ArticleMolecular & cellular proteomics : MCP2026
Antibiotic Resistance Detection and Concomitant Species Identification of ESKAPE Pathogens by Proteomics.
Article in Molecular & cellular proteomics : MCP, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Neglected ESKAPE Pathogens in Companion Animals: Diagnostic Bias, Zoonotic Blind Spots, and Phytotherapeutic Opportunities.Pathogens (Basel, Switzerland) · 2026Review
- PEPTiGEN: a tool for mining antimicrobial resistance PEPTides using GENe data of public available repositories.Bioinformatics (Oxford, England) · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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