Regulation of IFN Regulatory Factor 4 Expression in Human T Cell Leukemia Virus-I-Transformed T Cells
Bibliographic record
Abstract
IFN regulatory factor (IRF)-4 is a lymphoid/myeloid-restricted member of the IRF transcription factor family that plays an essential role in the homeostasis and function of mature lymphocytes. IRF-4 expression is tightly regulated in resting primary T cells and is transiently induced at the mRNA and protein levels after activation by Ag-mimetic stimuli such as TCR cross-linking or treatment with phorbol ester and calcium ionophore (PMA/ionomycin). However, IRF-4 is constitutively upregulated in human T cell leukemia virus type I (HTLV-I) infected T cells as a direct gene target for the HTLV-I Tax oncoprotein. In this study we demonstrate that chronic IRF-4 expression in HTLV-I-infected T lymphocytes is associated with a leukemic phenotype, and we examine the mechanisms by which continuous production of IRF-4 is achieved in HTLV-I-transformed T cells. IRF-4 expression in HTLV-1-infected cells is driven through activation of the NF-kappaB and NF-AT pathways, resulting in the binding of p50, p65, and c-Rel to the kappaB1 element and p50, c-Rel, and NF-ATp to the CD28RE element within the -617 to -209 region of the IRF-4 promoter. Furthermore, mutation of either the kappaB1 or CD28RE sites blocks Tax-mediated transactivation of the human IRF-4 promoter in T cells. These experiments constitute the first detailed analysis of human IRF-4 transcriptional regulation within the context of HTLV-I infection and transformation of CD4(+) T lymphocytes.
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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 | 0.000 |
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".