This project will focus on identification of aberrant cellular interactions and pathways caused by susceptibility genes that mediate a loss of immune tolerance to insulin-producing beta cells culminating in their destruction. These will provide potential targets for therapeutic intervention, as demonstrated by our work in the IL-2 and IL-6 pathways https://www.ncbi.nlm.nih.gov/pubmed/28747257; https://www.ncbi.nlm.nih.gov/pubmed/28284938). This knowledge will contribute to understanding how cell interactions are altered by disease genes, an essential step for prioritizing potential immune-modulating agents to be investigated in experimental studies in type 1 diabetes (T1D) patients.
The strongest genetic effect in T1D is encoded by the HLA class II genes, which encode peptide-binding molecules recognised by T cell receptors (TCR), the engagement of which leads to T cell activation. This HLA-peptide interaction with TCR is central to the initial selection or deletion of T cells in the thymus, and yet its precise configuration remains only partly understood in T1D. For example, there is a common HLA haplotype, DR*15-DQ*0602 that is highly protective for T1D and we assume that is through thymic deletion of potentially autoreactive beta-cell antigens. Yet evidence for this model is still lacking and even the identity of the autoantigenic peptides is uncertain. Very recently, accurate and robust sequencing and determination of the TCR repertoire has been reported. This exciting, albeit long awaited, development allows us to test the HLA-peptide-TCR selection hypothesis in our clinical PBMC samples in naïve and autoantigen-activated T cells, in bulk populations and single cells. This will lead to the identification of the primary TCR chain receptor amino acid sequences and their primary autoantigenic epitopes, which could underpin future diagnostics, trial monitoring and patient stratification.
There will be opportunity to learn and develop skills in a wide range of molecular and immunological techniques: flow cytometry, RNA sequence expression, protein expression using ELISA, western blotting and microscopy, molecular biology and mass spectrometry. There is also a significant bioinformatics component to this project.
Project reference number: 879
|Professor John A Todd FRS FMedSci||Wellcome Trust Centre for Human Genetics||Oxford University, Henry Wellcome Building of Genomic Medicine||GBRfirstname.lastname@example.org|
T cell receptor (TCR) sequences are very diverse, with many more possible sequence combinations than T cells in any one individual. Here we define the minimal requirements for TCR antigen specificity, through an analysis of TCR sequences using a panel of peptide and major histocompatibility complex (pMHC)-tetramer-sorted cells and structural data. From this analysis we developed an algorithm that we term GLIPH (grouping of lymphocyte interactions by paratope hotspots) to cluster TCRs with a high probability of sharing specificity owing to both conserved motifs and global similarity of complementarity-determining region 3 (CDR3) sequences. We show that GLIPH can reliably group TCRs of common specificity from different donors, and that conserved CDR3 motifs help to define the TCR clusters that are often contact points with the antigenic peptides. As an independent validation, we analysed 5,711 TCRβ chain sequences from reactive CD4 T cells from 22 individuals with latent Mycobacterium tuberculosis infection. We found 141 TCR specificity groups, including 16 distinct groups containing TCRs from multiple individuals. These TCR groups typically shared HLA alleles, allowing prediction of the likely HLA restriction, and a large number of M. tuberculosis T cell epitopes enabled us to identify pMHC ligands for all five of the groups tested. Mutagenesis and de novo TCR design confirmed that the GLIPH-identified motifs were critical and sufficient for shared-antigen recognition. Thus the GLIPH algorithm can analyse large numbers of TCR sequences and define TCR specificity groups shared by TCRs and individuals, which should greatly accelerate the analysis of T cell responses and expedite the identification of specific ligands. Hide abstract