Abstract
The increasing availability of genotype–phenotype maps for different combinations of mutations has empowered evolutionary biologists with the tools to interrogate the predictability of adaptive evolution, especially in the context of the evolution of antimicrobial resistance. Large microbial populations are known to generate competing beneficial mutations, but determining how these mutations contribute to the adaptive trajectories that are most likely to be followed remains a challenge. Despite a recognition that there may also be competition between successive alleles on the same trajectory, prior studies have not fully considered how this impacts adaptation rates along, or likelihood of following, individual trajectories. Here, we develop a metric that quantifies the competition between successive alleles along adaptive trajectories and show how this competition largely governs the rate of evolution in simulations on empirical fitness landscapes for proteins involved in drug resistance in two species of malaria (Plasmodium falciparum and P. vivax). Our findings reveal that a trajectory with a larger-than-average initial fitness increase may have smaller fitness increases in later steps, which slows adaptation. In some circumstances, these trajectories may be outcompeted by alleles on faster alternative trajectories that are being explored simultaneously. The ability to predict adaptation rates along accessible trajectories has implications for efforts to manage antimicrobial resistance in real-world settings and for the broader intellectual pursuit of predictive evolution in complex adaptive fitness landscapes for a variety of application domains.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Wright, S. The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In Proc. 4th Int. Congress Genetics (ed. Jones, D. F.) Vol. 1, 356–366 (The Genetics Society of America, 1932).
Kauffman, S., Lobo, J. & Macready, W. G. Optimal search on a technology landscape. J. Econ. Behav. Organ. 43, 141–166 (2000).
Holland, J. H. Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial intelligence (Univ. Michigan Press, 1975).
Marion, R. The Edge of Organization: Chaos and Complexity Theories of Formal Social Systems (Sage Publications, 1999).
Weinreich, D. M., Delaney, N. F., DePristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006).
Lozovsky, E. R. et al. Stepwise acquisition of pyrimethamine resistance in the malaria parasite. Proc. Natl Acad. Sci. USA 106, 12025–12030 (2009).
de Visser, J. A. G. & Krug, J. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15, 480–490 (2014).
Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M. & Tans, S. J. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).
Palmer, A. C. & Kishony, R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat. Rev. Genet. 14, 243–248 (2013).
Carneiro, M. & Hartl, D. L. Adaptive landscapes and protein evolution. Proc. Natl Acad. Sci. USA 107, 1747–1751 (2010).
Weinreich, D. M., Lan, Y., Wylie, C. S. & Heckendorn, R. B. Should evolutionary geneticists worry about higher-order epistasis? Curr. Opin. Genet. Dev. 3, 700–707 (2013).
Tan, L., Serene, S., Chao, H. X. & Gore, J. Hidden randomness between fitness landscapes limits reverse evolution. Phys. Rev. Lett. 106, 198102 (2011).
Jiang, P.-P., Corbett-Detig, R. B., Hartl, D. L. & Lozovsky, E. R. Accessible mutational trajectories for the evolution of pyrimethamine resistance in the malaria parasite plasmodium vivax. J. Mol. Evol. 77, 81–91 (2013).
Ogbunugafor, C. B., Wylie, C. S., Diakite, I., Weinreich, D. M. & Hartl, D. L. Adaptive landscape by environment interactions dictate evolutionary dynamics in models of drug resistance. PLoS Comput. Biol. 12, e1004710 (2016).
Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 44, 101–105 (2012).
Palmer, A. C. et al. Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes. Nat. Commun. 6, 7385 (2015).
De Visser, M. et al. Diminishing returns from mutation supply rate in asexual populations. Science 283, 404–406 (1999).
Miralles, R., Gerrish, P. J., Moya, A. & Elena, S. F. Clonal interference and the evolution of RNA viruses. Science 285, 1745–1747 (1999).
Paget-McNicol, S. & Saul, A. Mutation rates in the dihydrofolate reductase gene of plasmodium falciparum. Parasitology 122, 497–505 (2001).
Dondorp, A. M. et al. Estimation of the total parasite biomass in acute falciparum malaria from plasma pfhrp2. PLoS Med. 2, e204 (2005).
Gerrish, P. J. & Lenski, R. E. The fate of competing beneficial mutations in an asexual population. Genetica 102, 127–144 (1998).
Desai, M. M., Fisher, D. S. & Murray, A. W. The speed of evolution and maintenance of variation in asexual populations. Curr. Biol. 17, 385–394 (2007).
Jain, K., Krug, J. & Park, S.-C. Evolutionary advantage of small populations on complex fitness landscapes. Evolution 65, 1945–1955 (2011).
Ochs, I. E. & Desai, M. M. The competition between simple and complex evolutionary trajectories in asexual populations. BMC Evol. Biol. 15, 1 (2015).
Rozen, D. E., Habets, M. G., Handel, A. & de Visser, J. A. G. Heterogeneous adaptive trajectories of small populations on complex fitness landscapes. PLoS ONE 3, e1715 (2008).
Kryazhimskiy, S., Rice, D. P. & Desai, M. M. Population subdivision and adaptation in asexual populations of saccharomyces cerevisiae. Evolution 66, 1931–1941 (2012).
Nahum, J. R. et al. A tortoise–hare pattern seen in adapting structured and unstructured populations suggests a rugged fitness landscape in bacteria. Proc. Natl Acad. Sci. USA 112, 7530–7535 (2015).
Nwakanma, D. C. et al. Changes in malaria parasite drug resistance in an endemic population over a 25-year period with resulting genomic evidence of selection. J. Infect. Dis. 209, 1126–1135 (2013).
Ogbunugafor, C. B. & Hartl, D. A pivot mutation impedes reverse evolution across an adaptive landscape for drug resistance in Plasmodium vivax . Malaria J. 15, 1 (2016).
Eppstein, M. J. & Ogbunugafor, C. B. Quantifying deception: A case study in the evolution of antimicrobial resistance. In Proc. 2016 Genet. Evol. Comput. Conf. 101–108 (ACM, 2016).
Brown, K. M. et al. Compensatory mutations restore fitness during the evolution of dihydrofolate reductase. Molec. Biol. Evol. 27, 2682–2690 (2010).
Costanzo, M. S., Brown, K. M. & Hartl, D. L. Fitness trade-offs in the evolution of dihydrofolate reductase and drug resistance in Plasmodium falciparum . PLoS ONE 6, e19636 (2011).
Fisher, R. A. The Genetical Theory of Natural Selection (Oxford Univ. Press, 1930).
Wright, S. Evolution in mendelian populations. Genetics 16, 97–159 (1931).
Taylor, P. D. & Jonker, L. B. Evolutionary stable strategies and game dynamics. Math. Biosci. 40, 145–156 (1978).
Acknowledgements
The authors thank J. Bagrow, C. Goodnight, S. Scarpino, D. Weinrich and J. Weitz for helpful discussions, and S. Heinrich and J. Payne for valuable comments on the manuscript. C.B.O. was supported by the Ford Foundation Postdoctoral Fellowship and the George Washington Henderson Fellowship Program at the University of Vermont.
Author information
Authors and Affiliations
Contributions
C.B.O. and M.J.E. conceived the experiments, M.J.E. wrote the code, performed and analysed the experiments, and created the figures and tables. C.B.O. and M.J.E. interpreted the results and wrote the paper.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Supplementary information
Supplementary information
Supplementary Tables 1–6, Supplementary Figures 1–8 and Supplementary References. (PDF 446 kb)
Rights and permissions
About this article
Cite this article
Ogbunugafor, C., Eppstein, M. Competition along trajectories governs adaptation rates towards antimicrobial resistance. Nat Ecol Evol 1, 0007 (2017). https://doi.org/10.1038/s41559-016-0007
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41559-016-0007
This article is cited by
-
Environmental modulation of global epistasis in a drug resistance fitness landscape
Nature Communications (2023)
-
A trimethoprim derivative impedes antibiotic resistance evolution
Nature Communications (2021)
-
The causes of evolvability and their evolution
Nature Reviews Genetics (2019)