Abstract
Predicting the behavior of living organisms is an enormous challenge given their vast complexity. Efforts to model biological systems require large datasets generated by physical binding experiments and perturbation studies. Genetic perturbations have proven important and are greatly facilitated by the advent of comprehensive mutant libraries in model organisms. Small-molecule chemical perturbagens provide a complementary approach, especially for systems that lack mutant libraries, and can easily probe the function of essential genes. Though single chemical or genetic perturbations provide crucial information associating individual components (for example, genes, proteins or small molecules) with pathways or phenotypes, functional relationships between pathways and modules of components are most effectively obtained from combined perturbation experiments. Here we review the current state of and discuss some future directions for 'combination chemical genetics', the systematic application of multiple chemical or mixed chemical and genetic perturbations, both to gain insight into biological systems and to facilitate medical discoveries.
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References
Stelling, J. Sauer, U., Szallasi, Z., Doyle, F.J. III & Doyle, J. Robustness of cellular functions. Cell 118, 675–685 (2004).
Koonin, E.V., Wolf, Y.I. & Karev, G.P. The structure of the protein universe and genome evolution. Nature 420, 218–223 (2002).
Hood, L., Heath, J.R., Phelps, M.E. & Lin, B. Systems biology and new technologies enable predictive and preventative medicine. Science 306, 640–643 (2004).
Davidson, E.H. et al. A genomic regulatory network for development. Science 295, 1669–1678 (2002).
Silva, J.M. et al. Second-generation shRNA libraries covering the mouse and human genomes. Nat. Genet. 37, 1281–1288 (2005).
Moffat, J. et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283–1298 (2006).
Stockwell, B.R. Exploring biology with small organic molecules. Nature 432, 846–854 (2004).
Boone, C., Bussey, H. & Andrews, B.J. Exploring genetic interactions and networks with yeast. Nat. Rev. Genet. 8, 437–449 (2007).
Sharom, J.R., Bellows, D.S. & Tyers, M. From large networks to small molecules. Curr. Opin. Chem. Biol. 8, 81–90 (2004).
Segrè, D., Deluna, A., Church, G.M. & Kishony, R. Modular epistasis in yeast metabolism. Nat. Genet. 37, 77–83 (2005).
Wong, S.L. et al. Combining biological networks to predict genetic interactions. Proc. Natl. Acad. Sci. USA 101, 15682–15687 (2004).
Zhang, L.V. et al. Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network. J. Biol. 4, 6 (2005).
Kamath, R.S. et al. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421, 231–237 (2003).
Butland, G. et al. eSGA: E. coli synthetic genetic array analysis. Nat. Methods 5, 789–795 (2008).
Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).
Giaever, G. et al. Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc. Natl. Acad. Sci. USA 101, 793–798 (2004).
Lum, P.Y. et al. Discovering modes of action for therapeutic compounds using a genome-wide screen of yeast heterozygotes. Cell 116, 121–137 (2004).
Yeh, P., Tschumi, A.I. & Kishony, R. Functional classification of drugs by properties of their pairwise interactions. Nat. Genet. 38, 489–494 (2006).
Lehár, J. et al. Chemical combination effects predict connectivity in biological systems. Mol. Syst. Biol. 3, 80 (2007).
Kawasumi, M. & Nghiem, P. Chemical genetics: elucidating biological systems with small-molecule compounds. J. Invest. Dermatol. 127, 1577–1584 (2007).
Oprea, T.I., Tropsha, A., Faulon, J.L. & Rintoul, M.D. Systems chemical biology. Nat. Chem. Biol. 3, 447–450 (2007).
Overington, J.P., Al-Lazikani, B. & Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006).
Wishart, D.S. et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36, D901–D906 (2008).
Olah, M. et al. in Chemical Biology: from Small Molecules to Systems Biology and Drug Design Vol. 2 (eds. Schreiber, S.L., Kapoor, T. & Wess, G.) 760–779 (Wiley-VCH GmbH, Weinheim, Germany, 2007).
Feher, M. & Schmidt, J.M. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J. Chem. Inf. Comput. Sci. 43, 218–227 (2003).
Yagoda, N. et al. RAS-RAF-MEK-dependent oxidative cell death involving voltage-dependent anion channels. Nature 447, 864–868 (2007).
Yang, W.S. & Stockwell, B.R. Synthetic lethal screening identifies compounds activating iron-dependent, nonapoptotic cell death in oncogenic-RAS-harboring cancer cells. Chem. Biol. 15, 234–245 (2008).
Stegmaier, K. et al. Gene expression-based high-throughput screening(GE-HTS) and application to leukemia differentiation. Nat. Genet. 36, 257–263 (2004).
Chen, S. et al. Self-renewal of embryonic stem cells by a small molecule. Proc. Natl. Acad. Sci. USA 103, 17266–17271 (2006).
Gangadhar, N.M., Firestein, S.J. & Stockwell, B.R. A novel role for jun N-terminal kinase signaling in olfactory sensory neuronal death. Mol. Cell. Neurosci. 38, 518–525 (2008).
Koehler, A.N., Shamji, A.F. & Schreiber, S.L. Discovery of an inhibitor of a transcription factor using small molecule microarrays and diversity-oriented synthesis. J. Am. Chem. Soc. 125, 8420–8421 (2003).
Hughes, T.R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).
Perlman, Z.E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).
Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
Avery, L. & Wasserman, S. Ordering gene function: the interpretation of epistasis in regulatory hierarchies. Trends Genet. 8, 312–316 (1992).
Anastassiou, D. Computational analysis of the synergy among multiple interacting genes. Mol. Syst. Biol. 3, 83 (2007).
Mani, R., St Onge, R.P., Hartman, J.L. IV, Giaever, G. & Roth, F.P. Defining genetic interaction. Proc. Natl. Acad. Sci. USA 105, 3461–3466 (2008).
Carter, G.W. et al. Prediction of phenotype and gene expression for combinations of mutations. Mol. Syst. Biol. 3, 96 (2007).
Greco, W.R., Bravo, G. & Parsons, J.C. The search for synergy: a critical review from a response surface perspective. Pharmacol. Rev. 47, 331–385 (1995).
Chou, T.C. & Talalay, P. Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv. Enzyme Regul. 22, 27–55 (1984).
Jackson, R.C. Amphibolic drug combinations: the design of selective antimetabolite protocols based upon the kinetic properties of multienzyme systems. Cancer Res. 53, 3998–4003 (1993).
Araujo, R.P., Petricoin, E.F. & Liotta, L.A. A mathematical model of combination therapy using the EGFR signaling network. Biosystems 80, 57–69 (2005).
Costanzo, M., Giaever, G., Nislow, C. & Andrews, B. Experimental approaches to identify genetic networks. Curr. Opin. Biotechnol. 17, 472–480 (2006).
Elena, S.F. & Lenski, R.E. Test of synergistic interactions among deleterious mutations in bacteria. Nature 390, 395–398 (1997).
Parsons, A.B. et al. Exploring the mode-of-action of bioactive compounds by chemical-genetic profiling in yeast. Cell 126, 611–625 (2006).
Dorer, R.K. et al. A small-molecule inhibitor of Mps1 blocks the spindle-checkpoint response to a lack of tension on mitotic chromosomes. Curr. Biol. 15, 1070–1076 (2005).
Ericson, E. et al. Off-target effects of psychoactive drugs revealed by genome-wide assays in yeast. PLoS Genet. 4, e1000151 (2008).
Jiang, B. et al. PAP inhibitor with in vivo efficacy identified by Candida albicans genetic profiling of natural products. Chem. Biol. 15, 363–374 (2008).
Eggert, U.S. et al. Parallel chemical genetic and genome-wide RNAi screens identify cytokinesis inhibitors and targets. PLoS Biol. 2, e379 (2004).
MacKeigan, J.P., Murphy, L.O. & Blenis, J. Sensitized RNAi screen of human kinases and phosphatases identifies new regulators of apoptosis and chemoresistance. Nat. Cell Biol. 7, 591–600 (2005).
Wei, G. et al. Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell 10, 331–342 (2006).
Hillenmeyer, M.E. et al. The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320, 362–365 (2008).
Hoon, S. et al. An integrated platform of genomic assays reveals small-molecule bioactivities. Nat. Chem. Biol. 4, 498–506 (2008).
Keith, C.T., Borisy, A.A. & Stockwell, B.R. Multicomponent therapeutics for networked systems. Nat. Rev. Drug Discov. 4, 71–78 (2005).
Kaelin, W.G. Jr., The concept of synthetic lethality in the context of anticancer therapy. Nat. Rev. Cancer 5, 689–698 (2005).
Zimmermann, G.R., Lehár, J. & Keith, C.T. Multi-target therapeutics: when the whole is greater than the sum of the parts. Drug Discov. Today 12, 34–42 (2007).
Borisy, A.A. et al. Systematic discovery of multicomponent therapeutics. Proc. Natl. Acad. Sci. USA 100, 7977–7982 (2003).
Kvien, T.K. et al. Efficacy and safety of a novel synergistic drug candidate - CRx-102 - in hand osteoarthritis. Ann. Rheum. Dis. 67, 942–948 (2008).
Dresser, G.K., Spence, J.D. & Bailey, D.G. Pharmacokinetic-pharmacodynamic consequences and clinical relevance of cytochrome P450 3A4 inhibition. Clin. Pharmacokinet. 38, 41–57 (2000).
Li, Y. et al. Gene expression profiling revealed novel molecular targets of docetaxel and estramustine combination treatment in prostate cancer cells. Mol. Cancer Ther. 4, 389–398 (2005).
Li, Y. et al. Gene expression profiling revealed novel mechanism of action of Taxotere and Furtulon in prostate cancer cells. BMC Cancer 5, 7 (2005).
Daigeler, A. et al. Synergistic apoptotic effects of taurolidine and TRAIL on squamous carcinoma cells of the esophagus. Int. J. Oncol. 32, 1205–1220 (2008).
Austin, C.P., Brady, L.S., Insel, T.R. & Collins, F.S. NIH Molecular Libraries Initiative. Science 306, 1138–1139 (2004).
Seiler, K.P. et al. ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic Acids Res. 36, D351–D359 (2008).
Tolliday, N. et al. Small molecules, big players: the National Cancer Institute's Initiative for Chemical Genetics. Cancer Res. 66, 8935–8942 (2006).
Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006).
Root, D.E., Flaherty, S.P., Kelley, B.P. & Stockwell, B.R. Biological mechanism profiling using an annotated compound library. Chem. Biol. 10, 881–892 (2003).
Taunton, J., Hassig, C.A. & Schreiber, S.L. A mammalian histone deacetylase related to the yeast transcriptional regulator Rpd3p. Science 272, 408–411 (1996).
Miller, L.W. & Cornish, V.W. Selective chemical labeling of proteins in living cells. Curr. Opin. Chem. Biol. 9, 56–61 (2005).
Lefurgy, S. & Cornish, V. Finding Cinderella after the ball: a three-hybrid approach to drug target identification. Chem. Biol. 11, 151–153 (2004).
MacBeath, G. & Schreiber, S.L. Printing proteins as microarrays for high-throughput function determination. Science 289, 1760–1763 (2000).
Lopez, A., Parsons, A.B., Nislow, C., Giaever, G. & Boone, C. Chemical-genetic approaches for exploring the mode of action of natural products. Prog. Drug Res. 66, 237, 239–71 (2008).
Campillos, M., Kuhn, M., Gavin, A.C., Jensen, L.J. & Bork, P. Drug target identification using side-effect similarity. Science 321, 263–266 (2008).
Melnick, J.S. et al. An efficient rapid system for profiling the cellular activities of molecular libraries. Proc. Natl. Acad. Sci. USA 103, 3153–3158 (2006).
MacDonald, M.L. et al. Identifying off-target effects and hidden phenotypes of drugs in human cells. Nat. Chem. Biol. 2, 329–337 (2006).
Schreiber, S.L. Target-oriented and diversity-oriented organic synthesis in drug discovery. Science 287, 1964–1969 (2000).
Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).
Kitagawa, M. et al. Complete set of ORF clones of Escherichia coli ASKA library (a complete set of E. coli K-12 ORF archive): unique resources for biological research. DNA Res. 12, 291–299 (2005).
Brandner, C.J. et al. The ORFeome of Staphylococcus aureus v 1.1. BMC Genomics 9, 321 (2008).
Dricot, A. et al. Generation of the Brucella melitensis ORFeome version 1.1. Genome Res. 14, 2201–2206 (2004).
Parrish, J.R. et al. High-throughput cloning of Campylobacter jejuni ORfs by in vivo recombination in Escherichia coli. J. Proteome Res. 3, 582–586 (2004).
Murthy, T. et al. A full-genomic sequence-verified protein-coding gene collection for Francisella tularensis. PLoS ONE 2, e577 (2007).
Matsuyama, A. et al. ORFeome cloning and global analysis of protein localization in the fission yeast Schizosaccharomyces pombe. Nat. Biotechnol. 24, 841–847 (2006).
Xu, D. et al. Genome-wide fitness test and mechanism-of-action studies of inhibitory compounds in Candida albicans. PLoS Pathog. 3, e92 (2007).
Hoyer, L.L. & Konopka, J. Candida here, and Candida there, and Candida everywhere! Future Microbiol. 3, 271–273 (2008).
Boutros, M. et al. Genome-wide RNAi analysis of growth and viability in Drosophila cells. Science 303, 832–835 (2004).
Reboul, J. et al. C. elegans ORFeome version 1.1: experimental verification of the genome annotation and resource for proteome-scale protein expression. Nat. Genet. 34, 35–41 (2003).
Foley, E. & O'Farrell, P.H. Functional dissection of an innate immune response by a genome-wide RNAi screen. PLoS Biol. 2, E203 (2004).
Jones, G.M. et al. A systematic library for comprehensive overexpression screens in Saccharomyces cerevisiae. Nat. Methods 5, 239–241 (2008).
Brasch, M.A., Hartley, J.L. & Vidal, M. ORFeome cloning and systems biology: standardized mass production of the parts from the parts-list. Genome Res. 14, 2001–2009 (2004).
Tischler, J., Lehner, B. & Fraser, A.G. Evolutionary plasticity of genetic interaction networks. Nat. Genet. 40, 390–391 (2008).
Gardner, T.S., di Bernardo, D., Lorenz, D. & Collins, J.J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102–105 (2003).
Nelander, S. et al. Models from experiments: combinatorial drug perturbations of cancer cells. Mol. Syst. Biol. 4, 216 (2008).
Haggarty, S.J., Clemons, P.A. & Schreiber, S.L. Chemical genomic profiling of biological networks using graph theory and combinations of small molecule perturbations. J. Am. Chem. Soc. 125, 10543–10545 (2003).
St Onge, R.P. et al. Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions. Nat. Genet. 39, 199–206 (2007).
Musso, G. et al. The extensive and condition-dependent nature of epistasis among whole-genome duplicates in yeast. Genome Res. 18, 1092–1099 (2008).
Lee, M.S. et al. The novel combination of chlorpromazine and pentamidine exerts synergistic antiproliferative effects through dual mitotic action. Cancer Res. 67, 11359–11367 (2007).
Lehár, J., Krueger, A., Zimmermann, G. & Borisy, A. High-order combination effects and biological robustness. Mol. Syst. Biol. 4, 215 (2008).
Deutscher, D., Meilijson, I., Kupiec, M. & Ruppin, E. Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nat. Genet. 38, 993–998 (2006).
Southan, C., Varkonyi, P. & Muresan, S. Complementarity between public and commercial databases: new opportunities in medicinal chemistry informatics. Curr. Top. Med. Chem. 7, 1502–1508 (2007).
Stobaugh, R.E. Chemical Abstracts Service Chemical Registry System. 11. Substance-related statistics: update and additions. J. Chem. Inf. Comput. Sci. 28, 180–187 (1988).
Gelperin, D.M. et al. Biochemical and genetic analysis of the yeast proteome with a movable ORF collection. Genes Dev. 19, 2816–2826 (2005).
Lamesch, P. et al. hORFeome v3.1: a resource of human open reading frames representing over 10,000 human genes. Genomics 89, 307–315 (2007).
Acknowledgements
B.R.S. is supported by a Beckman Young Investigator Award from the Arnold and Mabel Beckman Foundation and by the NIH (CA097061 and GM085081). C.N. and G.G. are supported by the Canadian Institutes of Health Research (CIHR MOP-81340 and CIHR MOP-84305, respectively).
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J.L. is employed by and has a significant number of options in CombinatoRx, which is engaged in combination chemical genetics as described. B.R.S. is a founder of and holds significant shares in CombinatoRx.
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Lehár, J., Stockwell, B., Giaever, G. et al. Combination chemical genetics. Nat Chem Biol 4, 674–681 (2008). https://doi.org/10.1038/nchembio.120
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DOI: https://doi.org/10.1038/nchembio.120
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