Human T-Cell Leukemia Virus Type 1 (HTLV-1) bZIP Protein Interacts with the Cellular Transcription Factor CREB To Inhibit HTLV-1 Transcription
Bibliographic record
Abstract
The complex human T-cell leukemia virus type 1 (HTLV-1) retrovirus encodes several proteins that are unique to the virus within its 3'-end region. Among them, the viral transactivator Tax and posttranscriptional regulator Rex are well characterized, and both positively regulate HTLV-1 viral expression. Less is known about the other regulatory proteins encoded in this region of the provirus, including the recently discovered HBZ protein. HBZ has been shown to negatively regulate basal and Tax-dependent HTLV-1 transcription through its ability to interact with specific basic-leucine zipper (bZIP) proteins. In the present study, we found that HBZ reduces HTLV-1 transcription and virion production. We then characterized the interaction between HBZ and the cellular transcription factor CREB. CREB plays a critical role in Tax-mediated HTLV-1 transcription by forming a complex with Tax that binds to viral cyclic AMP-response elements (CREs) located within the viral promoter. We found that HBZ and CREB interact in vivo and directly in vitro, and this interaction occurs through the bZIP domain of each protein. We also found that CREM-Ia and ATF-1, which share significant homology in their bZIP domains with the bZIP domain of CREB, interact with HBZ-bZIP. The interaction between CREB and HBZ prevents CREB binding to the viral CRE elements in vitro and in vivo, suggesting that the reduction in HTLV-1 transcription by HBZ is partly due to the loss of CREB at the promoter. We also found that HBZ displaces CREB from a cellular CRE, suggesting that HBZ may deregulate CREB-dependent cellular gene expression.
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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) | 0.001 | 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".