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

Nature volume 575, pages 494–499 (2019)Cite this article

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Abstract

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.

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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 (https://github.com/icvijovic/lineage-tracking). Source Data for Figs. 2–4 are provided with the paper.

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Acknowledgements

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.

Author information

Author notes

  1. These authors contributed equally: Alex N. Nguyen Ba, Ivana Cvijović, José I. Rojas Echenique

Authors and Affiliations

  1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

    Alex N. Nguyen Ba, Ivana Cvijović, José I. Rojas Echenique, Katherine R. Lawrence, Artur Rego-Costa & Michael M. Desai

  2. Graduate Program in Systems Biology, Harvard University, Cambridge, MA, USA

    Ivana Cvijović

  3. NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA, USA

    Ivana Cvijović & Michael M. Desai

  4. Quantitative Biology Initiative, Harvard University, Cambridge, MA, USA

    Ivana Cvijović & Michael M. Desai

  5. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Katherine R. Lawrence

  6. Joint Initiative for Metrology in Biology, SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA

    Xianan Liu & Sasha F. Levy

  7. Laufer Center for Physical and Quantitative Biology, Department of Biochemistry, Stony Brook University, Stony Brook, NY, USA

    Xianan Liu & Sasha F. Levy

  8. Department of Physics, Harvard University, Cambridge, MA, USA

    Michael M. Desai

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Contributions

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

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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). https://doi.org/10.1038/s41586-019-1749-3

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