Candela Ana, Rodriguez-Temporal David, Blázquez-Sánchez Mario, Arroyo Manuel J, Marín Mercedes, Alcalá Luis, Bou Germán, Rodríguez-Sánchez Belén, Oviaño Marina
Clinical Microbiology Department, Complexo Hospitalario Universitario A Coruña, Institute of Biomedical Research A Coruña (INIBIC), A Coruña, Spain.
Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio Marañón and Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
Eur J Clin Microbiol Infect Dis. 2025 Feb;44(2):417-425. doi: 10.1007/s10096-024-05023-2. Epub 2024 Dec 17.
Clostridioides difficile is the main cause of antibiotic related diarrhea and some ribotypes (RT), such as RT027, RT181 or RT078, are considered high risk clones. A fast and reliable approach for C. difficile ribotyping is needed for a correct clinical approach. This study analyses high-molecular-weight proteins for C. difficile ribotyping with MALDI-TOF MS.
Sixty-nine isolates representative of the most common ribotypes in Europe were analyzed in the 17,000-65,000 m/z region and classified into 4 categories (RT027, RT181, RT078 and 'Other RTs'). Five supervised Machine Learning algorithms were tested for this purpose: K-Nearest Neighbors, Support Vector Machine, Partial Least Squares-Discriminant Analysis, Random Forest (RF) and Light-Gradient Boosting Machine (GBM).
All algorithms yielded cross-validation results > 70%, being RF and Light-GBM the best performing, with 88% of agreement. Area under the ROC curve of these two algorithms was > 0.9. RT078 was correctly classified with 100% accuracy and isolates from the RT181 category could not be differentiated from RT027.
This study shows the possibility of rapid discrimination of relevant C. difficile ribotypes by using MALDI-TOF MS. This methodology reduces the time, costs and laboriousness of current reference methods.
艰难梭菌是抗生素相关性腹泻的主要病因，一些核糖体分型（RT），如RT027、RT181或RT078，被认为是高风险克隆株。为了采取正确的临床治疗方法，需要一种快速可靠的艰难梭菌核糖体分型方法。本研究分析了用于艰难梭菌核糖体分型的高分子量蛋白质的基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）。
对代表欧洲最常见核糖体分型的69株分离株在17,000-65,000 m/z区域进行分析，并分为4类（RT027、RT181、RT078和“其他RTs”）。为此测试了五种监督式机器学习算法：K近邻算法、支持向量机、偏最小二乘判别分析、随机森林（RF）和轻梯度提升机（GBM）。
所有算法的交叉验证结果均>70%，其中RF和Light-GBM表现最佳，一致性为88%。这两种算法的ROC曲线下面积>0.9。RT078的正确分类准确率为100%，RT181类别的分离株无法与RT027区分开来。
本研究表明使用MALDI-TOF MS快速区分相关艰难梭菌核糖体分型是可能的。这种方法减少了当前参考方法的时间、成本和繁琐程度。
