Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSYNMR measurements and cumulative Bondomer simulation
Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSYNMR measurements and cumulative Bondomer simulation
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DOI：
10.1002/bit.10648
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发表时间：
2003-07-05
影响因子：
3.8
通讯作者：
Heijnen, JJ
中科院分区：
文献类型：
--
作者：
van Winden, WA;van Gulik, WM;Heijnen, JJ
目前有两种替代方法可用于分析代谢网络中的通量：(1)将净转化率的测量与一组代谢物平衡（包括辅因子平衡）结合起来，或(2)忽略辅因子平衡并将所得自由通量拟合到测量的c -13标记数据中。本研究将这两种方法应用于以氨或硝酸盐为氮源生长的青霉菌糖酵解和戊糖磷酸途径的通量，期望得到不同的戊糖磷酸途径通量。所提出的通量分析是基于广泛的2D [C-13， (1)1H] COSY数据集。采用了一种新的概念来模拟这类c -13标记数据：累积粘结模型。基于c -13标记的通量分析结果与纯代谢物平衡方法的结果有很大不同。使用c -13标记数据确定的通量显示高度依赖于所选择的代谢网络。通过增加转酮醇酶和转醛缩酶反应来扩展传统的非氧化戊糖磷酸途径，通过果糖6-磷酸醛缩酶/二羟丙酮激酶反应序列来扩展糖酵解，或者在模型中加入磷酸烯醇丙酮酸羧激酶反应，大大提高了测量数据和模拟NMR数据的拟合性。使用扩展版的非氧化戊糖磷酸途径模型得到的结果表明，转酮醇酶和转醛醇酶反应不需要假设可逆，以获得很好的拟合c -13标记数据。使用实际测量误差对基于c -13标记的通量分析结果进行严格的统计检验，对于验证假设的代谢模型至关重要。(C) 2003 Wiley期刊有限公司
At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured C-13-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [C-13, (1)1H] COSY data. A new concept is applied for simulation of this type of C-13-labeling data: cumulative bondomer modeling. The outcomes of the C-13-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using C-13-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the C-13-labeling data. Strict statistical testing of the outcomes of C-13-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model. (C) 2003 Wiley Periodicals, Inc.