T-cell Activation Leads to Poor Activation of the HIV-1 Clade E Long Terminal Repeat and Weak Association of Nuclear Factor-κB and NFAT with Its Enhancer Region
Bibliographic record
Abstract
The enhancer region in the human immunodeficiency virus type 1 (HIV-1) 5'-long terminal repeat (LTR) is very important for viral transcription. This promoter sequence binds both nuclear factor-kappaB and NFAT, two important modulators of HIV-1 gene expression. Previous studies have indicated that the enhancer regions of the different HIV-1 clade LTRs differ in their number of NF-kappaB-binding sites. In this study, we have compared the activation potential of the different HIV-1 clade and HIV-2 LTRs and assessed their interaction with NFAT and NF-kappaB. In T-cell lines and primary CD4(+) T-cells, the results showed that the HIV-1 clade E LTR (with a single NF-kappaB-binding site) was the weakest LTR regardless of the tested activators, whereas the HIV-2 LTR was the most responsive LTR. The clade E enhancer region was also demonstrated to be the weakest enhancer region in transfection experiments with luciferase reporter-based vectors. Electrophoretic mobility shift assays with extracts from activated CD4(+) T-cells indicated that, although NF-kappaB and NFAT bound all enhancers, HIV-1 clade E and HIV-2 LTR enhancers were poor binding targets for these two factors. Weak NFAT binding to clade E enhancers was also confirmed using NFAT1-expressing 293T cells in competition experiments. We have also shown the absence of interaction of NF-kappaB or NFAT with the third NF-kappaB repeat present in clade C. However, the clade C enhancer bound NFAT more efficiently than all other enhancer regions tested. Our results hence demonstrate for the first time that differences in the binding of NF-kappaB and NFAT to the enhancer regions could be responsible for some of the observed variation in HIV-1 clade LTR activation, whereas HIV-2 LTR activation seems mostly independent of these interactions.
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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 | 0.001 |
| 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".