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RESEARCH ARTICLE
Ribosomal DNA and Plastid Markers Used to
Sample Fungal and Plant Communities from
Wetland Soils Reveals Complementary Biotas
Teresita M. Porter
1
*, Shadi Shokralla
2
, Donald Baird
3
, G. Brian Golding
1
,
Mehrdad Hajibabaei
2
1McMaster University, Biology Department, Hamilton, ON, L8S 4K1, Canada, 2Biodiversity Institute of
Ontario & Department of Integrative Biology, University of Guelph, Guelph, ON, N1G 2W1, Canada,
3Environment Canada @ Canadian Rivers Institute, University of New Brunswick, Fredericton, NB, E3B
6E1, Canada
*terri@evol.mcmaster.ca
Abstract
Though the use of metagenomic methods to sample below-ground fungal communities is
common, the use of similar methods to sample plants from their underground structures is
not. In this study we use high throughput sequencing of the ribulose-bisphosphate carboxyl-
ase large subunit (rbcL) plastid marker to study the plant community as well as the internal
transcribed spacer and large subunit ribosomal DNA (rDNA) markers to investigate the fun-
gal community from two wetland sites. Observed community richness and composition var-
ied by marker. The two rDNA markers detected complementary sets of fungal taxa and total
fungal composition clustered according to primer rather than by site. The composition of the
most abundant plants, however, clustered according to sites as expected. We suggest that
future studies consider using multiple genetic markers, ideally generated from different
primer sets, to detect a more taxonomically diverse suite of taxa compared with what can be
detected by any single marker alone. Conclusions drawn from the presence of even the
most frequently observed taxa should be made with caution without corroborating lines of
evidence.
Introduction
Fungi are important members of ecosystem functioning and play critical roles in nutrient
cycling as symbionts, saprotrophs, and pathogens [1]. Below-ground mycorrhizal fungi in par-
ticular, may physically link the roots of different plant species and help to regulate plant diver-
sity [2–3]. When monitoring fungal and plant communities from bulk soil using DNA-based
methods, actively growing fungal mycelia and plant roots are detected as well as inactive propa-
gules such as fungal sclerotia, plant rhizomes, spores, and seeds. However, even inactive por-
tions of the below-ground community may have important future impacts. For example,
fungal pathogens can affect the composition of the plant seed bank and subsequent plant
PLOS ONE | DOI:10.1371/journal.pone.0142759 January 5, 2016 1/18
a11111
OPEN ACCESS
Citation: Porter TM, Shokralla S, Baird D, Golding
GB, Hajibabaei M (2016) Ribosomal DNA and Plastid
Markers Used to Sample Fungal and Plant
Communities from Wetland Soils Reveals
Complementary Biotas. PLoS ONE 11(1): e0142759.
doi:10.1371/journal.pone.0142759
Editor: Olivier Lespinet, Université Paris-Sud,
FRANCE
Received: August 7, 2015
Accepted: October 22, 2015
Published: January 5, 2016
Copyright: © 2016 Porter et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: Raw sequence data
are available from NCBI SRA SRP066030.
Funding: All the coauthors were funded by the
Government of Canada through Genome Canada
and the Ontario Genomics Institute through the
Biomonitoring 2.0 (www.biomonitoring2.org) project
(OGI-050). The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
recruitment [4–5]. Additionally, fungal mutualists and saprophytes in the fungal spore bank
contribute to the rapid turnover of the microbial community in soils in response to disturbance
or a change in seasons [6–9].
Due to the recalcitrance of many fungi towards cultivation using standard methods, and an
abundance of vegetatively growing fungi with a paucity of characters for morphology-based
identification, mycologists were early adopters of PCR-based detection and DNA-based identi-
fication methods [10–12]. Many fungal metagenomic studies using standard Sanger sequenc-
ing, and now high throughput sequencing, have been conducted in a variety of environments
including bulk soil such as [7,13–15]. In contrast, PCR-based studies to monitor underground
plant parts are rare [16–18]. Since plants and fungi co-exist in the same soil matrix these taxa
can be studied in tandem to gain a more holistic understanding of below-ground communities
in general and plant-fungal interactions in particular.
The internal transcribed spacer (ITS) region of nuclear encoded ribosomal DNA (rDNA)
has been proposed as a suitable fungal barcode [19]. The ITS region is comprised of the inter-
nal transcribed spacer 1 (ITS1), 5.8S rRNA gene, and the internal transcribed spacer 2 (ITS2)
with the greatest sequence variation in the ITS1 and ITS2 regions. Several studies have exam-
ined the implications of using ITS for species identification using high throughput sequencing
and have found that numerous methodological biases exist [20–26]. Despite these challenges,
many ITS rDNA reference sequences are available in the AFTOL (Assembling the Fungal Tree
of Life), UNITE, and GenBank sequence databases and tools have been developed to facilitate
the use of ITS for fungal metagenomic studies [27–31].
Large subunit (LSU) rDNA contains variable domains at the 5’end as well as highly con-
served regions at the 3’end suitable for taxonomically diverse phylogenetic analyses as well as
species- to family-level classifications. LSU rDNA reference sequences are also available
through the AFTOL, UNITE, and GenBank databases. LSU rDNA is particularly heavily sam-
pled for mushroom-forming fungi [32–33] and has been used as a 'barcoding' marker for yeasts
[34–35]. Previous fungal metagenomic studies of various soils have also used this region [36–
38]. Similar to studies with ITS, methodological biases also exist with the use of LSU rDNA in
metagenomic studies [39].
The ribulose-bisphosphate carboxylase large subunit (rbcL) plastid gene is one of two pro-
posed plant barcoding markers [40]. This multi-copy protein-coding gene is relatively con-
served and suitable for phylogenetic studies [41] and it has been shown to resolve species in
85% or more of cases when using BLAST against GenBank sequences [42–43]. Though the
rbcL marker may not be able to identify all plants to the species level on its own, it was one of
the first plant barcoding markers to be used in a multigene identification approach [43].
Because the diversity of plants was expected to be quite tractable compared to fungal diversity,
we only used a single marker, rbcL, to survey plant diversity. The rbcL marker is well repre-
sented in the NCBI GenBank nucleotide database.
Most metagenomic studies focusing on soil fungal communities involve the use of a single
DNA marker. Because we knew that fungal diversity would likely be orders of magnitude
higher than plant diversity in soil, we chose to use two fungal markers to increase our chances
of detecting as much of this diversity as possible. To the best of our knowledge this is the first
study to use two DNA markers (ITS + LSU) with largely fungal-specific primers as well as a
plant-specific marker (rbcL) to monitor both the fungal and plant communities from the same
soil samples simultaneously. We hypothesized that the fungal community detected by ITS and
LSU rDNA would be largely similar, and that the use of the ITS + LSU + rbcL markers would
together detect a richer assortment of organisms than any single marker. This study character-
izes the reproducibility and taxonomic breath detected by these various markers and highlights
areas of potential concern for future metagenomic and biomonitoring studies.
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
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Materials and Methods
Field sampling
We sampled soil cores from two key wetland areas within the Peace-Athabasca Delta in Wood
Buffalo National Park in northern Alberta, Canada. Field permits were granted by Parks Can-
ada and samples were collected by Environment Canada and Parks Canada staff. The fieldwork
did not involve endangered or protected species. Site A falls within Egg Lake (N 58° 54.535’W
111° 25.398’) and site B falls within Johnny’s Cabin Pond (N 58° 29.688’W 111° 30.773’).
These deltaic wetland sites are currently threatened by industrial hydro-electric development
and potential downstream oil sands contamination [44]. Physical and chemical analyses of
these two samples are summarized in Table 1.
Soil samples were collected in August 2010 using the following method: for each sampling
site the top 10cm of soil was sampled at three sampling locations within the site approximately
100 to 200 meters apart. In each sampling location, three soil cores were sampled within one
square meter. Each soil core was then stored in 50 ml sterile Falcon tubes. Samples were frozen
on dry ice in the field and stored in a -70°C freezer until shipped to the Hajibabaei laboratory
at the University of Guelph, Ontario for processing.
Sample processing
Frozen soil core samples were homogenized and one gram of each soil core was used for total
DNA extraction using a PowerSoil DNA isolation kit (cat.# 12888–100, MO BIO Laboratories,
Inc., California, USA). Ten extractions (100 mg each) were done for each soil core and each
extraction was eluted with 50 μL of molecular biology grade water. DNA extracts of each sam-
ple were pooled and used for further amplification. The ITS rDNA region (~ 600 bp) was tar-
geted using the fungal specific ITS1F and ITS4 primers [45–46]. The 5'-LSU rDNA region (~
900 bp) was targeted using the largely fungal specific LR0R_F and LR5-F primers [47]. The
rbcL region (~ 600 bp) was targeted for plant identification using the primers rbcLa-F and
rbcLa-R [48–49].
Marker amplification was done in a two-step PCR regime, the first PCR round was done
using target specific primers (without the 454 tail). The second PCR round used the same
primer sets with hybrid 454 fusion-tailed primers and specifically designed multiplex identifier
(MID) tag. Each PCR contained 2 μL DNA template, 17.5 μL molecular biology grade water,
Table 1. Physical and chemical soil measurements.
Sample ID Site A Site B
Total Carbon (% dry) 36.7 0.771
Inorganic Carbon (% dry) 0.22 0.3
Organic Carbon (% dry) 36.5 0.471
Organic matter "Walkley-Black" (% dry) 63 1.1
Phosphorus (mg/L soil dry) 11 5.2
Magnesium (mg/L soil dry) 750 270
Potassium (mg/L soil dry) 170 59
Manganese (mg/L soil dry) 4.6 28
Zinc (mg/L soil dry) 12 1.1
pH 6.3 8.1
% soil moisture (% dry) 413.24 32.09
Nitrogen (% dry) 2.52 <0.05
doi:10.1371/journal.pone.0142759.t001
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
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2.5 μL 10x reaction buffer, 1 μl 50x MgCl
2
(50 mM), 0.5 μL dNTP mix (10 mM), 0.5 μL forward
primer (10 mM), 0.5 μL reverse primer (10 mM), and 0.5 μL Invitrogen Platinum Taq poly-
merase (5 U/μL) in a total volume of 25 μL. PCR conditions were 95°C for 5 min; 15 cycles of
94°C for 40 s, (52°C for ITS, 48°C for LSU and 55°C for rbcL) for 1 min, and 72°C for 30s; and
72°C for 5 min. Amplicons were purified with Qiagen MinElute PCR purification columns and
eluted in 50 μL molecular biology grade water. The purified amplicons from the first PCR
round were used as template in the second PCR round using 454 fusion tailed and MID-tagged
primers in a 30-cycle amplification regime. An Eppendorf Mastercycler ep gradient S thermal
cycler was used for all PCR reactions. Negative controls were included in all experiments.
454 Pyrosequencing
The three indexed markers amplified from each soil core were purified and fluorometrically
quantified. Equimolar amounts of the MID-generated amplicons were combined and
sequenced on the 454 Genome Sequencer FLX System (Roche Diagnostics) following the
amplicon sequencing protocol with GS Titanium chemistry. Amplicons of each soil core were
bidirectionally sequenced in 2 (1/16) regions of a full sequencing run (70 x 75 pico titer plate).
Further details of the 454 pyrosequencing run are available by request from the corresponding
author. Raw sequence data is available through the NCBI SRA: SRP066030
Bioinformatic methods
A semi-automated Perl pipeline was created. Raw reads were sorted by primer sequences for
the ITS, LSU, and rbcL markers using AGREP version 2.04 allowing 1 mismatch. Sorted reads
were quality-trimmed using SeqTrim [50] with a 10 bp sliding window, excluding windows
with an average Phred score less than 20, and removing reads less than 80 bp after trimming.
Quality-trimmed reads were sorted by average read quality then clustered into operational
taxonomic units (OTUs) with USEARCH version 4.0.43 [51]. Clustering reads into OTUs
allowed us to retain many sequence types representing an array of taxonomic groups, while
absorbing some of the diversity represented by intraspecific variation. Rare OTUs comprised
of only one or two reads (singletons and doubletons) were excluded from downstream analyses
to avoid analyzing diversity generated by sequencing error [52–54]. These precautions allowed
us to dereplicate our dataset, account for potential chimeras and other sequencing artefacts,
and facilitate downstream analyses by being conservative with our inclusion of rare sequence
types. A variety of sequence similarity cutoffs were tested (S1 Text,S2 Fig) and we ultimately
used a 97% sequence similarity cutoff to delimit OTUs for the ITS marker and for the 5’LSU,
and a 95% similarity cutoff for the 3’LSU and rbcL marker. The 5’and 3’sequence reads were
initially analyzed separately to avoid any possible double-counting of the same PCR-template
that might inflate richness values. OTUs generated by USEARCH were reformatted using cus-
tom Perl scripts so that statistical analyses could be run with MOTHUR v.1.15.0 [55]. OTU
classifications were carried out using BLAST (blastall version 2.2.15) against a local installation
of the ‘nt’GenBank database [December 10, 2010] using default parameters with 8 processors
per job [56]. To minimize the number of incorrect annotations based on a best BLAST hit
approach, this was followed by lowest common ancestor (LCA) parsing using MEGAN version
4.40.6 [23,57]. We used the following LCA filter settings: minimum support = 1, minimum
score = 100, top percent = 1%, win score = 0.0.
Taxonomic comparisons were also performed using MEGAN using three ecological indices.
The ITS, LSU, and rbcL datasets were normalized, MEGAN classifications were summarized to
the order level, and comparisons were visualized using multi-dimensional scaling plots. The
Bray-Curtis dissimilarity statistic measures the number of species unique to either of two sites
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
PLOS ONE | DOI:10.1371/journal.pone.0142759 January 5, 2016 4/18
divided by the total number of species in both sites where each species is equally-weighted [58].
The non-parametric Goodall similarity index, however, gives more weight to differences
between rare taxa [59]. This method has been found to be particularly appropriate for compar-
ing microbial metagenomic datasets characterized by large numbers of rare taxa [60]. The Uni-
Frac measure emphasizes the amount of branch length unique to either of two sites compared
with the amount of branch length shared by both sites in a phylogeny. This is interpreted as
representing evolution among lineages unique to each site that may reflect adaption to a spe-
cific environment [61]. MEGAN calculates a simplified UniFrac distance metric based on the
NCBI taxonomy.
Differences among the number of observed OTUs from the ITS and LSU datasets from both
sites were compared using MEGAN. The directed homogeneity ‘up’test was used on normal-
ized data with the Bonferroni correction for multiple comparisons.
Results
Sampling consistency and effort
The number and average length of reads and OTUs that were sorted, filtered, and clustered for
each marker is shown S1 Table. The average number of OTUs sampled from three replicate
libraries was relatively similar within each primer and site combination (Table 2). Additionally,
the amount of OTU overlap recovered among three replicate soil samples was high, with rela-
tively few OTUs unique to each replicate (Table 3). These replicates were combined in subse-
quent analyses.
We assessed sampling effort by plotting rarefaction curves. We did not rarefy to a standard
number of reads because we wanted to see how each marker performed individually without
subsampling the data. The number of detected OTUs can still be fairly compared across mark-
ers in our plotted curves for any standard number of reads less than or equal to the smallest
library size. For each primer and site combination, curves reach a plateau indicating sampling
saturation (Fig 1). We assessed the presence of the same OTUs between both sites for each
primer by plotting their read frequency distribution (S3 Fig). For each primer, we observed a
few OTUs represented by many reads, and many OTUs represented by only a few reads each.
For each OTU, a different number of reads were detected from each site. These data, however,
are not necessarily quantitative [47].
Taxonomic classifications
It has been previously observed that the LCA algorithm used in MEGAN may classify reads to
high level taxonomic ranks and may not classify all sequences [23,62]. The proportion of reads
that could be classified to any taxonomic rank by MEGAN is shown in Table 4. MEGAN classi-
fied 94% (5113) of ITS OTUs, 97% (1658) of LSU OTUs, and 86% (529) of rbcL OTUs. We
also assessed the number of OTUs assigned to various taxonomic ranks and the number of cat-
egories present at each rank for each marker (S4 Fig). Although MEGAN was able to classify
nearly all our OTUs, the number of OTUs classified to the species-level represents only a frac-
tion of the OTUs classified to more inclusive taxonomic ranks, particularly for ITS and LSU.
At the species and genus levels, ITS detected more categories than LSU, however, from the fam-
ily to kingdom levels the number of detected categories is similar for both markers. Overall,
more OTUs are detected by ITS and LSU than for rbcL, indicating a generally high fungi to
plant ratio similar to that observed from other studies [63]. With MEGAN results summarized
to the genus level, we were also able to directly compare the number of taxonomic categories
recovered from each marker (Table 5). The number of categories detected by any single marker
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
PLOS ONE | DOI:10.1371/journal.pone.0142759 January 5, 2016 5/18
was less than any two-marker combination and three markers combined detected the greatest
number of categories.
Dataset comparisons
A comparison of the taxonomic content of the datasets using several different ecological indi-
ces in MEGAN is shown in Fig 2. When ITS and LSU taxa are summarized at the order level,
three of the ecological indices give different cluster patterns depending on what component of
diversity is emphasized by each measure. Giving more weight to distances among rare taxa
with the Goodall index emphasizes differences in the taxonomic composition of the ITS data-
sets compared with the Bray-Curtis statistic where each taxon is equally-weighted. With the
UniFrac metric ITS and LSU datasets cluster by primer emphasizing the presence of unique
taxonomic lineages. When the ITS and LSU datasets are summarized at increasingly more
exclusive taxonomic levels from phylum to order, the clustering pattern breaks down such that
eventually each dataset clusters mainly by primer. When the UniFrac metric is used with the
ITS + LSU + rbcL datasets, points cluster by marker with sub-clustering by primer for the ITS
and LSU datasets. When only the most frequently observed taxa are considered, datasets cluster
mainly by marker without any sub-clustering by primer. For the rbcL dataset, where only the
most frequently observed taxa were analyzed, datasets show some sub-clustering by site.
A summary of the OTUs from each marker and classified by MEGAN is shown in Fig 3.A
detailed breakdown of classifications is available in S2 and S3 Tables. Only the ITS primers
recovered OTUs classified as Fungi/Metazoa incertae sedis, Katablepharidiophyta (heterotro-
phic flagellates), Rhizaria (unicellular eukaryotes, protists, amoeboids, flagellates), and
Table 2. Average OTU richness from three soil sample replicates.
5’primer 3’primer
Marker Site A Site B Site A Site B
ITS 1138 ±14 1034 ±39 590 ±8 583 ±10
LSU 242 ±3 325 ±6 320 ±2 292 ±9
rbcL 76 ±332±292±254±1
doi:10.1371/journal.pone.0142759.t002
Table 3. Proportion (number of OTUs excluding singletons and doubletons) of overlap from three soil
sample replicates.
5’primer 3’primer
Marker Site A Site B Site A Site B
OTUs present in three replicates
ITS 69% (890) 68% (799) 68% (461) 68% (452)
LSU 63% (177) 63% (238) 76% (266) 72% (233)
rbcL 88% (70) 74% (26) 77% (78) 83% (48)
OTUs present in any two replicates
ITS 28% (354) 28% (328) 26% (175) 27% (181)
LSU 32% (91) 32% (122) 22% (78) 26% (85)
rbcL 11% (9) 23% (8) 20% (20) 16% (9)
OTUs unique to a single replicate
ITS 3% (38) 3% (40) 5% (37) 5% (31)
LSU 5% (14) 5% (17) 2% (7) 2% (7)
rbcL 1% (1) 3% (1) 3% (3) 2% (1)
doi:10.1371/journal.pone.0142759.t003
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
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Fig 1. Rarefaction curves. Data are shown for 5’and 3’fragments sampled from two sites (A and B) for three loci: (a) ITS, (b) LSU, and (c) rbcL.
doi:10.1371/journal.pone.0142759.g001
Table 4. MEGAN classification summary (to any taxonomic rank).
MEGAN
Marker Total OTUs Assigned Unassigned No hits
ITS 5454 94% (5113) 2% (98) 4% (243)
LSU 1710 97% (1658) 0% (6) 3% (46)
rbcL 612 86% (529) 0% (0) 14% (83)
doi:10.1371/journal.pone.0142759.t004
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
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Rhodophyta (red algae). Only the LSU primers recovered OTUs classified as Alveolata (mostly
single celled eukaryotes, protists, protozoa, flagellates) and stramenopiles (mostly algae and fil-
amentous Oomycetes). Only the rbcL primers recovered OTUs classified as Bacteria. This last
likely represents the presence of RuBisCO or RuBisCO-like proteins in these Bacteria [41,64].
The ITS + LSU markers both recovered OTUs classified as Fungi (yeasts, moulds, mushrooms)
and Metazoa (multicellular eukaryotes, animals). The ITS + LSU + rbcL markers each recov-
ered OTUs classified as Viridiplantae (plants), though the greatest number and diversity of
plants by far was detected with rbcL.
The top ten most frequently sampled MEGAN categories summarized at the order rank are
shown in Table 6. With ITS, only fungal orders are most frequently sampled. With LSU, both
fungi and nematodes are frequently sampled. The communities retrieved using the ITS and
LSU markers were not found to differ among sites and the taxa listed here were present in both
sites. For many categories, the number of OTUs detected by the ITS and LSU marker are signif-
icantly different. Five of the most frequently observed orders detected by both ITS and LSU are
the Pezizales (moulds, morels, and cup fungi), Helotiales, Pleosporales, Agaricales (mush-
room-forming fungi), and Hypocreales. The mitosporic Ascomycota category, frequently sam-
pled by the ITS and LSU markers, includes a heterogeneous group of asexual Ascomycota
fungi for which a sexual stage is unknown or does not exist. These groups represent an array of
saprotrophic, mycorrhizal, pathogenic, endophytic, and lichen-forming taxa. Taxa from the
Capnodiales, Glomerales (arbuscular mycorrhizal), Thelphorales (ectomycorrhizal), and Tre-
mellales (jelly fungi and yeasts) were most frequently found with ITS. Taxa from the Tylench-
ida and Rhabditida (nematodes), Polyporales (bracket fungi), Sordariales (saptrotrophic
fungi), Platygloeales (saprotrophic and plant parasitic fungi), and Chytridiales (aquatic fungi
with flagellated zoospores) were most frequently sampled with LSU. Although site A was much
more wet than site B, the number of OTUs of aquatic fungi in the Chytridiales was similar.
Among the most frequently observed rbcL OTUs, are orders of plants expected to be found in
wet habitats, especially site A, such as the ubiquitous Poales (grasses and sedges), the Acorales
(grass-like evergreen plants), as well as the spore-dispersed Equisetales (horsetails) and Bryales
(mosses).
The top ten most frequently sampled OTUs summarized to species rank by MEGAN are
shown in S4 Table. A mixture of fungi comprised of mycorrhizal symbionts, saprotrophs, and
parasitic species; as well as plants known to be mycorrhizal and expected to be abundant in a
wetland habitat, appear among the most frequently observed OTUs. Additionally, two
Table 5. Number of MEGAN categories at the genus rank.
5' primer 3' primer
Marker Site A Site B Site A Site B
Single markers
ITS 216 182 138 130
LSU 89 100 113 113
rbcL 20 13 22 19
Two-marker combinations
ITS+LSU 268 244 212 205
ITS+rbcL 236 195 160 149
LSU+rbcL 109 113 135 131
Three markers
ITS+LSU+rbcL 288 257 234 224
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Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
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nematode species, one alveolate, and one stramenopile species were identified. Only two
MEGAN classified species of fungi, Peziza badia (cup fungus) and Pterula echo (coral fungus),
were frequently detected by both ITS and LSU. With rbcL, we were only able to classify some
of the most abundant taxa to the genus level using MEGAN. The genus Typha, bulrushes or
cattails, was the most frequently sampled rbcL OTU. The genus Acorus, grasslike evergreen
plants, was the second most frequently sampled rbcL OTU.
Fig 2. Comparison of the taxonomic content among the metagenomic datasets. Normalized reads were used to compare datasets in MEGAN for a
variety of ecological indices including the Bray-Curtis metric, Goodall ecological index, and a simplified Unifrac metric. Each marker is indicated by circled
points: ITS (blue), LSU (red), and rbcL (green). Datasets generated using the forward 5’primer (+) or the reverse 3’primer (-) from two sites A (black) and B
(grey) are shown.
doi:10.1371/journal.pone.0142759.g002
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
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Site A versus Site B
Though the ITS and LSU markers did not distinguish between the two wetland sites, the rbcL
marker did. rbcL OTU richness differed significantly between sites. On average 76–92 rbcL
OTUs were detected from Site A and 32–54 rbcL OTUs were detected from Site B. Addition-
ally, the taxonomical composition of rbcL OTUs differed among sites. Site A is characterized
by the presence of bacterial rbcL OTUs classified in the Chromatiales, Caulobacteriales, and
Rhizobiales. Among the most frequently sampled rbcL OTUs, only the Rosales and Brassicales
were detected from site A. Site B is characterized by the presence of mosses in the Grimmiales
(moss that grows on rocks) and Pottiales.
Discussion
Marker specificity
Whole genome shotgun metagenomic approaches can utilize data from an array of markers
selected a posteriori to track taxonomic groups of taxa. Using this approach previous work in
the literature was able to track genus- to phylum-level Bacterial groups using six markers [65].
Though this method avoids the use of potentially biased primer-based amplification, it
Fig 3. Taxonomic distribution of MEGAN-classified OTUs. Taxonomic distributions are summarized for the Eukaryota at the Kingdom rank, for the Fungi
at the phylum rank, for the Metazoa at the phylum rank, and for the Viridiplantae at the order rank. Each dataset (columns) shows meters representing the
absolute number of reads classified to various taxonomic ranks (rows/leaves).
doi:10.1371/journal.pone.0142759.g003
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
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generates data from many loci that lack reference databases to allow species level identification.
The alternative approach is to select markers a priori based on the availability of existing refer-
ence databases. Although the ability to link data from multiple markers to specific individuals
is often lost using metagenomic methods, the data can be used to provide corroborating evi-
dence for species presence and prevalence as we have done here.
Table 6. The most frequent MEGAN categories at the order rank for each marker.
MEGAN Node / Order
a
Number of OTUs (Site A, Site B)
ITS LSU
Pezizales (Fungi, Ascomycota)*330 (168, 162) 98 (44, 54)
Helotiales (Fungi, Ascomycota)*259 (132, 127) 80 (34, 46)
Pleosporales (Fungi, Ascomycota)*198 (97, 101) 42 (17, 25)
Agaricales (Fungi, Basidiomycota) 194 (100, 94) 37 (17, 20)
Hypocreales (Fungi, Ascomycota) 157 (78, 79) 66 (31, 35)
Capnodiales (Fungi, Ascomycota)*121 (61, 60) 22 (12, 10)
Glomerales (Fungi, Glomeromycota)*116 (61, 55) 3 (2, 1)
Mitosporic Ascomycota (Fungi, Ascomycota)*114 (57, 57) 32 (14, 18)
Thelphorales (Fungi, Basidiomycota)*111 (55, 56) 15 (7, 8)
Tremellales (Fungi, Basidiomycota)*106 (58, 48) 21 (10, 11)
LSU ITS
Pezizales (Fungi, Ascomycota)*98 (44, 54) 330 (168, 162)
Helotiales (Fungi, Ascomycota)*80 (34, 46) 259 (132, 127)
Hypocreales (Fungi, Ascomycota) 66 (31, 35) 157 (78, 79)
Tylenchida (Metazoa, Nematoda)*57 (25, 32) 0 (0, 0)
Polyporales (Fungi, Basidiomycota)*45 (19, 26) 82 (41, 41)
Pleosporales (Fungi, Ascomycota)*42 (17, 25) 198 (97, 101)
Agaricales (Fungi, Basidiomycota) 37 (17, 20) 194 (100, 94)
Rhabditida (Metazoa, Nematoda)*37 (17, 20) 0 (0, 0)
Sordariales (Fungi, Ascomycota)*34 (17, 17) 81 (30, 51)
Mitosporic Ascomycota (Fungi, Ascomycota)*32 (14, 18) 114 (57, 57)
Platygloeales (Fungi, Basidiomycota)*32 (19, 13) 46 (25, 21)
Chytridiales (Fungi, Chytridiomycota)*24 (12, 12) 75 (36, 39)
rbcL
Poales (Viridiplantae, Streptophyta) 133 (107, 26)
Acorales (Viridiplantae, Streptophyta) 64 (27, 37)
Equisetales (Viridiplantae, Streptophyta) 57 (30, 27)
Lamiales (Viridiplantae, Streptophyta) 48 (30, 18)
Bryales (Viridiplantae, Streptophyta) 43 (33, 10)
Malpighiales (Viridiplantae, Streptophyta) 40 (21, 19)
Asterales (Viridiplantae, Streptophyta) 37 (23, 14)
Ricciales (Viridiplantae, Streptophyta) 32 (20, 12)
Rosales (Viridiplantae, Streptophyta) 11 (11, 0)
Brassicales (Viridiplantae, Streptophyta) 7 (7, 0)
a
Results from the ITS and LSU markers are compared at each MEGAN node using the directed
homogeneity ‘up’test performed in MEGAN using normalized datasets with Bonferroni-corrected
comparisons.
Significant differences in the number of observed ITS and LSU OTUs are indicated by an asterisk (*).
doi:10.1371/journal.pone.0142759.t006
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
PLOS ONE | DOI:10.1371/journal.pone.0142759 January 5, 2016 11 / 18
We hypothesized that the ITS and LSU rDNA markers would recover similar sets of fungal
taxa and that the ITS + LSU + rbcL markers together would recover a richer assortment of
taxa than any marker on its own. We produced thousands of OTUs from the ITS, LSU, and
rbcL markers that we directly compared showing a significant “rare biosphere”[66]. We
observed some similarity between the ITS and LSU datasets when taxa were compared at the
most inclusive taxonomic levels, however, this similarity breaks down at more specific taxo-
nomic levels even among the most frequently observed taxa. Each marker detected a taxo-
nomically distinct community that varied more by primer than by site, particularly for the
rDNA markers. To date, only a single fungal study that we are aware of has used more than
one rDNA region, SSU and ITS, to survey hundreds of fungal sequence types from bulk soil
using Sanger sequencing [13]. A previous fungal study also showed that using alternative
primers can affect the recovered richness and community composition of root tips that were
sequenced both individually and from a pooled sample [52]. Our study supports their asser-
tion and shows how community richness, overall taxonomic composition, and even the pres-
ence of the most frequently encountered taxa may differ according to the primer and marker
used for monitoring. Recent studies in arthropods have shown support for multiple primer
and multiple gene frameworks [67–68].
Classification complexities
How can we explain our inability to detect differences among sites using the ITS and LSU
markers? First, fungi are significantly more diverse than plants and our fungal sampling was
not exhaustive. Despite sequencing three soil sample replicates and producing saturated rare-
faction curves, the use of additional primer sets for each marker would likely recover additional
taxa [69]. Second, previous work has shown that partial sequences from the 5’and 3’ends of
the ITS region may BLAST to different species despite coming from the same full length
sequence. This type of BLAST result is often used to diagnose putative chimeras in full length
ITS sequences [70,71]. Using a dataset of fungal environmental sequences previous work in
the literature showed that 40% of partial ITS1 and ITS2 sequences from the same full length
query may BLAST to different species [22]. Using a well-annotated fungal ITS dataset gener-
ated from individual PCRs, it was shown that partial sequences from the 5’and 3’ends of the
same parent sequence had best BLAST matches to the correct species as well as to an incorrect
species in 6% of cases for 400 bp fragments and in 15% of cases for 50 bp fragments [23]. These
BLAST results may be best explained by lack of resolution among partial length ITS fragments,
insufficient database coverage, or incorrectly annotated database sequences. The consequence
of these observations is that taxonomic diversity recovered by the short fragments using differ-
ent primers in our study may be inflated. Third, intragenomic variation among multicopy
rDNA regions means that relaxed concerted evolution may result in sequences that are diver-
gent from the consensus or barcode sequence for a species [72–74]. This type of variation can
be detected from individuals by cloning and sequencing or from bulk soil DNA amplified with
mixed-template PCR [75]. As a consequence, there is poor database representation for these
rare alleles, and this may result in spurious BLAST matches to incorrect taxa. Fourth, the num-
ber of named fungal ITS sequences in GenBank available as references to identify new environ-
mental sequences is greatly exceeded by the number of unnamed environmental sequences
[76]. To improve the utility of reference databases, there has been a plea for increasing the
sequencing of type cultures and specimens as well as for the formal classification of environ-
mental sequences [76–78]. Progress towards automated sequence-based identification of fun-
gal ITS sequences has been made [79–80]. As the representation in reference databases
increases, so too will our ability to correctly classify taxa.
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
PLOS ONE | DOI:10.1371/journal.pone.0142759 January 5, 2016 12 / 18
Suggestions for future biomonitoring efforts
It is possible that next-generation sequencing platforms producing longer paired-end reads up
to 600 bp may be able to produce full length ITS sequences for most fungi to circumvent the
problem of working with partial ITS reads. The use of paired-end approaches would also allow
forward and reverse reads to be assembled, providing an additional level of quality assurance
[81]. In contrast to the rDNA markers, the rbcL marker did not show strong clustering by
primer, though species level identifications using MEGAN were not always possible due to the
conserved nature of this gene region. As such, it may be appropriate to use a second marker
such as matK to track below-ground plant structures and to corroborate rbcL results.
The general rules for setting up mixed-template PCRs that detect the greatest sample diver-
sity, particularly with 16S rDNA, have been known for some time and include using a low PCR
cycle number, longer elongation times, and pooling multiple PCR reactions [82–84]. It is clear
now that the use of multiple markers and even multiple amplicons for each marker, generated
using different primers, may also be a good way to address the issue of primer bias and detect
the broadest range of taxa from an environmental sample [69]. We suggest that future studies
consider these parameters carefully since the high throughput nature of next-generation
sequencing exaggerates these effects and even brute force sequencing will not detect maximum
diversity if the primers and PCR conditions do not facilitate this. In conclusion, high through-
put sequencing with multiple markers to study fungal and plant communities will be important
for biomonitoring efforts such as in the Alberta oil sands.
Supporting Information
S1 Fig. Characterizing the ITS1/ITS2 component of our ITS reads using the Fungal ITS
Extractor. After quality trimming and pooling all of our ITS reads, the proportion of reads in
the following categories are shown in (a) ITS1 and ITS2 regions were both detected (blue); only
the ITS1 region was detected (red); only the ITS2 region was detected (green); and neither the
ITS1 nor the ITS2 region was detected (purple). The number of reads of various lengths is
shown in (b) for the ITS1 region (red) and the ITS2 region (blue).
(PDF)
S2 Fig. Effect of sequence similarity cutoffs on the number of clustered OTUs. In (a),
sequence similarity cutoff values used with USEARCH are shown on the x-axis and the relative
number of recovered OTUs, with respect to the total number of OTUs recovered at 100%
sequence similarity, is shown on the y-axis. The following series are shown: the ITS region
(5’—blue, 3’–red), the LSU region (5’—green, 3’—purple), and the rbcL region (5’–teal, 3’—
orange). In (b), the increasing proportion of clustered OTUs is shown with increasing sequence
similarity cutoffs. Sequence similarity increases of 1% intervals are shown on the x-axis. The
resulting increase in the proportion of OTUs is shown on the y-axis using a log
2
scale. A cover-
line at 20% OTU increase is shown as a black dashed line.
(PDF)
S3 Fig. Frequency distributions comparing OTU recovery consistency among sites. Individ-
ual OTUs were plotted in rank order based on abundance at site A. Data are shown for three
loci (ITS, LSU, and rbcL) from 5’and 3’fragments. Blue represents site A and red represents
site B.
(PDF)
S4 Fig. Distribution and richness of MEGAN-classified OTUs at each rank. Data are shown
for three loci (ITS, LSU, rbcL) from 5’and 3’primers from two sites (A and B) combined: (a)
Fungal and Plant DNA Marker Sampling Reveals Complementary Biotas
PLOS ONE | DOI:10.1371/journal.pone.0142759 January 5, 2016 13 / 18
distribution of classified OTUs across ranks and (b) richness at each rank.
(PDF)
S5 Fig. Neighbor joining analysis of seed sequences classified as Pterula echo.ITS1 analysis
used 21 taxa, including four reference sequences from GenBank, and 279 aligned characters.
ITS2 analysis used 16 taxa, including four reference sequences, and 242 aligned characters.
Neighbor joining analysis used the Kimura two parameter model. 1000 neighbor joining boot-
strap (NJB) replicates were conducted and clades supported by greater than 60% NJB are
labeled at the nodes.
(PDF)
S1 Table. Raw read statistics for each library after sorting by primer sequence.
(DOCX)
S2 Table. Number of OTUs from order-level MEGAN classifications using GenBank taxon-
omy.
(DOCX)
S3 Table. Number of OTUs from species-level MEGAN classifications using GenBank tax-
onomy.
(DOCX)
S4 Table. The most frequent ITS, LSU, and rbcL categories summarized by MEGAN at the
species level.
(DOCX)
S1 Text. Supporting methods and results.
(DOCX)
Author Contributions
Conceived and designed the experiments: MH DB SS. Performed the experiments: SS. Ana-
lyzed the data: TP. Contributed reagents/materials/analysis tools: MH DB GBG. Wrote the
paper: TP SS DB GBG MH.
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