The role of IRF7 and NF-κB pathways in the induction of antiviral responses in chicken tracheal epithelial cells following exposure to TLR3 and 4 ligands
Bibliographic record
Abstract
Abstract With recent outbreaks of avian influenza viruses (AIV), there is a need for better understanding of host responses in the respiratory system to AIV in order to develop and improve prophylactic procedures for control of AIV. Toll-like receptors (TLRs) are molecules in the immune system that rapidly induce antiviral responses in the host. Activation of TLRs by their ligands leads to a variety of cellular responses, including the production of interferons (IFNs). However, there is little information about induced antiviral responses in chicken trachea following TLR ligand treatment. We cultured primary chicken tracheal epithelial cells (cTECs) to determine antiviral responses induced by TLR3 and 4 ligands. These cells were treated with polyI:C and LPS from Escherichia coli 026:B6, TLR3 and 4 ligands respectively, before AIV infection. We also used BX795 and celastrol, inhibitors of IRF7 and NF-κB pathways, respectively, in order to gain insight into the mechanisms involved in the induced antiviral responses. Our results showed that treatment of cTECs with TLR ligands reduced the replication of AIV in cTECs. Treatment of cTECs with polyI:C resulted in highest reduction of viral replication in cTECs followed by treatment of cTECs with LPS. BX795 increased AIV replication in polyI:C and LPS treated cells while celastrol increased AIV replication in polyI:C treated cells. Overall, these results provided evidence that cTECs are able to mount antiviral responses against AIV after TLR ligand stimulation. Moreover, we determined the role of IRF7 and NF-κB signalling pathways in the induction of antiviral responses. Studies are underway to examine the ability of cTECs to produce type I IFNs and the effect of other cells, such as macrophages, on cTECs.
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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) | 0.002 | 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".