Characterizing the regulation of the inducible HSP70 mRNA in yeasts and mammalian cells subjected to heat shock
Bibliographic record
Abstract
Cellular perturbations cause the proteins to misfold precluding their functions.In response to proteotoxic stress, cells downregulate gene expression by reducing cap-dependent translation.Concurrently, they upregulate the expression of a subset of molecular chaperones known as Heat Shock Proteins (HSPs) in charge of protein folding.The fast induction and rapid attenuation of the inducible HSP70 expression define the Heat Shock Response (HSR), which is critical for cell survival from stress.However, the ubiquitous expression of the inducible HSP70 is toxic and promotes tumorigenesis in mammalian cells.Thus, the fast degradation of the HSP70 mRNA allows for tailoring the HSP70 synthesis to the misfolded protein load in the cell is vital.Since the HSR is an evolutionarily conserved survival response, the induction and attenuation of the HSR have been studied in different model organisms.While the transcription of HSP70 mRNA is well studied, our initial studies revealed an important difference between yeast and mammalian cells on HSP70 mRNA translation.In yeast, the HSP70 synthesis peaks during heat shock, while in mammalian cells, it peaks during recovery.Based on this, we hypothesize that distinct regulatory elements in the sequence of the inducible HSP70 mRNA in yeast and mammalian cells regulate its translation upon heat shock and recovery.We discovered that in Saccharomyces cerevisiae, the coding sequence of the inducible HSP70, SSA4, is biased towards rare codons.These codons promote ribosome stalling and collisions on the mRNA that result in the control of Ssa4p expression by the ribosome quality control (RQC) mechanism.In RQC, the ribosomal protein Asc1p stabilizes the collided ribosomes triggering a series of downstream events to reduce translation and dissociate ribosomes.Our work elucidates the SSA4 coding sequence as a novel regulatory element and describes RQC as an
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".