整合组学数据剖析水稻籽粒中代谢途径及其调控网络

Metabolomics and other new emerging omics technologies provide opportunities to reconstruct metabolic pathways for an organism rapidly and automatically. However, the multidimensional omics data need to be integrated to achieve this objective and the lack of suitable bioinformatic tools and algorithms may hinder this expectation. In previous studies, we constructed platforms for genome-wide association studies (GWAS) and metabolomics studies in rice, and we reported comprehensive profiling of 840 metabolites in the leaves of rice at the five-leaf stage and a further metabolic GWAS based on these platforms. In this application, we intend to (1) develop automatic ab initio metabolic network construction algorithms; (2) generate metabolic profiles of mature seeds of rice and collect available data from rice and other species, together with the big data generated by us; (3) build comprehensive metabolic pathways and regulatory networks of mature seeds of rice based on the algorithms which we plan to develop; and (4) validate part of the metabolic networks. We expect that this project will not only be able to deepen our understanding of the regulation of metabolism in seeds of rice, but also provide useful bioinformatic tools and algorithms for other studies on metabolic networks.

代谢组及各种新开发的组学技术为快速解析、系统研究代谢途径及其调控网络创造了条件，但是目前尚缺少整合多种组学数据构建代谢调控网络的生物信息学方法。前期研究中，我们建立了水稻代谢组分析平台和包括529份品种的水稻关联分析群体。本申请拟获取该群体的成熟籽粒代谢组数据，通过关联分析剖析水稻籽粒中代谢物性状的遗传基础，开发整合多种组学数据从头构建代谢调控网络的生物信息学方法，获得水稻籽粒的代谢途径及其调控网络，最后进行部分实验验证。本项目的预期成果将能够加深我们对水稻籽粒中代谢途径及其调控的了解，并产生有助于开展其他代谢调控研究的生物信息学算法和工具。

提高作物的营养成分是育种的重要目标，然而目前作物中对代谢调控网络的了解尚比较粗浅。在本项目的资助下，我们开发了整合多组学数据鉴定水稻关联分析候选基因的生物信息学方法和软件；开发了整合高通量基因组、代谢组信息结合遗传学数据构建代谢途径及其调控网络的生物信息学方法和软件；获得了比较可靠的未报道的水稻代谢途径及其调控网络；探索并比较了水稻和玉米的代谢组及其遗传调控网络。最后，我们为开展作物营养成分育种提供了水稻品种中各种代谢物调控位点的候选基因及相应的分子标记。