Expression of TLR9 in mouse and human lung and its role in lung inflammation in chicken barn air exposure
Bibliographic record
Abstract
Workers exposed to intensive animal housing facility air show signs of occupationally‐related lung dysfunction. These respiratory symptoms are associated with endotoxin, but within these environments gram negative bacteria may constitute only a small portion of the microorganisms present. In contrast, unmethylated DNA can be found in all bacteria, some viruses, and mould. Immune responses to this unmethylated DNA are mediated through the TLR9 receptor. Therefore, we sought to test expression of TLR9 in mouse as well as human lungs, to see if mice would be an appropriate model for an exposure study. We hypothesized that the immune response seen in the lungs of mice exposed to barn air would be altered in a TLR9‐deficient animal towards a less inflammatory state. We used immunohistology, immuno‐electron microscopy and in situ hybridization to show expression of TLR9 on bronchial epithelium, alveolar septal cells, and alveolar macrophage of mouse and human lungs. Having established similar receptor expression, a barn exposure study was done using a TLR9‐deficient mouse model. Mice were exposed to chicken barn air for 8 hours/day for 1, 5, or 20 days. Examination of bronchiolar lavage and serum against a panel of cytokines (IL‐1β, IL‐6, IL‐10, IL‐12, TNF‐α, and IFN‐γ) showed no significant differences after one day exposure. TNF‐α (p=0.06) levels in TLR9‐deficient mice were reduced in blood and lavage fluids after 5 days, and somewhat reduced at 20 days of exposure (p=0.14), while IFN‐γ was also reduced at 5 days(p=0.06) and remained reduced after 20 days (p=0.05). A reduction in lung neutrophils at 20 days was also seen. Our data shows similar expression patterns of TLR9 in mouse and human lungs, and that barn dust DNA may contribute to inflammation induced following exposure to chicken barn air. Grant Funding Source : NSERC
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".