Research Report on Changes in the Metabolome of Senescent Myoblasts

Research Background

As age increases, the function of skeletal muscle gradually deteriorates, a phenomenon closely related to the senescence of muscle stem cells (satellite cells). Satellite cells play a key role in muscle injury repair. However, during the aging process, the function of these cells gradually diminishes, leading to a decline in muscle regeneration capacity. In recent years, scientists have discovered that cellular senescence is not only characterized by permanent cell cycle arrest but also accompanied by a phenomenon known as the “Senescence-Associated Secretory Phenotype” (SASP). SASP refers to the release of large amounts of metabolites and cytokines by senescent cells, which may negatively affect surrounding cells and tissues. However, research on changes in the metabolome of senescent skeletal muscle cells, especially their exometabolome, remains very limited. Therefore, this study aims to explore changes in the metabolome of senescent myoblasts, particularly the characteristics of their exometabolome, and investigate the relationship between these changes and SASP signaling.

Paper Source

This study was jointly completed by Michael Kamal, Meera Shanmuganathan, Zachery Kroezen, Sophie Joanisse, Philip Britz-McKibbin, and Gianni Parise. The research team comes from the Exercise Metabolism Research Group, Department of Chemistry and Chemical Biology at McMaster University in Canada, and the School of Life Sciences at the University of Nottingham in the UK. The paper was first published on December 27, 2024, in the journal American Journal of Physiology-Cell Physiology, with the DOI: 10.1152/ajpcell.00880.2024.

Research Workflow

1. Cell Culture and Senescence Induction

The study used C2C12 myoblasts as experimental subjects. These cells were obtained from the American Type Culture Collection and cultured in DMEM medium containing 10% fetal bovine serum and 1% penicillin/streptomycin. To induce cellular senescence, researchers treated the cells with bleomycin, a chemotherapeutic drug capable of causing DNA double-strand breaks, which has been proven to effectively induce cellular senescence. Cells at 50-60% confluence were treated with bleomycin or control solvent (DMSO) for 12 hours, then washed and replaced with fresh medium. Cells and conditioned media were collected after 24 hours for subsequent analysis.

2. Metabolomics Analysis

Researchers used capillary electrophoresis-mass spectrometry (CE-MS) for untargeted metabolomics analysis of metabolites in cells and conditioned media. Specific steps included: - Sample Pretreatment: Cells and conditioned media samples were slowly thawed on ice, mixed with methanol/water solution containing internal standards, vortexed, and centrifuged, and the supernatant was taken for analysis. - Metabolite Separation and Detection: Agilent 6230 time-of-flight mass spectrometer (TOF-MS) and Agilent G7100A capillary electrophoresis system were used for metabolite separation and detection. Analytical conditions included polar metabolite analysis in positive and negative ion modes and non-esterified fatty acid analysis in negative ion mode. - Data Analysis: MetaboAnalyst 6.0 software was used for data processing and statistical analysis, including principal component analysis (PCA), hierarchical clustering analysis (HCA), and gene-metabolite interaction network analysis.

3. Dose-Response Experiments of Metabolites

Researchers selected four metabolites significantly upregulated in the conditioned media of senescent cells for further experiments: trimethylamine-N-oxide (TMAO), xanthine, choline, and oleic acid. These metabolites were used to treat healthy myoblasts at physiological concentrations (1×) and supraphysiological concentrations (10×), assessing their effects on markers of cellular senescence and SASP gene expression. - Detection of Senescence Markers: Effects of metabolites on cellular senescence were evaluated through senescence-associated β-galactosidase (SA-β-gal) staining, cell proliferation assays (MTT method), p53 and p21 protein expression detection, and γ-H2AX protein expression detection. - Detection of SASP Gene Expression: Real-time quantitative PCR (qPCR) was used to detect the expression levels of SASP-related genes such as Ccl2, Cxcl12, Il33, and Cyr61.

4. Gene-Metabolite Interaction Network Analysis

To further explore the relationship between metabolites and senescence genes, researchers analyzed using the SenMayo gene set (a database containing genes associated with cellular senescence across various tissues and species). A gene-metabolite interaction network was constructed using Cytoscape software to screen metabolites highly associated with senescence genes.

Main Results

1. Significant Changes in the Exometabolome of Senescent Myoblasts

Through CE-MS analysis, researchers found significant changes in the exometabolome of senescent myoblasts, with 40 metabolites significantly upregulated in the conditioned media of senescent cells, mainly including non-esterified fatty acids and amino acid metabolites. The release of fatty acids such as oleic acid, arachidonic acid, and palmitic acid significantly increased. Additionally, changes in the intracellular metabolome were relatively minor, with only a few metabolites like arachidonic acid and tyrosine showing significant upregulation.

2. Effects of Metabolites on Cellular Senescence

Dose-response experiments showed that treating with TMAO, xanthine, choline, or oleic acid alone could not significantly induce cellular senescence. Although oleic acid treatment upregulated p53 mRNA expression, other senescence markers such as SA-β-gal activity and p21 protein expression did not change significantly. Moreover, oleic acid treatment significantly upregulated the expression of SASP genes like Ccl2, Cxcl12, and Il33, suggesting that oleic acid may play a role in SASP signal regulation.

3. Association Between Oleic Acid and Senescence Genes

Gene-metabolite interaction network analysis showed that oleic acid is highly associated with multiple senescence genes, including IL8, MMP9, and EDN1. These genes play important roles in the SASP signaling pathway, suggesting that oleic acid may regulate SASP by affecting the expression of these genes.

Research Conclusions

This study systematically analyzed changes in the exometabolome of senescent myoblasts for the first time, finding that senescent cells release large amounts of non-esterified fatty acids, especially oleic acid, which may play an important role in SASP signal regulation. Although these metabolites cannot independently induce cellular senescence, they may indirectly regulate the microenvironment of senescent cells by affecting the expression of SASP-related genes. This discovery provides new insights into understanding the molecular mechanisms of skeletal muscle aging and offers potential targets for developing therapeutic strategies against age-related diseases.

Research Highlights

1. First Systematic Analysis of Exometabolome Characteristics: This study detailed changes in the exometabolome of senescent myoblasts for the first time, revealing the important role of non-esterified fatty acids in the aging process.
2. Potential Regulatory Role of Oleic Acid: The study found that oleic acid can significantly upregulate the expression of multiple SASP-related genes, suggesting its potential role in SASP signal regulation.
3. Construction of Gene-Metabolite Interaction Network: Using the SenMayo gene set and Cytoscape software, researchers constructed a gene-metabolite interaction network, providing a new tool for understanding the role of metabolites in aging.

Research Significance

This study not only enriches our understanding of the mechanisms of skeletal muscle aging but also provides new ideas for developing therapeutic strategies against age-related diseases. In particular, the potential role of oleic acid in SASP signal regulation may offer new targets for future treatments of muscle aging and related diseases. Additionally, the untargeted metabolomics methods and gene-metabolite interaction network analysis used in this study provide experimental designs and technical routes that can be referenced by other aging-related research.