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 3

Ovary–peripheral organ crosstalk links reproductive and cardiometabolic phenotypes in PMOS.

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Ovary–peripheral organ crosstalk links reproductive and cardiometabolic ...
(A) Weighted gene coexpression network analysis of ovarian and adipose gene expression in PMOS mice. The image on the left indicates the tissue composition of each module, the center image shows the number of PMOS-associated differentially expressed genes in each tissue, and the image on the right shows correlations between module eigengenes and hormonal and cardiometabolic traits. Asterisks indicate statistically significant associations. Module 18 is highlighted based on correlations with testosterone, estradiol, and left ventricular ejection fraction. (B) Undirected network of module 18 genes. Node color indicates ovary-derived genes, adipose-derived genes, estradiol, testosterone, or cardiac ejection fraction. Edges represent statistically significant correlations, with red and blue indicating positive and negative correlations, respectively. Edge weight corresponds to correlation strength. Enriched ovarian and adipose pathways are shown by gene ratio. (C) Overlap between PMOS trait-correlated genes and aging-associated genes in human ovary. (D) Heatmap showing overlap between PMOS trait-associated genes and ovarian cell–type signatures. (E) Top overlapping granulosa cell genes shown by log2 fold change in aged versus young ovary. (F) Number of genes correlated with ovarian HDAC9 expression across tissues, stratified by age. (G) Gene set enrichment analysis of left ventricular pathways associated with ovarian HDAC9 expression by age group. (H) Ovarian Hdac9 expression plotted against left ventricular (LV) mass in control and PMOS mice. (I) Association of ovarian HDAC9 with IGFBP2 expression in human ovary and circulating IGFBP2 levels in PMOS and heart failure cohorts. Correlations were calculated using biweight midcorrelation. Differential expression was assessed using limma with FDR adjustment where indicated. Associations in A, B, F, H, and the ovarian correlation in I were calculated using biweight midcorrelation, with P values determined using bicorAndPvalue. In C, the Venn diagram shows the descriptive intersection of genes meeting P < 0.001 in the respective PMOS trait-correlation and human ovarian-aging analyses. Gene set enrichment analysis in G was performed using clusterProfiler, with adjusted P values shown. Differential protein abundance in I was assessed using limma with FDR adjustment.