Inflammasome-Independent NLRP3 Augments TGF-β Signaling in Kidney Epithelium
Bibliographic record
Abstract
Tubulointerstitial inflammation and fibrosis are strongly associated with the outcome of chronic kidney disease. We recently demonstrated that the NOD-like receptor, pyrin domain containing-3 (NLRP3) contributes to renal inflammation, injury, and fibrosis following unilateral ureteric obstruction in mice. NLRP3 expression in renal tubular epithelial cells (TECs) was found to be an important component of experimental disease pathogenesis, although the biology of NLRP3 in epithelial cells is unknown. In human and mouse primary renal TECs, NLRP3 expression was increased in response to TGF-β1 stimulation and associated with epithelial-mesenchymal transition (EMT) and the expression of α-smooth muscle actin (αSMA) and matrix metalloproteinase (MMP) 9. TGF-β1-induced EMT and the induction of MMP-9 and αSMA were significantly decreased in mouse Nlrp3(-/-) renal TECs, suggesting a role for Nlrp3 in TGF-β-dependent signaling. Although apoptosis-associated speck-like protein containing a CARD domain(-/-) TECs demonstrated a phenotype similar to that of Nlrp3(-/-) cells in response to TGF-β1, the effect of Nlrp3 on MMP-9 and αSMA expression was inflammasome independent, as IL-1β, IL-18, MyD88, and caspase-1 were dispensable. Smad2 and Smad3 phosphorylation in response to TGF-β1 was attenuated in Nlrp3(-/-) and apoptosis-associated speck-like protein containing a CARD domain(-/-) cells, accounting for the dampened EMT and TGF-β1 responsiveness in these cells. Consistent with these findings, overexpression of NLRP3 in 293T cells resulted in increased Smad3 phosphorylation and activity. Taken together, these data support a novel and direct role for NLRP3 in promoting TGF-β signaling and R-Smad activation in epithelial cells independent of the inflammasome.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".