Prediction of Antibiotic Resistance Evolution by Growth Measurement of All Proximal Mutants of Beta-Lactamase

Siyuan Feng, Zhuoxing Wu, Wanfei Liang, Xin Zhang, Xiujuan Cai, Jiachen Li, Lujie Liang, Daixi Lin, Nicole Stoesser, Yohei Doi, Lan Lan Zhong, Yan Liu, Yong Xia, Min Dai, Liyan Zhang, Xiaoshu Chen, Jian Rong Yang, Guo Bao Tian

研究成果: Article査読

抄録

The antibiotic resistance crisis continues to threaten human health. Better predictions of the evolution of antibiotic resistance genes could contribute to the design of more sustainable treatment strategies. However, comprehensive prediction of antibiotic resistance gene evolution via laboratory approaches remains challenging. By combining site-specific integration and high-throughput sequencing, we quantified relative growth under the respective selection of cefotaxime or ceftazidime selection in ∼23,000 Escherichia coli MG1655 strains that each carried a unique, single-copy variant of the extended-spectrum β-lactamase gene blaCTX-M-14 at the chromosomal att HK022 site. Significant synergistic pleiotropy was observed within four subgenic regions, suggesting key regions for the evolution of resistance to both antibiotics. Moreover, we propose PEARP and PEARR, two deep-learning models with strong clinical correlations, for the prospective and retrospective prediction of blaCTX-M-14 evolution, respectively. Single to quintuple mutations of blaCTX-M-14 predicted to confer resistance by PEARP were significantly enriched among the clinical isolates harboring blaCTX-M-14 variants, and the PEARR scores matched the minimal inhibitory concentrations obtained for the 31 intermediates in all hypothetical trajectories. Altogether, we conclude that the measurement of local fitness landscape enables prediction of the evolutionary trajectories of antibiotic resistance genes, which could be useful for a broad range of clinical applications, from resistance prediction to designing novel treatment strategies.

本文言語English
論文番号msac086
ジャーナルMolecular biology and evolution
39
5
DOI
出版ステータスPublished - 01-05-2022

All Science Journal Classification (ASJC) codes

  • 生態、進化、行動および分類学
  • 分子生物学
  • 遺伝学

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