Bhattacharyya Malay, Bandyopadhyay Sanghamitra
Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata - 700108, India.
Mol Biosyst. 2013 Mar;9(3):457-66. doi: 10.1039/c2mb25434d. Epub 2013 Jan 23.
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.
微小RNA（miRNA）是一类短链非编码RNA，对基因具有组织特异性调控活性。miRNA的表达谱分析是理解阿尔茨海默病（AD）病理学的重要步骤，AD是一种起源于大脑的神经退行性疾病。最近的研究表明，AD脑灰质（GM）和白质（WM）中富集的miRNA表现出差异表达。然而，尚未对GM和WM中miRNA对的差异共表达进行深入研究。如果两个基因（或miRNA）在多个样本中的表达谱变化相似，则称它们为共表达。在一种条件类型（或表型）下共表达的一对基因在另一种条件下可能不再共表达，或变为反式表达。这样的基因对被称为差异共表达。本文报道了在AD早期阶段的此类研究。首先构建了GM和WM中差异共表达miRNA的网络。对差异共表达特性的分析表明，这样的网络不可能有任何循环。我们使用切换的概念来区分两种不同类型的差异共表达模式——一对在GM中高度共表达但在WM中不再如此的miRNA，反之亦然。基于此，我们发现了被称为差异共表达切换树（DCST）的子结构，其在整个过程中具有相似的切换模式。miR-423-5p成为该网络的枢纽。我们提取了这些DCST中在整个过程中具有相似切换模式的子树。发现这些子结构在统计学和生物学上都具有重要意义。从DCST中获得的大量miRNA被发现与AD有关，其中大多数在WM中富集。因此，这项计算研究表明WM在AD早期进展中起着重要作用，这是一个迄今较少被认识到的事实。
