Quantitative evolutionary dynamics using high-resolution lineage tracking

This article has been updated (view changelog)

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1

    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 

  2. 2

    Herron, M. D. & Doebeli, M. Parallel evolutionary dynamics of adaptive diversification in Escherichia coli. PLoS Biol. 11, e1001490 (2013)

    CAS  Article  Google Scholar 

  3. 3

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

    CAS  ADS  Article  Google Scholar 

  4. 4

    Lang, G. I., Botstein, D. & Desai, M. M. Genetic variation and the fate of beneficial mutations in asexual populations. Genetics 188, 647–661 (2011)

    Article  Google Scholar 

  5. 5

    Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 999–1005 (2010)

    CAS  ADS  Article  Google Scholar 

  6. 6

    Shah, S. P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009)

    CAS  ADS  Article  Google Scholar 

  7. 7

    Mardis, E. R. et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N. Engl. J. Med. 361, 1058–1066 (2009)

    CAS  Article  Google Scholar 

  8. 8

    International Cancer Genome Consortium et al. International network of cancer genome projects. Nature 464, 993–998 (2010)

  9. 9

    Pleasance, E. D. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010)

    CAS  ADS  Article  Google Scholar 

  10. 10

    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)

    CAS  ADS  Article  Google Scholar 

  11. 11

    Young, B. C. et al. Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease. Proc. Natl Acad. Sci. USA 109, 4550–4555 (2012)

    CAS  ADS  Article  Google Scholar 

  12. 12

    Holden, M. T. G. et al. A genomic portrait of the emergence, evolution, and global spread of a methicillin-resistant Staphylococcus aureus pandemic. Genome Res. 23, 653–664 (2013)

    CAS  Article  Google Scholar 

  13. 13

    Desai, M. M., Walczak, A. M. & Fisher, D. S. Genetic diversity and the structure of genealogies in rapidly adapting populations. Genetics 193, 565–585 (2013)

    Article  Google Scholar 

  14. 14

    Neher, R. A. & Hallatschek, O. Genealogies of rapidly adapting populations. Proc. Natl Acad. Sci. USA 110, 437–442 (2013)

    CAS  ADS  Article  Google Scholar 

  15. 15

    Hegreness, M., Shoresh, N., Hartl, D. & Kishony, R. An equivalence principle for the incorporation of favorable mutations in asexual populations. Science 311, 1615–1617 (2006)

    CAS  ADS  Article  Google Scholar 

  16. 16

    Kao, K. C. & Sherlock, G. Molecular characterization of clonal interference during adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nature Genet. 40, 1499–1504 (2008)

    CAS  Article  Google Scholar 

  17. 17

    Imhof, M. & Schlötterer, C. Fitness effects of advantageous mutations in evolving Escherichia coli populations. Proc. Natl Acad. Sci. USA 98, 1113–1117 (2001)

    CAS  ADS  Article  Google Scholar 

  18. 18

    Gerrits, A. et al. Cellular barcoding tool for clonal analysis in the hematopoietic system. Blood 115, 2610–2618 (2010)

    CAS  Article  Google Scholar 

  19. 19

    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 

  20. 20

    Charlesworth, B. The good fairy godmother of evolutionary genetics. Curr. Biol. 6, 220 (1996)

    CAS  Article  Google Scholar 

  21. 21

    Berns, K. et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428, 431–437 (2004)

    CAS  ADS  Article  Google Scholar 

  22. 22

    Smith, A. M. et al. Quantitative phenotyping via deep barcode sequencing. Genome Res. 19, 1836–1842 (2009)

    CAS  Article  Google Scholar 

  23. 23

    Lu, R., Neff, N. F., Quake, S. R. & Weissman, I. L. Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding. Nature Biotechnol. 29, 928–933 (2011)

    CAS  Article  Google Scholar 

  24. 24

    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)

    CAS  Article  Google Scholar 

  25. 25

    Sternberg, N. & Hamilton, D. Bacteriophage P1 site-specific recombination. J. Mol. Biol. 150, 467–486 (1981)

    CAS  Article  Google Scholar 

  26. 26

    Austin, S., Ziese, M. & Sternberg, N. A novel role for site-specific recombination in maintenance of bacterial replicons. Cell 25, 729–736 (1981)

    CAS  Article  Google Scholar 

  27. 27

    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 

  28. 28

    Luria, S. E. & Delbrück, M. Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28, 491–511 (1943)

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Lang, G. I. & Murray, A. W. Estimating the per-base-pair mutation rate in the yeast Saccharomyces cerevisiae. Genetics 178, 67–82 (2008)

    CAS  Article  Google Scholar 

  30. 30

    Lynch, M. et al. A genome-wide view of the spectrum of spontaneous mutations in yeast. Proc. Natl Acad. Sci. USA 105, 9272–9277 (2008)

    CAS  ADS  Article  Google Scholar 

  31. 31

    Zhu, Y. O., Siegal, M. L., Hall, D. W. & Petrov, D. A. Precise estimates of mutation rate and spectrum in yeast. Proc. Natl Acad. Sci. USA 111, E2310–E2318 (2014)

    CAS  ADS  Article  Google Scholar 

  32. 32

    Joseph, S. B. & Hall, D. W. Spontaneous mutations in diploid Saccharomyces cerevisiae: more beneficial than expected. Genetics 168, 1817–1825 (2004)

    Article  Google Scholar 

  33. 33

    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)

    CAS  Article  Google Scholar 

  34. 34

    Gillespie, J. H. Molecular evolution over the mutational landscape. Evolution 38, 1116–1129 (1984)

    CAS  Article  Google Scholar 

  35. 35

    Orr, H. A. The distribution of fitness effects among beneficial mutations. Genetics 163, 1519–1526 (2003)

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Kassen, R. & Bataillon, T. Distribution of fitness effects among beneficial mutations before selection in experimental populations of bacteria. Nature Genet. 38, 484–488 (2006)

    CAS  Article  Google Scholar 

  37. 37

    Rokyta, D. R., Joyce, P., Caudle, S. B. & Wichman, H. A. An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus. Nature Genet. 37, 441–444 (2005)

    CAS  Article  Google Scholar 

  38. 38

    Rokyta, D. R. et al. Beneficial fitness effects are not exponential for two viruses. J. Mol. Evol. 67, 368–376 (2008)

    CAS  ADS  Article  Google Scholar 

  39. 39

    Gresham, D. et al. The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast. PLoS Genet. 4, e1000303 (2008)

    Article  Google Scholar 

  40. 40

    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)

    CAS  ADS  Article  Google Scholar 

  41. 41

    Salmon, S. E. & Smith, B. A. Immunoglobulin synthesis and total body tumor cell number in IgG multiple myeloma. J. Clin. Invest. 49, 1114–1121 (1970)

    CAS  Article  Google Scholar 

  42. 42

    Michaelson, J. S. et al. Predicting the survival of patients with breast carcinoma using tumor size. Cancer 95, 713–723 (2002)

    Article  Google Scholar 

  43. 43

    König, C., Simmen, H. P. & Blaser, J. Bacterial concentrations in pus and infected peritoneal fluid–implications for bactericidal activity of antibiotics. J. Antimicrob. Chemother. 42, 227–232 (1998)

    Article  Google Scholar 

  44. 44

    Wilson, M. L. & Gaido, L. Laboratory diagnosis of urinary tract infections in adult patients. Clin. Infect. Dis. 38, 1150–1158 (2004)

    Article  Google Scholar 

  45. 45

    Thomas, C. E., Ehrhardt, A. & Kay, M. A. Progress and problems with the use of viral vectors for gene therapy. Nature Rev. Genet. 4, 346–358 (2003)

    CAS  Article  Google Scholar 

  46. 46

    Bushman, F. et al. Genome-wide analysis of retroviral DNA integration. Nature Rev. Microbiol. 3, 848–858 (2005)

    CAS  Article  Google Scholar 

  47. 47

    Ran, F. A. et al. Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154, 1380–1389 (2013)

    CAS  Article  Google Scholar 

  48. 48

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013)

    CAS  ADS  Article  Google Scholar 

  49. 49

    Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nature Methods 9, 72–74 (2011)

    MathSciNet  Article  Google Scholar 

  50. 50

    Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D. & Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nature Methods 10, 999–1002 (2013)

    CAS  Article  Google Scholar 

Download references

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

Affiliations

Authors

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.

Ethics declarations

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)

PowerPoint slides

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing