Wang L Y, Hou X J, Sun H Y, Diao B W, Li J, Yan M Y
Yangxin County Center for Disease Control and Prevention of Shandong Province, Binzhou 251800,China.
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China School of Public Health, Shandong University, Jinan 250061, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2024 Sep 10;45(9):1266-1272. doi: 10.3760/cma.j.cn112338-20240314-00121.
To establish a matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) assay for the identification of common serotypes and provide etiology evidence for the early precise treatment of salmonellosis. A total of 500 strains were collected from different regions and sources and five predominant serotypes ( Typhi Paratyphi A Typhimurium Enteritidis and Indiana) of each strain was identified by agglutination test and whole-genome sequencing. The protein complex of the strains was extracted by using optimized pretreatment method to establish the fingerprint database of peptides for each serotype. The new serotyping assays were established by using different modules based on the mass spectra database. Additional 155 strains with specified serotypes and variant sources were used to test and evaluate the accuracy of the new typing assays. Five MALDI-TOF MS databases were established, and two new serotyping assays were established via peptide fingerprint mapping/matching and machine learning of the neuronal convolutional network respectively based on the databases. The results showed that the fingerprint matching approach could quickly identify five common serotypes in clinical practice compared with the machine learning method, the accuracy of fingerprint matching assay to identify five serotypes reached 100.00% and the serotyping can be conducted within a short time (15-20 minutes) and had a good reproducibility, while the machine learning method could not completely identify these serotypes. Moreover the sensitivity and specificity of fingerprint matching assay were all 100.00% respectively, while they were only 82.23% and 95.81% for machine learning method. The established serotyping assay based on MALDI-TOF MS in this study can easily, rapidly and accurately identify different serotypes of .
建立一种基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）分析法，用于鉴定常见血清型，并为沙门氏菌病的早期精准治疗提供病因学依据。从不同地区和来源收集了500株菌株，通过凝集试验和全基因组测序鉴定每株菌株的五种主要血清型（伤寒、甲型副伤寒、鼠伤寒、肠炎和印第安纳）。采用优化的预处理方法提取菌株的蛋白质复合物，建立每种血清型的肽指纹图谱数据库。基于质谱数据库，使用不同模块建立新的血清型分析方法。另外选取155株具有特定血清型和不同来源的菌株，用于测试和评估新分型方法的准确性。建立了五个MALDI-TOF MS数据库，并分别基于数据库通过肽指纹图谱映射/匹配和神经元卷积网络的机器学习建立了两种新的血清型分析方法。结果表明，与机器学习方法相比，指纹匹配方法在临床实践中能够快速鉴定出五种常见血清型，指纹匹配分析法鉴定五种血清型的准确率达到100.00%，血清分型可在短时间内（15 - 20分钟）完成，且具有良好的重复性，而机器学习方法不能完全鉴定这些血清型。此外，指纹匹配分析法的灵敏度和特异性分别为100.00%，而机器学习方法的灵敏度和特异性仅分别为82.23%和95.81%。本研究建立的基于MALDI-TOF MS的血清型分析方法能够轻松、快速、准确地鉴定不同血清型的[未提及具体细菌名称]。
