基因组尺度人类代谢网络的亚细胞及组织定位
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摘要
人体代谢系统的实验研究与人体内不同亚细胞结构和组织的功能是密切相关的,因而人体蛋白和代谢反应的亚细胞定位及组织分布是人体生物学研究和药物开发的重要研究对象。为了更好地理解人体代谢网络的复杂性,融合亚细胞和组织信息的人类代谢网络不可或缺。本文通过添加亚细胞定位信息、运输反应和组织定位信息对已构建的爱丁堡人类代谢网络模型(EHMN进行了扩展,并且在亚细胞定位的基础上对网络中错误的蛋白-反应关系进行了修正。首先蛋白亚细胞位置信息来自于不同的数据库,而EHMN中所有反应的位置信息则根据蛋白位置通过蛋白-反应关系获得,由此初步构建了亚细胞分室网络;之后,本文通过对每个途径中子网络的图论分析确定了网络中的空白和孤立反应并对其进行了修正;此外,本文基于文献及教科书中对途径位置的描述进一步校正了网络中反应的位置。最终初步分室网络中上百个反应的位置得到了修正,基于修正后反应的亚细胞位置,错误的蛋白-反应关系也得到了更正。亚细胞分室完成后,本文基于Recon 1模型、数据库以及代谢末端分析添加了1400多个运输反应,使网络中的各个亚细胞位置连通起来。为了验证分室网络的质量,本文通过途径分析检验了EHMN对近70种重要代谢物的合成和降解能力以及代谢位置,结果表明EHMN中这些代谢物的代谢过程与文献或教科书一致。在亚细胞分室网络的基础上,利用与亚细胞定位相同的方法,本文将组织信息也融合到EHMN中,从而构建了更加完整的包含亚细胞和组织定位的人类代谢网络。最后,本文从拓扑结构的角度对定位后的线粒体和脑两个子网络进行了功能验证,表明从拓扑结构出发的网络分析结果与子网络的功能特点相符。本文首次将图论分析的方法用于亚细胞及组织定位过程中并且利用途径分析工具对网络的可靠性进行分析,并开发了新的将分类树与模块化指标相结合的网络解耦方法对网络进行解耦以进行进一步的功能分析。亚细胞及组织定位后的网络可以从EHMN网站(www.ehmn.bioinformatics.ed.ac.uk)上下载并免费提供给学术研究。
Direct in vivo investigation of human metabolism is complicated by the distinct metabolic functions of various sub-cellular organelles and tissues.sub-cellular and tissue location of gene expression and metabolic reactions is an important issue in human biological research and biomedicine development. To better understand the complexity in the human metabolism, a human metabolic network with integrated sub-cellular and tissue location information is required. In this work, we extended the previously reconstructed Edinburgh Human Metabolic Network (EHMN) by integrating the sub-cellular location, transport reactions and tissue location. Firstly, protein subcelluar location information was obtained from various databases. Then all the reactions in EHMN were assigned to subcellular locations based on protein-reaction relationships to get a preliminary compartmentalized network. We investigated the localized sub-networks in each pathway to identify gaps and isolated reactions by connectivity analysis and refined the location information based on literature. As a result, location information for hundreds of reactions was revised and hundreds of incorrect protein-reaction relationships were corrected based on the sub-cellular location. Over 1400 transport reactions were added to link the location specific metabolic network. To validate the network, we have done pathway analysis to examine the capability of the network to synthesize or degrade certain key metabolites. Using a similar approach, we added the the tissue distribution information into EHMN to reconstruct a more complete sub-cellular and tissue localized human metabolic network. As an example, we further analyzed the biological functions of mitochondria and brain using a newly developed network decomposition method. The results showed good agreements between the structurally identified modules and the pathways classified based on biofunctions. The whole network can be downloaded from www.ehmn.bioinformatics.ed.ac.uk and free for academic use.
Direct in vivo investigation of human metabolism is complicated by the distinct metabolic functions of various sub-cellular organelles and tissues.sub-cellular and tissue location of gene expression and metabolic reactions is an important issue in human biological research and biomedicine development. To better understand the complexity in the human metabolism, a human metabolic network with integrated sub-cellular and tissue location information is required. In this work, we extended the previously reconstructed Edinburgh Human Metabolic Network (EHMN) by integrating the sub-cellular location, transport reactions and tissue location. Firstly, protein subcelluar location information was obtained from various databases. Then all the reactions in EHMN were assigned to subcellular locations based on protein-reaction relationships to get a preliminary compartmentalized network. We investigated the localized sub-networks in each pathway to identify gaps and isolated reactions by connectivity analysis and refined the location information based on literature. As a result, location information for hundreds of reactions was revised and hundreds of incorrect protein-reaction relationships were corrected based on the sub-cellular location. Over 1400 transport reactions were added to link the location specific metabolic network. To validate the network, we have done pathway analysis to examine the capability of the network to synthesize or degrade certain key metabolites. Using a similar approach, we added the the tissue distribution information into EHMN to reconstruct a more complete sub-cellular and tissue localized human metabolic network. As an example, we further analyzed the biological functions of mitochondria and brain using a newly developed network decomposition method. The results showed good agreements between the structurally identified modules and the pathways classified based on biofunctions. The whole network can be downloaded from www.ehmn.bioinformatics.ed.ac.uk and free for academic use.
引文
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