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Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance

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  • Jeff Maltas
  • Kevin B Wood

Abstract

Evolved resistance to one antibiotic may be associated with "collateral" sensitivity to other drugs. Here, we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant–drug combinations. We find that collateral effects are pervasive but difficult to predict because independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. Although phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in a four-drug uniform cycle.An experimental evolution study in bacteria offers a new approach to slowing antibiotic resistance by exploiting correlations that occur between evolved resistance levels to currently available drugs. The study reveals a new strategy that balances short-term inhibition of growth with infrequent use of antibiotics intended to steer pathogen populations to a more vulnerable future state.

Suggested Citation

  • Jeff Maltas & Kevin B Wood, 2019. "Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance," PLOS Biology, Public Library of Science, vol. 17(10), pages 1-34, October.
  • Handle: RePEc:plo:pbio00:3000515
    DOI: 10.1371/journal.pbio.3000515
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    References listed on IDEAS

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    1. Remy Chait & Allison Craney & Roy Kishony, 2007. "Antibiotic interactions that select against resistance," Nature, Nature, vol. 446(7136), pages 668-671, April.
    2. Jason Karslake & Jeff Maltas & Peter Brumm & Kevin B Wood, 2016. "Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-21, October.
    3. Joseph Peter Torella & Remy Chait & Roy Kishony, 2010. "Optimal Drug Synergy in Antimicrobial Treatments," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-9, June.
    4. Daniel Nichol & Joseph Rutter & Christopher Bryant & Andrea M. Hujer & Sai Lek & Mark D. Adams & Peter Jeavons & Alexander R. A. Anderson & Robert A. Bonomo & Jacob G. Scott, 2019. "Antibiotic collateral sensitivity is contingent on the repeatability of evolution," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    5. Daniel Nichol & Peter Jeavons & Alexander G Fletcher & Robert A Bonomo & Philip K Maini & Jerome L Paul & Robert A Gatenby & Alexander RA Anderson & Jacob G Scott, 2015. "Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-19, September.
    6. Nicole L. Podnecky & Elizabeth G. A. Fredheim & Julia Kloos & Vidar Sørum & Raul Primicerio & Adam P. Roberts & Daniel E. Rozen & Ørjan Samuelsen & Pål J. Johnsen, 2018. "Conserved collateral antibiotic susceptibility networks in diverse clinical strains of Escherichia coli," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
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    Cited by:

    1. Teemu Kuosmanen & Johannes Cairns & Robert Noble & Niko Beerenwinkel & Tommi Mononen & Ville Mustonen, 2021. "Drug-induced resistance evolution necessitates less aggressive treatment," PLOS Computational Biology, Public Library of Science, vol. 17(9), pages 1-22, September.
    2. Neythen J Treloar & Alex J H Fedorec & Brian Ingalls & Chris P Barnes, 2020. "Deep reinforcement learning for the control of microbial co-cultures in bioreactors," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-18, April.

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