# Delving into the soil and phytomicrobiome for disease suppression: A case study for the control of Fusarium Head Blight of cereals

Toan Bao Hung Nguyen, Amandine Henri-Sanvoisin, Gaétan Le Floch, Adeline Picot

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## To cite this version:

Toan Bao Hung Nguyen, Amandine Henri-Sanvoisin, Gaétan Le Floch, Adeline Picot. Delving into the soil and phytomicrobiome for disease suppression: A case study for the control of Fusarium Head Blight of cereals. Science of the Total Environment, 2025, 965, pp.178655. 10.1016/j.scitotenv.2025.178655. hal-05386738

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Submitted on 28 Nov 2025

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# Delving into the soil and phytomicrobiome for disease suppression: A case study for the control of Fusarium Head Blight of cereals

Toan Bao Hung Nguyen, Amandine Henri-Sanvoisin, Gaétan Le Floch, Adeline Picot*

ª Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France

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## HIGHLIGHTS

* Substrate type is the main driver shaping microbial communities in wheat fields.

* Residues act as a transient substrate between soil and grain microbiomes.

* The grain microbiome evolved rapidly during maturity.

* Correlation-based analyses revealed taxa negatively correlated to *Fusarium* spp.

* *Pantoea* was an indicator genera of mycotoxin-free wheat grains.

## GRAPHICAL ABSTRACT

<figure><img src="image_5.png" /><figcaption>3- Soil and phytomicrobiome diversity and dynamics</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>4- Comparative analysis between healthy and contaminated grains/residues</figcaption></figure>

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## ARTICLE INFO

Editor: Abasiofiok Mark Ibekwe

### Keywords:

* Microbiome dynamics
* Co-occurrence network
* Crop residues
* Soil
* Mycotoxin
* Biocontrol

## ABSTRACT

Fusarium Head Blight is one of the most devastating fungal diseases of cereals worldwide, causing significant yield losses and affecting grain quality. The predominant role of the interactions within the *Fusarium* communities as well as with members of the phytomicrobiome in disease onset and development has gained increasing attention. Understanding the diversity and dynamics of bacterial and fungal communities across different substrates colonized by *Fusarium* spp. in wheat fields can provide valuable insights into disease ecology and lead to the discovery of native microorganisms with biocontrol potential. In this study, the bacterial and fungal communities associated with soil, maize residues, and wheat grains, were studied based on metabarcoding sequencing of 16S rRNA and ITS2 regions in six wheat fields over two years and characterized by different levels of FHB disease pressure and mycotoxin contamination. Overall, the diversity and composition of microbial communities were primarily influenced by substrate type followed by geographic origins of fields and sampling time, notably for grains and residues while the soil microbiome was less impacted by environmental fluctuations. Notably, our findings suggest that crop residues function as a transient substrate between soil and wheat microbiomes. In addition, we found several taxa either strongly negatively correlated to *Fusarium* spp. and/or to levels of *Fusarium* DNA or mycotoxins in grains or residues, including *Cladosporium*, *Epicoccum*, *Paenibacillus*, *Curtobacterium*, *Pseudomonas*, *Pantoea*, and *Sphingomonas*, which could be potential antagonistic agents against *Fusarium* spp. Altogether, these findings provide novel insights into the field microbiome functioning and their complex interactions with the *Fusarium* communities.

<footnote>* Corresponding author.
E-mail address: adeline.picot@univ-brest.fr (A. Picot).</footnote>
# 1. Introduction

Fusarium Head Blight (FHB) is one of the most devastating diseases affecting cereals worldwide, leading to significant reductions in grain yield and quality, and posing serious safety concerns due to mycotoxin contamination (Summerell, 2019). At least seventeen *Fusarium* species were reported to be responsible for FHB of small-grain cereals (van der Lee et al., 2015), with *F. graminearum*, *F. poae*, *F. avenaceum*, and *F. culmorum* being the primary causal agents in Europe (Hellin et al., 2016; Nguyen et al., 2024). Species within the *Fusarium* genus often coexist and continuously interact with each other as well as with the plant and members of the phytomicrobiome, influencing both disease development and mycotoxin production (Karlsson et al., 2021). Risk factors for FHB infection primarily include precipitations during flowering, minimum to no tillage, crop rotation involving cereals or maize, and the use of susceptible cultivars (Dill-Macky and Jones, 2000; Edwards and Jennings, 2018; Wegulo et al., 2015). However, management strategies, mostly based on agricultural practices such as tillage, resistant cultivars, and fungicide application, have shown inconsistent efficacy in controlling FHB disease, particularly under high disease pressure (Wegulo et al., 2015).

Recent attention has shifted towards the interactions between plant pathogens and the plant-associated microbiome as a potential lever to reduce FHB incidence (Karlsson et al., 2021; Legrand et al., 2019). The microorganisms associated with all components of a plant, from the aboveground to the belowground, also referred to as the phytomicrobiome, play a crucial role in the health, growth, and resilience of plants to environmental stresses, including disease infections (Chen et al., 2023). Members of the phytomicrobiome can influence various aspects of disease progression, including fungal growth, virulence, and mycotoxin production (Chen et al., 2018b). Multiple beneficial microbial strains within the plant microbiome, particularly *Trichoderma* (Matarese et al., 2012), *Pseudomonas* (Palazzini et al., 2007), *Streptomyces* (Jung et al., 2013), and *Bacillus* (Chen et al., 2018a), have shown the ability to reduce FHB disease, notably through antibiosis, mycoparasitism, and/or competition for space and nutrients. On the other hand, microbiome disruption, for instance through certain fungicide application, can impair the microbial capacity to suppress pathogens, thereby enhancing *Fusarium* success and increasing mycotoxin accumulation (Simpson et al., 2001).

The use of biocontrol agents (BCAs) to reduce FHB has been intensively examined by many studies and, despite some promising results, only few of them are commercialized for FHB management worldwide. A better understanding of the complex pathogen-microbiome interactions at stake, as well as the microbiome dynamics, may provide opportunities to discover novel promising microorganisms with antagonistic activities that could be used as part of inoculative biocontrol strategies. Alternatively, manipulating the microbiome to promote disease-suppressive indigenous communities, another strategy known as conservation biocontrol, holds promise but also requires a deeper understanding of microbiome interactions and dynamics (Karlsson et al., 2021). So far, comparative analysis of microbiome in healthy versus FHB diseased tissues such as maize stalks (Köhl et al., 2015) or wheat spikes (Rojas et al., 2020), as well as in conducive versus suppressive soils to *F. graminearum* growth (Legrand et al., 2019; Todorović et al., 2024), has allowed pinpointing taxa more abundantly found in healthy tissues or soils. Additionally, random forest regression or co-occurrence networks were also used as a tool to identify negative correlations between *Fusarium* spp. and microbial taxa (Bakker and McCormick, 2019; Cobo-Díaz et al., 2019). Actually, in the context of FHB of cereals, three primary substrates colonized by FHB causal agents during their life cycle can be targeted to mitigate disease risk: previous crop residues, soil after residue decomposition, and cereal heads. While BCA treatments are usually applied directly to wheat heads, few studies have explored the efficacy of treating crop residues with potential BCAs to reduce survival of *Fusarium* species and their ability to produce perithecia and spores.

For instance, application of antagonistic strains, including *Trichoderma*, *Microsphaeropsis*, and *Clonostachys*, on crop residues have been shown to reduce the formation of perithecia or conidia of certain *Fusarium* species under controlled and field conditions (Bujold et al., 2001; Inch and Gilbert, 2011; Luongo et al., 2005; Schöneberg et al., 2015; Xue et al., 2009). Additionally, crop residues, due to their higher species richness compared to grain, offer a potential source for identifying interactions between pathogens and beneficial species (Kerdraon et al., 2019). However, the functions and diversity of microbial communities in residues, and their interaction with soils, remain largely underexplored. Furthermore, to the best of our knowledge, there is limited information on microbiome diversity and dynamics of residues left on the soil for several months after harvest, while the dynamics of grain-associated microbiomes during grain development are also poorly documented.

In this work, French fields under wheat cultivation, with maize as previous crop, were monitored over two consecutive years, with soil, maize residues, and wheat grains sampled at four different stages during the wheat growth cycle (that is, after maize harvest, during wheat seedling, 2-3 weeks after wheat flowering, and before harvest). The bacterial and fungal compositions were assessed using a metabarcoding sequencing of 16S rRNA and ITS2, respectively, while the effects of multiple factors, including geographical locations, soil characteristics, and climatic conditions, on the structure and dynamics of the soil and phytomicrobiome were investigated. A second objective of this study was to compare the microbiome dynamics and composition according to the levels of FHB disease pressure and levels of contamination in *Fusarium* spp. and mycotoxin, as previously described in Nguyen et al. (2024). Additionally, co-occurrence networks were employed as a preliminary step to identify potential antagonistic members within residue and wheat microbiomes.

# 2. Materials and methods

## 2.1. Field sampling

The field sampling strategy used for this study was previously described in Nguyen et al. (2024). Briefly, soil, maize residues, and wheat grains were collected from six soft winter wheat (*Triticum aestivum* L.) fields (F1 to F6) across six different farms in Brittany, France, over two consecutive wheat cycles (2020–2021 or Y1 and 2021–2022 or Y2). Weather data, including cumulative monthly precipitation, mean daily humidity, and temperature, were obtained from the nearest weather station to each field during grain flowering (from one week before to two weeks after flowering date) and grain growth (from one week before flowering until harvest) (MétéoFrance data). All fields were under a maize-wheat rotation (either maize silage or maize grain as a previous crop, Fig. S1) and minimum tillage practices, except under exceptional circumstances where ploughing was necessary (that is, F1 in 2020 and F2 in 2021). The residues of maize grain were crushed before incorporation into each field. Soil samples (top 5 cm) and aboveground maize residue fractions were collected at four time points: after maize harvest (October, T1), at the wheat seedling stage (January, T2), 2 to 3 weeks after wheat flowering (June, with flowering being defined as the onset of anther extrusion, T3), and before harvest (July, T4). Wheat heads and grains were collected 2 to 3 weeks after flowering and harvest, respectively. Soil samples were stored at –80 °C after removing stones and plant residues until DNA extraction. Maize residues were cut into small pieces and roots were discarded to avoid soil contamination. Maize residue and wheat head samples were stored at –80 °C, then lyophilized and ground using a Retsch MM400 Mixer Mill (Retsch, Haam, Germany) prior to DNA extraction. All samples were processed within 1 to 3 days of field collection.

Three soil samples per field collected at seedling were homogenized and 500 g were used to determine the physicochemical properties of soil at Capinov laboratory (Landerneau, France). The analysis was performed according to the French standards, including NF X31–107 (clay,
silt, and sand fractions), NF ISO 11465 (Humidity), NF ISO 10390 (pH), NF ISO 10694 (organic carbon), NF ISO 13878 (nitrogen), NF ISO 11263 (available phosphorus - OLSEN), NF X31-108 (exchangeable calcium, magnesium, potassium, sodium), NF X 31-120 (copper, ion, manganese, zinc), and NF X 21-130 (Cationic Exchange Capacity). The soil of the twelve wheat fields was classified using the K-means method to identify clusters with contrasting physicochemical properties. Principal component analysis was performed, allowing to identify the soil physicochemical properties that were the main contributors to the multivariate model.

The twelve wheat fields were grouped into three groups according to the levels of mycotoxin concentrations in wheat grains at harvest, as previously reported in Nguyen et al. (2024). The groups were: no mycotoxin contamination (F5Y1, F1 to F6 of Y2), moderate contamination (F1Y1, F2Y1, F4Y1), and severe contamination (F3Y1, F6Y1). In total, fifteen Fusarium mycotoxins were quantitated, including deoxynivalenol (DON), 15-acetyldeoxynivalenol (15ADON), 3-acetyldeoxynivalenol (3ADON), zearalenone (ZEA), nivalenol (NIV), moniliformin (MON), beauvericin (BEA), T-2, HT-2, enniatin (ENN) A, A1, B, B1, fumonisin B1 (FB1), and B2 (FB2) (Fig. S2). Deoxynivalenol was detected in 5 out of 6 fields in Y1 (except for F5), with grains of the most severely contaminated field, F6, also contaminated with NIV and ENNs. In contrast, harvested grains from Y2 were fusariotoxin-free, with concentrations below the limit of detection. The Fusarium communities of all samples were assessed using EF1α metabarcoding and species-specific qPCR for F. graminearum, F. avenaceum, and F. poae (Nguyen et al., 2024).

## 2.2. DNA extraction

Total DNA of soil samples were extracted from 2 × 0.5 g of soil using NucleoSpin® Kit for Soil (Macherey-Nagel, Düren, Germany) according to the manufacturer's instructions. For maize residues and wheat heads and grains, DNA was extracted from 100 mg of dried ground sample using FastDNA™ SPIN kit (MP Medicals, OH, USA), following the manufacturer's instructions. DNA quality and concentration were determined using a UV Spectrophotometer (Nanodrop1000, Thermo Fisher Scientific, MA, USA). DNA was stored at -20 °C until use.

## 2.3. Illumina MiSeq sequencing

Primers 341F (5'-CCTACGGGNGGCWGCAG-3') and 805R (5'-GAC-TACHVGGGTATCTAATCC-3') were used to amplify the variable regions V3 and V4 of 16S rRNA gene from bacteria (Herlemann et al., 2011). Primer ITS3 (5'-GCATCGATGAAGAACGCAGC-3') and ITS4_KYO1R (5'-TCCTCCGCTTWTGWWTWTC-3') were used to amplify the internal transcribed spacer 2 (ITS2) region from fungi (Toju et al., 2012). Amplicon libraries and Illumina MiSeq PE300 sequencing were performed at Génome Québec Innovation Centre, Montreal, Canada. Adapter FLD ill (forward sequence: 5'-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3' and reverse sequence: 5'-GTGACTGAGTTCAGACGTGTGCTCTTCCGATCT-3') was used. PCR mixtures were done in 8 µL of total volume, including 1 × Qiagen Buffer, 1.5 mM MgCl₂, 5 % DMSO, 0.2 mM dNTP mix, 0.01 U µL⁻¹ Qiagen HotStarTaq, 0.6 µM of each primer, and 8 pM of environmental DNA. PCR conditions were 15 min at 96 °C, followed by 35 cycles of 30 s at 96 °C, 30s at 52 °C, and 60 s at 72 °C, and a final extension of 10 min at 72 °C. The 16S and ITS2 gene sequencing from Y1 or Y2 were carried out in two separate runs with a sequencing depth of 40,000 reads each.

## 2.4. Processing of sequencing data

ITS2 or 16S rRNA sequencing data from Y1 and Y2 were gathered together for data processing, using the following pipeline. First, the quality profiles of reads were inspected first using plotQualityProfile function from DADA2 R package version 1.16, and poor-quality reads

were removed. Trimming parameters were determined beforehand by Figaro (https://github.com/Zymo-Research/figaro#figaro, accessed on 01 April 2022) (Weinstein et al., 2019) (estimated amplicon length set to 450 bp for 16S and 480 for ITS2 and minimum overlap length of 20 bases). Forward and reverse trim positions were 286 and 228 bp for 16S, and 299 and 241 for ITS2, respectively. The expected error values for both the forward and reverse sequences of bacteria and fungi were set to 3. Then forward and reverse read pairs were trimmed, denoised, and filtered using DADA2 pipeline (Callahan et al., 2016). Amplicon sequence variants (ASVs) were independently inferred from the forward and reverse using the run-specific error rates, and read pairs were merged with at least 12 bp overlap. Chimeras were also removed using removeBimeraDenovo function of DADA2. The average percentage of read retention were 80.6 % and 79.9 % after filtering, 57.7 % and 78.6 % after denoising, 29.8 % and 71.6 % after merging, and 25.5 % and 64.0 % after chimera removal for 16S and ITS2, respectively. Taxonomic assignment of the 16S dataset was performed using the assignTaxonomy function of the DADA2 package (based on the naive Bayesian classifier method, minBoot set to 50) with the SILVA version 138 as the reference dataset. Taxonomic assignment of ITS2 dataset was performed using Blastn against the UNITE version 2022 database. A manual inspection of taxonomic assignment was performed for 200 first ASVs using BlastN against NCBI Genbank nr/nt sequence database and a similar assignment was found between both methods. Last, the ASV tables were filtered by removing unassigned taxa, chloroplast, and mitochondria (accounting for 11.1 % and 4.1 % of ASVs for 16S and ITS2, respectively) as well as ASVs which minimum count across dataset was inferior to 10 (accounting for 23.9 % and 20.9 % of ASVs after the first filtering for 16S and ITS, respectively) respectively using filter_taxa_from_otu_table.py and filter_otus_from_otu_table.py scripts from QIIME 1 version 1.9.1 (Caporaso et al., 2010).

## 2.5. Microbial community analyses

Phyloseq R package version 1.38.0 was used to determine α- and β-diversity of genus-level datasets. Genus tables were generated by summing ASVs of the same genus-level taxonomy. α-diversity indexes, including observed genera, Shannon, and Simpson index, were calculated using rarefied genus tables based on the lowest number of reads. Student t-test was used to compare means of α-diversity indices. Rarefaction curves were constructed using ggrare function from the Phyloseq package. β-Diversity analysis was performed on Hellinger-transformed genus datasets. The effects of agricultural and climatic factors (e.g., fields, sampling stages, years, agriculture practices, climatic conditions, soil properties, and mycotoxin levels) on microbial structure were assessed using multivariate homogeneity of groups dispersions based on Bray-Curtis distances matrices (phyloseq) and Adonis test in vegan package version 2.6.4. Bray-Curtis distances capture dissimilarities in microbial community composition, considering both presence/absence and relative abundance of taxa. In addition, the weighted UniFrac distances, which use phylogenetic information to calculate community similarity between substrates, were calculated using Phyloseq package. Besides, a SourceTracker analysis was performed to estimate the contributions of the soil microbiome to the maize residue and grain microbiomes, as well as, the contribution of the maize residue microbiome to the grain microbiome, based on a Bayesian approach (https://github.com/caporaso-lab/sourcetracker2, accessed on 10 January 2025). Sankey diagram was generated to visualize the estimated contribution of potential sources of contamination using the plotly R package (version 4.10.4).

Statistical analyses and plotting were carried out in the R environment using version 4.1.3 (R Core Team, 2022).

The core microbiome of the three substrates (soil, residues, and grains) were identified using core_member function of microbiome R package (version 1.16.0), with cutoff level of relative abundance of 0.1 % and minimum percentage of samples in each substrate set at 50 %.
Venn diagram of the core taxa was created using the ggvenn R package version 0.1.10. Pairwise Wilcoxon rank-sum test was used to assess significant differences in the relative abundance of microbial taxa across different sampling time points. Pairwise Spearman's correlations were performed to determine significant pairwise associations between FHB disease (percentage of contaminated grains, DNA quantity of *F. graminearum*, and DON concentration), climatic conditions, and the relative abundance of the ten most dominant bacterial and fungal genera. A correlation matrix was produced using the corrplot function with the corrplot R package version 0.92. An indicator species analysis was conducted using multipatt function within the indicspecies R package (version 1.7.14) with 9999 permutations (De Cáceres and Legendre, 2009) to identify microbial members associated to one or several classes of mycotoxin levels. Indicator species were assessed based on specificity (probability that a site belongs to the particular environmental class when a taxon is present) and fidelity (probability of finding a taxon in sites that belong to a target class) (De Cáceres and Legendre, 2009). Significant co-occurrence patterns of bacterial, fungal genera, and *Fusarium* species per sampling stage were determined for each substrate and sampling time using Sparse Correlations for Compositional data (SparCC) algorithm, implemented in the SparCC python module (https://github.com/bio-developer/sparcc, accessed on 18 April 2024) (Friedman and Alm, 2012). Correlations were calculated using read counts of the ten most abundant genera (bacteria and fungi, obtained with 16S and ITS2 primers, respectively) or species (*Fusarium*, obtained with EF1 primers). ASVs assigned to the *Fusarium* genus (obtained with ITS2 primers) were excluded from the ASV table. Corresponding networks were visualized using Gephi 0.10.1 (Bastian et al., 2009b). Only correlations with an absolute SparCC's R-corr value >0.3 and *p*-value <0.05 were plotted.

# 3. Results

## 3.1. Identification of soil clusters

The principal component analysis (PCA) performed on the 12

samples × 20 physico-chemical parameters matrix explained 53.35 % of the total variability. The K-means analysis subdivided soil samples from the twelve wheat fields into four groups, including cluster 1 (F2Y1, F3Y1, F4Y1), cluster 2 (F5Y1, F6Y1, F4Y2, F5Y2, F6Y2), cluster 3 (F1Y1), and cluster 4 (F1Y2, F2Y2, F3Y2) (Fig. 1A). Differences in soil properties were based on geographic distances, with the fields located in eastern Brittany (F1, F2, and F3) clustered away from those in western Brittany (F4, F5, and F6). The eastern area was positively associated with higher levels of Mn, coarse silt, fine sand, and higher C/N ratio in soil, while the western one was characterized by coarse sand and higher levels of organic matter, phosphorus, nitrogen, potassium, zinc, copper, and total cation exchange capacity (CEC). A year effect was observed in the eastern fields, with more basicity, fine sand, C/N in Y1, and more Mn and coarse silt in Y2. Soil characteristics of F1Y1 were completely distinct from those of the other soils and characterized by a higher content of clay, fine silt, Fe, MgO, CaO, and Na₂O.

## 3.2. Diversity and dynamics of microbiota in soil, maize residues, and wheat grains throughout the wheat cycle

### 3.2.1. Microbial abundance

After the filtering process, a total of 3,456,653 and 9,532,870 sequences of the 16S rRNA and ITS2 genes, respectively, were obtained after metabarcoding sequencing of 140 soil, 123 residue, and 70 grain samples from two sampling years. After this, sequences were clustered into 61,557 and 19,691 bacterial and fungal ASVs. Sequences assigned to mitochondria, chloroplast, and unidentified kingdoms, along with ASVs containing fewer than 10 counts, were excluded from the analysis, representing 33.13 % and 24.11 % of the total bacterial and fungal ASVs. The final ASV tables comprised 41,163 and 14,943 bacterial and fungal ASVs. Both rarefaction curves showing the number of ASV as a function of sequencing depth reached a plateau (Fig. S3), indicating that the coverage was sufficient to capture the full range of diversity.

Across all soil, maize residues, and wheat grain samples collected during the two-year monitoring period, we identified a total of 780 bacterial and 797 fungal taxa. In soil samples, the most abundant

<figure><img src="image_11.png" /><figcaption>**Fig. 1.** Principal component analysis and K-means clustering based on the soil physicochemical properties of 12 wheat fields. (A) Biplot of the principal component analysis (PCA) highlighting the four most important clusters according to the K-means grouping; (B) Contribution of the 10 most important soil physico-chemical properties to the variance explained by the first axis of the PCA; (C) Contribution of the 10 most important soil physico-chemical properties to the variance explained by the second axis of the PCA. CEC: cation exchange capacity, Clay: < 0.002 mm, Fine silt: 0.002–0.02 mm, Coarse silt: 0.02–0.05 mm, Fine sand: 0.05–0.2 mm, Coarse sand: 0.2–2 mm.</figcaption></figure>
bacterial taxa included unknown Vicinamibacterales (mean relative abundance of 8.14 %), *Sphingomonas* (5.83 %), unknown Gemmatimonadaceae (3.77 %), unknown Vicinamibacteraceae (3.65 %), and unknown Cloroflexi KD4-96 (3.26 %) (Fig. 2A). Whatever the year, a notable decrease in *Sphingomonas* was observed throughout the wheat cycle (Table 1). Regarding the soil fungal communities, the most dominant taxa were *Mortierella* (5.36 %), *Cladosporium* (5.26 %),

unknown Chytridiomycota (5.09 %), *Schizothecium* (3.81 %), and *Botryotrichum* (2.93 %) (Fig. 2B). Notably, the relative abundance of *Ramophialophora* increased from 1.61 % (T1) to 7.59 % (T4) in Y1.

In maize residues, the bacterial communities were predominantly composed of *Sphingomonas* (8.33 %), unknown Microbacteriaceae (5.43 %), *Nocardioides* (4.81 %), *Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium* (3.56 %), and *Curtobacterium* (3.31 %) (Fig. 3A). An increase

<figure><img src="image_12.png" /><figcaption>**Fig. 2.** Barplots showing the relative abundance of the top 20 bacterial (A) and fungal (B) taxa in soil throughout the wheat cycle (after harvest of maize-T1, seedling-T2, 2–3 weeks after flowering-T3, and wheat harvest-T4) between Y1 (2021) and Y2 (2022).</figcaption></figure>
<caption>**Table 1**</caption>

Differences in relative abundance of bacterial and fungal genera found in soil, residues or wheat grains between early stages (T1 or T3) and harvest (T4) during two wheat cycles (Y1 and Y2).

<table><thead><tr><th colspan="2">Average relative abundance</th><th colspan="3">Y1</th><th colspan="3">Y2</th></tr><tr><th>Substrate</th><th>Genus</th><th>T1 or T3<sup>a</sup></th><th>T4</th><th>p<sup>b</sup></th><th>T1 or T3<sup>a</sup></th><th>T4</th><th>p<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="10">Soil</td><td>Sphingomonas</td><td>8.97</td><td>2.54</td><td>****</td><td>10.51</td><td>4.46</td><td>***</td></tr><tr><td>Ramophilophora</td><td>1.61</td><td>7.59</td><td>*</td><td></td><td></td><td></td></tr><tr><td>Cellulomonas</td><td>0.23</td><td>2.08</td><td>****</td><td>0.3</td><td>1.55</td><td>****</td></tr><tr><td>Devosia</td><td>0.43</td><td>1.9</td><td>****</td><td>0.77</td><td>2.49</td><td>***</td></tr><tr><td>Galbitalea</td><td>0.02</td><td>3.03</td><td>****</td><td>0.33</td><td>2.6</td><td>****</td></tr><tr><td>Marmoricola</td><td>0.13</td><td>2.76</td><td>****</td><td>0.31</td><td>3.55</td><td>****</td></tr><tr><td>Mycobacterium</td><td>1.18</td><td>4.09</td><td>****</td><td>1.48</td><td>3.07</td><td>****</td></tr><tr><td>Nocardioides</td><td>0.87</td><td>5.44</td><td>****</td><td>1.8</td><td>8.42</td><td>****</td></tr><tr><td>Curtobacterium</td><td>10.21</td><td>1.04</td><td>****</td><td>3.74</td><td>1.67</td><td>****</td></tr><tr><td>Flavobacterium</td><td>2.42</td><td>0.47</td><td>***</td><td>5.82</td><td>0.84</td><td>****</td></tr><tr><td rowspan="10">Residues</td><td>Pedobacter</td><td>3.16</td><td>0.41</td><td>***</td><td>4.91</td><td>1.4</td><td>****</td></tr><tr><td>Pseudomonas</td><td>2.32</td><td>0.44</td><td>****</td><td>4.43</td><td>0.56</td><td>****</td></tr><tr><td>Rahnella</td><td>6.39</td><td>0.12</td><td>****</td><td>6.2</td><td>0.04</td><td>****</td></tr><tr><td>Cladosporium</td><td>40.28</td><td>0.93</td><td>****</td><td>35.6</td><td>1.74</td><td>****</td></tr><tr><td>Fusarium</td><td>11.98</td><td>2.81</td><td>*</td><td>14.24</td><td>2.6</td><td>**</td></tr><tr><td>Sarocladium</td><td>7.7</td><td>0.72</td><td>****</td><td></td><td></td><td></td></tr><tr><td>Vishniacozyma</td><td>4.63</td><td>0.07</td><td>****</td><td>4.18</td><td>0.11</td><td>****</td></tr><tr><td>Dendryphion</td><td>0</td><td>3.13</td><td>****</td><td>0</td><td>4.51</td><td>****</td></tr><tr><td>Pyrenochaetopsis</td><td>0.09</td><td>3.69</td><td>****</td><td>0.22</td><td>4.21</td><td>****</td></tr><tr><td>Schizothecium</td><td>0</td><td>5.29</td><td>****</td><td>0.01</td><td>11.09</td><td>****</td></tr><tr><td rowspan="10">Grains</td><td>Torula</td><td>0.03</td><td>1.03</td><td>***</td><td>0.03</td><td>6.97</td><td>****</td></tr><tr><td>Erwinia</td><td>27.62</td><td>0.13</td><td>****</td><td></td><td></td><td></td></tr><tr><td>Pantoea</td><td></td><td></td><td></td><td>45.68</td><td>18.21</td><td>**</td></tr><tr><td>Pseudomonas</td><td>40.36</td><td>6.62</td><td>****</td><td>33.92</td><td>10.65</td><td>*</td></tr><tr><td>Curtobacterium</td><td>0.92</td><td>22.61</td><td>****</td><td>0</td><td>17.78</td><td>****</td></tr><tr><td>Frondihabitans</td><td>0</td><td>4.58</td><td>****</td><td>0.26</td><td>1.95</td><td>**</td></tr><tr><td>Hymenobacter</td><td>0.45</td><td>14.08</td><td>****</td><td>1.31</td><td>8.62</td><td>**</td></tr><tr><td>Massilia</td><td>0.74</td><td>4.08</td><td>***</td><td>1.35</td><td>6.96</td><td>**</td></tr><tr><td>Sphingomonas</td><td>5.85</td><td>18.93</td><td>****</td><td>2.67</td><td>11.98</td><td>***</td></tr><tr><td>Alternaria</td><td>1.3</td><td>12.97</td><td>****</td><td>0.52</td><td>26.1</td><td>****</td></tr><tr><td>Blumeria</td><td>30.96</td><td>6.2</td><td>****</td><td>27.27</td><td>1.05</td><td>****</td></tr><tr><td>Sporobolomyces</td><td>3.53</td><td>0.44</td><td>****</td><td>5.98</td><td>1.36</td><td>****</td></tr></tbody></table>

in the relative abundance of several bacterial genera was observed from T1 to T4, including *Cellulomonas*, *Devosia*, *Galbitalea*, *Marmoricola*, *Mycobacterium*, and *Nocardioides*. Conversely, a decline was noted in the relative abundance of several bacterial genera, including *Curtobacterium*, *Flavobacterium*, *Pedobacter*, *Pseudomonas*, and *Rahnella* (Table 1). The fungal composition of maize residues was primarily dominated by *Cladosporium* (17.48 %), *Fusarium* (8.71 %), *Sarocladium* (6.99 %), unknown Didymellaceae (6.44 %), and *Schizothecium* (4.26 %) (Fig. 3B). Over the wheat cycle, a decrease in the relative abundance of *Cladosporium*, *Sarocladium*, and *Vishniacozyma* was observed, while the relative abundance of *Dendryphion*, *Pyrenochaetopsis*, *Schizothecium*, and *Torula* increased (Table 1).

The diversity of bacterial and fungal communities in wheat grains was significantly lower compared to those in soil and maize residues (Figs. 2, 3, and 4). The most predominant bacterial genera in wheat grains were *Pseudomonas* (22.92 %), *Pantoea* (20.95 %), *Curtobacterium* (10.41 %), *Erwinia* (10.40 %), and *Sphingomonas* (10.01 %) (Fig. 4A). From T3 to T4, a notable decrease in the relative abundance of *Erwinia*, *Paenibacillus*, *Pantoea*, and *Pseudomonas* was found. Conversely, the relative abundance of certain bacterial genera increased, including *Curtobacterium*, *Frondihabitans*, *Hymenobacter*, *Massilia*, and *Sphingomonas* (Table 1). Among the fungal communities in wheat grains, the most dominant genera were *Cladosporium* (48.34 %), *Blumeria* (16.81 %), *Alternaria* (9.77 %), *Fusarium* (6.06 %), and *Sporobolomyces* (2.87 %) (Fig. 4B). The relative abundance of *Alternaria* increased over time, while that of *Blumeria* and *Sporobolomyces* decreased (Table 1).

### 3.2.2. Diversity metrics

To evaluate the diversity of wheat field microbiomes across different substrates and times, α-diversity indices (number of observed genera, Shannon and Simpson indices) were calculated at the genus level. Significant differences were found according to substrates, with soil and maize residue exhibiting significantly higher richness and diversity compared to the grain microbiome (*p* < 0.0001) (Figs. 5A and 6A). Bacterial communities in soil and residues displayed similar levels of richness and diversity (Fig. 5A, B, and C) (*p* > 0.05), whereas fungal communities in soil had significantly higher richness and evenness than those in residues (Fig. 6A, B, and C) (*p* < 0.01). The microbiomes of soil, maize residues, and wheat grains were clearly separated, based on PCoA on Bray-Curtis distances. The substrate effect explained 56.7 % and 44.1 % of the variation in fungal and bacterial microbiome composition, respectively (Adonis, *p* < 0.001). Interestingly, the PCoA plot based on both Bray-Curtis and weighted UniFrac distances (Figs. 5, 6 and S4) of the two first dimensions showed that soil and grain microbiomes were clustered away, while residues appeared to serve as a transient substrate between soil and grains, more especially for fungi. To further support this observation, core taxa in the three substrates were identified at genus level and included genera with a relative abundance of at least 0.1 % and present in at least 50 % of samples per substrate (Fig. 7). Residues shared microbial communities with both grains and soils and had no unique genera, whereas three genera were found unique to grains, and one genus was unique to soil. Fifteen bacterial and eighteen fungal genera were found to coexist across all three substrates (Table S1). Furthermore, SourceTracker analysis revealed that residues contributed substantially to the grain microbiome, accounting for an average of

<footnote><sup>a</sup> For maize residue and soil microbiome, the differences were compared between T1 (post-harvest of maize) and T4 (wheat harvest) while for grain microbiome, differences were compared between T3 (2–3 weeks after flowering) and T4.</footnote>

<footnote><sup>b</sup> *p* = test results of Wilcoxon rank sum test, accordingly: * = 0.05, ** = 0.01, *** = 0.001, and **** = 0.0001. Only the statistically significant differences are indicated.</footnote>
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 3.** Barplots showing the relative abundance of the top 20 bacterial (A) and fungal (B) taxa in maize residues throughout the wheat cycle (after harvest of maize-T1, seedling-T2, 2–3 weeks after flowering-T3, and wheat harvest-T4) between Y1 (2021) and Y2 (2022).</figcaption></figure>

33.66 % and 59.13 % of bacterial and fungal communities, respectively. On the other hand, the soil contributed to the residue microbiome by 3.47 % and 37.05 % for bacteria and fungi, respectively, while the soil contributions to the grain microbiome were lower (0.25 % and 3.60 %,

respectively Fig. S5).

Beyond substrate differences, the Adonis test based on Bray-Curtis distances was also used to compare microbiota structures according to other factors, including field location, sampling stage, sampling year,
<figure><img src="image_15.png" /><figcaption>Fig. 4. Barplots showing the relative abundance of the top 10 bacterial (A) and fungal (B) taxa in wheat grains throughout the grain filling and maturity period (from flowering-T3 to harvest-T4) between Y1 (2021) and Y2 (2022).</figcaption></figure>

maize type, residue management, and soil physicochemical properties (Table 2). Note that the wheat cultivars differed between years, reflecting local farming practices influenced by agronomic considerations, market trends and availability (Table S2). Soil microbiome composition was slightly influenced by field locations ($R^2$ = 0.158 for bacteria and 0.251 for fungi), soil physicochemical properties ($R^2$ = 0.103 and 0.142, respectively), and time throughout wheat cycle ($R^2$ = 0.220 and 0.126, *p* < 0.001). Other factors, such as residue management (tillage vs. minimum tillage), sampling year, and maize type (maize silage vs. grain) had a significant but very minor influence on the soil microbiome ($R^2$ < 0.1, *p* < 0.001). Compared to soil, both bacterial and fungal compositions in grains and residues were more strongly influenced by sampling time, with $R^2$ values around 0.5 (*p* < 0.001), as illustrated by the PCoA plots (Fig. 8).

### 3.3. Comparison of microbiome in mycotoxin-contaminated versus healthy wheat grains

α- and β-diversity indices of the grain microbiome (including the number of observed genera, Shannon and Simpson indices) were calculated at the genus level across samples grouped according to three levels of DON in grains at harvest, including none (below the limit of detection, LOD), moderate (mean concentration of 948 ng/g of dried grains ±579), and severe (6871 ± 3362 ng/g of dried grains), as defined in Nguyen et al. (2024). No significant differences either in terms of α- or β-diversity were found for bacteria (Fig. 9A) (*p* > 0.05). Meanwhile, fungal communities in severely contaminated samples exhibited significantly higher richness and evenness at T3 (but not T4) compared to non-contaminated samples (Fig. 9B). Additionally, PCoA based on Bray-Curtis distances demonstrated that mycotoxin contamination in grains was a significant factor driving the composition of fungal communities (Fig. 9D), with an Adonis $R^2$ value of 0.117 (*p* < 0.01, Table 2).

To go further, Spearman correlations were computed to determine pairwise correlations between FHB disease parameters, DON level, climatic data, and the grain microbiome composition (Fig. 10). Unsurprisingly, we found significant positive correlations between the relative abundance of *Fusarium* (determined by ITS2 metabarcoding) and FHB disease parameters (determined in Nguyen et al., 2024), including the percentage of grains contaminated with *F. graminearum* (regression coefficient $R^2$ = 0.368, *p* < 0.001), the DNA quantity of *F. graminearum* ($R^2$ = 0.734, *p* < 0.001), and DON concentration in grains at harvest ($R^2$ = 0.904, *p* < 0.001). Inversely, the relative abundances of *Pantoea*, *Pseudomonas*, *Massilia*, *P3OB-42*, *Cladosporium*, *Alternaria*, *Vishniacozyma*, and *Sporobolomyces* were strongly significantly negatively correlated to any FHB disease parameter, with Spearman correlation values ranging from -0.41 to -0.90 (Fig. 10). Besides, weather conditions, primarily precipitation and temperature, significantly contributed to shaping the grain microbiome. Remarkably, the relative abundance of *Sphingomonas*, *Hymenobacter*, *Blumeria*, and *Zymoseptoria* at harvest showed positive correlations with the cumulative daily precipitation from wheat flowering to harvest ($R^2$ = 0.249, 0.169, 0.274, and 0.434, respectively, *p* < 0.01), whereas *Cladosporium* and *Pantoea* were negatively affected ($R^2$ = 0.225 and 0.245, *p* < 0.01).

An indicspecies analysis was performed to identify microbial genera indicative of different levels of DON contamination in grains at harvest (Table 3). Unsurprisingly, the *Fusarium* genus, with a mean relative abundance of 6.06 %, was identified as a significant indicator of severe DON contamination in grains (indicator index = 0.560, *p* < 0.001). In contrast, *Pantoea* was a significant indicator of fusariotoxin-free wheat grains with an indicator index of 0.357 (*p* < 0.05). Several other minor microbial genera were also identified as marker of DON contamination levels, including *P3OB-42* for non-contaminated grains; *Paenibacillus*, *unknown Didymellaceae* genus, *Monographella*, and *Dissocinium* for moderate contamination; and *Deinococcus* for severe contamination.
<figure><img src="image_16.png" /><figcaption>**Fig. 5.** α- and β-diversity indices of the bacterial communities, calculated at genus level, in the three main agricultural substrates (soil, maize residues, and wheat grains) across the wheat cycle during a two-year monitoring period. (A) Number of observed genera by substrate, time and year, (B) Shannon index by substrate, and (C) Simpson index by substrate, time and year. (D) Principal coordinates analysis (PCoA) of bacterial communities by substrate, based on Bray-Curtis distances calculated with Hellinger-transformed data at genus level. All plots were created using the phyloseq package in R environment (v.4.1.3). T1: after harvest of maize, T2: at seedling, T3: 2–3 weeks after flowering of wheat, T4: at harvest of wheat, Y1: 2020–2021, Y2: 2021–2022.</figcaption></figure>

**3.4. Comparison of microbiome dynamics in mycotoxin contaminated versus mycotoxin-free maize residues**

Maize samples were categorized into two groups based on the presence of at least one mycotoxin ("Positive") or the absence of any mycotoxin below the LOD ("None"), according to the results obtained in Nguyen et al. (2024). PerMANOVA revealed low significant variations between bacterial or fungal community structure according to mycotoxin levels (Adonis $R^2$ = 0.050 for bacteria and 0.041 for fungi, *p* < 0.001). While some associations, both positive and negative, were identified between specific bacterial or fungal genera and mycotoxin concentrations or the DNA quantity of *Fusarium* species (e.g., *F. graminearum*, *F. avenaceum*, and *F. poae*) (Fig. 11), these correlations were weak, with low $R^2$ values below 0.2 in linear regression models. Notably, we observed a positive correlation between the levels of mycotoxin (DON, ZEA, and 15ADON) and the relative abundance of some genera such as *Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium*, *Curtobacterium*, *Flavobacterium*, *Pedobacter*, *Rahnella*, *Cladosporium*, *Fusarium*, and unidentified Dydimellaceae. Negative correlations were obtained with Spearman correlation values ranging from -0.43 to -0.86, including *Nocardioides*, *Mycobacterium*, *Galbitalea*, *Schizothecium*, *Heteroconium*, *Neosetophoma*, *Pyrenochaetopsis*, *Candida*, and

*Ramophialophora*. Indicator species analysis identified certain fungal genera indicative of fusariotoxin presence in maize residues, particularly *Fusarium* (indicator index = 0.262) and *Sarocladium* (0.195), as well as some bacterial genera such as *Pedobacter* (0.379) and *Flavobacterium* (0.216) (Table 4). Conversely, several bacterial and fungal genera were identified as indicators of mycotoxin-free maize residues, including *Nocardioides*, *Mycobacterium*, *Cellulomonas*, *Marmoricola*, *Schizothecium*, and *Dendryphion*.

**3.5. Microbial co-occurrence networks**

The complex interactions among the ten most abundant genera of bacteria, fungi, and *Fusarium* species in wheat grains and maize residues during the grain filling period were computed using SparCC co-occurrence networks (Figs. 12 and 13). Overall, network complexity was greater in residues compared to grains, with higher numbers of nodes (28–30 nodes for residues and 23–27 nodes for grains) and correlations (99–161 edges for residues and 49–85 edges for grains). Network complexity also increased over time, showing higher associations at T4 (harvest) compared to T3 (2–3 weeks after flowering) in both grains (49 and 53 edges at T3 vs. 85 and 67 edges at T4 in Y1 and Y2, respectively) and residues (99 and 111 edges at T3 vs. 131 and 161
<figure><img src="image_17.png" /><figcaption>**Fig. 6.** α- and β-diversity indices of the fungal communities, calculated at genus level, in three main agricultural substrates (soil, maize residues, and wheat grains) across the wheat cycle during a two-year monitoring period. (A) Number of observed genera (richness) by substrate, time and year, (B) Shannon index by substrate, and (C) Simpson index by substrate, time and year. (D) Principal coordinates analysis (PCoA) of fungal communities by substrate, based on Bray-Curtis distances calculated with Hellinger-transformed data at genus level. All plots were created using the phyloseq package in R environment (v.4.1.3). T1: after harvest of maize, T2: at seedling, T3: 2–3 weeks after flowering of wheat, T4: at harvest of wheat, Y1: 2020–2021, Y2: 2021–2022.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>**Fig. 7.** Venn diagrams showing the unique and shared bacterial (A) and fungal (B) genera among wheat grains, maize residues, and soil. The thresholds were set to include genera with a relative abundance of at least 0.1% of the total reads across all samples and found in at least 50% of samples in each substrate.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>**Fig. 8.** Principal coordinate analysis (PCoA) based on Bray-Curtis distances calculated at genus level after Hellinger transformation in soils (Fig. 8A and 8B for bacteria and fungi, respectively), residues (Fig. 8C and 8D for bacteria and fungi, respectively) and grains (Fig. 8E and 8F for bacteria and fungi, respectively) The individual samples are color-coordinated according to sampling times. T1: after harvest of maize, T2: at seedling, T3: 2–3 weeks after flowering of wheat, T4: before wheat harvest.</figcaption></figure>
<caption>**Table 2**</caption>

Results of the permutational multivariate analysis of variance (PerMANOVA, Adonis) using the Bray-Curtis dissimilarity index to determine the effects of various factors in the composition fungal and bacterial communities.

<table><thead><tr><th rowspan="3">Variables</th><th colspan="6">R<sup>2</sup> Adonis</th></tr><tr><th colspan="3">Bacteria</th><th colspan="3">Fungi</th></tr><tr><th>Soil</th><th>Residues</th><th>Grains</th><th>Soil</th><th>Residues</th><th>Grains</th></tr></thead><tbody><tr><td>Fields</td><td>0.158<br>***</td><td>0.073 *</td><td>p > 0,05</td><td>0.251<br>***</td><td>0.099<br>***</td><td>0.147<br>**</td></tr><tr><td>Time</td><td>0.220<br>***</td><td>0.521<br>***</td><td>0.532<br>***</td><td>0.126<br>***</td><td>0.485<br>***</td><td>0.486<br>***</td></tr><tr><td>Year</td><td>0.057<br>***</td><td>0.034 **</td><td>0.059<br>*</td><td>0.033<br>***</td><td>0.038<br>***</td><td>0.084<br>**</td></tr><tr><td>Time × Year</td><td>0.053<br>***</td><td>0.041<br>***</td><td>0.037<br>**</td><td>0.030<br>**</td><td>0.026 *</td><td>0.031<br>**</td></tr><tr><td>Maize types</td><td>0.020<br>***</td><td>p > 0,05</td><td>p > 0,05</td><td>0.044<br>***</td><td>0.028 **</td><td>0.041<br>*</td></tr><tr><td>Mycotoxin levels in grains</td><td>0.091<br>***</td><td>p > 0,05</td><td>p > 0,05</td><td>0.076<br>***</td><td>0.037 *</td><td>0.117<br>**</td></tr><tr><td>Mycotoxin levels in residues</td><td>–</td><td>0.050<br>***</td><td>–</td><td>–</td><td>0.041<br>***</td><td>–</td></tr><tr><td>Clusters of soil</td><td>0.103<br>***</td><td>–</td><td>–</td><td>0.142<br>***</td><td>–</td><td>–</td></tr></tbody></table>

Fields, sampling times, years of monitoring, maize types (maize grains vs silage), mycotoxin levels in harvested grains (none, moderate, severe), mycotoxin levels in maize residues (absence, presence), and clustering based on physicochemical properties of soil. Asterisks represent significance levels of Adonis test, accordingly: * = 0.05, ** = 0.01, and *** = 0.001. -: not analyzed.

edges at T4 in Y1 and Y2, respectively).

Furthermore, a number of bacterial and fungal genera associated with wheat grains and maize residues exhibited significant negative correlations ($r < -0.3$ and $p < 0.05$) with predominant *Fusarium* species, including *F. graminearum*, *F. poae*, and *F. avenaceum*. In grains, significant negative correlations were observed between one or more of these predominant *Fusarium* species and several bacterial genera, such as *Paenibacillus*, *Curtobacterium*, *Sphingomonas*, *Pantoea*, *Pseudomonas*, *Massilia*, *Methylobacterium*-Methylorubrum, *Erwinia*, and *Rhodococcus*. Similarly, several fungal genera, including an unknown Didymellaceae genus, *Zymoseptoria*, *Blumeria*, *Cladosporium*, *Sporobolomyces*, *Alternaria*, *Dioszegia*, and *Vishniacozyma*, were also negatively correlated with these *Fusarium* species, with *r* ranging from -0.305 to -0.757 (Fig. 12). Additionally, in residues, several other genera, such as *Pyrenochaetopsis*, *Sarocladium*, *Sphingomonas*, *Nocardioides*, and *Schizothecium*, exhibited strong negative associations with the predominant *Fusarium* species, with *r* ranging from -0.380 to -0.816 (Fig. 13).

# 4. Discussion

## 4.1. Microbiome diversity and dynamics

First, we found that the microbiome dynamics throughout the wheat cycle greatly varied between substrates (i.e., soil, residues, and grains) for both microbial groups (i.e., bacteria vs. fungi) (Table 2), underlining that the habitat was the primary factor shaping the field microbial communities. Higher α-diversity for both bacteria and fungi was observed in residue and soil microbiome compared to grain (Figs. 5 and 6). These results are consistent with previous studies indicating that microbial diversity in soil or rhizosphere environments is generally greater than in the phyllosphere or spikosphere (the niche around the spikes as firstly defined by Kavamura et al., 2021), including wheat (Gdanetz and Trail, 2017; Latz et al., 2021), sugarcane (de Souza et al., 2016), and rice (Knief et al., 2012). The open nature of the aerial parts of plants was hypothesized to expose the associated microbiome to a range of dynamic and heterogeneous environmental factors (Bashir et al., 2022). In contrast, residues and soil, protected by a higher canopy cover,

are supposed to provide a more stable habitat for bacterial and fungal communities, reducing exposure to environmental fluctuations and stresses (e.g., UV-light, wind, and rain). Therefore, this enhanced stability likely supports greater levels of microbial diversity. Another very likely explanation in our case is that grains were being formed when sampled, either 3 weeks after flowering (T3) or before harvest (T4), thus with little time for microbial colonization.

Beyond a logical substrate effect, we found that the soil microbiome was primarily field-specific, while year of experiment had little to no influence, again in agreement with the fact that soil microbiome is less affected by environmental changes than plants do. Our results also indicate that sampling time significantly affected bacterial and fungal (to a lesser extent) community composition, suggesting that temporal variations during crop cycle in microbial communities should not be overlooked (Table 2). A significant influence of the soil physicochemical properties on the microbiome ($R^2 = 0.103$ and $0.142$ for soil bacterial and fungal communities, respectively) was also found here, in line with numerous studies highlighting the sensibility of soil microbiome to abiotic factors, primarily soil pH, texture, and the composition of silt, sand, and clay particles (Abdul Rahman et al., 2021; Philippot et al., 2024). These soil static abiotic factors are themselves substantially affected by numerous factors, such as climatic conditions, environmental factors, plant species, and agricultural practices (Singh et al., 2009).

In contrast to soil, the microbiome of wheat grains and maize residues significantly varied throughout the wheat cycle (Table 2). In wheat grains, the sampling stages (T3 or T4, sampled one month apart) accounted for 53.2% and 48.6% of the variation in bacterial and fungal communities, respectively. The diversity of both bacterial and fungal communities significantly increased during the grain filling period. This increase in microbial diversity is very likely to be related to the expansion of niche breadth with numerous physiological changes occurring during grain filling. At 2–3 weeks after wheat flowering, wheat head were dominated by several bacterial genera such as *Erwinia*, *Paenibacillus*, *Pantoea*, *Pseudomonas*, and *Sphingomonas*, while the abundance of different genera increased at harvest, including *Curtobacterium*, *Frondiahabitans*, *Hymenobacter*, *Massilia*, and *Sphingomonas*. Likewise, similar shifts were observed in fungal communities with an increase of *Alternaria* and *Fusarium* and a decrease of *Blumeria* and *Sporobolomyces* at harvest compared to flowering (Fig. 4). During wheat grain filling and maturation, the grains accumulate carbohydrates, amino acids, and other organic compounds, creating a nutrient-rich environment with elevated moisture levels (Zhang et al., 2021). These conditions favor the colonization and growth of specific various bacterial and fungal genera, especially when the grain surface is more accessible. As the grain matures, physiological changes such as reduced moisture (down to 14% before harvest), the formation of robust protective layers, and the production of complex plant metabolites lead to shifts in bacterial and fungal composition (Zhang et al., 2021). Besides such impact of the plant host, variations in the microbiome during grain development also depend on the airborne inoculum in the field, itself dependent on environmental conditions including climatic ones. For instance, our findings revealed that *Cladosporium* and *Pantoea* relative abundance were enhanced at lower levels of precipitation. Likewise, an increase of the outdoor air concentration with *Cladosporium* spores was previously reported in warm and dry weather (Olsen et al., 2019). In contrast, high levels of precipitation during the grain filling period can result in the dominance of *F. graminearum*, while dryer conditions allowed the weaker pathogen *F. poae* to thrive at the expense of other *Fusarium* species, as was found in our past survey (Nguyen et al., 2024). Another factor that may influence the grain microbiome is wheat cultivar variability. In this study, cultivars were selected by farmers based on their preferences, resulting in the use of nine different cultivars in mono- or polycultures across six fields during two growing seasons. This diversity, combined with the limited number of fields, did not allow for a detailed analysis of cultivar-specific effects on the microbiome. Future research
<figure><img src="image_20.png" /><figcaption>**Fig. 9.** Boxplot showing α-diversity indices (number of genera, Shannon, and Simpson index) of bacterial (Fig. 9A) and fungal communities (Fig. 9B) in wheat grains at T3 (2–3 weeks after flowering) and T4 (harvest) according to the levels of mycotoxin contamination (deoxynivalenol, DON) in grains at harvest. The twelve wheat fields were classified into 3 levels of DON contamination: none (DON concentrations in grains were under limit of detection, n = 7), moderate (mean DON concentrations of 948 ± 579 ng/g of dried grains, n = 3), and severe (mean DON concentrations of 6871 ± 3362 ng/g of dried grain, n = 2). Principal coordinate analysis (PCoA) based on Bray-Curtis distances calculated at genus level after Hellinger transformation for grain bacterial (Fig. 9C) and fungal communities (Fig. 9D), among the three levels of mycotoxin contamination. Analyses were performed on genus level data. Asterisks represent significance levels of t-test, accordingly: \* = 0.05, \*\* = 0.01, \*\*\* = 0.001, and \*\*\*\* = 0.0001.</figcaption></figure>

with controlled experimental designs, including uniform cultivar selection, would help elucidate the role of cultivar variability in modulating the grain and residue microbiomes.

Regarding residues, our data supported the fact that they represent a transient substrate linking the soil and plant microbiomes, especially for fungi (Figs. 5 and 6), as previously described in Kerdraon et al. (2019). Besides, residue microbiomes were significantly influenced by time, explaining 52.1 % and 48.5 % of the variation in bacterial and fungal

compositions, respectively (Table 2). This temporal variation may reflect the gradual degradation of residues, with distinct clusters between the early (T1 and T2) and later (T3 and T4) stages of degradation. Bastian et al. (2009a) reported similar temporal shifts in microbial communities in wheat straw residues, driven by turnover between copiotrophic and oligotrophic organisms. The copiotrophs are responsive to high resource availability, such as carbon and nitrogen, actively participating in the first step of degradation process. As these resources
<figure><img src="image_21.png" /><figcaption>**Fig. 10.** Corrplot showing Spearman pairwise correlations between the ten most dominant bacterial and fungal genera in grains at harvest (obtained with metabarcoding sequencing of 16S rRNA and ITS2) and FHB disease parameters obtained by Nguyen et al. (2024) (percentage of grains contaminated with *F. graminearum*, DNA quantity of *F. graminearum* (log10(DNA +1) ng/20 ng total DNA, DON (deoxynivalenol) concentration (ng/g) in grains), and climatic conditions (total daily precipitation and temperature from flowering to harvest). Only statistically significant correlations (*p* < 0.05) are presented. Blue and red colors indicate positive and negative correlations, respectively. The color density is proportional to the level of correlation.</figcaption></figure>

become depleted over time, copiotrophs are gradually outcompeted by oligotrophic species, which are better suited to nutrient-limited conditions (Ho et al., 2017). In the present study, several common maize-associated genera displayed a decline in relative abundance as residue degradation progressed, including fungi (*Cladosporium*, *Fusarium*, and *Sarocladium*) and bacteria (*Pedobacter*, *Curtobacterium*, *Pseudomonas*, and *Flavobacterium*). In line with our study, these genera (except *Curtobacterium*) were also reported as the most abundant genera in maize residue samples collected during the initial degradation stage (Cobo-Díaz et al., 2022). The high abundance of these microbial genera in crop residues may be attributed to their roles in early-stage plant degradation, in particular fungi. For instance, *Fusarium* species are able to produce excellent lignocellulose degradation-related enzymes, facilitating the breakdown of plant cell walls (Huang et al., 2015), while bacteria such as *Pseudomonas*, *Pedobacter*, and *Flavobacterium* also contribute to the degradation of plant lignin, (hemi)cellulose and carboxymethylcellulose through lignocellulose degrading enzyme secretion (Jiménez et al., 2014; López-Mondéjar et al., 2016; Shi et al., 2021; Xu et al., 2021).

As residue degradation advanced, these presumptive copiotrophs were gradually replaced by more generalist species capable of colonizing residues from various plant sources (Kerdraon et al., 2019). Most of these generalists are known to be soil-borne microorganisms, such as *Dendryphion* (Chinn, 1973), *Schizothecium* (Li et al., 2023), *Torula* (Rohilla and Salar, 2012), and *Nocardioides* (Piutti et al., 2003).

Besides, the amounts of maize residues (e.g., grain vs. whole-plant silage) accounted for significant but minor variations in the microbial community structure of residues and soil. This finding suggests that, under our experimental conditions, the residue biomass on the soil does not substantially affect the colonization by microorganisms involved in cellulose and hemicellulose degradation on non-buried crop residues. Instead, this degradation process is mainly influenced by cultivation techniques (e.g., tillage vs. reduced tillage), which affect residue breakdown under aerobic or anaerobic conditions (Cookson et al., 1998).

## 4.2. Comparative analysis of healthy versus Fusarium-contaminated microbiome

A second important aspect of our study was to compare the microbiome dynamics and diversity in fields characterized by different levels of FHB and fusariotoxin contamination. Although the presence of fusariotoxins, such as DON, 15ADON, ZEA, NIV, ENN B, and fumonisins, had low influence on the overall bacterial and fungal composition of the residues, we identified certain specific genera that were strongly negatively or positively correlated with the detection of DON, ZEA, and 15ADON in grains and/or residues. Noteworthy, we observed that *Cladosporium* exhibited different interactions with DON across different substrates (grains vs. residues) (Figs. 10 and 11). In grains, *Cladosporium*

<caption>**Table 3**</caption>

Indicator genera analysis of bacteria and fungi in grains according to levels of mycotoxin contamination in grains at harvest.

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Genus</th><th rowspan="2">Mean relative abundance in grains (%)</th><th colspan="4">Indicator index according to classes</th></tr><tr><th>None</th><th>Moderate</th><th>Severe</th><th>Moderate and severe</th></tr></thead><tbody><tr><td rowspan="2">Bacteria</td><td><em>Pantoea</em></td><td>20.95</td><td>0.357*</td><td></td><td></td><td></td></tr><tr><td><em>P3OB-42</em><br>(Myxococcaceae family)</td><td>0.95</td><td>0.394**</td><td></td><td></td><td></td></tr><tr><td rowspan="10">Fungi</td><td><em>Paenibacillus</em></td><td>1.25</td><td></td><td>0.328**</td><td></td><td></td></tr><tr><td><em>Deinococcus</em></td><td>0.02</td><td></td><td></td><td>0.301*</td><td></td></tr><tr><td>Unidentified Didymellaceae</td><td>0.10</td><td>0.573***</td><td></td><td></td><td></td></tr><tr><td><em>Monographella</em></td><td>1.28</td><td>0.427***</td><td></td><td></td><td></td></tr><tr><td><em>Dissconium</em></td><td>0.01</td><td>0.354**</td><td></td><td></td><td></td></tr><tr><td><em>Fusarium</em></td><td>6.06</td><td></td><td>0.560***</td><td></td><td></td></tr><tr><td><em>Phaeosphaeria</em></td><td>0.04</td><td></td><td></td><td>0.403**</td><td></td></tr><tr><td><em>Akanthomyces</em></td><td>0.19</td><td></td><td></td><td>0.296*</td><td></td></tr></tbody></table>

Associations were calculated using indicspecies R packages. Asterisks represent significance levels where: * *p* < 0.05, ** *p* < 0.01, and *** *p* < 0.001.
<figure><img src="image_22.png" /><figcaption>**Fig. 11.** Corrplot showing Spearman pairwise correlations between the ten most dominant bacterial and fungal genera (obtained with metabarcoding sequencing of 16S rRNA and ITS2), levels of fusariotoxins (ng/g of dried residues), and the relative abundance of three *Fusarium* species (including *Fusarium avenaceum*, *F. graminearum*, and *F. poae*) in maize residues across the wheat cycle (obtained with metabarcoding sequencing of EF1α). DON: deoxynivalenol, ZEA: zearalenone, NIV: nivalenol, 15ADON: 15-deoxynivalenol, ENNB: enniatin B, FB1: fumonisin B1, FB2: fumonisin B2. Only statistically significant correlations (*p* < 0.05) are presented. Blue and red colors indicate positive and negative correlations, respectively. The color density is proportional to the level of correlation.</figcaption></figure>

showed negative correlations with DON and FHB disease parameters, probably because of different physiological characteristics in relation to climatic preference, as discussed above. In residues, this species displayed positive correlations with the levels of mycotoxins (DON, 15ADON, and ZEA). This discrepancy may be related to the saprophytic and pathogenic nature of *Cladosporium*. Overall, these findings suggest that variations in microbial communities within residues may not be predominantly driven by the presence of mycotoxins or inversely. Other factors, particularly sampling stages, had a more significant role in shaping the microbial composition of maize residues. However, our study focused exclusively on bacteria and fungi. Since a wide range of macro-organisms, mainly from soil fauna, such as earthworms (Abid et al., 2021; Friberg et al., 2005; Wolfarth et al., 2016, 2011), and nematodes (Friberg et al., 2005), were shown to be influenced by *Fusarium* and mycotoxins, it would be of interest to extend these kinds of survey to these organisms to provide a broader understanding of how mycotoxins impact ecosystems.

We also investigated the interactions between the field microbiome and FHB causal agents, with the aim to identify potential antagonists, either in residues or grains. Co-occurrence networks of residue- and grain-associated microbiome were thus analyzed to identify potential antagonists against the predominant *Fusarium* species (i.e., *F. graminearum*, *F. poae*, and *F. avenaceum*) under natural fields

conditions. Microbial interactions were assessed at 2–3 weeks after flowering and harvest. Multiple fungal and bacterial genera were found to be negatively correlated with *Fusarium* species, including *Cladosporium*, *Epicoccum*, *Paenibacillus*, *Curtobacterium*, *Pseudomonas*, *Pantoea* (also found as an indicator of fusariotoxin-free grains), and *Sphingomonas* (Figs. 12 and 13). Among this list of taxa, many of them have already been shown to exhibit anti-*Fusarium* activities. First, *Epicoccum* species, belonging to the Didymellaceae family, can function as endophytes, saprophytes, or pathogens, with some strains acting as biocontrol agents (Taguiam et al., 2021). *Epicoccum nigrum* is the most commonly reported species that is able to inhibit various plant pathogens in vitro, greenhouse, and field trials, including *F. graminearum* (Jensen et al., 2016). Besides, strains of *Paenibacillus* have also been demonstrated to promote plant growth and inhibit the mycelial growth of *F. oxysporum* in tomato plants under greenhouse conditions (Xu and Kim, 2014). Additionally, a strain of *Paenibacillus* isolated from the rhizosphere of wheat field exhibited good inhibitory effects against a panel of *Fusarium* species, including *F. graminearum*, *F. verticillioides*, *F. oxysporum*, and *F. proliferatum*, through the production of antibiotic compounds such as surfactin and iturine (Ran et al., 2023). In contrast, no evidence has been reported of *Curtobacterium* being used as a biocontrol agent against *Fusarium* species. Certain *Curtobacterium* species are, however, identified as plant pathogens, affecting crops such as maize (López-Ramírez et al.,
<table><caption>**Table 4**
Indicator of bacterial and fungal genera of mycotoxin contaminated maize residues throughout the wheat cycle.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Genus</th><th rowspan="2">Mean relative abundance</th><th colspan="2">Indicator index according to classes</th></tr><tr><th>None</th><th>Positive</th></tr></thead><tbody><tr><td rowspan="15">Bacteria</td><td>Unidentified</td><td>5.43 %</td><td></td><td>0.191*</td></tr><tr><td>Microbacteriaceae</td><td></td><td></td><td></td></tr><tr><td>Allorhizobium-<br/>Neorhizobium-<br/>Pararhizobium-Rhizobium</td><td>3.56 %</td><td></td><td>0.235*</td></tr><tr><td>Flavobacterium</td><td>3.21 %</td><td></td><td>0.216*</td></tr><tr><td>Pedobacter</td><td>2.74 %</td><td></td><td>0.379***</td></tr><tr><td>Rahnella</td><td>2.21 %</td><td></td><td>0.24**</td></tr><tr><td>Pseudomonas</td><td>1.61 %</td><td></td><td>0.301**</td></tr><tr><td>Dyadobacter</td><td>1.01 %</td><td></td><td>0.186*</td></tr><tr><td>Massilia</td><td>0.89 %</td><td></td><td>0.249**</td></tr><tr><td>Stenotrophomonas</td><td>0.72 %</td><td></td><td>0.239**</td></tr><tr><td>Kineococcus</td><td>0.52 %</td><td></td><td>0.233*</td></tr><tr><td>Chryseobacterium</td><td>0.49 %</td><td></td><td>0.31***</td></tr><tr><td>Seriniibacter</td><td>0.44 %</td><td></td><td>0.303***</td></tr><tr><td>Clavibacter</td><td>0.43 %</td><td></td><td>0.29**</td></tr><tr><td>Novosphingobium</td><td>0.43 %</td><td></td><td>0.223*</td></tr><tr><td>Aureimonas</td><td>0.29 %</td><td></td><td>0.198*</td></tr><tr><td>Pseudoclavibacter</td><td>0.24 %</td><td></td><td>0.216*</td></tr><tr><td>Gluconobacter</td><td>0.22 %</td><td></td><td>0.206*</td></tr><tr><td>Rathayibacter</td><td>0.22 %</td><td></td><td>0.187*</td></tr><tr><td>Schumannella</td><td>0.20 %</td><td></td><td>0.219*</td></tr><tr><td>Salana</td><td>0.05 %</td><td></td><td>0.221*</td></tr><tr><td>Falsirhodobacter</td><td>0.05 %</td><td></td><td>0.195*</td></tr><tr><td>Pseudokineococcus</td><td>0.02 %</td><td></td><td>0.242*</td></tr><tr><td>Candidatus-Udaeobacter</td><td>0.01 %</td><td></td><td>0.212*</td></tr><tr><td>Luteimonas</td><td>0.01 %</td><td></td><td>0.183*</td></tr><tr><td>Fusarium</td><td>8.71 %</td><td></td><td>0.262**</td></tr><tr><td>Sarocladium</td><td>6.99 %</td><td></td><td>0.195*</td></tr><tr><td>Unidentified</td><td>6.44 %</td><td></td><td>0.184*</td></tr><tr><td>Didymellaceae</td><td></td><td></td><td></td></tr><tr><td>Botrytis</td><td>0.03 %</td><td></td><td>0.187*</td></tr><tr><td>Colletotrichum</td><td>0.03 %</td><td></td><td>0.169*</td></tr><tr><td>Curvibasidium</td><td>0.01 %</td><td></td><td>0.201**</td></tr><tr><td>Fusariella</td><td>0.01 %</td><td></td><td>0.193*</td></tr><tr><td rowspan="15">Fungi</td><td>Nocardioides</td><td>4.81 %</td><td>0.302**</td><td></td></tr><tr><td>Mycobacterium</td><td>2.36 %</td><td>0.322***</td><td></td></tr><tr><td>Galbitalea</td><td>2.05 %</td><td>0.287**</td><td></td></tr><tr><td>Marmoricola</td><td>1.82 %</td><td>0.295**</td><td></td></tr><tr><td>Cellulomonas</td><td>1.35 %</td><td>0.276**</td><td></td></tr><tr><td>Caulobacter</td><td>1.21 %</td><td>0.278**</td><td></td></tr><tr><td>Amnibacterium</td><td>0.88 %</td><td>0.21*</td><td></td></tr><tr><td>Tardiphaga</td><td>0.86 %</td><td>0.208*</td><td></td></tr><tr><td>Tundrisphaera</td><td>0.84 %</td><td>0.333***</td><td></td></tr><tr><td>Rhodopseudomonas</td><td>0.82 %</td><td>0.202*</td><td></td></tr><tr><td>Pedococcus.Phycicoccus</td><td>0.68 %</td><td>0.221*</td><td></td></tr><tr><td>Kineosporia</td><td>0.55 %</td><td>0.242**</td><td></td></tr><tr><td>Unidentified</td><td>0.52 %</td><td>0.246**</td><td></td></tr><tr><td>Intrasporangiaceae</td><td></td><td></td><td></td></tr><tr><td>CL500-29 marine group</td><td>0.46 %</td><td>0.343***</td><td></td></tr><tr><td>Parafrigoribacterium</td><td>0.44 %</td><td>0.196*</td><td></td></tr><tr><td>Iamia</td><td>0.41 %</td><td>0.317***</td><td></td></tr><tr><td>Solirubrobacter</td><td>0.39 %</td><td>0.374***</td><td></td></tr><tr><td>Ancylobacter</td><td>0.33 %</td><td>0.196*</td><td></td></tr><tr><td>Chthoniobacter</td><td>0.29 %</td><td>0.34***</td><td></td></tr><tr><td>Unidentified WD2101 soil group</td><td>0.28 %</td><td>0.206*</td><td></td></tr><tr><td>Streptomyces</td><td>0.28 %</td><td>0.167**</td><td></td></tr><tr><td>Conexibacter</td><td>0.28 %</td><td>0.225*</td><td></td></tr><tr><td>Unidentified</td><td>0.27 %</td><td>0.263**</td><td></td></tr><tr><td>Caulobacteraceae</td><td></td><td></td><td></td></tr><tr><td>Unidentified JG30-KF-CM45</td><td>0.27 %</td><td>0.22*</td><td></td></tr><tr><td>Rhizobacter</td><td>0.26 %</td><td>0.198*</td><td></td></tr><tr><td>Unidentified Birii41</td><td>0.22 %</td><td>0.192*</td><td></td></tr><tr><td>IMCC26207</td><td>0.21 %</td><td>0.24**</td><td></td></tr><tr><td>Polaromonas</td><td>0.19 %</td><td>0.206*</td><td></td></tr><tr><td>Unidentified 67.14</td><td>0.19 %</td><td>0.278**</td><td></td></tr><tr><td>Gemmata</td><td>0.19 %</td><td>0.378***</td><td></td></tr><tr><td>Unidentified</td><td>0.18 %</td><td>0.216*</td><td></td></tr><tr><td>Sphingomonadaceae</td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 4 (continued)**</caption><thead><tr><th rowspan="2">Genus</th><th rowspan="2">Mean relative abundance</th><th colspan="2">Indicator index according to classes</th></tr><tr><th>None</th><th>Positive</th></tr></thead><tbody><tr><td>Unidentified</td><td>0.12 %</td><td></td><td>0.23*</td></tr><tr><td>Micropepsaceae</td><td></td><td></td><td></td></tr><tr><td>Reyranella</td><td>0.12 %</td><td></td><td>0.275**</td></tr><tr><td>Sphingoaurantiacus</td><td>0.10 %</td><td></td><td>0.226*</td></tr><tr><td>Unidentified Microtrichales</td><td>0.07 %</td><td></td><td>0.268**</td></tr><tr><td>Haliangium</td><td>0.06 %</td><td></td><td>0.194*</td></tr><tr><td>Dongia</td><td>0.06 %</td><td></td><td>0.299**</td></tr><tr><td>Gemmatimonas</td><td>0.05 %</td><td></td><td>0.211*</td></tr><tr><td>Unidentified</td><td>0.05 %</td><td></td><td>0.296**</td></tr><tr><td>Roseiflexaceae</td><td></td><td></td><td></td></tr><tr><td>Hyphomicrobium</td><td>0.04 %</td><td></td><td>0.229*</td></tr><tr><td>Lacunisphaera</td><td>0.03 %</td><td></td><td>0.241**</td></tr><tr><td>Herpetosiphon</td><td>0.03 %</td><td></td><td>0.236**</td></tr><tr><td>Unidentified</td><td>0.03 %</td><td></td><td>0.174*</td></tr><tr><td>Streptomycetaceae</td><td></td><td></td><td></td></tr><tr><td>Unidentified PeM15</td><td>0.02 %</td><td></td><td>0.255**</td></tr><tr><td>Buttiauxella</td><td>0.02 %</td><td></td><td>0.181*</td></tr><tr><td>Candidatus.Alysiosphaera</td><td>0.02 %</td><td></td><td>0.189*</td></tr><tr><td>Unidentified</td><td>0.02 %</td><td></td><td>0.165**</td></tr><tr><td>Solirubrobacteraceae</td><td></td><td></td><td></td></tr><tr><td>Ellin6055</td><td>0.02 %</td><td></td><td>0.193*</td></tr><tr><td>Unidentified Devosiaceae</td><td>0.02 %</td><td></td><td>0.201*</td></tr><tr><td>Ohtaekwangia</td><td>0.02 %</td><td></td><td>0.189*</td></tr><tr><td>Caenimonas</td><td>0.02 %</td><td></td><td>0.181*</td></tr><tr><td>Zavarzinella</td><td>0.01 %</td><td></td><td>0.185*</td></tr><tr><td>Fimbriimonas</td><td>0.01 %</td><td></td><td>0.224**</td></tr><tr><td>Geminicoccus</td><td>0.01 %</td><td></td><td>0.195*</td></tr><tr><td>Methylibium</td><td>0.01 %</td><td></td><td>0.21*</td></tr><tr><td>Puia</td><td>0.01 %</td><td></td><td>0.161*</td></tr><tr><td>Dyella</td><td>0.01 %</td><td></td><td>0.174*</td></tr><tr><td>YC-ZSS-LKJ147</td><td>0.00 %</td><td></td><td>0.18*</td></tr><tr><td>Unidentified Nocardiaceae</td><td>0.00 %</td><td></td><td>0.183*</td></tr><tr><td>Unidentified</td><td>0.00 %</td><td></td><td>0.159*</td></tr><tr><td>Methylophilaceae</td><td></td><td></td><td></td></tr><tr><td>Schizothecium</td><td>4.26 %</td><td></td><td>0.192*</td></tr><tr><td>Dendryphion</td><td>1.80 %</td><td></td><td>0.221*</td></tr><tr><td>Unidentified</td><td>0.82 %</td><td></td><td>0.235*</td></tr><tr><td>Sordariomycetes</td><td></td><td></td><td></td></tr><tr><td>Tubaria</td><td>0.47 %</td><td></td><td>0.118*</td></tr><tr><td>Trematosphaeria</td><td>0.40 %</td><td></td><td>0.2*</td></tr><tr><td>Neoschizothecium</td><td>0.36 %</td><td></td><td>0.267***</td></tr><tr><td>Apodus</td><td>0.24 %</td><td></td><td>0.256**</td></tr><tr><td>Camposporium</td><td>0.23 %</td><td></td><td>0.218**</td></tr><tr><td>Unidentified</td><td>0.22 %</td><td></td><td>0.195*</td></tr><tr><td>Lentitheciaceae</td><td></td><td></td><td></td></tr><tr><td>Lachnum</td><td>0.11 %</td><td></td><td>0.114*</td></tr><tr><td>Tumularia</td><td>0.05 %</td><td></td><td>0.211**</td></tr><tr><td>Cercophora</td><td>0.05 %</td><td></td><td>0.198*</td></tr><tr><td>Unidentified Xylariaceae</td><td>0.04 %</td><td></td><td>0.144*</td></tr><tr><td>Orbilia</td><td>0.04 %</td><td></td><td>0.183*</td></tr><tr><td>Subulicystidium</td><td>0.03 %</td><td></td><td>0.162**</td></tr><tr><td>Junewangia</td><td>0.02 %</td><td></td><td>0.261***</td></tr><tr><td>Unidentified</td><td>0.02 %</td><td></td><td>0.185**</td></tr><tr><td>Lasiosphaeriaceae</td><td></td><td></td><td></td></tr><tr><td>Dendrophoma</td><td>0.02 %</td><td></td><td>0.187*</td></tr><tr><td>Unidentified Tubeufiaceae</td><td>0.02 %</td><td></td><td>0.193*</td></tr><tr><td>Chaetosphaeria</td><td>0.01 %</td><td></td><td>0.149*</td></tr><tr><td>Unidentified Clavulinaceae</td><td>0.01 %</td><td></td><td>0.213**</td></tr><tr><td>Helgardiomyces</td><td>0.01 %</td><td></td><td>0.17**</td></tr><tr><td>Unidentified</td><td>0.00 %</td><td></td><td>0.198*</td></tr><tr><td>Tremellodendropsidales</td><td></td><td></td><td></td></tr><tr><td>Pleurotheciella</td><td>0.00 %</td><td></td><td>0.183*</td></tr><tr><td>Ceratocystis</td><td>0.00 %</td><td></td><td>0.199*</td></tr><tr><td>Pseudoechria</td><td>0.00 %</td><td></td><td>0.18*</td></tr><tr><td>Phyllozyma</td><td>0.00 %</td><td></td><td>0.211*</td></tr></tbody></table>

Associations were calculated using indic species R packages Asterisks represent significance levels where: * *p* < 0.05, ** *p* < 0.01, and *** *p* < 0.001.

2022), sugar beet (Chen et al., 2007), and onion (Khanal et al., 2023). Isolates of *Pseudomonas* also had the capacity to enhance plant growth and contribute to biocontrol against Fusarium wilt caused by *F. oxysporum* in chickpea (Karimi et al., 2012), tomato (Bolwerk et al.,
Y1

<figure><img src="image_23.png" /></figure>

Y2

<figure><img src="image_24.png" /></figure>

T3

<figure><img src="image_25.png" /></figure>

T4

<figure><img src="image_26.png" /></figure>

**Fig. 12.** SparCC network plots of co-occurrence and co-exclusion between the top 10 bacteria (obtained with metabarcoding sequencing of 16S rRNA), fungi (ITS2), and *Fusarium* species (EF1α), found in wheat grains throughout the grain filling and maturation period, from T3 (2–3 weeks after flowering) to T4 (harvest) during a two-year survey (Y1 and Y2). Only genera with SparCC significant (*p* < 0.05, 100 bootstrapping random permutations) and correlations with magnitude > 0.3 (positive correlation, blue edges) or < −0.3 (negative correlations, red edges) were included into the network plots. Each node represents a bacterial (in green) or fungal (in orange) taxa or a *Fusarium* species (in orange). Node size is proportional to the number of connections (both positive and negative correlations) and edge thickness between nodes indicates the magnitude of correlation.

2003), and cucumber (Islam et al., 2018). Additionally, co-inoculation of *Pseudomonas simiae* and *F. graminearum* on wheat ears under controlled climate chamber conditions showed significant effects in delaying or blocking the production of *Fusarium* mycotoxins (Hoffmann et al., 2021). This effect on mycotoxin biosynthesis was attributed to the production of secondary metabolites of *Pseudomonas*, such as phenazine-1-carboxamide, which exhibit strong antagonistic properties against fungal plant pathogens. Additionally, the antifungal effects of *Pseudomonas* have been linked to other organic compounds, including side-rophores, lytic enzymes, hydrogen cyanide, ammonia, and organic volatiles (Loper et al., 2012; Yasmin et al., 2017). Selected strains of *Sphingomonas* have been found to suppress the growth of *Fusarium* pathogens and resulted in increasing the population size of epiphytic *Pseudomonas* (Wachowska et al., 2013). Species of *Pantoea*, such as

*P. agglomerans*, exhibited fungicidal activity against plant pathogens of *Fusarium* genus by targeting fungal lipid rafts that reduce fungal growth, perithecium formation, and infectivity (Xu et al., 2022).

## 5. Conclusion

This study provides valuable and comprehensive insights into the ecosystem services provided by the soil and phytomicrobiome, in particular its role in the control of FHB and the impact on mycotoxin contamination in cereal grains. Our findings reveal that microbial diversity and composition are primarily influenced by substrate type, with crop residues functioning as a transient substrate linking the soil and wheat microbiomes. Through comparison of microbiomes according to the levels of contamination by *Fusarium* spp. and their associated toxins
<figure><img src="image_27.png" /><figcaption>**Fig. 13.** SparCC network plots of co-occurrence and co-exclusion between the top 10 bacteria (obtained with metabarcoding sequencing of 16S rRNA), fungi (ITS2), and *Fusarium* species (EF1α), found in maize residues throughout the grain filling and maturation period, from T3 (2–3 weeks after flowering) to T4 (harvest) during a two-year survey (Y1 and Y2). Only genera with SparCC significant (*p* < 0.05, 100 bootstrapping random permutations) and correlations with magnitude > 0.3 (positive correlation, blue edges) or < −0.3 (negative correlations, red edges) were included into the network plots. Each node represents a bacterial (in green) or fungal (in orange) taxa or a *Fusarium* species (in orange). Node size is proportional to the number of connections (both positive and negative correlations) and edge thickness between nodes indicates the magnitude of correlation.</figcaption></figure>

and co-occurrence network analysis, we identified various potential anti-*Fusarium* taxa, including *Pantoea*, *Pseudomonas*, and *Epicoccum*, suggesting several practical applications in agricultural production. Indeed, the identification of such potential biocontrol agents offers opportunities for either inoculative-based approaches or conservation-based ones where agricultural practices stimulating such resident microorganisms of interest are used. Experimental validations are now needed to demonstrate their involvement, either directly or indirectly, in *Fusarium* suppression. This comprehensive investigation contributes to a better understanding of microbiome dynamics within an agro-ecosystem at a very fine scale, providing crucial insights for integrated disease management strategies to control the impact of Fusarium Head Blight on cereal crops.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.178655.

### CRediT authorship contribution statement

**Toan Bao Hung Nguyen:** Writing – original draft, Visualization, Validation, Investigation, Formal analysis, Data curation, Conceptualization. **Amandine Henri-Sanvoisin:** Investigation. **Gaétan Le Floch:** Writing – review & editing, Supervision, Data curation, Conceptualization. **Adeline Picot:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

### Funding

This research was financially supported by the Agence Nationale de la Recherche, grant number ANR-20-CE32-0008.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

We thank the farmers for their participation and kindly giving us access to their fields to take samples.

### Data availability

The raw reads of the 16S rRNA and ITS2 metabarcoding presented in this study have been deposited at the NCBI and are openly available under the Bioproject PRJNA1176466.
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