How the tortoise beats the hare: Slow and steady adaptation in structured populations suggests a rugged fitness landscape in bacteria

Abstract In the context of Wright’s adaptive landscape, genetic epistasis can yield a multipeaked or “rugged” topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to rapidly fix on one, which may not be the highest peak. Contrarily, beneficial mutations in a population with spatially restricted migration take longer to fix, allowing distant parts of the population to explore the landscape semi-independently. Such a population can simultaneous discover multiple peaks and the genotype at the highest discovered peak is expected to fix eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the Tortoise-Hare fable, the structured population (Tortoise) starts relatively slow, but eventually surpasses the unstructured population (Hare) in average fitness. In contrast, on single-peak landscapes (e.g., systems lacking epistasis), all uphill paths converge. Given such “smooth” topography, breadth of search is devalued, and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the Tortoise-Hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we then explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a Tortoise-Hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wright’s Shifting Balance Process. Significance Statement: Adaptive landscapes are a way of describing how mutations interact with each other to produce fitness. If an adaptive landscape is rugged, organisms achieve higher fitness with more difficulty because the mutations to reach high fitness genotypes may not be always beneficial. By evolving populations of Escherichia coli with different degrees of spatial structure, we identified a Tortoise-Hare pattern, where structured populations were initially slower, but overtook less structured populations in mean fitness. These results, combined with genetic sequencing and computational simulation, indicate this bacterial adaptive landscape is rugged. Our findings address one of the most enduring questions in evolutionary biology, in addition to, providing insight into how evolution may influence medicine and engineering.

[1]  Arthur W. Covert,et al.  Intermediate Migration Yields Optimal Adaptation in Structured, Asexual Populations , 2014, bioRxiv.

[2]  D. Schwab,et al.  Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes , 2013, PLoS Comput. Biol..

[3]  Joachim Krug,et al.  Patterns of Epistasis between Beneficial Mutations in an Antibiotic Resistance Gene , 2013, Molecular biology and evolution.

[4]  Benjamin Kerr,et al.  Evolutionary rescue from extinction is contingent on a lower rate of environmental change , 2013, Nature.

[5]  Shifting-balance Theory of Evolution , 2013 .

[6]  Michael M. Desai,et al.  POPULATION SUBDIVISION AND ADAPTATION IN ASEXUAL POPULATIONS OF SACCHAROMYCES CEREVISIAE , 2012, Evolution; international journal of organic evolution.

[7]  Sebastian Bonhoeffer,et al.  Exploring the Complexity of the HIV-1 Fitness Landscape , 2012, PLoS genetics.

[8]  R. Kassen,et al.  Adaptive landscapes in evolving populations of Pseudomonas fluorescens in simple environments , 2010 .

[9]  R. Lenski,et al.  Negative Epistasis Between Beneficial Mutations in an Evolving Bacterial Population , 2011, Science.

[10]  Nigel F. Delaney,et al.  Diminishing Returns Epistasis Among Beneficial Mutations Decelerates Adaptation , 2011, Science.

[11]  Mark M. Tanaka,et al.  ESCAPING AN EVOLUTIONARY LOBSTER TRAP: DRUG RESISTANCE AND COMPENSATORY MUTATION IN A FLUCTUATING ENVIRONMENT , 2011, Evolution; international journal of organic evolution.

[12]  C. Adami,et al.  Impact of epistasis and pleiotropy on evolutionary adaptation , 2009, Proceedings of the Royal Society B: Biological Sciences.

[13]  A. Gardner,et al.  Diminishing Returns From Beneficial Mutations and Pervasive Epistasis Shape the Fitness Landscape for Rifampicin Resistance in Pseudomonas aeruginosa , 2010, Genetics.

[14]  Mauricio O. Carneiro,et al.  Adaptive landscapes and protein evolution , 2010, Proceedings of the National Academy of Sciences.

[15]  J. Krug,et al.  Exploring the Effect of Sex on Empirical Fitness Landscapes , 2009, The American Naturalist.

[16]  Pei-Chann Chang,et al.  Sub-population genetic algorithm with mining gene structures for multiobjective flowshop scheduling problems , 2007, Expert Syst. Appl..

[17]  K. Pepin,et al.  VARIABLE EPISTATIC EFFECTS BETWEEN MUTATIONS AT HOST RECOGNITION SITES IN φX174 BACTERIOPHAGE , 2007, Evolution; international journal of organic evolution.

[18]  D. J. Kiviet,et al.  Empirical fitness landscapes reveal accessible evolutionary paths , 2007, Nature.

[19]  Nigel F. Delaney,et al.  Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins , 2006, Science.

[20]  Stephen P. Miller,et al.  The Biochemical Architecture of an Ancient Adaptive Landscape , 2005, Science.

[21]  Zbigniew Skolicki,et al.  The influence of migration sizes and intervals on island models , 2005, GECCO '05.

[22]  R. Watson,et al.  PERSPECTIVE: SIGN EPISTASIS AND GENETIC COSTRAINT ON EVOLUTIONARY TRAJECTORIES , 2005, Evolution; international journal of organic evolution.

[23]  H. A. Orr,et al.  The genetic theory of adaptation: a brief history , 2005, Nature Reviews Genetics.

[24]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[25]  D. Andersson,et al.  Biological cost and compensatory evolution in fusidic acid‐resistant Staphylococcus aureus , 2001, Molecular microbiology.

[26]  D. Andersson,et al.  The cost of antibiotic resistance from a bacterial perspective. , 2000, Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy.

[27]  B. Levin,et al.  Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria. , 2000, Genetics.

[28]  B. Levin,et al.  The biological cost of antibiotic resistance. , 1999, Current opinion in microbiology.

[29]  M. Wade,et al.  PERSPECTIVE: THE THEORIES OF FISHER AND WRIGHT IN THE CONTEXT OF METAPOPULATIONS: WHEN NATURE DOES MANY SMALL EXPERIMENTS , 1998, Evolution; international journal of organic evolution.

[30]  S. Gavrilets Evolution and speciation on holey adaptive landscapes. , 1997, Trends in ecology & evolution.

[31]  Bastien Chopard,et al.  Parallel Genetic Programming and its Application to Trading Model Induction , 1997, Parallel Comput..

[32]  John R. Koza,et al.  Automated synthesis of analog electrical circuits by means of genetic programming , 1997, IEEE Trans. Evol. Comput..

[33]  N. Barton,et al.  PERSPECTIVE: A CRITIQUE OF SEWALL WRIGHT'S SHIFTING BALANCE THEORY OF EVOLUTION , 1997, Evolution; international journal of organic evolution.

[34]  M W Feldman,et al.  Population structure, fitness surfaces, and linkage in the shifting balance process. , 1995, Genetical research.

[35]  R. Lenski,et al.  Evidence for multiple adaptive peaks from populations of bacteria evolving in a structured habitat. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[36]  M J Wade,et al.  Wright's shifting balance theory: an experimental study , 1991, Science.

[37]  P. Valentin‐Hansen,et al.  Analysis of the tsx gene, which encodes a nucleoside-specific channel-forming protein (Tsx) in the outer membrane of Escherichia coli. , 1990, Gene.

[38]  E. D. Weinberger,et al.  The NK model of rugged fitness landscapes and its application to maturation of the immune response. , 1989, Journal of theoretical biology.

[39]  S. Wright Surfaces of Selective Value Revisited , 1988, The American Naturalist.

[40]  S. Kauffman,et al.  Towards a general theory of adaptive walks on rugged landscapes. , 1987, Journal of theoretical biology.

[41]  J. Gillespie MOLECULAR EVOLUTION OVER THE MUTATIONAL LANDSCAPE , 1984, Evolution; international journal of organic evolution.

[42]  M. Nei,et al.  Mathematical model for studying genetic variation in terms of restriction endonucleases. , 1979, Proceedings of the National Academy of Sciences of the United States of America.

[43]  W Bossert,et al.  Mathematical optimization: are there abstract limits on natural selection? , 1967, The Wistar Institute symposium monograph.

[44]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[45]  R. A. Fisher,et al.  The Genetical Theory of Natural Selection , 1931 .