High Glucose Up-regulates ADAM17 through HIF-1α in Mesangial Cells
Bibliographic record
Abstract
We previously showed that ADAM17 mediates high glucose-induced matrix production by kidney mesangial cells. ADAM17 expression is increased in diabetic kidneys, suggesting that its up-regulation may augment high glucose profibrotic responses. We thus studied the effects of high glucose on ADAM17 gene regulation. Primary rat mesangial cells were treated with high glucose (30 mm) or mannitol as osmotic control. High glucose dose-dependently increased ADAM17 promoter activity, transcript, and protein levels. This correlated with augmented ADAM17 activity after 24 h versus 1 h of high glucose. We tested involvement of transcription factors shown in other settings to regulate ADAM17 transcription. Promoter activation was not affected by NF-κB or Sp1 inhibitors, but was blocked by hypoxia-inducible factor-1α (HIF-1α) inhibition or down-regulation. This also prevented ADAM17 transcript and protein increases. HIF-1α activation by high glucose was shown by its increased nuclear translocation and activation of the HIF-responsive hypoxia-response element (HRE)-luciferase reporter construct. Assessment of ADAM17 promoter deletion constructs coupled with mutation analysis and ChIP studies identified HIF-1α binding to its consensus element at -607 as critical for the high glucose response. Finally, inhibitors of epidermal growth factor receptor (EGFR) and downstream PI3K/Akt, or ADAM17 itself, prevented high glucose-induced HIF-1α activation and ADAM17 up-regulation. Thus, high glucose induces ADAM17 transcriptional up-regulation in mesangial cells, which is associated with augmentation of its activity. This is mediated by HIF-1α and requires EGFR/ADAM17 signaling, demonstrating the potentiation by ADAM17 of its own up-regulation. ADAM17 inhibition thus provides a potential novel therapeutic strategy for the treatment of diabetic nephropathy.
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) | 0.000 | 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 | 0.000 |
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 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".