Investigating the impact of microbiome variation on the T cell repertoire in immune-mediated inflammatory diseases

PROJECT OVERVIEW

Recent studies have highlighted the potential for symbiotic gut microbes to shape host T cell repertoires through major histocompatibility complex (MHC)-mediated antigen presentation [1,2], and advances in computational modelling are making it increasingly possible to gain biological insight through accurate in silico prediction of microbial antigen-T cell receptor (TCR) interactions [3]. 

These advances provide novel opportunities to systematically address the impact of microbiome variation on T cell biology. For example, through exploring how shifts in the gut microbiome composition (b diversity) or loss of complexity (a diversity) impact on the diversity of peptide-MHC complexes and hence shape the host T-cell repertoire. This, in turn, paves the way for addressing broader questions, such as how immune homeostasis is impacted by environmental influences on microbiome composition, and how microbiome-mediated variation in host T cell immunity shapes subsequent response to immune challenge. For example, in immune-mediated inflammatory diseases (IMIDs) such as inflammatory bowel disease (IBD) and Ankylosing Spondylitis (AS) it is thought that interactions between specific microbial antigens and human leukocyte antigen (HLA) MHC Class I molecules may play important roles in disease pathogenesis [4].

This project will initially focus on developing and validating computational pipelines for microbial epitope prediction from meta-omic datasets (i.e. metagenome, metatranscriptome, metaproteome).  It will then apply these pipelines to predict how microbiome-TCR interactions vary in isogenic mice inoculated with a diverse set of minimal microbial communities under germ-free conditions. Predictions will be validated using metagenomic, single-cell and bulk TCR sequencing approaches and a role for candidate antigen-TCR interactions in disease pathogenesis investigated in in vivo models of inflammatory disease. Finally, we will apply the validated approaches to data from human IMIDs.

KEYWORDS

Microbiome, T cells, Computational Biology, 

TRAINING OPPORTUNITIES

This project would suit somebody with a strong interest in immunology and the microbiome, as well as in applying computational and state-of-the art artificial intelligences (AI) methods, such as neural networks and large language models (LLMs) to study the molecular basis of inflammatory disease.

The supervisory team will provide direct training in scripting (Python, R), computational pipeline development, machine-learning and the analysis and interpretation of (meta-)genomic datasets. Close integration with the Oxford Centre for Microbiome Studies (https://www.kennedy.ox.ac.uk/platforms-and-technologies/microbiome) and Single Cell & Spatial Genomics Facility (https://www.kennedy.ox.ac.uk/platforms-and-technologies/single-cell-and-spatial-genomics) will provide opportunities to directly engage with state-of-the-art laboratory techniques for immune and microbiome research. This includes the opportunity to run gnotobiotic mouse experiments. Close integration with industry partners (Roche) will additionally provide access to significant machine learning resources and opportunities to experience research in both an academic and industry setting.

As part of the Kennedy Institute of Rheumatology the successful applicant will be exposed to world-leading institute for discovery science and early-stage clinical research. 

KEY PUBLICATIONS

[1] Pedersen et al. 2022 Immunity 55, 1909–1923 https://doi.org/10.1016/j.immuni.2022.08.016

[2] Nagashima et al. 2023 Nature 621, 162–170 https://doi.org/10.1038/s41586-023-06431-8 

[3] Thakur Ed. 2024 Methods in Molecular Biology 2813 pp. 245-280 https://doi.org/10.1007/978-1-0716-3890-3

[4] Penkava et al. 2024 Rheumatology 63 pp ii4-ii6 https://doi.org/10.1093/rheumatology/keae522 

THEMES

Microbiome & Immunity, Data Science, Inflammatory Disease

CONTACT INFORMATION OF ALL SUPERVISORS

jethro.johnson@kennedy.ox.ac.uk

stephen.sansom@kennedy.ox.ac.uk

petar.scepanovic@roche.com

yang.luo@kennedy.ox.ac.uk