Chronic diseases such as cardiovascular disease, diabetes or COPD and cancer account for the great majority of human illness and death. They are characterised by persistent inflammation, leading to tissue damage and ultimately organ failure. Yet we know surprisingly little about what and how cellular damage lead to the establishment of these disease states. We also do not understand the interplay between genes, gene expression, transcription and translation and the cellular environment, despite decades of intense research.
Recent evidence has revealed that the pathways involved in chronic inflammation and its regulation are more ancient than previously thought and in some cases precede the emergence of animals (Metazoa) from unicellular ancestors. Therefore, an exciting possibility is that cellular stress from environmental damage, chronic inflammation or a combination of both triggers a partial reversion to unicellular-like behaviour. This may happen, for example, through the suppression of the expression of the multicellular genes that ensure cell-cell cooperation/communication in animals, or through the over-expression of unicellular genes. Better understanding of these phenomena may lead to potential breakthroughs in the management of many chronic diseases.
We hypothesise that pathways involved in chronic inflammation are driven by gene expression patterns that resemble a switch between uni- and multicellularity. We want to test this hypothesis using a combination of wet lab experiments, bioinformatics approaches, computer models and mathematical analysis. It would suit a student with a strong interest in interdisciplinary, translational research.
Year 1: To develop a computational pipeline integrating gene expression datasets publickly available and establish a signature of gene expression that characterizes and allows comparing uni- vs multi-cellularity. You will learn computational modelling and mathematical analysis contextualizing this in evolutionary relationships.
Years 2 and 3: To test the presence/absence of these pathways in disease datasets (mainly respiratory related) and determine candidates that are distinctive and may drive the observed nodes of expression in Objective 1. The student will also commence wet-lab experiments using cell lines and primary cells where modulation using siRNAs/CRISPR systems and sequencing will determine their role.