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

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.

Signalling pathways driven by protein and lipid kinases regulate fundamental biochemical processes in all organisms. Our understanding of kinase signalling is increasing rapidly in part due to advances in phosphoproteomic techniques based on mass spectrometry (MS) which can now detect and quantify thousands of phosphorylation sites in cells. However, the functional role of the majority of phosphorylation sites detectable by MS remains unknown; therefore, at present we can only harness a small fraction of the biological information that could in principle be derived from phosphoproteomics experiments.
The aim of this application is to systematically classify phosphorylation sites into groups so that these can serve as markers of signalling network circuitry. This will allow (i) the identification of novel signalling pathways and (ii) measuring the wiring of such networks routinely and systematically in cells and tissues.
Phosphorylation sites will be classified based on their patterns of modulation by a large panel of kinase inhibitors and activators of cell signalling in three different cell lines. These experiments will be performed using MS-based phosphoproteomics. The groups of phosphorylation sites resulting from this classification will be assembled in a database and characterized by a bioinformatics study that will investigate the relationships between these phosphorylation groups and kinases. This part of the work will also compare the classification of phosphorylation sites arising from this work across three different cell lines; this information will shed light into pathways likely to be core for all cells relative to those that may be cell type specific. The utility of phosphorylation site classification will be illustrated by measuring the remodelling of the newly characterized signalling network in cells that are undergoing changes in their physiology as a result of being treated with inhibitors of signalling.

Pharmaceutical/biotechnology companies:
The outputs of this research may benefit pharmaceutical companies developing kinase inhibitors. This research is likely to have an impact in our understanding of cell signalling, a cell biological process that is implicated in the onset and progression of several diseases of increasing concern in an ageing population, such as neurodegeneration, metabolic syndromes, autoimmune conditions and cancer. Indeed, kinase inhibitors are one of the mayor drug classes currently pursued by the pharmaceutical industry and new programs to develop kinase inhibitors are continuously being created.
The resources created as part of this application will allow measuring cell signalling networks with increased depth and in an integrative manner. These tools are likely to be useful for advancing drug development programs that target kinases and other enzymes involved in cell signalling. Knowledge on the precise effects of such compounds in cells (including target and off-target effects) and how different inhibitors relate to each other is likely to have an impact in these drug development programs. A potential application of the resources developed here includes mapping phosphorylation modulated by a novel compound to the databases developed here. This would allow comparing the in vivo selectivity of such novel compound to those already developed.

Clinical Researchers:
This research may also benefit clinical researchers trying to advance targeted therapies based on inhibitors of cell signalling. An output of this project will be the identification of signatures of kinase target activity. These could be used to assess the activity of the drug targets in cells and tissues. Therefore, an additional potential impact of this project is that the results could be used to measure how active drug targets are in cells and thus inform clinical trials based on kinase inhibitors. This aspect of the work would require additional research to develop clinical grade assays and to link the signatures identified in this work to specific clinical outcomes.

NHS:
In the longer term, this research could have an impact on costs savings for the NHS. As outlined above, kinase inhibitors are one of the mayor classes of novel targeted drugs for the treatment of several conditions. These drugs are often highly effective, but not all patients respond equally well to kinase-based therapies. This is a problem for the NHS because these drugs are very expensive (treatments are in excess of £30,000 per patient per year). Therefore, the ability to identify biomarkers of responses, so that only the subpopulation of patients that are likely to benefit are treated, is of paramount importance for the approval of some of these targeted therapies by NICE, benefiting patients and thus society. This research will advance technology to measuring the signalling network modulated by kinase inhibitors, which are already in the clinic or in advanced stages of clinical development. Our preliminary data shows that these measurements are a reflection of phenotype (Figure 3 in case for support). Therefore, with further work, the technological solution proposed here could have an impact in predicting responses to kinase drugs, thus contributing to advance personalized therapies that target the kinase network.

Other fields:
This research could also indirectly impact fields in which predicting phenotypes from molecular data is important. An example includes industrial biotechnology where understanding signalling in microorganisms may lead to new ways of engineering them to optimize yields and production processes. In agriculture, an understanding of signalling events that occur during plan disease may in the future lead to better ways of treating and/or preventing infection with pathogens, which in turn may lead to improving crop yields.