Literature DB >> 23344858

Studying the differential co-expression of microRNAs reveals significant role of white matter in early Alzheimer's progression.

Malay Bhattacharyya1, Sanghamitra Bandyopadhyay.   

Abstract

MicroRNAs (miRNAs) are a class of short non-coding RNAs, which show tissue-specific regulatory activity on genes. Expression profiling of miRNAs is an important step for understanding the pathology of Alzheimer's disease (AD), a neurodegenerative disorder originating in the brain. Recent studies highlight that miRNAs enriched in gray matter (GM) and white matter (WM) of AD brains show differential expression. However, no in-depth study has yet been conducted on analysing the differential co-expression of pairs of miRNAs over GM and WM. Two genes (or miRNAs) are said to be co-expressed if their expression profiles change similarly over a number of samples. A pair of co-expressed genes under a condition type (or phenotype) may not remain co-expressed, or get contra-expressed, under another condition. Such pairs of genes are referred to as differentially co-expressed. Such an investigation in the early stage of AD is reported in this article. A network of differentially co-expressed miRNAs in GM and WM is first built. Analysis of the differential co-expression property reveals that such a network can not have any cycle. We use the notion of switching to distinguish two distinct types of differential co-expression patterns - a pair of miRNAs that are highly co-expressed in GM but does not remain so in WM, and vice versa. Based on this, we find the substructures, referred to as differentially co-expressed switching tree (DCST), that throughout have similar pattern of switching. The miR-423-5p emerges as a hub of the network. We extract subtrees of these DCSTs that have similar switching pattern throughout. These substructures are found to be both statistically and biologically significant. A large number of miRNAs obtained from the DCSTs are found to have association with AD, most of which are enriched in WM. This computational study therefore indicates a significant role of WM in early AD progression, a hitherto less acknowledged fact.

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Year:  2013        PMID: 23344858     DOI: 10.1039/c2mb25434d

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  14 in total

1.  Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation.

Authors:  Hui Yu; Ramkrishna Mitra; Jing Yang; YuanYuan Li; ZhongMing Zhao
Journal:  Sci China Life Sci       Date:  2014-10-18       Impact factor: 6.038

2.  Human miRNome profiling identifies microRNAs differentially present in the urine after kidney injury.

Authors:  Krithika Ramachandran; Janani Saikumar; Vanesa Bijol; Jay L Koyner; Jing Qian; Rebecca A Betensky; Sushrut S Waikar; Vishal S Vaidya
Journal:  Clin Chem       Date:  2013-10-23       Impact factor: 8.327

3.  Identification of candidate miRNA biomarkers for pancreatic ductal adenocarcinoma by weighted gene co-expression network analysis.

Authors:  M Giulietti; G Occhipinti; G Principato; F Piva
Journal:  Cell Oncol (Dordr)       Date:  2017-02-15       Impact factor: 6.730

Review 4.  Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders.

Authors:  C Gaiteri; Y Ding; B French; G C Tseng; E Sibille
Journal:  Genes Brain Behav       Date:  2013-12-10       Impact factor: 3.449

5.  MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer.

Authors:  Li Zhu; Ying Ding; Cho-Yi Chen; Lin Wang; Zhiguang Huo; SungHwan Kim; Christos Sotiriou; Steffi Oesterreich; George C Tseng
Journal:  Bioinformatics       Date:  2017-04-15       Impact factor: 6.937

6.  Invariance and plasticity in the Drosophila melanogaster metabolomic network in response to temperature.

Authors:  Ramkumar Hariharan; Jessica M Hoffman; Ariel S Thomas; Quinlyn A Soltow; Dean P Jones; Daniel E L Promislow
Journal:  BMC Syst Biol       Date:  2014-12-24

7.  Expression data analysis to identify biomarkers associated with asthma in children.

Authors:  Wen Xu
Journal:  Int J Genomics       Date:  2014-03-27       Impact factor: 2.326

8.  Dynamic protein interaction modules in human hepatocellular carcinoma progression.

Authors:  Hui Yu; Chen-Ching Lin; Yuan-Yuan Li; Zhongming Zhao
Journal:  BMC Syst Biol       Date:  2013-12-09

9.  DCGL v2.0: an R package for unveiling differential regulation from differential co-expression.

Authors:  Jing Yang; Hui Yu; Bao-Hong Liu; Zhongming Zhao; Lei Liu; Liang-Xiao Ma; Yi-Xue Li; Yuan-Yuan Li
Journal:  PLoS One       Date:  2013-11-20       Impact factor: 3.240

10.  Predicted overlapping microRNA regulators of acetylcholine packaging and degradation in neuroinflammation-related disorders.

Authors:  Bettina Nadorp; Hermona Soreq
Journal:  Front Mol Neurosci       Date:  2014-02-10       Impact factor: 5.639

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