High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli

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.

A novel decoy strategy for polymyxin resistance in Acinetobacter baumannii

“…It is worth noting that antibiotic-induced genomic alterations including deletions, rearrangements, and amplification have been reported in many laboratory-evolved antibiotic resistant bacterial strains (Sandergren and Anderson, 2009). Several laboratory-evolved antibiotic resistant Escherichia coli strains also appeared to carry more than 20 mutations (Maeda et al, 2020).…”

“…It is worth noting that antibiotic-induced genomic alterations including deletions, rearrangements, and amplification have been reported in many laboratory-evolved antibiotic-resistant bacterial strains ( Sandegren and Andersson, 2009 ). Several laboratory-evolved antibiotic-resistant E. coli strains also appeared to carry more than 20 mutations ( Maeda et al, 2020 ). Tetracycline-induced genome rearrangements led to the loss of 6.1 kb having seven full-length and two truncated genes in E. coli strains ( Hoeksema et al, 2018 ).…”

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

“…The recent discovery of tight matches between mutational, genetic, and macroevolutionary variances in Drosophilid wing shape (Houle et al, 2017) is exemplary of a successful phenomic project. Second, large-scale empirical studies of the genotype-phenotype map will finally become possible, because of the availability of high-throughput phenotypic data and analytical framework to deal with big data (Pitchers et al, 2019;Zheng et al, 2019;Maeda et al, 2020). Third, studies of fossil time-series will gain opportunities to document and analyze the dynamics of long-term phenotypic evolution with unprecedented temporal resolution (Brombacher et al, 2017;Liow et al, 2017).…”

Identification of bacterial drug-resistant cells by the convolutional neural network in transmission electron microscope images

“…The results obtained using Grad-CAM suggested that the morphological features of the ENX-resistant strains could be found mainly at the envelope, and this led us to consider which genes might be associated with the morphological characteristics of these strains. (3) in Methods]. We retained pairs of genes and image features meeting the condition that the absolute correlation coefficient between them was equal to or greater than 0.999.…”

“…Laboratory-based bacterial evolution is a powerful tool for investigating the dynamics of acquired drug resistance. 1,2,3 In these experiments, bacterial cells are exposed to fixed concentrations of drugs, around which cell growth is partially or completely inhibited such that a selective advantage for resistant strains is maintained. Recently, Suzuki et al performed laboratory evolution of Escherichia coli under various drug treatment conditions to obtain resistant strains, including those resistant to quinolones such as enoxacin (ENX).…”