Lithocholic acid extends longevity of chronologically aging yeast only if added at certain critical periods of their lifespan
Bibliographic record
Abstract
Our studies revealed that LCA (lithocholic bile acid) extends yeast chronological lifespan if added to growth medium at the time of cell inoculation. We also demonstrated that longevity in chronologically aging yeast is programmed by the level of metabolic capacity and organelle organization that they developed before entering a quiescent state and, thus, that chronological aging in yeast is likely to be the final step of a developmental program progressing through at least one checkpoint prior to entry into quiescence. Here, we investigate how LCA influences longevity and several longevity-defining cellular processes in chronologically aging yeast if added to growth medium at different periods of the lifespan. We found that LCA can extend longevity of yeast under CR (caloric restriction) conditions only if added at either of two lifespan periods. One of them includes logarithmic and diauxic growth phases, whereas the other period exists in early stationary phase. Our findings suggest a mechanism linking the ability of LCA to increase the lifespan of CR yeast only if added at either of the two periods to its differential effects on various longevity-defining processes. In this mechanism, LCA controls these processes at three checkpoints that exist in logarithmic/diauxic, post-diauxic and early stationary phases. We therefore hypothesize that a biomolecular longevity network progresses through a series of checkpoints, at each of which (1) genetic, dietary and pharmacological anti-aging interventions modulate a distinct set of longevity-defining processes comprising the network; and (2) checkpoint-specific master regulators monitor and govern the functional states of these processes.
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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".