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Epistatic interaction maps relative to multiple metabolic phenotypes.

Snitkin ES, Segrè D - PLoS Genet. (2011)

Bottom Line: Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact.Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes.The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.

View Article: PubMed Central - PubMed

Affiliation: Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America.

ABSTRACT
An epistatic interaction between two genes occurs when the phenotypic impact of one gene depends on another gene, often exposing a functional association between them. Due to experimental scalability and to evolutionary significance, abundant work has been focused on studying how epistasis affects cellular growth rate, most notably in yeast. However, epistasis likely influences many different phenotypes, affecting our capacity to understand cellular functions, biochemical networks adaptation, and genetic diseases. Despite its broad significance, the extent and nature of epistasis relative to different phenotypes remain fundamentally unexplored. Here we use genome-scale metabolic network modeling to investigate the extent and properties of epistatic interactions relative to multiple phenotypes. Specifically, using an experimentally refined stoichiometric model for Saccharomyces cerevisiae, we computed a three-dimensional matrix of epistatic interactions between any two enzyme gene deletions, with respect to all metabolic flux phenotypes. We found that the total number of epistatic interactions between enzymes increases rapidly as phenotypes are added, plateauing at approximately 80 phenotypes, to an overall connectivity that is roughly 8-fold larger than the one observed relative to growth alone. Looking at interactions across all phenotypes, we found that gene pairs interact incoherently relative to different phenotypes, i.e. antagonistically relative to some phenotypes and synergistically relative to others. Specific deletion-deletion-phenotype triplets can be explained metabolically, suggesting a highly informative role of multi-phenotype epistasis in mapping cellular functions. Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact. Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes. The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.

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A schematic representation of the 3D epistatic map.The 3D epistatic map is represented as a 3-dimensional entity (perturbation by perturbation by phenotype). Each “slice” of the 3D epistatic map represents an epistatic interaction network, created with respect to a single phenotype. Previous genome-scale studies of epistasis in yeast metabolism have focused on a single “slice”, whose interactions were computed with respect to the biomass production phenotype.
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pgen-1001294-g001: A schematic representation of the 3D epistatic map.The 3D epistatic map is represented as a 3-dimensional entity (perturbation by perturbation by phenotype). Each “slice” of the 3D epistatic map represents an epistatic interaction network, created with respect to a single phenotype. Previous genome-scale studies of epistasis in yeast metabolism have focused on a single “slice”, whose interactions were computed with respect to the biomass production phenotype.

Mentions: In this work we report the computational study of epistatic interactions in a flux balance model of metabolism that is simple enough to allow an exhaustive computation of all possible perturbations relative to all possible phenotypes, but at the same time realistic enough to provide meaningful biological insight. Specifically, we use an experimentally informed variant of the method of minimization of metabolic adjustment (MOMA) in a genome-scale metabolic network model of Saccharomyces cerevisiae [24] to predict all steady state metabolic reaction rates (fluxes) in response to all possible single and double enzyme gene deletions. By comparing single and double mutant values for all fluxes and defining appropriate metrics, we construct an epistatic map for each flux phenotype (Figure 1). This multi-phenotype genetic interaction map allows us to explore for the first time the properties and significance of epistasis across a combinatorial set of perturbations and phenotypes.


Epistatic interaction maps relative to multiple metabolic phenotypes.

Snitkin ES, Segrè D - PLoS Genet. (2011)

A schematic representation of the 3D epistatic map.The 3D epistatic map is represented as a 3-dimensional entity (perturbation by perturbation by phenotype). Each “slice” of the 3D epistatic map represents an epistatic interaction network, created with respect to a single phenotype. Previous genome-scale studies of epistasis in yeast metabolism have focused on a single “slice”, whose interactions were computed with respect to the biomass production phenotype.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3037399&req=5

pgen-1001294-g001: A schematic representation of the 3D epistatic map.The 3D epistatic map is represented as a 3-dimensional entity (perturbation by perturbation by phenotype). Each “slice” of the 3D epistatic map represents an epistatic interaction network, created with respect to a single phenotype. Previous genome-scale studies of epistasis in yeast metabolism have focused on a single “slice”, whose interactions were computed with respect to the biomass production phenotype.
Mentions: In this work we report the computational study of epistatic interactions in a flux balance model of metabolism that is simple enough to allow an exhaustive computation of all possible perturbations relative to all possible phenotypes, but at the same time realistic enough to provide meaningful biological insight. Specifically, we use an experimentally informed variant of the method of minimization of metabolic adjustment (MOMA) in a genome-scale metabolic network model of Saccharomyces cerevisiae [24] to predict all steady state metabolic reaction rates (fluxes) in response to all possible single and double enzyme gene deletions. By comparing single and double mutant values for all fluxes and defining appropriate metrics, we construct an epistatic map for each flux phenotype (Figure 1). This multi-phenotype genetic interaction map allows us to explore for the first time the properties and significance of epistasis across a combinatorial set of perturbations and phenotypes.

Bottom Line: Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact.Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes.The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.

View Article: PubMed Central - PubMed

Affiliation: Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America.

ABSTRACT
An epistatic interaction between two genes occurs when the phenotypic impact of one gene depends on another gene, often exposing a functional association between them. Due to experimental scalability and to evolutionary significance, abundant work has been focused on studying how epistasis affects cellular growth rate, most notably in yeast. However, epistasis likely influences many different phenotypes, affecting our capacity to understand cellular functions, biochemical networks adaptation, and genetic diseases. Despite its broad significance, the extent and nature of epistasis relative to different phenotypes remain fundamentally unexplored. Here we use genome-scale metabolic network modeling to investigate the extent and properties of epistatic interactions relative to multiple phenotypes. Specifically, using an experimentally refined stoichiometric model for Saccharomyces cerevisiae, we computed a three-dimensional matrix of epistatic interactions between any two enzyme gene deletions, with respect to all metabolic flux phenotypes. We found that the total number of epistatic interactions between enzymes increases rapidly as phenotypes are added, plateauing at approximately 80 phenotypes, to an overall connectivity that is roughly 8-fold larger than the one observed relative to growth alone. Looking at interactions across all phenotypes, we found that gene pairs interact incoherently relative to different phenotypes, i.e. antagonistically relative to some phenotypes and synergistically relative to others. Specific deletion-deletion-phenotype triplets can be explained metabolically, suggesting a highly informative role of multi-phenotype epistasis in mapping cellular functions. Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact. Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes. The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.

Show MeSH
Related in: MedlinePlus