Gene Expression and Antiviral Activity of Alpha/Beta Interferons and Interleukin-29 in Virus-Infected Human Myeloid Dendritic Cells
Bibliographic record
Abstract
Dendritic cells (DCs) respond to microbial infections by undergoing phenotypic maturation and by producing multiple cytokines. In the present study, we analyzed the ability of influenza A and Sendai viruses to induce DC maturation and activate tumor necrosis factor alpha (TNF-alpha), alpha/beta interferon (IFN-alpha/beta), and IFN-like interleukin-28A/B (IFN-lambda2/3) and IL-29 (IFN-lambda1) gene expression in human monocyte-derived myeloid DCs (mDC). The ability of influenza A virus to induce mDC maturation or enhance the expression of TNF-alpha, IFN-alpha/beta, interleukin-28 (IL-28), and IL-29 genes was limited, whereas Sendai virus efficiently induced mDC maturation and enhanced cytokine gene expression. Influenza A virus-induced expression of TNF-alpha, IFN-alpha, IFN-beta, IL-28, and IL-29 genes was, however, dramatically enhanced when cells were pretreated with IFN-alpha. IFN-alpha priming led to increased expression of Toll-like receptor 3 (TLR3), TLR7, TLR8, MyD88, TRIF, and IFN regulatory factor 7 (IRF7) genes and enhanced influenza-induced phosphorylation and DNA binding of IRF3. Influenza A virus also enhanced the binding of NF-kappaB to the respective NF-kappaB elements of the promoters of IFN-beta and IL-29 genes. In mDC IL-29 induced MxA protein expression and possessed antiviral activity against influenza A virus, although this activity was lower than that of IFN-alpha or IFN-beta. Our results show that in human mDCs viruses can readily induce the expression of IL-28 and IL-29 genes whose gene products are likely to contribute to the host antiviral response.
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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".