Communications between Mitochondria, the Nucleus, Vacuoles, Peroxisomes, the Endoplasmic Reticulum, the Plasma Membrane, Lipid Droplets, and the Cytosol during Yeast Chronological Aging
Bibliographic record
Abstract
Studies employing the budding yeast Saccharomyces cerevisiae as a model organism have provided deep insights into molecular mechanisms of cellular and organismal aging in multicellular eukaryotes and have demonstrated that the main features of biological aging are evolutionarily conserved. Aging in S. cerevisiae is studied by measuring replicative or chronological lifespan. Yeast replicative aging is likely to model aging of mitotically competent human cell types, while yeast chronological aging is believed to mimic aging of post-mitotic human cell types. Emergent evidence implies that various organelle-organelle and organelle-cytosol communications play essential roles in chronological aging of S. cerevisiae. The molecular mechanisms underlying the vital roles of intercompartmental communications in yeast chronological aging have begun to emerge. The scope of this review is to critically analyze recent progress in understanding such mechanisms. Our analysis suggests a model for how temporally and spatially coordinated movements of certain metabolites between various cellular compartments impact yeast chronological aging. In our model, diverse changes in these key metabolites are restricted to critical longevity-defining periods of chronological lifespan. In each of these periods, a limited set of proteins responds to such changes of the metabolites by altering the rate and efficiency of a certain cellular process essential for longevity regulation. Spatiotemporal dynamics of alterations in these longevity-defining cellular processes orchestrates the development and maintenance of a pro- or anti-aging cellular pattern.
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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 | 0.001 | 0.001 |
| 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".