發(fā)布時間：2018-05-16 18:49

  本文選題：水稻 + 生物信息學�。� 參考：《華中農(nóng)業(yè)大學》2017年博士論文

【摘要】：生物信息學在水稻代謝組學研究中發(fā)揮著越來越重要的作用,尤其是近幾年來隨著高通量和高分辨的代謝檢測技術(shù)的不斷發(fā)展,我們可以結(jié)合多種生物信息技術(shù)和統(tǒng)計學方法,對高通量的代謝數(shù)據(jù)進行有效的分析,以揭示水稻代謝物潛在的遺傳特征。同時生物信息學還可以將代謝組與基因組、轉(zhuǎn)錄組、蛋白組等其他組學緊密結(jié)合在一起,以促進水稻功能基因組學的深入研究。在本研究中,通過結(jié)合主成分分析(PCA)、層次聚類分析(HCA)、以及相關(guān)性分析等生物信息方法來研究水稻代謝物的自然變異情況,并利用基于代謝組的全基因組關(guān)聯(lián)分析(m GWAS)來探究代謝物生物合成途徑中潛在的遺傳基礎(chǔ)。首先從代謝水平分析了近900種代謝物在水稻不同亞群體之間(秈稻和粳稻)以及不同組織之間(葉片和種子)的積累情況,發(fā)現(xiàn)水稻代謝物的積累不僅在秈粳群體之間具有顯著的差異,而且也具有明顯的組織特異性。為了從遺傳的角度來解釋這種現(xiàn)象,我們分別在不同群體和不同組織中進行比較m GWAS分析,結(jié)果表明水稻代謝物在不同群體中的遺傳調(diào)控具有顯著的差異性,同時也發(fā)現(xiàn)水稻代謝物在不同組織中的遺傳調(diào)控具有特異性,并且認為產(chǎn)生這種特異性的主要原因是基因的組織特異性表達。利用多種生物信息方法將m GWAS的結(jié)果與基因組、轉(zhuǎn)錄組、代謝組及其他組學的數(shù)據(jù)有效的整合,可以促進水稻功能基因組學的研究,包括群體遺傳變異的分析、基于轉(zhuǎn)錄組數(shù)據(jù)的共表達分析、序列相似性的比較,以及基于GGM模型的代謝網(wǎng)絡(luò)的構(gòu)建等�；诖舜蠹s有30個新的候選基因和40個未知代謝物從水稻種子m GWAS的顯著位點中鑒定出來,其中基因Os04g11970進行了功能驗證,而且還有4個色胺和5-羥色胺的衍生物通過實驗解析出來。除此之外,水稻與玉米m GWAS之間的同源位點的共定位分析,可以揭示出具有相同或相似化學結(jié)構(gòu)的代謝物在水稻與玉米之間共同的遺傳調(diào)控,而且該方法可以將水稻m GWAS中大效應的位點與玉米m GWAS的高分辨率有效結(jié)合,從而大大提高水稻代謝遺傳基礎(chǔ)的研究,最終我們通過該方法鑒定出20個候選基因,其中Os06g18670已完成了功能驗證。最后基于代謝全基因組關(guān)聯(lián)分析(m GWAS)和農(nóng)藝性狀全基因組關(guān)聯(lián)分析(p GWAS)的并行研究,我們可以探究代謝物與農(nóng)藝性狀的遺傳關(guān)系。在本文中,鑒定出一些與種皮顏色、大小等農(nóng)藝性狀相關(guān)的候選基因,而且通過實驗證明了Os02g57760對葫蘆巴堿與粒寬兩種性狀的共同影響,從而為揭示代謝組與表型組之間的遺傳關(guān)系提供了直接證據(jù)。綜上所述,生物信息學對代謝組學的研究具有十分重要的作用�；诖�,可以發(fā)展出一種強大的分析工具,以用于研究植物功能基因組與代謝組之間的相互作用,尤其是對復雜農(nóng)藝性狀的低效QTL位點的克隆,并最終為水稻重要性狀的研究和作物遺傳改良提供新的思路。
[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.
【學位授予單位】：華中農(nóng)業(yè)大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：S511;Q811.4

【參考文獻】

相關(guān)期刊論文 前1條

1 Xuekui Dong;Wei Chen;Wensheng Wang;Hongyan Zhang;Xianqing Liu;Jie Luo;;Comprehensive profiling and natural variation of flavonoids in rice[J];Journal of Integrative Plant Biology;2014年09期

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本文編號：1898008