A large lung gene expression study identifying fibulin-5 as a novel player in tissue repair in COPD
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive, incurable lung disease characterised by abnormal tissue repair causing emphysema and small airways fibrosis. Since current therapy cannot modify this abnormal repair, it is crucial to unravel its underlying molecular mechanisms. Unbiased analysis of genome-wide gene expression profiles in lung tissue provides a powerful tool to investigate this. METHODS: We performed genome-wide gene expression profiling in 581 lung tissue samples from current and ex-smokers with (n=311) and without COPD (n=270). Subsequently, quantitative PCR, western blot and immunohistochemical analyses were performed to validate our main findings. RESULTS: 112 genes were found to be upregulated in patients with COPD compared with controls, whereas 61 genes were downregulated. Among the most upregulated genes were fibulin-5 (FBLN5), elastin (ELN), latent transforming growth factor β binding protein 2 (LTBP2) and microfibrillar associated protein 4 (MFAP4), all implicated in elastogenesis. Our gene expression findings were validated at mRNA and protein level. We demonstrated higher ELN gene expression in COPD lung tissue and similar trends for FBLN5 and MFAP4, and negative correlations with lung function. FBLN5 protein levels were increased in COPD lung tissue and cleaved, possibly non-functional FBLN5 protein was present. Strong coexpression of FBLN5, ELN, LTBP2 and MFAP4 in lung tissue and in silico analysis indicated cofunctionality of these genes. Finally, colocalisation of FBLN5, MFAP4 and LTBP2 with elastic fibres was demonstrated in lung tissue. CONCLUSIONS: We identified a clear gene signature for elastogenesis in COPD and propose FBLN5 as a novel player in tissue repair in COPD.
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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 | 0.001 |
| 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".