Characterising molecular and evolutionary properties of the virus-host interactome
David L Robertson
Our lab is interested in virus origins and evolution with particular focus on fast evolving RNA viruses, eg, SARS-CoV-2. Such viruses are dependent on host cells to replicate and do this by exploiting the molecular systems of the infected cell. In turn infection triggers an anti-viral response that’s evolved mechanisms to counteract and limit infection. These virus-host interactions form an intricate set of molecular, mostly protein-protein, interactions (PPIs) that can be modelled using networks. Such representations can help us characterise a virus species’ life cycle and identify putative drug targets – either virus or host molecules – to interfere with infection. Combining PPI data with existing knowledge of host cell organisation, in particular molecular pathways with gene expression data from infected cells yields insights into the broader perturbation of infected host cells. This project will involve using publicly available virus-host molecular interaction data (eg, from IntAct and VirHostNet) and combining this with datasets available at the CVR from experimental collaborators, eg, from RNASeq and single-cell transcriptome studies. One hypothesis to test is the extent to which the use of divergent evolutionary hosts by a virus is mediated by homologous host molecules. For example, arthropod-borne viruses (arboviruses) that successfully infect both insects and mammals could be used as a model system to test the limits of host-dependency factors, for example linked to dengue virus infection. The requirement to use evolutionary divergent host systems places constraints on the virus that can be exploited for the design of anti-viral measures and mechanistic understanding of host-switching/zoonosis. The rotation or PhD project will be designed in partnership with the student and other supervisor(s). It can be tailored to students with an interest in molecular biology, virology and computational and/or evolutionary biology. A possible direction is to exploit advances in artificial intelligence in collaboration with computer science colleagues.