Comparative transcriptomics in human COPD reveals dysregulated genes uniquely expressed in ferrets
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive lung disease with poor treatment options. However, most mouse models of COPD produce a primarily emphysematous disease not recapitulating clinically meaningful COPD features like chronic bronchitis. METHODS: Wild-type ferrets (Mustela putorius furo) were divided randomly into two groups: whole body cigarette smoke exposure and air controls. Ferrets were exposed to smoke from 1R6F research cigarettes, twice daily for six months. RNA-sequencing was performed on RNA isolated from lung tissue. Comparative transcriptomics analyses of COPD in ferrets, mice, and humans were done to find the uniquely expressed genes. Further, Real-time PCR was performed to confirmed RNA-Seq data on multiple selected genes. RESULTS: RNA-sequence analysis identified 420 differentially expressed genes (DEGs) that were associated with the development of COPD in ferrets. By comparative analysis, we identified 25 DEGs that are uniquely expressed in ferrets and humans, but not mice. Among DEGs, a number were related to mucociliary clearance (NEK-6, HAS1, and KL), while others have been correlated with abnormal lung function (IL-18), inflammation (TREM1, CTSB), or oxidative stress (SRX1, AHRR). Multiple cellular pathways were aberrantly altered in the COPD ferret model, including pathways associated with COPD pathogenesis in humans. Validation of these selected unique DEGs using real-time PCR demonstrated > absolute 2-fold changes in mRNA versus air controls, consistent with RNA-seq analysis. CONCLUSION: Cigarette smoke-induced COPD in ferrets modulates gene expression consistent with human COPD and suggests that the ferret model may be uniquely well suited for the study of aspects of the 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 | 0.000 | 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".