Systems approach to the analysis of T cell receptor signal transduction

T cells are white blood cells that continuously patrol the body in search of evidence of infection or cancer. Recognition by T cells occurs when receptors on the T cell surface directly bind to fragments or 'antigens' derived from infectious organisms or cancer cells. This recognition is particularly important because T cells, in addition to themselves killing infected or cancerous cells, also coordinate and control other elements of the immune response that are required to remove these threats. T cell antigen recognition is the key to understanding appropriate and helpful immune responses to dangerous antigens (e.g. from harmful bacteria) as well as inappropriate, unhelpful responses to innocuous or self-antigens, which results in allergic and autoimmune diseases, respectively. Although the T cell antigen receptor (TCR) is clearly very important it is still unclear how binding of the TCR to antigen leads to the activation of the T cell and how the TCR detects the rare dangerous antigens amongst the far more numerous harmless antigens. This study aims to address these questions using a systems medicine based approach combining mathematical modelling and range of experimental techniques. An improved understanding of T cell antigen recognition will enhance our ability to manipulate the immune response in order to improve vaccines, treat autoimmune and allergic diseases, and prevent rejection of organ transplants.

Antigen recognition by T cells is mediated by the multisubunit T cell antigen receptor (TCR) complex. Extensive research into signal transduction through the TCR has highlighted the complexity of this receptor. Subunits in the TCR complex involved in signal transduction contain 10 immuno-tyrosine based activation motifs (ITAMs), which are sequentially phosphorylated by the kinase Lck and dephosphorylated by phosphatases such as CD45. Phosphorylated ITAMs serve as docking sites for the cytosolic kinase ZAP-70, which has been shown to bind these ITAMs with differing affinities. Despite this extensive research, the purpose of multiple ITAMs, their sequential phosphorylation by Lck, and the differential affinity of ZAP-70 are unknown.

The aim of this project is to use a detailed systems medicine approach to integrate these known TCR properties into a mathematical model. Predictions from the model will be tested experimentally and the model will subsequently be revised. A preliminary mathematical model that includes these TCR properties has suggested that the combination of these features (e.g. ITAM multiplicity, sequential phosphorylation, and differential ZAP-70 affinity) may be important for conferring sensitivity and discriminatory ability to the process of antigen recognition. In order to test these predictions, the TCR ITAMs will be systematically mutated and rearranged, and proximal and distal readouts of TCR signal transduction analysed. Based on these experiments, the mathematical model will be revised and calibrated so that it can be used to make further predictions. In addition, we will investigate the role of basic-residue-rich motifs that have been recently implicated in controlling ITAM phosphorylation in the underlying mechanism of sequential ITAM phosphorylation.

Collectively, this combined mathematical and experimental work will enhance our understanding of the mechanisms and properties of T cell antigen recognition.

Our proposed research utilizes a systems medicine approach to relate molecular events proximal to the T cell receptor to the expected cellular response. We expect that our experimentally validated systems model will benefit translational research in both academia and industry.

Specialized impact. There are many scientists around the world who study the T cell receptor in the hope that a deeper understanding antigen induced TCR signalling can be exploited for the benefit of human health. We expect that these scientists will directly benefit from our work as it will provide a unifying quantitative framework for understanding multiple events associated with antigen induced TCR signalling.

Interdisciplinary impact. There are a growing number of projects aimed at translational research. In particular, a number of researchers are attempting to modulate immune responses by adoptively transferring modified T cells into animal models of various diseases. These modified T cells often have a chimeric antigen receptor (CAR), which contains engineered signalling chains to increase the potency of the receptor. A key output from the proposed project will be a calibrated mathematical model which can be used for in silico predictions of various T cell receptor and chimeric antigen receptor modifications. For example, the mathematical model has uncovered mechanisms by which the potency of the receptor can be enhanced and this information, once confirmed in experiments, can be used to generate novel therapies. We expect that this basic research leg work will set the stage for testing in animal models and will inform on the rational design of translational research projects.

Public impact. Many companies around the world are focused on developing novel immune based therapies to treat cancers, viral infections, autoimmune diseases, and allergies. The United Kingdom is home to world-leading companies in T cell receptor based immunotherapy, such as Immunocore and Adaptimmune. A collaboration has been initiated with these companies so that their reagents will facilitate our research and our findings and mathematical model can inform their own work. In this way, the company can maintain their competitive edge and potentially increase the rate at which such therapies enter the public domain.

Training. This project involves both experimental and mathematical components and will bring together scientists with diverse backgrounds. A specific goal of the project is to train experimentalists to use mathematical modeling and data fitting, which are tools essential for modern biomedical research. The applicants will run a workshop detailing the procedure of data analysis and fitting for biomedical scientists that will be open to all researchers.