Loss of maternal EED results in postnatal overgrowth
Bibliographic record
Abstract
BACKGROUND: Investigating how epigenetic information is transmitted through the mammalian germline is the key to understanding how this information impacts on health and disease susceptibility in offspring. EED is essential for regulating the repressive histone modification, histone 3 lysine 27 tri-methylation (H3K27me3) at many developmental genes. RESULTS: In this study, we used oocyte-specific Zp3-Cre recombinase (Zp3Cre) to delete Eed specifically in mouse growing oocytes, permitting the study of EED function in oocytes and the impact of depleting EED in oocytes on outcomes in offspring. As EED deletion occurred only in growing oocytes and females were mated to normal wild type males, this model allowed the study of oocyte programming without confounding factors such as altered in utero environment. Loss of EED from growing oocytes resulted in a significant overgrowth phenotype that persisted into adult life. Significantly, this involved increased adiposity (total fat) and bone mineral density in offspring. Similar overgrowth occurs in humans with Cohen-Gibson (OMIM 617561) and Weaver (OMIM 277590) syndromes, that result from de novo germline mutations in EED or its co-factor EZH2, respectively. Consistent with a role for EZH2 in human oocytes, we demonstrate that de novo germline mutations in EZH2 occurred in the maternal germline in some cases of Weaver syndrome. However, deletion of Ezh2 in mouse oocytes resulted in a distinct phenotype compared to that resulting from oocyte-specific deletion of Eed. CONCLUSIONS: This study provides novel evidence that altering EED-dependent oocyte programming leads to compromised offspring growth and development in the next generation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".