The Metabolomic Pathways of the Senescence‐Associated Secretory Phenotype in C2C12 Myoblasts
Bibliographic record
Abstract
Cellular senescence is considered a hallmark of aging that often occurs in tissues and cells in response to DNA damage. Most healthy cells become senescent after a fixed number of divisions or irreparable genomic damage, often because of external conditions such as circulating factors that are released by adjacent senescent cells. These secreted factors, known as the senescence‐associated secretory phenotype (SASP), are capable of in vivo reprogramming of otherwise healthy cells, ultimately inducing senescence. The involvement of cellular senescence in a variety of aging‐related diseases has been thoroughly examined, however the role of cellular senescence and the SASP in aging muscle remains understudied. Therefore, the objective of this study was to examine the role of metabolites as potential components of the SASP through an easily reproducible model of senescence in skeletal muscle myoblasts. C2C12 myoblasts were treated with an antitumour antibiotic, bleomycin, to cause DNA damage‐induced senescence, or with a vehicle control. Cells and associated media were collected 24 hours after treatment for metabolomic profiling using capillary‐electrophoresis – mass spectrometry (CE‐MS). Samples were compared to untreated myoblasts or fresh growth media and normalized to total cell count and protein content prior to analysis. Pathway analysis using the KEGG database revealed a substantial impact on amino acid metabolism within senescent cells, specifically phenylalanine and histidine metabolism. The media associated with senescent cells had a similar impact on amino acid metabolism, but also showed a significant impact on several polyunsaturated fatty acids, including arachidonic acid and docosahexaenoic acid. Notably, trimethylamine N‐oxide (TMAO), a metabolite previously shown to promote endothelial senescence/dysfunction and neuroinflammation, was detected at significantly higher levels in the media surrounding senescent myoblasts relative to vehicle‐treated controls (fold change = 2.878, p < 0.05). These data suggest that DNA damage‐induced senescence significantly impacts the metabolome of skeletal muscle cells. Future work will aim to evaluate the targeted impact of candidate metabolites as potential senescence inducers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".