The genetic and epigenetic landscapes of the epithelium in asthma
Bibliographic record
Abstract
Asthma is a global health problem with increasing prevalence. The airway epithelium is the initial barrier against inhaled noxious agents or aeroallergens. In asthma, the airway epithelium suffers from structural and functional abnormalities and as such, is more susceptible to normally innocuous environmental stimuli. The epithelial structural and functional impairments are now recognised as a significant contributing factor to asthma pathogenesis. Both genetic and environmental risk factors play important roles in the development of asthma with an increasing number of genes associated with asthma susceptibility being expressed in airway epithelium. Epigenetic factors that regulate airway epithelial structure and function are also an attractive area for assessment of susceptibility to asthma. In this review we provide a comprehensive discussion on genetic factors; from using linkage designs and candidate gene association studies to genome-wide association studies and whole genome sequencing, and epigenetic factors; DNA methylation, histone modifications, and non-coding RNAs (especially microRNAs), in airway epithelial cells that are functionally associated with asthma pathogenesis. Our aims were to introduce potential predictors or therapeutic targets for asthma in airway epithelium. Overall, we found very small overlap in asthma susceptibility genes identified with different technologies. Some potential biomarkers are IRAKM, PCDH1, ORMDL3/GSDMB, IL-33, CDHR3 and CST1 in airway epithelial cells. Recent studies on epigenetic regulatory factors have further provided novel insights to the field, particularly their effect on regulation of some of the asthma susceptibility genes (e.g. methylation of ADAM33). Among the epigenetic regulatory mechanisms, microRNA networks have been shown to regulate a major portion of post-transcriptional gene regulation. Particularly, miR-19a may have some therapeutic potential.
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.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".