Non-coding RNA-mediated endothelial-to-mesenchymal transition in human diabetic cardiomyopathy, potential regulation by DNA methylation
Bibliographic record
Abstract
Abstract Aims Diabetic cardiomyopathy (DCM) is a major complication of diabetes and a risk factor for cardiovascular disease. Endothelial dysfunction is central to DCM, and endothelial-to-mesenchymal transition (EndMT) is a key form of endothelial dysfunction in diabetes. EndMT in DCM has been well-studied in model systems and has been found to be epigenetically regulated by non-coding RNAs (ncRNAs). However, EndMT in DCM and its associated epigenetic changes need further characterization in human patients. It is also not known if ncRNAs are affected by changes in DNA methylation in DCM. This study aims to confirm in human hearts, the findings from animal and cell studies, and potentially provide novel insight into interactions between DNA methylation and ncRNAs in EndMT in DCM. Methods and results Heart tissues were collected from autopsy patients, fixed in formalin, and embedded in paraffin. Thin sections from paraffin-embedded tissues were used for histology and immunofluorescence analyses, where we confirmed that diabetic patients showed increased cardiac fibrosis that EndMT had occurred. Tissue curls from the paraffin-embedded tissues were used for RT-qPCR and methylation analyses. RT-qPCR quantitatively showed that EndMT occurs in the hearts of diabetics, and that EndMT in human hearts corresponded to changes in key ncRNAs. Methylation analysis showed that some of the EndMT-related ncRNAs were regulated by DNA promoter methylation, while others may be regulated through different epigenetic mechanisms. Conclusions We show that EndMT is a relevant pathological process in human hearts during DCM, and that its occurrence coincides with changes in relevant ncRNAs. We further find that interplay between DNA methylation and certain ncRNAs involved in the regulation of EndMT may contribute to the observed changes in ncRNA expression. These findings reinforce the role of EndMT in patients afflicted with DCM and underscore the complexities and importance of the interactions between different facets of epigenetic regulation.
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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".