Comparison of innate immune agonists for induction of tracheal antimicrobial peptide gene expression in tracheal epithelial cells of cattle
Bibliographic record
Abstract
Bovine respiratory disease is a complex of bacterial and viral infections of economic and welfare importance to the beef industry. Although tracheal antimicrobial peptide (TAP) has microbicidal activity against bacterial pathogens causing bovine respiratory disease, risk factors for bovine respiratory disease including BVDV and stress (glucocorticoids) have been shown to inhibit the induced expression of this gene. Lipopolysaccharide is known to stimulate TAP gene expression, but the maximum effect is only observed after 16 h of stimulation. The present study investigated other agonists of TAP gene expression in primary cultures of bovine tracheal epithelial cells. PCR analysis of unstimulated tracheal epithelial cells, tracheal tissue and lung tissue each showed mRNA expression for Toll-like receptors (TLRs) 1–10. Quantitative RT-PCR analysis showed that Pam3CSK4 (an agonist of TLR1/2) and interleukin (IL)-17A significantly induced TAP gene expression in tracheal epithelial cells after only 4–8 h of stimulation. Flagellin (a TLR5 agonist), lipopolysaccharide and interferon-α also had stimulatory effects, but little or no response was found with class B CpG ODN 2007 (TLR9 agonist) or lipoteichoic acid (TLR2 agonist). The use of combined agonists had little or no enhancing effect above that of single agonists. Thus, Pam3CSK4, IL-17A and lipopolysaccharide rapidly and significantly induce TAP gene expression, suggesting that these stimulatory pathways may be of value for enhancing innate immunity in feedlot cattle at times of susceptibility to disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".