Integrative analysis of metabolomics, transcriptomics and proteomics to study mechanisms that regulate lentiviral vector hepatocyte transduction

Liver transplantation is the only cure for many congenital and acquired diseases. However, multiple liver diseases are thought to be single-gene disorders that may be amenable to gene therapy via targeting of liver hepatocytes. The liver, therefore, represents an important target organ for developing new gene therapy approaches.
One challenge is that hepatocytes are generally quiescent; thus a recombinant lentiviral vector (LV) may have the advantage over other gene transfer vectors in being able to transduce non-dividing cells, and also to integrate in the cell genome permitting long-term transgene expression. Crucially, irrespective of the specific application, establishing efficient LV transduction will be imperative for successful liver gene therapy.
The specific focus of this project will be to study potential effects of LV transduction on cellular metabolism and signalling pathways in liver cells. Whereas transcriptomics and proteomics measure changes in genes and proteins, respectively, metabolomics allows the concurrent measurement of large numbers of cellular metabolites. Machine learning can be used to integrate data generated from all three technologies, and will be used to determine large-scale changes induced by the virus aiming to identify novel targets, pathways and mechanisms that affect/regulate efficient gene transfer in hepatocytes.