Characterisation of new cellular signalling systems using novel, rapid, high resolution protein electrophoresis

人类基因组的很大一部分，以及其他后生动物的基因组，编码的蛋白质能对细胞内外环境的变化做出快速反应。例如，约5%用于通过GTP结合蛋白操作的信号系统，约2.5%用于蛋白激酶。此外，还有数百个基因编码支持或响应这两个系统的蛋白质。在一系列实验(未发表)中，我们可以展示主要的G蛋白信号系统-Arf家族G蛋白-控制囊泡运输和细胞内运输、细胞骨架动力学和磷脂酰肌醇代谢的G蛋白-与非受体酪氨酸激酶Src家族之间不寻常和意想不到的联系，该家族包含在细胞生长、分化、细胞形状、迁移和生存中发挥作用的主要细胞癌基因，以及特定的细胞信号。我们可以证明ARF是以一种依赖于Arf激活状态的方式被Src激酶磷酸化的酪氨酸--在GDP结合的“非活性”胞浆形式中，ARF是较差的底物，而在GTP结合的膜相关的“活性”形式中，它们是很好的底物。已有的证据(及相关的分子生物学工具和方法)包括：i)体外纯净的重组重组GTP-Arf异构体被Src和Lck酶磷酸化，而不是GDP Arf Form；i)通过HA标记的GTP-Arf1和GTP-Arf6的转染和免疫沉淀(IP)证明Arf蛋白酪氨酸磷酸化，但不存在GDP形式；iii)通过IPIV)天然和野生型Arf IP对Arf1和Arf6上的磷酸化位点进行质谱鉴定，并用FRAP共聚焦显微镜证明了野生型和野生型Arf GFP融合蛋白的膜结合动力学的差异(磷酸化位点)。我们建议使用新开发的先进的、快速的蛋白质分离和定量技术来充分研究结合的激酶-G蛋白信号在新的耦合的Src-Arf系统中的重要性。该项目将有一个可管理的职权范围和现实的成功前景，在非常紧迫的4年时间框架内。这个项目在技术上、概念上和理论上都非常苛刻。例如，与激酶/磷酸酶磷酸化/去磷酸化循环相比，G蛋白的GDP/GTP、环境基金/GAP开关的动态特性(实际上)知之甚少(Stanley和Thomas 2016中的数学证明和模型)。因此，组合系统的动力学并不直观，因为GDP-Arf、GTP-Arf、GDP-磷酸Arf和GTP-磷酸Arf形式的四向相互转化在细胞信号领域是全新的。因此，我们需要以足够的速度和非常高的物种分辨率(附录1)同时分离和量化Arf蛋白质的所有四种状态的方法，以便能够在高采样的实验时间过程中研究动力学。时间序列数据将支持完全描述耦合系统动态的数学模型，这些数学模型将补充所描述的细胞生物和生化数据，并允许对这一独特的组合细胞信号基序进行完整的系统生物学理解。数学和计算方法将允许对系统的完整描述，以便与其他研究人员进行透明和明确的交流。

A very large fraction of the human genome, and the genomes of other metazoans, encodes proteins that respond rapidly to changes in external and internal cellular environments. For example about 5% is given over to signaling systems that operate through GTP binding proteins and about 2.5% accounts for protein kinases. In addition, many hundreds more genes code proteins that support or respond to these two systems. In a series of experiments (unpublished) we can demonstrate an unusual and unexpected link between a major G-protein signaling system - the Arf family G-proteins that control vesicular trafficking and intracellular transport, cytoskeletal dynamics and phosphoinositide metabolism - and the Src family of non-receptor tyrosine kinases that contains major cellular oncogenes with roles in cell growth, differentiation, cell shape, migration and survival, and specialised cell signals. We can show that Arfs are tyrosine phosphorylated by Src kinases in a manner dependent on the Arf activation state - in the GDP bound "inactive" cytosolic form Arfs are poor substrates and in the GTP bound, membrane associated "active" form they are excellent substrates for the the kinases. Proofs in place (and relevant molecular biological tools and methods available) include: i) in vitro phosphorylation of pure recombinant GTP-Arf isoforms by pure recombinant Src and Lck enzymes but not GDP Arf formsii) demonstration of Arf protein tyrosine phosphorylation using transfection and immunoprecipitation (IP) of HA-tagged GTP-Arf1 and GTP-Arf6 but not GDP forms.iii) mass spectrometric identification of the phosphorylation site on Arf1 and Arf6 following IPiv) IP of native, wild-type Arfs and demonstration of tyrosine phosphorylation.v) differences in membrane association dynamics of wild-type Arf GFP fusion proteins and those with YF mutants (phosphorylation site eliminated) using FRAP confocal microscopy. We propose to use newly developed advanced, rapid protein separation and quantification techniques to fully investigate the importance of combined kinase-G protein signalling in the new coupled Src-Arf system. The project will have a manageable remit and a realistic prospect for success within the very tight 4-year timeframe. This project is technically, conceptually and theoretically very demanding. For example the dynamic properties of the GDP/GTP, GEF/GAP switch of G-proteins is (in reality) poorly understood (mathematical proofs and models in Stanley and Thomas 2016) compared to the kinase/phosphatase phosphorylation/dephosphorylaton cycle. As a result the dynamics of the combined system are not intuitive since the four-way interconversion of GDP-Arf, GTP-Arf, GDP-phospho Arf and GTP-phospho Arf forms is entirely novel in the cell signalling field. We therefore need methods to simultaneously isolate and quantify all four states of the Arf proteins at sufficient speed and very high species resolution (Appendix 1) to allow dynamics to be studied over highly sampled experimental time courses. Time series data will underpin mathematical models completely describing the dynamics of the coupled system that will supplement the cell biological and biochemical data described and allow a complete systems biological understanding of this unique combined cell signalling motif. The mathematical and computational approach will allow the complete description of the system for transparent and unambiguous communication to other researchers.