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

点击翻译按钮获取中文摘要

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.