Evidence map›Paper›PMID 35719905›Full record

ArticleFrontiers in genetics2021

Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia.

Jessica Schuster, George A Tollefson, Valeria Zarate, Anthony Agudelo, Joan Stabila, Ashok Ragavendran, James Padbury, Alper Uzun

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Jessica SchusterPediatrics, Women and Infants Hospital, Providence, RI, United States.
George A TollefsonPediatrics, Women and Infants Hospital, Providence, RI, United States.
Valeria ZaratePediatrics, Women and Infants Hospital, Providence, RI, United States.
Anthony AgudeloPediatrics, Women and Infants Hospital, Providence, RI, United States.
Joan StabilaPediatrics, Women and Infants Hospital, Providence, RI, United States.
Ashok RagavendranCenter for Computation and Visualization, Brown University, Providence, RI, United States.
James PadburyPediatrics, Women and Infants Hospital, Providence, RI, United States.
Alper UzunPediatrics, Women and Infants Hospital, Providence, RI, United States.
Women & Infants Hospital of Rhode Island · USBrown University · USProvidence College · US

Funding

Transcriptional Profiling of Exosomes Defines Molecular Phenotype of PreclampsiaP20GM121298 · NIGMS · WOMEN AND INFANTS HOSPITAL-RHODE ISLAND · PI SHARMA, SURENDRA · 2017 to 2021
$12.6M
NIGMS NIH HHS P20 GM121298
6 · The paper itself

Abstract

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.

Indexed as

complex diseasescomputational biologynetwork biologypreeclampsiaprotein protein interaction (PPI)

Identifiers

PMID35719905
PMCPMC9201216
OpenAlexW4281999618

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.