EGFR-PLCγ1 signaling mediates high glucose-induced PKCβ1-Akt activation and collagen I upregulation in mesangial cells
Bibliographic record
Abstract
Glomerular matrix accumulation is a hallmark of diabetic nephropathy. We have recently shown that epidermal growth factor receptor (EGFR) transactivation mediates high glucose (HG)-induced collagen I upregulation through PI3K-PKCbeta1-Akt signaling in mesangial cells (MC). Phospholipase Cgamma1 (PLCgamma1) interacts with activated growth factor receptors and activates classic PKC isoforms. We thus studied its role in HG-induced collagen I upregulation in MC. Primary rat MC were treated with HG (30 mM) or mannitol as osmotic control. Protein kinase activation was assessed by Western blotting and collagen I upregulation by Northern blotting. Diabetes was induced in rats by streptozotocin. HG treatment for 1 h led to PLCgamma1 membrane translocation and Y783 phosphorylation, both indicative of its activation. Mannitol was without effect. PLCgamma1 Y783 phosphorylation was also seen in cortex and glomeruli of diabetic rats. HG induced a physical association between EGFR and PLCgamma1 as identified by coimmunoprecipitation. PLCgamma1 activation required EGFR kinase activity since it was prevented by the EGFR inhibitor AG1478 or overexpression of kinase-inactive EGFR (K721A). Phosphoinositide-3-OH kinase inhibition also prevented PLCgamma1 activation. HG-induced Akt S473 phosphorylation, effected by PKCbeta1, was inhibited by the PLCgamma inhibitor U73122. PLCgamma1 inhibition or downregulation by small interference RNA also prevented HG-induced collagen I upregulation. Our results indicate that EGFR-PLCgamma1 signaling mediates HG-induced PKCbeta1-Akt activation and subsequent collagen I upregulation in MC. Inhibition of EGFR or PLCgamma1 may provide attractive therapeutic targets for the treatment of diabetic nephropathy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".