Systems genetics approaches model the heritable architecture of polyendocrine metabolic ovarian syndrome
Christy M. Nguyen, Leandro M. Velez, Youngseo Cheon, Cimone L. Jackson, Casey D. Johnson, Ian Tamburini, Mingqi Zhou, Erik Alvstad, Isoo Yoon, Farheen Dustagheer, Marie Li, Tvisha Gujjarlapudi, Kaitlene Ofilan, Neha Mishra, Evan G. Williams, Danica Kwan, Carlos H. Viesi, Naveena Ujagar, David G. Ashbrook, Alistair Senior, Marin E. Nelson, Nicholas R. Pannunzio, Selma Masri, Evgeny Z. Kvon, Grant MacGregor, Cholsoon Jang, Vittorio Sebastiano, Minji Byun, Changrui Xiao, Alexander S. Kauffman, Robert W. Williams, David E. James, Ivan Marazzi, Dequina Nicholas, Marcus Seldin
Christy M. Nguyen, Leandro M. Velez, Youngseo Cheon, Cimone L. Jackson, Casey D. Johnson, Ian Tamburini, Mingqi Zhou, Erik Alvstad, Isoo Yoon, Farheen Dustagheer, Marie Li, Tvisha Gujjarlapudi, Kaitlene Ofilan, Neha Mishra, Evan G. Williams, Danica Kwan, Carlos H. Viesi, Naveena Ujagar, David G. Ashbrook, Alistair Senior, Marin E. Nelson, Nicholas R. Pannunzio, Selma Masri, Evgeny Z. Kvon, Grant MacGregor, Cholsoon Jang, Vittorio Sebastiano, Minji Byun, Changrui Xiao, Alexander S. Kauffman, Robert W. Williams, David E. James, Ivan Marazzi, Dequina Nicholas, Marcus Seldin
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Research Article Endocrinology Metabolism Reproductive biology

Systems genetics approaches model the heritable architecture of polyendocrine metabolic ovarian syndrome

Abstract

Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is the most common endocrine disorder in women and is closely associated with complex diseases such as cardiovascular disease and type 2 diabetes. However, the mechanistic links between PMOS and its comorbidities remain poorly understood. Here, we present an integrative systems genetics platform that leverages genetic diversity in both mice and humans to dissect the drivers of PMOS and its associated complications. This framework uncovered conserved genetic and environmental factors underlying PMOS, identified susceptible cell types and organs, and elucidated mechanisms linking PMOS to subsequent pathologies. For instance, we showed that increased ovarian area contributes to both PMOS susceptibility and ovarian cancer progression, while specific ovary–heart signaling circuits modulate cardiac function with aging. We further identified ovarian SF3B1-mediated alternative splicing as a key mechanistic link between PMOS and metabolic traits. Pharmacologic inhibition of SF3B1 in mice reduced circulating testosterone, insulin, and glucose levels as well as fat mass expansion. Transcriptomics analysis of ovaries from mice and experiments using human cell lines localized these effects to exon skipping events in granulosa cells. Together, this study offers a mechanistic framework for modeling the diversity of PMOS pathologies and uncovers SF3B1-mediated splicing as a link between ovary function and systemic metabolism.

Authors

Christy M. Nguyen, Leandro M. Velez, Youngseo Cheon, Cimone L. Jackson, Casey D. Johnson, Ian Tamburini, Mingqi Zhou, Erik Alvstad, Isoo Yoon, Farheen Dustagheer, Marie Li, Tvisha Gujjarlapudi, Kaitlene Ofilan, Neha Mishra, Evan G. Williams, Danica Kwan, Carlos H. Viesi, Naveena Ujagar, David G. Ashbrook, Alistair Senior, Marin E. Nelson, Nicholas R. Pannunzio, Selma Masri, Evgeny Z. Kvon, Grant MacGregor, Cholsoon Jang, Vittorio Sebastiano, Minji Byun, Changrui Xiao, Alexander S. Kauffman, Robert W. Williams, David E. James, Ivan Marazzi, Dequina Nicholas, Marcus Seldin

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Figure 2

Genetic variation in ovarian morphology defines distinct cellular and molecular states with clinical relevance.

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Genetic variation in ovarian morphology defines distinct cellular and mo...
(A–H) Representative ovarian histology across 4 mouse strains, C3H/HeJ, C57BL/6J, FVB/NJ, and BALB/cJ, under control (A–D) or letrozole-induced PMOS (E–H) conditions. Stars, squares, and triangles indicate corpora lutea, antral follicles, and cysts, respectively. Scale bars: 500 μm. (I) Gene set enrichment analysis network of pathways enriched from differentially expressed ovarian genes in PMOS versus control mice. Edges represent shared genes between pathways. Node size indicates gene count, and node color indicates FDR. (J) Cell-type distribution of trait-associated genes in control and PMOS mice across ovarian morphological and hormonal traits, based on ovarian cell–type annotation. Bar colors indicate granulosa, luteal, macrophage, vascular endothelial, and thecal cell–specific transcriptional signatures. (K and L) Correlation coefficients of genes within the top enriched pathways associated with ovarian cyst number (K) or total ovarian area (L) across strains. Colors indicate FDR for each pathway, calculated from the enrichment statistics. (M and N) Gene expression signatures derived from positively correlated genes in K and L were evaluated in TCGA ovarian cancer samples. Images on the left show mean expression in tumor versus surrounding tissue and counts of deceased individuals used for survival analysis. Images on the right show Kaplan-Meier curves for patient survival, stratified by high and low expression relative to the population mean, using genes correlated with ovarian cyst number (M) or ovarian area (N). Tumor versus surrounding tissue P values were calculated using the Wilcoxon test. Survival P values were calculated using the log-rank test.