Bradykinin inhibits high glucose- and growth factor-induced collagen synthesis in mesangial cells through the B2-kinin receptor
Bibliographic record
Abstract
Mesangial matrix expansion is an early lesion leading to glomeruloclerosis and chronic renal diseases. A beneficial effect is achieved with angiotensin I-converting enzyme inhibitors (ACEI), which also favor bradykinin (BK) B2 receptor (B2R) activation. To define the underlying mechanism, we hypothesized that B2R activation could be a negative regulator of collagen synthesis in mesangial cells (MC). We investigated the effect of BK on collagen synthesis and signaling in MC. Inflammation was evaluated by intercellular adhesion molecule-1 (ICAM-1) expression. BK inhibited collagen I and IV synthesis stimulated by high glucose, epithelial growth factor (EGF), and transforming growth factor-β (TGF-β) but did not alter ICAM-1. Inhibition of collagen synthesis was B2R but not B1R mediated. PKC or phosphatidylinositol 3-kinase (PI3K) inhibitors mimicked the BK effect. B2R activation inhibited TGF-β- and EGF-induced Erk1/2, Smad2/3, Akt S473, and EGFR phosphorylation. A phosphatase inhibitor prevented BK effects. The in vivo impact of B2R on mesangial matrix expansion was assessed in streptozotocin-diabetic rodents. Deletion of B2R increased mesangial matrix expansion and albuminuria in diabetic mice. In diabetic rats, matrix expansion and albuminuria were prevented by ACEI but not by ACEI and B2R antagonist cotreatment. Consistently, the lowered BK content of diabetic glomeruli was restored by ACEI. In conclusion, deficient B2R activation aggravated mesangial matrix expansion in diabetic rodents whereas B2R activation reduced MC collagen synthesis by a mechanism targeting Erk1/2 and Akt, common pathways activated by EGF and TGF-β. Taken together, the data support the hypothesis of an antifibrosing effect of B2R activation.
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".