Increasing procaspase 8 expression using repurposed drugs to induce HIV infected cell death in ex vivo patient cells
Bibliographic record
Abstract
HIV persists because a reservoir of latently infected CD4 T cells do not express viral proteins and are indistinguishable from uninfected cells. One approach to HIV cure suggests that reactivating HIV will activate cytotoxic pathways; yet when tested in vivo, reactivating cells do not die sufficiently to reduce cell-associated HIV DNA levels. We recently showed that following reactivation from latency, HIV infected cells generate the HIV specific cytotoxic protein Casp8p41 which is produced by HIV protease cleaving procaspase 8. However, cell death is prevented, possibly due to low procaspase 8 expression. Here, we tested whether increasing procaspase 8 levels in CD4 T cells will produce more Casp8p41 following HIV reactivation, causing more reactivated cells to die. Screening 1277 FDA approved drugs identified 168 that increased procaspase 8 expression by at least 1.7-fold. Of these 30 were tested for anti-HIV effects in an acute HIVIIIb infection model, and 9 drugs at physiologic relevant levels significantly reduced cell-associated HIV DNA. Primary CD4 T cells from ART suppressed HIV patients were treated with one of these 9 drugs and reactivated with αCD3/αCD28. Four drugs significantly increased Casp8p41 levels following HIV reactivation, and decreased total cell associated HIV DNA levels (flurbiprofen: p = 0.014; doxycycline: p = 0.044; indomethacin: p = 0.025; bezafibrate: P = 0.018) without effecting the viability of uninfected cells. Thus procaspase 8 levels can be increased pharmacologically and, in the context of HIV reactivation, increase Casp8p41 causing death of reactivating cells and decreased HIV DNA levels. Future studies will be required to define the clinical utility of this or similar approaches.
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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) | 0.003 | 0.001 |
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".