Related Experiment Video
Updated: May 27, 2025
Cell-Specific Paired Interrogation of the Mouse Ovarian Epigenome and Transcriptome
Published on: February 24, 2023
Harnessing omics data for drug discovery and development in ovarian aging
Fengyu Zhang1,2, Ming Zhu2, Yi Chen2
1Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai, China.
Background:
Ovarian aging occurs earlier than the aging of many other organs and has a lasting impact on women's overall health and well-being. However, effective interventions to slow ovarian aging remain limited, primarily due to an incomplete understanding of the underlying molecular mechanisms and drug targets. Recent advances in omics data resources, combined with innovative computational tools, are offering deeper insight into the molecular complexities of ovarian aging, paving the way for new opportunities in drug discovery and development.
Objective And Rationale:
This review aims to synthesize the expanding multi-omics data, spanning genome, transcriptome, proteome, metabolome, and microbiome, related to ovarian aging, from both tissue-level and single-cell perspectives. We will specially explore how the analysis of these emerging omics datasets can be leveraged to identify novel drug targets and guide therapeutic strategies for slowing and reversing ovarian aging.
Search Methods:
We conducted a comprehensive literature search in the PubMed database using a range of relevant keywords: ovarian aging, age at natural menopause, premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), genomics, transcriptomics, epigenomics, DNA methylation, RNA modification, histone modification, proteomics, metabolomics, lipidomics, microbiome, single-cell, genome-wide association studies (GWAS), whole-exome sequencing, phenome-wide association studies (PheWAS), Mendelian randomization (MR), epigenetic target, drug target, machine learning, artificial intelligence (AI), deep learning, and multi-omics. The search was restricted to English-language articles published up to September 2024.
Outcomes:
Multi-omics studies have uncovered key mechanisms driving ovarian aging, including DNA damage and repair deficiencies, inflammatory and immune responses, mitochondrial dysfunction, and cell death. By integrating multi-omics data, researchers can identify critical regulatory factors and mechanisms across various biological levels, leading to the discovery of potential drug targets. Notable examples include genetic targets such as BRCA2 and TERT, epigenetic targets like Tet and FTO, metabolic targets such as sirtuins and CD38+, protein targets like BIN2 and PDGF-BB, and transcription factors such as FOXP1.
Wider Implications:
The advent of cutting-edge omics technologies, especially single-cell technologies and spatial transcriptomics, has provided valuable insights for guiding treatment decisions and has become a powerful tool in drug discovery aimed at mitigating or reversing ovarian aging. As technology advances, the integration of single-cell multi-omics data with AI models holds the potential to more accurately predict candidate drug targets. This convergence offers promising new avenues for personalized medicine and precision therapies, paving the way for tailored interventions in ovarian aging.
Registration Number:
Not applicable.
Insights
Ovarian aging research is advancing with multi-omics data, identifying key molecular mechanisms and potential drug targets for interventions. This progress offers new avenues for personalized medicine to slow or reverse ovarian aging.
Area of Science:
- Reproductive biology and aging research.
- Genomics, transcriptomics, proteomics, metabolomics, and microbiome studies.
- Computational biology and bioinformatics applications.
Background:
- Ovarian aging significantly impacts women's health but lacks effective interventions due to incomplete understanding of molecular mechanisms.
- Recent advances in omics data and computational tools provide deeper insights into ovarian aging complexities.
- This facilitates new opportunities for drug discovery and development targeting ovarian aging.
Purpose of the Study:
- To synthesize multi-omics data (genome, transcriptome, proteome, metabolome, microbiome) related to ovarian aging.
- To explore how omics datasets can identify novel drug targets for slowing or reversing ovarian aging.
- To integrate tissue-level and single-cell perspectives for a comprehensive understanding.
Main Methods:
- Comprehensive literature search in PubMed up to September 2024.
- Keywords included ovarian aging, menopause, POI, DOR, various omics fields, and computational methods (AI, machine learning).
- Inclusion of GWAS, WES, PheWAS, and Mendelian randomization studies.
Main Results:
- Multi-omics studies identified key ovarian aging mechanisms: DNA damage, inflammation, mitochondrial dysfunction, and cell death.
- Integration of multi-omics data revealed critical regulatory factors and potential drug targets.
- Examples of identified targets include genetic (BRCA2, TERT), epigenetic (Tet, FTO), metabolic (sirtuins, CD38+), protein (BIN2, PDGF-BB), and transcription factors (FOXP1).
Conclusions:
- Advanced omics technologies, particularly single-cell and spatial transcriptomics, offer valuable insights for treatment decisions and drug discovery.
- Integration of single-cell multi-omics data with AI models can enhance prediction of candidate drug targets.
- This convergence paves the way for personalized medicine and precision therapies for ovarian aging.
