S86 Integration of IPF-associated SNPs with differential DNA methylation and gene expression shows cell-type specific mechanisms in airway and parenchymal fibroblasts
Bibliographic record
Abstract
Rationale Idiopathic Pulmonary Fibrosis (IPF) is linked to over 35 genetic risk loci, but their contribution to disease mechanisms remains unclear. DNA methylation (DNAm) is a cell-type specific molecular mechanism that may mediate genetic risk by altering cell type specific gene expression (GE). We previously demonstrated widespread DNAm-associated GE alterations in IPF versus non-IPF fibroblasts derived from airways (AF) and parenchyma (PF). Here, we aimed to test the hypothesis that cell type specific GE is associated with IPF-associated SNPs, with potential modulation by DNAm. Methods DNA and RNA were isolated from AF (8 non-IPF, 8 IPF) and PF (14 non-IPF, 8 IPF) at passage 4 (AllPrep DNA/RNA Mini Kit, Qiagen). DNA was profiled using the Illumina HumanMethylation EPIC array. RNA expression was assessed using Affymetrix Human Gene 2.1ST Array. We analysed the dataset with a new focus on integrating genetic risk. Firstly, IPF associated genes linked to DNAm were overlapped with SNP associated genes. A direct link between DNAm and GE for overlapping genes was assessed via DNMT (5-aza-2′-deoxycytidine, 10−10-10−6 M) and TET (Ten-Eleven Translocation-in-C35, 5×10−7, 1×10-6, 5×10-6 and 1×10-5 M) inhibition in vitro, in PF from 3 IPF and 3 non-IPF donors. GE was assessed by qPCR. Secondly, we screened ±500 kb regions around each of the 35 IPF-associated SNPs for associated transcriptome changes, by linear modelling. Results Four genes; DSP, STMN3, FAM13A, and TERC, were identified as SNP-associated genes that overlapped with DNAm-associated differentially expressed genes. TET inhibition did not show any impact on GE. DNMT inhibition showed significant reduction in expression of FAM13A in non-IPF cells. Our analysis of ±500 kb regions around IPF-associated SNPs revealed 64 genes differentially expressed between AF and PF in IPF, highlighting cell-type specific regulatory effects of genetic risk loci. Conclusion Our integrative approach highlights an overlap between genetic risk loci and DNAm-associated GE changes in IPF. We observe fibroblast subtype-specific differences in GE near IPF-associated SNPs, though whether these effects are mediated by DNAm is to be further studied. These findings support the potential importance of epigenetic mechanisms in IPF pathogenesis and highlight the need for cell-type specific epigenetic analyses.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.013 | 0.001 |
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".