Sajitz-Hermstein Max, Töpfer Nadine, Kleessen Sabrina, Fernie Alisdair R, Nikoloski Zoran
Systems Biology and Mathematical Modeling Group.
Department of Plant and Environmental Sciences, Faculty of Biochemistry, Weizmann Institute of Science, Rehovot, 7610001, Israel.
Bioinformatics. 2016 Sep 1;32(17):i755-i762. doi: 10.1093/bioinformatics/btw465.
Understanding the rerouting of metabolic reaction fluxes upon perturbations has the potential to link changes in molecular state of a cellular system to alteration of growth. Yet, differential flux profiling on a genome-scale level remains one of the biggest challenges in systems biology. This is particularly relevant in plants, for which fluxes in autotrophic growth necessitate time-consuming instationary labeling experiments and costly computations, feasible for small-scale networks.
Here we present a computationally and experimentally facile approach, termed iReMet-Flux, which integrates relative metabolomics data in a metabolic model to predict differential fluxes at a genome-scale level. Our approach and its variants complement the flux estimation methods based on radioactive tracer labeling. We employ iReMet-Flux with publically available metabolic profiles to predict reactions and pathways with altered fluxes in photo-autotrophically grown Arabidopsis and four photorespiratory mutants undergoing high-to-low CO2 acclimation. We also provide predictions about reactions and pathways which are most strongly regulated in the investigated experiments. The robustness and variability analyses, tailored to the formulation of iReMet-Flux, demonstrate that the findings provide biologically relevant information that is validated with external measurements of net CO2 exchange and biomass production. Therefore, iReMet-Flux paves the wave for mechanistic dissection of the interplay between pathways of primary and secondary metabolisms at a genome-scale.
The source code is available from the authors upon request.
Supplementary data are available at Bioinformatics online.
了解扰动后代谢反应通量的重新路由，有可能将细胞系统分子状态的变化与生长改变联系起来。然而，在全基因组水平上进行差异通量分析仍然是系统生物学面临的最大挑战之一。这在植物中尤为相关，因为自养生长中的通量需要耗时的非稳态标记实验和昂贵的计算，这对于小规模网络来说是可行的。
在这里，我们提出了一种计算和实验上都很简便的方法，称为iReMet-Flux，它将相对代谢组学数据整合到代谢模型中，以预测全基因组水平上的差异通量。我们的方法及其变体补充了基于放射性示踪剂标记的通量估计方法。我们将iReMet-Flux应用于公开可用的代谢谱，以预测光合自养生长的拟南芥和四个经历高到低二氧化碳适应的光呼吸突变体中通量发生改变的反应和途径，并提供了在研究实验中调控最为强烈的反应和途径的预测。针对iReMet-Flux的公式进行的稳健性和变异性分析表明，这些发现提供了与生物学相关的信息，并通过净二氧化碳交换和生物量生产的外部测量得到了验证。因此，iReMet-Flux为在全基因组水平上对初级和次级代谢途径之间的相互作用进行机制剖析铺平了道路。
可根据作者要求提供源代码。
补充数据可在《生物信息学》在线获取。
