Molecular omics · 2022
Integrated proteomic and metabolomic analyses of the mitochondrial neurodegenerative disease MELAS.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАПоддержка правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Li Haorong, Uittenbogaard Martine, Navarro Ryan, Ahmed Mustafa, Gropman Andrea, Chiaramello Anne, Hao Ling
Аннотация
MELAS (mitochondrial encephalomyopathy, lactic acidosis, stroke-like episodes) is a progressive neurodegenerative disease caused by pathogenic mitochondrial DNA variants. The pathogenic mechanism of MELAS remains enigmatic due to the exceptional clinical heterogeneity and the obscure genotype-phenotype correlation among MELAS patients. To gain insights into the pathogenic signature of MELAS, we designed a comprehensive strategy integrating proteomics and metabolomics in patient-derived dermal fibroblasts harboring the ultra-rare MELAS pathogenic variant m.14453G>A, specifically affecting the mitochondrial respiratory complex I. Global proteomics was achieved by data-dependent acquisition (DDA) and verified by data-independent acquisition (DIA) using both Spectronaut and the recently launched MaxDIA platforms. Comprehensive metabolite coverage was achieved for both polar and nonpolar metabolites in both reverse phase and HILIC LC-MS/MS analyses. Our proof-of-principle MELAS study with multi-omics integration revealed OXPHOS dysregulation with a predominant deficiency of complex I subunits, as well as alterations in key bioenergetic pathways, glycolysis, tricarboxylic acid cycle, and fatty acid β-oxidation. The most clinically relevant discovery is the downregulation of the arginine biosynthesis pathway, likely due to blocked argininosuccinate synthase, which is congruent with the MELAS cardinal symptom of stroke-like episodes and its current treatment by arginine infusion. In conclusion, we demonstrated an integrated proteomic and metabolomic strategy for patient-derived fibroblasts, which has great clinical potential to discover therapeutic targets and design personalized interventions after validation with a larger patient cohort in the future.