Cells collectively reshape habitability of temperature by helping each other replicate
Bibliographic record
Abstract
SUMMARY How the rising global temperatures affect organisms is a timely question. The conventional view is that high temperatures cause microbes to replicate slowly or die, both autonomously. Yet, microbes co-exist as a population, raising the underexplored question of whether they can cooperatively combat rising temperatures. Here we show that, at high temperatures, budding yeasts help each other and future generations of cells replicate by secreting and extracellularly accumulating glutathione - a ubiquitous heat-damage-reducing antioxidant. Yeasts thereby collectively delay and can halt population extinctions at high temperatures. As a surprising consequence, even for the same temperature, a yeast population can either exponentially grow, never grow, or grow after unpredictable durations (hours-to-days) of stasis, depending on its population density. Despite the conventional theory stating that heat-shocked cells autonomously die and cannot stop population extinctions, we found that non-growing yeast-populations at high temperatures - due to cells cooperatively accumulating extracellular glutathione - continuously decelerate and can eventually stop their approach to extinction, with higher population-densities stopping faster. We show that exporting glutathione, but not importing, is required for yeasts to survive high temperatures. Thus, cooperatively eliminating harmful extracellular agents – not glutathione’s actions inside individual cells – is both necessary and sufficient for surviving high temperatures. We developed a mathematical model - which is generally applicable to any cells that cooperatively replicate by secreting molecules - that recapitulates all these features. These results show how cells can cooperatively extend boundaries of life-permitting temperatures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".