Campos-Ruiz Miriam, Wang-Wang Jun Hao, Bordoy Antoni E, Rodríguez-Ponga Beatriz, Pagan Natalia, Hidalgo Jessica, Quesada María Dolores, Giménez Montserrat, Cardona Pere-Joan
Microbiology Department, Laboratori Clínic Metropolitana Nord, Germans Trias i Pujol University Hospital, Badalona, Spain.
Genetics and Microbiology Department, Universitat Autònoma de Barcelona, Bellaterra, Spain.
Front Microbiol. 2025 Apr 23;16:1565888. doi: 10.3389/fmicb.2025.1565888. eCollection 2025.
is the leading cause of community-acquired pneumonia and remains a significant contributor to bacteremia and meningitis, collectively known as invasive pneumococcal disease (IPD). Certain serotypes are more strongly associated with severe illness and antimicrobial resistance. Accurate serotyping is essential for effective IPD surveillance and vaccine development. Fourier-transform infrared (FTIR) spectroscopy has emerged as a valuable tool for differentiating among serotypes across various isolates. We analyzed 150 pneumococcal strains isolated from a tertiary hospital in Barcelona, Catalonia, Spain, between 2016 and 2023, representing 32 serotypes associated with IPD. Forty-nine samples (33%) exhibited serotypes included in PCV13 vaccine. Each strain was classified using (A) FTIR-based clustering and (B) FTIR machine-learning-based PneumoClassifier algorithm. The results were compared to the Quellung reaction, the gold standard methodology. Clustering method grouped correctly PCV13-serotypes 1, 3, and 19F and non-PCV13 serotypes 6C, 7BC, 17F, 24F, 31, and 35B (48/150). PneumoClasifier algorithm successfully grouped all PCV13-serotypes (49/49) including some of the most virulent described serotypes, such as 1, 6B, 7F, and 14. Among non-PCV13 serotypes, it correctly classified 73 out of 101 isolates (72.3%). However, 12F, 15AB, 16F, 17F, 23A, and 24F were misclassified. Overall, PneumoClassifier achieved an accuracy of 122/150 (79.80%) in serotyping pneumococcal strains, demonstrating higher concordance with Quellung (adjusted Rand index: 0.717, adjusted Wallace coefficient: 0.636) compared to the clustering approach (0.397 and 0.378, respectively) ( < 0.001). FTIR has proven to be a rapid, user-friendly, cost-effective, and practical technique, making it a promising first-line tool for serotyping.
是社区获得性肺炎的主要病因，并且仍然是菌血症和脑膜炎的重要致病因素，这两种疾病统称为侵袭性肺炎球菌病（IPD）。某些血清型与严重疾病和抗菌药物耐药性的关联更为密切。准确的血清分型对于有效的IPD监测和疫苗开发至关重要。傅里叶变换红外（FTIR）光谱已成为区分各种分离株血清型的宝贵工具。我们分析了2016年至2023年期间从西班牙加泰罗尼亚巴塞罗那一家三级医院分离出的150株肺炎球菌菌株，这些菌株代表了与IPD相关的32种血清型。49个样本（33%）呈现出包含在13价肺炎球菌结合疫苗（PCV13）中的血清型。每种菌株都使用（A）基于FTIR的聚类和（B）基于FTIR机器学习的肺炎球菌分类器算法进行分类。将结果与金标准方法荚膜肿胀反应进行比较。聚类方法正确地将PCV13血清型1、3和19F以及非PCV13血清型6C、7BC、17F、24F、31和35B分组（150株中的48株）。肺炎球菌分类器算法成功地将所有PCV13血清型（49/49）分组，包括一些描述的最具毒性的血清型，如1、6B、7F和14。在非PCV13血清型中，它正确地将101株分离株中的73株（72.3%）分类。然而，12F、15AB、16F、17F、23A和24F被错误分类。总体而言，肺炎球菌分类器在肺炎球菌菌株血清分型中的准确率为150株中的122株（79.80%），与荚膜肿胀反应相比，显示出更高的一致性（调整兰德指数：0.717，调整华莱士系数：0.636），而聚类方法分别为0.397和0.378（P<0.001）。FTIR已被证明是一种快速、用户友好、经济高效且实用的技术，使其成为血清分型有前景的一线工具。
