Strain-dependent pulmonary gene expression profiles of a cystic fibrosis mouse model
Bibliographic record
Abstract
Cystic fibrosis (CF) lung disease severity is influenced by unknown genetic factors apart from the disease causative gene, cystic fibrosis transmembrane conductance regulator (CFTR). Previous studies have shown the C57BL/6J congenic Cftr(-/-) (B6 CF) mouse to develop a fibrotic lung disease compared with both CF mice of the BALB/c background and wild-type animals. In this report, gene expression profiling with microarrays was used to identify genes differentially expressed in the lungs of B6 and BALB CF mice compared with non-CF littermates. Seven hundred two genes or expressed sequence tags (ESTs) were identified to be differentially expressed between the B6 CF and non-CF control lungs (P < 0.05), and, by Gene Ontology classification, the B6 CF response included the cell proliferation categories of DNA metabolism and mitosis. In the response of BALB mice to nonfunctional Cftr, 943 genes/ESTs were differentially expressed compared with controls. The biological processes of apoptosis and T and B cell proliferation were prominent in the gene list of the BALB CF strain. In support of this strain difference, increased T lymphocyte infiltration was evident in the lungs of BALB CF mice, through immunohistochemical staining, compared with the lungs from both B6 CF and non-CF control mice. Four hundred forty-four genes/ESTs were differentially expressed between B6 CF and BALB CF mice (P < 0.05, fold > 2), including 56 that map to previously identified linkage intervals. These results suggest that the variable severity of CF lung disease in this mouse model is controlled by multiple genetic factors, including those of an immune response.
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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 | 0.001 |
| 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".