Clostridioides difficile 高致病性与非高致病性核糖型的鉴别：MALDI-TOF 质谱与机器学习

Aug 6, 2026

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Hypervirulent ribotypes (HVRTs) of Clostridioides difficile, including ribotype 027, are epidemiologically important. This study assessed whether MALDI-TOF can differentiate HVRT strains from non-HVRT strains commonly found in Europe. MALDI-TOF spectra from 157 clinical isolates served as training data, with an additional 83 isolates for validation. Direct spectral comparison was insufficient, while specific machine learning models achieved >95% accuracy and enabled sub-clustering of three HVRT subgroups. MALDI-TOF combined with ML offers rapid identification of major European HVRTs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Introduction

Material and methods

Strain collection and cultivation

Protein extraction, spectra acquisition, and species confirmation

Results

Discussion

European Journal of Clinical Microbiology & Infectious Diseases (2023) 42:1373–1381 [https://doi.org/10.1007/s10096-023-04665-y](https://doi.org/10.1007/s10096-023-04665-y) Discrimination between hypervirulent and non‑hypervirulent ribotypes of Clostridioides difficile by MALDI‑TOF mass spectrometry and machine learning Ahmed Mohamed Mostafa Abdrabou1,2,3 · Issa Sy1 · Markus Bischoff1,3 · Manuel J. Arroyo4 · Sören L. Becker1 · Alexander Mellmann3,5 · Lutz von Müller3,6 · Barbara Gärtner1,3 · Fabian K. Berger1,3 Received: 20 February 2023 / Accepted: 3 September 2023 / Published online: 18 September 2023 © The Author(s) 2023 Abstract 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. Keywords Clostridium difficile · Ribotypes · Anaerobic bacteria · MALDI-TOF mass spectrometry · Proteomic signature · Machine learning · Identification Ahmed Mohamed Mostafa Abdrabou and Issa Sy contributed equally to this article. * Ahmed Mohamed Mostafa Abdrabou [ahmed.mostafa@uks.eu](ahmed.mostafa@uks.eu); [ahmedmostafa2020@mans.edu.eg](ahmedmostafa2020@mans.edu.eg) 1 Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421 Homburg, Saar, Germany 2 Medical Microbiology and Immunology Department, Faculty of Medicine, Mansoura University, El Gomhouria Street, Mansoura 35516, Egypt 3 National Reference Center for Clostridioides (Clostridium) difficile, Homburg-Münster-Coesfeld, Germany 4 Clover Bioanalytical Software, Av. del Conocimiento, 41, 18016 Granada, Spain 5 Institute of Hygiene, University of Münster, Robert-Koch-Straße 41, 48149 Münster, Germany 6 Christophorus Kliniken Coesfeld, Coesfeld, Germany Introduction Clostridioides difficile is a significant cause of nosocomial diarrhea in industrialized nations [1] . Hypervirulent ribotypes (HVRTs) such as RT027 have influenced the global molecular epidemiology of C. difficile [2] leading to a higher disease burden [3] . RT027 has caused numerous outbreaks in Europe and the USA [4] . However, on a global scale, other HVRTs exist, e.g., RT023 being considered an emerging HVRT [5], and RT045 that might confer a zoonotic potential [6] . Besides the toxins A and B (genes: tcdA, tcdB) destroying the actin cytoskeleton, HVRT strains usually harbor a third toxin (binary toxin, gene: cdtAB) that increases bacterial adhesion through microtubular protrusions [7, 8] . Several typing techniques have been developed to identify RTs of higher importance. These include in particular ribotyping [9] and whole genome sequencing (WGS) [10] . However, both methods are comparably time-and resourceconsuming and therefore usually not available in most laboratories. Matrix-assisted laser desorption ionization timeof-flight (MALDI-TOF) mass spectrometry (MS) is widely distributed and an easy-to-use tool for the identification of bacteria [11], which is also used for bacterial subtyping [12] . Machine learning (ML) can further expand its capabilities, by training algorithms on a variety of databases garnered from analysis of bacterial proteins.

FAQs

直接比较 MALDI-TOF 光谱能否区分高致病性与非高致病性的核糖型？

不能。研究显示，HVRT 与非HVRT 的直接光谱比较不足以实现有效区分。

本研究使用了哪些数据集来训练和验证模型？

训练集包含 157 株临床分离株，覆盖欧洲流行且具有流行病学意义的 HVRT 与非HVRT；验证集另外包含 83 株用于独立评估。

MALDI-TOF 联合机器学习的主要效果是什么？

在特定机器学习模型下，HVRT 与非HVRT 可实现 >95% 的准确率，并可对三个 HVRT 亚群进行亚聚类，从而实现快速鉴别。

Aug 6, 2026

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