Impact of latency‐reversing agents on human macrophage physiology
Bibliographic record
Abstract
Abstract Introduction HIV‐1 eradication is hindered by the presence of inducible long‐lived reservoirs of latently infected cells which rapidly disseminate viral particles upon treatment interruption. Eliminating these reservoirs by the so‐called shock and kill strategy represents a crucial concept toward an HIV‐1 cure. Several molecules called latency‐reversing agents (LRAs) are under intensive investigations to reactivate virus gene expression. These studies are mainly conducted on CD4+ T cells where LRAs are well tolerated and did not induce global cellular activation. However, despite their broad spectrum, the putative impact of LRAs on other cellular reservoirs such as macrophages is still ill‐defined. Methods We investigated the impact of the protein kinase C (PKC) activator bryostatin‐1, bromodomain inhibitor JQ1 and histone deacetylase inhibitor romidepsin used either alone or in combination on human primary monocyte‐derived macrophages (MDMs). Results We demonstrate that bryostatin‐1, JQ1, and romidepsin or their combinations are not toxic at nanomolar concentrations but induce metabolic and morphologic alterations of MDMs. Bryostatin‐1 triggered the secretion of pro‐inflammatory cytokines, while JQ‐1 decreased it. Phagocytosis and endocytosis were modestly impaired upon bryostatin‐1 treatment whereas efferocytosis was markedly downregulated by romidepsin. Despite its pro‐inflammatory profile, bryostatin‐1 did not induce classically activated macrophage markers. Finally, we reveal that conditioned medium from bryostatin‐1‐treated macrophages did not potentiate its reactivation feature. Conclusions Our study reveals that LRAs can diversely impact basic physiologic features of human primary macrophages and could potentially decrease reactivation of nearby CD4+ T cells latently infected with HIV‐1. Our observations further stress the need to include different cell populations when assessing HIV‐1 cure strategies.
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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) | 0.002 | 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".