A targeted reactivation of latent HIV-1 using an activator vector in patient samples from acute infection
Bibliographic record
Abstract
BACKGROUND: During combined anti-retroviral treatment, a latent HIV reservoir persists within resting memory CD4 T cells that initiates viral recrudescence upon treatment interruption. Strategies for HIV-1 cure have largely focused on latency reversing agents (LRAs) capable of reactivating and eliminating this viral reservoir. Previously investigated LRAs have largely failed to achieve a robust latency reversal sufficient for reduction of latent HIV pool or the potential of virus-free remission in the absence of treatment. METHODS: We utilize a polyvalent virus-like particle (VLP) formulation called Activator Vector (ACT-VEC) to 'shock' provirus into transcriptional activity. Ex vivo co-culture experiments were used to evaluate the efficacy of ACT-VEC in relation to other LRAs in individuals diagnosed and treated during the acute stage of infection. IFN-γ ELISpot, qRT-PCR and Illumina MiSeq were used to evaluate antigenicity, latency reversal, and diversity of induced virus respectively. FINDINGS: patients diagnosed and treated at acute/early infection, we demonstrate that ACT-VEC can reverse latency in HIV infected CD4 T cells to a greater extent than other major recall antigens as stimuli or even mitogens such as PMA/Iono. Furthermore, ACT-VEC activates more latent HIV-1 than clinically tested HDAC inhibitors or protein kinase C agonists. INTERPRETATION: Taken together, these results show that ACT-VEC can induce HIV reactivation from latently infected CD4 T cells collected from participants on first line combined antiretroviral therapy for at least two years after being diagnosed and treated at acute/early stage of infection. These findings could provide guidance to possible targeted cure strategies and treatments. FUNDING: NIH and CIHR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".