Mapping the Peaks: Fitness Landscapes of the Fittest and the Flattest

Populations exposed to a high mutation rate harbor abundant deleterious genetic variation, leading to depressed mean fitness. This reduction in mean fitness presents an opportunity for selection to restore fitness through the evolution of mutational robustness. In extreme cases, selection for mutational robustness can lead to flat genotypes (with low fitness but high robustness) outcompeting fit genotypes (with high fitness but low robustness)—a phenomenon known as survival of the flattest. While this effect was previously explored using the digital evolution system Avida, a complete analysis of the local fitness landscapes of fit and flat genotypes has been lacking, leading to uncertainty about the genetic basis of the survival-of-the-flattest effect. Here, we repeated the survival-of-the-flattest study and analyzed the mutational neighborhoods of fit and flat genotypes. We found that the flat genotypes, compared to the fit genotypes, had a reduced likelihood of deleterious mutations as well as an increased likelihood of neutral and, surprisingly, of lethal mutations. This trend holds for mutants one to four substitutions away from the wild-type sequence. We also found that flat genotypes have, on average, no epistasis between mutations, while fit genotypes have, on average, positive epistasis. Our results demonstrate that the genetic causes of mutational robustness on complex fitness landscapes are multifaceted. While the traditional idea of the survival of the flattest emphasized the evolution of increased neutrality, others have argued for increased mutational sensitivity in response to strong mutational loads. Our results show that both increased neutrality and increased lethality can lead to the evolution of mutational robustness. Furthermore, strong negative epistasis is not required for mutational sensitivity to lead to mutational robustness. Overall, these results suggest that mutational robustness is achieved by minimizing heritable deleterious variation.

[1]  M. Eigen,et al.  The hypercycle. A principle of natural self-organization. Part A: Emergence of the hypercycle. , 1977, Die Naturwissenschaften.

[2]  D. Butcher,et al.  Muller's ratchet, epistasis and mutation effects. , 1995, Genetics.

[3]  D. Krakauer,et al.  Redundancy, antiredundancy, and the robustness of genomes , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Christoph Adami,et al.  Selection for mutational robustness in finite populations. , 2006, Journal of theoretical biology.

[5]  M. Eigen,et al.  What is a quasispecies? , 2006, Current topics in microbiology and immunology.

[6]  Robert T. Pennock,et al.  The evolutionary origin of complex features , 2003, Nature.

[7]  Peter Schuster,et al.  A principle of natural self-organization , 1977, Naturwissenschaften.

[8]  Cedric E. Ginestet.  ggplot2: Elegant Graphics for Data Analysis , 2011 .

[9]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[10]  P. Keightley,et al.  A Comparison of Models to Infer the Distribution of Fitness Effects of New Mutations , 2013, Genetics.

[11]  Christoph Adami,et al.  Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms , 2016, bioRxiv.

[12]  A. Lander,et al.  Survival of the Curviest: Noise-Driven Selection for Synergistic Epistasis , 2016, PLoS genetics.

[13]  Laurence D. Hurst,et al.  Metabolic trade-offs and the maintenance of the fittest and the flattest , 2011, Nature.

[14]  G. Wagner,et al.  EVOLUTION AND DETECTION OF GENETIC ROBUSTNESS , 2003 .

[15]  M. Archetti,et al.  Survival of the steepest: hypersensitivity to mutations as an adaptation to soft selection , 2009, Journal of evolutionary biology.

[16]  C. Ofria,et al.  Task-switching costs promote the evolution of division of labor and shifts in individuality , 2012, Proceedings of the National Academy of Sciences.

[17]  C. Ofria,et al.  Genome complexity, robustness and genetic interactions in digital organisms , 1999, Nature.

[18]  Claus O Wilke,et al.  Quasispecies theory in the context of population genetics , 2005, BMC Evolutionary Biology.

[19]  Giovanni Strona,et al.  Environmental change makes robust ecological networks fragile , 2016, Nature Communications.

[20]  Raul Andino,et al.  The role of mutational robustness in RNA virus evolution , 2013, Nature Reviews Microbiology.

[21]  M. Eigen.  Selforganization of matter and the evolution of biological macromolecules , 1971, Naturwissenschaften.

[22]  C. Adami,et al.  Evolution of Biological Complexity , 2000, Proc. Natl. Acad. Sci. USA.

[23]  Raul Andino,et al.  Quasispecies Theory and the Behavior of RNA Viruses , 2010, PLoS pathogens.

[24]  D. Weinreich,et al.  Natural selection can favor ratchet robustness over mutational robustness , 2017, bioRxiv.

[25]  M. Whitlock,et al.  Mutation Load: The Fitness of Individuals in Populations Where Deleterious Alleles Are Abundant , 2012 .

[26]  Arthur W. Covert,et al.  Experiments on the role of deleterious mutations as stepping stones in adaptive evolution , 2013, Proceedings of the National Academy of Sciences.

[27]  Michael Lachmann,et al.  Quasispecies Made Simple , 2005, PLoS Comput. Biol..

[28]  P. Schuster,et al.  Stationary mutant distributions and evolutionary optimization. , 1988, Bulletin of mathematical biology.

[29]  Robert T. Pennock.  Models, simulations, instantiations, and evidence: the case of digital evolution , 2007, J. Exp. Theor. Artif. Intell..

[30]  C. Ofria,et al.  Evolution of digital organisms at high mutation rates leads to survival of the flattest , 2001, Nature.

[31]  Brendan D. O'Fallon,et al.  Quasi-species evolution in subdivided populations favours maximally deleterious mutations , 2007, Proceedings of the Royal Society B: Biological Sciences.

[32]  J. Crow.  Some possibilities for measuring selection intensities in man. , 1989, Human biology.

[33]  C. Wilke,et al.  Interaction between directional epistasis and average mutational effects , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[34]  Charles Ofria,et al.  Avida , 2004, Artificial Life.

[35]  Ricard V Solé,et al.  The Fittest versus the Flattest: Experimental Confirmation of the Quasispecies Effect with Subviral Pathogens , 2006, PLoS pathogens.

[36]  Andrés Moya,et al.  Validating viral quasispecies with digital organisms: a re-examination of the critical mutation rate , 2005, BMC Evolutionary Biology.

[37]  M. Huynen,et al.  Neutral evolution of mutational robustness. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[38]  A. Kondrashov,et al.  Muller's ratchet under epistatic selection. , 1994, Genetics.

[39]  C. Adami,et al.  Evolution of drift robustness in small populations , 2016, Nature Communications.

[40]  Rafael Sanjuán,et al.  Mechanisms of genetic robustness in RNA viruses , 2006, EMBO reports.

[41]  David B. Knoester,et al.  The Evolutionary Origin of Somatic Cells under the Dirty Work Hypothesis , 2014, PLoS biology.

[42]  C. Wilke,et al.  Evolution of mutational robustness. , 2003, Mutation research.

[43]  Claus O. Wilke,et al.  Adaptive evolution on neutral networks , 2001, Bulletin of mathematical biology.

[44]  Jeffrey A. Edlund,et al.  Evolution of Robustness in Digital Organisms , 2004, Artificial Life.