SREBP‐1 Regulates TGFβ Signaling in Kidney Mesangial Cells
Bibliographic record
Abstract
Diabetic nephropathy (DN) is the most common cause of end‐stage renal disease worldwide. A key mediator in the progression of DN is the proinflammatory cytokine TGFβ. TGFβ signals through activation of Type I and II receptors (TβR1 and TβR2), followed by activation of Smads 2 and 3. Sterol regulatory element binding protein (SREBP) is a potent transcription factor known to regulate fatty acid and cholesterol biosynthesis, which has recently been implicated in the pathogenesis of DN. The aim of our study was to determine whether SREBP influenced TGFβ signaling in primary kidney mesangial cells (MC). We found that inhibition of SREBP with the small molecule inhibitor fatostatin blocked TGFβ‐induced C‐terminal phosphorylation of Smad2/3 and of Smad3 activation as assessed by SBE‐luciferase. Overexpression of dominant negative SREBP1 also attenuated Smad2/3 phosphorylation. This inhibition was not the result of an increased rate of Smad2/3 dephosphorylation, nor of Smad2/3 degradation. Rather, SREBP inhibition resulted in the loss of TβR1 protein expression without affecting its transcript levels. This appeared to be mediated by increased recruitment of Smad7 to TβRI with increased proteasomal‐mediated degradation of the receptor. Our data suggest that SREBP‐1 regulates TGFβ signaling through effects on its type I receptor. The mechanism of this regulation is being elucidated in ongoing studies.
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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) | 0.001 | 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".