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

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 rate of decline in reproductive potential and lay the groundwork for future interventions to delay early or premature menopause, alleviate symptoms, and reduce the risk of future menopause-related diseases. Our dynamic spatially-informed network-based models could be applied to study aging in other organs, e.g., brain.