Steady antibiotic overuse has led to the rise and spread of multidrug-resistant bacteria, and can potentially reduce the number of therapeutic options against several dangerous human pathogens. Resistance seriously impacts the effectiveness of treatment, and increases the risk of complications and fatal outcome (Mensah Abrampah et al., 2018). Strict policies that aim to restrict antimicrobial usage in clinical settings may offer a solution to this problem (Guay, 2008). Such strategies implicitly presume that resistance leads to reduced bacterial fitness in an antibiotic-free environment, and therefore these resistant populations should be rapidly outcompeted by antibiotic-sensitive variants. In theory, the extent of fitness costs determines the long-term stability of resistance, and consequently, the rate by which the frequency of resistant bacteria decreases in an antibiotic-free environment. Resistance mutations frequently incur fitness costs in the laboratory (Andersson and Levin, 1999; Levin et al., 1997), as such mutations impair essential cellular processes, such as transcription, translation, or cell-wall biogenesis. There is a negative correlation between measured fitness costs and prevalence in clinical settings (Basra et al., 2018; Praski Alzrigat et al., 2017; Trindade et al., 2009). This would suggest that fitness costs shape the propagation of antibiotic resistant bacteria in the clinics. However, in other cases, such deleterious side effects of resistance mutations are undetectable, and resistance can even confer benefits in specific, antibiotic-free environmental settings (Maharjan and Ferenci, 2017).

It is frequently assumed that such compensatory mutations mitigate the fitness costs of resistance mutations without affecting the level of resistance. As the range of targets for compensation is much broader, compensatory mutations are more likely than the reversion of resistance mutations. If compensatory mutations are indeed widespread, pathogens can reach both high level of resistance and high fitness. For these reasons, reversion to the original antibiotic-sensitive state under prudent antibiotic use may proceed so slowly that it will have no practical importance in the clinic (Andersson, 2006; Andersson and Hughes, 2010; Nicoloff et al., 2019; Schrag et al., 1997).

Several prior laboratory studies support the argument above. It has been reported that antibiotic resistance is stably maintained as a result of compensatory mutations (Björkman et al., 1999; Johanson et al., 1996; Marcusson et al., 2009), however, instances of compensatory mutations have not been generalized to clinical settings. Several published clinical studies indicate that limited antibiotic use can cause rapid changes in the frequency of resistance. In certain cases, resistance tends to decline both in individual patients and at the community level following restricted antibiotic use (Butler et al., 2007; Dagan et al., 2008; Gottesman et al., 2009; Kristinsson, 1997). For example, the frequency of erythromycin-resistant Streptococcus pyogenes steadily declined as a result of reduced use of macrolides in Finnish hospitals (Seppälä et al., 1997). By contrast, months of reduction of sulfamethoxazole clinical treatment in the United Kingdom failed to reach reduced resistance levels (Enne et al., 2001).

This disagreement between clinical observations and laboratory studies could have multiple reasons. First, antibiotic treatments frequently fail to completely eradicate antibiotic-sensitive bacteria from the population. Following treatment, antibiotic sensitive bacteria with high fitness could rapidly spread in the population, leading to rapid loss of resistance. Second, compensatory evolution may be limited in nature and in clinical settings (Brandis et al., 2012; Hall and MacLean, 2011; Vogwill and MacLean, 2015). Indeed, most laboratory studies focused on resistance to a single drug (Brandis et al., 2012; Maisnier-Patin et al., 2002; Qi et al., 2016), thus compensatory evolution in multidrug-resistant bacteria has remained largely unexplored (Vogwill and MacLean, 2015; but see Moura de Sousa et al., 2017). This latter shortcoming is especially noteworthy, because several multidrug-resistant bacteria are difficult to treat with current antibiotic treatment, and therefore the issue of compensatory evolution is especially relevant. Indeed, there is an urgent need to understand the mechanisms that mitigate the costs of multiple – potentially epistatically interacting – mutations in multidrug-resistant pathogens (Trindade et al., 2009; Wong, 2017), which clearly cannot be achieved in studies focusing on single mutants only.

In this work, we have studied 23 drug-resistant E. coli strains that carry 2 to 13 mutations (Lázár et al., 2014). We have found that 60 days of laboratory evolution under antibiotic-free conditions has led to a rapid decline of resistance to certain, but not all antibiotics. This decline in resistance has not resulted from strict reversion mutations that recapitulate wild-type bacteria at the molecular level. Rather, the mutated genes are functionally related, but not identical to those conferring antibiotic resistance. Notably, these mutations perturb membrane-permeability and the activity of regulons involved in defense against drugs and related stresses. They partially restore wild-type fitness in the antibiotic-free medium, and also reduce the level of antibiotic-resistance, at least against certain antibiotics. These considerations could help to identify antibiotics where restricting antimicrobial usage could be a useful policy.

It is an open issue whether restricting antimicrobial usage would contribute to the elimination of multidrug-resistant bacteria. Although resistance mutations frequently have associated fitness costs, such costs may decline subsequently through the accumulation of compensatory mutations. It has been argued that such compensatory mutations mitigate the fitness costs of resistance mutations without affecting the level of resistance (Andersson and Hughes, 2010), suggesting that limiting antibiotic usage may not have much practical utility in clinical settings. However, most prior laboratory studies focused on bacteria carrying a single resistance mutation, whereas antibiotic-resistant clinical isolates usually carry multiple resistance mutations (Vogwill and MacLean, 2015). This issue is all the more relevant, as epistasis is prevalent between antibiotic-resistance mutations (Wong, 2017).

A specific case of compensation is molecular reversion. In this case, the mutation responsible for molecular reversion restores the wild-type, antibiotic-susceptible genetic sequence, and thereby eliminates the fitness costs associated with the resistance mutation. However, molecular reversion is assumed to be generally rare, as it requires very specific and mutational events (Andersson and Hughes, 2010; Durão et al., 2018). On the other hand, case studies indicate that phenotypic reversion can also occur, when the original resistance mutation is maintained, but acquisition of additional mutations simultaneously reduces fitness costs and increases antibiotic-susceptibility. For example, streptomycin-resistance is frequently mediated by resistance mutations in the ribosomal protein gene rpsL, but compensatory mutations in other genes involved in translation yield reversion to streptomycin-sensitivity (Moura de Sousa et al., 2017). As the molecular targets for phenotypic reversion are relatively broad, reversion to the antibiotic-susceptible state may be far more likely than previously appreciated.

To test the theory of phenotypic reversion, we studied laboratory evolved drug-resistant E. coli strains carrying 2 to 13 mutations and initially displaying reduced fitness compared to the wild-type strain. We found that 60 days of laboratory evolution in an antibiotic-free environment led to a rapid fitness improvement in 51% of the antibiotic-resistant lineages (some of which approximated wild-type fitness).

Fitness may increase during the course of laboratory evolution as a result of general adaptation to the environment and/or accumulation of compensatory mutations that mitigate the deleterious side effects of resistance. The second option is more realistic, as several mutations that had accumulated during laboratory evolution affected genes involved in bacterial defense mechanisms against antibiotics or in general stress-responses (eg. rpoS promoter region/nlpD gene (Stoebel et al., 2009), potD (Shah et al., 2011), soxSR (Jain and Saini, 2016) (Supplementary file 1). An in-depth genetic analysis has also demonstrated epistatic interactions between resistance- and putative compensatory mutations in marR and envZ. These mutations had accumulated during laboratory evolution and increased fitness in the respective antibiotic-resistant strain, but reduced fitness in the wild-type (Figure 5A and Figure 5—figure supplement 2A, Figure 5—source data 1). This latter finding also suggests that compensatory mutations themselves have associated fitness costs, preventing antibiotic-resistant bacteria to reach the full fitness of sensitive variants.

Crucially, we have demonstrated that drug-resistance declines in an antibiotic-free laboratory environment. In as few as 480 generations, 64.7% of drug-resistant E. coli strains showed elevated susceptibilities to at least one antibiotic investigated (Figure 3—source data 1). We did not observe bona fide reversion mutations in laboratory evolved bacteria (Durão et al., 2018). This is not unexpected, as compensation via the accumulation of additional mutations elsewhere in the genome is far more likely to occur.

Detailed genetic analysis of the mar regulon also supports the phenotypic reversion hypothesis. MarR is a transcriptional regulatory protein that controls the activity of the mar regulon in E. coli through the repression of marA. The mar regulon participates in controlling several genes involved in antibiotic-resistance, including the AcrA/AcrB/TolC multidrug-efflux system (Figure 6). In response to antibiotic stresses (e.g. doxycycline or ciprofloxacin), marR is regularly mutated both in clinical and in laboratory settings, leading to increased expression of marA and other members of the mar regulon (Praski Alzrigat et al., 2017). Here we have focused on a multidrug-resistant laboratory evolved E. coli strain that carries a mutation in the protein coding sequence of marR. This resistance mutation has an associated fitness cost (Figure 5—source data 1), promoting the accumulation of further mutations. Our study indicates that this can be achieved by a compensatory mutation in the promoter region of the mar operon. This compensatory mutation increases bacterial fitness, susceptibility to multiple antibiotics alike, and restores wild-type-like membrane permeability, probably through changing the activity of the mar regulon. In a follow-up work we are planning to study this phenomenon in detail.

Figure 6

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It is important to emphasize that loss of antibiotic resistance is not equally likely across antibiotic-resistant strains. For example, in our study, resistance to doxycycline and tetracycline was frequently lost, while aminoglycoside-resistance was generally maintained during the course of laboratory evolution (Figure 3C and D).

Our findings appear to be consistent with clinical data. For instance, a Finnish retrospective study assessed the proportion of quinolone-susceptible E. coli urine isolates before and after a nationwide restriction of ciprofloxacin use was implemented in Finland. The research revealed that a reduced consumption of quinolone antibiotics resulted in a significant decrease in quinolone-resistance of E. coli (Gottesman et al., 2009). Our laboratory study also shows that ciprofloxacin-resistance has declined in 66% of initially resistant populations, following a long-term exposure to antibiotic-free medium (Figure 3D). Another study examined the impact of a 24 month voluntary restriction on the use of trimethoprim-containing drugs in Sweden on the prevalence of trimethoprim-resistant E. coli isolated from urinary-tract infections. All clinical isolates were found to retain their resistance levels and carried mutation in folA, the target gene of trimethoprim, even after 24 months of trimethoprim restriction (Brolund et al., 2010). In agreement with this clinical study, we have found that all five trimethoprim resistant E. coli strains with a folA resistance mutation have maintained their resistance following laboratory evolution in antibiotic-free medium (Figure 3-source data, Figure 4—source data 1). These considerations must be taken with some caution, as comparison of clinical and laboratory data is not straightforward. For instance, restricted usage of certain antibiotics in hospitals cannot completely eliminate antibiotic selection in a given region due to lack of isolation and cross-resistance between antibiotics.

In summary, three main patterns indicate that phenotypic reversion to an antibiotic-susceptible state could be common during compensatory evolution. In our study, we have observed i) rapid fitness increase in antibiotic free-medium, ii) associated loss of antibiotic resistance, and we iii) identified specific mutations that simultaneously change both characteristics.

Our findings suggest that restricting antimicrobial usage could be a useful policy to control the increasing rise and spread of multidrug-resistant bacteria, but it may work for certain antibiotics only. We should also emphasize, however, that all our evolutionary experiments were performed in devoid of any antibiotics. The next logical step is to study the occurrence of phenotypic reversion during exposure to temporarily changing antibiotic treatments or sublethal antibiotic dosages as it may occur in real-life situations (Andersson and Hughes, 2014). Our study leaves open the question about the extent by which initial fitness costs of resistance mutations and/or intensity of antibiotic selection shapes subsequent compensatory evolution and associated loss of resistance. Finally, our study has focused on chromosomal resistance mutations. It is still unclear whether resistance-conferring plasmids are generally lost or the associated fitness costs are mitigated by genomic mutations (Dahlberg and Chao, 2003).

Sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under the accession number of PRJNA529335.

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Thank you for submitting your article "Rapid decline of bacterial multidrug-resistance in an antibiotic-free environment through phenotypic reversion" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Reviewing Editor and Patricia Wittkopp as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The authors examine the loss of antibiotic resistance after the termination of experimental evolution that lead to adaptation and increased antibiotic resistance. They examine the cost of resistance and the role of compensatory mutations (or reversions) in alleviating this cost. The three reviewers appreciated the quality of the work and its general interest, but they raised a number of major concerns that can be addressed by modifying the text, reanalyzing some of the results and potentially adding relatively minor experiments. Some of these major concerns relate to the descriptive and qualitative nature of some of the analyses shown that are critical to supporting the conclusions of the study. Others relate to the quality of the presentation and of the discussion of the results as they relate to previous work or to the limitation of the study.

Essential revisions:

1) The manuscript is generally well written and referenced satisfyingly. However, the introduction is somewhat lagging behind the rest of the text. Claims in the introduction are not always supported by appropriate citations and quality of the writing could be improved in this section. The authors are referred to some specific comments below for examples of elements of the introduction that could be improved. For instance, the introduction describes an important issue related to the fitness cost of antimicrobial resistance, which is the disconnect between the results of experimental evolution studies and clinical observations. Whether resistance to antimicrobials really does carry a fitness cost, and whether it may or may not be maintained in populations of pathogens thanks to compensatory evolution is indeed controversial, and contradictory evidence currently prevents a definitive answer to this question. This manuscript does not directly address this issue, yet the introduction would suggest otherwise. The authors are invited to revise the introduction to better expose the focus and scope of the study.

2) Results on the loss of resistance appear overly qualitative, at least in the format used in Figure 3 and in the text (subsection “Rapid loss of multi-drug resistance in an antibiotic-free medium”). We believe a synthetic quantitative analysis may be applied to the results provided in Supplementary file 3 and presented in a visually compelling manner. A quantitative measure of loss in resistance and its causal link or not with loss of cost would be more convincing, and would help readers grasp the magnitude of this phenotypic change. The authors already have MIC data: it is just a matter of using it in a transparent data visualization format, which may still use the qualitative categories, but perhaps as an added layer on top of the quantitative data, rather than as the sole reported measure.

3) As presented, the results suggest association between fitness recovery and resistance loss. The manuscript would be enhanced by a more formal statistical analysis of this association.

4) The Discussion section provides a list of evidence that the authors claim supports a driving role for compensatory evolution in adaptation to the antibiotic-free environment. We think that 2 out of the 3 evidences mentioned does not support that claim. The first reads: "First, our analysis has revealed major differences in relative fitness across strains adapted to different antibiotics." We fail to see how differences in fitness between strains supports any role for compensatory evolution. It rather suggests a diversity of molecular mechanisms, which may or may not involve compensation. In other words, this phenomenon appears unrelated to compensation. The next claim which we believe is not supported by the stated evidence is the following: "Third, an in-depth genetic analysis has demonstrated that mutations in marR and envZ increased fitness in the respective antibiotic-resistant strain, but reduced fitness in the wild-type". This is evidence for epistasis, but not for compensation per se.

5) In comparison to the extensive analysis done in Lazar Nat Communications 2014, I would have preferred a similar detailed analysis of the evolutionary experiments in this follow-up work. The broad conclusions are expected, but there are insights which this work could uniquely address. For one, it is interesting to know how the fitness cost and resistance level vary with generation-while there is a broad phenotypic reversion of fitness cost, does this happen early on the evolution, does this reversion occur at the same time that resistance is reacquired (or not)? Additionally, what is the reproducibility/repeatability of these time courses across the replicates?

6) I am curious to know when the mutations found at the end of the experiment (listed in Supplementary file 4) occurred during the lab evolution. Moreover, do the occurrence of specific mutations correlate with the time-changes of fitness cost and resistance level throughout the evolution? This is particularly crucial because there are multiple mutations at the end of the lab evolution and correlating these with fitness cost and resistance level could determine causality and epistasis between mutations.

7) Why do many of the T60 lines have worse fitness after evolution in antibiotic-free medium? One would expect the fitness to at least stay the same or improve.

8) Subsection “Laboratory evolution of antibiotic-resistant strains in an antibiotic-free medium”: "adaptation is mainly driven by the set of resistance mutations present in the T0 strains." Isn't it also possible that mutations are stochastic, and some populations would have higher fitness by chance alone?

9) Figure 3 and subsection “Rapid loss of multi-drug resistance in an antibiotic-free medium”: Are these differences in ratios statistically significant?

10) Subsection “Compensatory mutations rather than reversions dominate”: "identify resistance-conferring SNPs in the T0 population that revert back". What is the likelihood of the exact mutation occurring and invading the population? I would guess this is very low, so searching for the exact mutation would not be a fruitful exercise. How about other mutations in the same genes as those where alterations conferred resistance? The analysis of compensatory mutations reveal that they are located elsewhere in the genome, i.e. not in the resistance genes themselves. This analysis appears superficial. Are these genes somehow functionally related? Part of the same modules? Related by genetic or other types of interactions?

11) Is there any direct evidence that resistance in the clinic comes at a major fitness cost? Also, the role of HGT is not mentioned, although this is a common means of low-cost resistance. Discussion section: The comparison to clinical restriction is problematic for a few reasons. (1) The clinic has an ecological component where the sensitive strain may still persist and invade the resistant strain once antibiotic selection pressure is ceased. (2) Countries are not isolated, so it is unclear that restriction in one place would eliminate pressure on the population as a whole. (3) Many antibiotic adaptations impart cross-resistance, so continued use of other antibiotics may diminish the effect of restricting another.

12) Parallel evolution of the wild type would have provided a useful control to distinguish the change in fitness specifically associated with antibiotic resistance phenotypes from general adaptation to the physicochemical environment of the experiment. Continued propagation in presence of antibiotic would also have provided a useful point of comparison. While these data would have enhanced the manuscript, in their absence, a discussion of expected results from these controls may add depth and perspective to the text.

13) The authors should clarify the caveats and limitations of the study. For example, the results here strongly depend on the fitness cost of resistant mutants acquired from previous evolution in strong selection regime (high antibiotic concentration and large population size) (Lazar Nat Communications 2014). Presumably, the trade-off between resistance level and fitness cost will be different in the low-selection regime and sub-inhibitory concentrations. Thus, the finding of rapid loss of fitness cost may not apply to resistance-conferring mutations evolved under this regime.

Essential revisions:

1) The manuscript is generally well written and referenced satisfyingly. However, the introduction is somewhat lagging behind the rest of the text. Claims in the introduction are not always supported by appropriate citations and quality of the writing could be improved in this section. The authors are referred to some specific comments below for examples of elements of the introduction that could be improved. For instance, the introduction describes an important issue related to the fitness cost of antimicrobial resistance, which is the disconnect between the results of experimental evolution studies and clinical observations. Whether resistance to antimicrobials really does carry a fitness cost, and whether it may or may not be maintained in populations of pathogens thanks to compensatory evolution is indeed controversial, and contradictory evidence currently prevents a definitive answer to this question. This manuscript does not directly address this issue, yet the introduction would suggest otherwise. The authors are invited to revise the introduction to better expose the focus and scope of the study.

Thank you for the suggestion. We modified the introduction with the aim to better describe the reasons for the controversy surrounding implications of clinical and laboratory studies on cost of resistance and the efficacy of compensatory evolution. We also put effort to cite more relevant papers on the subject.

2) Results on the loss of resistance appear overly qualitative, at least in the format used in Figure 3 and in the text (subsection “Rapid loss of multi-drug resistance in an antibiotic-free medium”). We believe a synthetic quantitative analysis may be applied to the results provided in Supplementary file 3 and presented in a visually compelling manner. A quantitative measure of loss in resistance and its causal link or not with loss of cost would be more convincing, and would help readers grasp the magnitude of this phenotypic change. The authors already have MIC data: it is just a matter of using it in a transparent data visualization format, which may still use the qualitative categories, but perhaps as an added layer on top of the quantitative data, rather than as the sole reported measure.

We fully agree and thank you for the suggestion. We designed a new figure depicting resistance level of each T0 and the corresponding T60 strains (Figure 3A).

3) As presented, the results suggest association between fitness recovery and resistance loss. The manuscript would be enhanced by a more formal statistical analysis of this association.

Thank you for the suggestions. As suggested, we also tested the association of relative fitness and the change of resistance against the antibiotic the T0 lines were originally adapted to. Accordingly, we generated a new figure depicting the correlation between fitness recovery and resistance loss. We found a significant negative correlation between fitness improvement and resistance level (Figure 4A).

4) The Discussion section provides a list of evidence that the authors claim supports a driving role for compensatory evolution in adaptation to the antibiotic-free environment. We think that 2 out of the 3 evidences mentioned does not support that claim. The first reads: "First, our analysis has revealed major differences in relative fitness across strains adapted to different antibiotics." We fail to see how differences in fitness between strains supports any role for compensatory evolution. It rather suggests a diversity of molecular mechanisms, which may or may not involve compensation. In other words, this phenomenon appears unrelated to compensation. The next claim which we believe is not supported by the stated evidence is the following: "Third, an in-depth genetic analysis has demonstrated that mutations in marR and envZ increased fitness in the respective antibiotic-resistant strain, but reduced fitness in the wild-type". This is evidence for epistasis, but not for compensation per se.

We modified the text as follows:

“Fitness may increase during the course of laboratory evolution as a result of general adaptation to the environment and/or accumulation of compensatory mutations that mitigate the deleterious side effects of resistance. The second option is more realistic, as several mutations that had accumulated during laboratory evolution affected genes involved in bacterial defense mechanisms against antibiotics or in general stress-responses (e.g. rpoS promoter region/nlpD gene (Stoebel et al., 2009), potD (Shah et al., 2011), soxSR (Jain and Saini, 2016) (Supplementary file 4). An in-depth genetic analysis has also demonstrated epistatic interactions between resistance and putative compensatory mutations in marR and envZ. These mutations had accumulated during laboratory evolution and increased fitness in the respective antibiotic-resistant strain, but reduced fitness in the wild-type (Figure 5A and Figure 5 —figure supplement 1A).”

5) In comparison to the extensive analysis done in Lazar Nat Communications 2014, I would have preferred a similar detailed analysis of the evolutionary experiments in this follow-up work. The broad conclusions are expected, but there are insights which this work could uniquely address. For one, it is interesting to know how the fitness cost and resistance level vary with generation-while there is a broad phenotypic reversion of fitness cost, does this happen early on the evolution, does this reversion occur at the same time that resistance is reacquired (or not)?

Thank you for the suggestion. Due to a strict deadline on submission of the revised manuscript, we focused on four randomly selected lineages only (CIP5a, DOX3a, TMP9e and TOB9e) and measured resistance levels and fitness at three timepoints: the beginning of, the midpoint and the end of the course of laboratory evolution. We found that at certain time points, decline in resistance and fitness recovery are coupled to each other.

Additionally, what is the reproducibility/repeatability of these time courses across the replicates?

We compared the fitness improvements between T60 evolved lines founded from the same T0 genotype versus those founded from different genotypes. As expected, the coefficient of variation was significantly lower (Mann-Whitney U-test, p < 0.01) for T60 lines founded from the same T0 genotype (median CV = 6.95%) compared to that of founded from different T0 genotypes (median CV = 11.6%).

6) I am curious to know when the mutations found at the end of the experiment (listed in Supplementary file 4) occurred during the lab evolution. Moreover, do the occurrence of specific mutations correlate with the time-changes of fitness cost and resistance level throughout the evolution? This is particularly crucial because there are multiple mutations at the end of the lab evolution and correlating these with fitness cost and resistance level could determine causality and epistasis between mutations.

The issues raised here are indeed very interesting but are beyond the scope of the manuscript. Answering them demands population genomic sequencing and subsequent in-depth phenotypic analyses of clones and populations at different timepoints of laboratory evolution.

7) Why do many of the T60 lines have worse fitness after evolution in antibiotic-free medium? One would expect the fitness to at least stay the same or improve.

This is not entirely unexpected, as the calculated bacterial growth parameters are unlikely to capture all aspects of bacterial fitness.

8) Subsection “Laboratory evolution of antibiotic-resistant strains in an antibiotic-free medium”: "adaptation is mainly driven by the set of resistance mutations present in the T0 strains." Isn't it also possible that mutations are stochastic, and some populations would have higher fitness by chance alone?

There are several reasons to believe that adaptive mutations accumulated during the course of evolution. First, the timescale of laboratory evolution was too limited for fixation of neutral mutations driven by stochastic changes in gene frequencies. Second, the accumulated mutations were far from being random: the corresponding genes are mainly involved in bacterial defense mechanisms against antibiotics or in general stress-responses. Finally, reconstruction of putative compensatory mutations increased fitness in a genotype specific manner, see main text Figure 5A.

9) Figure 3 and subsection “Rapid loss of multi-drug resistance in an antibiotic-free medium”: Are these differences in ratios statistically significant?

Yes, they are. We inserted this result into the corresponding figure legend (Figure 3B):

“Figure 3C and 3D show the number of T60 lines with resistance maintained/lost to the range of antibiotics tested, as absolute number versus ratio, respectively. Doxycycline (DOX) and tetracycline (TET) resistance was frequently lost (Fisher’s Exact test: odds ratio = 0.05, p < 0.001 for DOX and odds ratio = 0.20, p < 0.01 for TET), while aminoglycoside (kanamycin – KAN, tetracycline – TET) resistance was generally maintained in the T60 lines (Fisher’s Exact test: odds ratio = 14.29, p < 0.001 for KAN, odds ratio = 6.5, p < 0.01 for TOB).”

10) Subsection “Compensatory mutations rather than reversions dominate”: "identify resistance-conferring SNPs in the T0 population that revert back". What is the likelihood of the exact mutation occurring and invading the population? I would guess this is very low, so searching for the exact mutation would not be a fruitful exercise. How about other mutations in the same genes as those where alterations conferred resistance? The analysis of compensatory mutations reveal that they are located elsewhere in the genome, i.e. not in the resistance genes themselves. This analysis appears superficial. Are these genes somehow functionally related? Part of the same modules? Related by genetic or other types of interactions?

Due to the low number of mutations that have accumulated during the course of laboratory evolution, we could not perform a rigorous statistical analysis to test this issue. Nevertheless, we provide several examples on functional relatedness between resistance genes mutated in T0 and genes mutated during lab evolution (i.e. found in T60 strains only, Figure 5—figure supplement 1.).

ompR-EnvZ (FOX8b): A cefoxitin-resistant T0 line carries a resistance-conferring mutation in the transcriptional regulatory protein OmpR. OmpR modulates the expression of major outer membrane protein genes, and forms a two-component regulatory system with the sensory histidine kinase EnvZ, a protein mutated in the corresponding T60 line.

PhoQ/SoxR – PhoP/SoxS (TMP9c): A trimetoprim-resistant T0 lines carries a mutation in the sensory histidine kinase PhoQ and the redox‑sensitive transcriptional activator SoxR, whereas the T60 line carried compensatory mutations in genes phoP (response regulator in two‑component regulatory system with PhoQ) and soxS (superoxide response regulon transcriptional activator).

AcrR-MarR (CIP5a): Mutation in the transcriptional repressor AcrR most likely confers ciprofloxacin-resistance in T0 through activating the AcrA/AcrB/TolC multidrug-efflux system, while a compensatory mutation appeared in MarR, that controls the activity of the mar regulon. The mar regulon is responsible for regulatingseveral genes involved in antibiotic-resistance, including those that encode components of the AcrA/AcrB/TolC multidrug-efflux system.

11) Is there any direct evidence that resistance in the clinic comes at a major fitness cost?

Yes, there is some prior data supporting these claims, and we cite the relevant publications in the main text, such as: Prabh et al., 2018 and Alzigat et al., 2017

Also, the role of HGT is not mentioned, although this is a common means of low-cost resistance.

We briefly mention HGT in the Discussion section:

“Finally, our study has focused on chromosomal resistance mutations. It is still unclear whether resistance-conferring plasmids are generally lost or the associated fitness costs are mitigated by genomic mutations (Dahlberg and Chao, 2003).”

Discussion section: The comparison to clinical restriction is problematic for a few reasons. (1) The clinic has an ecological component where the sensitive strain may still persist and invade the resistant strain once antibiotic selection pressure is ceased. (2) Countries are not isolated, so it is unclear that restriction in one place would eliminate pressure on the population as a whole. (3) Many antibiotic adaptations impart cross-resistance, so continued use of other antibiotics may diminish the effect of restricting another.

We readily admit these caveats. As regards (1), we write in the Introduction:

“This disagreement between clinical observations and laboratory studies could have multiple reasons. First, antibiotic treatments frequently fail to completely eradicate antibiotic-sensitive bacteria from the population. Following treatment, antibiotic sensitive bacteria with high fitness could rapidly spread in the population, leading to rapid loss of resistance.”

As regards 2) and 3), we write in the Discussion section:

“These considerations must be taken with some caution, as comparison of clinical and laboratory data is not straightforward. For instance, restricted usage of certain antibiotics in hospitals cannot completely eliminate antibiotic selection in a given region due to lack of isolation and cross-resistance between antibiotics.”

12) Parallel evolution of the wild type would have provided a useful control to distinguish the change in fitness specifically associated with antibiotic resistance phenotypes from general adaptation to the physicochemical environment of the experiment. Continued propagation in presence of antibiotic would also have provided a useful point of comparison. While these data would have enhanced the manuscript, in their absence, a discussion of expected results from these controls may add depth and perspective to the text.

As noted above, we addressed this issue as follows:

“Fitness may increase during the course of laboratory evolution as a result of general adaptation to the environment and/or accumulation of compensatory mutations that mitigate the deleterious side effects of resistance. The second option is more realistic, as several mutations that had accumulated during laboratory evolution affect genes involved in antibiotic or general stress response (e.g. rpoS promoter region/nlpD gene (Stoebel et al., 2009), potD (Shah et al., 2011), soxSR (Jain and Saini, 2016) (Supplementary file 4). An in-depth genetic analysis has also demonstrated epistatic interactions between resistance- and putative compensatory mutations in marR and envZ. These mutations had accumulated during laboratory evolution and increased fitness in the respective antibiotic-resistant strain, but reduced fitness in the wild-type (Figure 5A and Figure 5—figure supplement 2A).”

13) The authors should clarify the caveats and limitations of the study. For example, the results here strongly depend on the fitness cost of resistant mutants acquired from previous evolution in strong selection regime (high antibiotic concentration and large population size) (Lazar Nat Communications 2014). Presumably, the trade-off between resistance level and fitness cost will be different in the low-selection regime and sub-inhibitory concentrations. Thus, the finding of rapid loss of fitness cost may not apply to resistance-conferring mutations evolved under this regime.

We describe the caveats of our work in more detail, as follows:

“We should also emphasize, however, that all our evolutionary experiments were performed in devoid of any antibiotics. The next logical step is to study the occurrence of phenotypic reversion during exposure to temporarily changing antibiotic treatments or sublethal antibiotic dosages as it may occur in real-life situations (Andersson and Hughes, 2014). Our study leaves open the question about the extent by which initial fitness costs of resistance mutations and/or intensity of antibiotic selection shapes subsequent compensatory evolution and associated loss of resistance. Finally, our study has focused on chromosomal resistance mutations. It is still unclear whether resistance-conferring plasmids are generally lost or the associated fitness costs are mitigated by genomic mutations (Dahlberg and Chao, 2003).

Author details

1. Anett Dunai

   1. Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary
   2. Doctoral School in Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing, Interpretation of data

   Contributed equally with

   Réka Spohn and Zoltán Farkas

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6270-9603

2. Réka Spohn

   Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

   Contribution

   Conceptualization, Formal analysis, Supervision, Investigation, Methodology, Writing—original draft

   Contributed equally with

   Anett Dunai and Zoltán Farkas

   Competing interests

   No competing interests declared

3. Zoltán Farkas

   Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing, Interpretation of data

   Contributed equally with

   Anett Dunai and Réka Spohn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5085-3306

4. Viktória Lázár

   Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

   Present address

   Faculty of Biology, Technion – Israel Institute of Technology, Haifa, Israel

   Contribution

   Conceptualization, Formal analysis, Supervision, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Ádám Györkei

   Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

   Contribution

   Formal analysis, Interpretation of data

   Competing interests

   No competing interests declared

6. Gábor Apjok

   1. Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary
   2. Doctoral School in Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary

   Contribution

   Methodology

   Competing interests

   No competing interests declared

7. Gábor Boross

   Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

   Present address

   Department of Biology, Stanford University, Stanford, United States

   Contribution

   Formal analysis, Methodology, Interpretation of data

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7208-5678

8. Balázs Szappanos

   Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

   Contribution

   Formal analysis, Interpretation of data

   Competing interests

   No competing interests declared

9. Gábor Grézal

   Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

   Contribution

   Formal analysis, Visualization, Interpretation of data

   Competing interests

   No competing interests declared

10. Anikó Faragó

    1. Doctoral School in Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary
    2. Department of Biochemistry and Molecular Biology, University of Szeged, Szeged, Hungary

    Contribution

    Resources, Bioinformatic analysis of whole genome sequencing data

    Competing interests

    No competing interests declared

11. László Bodai

    Department of Biochemistry and Molecular Biology, University of Szeged, Szeged, Hungary

    Contribution

    Resources, Bioinformatic analysis of whole genome sequencing data

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8411-626X

12. Balázs Papp

    Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

    Contribution

    Resources, Funding acquisition, Writing—review and editing

    Competing interests

    No competing interests declared

13. Csaba Pál

    Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing

    For correspondence

    cpal@brc.hu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5187-9903

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