BCL-2 antagonism sensitizes cytotoxic T cell–resistant HIV reservoirs to elimination ex vivo
Bibliographic record
Abstract
Curing HIV infection will require the elimination of a reservoir of infected CD4+ T cells that persists despite HIV-specific cytotoxic T cell (CTL) responses. Although viral latency is a critical factor in this persistence, recent evidence also suggests a role for intrinsic resistance of reservoir-harboring cells to CTL killing. This resistance may have contributed to negative outcomes of clinical trials, where pharmacologic latency reversal has thus far failed to drive reductions in HIV reservoirs. Through transcriptional profiling, we herein identified overexpression of the prosurvival factor B cell lymphoma 2 (BCL-2) as a distinguishing feature of CD4+ T cells that survived CTL killing. We show that the inducible HIV reservoir was disproportionately present in BCL-2hi subsets in ex vivo CD4+ T cells. Treatment with the BCL-2 antagonist ABT-199 was not sufficient to drive reductions in ex vivo viral reservoirs when tested either alone or with a latency-reversing agent (LRA). However, the triple combination of strong LRAs, HIV-specific T cells, and a BCL-2 antagonist uniquely enabled the depletion of ex vivo viral reservoirs. Our results provide rationale for novel therapeutic approaches targeting HIV cure and, more generally, suggest consideration of BCL-2 antagonism as a means of enhancing CTL immunotherapy in other settings, such as cancer.
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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 | 0.000 |
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".