Environmental bioaerosol antigen <i>Methanosphaera stadtmanae</i> induces a TH17 inflammatory lung response via TLR4 signaling
Bibliographic record
Abstract
Abstract Bioaerosols in occupational environments are highly associated with the development of inflammatory lung diseases. The archaea specie Methanosphaera stadtmanae (MSS) is found in high concentrations in poultries, dairy farms and swine confinement buildings bioaerosols (up to 108 archaea/m3). MSS induces a strong specie-dependent inflammatory lung response in mice, characterized by T cells, eosinophils, neutrophils, and IgG production. However, the polarity of lung response induced by MSS and the mechanisms underlying this inflammatory response remain unknown. Using a mouse model, we show that MSS induced a weak TH2 (CD4+/IL-13+T cells) strong TH17 (CD4+/IL-17A+T cells) lung response, characterized by IgG1 (but not IgG2a and IgE) production. Moreover, mice did not develop airway hyperresponsiveness following MSS exposure. Interestingly, increasing MSS quantity led to a lower eosinophil count associated with a decreased TH2 response. Using transgenic mice, we found that eosinophils, mast cells and ILC2 cells are not required for the TH2 and TH17 inflammatory response to MSS. However, Tlr4 −/− mice (but not Tlr2 −/− ) had reduced airway inflammation compared to WT mice after exposure to MSS, indicating a crucial role for TLR4 activation in this specific response. Finally, heat denaturation and zymography studies suggested TLR4 activation likely occurs through MSS protein antigen recognition rather than by enzymatic activity and cell damage. We conclude that MSS induces a TH17 inflammatory lung response via TLR4 signaling.
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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 | 0.000 |
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".