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

被引:27
作者
Bhattacharyya, Malay [1 ]
Bandyopadhyay, Sanghamitra [1 ]
机构
[1] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, India
关键词
DISEASE PROGRESSION; EXPRESSION; GENE;
D O I
10.1039/c2mb25434d
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
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.
引用
收藏
页码:457 / 466
页数:10
相关论文
共 36 条
[1]   miRGen 2.0: a database of microRNA genomic information and regulation [J].
Alexiou, Panagiotis ;
Vergoulis, Thanasis ;
Gleditzsch, Martin ;
Prekas, George ;
Dalamagas, Theodore ;
Megraw, Molly ;
Grosse, Ivo ;
Sellis, Timos ;
Hatzigeorgiou, Artemis G. .
NUCLEIC ACIDS RESEARCH, 2010, 38 :D137-D141
[2]   A Biologically Inspired Measure for Coexpression Analysis [J].
Bandyopadhyay, Sanghamitra ;
Bhattacharyya, Malay .
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2011, 8 (04) :929-942
[3]   MicroRNAs: Target Recognition and Regulatory Functions [J].
Bartel, David P. .
CELL, 2009, 136 (02) :215-233
[4]   LOCAL CONTROL OF NEURITE DEVELOPMENT BY NERVE GROWTH-FACTOR [J].
CAMPENOT, RB .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 1977, 74 (10) :4516-4519
[5]   Differential coexpression analysis using microarray data and its application to human cancer [J].
Choi, JK ;
Yu, US ;
Yoo, OJ ;
Kim, S .
BIOINFORMATICS, 2005, 21 (24) :4348-4355
[6]   Statistical methods for gene set co-expression analysis [J].
Choi, YounJeong ;
Kendziorski, Christina .
BIOINFORMATICS, 2009, 25 (21) :2780-2786
[7]   Transcriptional regulation of BACE1, the β-amyloid precursor protein β-secretase, by Sp1 [J].
Christensen, MA ;
Zhou, WH ;
Qing, H ;
Lehman, A ;
Philipsen, S ;
Song, WH .
MOLECULAR AND CELLULAR BIOLOGY, 2004, 24 (02) :865-874
[8]   Progression of cerebral white matter lesions in Alzheimer's disease: a new window for therapy? [J].
de Leeuw, FE ;
Barkhof, F ;
Scheltens, P .
JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY, 2005, 76 (09) :1286-1288
[9]  
Duff Karen, 2004, Briefings in Functional Genomics & Proteomics, V3, P47, DOI 10.1093/bfgp/3.1.47
[10]  
Fang G, 2010, BIOCOMPUT-PAC SYM, P145