Novel circRNA-miRNA-mRNA networks regulated by maternal exercise in fetal hearts of pregestational diabetes
Bibliographic record
Abstract
BACKGROUND: Maternal exercise lowers the incidence of congenital heart defects (CHDs) induced by pregestational diabetes. However, the molecular mechanisms underlying the beneficial effects of maternal exercise remain unclear. The present study aimed to identify circular RNA (circRNA), microRNA (miRNA) and mRNA networks that are regulated by maternal exercise in fetal hearts of pregestational diabetes. METHODS: Pregestational diabetes was induced in adult C57BL/6 female mice by streptozotocin. The expression profiles of circRNAs, miRNAs and mRNAs in E10.5 fetal hearts of offspring of control and diabetic mothers with or without exercise were analyzed using next generation sequencing. circRNA-miRNA-mRNA networks in fetal hearts were mapped and key candidate transcripts were verified by qPCR analysis. RESULTS: Pregestational diabetes dysregulated the expression of 206 circRNAs, 66 miRNAs and 391 mRNAs in fetal hearts. Maternal exercise differentially regulated 188 circRNAs, 57 miRNAs and 506 mRNAs in fetal hearts of offspring of pregestational diabetes. A total of 5 circRNAs, 12 miRNAs, and 28 mRNAs were incorporated into a final maternal exercise-associated regulatory network in fetal hearts of offspring of maternal diabetes. Notably, maternal exercise normalized the dysregulated circ_0003226/circ_0015638/miR-351-5p and circ_0002768/miR-3102-3p.2-3p pairs in fetal hearts of pregestational diabetes. CONCLUSION: Maternal exercise reverses the dysregulated circ_0003226/circ_0015638/miR-351-5p and circ_0002768/miR-3102-3p.2-3p pairs, and partially normalizes circRNA, miRNA, and mRNA expression profiles in fetal hearts of pregestational diabetes. These findings shed new light on the potential mechanisms of the beneficial effects of maternal exercise on the developing heart in diabetic pregnancies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".