Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Laboratory Medicine, Translational Metabolic Laboratory (TML), Radboud University Medical Center, Nijmegen, The Netherlands.
J Inherit Metab Dis. 2022 Jul;45(4):682-695. doi: 10.1002/jimd.12522. Epub 2022 May 22.
Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway-based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature-based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway-based interpretation of UM data in IMD diagnostics context.
非靶向代谢组学（UM）允许在单次分析运行中同时测量数百种代谢物。UM 生成的数据量非常大，这使其在患者诊断中的应用受到限制，因为手动解释所有特征是不可行的。在这里，我们描述了一种基于途径的代谢物集富集分析方法在 UM 数据中对相关生物途径进行优先级排序的应用。我们在一组 55 名患有已诊断遗传性代谢紊乱 (IMD) 的患者中验证了我们的方法，并表明它通过将特征置于生物背景中，补充了基于特征的生物标志物优先级排序。此外，我们发现通过采用跨不同 IMD 共享的富集途径，我们可以识别出常见的药物和化合物，否则这些药物和化合物可能会在富集方法中掩盖真正的疾病生物标志物。最后，我们证明了该方法在识别已知 IMD 的新候选生物标志物方面的潜力。我们的结果表明，在 IMD 诊断背景下，基于途径的 UM 数据分析具有附加价值。
