二代测序数据的处理及在微进化与肿瘤代谢中的应用

发布时间：2018-05-03 23:27

本文选题：二代测序数据 + 核小体定位　；参考：《中国科学技术大学》2016年博士论文

【摘要】：随着二代测序技术的普及与发展,与其相关的组学数据不断涌现。近年来,对这些组学数据的整合利用逐渐成为生物学研究的热点与难点。因为,越是复杂的生物学问题,越需要对不同水平的数据进行整合分析。本论文的绪论部分综述了测序技术的原理和发展过程,给出了二代测序数据的处理流程与常用实用软件,阐述了二代测序数据在组学研究中的重要地位。我的研究围绕对二代测序数据与不同水平的组学数据进行整合处理与运用,进行了以下2个相对独立的研究：1.核小体定位的微进化与表达噪音的关联研究基因表达变异为生物的适应性进化提供了大量且丰富的遗传资源。目前,许多个体之间的基因表达变异无法通过基因组序列突变来解释,因此表观遗传学被认为与这种微尺度的基因表达变异密切相关。核小体作为染色质的基本单元,对核小体的研究也是近年来表观遗传学非常热门的领域之一。核小体在基因组上处于动态变化之中,特别是在基因的启动子区域,大量的研究表明核小体对基因表达起着至关重要的作用。核小体的这种动态变化也称之为核小体定位的微进化,其与基因表达变异如何关联的机制还不清楚。本研究旨在探讨核小体定位的微进化与基因表达噪音的联系,整合分析利用二代测序技术获得的全基因组核小体组测序数据与基因表达数据,深入的研究了面包酵母核小体定位的微进化机制。通过比较一株酵母亲本及其1740代的五个样本的核小体定位信息和基因表达及其噪音,发现子代和亲本之间的核小体定位有明显的位移,而且主要影响低表达基因的表达噪音,从而降低整个基因组的表达噪音,为复杂性状的进化机制提供了全新的解释。2.基于转录组与蛋白组数据的肝癌个性化定量代谢网络分析肝脏是人体中最大的代谢器官,负责人体内多种代谢物的合成与分解。肝癌指的是肝脏组织细胞恶性增殖,是世界上死亡率最高的癌症之一。中国肝癌的发病率和死亡率均远超世界平均水平。因此,肝癌有着中国特色肿瘤之称。近年来,全基因组水平上代谢网络模拟技术不断发展,构建的代谢网络数量也逐渐增加。本实验室开发了一套个性化定量模拟代谢网络的个性化定量代谢网络模拟CPQMM)方法。PQMM法能很好的用于各种组织的特异性代谢网络模拟,并突破了之前构建代谢网络不能定量模拟的瓶颈。基于PQMM,我们整合利用TCGA的二代测序数据以及HPA的蛋白组数据,对肝癌组织的代谢进行全基因组水平的个性化定量模拟,结果揭示肝癌组织快速生长是因为其细胞代谢发生了巨大的变化,正常肝脏所进行的代谢显著降低,而与生长相关的代谢途径,如磷酸戊糖途径、氨基酸合成等显著提高,同时肝癌组织的ATP合成速率显著上升,这与其谷氨酸分解代谢速率提高相关。不仅如此,我们基于肝癌组织的代谢速率,将肝癌分为明显的两类。这两类肝癌的生存曲线具有显著差异,显示存活期较短的肝癌样本具有更高的ATP合成速率,存活期较长的样本可能与其保留了部分正常肝脏代谢功能,如胆固醇代谢、脂肪酸氧化、线粒体功能等有关。我们的研究是首次对肝癌与正常肝脏进行定量的个性化代谢模拟,模拟结果与实验数据高度吻合,这对肝癌的预防以及靶向治疗具有重要的理论指导意义。
[Abstract]:With the popularity and development of the two generation sequencing technology, the related data are constantly emerging. In recent years, the integration and utilization of these data has gradually become a hot and difficult problem in biological research. The more complex biological problems, the more need to integrate and analyze different levels of data. The introduction part of this paper is a summary of this paper. The principle and development process of sequencing technology, the processing flow and common practical software of the two generation sequencing data are given, and the important status of the two generation sequencing data in the study of the omics is expounded. My research focuses on the integration and application of the two generation sequencing data and the different level of the group data. The following 2 relative independent studies have been carried out. 1. the correlation between microevolution and expression noise of nucleosome localization research gene expression variation provides abundant and abundant genetic resources for adaptive evolution of organisms. At present, the gene expression variation among many individuals can not be explained by the mutation of the genome sequence, so the epigenetic inheritance is considered to be expressed with this microscale gene expression. The nucleosome is the basic unit of chromatin and the study of nucleosome is also one of the most popular fields in epigenetics in recent years. The nucleosome is in the dynamic change in the genome, especially in the promoter region of the gene, and a large number of studies have shown that the nucleosomes play a vital role in gene expression. This dynamic change of nucleosome, also known as the micro evolution of nucleosome localization, is not clear about the mechanism associated with gene expression variation. This study aims to explore the relationship between the micro evolution of nucleosome localization and the noise of gene expression, and to integrate the analysis of the sequencing data and genes of the whole genome nucleosome group obtained by the two generation sequencing technology. By comparing the nucleosome location information and gene expression and noise of five samples of a yeast parent and its 1740 generation, it is found that the nucleosome localization between the progeny and the parent has obvious displacement, and mainly affects the expression noise of the low expression gene. It reduces the expression noise of the whole genome and provides a new explanation for the evolutionary mechanism of complex traits..2. based on the data of the transcriptional and protein groups, the individual quantitative metabolic network of liver cancer is the largest metabolic organ in the human body, responsible for the synthesis and decomposition of a variety of metabolites in the human body. Liver cancer refers to the liver tissue cells. Malignant proliferation is one of the most fatal cancers in the world. The incidence and mortality of liver cancer in China are far beyond the world average. Therefore, liver cancer is known as a Chinese characteristic tumor. In recent years, the metabolic network simulation technology has been developed at the whole genome level and the number of metabolic networks is gradually increasing. The individualized quantitative metabolic network simulation of the personalized quantitative metabolic network (CPQMM) method.PQMM method can be used to simulate the specific metabolic network of various organizations, and break through the bottleneck of building the metabolic network that can not be quantified before. Based on PQMM, we integrate the two generation of TCGA sequencing data and the protein group data of HPA, The metabolism of liver cancer tissue was simulated by the whole genome level. The results revealed that the rapid growth of liver cancer tissue was caused by great changes in the metabolism of the cells. The metabolism of normal liver was significantly reduced, and the metabolic pathways related to growth, such as the amino acid phosphate pathway and the synthesis of amino acids, were significantly improved. The ATP synthesis rate of cancer tissue is significantly increased, which is related to the increase of the rate of glutamic acid catabolism. Not only so, we divide the liver cancer into two distinct groups based on the metabolic rate of the liver cancer tissue. The survival curves of the two types of hepatoma have significant differences, indicating that the shorter survival time of the liver cancer samples have higher ATP synthesis rate and survival time. The longer samples may be related to the retention of some normal liver metabolic functions, such as cholesterol metabolism, fatty acid oxidation, and mitochondrial function. Our study is the first quantitative individualized metabolic simulation of liver cancer and normal liver. The simulation results are highly consistent with the experimental data, which is a preventive and targeted therapy for liver cancer. Important theoretical guiding significance.

【学位授予单位】：中国科学技术大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：R730.2

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