基于生物信息学技术筛选慢性乙型肝炎血液相关基因的研究

发布时间：2018-08-19 18:27

【摘要】：慢性乙型肝炎是由乙型肝炎病毒引起的一种世界性疾病,最初被称作血清性肝炎。在亚非等发展中国家发病率高,是中国的地方病。全世界约三分之一人一生中都曾一度感染过慢性乙型肝炎,其中无症状乙肝病毒携带者(HBsAg携带者)超过3.5亿,我国约占1.3亿。多数无症状,其中1/3出现肝损害的临床表现。目前我国有乙肝患者3000万。乙肝的特点为起病较缓,以亚临床型及慢性型较常见。无黄疸型HBsAg持续阳性者易慢性化。本病主要通过血液、母婴和性接触进行传播。“慢性肝炎→肝纤维化→肝硬化→肝癌”是肝脏疾病演化的一条途径,对病患的生存形成相当大的威胁。乙肝所致的慢性肝炎在各种病因所致的慢性肝炎中约占80%～90%。慢性肝炎持续时间可长达数年,甚至数十年。本病通常表现较轻,不产生任何症状或明显的肝损害,但有些病例,持续的炎症会缓慢地损伤肝脏,最终导致肝硬化和肝癌,而肝癌患者预后极差,治疗方法较少。目前,乙肝疫苗的应用是预防和控制乙型肝炎的主要措施,虽全面接种乙肝疫苗可以使乙肝患病率大幅度下降,但由于我国乙肝感染患者基数庞大,新的乙肝患者还在不断出现,且在经济和社会发展不如我们的国家,乙肝还在继续蔓延。因此,寻找有效治疗乙肝的方法是医务人员和科研工作者急需解决的难题。基因芯片作为21世纪生命科学领域的一项重要的技术平台,是筛选差异表达相关基因的有效手段,具有高通量和快速测量等优点。基因表达谱是指通过构建处于某一特定状态下的细胞或组织的非偏性cDNA文库,大规模cDNA测序,收集cDNA序列片段、定性、定量分析其mRNA群体组成,从而描绘该特定细胞或组织在特定状态下的基因表达种类和丰度信息,这样编制成的数据表就称为基因表达谱。由于表达谱芯片在研究细胞基因表达模式上具有的优势,利用它可获取肿瘤细胞生长的各期以及肿瘤发生与发展过程中相关基因的表达模式变化,因此,基因表达谱芯片已广泛应用于肿瘤发生机制、早期诊断、肿瘤基因分型、指导治疗及评估预后等研究领域。随着表达谱芯片技术的广泛开展,产生了丰富的、海量的、复杂的生物信息数据。如何解读芯片上成千上万个基因点的杂交信息,揭示其中蕴含的生命特征和规律,己成为限制基因芯片技术应用和发展的主要“瓶颈”。生物信息学是现代生物学与医学科学(如生物化学、细胞生物学、发育生物学、遗传学、基因组学、生理学)和信息科学、计算机科学、生物统计学、数学等学科相互渗透并高度交叉形成的一门新兴前沿学科,最初常被称为基因组信息学,它以生物芯片研究为基础,以获取、加工、储存、管理、检索、分配、分析和释读生物学实验信息为手段,综合运用数学、计算机科学和生物学工具,以达到理解数据中所蕴含的生物学含义的目的。由于目前研究对象主要集中于核苷酸和氨基酸两方面,所以,狭义地说,生物信息学就是将计算机科学和数学应用于生物大分子信息的获取、加工、存储、分类、检索与分析,以达到理解这些生物大分子信息的生物学意义的交叉学科。生物信息学的主要研究内容包括生物分子数据的收集与管理、数据库搜索与序列比较、基因组序列信息分析、基因表达数据的分析与处理、蛋白质结构预测、系统发生分析、比较基因组学等方面。具体地说,生物信息学的研究范围包括以下几个方面：①基因组相关信息的获取、储存、管理、处理、分配与解释。②新基因的发现与定位、功能注释、调控机制和网络关系等研究。③非编码区信息结构分析。④生物进化的研究。⑤完整基因组的比较研究。⑥基因组信息分析的方法研究。⑦功能基因组相关信息分析,包括与大规模基因表达谱分析相关的算法、软件研究,基因表达调控网络的研究等。⑧蛋白质分子空间结构的预测、模拟和分子设计。⑨药物设计以及应用发展研究等。生物信息学是当今生命科学和自然科学的重大前沿领域之一,同时也是21世纪自然科学的核心领域之一,其研究重点主要体现在基因组学和蛋白质组学两方面。目前基因组学的研究出现了几个重心的转移：一是将已知基因的序列与功能联系在一起的功能基因组学研究。二是从作图为基础的基因分离转向以序列为基础的基因分离。三是从研究疾病的起因转向探索发病机理。四是从疾病诊断转向疾病易感性研究。其中,功能基因组学随着人类基因组计划绘图与测序工作(即结构基因组学)的完成已成为新的研究热点。在肿瘤研究中,目前常用的分析方法包括序列比对、统计分析、可视化作图、生物聚类、通路分析及启动子预测等。在分子水平进行数据挖掘、对疾病进行阐述,为肿瘤分子发病机理的研究开阔了新的研究思路,从而更加全面地从整体上对疾病进行研究。蛋白质组学的研究主要集中在以下方面：(1)蛋白质三维结构预测。(2)基于基因组上下文预测蛋白质相互作用,如基因邻居(gene "neighbor")、系统发生谱(phylogenetic profiles)和基因融合(Rosetta stone method)方法。(3)利用生物信息学对蛋白质分子进行理论模拟与结构预测,从而为药物分子设计提供依据。本研究内容主要分为以下三个部分：第一部分：慢性乙型肝炎基因表达谱芯片研究。收集慢性乙型肝炎患者及未患病志愿者血液标本各三例,采用美国Affymetrix公司的Human Genome U133Plus2.0Array基因表达谱芯片,按一步法抽提慢性乙型肝炎患者及志愿者血液白细胞总RNA,分离纯化,经逆转录合成掺入生物素标记的cDNA合成探针,与芯片杂交和严格洗片后,用荧光扫描仪扫描芯片荧光信号图像,分析慢性乙型肝炎患者与正常人中差异表达的基因。扫描仪结果经仔细分析后,发现有37542条基因被检出。经检验,芯片结果准确有效,可用于进一步分析利用。第二部分：慢性乙型肝炎发病相关基因的筛选。本研究利用基因芯片数据分析软件BRB-ArrayTools4.2.1,对第一部分获得的慢性乙型肝炎基因表达谱芯片数据进行挖掘,并进行生物信息学分析,探讨基于基因表达谱的途径筛选肿瘤发病相关基因。首先将芯片数据导入BRB-ArrayTools4.2.1,进行数据筛选,然后通过层次聚类,分组分析找出差异表达基因,进行GO分析和KEGG通路分析。结果得到51个差异表达基因,其中上调31个,下调20个。经功能注释后通路分析发现,它们主要被富集到四个生物通路中：白介素信号通路,炎症趋化因子和细胞因子介导的信号通路,凋亡信号通路以及FAS信号通路。第三部分：慢乙肝发病相关基因的蛋白质相互作用网络分析。对于芯片数据,获得差异表达基因仅是第一步,但这些差异表达基因的转录调控关系及所表达的蛋白质间的相互作用并不能从基因表达谱中分析到,更重要的是如何对这些差异表达基因进行恰如其分的注释,以揭示这些基因在特定条件下的相互关系。本章利用GATHER、STRING、Cytoscape等方法对差异基因进行生物学分析和相互作用网络图谱分析。结果发现,上调基因主要被富集到细胞黏附、Wnt信号通路、内分泌系统发展等通路中。而下调基因刚多被富集到机体免疫应答,防御反射,对生物刺激等各种体内外刺激的应答等生物学功能。为进一步了解差异基因间的相互作用网络,用STRING在线工具对51个慢性乙型肝炎相关差异基因编码的蛋白质间的相互作用进行了分析,结果发现这些基因编码的蛋白间的相互作用主要集中在11个蛋白。通过Cytoscape构建蛋白质相互作用网络进行进一步分析,发现上调基因中六个编码蛋白质与这些差异基因有密切联系,它们是：FLNC, MDK, TK1, THBS1, MAPK12和CD93。其中,MDK,TKl等基因与STRING分析结果一致,再次证明了它们的重要性。综上所述,本研究在芯片实验基础上,利用生物信息学方法有效地分析了慢性乙型肝炎发病的基因芯片数据,运用基因表达谱数据分析工具、生物信息学工具及文献挖掘工具对慢性乙型肝炎有关差异基因进行了深入的生物信息学分析,成功筛选到11个慢性乙型肝炎异常表达的结点基因,6个与差异表达基因密切联系的蛋白质。这些基因在慢性乙型肝炎从发病的过程中可能起重要作用,对这些基因从改变的幅度和本身的功能进行更深入的分析探索将是下一步工作的重点；在生物通路方面,亦发现了几条贯穿慢性乙型肝炎发病的生物通路,各条信号通路之间可能相互联系、相互影响、相互交叉形成了复杂的有机信号网,共同对慢性乙型肝炎的发生起作用。这些研究结果为进一步认识慢性乙型肝炎的分子发病机制、药物研发及治疗提供有意义的探索和依据。
[Abstract]:Chronic hepatitis B (CHB) is a worldwide disease caused by hepatitis B virus (HBV), originally known as serum hepatitis. It has a high incidence in Asia, Africa and other developing countries, and is endemic in China. About one third of the world's people have been infected with CHB once in their lifetime, including asymptomatic hepatitis B virus carriers (HBsAg carriers). At present, 30 million hepatitis B patients in China are characterized by mild onset, which is more common in subclinical and chronic types. Persistent positive HBsAg patients without jaundice tend to be chronically transmitted mainly through blood, mother-to-child and sexual contact. Hepatitis liver fibrosis cirrhosis liver cancer is an evolutionary pathway of liver disease, which poses a considerable threat to the survival of patients. Chronic hepatitis caused by hepatitis B accounts for about 80% to 90% of the chronic hepatitis caused by various causes. Chronic hepatitis can last for several years, even decades. The disease is usually mild and does not develop. Any symptoms or obvious liver damage, but in some cases, persistent inflammation will slowly damage the liver, leading to cirrhosis and liver cancer, and liver cancer patients with poor prognosis, less treatment. At present, the use of hepatitis B vaccine prevention and control of hepatitis B is the main measure, although full vaccination of hepatitis B vaccine can make a large incidence of hepatitis B. However, due to the large base of hepatitis B patients in China, new hepatitis B patients are still emerging, and the economic and social development is not as good as our country, hepatitis B is still spreading. Therefore, to find effective treatment of hepatitis B is an urgent problem for medical staff and researchers.
As an important technology platform in the field of life science in the 21st century, gene chip is an effective method for screening differentially expressed genes with the advantages of high throughput and rapid measurement. Column fragments, which qualitatively and quantitatively analyze the composition of their mRNA population to describe the type and abundance of gene expression in a particular cell or tissue in a particular state, are called gene expression profiles. Gene expression profiles have been widely used in tumor genesis, early diagnosis, tumor genotyping, guiding treatment and evaluating prognosis. With the development of gene expression profiles, abundant, massive and complex genes have been produced. How to interpret the hybridization information of thousands of gene spots on the chip and reveal the life characteristics and laws contained therein has become the main "bottleneck" that restricts the application and development of gene chip technology.
Bioinformatics is a new frontier subject, originally known as genomic informatics, which is formed by the mutual penetration and highly intersection of modern biology and Medical Sciences (such as biochemistry, cell biology, developmental biology, genetics, genomics, physiology) and information science, computer science, biostatistics, mathematics and so on. Biochip research is based on the acquisition, processing, storage, management, retrieval, distribution, analysis and interpretation of biological experimental information by means of a combination of mathematical, computer science and biological tools to achieve understanding of the biological implications contained in the data. Therefore, in a narrow sense, bioinformatics is an interdisciplinary subject that applies computer science and mathematics to the acquisition, processing, storage, classification, retrieval and analysis of biomolecular information in order to understand the biological significance of these biomolecular information.
The main research contents of bioinformatics include the collection and management of biomolecular data, database search and sequence comparison, genome sequence information analysis, gene expression data analysis and processing, protein structure prediction, phylogenetic analysis, comparative genomics and so on. Several aspects: (1) acquisition, storage, management, processing, distribution and interpretation of genome-related information; (2) discovery and localization of new genes, functional annotations, regulatory mechanisms and network relationships; (3) analysis of information structure in non-coding regions; (4) study of biological evolution; (6) comparative study of complete genomes; (3) study on methods of genome information analysis _Functional genome related information analysis, including large-scale gene expression profile analysis related algorithms, software research, gene expression regulation network research, etc. Protein molecular spatial structure prediction, simulation and molecular design. _Drug design and application development research.
Bioinformatics is one of the Important Frontiers of life science and natural science, and also one of the core fields of Natural Science in the 21st century. Its research focuses mainly on genomics and proteomics. Functional genomics research that can be linked. Second, the shift from mapping-based gene isolation to sequence-based gene isolation. Third, the shift from the study of the causes of disease to the exploration of pathogenesis. Fourth, the shift from disease diagnosis to disease susceptibility research. Among them, functional genomics with the mapping and sequencing of the human genome project In cancer research, the common analytical methods include sequence alignment, statistical analysis, visual mapping, biological clustering, pathway analysis and promoter prediction. Data mining at the molecular level is used to illustrate the disease and to open up the study of molecular pathogenesis of cancer. Proteomics research focuses on the following aspects: (1) protein three-dimensional structure prediction. (2) prediction of protein interactions based on genomic context, such as gene neighbors, phylogenetic profiles, and bases. Rosetta stone method. (3) Bioinformatics is used to simulate and predict the structure of protein molecules, so as to provide a basis for drug molecular design.
The research contents are divided into three parts.
Part 1: Chronic hepatitis B gene expression profiling chip. Three blood samples from patients with chronic hepatitis B and three healthy volunteers were collected. Total RNA of leukocytes from patients and volunteers with chronic hepatitis B was extracted by one-step method using human Genome U133Plus2.0Array gene expression profiling chip of Affymetrix Company in the United States. After hybridization and strict preparation, the differentially expressed genes in patients with chronic hepatitis B and normal persons were analyzed by fluorescence scanner. The scanner results showed that 37542 genes were detected after careful analysis. The results are accurate and effective, and can be used for further analysis and utilization.
Part two: Screening of genes related to the pathogenesis of chronic hepatitis B. In this study, the microarray data of chronic hepatitis B gene expression profiles obtained in the first part were mined by using the microarray data analysis software BRB-Array Tools 4.2.1, and bioinformatics analysis was carried out to explore the way of screening tumor-related genes based on gene expression profiles. The microarray data were imported into BRB-Array Tools 4.2.1 for data screening, then differentially expressed genes were identified by hierarchical clustering, grouping analysis, GO analysis and KEGG pathway analysis. Interleukin signaling pathway, inflammatory chemokine and cytokine mediated signaling pathway, apoptosis signaling pathway and FAS signaling pathway.
Part 3: Protein-protein interaction network analysis of genes associated with chronic hepatitis B. For microarray data, obtaining differentially expressed genes is only the first step, but the transcriptional regulation of these differentially expressed genes and the interactions between the expressed proteins can not be analyzed from the gene expression profiles, and more importantly, how to do these In this chapter, we used GATHER, STRING, Cytoscape and other methods to analyze the biology of differentially expressed genes and their interaction network map. The results showed that the up-regulated genes were mainly enriched in cell adhesion, Wnt signaling pathway and endocrine system. The down-regulated genes are mostly enriched in the immune response, defense reflex, response to various stimuli in vivo and in vitro. To further understand the interaction network between different genes, the STRING online tool was used to identify the proteins encoded by 51 different genes related to chronic hepatitis B. Interactions between the proteins encoded by these genes were analyzed and found to be mainly concentrated in 11 proteins. Further analysis of the protein-protein interaction network constructed by Cytoscape revealed that six of the up-regulated genes encoded proteins were closely related to these genes: FLNC, MDK, TK1, THBS1, MAP. K12 and CD93., MDK, TKl and other genes are consistent with the results of STRING analysis, proving their importance again.
To sum up, on the basis of microarray experiment, this study used bioinformatics method to analyze the gene chip data of chronic hepatitis B. Using gene expression profiling data analysis tools, bioinformatics tools and literature mining tools, we carried out in-depth bioinformatics analysis of differentially related genes of chronic hepatitis B. Eleven abnormally expressed nodal genes and six proteins closely related to differentially expressed genes were successfully screened. These genes may play an important role in the pathogenesis of chronic hepatitis B. Further analysis of the extent and function of these genes will be the next step. In the aspect of biological pathways, several biological pathways are also found, which may be interrelated and interacted with each other, forming a complex organic signal network, and play a role in the pathogenesis of chronic hepatitis B. These results provide a further understanding of chronic hepatitis B. It provides meaningful exploration and evidence for molecular pathogenesis, drug development and treatment.
【学位授予单位】：南方医科大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：R512.62

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