Integrated transcriptomics unveils mitochondrial oxidative phosphorylation dysfunction as a shared mechanism in sarcopenia and obesity.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41053312", "kind": "science-record", "revision": 4, "content_hash": "sha256:6e5b4605f0aca8e24d4ccfdc86f88c15f5c25799f64644f19ff184c4d41364ea", "created_at": "2026-11-01T20:31:06Z", "updated_at": "2026-11-01T22:57:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated transcriptomics unveils mitochondrial oxidative phosphorylation dysfunction as a shared mechanism in sarcopenia and obesity.", "doi": "10.1038/s41598-025-18824-y", "pmid": "41053312", "authors": ["Ruixuan Yu", "Lei Miao", "Jinchao Wang", "Dingding Wang", "Yajun Liu", "Ling Wang", "Renxian Wang"], "journal": "Scientific reports", "year": 2025, "abstract": "Sarcopenia and obesity, two prevalent metabolic disorders in aging populations, often coexist and share overlapping pathophysiological mechanisms, yet the molecular mechanisms underlying their comorbidity remain elusive. This study aimed to identify key gene expression signatures and pathways underlying their comorbidity through integrative transcriptomic and bioinformatics analyses. Gene expression datasets from sarcopenia (GSE111016, skeletal muscle) and obesity (GSE152991, adipose tissue) were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified using the limma package, and 208 common differentially expressed genes (CDEGs) were selected via Venn diagram intersection. Functional enrichment analyses (GO and KEGG) were performed to explore shared biological processes and pathways. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and key CDEGs were identified via ten topological algorithms (e.g., MCC, Degree) in the CycloHubba plugin. Pearson correlation analysis and qPCR were used to validate gene co-expression patterns and expression levels in tissue samples. GO and KEGG analyses revealed that CDEGs were significantly enriched in mitochondrial oxidative phosphorylation, electron transport chain, and thermogenesis pathways, with overlap in neurodegenerative disease pathways. The PPI network and multi-algorithm integration identified four key CDEGs: SDHB, SDHD, ATP5F1A, and ATP5F1B, all of which are components of mitochondrial respiratory chain complexes. These genes exhibited strong positive correlations (r > 0.86, p < 10⁻¹²) in both datasets and were significantly downregulated in sarcopenia and obesity tissues, as validated by qPCR. This study confirms mitochondrial dysfunction, particularly impaired oxidative phosphorylation, as a common pathological mechanism linking sarcopenia and obesity. The key genes SDHB, SDHD, ATP5F1A, and ATP5F1B represent potential therapeutic targets for managing these comorbid metabolic disorders. Future research should explore their functional roles in energy metabolism and cross-tissue crosstalk to develop targeted interventions.", "claims": [{"id": "claim-method", "statement": "The R language “limma” package was used to analyze the data from GSE152991 and GSE111016 to identify DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41053312", "locator": "sections[4] \"Identification of common DEGs between sarcopenia and obesity\" paragraph 2", "quote": "The R language “limma” package was used to analyze the data from GSE152991 and GSE111016 to identify DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression datasets (GSE111016 and GSE152991) used in this study are publicly available in the Gene Expression Omnibus (GEO) database. 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Pearson correlation analysis and qPCR were used to validate gene co-expression pat", "copenia and obesity tissues, as validated by qPCR. This study confirms mitochondrial dysfuncti", "nces provided in Table 1). Quantitative PCR (qPCR) was conducted using LightCycler 480 SYBR Gr", "lly significant. Table 1Primer sequences for qPCR analysis.Gene NameForward Primer (5’→3’)Reve"], "order_basis": "paper_text_order:1146", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The R language “limma” package was used to analyze the data from GSE152991 and GSE111016 to identify DEGs."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}