Abstract
High mutation rates select for the evolution of mutational robustness where populations inhabit flat fitness peaks with little epistasis, protecting them from lethal mutagenesis. Recent evidence suggests that a different effect protects small populations from extinction via the accumulation of deleterious mutations. In drift robustness, populations tend to occupy peaks with steep flanks and positive epistasis between mutations. However, it is not known what happens when mutation rates are high and population sizes are small at the same time. Using a simple fitness model with variable epistasis, we show that the equilibrium fitness has a minimum as a function of the parameter that tunes epistasis, implying that this critical point is an unstable fixed point for evolutionary trajectories. In agent-based simulations of evolution at finite mutation rate, we demonstrate that when mutations can change epistasis, trajectories with a subcritical value of epistasis evolve to decrease epistasis, while those with supercritical initial points evolve towards higher epistasis. These two fixed points can be identified with mutational and drift robustness, respectively.
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Notes
The present model in which fitness declines as a function of genetic distance from the wild type (modulated by epistasis) gives rise to conclusions similar to what Fisher’s geometric model would predict, even though in Fisher’s model the distance from wild type is phenotypic rather than genetic (Tenaillon et al. 2007).
Note that while technically the low-q fixed point is \(q=0\), this value cannot be attained in any realistic population as such a landscape is completely neutral (\(f=1\)) in this limit.
References
Adami C (1998) Introduction to artificial life. Springer, New York
Adami C, Ofria C, Collier TC (2000) Evolution of biological complexity. Proc Natl Acad Sci 97:4463–4468
Aita T, Morinaga S, Husimi Y (2004) Thermodynamical interpretation of evolutionary dynamics on a fitness landscape in a evolution reactor. I Bull Math Biol 66:1371–1403
Barton NH (1995) A general model for the evolution of recombination. Genet Res 65:123–145
Barton NH, Coe JB (2009) On the application of statistical physics to evolutionary biology. J Theor Biol 259:317–324
Beerenwinkel N, Pachter L, Sturmfels B, Elena SF, Lenski RE (2007) Analysis of epistatic interactions and fitness landscapes using a new geometric approach. BMC Evol Biol 7:60
Bonhoeffer S, Chappey C, Parkin NT, Whitcomb JM, Petropoulos CJ (2004) Evidence for positive epistasis in HIV-1. Science 306:1547–1550
Bull JJ, Sanjuan R, Wilke CO (2007) Theory of lethal mutagenesis for viruses. J Virol 81:2930–2939
Burch CL, Chao L (2004) Epistasis and its relationship to canalization in the RNA virus phi 6. Genetics 167:559–567
Charlesworth B (1976) Recombination modification in a fluctuating environment. Genetics 83:181–195
de Visser JA, Hoekstra RF, van den Ende H (1997) An experimental test for synergistic epistasis and its application in chlamydomonas. Genetics 145:815–819
Elena SF, Lenski R (1997) Test of synergistic interactions among deleterious mutations in bacteria. Nature 390:395–397
Franklin J, LaBar T, Adami C (2019) Mapping the peaks: fitness landscapes of the fittest and the flattest. Artif Life 25:250–262
Gibson G, Wagner G (2000) Canalization in evolutionary genetics: a stabilizing theory? BioEssays 22:372–380
Goyal S, Balick DJ, Jerison ER, Neher RA, Shraiman BI, Desai MM (2012) Dynamic mutation-selection balance as an evolutionary attractor. Genetics 191:1309–1319
Gros P-A, Le Nagard H, Tenaillon O (2009) The evolution of epistasis and its links with genetic robustness, complexity and drift in a phenotypic model of adaptation. Genetics 182:277–293
Haigh J (1978) The accumulation of deleterious genes in a population—Muller’s ratchet. Theor Popul Biol 14:251–267
Iwasa Y (1988) Free fitness that always increases in evolution. J Theor Biol 135:265–281
Jasnos L, Korona R (2007) Epistatic buffering of fitness loss in yeast double deletion strains. Nat Genet 39:550–554
Kirby LE, Koslowsky D (2017) Mitochondrial dual-coding genes in Trypanosoma brucei. PLoS Negl Trop Dis 11:e0005989
Koffi M, De Meeûs T, Bucheton B, Solano P, Camara M, Kaba D, Cuny G, Ayala FJ, Jamonneau V (2009) Population genetics of Trypanosoma brucei gambiense, the agent of sleeping sickness in western africa. Proc Natl Acad Sci USA 106:209–214
Kondrashov AS (1982) Selection against harmful mutations in large sexual and asexual populations. Genet Res 40:325–332
Kondrashov AS (1988) Deleterious mutations and the evolution of sexual reproduction. Nature 336:435–440
Kondrashov AS (1994) Muller’s ratchet under epistatic selection. Genetics 136:1469–1473
LaBar T, Adami C (2017) Evolution of drift robustness in small populations of digital organisms. Nat Commun 8:1012
Lan Y, Trout A, Weinreich D M, Wylie C S (2017) Natural selection can favor the evolution of ratchet robustness over evolution of mutational robustness. bioRxiv 122087
Lynch M, Bürger R, Butcher D, Gabriel W (1993) The mutational meltdown in asexual populations. J Hered 84:339–344
McCandlish DM, Stoltzfus A (2014) Modeling evolution using the probability of fixation: history and implications. Q Rev Biol 89:225–252
Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, Rocklin M, Kumar A, Ivanov S, Moore JK, Singh S, Rathnayake T, Vig S, Granger BE, Muller RP, Bonazzi F, Gupta H, Vats S, Johansson F, Pedregosa F, Curry MJ, Terrel AR, Roučka Š, Saboo A, Fernando I, Kulal S, Cimrman R, Scopatz A (2017) SymPy: symbolic computing in Python. PeerJ Comput Sci 3:e103
Oberle M, Balmer O, Brun R, Roditi I (2010) Bottlenecks and the maintenance of minor genotypes during the life cycle of Trypanosoma brucei. PLoS Pathog 6:e1001023
Ofria C, Bryson DM, Wilke CO (2009) Avida: a software platform for research in computational evolutionary biology. In: Komosinski M, Adamatzky A (eds) Artificial life models in software. Springer, London, pp 3–35
Oliphant TE (2006) A guide to NumPy. Trelgol Publishing, New York
Østman B, Hintze A, Adami C (2012) Impact of epistasis and pleiotropy on evolutionary adaptation. Proc R Soc B 279:247–256
Python Software Foundation (2019) The Python language reference. Python Software Foundation, Wilmington
R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Sanjuán R, Cuevas JM, Moya A, Elena SF (2005) Epistasis and the adaptability of an RNA virus. Genetics 170:1001–1008
Sanjuán R, Cuevas JM, Furió V, Holmes EC, Moya A (2007) Selection for robustness in mutagenized RNA viruses. PLoS Genet 3:e93
Scharloo W (1991) Canalization: genetic and developmental aspects. Annu Rev Ecol Syst 22:65–93
Sella G, Hirsh AE (2005) The application of statistical physics to evolutionary biology. Proc Natl Acad Sci USA 102:9541–9546
Speijer D (2006) Is kinetoplastid pan-editing the result of an evolutionary balancing act? IUBMB Life 58:91–96
Tenaillon O, Silander OK, Uzan J-P, Chao L (2007) Quantifying organismal complexity using a population genetic approach. PLoS ONE 2:e217
Westy SA, Lively CM, Read AF (1999) A pluralist approach to sex and recombination. J Evol Biol 12:1003–1012
Wickham H, Averick M, Bryan J, Chang W, D’Agostino McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Lin Pedersen T, Miller E, Milton Bache S, Müller K, Ooms J, Robinson D, Paige Seidel D, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019) Welcome to the tidyverse. J Open Source Softw 4:1686
Wilke CO, Adami C (2001) Interaction between directional epistasis and average mutational effects. Proc R Soc Lond B 268:1469–1474
Wilke CO, Adami C (2003) Evolution of mutational robustness. Mutat Res 522:3–11
Wilke CO, Wang JL, Ofria C, Lenski RE, Adami C (2001) Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature 412:331–333
Wolf JB, Brodie ED III, Wade MJ (eds) (2000) Epistasis and the evolutionary process. Oxford University Press, Oxford
Acknowledgements
We are grateful to an anonymous reviewer who drew our attention to the existence of the weaker secondary minimum of Eq. (2) at high epistasis. This work was supported in part by the National Science Foundation’s BEACON Center for the Study of Evolution in Action, under Contract No. DBI-0939454.
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Handling editor: David Liberles.
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Sydykova, D.K., LaBar, T., Adami, C. et al. Moderate Amounts of Epistasis are Not Evolutionarily Stable in Small Populations. J Mol Evol 88, 435–444 (2020). https://doi.org/10.1007/s00239-020-09942-4
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DOI: https://doi.org/10.1007/s00239-020-09942-4
Keywords
- Epistasis
- Drift robustness
- Mutational robustness
- Small populations