# Dynamic response of soil microbial communities and network to hymexazol exposure

Cara Meyer, Mathilde Jeanbille, Marie-Christine Breuil, David Bru, Kristin Höfer, Claudio Screpanti, Laurent Philippot

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Cara Meyer, Mathilde Jeanbille, Marie-Christine Breuil, David Bru, Kristin Höfer, et al.. Dynamic response of soil microbial communities and network to hymexazol exposure. Science of the Total Environment, 2024, 957, pp.177557. 10.1016/j.scitotenv.2024.177557. hal-04893623

HAL Id: hal-04893623

https://institut-agro-dijon.hal.science/hal-04893623v1

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# Dynamic response of soil microbial communities and network to hymexazol exposure

Cara Meyer<sup>a,b</sup>, Mathilde Jeanbille<sup>a</sup>, Marie-Christine Breuil<sup>a</sup>, David Bru<sup>a</sup>, Kristin Höfer<sup>b</sup>, Claudio Screpanti<sup>b</sup>, Laurent Philippot<sup>a,*</sup>

## HIGHLIGHTS

* Bacterial and protistan community composition altered by hymexazol

* Hymexazol affected 8 % bacterial, 22 % fungal, and 13 % protistan most abundant OTUs.

* Affected OTUs varied over time with most responding at only one time point.

* Network nodes and edges increased in hymexazol treatments relative to control.

* Hymexazol transiently increased AOM abundance and soil nitrate concentration.

## GRAPHICAL ABSTRACT

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

Fungicide
hymexazol applied
to soil microcosms
at 1x or 10x field
rate

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

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

OTUs significantly
affected by hymexazol
for duration of 60-day
experiment

## ARTICLE INFO

Editor: Abasiofiok Mark Ibekwe

### Keywords:

Pesticide

Fungicide

Nitrification

Nitrogen cycling

Protists

Bacteria

Fungi

## ABSTRACT

Fungicides are an essential component of current agricultural practices, but their extensive use has raised concerns about their effects on non-target soil microorganisms, which carry out essential ecosystem functions. However, despite the complexity of microbial communities, many studies investigating their response to fungicides focus only on bacteria or fungi at one point in time. In this study, we used amplicon sequencing to assess the effect of the fungicide hymexazol on the diversity, composition, and co-occurrence network of soil bacteria, fungi, and protists at 7, 21, and 60 days after application. We found that hymexazol had very little effect on microbial alpha-diversity, but that microbial community composition and OTU differential abundance were altered over the duration of the experiment, even after hymexazol concentrations were undetectable. The co-occurrence patterns within and between microbial kingdoms were affected by hymexazol dose, suggesting that indirect effects may play a role in the microbial community response. Nitrogen cycling was also affected, with a transient hymexazol-associated increase in the abundance of ammonia-oxidizing microorganisms and soil nitrate concentration. These findings highlight that the effects of fungicides on soil microorganisms are dynamic and extensive, spanning several taxonomic kingdoms.

<footnote><sup>a</sup> Université Bourgogne, INRAE, Institut Agro Dijon, Agroécologie, 17 rue Sully, 21000 Dijon, France</footnote>

<footnote><sup>b</sup> Syngenta Crop Protection Research Stein, Schaffhauserstrasse 101, 4332 Stein, Switzerland</footnote>

<footnote>* Corresponding author.
E-mail address: laurent.philippot@inrae.fr (L. Philippot).</footnote>
# 1. Introduction

Fungal plant pathogens lead to an annual loss of up to 30 % of crop yield worldwide (Fisher et al., 2018). Current agricultural systems rely heavily on fungicide application to control these pathogens and protect crop yield and quality (Fones et al., 2020). Although fungicides target pathogenic fungi, they can also have significant impacts on non-target organisms (Zubrod et al., 2019), including soil microorganisms such as non-pathogenic fungi (dela Cruz et al., 2022), bacteria (Imfeld and Vuilleumier, 2012; Lu et al., 2024), and protists (Ekelund et al., 2000). As soil microorganisms are essential for critical ecosystem functions, including biogeochemical cycling and plant productivity, it is crucial to understand the effects of fungicide application on soil microbial communities and their functions (Karpouzas et al., 2022).

The impacts of fungicides on soil microbial communities extend beyond immediate effects, but unfold over time. For example, in a meta-analysis Puglisi (2012) found that fungicides applied at the recommended field application rate induced significant, non-transient changes in microbial community composition in 87 % of 107 studies. The degradation of fungicides over time can reduce the exposure of the microbial community to the parent compound but the fungicide breakdown products can continue to affect the microbial community (Karas et al., 2018; Papadopoulou et al., 2016) and can sometimes be more toxic than the parent compound (Vasileiadis et al., 2018). During pesticide biodegradation, some microorganisms, particularly bacteria, can use fungicides as nutrient sources and thus gain a competitive advantage over their competitors (Copley, 2009; Fenner et al., 2013). Furthermore, biotic interactions between microorganisms can lead to indirect fungicide effects on organisms which are not themselves sensitive to the fungicide (Meyer et al., 2024), thus amplifying the magnitude and duration of observed effects (Kattwinkel et al., 2015).

Most studies that assess the microbial community response to fungicide application over time focus either on bacteria and/or fungi (El Azhari et al., 2018; Karas et al., 2018; Riedo et al., 2023; Storck et al., 2018). However, soil microbial communities are extremely complex and amount to more than the sum of their parts (Fierer, 2017). The interactions within and between different microbial kingdoms play a key role in soil microbial community assembly and functioning (Durán et al., 2018; Faust and Raes, 2012; van der Heijden et al., 2016). Soil protists, in particular, play a significant role as they are major consumers of soil bacteria and fungi (Geisen, 2016) and therefore key top-down regulators of soil microbial community composition (Gao et al., 2019; Jeanbille et al., 2024). Their role as predators has important implications for soil functions such as nutrient cycling and plant health (Geisen et al., 2018; Trap et al., 2016). Furthermore, fungicides have been shown to affect bacterial populations by altering predation and competition from protists in aquatic systems (Lu et al., 2019; Staley et al., 2014). However, very little is known about how pesticides, such as fungicides, affect soil microbes across multiple kingdoms as well as their interactions.

In this study, we aimed to characterize the interkingdom microbial community response over time to application of the fungicide hymexazol — a systemic fungicide used to control a range of fungal and oomycete plant pathogens such as Fusarium and Pythium (Ypema, 2003) through inhibition of DNA/RNA synthesis (Takahashi et al., 1974). We treated soil microcosms with either water (control), the recommended field rate of hymexazol, or 10 times this rate and observed the effect on diversity, composition, and co-occurrence networks within and between soil bacterial, fungal, and protistan communities. The microbial community response to hymexazol 7, 21, and 60 days post-application was assessed by 16S, ITS, and 18S amplicon sequencing and quantitative polymerase chain reaction (qPCR) followed by differential abundance and network analyses. In order to assess whether changes in the microbial community impacted soil functioning, we also monitored nitrogen cycling by measurement of soil ammonium and nitrate concentrations as well as quantification of bacterial and archaeal ammonia oxidizers by qPCR. Additionally, the dissipation of hymexazol

over the incubation period was assessed with liquid chromatography-tandem mass spectrometry (LC-MS/MS).

# 2. Materials and methods

## 2.1. Soil sampling and experimental design

The soil was collected in Bretenière, France (47.234715°N, 5.109561°E) in September 2021, sieved to a maximum particle size of 4 mm. The soil properties were 35 % clay, 59 % silt, and 6 % sand, pH 8, carbon and nitrogen content 31.6 and 2.7 g kg⁻¹ dry soil, respectively. Microcosms were prepared with 60 g soil and treated with either sterile water as a control, the recommended field application rate of hymexazol (1×), or 10 times this rate (10×) dissolved in sterile water. Hymexazol was applied at a rate of 3.6 and 36 µg g⁻¹ for 1× and 10× the recommended field application rate, respectively. The microcosms were closed with sterile stoppers which allowed for gas exchange and incubated at 20 °C with soil moisture maintained between 40 and 70 % of soil water holding capacity. Three microcosms of each treatment were harvested at 0, 1, 3, 7, 14, 21, 28, and 60 days (T0 to T60 hereafter) for quantification of hymexazol residues. At days 7, 21, and 60 and for the control at day 0, ten replicates were harvested for sequencing of the 16S rRNA and 18S rRNA genes and the ITS2 region. Inorganic N pools were also extracted from three of these replicates on these days.

## 2.2. Sequencing of microbial community

DNA was extracted from 250 mg of soil using the DNeasy 96 PowerSoil Pro Kit (Qiagen, Hilden, Germany). Amplicons were generated in a two-step polymerase chain reaction (PCR) approach (Berry et al., 2011). In the first step, the V3–V4 hypervariable region of the 16S rRNA gene was amplified by PCR with the 341F (5'-CCTACGGGRSGCAGCAG-3') and the 805R (5'-GACTACCAGGGTATCTAAT-3') fusion primers (Takahashi et al., 2014). The V4 hypervariable region of the 18S rRNA gene was amplified with the EK-565F (5'-GCAGTTAAAAAGCTCGTAGT-3') and 18S-EUK-1134R-UNonMet (5'-TTTAAGTTTCAGCCTTGCG-5') primers (Bower et al., 2004). The ITS2 region was amplified with ITS3F (5'-GCATCGATGAAGAACGCAGC-3') and ITS4R (5'-TCCTCSSCTTATTGATATGC-3') (White et al., 1990). All libraries were amplified with overhang adapters (forward: TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG, adapter: GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG) to allow the subsequent addition of multiplexing index-sequences. The thermal cycling conditions for the first PCR step were as follows: 98 °C for 3 min followed by cycles of 98 °C for 30 s, 55 °C (58 °C for the 18S rRNA gene) for 30 s, and 72 °C for 30 s (25 cycles for 16S and 18S and 30 cycles for ITS) and a final extension for 10 min at 7 °C. For the second PCR step, duplicate first-step PCR products were pooled and used as a template for an additional amplification which added multiplexing index sequences to the overhang adapters so that each sample had a unique combination of multiplex primers. Thermal cycling conditions for this second PCR step were as follows: 8 °C for 3 min followed by 98 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s (8 cycles for 16S and 18S and 10 cycles for ITS) and a final extension for 10 min at 72 °C. Duplicate second-step PCR products were pooled and amplicon size was visualized in a 2 % agarose gel. Amplicons were cleaned and pooled using the SequalPrep Normalization Plate (96) Kit (Invitrogen, Carlsbad, CA, USA). Sequencing of the 16S rRNA gene and ITS region was performed with Illuminal Miseq 2 × 250 bp and the 18S rRNA gene with 2 × 300 bp paired-end analysis using the MiSeq reagent kit v2 (500 cycles). Demultiplexing and trimming of Illumina adaptors and barcodes was carried out with Illumina MiSeq Reporter software (version 2.5.1.3).

## 2.3. Bioinformatic analysis

Sequencing data for the 16S rRNA and 18S rRNA genes and the ITS2 region were analyzed using an in-house Python pipeline (available upon
request). Briefly, sequences were assembled using PEAR (Zhang et al., 2014) with default settings. Further quality checks were conducted using the QIIME pipeline (Caporaso et al., 2010) and short sequences were removed (<400 bp for 16S rRNA, <475 bp for 18S rRNA, and <300 bp for ITS). Reference based and de novo chimera detection, as well as OTUs clustering were performed using VSEARCH (Rognes et al., 2016) and the adequate reference databases (SILVA representative set of sequences (Yilmaz et al., 2014) for 16S, the PR2 sequence database (Guillou et al., 2013) for 18S, and UNITE's ITS2 reference dynamic dataset (Nilsson et al., 2019) for ITS). The identity thresholds were set at 94 % for the 16S rRNA gene based on replicate sequencing of a bacterial mock community (Engelhardt et al., 2018) and 97 % for the 18S rRNA gene and ITS2 region for which we did not have mock communities. Representative sequences for each OTU were aligned using Infernal (Nawrocki and Eddy, 2013) and the phylogenetic tree for the 16S rRNA and 18S rRNA genes were constructed using FastTree (Price et al., 2010). Taxonomy was assigned using UCLUST (Edgar, 2010) and the SILVA database (138.1/2020) (Quast et al., 2013) for the 16S sequences and PR2 database (version 4.11.1) for the 18S sequences. For ITS, the taxonomy assignment was performed using BLAST and the UNITE reference database (v.7–08/2016) (Nilsson et al., 2019). Raw 16S, 18S, and ITS sequences were deposited at the NCBI under the BioProjects PRJNA1027766, PRJNA1142449, and PRJNA1028401, respectively. Protistan OTUs were obtained by filtering the 18S dataset to remove non-protistan OTUs classified as Fungi, Embryophaceae, or unclassified. Alpha-diversity metrics were calculated based on multiple rarefactions of the OTU tables (10 iterations of 11,000 bacterial sequences, 3000 protistan sequences, and 2000 fungal sequences). Subsequent statistical tests were carried out using the mean for each sample from these 10 iterations.

## 2.4. Quantification of microbial communities

The abundances of total bacterial and fungal microbial communities were estimated by real-time quantitative PCR (qPCR) assays using 16S rRNA gene and ITS2 region primers described by Muyzer et al. (1993) and White et al. (1990), respectively. Abundances of N-cycle microbial guilds were estimated using the amoA gene to quantify bacterial (AOB) and archaeal (AOA) ammonia-oxidizers (Bru et al., 2011). qPCR reactions were carried out in a ViiA7 instrument (Life Technologies, Carlsbad, CA, USA) in a 15 µL reaction volume containing 7.5 µL of Takyon Master Mix (Eurogentec, Liège, Belgium), 1 µM of each primer, 250 ng of T4 gene 32 (MP Biomedicals, Santa Ana, CA, USA), and 1.5 ng of DNA. Two independent runs were performed for each real-time PCR assay. Standard curves were obtained using serial dilutions of linearized plasmids containing appropriated cloned targeted genes from bacterial strains or environmental clones. PCR efficiency for the different assays ranged from 86 % to 99 %. No-template controls gave null or negligible values. Inhibition of the qPCR assay was tested by mixing soil DNA extracts with either control plasmid DNA (pGEM-T Easy Vector, Promega, Madison, WI, USA) or water. No inhibition was detected in any case.

## 2.5. Measurements of inorganic pools

Soil inorganic nitrogen pools (NO₃⁻ and NH₄⁺) for 3 replicates of each treatment at T7, T21, T60, and the no hymexazol control at T0 were extracted by adding 35 mL 0.5 M K₂SO₄ to 7 g fresh soil, shaking for 1 h at 80 rpm at room temperature, filtering, and were subsequently kept frozen until quantification by colorimetry according to ISO 14256-2:2005.

## 2.6. Quantification of hymexazol residues

Hymexazol was extracted from 9 g of each soil sample with 45 mL 50 % acetonitrile in water, shaken for 1 h, then centrifuged 5 min at 3000 rpm. The final concentrations of hymexazol were based on dry soil.

Hymexazol concentrations were analyzed with a Xevo TQ-XS Triple Quadrupole Mass Spectrometer connected to an Acquity UPLC IClass Plus (both Waters Corporation). The residues of hymexazol were chromatographically separated on an Acquity UPLC HSS T3 column (1.8 µm particle size, 30 × 2.1 mm; Waters Corporation) by a gradient from 20 % to 100 % Eluent B (Euent A: 90 % water, 10 % Methanol with 0.1 % Formic acid; Eluent B: Acetonitrile with 0.1 % Formic acid). Flowrate was 0.75 mL/min. The spectra were recorded on the Xevo TQ-XS Triple Quadr Mass Spectrometer. The samples were measured in gradient elution mode with the fluctuating flow rates. The gradient flow was separated into the following steps: initial flow 1.0 (mL/min) with 20 % B until 0.10 min; until 0.20 min to 25 % B; till 1.20-time flow rate change to 0.750 (mL/min) with 30 % B; from 1.20 min to 1.45 to 100 % B; until 1.45 min to 20 % eluent B; until the end of the run of 2 min flow rate 0.050 (mL/min) 50 % B isocratic. The column temperature was kept at 60 ± 5 °C and the sample injection volume was 2 µL. For the MS detection conditions the Desolvation Gas Flow: 1000 L/h with its temperature at 500 °C was used. The flow rate of cone was set with 150 L/h with a capillary voltage of 3 kV, a source temperature of 150 °C and a Cone Voltage of 15–60 V. Hymexazol residues were quantified by multiple reaction monitoring (MRM).

## 2.7. Statistical analyses

Statistical analyses were carried out using R version 4.2.1 (R Core Team, 2022). Differences between treatments in the abundances of the ITS2 region, 16S rRNA gene, bacterial and archaeal amoA genes (n = 10) were tested using a Welch ANOVA with Games Howell post-hoc test using a Tukey adjustment for multiple comparisons. Differences between treatments microbial alpha-diversity indices (n = 10) and concentrations of ammonium and nitrate (n = 3) were tested using ANOVAs followed by Tukey's honestly significant difference test. Principal coordinates analyses (PCoA) were performed on bacterial and protistan weighted UniFrac distance matrices (Lozupone et al., 2011) and the fungal Bray-Curtis dissimilarity matrix to visualize treatment effects on community composition. Permutational multivariate analysis of variance (PerMANOVA) (Anderson, 2001) was carried out on these matrices to detect significant differences in community composition between treatments (n = 10) using the adonis2 function implemented in the vegan package (version 2.6.2) (Oksanen et al., 2022) and pairwise. adonis function from the pairwiseAdonis package (version 0.4) (Arbizu, 2017).

## 2.8. Identification of OTUs affected by hymexazol

Differential abundance analysis was performed to estimate the effect of each treatment on the relative abundance of the OTUs using a generalized linear mixed model (Huet et al., 2023). As zero counts in sequencing datasets may inflate the number of false positives for the differential abundance analysis, low-abundance OTUs were first removed by discarding OTUs with (i) <0.1 % relative abundance across all samples and (ii) OTUs present in <60 % of replicates of at least one treatment, which resulted in 572 bacterial OTUs, 280 protistan OTUs, and 333 fungal OTUs. For each time point (T7, T21, and T60), we ran the model combining a generalized linear model, which allows linear regression on data that does not follow a normal distribution as abundance data typically follow a Poisson distribution, with a mixed model which contains both fixed effects (treatment effects) and random effects (sampling effects). Considering that an OTU of abundance Y, in any replicate k of any treatment i, follows a Poisson law of parameter Λ as Y ~ P(Λ), we used the following model (Eq. 1):

$$ \log(\Lambda_{ik}) = o_{ik} + \mu + \alpha_{i} + Z_{ik}, Z_{ik1 \le k \le 10} \quad \text{iid} \sim N(0, \sigma^{2}) \quad (1) $$

where o is the offset for each sample calculated as the log of the sample read sum, α is the effect of the treatments, and Z is the random sampling
effect modeling the data overdispersion. $i = \{1,...,3\}$ represents the pesticide dose and $k = \{1,...,10\}$ represents the replicates. The analysis was performed using the glmer function of the *lme4* package (version 1.1.30) (Bates et al., 2015). Subsequently, we performed a post-hoc Tukey test with the emmeans function of the *emmeans* package (version 1.7.5) (Lenth, 2022) implementing multiple comparisons. Within each time point, we selected pairwise comparisons for each OTU between each dose and the no pesticide control for that time point. The *p* values were then adjusted using the false discovery rate (FDR) (Benjamini and Hochberg, 1995). Significant comparisons resulted from an adjusted *p* value < 0.05. Significantly affected bacterial, fungal, and protistan OTUs were used to build pruned trees using the ape package version 5.6.2 and were visualized using the Interactive Tree of Life (iTOL) webserver (Letunic and Bork, 2019).

## 2.9. Co-occurrence networks

Networks were constructed based on centered log-ratio (CLR) normalized OTU count data with low abundance OTU filtered out as described above. Bayesian estimation of the sparse read counts using the CoDaSeq package version 0.99.6 (Gloor and Reid, 2016) was first computed before the CLR transformations with the zCompositions package version 1.3.4 (Palarea-Albaladejo and Martín-Fernández, 2015), in order to account for compositionality of the data (Gloor et al., 2017). The CLR transformations were computed on ITS, 18S rRNA and 16S rRNA abundance tables independently before merging the tables for co-occurrence network inference. To examine the effect of hymexazol on the microbial networks, independent control, 1× dose and 10× dose networks were constructed based on Pearson correlations. The random matrix theory approach provided by the RMThreshold package version 1.1 was used (Menzel, 2016) to sort out the correlations considered as noise from the signal correlations, based on eigenvalue spacing distribution shift. The thresholds of the noise versus signal correlations were determined at a Pearson's product moment correlation coefficient (R²) of 0.66 for the three networks. All correlations with R² > 0.66 had a false discovery rate (FDR) adjusted *p*-value < 0.05. Networks were visualized in Cytoscape version 3.10.1.

# 3. Results

## 3.1. Dissipation of hymexazol

Soil hymexazol concentration decreased rapidly over time for both the 1× and 10× doses. At T0, mean hymexazol concentration was 3.6 and 41.4 µg g⁻¹ dry soil for 1× and 10×, respectively. By the first sequencing time point at T7, mean hymexazol concentration had decreased to 0.34 and 8.1 µg g⁻¹ dry soil for 1× and 10×, respectively. Hymexazol was then no longer detected at T14 and T21 for the 1× and 10× doses, respectively (Supp. Fig. 1a). The time required for 50 % dissipation (DT₅₀) of hymexazol was calculated with a single first-order kinetic model which best fit the measured data. The DT₅₀ was determined to be 1.6 and 2.9 days at 1× and 10×, respectively (Fig. 1).

## 3.2. Influence of hymexazol on microbial abundance over time

Total bacterial, fungal, and AOB abundance did not change significantly over time, while AOA abundance increased from T0 to T60 (Fig. 2c; Tukey's test, adjusted *p* < 0.05). The addition of hymexazol affected the total bacterial abundance only at T21 for the 10× dose with a 41 % increase in the 16S gene copy number per g dry soil (Supp. Fig. 2a; Tukey's test, adjusted *p* < 0.05). In contrast, the fungal abundance was not affected by hymexazol addition, whatever the dose (Supp. Fig. 2b; Tukey's test, adjusted *p* > 0.05). A significant but transient effect of 10× hymexazol was also observed at T7 for both the abundance of AOB and the proportion of AOB in the total bacterial community with increases of 152 % and 103 %, respectively, compared to the control

<figure><img src="image_7.png" /><figcaption>**Fig. 1.** Hymexazol dissipation in soil. Percentage of initial concentration of hymexazol at 1× (red circles) and 10× (blue triangles) over time. Dissipation data were fitted to the single first-order kinetic model which provided the best fit to the data. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

(Fig. 2a and b; Tukey's test, adjusted *p* < 0.05). The AOA abundance also increased after hymexazol addition at both the 1× and 10× doses by 21 % and 138 %, respectively, at T7 and by 50 % and 48 %, respectively, at T21 (Fig. 2c; Tukey's test, adjusted *p* < 0.05). Additionally, there was a significant interaction effect of time and dose on the proportion of AOB in the bacterial community, total AOB abundance, and total AOA abundance (ANOVA, *p* < 0.05). Across all treatments, the ratio of AOB to AOA abundance was approximately 0.1, although there was a small but significant increase in this ratio at T60 in the 10× dose (Supp. Fig. 2c; Tukey's test, adjusted *p* < 0.05).

## 3.3. Temporal dynamics of microbial alpha-diversity

Temporal shifts in alpha-diversity were observed for bacteria and protists, but not fungi. The number of observed bacterial and protistan OTUs showed a small but significant increase from T0 to T60 by 4 % and 12 %, respectively (Supp. Fig. 3a and c; Tukey's test, adjusted *p* < 0.05). For protists, Simpson's reciprocal index increased by 28 % from T7 to T21 and again by 18 % from T21 to T60 (Supp. Fig. 3f; Tukey's test, adjusted *p* < 0.05). Hymexazol addition only affected alpha-diversity for protists, with a 19 % decrease in the Simpson's Reciprocal Index from the control to the 10× dose at T60 (Supp. Fig. 3f; Tukey's test, adjusted *p* < 0.05).

## 3.4. Effect of time and hymexazol on microbial community composition

Time had a small but significant effect on bacterial and fungal beta-diversity, accounting for 14 % and 9 % of the variation in the composition of bacterial and fungal communities, respectively (PerMANOVA, *p* < 0.001; Fig. 3b and d). Actinobacteria, Alphaproteobacteria and Acidobacteria constituted 35 %, 15 %, and 14 % of the bacterial community at T0, but showed no significant temporal variation in their relative abundances (Fig. 3a). Similarly, Ascomycota dominated the T0 fungal community with a mean relative abundance of 62 %, but this did not vary significantly with time (Fig. 3c). In contrast, time had a larger effect on protistan community composition, accounting for 28 % of the variation in protistan weighted UniFrac distances (PerMANOVA, *p* < 0.001; Fig. 3f). At T0, Cercozoa and Stramenopiles represented 44 % and 17 % of the protistan community, respectively (Fig. 3e). Small yet significant changes in the relative abundance of these phyla were observed with time (fdr-adjusted *p* < 0.05). Cercozoan relative abundance
<figure><img src="image_8.png" /><figcaption>**Fig. 2.** Time and hymexazol effects on microbial abundance (a) Copies of amoA per gram of dry soil for AOB (n = 10) and (b) ratio of AOB amoA to 16S rRNA gene copy number (n = 10) and (c) copies of amoA per gram of dry soil for AOA (n = 10). For all panels, statistical differences (Tukey's test, p adj < 0.05) between hymexazol doses within each time point are indicated with lower-case letters above boxplots and statistical differences (Tukey's test, p adj < 0.05) between the controls at different time points are indicated with upper-case letters below the boxplots. The length of the box represents the interquartile range (IQR). The black line in the center of the box indicates the median, whiskers indicate the minimum and the maximum. Outliers are indicated by small dots above or below the boxes.</figcaption></figure>

decreased at T7 to 42 % and then increased up to 49 % at T60. In
contrast, Stramenopiles relative abundance increased to 20 % at T7 and
T21 and then decreased to 16 % at T60.

Hymexazol exposure affected beta-diversity to a lesser extent than
time, accounting for 4 %, 3 %, and 7 % of variation in community
composition for bacteria, fungi, and protists, respectively (PerMANOVA,
p < 0.01). Hymexazol significantly impacted microbial community
composition, but this effect differed across time points. For bacteria, the
relative abundance of Gammaproteobacteria was significantly lower at
10× in all time points and also at 1× at T7 (fdr-adjusted p < 0.05). Of the
protistan phyla, Cercozoa relative abundance increased significantly at
10× at all time points (fdr-adjusted p < 0.05). Stramenopiles relative
abundance decreased at 10× at T7 and at both 1× and 10× at T21 (fdr-
adjusted p < 0.05). Hymexazol did not affect the relative abundance of
any fungal phyla.

**3.5. Differential abundance of microbial taxa in response to hymexazol over time**

A generalized linear mixed model was used to identify the microbial
OTUs which exhibited significant changes in relative abundance after
hymexazol application (fdr-adjusted *p* < 0.05) at each time point. Over
all time points and doses, 47 (8 %) of the 572 most abundant bacterial
OTUs (Fig. 4a), 73 (22 %) of the 333 most abundant fungal OTUs
(Fig. 4b), and 35 (13 %) of the 280 most abundant protistan OTUs
(Fig. 4c) were affected by hymexazol addition (fdr-adjusted *p* < 0.05)
regardless of the dose. We also observed dose-dependent responses with
more affected OTUs (Fig. 4f) and a trend of higher mean log2-fold
changes at 10× than 1× (Fig. 4e) for all microbial communities. Anal-
ysis of the taxonomic affiliation of the OTUs that were affected by
hymexazol revealed that while most of the hymexazol-affected Gam-
maproteobacteria decreased in relative abundance (Fig. 4a), all other
microbial phyla either had very few affected OTUs or showed a more
varied response, with some taxa increasing and others decreasing in
relative abundance (Figs. 4a, b, and c). Hymexazol-affected OTUs also
showed a dynamic response over the three time points. For all microbial
groups, the majority of hymexazol-affected OTUs responded at only one
of the three time points, while very few OTUs responded across two time
points and even fewer responded at all three time points (Fig. 4d).
However, the number and direction of OTU responses remained rela-
tively constant across time points for bacteria and protists, while more
fungal OTUs were affected at T21 (Fig. 4f).

**3.6. Co-occurrence networks**

In order to investigate the effect of hymexazol on biotic interactions,
we inferred interkingdom networks for each dose (control, 1×, and
10×). The 1× and 10× networks contained more edges and nodes than
the control network, with the highest number in the 1× network
(Fig. 5a). In particular, within the same domain, the total number of
edges between fungal taxa increased from 60 to 137 and 106 at 1× and
10×, respectively (Fig. 5b). Edges between bacterial taxa increased from
69 to 108 and 82 at 1× and 10×, respectively, with negative edges
increasing in particular at 1×. Edges between protistan taxa decreased
from 41 to 20 at 10×. Regarding the interkingdom edges, the largest
changes were observed between bacteria and protists with an increase
from 4 to 32 and 11 at 1× and 10×, respectively.

**3.7. Hymexazol effect on nitrogen cycling over time**

Soil ammonium-N (NH<sub>4</sub><sup>+</sup>-N) concentrations showed a trend of decreasing over time, with the concentrations in all samples below the detection limit at T60 (Fig. 6a), however this trend was not significant. Nitrite-N (NO<sub>3</sub><sup>-</sup>-N) concentrations increased significantly over time with the mean concentration in the T60 control 134 times that of the T0 control (Fig. 6b; Tukey's test, adjusted *p* < 0.05). Hymexazol dose did not significantly impact NH<sub>4</sub><sup>+</sup>-N concentrations, but did impact NO<sub>3</sub><sup>-</sup>-N concentrations, with a higher concentration in 10× than 1× and the control at T21 (Tukey's test, adjusted *p* < 0.05).

**4. Discussion**

Soil microorganisms are vital contributors to key ecosystem func-
tions such as biogeochemical cycling and plant productivity (Averill
et al., 2022; Wagg et al., 2019). Many recent studies have assessed the
impact of fungicides on soil bacteria and fungi (El Azhari et al., 2018;
Karas et al., 2018; Riedo et al., 2023; Storck et al., 2018), but soil pro-
tists, which are essential players in microbial ecosystems (Gao et al.,
2019), have not been as thoroughly investigated despite their known
sensitivity to pollutants (Ekelund et al., 2000; Wu et al., 2022). More-
over, the complex interactions between bacteria, fungi, and protists can
be affected by fungicide exposure (Fournier et al., 2020) and the effects
of fungicides on microbial communities vary over time (Riedo et al.,
2023). Here, we studied the effect of the fungicide hymexazol on bac-
terial, fungal, and protistan soil communities over time. Hymexazol is
<figure><img src="image_9.png" /><figcaption>**Fig. 3.** Time and hymexazol effects on microbial community composition. (a) Mean relative abundance of each bacterial phylum or class for Proteobacteria within each treatment. Phyla or classes with relative abundance below 0.5 % were grouped together. (b) Principal coordinates analysis (PcoA) of the weighted UniFrac distance matrix of the bacterial community. (c) Mean relative abundance of each fungal phylum within each treatment. Phyla with relative abundance below 0.5 % were grouped together. (d) Principal coordinates analysis (PcoA) of the Bray-Curtis dissimilarity matrix of the fungal community. (e) Mean relative abundance of each protistan phylum. Phyla with relative abundance below 0.5 % were grouped together. (f) Principal coordinates analysis (PcoA) of the weighted UniFrac distance matrix of the protistan community.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 4.** Distribution and response of microbial OTUs significantly affected by hymexazol exposure. The taxonomic assignment of each (a) bacterial, (b) fungal, and (c) protistan OTU at the class or phylum level is shown by the inner ring colored according to the legend. Each of the three heatmap rings shows for each dose of hymexazol the Log2-fold changes of the significantly increasing OTUs (blue shades) and decreasing OTUs (red shades) in each time point compared to the control for that time point. (d) Percentage of most abundant bacterial, fungal, and protistan OTUs increasing or decreasing in relative abundance in response to hymexazol at each time point. The small numbers in black above each bar shows the ratio of OTUs increasing to those decreasing in relative abundance. (e) Log2-fold changes of the significantly affected OTUs at each time point. Black bars indicate the mean negative and positive log2-fold changes. (f) Number of bacterial, fungal, and protistan OTUs which were affected by hymexazol at only one time point, two time points, or all three time points. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
a

<figure><img src="image_11.png" /><figcaption>**Fig. 5.** Co-occurrence networks and edge affiliations specific to each hymexazol dose (a) Co-occurrence networks of bacteria, fungi, and protists for control, 1×, and 10× doses. Nodes are colored according to phylum or class for Proteobacteria. Node shape indicates whether node is bacteria, fungus, or protist. Positive covariances are represented by blue edge stroke and negative by red edge stroke. Modules with less than 3 nodes are not pictured. (b) Overall number of edges within and between fungi, bacteria, and protists for each dose. Positive edges are shaded blue and negative edges shaded red. Small black number within or above bar indicates number of positive edges and the second black number above indicates number of negative edges, when applicable. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 6.** Time and hymexazol effects on inorganic nitrogen pools. (a) NH₄⁺-N (n = 3) and (b) NO₃⁻-N concentrations in µg g⁻¹ dry soil (n = 3). For both panels, statistical differences (Tukey's test, p adj < 0.05) between hymexazol doses within each time point are indicated with lower-case letters above plots and statistical differences (Tukey's test, p adj < 0.05) between the controls at different time points are indicated with upper-case letters below the plots. Points show the concentration for each sample and the black crossbar represents the median concentration for each treatment.</figcaption></figure>

used worldwide to treat fungal and oomycete diseases of various crops such as rice, sugar beet, vegetables, and cucurbits. For example, >2 million ha of hymexazol-treated sugar beet seed are sown annually in Europe (Harveson et al., 2007). We found that hymexazol dose had very little effect on alpha-diversity regardless of the microbial community, in agreement with several other studies (El Azhari et al., 2018; Papado-poulou et al., 2016; Storck et al., 2018). However, alpha-diversity alone provides limited insight into the underlying ecology of a community (Shade, 2017). Community composition is often a more comprehensive indicator of biodiversity change as it is sensitive to changes in species abundance and identity (Hillebrand et al., 2018). Indeed, we observed a significant effect of hymexazol addition on bacterial, fungal, and especially protistan beta-diversity. Moreover, community composition was affected by hymexazol in a dose-dependent manner, with more significant changes in the mean relative abundance of Gammaproteobacteria, Cercozoa, and Stramenopiles at 10× than at 1×. This was supported by the differential abundance analysis showing overall more and stronger effects of hymexazol on OTU relative abundance at 10× than 1×. In line with our findings, pure culture studies reported direct toxicity of fungicides to bacteria (Flores et al., 2014; Marinho et al., 2020) and non-target fungi (Laatikainen and Heinonen-Tanski, 2002) with increasing effects at higher doses (Adetutu et al., 2008). Moreover, direct toxicity of fungicides to protists has been reported (Ekelund et al., 2000) and, as such, hymexazol is often used to control diseases caused by protistan oomycetes e.g. *Pythium* and *Aphanomyces* (Kato et al., 1990; Ypema, 2003). On the other hand, fungicides also have positive effects on microorganisms, as some bacteria and fungi are able to degrade fungicides, which are used as a source of nutrients and energy (Copley, 2009; Fenner et al., 2013). Accordingly, we observed hymexazol-related increases in relative abundance of bacterial, fungal, and protistan OTUs with log2-fold changes of up to 2, 6, and 4, respectively.

Hymexazol dissipation in soil is mainly driven by microbial metabolism (Nakanishi et al., 1974). In this study, hymexazol dissipation was rapid with a DT₅₀ of <3 days, in agreement with other studies which have found DT₅₀ values for hymexazol ranging from 2 to 25 days (Tomlin and British Crop Protection Council, 1997). Residual hymexazol concentrations were below the detection limit for both the 1× and 10× doses by T21. Thus, the soil microbial community was exposed to hymexazol for a relatively short period of time, yet effects of hymexazol

on OTU relative abundance were still observed through the end of the 60-day incubation period. This is in accord with meta-analyses showing that soil microbial community composition is often persistently affected by pesticide application, even when study durations last 60 days or longer (Kattwinkel et al., 2015; Puglisi, 2012). However, nearly all microbial OTUs whose relative abundance were affected by hymexazol had a different response across time points. Indeed, some OTUs were affected by hymexazol only at T7 or T21, while others were affected only at T60, long after soil hymexazol concentrations were below the detection limit. Thus, the dynamic of OTU responses to hymexazol was not correlated with the concentration of hymexazol over time. Although fungicide residue concentrations in soil significantly influence microbial community structure (Lu et al., 2024), our result indicates that the microbial community response to fungicides is also determined by other factors. One such factor may be indirect effects of fungicides mediated by biotic interactions between microorganisms. These indirect effects of pesticide exposure on microbial soil communities can be even more widespread than their direct effects (Meyer et al., 2024) and can increase the magnitude and duration of fungicide effects (Kattwinkel et al., 2015). Thus, accounting for biotic interactions between microorganisms is crucial for understanding how microbial communities respond to fungicide application and other disturbances.

Next, we examined how addition of hymexazol affected interkingdom networks. Our network analysis revealed hymexazol-induced changes in intra- and interkingdom co-occurrence patterns. The overall increased number of nodes and edges in the 1× and 10× networks relative to the control indicate that hymexazol influences the interactions between microorganisms. This is in agreement with other studies showing fungicide effects on soil microbial networks (Fournier et al., 2020; Zhang et al., 2021). The increase in positive fungal edges in the 1× and 10× networks could indicate that hymexazol stimulates mutualist or commensalist relationships such as syntrophy or cross-feeding (Faust and Raes, 2012), possibly during the biodegradation of hymexazol. Indeed, fungi are involved in the biodegradation of many organic pollutants (Harms et al., 2011) and some are capable of using the breakdown products of these compounds for energy and nutrients (Murphy and Perry, 1984; Prenafeta-Boldú et al., 2006). Interestingly, we observed a stronger effect of hymexazol on the 1× network than the 10× network. In a previous work, we showed that indirect effects were
more widespread at lower doses of hymexazol, while direct effects played a greater role in the community response at higher doses (Meyer et al., 2024). For example, the greater number of edges between bacteria and protists at 1× could indicate that the predator/prey relationship is affected more at 1× than at 10×. At the 10× dose, direct toxicity may have a greater effect on both bacteria and protists, thus weakening this predator/prey interaction.

Hymexazol application also affected nitrogen cycling, with increased abundance of ammonia-oxidizing microorganisms (AOM) and soil nitrate concentration. This result contrasts with many studies which have reported decreases in AOM abundance and nitrification rates with fungicide application (Feld et al., 2015; Karas et al., 2018; Katsoula et al., 2020; Sim et al., 2022; Vasileiadis et al., 2018). Previous work has suggested that AOM may be indirectly affected by hymexazol application through biotic interactions with other microorganisms (Meyer et al., 2024), providing a possible explanation for this discrepancy. For example, ammonium released by hymexazol-degrading microorganisms (Nakanishi et al., 1974) could subsequently act as a substrate for ammonia-oxidizers. Accordingly, many studies have observed increased abundance of AOM after amending soil with inorganic ammonium (Cavagnaro et al., 2008; Habteselassie et al., 2013; Okano et al., 2004; Verhamme et al., 2011) or with organic N which was mineralized to ammonia by other microorganisms (Hink et al., 2018; Levićnik-Höfferle et al., 2012). Due to their sensitivity to pesticides and their key role in the nitrogen cycle, AOM have been proposed as potential bioindicators for pesticide risk assessment to soil microorganisms (Karpouzas, 2022). Although AOM have often been observed to decrease in abundance in response to pesticide exposure, our contrasting results nevertheless demonstrate their responsiveness to environmental changes such as pesticide exposure and confirm their utility as bioindicators for soil monitoring (Wessén and Hallin, 2011).

## 5. Conclusion

This study demonstrates that the fungicide hymexazol affects not only various kingdoms within the soil microbial community, but also the co-occurrence patterns between them. Although hymexazol had very little impact on microbial alpha-diversity, it altered microbial community composition for the duration of the 60-day incubation period, even after hymexazol concentrations decreased below the detection limit. This indicates that microbial community responses to fungicides may be influenced not only by residue concentrations over time, but also by indirect effects mediated through, for example, shifts in biotic interactions. Our network analysis supported this conclusion by showing that microbial co-occurrences were altered by hymexazol. In addition, hymexazol impacted nitrogen cycling by increasing the abundance of AOM and soil nitrate concentration. Our findings highlight the complexity of microbial community responses to fungicides and underscore the need for considering interkingdom microbial interactions in assessing the impacts of fungicide application on soil microorganisms.

### CRediT authorship contribution statement

**Cara Meyer:** Writing – original draft, Visualization, Investigation, Formal analysis, Data curation, Conceptualization. **Mathilde Jeanbille:** Writing – review & editing, Visualization, Supervision, Investigation, Formal analysis, Data curation, Conceptualization. **Marie-Christine Breuil:** Resources, Investigation. **David Bru:** Resources, Investigation. **Kristin Höfer:** Resources, Methodology, Investigation. **Claudio Screpanti:** Writing – review & editing, Supervision, Conceptualization. **Laurent Philippot:** Writing – original draft, Validation, Supervision, Project administration, Funding acquisition, Conceptualization.

### 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

This ARISTO project was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956496. The authors would like to thank Aymé Spor for assistance with the statistical analyses.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2024.177557.

### Data availability

Data will be made available on request.

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