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利用整合的“组学”方法从模式植物到作物的氮代谢的网络观点。

A network perspective on nitrogen metabolism from model to crop plants using integrated 'omics' approaches.

机构信息

RIKEN Center for Sustainable Resource Science (CSRS), 1-7-22 Suehirocho, Tsurumi, Yokohama 230-0045, Japan JST, National Bioscience Database Center (NBDC), 5-3, Yonbancho, Chiyoda, Tokyo 102-0081, Japan.

RIKEN Center for Sustainable Resource Science (CSRS), 1-7-22 Suehirocho, Tsurumi, Yokohama 230-0045, Japan Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan Japan Science and Technology Agency (JST), Precursory Research for Embryonic Science and Technology (PRESTO), 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan

出版信息

J Exp Bot. 2014 Oct;65(19):5619-30. doi: 10.1093/jxb/eru322. Epub 2014 Aug 16.

Abstract

Nitrogen (N), as an essential element in amino acids, nucleotides, and proteins, is a key factor in plant growth and development. Omics approaches such as metabolomics and transcriptomics have become a promising way to inspect complex network interactions in N metabolism and can be used for monitoring the uptake and regulation, translocation, and remobilization of N. In this review, the authors highlight recent progress in omics approaches, including transcript profiling using microarrays and deep sequencing, and show recent technical developments in metabolite profiling for N studies. Further, network analysis studies including network inference methods with correlations, information-theoretic measures, and a network concept to examine gene expression clusters in relation to N regulatory systems in plants are introduced, and integrating network inference methods and integrated networks using multiple omics data are discussed. Finally, this review summarizes recent omics application examples using metabolite and/or transcript profiling analysis to elucidate the regulation of N metabolism and signalling and the coordination of N and carbon metabolism in model plants (Arabidopsis and rice), crops (tomato, maize, and legumes), and trees (Populus).

摘要

氮(N)作为氨基酸、核苷酸和蛋白质中的必需元素,是植物生长和发育的关键因素。组学方法,如代谢组学和转录组学,已成为研究 N 代谢复杂网络相互作用的一种有前途的方法,可用于监测 N 的吸收和调节、转运和再利用。在这篇综述中,作者强调了组学方法的最新进展,包括使用微阵列和深度测序进行转录谱分析,并展示了 N 研究中代谢物谱分析的最新技术发展。此外,还介绍了包括基于相关性、信息论度量和网络概念的网络推断方法在内的网络分析研究,以研究与植物 N 调节系统相关的基因表达簇,并讨论了整合网络推断方法和使用多种组学数据的综合网络。最后,本文综述了最近使用代谢物和/或转录谱分析阐明 N 代谢和信号调节以及 N 和碳代谢在模式植物(拟南芥和水稻)、作物(番茄、玉米和豆科植物)和树木(杨树)中的协调的组学应用实例。

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