Making Sense out of Antisense Transcription in Human T-Cell Lymphotropic Viruses (HTLVs)
Bibliographic record
Abstract
Retroviral gene expression generally depends on a full-length transcript that initiates in the 5' long terminal repeat (LTR), which is either unspliced or alternatively spliced. We and others have demonstrated the existence of an antisense transcript initiating in the 3' LTR of the Human T-cell Leukemia Virus type 1 (HTLV-1) that is involved in the production of HBZ (HTLV-1 basic leucine zipper (bZIP) factor). HBZ is a Fos-like factor capable of inhibiting Tax-mediated activation of the HTLV-1 LTR by interacting with the cellular transcription factor cAMP-response element-binding protein (CREB) and the pleiotropic cellular coactivators p300/CBP. HBZ can also activate cellular transcription through its interaction with p300/CBP. Interestingly, HBZ has also been found to promote T-lymphocyte proliferation. By down-regulating viral expression and by stimulating T-cell proliferation, HBZ could be essential in the establishment of a chronic infection. Antisense transcription also occurs in the closely related HTLV-2 retrovirus as well as in the recently discovered HTLV-3 and HTLV-4. These antisense transcripts are also involved in the production of retroviral proteins that we have termed Antisense Protein of HTLVs (APH). Like HBZ, the APH proteins are localized in the nucleus of transfected cells and repress Tax-mediated viral transcription.
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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) | 0.001 | 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".