Utilizing ITS1 and ITS2 to study environmental fungal diversity using pyrosequencing

R E S EA RCH AR T I C L E

Utilizing ITS1 and ITS2 to study environmental fungal diversityusing pyrosequencing

C�ecile Monard1, Stephan Gantner1,2 & Jan Stenlid1

1Department of Forest Mycology and Plant Pathology, Uppsala BioCenter, Swedish University of Agricultural Sciences, Uppsala, Sweden and2Department of Ecology and Genetics, Limnology, Uppsala University, Uppsala, Sweden

Correspondence: C�ecile Monard,

Department of Forest Mycology and Plant

Pathology, Uppsala BioCenter, Swedish

University of Agricultural Sciences, PO Box

7026, SE-75007 Uppsala, Sweden. Tel.: +46

(0) 18 67 27 25; fax: +46 (0) 18 67 35 99;

e-mail: [email protected]

Present address: Stephan Gantner, Leibniz

Institute for Science and Mathematics

Education (IPN), Kiel, Germany

Received 8 May 2012; revised 9 November

2012; accepted 21 November 2012.

DOI: 10.1111/1574-6941.12046

Editor: Ian C. Anderson

Keywords

454 pyrosequencing; community structure;

species richness; Sørensen index; minimum

read length.

Abstract

The shorter reads generated by high-throughput sequencing has led to a focus

on either the ITS1 or the ITS2 sublocus in fungal diversity analyses. Our study

aimed to determine how making this choice would influence the datasets

obtained and our vision of environmental fungal diversity. DNA was extracted

from different environmental samples (water, sediments and soil) and the total

internal transcribed spacer (ITS) locus was amplified. 454-sequencing was per-

formed targeting both ITS1 and ITS2. No significant differences in the number

of sequences, operational taxonomic units (OTUs) and in the dominant OTUs

were detected but less diversity was observed in the ITS2 dataset. In the soil

samples, differences in the fungal taxonomic identification were observed, with

more Basidiomycota in the ITS1 dataset and more Ascomycota in the ITS2 data-

set. Only one-third of the OTUs were detected in both datasets which could be

due to (1) more short sequences removed in the ITS2 dataset, (2) different tax-

onomic affiliation depending on the sublocus used as BLASTn query and/or

(3) selectivity in how a primer amplifies the true community. Although ITS1

and ITS2 datasets led to similar results at the fungal community level, for fur-

ther in-depth diversity analysis this study suggests the analysis of both ITS

regions, as they provided different information and were complementary.

Introduction

While only a small part of fungal diversity can be

accessed using culture-based approaches, molecular tools

allow the study of uncultured fungi and so have signifi-

cantly improved our understanding of fungal ecology

during the last 20 years (Anderson & Cairney, 2004).

Direct DNA and/or RNA extraction followed by gene-spe-

cific amplification through PCR allows focusing on the

diverse fungal community or on a targeted function pres-

ent in a particular environmental sample. Community

fingerprinting and cloning-sequencing techniques have

been widely used to study fungal diversity in a broad

variety of samples (Anderson & Cairney, 2004). However,

recent developments in high-throughput sequencing tech-

niques may be more useful to study high-diversity fungal

systems. Their application to microbial ecology allows the

recovery of a huge number of sequences from different

environmental samples at the same time, and in-depth

analysis of microbial diversity (Cardenas & Tiedje, 2008).

Initially applied to study bacterial diversity (Roesch et al.,

2007; Acosta-Mart�ınez et al., 2008), these new sequencing

technologies have recently been applied to fungal ecology

(Wallander et al., 2010; Blaalid et al., 2012).

Fungal diversity is studied through analysis of either

the small subunit (SSU, 18S rRNA gene) or the internal

transcribed spacer (ITS) region of the nuclear ribosomal

RNA. Specific primers for amplification of these two

regions have been developed (White et al., 1990; Gardes

& Bruns, 1993) and used to study fungal diversity in

complex substrates such as soil or plant tissue (Peltoni-

emi et al., 2009; Vega et al., 2010). However, the 18S

rRNA gene region does not evolve rapidly enough to

identify fungi at low taxonomic levels. The ITS region,

which shows a high rate of evolution resulting in greater

sequence variation between closely related species, is thus

used as a DNA barcode for fungal identification (Horton

& Bruns, 2001; K~oljalg et al., 2005; Schoch et al., 2012).

FEMS Microbiol Ecol && (2012) 1–11 ª 2012 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

MIC

ROBI

OLO

GY

EC

OLO

GY

https://www.researchgate.net/publication/224967821_Changes_in_the_root-associated_fungal_communities_along_a_primary_succession_gradient_analysed_by_454_pyrosequencing?el=1_x_8&enrichId=rgreq-b78aa509-430f-49d1-9eac-17868d85c896&enrichSource=Y292ZXJQYWdlOzIzMzc2NjgzNTtBUzo5OTMxNjQzMzY4NjU0MUAxNDAwNjkwMjkxNjEx

Moreover because the ITS region is multi-copy (Vilgalys

& Gonzalez, 1990), it allows amplification from samples

containing a low concentration of DNA. As a conse-

quence of its widespread use, many ITS sequences are

already available, providing a large reference database for

taxonomic identification (Nilsson et al., 2009) even if its

quality is critical to successful identification (Nilsson

et al., 2006; Bidartondo, 2008). Within the ITS region,

the ITS1 sublocus evolved slightly more rapidly with a

more variable length than the ITS2 sublocus, while the

5.8S fragment embedded by these two subloci is highly

conserved (Hillis & Dixon, 1991; Hershkovitz & Lewis,

1996). It has been shown that on average the variability

of ITS1 exceeds that of ITS2 (Nilsson et al., 2008), and

thus depending on which part of the ITS region is tar-

geted, the sequence analysis from fungal communities

may differ. Moreover, such differences may not always be

biologically relevant and could rely on methodological

biases specific to each ITS sublocus. This could be of con-

cern using pyrosequencing because, even if the new tech-

nology promises sequences up to 1000 bp in size, the

average maximum read length of the sequences obtained

until now is of 450 bp while the entire ITS region can

vary from 450 to 800 bp. Moreover, although Illumina/

Solexa technology, which allows cheaper and higher-

throughput sequencing, may be more frequently used in

fungal ecological studies in the future, it generates short

sequences that are unable to cover the entire ITS region.

As a consequence, most of the fungal diversity studies

using pyrosequencing technology have focused on either

ITS1 or ITS2. The ITS1F and ITS2 primers (White et al.,

1990; Gardes & Bruns, 1993) targeting the ITS1 region

were, for example, used to perform 454 high-throughput

pyrosequencing analyses of fungal communities in Quer-

cus macrocarpa phyllosphere and in other forest soils har-

bouring different plantations (Bu�ee et al., 2009;

Jumpponen & Jones, 2009). And the ITS2 region has

been used to analyse the vertical distribution of fungi in

prairie soil (Jumpponen et al., 2010) and to determine

the ectomycorrhizal diversity in spruce forest (Wallander

et al., 2010). In all these studies, the choice to focus on

either ITS1 or ITS2 regions seems to be more arbitrary

than based on their suitability to better investigate fungal

diversity in these particular environments. In dust sam-

ples spiked with known quantities and identities of fungi,

Amend et al. (2010) compared the pyrosequencing results

obtained from ITS1F and ITS4 primers (White et al.,

1990; Gardes & Bruns, 1993) and containing the ITS1 or

ITS2 sublocus, respectively. They concluded that the

sequencing orientation affected the operational taxonomic

unit (OTU) clustering for the species added. Nilsson et al.

(2009), using in silico analysis with the fungal sequences

annotated in the International Nucleotide Sequence

Databases, observed that fungal diversity determined by

analysing the whole ITS region or either the ITS1 or the

ITS2 sublocus may be different. These results underline

that comparison of fungal ecology studies using pyrose-

quencing technology are limited by the chosen sequencing

orientation. As proposed by Nilsson et al. (2009), there is

a need to standardize the targeted ITS sublocus in high-

throughput sequencing studies of fungal ecology.

The aim of the present study was to analyse and com-

pare the ITS1 and ITS2 datasets obtained using 454

pyrosequencing targeting ITS amplicons of fungal rDNA

extracted from different environmental samples (water,

sediments and soil). According to previous studies (Nils-

son et al., 2009; Amend et al., 2010; Orgiazzi et al.,

2012), we hypothesized that the two datasets generated

should be different. As the size of the ITS region is highly

variable among the fungal domain, we believed that the

minimum read length used to quality-check the sequences

should be an important factor influencing fungal taxo-

nomic identification. We first determined to what extent

the sequencing orientation (ITS1F – forward or ITS4 –reverse) and the minimum read length threshold influ-

enced the quantitative and qualitative yield of the

sequence analysis. We then compared the fungal diversity

detected by analysing the ITS1 and ITS2 subloci in differ-

ent soil samples.

Materials and methods

Environmental samples

Soil cores (22 cm depth 9 3 cm diameter) were sampled

in October 2009 at five different sites around Lake Erken

in Sweden (59°51′N, 18°36′E). The sites differed in their

vegetation and were named as ‘Dry hill’ (DH), ‘Conifer

forest’ (CF), ‘Flooded area’ (FL), ‘Shore’ (SH) and ‘Agri-

cultural soil’ (AS). For each site, three replicates were

sampled. According to the soil profile, each soil core was

subsampled either into: (1) a top (0–5 cm depth), a med-

ium (8–13 cm depth) and a lower (17–22 cm depth)

layer (DH, CF and FL-Top/-Med/-Low); and (2) a top

(0–5 cm depth) and a lower (17–22 cm depth) layer

(AS-Top/-Low). The FL-Low layer was visually a clay-rich

mineral soil. The SH cores were divided into five equal

layers as they were composed of a superposition of rich

organic layers (SH-1/-3/-5) and sandy layers (SH-2/-4).

Each subsample was homogenized by sieving (2-mm

mesh size).

Water samples were obtained from the lake side, and

from the surface (1 m depth) and at 10 m depth in the

middle of the lake. Three replicates of 500 mL of water

were immediately filtered through 0.22-lm pore-size

polycarbonate filters. Sediment samples (50 mL) were

ª 2012 Federation of European Microbiological Societies FEMS Microbiol Ecol && (2012) 1–11Published by Blackwell Publishing Ltd. All rights reserved

2 C. Monard et al.

sampled at the lake side and in the middle of the lake

(21 m depth) with three replications. All samples (soil

cores, filters and sediments) were stored at �20 °C for

subsequent molecular analysis. In summary, each site and

soil/water depth was replicated three times for subsequent

DNA extraction, amplification and sequencing as detailed

below.

DNA extraction

DNA from soil and pelleted sediments was extracted from

4 9 0.5 g extraction-replicates and DNA from water was

extracted by dividing the filters into four pieces and DNA

was independently extracted from each one. The Griffiths

protocol (Griffiths et al., 2000) was used with the follow-

ing modifications: (1) cell lysis was performed in the

presence of 0.5 g of 106-lm glass beads and two 2-mm

glass beads and using the Precellys 24® bead beating sys-

tem for 3 min with a freezing step in liquid nitrogen each

minute, (2) glycogen was added for the nucleic acids pre-

cipitation step performed overnight at �20 °C and (3)

nucleic acids were pelleted by centrifugation at 18 000 g

for 30 min at 4 °C and resuspended in DNase–RNase-free water. DNA quality and quantity were checked at

260 nm (NanoDrop Technologies). All four extraction-

replicates were pooled and stored at �70 °C.

PCR amplification and sequencing

The DNAs were diluted 100 and 1000 times and 4 lLwas used as template for PCR. The PCR mix consisted if

2.5 units of DreamTaq green DNA polymerase (Fermen-

tas), 1 9 PCR buffer supplied by the manufacturer,

1.6 mM MgCl2, 80 lM dNTP, 1.6 lg bovine serum albu-

min, 0.4 lM of each primer and H2O to a final volume

of 40 lL. Fungal ITS amplification was performed using

the ITS1F (Gardes & Bruns, 1993) and ITS4 (White et al.,

1990) primers containing a unique additional 6-bp bar-

code used to tag each PCR product according to the

original environmental sample (Supporting Information,

Table S1). Samples were initially denatured for 5 min at

94 °C, then amplified by using 26–31 cycles of 94 °C for

30 s, 55 °C for 30 s and 72 °C for 30 s. A final extension

of 7 min at 72 °C was added at the end of the pro-

gramme. The number of PCR cycles was determined

according to previous quantitative PCRs performed on

our DNA samples (data not shown). All diluted DNA

extracts were amplified in duplicate.

Each PCR sample was purified using the GeneJet puri-

fication kit (Fermentas) following the manufacturer’s

instructions and quantified using a Qubit Fluorometer

(Invitrogen) and an equal amount of DNA (25 ng) from

each sample and each DNA dilution was pooled. To

remove potential primer dimers, the pooled DNA was

finally gel purified using the Qiaquick gel extraction kit

(Qiagen). The final sample was sent to GATC Biotech for

the ligation of the 454-sequencing adaptors ‘A’ and ‘B’

and the sequencing from both ITS1F and ITS4 sides using

a 454 Genome Sequencer FLX (Roche) machine.

Processing of pyrosequencing data

Data were processed using the SCATA pipeline (http://

scata.mykopat.slu.se) looking for either the ITS1F or ITS4

primers. The quality check was performed using different

minimum read lengths varying from 100 to 250 bp.

Sequences missing valid primer sequence or DNA Tag

and sequences with low quality (average read quality

below 20) were removed. Homopolymers of > 3 bp were

collapsed. OTUs were defined at the 98.5% similarity level

(over at least 90% of the alignment length) using single

linkage clustering. The 98.5% sequence similarity level

was chosen according to Wallander et al. (2010) and to

the study performed by Blaalid et al. (2012) who showed

that below 99% similarity the number of non-singleton

OTUs does not change significantly. All singletons were

removed because they have been shown to contain a high

number of technical artefacts (Unterseher et al., 2011).

OTUs were taxonomically identified from their most

abundant sequence using NCBI-BLASTn and the Gen-

Bank database (Altschul et al., 1997). The ITS1 and ITS2

subloci were compared based on their closest NCBI data-

base match and after extraction with the ITS extractor

tool (Nilsson et al., 2010) using the same criterion as

those of the single linkage clustering.

Rarefaction curves were generated by aRarefactWin

(Analytic Rarefaction 1.3, http://strata.uga.edu/software/

index.html). Diversity in the different datasets and sam-

ples was estimated using the Shannon’s diversity index H′and the species richness S was expressed as the number

of OTUs. The similarity between replicates, depths and

sites was measured using the Sørensen index (Legendre &

Legendre, 1998).

Statistical analysis

The nonparametric Mann–Whitney test was used to com-

pare the ITS1 and ITS2 datasets (number of sequences

and OTUs for the different minimum read lengths) as

they presented fewer than 30 values. Non-parametric

MANOVAs (Anderson, 2001), performed with the PRIMER 6

software, were used to determine the impact of site, depth

and ITS dataset on H′ and S. Sørensen indexes were anal-

ysed with one-way ANOVA using MINITAB software (version

16). Detrented correspondence analysis (DCA) was per-

formed to analyse the fungal community structure in the


ITS1 vs. ITS2 pyrosequencing 3

soil layers of the different sites (CANOCO software, Wagen-

ingen, the Netherlands). The Kendall tau rank correlation

test was performed using SAS (SAS Inc., Cary, NC).

Results

Total data analysis

According to the pyrosequencing strategy performed

herein, two datasets (ITS1 and ITS2) were generated for

each minimum read length tested (from 100 to 250 bp),

corresponding to the ITS1F and ITS4 sequencing orienta-

tion. By using 100–250 bp as minimum read length

threshold, 190 102 (4542 OTUs) to 67 246 sequences

(1563 OTUs) were retained in the ITS1 dataset and

194 711 (4671 OTUs) to 39 124 sequences (1239 OTUs)

were obtained in the ITS2 dataset. Both the number of

sequences and the number of OTUs decreased when the

minimum read length increased, but no significant differ-

ences were observed between the ITS1 and the ITS2 data-

sets (Mann–Whitney: W = 284, P = 0.462; W = 276,

P = 0.665, respectively). Significant lower diversity was

observed in the ITS2 dataset as Shannon’s diversity index

(H′) varied from 5.43 to 5.03 when the minimum read

length increased from 100 to 250 bp compared to a varia-

tion from 5.41 to 5.20 in the ITS1 dataset (Mann–Whit-

ney: W = 336, P < 0.01). The rarefaction curves for the

two datasets always reached saturation even at the highest

minimum read length tested, indicating that the sampling

coverage as well as the sequencing effort were high

enough (Fig. 1).

Dominant OTU identification in the total

dataset

The ranks of the 20 dominant OTUs in the ITS1 and

ITS2 datasets obtained using 100–250 bp as minimum

read lengths for quality check were compared (Table 1).

Four of these dominant OTUs (Inocybe sp., Russulaceae,

Penicillium sp. and Geomyces sp.) were specific to the

ITS1 dataset (among the 20 dominant ones; Table 1) and

eight (unknown fungus, Trichosporon sp., Suillus luteus,

Hydnum sp., Neonectria sp., Argyrodiaptomus furcatus,

Cercophora sp. and Inocybe sp.) were specific to the ITS2

dataset (among the 20 dominant ones; Table 1). By tak-

ing into account all the minimum read lengths, an aver-

age of 16.6 of the 20 dominant OTUs were detected in

both ITS1 and ITS2 datasets. The OTUs identified as

Trichosporon sp. and Preussia sp. were detected in the two

datasets as either the first or the second most represented

OTU regardless of the minimum read length considered

(Table 1). We observed that when the minimum read

length increased, some OTUs were more represented (e.g.

Pseudeurotium sp. in the ITS1 dataset, Pilidium concavum

in the ITS2 dataset). The opposite was also observed with

a decrease of some OTU abundances when the minimum

read length increased (e.g. Mortierella sp. in the two data-

sets; Table 1). However, the rank of the 20 dominant

OTUs at the different minimum read lengths obtained

from the ITS1 and the ITS2 datasets were not indepen-

dent (Kendall tau rank correlation, τ = 0.685,

P < 0.0001) meaning that there was no significant effect

of the ITS sublocus.

Comparing ITS1 and ITS2 analysis in a fungal

diversity study of soil samples

For the soil samples (AS, DH, CF, FL and SH) we com-

pared the sequencing results using OTUs detected in the

three core replicates and using minimum read lengths of

100 and 250 bp. From the 504 unique OTUs detected in

the four different datasets (ITS1-100 bp; ITS1-250 bp;

ITS2-100 bp; ITS2-250 bp), 45.6% of them were only

detected at the lowest read length. By using this thresh-

old, it appeared that the pipeline clustered two OTUs as

different while they were actually identical. As this was

not observed when the minimum read length was

increased up to 250 bp, we focussed on the effect of the

sequencing orientation in the datasets obtained using

250 bp as a minimum read length.

By compiling the ITS1 and ITS2 datasets, 272 unique

OTUs were defined, among which 111 were specific to

the ITS1 dataset, 67 were specific to the ITS2 dataset and

94 were common to both (GenBank accession numbers

JX915257–JX915623). Among these 94 common OTUs,

72.5% were detected at the same soil site and depth in

both datasets. Only one OTU did not correspond to fun-

gal DNA. Significant numbers of OTUs (37.6% and

35.4% in the ITS1 and ITS2 datasets, respectively)

lacked any close sequence information in the database for

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 50 000 100 000 150 000 200 000

OTU

s

Sequences

250bp

200bp

150bp

100bp

Fig. 1. Rarefaction curves for the datasets obtained from ITS1F (line)

and ITS4 (dots) primers using 100, 150, 200 or 250 bp as minimum

read length for the quality trimming of the sequences.


4 C. Monard et al.

Table

1.Ran

kofthe20dominan

tOTU

sdetectedin

theITS1

andITS2

datasetsbyincreasingtheminim

um

read

length

ofthesequen

cequalitycheck



Table

1.Continued


6 C. Monard et al.

identification. The different number of reads obtained

among samples reflected the variations in 454-sequencing

performance and was not related to the amount of DNA

extracted from soil (Table 2).

The proportions of Ascomycota and Basidiomycota

detected in the ITS1 and ITS2 datasets were 62.1% and

19.9%, and 67.1% and 15.5%, respectively. Mucoromycoti-

na were represented by 4.9% and 3.7% of the sequences

in the ITS1 and ITS2 datasets, respectively, and Chytridio-

mycotina by 3.4% and 1.9%. In the ITS1 dataset, Tricho-

sporon sp. (ITS1_111) was dominant (11.4% of the ITS1

sequences) while the ITS2 dataset was dominated by

Davidiella sp. (ITS_192) (7.5% of the ITS2 sequences).

The AS and DH sites were dominated by the same OTU

in both ITS1 and ITS2 datasets [Phoma sp. (ITS_250)

and Preussia sp. (ITS_184), respectively; Table 2].

The total diversity expressed as Shannon’s diversity

index (H′) was similar in the two datasets (4.06 for ITS1

and 3.95 for ITS2). When comparing the different soil

samples (site and depth), no significant effect of the ITS

sublocus on H′ was observed (NPMANOVA: F = 1.46,

P = 0.2646; Tables 3 and 4). However, differences in H′between the two datasets became obvious for specific

samples (AS-Top, CF-Low and SH, Table 3) and, for

example, the negative depth impact on fungal diversity

was not observed for the AF samples in the ITS2 dataset

(Table 3). Species richness (S) was significantly different

between the ITS1 and ITS2 datasets (NPMANOVA: F = 3.81,

P = 0. 019; Tables 3 and 4). Considering the different soil

samples (site and depth), S was higher in the ITS1 dataset

(up to 2.8 times higher) except in DH-Top and FL-Top

(Table 3). However, compared with the impact of the

sampling site and depth on fungal diversity and species

richness (NPMANOVA for H′: F = 4.55, P < 0.001; F = 1.84,

P = 0.0114, respectively; NPMANOVA for S: F = 17.91,

P < 0.001; F = 4.59, P < 0.001, respectively; Table 4), the

impact of the ITS sublocus was lower, even if significant

for S.

Sørensen similarities of fungal OTUs between layers,

soil cores and sites were calculated from the total ITS1

and ITS2 datasets (Fig. S1). No differences were observed

between the two datasets (ANOVA: F = 2.52, P = 0.11),

and, in both of them, we observed the same tendencies

with a significantly higher variation of diversity between

layers than between soil cores (ANOVA: F = 14.76,

P < 0.001; F = 15.94, P < 0.001, in ITS1 and ITS2 data-

sets, respectively).

The structures of the fungal community were anal-

ysed using DCA (Fig. 2). They were similar in the

two datasets except for the SH3 sample (Fig. 2a). The

fungal communities recovered in this soil layer from

the ITS2 dataset were dominated by one OTU

(Tetracladium sp., ITS2_171) which was highly repre-

sented (1.7% of the total sequences) and exclusively

detected in this sample. When performing the DCA

excluding this sample, the fungal communities

obtained from the two datasets tended to group

together (Fig. 2b).

Table 2. Number of sequences and identification of the dominant OTU (closest NCBI database match) and its proportion in each soil site (AS,

DH, CF, FL, SH, ‘Shore’) for the ITS1 and the ITS2 datasets

AS DH CF FL SH

ITS1 Number of total sequences 1036 5978 12922 7223 1730

Main OTU Phoma sp. Preussia sp. Trichosporon sp. Pilidium sp. Phoma sp.

NCBI accession number AJ890436 GU062204 JF519094 AY487097 AJ890436

Proportion in the site (% of sequences) 21.4 27.7 19.7 7.1 20.6

ITS2 Number of total sequences 444 3450 4344 4687 1588

Main OTU Phoma sp. Preussia sp. Geomyces sp. Davidiella sp. Tetracladium sp.

NCBI accession number AJ890436 GU062204 JF439476 HM136631 GU055705

Proportion in the site (% of sequences) 33.8 22.4 58.6 15.0 46.7

Table 3. Species richness expressed as the number of OTUs (S) and

Shannon’s diversity index (H′) in the different soil samples (AS, DH,

CF, FL, SH, ‘Shore’) and the different depths calculated from the two

ITS1 and ITS2 datasets and S common to the two datasets

Samples S ITS1 S ITS1 & S ITS2 S ITS2 H′ ITS1 H′ ITS2

AS-Top 24 4 6 3.02 1.73

AS-Low 6 6 6 2.14 2.16

CF-Top 46 38 21 3.50 3.36

CF-Med 42 30 16 3.04 3.15

CF-Low 22 9 3 2.79 1.71

DH-Top 23 33 30 2.79 3.13

DH-Med 29 11 8 2.71 2.76

DH-Low 8 3 2 1.17 0.84

FL-Top 33 27 41 3.55 3.81

FL-Med 22 7 8 2.78 2.27

FL-Low 8 10 5 2.14 1.47

SH-1 6 1 2 0.96 0.18

SH-2 2 1 0 0.61 0.00

SH-3 2 1 2 1.05 0.29

SH-4 2 2 2 0.90 1.22

SH-5 11 4 3 2.25 1.73



Discussion

Fungal diversity analysis through the use of high-through-

put sequencing techniques is becoming more common

but most datasets now generated allow only short-length

sequences of the ITS region. Thus, at present, one has to

choose to focus on either the ITS1 or the ITS2 sublocus.

It has been shown in silico that fungal taxonomic identifi-

cation can differ depending on the ITS sublocus analysed

(Nilsson et al., 2009) and recently Orgiazzi et al. (2012)

proposed to independently target both ITS1 and ITS2

subloci to assess fungal biodiversity in environmental

samples. In the present study, we aimed to determine to

what extent the 454 pyrosequencing orientation of ITS

amplicons influenced both dataset recovery (quantitatively

and qualitatively) and detected fungal diversity in differ-

ent environmental samples. Moreover, because the ITS

sequences would be more or less informative for further

taxonomic identification depending on their length, we

chose to compare different minimum read lengths as

thresholds during the sequence quality check on a scale

varying from 100 to 250 bp. The choice of the minimum

read length is crucial as the reads should be long enough

to allow fungal identification at the genus level but not

too demanding in order to obtain enough sequences for

ecological analyses. Bu�ee et al. (2009) reported that a

mean read length of 252 bp for the ITS1 sublocus was

long enough and sufficiently polymorphic for fungal

identification. In the present study we examined the effect

of minimum read length on the 20 dominant OTUs in

the total dataset and observed that the rank of the five

most abundant OTUs was not affected. However, even if

no significant differences were observed, it seems that, by

increasing the minimum read length, the ranking tended

to be more accurate and stable, particularly in the ITS1

dataset, probably due to higher identification precision.

Moreover, the clustering process was not optimal when

the minimum read length was too low because the

sequences were not polymorphic enough. By choosing

250 bp for further analyses, we avoided such biases in

comparing ITS1 and ITS2 sequencing orientations and

considered that, even if some sequences were missed, the

information would be more robust. The same strategy

was followed when focusing on the OTUs present in the

three field replicates.

Our results showed that sequencing orientation influ-

enced fungal taxonomic identification in the different soil

samples (site and depth) even if the total dataset and the

entire fungal community structure were not deeply

affected. In the total dataset, the numbers of sequences

and OTUs were always lower in the ITS2 dataset but the

sampling and sequencing efforts were always high enough

to cover the entire fungal diversity found in such

environmental samples, so this should not impact fur-

4–1

5–1

5–1

4–1

ITS1 ITS2AS-TopDH-TopCF-Top

SH1-3-5FL-Top

AS-LowDH-LowCF-Low

SH-2-4

FL-Low

DH-MedCF-MedFL-Med

12.7%

7.3%

12.1%

8.1%

(a) (b)

Fig. 2. DCA of the fungal communities in the

ITS1 and ITS2 datasets before (a) and after (b)

removal of the SH3 sample of the ITS2

dataset.

Table 4. Non-parametric MANOVA on Bray–Curtis distances for Shannon’s

diversity index (a) and species richness (b) in the five sites at the different

depths and in the ITS1 and ITS2 datasets

Source df SS MS F P

(a)

Site 4 11 270 2818 4.55 0.0008

Depth (site) 11 12 557 1142 1.84 0.0114

ITS 1 902 902 1.46 0.2646

Residual 15 9288 619

Total 31 34 018

(b)

Site 4 27 144 6786 17.91 0.0002

Depth (site) 11 19 117 1738 4.59 0.0002

ITS 1 1442 1442 3.81 0.019

Residual 15 5683 379

Total 31 53 387


8 C. Monard et al.

ther analyses. From the overall (total dataset – all the

minimum read lengths) to the specific (soil samples –250 bp) analysis, the mean fungal diversity in the ITS1

dataset was always higher than in the ITS2 dataset, in

accordance with the average higher sequence variation in

ITS1 than in ITS2 (Nilsson et al., 2008). Moreover, the

presence of a group I intron at the 3′ extremity of the

18S rRNA gene (Hibbett, 1996) and thus amplified by the

ITS1F primer may explain the higher fungal diversity

observed in the ITS1 dataset. As observed by Bellemain

et al. (2010), the ITS1F primer can generate a high pro-

portion of mismatches, which may also increase the

observed fungal diversity in the ITS1 dataset.

The higher variability of the ITS1 sublocus combined

with the higher variability of the Basidiomycota ITS

region compared with Ascomycota (Nilsson et al., 2008)

may explain the highest abundance of Basidiomycota

detected in the ITS1 dataset. Thus, for a fixed similarity

level for OTU determination (98.5% in our case), the

ITS1 sublocus would be more informative and precise in

the taxonomic identification towards Basidiomycota than

Ascomycota. With the ITS2 sublocus being shorter within

the Ascomycota than the Basidiomycota (Bellemain et al.,

2010), this should have led to higher hits for Ascomycota

in the ITS2 dataset than in the ITS1 dataset. These obser-

vations indicate that the analyses of both ITS1 and ITS2

sequences are complementary.

The complementarity of the ITS1 and ITS2 datasets in

fungal diversity analysis was also observed in the soil

samples although the diversity indexes were not signifi-

cantly different. The significant impact of the ITS dataset

on the species richness as well as the fact that only one-

third of the OTUs was detected in both ITS1 and ITS2

datasets reflected differences in the fungal diversity

observed in the two datasets. Additional to biological

variations, this un-matching between the two datasets

may be due to methodological biases such as (1) a greater

amount of short sequences removed in the ITS2 dataset

than in the ITS1 dataset, (2) a different taxonomic affilia-

tion of the sequences depending on the sublocus used as

BLASTn query as observed by Nilsson et al. (2009) and/

or (3) selectivity in how a primer amplifies the true com-

munity, which can be addressed partly by using different

sets of primers (Ihrmark et al., 2012; Toju et al., 2012).

These differences between the two datasets led to the

observation of a different fungal diversity in the soil sam-

ples. Only two soil sites (AS and DH) were dominated by

the same OTU in the two datasets. Moreover, it may

induce contradictory ecological conclusions such as the

depletion of fungal diversity with depth, as observed by

O’Brien et al. (2005), which was not observed in the AF

samples when using the ITS2 sublocus for analysis. How-

ever, even if significant, the impact of the ITS dataset on

the species richness was lower than that of site and depth

(lower mean square values) and, when agreements were

observed between the two datasets, the common OTUs

were frequently detected in exactly the same sample

(same site and depth) and their abundance changed with

depth in the same way. Analysis of either the ITS1 or the

ITS2 dataset independently allowed us to observe (1) the

same spatial diversification through similar fungal com-

munity structures, (2) the same specific diversity in the

highly selective mineral soils (FL-Low, SH-2 and SH-4,

dominated by two species: Davidiella sp. and Pilidium

sp.), (3) the highest fungal diversity in the CF and in the

FL and (4) the same variation of diversity between layers,

soil cores and sites.

Conclusion

Given the results of the present study, the choice of ITS

sequencing orientation would depend on the depth of the

final analysis because at the fungal community level, both

ITS1 and ITS2 datasets presented similar results while at

the species/genus level only one-third of the OTUs were

common to both datasets. When considering comparison

studies of the fungal diversity in different environmental

systems, the analysis of one of the two ITS subloci should

be adequate because the impact of the environmental

factors was higher then that of the ITS dataset analysed.

As we found higher diversity and greater number of

sequences in the ITS1 dataset, it appeared to be a better

choice for sequencing. However, given that the variabil-

ity among the two ITS subloci depends on the fungal

species (Nilsson et al., 2008) and according to the huge

fungal diversity we observed, the ITS1 and ITS2 datasets

appear to be complementary and, as proposed by Orgi-

azzi et al. (2012), analysis of both subloci in parallel

would give the best vision of fungal diversity in environ-

mental samples.

References

Acosta-Mart�ınez V, Dowd S, Sun Y & Allen V (2008) Tag-

encoded pyrosequencing analysis of bacterial diversity in a

single soil type as affected by management and land use.

Soil Biol Biochem 40: 2762–2770.Altschul SF, Madden TL, Sch€affer AA, Zhang J, Zhang Z,

Miller W & Lipman DJ (1997) Gapped BLAST and

PSI-BLAST: a new generation of protein database search

programs. Nucleic Acids Res 25: 3389–3402.Amend AS, Seifert KA & Bruns TD (2010) Quantifying

microbial communities with 454 pyrosequencing: does read

abundance count? Mol Ecol 19: 5555–5565.Anderson MJ (2001) A new method for non-parametric

multivariate analysis of variance. Austral Ecol 26: 32–46.



Anderson IC & Cairney JWG (2004) Diversity and ecology of

soil fungal communities: increased understanding through

the application of molecular techniques. Environ Microbiol

6: 769–779.Bellemain E, Carlsen T, Brochmann C, Coissac E, Taberlet P &

Kauserud H (2010) ITS as an environmental DNA barcode

for fungi: an in silico approach reveals potential PCR biases.

BMC Microbiol 10: 189.

Bidartondo MI (2008) Preserving accuracy in GenBank. Science

319: 1616.

Blaalid R, Carlsen TOR, Kumar S, Halvorsen R, Ugland KI,

Fontana G & Kauserud H (2012) Changes in the root-

associated fungal communities along a primary succession

gradient analysed by 454 pyrosequencing. Mol Ecol 21:

1897–1908.Bu�ee M, Reich M, Murat C, Morin E, Nilsson RH, Uroz S &

Martin F (2009) 454 Pyrosequencing analyses of forest soils

reveal an unexpectedly high fungal diversity. New Phytol

184: 449–456.Cardenas E & Tiedje JM (2008) New tools for discovering and

characterizing microbial diversity. Curr Opin Biotechol 19:

544–549.Gardes M & Bruns TD (1993) ITS primers with enhanced

specificity for Basidiomycetes – application to the identification

of mycorrhizae and rusts.Mol Ecol 2: 113–118.Griffiths RI, Whiteley AS, O’Donnell AG & Bailey MJ (2000)

Rapid method for coextraction of DNA and RNA from

natural environments for analysis of ribosomal DNA- and

rRNA-based microbial community composition. Appl

Environ Microbiol 66: 5488–5491.Hershkovitz MA & Lewis LA (1996) Deep-level diagnostic

value of the rDNA-ITS region. Mol Biol Evol 13: 1276–1295.Hibbett DS (1996) Phylogenetic evidence for horizontal

transmission of group I introns in the nuclear ribosomal DNA

of mushroom-forming fungi.Mol Biol Evol 13: 903–917.Hillis DM & Dixon MT (1991) Ribosomal DNA: molecular

evolution and phylogenetic inference. Q Rev Biol 66:

411–453.Horton TR & Bruns TD (2001) The molecular revolution in

ectomycorrhizal ecology: peeking into the black-box. Mol

Ecol 10: 1855–1871.Ihrmark K, B€odeker ITM, Cruz-Martinez K et al. (2012) New

primers to amplify the fungal ITS2 region – evaluation by

454-sequencing of artificial and natural communities. FEMS

Microbiol Ecol 82: 666–677.Jumpponen A & Jones KL (2009) Massively parallel 454

sequencing indicates hyperdiverse fungal communities in

temperate Quercus macrocarpa phyllosphere. New Phytol

184: 438–448.Jumpponen A, Jones KL & Blair J (2010) Vertical distribution

of fungal communities in tallgrass prairie soil. Mycologia

102: 1027–1041.K~oljalg U, Larsson K-H, Abarenkov K et al. (2005) UNITE: a

database providing web-based methods for the molecular

identification of ectomycorrhizal fungi. New Phytol 166:

1063–1068.

Legendre P & Legendre L (1998) Numerical Ecology. Elsevier,

BV, Amsterdam.

Nilsson RH, Ryberg M, Kristiansson E, Abarenkov K, Larsson

K-H & K~oljalg U (2006) Taxonomic reliability of DNA

sequences in public sequence databases: a fungal perspective.

PLoS ONE 1: e59.

Nilsson RH, Kristiansson E, Ryberg M, Hallenberg N &

Larsson K-H (2008) Intraspecific ITS variability in the

kingdom fungi as expressed in the international sequence

databases and its implications for molecular species

identification. Evol Bioinform Online 4: 193–201.Nilsson RH, Ryberg M, Abarenkov K, Sj€okvist E & Kristiansson

E (2009) The ITS region as a target for characterization of

fungal communities using emerging sequencing technologies.

FEMS Microbiol Lett 296: 97–101.Nilsson RH, Veldre V, Hartmann M, Unterseher M, Amend A,

Bergsten J, Kristiansson E, Ryberg M, Jumpponen A &

Abarenkov K (2010) An open source software package for

automated extraction of ITS1 and ITS2 from fungal ITS

sequences for use in high-throughput community assays and

molecular ecology. Fungal Ecol 3: 284–287.O’Brien HE, Parrent JL, Jackson JA, Moncalvo J-M & Vilgalys R

(2005) Fungal community analysis by large-scale sequencing of

environmental samples. Appl Environ Microbiol 71: 5544–5550.Orgiazzi A, Lumini E, Nilsson RH, Girlanda M, Vizzini A,

Bonfante P & Bianciotto V (2012) Unravelling soil fungal

communities from different Mediterranean land-use

backgrounds. PLoS ONE 7: e34847.

Peltoniemi K, Fritze H & Laiho R (2009) Response of fungal

and actinobacterial communities to water-level drawdown in

boreal peatland sites. Soil Biol Biochem 41: 1902–1914.Roesch LFW, Fulthorpe RR, Riva A, Casella G, Hadwin AKM,

Kent AD, Daroub SH, Camargo FAO, Farmerie WG &

Triplett EW (2007) Pyrosequencing enumerates and

contrasts soil microbial diversity. ISME J 1: 283–290.Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL,

Levesque CA, Chen W, Consortium FB (2012) Nuclear

ribosomal internal transcribed spacer (ITS) region as a

universal DNA barcode marker for Fungi. P Natl Acad Sci

USA 109: 6241–6246.Toju H, Tanabe AS, Yamamoto S & Sato H (2012) High-

coverage ITS primers for the DNA-based identification of

Ascomycetes and Basidiomycetes in environmental samples.

PLoS ONE 7: e40863.

Unterseher M, Jumpponen ARI, €OPik M, Tedersoo L, Moora

M, Dormann CF & Schnittler M (2011) Species abundance

distributions and richness estimations in fungal

metagenomics – lessons learned from community ecology.

Mol Ecol 20: 275–285.Vega FE, Simpkins A, Aime MC, Posada F, Peterson SW,

Rehner SA, Infante F, Castillo A & Arnold AE (2010)

Fungal endophyte diversity in coffee plants from Colombia,

Hawaii, Mexico and Puerto Rico. Fungal Ecol 3: 122–138.Vilgalys R & Gonzalez D (1990) Organization of ribosomal

DNA in the basidiomycete Thanatephorus praticola. Curr

Genet 18: 277–280.


10 C. Monard et al.

Wallander H, Johansson U, Sterkenburg E, Brandstr€om

Durling M & Lindahl BD (2010) Production of

ectomycorrhizal mycelium peaks during canopy closure in

Norway spruce forests. New Phytol 187: 1124–1134.White TJ, Bruns TD, Lee SB & Taylor JW (1990) Amplification

and direct sequencing of fungal ribosomal RNA genes for

phylogenetics. PCR Protocols–A Guide to Methods and

Applications (Innis MA, Gelfand DH, Sninsky JJ & White TJ,

eds), pp. 315–322. Academic Press, San Diego, CA.

Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Fig. S1. Similarity of fungal OTUs (Sørensen similarity

values) between layers (within one soil core), between soil

cores (within site) and between sites for the ITS1 (filled

circles) and ITS2 (open triangles) datasets.

Table S1. List of the barcodes used with the ITS1F and

ITS4 primers according to the different soil samples.

Table S2. Number of sequences in the ITS1 and ITS2

databases obtained for each soil sample using 100 and

250 bp as minimum read length.

Table S3. Identification of the fungal OTUs from the

ITS1 dataset, their abundance and distribution among the

AF, CF, DH, FL and SH sites at the different depths and

their proportion among the all dataset (ITS1 and ITS2)

and among the ITS1 dataset.

Table S4. Identification of the fungal OTUs from the

ITS2 dataset, their abundance and distribution among the

AF, CF, DH, FL and SH sites at the different depths and

their proportion among the all dataset (ITS1 and ITS2)

and among the ITS2 dataset.



Documents

Utilizing ITS1 and ITS2 to study environmental fungal diversity using pyrosequencing