Combination Immune Checkpoint Blockade to Reverse HIV Latency
Bibliographic record
Abstract
Abstract In people living with HIV on antiretroviral therapy, HIV latency is the major barrier to a cure. HIV persists preferentially in CD4+ T cells expressing multiple immune checkpoint (IC) molecules, including programmed death (PD)-1, T cell Ig and mucin domain-containing protein 3 (TIM-3), lymphocyte associated gene 3 (LAG-3), and T cell immunoreceptor with Ig and ITIM domains (TIGIT). We aimed to determine whether these and other IC molecules have a functional role in maintaining HIV latency and whether blocking IC molecules with Abs reverses HIV latency. Using an in vitro model that establishes latency in both nonproliferating and proliferating human CD4+ T cells, we show that proliferating cells express multiple IC molecules at high levels. Latent infection was enriched in proliferating cells expressing PD-1. In contrast, nonproliferating cells expressed IC molecules at significantly lower levels, but latent infection was enriched in cells expressing PD-1, TIM-3, CTL-associated protein 4 (CTLA-4), or B and T lymphocyte attenuator (BTLA). In the presence of an additional T cell–activating stimulus, staphylococcal enterotoxin B, Abs to CTLA-4 and PD-1 reversed HIV latency in proliferating and nonproliferating CD4+ T cells, respectively. In the absence of staphylococcal enterotoxin B, only the combination of Abs to PD-1, CTLA-4, TIM-3, and TIGIT reversed latency. The potency of latency reversal was significantly higher following combination IC blockade compared with other latency-reversing agents, including vorinostat and bryostatin. Combination IC blockade should be further explored as a strategy to reverse HIV latency.
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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) | 0.001 | 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 | 0.000 |
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".