1. Author Response:

    Reviewer #1:

    This paper sought to dissect the relative impact of history, selection, and chance, on the evolution of antibiotic resistance in the clinically relevant species Acinetobacter baumannii. The authors conducted adaptive evolutions of A. baumannii isolates that had been previously adapted to diverse environments, thus establishing distinct histories. The authors show that the impact of history becomes increasingly diminished as selection strength increases, and several specific observations were made about resistance to beta lactams and their collateral effects of ciprofloxacin resistance. Overall the question being asked is important and the observations made are quite interesting. However, the analysis lacks sufficient depth to draw specific conclusions, and many confounding effects (such as the lack of propagation in a drug-free environment) are not taken into account.

    Thanks for the comments. We have included the assumptions and limitations of our study, toned down some conclusions., and clarified that we propagated a control in a drug-free environment, which wasn’t clear in the previous version of the manuscript.

    Minor comments:

    The authors seem to cite themselves an inappropriate amount of times for key findings, and many highly established evolutionary studies on this very topic were not included. For example in line 79 - mutation rate is a well documented parameter that has been estimated long before their work in 2019. Likewise, there have been a large number of studies that leverage population data that were not included.

    Thanks for the comment. We have carefully reviewed the citations and included or deleted some references better reflecting the state-of-the-art of the field. To clarify, the citation in line 79 is fully justified. We agree that mutation rate is a well-documented parameter, and in the cited paper we used previous literature to analyze the probability that each base was mutated in an 80 generations experimental evolution propagating Acinetobacter baumannii with pops sizes higher than 1 × 107 that it is exactly the experimental setup of the current manuscript. Nevertheless, we agree with the reviewer and we have added more citations and reduced the number of citations of ourselves.

    Reviewer #2:

    The experimental design in this manuscript is exquisite. Its is simple in rationale yet also very clever and the work is performed to an excellent standard. The authors clearly address the extent to which history, chance and selection lead to the evolution of AMR, and it is all the more stronger that this is done in a real MDR clinical pathogen (A. baumannii) rather than lab E. coli.

    The work shows that history can influence AMR evolution, but that clearly natural selection is a dominant driver. This provides clear unambiguous data on the importance of antibiotic exposure on the evolution of AMR and will interest evolutionary biologists, microbiologists and clinicians.

    We are proud of this summary, and we would like to acknowledge Travisano, Lenski and coworkers for the elegant, simple and clever experimental design described in 1995, which was foundational for our study 25 years later.

    Reviewer #3:

    The manuscript by Santos-Lopez and colleagues investigates the roles of history, chance, and selection on the evolution of antibiotic resistance in the pathogen A. Baumannii. In previous work, they showed that the genotypic and phenotypic evolution of (fluoroquinolone) resistance differed between well-mixed and spatially extended (biofilm) environments; this work uses laboratory evolution experiments to investigate further evolution in response to new (beta lactam) drugs. Their experimental design is based on a simple but elegant assay for distinguishing the impact of previous adaptation ("history"), random deviations across replicate populations ("chance"), and selective pressure from the newly applied drug ("selection"). They found that while prior history of selection (including prior growth environment) often impacts evolution of resistance to a new drug, increasing concentrations of that drug generally reduced historical contingencies-that is, the prior selecting conditions became less influential on the new adaptation trajectories (quantified by MIC-based direct and collateral resistances). They also performed extensive population sequencing of the evolved populations and similarly quantified the effects of history, chance, and selection using aggregate measures of genome similarity based on Manhattan distance metrics. Notably, they found that strains originally selected in structured environments exhibited genetic reversion and a corresponding loss of resistance to the initial drug.

    Overall, this study addresses an interesting and important problem. It is well designed, with careful attention to both the phenotypic and genotypic analysis of evolved strains, and the results contribute new insight into the trade-offs associated with antibiotic resistance in an ESKAPE pathogen. I enjoyed reading this work. My comments below are suggestions to improve the paper and can be addressed by additional clarification and/or discussion of the limitations of the approach.

    Thanks for this comment, which, in our opinion, is a perfect summary of the two manuscripts that compose this research.

    Minor:

    • Define / cite ESKAPE pathogens for readers not familiar

    We have included the definition (Lines 104-106)

    • Why choose the Manhattan metric? It is not unreasonable, but I am wondering 1) if there is a deeper theoretical justification and 2) whether other metrics could be expected to give similar qualitative results.

    In previous experiments (Turner et al. 2018) we have used Bray-Curtis similarity as a metric for genetic difference between populations. However, Bray-Curtis and other related metrics calculate an average similarity across the genes with mutations. For assessing the roles of history, chance and adaptation, we needed an additive metric where the difference between populations strictly increases as more mutations occur. Of the commonly used distance metrics, Manhattan distance was a logical choice over Euclidean distance because each mutation independently adds to the genetic distance between populations. Hamming distance would consider only the presence or absence of mutations, not their frequency.

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  2. Evaluation Summary:

    This work, which will be of value to evolutionary and clinical microbiologists, uses a well-designed experimental evolution strategy to tease apart contributions of history, chance, and selection on the evolution of antibiotic resistance in A. baumannii, an important microbial pathogen. While relevant, the work will benefit from further clarification regarding some of the concepts and procedures used and revision of some of the interpretations.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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  3. Reviewer #1 (Public Review):

    This paper sought to dissect the relative impact of history, selection, and chance, on the evolution of antibiotic resistance in the clinically relevant species Acinetobacter baumannii. The authors conducted adaptive evolutions of A. baumannii isolates that had been previously adapted to diverse environments, thus establishing distinct histories. The authors show that the impact of history becomes increasingly diminished as selection strength increases, and several specific observations were made about resistance to beta lactams and their collateral effects of ciprofloxacin resistance. Overall the question being asked is important and the observations made are quite interesting. However, the analysis lacks sufficient depth to draw specific conclusions, and many confounding effects (such as the lack of propagation in a drug-free environment) are not taken into account.

    Minor comments:

    The authors seem to cite themselves an inappropriate amount of times for key findings, and many highly established evolutionary studies on this very topic were not included. For example in line 79 - mutation rate is a well documented parameter that has been estimated long before their work in 2019. Likewise, there have been a large number of studies that leverage population data that were not included.

    Read the original source
    Was this evaluation helpful?
  4. Reviewer #2 (Public Review):

    The experimental design in this manuscript is exquisite. Its is simple in rationale yet also very clever and the work is performed to an excellent standard. The authors clearly address the extent to which history, chance and selection lead to the evolution of AMR, and it is all the more stronger that this is done in a real MDR clinical pathogen (A. baumannii) rather than lab E. coli.

    The work shows that history can influence AMR evolution, but that clearly natural selection is a dominant driver. This provides clear unambiguous data on the importance of antibiotic exposure on the evolution of AMR and will interest evolutionary biologists, microbiologists and clinicians.

    Read the original source
    Was this evaluation helpful?
  5. Reviewer #3 (Public Review):

    The manuscript by Santos-Lopez and colleagues investigates the roles of history, chance, and selection on the evolution of antibiotic resistance in the pathogen A. Baumannii. In previous work, they showed that the genotypic and phenotypic evolution of (fluoroquinolone) resistance differed between well-mixed and spatially extended (biofilm) environments; this work uses laboratory evolution experiments to investigate further evolution in response to new (beta lactam) drugs. Their experimental design is based on a simple but elegant assay for distinguishing the impact of previous adaptation ("history"), random deviations across replicate populations ("chance"), and selective pressure from the newly applied drug ("selection"). They found that while prior history of selection (including prior growth environment) often impacts evolution of resistance to a new drug, increasing concentrations of that drug generally reduced historical contingencies-that is, the prior selecting conditions became less influential on the new adaptation trajectories (quantified by MIC-based direct and collateral resistances). They also performed extensive population sequencing of the evolved populations and similarly quantified the effects of history, chance, and selection using aggregate measures of genome similarity based on Manhattan distance metrics. Notably, they found that strains originally selected in structured environments exhibited genetic reversion and a corresponding loss of resistance to the initial drug.

    Overall, this study addresses an interesting and important problem. It is well designed, with careful attention to both the phenotypic and genotypic analysis of evolved strains, and the results contribute new insight into the trade-offs associated with antibiotic resistance in an ESKAPE pathogen. I enjoyed reading this work. My comments below are suggestions to improve the paper and can be addressed by additional clarification and/or discussion of the limitations of the approach.

    Minor:

    - Define / cite ESKAPE pathogens for readers not familiar
    - Why choose the Manhattan metric? It is not unreasonable, but I am wondering 1) if there is a deeper theoretical justification and 2) whether other metrics could be expected to give similar qualitative results.

    Read the original source
    Was this evaluation helpful?