High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli

Tomoya Maeda (), Junichiro Iwasawa, Hazuki Kotani, Natsue Sakata, Masako Kawada, Takaaki Horinouchi, Aki Sakai, Kumi Tanabe and Chikara Furusawa ()
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Tomoya Maeda: RIKEN Center for Biosystems Dynamics Research
Junichiro Iwasawa: The University of Tokyo
Hazuki Kotani: RIKEN Center for Biosystems Dynamics Research
Natsue Sakata: RIKEN Center for Biosystems Dynamics Research
Masako Kawada: RIKEN Center for Biosystems Dynamics Research
Takaaki Horinouchi: RIKEN Center for Biosystems Dynamics Research
Aki Sakai: RIKEN Center for Biosystems Dynamics Research
Kumi Tanabe: RIKEN Center for Biosystems Dynamics Research
Chikara Furusawa: RIKEN Center for Biosystems Dynamics Research

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Utilizing machine learning techniques, we analyze the phenotype–genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identification of trade-off relationships associated with drug resistance. We also report a decelerated evolution of β-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to β-lactams compared to strains directly selected by β-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance.

Date: 2020
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DOI: 10.1038/s41467-020-19713-w

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