Bottom-up Metabolic Reconstruction of Arabidopsis and Its Application to Determining the Metabolic Costs of Enzyme Production[W]
Bottom-up Metabolic Reconstruction of Arabidopsis and Its Application to Determining the Metabolic Costs of Enzyme Production[W]
复制标题
拟南芥自下而上的代谢重建及其在确定酶生产代谢成本中的应用[W]
作者:
Anne Arnold;Z. Nikoloski
A large-scale metabolic model of Arabidopsis with three condition-specific biomass compositions is assembled in a bottom-up fashion to estimate the enzyme costs of Rubisco. 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.
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影响因子:
20.3
作者:
Kalathottukaren, Manu Thomas;Abraham, Libin;Kizhakkedathu, Jayachandran N.
通讯作者:
Kizhakkedathu, Jayachandran N.
影响因子:
56.9
作者:
BACHMAIR, A;FINLEY, D;VARSHAVSKY, A
通讯作者:
VARSHAVSKY, A
影响因子:
46.9
作者:
通讯作者:
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DOI:
10.1073/pnas.1215142110
发表时间:
2013-02-19
影响因子:
11.1
作者:
Pick, Thea R.;Braeutigam, Andrea;Weber, Andreas P. M.
通讯作者:
Weber, Andreas P. M.