Single-nucleus multi-omics implicates androgen receptor signaling in cardiomyocytes and NR4A1 regulation in fibroblasts during atrial fibrillation
Bibliographic record
Abstract
The dysregulation of gene expression programs in the human atria during persistent atrial fibrillation (AF) is not completely understood. Here, we reanalyze bulk RNA-sequencing datasets from two studies (N = 242) and identified 755 differentially expressed genes in left atrial appendages of individuals with persistent AF and non-AF controls. We combined the bulk RNA-sequencing differentially expressed genes with a left atrial appendage single-nucleus multi-omics dataset to assign genes to specific atrial cell types. We found noncoding genes at the IFNG locus (LINC01479, IFNG-AS1) strongly dysregulated in cardiomyocytes. We defined a gene expression signature potentially driven by androgen receptor signaling in cardiomyocytes from individuals with AF. Cell-type-specific gene expression modules suggested an increase in T cell and a decrease in adipocyte and neuronal cell gene expression in AF. Lastly, we showed that reducing NR4A1 expression, a marker of a poorly characterized human atrial fibroblast subtype, fibroblast activation markers, extracellular matrix remodeling and cell proliferation decreased.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".