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.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Applying molecular and genetic methods to trees and their fungal communities
Applied Microbiology and Biotechnology Open Access 29 March 2023
-
Global genomic analyses of wheat powdery mildew reveal association of pathogen spread with historical human migration and trade
Nature Communications Open Access 26 July 2022
-
Genomic biosurveillance detects a sexual hybrid in the sudden oak death pathogen
Communications Biology Open Access 19 May 2022
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout




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
Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010).
Bennett, P. M. Plasmid encoded antibiotic resistance: acquisition and transfer of antibiotic resistance genes in bacteria. Br. J. Pharmacol. 153, S347–S357 (2008).
Stukenbrock, E. H. The role of hybridization in the evolution and emergence of new fungal plant pathogens. Phytopathology 106, 104–112 (2016).
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).
Barton, N. H. The role of hybridization in evolution. Mol. Ecol. 10, 551–568 (2001).
Schardl, C. L. & Craven, K. D. Interspecific hybridization in plant-associated fungi and oomycetes: a review. Mol. Ecol. 12, 2861–2873 (2003).
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).
Olson, A. & Stenlid, J. Pathogenic fungal species hybrids infecting plants. Microbes Infect. 4, 1353–1359 (2002).
Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).
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).
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).
Brasier, C. The rise of the hybrid fungi. Nature 405, 134–135 (2000).
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).
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).
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).
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).
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).
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).
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).
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).
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).
Brasier, C. M. A cytoplasmically transmitted disease of Ceratocystis ulmi. Nature 305, 220–223 (1983).
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).
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).
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).
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).
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).
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).
Eilertson, K. E., Booth, J. G. & Bustamante, C. D. SnIPRE: selection inference using a Poisson random effects model. PLoS Comput. Biol. 8, e1002806 (2012).
Newsham, K. K. et al. Relationship between soil fungal diversity and temperature in the maritime Antarctic. Nat. Clim. Change 6, 182–186 (2016).
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).
Brasier, C. M. Ophiostoma novo-ulmi sp. nov., causative agent of current Dutch elm disease pandemics. Mycopathologia 115, 151–161 (1991).
Mitchell, A. G. & Brasier, C. M. Contrasting structure of European and North American populations of Ophiostoma ulmi. Mycol. Res. 98, 576–582 (1994).
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).
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).
Estoup, A. et al. Is there a genetic paradox of biological invasion? Annu. Rev. Ecol. Evol. Syst. 47, 51–72 (2016).
Gladieux, P. et al. The population biology of fungal invasions. Mol. Ecol. 24, 1969–1986 (2015).
Menardo, F. et al. Hybridization of powdery mildew strains gives rise to pathogens on novel agricultural crop species. Nat. Genet. 48, 201–205 (2016).
Brasier, C. M. Rapid changes in genetic structure of epidemic populations of Ophiostoma ulmi. Nature 332, 538–541 (1988).
Brasier, C. M. Low genetic diversity of the Ophiostoma novo-ulmi population in North America. Mycologia 88, 951–964 (1996).
Milgroom, M. G. & Brasier, C. M. Potential diversity in vegetative compatibility types of Ophiostoma novo-ulmi in North America. Mycologia 89, 722–726 (1997).
Philibert, A. et al. Predicting invasion success of forest pathogenic fungi from species traits: predicting fungal invaders. J. Appl. Ecol. 48, 1381–1390 (2011).
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).
Desprez-Loustau, M.-L. et al. The fungal dimension of biological invasions. Trends Ecol. Evol. 22, 472–480 (2007).
Rogers, H. J., Buck, K. W. & Brasier, C. M. in Fungal Virology (ed. Buck, K. W.) 209–220 (CRC Press, 2017).
Fijarczyk, A., Dudek, K., Niedzicka, M. & Babik, W. Balancing selection and introgression of newt immune-response genes. Proc. Biol. Sci. 285, 20180819 (2018).
Enard, D. & Petrov, D. A. Evidence that RNA viruses drove adaptive introgression between Neanderthals and modern humans. Cell 175, 360–371.e13 (2018).
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).
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).
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).
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).
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).
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).
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).
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).
Sanglard, D. Emerging threats in antifungal-resistant fungal pathogens. Front. Med. 3, 11 (2016).
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).
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).
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).
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).
Overeem, J. C. & Elgersma, D. M. Accumulation of mansonones E and F in Ulmus Hollandica infected with Ceratocystis ulmi. Phytochemistry 9, 1949–1952 (1970).
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).
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).
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).
Eades, C. J. Characterization of the Alpha-Mannosidase Gene Family in Filamentous Fungi (University of Victoria, 2001).
Gauthier, G. M. Dimorphism in fungal pathogens of mammals, plants, and insects. PLoS Pathog. 11, e1004608 (2015).
Möller, M. & Stukenbrock, E. H. Evolution and genome architecture in fungal plant pathogens. Nat. Rev. Microbiol. 15, 756–771 (2017).
Zolan, M. E. & Pukkila, P. J. Inheritance of DNA methylation in Coprinus cinereus. Mol. Cell. Biol. 6, 195–200 (1986).
Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2014).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
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).
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).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Quesneville, H. et al. Combined evidence annotation of transposable elements in genome sequences. PLoS Comput. Biol. 1, 166–175 (2005).
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).
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
Bushnell, B., Rood, J. & Singer, E. BBMerge—accurate paired shotgun read merging via overlap. PLoS ONE 12, e0185056 (2017).
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997v2 (2013).
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).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).
Chiang, C. et al. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat. Methods 12, 966–968 (2015).
Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).
Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).
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).
Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2006).
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15, 356 (2014).
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).
R Core Development Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
Martin, S. H. & Van Belleghem, S. M. Exploring evolutionary relationships across the genome using topology weighting. Genetics 206, 429–438 (2017).
Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).
Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).
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).
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).
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).
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
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).
Tange, O. et al. GNU parallel: the command-line power tool. USENIX Magazine 36, 42–47 (2011).
Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Götz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 36, 3420–3435 (2008).
Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
Sperschneider, J. et al. EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytol. 210, 743–761 (2016).
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).
Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).
McDonald, J. H. & Kreitman, M. Adaptive protein evolution at the Adh locus in Drosophila. Nature 351, 652–654 (1991).
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).
Grigoriev, I. V. et al. The genome portal of the Department of Energy Joint Genome Institute. Nucleic Acids Res. 40, D26–D32 (2012).
Cabanettes, F. & Klopp, C. D-GENIES: dot plot large genomes in an interactive, efficient and simple way. PeerJ 6, e4958 (2018).
Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).
Bernier, L. & Hubbes, M. Mutations in Ophiostoma ulmi induced by N-methyl-N′-nitro-N-nitrosoguanidine. Can. J. Bot. 68, 225–231 (1990).
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).
Bates, D., Sarkar, D., Bates, M. D. & Matrix, L. The lme4 package. R package version 2 (2007).
Akaike, H. in Selected Papers of Hirotugu Akaike (eds Parzen, E., Tanabe, K. & Kitagawa, G.) 215–222 (Springer, 1974).
Lenth, R. Emmeans: Estimated marginal means, aka least-squares means. R package version 1 (2018).
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
Authors and Affiliations
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
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.
Supplementary Tables
Supplementary Tables 1–15.
Supplementary Video 1
Supplementary Video 1
Supplementary Video 2
Supplementary Video 2
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41559-020-1133-6
This article is cited by
-
Complexities underlying the breeding and deployment of Dutch elm disease resistant elms
New Forests (2023)
-
Applying molecular and genetic methods to trees and their fungal communities
Applied Microbiology and Biotechnology (2023)
-
Genomic biosurveillance detects a sexual hybrid in the sudden oak death pathogen
Communications Biology (2022)
-
The alien invasive forest pathogen Heterobasidion irregulare is replacing the native Heterobasidion annosum
Biological Invasions (2022)
-
Global genomic analyses of wheat powdery mildew reveal association of pathogen spread with historical human migration and trade
Nature Communications (2022)