Proinflammatory Cytokine Gene Induction by Human T-Cell Leukemia Virus Type 1 (HTLV-1) and HTLV-2 Tax in Primary Human Glial Cells
Bibliographic record
Abstract
Infection with human T-cell leukemia virus type 1 (HTLV-1) can result in the development of HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP), a chronic inflammatory disease of the central nervous system (CNS). HTLV-2 is highly related to HTLV-1 at the genetic level and shares a high degree of sequence homology, but infection with HTLV-2 is relatively nonpathogenic compared to HTLV-1. Although the pathogenesis of HAM/TSP remains to be fully elucidated, previous evidence suggests that elevated levels of the proinflammatory cytokines in the CNS are associated with neuropathogenesis. We demonstrate that HTLV-1 infection in astrogliomas results in a robust induction of interleukin-1beta (IL-1beta), IL-1alpha, tumor necrosis factor alpha (TNF-alpha), TNF-beta, and IL-6 expression. HTLV encodes for a viral transcriptional transactivator protein named Tax that also induces the transcription of cellular genes. To investigate and compare the effects of Tax1 and Tax2 expression on the dysregulation of proinflammatory cytokines, lentivirus vectors were used to transduce primary human astrocytomas and oligodendrogliomas. The expression of Tax1 in primary human astrocytomas and oligodendrogliomas resulted in significantly higher levels of proinflammatory cytokine gene expression compared to Tax2. Notably, Tax1 expression uniquely sensitized primary human astrocytomas to apoptosis. A Tax2/Tax1 chimera encoding the C-terminal 53 amino acids of the Tax1 fused to the Tax2 gene (Tax(221)) demonstrated a phenotype that resembled Tax1, with respect to proinflammatory cytokine gene expression and sensitization to apoptosis. The patterns of differential cytokine induction and sensitization to apoptosis displayed by Tax1 and Tax2 may reflect differences relating to the heightened neuropathogenicity associated with HTLV-1 infection and the development of HAM/TSP.
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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".