Crosstalk Between Pyroptosis and Apoptosis in Hepatitis C Virus-induced Cell Death
Bibliographic record
Abstract
Extensive inflammation in the liver is known to contribute to the pathogenesis of hepatitis C virus (HCV) infection. Apoptosis has, for a long time, been known to act as a mechanism of hepatocyte death, but our previous research also identified inflammasome-mediated pyroptosis in infected and uninfected bystander cells as an additional mechanism of HCV-induced cytopathicity. The purpose of this study was to investigate the mechanism of HCV-induced cell death and to determine the timing and relative contributions of apoptosis and pyroptosis during HCV infection. In a model employing a cell culture-adapted strain of JFH-1 HCV and Huh-7.5 hepatocyte-like cells, we found that pyroptosis occurred earlier than did apoptosis during infection. CRISPR knockout of NLRP3 resulted in decreased caspase-1 activation, but not complete elimination, indicating multiple sensors are likely involved in HCV-induced pyroptosis. Knockout of gasdermin-D resulted in increased activation of apoptosis-related caspase-3, suggesting potential crosstalk between the two cell death pathways. An unexpected decrease in activated caspase-1 levels was observed when caspase-3 was knocked out, implying that caspase-3 may have a role in the initiation of pyroptosis, at least in the context of HCV infection. Lower viral titres in culture fluids and increased ratios of intracellular to extracellular levels of infectious virus were observed in knockout versus wild-type Huh-7.5 cells, suggesting that HCV may induce programmed cell death in order to enhance virus release from infected cells. These results contribute to the understanding of HCV pathogenesis and add to the increasing volume of literature suggesting various programmed cell death pathways are not mutually exclusive.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".