Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The genome of homosporous maidenhair fern sheds light on the euphyllophyte evolution and defences

Nature Plants volume 8, pages 1024–1037 (2022)Cite this article

Subjects

Abstract

Euphyllophytes encompass almost all extant plants, including two sister clades, ferns and seed plants. Decoding genomes of ferns is the key to deep insight into the origin of euphyllophytes and the evolution of seed plants. Here we report a chromosome-level genome assembly of Adiantum capillus-veneris L., a model homosporous fern. This fern genome comprises 30 pseudochromosomes with a size of 4.8-gigabase and a contig N50 length of 16.22 Mb. Gene co-expression network analysis uncovered that homospore development in ferns has relatively high genetic similarities with that of the pollen in seed plants. Analysing fern defence response expands understanding of evolution and diversity in endogenous bioactive jasmonates in plants. Moreover, comparing fern genomes with those of other land plants reveals changes in gene families important for the evolutionary novelties within the euphyllophyte clade. These results lay a foundation for studies on fern genome evolution and function, as well as the origin and evolution of euphyllophytes.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Life cycle and intra-gametophytic selfing of A. capillus-veneris.
Fig. 2: A. capillus-veneris de novo genome assembly.
Fig. 3: Expression profile and gene network analysis during homosporangium development.
Fig. 4: JA biosynthesis pathway and defence-related metabolites in A. capillus-veneris.
Fig. 5: Gene family dynamics enabling adaptive features in euphyllophytes.
Fig. 6: BRI1-BRL gene family evolution.

Data availability

The A. capillus-veneris genome assembly, genome annotation and all the raw sequencing data have been deposited at NCBI, under the BioProject accession number PRJNA593372 (genome assembly and annotation) and PRJNA593361 (transcriptome raw sequence data). The CDS and peptide files are available from https://figshare.com/s/47be9fe90124b22d3c0e. The referenced dataset for Arabidopsis LEC1 co-expression analysis is under the accessions: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12404 (DOI: 10.1073/pnas.1707957114). Source data are provided with this paper.

Code availability

All custom codes are available for research purposes from the corresponding authors upon request.

References

  1. Sessa, E. B. & Der, J. P. in Advances in Botanical Research (ed Rsening, S. A.) 215–254 (Academic Press, 2016).

  2. The Pteridophyte Phylogeny Group. A community-derived classification for extant lycophytes and ferns. J. Syst. Evol. 54, 563–603 (2016).

    Article  Google Scholar 

  3. Pryer, K. M. et al. Horsetails and ferns are a monophyletic group and the closest living relatives to seed plants. Nature 409, 618–622 (2001).

    Article  CAS  PubMed  Google Scholar 

  4. Chang, C., Bowman, J. L. & Meyerowitz, E. M. Field guide to plant model systems. Cell 167, 325–339 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wolf, P. G. et al. An exploration into fern genome space. Genome Biol. Evol. 7, 2533–2544 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Szövényi, P., Gunadi, A. & Li, F. W. Charting the genomic landscape of seed-free plants. Nat. Plants 7, 554–565 (2021).

    Article  PubMed  Google Scholar 

  7. Clark, J. et al. Genome evolution of ferns: evidence for relative stasis of genome size across the fern phylogeny. New Phytol. 210, 1072–1082 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Li, F. W. et al. Fern genomes elucidate land plant evolution and cyanobacterial symbioses. Nat. Plants 4, 460–472 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Huang, X. et al. The flying spider-monkey tree fern genome provides insights into fern evolution and arborescence. Nat. Plants 8, 500–512 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sessa, E. B. et al. Between two fern genomes. Gigascience 3, 15 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Li, X., Fang, Y. H., Yang, J., Bai, S. N. & Rao, G. Y. Overview of the morphology, anatomy, and ontogeny of Adiantum capillus-veneris: an experimental system to study the development of ferns. J. Syst. Evol. 51, 499–510 (2013).

    Article  Google Scholar 

  12. Tsuboi, H., Suetsugu, N., Kawai-Toyooka, H. & Wada, M. Phototropins and neochrome1 mediate nuclear movement in the fern Adiantum capillus-veneris. Plant Cell Physiol. 48, 892–896 (2007).

    Article  CAS  PubMed  Google Scholar 

  13. Wada, M. Chloroplast and nuclear photorelocation movements. Proc. Jpn. Acad. B 92, 387–411 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Shen, H. et al. Large-scale phylogenomic analysis resolves a backbone phylogeny in ferns. Gigascience 7, 1–11 (2018).

    Article  PubMed  Google Scholar 

  15. Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  17. Leebens-Mack, J. H. et al. One thousand plant transcriptomes and the phylogenomics of green plants. Nature 574, 679–685 (2019).

    Article  Google Scholar 

  18. Huang, C. H., Qi, X., Chen, D., Qi, J. & Ma, H. Recurrent genome duplication events likely contributed to both the ancient and recent rise of ferns. J. Integr. Plant Biol. 62, 433–455 (2020).

    Article  PubMed  Google Scholar 

  19. Sensalari, C., Maere, S. & Lohaus, R. ksrates: positioning whole-genome duplications relative to speciation events in KS distributions. Bioinformatics 38, 530–532 (2022).

    Article  CAS  Google Scholar 

  20. Sato, N. & Furuya, M. The composition of lipids and fatty acids determined at various stages of haploid and diploid generations in the fern Adiantum capillus-veneris. Physiol. Plant. 62, 139–147 (1984).

    Article  CAS  Google Scholar 

  21. Alamillo, J. M. & Bartels, D. Effects of desiccation on photosynthesis pigments and the ELIP-like dsp 22 protein complexes in the resurrection plant Craterostigma plantagineum. Plant Sci. 160, 1161–1170 (2001).

    Article  CAS  PubMed  Google Scholar 

  22. Manfre, A. J., LaHatte, G. A., Climer, C. R. & Marcotte, W. R. Seed dehydration and the establishment of desiccation tolerance during seed maturation is altered in the Arabidopsis thaliana Mutant atem6-1. Plant Cell Physiol. 50, 243–253 (2008).

    Article  PubMed  Google Scholar 

  23. Alves, M. S., Fontes, E. P. B. & Fietto, L. G. EARLY RESPONSIVE to DEHYDRATION 15, a new transcription factor that integrates stress signaling pathways. Plant Signal. Behav. 6, 1993–1996 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kagaya, Y. et al. LEAFY COTYLEDON1 controls seed storage protein genes through its regulation of FUSCA3 and ABSCISIC ACID INSENSITIVE3. Plant Cell Physiol. 46, 399–406 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. West, M. A. L. et al. LEAFY COTYLEDON1 is an essential regulator of late embryogenesis and cotyledon identity in Arabidopsis. Plant Cell 6, 1731–1745 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Xie, Z. et al. Duplication and functional diversification of HAP3 genes leading to the origin of the seed-developmental regulatory gene, LEAFY COTYLEDON1 (LEC1), in nonseed plant genomes. Mol. Biol. Evol. 25, 1581–1592 (2008).

    Article  CAS  PubMed  Google Scholar 

  27. Sreenivasulu, N. & Wobus, U. Seed-development programs: a systems biology-based comparison between dicots and monocots. Annu. Rev. Plant Biol. 64, 189–217 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Braybrook, S. A. & Harada, J. J. LECs go crazy in embryo development. Trends Plant Sci. 13, 624–630 (2008).

    Article  CAS  PubMed  Google Scholar 

  29. Pelletier, J. M. et al. LEC1 sequentially regulates the transcription of genes involved in diverse developmental processes during seed development. Proc. Natl Acad. Sci. USA 114, E6710–E6719 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mertens, J., Aliyu, H. & Cowan, D. A. LEA proteins and the evolution of the WHy domain. Appl. Environ. Microbiol. 84, e00539-18 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Olvera-Carrillo, Y., Luis Reyes, J. & Covarrubias, A. A. Late embryogenesis abundant proteins: versatile players in the plant adaptation to water limiting environments. Plant Signal. Behav. 6, 586–589 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Fatihi, A. et al. Deciphering and modifying LAFL transcriptional regulatory network in seed for improving yield and quality of storage compounds. Plant Sci. 250, 198–204 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Kirkbride, R. C., Fischer, R. L. & Harada, J. J. LEAFY COTYLEDON1, a key regulator of seed development, is expressed in vegetative and sexual propagules of Selaginella moellendorffii. PLoS ONE 8, e67971 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Markham, K., Chalk, T. & Stewart, C. N. Jr. Evaluation of fern and moss protein-based defenses against phytophagous insects. Int. J. Plant Sci. 167, 111–117 (2006).

    Article  CAS  Google Scholar 

  35. Hendrix, S. D. An evolutionary and ecological perspective of the insect fauna of ferns. Am. Nat. 115, 171–196 (1980).

    Article  Google Scholar 

  36. Ali, M. S. & Baek, K. H. Jasmonic acid signaling pathway in response to abiotic stresses in plants. Int. J. Mol. Sci. 21, 621 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  37. Monte, I. et al. Ligand-receptor co-evolution shaped the jasmonate pathway in land plants. Nat. Chem. Biol. 14, 480–488 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Scholz, J. et al. Biosynthesis of allene oxides in Physcomitrella patens. BMC Plant Biol. 12, 228 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Koo, A. J. K., Gao, X., Daniel Jones, A. & Howe, G. A. A rapid wound signal activates the systemic synthesis of bioactive jasmonates in Arabidopsis. Plant J. 59, 974–986 (2009).

    Article  CAS  PubMed  Google Scholar 

  40. Reymond, P., Weber, H., Damond, M. & Farmer, E. E. Differential gene expression in response to mechanical wounding and insect feeding in Arabidopsis. Plant Cell 12, 707–719 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. de Vries, S. et al. Jasmonic and salicylic acid response in the fern Azolla filiculoides and its cyanobiont. Plant. Cell Environ. 41, 2530–2548 (2018).

    Article  PubMed  Google Scholar 

  42. Schüler, G. et al. Coronalon: a powerful tool in plant stress physiology. FEBS Lett. 563, 17–22 (2004).

    Article  PubMed  Google Scholar 

  43. Pratiwi, P. et al. Identification of jasmonic acid and jasmonoyl-isoleucine, and characterization of AOS, AOC, OPR and JAR1 in the model lycophyte Selaginella moellendorffii. Plant Cell Physiol. 58, 789–801 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. Fan, S. et al. Limonin: a review of its pharmacology, toxicity, and pharmacokinetics. Molecules 24, 3679 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  45. Kim, S. et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 47, D1102–D1109 (2019).

    Article  PubMed  Google Scholar 

  46. Sherer, T. B. et al. Mechanism of toxicity in rotenone models of Parkinson’s disease. J. Neurosci. 23, 10756–10764 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang, H. & Mao, H. On the origin and evolution of plant brassinosteroid receptor kinases. J. Mol. Evol. 78, 118–129 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Caño-Delgado, A. et al. BRL1 and BRL3 are novel brassinosteroid receptors that function in vascular defferentiation in Arabidopsis. Development 131, 5341–5351 (2004).

    Article  PubMed  Google Scholar 

  49. Zheng, B. et al. EMS1 and BRI1 control separate biological processes via extracellular domain diversity and intracellular domain conservation. Nat. Commun. 10, 4165 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ceserani, T., Trofka, A., Gandotra, N. & Nelson, T. VH1/BRL2 receptor-like kinase interacts with vascular-specific adaptor proteins VIT and VIK to influence leaf venation. Plant J. 57, 1000–1014 (2009).

    Article  CAS  PubMed  Google Scholar 

  51. Shiu, S. H. et al. Comparative analysis of the receptor-like kinase family in Arabidopsis and rice. Plant Cell 16, 1220–1234 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Marchant, D. B. et al. The C-Fern (Ceratopteris richardii) genome: insights into plant genome evolution with the first partial homosporous fern genome assembly. Sci. Rep. 9, 18181 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ranallo-Benavidez, T. R., Jaron, K. S. & Schatz, M. C. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat. Commun. 11, 1432 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gawel, N. J. & Jarret, R. L. A modified CTAB DNA extraction procedure for Musa and Ipomoea. Plant Mol. Biol. Rep. 9, 262–266 (1991).

    Article  CAS  Google Scholar 

  55. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Du, H. & Liang, C. Assembly of chromosome-scale contigs by efficiently resolving repetitive sequences with long reads. Nat. Commun. 10, 5360 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Rice, A. et al. The Chromosome Counts Database (CCDB) – a community resource of plant chromosome numbers. New Phytol. 206, 19–26 (2015).

    Article  PubMed  Google Scholar 

  62. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Ellinghaus, D., Kurtz, S. & Willhoeft, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinformatics 9, 18 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Ou, S. & Jiang, N. LTR_retriever: a highly accurate and sensitive program for identification of long terminal repeat retrotransposons. Plant Physiol. 176, 1410–1422 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Rice, P., Longden, I. & Bleasby, A. EMBOSS: the european molecular biology open software suite. Trends Genet. 16, 276–277 (2000).

    Article  CAS  PubMed  Google Scholar 

  69. Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).

    Article  CAS  PubMed  Google Scholar 

  70. Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28, 45–48 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Berardini, T. Z. et al. The Arabidopsis information resource: making and mining the ‘gold standard’ annotated reference plant genome. Genesis 53, 474–485 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Conesa, A. & Götz, S. Blast2GO: a comprehensive suite for functional analysis in plant genomics. Int. J. Plant Genomics 2008, 619832 (2008).

    Article  PubMed  Google Scholar 

  74. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

    Article  CAS  PubMed  Google Scholar 

  75. Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C. & Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182–W185 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zwaenepoel, A. & Van de Peer, Y. wgd—simple command line tools for the analysis of ancient whole-genome duplications. Bioinformatics 35, 2153–2155 (2019).

    Article  CAS  PubMed  Google Scholar 

  77. Enright, A. J. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  80. Proost, S. et al. i-ADHoRe 3.0—fast and sensitive detection of genomic homology in extremely large data sets. Nucleic Acids Res. 40, e11 (2012).

    Article  CAS  PubMed  Google Scholar 

  81. Lynch, M. & Conery, J. S. The evolutionary demography of duplicate genes. J. Struct. Funct. Genomics 3, 35–44 (2003).

    Article  CAS  PubMed  Google Scholar 

  82. Bowman, J. L. et al. Insights into land plant evolution garnered from the Marchantia polymorpha genome. Cell 171, 287–304 (2017).

    Article  CAS  PubMed  Google Scholar 

  83. Rensing, S. A. et al. The Physcomitrella genome reveals evolutionary insights into the conquest of land by plants. Science 319, 64–69 (2008).

    Article  CAS  PubMed  Google Scholar 

  84. Jo Ann Banks. et al. The compact Selaginella genome identifies changes in gene content associated with the evolution of vascular plants. Science 332, 960–963 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Liu, H. et al. The nearly complete genome of Ginkgo biloba illuminates gymnosperm evolution. Nat. Plants 7, 748–756 (2021).

    Article  CAS  PubMed  Google Scholar 

  86. Zimin, A. V. et al. An improved assembly of the loblolly pine mega-genome using long-read single-molecule sequencing. Gigascience 6, 1–4 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Nystedt, B. et al. The Norway spruce genome sequence and conifer genome evolution. Nature 497, 579–584 (2013).

    Article  CAS  PubMed  Google Scholar 

  88. Albert, V. A. et al. The Amborella genome and the evolution of flowering plants. Science 342, 1241089 (2013).

    Article  Google Scholar 

  89. Gonzali, S. et al. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–815 (2000).

    Article  Google Scholar 

  90. Huang, S. et al. The genome of the cucumber, Cucumis sativus L. Nat. Genet. 41, 1275–1281 (2009).

    Article  CAS  PubMed  Google Scholar 

  91. Jaillon, O. et al. The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449, 463–467 (2007).

    Article  CAS  PubMed  Google Scholar 

  92. Yu, J. et al. The genomes of Oryza sativa: a history of duplications. PLoS Biol. 3, 0266–0281 (2005).

    Article  CAS  Google Scholar 

  93. Liu, J. et al. Gapless assembly of maize chromosomes using long-read technologies. Genome Biol. 21, 121 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Cheng, S. et al. Genomes of subaerial Zygnematophyceae provide insights into land plant evolution. Cell 179, 1057–1067 (2019).

    Article  CAS  PubMed  Google Scholar 

  95. Hori, K. et al. Klebsormidium flaccidum genome reveals primary factors for plant terrestrial adaptation. Nat. Commun. 5, 3978 (2014).

    Article  CAS  PubMed  Google Scholar 

  96. Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Zheng, Y. et al. iTAK: a program for genome-wide prediction and classification of plant transcription factors, transcriptional regulators, and protein kinases. Mol. Plant 9, 1667–1670 (2016).

    Article  CAS  PubMed  Google Scholar 

  98. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  101. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Shannon, P. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Finn, R. D., Miller, B. L., Clements, J. & Bateman, A. iPfam: a database of protein family and domain interactions found in the Protein Data Bank. Nucleic Acids Res. 42, D364–D373 (2014).

    Article  CAS  PubMed  Google Scholar 

  104. Letunic, I. & Bork, P. 20 years of the SMART protein domain annotation resource. Nucleic Acids Res. 46, D493–D496 (2018).

    Article  CAS  PubMed  Google Scholar 

  105. Kall, 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).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Lewis, T. E. et al. Gene3D: extensive prediction of globular domains in proteins. Nucleic Acids Res. 46, D435–D439 (2018).

    Article  CAS  PubMed  Google Scholar 

  107. Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).

    Article  CAS  PubMed  Google Scholar 

  108. Robert, X. & Gouet, P. Deciphering key features in protein structures with the new ENDscript server. Nucleic Acids Res. 42, W320–W324 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank X. Zhang (Institute of Botany, Chinese Academy of Sciences) for providing helpful advice and suggestions, G. Rao (Peking University) for providing the homozygous A. capillus-veneris plant and DNA sequencing sample, Y. Wu (AGIS, CAAS) for providing the lncRNA determination pipeline, J. Zhao (Boyce Thompson Institute) for converting the references to a uniform format, and W. Wang and L. Xu (Tsinghua University) for assistance with phytohormone and metabolites detection. This work was supported by the National Key Research and Development Program of China (grant no. 2019YFA0906200 to S.H.); Science, Technology and Innovation Commission of Shenzhen Municipality (grant no. ZDSYS20200811142605017 to J.Y.); the Elite Young Scientists Program of CAAS (to J.Y.); the Agricultural Science and Technology Innovation Program (to J.Y.); the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 833522 to Y.V.d.P.); Methusalem funding from Ghent University (grant no. BOF.MET.2021.0005.01 to Y.V.d.P.); a postdoctoral fellowship from the Special Research Fund of Ghent University (grant no. BOFPDO2018001701 to Z.L.); and the Research Foundation – Flanders (FWO) (grant no. 3G032219 to H.C.).

Author information

Author notes

  1. These authors contributed equally: Yuhan Fang, Xing Qin, Qinggang Liao, Ran Du.

Authors and Affiliations

  1. Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

    Yuhan Fang, Xing Qin, Qinggang Liao, Ran Du, Xizhi Luo, Qian Zhou, Pengbo Sun, Li Wang, Shifeng Cheng, Xingtan Zhang, William John Lucas, Sanwen Huang & Jianbin Yan

  2. Peng Cheng Laboratory, Artificial Intelligence Research Center, Shenzhen, China

    Qian Zhou

  3. Department of Plant Biotechnology and Bioinformatics, Ghent University and VIB Center for Plant Systems Biology, Ghent, Belgium

    Zhen Li, Hengchi Chen & Yves Van de Peer

  4. State Key Laboratory of Protein and Plant Gene Research, Quantitative Biology Center, College of Life Sciences, Peking University, Beijing, China

    Wanting Jin, Yaning Yuan & Shunong Bai

  5. Eastern China Conservation Centre for Wild Endangered Plant Resources, Shanghai Chenshan Botanical Garden, Shanghai, China

    Rui Zhang & Jiao Zhang

  6. Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China

    Xueyong Yang & Zhonghua Zhang

  7. The Orchid Conservation and Research Centre of Shenzhen, Shenzhen, China

    Yuehong Yan

  8. College of Horticulture, Qingdao Agricultural University, Qingdao, China

    Zhonghua Zhang

  9. College of Horticulture, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China

    Yves Van de Peer

  10. Centre for Microbial Ecology and Genomics, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa

    Yves Van de Peer

  11. Department of Plant Biology, College of Biological Sciences, University of California, Davis, CA, USA

    William John Lucas

Authors
  1. Yuhan Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xing Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qinggang Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ran Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xizhi Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hengchi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wanting Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yaning Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Pengbo Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jiao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Shifeng Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Xueyong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yuehong Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Xingtan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Zhonghua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Shunong Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Yves Van de Peer
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. William John Lucas
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Sanwen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Jianbin Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.H., S.B., Y.F. and J.Y. conceived the study. Y.F. and J.Y. designed and managed the major scientific objectives. S.H., Y.F., W.J.L. and J.Y. coordinated the project. Y.F., W.J., Y. Yuan, L.W., X.Y. and X.Q. managed the plant materials. Q.Z. and P.S. assembled the genome and estimated the genome size. Y.F., Q.L., X.L., X.Q., Q.Z., P.S., L.W. and Z.Z. annotated the genomes. H.C., Z.L. and Y.V.d.P performed the WGD calling. Y.F., Q.L. and X.L. identified the repetitive elements, non-coding RNAs and lncRNAs. Y.F., Q.L., Y. Yan, R.Z., J.Z. and S.C. clustered the gene families and conducted the related phylogenetic analysis. Y.F. and Q.L. constructed the co-expression network of AdcLEC1. Y.F., Q.L., X.Q. and X.L. carried out the RNA-seq analysis on homosporous development and jasmonate biosynthesis genes. J.Y. and R.D. performed the jasmonate biosynthesis and signalling analyses. R.D., Y.F., W.J. and Y. Yuan contributed to hormone and metabolome sample preparation. R.D. carried out metabolome detection and characterized the function of coronatine-inducible metabolites. Y.F. and J.Y. led the article preparation, together with S.H., W.J.L., Y.V.d.P., Q.L., R.D., Z.L., X.Q., X.L. and X.Z. All authors read and approved the final article.

Corresponding authors

Correspondence to Yuhan Fang or Jianbin Yan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Yuannian Jiao, Fay-Wei Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Analyses of whole genome duplication (WGD) events.

a, Self-alignment dot plot based on paralogous pairs in collinear blocks. b, The distribution of KS of the whole paranome in A. capillus-veneris. c, The distribution of KS of the syntenic paralogous pairs in A. capillus-veneris. d, The analysis of ksrates for the whole paranome of A. capillus-veneris before rate adjustment. Light grey histogram and kernel density estimation (KDE) curve are plotted for the whole paranome KS distribution. The estimated mean mode and standard deviation from 200 bootstrapped KDEs of each orthologous distribution between A. capillus-veneris and other species is shown as dashed red lines and boxes with corresponding KS values denoted in the legend. e, The analysis of ksrates for the whole paranome of A. capillus-veneris after rate adjustment. The whole paranome KS distributions are overlaid with rate-adjusted divergence events in coloured vertical lines and boxes. The overall mixture model in the dark solid KDE curve consists of an exponential component in dotted grey curve and optimized log-normal components in dashed grey curves. Each log-normal component is labelled with a letter, shown as vertical dashed grey lines with circular labels. Rate-adjusted mode estimates of orthologous KS distributions between A. capillus-veneris and other species, representing speciation events, are drawn as numbered vertical long-dashed lines and associated coloured boxes showing the mean and the standard deviation. Lines representing the same speciation event in the phylogeny share colour and numbering while here only one speciation event is involved with A. capillus-veneris. Horizontal arrows in figure legends ‘divergence with’ part indicate the KS shifts resulted from the substitution rate adjustments. f, The analysis of ksrates for anchor pairs of A. capillus-veneris after rate adjustment. The KS distributions of two anchor pair clusters, namely a and b in filled blue and red KDE curves with associated peak, derived from the lognormal mixture modeling of median KS values for the collinear segment pairs, are shown as a grey histogram. Rate-adjusted mode estimates of orthologous KS distributions between A. capillus-veneris and other species, representing speciation events, are drawn as numbered vertical long-dashed lines, and associated coloured boxes showing the mean and the standard deviation. Lines representing the same speciation event in the phylogeny share colour and numbering while here only one speciation event is involved with A. capillus-veneris. Horizontal arrows in figure legends ‘divergence with’ part indicate the KS shifts resulted from the substitution rate adjustments.

Extended Data Fig. 2 Comparison of the genome features of A. capillus-veneris with those of A. filiculoides and S. cucullata.

a, Lengths of different repeat components in the indicated eight representative species across land plants. b, Distributions of nucleotide distance (D) calculated for LTR among three ferns, A. capillus-veneris, A. filiculoides and S. cucullata. c, Violet plot showing gene characteristics of ferns. The upper and lower edge of white frame in the violin plot represent the 75% and 25% quartiles, the central line denotes the median value, and the black square shows the mean value. The upper and lower terminal of line in the violin indicated the upper adjacent value and lower adjacent value, 1.5 × the interquartile range and outliers (solid points).

Extended Data Fig. 3 Time course of the mechanical wound-induced response of JA in Ginkgo biloba.

Leaves of G. biloba were wounded with a hemostat. Damaged leaves were harvested at the indicated time points after wounding and analysed for jasmonates (the precursor OPDA [▲], jasmonic acid (JA) [●], and JA-Ile [■]) accumulation by HPLC-MS; each data point represents the mean ± SD of five biological replicates.

Extended Data Fig. 4 The maximum-likelihood tree of the BRI1-BRL and EMS1 gene family in land plants.

The domains of BRI1-BRL (TM, LRR, ID, and KD) and its closest gene family EMS1 (TM, LRR, and KD) were identified from all main land plant groups (in different colours). Maximum-likelihood tree was constructed with parameters: WAG + F + R6 model and 1,000 bootstrap replicates. Whole genome assemblies from 24 species were used for identification of BRI1-BRL and EMS1 homologs, including 9 bryophytes (Mpo, Marchantia polymorpha; Cpl, Calohypnum plumiforme; Fan, Fontinalis antipyretica; Cpu, Ceratodon purpureus; Ppa, Physcomitrella patens; Psc, Pleurozium schreberi; Aan, Anthoceros angustus; Aag, Anthoceros agrestis; Apu, Anthoceros punctatus), 3 lycophytes (Ita, Isoetes taiwanensis; Smo, Selaginella moellendorffii; Sle, Selaginella lepidophylla), 3 ferns (Adc, Adiantum capillus-veneris; Afi, Azolla filiculoides; Scu, Salvinia cucullata), and 9 seed plants (Pab, Picea abies; Pta, Pinus taeda; Gbi, Ginkgo biloba; Ath, Arabidopsis thaliana; Atr, Amborella trichopoda; Csa, Cucumis sativus; Osa, Oryza sativa; Vvi, Vitis vinifera; Zma, Zea mays). The detailed information is provided in Supplementary Data 19.

Extended Data Fig. 5 The island domain of the BRI1-BRL gene family in land plant.

The island domains were extracted from peptide of BRI1-BRL homologues and aligned by MUSCLE. The conserved sites were indicated with red boxes.

Supplementary information

Supplementary Information

Supplementary Text, Figs. 1–5 and Tables 1–14.

Reporting Summary

Supplementary Data 1

Supplementary Data 1–20.

Source data

Source Data Fig. 3

Statistical source data for Fig. 3b,c.

Source Data Fig. 4

Statistical source data for Fig. 4d,e.

Source Data Fig. 5

Statistical source data for Fig. 5.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, Y., Qin, X., Liao, Q. et al. The genome of homosporous maidenhair fern sheds light on the euphyllophyte evolution and defences. Nat. Plants 8, 1024–1037 (2022). https://doi.org/10.1038/s41477-022-01222-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-022-01222-x

This article is cited by

Search

Advanced search

Quick links

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