The DNA repair transcriptome in severe COPD
Bibliographic record
Abstract
Inadequate DNA repair is implicated in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the mechanisms that underlie inadequate DNA repair in COPD are poorly understood. We applied an integrative genomic approach to identify DNA repair genes and pathways associated with COPD severity.We measured the transcriptomic changes of 419 genes involved in DNA repair and DNA damage tolerance that occur with severe COPD in three independent cohorts (n=1129). Differentially expressed genes were confirmed with RNA sequencing and used for patient clustering. Clinical and genome-wide transcriptomic differences were assessed following cluster identification. We complemented this analysis by performing gene set enrichment analysis, Z-score and weighted gene correlation network analysis to identify transcriptomic patterns of DNA repair pathways associated with clinical measurements of COPD severity.We found 15 genes involved in DNA repair and DNA damage tolerance to be differentially expressed in severe COPD. K-means clustering of COPD cases based on this 15-gene signature identified three patient clusters with significant differences in clinical characteristics and global transcriptomic profiles. Increasing COPD severity was associated with downregulation of the nucleotide excision repair pathway.Systematic analysis of the lung tissue transcriptome of individuals with severe COPD identified DNA repair responses associated with disease severity that may underlie COPD pathogenesis.
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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 | 0.000 | 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) | 0.000 | 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".