Transcription Factor Co-expression Networks of Adipose RNA-Seq Data Reveal Regulatory Mechanisms of Obesity

被引:9
|
作者
Skinkyte-Juskiene, Ruta [1 ]
Kogelman, Lisette J. A. [1 ,2 ]
Kadarmideen, Haja N. [1 ,3 ]
机构
[1] Univ Copenhagen, Fac Hlth & Med Sci, Dept Vet & Anim Sci, Gronnegardsvej 7, DK-1870 Frederiksberg C, Denmark
[2] Rigshosp Glostrup, Glostrup Res Inst, Dept Neurol, Danish Headache Ctr, Nordre Ringvej 69, DK-2600 Glostrup, Denmark
[3] Tech Univ Denmark, Sect Syst Genom, Dept Bio & Hlth Informat, Bldg 208, DK-2800 Lyngby, Denmark
基金
欧盟第七框架计划;
关键词
Obesity; Transcription factors; WGCNA; Transcriptomics; RNA-Seq; Gene networks; VITAMIN-D-RECEPTOR; GENE POLYMORPHISMS; PROVIDES INSIGHTS; TISSUE; ASSOCIATION; DISEASE; BONE; MACROPHAGES; DYSFUNCTION; PROTEINS;
D O I
10.2174/1389202918666171005095059
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Background: Transcription Factors (TFs) control actuation of genes in the genome and are key mediators of complex processes such as obesity. Master Regulators (MRs) are the genes at the top of a regulation hierarchy which regulate other genes. Objective: To elucidate clusters of highly co-expressed TFs (modules), involved pathways, highly interconnected TFs (hub-TFs) and MRs leading to obesity and leanness, using porcine model for human obesity. Methods: We identified 817 expressed TFs in RNA-Sequencing dataset representing extreme degrees of obesity (DO; lean, obese). We built a single Weighted Transcription Factor Co-expression Network (WTFCN) and TF sub-networks (based on the DO). Hub-TFs and MRs (using iRegulon) were identified in biologically relevant WTFCNs modules. Results: Single WTFCN detected the Red module significantly associated with DO (P < 0.03). This module was enriched for regulation processes in the immune system, e.g.: Immune system process (Padj = 2.50E-06) and metabolic lifestyle disorders, e.g. Circadian rhythm - mammal pathway (Padj = 2.33E-11). Detected MR, hub-TF SPII was involved in obesity, immunity and osteoporosis. Within the obese sub-network, the Red module suggested possible associations with immunity, e.g. TGF-beta signaling pathway (Padj = 1.73E-02) and osteoporosis, e.g. Osteoclast differentiation (Padj = 1.94E02). Within the lean sub-network, the Magenta module displayed associations with type 2 diabetes, obesity and osteoporosis e.g. Notch signaling pathway (Padj = 2.40E-03), osteoporosis e.g. hub-TF VDR (a prime candidate gene for osteoporosis). Conclusion: Our results provide insights into the regulatory network of TFs and biologically relevant hub TFs in obesity.
引用
下载
收藏
页码:289 / 299
页数:11
相关论文
共 50 条
  • [31] Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data
    Mingzhu Zhu
    Jeremy L Dahmen
    Gary Stacey
    Jianlin Cheng
    BMC Bioinformatics, 14
  • [32] Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification
    Al-Aamri, Amira
    Kudlicki, Andrzej S. S.
    Maalouf, Maher
    Taha, Kamal
    Homouz, Dirar
    BIOLOGY-BASEL, 2023, 12 (04):
  • [33] Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data
    Zhu, Mingzhu
    Dahmen, Jeremy L.
    Stacey, Gary
    Cheng, Jianlin
    BMC BIOINFORMATICS, 2013, 14
  • [34] Recursive Indirect-Paths Modularity (RIP-M) for Detecting Community Structure in RNA-Seq Co-expression Networks
    Rahmani, Bahareh
    Zimmermann, Michael T.
    Grill, Diane E.
    Kennedy, Richard B.
    Oberg, Ann L.
    White, Bill C.
    Poland, Gregory A.
    McKinney, Brett A.
    FRONTIERS IN GENETICS, 2016, 7
  • [35] Ranking genome-wide correlation measurements improves microarray and RNA-seq based global and targeted co-expression networks
    Franziska Liesecke
    Dimitri Daudu
    Rodolphe Dugé de Bernonville
    Sébastien Besseau
    Marc Clastre
    Vincent Courdavault
    Johan-Owen de Craene
    Joel Crèche
    Nathalie Giglioli-Guivarc’h
    Gaëlle Glévarec
    Olivier Pichon
    Thomas Dugé de Bernonville
    Scientific Reports, 8
  • [36] Ranking genome-wide correlation measurements improves microarray and RNA-seq based global and targeted co-expression networks
    Liesecke, Franziska
    Daudu, Dimitri
    de Bernonville, Rodolphe Duge
    Besseau, Sebastien
    Clastre, Marc
    Courdavault, Vincent
    de Craene, Johan-Owen
    Creche, Joel
    Giglioli-Guivarc'h, Nathalie
    Glevarec, Gaelle
    Pichon, Olivier
    de Bernonville, Thomas Duge
    SCIENTIFIC REPORTS, 2018, 8
  • [37] Co-expression Networks Identify DHX15 RNA Helicase as a B Cell Regulatory Factor
    Detanico, Thiago
    Virgen-Slane, Richard
    Steen-Fuentes, Seth
    Lin, Wai W.
    Rhode-Kurnow, Antje
    Chappell, Elizabeth
    Correa, Ricardo G.
    DiCandido, Michael J.
    Mbow, M. Lamine
    Li, Jun
    Ware, Carl F.
    FRONTIERS IN IMMUNOLOGY, 2019, 10
  • [38] DIFFERENTIAL DISTRIBUTION OF SCHIZOPHRENIA RISK GENES WITHIN GENE CO-EXPRESSION NETWORKS CONSTRUCTED FROM RNA-SEQ DATA (POSTMORTEM DLPFC) OF AFFECTED AND UNAFFECTED INDIVIDUALS
    Radulescu, Eugenia
    Straub, Richard E.
    Jaffe, Andrew E.
    Shin, Joo Heon
    Chen, Qiang
    Weinberger, Daniel R.
    EUROPEAN NEUROPSYCHOPHARMACOLOGY, 2019, 29 : S946 - S947
  • [39] Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems
    Angelini, Claudia
    Costa, Valerio
    FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 2014, 2
  • [40] Inferring Transcription Factor Variation of Murine Cardiogenesis From Single Cell RNA-seq Data
    Gong, Wuming
    Koyano-Nakagawa, Naoko
    Pan, Wei
    Garry, Mary G.
    Garry, Daniel J.
    CIRCULATION, 2017, 136