Hypervirulent ribotypes (HVRTs) of Clostridioides difficile such as ribotype (RT) 027 are epidemiologically important. This study evaluated whether MALDI-TOF can distinguish between strains of HVRTs and non-HVRTs commonly found in Europe. Obtained spectra of clinical C. difficile isolates (training set, 157 isolates) covering epidemiologically relevant HVRTs and non-HVRTs found in Europe were used as an input for different machine learning (ML) models. Another 83 isolates were used as a validation set. Direct comparison of MALDI-TOF spectra obtained from HVRTs and non-HVRTs did not allow to discriminate between these two groups, while using these spectra with certain ML models could differentiate HVRTs from non-HVRTs with an accuracy >95% and allowed for a sub-clustering of three HVRT subgroups (RT027/RT176, RT023, RT045/078/126/127). MALDI-TOF combined with ML represents a reliable tool for rapid identification of major European HVRTs.
Keywords: Anaerobic bacteria; Clostridium difficile; Identification; MALDI-TOF mass spectrometry; Machine learning; Proteomic signature; Ribotypes.
© 2023. The Author(s).