Newly collected specimens used in this study were obtained with permission from public and private lands. The permit for collecting specimens in Catoctin Mountain National Park was provided by the United States National Park Service to Steven Stephenson (CATO-2006-SCI-0005) and the permit for collecting specimens in Algonquin Provincial Park was provided by Ontario Parks to Jean-Marc Moncalvo (s.n.).

Approximately six hundred and fifty mushrooms in three classes of Agaricomycotina (Tremellomycetes, Dacrymycetes, Agaricomycetes) were newly collected from Ontario, Québec, and Maryland for this study. Species identifications were made comparing macro-and micro-morphological features with species descriptions in the relevant taxonomic literature (too numerous to list). Vouchers of all newly collected specimens are deposited in the fungal herbarium of the Royal Ontario Museum (TRTC). Additional specimens used in this study were borrowed from other herbaria (MIN, HSC, BUF, LIP, SFSU, WTU, KUN, TMI, UC, USJ, and NY).

DNA was extracted following various standard protocols [28], [29] or using a new rapid DNA isolation protocol developed in our lab [30]. Approximately 530-666 bp of exon from the 5′ end of the COI gene was amplified by designing taxonomically nonspecific primers (Table 1 and Figure 1) based on an alignment of fungal COI sequences in GenBank (Cryptococcus neoformans AY560609, Moniliophthora perniciosa NC005927, Schizophyllum commune AF402141, Agrocybe aegerita AF010257). PCR amplification was achieved using a cycling program with an initial denaturation of 95C for 2 min, followed by 30 cycles of denaturation at 94 C for 45 sec, annealing at 60 C for 45 sec, and extension at 72 C for 1 min 10 sec, followed by a final extension at 72 C for 7 min and an indefinite refrigeration at 4 C. New primers “COXBOL1-F” and “COXBOL1-R” were also designed for improved amplification of COI from genomic DNA for Boletales (Table 1) based on an initial alignment of Boletales sequences obtained using general primers. A touchdown program was used with the Boletales-specific primers: initial denaturation step for 2 min at 94 C, followed by 5 cycles of 94 C (30 sec), 55 C (30 sec), and 72 C (1 min), followed by 25 cycles of 94 C (30 sec), 50 C (30 sec), and 72 C (1 min), and ending with a final extension step at 72 C for 7 min. The ITS region was amplified and sequenced with primers ITS1F and ITS4 using standard protocols [31], [32] or with primers ITS8F and ITS6R using a new high-throughput protocol [30]. PCR products were visualized by UV fluorescence after running out 3-25 µL PCR products in a 1% agarose gel containing 0.005% ethidium bromide. Prior to sequencing, positive PCRs were cleaned one of two ways: 1) by incubating samples for 15 min at 37C then 15 min at 80 C after adding 0.4 volumes of a mixture containing shrimp alkaline phosphatase (0.05 units/µL) and exonuclease I (0.05 units/µL) in water, 2) loading the entire PCR in a single lane, running the gel at 90 V for ca. 1 h, cutting the band from the gel using a clean razor, placing the excised gel in the top of a disposable pipette tip with a filter and trimmed to ∼1 cm in length, placing the pipette tip in a 1.5 mL microfuge tube, and collecting the liquid in the gel by spinning the tube for 10 min at 10,000 g. Unidirectional dye-terminator sequencing used the ABI BigDye kit (Foster City, CA) and reactions were run on an ABI PRISM 3100 DNA Analyzer in the Department of Natural History at the Royal Ontario Museum, Toronto, after ethanol precipitation and resuspension in HiDi formamide. Sequences were edited using Sequencher3.1 (GeneCodes, Ann Arbor, MI) and are available as a project on the Barcode of Life Data Systems online database [“Evaluating COI in mushrooms and allies (Agaricomycotina)” Project code YYY; www.barcodinglife.org; 33] and through GenBank [Accession numbers JN020964-JN021114 (ITS), JN029360-JN029526 (COI); 34]. Additional sequences obtained from GenBank that were used in this study include the following accession numbers: EU231946, EU231948, EU231949, EU231954, EU231958, EU231966, EU231969, EU231971, EU231980, EU231982, EU231983, EU231984, EU231985, EU231990, EU231991, EU231992.

Our sampling included ca. 184 species in 73 genera (as identified by morphological traits). More than 90% of specimens (144 spp. in 49 genera) are members of the principal gilled-mushroom subclass Agaricomycetidae. Of the remaining, ca. 40 species belong to the Tremellomycetes, Dacrymycetes, and other orders of Agaricomycetes excluding the subclass Phallomycetidae [44].

Only 204 specimens (31.4%) yielded COI sequences, representing 57.7% and 61.3% of all species and genera sampled, respectively. The most successful primer combinations for COI amplification were 11F/2eR (615 bp amplicon), 12F/4R (529 bp amplicon), and 8F/2eR (666 bp amplicon). Most COI sequences used for this study were generated using combinations 11F/2eR or 12F/4R and trimmed to approximately 450 bp. Introns were found in 14 sequences (∼7% of those that amplified) in 18 species from 12 genera. Introns occurred in nine different locations (Fig. 1). Identical insertion/deletion sites occurred in sequences from specimens of different genera (e.g., Marasmius androsaceus, and Lepiota clypeolaria have an intron inserted at position 4), but these locations were not conserved within genera (e.g., Laccaria). In addition, presence/absence of intron was not always consistent between multiple specimens of the same species (e.g., Amanita fulva).

A total of 167 specimens from 102 morphospecies of Agaricomycetidae, including 97 taxa belonging to the Boletales and Agaricales and 5 taxa belonging to the Polyporales (Ganoderma, Merulius, Polyporus), Russulales (Albatrellus), and Thelephorales (Sarcodon), were selected to compare COI with ITS. Thirty-six of the 102 morphospecies were represented by more than one sequence (2–9 sequences/species). Phylogenetic analysis of the COI dataset resulted in a moderately supported tree (aLRT≥0.95) and generally reflects the known topology of the Agaricomycetidae, except for the placement of Polyporus badius near Suillus and a non-monophyletic Boletales.

Intra- and inter-specific divergence was calculated for 30 species represented by more than one sequence in both the COI and ITS datasets based on global alignments of each gene (Table 2). For COI, the mean±standard error intra-specific divergence was 0.0009±0.0004 (0.0000 median, 0.0064 maximum) and mean±standard error inter-specific divergence was 0.0569±0.0079 (0.0428 median, 0.0043 minimum). For ITS, the mean±standard error intra-specific divergence was 0.0063±0.0030 (0.0011 median, 0.0747 maximum). In our calculation, inter-specific divergence for ITS was set at 21% when the divergence was greater than 20% (a minimum estimate of divergence); mean±standard error inter-specific divergence was 0.1499±0.0156 (0.2100 median, 0.0258 minimum).

A side-by-side comparison reveals that COI and ITS distances are qualitatively similar (Table 2). Except for three cases in Boletus spp., all ratios of inter- to intra-specific divergences for COI and ITS were greater than one, the minimum threshold for a barcode locus (Table 2). In general, intra-specific divergences were slightly higher for ITS than COI. Inter-specific divergence was also higher for ITS than COI except between Amanita flavoconia and A. rubescens, where minimum inter-specific divergence was great for COI than ITS. The highest maximum intra-specific divergence was 7.47% for ITS (Hygrocybe miniata) and 0.64% for COI (Entoloma sp. parasite on T. focale and Hygrocybe miniata). The lowest minimum inter-specific divergence for ITS was 0.96% (Boletus regineus) and 0.20% for COI (Boletus spp.).

COI and ITS largely agree on terminal taxa, which corresponds well with morphospecies (Fig. 2). Discrepancy between terminal taxa recovered by COI and ITS was limited to the porcini group of Boletus where intraspecific sampling was most extensive. In one case, ITS recovers B. nobilissimus as distinct from B. quercophilus, but COI does not. Similarly, ITS recovers a monophyletic B. edulis sensu stricto [45], whereas COI groups B. rex-veris with one specimen of B. edulis.

Like Vialle et al. [24] in a different group of basidiomycetes (rusts), we encountered low PCR success with COI in mushrooms. Even using multiple combinations of primers, our success rate was poor. Post-PCR processing of COI resulted in even worse success rates because multiple, large introns made sequencing difficult. In our set of sequences, COI exons may be interrupted by one or more large introns (∼1500 bp) with variable positions that are not phylogenetically conserved (Fig. 1, 2). Thus, even after considerable effort we succeeded in generating COI sequences for only ∼30% of the specimens, a rate much lower than typically observed for ITS [e.g, 30]. We also encountered possible paralogous copies of COI from some specimens that were sequenced twice or more (data not shown), confirming in mushrooms similar problems recently reported with this locus for Fusarium [23]. Moreover, unlike the ITS, designing universal COI primers for mushrooms (let alone all fungi) is not possible. Thus, each order, family, or even genus would probably require extensive troubleshooting and primer optimization, which is time-consuming, costly, antithetical to high-throughput data acquisition, and impractical for detection of fungi from environmental samples.

Overall, the performances of COI and ITS as barcode markers for mushrooms are similar. Both generally exhibit low intra-specific and high inter-specific divergences in our dataset and both were able to distinguish most of the morphospecies we sampled (Table 2; Fig. 2). However, COI failed to recover two morphospecies (Boletus edulis and B. nobilissimus) that are recovered as reciprocally monophyletic by ITS, in agreement with a recent multigene phylogeny [45]. Moreover, the ratio of maximum intraspecific to minimum interspecific distance was greater for ITS than COI in 27/33 comparisons (82%; Table 2), indicating that ITS has a more pronounced “gap” between species. Amplification and sequencing notwithstanding, ITS appears to be better than COI as a primary DNA barcode locus for mushrooms.

The inability to produce a reliable alignment of ITS sequences across a divergent set of taxa is a major disadvantage of using ITS as a barcode. We avoided relying on a global alignment of ITS sequences by first identifying “alignment groups” where assessment of homologous characters for a set of sequences is trivial using widely used alignment software. Our approach relied on the contig assembly algorithm employed by Sequencher, which is normally used to assemble complementary sequence trace files output from ABI sequencers. By eliminating the ∼120 bp of the largely invariable 5.8S RNA subunit, increasing the minimum overlap to the maximum (100 bp), and using a threshold of 80% similarity, we avoided the problem of underestimating sequence divergence among a set of sequences, thereby minimizing ambiguous homology assessment. However, there are two related problems with this method: 1) contig folders must contain sets of sequences with 80% or better similarity, but some sequences may be 80% similar to two or more sets of sequences and could legitimately reside in more than one alignment group, and 2) the contig assembly algorithm is likely to be order-dependent (the assembly algorithm in Sequencher is proprietary and therefore unknown), so when a sequence could reside in two folders it may always preferentially be placed in the first contig folder encountered even if its closest relative is in another folder. To explore whether or not the algorithm employed by Sequencher was biasing our contig assembly, we added to the ITS dataset dummy sequences where we modified the number of substitutions (randomly distributed) and gaps (randomly and evenly distributed or at a single site of extension) of the sequence with the greatest inter-specific divergence (Entoloma aff. sericeum ALG-07-108) from one of the contig folders. Our dummy sequences ranged from 1–20% divergent from the original. Except for two situations, where gaps are randomly distributed across over 15% or more of the sequence and when gaps are evenly distributed across 12% or more of the sequences (an extra base inserted every 8^th position), Sequencher always correctly placed the two closest sequences together. Although the two cases where Sequencher did not correctly assign the two closest sequences together is of some concern, these synthetic scenarios are not very likely to be encountered with real biological material and we did not observe any misleading bias using the real data.

Ideally, a global alignment would be possible for phylogenetic-based methods of identification and this is still a characteristic that may be sought in alternative barcoding loci for mushrooms. But the competency of ITS as a barcode demonstrated here and elsewhere, as well as the wealth of ITS data that can be mined from publicly accessible databases such as GenBank and UNITE [46], further recommends it over COI for mushrooms and other fungi [5].

The barcode locus that was been chosen for animals, COI, will not work for mushrooms, rusts [24], and probably most other fungi [5], primarily due to the variable and unpredictable presence of large introns in the barcode region. Alternatively, the widely used ITS regions work well for identifying mushroom species, as has been determined empirically by mycologists and other fungal researchers for over a decade. Although ITS has limitations [42], [47], there is to date no better single molecular marker for barcoding mushroom species, and its versatility makes it possible to survey and discover new fungi from environment samples. With the development of high-throughput methods for producing ITS barcodes from mushrooms [30] and the relatively low cost of traditional (Sanger) sequencing, coupled with a cross-discipline desire for reliable molecular tools for the identification of environmental samples [48], the foundations have been laid for a worldwide fungal barcoding campaign. It is time to move forward with a global fungal barcoding initiative that adheres to standard protocols, from processing of new field collections to generating barcodes to integrating barcodes with taxonomy.