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Genomic evidence of human selection on Vavilovian mimicry

Nature Ecology & Evolution volume 3, pages 1474–1482 (2019)Cite this article

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Abstract

Vavilovian mimicry is an evolutionary process by which weeds evolve to resemble domesticated crop plants and is thought to be the result of unintentional selection by humans. Unravelling its molecular mechanisms will extend our knowledge of mimicry and contribute to our understanding of the origin and evolution of agricultural weeds, an important component of crop biology. To this end, we compared mimetic and non-mimetic populations of Echinochloa crus-galli from the Yangtze River basin phenotypically and by genome resequencing, and we show that this weed in rice paddies has evolved a small tiller angle, allowing it to phenocopy cultivated rice at the seedling stage. We demonstrate that mimetic lines evolved from the non-mimetic population as recently as 1,000 yr ago and were subject to a genetic bottleneck, and that genomic regions containing 87 putative plant architecture-related genes (including LAZY1, a key gene controlling plant tiller angle) were under selection during the mimicry process. Our data provide genome-level evidence for the action of human selection on Vavilovian mimicry.

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Fig. 1: Morphology associated with Vavilovian mimicry in E. crus-galli and sampling.
Fig. 2: Phylogenetic and phenotypic differentiation of E. crus-galli in the Yangtze River basin.
Fig. 3: Genomic signatures of positive selection during the mimicry process in E. crus-galli.
Fig. 4: Haplotype diversity of 455 variations within the 87 plant architecture-related genes in NMC and MIC.
Fig. 5: LA1 was putatively under positive selection during mimicry evolution in E. crus-galli.

Data availability

The genomic resequencing and RNA-seq data included in this study were deposited into the BIG data centre (https://bigd.big.ac.cn/) under accession number PRJCA001519.

Code availability

The custom scripts and pipelines used in this study have been deposited in Github (https://github.com/bioinplant/Vavilovian_mimicry).

References

  1. Barrett, S. C. H. Crop mimicry in weeds. Econ. Bot. 37, 255–282 (1983).

    Article  Google Scholar 

  2. Pasteur, G. A classificatory review of mimicry systems. Annu. Rev. Ecol. Syst. 13, 169–199 (1982).

    Article  Google Scholar 

  3. Vavilov, N. I. The origin, variation, immunity and breeding of cultivated plants (translation by K. S. Chester). Chron. Bot. 13, 1–366 (1951).

    Google Scholar 

  4. McElroy, J. S. Vavilovian mimicry: Nikolai Vavilov and his little-known impact on weed science. Weed Sci. 62, 207–216 (2014).

    Article  CAS  Google Scholar 

  5. Dasmahapatra, K. K. et al. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature 487, 94–98 (2012).

    Article  CAS  PubMed Central  Google Scholar 

  6. Iijima, T. et al. Parallel evolution of batesian mimicry supergene in two Papilio butterflies, P. polytes and P. memnon. Sci. Adv. 4, eaao5416 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Jay, P. et al. Supergene evolution triggered by the introgression of a chromosomal inversion. Curr. Biol. 28, 1839–1845 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Kunte, K. et al. doublesex is a mimicry supergene. Nature 507, 229–232 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Martin, A. et al. Diversification of complex butterfly wing patterns by repeated regulatory evolution of a Wnt ligand. Proc. Natl Acad. Sci. USA 109, 12632–12637 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nadeau, N. J. Genes controlling mimetic colour pattern variation in butterflies. Curr. Opin. Insect Sci. 17, 24–31 (2016).

    Article  PubMed  Google Scholar 

  11. Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nishikawa, H. et al. A genetic mechanism for female-limited Batesian mimicry in Papilio butterfly. Nat. Genet. 47, 405–409 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Reed, R. D. et al. optix drives the repeated convergent evolution of butterfly wing pattern mimicry. Science 333, 1137–1141 (2011).

    Article  CAS  PubMed  Google Scholar 

  14. Timmermans, M. J. T. N. et al. Comparative genomics of the mimicry switch in Papilio dardanus. Proc. R. Soc. B 281, 20140465 (2014).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  15. Zhang, W., Dasmahapatra, K. K., Mallet, J., Moreira, G. R. P. & Kronforst, M. R. Genome-wide introgression among distantly related Heliconius butterfly species. Genome Biol. 17, 25 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Gianoli, E. & Carrasco-Urra, F. Leaf mimicry in a climbing plant protects against herbivory. Curr. Biol. 24, 984–987 (2014).

    Article  CAS  PubMed  Google Scholar 

  17. Pannell, J. R. & Farmer, E. E. Mimicry in plants. Curr. Biol. 26, R784–R785 (2016).

    Article  CAS  PubMed  Google Scholar 

  18. Schaefer, H. M. & Ruxton, G. D. Deception in plants: mimicry or perceptual exploitation? Trends Ecol. Evol. 24, 676–685 (2009).

    Article  PubMed  Google Scholar 

  19. Schluter, P. M. & Schiestl, F. P. Molecular mechanisms of floral mimicry in orchids. Trends Plant Sci. 13, 228–235 (2008).

    Article  CAS  PubMed  Google Scholar 

  20. Barlow, B. A. & Wiens, D. Host-parasite resemblance in Australian mistletoes: case for cryptic mimicry. Evolution 31, 69–84 (1977).

    Article  PubMed  Google Scholar 

  21. Kellner, A., Ritz, C. M., Schlittenhardt, P. & Hellwig, F. H. Genetic differentiation in the genus Lithops L. (Ruschioideae, Aizoaceae) reveals a high level of convergent evolution and reflects geographic distribution. Plant Biol. 13, 368–380 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. Barrett, S. C. H. & Wilson, B. F. Colonizing ability in the Echinochloa crus-galli complex (barnyard grass). 2. Seed biology. Can. J. Bot. 61, 556–562 (1983).

    Article  Google Scholar 

  23. Barrett, S. C. H. & Wilson, B. F. Colonizing ability in the Echinochloa crus-galli complex (barnyard grass). 1. Variation in life history. Can. J. Bot. 59, 1844–1860 (1981).

    Article  Google Scholar 

  24. Barrett, S. C. H. in Applied Population Biology (eds Jain, S. K. & Botsford, L.) 91–120 (Kluwer Academic, 1992).

  25. Barrett, S. C. H. in Weed Management in Agroecosystems: Ecological Approaches (eds Altieri, M. & Liebman, M. Z.) 57–75 (CRC, 1988).

  26. Tominaga, T. & Fujimoto, T. Awn of darnel (Lolium temulentum L.) as an anthropogenic dispersal organ: a case study in Malo, south-western Ethiopia. Weed Biol. Manage. 4, 218–221 (2004).

    Article  Google Scholar 

  27. Senda, T. & Tominaga, T. Inheritance mode of the awnlessness of darnel (Lolium temulentum L.). Weed Biol. Manage. 3, 46–48 (2003).

    Article  Google Scholar 

  28. Fuller, D. Q. & Stevens, C. J. Open for competition: domesticates, parasitic domesticoids and the agricultural niche. Archaeol. Int. 20, 110–121 (2017).

    Article  Google Scholar 

  29. Guo, L. B. et al. Echinochloa crus-galli genome analysis provides insight into its adaptation and invasiveness as a weed. Nat. Commun. 8, 1031 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Fuller, D. Q. & Qin, L. Water management and labour in the origins and dispersal of Asian rice. World Archaeol. 41, 88–111 (2009).

    Article  Google Scholar 

  31. Aoki, D. & Yamaguchi, H. Genetic relationship between Echinochloa crus-galli and Echinochloa oryzicola accessions inferred from internal transcribed spacer and chloroplast DNA sequences. Weed Biol. Manage. 8, 233–242 (2008).

    Article  CAS  Google Scholar 

  32. Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Li, P. J. et al. LAZY1 controls rice shoot gravitropism through regulating polar auxin transport. Cell Res. 17, 402–410 (2007).

    Article  CAS  PubMed  Google Scholar 

  34. Wang, Y. D., Zhang, T., Wang, R. C. & Zhao, Y. D. Recent advances in auxin research in rice and their implications for crop improvement. J. Exp. Bot. 69, 255–263 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Strohm, A. K., Baldwin, K. L. & Masson, P. H. Multiple roles for membrane-associated protein trafficking and signaling in gravitropism. Front. Plant Sci. 3, 274 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wang, B., Smith, S. M. & Li, J. Y. Genetic regulation of shoot architecture. Annu. Rev. Plant Biol. 69, 437–468 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Wang, Y. & Li, J. Molecular basis of plant architecture. Annu. Rev. Plant Biol. 59, 253–279 (2008).

    Article  CAS  PubMed  Google Scholar 

  38. Wu, X., Tang, D., Li, M., Wang, K. & Cheng, Z. Loose plant Architecture1, an INDETERMINATE DOMAIN protein involved in shoot gravitropism, regulates plant architecture in rice. Plant Physiol. 161, 317–329 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Zhang, N. et al. A core regulatory pathway controlling rice tiller angle mediated by the LAZY1-dependent asymmetric distribution of auxin. Plant Cell 30, 1461–1475 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hu, X. M. et al. The U-Box E3 ubiquitin ligase TUD1 functions with a heterotrimeric G alpha subunit to regulate brassinosteroid-mediated growth in rice. PLoS Genet. 9, e1003391 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Qiao, S. L. et al. The RLA1/SMOS1 transcription factor functions with OsBZR1 to regulate brassinosteroid signaling and rice architecture. Plant Cell 29, 292–309 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tong, H. N. et al. DWARF AND LOW-TILLERING acts as a direct downstream target of a GSK3/SHAGGY-like kinase to mediate brassinosteroid responses in rice. Plant Cell 24, 2562–2577 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yamamuro, C. et al. Loss of function of a rice brassinosteroid insensitive1 homolog prevents internode elongation and bending of the lamina joint. Plant Cell 12, 1591–1605 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Dong, Z. B. et al. Maize LAZY1 mediates shoot gravitropism and inflorescence development through regulating auxin transport, auxin signaling, and light response. Plant Physiol. 163, 1306–1322 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Yoshihara, T. & Iino, M. Identification of the gravitropism-related rice gene LAZY1 and elucidation of LAZY1-dependent and -independent gravity signaling pathways. Plant Cell Physiol. 48, 678–688 (2007).

    Article  CAS  PubMed  Google Scholar 

  46. Yoshihara, T., Spalding, E. P. & Iino, M. AtLAZY1 is a signaling component required for gravitropism of the Arabidopsis thaliana inflorescence. Plant J. 74, 267–279 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. You, X. The rice production and farming technique in the Song Dynasty. Chin. J. Rice Sci. 1, 35–41 (1986).

    Google Scholar 

  48. Patel, R. K. & Jain, M. NGS QC toolkit: a toolkit for quality control of next generation sequencing data. PLoS ONE 7, e30619 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strainw1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  56. Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Molina, J. et al. Molecular evidence for a single evolutionary origin of domesticated rice. Proc. Natl Acad. Sci. USA 108, 8351–8356 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Tajima, F. Statistical-method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Pfeifer, B., Wittelsburger, U., Ramos-Onsins, S. E. & Lercher, M. J. PopGenome: an efficient swiss army knife for population genomic analyses in R. Mol. Biol. Evol. 31, 1929–1936 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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  PubMed Central  Google Scholar 

  63. Shin, J. H., Blay, S., McNeney, B. & Graham, J. LDheatmap: an R function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. J. Stat. Softw. 16, Code Snippet 3 (2006).

    Article  Google Scholar 

  64. Huang, X. et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497–501 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Qiu, J. et al. Genomic variation associated with local adaptation of weedy rice during de-domestication. Nat. Commun. 8, 15323 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43–49 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank H. Yamaguchi, S. Ge and G. Chen for their useful comments. This work was supported by the National Natural Science Foundation (grant number 9143511), the Zhejiang Natural Science Foundation (grant number LZ17C130001), the Jiangsu Collaborative Innovation Center for Modern Crop Production, the 111 Project (grant number B17039) and the China Agriculture Research System (grant number CARS-01-02A).

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Author notes

  1. These authors contributed equally: Chu-Yu Ye, Wei Tang, Dongya Wu.

Authors and Affiliations

  1. Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China

    Chu-Yu Ye, Dongya Wu, Lei Jia, Jie Qiu, Meihong Chen, Lingfeng Mao, Feng Lin, Haiming Xu & Longjiang Fan

  2. State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China

    Wei Tang, Xiaoyue Yu & Yongliang Lu

  3. State Key Laboratory of Plant Genomics and National Center for Plant Gene Research (Beijing), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Yonghong Wang

  4. Department of Biology, Washington University in St. Louis, St. Louis, MO, USA

    Kenneth M. Olsen

  5. Department of Biology, University of Virginia, Charlottesville, VA, USA

    Michael P. Timko

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  1. Chu-Yu Ye
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Contributions

L.F. conceived the study. D.W., C.-Y.Y., L.J., J.Q., M.C. and F.L. analysed the data. W.T., Y.L. and X.Y. performed the phenotyping. M.P.T., K.M.O., Y.W. and H.X. advised on the data analysis. M.P.T. edited the manuscript. C.-Y.Y., L.F. and D.W. wrote the manuscript. All authors read and contributed to the manuscript.

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Correspondence to Longjiang Fan.

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Ye, CY., Tang, W., Wu, D. et al. Genomic evidence of human selection on Vavilovian mimicry. Nat Ecol Evol 3, 1474–1482 (2019). https://doi.org/10.1038/s41559-019-0976-1

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