Bioinformatic analysis of molecular network of glucosinolate biosynthesis
Bioinformatic analysis of molecular network of glucosinolate biosynthesis
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DOI：
10.1016/j.compbiolchem.2010.12.002
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发表时间：
2011-02-01
影响因子：
3.1
通讯作者：
Chen, Sixue
中科院分区：
文献类型：
--
作者：
Chen, Yazhou;Yan, Xiufeng;Chen, Sixue
硫代葡萄糖苷是拟南芥次生代谢产物的主要组成部分，在植物与病原菌和昆虫的相互作用中起着重要作用。硫代葡萄糖苷研究的进展已经确定了生物合成途径。然而，硫代葡萄糖苷途径和其他分子途径之间的串扰和相互作用在很大程度上是未知的。本文利用三种生物信息学工具对硫代葡萄糖网络中的新组分和通路联系进行了研究。虽然没有一个软件工具是完美的预测芥子油苷基因，所有的工具产生的结果的组合导致所有已知的芥子油苷基因的成功预测。该方法被用于预测硫甙网络中的新基因。共发现了330个与硫代葡萄糖苷生物合成相关的基因。其中64个基因被选择来构建硫代葡萄糖甙网络，因为所有软件工具都预测了它们与至少一个已知硫代葡萄糖甙基因的个体连接。候选基因突变体的微阵列数据用于验证结果。由硫甙种子基因预测的9个基因的突变体都表现出硫甙基因表达的变化。其中四个基因在功能上与芥子油苷生物合成相互作用。这些结果表明，我们采取的方法提供了一个强大的方式来揭示新的球员在芥子油苷网络。硫代葡萄糖苷生物合成的计算机网络的创建将允许产生许多可测试的假设，并最终实现预测生物学。(C)2010爱思唯尔有限公司版权所有。
Glucosinolates constitute a major group of secondary metabolites in Arabidopsis, which play an important role in plant interaction with pathogens and insects. Advances in glucosinolate research have defined the biosynthetic pathways. However, cross-talk and interaction between glucosinolate pathway and other molecular pathways are largely unknown. Here three bioinformatics tools were used to explore novel components and pathway connections in glucosinolate network. Although none of the software tools were prefect to predict glucosinolate genes, combination of results generated by all the tools led to successful prediction of all known glucosinolate genes. This approach was used to predict new genes in glucosinolate network. A total of 330 genes were found with high potential to relate to glucosinolate biosynthesis. Among them 64 genes were selected to construct glucosinolate network because their individual connection to at least one known glucosinolate gene was predicted by all the software tools. Microarray data of candidate gene mutants were used for validation of the results. The mutants of nine genes predicted by glucosinolate seed genes all exhibited changes in the expression of glucosinolate genes. Four of the genes have been well-known to functionally interact with glucosinolate biosynthesis. These results indicate that the approach we took provides a powerful way to reveal new players in glucosinolate networks. Creation of an in silico network of glucosinolate biosynthesis will allow the generation of many testable hypotheses and ultimately enable predictive biology. (C) 2010 Elsevier Ltd. All rights reserved.