【作者】 陈伟；

【导师】 罗杰；

【作者基本信息】 华中农业大学 ， 生物化学与分子生物学， 2015， 博士

【摘要】 植物代谢物对于植物自身和人类的营养健康都非常重要。尽管近年来代谢组学领域的研究有了很大的进展,然而,在植物代谢组的遗传调控和自然变异方面很大程度上都是未知的。目前,通过建立MS2T(二级质谱标签)数据库极大地促进了以LC-MS(液相色谱-质谱联用)为基础的代谢组学的发展,同时也利用标准品发展了广谱定向的代谢组学研究方法。在此基础上,我们发展了一种步进MIM-EPI(多离子监测模式-增强子离子扫描)的方法来构建MS2T数据库,并且我们通过步进MIM的方法来触发二级质谱离子的收集。首先,我们总共获得了698种几乎没有重复结果的二级质谱数据,并且鉴定了其中的135种代谢产物。然后利用这些MS2T数据库中的代谢物,结合MRM(多反应监测模式)对277个定向的代谢产物(包括一些植物激素)进行了相对定量。进一步对这些代谢产物在水稻叶片中的脱水应激反应和自然变异进行研究,发现ABA(脱落酸)以及多胺类的物质在旱胁迫下有明显的应激反应,而碳糖糖基化的黄酮可以作为一种潜在的籼稻和粳稻分型的标签物质来进行研究。这些结果表明我们新建立的水稻MS2T数据库和广谱定向的代谢组学研究方法可以作为一个功能基因组学研究的工具。为了研究水稻代谢组的遗传调控,我们利用RIL(重组自交系)群体,对900多种代谢产物定位了2800多个m QTL(代谢数量性状座位),通过分析两个组织(抽穗期剑叶和发芽72h种子)的代谢谱及m QTL的定位结果,揭示了代谢谱的积累模式在不同组织之间的差异,以及控制代谢物含量的复杂的遗传调控模式。根据m QTL定位的结果,我们进一步结合转录组以及生物信息学分析筛选到了24个候选基因,包括一些可以调控或者影响重要农艺性状和生物学过程的基因,并对其中部分进行了功能验证。该研究为水稻功能基因组学(特别是代谢组学)研究提供了大量高质量的数据和结果,深化了人们对水稻代谢组的遗传基础的理解,有助于搭建基因组和表型组之间的桥梁。为了进一步解析水稻代谢组自然变异的遗传和生化基础,我们对529份水稻自然品种中所检测到的840种代谢产物进行了m GWAS(代谢全基因组关联分析)。首先通过自然品种代谢谱的分析,发现水稻代谢组在种内及亚种间存在巨大差异,揭示出逆境应答代谢组在亚种分化中的可能作用。进一步对所定位到的数百个高精度、大效应的控制代谢物含量的自然变异位点进行分析,找到了36个影响水稻生长发育、逆境生理及营养品质形成过程重要代谢物的候选基因。在此基础上,通过遗传转化及生化分析,鉴定了其中5个基因的功能,并进一步重构了水稻逆境抗性及营养成分相关的重要代谢途径。该研究成功表明水稻代谢组的遗传和生化基础研究可以作为遗传改良的有力的工具。更多还原

【Abstract】 Plant metabolites are crucial for both plant life and human nutrition. Despite recent advance in metabolomics, natural variation and the underlying genetic control of plant metabolome remain largely unknown.Liquid chromatography–mass spectrometry(LC-MS)-based metabolomics has been facilitated by the construction of MS2 spectral tag(MS2T) library from the total scan ESI MS/MS data, and the development of widely targeted metabolomics method using MS/MS data gathered from authentic standards. Here, a novel strategy called stepwise multiple ion monitoring-enhanced product ions(stepwise MIM-EPI) was developed to construct the MS2 T library, in which stepwise MIM was used as survey scans to trigger the acquisition of EPI. A total number of 698(almost) non-redundant metabolites with MS2 spectra were obtained, of which 135 metabolites were identified/annotated. Integrating the data gathered from our MS2 T library and other available multiple reaction monitoring(MRM) information, a widely targeted metabolomics method was developed to quantify 277 metabolites, including some phytohormones. Evaluation the dehydration responses and natural variations of these metabolites in rice leaf not only suggested the coordinated regulation of abscisic acid(ABA) with metabolites such as serotonin derivative(s), polyamine conjugates under drought stress, and also revealed some C-glycosylated flavones as the potential markers for the discrimination of indica and japonica rice subspecies. The new MS2 T library construction and widely targeted metabolomics strategy could be used as a tool for rice functional genomics.To explore the genetic control of plant metabolome, we performed a genetical genomics analysis of the rice metabolome which provided high resolution for over 2800 quantitative trait loci(QTL) for 900 metabolites. Distinct and overlapped accumulation was observed and complex genetic regulation of metabolism was revealed in two different tissues. Data mining has associated 24 candidate genes with various m QTLs, including ones controlling and/or regulating important morphological traits and biological processes, and the corresponding pathways were reconstructed by updating in vivo functions of previously identified and newly assigned genes. This study demonstrated a powerful tool and provided vast amount of high-quality data for understanding plant metabolome which may help bridge the gap between the genome and phenome.To further elucidate the genetic and biochemical bases of natural variation of rice metabolome, we report a comprehensive profiling for 840 metabolites and a further metabolic genome-wide association study(m GWAS) based on ~6.4 million SNPs obtained from 529 diverse accessions of Oryza sativa. We identified hundreds of common variants influencing numerous secondary metabolites with large effects at high resolution. Significant heterogeneity was observed in natural variation of metabolites and their underlying genetic architectures associated with different subspecies of rice. Data mining revealed 36 candidate genes modulating levels of metabolites that are of potential physiological and nutritional importance. As a proof-of-concept, we functionally identified or annotated 5 candidate genes. Our study provides insights into genetic and biochemical bases of rice metabolome and can be used as a powerful complementary tool to classical phenotypic traits mapping for rice improvement.更多还原