Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments.
As failure rates for traditional antimicrobial therapies escalate, recent focus has shifted to evolution-based therapies to slow resistance. Collateral sensitivity-the increased susceptibility to one drug associated with evolved resistance to a different drug-offers a potentially exploitable evoluti...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Biology |
| Online Access: | https://doi.org/10.1371/journal.pbio.3002970 |
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| author | Jeff Maltas Anh Huynh Kevin B Wood |
| author_facet | Jeff Maltas Anh Huynh Kevin B Wood |
| author_sort | Jeff Maltas |
| collection | DOAJ |
| description | As failure rates for traditional antimicrobial therapies escalate, recent focus has shifted to evolution-based therapies to slow resistance. Collateral sensitivity-the increased susceptibility to one drug associated with evolved resistance to a different drug-offers a potentially exploitable evolutionary constraint, but the manner in which collateral effects emerge over time is not well understood. Here, we use laboratory evolution in the opportunistic pathogen Enterococcus faecalis to phenotypically characterize collateral profiles through evolutionary time. Specifically, we measure collateral profiles for 400 strain-antibiotic combinations over the course of 4 evolutionary time points as strains are selected in increasing concentrations of antibiotic. We find that at a global level-when results from all drugs are combined-collateral resistance dominates during early phases of adaptation, when resistance to the selecting drug is lower, while collateral sensitivity becomes increasingly likely with further selection. At the level of individual populations; however, the trends are idiosyncratic; for example, the frequency of collateral sensitivity to ceftriaxone increases over time in isolates selected by linezolid but decreases in isolates selected by ciprofloxacin. We then show experimentally how dynamic collateral sensitivity relationships can lead to time-dependent dosing windows that depend on finely timed switching between drugs. Finally, we develop a stochastic mathematical model based on a Markov decision process consistent with observed dynamic collateral profiles to show measurements across time are required to optimally constrain antibiotic resistance. |
| format | Article |
| id | doaj-art-42f65496910e445e81fb9d228a480126 |
| institution | Kabale University |
| issn | 1544-9173 1545-7885 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Biology |
| spelling | doaj-art-42f65496910e445e81fb9d228a4801262025-01-17T05:30:43ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852025-01-01231e300297010.1371/journal.pbio.3002970Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments.Jeff MaltasAnh HuynhKevin B WoodAs failure rates for traditional antimicrobial therapies escalate, recent focus has shifted to evolution-based therapies to slow resistance. Collateral sensitivity-the increased susceptibility to one drug associated with evolved resistance to a different drug-offers a potentially exploitable evolutionary constraint, but the manner in which collateral effects emerge over time is not well understood. Here, we use laboratory evolution in the opportunistic pathogen Enterococcus faecalis to phenotypically characterize collateral profiles through evolutionary time. Specifically, we measure collateral profiles for 400 strain-antibiotic combinations over the course of 4 evolutionary time points as strains are selected in increasing concentrations of antibiotic. We find that at a global level-when results from all drugs are combined-collateral resistance dominates during early phases of adaptation, when resistance to the selecting drug is lower, while collateral sensitivity becomes increasingly likely with further selection. At the level of individual populations; however, the trends are idiosyncratic; for example, the frequency of collateral sensitivity to ceftriaxone increases over time in isolates selected by linezolid but decreases in isolates selected by ciprofloxacin. We then show experimentally how dynamic collateral sensitivity relationships can lead to time-dependent dosing windows that depend on finely timed switching between drugs. Finally, we develop a stochastic mathematical model based on a Markov decision process consistent with observed dynamic collateral profiles to show measurements across time are required to optimally constrain antibiotic resistance.https://doi.org/10.1371/journal.pbio.3002970 |
| spellingShingle | Jeff Maltas Anh Huynh Kevin B Wood Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments. PLoS Biology |
| title | Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments. |
| title_full | Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments. |
| title_fullStr | Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments. |
| title_full_unstemmed | Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments. |
| title_short | Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments. |
| title_sort | dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments |
| url | https://doi.org/10.1371/journal.pbio.3002970 |
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