Autophagy-enhancing drugs limit mucosal HIV-1 acquisition and suppress viral replication ex vivo
Bibliographic record
Abstract
Abstract Current direct-acting antiviral therapies are highly effective in suppressing HIV-1 replication. However, mucosal inflammation undermines prophylactic treatment efficacy, and HIV-1 persists in long-lived tissue-derived dendritic cells (DCs) and CD4+ T cells of treated patients. Host-directed strategies are an emerging therapeutic approach to improve therapy outcomes in infectious diseases. Autophagy functions as an innate antiviral mechanism by degrading viruses in specialized vesicles. Here, we investigated the impact of pharmaceutically enhancing autophagy on HIV-1 acquisition and viral replication. To this end, we developed a human tissue infection model permitting concurrent analysis of HIV-1 cellular targets ex vivo. Prophylactic treatment with autophagy-enhancing drugs carbamazepine and everolimus promoted HIV-1 restriction in skin-derived CD11c+ DCs and CD4+ T cells. Everolimus also decreased HIV-1 susceptibility to lab-adapted and transmitted/founder HIV-1 strains, and in vaginal Langerhans cells. Notably, we observed cell-specific effects of therapeutic treatment. Therapeutic rapamycin treatment suppressed HIV-1 replication in tissue-derived CD11c+ DCs, while all selected drugs limited viral replication in CD4+ T cells. Strikingly, both prophylactic and therapeutic treatment with everolimus or rapamycin reduced intestinal HIV-1 productive infection. Our findings highlight host autophagy pathways as an emerging target for HIV-1 therapies, and underscore the relevancy of repurposing clinically-approved autophagy drugs to suppress mucosal HIV-1 replication.
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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) | 0.001 | 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".