Genetic Influences on Asthma Susceptibility in the Developing Lung
Bibliographic record
Abstract
Asthma is the leading serious pediatric chronic illness in the United States, affecting 7.1 million children. The prevalence of asthma in children under 4 years of age has increased dramatically in the last 2 decades. Existing evidence suggests that this increase in prevalence derives from early environmental exposures acting on a pre-existing asthma-susceptible genotype. We studied the origins of asthma susceptibility in developing lung in rat strains that model the distinct phenotypes of airway hyperresponsiveness (Fisher rats) and atopy (brown Norway [BN] rats). Postnatal BN rat lungs showed increased epithelial proliferation and tracheal goblet cell hyperplasia. Fisher pups showed increased lung resistance at age 2 weeks, with elevated neutrophils throughout the postnatal period. Diverse transcriptomic signatures characterized the distinct respiratory phenotypes of developing lung in both rat models. Linear regression across age and strain identified developmental variation in expression of 1,376 genes, and confirmed both strain and temporal regulation of lung gene expression. Biological processes that were heavily represented included growth and development (including the T Box 1 transcription factor [Tbx5], the epidermal growth factor receptor [Egfr], the transforming growth factor beta-1-induced transcript 1 [Tgfbr1i1]), extracellular matrix and cell adhesion (including collagen and integrin genes), and immune function (including lymphocyte antigen 6 (Ly6) subunits, IL-17b, Toll-interacting protein, and Ficolin B). Genes validated by quantitative RT-PCR and protein analysis included collagen III alpha 1 Col3a1, Ly6b, glucocorticoid receptor and Importin-13 (specific to the BN rat lung), and Serpina1 and Ficolin B (specific to the Fisher lung). Innate differences in patterns of gene expression in developing lung that contribute to individual variation in respiratory phenotype are likely to contribute to the pathogenesis of asthma.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".