Integrative multi-omics profiling reveals the molecular subtypes and circulating biomarkers for pediatric mitochondrial disease

Abstract

Abstract Pediatric mitochondrial disease (PMD) refers the MD happened before 14 years old, which is a collection of rare, heterogenies and lethal syndromes. However, PMD still lacks molecular subtypes and a noninvasive diagnostic biomarker for precise medication and early diagnosis. By using multi-omics analyses for the discovery cohort, the molecular subtypes and robust biomarkers firstly discovered. The biomarkers further validated in an independent cohort. We found multiple energetic pathways altered in the PMD plasma (proteomics and metabolomics) and blood cells (transcriptomes), indicating the qualification of working pipelines. Some pathways were discovered without expectation may provide new insight into PMD pathogenesis. Molecular subtypes modeling revealed that PMD can be calcified into “AA-META”, “LIP-META” and “MIDDLE-META”, interestingly, the “AA-META” correlated with severe symptoms with a higher rate of neurologic and cardiac affected. Based on three machine learning algorithms, we discovered a panel of biomarkers with 13 molecules (1 gene, 2 proteins, and 10 metabolites), including classic (lactate, pyruvate) and novel biomarkers, showed more effective diagnosis rate of PMD (AUC=0.947) than reported ones. Overall, our work defined molecular subtypes of PMD and established a new panel of biomarkers for the precision diagnosis of PMD.