SARS CoV-2 mRNA vaccination exposes latent HIV to Nef-specific CD8+ T-cells
Bibliographic record
Abstract
Abstract Efforts to cure HIV have focused on reactivating latent proviruses to enable elimination by CD8 + cytotoxic T-cells. Clinical studies of latency reversing agents (LRA) in antiretroviral therapy (ART)-treated individuals have shown increases in HIV transcription, but without reductions in virologic measures, or evidence that HIV-specific CD8 + T-cells were productively engaged. Here, we show that the SARS-CoV-2 mRNA vaccine BNT162b2 activates the RIG-I/TLR – TNF – NFκb axis, resulting in transcription of HIV proviruses with minimal perturbations of T-cell activation and host transcription. T-cells specific for the early gene-product HIV-Nef uniquely increased in frequency and acquired effector function (granzyme-B) in ART-treated individuals following SARS-CoV-2 mRNA vaccination. These parameters of CD8 + T-cell induction correlated with significant decreases in cell-associated HIV mRNA, suggesting killing or suppression of cells transcribing HIV. Thus, we report the observation of an intervention-induced reduction in a measure of HIV persistence, accompanied by precise immune correlates, in ART-suppressed individuals. However, we did not observe significant depletions of intact proviruses, underscoring challenges to achieving (or measuring) HIV reservoir reductions. Overall, our results support prioritizing the measurement of granzyme-B-producing Nef-specific responses in latency reversal studies and add impetus to developing HIV-targeted mRNA therapeutic vaccines that leverage built-in LRA activity.
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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) | 0.003 | 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".