MicroRNA Expression Profiling in HCV-Infected Human Hepatoma Cells Identifies Potential Anti-Viral Targets Induced by Interferon-α
Bibliographic record
Abstract
OBJECTIVE: Increasing evidence suggests that miRNAs have a profound impact on host defense to Hepatitis C virus (HCV) infection and clinical outcome of standard HCV therapy. In this study, we investigated modulation of miRNA expression in Huh7.5 hepatoma cells by HCV infection and in vitro interferon-αtreatment. METHODS: MiRNA expression profiling was determined using Human miRNA TaqMan® Arrays followed by rigorous pairwise statistical analysis. MiRNA inhibitors assessed the functional effects of miRNAs on HCV replication. Computational analysis predicted anti-correlated mRNA targets and their involvement in host cellular pathways. Quantitative RTPCR confirmed the expression of predicted miRNA-mRNA correlated pairs in HCV-infected Huh7.5 cells with and without interferon-α. RESULTS: Seven miRNAs (miR-30b, miR-30c, miR-130a, miR-192, miR-301, miR-324-5p, and miR-565) were down-regulated in HCV-infected Huh7.5 cells (p<0.05) and subsequently up-regulated following interferon-α treatment (p<0.01). The miR-30(a-d) cluster and miR-130a/301 and their putative mRNA targets were predicted to be associated with cellular pathways that involve Hepatitis C virus entry, propagation and host response to viral infection. CONCLUSIONS: HCV differentially modulates miRNAs to facilitate entry and early establishment of infection in vitro. Interferon-α appears to neutralize the effect of HCV replication on miRNA regulation thus providing a potential mechanism of action in eradicating HCV from hepatocytes.
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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".