Mapping the Human Phosphatome on Growth Pathways

Molecular Systems Biology 8; Article number 603; doi:10.1038/msb.2012.36 Citation: Molecular Systems Biology 8:603 & 2012 EMBO and Macmillan Publishers Limited All rights reserved 1744-4292/12 www.molecularsystemsbiology.com Mapping the human phosphatome on growth pathways Francesca Sacco1,*, Pier Federico Gherardini1, Serena Paoluzi1, Julio Saez-Rodriguez2, Manuela Helmer-Citterich1, Antonella Ragnini-Wilson1,3, Luisa Castagnoli1 and Gianni Cesareni1,4,* 1 Department of Biology, University of Rome ‘Tor Vergata’, Rome, Italy, 2 EMBL-EBI, Hinxton, UK, 3 High-throughput Microscopy Facility, Department of Translational and Cellular Pharmacology, Consorzio Mario Negri Sud, SM Imbaro, Italy and 4 IRCCS Fondazione Santa Lucia, Rome, Italy * Corresponding authors. F Sacco or G Cesareni, Department of Biology, University of Rome ‘Tor Vergata’, Via della Ricerca Scientifica, Rome 00133, Italy. Tel.: þ 39 0672594315; Fax: þ 39 06202350; E-mail: [email protected] or [email protected] Received 5.3.12; accepted 10.7.12 Large-scale siRNA screenings allow linking the function of poorly characterized genes to phenotypic readouts. According to this strategy, genes are associated with a function of interest if the alteration of their expression perturbs the phenotypic readouts. However, given the intricacy of the cell regulatory network, the mapping procedure is low resolution and the resulting models provide little mechanistic insights. We have developed a new strategy that combines multiparametric analysis of cell perturbation with logic modeling to achieve a more detailed functional mapping of human genes onto complex pathways. A literature-derived optimized model is used to infer the cell activation state following upregulation or downregulation of the model entities. By matching this signature with the experimental profile obtained in the high-throughput siRNA screening it is possible to infer the target of each protein, thus defining its ‘entry point’ in the network. By this novel approach, 41 phosphatases that affect key growth pathways were identified and mapped onto a human epithelial cell-specific growth model, thus providing insights into the mechanisms underlying their function. Molecular Systems Biology 8: 603; published online 14 August 2012; doi:10.1038/msb.2012.36 Subject Categories: metabolic and regulatory networks; signal transduction Keywords: cancer; computational biology; functional genomics; imaging; modeling Introduction specific biological domain. In addition, it is often difficult to reconcile all the, sometimes conflicting, findings in different Phenotypic analysis of cell response after perturbations by experimental systems and summarize them in a trusted small interfering RNAs has established itself as a powerful signaling network, by modeling the biochemical reactions strategy to explore the function of entire gene families in large- underlying signal propagation in a specific cell system. Finally, scale screenings (Kiger et al, 2003; MacKeigan et al, 2005; as the complexity of the model grows, it becomes practically Conrad and Gerlich, 2010; Horn et al, 2011). Using this impossible to deduce the functional outcome of network approach, genes are connected to a function of interest, if perturbations without the assistance of computable models. the alteration of the cellular levels of their products perturbs a Such models can be derived by a variety of different phenotypic readout associated with the function under approaches (Kestler et al, 2008). A first strategy uses systems investigation. However, this approach only provides correla- of coupled differential equations (Aldridge et al, 2006). In tive information functionally linking the interfered gene and these models, each molecular reaction is specified using a the activation level of the phenotypic readout. Indeed, given kinetic law relating the concentrations of reactant and the extensive cross-talk among different signaling pathways, products. These reactions are governed by rate constants that the same readout alteration may be caused by perturbations of must be estimated by optimizing the fit with a set of radically different branches of the cell regulatory network. By experimental data. Due to the large number of parameters measuring multiple readouts it is sometimes possible to make this step can prove to be a very complex task. educated guesses about the network node that is the most An alternative approach represents signaling models as likely primary target of the induced perturbation (Jorgensen logic networks. In this case, the pathway of interest is drawn as et al, 2009). However, there are a number of issues that limit a signed direct graph where nodes represent proteins and this approach when applied on a large scale. edges specify activatory/inhibitory relationships between them. First the large number of functional (activation/inactivation) The effect of edges is combined in logic gates (AND/OR). This relationships between gene products that have been reported strategy generally yields discrete models that are less flexible in the literature is overwhelming, even for an expert of a than those obtained with differential equations, yet much easier & 2012 EMBO and Macmillan Publishers Limited Molecular Systems Biology 2012 1 Mapping the human phosphatome on growth pathways F Sacco et al to understand and compute. Recently, methods have been activities of pathways that regulate cell growth such as the developed to optimize the structure of these models against MAP kinase (ERK and p38), mTOR, NFkB pathways and experimental data (Saez-Rodriguez et al, 2009). autophagy (Hennessy et al, 2005; Viatour et al, 2005; Roberts Whatever the approach used biological signaling models and Der, 2007; Wagner and Nebreda, 2009; Steeves et al, 2010). provide detailed descriptions of a system of interest, but are By combining the results of the siRNA screening with necessarily limited in size due to their complexity. Conversely, modeling and simulation, B67% of the protein phosphatase large-scale perturbation screenings provide a higher level view hits were mapped onto the growth model. Some of the hit of a larger number of proteins. In this work, we aim to bridge genes, when deregulated, cause alterations in the timing of the the gap between these two worlds in order to obtain a higher cell cycle and therefore are new potential oncogenes or detail mapping of gene products onto complex pathways on a oncosuppressors. large scale. Our approach is based on the combination of multiparametric siRNA screening with modeling and simula- Results tion. We developed the strategy with the aim of characterizing protein phosphatases. Experimental strategy Phosphorylation is a pervasive post-translational modifica- To map protein phosphatases onto growth pathways, we tion that contributes to form regulatory switches by modulat- combined experimental characterization of the cell states upon ing the activity of key enzymes and promoting the formation of perturbation of phosphatases activity and logic modeling of supramolecular complexes (Barford et al, 1998). Phosphatases pathways relevant for cell growth. The strategy involves the work together with kinases to modulate the phosphorylation steps schematically summarized below and illustrated in of a large number of tyrosine, serine and threonine residues on Figure 1. most eukaryotic proteins. We focused on protein phosphatases because of their broad yet poorly understood regulatory (1) Perform a high-content siRNA screening of the human function in signaling pathways (Barford et al, 1998; Sacco phosphatome to identify phosphatases (hits) whose et al, 2012). Indeed till recently protein phosphatases have activities modulate five readouts monitored by automated been considered uninteresting housekeeping enzymes and fluorescence microscopy. The cell states after inhibition of have received less attention compared with kinases (Bardelli each phosphatase are represented as vectors with coordi- and Velculescu, 2005). However, evidence accumulated over nates corresponding to the measured readout values the past decades have indicated that this enzyme class plays an (Figure 1Aa–e). important regulatory role and that the deregulation of the (2) Collect from the literature and from pathway databases concentration or activity of specific phosphatases correlates information describing the functional relationships with a variety of human disorders (Wera and Hemmings, 1995; between signaling proteins in the pathways of interest. This Tonks, 2006). Approximately, 40% of protein phosphatases are allows the assembly of a prior knowledge network which is implicated in tumor development, highlighting the central role of represented as a signed directed model, where edges have this enzyme group in growth regulation and identifying some sign (activating or inhibitory) and directionality (enzyme– membersasnewtherapeutictargets(Julienet al,2011;Liberti substrate relationships). This naı¨ve network integrates et al,2012).However,themolecularmechanismsleadingto information obtained in different cellular systems under tumorigenesis have been characterized only for a few of these distinct experimental conditions (Figure 1Ba). potential oncogenes and oncosuppressors, whereas the majority (3) Optimize the model by training it against an independent of them still awaits to be placed in the intricate functional protein set of experimental data obtained by measuring the network underlying cell physiology. activation of a large number of nodes under different To

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