Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes

BMC Medicine

Research Article

Tyler Lovelace¹  Panayiotis V. Benos²  W. Tony Parks³  Samantha Piekos⁴  Leroy Hood⁴  Nathan D. Price⁵  Elena Sadovsky⁶  Zhishen Cao⁶  Oren Barak⁷  Yingshi Ouyang⁷  Tianjiao Chu⁷  Jean-Francois Mouillet⁷  Yoel Sadovsky⁸ 

[1] Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, 15260, Pittsburgh, PA, USA;Joint CMU-Pitt PhD Program in Computational Biology, 800 Murdoch Building, 3420 Forbes Avenue, 15260, Pittsburgh, PA, USA;Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, 15260, Pittsburgh, PA, USA;Joint CMU-Pitt PhD Program in Computational Biology, 800 Murdoch Building, 3420 Forbes Avenue, 15260, Pittsburgh, PA, USA;Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Road, 32610, Gainesville, FL, USA;Department of Laboratory Medicine and Pathobiology, University of Toronto, Simcoe Hall, 1 King’s College Circle, M5S 1A8, Toronto, ON, Canada;Institute for Systems Biology, 401 Terri Avenue North, 98109, Seattle, WA, USA;Institute for Systems Biology, 401 Terri Avenue North, 98109, Seattle, WA, USA;Thorne HealthTech, 152 West 57th Street, 10019, New York, NY, USA;Magee-Womens Research Institute, 204 Craft Avenue, 15213, Pittsburgh, PA, USA;Magee-Womens Research Institute, 204 Craft Avenue, 15213, Pittsburgh, PA, USA;Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, 300 Halket Street, 15213, Pittsburgh, PA, USA;Magee-Womens Research Institute, 204 Craft Avenue, 15213, Pittsburgh, PA, USA;Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, 300 Halket Street, 15213, Pittsburgh, PA, USA;Department of Microbiology and Molecular Genetics, University of Pittsburgh, 450 Technology Drive, 15219, Pittsburgh, PA, USA;

关键词: Pregnancy;    Placenta;    Multiomics;    Similarity network fusion;   

DOI  :  10.1186/s12916-023-03054-8

 received in 2023-06-30, accepted in 2023-08-29,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundPlacental dysfunction, a root cause of common syndromes affecting human pregnancy, such as preeclampsia (PE), fetal growth restriction (FGR), and spontaneous preterm delivery (sPTD), remains poorly defined. These common, yet clinically disparate obstetrical syndromes share similar placental histopathologic patterns, while individuals within each syndrome present distinct molecular changes, challenging our understanding and hindering our ability to prevent and treat these syndromes.MethodsUsing our extensive biobank, we identified women with severe PE (n = 75), FGR (n = 40), FGR with a hypertensive disorder (FGR + HDP; n = 33), sPTD (n = 72), and two uncomplicated control groups, term (n = 113), and preterm without PE, FGR, or sPTD (n = 16). We used placental biopsies for transcriptomics, proteomics, metabolomics data, and histological evaluation. After conventional pairwise comparison, we deployed an unbiased, AI-based similarity network fusion (SNF) to integrate the datatypes and identify omics-defined placental clusters. We used Bayesian model selection to compare the association between the histopathological features and disease conditions vs SNF clusters.ResultsPairwise, disease-based comparisons exhibited relatively few differences, likely reflecting the heterogeneity of the clinical syndromes. Therefore, we deployed the unbiased, omics-based SNF method. Our analysis resulted in four distinct clusters, which were mostly dominated by a specific syndrome. Notably, the cluster dominated by early-onset PE exhibited strong placental dysfunction patterns, with weaker injury patterns in the cluster dominated by sPTD. The SNF-defined clusters exhibited better correlation with the histopathology than the predefined disease groups.ConclusionsOur results demonstrate that integrated omics-based SNF distinctively reclassifies placental dysfunction patterns underlying the common obstetrical syndromes, improves our understanding of the pathological processes, and could promote a search for more personalized interventions.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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