College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China.
Key Lab of Animal Bacteriology, Ministry of Agriculture, Nanjing 210095, China.
J Appl Microbiol. 2023 Feb 16;134(2). doi: 10.1093/jambio/lxac075.
Combining MALDI-TOF MS and machine learning to establish a new rapid method to identify two important serotypes of Rimerella anatipestifer.
MALDI-TOF MS was performed on 115 R. anatipestifer strains (serotype 1, serotype 2, and other serotypes) to explore its ability to identify serotypes of R. anatipestifer. Raw spectral data were generated in diagnostic mode; these data were preprocessed, clustered, and analysed using principal component analysis. The results indicated that MALDI-TOF MS completely differentiated serotype 1 from serotype 2 of R. anatipestifer; the potential serotype-associated m/z loci are listed. Furthermore, Random Forest and Support Vector Machine were used for modelling to identify the two important serotypes, and the results of cross-validation indicated that they had ∼80% confidence to make the right classification.
We proved that MALDI-TOF MS can differentiate serotype 1 from serotype 2 of R. anatipestifer. Additionally, the identification models established in this study have high confidence to screen out these two important serotypes from other serotypes.
结合 MALDI-TOF MS 和机器学习建立一种新的快速方法，以鉴定两种重要的鸭疫里默氏杆菌血清型。
对 115 株鸭疫里默氏杆菌（血清型 1、血清型 2 和其他血清型）进行 MALDI-TOF MS 分析，以探索其鉴定鸭疫里默氏杆菌血清型的能力。采用诊断模式获取原始光谱数据；对这些数据进行预处理、聚类，并采用主成分分析进行分析。结果表明，MALDI-TOF MS 可完全区分血清型 1 和血清型 2 的鸭疫里默氏杆菌；列出了潜在的与血清型相关的 m/z 位点。此外，采用随机森林和支持向量机进行建模以鉴定这两种重要的血清型，交叉验证结果表明，它们有 80%左右的置信度可做出正确的分类。
我们证明 MALDI-TOF MS 可区分血清型 1 和血清型 2 的鸭疫里默氏杆菌。此外，本研究建立的鉴定模型具有很高的置信度，可从其他血清型中筛选出这两种重要的血清型。
