TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it