Article

Distributions of epistasis in microbes fit predictions from a fitness landscape model

University of Lausanne, Lausanne, Vaud, Switzerland
Nature Genetics (Impact Factor: 29.65). 05/2007; 39(4):555-60. DOI: 10.1038/ng1998
Source: PubMed

ABSTRACT 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.

Full-text

Available from: Santiago F Elena, Apr 26, 2015
0 Followers
 · 
91 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: When the effect of the state of one gene is dependent on the state of another gene in more than an additive or a neutral way, the phenomenon is termed epistasis. In particular, positive epistasis signifies that the impact of the double deletion is less severe than the neutral combination, while negative epistasis signifies that the double deletion is more severe. Epistatic interactions between genes affect the fitness landscape of an organism in its environment and are believed to be important for the evolution of sex and the evolution of recombination. Here we use large-scale computational metabolic models of microorganisms to study epistasis computationally using Flux Balance Analysis (FBA). We study what the effects of the environment are on epistatic interactions between metabolic genes in three different microorganisms: the model bacterium E. coli, the cyanobacteria Synechocystis PCC6803 and the model green algae, C. reinhardtii. Prior studies have shown that under standard laboratory conditions epistatic interactions between metabolic genes are dominated by positive epistasis. We show here that epistatic interactions depend strongly upon environmental conditions, i.e. the source of carbon, the carbon/oxygen ratio, and for photosynthetic organisms, the intensity of light. By a comparative analysis of flux distributions under different conditions, we show that whether epistatic interactions are positive or negative depends upon the topology of the carbon flow between the reactions affected by the pair of genes being considered. Thus complex metabolic networks can show epistasis even without explicit interactions between genes, and the direction and the scale of epistasis are dependent on network flows. Our results suggest that the path of evolutionary adaptation in fluctuating environments is likely to be very history dependent because of the strong effect of the environment on epistasis.
    Molecular BioSystems 07/2014; 10(10). DOI:10.1039/c4mb00181h · 3.18 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Empirical evidence for diminishing fitness returns of beneficial mutations supports Fisher's geometric model. We show that a similar pattern emerges through the phenomenon of regression-to-the-mean and that few studies correct for it. Although biases are often small, regression-to-the-mean has overemphasized diminishing returns and will hamper cross-study comparisons unless corrected for.
    Genetics 10/2014; DOI:10.1534/genetics.114.169870 · 4.87 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The genotype-fitness map (that is, the fitness landscape) is a key determinant of evolution, yet it has mostly been used as a superficial metaphor because we know little about its structure. This is now changing, as real fitness landscapes are being analysed by constructing genotypes with all possible combinations of small sets of mutations observed in phylogenies or in evolution experiments. In turn, these first glimpses of empirical fitness landscapes inspire theoretical analyses of the predictability of evolution. Here, we review these recent empirical and theoretical developments, identify methodological issues and organizing principles, and discuss possibilities to develop more realistic fitness landscape models.
    Nature Reviews Genetics 06/2014; 15(7):480-490. DOI:10.1038/nrg3744 · 39.79 Impact Factor