Accessory genes define species-specific routes to antibiotic resistance.

IF 3.3 2区 生物学 Q1 BIOLOGY
Life Science Alliance Pub Date : 2024-01-16 Print Date: 2024-04-01 DOI:10.26508/lsa.202302420
Lucy Dillon, Nicholas J Dimonaco, Christopher J Creevey
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引用次数: 0

Abstract

A deeper understanding of the relationship between the antimicrobial resistance (AMR) gene carriage and phenotype is necessary to develop effective response strategies against this global burden. AMR phenotype is often a result of multi-gene interactions; therefore, we need approaches that go beyond current simple AMR gene identification tools. Machine-learning (ML) methods may meet this challenge and allow the development of rapid computational approaches for AMR phenotype classification. To examine this, we applied multiple ML techniques to 16,950 bacterial genomes across 28 genera, with corresponding MICs for 23 antibiotics with the aim of training models to accurately determine the AMR phenotype from sequenced genomes. This resulted in a >1.5-fold increase in AMR phenotype prediction accuracy over AMR gene identification alone. Furthermore, we revealed 528 unique (often species-specific) genomic routes to antibiotic resistance, including genes not previously linked to the AMR phenotype. Our study demonstrates the utility of ML in predicting AMR phenotypes across diverse clinically relevant organisms and antibiotics. This research proposes a rapid computational method to support laboratory-based identification of the AMR phenotype in pathogens.

附属基因决定了抗生素耐药性的物种特异性途径。
要针对这一全球性负担制定有效的应对策略,就必须深入了解抗菌素耐药性(AMR)基因携带与表型之间的关系。AMR 表型往往是多基因相互作用的结果;因此,我们需要超越目前简单的 AMR 基因识别工具的方法。机器学习(ML)方法可以应对这一挑战,并允许开发用于 AMR 表型分类的快速计算方法。为了研究这一点,我们将多种 ML 技术应用于 28 个属的 16,950 个细菌基因组,以及 23 种抗生素的相应 MICs,目的是训练模型,以便从测序基因组中准确确定 AMR 表型。结果,AMR 表型预测准确率比单纯的 AMR 基因鉴定提高了 1.5 倍以上。此外,我们还发现了 528 种独特的(通常是物种特异性的)抗生素耐药性基因组途径,包括以前与 AMR 表型无关的基因。我们的研究证明了 ML 在预测各种临床相关生物和抗生素的 AMR 表型方面的实用性。这项研究提出了一种快速计算方法,以支持基于实验室的病原体 AMR 表型鉴定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Life Science Alliance
Life Science Alliance Agricultural and Biological Sciences-Plant Science
CiteScore
5.80
自引率
2.30%
发文量
241
审稿时长
10 weeks
期刊介绍: Life Science Alliance is a global, open-access, editorially independent, and peer-reviewed journal launched by an alliance of EMBO Press, Rockefeller University Press, and Cold Spring Harbor Laboratory Press. Life Science Alliance is committed to rapid, fair, and transparent publication of valuable research from across all areas in the life sciences.
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