TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma
Bibliographic record
Abstract
BACKGROUND: Mesenchymal fibroblasts are ubiquitous cells that maintain the extracellular matrix of organs. Within the lung, airway and parenchymal fibroblasts are crucial for lung development and are altered with disease, but it has been difficult to understand their roles due to the lack of distinct molecular markers. We studied genome-wide DNA methylation and gene expression in airway and parenchymal lung fibroblasts from healthy and asthmatic donors, to identify a robust cell marker and to determine if these cells are molecularly distinct in asthma. RESULTS: Airway (N = 8) and parenchymal (N = 15) lung fibroblasts from healthy individuals differed in the expression of 158 genes, and DNA methylation of 3936 CpGs (Bonferroni adjusted p value < 0.05). Differential DNA methylation between cell types was associated with differential expression of 42 genes, but no single DNA methylation CpG feature (location, effect size, number) defined the interaction. Replication of gene expression and DNA methylation in a second cohort identified TWIST1 gene expression, DNA methylation and protein expression as a cell marker of airway and parenchymal lung fibroblasts, with DNA methylation having 100% predictive discriminatory power. DNA methylation was differentially altered in parenchymal (112 regions) and airway fibroblasts (17 regions) with asthmatic status, with no overlap between regions. CONCLUSIONS: Differential methylation of TWIST1 is a robust cell marker of airway and parenchymal lung fibroblasts. Airway and parenchymal fibroblast DNA methylation are differentially altered in individuals with asthma, and the role of both cell types should be considered in the pathogenesis of asthma.
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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 | 0.000 | 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 | 0.000 |
| 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".