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.

Adaptive laboratory evolution (ALE) is a useful experimental methodology for fundamental scientific research and industrial applications to create microbial cell factories. By using ALE, cells are adapted to the environment that researchers set based on their objectives through the serial transfer of cell populations in batch cultivations or continuous cultures and the fitness of the cells (i.e., cell growth) under such an environment increases. Then, omics analyses of the evolved mutants, including genome sequencing, transcriptome, proteome and metabolome analyses, are performed. It is expected that researchers can understand the evolutionary adaptation processes, and for industrial applications, researchers can create useful microorganisms that exhibit increased carbon source availability, stress tolerance, and production of target compounds based on omics analysis data. In this review article, the methodologies for ALE in microorganisms are introduced. Moreover, the application of ALE for the creation of useful microorganisms as cell factories has also been introduced.

Laboratory evolution studies, particularly with Escherichia coli, have yielded invaluable insights into the mechanisms of antimicrobial resistance (AMR). Recent investigations have illuminated that, with repetitive antibiotic exposures, bacterial populations will adapt and eventually become tolerant and resistant to the drugs. Through intensive analyses, these inquiries have unveiled instances of convergent evolution across diverse antibiotics, the pleiotropic effects of resistance mutations, and the role played by loss-of-function mutations in the evolutionary landscape. Moreover, a quantitative analysis of multidrug combinations has shed light on collateral sensitivity, revealing specific drug combinations capable of suppressing the acquisition of resistance. This review article introduces the methodologies employed in the laboratory evolution of AMR in bacteria and presents recent discoveries concerning AMR mechanisms derived from laboratory evolution. Additionally, the review outlines the application of laboratory evolution in endeavors to formulate rational treatment strategies.

Antibiotic treatment generally results in the selection of resistant bacterial strains, and the dynamics of resistance evolution is dependent on complex interactions between cellular components. To better characterize the mechanisms of antibiotic resistance and evaluate its dependence on gene regulatory networks, we performed systematic laboratory evolution of Escherichia coli strains with single-gene deletions of 173 transcription factors under three different antibiotics. This resulted in the identification of several genes whose deletion significantly suppressed resistance evolution, including arcA and gutM. Analysis of double-gene deletion strains suggested that the suppression of resistance evolution caused by arcA and gutM deletion was not caused by epistatic interactions with mutations known to confer drug resistance. These results provide a methodological basis for combinatorial drug treatments that may help to suppress the emergence of resistant pathogens by inhibiting resistance evolution.

Drug-resistant tuberculosis (TB) is a growing public health problem. There is an urgent need for information regarding cross-resistance and collateral sensitivity relationships among drugs and the genetic determinants of anti-TB drug resistance for developing strategies to suppress the emergence of drug-resistant pathogens. To identify mutations that confer resistance to anti-TB drugs in Mycobacterium species, we performed the laboratory evolution of nonpathogenic Mycobacterium smegmatis, which is closely related to Mycobacterium tuberculosis, against ten anti-TB drugs. Next, we performed whole-genome sequencing and quantified the resistance profiles of each drug-resistant strain against 24 drugs. We identified the genes with novel meropenem (MP) and linezolid (LZD) resistance-conferring mutation, which also have orthologs, in M. tuberculosis H37Rv. Among the 240 possible drug combinations, we identified 24 pairs that confer cross-resistance and 18 pairs that confer collateral sensitivity. The acquisition of bedaquiline or linezolid resistance resulted in collateral sensitivity to several drugs, while the acquisition of MP resistance led to multidrug resistance. The MP-evolved strains showed cross-resistance to rifampicin and clarithromycin owing to the acquisition of a mutation in the intergenic region of the Rv2864c ortholog, which encodes a penicillin-binding protein, at an early stage. These results provide a new insight to tackle drug-resistant TB.