Both for understanding mechanisms of disease and for the design of transgenes, it is important to understand the determinants of ribosome velocity, as changes in the rate of translation are important for protein folding, error attenuation, and localization. While there is great variation in ribosomal occupancy along even a single transcript, what determines a ribosome' s occupancy is unclear. We examine this issue using data from a ribosomal footprinting assay in yeast. While codon usage is classically considered a major determinant, we find no evidence for this. By contrast, we find that positively charged amino acids greatly retard ribosomes downstream from where they are encoded, consistent with the suggestion that positively charged residues interact with the negatively charged ribosomal exit tunnel. Such slowing is independent of and greater than the average effect owing to mRNA folding. The effect of charged amino acids is additive, with ribosomal occupancy well-predicted by a linear fit to the density of positively charged residues. We thus expect that a translated poly-A tail, encoding for positively charged lysines regardless of the reading frame, would act as a sandtrap for the ribosome, consistent with experimental data.
While it is known that there is great variation in ribosomal velocity along even a single transcript [1], what determines how fast a transcript (or part thereof) is processed is unresolved. Resolving this issue is important for understanding causes of disease and for the generation of transgenes, as changes in the local translation rate along mRNAs have been implicated in the regulation of protein folding [2], error attenuation processes such as no-go decay in yeast [3], transcription attenuation in bacterial systems [4], and correct protein localization [5], [6]. For some time it has been hypothesized [7]–[10], and commonly assumed (e. g. , [11], [12]), that codons matching rare tRNAs slow ribosomes along transcripts due to differential tRNA availability. The supposition is that codons corresponding to less abundant tRNAs are translated at slower rates as the ribosome must pause while the appropriate tRNA becomes available. This, for example, is held up to explain the usage of codons specified by the most abundant tRNAs in the most highly expressed genes [13], [14]. Although the notion that rare codons must stall ribosomes is commonplace, recent work has started to undermine the supposition that differential usage of synonymous codons will significantly alter the rate of ribosomal translocation within a transcript under normal conditions [15]–[17]. Indeed, much of the evidence cited as support for an effect on translational speed is questionable (see Note S1) and many of the patterns attributed to selection for translational speed are better explained in terms of selection on codon usage for translational accuracy [18]–[21]. Codon usage, however, is not the only potential factor affecting elongation speed. Double-stranded mRNA hairpin or pseudoknot structures are thought to impede progress of the ribosome [22], [23]. The generality of this during elongation, however, is unclear, as other studies [24] suggest that the ribosome can more readily melt moderately stable secondary structures once initiation has taken place. While the above factors consider ribosomal velocity to be modulated by properties of the mRNA, much less attention has been paid to the possibility that the resultant protein might impact translation rates. However, recent experimental work on recombinant peptides has shown that positive charges on the newly synthesized peptide might slow ribosomes [25], [26]. This is conjectured to be owing to an electrostatic interaction between the cation in the emerging polypeptide and the negatively charged exit tunnel of the ribosome [25], [26]. Following on from this, it has been suggested that positive charges, codon usage bias, and transcript folding play a role in ribosomal stalling at 5′ transcript ends [27], [28]. Here we ask not whether certain features can sometimes modulate translation speed along a transcript (e. g. , when grossly overrepresented in transgenes; see Note S1), but if they do as evolved in endogenous genes when expressed at “normal” levels, and to what extent. Ribosomally protected mRNA footprints from an experimental Saccharomyces cerevisiae dataset [29] enable us to profile the location of ribosomes across the S. cerevisiae transcriptome. Under the assumption that ribosomal densities inversely reflect ribosomal velocity [30], [31], we independently examine the effects of codon usage, mRNA folding, and positive charge on ribosomal speed throughout endogenous yeast genes. We show that positive charges in the nascent peptide slow the ribosome along transcripts in an additive manner in vivo, and that this slowing effect cannot be accounted for by mRNA structure, and even far surpasses that (if any) induced by codon usage bias. Within transcripts, those regions with the highest ribosomal occupancy are those most likely to be just downstream of positively charged residues. The cation sandtrap effect has potential relevance for the evolution of the poly-A tail, specifying as it does a series of positively charged amino acids if translated.
Ribosomal footprint data [29] allow us to examine changes in the rate of translation given the assumption that the slower a ribosome travels along a given portion of a transcript, the more likely it is to be found there at any point in time [30], [31]. In the case of codon usage, we expect to see any possible ribosomal stalling centered over the rare codon (s) while the ribosome awaits a tRNA to enter its A-site. Hence to examine the effect of a sequence feature such as rare codons on the speed of translation, we calculate the relative change in stringently mapped ribosomal densities that occurs within a single transcript as ribosomes begin to translate regions of transcript enriched for rare codons (see Methods and Figure 1). To this end, within each transcript we compared the ribosomal occupancy at codon positions (rpos) in the vicinity of clusters of rare codons (rpos) to the average ribosomal occupancy of the 30 codons immediately preceding the first rare codon in the cluster (rprec30). We then averaged the relative increase or decrease in ribosomal occupancy across transcript sections aligned by rare codon clusters. A mean rpos/rprec30 after the clusters >1 indicates a denser sampling of ribosomal footprints on average and hence slowing at that codon position, while a mean rpos/rprec30<1 denotes sparser ribosomal coverage, consistent with acceleration. In our main analysis we make use of the tAI (range 0–1) as a measure of codon optimality as this metric uniquely reflects the tRNA pool. The tAI of a sequence is defined as the geometric mean of the relative adaptiveness of its constituent codons to the tRNA pool available in that organism [32]. A higher tAI indicates the codon has a high abundance of decoding isoacceptor tRNAs and, according to the codon usage hypothesis of translational speed, should be translated faster on account of its ready coupling with an aminoacylated tRNA. A lower tAI conversely indicates a codon that is matched by a low number of tRNAs and is therefore putatively slowly translated and nonoptimal. Here we define “rare” codons to be those in the lowest quartile of tAI values (Methods, “The Average Effect of Codon Usage on Ribosomal Densities”) (see also Figures S1, S2, S3 and Table S1 for analysis of rare codons defined according to genomic frequency). Our results show inconsistent trends in ribosomal occupancy after rare codon clusters when all clusters of a given size are aligned and the average increase in ribosomal density after the cluster (here uncontrolled for covariates) is plotted (Figure 2A). This inconsistency is still apparent when we consider rare codons to be not those with a low tAI but those that are genomically infrequent (Figure S1). If there is any slowing due to rare codons, we should expect an increase in the amount of slowing along the mRNA as the number of rare codons increases. However, no such trend is evident (Figure 3A). This lack of influence of rare codon usage on ribosomal speed is not owing to a covariance between rare codon clusters and expression levels (Table S2). Shifting the location of the “preceding 30 codons” we use to normalize footprint values slightly upstream, to accommodate the 5′ portion of the ribosome potentially slowed over a rare codon, still detects no slowing due to codon usage (Figure S4). As it has been postulated that tandem nonoptimal codons may more strongly inhibit progression of the ribosome than scattered rare codons [33], [34], we also investigated whether consecutive rare codons (adjacent codons, each from the lowest quartile of tAI values) may be affecting ribosomal velocity. Examining changes in ribosomal densities after pairs, triplets, and so forth of rare codons, however, also indicates that consecutive rare codons do not systematically slow ribosomes (Figure S5 and Figure 3B). We achieve similar findings when defining rare codons according to their genomic frequency (Figure S2). If the above results are correct, then we should also find that codon usage cannot explain ribosomal slowing when we compare sites within a given mRNA. Upon locating the highest and lowest ribosomal occupancy portions within a given mRNA, we determined whether the denser region was associated with a putative ribosome-slowing feature: lower tAI, or more rare codon pairs or rare 6-mers (two adjacent in-frame codons that, as a pair, come from the lowest 10% of all 6-mers within the genome) (see Methods, “The Relative Contributions of Charge, Folding, and Codon Usage to Extremes of Slowing Within Transcripts”). Considering all transcripts, the most slowly translated region within an mRNA in fact tends to be comprised of more optimal codons or fewer rare pairs, suggesting low codon optimality does not cause slowing (Table 1A, B and Figure S6). These results are not affected if we consider suboptimal codons to be those that are genomically infrequent (Table S1 and Figure S3). Nor do we find that transcript similarity to the yeast Kozak sequence can explain slowing within these regions (Figure S7 and Table S3). Additionally, as the difference in ribosomal occupancy between the two intra-transcript windows increases (and hence the presumed difference in the inferred ribosomal velocities between the two windows grows all the more), the already low proportion of transcripts for which tAI, genomic infrequency, or presence of rare pairs could possibly explain ribosomal pausing in fact decreases (Table 1A, B and Table S1). In other words, in transcripts that have the greatest differences in ribosomal densities along their length (as inferred from the highest and lowest ribosomal occupancy windows), and hence that contain the greatest degree of internal slowing relative to maximum translation speed, the most ribosomally occluded windows are even more likely to be comprised of more optimal codons. This indicates that not only is low codon optimality incapable of explaining ribosomal slowing in general, it is even less capable of explaining the greatest relative slowing within a transcript. We note that the decrease in the ability of codon usage to explain slowing in the upper quantiles (Table 1A) is simply a side effect of differential amino acid usage between the two windows. When we control for differential amino acid content between the two windows, we no longer see the decrease in the ability of codon usage to explain slowing, but codon usage still remains unable to explain the slowing that is observed in any of the quantiles (Table S4). Thus, in addition to the above finding that codon usage becomes less able to explain slowing as the degree of slowing grows (as deduced from observed transcripts), this amino-acid-controlled analysis suggests that even if amino acid sequence had evolved in any other way, codon usage would still not be a factor in the slowing of ribosomes. It is possible that codon usage could have different effects during different times of cell cycle if tRNA levels fluctuate [35]. We do not, however, detect a systematic influence of codon usage on ribosomal speed even under amino acid starvation conditions (Figures S8, S9, S10 and Table S5) when presumably tRNA charging levels are lower, making codon usage potentially more rate-limiting [36], [37]. If neither codon usage nor consecutive rare codons can explain variation in ribosomal speed, then what can? As it has been suggested that transcript structure can impede ribosomes along the length of the transcript [28], we next investigated whether RNA structure might be the major contributor to slowing. We used empirically determined (rather than computationally predicted) RNA structure data (PARS values, see Methods, “The Average Effect of Transcript Structure on Ribosomal Densities”) [38]. S. cerevisiae protein-coding sequences were scanned for stretches whose average PARS value was 0 or negative (and hence tending to be single-stranded), which were immediately followed by a block of codons whose average PARS value was positive (i. e. , with propensity for double-strandedness). The general contribution of folding to slowing was examined by calculating the relative change in ribosomal density (rpos/rprec30) at each position of the identified region of a transcript, where rprec30 is the average ribosomal occupancy in the single-stranded block. We then take the average of this ratio across transcripts aligned by identified blocks of structure. The method is similar to that used above with codons, but with one complication. In the case of codon usage, we have a prior expectation that any ribosomal pausing should occur while the ribosome is positioned over the “slow” codon. It is not immediately clear, however, where along the transcript we should expect any structure-induced pausing to take place. After translating an unstructured span of mRNA, will the ribosomal active site be able to get very close to the first double-stranded ribonucleotide it meets before it is finally slowed, or might pausing take place more 5′ if the ribosome progression is sterically occluded at some distance upstream? We investigated both hypotheses. We cannot immediately distinguish between the possibilities that mRNA folding has an effect on ribosomal progression either upon or upstream of the folded ribonucleotides in question, as some degree of pausing is observed in both cases (Figure 4). But how strong is this slowing effect? Could mRNA folding account for the bulk of the variance in ribosomal speed observed along transcripts? We find, again comparing the slowest and fastest translated regions within a given mRNA, that not only is secondary structure incapable of systematically explaining the slowest regions of translation, but the presence of secondary structure decreases as the difference between the ribosomal density (i. e. , difference in translation speed) of the two intra-transcript windows increases (Table 1C). Hence we conclude something other than mRNA folding must be responsible for the greatest slowing within transcripts. We performed a parallel version of the codon cluster analysis to look for changes in ribosomal density after differently sized clusters of encoded positive charges (see Methods, “The Average Effect of Positive Charge on Ribosomal Densities”), calculating the average relative change in ribosomal density within a transcript (rpos/rprec30) after positively charged residues (lysine, arginine, or histidine) are added to a nascent peptide chain. The effect, note, should be a stalling after the codon specifying the charged amino acid as the stalling process is hypothesized to be an interaction between the charged amino acid and the charged exit tunnel [26], [39]. We find that a single positive charge will slow the ribosome relative to the preceding sequence (Figure 5), regardless of whether the codon encoding the residue is A/G- or C-rich (Figure S11). Our findings show that at maximum (in real transcripts), ribosomes are more than twice as likely to be found at a given region of the transcript as before the addition of the cation to the polypeptide (Figure 5). The higher the density of positive charges in a peptide, the proportionally greater the effect (Figure 3C), in agreement with experimental findings that increasing the number of positive charges locally correspondingly increases ribosomal dwell time [26]. Our estimation of charge-induced pausing is conservative since some ribosomal density after charges is not included in the analysis if the mean ribosomal occupancy of the 30 codons preceding a charged cluster is 0 for a given transcript (our method in this case would require division by 0). We can also test whether charge is responsible for slowing by noting that the pKa, and hence overall net charge, of histidine is lower than that of either arginine or lysine at physiological pH. Thus we should expect a weaker slowing effect due to histidine residues being added to the polypeptide. When we re-calculate the slowing effect after a single positive charge (as shown in Figure 5, first panel), but separate the single charges according to whether or not they are histidine, we indeed observe that histidine causes weaker slowing (Figure S12). The slowing effect after a single histidine residue, as calculated using the area under the curve method, is anywhere from 25%–78% (95% CI) of the slowing found after a single lysine or arginine. As histidine is used much less frequently than either of the other positively charged residues, we consider slowing after single positive charges to be the best comparator due to the larger sample sizes available. When we separate larger positive charge clusters according to their histidine content (at least one histidine in the two- or three-charge clusters, and at least two histidines in the four- or five-charge clusters), we note that the slowing due to the histidine-enriched group is always lesser than that after the histidine-free group (Figure S12). If charge is a major determinant of ribosomal slowing, then it should be capable of explaining the regions of greatest translational pausing within transcripts (see Methods, “The Relative Contributions of Charge, Folding, and Codon Usage to Extremes of Slowing within Transcripts”). We find this is indeed the case. Of all the putative slowing features we consider, only positive charge is more often associated with the higher occupancy window within each transcript (Note S2). Breaking the comparisons into quantiles according to the magnitude of difference in ribosomal occupancy between each pair of windows further reveals that positive charge is the feature most often responsible for not just slowing when comparing between transcripts, but the greatest magnitude of slowing within any given mRNA. As the difference in ribosomal occupancy between the two windows increases, the window with the higher ribosomal occupancy tends increasingly to be the one with more positive charges (Table 1D). In fact the only clearly significantly overused amino acid in the higher occupancy windows is lysine, which is positively charged (Figure S13). This increase in ribosomal occupancy cannot be explained by physiochemical properties of other amino acids, namely hydropathy, negative charge, or polarity (Tables S6, S7, S8, S9). We note that even when both windows in a transcript have the same number of charges each, there is no predominant influence of tAI, rare codon pairs, or RNA structure on ribosomal slowing (Tables S10, S11, S12). The positive charge effects seen above could potentially be explained as covariate to codon usage bias, were, for example, codons specified by rare tRNAs especially abundant near those specifying positively charged residues. Given the absence of evidence for codon usage bias to affect translation rates, this now seems unlikely. To nonetheless test whether this is the case, we examined patterns of codon usage in the vicinity of positive charges similarly to the manner in which we investigated changes in ribosomal occupancy after positively charged clusters above. Thus if nonoptimal codon usage were causing the slowing patterns after encoded positive charges observed in Figure 1, we should see, on average, a relative decrease in tAI in those sites with elevated ribosomal occupancy. Contrary to this expectation, however, the trend for ribosomal occupancy to increase after positive charges (Figure 5) is independent of patterns of codon usage (Figure S14). It is also possible the slowing effects observed after positive charge clusters in Figure 5 occur ancillary to mRNA secondary structure, as such structure may have some slowing effect (Figure 4). Again we allow for mRNA folding to impede the flow of ribosomes starting either locally or 10 codons upstream (in the case that local double-strandedness creates a structure within the transcript that sterically occludes ribosomes from progressing further toward codons within the folded structure). We find that patterns of transcript secondary structure near positive charge clusters are unable to explain the pausing after translation of positive charges (Figure S14). Hence we argue that mRNA folding cannot explain the slowing seen in Figure 5, which is perhaps not surprising given its apparently weak effect on the whole (Figure 4). Given that positive charge slows ribosomes, we should expect that some of the (relatively weaker and/or inconsistent) ribosomal slowing at rare codon clusters or transcript secondary structure might in fact be due to the presence of uncontrolled-for positive charge. We find this to be the case. When groups of rare codons that are followed by either a lesser or greater number of positive charges are plotted separately, it is clear that rare codon clusters do not in and of themselves slow ribosomes (Figure 2B) but that the apparent (yet unsystematic) slowing in Figure 2A is in fact due to the presence of positive charge after some of the codon clusters (Figure 2C). Similarly, sorting by the number of positive charges present after a cluster reveals that some of the slowing observed at structured regions of transcript is likely due to previously unaccounted-for positive charge (Figure 4).
We find that codon usage and transcript secondary structure do not substantially affect ribosomal velocities systematically across endogenously occurring transcripts. Although it has been suggested that amino acid starvation might increase the ability of codon usage to modulate ribosomal speed [37], we find no such effect upon examination of ribosomal footprints taken from amino-acid-starved yeast (Figures S8, S9, S10 and Table S5). We do not, however, wish to assert that codon usage and RNA structure can never affect translation rates. Certain secondary structure configurations may substantially impact ribosomal flow. As regards codon usage, if we return to the original logic by which codon usage was thought to affect translation rates, we can both see where the prior logic was misleading and in turn can predict when codon usage should slow ribosomes. The classical logic supposes that because common codons are specified by abundant tRNAs, the waiting time for the ribosome to capture the necessary tRNA must be lower for “optimal” or common codons. The key parameter, however, to determine waiting time is not the absolute tRNA abundance (as often considered) but the tRNA availability. We note, similarly to Qian et al. [16], that if codons are used in proportion to tRNA availability [40], then this could dampen any pausing effect, since rare codons matching rare tRNAs will not be as rate-limiting as if they were used more often. Put differently, if highly abundant transcripts all require the same tRNA, then this acts as a drain on the availability of that tRNA. This can be described in terms of supply and demand economics. In the case of rare codons in lowly expressed transcripts, the supply (the pool of tRNA) is small and the demand (number of codons requiring that tRNA at any given time) low. For a common codon in an abundant transcript, the supply (tRNA pool) is large but the demand is also large. We can then imagine an equilibrium situation in which the ribosome waiting time is the same for all codons as the demand (absolute codon abundance in transcripts) and supply of tRNAs are balanced. This is consistent with our observation that, under normal growth conditions, codon usage does not predict ribosome occupancy. However, the same model can predict that under abnormal conditions, we might see an effect as the situation has been forced far out of supply–demand equilibrium. Greatly overexpressing a transcript rich in rarely used codons should slow the ribosome as the demand for the rare tRNAs now exceeds supply. Likewise, we expect that gross modification of tRNA pools should have gross effects on translational speed as the system has been shifted away from the demand–supply equilibrium. This distinction between normal (equilibrium) and experimentally forced (nonequilibrium) conditions makes good sense of the prior literature, where reports of an effect of codon usage on translational velocity involved experimentally forced conditions (for review, see Note S1). Further evidence that the impact of codon/tRNA abundance is buffered comes from the report that some codons whose aminoacyl-tRNAs are selected either intrinsically rapidly or slowly by the ribosome have either low or high tRNA concentrations within the cell, respectively [41], suggesting that intrinsic differences in the translation speeds of certain codons are not accentuated but rather compensated for. The evidence for codon usage/tRNA buffering indirectly suggests either that some property other than speed causes selection on codon usage (e. g. , accuracy of translation [18]–[21]) or that selection for speed occurs when the demand–supply balance is perturbed, for example when selection acts on growth rates and favor duplications of tRNAs. That codon usage also has little or no effect on ribosome velocity in mammals [15] as well as yeast is then, in retrospect, perhaps not so unexpected. Our results are consistent with the interaction of the cations in the protein with the ribosomal exit tunnel [25], [26], a model supported by the stalling being displaced from the location on the mRNA of the codons specifying the positive charge. Our results also indicate that positive charge, more than other chemical or biophysical properties of amino acids (see Tables S6, S7, S8, S9), is key. While some highly conserved amino acid sequences have been shown to interact with the ribosomal tunnel to stall translation in order to regulate the specific gene product they control (see, e. g. , [42]–[44]), our results suggest a fundamental feature of proteins that slows ribosomes regardless of sequence context (either the local amino acid sequence or the gene in which they reside) and without the addition of trans acting factors. A general slowing of translation due to positive charge has ramifications for the evolution of the poly-A tail. If translated, the poly-A tails results in a long run of positively charged lysines. This is expected to stall run-on ribosomes [39]. This stalling may glue the aberrantly translated peptide to the ribosome, preventing potentially toxic products from diffusing into the cell and/or permit tagging of the peptide in the nascent chain–ribosome complex with a signal for degradation, as observed [26], [39]. Our results are consistent with translation of poly-A tails stalling ribosomes. Extrapolating the linear trend for larger clusters of positive charges to additively slow ribosomes (reported in Figure 3C), we note that a poly-A tail of 80 consecutive adenines (∼27 lysines) in yeast [45] should slow translation at least 4-fold more than that observed in clusters of six or more positive charges (Figure 3C), probably halting it. This is in line with experimental work showing that while nonstop mRNAs without poly-A tails are efficiently translated [46], translation of polyadenylated mRNAs lacking stop codons or full 3′UTRs is repressed after initiation [47]. Similarly, inserting a poly-A tract into a coding sequence represses translation post-initiation, but not on account of rapid mRNA decay [39]; a similar finding was reported for 3′ poly-A tails [48]. Recently, it was shown that translation of 12 consecutive basic amino acids inserted into a reporter gene causes not only translation arrest but degradation of the polypeptide [49]. Why is the tail poly-lysine if any positive charge will do? The reason is likely to be found at the DNA sequence level. Of all codons encoding positive charges, only lysine possesses a codon that is a triplet repeat of a single nucleotide (AAA) and therefore may be added simply and sequentially by a single enzyme. Moreover, the triplet repeats form a homogenous run of adenines, meaning that positive charges will still be added to the nascent chain (and hence stall ribosomes) no matter how the stop codon is missed, be it by failure to interpret the stop when in-frame or owing to frame-shifting. This may have less relevance in species with long 3′UTRs, in which an alternative stop may be found with the UTR, but in the ancestor in which the poly-A tail evolved, if 3′UTRs were short, then this sandtrap for ribosomes may have been of considerable benefit. It is noteworthy that bacteria, which for the most part lack poly-A tails, have an alternative mechanism (tmRNA) to tag and destroy proteins resulting from frameshifting or stop codon readthrough [50]. Stalling initiated by positive charges resulting from translation of poly-A tails in eukaryotes and tmRNA system in prokaryotes may be functionally equivalent modes of error correction [51].
Both sequenced ribosomally protected fragments and sequenced fragmented total mRNA for S. cerevisiae dataset GSE13750 [29] were downloaded from the NCBI Gene Expression Omnibus at www. ncbi. nlm. nih. gov/projects/geo. The rich media and amino-acid-starved sets were considered separately. Annotations of the S. cerevisiae S288C genome as available on June 22,2008 (the build used by Ingolia et al. [29]) were obtained from the Saccharomyces cerevisiae Genome Database (www. yeastgenome. org). Only protein-coding sequences of nondubious classification were considered, giving 6,262 genes for potential analysis. Any sequences containing nonsense codons or that were not multiples of three were excluded. The sequences were further filtered to only allow the standard or alternative start codons indicated in NCBI genetic code Table 1 from http: //www. ncbi. nlm. nih. gov/Taxonomy/taxonomyhome. html/index. cgi? chapter=tgencodes, leaving 6,215 sequences for analysis. The chromosomal location and coordinates of the sequenced fragments given in the original dataset were used in combination with the start and stop coordinates of genes from the annotations to determine which fragments map to which genes, and in the case of the footprint fragments, where along the coding sequence the protected area lies. Since the probability of sequencing error in a stretch of ∼28 nucleotides is quite low (for runs <50 bp on Genome Analyzer 2, error rates are expected to be around 1%), only one mismatch between the sequenced fragment and reference genome sequence was allowed. All fragment counts were taken as the average value of the two experimental replicates. Fragments that were sequenced at least once in one replicate but not listed in the other were marked as having an expression count of 0 at analogous positions in the latter replicate. In the case of fragments that map to more than one possible genomic location, it is impossible to tell which are the true areas covered by ribosomes. In order to avoid the introduction of false-positive ribosomal occupancies en masse into the dataset, which could systematically bias the types of sequences that are occluded, only footprints that mapped uniquely to one location in the reference genome were considered. In line with Ingolia et al. [29], we assigned footprints to protein-coding genes of nondubious classification if the first base of the footprint mapped to 16 nt before the first base or 14 nt before the last base of the gene, in order to take account of which area of the footprint is likely in the ribosomal active site. Since the chance of sequencing another fragment from a stretch of coding sequence increases as a function of gene length, mRNA fragment counts were normalized by dividing by gene length for the relevant gene. In addition, the footprint counts were then divided by the normalized mRNA counts mapping to that gene to obtain per-transcript ribosomal densities (indexed by location along the transcript). We performed this normalization by mRNA to ensure that differences in occupancies we calculate (see Methods, “The Average Effect of Positive Charge on Ribosomal Densities”) are not an artefact of mRNA levels. This left us with a final 5,430 filtered genes with footprint coverage mapped per codon pair per transcript. We note that there are two previous studies [27], [28] that examined this ribosomal footprint data [29] and found a role for codon usage in modulating ribosomal speeds, mainly by detecting a correlation between the local codon optimality along a transcript and the corresponding local density of ribosomal footprints. We cannot offer a reason why these studies produce such a finding, namely because they do not detail their methodology concerning the ribosomal footprint data, including whether they used all or just a subset of all the sequenced footprints (e. g. , dependent on footprint length, the number of mismatches to the genome reference sequence allowed, or the number of places in the genome to which a single footprint could simultaneously map). Another study [16] that examined the same data contradicted the finding that codon usage affects ribosome velocity, highlighting the importance of methodology in the analysis of ribosomal profiling data. This opposing study [16], however, may have mapped footprints to multiple genomic locations and also considered only footprints 28 nt in length in an attempt to precisely map which codon is in the A-site and hence selecting an aminoacylated tRNA. We are not confident that the interpretation of ribosomal footprint data allows for such specificity, as the interpretation of where the A- and P-sites are along the ribosomal footprint seems to be inferred from the average footprint length obtained during initiation and termination [29], whereas the conformation of the ribosome and hence footprint obtained during elongation may differ. Also, it has since been noted that elongation inhibitors, such as cyclohexamide, which was used in the creation of the dataset under consideration [29], alter the conformation of the ribosome, leading to advised caution in determining position-specificity from individual footprints [15]. For these reasons, we consider it optimal to stringently map footprints to a single location in the genome, thus preventing the introduction of a false correlation between certain codons and ribosomal density, and to consider all of the sequence that is occluded by the footprint instead of attempting to pinpoint the location of a structural site in the ribosome from the artefact of the footprint. To determine what determines occupancy, we could consider some general linear model in which we employ multiple parameters (local codon usage, local RNA stability, and local charge density) to predict occupancy on a codon-by-codon basis. However, such models assume that the data points are independent. Owing to the nature of the data (ribosomes sit over spans of sequence), the occupancy seen at one codon by necessity is nonindependent of that seen at neighboring codons. Thus, such methods are not generally valid. To overcome the nonindependence problem, we do not consider each codon as a separate data point. Rather we consider the dimensions of the spans of increased relative occupancy and consider how trends in the dimensions of these spans correlate with the density of the potentially slowing feature in question, an approach that we outline in Figure 1 and below. We consider for any feature (e. g. , a cluster of codons or positive charges) the start position of this feature (position x = 0). We then define for each codon at and after the start of the feature (x≥0) how the occupancy is related to the mean occupancy of the 30 codons upstream of x = 0 within the same mRNA as the feature. We define the relative occupancy of any given codon (rpos/rprec30) as its occupancy (rpos) divided by the mean occupancy of the 30 codons prior to the considered feature (rprec30). For plotting purposes, we also normalize the occupancy of all codons 5′ of the focal position at x = 0 by the same rprec30 value. Dividing by pre-cluster ribosomal densities to obtain a ratio normalizes for differences between transcripts such as expression level, accommodates and normalizes for differences in ribosomal density that may be caused by characteristics of upstream sequence, and allows for comparisons of the relative change in ribosomal movement across different mRNAs. These relative occupancy ratios, which we calculate surrounding every identified feature, thus represent the speeding (if the ratio is <1) or slowing (if the ratio is >1) of ribosomes after a given feature as they translate that portion of that gene. After calculating the relative ribosomal occupancy ratios surrounding a feature within all available mRNAs, we then align these mRNAs by the start of that feature and calculate the average relative occupancy ratios across transcripts. We plot these averages such that y at codon x = 1 is the mean ratio of all the observations across multiple RNAs at x = 1, codon x = 2 is the mean ratio across multiple RNAs at x = 2, and so on. These plots then present a span of increased relative occupancy or of decreased relative occupancy following the start of the feature at x = 0 across all instances of that feature available to our analysis. As tRNA gene copy number has been shown to strongly correlate with tRNA abundance [27], [52], preferential use of codons that base-pair to the anticodons of high-copy tRNAs is taken to reflect adaptation of coding sequence to the tRNA pool and hence optimal codon usage for translational efficiency and/or accuracy. Each codon can then be ascribed an adaptiveness value (Wi) [32]. The tRNA adaptation index (tAI) is the geometric mean of the scores for the constituent codons and is then a measure of the degree to which protein-coding genes use codons corresponding to tRNA isoacceptors with high gene copy numbers within a given genome (although, see also Figures S1, S2, S3 and Table S1 for analyses of rare codons where “rare” is defined as genomically infrequent) [32]. The codonR package to calculate tAI was downloaded from http: //people. cryst. bbk. ac. uk/~fdosr01/tAI/index. html on May 7,2011. Yeast tRNA genes were obtained from the UCSC Table Browser [53] at http: //genome. ucsc. edu/cgi-bin/hgTables. Statistical calculations for the tAI (and for other analyses generally) were done in R [54]. As a gene with just one codon would have a tAI value equal to Wi of that codon, we refer in the text to a codon' s tAI value. In the main text we define rare codons to be those in the lowest quartile of Wi values as derived for yeast (CGA, ATA, CTT, CTG, CTC, CGG, AGT, CCC, GCG, AGC, CCT, TCG, TGT, ACG, and GTG). We interchangeably use “rare” for “non-optimal” as the frequency of codon usage in yeast is roughly proportional to the numbers of tRNAs that can decode them [40]. Protein-coding sequences were scanned for single rare codons; two rare codons anywhere within a five-codon stretch, three rare codons within eight codons, four or five within 10, and six or more within 16. The cluster specifications outlined here were chosen to maximize cluster sample sizes while incorporating the following caveat: we required that a block of 30 non-rare codons had to precede the identified codon clusters and that no other rare codons could be present in the next 30 codons apart from those in the identified cluster. When investigating consecutive rare codon clusters in a parallel analysis, we required that no consecutive rare codons could be present in the surrounding ∼60 codons apart from those in the identified cluster (single rare codons were permitted as otherwise sample sizes would be far too small). As noted above, the first rare codon in the cluster is always considered to be at position x = 0. As there are not enough rare codon clusters that are isolated from the ribosome-slowing effects of positive charges, we were unable to introduce the requirement that no positive charges be present in the vicinity of the rare codon cluster. Instead, we split the rare codon clusters into two groups—those that had two or more positive charges coded for in the sequence following the rare cluster, and those that had either zero or one positive charge—and plotted the results for these groups separately. As noted above, we perform a normalization with respect to the local ribosomal occupancy. Within a given mRNA, the relative increase or decrease in ribosomal density (rpos/rprec30) at each position surrounding a rare cluster was calculated by dividing the measured ribosomal density at each codon position (rpos) by the average ribosomal occupancy of the thirty codons preceding the first rare codon in the cluster (at position x = 0) within that same mRNA (rprec30). The average relative change in ribosomal occupancy (mean rpos/rprec30) at a given position during/after a cluster was then calculated by aligning all identified regions of a given cluster size according to the first codon present in each cluster and calculating the average ratio (i. e. , increase or decrease in measured ribosomal occupancy) in positions increasingly distant from the aligned clusters (a schematic of this approach is contained in Figure 1). We used experimentally and not computationally determined RNA structure data. By exposing transcripts independently to endonucleases specific for single- and double-stranded RNA, the degree to which individual nucleotides of an mRNA are involved in intramolecular secondary structure has been experimentally quantified [38]. The resulting metric is “parallel analysis of RNA structure” (PARS) values, with higher values (positive) indicating a propensity for secondary structure and lower (negative) values signifying lack thereof. The authors show the PARS metrics along transcripts in yeast globally correlate with the degree of single- versus double-strandedness predicted by the Vienna Package. PARS values for yeast transcripts were downloaded at http: //genie. weizmann. ac. il/pubs/PARS10/pars10_catalogs. html. PARS values corresponding to CDS regions were determined relative to the local coordinate file from the same website. S. cerevisiae protein-coding sequences were scanned for stretches 30 codons in length whose average PARS value was 0 or negative (and hence tending to be single-stranded), which were immediately followed by a block 31 codons in length whose average PARS value was positive (i. e. , with propensity for double-strandedness). To ensure a clear transition from single- to double-stranded structure upon averaging across transcripts, we added the requirement that the last codon in the first 30-block have a negative PARS value and that the first codon in the subsequent 31-block have a positive PARS value. Only nonoverlapping blocks (61 codons in length) were retained, with priority given to those with the highest combined number of negative PARS value in the first 30 codons and positive PARS value in the latter 31 codons. The general contribution of folding to slowing was then examined by calculating rpos/rprec30 (as described above) at each position and then taking the average across aligned single-stranded into double-stranded blocks. In the first instance of such a test, we investigated the hypothesis that the ribosome closely approaches the base of the double-stranded structure such that the ribosome is positioned closely over the first double-stranded ribonucleotide (i. e. , at the beginning of the 31st codon out of 61) by the time slowing occurs. Here the first 30 codons in the identified block are classed as the preceding 30 codons before slowing might occur. We then repeated the analysis examining whether pausing of the ribosome might occur somewhat further upstream—for example, if the mass of the ribosome sterically hinders it from progressing at its normal rate even before the double-stranded ribonucleotide approaches the active site. In this second analysis, we used the same identified blocks as above, but moved the potential point of slowing to 10 codons upstream of the first codon with a positive PARS score. Hence the preceding 30 codons used in this case to normalize nearby ribosomal densities were also shifted 10 codons upstream as well. Changes in rates of translation were measured by calculating the relative change in ribosomal densities that occurs within a transcript, on average, after positively charged residues (lysine, arginine, or histidine) are added to the nascent peptide chain. Such an effect should be observed at or after the encoded charge (s) in the mRNA as the positively charged amino acid travels down the exit tunnel. To test for an additive effect of charge on ribosomal density, S. cerevisiae protein-coding sequences were scanned for single positively charged amino acids, two positively charged residues anywhere within five amino acids, three positively charged residues within eight amino acids, four or five positively charged amino acids within 10 amino acids, and six or more positive charges within 16 amino acids, with the first positively charged residue always considered to be at x = 0. As in the case of the rare codon cluster analyses, these loosely defined cluster specifications were chosen to maximize the sample sizes available of clusters containing different numbers of positive charges. To eliminate interference from charged amino acids outside these charged clusters, we required that a block of 30 non-positively charged amino acids precede the identified positive-charge clusters, and that no other positively charged amino acids be present in the next 30 amino acids apart from those in the identified cluster. Thirty residues were chosen as this is approximately the length of extended peptide that the ribosomal exit tunnel can accommodate [55], [56]. Thirty non-basic residues therefore should provide a baseline ribosomal occupancy reading, and hence inference of the speed of translation, before the positively charged residues are added to the peptide chain and enter the exit tunnel. The relative increase or decrease in ribosomal density at each position (rpos/rprec30) was calculated for each transcript with an encoded positive-charge cluster. The average relative change in ribosomal occupancy (mean rpos/rprec30) at a given position during/after a cluster was then calculated across regions aligned by similar-sized clusters (see also Figure 1 for a visual of this approach). Regarding our methodology, we find that noise in footprint density is not a problem for our analysis as we see similar findings when we consider genes with either low or high footprint coverage (Figure S15). The above methods start by locating the appropriate putative ribosome-slowing feature within transcripts and then measures changes in ribosomal occupancy surrounding them. A complementary approach is to look to large changes in ribosomal density and then ask whether positive charges or rare codons are more often associated with the denser, putatively more slowly translated regions. Such an approach is best carried out on a within-mRNA level, as this normalizes for differences in overall expression levels across genes. Within each gene for which we retained ribosomal protection data (see Methods, “Ribosomal Density Data”), we located the two nonoverlapping 10-codon windows (approximately the length of RNA a ribosome footprint spans [29]) with the highest and lowest average ribosomal occupancy in that transcript. To circumvent the arbitrariness of choosing the location of the low-occupancy 10-codon window in a transcript for which there may be multiple possible windows with no footprint data available (i. e. , a footprint count of 0), we added the requirement that experimental protection data exist for each codon in the window. For each window in the pair, we recorded the average ribosomal occupancy as well as (1) the tAI; (2) the number of adjacent, nonoverlapping pairs of rare codons; (3) the number of positive charges encoded within and up to five codons upstream of the window (since a charge added while the ribosome was a few codons upstream should still be present within the exit tunnel); (4) the number of rare 6-mers (defined to be the lowest 10% of all possible in-frame 6-nt sequences within open reading frames); and (5) propensity for transcript secondary structure. We included 6-mers here, as while individual codons may be rare, it does not necessarily follow that two adjacent rare codons are just as rare of a combination, and thus examining the contribution of rare 6-mers provides an extra level of stringency in assessing the role of codon usage. As secondary structure either at the codon in question or downstream of the codon in question might pause ribosomes (see Results), we considered the PARS values not only within but also for an additional 10 codons downstream of each originally identified 10-codon window. If codon usage bias is modulating ribosomal speed, we expect to observe the main effect over and locally surrounding the codon in question, whereas we expect, if charge indeed is influencing ribosomal velocity, to observe a downstream effect of positive charge on ribosomal density as the cation travels further down the negatively charged exit tunnel. Thus we also counted any positive charges encoded in the five codons immediately preceding the identified windows since a charge added while the ribosome was a few codons upstream should still be present within the exit tunnel. Conversely, as secondary structure either at the codon in question or downstream of the codon in question might pause ribosomes (see Results), we considered the PARS values not only within but also for an additional 10 codons downstream of each originally identified 10-codon window. Since the interpretation of PARS values may be somewhat more labile (as not only the magnitude but the sign of the values may have meaning), we tested whether double-stranded structure might associate with the more dense window in two different ways. We used two methods of measuring propensity for transcript structure. In the first method (“PARS score”), an average PARS value for a window ≤0 means the window is single-stranded, and an average PARS value >0 means the window is double-stranded; the exact magnitude of the PARS value is disregarded beyond this. In the second method (“conservative PARS score”), smaller changes in the magnitude of PARS values count more: the mean PARS value is calculated for each extended (20-codon) window, and the means are then compared. The ability of each metric to explain the difference in average ribosomal occupancies between the two windows was then assessed by asking how often the window with more of a potentially ribosome-slowing feature was also the window with greater occupancy. For example, if the window with the higher ribosomal occupancy paired with the less optimal (lower) tAI—which would be expected if less optimal codons do in fact slow ribosomes—then the gene was assigned a tAI score of 1; if the higher occupancy window paired with the more optimal tAI, indicating tAI is not a good predictor of increased occupancy, the a tAI score of −1 was assigned; and if the tAI was the same in the two windows, a score of 0 was given. Similar tests were performed independently on the number of rare codon pairs, PARS metrics, and number of positive charges associated with each window, with more rare pairs/more positive charges in the more occupied window—and hence potentially capable of explaining the elevated ribosomal density—each being scored 1, fewer being scored −1, and the same number in each window scored 0. There are two potential complications of this method that we are able to address and dismiss. Firstly, although there is a tendency for ribosomal occupancy to decrease, on average, along the length of transcript [29], the correlations we report hold when we test only transcripts in which the high ribosomal occupancy windows are downstream of the low ribosomal occupancy windows (higher occupancy and increased positive charge, Spearman rho 0. 12, p = 0. 00031; higher occupancy and an excess of rare pairs, Spearman p = 0. 62; an excess of rare 6-mers, rho = −0. 06, p = 0. 07; higher occupancy and lower tAI, Spearman rho −0. 15, p = 2. 4e-05). This, along with the additive pattern in Figure 3C, shows that the correlation between positive charge and increased ribosomal density is not methodological artefact. Secondly, a window might have an apparently low average ribosomal occupancy if in fact there were ribosomal footprints that should have been assigned to that region of the transcript, but which was ultimately excluded from the analysis if the footprint mapped to multiple genomic locations. To this we note that the same analysis, when redone allowing the low-occupancy window to have a footprint count of zero, still gives similar results (Table S13). To further test this possibility, we created a list of nonredundant locations. These are sites in each transcript for which all mapping footprints were uniquely mapping to that location. In other words, no footprint data were excluded from being mapped to these sites because it also mapped somewhere else in the genome. Redoing the window comparisons analysis using the nonredundant locations, we find the results (Figures S16, S17 and Table S14) qualitatively match our original results using the dataset described above (see Methods, “Ribosomal Density Data”). Hence, we consider that our method fairly infers the contribution of different sequence features to ribosomal slowing.
Ribosomes do not synthesize protein at a constant rate along transcripts, and changes in translation speed can have knock-on consequences for the expression of that protein, even altering its folding or subcellular localization. It has long been thought that RNA-level features modulate translation rates, whether by delays incurred through the presence of codons that require relatively rare tRNAs, or by regions of mRNA folding that physically impede ribosomal progression. We find on the contrary that it is not RNA-level features but positive charges in the already translated protein that most retard ribosomes, possibly by interacting with the negatively charged ribosomal exit tunnel. We show that positive charge explains the sites where ribosomes stall most commonly within transcripts. We also show why, if protein charge were not considered, one could be misled into suspecting a role for non-optimal codons. Finally, we observe that the poly-A tail provides a massively positively charged terminus no matter in which frame it is translated. A missed stop codon or frameshifting would then lead to a stalled ribosome, which is consistent with experimental data.
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Positively Charged Residues Are the Major Determinants of Ribosomal Velocity