Lung Microbiome Is Influenced by the Environment and Asthmatic Status in an Equine Model of Asthma
Bibliographic record
Abstract
There is evidence that the lung microbiome differs between patients with asthma and healthy humans, but the effect of environmental conditions and medication is unknown and difficult to study. Equine asthma is a naturally occurring chronic airway disease characterized by reversible airway inflammation and bronchoconstriction upon exposure to inhaled antigens. In the present study, we evaluated the effect that environmental conditions and disease status have on pulmonary, nasal, and oral microbiomes. Six asthmatic and six healthy horses were studied while at pasture ("low antigen exposure"), as well as when being housed indoors and fed good-quality hay ("moderate exposure") and poor-quality hay ("high exposure"). At each time point, lung function was recorded; BAL, oral, and nasal rinses were collected; and 16S rRNA gene sequencing was performed. Asthmatic horses developed airway obstruction and inflammation under moderate and high antigen exposure conditions, whereas nonasthmatic horses showed mild inflammation under high antigen exposure, without bronchoconstriction. Lung, oral, and nasal communities clustered by environmental condition, but only lung communities were different between healthy and asthmatic horses. The association between asthma and lung microbiome was strongest in horses under moderate antigen exposure. Pulmonary, oral, and nasal microbiomes are influenced by environmental conditions, but only the pulmonary microbiome differs between horses with and without asthma. This difference, seen mainly when airway inflammation was present in horses with asthma but not in control animals, suggests that the altered lung microbiome in asthma might not be inherent but coincident with inflammation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".