Regulation of tracheal antimicrobial peptide gene expression in airway epithelial cells of cattle
Bibliographic record
Abstract
β-defensins are an important element of the mucosal innate immune response against bacterial pathogens. Tracheal antimicrobial peptide (TAP) has microbicidal activity against the bacteria that cause bovine respiratory disease, and its expression in tracheal epithelial cells is upregulated by bacterial products including lipopolysaccharide (LPS, a TLR4 agonist), Pam3CSK4 (an agonist of Toll-like receptor 2/1), and interleukin (IL)-17A. The objectives of this study were to identify the signalling pathway by which LPS, Pam3CSK4 and IL-17A induce TAP gene expression, and to determine the effect of glucocorticoid as a model of stress on this epithelial innate immune response. In primary cultures of bovine tracheal epithelial cells (bTEC), LPS, Pam3CSK4 and IL-17A each stimulated TAP gene expression. This effect was abrogated by caffeic acid phenylester (CAPE), an inhibitor of NF-κB. Similarly, western analysis showed that LPS, Pam3CSK4 and IL-17A each induced translocation of NF-κB p65 from the cytoplasm to the nucleus, but pre-treatment with CAPE inhibited this response. Finally, pre-treatment of bTEC with the glucocorticoid dexamethasone abolished the stimulatory effect of LPS, Pam3CSK4 and IL-17A on upregulation of TAP gene expression. These findings indicate that NF-κB activation is necessary for induction of TAP gene expression by LPS (a TLR4 agonist), Pam3CSK4 (a TLR2/1 agonist), or IL-17A. Furthermore, this stimulatory response is inhibited by glucocorticoid, suggesting this as one mechanism by which stress increases the risk of bacterial pneumonia. These findings have implications for understanding the pathogenesis of stress-associated bacterial pneumonia, and for developing methods to stimulate innate immune responses in the respiratory tract of cattle.
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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) | 0.001 | 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".