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Quantitative evolutionary dynamics using high-resolution lineage tracking

Nature volume 519, pages 181–186 (2015)Cite this article

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Abstract

Evolution of large asexual cell populations underlies ∼30% of deaths worldwide, including those caused by bacteria, fungi, parasites, and cancer. However, the dynamics underlying these evolutionary processes remain poorly understood because they involve many competing beneficial lineages, most of which never rise above extremely low frequencies in the population. To observe these normally hidden evolutionary dynamics, we constructed a sequencing-based ultra high-resolution lineage tracking system in Saccharomyces cerevisiae that allowed us to monitor the relative frequencies of ∼500,000 lineages simultaneously. In contrast to some expectations, we found that the spectrum of fitness effects of beneficial mutations is neither exponential nor monotonic. Early adaptation is a predictable consequence of this spectrum and is strikingly reproducible, but the initial small-effect mutations are soon outcompeted by rarer large-effect mutations that result in variability between replicates. These results suggest that early evolutionary dynamics may be deterministic for a period of time before stochastic effects become important.

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Figure 1: Lineage tracking with random barcodes.
Figure 2: Inferring the fitnesses and establishment times from lineage trajectories.
Figure 3: Fitness effects, establishment times, and population dynamics.
Figure 4: The need for high frequency resolution.

Change history

  • 11 March 2015

    A minor change was made to the Acknowledgements.

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Acknowledgements

The authors thank M. Siegal, K. Schwartz, B. Dunn, M. Jaffe, D. Kvitek, J. Thompson, D. Sellis, and Y. Zhu for discussions. FACS was performed at the Stanford Shared FACS Facility. We would like to acknowledge funding support from NIH grants R01 HG003328, 5-T32-HG-44-17 and R25 GM067110, NSF grants DMS-1120699 and PHY-1305433, Bio-X IIP6-63 grant from Stanford University, Gordon and Betty Moore Foundation grant no. 2919, and The Louis and Beatrice Laufer Center.

Author information

Author notes

  1. Sasha F. Levy and Jamie R. Blundell: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Genetics, Stanford University, Stanford, 94305-5120, California, USA

    Sasha F. Levy & Gavin Sherlock

  2. Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, 11794–5252, New York, USA

    Sasha F. Levy

  3. Department of Biochemistry and Cellular Biology, Stony Brook University, Stony Brook, 11794–5215, New York, USA

    Sasha F. Levy

  4. Department of Applied Physics, Stanford University, Stanford, 94305, California, USA

    Jamie R. Blundell & Daniel S. Fisher

  5. Department of Biology, Stanford University, Stanford, 94305, California, USA

    Jamie R. Blundell, Sandeep Venkataram, Dmitri A. Petrov & Daniel S. Fisher

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  1. Sasha F. Levy
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Contributions

S.F.L. conceived of the barcoding system. S.F.L. and G.S. designed the barcoding system and evolution experiments. S.F.L., J.R.B., D.A.P., G.S. and D.S.F. developed the project vision. S.F.L. performed the barcoding and evolution experiments. S.V. and D.A.P. designed the pairwise competition assays. S.V. performed the pairwise competition assays. J.R.B. and D.S.F. developed theory and analysed the data. J.R.B. and S.F.L. wrote the paper. All authors edited the paper.

Corresponding authors

Correspondence to Daniel S. Fisher or Gavin Sherlock.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Total population size over time.

A single ancestral cell is grown for ∼32 generations to ∼1010 cells before barcodes are inserted. Cells that incorporate a barcode are grown for another 16 generations. The population is then divided into two replicates (E1 and E2) at t = 0. Beneficial mutations that occurred before barcoding can be sampled into both replicates.

Extended Data Figure 2 Inferring the fitnesses and establishment times from lineage trajectories.

a, Selected lineage trajectories and the mean fitness trajectory from replicate E2. b, The distribution of lineage sizes over time, for lineages that begin with ∼100 ± 2 cells (vertical line). Adaptive lineages (red) begin to expand above the neutral expectation (black curve) and push neutral lineages to lower cell numbers (blue). c, The posterior probability distribution over s and τ for an adaptive lineage in E2. d, The measured trajectory of this lineage in E1 (unadaptive, blue circles) and E2 (adaptive, red circles) compared with the predicted trajectory with largest probability in E1 (blue line) and E2 (red line).

Source data

Extended Data Figure 3 Fitness effects and establishment times for replicate E2.

a, Scatter plot of τ and s of all ∼14,000 beneficial mutations (circles) identified in E2. Circle area represents the size of the lineage at generation 88. Purple circles indicate lineages with mutations that occurred in the period of common growth (t < 0) that were sampled into, and established in, E1 and E2. Green circles indicate lineages that were identified as adaptive in only one replicate and likely contain mutations that arose after t = 0. Lines indicate the time limits before which mutations must occur in order to establish (large dash) or be observed (small dash). These limits trail the mean fitness (solid line) by ∼1/s generations. Inset, the spectrum of mutation rates, μ(s), as a function of fitness effect, s inferred from mutations that likely occurred after t = 0 (Supplementary Information section 10.2). The y axis is the mutation rate density, so the mutation rate to a range, Δs, is obtained by multiplying this by Δs. The total beneficial mutation rate to s > 5% is inferred to be ∼1 × 10−6 and is consistent across replicates. The observed spectrum is not exponential (grey line, with the error range shaded). b, The distribution of the number of adaptive cells binned by their fitness over time. As the mean fitness (grey curtain) surpasses the fitness of a subpopulation, cells with that fitness begin to decline in frequency.

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data, Supplementary Tables 1-2, Supplementary Figures 1-49 and Supplementary references – see contents page for more details. (PDF 23847 kb)

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Levy, S., Blundell, J., Venkataram, S. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015). https://doi.org/10.1038/nature14279

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