HIV and HCV Cooperatively Promote Hepatic Fibrogenesis via Induction of Reactive Oxygen Species and NFκB
Bibliographic record
Abstract
HIV/HCV coinfection leads to accelerated hepatic fibrosis progression, with higher rates of cirrhosis, liver failure, and liver death than does HCV mono-infection. However, the profibrogenic role of HIV on hepatocytes and hepatic stellate cells (HSC) has not been fully clarified. We hypothesized that HIV, HCV induce liver fibrosis through altered regulation of the production of extracellular matrix and matrix metalloproteinases. We examined the fibrogenesis- and fibrolysis-related gene activity in LX2 HSC and Huh7.5.1 cells in the presence of inactivated CXCR4 and CCR5 HIV, as well as HCV JFH1 virus. The role of reactive oxygen species (ROS) upon fibrosis gene expression was assessed using the ROS inhibitor. Fibrosis-related transcripts including procollagen α1(I) (CoL1A), TIMP1, and MMP3 mRNA were measured by qPCR. TIMP1 and MMP3 protein expression were assessed by ELISA. We found that inactivated CXCR4 HIV and CCR5 HIV increased CoL1A, and TIMP1 expression in both HSC and Huh7.5.1 cells; the addition of JFH1 HCV further increased CoL1A and TIMP1 expression. CXCR4 HIV and CCR5 HIV induced ROS production in HSC and Huh7.5.1 cells which was further enhanced by JFH1 HCV. The ROS inhibitor DPI abrogated HIV-and HCV-induced CoL1A and TIMP1 expression. HIV and HCV-induced CoL1A and TIMP1 expression were also blocked by NFκB siRNA. Our data provide further evidence that HIV and HCV independently regulate hepatic fibrosis progression through the generation of ROS; this regulation occurs in an NFκB-dependent fashion. Strategies to limit the viral induction of oxidative stress are warranted to inhibit fibrogenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".