Regulation of Human Immunodeficiency Virus Type 1 Gene Expression by Clade-Specific Tat Proteins
Bibliographic record
Abstract
The major group of human immunodeficiency virus type 1 (HIV-1) strains that comprise the current global pandemic have diversified during their worldwide spread into at least 10 distinct subtypes, or clades. Subtype C predominates in sub-Saharan Africa and is responsible for the majority of worldwide HIV-1 infections, subtype B predominates in North America and Europe, and subtype E is prevalent in Southeast Asia. Significant amino acid variations have been observed among the clade-specific Tat proteins. For the present study, we examined clade-specific interactions between Tat, transactivation-responsive (TAR) element, and P-TEFb proteins and how these interactions may modulate the efficiency of HIV-1 transcription. Clade-specific Tat proteins significantly modified viral gene expression. Tat proteins derived from HIV-1 clades C and E were strong transactivators of long terminal repeat (LTR) activity; Tat E also had a longer half-life than the other Tat proteins and interacted more efficiently with the stem-loop TAR element. Chimeric Tat proteins harboring the Tat E activation domain were strong transactivators of LTR expression. While Tat B, C, and E were able to rescue a Tat-defective HIV-1 proviral clone, Tat E was significantly more efficient at rescue than Tat C, possibly due to the relative stability of the Tat protein. Swapping the activation domains of Tat B, C, and E identified the cyclin T1 association domain as a critical determinant of the transactivation efficiency and of Tat-defective HIV-1 provirus rescue.
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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) | 0.000 | 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".