Adjusting extracellular pH restores proteostasis and extends lifespan in a yeast model of polyglutamine toxicity
Bibliographic record
Abstract
ABSTRACT Impaired proteostasis is a hallmark of aging and is associated with several neurodegenerative diseases, including Huntington’s Disease (HD) where the polyglutamine (polyQ) expanded Huntingtin aggregates to form insoluble inclusions bodies (IBs) associated with neurotoxicity. Chronological lifespan (CLS) in yeast resembles many aspects of aging of non-dividing cells such as neurons. During chronological aging, acidification of the culture media due accumulation of acetic acid is one of the major cell-extrinsic factors contributing to age-related cell death. Thus, buffering media pH to prevent acidification significantly extends longevity. Here, we found that cells expressing pathogenic polyQ expansion proteins display increased sensitivity to acetic acid and shortened CLS. Buffering media pH promotes both polyQ aggregation into IBs and promotes longevity. We also found that growth at alkaline pH induces the activation of heat shock response (HSR) in young cells. Such hormetic HSR activation subsequently allowed aged cells to mount a proper HSR in response to stresses such as heat shock or polyQ misfolding, leading to lifespan extension. Our study thus provides new insight into how pH can promote proteotoxic stress resistance and longevity by modulating the HSR.
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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 | 0.001 |
| 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".