Martin G, Elena SF, Lenormand T. Distributions of epistasis in microbes fit predictions from a fitness landscape model. Nature Genet 39: 555-560

University of Lausanne, Lausanne, Vaud, Switzerland
Nature Genetics (Impact Factor: 29.35). 05/2007; 39(4):555-60. DOI: 10.1038/ng1998
Source: PubMed


How do the fitness effects of several mutations combine? Despite its simplicity, this question is central to the understanding of multilocus evolution. Epistasis (the interaction between alleles at different loci), especially epistasis for fitness traits such as reproduction and survival, influences evolutionary predictions "almost whenever multilocus genetics matters". Yet very few models have sought to predict epistasis, and none has been empirically tested. Here we show that the distribution of epistasis can be predicted from the distribution of single mutation effects, based on a simple fitness landscape model. We show that this prediction closely matches the empirical measures of epistasis that have been obtained for Escherichia coli and the RNA virus vesicular stomatitis virus. Our results suggest that a simple fitness landscape model may be sufficient to quantitatively capture the complex nature of gene interactions. This model may offer a simple and widely applicable alternative to complex metabolic network models, in particular for making evolutionary predictions.

Download full-text


Available from: Santiago F Elena,
27 Reads
  • Source
    • "From this initial use in theories of adaptation , the FGM can also serve as a null model to fit and interpret empirical DFEs. Indeed, this model was shown to capture the DFE of single mutants (Martin and Lenormand 2006b), of epistasis (Martin et al. 2007) or of dominance (Manna et al 2011). With respect to environmental effects, which is our focus here, (Martin and Lenormand 2006a) showed that mutation accumulation data was consistent with a concave, nearly Gaussian phenotype-fitness function with constant shape and varying optima across environments . "
    [Show abstract] [Hide abstract]
    ABSTRACT: When are mutations beneficial in one environment and deleterious in another? More generally, what is the relationship between mutation effects across environments? These questions are crucial to predict adaptation in heterogeneous conditions in a broad sense. Empirical evidence documents various patterns of fitness effects across environments but we still lack a framework to analyse these multivariate data. In this paper, we extend Fisher's geometrical model to multiple environments determining distinct peaks. We derive the fitness distribution, in one environment, among mutants with a given fitness in another and the bivariate distribution of random mutants' fitnesses across two or more environments. The geometry of the phenotype-fitness landscape is naturally interpreted in terms of fitness trade-offs between environments. These results may be used to fit/predict empirical distributions or to predict the pattern of adaptation across heterogeneous conditions. As an example, we derive the genomic rate of substitution and of adaptation in a metapopulation divided into two distinct habitats in a high migration regime and show that they depend critically on the geometry of the phenotype-fitness landscape. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Evolution 04/2015; 69(6). DOI:10.1111/evo.12671 · 4.61 Impact Factor
  • Source
    • "anjuan and Elena , 2006 ; Martin et al . , 2007 ; Aylor and Zeng , 2008 ; Perfeito et al . , 2011 ; Walkiewicz et al . , 2012 ) . Indeed , studies of whole viruses , bacteria etc . have revealed more epistasis among mutations than studies looking at the protein level ( Bonhoeffer et al . , 2004 ; Michalakis and Roze , 2004 ; Segre et al . , 2005 ; Martin et al . , 2007 ; Kryazhimskiy et al . , 2011 ; Breen et al . , 2012 ; Kachanovsky et al . , 2012 ; Kouyos et al . , 2012 ; Flynn et al . , 2013 ) . However even in a simplified model of enzyme evolution , in which enzymatic activity in the cell is directly related to fitness ( as is the case for essential metabolic enzymes or antibiotic resistance mar"
    [Show abstract] [Hide abstract]
    ABSTRACT: The wealth of distinct enzymatic functions found in nature is impressive and the on-going evolutionary divergence of enzymatic functions continues to generate new and efficient catalysts, which can be seen through the recent emergence of enzymes able to degrade xenobiotics. However, recreating such processes in the laboratory has been met with only moderate success. What are the factors that lead to suboptimal research outputs? In this review, we discuss constraints on enzyme evolution, which can restrict evolutionary trajectories and lead to evolutionary dead-ends. We highlight recent studies that have used experimental evolution to mimic different aspects of enzymatic adaptation under simple, controlled settings to shed light on evolutionary dynamics and constraints. A better understanding of these constraints will lead to the development of more efficient strategies for directed evolution and enzyme engineering. J. Exp. Zool. (Mol. Dev. Evol.) 9999B: 1-20, 2014. © 2014 Wiley Periodicals, Inc.
    Journal of Experimental Zoology Part B Molecular and Developmental Evolution 11/2014; 322(7). DOI:10.1002/jez.b.22562 · 2.31 Impact Factor
  • Source
    • "The second component follows the distribution of neutral epistasis for traits. This can be approximated by a normal distribution with mean and variance (Martin et al. 2007). The first component is the product of two independent random variables following the distribution given by (S2a). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The fitness landscape - the mapping between genotypes and fitness - determines properties of the process of adaptation. Several small genetic fitness landscapes have recently been built by selecting a handful of beneficial mutations and measuring fitness of all combinations of these mutations. Here we generate several testable predictions for the properties of these landscapes under Fisher's geometric model of adaptation (FGMA). When far from the fitness optimum, we analytically compute the fitness effect of beneficial mutations and their epistatic interactions. We show that epistasis may be negative or positive on average depending on the distance of the ancestral genotype to the optimum and whether mutations were independently selected or co-selected in an adaptive walk. Using simulations, we show that genetic landscapes built from FGMA are very close to an additive landscape when the ancestral strain is far from the optimum. However, when close to the optimum, a large diversity of landscape with substantial ruggedness and sign epistasis emerged. Strikingly, landscapes built from different realizations of stochastic adaptive walks in the same exact conditions were highly variable, suggesting that several realizations of small genetic landscapes are needed to gain information about the underlying architecture of the global adaptive landscape.
Show more