Gene expression profiles in sporadic ALS fibroblasts define disease subtypes and the metabolic effects of the investigational drug EH301. Issue 20 (2nd June 2022)

• Record Type: Journal Article
• Authors: Fels, Jasmine A; Casalena, Gabriella; Konrad, Csaba; Holmes, Holly E; Dellinger, Ryan W; Manfredi, Giovanni
• Is Part Of: Human molecular genetics. Volume 31:Issue 20(2022)
• Page Start: 3458
• Page End: 3477
• Publication Date: 2022-06-02
• Subjects: Human molecular genetics -- Periodicals; Human chromosome abnormalities -- Periodicals; 572.8
• DOI: 10.1093/hmg/ddac118
• Languages: English
• ISSNs: 0964-6906

Abstract

Abstract: Metabolic alterations shared between the nervous system and skin fibroblasts have emerged in amyotrophic lateral sclerosis (ALS). Recently, we found that a subgroup of sporadic ALS (sALS) fibroblasts (sALS1) is characterized by metabolic profiles distinct from other sALS cases (sALS2) and controls, suggesting that metabolic therapies could be effective in sALS. The metabolic modulators nicotinamide riboside and pterostilbene (EH301) are under clinical development for the treatment of ALS. Here, we studied the transcriptome and metabolome of sALS cells to understand the molecular bases of sALS metabotypes and the impact of EH301. Metabolomics and transcriptomics were investigated at baseline and after EH301 treatment. Moreover, weighted gene coexpression network analysis (WGCNA) was used to investigate the association of the metabolic and clinical features. We found that the sALS1 transcriptome is distinct from sALS2 and that EH301 modifies gene expression differently in sALS1, sALS2 and the controls. Furthermore, EH301 had strong protective effects against metabolic stress, an effect linked to the antiinflammatory and antioxidant pathways. WGCNA revealed that the ALS functional rating scale and metabotypes are associated with gene modules enriched for the cell cycle, immunity, autophagy and metabolic genes, which are modified by EH301. The meta-analysis of publicly available transcriptomic data from induced motor neurons by Answer ALS confirmed the functional associations of genes correlated with disease traits. A subset of genes differentially expressed in sALS fibroblasts was used in a machine learning model to predict disease progression. In conclusion, multiomic analyses highlighted the differential metabolic and transcriptomic profiles in patient-derived fibroblast sALS, which translate into differential responses to the investigational drug EH301.