Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.

Antimicrobial resistance (AMR) poses an increasing challenge for therapy and clinical management of bacterial infections. Currently, antimicrobial resistance detection relies on phenotypic assays, which are performed independently from species identification. Sequencing-based approaches are possible alternatives for AMR detection, although the analysis of proteins should be superior to gene or transcript sequencing for phenotype prediction as the actual resistance to antibiotics is almost exclusively mediated by proteins. In this proof-of-concept study, we present an unbiased proteomics workflow for detecting both bacterial species and AMR-related proteins in the absence of secondary antibiotic cultivation within <4 h from a primary culture. The workflow was designed to meet the needs in clinical microbiology. It introduces a new data analysis concept for bacterial proteomics, and a software (rawDIAtect) for the prediction and reporting of AMR from peptide identifications. The method was validated using a sample cohort of 7 bacterial species and 11 AMR determinants represented by 13 protein isoforms, which resulted in a sensitivity of 98% and a specificity of 100%.