Maltas Jeff, Huynh Anh, Wood Kevin B
Department of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Physics, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS Biol. 2025 Jan 8;23(1):e3002970. doi: 10.1371/journal.pbio.3002970. eCollection 2025 Jan.
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.
随着传统抗菌疗法的失败率不断攀升，近期的研究重点已转向基于进化的疗法以减缓耐药性。旁系敏感性——对一种药物的易感性增加与对另一种药物产生进化耐药性相关——提供了一种潜在可利用的进化限制，但旁系效应随时间出现的方式尚未得到充分理解。在此，我们利用机会致病菌粪肠球菌的实验室进化，通过进化时间从表型上表征旁系特征。具体而言，当菌株在不断增加的抗生素浓度中进行选择时，我们在4个进化时间点的过程中测量了400种菌株 - 抗生素组合的旁系特征。我们发现，在全球层面——当所有药物的结果合并在一起时——在适应的早期阶段，旁系耐药性占主导，此时对选择药物的耐药性较低，而随着进一步选择，旁系敏感性变得越来越可能。然而，在个体群体层面，趋势是特异的；例如，在由利奈唑胺选择的分离株中，对头孢曲松的旁系敏感性频率随时间增加，而在由环丙沙星选择的分离株中则降低。然后，我们通过实验展示了动态旁系敏感性关系如何导致依赖于药物之间精确时间切换的时间依赖性给药窗口。最后，我们基于马尔可夫决策过程开发了一个随机数学模型，该模型与观察到的动态旁系特征一致，以表明需要跨时间进行测量以最佳地限制抗生素耐药性。
