Development of a method for heat shock stress assessment in yeast based on transcription of specific genes
Bibliographic record
Abstract
All living cells, including yeast cells, are challenged by different types of stresses in their environments and must cope with challenges such as heat, chemical stress, or oxidative damage. By reversibly adjusting the physiology while maintaining structural and genetic integrity, cells can achieve a competitive advantage and adapt environmental fluctuations. The yeast Saccharomyces cerevisiae has been extensively used as a model for study of stress responses due to the strong conservation of many essential cellular processes between yeast and human cells. We focused here on developing a tool to detect and quantify early responses using specific transcriptional responses. We analyzed the published transcriptional data on S. cerevisiae DBY strain responses to 10 different stresses in different time points. The principal component analysis (PCA) and the Pearson analysis were used to assess the stress response genes that are highly expressed in each individual stress condition. Except for these stress response genes, we also identified the reference genes in each stress condition, which would not be induced under stress condition and show stable transcriptional expression over time. We then tested our candidates experimentally in the CEN.PK strain. After data analysis, we identified two stress response genes (UBI4 and RRP) and two reference genes (MEX67 and SSY1) under heat shock (HS) condition. These genes were further verified by real-time PCR at mild (42°C), severe (46°C), to lethal temperature (50°C), respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".