Direct non-productive HIV-1 infection in a T-cell line is driven by cellular activation state and NFκB
Bibliographic record
Abstract
BACKGROUND: Molecular latency allows HIV-1 to persist in resting memory CD4+ T-cells as transcriptionally silent provirus integrated into host chromosomal DNA. Multiple transcriptional regulatory mechanisms for HIV-1 latency have been described in the context of progressive epigenetic silencing and maintenance. However, our understanding of the determinants critical for the establishment of latency in newly infected cells is limited. RESULTS: In this study, we used a recently described, doubly fluorescent HIV-1 latency model to dissect the role of proviral integration sites and cellular activation state on direct non-productive infections at the single cell level. Proviral integration site mapping of infected Jurkat T-cells revealed that productively and non-productively infected cells are indistinguishable in terms of genomic landmarks, surrounding epigenetic landscapes, and proviral orientation relative to host genes. However, direct non-productive infections were inversely correlated with both cellular activation state and NFκB activity. Furthermore, modulating NFκB with either small molecules or by conditional overexpression of NFκB subunits was sufficient to alter the propensity of HIV-1 to directly enter a non-productive latent state in newly infected cells. Importantly, this modulatory effect was limited to a short time window post-infection. CONCLUSIONS: Taken together, our data suggest that cellular activation state and NFκB activity during the time of infection, but not the site of proviral integration, are important regulators of direct HIV-1 non-productive infections.
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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) | 0.002 | 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".