Comparison of methylation profiles in human blood and lung tissue identifies tissue specific CpG methylation sites
Bibliographic record
Abstract
Differences in methylation may contribute to the etiology of asthma, COPD and other lung related traits. As methylation patterns may be tissue specific we evaluated tissue specific methylation (TSM) patterns between blood and lung tissue. Methods: Using the Illumina Infinium HumanMethylation450K bead chip array, 36 paired samples (blood and lung tissue from the same individual) and 22 lung only samples, methylation levels were assessed using beta values. Correlations between beta values were examined using principal components, heatmaps, and a mixture model. Results: 49,376 CpG sites demonstrated tissue specific methylation. 31 CpG sites demonstrated extreme differences with complete methylation in lung tissue but no methylation in blood tissue. The remaining 49,345 CpG sites demonstrated more modest differences. Next we examined genes associated with lung related diseases. At ORMDL3 the patterns vary similar but there is poor concordance at sites within the TSLP gene (figure 1), this can be better seen in figure 2. Conclusion: We have identified 49,376 CpG sites with tissue specific methyation, including CpG sites within TSLP.
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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 | 0.001 |
| 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) | 0.002 | 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".