The HDAC inhibitor, vorinastat, prevents TGF‐β1 induced EMT and apoptosis in human renal proximal tubular cells
Bibliographic record
Abstract
Renal interstitial fibrosis is characterized by renal fibroblast proliferation. This process involves renal tubular epithelial cells adopting a fibroblast morphology through epithelial‐to‐mesenchymal transition (EMT). EMT produces phenotypic changes such as epithelial cell adherins junction loss and de novo α‐smooth muscle actin (SMA) expression. TGF‐ß1 has been shown to stimulate EMT. Identifying agents that inhibit TGF‐ß1 induced EMT may prevent patient progression towards chronic renal failure (CRF). Previous work from our laboratory has shown that 4‐phenylbuterate (4‐PBA), a histone deacetylase inhibitor (HDACi), inhibited EMT. In this study, we hypothesized that a broad spectrum HDACi, vorinastat, would inhibit TGF‐ß1 induced EMT and apoptosis in human proximal tubular epithelium (hPTE). We tested vorinastat and 4‐PBA on TGF‐ß1 induced EMT in hPTE. TGF‐ß1 induced EMT was inhibited by both 1mM 4‐PBA and 5μM vorinastat as shown by cadherin junction preservation and reduced de novo α‐SMA expression. Vorinastat was also shown to prevent TGF‐ß1‐induced decrease of E‐cadherin and increase of type I collagen transcript levels. Further, 4‐PBA and vorinastat inhibited TGF‐ß1 induced apoptosis in hPTE. These findings suggest that HDACi prevent EMT and apoptosis induced by TGFß1 and may reduce the progression of patients with chronic kidney disease towards CRF. Funding, CIHR OSO‐115895.
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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".