The green tea polyphenol, epigallocatechin-3-gallate, inhibits hepatitis C virus entry
Bibliographic record
Abstract
UNLABELLED: Hepatitis C virus (HCV) is a major cause of liver cirrhosis and hepatocellular carcinoma. Current antiviral therapy fails to clear infection in a substantial proportion of cases. Drug development is focused on nonstructural proteins required for RNA replication. Individuals undergoing orthotopic liver transplantation face rapid, universal reinfection of the graft. Therefore, antiviral strategies targeting the early stages of infection are urgently needed for the prevention of HCV infection. In this study, we identified the polyphenol, epigallocatechin-3-gallate (EGCG), as an inhibitor of HCV entry. Green tea catechins, such as EGCG and its derivatives, epigallocatechin (EGC), epicatechin gallate (ECG), and epicatechin (EC), have been previously found to exert antiviral and antioncogenic properties. EGCG had no effect on HCV RNA replication, assembly, or release of progeny virions. However, it potently inhibited Cell-culture-derived HCV (HCVcc) entry into hepatoma cell lines as well as primary human hepatocytes. The effect was independent of the HCV genotype, and both infection of cells by extracellular virions and cell-to-cell spread were blocked. Pretreatment of cells with EGCG before HCV inoculation did not reduce HCV infection, whereas the application of EGCG during inoculation strongly inhibited HCV infectivity. Moreover, treatment with EGCG directly during inoculation strongly inhibited HCV infectivity. Expression levels of all known HCV (co-)receptors were unaltered by EGCG. Finally, we showed that EGCG inhibits viral attachment to the cell, thus disrupting the initial step of HCV cell entry. CONCLUSION: The green tea molecule, EGCG, potently inhibits HCV entry and could be part of an antiviral strategy aimed at the prevention of HCV reinfection after liver transplantation.
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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".