Limits...
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.

Show MeSH

Related in: MedlinePlus

Evolutionary significance of epistasis with respect to metabolic flux phenotypes.The number of antagonistic and synergistic epistatic interactions for each of 39 genes was determined across each of the 293 flux phenotypes. (A) For each phenotype, the number of synergistic and antagonistic interactions involving the genes was correlated separately with the Ka/Ks of the genes, yielding distributions of Spearman Rank Correlation ρ values for synergistic (red) and antagonistic (blue) interactions. Both distributions are significantly skewed towards negative ρ values (sign test p = 8.5×10−25 (synergistic), 2.2×10−54 (antagonistic)). The red and blue circles indicate the correlation coefficient when the total numbers of synergistic (ρ = −0.27, p = 0.09, n = 39) and antagonistic (ρ = −0.41 p = 0.01, n = 39) interactions across all phenotypes are considered. (B) The above correlations were repeated, but controlling for the codon bias of the genes using partial correlation analysis. The distributions of ρ move closer to zero, with only the distribution for antagonistic interactions remaining significantly different from zero (sign test p = 0.07 (synergistic), 2.3×10−31 (antagonistic)).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3037399&req=5

pgen-1001294-g007: Evolutionary significance of epistasis with respect to metabolic flux phenotypes.The number of antagonistic and synergistic epistatic interactions for each of 39 genes was determined across each of the 293 flux phenotypes. (A) For each phenotype, the number of synergistic and antagonistic interactions involving the genes was correlated separately with the Ka/Ks of the genes, yielding distributions of Spearman Rank Correlation ρ values for synergistic (red) and antagonistic (blue) interactions. Both distributions are significantly skewed towards negative ρ values (sign test p = 8.5×10−25 (synergistic), 2.2×10−54 (antagonistic)). The red and blue circles indicate the correlation coefficient when the total numbers of synergistic (ρ = −0.27, p = 0.09, n = 39) and antagonistic (ρ = −0.41 p = 0.01, n = 39) interactions across all phenotypes are considered. (B) The above correlations were repeated, but controlling for the codon bias of the genes using partial correlation analysis. The distributions of ρ move closer to zero, with only the distribution for antagonistic interactions remaining significantly different from zero (sign test p = 0.07 (synergistic), 2.3×10−31 (antagonistic)).

Mentions: So far, we have shown that epistatic interactions between gene deletions relative to metabolic flux phenotypes are ubiquitous, and can provide an understanding of the relationships between different processes in the cell. The ubiquity of epistasis relative to metabolic flux phenotypes brought to our attention the possibility that these complex network-level functional interdependencies might impose constraints on evolutionary trajectories. We hypothesized that this phenomenon might manifest itself in the form of increased evolutionary constraints on enzymes that are involved in many epistatic links with other genes. Such a relationship between epistatic connectivity and evolutionary rate has been recently observed in the experimentally constructed global genetic interaction network with respect to growth rate in yeast [7]. Thus, we set out to explore whether predicted connectivity with respect to metabolic phenotypes other than growth rate are also correlated with evolutionary constraint. To this end we calculated the Spearman rank correlation between the number of interactions in which different genes participate and the evolutionary rates of such genes, as measured by their non-synonymous to synonymous substitution ratios. This correlation was calculated separately for synergistic and antagonistic interactions relative to each of the 293 flux phenotypes, for a total of 586 correlations. The distributions of correlation coefficients for synergistic and antagonistic interactions across all phenotypes are shown in Figure 7A. Both distributions significantly deviate from zero, with an overall bias towards negative correlations (Sign test, p = 8.5×10−25 (synergistic), 2.2×10−54 (antagonistic), n = 293). This trend towards negative correlations suggests that genes involved in many interactions with respect to metabolic flux phenotypes do indeed evolve slower.


Epistatic interaction maps relative to multiple metabolic phenotypes.

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

Evolutionary significance of epistasis with respect to metabolic flux phenotypes.The number of antagonistic and synergistic epistatic interactions for each of 39 genes was determined across each of the 293 flux phenotypes. (A) For each phenotype, the number of synergistic and antagonistic interactions involving the genes was correlated separately with the Ka/Ks of the genes, yielding distributions of Spearman Rank Correlation ρ values for synergistic (red) and antagonistic (blue) interactions. Both distributions are significantly skewed towards negative ρ values (sign test p = 8.5×10−25 (synergistic), 2.2×10−54 (antagonistic)). The red and blue circles indicate the correlation coefficient when the total numbers of synergistic (ρ = −0.27, p = 0.09, n = 39) and antagonistic (ρ = −0.41 p = 0.01, n = 39) interactions across all phenotypes are considered. (B) The above correlations were repeated, but controlling for the codon bias of the genes using partial correlation analysis. The distributions of ρ move closer to zero, with only the distribution for antagonistic interactions remaining significantly different from zero (sign test p = 0.07 (synergistic), 2.3×10−31 (antagonistic)).
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1001294-g007: Evolutionary significance of epistasis with respect to metabolic flux phenotypes.The number of antagonistic and synergistic epistatic interactions for each of 39 genes was determined across each of the 293 flux phenotypes. (A) For each phenotype, the number of synergistic and antagonistic interactions involving the genes was correlated separately with the Ka/Ks of the genes, yielding distributions of Spearman Rank Correlation ρ values for synergistic (red) and antagonistic (blue) interactions. Both distributions are significantly skewed towards negative ρ values (sign test p = 8.5×10−25 (synergistic), 2.2×10−54 (antagonistic)). The red and blue circles indicate the correlation coefficient when the total numbers of synergistic (ρ = −0.27, p = 0.09, n = 39) and antagonistic (ρ = −0.41 p = 0.01, n = 39) interactions across all phenotypes are considered. (B) The above correlations were repeated, but controlling for the codon bias of the genes using partial correlation analysis. The distributions of ρ move closer to zero, with only the distribution for antagonistic interactions remaining significantly different from zero (sign test p = 0.07 (synergistic), 2.3×10−31 (antagonistic)).
Mentions: So far, we have shown that epistatic interactions between gene deletions relative to metabolic flux phenotypes are ubiquitous, and can provide an understanding of the relationships between different processes in the cell. The ubiquity of epistasis relative to metabolic flux phenotypes brought to our attention the possibility that these complex network-level functional interdependencies might impose constraints on evolutionary trajectories. We hypothesized that this phenomenon might manifest itself in the form of increased evolutionary constraints on enzymes that are involved in many epistatic links with other genes. Such a relationship between epistatic connectivity and evolutionary rate has been recently observed in the experimentally constructed global genetic interaction network with respect to growth rate in yeast [7]. Thus, we set out to explore whether predicted connectivity with respect to metabolic phenotypes other than growth rate are also correlated with evolutionary constraint. To this end we calculated the Spearman rank correlation between the number of interactions in which different genes participate and the evolutionary rates of such genes, as measured by their non-synonymous to synonymous substitution ratios. This correlation was calculated separately for synergistic and antagonistic interactions relative to each of the 293 flux phenotypes, for a total of 586 correlations. The distributions of correlation coefficients for synergistic and antagonistic interactions across all phenotypes are shown in Figure 7A. Both distributions significantly deviate from zero, with an overall bias towards negative correlations (Sign test, p = 8.5×10−25 (synergistic), 2.2×10−54 (antagonistic), n = 293). This trend towards negative correlations suggests that genes involved in many interactions with respect to metabolic flux phenotypes do indeed evolve slower.

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