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 1

Systems genetics approach for modeling PMOS phenotypic diversity.

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Systems genetics approach for modeling PMOS phenotypic diversity. (A) St...
(A) Study overview illustrating integration of genetically diverse mouse models with human datasets to uncover molecular drivers of PMOS phenotypic diversity (42–48, 54, 64, 65). (B) Plasma testosterone, corpus luteum number, and percentage of mice in each estrous cycle stage at endpoint in control and letrozole-induced PMOS mice (n = 50–53 mice per group). P values were determined by unpaired 2-tailed Student’s t test. (C) Phenotypic variation across genetically diverse mouse strains, including fat mass, left ventricular ejection fraction, and glucose clearance, measured as area under the curve, in control and PMOS mice (n = 3–4 mice per strain per group). In B and C, points represent individual mice; boxes show the median and interquartile range, with whiskers extending to 1.5× the interquartile range. (D) Percentage of variance in PMOS-associated traits attributed to genetic, PMOS induction, gene-by-PMOS interaction, and residual components. (E) Mapping of mouse phenotypic traits and their direction across strains aligned to human Rotterdam criteria to define corresponding mouse models of PMOS subtypes. Arrows indicate direction of change in PMOS relative to control mice; filled arrows indicate traits included in defining each subtype, and open arrows indicate traits not included. (F) Representative mouse strains modeling Rotterdam-aligned PMOS subtypes, shown by log2 fold change in PMOS relative to control mice for circulating testosterone, corpus luteum number, and ovarian area. Dashed lines indicate no change.