MALDI-TOF MS: An alternative approach for ribotyping Clostridioides difficile isolates in Brazil

Contribution of MALDI-TOF mass spectrometry and machine learning including deep learning techniques for the detection of virulence factors of Clostridioides difficile strains

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.

Discrimination between hypervirulent and non-hypervirulent ribotypes of Clostridioides difficile by MALDI-TOF mass spectrometry and machine learning

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.

Make It Less difficile: Understanding Genetic Evolution and Global Spread of Clostridioides difficile

Clostridioides difficile is an obligate anaerobic pathogen among the most common causes of healthcare-associated infections. It poses a global threat due to the clinical outcomes of infection and resistance to antibiotics recommended by international guidelines for its eradication. In particular, C. difficile infection can lead to fulminant colitis associated with shock, hypotension, megacolon, and, in severe cases, death. It is therefore of the utmost urgency to fully characterize this pathogen and better understand its spread, in order to reduce infection rates and improve therapy success. This review aims to provide a state-of-the-art overview of the genetic variation of C. difficile, with particular regard to pathogenic genes and the correlation with clinical issues of its infection. We also summarize the current typing techniques and, based on them, the global distribution of the most common ribotypes. Finally, we discuss genomic surveillance actions and new genetic engineering strategies as future perspectives to make it less difficile.

Clostridioides difficile in Latin America: A comprehensive review of literature (1984-2021)

This narrative review summarizes literature on C. difficile and C. difficile infections (CDI) that emerged from Latin America (LA) between 1984 and 2021. The revised information includes papers in English, Spanish, or Portuguese that were retrieved from the databases Pubmed, Scopus, Web of Science, Google Scholar, Scielo, and Lilacs. Information is presented chronologically and segregated in subregions, focusing on clinical presentation, risk factors, detection and typing methods, prevalence and incidence rates, circulating strains, and, when available, phenotypic traits, such as antimicrobial susceptibility patterns. Studies dealing with cases, clinical aspects of CDI, and performance evaluations of diagnostic methods predominated. However, they showed substantial differences in case definitions, measuring units, populations, and experimental designs. Although a handful of autochthonous strains were identified, predominantly in Brazil and Costa Rica, the presentation and epidemiology of CDI in LA were highly comparable to what has been reported in other regions of the world. Few laboratories isolate and type this bacterium and even less generate whole genome sequences or perform basic science on C. difficile. Less than ten countries lead academic productivity on C. difficile or CDI-related topics, and information from various countries in Central America and the Caribbean is still lacking. The review ends with a global interpretation of the data and recommendations to further develop and consolidate this discipline in LA.