Integrated MicroRNA–mRNA Sequencing Analysis Identifies Regulators and Networks Involved in Feline Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Cardiac remodeling in feline hypertrophic cardiomyopathy (HCM) is poorly understood. To investigate underlying molecular mechanisms, we determined microRNA-mRNA interactions, regulatory networks, and upstream regulators using left ventricle (LV) and left atrium (LA) mRNA and microRNA sequencing datasets from cats with HCM and controls. Upstream regulators, molecules, and pathways associated with ischemia, inflammation, fibrosis, and cellular changes were observed in the HCM heart. In both the HCM LV and LA, TNFα, IL1β, and TGFβ were identified as upstream regulators, along with FGF23, THBS4, and FAMB177 as the top increased molecules. Relevant microRNAs included upstream regulator miR-132, enriched miR-124-3p, miR-122b-3p, miR-146-5p (HCM LV and LA), miR-370, miR-1185-5p, miR-12194-3p (HCM LV), miR-153-3p, miR-185-5p, and miR-185-3p (HCM LA). Macrophage pathways were activated, and granulocyte and agranulocyte adhesion and diapedesis were the most connected pathways. The HIF1α signaling pathway in the HCM LV, upstream regulators miR-1-3p and miR-204, and reduced miR-29 and miR-122-5p suggest cardioprotective mechanisms. Observed in the healthy heart and suspected to be involved in cardiac homeostasis were upstream regulators miR-96, inhibited WNT3A and miR-145, as well as miR-140-5p, miR-141-3p, miR-208b-3p, and miR-885-3p. This study provides further insights into the pathogenesis of HCM, and identifies the factors involved in the maintenance of a healthy LV and LA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".