Assessing patterns of genetic association between autoimmune diseases to date reveals heterogeneity, but also a few key variants that are emerging as immunopathological foci. The aim of this project is to investigate shared molecular circuits and cellular mechanisms across autoimmune conditions to identify and study immunological ‘hubs’ that may be targeted therapeutically. Cytokine signalling is central to immune responsiveness and plays a fundamental role in autoimmune diseases. Thus, several of the ‘immunological ‘hubs’ implicated in these conditions are genes involved in cytokine signalling. The project will involve interrogating the relationship between disease-associated genetic variation and the dynamics of immune cell signalling. Elucidating this relationship can have implications for the fine-tuning of signalling cascades, and by extension for precision medicine in the context of patient stratification, prognostication, and optimal drug dosages to balance efficacy and side effects. The project will be well suited to a student interested in the combination of wet‐lab research and bioinformatics/data analysis.
The proposed project will involve: primary human cell culture, FACS, flow and mass cytometry, single-cell RNA sequencing, ATAC-Seq, chromatin-conformation capture, ChIP-Seq, transfection and viral transduction, CRISPR‐Cas9 genome engineering, and computational biology. The student will additionally receive training in scientific writing and communication through oral presentations to the scientific community at international conferences, as well as to the general public through public engagement opportunities.
Project reference number: 942
|Dr Calliope Dendrou||Wellcome Trust Centre for Human Genetics||Oxford University, Henry Wellcome Building of Genomic Medicine||GBRfirstname.lastname@example.org|
Nat. Med., 19 (2), pp. 138-9. | Read more2013. Weighing in on autoimmune disease: Big data tip the scale.
Thousands of genetic variants have been identified, which contribute to the development of complex diseases, but determining how to elucidate their biological consequences for translation into clinical benefit is challenging. Conflicting evidence regarding the functional impact of genetic variants in the tyrosine kinase 2 (TYK2) gene, which is differentially associated with common autoimmune diseases, currently obscures the potential of TYK2 as a therapeutic target. We aimed to resolve this conflict by performing genetic meta-analysis across disorders; subsequent molecular, cellular, in vivo, and structural functional follow-up; and epidemiological studies. Our data revealed a protective homozygous effect that defined a signaling optimum between autoimmunity and immunodeficiency and identified TYK2 as a potential drug target for certain common autoimmune disorders. Hide abstract
Neuroinflammation is emerging as a central process in many neurological conditions, either as a causative factor or as a secondary response to nervous system insult. Understanding the causes and consequences of neuroinflammation could, therefore, provide insight that is needed to improve therapeutic interventions across many diseases. However, the complexity of the pathways involved necessitates the use of high-throughput approaches to extensively interrogate the process, and appropriate strategies to translate the data generated into clinical benefit. Use of 'big data' aims to generate, integrate and analyse large, heterogeneous datasets to provide in-depth insights into complex processes, and has the potential to unravel the complexities of neuroinflammation. Limitations in data analysis approaches currently prevent the full potential of big data being reached, but some aspects of big data are already yielding results. The implementation of 'omics' analyses in particular is becoming routine practice in biomedical research, and neuroimaging is producing large sets of complex data. In this Review, we evaluate the impact of the drive to collect and analyse big data on our understanding of neuroinflammation in disease. We describe the breadth of big data that are leading to an evolution in our understanding of this field, exemplify how these data are beginning to be of use in a clinical setting, and consider possible future directions. Hide abstract
Genetic discovery from the multitude of phenotypes extractable from routine healthcare data can transform understanding of the human phenome and accelerate progress toward precision medicine. However, a critical question when analyzing high-dimensional and heterogeneous data is how best to interrogate increasingly specific subphenotypes while retaining statistical power to detect genetic associations. Here we develop and employ a new Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to analyze genetic variants against UK Biobank disease phenotypes derived from self-reporting and hospital episode statistics. Our method displays a more than 20% increase in power to detect genetic effects over other approaches and identifies new associations between classical human leukocyte antigen (HLA) alleles and common immune-mediated diseases (IMDs). By applying the approach to genetic risk scores (GRSs), we show the extent of genetic sharing among IMDs and expose differences in disease perception or diagnosis with potential clinical implications. Hide abstract