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 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) | 0.003 | 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".