Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions. Although extensive regulatory pathways and protein interaction data sets exist which represent such networks, few have formally considered quantitative proteomics data to validate and extend them. We generate and consider such data here using a label-free proteomics strategy to quantify alterations in protein abundance for S. cerevisiae when grown on minimal media using glucose, galactose, maltose and trehalose as sole carbon sources. Using a high quality-controlled subset of proteins observed to be differentially abundant, we constructed a proteome-informed network, comprising 1850 transcription factor interactions and 37 chaperone interactions, which defines the major changes in the cellular proteome when growing under different carbon sources. Analysis of the differentially abundant proteins involved in the regulatory network pointed to their significant roles in specific metabolic pathways and function, including glucose homeostasis, amino acid biosynthesis, and carbohydrate metabolic process. We noted strong statistical enrichment in the differentially abundant proteome of targets of known transcription factors associated with stress responses and altered carbon metabolism. This shows how such integrated analysis can lend further experimental support to annotated regulatory interactions, since the proteomic changes capture both magnitude and direction of gene expression change at the level of the affected proteins. Overall this study highlights the power of quantitative proteomics to help define regulatory systems pertinent to environmental conditions.

中文翻译：

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酿酒酵母中蛋白质组整合的碳源依赖性遗传调控网络。

集成的监管网络可以成为检查和测试细胞系统特性的强大工具，例如对分子生物经济的环境影响进行建模，其中蛋白质水平会根据生长条件的变化而发生变化。尽管存在代表此类网络的广泛调控途径和蛋白质相互作用数据集，但很少有人正式考虑使用定量蛋白质组学数据来验证和扩展它们。我们在这里使用无标记蛋白质组学策略来生成和考虑此类数据，以定量在使用葡萄糖，半乳糖，麦芽糖和海藻糖作为唯一碳源的基本培养基上生长时，酿酒酵母蛋白质丰度的变化。利用观察到的差异丰富的高质量蛋白质控制子集，我们构建了蛋白质组信息网络，包含1850个转录因子相互作用和37个伴侣相互作用，它们定义了在不同碳源下生长时细胞蛋白质组的主要变化。对涉及调节网络的差异丰富的蛋白质的分析指出，它们在特定代谢途径和功能（包括葡萄糖稳态，氨基酸生物合成和碳水化合物代谢过程）中的重要作用。我们注意到在与应激反应和碳代谢改变有关的已知转录因子靶标的差异丰富蛋白质组中，统计学上的丰富性。这说明了这种综合分析如何为注释的监管互动提供进一步的实验支持，因为蛋白质组学改变捕获了受影响蛋白水平上基因表达变化的幅度和方向。总体而言，这项研究突出了定量蛋白质组学在帮助定义与环境条件相关的监管系统方面的力量。