Targeting Autophagy Augments Berberine-Mediated Cell Death in Human Hepatoma Cells Harboring Hepatitis C Virus RNA
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC), including hepatitis C virus (HCV)-induced HCC, is a deadly disease highly refractory to chemotherapy, thus requiring the continuous identification of novel treatment strategies. Berberine (BBR) has been previously reported to inhibit hepatoma cell growth, but the main type of cell death elicited by BBR, and whether the alkaloid can inhibit hepatoma cells carrying HCV genomes, is unclear. Herein, we show that BBR treatment induced a biphasic cell death irrespective of the presence of HCV subgenomic replicon RNA, first triggering apoptosis that then progressed to necrosis between 24 and 48 h post-treatment. Furthermore, BBR treatment potentiated the HCV replicon-induced reactive oxygen species (ROS) production, inhibition of which with an antioxidant attenuated the cell death that was elicited by BBR in these cells. Moreover, BBR dampened the autophagic response in HCV RNA-positive or negative hepatoma cells, and pharmacological inhibition of autophagy conversely augmented the BBR-induced cell death. Finally, BBR inhibited the growth of Huh-7 cells that were persistently infected with the full-length genome HCV particles, and concomitant pharmacological inhibition of autophagy potentiated the killing of these cells by BBR. Our findings suggest that combining BBR with the inhibition of autophagy could be an attractive treatment strategy against HCC, irrespective of the presence of the HCV genome.
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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".