Pharmacologic HIV-1 Nef blockade promotes CD8 T cell–mediated elimination of latently HIV-1–infected cells in vitro
Bibliographic record
Abstract
Eradication of the HIV-1 latent reservoir represents the current paradigm to developing a cure for AIDS. HIV-1 has evolved multiple mechanisms to evade CD8 T cell responses, including HIV-1 Nef-mediated downregulation of MHC-I from the surface of infected cells. Nef transcripts and protein are detectable in samples from aviremic donors, suggesting that Nef expression in latently HIV-1-infected CD4 T cells protects them from immune-mediated clearance. Here, we tested 4 small molecule inhibitors of HIV-1 Nef in an in vitro primary CD4 T cell latency model and measured the ability of autologous ex vivo or HIV-1 peptide-expanded CD8 T cells to recognize and kill latently infected cells as a function of inhibitor treatment. Nef inhibition enhanced cytokine secretion by autologous CD8 T cells against latently HIV-1-infected targets in an IFN-γ release assay. Additionally, CD8 T cell-mediated elimination of latently HIV-1-infected cells was significantly enhanced following Nef blockade, measured as a reduction in the frequency of infected cells and Gag protein in cultures following viral outgrowth assays. We demonstrate for the first time to our knowledge that Nef blockade, in combination with HIV-specific CD8 T cell expansion, might be a feasible strategy to target the HIV-1 latent reservoir that should be tested further in vivo.
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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) | 0.001 | 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".