Mapping the Fitness Landscape of Gene Expression Uncovers the Cause of Antagonism and Sign Epistasis between Adaptive Mutations

Marx CJ (2014) Mapping the Fitness Landscape of Gene Expression Uncovers the cause of Antagonism and Sign Epistasis between Adaptive Mutations. PLoS Genet 10(2): e1004149. doi:10.1371/journal.pgen.1004149 Mapping the Fitness Landscape of Gene Expression Uncovers the cause of Antagonism and Sign Epistasis between Adaptive Mutations Hsin-Hung Chou 0 1 Nigel F. Delaney 0 1 Jeremy A. Draghi 0 1 Christopher J. Marx 0 1 Mark Achtman, Warwick Medical School, United Kingdom 0 a Current address: Department of Biochemistry, University of Cambridge , Cambridge , United Kingdom. b Current address: Department of Molecular Biology, Massachusetts General Hospital , Boston , Massachusetts, United States of America. c Current address: Department of Biological Sciences, University of Idaho , Moscow, Idaho , United States of America 1 1 Department of Organismic and Evolutionary Biology, Harvard University , Cambridge, Massachusetts , United States of America, 2 Institute of Molecular Systems Biology, ETH Zurich , Zurich , Switzerland , 3 Department of Zoology, University of British Columbia , Vancouver , Canada , 4 Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, 5 Faculty of Arts and Sciences Center for Systems Biology, Harvard University , Cambridge, Massachusetts , United States of America How do adapting populations navigate the tensions between the costs of gene expression and the benefits of gene products to optimize the levels of many genes at once? Here we combined independently-arising beneficial mutations that altered enzyme levels in the central metabolism of Methylobacterium extorquens to uncover the fitness landscape defined by gene expression levels. We found strong antagonism and sign epistasis between these beneficial mutations. Mutations with the largest individual benefit interacted the most antagonistically with other mutations, a trend we also uncovered through analyses of datasets from other model systems. However, these beneficial mutations interacted multiplicatively (i.e., no epistasis) at the level of enzyme expression. By generating a model that predicts fitness from enzyme levels we could explain the observed sign epistasis as a result of overshooting the optimum defined by a balance between enzyme catalysis benefits and fitness costs. Knowledge of the phenotypic landscape also illuminated that, although the fitness peak was phenotypically far from the ancestral state, it was not genetically distant. Single beneficial mutations jumped straight toward the global optimum rather than being constrained to change the expression phenotypes in the correlated fashion expected by the genetic architecture. Given that adaptation in nature often results from optimizing gene expression, these conclusions can be widely applicable to other organisms and selective conditions. Poor interactions between individually beneficial alleles affecting gene expression may thus compromise the benefit of sex during adaptation and promote genetic differentiation. - Funding: HC acknowledges support by an EMBO Long-Term Fellowship (ALTF 132-2010). This work was funded by an NIH award to CJM (GM078209).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. The concept of a fitness landscape unites the three levels of evolutionary change genotype, phenotype, and fitness into a mathematical picture of the potential for, and constraints upon, adaptive evolution. By mapping genotypes to a measure of fitness, fitness landscapes guide our understanding of how epistasis nonlinear interactions between the fitness effects of mutations shapes evolution. Strong epistasis implies that landscapes are rugged, with many peaks, or locally optimally genotypes [1,2]. The magnitude and form of epistasis is predicted to determine the number of evolutionary trajectories [3,4], the rate and repeatability of adaptation [57], and the benefit of sex [8]. Recent experimental work with a wide variety of model organisms has revealed diminishing returns as a general trend of adaptation [9 13], with relatively few cases of synergy [11,14] or sign epistasis [15] (i.e., the same mutation being beneficial or deleterious in different contexts [16]). Antagonism between adaptive mutations might imply that these populations are summiting peaks in their fitness landscapes with just a handful of genetic changes. This explanation might lead to further trends, such as a negative relationship between the initial selective coefficient of a mutation and its epistatic interactions that could prove to be a useful predictor of a saturating process of adaptation [17]. In order to definitely link diminishing returns to the ascent of local peaks, as well as to understand the existence of the peaks themselves, we must understand the phenotypes that link genotype and fitness in the adaptive landscape. Mathematically convenient formulations such as Fishers geometric model for adaptation near a single peak [18] have been used to interpret the trend toward antagonism [19]. This approach assumes stabilizing selection a priori. What remains unclear is what types of physiological interactions give rise to fitness landscapes of varying shape and form, as well the constraints upon mutational changes to underlying phenotypes. Models of metabolic pathways have been amongst the most successful in translating underlying biochemical phenotypes to The pace and outcome of a series of adaptive steps in an evolving lineage depends upon how well different beneficial mutations stack on top of each other. We found that independent beneficial mutations that affected gene expression for a metabolic pathway did not work well together, and were often jointly deleterious. The most beneficial mutations interacted the most poorly with others, which was a trend we found common in other biological systems. Through generating a model that accounted for enzymatic benefits and expression costs, we uncovered that this antagonism was caused by a phenotype to fitness mapping that had an intermediate peak. This allowed us to predict the fitness effect of double mutants and to uncover that the single winning mutations tended to move straight to the peak in a single step. These findings demonstrate the importance of considering the phenotypic changes that cause nonlinear interactions between mutations upon fitness, and thus influence how populations evolve. fitness. The contribution of enzyme activities upon metabolic flux has been formalized via Metabolic Control Analysis (MCA) [20,21]. The ability of this approach to predict the fitness consequences of changes in enzyme properties has been verified in experimental systems that vary from Escherichia coli in lactoselimited chemostats to the flight properties of butterflies (reviewed in [22]). Turning to multiple (...truncated)