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Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana

Abstract

As Earth is currently experiencing dramatic climate change, it is of critical interest to understand how species will respond to it. The chance of a species withstanding climate change is likely to depend on the diversity within the species and, particularly, whether there are sub-populations that are already adapted to extreme environments. However, most predictive studies ignore that species comprise genetically diverse individuals. We have identified genetic variants in Arabidopsis thaliana that are associated with survival of an extreme drought event—a major consequence of global warming. Subsequently, we determined how these variants are distributed across the native range of the species. Genetic alleles conferring higher drought survival showed signatures of polygenic adaptation and were more frequently found in Mediterranean and Scandinavian regions. Using geo-environmental models, we predicted that Central European, but not Mediterranean, populations might lag behind in adaptation by the end of the twenty-first century. Further analyses showed that a population decline could nevertheless be compensated by natural selection acting efficiently over standing variation or by migration of adapted individuals from populations at the margins of the species’ distribution. These findings highlight the importance of within-species genetic heterogeneity in facilitating an evolutionary response to a changing climate.

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Fig. 1: Terminal drought treatment and phenotyping of 211 accessions.
Fig. 2: Population structure and history of 762 high-quality genomes.
Fig. 3: aGWA of drought survival and environmental predictions.

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References

  1. Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).

    Article  CAS  PubMed  Google Scholar 

  2. Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl Acad. Sci. USA 102, 8245–8250 (2005).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Jezkova, T. & Wiens, J. J. Rates of change in climatic niches in plant and animal populations are much slower than projected climate change. Proc. R. Soc. B Biol. Sci. 283, 20162104 (2016).

    Article  Google Scholar 

  4. Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).

    Article  PubMed  Google Scholar 

  5. Hereford, J. A quantitative survey of local adaptation and fitness trade-offs. Am. Nat. 173, 579–588 (2009).

    Article  PubMed  Google Scholar 

  6. Turesson, G. The species and the variety as ecological units. Hereditas 3, 100–113 (1922).

    Article  Google Scholar 

  7. Hancock, A. M. et al. Adaptation to climate across the Arabidopsis thaliana genome. Science 334, 83–86 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Fournier-Level, A. et al. A map of local adaptation in Arabidopsis thaliana. Science 334, 86–89 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. Lasky, J. R. et al. Characterizing genomic variation of Arabidopsis thaliana: the roles of geography and climate. Mol. Ecol. 21, 5512–5529 (2012).

    Article  PubMed  Google Scholar 

  10. Siepielski, A. M. et al. Precipitation drives global variation in natural selection. Science 355, 959–962 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2012).

    Article  Google Scholar 

  12. Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).

    Article  PubMed  Google Scholar 

  13. Lee-Yaw, J. A. et al. A synthesis of transplant experiments and ecological niche models suggests that range limits are often niche limits. Ecol. Lett. 19, 710–722 (2016).

    Article  PubMed  Google Scholar 

  14. Berg, J. J. & Coop, G. A population genetic signal of polygenic adaptation. PLoS Genet. 10, e1004412 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  15. Dormann, C. F. et al. Correlation and process in species distribution models: bridging a dichotomy. J. Biogeogr. 39, 2119–2131 (2012).

    Article  Google Scholar 

  16. 1001 Genomes Consortium. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166, 481–491 (2016).

    Article  Google Scholar 

  17. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Article  Google Scholar 

  18. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  19. Mojica, J. P. et al. Genetics of water use physiology in locally adapted Arabidopsis thaliana. Plant Sci. 251, 12–22 (2016).

    Article  CAS  PubMed  Google Scholar 

  20. Ingram, J. & Bartels, D. The molecular basis of dehydration tolerance in plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 47, 377–403 (1996).

    Article  CAS  PubMed  Google Scholar 

  21. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Schiffels, S. & Durbin, R. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 46, 919–925 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Lee, C.-R. et al. On the post-glacial spread of human commensal Arabidopsis thaliana. Nat. Commun. 8, 14458 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Atwell, S. et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Gibson, G. Rare and common variants: twenty arguments. Nat. Rev. Genet. 13, 135–145 (2011).

    Article  Google Scholar 

  28. Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Hedrick, P. W. Genetic polymorphism in heterogeneous environments: the age of genomics. Annu. Rev. Ecol. Evol. Syst. 37, 67–93 (2006).

    Article  Google Scholar 

  30. Pavlidis, P., Živkovic, D., Stamatakis, A. & Alachiotis, N. SweeD: likelihood-based detection of selective sweeps in thousands of genomes. Mol. Biol. Evol. 30, 2224–2234 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Pritchard, J. K., Pickrell, J. K. & Coop, G. The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation. Curr. Biol. 20, R208–R215 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Josephs, E. B., Stinchcombe, J. R. & Wright, S. I. What can genome-wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits? New Phytol. 214, 21–33 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Lawson, D. J., Hellenthal, G., Myers, S. & Falush, D. Inference of population structure using dense haplotype data. PLoS Genet. 8, e1002453 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Shriner, D., Adeyemo, A., Ramos, E., Chen, G. & Rotimi, C. N. Mapping of disease-associated variants in admixed populations. Genome Biol. 12, 223 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Tardieu, F. Any trait or trait-related allele can confer drought tolerance: just design the right drought scenario. J. Exp. Bot. 63, 25–31 (2012).

    Article  CAS  PubMed  Google Scholar 

  36. Ludlow, M. M. in Structural and Functional Responses to Environmental Stress (eds Kreeb, K. H, Richter, H. & Minckley, T. M.) 269–281 (SPB Academic, The Hague, 1989).

  37. Kenney, A. M., McKay, J. K., Richards, J. H. & Juenger, T. E. Direct and indirect selection on flowering time, water-use efficiency (WUE, δ13C), and WUE plasticity to drought in Arabidopsis thaliana. Ecol. Evol. 4, 4505–4521 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  38. Bac-Molenaar, J. A., Granier, C., Keurentjes, J. J. B. & Vreugdenhil, D. Genome-wide association mapping of time-dependent growth responses to moderate drought stress in Arabidopsis. Plant Cell Environ. 39, 88–102 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Vasseur, F., Wang, G., Bresson, J., Schwab, R. & Weigel, D. Image-based methods for phenotyping growth dynamics and fitness in large plant populations. Preprint at https://www.biorxiv.org/content/early/2017/10/25/208512 (2017).

  40. Juenger, T. E. et al. Identification and characterization of QTL underlying whole-plant physiology in Arabidopsis thaliana: δ13C, stomatal conductance and transpiration efficiency. Plant Cell Environ. 28, 697–708 (2005).

    Article  CAS  Google Scholar 

  41. McKay, J. K., Richards, J. H. & Mitchell-Olds, T. Genetics of drought adaptation in Arabidopsis thaliana: I. Pleiotropy contributes to genetic correlations among ecological traits. Mol. Ecol. 12, 1137–1151 (2003).

    Article  CAS  PubMed  Google Scholar 

  42. Jarzyniak, K. M. & Jasiński, M. Membrane transporters and drought resistance—a complex issue. Front. Plant Sci. 5, 687 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  43. Swindell, W. R. The association among gene expression responses to nine abiotic stress treatments in Arabidopsis thaliana. Genetics 174, 1811–1824 (2006).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Pauls, S. U., Nowak, C., Bálint, M. & Pfenninger, M. The impact of global climate change on genetic diversity within populations and species. Mol. Ecol. 22, 925–946 (2013).

    Article  PubMed  Google Scholar 

  45. Brown, J. L. et al. Predicting the genetic consequences of future climate change: the power of coupling spatial demography, the coalescent, and historical landscape changes. Am. J. Bot. 103, 153–163 (2016).

    Article  PubMed  Google Scholar 

  46. Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).

    Article  PubMed  Google Scholar 

  47. Catullo, R. A., Ferrier, S. & Hoffmann, A. A. Extending spatial modelling of climate change responses beyond the realized niche: estimating, and accommodating, physiological limits and adaptive evolution. Glob. Ecol. Biogeogr. 24, 1192–1202 (2015).

    Article  Google Scholar 

  48. Moritz, C. & Agudo, R. The future of species under climate change: resilience or decline? Science 341, 504–508 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).

    Article  Google Scholar 

  51. Fournier-Level, A. et al. Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 113, E2812–E2821 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Roux, F., Giancola, S., Durand, S. & Reboud, X. Building of an experimental cline with Arabidopsis thaliana to estimate herbicide fitness cost. Genetics 173, 1023–1031 (2006).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Acknowledgements

We thank R. Wedegärtner for assistance with the greenhouse drought experiment, I. Henderson for the recombination map, and the Petrov, Coop, Ross-Ibarra, Gaut, Schmitt, Weigel and Burbano laboratories for discussions. We thank J. Lasky, X. Picó, A. Hancock, H. Thomassen, T. Mitchell-Olds, J. Mujica, P. Lang and D. Seymour for comments. This work was supported by the President’s Fund of the Max Planck Society, project ‘Darwin’ to H.A.B., as well as central Max Planck Society funds and the European Research Council (AdG IMMUNEMESIS) to D.W.

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Contributions

M.E.-A. conceived and designed the project. G.W. and F.V. helped with and advised on image phenotyping and F.V. provided additional phenotypes. M.E.-A. and W.D. performed chromosome painter analyses. M.E.-A. performed the drought experiment, processed the image data, and designed and carried out the statistical analyses. D.W. and H.A.B. advised and oversaw the project. M.E.-A. wrote the first draft and, together with H.A.B. and D.W., wrote the final manuscript with input from all authors.

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Correspondence to Detlef Weigel.

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Supplementary Methods and Supplementary Figures 1–17.

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Supplementary Video 1

19-frame time series of green-segmented images for one exemplary tray.

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Exposito-Alonso, M., Vasseur, F., Ding, W. et al. Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana . Nat Ecol Evol 2, 352–358 (2018). https://doi.org/10.1038/s41559-017-0423-0

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