The mammalian longevity associated acetylome
Bibliographic record
Abstract
Despite extensive studies at the genomic, transcriptomic and metabolomic levels, the underlying mechanisms regulating longevity are incompletely understood. Post-translational protein acetylation is suggested to regulate aspects of longevity. To further explore the role of acetylation, we develop the PHARAOH computational tool based on the 100-fold differences in longevity within the mammalian class. Analyzing acetylome and proteome data across 107 mammalian species identifies 482 and 695 significant longevity-associated acetylated lysine residues in mice and humans, respectively. These sites include acetylated lysines in short-lived mammals that are replaced by permanent acetylation or deacetylation mimickers, glutamine or arginine, respectively, in long-lived mammals. Conversely, glutamine or arginine residues in short-lived mammals are replaced by reversibly acetylated lysine in long-lived mammals. Pathway analyses highlight the involvement of mitochondrial translation, cell cycle, fatty acid oxidation, transsulfuration, DNA repair and others in longevity. A validation assay shows that substituting lysine 386 with arginine in mouse cystathionine beta synthase, to attain the human sequence, increases the pro-longevity activity of this enzyme. Likewise, replacing the human ubiquitin-specific peptidase 10 acetylated lysine 714 with arginine as in short-lived mammals, reduces its anti-neoplastic function. Overall, in this work we propose a link between the conservation of protein acetylation and mammalian longevity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".