Phosphoproteome dynamics of Saccharomyces cerevisiae under heat shock and cold stress
Bibliographic record
Abstract
The ability of cells and organisms to survive and function through changes in temperature evolved from their specific adaptations to nonoptimal growth conditions. Responses to elevated temperatures have been studied in yeast and other model organisms using transcriptome profiling and provided valuable biological insights on molecular mechanisms involved in stress tolerance and adaptation to adverse environment. In contrast, little is known about rapid signaling events associated with changes in temperature. To gain a better understanding of global changes in protein phosphorylation in response to heat and cold, we developed a high temporal resolution phosphoproteomics protocol to study cell signaling in Saccharomyces cerevisiae. The method allowed for quantitative analysis of phosphodynamics on 2,777 phosphosites from 1,228 proteins. The correlation of kinetic profiles between kinases and their substrates provided a predictive tool to identify new putative substrates for kinases such as Cdc28 and PKA. Cell cycle analyses revealed that the increased phosphorylation of Cdc28 at its inhibitory site Y19 during heat shock is an adaptive response that delays cell cycle progression under stress conditions. The cellular responses to heat and cold were associated with extensive changes in phosphorylation on proteins implicated in transcription, protein folding and degradation, cell cycle regulation and morphogenesis.
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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".