Bottom-up Metabolic Reconstruction of Arabidopsis and Its Application to Determining the Metabolic Costs of Enzyme Production
Bottom-up Metabolic Reconstruction of Arabidopsis and Its Application to Determining the Metabolic Costs of Enzyme Production
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DOI：
10.1104/pp.114.235358
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发表时间：
2014-07-01
期刊：
影响因子：
7.4
通讯作者：
Nikoloski, Zoran
中科院分区：
文献类型：
--
作者：
Arnold, Anne;Nikoloski, Zoran
植物代谢的大规模建模提供了比较和对比不同的细胞和环境场景的可能性，最终目的是识别相应植物行为的潜在成分。现有的拟南芥（Arabidopsis thaliana）模型是自上而下组装的，其中起点是注释的基因组，特别是代谢基因。因此，死端代谢物和阻断的反应可能会出现，随后通过使用间隙填充算法结合物种非特异性基因来解决。在这里，我们提出了一个自下而上组装的大规模模型，该模型仅依赖于特定的注释，并导致仅包含手动策划的反应。虽然现有的模型在很大程度上是通过采用一个单一的生物质反应条件非特异性，我们提供了三个生物质组成，涉及到现实的和经常检查的情况下：碳限制，氮限制，和最佳的生长条件。比较分析表明，提出的拟南芥核心模型表现出相当的效率，碳利用和灵活性，现有的网络替代品。此外，该模型被用来量化的能量需求的氨基酸和酶从头合成的光合自养生长条件。以世界上最丰富的蛋白质Rubisco为例，我们根据ATP需求确定其合成成本。这反过来又使我们能够探索拟南芥蛋白质合成和生长之间的权衡。总而言之，该模型提供了一个坚实的基础，完全物种特异性的高通量数据，如基因表达水平的整合，并在计算机代谢工程策略的条件特定的调查。
Large-scale modeling of plant metabolism provides the possibility to compare and contrast different cellular and environmental scenarios with the ultimate aim of identifying the components underlying the respective plant behavior. The existing models of Arabidopsis (Arabidopsis thaliana) are top-down assembled, whereby the starting point is the annotated genome, in particular, the metabolic genes. Hence, dead-end metabolites and blocked reactions can arise that are subsequently addressed by using gap-filling algorithms in combination with species-unspecific genes. Here, we present a bottom-up-assembled, large-scale model that relies solely on Arabidopsis-specific annotations and results in the inclusion of only manually curated reactions. While the existing models are largely condition unspecific by employing a single biomass reaction, we provide three biomass compositions that pertain to realistic and frequently examined scenarios: carbon-limiting, nitrogen-limiting, and optimal growth conditions. The comparative analysis indicates that the proposed Arabidopsis core model exhibits comparable efficiency in carbon utilization and flexibility to the existing network alternatives. Moreover, the model is utilized to quantify the energy demand of amino acid and enzyme de novo synthesis in photoautotrophic growth conditions. Illustrated by the case of the most abundant protein in the world, Rubisco, we determine its synthesis cost in terms of ATP requirements. This, in turn, allows us to explore the tradeoff between protein synthesis and growth in Arabidopsis. Altogether, the model provides a solid basis for completely species-specific integration of high-throughput data, such as gene expression levels, and for condition-specific investigations of in silico metabolic engineering strategies.