Identification of Novel HIV-1 Latency-Reversing Agents from a Library of Marine Natural Products
Bibliographic record
Abstract
model of HIV-1 latency, we screened 257 pure compounds from a marine natural product library and identified 4 (psammaplin A, aplysiatoxin, debromoaplysiatoxin, and previously-described alotaketal C) that induced expression of latent HIV-1 provirus in both cell line and primary cell models. Notably, aplysiatoxin induced similar levels of HIV-1 expression as prostratin but at up to 900-fold lower concentrations and without substantial effects on cell viability. Psammaplin A enhanced HIV-1 expression synergistically when treated in combination with the protein kinase C (PKC) activator prostratin, but not the histone deacetylase inhibitor (HDACi) panobinostat, suggesting that psammaplin A functions as a latency-reversing agent (LRA) of the HDACi class. Conversely, aplysiatoxin and debromoaplysiatoxin synergized with panobinostat but not prostratin, suggesting that they function as PKC activators. Our study identifies new compounds from previously untested marine natural products and adds to the repertoire of LRAs that can inform therapeutic “shock-and-kill”-based strategies to eliminate latent HIV-infected reservoirs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".