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Convergent molecular evolution among ash species resistant to the emerald ash borer

Abstract

Recent studies show that molecular convergence plays an unexpectedly common role in the evolution of convergent phenotypes. We exploited this phenomenon to find candidate loci underlying resistance to the emerald ash borer (EAB, Agrilus planipennis), the United States’ most costly invasive forest insect to date, within the pan-genome of ash trees (the genus Fraxinus). We show that EAB-resistant taxa occur within three independent phylogenetic lineages. In genomes from these resistant lineages, we detect 53 genes with evidence of convergent amino acid evolution. Gene-tree reconstruction indicates that, for 48 of these candidates, the convergent amino acids are more likely to have arisen via independent evolution than by another process such as hybridization or incomplete lineage sorting. Seven of the candidate genes have putative roles connected to the phenylpropanoid biosynthesis pathway and 17 relate to herbivore recognition, defence signalling or programmed cell death. Evidence for loss-of-function mutations among these candidates is more frequent in susceptible species than in resistant ones. Our results on evolutionary relationships, variability in resistance, and candidate genes for defence response within the ash genus could inform breeding for EAB resistance, facilitating ecological restoration in areas invaded by this beetle.

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Fig. 1: Fraxinus species’ resistance to EAB in bioassays.
Fig. 2: Species-tree for the genus Fraxinus.
Fig. 3: Predicted protein structure for OG15551.

Data availability

Underlying data for Fig. 1 are available in Supplementary Tables 1 and 2. All trimmed read data and genome assemblies have been deposited in the European Nucleotide Archive under accession no. PRJEB20151. The genome assemblies are also available to download at: http://www.ashgenome.org.

Code availability

The custom scripts used is this study have been deposited in GitHub: https://github.com/lkelly3/eab-ms-scripts.

References

  1. 1.

    Pautasso, M., Aas, G., Queloz, V. & Holdenrieder, O. European ash (Fraxinus excelsior) dieback – a conservation biology challenge. Biol. Conserv. 158, 37–49 (2013).

    Google Scholar 

  2. 2.

    MacFarlane, D. W. & Meyer, S. P. Characteristics and distribution of potential ash tree hosts for emerald ash borer. For. Ecol. Manage. 213, 15–24 (2005).

    Google Scholar 

  3. 3.

    Boyd, I. L., Freer-Smith, P. H., Gilligan, C. A. & Godfray, H. C. J. The consequence of tree pests and diseases for ecosystem services. Science 342, 1235773 (2013).

    CAS  PubMed  Google Scholar 

  4. 4.

    Herms, D. A. & McCullough, D. G. Emerald ash borer invasion of North America: history, biology, ecology, impacts, and management. Annu. Rev. Entomol. 59, 13–30 (2014).

    CAS  PubMed  Google Scholar 

  5. 5.

    Orlova-Bienkowskaja, M. J. Ashes in Europe are in danger: the invasive range of Agrilus planipennis in European Russia is expanding. Biol. Invasions 16, 1345–1349 (2014).

    Google Scholar 

  6. 6.

    McCullough, D. G. Challenges, tactics and integrated management of emerald ash borer in North America. Forestry 93, 197–211 (2019).

  7. 7.

    Drogvalenko, A. N., Orlova-Bienkowskaja, M. J. & Bieńkowski, A. O. Record of the emerald ash borer (Agrilus planipennis) in Ukraine is confirmed. Insects 10, 338 (2019).

  8. 8.

    Semizer-Cuming, D., Krutovsky, K. V., Baranchikov, Y. N., Kjӕr, E. D. & Williams, C. G. Saving the world’s ash forests calls for international cooperation now. Nat. Ecol. Evol. 3, 141–144 (2019).

    PubMed  Google Scholar 

  9. 9.

    Evans, H. F., Williams, D., Hoch, G., Loomans, A. & Marzano, M. Developing a European toolbox to manage potential invasion by emerald ash borer (Agrilus planipennis) and bronze birch borer (Agrilus anxius), important pests of ash and birch. Forestry 93, 187–196 (2020).

  10. 10.

    Baranchikov, Y., Mozolevskaya, E., Yurchenko, G. & Kenis, M. Occurrence of the emerald ash borer, Agrilus planipennis in Russia and its potential impact on European forestry. Bull. OEPP 38, 233–238 (2008).

    Google Scholar 

  11. 11.

    Zhao, T. et al. Induced outbreaks of indigenous insect species by exotic tree species. Acta Entomol. Sin. 50, 826–833 (2007).

    Google Scholar 

  12. 12.

    Liu, H. et al. Exploratory survey for the emerald ash borer, Agrilus planipennis (Coleoptera: Buprestidae), and its natural enemies in China. Great Lakes Entomol. 36, 191–204 (2003).

    Google Scholar 

  13. 13.

    Wei, X. et al. Emerald ash borer, Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), in China: a review and distribution survey. Acta Entomol. Sin. 47, 679–685 (2004).

    Google Scholar 

  14. 14.

    Orlova-Bienkowskaja, M. J. & Volkovitsh, M. G. Are native ranges of the most destructive invasive pests well known? A case study of the native range of the emerald ash borer, Agrilus planipennis (Coleoptera: Buprestidae). Biol. Invasions 20, 1275–1286 (2018).

    Google Scholar 

  15. 15.

    Showalter, D. N., Villari, C., Herms, D. A. & Bonello, P. Drought stress increased survival and development of emerald ash borer larvae on coevolved Manchurian ash and implicates phloem-based traits in resistance. Agric. For. Entomol. 20, 170–179 (2018).

    Google Scholar 

  16. 16.

    Whitehill, J. G. A. et al. Interspecific proteomic comparisons reveal ash phloem genes potentially involved in constitutive resistance to the emerald ash borer. PLoS ONE 6, e24863 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Whitehill, J. G. A. et al. Interspecific comparison of constitutive ash phloem phenolic chemistry reveals compounds unique to Manchurian ash, a species resistant to emerald ash borer. J. Chem. Ecol. 38, 499–511 (2012).

    CAS  PubMed  Google Scholar 

  18. 18.

    Lane, T. et al. The green ash transcriptome and identification of genes responding to abiotic and biotic stresses. BMC Genomics 17, 702 (2016).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Sackton, T. B. et al. Convergent regulatory evolution and loss of flight in paleognathous birds. Science 364, 74–78 (2019).

    CAS  PubMed  Google Scholar 

  20. 20.

    Arnold, B. J. et al. Borrowed alleles and convergence in serpentine adaptation. Proc. Natl Acad. Sci. USA 113, 8320–8325 (2016).

    CAS  PubMed  Google Scholar 

  21. 21.

    Hu, Y. et al. Comparative genomics reveals convergent evolution between the bamboo-eating giant and red pandas. Proc. Natl Acad. Sci. USA 114, 1081–1086 (2017).

    CAS  PubMed  Google Scholar 

  22. 22.

    Yang, X. et al. The Kalanchoë genome provides insights into convergent evolution and building blocks of crassulacean acid metabolism. Nat. Commun. 8, 1899 (2017).

  23. 23.

    Hill, J. et al. Recurrent convergent evolution at amino acid residue 261 in fish rhodopsin. Proc. Natl Acad. Sci. USA 116, 18473–18478 (2019).

    CAS  PubMed  Google Scholar 

  24. 24.

    Zhen, Y., Aardema, M. L., Medina, E. M., Schumer, M. & Andolfatto, P. Parallel molecular evolution in an herbivore community. Science 337, 1634–1637 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Wallander, E. Systematics and floral evolution in Fraxinus (Oleaceae). Belg. Dendrol. Belg. 2012, 39–58 (2012).

    Google Scholar 

  26. 26.

    Koch, J. L., Carey, D. W., Mason, M. E., Poland, T. M. & Knight, K. S. Intraspecific variation in Fraxinus pennsylvanica responses to emerald ash borer (Agrilus planipennis). New For. (Dordr.) 46, 995–1011 (2015).

    Google Scholar 

  27. 27.

    Sollars, E. S. A. et al. Genome sequence and genetic diversity of European ash trees. Nature 541, 212–216 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Cruz, F. et al. Genome sequence of the olive tree, Olea europaea. Gigascience 5, 29 (2016).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Hellsten, U. et al. Fine-scale variation in meiotic recombination in Mimulus inferred from population shotgun sequencing. Proc. Natl Acad. Sci. USA 110, 19478–19482 (2013).

  30. 30.

    Tomato Genome Consortium. The tomato genome sequence provides insights into fleshy fruit evolution. Nature 485, 635–641 (2012).

    Google Scholar 

  31. 31.

    Wright, J. W. New chromosome counts in Acer and Fraxinus. Morris Arb. Bull. 8, 33–34 (1957).

    Google Scholar 

  32. 32.

    Bernards, M. A. & Båstrup-Spohr, L. in Induced Plant Resistance to Herbivory (ed. Schaller, A.) 189–211 (Springer, 2008).

  33. 33.

    Stahl, E., Hilfiker, O. & Reymond, P. Plant–arthropod interactions: who is the winner? Plant J. 93, 703–728 (2018).

    CAS  PubMed  Google Scholar 

  34. 34.

    Abdulrazzak, N. et al. A coumaroyl-ester-3-hydroxylase insertion mutant reveals the existence of nonredundant meta-hydroxylation pathways and essential roles for phenolic precursors in cell expansion and plant growth. Plant Physiol. 140, 30–48 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Rupasinghe, S., Baudry, J. & Schuler, M. A. Common active site architecture and binding strategy of four phenylpropanoid P450s from Arabidopsis thaliana as revealed by molecular modeling. Protein Eng. 16, 721–731 (2003).

    CAS  PubMed  Google Scholar 

  36. 36.

    Dolan, W. L. & Chapple, C. Conservation and divergence of mediator structure and function: insights from plants. Plant Cell Physiol. 58, 4–21 (2017).

    CAS  PubMed  Google Scholar 

  37. 37.

    Bonawitz, N. D. et al. Disruption of mediator rescues the stunted growth of a lignin-deficient Arabidopsis mutant. Nature 509, 376–380 (2014).

    CAS  PubMed  Google Scholar 

  38. 38.

    Dolan, W. L. & Chapple, C. Transcriptome analysis of four Arabidopsis thaliana mediator tail mutants reveals overlapping and unique functions in gene regulation. G3 (Bethesda) 8, 3093–3108 (2018).

    CAS  Google Scholar 

  39. 39.

    Xu, Z. et al. Functional genomic analysis of Arabidopsis thaliana glycoside hydrolase family 1. Plant Mol. Biol. 55, 343–367 (2004).

    CAS  PubMed  Google Scholar 

  40. 40.

    Rigsby, C. M., Herms, D. A., Bonello, P. & Cipollini, D. Higher activities of defense-associated enzymes may contribute to greater resistance of Manchurian ash to emerald ash borer than a closely related and susceptible congener. J. Chem. Ecol. 42, 782–792 (2016).

    CAS  PubMed  Google Scholar 

  41. 41.

    Villari, C., Herms, D. A., Whitehill, J. G. A., Cipollini, D. & Bonello, P. Progress and gaps in understanding mechanisms of ash tree resistance to emerald ash borer, a model for wood-boring insects that kill angiosperms. New Phytol. 209, 63–79 (2016).

    CAS  PubMed  Google Scholar 

  42. 42.

    Erb, M. & Reymond, P. Molecular interactions between plants and insect herbivores. Annu. Rev. Plant Biol. 70, 527–557 (2019).

    CAS  PubMed  Google Scholar 

  43. 43.

    Huang, J., Zhu, C. & Li, X. SCFSNIPER4 controls the turnover of two redundant TRAF proteins in plant immunity. Plant J. 95, 504–515 (2018).

    CAS  PubMed  Google Scholar 

  44. 44.

    Hua, Z. & Vierstra, R. D. The cullin-RING ubiquitin-protein ligases. Annu. Rev. Plant Biol. 62, 299–334 (2011).

    CAS  PubMed  Google Scholar 

  45. 45.

    Erb, M., Meldau, S. & Howe, G. A. Role of phytohormones in insect-specific plant reactions. Trends Plant Sci. 17, 250–259 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Berens, M. L., Berry, H. M., Mine, A., Argueso, C. T. & Tsuda, K. Evolution of hormone signaling networks in plant defense. Annu. Rev. Phytopathol. 55, 401–425 (2017).

    CAS  PubMed  Google Scholar 

  47. 47.

    Lin, S.-H. et al. Mutation of the Arabidopsis NRT1.5 nitrate transporter causes defective root-to-shoot nitrate transport. Plant Cell 20, 2514–2528 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Huysmans, M., Lema, A. S., Coll, N. S. & Nowack, M. K. Dying two deaths – programmed cell death regulation in development and disease. Curr. Opin. Plant Biol. 35, 37–44 (2017).

    CAS  PubMed  Google Scholar 

  49. 49.

    Bellin, D., Asai, S., Delledonne, M. & Yoshioka, H. Nitric oxide as a mediator for defense responses. Mol. Plant Microbe Interact. 26, 271–277 (2013).

    CAS  PubMed  Google Scholar 

  50. 50.

    Zebelo, S. A. & Maffei, M. E. Role of early signalling events in plant–insect interactions. J. Exp. Bot. 66, 435–448 (2015).

    CAS  PubMed  Google Scholar 

  51. 51.

    Seifi, H. S. & Shelp, B. J. Spermine differentially refines plant defense responses against biotic and abiotic stresses. Front. Plant Sci. 10, 117 (2019).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Whitehill, J. G. A., Rigsby, C., Cipollini, D., Herms, D. A. & Bonello, P. Decreased emergence of emerald ash borer from ash treated with methyl jasmonate is associated with induction of general defense traits and the toxic phenolic compound verbascoside. Oecologia 176, 1047–1059 (2014).

    PubMed  Google Scholar 

  53. 53.

    Nelson, R., Wiesner-Hanks, T., Wisser, R. & Balint-Kurti, P. Navigating complexity to breed disease-resistant crops. Nat. Rev. Genet. 19, 21–33 (2018).

    CAS  PubMed  Google Scholar 

  54. 54.

    Radville, L., Chaves, A. & Preisser, E. L. Variation in plant defense against invasive herbivores: evidence for a hypersensitive response in eastern hemlocks (Tsuga canadensis). J. Chem. Ecol. 37, 592–597 (2011).

    CAS  PubMed  Google Scholar 

  55. 55.

    Hilker, M. & Fatouros, N. E. Resisting the onset of herbivore attack: plants perceive and respond to insect eggs. Curr. Opin. Plant Biol. 32, 9–16 (2016).

    PubMed  Google Scholar 

  56. 56.

    Kim, C. Y., Bove, J. & Assmann, S. M. Overexpression of wound-responsive RNA-binding proteins induces leaf senescence and hypersensitive-like cell death. New Phytol. 180, 57–70 (2008).

    CAS  PubMed  Google Scholar 

  57. 57.

    Bollhöner, B. et al. The function of two type II metacaspases in woody tissues of Populus trees. New Phytol. 217, 1551–1565 (2018).

    PubMed  Google Scholar 

  58. 58.

    Altmann, S. et al. Transcriptomic basis for reinforcement of elm antiherbivore defence mediated by insect egg deposition. Mol. Ecol. 27, 4901–4915 (2018).

    CAS  PubMed  Google Scholar 

  59. 59.

    Rebek, E. J., Herms, D. A. & Smitley, D. R. Interspecific variation in resistance to emerald ash borer (Coleoptera: Buprestidae) among North American and Asian ash (Fraxinus spp.). Environ. Entomol. 37, 242–246 (2008).

    PubMed  Google Scholar 

  60. 60.

    Wei, Z. & Green, P. S. Fraxinus. Flora China 15, 273–279 (1996).

    Google Scholar 

  61. 61.

    Davidson, C. G. ‘Northern Treasure’ and ‘Northern Gem’ hybrid ash. HortScience 34, 151–152 (1999).

    Google Scholar 

  62. 62.

    Koch, J. L. et al. Strategies for selecting and breeding EAB-resistant ash. In Proc. 22nd US Department of Agriculture Interagency Research Symposium on Invasive Species (eds McManus, K. A. & Gottschalk, K. W.) 33–35 (US Department of Agriculture, Forest Service, Northern Research Station, 2011).

  63. 63.

    Duan, J. J., Larson, K., Watt, T., Gould, J. & Lelito, J. P. Effects of host plant and larval density on intraspecific competition in larvae of the emerald ash borer (Coleoptera: Buprestidae). Environ. Entomol. 42, 1193–1200 (2013).

    PubMed  Google Scholar 

  64. 64.

    Cappaert, D., McCullough, D. G., Poland, T. M. & Siegert, N. W. Emerald ash borer in North America: a research and regulatory challenge. Am. Entomol. 51, 152–165 (2005).

    Google Scholar 

  65. 65.

    Chamorro, M. L., Volkovitsh, M. G., Poland, T. M., Haack, R. A. & Lingafelter, S. W. Preimaginal stages of the emerald ash borer, Agrilus planipennis Fairmaire (Coleoptera: Buprestidae): an invasive pest on ash trees (Fraxinus). PLoS ONE 7, e33185 (2012).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Pellicer, J., Kelly, L. J., Leitch, I. J., Zomlefer, W. B. & Fay, M. F. A universe of dwarfs and giants: genome size and chromosome evolution in the monocot family Melanthiaceae. New Phytol. 201, 1484–1497 (2014).

    CAS  PubMed  Google Scholar 

  67. 67.

    Loureiro, J., Rodriguez, E., Dolezel, J. & Santos, C. Two new nuclear isolation buffers for plant DNA flow cytometry: a test with 37 species. Ann. Bot. 100, 875–888 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Doležel, J., Binarová, P. & Lucretti, S. Analysis of nuclear DNA content in plant cells by flow cytometry. Biol. Plant. 31, 113–120 (1989).

    Google Scholar 

  69. 69.

    Bennett Michael, D. & Smith, J. B. Nuclear DNA amounts in angiosperms. Philos. Trans. R. Soc. Lond. B 334, 309–345 (1991).

    Google Scholar 

  70. 70.

    Whittemore, A. T. & Xia, Z.-L. Genome size variation in elms (Ulmus spp.) and related genera. HortScience 52, 547–553 (2017).

    Google Scholar 

  71. 71.

    Doležel, J. et al. Plant genome size estimation by flow cytometry: inter-laboratory comparison. Ann. Bot. 82, 17–26 (1998).

    Google Scholar 

  72. 72.

    Greilhuber, J. & Obermayer, R. Genome size and maturity group in Glycine max (soybean). Heredity 78, 547–551 (1997).

    Google Scholar 

  73. 73.

    Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 19, 11–15 (1987).

    Google Scholar 

  74. 74.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  75. 75.

    Joshi, N. A. & Fass, J. N. Sickle: a sliding-window, adaptive, quality-based trimming tool for fastq files (2011); https://github.com/najoshi/sickle

  76. 76.

    Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Boetzer, M., Henkel, C. V., Jansen, H. J., Butler, D. & Pirovano, W. Scaffolding pre-assembled contigs using SSPACE. Bioinformatics 27, 578–579 (2011).

    CAS  PubMed  Google Scholar 

  78. 78.

    Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

    Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).

  80. 80.

    Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Keilwagen, J. et al. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 44, e89 (2016).

    PubMed  PubMed Central  Google Scholar 

  82. 82.

    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  PubMed  PubMed Central  Google Scholar 

  83. 83.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Altenhoff, A. M., Gil, M., Gonnet, G. H. & Dessimoz, C. Inferring hierarchical orthologous groups from orthologous gene pairs. PLoS ONE 8, e53786 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Altenhoff, A. M. et al. The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements. Nucleic Acids Res. 43, D240–D249 (2015).

    CAS  PubMed  Google Scholar 

  86. 86.

    Goodstein, D. M. et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 40, D1178–D1186 (2012).

    CAS  PubMed  Google Scholar 

  87. 87.

    Wallander, E. Systematics of Fraxinus (Oleaceae) and evolution of dioecy. Plant Syst. Evol. 273, 25–49 (2008).

    Google Scholar 

  88. 88.

    Hinsinger, D. D. et al. The phylogeny and biogeographic history of ashes (Fraxinus, Oleaceae) highlight the roles of migration and vicariance in the diversification of temperate trees. PLoS ONE 8, e80431 (2013).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Sela, I., Ashkenazy, H., Katoh, K. & Pupko, T. GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters. Nucleic Acids Res. 43, W7–W14 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000).

    CAS  PubMed  Google Scholar 

  92. 92.

    Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Ané, C., Larget, B., Baum, D. A., Smith, S. D. & Rokas, A. Bayesian estimation of concordance among gene trees. Mol. Biol. Evol. 24, 412–426 (2007).

    PubMed  Google Scholar 

  94. 94.

    Larget, B. R., Kotha, S. K., Dewey, C. N. & Ané, C. BUCKy: gene tree/species tree reconciliation with Bayesian concordance analysis. Bioinformatics 26, 2910–2911 (2010).

    CAS  PubMed  Google Scholar 

  95. 95.

    Castoe, T. A. et al. Evidence for an ancient adaptive episode of convergent molecular evolution. Proc. Natl Acad. Sci. USA 106, 8986–8991 (2009).

    CAS  PubMed  Google Scholar 

  96. 96.

    Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

    CAS  PubMed  Google Scholar 

  97. 97.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

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

    PubMed  PubMed Central  Google Scholar 

  99. 99.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Martin, M. et al. WhatsHap: fast and accurate read-based phasing. Preprint at bioRxiv https://doi.org/10.1101/085050 (2016).

  102. 102.

    Milne, I. et al. Using Tablet for visual exploration of second-generation sequencing data. Brief. Bioinform. 14, 193–202 (2013).

  103. 103.

    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Pond, S. L. K., Frost, S. D. W. & Muse, S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics 21, 676–679 (2005).

    CAS  PubMed  Google Scholar 

  105. 105.

    Kosakovsky Pond, S. L., Posada, D., Gravenor, M. B., Woelk, C. H. & Frost, S. D. W. Automated phylogenetic detection of recombination using a genetic algorithm. Mol. Biol. Evol. 23, 1891–1901 (2006).

    PubMed  Google Scholar 

  106. 106.

    Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2013).

    CAS  PubMed  Google Scholar 

  107. 107.

    Liu, Z. et al. Evolutionary interplay between sister cytochrome P450 genes shapes plasticity in plant metabolism. Nat. Commun. 7, 13026 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Altenhoff, A. M. et al. The OMA orthology database in 2018: retrieving evolutionary relationships among all domains of life through richer web and programmatic interfaces. Nucleic Acids Res. 46, D477–D485 (2018).

    CAS  PubMed  Google Scholar 

  109. 109.

    Crooks, G. E., Hon, G., Chandonia, J.-M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. 110.

    Alexa, A., Rahnenführer, J. & Lengauer, T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22, 1600–1607 (2006).

    CAS  PubMed  Google Scholar 

  111. 111.

    Alexa, A. & Rahnenfuhrer, J. topGO: enrichment analysis for gene ontology R Package v.2.32.0 (2016).

  112. 112.

    R Core Team et al. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2013).

  113. 113.

    Petersen, T. N., Brunak, S., von Heijne, G. & Nielsen, H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods 8, 785–786 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Käll, L., Krogh, A. & Sonnhammer, E. L. L. Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server. Nucleic Acids Res. 35, W429–W432 (2007).

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Källberg, M. et al. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 7, 1511–1522 (2012).

    PubMed  PubMed Central  Google Scholar 

  116. 116.

    Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

    Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119.

    Dallakyan, S. & Olson, A. J. Small-molecule library screening by docking with PyRx. Methods Mol. Biol. 1263, 243–250 (2015).

    CAS  PubMed  Google Scholar 

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Acknowledgements

This research used Queen Mary’s Apocrita HPC facility, supported by QMUL Research-IT (https://doi.org/10.5281/zenodo.438045). We thank J. Carlson for providing F. pennsylvanica DNA; T. Baxter, S. Brockington, P. Brownless, D. Crowley, S. Honey, R. Irvine, R. Jinks, P. Jones, T. Kirkham, H. McAllister, I. Parkinson and S. Redstone for help with obtaining Fraxinus materials from UK collections; T. Poland for providing EAB eggs; M. Miller for propagating trees for the bioassays; R. Matko for preparation of voucher specimens; J. Pellicer for advice on flow cytometry; P. Howard and M. Struebig for advice on DNA extractions; J. Keilwagen for help with GeMoMa; K. Davies and J. Parker for help with convergence analysis software; the Evolution Labchat group and Rossiter Lab at QMUL for discussions; and R. Rose and J. Sayers for advice on protein-modelling analyses. This project was funded by the Living with Environmental Change Tree Health and Plant Biosecurity Initiative – Phase 2 (grant no. BB/L012162/1), funded jointly by BBSRC, Defra, ESRC, Forestry Commission, NERC and the Scottish Government. R.J.A.B. acknowledges additional support from the DEFRA Future Proofing Plant Health scheme. R.J.A.B. and L.J.K. acknowledge additional support from the Erica Waltraud Albrecht Endowment Fund. W.J.P. was funded by the Walsh Scholarship Programme of the Department of Agriculture, Food and the Marine, Ireland. E.D.C. was supported by the Marie Skłodowska-Curie Individual Fellowship ‘FraxiFam’ (grant agreement no. 660003).

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R.J.A.B. conceived and oversaw the project. L.J.K. and R.J.A.B. wrote the manuscript, with input from J.L.K., W.J.P. and S.J.R. L.J.K. conducted gene annotation, orthologue inference, convergence analyses, calling and analysis of variants, GO enrichment analysis and phylogenetic analyses. L.J.K., W.C. and A.T.W. performed genome size estimation by flow cytometry. L.J.K., W.C., E.D.C. and D.W.C. extracted DNA. L.J.K. and E.D.C. assembled the genomes. J.L.K. conceived and oversaw the EAB bioassays. D.W.C. conducted the EAB bioassays. J.L.K. and M.E.M. analysed EAB bioassay data. W.J.P. conducted protein-modelling analyses. S.J.R. advised on convergence analyses.

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Correspondence to Laura J. Kelly or Richard J. A. Buggs.

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Extended data

Extended Data Fig. 1 Predicted protein structures for selected candidate loci.

a, Predicted protein structure for OG36502, modelled using the protein sequence for Fraxinus platypoda. The serine/asparagine variant at the site where convergence was detected is highlighted; the serine is a putative phosphorylation site. b, Predicted protein structure for OG40061, modelled using the protein sequence for F. mandshurica. The asparagine/serine variant at the site where convergence was detected is highlighted; the serine is a putative phosphorylation site. The putative substrate, NADP, is shown docked within the predicted active site. c, Predicted protein structure for OG38407, modelled using the protein sequence for F. mandshurica. The aspartic acid/asparagine variant at the site where convergence was detected is highlighted; the site falls within a leucine rich repeat region (LRR; shaded blue) which is predicted to span from position 111–237 within the protein sequence (detected using the GenomeNet MOTIF tool (www.genome.jp/tools/motif/), searching against the NCBI-CDD and Pfam databases with default parameters; the LRR region was identified as positions 111–237 with an e-value of 1e-05). d, Predicted protein structure for OG21033, modelled using the protein sequence for F. platypoda. The lysine/glutamine at the site where convergence was detected is highlighted. The putative substrate, β-D-Glcp-(1 → 3)-β-D-GlcpA-(1 → 4)-β-D-Glcp, is shown docked within the predicted active site.

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Supplementary Information

Supplementary Figs. 1–2 and Notes 1–6.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–10.

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Kelly, L.J., Plumb, W.J., Carey, D.W. et al. Convergent molecular evolution among ash species resistant to the emerald ash borer. Nat Ecol Evol 4, 1116–1128 (2020). https://doi.org/10.1038/s41559-020-1209-3

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