A JNK‐dependent pathway is required for HIV‐Vpr‐induced apoptosis in human monocytic cells
Bibliographic record
Abstract
HIV‐1 Vpr causes apoptosis in different kinds of cells including lymphocytes, monocytes, and neuronal cells. Persistently infected monocytic cells serve as a major reservoir of HIV at all stages of disease. It is believed that one of the reasons for monocytes in vivo to be protected from apoptosis is by secretion of different kinds of cytokines in response to HIV infection. C terminal moiety of Vpr is known to cause apoptosis in variety of cell types including monocytes. Herein we have demonstrated that Vpr c terminal peptide (52–96) causes apoptosis in monocytes and monocytic cell lines and the process was caspase dependent. Further, knowing the role of MAPKs in regulation of cell death, we determined the involvement of MAPKs in Vpr induced apoptosis in monocytes. Our results suggest that synthetic Vpr (52–96) and (1–45) peptides induced phosphorylation of all the MAPKs, whereas only Vpr (52–96) peptide induced apoptosis in monocytic cells. The results were confirmed by using JNK stealth RNA. Using a variety of strategies to manipulate JNK activity, we provided evidence that JNK activation is important in mediating Vpr induced apoptosis. Furthermore, Vpr induced apoptosis was mediated by downregulation of antiapoptotic genes Bcl2 and c‐IAP1 through activation of upstream JNK MAPK. Understanding the mechanism of apoptosis induced by Vpr will be beneficial for the development of therapeutic approaches.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".