We combine genomic analysis with clinical data to understand how genetics contributes to the variation between patients in their disease severity and response to treatments.
Through clinical and industry collaborations, we analyse gene expression datasets from cohorts of patients with diseases that involve systemic inflammation such as sepsis and autoimmune diseases. The main aim is to identify gene expression signatures for predicting disease severity and treatment response as well as using expression quantitative trait locus (eQTL) mapping to understand the role of regulatory DNA in patient heterogeneity.
Patient stratification from transcriptomic profiling
Sepsis is a life-threatening condition that occurs when the body's immune system damages it's own tissues and organs while trying to fight an infection. There is great individual variation in the way that people respond making sepsis a challenging disease to diagnose and treat. The underlying infection can be treated with antibiotics and failing organs can be supported but we still don't have treatments that tackle the immune response that is damaging the organs. We use transcriptomic profiling to stratify patients and further our understanding of the individual response to sepsis. We are exploring the transcriptomic response in patients presenting to the emergency room with suspected infection (BioAID) and in patients with sepsis admitted to the ICU (GAinS).
Role of regulatory DNA variants in patient heterogeneity
We integrate genetic information with our transcriptomic profiles to understand the role of genetics in the variation that we observe. We map expression quantitative trait locus (eQTL) in cohorts of patients and use eQTL interactions to understand how the environment can modulate these regulatory effects. We are interested in interactions both with disease severity and treatment with a drug.
Drug target prioritisation from single-cell eQTL mapping
SLE is an autoimmune disease that can affect virtually any organ system in the body. This clinical heterogeneity has made the development of new drugs for treatment very challenging. To enable the development of new therapies, we are interested in better understanding the molecular mechanisms contributing to the disease pathophysiology. In collaboration with Open Targets, we are generating single-cell RNA-sequencing for a cohort of SLE patients. This will allow us to map eQTL at the single-cell level and gain mechanistic insights that can inform novel target identification for future drugs.