Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy
Abstract
1. Introduction
2. Methods
2.1. Search Strategy and Selection Criteria
2.2. Eligibility Criteria
2.3. Data Extraction and Synthesis
2.4. Risk of Bias and Limitations
3. The Neurogenic Paradigm in Sarcopenia Pathogenesis: From Synaptic Dysfunction to Systemic Neurodegeneration
3.1. Architecture of the Neuromuscular Axis and Its Remodeling in Aging and Sarcopenia
3.2. Molecular Determinants of Neurogenic Muscle Atrophy: Neurotrophic Factors and Signaling Cascades
3.2.1. Brain-Derived Neurotrophic Factor (BDNF) in the Maintenance of NMJ Structure and Function
3.2.2. Neurotrophin-4 (NT-4) and TrkB Signaling in Maintaining Neuromuscular Synapse Stability
3.2.3. NGF and NT-3: Additional Regulators of the Neuromuscular Axis Neuritrophin-3 (NT-3)
3.2.4. Nerve Growth Factor (NGF)
3.2.5. GDNF and Its Role in Supporting the Neuromuscular System
3.2.6. IGF-1 as a Key Anabolic Factor and Potential Biomarker of Sarcopenia
3.2.7. Regulatory Role of Irisin in Muscle Homeostasis and Sarcopenia Development
3.3. Sarcopenia as a Component of the Neurodegenerative Disease Continuum: Shared Pathogenic Mechanisms with Alzheimer’s and Parkinson’s Diseases
3.3.1. Alzheimer’s Disease
3.3.2. Parkinson’s Disease
3.4. Summary: The Neuromuscular Junction as a Critical Link in the Pathogenesis of Muscle Failure
4. Genomic Landscapes Unveiled by NGS: Insights into Neurogenic Sarcopenia
4.1. Genetic Determinants of Sarcopenia Development and Progression
4.2. Epigenetic Regulation in Sarcopenia: The Role of DNA Methylation and Post-Translational Histone Modifications
5. Transcriptomic Signatures from NGS: Dysregulated Pathways in Neuromuscular Crosstalk
5.1. Systemic Analysis of Muscle Tissue Transcriptome: Dysregulation of Key Signaling Pathways and Biomarker Identification
5.2. Analysis of Cellular Heterogeneity by Single-Cell Sequencing: The Contribution of Cellular Populations to Sarcopenia Development
6. Translational Implications and Future Directions: From NGS Data to Clinical Applications
6.1. Contemporary Experimental Models: Platforms for Modeling Sarcopenia In Vitro and In Vivo
6.2. Biomarker Identification Strategies: Integration of Multi-Omics Data for Diagnosis and Treatment
6.3. Personalized Medicine Technologies: Application of Artificial Intelligence and Machine Learning for Analyzing Multidimensional Datasets in Sarcopenia
7. Clinical Implications and Translational Outlook
8. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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| Factor | Mechanism | Role |
|---|---|---|
| BDNF | Signaling via TrkB and p75NTR receptors; modifies the presynaptic exocytotic machinery (phosphorylation of Munc18-1, SNAP-25) and influences downstream kinases (PKC-βI/PKCε) | Involved in maintaining NMJ integrity and function; altered levels are associated with sarcopenia markers in humans and age-related NMJ changes in animal models |
| NT-4 | Shares the receptor pathway with BDNF via TrkB and p75NTR, influencing presynaptic exocytosis and the activity of PKC-dependent pathways | A postsynaptically released factor involved in neuroprotection and regulation of neurotransmission at the NMJ; an altered BDNF/NT-4 ratio is noted with aging |
| NGF | Acts through high-affinity TrkA and p75NTR receptors; supports neuronal survival and differentiation | Influences neuronal survival and motoneuron status during aging; identified as a positive regulator of muscle mass |
| NT-3 | Acts through high-affinity TrkC/TrkA and p75NTR receptors; supports neuronal survival and differentiation | |
| GDNF | Acts via the Ret/GFRα receptor system, stimulating pro-survival neuronal signals and maintaining synaptic stability | Supports motoneuron survival and helps preserve innervation; implicated in neuromuscular plasticity |
| NRG4 | Adipokine-like action; improves insulin sensitivity and indirectly contributes to the preservation of muscle mass | Levels decrease with age; administration of recombinant NRG4 in aged mice improves signs of sarcopenia and metabolic disorders |
| IGF-1 | Activation of IGF-1R → PI3K/AKT mechanisms stimulates protein synthesis and suppresses catabolic pathways | Anabolic factor promoting muscle cell growth and survival; protects against age-related loss of mass and strength |
| Irisin | Secreted myokine with anti-inflammatory and neuroprotective effects; modulates mitochondrial function and metabolism | Positive regulator of muscle function; its levels correlate with physical performance in the elderly |
| Link to Parkinson’s Disease (PD) | Link to Alzheimer’s Disease (AD) | Specific Processes and Manifestations | Pathogenic Mechanism |
|---|---|---|---|
| Pathological α-synuclein aggregation in motor neuron axons and NMJs, directly disrupting their structure and function, leading to denervation. | Altered NMJ structure (fragmentation, partial denervation) in AD models (3xTgAD mice). Accumulation of APP and β-amyloid in muscle tissue. | Muscle fiber denervation, NMJ degeneration, motor neuron dysfunction. | Neurogenic Mechanisms and Neuromuscular Junction (NMJ) Dysfunction |
| α-Synuclein impairs the mitochondrial importer TOM40. Decreased expression of oxidative phosphorylation genes in muscle. Sharp increase in ROS production. | Disrupted neuronal energy homeostasis, ROS accumulation. Mitochondrial dysfunction in skeletal muscle leads to reduced strength and apoptosis activation. | Disrupted dynamics, biogenesis, and mitophagy; accumulation of mtDNA damage; reduced energy production. | Mitochondrial Dysfunction |
| Oxidative stress and inflammation create a self-sustaining pathological cycle that enhances α-synuclein aggregation and muscle tissue damage. | Pro-inflammatory mediators in AD exert systemic effects on skeletal muscle, creating a vicious cycle that exacerbates sarcopenia. | Chronic low-grade inflammation (inflammaging); release of pro-inflammatory cytokines. | Neuroinflammation/Systemic Inflammation |
| Key role of α-synuclein: its aggregates in NMJs impair acetylcholine release and increase abnormal mitochondria count. | Accumulation of β-amyloid and APP in muscle tissue, contributing to dysfunction. | Accumulation and aggregation of specific pathological proteins with direct toxic effects. | Pathological Protein Aggregates |
| α-Synuclein reduces basal respiratory and glycolytic capacity of MuSCs, impairing their migration and fusion, critically compromising muscle regeneration. | May be a consequence of the general neurogenic and inflammatory context. | Dysfunction of muscle satellite cells (MuSCs), impaired metabolism, proliferation, and fusion capacity. | Impaired Regenerative Potential |
| NA | Identified as a key mechanism of age-related motor neuron loss. Physical activity can modulate this process. | Loss of nuclear membrane integrity, increased permeability, accumulation of toxic proteins in the nucleus. | Impaired Nucleocytoplasmic Transport |
| Notes/Replication | Phenotype Assessed | Biological Function/Pathway | Key Variant(s)/Gene(s) | Design and Sample Size | Study/Population |
|---|---|---|---|---|---|
| Sub-GWS; replicated in UK Biobank | WLBM | Ubiquitin–proteasome; lipid metabolism | rs740681 (FZR1), SOAT2 | WES | Han Chinese (n = 101) [137] |
| Significant in discovery, nominal in replication | WLBM | Actin signaling, glycoprotein biosynthesis, ATPase | rs3732593 (3p27.1; MCF2L2, B3GNT5, ATP11B) | GWAS | Framingham Heart Study (n = 6004) [138,139] |
| PRS ≥ 4 alleles: very high OR (630.6) | Clinical diagnosis | Cholesterol binding, apoptosis | rs10282247 (OSBPL3), rs7022373 (ACER2) | GWAS (clin. sarcopenia) | Taiwanese elderly [140] |
| eQTL confirmed expression in skeletal muscle | LBM, ASM | mRNA destabilization, immune signaling | rs1187118, rs3768582 (RPS10, NUDT3, NCF2, SMG7, ARPC5) | GWAS meta-analysis | Korean cohorts (n = 6961) [141] |
| 20 novel loci identified | Grip strength, walking speed | Glycogen synthesis, myogenesis, Ca2+ homeostasis | PPP1R3A, ZBTB38, ATP2A1 | MTAG + TWAS | UK Biobank (n = 217,822) [145] |
| Partial overlap with strength traits | Weakness (clinical) | Immune regulation, TGF-β signaling | HLA-DQA1, GDF5 | GWAS | Muscle weakness GWAS (n = 256,523) [146] |
| Functional validation in model organisms | Muscle mass | Sex-specific loci; mitochondrial/structural pathways | LINC01661/PRMT6, RCC2P8/COL25A1, DMAC1, EMP2, SSUH2 | WGS | Multi-ancestry WGS (n = 10,729) [147] |
| Protective allele in NE Asians | Sarcopenia diagnosis | Myogenic differentiation, immune protection | SLC41A3, HLA-DPB102:01 | WGS | Japanese cohort (n = 129) [148] |
| OR ≈ 1.98; physical activity mitigates risk | Sarcopenia risk | Lipid metabolism, cytoskeleton, signaling | FADS2, MYO10, KCNQ5, DOCK5, LRP1B | PRS | Korean case–control (1368 vs. 15,472) [149] |
| Significance and Future Directions | AI/ML Methods | Data Type |
|---|---|---|
| Laid the groundwork for diagnostic automation and identification of clinical predictors of sarcopenia | Deep learning architectures (U-Net, nnU-Net, AutoSAM); semantic segmentation (DeepLabV3+ with EfficientNetV2-L); image classification (EfficientNetV2-L); ensemble methods (Random Forest) | Clinical Data and Medical Images |
| Opened prospects for early detection and prediction of sarcopenia as a complication of other diseases based on metabolic profiles | LASSO, SVM-RFE и RF | Metabolomic Data |
| Highlighted the necessity of accounting for population characteristics. Deepened understanding of molecular mechanisms via key signaling pathways | Specialized neural network architectures (DSnet-v1); ensemble methods (Random Forest, XGBoost, AdaBoost) followed by deep neural network training | Transcriptomic Data |
| Provided a molecular basis for understanding the pathogenetic mechanisms of sarcopenia | LASSO, SVM-RFE | Epigenetic Data |
| The most promising direction. Enables identification of novel drug targets and repurposing candidates, unraveling complex molecular interaction networks | MTA-MO (Multi-Task Attention-aware for Multi-Omics data); ANNi (Artificial Neural Network Inference) | Integrative Multi-Omics Data |
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Kupriyanova, D.; Bilyalov, A.; Filatov, N.; Brovkin, S.; Shestakov, D.; Bodunova, N.; Gusev, O. Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy. Int. J. Mol. Sci. 2025, 26, 11185. https://doi.org/10.3390/ijms262211185
Kupriyanova D, Bilyalov A, Filatov N, Brovkin S, Shestakov D, Bodunova N, Gusev O. Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy. International Journal of Molecular Sciences. 2025; 26(22):11185. https://doi.org/10.3390/ijms262211185
Chicago/Turabian StyleKupriyanova, Darya, Airat Bilyalov, Nikita Filatov, Sergei Brovkin, Dmitrii Shestakov, Natalia Bodunova, and Oleg Gusev. 2025. "Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy" International Journal of Molecular Sciences 26, no. 22: 11185. https://doi.org/10.3390/ijms262211185
APA StyleKupriyanova, D., Bilyalov, A., Filatov, N., Brovkin, S., Shestakov, D., Bodunova, N., & Gusev, O. (2025). Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy. International Journal of Molecular Sciences, 26(22), 11185. https://doi.org/10.3390/ijms262211185

