DNA methylation pattern of bovine blastocysts associated with hyperinsulinemia in vitro
Bibliographic record
Abstract
Insulin functions as a regulator of metabolism and plays an important role in reproduction. Hyperinsulinemia is often observed in patients with obesity and diabetes type 2 and is known to impair fertility, but the underlying molecular mechanisms are only partly understood. Metabolic programming through epigenetic mechanisms such as DNA methylation during embryonic development can lead to health implications for the offspring later in life. Our aim was to study the potential effect of hyperinsulinemia on gene expression and DNA methylation of embryos by adding insulin (0.1 µg/ml = INS0.1 or 10 µg/ml = INS10) during in vitro oocyte maturation by using the EmbryoGENE DNA methylation array for a study of the bovine epigenome. Our results showed significant differences between blastocysts originating from insulin-treated oocytes compared with untreated control blastocysts. In total, 13,658 and 12,418 probes were differentially methylated (DM) in INS0.1 and INS10, respectively, with an overlap of 3,233 probes in the DM regions (DMR) for both insulin groups. Genes related to pathways such as lipid metabolism, growth and proliferation, mitochondrial function, and oxidative stress responses were influenced at both the epigenetic and transcriptomic levels. In addition, imprinted genes and genes with functions in the epigenetic machinery were among the DMRs. This study identified DMRs correlated to differential expression of genes involved in metabolic regulation and should help to improve our knowledge of the underlying molecular mechanisms of metabolic imbalance.
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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".