A network-based atlas of human skeletal muscle aging - PubMed
3 hours ago
- #skeletal muscle aging
- #network modeling
- #transcriptomics
- A network-based atlas of human skeletal muscle aging was created using deep transcriptomic profiles of 1,675 human muscle biopsies and single-cell spatial transcriptomic technologies.
- Five Quantitative Network Models (QNMs) were built using over 40 trillion calculations and 930 human muscle transcriptomes to model aging and the influence of load status.
- Differential expression (DE) signatures for atrophy, hypertrophy, and cardio-respiratory adaptation were integrated with single-cell RNAseq and cell-specific bulk profiles.
- Over 3,000 genes were identified as differentially expressed with muscle age, equally up and down-regulated.
- A novel pre-frailty signature in elderly subjects showed strong overlap with the response of healthy muscle during experimental atrophy.
- Hypertrophy signature in elderly muscle opposed the age-regulated transcriptome, unlike in young muscle.
- Non-responders to hypertrophy or cardio-respiratory gains had distinct genome-level responses to exercise.
- QNM revealed cell-specific processes in endothelial cells and fibroblasts, including novel interactions between insulin sensitivity, age, and senescence.
- 286 hub genes were consistent in both young and old muscle network models, with 27% having known roles in muscle biology.
- Top 50 hub genes included 45% protein-coding genes, with 80% newly linked to human muscle biology.
- Spatial muscle fiber-type profiling located key aging, frailty, and load-responsive genes to individual cell types.
- A machine-learning model prioritized network features over DE signatures to explain muscle aging.
- Genome-level modeling produced a transcriptomic 'age clock' invariant to muscle load status in people over 50 years.
- An unprecedented level of consistently aligned genomic data and QNMs with over 7,000 searchable modules were released.