Predictive Signatures of 19 Antibiotic-Induced Escherichia coli Proteomes.

Abstract

Identifying the mode of action (MOA) of antibacterial compounds is the fundamental basis for the development of new antibiotics, and the challenge increases with the emerging secondary and indirect effect from antibiotic stress. Although various omics-based system biology approaches are currently available, enhanced throughput, accuracy, and comprehensiveness are still desirable to better define antibiotic MOA. Using label-free quantitative proteomics, we present here a comprehensive reference map of proteomic signatures of Escherichia coli under challenge of 19 individual antibiotics. Applying several machine learning techniques, we derived a panel of 14 proteins that can be used to classify the antibiotics into different MOAs with nearly 100% accuracy. These proteins tend to mediate diverse bacterial cellular and metabolic processes. Transcriptomic level profiling correlates well with protein expression changes in discriminating different antibiotics. The reported expression signatures will aid future studies in identifying MOA of unknown compounds and facilitate the discovery of novel antibiotics.