Department of Clinical Laboratory, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kong Jiang Road, Shanghai, 200092, China.
Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, 100080, China.
Ann Clin Microbiol Antimicrob. 2022 Nov 18;21(1):50. doi: 10.1186/s12941-022-00541-3.
The clinical significance of group B streptococcus (GBS) was different among different clonal complexes (CCs), accurate strain typing of GBS would facilitate clinical prognostic evaluation, epidemiological investigation and infection control. The aim of this study was to construct a practical and facile CCs prediction model for S. agalactiae.
A total of 325 non-duplicated GBS strains were collected from clinical samples in Xinhua Hospital, Shanghai, China. Multilocus sequence typing (MLST) method was used for molecular classification, the results were analyzed to derive CCs by Bionumeric 8.0 software. Antibiotic susceptibility test was performed using Vitek-2 Compact system combined with K-B method. Multiplex PCR method was used for serotype identification. A total of 45 virulence genes associated with adhesion, invasion, immune evasion were detected by PCR method and electrophoresis. Three types of features, including antibiotic susceptibility (A), serotypes (S) and virulence genes (V) tests, and XGBoost algorithm was established to develop multi-class CCs identification models. The performance of proposed models was evaluated by the receiver operating characteristic curve (ROC).
The 325 GBS were divided into 47 STs, and then calculated into 7 major CCs, including CC1, CC10, CC12, CC17, CC19, CC23, CC24. A total of 18 features in three kinds of tests (A, S, V) were significantly different from each CC. The model based on all the features (S&A&V) performed best with AUC 0.9536. The model based on serotype and antibiotic resistance (S&A) only enrolled 5 weighed features, performed well in predicting CCs with mean AUC 0.9212, and had no statistical difference in predicting CC10, CC12, CC17, CC19, CC23 and CC24 when compared with S&A&V model (all p > 0.05).
The S&A model requires least parameters while maintaining a high accuracy and predictive power of CCs prediction. The established model could be used as a promising tool to classify the GBS molecular types, and suggests a substantive improvement in clinical application and epidemiology surveillance in GBS phenotyping.
B 群链球菌（GBS）在不同克隆复合体（CC）中的临床意义不同，准确的菌株分型有助于临床预后评估、流行病学调查和感染控制。本研究旨在构建一种实用且简便的用于鉴定无乳链球菌 CC 的预测模型。
本研究共收集了来自中国上海新华医院的 325 株非重复 GBS 菌株。采用多位点序列分型（MLST）方法进行分子分类，通过 Bionumeric 8.0 软件分析结果得出 CC。采用 Vitek-2 Compact 系统结合 K-B 法进行抗生素药敏试验。采用多重 PCR 法进行血清型鉴定。采用 PCR 电泳法检测与粘附、侵袭、免疫逃逸相关的 45 种毒力基因。建立了包括抗生素敏感性（A）、血清型（S）和毒力基因（V）检测以及 XGBoost 算法在内的三种类型的特征，用于开发多类 CC 识别模型。通过受试者工作特征曲线（ROC）评估所提出模型的性能。
325 株 GBS 分为 47 个 ST，进一步计算为 7 个主要 CC，包括 CC1、CC10、CC12、CC17、CC19、CC23、CC24。三种检测方法（A、S、V）中的 18 个特征在各 CC 之间存在显著差异。基于所有特征（S&A&V）的模型表现最佳，AUC 为 0.9536。仅基于血清型和抗生素耐药性（S&A）的模型纳入了 5 个有意义的特征，在预测 CC 方面表现良好，AUC 均值为 0.9212，与 S&A&V 模型相比，在预测 CC10、CC12、CC17、CC19、CC23 和 CC24 时无统计学差异（均 P>0.05）。
S&A 模型需要的参数最少，同时保持了 CC 预测的高准确性和预测能力。该模型可作为一种有前途的工具，用于对 GBS 分子类型进行分类，并为 GBS 表型的临床应用和流行病学监测提供实质性改进。
