HB-EGF release mediates glucose-induced activation of the epidermal growth factor receptor in mesangial cells
Bibliographic record
Abstract
Glomerular matrix accumulation is a hallmark of diabetic nephropathy. We showed that transactivation of the epidermal growth factor receptor (EGFR) is an important mediator of matrix upregulation in mesangial cells (MC) in response to high glucose (HG). Here, we study the mechanism of EGFR transactivation. In primary MC, EGFR transactivation by 1 h of HG (30 mM) was unaffected by inhibitors of protein kinase C, reactive oxygen species, or the angiotensin II AT1 receptor. However, general metalloprotease inhibition, as well as specific inhibitors of heparin-binding EGF-like growth factor (HB-EGF), prevented both EGFR and downstream Akt activation. HB-EGF was released into the medium by 30 min of HG, and this depended on metalloprotease activity. One of the metalloproteases shown to cleave proHB-EGF is ADAM17 (TACE). HG, but not an osmotic control, activated ADAM17, and its inhibition prevented EGFR and Akt activation and HB-EGF release into the medium. siRNA to either ADAM17 or HB-EGF prevented HG-induced EGFR transactivation. We previously showed that EGFR/Akt signaling increases transforming growth factor (TGF)-β1 transcription through the transcription factor activator protein (AP)-1. HG-induced AP-1 activation, as assessed by EMSA, was abrogated by inhibitors of metalloproteases, HB-EGF and ADAM17. HB-EGF and ADAM17 siRNA also prevented AP-1 activation. Finally, these inhibitors and siRNA prevented TGF-β1 upregulation by HG. Thus, HG-induced EGFR transactivation in MC is mediated by the release of HB-EGF, which requires activity of the metalloprotease ADAM17. The mechanism of ADAM17 activation awaits identification. Targeting upstream mediators of EGFR transactivation including HB-EGF or ADAM17 provides novel therapeutic targets for the treatment of diabetic nephropathy.
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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".