HIV-1 Vpr up-regulates expression of ligands for the activating NKG2D receptor and promotes NK cell–mediated killing
Bibliographic record
Abstract
HIV up-regulates cell-surface expression of specific ligands for the activating NKG2D receptor, including ULBP-1, -2, and -3, but not MICA or MICB, in infected cells both in vitro and in vivo. However, the viral factor(s) involved in NKG2D ligand expression still remains undefined. HIV-1 Vpr activates the DNA damage/stress-sensing ATR kinase and promotes G(2) cell-cycle arrest, conditions known to up-regulate NKG2D ligands. We report here that HIV-1 selectively induces cell-surface expression of ULBP-2 in primary CD4(+) T lymphocytes by a process that is Vpr dependent. Importantly, Vpr enhanced the susceptibility of HIV-1-infected cells to NK cell-mediated killing. Strikingly, Vpr alone was sufficient to up-regulate expression of all NKG2D ligands and thus promoted efficient NKG2D-dependent NK cell-mediated killing. Delivery of virion-associated Vpr via defective HIV-1 particles induced analogous biologic effects in noninfected target cells, suggesting that Vpr may act similarly beyond infected cells. All these activities relied on Vpr ability to activate the ATR-mediated DNA damage/stress checkpoint. Overall, these results indicate that Vpr is a key determinant responsible for HIV-1-induced up-regulation of NKG2D ligands and further suggest an immunomodulatory role for Vpr that may not only contribute to HIV-1-induced CD4(+) T-lymphocyte depletion but may also take part in HIV-1-induced NK-cell dysfunction.
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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".