High-throughput experimental evolution reveals the mutation-driven landscape of high-level clinical resistance acquired under low-level environmental antibiotic stresses

Abstract

Potential pathogens consistently exposed to low-level environmental antibiotics could derive high-level clinically relevant resistance detrimental to the human health. However, the underlying evolutionary landscapes remain poorly understood. We conducted a high-throughput experimental evolution study by exposing an environmentally isolated pathogenic Escherichia coli strain to 96 typical antibiotics at 10 g l-1 for 200 generations. Antibiotic resistance phenotypic (IC90 against 8 clinically used antibiotics) and genetic changes of the evolved populations were systematically investigated, revealing a universal increase in antibiotic resistance (up to 349-fold), and mutations in 2,432 genes. Transposon sequencing was further employed to verify genes potentially associated with resistance. A core set of mutant genes conferring high-level resistance was analyzed to elucidate their resistance mechanisms by analyzing the functions of interacted genes within the gene co-fitness network and performing gene knockout validations. We developed machine-learning models to predict antibiotic resistance phenotypes from antibiotic structures and genomic mutations, enabling the resistance predictions for another 569 antibiotics. Importantly, 14.6% of the 481 key mutations were observed in clinical and environmental E. coli isolates retrieved from the NCBI database, and several were over-represented in >500 clinical isolates. Deciphering the evolutionary landscapes underlying resistance exposed to low-level environmental antibiotics is crucial for evaluating the emergence and risks of environment-originated clinical antibiotic resistance.