Toll-like receptor ligands induce the expression of interferon-gamma and interleukin-17 in chicken CD4+ T cells
Bibliographic record
Abstract
BACKGROUND: Toll-like receptors (TLRs) are evolutionarily conserved pattern recognition receptors that mediate host responses to pathogens. To date, at least 10 different TLRs have been identified in chickens including TLR2, which binds lipopeptides and other similar ligands such as Pam3CSK4, TLR3, which binds double stranded RNA as well as synthetic molecules such as poly I:C, TLR4, which binds lipopolysaccharide (LPS), and TLR21, which binds CpG DNA motifs. In mammals, TLRs have been detected on CD4+ T cells where they mediate cellular survival, proliferation and the production of cytokines. However, the TLR-mediated responses in chicken CD4+ T cells remain to be determined. As such, the objective of the present study was to elucidate the kinetics of cytokine response to several different TLR ligands in chicken CD4+ T cells. RESULTS: The results suggest that these cells express TLRs 2, 3, 4 and 21 at the transcript level, and treatment with ligands for these TLRs significantly influenced the expression of the cytokines interferon (IFN)-γ and interleukin (IL)-17, but not IL-4, IL-10 and IL-13. Specifically, treatment with Pam3CSK4, poly I:C and LPS up-regulated IFN-γ transcripts, while CpG ODN significantly down-regulated them. In contrast, at least one dose of each of the TLR ligands, except for Pam3CSK4, significantly down-regulated IL-17 transcripts. CONCLUSION: Chicken CD4+ T cells respond to ligands for TLRs 2, 3, 4 and 21 by up-regulating or down-regulating cytokine transcripts. Future studies may consider exploring how these TLR ligands may modulate other effector functions in chicken CD4+ T cells, as well as in other T cell subsets such as CD8+ T cells.
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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 | 0.001 |
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".