Podocyte‐derived microparticles promote proximal tubule fibrotic signaling via p38 MAPK and CD36
Bibliographic record
Abstract
Tubulointerstitial fibrosis is a hallmark of advanced diabetic kidney disease that is linked to a decline in renal function, however the pathogenic mechanisms are poorly understood. Microparticles (MPs) are 100-1000 nm vesicles shed from injured cells that are implicated in intercellular signalling. Our lab recently observed the formation of MPs from podocytes and their release into urine of animal models of type 1 and 2 diabetes and in humans with type 1 diabetes. The purpose of the present study was to examine the role of podocyte MPs in tubular epithelial cell fibrotic responses. MPs were isolated from the media of differentiated, untreated human podocytes (hPODs) and administered to cultured human proximal tubule epithelial cells (PTECs). Treatment with podocyte MPs increased p38 and Smad3 phosphorylation and expression of the extracellular matrix (ECM) proteins fibronectin and collagen type IV. MP-induced responses were attenuated by co-treatment with the p38 inhibitor SB202190. A transforming growth factor beta (TGF-β) receptor inhibitor (LY2109761) blocked MP-induced Smad3 phosphorylation and ECM protein expression but not p38 phosphorylation suggesting that these responses occurred downstream of p38. Finally, blockade of the class B scavenger receptor CD36 completely abrogated MP-mediated p38 phosphorylation, downstream Smad3 activation and fibronectin/collagen type IV induction. Taken together our results suggest that podocyte MPs interact with proximal tubule cells and induce pro-fibrotic responses. Such interactions may contribute to the development of tubular fibrosis in glomerular disease.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".