Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

被引:5
|
作者
Creanza, Teresa Maria [1 ,2 ]
Liguori, Maria [3 ]
Liuni, Sabino [3 ]
Nuzziello, Nicoletta [3 ,4 ]
Ancona, Nicola [1 ]
机构
[1] Natl Res Council Italy, Inst Intelligent Syst Automat, I-70126 Bari, Italy
[2] Univ Turin, Ctr Complex Syst Mol Biol & Med, I-10123 Turin, Italy
[3] Natl Res Council Italy, Inst Biomed Technol, I-70126 Bari, Italy
[4] Univ Bari, Dept Basic Med Sci Neurosci & Sense Organs, I-70126 Bari, Italy
来源
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES | 2016年 / 17卷 / 06期
关键词
gene expression; multiple sclerosis; gene networks; INTERFERON-BETA; TRANSLATION INITIATION; TRANSCRIPTION FACTORS; PARKINSONS-DISEASE; PERIPHERAL-BLOOD; MESSENGER-RNA; EXPRESSION; ASSOCIATION; SUSCEPTIBILITY; SUPPRESSES;
D O I
10.3390/ijms17060936
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
Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-beta treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment.
引用
收藏
页数:24
相关论文
共 50 条
  • [31] Comparative analysis of weighted gene co-expression networks in human and mouse
    Eidsaa, Marius
    Stubbs, Lisa
    Almaas, Eivind
    PLOS ONE, 2017, 12 (11):
  • [32] Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks
    Carlson, MRJ
    Zhang, B
    Fang, ZX
    Mischel, PS
    Horvath, S
    Nelson, SF
    BMC GENOMICS, 2006, 7 (1)
  • [33] Differential co-expression analysis of venous thromboembolism based on gene expression profile data
    Ming, Zhibing
    Ding, Wenbin
    Yuan, Ruifan
    Jin, Jie
    Li, Xiaoqiang
    EXPERIMENTAL AND THERAPEUTIC MEDICINE, 2016, 11 (06) : 2193 - 2200
  • [34] Identification of ferroptosis-related gene signatures associated with multiple sclerosis using weighted gene co-expression network analysis
    Gu, Si-Chun
    Yuan, Can-Xing
    Gu, Chao
    MEDICINE, 2022, 101 (51)
  • [35] Consensus and Meta-analysis regulatory networks for combining multiple microarray gene expression datasets
    Steele, Emma
    Tucker, Allan
    JOURNAL OF BIOMEDICAL INFORMATICS, 2008, 41 (06) : 914 - 926
  • [36] Functional meta-analysis of double connectivity in gene coexpression networks in mammals
    Gustin, Marie-Paule
    Paultre, Christian Z.
    Randon, Jacques
    Bricca, Giampiero
    Cerutti, Catherine
    PHYSIOLOGICAL GENOMICS, 2008, 34 (01) : 34 - 41
  • [37] Differential Gene Co-expression Network using BicMix
    Wibawa, N. A.
    Bustaman, Alhadi
    Siswantining, Titin
    PROCEEDINGS OF THE SYMPOSIUM ON BIOMATHEMATICS (SYMOMATH) 2018, 2019, 2084
  • [38] Avoiding pitfalls in gene (co)expression meta-analysis
    Ostlund, Gabriel
    Sonnhammer, Erik L. L.
    GENOMICS, 2014, 103 (01) : 21 - 30
  • [39] Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA)
    Zhang, Tianjiao
    Wong, Garry
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2022, 20 : 3851 - 3863
  • [40] Protein co-expression with axonal injury in multiple sclerosis plaques
    Maria Diaz-Sanchez
    Kelly Williams
    Gabriele C. DeLuca
    Margaret M. Esiri
    Acta Neuropathologica, 2006, 111 : 289 - 299