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捕获植物 TOR 激酶的磷酸化和蛋白质相互作用图谱。

Capturing the phosphorylation and protein interaction landscape of the plant TOR kinase.

机构信息

Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.

VIB Center for Plant Systems Biology, Ghent, Belgium.

出版信息

Nat Plants. 2019 Mar;5(3):316-327. doi: 10.1038/s41477-019-0378-z. Epub 2019 Mar 4.

Abstract

The target of rapamycin (TOR) kinase is a conserved regulatory hub that translates environmental and nutritional information into permissive or restrictive growth decisions. Despite the increased appreciation of the essential role of the TOR complex in plants, no large-scale phosphoproteomics or interactomics studies have been performed to map TOR signalling events in plants. To fill this gap, we combined a systematic phosphoproteomics screen with a targeted protein complex analysis in the model plant Arabidopsis thaliana. Integration of the phosphoproteome and protein complex data on the one hand shows that both methods reveal complementary subspaces of the plant TOR signalling network, enabling proteome-wide discovery of both upstream and downstream network components. On the other hand, the overlap between both data sets reveals a set of candidate direct TOR substrates. The integrated network embeds both evolutionarily-conserved and plant-specific TOR signalling components, uncovering an intriguing complex interplay with protein synthesis. Overall, the network provides a rich data set to start addressing fundamental questions about how TOR controls key processes in plants, such as autophagy, auxin signalling, chloroplast development, lipid metabolism, nucleotide biosynthesis, protein translation or senescence.

摘要

雷帕霉素靶蛋白(TOR)激酶是一个保守的调控枢纽,它将环境和营养信息转化为允许或限制生长的决策。尽管人们越来越认识到 TOR 复合物在植物中的重要作用,但尚未进行大规模的磷酸化蛋白质组学或相互作用组学研究来绘制植物中的 TOR 信号事件图谱。为了填补这一空白,我们在模式植物拟南芥中结合了系统磷酸化蛋白质组学筛选和靶向蛋白质复合物分析。一方面,磷酸化蛋白质组学和蛋白质复合物数据的整合表明,这两种方法都揭示了植物 TOR 信号网络的互补子空间,从而能够在全蛋白质组范围内发现上游和下游网络组件。另一方面,这两个数据集之间的重叠揭示了一组候选的直接 TOR 底物。整合的网络嵌入了进化保守和植物特异性的 TOR 信号成分,揭示了与蛋白质合成的复杂相互作用。总的来说,该网络提供了丰富的数据集,可用于开始解决有关 TOR 如何控制植物中关键过程(如自噬、生长素信号、叶绿体发育、脂质代谢、核苷酸生物合成、蛋白质翻译或衰老)的基本问题。

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