unique, divergent Borneo haplotype(s), as we observed. Conversely, the introduction hypothesis assumes an introduction at 500 years ago or less, which approximates zero time on a scale of mtDNA d-loop evolution, and hence requires Borneo and source population haplotypes to be identical. This was not observed. Second, the estimated divergence time between the Borneo haplotype and other Asian elephant haplotypes is concordant with a mid- to late-Pleistocene isolation of elephants on Borneo and the vicariant history of the island (MacKinnon et al. 1996). Third, all observed ‘Sundaland' haplotypes, including Borneo's, were of the β clade, had basal relationships to that clade in a phylogenetic tree, and were independently derived from internal nodes in a haplotype network, suggesting an ancient isolation of these lineages on Borneo, Sumatra, and peninsular Malaysia. Thus, the Borneo haplotype fits a pattern of distribution and relatedness to other ‘Sundaland' haplotypes that is congruent with an ancient colonisation of the Sunda region by β clade and subsequent allopatric divergence of populations on its larger landmasses.
Microsatellite data also support the indigenous hypothesis. If the Borneo population originated from animals introduced in the 16th–18th centuries, it would have reached its mid-20th-century size of approximately 2,000 individuals (deSilva 1968) in fewer than 30 generations, assuming an Asian elephant generation time of 15–20 years (Sukumar 1989). Thus, the Borneo population would have experienced a rapid demographic expansion after the ‘recent’ bottleneck caused by the founder-event of introduction. We did not observe a heterozygote excess or a mode-shift distortion in allele frequency distribution in the Borneo population, suggesting that the population did not undergo a recent bottleneck and hence did not arise from a few introduced animals. However, this result by itself is not conclusive, since with a sample size of 15 and five loci, the test for heterozygosity excess has low power and bottlenecks may not be detected (Luikart et al. 1998b). We observed extremely low genetic diversity at Borneo elephant microsatellite loci, including fixation at three of the five loci. Sequential founder-events or persistent small population size, as would be expected in a small population isolated since the Pleistocene, would lead to substantial loss of genetic variation (Nei et al. 1975) and hence is consistent with the data. Successful founding of a population by a very few individuals from a single introduction could also result in a severe bottleneck. However, given the adversities faced by translocated elephants (Fernando 1997) and the importance of social structure in the reproduction and survival of elephants (Fernando and Lande 2000; McComb et al. 2001), such an explanation is unlikely.
In the assignment tests, all five Borneo genotypes, which included free-ranging as well as captive animals, were assigned to Borneo with significantly higher likelihoods than to other populations and with extremely low likelihoods to the putative source populations. An introduced population may be highly divergent from the source population in terms of F statistics (Williams et al. 2002) due to allelic loss from founder-events. However, the probability of loss for a particular allele is inversely proportional to its frequency in the founder and hence the source population. Thus, genotypes in an introduced population would retain a high likelihood of assignment to the source population, enabling its identification from among a number of candidate populations. Therefore, the assignment tests strongly suggest that the Borneo elephants were not derived from another population in the recent past.
Thus, microsatellite data strongly suggest a Pleistocene colonisation, independent evolution through a long period of isolation, and long-term small population size for the Borneo population. It strongly rejects a recent origin from any of the putative source populations.
Mitochondrial and microsatellite analyses indicate that Borneo's elephants are indigenous to Borneo, have undergone independent evolution since a Pleistocene colonisation, and are not descended from animals introduced by humans. The evolutionary history of Borneo's elephants warrants their recognition as a separate ESU (Moritz 1994). Thus, they should not be cross-bred with other Asian elephants in ex situ management. The genetic distinctiveness and evolutionary history of Borneo elephants support their recognition as a unique subspecies. However, one of the reasons E. maximus borneensis was subsumed under E. m. indicus and E. m. sumatrensis was the inadequacy of the original description of E. m. borneensis in terms of the morphological characters assessed and sample size. Therefore, we suggest that a formal reinstatement of the E. m. borneensis taxa await a detailed morphological analysis of Borneo elephants and their comparison with other populations.
While Borneo's elephants appear to be genetically depauperate, through a long history of isolation and inbreeding, they may have purged deleterious recessive alleles from their genome and decreased their genetic load, thus becoming less susceptible to inbreeding depression. We recommend research on reproductive rates, juvenile survival, and other indicators of detrimental effects of inbreeding such as sperm deformities, sperm mobility, and genetic diversity at MHC loci. While increasing genetic diversity by introducing a small number of elephants from other populations (Whitehouse and Harley 2001) may have to be considered if deleterious inbreeding effects are evident, in the absence of such findings Borneo's elephants should be managed separately from other Asian elephants.
Materials and Methods
Samples.
Samples consisted of dung from free-ranging and dung or blood from captive elephants. Sample collection, storage, and DNA extraction followed published protocols (Fernando et al. 2000, 2003). For mitochondrial and microsatellite analysis, respectively, 20 and 15 samples from Borneo (nine blood samples from elephants captured for management purposes—eight from the Kretam area and one individual originating from around Lahad Datu—and the rest from dung samples from free-ranging elephants collected during a survey of the Kinabatangan watershed) were compared with 317 and 136 samples from across the current Asian elephant range, Sri Lanka (n = 81, 20), India (n = 81, 20), Bhutan (n = 13, 13), Bangladesh (n = 30, 20), Thailand (n = 8, 8), Cambodia (n = 30, 20), Vietnam (n = 5, 0), Laos (n = 20, 6), Indonesia (Sumatra) (n = 40, 20), and peninsular Malaysia (n = 9, 9). Vietnam was excluded from the microsatellite analysis owing to nonamplification of a number of samples.
mtDNA amplification and sequencing.
Approximately 630 bp of mtDNA, including the left domain of the d-loop, were amplified using published primers (Fernando et al. 2000). PCR products were sequenced in both directions, using internal sequencing primers MDLseq-1 (CCTACAYCATTATYGGCCAAA) and MDLseq-2 (AGAAGAGGGACACGAAGATGG), and resolved in 4% polyacrylamide gels in an ABI 377 automated sequencer (Perkin-Elmer, Wellesley, Massachusetts, United States).
mtDNA phylogenetic analysis.
We used 600 bp of the amplified segment in the analysis. Sequences were aligned and edited using SEQUENCHER version 3.1.1 (GeneCodes Corporation, Ann Arbor, Michigan, United States). Sequences were deposited in GenBank (accession numbers AY245538 and AY245802 to AY245827). Phylogenetic analyses were conducted using PAUP* version 4.0 (Swofford 1998). Three African elephant (Loxodonta africana) sequences from zoo animals in the United States were used as an out-group. Genetic distances among sequences were calculated using uncorrected p distances. Maximum-parsimony analysis was conducted using a heuristic search with random stepwise addition of taxa, tree bisection/reconnection branch swapping, and equal weighting; neighbour joining, with Kimura two-parameter distances; and maximum likelihood, using empirical base frequencies and estimated values for the shape parameter for among-site rate variation and transition/transversion ratios. A network of haplotypes was created using statistical parsimony in the software TCS version 1.13 (Clement et al. 2001).
Microsatellite amplification.
Samples were screened with five published microsatellite loci, EMX-1 to EMX-4 (Fernando et al. 2001) and LafMS03 (Nyakaana and Arctander 1998). Forward primers were fluorescent labelled (FAM, HEX, or TET), samples were amplified in 12.5 μl volumes with relevant cycling profiles (Fernando et al. 2001), and 1 μl of PCR product was mixed with 0.2 μl of loading-dye and 0.5 μl of Tamra 500 size standard (Applied Biosystems, Foster City, California, United States) and was resolved in 4% polyacrylamide gels in an ABI 377 automated sequencer. Alleles were scored using GENESCAN software (Applied Biosystems) and published guidelines (Fernando et al. 2003).
Microsatellite data analysis.
Deviations from Hardy–Weinberg equilibrium for each locus and population were tested using the exact Hardy–Weinberg test as implemented in GENEPOP 3.2 (Raymond and Rousset 1995), with the complete enumeration method (Louis and Dempster 1987) for loci with fewer than four alleles and with the Markov chain method (Guo and Thompson 1992) (dememorization: 1000; batches: 100; iterations per batch: 1000) for loci with more than four alleles. GENEPOP was also used to test for linkage disequilibrium between loci, using the Markov chain method. Population differentiation was tested with estimates of Wright's fixation index (Weir and Cockerham 1984), FST, using the program Arlequin version 2 (Schneider et al. 2000).
Evidence for a recent bottleneck in the Borneo population in terms of a heterozygote excess (Cornuet and Luikart 1996) or a mode-shift distortion in allele frequencies (Luikart et al. 1998a) was conducted using the program BOTTLENECK version 1.2.02 (Piry et al. 1997) and a graphical method (Luikart et al. 1998a).
Assignment tests were performed using WHICHRUN version 4.1 (Banks and Eichert 2000). Assuming Hardy–Weinberg equilibrium in each baseline population and linkage equilibrium between loci, the likelihood that an individual originates from a particular population is the Hardy–Weinberg frequency of the individual's genotype at that locus, in that population. This likelihood was multiplied across loci to obtain a multilocus assignment likelihood of the test individual to each population, and the population with the highest value was identified as the ‘most-likely’ source population. To test for statistical significance of the most-likely source population, this assignment likelihood was compared with the distribution of assignment likelihoods of the other populations. Maximum-likelihood ratios were calculated as the ratio between the likelihood of assignment to the most-likely population to that for a particular population.
Supporting Information
Accession Numbers
The GenBank accession numbers for the sequences reported in this paper are AY245538 and AY245802 to AY245827.
We would like to thank Susan Mikota, Peter Malim, Eric Wickramakayake, Richard Lair, Jayantha Jayewardene, L. K. A. Jayasinghe, Manori Gunawardene, H. K. Janaka, Chandana Rajapakse, Ashoka Dangolla, Raman Sukumar, Ajay Desai, Christy Williams, Ainun Nishat, Mohsinuzzman Chowdhury, Mike Keele, Jeff Briscoe, Steve Osofsky, Karl Stromayer, Andrew Maxwell, Ou Ratanak, Lic Vuthy, Joe Heffernen, Rob Tizard, Tom Dillon, Vongphet, Buntjome, Kari Johnson, Heidi Riddle, Simon Hedges, Martin Tyson, Joshua Ginsberg, the Sabah Wildlife Department, Fauna and Flora International, the Department of Wildlife Conservation Sri Lanka, the International Union for Conservation of Nature and Natural Resources Bangladesh, the Zoological Gardens and Wildlife Rescue Centre Cambodia, Angkor Village Resort Elephant Farm (Siem Reap, Cambodia), the Seblat Elephant Training Centre (Bengkulu, Sumatra), Have Trunk Will Travel, the Department of Forests and Wildlife Cambodia, the Wildlife Trust, the World Wildlife Fund (WWF) Malaysia, WWF Vietnam, the Wildlife Conservation Society, the Portland (Oregon) Zoo, the Los Angeles Zoo, the Singapore Zoo, and the Pinnawela Elephant Orphanage Sri Lanka for help in obtaining samples; and Jennifer Pastorini, Ajay Desai, and two anonymous reviewers for comments on an earlier version of the manuscript. This study was conducted in partnership with the WWF's Asian Rhino and Elephant Action Strategy (AREAS) Programme and through additional collaboration with Wildlife Trust's Indian and Sri Lankan Elephant Programs and the Wildlife Conservation Society's Indonesia–Sumatran Elephant Project and Field Veterinary Program. It was made possible by grants from Ms. Nancy Abraham, the WWF United States, WWF for Nature (WWF International), the United States Fish and Wildlife Service's Asian Elephant Conservation Fund, and the Center for Environmental Research and Conservation Seed Grant Program and by additional support from the Laboratory of Genetic Investigation and Conservation, Columbia University. We dedicate this paper to the memory of our coauthor Annelisa Kilbourn, whose untimely death during her work in Gabon is a great loss to conservation.
Conflicts of Interest. The authors have declared that no conflicts of interest exist.
Author Contributions. This project is part of an ongoing multicentre collaboration on elephant conservation. All authors on this manuscript contributed substantively to the work described herein.
Academic Editor: Craig Moritz, University of California, Berkeley.
Δ In the online version of this article published on August 18, Annelisa Kilbourn's affiliation was incorrectly identified as the Sabah Wildlife Department. Her proper affiliation is shown here.
Abbreviations
ESUevolutionary significant unit
mtDNAmitochondrial DNA
MUmanagement unit.
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| 12929206 | PMC176546 | CC BY | 2021-01-05 08:21:06 | no | PLoS Biol. 2003 Oct 18; 1(1):e6 | utf-8 | PLoS Biol | 2,003 | 10.1371/journal.pbio.0000006 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0000007SynopsisEcologyEvolutionGenetics/Genomics/Gene TherapyZoologyMammalsBorneo Elephants: A High Priority for Conservation Synopsis10 2003 18 8 2003 18 8 2003 1 1 e7Copyright: © 2003 Public Library of Science.2003This 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.
DNA Analysis Indicates That Asian Elephants Are Native to Borneo and Are Therefore a High Priority for Conservation
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A new study settles a long-standing dispute about the genesis of an endangered species. With scant fossil evidence supporting a prehistoric presence, scientists could not say for sure where Borneo's elephants came from. Did they descend from ancient prototypes of the Pleistocene era or from modern relatives introduced just 300–500 years ago? That question, as Fernando et al. report in this issue, is no longer subject to debate.
Applying DNA analysis and dating techniques to investigate the elephants' evolutionary path, researchers from the United States, India, and Malaysia, led by Don Melnick of the Center for Environmental Research and Conservation at Columbia, demonstrate that Borneo's elephants are not recent arrivals. They are genetically distinct from other Asian elephants and may have parted ways with their closest Asian cousins when Borneo separated from the mainland, effectively isolating the Borneo elephants some 300,000 years ago.
In the 1950s, Borneo elephants had been classified as a subspecies of Asian elephants (either Indian or Sumatran) based on anatomical differences, such as smaller skull size and tusk variations. This classification was later changed, partly because of the popular view that these animals had descended from imported domesticated elephants. Until now, there was no solid evidence to refute this belief and no reason to prioritize the conservation of Borneo elephants.
Their new status, as revealed by this study, has profound implications for the fate of Borneo's largest mammals. Wild Asian elephant populations are disappearing as expanding human development disrupts their migration routes, depletes their food sources, and destroys their habitat. Recognizing these elephants as native to Borneo makes their conservation a high priority and gives biologists important clues about how to manage them.
Borneo elephant
| 0 | PMC176547 | CC BY | 2021-01-05 08:21:06 | no | PLoS Biol. 2003 Oct 18; 1(1):e7 | utf-8 | PLoS Biol | 2,003 | 10.1371/journal.pbio.0000007 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0000011SynopsisGenetics/Genomics/Gene TherapyInfectious DiseasesMicrobiologyPlasmodiumMonitoring Malaria: Genomic Activity of the Parasite in Human Blood Cells Synopsis10 2003 18 8 2003 18 8 2003 1 1 e11Copyright: © 2003 Public Library of Science.2003This 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.
Microarray Analysis: Genome-Scale Hypothesis Scanning
The Transcriptome of the Intraerythrocytic Developmental Cycle of Plasmodium falciparum
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Every year, malaria kills as many as 2.5 million people. Of these deaths, 90% occur in sub-Saharan Africa, and most are children. While four species of the single-celled organism Plasmodium cause malaria, Plasmodium falciparum is the deadliest. Harbored in mosquito saliva, the parasite infects its human host as the mosquito feeds on the victim's blood.
Efforts to control the disease have taken on an increased sense of urgency, as more P. falciparum strains show resistance to antimalarial drugs. To develop new drugs and vaccines that disable the parasite, researchers need a better understanding of the regulatory mechanisms that drive the malarial life cycle. Joseph DeRisi and colleagues now report significant progress toward this goal by providing the first comprehensive molecular analysis of a key phase of the parasite's life cycle.
While P. falciparum is a single-celled eukaryotic (nucleated) organism, it leads a fairly complicated life, assuming one form in the mosquito, another when it invades the human liver, and still another in human red blood cells (erythrocytes). The intraerythrocytic developmental cycle (IDC) is the stage of the P. falciparum lifecycle associated with the clinical symptoms of malaria. Using data from the recently sequenced P. falciparum genome, the researchers have tracked the expression of all of the parasite's genes during the IDC.
The pattern of gene expression (which can be thought of as the internal operating system of the cell) during the IDC is strikingly simple. Its continuous and clock-like progression of gene activation is reminiscent of much simple life forms—such as a virus or phage—while unprecedented for a free living organism. Virus and phage behave like a “just-in-time” assembly line: components are made only as needed, and only in the amount that is needed. In this respect, malaria resembles a glorified virus.
Given the remarkable coupling of the timing of gene activation with gene function, as shown in this paper, this understanding could help identify the biological function of the 60% of genes in P. falciparum that encode proteins of unknown function.
P. falciparum appears to be ultra-streamlined and exquisitely tuned to perform a single job: consume, replicate, and invade. The simple program regulating the life of P. falciparum may hold the key to its downfall as any perturbation of the regulatory program will likely have dire consequences for the parasite. This offers renewed hope for the design of inhibitory drugs targeted at the regulatory machinery that would irreparably foul the parasite's regulatory program, ultimately resulting in its death.
Gene expression profile of P. falciparum
| 0 | PMC176548 | CC BY | 2021-01-05 08:21:04 | no | PLoS Biol. 2003 Oct 18; 1(1):e11 | utf-8 | PLoS Biol | 2,003 | 10.1371/journal.pbio.0000011 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0000013Research ArticleCell BiologyDevelopmentGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyNeurosciencePhysiologyDrosophila
Drosophila Free-Running Rhythms Require Intercellular Communication Damping Transcriptional RhythmsPeng Ying
1
2
Stoleru Dan
1
Levine Joel D
1
Hall Jeffrey C
1
Rosbash Michael rosbash@brandeis.edu
1
2
1Department of Biology, Brandeis UniversityWaltham, MassachusettsUnited States of America2Howard Hughes Medical Institute, Brandeis UniversityWaltham, MassachusettsUnited States of America10 2003 15 9 2003 15 9 2003 1 1 e1320 6 2003 4 8 2003 Copyright: ©2003 Peng et al.2003This 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.
Biological Clock Depends on Many Parts Working Together
Robust self-sustained oscillations are a ubiquitous characteristic of circadian rhythms. These include Drosophila locomotor activity rhythms, which persist for weeks in constant darkness (DD). Yet the molecular oscillations that underlie circadian rhythms damp rapidly in many Drosophila tissues. Although much progress has been made in understanding the biochemical and cellular basis of circadian rhythms, the mechanisms that underlie the differences between damped and self-sustaining oscillations remain largely unknown. A small cluster of neurons in adult Drosophila brain, the ventral lateral neurons (LNvs), is essential for self-sustained behavioral rhythms and has been proposed to be the primary pacemaker for locomotor activity rhythms. With an LNv-specific driver, we restricted functional clocks to these neurons and showed that they are not sufficient to drive circadian locomotor activity rhythms. Also contrary to expectation, we found that all brain clock neurons manifest robust circadian oscillations of timeless and cryptochrome RNA for many days in DD. This persistent molecular rhythm requires pigment-dispersing factor (PDF), an LNv-specific neuropeptide, because the molecular oscillations are gradually lost when Pdf01 mutant flies are exposed to free-running conditions. This observation precisely parallels the previously reported effect on behavioral rhythms of the Pdf01 mutant. PDF is likely to affect some clock neurons directly, since the peptide appears to bind to the surface of many clock neurons, including the LNvs themselves. We showed that the brain circadian clock in Drosophila is clearly distinguishable from the eyes and other rapidly damping peripheral tissues, as it sustains robust molecular oscillations in DD. At the same time, different clock neurons are likely to work cooperatively within the brain, because the LNvs alone are insufficient to support the circadian program. Based on the damping results with Pdf01 mutant flies, we propose that LNvs, and specifically the PDF neuropeptide that it synthesizes, are important in coordinating a circadian cellular network within the brain. The cooperative function of this network appears to be necessary for maintaining robust molecular oscillations in DD and is the basis of sustained circadian locomotor activity rhythms.
Circadian rhythms are characterized by robust molecular oscillations, which are shown here to require a brain region-specific neuropeptide, PDF, for maintenance and coordination
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Introduction
Circadian rhythms of diverse organisms are based on similar intracellular molecular feedback loops (Dunlap 1999; Allada et al. 2001; Panda et al. 2002). Based on this view, it is believed that one or a small number of clock cells are sufficient for self-sustained rhythms (Dunlap 1999). This is despite the complex cellular organizations of many tissues, organisms, and systems (Kaneko and Hall 2000; Schibler and Sassone-Corsi 2002).
In Drosophila, circadian clocks have been identified in a diverse range of cell types throughout the head and the body (Glossop and Hardin 2002; Hall 2003). However, the clocks in different cells are considered nonidentical (Krishnan et al. 2001; Glossop and Hardin 2002; Levine et al. 2002a; Schibler and Sassone-Corsi 2002). In many tissues, molecular oscillations undergo rapid damping without environmental timing cues (Hardin 1994; Plautz et al. 1997; Stanewsky et al. 1997; Giebultowicz et al. 2000). This is similar to the damping of in vitro rhythms in some mammalian tissues (Balsalobre et al. 1998; Schibler and Sassone-Corsi 2002). In contrast, the Drosophila “core pacemaker” is believed to maintain robust oscillations for a long time in constant darkness (DD) with little or no damping, such that circadian behaviors can persist under such conditions (Dowse et al. 1987). Indeed, self-sustaining oscillations are a defining characteristic of true circadian rhythms and are believed to be required of a fully functional rhythmic cell. The differences between the “core pacemaker” and the clock machinery within damping cells or systems are unknown.
The six clusters of approximately 100 clock neurons in the adult Drosophila brain are well characterized (Kaneko and Hall 2000). Recent studies have focused principally on one of these groups, the small ventral lateral neurons (s-LNvs), as the best “core pacemaker” candidate for the following reasons: (1) in the developmental mutant disco, the presence of LNvs correlates with the maintenance of behavior rhythmicity (Helfrich-Förster 1997); (2) LNvs specifically express the neuropeptide pigment-dispersing factor (PDF), and the Pdf01-null mutant loses behavioral rhythmicity under DD conditions (Renn et al. 1999); (3) genetic ablation of the LNvs by expressing proapoptotic genes causes the loss of rhythmicity in DD (Renn et al. 1999); and (4) the s-LNvs maintain robust molecular oscillations for at least for 2 days in DD (Yang and Sehgal 2001; Shafer et al. 2002), in contrast to at least some other brain neurons and nonneuronal tissues. This final property suggests that these cells might fulfill the self-sustaining criterion for the “core pacemaker.” Indeed, the s-LNvs have been proposed to the primary pacemaker cells that generate locomotor activity rhythms (Helfrich-Förster 1997; Renn et al. 1999; Emery et al. 2000). Consistent with this cell-autonomous view of circadian rhythmicity, it has been shown that the LNvs possess all components of a fully functional, independent circadian clock: the photoreceptor cryptochrome, the rhythm-generating feedback loops, and a putative output factor, the neuropeptide PDF (Emery et al. 2000). Our pursuit of the self-sustaining “core pacemaker” of the Drosophila circadian system began with a test of the s-LNv cell-autonomous clock hypothesis.
Results
LNvs Cannot Support Circadian Behavior Independently
To test whether the LNvs can support free-running circadian locomotor activity rhythms independently of other functional clock cells, we restricted pacemaker activity to these few PDF-expressing cells. CYCLE (CYC) is a bHLH–PAS protein (Rutila et al. 1998) and forms a heterodimeric transcription factor with CLOCK (CLK), another bHLH–PAS protein (Allada et al. 1998). CYC is an essential component of the Drosophila circadian oscillator transcriptional feedback loop (Glossop et al. 1999). The cyc01 nonsense mutation completely eliminates molecular oscillations, and the direct target genes period (per) and timeless (tim) mRNAs are essentially undetectable (Rutila et al. 1998). Behavioral rhythms are also absent in the cyc01 homozygous mutant strain (Rutila et al. 1998). We rescued cyc01 specifically in the LNvs, by using a well-characterized pdf–GAL4 driver (Renn et al. 1999) in combination with a UAS–CYC transgene to express ectopically wild-type CYC. Since CYC is apparently not a rate-limiting component of active dCLK–CYC complexes (Bae et al. 2000) and does not undergo molecular oscillations itself (Rutila et al. 1998), we expected that CYC overexpression would not cause circadian oscillator dysfunction. Indeed, the presence of the two transgenes did not affect locomotor activity rhythms in a wild-type background (Figure 1C, right panel).
Figure 1 Rescuing Molecular Oscillations within the LNvs Is Not Sufficient to Rescue Locomotor Activity Rhythms
The rescued mutant genotype is y w;pdf–GAL4;UAS–CYC,cyc01/cyc01. The flies were entrained in standard LD conditions and timepoints taken. Molecular oscillations were examined by whole-mount in situ hybridization of the tim gene. Double staining with a Pdf probe was used to label the LNvs neuronal group.
(A and B) These show representative duplicate experiments. No tim mRNA signal is detectable in the dorsal region of the brain. The lower arrows point to the s-LNvs and the upper arrows to the l-LNvs. (A) Brain taken at timepoint ZT3. Panels shown from left to right are Pdf (green, FITC labeled), tim (red, Cy3 labeled), and an image overlay. (B) Brain taken at timepoint ZT15. Panels shown from left to right are Pdf (green, FITC labeled), tim (red, Cy3 labeled), and an image overlay.
(C) The double-plotted actograms of rescue mutant and control flies in a standard LD:DD behavior assay. The colors on the background indicate the lighting conditions of the behavior monitors (white, lights on; light blue, lights off). In the actogram, the average locomotor activity of the group of flies is plotted as a function of time. The left panel shows the actogram of the rescued mutant flies (y w;pdf–GAL4/+;UAS–CYC,cyc01/cyc01, n = 30). RI (rhythm index; Levine et al. 2002a) = 0.14. The right panel shows the actogram for the rescued wild-type (control) flies (y w;pdf–GAL4/+;UAS–CYC/+, n = 32, RI = 0.61).
The rescued mutant flies (pdf–GAL4;UAS–CYC,cyc01/cyc01) were examined by two independent criteria. First, molecular oscillations were assayed by in situ hybridization with a tim probe (Figure 1A and 1B). tim RNA levels undergo robust cycling in wild-type flies, with a trough at ZT3 and a peak at ZT15 (Sehgal et al. 1994). This is also true within all individual clock neurons (Zhao et al. 2003). tim mRNA cycled in the LNvs (Figure 1A and B), indicating successful rescue of the molecular oscillator within these cells. The fact that other clock neurons were still tim mRNA-negative (Figure 1A and B) suggests that CYC and the rest of the molecular machinery can function cell autonomously, at least in the LNvs under these light–dark (LD) conditions. The observed oscillations are also not passively driven by light, since they persisted in DD, at least in the s-LNvs (Figure S1). Second, locomotor activity rhythms were examined by standard behavioral criteria. The transgenic flies were completely arrhythmic in DD. They were also arrhythmic under LD conditions, as the flies failed to anticipate the discontinuous transitions from light to dark or from dark to light (see Figure 1C, left panel; Rutila et al. 1998). In summary, the behavioral phenotypes were indistinguishable from those of the parental cyc01 mutant strain.
Brain Clock Neurons Manifest Robust Molecular Oscillations in DD
The insufficiency of LNv molecular rhythmicity indicates that one or more additional groups of rhythmic clock neurons are required for behavioral rhythmicity. We considered that robust molecular cycling under extended constant darkness conditions might be a good criterion for identifying these cell groups, because prior biochemical studies showed that some head and brain locations undergo damping of molecular oscillations under free-running conditions (Hardin 1994; Stanewsky et al. 1997). This conclusion has been extended by more recent immunohistochemical observations (Yang and Sehgal 2001; Shafer et al. 2002). The criterion of maintaining persistent and robust molecular rhythms in DD therefore suggests that only a limited set of brain locations are likely to be free-running pacemaker candidates. In order to identify these neurons, we assayed fly brains by tim in situ hybridization after 8 days in DD. To our surprise, we found that all tim-expressing brain cell groups (including both large ventral lateral neurons [l-LNvs] and small ventral lateral neurons [s-LNvs], doral lateral neurons [LNds], and all three groups of dorsal neurons [DNs]) still cycle robustly at this time (Figure 2). Previous studies have reported that the l-LNvs fail to maintain oscillations at the beginning of DD (Yang and Sehgal 2001; Shafer et al. 2002). We have reproduced these observations, but noticed that the l-LNvs “adapt” to constant conditions by becoming rhythmic once again after about 2 days in DD (data not shown). These results clearly distinguish the brain from the eyes and other peripheral tissues, which rapidly lose coherent molecular oscillations under free-running conditions (Hardin 1994; Plautz et al. 1997; Stanewsky et al. 1997; Giebultowicz et al. 2000). Although this approach failed to identify the additional neuronal groups necessary for behavioral rhythms, it suggests that many of these brain neuronal groups might act together in a network to support robust rhythms.
Figure 2 All Brain Clock Neuronal Groups Maintain Robust Oscillations of tim RNA Levels in DD
Wild-type flies were entrained for at least 3 days and then released into DD. tim RNA was assayed at trough (left panels) and peak (right panels) timepoints by whole-mount in situ hybridization. Wild-type flies in LD (A) were compared with the eighth day of DD (B). On the eighth day of DD, the locomotor activities of the fly population were still in close synchrony, without any obvious phase spreading (data not shown). Left panels, brains at ZT3 (A) or CT3 (B); right panels, brains from ZT15 (A) or CT15 (B). Both (A) and (B) are representative of three replicate experiments.
Sustained Molecular Oscillation in Constant Darkness Requires PDF
This association between robust molecular oscillations in all brain clock cells and behavioral rhythms in DD also made us consider the role of the neuropeptide PDF. The Pdf01 mutant strain is unique among identified Drosophila circadian mutants, as it has little effect under LD conditions, but loses behavioral rhythmicity gradually and specifically in DD (Renn et al. 1999). This phenotype might reflect a disassociation between behavioral rhythmicity and the underlying molecular oscillations, as predicted from the role of PDF as a circadian output signal; it is proposed to connect the molecular oscillation in the LNvs to locomotor activity (Renn et al. 1999).
We considered a completely different interpretation, namely, that PDF contributes to the functional integration of several brain clock neuronal groups, which is necessary to sustain molecular as well as behavioral rhythmicity under constant conditions. This fits well with previous studies of PDF in other organisms (Rao and Riehm 1993; Petri and Stengl 1997). In contrast to the canonical output model, this possibility suggests that the Pdf01 mutant might manifest unusual molecular oscillations within clock neurons, especially under DD conditions. To address this issue experimentally, we examined Pdf01 mutant flies by tim in situ hybridization.
In Pdf01 flies, all clock neurons had robust tim RNA oscillations in LD, and the cycling phase and amplitude were comparable to those of wide-type flies (Figure 3A). The mutant flies were then released into DD and assayed at various times thereafter. In the first day of DD, cycling was similar to that observed in LD (Figure 3B). By the fourth day of DD, however, the cycling amplitude was much reduced in all clock neurons (Figure 3C and 3D). This was most evident from the unusually high signal in the CT2 sample; in wild-type flies, no tim signal was detected in any clock neuron at this timepoint (Figure 3C, left panels). There was also a reduced signal strength at the peak time, CT14 (Figure 3C, fourth panel from the left). The result parallels the damping of behavioral rhythms in the Pdf01 mutant strain (Renn et al. 1999).
Figure 3 Molecular Oscillations of tim RNA Damp in DD in the Pdf01 Mutant
tim RNA oscillations were examined in the Pdf01 mutant under both LD (A) and different days in DD ([B] and [C]), by whole-mount in situ hybridization. (A), (B), and (C) are representative images from replicas of three experiments.
(A) The left panel is from ZT3, and the right panel is from ZT15. A normal tim oscillation profile is observed compared to that of wild-type (see Figure 2A).
(B) Brains from the Pdf01 mutant in the first day of DD. Left panel, CT3; right panel, CT15. Oscillations are comparable to those in LD.
(C) Brains taken in the fourth day of DD. Six timepoints were taken throughout the circadian day. The sequence of panels from left to right is CT2, 6, 10, 14, 18, and 20, respectively. Wild-type brains (top row) were assayed in parallel with those from the Pdf01 mutant (bottom row). See text for details.
(D) Quantification of (C). Relative intensities are taken from normalized mean pixel intensities. Different clock neuronal groups were quantified independently and compared between wild-type (blue curves) and Pdf01 mutant (purple curves). The panels from left to right are quantification of tim RNA oscillation in the DNs, in the LNds, and in the LNvs. Reduced cycling amplitude and a significant advanced phase were observed in the fourth day of DD. See text for details.
Despite the gradual fading of locomotor activity rhythms in DD, a significant fraction of Pdf01 mutant flies is still weakly rhythmic after 4 d of DD (Renn et al. 1999). By tracking their locomotor activity phases, we observed that most of them had accumulated an approximately 4-hour phase advance relative to wild-type flies by the fourth day in DD. This is consistent with the measured ca. 23-hour periods of these weakly rhythmic flies (1-hour phase advanced per day for 4 days) as well as their advanced evening activity peak in LD (Renn et al. 1999). Quantitation of the tim in situ hybridization signal showed that there was a comparable one-point (4 h) advance in the peak of tim RNA and also confirmed the reduced cycling amplitude (Figure 3D). In order to eliminate the possibility that the observed damping is caused by the asynchrony of the Pdf01 fly population, locomotor activities were tracked in real time. Individual flies were then removed from the monitors to assay tim RNA levels. Identical damped molecular oscillations were also observed in this case (data not shown). Taken together, the results indicate an excellent quantitative correspondence in phase and amplitude between the tim RNA rhythms and the behavioral rhythms in all clock neurons of the Pdf01 strain.
To extend these observations, we also assayed cryptochrome (cry) mRNA oscillations by in situ hybridization. cry is expressed in a similar clock neuron pattern to tim, but it has a peak expression at ZT2 and a trough at ZT14 (Emery et al. 1998; Zhao et al. 2003). This phase is opposite to that of tim and other CLK–CYC direct target genes and reflects the fact that cry is only indirectly regulated by this heterodimeric transcription factor; CLK–CYC directly regulates the transcription factors PDP1 and VRILLE, which then regulate cry (Cyran et al. 2003; Glossop et al. 2003). Despite these differences between tim and cry, a similar result was obtained for cry in the Pdf01 strain in the fourth day of DD (Figure 4), i.e., a reduced cycling amplitude compared to the fourth day of DD in a wild-type strain. This is suggested by the in situ pictures and is strongly indicated by the quantitation (Figure 4). The correspondence between the tim and cry mRNA patterns indicates that the entire circadian transcriptional program damps in the mutant strain in DD, which underlies the behavioral damping.
Figure 4
cry RNA Oscillation Amplitude Is Also Reduced by the Fourth Day of DD in the Pdf01 Mutant
cry RNA expression in the brain was examined at the fourth day of DD by whole-mount in situ hybridization using a cry probe. Timepoints were taken every 4 hours throughout the circadian day. The sequence of panels from left to right is CT2, 6, 10, 14, 18, and 20, respectively. Wild-type brains (top row) were analyzed in parallel with those from the Pdf01 mutant (bottom row). Shown are representative images from duplicate experiments. Quantification of cry RNA oscillations in different cell groups is as shown in Figure 3. Ubiquitous damping of the cycling amplitude in the different cell groups was observed in the Pdf01 mutant.
PDF Is Likely to Act upon Clock Neurons Directly
It is noteworthy that the mRNA oscillations damp uniformly in the Pdf01 mutant strain, including the PDF-expressing LNvs (see Figures 3 and 4). Since PDF is a neuropeptide (Rao and Riehm 1993), it is unlikely to exert a direct intracellular effect on the LNv transcriptional machinery. A more conservative interpretation is that PDF maintains intercellular communication between individual LNv neurons (Petri and Stengl 1997) and/or between the LNvs and other cells; the communication is essential for self-sustained molecular rhythms within the LNvs. Although this “feedback” could be quite indirect, the l-LNvs project to the contralateral LNvs through the posterior optic tract. Moreover, the s-LNvs project dorsally to the superior protocerebrum, the location of the DNs. (Helfrich-Förster 1995). These anatomic features suggest that PDF might bind directly to clock neurons.
To test this hypothesis, in vitro biotinylated PDF peptide was incubated with fixed adult brains under near physiological conditions. The bound peptide was then detected in situ with a streptavidin-conjugated enzymatic amplification reaction. The vast majority of the signal localized with numerous cells at the periphery of medulla (Figure 5A). This is exactly where the l-LNvs send large arborizations as their centrifugal projections (Helfrich-Förster 1995). Importantly, signal was also detected coincident with the LNvs (Figure 5B) and likely DN3 clock neurons (Figure 5C) within the superior protocerebrum region, i.e., the bound peptide colocalized with GFP when the brains were from a strain with GFP-labeled clock neurons. Staining intensity was temporally constant; i.e., there was no systematic variation in signal intensity with circadian time. Although we obtained identical results with two differently biotinylated PDF peptides and there was no staining with two other biotinylated control peptides, we had difficulty to compete specifically the signal with nonbiotinylated PDF (see Materials and Methods). Moreover, PDF peptide staining of clock neurons was not reliably detected in every brain, in contrast to optic lobe staining. Nonetheless, we never detected peptide staining of other neurons in the vicinity of the LNvs; i.e., signal in this region of the brain was always coincident with the GFP-labeled LNvs. The peptide staining therefore suggests that PDF acts on the LNvs in an autocrine or paracrine fashion as well as on other clock neurons, but the results do not exclude additional, more indirect modes of action.
Figure 5 A PDF Peptide Binds to Many Cells, Including Several Clock Neuronal Groups
In vitro biontinylated PDF peptide was used to visualize the peptide binding locations (middle panels, with Cy3) in the brain (see Materials and Methods for details). We used membrane-bound GFP (green panels on the left) to label specific circadian neurons as well as their projections (right panels show the overlay of both channels).
(A) The brain is from flies with labeled LNvs (y w,UAS–mCD8iGFP;pdf–GAL4). Numerous cells at the periphery of the medulla have the vast majority of the bound PDF peptide signal within the brain. This region receives widespread dendritic arborizations from the l-LNvs.
(B) Bound PDF peptide was also detected on the surface of LNvs at a lower intensity. LNv cell bodies were labeled using UAS–mCD8iGFP;pdf–GAL4. Since the signal from the Cy3 channel was much weaker than the GFP signal, we reduced the output gain from the GFP channel. Sequential scanning was used to prevent cross-talk between the two channels.
(C) y w,UAS–mCD8iGFP;tim–GAL4/+ flies were used to label all circadian neurons. In the dorsal region shown in this series, the arrow points to a group of DN3 neurons.
Discussion
The strong behavioral phenotype of the Pdf01 mutant strain in DD indicates that PDF makes an important contribution to free-running circadian rhythms. It was, however, unanticipated that the Pdf01 mutant would have an additional effect on transcriptional oscillations within most if not all clock neurons. This observation extends the tight parallel between strong behavioral rhythms and robust transcriptional rhythms and suggests that the behavioral damping is due to the transcriptional damping (Marrus et al. 1996). In contrast to this strong effect of the Pdf01 mutation on free-running rhythms, the molecular as well as behavioral rhythms of these mutant flies are nearly normal under LD conditions. We now interpret this difference to indicate that intercellular communication among different clock cells and neuronal groups is less important when they can independently receive photic information via cryptochrome. This probably serves not only to synchronize clock neurons but also to reinforce and strengthen the molecular oscillation (Emery et al. 1998; Stanewsky et al. 1998).
The damping phenotype includes the LNvs, which have been proposed to be the principal pacemaker neurons in Drosophila (Helfrich-Förster 1997; Renn et al. 1999). Their counterparts in mammals, the suprachiasmatic nucleus (SCN) neurons, can support circadian rhythms independently (e.g., Sujino et al. 2003). However, our data indicate that the LNvs cannot support locomotor activity rhythms without other clock cell groups (see Figure 1). A similar attempt to rescue behavioral rhythms of an arrhythmic Clk mutant also failed (Allada et al. 2003). Although the negative result shown here might be due to developmental defects of the cyc01 mutation (Park et al. 2000), the conclusion fits well with a role for PDF in functional cooperation between individual neuronal groups. Indeed, it appears that PDF secretion comprises much of what the LNvs contribute to rhythms, as the phenotype of flies missing the LNvs is virtually identical to that of the Pdf01 strain (Renn et al. 1999). There is less known about the roles of other clock neurons, although they do have specific wiring properties (Kaneko and Hall 2000) as well as specific sets of gene expression profiles (unpublished data). An additional indication that other clock neurons contribute to locomotor activity rhythms is that LD behavioral rhythms do not require the LNvs (Hardin et al. 1992; Renn et al. 1999). As the Pdf01 strain also has a strong effect on geotaxis (Toma et al. 2002), clock neurons may even contribute to other behavioral modalities.
The staining pattern suggests that the PDF ligand contacts a receptor on the surface of clock neurons, including the LNvs themselves. This is consistent with the notion that PDF acts as an important intercellular cell communication molecule within the Drosophila circadian system. The dorsal projections of the s-LNvs stain rhythmically with anti-PDF antibodies, and it has been suggested that released PDF affects dorsal clock neurons (Helfrich-Förster et al. 2000). Indeed, ectopic expression of PDF in neurons that project to the dorsal brain region causes severe rhythm defects, suggesting that misregulation of this signaling causes circadian system dysfunction (Helfrich-Förster et al. 2000). Our staining with a PDF peptide indicates that the PDF signaling to the DNs may be direct. Although rhythmic PDF staining is restricted to the s-LNv terminals (Park et al. 2000), this could be because a smaller fraction of PDF is released from the l-LNv terminals. Some of these processes follow the posterior optic track to the opposite side of the brain. Taken together with the LNv peptide staining, it is likely that PDF from the l-LNvs signals contralaterally and positively influences clock cells on the opposite side of the brain. A very recent study of the Drosophila prothoracic gland (PG) clock and eclosion rhythms suggests that the LNvs also control the PG clock via PDF signaling (Myers et al. 2003). This raises the possibility that PDF not only synchronizes brain clock neurons, but also keeps peripheral clocks in pace with the core brain network.
The Pdf01 molecular phenotype implies that the wild-type organization of the system normally supports the individual clock cells as well as the entire circadian program in DD. Although we do not know that all molecular aspects of rhythms damp in DD in Pdf01 flies, we suggest that damped transcriptional rhythms are the intracellular default state in Drosophila and are manifest without the driving and entraining LD cycle or without a functionally integrated clock network. This view is also consistent with recent studies showing that electrical silencing of clock neurons eliminates free-running molecular as well as behavioral rhythms (Nitabach et al. 2002). It will be interesting to learn how PDF signaling connects to the intracellular transcriptional machinery.
We note that communication among clock neurons is likely to be important in other organisms. The ability of PDF to phase-shift the cockroach circadian clock (Petri and Stengl 1997) is more consistent with our proposal than with a simple role in clock output. A recent study of VPAC(2) receptor knock-out mice (Harmar et al. 2002) showed that these mice fail to sustain behavioral rhythms and have molecular rhythms defects within the SCN. This raises the intriguing possibility that SCN neurons as well as Drosophila clock neurons may require network integration to sustain free-running intracellular oscillations.
Materials and Methods
Drosophila genetics.
Full-length cyc cDNA was obtained from BDGP cDNA clone GM02625 and was tagged with hemagglutinin (HA) epitope by PCR cloning. CYC–HA was subsequently cloned into pUAST to generate pUAS–CYC–HA. The transformation plasmid was used to generate transgenetic flies. A third chromosome insertion line (UAS–CYC–HA15) was used subsequently. All wild-type flies and specimens were taken from a Canton-S stock.
The circadian driver lines pdf–GAL4 (Renn et al. 1999), tim–GAL4 (Kaneko and Hall 2000), as well as the cyc01 (Rutila et al. 1998) and Pdf01 (Renn et al. 1999) mutant strains have been previously described. All molecular and behavioral analyses were conducted on flies entrained at 25°C.
GFP expression analysis.
To visualize the axon projections from circadian neurons, a UAS–mCD8GFP line labeling the cell membrane was crossed with various circadian GAL4 drivers. The progeny brains were dissected in PBS and fixed in 3.7% paraformaldehyde in PEM. After rinses in PBS plus 0.3% Triton and PBS, brains were mounted in Vectashield mounting medium (Vector Laboratories, Burlingame, California, United States) and imaged on a Leica laser scanning confocal microscope. Optical sections were taken at 1–2 μm intervals and used to construct a maximum projection image for each brain.
In situ mRNA hybridization on adult brain whole mounts.
In situ hybridization of tim and cry was done as described previously (Zhao et al. 2003). The maximum projection images taken from a Leica laser scanning confocal microscope were used for the quantification. The quantification was done using three brain images per sample with Leica confocal software. The mean pixel intensities of cell groups were normalized by subtracting the average of two general background areas in the brain.
Behavioral analysis.
Flies were entrained for 3–5 d in 12 h light:12 h dark (LD) conditions before release into DD. Locomotor activities of individual flies were monitored using Trikinetics Drosophila Activity Monitors (Waltham, Massachusetts, United States). The analysis was done by using a signal processing toolbox (Levine et al. 2002b). Autocorrelation and spectral analysis were used to assess rhythmicity and to estimate the period. The phase information was extracted using circular statistics (Levine et al. 2002b). In some cases, the phases of individual Pdf01 flies were also examined by inspection.
In vitro peptide binding assay.
Biotinylation of the PDF peptide was with EZ-Link Sulfo–NHS–LC–Biotin reagent (Pierce Biotechnology, Rockford, Illinois, United States), following the manufacturer's instruction. Excess biotinylation reagent was removed by prolonged incubation in Tris–HCl buffer (1 M [pH 7.5]) followed by protein purification through a Polyacrylamide 1800 desalting column (Pierce Biotechnology). A control neuropeptide, allatostatin I (Sigma-Aldrich, St. Louis, Missouri, United States), was biotinylated using the same method. A second control was a synthetic, biotinylated peptide derived from the Drosophila PER protein (a gift from P. Nawathean). In addition, a new N-terminus biotinylated PDF peptide was chemically synthesized de novo (Sigma-Aldrich). Identical results were obtained with the two PDF peptides, and no specific signal was obtained with the two control peptides.
To detect the binding of the neuropeptide in the CNS of Drosophila, brains were dissected in PBS and fixed in 3.7% paraformaldehyde in PEM for 30 min. After they were rinsed in PBS plus 0.3% Triton and blocked using 1% FBS or BSA, biotinylated peptide was incubated with the brains at a final concentration of 0.2 μg/ml. The brains were washed thoroughly with TNT (0.1 M Tris–HCl [pH 7.5], 0.15 M NaCl, 0.05% Tween 20). The bound peptide was subsequently detected through the biotin label using streptavidin–HRP (NEN LifeScience, now Perkin-Elmer, Torrance, California, United States) and fluorescent tyramides (NEN LifeScience). A detailed protocol is provided as Protocol S1. For the competition assay, unlabeled peptide was added at a 200- to 5000-fold concentration increase in the blocking step; subsequent steps were as described above.
Supporting Information
Figure S1 Rescued Molecular Oscillations Persist during DD in the s-LNvs
The “rescued” mutant y w; pdf–GAL4;UAS–CYC,cyc01/cyc01 was released into DD after entrainment and assayed by tim whole-mount in situ hybridization on the fourth day of DD. A Pdf probe was used to label the LNv group. Brains were taken at two opposite timepoints, CT3 (top panels) and CT15 (bottom panels). From left to right are Pdf (green, FITC labeled), tim (red, Cy3 labeled), and an image overlay. The lower arrows point to the s-LNvs and the upper arrows to l-LNvs. Whereas the l-LNvs show barely visible tim RNA oscillations under these conditions, the s-LNvs are obviously cycling. This difference suggests that the l-LNvs might damp more rapidly or be more light-dependent than the s-LNvs in this unusual genotype. (7.1 MB PDF).
Click here for additional data file.
Protocol S1 Short Protocol for Neuropeptide Biotinylation and Receptor Detection
(23 KB DOC).
Click here for additional data file.
We thank our colleagues Joan Rutila for making the UAS–CYC–HA transgenetic flies; Jie Zhao for help with the whole-mount in situ hybridization; Patrick Emery and Mike McDonald for inspiration and helpful discussions; Paul Taghert, Orie Shafer, Ravi Allada, and Ralf Stanewsky for critical readings of the manuscript and exchanging unpublished results. We also thank Ed Dougherty and National Institutes of Health (NIH) grant S10 RR16780 for assistance in confocal microscopy and Heather Felton for administrative assistance. The work was supported in part by NIH grants GM33205 and NS44232 to MR and JCH.
Conflicts of Interest. The authors have declared that no conflicts of interest exist.
Author Contributions. YP, DS, JCH, and MR conceived and designed the experiments. YP, DS, and JDL performed the experiments. YP, DS, JDL, and MR analyzed the data. YP, DS, JDL, and MR contributed reagents/materials/analysis tools. YP, DS, and MR wrote the paper.
Academic Editor: Ueli Schibler, University of Geneva.
Abbreviations
clk
clock
cry
cryptochrome
cyc
cycle
CTcircadian time
DDconstant darkness
DNdorsal neuron
HAhemagglutinin
LDlight–dark
l-LNvlarge ventral lateral neuron
LNddorsal lateral neuron
LNvventral lateral neuron
PDFpigment-dispersing factor
per
period
PGprothoracic gland
SCNsuprachiasmatic nucleus
s-LNvsmall ventral lateral neuron
tim
timeless
ZTZeitgeber time.
==== Refs
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| 12975658 | PMC193604 | CC BY | 2021-01-05 08:21:03 | no | PLoS Biol. 2003 Oct 15; 1(1):e13 | utf-8 | PLoS Biol | 2,003 | 10.1371/journal.pbio.0000013 | oa_comm |
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"\n==== Front\nPLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science(...TRUNCATED) | 0 | PMC193606 | CC BY | 2021-01-05 08:21:04 | no | PLoS Biol. 2003 Oct 15; 1(1):e23 | utf-8 | PLoS Biol | 2,003 | 10.1371/journal.pbio.0000023 | oa_comm |
"\n==== Front\nPLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science(...TRUNCATED) | 0 | PMC193607 | CC BY | 2021-01-05 08:21:04 | no | PLoS Biol. 2003 Oct 15; 1(1):e31 | utf-8 | PLoS Biol | 2,003 | 10.1371/journal.pbio.0000031 | oa_comm |
"\n==== Front\nBMC Cell Biol\nBMC Cell Biol\nBMC Cell Biology\n1471-2121\nBioMed Central London\n\n1(...TRUNCATED) | 12969509 | PMC212319 | CC BY | 2022-01-04 23:15:02 | no | BMC Cell Biol. 2003 Sep 11; 4:13 | utf-8 | BMC Cell Biol | 2,003 | 10.1186/1471-2121-4-13 | oa_comm |
"\n==== Front\nPLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science(...TRUNCATED) | 14551903 | PMC212687 | CC BY | 2021-01-05 08:21:03 | no | PLoS Biol. 2003 Oct 13; 1(1):e1 | utf-8 | PLoS Biol | 2,003 | 10.1371/journal.pbio.0000001 | oa_comm |
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