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Epistatic interaction maps relative to multiple metabolic phenotypes.

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.

SUBMITTER: Snitkin ES

PROVIDER: S-EPMC3037399 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Epistatic interaction maps relative to multiple metabolic phenotypes.

Snitkin Evan S ES Segrè Daniel D

PLoS genetics 20110210 2

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, an ...[more]

PMID: 21347328

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Project description:BackgroundEpistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.ResultsWe identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. We compare local and global imputation approaches across a variety of distinct E-MAP datasets, showing that both are competitive and preferable to filling in with zeros. In addition we show that these methods are effective in an E-MAP from a different species, suggesting that pairwise imputation techniques will be increasingly useful as analogous epistasis mapping techniques are developed in different species. We show that strongly alleviating interactions are significantly more difficult to predict than strongly aggravating interactions. Finally we show that imputed interactions, generated using nearest neighbor methods, are enriched for annotations in the same manner as measured interactions. Therefore our method potentially expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers.ConclusionsWe address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.

Project description:Brucella spp. are facultative intracellular pathogens that cause chronic brucellosis in humans and animals. The virulence of Brucella primarily depends on its successful survival and replication in host cells. During invasion of the host tissue, Brucella is simultaneously subjected to a variety of harsh conditions, including nutrient limitation, low pH, antimicrobial defenses, and extreme levels of reactive oxygen species (ROS) via the host immune response. This suggests that Brucella may be able to regulate its metabolic adaptation in response to the distinct stresses encountered during its intracellular infection of the host. An investigation into the differential proteome expression patterns of Brucella grown under the relevant stress conditions may contribute toward a better understanding of its pathogenesis and adaptive response. Here, we utilized a mass spectrometry-based label-free relative quantitative proteomics approach to investigate and compare global proteomic changes in B. abortus in response to eight different stress treatments. The 3 h short-term in vitro single-stress and multi-stress conditions mimicked the in vivo conditions of B. abortus under intracellular infection, with survival rates ranging from 3.17 to 73.17%. The proteomic analysis identified and quantified a total of 2,272 proteins and 74% of the theoretical proteome, thereby providing wide coverage of the B. abortus proteome. By including eight distinct growth conditions and comparing these with a control condition, we identified a total of 1,221 differentially expressed proteins (DEPs) that were significantly changed under the stress treatments. Pathway analysis revealed that most of the proteins were involved in oxidative phosphorylation, ABC transporters, two-component systems, biosynthesis of secondary metabolites, the citrate cycle, thiamine metabolism, and nitrogen metabolism; constituting major response mechanisms toward the reconstruction of cellular homeostasis and metabolic balance under stress. In conclusion, our results provide a better understanding of the global metabolic adaptations of B. abortus associated with distinct environmental stresses. The identification of proteins necessary for stress resistance is crucial toward elucidating the infectious process in order to control brucellosis, and may facilitate the discovery of novel therapeutic targets and effective vaccines.

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Epistatic interaction maps relative to multiple metabolic phenotypes.

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Epistatic interaction maps relative to multiple metabolic phenotypes.

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