Introduction

Fusarium head blight (FHB) is a major cereal disease causing severe damage to crops worldwide including Europe. In addition to reducing grain yield and quality, the harvested grains are often contaminated with mycotoxins that are a major health and food safety concern due to their toxicity to humans and other animals. Up to 17 fungal species have been associated with FHB [1], belonging either to the toxinogenic genus Fusarium or to the non-toxinogenic genus Microdochium. For example, in France, the most common species associated with FHB on soft wheat, durum wheat and barley are F. graminearum sensu lato, F. avenaceum, F. poae and M. nivale, but species such as F. tricinctum and F. culmorum have also been found frequently, along with less frequent species such as F. equiseti, F. acuminatum, F. sambucinum, F. sporotrichioides, F. verticillioides, F. heterosporum, F. subglutinans and F. oxysporum [2, 3]. These Fusarium species are frequently associated with one another in the infected grains and constitute a disease complex [4]. A similar species profile was also found in other European countries, with local and temporal specificities due to several factors such as climatic conditions, agronomic factors or host genotype [5–12].

Most Fusarium species are able to produce one or more mycotoxins with varying degrees of toxicity [5]. Fusarium species involved in FHB differ in their epidemiology, pathogenicity, ability to produce mycotoxins, and fungicide sensitivity. However, in a context of strong political and public awareness on the issues of food, health and environmental safety, the current trend is to reduce the quantities of fungicides used on crops, while maintaining efficient and sustainable agriculture. Climate change and evolving cultural farming practices may also contribute to the modification of Fusarium species distribution and in turn, mycotoxin content. Information on the distribution and predominance of the different Fusarium species in a given geographical area, and in response to various environmental and cultural factors, is therefore of great importance for managing the disease and evaluating mycotoxin contamination risks.

The analysis of Fusarium species composition in field samples is mainly performed using morphological identification or species-specific PCR-based methods [2, 13–15]. Morphological identification of Fusarium species is based on the microscopic observation of various criteria (mycelia, growth, macroconidia, microconidia, etc.), and requires time and high taxonomic skills. In addition, competition between species during the isolation step can lead to an underestimation of the occurrence of slower-growing species and of morphologically indistinguishable (i.e. cryptic) species, like the Fusarium graminearum species complex (FGSC) occurring on cereal spikes and grains. Species-specific PCR-based methods are reliable methods to detect/quantify various Fusarium species in field samples [13–15], but constitute a priori approaches restricted to known and specific targets. Likewise, target-specific PCR tests cannot be used to search for emerging, i.e. ill-described or yet undescribed, Fusarium species.

Typically, identification of fungal species can be achieved by analysis of one or a combination of DNA sequences [16], but still requires preliminary isolation of the fungus in pure culture before amplification by PCR and sequencing of the sequence. The development of more efficient and more affordable high-throughput sequencing techniques now enables the generation of thousands of DNA sequences from environmental samples, without any isolation and culturing step required. Metabarcoding, i.e. the simultaneous amplification and sequencing of barcode sequences directly from environmental samples, has become a very popular and powerful technique to explore the diversity of microorganisms, including slow-competing or uncultivable taxa. Numerous studies have recently described or re-assessed fungal diversity in samples as diverse as soil, rhizosphere, phyllosphere or field samples using metabarcoding [17–24]. High-throughput sequencing methods are evolving at a fast pace, and Illumina MiSeq sequencing is currently the most successful and extensively used technology globally due to its low error rate and the lowest cost per million bases, with a high number of reads generated. It also has a major advantage over other technologies by generating the longer DNA sequences required for barcoding.

For fungi, the internal transcribed spacer (ITS) region of rDNA has been recognized as the official barcode [16]. ITS sequences are largely represented in international databases [16, 25] and have often been used in fungal diversity studies [17, 26]. However, this barcode does not always enable species discrimination for some genera, in particular within Fusarium [27]. The translation elongation factor 1-α (EF1α) gene, which is involved in the protein translation machinery of eukaryotic cells, has proven to be efficient to distinguish related Fusarium species [28, 29], and was selected as the barcode of choice for the FUSARIUM-ID online identification tool [27]. Owing to its phylogenetic utility and the fact that it is present as a single copy in Fusarium, this gene appears to be a good candidate for Fusarium metabarcoding approaches.

In this study, two PCR primers were designed to amplify an informative region of the EF1α gene in the genus Fusarium and a bioinformatic pipeline was developed to assess overall Fusarium diversity in field samples by metabarcoding. Various control samples were used to validate the precision, sensitivity and robustness of the technique. As a proof of principle, analyses were performed on field samples and the results were compared with real-time PCR species-specific detection tests.

Materials and methods

Biological samples

Mock communities.

Five different fungal mock communities were prepared (Table 1) to test the efficacy of the analytical process (i.e. PCR primer specificity, PCR reaction, bioinformatic pipeline) in differentiating Fusarium species. Mock communities 1 to 4 included various Fusarium species and other fungal plant pathogens from diverse taxonomic groups from the Anses, ARVALIS-Institut du végétal and INRA laboratory collections. All of the strains used in this publication were single-spored. They were characterised by morphological identification when possible and by sequencing of at least two genes: ITS and EF1α (S1 Fig). The sequences of ITS, EF1α and some of RNA polymerase II second largest subunit (RPB2) are publicly available on R-Syst::fungi database (http://138.102.89.206/new_rsyst_chp/, https://github.com/r-syst/databases/tree/master/r-syst::fungi). To search for these sequences enter internal lab id (available S1 Table column 2) in ‘advance search’ ‘strain’ and then click to view the results. Using Phylogeny.fr [30] and the following set up: MUSCLE alignment, Gblock curation, Maximum Likelihood PhyML, a phylogenic tree was built with EF1α sequences of our strains and EF1α sequences from collection strains of the Fusarium ID database [27] (S2 Fig). To confirm the name, the same way an RPB2 phylogenic tree was obtained of some of our strains (S3 Fig). Mock community 5 contained 10 species in the FGSC previously obtained from the ARS culture collection (USDA ARS, Peoria, Illinois, USA). To prepare each mock community, 100 ng of DNA of each species were mixed in sterile water, except for mock community 5 for which 10 ng of DNA of each species were mixed.

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Table 1. DNA mock communities description.

https://doi.org/10.1371/journal.pone.0207988.t001

Artificially infected grains.

Infected grain communities were prepared (Table 2) to (i) test the efficacy of the analytical process to differentiate Fusarium species in complex samples, (ii) verify consistency between replicates and DNA extractions, and (iii) evaluate the sensitivity of the technique. Healthy wheat grains were infected in vitro with Fusarium strains from the ANSES, ARVALIS and INRA laboratory collections. Each Fusarium strain was grown on potato dextrose agar (PDA) plates for 7 to 10 days. Wheat grains were sterilized by autoclaving, and plated on PDA. For each Fusarium strain, a series of plates containing 6 to 8 wheat grains was prepared. Three plugs taken from the edge of the fungal cultures on PDA were evenly placed on each plate containing the grains, and incubated for 10 days to allow fungal colonization of the grains. After infection, grains were dried for 24 h at 50°C.

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Table 2. Description of artificially infected grain samples prepared in this study.

https://doi.org/10.1371/journal.pone.0207988.t002

A first set of 100 grain samples containing 10 grains infected with F. graminearum (LSV M808) and 1 grain infected with F. langsethiae (LSV M846), F. poae (LSV M861) and F. tricinctum (LSV M860) in different proportions was prepared (Table 2). Each sample was prepared with durum wheat (cultivar CG09) and soft wheat (cultivar Galopain). The samples in their entirety were ground in a grinding jar using a TissueLyser (Qiagen, France). The resulting powder (particle size not determined) was collected in a 50 mL falcon tube and mixed manually before collecting 200mg for DNA extraction.

A second set of artificially infected samples was prepared by mixing 8 grains individually infected with F. poae (FP1), F. langsethiae (FL33), F. sp. FTSC (FT12), F. culmorum (C17), F. avenaceum (Fa), F. graminearum (FG20), F. subglutinans (FU12238) and F. verticillioides (FU09041), respectively, with a total of 100, 200, 500, 1000, 2000, 5000 and 10,000 healthy grains (Table 2). Each sample was prepared with durum wheat (cultivar Miradou). The samples in their entirety were ground using a PULVERISETTE 14 Variable-Speed Rotor Mill (Fritsch, Germany). The particle size of the flour was approximately 5μm.

Field samples.

In 2014 and 2015, 65 field samples of durum wheat, soft wheat and barley were collected in different locations in France. Samples were selected to represent a variety of cereal-growing regions with different climates and cultural farming practices, and following different chemical treatment strategies (S4 Fig). The samples were used to test the applicability of the protocol and analysis steps. No permits were required for the field samplings and the authors have permission to conduct the study. Each sample, corresponding to 3 kg of grains, was homogenised and ground using a hammer mill (TITAN 2000, fao). Then, 100 g of flour was ground again using an MM400 Retsch mixer mill (Retsch, Germany). The particle size of the flour was approximately 5μm.

Methods

DNA extraction.

For the mock communities, fungal DNA was obtained from aerial mycelium aseptically scraped from cultures grown on either PDA or malt agar. The mycelium was lyophilized for 24h in an Edwards Modulyo 4K Lyophilizer (Edwards, United Kingdom) and 100 mg of lyophilized mycelium was placed in a Fast-Prep tube (2 mL) containing 130 mg of glass beads (4.5 mm in diameter; Dutscher, France) and ground using FastPrep (MP Biomedicals, France) for 30s at maximum shaking frequency. DNA was extracted using a DNeasy Plant Mini kit (Qiagen, France) according to the manufacturer’s instructions, except that the incubation time was extended to 1 h at 65°C, and the volumes of AP1 and P3 buffers were doubled.

DNA was extracted in the same way from both artificially infected grains and field grain samples. Three independent extractions were performed per sample for artificially infected samples with four species (Table 2), whereas only a single extraction was carried out for artificially infected samples with eight species (F8 100 to F8 10,000; Table 2) and field samples. 100 mg of the homogenized flour was re-suspended in 400 μL of AP1buffer (DNeasy Plant Mini kit, Qiagen, France) and sonicated for 30s. Then, 10 μL of proteinase K (10 mg/mL) and 10 μL of RNase A (Qiagen) were added to the suspension. After 20 minutes of incubation at 65°C, the protocol recommended by the manufacturer was followed. All DNA extracts were quantified using a Qubit 2.0 Fluorometer (Invitrogen, USA).

Primer design.

A local database of partial EF1α gene sequences from different Fusarium species was constructed, containing sequences downloaded from the Fusarium ID database [27]. In total, the database contained 162 sequences, covering 74 species. Using Phylogeny.fr [30], a phylogenic tree was built and 6 divergent sequences were selected and aligned using MultAlin [31] with EF1α sequences from other genera such as Zymoseptoria, Puccinia, Microdochium, Parastagonospora, and Leptosphaeria in order to find regions i) sufficiently conserved in Fusarium to design a genus-specific primer pair, and ii) flanking a DNA region showing a high level of interspecific polymorphism within the genus Fusarium. Several sets of candidate primers were designed and their specificity was first assessed in silico by BLAST with the NCBI database, then in vitro by PCR using DNA from a panel of 54 strains including 31 Fusarium species and 19 other genera of fungi (S5 Fig). The best primer pair was then selected: EF1-F2 (5’TCATCGGCCACGTCGACTCT3’) and EF1-R3 (5’TACCAGCCTCGAACTCACCA3’) (S6 Fig).

PCR and Illumina sequencing.

In order to obtain a PCR fragment suitable for Illumina MiSeq sequencing, primers EF1-F2 and EF1-R3 were tagged with specific sequences (PYROFUS-EF1-F2-MISEQ: 5’CTTTCCCTACACGACGCTCTTCCGATCTATTAACCCTCACTAAAGGGATCATCGGCCACGTCGACTCT3’ and PYROFUS-EF1-R3-MISEQ 5’GGAGTTCAGACGTGTGCTCTTCCGATCTCCCTATAGTGAGTCGTATTATACCAGCCTCGAACTCACCA3’). The PCR reaction contained 1 x mastermix for the Type-it Microsatellite PCR Kit (Qiagen, France), 5 μl Q-Solution and 0.25 μM of each primer in a total volume of 50 μL. PCR reactions were performed with either 60 ng (mock communities) or 125 ng (artificially infected grains and field samples) of DNA. PCR reactions were performed using an Applied Biosystems 9700 thermocycler. The amplification parameters consisted of an initial denaturation step at 95°C for 5 min; then either 31 or 35 cycles of denaturation at 95°C for 60 s, annealing at 65°C for 90 s, and elongation at 72°C for 60 s; and a final extension step at 72°C for 10 min. The size of the final PCR amplicon before sequencing was around 750 bp. For field samples, in order to obtain a sufficient amount of PCR product for Illumina MiSeq sequencing, 3 or 6 independent PCRs of the same sample where pooled before purification. The PCR products were purified by PEG-8000 precipitation and concentration (26.2% polyethylene glycol 8000, 6.6 mM MgCl₂ and 0.6 M NaOAc) and sent to the GenoToul platform (INRA Toulouse) for Illumina MiSeq (V3 2 x 250 bp) sequencing. Three independent sequencing runs were performed.

Species detection by real-time PCR.

Total DNA from each of the 65 field samples was analyzed by species-specific real-time PCR targeting F. avenaceum, F. culmorum, F. graminearum, F. langsethiae, F. poae, F. sporotrichioides and F. tricinctum [32]. The artificially infected samples were also analyzed with these tests in order to verify the presence of the Fusarium species inoculated. The primers and probes used were custom-synthesized by Eurogentec (Seraing, Belgium) and are listed in Table 3.

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Table 3. Primer and probe sequences and real-time PCR amplification conditions for each Fusarium species (table extracted from [32]).

https://doi.org/10.1371/journal.pone.0207988.t003

Real-time PCR assays were performed using 2 x qPCR MasterMix with ROX and Uracil N Glycosylase (UNG) from Eurogentec (Angers, France). PCR reactions were performed in a total of 20 μL consisting of 12.5 μL of mastermix, with primers and probes at the concentrations described in Table 3, and 5 μL of DNA extract (100 ng for field samples) were added to the mix. Standard calibration curves were constructed with DNA from a pure culture of the target species with concentrations ranging from 1 ng/μL to 0.001 ng/μL and were included in each experiment. Real-time PCR reactions were performed on an ABI PRISM 7900 Sequence Detection System (Applied Biosystems, Foster City, CA, USA) in Applied Biosystem 96-well plates. Cycling conditions included an initial denaturation step at 95°C for 10 min, followed by 40 cycles each at 95°C for 15 s then one minute in specific hybridization/polymerization conditions (Table 3). Each DNA template was analyzed in triplicate.

Filtering, processing and assigning a taxonomic classification to sequences.

A stringent pipeline taking as a basis for the MiSeq SOP [33] was used to filter and process raw sequences utilizing MOTHUR v.1.36 [34] (S7 Fig). After FastQC analysis [35], the reverse sequences were associated with poor quality files. Around 100 bp of each read received quality scores lower than 20. We eliminated these poor-quality reads from further analyses. By contrast, forward sequences met good quality standards and were processed. First, only the sequences showing the expected primer and barcode sequences were retained. Then, sequences with low quality (shorter than 100 bp or including low quality scores) were removed. After preprocessing the sequences, multiple sequence files were merged and unique sequences represented by singletons removed. Chimeric sequences were removed with the Uchime tool [36] available in MOTHUR, using self as the reference. Uncorrected pairwise distances were determined between the remaining sequences with the Needleman alignment method [37] with match 1.0, mismatch -1.0, gapopen -2.0 and gapextend 1.0 using 1000 iterations. The distance matrix was then clustered with the average neighbor algorithm to assign sequences to operational taxonomic units (OTUs).

A custom EF1α gene fungal reference database with 9,196 sequences was created by extracting Fungal EF1α sequences from GenBank using the query: (fungi[All Fields] OR fungi[Organism] OR fusarium[Title]) AND (translation elongation factor[Title] OR tef1[Title] OR translation elongation factor-1 alpha[Title] OR elongation[Title] OR EF1-a[Title] OR tef-1[Title]) AND (CBS*[Title] OR NRRL*[Title] OR MUCL*[Title] OR voucher*[Title] OR DAOM*[Title] OR MAFF*[Title] OR MIAE*[Title] OR IPO*[Title] OR ATCC*[Title] OR LMSA*[Title]) AND (100[SLEN]: 3000[SLEN]) NOT Fusarium sp.[Title] NOT IonTorrent[All Fields] NOT (TEF3[Title] OR translation elongation factor 3[Title] OR tef-3[Title] OR elongation factor 3[Title] OR EF-3[Title]). An additional set of partial EF1α gene sequences was generated in-house by PCR, using the EF1-F2/EF1-R3 primers and DNA from the Fusarium strains available in the INRA and ANSES collections. The custom EF1α gene fungal reference database was called ‘merge.reference.fasta’ in the pipeline described in the S7 Fig. There were nine taxonomic levels associated with each fungal sequence in the following order: kingdom, phylum, subphylum, class, subclass, order, family, genus, and species, with undefined levels recorded as unclassified. The classification of taxonomic levels for sequences was manually curated from the Mycobank database [38]. The curated taxonomy file was called ‘merge.taxonomy.fasta’ in the pipeline described in the S7 Fig. The consensus taxonomy for representative sequences for each OTU was performed using MOTHUR’s classifier [39] to assign the taxonomy of sequences from the custom EF1α fungal reference database using 2% divergence. An excel database was created containing OTU number, read count per OTU per sample, representative sequence name, representative sequence, and OTU representative assignation. Then, the OTUs were carefully checked and assigned to the species taxonomic level for Fusarium OTUs and genus taxonomic level for non-Fusarium OTUs. OTUs with the same species or genus assignment were merged and were represented as a percentage of the total sequences in the sample, with a minimum acceptance confidence level. Errors in the sequence of mock communities were calculated using Sanger sequences as references (MOTHUR v.1.36; [34]). The calculated errors were then used to determine the minimum acceptance confidence level to retain OTUs (S7 Fig).

Statistical analysis.

A linear regression model in R version 3.3 with Rcmdr [40] was used to evaluate the influence of PCR cycle number, run and PCR pooling on the percentage of final Fusarium OTUs.

To compare the diversity present in each wheat sample, only Fusarium species determined from OTUs as described above were considered, as the experiment was developed specifically for Fusarium analysis. Analyses were performed with R (R version 3.3.1 (2016-06-21)) using the ‘Phyloseq’ R package specifically designed for analysis of microbiological communities [41]. Using ‘Phyloseq’, a Heatmap of abundance of each species per sample was generated.

Results

Analysis of the DNA mock communities

The five fungal mock communities were used as positive controls in three independent sequencing experiments. In these three runs, a total of 3,491,565 raw reads were generated for the mock communities. After filtering, 81,604 (2.3% of the raw sequences) were kept for the analysis. Mock communities were used to determine error rates. As the EF1α Sanger sequence of each strain included in each mock community was available, a maximum error rate generated by Illumina MiSeq sequencing was calculated for each mock community using Mothur (S7 Fig). In our experiments, the maximum error rate obtained was 0.3%. In this respect, for each sample analyzed (artificially infected grain samples and field samples), OTUs representing less than 0.3% of the total reads were removed from the data.

For each OTU, results were expressed as a percentage of sequences out of the total number of sequences analyzed per sample. The expected EF1α sequences from all the Fusarium species added in mock 1, mock 2, mock 3 and mock 4 could be identified (Fig 1). The percentage of Fusarium OTUs did not differ significantly (p > 0.05) between the different PCR cycle conditions tested (31 or 35 cycles), and between runs (3 independent runs). Two independent PCRs were also performed with DNA from the same mock community and sequenced in one run, and no difference could be observed between the percentages of Fusarium OTUs retrieved. Sequences from a few non-Fusarium species that were included in the mock communities were identified: Phaeoacremonium (mean 1.0%, SEM 0.1%) for mock 1, Cladosporium (mean 7.3%, SEM 2.9%), Colletotrichum (mean 10.7%, SEM 2.8%), Eutypa (mean 0.4%, SEM 0.3%), and Pseudocercosporella (mean 0.4%, SEM 0.1%) for mock 2, and Leptosphaeria (mean 2.2%, SEM 0.2%) for mock 3, Zymospetoria (mean 0.2%, SEM 0.002%) for mock 4. No other species/genera were identified.

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Fig 1. Boxplots of relative abundance of reads per species for mock communities 1 to 4.

For each species, each Boxplot includes 8 independent samples for each mock (two PCR cycle conditions and 2 replicates for sequencing run 1, one PCR cycle condition and 3 replicates for sequencing run 2, one PCR cycle condition and 1 replicate for sequencing run 3).

https://doi.org/10.1371/journal.pone.0207988.g001

In mock community 5, which contained 10 species from the FGSC, only three species could be identified based on the sequence analyses: F. acaciae-mearnsii (mean 5.3%, SEM 0.1%), F. cortaderiae (mean 10.9%, SEM 0.1%), and F. meridionale (mean 5.6%, SEM 0.1%). All the other reads were assigned to F. graminearum (77.8%, SEM 0.1%). This correlates with the differences observed in the 200 bp sequence of the EF1α gene originating from Illumina Miseq sequencing, as F. cortaderiae, F. meridionale and F. acaciae-mearnsii have 4, 3 and 3 base pair differences, respectively. The other species of the FGSC showed fewer than 3 base pair differences in the 200 bp sequence of the EF1α gene used for the analysis, and could not be differentiated using the 2% sequence divergence threshold.

Analysis of the artificially infected grain samples

A total of 6,995,829 raw sequences were generated for the infected grain communities, and after filtering, 172,894 unique sequences were kept (2.4% of the raw sequences).

In samples contaminated with 4 species, the results showed that all these species could be retrieved in the mixed samples, regardless of the level and type of artificial contamination, the wheat varieties or the DNA extraction replicate (Fig 2). The results obtained comparing these 24 DNA samples (4 types of artificial contamination, 2 varieties, 3 extractions), sequenced in one run, were reproducible: F. graminearum (mean 89.4%, SEM 4.5%), F. langsethiae (mean 7.2%, SEM 2.5%), F. poae (mean 5%, SEM 1.6%), and F. tricinctum (mean 1.6%, SEM 0.5%). F. graminearum represented the highest percentage of reads as 10 grains infected with F. graminearum were mixed with only one single grain infected with the other species. EF1α sequences of F. poae, F. tricinctum or F. langsethiae could be detected in samples with only one single grain infected, mixed with 100 healthy grains. Real-time PCR analysis confirmed the presence of the 4 different Fusarium species in the samples (data not shown).

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Fig 2. Percentage of sequences in Fusarium OTUs retrieved from artificially infected grain samples.

G10 = 10 grains infected with F. graminearum; L1 = 1 grain infected with F. langsethiae; P1 = 1 grain infected with F. poae; T1 = 1 grain infected with F. tricinctum; CG09 and GAL = wheat varieties. The scale was adjusted as F. graminearum accounted for more than 75% of the reads for each sample.

https://doi.org/10.1371/journal.pone.0207988.g002

As the results were reproducible regardless of the wheat variety, it was decided to use only one variety to assess the sensitivity of the technique (F8-100 to F8-10,000, Table 2). In addition, as no significant effect of extraction and PCR cycle number were observed during the preliminary tests, these samples were extracted once, and tested with PCR cycling condition described in the materials and methods. The products of 6 independent PCRs were pooled per sample to improve sensitivity. EF1α sequences of F. avenaceum, F. graminearum, F. langsethiae, F. poae, F. subglutinans, Fusarium sp. (FTSC) and F. verticillioides were identified in all samples (99.3% ± 0.74% SEM all sequences) (Fig 3). No sequences of F. culmorum were observed. A two- to four-fold increase in non-Fusarium sequences was observed with higher levels of dilutions (F8-5000 and F8-10,000). In the control sample, 38,977 sequences were kept after filtering, of which 92.1% did not correspond to Fusarium OTUs. 4.6% of the reads were identified as Fusarium thapsinum and 1.9% as F. verticillioides. F. thapsinum was not identified in the other samples although the wheat seeds used all originated from the same sample.

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Fig 3. Percentage of sequences in Fusarium OTUs retrieved from artificially-infected grain samples.

Eight grains infected with F. poae, F. langsethiae, Fusarium sp. (FTSC), F. culmorum, F. avenaceum, F. graminearum, F. subglutinans and F. verticillioides were added to increased the number of uninfected grains. The same lot of uninfected grains was used as the control sample.

https://doi.org/10.1371/journal.pone.0207988.g003

Analysis of field samples

In all, 65 field samples were analyzed using the protocol developed in this study. Samples were collected from 12 fields of durum wheat, 30 fields of soft wheat, and 23 fields of barley in 2014 and 2015 (S1 Fig). A total of 5,115,242 raw sequences were generated. A total of 141,103 unique sequences remained after filtering (2.8% of the raw sequences). In order to obtain a sufficient amount of PCR amplicons before Illumina sequencing, independent PCR amplifications of the same DNA template were pooled. A first test showed that the percentage of Fusarium OTUs or the total percentage of non-Fusarium OTUs did not differ significantly (p > 0.5) between pooling 3 versus 6 PCR reactions before sequencing. Thus, 6 independent PCRs were performed for all samples and pooled before sequencing as this increased the amount for detection. The Heatmap (Fig 4) shows the abundance of each species detected. The abundance corresponds to the percentage of the number of reads per species out of the total number of reads in a sample. Up to 11 Fusarium species could be detected in wheat and up to 16 in barley. F. graminearum and F. poae were present in the majority of the samples. Fusarium sp. (FTSC), F. culmorum, F. langsethiae, F. verticillioides and F. avenaceum were also detected in many samples, regardless of the type of cereal. Some Fusarium species were only detected in wheat, such as F. proliferatum, whereas by contrast, other Fusarium species, such as F. thapsinum, F. torulosum, F. subglutinans, F. redolens, F. oxysporum, and F. mahasenii, were only observed in barley.

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Fig 4. Heatmap analysis of Fusarium species recovered from field samples.

The colour reflects the read abundance: from fewer reads in yellow to increased number of reads in red.

https://doi.org/10.1371/journal.pone.0207988.g004

The DNA extracts from the 65 field samples were also analyzed by seven species-specific real-time PCR tests targeting F. graminearum, F. culmorum, F. poae, F. tricinctum, F. langsethiae, F. sporotrichioides and F. avenaceum, respectively (S1 Table). These real-time PCR tests were developed in 2009 using the Fusarium species taxonomy that was in force at that time. Since then, new cryptic species have been described, like F. sibiricum which is closely related to F. sporotrichioides, or taxa from the F. graminearum or F. tricinctum species complex. These cryptic species will therefore not be differentiated with the real-time PCR tests used here. Comparison of the real-time PCR results and the metabarcoding analyses is summarized in Table 4. Only a few discrepancies were observed. The level of discrepancy varied according to the Fusarium species. Similar results were obtained using the two techniques for F. culmorum, F. poae and F. sporotrichioides (< 8% of discrepant results), whereas a higher percentage of discrepant results was observed for F. avenaceum, F. graminearum, F. langsethiae and F. tricinctum (18.46% to 36.92%). Except for F. tricinctum, the metabarcoding always detected more positive samples than real-time PCR did.

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Table 4. Discrepancy analysis results for 65 field cereal samples by real-time PCR and PCR/Illumina sequencing.

https://doi.org/10.1371/journal.pone.0207988.t004

This table summarised the number of samples in which Fusarium species were detected by qPCR or the metabarcoding techniques developed in this paper. For each species, the number of samples that were positively detected by both qPCR and the metabarcoding technique (+/+), the number samples negatively detected by both techniques (-/-) and the number of samples positively detected only by qPCR (+/-) or only by metabarcoding (-/+) are presented. A percentage of discrepancies in detection is calculated at the bottom of the columns for each species.

EF1α sequences from other genera were also identified. The percentage of reads for non-Fusarium species varied from 5% to 95% of total reads after filtering (Fig 5). The percentage of non-Fusarium reads increased when contamination by Fusarium species was low. For example, small amounts of Fusarium species were detected by real-time PCR in samples S_2742, S_2734 and S_2736, in which up to 95% of non-Fusarium species were identified by metabarcoding. The non-Fusarium taxa identified in durum wheat were Alternaria, Cladosporium, Colletotrichum, Ilyonectria, and Lewia; whereas Alternaria, Campylocarpon, Chalastospora, Cladosporium, Colletotrichum, Diaporthe, Lewia, Trichoderma and Zymoseptoria were detected in bread wheat and Alternaria, Cladosporium, Colletotrichum, Gnomoniopsis, Lewia, Trichoderma and Zymoseptoria were detected in barley.

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Fig 5. Percentage of Fusarium and non-Fusarium reads per sample after bioinformatic analyses.

Percentage of Fusarium reads are presented in black and percentage of non Fusarium reads in hatched per field sample.

https://doi.org/10.1371/journal.pone.0207988.g005

Discussion

This study presents the development and validation of a metabarcoding-based protocol to assess Fusarium diversity on cereals. The protocol includes a method for extracting good quality DNA, followed by PCR amplification of a genus-specific, polymorphic region of the EF1α gene, followed by Illumina Miseq sequencing of the amplicons and analysis of the reads to identify Fusarium species. A new specific PCR primer pair was designed that targets the EF1α gene, allowing species-level discrimination, which confirms the potential of this gene previously reported [27]. In metabarcoding techniques, the initial PCR is an important step to correctly describe a community due to bias resulting from primer mismatches or PCR inhibitors [42]. As we selected conserved primers preferentially targeting the EF1α gene from species that belong to a single genus, Fusarium, primer mismatch should be avoided. An advantage of using the EF1α gene to study Fusarium communities is that it appears to be present as a single copy in this genus [27], thus circumventing problems caused by the occurrence of non-orthologous copies or a variable copy number among species, like in the case of the ITS gene, which is mainly used for metabarcoding [18]. Recently specific primers for the EF1α gene were used to assess Fusarium diversity in field samples from soil and wheat kernels in association with 454 pyrosequencing [24]. In a different study [22], the authors used another single-copy protein-coding locus (RPB2) to target the genus Fusarium associated with the rhizosphere of native grassland plants using 454 technology [22]. In our study, Illumina Miseq technology was used in order to deepen the sequencing compared to 454 technology. In addition, Illumina technology enabled the simultaneous processing of a larger number of samples. Indeed, in our study, up to 154 field samples could be sequenced at the same time, with a mean number of 88,663 reads per sample before bioinformatic analysis.

DNA mock communities and artificially infected grain samples were used to validate our metabarcoding protocol. A sufficient number of reads could be retrieved and assigned to species in our DNA mock communities using the forward Illumina MiSeq sequences. Reverse Illumina MiSeq sequences could have given complementary information, but could not be used due to poor quality. One difficulty using high-throughput sequencing technologies is to validate OTUs with low numbers of reads, as they can be the result of PCR and/or sequencing errors [43]. DNA mock communities were used to determine an error rate, which was calculated by comparing the Sanger sequence of each strain included in each mock community with the sequence of the OTUs obtained. The maximum error rate for the 5 mock communities was estimated to be 0.3%. Thus, to ensure that correct OTUs were retained in all samples, all OTUs with a number of reads below 0.3% of the total reads per sample were removed. Rare OTUs were thus removed from all samples with a similar rule in order to avoid overestimation of the diversity present in the samples. EF1α sequences from all 20 Fusarium species used were successfully retrieved after analysis of the DNA mock communities. In addition, 3 species out of 10 in the FGSC (F. cortaderiae, F. meridionale and F. acaciae-mearnsii) could also be identified using our protocol. Interestingly, even though absolute quantification is not possible due to PCR bias, all Fusarium species in mock communities 2 and 4 yielded approximately identical percentages of total reads. This might be due to the fact that EF1α used for barcoding is a single copy gene. However, the amplification efficiency of the primers developed in this study might be different and may depend on the target species. In mock community 3, F. culmorum accounted for only 5% of the reads.

All Fusarium species, except F. culmorum, could be detected in the artificially infected grain samples. Several markers of the strain used for inoculation were sequenced and the sequences obtained were identical to the sequences of other F. culmorum strains. However, F. culmorum was detected in 24 field samples showing that our metabarcoding protocol is also able to specifically identify F. culmorum in complex samples. A close look at the results for artificially infected grain samples showed that the OTU assigned to F. graminearum had a score of 56. This score is a bootstrap value; the higher it is, the greater the probability that the correct name is given to an OTU. The other OTUs had a score of 75–100. After careful checking, some reads grouped in this OTU could be assigned to F. culmorum. The errors seemed to be linked to a PCR chimera between F. graminearum and F. culmorum not recognized by the bioinformatic pipeline. However, careful examination of the final identification scoring should prevent these errors.

The analysis of a sample using our metabarcoding approach will yield a large batch of barcode sequences. Assignation to a species name using a barcode sequence will first depend on the quality of the reference database used and second on the phylogenic resolution of the chosen barcode. The phylogenic resolution of the EF1α barcode used in this publication allowed us to identify species commonly present in FHB disease. However some closely related (or cryptic-) species might be difficult to differentiate due to barcode resolution. For example, to the EF1α region that was used here will not enable to discriminate some strains of the FGSC complex (F. vorosii, F. brasilicum, F. gerlachii) from F. graminearum. Another example are the closely related species F. sibiricum and F. sporotrichioides [44]. It is obvious that the quality of the overall assignation pipeline will depend on the quality of the reliability of the DNA sequence database used, and on the fact that the species names are regularly updated according to the state of the art regarding taxonomy in this intensively studied genus.

As the protocol proposed was aimed at assessing Fusarium diversity on field samples, it was important to test the reproducibility and the sensitivity of the technique. In this study, metabarcoding proved to be reproducible using DNA mock community samples, independent of the amplification or the sequencing run. Numbers of PCR cycles have been reported as an important factor increasing contamination [45], but no such effects were observed in our experiments by testing different cycle numbers. Analysis of artificially infected wheat samples showed the high sensitivity of metabarcoding for Fusarium. One infected grain could be detected in a total of 10,000 grains.

As a proof of principle, further analyses were performed on 65 cereal samples harvested across the major cereal-growing regions of France. Overall, 17 different Fusarium species were detected in these samples of barley, durum and soft wheat. In wheat samples, F. graminearum and F. poae were the dominant species, followed by Fusarium sp. (FTSC), F. culmorum and F. avenaceum, which are common Fusarium species previously recovered from wheat in France [2, 3].In barley samples, F. poae and Fusarium sp. (FTSC) were the dominant species, followed by F. langsethiae, F. graminearum and F. avenaceum. These species were previously reported from barley samples collected in France, except F. langsethiae [2, 3]. As F. langsethiae was described in 2004 [46] and it resembles F. poae morphologically, F. langsethiae was probably present on barley sampled in France in 2004 but was likely assigned to F. poae in a previous survey [2]. To our knowledge, this is the first time species such as F. thapsinum, F. torulosum, F. redolens and F. mahasenii have been reported on barley, and F. proliferatum on wheat collected in France. In a survey conducted on FGSC diversity in France in 2011, F. graminearum sensu stricto was predominant on wheat, barley and maize, but three isolates of F. cortaderiae and two isolates representing F. graminearum × F. boothii hybrids were also identified from maize [47]. The tool developed in this study should recover the F. cortaderiae isolates in France but not the hybrid. As the symptoms on cereal grains are not specific to the Fusarium species causing the disease, real-time PCR tests have been developed for Fusarium species detection and quantification in field samples [15]. Some of these tests are multiplexed in order to reduce the costs of analysis [48, 49]. Using our metabarcoding protocol, up to 12 Fusarium species could be detected in field samples and up to 150 samples could be processed at the same time. Species like F. torulosum, F. sambucinum and 3 species of the FGSC were also detected. As far as we know, no specific real-time PCR tests are available for these species. It is difficult to compare real-time PCR tests with the metabarcoding technique to detect Fusarium species because the efficiency of the species-specific primers used in real-time PCR tests and the length of the amplified fragment vary depending on the species. We used the real-time PCR only to verify that Fusarium species were present in our field samples. Discrepancies were observed between the results of the two techniques, with up to 37% non-corresponding detection observed (Table 4). In the majority of cases, discrepancies were due to detection of species by metabarcoding but not by real-time PCR. It is difficult to conclude whether these correspond to true or false positives. Due to PCR amplification biases, each technique may be favourable to the detection of one species rather than another. A possible explanation for these discrepant results may be that the limit of detection of each test (metagenomics and the different species-specific qPCR test) may be variably affected by the presence of inhibiting compounds in the DNA extracts. Since the level of contamination by some of the Fusarium species was sometimes very low, it is likely that some of the analyses were at the limit of detection for some of the targets.

Some of the reads generated were not assigned to species of Fusarium. As the identification of other genera was not included in the objectives of this study, the corresponding OTUs were only assigned to the genus level even though a species name could be determined. Importantly, when low Fusarium contamination was found in field samples, the amounts of non-Fusarium species increased. It is also interesting to note that in artificially infected samples, when Fusarium contamination decreased, an increase in non-Fusarium species was observed. This could be due to the lack of target species that decrease competition for the primer, leading to less specific amplification. However, it was shown that reagent and laboratory contamination increased in the results, especially when there was a low amount of starting material [50]. As a result, special care should be taken when performing metabarcoding experiments.

Conclusion

Using mock communities and artificially infected samples, a protocol was developed to assess the composition of Fusarium species in field samples without a priori criteria. For the first time, the sensitivity and reproducibility of the technique proved to be suitable to detect most of the Fusarium species with low contamination levels (up to 1 seed in 10,000). Moreover, 3 species out of the 10 tested in the FGSC could be distinguished. The possibility of multiplexing samples makes this protocol suitable for large-scale Fusarium epidemiological surveys, such as those studying the impact of climate change, or of evolving cultural farming practices.

Supporting information

Acknowledgments

This work was part of the PSPE 2018 program (Pour et Sur le Plan Ecophyto 2018). We would like to thank the GeT Genotoul platform in Toulouse, Midi-Pyrénées for the MiSeq sequencing experiments. We are grateful to the Genotoul Bioinformatics platform in Toulouse, Midi-Pyrénées for providing help computing and data storage.