Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia.

complex diseases computational biology network biology preeclampsia protein protein interaction (PPI)

Journal

Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621

Informations de publication

Date de publication:
2021
Historique:
received: 28 08 2021
accepted: 03 11 2021
entrez: 20 6 2022
pubmed: 21 6 2022
medline: 21 6 2022
Statut: epublish

Résumé

Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.

Identifiants

pubmed: 35719905
doi: 10.3389/fgene.2021.765985
pii: 765985
pmc: PMC9201216
doi:

Types de publication

Journal Article

Langues

eng

Pagination

765985

Informations de copyright

Copyright © 2022 Schuster, Tollefson, Zarate, Agudelo, Stabila, Ragavendran, Padbury and Uzun.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Jessica Schuster (J)

Pediatrics, Women and Infants Hospital, Providence, RI, United States.
Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States.

George A Tollefson (GA)

Pediatrics, Women and Infants Hospital, Providence, RI, United States.

Valeria Zarate (V)

Pediatrics, Women and Infants Hospital, Providence, RI, United States.

Anthony Agudelo (A)

Pediatrics, Women and Infants Hospital, Providence, RI, United States.

Joan Stabila (J)

Pediatrics, Women and Infants Hospital, Providence, RI, United States.

Ashok Ragavendran (A)

Center for Computation and Visualization, Brown University, Providence, RI, United States.
Computational Biology of Human Disease, Brown University, Providence, RI, United States.

James Padbury (J)

Pediatrics, Women and Infants Hospital, Providence, RI, United States.
Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States.
Center for Computational Molecular Biology, Brown University, Providence, RI, United States.

Alper Uzun (A)

Pediatrics, Women and Infants Hospital, Providence, RI, United States.
Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States.
Computational Biology of Human Disease, Brown University, Providence, RI, United States.
Center for Computational Molecular Biology, Brown University, Providence, RI, United States.

Classifications MeSH