Systematic classification of phosphorylation sites for an integrative analysis of kinase signalling

用于激酶信号传导综合分析的磷酸化位点的系统分类

• 批准号: BB/M006174/1
• 负责人: Pedro Cutillas
• 金额: $ 63.22万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM006174%2F1
• 关键词: Systematic; classification; phosphorylation; sites; integrative

A series of biochemical events in cells known as signalling pathways play important roles in the regulation of normal physiological functions in all organisms. Examples of processes regulated by signalling pathways include the movement of bacteria towards a food source, budding of yeast, the response of plants to pathogens, and sugar metabolism in mammals. Therefore, the ability to monitor signalling pathways is important for understanding the biochemistry of essentially all living beings. The activity of these pathways is driven by a group of enzymes known as kinases which attach a type of chemical group, known as phosphate, to other proteins. There are more than 500 different protein kinases in humans and their relationship with each other and with other proteins is very complex. The activity of protein kinases can be detected in cells by analysing phosphates attached to other proteins. Modern methods based on a technique named mass spectrometry (MS) can now detect several thousands of such phosphorylation events. This technique is known as phosphoproteomics and the information provided by this method has the potential to reveal an immense new set of knowledge on how kinases are regulated in cells and how these are altered in disease. To maximize the information that can be derived from phosphoproteomics data, we recently developed a computational approach named Kinase Substrate Enrichment Analysis (KSEA), which links the phosphorylation sites identified by MS to the kinases acting upstream. KSEA algorithms then calculate the enrichment of substrates belonging to given kinases in the dataset. We found that values given by KSEA can be used to measure the activities of all kinases for which substrates are known. However, only about 10% of phosphorylation sites detectable by MS are annotated with the kinases acting upstream. Therefore, only a small fraction of the data obtained in a phosphoproteomic experiment are actually informative for understanding cell biochemistry.To address this issue, in this application we aim to assemble a database of phosphorylation sites annotated with the signalling pathway they belong to. Our hypothesis is that, when used together with KSEA, this database of phosphorylation sites will have the ability to measure signalling with unprecedented depth, thus significantly advancing our understanding of the fundamental properties of biological systems. We will initially focus on signalling pathways operating in human cells but the same approaches could be used to advance the understanding of signalling in other organisms. The database of signalling pathways will be built by classifying phosphorylation events on proteins based on whether these are increased or decreased by drugs that target kinases and by their patterns of modulation by agents known to activate protein kinases. These experiments are now possible because a large array of kinase inhibitors have recently been developed, to be used as drugs to treat diseases such as cancer and inflammation, and because of the recent development of techniques for quantitative phosophoproteomics. We expect to treat cells with at least 100 kinase inhibitors, targeting a minimum of 50 different kinases.By performing this classification systematically and in different cells lines, we will identify relationships between different kinases and will discriminate signalling events that are core for several cell types from those that are cell type specific. Systematic classification of phosphorylation sites will also identify markers of signalling that can be used to measure how these events are remodelled in cells that have changed their characteristics due to disease or because they have become insensitive to therapy. We also hypothesise that a classification of phosphorylation sites based on their patterns of modulation by kinase inhibitors will be useful in constructing models to predict the best kinase inhibitors that can modify a given phenotype.

细胞中的一系列生物化学事件被称为信号通路，在所有生物体的正常生理功能的调节中发挥重要作用。由信号通路调节的过程的例子包括细菌向食物源的移动、酵母的出芽、植物对病原体的反应以及哺乳动物的糖代谢。因此，监测信号通路的能力对于理解基本上所有生物的生物化学是重要的。这些途径的活性是由一组称为激酶的酶驱动的，这些激酶将一种称为磷酸盐的化学基团连接到其他蛋白质上。人类有500多种不同的蛋白激酶，它们之间以及与其他蛋白质之间的关系非常复杂。蛋白激酶的活性可以通过分析附着在其他蛋白质上的磷酸盐来检测。基于一种名为质谱（MS）的技术的现代方法现在可以检测到数千个这样的磷酸化事件。这种技术被称为磷酸化蛋白质组学，这种方法提供的信息有可能揭示一系列关于激酶在细胞中如何调节以及在疾病中如何改变的新知识。为了最大限度地利用磷酸化蛋白质组学数据，我们最近开发了一种名为激酶底物富集分析（KSEA）的计算方法，该方法将MS鉴定的磷酸化位点与上游激酶联系起来。KSEA算法然后计算属于数据集中给定激酶的底物的富集。我们发现，KSEA给出的值可以用来测量所有激酶的活动，其底物是已知的。然而，只有约10%的磷酸化位点可检测的MS注释的激酶上游。因此，在磷酸化蛋白质组学实验中获得的数据中只有一小部分实际上是用于理解细胞biochemics.To解决这个问题，在本申请中，我们的目标是组装一个磷酸化位点数据库，并注释它们所属的信号通路。我们的假设是，当与KSEA一起使用时，这个磷酸化位点数据库将能够以前所未有的深度测量信号，从而大大推进我们对生物系统基本特性的理解。我们最初将专注于在人类细胞中运作的信号通路，但同样的方法可以用于促进对其他生物体中信号传导的理解。信号通路的数据库将通过对蛋白质上的磷酸化事件进行分类来建立，所述分类基于这些磷酸化事件是否被靶向激酶的药物增加或减少，以及通过已知激活蛋白激酶的试剂的调节模式。这些实验现在是可能的，因为最近开发了大量的激酶抑制剂，可用作治疗癌症和炎症等疾病的药物，并且因为最近开发了定量磷酸蛋白质组学技术。我们希望用至少100种激酶抑制剂处理细胞，靶向至少50种不同的激酶。通过在不同的细胞系中系统地进行这种分类，我们将确定不同激酶之间的关系，并将区分几种细胞类型的核心信号事件和细胞类型特异性的信号事件。磷酸化位点的系统分类还将鉴定信号传导的标志物，其可用于测量这些事件在由于疾病或因为它们已经变得对治疗不敏感而改变其特征的细胞中是如何重塑的。我们还假设，磷酸化位点的分类基于其模式的激酶抑制剂的调制将是有用的，在构建模型，以预测最好的激酶抑制剂，可以修改一个给定的表型。

项目成果

Endothelial cell rearrangements during vascular patterning require PI3-kinase-mediated inhibition of actomyosin contractility.

• DOI: 10.1038/s41467-018-07172-3
• 发表时间: 2018-11-16
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Angulo-Urarte A;Casado P;Castillo SD;Kobialka P;Kotini MP;Figueiredo AM;Castel P;Rajeeve V;Milà-Guasch M;Millan J;Wiesner C;Serra H;Muixi L;Casanovas O;Viñals F;Affolter M;Gerhardt H;Huveneers S;Belting HG;Cutillas PR;Graupera M
• 通讯作者: Graupera M

Withanolide Metabolites Inhibit PI3K/AKT and MAPK Pro-Survival Pathways and Induce Apoptosis in Acute Myeloid Leukemia Cells.

• DOI: 10.3390/biomedicines8090333
• 发表时间: 2020-09-06
• 期刊: Biomedicines
• 影响因子: 4.7
• 作者: Akhtar N;Baig MW;Haq IU;Rajeeve V;Cutillas PR
• 通讯作者: Cutillas PR

Oxidative Stress in Cells with Extra Centrosomes Drives Non-Cell-Autonomous Invasion.

• DOI: 10.1016/j.devcel.2018.10.026
• 发表时间: 2018-11-19
• 期刊: Developmental cell
• 影响因子: 11.8
• 作者: Arnandis T;Monteiro P;Adams SD;Bridgeman VL;Rajeeve V;Gadaleta E;Marzec J;Chelala C;Malanchi I;Cutillas PR;Godinho SA
• 通讯作者: Godinho SA

Integrative phosphoproteomics defines two biologically distinct groups of KMT2A rearranged acute myeloid leukaemia with different drug response phenotypes.

• DOI: 10.1038/s41392-022-01288-1
• 发表时间: 2023-02-27
• 期刊: SIGNAL TRANSDUCTION AND TARGETED THERAPY
• 影响因子: 39.3
• 作者: Casado, Pedro;Rio-Machin, Ana;Miettinen, Juho J.;Bewicke-Copley, Findlay;Rouault-Pierre, Kevin;Krizsan, Szilvia;Parsons, Alun;Rajeeve, Vinothini;Miraki-Moud, Farideh;Taussig, David C.;Boedoer, Csaba;Gribben, John;Heckman, Caroline;Fitzgibbon, Jude;Cutillas, Pedro R.
• 通讯作者: Cutillas, Pedro R.

Proteomic and genomic integration identifies kinase and differentiation determinants of kinase inhibitor sensitivity in leukemia cells.

• DOI: 10.1038/s41375-018-0032-1
• 发表时间: 2018-08
• 期刊: Leukemia
• 影响因子: 11.4
• 作者: Casado P;Wilkes EH;Miraki-Moud F;Hadi MM;Rio-Machin A;Rajeeve V;Pike R;Iqbal S;Marfa S;Lea N;Best S;Gribben J;Fitzgibbon J;Cutillas PR
• 通讯作者: Cutillas PR