Targeted sequencing analysis of <i>ACVR2A</i> gene identifies novel risk variants associated with preeclampsia

Glotov, Andrey S.; Казаков, С. В.; Vashukova, Elena S.; Pakin, Vladimir S.; Danilova, Maria M.; Nasykhova, Yulia A.; Masharsky, Aleksey E.; Mozgovaya, Elena V.; Еремеева, Д. Р.; Zainullina, Marina S.; Baranov, V. S.

doi:10.1080/14767058.2018.1449204

Cited by 11 publications

(8 citation statements)

References 37 publications

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“…There have been several sequencing efforts, including whole genome, whole exome and targeted sequencing, on an array of preeclampsia phenotypes from diverse populations ( Johnson et al, 2012 ; Emmery et al, 2016 ; Kaartokallio et al, 2016 ; Thomsen et al, 2017 ; Gammill et al, 2018 ; Hansen et al, 2018 ; Soellner et al, 2018 ; Glotov et al, 2019 ; Melton et al, 2019 ; Steinthorsdottir et al, 2020 ; Zhang et al, 2020 ). There is no consensus among the published results in regards to associated genes and variants.…”

Section: Discussionmentioning

confidence: 99%

Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

et al. 2022

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“…ANK2 mainly links to the integral membrane proteins and is essential for maintaining cardio-myocytes Na/Ca exchanger 1 stability ( 19 - 21 ). ACVR2A is the host gene of hsa_circ_0001073 associate with preeclampsia ( 22 , 23 ). The predicted microRNAs’ functions are highly involved in multiple tumor regulation.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of potential glioblastoma circular RNA sponge network

Zhao¹,

Zhang²,

Yang³

et al. 2022

Transl Cancer Res TCR

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Background Circular RNA is emerging functional molecule for glioblastoma. However, the function and regulatory of circular RNA (circRNA) remains unclear. In this study, the circRNA sequencing and array data of glioblastoma were analyzed by multiple bioinformatics methods to establish a potential molecular sponge mechanism regulation network. Methods Gene Expression Omnibus datasets were used to extract circRNAs. CircInteractome was used to predict microRNAs binding to circRNAs. Chinese Glioma Gene Atlas database was used to screen the microRNAs with expression and survival trends. MiRabel database was used to predict potential gene targets of microRNAs. The Cancer Genome Atlas database was used to screen the gene targets of sponge network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were to explain the gene targets functions. R software, Cytoscape software and Bioinformatics website were used to establish the network and visualize the results. Results Hsa_circ_0000219, hsa_circ_0001073 and hsa_circ_0070700 were selected from more than 2000 differentially expressed circRNAs of Gene Expression Omnibus Series (GSE) GSE146463, GSE92322 and GSE86202 datasets. Hsa-miR-1248 and hsa-miR-1290 were up regulated and related to glioblastoma poor prognosis. Targets of these microRNAs including ARHGEF7, CELA2b, RNF11, YPEL1 and ZNF37a were also screened via expression and survival data. Gene targets function were mainly enriched in signal transduction, cell plasma membrane, ATP binding and calcium signaling pathway. Conclusions A circRNA molecular sponge regulatory network including hsa-miR-1248 and hsa-miR-1290 has been established. In this network, hsa_circ_0001073, hsa_circ_0070700, hsa_circ_0000219, hsa-miR-1248, hsa-miR-1290, and RNF11 may have the potential being emerging glioblastoma therapeutic targets. However, their function and significance for glioblastoma need further experiments to verify.

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“…There have been several sequencing efforts including whole genome, whole exome and targeted sequencing on an array of preeclampsia phenotypes from diverse populations 37,[55][56][57][58][59][60][61][62][63][64] . There is no consensus among the published results in regards to associated genes and variants.…”

Section: Discussionmentioning

confidence: 99%

Integrated Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

Schuster

Tollefson

Zarate

et al. 2020

Preprint

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To identify clusters of patients with shared networks of genes associated with early onset severe preeclampsia from whole exome sequencing data through novel bioinformatic analysis.We performed a case-control study using whole exome sequencing (WES) on early onset preeclamptic mothers with severe features delivering < 34 weeks and mothers who delivered ≥ 37 weeks. Genotype testing identified variants that were differentially abundant between cases and controls. A Protein-Protein interaction (PPI) analysis and visualization tool, Proteinarium, was implemented to identify clusters of patients with shared networks associated with severe preeclampsia.A total of 61 early onset preeclamptic women with severe features and 82 race and ethnicity matched control women at term were sequenced. We identified 8,867 predicted deleterious variants. 21 of these variants were nominally associated with preeclampsia by genotype testing. Using Proteinarium129 out of the 143 sequenced patients were assigned to statistically significant clusters, Cluster A and B (p< 0.0001). Case dominated Cluster A contained 47 of the 61 case subjects. There were 13 unique genes in the PPI network of Cluster A compared to control dominated Cluster B. Amongst these unique genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia.Our network analysis identified genes that were unique to a large cluster of patients with shared networks that provide insights for severe preeclampsia. We also identified genes imputed from the interactome that may otherwise have not been identified by conventional analysis. Strict phenotyping of both cases and controls improved the likelihood of identifying these otherwise difficult to find genetic associations. These uniquely identified genes and their associated variants are potential candidates for developing polygenic risk scores for severe preeclampsia. These results support our hypothesis on the genetic architecture of complex diseases and are generalizable to other phenotypes.

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Targeted sequencing analysis of ACVR2A gene identifies novel risk variants associated with preeclampsia

Abstract: Our data suggest that some variants in ACVR2A gene are associated with PE. But more studies are required to reveal the role of ACVR2A gene in the pathogenesis of this disease during pregnancy.

Cited by 11 publications

References 37 publications

Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

Bioinformatics analysis of potential glioblastoma circular RNA sponge network

Integrated Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

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