Epistasis between two genetic loci indicates an interaction between them, i.e. a combined effect on phenotype that defies expectations based on their individual effects. The availability of computer simulations and high-throughput technologies makes it possible to explore simultaneously several epistatic interactions, giving rise to epistatic interaction networks. These networks play an increasingly central role in explaining pathway functions and evolutionary adaptation, as well as in the study of multi- trait genetic diseases and in the development of drug combination therapies. For these reasons, a growing number of experimental and computational efforts focus on the collection, simulation and analysis of epistatic interaction data. Yet, an often neglected matter is the importance of the choice of the phenotype relative to which the interaction between two genes is defined. The limitation to a single phenotype is largely a consequence of the combinatorial complexity of exploring many possible genetic variants and phenotypes. Here, we propose to take advantage of experimentally-driven in silico genome- scale models of the metabolic network of the yeast S. cerevisiae to generate and study the first epistatic interaction map for all possible phenotypes and perturbations in a biological network. The perturbations to the system will be the deletions of metabolic enzyme genes, and the phenotypes will consist of all computable variables of the system, i.e. all intracellular and transport metabolic reaction rates (fluxes). Specifically, we will compute all fluxes (phenotypes) for all single and double perturbations (gene deletions) under a set of predefined environmental conditions, choosing an appropriate epistasis metric, and then deriving the three-dimensional matrix of interactions (Aim 1). The set of all flux phenotypes will constitute a functional fingerprint containing dependencies between metabolic genes, which can be used for planning subsequent experiments and for biomedically relevant applications (like predicting disease and developing therapies). Next, we will test a significant number of these predictions by using high throughput methods to construct the appropriate strains and a robust set of assays to measure selected flux phenotypes in a large number of single and double yeast mutants (Aim 2). Finally, we will implement an online platform for multi-phenotype epistasis analyses through which users will be able not only to download data and software, but also to perform novel calculations and generate user-specific predictions and maps (Aim 3). We expect that, compared to single phenotype maps, our multi-phenotype map will reveal novel interactions and will convey a much richer view of the relationships between processes. The work we are proposing will lay the theoretical, computational and interactive visualization foundations for the analysis of multi-phenotype epistatic interaction data in biological systems.

Public Health Relevance

/Relevance Complex networks of interactions between genes are ubiquitous in biological systems, posing fundamental barriers that severely limit our capacity to address major biomedical challenges, such as complex genetic diseases as well as drug interactions and side- effects. This proposal will address this problem by generating a new computational representation of genetic networks, which will help predict, visualize and experimentally screen biomedically relevant interactions.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Dunsmore, Sarah
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Boston University
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Sirr, Amy; Scott, Adrian C; Cromie, Gareth A et al. (2018) Natural Variation in SER1 and ENA6 Underlie Condition-Specific Growth Defects in Saccharomyces cerevisiae. G3 (Bethesda) 8:239-251
Zhao, Qi; Stettner, Arion I; Reznik, Ed et al. (2016) Mapping the landscape of metabolic goals of a cell. Genome Biol 17:109
Zomorrodi, Ali R; Segrè, Daniel (2016) Synthetic Ecology of Microbes: Mathematical Models and Applications. J Mol Biol 428:837-61
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Sirr, Amy; Cromie, Gareth A; Jeffery, Eric W et al. (2015) Allelic variation, aneuploidy, and nongenetic mechanisms suppress a monogenic trait in yeast. Genetics 199:247-62
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Reznik, Ed; Yohe, Stefan; Segrè, Daniel (2013) Invariance and optimality in the regulation of an enzyme. Biol Direct 8:7
Reznik, Ed; Mehta, Pankaj; Segrè, Daniel (2013) Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools. PLoS Comput Biol 9:e1003195
Byrne, David; Dumitriu, Alexandra; Segre, Daniel (2012) Comparative multi-goal tradeoffs in systems engineering of microbial metabolism. BMC Syst Biol 6:127

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