Combination chemical genetics

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.

Figure 1

Combined perturber studies in the context of forward and reverse genetics. (a) In classical and chemical genetics, “forward” screens use many uncharacterized perturbers and a known phenotype to discover genes or proteins that affect that phenotype, and “reverse” studies test many phenotypes using a perturbagen with a known target to determine which phenotypes are affected by that target. In both cases, the questions under investigation center on the function of individual genes or proteins. (b) For combination chemical genetics, the focus of investigations shifts from individual targets to interactions between them or conditional target dependencies, and the perturbations are applied as combinations. Here forward screens use combinations of many perturbagens to discover interactions, and reverse studies involve modulating a known interaction with a set of probes targeting its components to determine which of many tested phenotypes are affected by that interaction. Figure adapted from ref. .

Figure 2

Measuring synergy for chemical combinations. (a) Continuous perturbations with sigmoidal response curves can cooperate either to boost the high-dose effect to new levels or to shift the effective concentration to lower doses, and the optimal dosing ratio is usually not known. A factorial dose matrix design can capture all of these possibilities. (b) The resulting interaction can be analyzed using the full three-dimensional response surface or using an isobologram to measure linear dose shifting at a chosen effect level via a combination index CI. For this example, we show a strongly synergistic antibiotic combination that targets folate metabolism enzymes. (c) Synergy reference models will differ depending on the null-effect assumption. Bliss independence (multiplicative epistasis) or Gaddum's non-interaction (Bateson masking) are generally used to analyze genetic epistasis, and Loewe dose additivity is most widely used for drug combinations. The multiplicative model produces stronger effects than either of the single agents at high combined doses, whereas masking simply follows the strongest single agent at corresponding doses. In dose-additive combinations, the agents cooperate in the same way as increasing the dose of a single drug. All three models can be adapted for analyzing pairs of agonists and generalized for three or more agents.

Figure 3

The response shape in dose matrix experiments depends on target connectivity. In simulations of multiply inhibited metabolic networks, the response surface morphology depended strongly on how the inhibited targets were connected in the network. Here we show four representative target connectivities, where substrates (green symbols) are metabolized by reactions (black arrows), and the reaction enzymes (white circles) are modulated by inhibitors (red markers). The resulting response surfaces from dynamic simulations are shown to the right of each such pathway. (a,b) Inhibitor pairs with parallel targets produced either saturated (a) or masking (b) effects in combination, depending on whether the targets affected independent alternatives or codependent ingredients of the final reaction. (c,d) Inhibiting serial targets along a pathway yielded multiplicative effects (c) for partially effective single agents, but serial targets in pathways regulated by negative feedback (d) produced strong dose shifting like that seen in our antibacterial example (Fig. 2b). Each of these cases represents a mechanistic hypothesis relating response shape to target connectivity, generated by the pathway simulations.

Figure 4

Designing combination chemical genetics experiments. (a) The dose sampling possible for individual pairs depends on whether the perturbagens are discrete (for example, knockouts) or continuous (for example, chemicals, overexpression or RNAi). When the perturbers have known cellular targets, interactions can be described in terms of those targets. (b) Following the practices of chemical genetics, CCG involves many such experiments, either testing multiple combinations against a few phenotypes to discover synergistic interactions (forward) or testing a few combinations against many phenotypes to characterize the function of an interaction (reverse). These approaches can be integrated by collecting profiles across many combinations for a comparable number of phenotypes, allowing profiles in either direction to be compared for similarity or selective effects.