Jadhav Snehal R, Shah Rohan M, Karpe Avinash V, Barlow Robert S, McMillan Kate E, Colgrave Michelle L, Beale David J
Consumer-Analytical-Safety-Sensory (CASS) Food Research Centre, School of Exercise and Nutrition Sciences, Deakin University, Burwood, VIC 3125, Australia.
Department of Chemistry and Biotechnology, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.
Metabolites. 2021 Jan 25;11(2):67. doi: 10.3390/metabo11020067.
Shiga toxigenic (STEC) are an important cause of foodborne disease globally with many outbreaks linked to the consumption of contaminated foods such as leafy greens. Existing methods for STEC detection and isolation are time-consuming. Rapid methods may assist in preventing contaminated products from reaching consumers. This proof-of-concept study aimed to determine if a metabolomics approach could be used to detect STEC contamination in spinach. Using untargeted metabolic profiling, the bacterial pellets and supernatants arising from bacterial and inoculated spinach enrichments were investigated for the presence of unique metabolites that enabled categorization of three risk groups. A total of 109 and 471 metabolite features were identified in bacterial and inoculated spinach enrichments, respectively. Supervised OPLS-DA analysis demonstrated clear discrimination between bacterial enrichments containing different risk groups. Further analysis of the spinach enrichments determined that pathogen risk groups 1 and 2 could be easily discriminated from the other groups, though some clustering of risk groups 1 and 2 was observed, likely representing their genomic similarity. Biomarker discovery identified metabolites that were significantly associated with risk groups and may be appropriate targets for potential biosensor development. This study has confirmed that metabolomics can be used to identify the presence of pathogenic likely to be implicated in human disease.
产志贺毒素大肠杆菌（STEC）是全球食源性疾病的一个重要病因，许多疫情爆发都与食用受污染食品有关，如绿叶蔬菜。现有的STEC检测和分离方法耗时较长。快速方法可能有助于防止受污染产品流向消费者。这项概念验证研究旨在确定代谢组学方法是否可用于检测菠菜中的STEC污染。通过非靶向代谢谱分析，研究了细菌富集物以及接种菠菜富集物产生的细菌沉淀和上清液中是否存在独特的代谢物，这些代谢物可用于对三个风险组进行分类。在细菌富集物和接种菠菜富集物中分别鉴定出了总共109个和471个代谢物特征。有监督的OPLS-DA分析表明，含有不同风险组的细菌富集物之间存在明显区分。对菠菜富集物的进一步分析确定，病原体风险组1和2可以很容易地与其他组区分开来，不过观察到风险组1和2有一些聚类，这可能代表了它们的基因组相似性。生物标志物发现确定了与风险组显著相关的代谢物，这些代谢物可能是潜在生物传感器开发的合适靶点。这项研究证实，代谢组学可用于识别可能与人类疾病有关的病原体的存在。
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