Modular epistasis in yeast metabolism

Segrè, Daniel; DeLuna, Alexander; Church, George M.; Kishony, Roy

doi:10.1038/ng1489

Cited by 547 publications

(679 citation statements)

References 28 publications

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“…Manifestations of these dependencies at the genetic and physiological level are pleiotropy, where a single gene or mutation has an effect on multiple phenotypic traits, and epistasis, which classifies the interaction between genes or mutations in their effect on fitness. The molecular basis for epistatic effects often lies in the physical interactions within or between gene products (Kogenaru et al 2009), but it can for example also result from the interplay between protein stability and catalytic activity (DePristo et al 2005), from Watson-Crick base pairing within an RNA molecule (Kirby et al 1995), or from interactions between modular metabolic networks (Segre et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Reciprocal sign epistasis is a necessary condition for multi-peaked fitness landscapes

Poelwijk¹,

Tănase‐Nicola²,

Kiviet³

et al. 2011

Journal of Theoretical Biology

198

260

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Having multiple peaks within fitness landscapes critically affects the course of evolution, but whether their presence imposes specific requirements at the level of genetic interactions remains unestablished. Here we show that to exhibit multiple fitness peaks, a biological system must contain reciprocal sign epistatic interactions, which are defined as genetic changes that are separately unfavorable but jointly advantageous. Using Morse theory, we argue that it is impossible to formulate a sufficient condition for multiple peaks in terms of local genetic interactions. These finding indicate that systems incapable of reciprocal sign epistasis will always possess a single fitness peak. However, reciprocal sign epistasis should be pervasive in nature as it is a logical consequence of specificity in molecular interactions. The results thus predict that specific molecular interactions may yield multiple fitness peaks, which can be tested experimentally. 2 1. Introduction.

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Section: Introductionmentioning

confidence: 99%

Reciprocal sign epistasis is a necessary condition for multi-peaked fitness landscapes

Poelwijk¹,

Tănase‐Nicola²,

Kiviet³

et al. 2011

Journal of Theoretical Biology

198

260

View full text Add to dashboard Cite

show abstract

“…For example, studies of modular epistasis in yeast metabolism suggest that epistasis extend beyond functional modules of genes and frequently involves interactions between, rather than within, functional modules (Segre et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Interactions between genes involved in the antioxidant defence system and breast cancer risk

Oestergaard¹,

Tyrer

Cebrián

et al. 2006

Br J Cancer

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The aim of the study is to examine the association between multilocus genotypes across 10 genes encoding proteins in the antioxidant defence system and breast cancer. The 10 genes are SOD1, SOD2, GPX1, GPX4, GSR, CAT, TXN, TXN2, TXNRD1 and TXNRD2. In all, 2271 cases and 2280 controls were used to examine gene -gene interactions between 52 single nucleotide polymorphisms (SNPs) that are hypothesised to tag all common variants in the 10 genes. The statistical analysis is based on three methods: unconditional logistic regression, multifactor dimensionality reduction and hierarchical cluster analysis. We examined all two-and three-way combinations with unconditional logistic regression and multifactor dimensionality reduction, and used a global approach with all SNPs in the hierarchical cluster analysis. Single-locus studies of an association of genetic variants in the antioxidant defence genes and breast cancer have been contradictory and inconclusive. It is the first time, to our knowledge, the association between multilocus genotypes across genes coding for antioxidant defence enzymes and breast cancer is investigated. We found no evidence of an association with breast cancer with our multilocus approach. The search for two-way interactions gave experimentwise significance levels of P ¼ 0. [a442g]) for the unconditional logistic regression and multifactor dimensionality reduction, respectively. In the hierarchical cluster analysis neither the average across four rounds with replacement of missing values at random (P ¼ 0.12) nor a fifth round with more balanced proportion of missing values between cases and controls (P ¼ 0.17) was significant.

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“…With metabolic pathways showing a high level of connectivity to numerous physiological and regulatory processes, this raises the potential for interactions in genes across these pathways and processes [41]. However, little is known about the style of these interactions, are they largely additive suggesting that they can be readily modeled or predicted.…”

Section: Quantitative Trait Locus Mapping To Reverse Engineer the Shamentioning

confidence: 99%

Synthetic biology of metabolism: using natural variation to reverse engineer systems

Kliebenstein

2014

Current Opinion in Plant Biology

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A goal of metabolic engineering is to take a plant and introduce new or modify existing pathways in a directed and predictable fashion. However, existing data does not provide the necessary level of information to allow for predictive models to be generated. One avenue to reverse engineer the necessary information is to study the genetic control of natural variation in plant primary and secondary metabolism. These studies are showing that any engineering model will have to incorporate information about 1000s of genes in both the nuclear and organellar genome to optimize the function of the introduced pathway. Further, these genes may interact in an unpredictable fashion complicating any engineering approach as it moves from the one or two gene manipulation to higher order stacking efforts. Finally, metabolic engineering may be influenced by a previously unrecognized potential for a plant to measure the metabolites within it. In combination, these observations from natural variation provide a beginning to help improve current efforts at metabolic engineering. Introduction A significant goal of synthetic biology or biological engineering is to take an existant organism and alter it in a directed and predictive fashion to introduce a new phenotype that had not previously existed in that organism. Some of these efforts start with introducing a new metabolic pathway into an organism to provide a better source of a commercially important chemical or to make new chemicals entirely [1][2][3]. Other efforts are focused at taking agronomically important traits such as defensive chemicals, nitrogen fixation or perennial growth from one species and transferring these traits into other species that do not contain them [4 ,5,6]. However these efforts while exciting frequently run into great difficulty and for this review article, I will focus on one the potential of using information from natural variation studies to facilitate these efforts for metabolic engineering.To predictively engineer a new metabolic pathway into a plant with optimal efficiency requires a base level of knowledge that likely does not exist to any full level. To generate full predictive ability we would need much deeper understanding of the following questions. First, how many genes within the plant need to be manipulated to facilitate the integration of the new pathway into the organisms metabolic and regulatory network. How do these genes interact, are they solely additive or is there evidence of more complex epistasis that complicates predictive capacity? Finally, are these genes solely in the nuclear genome or is there causal variation also in the organellar genomes? Most efforts to answer these questions focus largely on forward or single-gene reverse genetics approaches which are highly time consuming and difficult to scale up to the necessary level.An alternative approach to understanding a system is to use the concept of reverse engineering [7,8]. The goal of reverse engineering is to take a functioning system, say an existing metabolic pathway, ...

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Modular epistasis in yeast metabolism

Cited by 547 publications

References 28 publications

Reciprocal sign epistasis is a necessary condition for multi-peaked fitness landscapes

Reciprocal sign epistasis is a necessary condition for multi-peaked fitness landscapes

Interactions between genes involved in the antioxidant defence system and breast cancer risk

Synthetic biology of metabolism: using natural variation to reverse engineer systems

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