A thousand empirical adaptive landscapes and their navigability

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.

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: Adaptive landscapes of TF binding affinity.
Figure 2: The navigability of adaptive landscapes of TF binding affinity.
Figure 3: In vivo binding site abundance correlates with landscape navigability.
Figure 4: Gene expression increases along accessible mutational paths and reflects landscape topography.
Figure 5: Global peak breadth influences the diversity of TF binding sites in the yeast genome.

References

  1. 1

    Wright, S. The roles of mutation, inbreeding, crossbreeding and selection in evolution. in Proc. Sixth Int. Congress Genetics Vol. 1 (ed. Jones, D. F. ) 356–366 (The Genetics Society of America, 1932).

    Google Scholar 

  2. 2

    Szendro, I. G., Schenk, M. F., Franke, J., Krug, J. & de Visser, J. A. G. M. Quantitative analyses of empirical fitness landscapes. J. Stat. Mech-Theory E. 2013, P01005 (2013).

    Article  Google Scholar 

  3. 3

    Kauffman, S. & Levin, S. Towards a general theory of adaptive walks on rugged landscapes. J. Theor. Biol. 128, 11–45 (1987).

    CAS  Article  Google Scholar 

  4. 4

    Rowe, W. et al. Analysis of a complete DNA-protein affinity landscape. J. R. Soc. Interface 7, 397–408 (2010).

    CAS  Article  Google Scholar 

  5. 5

    Jiménez, J. I., Xulvi-Brunet, R., Campbell, G. W., Turk-MacLeod, R. & Chen, I. A. Comprehensive experimental fitness landscape and evolutionary network for small RNA. Proc. Natl Acad. Sci. USA 110, 14984–14989 (2013).

    Article  Google Scholar 

  6. 6

    Wray, G. A. The evolutionary significance of cis-regulatory mutations. Nat. Rev. Genet. 8, 206–216 (2007).

    CAS  Article  Google Scholar 

  7. 7

    Gertz, J., Siggia, E. D. & Cohen, B. A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457, 215–218 (2009).

    CAS  Article  Google Scholar 

  8. 8

    Shultzaberger, R. K., Malashock, D. S., Kirsch, J. F. & Eisen, M. B. The fitness landscapes of cis-acting binding sites in different promoter and environmental contexts. PLoS Genet. 6, e1001042 (2010).

    Article  Google Scholar 

  9. 9

    Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).

    CAS  Article  Google Scholar 

  10. 10

    Gerland, U. & Hwa, T. On the selection and evolution of regulatory DNA motifs. J. Mol. Evol. 55, 386–400 (2002).

    CAS  Article  Google Scholar 

  11. 11

    Berg, J., Willmann, S. & Lässig, M. Adaptive evolution of transcription factor binding sites. BMC Evol. Biol. 4, 42 (2004).

    Article  Google Scholar 

  12. 12

    Maerkl, S. J. & Quake, S. R. A systems approach to measuring the binding energy landscapes of transcription factors. Science 315, 233–237 (2007).

    CAS  Article  Google Scholar 

  13. 13

    Mustonen, V., Kinney, J., Callan, C. G. & Lässig, M. Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites. Proc. Natl Acad. Sci. USA 105, 12376–12381 (2008).

    CAS  Article  Google Scholar 

  14. 14

    Haldane, A., Manhart, M. & Morozov, A. V. Biophysical fitness landscapes for transcription factor binding sites. PLoS Comput. Biol. 10, e1003683 (2014).

    Article  Google Scholar 

  15. 15

    Carlson, C. D. et al. Specificity landscapes of DNA binding molecules elucidate biological function. Proc. Natl Acad. Sci. USA 107, 4544–4549 (2010).

    CAS  Article  Google Scholar 

  16. 16

    Weghorn, D. & Lässig, M. Fitness landscape for nucleosome positioning. Proc. Natl Acad. Sci. USA 110, 10988–10993 (2013).

    CAS  Article  Google Scholar 

  17. 17

    Buenrostro, J. D. et al. Quantitative analysis of RNA–protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes. Nat. Biotechnol. 32, 562–568 (2014).

    CAS  Article  Google Scholar 

  18. 18

    Newburger, D. E. & Bulyk, M. L. UniPROBE: an online database of protein binding microarray data on protein–DNA interactions. Nucleic Acids Res. 37, D77–D82 (2009).

    CAS  Article  Google Scholar 

  19. 19

    Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    CAS  Article  Google Scholar 

  20. 20

    Badis, G. et al. Diversity and complexity in DNA recognition by transcription factors. Science 324, 1720–1723 (2009).

    CAS  Article  Google Scholar 

  21. 21

    Berger, M. F. et al. Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat. Biotechnol. 24, 1429–1435 (2006).

    CAS  Article  Google Scholar 

  22. 22

    Payne, J. L. & Wagner, A. The robustness and evolvability of transcription factor binding sites. Science 343, 875–877 (2014).

    CAS  Article  Google Scholar 

  23. 23

    Zhu, C. et al. High-resolution DNA-binding specificity analysis of yeast transcription factors. Genome Res. 19, 556–566 (2009).

    CAS  Article  Google Scholar 

  24. 24

    Nakagawa, S., Gisselbrecht, S. S., Rogers, J. M., Hartl, D. L. & Bulyk, M. L. DNA-binding specificity changes in the evolution of forkhead transcription factors. Proc. Natl Acad. Sci. USA 110, 12349–12354 (2013).

    CAS  Article  Google Scholar 

  25. 25

    Maynard Smith, J. Natural selection and the concept of a protein space. Nature 225, 563–564 (1970).

    Article  Google Scholar 

  26. 26

    Lehner, B. Molecular mechanisms of epistasis within and between genes. Trends Genet. 27, 323–331 (2011).

    CAS  Article  Google Scholar 

  27. 27

    Poelwijk, F. J., Tănase-Nicola, S., Kiviet, D. J. & Tans, S. J. Reciprocal sign epistasis is a necessary condition for multi-peaked fitness landscapes. J. Theor. Biol. 272, 141–144 (2011).

    Article  Google Scholar 

  28. 28

    Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    CAS  Article  Google Scholar 

  29. 29

    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  Article  Google Scholar 

  30. 30

    Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).

    CAS  Article  Google Scholar 

  31. 31

    Stergachis, A. B. et al. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature 515, 365–370 (2014).

    CAS  Article  Google Scholar 

  32. 32

    Hesselberth, J. R. et al. Global mapping of protein–DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).

    CAS  Article  Google Scholar 

  33. 33

    Lynch, M. & Hagner, K. Evolutionary meandering of intermolecular interactions along the drift barrier. Proc. Natl Acad. Sci. USA 112, E30–E38 (2015).

    CAS  Article  Google Scholar 

  34. 34

    MacArthur, S. & Brookfield, J. F. Y. Expected rates and modes of evolution of enhancer sequences. Mol. Biol. Evol. 21, 1064–1073 (2004).

    CAS  Article  Google Scholar 

  35. 35

    Bergström, A. et al. A high-definition view of functional genetic variation from natural yeast genomes. Mol. Biol. Evol. 31, 872–888 (2014).

    Article  Google Scholar 

  36. 36

    MacIsaac, K. D. et al. An improved map of conserved regulatory sites for Saccharomyces cerevisiae . BMC Bioinformatics 7, 113 (2006).

    Article  Google Scholar 

  37. 37

    Gompel, N., Prud’homme, B., Wittkopp, P. J., Kassner, V. A. & Carroll, S. B. Chance caught on the wing: cis-regulatory evolution and the origin of pigment patterns in Drosophila . Nature 433, 481–487 (2005).

    CAS  Article  Google Scholar 

  38. 38

    Rister, J. et al. Single-base pair differences in a shared motif determine differential Rhodopsin expression. Science 350, 1258–1261 (2015).

    CAS  Article  Google Scholar 

  39. 39

    Siggers, T. & Gordân, R. Protein–DNA binding: complexities and multi-protein codes. Nucleic Acids Res. 42, 2099–2111 (2014).

    CAS  Article  Google Scholar 

  40. 40

    Li, X. Y. et al. Transcription factors bind thousands of active and inactive regions in the Drosophila blastoderm. PLoS Biol. 6, 0365–0388 (2008).

    CAS  Google Scholar 

  41. 41

    Fisher, W. W. et al. DNA regions bound at low occupancy by transcription factors do not drive patterned reporter gene expression in Drosophila . Proc. Natl Acad. Sci. USA 109, 21330–21335 (2012).

    CAS  Article  Google Scholar 

  42. 42

    Mustonen, V. & Lässig, M. From fitness landscapes to seascapes: non-equilibrium dynamics of selection and adaptation. Trends Genet. 25, 111–119 (2009).

    CAS  Article  Google Scholar 

  43. 43

    Arbiza, L. et al. Genome-wide inference of natural selection on human transcription factor binding sites. Nat. Genet. 45, 723–729 (2013).

    CAS  Article  Google Scholar 

  44. 44

    Mustonen, V. & Lässig, M. Evolutionary population genetics of promoters: predicting binding sites and functional phylogenies. Proc. Natl Acad. Sci. USA 102, 15936–15941 (2005).

    CAS  Article  Google Scholar 

  45. 45

    Swanson, C. I., Schwimmer, D. B. & Barolo, S. Rapid evolutionary rewiring of a structurally constrained eye enhancer. Curr. Biol. 21, 1186–1196 (2011).

    CAS  Article  Google Scholar 

  46. 46

    Grönlund, A., Lötstedt, P. & Elf, J. Transcription factor binding kinetics constrain noise suppression via negative feedback. Nat. Commun. 4, 1864 (2013).

    Article  Google Scholar 

  47. 47

    Ramos, A. I. & Barolo, S. Low-affinity transcription factor binding sites shape morphogen responses and enhancer evolution. Phil. Trans. R. Soc. B. 368, 20130018 (2013).

    Article  Google Scholar 

  48. 48

    Crocker, J. et al. Low affinity binding site clusters confer hox specificity and regulatory robustness. Cell 160, 191–203 (2015).

    CAS  Article  Google Scholar 

  49. 49

    Berger, M. F. & Bulyk, M. L. Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors. Nat. Protoc. 4, 393–411 (2009).

    CAS  Article  Google Scholar 

  50. 50

    Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    CAS  Article  Google Scholar 

  51. 51

    van Helden, J., André, B. & Collado-Vides, J. Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J. Mol. Biol. 281, 827–842 (1998).

    CAS  Article  Google Scholar 

  52. 52

    Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    CAS  Article  Google Scholar 

  53. 53

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  Article  Google Scholar 

  54. 54

    Dawid, A., Kiviet, D. J., Kogenaru, M., de Vos, M. & Tans, S. J. Multiple peaks and reciprocal sign epistasis in an empirically determined genotype-phenotype landscape. Chaos 20, 26105 (2010).

    Article  Google Scholar 

  55. 55

    Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M. & Tans, S. J. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).

    CAS  Article  Google Scholar 

  56. 56

    Franke, J., Klözer, A., de Visser, J. A. G. M. & Krug, J. Evolutionary accessibility of mutational pathways. PLoS Comput. Biol. 7, e1002134 (2011).

    CAS  Article  Google Scholar 

  57. 57

    Parker, D. S., White, M. A., Ramos, A. I., Cohen, B. A. & Barolo, S. The cis-regulatory logic of Hedgehog gradient responses: key roles for gli binding affinity, competition, and cooperativity. Sci. Signal. 4, ra38 (2011).

    Article  Google Scholar 

  58. 58

    Zhao, Y. & Stormo, G. D. Quantitative analysis demonstrates most transcription factors require only simple models of specificity. Nat. Biotechnol. 29, 480–483 (2011).

    CAS  Article  Google Scholar 

  59. 59

    Morris, Q., Bulyk, M. L. & Hughes, T. R. Jury remains out on simple models of transcription factor specificity. Nat. Biotechnol. 29, 483–484 (2011).

    CAS  Article  Google Scholar 

  60. 60

    Weinreich, D. M., Watson, R. A. & Chao, L. Perspective: sign epistasis and genetic constraint on evolutionary trajectories. Evolution 59, 1165–1174 (2005).

    CAS  PubMed  Google Scholar 

Download references

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

Affiliations

Authors

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.

Corresponding author

Correspondence to Andreas Wagner.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Figures 1–33; Supplementary Analyses, Results and Discussion (PDF 1981 kb)

Supplementary Table 1

Information about the 1,137 transcription factors and their landscapes. (XLSX 212 kb)

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. (XLSX 10 kb)

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. (XLSX 54 kb)

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. (XLSX 57 kb)

Supplementary Table 5

Analysis of covariance (ANCOVA) and its underlying assumptions. (XLSX 53 kb)

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. (XLSX 41 kb)

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. (XLSX 51 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aguilar-Rodríguez, J., Payne, J. & Wagner, A. A thousand empirical adaptive landscapes and their navigability. Nat Ecol Evol 1, 0045 (2017). https://doi.org/10.1038/s41559-016-0045

Download citation

Further reading