[WCB-9] High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli
The emergence of antibiotic resistance and multidrug-resistant bacteria is a growing global health concern. Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Using an automated culture system, we performed laboratory evolution of Escherichia coli under 95 heterogenous stress conditions which allowed us to elucidate the molecular mechanisms associated with resistance acquisition to antibiotics and non-antibiotics stressors. Changes in the transcriptome, genomic sequence, and resistance profile in the evolved strains were quantified, resulting in a multiscale dataset for analyzing stress resistance. By analyzing the gene expression-resistance map through machine learning techniques, modular phenotypic classes both in the gene expression space and the stress resistance space were identified, suggesting close interactions between changes in gene expression and stress resistance. The underlying biological processes corresponding to each state were then analyzed by introducing the representative mutations to the parent strain, leading to the identification of trade-off relationships associated with drug resistance. These findings provide a quantitative understanding of evolutionary constraints in adaptive evolution, leading to the basis for predicting and controlling antibiotic resistance.