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Investigation of maldi-tof mass spectrometry for assessing the molecular diversity of campylobacter jejuni and comparison with mlst and cgmlst: A luxembourg one-health study

par Feucherolles, Maureen;Nennig, Morgane;Becker, Sören Leif;Martiny, Delphine ;Losch, Serge;Penny, Christian;Cauchie, Henry Michel;Ragimbeau, Catherine
Référence Diagnostics, 11, 11, 1949
Publication Publié, 2021-11

Article révisé par les pairs

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Résumé :

There is a need for active molecular surveillance of human and veterinary Campylobacter infections. However, sequencing of all isolates is associated with high costs and a considerable workload. Thus, there is a need for a straightforward complementary tool to prioritize isolates to sequence. In this study, we proposed to investigate the ability of MALDI-TOF MS to pre-screen C. jejuni genetic diversity in comparison to MLST and cgMLST. A panel of 126 isolates, with 10 clonal complexes (CC), 21 sequence types (ST) and 42 different complex types (CT) determined by the SeqSphere+ cgMLST, were analysed by a MALDI Biotyper, resulting into one average spectra per isolate. Concordance and discriminating ability were evaluated based on protein profiles and different cut-offs. A random forest algorithm was trained to predict STs. With a 94% similarity cut-off, an AWC of 1.000, 0.933 and 0.851 was obtained for MLSTCC, MLSTST and cgMLST profile, respectively. The random forest classifier showed a sensitivity and specificity up to 97.5% to predict four different STs. Protein profiles allowed to predict C. jejuni CCs, STs and CTs at 100%, 93% and 85%, respectively. Machine learning and MALDI-TOF MS could be a fast and inexpensive complementary tool to give an early signal of recurrent C. jejuni on a routine basis.