Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics.
Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics.
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DOI：
10.1038/srep46249
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发表时间：
2017-04-07
影响因子：
4.6
通讯作者：
Palsson BO
中科院分区：
文献类型：
--
作者：
Bordbar A;Yurkovich JT;Paglia G;Rolfsson O;Sigurjónsson ÓE;Palsson BO
代谢组学数据的可用性不断增加，需要新的方法来进行更深入的数据分析和解释。我们提出了一种通量平衡分析方法，该方法允许通过整合时程绝对定量代谢组学来计算细胞尺度上的动态细胞内代谢变化。这种方法被称为“非稳态通量平衡分析”(uFBA)，适用于四种细胞系统：三种动态和一种稳态作为阴性对照。对比 uFBA 和 FBA 预测，发现 uFBA 在预测红细胞、血小板和酿酒酵母的动态代谢通量状态方面更准确。值得注意的是，只有 uFBA 预测储存的红细胞代谢 TCA 中间体以再生重要的辅助因子，例如 ATP、NADH 和 NADPH。这些途径使用预测随后通过 13C 同位素标记和储存的红细胞中的代谢流分析进行了验证。 uFBA 利用时程代谢组学数据，提供了一种在细胞尺度上预测动态系统代谢生理学的准确方法。
The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.