Signals and systems

Meeting repor t Signals and systems Nevan J Krogan* and Timothy R Hughes 0 Banting and Best Department of Medical Research, University of Toronto , Toronto, ON, M5S 3E1 , Canada 1 Department of Cellular and Molecular Pharmacology, University of California , San Francisco, CA 94143 , USA The boundaries between traditional notions of cellular signaling and more genomic and systematic approaches to biology are becoming increasingly blurred, and the recent Keystone conference on signaling networks reflected an expanded view of signaling. It is becoming increasingly accepted that genes, proteins, cells, and organisms function as components of larger systems, rather than independent activities contributing to a single defined outcome, and many presentations at the conference reflected this. If there was a single theme, it was the heavy reliance on technical approaches in functional genomics, proteomics, and computational biology, such that conceptual and technical discussions often dominated the resulting biology. - RNA interference screening The impact of technology on the study of signaling networks was most evident in the widespread application of RNA interference (RNAi) screens. Screens to study signaling networks in multicellular organisms using RNAi technology are being performed with success, although several hurdles clearly remain. One of these is the apparently high rate of false positives. In his keynote lecture, Norbert Perrimon (Harvard Medical School, Boston, USA) predicted an uncomfortably high rate of false positives due to off-target effects, at least in Drosophila. Consistent with this, Phil Beachy (Johns Hopkins University School of Medicine, Baltimore, USA) described an ongoing RNAi screen in Drosophila looking for proteins involved in the Wingless signaling pathway. Following efforts to study a previously uncharacterized gene identified by this screen, it was noted that there were 16 bases in the interfering RNA that were identical to armadillo, a known gene in the pathway, suggesting that it is an unanticipated off-target that could completely explain the phenotype conferred by the interfering RNA. Perrimon proposed that incorporating other types of data, such as protein-protein interaction information, can help decipher which hits are physiologically relevant. Rene Bernards (Netherlands Cancer Institute, Amsterdam, The Netherlands) commented that reporting lists of unconfirmed hits from genome-wide RNAi screens in humans might be detrimental to future work by creating distractions from more productive lines of research, and that scientists (and journals) perhaps need to be more mindful of this. Despite these limitations, work presented on Caenorhabditis elegans showed unquestionably that useful biological information could be extracted from RNAi screens. Gary Ruvkun (Massachusetts General Hospital, Boston, USA) and colleagues have been involved in a variety of screens looking for factors involved in lifespan, molting, defects in fat storage, and the RNAi pathway itself. Ruvkun described the complete list of hits from the molting screen as a rogues gallery of phenotypes and molecular functions. One could argue that this might be evidence not so much of a high false-positive rate as of the dependence of a system on many components and processes; a corollary of this is that perturbation of an individual gene can have a myriad of physiological effects. Andrew Fraser (Wellcome Trust Sanger Institute, Cambridge, UK) presented work that supports this view. His group has been performing synthetic interaction screens in C. elegans in which previously characterized worm mutants defective in the epidermal growth factor (EGF)-Ras-RafMAP kinase pathway are treated with libraries encoding double-stranded RNAs, in order to identify synthetic genetic interactions. A significant outcome of this work was that RNAi of genes in certain functional categories results in synthetic effects with a high proportion of all mutants tested, suggesting that some cellular functions buffer or canalize many physiological traits, rather than perform a single physiological function. David Sabatini (Massachusetts Institute of Technology, Cambridge, USA) presented a thorough overview of technical aspects of the development and use of genome-scale lentiviral RNAi libraries for human and mouse. A consortium of groups in the public and private sectors is contributing to the creation of the libraries, which are sold by both Sigma (St. Louis, USA) and Open Biosystems (Huntsville, USA) and are available as glycerol stocks, plasmids and viruses. Sabatini commented that while this effort is still incomplete, some functional categories (for example, transcription factors) are fairly comprehensively covered. The efficacy of the system has been validated by targeting tyrosine kinases, and Sabatini reported that 90% of these genes have at least one target that results in a significant knockdown of expression. Sabatini also discussed issues of high-content screening, such as image archiving (a screen can generate 1 Tb of images), image analysis, and defining a hit. He described Cell Profiler [http://jura.wi.mit.edu/cellprofiler/], a free, open-source software package for analysis of thousands of cell images, which among other statistics can output cell counts, DNA content and mitotic index. Sabatini gave his view on how to define true positives: as a rule of thumb, he suggested requiring two independent hairpins that work (the consortium library contains several for each gene), dose dependence, and complementation if possible. Finally, he noted that as antibodies are not available for the majority of human proteins, it is currently difficult to ascertain whether protein levels are impacted by RNAi. On a similar theme, Sumit Chanda (Genomic Institute of the Novartis Research Foundation, San Diego, USA) pointed out that 50% of work published in humans corresponds to less than 10% of the genome, with few (or no) publications for most known and predicted genes, which provides a justification for comprehensive screening efforts. Chemical screening Chemicals have long been used to perturb specific biological activities in experiments, and they have a particular appeal because of their potential application as drugs. Justin Lamb (Broad Institute, Cambridge, USA) described an ongoing effort to construct a transcriptional connectivity map for biomedical discovery, which relates microarray gene-expression profiles that are due to drug effects, disease, and gene perturbations. An initial goal is to find off-target uses for existing drugs; Lambs group is currently analyzing 1,500 drugs approved by the US Food and Drug Administration (FDA), 500 non-drug bioactive compounds used as research tools, and the effects of the knockdown of 500 potential drug targets using lentivirally delivered short hairpin RNAs (shRNAs). A distinguishing feature of the connectivity map is the use of the Kalmogorov-Smirnov statist (...truncated)