Analysis of high-throughput data increasingly relies on pathway annotation and functional information derived from Gene Ontology. This approach has limitations, in particular for the analysis of network dynamics over time or under different experimental conditions, in which modules within a network rather than complete pathways might respond and change. We report an analysis framework based on protein complexes, which are at the core of network reorganization. We generated a protein complex resource for human, Drosophila, and yeast from the literature and databases of protein-protein interaction networks, with each species having thousands of complexes. We developed COMPLEAT (http://www.flyrnai.org/compleat), a tool for data mining and visualization for complex-based analysis of high-throughput data sets, as well as analysis and integration of heterogeneous proteomics and gene expression data sets. With COMPLEAT, we identified dynamically regulated protein complexes among genome-wide RNA interference data sets that used the abundance of phosphorylated extracellular signal-regulated kinase in cells stimulated with either insulin or epidermal growth factor as the output. The analysis predicted that the Brahma complex participated in the insulin response.
RNA interference (RNAi) is an effective tool for genome-scale, high-throughput analysis of gene function. In the past five years, a number of genome-scale RNAi high-throughput screens (HTSs) have been done in both Drosophila and mammalian cultured cells to study diverse biological processes, including signal transduction, cancer biology, and host cell responses to infection. Results from these screens have led to the identification of new components of these processes and, importantly, have also provided insights into the complexity of biological systems, forcing new and innovative approaches to understanding functional networks in cells. Here, we review the main findings that have emerged from RNAi HTS and discuss technical issues that remain to be improved, in particular the verification of RNAi results and validation of their biological relevance. Furthermore, we discuss the importance of multiplexed and integrated experimental data analysis pipelines to RNAi HTS.
Receptor tyrosine kinase (RTK) signalling through extracellular-signal-regulated kinases (ERKs) has pivotal roles during metazoan development, underlying processes as diverse as fate determination, differentiation, proliferation, survival, migration and growth. Abnormal RTK/ERK signalling has been extensively documented to contribute to developmental disorders and disease, most notably in oncogenic transformation by mutant RTKs or downstream pathway components such as Ras and Raf. Although the core RTK/ERK signalling cassette has been characterized by decades of research using mammalian cell culture and forward genetic screens in model organisms, signal propagation through this pathway is probably regulated by a larger network of moderate, context-specific proteins. The genes encoding these proteins may not have been discovered through traditional screens owing, in particular, to the requirement for visible phenotypes. To obtain a global view of RTK/ERK signalling, we performed an unbiased, RNA interference (RNAi), genome-wide, high-throughput screen in Drosophila cells using a novel, quantitative, cellular assay monitoring ERK activation. Here we show that ERK pathway output integrates a wide array of conserved cellular processes. Further analysis of selected components-in multiple cell types with different RTK ligands and oncogenic stimuli-validates and classifies 331 pathway regulators. The relevance of these genes is highlighted by our isolation of a Ste20-like kinase and a PPM-family phosphatase that seem to regulate RTK/ERK signalling in vivo and in mammalian cells. Novel regulators that modulate specific pathway outputs may be selective targets for drug discovery.
Here, I discuss how RNAi screening can be used effectively to uncover gene function. Specifically, I discuss the types of high-throughput assays that can be done in Drosophila cells and in vivo, RNAi reagent design and available reagent collections, automated screen pipelines, analysis of screen results, and approaches to RNAi results verification.