Airway Mucosal Host Defense Is Key to Genomic Regulation of Cystic Fibrosis Lung Disease Severity
Bibliographic record
Abstract
RATIONALE: The severity of cystic fibrosis (CF) lung disease varies widely, even for Phe508del homozygotes. Heritability studies show that more than 50% of the variability reflects non-cystic fibrosis transmembrane conductance regulator (CFTR) genetic variation; however, the full extent of the pertinent genetic variation is not known. OBJECTIVES: We sought to identify novel CF disease-modifying mechanisms using an integrated approach based on analyzing "in vivo" CF airway epithelial gene expression complemented with genome-wide association study (GWAS) data. METHODS: Nasal mucosal RNA from 134 patients with CF was used for RNA sequencing. We tested for associations of transcriptomic (gene expression) data with a quantitative phenotype of CF lung disease severity. Pathway analysis of CF GWAS data (n = 5,659 patients) was performed to identify novel pathways and assess the concordance of genomic and transcriptomic data. Association of gene expression with previously identified CF GWAS risk alleles was also tested. MEASUREMENTS AND MAIN RESULTS: Significant evidence of heritable gene expression was identified. Gene expression pathways relevant to airway mucosal host defense were significantly associated with CF lung disease severity, including viral infection, inflammation/inflammatory signaling, lipid metabolism, apoptosis, ion transport, Phe508del CFTR processing, and innate immune responses, including HLA (human leukocyte antigen) genes. Ion transport and CFTR processing pathways, as well as HLA genes, were identified across differential gene expression and GWAS signals. CONCLUSIONS: Transcriptomic analyses of CF airway epithelia, coupled to genomic (GWAS) analyses, highlight the role of heritable host defense variation in determining the pathophysiology of CF lung disease. The identification of these pathways provides opportunities to pursue targeted interventions to improve CF lung health.
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How this classification was reachedexpand
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".