肝脏质膜蛋白质表达谱的构建及功能研究
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摘要
真核细胞由生物膜分隔成行使不同功能的细胞器。尽管这些细胞器研究由来已久,但其蛋白质组成仍然不甚清楚。基于质谱的蛋白质组学技术的发展,使从整体上研究不同细胞器的蛋白质组成及功能成为可能。借助蛋白质组研究的手段,可以规模化的发掘存在于细胞器的蛋白质信息。通过蛋白质鉴定及数据发掘,我们期望更进一步的全面系统地揭示细胞器中所蕴含的生命信息。因此,亚细胞蛋白质组的研究一直是蛋白质组研究的重要分支,更是人类肝脏蛋白质组计划(Human Liver Proteome Project)的重要组成部分。
首先,本研究以肝脏质膜为研究对象,利用蛋白质组技术,构建人肝脏质膜蛋白质表达谱。其次,由于细胞器之间存在着天然的联系,造成分离过程中不可避免的交叉污染,而质谱鉴定的高灵敏性,使得这些相对微量的污染蛋白质得以鉴定,从而造成了细胞器定位的误判。因此我们建立了一种机械学习的方法来校正蛋白质的亚细胞定位。其三,在质膜蛋白质表达谱及精确定位研究的基础上,我们利用新构建的肝脏质膜蛋白质数据集,对肝脏质膜的功能特性进行了分析。其四,规模化的蛋白质组研究在提供参考数据集的同时,也对数据的发掘提出了新的要求,而亚细胞蛋白质组提供了一个细胞器研究的平台,不同类型的大规模实验数据可以与其整合,来共同描绘细胞器的功能,我们通过整合蛋白质相互作用数据构建细胞器功能模块这一全新的方式,实现了对细胞器功能的系统挖掘。其五,众所周知,利用质谱数据对基因组进行注释,是蛋白质组研究的一个亮点和难点。我们尝试通过小鼠肝脏质谱数据搜索EST数据库对小鼠基因组进行注释,为下一步人肝脏蛋白质组数据进行人类基因组注释打下了良好的基础。具体研究思路及重要结果简述如下。
首先,建立了肝脏细胞器联合分离的方法,并以此为基础,构建了人肝脏质膜蛋白质表达谱。我们以C57BL/6J小鼠肝脏为材料,建立了一套从同一组织匀浆液中,同时分离质膜、线粒体、粗面内质网、滑面内质网、细胞核、胞浆六个亚细胞组分的方法。通过电镜和免疫印迹等方法,对分离的细胞器进行了形态学观察和纯度评价,得到的细胞器结构完整,纯度较高。进而摸索建立了人肝脏细胞器的分离方法。进一步提取人肝脏质膜蛋白质,通过SDS-PAGE分离蛋白质、胶内酶切肽提取、nano-LC-ESI-MS/MS鉴定的技术策略,构建了目前国际上最大规模的人肝脏质膜蛋白质表达谱。在95%置信度、至少双肽段匹配(95P2)的数据标准下,共鉴定到1381个人肝脏质膜蛋白质;通过校正的质谱图数提取蛋白质的定量信息,并进一步对定量准确性进行了评估。
其次,对肝脏亚细胞蛋白质表达谱的细胞器定位进行了探索。基于鉴定蛋白质在不同亚细胞组分中的定量信息,并结合各细胞器特征蛋白(金标准),通过KNN模块初步确定蛋白质的亚细胞定位。继而,利用贝叶斯模型进一步整合五种基于蛋白质序列信息的定位预测方法(pTAGET,Proteome Analyst,WoLFPSORT,TargetP,NUCLEO),建立了一个新的蛋白质定位方法,与已有亚细胞定位预测方法相比,其准确性显著提高。将此方法用于肝脏细胞器蛋白质组数据(含有6311个蛋白质)分析,确定了肝脏各细胞器的蛋白质数据集。通过与SWISS-PROT等数据库比较,发现了4966个肝脏蛋白质的新定位,包括两类,一是数据库未给出蛋白质的定位,我们首次发现了它的定位(4433个);另一类是数据库虽给出了它的定位,但我们发现了它的新定位(533个)。接着,我们选取四个蛋白质进行了荧光定位实验验证,结果全部与预测结果一致。
其三,利用肝脏质膜蛋白质数据集,对肝脏质膜的功能特性进行了分析。通过贝叶斯模型的定位方法,共得到871个质膜蛋白质,其中84%为新的质膜定位蛋白质。在多定位中,质膜和内质网共定位蛋白质所占的比例最大(215个,24.68%),其中主要是参与分泌途径的蛋白质分子,体现了肝脏旺盛的分泌能力。本研究发现了许多参与重要生理过程的蛋白质,包括信号转导的受体、离子转运的通道蛋白和转运体等具有质膜定位,提示了质膜上存在着极为复杂的生物学过程。进一步分析质膜蛋白质表达谱中的新蛋白,我们发现了29个假想蛋白质,其中10个蛋白质同时具有C1-set和V-set结构域,结合定位和结构域信息推测它们可能属于免疫球蛋白超家族,参与免疫反应过程。
其四,构建了各细胞器的蛋白质功能模块,以此为基础对细胞器蛋白质的新功能进行了挖掘。我们采用系统生物学的手段,将细胞器蛋白质表达谱中得到的蛋白质定量、定位信息与蛋白质相互作用网络结合,构建细胞器相互作用子网络,并通过MCODE聚类软件,将其拆分为蛋白质模块,利用Gene Ontology和文献搜索对模块功能进行注释,实现了对细胞器功能的系统注释。基于同一模块的蛋白质相互作用紧密、很可能属于同一复合体或参与同一生物学过程,因此具有类似的功能。我们赋予了肝脏中151个新蛋白或已知蛋白潜在的新功能,其中发现其中一个假想蛋白分析可能属于呼吸链复合体Ⅰ的新成员。我们的功能模块分析表明细胞器蛋白质组不仅数据集本身具有重要价值,而且与其它类型数据集整合可以对细胞器及其功能进行新的阐释。
最后,利用小鼠肝脏细胞器表达谱的质谱数据对其基因组进行了注释和补充。以蛋白质组数据实现对基因组的注释是蛋白质组学的重要研究方向。我们采用小鼠肝脏细胞器的蛋白质组数据对此进行了初步的尝试。质谱结果通过EST数据库重新搜索,与IPI数据库比对,发现了486个新的肽段。进一步将其与小鼠基因组进行匹配,根据其在基因组上的位置以及与编码蛋白基因的关系进行分类,发现了96个新的蛋白编码区,152个单氨基酸突变和102个与基因组不匹配肽段和136个IPI蛋白库中未包含肽段。为质谱数据的深度发掘做了有益的探索。
综上,我们提供了一个从同一肝脏组织匀浆液中同时分离多个细胞器的方法,得到的细胞器结果完整,纯度高。以此为基础,构建了1381个蛋白质构成的人肝脏质膜蛋白质表达谱。建立了一个结合质谱定量和序列信息的蛋白质定位预测方法,并用于构建细胞器蛋白质组数据集,发现了4966个新的蛋白质定位。进一步,对质膜蛋白质数据集进行了功能分析,发现了10个可能的免疫球蛋白超家族成员。通过整合蛋白质相互作用网络,构建了细胞器的功能模块图谱,并以此为基础,对151个新蛋白及已知蛋白质的新功能给予了系统分析。通过搜索EST数据库,发现了486个新的肽段,其中包括96个新的蛋白质编码区。
Eukaryotic cells include many dynamic membrane-bounded organelles that carry out distinct functions. Although these cell compartments have been studied for a long time, the diversity of proteins in different organelles remains unclear. With the development of mass spectrometry, sub-cellular proteome has become one of the most important fields of proteomics, which provide a powerful tool to give a survey of the proteins composition in different organelles. What’s more, organelle proteomics is also a crucial part of Human Liver Proteome Project.
Here, we combined sub-cellular fractionation with proteomic technique to explore the protein composition of liver plasma membrane. In addition, the separated organelles always contain cross-contaminant by other compartments and could be identified by high sensitive mass spectrometer easily, which may lead to miss-assessment of protein localization. To address this issue, we used a machine-learning stratry to assign protein subcellular localization. Based on this method, the datasets of liver plasma membrane proteins was confirmed, and the function of those proteins was analysis. More importantly, organelle proteomic maps provides a‘‘cell biological scaffold’’on which other functional genomics data can be layered. And the integration of many diverse data sets can help accquire a novel view of the organelles.
Combined with protein-protein interaction data, protein models were assembled for annotation organelles and assigning function to uncharacterized proteins. Moreover, it is known to all that genome annotation by proteomic data is an important and difficult field. Here, combining mouse sub-cellular proteome and EST sequence database, we found new protein-code genes and novel gene models, which could be used as a preparation for the next step of human genome annotation. The detailed research ideas and main results outlined as follows.
Firstly,we established a method to obtain multiple organelles from same liver homogenate. C57BL/6J mouse liver was chosen as a model to explore the optimum method for sub-cellular preparation. The method could obtain the multiple fractions including plasma membrane, mitochondria, nucleus, rough and smooth endoplasmic reticulum and cytosol from a single homogenate. We systematically evaluated the purity, efficiency and integrity by western blot and transmission electron microscope. Subsequently, highly purified plasma membranes from human liver were used to compile a protein expression profile with strategy that combined SDS-PAGE separation with liquid chromatography gas phase fractionation (GPF) tandem mass spectrometry analysis. Totally, 1381 human proteins were identified with 95% confidence and minimum two peptides match. And the quantitation of proteins was obtained by the number of mass spectra.
Secondly, the sub-cellular localization of human liver protein was analyzed. We developed a machine-learning method named KNN (K Nearest Neighbor) which was based on the similarity of quantitative curve in different compartment comparing to organelle marker protein. In addition, Bayesian model was employed, which combined KNN with five sub-cellular localization prediction methods (pTAGET,Proteome Analyst,WoLFPSORT,TargetP and NUCLEO), to assign the sub-cellular localization of human liver proteins. Comparing with the well-known organelles marker proteins, we found 4966 new sub-cellular localizations, including two categories: one was that the proteins’localization was never found before, and the other was that new localization was given to the well-reported proteins. At last, four new protein localizations were selected and confirmed by experiments.
Thirdly, the function of human liver plasma membrane was analyzed. Based on Bayes model, a total of 871 proteins were found, 84% of which were firstly given the localization to plasma membrane. Except the single localization, the plasma membrane and endoplasmic reticulum-targeted proteins had the largest proportion (215, 24.68%). Those protiens were mainly involved in the protein secretory pathway, which reflect the strong secretion function of the liver. In the function analysis, the biological characterization was described systematically in liver plasma membrane, in which many proteins involve in signal transduction, ion transport and protein modifing. Further more, we found 29 the hypothetical proteins, of which 10 proteins had both C1-set and V-set domains. By combining with sub-cellular localization and domain information, we predicted those proteins may belong to the immune globulin superfamily, which involved in the process of immune response.
Fourthly, the function modules of different organelle were constructed for predicting of protein function. The aim of organelle proteome research is not only to provide an organelle reference map, but also to form new knowledge. Here, we combined the human liver sub-cellular proteome with the protein interaction network to acquire the organelle specific protein network, which was clustered into function modules by MCODE. Based on those modules, the relations of organelle localization, protein quantitative and function were explored. Importantly, the members of protein module had similar function, which could be used as indicator for unknown member. Based on this principle, the new function of 151 proteins were predicted, one of which was a novel protein involving in mitochondria Respiratory chain complex I. We had demonstrated that the localization data presented here was not only valuable by itself but could be combined with other large-scale datasets to gain unanticipated insights.
Finally, the mouse genome was annotated by mouse liver proteomic data. Genome annotation was an important field of proteomic study, by which novel gene and gene mode could be found. By searching mouse EST sequence database and blasting with mouse genome, 486 novel peptides were obtained including 96 novel protein-coding genes 152 novel amino acids mutations,102 novel peptides matching no genome sequence and 136 new peptides never existing in IPI database.
In conclusion, we established a method that could separate multiple fractions from a single liver homogenate and identified 1381 human liver plasma membrane proteins. After that, a new sub-cellular localization strategy was used, and 4966 new localizations was found,four of which were confirmed by experiments. Then, the function of plasma membrane proteins was analysis, and 10 hypothetical plasma membrane proteins may belong to the immune globulin superfamily. Subsequently, the function modules of different organelle were constructed to predict 151 new proteins function. At last, 486 novel peptides, including 96 novel protein-coding genes, were found by searching the mouse EST database.
首先,本研究以肝脏质膜为研究对象,利用蛋白质组技术,构建人肝脏质膜蛋白质表达谱。其次,由于细胞器之间存在着天然的联系,造成分离过程中不可避免的交叉污染,而质谱鉴定的高灵敏性,使得这些相对微量的污染蛋白质得以鉴定,从而造成了细胞器定位的误判。因此我们建立了一种机械学习的方法来校正蛋白质的亚细胞定位。其三,在质膜蛋白质表达谱及精确定位研究的基础上,我们利用新构建的肝脏质膜蛋白质数据集,对肝脏质膜的功能特性进行了分析。其四,规模化的蛋白质组研究在提供参考数据集的同时,也对数据的发掘提出了新的要求,而亚细胞蛋白质组提供了一个细胞器研究的平台,不同类型的大规模实验数据可以与其整合,来共同描绘细胞器的功能,我们通过整合蛋白质相互作用数据构建细胞器功能模块这一全新的方式,实现了对细胞器功能的系统挖掘。其五,众所周知,利用质谱数据对基因组进行注释,是蛋白质组研究的一个亮点和难点。我们尝试通过小鼠肝脏质谱数据搜索EST数据库对小鼠基因组进行注释,为下一步人肝脏蛋白质组数据进行人类基因组注释打下了良好的基础。具体研究思路及重要结果简述如下。
首先,建立了肝脏细胞器联合分离的方法,并以此为基础,构建了人肝脏质膜蛋白质表达谱。我们以C57BL/6J小鼠肝脏为材料,建立了一套从同一组织匀浆液中,同时分离质膜、线粒体、粗面内质网、滑面内质网、细胞核、胞浆六个亚细胞组分的方法。通过电镜和免疫印迹等方法,对分离的细胞器进行了形态学观察和纯度评价,得到的细胞器结构完整,纯度较高。进而摸索建立了人肝脏细胞器的分离方法。进一步提取人肝脏质膜蛋白质,通过SDS-PAGE分离蛋白质、胶内酶切肽提取、nano-LC-ESI-MS/MS鉴定的技术策略,构建了目前国际上最大规模的人肝脏质膜蛋白质表达谱。在95%置信度、至少双肽段匹配(95P2)的数据标准下,共鉴定到1381个人肝脏质膜蛋白质;通过校正的质谱图数提取蛋白质的定量信息,并进一步对定量准确性进行了评估。
其次,对肝脏亚细胞蛋白质表达谱的细胞器定位进行了探索。基于鉴定蛋白质在不同亚细胞组分中的定量信息,并结合各细胞器特征蛋白(金标准),通过KNN模块初步确定蛋白质的亚细胞定位。继而,利用贝叶斯模型进一步整合五种基于蛋白质序列信息的定位预测方法(pTAGET,Proteome Analyst,WoLFPSORT,TargetP,NUCLEO),建立了一个新的蛋白质定位方法,与已有亚细胞定位预测方法相比,其准确性显著提高。将此方法用于肝脏细胞器蛋白质组数据(含有6311个蛋白质)分析,确定了肝脏各细胞器的蛋白质数据集。通过与SWISS-PROT等数据库比较,发现了4966个肝脏蛋白质的新定位,包括两类,一是数据库未给出蛋白质的定位,我们首次发现了它的定位(4433个);另一类是数据库虽给出了它的定位,但我们发现了它的新定位(533个)。接着,我们选取四个蛋白质进行了荧光定位实验验证,结果全部与预测结果一致。
其三,利用肝脏质膜蛋白质数据集,对肝脏质膜的功能特性进行了分析。通过贝叶斯模型的定位方法,共得到871个质膜蛋白质,其中84%为新的质膜定位蛋白质。在多定位中,质膜和内质网共定位蛋白质所占的比例最大(215个,24.68%),其中主要是参与分泌途径的蛋白质分子,体现了肝脏旺盛的分泌能力。本研究发现了许多参与重要生理过程的蛋白质,包括信号转导的受体、离子转运的通道蛋白和转运体等具有质膜定位,提示了质膜上存在着极为复杂的生物学过程。进一步分析质膜蛋白质表达谱中的新蛋白,我们发现了29个假想蛋白质,其中10个蛋白质同时具有C1-set和V-set结构域,结合定位和结构域信息推测它们可能属于免疫球蛋白超家族,参与免疫反应过程。
其四,构建了各细胞器的蛋白质功能模块,以此为基础对细胞器蛋白质的新功能进行了挖掘。我们采用系统生物学的手段,将细胞器蛋白质表达谱中得到的蛋白质定量、定位信息与蛋白质相互作用网络结合,构建细胞器相互作用子网络,并通过MCODE聚类软件,将其拆分为蛋白质模块,利用Gene Ontology和文献搜索对模块功能进行注释,实现了对细胞器功能的系统注释。基于同一模块的蛋白质相互作用紧密、很可能属于同一复合体或参与同一生物学过程,因此具有类似的功能。我们赋予了肝脏中151个新蛋白或已知蛋白潜在的新功能,其中发现其中一个假想蛋白分析可能属于呼吸链复合体Ⅰ的新成员。我们的功能模块分析表明细胞器蛋白质组不仅数据集本身具有重要价值,而且与其它类型数据集整合可以对细胞器及其功能进行新的阐释。
最后,利用小鼠肝脏细胞器表达谱的质谱数据对其基因组进行了注释和补充。以蛋白质组数据实现对基因组的注释是蛋白质组学的重要研究方向。我们采用小鼠肝脏细胞器的蛋白质组数据对此进行了初步的尝试。质谱结果通过EST数据库重新搜索,与IPI数据库比对,发现了486个新的肽段。进一步将其与小鼠基因组进行匹配,根据其在基因组上的位置以及与编码蛋白基因的关系进行分类,发现了96个新的蛋白编码区,152个单氨基酸突变和102个与基因组不匹配肽段和136个IPI蛋白库中未包含肽段。为质谱数据的深度发掘做了有益的探索。
综上,我们提供了一个从同一肝脏组织匀浆液中同时分离多个细胞器的方法,得到的细胞器结果完整,纯度高。以此为基础,构建了1381个蛋白质构成的人肝脏质膜蛋白质表达谱。建立了一个结合质谱定量和序列信息的蛋白质定位预测方法,并用于构建细胞器蛋白质组数据集,发现了4966个新的蛋白质定位。进一步,对质膜蛋白质数据集进行了功能分析,发现了10个可能的免疫球蛋白超家族成员。通过整合蛋白质相互作用网络,构建了细胞器的功能模块图谱,并以此为基础,对151个新蛋白及已知蛋白质的新功能给予了系统分析。通过搜索EST数据库,发现了486个新的肽段,其中包括96个新的蛋白质编码区。
Eukaryotic cells include many dynamic membrane-bounded organelles that carry out distinct functions. Although these cell compartments have been studied for a long time, the diversity of proteins in different organelles remains unclear. With the development of mass spectrometry, sub-cellular proteome has become one of the most important fields of proteomics, which provide a powerful tool to give a survey of the proteins composition in different organelles. What’s more, organelle proteomics is also a crucial part of Human Liver Proteome Project.
Here, we combined sub-cellular fractionation with proteomic technique to explore the protein composition of liver plasma membrane. In addition, the separated organelles always contain cross-contaminant by other compartments and could be identified by high sensitive mass spectrometer easily, which may lead to miss-assessment of protein localization. To address this issue, we used a machine-learning stratry to assign protein subcellular localization. Based on this method, the datasets of liver plasma membrane proteins was confirmed, and the function of those proteins was analysis. More importantly, organelle proteomic maps provides a‘‘cell biological scaffold’’on which other functional genomics data can be layered. And the integration of many diverse data sets can help accquire a novel view of the organelles.
Combined with protein-protein interaction data, protein models were assembled for annotation organelles and assigning function to uncharacterized proteins. Moreover, it is known to all that genome annotation by proteomic data is an important and difficult field. Here, combining mouse sub-cellular proteome and EST sequence database, we found new protein-code genes and novel gene models, which could be used as a preparation for the next step of human genome annotation. The detailed research ideas and main results outlined as follows.
Firstly,we established a method to obtain multiple organelles from same liver homogenate. C57BL/6J mouse liver was chosen as a model to explore the optimum method for sub-cellular preparation. The method could obtain the multiple fractions including plasma membrane, mitochondria, nucleus, rough and smooth endoplasmic reticulum and cytosol from a single homogenate. We systematically evaluated the purity, efficiency and integrity by western blot and transmission electron microscope. Subsequently, highly purified plasma membranes from human liver were used to compile a protein expression profile with strategy that combined SDS-PAGE separation with liquid chromatography gas phase fractionation (GPF) tandem mass spectrometry analysis. Totally, 1381 human proteins were identified with 95% confidence and minimum two peptides match. And the quantitation of proteins was obtained by the number of mass spectra.
Secondly, the sub-cellular localization of human liver protein was analyzed. We developed a machine-learning method named KNN (K Nearest Neighbor) which was based on the similarity of quantitative curve in different compartment comparing to organelle marker protein. In addition, Bayesian model was employed, which combined KNN with five sub-cellular localization prediction methods (pTAGET,Proteome Analyst,WoLFPSORT,TargetP and NUCLEO), to assign the sub-cellular localization of human liver proteins. Comparing with the well-known organelles marker proteins, we found 4966 new sub-cellular localizations, including two categories: one was that the proteins’localization was never found before, and the other was that new localization was given to the well-reported proteins. At last, four new protein localizations were selected and confirmed by experiments.
Thirdly, the function of human liver plasma membrane was analyzed. Based on Bayes model, a total of 871 proteins were found, 84% of which were firstly given the localization to plasma membrane. Except the single localization, the plasma membrane and endoplasmic reticulum-targeted proteins had the largest proportion (215, 24.68%). Those protiens were mainly involved in the protein secretory pathway, which reflect the strong secretion function of the liver. In the function analysis, the biological characterization was described systematically in liver plasma membrane, in which many proteins involve in signal transduction, ion transport and protein modifing. Further more, we found 29 the hypothetical proteins, of which 10 proteins had both C1-set and V-set domains. By combining with sub-cellular localization and domain information, we predicted those proteins may belong to the immune globulin superfamily, which involved in the process of immune response.
Fourthly, the function modules of different organelle were constructed for predicting of protein function. The aim of organelle proteome research is not only to provide an organelle reference map, but also to form new knowledge. Here, we combined the human liver sub-cellular proteome with the protein interaction network to acquire the organelle specific protein network, which was clustered into function modules by MCODE. Based on those modules, the relations of organelle localization, protein quantitative and function were explored. Importantly, the members of protein module had similar function, which could be used as indicator for unknown member. Based on this principle, the new function of 151 proteins were predicted, one of which was a novel protein involving in mitochondria Respiratory chain complex I. We had demonstrated that the localization data presented here was not only valuable by itself but could be combined with other large-scale datasets to gain unanticipated insights.
Finally, the mouse genome was annotated by mouse liver proteomic data. Genome annotation was an important field of proteomic study, by which novel gene and gene mode could be found. By searching mouse EST sequence database and blasting with mouse genome, 486 novel peptides were obtained including 96 novel protein-coding genes 152 novel amino acids mutations,102 novel peptides matching no genome sequence and 136 new peptides never existing in IPI database.
In conclusion, we established a method that could separate multiple fractions from a single liver homogenate and identified 1381 human liver plasma membrane proteins. After that, a new sub-cellular localization strategy was used, and 4966 new localizations was found,four of which were confirmed by experiments. Then, the function of plasma membrane proteins was analysis, and 10 hypothetical plasma membrane proteins may belong to the immune globulin superfamily. Subsequently, the function modules of different organelle were constructed to predict 151 new proteins function. At last, 486 novel peptides, including 96 novel protein-coding genes, were found by searching the mouse EST database.
引文
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