Department of Biochemical Engineering, School of Chemical Engineering & Technology, Tianjin University, Tianjin 300072, China.
BMC Bioinformatics. 2010 Jul 22;11:393. doi: 10.1186/1471-2105-11-393.
Direct in vivo investigation of human metabolism is complicated by the distinct metabolic functions of various sub-cellular organelles. Diverse micro-environments in different organelles may lead to distinct functions of the same protein and the use of different enzymes for the same metabolic reaction. To better understand the complexity in the human metabolism, a compartmentalized human metabolic network with integrated sub-cellular location information is required.
We extended the previously reconstructed Edinburgh Human Metabolic Network (EHMN) [Ma, et al. Molecular Systems Biology, 3:135, 2007] by integrating the sub-cellular location information for the reactions, adding transport reactions and refining the protein-reaction relationships based on the location information. Firstly, protein location information was obtained from Gene Ontology and complemented by a Swiss-Prot location keywords search. Then all the reactions in EHMN were assigned to a location based on the 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 information from literature. As a result, location information for hundreds of reactions was revised and hundreds of incorrect protein-reaction relationships were corrected. 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. Compared with a previously published human metabolic network (Human Recon 1), our network contains over 1000 more reactions assigned to clear cellular compartments.
By combining protein location information, network connectivity analysis and manual literature search, we have reconstructed a more complete compartmentalized human metabolic network. The whole network is available at http://www.ehmn.bioinformatics.ed.ac.uk and free for academic use.
直接对人体代谢进行体内研究很复杂，因为不同亚细胞细胞器具有不同的代谢功能。不同细胞器中的不同微环境可能导致同一蛋白质具有不同的功能，并且同一代谢反应使用不同的酶。为了更好地理解人体代谢的复杂性，需要构建一个具有整合的亚细胞位置信息的模块化人体代谢网络。
我们通过整合反应的亚细胞位置信息、添加转运反应以及根据位置信息细化蛋白-反应关系，扩展了之前构建的爱丁堡人类代谢网络（EHMN）[Ma 等人，《分子系统生物学》，3：135，2007]。首先，从基因本体论（GO）获取蛋白质位置信息，并通过 Swiss-Prot 位置关键字搜索进行补充。然后，根据蛋白-反应关系将 EHMN 中的所有反应分配到一个位置，以获得初步的模块化网络。我们通过连通性分析研究每条途径中的局部子网络，以识别间隙和孤立反应，并根据文献信息细化位置信息。结果，修订了数百个反应的位置信息，并纠正了数百个错误的蛋白-反应关系。添加了 1400 多个转运反应以连接特定位置的代谢网络。为了验证网络，我们进行了途径分析，以检查网络合成或降解某些关键代谢物的能力。与之前发表的人类代谢网络（Human Recon 1）相比，我们的网络包含了 1000 多个分配到明确细胞区室的反应。
通过整合蛋白质位置信息、网络连通性分析和手动文献搜索，我们重新构建了一个更完整的模块化人体代谢网络。整个网络可在 http://www.ehmn.bioinformatics.ed.ac.uk 上获取，可免费供学术使用。
