# Suboptimal pre-anthesis water status mitigates wheat susceptibility to fusarium head blight and triggers specific metabolic responses

Larissa Adamik, Paul Samir Dou, Géraldine Philippe, Richard Blanc, Pedro

Vásquez-Ocmín, Guillaume Marti, Thierry Langin, Ludovic Bonhomme

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Larissa Adamik, Paul Samir Dou, Géraldine Philippe, Richard Blanc, Pedro Vásquez-Ocmín, et al. Suboptimal pre-anthesis water status mitigates wheat susceptibility to fusarium head blight and triggers specific metabolic responses. Scientific Reports, 2025, 15 (1), pp.11773. 10.1038/s41598-025-96159-4. hal-05075062

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# Suboptimal pre-anthesis water status mitigates wheat susceptibility to fusarium head blight and triggers specific metabolic responses

Larissa Adamik<fnref n="1" />, Paul Samir Dou<fnref n="1" />,², Géraldine Philippe<fnref n="1" />, Richard Blanc<fnref n="1" />, Pedro Vásquez-Ocmín³, Guillaume Marti³,⁴, Thierry Langin<fnref n="1" /> & Ludovic Bonhomme ID <fnref n="1" />✉

The impact of abiotic challenges on plant physiology reshapes plant-pathogen interactions by modulating the plant immune responses. In wheat, the development of Fusarium Head Blight (FHB) is heavily influenced by environmental conditions, especially during the pre-anthesis stage, just before fungal infection occurs. The early stages of infection are thus likely conditioned by prior environmental changes with consequences on the disease outcome that require further characterization. In this study, we aimed to assess the impact of pre-anthesis water depletion followed by rapid rehydration at inoculation on the expression of FHB-related molecular determinants with emphasis on susceptibility factors and metabolism-related processes. Water depletion altered plant physiology and its effects remained detectable after three days after rehydration, leading to significantly reduced FHB symptoms. Dual-transcriptomics, combined with untargeted metabolomics, revealed two key findings including (i) extensive metabolic changes specific to prior water stress, and (ii) the strong conservation of previously identified candidate susceptibility genes regulation. Considering the combined stress effects, a unique response signature emerged, highlighting that immune responses are strongly interwoven with physiological adjustments. Our findings provide new insights into the trade-offs that plants make under multiple challenges and identify original wheat metabolic determinants that may improve FHB resistance even in suboptimal physiological conditions.

**Keywords** Bread wheat, FHB susceptibility, Water stress, Specialized metabolism, Multi-stress genomics

Plant-pathogen interactions depend on their abiotic environment<fnref n="1" />. Due to the environmental impacts of global change, these interactions are already being reshaped and will continue to evolve as climatic events intensify. These outcomes can arise from direct effects on organism physiology, with either positive or negative consequences. Such shifts have clear implications for plant disease epidemiology, as various abiotic stresses have already been shown to affect the survival of crop pathogens²,³. Other outcomes can also be more discreet and complex to delineate, especially when environmental changes alter the molecular and physiological determinants of host-microbe interactions. Several studies have already demonstrated that components of climate change, such as increased CO₂ concentration, high humidity or elevated temperatures, can jeopardize plant immunity and even compromise the effectiveness of basal defenses and the expression of known resistance genes⁴,⁵. Further researches also suggest that physiological changes in plants exposed to degraded environments can promote unique responses unpredictable from knowledge acquired under optimal conditions or from model plants⁶⁻⁸. In crop species such as bread wheat, the responses to combined stress remains largely unexplored. Wheat studies on abiotic x biotic stress interactions have been predominantly addressed through epidemiological approaches, while molecular-level investigations remain insufficient⁸,⁹. Expanding our understanding of the molecular

<footnote n="1">¹Université Clermont Auvergne, INRAE, GDEC, 63000 Clermont-Ferrand, France. ²Université Clermont Auvergne, INRAE, UMR Herbivores, VetAgroSup, Saint-Genès- Champanelle, France. ³MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France. ⁴Laboratoire de Recherche en Sciences Végétales, Metatoul-Agromix Platform, Université de Toulouse, CNRS, INP, 24 Chemin de Borde Rouge, Auzeville, Auzeville-Tolosane 31320, France. ✉email: ludovic.bonhomme@inrae.fr</footnote>
determinants underlying these responses will be crucial for elucidating the links between immunity expression and environmental adaptation and will guide further researches to identify novel regulatory mechanisms with potentially high adaptive value in fluctuating environments.

In wheat, Fusarium head blight (FHB) is one of the most devastating diseases leading to substantial yield losses, reduced grain quality, and contamination with harmful mycotoxins that render the grain unsuitable for human and animal consumption¹⁰. FHB epidemics are highly dependent on environmental factors, with temperature, rainfall, and humidity being the most influential parameters, especially during the pre-anthesis and anthesis stages when the infection occurs¹⁰⁻¹². Multiple weather-based forecasting models have been established in order to adapt crop management practices reducing cost associated to FHB damages¹³. Unfortunately, these strategies are insufficient to effectively control the consequences of the disease while new genetic determinants, tailored more closely to environmental conditions, are a promising alternative. Although complete resistance to FHB has not yet been identified so far, several works demonstrated that some plant functions and the expression of specific genes, known as susceptibility genes (S genes), are crucial for promoting FHB¹⁴,¹⁵. S genes are supposed to control host's basal processes that the fungus hijacks for its own benefit and as such, their regulation under changing environmental conditions likely influences the outcome of the disease. To date, very little is known about the impact of environmental stress on susceptibility determinants, but a few studies have demonstrated their complex effects on FHB progress. For instance, the metabolic response to FHB under varying ambient CO₂ concentrations showed adverse effects in a "resistant" cultivar, while it improved responses in a susceptible cultivar¹⁶. This highlights the necessity of disentangling the combined regulatory effects of multiple stresses on key components of wheat immunity, as well as understanding how physiological disturbances influence the expression of susceptibility mechanisms.

A major driver of susceptibility in plants is their metabolism products, since the expression of S genes in fine can alter metabolic processes that favor fungal development. Susceptibility-promoting metabolic compounds include primary metabolites involved in nutrient allocation, as well as a range of specialized metabolites such as those intervening in membrane and cytoskeleton reorganization¹⁷,¹⁸. In particular, specialized metabolism is at the crossroad of plant response and adaptation to both biotic and abiotic stress¹⁹⁻²¹, demonstrating for instance a crucial role in enhancing wheat immunity to the *Puccinia striiformis* pathogen²². Although further works proved its concrete involvement in key adjustments of the combined stress responses²³⁻²⁵, limited information is available in wheat facing FHB, especially under conditions of cumulative abiotic stresses.

Water deficit is one of the most prevalent abiotic challenges in fields before pre-anthesis, while FHB is usually developing after rain events at the flowering stage²⁶⁻²⁸. In this context, this study aims to evaluate the expression of FHB-related determinants after exposure to water stress followed by rapid rehydration. It hypothesizes that pre-anthesis soil water depletion may reshape the expression of basal plant immunity processes ans susceptibility determinants. A particular emphasis has been placed on known susceptibility determinants¹⁵ and on metabolism-related processes, given their central role in both biotic and abiotic stress responses. Our approach has enabled to trace robust responses set up independently of the plant's initial physiological state, as well as responses that were specifically influenced by the growing conditions preceding inoculation.

# Materials and methods

## Plant production and inoculum Preparation

The French *Triticum aestivum* cv. Recital was selected for this study because of its high susceptibility to FHB as already shown in previous studies²⁹,³⁰ and intermediate performance under water limiting conditions³¹. Seeds were provided by the INRAE Small Grain Cereals Biological Resources Centre (Clermont-Ferrand, France). They were sown in plastic pots containing seedling soil and kept at 6 °C for 5 days for stratification. Germinated seedlings were transferred to 23 °C and grown for 2 weeks. Seedlings were then vernalized at 6 °C for 8 weeks before transplanting in 4 L plastic pots containing compost for transplanting (Klasmann/Deilmann) and transferring to a growth chamber with a photoperiod of 12 h per day, 17 °C day and 15 °C night temperatures to enable tillering. The lighting was provided by 40 sodium lamps of 400 W each resulting in a luminosity of 520µmol m⁻² s⁻¹. Automated irrigation was used to water plants at field capacity level. Relative air humidity was 80%. After 3 weeks, plants were submitted to final conditions that consisted of 85% relative air humidity, 16 h photoperiod and 21 °C day and 17 °C night temperatures maintaining same lighting as before.

The French *Fusarium graminearum* strain MDC_Fg1 was selected for its aggressiveness in wheat cultivars²⁹,³⁰,³²,³³. Inoculum was prepared from isolates stored at -70 °C prior to the experiment. Before use, the isolate was grown on potato dextrose agar (PDA) medium (39 g PDA per L of sterilized water) in dark at 23 °C for nine days. Mycelium plugs were then given to sterile mung bean broth (40 g of mung bean per liter of osmosis water at initial temperature of 95 °C) and kept for three weeks on a shaker plate (150 rpm) for the production of macroconidia. Spore development was verified under the microscope (Olympus BH2-RFCA). Spores were separated by filtration and stored in sterilized water at 4 °C until use.

## Experimental design and plant measurements

Plants were submitted to four distinct treatments including (i) a pre-anthesis water restriction, (ii) *F. graminearum* inoculation, (iii) the succession of both (water depletion and *F. graminearum* inoculation) and (iv) the control condition receiving full irrigation and mock inoculation (Fig. 1). Treatments were organized in a randomized complete block design with four blocks. For induction of water depletion, automated irrigation was suspended 12 days before mid-anthesis. Soil water deficiency was maintained when reaching 11,5% of field capacity by daily manual weighing and watering. Rehydration to field capacity took place at mid-anthesis before restoring automated watering. Inoculation with *F. graminearum* MDC_Fg1 strain was carried out at mid-anthesis on the twelve central spikelets of 3 spikes per plant. For this, prepared macrospores were diluted with tap water to a concentration of 10⁵ spores/mL and 10µL of this solution were deposited in the floral cavity of spikelets with
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Experimental design and contrasts of interest that were considered in the statistical model for transcriptomic and metabolomic data analysis. FHB stands for Fusarium head blight.</figcaption></figure>

a micro-pipette. Symptoms were assessed visually based on the symptom scale from ref³⁴, every 24 h from 0 to 240 h post inoculation (hpi), which attributes scores from 0 – no visible symptoms, to 4 – development of aerial mycelium and complete spikelet bleaching. Yields of photosystem II were estimated using a MINI-PAM instrument (Waltz) based on fluorescence measurements on flag leaves every mid-day from the eleventh day before mid-anthesis until 7 days post inoculation. At 72hpi, the inoculated sections of two spikes of four plant replicates per treatment were collected, immediately frozen into liquid nitrogen and stored at -70 °C for further mRNA and metabolite extraction. Supplemental 17 plants underwent the described water depletion regime and served for water potential analysis. Water potential was measured with a Scholander-type pressure chamber (PMS instrument company, model 600) on flag leaves randomly sampled on days 0, 2, 4, 5, 6, 8, and 12 after water depletion induction.

## Transcriptomic analyses

### RNA extraction and sequencing

Frozen samples, comprising four replicates for each of the four treatments, were ground to fine powder of which 100 mg were used for extraction of total RNA with TRIazol protocol (TRI reagent, Sigma-Aldrich, St. Louis, MO, USA) and FastDNAse treatment (TURBO DNA-freeTM Kit, Thermo Fisher Scientific, Waltham, MA, USA). Sample quality was supervised with gel electrophoresis (1% agarose gel in Tris-Acetate-EDTA buffer). RNA quantities were assessed with Qubit RNA BR Assay Kit (Invitrogen). A volume of 50µL containing 5 µg of RNA were used for cDNA library generation. GeT PlaGe (GenoToul INRAE Auzeville, France) generated cDNA library and carried out sequencing with Illumina technology (NovaSeq6000) with the same procedure as in Rocher et al. (2024). Paired-end sequences of 150 base pairs were generated two times on the entire library.

### RNA-seq bioinformatics

Computations have been performed on the supercomputer facilities of the Mésocentre Clermont-Auvergne of the University Clermont Auvergne. Adapter residues and polyA/T were excluded with TrimGalore v0.6.5³⁵ and data cleaning was carried out with the same software fixing a lower limit for phred scores at 20 and for read length at 60 bases. Sequences were mapped with STAR v2.7.1a³⁶ on bacterial, viral (homemade databases built from genomes retrieved from NCBI, see ref³⁰), and human genome (NCBI reference genome sequence), those reads were subtracted from the files for the following operations. SortMeRNA v4.2.0³⁷ permitted to suppress
reads containing ribosomal RNA from *Triticum aestivum* and *F. graminearum* (homemade database³⁰). Further, STAR was used to map remaining reads on wheat genome IWGSC RefSeq v2.1 that was previously concatenated with the genome of *F. graminearum* MDC_Fg1³². Genes were assigned to wheat and *F. graminearum* separately and reads were counted with featureCounts v2.0.1. Only uniquely mapped reads were considered for further data analysis.

## Metabolomic approach

### Biphasic extraction of total metabolites

From the same samples as used for transcriptomics, approximately 100 mg of powder was first extracted with 700 µL of a methyl tert-butyl ether (MtBE) / methanol (MeOH) (75:25, v/v) mixture, then vortexed and sonicated for 3 min. Subsequently, 700 µL of a water/MeOH (75:25, v/v) mixture was added, followed by vortexing and sonication for another 3 min. The extract was centrifuged at 5 °C and approximately 14,000 rpm for 3 min, resulting in the formation of two phases: an upper apolar phase and a lower polar phase. The polar phase was collected in pre-weighed Eppendorf tubes and dried in a SpeedVac at 37 °C overnight. The final concentration for untargeted metabolomic analyses was performed at 20 mg/mL in MeOH/water (80:20). A quality control (QC) sample was made by pooling aliquots from each extract.

### LC-MS based chemical profiling

Ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC–HRMS) analyses were performed on a Q Exactive Plus quadrupole-Orbitrap mass spectrometer, equipped with a heated electrospray ionization probe (HESI II) and coupled to a U-HPLC Vanquish H system (Thermo Fisher Scientific, Hemel Hempstead, U.K.). Reverse phase separation was achieved on a Luna Omega Polar C18 column (150 mm × 2.1 mm i.d., 1.6 µm, Phenomenex, Sartrouville, France) equipped with a guard column. The mobile phase A (MPA) consisted of water with 0.05% formic acid (FA), while the mobile phase B (MPB) was acetonitrile with 0.05% FA. The solvent gradient was as follow: 98% MPA (0–0.5 min), 98% MPA to 30% MPB (0.5–8 min), 98% MPB (8–9 min), 98% MPB (9–12 min), and 98% MPA (12.1–14 min). The flow rate was maintained at 0.4 mL/min, with the column temperature set to 40 °C, the autosampler temperature at 5 °C, and the injection volume fixed to 1 µL.

Mass detection was performed in positive and negative ionization modes (PI and NI) at a resolution of 35,000 power [full width at half-maximum (fwhm) at 400 m/z] for MS1 and 17,500 for MS2. The automatic gain control (AGC) target was set to $1 \times 10^6$ for full scan MS1 and $1 \times 10^5$ for MS2. Ionization spray voltages were set to 3.5 kV for PI and 2.6 kV for NI, with the capillary temperature maintained at 256 °C. The mass scanning range was m/z 100–1500. Each full MS scan was followed by data-dependent acquisition of MS/MS spectra for the four most intense ions using stepped normalized collision energy of 20, 40, and 60 eV.

### LC-MS data processing

UHPLC-HRMS raw data were processed with MS-DIAL version 4.9³⁸ for mass signal extraction between 100 and 1500 Da from 0.5 to 12 min. The MS1 and MS2 tolerances were set to 0.01 and 0.05 Da, respectively, in centroid mode. The optimized detection thresholds were set to $1 \times 10^5$ for MS1 and 10 for MS2. Peaks were aligned on a quality control (QC) reference file with a retention time tolerance of 0.1 min and a mass tolerance of 0.015 Da. Peak annotation was performed with an in-house database built on MS-FINDER model³⁹.

MS-DIAL data were then cleaned with the MS-CleanR workflow version 1.0⁴⁰ applying all filters with a minimum blank ratio set to 0.8, a maximum relative standard deviation (RSD) set to 40, and a relative mass defect (RMD) ranging from 50 to 3 000. The maximum mass difference for feature relationships detection was set to 0.005 Da, and the maximum RT difference to 0.025 min. The Pearson correlation links were considered with a correlation $\ge$ 0.8 and statistically significant with $\alpha$ = 0.05. For each cluster, the two most relevant peaks based on intensity and connectivity were retained. The selected features were annotated with MS-FINDER version 3.61. The MS1 and MS2 tolerances were respectively set to 5 and 10 ppm. Formula finder was only processed with C, H, O, N, P, and S atoms. Databases (DBs) based on *Triticum* and *Fusarium* (genus), *Poaceae* and *Nectriaceae* (family), were constituted using the dictionary of natural product (DNP, CRC press, DNP on DVD v. 30.1). The internal generic DBs from MS-FINDER included KNApSAcK, PlantCyc, NANPDB, UNPD, COCONUT, YMDB, ECMDB, ChEBI and FooDB. Annotation prioritization was performed by ranking genus and family DBs and finally generic DBs, using the final MS-CleanR step.

The resulting feature table was reshaped using a in house KNIME (v 4.6) workflow⁴¹ to enrich data with annotation level according to the metabolomic standard initiative⁴² and the chemical ontology based on Classyfire⁴³ and NP Classifier⁴⁴. Plant and fungal annotated metabolites were filtered separately and only features with intensities different from signal base line referring to extraction blanks were retained for this work.

## Statistical analysis and data mining

All statistical analysis was conducted with R (version 4.3.1) exploited by RStudio (version 2023.09.1 + 494). Field capacity loadings were calculated based on daily pot weight measurements. Daily means and standard errors of field capacity loadings, photosystem II yields and symptom scores of distinct treatments were calculated with summarySE from Rmisc package (n=4). Results were plotted using ggplot2. Significant differences were analyzed with ANOVA and Tukey's Honestly Significant Difference test (n=4, $\alpha$ = 0.05) after visual validation of test assumptions. Analysis of transcriptomic and metabolomic was performed as two-by-two comparisons, for biological relevance the following contrasts were considered: Effects of water treatment in healthy plants, FHB effect in well-watered plants, FHB effect in previously water depleted plants and the effect of previous water depletion and FHB compared to absolute control (well-watered, mock) (Fig. 1).
Statistical analysis of transcriptomic data (n=4) was conducted with the package DiCoExpress package⁴⁵. DiCoExpress analysis was conducted in two steps, one focusing on the wheat assigned reads, the other on the *Fusarium* assigned ones. A count per million (CPM) cutoff of 4 was chosen for both datasets and counts were normalized according to library size with the trimmed mean of M values (TMM). Genes were retained as differentially expressed with α = 0.01. UpSet plots were used for data representation⁴⁶. For wheat analysis, all Chinese Spring IWGSC RefSeq v2.1 nucleic sequences annotated with the Gene Ontology (GO) term GO:0008152 "metabolic process" or its 55 daughter GOterms, were retrieved from Ensembl! Plants by biomart request. All differentially regulated genes from DiCoExpress output in the focal contrasts were filtered for annotation to metabolic process ontology further referred to as differentially expressed metabolism-related genes (DEGs). Generic and specifically expressed genes were identified based on the compare table output from DiCoExpress differential analysis. One-sided Fisher's exact test (α=0.05) was performed on accession counts associated to GOterm "metabolic process" and differential expression. Hierarchical cluster analysis (HCA) was performed using ComplexHeatmap package⁴⁷ on all DEGs and DEmGs. Generic and specific DEmG lists were used for enrichment analysis, which was conducted using agriGO v2.0⁴⁸ (α=0.05). Results from enrichments were investigated for metabolic pathways corresponding to NP Classifier pathways from the metabolomic approach. Wheat susceptibility factors for FHB and master regulators of the transcription factor regulation network were retrieved from ref¹⁵, and were searched in the dataset of this study. Metabolite accumulation was analyzed by distinguishing qualitative variations (presence/absence) from quantitative variations. For qualitative analysis, all observations were rendered binary (presence = 1; absence = 0) and multiple Fisher tests (fisher.test) were performed (n = 4). Adjusted p-values with false discovery rate (fdr) correction were then calculated and values lower than 0.05 were considered as significant. Quantitative analysis was performed on all features except significant from qualitative analysis. Analysis of variance (Anova type II from car package) was performed after visual validation of test assumptions and features with adjusted p-values below 0.01 were considered as significant. Tukey tests (α = 0.05) were performed (TukeyHSD) in order to identify differences among considered contrasts and fold changes were calculated. Effects of different contrasts were compared by UpSet plot in order to determine generic and specific responses. Therefore, frequencies of shared and specific differentially accumulated metabolites in NP Classifier pathways were calculated. Generic and specific differentially accumulated metabolites were analyzed by HCA and their pathways were retrieved from NP Classifier⁴⁴.

## Results

### Pre-anthesis water limitation altered host physiology along with reduced FHB symptoms

As compared to well-watered conditions, water deficit conditions initiated 12 days before mid-anthesis, caused a significant decrease in the relative field capacity loadings (FC) within the first 5 days (Fig. 2a).

This yielded to a significant and constant difference in FC over at least the last six days prior infection and reaching 11.5% of that measured initially, while FC remained between 75 and 100% in well-watered (WW) plants. Resuming irrigation at mid-anthesis had an immediate recovery effect on FC and led to comparable values with WW plants (Fig. 2a). As a consequence of the twelve-day water depletion, the midday water potential measured in pre-anthesis water deficit (PWD) plants decreased to an average of -1.6 MPa as compared to -0.8 MPa observed before water depletion treatment (Supplementary Fig. S1). At the time of sampling (3 days post-inoculation, 3dpi), PSIIY displayed a clear and significant drop of nearly 10% in PWD plants as compared to the WW plants, whether the plants were inoculated with *F. graminearum* or not (Fig. 2b). Inoculation of *F. graminearum* in WW and in PWD plants did not change the PSIIY (Fig. 2b) as compared to the mock inoculated plants. Monitoring of FHB symptoms revealed a PWD-specific dynamic with an overall decrease of FHB development. Differences were significantly observed starting from 72hpi (p-value = 0.029) and resulted, at the end of the FHB progression, in yellowing spikes in PWD (average score symptom < 1.2) while WW plants displayed fully bleached spikelets (Fig. 2c).

### Transcriptomic profiling of the responses to preceding water deficit, to the FHB progress and to their cumulative effects

Dual-transcriptomic approach enabled the assignation of 90% of reads to *Triticum aestivum* (Supplementary Table S1). Differential gene expression analysis surveyed at the single time-point 72hpi, was based on 39,998 gene IDs that passed quality control from DiCoExpress. Considering all examined contrasts, it allowed the identification of 7,165 differentially expressed genes (DEGs) (p-values < 0.01) among which only 14 DEGs were found to be significant for the individual PWD effect (Fig. 3a). DEGs shown to be significantly enriched (p-value < 2.2*10⁻¹⁶) in the gene ontology (GO) term GO:0008152 "metabolic process", gathered nearly half of the DEG set.

*Wheat transcriptomic responses to FHB infection include specific alterations after contrasting water regimes*

FHB Infection of WW plants evidenced 2,518 DEGs while 5,711 DEGs were found in PWD plants (Fig. 3a). More than 90% of the FHB-responsive genes in WW plants were also observed in the PWD plants and only 153 DEGs were specific to the WW condition. On the contrary, among FHB-responsive genes in PWD plants, nearly 25% (1,369 DEGs) were specific to the PWD condition. Narrowing down to DEGs harboring metabolic process GO terms (Differentially-expressed metabolism-related genes, DEmGs) resulted in 1,222 and 2,789 FHB-responsive genes in WW and in PWD plants, respectively. Out of these, 48 were specific to the WW condition and 660 to the PWD condition (Fig. 3a). DEmGs specific to the FHB response in WW conditions included mainly up-regulated genes (Fig. 4b) and were enriched in genes involved in carbohydrate metabolic process (28.9% of DEmGs) (Fig. 5a, Supplementary Table S2). DEmGs specific to the FHB response in PWD plants displayed a balanced proportion of up-regulated and downregulated genes (Fig. 4c) and were enriched in genes involved in
<figure><img src="image_4.png" /></figure>

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

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

**Fig. 2.** Characterization of water depletion and FHB development during experimentation. (a) Mean field capacity loading measured during the twelve days preceding mid-anthesis and over the 7 following days. Values are represented with standard errors for well-watered condition (dark blue lines) and for water limited condition (light blue lines). (b) Box plot of photosystem II yields measured in flag leaves at mid-day for **mock (dark lines)** and *F. graminearum* infected (grey lines) plants. Values are shown at 3 days post inoculation for plants submitted to both water regimes. P-value originates from Tukey's Honestly Significant Difference test. (c) Evolution of mean symptom scores and their standard errors following infection with the MDC_Fg1 strain under well-watered conditions and pre-anthesis water limited conditions.

carbohydrate metabolic process (25.4% of DEmGs), in peptide and cellular amino acid metabolic process (8.2% and 5.6%) as well as in phenylpropanoid metabolic process (6.5%) (Fig. 5a, Supplementary Table S3).

*Transcriptional wheat Spike responses to cumulative FHB and PWD effects included FHB-generic responses and*
*revealed new regulations*

In comparison to the mock-inoculated WW plants, gene expression profiling in plants subjected to both PWD and FHB evidenced 5,577 DEGs among which 1,292 were specific to the stress cumulation (Fig. 3A). Narrowing down this gene set to the DEmGs yielded 2,601 genes, of which 489 were specific to this cumulative response. Cumulative-stress specific responses consisted in a majority of down-regulations (Fig. 4d) with 20% belonging to genes involved in carbohydrate metabolic process, 9.9% and 7.2% in peptide and cellular amino acid metabolic process (Fig. 5a, Supplementary Table S4). Considering the whole set of DEGs responding to stress cumulation, most were shared with FHB-responsive genes evidenced in WW and PWD condition (2,302 DEGs). Almost half of these genes were DEmGs (1,135 genes) (Fig. 3a) and will further be addressed as FHB-generic DEGs/DEGs. FHB-generic DEmGs consisted essentially in up-regulations (Fig. 4a) and were enriched in genes involved in carbohydrate metabolic process (GO:0005975, 19.8% of DEmGs), in peptide and cellular amino acid metabolic process (GO:0006518, 10%; GO:0006520, 12.6%), and in phenylpropanoid metabolic process (GO:0009698, 11.5%). Other key pathways as fatty acid metabolic process and terpenoid metabolic process were enriched to a lesser extent (4.8% and 2.8% respectively) (Fig. 5a, Supplementary Table S5).

*Effect of preceding soil water depletion on *F. graminearum* gene expression*

Dual-transcriptomic approach enabled the identification of 13,166 *F. graminearum* gene accessions, of which 9,380 passed quality control and were used for differential expression analysis. No significant difference in the number of fungal transcripts/reads was observed in the PWD infected plants compared to the WW ones. When comparing fungal gene expression profiles between the two water regimes, only 10 genes were up-regulated under
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Upset plots showing the number of differentially-expressed genes (DEG) and differentially-accumulated metabolites (DAM) observed following pre-anthesis water depletion in healthy plants (PWD effect), Fusarium Head Blight infection of well-watered plants (FHB effect after WW), FHB infection effect after pre-anthesis water depletion (FHB effect after PWD) and effects of cumulative PWD and FHB effects compared to well-watered healthy plants (cumulative effect). (a) Plots are given for all differentially expressed genes and for a narrowed set of genes annotated to GO:0008152 "metabolic process" and daughter GeneOntology (GO) terms as well as for (b) metabolomic features. Upset plots were created according to the intersections between DEGs of focal contrasts.</figcaption></figure>

PWD as compared to WW (Supplementary Table S6). These genes were annotated *inter alia* to carbohydrate metabolism of the fungus and, cellulose-binding related processes (Supplementary Table S7).

*Meta-analysis of the putative susceptibility gene set previously identified by Rocher et al.¹⁵*

Using a similar transcriptomic analysis, a previous study identified the core FHB-responsive genes of bread wheat cultivars with contrasting susceptibility¹⁵ demonstrating consistent regulation patterns in all cultivars and two peculiar expression profiles. This gene set included 233 known susceptibility (S) genes and the computation of the complete gene regulatory network yielded the characterization of 427 master regulators of these core FHB-responses. In the present study and although it focused only on the 72hpi time point, nearly 90% of these known S genes were successfully re-profiled (Fig. 6a) and almost half of them exhibited also a significant differential expression in response to FHB in WW (65.6%) and/or PWD conditions (93.8%). Identified changes between mock-inoculated and FHB plants after WW and PWD treatment were systematically consistent with the previous study of Rocher et al.¹⁵ for all gene accessions and none of these genes was significantly deregulated by the previous irrigation regimes (Supplementary Table S8). Similarly, the expression of nearly 75% of the master regulators were also re-profiled (Fig. 6b). As a whole, 42% of all tracked transcription factors (89.4% responding to FHB after WW and 59.1% after PWD) were part of the DEGs. Globally, regulation patterns were also well conserved regardless of the preceding water treatments, with a few exceptions (Fig. 6b, Supplementary Table S9).

**Metabolomic profiling of the responses to preceding water deficit, to the FHB progress and to their cumulative effects**

Untargeted metabolomics enabled the identification of changes in spike metabolism at 72hpi based on 2,489 ion peaks (724 and 1765 ions in negative and positive mode, respectively). These resulted in 376 metabolic features, after LC-MS data processing, filtering for wheat specific features and narrowing the dataset to reproducible detections. Considering all examined contrasts, 93 differentially accumulated metabolites (DAMs) were identified (Fig. 3b). The sensitivity and resolution of untargeted metabolomics was obviously not sufficient to detect fungal metabolic changes due to relatively low abundances of the fungal metabolites in infected samples and to uncertainties in the assignment (between 5 and 36 ions with no reproducible values).

*Wheat metabolomic profiles remain altered 3 days after the end of the water depletion period*

The preceding water depletion impact in healthy plants induced the differential accumulation of 43 metabolites, of which seven were specific to this effect (Fig. 3b). Later were composed of one unannotated feature, two alkaloids, one peptide, one carbohydrate and two phenylpropanoids of which one is a derivate of salicylic acid. The remaining DAMs were also observed in the FHB effect upon PWD, in the cumulative effect of FHB and
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Heat maps of the expression patterns of differentially expressed metabolism-related genes (DEGs) and differentially accumulated metabolites (DAMs). (a) Elements responding generically to Fusarium Head Blight (FHB), i.e. evidenced in FHB effect after well watered (WW) conditions, in FHB effect after pre-anthesis water depletion (PWD) and in cumulative effect; (b) responding specifically to FHB after WW; (c) responding specifically to FHB after PWD and (d) responding specifically to cumulative effects of PWD and FHB infection. Colors represent z-scores of normalized counts from DiCoExpress. Red dot on the top right of each heat map refer to the groups of the Upset plots in Fig. 3.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**b** NP Classifier pathways for DAMs</figcaption></figure>

<figure><img src="image_10.png" /></figure>

**Fig. 5.** Bar plots representing distribution of functional annotations of pre-anthesis water depletion (PWD) and Fusarium Head Blight (FHB) responsive elements. (a) GeneOntology (GO) annotations of differentially expressed metabolism-related genes (DEGs) and (b) metabolite annotation to NP Classifier pathways of the former identified differentially accumulated metabolites (DAMs). Visual representations correspond to the number of FHB generic (black bar) and specific responsive elements (FHB effect after WW, FHB effect after PWD (grey bars) and □ for cumulative effects of PWD and FHB).
<figure><img src="image_11.png" /><figcaption>**Fig. 6.** Heat maps of the expression patterns of genes related to Fusarium Head Blight (FHB) susceptibility retrieved from the study of Rocher et al.¹⁵. (a) Known FHB susceptibility factors and (b) master regulators of FHB response network.</figcaption></figure>

PWD, and eight were common to all studied effects, including two unannotated features, two annotated to phenylpropanoid pathway, one fatty acid, one terpenoid, one carbohydrate and one polyketide.

*Pre-anthesis water depletion changed wheat metabolomic adjustments to FHB infection*

Upon continuous WW conditions, FHB infection promoted 29 DAMs (Fig. 3b), of which six were specific to this comparison and proved to be systematically more abundant after *F. graminearum* inoculation (Fig. 4b). They were annotated to phenylpropanoids, alkaloids and, amino acid and peptide pathways (Fig. 5b). After PWD, the FHB effect significantly induced 59 DAMs. Five of them were specifically observed after this water regime, including three that displayed an increasing abundance after *F. graminearum* inoculation (Fig. 4c). These specific DAMs belonged to the phenylpropanoids and alkaloids (Fig. 5b).

*Metabolomics responses to cumulative FHB and PWD effects in wheat spikes included specific metabolite accumulations and maintained part of the FHB-generic ones*

The comparison of mock-inoculated WW plants with plants subjected to cumulated PWD and FHB evidenced 75 DAMs of which 16 were specific to the stress cumulation (Fig. 3b). Cumulative-stress specific responses consisted predominantly in decreases of metabolite accumulation (Fig. 4d), with most DAMs belonging to the phenylpropanoid pathway, and some to alkaloids, peptides, fatty acids and polyketides (Fig. 5b). Considering all DAMs responding to stress cumulation, an important number was shared with PWD effect and FHB effect after PWD (23 DAMs i.e., 30% of the total). Another substantial part was shared with FHB-responsive metabolites evidenced in WW and PWD conditions (15 DAMs i.e., 20% of the total) and will further be referred to as FHB-generic DAMs. FHB-generic DAMs exhibited mainly accumulation increases (Fig. 4a) with seven DAMs contributing to the phenylpropanoid pathway (Fig. 5b). Metabolites contributing to alkaloid, carbohydrate, fatty acid and terpenoid pathways were also affected.

Discussion

In this study, wheat plants were submitted to different pre-anthesis water regimes followed by *F. graminearum* inoculation to induce FHB, with the aim of comparing their transcriptomic and metabolomic profiles. This approach allowed to analyze the impact of water stress consequences on the FHB-responsive genes and metabolites, as well as to decipher specific features of the cumulative stress responses. Final FHB severity scores consistently decreased after the water depletion treatment, suggesting that prior water regimes partially influenced the disease outcome. In the plants, transcriptomic profiles did not corroborate wide gene regulation adjustments associated with water depletion 72 h after the recovery to full irrigation. However, both ecophysiological parameters and metabolite accumulations, refer to a still altered physiological state. Such impacts may rely on a recovery phase during which the plant restores baseline homeostasis, comparable to its pre-water stress state, as previously showed with similar durations of water restrictions⁴⁹. Physiological adjustments specific to this recovery phase create unique conditions, positioning our stress treatments at the intersection of stress succession and stress combination.
## The core-molecular dialog between wheat and *F. graminearum* is conserved while plant physiological status differs

The consistency of the FHB-responsive metabolomics and transcriptomics profiles observed across different plant physiological statuses provides evidence that the wheat response to *F. graminearum* is particularly robust. Several responsive elements were not only conserved regardless of the water regimes, but their deregulation conserved its direction and magnitude. Consistently, fungal transcriptome profiles remained unchanged, despite the known influence of environmental factors on pathogen virulence⁵⁰. This suggests that *F. graminearum* did not respond to the pre-anthesis soil water depletion and did not appear to remodel its infection strategy or its main drivers, even as plant ecophysiology underwent specific adjustments to cope with environmental changes. Similar FHB-responsive features have already been observed in contrasting wheat cultivars and using different *F. graminearum* strains¹⁵,³⁰,⁵¹, our results extend this conservation to different growth conditions strengthening the hypothesis that the *F. graminearum* infection program depends on robust mechanisms. Furthermore, susceptibility gene candidates¹⁵, also proved to be still regulated in our study with similar patterns after the two different water regimes. This suggest that core susceptibility-related processes are consistently activated, regardless of the host's physiological status, emphasizing the particular value of these elements in identifying relevant candidate genes for breeding FHB-resistant plants. Furthermore, the conserved activation of a significant portion of the core molecular dialog, along with a range of susceptibility-related processes, does not necessarily conflict with the occurrence of different symptom developments. Instead, specific adjustments and variations of gene regulations and metabolite accumulations may help explain the symptom discrepancies.

## Concurrent physiological adjustments promote specific wheat responses to FHB and improve plant immunity

Altered physiology after pre-anthesis water depletion remodels infection responses with more diverse and fine-tuned responses, including new regulations detectable at both the gene expression and metabolite accumulation levels. Gene expression changes were no longer exclusively associated with up-regulations of carbohydrate metabolic processes but also down-regulations and differential regulation of cellular amino acid and peptide metabolic process as well as phenylpropanoid metabolic process. Expression of metabolism-related genes involved in the defense response was shown to be widely rearranged when the plant's physiological state was altered. Even though it remains challenging how the rearrangement of defense responses optimizes the response to FHB, these differential adjustments may enhance the effectiveness of the response. Down-regulation of carbohydrate metabolism after pre-anthesis water deficit could be less favorable for the fungus. Changing expression of genes involved in cellular amino acid and peptides metabolic process confirm the importance of post-transcriptional modifications in this interaction. Furthermore, both transcriptomics and metabolomics agreed with the central role of regulations in the phenylpropanoid pathway, leading to differential accumulation of cinnamic acid and salicylic acid derivates that could explain a different symptomatic progression. Cinnamic acid is a well-documented anti-fungal molecule⁵² and an FHB reducing action of the salicylic acid, a key phytohormone mediating responses to biotic and abiotic stress⁵³, has already been reported⁵⁴. The specific FHB responses promoted by the pre-anthesis water deficit also included more transcriptional down-regulations and lower metabolite accumulations, as well as abolished part of the specific up-regulations described under control conditions. Altogether, the fine tuning of regular FHB responses and the regulation of new and specific response after the pre-anthesis water stress suggest that the plant manages new compromises between the physiological adjustment to abiotic stimulus and the immune-related processes. Similar to the well-documented trade-off between growth and defense⁵⁵, our results depict that the plant could prioritize specific responses allocating resources more efficiently through different basal expression of genes and altered metabolic status before infection. Such a susceptibility-adjustment compromise was indirectly discussed in the FHB risk assessment model⁵⁶ where one of the best predictors influencing FHB susceptibility was water availability seven days before potential infection which implies an altered plant physiological state to which the plants adjust at the moment of infection.

## Disentangling unique combinatory effects from infection effects among contrasting physiologies supports the idea that cumulative stresses should be considered as a new and unique stress

Part of the cumulative pre-anthesis water stress and FHB responses included generic FHB response and FHB responses in water stressed plants, while substantial unique regulations were found to be specific of the combined effects of both stresses. This specificity of responses primarily consisted of down-regulations, corroborated by large accumulation decreases at the metabolomic level. Remodeling of the carbohydrate metabolism undermines the hypothesis of altered energy allocation. Amino acid and peptide metabolism were also impacted by unique combinatory effects, which is in line with the differential accumulation of N-acetylleucine. The most important accumulation changes were found in the phenylpropanoid pathway, including a coumaric acid derivate known for its adverse effects on FHB⁵⁷. Although response specificity of combined stress has already been reported in several studies⁵⁸⁻⁶³, our results reveal a distinct unique response to cumulative stress, which is different from the action of a focal stress on non-focal stress effects. This corroborates the idea that stress combinations should be considered as a new stress of distinct nature⁷,⁸,⁶⁴,⁶⁵.

## Conclusion

To conclude, by combining pre-anthesis water deficit and Fusarium head blight experiments, our study demonstrated that disease development remains persistent regardless of the preceding water regime, probably as a result of the robust expression of a highly conserved set of susceptibility genes and their regulators. However, a preceding stressor proved to change the intensity of the disease, triggering differential stress responses at both
ecophysiological and molecular level. While no evidence was found for any adjustment in the fungus' infectious strategy, our results suggest that the increased resistance to FHB, or at least the improved plant immune response, after pre-anthesis water stress could be more likely attributable to changes in the plant physiology rather than to direct adverse effects on the fungus. Although unique and specific regulatory responses were evidenced upon cumulative stress, notably affecting wheat spike metabolism, their role in regulating adaptive responses to combined stress undoubtedly requires further functional validation. Further researches are needed to understand their potential role in mitigating FHB under fluctuating abiotic conditions. As such, these findings open new avenues for identifying original and sustainable sources of resistance.

## Data availability

All generated data which was analyzed for and included in this study is available in supplementary files or in publicly available platforms. Read data obtained from RNA-Seq was deposited in the form of raw fastq files in the European Nucleotide Archive (ENA) database under the study reference number PRJEB79249. Metabolomic raw data that supports this work was deposited in the MassIVE database from the Center for Computational Mass Spectrometry, project reference number MSV000095730 (doi:10.25345/C5N01053G).

Received: 3 December 2024; Accepted: 26 March 2025

Published online: 06 April 2025

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## Acknowledgements

The authors thank the Mésocentre of the Université Clermont-Auvergne for computer calculation and storage facilities. The authors acknowledge GeT-PlaGe GenoToul (https://get.genotoul.fr) for the performance of cDNA library construction and RNA sequencing. The authors are also grateful to Christophe Sallaud and Freddy Boutrot (Limagrain Europe) for proofreading the mansucript. This research and PhD grant (LA) were funded by the Industrial Chair "Adaptation of wheat to combinations of biotic and abiotic stress". The authors also acknowledge the support received from the Agence Nationale de la Recherche of the French government through the program "Investissements d'Avenir" (16-IDEX-0001 CAP 20-25; International Research Center on Sustainable AgroEcosystems and CAP2025 Academy).

## Author contributions

LA, TL and LB conceived and designed research. LA and GP conducted experiments. LA, PSD and LB analyzed the data. PVO, GM and LA conducted metabolomics. LA and PSD carried out transcriptomics data analysis. LA and LB wrote the manuscript. All authors read and approved the manuscript.

## Declarations

### Competing interests

The authors declare no competing interests.

### Additional information

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1038/s41598-025-96159-4.

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