Rapid Classification of Clostridioides difficile Strains Using MALDI-TOF MS Peak-Based Assay in Comparison with PCR-Ribotyping

Evaluation of MALDI-TOF mass spectrometry coupled with ClinProTools as a rapid tool for toxin-producing Clostridioides difficile

INTRODUCTION

The performance of MALDI-TOF MS combined with analysis platform for identification of toxin-producing Clostridiodes difficile is yet to be known.

METHODS

Between August 2018 and September 2020, 61 isolates from stool specimens of patients with C. difficile-associated diarrhea were analyzed using the MALDI Biotyper system. A C. difficile toxin-producer detection model was developed using ClinProTools. The model was validated using 28 known strains that differed from the isolates used to develop the model.

RESULTS

The sensitivity and specificity of the Genetic Algorithm (GA) model using isolates grown on Brucella with hemin and vitamin K (BHK) agar plates were 91.7% and 44.4%, respectively. When isolates grown on cycloserine-cefoxitin mannitol agar were analyzed by the model, sensitivity and specificity were 6.3% and 100%, respectively. The GA model using BHK medium showed the highest discriminatory performance in detection of toxin-producing C. difficile. However, a discrepancy in detection of toxin-producing C. difficile was observed in the results generated when the model was being developed and when the model was validated which suggests that incubation conditions may have affected the results.

CONCLUSION

MALDI-TOF analysis using ClinProTools has a potential to be a cost-effective tool for rapid diagnosis and contribute to antimicrobial stewardship by differentiating toxin-producing C. difficile from non-producers.

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.

MALDI-TOF MS: A Reliable Tool in the Real Life of the Clinical Microbiology Laboratory

Matrix-Assisted Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) in the last decade has revealed itself as a valid support in the workflow in the clinical microbiology laboratory for the identification of bacteria and fungi, demonstrating high reliability and effectiveness in this application. Its use has reduced, by 24 h, the time to obtain a microbiological diagnosis compared to conventional biochemical automatic systems. MALDI-TOF MS application to the detection of pathogens directly in clinical samples was proposed but requires a deeper investigation, whereas its application to positive blood cultures for the identification of microorganisms and the detection of antimicrobial resistance are now the most useful applications. Thanks to its rapidity, accuracy, and low price in reagents and consumables, MALDI-TOF MS has also been applied to different fields of clinical microbiology, such as the detection of antibiotic susceptibility/resistance biomarkers, the identification of aminoacidic sequences and the chemical structure of protein terminal groups, and as an emerging method in microbial typing. Some of these applications are waiting for an extensive evaluation before confirming a transfer to the routine. MALDI-TOF MS has not yet been used for the routine identification of parasites; nevertheless, studies have been reported in the last few years on its use in the identification of intestinal protozoa, Plasmodium falciparum, or ectoparasites. Innovative applications of MALDI-TOF MS to viruses' identification were also reported, seeking further studies before adapting this tool to the virus's diagnostic. This mini-review is focused on the MALDI-TOF MS application in the real life of the diagnostic microbiology laboratory.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for differential identification of adult Schistosoma worms

BACKGROUND

Schistosomiasis is a major neglected tropical disease that affects up to 250 million individuals worldwide. The diagnosis of human schistosomiasis is mainly based on the microscopic detection of the parasite's eggs in the feces (i.e., for Schistosoma mansoni or Schistosoma japonicum) or urine (i.e., for Schistosoma haematobium) samples. However, these techniques have limited sensitivity, and microscopic expertise is waning outside endemic areas. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become the gold standard diagnostic method for the identification of bacteria and fungi in many microbiological laboratories. Preliminary studies have recently shown promising results for parasite identification using this method. The aims of this study were to develop and validate a species-specific database for adult Schistosoma identification, and to evaluate the effects of different storage solutions (ethanol and RNAlater) on spectra profiles.

METHODS

Adult worms (males and females) of S. mansoni and S. japonicum were obtained from experimentally infected mice. Species identification was carried out morphologically and by cytochrome oxidase 1 gene sequencing. Reference protein spectra for the creation of an in-house MALDI-TOF MS database were generated, and the database evaluated using new samples. We employed unsupervised (principal component analysis) and supervised (support vector machine, k-nearest neighbor, Random Forest, and partial least squares discriminant analysis) machine learning algorithms for the identification and differentiation of the Schistosoma species.

RESULTS

All the spectra were correctly identified by internal validation. For external validation, 58 new Schistosoma samples were analyzed, of which 100% (58/58) were correctly identified to genus level (log score values ≥ 1.7) and 81% (47/58) were reliably identified to species level (log score values ≥ 2). The spectra profiles showed some differences depending on the storage solution used. All the machine learning algorithms classified the samples correctly.

CONCLUSIONS

MALDI-TOF MS can reliably distinguish adult S. mansoni from S. japonicum.

Characterization of Clostridioides difficile Strains from an Outbreak Using MALDI-TOF Mass Spectrometry

The epidemiology of Clostridioides difficile infection (CDI) has changed over the last two decades, due to the emergence of C. difficile strains with clinical relevance and responsible for nosocomial outbreaks with severe outcomes. This study reports an outbreak occurred in a Long-term Care Unit from February to March 2022 and tracked by using a Matrix-Assisted Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) typing approach (T-MALDI); subsequently, a characterization of the toxigenic and antimicrobial susceptibility profiles of the C. difficile isolates was performed. A total of 143 faecal samples belonging to 112 patients was evaluated and C. difficile DNA was detected in 51 samples (46 patients). Twenty-nine C. difficile isolates were obtained, and three different clusters were revealed by T-MALDI. The most representative cluster accounted 22 strains and was considered to be epidemic, in agreement with PCR-Ribotyping. Such epidemic strains were susceptible to vancomycin (MIC ≤ 0.5 mg/mL) and metronidazole (MIC ≤ 1 mg/mL), but not to moxifloxacin (MIC > 32 mg/mL). Moreover, they produced only the Toxin A and, additionally, the binary toxin. To our knowledge, this is the first reported outbreak referable to a tcdA+/tcdB-/cdt+ genotypic profile. In light of these results, T-MALDI is a valid and rapid approach for discovering and tracking outbreaks.

Clostridioides difficile: Current overview and future perspectives

The most common world-wide cause of antibiotic-associated infectious diarrhea and colitis is the toxin producing bacterium, Clostridioides difficile (C. difficile). Here we review the background and characteristics of the bacterium and the toxins produced together with the epidemiology and the complex pathogenesis that leads to a broad clinical spectrum of disease. The review describes the difficulties faced in obtaining a quick and accurate diagnosis despite the range of sensitive and specific diagnostic tools available. We also discuss the problem of disease recurrence and the importance of disease prevention. The high rates of infection recurrence mean that treatment strategies are constantly under review and we outline the diverse treatment options that are currently in use and explore the emerging treatment options of pulsed antibiotic use, microbial replacement therapies and the use of monoclonal antibodies. We summarize the future direction of treatment strategies which include the development of novel antibiotics, the administration of oral polyclonal antibody formulations, the use of vaccines, the administration of competitive non-toxigenic spores and the neutralization of antibiotics at the microbiota level. Future successful treatments will likely involve a combination of therapies to provide the most effective and robust approach to C. difficile management.

Detection of potential enteric pathogens in children with severe acute gastroenteritis using the filmarray: Results from a three - years hospital-based survey in Northern Italy

Acute gastroenteritis (AGE) are leading causes of morbidity and mortality in children. Therefore, rapid pathogens identification is needed. The AGE aetiology was investigated from 2018 to 2020 in 2,066 children in Parma (Italy) by FilmArray Gastrointestinal Panel and Enterovirus-targeting RT-PCR. Pathogens were detected in 1,162 (56.2%) stool samples from as many children; 798 (68.7%) were single and 364 (31.3%) mixed infections (68.7% vs 31.3%, P < 0.0001). Children aged 0-5 years showed the highest infection incidence (66.1%). The most frequent pathogens were Enteropathogenic Escherichia coli (EPEC; 19.14%), Clostridioides difficile (10.42%), Norovirus (10.36%), Enterovirus (9.44%), and Campylobacter (9.21%). EPEC, Campylobacter, enteroaggregative E. coli, Norovirus, and Rotavirus showed seasonality. The incidence of pathogens detected decreased between 2018 and 2020 (42.7% vs 20.8%, P < 0.0001), seemingly for the preventive measures imposed by the severe acute respiratory syndrome coronavirus-2 pandemic. A putative aetiology in half the children examined and an estimate of enteric pathogens epidemiology were assessed.

Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry

Colistin resistance is one of the major threats for global public health, requiring reliable and rapid susceptibility testing methods. The aim of this study was the evaluation of a MALDI-TOF mass spectrometry (MS) peak-based assay to distinguish colistin resistant (colR) from susceptible (colS) Escherichia coli strains. To this end, a classifying algorithm model (CAM) was developed, testing three different algorithms: Genetic Algorithm (GA), Supervised Neural Network (SNN) and Quick Classifier (QC). Among them, the SNN- and GA-based CAMs showed the best performances: recognition capability (RC) of 100% each one, and cross validation (CV) of 97.62% and 100%, respectively. Even if both algorithms shared similar RC and CV values, the SNN-based CAM was the best performing one, correctly identifying 67/71 (94.4%) of the E. coli strains collected: in point of fact, it correctly identified the greatest number of colS strains (42/43; 97.7%), despite its lower ability in identifying the colR strains (15/18; 83.3%). In conclusion, although broth microdilution remains the gold standard method for testing colistin susceptibility, the CAM represents a useful tool to rapidly screen colR and colS strains in clinical practice.