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Our results show that interferences between pairs of loci are negative: a recombination event between two loci apparently increases the probability of recombination between another pair of loci. We believe that the most parsimonious explanation of these negative interferences is based on the way the infection builds up within plant hosts. Indeed, one can divide infected host cells into those infected by a single virus genotype and those infected by more than one viral genotype. In the former, analogous to clonal propagation, recombination is undetectable. In the latter, recombination is not only detectable but, as our results indicate, very frequent. Samples consisting of viruses resulting from a mixture of these two types of host cell infections will thus contain viruses with no recombination and viruses with several recombination events, thus yielding an impression of negative interference. These conceptual arguments are supported by mathematical models. It is indeed easy to show (detailed results not shown) that if a proportion F of the population reproduces clonally, analogous to single infections, while the remaining reproduces panmictically, negative interferences could be inferred even if they do not exist. For example, assuming a three-locus model with real recombination rates r 1 and r 2 and interference i 12 , the ''apparent'' recombination and interference parameters, would be r 1 = (1 À F)r 1 , r 2 = (1 À F)r 2 , and i 12 = À(F À i 12 )/(1 À F). Interestingly, this example also shows that our estimates of the recombination rate are conservative: that a fraction F of host cells are singly infected while others are multiply infected leads to an underestimation of the recombination rate. As judged by r 1 , r 2 , and r 3 , calculated between markers a-b, b-c, and c-d, respectively, we found evidence for recombination through the entire CaMV genome. The values for r 1, r 2 , and r 3 are remarkably similar, hence the recombination sites seem to be evenly distributed along the genome. We considered the template-switching model as the major way recombinants are created in CaMV. As already mentioned in the Introduction, hot spots of template switching have been predicted at the position of the 59 extremities of the 35S and 19S RNAs [21, 36, 42] . If other recombination mechanisms, such as that associated with second-strand DNA synthesis or with the host cell DNA repair machinery, act significantly, hot The various parameters are as follows: r1, recombination rate between markers a and b; r2, recombination rate between markers b and c; r3, recombination rate between markers c and d; i12, interference between crossovers in segments a-b and b-c; i23, interference between crossovers in segments b-c and c-d; i13, interference between crossovers in segments a-b and c-d; i123, second-order interference accounting for residual interference. The recombination rates are the maximum likelihood estimates (6 95% confidence intervals). The interference parameters were obtained numerically as explained in the Materials and Methods. DOI: 10.1371/journal.pbio.0030089.t002 spots would be expected at the positions of the sequence interruption D1, D2, and D3 [43] . Due to the design of our experiment and the position of the four markers, we have no information on putative hot spots at positions corresponding to the 59 end of the 35S RNA and to D1 (at nucleotide position 0). Nevertheless, the putative hot spots at the 59 end of the 19S RNA and at D2 and D3 (nucleotide positions 4,220 and 1,635, respectively) fall between marker pairs c-d, b-c, and a-b, respectively. Our results indicate that either these hot spots are quantitatively equivalent-though predicted by different recombination mechanisms-or, more likely, that they simply do not exist. Whatever the explanation, what we observe is that the CaMV can exchange any portion of its genome, and thus any gene thereof, with an astonishingly high frequency during the course of a single host infection. To our knowledge, the viral recombination rate has never previously been quantified experimentally for a plant virus [3] . In contrast, retroviruses and particularly HIV-1 have been extensively investigated in that sense. As we have already discussed for these latter cases, the quantification of the intrinsic recombination rate was carried out in artificially coinfected cell cultures. The estimated intrinsic per nucleotide per generation recombination rate in HIV-1 is on the order of 10 À4 [14, 15, 19] , less than one order of magnitude higher than our estimation for CaMV. Because for various reasons detailed above we probably underestimate the within-host CaMV recombination rate, we believe that the intrinsic recombination rate in CaMV is higher and perhaps on the order of that of HIV. |
Other pararetroviruses such as plant badnaviruses or vertebrate hepadnaviruses have a similar cycle within their host cells, including steps of nuclear minichromosome, genomic size RNA synthesis, and reverse transcription and encapsidation. Nevertheless, vertebrate hepadnaviruses (e.g., hepatitis B virus) infect hosts that are very different from plants in their biology and physiology, and this could lead to a totally different frequency of cell co-infection during the development of the virus populations. Thus, even though our results can be informative for other pararetroviruses because of the viruses' shared biological characteristics, they should not be extrapolated to vertebrate pararetroviruses without caution. |
Viral isolates. We used the plasmid pCa37, which is the complete genome of the CaMV isolate Cabb-S, cloned into the pBR322 plasmid at the unique SalI restriction site [44] . To analyze recombination in different regions of the genome, we introduced four genetic markers: a, b, c, and d, at the positions 881, 3,539, 5,365, and 6,943, respectively, thus approximately at four cardinal points of the CaMV circular double-stranded DNA of 8,024 bp ( Figure 1 ). All markers, each corresponding to a single nucleotide change, were introduced by PCR-directed mutagenesis in pCa37, and resulted in the duplication of previously unique restriction sites BsiWI, PstI, MluI, and SacI in a plasmid designated pMark-S. Because, in this study, we targeted the possible exchange of genes between viral genomes, all markers a, b, c, and d were introduced within coding regions corresponding to open reading frames I, IV, V, and VI, respectively. Another important concern was to quantify recombination in the absence of selection, i.e., to create neutral markers. Consequently all markers consist of synonymous mutations (see below). |
Production of viral particles and co-inoculation. To generate the parental virus particles, plasmids pCa37 and pMark-S were mechanically inoculated into individual plants as previously described [33] . All plants were turnips (B. rapa cv, ''Just Right'') grown under glasshouse conditions at 23 8C with a 16/8 (light/dark) photoperiod. |
Thirty days post-inoculation, all symptomatic leaves were harvested and viral particles were purified as described earlier [45] . |
The resulting preparations of parental viruses, designated Cabb-S and Mark-S, were quantified by spectrometry using the formula described by Hull et al. [46] . We fixed the initial frequency of markers to a value of 0.5, and a solution containing 0.1 mg/ml of virus particles of both Cabb-S and Mark-S at a 1:1 ratio was prepared. Plantlets were co-infected by mechanical inoculation of two to three leaves with 20 ll of this virus solution, using abrasive Celite AFA (Fluka, Ronkonkoma, New York, United States). The mixed CaMV population was allowed to grow during 21 d of systemic infection. |
Estimation of marker frequency within mixed virus populations. We designed an experimental protocol for quantifying marker frequency within a mixed Cabb-S/Mark-S virus population after a single passage in a host plant. Twenty-four individual plants, inoculated as above with equal amounts of Cabb-S and Mark-S, were harvested 21 d post-inoculation, when symptoms were fully developed. The viral DNA was purified from 200 mg of young newly formed infected leaves according to the protocol described previously [47] . After the precipitation step of this protocol, the viral DNA was resuspended and further purified with the Wizard DNA clean-up kit (Promega, Fitchburg, Wisconsin, United States) in TE 1X (100 mM Tris-HCl and 10 mM EDTA [pH 8]). Aliquots of viral DNA preparations were digested by restriction enzymes corresponding either to marker a, b, c, or d and submitted to a 1% agarose gel electrophoresis, colored by ethydium bromide and exposed to UV. Each individual restriction enzyme cut once in Cabb-S DNA and twice in Mark-S, thus generating DNA fragments of different sizes attributable to one or the other in the mixed population of CaMV genomes. After scanning the agarose gels, we estimated the relative frequency of the two genotypes in each viral DNA preparation and at each marker position, by densitometry using the NIH 1.62 Image program. The statistical analyses of the frequency of the four markers are described below. |
Isolation of individual CaMV genomes and identification of recombinants. To identify and quantify the recombinants within the CaMV mixed populations, aliquots from ten of the 24 viral DNA preparations described above were digested by the restriction enzyme SalI, and directly cloned into pUC19 at the corresponding site. In each of the ten viral populations analyzed, 50 full-genome-length clones were digested separately by BsiWI, PstI, MluI, and SacI, to test for the presence of marker a, b, c, and d, respectively. In this experiment, with the marker representing an additional restriction site, we could easily distinguish between the Cabb-S and the Mark-S genotype at all four marker positions, upon agarose gel (1%) electrophoresis of the digested clones. Clones with none or all four markers were parental genotypes, whereas clones harboring 1, 2, or 3 markers were clearly recombinants. Due to the very high number of recombinants detected, markers eventually appearing or disappearing due to spontaneous mutations were neglected. |
Statistical analysis. Here we present the different methods we used to quantify recombination in the CaMV genome. Because all these methods assume that the different markers are neutral, we first discuss assumption. |
We used two datasets to test the neutrality of markers, both resulting from plants co-infected with a 1:1 ratio of Mark-S and Cabb-S. The first consisted of viral DNA densitometry data derived from 24 plants (described above), where for each plant we have an estimate of the frequency of each marker in the genome population. The second consisted of the restriction of 50 individual full-genome-length viral clones obtained from one co-infected plant (described above), yielding an estimate of the frequency of each marker, and this was repeated on ten different plants. The frequencies of the different markers were 0.508, 0.501, 0.516, and 0.507 for markers a, b, c, and d in the first dataset and 0.521, 0.518, 0.514, and 0.524 in the second dataset. We tested whether these frequencies were significantly different from the expected value under neutrality, 0.5, using either t-tests, for datasets where normality could not be rejected (seven out of eight cases), or Wilcoxon signed-rank non-parametric tests otherwise (marker c in the first dataset). In all cases p-values were larger than 0.05. |
There are several cautionary remarks regarding these analyses. First, in all cases we found an excess of markers. Unfortunately, the two datasets cannot be regarded as independent because, even though the methods through which the frequency estimates were obtained were different, the plants used in the second dataset were a subset of the plants of the first. We thus have only four independent estimates in each case, and there is minimal power to detect significant deviations from neutrality with such a small sample size. It should be noted at this stage that deviations from the expected value could also be caused either by slight deviations from the 1:1 ratio in the infecting mixed solution, or by deviations from that ratio in the frequency of the viral particles that actually get into the plants. Second, because of the relatively small sample sizes and low statistical power, the tests presented above could have detected only large deviations. |
The results clearly show, however, precisely that the markers do not have large effects, if any, and that therefore recombination estimates would be affected only very slightly by any hypothetical selective effects of the markers. Because of this, along with the fact that the introduced markers provoke silent substitutions in the CaMV genome, we assumed that markers were effectively neutral in the rest of the analysis. |
The dataset used to estimate the recombination frequency consisted of the 500 full-genome-length viral clones (50 from each of ten co-infected plants) individually genotyped for each of the four markers. As discussed in detail in the Results, recombination was very frequent and concerned all four markers. Indeed, approximately half of the genotyped clones exhibited a recombinant genotype. It was therefore meaningful to try to obtain quantitative estimates of recombination from our data. |
Our aim was to analyze viral recombination in a live host. Consequently, we had to deal with the fact that more than one viral replication cycle occurred during the 21 d that infection lasted in our experiment (we had to wait that long for the disease to develop and to be able to recover sufficient amounts of viral DNA from each infection). Based on the kinetics of gene expression [40] , we postulate that each replication cycle lasts between 2 and 3 d, and that therefore seven to ten cycles occurred between infection and the sampling time. In case this assumption is incorrect, we did calculations assuming five, seven, ten, or 20 replication cycles during these 21 d. As shown, the results were not affected qualitatively, and only slightly quantitatively. It is important to note that we assumed that recombination occurred through a template-switching mechanism, and that therefore, from a recombination point of view, the CaMV genome is linear. The reverse transcription starts and finishes at the position 0 in Figure 1 , which is the point of circularization of the DNA genome. This implies that changes between contiguous markers a-b, b-c, and c-d can be considered as true recombination whereas those between a and d cannot, as they may simply stem from circularization of DNA, during the synthesis of which the polymerase has switched template once anywhere between a-b, b-c, or c-d. |
To estimate the recombination rate between markers, we wrote recurrence equations describing the change in frequency of each genotype over one generation, assuming random mating and no selection (i.e., the standard Wright-Fisher population genetics model). We then expressed the frequency of all possible genotypes n generations later as a function of their initial frequency and of the recombination parameters. Subsequently we calculated the maximum likelihood estimates of the recombination parameters and their asymptotic variances given initial frequencies (we assumed that the two ''parental'' genotypes, Mark-S and Cabb-S, had equal initial frequencies of 0.5 and that all other genotypes had initial frequencies of zero) and frequencies after n generations (the observed frequencies; as stated above we used different values of n). All algebraic and numerical calculations were carried out with the software Mathematica. |
The recombination parameters are the recombination rates between two adjacent loci, e.g., r 1 for the recombination rate between markers a and b, and the interference coefficients, e.g., i 12 for interference between recombination events in the segments between markers a and b and b and c. To define these parameters we followed Christiansen [48] , and in particular the recombination distributions for two, three, and four loci (respectively, Tables 2.7, 2.8, and 2.9 of [48] ). It is important to realize that given the definitions of these parameters, the estimator of the recombination rate between two loci is not affected by the number of loci considered. In other words, we obtain the same estimation of the recombination rate between markers a and b whether we consider genotypic frequencies at just these two loci, or the frequencies at these two loci plus a third locus, or the complete information to which we have access, the fourmarker genotypes. Information on additional loci only affects the estimates of the interference coefficients. |
It proved impossible to carry out the calculations for four loci algebraically. Instead, we used a computer program to calculate the expected genotypic frequencies at all four loci after n generations, given the above stated initial frequencies and specified recombination parameters. For each combination of recombination parameters we calculated a Euclidean distance between the vector of the expected genotypic frequencies and the observed genotypic frequencies, and considered that the estimated recombination parameters were those yielding the minimal Euclidean distance. In all cases, the estimated recombination rates between pairs of loci were equal to the second decimal to those estimated algebraically from data for three or two loci. Torsional restraint: a new twist on frameshifting pseudoknots mRNA pseudoknots have a stimulatory function in programmed −1 ribosomal frameshifting (−1 PRF). Though we previously presented a model for how mRNA pseudoknots might activate the mechanism for −1 PRF, it did not address the question of the role that they may play in positioning the mRNA relative to the ribosome in this process [E. P. Plant, K. L. M. Jacobs, J. W. Harger, A. Meskauskas, J. L. Jacobs, J. L. Baxter, A. N. Petrov and J. D. Dinman (2003) RNA, 9, 168–174]. A separate ‘torsional restraint’ model suggests that mRNA pseudoknots act to increase the fraction of ribosomes directed to pause with the upstream heptameric slippery site positioned at the ribosome's A- and P-decoding sites [J. D. Dinman (1995) Yeast, 11, 1115–1127]. Here, experiments using a series of ‘pseudo-pseudoknots’ having different degrees of rotational freedom were used to test this model. The results of this study support the mechanistic hypothesis that −1 ribosomal frameshifting is enhanced by torsional resistance of the mRNA pseudoknot. The structure of an RNA molecule is widely recognized to play a role in many processes, including structurally organizing complex RNAs, the assembly of ribonucleoprotein complexes, and in translational recoding and regulation [reviewed in (1) ]. One common RNA folding motifs is a pseudoknot, the folding back of a single-stranded RNA onto itself to form two helical structures with single-stranded loops joining them (2) . Many such structures can be inferred from RNA sequences and frameshifting function has been demonstrated for some of these [reviewed in (3) (4) (5) ]. However, though much theoretical progress has been made in understanding how mRNA pseudoknots promote efficient À1 ribosomal frameshifting (6), a complete understanding of this mechanism remains untested. |
Programmed À1 ribosomal frameshift signals are typically divided into three components. From 5 0 to 3 0 these are (i) a 'slippery site' in the form N NNW WWH, where N must be a stretch of any three identical nucleotides, where W is either three A or U residues, and H is A, C or U (spacing indicates the unshifted zero frame), (ii) a spacer region and (iii) an mRNA structural element, most often a pseudoknot. The general model posits that upon encountering the mRNA pseudoknot, an elongating ribosome is forced to pause such that the anticodons of its A-and P-site tRNAs are base-paired with the zero-frame codons of the slippery site. The nature of the tRNA-mRNA interactions is such that a relative slip of À1 nucleotide still allows base-pairing in the non-wobble positions. The slippage occurs during the ribosomal pause, and it has been shown that changes affecting ribosome pause times affect frameshift efficiencies [reviewed in (7) ]. An important observation is that even though mRNA pseudoknots and energetically equivalent stem-loop structures appear to promote ribosome pausing with equal effectiveness, mRNA pseudoknots are more efficient at promoting À1 PRF (8). Our '9 Å ' model (6) provided a refinement of the original 'simultaneous slippage' (9, 10) model of frameshifting by suggesting that rather than the entire ribosome having to slip one base in the 5 0 direction, slippage could be accomplished by moving the small section of mRNA in the downstream tunnel by one base in the 3 0 direction. We have proposed that this is accomplished by the bulky and difficult to unwind mRNA pseudoknot structures becoming wedged in the downstream entrance tunnel of the ribosome, preventing the downstream region of the mRNA from being pulled into the ribosome by the equivalent of one base during the accommodation step of elongation. This blockage would introduce tension into the spacer region, which could be resolved by unpairing the mRNA from the tRNAs, allowing the mRNA to slip 1 nt backwards, resulting in a net shift of reading frame by À1 base. |
Though the 9 Å model provides a partial explanation for why mRNA pseudoknots promote programmed À1 ribosomal frameshifting (À1 PRF) more efficiently than simple *To whom correspondence should be addressed. Tel: +1 301 405 0981; Fax: +1 301 314 9489; Email: dinman@umd.edu ª The Author 2005. Published by Oxford University Press. All rights reserved. |
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org stem-loop structures, it does not answer the question of how the mRNA pseudoknot directs the ribosome to pause at the correct position along the mRNA. A complementary 'torsional restraint' model addresses this issue (11) . When a stem-loop structure is unwound by an elongating ribosome, unwinding of the stem forces the loop to rotate. Since a simple stem-loop is not restrained, the loop can rotate freely and only the base pairs within the stem resist ribosomal movement, and thus the potential energy of unwinding should be distributed along the length of Stem 1 ( Figure 1A ). However, if the loop is anchored or restrained, as it is in a pseudoknot by Stem 2, since the intrinsic ribosomal helicase is processive (12) , Stem 1 cannot be fully unwound until Stem 2 is first denatured. Mechanically, as the ribosome begins to unwind the base of Stem 1, Stem 2 forces the supercoiling in the remainder of Stem 1, providing extra resistance to ribosome movement. At some specific point, the resistance to ribosome movement provided by the supercoiling counteracts the forward movement of the ribosome, increasing the likelihood that the ribosome will stop at a precise point along the mRNA. Energetically, since full unwinding of Stem 1 is dependent on complete denaturation of Stem 2, the potential energy of unwinding of the pseudoknot structure should similarly be directed toward one point. Viewed either mechanically or energetically, this point is where ribosomes will be directed to specifically pause on the mRNA. If it occurs with the tRNAs in ribosomal A-and P-sites positioned at the slippery site, then frameshifting is stimulated. This is summarized in Figure 1B . The efficiency of À1 PRF can thus be viewed as a function of (i) the fraction of ribosomes paused over the slippery site and (ii) the rate at which the structure can be denatured. There is increasing evidence from single molecule experiments that unfolding occurs in quick 'rips' at a particular force (13) , suggesting that in the case of unfolding pseudoknots, frameshifting efficiency is related to both the energy barriers to unfolding the pseudoknot structure and the resistance of the structure against the force of the ribosome. In the context of the torsional restraint model, this resistance is dependent on the ability of Stem 2 to remain intact while Stem 1 is being unwound. |
There is experimental data that indirectly support this model: (i) disruption of the first 3 bp of Stem 1, which would displace the ribosome's pause site to a point 3 0 of the slippery site, has been shown to eliminate frameshifting (14) ; (ii) destabilizing Stem 2, which would allow it to be unwound more readily, has been shown to result in decreased frameshifting efficiency (15) (16) (17) ; (iii) replacing bulges in Stem 1 with base pairs would increase the energy required to unwind the first three bases, and a longer ribosomal pause over the slippery site would follow, yielding increased efficiencies in À1 frameshifting (15, 18) ; (iv) destabilizing the base of Stem 1 by replacing G:C base pairs with A:U pairs decreases À1 frameshifting efficiencies (19, 20) ; (v) the model eliminates the need for a 'pseudoknot recognizing factor', the evidence of which has not been forthcoming in either competition assays in in vitro translation systems (21) or by gel retardation assays (J. D. Dinman, unpublished data); and (vi) elimination of a potential torsion-restraining Stem 2, but not of a non-torsion-restraining Stem 2 in HIV-1, resulted in decreased À1 PRF efficiencies (22) . |
Though all of the cited studies support the torsional restraint model, none has directly addressed it. In the experiments presented in this study, a series of 'pseudo-pseudoknot' containing reporter constructs were used to test the torsional restraint hypothesis. In vitro frameshifting assays show that frameshifting can be significantly stimulated by limiting the rotational freedom of the loop region of a stem-loop structure, and that the degree of rotational freedom of Stem 1 is important in determining the extent of À1 PRF. Furthermore, mRNA toeprint analyses reveal a pseudo-pseudoknot-specific strong stop 16 nt 3 0 of the slippery site, consistent with this structure being able to direct ribosomes to pause with their A-and P-sites positioned at the slippery site. |
All synthetic DNA oligonucleotides were purchased by IDT (Coralville, IA). The modified L-A viral À1 PRF signal containing the G GGU UUA slippery site followed by a simple stem-loop was amplified from pJD18 (23) using the primers luc5 0 b (5 0 -CCCCAAGCTTATGACTTCTAGGCAGGGTTT-AGG-3 0 ) and luc3 0 b (5 0 -CCCCCCATGGGACGTTGTAAA-AACGACGGGATC-3 0 ). These were digested with HindIII and NcoI (restriction sites are underlined) and cloned into the firefly luciferase reporter plasmid pT7-LUC minus 3 0untranslated region-A50 (24) . In the resulting reporter construct (pJD214-18), expression of firefly luciferase requires a À1 frameshift, and the 5 0 sequence of the Stem 2 is not able to base pair with the 3 0 sequence, so that only a stem-loop rather than a pseudoknot is able to form. The same primers were used to amplify DNA from pJDRC (23) to make pJD214-Ry. In this construct, complementary mutations (5 0 -GCUGGC-3 0 to 5 0 -CGACCG-3 0 ) in the 3 0 acceptor sequence of the pseudoknot-forming region of Stem 2 allow the formation of an mRNA pseudoknot that has previously been shown to promote frameshifting at the same frequency as the wild type (23) . The primer luc5 0 CON (5 0 -CCCCAAGCTTATGACTTC-TAGGCAAGGGTTTAGG-3 0 ) contains an additional A nucleotide upstream of the slippery site and was used to make pJD214-0, the zero-frame control. To eliminate the possibility of internal initiation occurring at the luciferase initiation codon downstream of the frameshift signals, the AUG codon was changed to AUA. The Stratagene Quik-Change kit was used to mutate pJD214-18 and pJD214-Ry into pJD336-18 and pJD366-Ry, respectively, using the oligonucleotides 5 0 -GGCGTTCTTCTATGGGACGTTGTA-AAAACGGATC-3 0 and 5 0 -GATCCGTCGTTTTTACAACG-TCCCATAGAAGACGCC-3 0 (the mutated codon is underlined). pJD366-18 was further mutated to make a zero-frame control by placing an A upstream of the slippery site using the oligonucleotides 5 0 -TGACTTCTAGGCAAGGGTTTAGGAG-TG and 5 0 -CACTCCTAAACCCTTGCCTAGAAGTCA (the inserted base is underlined). A series of synthetic DNA oligonucleotides were designed to join the loop acceptor region of mRNA transcribed from pJD366-18 to the downstream region that forms the pseudoknot in the wild-type L-A À1 PRF signal. In the J-oligos, the 3 0 sequences base pair with the loop of the mRNA transcribed from pJD366-18, and the 5 0 regions of these oligos base pair with the downstream sequence. This orientation is reversed for the R-oligos. These general orientations are shown in Figure 3B . The naming of the oligonucleotide names refers to the number of additional residues placed between the regions of complementarity. The bases complementary to the pJD366-18 sequence are underlined. |
Plasmid DNAs were prepared using the Qiagen mini-prep kits and were linearized with DraI in a total volume of 20 ml. Proteins were eliminated by the addition of 2 ml of 1 mg/ml proteinase K and SDS to a final concentration of 0.5% followed by digestion at 50 C for 30 min. Volumes were then increased to 100 ml, extracted twice with phenol/chloroform, and DNA was precipitated with 10 ml NH 4 Ac and 250 ml ethanol. The purified DNA was resuspended in DEPCtreated H 2 O. To prepare synthetic mRNAs, 2 ml of purified linear DNAs were used for in vitro transcription using the Ambion T7 mMachine mMessage kit. RNAs were precipitated using 30 ml DEPC H 2 O and 25 ml LiAc. The RNA was resuspended in 11 ml DEPC H 2 O (1 ml in 500 would give an OD 260 of 0.05-00.1; 1-2 mg/ml). |
To anneal the oligonucleotides with the mRNA, J-oligos, R-oligos or the equivalent volumes of dilution buffer alone (20 mM Tris, pH 7.4, 2 mM MgCl 2 and 50 mM EDTA final concentration) were added to synthetic mRNA (0.5 mg), and the mixtures were first incubated in a 70 C heating block for 10 min; the block was then removed and allowed to cool to 37 C (30 min), after which they were briefly spun down and incubated on ice. In all experiments, the molar ratios of J-and R-oligos to synthetic mRNAs were 100:1. In experiments using the competing oligonucleotide (C-oligo), this was added to either 0.5:1 or 1:1 molar ratios with either J-or R-oligonucleotides. Reticulocyte lysates were thawed on ice, 15 ml of Àmet and 15 ml of Àleu master mixes plus 20 ml of H 2 O were added to 400 ml of lysate, and 19 ml of this was added to each annealed reaction to start the in vitro translation reactions. These were incubated at 30 C for 60 min (the reaction reached a plateau after 30-35 min where the greatest difference was seen between the zero-frame controls and the frameshifting plasmids) (data not shown), and the reactions were then placed on ice. An aliquot of 7.5 ml from each in vitro translation reaction was added to 50 ml of the prewarmed luciferase reagent, and luminescence readings were taken after a 3 s delay for 15 s in triplicate using a Turner 20/20 Luminometer. |
Synthetic transcripts generated from DraI-digested pJD366-18 ($1.7 kb) were 5 0 end labeled using [g-32 P]CTP. These RNAs (4 ml) were incubated with 1 ml of annealing buffer and either 1 ml of H 2 O or 1 ml of an oligo (0.25 ng) at 70 C. The heating block was allowed to cool at room temperature for 40 min before 8 ml of RNaseH buffer was added (20 mM HEPES, 50 mM KCl, 10 mM MgCl 2 and 1 mM DTT). An aliquot of 1 ml of enzyme was added (mung bean nuclease, RNaseH or RNaseT1) and the reactions incubated at 37 C for 1 h. The reactions were stopped by adding 4 ml of stop solution, the products separated through a 6% polyacrylamide-urea denaturing gel and visualized by autoradiography. |
mRNA toeprinting JD366-18 mRNA (1 mg in 8 ml) was annealed with 2 ml of 3 0 end-labeled toeprinting primer (5 0 -CGTACGTGATCTTCA-CC-3 0 , complementary to sequence 240 bp 3 0 of the slippery site) as described above. This was added to 15 ml of lysate (200 ml Ambion retic lysate, 7.5 ml of each master mix [Àleu and Àmet] and 70 ml of 250 mM KCl), except for 2 ml, which was added to 15 ml of RT buffer [50 mM Tris-HCl (25), 40 mM KCl, 6 mM MgCl 2 , 5 mM DTT and 575 mM dNTPs] to be used as a no-ribosome control. In vitro translation reactions were incubated at room temperature for 10 min, which was empirically determined to provide the optimum amount of time to allow ribosomes to initiate translation and pause at the frameshift signal. Subsequently, 15 ml of RT buffer containing RNasin inhibitor and cycloheximide (to a final concentration of 100 ng/ml) was added to stop translation. To this, 2 ml of Superscript II (Invitrogen) was added and the reaction incubated at room temperature for 10 min. Reactions were terminated by phenol:chloroform extraction and 15 ml of stop solution added. The toeprinting primer was also used in conjunction with pJD366-18 to produce sequencing ladders by standard dideoxynucleotide chain termination methods using Sequenase (USB). Products were separated though 6% polyacrylamide-urea denaturing gels and visualized using a Storm phosphorImager (Pharmacia). |
Pseudo-pseudoknots stimulate frameshifting, and frameshifting efficiency changes with the degree of pseudo-pseudoknot rotational freedom |
We previously showed in intact yeast cells that the pseudoknot containing mRNA produced from pJDRC was able to promote efficient À1 PRF, whereas one in which only a stem-loop can form, transcribed from pJD18, could not (23) . As a first step in this study, we tested the ability of synthetic mRNAs produced from pJD366-RC and from pJD366-18, two plasmids derived from these parental constructs, to promote À1 PRF. Total luciferase activities produced from these synthetic mRNAs were divided by the luciferase activity produced from the zero-frame control plasmid, pJD366-0, and multiplied by 100% to determine À1 PRF efficiencies. The results show that the trends observed in yeast were replicated in vitro, i.e. JD366-RC mRNA promoted $8% efficiency of À1 PRF as compared with $1.1% promoted by JD366-18 mRNA (Figure 2 ). |
The 'torsional restraint' model predicts that conditions that would inhibit the rotational freedom of the loop region of the pJD366-18-derived mRNA should result in enhanced À1 PRF efficiency. The strategy used in this study was to anneal this mRNA with synthetic oligonucleotides complementary to both the loop region and to the sequence downstream that is normally involved in pseudoknot formation. These 'pseudopseudoknots' would be predicted to restore a pseudoknot-like structure to the mRNA. This is diagrammed in Figure 3A . Two different classes of oligonucleotides having different orientations relative to the mRNA were used to this end: 'joining' (J-) and 'reverse' (R-) oligos. The orientation of the J-oligos promotes the formation of a structure containing the equivalent of a Loop 2 region, while that of the R-oligos promotes a Loop 1 equivalent. The model also predicts that pseudo-pseudoknots having different degrees of rotational freedom should promote different frequencies of ribosome pausing over the slippery site, resulting in different efficiencies of À1 PRF. In order to control this parameter, increasing numbers of nucleotides were inserted between the mRNA hybridizing regions of the J-and R-oligos. The additional non-complementary bases in the J-oligos are 3 0 to the stem-loop residues involved in Stem 2, thus effectively increasing Loop 2. Similarly, the additional non-complementary bases in the R-oligos are 5 0 to the loop acceptor residues and correspond to an increased Loop 1. The structure of the stem-loop of pJD366-18 and its maximum base-paired interactions with representative J-and R-oligos are shown in Figure 3B . To demonstrate that an oligonucleotide-mRNA hybrid was capable of forming under the assay conditions, the J1-oligo was incubated with 5 0 [ 32 P]labeled JD366-18 mRNA and subjected to RNaseH digestion. Digestion of the RNA-DNA hybrid resulted in a labeled 110 nt fragment, demonstrating that the oligonucleotide bound to the mRNA at the position of the pseudoknot (Figure 4) . |
Having demonstrated the utility of the in vitro frameshifting assay and that the J-and R-series of oligonucleotides were able to hybridize with synthetic mRNA produced from pJD366-18, the next step was to monitor frameshifting efficiencies promoted by these hybrid species. Significant increases in frameshifting were observed with the incubation of pJD366-18 mRNA with oligonucleotides J1 ($10%) and J2 ($35%), while only modest increases were seen with J3 and J4 ( Figure 5 ). These findings are consistent with the notion that changes in the degree of rotational freedom of the structure would affect the distribution of paused ribosomes in the vicinity of the slippery site. One potential complication with the J-oligos is the possibility that they could interact with the Loop 2-Stem 1 region. In the R-oligos, the additional bases are distal to any possible Loop 2-Stem 1 interactions and would be more analogous to increasing Loop 1. The R-oligos stimulated À1 PRF to an even higher extent than the J-oligos ( Figure 5 ). Importantly, increasing the length of the bridging regions in these oligonucleotides (R1 to R3), which is predicted to increase the rotational freedom of the stem-loop, resulted in decreased frameshifting activity as predicted by the torsional resistance model. However, addition of three residues between the two binding regions of the R-oligo (R4) resulted in an unexpected increase in frameshifting with a very large amount of variation. |
In a series of control experiments, 8 nt oligos complementary to the 5 0 (Loop 1) and 3 0 Stem 2 forming regions of the pseudo-pseudoknot were hybridized to the SL mRNA and À1 PRF assays were performed. Neither of these were able to stimulate À1 PRF, even at concentrations in 100-fold molar excess to the mRNA template (data not shown). Though supportive of our central hypothesis, it is also possible that these results were due to the thermodynamic instability of the RNA:DNA duplexes through the course of the experimental protocol. |
To determine whether the stimulation of frameshifting was specifically due to the bridging of the stem-loop with downstream sequence (the pseudo-pseudoknot), as opposed to the nonspecific presence of an RNA:DNA hybrid, the competing oligonucleotide (C-oligo) was designed to form a 15 bp duplex with JD366-18 mRNA, including the 3 0 Stem 2 forming region, which was expected to significantly out compete either the J-or R-oligos from binding to this site, thus disrupting formation of the pseudo-pseudoknot (see Figure 3A ). Additionally, in the presence of the C-oligo, the J-and R-oligos were still predicted to hybridize with the 5 0 Stem 2 forming region, enabling us to address the question of whether this interaction alone was able to stimulate frameshifting. The results demonstrate that the addition of the C-oligo severely inhibited the abilities of both the J-and R-oligos to promote efficient frameshifting ( Figure 6 ). These findings demonstrate that (i) frameshifting was specifically stimulated by bridging of the 5 0 and 3 0 Stem 2 forming regions by the J-and R-oligos, and (ii) that the presence of an RNA:DNA hybrid at the 5 0 Stem 2 forming region was not sufficient to stimulate frameshifting by itself. |
The torsional restraint model predicts that pseudoknots should direct elongating ribosomes to pause at one specific location 1 2 3 4 5 6 7 8 . Efficient frameshifting is stimulated by pseudo-pseudoknots. In vitro translation assays were performed in retic lysates with mRNAs derived from pJD366-18 (SL) to which J-or R-oligos were annealed. Luciferase activities were divided by those obtained using mRNAs generated from pJD366-0, and the resulting ratios were multiplied by 100 to calculate percent frameshifting. The averages of three independent experiments performed in triplicate are shown. Error bars denote standard deviation. on the mRNA, rather than being distributed along Stem 1. We used mRNA toeprint assays to test this hypothesis. In mRNA toeprint reactions, the movement of reverse transcriptase is blocked by paused ribosomes, resulting in a strong stop positioned $16-18 nt 3 0 of the P-site of eukaryotic ribosomes (25) . Synthetic JD366-18 mRNAs were annealed with the sequencing oligonucleotide and either J1, R1 or no second oligo, and these were then used for in vitro translation reactions. After a period of time (10 min were empirically determined to be optimal), elongation reactions were stopped by the addition of cycloheximide, and reverse transcription reactions were initiated on the sequencing oligonucleotides. In parallel, control reverse transcription reactions were carried out using synthetic JD366-18 mRNA and oligonucleotides, but without in vitro translation. The results are consistent with the model, showing that the J1-and R1-oligos specifically promoted one strong reverse transcriptase stop 16 nt 3 0 of the P-site of the slippery site only the in the in vitro translation reactions ( Figure 7 ). As further predicted by the model, a broad distribution of stops of equal intensities was observed in this region with JD366-18 mRNA alone (Figure 7, lane 1) . Importantly, the +16 stop was not observed when toeprint reactions were carried in the absence of ribosomes. Additional strong stops were also of interest. One corresponding to the 3 0 end of the base of Stem 1 was observed in all samples, consistent with the presence of this structure. Both J-and R-oligo-specific pauses were also observed. The reason for the strong pause in the J-oligo is unknown. The R-oligo-specific pause is perhaps more revealing. It occurs at the 3 0 end of the RNA:DNA hybrid formed by this oligo and the mRNA, a structure that should also promote pausing of reverse transcriptase. |
The results presented in this study provide strong support for the torsional restraint model of programmed À1 frameshifting. Specifically, we demonstrated that RNA:DNA hybrids that mimic mRNA pseudoknots can significantly stimulate frameshifting. As predicted by the model, changing the rotational freedom of the structure by altering the lengths in the J1-and R1-oligos between the 5 0 and 3 0 mRNA hybridizing regions resulted in changes in their abilities to stimulate À1 frameshifting. The demonstration that these 'pseudopseudoknot' structures cause elongating ribosomes to specifically pause with their A-and P-sites positioned at the slippery site provides independent evidence in support of the model. In the case of the J-oligo series, frameshifting was best stimulated by J2, suggesting the structure created and the rotational freedom allowed by it was optimal for À1 PRF. The experimental design is such that we assume a similar rate of unfolding for each oligo as the predicted maximum base pairing is the same for them all. However, we do note that the type of nucleotides separating the two, separately paired regions of the oligos, and their presentation, may play a role in À1 PRF efficiency. The recent NMR structural solution of the SRV-1 pseudoknot revealed a highly structured Loop 2-Stem 1 interface including base triples involving an A residue at the 3 0 end of Loop 2 (26) . The additional base in the J2oligonucleotide is also an A. Mutagenesis experiments in this region by other groups showed, for example, that replacing the 3 0 base in Loop 2 of IBV with an A residue promoted a significant increase in frameshifting efficiency (27) , and mutation or removal of the A residue at the 3 0 base in Loop 2 of the BWYV pseudoknot reduced frameshifting levels (17) . This part of the pseudoknot has been proposed to be important in a frameshifting model where differential transition state energy barriers (due to small differences in local structure, stability or dynamics) are the primary determinant of frameshifting efficiency (3). Indeed, a Loop 2-Stem 1 triplex interaction seen in smaller frameshifting pseudoknots from luteoviruses has been shown to be critical for À1 PRF, and that similar pseudoknots lacking the triplex are less efficient at frameshifting [(28) and references therein]. This extra structural feature would limit the rate of unfolding and provide extra anchoring of Stem 2 as the ribosome attempts to unwind Stem 1, i.e. it too would help to provide additional torsional restraint. It is also possible that although the J3-and J4oligonucleotides also help to form a pseudo-pseudoknot, the additional bases may interfere with the stabilization of Stem 1. |
With the R-oligos, a general correlation was observed between minimization of rotational freedom and frameshifting efficiency, though this was not the case of the R4-oligo. Since the stability of the pseudo-pseudoknot generated with R4 should be similar to that of the other oligonucleotides based on the base-pairing, this result suggests that there are additional considerations to be uncovered with regard to the Frameshifting (% stimulated by R1) Figure 6 . Competition for J-or R-oligo binding sites inhibits its ability to promote efficient frameshifting. mRNA transcribed from pJD366-18 (SL) was annealed with either J-or R-oligos alone, or in combination with different concentrations of competing (C-) oligos (in ratios of 2:1 or equimolar as indicated). Sample marked SL is mRNA alone. Luciferase activities generated from in vitro translation reactions in rabbit reticulocyte lysates were divided by those obtained using mRNAs generated from pJD366-0, and the resulting ratios were multiplied by 100 to calculate percent frameshifting. |
pseudoknot structure influencing frameshifting. Addition of residues in the R-oligos was analogous to lengthening Loop 1, which is typically short in À1 frameshifting pseudoknots. Limited and conflicting data are available on the importance of Loop 1 in À1 frameshifting pseudoknots. In one study, addition of three A bases to Loop 1 did not affect frameshifting efficiency (15) , while in another all the mutations made in this region were detrimental to frameshifting efficiency (17) . Given the complex interactions occurring between the helices and loops in this region, we cannot yet account for why the R4-oligo stimulated frameshifting so efficiently and with such variable results. Examination of the RNA toeprint data presented here reveals that both of the pseudo-pseudoknot structures formed by the J1-and R1-oligos promoted strong stops of the reverse transcriptase $16 nt 3 0 of the P-site codon of the slippery site, consistent with the hypothesis that the presence of Stem 2 forces ribosomes to pause with their A-and P-sites positioned over the slippery site. Previous studies mapping the lagging edge of paused ribosomes, i.e. mRNA heelprint studies, did not reveal any striking differences between the effects of pseudoknots versus stem-loops (8, 16) . Interestingly, using this method, the ribosomal pauses appeared distributed over a broader stretch of mRNA ($4 nt) than observed here. It is possible that some critical level of resolution is lost in the requirement for many additional manipulations of substrates using the mRNA heelprint as compared with the toeprint methods. |
A remaining question centers on whether the role of the RNA pseudoknot in À1 PRF is passive or active. In the '9 Å solution' (6), the frameshift mechanism is activated by movement of the A-site codon-anticodon complex by 1 base in the 5 0 direction upon accommodation. As currently described, the mRNA pseudoknot merely passively blocks entry of the downstream message into the ribosome, resulting in stretching of the segment of mRNA located between the codon-anticodon complex and the pseudoknot. By this model, all of the energetic input for the frameshift is derived from hydrolysis of GTP by eEF1A. However, it is possible that the pseudoknot may also actively contribute to the frameshift mechanism. Specifically, pulling the downstream message into the ribosome at accommodation could result in unwinding of Stem 1 of the pseudoknot by one additional base pair. The energetic cost of so doing would be to introduce an equivalent amount of torsional resistance into Stem 2. If Stem 2 were to release this resistance by 'pulling back', the base pair in Stem 1 would be re-formed, which in turn would contribute to the energy required to dissociate the A-and P-site codonanticodon complexes from the zero-frame. This would be followed by slippage of the mRNA by 1 base in the 3 0 direction relative to the ribosome, followed by the formation of À1 frame codon-anticodon complexes. As such, the proposed active role for the mRNA pseudoknot would further reduce the energetic barrier to À1 PRF. In sum, we suggest that the 'torsional restraint model' can be combined with the '9 Å solution' to mechanistically explain the original 'simultaneous |
3' mRNA + Ribos. mRNA Figure 7 . Pseudo-pseudoknots direct ribosomes to pause over the slippery site. mRNAs generated from pJD366-18 (SL) were annealed with the sequencing oligonucleotide and either J1-, R1-or no oligo (lanes 1, 2 and 3, respectively), and these were then used for in vitro translation reactions. Reactions were stopped after 10 min by the addition of cycloheximide, and reverse transcription reactions were initiated on the sequencing oligonucleotides. In parallel, control reverse transcription reactions were carried out using synthetic JD366-18 mRNA and oligonucleotides, but without in vitro translation (lanes 4-6). The positions of the slippery site, loops and stems of the pseudo-pseudoknots are indicated next to a sequencing reaction. Arrowheads indicate positions of reverse transcriptase strong stops and these are mapped to a representation of the stem-loop structure of pJD366- 18. slippage' model of À1 PRF (9, 10) . In other words, the 9 Å solution + torsional restraint = simultaneous slippage. |
Two recent publications have also shown that oligonucleotide:mRNA duplexes can stimulate efficient À1 ribosomal frameshifting (29, 30) . These studies differed from the present one in a number of ways, particularly insofar as they examined the effects duplex structures immediately 3 0 of the slippery site rather than addressing mRNA pseudoknot related questions. The findings support the notion that the specific location of ribosome pausing on the mRNA plays a critical role in determining frameshifting, though they do come with caveats, e.g. neither study directly mapped ribosomal pausing, and the use of different slippery sites and downstream contexts likely contributed to disparate findings for the optimal distances between the 3 0 ends of slippery sites and 5 0 ends of frameshift-stimulating oligonucleotides. Although potentially useful therapeutically there are no known natural examples of frameshifting stimulated in this manner, and thus these results do not affect the hypothesis presented here. However, these studies are important in that they raise the possibility for a new role for micro-RNAs in regulating gene expression, and for therapeutic approaches to correcting inborn errors of metabolism due to the presence of frameshift mutations. Correcting errors in synthetic DNA through consensus shuffling Although efficient methods exist to assemble synthetic oligonucleotides into genes and genomes, these suffer from the presence of 1–3 random errors/kb of DNA. Here, we introduce a new method termed consensus shuffling and demonstrate its use to significantly reduce random errors in synthetic DNA. In this method, errors are revealed as mismatches by re-hybridization of the population. The DNA is fragmented, and mismatched fragments are removed upon binding to an immobilized mismatch binding protein (MutS). PCR assembly of the remaining fragments yields a new population of full-length sequences enriched for the consensus sequence of the input population. We show that two iterations of consensus shuffling improved a population of synthetic green fluorescent protein (GFPuv) clones from ∼60 to >90% fluorescent, and decreased errors 3.5- to 4.3-fold to final values of ∼1 error per 3500 bp. In addition, two iterations of consensus shuffling corrected a population of GFPuv clones where all members were non-functional, to a population where 82% of clones were fluorescent. Consensus shuffling should facilitate the rapid and accurate synthesis of long DNA sequences. Methods for the automated chemical synthesis of oligonucleotides (1, 2) and their assembly into long double-stranded DNA (dsDNA) sequences by PCR (3, 4) and LCR (5) have enabled the chemical synthesis of genes and even entire viral genomes (6, 7) . These technological advances have helped spur the formation of the new field of synthetic biology (8) , which aims at defining the functional units of living organisms through the modular engineering of synthetic organisms. In addition, the demand for fully synthetic gene length DNA fragments of defined sequence has dramatically increased in recent years for use in applications such as codon optimization (9), construction of DNA vaccines (10) , de novo synthesis of novel biopolymers (11) , or simply to gain access to known DNA sequences when original templates are unavailable. The future demand for long synthetic DNA is likely to dramatically increase when it becomes cheaper/faster to synthesize a desired sequence than to obtain it by other means. |
The assembly of DNA is currently limited by the presence of random sequence errors in synthetic oligonucleotides that arise from side reactions during synthesis (incomplete couplings, misincorporations, etc.) and resulting in 1-3 errors/kb (7, 12, 13) . The deleterious impact of these errors becomes more significant as the desired lengths of synthetic DNA increase. Indeed, in the remarkable assembly of the PhiX 174 bacteriophage genome (5386 bp) using gel-purified, synthetic oligonucleotides, the products contained an average of $2 lethal errors/kb resulting in 1 plaque-forming genomes per 20 000 clones (7) . A functional selection (plaque formation) was required in this study to identify a clone with the correct sequence. Thus, error reduction/correction is a requirement for the efficient production of long synthetic DNA of defined sequence. However, the process of sequencing multiple clones and manual correction of errors is both costly and time consuming. |
Several methods have been reported for the removal of error-containing sequences in populations of DNA. These methods rely upon the selective destruction (14, 15) or physical separation (16, 17) of mismatch-containing heteroduplexes. Smith and Modrich (14) reported the selective destruction of error-containing sequences in PCR products by generating dsDNA breaks upon overdigestion with the Escherichia coli mismatch-specific endonuclease MutHLS (18) . Gel purification and cloning of the remaining full-length DNA resulted in an apparent 10-fold reduction in the error rate for PCR products. However, the existing approaches are not well suited for error removal in long synthetic DNA sequences where virtually all members in the population contain multiple errors. The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org Error correction with MutS is outlined in Figure 1 . The population of DNA molecules containing random errors is first re-hybridized to expose synthesis errors as mismatches ( Figure 1A ). Duplexes containing mismatches can then be removed from the population by affinity capture with immobilized MutS ( Figure 1B) , a process we term coincidence filtering, since both strands of the duplex must match to pass this filtering step. For long synthetic DNA sequences or for sequences with high error rates, coincidence filtering is ineffective, since the likelihood of both strands being perfectly matched after re-hybridization is very low. To generalize MutS error filtering for application on synthetic DNA, the synthetic DNA is cleaved into small overlapping fragments before MutS filtering. Fragments containing mismatches are selectively removed through absorption to an immobilized maltose-binding protein (MBP)-Thermus aquaticus (Taq) MutS-His 6 fusion protein (MBP-MutS-H6) (18) (19) (20) . The remaining mixture of fragments (enriched with fragments of the correct sequence) serves as a template for assembly PCR to produce the full-length product ( Figure 1C ). This process can be iterated until the consensus sequence emerges as the dominant species in the population. This approach is equivalent to DNA shuffling (21) with additional mismatch exposure and removal steps. |
In this report, we assemble GFPuv from synthetic oligonucleotides and apply both coincidence filtering and consensus shuffling protocols to reduce errors in the resultant DNA populations. The error rates are characterized by gene function (fluorescence) and by DNA sequencing. We also provide a mathematical model describing the error reduction protocols to aid predictions about parameters influencing their effectiveness. |
Chemicals were from Sigma. Restriction enzymes were from Promega and New England Biolabs. KOD Hot Start DNA Polymerase was from Novagen. Amylose resin was from NEB (catalog no. E8021S). Ni-NTA resin was from Novagen (catalog no. 70666). Ultrafiltration device from Millipore (catalog no. UFC900524). Slide-A-Lyzer dialysis membrane was from Pierce (catalog no. 66415). |
Full-length Taq MutS was amplified from template pETMutS (22) with primers 5 0 -AAA AAA CAT ATG GAA GGC ATG CTG AAG G-3 0 and 5 0 -AAA AAT AAG CTT CCC CTT CAT GGT ATC CAA GG-3 0 and cloned into the Nde1/HindIII sites of vector pIADL14 (23) to give plasmid pMBP-MutS-H6. |
E.coli strain BL21(DE3) transformed with pMBP-MutS-H6 was grown to OD 600 $1.0 and induced using 1 mM isopropylb-D-thiogalactopyranoside for 4 h at 37 C. Cells from 4 l of culture were pelleted and resuspended in 60 ml of buffer A (20 mM Tris-HCl, pH 7.4, 300 mM NaCl, 1 mM EDTA, 1 mM DTT and 1 mM phenylmethlysulfonyl fluoride). Cell suspension was sonicated on ice and insoluble material was removed by centrifugation at 50 000 g for 10 min at 4 C. Supernatant was applied to 5 ml amylose resin pre-equilibrated in buffer A. Bound MBP-MutS-H6 was washed three times using 20 ml buffer B (20 mM Tris-HCl, pH 7.4, 300 mM NaCl) and stored The re-hybridized gene synthesis products are fragmented, and error containing fragments are precipitated by MBP-MutS-H6 immobilized on amylose support. Error reduced fragments (orange, blue and red) are reassembled into the full-length gene followed by PCR amplification to generate error reduced products. Primers: black lines. |
overnight at 4 C. MBP-MutS-H6 was eluted using 20 ml buffer B + 10 mM maltose. Eluate was applied to $4 ml of Ni-NTA resin pre-equilibrated in buffer B. Bound MBP-MutS-H6 was washed four times using 20 ml buffer B + 25 mM imidazole. Bound MBP-MutS-H6 was eluted using buffer B + 1 M imidazole. Eluate was concentrated via ultrafiltration using Amicon Ultra 5 kDa MWCO at 4 C. Concentrated sample was dialyzed extensively against 2· storage buffer (100 mM Tris-HCl, pH 7.5, 200 mM NaCl, 0.2 mM EDTA and 0.2 mM DTT) using a Slide-A-Lyzer 10 kDa MWCO cassette at 4 C. Protein concentration was determined using A 280 and a calculated extinction coefficient of 119 070 M À1 cm À1 . Dialyzed sample was diluted using an equal volume of glycerol and stored at À20 |
Oligonucleotides were purchased from Qiagen with 'salt-free' purification. Sequence 261-1020 of pGFPuv (GenBank accession no. U62636 with T357C, T811A and C812G base substitutions) was assembled using 40mer (37) and 20mer (2) oligonucleotides with 20 bp overlap (Supplementary Table 1 ). Assembly reactions contained the following components: 64 nM each oligonucleotide, 200 mM dNTPs, 1 mM MgSO 4 , 1· buffer and 0.02 U/ml KOD Hot Start DNA Polymerase. Assembly was carried out using 25 cycles of 94 C for 30 s, 52 C for 30 s and 72 C for 2 min. PCR amplification of assembly products contained the following components: 10-fold dilution of assembly reaction, 25 mM of 20 bp outside primers, 200 mM dNTPs, 1 mM MgSO 4 , 1· buffer and 0.02 U/ml KOD Hot Start DNA Polymerase. PCR was carried out using 35 cycles of 94 C for 30 s, 55 C for 30 s and 72 C for 1 min followed by a final extension at 72 C for 10 min. PCR products were purified using the Qiagen QIAquick PCR purification kit with elution in dH 2 O followed by speed-vac concentration. Assuming an error rate of 1 · 10 À6 /bp/duplication for KOD DNA polymerase (24) , 35 cycles of PCR would be expected to introduce $0.053 mutations per assembled GFPuv molecule. |
Assembled GFPuv was diluted to 250 ng/ml in 10 mM Tris-HCl, pH 7.8, 50 mM NaCl and heated to 95 C for 5 min followed by cooling 0.1 C/s to 25 C. Heteroduplex for consensus filtering was split into three pools and digested to completion with NlaIII (NEB), TaqI (NEB) or NcoI plus XhoI (Promega) for 2 h following the manufacturer's protocols. Digests were purified using the Qiagen QIAquick PCR purification kit with elution in dH 2 O. Samples were pooled and the concentration was determined by measuring A 260 . |
MBP-MutS-H6 binding reactions contained $11.5 ng/ml DNA and $950 nM MBP-MutS-H6 dimers in 1· binding buffer (20 mM Tris-HCl, pH 7.8, 10 mM NaCl, 5 mM MgCl 2 , 1 mM DTT and 5% glycerol). Reactions were allowed to incubate at room temperature for 10 min before incubation for 30 min with an equal volume of amylose resin pre-equilibrated in 1· binding buffer. Protein-DNA complexes were removed by low-speed centrifugation and aliquots of supernatant were removed for subsequent processing. |
Supernatant (50 ml) from consensus filtering experiments was desalted using Centri-Sep spin columns (Princeton Separations) and concentrated. Purified and concentrated DNA fragments were reassembled as above with aliquots removed at varying cycles. Aliquots of assembly reactions were resolved on 2% agarose gels to monitor the reassembly process. Aliquots showing predominantly reassembled fulllength GFPuv were PCR amplified as above. Aliquots of supernatant from coincidence filtering experiments were diluted 10-fold and PCR amplified as above. PCR products were digested with BamHI/EcoRI and ligated into the 2595 bp BamHI-EcoRI fragment of pGFPuv. Ligations were transformed into E.coli DH5 and fluorescent colonies were scored using a handheld 365 nm ultraviolet (UV) lamp. |
Preparation of substrate for consensus shuffling from 10 non-fluorescent GFPuv clones Ten non-fluorescent GFPuv clones were pooled in equal amounts. The nature and location of the mutations in these clones is shown in Figure 4 . The GFP coding region was PCR amplified from the mixture and submitted to the consensus shuffling protocol with and without the application of the MBP-MutS-H6 error filter. |
To create an error filter, we constructed a fusion protein between MBP (19) and the mismatch binding protein from T.aquaticus (22) with a C-terminal His 6 tag (MBP-MutS-H6). MBP-MutS-H6 was overexpressed and purified from E.coli to >95% purity (Supplementary Figure 1) . MBP-MutS-H6 immobilized on amylose resin was shown to selectively retain a 40mer heteroduplex containing a deletion mutation over wild-type homoduplex (Supplementary Figure 2) . |
To demonstrate error correction, unpurified 40mer oligonucleotides were assembled by PCR (3) to produce a 760 bp gene encoding green fluorescent protein (25) (GFPuv). Two independent preparations of GFPuv containing typical gene synthesis errors (Figure 3 and Table 1 ) were re-hybridized and subjected to two iterations of coincidence filtering or consensus shuffling. For consensus shuffling, the GFPuv assembly product was split into three pools and digested into sets of overlapping fragments using distinct Type II restriction enzymes ( Figure 2 ). The digests were pooled and subjected to error filtering with or without added MBP-MutS-H6. The unbound fragments were reassembled into full-length products and PCR amplified. For coincidence filtering, unbound fulllength GFPuv was PCR amplified following treatment with the error filter. After cloning in E.coli, error rates were estimated by scoring colonies for fluorescence under a handheld UV lamp (Figure 3) . The actual error rates of the input and consensus shuffled populations were determined by sequencing plasmid DNA from randomly selected colonies (Figure 3) . The results show that two rounds of consensus shuffling increased the percentage of fluorescent colonies from $60 to >90% and Table 1 . Sequence errors in input and consensus shuffled DNA Table 1 . |
Although DNA shuffling has traditionally been used to create diversity through the combinatorial shuffling of mutations in a population, DNA shuffling also creates a sub-population of sequences with a reduction in diversity, as correct fragments can recombine to produce error-free sequences. Indeed, with consensus shuffling, it is possible to start with a population of DNA molecules wherein every individual in the population contains errors and create a new population where the dominant sequence is error free. To demonstrate this, 10 nonfluorescent GFPuv clones, each containing a deletion mutation (Figure 4) , were pooled and subjected to either DNA shuffling alone or two iterations of consensus shuffling. Products were cloned in E.coli, and the percentage of fluorescent colonies was monitored as an indication of progress toward the consensus sequence. DNA shuffling alone (no MBP-MutS-H6) increased the percentage of fluorescent colonies to 30% (387 colonies total) similar to a previous report (26) . Two rounds of consensus shuffling gave a new population that was 82% fluorescent (551 colonies total), indicating that the dominant species was likely the consensus sequence of the input population. |
Both consensus shuffling and coincidence filtering protocols were effective in reducing errors in synthetic GFPuv populations ( Figure 3 ). In both cases, two iterations of either consensus shuffling or coincidence filtering increased fluorescent colonies from average values of $60 to >90%. Sequencing data from two independent experiments showed a 4.3-and 3.5-fold reduction in the error rate for the consensus shuffled populations compared with the input populations giving final error rates of 0.3 and 0.28 errors/kb, respectively. These results demonstrate the usefulness of the MBP-MutS-H6 error filter in both consensus shuffling and coincidence filtering protocols. Taq MutS has previously been shown to bind to deletion mutations with high affinity (27) , a mutation common in synthetic DNA. However, it is important to note that Taq MutS has lower affinity for specific point mutations and binds weakly to homoduplex DNA (27) . These factors may limit the stepwise efficiency of the error filter. Moreover, specific point mutations may be refractory to removal even after multiple rounds of consensus shuffling. Two rounds of consensus shuffling using the MBP-MutS-H6 error filter proved most effective in reducing deletions and G/C to A/T transitions, consistent with previous reports for the selectivity of Taq MutS (27) . However, it must be emphasized that each synthetic oligonucleotide point mutation would generate two heteroduplex DNA molecules containing unique mismatches after PCR amplification and re-hybridization ( Figure 1A and Table 1 ). For example, a G to A transition mutation in a synthetic oligonucleotide would generate heteroduplexes with G-T or A-C mismatches after PCR amplification and re-hybridization. For consensus shuffling, either of these mismatch containing heteroduplexes could evade precipitation by the MBP-MutS-H6 error filter and participate in the reassembly of full-length GFPuv. Therefore, Table 1 lists the pair of mismatches that could give rise to the observed transition or transversion mutation. These results show that the MBP-MutS-H6 error filter was most effective at removing insertion/deletion loops and G-T/A-C mismatches from the population. |
It should be possible to generalize the consensus shuffling protocol to a large number of synthetic DNA constructs. GFPuv was chosen as the synthetic construct in this study for its advantages as a fluorescent reporter gene. This allowed easy optimization of our protocol without the need to sequence thousands of base pairs of DNA. We expect the results reported here for consensus shuffling to readily translate to synthetic DNA constructs of varied sequence, greater overall length and/ or higher initial errors/kb. Synthetic DNA constructs of varied sequence can be digested into a defined set of fragments using Type II restriction enzymes or fragmented into any desired size range using controlled DNase I digestion (26) . Digestion and reassembly of a large number of different genes is expected to be as robust as the protocol of DNA shuffling (28) , which has been broadly applied to a variety of gene sequences. Synthetic DNA constructs larger than GFPuv are expected to be amenable to error correction by consensus shuffling, as the error filtering is conducted on gene fragments before reassembly of the full-length gene. Thus, the errors/kb data presented in this study are expected to translate to larger genes with similar initial errors/kb (excepting mutations introduced by PCR amplification following the final application of the error filter). Synthetic DNA constructs of higher initial errors/kb are expected to be amenable for error correction by consensus shuffling. However, these constructs will require digestion into smaller sized gene fragments that may affect the efficiency of error correction. In contrast to consensus shuffling, an increase in the size of the synthetic DNA product or an increase in errors/kb would preclude the use of the coincidence filtering protocol, as every molecule in the population would contain one or more errors. As proof of the utility of the consensus shuffling protocol, 10 non-fluorescent GFPuv clones containing one or more errors (Figure 4 ) were converted into a population where 82% of the clones were fluorescent. It is important to note that DNA shuffling alone shows an improvement in percent fluorescent colonies in this example (from 0 to 30%). For synthetic DNA populations, DNA shuffling alone shows no improvement in percent fluorescent colonies (see Figure 3 'no MutS' treatments). DNA shuffling alone improves the overall number of correct sequences only for small DNA populations with low error rates. For example, when shuffling 10 clones with a unique mutation in each clone, one would expect the fraction of correct products to be (9/10) 10 = 35% (26), very close to the value of 30% that we observed. A mathematical model describing the error rates for shuffling and error filtering of synthetic DNA populations is presented below. |
To estimate some parameters of consensus shuffling and coincidence filtering, a simple mathematical model (Equations 1-6) was constructed. An input population of dsDNA molecules of length N, containing E errors/base is re-hybridized, fragmented into shorter dsDNA fragments of average length S, error filtered and reassembled. P(F) is the probability a fragment of length S will have a correct sequence. We determine the probability that re-hybridized duplexes will have zero (C), one (H ) or both (I ) strands with errors. |
Equation 5 estimates the probability that a fragment will be correct after a cycle of MutS filtering, P(F 0 ), by applying a MutS selectivity factor (M ) to adjust the relative amounts of mismatch containing duplexes (I, H ) while accounting for the total fraction of correct strands in the re-hybridized duplexes. The probability of obtaining an error free assembly product, P(A), is then given by Equation 6 . |
From our consensus shuffling error rate data (Figure 3 ), we estimate the MutS selectivity factor M to be $2.2. Figure 5 shows some predictions that emerge from this model assuming typical length (2 kb), fragment sizes (200 bp) and error rates (1.8 errors/kb). Consensus shuffling is predicted to be most effective with smaller fragment sizes ( Figure 5A ). As mentioned above, smaller fragment sizes could be obtained by controlled digestion with DNase I (21) . In addition, multiple iterations of MutS filtering can have dramatic results on populations with few correct sequences ( Figure 5B ), although the model does not account for the differing specificity of MutS toward the various types of mismatches. The model also predicts that even modest improvements in the MutS selectivity factor through optimization of the MutS-DNA binding conditions and/or the use of a combination of MutS homologs with varying mismatch specificity (29) could dramatically improve the consensus shuffling protocol ( Figure 5C ). Coincidence filtering (N = S) is predicted to be effective for populations with low error rates per clone ( Figure 5D ) but becomes ineffective when the majority of re-hybridized duplexes contain mismatches. |
We have demonstrated consensus shuffling and coincidence filtering as experimental methods to significantly reduce errors in synthetic DNA. Consensus shuffling should be generally applicable for error correction on synthetic genes of typical lengths and error rates. Two iterations of consensus shuffling ($6 h/iteration) generated a population with $1 error/3500 bp. This reduction in error rate will allow the identification of a correct clone after sequencing DNA from a reduced number of colonies. Coincidence filtering is a simple and effective procedure to reduce errors in synthetic DNA populations with low error rates per clone. These methods should significantly increase the speed and decrease the cost of production of synthetic genes. |
Note: While this manuscript was under review, Carr et al. (30) independently reported the application of Taq MutS in protocols for error reduction on synthetic DNA. Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces While it is universally accepted that intact RNA constitutes the best representation of the steady-state of transcription, there is no gold standard to define RNA quality prior to gene expression analysis. In this report, we evaluated the reliability of conventional methods for RNA quality assessment including UV spectroscopy and 28S:18S area ratios, and demonstrated their inconsistency. We then used two new freely available classifiers, the Degradometer and RIN systems, to produce user-independent RNA quality metrics, based on analysis of microcapillary electrophoresis traces. Both provided highly informative and valuable data and the results were found highly correlated, while the RIN system gave more reliable data. The relevance of the RNA quality metrics for assessment of gene expression differences was tested by Q-PCR, revealing a significant decline of the relative expression of genes in RNA samples of disparate quality, while samples of similar, even poor integrity were found highly comparable. We discuss the consequences of these observations to minimize artifactual detection of false positive and negative differential expression due to RNA integrity differences, and propose a scheme for the development of a standard operational procedure, with optional registration of RNA integrity metrics in public repositories of gene expression data. Purity and integrity of RNA are critical elements for the overall success of RNA-based analyses, including gene expression profiling methods to assess the expression levels of thousands of genes in a single assay. Starting with low quality RNA may strongly compromise the results of downstream applications which are often labor-intensive, time-consuming and highly expensive. However, in spite of the need for standardization of RNA sample quality control, presently there is no real consensus on the best classification criteria. Conventional methods are often not sensitive enough, not specific for single-stranded RNA, and susceptible to interferences from contaminants present in the sample. For instance, when using a spectrophotometer, a ratio of absorbances at 260 and 280 nm (A 260 :A 280 ) greater than 1.8 is usually considered an acceptable indicator of RNA purity (1, 2) . However, the A 260 measurement can be compromised by the presence of genomic DNA leading to over-estimation of the actual RNA concentration. On the other hand, the A 280 measurement will estimate the presence of protein but provide no hint on possible residual organic contaminants, considered at 230 nm (3) (4) (5) . Pure RNA will have A 260 :A 230 equal to A 260 :A 280 and >1.8 (1) . A second check involves electrophoresis analysis, routinely performed using agarose gel electrophoresis, with RNA either stained with ethidium bromide (EtBr) (6) (7) (8) (9) , or the more sensitive SYBR Green dye (10) . The proportion of the ribosomal bands (28S:18S) has conventionally been viewed as the primary indicator of RNA integrity, with a ratio of 2.0 considered to be typical of 'high quality' intact RNA (1) . However, these methods are highly sample-consuming, using 0.5-2 mg total RNA and often not sensitive enough to detect slight RNA degradation. Today, microfluidic capillary electrophoresis with the Agilent 2100 bioanalyzer (Agilent Technologies, USA) has become widely used, particularly in the gene expression profiling platforms (11, 12) . It requires only a very small amount of RNA sample (as low as 200 pg), the use of a size standard during electrophoresis allows the estimation of sizes of RNA bands and the measurement appears relatively unaffected by contaminants. Integrity of *To whom correspondence should be addressed. Tel: The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org the RNA may be assessed by visualization of the 28S and 18S ribosomal RNA bands ( Figure 1A and B); an elevated threshold baseline and a decreased 28S:18S ratio, both are indicative of degradation. A broad band shows DNA contamination ( Figure 1C ). As it is apparent from a review of the literature, the standard of a 2.0 rRNA ratio is difficult to meet, especially for RNA derived from clinical samples, and it now appears that the relationship between the rRNA profile and mRNA integrity is somewhat unclear (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) . On the one hand, this may reflect unspecific damage to the RNA, including sample mishandling, postmortem degradation, massive apoptosis or necrosis, but it can reflect specific regulatory processes or external factors within the living cells. Altogether, it appears that total RNA with lower rRNA ratios is not necessarily of poor quality especially if no degradation products can be observed in the electrophoretic trace ( Figure 1D ). |
For all these reasons, the development of a reliable, fully integrated and automated system appropriate for numeric evaluation of RNA integrity is highly desirable. Standardized RNA quality assessment would allow a more reliable comparison of experiments and facilitate exchange of biological information within the scientific community. With that prospect in mind, and with the aim of anticipating future standards by pre-normative research, we identified and tested two software packages recently developed to gauge the integrity of RNA samples with a user-independent strategy: one open source, the degradometer software for calculation of the degradation factor and 'true' 28S:18S ratio based on peak heights (24) and the freely available RIN algorithm of the Agilent 2100 expert software, based on computation of a 'RNA Integrity Number' (RIN) (25) . Both tools were developed separately to extract information about RNA integrity from microcapillary electrophoretic traces and produce a userindependent metrics. Using these tools, we assessed the purity and integrity of 414 RNA samples, derived from 14 different human adult tissues and cell lines, many of which representing tumors. Those results were compared with conventional RNA quality measurement approaches as well as with highly expert human interpretation. We evaluated the simplicity for users and examined the potential, accuracy and efficiency of each method to contribute to standardization of RNA integrity assessment upstream of biological assays. These procedures were further validated by real-time RT-PCR quantitation of the expression levels of three housekeeping genes, using the same RNA samples, at different levels of degradation. |
Total RNA was prepared from human cell lines (especially from the ATCC bio-resource center, N = 50) and tissue samples (clinical samples, N = 285) from 13 different human adult tissue types, i.e. blood, brain, breast, colon, epithelium, kidney, lymphoma, lung, liver, muscle, prostate, rectum and thyroid. RNA purification was performed by cesium chloride ultracentrifugation according to Chomczynski and Sacchi (26) , by phenol-based extraction methods (TRIzol reagent, Invitrogen, USA), or silica gel-based purification methods (RNeasy Mini Kit, Qiagen, Germany; Strataprep kit, Stratagene, USA or SV RNA isolation kit, Promega, USA) according to the manufacturer's instructions with some modifications. Material was maintained at À80 C with minimal handling. RNA extraction was carried out in an RNase-free environment (see Supplementary Table 1 online) . |
The commercially available RNA samples were the 'Universal Human Reference' (N = 75) distributed by Stratagene (USA), and human brain (N = 2) and muscle (N = 2) RNAs supplied by Clontech (USA). |
Once extracted, RNA concentration and purity was first verified by UV measurement, using the Ultrospec3100 pro (Amersham Biosciences, USA) and 5 mm cuvettes. The absorbance (A) spectra were measured from 200 to 340 nm. A 230 , A 260 and A 280 were determined. A 260 :A 280 and A 260 :A 230 ratios were calculated. For microcapillary electrophoresis measurements, the Agilent 2100 bioanalyzer (Agilent Technologies, USA) was used in conjunction with the RNA 6000 Nano and the RNA 6000 Pico LabChip kits. In total, 39 assays were run in accordance with the manufacturer's instructions (see Supplementary Notes online). To evaluate the reliability of the classifier systems described in this study, replicate runs were done on a set of 56 RNA samples loaded on different chips, resulting in 2 (N = 41), 3 (N = 12), 7 (N = 2) and 50 (N = 1) data points per sample. |
Human RNA integrity categorization RNA integrity checking was performed by expert operators who classified each total RNA sample within a predefined discrete category from 1 to 5, examining the integrity of the RNA from electropherograms (see Supplementary Table 2 online). A low number indicates high integrity. Reference criteria parameters include ribosomal peaks definition, baseline flatness, existence of additional or noise peaks between ribosomal peaks, low molecular weight species contamination and genomic DNA presence suspicion. A smearing of either 28S and 18S peaks, or a decrease in their intensity ratio indicate degradation of the RNA sample and results in the classification into the higher categories. To evaluate the robustness of this human interpretation, five highly experienced operators, trained in these cataloging steps, separately classified a subset of 33 samples from breast cancers. It included samples with varying levels of integrity: intact RNA (33%), low quality samples (20%) and a wide range of degradation (47%). |
Bioanalyzer electrophoretic data were exported in the degradometer software folder (.cld format). For comparison of samples, the original data were re-scaled by the classifier system, first along the time-axis to compensate for differences in migration time, then along the fluorescence intensity-axis to compensate for variation in total RNA amount. As a result, fluorescence curves that have the same shape will have the same peak heights after re-scaling. Then, Degradation Factors (DegFact) and corrected 28S:18S ratios were calculated (see Supplementary Table 3 online) using the mathematical model developed by Auer et al. (24) , examining additional 'degradation peak signals' appearing in the lower molecular weight range and comparing them to ribosomal peak heights. Calculation of the DegFact is based on a numbering of continuous metrics, ranging from 1 to ¥; increasing DegFact values correspond to more degradation, and a new group of integrity is defined after 8 graduation steps. Once the classification of the RNA samples is completed, 4 groups of integrity are displayed, 3 showing an alert warning indicative of some measurable degradation (Yellow: 8-16, Orange: 16-24 and Red: >24), while all non-reliable data come together and form the fourth group (Black). We introduced a fifth class labeled White (<8), when no alert was produced by the software. |
Software and manual are freely available at http://www. dnaarrays.org/downloads.php. Degradometer version 1.4.1 (released in May 2004) of the software was used. |
Bioanalyzer electrophoretic sizing files (.cld format) collected with biosizing software version A.02.12.SI292 (released in March 2003) were imported in the Agilent 2100 expert software (RIN beta release). The RIN algorithm allows calculation of RNA integrity using a trained artificial neural network based on the determination of the most informative features that can be extracted from the electrophoretic traces out of 100 features identified through signal analysis. The selected features which collectively catch the most information about the integrity levels include the total RNA ratio (ratio of area of ribosomal bands to total area of the electropherogram), the height of the 18S peak, the fast area ratio (ratio of the area in the fast region to the total area of the electropherogram) and the height of the lower marker. |
A total of 1300 electropherograms of RNA samples from various tissues of three mammalian species (human, mouse and rat), showing varying levels of degradation and an adaptive learning approach were used in order to assign a weight factor to the relevant features that describe the RNA integrity. A RIN number is computed for each RNA profile (see Supplementary Table 4 online) resulting in the classification of RNA samples in 10 numerically predefined categories of integrity. The output RIN is a decimal or integer number in the range of 1-10: a RIN of 1 is returned for a completely degraded RNA samples whereas a RIN of 10 is achieved for intact RNA sample. |
In some cases, the measured electropherogram signals are of an unusual shape, showing for example peaks at unexpected migration times, spikes or abnormal fluctuation of the baseline. In such cases, a reliable RIN computation is not possible. Several separate neural networks were trained to recognize such anomalies and display a warning to the user or even suppress the display of a RIN number. Combining the results of the neural network for the RIN computation and the neural networks to detect anomalies, the RIN algorithm achieves a mean square error of 0.1 and a mean absolute error of 0.25 on an independent test set. |
The beta release of the software and manual are freely available at http://www.agilent.com/chem/RIN. Agilent 2100 expert version B.01.03.SI144 (released in November 2003) of the software was used. |
Expression levels of three housekeeping genes (HKG)-GAPD, GUSB and TFRC-were measured by quantitative PCR using the TaqMan Gene Expression Assays according to the manufacturer's instructions (Applied Biosystems, USA). Sixteen aliquots of a unique batch of RNA sample (Universal Human Reference RNA, Stratagene, USA) of various levels of integrity (cf. Table 1 ) were used to test the influence of RNA quality on the relative expression of those three genes. In parallel, a 5 0 to 3 0 comparison was done using two separate GUSB and TFRC TaqMan probes. |
An homogeneous quantity (0.8-1 mg) of the RNA samples was subjected to a reverse transcription step using the highcapacity cDNA archive kit (Applied Biosystems, USA) as described by the manufacturer. Single-stranded cDNA products were then analyzed by real-time PCR using the TaqMan Gene Expression Assays according to the manufacturer's instructions (Applied Biosystems, USA). Single-stranded cDNA products were analyzed using the ABI PRISM 7700 Sequence Detector (Applied Biosystems, USA). The efficiency and reproducibility of the reverse transcription were tested using 18S rRNA TaqMan probes. Five assays were used, GAPDH-5 0 (Hs99999905_m1), GUSB-5 0 (Hs00388632_gH), GUSB-3 0 (Hs99999908_m1), TFRC-5 0 (Hs00951086_m1) and TFRC-3 0 (Hs00951085_m1). In each case, duplicate threshold cycle (Ct) values were obtained and averaged; then expression levels were evaluated by a relative quantification method (27) . |
The fold change in one tested HKG (target gene) was normalized to the 18S rRNA (reference gene) and compared to the highest quality sample (calibrator sample), using the following formula: Fold change = 2 ÀDDCt , where DDCt = (C t-target À C t-reference ) sample-n À (C t-target À C t-reference ) calibrator-sample . Sample-n corresponds to any sample for the target gene normalized to the reference gene and calibrator-sample represents the expression level (1·) of the target gene normalized to the reference gene considering the highest quality sample. Mean 2 ÀDDCt and SD were calculated, considering the samples either individually or grouped by quality metrics categories, based on RIN metrics or DegFact values, together with the lower and upper bound mean of 95% Intervals of Confidence (IC). Using this analysis, if the expression levels of the HKG are not affected by the RNA degradation, the values of the mean fold change at each condition should be very close to 1 (since 2 0 = 1) (27) . |
Descriptive statistics were executed using the XLSTAT software, version 7.1 (Addinsoft, USA), P = 0.05. Mean, SD and coefficient of variation (variation or CV) between and within groups of samples were calculated, together with a measure of the dispersion (range), inter-quartile range (1st and 3rd quartiles, Q1-Q3) and evaluation of the lower and upper bound mean of 95% Interval of Confidence (IC). Comparative statistical analyses between groups were completed, P = 0.05, using non-parametric statistical tests: two-independent Mann-Whitney U-test and k-independent Kruskal-Wallis test. |
We analyzed 414 total RNA sample profiles from various human tissues (69%) and cell lines (31%) of either tumoral (85%) or normal (15%) origin, with varying levels of RNA integrity. Supplementary Table 1 online for details). Significant differences in A 260 :A 280 ratios were observed between specific groups of samples (i.e. tumoral versus normal or tissues versus cell lines). For instance, RNA extracted from normal samples displayed an improved ratio of 1.97, with 97% falling within the desired range ( Figure 2A ). In contrast, the distribution of A 260 :A 280 ratios was not found to correlate with either purification methods or tissues of origin. |
RNA integrity was further assessed by resolving the 28S and 18S ribosomal RNA bands using the Agilent 2100 bioanalyzer and the RNA 6000 protocol. The analysis was done on 399 RNA profiles; data from 15 samples was not obtained due to device problems during the runs. The system automatically provided 28S:18S ratios for 348 (87%) of the 399 profiles. Figure 2B shows the distribution of the 28S:18S computed values, with a median ratio around 1.7 and a variation of 54% from the mean (IC 1.9-2.1 and Q1-Q3 1.4-2.5). In addition, a significant degree of variability of the 28S:18S ratio (19-24%) was found for identical samples from replicate runs (2-50 times). Among those RNA samples, 28S:18S ratios of 2.0 or greater were rare, less than 44% of the values measured being within the theoretically desired range, except for the samples prepared from cultured cells ( Figure 2B ). The integration failed in the remaining 51 cases, displaying an atypical migration, with no clear 28S and 18S rRNA bands, and no 28S:18S ratio was computed (data not shown). |
Expert operators categorized the set of RNA samples by inspecting the electrophoretic traces of successful assays. Over the 399 RNA profiles checked, 379 (95%) were scored within predefined categories ( Figure 2C ), namely good [Human Categorization (HC)-level 1], regular (HC-level 2), moderate (HC-level 3), low (HC-level 4) and degraded (HC-level 5). The remaining 20 (5%) were flagged as displaying a temperature-sensitive profile: RNA samples initially found intact became highly degraded when heated, although no RNase contamination was observed (data not shown). |
Estimation of the robustness of this cataloging was done through comparison of qualifying criteria using a set of 33 breast cancer samples (see Materials and Methods). Integrity of the samples was evaluated independently by five expert operators, and categorization was found highly reliable with a coefficient variation (CV) $16%. This is low considering that individual interpretation is involved, but can be explained by the fact that very experienced operators accomplished the scoring based on a clearly defined set of instructions, thus limiting frequently observed subjective visual interpretation and inconsistency of human categorization. Predictably, a 28S:18S ratio of 2.0 denoted high quality for a majority of RNA samples, 91% being classified in HC-levels 1 to 3. However, 83% of total RNAs with 28S:18S > 1.0 but a low baseline between the 18S and 5S rRNA or front marker were also classified in HC-levels 1-3 (see Figure 1D ) and could be considered suitable for most downstream applications. |
RNA degradation was first assessed using the degradometer software (see Materials and Methods). Over the 399 RNA profiles checked, all were scored in one of the five predefined classes ( Figure 3A) . Altogether, 334 (84%) Degradation Factors (DegFact) values were computed, the remaining 65 RNA samples (16%) displaying profiles that could not be interpreted reliably; no DegFact values could be scored, and samples were flagged in the Black category ( Figure 3A ). Most of them (80%) correspond to samples previously classified by our operators as degraded (HC-level 5). The remaining cases had an average degradation factor of 7.5 (IC 6.7-8.3) with large variations over the entire set of samples (over 103% from the mean, range 1-52). A lower variability was persistently found when identical samples from replicate runs were considered, resulting in observed DegFact values with a 26-32% CV. In addition, statistically significant differences were found between DegFact values of samples sorted by types. The highest DegFact values were found characteristic of tissue samples, 41% of them displaying a DegFact > 8, as compared with 6% for the cell lines (data not shown). |
Remarkably, we found a significant linear relationship between the DegFact values distribution and the explicit human categorization. Most HC classes corresponded to an unambiguous DegFact distribution ( Figure 3B ), while HClevels 2 and 3 form a single class: HC-level 1, mean DegFact of 3.3, SD of 2.8 (IC 2.8-3.7); HC-level 2 and 3, mean Deg-Fact of 8.8, SD of 6.8 (IC 7.5-10.2); HC-level 4, mean DegFact of 15.9, SD of 7.8 (IC 12.7-19.1); HC-level 5, mean DegFact of 26.0, SD of 7.5 (IC 21.9-30.1). It is worth mentioning that the normalized heights of 18S and 28S peaks, and the interval between them after rescaling gradually decrease and then reverse with increasing degradation ( Figure 3B ). |
Integrity of RNA samples was measured in parallel based on the RNA Integrity Number metrics using an artificial neural network trained to distinguish between different RNA integrity levels by examining the shape of the microcapillary electrophoretic traces (see Materials and Methods). Over the 399 RNA profiles checked, 363 (91%) were scored successfully ( Figure 4A) , with an average RIN of 7.7 (IC 7.4-8.0). The remaining 36 (9%) samples were associated with various unexpected signals, disturbing computation of the RIN using default anomaly detection parameters. In each case, a flag alert was added corresponding to critical anomalies including unexpected data in sample type, (or) ribosomal ratio, (or) baseline and signal in the 5S region (data not shown). |
RIN categorization was found regular, variability between replicate runs, compared to the other methods, being consistently very small (CV 8-12%). As expected, the highest RIN were characteristic of cell line samples, 72% of them displaying a RIN > 9, as compared with 47% for the tissue samples (data not shown). |
A first group, corresponding to 295 (82%) of the 363 RNA profiles, was analyzed using the default settings of the RIN system, but with a lower threshold of RNA quantity loaded (20 ng) for reliable detection of anomalies than that recommended by the manufacturer (50 ng). A significant linear relationship was found between the RIN number and both the explicit human classification provided by our operators, Figure 3 . RNA degradation characterization. Integrity of 399 RNA sample profiles was scored using the degradometer software. (A) A total of 334 RNA profiles were successfully categorized into 5 predefined alert classes using a mathematical model that quantifies RNA degradation and computes a degradation factor (DegFact). Four classes (White, Yellow, Orange and Red) are associated with different levels of degradation. A fifth class, Black alert corresponds to samples that the system was not able to qualify with accuracy (n.d.). The distribution is represented by the number of records in each class. (B) Comparative analysis was done using human evaluation (x-axis) based on electrophoresis analysis as a reference for RNA integrity classification; observations of rRNA peak heights and DegFact values were taken at each of the 5 HC levels. Histograms refer to the mean 28S and 18S rRNA peak heights and 95% confidence intervals (fluorescence intensities; left scale). Mean DegFact values and 95% confidence intervals (arbitrary unit, right scale) are plotted with the means joined. and the DegFact values calculated by the degradometer software ( Figure 4B ). Each distinct HC class corresponds to an explicit RIN number, with HC-levels 2 and 3 forming once again a single class: HC-level 1, mean RIN of 9.6, SD of 0.7 (IC 9.5-9.7); HC-level 2 and 3, mean RIN of 8.6, SD of 0.9 (IC 8.4-8.9); HC-level 4, mean RIN of 6.1, SD of 1.5 (IC 5.2-7.1); HC-level 5, mean RIN of 3.7, SD of 2.0 (IC 2.9-4.5). |
For the remaining 68 samples (assay done with <20 ng of RNA), two separate groups were considered: 41 samples with a computed RIN below 5.0, and 27 above 7.0. All samples in the first group were derived from RNA 6000 Nano assays, with mean RNA quantities loaded below 10 ng (Q1-Q3, 5-12 ng), i.e. below the lower limit of quantitation indicated by the manufacturer. All but 8 of these samples were estimated by our operators to be of poor quality (HC-level 4; N = 3) or degraded (HC-level 5; N = 30), and all but 4 were flagged Black by the degradometer software and no DegFact values were scored. These RNA profiles could not be interpreted reliably, possibly due to either the low RNA concentration or the unusual migration behavior and shifted baseline values of degraded samples. Thus, the two automated systems were in disagreement for these samples; while human interpretation was in most cases in agreement with the RIN system, with less than 20% of inconsistency. In the second group of 27 samples, 20 of the profiles were derived from RNA 6000 Pico assays with RNA quantities loaded being on average below 4 ng (Q1-Q3, 0.5-0.8 ng), which is within the manufacturer specifications. All but 3 of them were estimated by our operators to range from high (HC-level 1; N = 12) to correct (HC-level 2 and 3; N = 12) quality levels. In addition, all RNA profiles except 1 were scored by the degradometer software, most of them displaying an alert flag (N = 20); some slight degradation was detected, associated to a low mean DegFact value of 9.7 (IC 8.1-11.3; Q1-Q3, 6.2-12.6). Thus, both automated systems and human interpretations agreed in most of these cases, with <11% of inconsistency. |
The influence of RNA quality categorization obtained with both user-independent classifiers on gene expression profiling was explored using real-time RT-PCR. The expression levels of three housekeeping genes (HKG)-GAPDH, GUSB and TFRC-were measured in 16 aliquots of a unique RNA displaying various integrity metrics ( Table 1 ). The mean correlation coefficient (r) between the threshold cycle (Ct) among the 16 samples and both quality metrics was found high: r = À0.87 considering the RIN metrics and r = 0.85 considering the DegFact values. The values of the mean fold changes, calculated according to the 2 ÀDDCt quantification method (see Materials and Methods), were found lower than 1.0, corresponding to the expression level (1·) in the sample exhibiting the highest RNA quality (Table 2 and Figure 5 ). Considering that HKG expression was measured relative to the reference sample, an obvious decline of the relative expression levels was observed, up to 24, 70 and 82%, in samples categorized according to the RIN metrics ( Figure 5A) and DegFact values ( Figure 5B ). These results indicate that 2-to 7-fold differences may be expected in the relative expression levels of genes in samples that differ only by their quality (Table 2 ). These fold differences are much larger than those measured for RNA samples of comparable integrity, consistently lower than 1.6 (Table 2 and Figure 5 ). In addition, an unambiguous gap in the distribution may be defined ( Figure 5A and B) , distinguishing the RNA samples of the higher quality categories (RIN > 8 and DegFact values < 7) from those of the lower categories (RIN < 8 and DegFact values > 12). |
It would be expected that measuring expression of an intact mRNA would yield approximately equal results regardless of the region being probed, and if mRNA fragmentation had occurred, then some sequences may be more abundant than others. We thus tested the effect of PCR probe location on the RNAs. The 5 0 and 3 0 GUSB probes, separated by 1209 nt, were associated with highly similar threshold cycle (Ct) measures (r = 0.98, b parameter = 0.88) ( Figure 5C ). Similar results were obtained for TFRC, with probes separated by 2066 nt (r = 0.84, b parameter = 0.92, data not shown). It seems therefore that the region being probed is not a source of variation in our results. |
It is universally accepted that RNA purity and integrity are of foremost importance to ensure reliability and reproducibility of downstream applications. In the biomedical literature (PubMed, November 2004), from the 485 090 articles that relate to RNA, and the 287 515 or 40 395 including respectively the 'quality' or 'integrity' term, less than 100 were found to contain 'RNA quality' or 'RNA integrity' terms. Interestingly, half of them were published between 2001 and 2004; but none is proposing a standard operational procedure for RNA quality assessment to the scientific community. Except for two studies (24, 25) , those reports are based on 10 to 15 years old methods (1), indicating that they represent the established and currently mostly used methods. Our results strongly challenge the reliability and usefulness of those conventional methods, demonstrating their inconsistency to evaluate RNA quality. |
First, the A 260 :A 280 and A 260 :A 230 ratios are reflecting RNA purity, but are not informative regarding the integrity of the RNA. Available RNA extraction and purification methods yield highly pure RNA with very little DNA or other contaminations, resulting most often in both ratios )1.8, although 18% of the samples were found degraded and 7% more of poor quality. The high A 260 :A 280 ratios are indicative of limited protein contaminations, whereas high A 260 :A 230 ratios are indicative of an absence of residual contamination by organic compounds such as phenol, sugar or alcohol, which could be highly detrimental to downstream applications. Nonetheless, samples displaying low A 260 :A 230 ratios ((1.8) did not exhibit any inhibition during downstream applications, such as cDNA synthesis and labeling or in vitro transcription (data not shown). Second, due to a lack of reliability, the 28S:18S rRNA ratios may not be used as a gold standard for assessing RNA integrity. When ribosomal ratios were calculated from identical samples but through independent runs, a large degree of variability (CV 19-24%) was observed. Moreover, using the biosizing software, we found 28S:18S rRNA ratios evaluation compromised by the fact that their calculation is based on area measurements and therefore heavily dependent on definition of start and end points of peaks. In 13% of the cases, the system was unable to localize the ribosomal peaks, and therefore no 28S:18S ratios were computed. For the remaining samples, no clear correlation between 28S:18S ratios and RNA integrity was found although RNAs with 28S:18S >2.0 were usually of high quality. Most of the RNAs we studied (83%), displaying a 28S:18S > 1.0, could be considered of good quality. Interestingly, Auer et al. (24) in a study on 19 tissues from seven organisms, reported that an objective measurement of the RNA integrity may possibly be done through comparison of re-scaled 28S and 18S peak heights, but not of the corresponding areas. Actually, we observed a linear relationship between RNA integrity and differences in normalized 28S and 18S peak heights. Increased degradation resulted in a significant decrease in the scaled corrected heights of the ribosomal peaks, with inversion of the ratio at the highly degraded stages (cf. Figure 3B ). In comparison to the area computation, 28S:18S rRNA re-scaled peak height measurement produced more consistent values, with a CV reduced to 12-14%, and displayed clear concentration-independent values (see Supplementary Tables 1 and 3 online) . Human evaluation of the integrity of RNA through visual inspection of the electrophoresis profiles provided very consistent data. Variability between classifications produced by five independent expert operators (CV 16%) was lower than with automated management of more conventional control 28S:18S area values (CV 19-24%). It is, however, very time-consuming and strongly dependent on individual competence. Even with highly trained specialists, 5% of the set of RNA samples could not be allocated to any of the five predefined categories; their corresponding profiles were considered by our experts as atypical, displaying a temperature-sensitive shape (data not shown). |
These strategies appear unsuitable for standardization and quality control of RNA integrity assessment, which require simple but consistent expert-independent classification, facilitating information exchanges between laboratories. The N-value corresponds to the number of samples by category. The mean quality metrics, i.e. RIN and DegFact and the mean fold change (2 ÀDDCt ) relative to the reference sample are indicated, together with the 95% confidence intervals. Observed technical variation (IC-rep, P = 0.05) is also specified, considering duplicate (two per gene per target sample) and replicate (six per gene per calibrator sample) measures. The reference sample exhibits a RIN of 9, a DegFact value of 4.9 and by default mean fold change set to 1. The observed decrease in the expression (relative expression, %) relative to the reference sample is calculated. The fold differences refer to the fold-ratios that are expected in the expression levels for a gene, across categories (between categories), given that the samples only differ by their quality, and within each category (within categories), considering RNA of comparable integrity. The fold-ratios (technical variation) that may be expected by chance in the gene expression levels, P = 0.05, from some technical reasons, are also considered. |
We therefore investigated the performance of two recently developed user-independent software algorithms (24, 25) . The degradometer software provided a reliable evaluation of RNA integrity based on the identification of additional 'degradation peak signals' and their integration in a mathematical calculation together with the ribosomal peak heights. It allowed characterization of the integrity of 84% of the samples tested, one-third with an alert flag, which was first found to be fairly informative, as it strongly reduces the complexity of the metrics by introducing three distinct classes labeled Yellow, Orange and Red, and can be used as a first straightforward simple filtering step. However, degradation factors (DegFact) metrics yield precise measures with less than 32% CV and are much more valuable than flag alerts for the purpose of standardization. The same is true for the RNA Integrity Number 'RIN' software which allowed the characterization of the integrity of 91% of the RNA samples tested, with a RIN value for 363 RNA sample profiles with less than 12% CV. In general, there was a good agreement between the human classification, the degradation factor and the RIN (see Figure 4B ). This provided a cross-validation of the user-independent qualification systems tested. Both resulted in the refinement of human interpretations, validating four statistically relevant classes of samples, namely good (HC-level 1), regular/ moderate (HC-level 2 and 3), poor (HC-level 4) and degraded (HC-level 5). Moreover, the 5% RNA samples previously flagged by the operators as displaying an atypical temperature-sensitive shape were unambiguously assigned to one or the other category of samples [RIN = 7.3 (IC 6.8-7.8); DegFact = 11.9 (IC 9.5-14.2); data not shown]. Altogether, we found the degradometer and RIN algorithms to be highly reliable user-independent methods for automated assessment of RNA degradation and integrity. The RIN system is a slightly more informative tool, able to compute assessment metrics for 91% of the RNA profiles, compared to 84% with the degradometer software; the remaining being flagged respectively as N/A or Black alert. For samples available below a low limit of 20 ng (N = 80) the RIN system provided Figure 6 . Workflow of operational procedure for RNA quality assessment. Integrity of the RNA, once extracted and purified from cell lines, clinical or biological tissues samples, is controlled from the widely used bioanalyzer electrophoretic traces. As standard part of the Agilent analysis software (25), a RIN metrics is first calculated, scoring each RNA sample into 10 numerically predefined categories of integrity (RIN, from 1 to 10; N is a threshold value). As an independent control, a degradation factor metrics (DegFact, from 1 to ¥; N 0 is a threshold value) may optionally be allocated to each RNA sample using the bioanalyzer-independent degradometer software (24) . In a standard operating procedure, RIN and/or DegFact metrics will first be used as a standard exchange language to document RNA integrity and degradation, second to classify the RNA in homogeneous groups, and finally to select samples of comparable RNA integrity to improve the scheme of meaningful downstream experiments. The standard operating procedure will benefit from feedback information that will help users to define threshold integrity metrics values based on the results of RNA-based analyses. metric values for 85% of them, compared to only 46% with the degradometer software. Similarly, the RIN system was able to provide metric values for 81% of poor quality samples (including low quality and degraded samples; N = 96), whereas the degradometer software could classify only 44% of them. Another advantage with the RIN classifier is that, if there are critical anomalies detected (including genomic DNA contamination, wavy baseline, etc.), threshold settings may be changed and a reliable RIN value computed. This was the case for 25 of the 363 RNA sample profiles successfully classified by the system. |
While intact RNA obviously constitutes the best representation of the natural state of the transcriptome, there are situations in which gene expression analysis may be desirable even on partially degraded RNA. Some studies report collection of reasonable microarray data from RNA samples of impaired quality (28) , leading to meaningful results if used carefully. Moreover, Auer et al. (24) recently concluded that degradation does not preclude microarray analysis if comparison is done using samples of comparable RNA integrity. We confirmed the direct influence of the RNA quality on the distribution of gene expression levels, by detecting using Q-PCR a significant (up to 7-fold) difference in the relative expression of genes in samples of slightly decreased RNA integrity, which is much larger than the variation within comparable RNA quality categories (cf. Figure 5 and Table 2 ). This may correlate with ratio discrepancies in gene expression experiments, and therefore with false positive and false negative rates of differential gene expression when comparing two samples. Therefore, computing reliable metrics of RNA integrity, even if the RNA is found to be partially degraded, may be highly valuable. The straight and unambiguous relationships established between human interpretations and both RIN and DegFact distributions indicates that, using these metrics, it should be possible to distinguish specific samples that are too disparate to be included in comparative gene expression analyses without compromising the results. Although the information provided by these user-independent classifiers is not a guarantee for successful downstream experiments, it gives a more comprehensive picture of the samples and can be used as a safeguard against performing useless and costly experiments. |
Thus, the RIN system may be used as simple metrics that can be easily integrated in any sample tracking information system for definition of standard operating procedures under quality assurance following a scheme such as the one described in Figure 6 . In this context, we suggest that the growing number of laboratories performing RNA Quality Control by microcapillary electrophoresis should be offered the option to report objective RNA quality metrics as part of the 'Minimum Information About a Microarray Experiment' MIAME standards (29) . Through registration of RNA profiles in a public electronic repository, such standardized information should enable and facilitate comparisons of RNA-based bioassays performed across laboratories with RNA samples of similar quality, in much the same way as sequencing traces are compared. Factors affecting translation at the programmed −1 ribosomal frameshifting site of Cocksfoot mottle virus RNA in vivo The ratio between proteins P27 and replicase of Cocksfoot mottle virus (CfMV) is regulated via a −1 programmed ribosomal frameshift (−1 PRF). A minimal frameshift signal with a slippery U UUA AAC heptamer and a downstream stem–loop structure was inserted into a dual reporter vector and directed −1 PRF with an efficiency of 14.4 ± 1.9% in yeast and 2.4 ± 0.7% in bacteria. P27-encoding CfMV sequence flanking the minimal frameshift signal caused ∼2-fold increase in the −1 PRF efficiencies both in yeast and in bacteria. In addition to the expected fusion proteins, termination products ending putatively at the frameshift site were found in yeast cells. We propose that the amount of premature translation termination from control mRNAs played a role in determining the calculated −1PRF efficiency. Co-expression of CfMV P27 with the dual reporter vector containing the minimal frameshift signal reduced the production of the downstream reporter, whereas replicase co-expression had no pronounced effect. This finding allows us to propose that CfMV protein P27 may influence translation at the frameshift site but the mechanism needs to be elucidated. The principal mechanism of translation is the accurate decoding of the triplet codon sequences in one reading frame of mRNA. Specific signals built into the mRNA sequences can cause deviations from this rule. Viruses exploit several translational 'recoding' mechanisms, including translational hopping, stop codon readthrough and programmed ribosomal frameshifting (PRF) [reviewed in (1, 2) ], for regulating the amount of proteins produced from their polyproteins. For positive-stranded RNA viruses, À1 PRF is the prevailing recoding mechanism and an essential determinant of the stoichiometry of synthesized viral proteins. Most viral À1 PRF signals are regulating the production of replication-associated proteins. Depending on the virus, the efficiency of À1 PRF can vary between 1 and 40% (3) , and changes in the efficiency can inhibit virus assembly and replication (4) (5) (6) . Therefore, À1 PRF can be regarded as a potential target for antiviral agents (4, 7) . However, the development of efficient antiviral drugs is still hindered, since little is known about the trans-acting factors and the biophysical parameters affecting the À1 PRF efficiencies. Database searches have identified putative frameshift signals from a substantial number of chromosomally encoded eukaryotic mRNAs (8) . Thus, À1 PRF may also have an impact on the complexity of the proteome of several eukaryotic organisms. |
Two cis-acting signals, a slippery heptamer X XXY YYZ (the incoming reading frame indicated) and a downstream secondary structure, direct the slippage and are therefore essential for this event (9) . À1 PRF takes place after the accommodation step in the slippery sequence by simultaneous slippage of both tRNAs into the overlapping À1 frame XXX YYY (9, 10) . The sequence of the heptamer allows postslippage base-pairing between the non-wobble bases of the tRNAs and the new À1 frame codons of the mRNA. Downstream RNA secondary structures [reviewed in (11) ] force the ribosomes to pause, and place the ribosomal A-and P-sites correctly over the slippery sequence (12) . However, the pausing of the ribosomes is not sufficient for À1 PRF to occur (13) ; in fact, the duration of the halt does not necessarily correlate with the level of the À1 PRF observed (12) . Crystallographic, molecular, biochemical and genetic studies suggest that a pseudoknot restricts the movement of the mRNA during the tRNA accommodation step of elongation by filling the entrance of the ribosomal mRNA tunnel (14) . This restriction can be eased either by unwinding the pseudoknot, which allows the mRNA to move forward, or by a slippage of the mRNA one nucleotide backwards. Chemical agents such as *To whom correspondence should be addressed. Tel: +358 9 19158342 ; Fax: +358 9 19158633; Email: kristiina.makinen@helsinki.fi ª The Author 2005. Published by Oxford University Press. All rights reserved. |
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org antibiotics, certain mutations in the translation apparatus, and in translation elongation factors that change the translation fidelity and kinetics, have been shown to influence À1 PRF efficiency [(10,15) ; reviewed in (16) ]. |
The parameters known to contribute to the efficiency of À1 PRF are the sequence of the slippery heptamer, the downstream secondary structure, and the length and sequence of the spacer between the two cis-acting signals. Up-and downstream sequences such as termination codons in the vicinity of the À1 PRF signals, or even several kilobases away from them, can affect the À1 PRF efficiencies (3, (17) (18) (19) (20) (21) (22) . A specific sequence in the Barley yellow dwarf virus (BYDV) 3 0 untranslated region (UTR), 4 kb downstream from the slippage site, is vital for À1 PRF (6, 19) . A stimulating effect is achieved through the formation of a tertiary structure, where complementary nucleotides from the 3 0 UTR base pair with a single-stranded bulge in the cis-acting stem-loop (6). Human immunodeficiency virus (HIV) was also shown to require a more complex secondary structure instead of a simple stemloop for optimal À1 PRF in vivo (21, 22) . These investigations suggest that À1 PRF studies carried out with minimal frameshift signals may lead to inaccurate estimates of the stoichiometry of synthesized viral protein products during infection. |
Cocksfoot mottle virus (CfMV; genus Sobemovirus) infects a few monocotyledonous plant species such as barley, oats and wheat. It has a monopartite, single-stranded, 4082 nt long, positive-sense RNA genome (23, 24) . The polyprotein of CfMV is translated from two overlapping open reading frames (ORFs) 2A and 2B by a À1 PRF mechanism (25) . In this study, we wanted to determine the in vivo À1 PRF efficiency guided by the CfMV U UUA AAC heptamer and the stem-loop structure. In addition to the minimal signal (18) , we decided to test the effect of flanking CfMV sequences for their ability to contribute to À1 PRF. We found that the surrounding viral sequences promoted more efficient À1 PRF than the minimal signal sequence in vivo when measured with the dual reporter vector system developed by Stahl et al. (26) . Therefore, we carried out an expression pattern and deletion analysis to understand the molecular basis of the observed upregulation. In addition, we critically analysed the suitability of the implemented experimental system for this type of a recoding study. An interesting possibility is that the viral proteins produced via À1 PRF could regulate À1 PRF. This hypothesis was tested by co-expressing the CfMV proteins P27 and replicase together with the dual reporter vectors. |
Three regions from the CfMV polyprotein ORFs ( Figure 1A) were cloned into the NheI and BclI sites between the lacZ and the luc ORFs in pAC74 (26) . This dual reporter vector was a generous gift from Dr J. Rousset of the Universite Paris-Sud, France. The inserted sequences 1602-1720 (A region), 1386-2137 (B region) and 1551-1900 (C region), were amplified by PCR using pAB-21 as a template (18) . Primers were used to introduce NheI and BglII sites to the flanking ends of the inserts. Since NheI digestion removed lacZ ORF, it was reintroduced into the plasmids as a final cloning step. The resulting plasmids were named pAC-A, pAC-B and pAC-C. |
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