HIV-1 latency reversing agents converge on phosphoregulation of nuclear protein complexes
Bibliographic record
Abstract
ABSTRACT Despite the success of antiretroviral therapy (ART), HIV-1 persists in latently infected cells, posing a central barrier to a cure. “Shock-and-kill” strategies using latency-reversing agents (LRAs) have shown some promise in reactivating viral gene expression ex vivo , but have yielded little clinical efficacy, underscoring the need for deeper insight into the molecular mechanisms that govern reactivation. Here, we performed deep quantitative phosphoproteomics of J-Lat 10.6 cells treated with diverse LRAs: SAHA, PMA, or prostratin. We identified 48,476 confidently localized phosphorylation sites mapping to 6,672 proteins, with SAHA inducing the most extensive changes. Regulated phosphoproteins were enriched in chromatin organization, transcription, RNA processing, nuclear transport, and cytoskeletal remodeling. Although LRAs regulated overlapping pathways, they elicited divergent kinase activities and site-specific phosphorylation patterns. A reproducible core of 3,502 phosphorylation sites on 1,432 proteins mapped to 39 nuclear protein complexes, including the spliceosome, Mediator, NF-κB, and RNA polymerase II. Remarkably, 20 protein complexes were phosphoregulated by all three LRAs, but at distinct sites, revealing convergence on shared nuclear machinery through distinct mechanisms. This study provides a comprehensive map of protein complex phosphorylation remodeling during HIV-1 reactivation and highlights signaling mechanisms that could guide the rational design of next-generation LRAs with improved efficacy and reduced toxicity.
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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".