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The dynamics of adaptive genetic diversity during the early stages of clonal evolution

Abstract

The dynamics of genetic diversity in large clonally evolving cell populations are poorly understood, despite having implications for the treatment of cancer and microbial infections. Here, we combine barcode lineage tracking, sequencing of adaptive clones and mathematical modelling of mutational dynamics to understand adaptive diversity changes during experimental evolution of Saccharomyces cerevisiae under nitrogen and carbon limitation. We find that, despite differences in beneficial mutational mechanisms and fitness effects, early adaptive genetic diversity increases predictably, driven by the expansion of many single-mutant lineages. However, a crash in adaptive diversity follows, caused by highly fit double-mutant ‘jackpot’ clones that are fed from exponentially growing single mutants, a process closely related to the classic Luria–Delbrück experiment. The diversity crash is likely to be a general feature of asexual evolution with clonal interference; however, both its timing and magnitude are stochastic and depend on the population size, the distribution of beneficial fitness effects and patterns of epistasis.

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Raw barcode read counts are contained in the Supplementary Data 1. Variant calls for sequenced clones are contained in Supplementary Data 2. All other data are available on request.

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Acknowledgements

We wish to thank all members of the Levy, Sherlock, Fisher and Blundell laboratories for useful discussions and comments. J.R.B. is supported by grant NSF PHY-1545840, Stand Up To Cancer, the Louis and Beatrice Laufer Center and by the CRUK Cambridge Center; D.S.F. by grants NSF PHY-1305433, NSF PHY-1545840, Stand Up 2 Cancer and NIH R01 HG003328; G.S. by NIH grants R01 HG003328 and GM110275; S.F.L by grant NIH R01 HG008354, the Louis and Beatrice Laufer Center, the National Institute of Standards and Technology and the U.S. Department of Energy under contract number DE-AC02-76SF00515. All data are available on request. The identification of any specific commercial products is for the purpose of specifying a protocol and does not imply a recommendation or endorsement by the National Institute of Standards and Technology.

Author information

Conceptualization, J.R.B, D.S.F, G.S., S.F.L; methods, J.R.B., S.F.L.; software, J.R.B.; formal analysis, J.R.B., D.S.F; investigation, J.R.B., K.S., D.F., S.F.L.; resources, G.S., S.F.L; curation, K.S., J.R.B.; writing—original draft, J.R.B., S.F.L.; writing—reviewing and editing, J.R.B, D.S.F, G.S., S.F.L; visualization, J.R.B.; supervision, S.F.L.; funding acquision, J.R.B, D.S.F, G.S., S.F.L.

Competing interests

The authors declare no competing interests.

Correspondence to Jamie R. Blundell or Sasha F. Levy.

Supplementary information

Supplementary Information

Supplementary Information sections 1–8, including Supplementary Figures 1–14, Supplementary Tables 1–4 and Supplementary References

Reporting Summary

Supplementary Dataset 1

De-duplicated barcode read counts from carbon-limited environment, replicate 1. Column headings are the format T.DS where T, generation; D, DNA prep and PCR replicate; and S, sequencing replicates. So, for example: 0.51, 0.52 are both generation 0, and were prepped together, but sequenced independently (1 versus 2). 0.51 and 0.61 are the same time point but were prepped and sequenced independently. Note generation 0 is the same for replicate 1 and 2

Supplementary Dataset 2

De-duplicated barcode read counts from carbon-limited environment, replicate 2. Column headings are the format T.DS where T, generation; D, DNA prep and PCR replicate; and S, sequencing replicates. So, for example: 0.51, 0.52 are both generation 0, and were prepped together, but sequenced independently (1 versus 2). 0.51 and 0.61 are the same time point but were prepped and sequenced independently. Note generation 0 is the same for replicate 1 and 2

Supplementary Dataset 3

De-duplicated barcode read counts from nitrogen-limited environment, replicate 1. Column headings are the format T.DS where T, generation; D, DNA prep and PCR replicate; and S, sequencing replicates

Supplementary Dataset 4

De-duplicated barcode read counts from nitrogen-limited environment, replicate 2. Column headings are the format T.DS where T, generation; D, DNA prep and PCR replicate; and S, sequencing replicates

Supplementary Dataset 5

Estimates for the likelihood of being adaptive, the fitness effect (s) and the establishment time (τ), for lineages in the carbon-limited environment

Supplementary Dataset 6

Estimates for the likelihood of being adaptive, the fitness effect (s) and the establishment time (τ), for lineages in the nitrogen-limited environment

Supplementary Dataset 7

List of adaptive-clone sequencing results from sequencing of adaptive clones in carbon-limitation. Entries indicate the replicate (2M3 = replicate 1, 4M3 = replicate 2), the barcode lineage to which that cell belonged and the gene(s) mutated

Supplementary Dataset 8

List of adaptive-clone sequencing results from sequencing of adaptive clones in nitrogen-limitation. Entries indicate the replicate (N1 = replicate 1), the barcode lineage to which that cell belonged and the gene(s) mutated

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Fig. 1: Muller plots of adaptive lineages.
Fig. 2: Barcode-directed whole-genome sequencing of adaptive clones to find the mutational targets underlying the distribution of beneficial fitness effects (mDFE).
Fig. 3: The dynamics of adaptive genetic diversity in the fitness-staircase model.
Fig. 4: Exponential feeding of double mutants causes a diversity crash.
Fig. 5: Simulations of diploid dynamics using the additive and ‘categorical’ epistasis models.