Inactivation of eRF1 promotes readthrough of both normal and premature stop codons and accumulation of ribosomes at those codons

We performed ribosome profiling and total RNA sequencing of isogenic wild-type (WT) and eRF1 mutant yeast cells. The latter harbor the sup45-2 (subsequently referred to as sup45-ts) mutation that minimally affects eRF1 function when cells are grown at 25°C, but renders eRF1 inactive within 30 minutes of growth at 37°C [39]. Reads between replicates were reproducible, with Pearson’s correlation coefficients (r) of 0.98 and 0.99 on average for ribosome profiling and RNA-Seq, respectively (S1 Fig). In addition to the profiling data obtained from these experiments, we also analyzed published ribosome profiling data obtained from yeast cells depleted of eRF1 via specific transcription shutoff [40] (S1 Table). For comparison to ribosome occupancies in the 3’-UTR that are not due to termination defects, we included analyses of ribosome profiling data obtained from cells that are depleted of the ribosome recycling factor, Rli1 [41] (S1 Table). In these cells, full-length newly synthesized protein is properly released, but the ribosomes fail to recycle, then reinitiate randomly downstream of the stop codon, and generally produce a peptide independent of the original ORF [41]. To evaluate 3’-UTR translation, ribosome footprints mapped to genes that have no experimental UTR annotations and genes that overlap by more than 18 base pairs on the same mRNA strand (where read assignment is ambiguous) were discarded from further analyses. With these filters, we were left with 2,693 genes, or ~41% of the yeast genome, for analyses. This set of genes is referred as the reference gene set.

To explore the consequence of eRF1 inactivation on translation, ribosome footprints were mapped relative to their respective start or stop codons based on their P-site locations and quantified (Fig 1A–1D). In all strains, footprints in the coding (CDS) region showed 3-nt periodicity, a profile expected of translating ribosomes. The absence of footprints at the penultimate codon of the open reading frames (ORFs) in SUP45 cells at 25°C indicated that, with the stop codon in the A site, termination was completed, and ribosomes were dissociated from the mRNAs before they could be captured. The presence of footprints at this position in sup45-ts cells at 25°C suggested that the mutation slowed down termination enough for the ribosomes to be captured. When eRF1 was inactivated in sup45-ts cells grown at 37°C, or depleted in sup45-d cells, a high number of footprints was observed at the penultimate codon, indicating that ribosomes were stalling as they awaited successful termination. Similarly, the high number of footprints in rli1-d cells was also consistent with ribosome stalling, as the stop codon had entered the ribosomal A site, but a factor needed for successful recycling was lacking. Additionally, the peaks at approximately 30 nt upstream of the stop codon in sup45-ts cells at 37°C and sup45-d samples identified ribosomes stacking against those stalled at the stop codon. As a consequence of failed termination or recycling, the mutant libraries, especially from the sup45-ts cells at 37°C, had increased footprint reads in the 3’-UTR region compared to those of WT samples (Fig 1A–1D, insets), implying stop codon readthrough, frameshifting, or reinitiation events. Although inactivation (sup45-ts cells at 37°C) and depletion (sup45-d) of eRF1 both caused loss of eRF1 function, these two different strategies likely resulted in loss of eRF1 function to different extent and yielded slightly different metagene profiles. While the sup45-ts strain lost normal eRF1 function within 30 minutes of the temperature shift, the sup45-d strain needed 9 hours of growth after transcription shutoff to gradually dilute the functional eRF1 protein level. In eRF1 depleted cells, although the level of functional eRF1 was significantly decreased, small amount of eRF1 still existed and was fully capable of promoting termination. The residual functional eRF1 in sup45-d cells likely resulted in a diminished quantity of 3’-UTR reads compared to those obtained with the sup45-ts strain. In addition to increased reads near the stop codons, all the mutant samples also showed increased footprint reads at the start codon and decreased footprint reads in the CDS compared to their WT counterparts. These observations are in agreement with the notion that termination and recycling steps are linked to translation initiation [3].

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Fig 1. Ribosome occupancy at and beyond the stop codon increases in yeast cells defective in translation termination or ribosome recycling.

A-D. Ribosome footprints (2,693 genes with 3’-UTR annotations and without sequence overlapping) normalized to the total number of footprints in the indicated nt window (-20 to 40 nt around the start codon and -40 to 80 nt around the stop codon) aligned at their start or canonical stop codons. Footprints were plotted by the position of their P-sites. Each panel contains the data from WT and its respective mutant. The elevated number of footprints at the penultimate codon of the ORFs in mutant samples demonstrates ribosome stalling when the stop codon was in the A site. (The footprints at this codon and their shift to reading frame +1 in other yeast strains obtained from published data may be attributable to the differences in strain background or sequencing library preparation procedures.) Inset: Magnified view of the region in the box, showing increased 3’-UTR ribosome occupancy in sup45-ts cells at 37°C, sup45-d, and rli1-d cells relative to their respective WT. E-F Ribosome footprints normalized to the respective mRNA levels mapped across the ORF of two PTC-containing alleles, ade2-1 (E) and can1-100 (F), in SUP45 and sup45-ts strains at 25°C and 37°C.

https://doi.org/10.1371/journal.pgen.1009538.g001

We also examined the effects of eRF1 inactivation on premature translation termination. Two alleles in our strain background, ade2-1 and can1-100, contain premature termination codons (PTCs). Their gene specific profiles revealed an accumulation of ribosomes 5’ to the respective PTCs and increased ribosome density in the part of the CDS following the PTCs in sup45-ts cells grown at 37°C (compared to sup45-ts at 25°C or to WT cells at either temperature) (Fig 1E and 1F). This observation is further supported by computing the ratio of in-frame footprint counts downstream vs. upstream of PTCs, a measurement of readthrough efficiency that automatically takes into account the differences in mRNA levels between samples (S2A Fig). For the PTC in ade2-1, sup45-ts cells grown at 37°C showed ~32-fold and 17-fold higher readthrough efficiencies relative to WT cells grown at 37°C and sup45-ts cells grown at 25°C, respectively. For can1-100, sup45-ts cells grown at 37°C showed >100-fold and 26-fold higher readthrough efficiencies relative to WT cells grown at 37°C and sup45-ts cells grown at 25°C, respectively. Together, these data show that readthrough of PTCs also occurs in the absence of functional eRF1. Further evidence for PTC readthrough includes the gene-specific profiles for RPL28 and RPS0A (S2B and S2C Fig). Intron-containing transcripts from both of these genes enter the cytoplasm and their translation stalls and triggers NMD (nonsense-mediated mRNA decay) when ribosomes encounter PTCs within the respective introns [42,43]. S2B and S2C Fig show that, for both transcripts, ribosome profiling reads that were generally absent from the respective intron regions in sup45-ts cells at 25°C or in WT cells at either temperature were abundant in sup45-ts cells grown at 37°C and accumulated mostly at in-frame stop codons (yellow rectangles).

To determine whether the increased ribosome density in regions following the stop codons in eRF1 mutants was due to stop codon readthrough rather than frameshifting or reinitiation events, we calculated the fraction of reads in each of the three reading frames in different mRNA regions: 5’-UTR, CDS, the 3’-UTR region between the canonical stop codon and the first downstream in-frame stop codon (“extension”), and the rest of the 3’-UTR (“distal 3’-UTR”) (Fig 2A). If readthrough occurred, we expected i) the amount of reading frame 0 footprints, which represent in-frame translation in the CDS, to also be the dominant reading frame in the extension and ii) the amount of frame 0 footprints in the extension from sup45-ts cells grown at 37°C to be higher than that of their WT counterparts. Indeed, we saw that frame 0, which was dominant in the CDS, was also dominant in the extension, but not in the distal 3’-UTR of most strains (Fig 2B). It is notable that the amount of reading frame 0 footprints in the extension region was less pronounced than that in the CDS, possibly because other recoding or recycling events, such as frameshifting or reinitiation, could also happen at the stop codon that contributed to the fraction of out-of-frame footprints. Nevertheless, compared to the reading frame fraction in the extension region of their WT counterparts, sup45-ts at 37°C displayed notable enrichment of frame 0 reads while sup45-d and sup45-ts at 25°C showed only slight increases of frame 0 reads relative to their WT counterparts (Fig 2B). On the other hand, rli1-d had a reading frame preference comparable to its WT counterpart (Fig 2B). These results indicate that, in cells with inactivated eRF1, a considerable proportion of footprints in the extension region of the mRNAs are caused by stop codon readthrough.

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Fig 2. Readthrough events were characterized by maintenance of reading frame when ribosomes bypass the stop codon.

A. Example of ribosome footprint count plot with labeled mRNA regions of interest. B. Fractions of reads in each of the three reading frames for different mRNA regions. The results of each sample were the average of 2 or 3 replicates, with black dots indicating individual data points. The dashed line drawn at 0.33 is the theoretical fraction at which all 3 reading frames are represented in equal amounts.

https://doi.org/10.1371/journal.pgen.1009538.g002

In summary, the increased in-frame footprints in the extension region of the 3’-UTR in eRF1 mutants indicated that a lack of functional eRF1 led to stop codon readthrough globally. Hence, we developed formal criteria for the quantitation of readthrough of individual mRNAs and a bioinformatic scheme to assess the genome-wide positive and negative cis-acting effects of neighboring sequences to the readthrough of stop codons.

Analysis pipeline development

To dissect the relationships between readthrough efficiency (Y variable) and mRNA or nascent peptide features (X variables), we used the random forest machine learning approach [44,45] to first narrow down the candidate features. We defined each of the variables as follows:

Defining readthrough efficiency (Y variable).

Since termination efficiency is not directly measurable by ribosome profiling we calculated readthrough efficiency and utilized the generally inverse relationship of termination and readthrough (shown experimentally by Wu et al. (2020) [38]) to infer termination efficiency. Our subsequent interpretation of the results was based on this relationship, where high readthrough efficiency would point to low termination efficiency and vice versa in this experimental context. Readthrough efficiency in the context of ribosome profiling can be loosely defined as the ratio of ribosome density (footprint count normalized to the length of the region) in the 3’-UTR to ribosome density in the CDS; however, the nature of the mutants should be taken into account to ensure accuracy and minimize noise. First, due to ribosome stalling and queuing at the start and stop codons in mutant strains (Fig 1A), ribosome density in the CDS may be overestimated; therefore, we excluded footprints whose P-sites fall into the first 15 and the last 33 nucleotides of the CDS of each gene in all samples. Although the stalling of the ribosomes also occurred at the first downstream stop codon in the 3’-UTR, we could not use the same rules of footprint exclusion here because the lengths of many extension regions are smaller than 33 nucleotides. Thus, the apparent readthrough level could be somewhat overestimated. Second, as readthrough is by definition in-frame with the CDS (Fig 2B), only footprints that were in frame 0 were considered. The adjusted readthrough efficiency calculation, log-transformed, is shown in Fig 3A.

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Fig 3. Definition of X and Y Variables.

A. Readthrough efficiency calculation formula, which takes into account the ribosome pile-up at NTCs and reading frame preference. B. mRNA and nascent peptide features of interest in the context of the ribosome and related translation termination components. Previously identified variables are colored and labeled. Hypothesized variables are in purple and marked with “?”. Details of how each variable is measured are in Table 1. Information regarding the effects of particular nucleotides on readthrough efficacy is taken from a recent review, as is the general format of the schematic [5].

https://doi.org/10.1371/journal.pgen.1009538.g003

Additional sources of noise in the data include: 1) poorly-expressed genes that result in an exaggeration of the readthrough efficiency ratios and 2) genes lacking footprints in the 3’-UTR region, for which it was challenging to distinguish lack of readthrough from insufficient sequencing depth. Inclusion of these genes confounded previous analyses and led to conflicting results. For instance, genes belonging in the “leaky” set (genes that had 3’-UTR reads, i.e. readthrough) were highly expressed compared to genes in the “non-leaky set” (genes lacking 3’-UTR reads), but within the “leaky” set, readthrough rate was negatively correlated with gene expression [21]. Therefore, we discarded genes with both RPKM of CDS < 5 and RPKM of 3’-UTR < 0.5 [41] from further analyses.

Defining mRNA and nascent peptide features (X variables).

To date, mRNA features affecting stop codon readthrough have frequently been identified 3’ of the stop codon, but we also considered the possibility that mRNA sequence on the 5’ side of the stop codon and the nascent peptide in the exit tunnel may contribute to variations in readthrough efficiencies (Fig 3B). These features can either be directly documented or inferred from the mRNA sequences (Table 1). Therefore, we used established algorithms to predict RNA secondary structure [46] and nascent peptide α-helix formation [47].

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Table 1. Assessed X variables and their modes of measurement.

https://doi.org/10.1371/journal.pgen.1009538.t001

Other than the mRNA features listed in Table 1, many characteristics of the mRNAs (such as expression levels, codon optimality, translation efficiency, and gene length) were previously correlated with readthrough efficiency measured by ribosome profiling [21,31]. We performed similar analyses involving pairwise correlation between readthrough efficiency, gene expression level (determined by RSEM of RNA-Seq data), translation efficiency (ribosome density divided by gene expression level), length of transcript and mRNA regions, and codon optimality (tRNA adaptation index [48,49]) (S3 Fig). We found that, except for sup45-ts at 37°C, readthrough efficiency was negatively correlated with gene expression and codon optimality, consistent with previous results [21,31]. This correlation may have an evolutionary, rather than a mechanistic, explanation; for example, the inverse relationship between gene expression and readthrough efficiency may result from more deleterious effects of readthrough from highly expressed genes than from poorly expressed genes, and thus highly expressed genes evolved to have lower readthrough efficiency [31]. Since gene expression level was positively correlated with codon optimality across the ORF, where mRNAs with optimal codons are more stable and hence more abundant than those with non-optimal codons [50], we could not make a distinction whether the observed negative correlation between readthrough efficiency and codon optimality was simply due to increased mRNA abundance or was instead mechanistically relevant to the readthrough process. We therefore did not include gene expression and codon optimality in further analyses. Moreover, readthrough measurements in general should be interpreted with caution and due consideration given the caveats described above.

Random forest analyses reveal stop codon identity, P-site amino acid identity, and 3’-UTR length as important factors in predicting readthrough efficiency

In order to identify without bias the mRNA and nascent peptide features affecting readthrough efficiency, we used two independent approaches involving the random forest algorithm [44,45]: 1) random forest regression to predict readthrough efficiency based on the X variables, and 2) random forest classification to differentiate between “High” (top 15%) and “Low” (bottom 15%) readthrough genes based on the X variables. The number of genes in each type of analysis can be found in S2 Table. A regression model and a classification model with 5-fold cross-validation were created for each sample using the same tuning parameters. For the regression model, we used Normalized Root Mean Squared Error (NRMSE) as a measurement of predictive ability of X variables. We observed NRMSE of approximately 0.14 across all samples, indicating that the average difference between predicted and actual readthrough efficiency is 14% (Fig 4A). For the classification model, Area Under the Receiver Operating Characteristics (AUROC) was used as a measurement of model capability at distinguishing between the “High” and “Low” groups. AUROC values ranging from 0.72–0.82, 0.76 on average, were observed across all samples; therefore, the models had ~76% chance of classifying genes into the correct group (Fig 4B).

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Fig 4. Random forest identified stop codon identity, P-site amino acid identity, and 3’-UTR length as factors critical in readthrough efficiency prediction.

A. Performance metric for random forest regression displaying average Normalized Root Mean Squared Error (NRMSE) ± standard deviation across 5-fold cross-validation. B. Performance metric for random forest classification displaying average Area Under the Receiving Operator Curve (AUROC) ± standard deviation across 5-fold cross-validation. The value of 0.5 (dashed line) means the classification by the input X variables is no better than random chance. C. and D. Feature importance extracted from the random forest models. The relative importance of a feature is represented by percent increase in mean squared error (%IncMSE) for regression (C) and Mean Decrease Accuracy (MDA) for classification (D), which is percent increase in error that results from permuting the feature. The higher the %IncMSE or MDA is, the more crucial the feature is in predicting readthrough efficiency or correctly classifying genes into groups. Arbitrary continuous and discrete values randomly assigned to the genes (random number and random factor) are used as negative controls.

https://doi.org/10.1371/journal.pgen.1009538.g004

To determine which of the X variables were most responsible for the predictions, we permuted each X variable for the regression and classification models and assessed its importance by calculating either percent increase in mean squared error (%IncMSE) or Mean Decrease Accuracy (MDA). %IncMSE is the calculation of percent increase in prediction error, while MDA is the percent increase in misclassification, when the X variable was permuted. Thus, the higher the %IncMSE or MDA value is for an X variable, the more critical that variable is in predicting readthrough efficiency. We performed hierarchical clustering of these values across samples and found that in general, the eRF1 mutants were more similar to each other than to other strains in both regression and classification approaches (Fig 4C and 4D).

In order to determine which X variables exhibited meaningful %IncMSE or MDA values, we established their baselines (negative controls) by randomly assigning one numeric value between 1–100 (random number) and one of four categorical values (random factor) to each gene before model training. Because the assignment was random, these two variables should have no influence on the prediction. Indeed, random number and random factor were among the X variables with %IncMSE and MDA values close to zero (Fig 4C and 4D). Reassuringly, in both the regression and classification approaches, the variables previously known to affect readthrough efficiency, such as the identity of a stop codon, nucleotide (nt) +4 and +5 were among the variables with %IncMSE and MDA values above the baseline in most samples, arguing that our pipeline was capable of distinguishing relevant mRNA features. In addition to the stop codon identities, 3’-UTR length and the P-site amino acid (or tRNA or codon) also had one of the highest %IncMSE and MDA values in most samples. The nt -3, and -2 in the P-site only had high MDA values in the ribosome recycling mutant, rli1-d, suggesting that they may play a role in recycling; however, this is only true in the classification model.

Interestingly, the first downstream stop codon in the 3’-UTR (“1st 3’-UTR stop” in Fig 4C and 4D) had high %IncMSE and MDA values only in sup45-ts at 37°C and sup45-d samples. This observation is due to ribosome pile-up at those stop codons, as with canonical stop codons (Fig 1B and 1C). The identity of this stop codon affects the read count in the extension region–its high %IncMSE and MDA value reinforces the role of stop codon identity in readthrough but not mechanistic relevance to readthrough occurring specifically at the canonical stop codon.

In summary, random forest analyses reveal identities of the stop codon, P-site amino acid (or tRNA or nucleotides), and 3’-UTR length as mRNA features that control genome-wide readthrough efficiency.

Readthrough-promoting stop codons and nucleotides at positions +4 to +9 occur at a genome-wide level

Previous studies using synthetic dual reporters have shown that the UGA stop codon and certain nucleotides at positions -2, -1, +4 to +9 are the most permissive features for stop codon readthrough (Fig 3B) [5,8,16,22–28]. Since the random forest analyses also identified the stop codon identity and some of these nucleotides as features that, when permuted, led to higher prediction error than baseline, we wondered whether these same stop codon and nucleotide identities were also readthrough-permissive or readthrough-inhibiting at a genome-wide level. To answer this question, the genes were divided into 3 groups for analysis of stop codon identity or 4 groups for analyses of individual nucleotides at each position and Wilcoxon’s rank sum test was used to compare the median of genes in each group to the overall sample median. The differences between group median and sample median were shown as a heatmap (Fig 5A). The significance values from Wilcoxon’s rank sum test were represented by tile size, where a larger tile depicted p-value < 0.05. As expected, the distribution of random factor variables was not significantly different between the groups and sample median and showed no particular pattern of higher or lower readthrough efficiency across all samples.

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Fig 5. Known individual effects of stop codon and surrounding nucleotide identities on readthrough efficiency occur at a genome-wide level.

A. Heatmap of median readthrough efficiency of genes containing particular stop codon or nucleotide relative to median readthrough efficiency of all genes in the sample. Positive value (red) indicates that the group of genes had higher readthrough efficiencies compared to the sample median, while negative value (blue) indicates lower readthrough efficiencies. Wilcoxon’s rank sum test was used to determine whether the difference in group and sample media was significant. Significant difference was represented as a larger tile. B. Heatmap demonstrating over-representation (red) or under-representation (blue) of a stop codon or nucleotide in “High” or “Low” readthrough groups relative to the frequency in the reference gene set. χ² goodness of fit test with Bonferroni correction was performed to compare usage distribution between the “High” readthrough group, “Low” readthrough group, and the reference. Significant difference was represented as a larger tile. Grey tiles signify that there was zero observation of that nucleotide (essentially under-represented), and log₂ calculation and statistical analysis could not be performed.

https://doi.org/10.1371/journal.pgen.1009538.g005

The variables that showed the strongest pattern and significance were the stop codon and the nucleotide immediately downstream (nt +4). Consistent with the notion that UGA is the most readthrough-permissive stop codon and UAA is the strongest terminator [23], genes with UAA as stop codons had significantly lower readthrough efficiencies while genes with UGA had significantly higher readthrough efficiencies compared to the sample median in all samples except for rli1-d cells. For nt +4, genes with C at this position had significantly higher readthrough efficiencies, and those with G had slightly lower readthrough efficiencies. These observations are in line with previous data where C was reported to allow the most readthrough and G the least [5,23,26,27] and are supported by structural evidence showing preferential base stacking of purines at nt +4 with 18S rRNA, enhancing the stop codon recognition by eRF1 [33].

For nucleotides further into the mRNA channel 3’ of the stop codon (nt +5 to +9), only nt +9 showed clear trends: genes with A had higher readthrough efficiencies (i.e., readthrough-permissive), consistent with previous reports, and genes with U had lower readthrough efficiencies (i.e., readthrough-inhibiting). At position +5, although the differences between groups’ readthrough efficiencies and overall readthrough efficiencies were not significant in every sample, genes containing adenine had a uniform pattern of higher readthrough efficiency compared to sample median (unlike random factor), indicating that A at nt +5 was relatively readthrough-permissive.

Adenine at nt -1 and -2 has been shown to be readthrough-permissive [24,25], but this was not reflected in our current analysis (Fig 5A). Instead, we observed that U at nt -1 was readthrough-inhibiting, and A and C but not G and U at nt -3 were readthrough-permissive. It is noteworthy that nt -3 was identified as having a significant role along with nt -2; this may mean that the amino acid they encode or its tRNA, rather than the nucleotides themselves, mechanistically affect the termination process. Thus, we also explored these three nucleotides as a codon (see below).

It is notable that most of the patterns for nt -3 and -2 in rli1-d sample did not match with other samples. In fact, some of them appeared to be the opposite. Since other samples, even the sup45 mutants, had wild-type Rli1, the opposite patterns in rli1-d sample may suggest that nt -3 and -2 (or the amino acid they encode) played a role in a ribosome recycling step rather than the termination step. This also influenced the number of ribosomes translated into the 3’-UTR in rli1-d, as shown in the feature importance plot (Fig 4D).

To see whether we could extract more comprehensive information on these nucleotides, we used a different approach involving only the “High” and “Low” readthrough genes used for the classification models. χ² goodness of fit tests with Bonferroni correction were used to compare the frequencies of UGA, UAG, UGA, A, C, G, and U at each nucleotide position between the “High” readthrough group, “Low” readthrough group, and the reference gene set (2,693 genes). For data presentation purposes, the frequencies were converted into fractions and log₂ ratios of “High” or “Low” groups to the reference were computed, as shown in the heatmap (Fig 5B). A positive log₂ ratio (red) indicates an over-representation while a negative log₂ ratio (blue) indicates an under-representation of X in the group compared to reference. The patterns, especially those of the “Low” readthrough group, corroborated our previous approach (Fig 5A). For instance, genes with C at the nt +4 position had higher readthrough efficiencies than the sample median (Fig 5A), and within the “Low” readthrough group (Fig 5B), C was severely under-represented (blue tile) or even absent (grey tile). Moreover, we were able to rank the nucleotides from readthrough-permissive to readthrough-inhibiting using this approach. Although the patterns of either groups were not significantly different from reference based on the χ² analysis, uniform patterns could be observed across all samples for most nucleotide positions (compared to random factor). For nt +5, the pattern allowed us to extrapolate that following readthrough-permissive G, were increasingly readthrough-inhibiting A, U, and C (which was most associated with termination). This information was difficult to obtain in the dual reporter assays because readthrough events were so low that they were indistinguishable from each other. Information compiled for other nucleotide positions is shown in Table 2.

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Table 2. Comparison of nt -3 to +9 effects (based on Figs 5 and 6) to previous data [5].

The order of nucleotide/stop codon is from most to (“>”) least permissive to readthrough.

https://doi.org/10.1371/journal.pgen.1009538.t002

Next, we asked how particular combinations of stop codon and nt +4 identities, the two strongest variables among all the nucleotides, affect readthrough efficiency together. Genes were divided into groups based on their stop codon and nt +4 identities and the same analysis as described in Fig 5A was performed. We found that genes containing UGA, the most readthrough-permissive stop codon, followed by C, the most readthrough-permissive nt +4, had the highest readthrough efficiency relative to the overall median. UGA followed by G, the least readthrough-permissive nt +4, reduced readthrough efficiency but not to the level of overall median (Fig 6A). On the other hand, genes containing the most readthrough-inhibiting combination, UAAG, had the lowest readthrough efficiencies while UAAC brought readthrough up to a level that was equal to that of the overall median. With regard to UGAC association with the highest readthrough efficiency, we noticed that this trend was not true for the sup45-ts strain at 37°C where, unlike in other strains, genes containing UGAC did not have higher readthrough efficiency than the median (Fig 6A). This result suggests that in the absence of functional eRF1, some combinations of stop codon and nt +4 identities had limited effects on readthrough efficiency variation.

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Fig 6. Readthrough-permissive combinations of stop codon and nt +4 identities have additive effects on readthrough efficiency and are avoided in the genome.

A. Heatmap of median readthrough efficiency of genes containing particular combination of stop codon and nt +4 relative to median readthrough efficiency of all genes in the sample. Statistical analysis as in A. B. Heatmap showing residuals of χ² test of independence between stop codon and nt +4 identities. Significant values (p < 0.05), represented by larger tile size, indicate the significant association between stop codon and nt +4 identities. Positive (red) residuals specify positive association (attraction) and negative (blue) residuals negative association (repulsion) between variables.

https://doi.org/10.1371/journal.pgen.1009538.g006

To test whether particular stop codon and nt +4 identities tend to occur together at different readthrough rates, a χ² test of independence was performed using all genes (“All”), genes in the “High” readthrough group, or genes in the “Low” readthrough group (Fig 6B). We found that in general (“All”), the association between stop codon and nt +4 identities was significant in all samples. Readthrough-inhibiting features, UAA and G, appeared together more frequently than the expected frequency (positive residuals) while readthrough-promoting features, UGA and C, tended to repel each other (negative residuals) (Fig 6B, left panel). Similar but weaker associations were seen among genes with “Low” readthrough, but no association was observed among those with “High” readthrough. From an evolutionary standpoint, this analysis supported a previous proposal stating that genes have evolved to minimize stop codon readthrough [31], as low readthrough features tended to coexist but high readthrough features tended to exclude each other.

To summarize, we showed that readthrough-promoting stop codon and nucleotides at positions +4 to +9 previously determined in reporter assays also occur at a genome-wide level, with some combinations of stop codon and nt +4 identities selected for or against each other. Additionally, we identified readthrough-inhibiting nucleotides refractory to previous studies due to the detection limits of reporter assays.

Specific combinations of nucleotides and tRNA in the ribosomal P site may influence readthrough efficiency

Random forest identified the P-site amino acid to be a key factor in readthrough efficiency prediction, and we observed patterns of nucleotide usages at position -3, -2, and -1; however, it was unclear whether readthrough was influenced by the nucleotide themselves, the amino acid they encode, the tRNA that decoded them, or a combination of these three possibilities. Therefore, we performed analysis as described in Fig 5A, but as codons instead of individual nucleotide positions (Fig 7A) and were able to identify codons associated with genes having significant increases or decreases in readthrough efficiencies compared to the sample median.

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Fig 7. Specific codons in the P site were associated with higher or lower readthrough efficiencies.

A. Heatmap of median readthrough efficiency of genes containing a particular triplet codon in the ribosomal P site. Positive value (red) indicates that the group of genes had higher readthrough efficiencies compared to sample median, while negative value (blue) indicates lower readthrough efficiencies. Wilcoxon’s rank sum test was used to determine whether the difference in group and sample media was significant. Significant difference was represented as a larger tile. B. General information regarding the P-site codons. The Y-axis shows frequency of codon usage in the P site among the reference gene set. Color of the bar represents tRNA adaptation index (tAI), a measurement for codon optimality where 0 is non-optimal and 1 is optimal [48,49]. Below the bar plots are wobble pair information and anti-codon modifications. C. Heatmap showing residuals of χ² test of independence between stop codon and P-site codon identities. Significant values (p < 0.05), represented by larger tile size, indicate the significant association between stop codon and P-site codon identities. Positive (red) residuals specify positive association (attraction) and negative (blue) residuals negative association (repulsion) between variables. Interpretation of the results should be done with caution as the analysis suffered from small sample size.

https://doi.org/10.1371/journal.pgen.1009538.g007

From this analysis, two particular nucleotide combinations were likely responsible for low readthrough efficiencies, namely the C/G and U combinations and the UU and A/C combinations. In the first group, CGU and GCU were codons that appeared in genes with significantly lower readthrough efficiencies in most samples even though they code for different amino acids and were clearly distinct from their respective synonymous codons. Remarkably, genes containing CUG (leucine), which had exactly the same nucleotide composition, showed higher readthrough efficiencies compared to sample median. These opposite trends suggested that not only specific combinations of nucleotides, but also specific positional arrangements have to be optimal for efficient termination. In the second group genes containing UUA and UUC had lower readthrough efficiencies but only in WT conditions (WT strains and sup45-ts at 25°C). Similar to the C/GU case, UUA and UUC had the same pattern despite coding for different amino acids and were distinct from their respective synonymous codons.

In both cases, the amino acid identity was ruled out as a possible mechanism. We wondered whether these two groups of codons share certain properties that may explain their occurrence in low readthrough genes and explored P-site codon usage frequency in the reference set, tRNA adaptation index (tAI) [48,49], tRNA anti-codon modifications [51], and wobble pairs (Fig 7B). None of these properties were shared exclusively among the four codons, suggesting the mechanisms by which UUA/UUC and CGU/GCU influence readthrough efficiency were different. Nevertheless, the role of tRNA properties could not be ruled out. It is likely that the nucleotides and tRNAs together interact with the translation termination components in such a way that is optimal for a termination event to occur.

At the other end of the spectrum, codons that appeared in genes with significantly higher readthrough efficiencies include AUA, ACA, ACC, CUG, and GAC. Arguments similar to those made for the low readthrough set of codons applied here. First, the amino acid identity could be ruled out. Although ACA and ACC encode the same amino acid (threonine), it could be ruled out as a possible mechanism because other threonine codons showed different patterns. All five codons did not share any tRNA properties in common; however, individualized nucleotide-tRNA property combinations could not be ruled out as possible mechanisms.

Next, we performed a χ² test of independence to determine whether the P-site codons with prominent effects on readthrough efficiencies tended to appear in conjunction with particular stop codons. Although statistical analysis here suffered from small sample size and the results should be interpreted with caution, we could still observe a similar trend as seen with stop codon and nt +4 in that readthrough-inhibiting features attracted one another. We found the most readthrough-inhibiting P-site codons, CGU and GCU, coexisted more frequently with UAA than UAG or UGA stop codons (Fig 7C). On the other hand, ACA, which was found in genes having high readthrough efficiencies, coexisted more frequently with UAG than UAA.

As with nt -3 and -2, many P-site codons (e.g., GAC, CUA, CUC, UUA) in the rli1-d mutant had either the opposite effect on readthrough efficiencies or opposite level of significance compared to the eRF1 mutants and WT cells. Thus, it is possible that these codons affect the recycling step rather than termination step.

In summary, we identified four codons (CGU, GCU, UUA, and UUC) associated with lower readthrough efficiencies and five codons (AUA, ACA, ACC, CUG, and GAC) associated with higher readthrough efficiencies. It is unlikely that amino acid identity by itself is the reason for this observation; rather, specific combinations of nucleotide and tRNA properties likely influence termination and readthrough.

Readthrough efficiency increased with 3’-UTR lengths in the eRF1 mutants, but not in wild-type or recycling factor mutant cells

Poly(A)-binding protein (PABP, Pab1 in yeast) has been shown previously to interact with eRF3, enhancing termination efficiency in vitro [36]. Consistent with these observations, deletion of the PAB1 gene in yeast, or extension of the mRNA 3’-UTR length downstream of a PTC, leads to inefficient termination and readthrough in vivo [38]. Accordingly, we asked whether readthrough efficiency and 3’-UTR length are positively correlated. A weak but significant positive correlation between readthrough efficiency and 3’-UTR length was observed in sup45-ts cells at 37°C and sup45-d cells while a negative correlation was observed in the respective WT strains (Fig 8). These results hinted that Pab1’s expected role in aiding termination became prominent only when eRF1 activity was inefficient. Although this result may be prone to artifacts, as 3’-UTR length also correlates with other features of the mRNAs (S3 Fig), our finding was consistent with multiple studies linking the distance from the stop codon to Pab1 and termination efficiency, such as recent studies of context dependent translation termination in Ciliates [37].

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Fig 8. Readthrough efficiency increased with 3’-UTR lengths in eRF1 mutants, but not in wild-type or ribosome recycling factor mutant cells.

Scatter plots of readthrough efficiency vs. 3’-UTR length. Spearman’s correlation coefficient (ρ) and p-value were calculated for each sample.

https://doi.org/10.1371/journal.pgen.1009538.g008

Yeast strains and culture growth conditions

Yeast strains used in this study are in the W303 background. The temperature-sensitive sup45-2 strain (HFY1218) was derived from the wild-type strain (HFY114) [57] by the pop-in/pop-out gene replacement technique. Cells were grown in 1 L of YPD at 25°C with shaking. When the OD₆₀₀ of the culture reached 0.6–0.8, its temperature was shifted as follows: Cells were collected by centrifugation at 5,000 rpm for 5 min at room temperature (RT) using a JA10 rotor in a Beckman Coulter Avanti J-E centrifuge, resuspended in 400 ml of fresh YPD media, transferred to a new flask, and incubated in a 25°C water bath with shaking. After 20 min of incubation, 400 ml of pre-warmed (57°C) YPD were added to the flask, and cells were incubated in a 37°C water bath for 30 minutes with shaking. As a control for the temperature shift procedure, the same protocol was carried out except that YPD was pre-warmed to 25°C and the subsequent 30-minute incubation was at 25°C.

Library preparation and sequencing

Ribosome profiling and RNA-seq libraries were prepared as described previously in our library preparation protocol for immunoprecipitated ribosomes [58]. Briefly, 1 L of cells of OD₆₀₀ between 0.6 and 0.8 were harvested by rapid vacuum filtration and flash-frozen in liquid nitrogen in the presence of Footprinting Buffer (20 mM Tris-HCl pH 7.4, 150 mM NaCl, 5 mM MgCl₂) plus 1% TritonX-100, 0.5 mM DTT, 1 mM phenylmethylsulfonyl fluoride (PMSF) and 1X protease inhibitors. Cells were lysed in a Cryomill (Retsch) at 5 Hz for 2 min, then 10 Hz for 15 min. Lysates were clarified by ultracentrifugation in a Beckman Coulter Optima L-90K Ultracentrifuge at 18,000 rpm for 10 min at 4°C, using a 50Ti rotor. Centrifugation was repeated for the supernatant at 18,000 rpm for 15 min at 4°C.

For ribosome profiling [59], lysates were digested with RNaseI (Invitrogen, #AM2294), then layered onto a 1 M sucrose cushion in Footprinting Buffer plus 0.5 mM DTT and centrifuged in a Beckman Optima TLX Ultracentrifuge at 60,000 rpm for 1 hour at 4°C using a TLA100.3 rotor to isolate 80S ribosomes. Ribosome-protected fragments were extracted using a miRNeasy kit (QIAgen, #217004) and depleted of rRNA using a Yeast Ribo-Zero Gold rRNA removal kit (Illumina, #MRZY1324) according to the manufacturer’s protocol. Multiplexed cDNA libraries were prepared with the NEXTFlex Small RNA-Seq Kit v3 (BIOO Scientific, #NOVA-5132-06) according to the manufacturer’s protocol and sequenced (single-end, 75 cycles) on a NextSeq500.

For RNA-Seq, total RNAs were extracted from lysates using a miRNeasy kit (QIAgen, #217004), depleted of genomic DNA contamination using Baseline-Zero DNase (Epicentre, #DB0715K), and depleted of rRNA using a Yeast Ribo-Zero Gold rRNA removal kit (Illumina, #MRZY1324) according to the manufacturer’s protocol. Multiplexed cDNA libraries were prepared with a TruSeq Stranded mRNA sample prep kit (Illumina, #RS-122-2101) according to the manufacturer’s protocol and sequenced (single-end, 75 cycles) on a NextSeq500.

Reads processing and alignment

The following software packages were used to process RNA- and ribo-seq library sequences: fastqc v0.10.1 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/); cutadapt v1.9 [60]; samtools v0.1.19 [61]; bowtie v1.0.0 [62], cufflinks v2.2.1 [63]; bam2fastq v1.1.0 (https://gsl.hudsonalpha.org/information/software/bam2fastq); RSEM v1.3.0 [64].

A customized yeast transcriptome was constructed from the S288C reference genome sequence and annotations downloaded from the Saccharomyces Genome Database (https://www.yeastgenome.org) on September 10, 2015 (S288C_reference_genome_R64-2-1_20150113). Annotations for the following classes of transcripts were parsed from the genomic gff3-formatted annotation file and processed separately: protein coding genes (6551); intron-containing genes (272); genes with 5’-UTR introns (24); frameshifted genes (47); blocked and pseudogenes (18), non-coding RNAs (ncRNAs) (16). For all but the last class, 5’- and 3’-UTR entries were added to the individual gff files employing the UTR annotations from Nagalakshmi et al. [65]. 5’- and 3’-UTR information was available for 2840 and 2849 genes, respectively. In cases where no UTR was annotated, we used the 75^th percentile lengths of 97 and 173 nt, respectively. Unspliced versions (pre-mRNAs) of all intron-containing genes were defined as a continuous exon extending from the start of the 5’-UTR to end of the 3’-UTR. After editing the individual parsed gff files, they were concatenated, and a fasta file with sequences of all 6952 transcripts was generated using the cufflinks gffread program. Information included in the fasta header lines generated by gffread was used to construct a transcript-to-gene-map file for use with RSEM, which was used for gene- and isoform-level quantification of transcripts. The fasta file is available at https://github.com/Jacobson-Lab/yeast_transcriptome_v5. A modified transcriptome was used for riboWaltz (see Data Analyses section). The riboWaltz transcriptome contained only the spliced transcripts of protein coding genes (6551) and genes with 5’-UTR introns (24). In addition, the riboWaltz transcriptome considered the stop codon as a part of the 3’-UTR region rather than the CDS region, thus the length of CDS and 3’-UTR regions of each gene were adjusted accordingly.

For ribosome profiling libraries, de-multiplexed sequences were first processed by cutadapt to filter out low quality reads and read lengths shorter than 23 nt while trimming adapter sequences (cutadapt -a TGGAATTCTCGGGTGCCAAGG -q 10—trim-n -m 23). Reads aligning to non-protein coding RNAs (rRNA, tRNA, snoRNA, and other non-protein coding RNA sequences) were removed by aligning with bowtie (-m 100–3 4–5 4 -n 2 -l 15—un) and retaining only unaligned reads. The random 4-mers introduced on both sides of each RPF during library preparation were trimmed, concatenated, and stored as an identifier “barcode” used to remove PCR duplicates by aligning reads to the transcriptome (bowtie -m 10 -n 2 -l 15 -S—best—strata) and retaining only a single barcode (and associated read) at each position where multiple reads aligned. After duplicates removal, the SAM-formatted alignment file was converted either to BAM format using samtools or to fastq format using bam2fastq for use in downstream analyses. Transcript abundance measurements were determined by RSEM with settings—seed-length 15—bowtie-m 10. Reads processing and alignment statistics can be found in S3 Table.

The TruSeq stranded RNA-Seq libraries were processed using RSEM with no additional pre-processing.

Public ribosome profiling data sets

Public ribosome profiling data of sup45-d [40], rli1-d [41], and their WT counterparts were downloaded from NCBI’s Gene Expression Omnibus (GEO) database. Yeast strain genotypes, cell growth conditions, accession numbers of the data series and specific sequencing runs are outlined in S1 Table. After download, reads were adapter-trimmed and length-filtered by cutadapt (cutadapt -a CTGTAGGCACCATCAAT -q 10—trim-n -m 15), after which reads aligning to non-protein coding RNAs were removed (bowtie -m 100 -n 2 -l 15—un) and remaining reads aligned to the transcriptome exactly as described above, with the omission of the PCR duplicate removal step. Reads processing and alignment statistics can be found in S3 Table.

Data analyses

Analyses of ribosome profiling data were performed in the R software environment (versions 3.5 and 3.6). RPFs of 20–23 nt and 27–32 nt in length were used for the analyses. P-site offsets of reads in ribosome profiling libraries were assigned using the R package riboWaltz [66], the results of which are provided in S4 Table. After assigning P-site location, reads from replicate libraries were combined. In addition to riboWaltz, the following packages were used for data analysis and visualizations: data.table, dplyr, reshape2, seqinr, ggplot2, and scales. Built-in base R functions, rcompanion, and ggpubr were used for statistical analyses. randomForest and caret were used to generate and analyze random forest models.

For analyses results that were visualized in a heatmap form, extreme results were made to fit the indicated scale in order to not jeopardize the visualization of most of the results. The actual results for each figure and associated R codes used to generate the figure are provided for reference at https://github.com/Jacobson-Lab/sup45-ts_readthrough.

Random forest models

Random forest analyses [44] were implemented in the R environment using R packages randomForest [45] and caret [67]. A classification (predicts readthrough groups) and a regression (predicts readthrough efficiency) model were created for each sample. Each model was trained with 100 trees (parameter ntree = 100) and 81 mRNA features. The number of features chosen to split at each tree node varied (parameter mtry ranging from 1 to 81, increasing by 10), with the number resulting in the highest accuracy automatically selected by the program. Model accuracy was evaluated by 5-fold cross-validation using receiver operating characteristic (ROC) as the performance metric for classification model and root mean squared error (RMSE) for regression model. To report performance metric for classification, area under the ROC curve (AUROC) for each fold was extracted, and the average ± standard deviation across 5 folds was calculated. For regression, RMSE was normalized by the range of the Y variable (readthrough efficiency) for each fold, and the average ± standard deviation across 5 folds was calculated.

Interpretation of each performance metric is as follows. AUROC is a common metric used to assess classification model. An ROC curve is generated by plotting true positive rates (TPR) against false positive rates (FPR) of multiple classification thresholds. A straight diagonal line indicates that TPR equals to FPR, resulting in area under the curve (AUROC) of 0.5, and means the chance that the model can classify genes into the correct groups is 50%–this is no better than random chance. Thus, the higher AUROC is than 0.5, the better the model performs, and AUROC of 1 indicates a perfect classification model [68]. Acceptable value for AUROC depends on the context of the study. For our study, we think the AUROC of ~0.7–0.8 is acceptable.

Root mean squared error (RMSE), one of the performance metrics for regression, is the average of the distance between the predicted values and the actual values. The unit of RMSE is the same as the Y variable. Whether the error is considered large or small depends on the range of the data. For instance, an average error of ± 0.5 may be considered small for the data ranging from 0–100, but considered large for the data ranging from 0–1. Therefore, in order to easily assess the errors across samples, we normalized the RMSE by the range of the data, where the resulting normalized RMSE (NRMSE) displays the average fraction of error relative to data range.

After the random forest models were trained, feature importance for each X variable was extracted with the importance() function. We selected Mean Decrease Accuracy (MDA) and % Increase in Mean Squared Error (%IncMSE) as measures of feature importance for classification and regression models, respectively.