Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action

Abstract

We systematically analyzed the relationships between gene fitness profiles (co-fitness) and drug inhibition profiles (co-inhibition) from several hundred chemogenomic screens in yeast. Co-fitness predicted gene functions distinct from those derived from other assays and identified conditionally dependent protein complexes. Co-inhibitory compounds were weakly correlated by structure and therapeutic class. We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing. Fitness data provide a novel, systems-level perspective on the cell.

Figure 1

Predicting shared gene functions using co-fitness and other datasets. (a) Precision-recall curve for each of four high-throughput datasets, illustrating the prediction accuracy of each dataset to expert-curated reference interactions [13]. The optimal dataset has both high precision and high coverage (a point in the upper right corner). TP is the number of true positive interactions captured by the dataset, FP is the number of false positives, and FN is the number of false negatives. Synthetic lethality networks have only one value for precision and coverage because their links are binary. Correlation-based networks, including co-fitness, co-expression, and physical interactions, use an adjustable correlation threshold to define interactions: each point corresponds to one threshold. (b) Each cell in the matrix summarizes the precision that each dataset achieved for each function, ranging from low (black) to high (red), hierarchically clustered on both axes. (c-f) Individual precision-recall curves for four of the gene categories, from which the values for (b) were calculated. The remaining 28 categories are shown in Supplementary Figure 2 in Additional file 1 in Additional file 1.

Figure 2

Compound clusters, extracted from genome-wide two-way clustering on the complete dataset (using all genes and all compounds). (a) Antifungal azoles in the heterozygous data, with high structural similarity. All induce sensitivity in strains deleted for ERG11, an azole target, and related pleiotropic drug resistance (PDR) transport-related genes; fluconazole (inset) did not appear in this cluster, though it is also thought to target Erg11. (b) Psychoactive compounds that target dopamine, serotonin, and acetylcholine receptors in human; these compounds cluster in the heterozygous dataset based on inhibition of small ribosomal subunit genes and Cox17, potential targets in both yeast and human. (c) Examples of drugs with similar homozygous fitness profiles; the similarity is due to shared sensitivity of strains deleted for multi-drug resistance (MDR) genes with roles in vesicle-mediated transport.

Figure 3

The limited correlation between Tanimoto structural similarity and co-fitness in the heterozygous and homozygous datasets suggests that chemical structure influences inhibition patterns but does not exclusively define them. Each point represents a pair of compounds; to allow for comparison between (a) heterozygous and (b) homozygous datasets, for this figure we used only pairs of compounds that were tested in both datasets.

Figure 4

The ability of co-inhibition to predict shared therapeutic use was higher for the homozygous than for the heterozygous dataset. As reference, we used a set of compound pairs with shared therapeutic use (WHO ATC level 3 code). As in Figure 3, we used only pairs of compounds that were tested in both the (a) heterozygous and (b) homozygous datasets.

Figure 5

Drug target prediction accuracy (ten-fold cross validation) using one of four algorithms: log₂ ratio fitness defect with a simple decision stump model (red); log₂ ratio fitness defect with a richer random forest model (green); the chemical structure similarity features with the random forest model (blue); and all features with the random forest model (purple). Each point represents a threshold for the algorithm. For the decision stump, each point represents a single log₂ ratio value, and for the random forest, each point represents the algorithm's decision as a mode of decision trees that use the available features (see Materials and methods). The accuracies of other algorithms are shown in Supplementary Figure 7 in Additional file 1. (a) Performance on the expert-curated reference set of compounds and their known interacting yeast proteins. (b) Performance on DrugBank protein-compound interactions (mostly human) mapped to yeast through protein homology.

Figure 6

Overexpression of Exo84 alleviates the sensitivity of the control to 27 μM nocodazole. The optical density at 595 nm over time for wild-type BY4743 cells harboring the Exo84 overexpression construct compared to that of controls (ctrl) transformed with plasmid lacking a gene insert (for details, see Materials and methods, and for replicates, see Supplementary Figure 10 in Additional file 1).