PGENETICS-D-25-00876

The Importance of Nonsense Errors: Estimating the Rate and Implications of Drop-Off Errors during Protein Synthesis

PLOS Genetics

Dear Dr. Cope,

Thank you for submitting your manuscript to PLOS Genetics. After careful consideration, we feel that it has merit but does not fully meet PLOS Genetics's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Kind regards,

Jianzhi Zhang

Academic Editor

PLOS Genetics

Kelly Dyer

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

Additional Editor Comments:

Reviewer 1 raised substantial concerns, especially regarding reliability of the NSE rate and the lack of necessary controls. We would be reluctant to send your revised manuscript back to the reviewers unless these and other main issues from the two reviewers are adequately addressed.

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1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full.

At this stage, the following Authors/Authors require contributions: Alexander L Cope, Denizhan Pak, and Michael Gilchrist. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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Reviewer #1: Translating is the most error-prone step in the central dogma. Translation errors, such as amino acid misincorporation and stop codon read-through, as well as their evolutionary and functional implications, have been extensively studied. In this manuscript, Cope, Pak and Gilchrist studied yet another type of translation error, nonsense errors (NSEs), defined as pre-stop codon drop-offs of translating ribosomes. A computational model was developed that infers various key parameters of translation dynamics, including, most importantly, rates of NSE. For each codon, they estimated NSE rates by fitting their model to one ribosome profiling dataset from yeast. It is argued that NSE is a form of cellular burden similar to ribosome pausing, as a result of which it is selected against. Given the recent discovery of the biological significance of translation errors, I believe the authors have proposed an intriguing and meaningful biological hypothesis. The analyses presented are, however, far from convincing, especially with regard to the reliability of the NSE rate and the lack of necessary controls with respect to the argued natural selection. Below are my comments in more detail.

1. The first and most important concern is the fact that the authors never really tested whether their NSE rate is (relatively) accurate. There may be no actual data that directly measure NSE, but the authors should at least try some data from indirect or manipulative experiments in order to demonstrate the reliability of their model on inferring NSE rates. I can think of a few possible directions. (i) The inferred NSE rate for a given codon should increase upon the KO/KD of some tRNA. For RNA editing events that result in premature stop codons, the NSE rate should presumably be similar to the fraction of edited mRNAs. (iii) Treat the actual stop codons as sense codons, since they have different termination efficiency/readthrough probability, the model should be able to capture this difference. (vi) Splicing or transcription termination errors might result in truncated mRNA. Perhaps corresponding RNA-seq can show a fraction of truncated mRNA, which should be matched by ribo-seq-estimated NSE.

2. I feel uneasy about the fact that the authors only focused on one ribo-seq dataset from 2016. It is possible to analyze much more ribo-seq data. Presumably, more recent datasets should be of higher quality, and at the very least higher sequencing throughput. As well, there has been other recently identified bias in traditional ribo-seq protocols, such as "Common cell lysis procedures distort ribosome profiling analyses of gene expression" (Could you please comment on this, and whether it impacts your conclusion?). The analysis of many more datasets, in many more species, is recommended.

3. I believe that almost all patterns concerning NSE rate (its variation, selection, etc.) can be explained by codon optimality, as if NSE occurs less often in optimal codons than in non-optimal codons. The question arises as to how the authors can determine whether their observed patterns are actually due to selection against translational burden due to NSE, or whether they are due to other factors, such as tRNA misreading rates or translational efficiency (or ribosome pausing, as the authors mention). High-expression genes, for example, are known to use optimal codons more frequently. In addition, Koonin and colleagues have demonstrated that the 5' end of a gene is more susceptible to other types of errors (e.g. frame shift) than the 3' end. According to them, errors at the 5' end are less costly than those at the 3' end, thereby suggesting that selection is taking place. The authors of this manuscript would benefit from doing a better job controlling these alternative hypotheses or at least explaining why the patterns they showed are not confounded by these other factors.

4. The model did not consider No-Go Decay (NGD) or non-sense mediated decay (NMD). I am not quite certain whether NSE will trigger NGD/NMD, and I am not certain why not accounting for NMD makes NSE rate estimation conservative (Line 574-575).

5. Line 27-30. Does the "simple exponential waiting time model" refer to the authors' own work in the manuscript? Or the work of others? In either case, please make it clear.

6. Do the releasing factors behave similarly to the tRNA (in terms of thermodynamics or kinetics of diffusion to the ribosome A site)? This needs to be clarified.

7. Line 91. According to this sentence, "b" represents elongation rate, whereas the next sentence suggests "c" represents elongation rate.

8. Line 111. It is necessary to provide support for the use of 15 nt offset, such as ribosome profiles near start and stop codons.

9. L130. Even finer-scale model fittings may be possible, such as assuming that the NSE rate is similar within some codon sites with similar elongate rates (decoding times). This may address differences in NSE between codons of the same type (assuming decoding time is a major determinant, which the author seems to agree with in L27-30).

10. L270. Two citations are missing their parentheses.

11. L312. There is ambiguity in the phrase "the drop," as it could mean that the ribosome density is lower than elsewhere, but the authors are actually referring to a negative first derivative. Please rephrase it in a clearer manner.

12. L364. Why is normalization performed in this manner? The cost of a nearly completed long protein is much greater than the cost of a nearly completed short protein. The length itself should be used without normalization in this logic.

13. L368. The test is not sufficiently specific. There is also a possibility that codons clustering around normalized position 0.5 may indicate that the codon appears only in the middle of the CDS and not at both ends. It appears that the authors are arguing for a near uniform distribution of those codons along the CDS.

Reviewer #2: The review is uploaded as an attachment

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Large-scale datasets should be made available via a public repository as described in the PLOS Genetics  data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

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Attachments

Attachment

Submitted filename: PlosGeneticsComments.pdf

PGENETICS-D-25-00876R1

The Importance of Nonsense Errors: Estimating the Rate and Implications of Drop-Off Errors during Protein Synthesis

PLOS Genetics

Dear Dr. Cope,

Thank you for submitting your revised manuscript to PLOS Genetics. Your manuscript was seen again by the original reviewers. While one of them is satisfied, the other is not. After careful consideration, we feel that it has merit but does not fully meet PLOS Genetics's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Apr 18 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosgenetics@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgenetics/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to any formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Jianzhi Zhang

Academic Editor

PLOS Genetics

Kelly Dyer

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I would like to thank the authors for their response. I will detail my additional comments following the author's numbering of my original comments, since some of them have been miscommunicated.

3. I am not asking for additional experiments. All my previously proposed additional tests are feasible with existing related data instead of new experiments.

4.i. “we do expect this to alter the NSE probabilities by increasing ribosome waiting times”. That is exactly what I meant. In my opinion, it is possible to examine the ribosome waiting times and NSE using public Ribo-seq data with tRNA abundance manipulated.

4.iii. I am not proposing a comparison between actual stop codons and NSE at coding codons. My proposal is to compare the three stop codons, whose "NSE" (read-throughrate) is known to differ. A good example of this is UGA, which has the highest read-through rate, so its "NSE" should be low.

5. Good to see similar results from other datasets.

6 and 7. My point is that the alternative explanation of the pattern supporting the selection argument is not sufficiently excluded. But the added paragraph in Discussion seem to be good enough,

12. It is not immediately clear to me that the goal of this work is to test whether NSE rates vary between codons. Indeed, only Fig1B/D , Fig2 and Fig6B are codon-centric, whereas other figures are more often codon site or gene-centric (although the authors could surely argue their gene-centric results are all based on their codon-distinguishing model), which I find more interesting. I suggest the authors clarify this major goal in the title. The authors may decide whether to take this suggestion or not.

15. Good to see that the conclusions are robust

16. Okay.

I am overall satisfied with other responses. I also noticed that there is a typo at L631. “Tthe”.

Reviewer #2: The authors have addressed the issues I raised in a reasonable way. I have no further comments.

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Large-scale datasets should be made available via a public repository as described in the PLOS Genetics  data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: None

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Dear Dr Cope,

We are pleased to inform you that your manuscript entitled "The Importance of Nonsense Errors: Estimating the Rates and Implications of Ribosome Drop-Off during Protein Synthesis" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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Comments from the reviewers (if applicable):

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: My thanks go out to the authors for taking the time to fully address my comments in the two rounds of review. It is particularly pleasing and convincing to see the two additional analyses under point 4 to be completed and supportive for this round. Congratulations to the authors on their excellent work !

Signed, Jian-Rong Yang

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics  data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

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Reviewer #1: Yes: Jian-Rong Yang

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