Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK.
J Exp Bot. 2012 Mar;63(6):2309-23. doi: 10.1093/jxb/err382. Epub 2011 Dec 3.
Steady-state (13)C metabolic flux analysis (MFA) is currently the experimental method of choice for generating flux maps of the compartmented network of primary metabolism in heterotrophic and mixotrophic plant tissues. While statistically robust protocols for the application of steady-state MFA to plant tissues have been developed by several research groups, the implementation of the method is still far from routine. The effort required to produce a flux map is more than justified by the information that it contains about the metabolic phenotype of the system, but it remains the case that steady-state MFA is both analytically and computationally demanding. This article provides an overview of principles that underpin the implementation of steady-state MFA, focusing on the definition of the metabolic network responsible for redistribution of the label, experimental considerations relating to data collection, the modelling process that allows a set of metabolic fluxes to be deduced from the labelling data, and the interpretation of flux maps. The article draws on published studies of Arabidopsis cell cultures and other systems, including developing oilseeds, with the aim of providing practical guidance and strategies for handling the issues that arise when applying steady-state MFA to the complex metabolic networks encountered in plants.
稳态 (13)C 代谢通量分析 (MFA) 是目前用于生成异养和混合营养植物组织中初级代谢区室网络通量图的首选实验方法。尽管已经有几个研究小组开发了用于植物组织的稳态 MFA 的统计稳健协议,但该方法的实施仍然远远不够常规。该方法产生的通量图所包含的关于系统代谢表型的信息非常有价值,但其仍然需要在分析和计算方面付出大量的努力。本文概述了实施稳态 MFA 的基本原则,重点介绍了负责标签再分配的代谢网络的定义、与数据收集相关的实验考虑因素、允许从标记数据推导出一组代谢通量的建模过程,以及通量图的解释。本文参考了已发表的拟南芥细胞培养物和其他系统(包括发育中的油籽)的研究,旨在为应用稳态 MFA 于植物中遇到的复杂代谢网络时出现的问题提供实用的指导和策略。