Moredun Research Institute, Pentlands Science Park, Penicuik, Mid Lothian, UK.
Biomathematics and Statistics Scotland (BioSS), James Clerk Maxwell Building, Peter Guthrie Tait Road, The King's Buildings, Edinburgh, Scotland EH9 3FD, UK.
Talanta. 2018 May 15;182:164-170. doi: 10.1016/j.talanta.2018.01.055. Epub 2018 Feb 20.
Whole cell MALDI is regularly used for the identification of bacteria to species level in clinical Microbiology laboratories. However, there remains a need to rapidly characterize and differentiate isolates below the species level to support outbreak management. We describe the implementation of a modified preparative approach for MALDI-MS combined with a custom analytical computational pipeline as a rapid procedure for subtyping Shigatoxigenic E. coli (STEC) and accurately identifying strain-specifying biomarkers. The technique was able to differentiate E. coli O157:H7 from other STEC. Within O157 serotype O157:H7 isolates were readily distinguishable from Sorbitol Fermenting O157 isolates. Overall, nine homogeneous groups of isolates were distinguished, each exhibiting distinct profiles of defining mass spectra features. This offers a robust analytical tool useable in reference/diagnostic public health scenarios.
全细胞 MALDI 常用于临床微生物学实验室对细菌进行种水平的鉴定。然而，仍然需要快速对种以下的分离物进行特征分析和区分，以支持暴发管理。我们描述了一种改良的 MALDI-MS 制备方法与自定义分析计算流程的结合，作为一种快速鉴定志贺毒素产生型大肠杆菌（STEC）和准确鉴定菌株特异性生物标志物的方法。该技术能够区分大肠杆菌 O157:H7 与其他 STEC。在 O157 血清型中，O157:H7 分离株很容易与山梨醇发酵 O157 分离株区分开来。总体而言，区分了 9 个具有相似特征的分离株组，每组都具有独特的定义质谱特征图谱。这为参考/诊断公共卫生场景提供了一种强大的分析工具。
