The bitter side of sweet: the role of Galectin-9 in immunopathogenesis of viral infections
Bibliographic record
Abstract
In recent years, a critical role for β-galactoside-binding protein, Galectin-9 (Gal-9) has emerged in infectious disease, autoimmunity, and cancer. It is a ligand for T cell immunoglobulin mucin domain 3 (Tim-3), a type-I glycoprotein that is persistently expressed on dysfunctional T cells during chronic viral infections. Gal-9 exerts its pivotal immunomodulatory effects by inducing apoptosis or suppressing effector functions via engagement with its receptor, Tim-3. Recent studies report elevation of circulating Gal-9 in humans infected with different viral infections. Interaction of soluble Gal-9 with Tim-3 expressed on the surface of activated CD4+ T cells renders them less susceptible to HIV-1 infection, while enhanced HIV infection occurs when Gal-9 interacts with a different receptor than Tim-3. This indicates the versatile role of Gal-9 in viral pathogenesis. For instance, higher expression of Tim-3 during chronic viral infection and elevation of plasma Gal-9 may have evolved to limit persistent immune activation and pathogenic T cells activity. In contrast, Gal-9 can suppress the effectiveness of immunity against viral infections. In agreement, Gal-9 knockout mice mount a more robust and vigorous virus-specific immune response in acute and chronic viral infections resulting in rapid viral clearance. In line with this observation, blocking Gal-9 signals to Tim-3-expressing T cells result in improved immune responses. Here we review the biological and immunological properties of Gal-9 in viral infections (HIV, HCV, HBV, HSV, CMV, influenza, and dengue virus). Manipulating Gal-9 signals may have immunotherapeutic potential and could represent an alternative approach for improving immune responses to viral infections/vaccines.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".