Estimating relative changes of metabolic fluxes.
Estimating relative changes of metabolic fluxes.
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DOI：
10.1371/journal.pcbi.1003958
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发表时间：
2014-11
影响因子：
4.3
通讯作者：
Locasale JW
中科院分区：
文献类型：
--
作者：
Huang L;Kim D;Liu X;Myers CR;Locasale JW
通量是新陈代谢的核心特征，而动态通量剖析(KFP)是一种有效的测量方法。为了推广它的适用性，我们提出了一种扩展的方法，只使用13C标记的代谢物的相对定量来估计通量的相对变化。这些特征是直接为更常见的实验量身定做的，这种实验只执行相对定量，并比较两种条件下的通量。我们称我们的扩展为rKFP。此外，我们还考察了常见的缺失数据和常见的建模假设对(R)KFP的影响，并提出了切实可行的建议。我们还研究了(R)KFP测量时间的选择，并提供了一个简单的配方。然后，我们将rKFP应用于从正常和缺糖条件下的细胞收集的13C标记的葡萄糖时间序列数据，估计糖酵解及其分支途径的相对通量变化。我们确定了一种适应性反应，在这种反应中，从头开始的丝氨酸生物合成受到损害，以维持糖酵解通量骨架。综上所述，这些结果极大地扩展了KFP的能力，适合于广泛的生物学应用。新陈代谢是所有生物过程的基础，对新陈代谢的定量研究对我们的理解至关重要。新陈代谢的中心特征，代谢通量，不能直接测量，通常通过建模来估计。然而，现有的建模方法受到参数表征不佳、精度不高或劳动强度的限制。出于这些限制，并认识到在两种条件之间比较通量的领域中最常见的目标，我们开发了现有方法的扩展，该方法采用同位素标记的代谢物的时间序列相对定量数据(一种现代代谢组学技术容易产生的数据)，并输出感兴趣的代谢网络中通量的相对变化。我们还仔细研究了模型构建和实验设计中的一些问题，提高了方法的可靠性和强度。我们将我们的方法应用于从正常和缺糖条件下的细胞收集的数据，展示了该方法的有效性，并得出了新的生物学见解。
Fluxes are the central trait of metabolism and Kinetic Flux Profiling (KFP) is an effective method of measuring them. To generalize its applicability, we present an extension of the method that estimates the relative changes of fluxes using only relative quantitation of 13C-labeled metabolites. Such features are directly tailored to the more common experiment that performs only relative quantitation and compares fluxes between two conditions. We call our extension rKFP. Moreover, we examine the effects of common missing data and common modeling assumptions on (r)KFP, and provide practical suggestions. We also investigate the selection of measuring times for (r)KFP and provide a simple recipe. We then apply rKFP to 13C-labeled glucose time series data collected from cells under normal and glucose-deprived conditions, estimating the relative flux changes of glycolysis and its branching pathways. We identify an adaptive response in which de novo serine biosynthesis is compromised to maintain the glycolytic flux backbone. Together, these results greatly expand the capabilities of KFP and are suitable for broad biological applications. Metabolism underlies all biological processes, and its quantitative study is crucial for our understanding. The central trait of metabolism, metabolic fluxes, cannot be directly measured and are estimated usually through modeling. Existing modeling methods, however, are limited by poorly-characterized parameters, crude precision, or labor-intensiveness. Motivated by these limitations, and recognizing a most common goal in the field of comparing the fluxes between two conditions, we develop an extension of an existing method that takes in time-series relative-quantitation data of isotope-labeled metabolites (a kind of data that modern metabolomic technologies readily generate), and outputs the relative changes of fluxes in the metabolic networks of interest. We also carefully examine some issues on model construction and experimental design, and improve the reliability and strength of the method. We apply our method to data collected from cells in normal and glucose-deprived conditions, demonstrate the efficacy of the method and arrive at new biological insight.
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影响因子：
64.8
作者：
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Wang, J
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Nomura DK
影响因子：
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作者：
Katada, Sayako;Imhof, Axel;Sassone-Corsi, Paolo
通讯作者：
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