Carnitine acetyltransferase deficiency mediates mitochondrial dysfunction‐induced cellular senescence in dermal fibroblasts
Bibliographic record
Abstract
Aging is accompanied by impaired mitochondrial function and accumulation of senescent cells. Mitochondrial dysfunction contributes to senescence by increasing the levels of reactive oxygen species and compromising energy metabolism. Senescent cells secrete a senescence-associated secretory phenotype (SASP) and stimulate chronic low-grade inflammation, ultimately inducing inflammaging. Mitochondrial dysfunction and cellular senescence are two closely related hallmarks of aging; however, the key driver genes that link mitochondrial dysfunction and cellular senescence remain unclear. Here, we aimed to elucidate a novel role of carnitine acetyltransferase (CRAT) in the development of mitochondrial dysfunction and cellular senescence in dermal fibroblasts. Transcriptomic analysis of skin tissues from young and aged participants showed significantly decreased CRAT expression in intrinsically aged skin. CRAT downregulation in human dermal fibroblasts recapitulated mitochondrial changes in senescent cells and induced SASP secretion. Specifically, CRAT knockdown caused mitochondrial dysfunction, as indicated by increased oxidative stress, disruption of mitochondrial morphology, and a metabolic shift from oxidative phosphorylation to glycolysis. Mitochondrial damage induced the release of mitochondrial DNA into the cytosol, which activated the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) and NF-ĸB pathways to induce SASPs. Consistently, fibroblast-specific CRAT-knockout mice showed increased skin aging phenotypes in vivo, including decreased cell proliferation, increased SASP expression, increased inflammation, and decreased collagen density. Our results suggest that CRAT deficiency contributes to aging by mediating mitochondrial dysfunction-induced senescence.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".