Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p
Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p
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DOI：
10.1073/pnas.0811091106
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发表时间：
2009-04-21
影响因子：
11.1
通讯作者：
Stephanopoulos, Gregory
中科院分区：
文献类型：
--
作者：
Moxley, Joel F.;Jewett, Michael C.;Stephanopoulos, Gregory
基因组测序极大地提高了我们理解细胞对干扰的反应的能力。整合系统范围的测量，如基因表达与蛋白质蛋白质相互作用和转录因子结合的网络揭示了细胞行为的关键见解。然而，系统生物学方法的潜力是有限的，尽管它们与细胞表型最密切相关，但在整合细胞功能水平的代谢测量方面存在困难。为了解决这一限制，我们开发了一种基于模型的方法来关联mRNA和代谢通量数据，该方法结合了来自相互作用网络模型和通量确定模型的信息。我们首先在不存在或存在全局调节剂Gcn 4p的情况下，在压力下连续培养的酵母中定量5，764种mRNA，54种代谢物和83种实验性C-13反应通量。虽然单独的mRNA表达不能直接预测代谢反应，但通过结合基于网络的氨基酸生物合成模型（mRNA通量一致性从r = 0.07到0.80），这种相关性得到了改善。该模型提供了一般生物学原理的证据：代谢通量的重新布线（即，使用不同的反应途径）通过转录调节和代谢物相互作用密度（即，成对代谢物-蛋白质相互作用的水平）作为关键的生物合成控制决定因素。此外，该模型预测通量重新布线的后续转录调节因子的研究，实验验证了额外的C-13为基础的通量测量。作为连接代谢控制和遗传调控网络的第一步，该模型强调了在大规模细胞模型中整合不同数据类型的重要性。我们预计，集中在代谢测量的综合方法将有助于构建更现实的细胞调控模型，以了解疾病和构建工业应用的菌株。
Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental C-13-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional C-13-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.