Transcriptomic profiling of canine atrial fibrillation models after one week of sustained arrhythmia
Bibliographic record
Abstract
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 (miRNAseq and mRNAseq) changes in the left atria of two robust canine AF-models after one 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 (e.g. extracellular matrix structure organization) but also many novel pathways (e.g. muscle structure development, striated muscle cell differentiation) that may play a role in tissue remodeling and/or cellular transdifferentiation. Of interest, we found dysregulation of a cluster of non-coding RNAs, including many microRNAs but also the MEG3 long non-coding 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".