SREBP-1 is a novel mediator of TGFβ1 signaling in mesangial cells
Bibliographic record
Abstract
Glomerular matrix accumulation is a hallmark of diabetic nephropathy. Recent studies showed that overexpression of the transcription factor SREBP-1 induces glomerulosclerosis. TGFβ1 is a key profibrotic mediator of glomerulosclerosis, but whether SREBP-1 regulates its effects is unknown. In kidney mesangial cells and in vivo, TGFβ1 activates SREBP-1. This requires SCAP, S1P, and PI3K/Akt signaling, but is independent of Smad3. Activation of the TGFβ1-responsive reporter plasmid p3TP-lux requires SREBP-1a, but not SREBP-1c, binding to an E-box adjacent to a Smad-binding element. SREBP-1a overexpression alone activates p3TP-lux. Smad3 is required for SREBP-1a transcriptional activation and TGFβ1 induces association between the two transcription factors. SREBP-1a K333 acetylation by the acetyltransferase CBP is required for Smad3 association and SREBP-1 transcriptional activity, and is also required for Smad3 transcriptional activity. Thus, both Smad3 and SREBP-1a activation cooperatively regulate TGFβ transcriptional responses. SREBP-1 inhibition provides a novel therapeutic strategy for diabetic kidney disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".