Development of shRNA screens to identify effectors of three complex traits: neighbour suppression of tumour growth and proliferation and protection from lipotoxicity in β-cells.

Abstract

RNA interference (RNAi) is a natural mechanism of cellular defence against exogenous double stranded RNA (dsRNA). The discovery of small dsRNA molecules which can be processed by the RNAi pathway in mammalian cells was one of the key advances in the study of functional genomics. These molecules can be designed to downregulate the expression of specific genes. Collections or libraries of dsRNA molecules targeting an extensive number of genes are now available. Using these libraries, numerous studies have implemented high-throughput screens for the study of molecular effectors of numerous phenotypes. The process of designing an RNAi screen requires the consideration of several critical factors during both the experimental and analysis phases. The experimental screen should aim to reproduce the biological phenomenon studied as closely as possible by choosing an adequate model and screening conditions. Phenotype evaluation and assessment of knockdown effects need careful consideration. The results obtained from large-scale RNAi screens are often complex. An analysis pipeline should be implemented which integrates the biological basis of the phenomenon and facilitates the interpretation of the data. This project designed and implemented an unbiased shRNA screen in two in vitro models relevant to carcinogenesis and diabetes. The first screen implemented used a model of neighbour suppression to study the molecular effectors of the response in tumorigenic cells to growth suppression cues from the surrounding tissue, a cellular interaction relevant in early tumorigenesis. The second screen studied two phenotypes relevant to diabetes: proliferation and resistance to lipotoxicity of β-cells in a reversibly immortalised  cell line. An integrative analysis pipeline was also developed to apply network biology and functional enrichment analysis methods for the interpretation of the data obtained from both screens.