Contribution of MALDI-TOF mass spectrometry and machine learning including deep learning techniques for the detection of virulence factors of Clostridioides difficile strains.
Alexandre GodmerQuentin Giai GianettoKillian Le NeindreValentine LatapyMathilda BastideMuriel EhmigValérie LalandeNicolas VezirisAlexandra AubryFrédéric BarbutCatherine EckertPublished in: Microbial biotechnology (2024)
Clostridioides difficile (CD) infections are defined by toxins A (TcdA) and B (TcdB) along with the binary toxin (CDT). The emergence of the 'hypervirulent' (Hv) strain PR 027, along with PR 176 and 181, two decades ago, reshaped CD infection epidemiology in Europe. This study assessed MALDI-TOF mass spectrometry (MALDI-TOF MS) combined with machine learning (ML) and Deep Learning (DL) to identify toxigenic strains (producing TcdA, TcdB with or without CDT) and Hv strains. In total, 201 CD strains were analysed, comprising 151 toxigenic (24 ToxA + B + CDT + , 22 ToxA + B + CDT + Hv + and 105 ToxA + B + CDT - ) and 50 non-toxigenic (ToxA - B - ) strains. The DL-based classifier exhibited a 0.95 negative predictive value for excluding ToxA - B - strains, showcasing accuracy in identifying this strain category. Sensitivity in correctly identifying ToxA + B + CDT - strains ranged from 0.68 to 0.91. Additionally, all classifiers consistently demonstrated high specificity (>0.96) in detecting ToxA + B + CDT + strains. The classifiers' performances for Hv strain detection were linked to high specificity (≥0.96). This study highlights MALDI-TOF MS enhanced by ML techniques as a rapid and cost-effective tool for identifying CD strain virulence factors. Our results brought a proof-of-concept concerning the ability of MALDI-TOF MS coupled with ML techniques to detect virulence factor and potentially improve the outbreak's management.
Keyphrases
- mass spectrometry
- escherichia coli
- clostridium difficile
- machine learning
- liquid chromatography
- deep learning
- capillary electrophoresis
- gas chromatography
- high performance liquid chromatography
- staphylococcus aureus
- pseudomonas aeruginosa
- high resolution
- klebsiella pneumoniae
- artificial intelligence
- biofilm formation
- big data
- antimicrobial resistance
- convolutional neural network
- risk factors
- cystic fibrosis
- simultaneous determination
- solid phase extraction