Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data

Abstract

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ∼80% of the known relationships without any false positives.

Author Summary

Cells have evolved elaborate pathways that allow them to optimally use available nutrients, for example, and alter gene expression in response to external challenges. The mapping of these pathways provides an understanding of cell function critical for advancements in a number of fields, from biofuel production to drug discovery. In this study, we developed a novel method to map pathways of genes that function in the cellular response to a given signal or stress. The method represents a significant advancement since it takes full advantage of modern genomics techniques to provide novel, detailed information about gene function, including the contribution from different genes individually, and in combination with other genes or pathways. We tested the method on a pathway in yeast whose human equivalent is associated with a serious and potentially fatal hereditary disease called galactosemia. We demonstrate that the method allows a highly accurate reconstruction of this pathway, correctly segregating genes with major and minor functions, and recapitulating the known mechanisms associated with the disease.