The spatiotemporal dynamics of a modular metabolic network that regulates longevity in yeast
Bibliographic record
Abstract
The yeast Saccharomyces cerevisiae is a valuable model for unveiling the mechanisms of cellular aging in multicellular eukaryotes. Yeast aging can be slowed down by calorie restriction (CR), a low‐calorie dietary regimen that extends life span and delays age‐related disorders in a wide spectrum of organisms. To establish the mechanisms underlying the anti‐aging effect of CR, we assessed the effect of a CR diet and numerous mutations extending life span on the metabolic history and age‐dependent organelle dynamics of chronologically aging yeast. We found that yeast merge a number of cellular processes, which we call modules, into a metabolic longevity network. Our findings imply that 1) yeast establish a diet‐ and genotype‐specific configuration of the network by setting up the rates of the processes taking place within each of its modules; 2) the establishment of a network's configuration occurs before yeast enter a non‐proliferative state; and 3) different network's configurations established prior to entry into a non‐proliferative state define different rates of survival following such entry. Thus, by designing a specific configuration of the modular longevity network prior to reproductive maturation, yeast define their life span. Implementing our knowledge, we identified five groups of novel anti‐aging small molecules that greatly extend yeast longevity by remodelling two key modules of the network.
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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) | 0.000 | 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".