A Doubly Fluorescent HIV-1 Reporter Shows that the Majority of Integrated HIV-1 Is Latent Shortly after Infection
Bibliographic record
Abstract
HIV-1 latency poses a major barrier to viral eradication. Canonically, latency is thought to arise from progressive epigenetic silencing of active infections. However, little is known about when and how long terminal repeat (LTR)-silent infections arise since the majority of the current latency models cannot differentiate between initial (LTR-silent) and secondary (progressive silencing) latency. In this study, we constructed and characterized a novel, double-labeled HIV-1 vector (Red-Green-HIV-1 [RGH]) that allows for detection of infected cells independently of LTR activity. Infection of Jurkat T cells and other cell lines with RGH suggests that the majority of integrated proviruses were LTR-silent early postinfection. Furthermore, the LTR-silent infections were transcriptionally competent, as the proviruses could be reactivated by a variety of T cell signaling agonists. Moreover, we used the double-labeled vector system to compare LTRs from seven different subtypes with respect to LTR silencing and reactivation. These experiments indicated that subtype D and F LTRs were more sensitive to silencing, whereas the subtype AE LTR was largely insensitive. Lastly, infection of activated human primary CD4(+) T cells yielded LTR-silent as well as productive infections. Taken together, our data, generated using the newly developed RGH vector as a sensitive tool to analyze HIV-1 latency on a single-cell level, show that the majority of HIV-1 infections are latent early postinfection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".