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Effort versus Reward: Preparing Samples for Fungal Community Characterization in High-Throughput Sequencing Surveys of Soils

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Next generation fungal amplicon sequencing is being used with increasing frequency to study fungal diversity in various ecosystems; however, the influence of sample preparation on the characterization of fungal community is poorly understood. We investigated the effects of four procedural modifications to library preparation for high-throughput sequencing (HTS). The following treatments were considered: 1) the amount of soil used in DNA extraction, 2) the inclusion of additional steps (freeze/thaw cycles, sonication, or hot water bath incubation) in the extraction procedure, 3) the amount of DNA template used in PCR, and 4) the effect of sample pooling, either physically or computationally. Soils from two different ecosystems in Minnesota, USA, one prairie and one forest site, were used to assess the generality of our results. The first three treatments did not significantly influence observed fungal OTU richness or community structure at either site. Physical pooling captured more OTU richness compared to individual samples, but total OTU richness at each site was highest when individual samples were computationally combined. We conclude that standard extraction kit protocols are well optimized for fungal HTS surveys, but because sample pooling can significantly influence OTU richness estimates, it is important to carefully consider the study aims when planning sampling procedures.
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RESEARCH ARTICLE
Effort versus Reward: Preparing Samples for
Fungal Community Characterization in High-
Throughput Sequencing Surveys of Soils
Zewei Song
1
*, Dan Schlatter
1
, Peter Kennedy
2
, Linda L. Kinkel
1
, H. Corby Kistler
1,3
,
Nhu Nguyen
2
, Scott T. Bates
1
1Department of Plant Pathology, University of Minnesota, Saint Paul, MN 55108, United States of America,
2Department of Plant Biology, University of Minnesota, Saint Paul, MN 55108, United States of America,
3USDA ARS Cereal Disease Laboratory, Saint Paul, MN 55108, United States of America
*songx208@umn.edu
Abstract
Next generation fungal amplicon sequencing is being used with increasing frequency to
study fungal diversity in various ecosystems; however, the influence of sample preparation
on the characterization of fungal community is poorly understood. We investigated the ef-
fects of four procedural modifications to library preparation for high-throughput sequencing
(HTS). The following treatments were considered: 1) the amount of soil used in DNA extrac-
tion, 2) the inclusion of additional steps (freeze/thaw cycles, sonication, or hot water bath in-
cubation) in the extraction procedure, 3) the amount of DNA template used in PCR, and 4)
the effect of sample pooling, either physically or computationally. Soils from two different
ecosystems in Minnesota, USA, one prairie and one forest site, were used to assess the
generality of our results. The first three treatments did not significantly influence observed
fungal OTU richness or community structure at either site. Physical pooling captured more
OTU richness compared to individual samples, but total OTU richness at each site was
highest when individual samples were computationally combined. We conclude that stan-
dard extraction kit protocols are well optimized for fungal HTS surveys, but because sample
pooling can significantly influence OTU richness estimates, it is important to carefully con-
sider the study aims when planning sampling procedures.
Introduction
Terrestrial ecosystems are inhabited by an astonishing diversity of soil microorganisms [1],
which play critical roles in global geochemical cycles and ecosystem functioning [2,3]. A large
portion of this diversity was essentially hidden from microbial ecologists until DNA-based
techniques eliminated the constraints of culture-dependent community surveys [4]. Since then,
the technology for sequencing DNA has advanced at a staggering rate, with progress currently
allowing for the generation of millions of microbial community sequences from environmental
samples in very short periods of time [5].
PLOS ONE | DOI:10.1371/journal.pone.0127234 May 14, 2015 1/13
OPEN ACCESS
Citation: Song Z, Schlatter D, Kennedy P, Kinkel LL,
Kistler HC, Nguyen N, et al. (2015) Effort versus
Reward: Preparing Samples for Fungal Community
Characterization in High-Throughput Sequencing
Surveys of Soils. PLoS ONE 10(5): e0127234.
doi:10.1371/journal.pone.0127234
Academic Editor: Gabriele Berg, Graz University of
Technology (TU Graz), AUSTRIA
Received: December 12, 2014
Accepted: April 12, 2015
Published: May 14, 2015
Copyright: This is an open access article, free of all
copyright, and may be freely reproduced, distributed,
transmitted, modified, built upon, or otherwise used
by anyone for any lawful purpose. The work is made
available under the Creative Commons CC0 public
domain dedication.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information files.
All the data we used in this manuscripts can be
calculated based on the OTU table in the supporting
materials. All richness data used in statistical analysis
can also be found in the supporting materials. The
data can be found in University of Minnesota Data
Repository through this link: http://conservancy.umn.
edu/handle/11299/170817
Funding: Funding was received from United States
Department of Agriculture (2011-67019-30200 http://
nifa.usda.gov/grants) grant to LLK & HCK; University
Soil fungi are an important component of terrestrial microbial diversity due to their consid-
erable influence on aboveground biodiversity and primary productivity, symbiotic relation-
ships with most land plants, and as drivers of the global soil carbon cycle [3,68]. The number
of publications using high-throughput sequencing (HTS) to characterize fungal communities
in soil is growing exponentially (S1 Fig), and these studies reveal a high degree of diversity and
endemism as well as functional redundancy among these fungi [911]. Although general out-
lines for the fungal community HTS workflow have been published [12], few studies have ex-
amined the protocol used in preparing samples for HTS, with some notable exceptions [13
15]. Such considerations are important, as standardization of HTS procedures will allow for
cross-study comparisons of microbial HTS datasets, which will ultimately benefit many emerg-
ing frontiers in microbial ecology [16].
Modification of sample preparation procedures has been previously shown to influence fun-
gal community characterization in culture-independent non-HTS studies. For example, in-
creasing the amount of soil used in DNA extraction [1719] or the use of sample pooling
methods [20] can enhance capture of rare species. Additional steps in extraction protocol have
also been shown to increase DNA yield [2123] as well as the number of species recovered
[2426]. Conversely, species richness gains can also be achieved through regulating DNA tem-
plate amount to improve PCR efficiency [12,27]. The influence of these different procedural
modifications on our ability to characterize the richness of soil fungal communities, however,
has not been sufficiently examined for the deep sequencing levels now possible using HTS.
To assess the role of modified procedures on species richness capture and address the need
for standardization, we examined sample preparation protocols used in HTS surveys of soil
fungi. Samples were collected at two sites in the Midwestern United States representing prairie
and forest soils. Specifically, we examined the effect of extracting DNA from different volumes
of soil (10 g, 1 g, 0.25 g) and using various extraction protocol modifications (freeze/thaw, soni-
cation, 65°C water bath) on recovered fungal richness. Along with these treatments, we also ex-
amined fungal richness outcomes using varying PCR DNA template amounts (10 ng, 20 ng,
40 ng) and compared different sample pooling methods (physical vs. computational) to assess
their ability to capture fungal community richness and composition at both sites.
Methods
Sample collection in Cedar Creek grant through Cedar Creek Ecosystem Science Reserve of the
University of Minnesota. Sample collection in Cloquet forest grant through Cloquet Forest
Center of the University of Minnesota.
Soil sampling
In fall 2013, soils were collected from two sites: the University of Minnesota Cedar Creek Eco-
system Science Reserve (CCR, 45°24'13" N, 93°11'20" W) and the University of Minnesota Clo-
quet Forestry Center (CFC, 46°40'45" N, 92°31'08" W). These sites represent distinct biome
types, prairie and forest, respectively. CCR has mean monthly temperature range from -10°C
to 22°C with an annual mean temperature at 6.7°C and an annual precipitation of 800 mm.
The soil is highly sandy (93%) of the Zimmerman series [28]. CFC has mean monthly tempera-
tures range from -14°C to 19°C with an annual mean temperature at 4°C and an annual precip-
itation of 761 mm. The soil is a loamy sand of the Omega and Cloquet series [29]. At CCR,
soils were collected from the base of Andropogon gerardii plants growing in monoculture
(n = 2 samples) and polyculture (n = 2 samples). For each sample, three 10 cm deep × 2.5 cm
wide cores were taken from the base of an individual A.gerardii plant and combined in the
field. At CFC, soils were collected under four replicate plots containing six tree species (Pinus
Standard Sample Preparations Suffice for Next Gen Amplicon Library
PLOS ONE | DOI:10.1371/journal.pone.0127234 May 14, 2015 2/13
of Minnesota MnDrive (http://mndrive.umn.edu/) grant
to LLK & STB; and National Science Foundation
(Grants EF 12-41895 grant to LLK and Long-Term
Ecological Research Network 0620652). 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.
strobus,Larix laricina,Picea glauca,Quercus rubra,Betula papyifera, and Acer saccharum). For
each sample, four 10 cm deep × 2.5 cm wide cores were taken one meter apart and combined
in the field. At each site, four individual plot sample replicates were gathered. All soil samples
were transported in coolers to the lab and stored at -20°C until sieving at 4 mm size to remove
fine roots and any part of small woody debris prior to DNA extraction.
Experimental design and DNA extraction
We designed our experiments to examine the effects of soil volume, DNA extraction modifica-
tions, PCR DNA template amount, and soil sample pooling on observed fungal richness and
community composition. To assess soil volume, DNA was extracted from 10 g, 1 g, and 0.25 g
of soil from each plot (12 samples sequenced per site, 24 samples sequenced in total). The
PowerMax Soil DNA kit (MO BIO Laboratories, Carlsbad, CA, USA) was used for 10 g extrac-
tions. For the 1 g extractions, the PowerSoil kit (MO BIO Laboratories, Carlsbad, CA, USA)
was used to extract DNA from four replicate 0.25 g portions of the same plot soil sample
(0.25 g is the standard quantity recommended for this kit by the manufacturer). DNA concen-
tration for each of the four replicate extractions were then quantified using a BioTek Synergy
H1 plate reader (BioTek, Winoosku, VT, USA) and mixed at equal amount to prepare a single
1gsample. An additional 0.25 g portion of the same plot soil sample was used in the Power-
Soil kit to represent the kit standardextraction. All extractions were carried out according to
the manufacturers instruction. Homogenizing was done, as specified in the kit protocol, by
vortexing 10 min on the MO BIO vortex adapters (MO BIO Laboratories, Carlsbad, CA, USA,
13000-V1-24 for PowerSoil kit, and 13000-V1-50 for PowerMax Soil kit).
In the DNA extraction kit modification experiment, 0.25 g samples of soil were added to the
PowerSoil bead-beaterextraction tubes containing the kit buffer. Samples were then subjected
to three different modification procedures, freeze/thaw, sonication, or heating via water bath
incubation (12 treatment samples sequenced per site, 24 treatment samples sequenced in
total). The freeze/thaw treatment consisted of three rounds of freezing and thawing by sub-
mersing tubes in liquid N
2
for 510 seconds followed by an incubation step in a 65°C water
bath for 10 min. The sonication treatment consisted of sonicating tubes for 10 min in a Bran-
son 8200 sonicator (Thomas Scientific, Swedesboro, NJ, USA). Finally, the heating treatment
consisted of incubating tubes in a 65°C water bath for 10 min. After each modification, DNA
extraction proceeded according to the kit protocol instructions. The standard 0.25 g soil extrac-
tion from the soil volume experiment above served as an unmodified control for comparison.
To assess the effects of soil sample pooling and DNA template amount, four individual plot
samples were combined in equal amounts (3 g from each sample) and thoroughly mixed to
form a bulkedsample (hereafter referred to as the physicalpool) for each site. From these
physical pool samples, DNA was extracted from 10 g, 1 g, and 0.25 g soil as describe above.
DNA extracted from each of these three different soil volumes was then used as template in in-
dividual PCR reactions containing different template DNA amounts (10 ng, 20 ng, and 40 ng).
Aliquots were prepared from a standardized 10 ng uL
-1
stock concentration and added to the
PCR reaction at 1 μL, 2 μL, and 4 μL volumes to achieve the desired quantity of DNA template
(9 samples sequenced per site, 18 samples sequenced in total). Along with all of the aforemen-
tioned samples, for each extraction kit that was used (PowerSoil and PowerMax Soil), a soil-
free extraction was conducted using the manufacturers protocol to serve as negative controls.
These negative controls allowed us to identify contaminants introduced during the extraction
process and remove them from the final analyses. Finally, a mockfungal community consist-
ing of DNA extracted from 25 known fungal taxa was also included in the sequencing run to
allow for internal optimization of the OTU clustering method [14].
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Fungal ITS amplification and Illumina sequencing
PCR amplification of the ITS1 region of the nuclear ribosomal RNA gene was conducted using
the general fungal primers ITS1-F and ITS2 adapted for Illumina sequencing [13]. Using other
primer combinations mays improve amplification of certain fungal groups [30]; however, test-
ing differences among primer pairs was not the aim of this study. Each 20 μL PCR reaction
consisted of 10 μL 2x Roche FastStart PCR Master (Roche, Indianapolis, IN, USA), 0.35 μLof
each forward and reverse primers (10 μM), 1.2 μL MgCl
2
(25 mM), and 2 μL template DNA
(20 ng, except in the DNA template amount experiment where 10 ng and 40 ng were also used,
see above). Triplicate PCR reactions were performed for each sample using three separate an-
nealing temperatures (50°C, 53°C, and 55°C)[14]. Thermocycling conditions included an ini-
tial denaturation step of 95°C for 10 min, 30 cycles of 95°C for 30s, annealing at 50°C, 53°C, or
55°C for 20 s, and extension at 72°C for 30 s, and a single final extension at 72°C for 8 minutes.
Triplicate PCR reactions at each annealing temperature were pooled, purified using the Agin-
court AMPure XP PCR purification kit (Beckman Coulter Inc., Brea, CA, USA). Purified PCR
products were quantified using the PicoGreen dsDNA assay (Life Technologies, Grand Island,
NY, USA) following the manufacturers instructions, and 30 ng of DNA from each sample was
combined into a single pool for Illumina MiSeq sequencing at the University of Minnesota Bio-
medical Genomics Center.
Data processing
Low quality ends of reads (<q20), primer sites, and adapter sites were trimmed from forward
MiSeq Illumina reads (match error rate of e = 0.2) using Cutadapt [31]. Trimmomatic [32] was
then used to trim from both ends (quality threshold set at 20) and filter out reads less than
125 bp long. Sequences with ambiguous bases and homopolymers greater than 9 bp were re-
moved in Mothur [33]. All sequences files were then combined into a single fasta file with valid
QIIME labels [34]. To generate a de novo database of operational taxonomic units (OTUs), se-
quences were dereplicated and singletons were removed in USEARCH [35]. Sequences were
then clustered at 97% similarity using the UPARSE algorithm implemented in USEARCH,
which included chimera detection and filtering. Subsequently, to reduce the number of spuri-
ous OTUs, a second round of clustering was conducted at 95% similarity using the UCLUST al-
gorithm in QIIME, as Nguyen et al. (2014) found that chainingOTU clustering algorithms
generated more accurate OTU bins in a similarly processed dataset [14]. The representative
OTU list after the chainingclustering were filtered against a customized UNITE database [36]
with UCLUST, but at a 75% similarity level to remove non-fungal sequences. Any OTU that
failed in the third clustering round were removed from the de novo OTU list and blasted
against another custom database that includes fungal voucher sequences and the UNITE data-
base [14]. OTUs that hit the custom database and passed a quality filter (match length >0.85,
similarity level >0.75%, and kingdom Fungiidentity in the taxonomic string) were added
back to the de novo OTU list. Mapping the original sequence file to the de novo OTU sequence
list generated an OTU table. Finally, the abundances of each OTU found in negative controls
were subtracted from all samples prior to rarefaction following Nguyen et al. (2014)[14].
Statistical analysis
All samples were rarefied to an equal sampling depth of 18,778 sequences, and observed fungal
OTU richness, Chao1, Simpson as well as Shannon indexes for each sample were calculated
using the QIIME alpha_diversity.py command. To assess differences among treatments, a se-
ries of one-way ANOVAs were applied on OTU richness with Tukeys multiple comparisons
in the R statistical software (www.r-project.org). Diversity estimates, such as Chao 1 was not
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used in our analyses due to the potential biases associated with them for next generation se-
quencing studies [37]; however, we have provided them in the supplementary materials (S1
File.) for general comparison. A Bray-Curtis dissimilarity matrix among samples was calculat-
ed, and community distance was visualized in non-metric multidimensional scaling (NMDS)
plots using the vegan [38] and ggplot2 [39] packages in R. The vegan package was also used to
assess potential community composition differences among treatments by testing with ADO-
NIS. For each site, the physical pooled samples sequenced from the standard 0.25 g soil sample
(using a 20 ng DNA template amount for PCR) were also compared to a corresponding pool of
the individual plot samples (0.25 g soil and 20 ng DNA template) achieved through computa-
tion. For this computationalpool, the number of each OTU recovered from the four individu-
al plot samples were summed, and the resultant composite pool was rarefied at the
18,778-sequence level. We also compared the average occurrence frequency of OTUs between
physical and individual pools. The occurrence frequency of each OTU is defined as the occur-
rence (sequence number 1) in either 9 physical pools or 24 individual pools in the form of
percentage in both site. The average occurrence frequency was calculated as the average of 20
OTUs around each individual OTU starting from the 10
th
(i.e., 1
st
to 20
th
OTU, 2
nd
to 21
st
OTU, ..., 100
th
to 119
th
...). Paired T-test was applied between each pair of the 20-OTUs sub-
set (α= 0.05). All T-tests were adjusted with Bonferroni correction with hypotheses n= 20.
Results and Discussion
High-throughput sequencing of soil fungal communities
After quality filtering and sequence processing, we obtained 2,958,381 sequences, with a range
of 18,778 to 78,509 sequences per sample. Rarefying at the lowest per sample recovery thresh-
old (18,778 sequences), we recovered a total of 2,009 unique fungal OTUs from our 66 soil
samples. These OTUs represented five known phyla (S2 Fig). We found a total of 1,273 and
1,436 OTUs in CCR (Cedar Creek Ecosystem Science Reserve) and CFC (Cloquet Forestry
Center) soils (33 samples at each site), respectively. On average, individual samples contained
~330 OTUs for CCR and ~390 OTUs for CFC. Due to substantial community turnover among
samples at each site, soils at CCR and CFC harbored a very high degree of total fungal richness,
with rarefaction curves failing to achieve an asymptote (S3 Fig). These results agree with other
high-throughput sequencing studies [11,4042] that consistently reveal extremely high levels
of fungal richness in soils of various ecosystems. In fact, only one study to date has successfully
saturated the rarefaction curve for soil fungi, recovering a total of 1,002 OTUs from a single
biome type [43], a richness level that is considerably smaller than what we detected at our sites.
Treatment effects on fungal alpha diversity
Extracting DNA from larger volumes of soil did not result in significantly higher OTU richness
for either site (Fig 1A and 1B for richness and S1 Table for other indexes), although average
richness did increase slightly with more soil used in the extraction. Extracting DNA from more
soil may be desirable if detecting low abundance taxa is critical. For example, methods for early
detection of soil-borne plant pathogens have been developed that require using large volumes
of soil [1719]. In support of this approach, we were able to detect potential plant pathogens,
such as Cochliobolus sativus, in our 10 g and 1 g soil extraction samples that were not present
when only 0.25 g of soil were used. However, our data suggest that extracting DNA from small
soil volumes (e.g., the standard 0.25 g recommended in widely-used commercial extraction
kits) is sufficient for most HTS applications in which a general characterization of soil fungal
richness is required.
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Including additional steps in the extraction protocol, such as sonication treatments, freeze/
thaw regimes, or period of incubation in a hot water bath, all of which are thought to increase
DNA yields and recovered richness, likewise did not significantly impact observed fungal rich-
ness (Fig 1C and 1D for richness & S1 Table for other diversity indexes). Although previous
studies have found significant effects of DNA extraction method on recovered fungal and bac-
terial richness [21,2426,4446], those studies used older molecular techniques (e.g., RFLP and
DGGE) that are likely more sensitive to biases due to the limited depth at which they survey
the communities [4749]. While our results suggest unmodified protocols from standard DNA
extraction kits is sufficient for general characterizing fungal richness of soil samples in HTS
surveys, we reiterate that study aims must be considered. For example, more stringent DNA
Fig 1. Rarefied OTU richness estimates of soil fungi by treatment. A-B. Amount of soil extracted for CCR
and CFC, respectively (n = 5 for each treatment). C-D. Extraction modification for CCR and CFC, respectively
(n = 4 for each treatment). E-F. Amount of DNA template in each PCR reactions for CCR and CFC,
respectively (n = 3 for each treatment). The same letter above each bar indicates no significant difference
(P0.01) in ANOVA using Tukeys multiple comparison tests (α= 0.05).
doi:10.1371/journal.pone.0127234.g001
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extraction treatments may be necessary to detect fungi exhibiting resistant structures [5052];
however, facilitating extraction of DNA from such structures (e.g., resistant spores) is often un-
desirable as the associated fungal taxa may represent transient, rather than living, components
of the community.
As with the other preparation treatments, the amount of DNA template used in PCR reac-
tions (10 ng, 20 ng, or 40 ng) did not significantly influence recovered fungal richness in our
samples either (Fig 1E and 1F for richness & S1 Table for other indexes). While significant ef-
fects on fungal richness were not detected overall, opposing trends were noted between sites,
with slightly lower richness observed for the highest template amount at CCR and slightly
higher richness recorded for higher template amounts at CFC. It is possible that these results
are related to the fact that increasing DNA template amount can adversely affect PCR by also
increasing the concentrations of inhibitors [27,53,54] and Lindahl et al. (2013) noted that dilut-
ing DNA template for PCR steps in HTS library preparation is often required [12]. While we
did not directly measure potential inhibitors in our samples, our results do highlight the often
inconsistent results of increasing DNA templates amounts among different sample types as
well as the need for template optimization in preparing HTS runs.
Sample pooling and treatment effects on fungal community composition
At both sites, physical pools generally had higher richness (Fig 2) and more unique OTUs (S4
Fig) when compared with each individual plot sample. Abundant OTUs (>1% of the total se-
quence abundance, consisted of ~70% sequences in total for both sites) appearing in each indi-
vidual plot sample were also more fully captured in the physical pools at both sites (Fig 3 and
S2 File.). Between the two pooling methods, computational pools of each site had higher rich-
ness measures than physical pools (Fig 2), due mostly to the contribution of unique OTUs
from all four individual plot samples (S4 Fig). A number of OTUs were also recovered in the
computation pool that were not present in the physical pool, while a smaller portion were
Fig 2. Soil fungal OTU richness of individual samples, physical pool, and computational pool. The
shorter bars inside of physical and computational pools indicated the number of unique OTUs that were not
shared between the two pools in each site. All samples used 0.25 g soil with standard DNA extraction and
PCR procedures.
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unique to the physical pool alone (Fig 2). Since soil from each of the study sites originated from
a very similar source (see methods), it is unlikely that the differences on unique OTUs could be
due to data process procedures or PCR amplifications. Our findings are similar to Branco et al.
(2013), who found that computational pooling yielded higher fungal OTU richness than physi-
cal sample pool and Dickie et al. (2010), who found different rare taxa in pooled and individual
samples [20,55]. Importantly, the high level of fungal community turnover demonstrated in
this study and others [11,4042] indicates the difficultly in achieving comprehensive character-
ization of soil fungal communities, even using the deep level of survey possible with HTS.
In addition to differences in richness, physically pooled and individual plot samples for each
site differed significantly in their fungal community structure (ADONIS based on Bray Curtis
and presence/absence distance matrixes respectively, F
4, 28
= 17.71 and 6.10 for CCR, F
4,28
=
81.06 and 6.71 for CFC, P= 0.001 for all tests). Our results are again similar to those of Branco
et al. (2013), who showed significant differences in fungal community structure between
Fig 3. Average occurrence frequency of OTUs in physical pools versus individual samples, and percent of total number of sequence, moving from
abundant (left) to rare (right) taxa. Physical pools are in red and blue points, individual samples are in green points, total number of sequence is in gray
points. The occurrence frequency of each OTU is defined as the occurrence (sequence number 1) in either 9 physical pools or 24 individual pools in the
form of percentage in both site. The average occurrence frequency shown in the plot was calculated as the average of 20 OTUs around each individual OTU.
In the physical pool, red points indicate significant difference (P0.05) in paired T-test between physical and individual pools (α= 0.05), while blue points
were not significantly different. All T-tests were adjusted with Bonferroni correction with hypotheses n= 20. The percent of total number of sequence was
defined as the sum of abundance percentage from 1
st
to current OTU in descending order of abundance.
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Fig 4. Nonmetric Multidimensional Scaling Plot of physical pools and individual plot samples from each site. Plots based on either Bray-Curtis
distance matrices generated from rarefied taxon abundances of A. CCR and B. CFC or from presence/absence matrix of C. CCR and D. CFC.
doi:10.1371/journal.pone.0127234.g004
Fig 5. Nonmetric Multidimensional Scaling Plot of samples from CCR and CFC. Plot is based on a Bray-
Curtis distance matrix generated from rarefied taxon abundances.
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individual versus physically pooled samples [20]; however, the differences among individual
samples taken at their site were not significant. In our study, significant compositional differ-
ences among individual samples are apparent in the distinct clustering patterns of the NMDS
plots (Fig 4). The importance of individual taxa, rather than their respective abundances, in
driving these clustering patterns (Fig 4) is demonstrated by the consistency of the results be-
tween the two different matrix types (Bray Curtis vs. presence/absence). In addition, the tight
clusters formed by the physical pool samples in the central ordinationspaces of Fig 4, suggests
the averaging effect that physical pooling had on the fungal communities recovered. Overall,
site differences in fungal community structure were much stronger than any other effect ob-
served within the sites (Fig 5).
Conclusions
When considering the effort applied in the sample preparation phase of this survey, we found
that the effort generally outweighed any associated rewards. Specifically, we found that sonica-
tion, freeze/thaw cycling, and heating during DNA extraction did not result in notable gains of
richness or structural characterization of soil fungal communities. Likewise, community-level
results were very similar regardless of the amount of soil used in the DNA extraction protocol
or the amount of DNA template used in the PCR reactions. We did, however, find that pooling
individual soil samples, either physically or computationally, resulted in higher richness. Al-
though computational pooling of samples provides a more comprehensive assessment of local
OTU richness than physical pooling, there are additional associated effort of preparation and
sequencing. We therefore suggest that for complete biotic inventories, collecting a high number
of individual samples at a given site would be preferable, while a single pooled sample from
many sites would be preferable for studies aimed at larger spatial scales. Overall, we conclude
that following standard protocols of widely used DNA extraction kits is sufficient to successful-
ly assess soil fungal community richness and composition in HTS surveys.
Supporting Information
S1 Fig. Number of publications related to fungal HTS studies from 2000 to 2013 found by
searching key wordsfungi OR fungal, "high throughput sequencing"in Google Scholar
(http://scholar.google.com/). The points plotted fit an exponential curve (α= 0.05; P0.01).
(TIF)
S2 Fig. Relative abundance of fungal phyla in Cedar Creek Reserve (CCR) and Cloquet For-
est Center (CFC) soils after rarefaction (n = 619,674 sequences/site).
(TIF)
S3 Fig. Taxon (OTU) accumulation curves in this study. Taxon (OTU) accumulation curves
depicting the rarefied richnessof fungal taxa as a function of increased sample sequencing ef-
fort at CCR (brown) and CFC (green) in the Midwestern United States (sample
depth = 18,778 sequences per sample).
(TIF)
S4 Fig. Number of unique and shared OTUs between individual samples or computational
pool compared with the physical pool. Samples using 0.25 g soil with standard extraction and
PCR procedure were used. Computational pool was generated with the four individual samples
and rarefied at the same level of richness (18,778 sequences).
(TIF)
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S1 Table. One-way ANOVA of alpha diversity indexes and ADONIS in the three treatments
on sequencing sample preparations.
(DOCX)
S1 File. Diversity indexes in different treatments.
(XLSX)
S2 File. OTU tables for CCR and CFC.
(XLSX)
Acknowledgments
We thank the University of Minnesota Supercomputing Institute (MSI) for providing
computing support.
Author Contributions
Conceived and designed the experiments: DS PK LK HCK NN STB. Performed the experi-
ments: ZS DS PK NN STB. Analyzed the data: ZS DS PK NN STB. Contributed reagents/mate-
rials/analysis tools: PK NN STB. Wrote the paper: ZS DS PK LK HCK NN STB.
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... It is perhaps surprising that homogenizing plant tissues before subsampling did not recover more species than homogenizing after subsampling for fungi as well, because with the former approach, more plant tissue is initially represented. Indeed, a previous study showed that sample pooling or homogenizing before subsampling resulted in a higher richness of soil fungi compared to equally sized individual samples 50 . In Song et al. (2015) 50 they also found that multiple individual subsamples, rather than the single homogenized subsample, resulted in higher richness. ...
... Indeed, a previous study showed that sample pooling or homogenizing before subsampling resulted in a higher richness of soil fungi compared to equally sized individual samples 50 . In Song et al. (2015) 50 they also found that multiple individual subsamples, rather than the single homogenized subsample, resulted in higher richness. This may suggest that the scale at which we are physically able to break down the particle size of plant tissues, as opposed to soil, is not always fine enough to sufficiently homogenize the fungi within. ...
... Indeed, a previous study showed that sample pooling or homogenizing before subsampling resulted in a higher richness of soil fungi compared to equally sized individual samples 50 . In Song et al. (2015) 50 they also found that multiple individual subsamples, rather than the single homogenized subsample, resulted in higher richness. This may suggest that the scale at which we are physically able to break down the particle size of plant tissues, as opposed to soil, is not always fine enough to sufficiently homogenize the fungi within. ...
Article
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Plants host diverse microbial communities, but there is little consensus on how we sample these communities, and this has unknown consequences. Using root and leaf tissue from showy milkweed ( Asclepias speciosa ), we compared two common sampling strategies: (1) homogenizing after subsampling ( 30 mg ), and (2) homogenizing bulk tissue before subsampling ( 30 mg ). We targeted bacteria, arbuscular mycorrhizal (AM) fungi and non-AM fungi in roots, and foliar fungal endophytes (FFE) in leaves. We further extracted DNA from all of the leaf tissue collected to determine the extent of undersampling of FFE, and sampled FFE twice across the season using strategy one to assess temporal dynamics. All microbial groups except AM fungi differed in composition between the two sampling strategies. Community overlap increased when rare taxa were removed, but FFE and bacterial communities still differed between strategies, with largely non-overlapping communities within individual plants. Increasing the extraction mass 10 × increased FFE richness ~ 10 ×, confirming the severe undersampling indicated in the sampling comparisons. Still, seasonal patterns in FFEs were apparent, suggesting that strong drivers are identified despite severe undersampling. Our findings highlight that current sampling practices poorly characterize many microbial groups, and increased sampling intensity is necessary for increase reproducibility and to identify subtler patterns in microbial distributions.
... The number of subsamples to be pooled depends on the research question and the size of the area, with 7-25 being optimal in most cases (Schwarzenbach et al., 2007). Both physical and analytical pooling improve richness and composition assessments of soil fungi (Schwarzenbach et al., 2007;Song et al., 2015) and reduce estimated variance (Dickie et al., 2018). However, pooling of physical samples may result in the loss of patchily occurring rare taxa (e.g., in extremely dilute fish eDNA samples with a detection threshold of 0.05% of total relative abundance at deep sequencing; Sato et al., 2017). ...
... It is usually undesirable to reach the full capacity of the DNA extraction kit, because several types of samples (e.g., peat soils, dead wood, debris-rich sediments and fleshy plant tissues) may absorb the liquid, or inhibitors may be coextracted. For well-homogenised soil samples, there are only minor differences in perceived richness when using DNA extracts from 0.25, 1 or 10 g material (Song et al., 2015), but increasing the volume through replicate extractions or through more material using "maxi" kits provides more reproducible estimates (Dickie et al., 2018). It is crucial to perform weighing and DNA extraction under a dedicated laminar flow in a room separated from the PCR laboratory to avoid cross-contamination and air contamination by amplicons. ...
... Mock community analysis provides additional insights into the qualitative (i.e., estimation of PCR/sequencing error rates) and quantitative capacity (i.e., biased amplification) to recover the original diversity. Positive controls and mock communities may consist of artificial synthesised molecules or DNA extracts of actual species known not to occur in the experimental system (Ihrmark et al., 2012;Song et al., 2015). A sophisticated mock community should comprise >10 species with variable G+C content, amplicon length and quantity based on actual marker copy numbers. ...
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The development of high‐throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By re‐analysing published datasets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full‐length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.
... But at the same time, it is often confounded with haphazard and subjective methods for convenience in practice. The choice of pooling or not, the type of pooled units (i.e., soil cores or data), and the number of (biological or technical) replicates required to accurately describe community richness have been discussed at length in various reviews (Balint et al., 2016;Glenn, 2011;Goodrich et al., 2014;Knight et al., 2012;Knight et al., 2018;Song et al., 2015;Zinger et al., 2019). It should be noted that in order to draw our conclusions, there is a hidden precursor assumption that widespread undersampling leads to underestimation of α diversity (Chao and Jost, 2015). ...
... Therefore, the higher diveristy proves the superiority of one method. In our study, we found that pooling replicates provided higher α diversity (including richness, Shannon entropy and inverse Simpson index) values compared with individual cores under high sequencing depths (Figs. 2, 3), this was consistent with results observed in previous studies for both bacteria and fungi (Branco et al., 2013;Song et al., 2015). When comparing the effect of biological and technical replication on α diversity, we found that biological replicates contribute more richness than technical replicates in Deblur algorithms, while both biological and technical replicates contributed similarly in UPARSE (Fig. 2). ...
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... For instance, Duarte et al. (2017), assessed the diversity of aquatic fungi across graded size of alder leaves and found that alpha diversity was positively influenced by increasing leaf area. Likewise, for microbes, Song et al. (2015) detected an increase in fungal OTU richness with increasing soil sample size from 0.25 g to 10 g in both prairie and forest soils. Therefore, increasing the number of soil sample pools may lead to a positive species/area relationship, and insufficient sampling may result in incorrect diversity estimations (Grey et al., 2018). ...
... Thus, the hypothesis 1 is accepted. These richness patterns are consistent with those reported in previous studies in agricultural fields and temperate forest sites, in which a positive relationship was detected between fungal diversity and increasing soil sample size (Ranjard et al., 2003;Song et al., 2015;Penton et al., 2016). Consequently, the number of samples pooled has important effects on the ecological interpretations also for fungal communities in soils, because insufficient sampling caused deviated richness values (Magurran, 2011). ...
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... Also, there are known examples of how experimental conditions (e.g. DNA extraction methods) and sequencing platforms affect metabarcoding results (Lindahl et al., 2013;Song et al., 2015;Ushio, 2019). Another reason may be that Glomeromycota do not produce airborne spores. ...
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... Regarding fungal identification, as fungi are ubiquitously spread, there are concerns regarding the sampling and processing methods in order to avoid contamination [76]. Fungal DNA extraction is performed by mechanical cell lysis; however, there are some difficulties including their recalcitrant chitinous cell walls and the interference of secondary metabolites with DNA extraction. ...
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Background: To date, most researchhas focused on the bacterial composition of the human microbiota. In this review, we synopsize recent data on the human mycobiome and cancer, highlighting specific cancer types based on current available evidence, presenting interesting perspectives and limitations of studies and laboratory methodologies. Recent findings: Head and neck cancer carcinoma (HNCC), colorectal carcinoma (CRC) and pancreatic ductal adenocarcinoma (PDA) have been associated with dissimilarities in the composition of mycobiota between cancer cases and non-cancer participants. Overall, fungal dysbiosis with decreased fungal richness and diversity was common in cancer patients; however, a specific mycobiotic signature in HNSCC or CRC has not emerged. Different strains of Candida albicans have been identified among cases with HNCC, whilst Lichtheimia corymbifera, a member of the Mucoraceae family, has been shown to predominate among patients with oral tongue cancer. Virulence factors of Candida spp. include the formation of biofilm and filamentation, and the secretion of toxins and metabolites. CRC patients present a dysregulated ratio of Basidiomycota/Ascomycota. Abundance of Malassezia has been linked to the occurrence and progression of CRC and PDA, particularly in animal models of PDA. Interestingly, Schizophyllum, a component of the oral mycobiome, may exhibit anti-cancer potential. Conclusion: The human mycobiome, per se, along with its interactions with the human bacteriome and the host, may be implicated in the promotion and progression of carcinogenesis. Fungi may be used as diagnostic and prognostic/predictive tools or treatment targets for cancer in the coming years. More large-scale, prospective, multicentric and longitudinal studies with an integrative multi-omics methodology are required to examine the precise contribution of the mycobiome in the etiopathogenesis of cancer, and to delineate whether changes that occur in the mycobiome are causal or consequent of cancer.
... However, a quantitative meta-analysis found only a weak relationship between the two (Lamb et al., 2019). Read abundance can be profoundly affected by methodological biases at several steps during metabarcoding procedures, starting from the choice of primers through wet-lab methods, including sequencing, to bioinformatic pipelines (Lindahl et al., 2013;Nguyen et al., 2015;Song et al., 2015;Taylor et al., 2016). However, in our study, the main pathways affecting plants directly and not through fungal parameters remained present in both best-fitting models. ...
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Arctic plants are affected by many stressors. Root-associated fungi are thought to influence plant performance in stressful environmental conditions. However, the relationships are not well-known; do the number of fungal partners, their ecological functions and community composition mediate the impact of environmental conditions and/or influence host plant performance? To address these questions, we used a common arctic plant as a model system: Bistorta vivipara. Whole plants (including root system, n = 214) were collected from nine locations in Spitsbergen. Morphometric features were measured as a proxy for plant performance and combined with metabarcoding datasets of their root-associated fungi (amplicon sequence variants, ASVs), edaphic and meteorological variables. Seven biological hypotheses regarding fungal influence on plant measures were tested using structural equation modelling. The best-fitting model revealed that local temperature affected plants both directly (negatively aboveground and positively below-ground) and indirectly - mediated by fungal richness and the ratio of symbio- and saprotrophic ASVs. The influence of temperature on host plants is therefore complex and should be examined further. Fungal community composition did not impact plant measurements and plant reproductive investment was not influenced by any fungal parameters. The lack of impact of fungal community composition on plant performance suggests that the functional importance of fungi is more essential for the plant than their identity.
... An additional limitation of these studies is the lack of more repetitions in NGS analysis. Although pooled samples are commonly used to identify soil microorganisms (Dokić et al., 2010;Sun et al., 2017), a higher number of repetitions might give a more accurate overview of fungal communities in the examined soils (Song et al., 2015). Another problem is the constantly changing taxonomy of fungi and the problem of matching the sequences obtained to specific genera (Purahong et al., 2019;Raja et al., 2017;Thines et al., 2018). ...
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Fungi play key roles in ecosystems as mutualists, pathogens, and decomposers. Current estimates of global species richness are highly uncertain, and the importance of stochastic vs. deterministic forces in the assembly of fungal communities is unknown. Molecular studies have so far failed to reach saturated, comprehensive estimates of fungal diversity. To obtain a more accurate estimate of global fungal diversity, we used a direct molecular approach to census diversity in a boreal ecosystem with precisely known plant diversity, and we carefully evaluated adequacy of sampling and accuracy of species delineation. We achieved the first exhaustive enumeration of fungi in soil, recording 1002 taxa in this system. We show that the fungus : plant ratio in Picea mariana forest soils from interior Alaska is at least 17:1 and is regionally stable. A global extrapolation of this ratio would suggest 6 million species of fungi, as opposed to leading estimates ranging from 616 000 to 1.5 million. We also find that closely related fungi often occupy divergent niches. This pattern is seen in fungi spanning all major functional guilds and four phyla, suggesting a major role of deterministic niche partitioning in community assembly. Extinctions and range shifts are reorganizing biodiversity on Earth, yet our results suggest that 98% of fungi remain undescribed and that many of these species occupy unique niches.
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