# Unbalanced roles of fungal aggressiveness and host cultivars in the establishment of the Fusarium head blight in bread wheat

Francis Fabre, Joerg Bormann, Serge Urbach, Sylvie Roche, Thierry Langin, Ludovic Bonhomme

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Francis Fabre, Joerg Bormann, Serge Urbach, Sylvie Roche, Thierry Langin, et al.. Unbalanced roles of fungal aggressiveness and host cultivars in the establishment of the Fusarium head blight in bread wheat. Frontiers in Microbiology, 2019, 10, pp.1-14. 10.3389/fmicb.2019.02857. hal-02935809

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# Unbalanced Roles of Fungal Aggressiveness and Host Cultivars in the Establishment of the Fusarium Head Blight in Bread Wheat

Francis Fabre<fnref n="1" />, Joerg Bormann², Serge Urbach³, Sylvie Roche⁴, Thierry Langin<fnref n="1" /> and Ludovic Bonhomme<fnref n="1" />*

## OPEN ACCESS

**Edited by:**
Fred Asiegbu,
University of Helsinki, Finland

**Reviewed by:**
Therese Ouellet,
Agriculture and Agri-Food Canada
(AAFC), Canada
Thomas Miedaner,
University of Hohenheim, Germany
Christina Cowger,
Plant Science Research Unit
(USDA-ARS), United States

*Correspondence:
Ludovic Bonhomme
ludovic.bonhomme@inra.fr

**Specialty section:**
This article was submitted to
Fungi and Their Interactions,
a section of the journal
Frontiers in Microbiology

**Received:** 30 August 2019
**Accepted:** 25 November 2019
**Published:** 11 December 2019

**Citation:**
Fabre F, Bormann J, Urbach S, Roche S, Langin T and Bonhomme L (2019) Unbalanced Roles of Fungal Aggressiveness and Host Cultivars in the Establishment of the Fusarium Head Blight in Bread Wheat. *Front. Microbiol.* 10:2857. doi: 10.3389/fmicb.2019.02857

Fusarium head blight (FHB), caused mainly by *Fusarium graminearum*, is the foremost destructive disease of cereals worldwide. Effector-like molecules produced by *F. graminearum* play key roles in the infection process and are assumed to be one of the essential components of the pathogen's aggressiveness. However, their nature and role in the disease are still largely misunderstood. As a mean to provide relevant information about the molecular determinism of *F. graminearum* aggressiveness, we surveyed three *F. graminearum* strains on three wheat cultivars contrasted by their susceptibility to FHB. *F. graminearum* strains revealed large differences in aggressiveness which were mostly unchanged when facing hosts of contrasted susceptibility, suggesting that their behavior rely on intrinsic determinants. Surveying the fungal mass progress and the mycotoxin production rate in the spikes did not evidence any simple relationship with aggressiveness differences, while clues were found through a qualitative and quantitative characterization of the three strain proteomes established in planta especially with regards to early synthesized putative effectors. Independently of the wheat cultivar, the three *F. graminearum* strains produced systematically the same protein set during the infection but substantial differences in their abundance enabled the categorization of fungal aggressiveness. Overall, our findings show that the contrasts in *F. graminearum* aggressiveness were not based on the existence of strain-specific molecules but rather on the ability of the strain to ensure their sufficient accumulation. Protein abundance variance was mostly driven by the strain genetics and part was also influenced by the host cultivar but strain by cultivar interactions were marginally detected, depicting that strain-specific protein accumulations did not depend on the host cultivar. All these data provide new knowledge on fungal aggressiveness determinants and provide a resourceful repertoire of candidate effector proteins to guide further research.

**Keywords:** Fusarium graminearum, Gibberella zeae, aggressiveness, proteomics, effector proteins, plant-pathogen interactions, bread wheat

<footnote n="1">¹ Université Clermont Auvergne, INRAE, UMR 1095 Genetics, Diversity and Ecophysiology of Cereals, Clermont-Ferrand, France, ² Department of Molecular Phytopathology, Institute of Plant Science and Microbiology, University of Hamburg, Hamburg, Germany, ³ Functional Proteomics Platform, Institute of Functional Genomics, CNRS UMR 5203 INSERM U661, Montpellier, France, ⁴ INRAE, Unité Experimentale 1375, Phénotypage au Champs des Céréales (PHACC), Clermont-Ferrand, France</footnote>
# INTRODUCTION

Plants are constantly subjected to biological pressures that could compromise their development. Plant and pathogen interests are antagonist, thus generating an evolutionary dynamic between the two, promoted by the confrontation of the plant resistance with the ability of the pathogen to induce disease through an important variety of pathogenicity factors. Aggressiveness constitutes the quantitative component of pathogenicity and is defined as the degree of damage caused by the pathogen to the host (Van der Plank, 1968; Pariaud et al., 2009; Delmas et al., 2016). When a plant-pathogen interaction is established, proteins called effectors are synthesized to promote pathogenicity by suppressing the host's immune response and inducing physiological manipulation of the plant (Kamoun, 2006). Effector proteins can be localized in the cell wall surface or secreted directly into the plant cells to target specific host functions (Kamoun, 2006; Lo Presti et al., 2015; Sperschneider et al., 2016; Jones et al., 2018). They can also play crucial roles in the degradation of complex compounds like the plant cell wall, in the initiation of infection and for obtaining nutrients from plant host reserves (Hao et al., 2019). Effector proteins are probably an essential component of the pathogen's aggressiveness, nevertheless the molecular mechanisms underlying their functions are for the most part unknown, especially in fungus (Yi and Valent, 2013; van Schie and Takken, 2014; Jia and Tang, 2015; Lo Presti et al., 2015).

One relevant pathosystem for assessing the importance of pathogen's aggressiveness during the interaction with the host is the relationship between bread wheat and *Fusarium graminearum* Schwabe (*Hypocreales: Nectriaceae*) (teleomorph: *Gibberella zeae*). *F. graminearum* is the most prominent causal agent of the FHB in Europe, Canada, and United States (McMullen et al., 1997; Brennan et al., 2003; Steiner et al., 2017). Severe outbreaks regularly result in significant yield losses (Parry et al., 1995; Xu and Nicholson, 2009; McMullen et al., 2012; Chen et al., 2019), as well as altering nutritional grain quality and inducing a major health problem throughout the food chain via grain contamination by mycotoxins (Liu et al., 2019). DON is the most commonly found toxin in cereals (Placinta et al., 1999). Previous works showed that DON could have a role in fungal spread beyond the initial infection (Bai et al., 2002) by facilitating the spreading of *F. graminearum* from spikelets into the rachis which might induce the switch from biotrophy to necrotrophy (Bönnighausen et al., 2018). DON is also known to allow the inhibition of host protein synthesis (Walter et al., 2010), and is believed to be an aggressiveness factor rather than a pathogenicity factor (Proctor et al., 1995; Pasquet et al., 2016).

Although *F. graminearum* strains are not all identical in their ability to induce disease (Carter et al., 2002; Goswami and Kistler, 2005), the molecular mechanisms and life traits

that determine the fungal aggressiveness level are always very controversial according to the authors. Classically, *F. graminearum* variation in aggressiveness is measured with severity variables such as the percentage of spikelets infected or the size of the visual symptom (Cumagun et al., 2004; Saville et al., 2012). Mycotoxins production is also considered as a FHB aggressiveness component (Proctor et al., 1997, 2002; Mesterházy, 2002; Burlakoti et al., 2007; Shin et al., 2018). Molecular approaches have also been used to characterize differences in *F. graminearum* strain aggressiveness at the genome (Carter et al., 2002; Gale et al., 2002; Cumagun et al., 2004; Laurent et al., 2017, 2018) or at the transcriptome scale (Harris et al., 2016; Puri et al., 2016). Many other studies have also identified *F. graminearum* genes involved in pathogenicity and some appeared to have only a quantitative effect (i.e., aggressiveness-related genes) (Pariaud et al., 2009). These genes encode secreted proteins and effectors that may play roles in the infection course (Krijger et al., 2014; Lu and Edwards, 2015; Chetouhi et al., 2016; Fabre et al., 2019). Furthermore, previous genomics studies have identified more than 600 genes coding for secreted proteins (Brown et al., 2012; King et al., 2015). Some have been identified at the proteome level (Lowe et al., 2015; Fabre et al., 2019) suggesting that *F. graminearum* could synthesize a large number of proteinous effectors.

In a previous study, we investigated the molecular dialogue dynamics taking place during the early stages of the FHB progress in bread wheat (Fabre et al., 2019). This has highlighted dual protein regulations between 48 hpi and 72 hpi both in *F. graminearum* and in wheat, emphasizing that regulated *F. graminearum* proteins could dynamically adjust to the plant physiological responses (Fabre et al., 2019). *Fusarium graminearum* effectors have been shown to be accumulated at specific stages of infection to achieve precise roles in the progress of the interaction, especially at 72 hpi during symptoms appearance (Fabre et al., 2019). However, this previous study was carried out on only one aggressive strain and one susceptible wheat cultivar. Evaluating the specificity of these proteome adjustments in hosts and pathogens contrasting for their susceptibility and aggressiveness, respectively, represents a powerful lever to identify the molecular determinants that drive the development of the disease.

The aim of this work is to question the potential links between the intrinsic characteristics of different *F. graminearum* strain proteomes with their respective aggressiveness in planta, as well as the impact of different wheat genetic backgrounds on the accumulation of the pathogen proteins. The qualitative and quantitative characterizations of the three *F. graminearum* proteomes at 72 hpi were performed with a special interest on effectors and proteins coded by known *F. graminearum* aggressiveness-related genes. In addition, three variables classically used for the *F. graminearum* aggressiveness determination were described, (i) the severity of symptoms induced by the strains, (ii) the fungal mass development in planta, and, (iii) the DON synthesis rate of each strain. The joint analysis of all these data provide new insights into the determinism of *F. graminearum* aggressiveness at the early stage of the disease progress.

<footnote>**Abbreviations:** ANOVA, Analysis of variance; Cv, Cultivar effect; DON, Deoxynivalenol; FDR, False discovery rate; FHB, Fusarium head blight; hpi, hours post-inoculation; S+Cv, Strain+Cultivar effect; S×Cv, Strain×Cultivar effect; S, Strain effect.</footnote>
# MATERIALS AND METHODS

## Plant Growth and *Fusarium graminearum* Inoculation

Experiments were conducted on three wheat cultivars of contrasting susceptibility to FHB, including in decreasing order of susceptibility cv. Recital, cv. Cadenza and cv. Renan as ranked from previous field observations. Recital and Renan are among the most contrasted cultivars of the french wheat collections (Gervais et al., 2003) while Cadenza is considered as intermediate. For each wheat cultivar, seeds were sown in buckets and kept at 20°C to allow germination. Vernalization was performed at 4°C for 8 weeks, then plantlets were transplanted in 4-L pots and transferred in a growth cabinet with optimal conditions to allow tillering and synchronized flowering. Twenty four plants per wheat cultivar were prepared for a total of 72 wheat plants divided in three randomized complete blocks in the growth cabinet and surrounded by additional plants to control any edge effects. Automatic watering was installed, and the daily photoperiod was set at 16-h daylight for a temperature of 20°C and 8-h darkness at 18°C. Relative humidity was maintain at 80% during day and night.

*Fusarium graminearum* strains MDC_Fg1, MDC_Fg13, and MDC_FgU1, all originating from France, were selected for their contrasting aggressiveness (based on field observations). The preparation of the *F. graminearum* inocula was performed as described in Fabre et al. (2019). The three strains were individually inoculated in six plants of each wheat cultivar i.e., six plants × 3 *F. graminearum* strains for a total of 18 plants per cultivar. For each cultivar, inoculation was performed at the mid-anthesis stage by depositing 10 µl of inoculum in the floral cavity of six contiguous spikelets located in the middle zone of three synchronized spikes per plant. The last six plants were inoculated with water and were used as control. For each cultivar×strain combination, the point-inoculated spikelets of the three spikes of three independent plants were specifically collected 72 h after the inoculation (hpi), while the ones of the three remaining plants were collected at 168 hpi. For each cultivar×strain combination, three biological replicates were designed at both time points; they corresponded to one individual plant which was characterized by the pool of all inoculated spikelets from three spikes. Fungal mass quantification in wheat spikes, *F. graminearum* DON synthesis rate and proteomics were performed on the same plant material.

## Symptom and Fungal Development Monitoring

Symptoms were monitored by visual inspection and scored every 24 h from 0 to 168 hpi (hours post-inoculation) using a 5-level rating scale (Figure 1). A score of 0 was given to spikelets which had no visible symptom, a score of 1 was given when the first yellowing spot appeared, a score of 2 when a brown spot was visible, a score of 3 when browning gained the whole spikelet and a score of 4 when aerial mycelium became visible. Until 72 hpi, symptom monitoring was profiled on the whole set of plants

(six plants for each cultivar×strain combination) while the range 96 hpi to 168 hpi was monitored from the remaining unsampled plants (three plants for each cultivar×strain combination).

Fungal mass was assessed at 72 and 168 hpi from the three biological replicates of each cultivar×strain combination, using qPCR method. For each biological replicate, the measurements made for each sample were performed nine times and were considered as technical replicates. Water-inoculated spikes were used as control. Frozen spikes from the same plant were mixed and finely ground under liquid nitrogen using pestle and mortar. The DNA-isolation was performed on 100 mg of ground material (i.e., 72 and 168 hpi) using CTAB buffer (100 mM Tris-HCl pH 8.0, 1.4 M NaCl, 20 mM EDTA and 2% cetyltrimethyl ammonium bromide). Samples were incubated at 65°C during 1 h and centrifuged at 10 000 g for 10 minutes at room temperature. DNA was recovered by adding 1 volume of chloroform and centrifuged as described above. Precipitation of the nucleic acids was performed using 1 volume of isopropanol and a centrifugation at 12 000 g for 20 minutes at 4°C. DNA has been cleaned with 70% ethanol and dissolved in 100 µL TE buffer (10 mM Tris HCl pH 8.0, 1 mM EDTA). The DNA concentrations of each sample were measured with the Tecan's high performance Safire2TM microplate reader. Concentrations of each sample were normalized by dilution to 100 ng/µL, 1 µl was used for qPCR using Roche LightCycler® 480 SYBR Green I Master for each samples. Amplification was performed on the *F. graminearum* β-tubulin gene (Forward: TGCTGTTCTGGTCGATCTTG/Reverse: GACGGAAGTTTGGACGTTG) as described in Nguyen et al. (2013). Fungal DNA mass was estimated using a *F. graminearum* DNA dilution range (0.1–100 ng).

## DON Synthesis Rate by the Three *F. graminearum* Strains

DON synthesis rate per fungal mass unit was measured following the methodology described by Nguyen et al. (2013). DON measurement was realized at 72 and 168 hpi from the three biological replicates of each cultivar×strain combination and using water-inoculated samples as control. Each measurement was repeated nine times as technical replicates. For each sample, 50 mg of fresh ground material was suspended in 500 µl of distilled water and the extract was vortexed and centrifuged. A volume of 50 µl of the supernatant was used for DON quantification using the DON ELISA technique (RIDAscreen DON kits; R-Biopharm AG, Darmstadt, Germany. Detection range: 0.2 ppm to 6 ppm) and following the manufacturer's instructions. In order to estimate the DON synthesis rate by fungal mass unit, each DON quantity determined by the ELISA method was subsequently normalized with the mass of mycelium quantified in the sample. Milligrams of mycelium per gram of fresh ground sample were computed by referring to the number of β-tubulin gene copies per milligram mycelium of the *F. graminearum* DNA dilution range (0.1–100 ng) as described in Voigt et al. (2007).
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Determination of the symptom severity scale. (A) The picture represents spikelets with type-0 symptoms corresponding to asymptomatic spikelets. (B) Representation of type-1 symptoms on spikelet with the first yellowing spot. (C) The picture illustrates the type-2 symptoms characterized by a spikelet harboring the first browning spot. (D) The picture illustrates the type-3 symptoms characterized by a fully burnished spikelet. (E) The picture illustrates the type-4 symptoms characterized by a total drying of the spikelet with the visible mycelium outside the plant organ.</figcaption></figure>

## Protein Extraction and LC-MS/MS Procedures

Denaturing protein extraction was achieved from the three biological replicates of each cultivar × strain combination collected at 72 hpi using the TCA/acetone procedure as described in Bonhomme et al. (2012). Protein solubilization was performed in an urea-thiourea buffer [6 M urea; 2 M thiourea; 100 mM ammonium bicarbonate; 1% Halt™ Protease Inhibitor Cocktail 100X (Thermo Fisher Scientific 78429); 0.1% ProteaseMAX™ Surfactant (Promega V2071)] by following the ratio 10 µL per mg of dry matter. Protein digestion were performed as described in Fabre et al. (2019). Tandem mass spectrometry analyses were achieved using a nanoESI Q Exactive™ HF-X Hybrid Quadrupole-Orbitrap™ Mass Spectrometer (Thermo Fisher Scientific 0726042) coupled with an Ultimate 3000 HPLC (Thermo Fisher Scientific). HPLC gradients and data acquisition parameters were set as described in Fabre et al. (2019).

## Identification and Quantification of Peptides and Proteins From MS/MS Data

Database searches were performed using X!Tandem<fnref n="1" />; 2010.01.01.4) specifying one possible miscleavage. Cys carboxyamidomethylation and Met oxidation were set as static and variable modification, respectively. Precursor mass and fragment mass tolerance were 10 ppm and 0.5 Da, respectively. Identifications were performed using a concatenated file including a contaminant database (trypsin, keratins, etc.), the MDC_Fg1 (13166 entries, 01/2019), MDC_Fg13 (13297 entries, 01/2019), and MDC_FgU1 (13014 entries, 01/2019) databases obtained from an in-lab re-sequencing of each F. graminearum strain (Alouane et al., 2018). To prevent peptides derived from plant proteins from being assigned to fungal proteins, a wheat database (IWGSC, v1.0 269472 entries, 04/2017) was also used for the identification. Identified proteins were parsed and grouped using the X!TandemPipeline v0.2.35 c++ (Langella et al., 2017). Data filtering was achieved according to a peptide E-value < 0.05. Proteins were reported when they displayed at least two different peptides in the same sample and when the protein E-value < 0.0001. The FDR (False Discovery Rate) at the

peptide level assessed from searches against reversed amino acid sequences for each protein was smaller than 0.8 × 10⁻⁶. Protein and gene ontology annotations of the identified F. graminearum proteins were performed following the same methodology described in Fabre et al. (2019). Secretion features, subcellular localization predictions and functional annotations of identified proteins were conducted as described in Fabre et al. (2019). Relative quantification of peptides was achieved using the MassChroQ software (Valot et al., 2011) by extracting ion chromatograms as described in Bonhomme et al. (2012). The normalization was performed by dividing ratios by the total peptide abundance value in each LC-MS/MS run. Subsequent statistical analyses were performed on log10-transformed normalized data.

## Statistical Analyses

Results were analyzed with the R programming language v3.4.4 (R Core Team, 2018). For each time point and after validating the absence of any block/replicate effect, symptom severity monitoring were performed using generalized linear model following Poisson distribution:

$$ \log(Y_{ijk}) = \mu + S_i + Cv_j + (S_i \times Cv_j) + \varepsilon_{ijk}, $$

while fungal mass, DON synthesis rate and abundance variations of F. graminearum proteins were traced using two-way ANOVA with following linear model, after checking the absence of any block/replicate effect:

$$ Y_{ijk} = \mu + S_i + Cv_j + (S_i \times Cv_j) + \varepsilon_{ijk}, $$

where $Y_{ijk}$ refers to individual values, $\mu$ is the general mean of the variable considered, $S_i$ is the effect of the F. graminearum strain inoculated, $Cv_j$ is the effect of the wheat cultivar infected, $S_i \times Cv_j$ is the interaction of the strain effect by the Cv, and $\varepsilon_{ijk}$ is the residual.

For each individual F. graminearum protein, p-values obtained from each effect were adjusted to control the FDR for independent test statistics (Benjamini and Hochberg, 1995). The FDR was < 0.05 corresponding, respectively, to p-values < 0.022, < 0.017, and < 0.00005 in the S, Cv and S × Cv effects, respectively. Fuzzy C-means clustering (Kumar and Futschik, 2007) of F. graminearum proteins showing significant

<footnote n="1">¹http://www.thegpm.org/TANDEM/</footnote>
A

<figure><img src="image_4.png" /></figure>

Recital

B

<figure><img src="image_5.png" /></figure>

Cadenza

C

<figure><img src="image_6.png" /></figure>

Renan

**FIGURE 2** | Symptom dynamics induced by the three *F. graminearum* strains on the three studied wheat cultivars (A) Recital, (B) Cadenza, and (C) Renan. Line plots show the course of the symptom severity observed for MDC_Fg1 (blue lines), MDC_Fg13 (pink lines), and MDC_FgU1 (green lines) on the three wheat cultivars. Values are means of six biological replicates until 72 hpi and means of three biological replicates from 96 hpi to 168 hpi. Each biological replicate was characterized from three spikes. Error bars indicate the confidence interval at 5% calculated for each time point independently.
<figure><img src="image_7.png" /></figure>

**FIGURE 3** | *Fusarium graminearum* strains development in infected wheat spikes. Fungal mass was assessed at 72 and 168 hpi in the cultivars Recital (A), Cadenza (B) and Renan (C), from the absolute quantification of the *F. graminearum* β-tubulin gene using qPCR (Nguyen et al., 2013). Values were estimated with a standard curve of 10-fold dilutions (ranging from 0.01–100 ng) of *F. graminearum* DNA purification for MDC_Fg1 (blue bars), MDC_Fg13 (pink bars) and MDC_FgU1 (green bars). Values are means of nine biological replicates computed for three biological replicates at 72 hpi and at 168 hpi. Each biological replicate was characterized from three spikes. Error bars indicate the confidence interval at 5% calculated for each time point independently.

<figure><img src="image_8.png" /></figure>

**FIGURE 4** | Fungal DON synthesis rate in infected wheat spikes. Bar graphs represent DON synthesis rate (in g of DON per kg⁻¹ of fungal mycelium) in wheat heads infected with MDC_Fg1 (blue bars), MDC_Fg13 (pink bars) and MDC_FgU1 (green bars) in the cultivars Recital (A), Cadenza (B), and Renan (C). Measurements were performed by enzyme-linked immunosorbent assay at 72 and 168 hpi, and normalized by the fungal mass quantified by qPCR. Values are means of nine biological replicates computed for three biological replicates at 72 hpi and at 168 hpi. Each biological replicate was characterized from three spikes. Error bars indicate the confidence interval at 5% calculated for each time point independently.

S2 and S+ Cv2 appeared to be enriched in proteins involved in “translation,” “ribosome” and “Protein synthesis” functions and Cluster S5 in proteins involved in “ATP binding” and “carbohydrate metabolic process” functions. These three clusters were also enriched in predicted effector proteins. In all other

clusters, fungal protein abundances were lower in MDC_Fg1 than in the two others strains. Cluster S+ Cv4 included proteins systematically of higher abundance for MDC_Fg13 while in the cluster S3 no significant difference was found between MDC_Fg13 and MDC_FgU1 protein abundances. No significant
<figure><img src="image_9.png" /><figcaption>**FIGURE 8** | Clustering of *F. graminearum* Strain+Cultivar\_effect protein abundance patterns. Clustering was computed using the fuzzy C-means methods using Z-score transformed values to identify homogeneous patterns. For each cluster, the average profile was represented by the line plot with the wheat cultivar in x-axis and a color code for the *F. graminearum* strains (blue: MDC\_Fg1; pink: MDC\_Fg13, and green: MDC\_FgU1). Ribbons indicate the confidence interval at 5% following the same color code than the line plot. The number of proteins included in each cluster are specified in the right upper corner (n).</figcaption></figure>

allowed for the establishment of a clear ranking of our strains on the three wheat cultivars. However, neither the strain's level of DON synthesis rate nor the fungal mass quantified in planta seems to explain the differences in symptom severity. For example, DON synthesis rate measured in the Recital samples inoculated with MDC_Fg13 was significantly higher than that measured in MDC_FgU1 samples while the symptom severity was not different at the same time. Several studies had already made the same observation (Bai et al., 2001; Birzele et al., 2002; Gilbert et al., 2002; Champeil et al., 2004; Alvarez et al., 2010; Hallen-Adams et al., 2011; He et al., 2019). We can also wonder about the links between the DON synthesis rate and symptom severity with the fungal mass present in the wheat spike. Previous studies showed a strong correlation between fungal biomass and DON production (Lamper et al., 2000; Bai et al., 2001) and between fungal development and symptom severity (Burlakoti et al., 2007) while other studies have not shown any obvious relationship between these parameters (Nicholson et al., 1998; Paul et al., 2005; Alvarez et al., 2010; Hallen-Adams et al., 2011). In this work, no clear correlation between fungal mass, DON

synthesis rate and symptom severity was found. The same fungal mass quantified in the three strains at 72 hpi produced different DON amount and induced different symptom severity in the three wheat cultivars. Moreover, in agreement with a previous study showing that the *F. graminearum* genes involved in DON synthesis were highly expressed in the early stages of fungus development (Hallen-Adams et al., 2011), our results suggest that in the most aggressive strain MDC_Fg1, a small amount of mycelium at 72 hpi produced a large amount of DON while a larger quantity of mycelium (168 hpi) resulted in a lower production. Individually, these parameters allow us to efficiently distinguish the different strains but they do not seem to be sufficient to explain their respective aggressiveness.

## Fusarium graminearum Strains Express the Same Genetic Program During Wheat Infection

The study of proteins, molecules directly responsible for cell activity, is essential for understanding plant-pathogen
synthesis rate and fungal mass monitoring, nor at the proteomics level where only two *Strain×Cultivar effect proteins* were detected. Although the use of only three wheat cultivars cannot represent the whole genetic variability for FHB susceptibility, this illustrates the pivotal role of the fungal component in the disease. *F. graminearum* is a broad-host pathogen (Harris et al., 2016) and it is known that pathogens able to infect multiple hosts have a reduced selection pressure to co-evolve with a particular host, especially when the hosts belong to the same species (Woolhouse et al., 2002). Previous work done on the interaction between a generalist pathogen and cereals has shown that *Strain×Cultivar* interactions could have only a minor effect, for example in *Puccinia coronata* f. sp. *avenae/Avena sativa* interaction (Bruns et al., 2012), in *Septoria tritici/Triticum aestivum* interaction (van Ginkel and Scharen, 1988) or in *Fusarium graminearum/Triticum aestivum* interaction (Bai and Shaner, 1996; Xue et al., 2004; Mesterházy et al., 2005). All together, these results indicate that the host cultivars similarly influence the three *F. graminearum* proteomes and that strain-specific protein accumulations did not depend on the host cultivar.

## CONCLUSION

This study expands our understanding of the determinants of *F. graminearum* aggressiveness during the early stage of bread wheat infection. Three *F. graminearum* strains express the same proteome *in planta* while differences were only detectable in protein abundance. Host background effect was only shown at the protein abundance level and in the DON synthesis rate without any change in the strain ranking. In addition, the absence of *Strain×Cultivar* specific interaction suggests a common infection strategy of the three strains and modulated identically by wheat cultivars of contrasted FHB susceptibility. Now, confirming these results using more strains and cultivars on a longer infection dynamics will be an additional work in order to understand the determinism of pathogen's aggressiveness during the interaction.

## REFERENCES

* Alouane, T., Rimbert, H., Fabre, F., Cambon, F., Langin, T., and Bonhomme, L. (2018). Genome sequence of *Fusarium graminearum* strain MDC_Fg1, isolated from bread wheat grown in France. *Microbiol. Resour. Announc.* 7:e01260-18. doi: 10.1128/MRA.01260-18

* Alvarez, C. L., Somma, S., Moretti, A., and Fernández Pinto, V. (2010). Aggressiveness of *Fusarium graminearum* sensu stricto isolates in wheat kernels in Argentina. *J. Phytopathol.* 158, 173–181. doi: 10.1111/j.1439-0434.2009.01596.x

* Bai, G.-H., Desjardins, A. E., and Plattner, R. D. (2002). Deoxynivalenol-nonproducing *Fusarium graminearum* causes initial infection, but does not cause disease spread in wheat spikes. *Mycopathologia* 153, 91–98. doi: 10.1023/A:1014419323550

* Bai, G.-H., Plattner, R., Desjardins, A., Kolb, F., and McIntosh, R. A. (2001). Resistance to Fusarium head blight and deoxynivalenol accumulation in wheat. *Plant Breed.* 120, 1–6. doi: 10.1046/j.1439-0523.2001.00562.x

## DATA AVAILABILITY STATEMENT

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Vizcaíno et al., 2016) partner repository with the dataset identifier PXD015139.

## AUTHOR CONTRIBUTIONS

TL and LB designed the research. FF and SR prepared the samples. FF, SU, and LB performed the proteomic experimentation. JB involved in deoxynivalenol synthesis rate measurements. FF and JB involved in qPCR measurements. FF and LB analyzed the data, prepared the figures, and wrote the manuscript. LB conceived and performed modeling.

## FUNDING

This research and FF Ph.D. grant were supported by the Region Auvergne-Rhône-Alpes and the Fonds Européen de Développement Régional (FEDER support: Nouveau Chercheur 2016 program).

## ACKNOWLEDGMENTS

We acknowledge Tarek Alouane, David Lopez, and Hélène Rimbert for their valuable help in bioinformatics. We thank Marie Dufresne (Institut of Plant Sciences – Paris-Saclay) for her helpful comments on our manuscript and Christopher Smith for his proofreading. We also thank Richard Blanc for his help in the plant cultivation.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2019.02857/full#supplementary-material

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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