The HIV Latency Reversal Agent HODHBt Enhances NK Cell Effector and Memory-Like Functions by Increasing Interleukin-15-Mediated STAT Activation
Bibliographic record
Abstract
Elimination of human immunodeficiency virus (HIV) reservoirs is a critical endpoint to eradicate HIV. One therapeutic intervention against latent HIV is “shock and kill.” This strategy is based on the transcriptional activation of latent HIV with a latency-reversing agent (LRA) with the consequent killing of the reactivated cell by either the cytopathic effect of HIV or the immune system. We have previously found that the small molecule 3-hydroxy-1,2,3-benzotriazin-4(3H)-one (HODHBt) acts as an LRA by increasing signal transducer and activator of transcription (STAT) factor activation mediated by interleukin-15 (IL-15) in cells isolated from aviremic participants. The IL-15 superagonist N-803 is currently under clinical investigation to eliminate latent reservoirs. IL-15 and N-803 share similar mechanisms of action by promoting the activation of STATs and have shown some promise in preclinical models directed toward HIV eradication. In this work, we evaluated the ability of HODHBt to enhance IL-15 signaling in natural killer (NK) cells and the biological consequences associated with increased STAT activation in NK cell effector and memory-like functions. We showed that HODHBt increased IL-15-mediated STAT phosphorylation in NK cells, resulting in increases in the secretion of CXCL-10 and interferon gamma (IFN-γ) and the expression of cytotoxic proteins, including granzyme B, granzyme A, perforin, granulysin, FASL, and TRAIL. This increased cytotoxic profile results in increased cytotoxicity against HIV-infected cells and different tumor cell lines. HODHBt also improved the generation of cytokine-induced memory-like NK cells. Overall, our data demonstrate that enhancing the magnitude of IL-15 signaling with HODHBt favors NK cell cytotoxicity and memory-like generation, and thus, targeting this pathway could be further explored for HIV cure interventions.
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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 | 0.000 |
| 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".