Proteasome inhibition induces hsp30 and hsp70 gene expression as well as the acquisition of thermotolerance in Xenopus laevis A6 cells
Bibliographic record
Abstract
Previous studies have shown that inhibiting the activity of the proteasome leads to the accumulation of damaged or unfolded proteins within the cell. In this study, we report that proteasome inhibitors, lactacystin and carbobenzoxy-L-leucyl-L-leucyl-L-leucinal (MG132), induced the accumulation of ubiquitinated proteins as well as a dose- and time-dependent increase in the relative levels of heat shock protein (HSP)30 and HSP70 and their respective mRNAs in Xenopus laevis A6 kidney epithelial cells. In A6 cells recovering from MG132 exposure, HSP30 and HSP70 levels were still elevated after 24 h but decreased substantially after 48 h. The activation of heat shock factor 1 (HSF1) may be involved in MG132-induced hsp gene expression in A6 cells since KNK437, a HSF1 inhibitor, repressed the accumulation of HSP30 and HSP70. Exposing A6 cells to simultaneous MG132 and mild heat shock enhanced the accumulation of HSP30 and HSP70 to a much greater extent than with each stressor alone. Immunocytochemical studies determined that HSP30 was localized primarily in the cytoplasm of lactacystin- or MG132-treated cells. In some cells treated with higher concentrations of MG132 or lactacystin, we observed in the cortical cytoplasm (1) relatively large HSP30 staining structures, (2) colocalization of actin and HSP30, and (3) cytoplasmic areas that were devoid of HSP30. Lastly, MG132 treatment of A6 cells conferred a state of thermotolerance such that they were able to survive a subsequent thermal challenge.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".