domain: sdv sde

text:
Anne-Laure Boutigny

Angelique Gautier

Ryan Basler

Florian Dauthieux

Stephen Leite

Romain Valade

Jaime Aguayo

Renaud Ioos

Valerie Laval
email: valerie.laval@inra.fr

Ange ´lique Gautier

Vale ´rie Laval

Metabarcoding targeting the EF1 alpha region to assess Fusarium diversity on cereals

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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 [START_REF] Parry | Fusarium ear blight (scab) in small grain cereals-a review[END_REF], 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 [START_REF] Ioos | Occurrence and distribution of Microdochium nivale and Fusarium species isolated from barley, durum and soft wheat grains in France from 2000 to 2002[END_REF][START_REF] Ioos | The effects of fungicides on Fusarium spp. and Microdochium nivale and their associated trichothecene mycotoxins in French naturally-infected cereal grains[END_REF]. These Fusarium species are frequently associated with one another in the infected grains and constitute a disease complex [START_REF] Bottalico | Fusarium diseases of cereals: species complex and related mycotoxin profiles in Europe[END_REF]. 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 [START_REF] Bottalico | Toxigenic Fusarium species and mycotoxins associated with head blight in small-grain cereals in Europe[END_REF][START_REF] Waalwijk | Major changes in Fusarium spp. in wheat in The Netherlands[END_REF][START_REF] Xu | Predominance and association of pathogenic fungi causing Fusarium ear blight in wheat in four European countries[END_REF][START_REF] Xu | Relationship between the fungal complex causing Fusarium head blight of wheat and environmental conditions[END_REF][START_REF] Audenaert | Fusarium head blight (FHB) in Flanders: population diversity, inter-species associations and DON contamination in commercial winter wheat varieties[END_REF][START_REF] Chandelier | Fusarium species and DON contamination associated with head blight in winter wheat over a 7-year period (2003-2009) in Belgium[END_REF][START_REF] Nielsen | Fusarium head blight of cereals in Denmark: species complex and related mycotoxins[END_REF][START_REF] Hietaniemi | Updated survey of the Fusarium species and toxins in Finnish cereal grains[END_REF].

Most Fusarium species are able to produce one or more mycotoxins with varying degrees of toxicity [START_REF] Bottalico | Toxigenic Fusarium species and mycotoxins associated with head blight in small-grain cereals in Europe[END_REF]. 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 [START_REF] Ioos | Occurrence and distribution of Microdochium nivale and Fusarium species isolated from barley, durum and soft wheat grains in France from 2000 to 2002[END_REF][START_REF] Doohan | The use of species-specific PCR-based assays to analyse Fusarium ear blight of wheat[END_REF][START_REF] Waalwijk | Quantitative detection of Fusarium species in wheat using TaqMan[END_REF][START_REF] Nicolaisen | Real-time PCR for quantification of eleven individual Fusarium species in cereals[END_REF]. 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 [START_REF] Doohan | The use of species-specific PCR-based assays to analyse Fusarium ear blight of wheat[END_REF][START_REF] Waalwijk | Quantitative detection of Fusarium species in wheat using TaqMan[END_REF][START_REF] Nicolaisen | Real-time PCR for quantification of eleven individual Fusarium species in cereals[END_REF], 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 [START_REF] Schoch | Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi[END_REF], 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 [START_REF] Bue ´e | 454 Pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity[END_REF][START_REF] Amend | Quantifying microbial communities with 454 pyrosequencing: does read abundance count?[END_REF][START_REF] Jumpponen | Seasonally dynamic fungal communities in the Quercus macrocarpa phyllosphere differ between urban and nonurban environments[END_REF][START_REF] Liu | Microbial community diversities and taxa abundances in soils along a seven-year gradient of potato monoculture using high throughput pyrosequencing approach[END_REF][START_REF] Arfi | Multiple markers pyrosequencing reveals highly diverse and host-specific fungal communities on the mangrove trees Avicennia marina and Rhizophora stylosa[END_REF][START_REF] Leblanc | Soil fungal communities respond to grassland plant community richness and soil edaphic[END_REF][START_REF] Sapkota | Host genotype is an important determinant of the cereal phyllosphere mycobiome[END_REF][START_REF] Karlsson | Genus-specific primers for study of Fusarium communities in field samples[END_REF]. 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 [START_REF] Schoch | Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi[END_REF]. ITS sequences are largely represented in international databases [START_REF] Schoch | Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi[END_REF][START_REF] Nilsson | An open source chimera checker for the fungal ITS region[END_REF] and have often been used in fungal diversity studies [START_REF] Bue ´e | 454 Pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity[END_REF][START_REF] Nilsson | The ITS region as a target for characterization of fungal communities using emerging sequencing technologies[END_REF]. However, this barcode does not always enable species discrimination for some genera, in particular within Fusarium [START_REF] Geiser | FUSARIUM-IDv.1.0: a DNA sequence database for identifying. Fusarium[END_REF]. 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 [START_REF] O'donnell | Gene genealogies reveal global phylogeographic structure and reproductive isolation among lineages of Fusarium graminearum, the fungus causing wheat scab[END_REF][START_REF] O'donnell | Multilocus genotyping and molecular phylogenetics resolve a novel head blight pathogen within the Fusarium graminearum species complex from Ethiopia[END_REF], and was selected as the barcode of choice for the FUSARIUM-ID online identification tool [START_REF] Geiser | FUSARIUM-IDv.1.0: a DNA sequence database for identifying. Fusarium[END_REF]. 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 speciesspecific 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 ve ´ge ´tal 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 [START_REF] Dereeper | Phylogeny.fr: robust phylogenetic analysis for the non-specialist[END_REF] 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 [START_REF] Geiser | FUSARIUM-IDv.1.0: a DNA sequence database for identifying. Fusarium[END_REF]

(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.

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.

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 [START_REF] Geiser | FUSARIUM-IDv.1.0: a DNA sequence database for identifying. Fusarium[END_REF]. In total, the database contained 162 sequences, covering 74 species. Using Phylogeny.fr [START_REF] Dereeper | Phylogeny.fr: robust phylogenetic analysis for the non-specialist[END_REF], a phylogenic tree was built and 6 divergent sequences were selected and aligned using MultAlin [START_REF] Corpet | Multiple sequence alignment with hierarchical clustering[END_REF] 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'TCATC GGCCACGTCGACTCT3') and EF1-R3 (5'TACCAGCCTCGAAC TCACCA3') (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 (PYROF US-EF1-F2-MISEQ: 5'CTTTC CCTACACGACGCTCTTCCGATCTATTAACCCTCACTAA AGGGATCATCGGCCACGTCGAC TCT3' and PYROFUS-EF1-R3-MISEQ 5'GGAGTTC AGACGTGTGCTCTTCCGATCT CCCTATAGTGAGTCGTATTATACCAGCCTCGAACTCACC A3'). 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 MgCl 2 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 [START_REF] Elbelt | Optimized real-time qPCR assays for detecting and quantifying the Fusarium and Microdochium species responsible for wheat head blight, as defined by MIQE guidelines[END_REF]. 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.

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 [START_REF] Kozich | Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform[END_REF] was used to filter and process raw sequences utilizing MOTHUR v.1.36 [START_REF] Schloss | Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities[END_REF] (S7 Fig) . After FastQC analysis [START_REF] Andrews | FastQC: a quality control tool for high throughput sequence data[END_REF], 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 [START_REF] Edgar | UCHIME improves sensitivity and speed of chimera detection[END_REF] available in MOTHUR, using self as the reference. Uncorrected pairwise distances were determined between the remaining sequences with the Needleman alignment method [START_REF] Needleman | A general method applicable to the search for similarities in the amino acid sequence of two proteins[END_REF] 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: 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 [START_REF] Mcmurdie | Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data[END_REF]. Using 'Phyloseq', a Heatmap of abundance of each species per sample was generated.

F. culmorum EF1-FC_F2 CGAATCGCCCTCACACG 300nM 62˚C EF1-FC-R2 GTGATGGTGCGCGCCTAG 300nM culmo2-EF1-R2 ATGAGCCCCACCAGAAAAATTACGACAA 100nM F. poae EF1-FP2_F CTCGAGCGATTGCATTTCTTT 300nM 60˚C EF1_FP2_R GGCTTCCTATTGACAGGTGGTT 300nM EF1-FP CGCGAATCGTCACGTGTCAATCAGTT 100nM F. sporotrichioides EF1-FS_F3 GGCTCATACCCCGCCG 300nM 60˚C EF1-FS_R2 GCGCCCATGTAAATGGATG 300nM EF1 FS TGGGAAGGGCAAAAGCGCCTGT 100nM F. langsethiae EF1 FL F3 GCCGTGTCGTAATTTTTTTTGTG 300nM 62˚C EF1 FL R3 AAATGGCTATGTGGGAAGGAAG 300nM EF1_FL GGGCTCATACCCCGCCACTCGA 300nM
F. avenaceum EF1-FA_F2 CATCTTGCTAACTCTTGACAGACCG 300nM 64˚C EF1-FA_R3 GGGTAATGAATGCGTTTCGAA 300nM Ef1 FA AGCGAGTCGTGGGAATCGATGGG 150nM F. tricinctum EF1-FT_F2 CCACGATTCGCTCCCTCAC 300nM 62˚C EF1-FT_R2 GGTAAGATACCCCACCAGAAAAA 300nM EF1 FT AGCGGGGTAATGGATGCGTTTCGAGT 150nM � All

Results

Design of a Fusarium-specific PCR primer pair

The different PCR primers were evaluated on a panel of DNA extracts representing 30 Fusarium species and 23 non-Fusarium species. Primers EF1-F2 and EF1-R3 were selected due to their specificity for the Fusarium genus (S5 Fig) , and their ability to generate a single sharp band after electrophoresis on an agarose gel (data not shown). The primers amplified a 640 bp region of the EF1α gene in the Fusarium species, displaying a high level of interspecific polymorphism. This region was successfully amplified with DNA from the 30 Fusarium species tested. No PCR amplification was observed with DNA from uninfected wheat. Weak amplification signals were observed for a few non-target taxa, such as Alternaria alternata, Botrytis cinerea, Sclerotinia sclerotiorum, Zymoseptoria tritici, Leptosphaeria maculans, Pseudocercosporella herpotrichoides, Phaeoacremonium mortoniae, Epicoccum nigrum, Eutypa lata, Cladosporium delicatulum. and Colletotrichum orbiculare. (S5 Fig).

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.

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).

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.

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. 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.

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.

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 [START_REF] Geiser | FUSARIUM-IDv.1.0: a DNA sequence database for identifying. Fusarium[END_REF]. 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 [START_REF] Tedersoo | Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi[END_REF]. 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 [START_REF] Geiser | FUSARIUM-IDv.1.0: a DNA sequence database for identifying. Fusarium[END_REF], 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 [START_REF] Amend | Quantifying microbial communities with 454 pyrosequencing: does read abundance count?[END_REF]. 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 [START_REF] Karlsson | Genus-specific primers for study of Fusarium communities in field samples[END_REF]. In a different study [START_REF] Leblanc | Soil fungal communities respond to grassland plant community richness and soil edaphic[END_REF], 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 [START_REF] Leblanc | Soil fungal communities respond to grassland plant community richness and soil edaphic[END_REF]. 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 [START_REF] Taylor | Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for Illumina amplicon sequencing[END_REF]. 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 [START_REF] Yli-Mattila | Fusarium sibiricum sp. nov, a novel type A trichothecene-producing Fusarium from northern Asia closely related to F. sporotrichioides and F. langsethiae[END_REF]. 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 [START_REF] Polz | Bias in Template-to-Product Ratios in Multitemplate PCR[END_REF], 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 [START_REF] Ioos | Occurrence and distribution of Microdochium nivale and Fusarium species isolated from barley, durum and soft wheat grains in France from 2000 to 2002[END_REF][START_REF] Ioos | The effects of fungicides on Fusarium spp. and Microdochium nivale and their associated trichothecene mycotoxins in French naturally-infected cereal grains[END_REF].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 [START_REF] Ioos | Occurrence and distribution of Microdochium nivale and Fusarium species isolated from barley, durum and soft wheat grains in France from 2000 to 2002[END_REF][START_REF] Ioos | The effects of fungicides on Fusarium spp. and Microdochium nivale and their associated trichothecene mycotoxins in French naturally-infected cereal grains[END_REF]. As F. langsethiae was described in 2004 [START_REF] Torp | Fusarium langsethiae sp. nov. on cereals in Europe[END_REF] 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 [START_REF] Ioos | Occurrence and distribution of Microdochium nivale and Fusarium species isolated from barley, durum and soft wheat grains in France from 2000 to 2002[END_REF]. 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 [START_REF] Boutigny | Diversity of the Fusarium graminearum species complex on French cereals[END_REF]. 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 [START_REF] Nicolaisen | Real-time PCR for quantification of eleven individual Fusarium species in cereals[END_REF]. Some of these tests are multiplexed in order to reduce the costs of analysis [START_REF] Yli-Mattila | Real-time PCR detection and quantification of Fusarium poae, F. graminearum, F. sporotrichioides and F. langsethiae in cereal grains in Finland and Russia[END_REF][START_REF] Bluhm | Multiplex real-time PCR detection of fumonisin-producing and trichothecene-producing groups of Fusarium species[END_REF]. 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 [START_REF] Salter | Reagent and laboratory contamination can critically impact sequence-based microbiome analyses[END_REF]. 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.

  (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

  probes were labelled with FAM fluorophore and BHQ1 Quencher https://doi.org/10.1371/journal.pone.0207988.t003 [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.Therewere 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[START_REF] Crous | MycoBank: an online initiative to launch mycology into the 21st century[END_REF]. 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[START_REF] Wang | Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy[END_REF] 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;[START_REF] Schloss | Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities[END_REF]). 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[START_REF] Fox | The R Commander: A Basic Statistics Graphical User Interface to R[END_REF] was used to evaluate the influence of PCR cycle number, run and PCR pooling on the percentage of final Fusarium OTUs.

Fig 1 .

 1 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

Fig 2 .

 2 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

Fig 3 .

 3 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

Fig 4 .

 4 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

Fig 5 .

 5 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

Table 1 . DNA mock communities description.

 1

	Mock 1		Mock 2		Mock 3
	Species	ID	Species	ID	Species	ID

Fusarium oxysporum FOR 4 Fusarium equiseti (FIESC 14) C33 Fusarium culmorum MTK0055 Fusarium sambucinum C39 Fusarium graminearum MTK0019 Fusarium poae MTK0005 Fusarium sp. (FSSC 11) FJMO Fusarium sambucinum C39 Fusarium pseudonygamai FoxFMO14

	Fusarium sp.	22374	Cladosporium delicatulum	C58	Fusarium sambucinum	C39
	Fusarium staphyleae	22316	Cladosporium sphaerospermum	96	Fusarium sporotrichioides	Fs23
	Alternaria alternata	C61	Colletotrichum orbiculare	C-orbiculare	Calonectria pseudonaviculata	93
	Botrytis cinerea	C87	Eutypa lata	0002-11	Epicoccum nigrum	100
	Helminthosporium tritici-repentis	MTK0038	Gaeumannomyces graminis var. tritici	MTK0077	Leptosphaeria maculans	102
	Phaeoacremonium mortoniae	0002-12	Microdochium nivale	Mn08N58	Microdochium majus	MTK0025
	Puccinia triticina	B77Saba	Pseudocercosporella herpotrichoides	MTK0049	Mortierella elongata	95
	Parastagonospora nodorum	MTK0081			Pseudonectria buxi	94
	Sclerotinia sclerotiorum	C62
	Mock 4		Mock 5
	Species	ID	Species	ID
	Fusarium culmorum	MTK0009	Fusarium cortaderiae	NRRL31171
	Fusarium langsethiae	Fl39	Fusarium meridionale	NRRL29010
	Fusarium sambucinum	C39	Fusarium acaciae-mearnsii	NRRL26755
	Fusarium subglutinans	Fsub69	Fusarium austroamericanum	NRRL28718
	Fusarium temperatum	Fpro32-2	Fusarium aethiopicum	NRRL46722
	Fusarium sp. (FTSC)	FT12	Fusarium brasilicum	NRRL31238
	Fusarium avenaceum	MTK0070	Fusarium vorosii	NRRL38208
	Fusarium verticillioides	Fmoni62	Fusarium gerlachii	NRRL38380
	Leptosphaeria biglobosa	C68	Fusarium mesoamericanum	NRRL25797
	Microdochium bolleyi	98	Fusarium asiaticum	NRRL13818
	Zymoseptoria tritici	MTK0030
	This table presents the species name and identification number (ID) of the strains used to prepare the 5 mock communities. DNA of these strains was extracted and
	mixed in equimolar quantity after Qubit quantification.
	https://doi.org/10.1371/journal.pone.0207988.t001

Table 2 . Description of artificially infected grain samples prepared in this study. Sample name Number of artificially infected grains Number of uninfected grains

 2

	G10 P1 L1	10	1	1	1
	T1

F. graminearum F. langsethiae F. poae F. sp. FTSC F. culmorum F. avenaceum F. subglutinans F. verticillioides

This table describe which Fusarium species were used to inoculate grain and the number of infected grains and uninfected grains per samples. https://doi.org/10.1371/journal.pone.0207988.t002

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

 3

	Target species	Name	Sequence �	Final concentration	Annealing temperature
	F. graminearum	EF1-FCFG_F	TCGATACGCGCCTGTTACC	300nM	62˚C
		EF1-FG_R	ATGAGCGCCCAGGGAATG	300nM
		grami2-EF1_rev	AGCCCCACCGGGAAAAAAATTACGACA	100nM

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

 4

	Detection	F.	F.	F.	F.	F.	F.	F.
	qPCR / metabarcoding	avenaceum	culmorum	graminearum	langsethiae	poae	sporotrichioides	tricinctum
	+/+	23	24	49	17	62	0	40
	-/-	26	40	4	24	1	60	11
	+/-	2	1	0	1	2	5	8
	-/+	14	0	12	23	0	0	6
	Discrepancies (in % of samples of non-corresponding	24.62	1.54	18.46	36.92	3.08	7.69	21.54
	detection out of total samples)

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

PLOS ONE | https://doi.org/10.1371/journal.pone.0207988January 11, 2019

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-Pyre ´ne ´es for the MiSeq sequencing experiments. We are grateful to the Genotoul Bioinformatics platform in Toulouse, Midi-Pyre ´ne ´es for providing help computing and data storage.

This work was supported by AAP PSPE 2012 (Appel à proposition Pour et Sur le Plan Ecophytro de Juin 2012) www.onema.fr.

All relevant data are within the paper and its Supporting Information file. Sanger DNA sequences are deposited in Rsyst::fungi database (http://138.102.89.206/new_ rsyst_chp/). Metabarcoding data are deposited in the European Nucleotide Archive (ENA) (accession number: PRJEB27158). QPCR protocol is published in https://doi.org/10.1101/272534.

Supporting information


Keywords: endosperm, hardness, microstructure, puroindoline, Triticum

Light transmission through wheat (T. aestivum L.) grain longitudinal cross sections of different thickness was used to study the endosperm microstructure and was shown to strictly follow a Beer-Lambert law allowing a non ambiguous quantification of the endosperm vitreousness. Therefore similar samples obtained from near-isogenic lines differing by hardness and grown in two distinct environments affecting their vitreousness were analyzed and confirmed the relationship between light transmission and the endosperm microstructure. In each sample, moreover analysis of light transmission within the different grain parts highlighted the greater compactness of the central endosperm cheeks in comparison with the distal and the proximal regions.

These results helps a better understanding of the endosperm microstructure.

Introduction

The protein-starch adhesion and the microstructure of the common wheat (Triticum aestivum L.) starchy endosperm were found to differ depending on genetic and environmental factors [START_REF] Turnbull | Endosperm texture in wheat[END_REF]. These factors are both found to affect the grain mechanical resistance i.e. endosperm hardness [START_REF] Haddad | Rheological 278 behaviour of wheat endospermproposal for classification based on the 279 rheological characteristics of endosperm test samples[END_REF] and thus play a key role in grain milling behavior. Recently, an effort was made to experimentally clarify the role of these factors using genetically well-defined grains grown under different cultural conditions [START_REF] Greffeuille | Grain characterization and milling be-283 haviour of near-isogenic lines differing by hardness[END_REF][START_REF] Oury | Bread 287 wheat milling behavior: effects of genetic and environmental factors, 288 and modeling using grain mechanical resistance traits[END_REF]. The main genetic locus controlling the starchy endosperm texture, called Ha, was located on the short arm of chromosome 5D [START_REF] Chantret | Molecular ba-292 sis of evolutionary events that shaped the hardness locus in diploid and 293 polyploid wheat species (Triticum and Aegilops)[END_REF] where two important genes encoding specific proteins called puroindoline A (PINA) and puroindoline B (PINB), were found [START_REF] Bhave | Molecular genetics of puroindolines and related 296 genes: allelic diversity in wheat and other grasses[END_REF][START_REF] Morris | Puroindolines: the molecular genetic basis of wheat grain 299 hardness[END_REF]. Presence of the wild-type alleles of both puroindoline genes (Pina-D1a/Pinb-D1a) leads to both functional PINA and PINB and results in a soft mechanical behavior, whereas mutation or deletion of one or both of the puroindoline genes was found to lead to a hard texture [START_REF] Morris | Puroindolines: the molecular genetic basis of wheat grain 299 hardness[END_REF]. Moreover, translocation of the wild type puroindoline genes in a durum background [START_REF] Morris | Transfer of soft ker-301 nel texture from Triticum aestivum to durum wheat, Triticum turgidum 302 ssp. durum[END_REF], which lacks D genome, leads to mechanical resistance and similar characteristics to soft common wheat i.e. a higher production of the finest particles with low starch damage after milling [START_REF] Morris | 304 D. Lafiandra, Soft kernel durum wheat a new bakery ingredient?[END_REF][START_REF] Murray | Effect of soft 307[END_REF]11]. Conversely, removal of the chromosome 5D distal part (which carries the puroindolines genes) in a soft hexaploid wheat led to hard vitreous grains [12].

Differences at the interface between protein and starch granules are suggested to explain the mechanical differences between soft and hard cultivars [13] and puroindolines are believed to affect the starch-protein adhesion. Indeed recent studies showed mechanical changes at the interface using near- Besides genetic factors, environmental conditions were found to affect the starchy endosperm appearance the so-called vitreousness [15] which is an optical property attributed to differences in endosperm porosity [16]. Grains are classified as mealy when the starchy endosperm is porous and appears white and floury, or as vitreous when it is transluscent and glassy. Vitreousness is generally estimated through examination of a number of grain cross-sections made with a Pohl grain cutter. However, this method is operator-dependent and time consuming. Therefore, other methods were recently developed for rapid classification of wheat grains depending on their vitreousness level.

Comment
Transmitted light images were found allowing correct classification according to vitreousness even if percentages of accuracy relative to the visual inspection differ between authors [START_REF] Neethirajan | Classification 331 of vitreousness in durum wheat using soft x-rays and transmitted light 332 images[END_REF][START_REF] Venora | Quality assessment of durum wheat 334 storage centres in Sicily: evaluation of vitreous, starchy and shrunken 335 kernels using an image analysis system[END_REF]. Soft X-ray, dual energy X-ray or light reflectance coupled with image analysis were also found to be potentially efficient in the differentiation between vitreous and non-vitreous grains [START_REF] Neethirajan | Classification 331 of vitreousness in durum wheat using soft x-rays and transmitted light 332 images[END_REF][START_REF] Neethirajan | Dual energy x-ray image 338 analysis for classifying vitreousness in durum wheat[END_REF][START_REF] Xie | Detecting vitreous wheat 341 kernels using reflectance and transmittance image analysis[END_REF]. Near infrared hyper-spectral imaging was also used to satisfactory classified vitreous from non-vitreous grains [START_REF] Gorretta | Determining vitreousness of durum wheat kernels using near 345 infrared hyperspectral imaging[END_REF][START_REF] Serranti | The development of a hyperspectral 348 imaging method for the detection of Fusarium-damaged, yellow berry 349 and vitreous Italian durum wheat kernels[END_REF]. These classification methods are rapid and non-destructive. However, they are based on reference wheat samples which need to be well characterized and are only valid for grains which share the same characteristics as the references (grain geometric characteristics, tissue thickness, colors, hardness, etc.). These recent methods are hardly used to quantify the level of vitreousness as a function of the microstructure.

In the present work, we chose grains from two near-isogenic lines with a

Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. ( 2018 Then, locally distinct regions within the endosperm were explored thanks to an original experimental assembly allowing measurement of transmitted light through grain cuts. It was thus possible to objectively compare the endosperm microstructure between the four wheat grain samples depending on genetic or environmental conditions, as well as within each type of sample depending on intra-grain location.

Material and Methods

Wheat grains

Near-isogenic lines (NIL) of Triticum aestivum L. were produced by Institut National de la Recherche Agronomique (INRA) and displayed either the wild-type Pinb-D1a or the mutated Pinb-D1b allele (leading to single amino acid change in PINB, Gly46Ser ), which respectively conferred to grains the soft or the hard phenotype. They were derived from a cross after selection of the two allelic forms at the F6:F7 selfing generation (F7 siblings issued from the same F6 parent plant, construction and genetic similarity testing detailed in [START_REF] Greffeuille | Grain characterization and milling be-283 haviour of near-isogenic lines differing by hardness[END_REF]). Grains from the two lines were cultivated in different locations in France, collected and cleaned to remove broken kernels or impurities and stored at 4 multiplied by the corresponding factor of vitreousness for that class, and the percentage of vitreousness in the grain sample was calculated according to the following equation:

N 1 ×0.00+N 2 ×0.25+N 3 ×0.50+N 4 ×0.75+N 5 ×1 N 1 +N 2 +N 3 +N 4 +N 5
, where the sum of the analyzed grains in each class was around 500. From a Student test the maximum error on this measurement was estimated, to be lower than 2 units.

Grains harvested from two different locations (Cappelle (50

• 29N/3 • 10E) and
Maule (48

• 54N/1 • 51E))
were retained for this study as they were found to display contrasting endosperm vitreousness [15].

Light transmission through endosperm samples

Wheat kernels were abraded longitudinally from the back and the ventral sides with a 240 grit extra fine sandpaper (grit size 58.5 µm). Abraded grains were placed above a plate light (flat dome Light, LFX-100, CCS, Japan) that diffuses uniform white LED lighting (Figure 1a). In order to avoid straight light the plate light was covered with a black paper except at the kernel position. Then light transmission through grain cut was collected in the visible range using a 400 µm diameter optical fiber and measured using a spectrometer (Ocean Optics USB 2000+XR extended range, FL, USA).

Statistical analysis 107

The statistical analysis, such as statistical tests and box-plots, were per-108 formed with R software (R Core Team (2016), R: A language and environ- Therefore, this law directly relates the attenuation of light to the optical properties of the bulk microstructure. It is worth noting that κ depends only on physical parameters without introducing any arbitrary variable.

I = I 0 exp(-κh) (1)
To check the validity of this law, we performed light transmission measurement at the center of HV kernel cheek cross-sections with thicknesses varying from 0.9 to 1.8 mm. Figure 2 The validity of the Beer-Lambert law to describe the transmission of light through the wheat endosperm gives way to characterize wheat propensity to transmit light with a sound physical parameter: the absorption coefficient.

In the following, we chose instead to use the transmission coefficient τ = 1/κ which increases with vitreousness. The four wheat samples were then analyzed to determine τ (and thus the vitreousness) with a specific focus on both inter-grain samples or intragrain sample variability. We performed measurements on 30 randomly selected grains for each wheat samples (s) working at a carefully abraded fixed 171

The statistical results showed that mealy grains displayed the lowest light 172 transmission coefficient, with no significant difference between soft and hard 173 lines. On the opposite, vitreous grains exhibit higher transmission coeffi-174 cients, which were influenced by the genetic origin of the grains. Indeed, HV 175 grains present higher light transmission coefficients than SV grains (Figure 176 3). using similar genetic material that the soft near-isogenic lines never exceed a vitreousness level of 60% in comparison with the hard near-isogenic lines which were found to display a broader range of vitreousness levels (up to 80%). Additionally a larger data spreading was found within the hard vitreous grain in accordance with the larger standard deviation observed in similar samples for mechanical properties [START_REF] Greffeuille | Grain characterization and milling be-283 haviour of near-isogenic lines differing by hardness[END_REF]. Our results also match with modeling of the endosperm that also revealed a more important effect of changes in the adhesion between starch and the protein network (related to the presence of wild or mutated puroindoline alleles) in the case of low porosity [27].

Moreover, light transmission analysis also highlighted the heterogeneity in the endosperm microstructure depending on the location within the grain.

The highest light transmission obtained in the central endosperm suggests a more compact organization in comparison with the distal or proximal regions respectively close to the brush or the germ extremities . These differences in compactness can be potentially related to the observed distinct behavior of the grain extremities against pathogen attack [28] or water, gibberelic acid and α-amylase diffusion along imbibition/germination [29, 30].

Conclusions

In this study, we first demonstrated a Beer-Lambert relationship for light transmission through the endosperm allowing to quantify and compare endosperm microstructure depending on both genetic background and environmental conditions. We confirmed the role of the environment in endosperm

  citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT isogenic lines differing only by the wild-type or mutated allele of the gene encoding PIN B [14].

  ). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT known profile of puroindoline alleles, encoding either native or mutated PINB in order to fix hardness due to the genetic background, respectively soft or hard, and selected two different environments from a previous study [15] allowing obtaining contrasted levels of vitreousness for the starchy endosperm.

Figure 1 :

 1 Figure 1: (a) Schematic representation of the experimental assembly used for the spectroscopic analysis of the grain microstructure. h is for the sample thickness (b) Example of the analyzed grains differing by vitreousness. Circles represent the six areas where the transmission spectra were collected into the starchy endosperm.

  Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT where I 0 and I are the incident and transmitted light beam respectively, κ is an absorption coefficient which depends on the material, and h is the thickness of the sample.

  shows the evolution of light transmission T = I/I 0 as a function of h for three different samples. The results are well fitted by the Beer-Lambert law (in dashed line) with κ = (1.13 ± 0.50) mm -1 .

Figure 2 :

 2 Figure 2: Evolution of the light transmission as a function of the grain thickness for three different HV samples (fit dashed line T (h) = e -1.13 h ). h is for the sample thickness.

thickness h s = ( 1 . 1 .

 11 58 ± 0.14) mm. This thickness was chosen to get easily reproducible cross-sections with a good compromise between stiffness and sensitivity to transmission measurement. τ values were directly derived from Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT the measurements of light transmission at this thickness, T (h s ), using equa-Inter wheat grain endosperm texture comparison 158 Inter-grain comparison was performed by comparing the transmitted light 159 between the same specific grain location in the different wheat samples (cen-160 tral areas of HV vs central areas of HM as an example). As far as the τ ob-161 servations did not fulfill a Gaussian distribution, non-parametric tests were 162 performed to compare samples. Wilcoxon-Mann-Whitney tests were imple-163 mented to compare the inter-grain observations (comparison of the median 164 of the transmission coefficient τ from an identical location between different 165 grain samples). Example of the inter-grain comparison corresponding to both 166 of the endosperm central areas is illustrated in Figure 3. The represented 167 boxplots highlighted the non-Gaussian and discontinuous distribution of the 168 transmission coefficient within the different samples with a few number of 169 grains displaying higher transmittance than the major part of the popula-170 tion in each case, clearly reflecting the natural sample heterogeneity.

177Figure 3 :

 3 Figure 3: Boxplot for intergrain comparison of the transmission coefficient τ for HV, HM, SV and SM in both of the central cheek endosperm. The graph summarizes the distribution of data as the following : boxes enclose 50 % of the data (n=60) with the median value for variables displayed as a line, boxes span from the first quartile to the third quartile and vertical lines display 1.5 times the interquartile distance from the box (with extreme values of whiskers [max(τ min , Q3-1.5(Q3-Q1)), min(τ max , Q3+1.5(Q3-Q1))] where Q1, Q3, τ max and τ min are the first and third quantiles, and max and min are the measured values for each texture respectively). Outliers are positioned outside the box and the vertical lines. *, ** and *** indicate significance of the differences with 90 %, 95 % or 99 % confidence level respectively, with n.s.= non significant effect. Each measurement was labeled with a dot.

182 3 . 2 .Figure 4 :

 324 Figure 4: Boxplot for intragrain comparison of the transmission coefficient τ for HV, HM, SV and SM in the different endosperm regions : distal near the brush, central into the grain cheeks or proximal close to the germ. *, **, *** and n.s. as in Figure 3.

202

  In this study, the endosperm microstructure was analyzed based on light 203 transmission measurements. As grain endosperm organization was found to 204 differ depending on the puroindoline allelic state which was suggested to play 205 a role in starch-protein network adhesion[START_REF] Bhave | Molecular genetics of puroindolines and related 296 genes: allelic diversity in wheat and other grasses[END_REF], near-isogenic grains differing 206 only by the puroindoline b allelic form (PinbD1a or PinbD1b) were used in 207 order to well define the genetic background. These wheat samples were grown 208 in two contrasted environments found to lead to distinct vitreousness level 209 [15] and thus to different level of wheat endosperm porosity. Here, we demon-210 strated for the first time that vitreousness can be non-ambiguously quantified 211 and compared from light transmission measurements through wheat samples 212 displaying standardized thickness. As light transmission was found to follow 213 a Beer-Lambert law, it is clearly the results of changes in the endosperm 214 Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT porosity as pores are known to scatter light. The statistical analysis showed that grains grown in an environment leading to mealy kernels but presenting distinct genetic background (as observed between mealy grains of distinct hardness, HM and SM) cannot be distinguished by light transmission because they display a high porosity level. Importance of the environment was also highlighted for grains within the same genetic background but grown in different locations that were found to display distinct vitreousness and thus light transmission (SM vs SV, HM vs HV). Consistency with these differences between wheat samples, presence of a pore network was shown to lead to weaker mechanical strength whatever the genetic background [3]. These results clearly highlight the importance of porosity or vitreousness characterization and of the identification of genetic background. Such a knowledge help us to get a more accurate understanding of the grain mechanical properties and thus the milling behavior as also previously noticed [15, 4]. Furthermore, SV and HV wheat grains displayed distinct vitreousness scores and transmission coefficients even if they were grown in the same environment. Therefore for vitreous grains, presence of both wild-type puroindolines results in a significant decrease of τ revealing a more porous microstructure. This observation possibly suggests that light transmission, and thus vitreousness, is affected by the different genetic background of these wheat samples. Indeed, the soft line containing the wild-type puroindolines would have a lower starch-protein adhesion due to the higher puroindoline content at the starch granule surface in the endosperm [25, 26]. The weak starch-protein adhesion could act as a network of internal microcracks. They may constitute another factor responsible for endosperm porosity that scatter the light and Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT result in lower light transmission. Recently, Oury et al.[15] demonstrated,

h•

  porosity and revealed a more pronounced effect of the genetic background for Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT [30] F. J. Bruggeman, K. R. Libbenga, B. Duijn, The diffusive transport of 376 gibberellins and abscisic acid through the aleurone layer of germinating 377 barley grain: a mathematical model, Planta 214 (2001) 89-96. 378 Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 M A N U S C R I P T A C C E P T E D Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 document Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullie grain quantitative evaluation of vitreousness by light transm Science, 83, 58-62. , DOI : 10.1016/j.jcs Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 Light transmission through the wheat endosperm follows a Beer-Lambert law • Similar endosperm light transmission was found in grains displaying low porosity • The highest transmittance was found with vitreous grains from hard genotype • The proximal or distal part of the endosperm was more porous than the center

Table 1 :

 1

	114
	115	(native puroindoline a and b alleles) or hard (mutated puroindoline b) phe-
	116	notype and grown in two contrasted environments resulting in different levels
		of vitreousness for the starchy endosperm. Wheat grains were characterized

109 ment for statistical computing. R Foundation for Statistical Computing, 110 Vienna, Austria. URL https://www.R-project.org/). 111 3. Results 112 Wheat samples were selected from a previous study [15].This study drove 113 us to select grains with a defined puroindoline genome leading to either soft 117 with a Pohl grain cutter as already described [23]. Considering the previous 118 Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal of Cereal Science, 83, 58-62. , DOI : 10.1016/j.jcs.2018.07.009 Soft Vitreous (SV) Pina-D1a/Pinb-D1a 48.2 ± 1.6 Hard Mealy (HM) Pina-D1a/Pinb-D1b 23.1 ± 1.1 Hard Vitreous (HV) Pina-D1a/Pinb-D1b 68.8 ± 1.7 Genomic profile and vitreousness of the wheat near-isogenic lines differing by hardness and displaying contrasted vitreousness (as determined with a Pohl grain cutter) 3.1. Wheat grain endosperm vitreousness fits with a Beer Lambert law Wheat starchy endosperm is a cohesive granular material [24] composed of various phases among which starch, protein matrix and voids occupy most of the volume. In such a material the pores induce differences in light transmission resulting in various vitreousness. For this type of porous materials the strong variation in optical indexes, which constitute massive scattering events, should follow the Beer-Lambert law:

Comment citer ce document : Chichti, E., Carrere, M., George, M., Delenne, J.-Y., Lullien-Pellerin, V. (2018). A wheat grain quantitative evaluation of vitreousness by light transmission analysis. Journal