Systematic Interactome Mapping and Genetic Perturbation Analysis of a C. elegans TGF-β Signaling Network

Tewari, Muneesh; Hu, Patrick J.; Ahn, Jin Sook; Ayivi-Guedehoussou, Nono; Vidalain, Pierre‐Olivier; Li, Siming; Milstein, Stuart; Armstrong, Chris M.; Boxem, Mike; Butler, Maurice D.; Busiguina, S.; Rual, Jean François; Ibarrola, Nieves; Chaklos, Sabrina T.; Bertin, Nicolas; Vaglio, Philippe; Edgley, Mark L.; King, Kevin V.; Albert, Patrice S.; Vandenhaute, Jean; Pandey, Akhilesh; Riddle, Donald L; Ruvkun, Gary; Vidal, Marc

doi:10.1016/s1097-2765(04)00033-4

Cited by 135 publications

(94 citation statements)

References 65 publications

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“…A N A LY S I S Open reading frame cloning and coaffinity purification experiments. Fulllength, sequence-verified mammalian gene collection (MGC) clones (Open Biosystems) were obtained for the indicated proteins and Gateway-cloned essentially as described 48 . Clones and primer sequences used for amplification are available in Supplementary Table 11 online.…”

Section: Interaction Network Graphingmentioning

confidence: 99%

Probabilistic model of the human protein-protein interaction network

et al. 2005

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A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genomewide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans⎯a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.We began by assembling a collection of genomic and proteomic data potentially useful in predicting human protein-protein interactions that included model organism protein-protein interactions 1 , protein domain assignments 2 , gene expression measurements in human tissue samples 3 and biological function annotations 4 ( Table 1). Based on previous reports, we suspected that (i) model organism interactions may suggest interactions among orthologous human proteins 5,6 , (ii) similar gene expression profiles across a panel of human tissue samples may identify interacting protein products 7,8 , (iii) protein domain pairs enriched among known human protein-protein interactions may suggest novel interactions 9 , (iv) shared functional annotations from Gene Ontology 4 may suggest physical interactions, and (v) that combining evidence from independent data sources may strongly predict protein-protein interactions [10][11][12] . To test these hypotheses, we applied a naive Bayes classifier 7 , a method well-suited for integrating disparate data types.A gold standard positive set (GSP) of 11,678 distinct protein-protein interactions among 5,505 proteins was queried from the Human Protein Reference Database (HPRD) 12 , a resource that contains known protein-protein interactions manually curated from the literature by expert biologists. A gold standard negative set (GSN) of 3,106,928 protein pairs was defined, in which one protein was assigned to the plasma membrane cellular component and the other to the nuclear cellular component by the Gene Ontology Consortium 4 . Although it is known that membrane proteins can occasionally interact with nuclear proteins, we demonstrated that there are far fewer known interactions within GSN than would be expected by chance (Supplementary Methods online). By averaging the number of interactions per protein in the GSP, we estimated the prior odds of interaction among two randomly selected proteins to be 1 in 381. This is likely an underestimate of the true prior odds because all protein-protein intera...

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Section: Interaction Network Graphingmentioning

confidence: 99%

Probabilistic model of the human protein-protein interaction network

et al. 2005

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“…Similar experimental and computational approaches have been used to generate small but more detailed maps for some specific biological processes, such as maps of the TGF-β pathway [26,27], or C. elegans early embryogenesis [19]. For further details on mapping molecular networks there are other more comprehensive reviews [15,28,29].…”

Section: Methods To Acquire Raw Data For Molecular Networkmentioning

confidence: 99%

Understanding biological functions through molecular networks

Han

2008

Cell Res

144

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“…3c). Similarly, vidal and colleagues integrated Y2H data with RNAi screens to refine a map of transforming growth factor-β (TGFb) signalling in C. elegans and to propose several DAF-7-TGFb modulators 64 . In both cases, the focus was on identification of proteins that act within the same or related pathways.…”

Section: Integration Of Ordered Interactionsmentioning

confidence: 99%

Integrating physical and genetic maps: from genomes to interaction networks

2007

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Physical and genetic mapping data have become as important to network biology as they once were to the Human Genome Project. Integrating physical and genetic networks currently faces several challenges: increasing the coverage of each type of network; establishing methods to assemble individual interaction measurements into contiguous pathway models; and annotating these pathways with detailed functional information. A particular challenge involves reconciling the wide variety of interaction types that are currently available. For this purpose, recent studies have sought to classify genetic and physical interactions along several complementary dimensions, such as ordered versus unordered, alleviating versus aggravating, and first versus second degree.The successful completion of the Human Genome Project depended crucially on the integration of genetic and physical maps 1 . Genetic maps, also known as gene linkage maps 2 , were constructed by measuring the meiotic recombination frequencies between different pairs of genetic markers. On the basis of many pairwise genetic distances, markers could be placed on a number line with short distances corresponding to low recombination frequencies. Conversely, physical maps were constructed by identifying the position of markers along the chromosome. Physical distances between markers were determined by techniques such as radiation hybrid mapping 3,4 , fluorescence in situ hybridization (FISH) 5 or, ultimately, automated DNA sequencing 6 . Genome assembly involved a multi-step procedure in which DNA fragments were cloned, sequenced and, on the basis of the markers they were found to contain, ordered relative to each other and to the genetic map 7,8 . Obtaining full coverage of the genome involved generating enough physical and genetic data so that the two maps could be reconciled. Following assembly, the physical and genetic maps were annotated and continuously updated with detailed information about functional elements 9 . For the physical sequence map, the primary annotation task was the identification of genes; for the genetic map, it was linking genes or their surrogate genetic markers with diseases of interest.Remarkably, the mapping cellular regulatory and signalling networks is now proceeding in much the same way 10,11 (FIG. 1). As for genomics, large-scale genetic and physical interaction mapping projects release enormous amounts of raw data that must be filtered and interpreted biologically (BOX 1). Integration of these two types of maps is important because they provide views that are highly complementary with regard to cellular structure and function: physical interactions dictate the architecture of the cell in terms of how direct associations between © 2007 Nature Publishing Group Correspondence to T.I. trey@bioeng.ucsd.edu. Competing interests statementThe authors declare no competing financial interests. NIH Public Access Author ManuscriptNat Rev Genet. Author manuscript; available in PMC 2010 January 25. Published in final edited form as:Nat Rev G...

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Systematic Interactome Mapping and Genetic Perturbation Analysis of a C. elegans TGF-β Signaling Network

Cited by 135 publications

References 65 publications

Probabilistic model of the human protein-protein interaction network

Probabilistic model of the human protein-protein interaction network

Understanding biological functions through molecular networks

Integrating physical and genetic maps: from genomes to interaction networks

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