Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS.

ABSTRACT:

Background

Clostridioides difficile is a major cause of hospital-acquired diarrhea and a driver of nosocomial outbreaks, yet rapid, accurate ribotype identification remains challenging. We sought to develop a MALDI-TOF MS-based workflow coupled with machine learning to distinguish epidemic toxigenic ribotypes (RT027 and RT181) from other strains in real time.

Results

We analyzed MALDI-TOF spectra from 379 clinical isolates collected across ten Spanish hospitals and identified seven discriminant biomarker peaks. Two peaks (2463 and 4993 m/z) were uniquely associated with RT027, while combinations of five additional peaks reliably identified RT181. Our classifiers-implemented both in the commercial Clover MSDAS platform and the open-access AutoCdiff web tool-achieved up to 100

SUBMITTER: Blazquez-Sanchez M 

PROVIDER: S-EPMC12273265 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature