Hybridization and introgression drive genome evolution of Dutch elm disease pathogens

Abstract

Hybridization and the resulting introgression can drive the success of invasive species via the rapid acquisition of adaptive traits. The Dutch elm disease pandemics in the past 100 years were caused by three fungal lineages with permeable reproductive barriers: Ophiostoma ulmi, Ophiostoma novo-ulmi subspecies novo-ulmi and Ophiostoma novo-ulmi subspecies americana. Using whole-genome sequences and growth phenotyping of a worldwide collection of isolates, we show that introgression has been the main driver of genomic diversity and that it impacted fitness-related traits. Introgressions contain genes involved in host–pathogen interactions and reproduction. Introgressed isolates have enhanced growth rate at high temperature and produce different necrosis sizes on an in vivo model for pathogenicity. In addition, lineages diverge in many pathogenicity-associated genes and exhibit differential mycelial growth in the presence of a proxy of a host defence compound, implying an important role of host trees in the molecular and functional differentiation of these pathogens.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Clustering analyses based on genomic variation of worldwide Ophiostoma species samples show among-lineage admixture.
Fig. 2: Introgression between DED-causing Ophiostoma lineages is frequent.
Fig. 3: The most diverse genomic regions in ONU derive from recent introgression from OU.
Fig. 4: Hybridization and introgression have a strong impact on growth rate and virulence.

Data availability

Read sequence data that support the findings of this study have been deposited in the Sequence Read Archive repository under BioProject number PRJNA566197. Genome sequence variant file (VCF) and phenotyping data are available on Figshare (https://doi.org/10.6084/m9.figshare.11663811).

Code availability

All custom codes, as well as descriptions, versions and URLs of all open-source software used in this study are provided at https://github.com/Landrylab/Ophiostoma_Hybridization_2019.

References

  1. 1.

    Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Bennett, P. M. Plasmid encoded antibiotic resistance: acquisition and transfer of antibiotic resistance genes in bacteria. Br. J. Pharmacol. 153, S347–S357 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Stukenbrock, E. H. The role of hybridization in the evolution and emergence of new fungal plant pathogens. Phytopathology 106, 104–112 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Depotter, J. R., Seidl, M. F., Wood, T. A. & Thomma, B. P. Interspecific hybridization impacts host range and pathogenicity of filamentous microbes. Curr. Opin. Microbiol. 32, 7–13 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Barton, N. H. The role of hybridization in evolution. Mol. Ecol. 10, 551–568 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Schardl, C. L. & Craven, K. D. Interspecific hybridization in plant-associated fungi and oomycetes: a review. Mol. Ecol. 12, 2861–2873 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Brasier, C. M. Rapid evolution of introduced plant pathogens via interspecific hybridization: hybridization is leading to rapid evolution of Dutch elm disease and other fungal plant pathogens. Bioscience 51, 123–133 (2001).

    Article  Google Scholar 

  8. 8.

    Olson, A. & Stenlid, J. Pathogenic fungal species hybrids infecting plants. Microbes Infect. 4, 1353–1359 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Fisher, M. C., Hawkins, N. J., Sanglard, D. & Gurr, S. J. Worldwide emergence of resistance to antifungal drugs challenges human health and food security. Science 360, 739–742 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Paoletti, M., Buck, K. W. & Brasier, C. M. Selective acquisition of novel mating type and vegetative incompatibility genes via interspecies gene transfer in the globally invading eukaryote Ophiostoma novo-ulmi. Mol. Ecol. 15, 249–262 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Brasier, C. The rise of the hybrid fungi. Nature 405, 134–135 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Brasier, C. M. The population biology of Dutch elm disease: its principal features and some implications for other host–pathogen systems. Adv. Plant Pathol. 5, 53–118 (1986).

    Google Scholar 

  14. 14.

    Brasier, C. M., Lea, J. & Rawlings, M. K. The aggressive and non-aggressive strains of Ceratocystis ulmi have different temperature optima for growth. Trans. Br. Mycol. Soc. 76, 213–218 (1981).

    Article  Google Scholar 

  15. 15.

    Brasier, C. M. Inheritance of pathogenicity and cultural characters in Ceratocystis ulmi; hybridization of protoperithecial and non-aggressive strains. Trans. Br. Mycol. Soc. 68, 45–52 (1977).

    Article  Google Scholar 

  16. 16.

    Pipe, N. D., Buck, K. W. & Brasier, C. M. Molecular relationships between Ophiostoma ulmi and the NAN and EAN races of O. novo-ulmi determined by RAPD markers. Mycol. Res. 99, 653–658 (1995).

    Article  Google Scholar 

  17. 17.

    Brasier, C. M. Comparison of pathogencity and cultural characteristics in the EAN and NAN aggressive subgroups of Ophiostoma ulmi. Trans. Br. Mycol. Soc. 87, 1–13 (1986).

    Article  Google Scholar 

  18. 18.

    Brasier, C. M. & Kirk, S. A. Designation of the EAN and NAN races of Ophiostoma novo-ulmi as subspecies. Mycol. Res. 105, 547–554 (2001).

    Article  Google Scholar 

  19. 19.

    Gibbs, J. N. & Brasier, C. M. Correlation between cultural characters and pathogenicity in Ceratocystis ulmi from Britain, Europe and America. Nature 241, 381–383 (1973).

    Article  Google Scholar 

  20. 20.

    Brasier, C. M. & Mehrotra Ophiostoma himal-ulmi sp. nov., a new species of Dutch elm disease fungus endemic to the Himalayas. Mycol. Res. 99, 205–215 (1995).

    Article  Google Scholar 

  21. 21.

    Brasier, C. M. & Kirk, S. A. Survival of clones of NAN Ophiostoma novo-ulmi around its probable centre of appearance in North America. Mycol. Res. 104, 1322–1332 (2000).

    Article  Google Scholar 

  22. 22.

    Brasier, C. M. A cytoplasmically transmitted disease of Ceratocystis ulmi. Nature 305, 220–223 (1983).

    Article  Google Scholar 

  23. 23.

    Brasier, C. M., Kirk, S. A., Pipe, N. D. & Buck, K. W. Rare interspecific hybrids in natural populations of the Dutch elm disease pathogens Ophiostoma ulmi and O. novo-ulmi. Mycol. Res. 102, 45–57 (1998).

    Article  Google Scholar 

  24. 24.

    Brasier, C. M. & Kirk, S. A. Rapid emergence of hybrids between the two subspecies of Ophiostoma novo-ulmi with a high level of pathogenic fitness. Plant Pathol. 59, 186–199 (2010).

    Article  CAS  Google Scholar 

  25. 25.

    Zhang, D.-X., Spiering, M. J., Dawe, A. L. & Nuss, D. L. Vegetative incompatibility loci with dedicated roles in allorecognition restrict mycovirus transmission in chestnut blight fungus. Genetics 197, 701–714 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Et-Touil, A., Dusabeyagasani, M., Bouvet, G. F., Brasier, C. M. & Bernier, L. Ophiostoma ulmi DNA naturally introgressed into an isolate of Ophiostoma novo-ulmi is clustered around pathogenicity and mating type loci. Phytoprotection 99, 1–11 (2019).

  27. 27.

    Nigg, M. & Bernier, L. From yeast to hypha: defining transcriptomic signatures of the morphological switch in the dimorphic fungal pathogen Ophiostoma novo-ulmi. BMC Genomics 17, 920 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Nigg, M., Laroche, J., Landry, C. R. & Bernier, L. RNAseq analysis highlights specific transcriptome signatures of yeast and mycelial growth phases in the Dutch elm disease fungus Ophiostoma novo-ulmi. G3 5, 2487–2495 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Eilertson, K. E., Booth, J. G. & Bustamante, C. D. SnIPRE: selection inference using a Poisson random effects model. PLoS Comput. Biol. 8, e1002806 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Newsham, K. K. et al. Relationship between soil fungal diversity and temperature in the maritime Antarctic. Nat. Clim. Change 6, 182–186 (2016).

    Article  Google Scholar 

  31. 31.

    Plourde, K. V. & Bernier, L. A rapid virulence assay for the Dutch elm disease fungus Ophiostoma novo-ulmi by inoculation of apple (Malus domestica ‘Golden Delicious’) fruits. Plant Pathol. 63, 1078–1085 (2014).

    Article  CAS  Google Scholar 

  32. 32.

    Brasier, C. M. Ophiostoma novo-ulmi sp. nov., causative agent of current Dutch elm disease pandemics. Mycopathologia 115, 151–161 (1991).

    Article  Google Scholar 

  33. 33.

    Mitchell, A. G. & Brasier, C. M. Contrasting structure of European and North American populations of Ophiostoma ulmi. Mycol. Res. 98, 576–582 (1994).

    Article  Google Scholar 

  34. 34.

    Bates, M. R., Buck, K. W. & Brasier, C. M. Molecular relationships between Ophiostoma ulmi and the NAN and EAN races of O. novo-ulmi determined by restriction fragment length polymorphisms of nuclear DNA. Mycol. Res. 97, 449–455 (1993).

    Article  CAS  Google Scholar 

  35. 35.

    Konrad, H., Kirisits, T., Riegler, M., Halmschlager, E. & Stauffer, C. Genetic evidence for natural hybridization between the Dutch elm disease pathogens Ophiostoma novo-ulmi ssp. novo-ulmi and O. novo-ulmi ssp. americana. Plant Pathol. 51, 78–84 (2002).

    Article  Google Scholar 

  36. 36.

    Estoup, A. et al. Is there a genetic paradox of biological invasion? Annu. Rev. Ecol. Evol. Syst. 47, 51–72 (2016).

    Article  Google Scholar 

  37. 37.

    Gladieux, P. et al. The population biology of fungal invasions. Mol. Ecol. 24, 1969–1986 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Menardo, F. et al. Hybridization of powdery mildew strains gives rise to pathogens on novel agricultural crop species. Nat. Genet. 48, 201–205 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Brasier, C. M. Rapid changes in genetic structure of epidemic populations of Ophiostoma ulmi. Nature 332, 538–541 (1988).

    Article  Google Scholar 

  40. 40.

    Brasier, C. M. Low genetic diversity of the Ophiostoma novo-ulmi population in North America. Mycologia 88, 951–964 (1996).

    Article  Google Scholar 

  41. 41.

    Milgroom, M. G. & Brasier, C. M. Potential diversity in vegetative compatibility types of Ophiostoma novo-ulmi in North America. Mycologia 89, 722–726 (1997).

    Article  Google Scholar 

  42. 42.

    Philibert, A. et al. Predicting invasion success of forest pathogenic fungi from species traits: predicting fungal invaders. J. Appl. Ecol. 48, 1381–1390 (2011).

    Article  Google Scholar 

  43. 43.

    Bazin, É., Mathé-Hubert, H., Facon, B., Carlier, J. & Ravigné, V. The effect of mating system on invasiveness: some genetic load may be advantageous when invading new environments. Biol. Invasions 16, 875–886 (2014).

    Article  Google Scholar 

  44. 44.

    Desprez-Loustau, M.-L. et al. The fungal dimension of biological invasions. Trends Ecol. Evol. 22, 472–480 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Rogers, H. J., Buck, K. W. & Brasier, C. M. in Fungal Virology (ed. Buck, K. W.) 209–220 (CRC Press, 2017).

  46. 46.

    Fijarczyk, A., Dudek, K., Niedzicka, M. & Babik, W. Balancing selection and introgression of newt immune-response genes. Proc. Biol. Sci. 285, 20180819 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Enard, D. & Petrov, D. A. Evidence that RNA viruses drove adaptive introgression between Neanderthals and modern humans. Cell 175, 360–371.e13 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Zhao, Z., Liu, H., Wang, C. & Xu, J.-R. Correction: comparative analysis of fungal genomes reveals different plant cell wall degrading capacity in fungi. BMC Genomics 15, 6 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Lah, L., Löber, U., Hsiang, T. & Hartmann, S. A genomic comparison of putative pathogenicity-related gene families in five members of the Ophiostomatales with different lifestyles. Fungal Biol. 121, 234–252 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Et-Touil, A., Brasier, C. M. & Bernier, L. Localization of a pathogenicity gene in Ophiostoma novo-ulmi and evidence that it may be introgressed from O. ulmi. Mol. Plant Microbe Interact. 12, 6–15 (1999).

    Article  CAS  Google Scholar 

  51. 51.

    Sinha, H. et al. Sequential elimination of major-effect contributors identifies additional quantitative trait loci conditioning high-temperature growth in yeast. Genetics 180, 1661–1670 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Eberlein, C. et al. The rapid evolution of an ohnolog contributes to the ecological specialization of incipient yeast species. Mol. Biol. Evol. 34, 2173–2186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Wu, Z. et al. A novel major facilitator superfamily transporter in Penicillium digitatum (PdMFS2) is required for prochloraz resistance, conidiation and full virulence. Biotechnol. Lett. 38, 1349–1357 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Lin, H.-C., Yu, P.-L., Chen, L.-H., Tsai, H.-C. & Chung, K.-R. A major facilitator superfamily transporter regulated by the stress-responsive transcription factor Yap1 is required for resistance to fungicides, xenobiotics, and oxidants and full virulence in Alternaria alternata. Front. Microbiol. 9, 2229 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Chen, L.-H., Yang, S. L. & Chung, K.-R. Resistance to oxidative stress via regulating siderophore-mediated iron acquisition by the citrus fungal pathogen Alternaria alternata. Microbiology 160, 970–979 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Sanglard, D. Emerging threats in antifungal-resistant fungal pathogens. Front. Med. 3, 11 (2016).

    Article  Google Scholar 

  57. 57.

    Ruocco, M. et al. Identification of a new biocontrol gene in Trichoderma atroviride: the role of an ABC transporter membrane pump in the interaction with different plant-pathogenic fungi. Mol. Plant Microbe Interact. 22, 291–301 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Stefanato, F. L. et al. The ABC transporter BcatrB from Botrytis cinerea exports camalexin and is a virulence factor on Arabidopsis thaliana. Plant J. 58, 499–510 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    DiGuistini, S. et al. Genome and transcriptome analyses of the mountain pine beetle–fungal symbiont Grosmannia clavigera, a lodgepole pine pathogen. Proc. Natl Acad. Sci. USA 108, 2504–2509 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Duchesne, L. C., Jeng, R. S. & Hubbes, M. Accumulation of phytoalexins in Ulmus americana in response to infection by a nonaggressive and an aggressive strain of Ophiostoma ulmi. Can. J. Bot. 63, 678–680 (1985).

    Article  CAS  Google Scholar 

  61. 61.

    Overeem, J. C. & Elgersma, D. M. Accumulation of mansonones E and F in Ulmus Hollandica infected with Ceratocystis ulmi. Phytochemistry 9, 1949–1952 (1970).

    Article  CAS  Google Scholar 

  62. 62.

    Ojeda Alayon, D. I. et al. Genetic and genomic evidence of niche partitioning and adaptive radiation in mountain pine beetle fungal symbionts. Mol. Ecol. 26, 2077–2091 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Proctor, R. H., Guries, R. P. & Smalley, E. B. Lack of association between tolerance to the elm phytoalexin mansonone E and virulence in Ophiostoma novo-ulmi. Can. J. Bot. 72, 1355–1364 (1994).

    Article  CAS  Google Scholar 

  64. 64.

    Melin, P., Schnürer, J. & Wagner, E. G. H. Proteome analysis of Aspergillus nidulans reveals proteins associated with the response to the antibiotic concanamycin A, produced by Streptomyces species. Mol. Genet. Genomics 267, 695–702 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Eades, C. J. Characterization of the Alpha-Mannosidase Gene Family in Filamentous Fungi (University of Victoria, 2001).

  66. 66.

    Gauthier, G. M. Dimorphism in fungal pathogens of mammals, plants, and insects. PLoS Pathog. 11, e1004608 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Möller, M. & Stukenbrock, E. H. Evolution and genome architecture in fungal plant pathogens. Nat. Rev. Microbiol. 15, 756–771 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Zolan, M. E. & Pukkila, P. J. Inheritance of DNA methylation in Coprinus cinereus. Mol. Cell. Biol. 6, 195–200 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2014).

  70. 70.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Comeau, A. M. et al. Functional annotation of the Ophiostoma novo-ulmi genome: insights into the phytopathogenicity of the fungal agent of Dutch elm disease. Genome Biol. Evol. 7, 410–430 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Forgetta, V. et al. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core facilities. J. Biomol. Tech. 24, 39–49 (2013).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Quesneville, H. et al. Combined evidence annotation of transposable elements in genome sequences. PLoS Comput. Biol. 1, 166–175 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Bushnell, B., Rood, J. & Singer, E. BBMerge—accurate paired shotgun read merging via overlap. PLoS ONE 12, e0185056 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997v2 (2013).

  80. 80.

    Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 14, 1394–1403 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Chiang, C. et al. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat. Methods 12, 966–968 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Abyzov, A., Urban, A. E., Snyder, M. & Gerstein, M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 21, 974–984 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15, 356 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Fumagalli, M., Vieira, F. G., Linderoth, T. & Nielsen, R. ngsTools: methods for population genetics analyses from next-generation sequencing data. Bioinformatics 30, 1486–1487 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    R Core Development Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  94. 94.

    Martin, S. H. & Van Belleghem, S. M. Exploring evolutionary relationships across the genome using topology weighting. Genetics 206, 429–438 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).

    Article  CAS  Google Scholar 

  99. 99.

    Jakobsson, M. & Rosenberg, N. A. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    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 

  102. 102.

    Tange, O. et al. GNU parallel: the command-line power tool. USENIX Magazine 36, 42–47 (2011).

    Google Scholar 

  103. 103.

    Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Götz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 36, 3420–3435 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Sperschneider, J. et al. EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytol. 210, 743–761 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  109. 109.

    Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. 110.

    McDonald, J. H. & Kreitman, M. Adaptive protein evolution at the Adh locus in Drosophila. Nature 351, 652–654 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Pfeifer, B., Wittelsbürger, 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 

  112. 112.

    Grigoriev, I. V. et al. The genome portal of the Department of Energy Joint Genome Institute. Nucleic Acids Res. 40, D26–D32 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Cabanettes, F. & Klopp, C. D-GENIES: dot plot large genomes in an interactive, efficient and simple way. PeerJ 6, e4958 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Bernier, L. & Hubbes, M. Mutations in Ophiostoma ulmi induced by N-methyl-N′-nitro-N-nitrosoguanidine. Can. J. Bot. 68, 225–231 (1990).

    Article  CAS  Google Scholar 

  116. 116.

    Pau, G., Fuchs, F., Sklyar, O., Boutros, M. & Huber, W. EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26, 979–981 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

    Bates, D., Sarkar, D., Bates, M. D. & Matrix, L. The lme4 package. R package version 2 (2007).

  118. 118.

    Akaike, H. in Selected Papers of Hirotugu Akaike (eds Parzen, E., Tanabe, K. & Kitagawa, G.) 215–222 (Springer, 1974).

  119. 119.

    Lenth, R. Emmeans: Estimated marginal means, aka least-squares means. R package version 1 (2018).

Download references

Acknowledgements

This work was funded by Genome Canada, Genome British Columbia and Génome Québec within the framework of project bioSAFE (Biosurveillance of Alien Forest Enemies; project number 10106). Additional funding was provided by the Canadian Food Inspection Agency, Natural Resources Canada and FPInnovations and by the NSERC Discovery Grant of C.R.L. We thank A. Dubé, A. Gagné, I. Giguère and A. Potvin for help with the experiments, W. Babik and the Landry laboratory for comments and discussions, the McGill University and the Centre d’Expertise et de Services Génome Québec and the Institut de Biologie Intégrative et des Systèmes bioinformatics platform for technical support.

Author information

Affiliations

Authors

Contributions

P.H., A.F., H.M., P.T., R.C.H. and C.R.L. designed the study. L.B. provided the isolates. P.H. and J.P. selected the isolates from the collection of L.B. P.H. performed the DNA extractions. P.H., G.C. and J.C. performed the phenotyping experiments. P.H., A.F. and H.M. performed the genome analyses. P.H. and A.F. wrote the manuscript. All authors contributed to manuscript editing. R.C.H. and C.R.L. supervised the research. C.R.L. overviewed the analyses and writing.

Corresponding authors

Correspondence to Richard C. Hamelin or Christian R. Landry.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–18.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–15.

Supplementary Video 1

Supplementary Video 1

Supplementary Video 2

Supplementary Video 2

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hessenauer, P., Fijarczyk, A., Martin, H. et al. Hybridization and introgression drive genome evolution of Dutch elm disease pathogens. Nat Ecol Evol 4, 626–638 (2020). https://doi.org/10.1038/s41559-020-1133-6

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing