Mistranslation suppresses mistranscription in eukaryotes

Domain: chemistry
Published: 2026-02-25
Editorial outcome: major_revision

Abstract
Phenotypic mutations include non-heritable sequence changes arising from synthetic errors such as mistranscription or mistranslation. Despite wide potential impact of both error types, their relationship remains under-explored. Using Circ-Seq and mass spectrometry data, we perform a genome-wide analysis of mistranscriptions and mistranslations in five model organisms, and find that genes with frequent mistranslations exhibit lower mistranscription rates. We hypothesize that this pattern is explainable by a negative epistasis between the two error types. We assess our hypothesis with systematic experimental measurements that confirm the prevalence of negative epistasis, as well as in silico evolutionary simulations that suggest the extra deleterious effects resulting from negative epistasis are sufficient to create selection against mistranscription in highly mistranslated genes. Moreover, empirical genomic analyses indicate that genes with frequent mistranslation purge nonsynonymous mistranscriptions more efficiently, and that highly translated transcripts show fewer mistranscriptions. Combined, our results reveal an unrecognized interaction between mistranscription and mistranslation.

Introduction
Biological activities critically depend on the accurate expression of genetic information, which is largely determined by the molecular processes of transcription and translation. The fidelity with which they convey genetic information is, however, not ideal. It has been estimated that mistranscription, which is the incorporation of a nucleotide other than that encoded by the DNA template, occurs at an average rate of 10−5 to 10−6 per nucleotide during transcription1,2. The rate of mistranslation, i.e., the incorporation of an amino acid not encoded by the codon being translated on the mRNA, is on average 10−3 to 10−4 per codon3,4.

Besides producing erroneous proteins with reduced functional capacity, the biological consequences of mistranscription and mistranslation go far beyond the associated waste of time, energy, and resources. For example, mistranscription has been shown to activate oncogene4, impair nucleotide and amino acid metabolism1, and shorten cellular lifespan5, among other effects6–8. Transcription error of skipping nucleotides produces toxic APP and UBB peptides, which are neuropathological markers of non-familial Alzheimer’s disease9,10. Mistranslation, on the other hand, causes protein misfolding11,12 and misinteraction13,14, induces proteotoxic stress by overwhelming the cell’s protein quality control system11, leads to neurodegenerative diseases15,16, and correlates with aging17–19. Theoretically, all these phenotypic consequences of mistranslation can also be caused by nonsynonymous mistranscription, although at a lower rate.

Given these phenotypic effects, it is not surprising that mistranscription and mistranslation also play important roles in the evolution of the genome. Natural selection against the cellular toxicity of mistranslated proteins has been suggested as the major force behind the more constrained sequence evolution of highly expressed genes, as otherwise they would produce a great number of erroneous proteins12–14,20–22. Additionally, mistranscription and mistranslation are both phenotypic mutations, which on the one hand decrease population-average fitness by creating deviations from the optimal phenotype23, but on the other hand facilitate evolution by creating diversity that ensures population survival in extreme environments24–26, purging slightly deleterious mutations through their negative epistasis with the mutations27, or promoting further heritable phenotypic changes28,29.

Despite the functional and evolutionary importance of both mistranscription and mistranslation and the inherent connection between these sources of erroneous proteins, very little is known about their relationship, as previous studies have focused on only one or the other, rather than both30,31. It may be hypothesized, for example, that mistranscription makes downstream sequences more susceptible to mistranslation32, or that functionally more important genes may be subjected to more stringent natural selection11,12,20,33 against transcription and translation errors, whereas less important genes may be subjected to a more relaxed selection against these errors. Considering this, comparing the rates of mistranscription and mistranslation across the genome should provide unique insight into the relationship between these two types of errors. As an example, both hypotheses mentioned above predict that mistranscription and mistranslation rates across different genes within the genome are positively correlated.

In this study, to investigate the relationship between mistranscription and mistranslation, we estimate the rate of mistranscription and mistranslation at the genome-scale in five model eukaryotes. Despite confirming the tenfold difference between mistranslation rates per codon and mistranscription rates per nucleotide, we also found that the difference between mistranslation and mistranscription probabilities per gene was less than threefold. Unexpectedly, we found strong evidence that genes with frequent mistranslation tend to have a lower mistranscription rate. We hypothesized that this pattern is created by the negative epistasis between the two types of errors, and tested this hypothesis via systematic experimental measurements of within-gene epistasis between genetic mutations. It is revealed that such epistasis is predominantly negative, which makes proteins affected simultaneously by both types of errors significantly more deleterious than expected. More importantly, an in silico simulation of molecular evolution suggests that the extra deleterious effects caused by the negative epistasis, when scaled by the error rates, can facilitate selection against mistranscription for genes with frequent mistranslation. Finally, our hypothesis is further corroborated by two independent lines of empirical evidence, including that genes with more frequent mistranslation purge nonsynonymous mistranscription more efficiently, and genes that are more frequently translated and therefore with higher penetration of mistranscriptions tend to exhibit a decreased mistranscription rate. Collectively, our results demonstrated a previously unknown interaction between mistranscription and mistranslation due to their negative epistasis.

Results
To detect mistranscription at genome scale, we used data from Circ-Seq, in which RNA template molecules are circularized by ligation to themselves and then reverse-transcribed to generate linear cDNA molecules with tandem copies of the original template sequence (Fig. 1A). A mistranscription can thereby be identified as recurring on all tandem copies, as opposed to reverse-transcription/PCR/sequencing errors occurring in only one copy1,34,35. We collected and analyzed Circ-Seq data from Homo sapiens, Mus Musculus, Drosophila melanogaster, Caenorhabditis elegans and Saccharomyces cerevisiae (Supplementary data 1), using a unified computational pipeline adapted from previous publications1,36,37 (Fig. 1A and “Methods”), and identified high-confidence mistranscription in 125, 99, 436, 75 and 37 protein coding genes in these five model organisms, respectively (Supplementary data 2). Consistent with previous reports1,36,37, the mistranscription rates of C>U, G>A, and U>C are almost always higher than other types of mistranscription (Fig. 1B). In H. sapiens, the gene-wise average per-nucleotide mistranscription rate of the genes with detected mistranscriptions exhibits a near-normal distribution, with 95% confidence interval at 1.2×10−5 to 1.3×10−5 (Fig. 1C. Other species in Supplementary Fig. S1). The low mistranscription rates and the rare overlap with polymorphisms (e.g., only 1.5% human mistranscriptions overlap with polymorphisms in dbSNP) are indicative of genuine mistranscriptions rather than genetic polymorphisms.

To identify mistranslation, we used a mass-spectrometry (MS)-based method31 that detects the mass shifts strictly explainable only by amino acid misincorporations but not by post-translational modifications (PTMs) or RNA editing (Fig. 1D. See “Methods”). The rate of a specific mistranslation event is calculated as the MS signal intensity of the misincorporation-containing peptide, divided by the sum of the MS signal intensities of the misincorporation-free peptides and the misincorporation-containing peptides. As a result of applying this method to high-throughput MS data from the five model organisms mentioned above (Supplementary data 3), we found mistranslations in 174 human genes, 190 mouse genes, 95 fly genes, 55 nematode genes, and 49 yeast genes (Supplementary data 4). The vast majority of the mistranslation events identified correspond to native codons being incorrectly decoded by near-cognate tRNAs rather than to artefacts such as polymorphisms (e.g., only 1.7% human mistranslations can be explained by polymorphisms in dbSNP), and the competition for decoding between cognate and near-cognate tRNAs significantly correlates with the mistranslation rate38,39 (Supplementary Fig. S2). In H. sapiens, the gene-wise average per-codon mistranslation rates for all genes with detected mistranslations exhibit a near-normal distribution with 95% confidence interval at 4.7×10−3 to 5.3×10−3 (Fig. 1E and Supplementary Fig. S3). Collectively, these results suggest that we are able to estimate mistranscription and mistranslation rates accurately enough to capture major biological signals.

Transcription is more accurate than translation, but not by much on a per-gene basis

The genome-scale estimation of mistranscription and mistranslation rates allows a direct comparison of the two, which indicates that the per-codon mistranslation rate (4.98×10−3) is on average 400-fold higher than the per-nucleotide mistranscription rate (1.25×10−5, Fig. 2A) in H. sapiens (other species in Supplementary Fig. S4A). This value is largely consistent with previous reports30,38–40. We have found, however, that when extrapolating (see “Methods”) the probability of errors per gene based on the average per-site error rate and the number of sites across the full coding sequence, the magnitudinal difference in mistranslation and mistranscription is reduced to only 2.9-fold (Fig. 2B. See also Supplementary Fig. S4B). Note here that the probability of errors per gene is defined as the probability of a full-length product of the gene containing at least one error of a specific type, and our extrapolation likely over-estimates mistranslation relative to mistranscription (see “Methods”). This pattern is mainly attributable to two factors. First, an mRNA is at least three times longer than the protein it encodes, even when non-coding regions such as UTRs and introns are not considered. Second, the near-linear accumulation of per-site error rates to per-gene error probabilities gradually decelerates as it approaches saturation (i.e., per-gene error probabilities of ~1). Therefore, the higher per-site mistranslation rate dictates that its accumulation with protein length rapidly decelerates, while the same accumulation remains near-linear for the lower per-site mistranscription rate (Supplementary Fig. S4C). We emphasize that while our estimation of average per-site error rate may be inaccurate due to variation in Circ-Seq/MS coverage, the these two factors underlying the narrowed difference between the per-gene probabilities of mistranslation and mistranscription are likely to remain valid due to their mathematical nature.

The above observation points to the possibility that some genes might have a per-gene probability of mistranscription higher than that of mistranslation. Indeed, among human genes with both types of errors detected, we found two genes exhibiting higher probabilities of mistranscription than mistranslation (Fig. 2C), namely EDIL3 (EGF-like repeats and discoidin domains 3) and SMOC1 (encoding SPARC Related Modular Calcium Binding 1). Interestingly, these two genes are closely associated with cancer invasion and metastasis. Specifically, EDIL3 is involved in epithelial-mesenchymal transition (EMT)41,42. Variations in EDIL3 sequence are significantly associated with basal subtype breast cancer43. SMOC1 has been suggested as a tumor suppressor whose mutation is associated with poor prognosis in glioma44. It is also worth noting that, as all residues in the mRNA/protein are susceptible to mistranscription/mistranslation, the prevalence of erroneous products should be comparable or even exceed many commonly observed post-transcriptional/post-translational modifications. These highlight the biomedical significance of mistranscription and mistranslation, and suggest a possible cross-talk between them, as both are substantial sources of phenotypic mutations.

Genes with high mistranslation rates tend to have a lower mistranscription rate

To directly compare mistranscription and mistranslation rates, we will hereinafter focus on the average per-site rates of mistranscription and mistranslation of a gene. And unless otherwise stated, results based on genes exhibiting at least one type of error are presented, whereas additional analyses that include genes exhibiting both types of errors, or all expressed genes regardless of the presence of errors, are shown in Supplementary Fig. S5. Gene-by-gene comparisons of mistranscription and mistranslation rates revealed that genes with high mistranslation rates tend to have a lower mistranscription rate (Fig. 3A–E). More importantly, such “trade-off” between mistranscription and mistranslation rates was consistently observed across all five model organisms, ranging from yeasts to humans, suggesting that the relationship is highly conserved. To further strengthen the reliability of this observation and eliminate the possibility of statistical artefacts, we assess this trade-off by four additional tests. First, we construct a 2×2 contingency table by classifying each gene into one of four categories based on its mistranscription and mistranslation rate, and calculated an odds ratio from the table (Fig. 3F). The smaller the odds ratio, the stronger the support for the trade-off. We found that the odds ratios from all five model organisms are all significantly smaller than 1 (Fig. 3F, P < 0.001, Fisher’s exact test). Second, we analyzed all genes with non-zero coverage in both Circ-Seq and MS, regardless whether they had mistranscription or mistranslation detected, and were able to replicate the negative correlation with Tobit model, which accounted for gene expression as a confounding factor for error detection (Supplementary Fig. S5A, B. See “Methods”). Third, using all genes with both mistranscription and mistranslation detected, we again observed the negative correlations. Specifically, although the results are statistically insignificant due to the reduced sample size, their average value is significantly more negative than expected (Supplementary Fig. S5C), and the categorical analyses by contingency table and odds ratio remained significant by the collective Mantel-Haenszel test (Supplementary Fig. S5D). Fourth, all correlation-based results above are readily reproducible by weighted correlations using the inverse of the standard errors of the estimated error rates as weights, thereby precluding biased conclusions due to large estimation errors (Supplementary Fig. S6). These results suggest a conserved trade-off relationship between mistranscription and mistranslation rates.

Negative epistasis between mistranscription and mistranslation may drive their anticorrelation

Our unexpected finding of a trade-off between mistranscription and mistranslation prompted us to investigate the underlying mechanism. While it is highly improbable that there would be a gene-specific molecular regulator that could sense one type of error and reverse-regulate the other, we instead sought to look for an evolutionary mechanism. Our attention was drawn to a recent study revealing that mistranslation facilitates the selective purging of slightly deleterious mutations due to its negative epistasis with the mutations27. This report led us to similarly hypothesize that mistranslation can help eliminate deleterious mistranscriptions and thus reduce a population’s error load via its negative epistasis with mistranscription in the same gene. In other words, gene products affected by both types of errors are far more harmful than expected, which will result in a stronger natural selection against mistranscription among genes with a higher mistranslation rate, thus creating a trade-off between mistranscription and mistranslation.

To test our hypothesis, a direct quantification of the negative epistasis was required to determine if it is sufficiently pervasive and strong to explain the mistranscription-mistranslation trade-off. Notably, this test should be conducted with three specific considerations in mind. First, negative epistasis can be caused by a greater-than-expected functional reduction of proteins containing both errors, but it can also be caused by the cellular toxicity11,12,14,33 of extra misfolded proteins resulting from simultaneous mistranslation and mistranscription. Here, the impact of functional reduction heavily depends on the specific genes considered, whereas the extra toxicity is likely to be independent of the gene’s function and therefore more generally applicable. Consequently, a gene with no native function in the cell should be tested so that the result is not dependent on the gene’s function. Second, since mistranslations and mistranscriptions can occur anywhere in the CDS, negative epistasis should be evaluated across the entire CDS rather than within some specific functional domains. Last but not least, fitness or growth rates should be measured directly rather than some other types of functional capacities (such as fluorescence), as fitness is most important for natural selection and evolution.

As such, we utilized two 3’-barcoded mutant libraries of green fluorescent protein (GFP) that we had previously constructed21 for examining the fitness effects of the single-nucleotide mutations (single mutants). The advantage of these libraries lies not only in their near-complete coverage of all single mutations across the CDS, but also in the fact that they maximize the percentage of molecules containing two single-nucleotide mutations (i.e., double mutants; see “Methods”), which is particularly suitable for measuring within-gene epistasis. In each library, the expression of GFP is driven by one of two promoters whose activity differs by ~ten-fold (PAGP1 or PTDH3)21, allowing for the assessment of how gene expression affects epistasis. The mutant libraries were bulk-transformed into S. cerevisiae BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) and integrated into the HO locus, and then subjected to PacBio-CCS to determine the correspondence between the barcodes and genotypes21. Together, we obtained unambiguous barcodes for 39238 single mutants and 31352 double mutants (Fig. 4A. See also Supplementary data 5).

Using each of these two strain pools of GFP mutants, we performed three replicates of competitive cocultures in Synthetic Complete (SC) media for seven days. The Wrightian fitness of any specific genotype relative to the wild type is estimated by Hi-Seq targeting the genotype barcodes in samples before and after the competitive coculture (“This study” in Fig. 4A. See also Supplementary Fig. S7, Supplementary data 6 and “Methods”). In the two libraries driven by PAGP1 and PTDH3, reliable fitness estimates were obtained for 2059 and 2180 single mutants, as well as 7631 and 14549 double mutants, among which 94.3% (7198 out of 7631) and 99.7% (14500 out of 14549) have both corresponding single mutants measured (Supplementary Fig. S7 and Supplementary data 7).

We then quantify the epistasis by focusing on one single mutation and all of its participating double mutants at a time. As an example, we extracted the fitness estimates for 12 double mutants containing the mutation C315T and one of other 12 single-nucleotide mutations whose fitness is also available. The observed fitness effect (selective coefficients) of each double mutant is then compared with its additive expectations derived from the individual effects of C315T and the other single-nucleotide mutation (Fig. 4B). The resulting differences (i.e., epistasis) were averaged for this focal single-nucleotide mutation (see “Methods”). Similarly, we individually evaluated all single-nucleotide mutations with participating double mutants and corresponding partner single mutants available, and found that the averaged epistasis is all negative (Fig. 4C “This study”). As a further confirmation that such pervasive epistasis is not limited to a specific

Methods
To detect mistranscription at genome scale, we used data from Circ-Seq, in which RNA template molecules are circularized by ligation to themselves and then reverse-transcribed to generate linear cDNA molecules with tandem copies of the original template sequence (Fig. 1A). A mistranscription can thereby be identified as recurring on all tandem copies, as opposed to reverse-transcription/PCR/sequencing errors occurring in only one copy1,34,35. We collected and analyzed Circ-Seq data from Homo sapiens, Mus Musculus, Drosophila melanogaster, Caenorhabditis elegans and Saccharomyces cerevisiae (Supplementary data 1), using a unified computational pipeline adapted from previous publications1,36,37 (Fig. 1A and “Methods”), and identified high-confidence mistranscription in 125, 99, 436, 75 and 37 protein coding genes in these five model organisms, respectively (Supplementary data 2). Consistent with previous reports1,36,37, the mistranscription rates of C>U, G>A, and U>C are almost always higher than other types of mistranscription (Fig. 1B). In H. sapiens, the gene-wise average per-nucleotide mistranscription rate of the genes with detected mistranscriptions exhibits a near-normal distribution, with 95% confidence interval at 1.2×10−5 to 1.3×10−5 (Fig. 1C. Other species in Supplementary Fig. S1). The low mistranscription rates and the rare overlap with polymorphisms (e.g., only 1.5% human mistranscriptions overlap with polymorphisms in dbSNP) are indicative of genuine mistranscriptions rather than genetic polymorphisms.

To identify mistranslation, we used a mass-spectrometry (MS)-based method31 that detects the mass shifts strictly explainable only by amino acid misincorporations but not by post-translational modifications (PTMs) or RNA editing (Fig. 1D. See “Methods”). The rate of a specific mistranslation event is calculated as the MS signal intensity of the misincorporation-containing peptide, divided by the sum of the MS signal intensities of the misincorporation-free peptides and the misincorporation-containing peptides. As a result of applying this method to high-throughput MS data from the five model organisms mentioned above (Supplementary data 3), we found mistranslations in 174 human genes, 190 mouse genes, 95 fly genes, 55 nematode genes, and 49 yeast genes (Supplementary data 4). The vast majority of the mistranslation events identified correspond to native codons being incorrectly decoded by near-cognate tRNAs rather than to artefacts such as polymorphisms (e.g., only 1.7% human mistranslations can be explained by polymorphisms in dbSNP), and the competition for decoding between cognate and near-cognate tRNAs significantly correlates with the mistranslation rate38,39 (Supplementary Fig. S2). In H. sapiens, the gene-wise average per-codon mistranslation rates for all genes with detected mistranslations exhibit a near-normal distribution with 95% confidence interval at 4.7×10−3 to 5.3×10−3 (Fig. 1E and Supplementary Fig. S3). Collectively, these results suggest that we are able to estimate mistranscription and mistranslation rates accurately enough to capture major biological signals.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper, which I looked forward to reading, reports on analyses on transcription and translation error rates in several\nmodel-system species. More details could probably be given on the molecular methodologies employed and their\nassumptions, and it will be a good idea to have a reviewer directly involved in the basic computational approaches weigh in.\nMy concerns are with a number of aspects of the analyses, which almost certainly introduce biases, and in doing so set up\nfalse expectations / motivations for the downstream studies reported later in the paper.\n1) The authors suggest that more highly expressed genes have elevated transcription error rates. This conclusion flies in the\nface of a recent paper by Li et al., possibly unavailable to the authors at the time of publication, that reports no such pattern\nfor a large number of species. Less than 1% of the protein-coding genes in the species examined were evaluated in the end.\nThis will need to be addressed and may be a simple consequence of the multiple biases in the analyses. I found this hard to\nbelieve, but the authors repeatedly stated that they only looked at such subsets of genes.\n2) For reasons that are unclear, the authors only report on the error rates for a handful of genes that presumably had enough\nreads to make such calls, but even then it appears that genes with no errors weren’t in the game. More significantly, if all\ngenes with no errors did not enter the final analyses, the estimates are clearly very biased.\n3) The authors report a ~100-fold difference in transcription and translation error rates, which is reasonably consistent with\nprior work. However, they then go into an exercise that leads them to conclude the difference is only about 3-fold on a gene-\nlevel basis. Whereas I can see the ratio would shrink owing to the use of nucleotides vs. codons, the rest of the argument\ndoes not make sense to me. And I also don’t follow the saturation argument – translation error rates are made on a per\ncodon basis, so there is no reason why after multiplying by the number of codons, the gen-level rate can’t exceed 1.0.\n4) I was left uncertain about the SEs of the single estimates, which almost certainly must be very large, as they are not\nreported. A back of the envelope derivation suggests the CV of the rate of a gene should be something on the order of the\nsquare root of the inverse of the number of mutations observed, which even if 25 are observed (I guess unlikely) would be\n~0.2. This is all relevant to the attempts to think about how gene-specific error rates of the two types covary.\n5) The kinds of negative correlations implied in Figure 3, and used to motivate the rest of the study appear to be totally\nartifactual. One issue is clear from the significance results reported, which have remarkably low numbers, and then simple\nvisual inspection of the means of the bins, which might not be significant (or if so, not by much). But there is a much bigger\nissue of bias here – the authors use only genes that have at least a single error of one of the types. This means that in there\nare likely a lot of zeros in the first bin to the left, but if these exist, then for a gene to be in the game, it by definition has to\nhave a nonzero translation error rate, which will virtually guarantee that the height of the first column has to be upwardly\nbiased, and as one can see if any relationship exists at all, it is because of this first bin. On the other hand, translation errors\nof zero can only appear in the next four to five bins, which will work in the opposite direction. I’m left with the impression that\nthe entire analysis of the negative correlation between the two error rates is artifactual (and that doesn’t include the issue of\nignoring all of the genes with no error rates). If they are unclear on these sorts of things, I suggest the authors consult a\nstatistician,\n6) The evolution simulations are very poorly described, and unfortunately also possibly irrelevant. As far as I can tell, the\nauthors are modeling asexual genomes, which the study species are not. They never tell us what the modifiers of the error\nrates are, nor do they tell us the rates at which mutations to error rates are arising. These things are problems of\nconsiderable significance, as the authors are trying to model gene-specific modifiers, which are known to be very difficult to\npromote by selection, and even more so in an actual recombining system.\n7) The question of epistasis is of potential interest, but as noted above, the motivation for this work is questionable given all\nof the other issues in the paper. Moreover, the reported study is focused on GFP, a protein not present in any of the study\ngenomes. I’m lost as to why this would be relevant to this particular study and note as well that there have been lots of other\nstudies of this nature already done with other model proteins, so even in a broader context, not much has been added here.\nUnfortunately, this confluence of issues leaves me much colder on this paper than I had hoped to be.", "reviewer_2": "The manuscript by Zhang et al. presents a compelling distinction between mistranslation and mistranscription rates, and the\nmost interesting finding is the negative correlation between these two types of errors across genomes of several eukaryotic\nspecies. The manuscript is well-written and the study hints at a potentially important observation. However, several issues\nregarding the robustness, interpretation, and biological realism of the model and simulations should be addressed. Here is\nthe list of points that I think would help authors to improve their work:\n1. Robustness of the correlations (Figure 3):\nThe correlation between mistranslation and mistranscription rates appears to be largely driven by a single bin (the lowest bin\nof mistranscription rate). If this bin is removed, it is likely that the correlation weakens substantially. The authors should\nexplicitly assess and report the robustness of these correlations to binning strategies and the presence of potential outliers.\nIncluding the standard deviation (SD) or coefficient of variation (CV) of mistranscription error rates across bins would help to\nget more ideas on the range and variability of this error rate. Additionally, would the reported Fisher’s exact test still yield\nsignificant results if the median was used instead of the mean to define bin thresholds? This would be important to assess\nthe influence of skewed distributions.\n2. Experimental epistasis measurements:\nThe experimental approach used to test for negative epistasis is elegant. However, the concept of negative epistasis in\nmolecular evolution is already well-established, with several previous studies linking it to biophysical fitness landscapes.\nTherefore, while this approach is technically sound, its novelty may be limited unless the authors can explicitly demonstrate\nhow their system provides new mechanistic insights beyond what has been previously shown.\n3. Evolutionary simulations:\nThe simulation framework appears to be correctly implemented in terms of fitness function, selection coefficients, and\nmutation steps. However, it is unclear how realistic the modeled evolutionary scenarios are. For instance, a widely\ndiscussed mechanism involves selection for protein stability (see: Nature ecology & evolution 6.8 (2022): 1155-1164.;\nGenome biology and evolution, 6(10), 2956-2967; Scientific reports 7.1 (2017): 15844). The proposed mechanism is that\nmutations that buffer against misfolding are selected for. Since mistranslation errors can increase the burden of unstable\nproteins, it has been argued that purifying selection indirectly acts on mistranslation rates through stability constraints (for\nexample: Cell reports 2.2 (2012): 249-256).\nOne way to model this more realistically would be to allow the parameter m in the fitness function w=exp⁡(−cm) to be a\nfunction of protein stability and the probability of misfolding, weighted by protein abundance. Introducing such an addition\nwould allow the simulation to reflect protein-level selection more explicitly. I encourage authors to review the simulation\nefforts since the publication of Drummond and Wilke’s paper (also cited by the authors) and discuss how they can make the\nfitness function more realistic.\n4. Negative epistasis in simulations\nIn the simulations, the introduction of a cross-term between mistranslation and mistranscription essentially enforces a\nstatistical dependence between the two error types. This may partly explain the observed correlation in simulation outputs,\nparticularly at equilibrium. When fitness reaches a steady state, any changes in one error rate must be compensated by\nchanges in the other to maintain overall fitness, thus enforcing a coupled dynamic. The authors should clarify whether the\nobserved correlations in simulations are emergent properties or model-imposed constraints.\n5. Missing consideration of transcription–translation decoupling\nOne key point absent from the current model is the weak correlation between mRNA and protein abundance across genes\nand species (see: Nature reviews genetics 13.4 (2012): 227-232.). Several studies suggest that mRNA levels often act as a\nbuffering mechanism, providing just enough transcript to ensure sufficient protein levels above a threshold. In this view, post-\ntranscriptional regulation plays a dominant role in determining protein output. Moreover, protein abundance is generally\nmore conserved across species than mRNA levels, indicating that purifying selection more strongly constrains protein-level\nexpression. Would authors think that these additional layers of regulation may affect their model and interpretation."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors did an excellent job addressing my concerns. Their response clearly distinguishes between correlations that\nare imposed by model structure and those that are truly emergent under strong negative epistasis. I am particularly satisfied\nthat they performed additional simulations to test the robustness of this relationship; these new analyses strengthen the\nmanuscript and provide a much clearer understanding of how epistasis shapes the joint evolution of mistranslation and\nmistranscription.", "reviewer_3": "Zhang et al collected publicly available data on Circ-Seq and Mass spec data to evaluate potential interactions between\nmistranscriptions and mistranslations. The authors proposed a novel and intriguing hypothesis regarding the negative\nepistasis between these two types of errors. They also made great efforts during revision to strengthen the statistical rigor of\nthe reported negative correlation between mistranscription and mistranslation rates. However, several issues related to data\nanalysis and interpretation remain to be addressed.\nAnalysis for identifying mistranscriptions\n1. The authors reanalyzed Circ-Seq datasets from previous studies (PMID: 36696440 and 36941254) but the number of\nprotein-coding genes with detected mistranscriptions reported in the present study (Line 108) seems to be lower than what\nreported in PMID: 36696440 and 36941254. The definition of “high-confidence mistranscription” is not clearly explained.\nAccording to the Methods (Lines 423–424), the authors apply an 80% read-coverage cutoff. It remains unclear how this\nthreshold affects the number of identified mistranscriptions and, consequently, the downstream analyses evaluating the\ncorrelation between mistranscription and mistranslation rates.\n2. The dataset analyzed in this study (e.g., PMID: 36941254) includes both wild-type and mutant RNA polymerases, the\nlatter exhibit induced and potentially biased mistranscription rates. The correlation analysis between mistranscription and\nmistranslation rates should therefore be performed using only wild-type data. It would be helpful for the authors to clarify\nwhich subset(s) of the data were included in their analyses.\n3. The authors performed several sanity checks (e.g., those shown in Fig. 1C and Fig. S1, as well as comparisons between\ngenetic polymorphisms and identified mistranscriptions; Lines 114–115). An additional validation that would further support\nthe robustness of their mistranscription-calling pipeline is to test whether the analysis can recapitulate the known induced G-\nto-A mistranscriptions observed in specific mutant RNA polymerases.\nRobustness of the analysis to evaluate the correlation between mistranscription and mistranslation rates\nThe authors performed analyses with statistical rigor, such as using raw data for correlation tests and grouped data solely for\nplot/visualization. In the revised manuscript, additional analyses, such as including genes with sufficient read coverages but\nno detected mistranscriptions and excluding genes in the first bins in Fig. 3A-E, indeed strengthened the reliability of the\nreported negative correlation between mistranscription and mistranslation rates. Given the potential negative correlation\nobserved here is the fundamental basis of this study, it needs to be further evaluated,\n1. To include genes with sufficient read coverages but no detected mistranscriptions, the authors used a bivariate Tobit\nmodel. One alternative approach to determine whether a gene should be included is to use the binomial probability of\nobserving at least one mistranscription given the gene’s cumulative read coverage and the average mistranscription error\nrate. It would be informative to assess whether such alternative approach yields consistent conclusions.\n2. Pearson’s and Spearman’s correlation tests were performed on the raw estimates of mistranscription and mistranslation\nrates. Because these estimates may differ substantially in their standard errors, it is unclear whether each raw data point was\ntreated equally or weighted, such as weighted by the inverse of standard errors, to evaluate the potential correlation?\n3. In Fig. 3E, the x-axis appears to use an uneven scale, with unequal spacing between 0–2 and 2–3. A numerical, uniformly\nscaled axis would be more appropriate and consistent with the visual presentation in Fig. 3A–3E.\n4. The pattern observed in yeast (Fig. 3E) appears particularly strong, yet the number of genes analyzed is the smallest. It is\nunclear whether the reported negative correlation would hold with a larger gene set. This goes back to one of my earlier\nquestions. What criteria were used to define high-confidence mistranscriptions, and how does this threshold influence the\nnumber of genes with detected errors and, consequently, the downstream correlation analyses between mistranscription and\nmistranslation?\nNegative epistasis among point genetic mutations\nA fundamental distinction between interactions among genetic mutations and those between\nmistranscription and mistranslation is that, genetic mutations occur at a similar rate while per site mistranscription rates and per codon\nmistranslation rates are orders of magnitude different. Additionally, genetic mutations are heritable across generations while\nthe other two types of errors tend to be transient. Given these differences, it is difficult to follow how the analyses in this\nsection directly support the argument for negative epistasis between mistranscription and mistranslation.\nInterpretation of the potential negative correlations between mistranscription and mistranslation rates\n1. Assuming there is a negative correlation between mistranscription and mistranslation rates, it is unclear why the authors\ninterpret this as mistranslation suppressing mistranscription rather than the reverse? Following the reasoning displayed in\nFig. 6A-6C, would the efficiency of purging deleterious mistranslations be higher for genes with high mistranscription rates?\nThe directionality of the proposed suppression therefore requires further justification.\n2. Mistranscriptions may largely have fitness effects after translation. Given the per-site mistranscription rates are 2–3 orders\nof magnitude lower than per-codon mistranslation rates, most amino acid–level errors should arise from mistranslation. It is\ntherefore unclear how a relatively small number of mistranscription-derived amino acid errors would generate sufficiently\nstrong fitness effects to be suppressed?\n3. Fig. 6D suggests a negative correlation between translation efficiency and mistranscription rates. Is there a positive\ncorrelation between translation efficiency and mistranslation rates? If so, the observed negative correlation between\nmistranscription and mistranslation rates could be explained by post-transcriptional or translation-dependent RNA quality\ncontrol rather than by negative epistasis between mistranscriptions and mistranslations.\nOthers\n1. In the simulations, the initial per-codon mistranslation rate is set to 10^(-4)(Line 266), whereas the authors’ empirical\nestimates suggest a rate closer to 10^(-3). It would be helpful to clarify the rationale for using a lower mistranslation rate in\nthe simulations.\n2. In Fig. 6A and Fig. 6B, the method used to calculate the expected ratios of nonsynonymous and synonymous\nmistranscriptions is not clearly described. Specifically: (a) Were all protein-coding genes in the genome included in\ngenerating the expectations, or only those with detected errors? (b) Were differences in codon composition and the twelve\ndistinct types of mistranscription substitution rates taken into account?"}, "author_response": "Point-by-point response to reviewers’ comments\nReviewer #2:\nOverall Comment:\nThe authors did an excellent job addressing my concerns. Their response clearly distinguishes\nbetween correlations that are imposed by model structure and those that are truly emergent under\nstrong negative epistasis. I am particularly satisfied that they performed additional simulations to\ntest the robustness of this relationship; these new analyses strengthen the manuscript and provide a\nmuch clearer understanding of how epistasis shapes the joint evolution of mistranslation and\nmistranscription.\nResponse:\nWe are very grateful for your positive comments regarding our work.\nReviewer #3:\nOverall Comment:\nZhang et al collected publicly available data on Circ-Seq and Mass spec data to evaluate potential\ninteractions between mistranscriptions and mistranslations. The authors proposed a novel and\nintriguing hypothesis regarding the negative epistasis between these two types of errors. They also\nmade great efforts during revision to strengthen the statistical rigor of the reported negative\ncorrelation between mistranscription and mistranslation rates. However, several issues related to\ndata analysis and interpretation remain to be addressed.\nResponse:\nThank you very much for acknowledging our improvement. Please find below our response to your\ncomments one by one.\nComment 1.1\nAnalysis for identifying mistranscriptions\n1. The authors reanalyzed Circ-Seq datasets from previous studies (PMID: 36696440 and 36941254)\nbut the number of protein-coding genes with detected mistranscriptions reported in the present study\n(Line 108) seems to be lower than what reported in PMID: 36696440 and 36941254. The definition\nof “high-confidence mistranscription” is not clearly explained. According to the Methods (Lines\n423–424), the authors apply an 80% read-coverage cutoff. It remains unclear how this threshold\naffects the number of identified mistranscriptions and, consequently, the downstream analyses\nevaluating the correlation between mistranscription and mistranslation rates.\nResponse:\nThank you for your comment. Indeed, the number of protein-coding genes with detected\nmistranscriptions is lower in our study than in previous studies (PMID: 36696440, 36941254). A\nprimary reason for this discrepancy is the difference between the analytical goals and methodology\nused. In particular, earlier studies focused on identifying and characterizing individual, discrete\nmistranscription events at the whole-transcriptome level. Thus, applying gene-level thresholds or\nfilters is pointless therein. We, however, require the gene-level mistranscription rates. In the event\nthat a gene is not sufficiently covered by the circ-seq reads, the mistranscription rate may be biased\nby the transcript segments that are covered rather than sufficiently represent the gene as a whole.\nTherefore, we applied a \"high-confidence mistranslation\" criterion that required circ-seq reads to\ncover at least 80% of the transcript.\nAs for the specific threshold used, per your suggestion, we tested read-coverage thresholds\nfrom 50% to 90%. As shown in Figure R1 (also added to the manuscript as Supplemental Figure\nS8), there was a consistent negative correlation between mistranscription rates and mistranslation\nrates across all thresholds tested. Therefore, our core finding - the inverse relationship between\ntranscriptional and translational errors - remains robust. In light of these results, we decided to\nadhere to the 80% cutoff in our manuscript.\nFigure R1. Different read-coverage cutoff of high-confidence mistranscriptions.\nThe Pearson’s and Spearman’s correlation between mistranscription rate and\nmistranslation rate, based on different read-coverage cutoffs for high-confidence\nmistranscriptions.\nComment 1.2\n2. The dataset analyzed in this study (e.g., PMID: 36941254) includes both wild-type and mutant\nRNA polymerases, the latter exhibit induced and potentially biased mistranscription rates. The\ncorrelation analysis between mistranscription and mistranslation rates should therefore be\nperformed using only wild-type data. It would be helpful for the authors to clarify which subset(s)\nof the data were included in their analyses.\nResponse:\nThank you for your comment. We confirm that the correlation analysis between mistranscription\nand mistranslation rates presented in our study was performed exclusively using the wild-type (WT)\ndatasets. We intentionally excluded data from mutant RNA polymerases to avoid any potential bias\nin mistranscription patterns.\nPer your comment, and to prevent any misunderstanding, we have explicitly clarified this in\nthe revised manuscript (Methods section) and have also updated Supplementary Table S1 to\nspecify the WT-derived accession codes used for this core analysis: SRR22944106, SRR27251376,\nSRR23095488, SRR23042841, and SRR23063524.\nSupplementary Table S1 - Datasets used to detect mistranscriptions\nType (usage) Species Reference Accession Number\nChung, C. et al. The fidelity of transcription in human cells.\nH. sapiens SRA:SRR22944106\nProc Natl Acad Sci U S A 120, e2210038120 (2023)\nCircle-Seq (to detect M. musculus SRA:SRR27251376\nmistranscription) D. melanogaster Chung, C. et al. Evolutionary conservation of the fidelity of SRA:SRR23095488\nC. elegans transcription. Nat Commun 14, 1547 (2023). SRA:SRR23042841\nS. cerevisiae SRA:SRR23063524\nComment 1.3\n3. The authors performed several sanity checks (e.g., those shown in Fig. 1C and Fig. S1, as well as\ncomparisons between genetic polymorphisms and identified mistranscriptions; Lines 114–115). An\nadditional validation that would further support the robustness of their mistranscription-calling\npipeline is to test whether the analysis can recapitulate the known induced G-to-A mistranscriptions\nobserved in specific mutant RNA polymerases.\nResponse:\nThank you for the valuable suggestion. We have performed the recommended validation analysis\nusing the S. cerevisiae dataset with specific mutant RNA polymerases (SRR23063467). As shown\nin the Figure R2 (below), our mistranscription-calling pipeline successfully recapitulates the\nexpected increase in the G-to-A mistranscription rate. This result provides strong, independent\nvalidation for the accuracy and robustness of our identification method. We decided not to include\nthis figure in our manuscript since we did not use this mutant strain in our analyses (see our response\nto Comment 1.2 above).\nFigure R2. Mistranscription profile of a yeast strain with mutated RNA polymerases\nknown induced G-to-A mutations.\nComment 2.1\nRobustness of the analysis to evaluate the correlation between mistranscription and\nmistranslation rates\nThe authors performed analyses with statistical rigor, such as using raw data for correlation tests\nand grouped data solely for plot/visualization. In the revised manuscript, additional analyses, such\nas including genes with sufficient read coverages but no detected mistranscriptions and excluding\ngenes in the first bins in Fig. 3A-E, indeed strengthened the reliability of the reported negative\ncorrelation between mistranscription and mistranslation rates. Given the potential negative\ncorrelation observed here is the fundamental basis of this study, it needs to be further evaluated,\n1. To include genes with sufficient read coverages but no detected mistranscriptions, the authors\nused a bivariate Tobit model. One alternative approach to determine whether a gene should be\nincluded is to use the binomial probability of observing at least one mistranscription given the gene’s\ncumulative read coverage and the average mistranscription error rate. It would be informative to\nassess whether such alternative approach yields consistent conclusions.\nResponse:\nThank you for this constructive suggestion. We have conducted additional analyses based on your\nrecommendations. Specifically, we evaluated whether the absence of mistranscription is due to\ninsufficient sequencing depth or to a low mistranscription rate. To achieve this goal, we modeled\nthe occurrence of mistranscription events using a binomial distribution, and calculated the\nprobability of observing zero mistranscription events based on the number of reads (number of trials\nin the binomial test) of the gene and the average error rate (\"success\" probability in the binomial\ntest). Those genes for which this probability was smaller than 0.05 were considered to have a low\nmistranscription rate rather than just insufficient sequencing depth (Figure R3A). When we\nincluded these genes regardless of their mistranslation rate into the analyses presented in Figure 3,\nwe found that the negative correlation between mistranslation rate and mistranscription was rate\nretained in all five organisms (Figure R3B). We therefore conclude that our conclusion remains\nvalid with this alternative approach.\nFigure R3. Robustness of the mistranscription–mistranslation anticorrelation using\na binomial- based inclusion criterion\n(A) For each gene with sufficient Circ-Seq coverage but no mistranscription detected, we\ncalculated the probability of observing zero mistranscription events given the gene’s\nnumber of reads (number of trials in the binomial test) and the average error rate (“success”\nprobability in the binomial test) were calculated based on a binomial distribution. A gene\nwith a binomial P < 0.05 (blue) is considered to have a low mistranscription rate.\nOtherwise, it is considered to have insufficient sequencing depth (red). (B) These\nSpearman’s correlations are based on Figure 3A-E, except that genes with actual low\nmistranscription rates were also included (regardless of their mistranslation rate).\nAlthough the raw correlations are not always significant, the average value is significantly\nmore negative than its random expectation as shown in the inset. Here, 1,000 average\ncorrelations are calculated using mistranslation and mistranscription rates independently\npermuted within the five species to determine the random expectation.\nNote that due to the complexity of mass-spectrums, the binomial criterion cannot be applied to\nMS-based mistranslation detection. This inequality between mistranslation and mistranscription\nprevents us from including this above result in our manuscript. The Tobit model-based results, which\nare applicable both to transcription and mistranslation, are instead used.\nComment 2.2\n2. Pearson’s and Spearman’s correlation tests were performed on the raw estimates of\nmistranscription and mistranslation rates. Because these estimates may differ substantially in their\nstandard errors, it is unclear whether each raw data point was treated equally or weighted, such as\nweighted by the inverse of standard errors, to evaluate the potential correlation?\nResponse:\nThank you for this important comment. In our original analyses, each gene was treated equally in\nSpearman’s and Pearson’s correlation tests without weighting. Per your comment, we attempted\nweighted correlation analyses, in which each data point was weighted by the inverse of its standard\nerror. As shown in the Figure R4 below, these weighted analyses yielded results highly consistent\nwith those obtained from the unweighted analyses, suggesting that our conclusion is robust. These\nnew results have been added to the manuscript as Supplemental Figure S6.\nFigure R4. Weighted correlations between mistranscription and mistranslation rates.\nTo take estimation errors into account when calculating correlations, we determined the standard\nerror (SE) of the per-gene error rate (E) by 𝑆𝐸(𝐸)≈√∑𝐿 𝜎2 . Here 𝜎 is the per-site SE as\n𝑖=1 𝑖\ndetermined by the empirical data. That is, binomial SE for mistranscription (standard error of the\nproportion of mistranscription-containing reads), and SE among replicated mass-spectrum for\nmistranslation. For 𝑆𝐸(𝐸) of genes with no errors detected, to be conservative, the median 𝑆𝐸(𝐸)\nof the genes with errors detected was used instead. Then using the inverse of the standard error of\nthe mistranslation or mistranscription rates as weights, the correlations between mistranscription\nrates and mistranslation rates are re-calculated. (A-B) Weighted Pearson’s (A) and Spearman’s (B)\ncorrelations among genes that exhibited at least one type of error. These are the weighted version of\nthe correlations shown in Figure 3A-E. (C-D) Weighted Pearson’s (C) and Spearman’s (D)\ncorrelations among all genes with non-zero coverage in both the Circ-Seq and mass spectrometry\ndatasets, regardless of whether an error was detected. These are the weighted version of the\ncorrelations shown in Figure S5A. (E-F) Weighted Pearson’s (E) and Spearman’s (F) correlations\namong genes with both types of errors detected. These are the weighted version of the correlations\nshown in Figure S5C. In C-F, although the raw correlations are not always significant, the average\nvalue in each panel is significantly more negative than its random expectation as shown in the\nrespective inset. Here, 1,000 average correlations are calculated using mistranslation and\nmistranscription rates independently permuted within the five species to determine the random\nexpectation.\nComment 2.3\n3. In Fig. 3E, the x-axis appears to use an uneven scale, with unequal spacing between 0–2 and 2–\n3. A numerical, uniformly scaled axis would be more appropriate and consistent with the visual\npresentation in Fig. 3A–3E.\nResponse:\nThe x-axes in Figure 3A-E are all meant to be even scale except at both ends, which are extend to\ninclude outliers. We have now clarified this in the figure legend, as pasted below.\nFigure 3. Genes with high mistranslation rates tend to have a lower mistranscription rate\n(A-E) The mistranscription and mistranslation rates are negatively correlated in H. sapiens (A),\nM. musculus (B), D. melanogaster (C), C. elegans (D), and S. cerevisiae (E). All genes with\neither mistranscription or mistranslation detected were grouped into bins defined by uniformly\nsized intervals of mistranscription rates, except for the first and last bins, which were extended\nto include outliers at the lower and upper ends of the distribution of mistranscription rates. …\nComment 2.4\n4. The pattern observed in yeast (Fig. 3E) appears particularly strong, yet the number of genes\nanalyzed is the smallest. It is unclear whether the reported negative correlation would hold with a\nlarger gene set. This goes back to one of my earlier questions. What criteria were used to define\nhigh-confidence mistranscriptions, and how does this threshold influence the number of genes with\ndetected errors and, consequently, the downstream correlation analyses between mistranscription and\nmistranslation?\nResponse:\nThank you for the question. Please refer to our response to your earlier Comment 1.1\nComment 3.1\nNegative epistasis among point genetic mutations\nA fundamental distinction between interactions among genetic mutations and those between\nmistranscription and mistranslation is that, genetic mutations occur at a similar rate while per site\nmistranscription rates and per codon mistranslation rates are orders of magnitude different.\nAdditionally, genetic mutations are heritable across generations while the other two types of errors\ntend to be transient. Given these differences, it is difficult to follow how the analyses in this section\ndirectly support the argument for negative epistasis between mistranscription and mistranslation.\nResponse:\nThank you. We have taken into consideration the error rate in estimating the effect of negative\nepistasis. Critically, the fitness effect of genetic mutations as estimated by the experiment have been\nscaled by the mistranscription and mistranslation rate when used to estimate the effect of epistasis\nbetween errors. This is explained in the Methods section as below.\nMore specifically, if we define the Wrightian fitness of a mutant relative to the wildtype as 𝑤,\nand the corresponding selective coefficient as 𝑠 =𝑤−1, we can estimate the epistasis between\ntwo single-nucleotide mutations in a gene as\n𝜀 =𝑠 −(𝑠 +𝑠 ) (1)\n𝐴𝐵 𝐴𝐵 𝐴 𝐵\nby assuming that the effects of independent mutations are additive. Here, 𝑠 and 𝑠 are the\n𝐴 𝐵\nselective coefficients of the single-nucleotide mutation A and B respectively, so (𝑠 +𝑠 ) is the\n𝐴 𝐵\nexpected effect when both A and B occur and have independent effects, and 𝑠 is the observed\n𝐴𝐵\nselective coefficient when both A and B occur. In the case of mistranscription and mistranslation,\nif mistranscription A occurs at a rate of 𝛽 and mistranslation B occurs at a rate of 𝛽 , we can express\n𝐴 𝐵\nthe total fitness effect in three parts. The first part is the effect of mistranscription as 𝑠 𝛽 (1−𝛽 ),\n𝐴 𝐴 𝐵\nwhere 𝛽 (1−𝛽 ) is the fraction of molecules with only mistranscription A but no mistranslation\n𝐴 𝐵\nB. Similarly, the second part, which represents the effect of mistranslation, is 𝑠 𝛽 (1−𝛽 ). Lastly,\n𝐵 𝐵 𝐴\nthe third part representing the effect of molecules containing both types of errors is 𝑠 𝛽 𝛽 . In\n𝐴𝐵 𝐴 𝐵\nsum, the total fitness effect of mistranscription and mistranslation can then be expressed as\n𝑠 =𝑠 𝛽 (1−𝛽 )+𝑠 𝛽 (1−𝛽 )+𝑠 𝛽 𝛽 =𝑠 𝛽 +𝑠 𝛽 + 𝜀 𝛽 𝛽 (2)\n𝐴 𝐴 𝐵 𝐵 𝐵 𝐴 𝐴𝐵 𝐴 𝐵 𝐴 𝐴 𝐵 𝐵 𝐴𝐵 𝐴 𝐵\nIf there is no epistasis, so that A and B just independently affect fitness (i.e. 𝜀 =0 ), the\n𝐴𝐵\nindependently combined fitness effect of mistranscription and mistranslation can be expressed as\n𝑠′=𝑠 𝛽 +𝑠 𝛽 (3)\n𝐴 𝐴 𝐵 𝐵\nThen the net contribution of epistasis between mistranscription and mistranslation is simply\n𝑠−𝑠′=𝜀 𝛽 𝛽 (4)\n𝐴𝐵 𝐴 𝐵\nBy this logic, we can transform the question of epistasis between transcription and translation errors\ninto a question of epistasis between two genetic mutations (i.e. 𝜀 ).\n𝐴𝐵\nComment 4.1\nInterpretation of the potential negative correlations between mistranscription and\nmistranslation rates\n1. Assuming there is a negative correlation between mistranscription and mistranslation rates, it is\nunclear why the authors interpret this as mistranslation suppressing mistranscription rather than the\nreverse? Following the reasoning displayed in Fig. 6A-6C, would the efficiency of purging\ndeleterious mistranslations be higher for genes with high mistranscription rates?\nThe directionality of the proposed suppression therefore requires further justification.\nResponse:\nThank you. We agree that the presence of a negative correlation between mistranscription and\nmistranslation rates alone does not reveal the direction of causality. In addition, we would like to\nemphasize that the two hypotheses (\"mistranslation suppresses mistranscription\" and\n\"mistranscription suppresses mistranslation\") are not mutually exclusive. It is possible for both to\nbe true at the same time! We have actually examined both directions of causality during the early\nstages of this project. Specifically, we require testable predictions that exclusively support one\ndirection (not necessarily reject the other). Unfortunately, after much contemplation, we only came\nup with two predictions that exclusively support the \"mistranslation suppresses mistranscription\"\nhypothesis, which were tested in the manuscript. As a result, we have decided to report the one\nhypothesis for which we can find evidence, and to leave the other direction for future research. We\nhave now explicitly mentioned that the other direction of causality is not rejected in Discussion,\nwhich is pasted below.\n… Fourth, the mistranscription-mistranslation anticorrelation may also be explained by an\nalternative, non-mutually exclusive hypothesis, namely that mistranscription suppresses\nmistranslation. However, we didn’t come up with predictions that exclusively support this\nalternative direction of causality, so we focused on the testable hypothesis. …\nOn reflection, we have concluded that equivalent predictions exclusively supporting the\n\"mistranscription suppresses mistranslation\" hypothesis are much more difficult to make. For\nexample, by comparing the synonymous versus nonsynonymous fraction of observed\nmistranscriptions, we can infer whether deleterious mistranscriptions are excessively purged – a\ncharacteristic of natural selection - which allows comparison of genes with different mistranslation\nrates. Inferences of this kind are impossible without the critical neutral control (or baseline) of\nsynonymous mistranscriptions. If we want to support the \"mistranscription suppresses\nmistranslation\" hypothesis, we need a reliable quantification of the strength of deleterious\nmistranslation being purged by natural selection. This is extremely difficult for two reasons. The\nfirst issue is that the baseline mistranslation rate varies across genes, which we do not have an\nassessment of, since the observed mistranslation rate is post-selection, whereas the baseline\nmistranslation rate is pre-selection. Second, although it might be possible to predict the destabilizing\neffects of mistranslation on proteins (certainly far from accurate), predicting fitness effects of these\nmistranslation events is still infeasible because genes have very different importance when it comes\nto their fitness contribution, not to mention potential dominant negative effects (as a result of\nmisinteractions, misfoldings, etc.).\nThe two additional predictions we tested in the last section of Results, are, on the other hand,\nexclusively supporting the \"mistranslation suppresses mistranscription\" hypothesis. Specifically, (i)\nwe used the ratio of nonsynonymous (N) to synonymous (S) mistranscription events (N/S) as a\nproxy for the strength of purging against deleterious mistranscriptions, and found that genes with\nfrequent mistranslation tend to have reduced N/S (Figure 6A-C). (ii) It is predicted that highly\ntranslated genes (i.e., those producing many protein molecules per mRNA) should experience\nstronger selective pressure to reduce mistranscription rates, because each mistranscription event is\namplified into many erroneous protein products. This is indeed observed (Figure 6D). Taken\ntogether, our conclusions are not based on correlation alone, but supported by multiple lines of\nfunctional genomic evidence.\nComment 4.2\n2. Mistranscriptions may largely have fitness effects after translation. Given the per-site\nmistranscription rates are 2–3 orders of magnitude lower than per-codon mistranslation rates, most\namino acid–level errors should arise from mistranslation. It is therefore unclear how a relatively\nsmall number of mistranscription-derived amino acid errors would generate sufficiently strong\nfitness effects to be suppressed?\nResponse:\nThank you for the question. There are two angles from which we would like to respond. First, the\nper-site mistranscription rates are indeed 2–3 orders of magnitude lower than per-codon\nmistranslation rates. However, error rate along does not capture the functional impact of errors. The\npossibility exists that an errorneous template (mRNA) may be translated repeatedly, thereby\nresulting in many erroneous proteins that may collectively exert a dosage-dependent non-linear\ncytotoxicity due to dominant negative effects or prolonged half-lives. Second, our argument is not\nthat selection act on the small number of amino acid errors resulting from mistranscription per se,\nbut rather on the super-additive fitness cost associated with the co-occurrence of mistranscription\nand mistranslation-induced errors on one molecule. In direct support of this argument, our DMS\nexperiment and evolutionary simulation indicated that the negative epistasis between errors can\nindeed result in an additional amount of deleterious effect that can be seen by natural selection.\nComment 4.3\n3. Fig. 6D suggests a negative correlation between translation efficiency and mistranscription rates.\nIs there a positive correlation between translation efficiency and mistranslation rates? If so, the\nobserved negative correlation between mistranscription and mistranslation rates could be explained\nby post-transcriptional or translation-dependent RNA quality control rather than by negative\nepistasis between mistranscriptions and mistranslations.\nResponse:\nThank you. The translation efficiency and mistranslation rates are slightly positively correlated in\nsome species by never significantly so. The correlations were presented as in Figure R5 below.\nFigure R5. Pearson’s and Spearman’s Correlations between translational efficiency\nand mistranslation rate\nWe are not 100% certain as to why the positive correlation between translation efficiency and\nmistranslation rates indicates the existence of a post-transcriptional or translation-dependent RNA\nquality control mechanism at work. Our best guess is that you are suggesting that post-\ntranscriptional or translation-dependent RNA quality control is more active in genes with higher\ntranslation efficiency. If our guess is correct, we would like to respond from three different\nperspectives.\nFirst, we agree that a post-transcriptional or translation-dependent RNA quality control could,\nin principle, contribute to the observed relationships among translation efficiency, mistranscription\nrates, and mistranslation rates. However, it should be noted that all current known molecular\nmechanisms of this kind monitor stop codon/termination related translation errors (NMD, No-go\ndecay, etc.). On the other hand, our study investiagtes amino acid misincorporations, a type of\nmistranslation that no known molecular mechanisms of RNA quality control monitor.\nSecond, the majority of mistranslations are caused by tRNA level problems (codon anticodon\nmismatch, tRNA mischarging, etc.). Some genes are more likely to mistranslate due to their CDS.\nTargeted degradation of the underlying mRNA by an RNA quality control mechanism of any type\ndoes not reduce mistranslation in either case since it cannot correct the tRNA-side problems, and\nthe CDS’ intrinsic mistranslation propensity remains in every mRNA transcribed from the gene.\nThere is therefore no theoretical expectation of a post-transcriptional or translation-dependent RNA\nquality control monitoring amino acid misincorporation.\nThird, even if this mechanism exists, it is a molecular mechanism/explanation, whereas the\nnegative epistasis we proposed is a genetic/evolutionary explanation. The two explanations are not\nmutually exclusive and can both be true. In the discussion, we devote one paragraph to the potential\nproximal molecular mechanism underlying the observed anticorrelation between mistranslation\nrates and mistranscription rates, which is pasted below.\nAlthough the presented empirical evidence unanimously supports an evolutionary genetic\nexplanation of the mistranscription-mistranslation anticorrelation, the proximal molecular\nmechanisms regulating the mistranscription and mistranslation rates of individual genes remain\nunexplored. In the case of mistranscription, two mechanisms have been proposed, namely\nassembly of RNA polymerase with or without fidelity factors, or altering the speed of RNA\nsynthesis30, albeit with limited empirical support. The codon usage bias22,56 and modulation of\ntranslational elongation speed14, on the other hand, have found various support for their\nregulatory role in the mistranslation rate. Regardless of the specific molecular mechanisms,\nfindings from this and other studies strongly suggest a local regulation of error rates for\nindividual genes, as there is clear genomic variation in mistranscription and mistranslation\nrates, prioritizing fidelity in some genes over others19,24,28. The observed variation in gene-\nspecific error rates also highlights potential use of phenotypic mutations to identify harmful\ngenetic mutations57. It should be noted, however, that this does not preclude the global\nregulation of mistranscription and mistranslation rates.\nComment 5.1\nOthers\n1. In the simulations, the initial per-codon mistranslation rate is set to 10^(-4)(Line 266), whereas\nthe authors’ empirical estimates suggest a rate closer to 10^(-3). It would be helpful to clarify the\nrationale for using a lower mistranslation rate in the simulations.\nResponse:\nThank you. To evaluate whether the choice of the initial per-codon mistranslation rate influences\nour simulation results, we repeated the simulations using a higher initial mistranslation rate of 10⁻³,\nwhich is closer to our empirical estimates. We found that increasing the initial mistranslation rate\ndid not qualitatively change the simulation outcomes. After adjusting this parameter, Figure R6A–\nB show that the overall pattern of negative correlation between mistranscription and mistranslation\nrates remains robust across a reasonable range of the toxicity coefficient (c) and mutation rates\ngoverning the evolution of error rates. Most importantly, in parallel control simulations in which\nepistatic interactions were removed, the mistranscription – mistranslation anticorrelation\ndisappeared (Figure R6C–D). This result mirrors our original findings and directly supports the\nconclusion that negative epistasis is essential for generating the observed anticorrelation between\ntranscriptional and translational error rates. Together, these additional simulations demonstrate that\nour conclusions are not sensitive to the initial choice of the mistranslation rate and remain consistent\nwhen using values closer to those inferred from empirical data.\nFigure R6. Evolutionary simulations support the role of negative epistasis in the\nmistranscription-mistranslation trade-off\n(A) The key results are presented as heatmaps for simulations with various combinations\nof toxicity coefficient (y axis) and probability of error rate changes per generation (x axis).\nThe fraction of individuals with a negative mistranscription-mistranslation correlation (A),\nand the averaged correlation coefficient of the simulated population (B) at the 20,000th\ngeneration are presented using the color scale on top of each panel, with an asterick\nindicating statistical significance (Bonferroni-adjusted P < 0.05, Binomial test for A and\nPearson’s correlation test for B). (C-D) Similar to A and B, except the simulation is\nconducted without epistasis.\nComment 5.2\n2. In Fig. 6A and Fig. 6B, the method used to calculate the expected ratios of nonsynonymous and\nsynonymous mistranscriptions is not clearly described. Specifically: (a) Were all protein-coding genes in the genome included in\ngenerating the expectations, or only those with detected errors? (b)\nWere differences in codon composition and the twelve distinct types of mistranscription substitution\nrates taken into account?\nResponse:\nThank you for this question. Only protein-coding genes with detected errors were considered. The\ncodon composition were taken into account because all simulated mistranscriptions happen in the\nobserved mistranscribed position. The twelve distinct types of mistranscription substitution rates\nwere also taken into account in generated the simulated mistranscriptions. We have now revised the\nrelated Methods section, which is pasted below.\nSimulated mistranscriptions\nTo assess the expected ratios of nonsynonymous (N) to synonymous (S) mistranscriptions\n(Figure 6A-B), we first extracted the protein coding genes with detected mistranscriptions. For\neach mistranscribed nucleotide of these genes, we randomly generated a simulated\nmistranscription event whose type of base substitution is sampled from the genome-wide\nfrequencies of the twelve distinct base substitution types (in empirically observed\nmistranscriptions). We then calculated the expected N/S ratio for each gene by determining\nwhether the simulated mistranscription events were synonymous or not.\nPoint-by-point response to reviewers’ comments\nReviewer #3:\nComment 1:\nThe authors have addressed all my concerns. Only one minor point, in line 349, should\n\"mistranslation rate in 21 species\" be \"mistranscription rate in 21 species\"?\nResponse:\nNice catch! We want to thank the reviewer again for taking the time to read our manuscript. It's now\nfixed."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have addressed all my concerns. Only one minor point, in line 349, should \"mistranslation rate in 21 species\" be \"mistranscription rate in 21 species\"?"}, "author_response": ""}]

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