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...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-025-06200-6 |
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| Summary: | 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% balanced accuracy in ribotype assignment and proved robust in real-time outbreak simulations. Conclusions This study demonstrates that MALDI-TOF MS combined with tailored machine learning can deliver rapid, high-precision ribotype identification for C. difficile. The freely available AutoCdiff models ( https://bacteria.id ) offer an immediately deployable solution for clinical laboratories, with the potential to enhance outbreak surveillance and control. |
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| ISSN: | 1471-2105 |