# Utilizing spatially-informed cell-specific multicellular genome-scale metabolic models to unveil the mechanisms that regulate the decline of the ovarian reserve

> **NIH NIH R21** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2024 · $625,898

## Abstract

PROJECT SUMMARY
This proposal seeks to understand and predict the onset and duration of the menopause transition using a
Systems Metabolic approach. Menopause, which is the complete cessation of menstruation, increases the risks
of cardiovascular disorders, osteoporosis, depression, and cognitive decline. The menopause transition,
occurring roughly 3-5 years before menopause, in same cases it can last up to 10 years, is marked by reduced
quality of life due to symptoms like hot flashes, sleep problems, migraines, lack of concentration, and irritability.
Current clinical tools such as Anti-Mullerian Hormone (AMH) or Follicle Stimulating Hormone (FSH) can detect
whether the menopause transition has already started but cannot estimate its commencement or duration.
Identifying better biomarkers is challenging due to the complex spatial and temporal heterogeneity of the ovary,
compounded by the presence of different cell types in the ovary such as somatic (e.g., estrogen producers),
stromal, immune, epithelial, and endothelial cells. To understand the role of ovarian spatiotemporal heterogeneity
in the menopause transition and the decline of reproductive potential, a systems approach is needed to consider
spatial and age-dependent inter- and intra-cellular signaling and metabolic communication within the ovary.
Genome-scale metabolic models (GMMs) are network-based systems approaches that have been used to study
inter- and intra-cellular metabolic communication in the ovary. While current ovarian GMMs are cell-specific and
multicellular, they have not accounted yet for cell location within the ovary or explored ovarian aging, both crucial
for understanding the menopause transition. We plan to address these limitations by generating spatially-
informed cell-specific multicellular GMMs) to identify ovarian-produced metabolites that can be secreted into
circulation and are significantly associated with the state of the menopause transition, and hence could serve as
novel biomarkers of reproductive potential its rate of decline. Our long-term goal is to create a platform for early
prediction of menopause transition onset and duration to reduce the risk of menopause-related diseases. Our
overarching hypothesis is that the integration of dynamic multi-omics data (single-cell and spatial transcriptomics
and non-targeted metabolomics) with prior metabolomic knowledge encoded into GMMs could serve to identify
novel metabolic markers of reproductive potential and its rate of decline. Test this, we aim to develop spatially-
informed cell-specific multicellular GMMs using publicly and newly collected single-cell and spatial
transcriptomics data from prepubertal (3 weeks) to reproductive aged mice (18 months); and identify ovarian-
synthesized metabolites measurable in circulation and significantly associated with reproduction potential and
its rate of decline. Success in this proposal could identify minimally-invasive biomarkers that can prospectively
predict the...

## Key facts

- **NIH application ID:** 10950490
- **Project number:** 1R21AG088610-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Beatriz Penalver Bernabe
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $625,898
- **Award type:** 1
- **Project period:** 2024-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10950490

## Citation

> US National Institutes of Health, RePORTER application 10950490, Utilizing spatially-informed cell-specific multicellular genome-scale metabolic models to unveil the mechanisms that regulate the decline of the ovarian reserve (1R21AG088610-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10950490. Licensed CC0.

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