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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2025-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06200-6 |
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| author | Mario Blázquez-Sánchez Alejandro Guerrero-López Ana Candela Albert Belenguer-Llorens José Miguel Moreno Carlos Sevilla-Salcedo María Sánchez-Cueto Manuel J. Arroyo Mark Gutiérrez-Pareja Vanessa Gómez-Verdejo Pablo M. Olmos Luis Mancera Patricia Muñoz Mercedes Marín Luis Alcalá David Rodríguez-Temporal Belén Rodríguez-Sánchez |
| author_facet | Mario Blázquez-Sánchez Alejandro Guerrero-López Ana Candela Albert Belenguer-Llorens José Miguel Moreno Carlos Sevilla-Salcedo María Sánchez-Cueto Manuel J. Arroyo Mark Gutiérrez-Pareja Vanessa Gómez-Verdejo Pablo M. Olmos Luis Mancera Patricia Muñoz Mercedes Marín Luis Alcalá David Rodríguez-Temporal Belén Rodríguez-Sánchez |
| author_sort | Mario Blázquez-Sánchez |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bbcfa345e223418cbfa944691d0b8c6d |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-bbcfa345e223418cbfa944691d0b8c6d2025-08-20T03:43:31ZengBMCBMC Bioinformatics1471-21052025-07-0126111510.1186/s12859-025-06200-6Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MSMario Blázquez-Sánchez0Alejandro Guerrero-López1Ana Candela2Albert Belenguer-Llorens3José Miguel Moreno4Carlos Sevilla-Salcedo5María Sánchez-Cueto6Manuel J. Arroyo7Mark Gutiérrez-Pareja8Vanessa Gómez-Verdejo9Pablo M. Olmos10Luis Mancera11Patricia Muñoz12Mercedes Marín13Luis Alcalá14David Rodríguez-Temporal15Belén Rodríguez-Sánchez16Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónSignal Processing and Communications Department, Universidad Carlos III de MadridClinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónSignal Processing and Communications Department, Universidad Carlos III de MadridComputer Science and Engineering Department, Universidad Carlos III de MadridSignal Processing and Communications Department, Universidad Carlos III de MadridClinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónClover Bioanalytical SoftwareClinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónSignal Processing and Communications Department, Universidad Carlos III de MadridSignal Processing and Communications Department, Universidad Carlos III de MadridClover Bioanalytical SoftwareClinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónClinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónClinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónClinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónClinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio MarañónAbstract 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.https://doi.org/10.1186/s12859-025-06200-6Clostridioides difficileClostridium difficileMALDI-TOF MSMachine learningRibotypingOutbreak |
| spellingShingle | Mario Blázquez-Sánchez Alejandro Guerrero-López Ana Candela Albert Belenguer-Llorens José Miguel Moreno Carlos Sevilla-Salcedo María Sánchez-Cueto Manuel J. Arroyo Mark Gutiérrez-Pareja Vanessa Gómez-Verdejo Pablo M. Olmos Luis Mancera Patricia Muñoz Mercedes Marín Luis Alcalá David Rodríguez-Temporal Belén Rodríguez-Sánchez Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS BMC Bioinformatics Clostridioides difficile Clostridium difficile MALDI-TOF MS Machine learning Ribotyping Outbreak |
| title | Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS |
| title_full | Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS |
| title_fullStr | Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS |
| title_full_unstemmed | Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS |
| title_short | Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS |
| title_sort | automated web based typing of clostridioides difficile ribotypes via maldi tof ms |
| topic | Clostridioides difficile Clostridium difficile MALDI-TOF MS Machine learning Ribotyping Outbreak |
| url | https://doi.org/10.1186/s12859-025-06200-6 |
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