Detection and Quantification of Antimicrobial-Resistant Cells in Aquatic Environments by Bioorthogonal Noncanonical Amino Acid Tagging.

Abstract

Aquatic environments are important reservoirs of antibiotic wastes, antibiotic resistance genes, and bacteria, enabling the persistence and proliferation of antibiotic resistance in different bacterial populations. To prevent the spread of antibiotic resistance, effective approaches to detect antimicrobial susceptibility in aquatic environments are highly desired. In this work, we adopt a metabolism-based bioorthogonal noncanonical amino acid tagging (BONCAT) method to detect, visualize, and quantify active antimicrobial-resistant bacteria in water samples by exploiting the differences in bacterial metabolic responses to antibiotics. The BONCAT approach can be applied to rapidly detect bacterial resistance to multiple antibiotics within 20 min of incubation, regardless of whether they act on proteins or DNA. In addition, the combination of BONCAT with the microscope enables the intuitive characterization of antibiotic-resistant bacteria in mixed systems at single-cell resolution. Furthermore, BONCAT coupled with flow cytometry exhibits good performance in determining bacterial resistance ratios to chloramphenicol and population heterogeneity in hospital wastewater samples. In addition, this approach is also effective in detecting antibiotic-resistant bacteria in natural water samples. Therefore, such a simple, fast, and efficient BONCAT-based approach will be valuable in monitoring the increase and spread of antibiotic resistance within natural and engineered aquatic environments.