Induction of galectin-1 expression by HTLV-I Tax and its impact on HTLV-I infectivity
Bibliographic record
Abstract
BACKGROUND: Cell-free Human T-cell Leukemia Virus type I (HTLV-I) virions are poorly infectious and cell-to-cell contact is often required to achieve infection. Other factors might thus importantly contribute in increasing infection by HTLV-I. Galectin-1 is a galactoside-binding lectin which is secreted by activated T lymphocytes. Several functions have been attributed to this protein including its capacity to increase cell-to-cell adhesion. Based on previous studies, we postulated that this protein could also accentuate HTLV-I infection. RESULTS: Herein, we demonstrate that galectin-1 expression and release are higher in HTLV-I-infected T cells in comparison to uninfected T cells. Furthermore, galectin-1 expression was activated in various cell lines expressing the wild type viral Tax protein while this induction was minimal upon expression of NF-kappaB activation-defective TaxM22. Cotransfection of these Tax expression vectors with galectin-1 promoter-driven luciferase constructs confirmed that Tax upregulated galectin-1 promoter activity. However, a NF-kappaB-independent mechanism was strongly favoured in this induction of galectin-1 expression as no activation of the promoter was apparent in Jurkat cells treated with known NF-kappaB activators. Using HTLV-I envelope pseudotyped HIV-1 virions, galectin-1 was shown to increase infectivity. In addition, a co-culture assay with HTLV-I-infected cells also indicated an increase in cell fusion upon addition of galectin-1. This effect was not mediated by factors present in the supernatant of the HTLV-I-infected cells. CONCLUSION: These data suggest that HTLV-I Tax increases galectin-1 expression and that this modulation could play an important role in HTLV-I infection by stabilizing both cell-to-cell and virus-cell interactions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".