The regulatory landscapes of human ovarian ageing
Bibliographic record
Abstract
Summary paragraph The ovary is the first organ to age in the human body, affecting both fertility and overall health in women 1-8 . However, the biological mechanisms underlying human ovarian ageing remain poorly understood. Here we performed single-nuclei multi-omics analysis of young and reproductively aged human ovaries to understand the molecular and cellular basis of ovarian ageing in humans. Our analysis reveals coordinated changes in transcriptomic output and chromatin accessibility across cell types during ageing, including elevated mTOR and MAPK signaling, decreased activity of the oxidative phosphorylation and DNA damage repair pathways, and an increased signature of cellular senescence. By constructing cell type-specific regulatory networks, we uncover enhanced activity of the transcription factor CEBPD across cell types in the aged ovary, with a corresponding significant loss of activity of most cell identity-associated transcription factors. Moreover, by performing integrative analyses of our single-nuclei multi-omics data with common genetic variants associated with age at natural menopause (ANM) from genome-wide association studies, we demonstrate a global impact of functional variants on changes in gene regulatory networks across ovarian cell types. Finally, we nominate about a dozen of functional non-coding variants, their target genes and cell types and regulatory mechanisms that underlie genetic association with ANM. This work provides a comprehensive multimodal landscape of human ovarian ageing and mechanistic insights into inherited variation of ANM.
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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 | 0.001 |
| 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 | 0.001 | 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".