Schwahn Kevin, Beleggia Romina, Omranian Nooshin, Nikoloski Zoran
Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
Front Plant Sci. 2017 Dec 18;8:2152. doi: 10.3389/fpls.2017.02152. eCollection 2017.
Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higher-order dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from and , we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.
代谢组学技术的最新进展已产生了高质量的（时间分辨的）代谢谱，且对代谢途径的覆盖范围不断扩大。这些数据谱代表了代谢网络中通常非线性动态过程的读数。然而，代谢谱在很大程度上是通过仅捕捉线性关系的基于回归的方法进行探索的，这使得难以确定数据反映潜在反应速率及其耦合程度的范围。在此，我们提出一种基于对数变换后的代谢谱正线性组合之间相关性的方法，称为化学计量相关分析（SCA）。对数变换是基于代谢网络可由质量作用定律及其导出的动力学进行建模这一证据。与现有的在成对代谢物之间建立关系的方法不同，SCA有助于发现两个以上代谢物之间的高阶依赖性。通过使用三羧酸循环的范例模型，我们表明高阶依赖性反映了反应物复合物浓度的耦合，捕捉了所采用的酶动力学之间的细微差异。使用来自[具体研究对象1]和[具体研究对象2]的时间分辨代谢谱，我们表明SCA可用于量化这些模式生物中严格反应背后反应物复合物耦合的差异，进而量化反应速率的差异。通过将SCA应用于野生和驯化小麦及番茄品种自然变异的数据，我们证明在这些物种中，驯化伴随着这种耦合的丧失。因此，将SCA应用于野生和驯化种群自然变异的代谢组学数据，为理解驯化及其与代谢网络的关系提供了一种机制性方法。
