Discrimination between hypervirulent and non-hypervirulent ribotypes of Clostridioides difficile by MALDI-TOF mass spectrometry and machine learning.
Ahmed Mohamed Mostafa AbdrabouIssa SyMarkus BischoffManuel J ArroyoSören L BeckerAlexander MellmannLutz von MüllerBarbara GärtnerFabian K BergerPublished in: European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology (2023)
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.
Keyphrases
- mass spectrometry
- clostridium difficile
- machine learning
- liquid chromatography
- gas chromatography
- klebsiella pneumoniae
- capillary electrophoresis
- high performance liquid chromatography
- high resolution
- genetic diversity
- escherichia coli
- density functional theory
- artificial intelligence
- ms ms
- rna seq
- big data
- multidrug resistant
- molecular dynamics