Evidence map›Paper›PMID 27534535›Full record

ArticleBMC medical genomics2016

Integrated analysis of omics data using microRNA-target mRNA network and PPI network reveals regulation of Gnai1 function in the spinal cord of Ews/Ewsr1 KO mice.

Chai-Jin Lee, Hongryul Ahn, Sean Bong Lee, Jong-Yeon Shin, Woong-Yang Park, Jong-Il Kim, Junghee Lee, Hoon Ryu, Sun Kim

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 28% 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

3 citing papers in PubMed, 7 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

9 authors at 5 institutions in 2 countries.

Chai-Jin LeeInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-747, Republic of Korea.
Hongryul AhnDepartment of Computer Science and Engineering, Seoul National University, Seoul, 151-744, Republic of Korea.
Sean Bong LeeDepartment of Pathology & Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA.
Jong-Yeon ShinGenome Medicine Institute and Department of Biochemistry, Seoul National University College of Medicine, Seoul, 110-799, Republic of Korea.
Woong-Yang ParkSamsung Genome Institute, Samsung Medical Center and Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, 135-710, Republic of Korea.
Jong-Il KimGenome Medicine Institute and Department of Biochemistry, Seoul National University College of Medicine, Seoul, 110-799, Republic of Korea.
Junghee LeeVA Boston Healthcare System, Boston, MA, 02130, USA.
Hoon RyuVA Boston Healthcare System, Boston, MA, 02130, USA. hoonryu@bu.edu.
Sun KimInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-747, Republic of Korea. sunkim.bioinfo@snu.ac.kr.
Seoul National University · KRBoston University · USSungkyunkwan University · KRTulane University · USVA Boston Healthcare System · US

Funding

Epigenetic Regulation of Heterochromatin Condensation in Huntington's DiseaseR01NS067283 · NINDS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI RYU, HOON · 2011 to 2015
$1.2M
NINDS NIH HHS R01 NS067283
6 · The paper itself

Abstract

backgroundMultifunctional transcription factor (TF) gene EWS/EWSR1 is involved in various cellular processes such as transcription regulation, noncoding RNA regulation, splicing regulation, genotoxic stress response, and cancer generation. Role of a TF gene can be effectively studied by measuring genome-wide gene expression, i.e., transcriptome, in an animal model of Ews/Ewsr1 knockout (KO). However, when a TF gene has complex multi-functions, conventional approaches such as differentially expressed genes (DEGs) analysis are not successful to characterize the role of the EWS gene. In this regard, network-based analyses that consider associations among genes are the most promising approach.

methodsNetworks are constructed and used to show associations among biological entities at various levels, thus different networks represent association at different levels. Taken together, in this paper, we report contributions on both computational and biological sides.

resultsContribution on the computational side is to develop a novel computational framework that combines miRNA-gene network and protein-protein interaction network information to characterize the multifunctional role of EWS gene. On the biological side, we report that EWS regulates G-protein, Gnai1, in the spinal cord of Ews/Ewsr1 KO mice using the two biological network integrated analysis method. Neighbor proteins of Gnai1, G-protein complex subunits Gnb1, Gnb2 and Gnb4 were also down-regulated at their gene expression level. Interestingly, up-regulated genes, such as Rgs1 and Rgs19, are linked to the inhibition of Gnai1 activities. We further verified the altered expression of Gnai1 by qRT-PCR in Ews/Ewsr1 KO mice.

conclusionsOur integrated analysis of miRNA-transcriptome network and PPI network combined with qRT-PCR verifies that Gnai1 function is impaired in the spinal cord of Ews/Ewsr1 KO mice.

Indexed as

Computational BiologyProtein Interaction MappingAnimalsCalmodulin-Binding ProteinsGene Expression ProfilingGene OntologyGene Regulatory NetworksGTP-Binding Protein alpha Subunits, Gi-GoMiceMice, KnockoutMicroRNAsRNA-Binding Protein EWSRNA-Binding ProteinsRNA, MessengerSequence Analysis, RNASpinal CordCalmodulin-Binding ProteinsEwsr1 protein, mouseGnai1 protein, mouseGTP-Binding Protein alpha Subunits, Gi-GoMicroRNAsRNA-Binding Protein EWSRNA-Binding ProteinsRNA, MessengerEWSEwsr1Gnai1MMIANetwork analysisPPI

Identifiers

PMID27534535
PMCPMC4989891
OpenAlexW2510768056

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.