Abstract
The upswing in US Food and Drug Administration and European Medicines Agency drug approvals in 2014 may have marked an end to the dry spell that has troubled the pharmaceutical industry over the past decade. Regardless, the attrition rate of drugs in late clinical phases remains high, and a lack of target validation has been highlighted as an explanation. This has led to a resurgence in appreciation of phenotypic drug screens, as these may be more likely to yield compounds with relevant modes of action. However, cell-based screening approaches do not directly reveal cellular targets, and hence target deconvolution and a detailed understanding of drug action are needed for efficient lead optimization and biomarker development. Here, recently developed functional genomics technologies that address this need are reviewed. The approaches pioneered in model organisms, particularly in yeast, and more recently adapted to mammalian systems are discussed. Finally, areas of particular interest and directions for future tool development are highlighted.
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References
Swinney, D.C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).
Garnier, J.P. Rebuilding the R&D engine in big pharma. Harvard Bus. Review 86, 66–70, 72–66, 128 (2008).
Bunnage, M.E., Gilbert, A.M., Jones, L.H. & Hett, E.C. Know your target, know your molecule. Nat. Chem. Biol. 11, 368–372 (2015).
Savitski, M.M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014).
Schenone, M., Dancik, V., Wagner, B.K. & Clemons, P.A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232–240 (2013).
Reinhold, W.C. et al. Using drug response data to identify molecular effectors, and molecular “omic” data to identify candidate drugs in cancer. Hum. Genet. 134, 3–11 (2015).
Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
Rix, U. & Superti-Furga, G. Target profiling of small molecules by chemical proteomics. Nat. Chem. Biol. 5, 616–624 (2009).
Shoichet, B.K. & Kobilka, B.K. Structure-based drug screening for G-protein-coupled receptors. Trends Pharmacol. Sci. 33, 268–272 (2012).
Andrusiak, K., Piotrowski, J.S. & Boone, C. Chemical-genomic profiling: systematic analysis of the cellular targets of bioactive molecules. Bioorg. Med. Chem. 20, 1952–1960 (2012).
Enserink, J.M. Chemical genetics: budding yeast as a platform for drug discovery and mapping of genetic pathways. Molecules 17, 9258–9273 (2012).
Ho, C.H. et al. Combining functional genomics and chemical biology to identify targets of bioactive compounds. Curr. Opin. Chem. Biol. 15, 66–78 (2011).
Giaever, G. et al. Genomic profiling of drug sensitivities via induced haploinsufficiency. Nat. Genet. 21, 278–283 (1999).
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).
Lee, A.Y. et al. Mapping the cellular response to small molecules using chemogenomic fitness signatures. Science 344, 208–211 (2014).
Ericson, E. et al. Off-target effects of psychoactive drugs revealed by genome-wide assays in yeast. PLoS Genet. 4, e1000151 (2008).
Yan, Z. et al. Yeast Barcoders: a chemogenomic application of a universal donor-strain collection carrying bar-code identifiers. Nat. Methods 5, 719–725 (2008).
Castoreno, A.B. et al. Small molecules discovered in a pathway screen target the Rho pathway in cytokinesis. Nat. Chem. Biol. 6, 457–463 (2010).
Parsons, A.B. et al. Exploring the mode-of-action of bioactive compounds by chemical-genetic profiling in yeast. Cell 126, 611–625 (2006).
Hillenmeyer, M.E. et al. The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320, 362–365 (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).
Luesch, H. et al. A genome-wide overexpression screen in yeast for small-molecule target identification. Chem. Biol. 12, 55–63 (2005).
Butcher, R.A. et al. Microarray-based method for monitoring yeast overexpression strains reveals small-molecule targets in TOR pathway. Nat. Chem. Biol. 2, 103–109 (2006).
Ho, C.H. et al. A molecular barcoded yeast ORF library enables mode-of-action analysis of bioactive compounds. Nat. Biotechnol. 27, 369–377 (2009).
Nishimura, S. et al. Marine antifungal theonellamides target 3β-hydroxysterol to activate Rho1 signaling. Nat. Chem. Biol. 6, 519–526 (2010).
Hoon, S. et al. An integrated platform of genomic assays reveals small-molecule bioactivities. Nat. Chem. Biol. 4, 498–506 (2008).
Perlstein, E.O. et al. Revealing complex traits with small molecules and naturally recombinant yeast strains. Chem. Biol. 13, 319–327 (2006).
Perlstein, E.O., Ruderfer, D.M., Roberts, D.C., Schreiber, S.L. & Kruglyak, L. Genetic basis of individual differences in the response to small-molecule drugs in yeast. Nat. Genet. 39, 496–502 (2007).
Wallace, I.M. et al. Compound prioritization methods increase rates of chemical probe discovery in model organisms. Chem. Biol. 18, 1273–1283 (2011).
Xu, D. et al. Genome-wide fitness test and mechanism-of-action studies of inhibitory compounds in Candida albicans. PLoS Pathog. 3, e92 (2007).
Pathania, R. et al. Chemical genomics in Escherichia coli identifies an inhibitor of bacterial lipoprotein targeting. Nat. Chem. Biol. 5, 849–856 (2009).
Nichols, R.J. et al. Phenotypic landscape of a bacterial cell. Cell 144, 143–156 (2011).
Chong, Y.T. et al. Yeast proteome dynamics from single cell imaging and automated analysis. Cell 161, 1413–1424 (2015).
Elbashir, S.M. et al. Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature 411, 494–498 (2001).
Kaelin, W.G. Jr. The concept of synthetic lethality in the context of anticancer therapy. Nat. Rev. Cancer 5, 689–698 (2005).
Hartwell, L.H., Szankasi, P., Roberts, C.J., Murray, A.W. & Friend, S.H. Integrating genetic approaches into the discovery of anticancer drugs. Science 278, 1064–1068 (1997).
Nijman, S.M. & Friend, S.H. Cancer. Potential of the synthetic lethality principle. Science 342, 809–811 (2013).
Kaelin, W.G. Jr. Molecular biology. Use and abuse of RNAi to study mammalian gene function. Science 337, 421–422 (2012).
Fece de la Cruz, F., Gapp, B.V. & Nijman, S.M. Synthetic lethal vulnerabilities of cancer. Annu. Rev. Pharmacol. Toxicol. 55, 513–531 (2015).
Brummelkamp, T.R. et al. An shRNA barcode screen provides insight into cancer cell vulnerability to MDM2 inhibitors. Nat. Chem. Biol. 2, 202–206 (2006).
Matheny, C.J. et al. Next-generation NAMPT inhibitors identified by sequential high-throughput phenotypic chemical and functional genomic screens. Chem. Biol. 20, 1352–1363 (2013).
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).
Arnoldo, A. et al. A genome scale overexpression screen to reveal drug activity in human cells. Genome Med 6, 32 (2014).
Muellner, M.K. et al. A chemical-genetic screen reveals a mechanism of resistance to PI3K inhibitors in cancer. Nat. Chem. Biol. 7, 787–793 (2011).
Jiang, H., Pritchard, J.R., Williams, R.T., Lauffenburger, D.A. & Hemann, M.T. A mammalian functional-genetic approach to characterizing cancer therapeutics. Nat. Chem. Biol. 7, 92–100 (2011).
Martins, M.M. et al. Linking tumor mutations to drug responses via a quantitative chemical-genetic interaction map. Cancer Discov 5, 154–167 (2015).
Pettitt, S.J. et al. A genetic screen using the PiggyBac transposon in haploid cells identifies Parp1 as a mediator of olaparib toxicity. PLoS ONE 8, e61520 (2013).
Carette, J.E. et al. Global gene disruption in human cells to assign genes to phenotypes by deep sequencing. Nat. Biotechnol. 29, 542–546 (2011).
Blomen, V.A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 10.1126/science.aac7557 (15 October 2015).
Wang, T. et al. Identification and characterization of essential genes in the human genome. Science science.aac7041 (15 October 2015).
Birsoy, K. et al. MCT1-mediated transport of a toxic molecule is an effective strategy for targeting glycolytic tumors. Nat. Genet. 45, 104–108 (2013).
Winter, G.E. et al. The solute carrier SLC35F2 enables YM155-mediated DNA damage toxicity. Nat. Chem. Biol. 10, 768–773 (2014).
Reiling, J.H. et al. A haploid genetic screen identifies the major facilitator domain containing 2A (MFSD2A) transporter as a key mediator in the response to tunicamycin. Proc. Natl. Acad. Sci. USA 108, 11756–11765 (2011).
Kell, D.B. & Oliver, S.G. How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion. Front. Pharmacol. 5, 231 (2014).
Doudna, J.A. & Charpentier, E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science 346, 1258096 (2014).
Mali, P., Esvelt, K.M. & Church, G.M. Cas9 as a versatile tool for engineering biology. Nat. Methods 10, 957–963 (2013).
Shalem, O., Sanjana, N.E. & Zhang, F. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16, 299–311 (2015).
Wang, T., Wei, J.J., Sabatini, D.M. & Lander, E.S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).
Gilbert, L.A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
Auerbach, C. in Chemical Mutagens 1–19 (Springer, 1973).
Jones, A.K., Buckingham, S.D. & Sattelle, D.B. Chemistry-to-gene screens in Caenorhabditis elegans. Nat. Rev. Drug Discov. 4, 321–330 (2005).
Dejonghe, W. & Russinova, E. Target identification strategies in plant chemical biology. Front. Plant Sci. 5, 352 (2014).
Heitman, J., Movva, N.R. & Hall, M.N. Targets for cell cycle arrest by the immunosuppressant rapamycin in yeast. Science 253, 905–909 (1991).
Huang, Z. et al. A functional variomics tool for discovering drug-resistance genes and drug targets. Cell Reports 3, 577–585 (2013).
Wacker, S.A., Houghtaling, B.R., Elemento, O. & Kapoor, T.M. Using transcriptome sequencing to identify mechanisms of drug action and resistance. Nat. Chem. Biol. 8, 235–237 (2012).
Kasap, C., Elemento, O. & Kapoor, T.M. DrugTargetSeqR: a genomics- and CRISPR-Cas9-based method to analyze drug targets. Nat. Chem. Biol. 10, 626–628 (2014).
Smurnyy, Y. et al. DNA sequencing and CRISPR-Cas9 gene editing for target validation in mammalian cells. Nat. Chem. Biol. 10, 623–625 (2014).
Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675–686 (2015).
Jae, L.T. Virus entry. Lassa virus entry requires a trigger-induced receptor switch. Science 344, 1506–1510 (2014).
Segawa, K. Caspase-mediated cleavage of phospholipid flippase for apoptotic phosphatidylserine exposure. Science 344, 1164–1168 (2014).
Nihongaki, Y., Kawano, F., Nakajima, T. & Sato, M. Photoactivatable CRISPR-Cas9 for optogenetic genome editing. Nat. Biotechnol. 33, 755–760 (2015).
Nihongaki, Y., Yamamoto, S., Kawano, F., Suzuki, H. & Sato, M. CRISPR-Cas9-based photoactivatable transcription system. Chem. Biol. 22, 169–174 (2015).
Qi, L.S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).
Kleinstiver, B.P. et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature 523, 481–485 (2015).
Swarts, D.C. et al. DNA-guided DNA interference by a prokaryotic Argonaute. Nature 507, 258–261 (2014).
Bürckstummer, T. et al. A reversible gene trap collection empowers haploid genetics in human cells. Nat. Methods 10, 965–971 (2013).
Andersson, B.S. et al. Ph-positive chronic myeloid leukemia with near-haploid conversion in vivo and establishment of a continuously growing cell line with similar cytogenetic pattern. Cancer Genet. Cytogenet. 24, 335–343 (1987).
Kotecki, M., Reddy, P.S. & Cochran, B.H. Isolation and characterization of a near-haploid human cell line. Exp. Cell Res. 252, 273–280 (1999).
Carette, J.E. et al. Haploid genetic screens in human cells identify host factors used by pathogens. Science 326, 1231–1235 (2009).
Carette, J.E. et al. Generation of iPSCs from cultured human malignant cells. Blood 115, 4039–4042 (2010).
Essletzbichler, P. et al. Megabase-scale deletion using CRISPR/Cas9 to generate a fully haploid human cell line. Genome Res. 24, 2059–2065 (2014).
Yi, M., Hong, N. & Hong, Y. Generation of medaka fish haploid embryonic stem cells. Science 326, 430–433 (2009).
Elling, U. et al. Forward and reverse genetics through derivation of haploid mouse embryonic stem cells. Cell Stem Cell 9, 563–574 (2011).
Leeb, M. & Wutz, A. Derivation of haploid embryonic stem cells from mouse embryos. Nature 479, 131–134 (2011).
Li, W. et al. Genetic modification and screening in rat using haploid embryonic stem cells. Cell Stem Cell 14, 404–414 (2014).
Yang, H. et al. Generation of haploid embryonic stem cells from Macaca fascicularis monkey parthenotes. Cell Res. 23, 1187–1200 (2013).
Mojica, F.J., Diez-Villasenor, C., Garcia-Martinez, J. & Soria, E. Intervening sequences of regularly spaced prokaryotic repeats derive from foreign genetic elements. J. Mol. Evol. 60, 174–182 (2005).
Pourcel, C., Salvignol, G. & Vergnaud, G. CRISPR elements in Yersinia pestis acquire new repeats by preferential uptake of bacteriophage DNA, and provide additional tools for evolutionary studies. Microbiology 151, 653–663 (2005).
Bolotin, A., Quinquis, B., Sorokin, A. & Ehrlich, S.D. Clustered regularly interspaced short palindrome repeats (CRISPRs) have spacers of extrachromosomal origin. Microbiology 151, 2551–2561 (2005).
Makarova, K.S., Grishin, N.V., Shabalina, S.A., Wolf, Y.I. & Koonin, E.V. A putative RNA-interference-based immune system in prokaryotes: computational analysis of the predicted enzymatic machinery, functional analogies with eukaryotic RNAi, and hypothetical mechanisms of action. Biol. Direct 1, 7 (2006).
Barrangou, R. et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science 315, 1709–1712 (2007).
Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).
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).
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I wish to thank H. Pickersgill of Life Science Editors for critical reading and editing of the manuscript.
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The author is cofounder and shareholder of Haplogen, GmbH. The company employs haploid genetics in the area of infectious disease.
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Nijman, S. Functional genomics to uncover drug mechanism of action. Nat Chem Biol 11, 942–948 (2015). https://doi.org/10.1038/nchembio.1963
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DOI: https://doi.org/10.1038/nchembio.1963
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