High-Throughput Evolution Unravels Landscapes of High-Level Antibiotic Resistance Induced by Low-Level Antibiotic Exposure

Abstract

AbstractPotential pathogens exposed to low-level environmental antibiotics could develop high-level clinically relevant antibiotic resistance detrimental to the health of the general population. However, the underlying evolutionary landscapes remain poorly understood. We conducted a high-throughput experimental evolution study by exposing an environmentally isolated pathogenicEscherichia colistrain to 96 typical antibiotics at 10 μg l−1for 20 days. Antibiotic resistance phenotypic (IC90against 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 environmentalE. coliisolates retrieved from the NCBI database, and several were over-represented in 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.