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High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast


In rapidly adapting asexual populations, including many microbial pathogens and viruses, numerous mutant lineages often compete for dominance within the population1,2,3,4,5. These complex evolutionary dynamics determine the outcomes of adaptation, but have been difficult to observe directly. Previous studies have used whole-genome sequencing to follow molecular adaptation6,7,8,9,10; however, these methods have limited resolution in microbial populations. Here we introduce a renewable barcoding system to observe evolutionary dynamics at high resolution in laboratory budding yeast. We find nested patterns of interference and hitchhiking even at low frequencies. These events are driven by the continuous appearance of new mutations that modify the fates of existing lineages before they reach substantial frequencies. We observe how the distribution of fitness within the population changes over time, and find a travelling wave of adaptation that has been predicted by theory11,12,13,14,15,16,17. We show that clonal competition creates a dynamical ‘rich-get-richer’ effect: fitness advantages that are acquired early in evolution drive clonal expansions, which increase the chances of acquiring future mutations. However, less-fit lineages also routinely leapfrog over strains of higher fitness. Our results demonstrate that this combination of factors, which is not accounted for in existing models of evolutionary dynamics, is critical in determining the rate, predictability and molecular basis of adaptation.

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Fig. 1: Renewable barcoding system and lineage dynamics.
Fig. 2: Inferred clonal dynamics.
Fig. 3: Travelling wave dynamics.
Fig. 4: Travelling wave dynamics and factors determining the success of mutant lineages.

Data availability

Raw sequencing reads have been deposited in the NCBI BioProject database under accession number PRJNA559526. All associated metadata, as well as the source code for the sequencing pipeline, downstream analyses, and figure generation, are available at GitHub ( Source Data for Figs. 2–4 are provided with the paper.


  1. Gerrish, P. J. & Lenski, R. E. The fate of competing beneficial mutations in an asexual population. Genetica 102–103, 127–144 (1998).

    Article  Google Scholar 

  2. 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).

    Article  CAS  Google Scholar 

  3. Miller, C. R., Joyce, P. & Wichman, H. A. Mutational effects and population dynamics during viral adaptation challenge current models. Genetics 187, 185–202 (2011).

    Article  Google Scholar 

  4. De Visser, J. A. G. M. et al. Diminishing returns from mutation supply rate in asexual populations. Science 283, 404–406 (1999).

    Article  ADS  Google Scholar 

  5. Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).

    Article  ADS  CAS  Google Scholar 

  6. McDonald, M. J., Rice, D. P. & Desai, M. M. Sex speeds adaptation by altering the dynamics of molecular evolution. Nature 531, 233–236 (2016).

    Article  ADS  CAS  Google Scholar 

  7. Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013).

    Article  ADS  CAS  Google Scholar 

  8. Kvitek, D. J. & Sherlock, G. Whole genome, whole population sequencing reveals that loss of signaling networks is the major adaptive strategy in a constant environment. PLoS Genet. 9, e1003972 (2013).

    Article  Google Scholar 

  9. Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).

    Article  ADS  Google Scholar 

  10. Tenaillon, O. et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature 536, 165–170 (2016).

    Article  ADS  CAS  Google Scholar 

  11. Neher, R. A. Genetic draft, selective interference, and population genetics of rapid adaptation. Annu. Rev. Ecol. Evol. Syst. 44, 195–215 (2013).

    Article  Google Scholar 

  12. Desai, M. M. & Fisher, D. S. Beneficial mutation selection balance and the effect of linkage on positive selection. Genetics 176, 1759–1798 (2007).

    Article  Google Scholar 

  13. Good, B. H., Rouzine, I. M., Balick, D. J., Hallatschek, O. & Desai, M. M. Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc. Natl Acad. Sci. USA 109, 4950–4955 (2012).

    Article  ADS  CAS  Google Scholar 

  14. Rouzine, I. M., Brunet, E. & Wilke, C. O. The traveling-wave approach to asexual evolution: Muller’s ratchet and speed of adaptation. Theor. Popul. Biol. 73, 24–46 (2008).

    Article  Google Scholar 

  15. Tsimring, L. S., Levine, H. & Kessler, D. A. RNA virus evolution via a fitness-space model. Phys. Rev. Lett. 76, 4440–4443 (1996).

    Article  ADS  CAS  Google Scholar 

  16. Hallatschek, O. The noisy edge of traveling waves. Proc. Natl Acad. Sci. USA 108, 1783–1787 (2011).

    Article  ADS  CAS  Google Scholar 

  17. Fisher, D. S. Asexual evolution waves: fluctuations and universality. J. Stat. Mech. 2013, P01011 (2013).

    MathSciNet  Google Scholar 

  18. Cvijović, I., Nguyen Ba, A. N. & Desai, M. M. Experimental studies of evolutionary dynamics in microbes. Trends Genet. 34, 693–703 (2018).

    Article  Google Scholar 

  19. Buskirk, S. W., Peace, R. E. & Lang, G. I. Hitchhiking and epistasis give rise to cohort dynamics in adapting populations. Proc. Natl Acad. Sci. USA 114, 8330–8335 (2017).

    Article  CAS  Google Scholar 

  20. Zanini, F. et al. Population genomics of intrapatient HIV-1 evolution. eLife 4, e11282 (2015).

    Article  Google Scholar 

  21. Lieberman, T. D. et al. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nat. Genet. 43, 1275–1280 (2011).

    Article  CAS  Google Scholar 

  22. Strelkowa, N. & Lässig, M. Clonal interference in the evolution of influenza. Genetics 192, 671–682 (2012).

    Article  CAS  Google Scholar 

  23. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    Article  CAS  Google Scholar 

  24. Nourmohammad, A., Otwinowski, J., Luksza, M., Mora, T. & Walczak, A. M. Fierce selection and interference in B-cell repertoire response to chronic HIV-1. Mol. Biol. Evol. 36, 2184–2194 (2019).

    Article  Google Scholar 

  25. Muller, H. Some genetic aspects of sex. Am. Nat. 66, 118–138 (1932).

    Article  Google Scholar 

  26. Maynard Smith, J. Evolution in sexual and asexual populations. Am. Nat. 102, 469–473 (1968).

    Article  Google Scholar 

  27. Good, B. H. & Desai, M. M. Deleterious passengers in adapting populations. Genetics 198, 1183–1208 (2014).

    Article  Google Scholar 

  28. Blundell, J. R. & Levy, S. F. Beyond genome sequencing: lineage tracking with barcodes to study the dynamics of evolution, infection, and cancer. Genomics 104, 417–430 (2014).

    Article  CAS  Google Scholar 

  29. Blundell, J. R. et al. The dynamics of adaptive genetic diversity during the early stages of clonal evolution. Nat. Ecol. Evol. 3, 293–301 (2019).

    Article  Google Scholar 

  30. Ludovico, P., Sousa, M. J., Silva, M. T., Leão, C. & Côrte-Real, M. Saccharomyces cerevisiae commits to a programmed cell death process in response to acetic acid. Microbiology 147, 2409–2415 (2001).

    Article  CAS  Google Scholar 

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We thank E. Jerison, A. Moses, A. Murray and members of the M.M.D. laboratory for comments on the manuscript. A.N.N.B. acknowledges support from NSERC; I.C. acknowledges support from the NSF-Simons Center for Mathematical and Statistical Analysis of Biology at Harvard University (NSF grant DMS-1764269) and the Harvard FAS Quantitative Biology Initiative; K.R.L. acknowledges support from the Fannie and John Hertz Foundation Graduate Fellowship Award and the NSF Graduate Research Fellowship Program; S.F.L. acknowledges support from the NIH (grants HG008354 and HL127522); M.M.D. acknowledges support from the Simons Foundation (grant 376196), the NSF (grant DEB-1655960) and the NIH (grant GM104239). Computational work was performed on the Odyssey cluster supported by the Research Computing Group at Harvard University.

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Authors and Affiliations



A.N.N.B., J.I.R.E. and M.M.D. designed the project; A.N.N.B. and J.I.R.E. constructed the barcoding system with assistance from X.L., S.F.L. and M.M.D.; A.N.N.B., J.I.R.E., K.R.L. and A.R.-C. conducted the experiments; A.N.N.B., J.I.R.E. and I.C. designed and conducted the bioinformatics analysis; I.C. developed theory and inference methods and analysed the data; I.C., M.M.D., A.N.N.B. and J.I.R.E. wrote the paper.

Corresponding author

Correspondence to Michael M. Desai.

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The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Peer review information Nature thanks David Gresham, Daniel Weinreich and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Allele frequency trajectories in the two populations, as detected by metagenomic sequencing.

a, b, In both the YPD (a) and the YPA (b) population, solid lines denote missense and nonsense mutations, and dotted lines denote synonymous mutations and those falling in intergenic regions. Lines are coloured according to the peak time of the trajectory. Note that a frequency of 50% (dotted line) corresponds to a mutation that fixes as a heterozygote.

Extended Data Fig. 2 Comparison of inferred and measured population mean fitness trajectories.

All fitness measurements and inferences refer to the evolution environment only. Trajectories have been offset to agree with the fitness assay at time point 3.100. Dots denote barcoding intervals. Shaded regions around the trajectories denote estimates of 95% confidence intervals for the inferred mean fitness trajectory, which often do not exceed the width of the lines (Supplementary Information section 6.1). In the case of the YPA population, lighter colours denote mean fitness trajectories over the last two epochs, offset to agree with fitness assays in the last time point (see Supplementary Information section 6.6 for a discussion of potential reasons for these discrepancies) FACS, fluorescence-activated cell sorting.

Extended Data Fig. 3 Predictors of the success of lineages.

The size of each dot denotes the number of later beneficial mutations that occur in the founding clonal background of a lineage (in the first half of the experiment).

Extended Data Fig. 4 Genetic variation over time.

a, Total number of lineages above a threshold frequency (0.01%) over time. Bars denote the number of new lineages that arise in each 100-generation interval. b, Genetic diversity within each population over time, as measured by entropy (Supplementary Information section 6.4). c, Variance in fitness over time. d, Fitness diversity within each population over time, as measured by fitness entropy. Fitness entropy quantifies how fitness variance is distributed among lineages (Supplementary Information section 6.4).

Supplementary information

Supplementary Information

This file contains Supplementary Information sections 1-7, including Supplementary Figures 1-18, Supplementary Tables 1-4, a Data Availability Statement, and a table of contents.

Reporting Summary

Supplementary Table 5

This file contains the list of all barcoded lineages and their frequencies in each sequencing time-point.

Supplementary Table 6

This file contains the lists of total lineage read counts in each sequencing time-point in each population.

Supplementary Table 7

This file contains a list of all mutations called from metagenomic sequence data

Source data

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Nguyen Ba, A.N., Cvijović, I., Rojas Echenique, J.I. et al. High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast. Nature 575, 494–499 (2019).

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