Th17 Cytokines Drive Liver Fibrosis Progression by Regulating TGF-β Signaling through Activation of MAPKs
Bibliographic record
Abstract
Abstract Activation of hepatic stellate cells (HSCs) is a key event in the initiation of liver fibrosis. We and others have demonstrated that IL-17A produced by Th17 cells promotes activation of HSCs. Th17 cells also produce IL-22, an enigmatic cytokine with pro-inflammatory and hepatoprotective properties. In addition, regulatory T cells (Treg) negatively modulate activation of HSCs. We hypothesized that liver fibrosis progression results from an alteration in the Th17/Treg ratio leading to an imbalance in pro-fibrotic Th17 cytokines in the liver. We examined ex vivo the frequency of Th17 and Treg populations and the cytokine profile of intrahepatic lymphocytes in liver biopsy samples (n=32). We observed increased Th17/Treg ratio in advanced as compared to moderate or no-fibrosis. Furthermore, we observed a bias towards Th17 cytokines in fibrotic livers with viral-hepatitis both in situ and ex vivo. All biopsies exhibited a 5-fold increase in IL-22 in fibrotic livers irrespective of aetiology. In vitro stimulation of HSCs with IL-22 sensitized them to suboptimal doses of TGF-β and activated p38 pathways. Chemical inhibition of p38 suppressed the pro-fibrogenic effect of IL-22. In vivo, lack of IL-22 signaling protected mice against liver fibrosis. IL-22RA1 Knockout mice exhibited reduced collagen deposition and expression of pro-fibrotic genes (ACTA2, LOXL2, TIMP-I, TGFb1, COL1A1) in comparison to wild-type littermates. Our results suggest a dysregulated Th17 response with increased IL-22 signaling in advanced fibrosis from all different aetiologies. Finally, we have identified IL-22 as a common factor in advanced liver fibrosis acting through sensitization of HSCs to TGF-β in a p38-dependent manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".