High-throughput laboratory evolution reveals global evolutionary constraints for antibiotic resistance

Understanding constraints which shape antibiotic resistance is key for predicting and controlling drug resistance. Here, we performed high-throughput laboratory evolution of Escherichia coli. The transcriptome, resistance, and genomic profiles for the evolved strains in 48 environments were quantitatively analyzed. By analyzing the quantitative datasets through interpretable machine learning techniques, the emergence of low dimensional phenotypic states within the 192 strains was observed. Further analysis revealed the underlying biological processes responsible for the distinct states. We also report a novel constraint which leads to decelerated evolution. These findings bridge the genotypic, gene expression, and drug resistance space, and lead to a comprehensive understanding of constraints for antibiotic resistance. Transcriptomes of the 192 evolved strains and the wild-type strain were compared by DNA microarray analysis. All strains were grown on the modified M9 medium and total RNA was isolated from the cells in the exponential growth phase.