Combination chemical genetics is an emerging field of research. Whether genetic and chemical perturbations are applied together or combinations are purely between chemicals, CCG allows the testing of interactions between cellular components to be studied in new contexts and with more detail than can be achieved with single agents or only genetic perturbations.
To make the best use of available resources, CCG researchers will need to agree on standards for the design, data collection and analysis of experiments, building on resources already established for chemical genetics. For example, the US National Institutes of Health (NIH) Molecular Library Initiative63
aims to create a publically available collection of biologically active compounds64
and experimental standards65
. For CCG, it will be helpful to coordinate common probe libraries that can facilitate comparisons between experiments, and to agree on combination effect reference models for analyses across combination studies. Interaction data can be collected in existing public repositories (for example, BioGRID66
), which will need to be equipped with cheminformatics tools to allow integration with the knowledge in chemical databases21
Chemical genetic studies will benefit from continued improvement and extension of available chemical libraries. Efforts to catalog the druggable genome for existing chemicals22
have led to collections of biologically active compounds with known targets23,67
. Ligands for protein targets can be identified using traditional affinity purification7
and live-cell target-labeling approaches69
. Yeast three-hybrid techniques70
and the use of immobilized chemical or protein arrays71
can reveal direct drug-protein binding events, and haploinsufficiency fitness tests15
can associate small molecules with their target genes, which will be especially helpful toward characterizing the mechanisms of bioactive natural products72
. Chemical genetics probe sets are also being extended by comparing response profiles across cell-based phenotypes33,67
, by gene expression profiling28,34
and by analysis of drug activities and side effects73
. Considerable progress has also been made with establishing libraries for specific target classes (for example, epidermal growth factor receptor kinases74
). For modulating the many targets that are not covered by current probe sets, it remains critical to develop libraries with greater chemical diversity. Current efforts to this end involve assembling bioactive molecular fragments75
, or using diversity-oriented synthesis76
to produce complex, natural product–like libraries.
Chemogenomics efforts are becoming increasingly complex and diverse with the introduction of expanded probe sets and higher content experimental platforms. The resources for genome-scale genetic perturbations in S. cerevisiae
and other yeasts have grown dramatically, and the set of model organisms has expanded to include bacteria77-82
, other yeasts83-85
and select vertebrates5,6
(). Genome-wide overexpression libraries are being constructed in yeast that can be applied in conjunction with knockout experiments to provide a complete characterization of genetic perturbations89
. Finally, the rapid adoption of RNAi technology promises a smooth transition from sequence to function in more complex cells and metazoans90
, and flow cytometry readouts permit high-throughput analysis of changes in cell-cycle state or the expression of cytoplasmic and cell-surface markers.
Drug discovery will remain a major driving force for combination chemical genetics, and we expect to see expanded efforts involving forward, reverse and integrated CCG approaches toward this goal. One advantage of forward CCG is that it is a purely empirical approach, allowing new biological interactions to be revealed. Another potential advantage is suggested by a recent comparison of synthetic lethality in yeast (with deletion alleles) and nematodes (using double RNAi), which concluded that synthetic lethal interactions are not conserved91
. This suggests that the kinds of interactions probed by CCG are likely to be organism and context specific, offering the possibility that combination therapies targeting such interactions may achieve higher levels of selectivity than single agents toward targeting infectious diseases or other context-dependent conditions such as cancer.
Considering theoretical simulations, an obvious extension of the current modeling is to simulate chemical combination effects at genome scales. This can be achieved by adapting the metabolic simulations10
previously used for genetic interaction predictions to allow partial inhibition rather than total knockouts for target genes. The response surfaces from such simulations could be compared to data from matched combination experiments, and inconsistencies could guide improvements in our understanding of the underlying biological network. Another promising approach is to use combination effects to infer topological models of the target network, using linear or nonlinear regression methods92
adapted for combination data constraints93
. This approach provides a data-driven complement to the model-driven predictions from a priori reaction networks, and combining both methods should provide an effective strategy for refining predictive biological models.
An area with considerable promise involves high-order combinations of three or more perturbagens. Current CCG studies in yeast extend to third order (combinations of three agents), using designs that (i) test pairwise combinations against single mutants94
or chemogenomic profiles53
, (ii) screen double mutants for sensitivity to drug treatments95
or varying conditions96
and (iii) investigate purely chemical combinations97
. All three designs generally find synergies that pairwise interactions could not fully account for. Theoretically, high-order combinations should yield ever more selective control of complex systems42
, and it should even be possible to use high-order testing to quantify the functional complexity of a biological system98
. Extending chemical genetic studies to yet higher order combinations should provide constraints on the limits of medically useful synergy98
as well as mechanistic insights into biological networks99
. Another dimension to explore is the effect on synergy due to nonsimultaneous drug application, both on heterogeneous and synchronized cell populations, which could reveal conditional dependencies on cellular state changes. Designing and analyzing high-order and phased combination experiments will be challenging, and it will be an area of considerable activity in coming years.
Combination chemical genetics brings together the traditions of genetic perturbation and synergistic drug discovery to enable the detailed study of network topology. This has been made possible by the assembly of large, diverse chemical libraries and comprehensive sets of genetic mutants or RNAi suppressors. This marriage of large-scale genomic approaches, synergy analysis and chemical genetic tools offers the promise of new insights into biology and a new avenue for drug discovery.