DPHIL OPPORTUNITIES AVAILABLE
Associate Professor of Systems Immunology, Member of Congregation
- WIMM Group Leader
- Principal Investigator in RDM
Systems and Quantitive T Cell Immunology (SaQTCI)
BIOGRAPHY and RESEARCH INTERESTS
Hashem Koohy awarded a PhD in Systems Biology from Warwick University in 2010. Hashem then went on to do a postdoctoral study at Sanger Institute Cambridge University working on transcriptional regulation and gene function. He then moved to the Babraham Institute to investigate epigenetic features associated with deterioration of adaptive immune system as a function of age using murine developing B cells incorporating multi-omics into cutting-edge machine-learning techniques.
He became a group head in 2017 at Human Immunology Unit (MRC Molecular Medicine, University of Oxford). The research in Koohy’s group is specifically focused on T cell recognition of pathogens in which multi-modal high throughput sequencing ‘big-data’ from the cutting-edge bulk and single cell technologies are incorporated into computational, machine-learning and statistical models to study antigen processing and presentation and T cell recognition and function. This has led to a number of national and international collaborations with high impact publications in a number of research themes.
Immune Correlates of heath and disease
In collaboration with Prof Simmons lab, we have tried to shed further light into immune homeostasis in gut/intestine and its violation upon a number of diseases. We started by profiling the composition and heterogeneity of human mesenchymal cells and identified new sub-population of cells with a transcriptomic signature suggestive of their role in regulating homeostasis in human intestinal epithelial cells, and how it is violated upon UC 1. Next, by single cell profiling of colonic epithelium we have revealed goblet cell drivers of barrier breakdown in IBD2 and compiled an unbiased atlas of human colonic CD8+ T cells in health and UC by leveraging combine transcriptomics, T cell repertoire and proteomics3. Most recently we have utilized single-cell sequencing and spatial transcriptomics to provide a comprehensive landscape of human intestinal development4.
Antigen processing/presentation and T cell response
Upon emergence of SARS-CoV-2, we focused on identifying immunogenic peptide from virus genome as potential targets for vaccine development5 and the extent to which the society may have cross-protection from other flu-like human coronaviruses6. In collaboration with Dr Shugay, we have more generally investigated the landscape of self-peptide presentation7 as well as evaluating the performance of existing tools for predicting T cell target peptides8.
Epigenetic contributors of Immunosenescence
Immunosenescence which is defined as age-associated deterioration of adaptive immune system causes serious health issues to aged adults eg high vulnerability to infections and loss of vaccines’ efficacy. It is therefore crucial to better understand mechanisms underpinning Immunosenescence. Towards this, we have illustrated an epigenetic regulatory mechanism correlated to V gene usage in mouse heavy 9 and light chain 10. We have also illustrated the down-regulation of insulin-like growth factor signalling as a hallmark of aging in developing B lymphocytes11.
Heterogeneity of response to personalized cancer immunotherapy
Although personalized cancer immunotherapies have revolutionized cancer treatment for a number of cancer types, not all patients respond the same way. Besides, some develop immune related adverse events such as checkpoint colitis. We have been therefore investigating various correlates of variation to checkpoint blocked treatment. In collaboration with Cerundolo lab, we have been exploiting longitudinal effect of different types of CBT on melanoma patients. In collaboration with Simmons lab, we are interrogating molecular signature and T cell antigen specificity in check point colitis.
A robust deep learning workflow to predict CD8 + T-cell epitopes.
Lee CH. et al, (2023), Genome Med, 15
A comparison of clustering models for inference of T cell receptor antigen specificity
Hudson D. et al, (2023)
Can we predict T cell specificity with digital biology and machine learning?
Hudson D. et al, (2023), Nat Rev Immunol, 1 - 11
SARS-CoV-2 peptides bind to NKG2D and increase NK cell activity.
Kim H. et al, (2021), Cell Immunol, 371