Transcriptomic Profiling of Canine Atrial Fibrillation Models After One Week of Sustained Arrhythmia
Bibliographic record
Abstract
Background: Atrial fibrillation (AF), the most common sustained arrhythmia, is associated with increased morbidity, mortality, and health care costs. AF develops over many years and is often related to substantial atrial structural and electrophysiological remodeling. AF may lack symptoms at onset, and atrial biopsy samples are generally obtained in subjects with advanced disease, so it is difficult to study earlier stage pathophysiology in humans. Methods: Here, we characterized comprehensively the transcriptomic (miRNA-seq and mRNA-seq) changes in the left atria of 2 robust canine AF models after 1 week of electrically maintained AF, without or with ventricular rate control via atrioventricular node-ablation/ventricular pacing. Results: Our RNA-sequencing experiments identified thousands of genes that are differentially expressed, including a majority that have never before been implicated in AF. Gene set enrichment analyses highlighted known (eg, extracellular matrix structure organization) but also many novel pathways (eg, muscle structure development, striated muscle cell differentiation) that may play a role in tissue remodeling and cellular trans-differentiation. Of interest, we found dysregulation of a cluster of noncoding RNAs, including many microRNAs but also the MEG3 long noncoding RNA orthologue, located in the syntenic region of the imprinted human DLK1-DIO3 locus. Interestingly (in the light of other recent observations), our analysis identified gene targets of differentially expressed microRNAs at the DLK1-DIO3 locus implicating glutamate signaling in AF pathophysiology. Conclusions: Our results capture molecular events that occur at an early stage of disease development using well-characterized animal models and may, therefore, inform future studies that aim to further dissect the causes of AF in humans.
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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 | 0.000 | 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 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".