Dataset Information

The regulatory landscapes of human ovarian ageing

ABSTRACT: The ovary is the first organ to age in the human body, affecting both fertility and overall health in women1-8. However, the biological mechanisms underlying human ovarian ageing remain poorly understood. Here we performed single-cell 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE202601 | GEO | 2023/03/12

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Thiele2013 - Ovary ovarian stroma cells The model of ovary ovarian stroma cells metabolism is derived from the community-driven global reconstruction of human metabolism (version 2.02, MODEL1109130000 ). This model is described in the article: A community-driven global reconstruction of human metabolism. Thiele I, et al . Nature Biotechnology Abstract: Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2x more reactions and ~1.7x more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. This model is hosted on BioModels Database and identified by: MODEL1310110029 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

The regulatory landscapes of human ovarian ageing

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets