IL-17A Enhances the Expression of Profibrotic Genes through Upregulation of the TGF-β Receptor on Hepatic Stellate Cells in a JNK-Dependent Manner
Bibliographic record
Abstract
Activation of hepatic stellate cells (HSCs) is a key event in the initiation of liver fibrosis, characterized by enhanced extracellular matrix production and altered degradation. Activation of HSCs can be modulated by cytokines produced by immune cells. Recent reports have implicated the proinflammatory cytokine IL-17A in liver fibrosis progression. We hypothesized that IL-17A may enhance activation of HSCs and induction of the fibrogenic signals in these cells. The human HSC line LX2 and primary human HSCs were stimulated with increasing doses of IL-17A and compared with TGF-β- and PBS-treated cells as positive and negative controls, respectively. IL-17A alone did not induce activation of HSCs. However, IL-17A sensitized HSCs to the action of suboptimal doses of TGF-β as confirmed by strong induction of α-smooth muscle actin, collagen type I (COL1A1), and tissue inhibitor of matrix metalloproteinase I gene expression and protein production. IL-17A specifically upregulated the cell surface expression of TGF-βRII following stimulation. Pretreatment of HSCs with IL-17A enhanced signaling through TGF-βRII as observed by increased phosphorylation of SMAD2/3 in response to stimulation with suboptimal doses of TGF-β. This enhanced TGF-β response of HSCs induced by IL-17A was JNK-dependent. Our results suggest a novel profibrotic function for IL-17A by enhancing the response of HSCs to TGF-β through activation of the JNK pathway. IL-17A acts through upregulation and stabilization of TGF-βRII, leading to increased SMAD2/3 signaling. These findings represent a novel example of cooperative signaling between an immune cytokine and a fibrogenic receptor.
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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".