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

被引:4
|
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
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
  • [1] META-ANALYSIS OF DIFFERENTIAL GENE CO-EXPRESSION: APPLICATION TO LUPUS
    Makashir, Sumit B.
    Kottyan, Leah C.
    Weirauch, Matthew T.
    PACIFIC SYMPOSIUM ON BIOCOMPUTING 2015 (PSB), 2015, : 443 - 454
  • [2] RANKING DIFFERENTIAL HUBS IN GENE CO-EXPRESSION NETWORKS
    Odibat, Omar
    Reddy, Chandan K.
    JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 2012, 10 (01)
  • [3] Comparative Transcriptome Analysis of Gene Co-Expression Networks in Relapsing-Remitting Multiple Sclerosis Patients and Healthy Controls
    Zhang, Mindy
    Chang, Yi-Chien
    Shankara, Srinivas
    Jacobs, Alan
    Godin, Jean
    Klinger, Katherine
    Madden, Stephen
    MULTIPLE SCLEROSIS JOURNAL, 2019, 25 : 157 - 157
  • [4] Loss of Connectivity in Cancer Co-Expression Networks
    Anglani, Roberto
    Creanza, Teresa M.
    Liuzzi, Vania C.
    Piepoli, Ada
    Panza, Anna
    Andriulli, Angelo
    Ancona, Nicola
    PLOS ONE, 2014, 9 (01):
  • [5] DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
    Thomas WH Lui
    Nancy BY Tsui
    Lawrence WC Chan
    Cesar SC Wong
    Parco MF Siu
    Benjamin YM Yung
    BMC Bioinformatics, 16
  • [6] DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
    Lui, Thomas W. H.
    Tsui, Nancy B. Y.
    Chan, Lawrence W. C.
    Wong, Cesar S. C.
    Siu, Parco M. F.
    Yung, Benjamin Y. M.
    BMC BIOINFORMATICS, 2015, 16
  • [7] (Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices
    Chowdhury, Hussain Ahmed
    Bhattacharyya, Dhruba Kumar
    Kalita, Jugal Kumar
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2020, 17 (04) : 1154 - 1173
  • [8] Revisiting Connectivity Map from a gene co-expression network analysis
    Liu, Wei
    Tu, Wei
    Li, Li
    Liu, Yingfu
    Wang, Shaobo
    Li, Ling
    Tao, Huan
    He, Huaqin
    EXPERIMENTAL AND THERAPEUTIC MEDICINE, 2018, 16 (02) : 493 - 500
  • [9] Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis
    Inge R Holtman
    Divya D Raj
    Jeremy A Miller
    Wandert Schaafsma
    Zhuoran Yin
    Nieske Brouwer
    Paul D Wes
    Thomas Möller
    Marie Orre
    Willem Kamphuis
    Elly M Hol
    Erik W G M Boddeke
    Bart J L Eggen
    Acta Neuropathologica Communications, 3
  • [10] MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer
    Zhu, Li
    Ding, Ying
    Chen, Cho-Yi
    Wang, Lin
    Huo, Zhiguang
    Kim, SungHwan
    Sotiriou, Christos
    Oesterreich, Steffi
    Tseng, George C.
    BIOINFORMATICS, 2017, 33 (08) : 1121 - 1129