4.7 Article

Integrated Multi-Omics Analysis for Inferring Molecular Players in Inclusion Body Myositis

Journal

ANTIOXIDANTS
Volume 12, Issue 8, Pages -

Publisher

MDPI
DOI: 10.3390/antiox12081639

Keywords

inclusion body myositis (IBM); metabolism; organic acids; nucleotides; biomarker

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In this study, an omics analysis of multiple samples from IBM patients was conducted to gain insights into the pathophysiology of the disease. The presence of amyloid beta peptide 1-42, imbalanced organic acid profile, and abnormal expression of metabolites and gene regulators were observed in various samples. The findings reported in this study provide novel insights into IBM and suggest potential biomarkers that need to be validated in larger cohorts.
Inclusion body myositis (IBM) is an acquired inflammatory myopathy affecting proximal and distal muscles that leads to weakness in patients over 50. It is diagnosed based on clinical and histological findings in muscle related to inflammation, degeneration, and mitochondria. In relation to IBM, a shortage of validated disease models and a lack of biomarkers and effective treatments constitute an unmet medical need. To overcome these hurdles, we performed an omics analysis of multiple samples from IBM patients (saliva, fibroblasts, urine, plasma, and muscle) to gain insight into the pathophysiology of IBM. Degeneration was evident due to the presence of amyloid beta peptide 1-42 (A beta 1-42) in the saliva of the analyzed IBM patients. The presence of metabolic disarrangements in IBM was indicated by an imbalanced organic acid profile in fibroblasts and urine. Specifically, abnormal levels of L-pyroglutamic and orotic acid were supported by the abnormal expression of related metabolites in plasma and urine (glutathione and pyrimidines) and the aberrant expression of upstream gene regulators (L2HGDH, IDH2, OPLAH, and ASL) in muscle. Combined levels of L-pyroglutamic and orotic acid displayed an outstanding biomarker signature in urine with 100% sensitivity and specificity. The confirmation of systemic metabolic disarrangements in IBM and the identification of novel biomarkers reported herein unveil novel insights that require validation in larger cohorts.

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