PKC-β1 Mediates Glucose-Induced Akt Activation and TGF-β1 Upregulation in Mesangial Cells
Bibliographic record
Abstract
Accumulation of glomerular matrix is a hallmark of diabetic nephropathy. The serine/threonine kinase Akt mediates glucose-induced upregulation of collagen I in mesangial cells through transactivation of the EGF receptor (EGFR). In addition, in renal tubular cells, glucose-induced secretion of TGF-beta requires phosphoinositide-3-OH kinase, suggesting a possible role for Akt in the modulation of TGF-beta expression, but the mechanisms of Akt activation and its involvement in TGF-beta regulation are unknown. Here, in primary mesangial cells, high glucose induced AktS473 phosphorylation, which correlates with its activation, in a protein kinase C beta (PKC-beta)-dependent manner. Glucose led to PKC-beta1 membrane translocation and association with Akt, and PKC-beta1 immunoprecipitated from glucose-treated cells phosphorylated recombinant Akt on S473. PKC is known to mediate glucose-induced TGF-beta1 upregulation through the transcription factor AP-1; here, inhibitors of phosphoinositide-3-OH kinase, PKC-beta and Akt, and dominant-negative Akt all prevented glucose-induced activation of AP-1 and upregulation of TGF-beta1. Finally, pharmacologic and dominant negative inhibition of EGFR blocked glucose-induced activation of PKC-beta1, phosphorylation of AktS473, activation of AP-1, and upregulation of TGF-beta1. In vivo, the PKC-beta inhibitor ruboxistaurin prevented Akt activation in the renal cortex of diabetic rats. In conclusion, PKC-beta1 is an Akt S473 kinase in glucose-treated mesangial cells, and TGF-beta1 transcriptional upregulation requires EGFR/PKC-beta1/Akt signaling. New therapeutic approaches for diabetic nephropathy may result from targeting components of this pathway, particularly the initial EGFR transactivation.
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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) | 0.001 | 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 | 0.001 |
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".