IFN-lambda 3 directly induces antiviral responses in human B and T cells leading to inhibition of HIV-1 infection of CD4+ T cells
Bibliographic record
Abstract
Abstract Type III interferons (IFN-lambdas(λ)) are the most recently discovered interferon cytokine family that inhibit viruses by signaling through a unique IFN-λR1/IL-10RB heterodimeric receptor. Until now, IFN-λs were thought to primarily act on anatomical barrier epithelial cells, neutrophils and a subset of dendritic cells, although the majority of studies have been performed in mice. Here, we examine regulation of IFN-λR1 expression and downstream effects of IFN-λ3 stimulation of primary human blood immune cells and compare them to lung or liver epithelial cells. IFN-λ3 directly bound and upregulated IFN-stimulated gene (ISG) expression in freshly purified human B cells and CD8+ T cells, but not monocytes, neutrophils, natural killer cells and CD4+ T cells. Despite similar IFNLR1 transcript levels in B cells and lung epithelial cells, lung epithelial cells bound more IFN-λ3, which resulted in a 50-fold greater ISG induction when compared to B cells. The reduced response of B cells could be explained by the higher expression of the soluble splice variant of IFN-λR1, which inhibited ISG induction when added with IFN-λ3 to peripheral blood mononuclear cells. Human CD4+ T cells gained responsiveness to IFN-λ3 upon T-cell receptor stimulation since activation signals potently, and specifically upregulated membrane-bound IFN-λR1 expression. Importantly, IFN-λ3 treatment of activated CD4+ T cells caused a significant decrease in human immunodeficiency virus-1 (HIV-1) infection. Collectively, our data demonstrate that IFN-λ3 directly interacts with the human adaptive immune system, and thus can promote antiviral immunity at mucosal and peripheral sites, unlike what has been previously shown in published mouse models.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".