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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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Epistatic networks with respect to growth rate and two other flux phenotypes.To explore the biological diversity underlying the different epistatic networks, the presence of synergistic and antagonistic interactions between pairs of metabolic processes were determined for (A) growth, (B) succinate secretion and (C) glycerol secretion phenotypes. These process interactions are visualized as networks, where nodes are biological processes and edges indicate that gene pairs in the two biological processes interact antagonistically (blue), synergistically (red) or mixed (yellow). Visualizing the interaction networks in this way demonstrates that the variability in interaction coverage found relative to different phenotypes is a consequence of phenotype-specific interactions among completely different biological processes. Specific process interactions observed in these networks are described in detail in Figure 6 and in Text S1.
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pgen-1001294-g005: Epistatic networks with respect to growth rate and two other flux phenotypes.To explore the biological diversity underlying the different epistatic networks, the presence of synergistic and antagonistic interactions between pairs of metabolic processes were determined for (A) growth, (B) succinate secretion and (C) glycerol secretion phenotypes. These process interactions are visualized as networks, where nodes are biological processes and edges indicate that gene pairs in the two biological processes interact antagonistically (blue), synergistically (red) or mixed (yellow). Visualizing the interaction networks in this way demonstrates that the variability in interaction coverage found relative to different phenotypes is a consequence of phenotype-specific interactions among completely different biological processes. Specific process interactions observed in these networks are described in detail in Figure 6 and in Text S1.

Mentions: To solidify the observation that different flux phenotypes reveal unique aspects of the functional relationships between genes, we next focus on the epistatic networks relative to two secretion phenotypes (succinate, Figure 5B, and glycerol, Figure 5C). We chose to focus on secretion flux phenotypes because they are the most tractable fluxes to measure experimentally, and hence potentially the most relevant for future experimental studies. Both of these secretion flux epistatic networks contain several interactions that are not detected relative to the growth phenotype (Figure 5A). In particular, in the succinate secretion network, the genes that are part of complex II of the electron transport chain (ETC II) display synergistic interactions with several other biological processes (Figure 5B). Among these interactions, which are indicative of an unexpectedly large increase in succinate secretion in the double mutant, the one between serine biosynthesis and ETCII has been reported in previous experimental efforts to overproduce succinate [33]. This interaction occurs because the predicted alternate pathway for serine biosynthesis produces succinate as a byproduct, and ETC II is the primary route through which this succinate is metabolized in the wild-type (Figure 6A, Figure S7). Thus, interactions with respect to succinate may in general probe the way in which TCA cycle intermediates are produced and consumed.


Epistatic interaction maps relative to multiple metabolic phenotypes.

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

Epistatic networks with respect to growth rate and two other flux phenotypes.To explore the biological diversity underlying the different epistatic networks, the presence of synergistic and antagonistic interactions between pairs of metabolic processes were determined for (A) growth, (B) succinate secretion and (C) glycerol secretion phenotypes. These process interactions are visualized as networks, where nodes are biological processes and edges indicate that gene pairs in the two biological processes interact antagonistically (blue), synergistically (red) or mixed (yellow). Visualizing the interaction networks in this way demonstrates that the variability in interaction coverage found relative to different phenotypes is a consequence of phenotype-specific interactions among completely different biological processes. Specific process interactions observed in these networks are described in detail in Figure 6 and in Text S1.
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1001294-g005: Epistatic networks with respect to growth rate and two other flux phenotypes.To explore the biological diversity underlying the different epistatic networks, the presence of synergistic and antagonistic interactions between pairs of metabolic processes were determined for (A) growth, (B) succinate secretion and (C) glycerol secretion phenotypes. These process interactions are visualized as networks, where nodes are biological processes and edges indicate that gene pairs in the two biological processes interact antagonistically (blue), synergistically (red) or mixed (yellow). Visualizing the interaction networks in this way demonstrates that the variability in interaction coverage found relative to different phenotypes is a consequence of phenotype-specific interactions among completely different biological processes. Specific process interactions observed in these networks are described in detail in Figure 6 and in Text S1.
Mentions: To solidify the observation that different flux phenotypes reveal unique aspects of the functional relationships between genes, we next focus on the epistatic networks relative to two secretion phenotypes (succinate, Figure 5B, and glycerol, Figure 5C). We chose to focus on secretion flux phenotypes because they are the most tractable fluxes to measure experimentally, and hence potentially the most relevant for future experimental studies. Both of these secretion flux epistatic networks contain several interactions that are not detected relative to the growth phenotype (Figure 5A). In particular, in the succinate secretion network, the genes that are part of complex II of the electron transport chain (ETC II) display synergistic interactions with several other biological processes (Figure 5B). Among these interactions, which are indicative of an unexpectedly large increase in succinate secretion in the double mutant, the one between serine biosynthesis and ETCII has been reported in previous experimental efforts to overproduce succinate [33]. This interaction occurs because the predicted alternate pathway for serine biosynthesis produces succinate as a byproduct, and ETC II is the primary route through which this succinate is metabolized in the wild-type (Figure 6A, Figure S7). Thus, interactions with respect to succinate may in general probe the way in which TCA cycle intermediates are produced and consumed.

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