Expression of cystic fibrosis lung disease modifier genes in human airway models
Bibliographic record
Abstract
BACKGROUND: Variation in respiratory response to cystic fibrosis (CF) small molecule therapies is due in part to the contribution of CF lung disease modifier genes. Cultured human bronchial epithelia (HBE) is the gold standard respiratory model for assessing CF therapeutic efficacy but it is hard to access. Cultured human nasal epithelia (HNE) is proposed as a more accessible surrogate model but it is unknown whether the expression profile of the modifier genes are comparable between HNE and HBE which we assess here. METHODS: RNA-sequencing was conducted on paired cultured and fresh HNE and HBE (n = 71 samples) collected from 21 individuals with CF. Genome-wide gene expression was first compared between cultured and fresh cells and then between cultured HNE and HBE based on an equivalence testing procedure we implemented. The co-expression relationships of CFTR and CF lung disease modifier genes were compared between cultured HNE and HBE to determine equivalent interactions. RESULTS: The culturing process had little impact on the expression level of CF lung disease modifier genes. Over 90% of expressed genes showed significant equivalent expression level across cultured HNE and HBE (expression fold-change<2, FDR<0.1), including CFTR and CF lung disease modifier genes. The difference in co-expression relationships among these genes was not significant (p-value=0.99), suggesting their functional interactions are likely to be consistent in the two models. CONCLUSIONS: Cultured HNE recapitulates the expression profile of CF lung disease modifier genes in cultured HBE, suggesting the biological processes involving these genes are likely to be consistent across the two models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".