The Viral Protein Tat Can Inhibit the Establishment of HIV-1 Latency
Bibliographic record
Abstract
The establishment of HIV-1 latency can result from limiting levels of transcription initiation or elongation factors, restrictive chromatin modifications, transcriptional interference, and insufficient Tat activity. Since the viral protein Tat can counteract many of these factors, we hypothesized that the presence of exogenous Tat during infection might inhibit the establishment of latency. This was explored using a Jurkat model of latency establishment and reactivation. PCR and reverse transcriptase PCR (RT-PCR) confirmed the latent state in this model and showed evidence of transcriptional interference. To address our hypothesis, cells undergoing infection were first exposed to either purified recombinant Tat or a transactivation-negative mutant. Only the former resulted in a modest inhibition of the establishment of latency. Next, Jurkat cells stably expressing intracellular Tat were used in our latency model to avoid limitations of Tat delivery. Experiments confirmed that intracellular Tat expression did not affect the susceptibility of these cells to viral infection. Eight weeks after infection, Jurkat cells expressing Tat harbored up to 1,700-fold fewer (P < 0.01) latent viruses than Jurkat cells that did not express Tat. Additionally, Tat delivered by a second virus was sufficient to reactivate most of the latent population. Our results suggest that inhibition of the establishment of latent infection is theoretically possible. In a hypothetical scenario of therapy that induces viral gene expression during acute infection, activation of viruses which would otherwise have entered latency could occur while concurrent highly active antiretroviral therapy (HAART) would prevent further viral spread, potentially decreasing the size of the established latent reservoir.
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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 | 0.000 |
| 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".