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拟南芥 microRNA-转录因子调控网络的构建与分析。

Construction and analysis of microRNA-transcription factor regulation network in Arabidopsis.

机构信息

Department of Applied Bioscience, College of Agronomy and Biotechnology, Hangzhou 310058, People's Republic of China.

Department of Bioinformatics, College of Life Sciences, Hangzhou, Zhejiang, People's Republic China.

出版信息

IET Syst Biol. 2014 Jun;8(3):76-86. doi: 10.1049/iet-syb.2013.0024.

Abstract

Transcription factors (TFs) and microRNAs (miRNAs) are two major types of regulators of gene expression, at transcriptional and post-transcriptional levels, respectively. By gathering their gene regulatory relationships, gene regulatory networks (GRNs) could be formed. A network motif is a type of connection pattern among a set of nodes which appears significantly more frequently than in random networks. Investigations of the network motifs often yield biological insights into the nature of the network. The previous study on miRNA-TF regulation networks concentrated on animals, and relied heavily on computational predictions. The authors collected data concerning miRNA regulation and transcriptional regulation relationships in Arabidopsis from publicly available databases, and further incorporated them with the protein-protein interaction data. All the data in the author's collection are supported by experiments. They screened the network motifs, whose size ranges between 1 and 4. The biological implications of the motifs were further analysed, and a flower development related network was constructed as an example. In this example, they illustrated the relevance of the network with the given process, and proposed the association of several genes with flowers by a network cluster identification. In this study, they analysed the properties of the GRN in Arabidopsis, and discussed their biological implications, as well as their potential applications.

摘要

转录因子 (TFs) 和 microRNAs (miRNAs) 是分别在转录和转录后水平上调节基因表达的两种主要类型的调节剂。通过收集它们的基因调控关系,可以形成基因调控网络 (GRN)。网络基元是一组节点之间的连接模式类型,其出现的频率明显高于随机网络。对网络基元的研究经常为网络的本质提供生物学见解。以前关于 miRNA-TF 调控网络的研究集中在动物上,并且严重依赖于计算预测。作者从公开数据库中收集了有关拟南芥 miRNA 调控和转录调控关系的数据,并进一步将其与蛋白质-蛋白质相互作用数据相结合。作者收集的数据全部都有实验支持。他们筛选了大小在 1 到 4 之间的网络基元。进一步分析了基元的生物学意义,并以花发育相关网络为例进行了构建。在这个例子中,他们说明了网络与给定过程的相关性,并通过网络聚类识别提出了几个与花相关的基因的关联。在这项研究中,他们分析了拟南芥中 GRN 的性质,并讨论了它们的生物学意义及其潜在应用。

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