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 5

H3B-8800 reprograms hyperandrogenism-induced dysregulation of granulosa cell transcriptional and splicing programs.

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H3B-8800 reprograms hyperandrogenism-induced dysregulation of granulosa ...
(A) Number of upregulated and downregulated genes across ovary, gonadal white adipose tissue (gWAT), and subcutaneous white adipose tissue (sWAT) in PMOS mice treated with H3B-8800 or vehicle. (B) Single-cell expression of H3B-8800–associated differentially expressed genes across ovarian cell types. (C) Heatmap of row-scaled gene expression in human granulosa KGN cells across control, DHT, and H3B-8800+DHT conditions (n = 3 per treatment group). Color indicates relative expression. (D and E) Gene set enrichment analysis of differentially expressed genes showing normalized enrichment scores for selected pathways in DHT-treated versus control KGN cells (D) and H3B-8800+DHT–treated versus DHT-treated KGN cells (E). Point color indicates direction of enrichment, and point size indicates –log10(P value). (F) Number of DHT-induced or DHT-suppressed differentially expressed genes, categorized by whether expression was reversed by H3B-8800 treatment. (G) Distribution of the top alternative splicing event types in human KGN cells and mouse ovarian tissue, including skipped exon (SE), mutually exclusive exon (MXE), and retained intron (RI) events. (H) Heatmap showing percentage spliced in values, scaled as z-scores, for representative alternative splicing events in GMEB1 and HDAC9 across control, DHT, and H3B-8800+DHT conditions (n = 3 per treatment group). Schematics depict exon structure and protein domains, showing restoration of alternative exon usage within functionally relevant regions. Differential expression was assessed using limma, and pathway enrichment was assessed by gene set enrichment analysis.