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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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Disease association of genes involved in many interactions across all metabolic flux phenotypes.The relevance of multi-phenotype epistasis to human disease was explored by comparing the total number of (A) antagonistic and (B) synergistic interactions across all phenotypes for those yeast genes with a human homolog in OMIM to those yeast genes with a human homolog that is not in OMIM. The distributions of interaction counts are displayed as box-plots with the boxes encompassing the 25th to 75th percentiles, and the line at the median value. Genes with a human homolog in OMIM have more antagonistic and synergistic interactions, with the difference for antagonistic interactions being significant at p<0.05 (Signed-rank test, p = 0.022 (antagonistic), p = 0.071 (synergistic), n1 = 30, n2 = 19).
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pgen-1001294-g008: Disease association of genes involved in many interactions across all metabolic flux phenotypes.The relevance of multi-phenotype epistasis to human disease was explored by comparing the total number of (A) antagonistic and (B) synergistic interactions across all phenotypes for those yeast genes with a human homolog in OMIM to those yeast genes with a human homolog that is not in OMIM. The distributions of interaction counts are displayed as box-plots with the boxes encompassing the 25th to 75th percentiles, and the line at the median value. Genes with a human homolog in OMIM have more antagonistic and synergistic interactions, with the difference for antagonistic interactions being significant at p<0.05 (Signed-rank test, p = 0.022 (antagonistic), p = 0.071 (synergistic), n1 = 30, n2 = 19).

Mentions: The implications of the current analysis are not limited to yeast. In fact, multi-phenotype epistatic interactions may be relevant to the manifestation and treatment of human disease. Given the previously discussed importance of multi-phenotype hub genes, it is likely that perturbations of these genes would have major effects in a biological network. Translating this observation to humans, we hypothesize that the disruption of more highly connected genes in the human metabolic network would be more likely to result in a disease state. We sought evidence for this by evaluating whether the epistatic connectivity of genes in the yeast model was predictive of the role of their human homologs in genetic diseases. Indeed, we observe a significant difference between the connectivity of yeast homologs of human genes that have been associated with a genetic disease, versus those that have not (Figure 8). While the statistical significance is limited due to the small sample size, this result provides support for the growing sentiment that majority of human genetic disorders are a consequence of complex interactions between numerous cellular components [1], [21].


Epistatic interaction maps relative to multiple metabolic phenotypes.

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

Disease association of genes involved in many interactions across all metabolic flux phenotypes.The relevance of multi-phenotype epistasis to human disease was explored by comparing the total number of (A) antagonistic and (B) synergistic interactions across all phenotypes for those yeast genes with a human homolog in OMIM to those yeast genes with a human homolog that is not in OMIM. The distributions of interaction counts are displayed as box-plots with the boxes encompassing the 25th to 75th percentiles, and the line at the median value. Genes with a human homolog in OMIM have more antagonistic and synergistic interactions, with the difference for antagonistic interactions being significant at p<0.05 (Signed-rank test, p = 0.022 (antagonistic), p = 0.071 (synergistic), n1 = 30, n2 = 19).
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1001294-g008: Disease association of genes involved in many interactions across all metabolic flux phenotypes.The relevance of multi-phenotype epistasis to human disease was explored by comparing the total number of (A) antagonistic and (B) synergistic interactions across all phenotypes for those yeast genes with a human homolog in OMIM to those yeast genes with a human homolog that is not in OMIM. The distributions of interaction counts are displayed as box-plots with the boxes encompassing the 25th to 75th percentiles, and the line at the median value. Genes with a human homolog in OMIM have more antagonistic and synergistic interactions, with the difference for antagonistic interactions being significant at p<0.05 (Signed-rank test, p = 0.022 (antagonistic), p = 0.071 (synergistic), n1 = 30, n2 = 19).
Mentions: The implications of the current analysis are not limited to yeast. In fact, multi-phenotype epistatic interactions may be relevant to the manifestation and treatment of human disease. Given the previously discussed importance of multi-phenotype hub genes, it is likely that perturbations of these genes would have major effects in a biological network. Translating this observation to humans, we hypothesize that the disruption of more highly connected genes in the human metabolic network would be more likely to result in a disease state. We sought evidence for this by evaluating whether the epistatic connectivity of genes in the yeast model was predictive of the role of their human homologs in genetic diseases. Indeed, we observe a significant difference between the connectivity of yeast homologs of human genes that have been associated with a genetic disease, versus those that have not (Figure 8). While the statistical significance is limited due to the small sample size, this result provides support for the growing sentiment that majority of human genetic disorders are a consequence of complex interactions between numerous cellular components [1], [21].

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