Key Points
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Since the turn of the century, high-throughput interaction mapping has emerged as an extremely useful approach for connecting genotype with phenotype. Such studies allow us to assign functionality to whole genes or proteins but not to specific domains or residues.
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Many naturally occurring mutations, including many of those that cause disease, result in the alteration of a single residue on a protein rather than the complete loss of function or a truncation. Understanding the consequences of such mutations requires higher-resolution interaction networks.
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Computational methods can be used to integrate structural information with existing protein–protein interaction networks to improve their resolution. Such approaches range from methods that identify the domains responsible for specific interactions to those that allow the complete determination of macromolecular structures.
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Experimental methods have been developed to identify 'edgetic' mutations that perturb one or more protein–protein interactions while leaving other interactions intact. Combining the location of these mutations with structural models identifies putative binding sites.
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Genetic and drug–gene interaction profiles can assess the functional consequences of perturbations to specific residues (including those that are post-translationally modified) even in the absence of detected changes to the protein–protein interaction network.
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High-throughput genetic interaction mapping has traditionally been used to study the consequences of knocking out whole genes, but it has recently been adapted to investigate the consequences of mutating specific residues in essential multifunctional protein complexes. In addition to assigning functionality to specific regions, this has shown that mutations that affect residues which are proximal in three-dimensional space frequently have similar interaction profiles, even when they affect different proteins.
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Although the majority of the high-throughput screens discussed have been carried out in model organisms, the same approaches are increasingly being used to study disease-associated mutations in humans.
Abstract
Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein–protein, genetic and drug–gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.
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References
Phillips, P. C. Epistasis – the essential role of gene interactions in the structure and evolution of genetic systems. Nature Rev. Genet. 9, 855–867 (2008).
Sharan, R., Ulitsky, I. & Shamir, R. Network-based prediction of protein function. Mol. Syst. Biol. 3, 88 (2007).
Barabasi, A. L. Scale-free networks: a decade and beyond. Science 325, 412–413 (2009).
Pawson, T. & Nash, P. Assembly of cell regulatory systems through protein interaction domains. Science 300, 445–452 (2003).
Beltrao, P. et al. Systematic functional prioritization of protein posttranslational modifications. Cell 150, 413–425 (2012).
Abecasis, G. R. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).
Furey, T. S. ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Nature Rev. Genet. 13, 840–852 (2012).
Karlebach, G. & Shamir, R. Modelling and analysis of gene regulatory networks. Nature Rev. Mol. Cell Biol. 9, 770–780 (2008).
Kholodenko, B. N., Hancock, J. F. & Kolch, W. Signalling ballet in space and time. Nature Rev. Mol. Cell Biol. 11, 414–426 (2010).
Choudhary, C. & Mann, M. Decoding signalling networks by mass spectrometry-based proteomics. Nature Rev. Mol. Cell Biol. 11, 427–439 (2010).
Ideker, T. & Krogan, N. J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012).
Linding, R. et al. Systematic discovery of in vivo phosphorylation networks. Cell 129, 1415–1426 (2007).
Ward, L. D. & Kellis, M. Interpreting noncoding genetic variation in complex traits and human disease. Nature Biotech. 30, 1095–1106 (2012).
Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008).
Tarassov, K. et al. An in vivo map of the yeast protein interactome. Science 320, 1465–1470 (2008).
Krogan, N. J. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).
Gavin, A. C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).
Wodak, S. J., Pu, S., Vlasblom, J. & Seraphin, B. Challenges and rewards of interaction proteomics. Mol. Cell Proteom. 8, 3–18 (2009).
von Mering, C. et al. Comparative assessment of large-scale data sets of protein–protein interactions. Nature 417, 399–403 (2002).
Bader, G. D. & Hogue, C. W. Analyzing yeast protein–protein interaction data obtained from different sources. Nature Biotech. 20, 991–997 (2002).
Cusick, M. E. et al. Literature-curated protein interaction datasets. Nature Methods 6, 39–46 (2009).
Dolinski, K., Chatr-Aryamontri, A. & Tyers, M. Systematic curation of protein and genetic interaction data for computable biology. BMC Biol. 11, 43 (2013).
Licata, L. et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 40, D857–D861 (2012).
Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816–D823 (2013).
Gomez, S. M., Noble, W. S. & Rzhetsky, A. Learning to predict protein–protein interactions from protein sequences. Bioinformatics 19, 1875–1881 (2003).
Zhang, Q. C. et al. Structure-based prediction of protein–protein interactions on a genome-wide scale. Nature 490, 556–560 (2012).
Jansen, R. et al. A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science 302, 449–453 (2003).
Wang, P. I. & Marcotte, E. M. It's the machine that matters: predicting gene function and phenotype from protein networks. J. Proteom. 73, 2277–2289 (2010).
Beltrao, P., Cagney, G. & Krogan, N. J. Quantitative genetic interactions reveal biological modularity. Cell 141, 739–745 (2010).
Tong, A. H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004).
Haber, J. E. et al. Systematic triple-mutant analysis uncovers functional connectivity between pathways involved in chromosome regulation. Cell Rep. 3, 2168–2178 (2013).
Collins, S. R., Roguev, A. & Krogan, N. J. Quantitative genetic interaction mapping using the E-MAP approach. Methods Enzymol. 470, 205–231 (2010).
Dixon, S. J. et al. Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes. Proc. Natl Acad. Sci. USA 105, 16653–16658 (2008).
Butland, G. et al. eSGA: E. coli synthetic genetic array analysis. Nature Methods 5, 789–795 (2008).
Roguev, A., Wiren, M., Weissman, J. S. & Krogan, N. J. High-throughput genetic interaction mapping in the fission yeast Schizosaccharomyces pombe. Nature Methods 4, 861–866 (2007).
Typas, A. et al. High-throughput, quantitative analyses of genetic interactions in E. coli. Nature Methods 5, 781–787 (2008).
Tong, A. H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001).
Ryan, C. J. et al. Hierarchical modularity and the evolution of genetic interactomes across species. Mol. Cell 46, 691–704 (2012).
Babu, M. et al. Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways. PLoS Genet. 7, e1002377 (2011).
Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).
Roguev, A. et al. Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322, 405–410 (2008).
Breslow, D. K. et al. A comprehensive strategy enabling high-resolution functional analysis of the yeast genome. Nature Methods 5, 711–718 (2008).
Davierwala, A. P. et al. The synthetic genetic interaction spectrum of essential genes. Nature Genet. 37, 1147–1152 (2005).
Mnaimneh, S. et al. Exploration of essential gene functions via titratable promoter alleles. Cell 118, 31–44 (2004).
Bassik, M. C. et al. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell 152, 909–922 (2013).
Horn, T. et al. Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nature Methods 8, 341–346 (2011).
Lin, Y. Y. et al. Functional dissection of lysine deacetylases reveals that HDAC1 and p300 regulate AMPK. Nature 482, 251–255 (2012).
Roguev, A. et al. Quantitative genetic-interaction mapping in mammalian cells. Nature Methods 10, 432–437 (2013).
Laufer, C., Fischer, B., Billmann, M., Huber, W. & Boutros, M. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nature Methods 10, 427–431 (2013).
Lehner, B., Crombie, C., Tischler, J., Fortunato, A. & Fraser, A. G. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nature Genet. 38, 896–903 (2006).
Byrne, A. B. et al. A global analysis of genetic interactions in Caenorhabditis elegans. J. Biol. 6, 8 (2007).
Ryan, C., Greene, D., Cagney, G. & Cunningham, P. Missing value imputation for epistatic MAPs. BMC Bioinformatics 11, 197 (2010).
Wong, S. L. et al. Combining biological networks to predict genetic interactions. Proc. Natl Acad. Sci. USA 101, 15682–15687 (2004).
Lu, X., Kensche, P. R., Huynen, M. A. & Notebaart, R. A. Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets. Nature Commun. 4, 2124 (2013).
Pandey, G. et al. An integrative multi-network and multi-classifier approach to predict genetic interactions. PLoS Comput. Biol. 6, e1000928 (2010).
Folger, O. et al. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7, 501 (2011).
Chang, M., Bellaoui, M., Boone, C. & Brown, G. W. A genome-wide screen for methyl methanesulfonate-sensitive mutants reveals genes required for S phase progression in the presence of DNA damage. Proc. Natl Acad. Sci. USA 99, 16934–16939 (2002).
Beyer, A., Bandyopadhyay, S. & Ideker, T. Integrating physical and genetic maps: from genomes to interaction networks. Nature Rev. Genet. 8, 699–710 (2007).
Sharan, R. & Ideker, T. Modeling cellular machinery through biological network comparison. Nature Biotech. 24, 427–433 (2006).
Mitra, K., Carvunis, A. R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nature Rev. Genet. 14, 719–732 (2013).
Bandyopadhyay, S., Kelley, R., Krogan, N. J. & Ideker, T. Functional maps of protein complexes from quantitative genetic interaction data. PLoS Comput. Biol. 4, e1000065 (2008).
Hillenmeyer, M. E. et al. Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action. Genome Biol. 11, R30 (2010).
Parsons, A. B. et al. Integration of chemical–genetic and genetic interaction data links bioactive compounds to cellular target pathways. Nature Biotech. 22, 62–69 (2004).
Kapitzky, L. et al. Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action. Mol. Syst. Biol. 6, 451 (2010).
Boxem, M. et al. A protein domain-based interactome network for C. elegans early embryogenesis. Cell 134, 534–545 (2008). This is a large-scale fragment-based protein–protein interaction screen that identifies the minimal regions of interaction for many interactions.
Fromont-Racine, M., Rain, J. C. & Legrain, P. Toward a functional analysis of the yeast genome through exhaustive two-hybrid screens. Nature Genet. 16, 277–282 (1997).
Goehler, H. et al. A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease. Mol. Cell 15, 853–865 (2004).
Guglielmi, B. et al. A high resolution protein interaction map of the yeast Mediator complex. Nucleic Acids Res. 32, 5379–5391 (2004).
LaCount, D. J. et al. A protein interaction network of the malaria parasite Plasmodium falciparum. Nature 438, 103–107 (2005).
Rain, J. C. et al. The protein–protein interaction map of Helicobacter pylori. Nature 409, 211–215 (2001).
Amberg, D. C., Basart, E. & Botstein, D. Defining protein interactions with yeast actin in vivo. Nature Struct. Biol. 2, 28–35 (1995). This is a pioneering study that highlights the use of integrating structural models with edgetic protein–protein interaction mapping.
Charloteaux, B. et al. Protein–protein interactions and networks: forward and reverse edgetics. Methods Mol. Biol. 759, 197–213 (2011).
Leanna, C. A. & Hannink, M. The reverse two-hybrid system: a genetic scheme for selection against specific protein/protein interactions. Nucleic Acids Res. 24, 3341–3347 (1996).
Shih, H. M. et al. A positive genetic selection for disrupting protein–protein interactions: identification of CREB mutations that prevent association with the coactivator CBP. Proc. Natl Acad. Sci. USA 93, 13896–13901 (1996).
Vidal, M., Brachmann, R. K., Fattaey, A., Harlow, E. & Boeke, J. D. Reverse two-hybrid and one-hybrid systems to detect dissociation of protein–protein and DNA–protein interactions. Proc. Natl Acad. Sci. USA 93, 10315–10320 (1996).
Walhout, A. J. M. et al. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 287, 116–122 (2000).
Zhong, Q. et al. Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5, 321 (2009).
Dreze, M. et al. 'Edgetic' perturbation of a C. elegans BCL2 ortholog. Nature Methods 6, 843–849 (2009).
Oren, M. & Rotter, V. Mutant p53 gain-of-function in cancer. Cold Spring Harb. Perspect. Biol. 2, a001107 (2010).
Lim, J. et al. Opposing effects of polyglutamine expansion on native protein complexes contribute to SCA1. Nature 452, 713–718 (2008).
Aloy, P. et al. Structure-based assembly of protein complexes in yeast. Science 303, 2026–2029 (2004).
Deng, M., Mehta, S., Sun, F. & Chen, T. Inferring domain–domain interactions from protein–protein interactions. Genome Res. 12, 1540–1548 (2002).
Kim, P. M., Lu, L. J., Xia, Y. & Gerstein, M. B. Relating three-dimensional structures to protein networks provides evolutionary insights. Science 314, 1938–1941 (2006).
Prieto, C. & De Las Rivas, J. Structural domain–domain interactions: assessment and comparison with protein–protein interaction data to improve the interactome. Proteins 78, 109–117 (2010).
Riley, R., Lee, C., Sabatti, C. & Eisenberg, D. Inferring protein domain interactions from databases of interacting proteins. Genome Biol. 6, R89 (2005).
Wang, H. et al. InSite: a computational method for identifying protein–protein interaction binding sites on a proteome-wide scale. Genome Biol. 8, R192 (2007).
Wang, X. et al. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nature Biotech. 30, 159–164 (2012). This study integrates high-throughput protein–protein interactions with three-dimensional structures of interacting interfaces to interpret human disease-associated mutations.
Schuster-Bockler, B. & Bateman, A. Protein interactions in human genetic diseases. Genome Biol. 9, R9 (2008).
Alber, F. et al. The molecular architecture of the nuclear pore complex. Nature 450, 695–701 (2007). This is a landmark example of the use of integrative approaches to determine the structure of a complex macromolecule — in this case, the nuclear pore complex that consists of 30 distinct proteins.
Lasker, K. et al. Molecular architecture of the 26S proteasome holocomplex determined by an integrative approach. Proc. Natl Acad. Sci. USA 109, 1380–1387 (2012).
Campos, M., Nilges, M., Cisneros, D. A. & Francetic, O. Detailed structural and assembly model of the type II secretion pilus from sparse data. Proc. Natl Acad. Sci. USA 107, 13081–13086 (2010).
Ward, A. B., Sali, A. & Wilson, I. A. Biochemistry. Integrative structural biology. Science 339, 913–915 (2013).
Finn, R. D., Marshall, M. & Bateman, A. iPfam: visualization of protein–protein interactions in PDB at domain and amino acid resolutions. Bioinformatics 21, 410–412 (2005).
Punta, M. et al. The Pfam protein families database. Nucleic Acids Res. 40, D290–301 (2012).
Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).
Yellaboina, S., Tasneem, A., Zaykin, D. V., Raghavachari, B. & Jothi, R. DOMINE: a comprehensive collection of known and predicted domain–domain interactions. Nucleic Acids Res. 39, D730–735 (2011).
Reimand, J., Hui, S., Jain, S., Law, B. & Bader, G. D. Domain-mediated protein interaction prediction: from genome to network. FEBS Lett. 586, 2751–2763 (2012).
Charles, G. M. et al. Site-specific acetylation mark on an essential chromatin-remodeling complex promotes resistance to replication stress. Proc. Natl Acad. Sci. USA 108, 10620–10625 (2011).
Fuchs, S. M., Kizer, K. O., Braberg, H., Krogan, N. J. & Strahl, B. D. RNA polymerase II carboxyl-terminal domain phosphorylation regulates protein stability of the Set2 methyltransferase and histone H3 di- and trimethylation at lysine 36. J. Biol. Chem. 287, 3249–3256 (2012).
Morrison, A. J. et al. Mec1/Tel1 phosphorylation of the INO80 chromatin remodeling complex influences DNA damage checkpoint responses. Cell 130, 499–511 (2007).
Mehta, M. et al. Individual lysine acetylations on the N terminus of Saccharomyces cerevisiae H2A.Z are highly but not differentially regulated. J. Biol. Chem. 285, 39855–39865 (2010).
Wang, A. Y., Aristizabal, M. J., Ryan, C., Krogan, N. J. & Kobor, M. S. Key functional regions in the histone variant H2A.Z C-terminal docking domain. Mol. Cell. Biol. 31, 3871–3884 (2011).
Kim, H. S. et al. An acetylated form of histone H2A.Z regulates chromosome architecture in Schizosaccharomyces pombe. Nature Struct. Mol. Biol. 16, 1286–1293 (2009).
Haarer, B., Viggiano, S., Hibbs, M. A., Troyanskaya, O. G. & Amberg, D. C. Modeling complex genetic interactions in a simple eukaryotic genome: actin displays a rich spectrum of complex haploinsufficiencies. Genes Dev. 21, 148–159 (2007).
Collins, S. R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007).
Zhang, Z., Shibahara, K. & Stillman, B. PCNA connects DNA replication to epigenetic inheritance in yeast. Nature 408, 221–225 (2000).
Ayyagari, R., Impellizzeri, K. J., Yoder, B. L., Gary, S. L. & Burgers, P. M. A mutational analysis of the yeast proliferating cell nuclear antigen indicates distinct roles in DNA replication and DNA repair. Mol. Cell. Biol. 15, 4420–4429 (1995).
Dai, J. et al. Probing nucleosome function: a highly versatile library of synthetic histone H3 and H4 mutants. Cell 134, 1066–1078 (2008). This paper describes both the systematic mutation of every individual residue of two histone proteins and the use of drug sensitivity screening to assess the functional effects of these mutations.
Matsubara, K., Sano, N., Umehara, T. & Horikoshi, M. Global analysis of functional surfaces of core histones with comprehensive point mutants. Genes Cells 12, 13–33 (2007).
Nakanishi, S. et al. A comprehensive library of histone mutants identifies nucleosomal residues required for H3K4 methylation. Nature Struct. Mol. Biol. 15, 881–888 (2008).
Huang, H. L. et al. HistoneHits: a database for histone mutations and their phenotypes. Genome Res. 19, 674–681 (2009). This paper reports a database that focuses on a specific protein family (histones) and that integrates the results of phenotyping screens of point mutants from several laboratories. It provides an interactive structure on which residues that are associated with specific phenotypes can be highlighted.
Braberg, H. et al. From structure to systems: high-resolution, quantitative genetic analysis of RNA polymerase II. Cell 154, 775–788 (2013). This study reports the functional dissection of RNA polymerase II by genetic interaction profiling of point mutants from multiple distinct subunits; it shows that the mutation of residues that are on distinct subunits but that are close together in the three-dimensional structure have similar genetic interaction profiles.
Alber, F., Forster, F., Korkin, D., Topf, M. & Sali, A. Integrating diverse data for structure determination of macromolecular assemblies. Annu. Rev. Biochem. 77, 443–477 (2008).
Hietpas, R., Roscoe, B., Jiang, L. & Bolon, D. N. Fitness analyses of all possible point mutations for regions of genes in yeast. Nature Protoc. 7, 1382–1396 (2012).
Roscoe, B. P., Thayer, K. M., Zeldovich, K. B., Fushman, D. & Bolon, D. N. Analyses of the effects of all ubiquitin point mutants on yeast growth rate. J. Mol. Biol. 425, 1363–1377 (2013).
McGary, K. L., Lee, I. & Marcotte, E. M. Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes. Genome Biol. 8, R258 (2007).
Lee, I. et al. Predicting genetic modifier loci using functional gene networks. Genome Res. 20, 1143–1153 (2010).
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nature Methods 7, 248–249 (2010).
Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature Protoc. 4, 1073–1081 (2009).
Jager, S. et al. Global landscape of HIV-human protein complexes. Nature 481, 365–370 (2012).
Shapira, S. D. et al. A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 139, 1255–1267 (2009).
Neveu, G. et al. Comparative analysis of virus–host interactomes with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase. Methods 58, 349–359 (2012).
Stark, C. et al. The BioGRID interaction database: 2011 update. Nucleic Acids Res. 39, D698–D704 (2011).
Koh, J. L. et al. DRYGIN: a database of quantitative genetic interaction networks in yeast. Nucleic Acids Res. 38, D502–507 (2010).
Fraser, J. S., Gross, J. D. & Krogan, N. J. From systems to structure: bridging networks and mechanism. Mol. Cell 49, 222–231 (2013).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Maher, M. C., Uricchio, L. H., Torgerson, D. G. & Hernandez, R. D. Population genetics of rare variants and complex diseases. Hum. Hered. 74, 118–128 (2012).
Gravel, S. et al. Demographic history and rare allele sharing among human populations. Proc. Natl Acad. Sci. USA 108, 11983–11988 (2011).
Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Lander, G. C., Saibil, H. R. & Nogales, E. Go hybrid: EM, crystallography, and beyond. Curr. Opin. Struct. Biol. 22, 627–635 (2012).
Russel, D. et al. Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies. PLoS Biol. 10, e1001244 (2012).
Bau, D. et al. The three-dimensional folding of the α-globin gene domain reveals formation of chromatin globules. Nature Struct. Mol. Biol. 18, 107–114 (2011).
Lasker, K., Topf, M., Sali, A. & Wolfson, H. J. Inferential optimization for simultaneous fitting of multiple components into a CryoEM map of their assembly. J. Mol. Biol. 388, 180–194 (2009).
Kaelin, W. G. Jr. The concept of synthetic lethality in the context of anticancer therapy. Nature Rev. Cancer 5, 689–698 (2005).
Ashworth, A., Lord, C. J. & Reis-Filho, J. S. Genetic interactions in cancer progression and treatment. Cell 145, 30–38 (2011).
Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).
Cheung, H. W. et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc. Natl Acad. Sci. USA 108, 12372–12377 (2011).
Krastev, D. B. et al. A systematic RNAi synthetic interaction screen reveals a link between p53 and snoRNP assembly. Nature Cell Biol. 13, 809–818 (2011).
Luo, J. et al. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137, 835–848 (2009). This is a genome-wide RNAi screen of isogenic human cell lines to identify genes that are synthetically lethal with a specific oncogenic mutation.
Wang, Y. et al. Critical role for transcriptional repressor Snail2 in transformation by oncogenic RAS in colorectal carcinoma cells. Oncogene 29, 4658–4670 (2010).
Miller, J. P. et al. A genome-scale RNA-interference screen identifies RRAS signaling as a pathologic feature of Huntington's disease. PLoS Genet. 8, e1003042 (2012).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).
McDermott, U. et al. Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling. Proc. Natl Acad. Sci. USA 104, 19936–19941 (2007).
Muellner, M. K. et al. A chemical–genetic screen reveals a mechanism of resistance to PI3K inhibitors in cancer. Nature Chem. Biol. 7, 787–793 (2011).
Dolma, S., Lessnick, S. L., Hahn, W. C. & Stockwell, B. R. Identification of genotype-selective antitumor agents using synthetic lethal chemical screening in engineered human tumor cells. Cancer Cell 3, 285–296 (2003).
Corcoran, R. B. et al. Synthetic lethal interaction of combined BCL-XL and MEK inhibition promotes tumor regressions in KRAS mutant cancer models. Cancer Cell 23, 121–128 (2013).
Ding, Q. et al. A TALEN genome-editing system for generating human stem cell-based disease models. Cell Stem Cell 12, 238–251 (2013).
Hockemeyer, D. et al. Genetic engineering of human pluripotent cells using TALE nucleases. Nature Biotech. 29, 731–734 (2011).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
Hillenmeyer, M. E. et al. The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320, 362–365 (2008).
Han, T. X., Xu, X. Y., Zhang, M. J., Peng, X. & Du, L. L. Global fitness profiling of fission yeast deletion strains by barcode sequencing. Genome Biol. 11, R60 (2010).
Simonis, N. et al. Empirically controlled mapping of the Caenorhabditis elegans protein–protein interactome network. Nature Methods 6, 47–54 (2009).
Guruharsha, K. G. et al. A protein complex network of Drosophila melanogaster. Cell 147, 690–703 (2011).
Giot, L. et al. A protein interaction map of Drosophila melanogaster. Science 302, 1727–1736 (2003).
Bakal, C. et al. Phosphorylation networks regulating JNK activity in diverse genetic backgrounds. Science 322, 453–456 (2008).
Hu, P. et al. Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biol. 7, e96 (2009).
Havugimana, P. C. et al. A census of human soluble protein complexes. Cell 150, 1068–1081 (2012).
Stelzl, U. et al. A human protein–protein interaction network: a resource for annotating the proteome. Cell 122, 957–968 (2005).
Rual, J. F. et al. Towards a proteome-scale map of the human protein–protein interaction network. Nature 437, 1173–1178 (2005).
Acknowledgements
The authors thank G. Cagney, D. Fitzpatrick and C. Maher for their comments and feedback. They also thank M. Shales and H. Braberg for suggestions and assistance with figures and K. Lasker for assistance with Box 3. C.J.R. is supported by ICON Plc and the University College Dublin Newman Fellowship Programme; P.C. is supported by a Howard Hughes Predoctoral Fellowship; A.S. is supported by the National Institutes of Health (R01 GM083960, U54 RR022220, U54 GM094662, P01 AI091575, and U01 GM098256); N.J.K. is supported by the US National Institutes of Health (P50 GM082250, R01 GM084448, P01 AI090935, P50G M081879, R01 GM098101, R01 GM084279 and P01 AI091575) and the Defense Advanced Research Projects Agency (DARPA-10-93-Prophecy-PA-008).
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FURTHER INFORMATION
Glossary
- Epistasis
-
A phenomenon whereby the phenotype associated with a mutation is altered by the presence or absence of additional mutations.
- Domains
-
Distinct functional or structural regions of a protein, which can fold independently of the rest of the protein. A protein may contain several domains, and the same domain may be present in different proteins.
- Post-translational modifications
-
(PTMs). The chemical modifications of a protein after its translation, which can change the enzymatic activity, subcellular localization or interaction partners of the protein.
- Exome sequencing
-
The targeted sequencing of only known protein-coding regions.
- Nonsense
-
Pertaining to a mutation that changes an amino acid codon to a stop codon.
- Missense
-
Pertaining to a mutation that changes the encoded amino acid.
- Synonymous
-
Pertaining to a mutation that does not change the encoded amino acid.
- Deletion libraries
-
Sets of mutant strains, each of which has a single gene removed. The removed gene is typically replaced with an antibiotic-resistant marker to allow easy selection in genetic experiments.
- Forward genetics
-
The classical genetics approach, in which the genotypes that are associated with particular phenotypes are identified.
- Reverse genetics
-
The inverse approach to forward genetics, in which phenotypes that are associated with a particular genotype are identified. Such approaches are exemplified by studies of knockout mutants.
- Alleles
-
Multiple forms of a gene that occur at a specific locus.
- Reverse Y2H
-
(Reverse yeast two-hybrid). A genetic strategy to select against specific protein–protein interactions.
- Histone
-
A family of proteins that package DNA into nucleosomes. They consist of a globular domain and a tail that is subject to extensive post-translational modifications.
- Pleiotropic
-
Pertaining to a gene that is associated with multiple distinct phenotypes.
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Ryan, C., Cimermančič, P., Szpiech, Z. et al. High-resolution network biology: connecting sequence with function. Nat Rev Genet 14, 865–879 (2013). https://doi.org/10.1038/nrg3574
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DOI: https://doi.org/10.1038/nrg3574
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