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A thousand empirical adaptive landscapes and their navigability

Nature Ecology & Evolution volume 1, Article number: 0045 (2017) | Download Citation

Abstract

The adaptive landscape is an iconic metaphor that pervades evolutionary biology. It was mostly applied in theoretical models until recent years, when empirical data began to allow partial landscape reconstructions. Here, we exhaustively analyse 1,137 complete landscapes from 129 eukaryotic species, each describing the binding affinity of a transcription factor to all possible short DNA sequences. We find that the navigability of these landscapes through single mutations is intermediate to that of additive and shuffled null models, suggesting that binding affinity—and thereby gene expression—is readily fine-tuned via mutations in transcription factor binding sites. The landscapes have few peaks that vary in their accessibility and in the number of sequences they contain. Binding sites in the mouse genome are enriched in sequences found in the peaks of especially navigable landscapes and the genetic diversity of binding sites in yeast increases with the number of sequences in a peak. Our findings suggest that landscape navigability may have contributed to the enormous success of transcriptional regulation as a source of evolutionary adaptations and innovations.

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Acknowledgements

J.A.-R. and J.L.P. acknowledge support through the Forschungskredit program of the University of Zurich (grant numbers FK-14-076 and K-74301-04-01). J.L.P. acknowledges additional support through the Ambizione program of the Swiss National Science Foundation. A.W. acknowledges support through the Swiss National Science Foundation (grant 31003A_146137) and the University Priority Research Program in Evolutionary Biology at the University of Zurich. We thank S. Bratulic, F. Khalid, A. Moya, Y. Schaerli and M. Toll-Riera for discussions and helpful comments on this manuscript.

Author information

Author notes

    • José Aguilar-Rodríguez
    •  & Joshua L. Payne

    These authors contributed equally to this work.

Affiliations

  1. Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland

    • José Aguilar-Rodríguez
    • , Joshua L. Payne
    •  & Andreas Wagner
  2. Swiss Institute of Bioinformatics, Quartier Sorge - Bâtiment Génopode, 1015 Lausanne, Switzerland

    • José Aguilar-Rodríguez
    • , Joshua L. Payne
    •  & Andreas Wagner
  3. The Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA

    • Andreas Wagner

Authors

  1. Search for José Aguilar-Rodríguez in:

  2. Search for Joshua L. Payne in:

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Contributions

J.A.-R., J.L.P. and A.W. designed the research. J.A.-R. and J.L.P. performed the research. J.A.-R., J.L.P. and A.W. analysed the data and wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Andreas Wagner.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Figures 1–33; Supplementary Analyses, Results and Discussion

Excel files

  1. 1.

    Supplementary Table 1

    Information about the 1,137 transcription factors and their landscapes.

  2. 2.

    Supplementary Table 2

    Sources for the DNase I hypersensitive regions, footprints, and RNA-seq data for the 14 cell and tissue types analysed in this study.Sources for the DNase I hypersensitive regions, footprints, and RNA-seq data for the 14 cell and tissue types analysed in this study.

  3. 3.

    Supplementary Table 3

    Spearman’s rank correlations between peak accessibility and the abundance of the highest-affinity site in DNase I footprints for the 14 cell and tissue types, in addition to Wilcoxon rank-sum tests comparing binding site abundance amongst single-peaked and multi-peaked landscapes.

  4. 4.

    Supplementary Table 4

    Spearman’s rank correlations between peak accessibility and the abundance of the highest-affinity site in DNase hypersensitive regions for 14 murine cell and tissue types, after having removed the DNase I footprints, in addition to Wilcoxon rank-sum tests comparing binding site abundance in the same hypersensitive regions amongst single-peaked and multi-peaked landscapes.

  5. 5.

    Supplementary Table 5

    Analysis of covariance (ANCOVA) and its underlying assumptions.

  6. 6.

    Supplementary Table 6

    Partial Spearman’s rank correlations with binding affinity between peak accessibility and the abundance of the highest-affinity site in DNase I footprints for the 14 cell and tissue types.

  7. 7.

    Supplementary Table 7

    Partial Spearman’s rank correlations with PWM information content between peak accessibility and the abundance of the highest-affinity site in DNase I footprints for 14 murine cell and tissue types.

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DOI

https://doi.org/10.1038/s41559-016-0045

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