Valproate protects cells from ER stress-induced lipid accumulation and apoptosis by inhibiting glycogen synthase kinase-3
Bibliographic record
Abstract
A wide range of agents and conditions are known to disrupt the ability of the endoplasmic reticulum (ER) to fold proteins properly, resulting in the onset of ER dysfunction/stress. We and others have shown that ER stress can induce intracellular lipid accumulation through the activation of the sterol responsive element binding proteins (SREBPs) and initiate programmed cell death by activation of caspases. It has been suggested that ER stress-induced lipid accumulation and cell death play a role in the pathogenesis of disorders including Alzheimer's disease, Parkinson's disease, type-1 diabetes mellitus and hepatic steatosis. Here we show that exposure of HepG2 cells to the branch chain fatty acid, valproate, increases cellular resistance to ER stress-induced dysfunction. Two distinctly different potential mechanisms for this protective effect were investigated. We show that exposure to valproate increases the expression of chaperones that assist in the folding of proteins in the ER including GRP78/BiP, GRP94, PDI and calreticulin as well as the cytosolic chaperone, HSP70. However, exposure of HepG2 cells to valproate does not decrease the apparent ER stress response in cells challenged with tunicamycin, A23187 or glucosamine, suggesting that valproate-conferred protection occurs downstream of ER dysfunction. Finally, we demonstrate that valproate directly inhibits the glycogen synthase kinases (GSK)-3alpha/beta. The ability of lithium, another inhibitor of GSK3alpha/beta to protect cells from ER stress-induced lipid accumulation suggests that GSK3 plays a central role in signaling downstream effects of ER stress. Strategies to protect cells from agents/conditions that induce ER stress may have potential in the treatment of the growing number of diseases and disorders linked to ER dysfunction.
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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".