ER Stress‐Induced SREBP‐2 Activation Contributes to Lipid Accumulation in Tubular Nephrotoxicity
Bibliographic record
Abstract
Disruption of protein folding in the endoplasmic reticulum (ER) leads to ER stress and activation of the unfolded protein response (UPR). Tubular damage caused by nephrotoxic drugs is associated with ER stress. Since ER stress is known to induce lipid dysregulation through the activation of sterol regulatory element binding proteins (SREBPs), we hypothesized that lipid accumulation observed in proximal tubules affected by nephrotoxic drugs is a consequence of ER stress‐induced SREBP activation. Mice injected with tunicamycin display vacuolization and ER stress in proximal tubules. The vacuoles contain lipids that co‐localize with increased SREBP‐2 expression and markers of ER stress. In kidney biopsies from cyclosporine A (CsA)‐treated patients we show by immunohistochemistry the co‐localization of SREBP‐2 and ER stress markers in the damaged tubules. Treatment of HK2 renal cells with tunicamycin, thapsigargin or CsA caused UPR activation and increased lipid content. SREBP‐2 activation was shown by the enhanced sterol‐regulatory element‐GFP fluorescence, increased mRNA of downstream genes, and the presence of the mature form of SREBP‐2 on Western blot. These outcomes were blocked by AEBSF, an inhibitor of SREBP‐2 processing. We conclude that lipid accumulation in diseased kidney is associated with SREBP‐2 activation by ER stress . Supported by Heart and Stroke Foundation grant NA6024.
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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) | 0.002 | 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".