Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT.
Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT.
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DOI：
10.1371/journal.pcbi.1002518
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发表时间：
2012
影响因子：
4.3
通讯作者：
Nielsen J
中科院分区：
文献类型：
--
作者：
Agren R;Bordel S;Mardinoglu A;Pornputtapong N;Nookaew I;Nielsen J
高通量分析方法的发展使医生有可能获得广泛的和患者特异性的数据集，如基因序列，基因表达谱或代谢物足迹。这为医疗保健开辟了一种新的方法，这种方法既个性化又基于系统级分析。基因组尺度的代谢网络提供了不同基因之间关系的机制描述，这对于分析和解释大型实验数据集是有价值的。在这里，我们描述了使用INIT（组织综合网络推理）算法为69种不同细胞类型和16种癌症类型生成基因组规模的活性代谢网络。INIT算法使用人类蛋白质组图谱中包含的关于蛋白质丰度的细胞类型特异性信息作为主要证据来源。生成的模型构成了建立人类代谢图谱的第一步，这将是对不同人类细胞类型代谢的全面描述（可在线访问），并将允许组织水平和生物体水平的模拟，以便更好地了解复杂疾病。癌症类型和健康细胞类型的活性代谢网络之间的比较分析允许鉴定构成癌症治疗的通用潜在药物靶标的癌症特异性代谢特征。许多严重的疾病都有很强的代谢成分。因此，病变细胞的异常代谢状态可能是治疗的目标。然而，代谢是一个高度复杂和相互关联的系统，其中数千个代谢反应在任何给定的细胞类型中同时发生。为了了解病变细胞的代谢与健康细胞的代谢有何不同，我们必须研究整个系统。我们已经开发出一种算法，它集成了几种类型的数据，以生成活跃的代谢网络;在给定的细胞类型中可能活跃的代谢反应的目录。我们将该算法应用于69种健康细胞类型和16种癌细胞类型的数据。这些代谢网络可以形成模拟器官之间代谢相互作用的基础，或者作为解释高通量数据的支架。我们使用这些网络在癌症和健康细胞类型之间进行分析，以确定构成潜在药物靶点的癌症特异性代谢特征。其中几个靶点已经为人们所知并在临床上使用，但我们也发现了尚未作为药物靶点进行研究的高级反应和代谢物。
Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment. Many serious diseases have a strong metabolic component. The abnormal metabolic states of diseased cells could therefore be targets for treatment. However, metabolism is a highly complex and interconnected system in which thousands of metabolic reactions occur simultaneously in any given cell type. In order to understand how metabolism of a diseased cell differs from its healthy counterpart we must therefore study the system as a whole. We have developed an algorithm that integrates several types of data in order to generate active metabolic networks; catalogues of the metabolic reactions that are likely to be active in a given cell type. We applied this algorithm to data for 69 healthy cell types and 16 cancer cell types. These metabolic networks can form the basis for simulation of metabolic interactions between organs or as scaffolds for interpretation of high-throughput data. We used these networks to perform an analysis between cancer and healthy cell types in order to identify cancer specific metabolic features that constitute potential drug targets. Several of the resulting targets were already known and used clinically, but we also found high-ranking reactions and metabolites which have not yet been investigated as drug targets.
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影响因子：
3
作者：
Hao T;Ma HW;Zhao XM;Goryanin I
通讯作者：
Goryanin I
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
14.9
作者：
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者：
Yamanishi Y
影响因子：
5.8
作者：
Hucka, M;Finney, A;Wang, J
通讯作者：
Wang, J
影响因子：
3.4
作者：
Dudakovic, Amel;Tong, Huaxiang;Hohl, Raymond J.
通讯作者：
Hohl, Raymond J.