Dear Dr Johnston,

Thank you for submitting your manuscript entitled "A natural history of AMR in Klebsiella pneumoniae: Global diversity, predictors, and predictions of evolutionary pathways" for consideration as a Research Article by PLOS Biology.

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Melissa Vazquez Hernandez, Ph.D.

Associate Editor

PLOS Biology

mvazquezhernandez@plos.org

Dear Iain,

Thank you for your patience while we considered your revised manuscript "A natural history of AMR in Klebsiella pneumoniae: Global diversity, predictors, and predictions of evolutionary pathways" for consideration as a Research Article at PLOS Biology under Portable Peer Review. Your revised study has now been evaluated by the PLOS Biology editors, the Academic Editor and one additional reviewer (Joachim Krug).

In light of the reviews, which you will find at the end of this email, we would like to invite you to revise your manuscript to address several points raised during evaluation. As you will see below, the additional reviewer is overall positive about the quality and relevance of the work and acknowledges that the previous concerns brought at the previous journal have been carefully addressed, particularly with the expanded treatment of reversibility. However, some important issues remain that require clarification before further consideration. In particular, our reviewer asks that you more clearly explain the computational challenges associated with incorporating reversibility into the modeling framework, to better guide readers through this conceptual limitation. In addition, s/he raises concerns about the interpretation of the predictive analysis presented in Figure 5, noting that the performance of the HyperTraPS model appears comparable to a simple prevalence-based null model. We therefore ask that you clarify how predictive performance is quantified and revise the text to more accurately reflect the strength of this result. More generally, we encourage you to carefully temper conclusions regarding inferred interactions between resistance features, given the acknowledged limitations of the approach.

In addition to these revisions, you may need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests shortly. If you do not receive a separate email within a few days, please assume that checks have been completed, and no additional changes are required.

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Sincerely,

Melissa

Melissa Vazquez Hernandez, Ph.D.

Associate Editor

PLOS Biology

mvazquezhernandez@plos.org

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Reviewer #1 (Joachim Krug):

The scope and significance of the work has been well described in the previous reports. As far as I can see, in the revision the authors have fully addressed the concerns raised by the previous reviewers, by adding material to the manuscript and the supplement, as well as through a new in-depth investigation of the key problem of incorporating reversibility of evolutionary pathways in the inference (which here corresponds to the possibility of acquired AMR features being lost along a trajectory), see Johnston 2026. The detailed engagement with the limitations of the HyperTraPS approach caused by its restriction to irreversible acquisition trajectories has the somewhat unfortunate consequence of weakening the conclusions that can be drawn with confidence, in particular with regard to possible interactions between resistance features that could guide the development of combination therapies. Nevertheless, the study remains an impressive demonstration of the state of the art in the field, and I recommend publication in PLoS Biology after the following (additional) comments and suggestions have been addressed.

MAJOR ISSUES

1. While the distinction between reversible and irreversible acquisition pathways is discussed in detail in the revised manuscript, it may not become clear to the reader why it is computationally difficult to incorporate reversibility. This is well explained in the new manuscript by Johnston 2026 - it is just a matter of the explosion in the number of possible pathways - and I suggest to include a similar discussion here. To provide some context, it may be helpful to refer to the seminal work of Weinreich and collaborators on trajectories towards high resistance in the TEM-1 beta-lactamase (Science 2006) and the follow-up work by De Pristo et al. (MBE 2007). Weinreich et al. found that out of the 5!=120 possible irreversible trajectories along which 5 mutations in TEM-1 could be acquired only 18 were 'selectively accessible' (= monotonically increasing in MIC). De Pristo et al. expanded the analysis to irreversible trajectories, finding an enormous increase of possible paths (from 120 to 18,651,552,840) but only a moderate increase in the number of accessible ones (from 18 to 27), because very long trajectories are unlikely to be accessible. Weinreichs problem is different from the present one in that in his case trajectories cannot revisit the same configuration (and therefore the number of pathways is finite even when reversions are included), but in practice this distinction hardly matters (in fact in the extensive theoretical work on this problem the constraint that accessible paths are self-avoiding can be ignored asymptotically for large hypercubes, see Schmiegelt and Krug (J. Math. Biol. 2023) for a rigorous treatment and Krug and Oros (Journal of Statistical Mechanics: Theory and Experiment 2024, Section 3) for a review).

2. An important change compared to the previous version of the manuscript is the choice of null model in the assessment of the predictive potential of the method in Figure 5. The new panel 5B shows that the prediction of the HyperTraPS is almost indistinguishable from that of a simple null model based only on the prevalence of traits. I have difficulty interpreting this figure and linking it to the statement in the text that the trained model outperforms the null model in 18% of cases (lines 359/360). In terms of predicting the exact rank, it seems from Fig. 5B that the two models perform equally well, getting it right in about 50% of cases. How is the full information in the histogram in Fig. 5B used to quantify the overall performance?

MINOR ISSUES

3. The abbreviation GBD (= Global Burden of Disease) is used throughout the paper but not defined.

4. line 231: 19th century -> 20th century

5. line 336: remove 'and'

Dear Iain,

Thank you for your patience while we considered your revised manuscript "A natural history of AMR in Klebsiella pneumoniae: Global diversity, predictors, and predictions of evolutionary pathways" for publication as a Research Article at PLOS Biology. This revised version of your manuscript has been evaluated by the PLOS Biology editors, the Academic Editor and the original reviewer.

Based on the review, we are likely to accept this manuscript for publication, provided you satisfactorily address the remaining editorial points. Please also make sure to address the following data and other policy-related requests.

1) We routinely suggest changes to titles to ensure maximum accessibility for a broad, non-specialist readership, and to ensure they reflect the contents of the paper. In this case, we would suggest a minor edit to the title, as follows. Please ensure you change both the manuscript file and the online submission system, as they need to match for final acceptance:

"Evolutionary modeling reveals global routes to antimicrobial resistance in Klebsiella pneumoniae"

2) Please add the weblink of the funding agencies in the Financial Disclosure statement in the manuscript details during submission.

3) We do not have a word limit. Please move the supplementary text, together with the references, to the main text which can provide the readers an easier access to all information.

4) You may be aware of the PLOS Data Policy, which requires that all data be made available without restriction: http://journals.plos.org/plosbiology/s/data-availability. For more information, please also see this editorial: http://dx.doi.org/10.1371/journal.pbio.1001797

Please supply the numerical values either in the a supplementary file or as a permanent DOI’d deposition for the following figures:

Figure 1ABC, 2AC, 3ABC, 4A-D, 5BC, S1ABC, S2, S3AB, S4A, S5

NOTE: the numerical data provided should include all replicates AND the way in which the plotted mean and errors were derived (it should not present only the mean/average values).

*If the figure were done using r, or any code, please provide the code necessary to replicate the figures.

5) Please cite the location of the data clearly in all relevant main and supplementary Figure legends, e.g. “The data underlying this Figure can be found in S1 Data” or “The data underlying this Figure can be found in https://doi.org/10.5281/zenodo.XXXXX”

6) Please provide the tree files for the phylogenetic trees in Figures .5A, S1AB, S6A-E Please make sure all relevant figures have scale bars.

7) Please ensure that your Data Statement in the submission system accurately describes where your data can be found and is in final format, as it will be published as written there

8) Thank you for providing the underlying code in GitHub. However, because Github depositions can be readily changed or deleted, please make a permanent DOI’d copy (e.g. in Zenodo) and provide this URL in the manuscript and Data Availability Statement.

As you address these items, please take this last chance to review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the cover letter that accompanies your revised manuscript.

In addition to these revisions, you may need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests shortly. If you do not receive a separate email within a few days, please assume that checks have been completed, and no additional changes are required.

We expect to receive your revised manuscript within two weeks.

To submit your revision, please go to https://www.editorialmanager.com/pbiology/ and log in as an Author. Click the link labelled 'Submissions Needing Revision' to find your submission record. Your revised submission must include the following:

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*Protocols deposition*

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please do not hesitate to contact me should you have any questions.

Sincerely,

Melissa

Melissa Vazquez Hernandez, Ph.D.

Associate Editor

mvazquezhernandez@plos.org

PLOS Biology

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REVIEWERS' COMMENTS

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Reviewer #1 (Joachim Krug):

My previous comments have been fully addressed in the revision.

Dear Iain

Thank you for the submission of your revised Research Article "Evolutionary inference reveals global natural histories and predicted pathways of antimicrobial resistance in Klebsiella pneumoniae" for publication in PLOS Biology, which came in as Portable Peer Review. On behalf of my colleagues and the Academic Editor, J. Arjan G. M. de Visser, I am pleased to say that we can in principle accept your manuscript for publication, provided you address any remaining formatting and reporting issues. These will be detailed in an email you should receive within 2-3 business days from our colleagues in the journal operations team; no action is required from you until then. Please note that we will not be able to formally accept your manuscript and schedule it for publication until you have completed any requested changes.

Please take a minute to log into Editorial Manager at http://www.editorialmanager.com/pbiology/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

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Thank you again for choosing PLOS Biology for publication and supporting Open Access publishing. We look forward to publishing your study.

Sincerely,

Melissa

Melissa Vazquez Hernandez, Ph.D., Ph.D.

Associate Editor

PLOS Biology

mvazquezhernandez@plos.org