PKCϵ Is a Unique Regulator for hsp90β Gene in Heat Shock Response
Bibliographic record
Abstract
An early event in cellular heat shock response is the transmittance of stress signals from the cell surface into the nuclei, resulting in the induction of heat shock proteins (Hsps). Protein kinase C (PKC) has been implicated as a key player in transducing stress signals. However, mechanism(s) by which PKC regulates heat shock-induced events remains largely unknown. Here we present data that pan-PKC inhibitor GF109203X, but not classic PKC inhibitor Gö6976, specifically repressed heat shock-induced accumulation of mRNA as well as promoter activity of hsp90 beta, but not hsp90 alpha, in Jurkat cells. Subcellular fractionation studies revealed that heat shock exclusively induced PKC-epsilon membrane translocation. Consistently, expression of a constitutively active PKC-epsilon(A159E) resulted in an enhanced promoter activity of hsp90 beta upon heat shock, whereas a dominant-negative PKC-epsilon(K437R) abolished this effect. In contrast, constitutively active-PKC-alpha or dominant-negative-PKC-alpha had no effects on heat shock induction of the gene. The effect of PKC-epsilon on hsp90 beta expression seems to be stimuli-specific, as phorbol myristate acetate-mediated hsp90 beta expression was PKC-epsilon-independent. We conclude that PKC-epsilon is specifically required in the signaling pathway leading to the induction of hsp90 beta gene in response to heat shock.
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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) | 0.003 | 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".