水稻代谢组学的生物信息学分析及遗传基础的研究

发布时间：2018-05-16 18:49

本文选题：水稻 + 生物信息学　；参考：《华中农业大学》2017年博士论文

【摘要】：生物信息学在水稻代谢组学研究中发挥着越来越重要的作用,尤其是近几年来随着高通量和高分辨的代谢检测技术的不断发展,我们可以结合多种生物信息技术和统计学方法,对高通量的代谢数据进行有效的分析,以揭示水稻代谢物潜在的遗传特征。同时生物信息学还可以将代谢组与基因组、转录组、蛋白组等其他组学紧密结合在一起,以促进水稻功能基因组学的深入研究。在本研究中,通过结合主成分分析(PCA)、层次聚类分析(HCA)、以及相关性分析等生物信息方法来研究水稻代谢物的自然变异情况,并利用基于代谢组的全基因组关联分析(m GWAS)来探究代谢物生物合成途径中潜在的遗传基础。首先从代谢水平分析了近900种代谢物在水稻不同亚群体之间(籼稻和粳稻)以及不同组织之间(叶片和种子)的积累情况,发现水稻代谢物的积累不仅在籼粳群体之间具有显著的差异,而且也具有明显的组织特异性。为了从遗传的角度来解释这种现象,我们分别在不同群体和不同组织中进行比较m GWAS分析,结果表明水稻代谢物在不同群体中的遗传调控具有显著的差异性,同时也发现水稻代谢物在不同组织中的遗传调控具有特异性,并且认为产生这种特异性的主要原因是基因的组织特异性表达。利用多种生物信息方法将m GWAS的结果与基因组、转录组、代谢组及其他组学的数据有效的整合,可以促进水稻功能基因组学的研究,包括群体遗传变异的分析、基于转录组数据的共表达分析、序列相似性的比较,以及基于GGM模型的代谢网络的构建等。基于此大约有30个新的候选基因和40个未知代谢物从水稻种子m GWAS的显著位点中鉴定出来,其中基因Os04g11970进行了功能验证,而且还有4个色胺和5-羟色胺的衍生物通过实验解析出来。除此之外,水稻与玉米m GWAS之间的同源位点的共定位分析,可以揭示出具有相同或相似化学结构的代谢物在水稻与玉米之间共同的遗传调控,而且该方法可以将水稻m GWAS中大效应的位点与玉米m GWAS的高分辨率有效结合,从而大大提高水稻代谢遗传基础的研究,最终我们通过该方法鉴定出20个候选基因,其中Os06g18670已完成了功能验证。最后基于代谢全基因组关联分析(m GWAS)和农艺性状全基因组关联分析(p GWAS)的并行研究,我们可以探究代谢物与农艺性状的遗传关系。在本文中,鉴定出一些与种皮颜色、大小等农艺性状相关的候选基因,而且通过实验证明了Os02g57760对葫芦巴碱与粒宽两种性状的共同影响,从而为揭示代谢组与表型组之间的遗传关系提供了直接证据。综上所述,生物信息学对代谢组学的研究具有十分重要的作用。基于此,可以发展出一种强大的分析工具,以用于研究植物功能基因组与代谢组之间的相互作用,尤其是对复杂农艺性状的低效QTL位点的克隆,并最终为水稻重要性状的研究和作物遗传改良提供新的思路。
[Abstract]:Bioinformatics plays an increasingly important role in the study of rice metabolomics, especially with the development of high-throughput and high-resolution metabolic detection techniques in recent years, we can combine a variety of bioinformatics and statistical methods. The high throughput metabolic data were analyzed effectively to reveal the potential genetic characteristics of rice metabolites. At the same time, bioinformatics can combine metabolites with genome, transcriptome, proteome and so on, so as to promote the further study of rice functional genomics. In this study, the natural variation of rice metabolites was studied by means of biological information methods such as principal component analysis (PCA), hierarchical cluster analysis (HAC), and correlation analysis. The genome-wide association analysis based on metabolites was used to explore the potential genetic basis of metabolite biosynthesis pathway. The accumulation of nearly 900 metabolites among different subpopulations (Indica and japonica) and between different tissues (leaves and seeds) was analyzed at the metabolic level. It was found that the accumulation of metabolites in rice was not only significantly different between indica and japonica populations, but also had obvious tissue specificity. In order to explain this phenomenon from the perspective of heredity, we compared m GWAS analysis in different populations and different tissues. The results showed that the genetic regulation of metabolites in different populations was significantly different. It is also found that the genetic regulation of rice metabolites in different tissues is specific, and it is believed that the main reason for this specificity is the tissue-specific expression of genes. The effective integration of m GWAS results with genomic, transcriptional, metabolic and other genomics data using a variety of biological information methods can facilitate the study of functional genomics in rice, including the analysis of population genetic variation. Coexpression analysis based on transcriptome data, comparison of sequence similarity, and construction of metabolic network based on GGM model. Based on this, about 30 new candidate genes and 40 unknown metabolites were identified from the significant sites of rice seed m GWAS, in which the gene Os04g11970 was functionally validated. Four derivatives of tryptamine and 5-hydroxytryptamine were analyzed experimentally. In addition, the co-localization of the homologous sites between rice and maize m GWAS may reveal the common genetic regulation of metabolites with the same or similar chemical structure between rice and maize. Moreover, this method can effectively combine the sites of large effect in rice m GWAS with the high resolution of maize m GWAS, thus greatly improving the genetic basis of rice metabolism. Finally, 20 candidate genes were identified by this method. Os06g18670 has completed the functional verification. Finally, we can explore the genetic relationship between metabolites and agronomic traits based on the parallel studies of metabolic genome-wide association analysis (m GWAS) and agronomic trait whole genome association analysis (GWAS). In this paper, some candidate genes related to agronomic traits such as seed coat color and seed coat size were identified, and the effects of Os02g57760 on the two traits of cucurbitine and grain width were proved by experiments. This provides direct evidence for revealing the genetic relationship between metabolic and phenotypic groups. In conclusion, bioinformatics plays an important role in the study of metabonomics. Based on this, a powerful analytical tool can be developed to study the interactions between functional genomes and metabolites in plants, especially the cloning of inefficient QTL loci for complex agronomic traits. Finally, it provides a new idea for the study of rice importance and crop genetic improvement.
【学位授予单位】：华中农业大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：S511;Q811.4

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