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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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Related in: MedlinePlus

Coverage of the 3D epistasis map.The total number of antagonistic and synergistic interactions for each phenotype are displayed as stacked bars, with the cumulative sum of interactions at a given point in the list being represented by a black circle. Phenotypes were sorted according to the number of unique interactions added to a cumulative tally. It should be noted that the first phenotype in the list is the biomass flux, which does not have the maximal number of interactions. It was placed first so as to clearly demonstrate the gain in interaction coverage by observing non-growth phenotypes. Only the first 83 phenotypes are shown, as the number of unique interactions reaches saturation at this point.
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pgen-1001294-g004: Coverage of the 3D epistasis map.The total number of antagonistic and synergistic interactions for each phenotype are displayed as stacked bars, with the cumulative sum of interactions at a given point in the list being represented by a black circle. Phenotypes were sorted according to the number of unique interactions added to a cumulative tally. It should be noted that the first phenotype in the list is the biomass flux, which does not have the maximal number of interactions. It was placed first so as to clearly demonstrate the gain in interaction coverage by observing non-growth phenotypes. Only the first 83 phenotypes are shown, as the number of unique interactions reaches saturation at this point.

Mentions: The intuition that different phenotypes convey complementary insight into the functional associations between genes and processes was confirmed in a quantitative manner by determining how many unique interactions each phenotype contributes to the 3D epistatic map. Figure 4 shows that the total number of interactions identified when considering all phenotypes is ∼8 times larger than can be identified relative to any individual phenotype, although the exact increase in interaction coverage is dependent on the threshold for defining a significant interaction (See Materials and Methods). Figure 4 also shows that 83 of the 293 total metabolic flux phenotypes are required to identify all unique epistatic interactions in yeast metabolism. Examining the distribution of metabolic processes where these 83 phenotypes come from (Figure S6), reveals that they are spread across all metabolic processes. This suggests that a set of phenotypes that represents all metabolic functions is required to identify all epistatic interactions. Conversely, this implies that different phenotypes are providing insights into unique aspects of the functional relationships between genes.


Epistatic interaction maps relative to multiple metabolic phenotypes.

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

Coverage of the 3D epistasis map.The total number of antagonistic and synergistic interactions for each phenotype are displayed as stacked bars, with the cumulative sum of interactions at a given point in the list being represented by a black circle. Phenotypes were sorted according to the number of unique interactions added to a cumulative tally. It should be noted that the first phenotype in the list is the biomass flux, which does not have the maximal number of interactions. It was placed first so as to clearly demonstrate the gain in interaction coverage by observing non-growth phenotypes. Only the first 83 phenotypes are shown, as the number of unique interactions reaches saturation at this point.
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1001294-g004: Coverage of the 3D epistasis map.The total number of antagonistic and synergistic interactions for each phenotype are displayed as stacked bars, with the cumulative sum of interactions at a given point in the list being represented by a black circle. Phenotypes were sorted according to the number of unique interactions added to a cumulative tally. It should be noted that the first phenotype in the list is the biomass flux, which does not have the maximal number of interactions. It was placed first so as to clearly demonstrate the gain in interaction coverage by observing non-growth phenotypes. Only the first 83 phenotypes are shown, as the number of unique interactions reaches saturation at this point.
Mentions: The intuition that different phenotypes convey complementary insight into the functional associations between genes and processes was confirmed in a quantitative manner by determining how many unique interactions each phenotype contributes to the 3D epistatic map. Figure 4 shows that the total number of interactions identified when considering all phenotypes is ∼8 times larger than can be identified relative to any individual phenotype, although the exact increase in interaction coverage is dependent on the threshold for defining a significant interaction (See Materials and Methods). Figure 4 also shows that 83 of the 293 total metabolic flux phenotypes are required to identify all unique epistatic interactions in yeast metabolism. Examining the distribution of metabolic processes where these 83 phenotypes come from (Figure S6), reveals that they are spread across all metabolic processes. This suggests that a set of phenotypes that represents all metabolic functions is required to identify all epistatic interactions. Conversely, this implies that different phenotypes are providing insights into unique aspects of the functional relationships between genes.

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