Abstract
Background
Massive forest decline has been observed almost everywhere as a result of negative anthropogenic and climatic effects, which can interact with pests, fungi and other phytopathogens and aggravate their effects. Climatic changes can weaken trees and make fungi, such as Armillaria more destructive. Armillaria borealis (Marxm. & Korhonen) is a fungus from the Physalacriaceae family (Basidiomycota) widely distributed in Eurasia, including Siberia and the Far East. Species from this genus cause the root white rot disease that weakens and often kills woody plants. However, little is known about ecological behavior and genetics of A. borealis. According to field research data, A. borealis is less pathogenic than A. ostoyae, and its aggressive behavior is quite rare. Mainly A. borealis behaves as a secondary pathogen killing trees already weakened by other factors. However, changing environment might cause unpredictable effects in fungus behavior.
Results
The de novo genome assembly and annotation were performed for the A. borealis species for the first time and presented in this study. The A. borealis genome assembly contained ~ 68 Mbp and was comparable with ~ 60 and ~ 79.5 Mbp for the A. ostoyae and A. mellea genomes, respectively. The N50 for contigs equaled 50,544 bp. Functional annotation analysis revealed 21,969 protein coding genes and provided data for further comparative analysis. Repetitive sequences were also identified. The main focus for further study and comparative analysis will be on the enzymes and regulatory factors associated with pathogenicity.
Conclusions
Pathogenic fungi such as Armillaria are currently one of the main problems in forest conservation. A comprehensive study of these species and their pathogenicity is of great importance and needs good genomic resources. The assembled genome of A. borealis presented in this study is of sufficiently good quality for further detailed comparative study on the composition of enzymes in other Armillaria species. There is also a fundamental problem with the identification and classification of species of the Armillaria genus, where the study of repetitive sequences in the genomes of basidiomycetes and their comparative analysis will help us identify more accurately taxonomy of these species and reveal their evolutionary relationships.
Keywords: Annotation, Armillaria, De novo sequencing, Fungal forest pathogen, Genome
Background
Massive forest decline as a result of negative anthropogenic and climatic effects, often aggravated by pests, fungi, and other phytopathogens, has been observed almost everywhere. Environmental changes, such as increased average annual temperatures, decreased precipitation, more frequent droughts, can weaken trees and make fungi much more destructive. Forest conservation has become a serious issue since the scale of plant death caused by phytopathogenic fungi is enormous. For instance, tree diseases have caused the loss of approximately 100 million elm trees in the United Kingdom and the United States, and the list can be continued. Among all phytopathogens, fungi cause 64% of infection-related species extinction and regional extirpation events [1].
The basidiomycete genus Armillaria plays a very important role in forest ecosystems worldwide and currently includes more than 40 officially described species [2, 3]. Armillaria species differ significantly in virulence, for example, some species, such as A. ostoyae, are the main cause of tree death while other species colonize plants already damaged by various factors (drought, pests, etc.) [4, 5]. Difference in pathogenicity has also been observed in A. ostoyae, however virulence variation of A. borealis has not been studied yet [6].
Armillaria borealis (Marxm. & Korhonen) is a fungus from the Physalacriaceae family (Basidiomycota) widely distributed in Eurasia, including Siberia and the Far East [1]. Species from this genus cause the root white rot disease that weakens and often kills woody plants [7]. Several phylogenetic and genomic studies on A. ostoyae have been carried out due to its high pathogenic potential and common occurrence [4, 8, 9], while little is known about ecological behavior of A. borealis. According to field research data, A. borealis is less pathogenic than A. ostoyae, and its aggressive behavior is rare. Mainly A. borealis behaves as a secondary pathogen killing trees already weakened by biotic and abiotic factors [10–13]. However, changing environment might cause unpredictable effects in fungi behavior.
Armillaria spp. impact on forest populations has both economic and ecological significance. They attack hundreds of different tree species (e.g., Abies, Picea, Pinus, Betula, Sorbus, Juglans, Malus, etc.) in both hemispheres under different climatic conditions, and are among the most destructive forest pathogens [2, 14, 15].
Identification of species and pathogenicity levels of Armillaria is crucial for forest conservation. The genomic data are needed to study the pathogenicity of pathogenic species and to better understand their impact on trees and the host-pathogen interactions. In addition, comparative genomics can help to resolve complex phylogeny of Armillaria species. It is worth noting that fungi genomic data are also important for industrial applications. For example, white rot Armillaria fungi are capable of lignin and cellulose decomposition, and they can be used to utilize the wood and paper production waste [16].
A. borealis is very important for the vast boreal forest ecosystems. However, despite the enormous influence of Armillaria species on forestry, horticulture, and agriculture, fungi of this genus and their pathogenicity are still not well-studied in this large region, which makes the presented genomic study very much needed.
There are already published genomic and proteomic data for A. mellea, A. solidipes, and A. ostoyae revealing the presence of plant cell wall degradation enzymes (PCWDE) and some secreted proteins [17–19]. Genomic analysis of other pathogenic basidiomycetes, such as Moniliophthora [20, 21], Heterobasidion [22], and Rhizoctonia [23], also revealed genes encoding PCWDE, as well as secreted enzymes and secondary metabolism effector proteins as putative pathogenicity factors. However, the life cycle and the distribution strategy of Armillaria members indicate that they may have evolved other additional mechanisms for pathogenicity, which along with other potential genomic mechanisms are not yet studied [24]. It is worth noting that the role and functional significance of mobile and highly repetitive elements (REs) are still not completely clear. Gradually accumulated data suggest that REs can play an important role in the evolutionary development of organisms, replication, and formation of nucleoprotein complexes, as well as affect gene expression [17]. Genomes of fungi are densely packed containing effector genes and transposable elements (TEs) [25–27]. It was reported that different fungal pathogens, such as Fusarium [28] and Verticillium [29], have similar genome architecture. So, it is expected that TEs may play important roles in host switching and adaptation to new ecological niches [30]. It was found in Magnaporthe oryzae that genes involved in host specialization were associated with TEs [31].
Results
Genome assembly
The A. borealis genome assembly contained ~ 67 Mbp and was comparable with ~ 60 and ~ 79.5 Mbp for the A. ostoyae and A. mellea genomes, respectively. The N50 for contigs equaled 50,554 bp (Table 1).
Table 1.
Assembly parameters of the Armillaria borealis genome
| Parameter | Contig |
|---|---|
| Number | 44,412 |
| Total length, bp | 66,792,428 |
| Maximum length, bp | 2,136,877 |
| N50, bp | 50,544 |
| N90, bp | 346 |
Completeness of the genome assembly
The BUSCO v3.1.0 software [32] was used to evaluate the completeness of the genome assembly. It showed that 94.7% of reference genes were captured as complete single-copy genes (Table 2). In addition, the RNA sequence reads were mapped to the genome assembly by TopHat v. 2.1.0 [33].
Table 2.
Results of the Armillaria borealis genome assembly assessment using BUSCO
| Genes | Number | Percentage |
|---|---|---|
| Complete (single-copy) | 1286 | 96.3 (94.7) |
| Fragmented | 17 | 1.3 |
| Missing | 32 | 2.4 |
| Total number | 1335 | 100 |
Content of the RNA-seq reads
The SortMeRNA v2.1 program [34] with the pre-installed eight rRNA databases (silva-bac-16 s-id90, silva-arc-16 s-id95, silva-euk-18 s-id95, silva-bac-23 s-id98, silva-arc-23 s-id98, silva-euk-28 s-id98, rfam-5 s-id98, and rfam-5.8 s-id98) from the SILVA rRNA database project (SILVA SSU and LSU Ref NR v.119; https://www.arb-silva.de) was used to check the content of the RNA sequences. The results are presented in Table 3. Both RNA samples contained high number of rRNA reads – 32.5 and 72.9%, respectively.
Table 3.
Content of the RNA-seq reads
| Parameter | Sample 1 | Sample 2 |
|---|---|---|
| Total number of reads (%) | 6,428,260 (100%) | 8,444,300 (100%) |
| Number of non-rRNA reads (%) | 4,339,634 (67.5%) | 2,288,932 (27.1%) |
| Number of rRNA reads (%) | 2,088,626 (32.5%) | 6,155,368 (72.9%) |
| Average read length, bp | 153 | 151 |
Genome gene annotation
Functional annotation revealed 21,969 protein coding genes, which was also comparable with 22,705 and 14,473 genes in A. ostoyae and A. mellea, respectively. Their gene ontology (GO) functional annotation is presented in Fig. 1 and Additional files 1, 2 and 3. The greatest number of annotated sequences was related to the functioning of the cell nucleus (Fig. 1).
Fig. 1.
Distribution of 21,969 protein coding genes found in the Armillaria borealis genome assembly at the three levels - molecular function (MF), biological processes (BP), and cellular components (CC), respectively, based on the GO functional annotation
The distribution of enzyme genes across main classes is represented in Fig. 2. Oxidoreductases and hydrolases were among the most abundant enzymes.
Fig. 2.
Distribution of enzyme genes found in the Armillaria borealis genome assembly across main classes
Repetitive element (RE) annotation
In total, 886 RE sequences were identified in the A. borealis genome assembly. However, 839 (94.7%) of them remained unrecognized based on the initial classification using RepeatModeler program (Table 4).
Table 4.
Initial classification of the repetitive elements (REs) identified in the A. borealis genome assembly
| RE type | RE family | Number |
|---|---|---|
| LTR retrotransposons | Copia | 5 |
| Gypsy | 32 | |
| Pao | 1 | |
| LINE-retrotransposons | Tad1 | 5 |
| Retrotransposons with a tyrosine recombinase (YR) | Ngaro | 3 |
| Simple sequence repeats | Simple Repeats | 1 |
| Classified | 47 (5.3%) | |
| Unclassified | 839 (94.7%) | |
| Total | 886 | |
The following RE types were identified: LTR retrotransposons (Copia, Gypsy, Pao), LINE-retrotransposons (Tad1), and retrotransposons with a tyrosine recombinase (Ngaro).
The TEclass classifier software allowed us to further partition sequences including initially unclassified into four main groups (Table 5). The additional comparative studies of repetitive sequences in the genomes of basidiomycetes are needed to further classify REs. The variety of the detected TE families was not very wide, excep the LTR TE family, which was widely represented in A. borealis. The Ty3/Gypsy and Ty1/Copia elements have been identified, and Gypsy was the most abundant among them.
Table 5.
Additional classification based on TEclass including initially unclassified repetitive elements (REs) identified in the A. borealis genome assembly
| RE type | Number | % |
|---|---|---|
| DNA-transposons | 79 | 9 |
| Retrotransposons: | ||
| LTR | 735 | 83 |
| LINE | 27 | 3 |
| Unclear | 18 | 2 |
| Total | 886 | 100 |
Discussion
Typical fungal nuclear genome sizes occupy an intermediate position between prokaryotes and other eukaryotes. On average, the size of the fungal genome is two orders of magnitude smaller than that of higher plants and varies from ~ 2.19 to ~ 3706 Mbp ( [35], see also DOE JGI Fungi Portal: https://mycocosm.jgi.doe.gov/mycocosm/home). The average genome size of Ascomycota and Basidiomycota divisions is ~ 36.91 and ~ 46.48 Mb, respectively [36]. The genome size of A. borealis (~ 66.79 Mbp) was within a range of genome sizes in closely related species. For example, the genomes of A. ostoyae and A. mellea were ~ 60 and ~ 79.5 Mbp, respectively [19, 37].
The genome sizes of our assembly and draft assembly of Armillaria borealis FPL87.14 v1.0 available in the US DoE JGI fungal genomics resource database (https://mycocosm.jgi.doe.gov/Armbor1) are quite comparable, ~ 66.8 vs. ~ 71.69 Mbp, respectively. The difference can be explained by the use of different sequencing technologies (Illumina MiSeq vs. PacBio) that resulted in significant differences in the number of scaffolds between two assemblies but did not influence much the number of coding sequences identified, 21,969 coding sequences in our assembly vs. 19,984 in Armillaria borealis FPL87.14 v1.0. Meanwhile, the genome size of A. gallica [19] is almost 19 Mbp bigger than in A. borealis. The number of genes identified in the A. gallica genome was also larger (~ 25,000 genes).
Enzymatic activities of plant cell wall degrading fungi are performed by complex mixtures of cellulases, hemicellulases, and ligninases [38]. Oxidoreductases and hydrolases are the most interesting among enzymes in Armillaria because they are involved in lignin (oxidoreductases) and cellulose (hydrolases) decomposition.
Among identified TE classes LTR elements were the most frequent, particularly in basidiomycete fungi [39]. It is also true for A. borealis. The effect of TE on lifestyle of white-rot fungi has not been studied in depth yet, but some studies revealed considerable difference in TE number among different Armillaria species [19]. Their detailed comparative analysis will be presented in a separate paper.
Conclusions
The destruction of forests by pathogenic fungi is one of the main problems of forest conservation. Further comprehensive studies of these fungi at genomic, transcriptomic, proteomic, and metabolomic levels are very much needed to identify causes and mechanisms of their increasing pathogenicity. Our study provides important genomic resources of sufficiently good quality for further detailed work on studying the genetics of pathogenicity of Armillaria and other fungi species. It should also help with in-depth evolutionary and phylogenomic analyses and better identification and classification of Armillaria species genus.
Methods
Sample collection and DNA sequencing
The active mycelia of A. borealis were collected from dead trees of Abies sibirica in 2015 in a mixed forest consisted mainly of Siberian fir, silver birch, Norway spruce, and Siberian stone pine and located 40 km to the northwest of Krasnoyarsk City, Russia (56.175847°N, 92.184933°E). The fresh mycelium was isolated from under the bark of the infested stems 50 cm above the soil surface using sterile tweezers and gloves to avoid contamination. Before DNA extraction, mycelium was fixed for 2 days at 4°С in RNAlater (Thermo Fisher Scientific Company, Waltham, Massachusetts, USA). Then RNAlater fixed mycelium was quickly ground in acid-washed and autoclaved mortar. DNA was isolated using a modified version of the hot-CTAB extraction at 65 °C [40], followed by chloroform (double washing). Total DNA was precipitated for an hour with isopropanol at 4 °C, centrifuged at 6500 g for 30 min at 4 °C, washed twice with 70% ethanol, and was eluted in 50 μl nuclease-free water. Integrity and amount of the isolated total DNA were examined by 1.5% (wt/vol) agarose gel electrophoresis, and using the NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific Company, Waltham, Massachusetts, USA). The amount of extracted DNA was also measured on the Invitrogen Qubit 4 Fluorometer (Thermo Fisher Scientific Company, Waltham, Massachusetts, USA).
The paired-end sequencing libraries with 500 bp long genomic DNA inserts were generated using Truseq DNA Sample Prep Kit according to the manufacturer’s instructions (Illumina, Inc., San Diego, CA, USA). MiSeq Reagent Kit v2 (500-cycles) was used to sequence on the Illumina MiSeq platform with 2 × 250 cycles at the Laboratory of Forest Genomics of Siberian Federal University (Genome Research and Education Center, Siberian Federal University, Krasnoyarsk, Russia).
RNA isolation and sequencing
Two samples were isolated from the active A. borealis mycelium from two dead trees of Abies sibirica, respectively, in 2015 and fixed for 2 days at 4°С in RNAlater (Thermo Fisher Scientific Company, Waltham, Massachusetts, USA). The distance between the trees was 2–10 m. The RNA was isolated using Qiagen RNeasy Mini Kit (Qiagen, Valencia, CA, USA).
The quality and concentration of the RNA were measured on Agilent 2100 Bioanalyzer using Agilent RNA 6000 Nano kit (Agilent Technologies, Inc., Santa Clara, CA, USA). Purified RNA with high quality was selected for further cDNA library construction. Purification of mRNA from total RNA was performed using Oligo (dT) magnetic beads. The mRNA treated with fragmentation buffer was used as a template for cDNA synthesis. A double-stranded cDNA library was constructed with the TruSeq RNA Library Prep Kit v2 (Illumina, Inc., San Diego, CA, USA). End-repair, A-tailing, adapter ligation, and library amplification were performed during cDNA library construction followed by cluster generation and sequencing on the Illumina MiSeq platform in the Laboratory of Forest Genomics (Genome Research and Education Center, Siberian Federal University, Krasnoyarsk, Russia) using MiSeq Reagent Kit v2 (2 × 150 cycles).
Genome assembly
The de novo genome assembly was performed using SPAdes 3.13.0 genome assembler (http://cab.spbu.ru/software/spades) on high-performance computing (HPC) system IBM × 3950 X6 with 96 CPU and 3 TB RAM using an iterative genome assembly module for short reads. K-mer values were automatically selected based on read length and data type [41].
Gene annotation
The gene annotation of the A. borealis genome was performed using BRAKER2 [42], which is a combination of GeneMark-ET [43] and AUGUSTUS [44], that uses genomic and RNA-Seq data to automatically generate full gene structure annotations in novel genomes. AUGUSTUS integrates the extrinsic evidence from protein homology information into the prediction. There were no protein data for A. borealis before this study, therefore protein sequences of a close relative A. ostoyae have been used [19].
The gene ontology (GO) functional annotation was carried out using Blast2GO [45]. This program worked directly with coding sequences in fasta format. First, the nucleotide sequences homologues to the A. borealis sequences were searched for in the BLAST database. Then, sequence mapping and annotation were carried out. In parallel, protein domains were detected using InterProScan [46].
Repetitive element (RE) annotation
Repetitive sequences were identified initially using the RepeatModeler program designed to perform de novo search [47]. The additional classification of RE sequences was done using TEclass program [48].
Supplementary information
Additional file 1: Figure S1. GO distribution of coding sequences found in the Armillaria borealis genome assembly at the biological processes (BP) level based on the GO functional annotation.
Additional file 2: Figure S2. GO distribution of coding sequences found in the Armillaria borealis genome assembly at the molecular function (MF) level based on the GO functional annotation.
Additional file 3: Figure S3. GO distribution of coding sequences found in the Armillaria borealis genome assembly at the cellular components (CC) level based on the GO functional annotation.
Acknowledgments
Authors are thankful to the editor and two anonymous reviewers for their suggestions that helped us improve the manuscript. We also acknowledge support by the German Research Foundation (DFG) and the Open Access Publication Funds of the University of Göttingen.
About this supplement
This article has been published as part of BMC Genomics Volume 21 Supplement 7, 2020: Selected Topics in “Systems Biology and Bioinformatics” - 2019: genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume-21-supplement-7.
Abbreviations
- BLAST
Basic Local Alignment Search Tool
- bp
Base pair
- CPU
Central processing unit
- DNA
Deoxyribonucleic acid
- LINE
Long interspersed nuclear element
- LTR
Long terminal repeat
- Mbp
Million base pair
- N50
A weighted median statistic such that 50% of the entire assembly is contained in contigs or scaffolds equal to or larger than this value
- N90
A weighted median statistic such that 90% of the entire assembly is contained in contigs or scaffolds equal to or larger than this value
- PCWDE
Plant cell wall degradation enzymes
- RAM
Random access memory
- RE
Repetitive element
- RNA
Ribonucleic acid
- rRNA
ribosomal RNA
- SINE
Short interspersed nuclear element
- TE
Transposable element
Authors’ contributions
KVK, YAP, NVO & INP designed the study. KVK, NVO & YAP administered the project. NVO carried out most of the sequencing. VVS, VSA, AIA, SIF & DAK carried out bioinformatics analysis. VVS & DAK provided computer support. INP & YAL provided fungal material. VSA, AIA, INP & KVK drafted the manuscript. VAS, VVS, SIF, AIA & YAL contributed to analysis and interpretation of data and revised the paper. All authors read and approved the final manuscript.
Funding
This work including the study and collection, analysis and interpretation of data, and writing the manuscript was supported by research grant № 14.Y26.31.0004 from the Government of the Russian Federation with partial funding from the Federal Research Center “Krasnoyarsk Scientific Center”, Siberian Branch, Russian Academy of Sciences (grants № 0287–2019-0002, № 0356–2016-0704, and № 0356–2019-0024). The funding agencies played no role in the design of the study and collection material, analysis and interpretation of data, and in writing the manuscript. Publication cost have been funded by the Open Access Publication Funds of the University of Göttingen.
Availability of data and materials
The A. borealis genome assembly and all sequences described in this study are available in GenBank under the accession number JAAGUC000000000, sample SAMN13920850, project PRJNA603128 (https://www.ncbi.nlm.nih.gov/bioproject/603128).
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary information accompanies this paper at 10.1186/s12864-020-06964-6.
References
- 1.Fisher MC, Henk DA, Briggs CJ, Brownstein JS, Madoff LC, McCra SL, Gurr SJ. Emerging fungal threats to animal, plant and ecosystem health. Nature. 2012;484:186–194. doi: 10.1038/nature10947. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Coetzee M, Wingfield BD, Wingfield MJ. Armillaria root-rot pathogens: species boundaries and global distribution. Pathogens. 2018;7(4):83. doi: 10.3390/pathogens7040083. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Baumgartner K, Coetzee MPA, Hoffmeister D. Secrets of the subterranean pathosystem of Armillaria. Mol Plant Pathol. 2011;12(6):515–534. doi: 10.1111/j.1364-3703.2010.00693.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Prospero S, Holdenrieder O, Rigling D. Comparison of the virulence of Armillaria cepistipes and Armillaria ostoyae on four Norway spruce provenances. For Pathol. 2004;34(1):1–14. [Google Scholar]
- 5.Marcais B, Breda N. Role of an opportunistic pathogen in the decline of stressed oak trees. J Ecol. 2006;94(6):1214–1223. [Google Scholar]
- 6.Morrison DJ, Pellow KW. Variation in virulence among isolates of Armillaria ostoyae. For Pathol. 2002;32(2):99–107. [Google Scholar]
- 7.Sahu N, Merényi Z, Bálint B, Kiss B, Sipos G, Owens R, Nagy LG. Hallmarks of basidiomycete soft- and white-rot in wood-decay-omics data of Armillaria. bioRxiv preprint. 2020. 10.1101/2020.05.04.075879. [DOI] [PMC free article] [PubMed]
- 8.Legrand P, Ghahari S, Guillaumin JJ. Occurrence of genets of Armillaria spp. in four mountain forests in Central France: the colonization strategy of Armillaria ostoyae. New Phytol. 1996;133(2):321–332. doi: 10.1111/j.1469-8137.1996.tb01899.x. [DOI] [PubMed] [Google Scholar]
- 9.Omdal DW, Shaw CG, III, Jacobi WR, Wager TC. Variation of pathogenicity and virulence of isolates of Armillaria ostoyae on eight tree species. Plant Dis. 1995;79(9):939–944. [Google Scholar]
- 10.Bendel M, Kienast F, Rigling D. Genetic population structure of three Armillaria species at the landscape scale: a case study from Swiss Pinus mugo forests. Mycol Res. 2006;110(6):705–712. doi: 10.1016/j.mycres.2006.02.002. [DOI] [PubMed] [Google Scholar]
- 11.Keča N, Solheim H. Ecology and distribution of Armillaria species in Norway. For Pathol. 2011;41(2):120–132. [Google Scholar]
- 12.Marxmüller H, Holdenrieder O. Frontiers in mycology. Wallingford, Oxon: CAB International; 1990. Armillaria mellea sl in Southern Bavaria; pp. 9–32. [Google Scholar]
- 13.Pavlov IN. Biotic and abiotic factors as causes of coniferous forests dieback in Siberia and Far East. Contemp Probl Ecol. 2015;8(4):440–456. [Google Scholar]
- 14.Cromey M, Drakulic J, Beal L, Waghorn I, Perry J, Clover GR. Susceptibility of garden trees and shrubs to Armillaria root rot. Plant Dis. 2020;104(2):483–492. doi: 10.1094/PDIS-06-19-1147-RE. [DOI] [PubMed] [Google Scholar]
- 15.Drakulic J, Gorton C, Perez-Sierra A, Clover G, Beal L. Associations between Armillaria species and host plants in UK gardens. Plant Dis. 2017;101(11):1903–1909. doi: 10.1094/PDIS-04-17-0472-RE. [DOI] [PubMed] [Google Scholar]
- 16.Abdel-Hamid AM, Solbiati JO, Cann IKO. Insights into lignin degradation and its potential industrial applications. Adv Appl Microbiol. 2013;82:1–28. doi: 10.1016/B978-0-12-407679-2.00001-6. [DOI] [PubMed] [Google Scholar]
- 17.Hirsch CD, Springer NM. Transposable element influences on gene expression in plants. Biochim Biophys Acta. 2017;1860(1):157–165. doi: 10.1016/j.bbagrm.2016.05.010. [DOI] [PubMed] [Google Scholar]
- 18.Ross-Davis AL, Steward JE, Hanna JW, Kim M-S, Knaus BJ, Cronn R, Rai H, Richardson BA, GI MD, Klopfenstein NB. Transcriptome of an Armillaria root disease pathogen reveals candidate genes involved in host substrate utilization at the host-pathogen interface. For Path. 2013;43(6):468–477. [Google Scholar]
- 19.Sipos G, Prasanna AN, Walter MC, O’Connor E, Bálint B, Krizsán K, et al. Genome expansion and lineage-specific genetic innovations in the forest pathogenic fungi Armillaria. Nat Ecol Evol. 2017;1(12):1931–1941. doi: 10.1038/s41559-017-0347-8. [DOI] [PubMed] [Google Scholar]
- 20.Meinhardt LW, Costa GGL, Thomazella DPT, Thomazella DP, Teixeira PJP, Carazzolle MF, et al. Genome and secretome analysis of the hemibiotrophic fungal pathogen, Moniliophthora roreri, which causes frosty pod rot disease of cacao: mechanisms of the biotrophic and necrotrophic phases. BMC Genomics. 2014;15(1):164. doi: 10.1186/1471-2164-15-164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mondego JMC, Carazzolle MF, Costa GG, Formighieri EF, Parizzi LP, Rincones J, Cotomacci C, Carraro DM, Cunha AF, Carrer H, Vidal RO, Estrela RC, Garcia O, Thomazella DPT, de Oliveira BV, ABL P, MCS R, MRR A, de Moraes MH, Castro LAB, Gramacho KP, Goncalves MS, Neto JPM, Neto AG, Barbosa LV, Guiltinan MJ, Bailey BA, Meinhardt LW, Cascardo JCM, Pereira GAG. A genome survey of Moniliophthora perniciosa gives new insights into Witches' Broom disease of cacao. BMC Genomics. 2008;9(1):548. doi: 10.1186/1471-2164-9-548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Olson Å, Aerts A, Asiegbu F, Belbahri L, Bouzid O, Broberg A, Canback B, Coutinho PM, Cullen D, Dalman K, Deflorio G, van Diepen LT, Dunand C, Duplessis S, Durling M, Gonthier P, Grimwood J, Fossdal CG, Hansson D, Henrissat B, Hietala A, Himmelstrand K, Hoffmeister D, Hogberg N, James TY, Karlsson M, Kohler A, Kues U, Lee YH, Lin YC, et al. Insight into trade-off between wood decay and parasitism from the genome of a fungal forest pathogen. New Phytol. 2012;194(4):1001–1013. doi: 10.1111/j.1469-8137.2012.04128.x. [DOI] [PubMed] [Google Scholar]
- 23.Hane JK, Anderson JP, Williams AH, Sperschneider J, Singh KB. Genome sequencing and comparative genomics of the broad host-range pathogen Rhizoctonia solani AG8. PLoS Genet. 2014;10(5):e1004281. doi: 10.1371/journal.pgen.1004281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Raffaele S, Kamoun S. Genome evolution in filamentous plant pathogens: why bigger can be better. Nat Rev Microbiol. 2012;10(6):417–430. doi: 10.1038/nrmicro2790. [DOI] [PubMed] [Google Scholar]
- 25.Muszewska A, Steczkiewicz K, Stepniewska-Dziubinska M, Ginalski K. Transposable elements contribute to fungal genes and impact fungal lifestyle. Sci Report. 2019;9(1):4307. doi: 10.1038/s41598-019-40965-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Moller M, Stukenbrock EH. Evolution and genome architecture in fungal plant pathogens. Nat Rev Microbiol. 2017;15:756–771. doi: 10.1038/nrmicro.2017.143. [DOI] [PubMed] [Google Scholar]
- 27.Dong S, Raffaele S, Kamoun S. The two-speed genomes of filamentous pathogens: waltz with plants. Curr Opin Genet Dev. 2015;35:57–65. doi: 10.1016/j.gde.2015.09.001. [DOI] [PubMed] [Google Scholar]
- 28.Sperschneider J, et al. Genome-wide analysis in three Fusarium pathogens identifies rapidly evolving chromosomes and genes associated with pathogenicity. Genome Biol Evol. 2015;7:1613–1627. doi: 10.1093/gbe/evv092. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Faino L, Seidl MF, Shi-Kunne X, Pauper M, van den Berg GC, Wittenberg AH, Thomma BP. Transposons passively and actively contribute to evolution of the two-speed genome of a fungal pathogen. Genome Res. 2018;26(8):1091–1100. doi: 10.1101/gr.204974.116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Ma L-J, et al. Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium. Nature. 2010;464:367–373. doi: 10.1038/nature08850. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Yoshida K, Saunders DG, Mitsuoka C, Natsume S, Kosugi S, Saitoh H, et al. Host specialization of the blast fungus Magnaporthe oryzae is associated with dynamic gain and loss of genes linked to transposable elements. BMC Genomics. 2016;17(1):370. doi: 10.1186/s12864-016-2690-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Simao FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19):3210–3212. doi: 10.1093/bioinformatics/btv351. [DOI] [PubMed] [Google Scholar]
- 33.Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–1111. doi: 10.1093/bioinformatics/btp120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28(24):3211–3217. doi: 10.1093/bioinformatics/bts611. [DOI] [PubMed] [Google Scholar]
- 35.Talhinhas P, Tavares D, Ramos AP, Gonçalves S, Loureiro J. Validation of standards suitable for genome size estimation of fungi. J Microbiol Methods. 2017;142:76–78. doi: 10.1016/j.mimet.2017.09.012. [DOI] [PubMed] [Google Scholar]
- 36.Mohanta TK, Bae H. The diversity of fungal genome. Biol Proced Online. 2015;17:8. doi: 10.1186/s12575-015-0020-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Collins C, Keane TM, Turner DJ, O’Keeffe G, Fitzpatrick DA, Doyle S. Genomic and proteomic dissection of the ubiquitous plant pathogen, Armillaria mellea: toward a new infection model system. J Proteome Res. 2013;12(6):2552–2570. doi: 10.1021/pr301131t. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Adejumo TO. Coker ME, Ogundeji JS, Adejoro DO. Qualitative determination of lignocellulolytic enzymes in eight wood-decomposing fungi. J Nat Sci Res. 2015;5(14):1–8. [Google Scholar]
- 39.Castanera R, Borgognone A, Pisabarro AG, Ramírez L. Biology, dynamics, and applications of transposable elements in basidiomycete fungi. Appl Microbiol Biotechnol. 2017;101(4):1337–1350. doi: 10.1007/s00253-017-8097-8. [DOI] [PubMed] [Google Scholar]
- 40.Devey ME, Bell JC, Smith DN, Neale DB, Moran GF. A genetic linkage map for Pinus radiata based on RFLP, RAPD, and microsatellite markers. Theor Appl Genet. 1996;92(6):673–679. doi: 10.1007/BF00226088. [DOI] [PubMed] [Google Scholar]
- 41.Bankevich A, Nurk S, Antipov D, Gurevich A, Dvorkin M, Kulikov AS, Lesin V, Nikolenko S, Pham S, Prjibelski A, Pyshkin A, Sirotkin A, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–477. doi: 10.1089/cmb.2012.0021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Hoff KJ, Lange S, Lomsadze A, Borodovsky M, Stanke M. BRAKER1: unsupervised RNA-seq-based genome annotation with GeneMark-ET and AUGUSTUS. Bioinformatics. 2015;32(5):767–769. doi: 10.1093/bioinformatics/btv661. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Lomsadze A, Burns PD, Borodovsky M. Integration of mapped RNA-seq reads into automatic training of eukaryotic gene finding algorithm. Nucleic Acids Res. 2014;42(15):e119. doi: 10.1093/nar/gku557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34(Suppl. 2):W435–W439. doi: 10.1093/nar/gkl200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–3676. doi: 10.1093/bioinformatics/bti610. [DOI] [PubMed] [Google Scholar]
- 46.Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweiler R, Lopez R. InterProScan: protein domains identifier. Nucleic Acids Res. 2005;33(Suppl. 2):W116–W120. doi: 10.1093/nar/gki442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.. Smit A, Hubley R. RepeatModeler-1.0.11. Institute for Systems Biology. http://www.repeatmasker.org.
- 48.Abrusán G. TEclass—a tool for automated classification of unknown eukaryotic transposable elements. Bioinformatics. 2009;25(10):1329–1330. doi: 10.1093/bioinformatics/btp084. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Figure S1. GO distribution of coding sequences found in the Armillaria borealis genome assembly at the biological processes (BP) level based on the GO functional annotation.
Additional file 2: Figure S2. GO distribution of coding sequences found in the Armillaria borealis genome assembly at the molecular function (MF) level based on the GO functional annotation.
Additional file 3: Figure S3. GO distribution of coding sequences found in the Armillaria borealis genome assembly at the cellular components (CC) level based on the GO functional annotation.
Data Availability Statement
The A. borealis genome assembly and all sequences described in this study are available in GenBank under the accession number JAAGUC000000000, sample SAMN13920850, project PRJNA603128 (https://www.ncbi.nlm.nih.gov/bioproject/603128).