Abstract (English):
Introduction. Barley can be infected with a broad variety of fungi, which can cause considerable loss of crop yield and reduce the quality of grain. Modern vision on the geographical and ecological distribution and biodiversity of micromycetes has been established by traditional, cultivation-based methods. However, more recently, molecular methods have shifted microbiological research to a new level, making it possible to investigate hidden taxonomical biodiversity. Study objects and methods. For this study, we determined the fungal biome on the surface and inside of barley grains using the traditional mycological method and the contemporary molecular method, which employed DNA metabarcoding based on NGS (nextgeneration sequencing) of the ITS2 region. We analyzed five cultivars that were collected in two subsequent crop seasons (2014, 2015). Results and discussion. DNA metabarcoding revealed 43 operational taxonomic units, while 17 taxa of genus or species level were recovered by the traditional method. DNA metabarcoding revealed several minor species and one predominant, presumably plantpathogenic Phaeosphaeria sp., which were not detected in the agar plate-based assay. Traditionally, Fusarium fungi were identified by mycological assay. However, the resolution of DNA metabarcoding was sufficient to determine main Fusarium groups divided by ability to produce toxic secondary metabolites. The combined list of Ascomycetes consisted of 15 genera, including 14 fungi identified to species level. The list of Basidiomycota derived from DNA metabarcoding data alone included 8 genera. Conclusion. It was found that crop season predetermines the fungal community structure; mycobiota on the surface and inside of grain was significantly different.

Keywords:
Barley, seed-borne fungi, infection, next-generation sequencing, rDNA, Alternaria, Fusarium

INTRODUCTION
Barley (Hordeum vulgare L.) is one of the major
cereal crops. It occupies fourth place among cereals
in the world and second place in Russia by production
quantity and cultivation area [1]. The importance of
barley has been accepted since ancient times and used
in the food, feed and brewing industries due to its
versatility, excellent adaptation capabilities and superior
properties [2].
The increased interest in barley as a source of food
and fodder has resulted in a huge number of studies of
associated microorganisms. It is known that barley can
be infected with a broad variety of plant-pathogenic and
toxigenic fungi, many of which may persist in grains.
The Bipolaris, Pyrenophora, Phaeosphaeria, Alternaria,
and Fusarium genera are considered to be prevailing
fungi in barley grain worldwide [3, 4]. Species of the last
two genera are well known as mycotoxin producers, with
Fusarium spp. being the most dangerous food and feed
contaminants.
Cultivation-based methods have traditionally
established modern vision on geographical and
ecological distribution and biodiversity of micromycetes.
These methods cannot provide accurate data on taxon
composition because some microorganisms do not have
specific characteristics to be identified, and some appear
to be noncultivable. Thus, data on the mycobiome of
many substrates, including barley grain, is likely to be
incomplete.

In recent decades, molecular methods have shifted
microbiological research to a new level, making it
possible to investigate hidden taxonomical biodiversity.
Next-generation sequencing (NGS), implemented
on various independent technical platforms, became
the most promising method for conducting research
projects aimed at revealing fungal or bacterial
composition [5–7]. Several studies have focused on a
variety of agricultural subjects [8–12]. Advances in this
field led to consideration that NGS-based methods are
suitable as incipient techniques for seed testing [13].
In Denmark, 454 pyrosequencing of the
internal transcribed spacer 1 (ITS1) of the nuclear
ribosomal DNA (rDNA) has been chosen to recover
the composition of fungal communities associated
with wheat grain [14]. NGS revealed a significantly
higher level of biodiversity than it was observed
in previous culturing studies. Another appropriate
454 pyrosequencing of both ITS regions was done
to study the mycobiome of barley grain in western
Canada [3]. It demonstrated that geographic location
and agronomical practices were the determining factors
explaining the observed differences in the fungal
communities associated with barley. Such studies may
contribute to a better understanding of fungal species
compositions in cereals. They may also lead to more
accurate food-quality testing and the precise design of
crop protection strategies that would reduce the level of
fungal contamination of agricultural products.
The objective of this study was to revise the
taxonomical variety of fungi contaminated the surface
and infected barley grains harvested in the northwestern
region of Russia. We hypothesized that grain mycobiome
could be significantly differ on the surface and inside
the grain, and the difference may depend on the crop
year. In our research we used the traditional agar platebased
method and the contemporary method based on
454 pyrosequencing of ITS2.
STUDY OBJECTS AND METHODS
Sampling. Grain samples of five spring barley cultivars
(Suzdalets, Krinichnyy, Moskovskiy 86, Tatum,
and Belgorodskiy 110) were received in 2014 and
2015 from the State Experimental Station (Volosovo,
Leningradskaya oblast, Russia, 59°31’N, 29°28’E).
Small grain cereals on this station were cultivated with
no fungicide treatments. The grain samples intended
for fungi isolation and DNA extraction were stored
separately at 4°C and –20°C respectively.
Extraction of DNA. Two representative subsamples
consisting of 500 grains were picked from each sample.
One subsample was placed in a 50 mL polypropylene
tube and subjected to superficial sterilization. The grains
were consistently washed with 20 mL of 2% sodium
hypochlorite solution containing 0.1% of sodium dodecyl
sulphate (SDS). They were then washed once with 5%
sodium hypochlorite, two times with deionized water
(diH2O), and finally rinsed with 98% ethanol. With
each step the mixtures were actively stirred up within
2 min, and the flushing solution was then decanted
without the grain. The ethanol was removed by burning
at regular stirring during 10 s. After this, the grains
were homogenized in sterile disposable chambers on a
Tube Mill Control (IKA, Germany) grinder. The other
subsample was similarly homogenized, but the step of
superficial sterilization was skipped.
Further, 240 mg of the ground subsamples were
transferred to a 2 mL Eppendorf tube, where DNA
was extracted with an AxyPrep Multisource Genomic
DNA Miniprep Kit (Axygen, USA), according to
the centrifugal protocol for plant tissues and fungal
mycelium. The DNA concentrations were measured
with a Qubit 2.0 (Thermo Fisher Scientific, USA)
using a dsDNA HS Assay Kit. Extracted DNA was
used for library preparation and subsequent universal
tailed amplicon sequencing, as described for the
454 Sequencing System.
Pyrosequencing and primary data analyses.
For amplicon library preparation we chose the
taxonomically significant ITS2 region, which
is commonly used, as well as ITS1, in DNA
metabarcoding studies of fungal diversity. To a
large extent, ITS1 and ITS2 have similar results
when used as DNA metabarcodes for fungi [15–17].
However, the ITS2 region lacks the insertions commonly
found in ITS1 and thus reduces length variation [18].
This is important, as length variation can bias
community pyrosequencing toward shorter amplicons.
Also, ITS2 is the best-represented fungal genomic
element in the public databases [19, 20]. Therefore, in
studies similar to our project, use of ITS1 obtained with
fungal specific primer (ITS1F) [21] can be helpful in
eliminating plant ITS amplification, and may turn out
to be the method of choice in cases of mixed plant and
fungal genomic DNA. However, it is necessary to take
into account that ITS1F (with constricted, specific range
toward exclusion of all eukarya except fungal taxa), may
not be able to amplify several fungal taxa because it is
hampered with a high degree of mismatches relative to
the target sequences [22, 23].
The ITS2 region was amplified with
eukaryote-specific ITS3 and ITS4 primers
(ITS3: GCATCGATGAAGAACGCAGC; ITS4:
TCCTCCGCTTATTGATATGC) [22, 24]. Multiplex
identifiers (MIDs) were attached to the primers’ ends to
carry out in consequence the simultaneous analysis of all
samples.
The amplicon library pool was sequenced with
454 pyrosequencing on the GS Junior sequencer
(Roche, USA) according to the recommendations of
the manufacturer [25]. The ITS2 locus reads were
processed by QIIME Version 1.6.0 (Quantitative Insights
into Microbial Ecology) [26]. To reduce the amount
of erroneous sequences and thus increase the accuracy of the whole pipeline, the denoising procedure was
employed [27].
Next steps included assigning multiplexed reads to
samples based on their specific MIDs (demultiplexing),
removing the low-quality or ambiguous reads,
truncating primers, and other accessorial sequences.
Chimeric sequences were detected using the UCHIME
algorithm with the Unite database [28–30]. All of the
reads were clustered into operational taxonomic units
(OTUs) at 97% sequence similarity using the UCLUST
method [31]. Representative sequences were chosen
according to their abundance between similar reads.
Low-abundance OTUs, which have less than four copies
(singletons, doubletons and tripletons), were deleted
over all of the analysis [32]. All 454 pyrosequencing
data of the present investigation are available through
the Sequence Read Archive (SRA) under BioProject
PRJNA353503, with run accession numbers from
SRR5022991 to SRR5023010 [33].
Phylogenetic and statistical analyses. Taxonomical
identification of representative sequences was carried
out by the BLAST method using Genbank databases [34,
35]. Query coverage ≥ 99% was recognized as
significant. Query identity of ≥ 99% was considered
identification at the species level; identity of ≥ 98–95%
was considered reliable identification at the genus level.
The smaller similarity Ribosomal Database Project
classifier, along with the Unite database (minimum
confidence at 0.9), were implemented to assign OTUs to
a higher taxonomic rank [29, 30, 36, 37].
Alignment of representative sequences was
carried out using MAFFT algorithm G-INS-1 [38].
A phylogenetic tree was conducted with MEGA5 using
the Maximum Likelihood method, based on the Tamura-
Nei model with 1000 bootstrap replicates [39–41].
Vegdist and hclust R functions were used for
computing Bray-Curtis dissimilarity indices and
UPGMA hierarchical clustering of OTUs, showing their
coexistence in the samples [42]. Heatmaps were generated
with QIIME 1.8.0 with log-transformed abundance data.
OTUs were sorted by phylogenetic or hierarchical trees.
Figure 1 Maximum likelihood consensus tree and fungal taxa heat map of sterilized and non-sterilized grain samples,
which were harvested in 2014 and 2015 years. Read counts of each OTU were weighted according to sum of reads
in the sample and log-transformed. White corresponds to low and blue to high number of reads Kri – Krinichnyy,
Suz – Suzdalets, B110 – Belgorodskiy 110, Tat – Tatum, and M86 – Moskovskiy 86

Beta diversity between samples was
calculated by beta_diversity.py script in QIIME with
unweighted UniFrac metric [26, 43]. To check the
robustness of estimated beta diversity, jackknifed
analysis, with 96 reads per sample depth and
100 replicates, was performed. The results were
visualized with Principal coordinate analysis (PCoA) in
a 2D scale plot.

Figure 2 Boxplots depicting relative abundance (%) of the taxonomic ranks in sterilized and non-sterilized grain samples
harvested in 2014 and 2015. All taxonomical ranks are marked by different colors. The boxplots consist of square boundaries
indicating the 25th and 75th percentiles. Whiskers indicate the 10th and 90th percentiles, and the line inside the box represents
the median. Outliers are not displayed

Bray-Curtis, weighted and unweighted UniFrac
dissimilarity indices were used for measuring the
strength and significance of sample groupings with
Permutational Multivariate Analysis of Variance
(PERMANOVA) and Analysis of Similarity (ANOSIM)
with script compare_categories.py [26].
Agar plate-based method of isolation and
identification of fungi. Representative subsamples (200
grains) of each cultivar were surface-sterilized by being
shaken for 2 min in 5% sodium hypochlorite. Then
they were rinsed twice in sterilized water. The surfacesterilized
grains were placed on 90 mm Petri dishes (10
grains per dish) with potato-sucrose agar (PSA) and
incubated at 24°C for 10–14 days. The isolated fungal
colonies from every grain were identified by visual and
microscopic observations according to Ellis, Gerlach
and Nirenberg, Lawrence, Rotondo, and Gannibal,
and Samson et al. [44–47]. To present data comparable
to those obtained with NGS, relative abundance was
calculated as the number of all isolates of a certain taxon
divided by the total number of fungal isolates (%). A
more conventional index of seed expertise, infection
frequency, was calculated as the number of grains
infected by the fungus (%).
RESULTS AND DISCUSSION
NGS-based identification of fungi. After
quality filtering and removal of nonfungal and
chimeric sequences, in total, 8484 fungal reads
were obtained and clustered into 43 operational
taxonomic units (OTUs). The number of observed
OTUs in the grain samples was ranged from 10
to 27. The estimated OTU richness was higher on
the s urface ( Chao1 = 2 4.3 ± 4 .4, A CE = 2 6.2 ± 4 .9
(2014); C hao1 = 3 0.7 ± 4 .2, A CE = 2 9.7 ± 1 .8 ( 2015)),
than inside (Chao1 = 15.1 ± 1.5, ACE = 16.8 ± 1.9 (2014);
Chao1 = 19.3 ± 2.0, ACE = 21.7 ± 1.7 (2015)) the barley
grains. The rarefaction curves pointed that the diversity
of some samples might be underestimated, although all
rarefaction curves were beyond the linear ranges.
All of the OTUs were assigned to Basidiomycota and
Ascomycota phyla (Fig. 1).
Primary Ascomycota prevail over Basidiomycota,
but in non-sterilized grain, the ascomycetous read
prevalence (percentage of reads) was significantly
lower (Fig. 2). The 26 OTUs from Ascomycota
belonged to the families Nectriaceae, Dothioraceae,
Microdochiaceae, Cladosporiaceae, Pleosporaceae,
Sclerotiniaceae, Didymellaceae, Phaeosphaeriaceae,
and unidentified Dothideomycetes, as well as
undefined groups within Helotiales and Hypocreales.
Seventeen basidiomycete OTUs belonged to yeastlike
fungi from Entylomataceae, Cystofilobasidiaceae,
and other undefined groups within Tremellales,
Cystofilobasidiales, and Sporidiobolales.
One of the most abundant OTU, otu166, assigned
as Dothideomycetes, failed to be identified to a more
precise taxonomical level. It has similar characteristics
(BLASTn 100% query coverage and identity) with
several GenBank sequences (e.g., EU552134, AJ279448,
HG935454) which can be joined together only at the
rank of class. More likely, it coincides with Epicoccum
nigrum, the one abundant Dothideomycete identified
during mycological analysis.
Eleven minor OTUs (Alternaria related otu44, otu53,
otu61, otu180, and otu190; Cryptococcus related otu29,
otu60, otu218, and otu264; Davidiella related otu123;
and Fusarium related otu89) had no close relation to any
known species but appeared in the same samples where
a major OTU of a certain genus was abundant. However,
potentially such satellite OTUs represent rare and/or
poorly studied species, but most likely they are technical
errors or sequence variances, which can occur despite all
filtering and trimming procedures.
From seven clustered OTUs that were assigned as
Alternaria, the most abundant OTUs, otu18 and otu304,
can refer to Alternaria and Pseudoalternaria sections
respectively (Fig. 3) [46, 48].
From ITS2 sequences combined into six OTUs and
designated as Fusarium, several OTUs can be readily
assigned as two synapomorphic (Fig. 4) clades, similar
to that described by Watanabe et al. [49]. Two OTUs
(F. poae – otu224 and Fusarium sp. – otu259) were
abundant, but four OTUs appeared as solitary sequences.
Distribution of Fusarium-related OTUs among
clusters corresponded to the prevalent toxic secondary
metabolite production. The first cluster consisted
of Fusarium fungi that are able to produce the
trichothecene group metabolites. The subcluster 1a
included F. sporotrichioides and F. langsethiae, which
are the main producers of type A trichothecenes
(like T-2 and HT-2 toxins); the subcluster 1b included
F. culmorum and F. graminearum the producers of
type B trichothecenes (like DON, or NIV). Species
F. poae (subcluster 1c) produces trichothecenes of types
Figure 3 Maximum likelihood tree of OTUs related to the
Alternaria genus. Bootstrap values based on 1000 replicates.
Bootstrap values less than 50% are not presented

Figure 4 Maximum likelihood tree of OTUs related to the
Fusarium genus. Bootstrap values based on 1000 replicates.
Bootstrap values less than 50% are not presented

A and B, and enniatins (ENNs). Fungus F. equiseti
(subcluster 1d) is able to produce ENNs, but according
to some authors, it can also produce a small amount
of type A trichothecenes [50, 51]. The subcluster
2 brought together Fusarium fungi that are able to
synthesize ENNs: F. tricinctum, F. avenaceum, and
F. lateritium [52].
Fourteen OTUs that were defined to the species
level belonged to Bipolaris sorokiniana, Fusarium
poae, Neoascochyta exitialis, Sarocladium strictum,
Cystofilobasidium macerans, Udeniomyces pannonicus,
Cryptococcus victoriae, Cryptococcus
tephrensis, Cryptococcus wieringae, Sporobolomyces
ruberrimus, Sporobolomyces roseus, Dioszegia
hungarica, Aureobasidium pullulans, and Tilletiopsis
washingtonensis.
In general, the mycobiome of nonsterilized barley
grains was characterized by a greater abundance of
Basidiomycetes in comparison with surface-sterilized
grains. The most abundant fungi in nonsterilized grains
were Davidiella (Cladosporium) spp. and Cryptoccocus
spp. After surface sterilization, the average abundance
of Fusarium, Alternaria, Pyrenophora, and
Phaeosphaeria, as well as fungi from Dothideomycetes,
increased, but the ratio of those taxa depended
on the year.
Comparison of taxonomical structure and relative
abundance between groups of samples combined by
crop year (2014/2015) and type of treatment (sterilized/
non-sterilized) reflected significant distinctions in
both cases (Fig. 5). Nevertheless, distinctions between
sterilized and non-sterilized grain mycobiota (ANOSIM
R = 0.64, 0.76, 0.69; P = 0.001, 0.001, 0.001;
PERMANOVA pseudo F = 9.89, 17.7, 10.93;
P = 0.001, 0.001, 0.001; data shown successively for
Bray-Curtis, Weighted and Unweighted UniFrac
community dissimilarity matrices) occurred to be more
strong, than those determined in successive crop years
(ANOSIM R = 0.53, 0.21, 0.37; P = 0.001, 0.02, 0.006;
PERMANOVA pseudo F = 8.03, 4.97, 5.02, P = 0.001,
0.019, 0.003).
The fungal species composition of non-sterilized
grains differed from the mycobiome of surfacesterilized
grains primary due to a higher abundance
of Basidiomycetes (Cryptococcus spp. and other
Tremellales, and Cystofilobasidium macerans and other
Cystofilobasidiaceae) and Davidiella ( Cladosporium
spp.) in the non-sterilized grains. All Basidiomycetes
disappeared or became sparse after surface sterilization.
The most abundant of them, Cryptococcus tephrensis
(otu204) and C. victoriae (otu124), were also revealed
inside grains but in fewer samples and in lesser amounts.
Several OTUs, e.g., Cryptococcus wieringae (otu183),
Mrakiella sp. (otu254), and Dioszegia sp. (otu303),
tended to present on seed surfaces during only one
year. Mycobiomes observed in two different growing
seasons differed by abundance of Pyrenophora sp.
in 2014 and Fusarium spp. and Phaeosphaeria sp. in
2015. The Alternaria, Bipolaris, and Epicoccum genera
were relatively abundant in both sample sets. More
detailed results of fungal coexistence in the samples are
introduced in Fig. 6.
Agar plate-based method of identification of
fungi. From 87 to 117 fungal isolates per 100 grains
were obtained from each sample. As a result of two
growing seasons, a total of 18 taxa of seed-borne fungi
were identified (Table 1). The taxonomic position of
some fungi was vague due to the lack of sporulation
(Mycelia sterilia). In both years, the appearance of
Alternaria, Bipolaris, Epicoccum, and Fusarium species 

Figure 6 Fungal taxa heat map of sterilized and non-sterilized grain samples harvested in 2014 and 2015. OTUs are grouped
according to UPGMA clusterization of Bray-Curtis dissimilarity matrix representing coexistence of OTUs within samples.
Read counts of each OTU were weighted according to sum of reads in the sample, and then the proportion of the OTU dominance
between samples was calculated and log-transformed. White corresponds to OTU absence in sample and red to OTU with high
relative abundance across the samples Kri –Krinichnyy, Suz – Suzdalets, B110 – Belgorodskiy 110, Tat – Tatum,
and M86 – Moskovskiy 86

was common. Species of the genus Pyrenophora were
found only in the 2014 growing season. Some potentially
toxigenic fungi, such as Penicillium, Aspergillus,
Cladosporium and unidentified Zygomycota, were found
only in a few samples. No Basidiomycetes were isolated
and identified by agar plate-based assay.
In both years, fungi of the genus Alternaria
predominated in barley grain samples. The members
of two sections, Alternaria and Infectoriae, were
determined. More precise identification was not
performed, since species concept is debatable for
Alternaria and Infectoriae [53–56] sections.
Contamination of the barley grains by Fusarium
spp. varied significantly in 2014 and 2015. In 2014, the
Fusarium infection frequency was low (0–4%) and
represented by five species, of which F. avenaceum
was the most frequent (infection frequency up to 2.5%,
relative abundance of isolates up to 2.2%). In 2015,
the infection of barley grains with Fusarium spp. was
considerably higher (infection frequency 14–19%,
isolate abundance 12–17%). Eight Fusarium species were
identified; four of them were common for both years.
Comparison of methods. In total, 43 OTUs assigned
as Ascomycota (26) and Basidiomycota (17) were
revealed by DNA metabarcoding. Only 14 OTUs were
assigned to species level. From those species, only two
were reoccurred in traditional mycological analysis. The
other 12 species either were not detected among isolates
grown up from grains on agar medium or were Mycelia
sterilia. At the same time, the conventional mycological
seed test revealed 17 Ascomycetes, including 11 species,
apart from some Zygomycetes and sterile Ascomycetes.
Basidiomycetes were not recovered by conventional
assay. Two species (Fusarium poae and Bipolaris
sorokiniana), one section (Alternaria sect. Alternaria),
and three genera (Davidiella [Cladosporium], Fusarium,
and Pyrenophora) were formally common for both
assays. In general, the list of undoubtedly identified
dominant taxa coincides with the results of the NGS
mycobiome study of Canadian barley grains [3].

The predominant OTUs from Alternaria were
identified as Alternaria and Pseudoalternaria sections
when Alternaria and Infectoriae sections were fixed
during mycological analysis. In both cases, taxa
were identified to the section level. Such precision
is sufficient for the majority of practical purposes,
e.g., for tests of seed, food, or feed-grain quality. The
big section Infectoriae and lately described section
Pseudoalternaria are morphologically similar and
phylogenetically close groups [46]. This obviously
can be the cause of errors, if identification is based on
morphological features.
Both methods similarly reflected a very low
abundance of Fusarium spp. in 2014 and a higher
quantity in 2015. Traditional mycological analysis
revealed nine Fusarium species. DNA metabarcoding
results were more limited; only one OTU was identified
as a certain species, F. poae, but the others were
assigned to a clade level. Phylogenetic resolution
derived from ITS2 is not useful in defining Fusarium
species. Recently, Fusarium-specific primers targeting
translation elongation factor 1 (TEF1) were evaluated
and successfully applied to analyze Fusarium
communities in soil and plant material [57].
The taxonomy of Fusarium fungi is confusing and
various classification systems have been proposed [58].
For Fusarium, chemotaxonomy is considered a
supplement to traditional morphology-based taxonomy.
Several fungal genes involved in trichothecene and
enniatins biosynthesis have been defined and used for
development of molecular assays aimed at identification.
In spite of the ITS sequences used in our analysis, the
results strongly suggested the division of fungi based on
their ability to produce metabolites. In the future, this
will provide an opportunity to predict the severity of
grain contamination by some mycotoxins according to
the number of certain identified OTUs.
Fusarium avenaceum, F. poae, F. tricinctum, and
F. sporotrichioides were the most abundant
representatives of the genus. They are the typical
pathogens of barley in northwestern Russia [59, 60].
Most likely, multi-copy otu259 discovered by DNA
metabarcoding is associated with F. avenaceum, which
occurred frequently on the barley grain.
Both methods revealed pathogenic fungi from
Pleosporaceae: Bipolaris and Pyrenophora. Those
fungi have different patterns of appearance through the
cropping seasons. DNA metabarcoding demonstrated
higher sensitivity. Pyrenophora sp. colonies were not
recovered in 2015 at all, but several respective reads
were obtained for 7 out of 10 samples.
Davidiella (Cladosporium) associated reads were
abundant in DNA metabarcoding assay in non-sterilized
samples but only single colonies were detected in
the agar plate-based test. Underestimation of relative
abundance of the fungus in the latter case can be result
of two reasons: rapidly spreading colonies suppress or
mask slowly growing fungi, and infected individual
grains contain not uniform quantity of fungal biomass
that appear as insufficient correlation between the number of infected grains and the amount of fungal
DNA in the whole sample.
Four fungal genera revealed by only DNA metabarcoding
contained agents of cereal diseases
(Neoascochyta, Botrytis, Microdochium, and
Phaeosphaeria). The first three taxa were represented by
solitary reads. Phaeosphaeria (otu106 and otu215) were
found in 14 of 20 samples. In 2015, in surface-sterilized
samples, the relative abundance of Phaeosphaeria
reads varied between 11 and 36%. Sequences of otu106
had the closest similarity (99%) with representative
sequences of Parastagonospora avenae ( Septoria
avenae or Stagonospora avenae), widespread fungus
causing leaf blotch of barley and some other cereals, and
Parastagonospora poagena, a recently described fungus
from Poa sp. [61, 62]. Less abundant OTU, otu215, had
a similarity of 98%, with several Phaeosphaeria species
and with some unidentified endophytes.
CONCLUSION
DNA metabarcoding, based on high-throughput
sequencing, is a sensitive and powerful method of grain
mycobiome analysis that provides large amounts of data.
However, at this time, not all fungi can be identified
to species level by molecular markers, especially by
rDNA sequences. In spite of universality, rDNA has
a limitation as a taxonomic marker. The resolution
of the ITS sequence-based method is not enough to
differentiate many fungal species. For instance, many
Fusarium species have nonorthologous copies of ITS2.
Many other important plant pathogenic and toxigenic
fungi also can be identified up to genus level, but that is
not always informative in the framework of mycological
seed expertise. Erroneous and chimerical sequences, as
well as the lack of reference sequences of many species,
still limits wide application of NGS-based technologies
in biodiversity studies.
The most complete and credible results can be
obtained when several approaches are implemented
simultaneously. Combining the results of DNA
metabarcoding and traditional culture-plating
assay allowed us to revise the diversity of fungi
colonizing on the surface of and inside barley grains in
Leningradskaya oblast (northwest Russia).
Fungal species diversity of barley grain revealed
by DNA metabarcoding formally exceeded the
traditional microbiological culture-based agar plating:
43 operational taxonomic units (OTUs) vs. 17 taxa
of genus or species level. DNA metabarcoding assay
allowed seven ascomycete taxa to be added to the total
list. Of those additional taxa, only Phaeosphaeria was
abundant internal fungus. Seventeen OTUs belonging
mainly to surface-seed-borne, yeastlike Basidiomycetes
were completely outside the scope of traditional analysis.
Meanwhile, routine mycological analysis, in contrast to
DNA metabarcoding, resulted in precise identification
of practically important Fusarium species. On the other
side, due to DNA analysis, one Alternaria taxon was
reidentified as Alternaria section Pseudoalternaria
instead of section Infectoriae.
CONTRIBUTION
Authors are equally related to the writing of the
manuscript and are equally responsible for plagiarism.
CONFLICT OF INTEREST
The authors declare that there is no conflict of
interest regarding the publication of this article.
ACKNOWLEDGEMENTS
The authors are grateful to A. Vagin (Volosovo State
Seed-Trial Ground, Russia) for providing seed samples
and key information on them, and to M. Gomzhina
(All-Russian Institute of Plant Protection) for technical
assistance. Pyrosequencing was conducted using the
equipment of the Core Centrum “Genomic Technologies,
Proteomics and Cell Biology” at All-Russia Research
Institute for Agricultural Microbiology (ARRIAM,
St. Petersburg, Russia).

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