The relation between age and airway epithelial barrier function
Bibliographic record
Abstract
BACKGROUND: The prevalence of age-associated diseases, such as chronic obstructive pulmonary disease (COPD), is increasing as the average life expectancy increases around the world. We previously identified a gene signature for ageing in the human lung which included genes involved in apical and tight junction assembly, suggesting a role for airway epithelial barrier dysfunction with ageing. AIM: To investigate the association between genes involved in epithelial barrier function and age both in silico and in vitro in the airway epithelium. METHODS: We curated a gene signature of 274 genes for epithelial barrier function and tested the association with age in two independent cohorts of bronchial brushings from healthy individuals with no respiratory disease, using linear regression analysis (FDR < 0.05). Protein-protein interactions were identified using STRING©. The barrier function of primary bronchial epithelial cells at air-liquid interface and CRISPR-Cas9-induced knock-down of target genes in human bronchial 16HBE14o-cells was assessed using Trans epithelial resistance (TER) measurement and Electric cell-surface impedance sensing (ECIS) respectively. RESULTS: In bronchial brushings, we found 55 genes involved in barrier function to be significantly associated with age (FDR < 0.05). EPCAM was most significantly associated with increasing age and TRPV4 with decreasing age. Protein interaction analysis identified CDH1, that was negatively associated with higher age, as potential key regulator of age-related epithelial barrier function changes. In vitro, barrier function was lower in bronchial epithelial cells from subjects > 45 years of age and significantly reduced in CDH1-deficient 16HBE14o-cells. CONCLUSION: The significant association between genes involved in epithelial barrier function and age, supported by functional studies in vitro, suggest a role for epithelial barrier dysfunction in age-related airway disease.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".