Th17/Treg ratio derived using DNA methylation analysis discriminates allergen-induced early from dual asthmatic responses
Bibliographic record
Abstract
Atopic allergic asthmatic individuals experience acute bronchoconstriction (early response) upon allergen exposure. Several hours after the initial exposure, some individuals exhibit a chronic late phase (dual responders, DRs) whereas others do not (early responders, ERs). The purpose of this study is to determine changes in Th17 and regulatory T (Treg) cell numbers and their associated gene expression profiles in whole blood between allergen-induced ERs and DRs. 14 participants with mild, atopic asthma (8 ERs and 6 DRs) underwent a cat allergen inhalation challenge as part of the AllerGen Clinical Investigator Collaborative. Whole blood was collected immediately prior to challenge (pre) and 2 hours post-challenge. DNA methylation analysis was used to measure the frequency of Th17, Treg, B and T cells (Epiontis, Germany). Whole blood transcriptome profiling was performed using Affymetrix GeneChip® Human Gene 1.0 ST Arrays. Statistical analysis was performed using R. Sum of the T cell and B cell frequencies obtained using the methylation assays strongly correlated (r = 0.95) with the lymphocyte frequency obtained using a hematolyzer. Allergen inhalation did not significantly (p>0.05) change Th17, Treg, B and T cell counts between ERs and DRs. However, the Th17/Treg ratio was significantly (p=0.03) different between ERs and DRs post challenge. 199 genes positively correlated with Th17 cells at an FDR of 5%. 463 genes positively correlated with Treg cells at an FDR of 5%. Th17 genes were inversely correlated with Treg genes. Th17/Treg ratio derived using DNA methylation analysis discriminates allergen-induced early from dual asthmatic responses. The inverse correlation between Th17 genes and Treg genes may be indicative of the inflammatory or suppressive phenotypes of these cells.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".