Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics

Abstract

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-ofconcept 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%.

Increasing Proteome Depth While Maintaining Quantitative Precision in Short-Gradient Data-Independent Acquisition Proteomics

“…cerevisiae (4198 proteins), and 5059 for Homo sapiens (6801 proteins). This demonstrates that almost complete bacterial proteomes are detectable in a 30 min gradient, which offers great opportunities for clinical microbiology including antimicrobial resistance detection and species identification as well as for molecular epidemiology of bacterial infections …”

“…This demonstrates that almost complete bacterial proteomes are detectable in a 30 min gradient, which offers great opportunities for clinical microbiology including antimicrobial resistance detection and species identification as well as for molecular epidemiology of bacterial infections. 22 The density distributions of the protein copy numbers per cell were calculated for HeLa, S. cerevisiae, and E. coli cells using the total protein approach. 21 The distributions represent all proteins which were consistently identified in three replicates from a 30 min DIA analysis.…”

Increasing proteome depth while maintaining quantitative precision in short gradient data-independent acquisition proteomics

“…It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 13, 2022. ; https://doi.org/10.1101/2022.09.12.507556 doi: bioRxiv preprint 5059 for Homo sapiens (HeLa). This demonstrates, that almost complete bacterial proteomes are detectable in a 30 min gradient, which offers great opportunities for clinical microbiology including antimicrobial resistance detection and species identification as well as for molecular epidemiology of bacterial infections [20][21] . The density distributions of the protein copy numbers per cell were calculated for HeLa, S. cerevisiae and E. coli cells using the total protein approach 19 .…”

“…The observed limits of detection (95th percentile) for the protein copy numbers per cell are 4 for E. coli, 79 for S. cerevisiae and 5059 for Homo sapiens (HeLa). This demonstrates, that almost complete bacterial proteomes are detectable in a 30 min gradient, which offers great opportunities for clinical microbiology including antimicrobial resistance detection and species identification as well as for molecular epidemiology of bacterial infections [20][21] . The density distributions of the protein copy numbers per cell were calculated for HeLa, S. cerevisiae and E. coli cells using the total protein approach 19 .…”

The Integration of Proteomics and Metabolomics Data Paving the Way for a Better Understanding of the Mechanisms Underlying Microbial Acquired Drug Resistance

“…Several methods using MALDI-TOF MS are employed in diagnostic laboratories to characterize drugresistance and susceptibility (38). In recent years label-free quantitative LC-MS/MS have been developed to provide a simplified sample preparation, where the whole proteome is digested into peptides without prior protein separation, to analyse complex proteomic samples and directly identify thousands of proteins quantitatively (Table 1) (29,39,(41)(42)(43)(44). Pournaras et al (45) in their comparative proteomic analysis of colistin-susceptible and resistant A. baumannii strains found proteins involved in antibiotic resistance and virulence were differential expressed in resistant strains.…”