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Keywords:

  • COX1;
  • Fun-BOL;
  • internal transcribed spacer (ITS);
  • mitochondrial cytochrome oxidase 1;
  • online identification databases

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. The shift from classical to molecular fungal taxonomy
  5. DNA barcode marker selection for fungi
  6. Existing mycological networks and barcode-like resources
  7. Acknowledgements
  8. Conflict of interest statement
  9. References

The use of DNA sequences for identifying fungi and fungus-like organisms predates the DNA barcoding movement by at least 10 years. A brief overview of the mycological shift from phenotypic to molecular taxonomy is provided. Exploration of the animal barcode marker, cytochrome oxidase 1, by Canadian mycologists has been fruitful for some fungi, but intron issues and lack of resolution in other taxa prevent its universal application. The momentum established by 15 years of research on the fungal nuclear ribosomal internal transcribed spacer (ITS) sequences will lead to a proposal to the Consortium for the Barcode of Life on the adoption of this marker as the fungal barcode. Existing mycological research networks should facilitate the rapid development of DNA barcoding of fungi once the marker issue is settled. Some available online fungal identification databases are briefly described.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. The shift from classical to molecular fungal taxonomy
  5. DNA barcode marker selection for fungi
  6. Existing mycological networks and barcode-like resources
  7. Acknowledgements
  8. Conflict of interest statement
  9. References

Long before the term ‘DNA barcoding’ assumed its present meaning, mycologists were developing DNA sequence databases to facilitate fungal identification (Bruns et al. 1991). Macrofungi, such as mushrooms, puff balls and bracket fungi are the ‘charismatic megaflora’ of this group, but most fungi are inconspicuous or microscopic and live without notice from humans. Even macrofungi mostly exist in a microscopic mode, invisibly growing, competing and evolving in soil and plants. Interpretation of microscopic features, the existence of morphologically similar species, and the speciose nature of the kingdom are perpetual challenges for mycologists and make the fungi a group where DNA barcoding is essential.

The 1990s was a turbulent decade in fungal systematics, as new molecular techniques challenged phenotypic taxonomy. The attitudes and feelings expressed in mycology would be familiar to proponents of DNA barcoding, which is still attacked using similar arguments. But the generational succession among mycologists, combined with cheaper and easier sequencing, have carried the day. Most professional mycological taxonomy now has a molecular dimension, and DNA sequence-based identification systems are being developed and adopted at an increasing rate.

By the stringent standards adopted recently for barcoding, most present initiatives for fungal identification by DNA sequencing would be characterized as ‘barcode-like’. There is informal standardization on a few species-level markers [the nuclear ribosomal internal transcribed spacer (ITS), and protein coding genes such as ribosomal polymerase B2, β-tubulin, calmodulin, etc.], but vouchering has not been rigorously enforced (Agerer et al. 2000), and online archiving of sequence traces has not been part of the culture. Here I will outline the establishment of DNA sequencing as the prime mover in fungal taxonomy, give a perspective on the present involvement of taxonomic mycologists in DNA barcoding, and speculate on how mycologists can apply their expertise to the development of this essential biological tool.

The shift from classical to molecular fungal taxonomy

  1. Top of page
  2. Abstract
  3. Introduction
  4. The shift from classical to molecular fungal taxonomy
  5. DNA barcode marker selection for fungi
  6. Existing mycological networks and barcode-like resources
  7. Acknowledgements
  8. Conflict of interest statement
  9. References

Mycologists are accustomed to the low profile fungi assume in biological discussions. Although yeast-derived breads and spirits nourish all of mankind, penicillin banished bacterial disease and cyclosporin enabled organ transplants, the public focuses on poisonous ‘toadstools’ and hysteria about moulds in houses. But fungi are everywhere, including in and on the animals that dominate discussions of barcoding.

The trends and fashions of 200 years of mycological taxonomy parallel those of other kingdoms. Gross phenotypic taxonomy was supplemented by microscopy and then in vitro culturing. Waves of ultrastructural, enzymatic and protein profiling were succeeded by the nucleic acid revolution. Fungi are now recognized as a eukaryotic monophyletic sister clade to the animals, characterized by an absorptive, heterotrophic (i.e. nonphotosynthetic) nutritional mode, cell walls including chitin, the production of cylindrical cells with apical growth (hyphae), with varying sexual and asexual forms of spore production. The legacy of taxonomic study is about 80 000 accepted species, traditionally divided into six main Divisions (the equivalent of phyla) differentiated by modes of sexual reproduction and ultrastructural and biochemical characters: Myxomycetes (now considered protists), Oomycetes (now in the Kingdom Straminipila), and the true fungi classified in the Chytrids, Zygomycetes, Ascomycetes and Basidiomycetes (McLaughlin et al. 2001a; McLaughlin et al. 2001b). A seventh pseudo-division, the Deuteromycetes or Fungi Imperfecti, referred to here as moulds and now often called anamorphic fungi, includes asexually reproducing fungi, some with known sexual states. They are now mostly classified phylogenetically among related sexual Ascomycetes (and to a lesser extent Basidiomycetes).

As with insects and bacteria, the overwhelming majority of fungi are probably undescribed. Estimates of > 15 million species are published, with most mycologists settling on 1–1.5 million as a best guess (Hawksworth 2001, 2004). Although there is an online catalogue of fungal names (Index Fungorum, coordinated with MycoBank, see below), there is no checklist of accepted fungal species names. Apart from macrofungi and lichens, checklists are not the common feature in mycology that they are in the botanical and zoological milieu.

The problem of cataloguing all fungi is more similar to the challenge of enumerating bacteria than it is to collecting insects. Compiling this catalogue will provide the major challenge to and application for fungal DNA barcoding. Studies of environmental DNA reveal the existence of many micro-organisms, including fungi, that do not grow using presently available culturing techniques. As of this writing, the GenBank ‘Top Organisms’ page lists ‘uncultured fungus’, ‘uncultured soil fungus’ and ‘uncultured endophytic fungus’ among the top five most frequently reported ‘species’, with about 7800 records in total, compared to about 5350 for Homo sapiens and 2725 for ‘uncultured bacterium’. We do not know what these fungi look like, how to collect specimens or isolate cultures of them. In the future, fungi will be most effectively detected and identified by studies of their DNA alone. Fungal barcoding primers will have to be robust in order not to overlook species. Identification databases will have to be reliable, and will probably include barcodes for many unnamed fungi. Mycologists will be key participants in the development of barcoding for total ecosystem profiling.

Inspired by molecular bacterial taxonomy, and the need to work with easily isolated or amplified nucleic acids, the initial phylogenetic and molecular identification work on fungi began with nuclear ribosomal genes. The now classic paper by White et al. (1990) included universal primers still widely used for amplifying three main components of the fungal ribosomal operon: (i) the large subunit (LSU, variously referred to as the 26S or 28S, and including two variable subregions called D1 and D2); (ii) the small subunit (SSU, or 18S), separated by (iii) the ITS, comprising two sections (ITS1, ITS2) that bracket the conserved 5.8S region. Because of the length limitations of manual sequencing, early studies of the fungal ITS often focused only on either the ITS1 or ITS2. The White et al. (1990) primers are remarkably robust, working with the vast majority of fungi.

The ITS became the default marker for species level studies for most fungi, with the notable exception of the yeasts, where the LSU became the standard for identification. The ITS varies in length considerably among major taxonomic groups (Table 1), and gives superior resolution in those groups with longer amplicons. Fungal ITS is indel rich, which makes it useful for the development of molecular diagnostics involving taxon specific oligonucleotides. However, it is difficult to align, which restricts its utility for phylogenetic reconstruction. It is beyond the aims of this paper to review the taxonomic use of the ITS, but as examples, effective species-level data sets for the ITS were published for the Oomycetes (Cooke et al. 2000 for Phytophthora, Lévesque & de Cock 2004 for Pythium), agarics (Aanen & Kuyper 2004 for Hebeloma, Frøslev et al. 2007 for Cortinarius, Kretzer et al. 1996 for Suillus) and lichens (Stenroos et al. 2002, for Cladonia). The movement to adopt the ITS as the fungal barcoding marker is discussed below.

Table 1.  Variation in the lengths of ITS amplicons (including 5.8S) from selected major fungal groups. The values were calculated using the Primer blast function at the National Center for Biotechnology Information (NCBI) using the ITS4 and ITS5 primers (White et al. 1990), with the searches restricted to the taxonomic groups shown
 Minimu mMaximumMeanMedianNo. of sequences
  1. Sequence lengths were calculated by subtracting the combined primer lengths and the number of bases from the end of primer ITS5 to the beginning of the ITS1, and from the beginning of the LSU to the start of primer ITS4. Number of sequences indicates only those recovered by the Primer blast function; many other ITS sequences exist for these taxa in GenBank but would not be recovered by this search because the accessions do not include the primer sequences.

Chytridiomycota381772573573 29
Mucoromycotina (Zygomycetes)540633577562 38
Glomeromycetes (AM fungi)494497495495  7
Ascomycota     
Saccharomycotina (yeasts)270763412443110
Taphrinomycotina613889810889  7
Lecanoromycetes (lichens)464829581512 79
Pezizomycotina454509465468177
Leotiomycetes (cup fungi)437951522491175
Dothideomycetes454973478468200
Basidiomycota     
Pucciniomycotina (rusts)499642598597200
Ustilaginomycotina (smuts)572699601587 24
Agaricomycotina (mushrooms etc.)397668589596 63
Oomycetes (Straminipila)493837654666 38

DNA barcode marker selection for fungi

  1. Top of page
  2. Abstract
  3. Introduction
  4. The shift from classical to molecular fungal taxonomy
  5. DNA barcode marker selection for fungi
  6. Existing mycological networks and barcode-like resources
  7. Acknowledgements
  8. Conflict of interest statement
  9. References

COX1

Only a small amount of COX1 data was available for fungi when the Canadian Barcode of Life Network began in 2003–2004. Participating mycologists investigated the feasibility of using COX1 as a fungal barcode, realizing there was little chance that the broader mycological community would accept it with ITS already functioning as a de facto barcode. The Assembling the Fungal Tree of Life Project (AFTOL, described below) did not include COX1, probably because of reports of introns, some of them mobile, in fungal mitochondrial genes. The length of fungal COX1 varies from 1584 bp to 22 kb, with the barcode region potentially from 642 bp to 12.3 kb, the size range reflecting the number and length of introns (Seifert et al. 2007). Not only would introns interfere with polymerase chain reaction (PCR), but the lack of conserved regions in existing sequences seemed to preclude universal primer design.

Seifert et al. (2007) sequenced the barcode region of COX1 for 360 strains from one group of the mould genus Penicillium using PCR primers designed from mitochondrial genomes for the ascomycete family Trichocomaceae. COX1 provided species-specific barcodes for about 66% of species, superior to ITS (about 25%), but poorer than the protein-coding gene BENA (about 80%). Introns were detected in only about 1% of strains, did not interfere with amplification, and had little impact on barcoding utility of COX1. Similar results were obtained for seven species of the soil-borne genus, Leohumicola (Nguyen & Seifert 2008), where similar species resolution with COX1 and ITS was observed, using newly designed COX1 primers for the subdivision Pezizomycotina. Introns occurred in only 5% of the strains.

Other work with COX1 on moulds has been disappointing. Geiser et al. (2007) reported insufficient variation for species recognition in the Aspergillus niger complex, phylogenetically closely related to Penicillium. There was evidence for multiple copies of COX1 in the published phylogram, although the possibility was not explored in the text. Gilmore et al. (2009) encountered paralogues of COX1 within individual strains of Fusarium species, revealed by the occurrence of double bases at some positions, or otherwise variable sequences among copies. Homologous copies had little species level resolution, with species assigned to different taxonomic sections sharing identical barcode sequences.

COX1 functions well as a barcode in the fungus-like Oomycete genera Pythium and Phytophthora (G. Robideau & C. A. Lévesque, personal communication). Pre-barcoding molecular phylogeny studies of Pythophthora by Martin & Tooley (2003) employed COX1 and COX2 genes as markers. No introns occur in the COX1 and COX2 genes of Oomycetes; therefore, primer design, amplification and sequencing have presented few unexpected problems. For these organisms, COX1 provides similar resolution to ITS and either could function as an acceptable barcode.

Two disparate basidiomycete groups are being studied in the Canadian Barcode of Life network. For both, introns are a major issue. Rusts are obligate plant pathogens that do not grow in culture; their DNA must be extracted either from freshly collected or archived herbarium specimens. For tree rusts, frequent introns inside the barcode region led to low amplification and sequencing success (Vialle et al. 2009). In Chrysomyxa and Melampsora species, amplification of the 300- to 600-bp coding fragment of COX1 required at least two primer pairs to bypass introns, but success was species dependent (M. Allaire and R. C. Hamelin, unpublished data). In these genera, species resolution obtained with COX1 barcodes was inferior to both the ITS and the D2 region of the LSU.

COX1 is also a difficult marker for mushrooms, where introns frequently occur in the barcode region (Vialle et al. 2009). After many attempts at optimizing conventional PCR, J.-M. Moncalvo & M. Didukh (personal communication) resorted to reverse transcriptase to sequence the COX1 barcode region from transcribed RNA isolated from freshly collected mushrooms. Such a practice might be feasible for ecological studies, but not for the development of a COX1 barcoding database from preserved specimens. For oyster mushrooms (Pleurotus spp.), species level resolution was disappointing and more species were distinguished with ITS than COX1.

The suggested adoption of the ITS

In May 2007, against the backdrop of the recently published article on the COX1 of Penicillium (Seifert et al. 2007), 37 mycologists from 12 countries met at the Smithsonian's Conservation and Research Centre at Front Royal, Virginia, for a discussion organized by Amy Rossman and Mary Palm, with the support of the Consortium of the Barcode of Life (CBOL), and funded by the A.F. Sloan Foundation. Rossman (2007) reviewed the deliberations of this meeting, which resulted in the participants unanimously endorsing the ITS as the fungal barcode. Considerable interest was also expressed in the LSU. An in-depth review of the fungal ITS and its potential as a DNA barcode is being prepared for presentation to the Scientific Advisory Board of CBOL, led by Dr Ursula Eberhardt, the fungal representative on the CBOL Scientific Advisory Board. A large group of mycologists will contribute published and unpublished data sets to test the barcoding utility of ITS, and the Canadian Barcode of Life Network participants have made their COX1 data available for comparison. The process is inclusive; interested parties not invited directly should contact Dr Eberhardt (u.eberhardt@cbs.knaw.nl).

For some taxonomic groups, mean ITS length is less than the 500 bp designated as the optimal lower limit for an effective DNA barcode (Table 1). For species-rich Ascomycete genera with these shorter amplicons, including the mould genera Cladosporium (Schubert et al. 2007), Penicillium (Skouboe et al. 1999) and Fusarium (O'Donnell & Cigelnik 1997), the ITS often has insufficient variation to unequivocally identify species. For these genera, a second barcoding gene (hopefully with as much standardization as possible across taxonomic groups) will be necessary for precise species identification.

There are presently about 70 000 ITS sequences in GenBank. Nilsson et al. (2006) estimated that 80% lack explicitly identified vouchers, which would exclude them from barcode status. Therefore, about 13 000 fungal ITS sequences must be evaluated for their eligibility for the BARCODE keyword. As noted below, AFTOL already has sequence traces online, but trace files for sequence databases of some other taxonomic groups described below could be uploaded. Some mycologists feel that if a grandfathering provision can be offered to selected legacy ITS data that lacks traces, then existing sequences based on type specimens or cultures, authoritative taxonomic revisions or reliable identifications can be considered validated barcodes. CBOL has pledged its assistance to deal with the legacy data issue. It will be critical for as many fungal taxonomists as possible to evaluate the quality of legacy data in their areas of expertise.

Existing mycological networks and barcode-like resources

  1. Top of page
  2. Abstract
  3. Introduction
  4. The shift from classical to molecular fungal taxonomy
  5. DNA barcode marker selection for fungi
  6. Existing mycological networks and barcode-like resources
  7. Acknowledgements
  8. Conflict of interest statement
  9. References

Mycologists have been vocal critics of the GenBank policy of forbidding third party annotation of sequence accessions, citing contamination of the database with sequencing errors, and sequences derived from misidentified vouchers (Bridge et al. 2003; Nilsson et al. 2006; Bidartondo et al. 2008). Long before barcoding databases such as BOLD were online, mycologists were developing identification databases as substitutes for increasingly unreliable blast search identifications through GenBank. These resources, and the research coordinating bodies that produced them, are described below, with URLs listed in Table 2. A common feature of many of these sites is the de novo generation of in-house sequences from authoritatively identified vouchers (with variable online access to meta-data on the vouchers). The databases do not employ the same gene regions, none include COX1, and about half include ITS.

Table 2.  Selected DNA barcode-like Internet resources for fungi, and organizations and research coordinating networks active in fungal DNA barcoding
NameFocusSequence data on-line*URL
  • *

    Abbreviations for genes: BENA,β-tubulin; ITS, internal transcribed spacer of the nuclear ribosomal DNA; LSU, large subunit of the nuclear ribosomal DNA; mtSSU, small subunit of the mitochondrial ribosomal DNA; RPB1 RPB2, ribosomal polymerase B-1 and B-2; SSU, small subunit of the nuclear ribosomal DNA; TEF1, translation elongation factor 1-α.

Assembling the Fungal Tree of Life (AFTOL)Fungal phylogenyLSU, SSUaftol.org
RPB1, RPB2
TEF1, mtSSU
ITS
CBS Fungal Biodiversity Centre (CBS) Identification databasesDermatophytesITShttp://www.cbs.knaw.nl [RIGHTWARDS ARROW] Databases
PenicilliumBENA
PhaeoacremoniumBENA
YeastsSSU, LSU, ITS
Medical fungiSSU, LSU, ITS, BENA, TEF1
Deep HyphaFungal phylogeny(see AFTOL)http://ocid.nacse.org/research/deephyphae
Fungal Environmental Sampling and Informatics Network (FESIN)Fungal ecology(see UNITE)http://www.bio.utk.edu/fesin
Fungi in Boreal Forests networkMycorrhizal fungi http://www.bio.uio.no/meb/fbfs/index.html
Fusarium-IDFusariumTEF1 RPB2 (planned)isolate.fusariumdb.org
Index FungorumNomenclatural dataNonehttp://www.indexfungorum.org
International Commission on the Taxonomy of Fungi (ICTF)Fungal taxonomyNonehttp://www.fungaltaxonomy.org
International Mycological Association (IMA)All mycologyNonehttp://www.ima-mycology.org
International Union of Microbiological SciencesAll microbiologyNonehttp://www.iums.org
International Subcommission on Fungal Barcoding (Fun-BOL)Fungal DNA barcodingNonehttp://www.fungaltaxonomy.org
MycoBankNomenclatural dataNonehttp://www.mycobank.org
TrichoKeyTrichodermaITShttp://www.isth.info
UNITERoot-associated fungi (identification)ITSunite.ut.ee

Research networks: Deep Hypha, AFTOL, Fungal Environmental Sampling and Informatics Network, UNITE and Fungi in Boreal Forests

Deep Hypha was a US National Science Foundation-funded Research Coordination Network started by American mycologists (Blackwell et al. 2006) that evolved into the international, multi-laboratory consortium AFTOL, with the goal of developing a complete phylogeny for the Kingdom Fungi (Spatafora 2005). More than 1000 fungal species were sampled, representing all higher taxonomic groups to the level of Order. AFTOL selected six genetic markers: the large and small ribosomal subunits (LSU, SSU), ribosomal polymerase B1 and B2 (RPB1, RPB2), translation elongation factor 1-α (TEF1), and the small subunit of the mitochondrial ribosomal operon (mtSSU). Reliable universal primers for some genes could not be designed for certain taxa, restricting parts of the resulting phylogenies to ribosomal genes. AFTOL sequenced ITS to provide a reference framework that could be fleshed out to a complete identification database by subsequent studies. A ribosomal gene phylogeny of the Kingdom (Lutzoni et al. 2004) was followed by the December 2007 issue of Mycologia, devoted entirely to AFTOL results. A comprehensive classification of Fungi based on phylogenetic results from AFTOL (Hibbett et al. 2007) was adopted by the Dictionary of the Fungi (Kirk et al. 2008), a standard reference source for mycologists. Subsequently, several AFTOL2 projects began, focused on intractable fungal classes requiring denser sampling and the sequencing of additional genes to answer fundamental questions of basal phylogeny.

The AFTOL project established a strong networking ethic among mycologists, particularly in North America. It formalized two standards now recognized as central to DNA barcoding, that is, the explicit designation of authoritatively identified vouchers, and online archiving of sequence traces. By developing a parallel ITS database for identification purposes, AFTOL effectively promoted the use of this marker as the standard for DNA barcoding.

With the completion of AFTOL and Deep Hypha, a second NSF-funded Research Coordination Network was established, focused on fungal ecology, and the bioinformatics relevant to direct environmental sampling. The Fungal Environmental Sampling and Informatics Network (FESIN) has an obvious interest in developing effective DNA barcoding databases for fungi. Their focus is on ribosomal markers, especially ITS and LSU; some of the participants have close interactions with the ITS-based UNITE database (described below). FESIN also has a very strong interest in microarrays, which are likely to exploit ITS barcodes or microcodes for species identification.

Hyphae of mycorrhizal fungi grow within plant roots and extend into surrounding soil, facilitating nutrient uptake for their plant symbionts. Sporadically or seasonally produced macroscopic fruiting bodies are required for classical identification; this always limited ecological studies of these fungi and made them an obvious choice for molecular identification systems. Kõljalg et al. (2005) created UNITE, a research network that resulted in the production of multi-stakeholder database for root-associated fungi which included only newly derived ITS sequences from authoritatively identified voucher specimens. The database now includes > 2750 ITS sequences from 1192 species representing 187 mycorrhizal genera. Although sequence traces are not presently online, they are available to bring this database into compliance with the barcode standard. The UNITE research network was succeeded by the ‘Fungi in Boreal Forests’ network.

Coordinating bodies: the International Mycological Association, the International Union of Microbiological Sciences and their subsidiaries, and the CBS Fungal Biodiversity Centre

On an international scale, mycologists are networked through the International Mycological Association (IMA), which organizes congresses every 4 years. The International Union of Microbiological Sciences (IUMS) also has a mycology division, which participates in large congresses with bacteriologists and virologists every 3 years. The IUMS structure includes so-called committees, commissions and federations (COMCOFS), formal groups with elected officers, appointed members from the international community, and written statutes. Among these is the International Commission on the Taxonomy of Fungi (ICTF), which also reports to the IMA. A main function is to encourage formation of commissions and subcommissions focused on specific taxonomic groups, some of which are described below. With their focus on the taxonomy of economically important fungi and their expert membership, these bodies should be obvious leaders and participants in fungal DNA barcoding.

The CBS Fungal Biodiversity Centre in Utrecht, the Netherlands, is an influential major mycological research institute, an internationally important culture collection, and a leader in web resources for fungal taxonomy. MycoBank (Crous et al. 2004), developed and hosted by CBS, now is owned by the International Mycological Association. Mycological journals require deposit of data in MycoBank before accepting taxonomic novelties. The flexibility of its underlying database, BioLomics, makes it likely that at least part of the fungal component of the envisaged DNA barcoding database will be affiliated with this site.

CBS hosts several taxon-specific identification databases based on pairwise searches against an in-house sequence database and the GenBank nucleotide file. The dermatophyte database, based on ITS, identifies agents of fungal skin diseases and is presently the only one to include sequence traces online. Additional databases are available for Penicillium subgenus Penicillium and Phaeoacremonium, based on the β-tubulin databases of Samson et al. (2004) and Mostert et al. (2006). Both are accompanied by synoptic keys for identification using in vitro colony growth characters, micromorphology, and in the case of Penicillium, mycotoxin profiles. The in-house CBS databases are all effectively vouchered by cultures in the collection, with the meta-data available at the same site.

An identification database for species of economically important plant pathogenic and mycotoxigenic mould genus Fusarium, called Fusarium ID was established by the ICTF Subcommission on Fusarium Systematics (which reports also to the International Society of Plant Pathology Fusarium committee). The development of an objective identification tool was essential to counteract the plague of misidentifications in the literature and GenBank. Based on translation elongation factor 1-α (TEF1) sequences from authoritatively identified vouchers, much of the data in Fusarium ID is original to its authors (Geiser et al. 2004). Meta-data for vouchers is not yet online. According to Geiser (personal communication), a second generation of Fusarium ID will include an additional identification marker, ribosomal polymerase B2 (RPB2).

The International Subcommission on Trichoderma and Hypocrea (ISTH) website includes an identification database based on a variant of the DNA barcode concept, in which diagnostic oligonucleotide segments (called hallmarks) were extracted from an ITS alignment, and the pattern of hallmarks was then used for identification (Druzhinina et al. 2005). The online database, now in its second version, is based on 979 sequences from 88 species, including 135 ITS haplotypes. The strains used for the database are all vouchered; although the sequences in the database are accessible, the traces are not online at this time. The site also includes a morphologically based identification key to species of this important mould genus, which includes many cellulolytic and potential biocontrol species.

From 2006–2008, CBOL actively promoted fungal DNA barcoding hoping to extend interest beyond the Canadian Barcode of Life network. Following the May 2007 meeting in Front Royal (discussed above), CBOL established an ad hoc committee on DNA barcoding in mycology, with Pedro Crous, Keith Seifert and John Taylor as interim co-chairs. Presently, the committee, informally known as Fun-BOL, continues as an ad hoc body of mycologists with an expressed interest in barcoding. However, its proposed status as a subcommission of the ICTF should result in elected officers and members, and will alleviate its ad hoc status. Interaction between Fun-BOL, FESIN and ICTF subcommissions should provide a structure for more effective promotion of and broader participation in fungal barcoding.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. The shift from classical to molecular fungal taxonomy
  5. DNA barcode marker selection for fungi
  6. Existing mycological networks and barcode-like resources
  7. Acknowledgements
  8. Conflict of interest statement
  9. References

I am grateful for funding from the Canadian Barcode of Life Network and Genome Canada through the Ontario Genomics Institute, NSERC, and other sponsors listed at http://www.bolnet.ca. Thanks to Richard Hamelin, André Lévesque, Jean-Marc Monvalvo, and their post docs and students, for permission to mention some of their unpublished data in this article, and to Tom Gräfenhan for comments on the manuscript.

Conflict of interest statement

  1. Top of page
  2. Abstract
  3. Introduction
  4. The shift from classical to molecular fungal taxonomy
  5. DNA barcode marker selection for fungi
  6. Existing mycological networks and barcode-like resources
  7. Acknowledgements
  8. Conflict of interest statement
  9. References

The authors have no conflict of interest to declare and note that the funders of this research had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. The shift from classical to molecular fungal taxonomy
  5. DNA barcode marker selection for fungi
  6. Existing mycological networks and barcode-like resources
  7. Acknowledgements
  8. Conflict of interest statement
  9. References
  • Aanen DK, Kuyper TW (2004) A comparison of the application of a biological and phenetic species concept in the Hebeloma crustuliniforme complex within a phylogenetic framework. Persoonia, 18, 285316.
  • Agerer R, Ammirati J, Blanz P et al . (2000) Always deposit vouchers. Mycological Research, 104, 642644.
  • Bidartondo MI, 255 others (2008) Preserving accuracy in GenBank. Science, 319, 1616.
  • Blackwell M, Hibbett DS, Taylor JW, Spatafora JW (2006) Research Coordination Networks: a phylogeny for kingdom Fungi (Deep Hypha). Mycologia, 98, 829837.
  • Bridge PD, Roberts PJ, Spooner BM, Pancha G (2003) On the unreliability of published DNA sequences. New Phytologist, 160, 4348.
  • Bruns TD, White TJ, Taylor JW (1991) Fungal molecular systematics. Annual Review of Ecology and Systematics, 22, 525564.
  • Cooke DEL, Drenth A, Duncan JM, Wagels G, Brasier CM (2000) A molecular phylogeny of Phytophthora and related oomycetes. Fungal Genetics and Biology, 30, 1732.
  • Crous PW, Gams W, Stalpers JA, Robert V, Stegehuis G (2004) MycoBank: an online initiative to launch mycology into the 21st century. Studies in Mycology, 50, 1922.
  • Druzhinina IS, Kopchinskiy AG, Komoj M, Bissett J, Szakacs G, Kubicek CP (2005) An oligonucleotide barcode for species identication in Trichoderma and Hypocrea. Fungal Genetics and Biology, 42, 813828.
  • Frøslev TG, Jeppesen TS, Læssøe T, Kjøller R (2007) Molecular phylogenetics and delimitation of species in Cortinarius section Calochroi (Basidiomycota, Agaricales) in Europe. Molecular Phylogenetics and Evolution, 44, 217227.
  • Geiser DM, Jimenez-Gasco MM, Kang S et al . (2004) Fusarium-ID v.1.0: a DNA sequence database for identifying Fusarium. European Journal of Plant Pathology, 110, 473479.
  • Geiser DM, Klich MA, Frisvad JC, Peterson SW, Varga J, Samson RA (2007) The current status of species recognition and identification in Aspergillus. Studies in Mycology, 59, 110.
  • Gilmore SR, Gräfenhan T, Louis-Seize G, Seifert KA (2009) Multiple copies of cytochrome oxidase 1 in species of the fungal genus Fusarium. Molecular Ecology Resources, 9 (Suppl. 1), 9098.
  • Hawksworth DL (2001) The magnitude of fungal diversity: the 1.5 million species estimate revisited. Mycological Research, 105, 14221432.
  • Hawksworth DL (2004) Fungal diversity and its implications for genetic resource collections. Studies in Mycology, 50, 918.
  • Hibbett DS, Binder M, Bischoff JF et al . (2007) A higher-level phylogenetic classification of the Fungi. Mycological Research, 111, 509547.
  • Kirk PM, Cannon PF, Minter DW, Stalpers JA (2008) Dictionary of the Fungi, 10th edn. CAB International, Wallingford, UK.
  • Kõljalg U, Larsson K-H, Abarenkov K et al . (2005) UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytologist, 166, 10631068.
  • Kretzer A, Li Y, Szaro T, Bruns TD (1996) Internal transcribed spacer sequences from 38 recognized species of Suillus sensu lato: Phylogenetic and taxonomic implications. Mycologia, 88, 776785.
  • Lévesque CA, De Cock AWAM (2004) Molecular phylogeny and taxonomy of the genus Pythium. Mycological Research, 108, 13631383.
  • Lutzoni F, Kauff F, Cox CJ et al . (2004) Assembling the fungal tree of life: progress classification and evolution of subcellular traits. American Journal of Botany, 91, 14461480.
  • Martin FN, Tooley PW (2003) Phylogenetic relationaship among Phytophthora species inferred from sequence analysis of mitochondrial encoded cytochrome oxidase I and II genes. Mycologia, 95, 269284.
  • McLaughlinDJ, McLaughlinEG, LemkePA, eds (2001a) The Mycota, an Advanced Treatise on Fungi as Experimental Systems for Basic and Applied Research, vol. 7A. Systematics and Evolution. Springer Verlag, Berlin, Heidelberg, New York.
  • McLaughlinDJ, McLaughlinEG, LemkePA, eds (2001b) The Mycota, an Advanced Treatise on Fungi as Experimental Systems for Basic and Applied Research, vol. 7B. Systematics and Evolution. Springer Verlag, Berlin, Heidelberg, New York.
  • Mostert L, Groenewald JZ, Summerbell RC, Gams W, Crous PW (2006) Taxonomy and pathology of Togninia (Diaporthales) and its Phaeoacremonium anamorphs. Studies in Mycology, 54, 1115.
  • Nguyen HDT, Seifert KA (2008) Description and DNA barcoding of three new species of Leohumicola from South Africa and the United States. Persoonia, 21, 5769.
  • Nilsson RH, Ryberg M, Kristiansson E, Abarenkov K, Larsson K-H, Kõljalg U (2006) Taxonomic Reliability of DNA Sequences in Public Sequence Databases: a Fungal Perspective. PLoS ONE, 1(1), e59.
  • O'Donnell K, Cigelnik E (1997) Two divergent intragenomic rDNA ITS2 types within a monophyletic lineage of the fungus Fusarium are nonorthologous. Molecular Phylogenetics and Evolution, 7, 103116.
  • Rossman A (2007) Report of the planning workshop for all fungi DNA Barcoding. Inoculum, 58(6), 15.
  • Samson RA, Seifert KA, Kuijpers AFA, Houbraken JAMP, Frisvad JC (2004) Phylogenetic analysis of Penicillium subgenus Penicillium using partial β-tubulin sequences. Studies in Mycology, 49, 175200.
  • Schubert K, Groenewald JZ, Braun U, et al . (2007) Biodiversity in the Cladosporium herbarum complex (Davidiellaceae, Capnodiales), with standardisation of methods for Cladosporium taxonomy and diagnostics. Studies in Mycology, 58, 105156.
  • Seifert KA, Samson RA, Dewaard JR et al . (2007) Prospects for fungus identification using CO1 DNA barcodes, with Penicillium as a test case. Proceedings of the National Academy of Sciences, USA, 104, 39013906.
  • Skouboe P, Frisvad JC, Taylor JW, Lauritsen D, Boysen M, Rossen L (1999) Phylogenetic analysis of nucleotide sequences from the ITS region of terverticillate Penicillium species. Mycological Research, 103, 873881.
  • Spatafora J (2005) Assembling the fungal tree of life (AFTOL). Mycological Research, 109, 755756.
  • Stenroos S, Hyvönen J, Myllys L, Thell A, Ahti T (2002) Phylogeny of the genus Cladonia s.lat. (Cladoniaceae, Ascomycetes) inferred from molecular, morphological, and chemical data. Cladistics, 18, 237278.
  • Vialle A, Feau N, Allaire M, Didukh M, Martin M, Moncalvo J-M, Hamelin RC (2009) In silico evaluation of mitochondrial genes as DNA barcode for Basidiomycota, Molecular Ecology Resources, 9 (Suppl. 1), 99113.
  • White TJ, Bruns T, Lee S, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenies. In: PCR Protocols: a Guide to Methods and Applications (eds InnisMA, GelfandDH, SninskyJJ, WhiteTJ), pp. 315322. Academic Press, San Diego, California.