Transcriptomics-Based Subphenotyping of the Human Placenta Enabled by Weighted Correlation Network Analysis in Early-Onset Preeclampsia With and Without Fetal Growth Restriction
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Published:2023-06
Issue:6
Volume:80
Page:1363-1374
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ISSN:0194-911X
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Container-title:Hypertension
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language:en
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Short-container-title:Hypertension
Author:
Ackerman William E.1ORCID, Buhimschi Catalin S.1, Brown Thomas L.2, Zhao Guomao1, Summerfield Taryn L.3, Buhimschi Irina A.1ORCID
Affiliation:
1. Department of Obstetrics and Gynecology, University of Illinois College of Medicine—Chicago, IL (W.E.A., C.S.B., G.Z., I.A.B.). 2. Department of Neuroscience, Cell Biology, and Physiology, Wright State University Boonshoft School of Medicine, Dayton, OH (T.L.B.). 3. Department of Obstetrics and Gynecology, The Ohio State University College of Medicine (T.L.S.).
Abstract
Background:
Placental disorders contribute to pregnancy complications, including preeclampsia and fetal growth restriction (FGR), but debate regarding their specific pathobiology persists. Our objective was to apply transcriptomics with weighted gene correlation network analysis to further clarify the placental dysfunction in these conditions.
Methods:
We performed RNA sequencing with weighted gene correlation network analysis using human placental samples (n=30), separated into villous tissue and decidua basalis, and clinically grouped as follows: (1) early-onset preeclampsia (EOPE)+FGR (n=7); (2) normotensive, nonanomalous preterm FGR (n=5); (2) EOPE without FGR (n=8); (4) spontaneous idiopathic preterm birth (n=5) matched for gestational age; and (5) uncomplicated term births (n=5). Our data was compared with RNA sequencing data sets from public databases (GSE114691, GSE148241, and PRJEB30656; n=130 samples).
Results:
We identified 14 correlated gene modules in our specimens, of which most were significantly correlated with birthweight and maternal blood pressure. Of the 3 network modules consistently predictive of EOPE±FGR across data sets, we prioritized a coexpression gene group enriched for hypoxia-response and metabolic pathways for further investigation. Cluster analysis based on transcripts from this module and the glycolysis/gluconeogenesis metabolic pathway consistently distinguished a subset of EOPE±FGR samples with an expression signature suggesting modified tissue bioenergetics. We demonstrated that the expression ratios of
LDHA
/
LDHB
and
PDK1
/
GOT1
could be used as surrogate indices for the larger panels of genes in identifying this subgroup.
Conclusions:
We provide novel evidence for a molecular subphenotype consistent with a glycolytic metabolic shift that occurs more frequently but not universally in placental specimens of EOPE±FGR.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Internal Medicine
Reference34 articles.
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