# Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics

## TL;DR

This study shows that proteomics can quickly and accurately detect antibiotic resistance and identify bacterial species in clinical samples, offering a faster alternative to traditional methods.

## Contribution

A proteomics workflow is introduced that enables rapid AMR detection and species identification without antibiotic cultivation.

## Key findings

- Proteomics achieved 100% specificity and 94.4% sensitivity for AMR detection.
- Species identification was accurate using LCMS1 barcoding from large databases.
- AMR gene family expression was detectable without antibiotic cultivation.

## Abstract

Antimicrobial resistance (AMR) is an increasing challenge for the therapy of bacterial infections. Currently, patient treatment is guided by antimicrobial susceptibility testing (AST) using phenotypic assays and species identification by MALDI-ToF biotyping. Bacterial phenotype prediction using omics technologies could offer several advantages over current diagnostic methods. It would allow species identification and AST to be combined in a single measurement, it would eliminate the need for secondary cultivation and could enable the prediction of phenotypes beyond AMR, such as virulence. In this study, the potential of proteomics for clinical microbiology was evaluated in an analysis of 126 clinical isolates covering 16 species, including all ESKAPE genera and 29 of the most common AMR gene families. For this purpose, a flexible workflow was developed, which enables reporting of the AMR phenotype and the species of primary cultures within 2h. Proteomics provided high specificity (100%) and sensitivity (94.4%) for AMR detection, while allowing species identification from very large sequence databases with high accuracy. The results show that proteomics is well-suited for phenotyping clinical bacterial isolates and has the potential to become a valuable diagnostic tool for clinical microbiology in the future.

•Panel of clinical isolates of all ESKAPE pathogens and most common AMR determinants.•Detection of AMR gene family expression is possible without antibiotic cultivation.•LCMS1 barcoding enables very rapid and accurate species identification.•Detection of AMR protein determinants by proteomics is specific and sensitive.•Future research should focus on improving AMR phenotype prediction.

Panel of clinical isolates of all ESKAPE pathogens and most common AMR determinants.

Detection of AMR gene family expression is possible without antibiotic cultivation.

LCMS1 barcoding enables very rapid and accurate species identification.

Detection of AMR protein determinants by proteomics is specific and sensitive.

Future research should focus on improving AMR phenotype prediction.

Antimicrobial resistance (AMR) is an increasing challenge for therapy of bacterial infections. In this study, a proteomics workflow is presented, which is used to analyze a panel of clinical bacterial isolates including all ESKAPE pathogens and most common AMR determinants. Results show that AMR gene family expression is detectable without the need for antibiotic cultivation with high specificity (100%) and sensitivity (94,4%). LCMS1 barcoding enables very rapid and accurate species identification from very large taxonomic databases.