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Network analysis of human muscle adaptation to aging and contraction.

ABSTRACT: Resistance exercise (RE) remains a primary approach for minimising aging muscle decline. Understanding muscle adaptation to individual contractile components of RE (eccentric, concentric) might optimise RE-based intervention strategies. Herein, we employed a network-driven pipeline to identify putative molecular drivers of muscle aging and contraction mode responses. RNA-sequencing data was generated from young (21±1 y) and older (70±1 y) human skeletal muscle before and following acute unilateral concentric and contralateral eccentric contractions. Application of weighted gene co-expression network analysis identified 33 distinct gene clusters ('modules') with an expression profile regulated by aging, contraction and/or linked to muscle strength. These included two contraction 'responsive' modules (related to 'cell adhesion' and 'transcription factor' processes) that also correlated with the magnitude of post-exercise muscle strength decline. Module searches for 'hub' genes and enriched transcription factor binding sites established a refined set of candidate module-regulatory molecules (536 hub genes and 60 transcription factors) as possible contributors to muscle aging and/or contraction responses. Thus, network-driven analysis can identify new molecular candidates of functional relevance to muscle aging and contraction mode adaptations.

SUBMITTER: Willis CRG

PROVIDER: S-EPMC6977671 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Network analysis of human muscle adaptation to aging and contraction.

Willis Craig R G CRG Ames Ryan M RM Deane Colleen S CS Phillips Bethan E BE Boereboom Catherine L CL Abdulla Haitham H Bukhari Syed S I SSI Lund Jonathan N JN Williams John P JP Wilkinson Daniel J DJ Smith Kenneth K Kadi Fawzi F Szewczyk Nathaniel J NJ Atherton Philip J PJ Etheridge Timothy T

Aging 20200107 1

Resistance exercise (RE) remains a primary approach for minimising aging muscle decline. Understanding muscle adaptation to individual contractile components of RE (eccentric, concentric) might optimise RE-based intervention strategies. Herein, we employed a network-driven pipeline to identify putative molecular drivers of muscle aging and contraction mode responses. RNA-sequencing data was generated from young (21±1 y) and older (70±1 y) human skeletal muscle before and following acute unilater ...[more]

PMID: 31910159

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Project description:BackgroundSarcopenia is an age-related muscle disease that increases the risk of falls, disabilities, and death. It is associated with increased muscle protein degradation driven by molecular signalling pathways including Akt and FOXO1. This study aims to identify genes, gene interactions, and molecular pathways and processes associated with muscle aging and exercise in older adults that remained undiscovered until now leveraging on an artificial intelligence approach called artificial neural network inference (ANNi).MethodsFour datasets reporting the profile of muscle transcriptome obtained by RNA-seq of young (21-43 years) and older adults (63-79 years) were selected and retrieved from the Gene Expression Omnibus (GEO) data repository. Two datasets contained the transcriptome profiles associated to muscle aging and two the transcriptome linked to resistant exercise in older adults, the latter before and after 6 months of exercise training. Each dataset was individually analysed by ANNi based on a swarm neural network approach integrated into a deep learning model (Intelligent Omics). This allowed us to identify top 200 genes influencing (drivers) or being influenced (targets) by aging or exercise and the strongest interactions between such genes. Downstream gene ontology (GO) analysis of these 200 genes was performed using Metacore (Clarivate™) and the open-source software, Metascape. To confirm the differential expression of the genes showing the strongest interactions, real-time quantitative PCR (RT-qPCR) was employed on human muscle biopsies obtained from eight young (25 ± 4 years) and eight older men (78 ± 7.6 years), partaking in a 6-month resistance exercise training programme.ResultsCHAD, ZDBF2, USP54, and JAK2 were identified as the genes with the strongest interactions predicting aging, while SCFD1, KDM5D, EIF4A2, and NIPAL3 were the main interacting genes associated with long-term exercise in older adults. RT-qPCR confirmed significant upregulation of USP54 (P = 0.005), CHAD (P = 0.03), and ZDBF2 (P = 0.008) in the aging muscle, while exercise-related genes were not differentially expressed (EIF4A2 P = 0.99, NIPAL3 P = 0.94, SCFD1 P = 0.94, and KDM5D P = 0.64). GO analysis related to skeletal muscle aging suggests enrichment of pathways linked to bone development (adj P-value 0.006), immune response (adj P-value <0.001), and apoptosis (adj P-value 0.01). In older exercising adults, these were ECM remodelling (adj P-value <0.001), protein folding (adj P-value <0.001), and proteolysis (adj P-value <0.001).ConclusionsUsing ANNi and RT-qPCR, we identified three strongly interacting genes predicting muscle aging, ZDBF2, USP54, and CHAD. These findings can help to inform the design of nonpharmacological and pharmacological interventions that prevent or mitigate sarcopenia.

Project description:PurposeWhile strengthening exercises are recommended for knee osteoarthritis (KOA) treatment, the optimal type of muscle contraction remains unclear, with current research showing conflicting results. This network meta-analysis (NMA) aims to evaluate the efficacy of lower limb strengthening exercises based on different muscle contraction characteristics for KOA patients and provide clinical references.MethodsWe conducted the NMA following the PRISMA-NMA. A comprehensive search of five databases (PubMed, Web of Science, CENTRAL, Embase, and SPORTDiscus) up to August 2024 identified randomized controlled trials (RCTs) investigating lower limb strengthening exercises in KOA patients. Control groups included receiving usual care, only providing health education, or no intervention at all. Outcomes analyzed included pain, physical function, quality of life, and muscle strength.ResultsForty-one studies (2,251 participants) were included. Twenty-eight studies used rigorous randomization; eighteen reported allocation concealment. All had high performance bias risk due to exercise interventions. Regarding efficacy, isokinetic exercise ranked highest in pain relief (SMD = 0.70, 95% CI: 0.50-0.91, SUCRA = 82.6%), function improvement (SMD = 0.75, 95% CI: 0.57-0.92, SUCRA = 96.1%), and enhancement in muscle strength (SMD = 0.56, 95% CI: 0.34-0.78, SUCRA = 90.1%). Isometric exercise ranked highest in improving quality of life (SMD = 0.80, 95% CI: 0.28-1.31, SUCRA = 90.5%). Mixed strengthening exercise ranked lowest across all outcomes. High-frequency interventions (≥5 times/week) showed superior pain relief compared with low-frequency (≤3 times/week) for isotonic, isometric, and isokinetic exercise.ConclusionThis NMA suggests isokinetic exercise may be most effective for pain, function, and muscle strength in KOA patients, while isometric exercise benefits quality of life most. Mixed strengthening exercise ranked lowest across all outcomes. High-frequency interventions appear more effective than low-frequency ones. These findings support personalized KOA treatment, considering efficacy, accessibility, and patient-specific factors. Study biases, heterogeneity, and other limitations may affect result reliability. Future research should focus on high-quality studies with standardized protocols and analyze dose-response relationships to refine KOA treatment strategies.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024582525, identifier: CRD42024582525.

Dataset Information

Network analysis of human muscle adaptation to aging and contraction.

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Network analysis of human muscle adaptation to aging and contraction.

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