Targeted HIV-1 Latency Reversal Using CRISPR/Cas9-Derived Transcriptional Activator Systems
Bibliographic record
Abstract
CRISPR/Cas9 technology is currently considered the most advanced tool for targeted genome engineering. Its sequence-dependent specificity has been explored for locus-directed transcriptional modulation. Such modulation, in particular transcriptional activation, has been proposed as key approach to overcome silencing of dormant HIV provirus in latently infected cellular reservoirs. Currently available agents for provirus activation, so-called latency reversing agents (LRAs), act indirectly through cellular pathways to induce viral transcription. However, their clinical performance remains suboptimal, possibly because reservoirs have diverse cellular identities and/or proviral DNA is intractable to the induced pathways. We have explored two CRISPR/Cas9-derived activator systems as targeted approaches to induce dormant HIV-1 proviral DNA. These systems recruit multiple transcriptional activation domains to the HIV 5' long terminal repeat (LTR), for which we have identified an optimal target region within the LTR U3 sequence. Using this target region, we demonstrate transcriptional activation of proviral genomes via the synergistic activation mediator complex in various in culture model systems for HIV latency. Observed levels of induction are comparable or indeed higher than treatment with established LRAs. Importantly, activation is complete, leading to production of infective viral particles. Our data demonstrate that CRISPR/Cas9-derived technologies can be applied to counteract HIV latency and may therefore represent promising novel approaches in the quest for HIV elimination.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".