Single nucleotide mapping of trait space reveals Pareto fronts that constrain adaptation

Abstract

Trade-offs constrain the improvement of performance of multiple traits simultaneously. Such trade-offs define Pareto fronts, which represent a set of optimal individuals that cannot be improved in any one trait without reducing performance in another. Surprisingly, experimental evolution often yields genotypes with improved performance in all measured traits, perhaps indicating an absence of trade-offs at least in the short term. Here we densely sample adaptive mutations in Saccharomyces cerevisiae to ask whether first-step adaptive mutations result in trade-offs during the growth cycle. We isolated thousands of adaptive clones evolved under carefully chosen conditions and quantified their performances in each part of the growth cycle. We too find that some first-step adaptive mutations can improve all traits to a modest extent. However, our dense sampling allowed us to identify trade-offs and establish the existence of Pareto fronts between fermentation and respiration, and between respiration and stationary phases. Moreover, we establish that no single mutation in the ancestral genome can circumvent the detected trade-offs. Finally, we sequenced hundreds of these adaptive clones, revealing new targets of adaptation and defining the genetic basis of the identified trade-offs.

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Fig. 1: Evolutionary constraints in trait-performance space.
Fig. 2: Experimental design and observation of local adaptation and trade-offs.
Fig. 3: Mapping of the evolutionarily accessible trait space.
Fig. 4: Pareto front geometry and potential changes over longer-term evolution.

Data availability

All sequencing data are deposited in Short Read Archive under Bioproject ID PRJNA515761. The remaining data are available in either the main text or the Supplementary tables. Figures 2 and 3 have associated raw data. All strains are readily available from the authors upon request.

Code availability

All code used in this manuscript is deposited in GitHub. The source code for computing these fitness estimates can be found at https://github.com/barcoding-bfa/fitness-assay-python. The source code for variant calling and annotation can be found at https://github.com/liyuping927/DNAscope-variants-calling.

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Acknowledgements

We thank A. Agarwala, G. Kinsler, C. McFarland and D. Fisher for discussions. We thank J. Blundell, K. Geiler-Samerotte, O. Kolodny, S. Kryazhimskiy, C. Li, F. Rosenzweig and S. Venkataram for comments on the manuscript. We thank all members in the Sherlock and Petrov labs for helpful suggestions. Y.L. is supported by the Stanford Center for Computational, Human and Evolutionary Genomics Predoctoral Fellowship. The work was supported by NIH grants (nos. R01 GM110275 and R35 GM131824), a NASA grant (no. NNX17AG79G to G.S.) and a NIH grant (no. R35GM118165 to D.A.P.).

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Contributions

Y.L., G.S. and D.A.P. were responsible for conceptualization. Y.L. performed the methodology. Formal analysis was carried out by Y.L. Investigation was performed by Y.L. Y.L. wrote the original draft. Y.L., D.A.P. and G.S. undertook the writing, review and editing. Supervision was carried out by D.A.P. and G.S.

Corresponding authors

Correspondence to Dmitri A. Petrov or Gavin Sherlock.

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Supplementary Information

Supplementary information

Supplementary Figs. 1–5.

Reporting Summary

Supplementary Table 1

Barcode counts of all lineages during the course of evolution.

Supplementary Table 2

Fitness measurements of isolated clones.

Supplementary Table 3

Genetic basis of genome-wide sequenced clones.

Supplementary Table 4

Viability measurement of FPK1 mutants and wild-type strains.

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Li, Y., Petrov, D.A. & Sherlock, G. Single nucleotide mapping of trait space reveals Pareto fronts that constrain adaptation. Nat Ecol Evol 3, 1539–1551 (2019). https://doi.org/10.1038/s41559-019-0993-0

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