Prediction of intracellular metabolic states from extracellular metabolomic data.
Prediction of intracellular metabolic states from extracellular metabolomic data.
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DOI：
10.1007/s11306-014-0721-3
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发表时间：
2015
期刊：
影响因子：
3.6
通讯作者：
Thiele, Ines
中科院分区：
文献类型：
--
作者：
Aurich, Maike K.;Paglia, Giuseppe;Rolfsson, Ottar;Hrafnsdottir, Sigrun;Magnusdottir, Manuela;Stefaniak, Magdalena M.;Palsson, Bernhard O.;Fleming, Ronan M. T.;Thiele, Ines
代谢模型可以提供一个机制框架来分析信息丰富的组学数据集，并越来越多地被用于研究人类疾病中的代谢变化。改变的代谢途径利用的表达是选择由细胞消耗和释放的代谢物。然而，与其他组学数据的方法相比，在代谢模型的背景下从细胞外测量推断细胞内代谢状态的方法仍然不发达。在这里，我们描述了这样一个综合分析的工作流程，强调细胞外代谢组学数据。我们证明，使用淋巴细胞白血病细胞系Molt-4和CCRF-CEM，我们的方法可以揭示细胞代谢的差异。我们的模型通过预测CCRF-CEM模型的更多糖酵解表型和Molt-4模型的更多氧化表型来解释代谢物摄取和分泌，这得到了我们的实验数据的支持。基因表达分析揭示了在这些中心代谢途径的关键调控步骤中基因产物的表达改变，并且文献查询强调了这些基因在癌症代谢中的作用。此外，计算机基因敲除鉴定了每个细胞系模型的独特控制点，例如，磷酸甘油酸脱氢酶用于Molt-4模型。因此，我们的工作流程非常适合于基于细胞外代谢组学数据的细胞代谢特征的表征，并且它允许基于定义的模型上下文将多个组学数据集整合到一个有凝聚力的图片中。本文的在线版本（doi：10.1007/s11306-014-0721-3）包含补充材料，可供授权用户使用。
Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context. The online version of this article (doi:10.1007/s11306-014-0721-3) contains supplementary material, which is available to authorized users.
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影响因子：
46.9
作者：
通讯作者：
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影响因子：
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作者：
Hyduke DR;Lewis NE;Palsson BØ
通讯作者：
Palsson BØ
影响因子：
3.4
作者：
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DAVEY, FR
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Barrett T;Troup DB;Wilhite SE;Ledoux P;Evangelista C;Kim IF;Tomashevsky M;Marshall KA;Phillippy KH;Sherman PM;Muertter RN;Holko M;Ayanbule O;Yefanov A;Soboleva A
通讯作者：
Soboleva A
影响因子：
11.5
作者：
Jerby, Livnat;Ruppin, Eytan
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Ruppin, Eytan