Mutually exclusive Th2 and Th17 bronchial gene expression signatures are associated with eosinophilia in asthma
Bibliographic record
Abstract
*JA and PB are co-senior authors Background: Th2 and Th17 cytokine-dependent inflammatory pathways are potential therapeutic targets in severe asthma. The extent of the activity of these two pathways in individual patients is unclear. Objective: To develop gene expression signatures of Th2 and Th17 cytokine activity in asthmatic airways and assess their relationships to airway pathophysiology, clinical disease expression, and biomarkers of cytokine activity. Methods: Th2 and Th17 cytokine gene expression metrics were developed in vitro in primary human bronchial epithelial cells and used to interrogate the activity of these pathways in endobronchial tissue from patients with clinically well-characterised asthma across a spectrum of severity (n=51). Results: We identified three clusters of cytokine gene expression in asthmatic airways: Th2-high, Th17-high and Th2/17-low. Th2-high and Th17-high patterns were mutually exclusive in individual patients, and were inversely correlated. However Th2 and Th17 signatures were both associated with airway eosinophilia. The Th17 signature was only detected in patients taking inhaled corticosteroids (ICS) while Th2-high and Th2/17-low patterns were present in ICS users and non-users. Only the Th2/17-low signature was associated with non-eosinophilic pathology. Conclusion: Patients with severe asthma may have either a Th2-high, Th17-high or Th2/17-low pattern of airway gene expression. Low levels of blood and sputum eosinophilia predict a Th2/17-low molecular phenotype, but all three molecular phenotypes may be associated with eosinophilic disease, which may have implications for therapeutic strategies targeting inflammatory cytokines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".