Novel RORγt inverse agonists limit IL-17-mediated liver inflammation and fibrosis
Bibliographic record
Abstract
Liver fibrosis is a global health problem. IL-17A has proven profibrogenic properties in liver disease making it an interesting therapeutic target. IL-17A is regulated by RORγt and produced by Th17 CD4+ and γδ-T cells. We hypothesized that blocking IL-17A production will limit fibrosis progression by reducing recruitment of inflammatory cells. Herein, we tested the therapeutic potential of 2 novel RORγt inverse agonists (2,3 derivatives of 4,5,6,7-tetrahydro-benzothiophene) in a mouse model of CCl4-induced liver injury. C57BL/6 mice received 2 weekly injections of CCl4 for 4 weeks. As of week 3, mice were treated with the 2 novel inverse agonists (TF-S10 and TF-S14) and GSK805 as a positive control. Mice treated with the inverse agonists showed reduced immune cells infiltrate around the portal and central veins. TF-S14 significantly reduced AST levels (P < 0.05), and all inhibitors led to an improvement in relative liver weight (liver index). Flow cytometry analysis demonstrated that all inhibitors reduced the numbers of intrahepatic lymphocytes (CD4+, CD8+, and γδ-T cells, P < 0.05), and myeloid (CD11b+) cells (P = 0.04), most significantly eosinophils (P < 0.05). Furthermore, IL-17A production by CD4+ and γδ-T cells was diminished (P < 0.05 and P < 0. 01, respectively). Finally, livers from inhibitors-treated mice showed decreased markers of hepatic stellate cell activation (desmin and ɑ-smooth muscle actin [ɑ-SMA]) and significantly reduced expression of the profibrogenic genes (Col1a1, Acta, Loxl2, and Tgfβ) (P < 0.001). This was accompanied by diminished collagen deposition as measured by Picrosirius Red staining (P < 0.001). In conclusion, our results suggest that inhibition of the IL-17A pathway could be a promising therapeutic strategy for liver fibrosis.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".