HCV Induces Oxidative and ER Stress, and Sensitizes Infected Cells to Apoptosis in SCID/Alb-uPA Mice
Bibliographic record
Abstract
Hepatitis C virus (HCV) is a blood-borne pathogen and a major cause of liver disease worldwide. Gene expression profiling was used to characterize the transcriptional response to HCV H77c infection. Evidence is presented for activation of innate antiviral signaling pathways as well as induction of lipid metabolism genes, which may contribute to oxidative stress. We also found that infection of chimeric SCID/Alb-uPA mice by HCV led to signs of hepatocyte damage and apoptosis, which in patients plays a role in activation of stellate cells, recruitment of macrophages, and the subsequent development of fibrosis. Infection of chimeric mice with HCV H77c also led an inflammatory response characterized by infiltration of monocytes and macrophages. There was increased apoptosis in HCV-infected human hepatocytes in H77c-infected mice but not in mice inoculated with a replication incompetent H77c mutant. Moreover, TUNEL reactivity was restricted to HCV-infected hepatocytes, but an increase in FAS expression was not. To gain insight into the factors contributing specific apoptosis of HCV infected cells, immunohistological and confocal microscopy using antibodies for key apoptotic mediators was done. We found that the ER chaperone BiP/GRP78 was increased in HCV-infected cells as was activated BAX, but the activator of ER stress-mediated apoptosis CHOP was not. We found that overall levels of NF-kappaB and BCL-xL were increased by infection; however, within an infected liver, comparison of infected cells to uninfected cells indicated both NF-kappaB and BCL-xL were decreased in HCV-infected cells. We conclude that HCV contributes to hepatocyte damage and apoptosis by inducing stress and pro-apoptotic BAX while preventing the induction of anti-apoptotic NF-kappaB and BCL-xL, thus sensitizing hepatocytes to apoptosis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".