Proteinase-activated Receptor-2 Transactivation of Epidermal Growth Factor Receptor and Transforming Growth Factor-β Receptor Signaling Pathways Contributes to Renal Fibrosis
Bibliographic record
Abstract
Chronic kidney diseases cause significant morbidity and mortality in the population. During renal injury, kidney-localized proteinases can signal by cleaving and activating proteinase-activated receptor-2 (PAR2), a G-protein-coupled receptor involved in inflammation and fibrosis that is highly expressed in renal tubular cells. Following unilateral ureteric obstruction, PAR2-deficient mice displayed reduced renal tubular injury, fibrosis, collagen synthesis, connective tissue growth factor (CTGF), and α-smooth muscle actin gene expression at 7 days, compared with wild-type controls. In human proximal tubular epithelial cells in vitro, PAR2 stimulation with PAR2-activating peptide (PAR2-AP) alone significantly up-regulated the expression of CTGF, a potent profibrotic cytokine. The induction of CTGF by PAR2-AP was synergistically increased when combined with transforming growth factor-β (TGF-β). Consistent with these findings, treating human proximal tubular epithelial cells with PAR2-AP induced Smad2/3 phosphorylation in the canonical TGF-β signaling pathway. The Smad2 phosphorylation and CTGF induction required signaling via both the TGFβ-receptor and EGF receptor suggesting that PAR2 utilizes transactivation mechanisms to initiate fibrogenic signaling. Taken together, our data support the hypothesis that PAR2 synergizes with the TGFβ signaling pathway to contribute to renal injury and fibrosis.
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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".