Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization.
Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization.
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作者：
抗生素耐药性是一项重大的医疗和公共卫生挑战，其特点是全球耐药菌株流行率的增加。传统观点认为，所有抗生素耐药性都是有问题的，即使不是病原体。共生细菌的耐药性会带来风险，因为耐药生物体可以提供耐药基因库，这些耐药基因可以水平转移到病原体，或者本身可能在未来引起机会性感染。虽然这些风险是真实存在的，但我们认为，共生耐药性也可以通过促进对病原体的持续生态抑制，在人类感染的抗生素治疗过程中产生益处。为了定义和说明这种替代概念视角，我们使用两种物种的数学模型来确定有益抵抗力的必要和充分的生态条件。我们表明，这些益处仅限于物种（或菌株）相互作用，其中共生体抑制病原体生长，并且当共生体与病原体竞争而不是捕食或以其他方式利用病原体时，益处最大化。通过识别共生抗性的好处，我们建议，与其严格最小化所有抗性，不如将抗性管理视为优化问题。我们讨论了两个应用环境中的影响：共生微生物组中的旁观者（非目标）选择和多种微生物感染的病原体治疗。
Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections.
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影响因子：
4.3
作者：
Day T;Read AF
通讯作者：
Read AF
DOI：
10.1038/nri3535
发表时间：
2013-11
期刊：
Nature reviews. Immunology
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1126/science.aad3292
发表时间：
2016-01-01
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Baym M;Stone LK;Kishony R
通讯作者：
Kishony R
影响因子：
1.9
作者：
Hesseler, Julia;Schmidt, Julia K.;Flockerzi, Dietrich
通讯作者：
Flockerzi, Dietrich
影响因子：
3.7
作者：
Eswarappa, Sandeepa M.;Estrela, Sylvie;Brown, Sam P.
通讯作者：
Brown, Sam P.