Connecting extracellular metabolomic measurements to intracellular flux states in yeast.
Connecting extracellular metabolomic measurements to intracellular flux states in yeast.
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DOI：
10.1186/1752-0509-3-37
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发表时间：
2009-03-25
影响因子：
--
通讯作者：
Herrgård MJ
中科院分区：
文献类型：
--
作者：
Mo ML;Palsson BO;Herrgård MJ
代谢组学已成为定量鉴定生理和疾病引起的生物状态的有力工具。特别是细胞外代谢组或代谢谱数据可以以非侵入性方式提供细胞内生理状态的深刻见解。我们使用了一个更新的基因组规模的代谢网络模型酿酒酵母，iMM 904，研究细胞外代谢组的变化如何可以用来研究细胞内代谢状态的系统变化。iMM 904代谢网络是基于现有的基因组规模网络iND 750重建的，包括904个基因和1,412个反应。该网络模型首先通过将2，888个计算机单基因缺失菌株生长表型预测与已发表的实验数据进行比较来验证。胞外代谢组数据测量响应环境和遗传扰动的铵同化途径，然后集成与iMM 904网络的形式相对溢流分泌的限制和通量采样方法被用来表征候选通量分布允许这些限制。预测的细胞内通量变化与已发表的细胞内代谢物水平和通量的测量结果一致。预测的细胞内通量变化的模式也可用于正确识别代谢网络中受到干扰的区域。我们的研究结果表明，整合定量细胞外代谢谱在一个约束为基础的框架，能够推断细胞内代谢通量状态的变化。类似的方法可以潜在地应用于分析与人类生理和疾病状态相关的生物流体代谢组变化。
Metabolomics has emerged as a powerful tool in the quantitative identification of physiological and disease-induced biological states. Extracellular metabolome or metabolic profiling data, in particular, can provide an insightful view of intracellular physiological states in a noninvasive manner. We used an updated genome-scale metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate how changes in the extracellular metabolome can be used to study systemic changes in intracellular metabolic states. The iMM904 metabolic network was reconstructed based on an existing genome-scale network, iND750, and includes 904 genes and 1,412 reactions. The network model was first validated by comparing 2,888 in silico single-gene deletion strain growth phenotype predictions to published experimental data. Extracellular metabolome data measured in response to environmental and genetic perturbations of ammonium assimilation pathways was then integrated with the iMM904 network in the form of relative overflow secretion constraints and a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints. Predicted intracellular flux changes were consistent with published measurements on intracellular metabolite levels and fluxes. Patterns of predicted intracellular flux changes could also be used to correctly identify the regions of the metabolic network that were perturbed. Our results indicate that integrating quantitative extracellular metabolomic profiles in a constraint-based framework enables inferring changes in intracellular metabolic flux states. Similar methods could potentially be applied towards analyzing biofluid metabolome variations related to human physiological and disease states.
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DOI：
10.1016/j.ijfoodmicro.2007.12.004
发表时间：
2008-03-31
影响因子：
5.4
作者：
Hjortmo, Sofia;Patring, Johan;Andlid, Thornas
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DOI：
10.1016/s0731-7085(03)00410-2
发表时间：
2003-12-04
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
7
作者：
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影响因子：
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