小鼠肝实质细胞蛋白质表达谱构建及功能分析

发布时间：2018-05-14 00:44

本文选题：肝脏细胞 + 蛋白质组　；参考：《中国人民解放军军事医学科学院》2012年博士论文

【摘要】：肝脏是人体内最大的腺体，其主要功能包括物质代谢、解毒、防御、激素系统稳态、血液存储和pH调节、造血和免疫调节等。肝脏由肝实质细胞、肝窦内皮细胞、枯否细胞和星状细胞等组成。细胞是肝脏复杂功能发挥的基础，尽管对于这些细胞的研究不少，其蛋白质组成并不清楚，在细胞水平的肝脏蛋白质组构建还没有进行。随着高精度质谱仪的发展，高通量规模化鉴定真核细胞的蛋白质组成为可能。利用生物信息学方法进行蛋白质鉴定和数据挖掘，全面系统地描绘细胞表达的蛋白，对理解不同类型细胞的特异功能具有重大意义。基于细胞的蛋白质组研究是蛋白质组学研究内容之一，也是人类肝脏蛋白质组计划（human liver proteome project，HLPP）的重要组成部分。本研究欲分离和纯化小鼠肝脏的不同细胞，同时构建不同细胞的蛋白质表达谱。对肝脏内的主要细胞类型-肝实质细胞蛋白质数据集进行了深入的挖掘，分析其功能特征。本研究通过系统生物学的方法对肝脏细胞蛋白质组和转录组进行比较分析，发现一批和肝脏细胞功能特异性相关的分子。通过比较细胞膜表面表达的CD分子，以寻找细胞特异性的表面标记物。同时，对这些细胞参与的KEGG通路进行比较，发现丙酮酸代谢通路可能存在实质细胞和非实质细胞细胞间协作，这对于理解肝脏细胞在代谢中的相互协作起重要提示作用。另外，选取通路中的多个分子进行验证，为我们的假说提供实验证据。细胞预分离方法提高了肝脏蛋白质组的覆盖率，新鉴定出许多以前在肝脏中没有鉴定出来的蛋白，而其中的一些蛋白质缺失功能信息，本研究通过3种方法对这些蛋白质进行了功能注释。首先，通过2步原位灌流结合密度梯度和免疫磁珠分选（magnetic activated cell sorting，，MACS）方法对肝脏细胞进行分离纯化，通过细胞学方法和免疫印迹评价细胞纯度，建立了细胞制备方法，为后续实验获得足够数量和纯度的细胞，用于蛋白质组表达谱构建。我们以C57BL/6J小鼠肝脏为材料，建立了一套从同一组织中同时获取肝实质细胞、肝窦内皮细胞、库否细胞和星状细胞4种细胞的方法。第二，构建基于SDS-PAGE联合胶内酶切和LTQ-FT质谱鉴定流程的无标记定量方法。通过筛选，选择牛血清白蛋白作为内参蛋白。以复杂样本作为背景，研究蛋白上样量和质谱相应信号关系。发现在一定范围内，蛋白上样量和质谱响应信号成线性关系，成功建立了复杂样本适用的蛋白质组无标定量体系。用APEX软件（absolute protein expressionmeasurements）对质谱数据进行定量。该软件利用校正的谱图计数对鉴定蛋白进行相对定量，用机器学习算法校正不同实验批次间误差和肽段理化性质不同引起的离子化效率差异，能够较为精确测量出蛋白质在不同细胞内的相对表达值。在95%置信度和双肽段鉴定的数据标准下，肝脏实质细胞中共鉴定到8,060个蛋白质，其中4,842个蛋白质有定量信息。第三，对鉴定肝实质细胞蛋白质的理化性质和丰度分布进行分析，发现代谢酶在肝实质细胞中占很大比重，提示其活跃的代谢能力。结合文献和GO（Gene Ontology）注释信息，对细胞中具有重要生理功能的亚蛋白质组进行分析，包括CYP450蛋白家族（cytochromeP450）、糖原结合蛋白、细胞极性相关蛋白和激酶，其中4个CYP450蛋白和9个细胞极性相关蛋白为首次在肝脏中鉴定到的蛋白。同时，结合蛋白质相互作用网络进行分析，发现这些亚蛋白质组可能参与的生物学功能。通过和小鼠血浆蛋白质组数据比较，发现一些在肝实质细胞中高表达，而在血浆中低表达的蛋白，这些蛋白质可能作为肝脏损伤的血清候选标记物。结合mRNA表达和蛋白质丰度信息，分析不同功能类别蛋白表达模式，发现维持细胞生存的核心功能基因其mRNA和蛋白质表达丰度的相关性较高，调控功能相关的基因其mRNA和蛋白质表达丰度的相关性普遍低。转氨酶、核糖体和染色体蛋白质其mRNA和蛋白质表达的丰度没有相关性。第四，肝脏细胞蛋白质组和转录组数据进行比较，发现细胞的特异功能特点。通过聚类分析、GO分析和KEGG通路分析，发现肝脏实质细胞主导物质和能量代谢、肝窦内皮主导物质交换、库否细胞主导免疫以及星状细胞主导细胞外基质组成。通过哺乳动物表型数据库（mammalian phenotype ontology，MPO）分析，发现4种细胞在不同肝病中发挥不同作用，这和细胞各自的特异功能特点是相关的。同时，对鉴定到的细胞表面CD分子进行比较，发现一批和细胞特异功能相关的分子，这些分子可能作为肝脏细胞新的细胞膜表面标记物，值得后续验证。第五，利用3种最新的功能注释算法Blast2GO、权重基因共表达分析（weighted geneco-expression analysis，WGCNA）和Endeavour，分别基于蛋白质序列、基因表达和公共数据库整合打分等3种方法，注释了实质细胞中的2,944个功能未知蛋白的功能。其中数目比较多的有82个膜整合蛋白、59个细胞核蛋白、40个胞浆蛋白和35个线粒体蛋白。为整合利用多种数据资源和算法进行蛋白功能注释做了初步探索。最后，在通路水平分析通路成员在不同细胞中表达情况，筛选出丙酮酸代谢通路可能在不同细胞间存在协作，对该通路中的候选分子进行WB验证。另外，还选取一些表达具有细胞特异性的分子进行验证。发现APOA4为肝实质细胞特异表达，ACYP1、ACYP2、ME2为非实质细胞特异表达，DOK2、IFNGR1为肝实质细胞和库否细胞特异表达，COLEC10主要在肝实质细胞和星状细胞表达。结合他人报道的转录组数据，进一步说明了这些分子表达的细胞特异性。这些细胞特异marker分子可能反映了其特异的功能，同时为相关肝病发生的机制研究提供细胞信息。
[Abstract]:The liver is the largest gland in the human body. Its main functions include material metabolism, detoxification, defense, homeostasis of hormone systems, blood storage and pH regulation, hematopoiesis and immunoregulation. The liver consists of liver parenchyma cells, hepatic sinusoidal endothelial cells, Kupffer cells and stellate cells. Cells are the basis for the complex functions of the liver, although these cells are used for these cells. With the development of the high precision mass spectrometer, it is possible to identify the protein composition of eukaryotic cells with the development of high precision mass spectrometer. The protein identification and data mining of the protein are used in the bioinformatics method, and the cell surface is systematically depicted. The protein is significant for understanding the specific functions of different types of cells. Cell based proteome research is one of the contents of proteomics research, and is also an important part of the human liver proteome project (HLPP).
The purpose of this study is to separate and purify the different cells of the liver of mice, and to construct the protein expression profiles of different cells. The main cell types in the liver, the protein data set of liver parenchyma cells, are deeply excavated and the functional characteristics of the liver cells are analyzed. The study of the liver cell proteome and transcriptional group by the method of systematic biology In comparison, we found a group of molecules associated with the function of the liver cells. By comparing the CD molecules expressed on the surface of the cell membrane to find the specific surface markers of the cells, and to compare the KEGG pathway involved in these cells, and the possible cooperation between the parenchymal and non parenchymal cells may be found in the pyruvate pathway. This plays an important role in understanding the interaction of the liver cells in the metabolism. In addition, a number of molecules in the pathway are selected to provide experimental evidence for our hypothesis. The cell pre separation method improves the coverage of the liver proteome, and a number of new proteins previously identified in the liver have been identified. Some of these proteins lack functional information. In this study, functional annotation of these proteins was carried out in 3 ways.
First, the liver cells were isolated and purified by 2 steps in situ perfusion combined with density gradient and magnetic activated cell sorting (MACS). Cell preparation methods were established by cytological method and immunoblotting to evaluate cell purity. Cells with sufficient quantity and purity were obtained for subsequent test, and used for protein groups. Expression profile construction. We use the C57BL/6J mouse liver as the material to establish a set of methods for the simultaneous acquisition of 4 kinds of cells from the same tissue, the liver parenchyma cells, the hepatic sinusoidal endothelial cells, the cell and the stellate cells.
Second, an unmarked quantitative method based on SDS-PAGE combined enzyme digestion and LTQ-FT mass spectrometry was constructed. Bovine serum albumin was selected as an internal reference protein by screening. The relationship between the amount of protein sample and the corresponding signal of mass spectrometry was studied with complex samples. It has successfully established a standard quantitative system for the protein group that is suitable for complex samples. The quantitative data of mass spectrometry is quantified by the APEX software (absolute protein expressionmeasurements). The software uses the corrected spectrum count to quantify the identification protein, and uses the machine learning method to correct the errors and peptides between different experimental batches. The difference in ionization efficiency caused by different properties can accurately measure the relative expression value of protein in different cells. Under the data standard of 95% confidence and dipeptide segment identification, 8060 proteins are identified in the liver parenchyma cells, of which 4842 proteins have quantitative information.
Third, analysis of the physical and chemical properties and abundance distribution of protein in the liver parenchyma cells, which make up a large proportion of the metabolites in the liver parenchyma cells, and indicate the active metabolic capacity. Combined with the literature and GO (Gene Ontology) annotation information, the subprotein groups with important physiological functions in the cells, including the CYP450 protein family, are analyzed. CytochromeP450, glycogen binding proteins, cell polar related proteins and kinases, of which 4 CYP450 proteins and 9 cell polar related proteins are proteins identified in the liver for the first time. Meanwhile, combined with protein interaction network analysis, it is found that these subgroups may be involved in biological functions. Through and mouse plasma Compared with the protein group data, some proteins with high expression in the liver parenchyma and low expression proteins in the plasma may be used as a candidate marker for the liver injury. In combination with the expression of mRNA and the information of protein abundance, the expression patterns of different functional categories of proteins are analyzed, and the core functional gene for the maintenance of cell survival is found to be the M The correlation between RNA and protein expression abundance is high, and the correlation of mRNA and protein expression abundances of regulatory function related genes is generally low. There is no correlation between the abundances of the mRNA and protein expression of the transaminase, ribosome and chromosome protein.
Fourth, the liver cell proteome and the transcriptional group were compared, and the specific functional characteristics of the cells were found. By cluster analysis, GO analysis and KEGG pathway analysis, the leading substance and energy metabolism of the liver parenchyma cells, the exchange of the leading substance in the hepatic sinusoid endothelium, the cell main immunity and the composition of the extracellular matrix dominated by stellate cells were found. After the analysis of the Mammalian Phenotype Ontology (MPO), we found that 4 cells play different roles in different liver diseases, which are related to the specific functional characteristics of the cells. At the same time, a group of identified molecules on the cell surface are compared, and a group of molecules related to cell specific functions are found, these molecules may be found. As a new marker for liver cell membrane, it is worthy of further validation.
Fifth, using the 3 most recent functional annotation algorithms Blast2GO, weight gene co expression analysis (weighted geneco-expression analysis, WGCNA) and Endeavour, respectively, based on 3 methods of protein sequence, gene expression and public database integration scoring, respectively, to annotate the function of 2944 functional unknown proteins in the essential fine cell. There are 82 membrane integrins, 59 nuclear proteins, 40 cytoplasmic proteins and 35 mitochondrial proteins, which have been preliminarily explored for the integration of various data resources and algorithms for protein functional annotation.
Finally, the expression of the pathway members in different cells was analyzed at the level of the pathway. It was found that the pyruvate metabolic pathway might cooperate in different cells, and the candidate molecules in the pathway were verified by WB. In addition, some molecules expressed with cell specificity were selected to be verified. It was found that APOA4 was a specific expression of liver parenchymal cells, ACY P1, ACYP2, and ME2 are non parenchymal cells specifically expressed, DOK2, IFNGR1 are specific expression of liver parenchyma cells and cell cells. COLEC10 is mainly expressed in liver parenchyma and stellate cells. Combined with the transcriptional data reported by others, the specific expression of these molecules is further illustrated. These specific marker molecules may reflect their specificity. Meanwhile, it can provide cell information for studying the mechanism of the occurrence of liver diseases.

【学位授予单位】：中国人民解放军军事医学科学院
【学位级别】：博士
【学位授予年份】：2012
【分类号】：R329

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