Higher levels of antibiotic resistance are less competitive: the hidden ecological cost of no-metabolic cost resistance

抗生素耐药性水平越高,竞争力就越弱:这是无代谢成本耐药性的隐性生态代价。

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Abstract

Antibiotic resistance is often assumed to be constrained by fitness costs that limit the spread of highly resistant strains. Yet, many resistance mechanisms - including enzymatic antibiotic degradation - can arise with little or no metabolic cost, raising an important question: why is extreme resistance not more widespread? Here, we show that community-level interactions impose a hidden ecological cost on high resistance. By performing experiments with simple communities comprised of antibiotic resistant clinical isolates and an antibiotic susceptible strain, we find that when exposed to betalactam antibiotics, strains with a higher degree of antibiotic resistance can promote the survival of cohabiting susceptible strains. Guided by mean-field modeling, we find that highly resistant bacteria accelerate detoxification of the shared environment, shortening the period during which resistance confers a competitive advantage. Experiments with an engineered strain with tunable resistance level confirm that susceptible cells grow best in the presence of highly resistant strains. Importantly, this effect does not require evolved cooperation or active enzyme secretion; experimental and modeling results show that unavoidable processes, such as cell death or passive leakage, prevent complete privatization of resistance, giving rise to "accidental cooperation". These findings suggest that resistance evolution is not only shaped by intrinsic cellular costs but also by ecological feedback that limits the benefits of incremental increases in resistance. This result may be reflected in the phenotypic responses of the clinical strains tested in this work, which fell into distinct low- and high-resistance classes with no intermediate phenotypes. Thus, this work demonstrates the important role of community dynamics in understanding the evolution of antibiotic resistance and treatment outcomes.

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