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质粒介导的抗菌药物耐药性的全基因组互作。

Global epistasis in plasmid-mediated antimicrobial resistance.

机构信息

Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain.

Institute of Functional Biology & Genomics, IBFG - CSIC, Universidad de Salamanca, Salamanca, Spain.

出版信息

Mol Syst Biol. 2024 Apr;20(4):311-320. doi: 10.1038/s44320-024-00012-1. Epub 2024 Feb 26.

Abstract

Antimicrobial resistance (AMR) in bacteria is a major public health threat and conjugative plasmids play a key role in the dissemination of AMR genes among bacterial pathogens. Interestingly, the association between AMR plasmids and pathogens is not random and certain associations spread successfully at a global scale. The burst of genome sequencing has increased the resolution of epidemiological programs, broadening our understanding of plasmid distribution in bacterial populations. Despite the immense value of these studies, our ability to predict future plasmid-bacteria associations remains limited. Numerous empirical studies have recently reported systematic patterns in genetic interactions that enable predictability, in a phenomenon known as global epistasis. In this perspective, we argue that global epistasis patterns hold the potential to predict interactions between plasmids and bacterial genomes, thereby facilitating the prediction of future successful associations. To assess the validity of this idea, we use previously published data to identify global epistasis patterns in clinically relevant plasmid-bacteria associations. Furthermore, using simple mechanistic models of antibiotic resistance, we illustrate how global epistasis patterns may allow us to generate new hypotheses on the mechanisms associated with successful plasmid-bacteria associations. Collectively, we aim at illustrating the relevance of exploring global epistasis in the context of plasmid biology.

摘要

细菌中的抗生素耐药性 (AMR) 是一个主要的公共卫生威胁,而可移动质粒在 AMR 基因在细菌病原体中的传播中起着关键作用。有趣的是,AMR 质粒与病原体之间的关联并非随机的,某些关联在全球范围内成功传播。基因组测序的爆发提高了流行病学计划的分辨率,拓宽了我们对细菌种群中质粒分布的理解。尽管这些研究具有巨大的价值,但我们预测未来质粒-细菌关联的能力仍然有限。最近的许多实证研究报告了遗传相互作用中的系统模式,从而在称为全局上位性的现象中实现了可预测性。在这种观点下,我们认为全局上位性模式有可能预测质粒和细菌基因组之间的相互作用,从而有助于预测未来成功的关联。为了评估这个想法的有效性,我们使用先前发表的数据来识别临床相关质粒-细菌关联中的全局上位性模式。此外,我们使用抗生素耐药性的简单机制模型来说明全局上位性模式如何使我们能够生成与成功的质粒-细菌关联相关的机制的新假设。总的来说,我们旨在说明在质粒生物学背景下探索全局上位性的相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86f7/10987494/1eccd26a053b/44320_2024_12_Figb_HTML.jpg

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