Chromatin landscape and enhancer-gene interaction differences between three cardiac cell types
Bibliographic record
Abstract
ABSTRACT Genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNP) associated with a specific traits and diseases, however, uncovering the true disease-relevant SNPs remains challenging. One limitation for prioritizing true disease-relevant SNPs from GWAS is that most of the identified SNPs are non-coding, making it difficult to unravel their mechanism of action. Nevertheless, mapping non-coding SNPs to enhancers is a validated approach to link SNPs to their target genes through the analysis of enhancer-gene interactions (EGI) and thus provide insight into their mechanism of action. While previous studies linking cardiac disease-relevant SNPs to enhancers and their target genes have focused on the principal cardiac cell type, cardiomyocytes (CMs), the analysis of other non-CM cell types has been largely ignored and has only gained attention recently. We hypothesize that characterizing cell-type-specific enhancer-gene interactions (EGIs) for these non-CMs, namely cardiac fibroblasts (CFs), endothelial cells (ECs), and smooth muscle cells (SMCs), followed by mapping cardiac-disease-associated non-coding SNPs to those enhancers will identify novel disease-relevant genes and provide insights for future mechanistic research. To identify the landscape of cell-type-specific EGIs in these cardiac cells, we have employed the activity-by-Contact (ABC) model. It integrates assay for transposase-accessible chromatin sequencing (ATAC-seq), H3K27ac chromatin immunoprecipitation with sequencing (ChIP-seq), and high-throughput chromosome conformation capture with H3K27ac immunoprecipitation (H3K27ac HiChIP) data to identify EGIs. We have identified the landscape of cell-type-specific EGIs in these cardiac cells. Furthermore, a higher similarity of the chromatin accessibility profile (ATAC-seq) between CF and SMC, compared to CF and EC, and SMC and EC was observed. Finally, overlapping identified EGIs with cardiac-disease-associated non-coding variants has allowed the identification of a QT-interval-associated SNP that is mapped to the enhancer region of an EC-specific EGI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 teacher head, 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".