Activation of the Connective Tissue Growth Factor (CTGF)-Transforming Growth Factor β 1 (TGF-β 1) Axis in Hepatitis C Virus-Expressing Hepatocytes
Bibliographic record
Abstract
BACKGROUND: The pro-fibrogenic cytokine connective tissue growth factor (CTGF) plays an important role in the development and progression of fibrosis in many organ systems, including liver. However, its role in the pathogenesis of hepatitis C virus (HCV)-induced liver fibrosis remains unclear. METHODS: In the present study, we assessed CTGF expression in HCV-infected hepatocytes using replicon cells containing full-length HCV genotype 1 and the infectious HCV clone JFH1 (HCV genotype 2) by real-time PCR, Western blot analysis and confocal microscopy. We evaluated transforming growth factor β1 (TGF-β1) as a key upstream mediator of CTGF production using neutralizing antibodies and shRNAs. We also determined the signaling molecules involved in CTGF production using various immunological techniques. RESULTS: We demonstrated an enhanced expression of CTGF in two independent models of HCV infection. We also demonstrated that HCV induced CTGF expression in a TGF-β1-dependent manner. Further dissection of the molecular mechanisms revealed that CTGF production was mediated through sequential activation of MAPkinase and Smad-dependent pathways. Finally, to determine whether CTGF regulates fibrosis, we showed that shRNA-mediated knock-down of CTGF resulted in reduced expression of fibrotic markers in HCV replicon cells. CONCLUSION: Our studies demonstrate a central role for CTGF expression in HCV-induced liver fibrosis and highlight the potential value of developing CTGF-based anti-fibrotic therapies to counter HCV-induced liver damage.
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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) | 0.001 | 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".