nutritional source. Altogether, these results suggest that the antibiotrophic strain studied can modulate its sulfamethazine-degrading function depending on microbial competition and resource accessibility, to become established in an agricultural soil. Most importantly, this work highlights an increased dispersal potential of antibiotrophs in antibiotic-polluted environments, as antibiotics can not only release existing trophic niches but also form new ones.

Keywords: sulfonamide, microbial ecotoxicology, bacterial community invasion, soil, antibiotic biodegradation

INTRODUCTION

Antibiotics and their residues deserve specific attention as pollutants of emerging concern. Most are slightly and partially metabolized in humans or animals prior to excretion (Lamshöft et al., 2007; Hawkshead, 2008), resulting in heavy contamination of wastewater and livestock manure (Kümmerer, 2001; Watkinson et al., 2007; Feng et al., 2017), where they can reach concentrations of up to micrograms per liter (Peng et al., 2006; Rodriguez-Mozaz et al., 2020) and milligrams per kilogram (Haller et al., 2002; Cid et al., 2018), respectively. The disposal processes applied to these matrices lack efficiency, and so the discharge of wastewater into aquatic ecosystems or manure spread on soils, a common agricultural practice for nutrient recycling and soil fertilization, contributes to the release of antibiotics into different environmental compartments (Watkinson et al., 2007; Heuer et al., 2011; Ghirardini et al., 2020).

By their mode of action and their targets, antibiotics have several ecotoxicological effects on environmental bacteria. First, the direct biocidal action of an antibiotic can alter microbial communities and disturb their function supporting ecosystem services (Costanzo et al., 2005; Ding and He, 2010; Hammesfahr et al., 2011; Pinna et al., 2012; Proia et al., 2013; Cycoń et al., 2019). For instance, tetracycline can alter the denitrification process of soil microbial communities (Shan et al., 2018), while sulfamethazine was shown to negatively impact the phosphatase activity and the respiration of soil microflora (Liu et al., 2009). Second, exposure to antibiotics can induce an adaptive response of microbial communities, notably through the development and spread of antibiotic-resistant bacteria and genes. Numerous studies have shown a positive correlation between antibiotic contamination and the occurrence of antibiotic-resistance genes in ecosystems receiving the above polluted matrices (Hawkshead, 2008; Zhang et al., 2009; Bengtsson-Palme and Larsson, 2016; Bengtsson-Palme et al., 2018). Contamination of ecosystems by antibiotics is thus a major health concern, as these environmental microbial communities constitute reservoirs of resistance that might potentially be transferred to pathogenic bacteria (Perry and Wright, 2013; Zrimec, 2020).

The adaptive capacities of environmental microorganisms toward organic pollutants are not only limited to the increased tolerance or resistance but also include the development of catabolic pathways (Pesce et al., 2020). In this way, the long-term exposure of microbial communities to these substances can lead to the emergence of bacteria able to use them as

nutrient sources (Copley, 2000; Janssen et al., 2005). Although the pesticide-degrading function has been extensively studied (Behki and Khan, 1986; Rani and Lalithakumari, 1994; Aislabie et al., 1997; Gisi et al., 1997), the existence of antibiotic-degrading bacteria was reported more recently (Barnhill et al., 2010; Xin et al., 2012; Topp et al., 2013; Leng et al., 2016; Crofts et al., 2018). The low environmental concentrations of antibiotics compared to pesticides, together with their biocidal effects on many microorganisms, have probably slowed down the development, emergence, and spread of antibiotic-degrading functions. However, the likelihood of such emerging abilities has risen with the increasing concentrations of antibiotics in the environmental compartments along with the decreasing sensitivity of microbial communities. The latest effect is due to the spread of a wide range of antibiotic resistances (Stoll et al., 2012; Bengtsson-Palme et al., 2018) and/or to phenotypic adaptation following chronic exposure to antibiotics (Schmitt et al., 2005; Corona and Martinez, 2013).

In the last decade, a handful of antibiotic-resistant bacteria able to use some antibiotics as nutritive sources were isolated from various environmental matrices repeatedly exposed to relatively high concentrations of antibiotics as agricultural soil (Topp et al., 2013), membrane bioreactor (Bouju et al., 2012), and activated sewage sludge (Lin et al., 2015). The degradative function, termed "antibiotrophy" (Woappi et al., 2016), is specific to an antibiotic or a class of antibiotics, such as β-lactams (Lin et al., 2015; Crofts et al., 2018) or erythromycin (Gao et al., 2015). The case of sulfonamide is currently the most documented (Bouju et al., 2012; Tappe et al., 2013; Topp et al., 2013; Ricken et al., 2017; Kim et al., 2019).

Among antibiotics, sulfonamides are synthetic bacteriostatic compounds largely used in veterinary and human medicine (Shah et al., 2013). They act as competitive inhibitors of the dihydropteroate synthase involved in the synthesis pathway of nucleic acids (Brown, 1962). The most documented resistance mechanism relies on four genes (*sul1*, *sul2*, *sul3*, and *sulA*), coding for dihydropteroate synthase isoproteins insensitive to sulfonamide (Wise and Abou-Donia, 1975; Yun et al., 2012). Both sulfonamides and *sul* genes are widely detected in different environmental matrices such as livestock manure, surface water, and river sediment (Pei et al., 2006; McKinney et al., 2010; Stoll et al., 2012). The dispersion potential of these plasmid-borne resistant genes is enhanced by their common association with integrons (Antunes et al., 2005; Wei et al., 2018), which also contain a variety of resistance genes toward many kinds of biocides (e.g., disinfectants, metals, and antibiotics). Exposure
to these biocides can therefore lead to a co-selection of sulfonamide resistances (Pei et al., 2006; Luo et al., 2010; McKinney et al., 2010; Hoa et al., 2011; Stoll et al., 2012). Sulfonamide antibiotrophy is a recent discovery. Reports on environmental sulfonamide-degrading bacteria are still few but increasing. Those already described originate from different geographically distant matrices, but are phylogenetically close, and their sulfonamide degradative pathway relies on the same cluster of genes (Ricken et al., 2017; Kim et al., 2019). It composed of two flavin-dependent monooxygenases (*sadA* and *sadB*) and one flavin mononucleotide reductase (*sadC*). Although genetic and metabolic studies have been conducted on isolated sulfonamide antibiotrophs, information is lacking on the ecological impact of this function for bacterial carriers in complex environmental bacterial communities. Manure amendment on soils, known not only to increase the fertility of soils but also to introduce biological contaminants and chemical pollutants, may also be involved in the environmental dispersion of antibiotrophy (Heuer et al., 2011; Tappe et al., 2013; Chen et al., 2019).

At first glance, the development of antibiotic degradation capacities might seem environmentally beneficial by increasing the self-purifying potential of ecosystems, contributing to the natural attenuation of their contamination by antibiotics. Likewise, it could be perceived as a bioremediation tool that could improve the removal of antibiotics from contaminated matrices before their discharge into the environment (Hirth et al., 2016; Sharma et al., 2018; Kumar et al., 2019; Silva et al., 2020). However, given that currently known antibiotrophs are also antibiotic-resistant, the question arises of whether antibiotrophy could lend them a selective advantage and so facilitate their implantation and dispersion in the environment.

We conducted a microcosm study to investigate the mechanisms driving the emergence and development of antibiotrophy in agricultural soils. Specifically, we investigated whether an allochthonous bacterium harboring both sulfonamide resistance and degradation genes could become established within natural microbial communities in soils subjected to both sulfonamide contamination and manure amendment. For this purpose, we considered four different soil types with and without added sulfamethazine (SMZ) and/or swine manure, and with and without inoculation with the sulfonamide-degrading and resistant bacterial strain, *Microbacterium* sp. C448. This model bacterium was isolated from experimental soils exposed for 10 years to an annual increasing dose of SMZ ranging from 1 to 10 mg kg⁻¹ (Topp et al., 2013). It presents the advantage of being well-characterized genetically and physiologically (Martin-Laurent et al., 2014). To assess the establishment of *Microbacterium* sp. C448 in soil communities, its abundance was measured, after 1 month of incubation, by qPCR assay targeting its *groL* gene sequence. The resulting effect of applied treatments on the soil community's genetic potential for sulfonamide degradation was assessed through the quantification of the *sadA* gene sequence. To determine whether the degradation potential was expressed, we measured the mineralization kinetics of ¹⁴C-SMZ during the incubation period and quantified the remaining extractable fraction of SMZ at the end of the incubation period. Finally, we assessed the effects of the eight treatments on the soil

microbial community structure and diversity by Illumina 16S rDNA sequencing.

# MATERIALS AND METHODS

## Collection and Analyses of Soils and Manure

Soils and manure were collected in the Bourgogne-Franche-Comté region of France in November 2018. Four soils, A, B, C, and D, selected for their different textures, were sampled from the upper 20 cm of different agricultural fields, air-dried, sieved (mesh size 5 mm), and stored in airtight plastic bags at 4°C until use. Soil A originated from an agricultural field frequently flooded by the nearby Saône River. Soil B came from the same plot but was less often flooded owing to its higher altitude. Soil C originated from the experimental farm of Epoisses. Soil D was sampled near a hog nursery and had received manure 6 months before the sampling. The pedological variables of the soils (pH, ion retention, grain size, and water content at pF 2.7) are given in **Supplementary Table 1**. Manure was collected from the covered pit of a hog nursery and stored in plastic bottles at 4°C until use. SMZ quantification was performed on soil and manure samples (**Supplementary Table 1**). SMZ concentration reached 0.94 µg L⁻¹ in manure.

## Microcosm Experiment Setup

The experiment consisted in incubating soil treated or not with manure, contaminated or not with SMZ, and inoculated or not with *Microbacterium* sp. C448. Considering eight conditions and five replicates per condition, 40 microcosms for each soil were prepared as described below. Microcosms containing 50 g dry weight (DW) of soils were prepared in glass bottles closed with air-permeable lids made of gauze and cotton (**Supplementary Figure 1**). They were incubated for 1 week at 20°C (±1°C) to gently activate the native soil microorganisms. Soil microcosms were humidified up to 80% of their water content at pF 2.7. To inoculate microcosms with *Microbacterium* sp. C448, the strain was first grown in LB-Miller liquid medium at 28°C under 130 rpm agitation. The grown cells were collected in their exponential phase (OD₆₀₀nm ~ 1 AU) and washed twice with NaCl 0.9%. SMZ sodium salt [SMZ, CAS number: 1981-58-4, 4-amino-N-(4,6-dimethyl-2-pyrimidinyl)benzenesulfonamide, Sigma-Aldrich, France] was diluted to 100 mg ml⁻¹ in water and filtered (0.2 µm). The manure was incubated overnight at room temperature (20 ± 1°C). Treatments were prepared a few minutes before their application on soils to reach nominal concentrations of 10⁴ cells of *Microbacterium* sp. C448 g⁻¹ dry soil, 100 mg of SMZ kg⁻¹ dry soil, and 13 ml of manure kg⁻¹ dry soil [eight treatments, five replicates (**Supplementary Figure 1**)]. The microcosms were incubated at room temperature in the dark for 1 month and maintained at a stable humidity by regular watering (80% of their water content at pF 2.7). At the end of the incubation, we subsampled each microcosm for DNA extraction and further molecular analyses.
# SMZ Fate Monitoring

## SMZ Mineralization Monitoring

For the soil treated with SMZ, the mineralization SMZ was monitored over the time of incubation by radiospirometry. To do so, the SMZ-treated microcosms were duplicated, with one series being exposed to ¹²C-SMZ and another one being exposed to a mixture of ¹²C- and ¹⁴C-SMZ (**Supplementary Figure 1**). These last ones were treated with 0.11 µCi of ¹⁴C-ring-labeled SMZ (¹⁴C-SMZ, CAS number: 57-68-1, radiochemical purity: >98%, specific radioactivity: X MBq mmol⁻¹) and placed in closed respirometer jars. ¹⁴CO₂ released from the ¹⁴C-SMZ degradation was trapped in 5 ml of 0.2 M NaOH solution and regularly analyzed by liquid scintillation counting using ACS II scintillation fluid (Amersham).

## SMZ Quantification

The initial concentration of SMZ in the matrices used in this study (soil and manure) as well as the SMZ remaining in the soil microcosms at the end of the incubation was measured as described below. To Nalgene PPCO 50-ml centrifuge tubes (checked for lack of SMZ sorption capacity) were added 6.0000 ± 0.0005 g of each previously dried and crushed SMZ-contaminated soil sample and 20 ml of MeOH/H₂O 4/1 (v/v) mixture. The tubes were stirred for 18 h using an orbital shaker (Heidolph Reax) at 50 rpm and room temperature. After centrifuging (12,500 × g for 15 min), the supernatant was filtered on a 0.45-µm syringe PVDF filter (after checking for lack of sorption) before injecting on HPLC. The four soils were spiked with standard SMZ solutions at different concentrations and underwent the same protocol. The recovery percentages ranged between 79 ± 1% and 93 ± 2% according to the soil. The antibiotic concentrations of soil liquid extracts were determined by HPLC on an Agilent 1,100 apparatus (Courtaboeuf, France) equipped with a reverse-phase column (C18 Zorbax Eclipse Plus column, 75 mm × 4.6 mm, 3.5 µm) at 22°C and a diode array detector set at λ = 260 and 298 nm. The mobile phase composed of aqueous H₃PO₄ (0.01% v/v, pH = 2.9) (A) and acetonitrile (B) at a flow rate of 1 ml min⁻¹. Gradient (linear): 0–5 min: 2% B; 5–8 min: 2–30% B; 8–10 min: 30–90% B; 10–10.5 min: 90–100% B; 10.5–11.5 min: 100% B; 11.5–13 min: 100–2% B. Injection volume: 10 µl. Each sample was analyzed twice (technical duplicate). Some samples were also treated with the LC/ESI-MS protocol (below) to check the SMZ concentrations. These were diluted 1/1000 before LC/MS analysis.

For the non-contaminated soil samples, the protocol was the same except that 50 µl of a 5000 µg L⁻¹ SMZ-d₄ (LCG standards) solution was added to each centrifuge tube. After centrifuging, the supernatant was concentrated in a Speed-Vac vacuum concentrator heated at 35°C. The residue was dissolved in 10 ml of distilled water. After adjusting the pH to 3.8 ± 0.2, samples were concentrated 40-fold on Oasis HLB 500-mg cartridges (Waters™) according to the manufacturer's recommendations (elution: 10 ml methanol). The antibiotic concentrations were determined by LC/ESI-MS on a Thermo Scientific UHPLC Ultimate 3000 RSLC coupled with an Orbitrap Q-Exactive analyzer. The analyses were carried out in positive

mode. The UHPLC was equipped with a Luna Omega Polar C18 column; 100 × 2.1 mm: 1.6 µm (Phenomenex) at 30°C with acetonitrile gradient +0.1% formic acid (Solvent A) and water +0.1% formic acid (Solvent B): 0–2.5 min: 30–64.5% A (linear); 2.5–2.6 min: 64.5–99% A (linear); 2.6–5 min: 99% A; 5–5.1 min: 99–30% A; 5.1–8 min: 30% A. Flow rate of 0.45 ml min⁻¹. For the mass spectrometer, gaseous N₂ was used as nebulizer gas (50 A.U.). The spray voltage was 3.0 kV.

# Soil DNA Extraction and Real-Time PCR

DNA was extracted from 250 mg of soil from each sample using the DNeasy PowerSoil-htp 96-well DNA isolation kit (Qiagen, France) following the manufacturer's instructions and stored at -20°C.

Real-time PCR reactions were carried out in a ViiA7 (Life Technologies, United States) in a 15-µl reaction volume. For *sada* and *rss* genes, the mixture contained 7.5 µl of Takyon MasterMix (Eurogentec, France), 1 µM of each primer (**Supplementary Table 2**), 250 ng of T4 gene 32 (QBiogene, France), and 3 ng of DNA. The quantification of *Microbacterium* sp. C448's *groL* required a specific probe. The reaction mixture contained 7.5 µl of Takyon Low ROX Probe 2X MasterMix dTTP blue (Eurogentec, France), 400 nM of each primer, 200 nM of the probe (**Supplementary Table 2**), 250 ng of T4 gene 32, and 3 ng of soil DNA. Each real-time PCR assay was performed twice independently. Standard curves were obtained using serial dilutions of linearized plasmids containing the appropriate cloned target genes from bacterial strains. The qPCR efficiencies ranged between 80 and 100%. Inhibition was assessed for each sample in accordance with ISO17601 (2016). No inhibition was detected in any case. The thermal cycling was performed as follows: 95°C for 10 min, 40 cycles of 95°C for 30 s, annealing (**Supplementary Table 2**) for 30 s, and 30 s at 60°C. Relative abundances were obtained by normalizing the copy number of genes *sada* and *groL* with bacterial 16S rRNA gene *rss* copies. The detection limit was 50 copies/ng of DNA for *sada* and *groL* and 5,000 copies/ng of DNA for *rss*. The threshold expressed by 1,000 copies of *rss* was determined by dividing the detection limit of the gene by the lowest quantification value of *rss*.

# 16S rDNA Sequencing and Bioinformatic Analysis

The V3–V4 hypervariable region of the bacterial 16S rRNA gene was amplified using a two-step PCR and sequenced on MiSeq (Illumina, 2 × 250 bp) as previously described (Spor et al., 2020).

A Jupyter Notebook developed in-house was used to analyze the sequence data. 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). Reference-based and *de novo* chimera detection together with clustering in OTUs were performed using VSEARCH (Rognes et al., 2016) and Greengenes' representative set of 16S rRNA sequences as the reference database. The identity thresholds were set at 94%. Representative sequences for each OTU were aligned
using Infernal (Nawrocki and Eddy, 2013) and a 16S rRNA phylogenetic tree was constructed using FastTree (Price et al., 2010). Taxonomy was assigned using RDP Classifier (Wang et al., 2007) and the latest released Greengenes database (v.05/2013; McDonald et al., 2012). α-Diversity metrics (PD whole tree, species richness, and equitability index) were determined from rarefied OTU tables of 25,700, 24,500, 28,300, and 23,100 sequences per sample for soils A, B, C, and D, respectively. Sequences were deposited in the SRA at NCBI under the accession number SUB8712901.

## Statistical Analyses

Statistical analyses were carried out using RStudio statistical software (version 1.2.5033). For each soil, we built an ad hoc analysis of variance (ANOVA) model to determine the effects of treatments on gene abundances and on the alpha diversity indices of bacterial communities. Normality and homogeneity of the residual distribution were inspected and log10-transformations were performed when necessary. Differences in response variable related to the treatment (antibiotic treatment, manure amendment, and strain inoculation) were detected using the following model:

$$Y_{ijkl} = \mu + A_i + T_j + I_k + A : T_{ij} + A : I_{ik} + T : I_{jk} + A : T : I_{ijk} + \varepsilon_{ijkl},$$

where $Y_{ijkl}$ is the response variable, A is the fixed effect of manure amendment ($i = 1,2$), T is the fixed effect of sulfamethazine treatment ($j = 1,2$), I is the fixed effect of inoculation by *Microbacterium* sp. C448 ($k = 1,2$), and $\varepsilon_{ijkl}$ are the residuals.

Tukey's test was performed to detect significant pairwise differences for each treatment (significance threshold set at $p < 0.05$).

The OTU table was also filtered to keep the dominant OTUs (relative abundance > 0.1%). For each soil, the OTUs that were significantly impacted by treatments were detected with the previous linear model. We opted for a custom model analysis to take into account the relative complexity of our experimental design that cannot be handled by classical analysis packages such as edgeR or DESeq2. A false discovery rate procedure was used to decrease substantially the probability of detecting false positives. The FDR significance threshold was set at 0.05.

## RESULTS AND DISCUSSION

### The Soils, Regardless of Manure Amendment, Did Not Have Innate Antibiotrophic Potential or Activity

After a 1-month incubation period, the antibiotrophic gene *sada* was not detected in the four soils even when they were amended with manure (Figure 1B, detection limit of 0.1 *sada* copies per 1,000 *rss*). Despite the exposure to SMZ, its abundance remained below the detection limit. This strongly suggests that the microbial communities of the four soils and manure do not initially possess bacteria carrying *sada*, although

some of them were previously exposed to low concentrations of SMZ (Supplementary Table 3). *sada* has only appeared in recent literature on sulfonamide-degrading bacteria isolated from environmental compartments after high and long-term exposure to sulfonamide antibiotics, such as bioreactor membranes (Bouju et al., 2012), active sludges (Reis et al., 2014), or manure-amended soils (Tappe et al., 2013; Chen et al., 2019). However, the lack of detection of the key gene coding for the first enzyme of the only currently established SMZ antibiotrophy pathway cannot exclude the possibility of antibiotrophic activity catalyzed by other still unknown enzymes. We also measured the antibiotrophic potential activity. These analyses relied on a similar microcosm experiment differing only by the addition of $^{14}$C-labeled SMZ to monitor the mineralization of its phenyl ring. The mineralization rates obtained for soils, regardless of manure amendment and SMZ treatment modalities, did not exceed 3% of initially added $^{14}$C-SMZ (Figure 2). Although an alternative low-efficiency SMZ-antibiotrophic pathway can explain this slight observed mineralization, it can be reasonably assumed that it results from residual mineralization. Nonspecific biotic and abiotic mechanisms can partially break complex molecules (Gold et al., 2000; Mitchell et al., 2015), promoting fortuitous biological degradation. Moreover, the mineralization rates did not increase during the 1 month of exposure of native soil microbial communities to a high concentration of SMZ. We note that the experimental conditions were insufficient to guarantee enhancement and development of antibiotrophic abilities.

### Microbacterium sp. C448 Needs SMZ to Become Established in the Soil Community

After a 1-month incubation period, quantification of the *Microbacterium* sp. C448 through the estimation of the abundance of its *groL* gene sequence (Figure 1A) indicated that it could establish in the four tested soils but only under SMZ exposure. The relative abundance of the strain was relatively homogenous in the four treated soils (from 1.3 ± 0.1 to 4.6 ± 1.6 *groL* copies per 1,000 copies of the 16S rRNA gene *rrs*), despite their different biotic and abiotic properties. Adding manure had either no impact (soils A and C) or a relatively limited one (soils B and D, $n = 5$, $p < 0.05$) on the relative abundance of the *groL* gene sequence. Extrapolating *groL* quantification and assuming a soil DNA extraction yield of 1, at least $10^6$ cells of *Microbacterium* sp. C448 per gram of dry soil were counted at the end of incubation in the SMZ-treated soils inoculated with $10^4$ cells per gram of dry soil, indicating growth. Conversely, in the SMZ-untreated soils, *groL* gene sequences were below the detection limit of 0.1 *groL* per 1,000 *rss*, suggesting that *Microbacterium* sp. C448, initially absent, did not become established in these conditions. This indicates a low competitiveness for this strain, which possesses a low invasion potential under SMZ-free conditions and highlights the mandatory presence of the antibiotic for its establishment. SMZ may therefore be the main environmental driver controlling the efficiency of the establishment of *Microbacterium* sp. C448, at least in our experimental conditions. Given that this strain is also known to resist and degrade other sulfonamides such
<figure><img src="image_54.png" /><figcaption>**FIGURE 1** | Relative abundances of *groL* (A) and *sadA* (B) gene sequences in the soil microcosms (four types of soil, A, B, C, and D, amended or not with manure, treated or not with SMZ, and inoculated or not with *Microbacterium* sp. C448). After 1 month of incubation, gene sequence abundances were measured by quantitative PCR and expressed per 1000 copies of the bacterial 16S rRNA gene *rss* (Tukey's test, *n* = 5, *p* < 0.05). BD: below the detection limit (0.1 copy per 1,000 16S rRNA gene).</figcaption></figure>

as sulfamethoxazole, sulfadiazine, or sulfadimethoxine (Martin-Laurent and Topp, unpublished data), this effect might hold for a wider range of antibiotics in this family.

Two main hypotheses could explain this selective advantage conferred by SMZ. The first is that the antibiotic may be used as a nutrient source exclusively dedicated to *Microbacterium* sp. C448, thus playing a major role in ecological selectivity

(Nishiyama et al., 1993; Karpouzas and Walker, 2000; Hong et al., 2007). Specialized microorganisms, when they are the only ones possessing the required catabolic enzymes to mineralize selected xenobiotics, are able to establish and perform mineralization even if they are initially scant in the microbial community (van Veen et al., 1997). The second hypothesis is that SMZ may release trophic niches by biocidal action
<figure><img src="image_55.png" /><figcaption>**FIGURE 2** | Kinetics of ¹⁴C-SMZ mineralization throughout the incubation for the four different types of soils, A, B, C, and D, treated with SMZ, amended or not with manure, and inoculated or not with *Microbacterium* sp. C448. Results are expressed in percentage of initially added ¹⁴C-SMZ.</figcaption></figure>

(Pinna et al., 2012; Reichel et al., 2015), thus reducing negative interactions such as trophic interactions or competition (van Elsas et al., 2012). These two non-exclusive hypotheses may explain why SMZ facilitates the establishment of the strain in the soil communities.

## The Presence of Genetic Potential for Antibiotrophy in Soil Does Not Guarantee Its Expression

The relative abundance of the antibiotrophic gene *sadA* (Figure 1B) followed trends similar to those of *groL*. *sadA* was detected only when *Microbacterium* sp. C448 was present (i.e., when *groL* was detected), with relative abundances fluctuating between 0.3 ± 0.1 and 1.1 ± 0.4 *sadA* copies per 1,000 copies of *rrs* gene. More than just indicating that *Microbacterium* sp. C448 was initially the only host of *sadA* gene, it suggests that it kept its antibiotrophic genetic potential after its establishment in the SMZ-treated soils, whether or not manure was added. This agrees with the idea that the antibiotrophic

function carried by *sadA* gene is probably useful in SMZ-polluted conditions.

Although previous studies have indicated that *sadA* is potentially mobile in other antibiotroph strains (Ricken et al., 2017; Reis et al., 2019), our results did not show any increase in the *sadA/groL* ratio (data not shown), suggesting an absence of horizontal gene transfer. This is consistent with the chromosomic localization of *sadA*, which makes its mobility *via* a horizontal gene transfer event unlikely (whole genome sequencing of *Microbacterium* sp. C448, Martin-Laurent et al., 2014).

The main outcome of *groL* and *sadA* gene sequence quantification is that installation of *Microbacterium* sp. C448 and its antibiotrophic gene *sadA* both succeed as long as they are under SMZ selection pressure. However, and since the gene's presence does not guarantee its functional expression (Browning and Busby, 2004; Pietramellara et al., 2008), qPCR assessments were complemented with functional analyses to assess potential ¹⁴C-SMZ mineralization. As stated above, mineralization rates obtained for conditions without *Microbacterium* sp. C448 were low and did not exceed 3% in any of the soils (Figure 2).
When *Microbacterium* sp. C448 was inoculated, a mineralization activity was observed for each soil, but it occurred only (in soils A, B, and D) or preferentially (in soil C) under manure spreading conditions (Figure 2). When the strain was applied simultaneously with manure, soils A, B, and D reached, respectively, 19.5 ± 1.2% (Figure 2), 15.3 ± 1.4% (Figure 2), and 5.6 ± 0.7% (Figure 2) of SMZ mineralization. Soil C was the only one where *Microbacterium* sp. C448 was able to express its mineralization potential without manure spreading (Figure 2). However, the mineralization rate was about half that with manure (5.5 ± 0.3% versus 12.6 ± 0.4%). Altogether, these results reveal that the presence of *Microbacterium* sp. C448 and its antibiotrophic gene sadA does not always result in effective antibiotrophy in SMZ-contaminated soils. In our experimental conditions, they reflect a modularity of the functional expression depending on the kind of soil and on manure addition what is commonly observed in particular for antibiotic degradation (Cycoń et al., 2019). This lack of mineralization did not seem to result from the absence of SMZ bioavailability. The quantification of the SMZ extractable fraction performed at the end of the incubation period indicates that the amount of bioavailable SMZ was not or only slightly affected by manure amendment (Table 1). *Microbacterium* sp. C448 thus potentially had access to as much SMZ with manure amendment as without. Moreover, the variation of free SMZ amount between soils does not seem to be correlated with the variation of mineralization between soils, since the soil C with the lowest SMZ concentration was not the one with the lowest degradation rate. Altogether, these observations provide a nuanced response to the hypothesis for how SMZ can give *Microbacterium* sp. C448 a selective advantage. Without manure, the absence of mineralization (or the drastically diminished mineralization compared to manure treatment for soil C) indicates that SMZ is not always essential as an exclusive dedicated nutrient source. We therefore investigated whether SMZ could also give this strain a selective advantage by its biocidal action to release ecological niches.

## Antibiotrophy Efficiency Depends on SMZ Biocidal Impact

The biocidal impact of SMZ on soil communities was evaluated by 16S rDNA sequencing to assess the resulting changes in the bacterial and archeal community diversity and structure. From this sequencing, more than 23,100 partial 16S rDNA sequences were analyzed from each microcosm, accounting for 15,195 OTUs. The overall diversity indices (richness, equitability, and observed species) in the four tested soils were not significantly impacted by SMZ addition, whether or not manure and/or *Microbacterium* sp. C448 were added (Supplementary Table 4). This suggests no or only transient effects of SMZ on community diversity, but it does not exclude impacts on the members of the community. Further OTU-level ANOVAs revealed that about 165 of the studied OTUs were significantly affected by SMZ in at least one of the soils (Figure 3A). This limited impact is coherent with the previous study of Cleary et al. (2016) in which few OTUs were significantly affected by the input of SMZ into soils. The response of the bacterial community to SMZ varied between soils.

<table><caption>**TABLE 1** | Concentrations of extractable SMZ measured after 1 month of incubation in the four different types of soils treated with SMZ, amended or not with manure, and inoculated or not with *Microbacterium* sp. C448 (C448).</caption><thead><tr><th></th><th></th><th>Concentration of extractable<br>SMZ (mg kg<sup>-1</sup> dry soil)</th></tr></thead><tbody><tr><td rowspan="4">Soil A</td><td>SMZ</td><td>54.1 ± 0.4</td></tr><tr><td>SMZ × C448</td><td>54.4 ± 1.7</td></tr><tr><td>SMZ × MANURE</td><td>45.4 ± 1.7</td></tr><tr><td>SMZ × C448 × MANURE</td><td>34.6 ± 1.8</td></tr><tr><td rowspan="4">Soil B</td><td>SMZ</td><td>21.8 ± 2.7</td></tr><tr><td>SMZ × C448</td><td>21.4 ± 0.8</td></tr><tr><td>SMZ × MANURE</td><td>21.0 ± 2.6</td></tr><tr><td>SMZ × C448 × MANURE</td><td>19.9 ± 1.5</td></tr><tr><td rowspan="4">Soil C</td><td>SMZ</td><td>0.9 ± 0.1</td></tr><tr><td>SMZ × C448</td><td>0.6 ± 0.0</td></tr><tr><td>SMZ × MANURE</td><td>1.1 ± 0.1</td></tr><tr><td>SMZ × C448 × MANURE</td><td>0.5 ± 0.0</td></tr><tr><td rowspan="4">Soil D</td><td>SMZ</td><td>6.4 ± 0.2</td></tr><tr><td>SMZ × C448</td><td>4.1 ± 0.1</td></tr><tr><td>SMZ × MANURE</td><td>6.8 ± 0.1</td></tr><tr><td>SMZ × C448 × MANURE</td><td>3.7 ± 0.1</td></tr></tbody></table>

The soil A community was clearly the most negatively impacted with 55 OTUs whose abundance was significantly reduced by the antibiotic, while the soil C community appeared to be the most robust one, with only eight OTUs negatively impacted (accounting for 14.1 and 2.3% of the bacterial community of untreated and non-amended soils, respectively). SMZ moderately affected soil B and D communities with, respectively, 15 and 20 OTUs with significantly reduced abundance (accounting for 5.3 and 2.7% of the bacterial community of untreated and non-amended soils, respectively). In parallel, the abundance of some OTUs was also increased, probably benefiting from the decrease in others. Pre-adaptation of bacterial communities to antibiotics could have been the main factor explaining the variations in the biocidal impact (Schmitt et al., 2004, 2005; Brandt et al., 2009). This was not the case here since the SMZ impact on the community of soil A was much stronger than on that of soil B, whereas they were exposed before the experiment to similar concentrations of SMZ (Supplementary Table 1). The second most probable factor explaining the variation of SMZ impact could be its bioavailability, since only the free fraction can be bioavailable for bacteria and exert its biocidal action. The SMZ quantification supports this hypothesis (Table 1). In the non-inoculated soils where no specific mineralization took place, the greatest SMZ available fraction was found in the most impacted soil (soil A, 54.1 ± 0.4% of SMZ available) and the lowest in the least impacted one (soil C, 0.9 ± 0.1%). The bioavailability of SMZ (and more generally of organic contaminants) relies on its intrinsic properties and on soil characteristics and especially clay content, organic carbon fraction, and pH (Supplementary Table 1). It is expected that their increases will reduce the bioavailability of SMZ in soil and therefore its biocidal activity (Gao and Pedersen, 2005; Lertpaitoonpan et al., 2009; Pinna et al., 2012). Our results are in line with this hypothesis since soil A,
<figure><img src="image_56.png" /><figcaption>**FIGURE 3** | Phylogenetic placement of bacterial OTUs significantly impacted by SMZ (A) or manure spreading (B) in the four different types of soils, A, B, C, and D, after 1 month of incubation. Discriminant OTUs between treatments were assessed by ANOVA (p < 0.05). The phylum-level affiliation of each OTU is indicated by different colors in the left strip. For each OTU in each soil, the impact of SMZ (A) or manure spreading (B) is represented by a two-strip heatmap to distinguish conditions without (left strip) and with (right strip) manure spreading (A) or SMZ (B). Red represents a negative impact of the treatment on the OTU abundance; blue, a positive impact. For each condition and OTU, the impact is reported as the difference in OTU abundance means between treated and non-treated samples divided by its maximum over the two conditions. Color intensities show the strength of the impact.</figcaption></figure>

which hosted the most impacted community, and soil C, which
hosted the least impacted one, exhibited, respectively, the lowest
and highest values for clay content, organic carbon fraction, and
pH (84, 5.2, and 5.6%, and 360, 13, and 6.9%, respectively).

Besides confirming that the biocidal activity of SMZ depends
on the soil's physical and chemical properties, microbial diversity
and structure analyses suggest that ecological niches may have
been freed after the reduction in abundance of some OTUs
following the SMZ exposure. *Microbacterium* sp. C448 could
have potentially colonized and used these unoccupied niches,
thus facilitating its establishment in the soil community. This
outcome supports the hypothesis of released niches, which could

explain the crucial positive role of SMZ for *Microbacterium* sp. C448 establishment even when it does not use this antibiotic as a nutrient source. The lack (or limited level) of SMZ mineralization observed in the absence of manure spreading, although the antibiotic was available, may indicate a niche preference of the strain for readily available organic nutrients as already shown in other studies dealing with inoculation of xenobiotics degraders in soils (Benimeli et al., 2008). The degradation of antibiotics like many other synthetic organic contaminants can be arduous as the successive steps leading to their complete degradation can release intermediate transformation products that can be toxic for the degrading bacteria involved. For instance, sulfur
dioxide, one dead-end product of SMZ mineralization (Ricken
et al., 2017), is known for its antibacterial properties (Malwal
et al., 2012). However, SMZ can constitute a nutritive source
of prime choice for *Microbacterium* sp. C448, especially if the
strain lacks niches to support its growth. The measurements of
SMZ mineralization potential for the soil C fits this hypothesis.
The slight mineralization of SMZ (5.5 ± 0.3%) and its reduced
biocidal action on the bacterial community suggest that the
release of trophic niches was insufficient to entirely support
*Microbacterium* sp. C448 growth, which therefore used its
antibiotic degradation in compensation.

Antibiotrophy Efficiency Depends on Soil Permissiveness to Invasion of Manure-Originating OTUs and Resulting Trophic Competition

In soils spread with manure, antibiotic mineralization was
enhanced although biocidal action of SMZ and therefore niche
releases occurred in a similar way (Figure 2). This suggests that
*Microbacterium* sp. C448 encountered difficulties in accessing
trophic niches released by SMZ and had to rely on antibiotic
degradation to become established.

We observed that adding manure significantly increased the
alpha diversity of bacteria and archaea in each SMZ-treated soil,
except for soil D (Supplementary Table 4). Phylogenetic diversity
values increased by about 5.7, 5.2, and 7.2% in soils A, B, and
C, respectively, with a respective gain of about 75, 68, and 116
observed OTUs. ANOVAs conducted on each OTU individually
indicated that 79 of the 15,195 OTUs were significantly increased
or decreased in response to manure application in at least one
of these four soils (Figure 3B). In detail, soil A was the most
affected, with a strong increase in abundance of 21 OTU (+80%
of their abundance at least) and a slight decrease in abundance
of 51 OTU (-60% at most). In the presence of manure, soils
B and C presented increasing abundances only for eight and
nine OTUs and decreasing abundances for one and two OTUs,
respectively. Interestingly, a cluster of seven OTUs presented the
same pattern in soils A, B, and C. They were below or near the
detection limit without manure. Their abundance significantly
increased in manure-spread soils. Some of these OTUs (two
γ-Proteobacteria and five Firmicutes including four Clostridiales)
are known as fecal markers (Hagey et al., 2019). Moreover, they
were abundant in manure (Supplementary Table 5). According
to Gravuer and Scow (2021), those observations are signs
that these OTUs originate manure and are soil invaders. This
supports the hypothesis that manure brought exogenous OTUs
that compete with *Microbacterium* sp. C448 for establishment
in soil. As stated above, *Microbacterium* sp. C448 seems to be
a poor competitor in the studied soils. Simultaneous inputs
of manure and the antibiotrophic strain could thus induce
detrimental competition for *Microbacterium* sp. C448, in favor
of using SMZ as a nutritional support of choice. This could
thus explain why manure spreading led to a strong stimulation
of SMZ mineralization, which reached 19.5, 15.3, and 12.6%
in soils A, B, and C, respectively (Figure 2). On the contrary,
the soil D community structure was only slightly affected by

manure. Its diversity indices were not changed (Supplementary Figure 4) and the OTU-level analyses indicated that only four OTUs were significantly decreased, with no detected increase in individual OTU abundances (Figure 3B). Whether or not manure was added, soil D exhibited most of the seven OTUs belonging to the above cluster that was abundant in soils A, B, and C spread with manure (Supplementary Table 5). This initial presence in soil D might be due to its agronomical history, which involved regular application of manure as organic fertilizer (the latest occasion being 6 months before the experiment). Based on these results, we can hypothesize that the previous in-field manure exposure led to a pre-colonization of soil communities by manure OTUs (Gravuer and Scow, 2021), thus limiting further microbial disturbance in response to the experimental amendment of manure. It therefore suggests that most of the ecological niches made available by the biocidal effect of SMZ in this soil remained free for *Microbacterium* sp. C448, thus explaining, at least partially, the low SMZ mineralization rate (5.6 ± 0.7%) measured in soil D spread with manure (Figure 2).

CONCLUSION

The sulfonamide-resistant function confers a selective advantage
in competitive environments impacted by the biocidal action of
SMZ. Our study strongly suggests that sulfonamide-degrading
capacities could provide an additional selective advantage to
sulfonamide-resistant bacteria by enhancing their dispersion
potential. Besides eliminating sensitive trophic competitors,
and so releasing niches, SMZ can constitute an exclusive
trophic niche for sulfonamide-degraders. Our study shows that
antibiotrophy is mainly beneficial when the SMZ biocidal activity
on competitors is insufficient. Indeed, antibiotrophy promoted
*Microbacterium* sp. C448 establishment when the impact of SMZ
on soil microbial diversity and structure was low and when
outsider competitors were added by manure amendment. The
emergence of antibiotrophy among antibiotic-resistant bacteria
might promote their dispersion and survival in the environment
and thus might constitute a health concern. Antibiotrophy would
be expected to provide an environmental benefit by contributing
to the self-purifying of antibiotic-contaminated ecosystems. Yet,
in our experiment, SMZ degradation was neither high nor
automatic in soils with antibiotrophs. Further research is needed
to better assess the role of the antibiotroph function in the
dispersion of antibiotic-resistant bacteria in the environment.
One of the possible ways to achieve this goal will be to compare
the dispersion potential of the wild-type strain to that of a mutant
unable to degrade and grow on SMZ.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online
repositories. The names of the repository/repositories
and accession number(s) can be found below:
https://www.ncbi.nlm.nih.gov/, SUB8712901. The latest accession in the SRA at NCBI number is: PRJNA684125.

for helpful discussions within the framework of the ANTIBIOTOX project.

## AUTHOR CONTRIBUTIONS

LB, SP, MD-L, and FM-L contributed to the conception and design of the study. LB, NR, and MD-L performed the laboratory experiments. LB, MD-L, AS, and AM analyzed the results. LB, SP, and MD-L wrote the first draft of the manuscript. LP, ML, and PB-H developed the analytical methods for the SMZ quantification in soil and manure and analyzed the results. LP and PB-H prepared the soil and manure samples, and ML carried out the LC-MS analyses. All authors contributed to manuscript revision and read and approved the submitted version.

## FUNDING

This research was funded by the French National Research Agency (ANTIBIOTOX project, ref: ANR-17-CE34-0003).

## ACKNOWLEDGMENTS

The authors thank Dr. Ed Topp and his team for previous characterization studies of *Microbacterium* sp. C448 and

## REFERENCES

Aislabie, J., Richards, N., and Boul, H. (1997). Microbial degradation of DDT and its residues—A review. *N. Z. J. Agric. Res.* 40, 269–282. doi: 10.1080/00288233.1997.9513247

Antunes, P., Machado, J., Sousa, J. C., and Peixe, L. (2005). Dissemination of sulfonamide resistance genes (sul1, sul2, and sul3) in Portuguese *Salmonella enterica* strains and relation with integrons. *Antimicrob. Agents Chemother.* 49, 836–839. doi: 10.1128/AAC.49.2.836-839.2005

Barnhill, A. E., Weeks, K. E., Xiong, N., Day, T. A., and Carlson, S. A. (2010). Identification of multiresistant *Salmonella* isolates capable of subsisting on antibiotics. *Appl. Environ. Microbiol.* 76, 2678–2680. doi: 10.1128/AEM.02516-09

Behki, R. M., and Khan, S. U. (1986). Degradation of atrazine by *Pseudomonas*: N-dealkylation and dehalogenation of atrazine and its metabolites. *J. Agric. Food Chem.* 34, 746–749. doi: 10.1021/jf00070a039

Bengtsson-Palme, J., Kristiansson, E., and Larsson, D. G. J. (2018). Environmental factors influencing the development and spread of antibiotic resistance. *FEMS Microbiol. Rev.* 42:fux053. doi: 10.1093/femsre/fux053

Bengtsson-Palme, J., and Larsson, D. G. J. (2016). Concentrations of antibiotics predicted to select for resistant bacteria: proposed limits for environmental regulation. *Environ. Int.* 86, 140–149. doi: 10.1016/j.envint.2015.10.015

Benimeli, C. S., Fuentes, M. S., Abate, C. M., and Amoroso, M. J. (2008). Bioremediation of lindane-contaminated soil by Streptomyces sp. M7 and its effects on Zea mays growth. *Int. Biodeter. Biodegr.* 61, 233–239. doi: 10.1016/j.ibiod.2007.09.001

Bouju, H., Ricken, B., Beffa, T., Corvini, P. F.-X., and Kolvenbach, B. A. (2012). Isolation of bacterial strains capable of sulfamethoxazole mineralization from an acclimated membrane bioreactor. *Appl. Environ. Microbiol.* 78, 277–279. doi: 10.1128/AEM.05888-11

Brandt, K. K., Sjøholm, O. R., Krogh, K. A., Halling-Sørensen, B., and Nybroe, O. (2009). Increased pollution-induced bacterial community tolerance to

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.643087/full#supplementary-material

**Supplementary Figure 1** | Experimental design of soil microcosm experiment with ¹²C-SMZ or ¹⁴C-SMZ, consisting of amendment or not with manure, treatment.

**Supplementary Table 1** | GPS coordinates and physical-chemical properties of the four different soils used in the study.

**Supplementary Table 2** | Primers and probe used in this study.

**Supplementary Table 3** | Mass balances of ¹⁴C-radioactivity in the four different soils treated with SMZ, amended or not with manure, and inoculated or not with *Microbacterium* sp. C448 (C448) obtained at the end of the month of incubation. Between 75% and 100% of the initially added radioactivity was retrieved. Results are expressed in percent of ¹⁴C-SMZ initially retrieved.

**Supplementary Table 4** | Alpha diversity indices of the four different soils amended or not with manure, treated or not with SMZ and inoculated or not with *Microbacterium* sp. C448 (C448).

**Supplementary Table 5** | Initial abundance and phylogeny (phylum, class, order) of the seven OTUs highly likely to come from manure in the four soils and in the manure.

sulfadiazine in soil hotspots amended with artificial root exudates. *Environ. Sci. Technol.* 43, 2963–2968. doi: 10.1021/es803546y

Brown, G. M. (1962). The biosynthesis of folic acid. II. Inhibition by sulfonamides. *J. Biol. Chem.* 237, 536–540.

Browning, D. F., and Busby, S. J. (2004). The regulation of bacterial transcription initiation. *Nat. Rev. Microbiol.* 2, 57–65. doi: 10.1038/nrmicro787

Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., et al. (2010). QIIME allows analysis of high-throughput community sequencing data. *Nat. Methods* 7, 335–336. doi: 10.1038/nmeth.f.303

Chen, J., Jiang, X., Tong, T., Miao, S., Huang, J., and Xie, S. (2019). Sulfadiazine degradation in soils: dynamics, functional gene, antibiotic resistance genes and microbial community. *Sci. Total Environ.* 691, 1072–1081. doi: 10.1016/j.scitotenv.2019.07.230

Cid, C., Álvarez-Esmorís, C., Paradelo Núñez, R., Nóvoa-Muñoz, J. C., Arias-Estévez, M., Álvarez-Rodríguez, E., et al. (2018). Occurrence of tetracyclines and sulfonamides in manures, agricultural soils and crops from different areas in Galicia (NW Spain). *J. Clean. Prod.* 197, 491–500. doi: 10.1016/j.jclepro.2018.06.217

Cleary, D. W., Bishop, A. H., Zhang, L., Topp, E., Wellington, E. M. H., and Gaze, W. H. (2016). Long-term antibiotic exposure in soil is associated with changes in microbial community structure and prevalence of class 1 integrons. *FEMS Microbiol. Ecol.* 92:fiw159. doi: 10.1093/femsec/fiw159

Copley, S. D. (2000). Evolution of a metabolic pathway for degradation of a toxic xenobiotic: the patchwork approach. *Trends Biochem. Sci.* 25, 261–265. doi: 10.1016/s0968-0004(00)01562-0

Corona, F., and Martinez, J. L. (2013). Phenotypic resistance to antibiotics. *Antibiotics* 2, 237–255. doi: 10.3390/antibiotics2020237

Costanzo, S. D., Murby, J., and Bates, J. (2005). Ecosystem response to antibiotics entering the aquatic environment. *Mar. Pollut. Bull.* 51, 218–223. doi: 10.1016/j.marpolbul.2004.10.038

Crofts, T. S., Wang, B., Spivak, A., Gianoulis, T. A., Forsberg, K. J., Gibson, M. K., et al. (2018). Shared strategies for β-lactam catabolism in the soil microbiome. *Nat. Chem. Biol.* 14, 556–564. doi: 10.1038/s41589-018-0052-1
Cycoń, M., Mrozik, A., and Piotrowska-Seget, Z. (2019). Antibiotics in the soil environment-degradation and their impact on microbial activity and diversity. *Front. Microbiol.* 10:338. doi: 10.3389/fmicb.2019.00338

Ding, C., and He, J. (2010). Effect of antibiotics in the environment on microbial populations. *Appl. Microbiol. Biotechnol.* 87, 925–941. doi: 10.1007/s00253-010-2649-5

Feng, L., Casas, M. E., Ottosen, L. D. M., Møller, H. B., and Bester, K. (2017). Removal of antibiotics during the anaerobic digestion of pig manure. *Sci. Total Environ.* 603–604, 219–225. doi: 10.1016/j.scitotenv.2017.05.280

Gao, J., and Pedersen, J. A. (2005). Adsorption of sulfonamide antimicrobial agents to clay minerals. *Environ. Sci. Technol.* 39, 9509–9516. doi: 10.1021/es050644c

Gao, P., Wei, X., Gu, C., Wu, X., Xue, G., Shi, W., et al. (2015). Isolation and characterization of an erythromycin-degrading strain and application for bioaugmentation in a biological aerated filter. *Water. Air. Soil Pollut.* 226:190. doi: 10.1007/s11270-015-2449-8

Ghirardini, A., Grillini, V., and Verlicchi, P. (2020). A review of the occurrence of selected micropollutants and microorganisms in different raw and treated manure - environmental risk due to antibiotics after application to soil. *Sci. Total Environ.* 707:136118. doi: 10.1016/j.scitotenv.2019.136118

Gisi, D., Stucki, G., and Hanselmann, K. W. (1997). Biodegradation of the pesticide 4,6-dinitro-ortho-cresol by microorganisms in batch cultures and in fixed-bed column reactors. *Appl. Microbiol. Biotechnol.* 48, 441–448. doi: 10.1007/s002530051077

Gold, R. S., Wales, M. E., Grimsley, J. K., and Wild, J. R. (2000). “Ancillary function of housekeeping enzymes: fortuitous degradation of environmental contaminants,” in *Enzymes in Action: Green Solutions for Chemical Problems*, eds B. Zwanenburg, M. Mikolajczyk, and P. Kielbasiński (Dordrecht: Springer Netherlands), 263–286. doi: 10.1007/978-94-010-0924-9_13

Gravuer, K., and Scow, K. M. (2021). Invader-resident relatedness and soil management history shape patterns of invasion of compost microbial populations into agricultural soils. *Appl. Soil Ecol.* 158:103795. doi: 10.1016/j.apsoil.2020.103795

Hagey, J., Bhatnagar, S., Heguy, J. M., Karle, B. M., Price, P. L., Meyer, D., et al. (2019). Fecal microbial communities in a large representative cohort of California dairy cows. *Front. Microbiol.* 10:1093. doi: 10.3389/fmicb.2019.01093

Haller, M. Y., Müller, S. R., McArdell, C. S., Alder, A. C., and Suter, M. J. F. (2002). Quantification of veterinary antibiotics (sulfonamides and trimethoprim) in animal manure by liquid chromatography-mass spectrometry. *J. Chromatogr.* A 952, 111–120. doi: 10.1016/s0021-9673(02)00083-3

Hammesfahr, U., Bierl, R., and Thiele-Bruhn, S. (2011). Combined effects of the antibiotic sulfadiazine and liquid manure on the soil microbial-community structure and functions. *J. Plant Nutr. Soil Sci.* 174, 614–623. doi: 10.1002/jpln. 201000322

Hawkshead, J. (2008). Hospital wastewater containing pharmaceutically active compounds and drug-resistant organisms: a source of environmental toxicity and increased antibiotic resistance. *J. Residuals Sci. Technol.* 5, 51.

Heuer, H., Schmitt, H., and Smalla, K. (2011). Antibiotic resistance gene spread due to manure application on agricultural fields. *Curr. Opin. Microbiol.* 14, 236–243. doi: 10.1016/j.mib.2011.04.009

Hirth, N., Topp, E., Dörfler, U., Stupperich, E., Munch, J. C., and Schroll, R. (2016). An effective bioremediation approach for enhanced microbial degradation of the veterinary antibiotic sulfamethazine in an agricultural soil. *Chem. Biol. Technol. Agr.* 3:29. doi: 10.1186/s40538-016-0080-6

Hoa, P. T. P., Managaki, S., Nakada, N., Takada, H., Shimizu, A., Anh, D. H., et al. (2011). Antibiotic contamination and occurrence of antibiotic-resistant bacteria in aquatic environments of northern Vietnam. *Sci. Total Environ.* 409, 2894–2901. doi: 10.1016/j.scitotenv.2011.04.030

Hong, Q., Zhang, Z., Hong, Y., and Li, S. (2007). A microcosm study on bioremediation of fenitrothion-contaminated soil using Burkholderia sp. FDS-1. *Int. Biodeter. Biodegr.* 59, 55–61. doi: 10.1016/j.ibiod.2006.07.013

Janssen, D. B., Dinkla, I. J. T., Poelarends, G. J., and Terpstra, P. (2005). Bacterial degradation of xenobiotic compounds: evolution and distribution of novel enzyme activities. *Environ. Microbiol.* 7, 1868–1882. doi: 10.1111/j.1462-2920. 2005.00966.x

Karpouzas, D. G., and Walker, A. (2000). Factors influencing the ability of *Pseudomonas* putida epl to degrade ethrophosins in soil. *Soil Biol. Biochem.* 32, 1753–1762. doi: 10.1016/S0038-0717(00)00093-6

Kim, D.-W., Thawng, C. N., Lee, K., Wellington, E. M. H., and Cha, C.-J. (2019). A novel sulfonamide resistance mechanism by two-component flavin-dependent monooxygenase system in sulfonamide-degrading actinobacteria. *Environ. Int.* 127, 206–215. doi: 10.1016/j.envint.2019.03.046

Kumar, M., Jaiswal, S., Sodhi, K. K., Shree, P., Singh, D. K., Agrawal, P. K., et al. (2019). Antibiotics bioremediation: perspectives on its ecotoxicity and resistance. *Environ. Int.* 124, 448–461. doi: 10.1016/j.envint.2018.12.065

Kümmerer, K. (2001). Drugs in the environment: emission of drugs, diagnostic aids and disinfectants into wastewater by hospitals in relation to other sources-a review. *Chemosphere* 45, 957–969. doi: 10.1016/s0045-6535(01)00144-8

Lamshöft, M., Sukul, P., Zühlke, S., and Spiteller, M. (2007). Metabolism of 14C-labelled and non-labelled sulfadiazine after administration to pigs. *Anal. Bioanal. Chem.* 388, 1733–1745. doi: 10.1007/s00216-007-1368-y

Leng, Y., Bao, J., Chang, G., Zheng, H., Li, X., Du, J., et al. (2016). Biotransformation of tetracycline by a novel bacterial strain *Stenotrophomonas maltophilia* DT1. *J. Hazard. Mater.* 318, 125–133. doi: 10.1016/j.jhazmat.2016.06.053

Lertpaitoonpan, W., Ong, S. K., and Moorman, T. B. (2009). Effect of organic carbon and pH on soil sorption of sulfamethazine. *Chemosphere* 76, 558–564. doi: 10.1016/j.chemosphere.2009.02.066

Lin, B., Lyu, J., Lyu, X., Yu, H., Hu, Z., Lam, J. C. W., et al. (2015). Characterization of cefalexin degradation capabilities of two *Pseudomonas* strains isolated from activated sludge. *J. Hazard. Mater.* 282, 158–164. doi: 10.1016/j.jhazmat.2014. 06.080

Liu, F., Ying, G.-G., Tao, R., Zhao, J.-L., Yang, J.-F., and Zhao, L.-F. (2009). Effects of six selected antibiotics on plant growth and soil microbial and enzymatic activities. *Environ. Pollut.* 157, 1636–1642. doi: 10.1016/j.envpol.2008.12.021

Luo, Y., Mao, D., Rysz, M., Zhou, Q., Zhang, H., Xu, L., et al. (2010). Trends in antibiotic resistance genes occurrence in the Haihe River, China. *Environ. Sci. Technol.* 44, 7220–7225. doi: 10.1021/es100233w

Malwal, S. R., Sriram, D., Yogeeswari, P., and Chakrapani, H. (2012). Synthesis and antimycobacterial activity of prodrugs of sulfur dioxide (SO2). *Bioorg. Med. Chem. Lett.* 22, 3603–3606. doi: 10.1016/j.bmcl.2012.04.048

Martin-Laurent, F., Marti, R., Waglechner, N., Wright, G. D., and Topp, E. (2014). Draft genome sequence of the sulfonamide antibiotic-degrading Microbacterium sp. strain C448. *Genome Announc.* 2, e1113–e1113. doi: 10.1128/genomeA.01113-13

McDonald, D., Price, M. N., Goodrich, J., Nawrocki, E. P., DeSantis, T. Z., Probst, A., et al. (2012). An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. *ISME J.* 6, 610–618. doi: 10.1038/ismej.2011.139

McKinney, C. W., Loftin, K. A., Meyer, M. T., Davis, J. G., and Pruden, A. (2010). tet and sul antibiotic resistance genes in livestock lagoons of various operation type, configuration, and antibiotic occurrence. *Environ. Sci. Technol.* 44, 6102–6109. doi: 10.1021/es9038165

Mitchell, S. M., Ullman, J. L., Teel, A. L., and Watts, R. J. (2015). Hydrolysis of amphenicol and macrolide antibiotics: chloramphenicol, florfenicol, spiramycin, and tylosin. *Chemosphere* 134, 504–511. doi: 10.1016/j.chemosphere.2014.08.050

Nawrocki, E. P., and Eddy, S. R. (2013). Infernal 1.1: 100-fold faster RNA homology searches. *Bioinformatics* 29, 2933–2935. doi: 10.1093/bioinformatics/btt509

Nishiyama, M., Senoo, K., and Matsumoto, S. (1993). gamma-1,2,3,4,5,6- Hexachlorocyclohexane-assimilating bacterium, *Sphingomonas paucimobilis* strain SS86, in soil. *Soil Biol. Biochem.* 25, 769–774.

Pei, R., Kim, S.-C., Carlson, K. H., and Pruden, A. (2006). Effect of river landscape on the sediment concentrations of antibiotics and corresponding antibiotic resistance genes (ARG). *Water Res.* 40, 2427–2435. doi: 10.1016/j.watres.2006. 04.017

Peng, X., Wang, Z., Kuang, W., Tan, J., and Li, K. (2006). A preliminary study on the occurrence and behavior of sulfonamides, ofloxacin and chloramphenicol antimicrobials in wastewaters of two sewage treatment plants in Guangzhou, China. *Sci. Total Environ.* 371, 314–322. doi: 10.1016/j.scitotenv.2006.07.001

Perry, J., and Wright, G. (2013). The antibiotic resistance "mobilome": searching for the link between environment and clinic. *Front. Microbiol.* 4:138. doi: 10. 3389/fmicb.2013.00138

Pesce, S., Ghiglione, J.-F., Topp, E., and Martin-Laurent, F. (2020). Editorial: microbial ecotoxicology. *Front. Microbiol.* 11:1342. doi: 10.3389/fmicb.2020. 01342
Pietramellara, G., Ascher-Jenull, J., Borgogni, F., Ceccherini, M. T., Guerri, G., and Nannipieri, P. (2008). Extracellular DNA in soil and sediment: fate and ecological relevance. *Biol. Fertil Soils* 45, 219–235. doi: 10.1007/s00374-008-0345-8

Pinna, M. V., Castaldi, P., Deiana, P., Pusino, A., and Garau, G. (2012). Sorption behavior of sulfamethazine on unamended and manure-amended soils and short-term impact on soil microbial community. *Ecotoxicol. Environ. Saf.* 84, 234–242. doi: 10.1016/j.ecoenv.2012.07.006

Price, M. N., Dehal, P. S., and Arkin, A. P. (2010). FastTree 2-approximately maximum-likelihood trees for large alignments. *PLoS One* 5:e9490. doi: 10.1371/journal.pone.0009490

Proia, L., Lupini, G., Osorio, V., Pérez, S., Barceló, D., Schwartz, T., et al. (2013). Response of biofilm bacterial communities to antibiotic pollutants in a Mediterranean river. *Chemosphere* 92, 1126–1135. doi: 10.1016/j.chemosphere.2013.01.063

Rani, N. L., and Lalithakumari, D. (1994). Degradation of methyl parathion by *Pseudomonas* putida. *Can. J. Microbiol.* 40, 1000–1006. doi: 10.1139/m94-160

Reichel, R., Michelini, L., Ghisi, R., and Sören, T.-B. (2015). Soil bacterial community response to sulfadiazine in the soil-root zone. *J. Plant Nutr. Soil Sci.* 178, 499–506. doi: 10.1002/jpln.201400352

Reis, A. C., Kolvenbach, B. A., Chami, M., Gales, L., Egas, C., Corvini, P. F.-X., et al. (2019). Comparative genomics reveals a novel genetic organization of the sad cluster in the sulfonamide-degrader 'Candidatus Leucobacter sulfamidivorax' strain GP. *BMC Genom.* 20:885. doi: 10.1186/s12864-019-6206-z

Reis, P., Reis, A., Ricken, B., Kolvenbach, B., Manaia, C., Corvini, P., et al. (2014). Biodegradation of sulfamethoxazole and other sulfonamides by *Achromobacter* denitrificans PR1. *J. Hazard. Mater.* 280, 741–749. doi: 10.1016/j.jhazmat.2014.08.039

Ricken, B., Kolvenbach, B. A., Bergesch, C., Benndorf, D., Kroll, K., Strnad, H., et al. (2017). FMNH2-dependent monooxygenases initiate catabolism of sulfonamides in *Microbacterium* sp. strain BR1 subsisting on sulfonamide antibiotics. *Sci. Rep.* 7:15783. doi: 10.1038/s41598-017-16132-8

Rodriguez-Mozaz, S., Vaz-Moreira, I., Varela Della Giustina, S., Llorca, M., Barceló, D., Schubert, S., et al. (2020). Antibiotic residues in final effluents of European wastewater treatment plants and their impact on the aquatic environment. *Environ. Int.* 140:105733. doi: 10.1016/j.envint.2020.105733

Rognes, T., Flouri, T., Nichols, B., Quince, C., and Mahé, F. (2016). VSEARCH: a versatile open source tool for metagenomics. *PeerJ* 4:e2584. doi: 10.7717/peerj.2584

Schmitt, H., Haapakangas, H., and van Beelen, P. (2005). Effects of antibiotics on soil microorganisms: time and nutrients influence pollution-induced community tolerance. *Soil Biol. Biochem.* 37, 1882–1892. doi: 10.1016/j.soilbio. 2005.02.022

Schmitt, H., van Beelen, P., Tolls, J., and van Leeuwen, C. L. (2004). Pollution-induced community tolerance of soil microbial communities caused by the antibiotic sulfachloropyridazine. *Environ. Sci. Technol.* 38, 1148–1153. doi: 10.1021/es034685p

Shah, S. S. A., Rivera, G., and Ashfaq, M. (2013). Recent advances in medicinal chemistry of sulfonamides. Rational design as anti-tumoral, anti-bacterial and anti-inflammatory agents. *Mini Rev. Med. Chem.* 13, 70–86.

Shan, J., Yang, P., Rahman, M. M., Shang, X., and Yan, X. (2018). Tetracycline and sulfamethazine alter dissimilatory nitrate reduction processes and increase N2O release in rice fields. *Environ. Poll.* 242, 788–796. doi: 10.1016/j.envpol.2018.07.061

Sharma, B., Dangi, A. K., and Shukla, P. (2018). Contemporary enzyme based technologies for bioremediation: a review. *J. Environ. Manag.* 210, 10–22. doi: 10.1016/j.jenvman.2017.12.075

Silva, V., Correia, S., Pereira, J. E., Igrejas, G., and Poeta, P. (2020). “Surveillance and environmental risk assessment of antibiotics and amr/args related with mrsa: one health perspective,” in *Antibiotics and Antimicrobial Resistance Genes: Environmental Occurrence and Treatment Technologies*, ed. M. Z. Hashmi (Cham: Springer International Publishing), 271–295. doi: 10.1007/978-3-030-40422-2_13

Spor, A., Camargo, A. R. O., Bru, D., Gaba, S., Garmyn, D., Gal, L., et al. (2020). Habitat disturbances modulate the barrier effect of resident soil microbiota on listeria monocytogenes invasion success. *Front. Microbiol.* 11:927. doi: 10.3389/fmicb.2020.00927

Stoll, C., Sidhu, J. P. S., Tiehm, A., and Toze, S. (2012). Prevalence of clinically relevant antibiotic resistance genes in surface water samples collected from Germany and Australia. *Environ. Sci. Technol.* 46, 9716–9726. doi: 10.1021/es302020s

Tappe, W., Herbst, M., Hofmann, D., Koeppchen, S., Kummer, S., Thiele, B., et al. (2013). Degradation of sulfadiazine by *Microbacterium* lacus strain SDZm4, isolated from lysimeters previously manured with slurry from sulfadiazine-medicated pigs. *Appl. Environ. Microbiol.* 79, 2572–2577. doi: 10.1128/AEM.03636-12

Topp, E., Chapman, R., Devers-Lamrani, M., Hartmann, A., Marti, R., Martin-Laurent, F., et al. (2013). Accelerated biodegradation of veterinary antibiotics in agricultural soil following long-term exposure, and isolation of a sulfamethazine-degrading sp. *J. Environ. Qual.* 42, 173–178. doi: 10.2134/jeq2012.0162

van Elsas, J. D., Chiurazzi, M., Mallon, C. A., Elhottovà, D., Krištúfek, V., and Salles, J. F. (2012). Microbial diversity determines the invasion of soil by a bacterial pathogen. *Proc. Natl. Acad. Sci. U.S.A.* 109, 1159–1164. doi: 10.1073/pnas.1109326109

van Veen, J. A., van Overbeek, L. S., and van Elsas, J. D. (1997). Fate and activity of microorganisms introduced into soil. *Microbiol. Mol. Biol. Rev.* 61, 121–135.

Wang, Q., Garrity, G. M., Tiedje, J. M., and Cole, J. R. (2007). Naïve bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. *Appl. Environ. Microbiol.* 73, 5261–5267. doi: 10.1128/AEM.00062-07

Watkinson, A., Murby, E. J., and Costanzo, S. (2007). Removal of antibiotics in conventional and advanced wastewater treatment: implications for environmental discharge and wastewater recycling. *Water Res.* 41, 4164–4176. doi: 10.1016/j.watres.2007.04.005

Wei, Z., Feng, K., Li, S., Zhang, Y., Chen, H., Yin, H., et al. (2018). Exploring abundance, diversity and variation of a widespread antibiotic resistance gene in wastewater treatment plants. *Environ. Int.* 117, 186–195. doi: 10.1016/j.envint. 2018.05.009

Wise, E. M., and Abou-Donia, M. M. (1975). Sulfonamide resistance mechanism in *Escherichia coli*: R plasmids can determine sulfonamide-resistant dihydropteroate synthases. *Proc. Natl. Acad. Sci. U. S. A.* 72, 2621–2625. doi: 10.1073/pnas.72.7.2621

Woappi, Y., Gabani, P., Singh, A., and Singh, O. V. (2016). Antibiotrophs: the complexity of antibiotic-subsisting and antibiotic-resistant microorganisms. *Crit. Rev. Microbiol.* 42, 17–30. doi: 10.3109/1040841X.2013.875982

Xin, Z., Fengwei, T., Gang, W., Xiaoming, L., Qiuxiang, Z., Hao, Z., et al. (2012). Isolation, identification and characterization of human intestinal bacteria with the ability to utilize chloramphenicol as the sole source of carbon and energy. *FEMS Microbiol. Ecol.* 82, 703–712. doi: 10.1111/j.1574-6941.2012.01440.x

Yun, M.-K., Wu, Y., Li, Z., Zhao, Y., Waddell, M. B., Ferreira, A. M., et al. (2012). Catalysis and sulfa drug resistance in dihydropteroate synthase. *Science* 335, 1110–1114. doi: 10.1126/science.1214641

Zhang, J., Kobert, K., Flouri, T., and Stamatakis, A. (2014). PEAR: a fast and accurate illumina paired-end reAd mergeR. *Bioinforma. Oxf. Engl.* 30, 614–620. doi: 10.1093/bioinformatics/btt593

Zhang, Y., Marrs, C. F., Simon, C., and Xi, C. (2009). Wastewater treatment contributes to selective increase of antibiotic resistance among *Acinetobacter* spp. *Sci. Total Environ.* 407, 3702–3706. doi: 10.1016/j.scitotenv.2009.02.013

Zrimec, J. (2020). Multiple plasmid origin-of-transfer regions might aid the spread of antimicrobial resistance to human pathogens. *Microbiologyopen* 9:e1129. doi: 10.1002/mbo.31129

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

Copyright © 2021 Billet, Pesce, Rouard, Spor, Paris, Leremboure, Mounier, Besse-Hoggan, Martin-Laurent and Devers-Lamrani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Environmental Concentrations of Sulfonamides Can Alter Bacterial Structure and Induce Diatom Deformities in Freshwater Biofilm Communities

Laura Kergoat<fnref n="1" />, Pascale Besse-Hoggan², Martin Leremboure², Jérémie Beguet³, Marion Devers³, Fabrice Martin-Laurent³, Matthieu Masson<fnref n="1" />, Soizic Morin⁴, Amélie Roinat<fnref n="1" />, Stéphane Pesce<fnref n="1" /> and Chloé Bonnineau<fnref n="1" />*

## OPEN ACCESS

**Edited by:**
Obulisamy Parthiba Karthikeyan,
University of Houston, United States

**Reviewed by:**
Nikolina Udikovic-Kolic,
Rudjer Boskovic Institute, Croatia
Yuyi Yang,
Chinese Academy of Sciences, China

**\*Correspondence:**
Chloé Bonnineau
chloe.bonnineau@inrae.fr

**Specialty section:**
This article was submitted to Microbiotechnology, a section of the journal Frontiers in Microbiology

**Received:** 18 December 2020

**Accepted:** 22 February 2021

**Published:** 07 May 2021

**Citation:**
Kergoat L, Besse-Hoggan P, Leremboure M, Beguet J, Devers M, Martin-Laurent F, Masson M, Morin S, Roinat A, Pesce S and Bonnineau C (2021) Environmental Concentrations of Sulfonamides Can Alter Bacterial Structure and Induce Diatom Deformities in Freshwater Biofilm Communities. *Front. Microbiol.* 12:643719.
doi: 10.3389/fmicb.2021.643719

Since the early 1920s, the intensive use of antibiotics has led to the contamination of the aquatic environment through diffuse sources and wastewater effluents. The antibiotics commonly found in surface waters include sulfamethoxazole (SMX) and sulfamethazine (SMZ), which belong to the class of sulfonamides, the oldest antibiotic class still in use. These antibiotics have been detected in all European surface waters with median concentrations of around 50 ng L⁻<fnref n="1" /> and peak concentrations of up to 4–6 µg L⁻<fnref n="1" />. Sulfonamides are known to inhibit bacterial growth by altering microbial production of folic acid, but sub-lethal doses may trigger antimicrobial resistance, with unknown consequences for exposed microbial communities. We investigated the effects of two environmentally relevant concentrations (500 and 5,000 ng L⁻<fnref n="1" />) of SMZ and SMX on microbial activity and structure of periphytic biofilms in stream mesocosms for 28 days. Measurement of sulfonamides in the mesocosms revealed contamination levels of about half the nominal concentrations. Exposure to sulfonamides led to slight, transitory effects on heterotrophic functions, but persistent effects were observed on the bacterial structure. After 4 weeks of exposure, sulfonamides also altered the autotrophs in periphyton and particularly the diversity, viability and cell integrity of the diatom community. The higher concentration of SMX tested decreased both diversity (Shannon index) and evenness of the diatom community. Exposure to SMZ reduced diatom species richness and diversity. The mortality of diatoms in biofilms exposed to sulfonamides was twice that in non-exposed biofilms. SMZ also induced an increase in diatom teratologies from 1.1% in non-exposed biofilms up to 3% in biofilms exposed to SMZ. To our knowledge, this is the first report on the teratological effects of sulfonamides on diatoms within periphyton. The increase of both diatom growth rate and mortality suggests a high renewal of diatoms under sulfonamide exposure. In conclusion, our

<footnote n="1">¹ INRAE, UR RiverLy, Villeurbanne, France, ² Université Clermont Auvergne, CNRS, Sigma Clermont, Institut de Chimie de Clermont-Ferrand, Clermont-Ferrand, France, ³ AgroSup Dijon, INRAE, Univ. Bourgogne Franche-Comté, Agroécologie, Dijon, France, ⁴ INRAE, UR EABX, Cestas, France</footnote>
study shows that sulfonamides can alter microbial community structures and diversity at concentrations currently present in the environment, with unknown consequences for the ecosystem. The experimental set-up presented here emphasizes the interest of using natural communities to increase the ecological realism of ecotoxicological studies and to detect potential toxic effects on non-target species.

**Keywords:** periphyton, antibiotic, teratogenicity, microbial ecotoxicology, sulfamethazine, sulfamethoxazole

INTRODUCTION

The number of scientific articles on the occurrence and impact of pharmaceutical residues in the environment has greatly increased since the early 2000s (Daughton, 2016), illustrating a growing concern with the wide dissemination of these biologically active substances in the environment and their potential impact on ecosystems. Pharmaceutical residues can reach freshwater ecosystems directly from aquaculture, run-off from agricultural soils amended with organic matter or from the effluents of wastewater treatment plants, pharmaceutical manufacturing plants and animal husbandry (Halling-Sørensen et al., 1998; Paíga et al., 2016; Mandaric et al., 2017). Numerous monitoring studies have shown the wide distribution of pharmaceutical products and their transformation products in European freshwaters at concentrations ranging from ng L⁻¹ up to µg L⁻¹, with a high proportion of antibiotics (for more details, see the reviews of Carvalho and Santos, 2016; Szymańska et al., 2019).

The work reported here focuses on sulfonamides, one of the oldest antibiotic classes still in use for both animal and human health care. Sulfonamides are bacteriostatic compounds that inhibit the growth of microbial organisms (e.g., protozoa, fungi, certain bacteria; Straub, 2016) that rely on folate synthesis for the synthesis of purine and pyrimidine nucleotides (McDermott et al., 2003). The occurrence of sulfonamides in European surface waters is well documented (e.g., Kovalakova et al., 2020): in a review on environmental risk assessment of sulfamethoxazole, Straub (2016) found that in most studies, sulfamethoxazole concentrations in surface water were <286 ng L⁻¹ with a median of 52 ng L⁻¹ (n = 5,536 surface water samples). A few measured environmental concentrations reached higher levels, up to 4 µg L⁻¹, such concentrations being mainly found in streams where wastewater treatment plant effluents were released (Straub, 2016). Sulfamethazine concentrations in European freshwaters are in the range of those observed for sulfamethoxazole (see Figure 1 in Charuaud et al., 2019). However, this antibiotic has also been found at concentrations of up to 6 µg L⁻¹ in the Llobregat river (Spain) by Díaz-Cruz et al. (2008) and up to 12 µg L⁻¹ in a Croatian river receiving industrial waste water (Bielen et al., 2017).

A major concern regarding the general contamination of freshwaters (and other environmental compartments) by sulfonamides (and other antibiotics) is the increasing occurrence of microbial antibiotic resistance in the environment (Martin-Laurent et al., 2019). At sub-lethal doses, antibiotics exert a selection pressure that will promote the emergence and development of antibiotic resistance (for a complete review

on antibiotic resistance mechanisms, see McDermott et al., 2003). Resistance to sulfonamides is generally conferred by the acquisition of an enzyme insensitive to sulfonamides that restores the folic acid synthesis pathway (McDermott et al., 2003). This enzyme is encoded by *sul* genes (i.e., *sul1*, *sul2*, and *sul3*), generally found on a plasmid, which facilitates their dissemination by horizontal gene transfer across microbial communities (Sköld, 2000; Perreten and Boerlin, 2003). Antibiotic resistance genes are frequently detected in natural microbial communities (Martínez, 2008) in both periphytic biofilms (e.g., Di Cesare et al., 2017; Guo et al., 2018) and sediments (e.g., Marti et al., 2013; Czekalski et al., 2014; Calero-Cáceres et al., 2017) with higher levels being detected downstream from the discharge of effluents of wastewater treatment plants (Proia et al., 2016). The dissemination and occurrence of antibiotic resistance genes in aquatic ecosystems are likely to be a threat for environmental health and have led several authors to qualify antibiotic resistance genes as emerging contaminants (Sanderson et al., 2016; O'Flaherty and Cummins, 2017).

Besides their implication in the dissemination of antibiotic resistance genes, antibiotics could have a direct toxic impact on aquatic organisms. Ecotoxicity of sulfamethoxazole in a wide range of aquatic model species is well known, with 236 entries for NOEC (no observed effect concentration) in the ECOTOX database of US EPA (U.S. Environmental Protection Agency, 2019). Focusing on endpoints directly related to population dynamics (i.e., growth, reproduction, survival, etc.), sulfamethoxazole NOECs range from 5.9 µg L⁻¹ for the cyanobacterium *Synechococcus leopoliensis* to 253 mg L⁻¹ for the protist *Tetrahymena pyriformis* (reviewed by Straub, 2016). Information on sulfamethazine ecotoxicity is scarce, with only three entries available for NOEC in the ECOTOX database of US EPA. Yang et al. (2008) reported a NOEC of 1 mg L⁻¹ for the growth of the microalga *Pseudokirchneriella subcapitata*. De Liguoro et al. (2009) reported a NOEC of 50 mg L⁻¹ for the survival of the crustacean *Daphnia magna*. The distribution of species sensitivity toward sulfonamides underlines a high sensitivity of cyanobacteria and microbial communities to this antibiotic (Straub, 2016; Charuaud et al., 2019).

In aquatic environments, antibiotics are generally present at lower concentrations than those producing bactericidal or bacteriostatic effects. Even so, this does not rule out an ecotoxicological risk for chronically exposed communities. For example, sub-lethal concentrations of antibiotics may act as signaling molecules (e.g., “danger signal,” Moon et al., 2017) and trigger specific responses (e.g., biofilm formation, Linares et al., 2006) different from the effects of inhibitory
concentrations (Fajardo and Martínez, 2008). Environmental concentrations of antibiotics might therefore affect natural microbial communities, with possible consequences for microbial diversity and functions and thereby for ecosystem processes supported by those communities.

Among aquatic microbial communities, periphytic biofilms are complex communities composed of bacteria, microalgae, fungi and protozoa, among other organisms. In lotic ecosystems, they play an essential role in important ecological processes including primary production and nutrient recycling (Battin et al., 2016). Previous studies have shown that environmental concentrations of antimicrobial agents and/or antibiotics can modify aquatic biofilm structure (e.g., triclosan in Lawrence et al., 2015) and function (e.g., tetracycline on bacterial productivity, Quinlan et al., 2011; triclosan on phosphate uptake capacity, Proia et al., 2013b). Microbial exposure to sulfamethoxazole and/or sulfamethazine has been shown to reduce microbial activity and/or inhibit extracellular enzymatic activities in soil (e.g., Liu et al., 2009; Frková et al., 2020). However, only a few authors have investigated the specific effects of these antibiotics on aquatic microbial community structure and functions (Yergeau et al., 2010, 2012; Johansson et al., 2014; Paumelle et al., 2021). Johansson et al. (2014) showed that sulfamethoxazole concentrations higher than 35 µg L⁻¹ could trigger modifications in C source utilization in marine biofilm exposed for 4 days. Yergeau et al. (2010) showed that 8 weeks exposure to environmental concentrations of sulfonamides (0.5 µg L⁻¹ of sulfamethoxazole or sulfamethazine) induced transcriptional changes in biofilm microbial communities. In a follow-up study, Yergeau et al. (2012) identified a negative impact of sulfamethoxazole on cyanobacteria abundance and on photosynthesis-related transcripts. Although sulfamethoxazole and sulfamethazine are structurally closely related and have the same modes of action, their effects on microbial communities are different in several ways (Yergeau et al., 2010, 2012).

Based on these findings, we made the following two hypotheses:

(1) Environmentally relevant concentrations of sulfamethoxazole and sulfamethazine will directly affect the bacterial structure (by exerting selection pressure toward resistant species) and functions (by reducing microbial activities) of periphytic biofilm.

(2) The ensuing changes to the bacterial community within the periphytic community will have indirect negative effects on the autotroph component.

To test these hypotheses, we investigated the effects of two levels of environmentally relevant concentrations of sulfamethazine (SMZ) and sulfamethoxazole (SMX), tested individually, on microbial activities and structure of natural periphytic biofilms cultivated in stream mesocosms for 28 days. The use of natural communities, such as periphytic biofilms, in controlled experiments is particularly useful for considering non-target species and biological interactions (Gessner and Tlili, 2016). This approach allowed us to increase the ecological realism of this ecotoxicological study. Microbial activities were assessed on a time scale by measuring functional parameters

of the heterotroph (extracellular enzymatic activities, microbial respiration) and autotroph (photosynthesis) components of the biofilm. Structural effects were studied at the end of the experiment by assessing the taxonomic structure of diatoms and the genetics of bacterial communities. After 28 days of exposure, the main effects of both sulfonamides were observed on bacterial structure and on the diversity, viability and cell integrity of the diatom community. In particular, exposure to SMZ induced an increase in diatom teratologies at both concentrations. Thus, our study shows that sulfonamides can alter microbial community structure and diversity at concentrations currently present in the environment.

# MATERIALS AND METHODS

## Biofilm Colonization

Natural biofilm was collected from the upstream Morcille river (Saint Joseph, 46°10′39.1″N 4°38′10.1″E) where the absence of any sulfonamide contamination in the sediment had previously been confirmed ([SMX] < 0.035 ng g⁻¹; [SMZ] < 0.005 ng g⁻¹). Biofilm was scraped from about 30 medium-sized stones and resuspended in river water before inoculation in aquaria to colonize glass slides (7.6 cm × 2.5 cm) for 22 days. Aquaria were filled with a mixture of demineralized water:groundwater (2:1 v/v, local groundwater used in previous experiments: Mahamoud Ahmed et al., 2018; Pesce et al., 2018) supplemented with K₂HPO₄ solution (0.1 mg L⁻¹). A submersible pump (NEWA Jet 600) ensured water recirculation in each aquarium, which was exposed to a stable temperature (20°C) and natural light.

## Mesocosm Experiment

The experiment was performed in 15 glass indoor channels (length × width × height = 83 × 10.5 × 10 cm) previously described (Supplementary Figure S1; Mahamoud Ahmed et al., 2018). The bottom of each channel was covered with 28 glass slides previously colonized by biofilm and filled with 10 L of recirculating water (2:1 v/v demineralized water:groundwater). Flow rate and temperature conditions were similar to those described above. Light-emitting diodes (Radiometrix modules, total luminous flux 25116 lm) provided stable light conditions (12/12 h light/dark photoperiod). Light intensity was recorded every hour by a data logger (HOBO® Pendant Temperature/light, Prosensor).

Three channels were exposed independently to one of the five different treatments tested:

1. Control: biofilm without antibiotic;

2. SMZ: biofilm exposed to SMZ at 500 ng L⁻¹ = 126 µM;

3. SMZ+: biofilm exposed to SMZ at 5,000 ng L⁻¹ = 1,266 µM;

4. SMZ: biofilm exposed to SMZ at 500 ng L⁻¹ = 139 µM;

5. SMZ+: biofilm exposed to 5,000 ng L⁻¹ = 1,391 µM.

These sulfonamide concentrations of 500 and 5,000 ng L⁻¹ were chosen to reflect environmental concentrations (Deng et al., 2018). Antibiotic stock solutions of SMZ (Sigma Aldrich, No-CAS: 723-46-6) or SMZ (Sigma Aldrich, No-CAS: 57-68-1) were
prepared in water (2:1 v/v demineralized water:groundwater) and frozen at -20°C (final concentration: 16.5 mg L⁻¹) before contamination.

To limit antibiotic loss by adsorption on the abiotic surfaces during the experiment, the experimental material was exposed to the corresponding concentrations of antibiotics for 4 days before the experiment started. Biofilm exposure to the five different treatments was then conducted for 4 weeks, and water was renewed every week using demineralized water:groundwater (2:1 v/v) phosphate-supplemented with K₂HPO₄ (final concentration 0.1 mg L⁻¹) and contaminated or not by SMX or SMZ according to the treatment.

## Water Analyses

Physical and chemical parameters of water from each channel were measured each week before water renewal. Dissolved oxygen, conductivity, pH and temperature were quantified using portable meters (WTW, Germany). Water samples were collected to determine the concentrations of silicon dioxide (SiO₂) according to NF T 90-007 and of chloride (Cl⁻), sulfate (SO₄²⁻), nitrite (NO₂⁻), nitrate (NO₃⁻) and orthophosphate (PO₄³⁻) ions according to EN ISO 10304-1.

Water samples (1 L) were also collected each week in each channel before water renewal and kept at -20°C before analyses of SMX and SMZ dissolved concentrations. The pH of a 100 mL water sample was adjusted to 4.0 ± 0.2 with HCl and 50 µL of 500 µg L⁻¹ SMX-d₄ and SMZ-d₄ solutions (Toronto Research Chemicals and LGC standards, respectively) was added. The sample was then concentrated 200-fold on an Oasis HLB-200 mg cartridge (Waters™) according to the manufacturer's recommendations (elution solvent methanol). The SMX and SMZ concentrations were determined by LC/ESI-MS on a Thermo Scientific UHPLC Ultimate 3000 RSLC coupled to an Orbitrap Q-Exactive Analyzer. The analyses were carried out in positive mode. The UHPLC was equipped with a Luna Omega Polar C18 column; 100 × 2.1 mm; 1.6 µm (Phenomenex) at 30°C with acetonitrile gradient + 0.1% formic acid (Solvent A) and water + 0.1% formic acid (Solvent B): 0-2.5 min: 30-64.5% A (linear); 2.5-2.6 min: 64.5-99% A (linear); 2.6-5 min: 99% A; 5-5.1 min: 99-30% A; 5.1-8 min: 30% A, flow rate 0.45 mL min⁻¹. For the mass spectrometer, gaseous N₂ was used as nebulizer gas (50 A.U.). The spray voltage was 3.0 kV.

## Biofilm Sampling

Biofilm was sampled before exposure (Day 0) and after 7, 14, 21, 28 days of exposure. For this purpose, glass slides were collected before weekly water renewal and then scraped with a razor blade. The detached biofilm was suspended in a known volume of demineralized water:groundwater (2:1 v/v). On the day of sampling, the measurements of exo-enzymatic activities, respiration, photosynthetic yield and chlorophyll *a* concentrations were directly made on fresh biofilms. Biofilm suspension was kept either frozen to determine bacterial community structure or with preservatives to determine algal composition and diatom taxonomy as described below.

# Analyses of Microbial Community Functions

## Respiration

Respiration was assessed under aerobic conditions on Days 0, 14, and 28 (Foulquier et al., 2013). When necessary, biofilm suspensions were diluted down to 2,000 µg of chlorophyll *a* L⁻¹. 10 mL of biofilm suspension was then placed in a glass flask hermetically closed and incubated for 3 h, in the dark, under gentle agitation, at 20°C. Carbon dioxide production was measured by gas chromatography (490 MicroGC, Agilent Technologies) after 4 h and 24 h of incubation at ambient temperature (20°C). Respiration was expressed in nmol CO₂ cm⁻² h⁻¹.

## Exo-Enzymatic Activities

At Days 0, 7, 14, 21, and 28, potential activities of three extracellular enzymes (β-glucosidase EC 3.2.1.21, phosphatase EC 3.1.3.1, and leucine aminopeptidase EC 3.4.11.1) were measured by fluorimetry using substrate analogs coupled with a fluorochrome: MUF-Glu (4-methylumbelliferyl-β-D-glucopyranoside M3633 Sigma-Aldrich, CAS No. 18997-57-4) for β-glucosidase, MUF-Pho (4-methylumbelliferyl-phosphate M8883 Sigma-Aldrich, CAS No. 3368-04-5) for phosphatase, and MCA-Leu (L-leucine-7 amido-4-methylcoumarin hydrochloride L2145 Sigma-Aldrich, CAS No. 62480-44-8.) for leucine aminopeptidase (Romani et al., 2004; Pesce et al., 2018). Enzyme activities were measured for a range of substrate concentrations (0-3,000 µM for MUF-Glu and MUF-Pho, 0-2,000 µM for MCA-Leu) to ensure activity measurement at optimal substrate concentration. When necessary, biofilm suspension was diluted down to 650 µg L⁻¹ of chlorophyll *a*. In a 96-well microplate, 70 µL of substrate and 150 µL of biofilm suspension were mixed and incubated at 15°C for 3 h 45 min. To stop the enzyme reaction, 20 µL of glycine buffer (0.05 M glycine, 0.2 M NH₄OH, pH 10.4) was added. Fluorescence was then measured with a microplate reader (Synergy HT BioTek Instruments) with excitation wavelength 360 nm and emission wavelength 460 nm. To convert fluorescence values into enzymatic activities, standard curves of MUF (Sigma M1381 CAS No. 90-33-5) for β-glucosidase and phosphatase and MCA (Sigma A9891, CAS No. 26093-31-2) for leucine aminopeptidase were plotted at each time. Activities were expressed in nanomoles of hydrolyzed compound h⁻¹ cm⁻².

## Chlorophyll a Concentration and Photosystem II Activity

At Days 0, 7, 14, 21, and 28, 3 mL of fresh biofilm suspension was used to estimate chlorophyll *a* (chl *a*) concentration and photosynthetic efficiency by multi-wavelength pulse-amplitude-modulated (PAM) fluorimetry using a PhytoPAM instrument (H. Walz, GmbH) (Schmitt-Jansen and Altenburger, 2008). Characterization of photosynthetic efficiency is based on the measurement of maximum photosynthetic yield of photosystem II (Φ<sub>PSII</sub>). Biofilm samples were adapted for a few minutes to actinic light and two saturating pulses were done. Parameter Φ<sub>PSII</sub> was calculated according to Genty et al. (1989)
$\Phi_{PSII} = \frac{F_m - F}{F_m}$ where $F_m$ is the maximal fluorescence yield after the saturating pulse and $F$ is the steady state of fluorescence.

# Analyses of Microbial Community Structure

## Algal Community Composition

The enumeration of cyanobacteria, green algae and diatom cells was performed at Days 0, 14, and 28. On the day of each sampling, 4 mL of biofilm suspension was mixed with 150 µL of formol (37%) and stored at 4°C before analysis.

Samples were exposed to ultrasounds for 7 min for correct homogenization and 100 µL was placed in a Nageotte counting cell. Counting was done for 10 microscopy fields (200 cells minimum). Density was expressed in cells cm⁻² (Morin et al., 2010).

Diatom species were identified as described in Morin et al. (2017) in the samples collected at Days 0 and 28 (n = 3 per treatment). Briefly, samples were exposed to boiling hydrogen peroxide (30%) and rinsed several times with distilled water. A few droplets were placed on slides and fixed with Naphtrax® (AFNOR NF EN 13946). Over 200 frustules per slide were counted (i.e., >600 individuals identified per treatment) and diversity indices (specific richness, Shannon, evenness) were calculated.

## Bacterial Community Structure

At Days 0, 7, 14, 21, and 28, 2 mL of biofilm suspension was centrifuged (6,500 × g, 2 min, ambient temperature) and after removing the supernatant, the pellets were stored at -80°C.

Total genomic DNA was extracted using FastDNA Spin kit for soil (MP Biomedicals, France) according to the manufacturer's instructions. Evolution of bacterial community structure was assessed using a molecular fingerprint technique based on the polymorphism length of the intergenic transcribed spacer (ITS) between rRNA 16S and 23S gene. Amplification by PCR adapted from Ranjard et al. (2001) coupled with an automated ribosomal intergenic spacer analysis (ARISA) was carried out. Amplification of the ITS 16S-23S region was performed using primers 5'-6-carboxyfluorescein (FAM)-labelled S-D-Bact-1552-b-S-20 (5'-TGCGGCTGGATCCCCCTCCTT-3') and L-D-Bact-132-a-A-18 (5'-CCGGGTTTCCCCATTCGG-3') (Normand et al., 1996). The reaction medium contained, in a total volume of 50 µL: 5µL of 10× Taq reaction buffer (GE Healthcare), 200 µM of each deoxynucleotide, 0.5 µM of each primer, 5% dimethylsulfoxide, 0.5 U of rTaq DNA polymerase (illustraTM rTaq DNA Polymerase GE Healthcare), bovine serum albumin (Sigma, 0.3 mg.mL⁻¹ final concentration) and 20 ng of DNA template. PCRs were obtained using a Thermal Cycler Tpersonal (Biometra, Göttingen, Germany) with the following program: initial denaturation at 94°C for 5 min, 35 cycles of denaturation (1 min at 94°C), annealing (1 min at 55°C), and extension (1 min at 72°C) and a final extension at 72°C for 10 min. DNA fragments were separated with sequencer ABI 3730xl (BIOfidal, Vaulx-en-Velin). An internal size standard G1200LIZ (20–1,200 pb) was used to normalize the data.

# Quantification of Antibiotic Resistance Gene sul1

Before quantification of sul1 and 16S rRNA sequence copy numbers, an inhibition test was carried out on each DNA sample as recommended by ISO 17601 (2016). qPCR reactions were carried out in a 15 µL reaction volume in a VIIa7 (Life Technologies, United States). For each reaction, the qPCR mixture contained 7.5 µL of Takyon MasterMix (Eurogentec, France), 1 µM of each primer [sul1 (51F: AAATGCTGCGAGTYGGMKCA and 280R: AACMACCAKCCTRCAGTCCG, Wei et al. (2018); 16S rRNA primers according to Petrić et al. (2011)], 250 ng of T4 gene 32 (QBiogene, France), and 3 ng of DNA. The thermal cycling was performed as follows: 95°C for 10 min, 40 cycles of 95°C for 30 s, annealing at 60°C for 30 s and extension at 72°C for 30 s with data acquisition. A melting curve step was then run with 15 s at 95°C, 1 min at 68°C and then increase in temperature of 0.5°C s⁻¹ to 95°C with data acquisition. Standard curves were obtained using serial dilutions of linearized plasmid containing sul1 amplicon. For each treatment, three independent replicates were analyzed. qPCR assays were duplicated. For each assay, three non-template samples were included. qPCR results were expressed as log of relative abundance of sul1 sequence copy number per 16S rRNA copy number.

# Data Analyses

Statistical analyses were performed with R software version 3.4.4 (R Core Team, 2020). Normality of the residuals and data homoscedasticity were checked using Shapiro-Wilk (Royston, 1982) and Fligner-Killeen (Conover et al., 1981) tests, respectively. For each sampling time, differences in microbial parameters between treatments were estimated by analysis of variance (ANOVA) followed by a Dunnett post-hoc test. Results were considered significant if *p* < 0.05.

ARISA electropherograms obtained after sequencing were analyzed using Peak Scanner Software (Applied Biosystems). To eliminate background noise, an “optimal divisor” (Osborne et al., 2006) of 1/1, 000 was used. An abundance matrix of operational taxonomic units was obtained using the interactive binner script (Ramette, 2009).

For both diatoms and bacteria, abundance matrices were used to perform structure analyses. Distances between samples were represented by a non-multidimensional scaling (nMDS) based on a Bray Curtis dissimilarity index, using R package FactoMineR (Le et al., 2008). The stress value was calculated as an indicator of the agreement between the 2D projection and the predicted value from the regression following Clarke and Warwick (2001). To check whether the sample clustering observed on the nMDS plot was significant, an analysis of variance using distance matrices was performed (ADONIS test, 999 permutations, package Vegan: Oksanen et al., 2019).

# RESULTS

## Physico-Chemical Parameters

Throughout the experiment, physical and chemical parameters in the channels remained stable and similar between treatments
with mean values (n = 60) of 19.4 ± 0.3°C for water temperature, 7.9 ± 0.1 for pH, 180.5 ± 5.9 µS cm⁻¹ for conductivity and 91.2 ± 1.3% for oxygen saturation. Most of these parameters were close to the characteristics of the sampling location in the Morcille river where pH was 7.3, oxygen saturation was 88.8%, and conductivity was 136.8 µS cm⁻¹ on the day of sampling. However, the water temperature was 4 times colder in the natural environment in January (5.1°C). Nutrient levels were also similar between treatments with mean values (n = 20) of 2.30 ± 0.13 mg L⁻¹ for silica, 2.78 ± 0.16 mg L⁻¹ for nitrate and 0.06 ± 0.02 mg L⁻¹ for phosphate in water before addition to the channels (data not shown).

## SMZ and SMX Concentrations

Sulfonamide concentrations in channel water varied according to the active substance and the level of contamination (Table 1). At Day 7, SMX concentrations were higher than SMZ ones for both the low (~220 ng SMX L⁻¹ vs. ~157 ng SMZ L⁻¹) and high (~4,286 ng SMX L⁻¹ vs. ~2,780 ng SMZ L⁻¹) contamination levels. However, whatever the sulfonamide and the contamination level considered, measured concentrations were lower than the expected ones (i.e., 500 ng L⁻¹ and 5,000 ng L⁻¹, respectively). In the high concentration treatments (SMZ+and SMX+), concentrations of both SMX and SMZ decreased continuously over time, with mean values of 2,783 ng L⁻¹ and 960 ng L⁻¹ at Day 28. SMZ and SMX concentrations were more stable in channels exposed to the nominal concentrations of 500 ng L⁻¹ and were relatively similar for the two studied active substances at the end of the experiment (179.8 ± 13.6 ng SMX L⁻¹ vs. 186.1 ± 104.0 ng SMZ L⁻¹).

<table><caption>**TABLE 1** | Sulfonamide concentrations measured in the water of the channels.</caption><thead><tr><th>Treatments</th><th>Sulfonamide</th><th>Expected concentrations (ng L<sup>-1</sup>)</th><th>Days</th><th>Measured concentrations (ng L<sup>-1</sup>)</th></tr></thead><tbody><tr><td>SMX</td><td>Sulfamethoxazole</td><td>500</td><td>7</td><td>220.1 ± 32.8</td></tr><tr><td>SMX</td><td>Sulfamethoxazole</td><td>500</td><td>14</td><td>204.1 ± 21.6</td></tr><tr><td>SMX</td><td>Sulfamethoxazole</td><td>500</td><td>21</td><td>189.9 ± 6.8</td></tr><tr><td>SMX</td><td>Sulfamethoxazole</td><td>500</td><td>28</td><td>179.8 ± 13.6</td></tr><tr><td>SMX+</td><td>Sulfamethoxazole</td><td>5000</td><td>7</td><td>4285.8 ± 266.9</td></tr><tr><td>SMX+</td><td>Sulfamethoxazole</td><td>5000</td><td>14</td><td>4135.8 ± 310.7</td></tr><tr><td>SMX+</td><td>Sulfamethoxazole</td><td>5000</td><td>21</td><td>3189.4 ± 74.7</td></tr><tr><td>SMX+</td><td>Sulfamethoxazole</td><td>5000</td><td>28</td><td>2783.0 ± 129.4</td></tr><tr><td>SMZ</td><td>Sulfamethazine</td><td>500</td><td>7</td><td>157.2 ± 13.1</td></tr><tr><td>SMZ</td><td>Sulfamethazine</td><td>500</td><td>14</td><td>105.8 ± 48.8</td></tr><tr><td>SMZ</td><td>Sulfamethazine</td><td>500</td><td>21</td><td>156.2 ± 61.7</td></tr><tr><td>SMZ</td><td>Sulfamethazine</td><td>500</td><td>28</td><td>186.1 ± 104.0</td></tr><tr><td>SMZ+</td><td>Sulfamethazine</td><td>5000</td><td>7</td><td>2779.5 ± 582.9</td></tr><tr><td>SMZ+</td><td>Sulfamethazine</td><td>5000</td><td>14</td><td>1011.5 ± 192.7</td></tr><tr><td>SMZ+</td><td>Sulfamethazine</td><td>5000</td><td>21</td><td>1376.7 ± 313.9</td></tr><tr><td>SMZ+</td><td>Sulfamethazine</td><td>5000</td><td>28</td><td>959.7 ± 288.3</td></tr></tbody></table>

Concentrations were measured every week of the 4-week experiment, before water renewal. Mean and standard deviations of measured SMX and SMZ concentrations (in ng L⁻¹) are indicated (n = 3, per each treatment).

## Heterotrophic Microbial Community

Sulfonamide contamination had few effects on the measured bacterial parameters, and most of the observed functional changes in response to sulfonamide exposure were transient.

Microbial respiration was similar between treatments and increased over time from 0.29 ± 0.09 nmol cm⁻² h⁻¹ at Day 0 (n = 3) to 14.88 ± 1.92 at Day 14 and 57.68 ± 11.10 nmol cm⁻² h⁻¹ at Day 28 (n = 15 channels, Supplementary Table S2).

Extra-cellular enzymatic activities exhibited various temporal trends independently of sulfonamide exposure (Table 2). The general trends observed were a decrease over time of microbial β-glucosidase activity (except at Day 14) and an increase over time of both phosphatase and leucine aminopeptidase activities. In unexposed biofilms (control), β-glucosidase activity was divided by almost 4 at the end of the experiment, while phosphatase activity was multiplied by 4 and leucine aminopeptidase by 6 (Table 2). In biofilms exposed to SMX or SMZ, β-glucosidase activity was mostly similar or lower than in unexposed biofilms. However, no significant difference (p > 0.05) was found between treatments (Table 2). During the first 14 days of the experiment, phosphatase activity was significantly lower (p < 0.05) in biofilms exposed to the sulfonamides tested than in unexposed biofilms (Table 2). In the last 14 days of exposure, phosphatase activity was slightly higher in biofilms exposed to the low and high concentrations of SMX than in unexposed biofilms (p < 0.05 at Day 21, Table 2). After 14 days of exposure, leucine aminopeptidase activity was almost 2 times higher in biofilms exposed to low SMX and SMZ concentrations than in unexposed communities (p < 0.05). However, this effect was transient, and no other significant difference was observed whatever the treatment or the sampling time (Table 2).

The antibiotic resistance gene *sul1* was detected in biofilms from all the treatments (including control). In all treatments the relative abundance of *sul1* was similar, with an average of 25.1 ± 5.3 *sul1* sequence copies per 1,000 16S rRNA sequence copy (n = 20, Supplementary Figure S2).

The ARISA performed after 28 days of exposure allowed retrieving 267 OTUs, bacterial community structure was slightly affected by the exposure to SMX and SMZ, at both the low and high concentrations (Figure 1). The permutational analysis of variance (ADONIS) shows that the exposure to sulfonamides explained 32% of the variance in the bacterial structure between samples (p < 0.01) and indicated a significant difference in bacterial structure between control biofilms, biofilms exposed to SMX and biofilms exposed to SMZ.

## Phototrophic Microbial Community

Sulfonamide exposure did not affect the effective quantum yield of photosystem II, which was relatively stable over time and between treatments (e.g., average yield 0.28 ± 0.05 at Day 28, Supplementary Table S1).

The relative proportions of the different algal groups (cyanobacteria, diatoms and green algae) were relatively constant over time and similar between treatments (Supplementary Figure S3). Nevertheless, cell densities of the different algal groups were different between treatments, with generally
<table><caption>**TABLE 2** | Phosphatase and leucine aminopeptidase activities (nmol MUF cm⁻² h⁻¹) of microbial communities non-exposed (control) and exposed to 500 or 5,000 ng L⁻¹ of sulfamethoxazole (SMX) or sulfamethazine (SMZ) over time.</caption><thead><tr><th>Activity (nmolMUF.cm<sup>-2</sup>.h<sup>-1</sup>)</th><th>Days</th><th>Control</th><th>SMX</th><th>SMX+</th><th>SMZ</th><th>SMZ+</th></tr></thead><tbody><tr><td rowspan="4">β-glucosidase</td><td>7</td><td>10.08 ± 3.63</td><td>7.95 ± 3.22</td><td>5.01 ± 2.72</td><td>4.26 ± 2.31</td><td>10.39 ± 1.95</td></tr><tr><td>14</td><td>7.56 ± 0.55</td><td>9.43 ± 1.04</td><td>5.22 ± 0.95</td><td>8.24 ± 2.71</td><td>7.35 ± 1.92</td></tr><tr><td>21</td><td>7.60 ± 3.34</td><td>4.16 ± 2.19</td><td>2.43 ± 0.55</td><td>6.00 ± 3.13</td><td>4.27 ± 1.34</td></tr><tr><td>28</td><td>2.66 ± 1.54</td><td>2.44 ± 1.76</td><td>2.23 ± 1.91</td><td>1.15 ± 0.05</td><td>1.22 ± 0.14</td></tr><tr><td rowspan="4">Phosphatase</td><td>7</td><td>12.57 ± 0.73</td><td>7.21 ± 0.40*</td><td>11.02 ± 0.84</td><td>6.18 ± 1.45*</td><td>9.34 ± 1.17*</td></tr><tr><td>14</td><td>11.39 ± 0.53</td><td>9.12 ± 0.15*</td><td>12.13 ± 1.03</td><td>9.11 ± 1.01*</td><td>8.56 ± 0.89*</td></tr><tr><td>21</td><td>14.01 ± 0.88</td><td>17.99 ± 1.35*</td><td>17.42 ± 0.93*</td><td>13.45 ± 2.22</td><td>13.34 ± 0.84</td></tr><tr><td>28</td><td>57.38 ± 26.42</td><td>65.10 ± 20.19</td><td>66.70 ± 18.22</td><td>49.78 ± 12.13</td><td>43.79 ± 8.13</td></tr><tr><td rowspan="4">Leucine aminopeptidase</td><td>7</td><td>3.47 ± 0.83</td><td>3.29 ± 0.39</td><td>3.44 ± 0.20</td><td>3.96 ± 0.42</td><td>3.37 ± 0.42</td></tr><tr><td>14</td><td>4.35 ± 0.07</td><td>7.43 ± 0.31*</td><td>4.17 ± 0.37</td><td>9.08 ± 1.97*</td><td>3.14 ± 0.27</td></tr><tr><td>21</td><td>13.68 ± 2.91</td><td>13.28 ± 2.67</td><td>15.96 ± 1.11</td><td>16.18 ± 5.61</td><td>9.81 ± 2.64</td></tr><tr><td>28</td><td>20.52 ± 4.49</td><td>19.64 ± 5.52</td><td>16.58 ± 1.78</td><td>17.85 ± 3.86</td><td>21.30 ± 4.41</td></tr></tbody></table>

Mean ± standard deviation of enzyme activities (nmol MUF cm⁻² h⁻¹) measured in the different treatments are indicated (n = 3 per treatment). Stars indicate significant differences between treatments and controls (Dunnett, *p* < 0.05).

<figure><img src="image_58.png" /><figcaption>**FIGURE 1** | nMDS representation of Bray-Curtis distance similarities between bacterial communities based on ARISA after 28 days of exposure to sulfonamides (sulfamethoxazole, SMX; sulfamethazine, SMZ). The stress value is 0.143. Controls are represented by white circles, communities exposed to SMX by squares and to SMZ by triangles. The level of exposure to sulfonamides is color-coded as follows: gray for exposure to 500 ng L⁻¹ and black for exposure to 5,000 ng L⁻¹. Ellipses correspond to the 95% confidence interval around the centroid of the following groups: control, exposed to SMX (including SMX and SMX+), exposed to SMZ (including SMZ and SMZ+).</figcaption></figure>

the highest cell densities observed in biofilms exposed to sulfonamides (Figure 2). Thus densities of cyanobacteria, diatoms and green algae were significantly higher (*p* < 0.05) in biofilms exposed to the low and high concentrations of SMX at Day 14 than in control biofilms (Figure 2).

All the microbial communities in biofilm were dominated by diatoms, whatever the treatment (Supplementary Figure S3 and Figure 2). Although sulfonamides did not have a clear effect on the structure of the diatom community (as

illustrated in the nMDS plot in Supplementary Figure S4), exposure to sulfonamides led to a decrease in diatom specific richness, diversity and evenness, depending on the treatment (Table 3). Exposure to low and high concentrations of SMZ significantly reduced the specific richness and the Shannon index (*p* < 0.05) of the biofilm but no significant effect was observed on the evenness. Neither the low nor the high concentration of SMX had a significant effect on the specific richness, but exposure to the highest tested concentration of SMX significantly reduced both Shannon and evenness indices (Table 3).

The physiology and morphology of diatoms were also affected by sulfonamides (Figure 3). At Days 14 and 28, diatom mortality was significantly higher in biofilms exposed to SMX than in control biofilms (*p* < 0.05, Figure 3A) and a similar trend was observed at Day 28 for biofilms exposed to both concentrations of SMZ (significant result only for biofilm exposed to 5,000 ng L⁻¹, Figure 3B). Diatom growth rates were also slightly higher in biofilms exposed to sulfonamides than in control biofilms despite the lack of significant difference (Figure 3C). In biofilms exposed to the highest sulfonamide concentrations tested, we observed an increase in the abundance of diatoms exhibiting deformities, in particular the abundance of teratological diatoms was more than 2 times higher in biofilms exposed to SMZ (reaching 3%) than in control biofilms (1.1%).

## DISCUSSION

Water contamination by environmentally realistic concentrations of SMZ and SMX affected both the heterotrophic and autotrophic communities of biofilms with various effects according to the sulfonamide and to the exposure level. Supporting our hypotheses, exposure to SMX, and to a lesser extent to SMZ, modified bacterial structure and impaired microbial enzyme functions. Moreover, sulfonamide exposure also had negative effects on the autotroph component of periphytic biofilm.
<figure><img src="image_59.png" /><figcaption>**TABLE 3** | Structure and diversity of diatom community non-exposed (control) and exposed to 500 or 5,000 ng L⁻¹ of sulfamethoxazole (SMX) or sulfamethazine (SMZ) after 28 days or exposure, represented by diversity index (specific richness, Shannon, evenness).</figcaption></figure>

<table><thead><tr><th>Diversity index</th><th>Control</th><th>SMX</th><th>SMX+</th><th>SMZ</th><th>SMZ+</th></tr></thead><tbody><tr><td>Specific richness</td><td>20.67 ± 1.08</td><td>16.67 ± 1.47</td><td>19.00 ± 2.55</td><td>15.00 ± 0.00*</td><td>13.67 ± 1.47*</td></tr><tr><td>Shannon</td><td>2.00 ± 0.10</td><td>1.77 ± 0.07</td><td>1.66 ± 0.16*</td><td>1.64 ± 0.07*</td><td>1.58 ± 0.04*</td></tr><tr><td>Evenness</td><td>0.65 ± 0.03</td><td>0.63 ± 0.03</td><td>0.56 ± 0.03*</td><td>0.61 ± 0.03</td><td>0.60 ± 0.01</td></tr></tbody></table>

Mean ± standard deviation of diversity indices estimated in the different treatments are indicated (n = 3 per treatment). Stars indicate significant differences between treatments and controls (Dunnett, p < 0.05).

Concerning the heterotrophic component of the biofilm, both sulfonamides modified phosphatase and leucine aminopeptidase extracellular enzyme activities in the first weeks of exposure, with no effect on β-glucosidase activity. Nevertheless, these effects were transient: after 4 weeks of exposure, all the measured extracellular enzymatic activities recovered and were similar between control communities and communities exposed to sulfonamides. Together with this functional recovery, small changes in the structure of the bacterial communities exposed to sulfonamides were also observed. These results suggest first a direct effect of the sulfonamides on the exposed communities, resulting in impairment of the bacterial functions as already observed in soil microbial communities exposed to SMX (Liu et al., 2016) or in biofilm exposed to river water strongly contaminated by pharmaceutical residues including antibiotics (Proia et al., 2013a). In addition, the functional recovery observed after 4 weeks of exposure, together with the changes in bacterial community structure, suggests that sulfonamide exposure acted as a selection pressure on the microbial communities, selecting the most tolerant species able to maintain the reference level of extracellular enzyme activities on exposure to sulfonamides.

Previous studies highlighted the impact of environmentally realistic levels of antibiotics, including sulfonamides, on the bacterial structure in soil (Liu et al., 2016), sediments (own unpublished results), or biofilm (Proia et al., 2013a).

In our study, bacterial structural changes following sulfonamide exposure were not associated with a higher relative abundance of the *sul1* resistance gene in microbial communities exposed to sulfonamides than in controls. The level of this antibiotic resistance gene remained stable throughout the experiment. This result is in agreement with the observation of Subirats et al. (2018) who found no significant difference in the relative abundance of *sul1* genes between control biofilms and biofilms exposed to a mixture of five contaminants, including sulfamethoxazole, at 0.1 mg L⁻¹ each (methylparaben, ciprofloxacin, diclofenac, erythromycin and sulfamethoxazole) for 28 days in mesocosms. To go beyond the community capacity to resist antibiotics (assessed here by the relative abundance of *sul1* gene in the total bacterial community) and to validate the above hypothesis of the selection of the most tolerant species, it would be of interest to assess the functional resistance of the community to the acute toxicity of antibiotics through
<figure><img src="image_60.png" /></figure>

**FIGURE 3** | Diatoms mortality (in%, on Day 14: A, and on Day 28: B), diatom growth rate (in division cm⁻², C), abundance of teratological diatoms (in%, on Day 28: D) of biofilms non-exposed (control) and exposed to sulfamethoxazole (500 ng L⁻¹: SMX, 5,000 ng L⁻¹: SMX+) or sulfamethazine (500 ng L⁻¹: SMZ, 5,000 ng L⁻¹: SMZ+). Significant differences (*p* < 0.05) between controls and biofilms exposed to antibiotics are indicated by stars.

pollution-induced community tolerance (PICT) measurements based on short-term toxicity tests (e.g., Corcoll et al., 2014).

Concerning the autotrophic component of the biofilm, the exposure to both sulfonamides led to an increase in cell density and diatom mortality with no impact on the autotrophic community structure based on the relative distribution of the main groups (i.e., diatoms, cyanobacteria and green algae). This stimulatory effect of low concentrations of SMX on algal growth has been previously observed in marine biofilms exposed to SMX (Johansson et al., 2014) and may be a consequence of temporarily decreased fitness of the heterotrophic compartment (e.g., Proia et al., 2011). The higher cell density observed in biofilm communities exposed to sulfonamides in our experiment is therefore likely to be due to the presence of tolerant species with high competitive abilities. The higher mortality values in these communities may reflect the presence and elimination of the sensitive species following SMX/SMZ exposure.

Sulfonamides did not affect only microalgal growth dynamics but also diatom morphology, since the percentage of teratologies was higher in communities exposed to the highest concentration

of sulfonamides tested than in non-exposed ones. Metals are known to induce deformities in diatoms (e.g., Lavoie et al., 2017; Tiam et al., 2019), but to our knowledge, this is the first report of a teratogenic effect of antibiotics on diatoms. There is no evidence in the literature that sulfonamides can directly cause diatom deformities, and the teratogenic effects of sulfamethoxazole exposure observed in our experiment could be due to indirect effects. The higher diatom mortality and cell density in exposed communities than in non-exposed ones suggest a higher renewal rate of diatom cells in those exposed communities. This higher renewal rate could be the result of greater growth rates of teratological individuals over normal ones (Coquillé and Morin, 2019).

The observed effects of sulfonamides on non-target species, such as diatoms, may also result from interactions between microalgae and bacteria within the periphytic biofilm. Indeed, previous studies have highlighted the tight link between these two complementary groups (e.g., in the carbon cycle: Battin et al., 2016), and the repercussion of direct toxic effects on one group to another (non-target) one (e.g., after exposure to the bactericide triclosan: Proia et al., 2011). The direct effect
of sulfonamides on bacteria may also change the heterotrophic community composition and the quality of bacterial exudates for microalgae and then lead to alterations in the morphology and survival of diatoms. Since Windler et al. (2014) showed that the absence of essential accompanying bacteria in laboratory cultures induced teratologies in diatoms, we can hypothesize that the structural changes in bacterial structure induced by sulfonamides led to the observed increase in diatom deformities. Further experiments are needed to better understand how sulfonamides might modulate microalgae-bacteria interactions within biofilms.

Altogether, our results highlight that even low concentrations of antibiotics can modify, at least transiently, microbial community structure and functions.

## CONCLUSION

Sulfamethazine and sulfamethoxazole had similar effects on biofilm communities after 4 weeks of chronic exposure to nominal concentrations of 500 and 5,000 ng L⁻¹, with exposure to the higher concentration generally resulting in stronger effects. Exposure to sulfonamides mainly affected bacterial structure within the heterotrophic component of biofilm. In the autotrophic compartment, those two antibiotics particularly affected the dominant algal component (diatoms), by increasing their mortality and inducing teratologies at the higher of the two concentrations tested. Our study shows that sulfonamides are susceptible to alter microbial community structure and diversity at concentrations frequently found in the environment. In particular, non-target species such as diatoms are likely to be affected by contamination by sulfonamides, with unknown functional consequences for the ecosystem.

## DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

## REFERENCES

Battin, T. J., Besemer, K., Bengtsson, M. M., Romani, A. M., and Packmann, A. I. (2016). The ecology and biogeochemistry of stream biofilms. *Nat. Rev. Microbiol.* 14, 251–263. doi: 10.1038/nrmicro.2016.15

Bielen, A., Šimatović, A., Kosić-Vukšić, J., Senta, I., Ahel, M., Babić, S., et al. (2017). Negative environmental impacts of antibiotic-contaminated effluents from pharmaceutical industries. *Water Res.* 126, 79–87. doi: 10.1016/j.watres. 2017.09.019

Calero-Cáceres, W., Méndez, J., Martín-Díaz, J., and Muniesa, M. (2017). The occurrence of antibiotic resistance genes in a Mediterranean river and their persistence in the riverbed sediment. *Environ. Pollut.* 223, 384–394. doi: 10. 1016/j.envpol.2017.01.035

Carvalho, I. T., and Santos, L. (2016). Antibiotics in the aquatic environments: a review of the European scenario. *Environ. Int.* 94, 736–757. doi: 10.1016/j. envint.2016.06.025

Charuaud, L., Jarde, E., Jaffrezic, A., Thomas, M.-F., and Le Bot, B. (2019). Veterinary pharmaceutical residues from natural water to tap water: sales, occurrence and fate. *J. Hazard. Mater.* 361, 169–186. doi: 10.1016/j.jhazmat. 2018.08.075

## AUTHOR CONTRIBUTIONS

CB and SP designed the experiment with the help of LK and AR. LK and AR led the experimental work during mesocosm exposure. PB-H, LK, and ML did the chemical analyses of sulfonamides. MM did the analyses of major ions in water surface. LK, AR, and CB did the functional microbial analyses. LK did the DNA extraction. FM-L, MD, and JB did the quantification of *sul1* genes. SM did all the analyses of algal community composition. LK and CB did the data treatment, statistical analyses, and wrote the first version of the manuscript. SP, FM-L, PB-H, and SM reviewed, completed, and improved the first version of the manuscript. All authors contributed to the article and approved the submitted version.

## FUNDING

This study was supported by the ANTIBIOTOX project (contract no ANR-17-CE34-0003) funded by the ANR (Agence Nationale de la Recherche).

## ACKNOWLEDGMENTS

We thank Bernadette Volat, Bernard Motte, Anaïs Charton, and Christophe Rosy for their participation in experiment maintenance, sampling, and analysis.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2021.643719/full#supplementary-material

Clarke, K. R., and Warwick, R. M. (2001). A further biodiversity index applicable to species lists: variation in taxonomic distinctness. *Mar. Ecol. Prog. Ser.* 216, 265–278. doi: 10.3354/meps216265

Conover, W. J., Johnson, M. E., and Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. *Technometrics* 23, 351–361. doi: 10.1080/00401706.1981. 10487680

Coquillé, N., and Morin, S. (2019). Fitness of teratological morphotypes and heritability of deformities in the diatom gomphonema gracile. *Ecol. Indic.* 106:105442. doi: 10.1016/j.ecolind.2019.105442

Corcoll, N., Acuña, V., Barceló, D., Casellas, M., Guasch, H., Huerta, B., et al. (2014). Pollution-induced community tolerance to non-steroidal anti-inflammatory drugs (NSAIDs) in fluvial biofilm communities affected by WWTP effluents. *Chemosphere* 112, 185–193.

Czekalski, N., Gascón, D., and Bürgmann, H. (2014). Wastewater as a point source of antibiotic-resistance genes in the sediment of a freshwater lake. *ISME J.* 8, 1381–1390. doi: 10.1038/ismej.2014.8

Daughton, C. G. (2016). Pharmaceuticals and the environment (PiE): evolution and impact of the published literature revealed by bibliometric analysis. *Sci. Total Environ.* 562, 391–426. doi: 10.1016/j.scitotenv.2016.03.109
De Liguoro, M., Fioretto, B., Poltronieri, C., and Gallina, G. (2009). The toxicity of sulfamethazine to *Daphnia* magna and its additivity to other veterinary sulfonamides and trimethoprim. *Chemosphere* 75, 1519–1524. doi: 10.1016/j.chemosphere.2009.02.002

Deng, Y., Li, B., and Zhang, T. (2018). Bacteria that make a meal of sulfonamide antibiotics: blind spots and emerging opportunities. *Environ. Sci. Technol.* 52, 3854–3868. doi: 10.1021/acs.est.7b06026

Di Cesare, A., Eckert, E. M., Rogora, M., and Corno, G. (2017). Rainfall increases the abundance of antibiotic resistance genes within a riverine microbial community. *Environ. Pollut.* 226, 473–478. doi: 10.1016/j.envpol.2017.04.036

Díaz-Cruz, M. S., García-Galán, M. J., and Barceló, D. (2008). Highly sensitive simultaneous determination of sulfonamide antibiotics and one metabolite in environmental waters by liquid chromatography-quadrupole linear ion trap-mass spectrometry. *J. Chromatogr. A* 1193, 50–59. doi: 10.1016/j.chroma.2008.03.029

Fajardo, A., and Martínez, J. L. (2008). Antibiotics as signals that trigger specific bacterial responses. *Curr. Opin. Microbiol.* 11, 161–167. doi: 10.1016/j.mib.2008.02.006

Foulquier, A., Volat, B., Neyra, M., Bornette, G., and Montuelle, B. (2013). Long-term impact of hydrological regime on structure and functions of microbial communities in riverine wetland sediments. *FEMS Microbiol. Ecol.* 85, 211–226. doi: 10.1111/1574-6941.12112

Frková, Z., Vystavna, Y., Koubová, A., Kotas, P., Grabicová, K., Grabic, R., et al. (2020). Microbial responses to selected pharmaceuticals in agricultural soils: microcosm study on the roles of soil, treatment and time. *Soil Biol. Biochem.* 149:107924. doi: 10.1016/j.soilbio.2020.107924

Genty, B., Briantais, J. M., and Baker, N. R. (1989). The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. *Biochim. Biophys. Acta* 990, 87–92. doi: 10.1016/s0304-4165(89)80016-9

Gessner, M. O., and Tlili, A. (2016). Fostering integration of freshwater ecology with ecotoxicology. *Freshwater Biol.* 61, 1991–2001. doi: 10.1111/fwb.12852

Guo, X.-P., Yang, Y., Lu, D.-P., Niu, Z.-S., Feng, J.-N., Chen, Y.-R., et al. (2018). Biofilms as a sink for antibiotic resistance genes (ARGs) in the Yangtze estuary. *Water Res.* 129, 277–286. doi: 10.1016/j.watres.2017.11.029

Halling-Sørensen, B., Nors Nielsen, S., Lanzky, P. F., Ingerslev, F., Holten Lützhoft, H. C., and Jørgensen, S. E. (1998). Occurrence, fate and effects of pharmaceutical substances in the environment - a review. *Chemosphere* 36, 357–393. doi: 10.1016/s0045-6535(97)00354-8

Johansson, C. H., Janmar, L., and Backhaus, T. (2014). Toxicity of ciprofloxacin and sulfamethoxazole to marine periphytic algae and bacteria. *Aquat. Toxicol.* 156, 248–258. doi: 10.1016/j.aquatox.2014.08.015

Kovalakova, P., Cizmas, L., McDonald, T. J., Marsalek, B., Feng, M., and Sharma, V. K. (2020). Occurrence and toxicity of antibiotics in the aquatic environment: a review. *Chemosphere* 251:126351. doi: 10.1016/j.chemosphere.2020.126351

Lavoie, I., Hamilton, P. B., Morin, S., Kim Tiam, S., Kahler, M., Gonçalves, S., et al. (2017). Diatom teratologies as biomarkers of contamination: are all deformities ecologically meaningful? *Ecol. Indic.* 82, 539–550. doi: 10.1016/j.ecolind.2017.06.048

Lawrence, J. R., Topp, E., Waiser, M. J., Tumber, V., Roy, J., Swerhone, G. D. W., et al. (2015). Resilience and recovery: the effect of triclosan exposure timing during development, on the structure and function of river biofilm communities. *Aquat. Toxicol.* 161, 253–266. doi: 10.1016/j.aquatox.2015.02.012

Le, S., Josse, J., and Husson, F. (2008). FactoMineR: an R package for multivariate analysis. *J. Stat. Soft.* 25, 1–18.

Linares, J. F., Gustafsson, I., Baquero, F., and Martinez, J. L. (2006). Antibiotics as intermicrobial signaling agents instead of weapons. *Proc. Natl. Acad. Sci. U.S.A.* 103, 19484–19489. doi: 10.1073/pnas.0608949103

Liu, A., Cao, H., Yang, Y., Ma, X., and Liu, X. (2016). Combinational effects of sulfomethoxazole and copper on soil microbial community and function. *Environ. Sci. Pollut. Res.* 23, 4235–4241. doi: 10.1007/s11356-015-4892-x

Liu, F., Ying, G.-G., Tao, R., Zhao, J.-L., Yang, J.-F., and Zhao, L.-F. (2009). Effects of six selected antibiotics on plant growth and soil microbial and enzymatic activities. *Environ. Poll.* 157, 1636–1642. doi: 10.1016/j.envpol.2008.12.021

Mahamoud Ahmed, A., Lyautey, E., Bonnineau, C., Dabrin, A., and Pesce, S. (2018). Environmental concentrations of copper, alone or in mixture with arsenic, can impact river sediment microbial community structure and functions. *Front. Microbiol.* 9:1852. doi: 10.3389/fmicb.2018.01852

Mandaric, L., Diamantini, E., Stella, E., Cano-Paoli, K., Valle-Sistac, J., Molins- Delgado, D., et al. (2017). Contamination sources and distribution patterns of pharmaceuticals and personal care products in Alpine rivers strongly affected by tourism. *Sci. Total Environ.* 590–591, 484–494. doi: 10.1016/j.scitotenv.2017.02.185

Marti, E., Jofre, J., and Balcazar, J. L. (2013). Prevalence of antibiotic resistance genes and bacterial community composition in a river influenced by a wastewater treatment plant. *PLoS One* 8:e78906. doi: 10.1371/journal.pone.0078906

Martínez, J. L. (2008). Antibiotics and antibiotic resistance genes in natural environments. *Science* 321, 365–367. doi: 10.1126/science.1159483

Martin-Laurent, F., Topp, E., Billet, L., Batisson, I., Malandain, C., Besse-Hoggan, P., et al. (2019). Environmental risk assessment of antibiotics in agroecosystems: ecotoxicological effects on aquatic microbial communities and dissemination of antimicrobial resistances and antibiotic biodegradation potential along the soil-water continuum. *Environ. Sci. Pollut. Res.* 26, 18930–18937. doi: 10.1007/s11356-019-05122-0

McDermott, P. F., Walker, R. D., and White, D. G. (2003). Antimicrobials: modes of action and mechanisms of resistance. *Int. J. Toxicol.* 22, 135–143. doi: 10.1080/10915810305089

Moon, K. H., Weber, B. S., and Feldmana, M. F. (2017). Subinhibitory concentrations of trimethoprim and sulfamethoxazole prevent biofilm formation by *Acinetobacter baumannii* through inhibition of csu pilus expression. *Antimicrob. Agents Chemother.* 61:e778-17.

Morin, S., Lambert, A. S., Rodriguez, E. P., Dabrin, A., Coquery, M., and Pesce, S. (2017). Changes in copper toxicity towards diatom communities with experimental warming. *J. Hazard. Mater.* 334, 223–232. doi: 10.1016/j.jhazmat.2017.04.016

Morin, S., Proia, L., Ricart, M., Bonnineau, C., Geiszinger, A., Ricciardi, F., et al. (2010). Effects of a bactericide on the structure and survival of benthic diatom communities. *Vie Milieu* 60, 109–116.

Normand, P., Ponsonnet, C., Nesme, X., Neyra, M., and Simonet, P. (1996). "ITS analysis of prokaryotes," in *Molecular Microbial Ecology Manual*, eds D. L. Akkermans, J. D. van Elsas, and F. J. de Brujin (Dordrecht: Kluwer Academic Publishers), 1-12. doi: 10.1016/b978-0-12-509001-8.50008-1

O'Flaherty, E., and Cummins, E. (2017). Antibiotic resistance in surface water ecosystems: presence in the aquatic environment, prevention strategies, and risk assessment. *Hum. Ecol. Risk Assess. Int. J.* 23, 299–322. doi: 10.1080/10807039.2016.1247254

Oksanen, J., Guillaume Blanchet, F., Friendly, M., Kindt, R., Legendre, L., McGlinn, D., et al. (2019). *vegan: Community Ecology Package. R Package Version 2.5-6*.

Osborne, C. A., Rees, G. N., Bernstein, Y., and Janssen, P. H. (2006). New threshold and confidence estimates for terminal restriction fragment length polymorphism analysis of complex bacterial communities. *Appl. Environ. Microbiol.* 72, 1270–1278. doi: 10.1128/aem.72.2.1270-1278.2006

Paiga, P., Santos, L. H. M. L. M., Ramos, S., Jorge, S., Silva, J. G., and Delerue-Matos, C. (2016). Presence of pharmaceuticals in the Lis river (Portugal): sources, fate and seasonal variation. *Sci. Total Environ.* 573, 164–177. doi: 10.1016/j.scitotenv.2016.08.089

Paumelle, M., Donnadieu, F., Joly, M., Besse-Hoggan, P., and Artigas, J. (2021). Effects of sulfonamide antibiotics on aquatic microbial community composition and functions. *Environ. Int.* 146:106198. doi: 10.1016/j.envint.2020.106198

Perreten, V., and Boerlin, P. (2003). A new sulfonamide resistance gene (sul3) in *Escherichia coli* is widespread in the pig population of Switzerland. *Antimicrob. Agents Chemother.* 47, 1169–1172. doi: 10.1128/aac.47.3.1169-1172.2003

Pesce, S., Lambert, A.-S., Morin, S., Foulquier, A., Coquery, M., and Dabrin, A. (2018). Experimental warming differentially influences the vulnerability of phototrophic and heterotrophic periphytic communities to copper toxicity. *Front. Microbiol.* 9:1424. doi: 10.3389/fmicb.2018.01424

Petrić, I., Bru, D., Udiković-Kolić, N., Hršak, D., Philippot, L., Martin-Laurent, F., et al. (2011). Evidence for shifts in the structure and abundance of the microbial community in a long-term PCB-contaminated soil under bioremediation. *J. Hazard. Mater.* 195, 254–260. doi: 10.1016/j.jhazmat.2011.08.036

Proia, L., Morin, S., Peipoch, M., Romaní, A. M., and Sabater, S. (2011). Resistance and recovery of river biofilms receiving short pulses of Triclosan and Diuron. *Sci. Total Environ.* 409, 3129–3137. doi: 10.1016/j.scitotenv.2011.05.013

Proia, L., Lupini, G., Osorio, V., Pérez, S., Barceló, D., Schwartz, T., et al. (2013a). Response of biofilm bacterial communities to antibiotic pollutants in a
Mediterranean river. *Chemosphere* 92, 1126-1135. doi: 10.1016/j.chemosphere. 2013.01.063

Proia, L., Vilches, C., Boninneau, C., Kantiani, L., Farré, M., Romaní, A. M., et al. (2013b). Drought episode modulates the response of river biofilms to triclosan. *Aquat. Toxicol.* 127, 36-45. doi: 10.1016/j.aquatox.2012.01.006

Proia, L., von Schiller, D., Sànchez-Melsió, A., Sabater, S., Borrego, C. M., Rodríguez-Mozaz, S., et al. (2016). Occurrence and persistence of antibiotic resistance genes in river biofilms after wastewater inputs in small rivers. *Environ. Pollut.* 210, 121-128. doi: 10.1016/j.envpol.2015.11.035

Quinlan, E. L., Nietch, C. T., Blocksom, K., Lazorchak, J. M., Batt, A. L., Griffiths, R., et al. (2011). Temporal dynamics of periphyton exposed to tetracycline in stream mesocosms. *Environ. Sci. Technol.* 45, 10684-10690. doi: 10.1021/es202004k

R Core Team (2020). *R: A Language and Environment for Statistical Computing*. Vienna: R Foundation for Statistical Computing.

Ramette, A. (2009). Quantitative community fingerprinting methods for estimating the abundance of operational taxonomic units in natural microbial communities. *Appl. Environ. Microbiol.* 75, 2495-2505. doi: 10.1128/aem.02409-08

Ranjard, L., Poly, F., Lata, J.-C., Mougel, C., Thioulouse, J., and Nazaret, S. (2001). Characterization of bacterial and fungal soil communities by automated ribosomal intergenic spacer analysis fingerprints: biological and methodological variability. *Appl. Environ. Microbiol.* 67, 4479-4487. doi: 10.1128/aem.67.10.4479-4487.2001

Romani, A. M., Guasch, H., Munoz, I., Ruana, J., Vilalta, E., Schwartz, T., et al. (2004). Biofilm structure and function and possible implications for riverine DOC dynamics. *Microb. Ecol.* 47, 316-328.

Royston, J. P. (1982). An extension of shapiro and Wilk's W test for normality to large samples. *J. R. Stat. Soc. Ser. C Appl. Stat.* 31, 115-124. doi: 10.2307/2347973

Sanderson, H., Fricker, C., Brown, R. S., Majury, A., and Liss, S. N. (2016). Antibiotic resistance genes as an emerging environmental contaminant. *Environ. Rev.* 24, 205-218. doi: 10.1139/er-2015-0069

Schmitt-Jansen, M., and Altenburger, R. (2008). Community-level microalgal toxicity assessment by multiwavelength-excitation PAM fluorometry. *Aquat. Toxicol.* 86, 49-58. doi: 10.1016/j.aquatox.2007.10.001

Sköld, O. (2000). Sulfonamide resistance: mechanisms and trends. *Drug Resist. Updat.* 3, 155-160. doi: 10.1054/drup.2000.0146

Straub, J. O. (2016). Aquatic environmental risk assessment for human use of the old antibiotic sulfamethoxazole in Europe. *Environ. Toxicol. Chem.* 35, 767-779. doi: 10.1002/etc.2945

Subirats, J., Timoner, X., Sànchez-Melsió, A., Balcázar, J. L., Acuña, V., Sabater, S., et al. (2018). Emerging contaminants and nutrients synergistically affect the spread of class 1 integron-integrase (intI1) and sul1 genes within stable

streambed bacterial communities. *Water Res.* 138, 77-85. doi: 10.1016/j.watres. 2018.03.025

Szymańska, U., Wiergowski, M., Soltyszewski, I., Kuzemko, J., Wiergowska, G., and Woźniak, M. K. (2019). Presence of antibiotics in the aquatic environment in Europe and their analytical monitoring: recent trends and perspectives. *Microchem. J.* 147, 729-740. doi: 10.1016/j.microc.2019.04.003

Tiam, S. K., Lavoie, I., Liu, F., Hamilton, P. B., and Fortin, C. (2019). Diatom deformities and tolerance to cadmium contamination in four species. *Environment* 6:102. doi: 10.3390/environments6090102

U.S. Environmental Protection Agency (2019). *ECOTOX User Guide: ECOTOXicology Knowledgebase System. Version 5.0.*

Wei, Z., Feng, K., Li, S., Zhang, Y., Chen, H., Yin, H., et al. (2018). Exploring abundance, diversity and variation of a widespread antibiotic resistance gene in wastewater treatment plants. *Environ. Int.* 117, 186-195. doi: 10.1016/j.envint. 2018.05.009

Windler, M., Bova, D., Kryvenda, A., Straile, D., Gruber, A., Kroth, P. G., et al. (2014). Influence of bacteria on cell size development and morphology of cultivated diatoms. *Phycol. Res.* 62, 269-281. doi: 10.1111/pre.12059

Yang, L.-H., Ying, G.-G., Su, H.-C., Stauber, J. L., Adams, M. S., and Binet, M. T. (2008). Growth-inhibiting effects of 12 antibacterial agents and their mixtures on the freshwater microalga Pseudokirchneriella subcapitata. *Environ. Toxicol. Chem.* 27, 1201-1208. doi: 10.1897/07-471.1

Yergeau, E., Lawrence, J. R., Waiser, M. J., Korber, D. R., and Greer, C. W. (2010). Metatranscriptomic analysis of the response of river biofilms to pharmaceutical products, using anonymous DNA microarrays. *Appl. Environ. Microbiol.* 76, 5432-5439. doi: 10.1128/aem.00873-10

Yergeau, E., Sanschagrin, S., Waiser, M. J., Lawrence, J. R., and Greer, C. W. (2012). Sub-inhibitory concentrations of different pharmaceutical products affect the meta-transcriptome of river biofilm communities cultivated in rotating annular reactors. *Environ. Microbiol. Rep.* 4, 350-359. doi: 10.1111/j.1758-2229.2012. 00341.x

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

Copyright © 2021 Kergoat, Besse-Hoggan, Leremboure, Beguet, Devers, Martin-Laurent, Masson, Morin, Roinat, Pesce and Bonnineau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Side Effects of Pesticides and Metabolites in Groundwater: Impact on Denitrification

Caroline Michel¹*, Nicole Baran¹, Laurent André¹,², Mickael Charron¹ and Catherine Joulian¹

¹ BRGM, DEPA (Direction de l'Éau, de l'Environnement, des Procédés et Analyses), Orléans, France, ² Université d'Orléans, CNRS, BRGM, UMR 7327 Institut des Sciences de la Terre d'Orléans, Orléans, France

The impact of two pesticides (S-metolachlor and propiconazole) and their respective main metabolites (ESA-metolachlor and 1,2,4-triazole) on bacterial denitrification in groundwater was studied. For this, the denitrification activity and the bacterial diversity of a microbial community sampled from a nitrate-contaminated groundwater were monitored during 20 days in lab experiments in the presence or absence of pesticides or metabolites at 2 or 10 µg/L. The kinetics of nitrate reduction along with nitrite and N₂O production all suggested that S-metolachlor had no or only little impact, whereas its metabolite ESA-metolachlor inhibited denitrification by 65% at 10 µg/L. Propiconazole and 1,2,4-triazole also inhibited denitrification at both concentrations, but to a lesser extent (29–38%) than ESA-metolachlor. When inhibition occurred, pesticides affected the reduction of nitrate into nitrite step. However, no significant differences were detected on the abundance of nitrate reductase *narG* and *napA* genes, suggesting an impact of pesticides/metabolites at the protein level rather than on denitrifying bacteria abundance. 16S rRNA gene Illumina sequencing indicated no major modification of bacterial diversity in the presence or absence of pesticides/metabolites, except for ESA-metolachlor and propiconazole at 10 µg/L that tended to increase or decrease Shannon and InvSimpson indices, respectively. General growth parameters suggested no impact of pesticides, except for propiconazole at 10 µg/L that partially inhibited acetate uptake and induced a decrease in microbial biomass. In conclusion, pesticides and metabolites can have side effects at environmental concentrations on microbial denitrification in groundwater and may thus affect ecosystem services based on microbial activities.

**Keywords:** denitrification, groundwater, chloroacetanilide, conazole, pesticides, metabolites, non-target effects

## INTRODUCTION

One consequence of the increasing use of pesticides is the presence of both parent molecules and transformation products (metabolites) in various environmental matrices and notably groundwater, with metabolites being even referred to as emerging groundwater contaminants (Jurado et al., 2012; Lapworth et al., 2012; Baran and Gourcy, 2013; Lopez et al., 2015). One drawback is that pesticides used in agriculture to target insects, fungi, or plants are now known to have side effects on non-target surface and subsurface living (micro)organisms (Iker et al., 2010;

**OPEN ACCESS**

**Edited by:**

Dimitrios Georgios Karpouzas,
University of Thessaly, Greece

**Reviewed by:**

Evangelia Papadopoulou,
University of Thessaly, Greece
Aqiang Ding,
Chongqing University, China

**\*Correspondence:**

Caroline Michel
c.michel@brgm.fr

**Specialty section:**

This article was submitted to
Microbiotechnology,
a section of the journal
Frontiers in Microbiology

**Received:** 01 February 2021

**Accepted:** 30 March 2021

**Published:** 13 May 2021

**Citation:**

Michel C, Baran N, André L, Charron M and Joulian C (2021) *Side Effects of Pesticides and Metabolites in Groundwater: Impact on Denitrification*. Front. Microbiol. 12:662727. doi: 10.3389/fmicb.2021.662727
Staley et al., 2015). The environmental consequence of such side effects of pesticides and metabolites on microbial ecosystems is that they can threaten the ecosystem services based on microbial activities in soil (e.g., litter degradation, plant growth, nutrient cycling, and degradation of pollutants) and groundwater (e.g., production of drinking water, nutrient cycling, degradation of contaminant) (Tuinstra and van Wensem, 2014; Griebler and Avramov, 2015).

Most studies on the side effects of pesticides have been conducted in soils, and the main impacts were noticed on microbial abundance, presence or absence of microbial species, increase or decrease in gene expression (mainly linked to the N cycle), and increase or decrease of functional diversity [activities measured using the Biolog EcoPlates (C cycle) or soil activities linked to P, N, S, and C cycles] (Hussain et al., 2009; Lo, 2010; Kalia and Gosal, 2011; Yang et al., 2011; Jacobsen and Hjelmsø, 2014). Moreover, the presence of pesticides was shown to usually lead to the selection of microorganisms having the ability to degrade them (Bælum et al., 2008; Lancaster et al., 2010). The modes of actions of pesticides are multiple, which could explain the diversity of side effects observed on microbial communities and activities (Saez et al., 2003, 2006). In addition, the effects of pesticides on soil microbial ecosystems mainly depend on the type of pesticides and their concentration, as well as time after application. They also depend on the microbial community structure and on the diversity of the microbial processes that are taking place in the studied soil. Physical and chemical factors such as the type of soil, pesticide concentration, presence of organic matter, and adsorption and desorption processes also influence the impact of pesticides on microbial communities (Hussain et al., 2009). It was also demonstrated that some metabolites obtained after the biological and physicochemical transformations of pesticides can be more persistent and/or more toxic than the parent molecules (Bollag and Kurek, 1980). Taking all together, these factors increase the difficulty to evaluate the risks associated with pesticides use and to predict the net effects of pesticides on soil ecosystem health (Yang et al., 2011; Staley et al., 2015). In addition, contradictory conclusions on the impacts of pesticides can be found among studies due to the difficulty to compare results from different works done with great differences in experimental setups, pesticide concentrations, and methods (Jacobsen and Hjelmsø, 2014). The bioavailability/sorption/biodegradation of pesticides can also vary a lot for the same pesticides according to the studied system (Jacobsen and Hjelmsø, 2014).

The very few studies conducted in groundwater have also underlined the potential side effects of pesticides on groundwater microbial communities. Results first suggested that the presence of pesticides could increase microbial biodiversity. For instance, Imfeld et al. (2018) obtained higher Shannon and Simpson diversity indices (from T-RFLP fingerprints) for groundwater communities in batch experiments amended with pesticides (metolachlor) compared with experiments without the addition of pesticides. de Liphay et al. (2004) also demonstrated that the diversity of colony morphotypes (culturable) in sediments from a subsurface aquifer exposed to herbicides (as mixture)

was higher compared with the non-exposed samples. These authors also observed an increase of the relative abundance of bacterial heterotrophs in aquifer sediment exposed to pesticides, but no specific bacterial species were found for herbicide-exposed samples. Janniche et al. (2012) also noticed high diversity indices from microbial community-level physiological profiles (CLPP) using EcoPlates™ in herbicide (isoproturon, atrazine, and acetochlor)-exposed groundwater. Studies, however, underlined the difficulties to determine *in situ* the impact of pesticides on groundwater biodiversity due to natural spatial and temporal variations of groundwater bacterial communities (de Liphay et al., 2004; Imfeld et al., 2018). Moreover, they suggested that biodiversity modifications induced by pesticides were not associated to changes in general catabolic properties. The role of the contamination history of pesticides on its impact on groundwater microbial communities has also been underlined. As an example, Iker et al. (2010) showed that in an aquifer impacted with atrazine over a long time, a new contamination with this pesticide leads to the decrease of the relative abundance of *amoA* gene and nitrite-oxidizing bacteria, even if ammonia is the primary degradation product of atrazine. On the opposite, in an aquifer never impacted with atrazine before, atrazine spiking led to an increase in the relative abundance of nitrite-oxidizing bacteria. In another study, Mauffret et al. (2017) showed that a triazine concentration of 1 µg/L was enough to induce modification of the bacterial community structure in non-contaminated groundwater batch experiments, but a 10 times higher (10 µg/L) triazine concentration was needed to obtain the same impact in historically contaminated groundwater.

The possibility to extrapolate all the knowledge obtained for soil communities to groundwater ecosystems is still difficult to establish. The first reason is that the concentrations of pesticides in groundwater are low (in hundreds of nanograms to micrograms per liter order) compared with those found in soil, with metabolite contents usually higher than parent molecules (Amalric et al., 2013; Lopez et al., 2015). The second reason is that aquifers are physically, chemically, and biologically different from soil. Groundwater is indeed characterized by a quite constant temperature (around 12–14°C in temperate climates), no sunlight, and a low easily available nutrient content (low organic carbon and oxygen availability) (Griebler and Lueders, 2009; Gregory et al., 2014; Taubert et al., 2019; Retter et al., 2021). Aquifers can be connected to the surface but this connection greatly varies from an aquifer to another, and transfer rates can be so slow that some aquifers can be considered as isolated environments (Hubalek et al., 2016). All of this represents the most important differences with soils and can greatly influence microbial diversity and activities. In particular, lithoautotrophs that fix CO₂ and oxidize inorganic electron donors as energy sources are an important part of groundwater microbial communities. Previous studies also support the idea that groundwater microbial diversity is different from that found in the overlying surface soil (Griebler and Lueders, 2009; Taubert et al., 2019) even if soil microorganisms can be transported into groundwater (Dibbern et al., 2014; Lazar et al., 2017). Moreover, groundwater is usually characterized by lower biodiversity and biomass than soil, and the presence of several novel microbial
phyla has also been demonstrated (Griebler and Lueders, 2009; Anantharaman et al., 2016). Finally, the composition of microbial communities varies across aquifers, which is mainly due to species sorting imposed by local environmental conditions (mineralogy, water chemistry, etc.) as well as other factors such as dispersal limitation and drift across areas, type of aquifer and its connection to the surface, anthropogenic activities, etc. (Gregory et al., 2014; Fillinger et al., 2019; Sonthiphand et al., 2019). When considering all of this, and due to the low nutrient content of aquifers, it can be expected that the addition of exogenous organic compounds such as pesticides and/or their metabolites even at low concentration has impacts on the microbial community in terms of biodiversity and/or activity. This is particularly true as pesticides and metabolites are quite persistent (due to low or no biodegradation) and tend to persist in groundwater. Indeed, even if the potential biodegradation of pesticides in aquifers was already demonstrated (Janniche et al., 2012) with the isolation of pesticide-degrading bacteria, the biodegradation rates of pesticides in aquifers are significantly lower than those observed in topsoil (Hoyle and Arthur, 2000; Albrechtsen et al., 2001; Grenni et al., 2009; Barra Caracciolo et al., 2010).

Among the most used pesticides in Europe that are found in groundwater, chloroacetanilide pesticides, used for the control of annual weeds, mainly on corn, sugar beet, and sunflower, are frequently detected together with their transformation products, notably ethane sulfonic, and oxanilic acids (Kalkhoff et al., 2012; Amalric et al., 2013; Baran and Gourcy, 2013; Sidoli et al., 2016). Among the chloroacetanilide family, S-metolachlor, an herbicide used worldwide, is one of the top five pesticides detected in France [with a maximal concentration of 20.9 µg/L in Amalric et al. (2013) and 0.95 µg/L in Lopez et al. (2015)] and in the EU (Loos et al., 2010). Its metabolite ESA-metolachlor is also widely detected in French groundwater [at a maximum of 4.8 µg/L in Amalric et al. (2013) and with an average concentration of 0.21 µg/L in Baran and Gourcy (2013)]. Triazole fungicides, widely used on fruits, vegetables, and cereals, are also frequently detected in groundwater. During the French national campaign of 2012, propiconazole was one of the most quantified fungicides (Lopez et al., 2015). One of the main metabolites of all triazole fungicides is 1,2,4-triazole, also used in Europe as a nitrification and urease inhibitor. Few data exist on its surveillance in France: its presence was reported in surface waters (Scheurer et al., 2016), and no quantification was done in groundwater during the French national campaigns, which might be due to a high limit of quantification (0.1 µg/L). Recent publications of compiled groundwater monitoring in the United States demonstrate once again the occurrence of these molecules in groundwater and the significant levels that can be observed there. As examples, Bexfield et al. (2021), on a panel of 1,204 sites, measured maximum concentrations of 0.0237, 1.419, and 2.509 µg/L for propiconazole, metolachlor, and ESA-metolachlor, respectively, and Fisher et al. (2021) (54 sites) obtained concentrations of 0.0254, 0.075, and 4.04 µg/L for propiconazole, metolachlor, and ESA-metolachlor, respectively. Both studies also provided new data on 1,2,4-triazole with a maximum concentration of 0.296 µg/L for Bexfield et al.

(2021) (with a detection frequency of 1.4%) and 0.436 µg/L for Fisher et al. (2021).

There is thus a need to study the impact of pesticides and metabolites on the microbial communities and the linked ecosystem services in groundwater. As mentioned above, pesticides/metabolites can affect the N cycle and thus microbial denitrification activity involved in ecosystem services such as the production of drinking water. The objective of this study was thus to evaluate in controlled conditions the impact of pesticides or metabolites on the potential denitrification activity and biodiversity of a groundwater microbial community. One herbicide (S-metolachlor) and one fungicide (propiconazole) belonging to two different chemical classes (respectively, chloroacetanilide and conazole) and their major metabolite ESA-metolachlor and 1,2,4-triazole, respectively, were selected. These two active substances were chosen due to their wide use in France and Europe and their occurrence in groundwater, as mentioned above. These four compounds were tested at two concentrations, 2 and 10 µg/L, similar to those measured in groundwater, in a batch experimental approach to investigate their impacts on groundwater microbial denitrification activity and biodiversity. Groundwater from an agricultural site historically submitted to nitrate use was chosen for this study. The denitrification activity was determined by following the evolution of nitrate, nitrite, and nitrous oxide concentrations, whereas variations of bacterial biodiversity between the start and the end of the experiment were determined thanks to genomic characterizations.

# MATERIALS AND METHODS

## Chemicals

Molecules were provided as pure chemicals by Dr. Ehrenstorfer (S-metolachlor), Sigma Aldrich (ESA-metolachlor), and HPC Standards (propiconazole and 1,2,4-triazole). Individual concentrated solutions (500 mg/L) were prepared in methanol and then diluted in sterile water to obtain a 10-mg/L solution used to spike groundwater samples. Two final concentrations of pesticides and metabolites were used in this study: 2 and 10 µg/L.

## Field Site and Groundwater Sampling and Chemical Analyses

Sampling was done on a catchment located in the NW of France (Plourhan, French Brittany) (Petelet-Giraud et al., 2021; Surdyk et al., 2021). Several piezometers have been drilled in 2006 in the sector. The geological setting corresponds to an amphibolite (metamorphic rock, basement rock).

In the frame of a monitoring campaign in October 2017, a groundwater sample (8 L) was collected from piezometer Pz3 after discarding three purge volumes and stabilization of parameters measured *in situ* (temperature, pH, redox potential, electrical conductivity, and dissolved oxygen). This piezometer has a depth of 19 m and a water table level fluctuating between 2 and 4 m below soil level (bsl) in average and is equipped with a screen between 7 and 19 m bsl. For the analyses of anions and major cations, a sample was collected in 100 ml PE bottles after filtration through a 0.45-µm filter. The sample for major
cation analyses was acidified to pH 2 with ultrapure nitric acid. All samples were cooled for transportation to the laboratory and stored at 4°C until chemical analysis. Pesticides and major ions were then analyzed at the laboratory (Table 1). Chemical analyses were performed by using ICP-AES for Ca²⁺, Na⁺, K⁺, Si²⁺, and Mg²⁺ (with 5% uncertainty); ion chromatography for Cl⁻, SO₄²⁻, and NO₃⁻ (with 10% uncertainty); colorimetric method based on NF ISO 15923-1 for NH₄⁺, PO₄³⁻, and NO₂⁻; and potentiometric methods according to NF EN ISO 9963-1 (1996) for HCO₃⁻ and CO₃²⁻ (with 5% uncertainty). Dissolved organic carbon (DOC) was quantified according to NF EN 1484 (1997) procedures. For pesticides, a 55-pesticides monitoring (occurrence and concentration), including mother molecules and metabolites from pesticides degradation, was performed by liquid chromatography-mass spectrometry (LC-MS/MS) following an online solid-phase extraction (Amalric et al., 2013). The chemical analysis done on Pz3 groundwater validated previous results and showed that this piezometer is impacted by nitrate (78 mg/L) but not by pesticides or metabolites (Table 1).

## Groundwater Incubation (Batch Experiments)

To study the impact of pesticides and metabolites at two concentrations on denitrification in groundwater, pesticide/metabolite-spiked batch experiments were undergone in triplicate. Flasks (300 ml) were filled in with 150 ml groundwater (to obtain a gas/liquid ratio of 50/50) in anoxic conditions (nitrogen atmosphere). Taking into account nitrate concentration in the piezometer (Table 1), flasks were also supplemented with nitrate to reach a final NO₃⁻ concentration

of 100 mg/L. Pesticides and metabolites were added at a final concentration of 2 or 10 µg/L with the exception of positive control flasks (denitrification in the absence of pesticides/metabolites) and abiotic test flasks (addition of sodium azide) (Cabrol et al., 2017). Acetylene was added in the gas phase (10% acetylene/90% nitrogen) to inhibit the last step of the denitrification pathway, i.e., nitrous oxide (N₂O) reduction into nitrogen gas, leading to N₂O accumulation instead of N₂ production (Milenkovski et al., 2010; Crouzet et al., 2016). Acetate (C₂H₃NaO₂) was added as a carbon source in each flask at a final concentration of 100 mg/L. This initial acetate concentration was defined in order to cover the carbon needs of heterotrophic bacteria (catabolism and anabolism) during the denitrification process and to avoid any substrate limitation in relation with initial nitrate concentration (André et al., 2011). Flasks were incubated during 20 days in the dark at 25°C under stirring (100 rpm) to favor water contact with acetylene. At regular times (every 2–3 days), gas sampling (5 ml, using a Vacuette tube system) for N₂O quantification and water sampling (8 ml) for nitrate, nitrite, and acetate quantification were done. To circumvent the depression linked to gas and water sampling, an equal volume of nitrogen (90%)/acetylene (10%) gas was added after each sampling. Nitrate and acetate were quantified by ionic chromatography (Dionex IC3000-SP-EG-DC system equipped with an AS50 autosampler and a conductimetric detector) according to the NF EN ISO 10304-1 (2009) method. Nitrite was analyzed by colorimetry according to the NF ISO 15923-1 (2014) method. N₂O was analyzed by gas chromatography using a Varian CP-3800 GC equipped with a gas injection valve and an electron capture detector. Finally, after 20 days of incubation, 20 ml of water was sampled, filtered (0.22 µm), and stored at –20°C for molecular analyses.

<table><caption>TABLE 1 | Water chemistry and physical properties of groundwater collected from well Pz3 in October 2017.</caption><tr><td>Water level (from topsoil) (m)</td><td>4</td></tr><tr><td>Temperature (°C)</td><td>12</td></tr><tr><td>pH (in situ measurement)</td><td>6.1</td></tr><tr><td>Conductivity (mS/cm)</td><td>455</td></tr><tr><td>Redox potential (Eh, mV)</td><td>223</td></tr><tr><td>Dissolved oxygen (mg/L)</td><td>1.8</td></tr><tr><td>Ca (calcium) (mg/L)</td><td>29</td></tr><tr><td>Cl (chloride) (mg/L)</td><td>50</td></tr><tr><td>HCO<sub>3</sub> (bicarbonates) (mg/L)</td><td>61</td></tr><tr><td>CO<sub>3</sub> (carbonates) (mg/L)</td><td>&lt;LQ</td></tr><tr><td>K (potassium) (mg/L)</td><td>1.4</td></tr><tr><td>Mg (magnesium) (mg/L)</td><td>9.2</td></tr><tr><td>Ammonium (as NH<sub>4</sub>) (mg/L)</td><td>&lt;LQ</td></tr><tr><td>Nitrite (as NO<sub>2</sub>) (mg/L)</td><td>&lt;LQ</td></tr><tr><td>Nitrate (as NO<sub>3</sub>) (mg/L)</td><td>79</td></tr><tr><td>Na (natrium) (mg/L)</td><td>43</td></tr><tr><td>P (phosphate as PO<sub>4</sub>) (mg/L)</td><td>0.1</td></tr><tr><td>S (sulfur as SO<sub>4</sub>) (mg/L)</td><td>18</td></tr><tr><td>Si (silica as SiO<sub>2</sub>) (mg/L)</td><td>34</td></tr><tr><td>Dissolved organic carbon (DOC) (mg/L)</td><td>&lt;LQ</td></tr><tr><td>Pesticides and metabolites (mg/L)</td><td>&lt;LQ</td></tr></table>

LQ, limit of quantification.

## Molecular Analyses

Microbial DNA was extracted from frozen filters using the FastDNA™ Spin Kit for Soil (MP Biomedicals, United States) according to the manufacturer's recommendations with a FastPrep®-24 instrument at a speed of 5 ms⁻¹ for 30 s and quantified using the Quantifluor dsDNA sample kit and the Quantus fluorimeter, according to the manufacturer's instructions (Promega, United States). The abundance of the bacterial universal marker (16S rRNA gene) and of nitrate-reducing bacterial markers *narG* and *napA* genes was assessed by duplicated real-time quantitative PCR (qPCR). The reaction mixture contained 1× SSO Advanced Supermix (Bio-Rad), 0.4 µM of each primer, 100 ng of T4gp32 (MP Biomedicals), 2 µl of 0.03–0.5 ng/µl of template DNA, and qs 20 µl of nuclease-free water. For 16S rRNA gene, primers 341F (5'-CCTACGGGAGGCAGCAG-3') and 515R (5'-ATTACCGGGCTGCTGGCA-3') (López-Gutiérrez et al., 2004; Crouzet et al., 2016) and the following thermocycling conditions were used: 95°C for 3 min, 35 cycles of 95°C for 30 s, 60°C for 30 s, 72°C for 30 s, and a data acquisition step at 80°C for 30 s at each cycle. For *narG* and *napA* genes, the respective primer sets *narG*-F (5'-TCGCCSATYCCGGCSATGTC-3') and *narG*-R (5'-GAGTTGTACCAGTCRGCSGAYTCSG-3') and V17m (5'-TGGACVATGGGYTTYAAYC-3') and *napA*4r
(5'-ACYTCRCGHGCVGTRCCRCA-3') described in Bru et al. (2007) and the following thermocycling conditions were used: 3 min at 95°C; 6 cycles of 30 s at 95°C, 30 s at 63°C (narG) or 61°C (napA) with a touchdown of -1°C by cycle, 30 s at 72°C; 34 (narG) or 40 (napA) cycles of 30 s at 95°C, 30 s at 58°C (narG) or 56°C (napA), 30 s at 72°C, 30 s at 80°C. Standard curves were obtained from serial 10-fold dilutions of linearized plasmids containing known copy numbers of the target gene. No-template controls were run for each qPCR assay. qPCR was run in a CFX Connect Real-Time PCR Detection and data were analyzed with the CFX Manager 3.1 software (Bio-Rad).

Diversity of the bacterial community was determined by 16S rRNA gene Illumina sequencing. Amplicon libraries and sequences were generated by the MetaHealth CIRAD platform (Montpellier, France) using a modified version of the Illumina 16S "Metagenomic" Sequencing Library Preparation Protocol. Briefly, the 16S rRNA V3-V4 gene region was targeted for PCR amplification, in a nested PCR strategy using primers 341F (5'-CCTACGGGNGGCWGCAG-3') and 785R (5'-GACTACHVGGGTATCTAATCC-3') modified with Illumina-specific overhang sequences for barcoding. The first PCRs were run in duplicate using the Phusion Flash High-Fidelity PCR Master Mix (Thermo Fisher), and 10-fold diluted PCR products were then subjected to the second PCR to implement dual barcodes. DNA library was obtained after two successive purifications (Wizard® DNA Clean-Up System kit, Promega) of the second PCR products. Library quantitation was performed by running the library on a D5000 ScreenTape bioanalyzer (Agilent). Sequencing was performed on an Illumina MiSeq platform with MCS v2.6.2.1. For Fastq generation, base calling and associated quality scores were done with Illumina RTA 1.18.54, and demultiplexing was done with MiSeq Reporter 2.6.2.3. Fastq quality control was evaluated with FastQC v0.11.7 (Andrews, 2010) and summarized with multiqc v1.5 (Ewels et al., 2016).

Fastq sequences were processed using the FROGS bioinformatics pipeline (Escudie et al., 2017) implemented into the GenoToul Galaxy platform (Afgan et al., 2018). In brief, after denoising and primer and adapter removal, paired reads were merged with VSEARCH and clustered into OTU with SWARM and an aggregation distance of 3. After chimera removal and filtering for OTU abundance (threshold of 0.00005%), taxonomic affiliation was performed using BLASTn and the 132 Silva database. Filtration on taxonomic affiliation was done at minimum identity of 98.2% and minimum coverage of 99%. Random resampling of the sequences obtained from the 24 samples was applied to have an equal number of 11,200 good-quality sequences per sample. The FROGS implemented Phyloseq R package was used for OTU structure visualization, rarefaction curve, and diversity index calculations. Shannon's index (H') (heterogeneity of microbial community: H' is minimal if all the individuals of a community belong to the same species, or if in a community, all the present species are represented by one individual except for one species which is represented by the remaining individuals of the community) and Simpson's index (1/D in this paper: probability that two individuals picked at random do not belong to the same species; diversity is low if 1/D is low and vice versa) were calculated.

## Statistics

For statistical analyses, boxplots were calculated from triplicate flasks with data obtained during the 0-7-day period and corresponding to the maximal rates and using R4.0.1 and RStudio (R Development Core Team, 2009)<fnref n="1" />. Data (n = 3) were analyzed using the non-parametric Kruskal-Wallis test. Difference was considered significant at *p* value < 0.05. Principal component analysis (PCA) was calculated using XLSTAT (Pearson correlation matrix).

## RESULTS

### Impact of Pesticides and Metabolites on Potential Denitrification Activity

The impact of pesticides on denitrification was tested in batch conditions in the presence of fungicide (propiconazole) and herbicide (S-metolachlor) or their major metabolites (1,2,4-triazole and ESA-metolachlor, respectively) at two concentrations (2 and 10 µg/L). In presence of acetate and since the last denitrification step was inhibited with the use of acetylene leading to N₂O accumulation, the denitrification pathway studied is as follows:

$$ \text{Step1: } \mathrm{NO}_3^- + 1/4\mathrm{CH}_3\mathrm{COO}^- + 1/4\mathrm{H}^+ \rightarrow \mathrm{NO}_2^- + 1/2\mathrm{CO}_2(\mathrm{aq}) + 1/2\mathrm{H}_2\mathrm{O} $$

$$ \text{Step2: } \mathrm{NO}_2^- + 1/8\mathrm{CH}_3\mathrm{COO}^- + 9/8\mathrm{H}^+ \rightarrow \mathrm{NO} + 1/4\mathrm{CO}_2(\mathrm{aq}) + 3/4\mathrm{H}_2\mathrm{O} $$

$$ \text{Step3: } \mathrm{NO} + 1/8\mathrm{CH}_3\mathrm{COO}^- + 1/8\mathrm{H}^+ \rightarrow 1/2 \mathrm{N}_2\mathrm{O} + 1/4\mathrm{CO}_2(\mathrm{aq}) + 1/4\mathrm{H}_2\mathrm{O} $$

The following parameters were quantified: nitrate reduction, nitrite production (step 1), and N₂O production (step 3) rates (Figures 1, 2); these parameters are specific to the denitrification pathway. NO produced during step 2 is a short-lived compound difficult to analyze. Indeed, NO is immediately reduced to nitrous oxide (N₂O) by cytochrome c (cNOR) or quinone (qNOR) membrane-bound reductases (Santana et al., 2017). For this reason, NO was not quantified and we focused on the other nitrogen-bearing compounds. The nitrogen balance was verified and validated for all the batch experiments with a constant nitrogen concentration all along the experiment (Table 2). No nitrate reduction and no nitrite and N₂O production were observed in abiotic test flasks.

The nitrate reduction rate measured in the absence of pesticides or metabolites (control) was about 5.6 × 10⁻⁵ g N/L/h (Figure 1A). With S-metolachlor (at both concentrations) and ESA-metolachlor at 2 µg/L, the kinetic rates were similar. However, ESA-metolachlor at 10 µg/L had the most significant impact on nitrate reduction rate among all the tested conditions, as on average a decrease of 65% was observed. For triazoles,

<footnote n="1">¹http://www.R-project.org</footnote>
<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

**FIGURE 1** | Impact of pesticides and metabolites on the potential denitrification activity (batch experiments) measured as the impact on nitrate (as NO₃⁻) reduction (A), nitrite (as NO₂⁻) production (B), and N₂O production (C) rates. Significant differences between conditions were searched applying the Kruskal-Wallis non-parametric test and are mentioned as a, b, c, d, and e letters. No pesticide: control (absence of pesticides or metabolites); Met: metolachlor; ESA-met: ESA-metolachlor Propico: propiconazole; Triazol: 1,2,4 triazole. 2 and 10 are pesticide or metabolite concentrations in µg/L. No nitrate reduction nor nitrite and N₂O production took place in abiotic test batch (data not shown).
<figure><img src="image_65.png" /><figcaption>**FIGURE 2** | Kinetics of nitrate consumption (**top**), nitrite production (**middle**), and N₂O production (**bottom**) measured in batch experiments in the absence (no pest) or presence of herbicides [Left: Metolachlor (Met) or its metabolite ESA-metolachlor (ESA-met)] or fungicides [Right: Propiconazole (Propico) or its metabolite 1,2,4-triazole (Triazol)] at 2 or 10 µg/L. Abiotic control were performed in the presence of azide (cf. "Materials and Methods" section).</figcaption></figure>

both propiconazole and 1,2,4-triazole decreased the nitrate reduction rate by 29–38% at both concentrations so that high concentrations (10 µg/L) did not result in higher impacts.

In the presence of pesticides or metabolites, a decrease in nitrite production compared with the control (estimated at $4.5 \times 10^{-5}$ g N/L/h) was observed (Figure 1B). With S-metolachlor, at both concentrations, the nitrite production rates decreased by about 10% with respect to the control, but this weak decrease was statistically not significant. With the other compounds, the decrease was significant and could reach almost 50%. As a reminder, the nitrite concentrations

measured in flasks correspond to a mean value taking into account nitrite production due to nitrate reduction and nitrite consumption due to nitrite reduction into NO and then N₂O.

The production rate of N₂O (Figure 1C) was statistically not affected by the presence of metolachlor and ESA-metolachlor at 2 µg/L since the values were similar to the ones of the control (production close to $3.4 \times 10^{-4}$ g N/L/h). In contrast, N₂O production rate was negatively affected by the presence of ESA-metolachlor at 10 µg/L (by 70%) and triazole at both concentrations (by 40–70%).
<table><caption>**TABLE 2** | Nitrogen balance and transformation and acetate concentration during denitrification experiments (batch, n = 3) in the presence or absence of pesticides or metabolites (at 2 or 10 µg/L) (here after 7 days of incubation).</caption><thead><tr><th rowspan="2">Treatment</th><th colspan="3">Nitrogen (after 7 days batch experiments)</th><th rowspan="2">Acetate concentration (mg/L) (after 7 days batch experiments)</th></tr><tr><th>As N-NO<sub>3</sub><sup>-</sup> (%)</th><th>As N-NO<sub>2</sub> (%)</th><th>As N-N<sub>2</sub>O (%)</th></tr></thead><tbody><tr><td>Abiotic control</td><td>100</td><td>0</td><td>0</td><td>130.1 ± 4.8</td></tr><tr><td>No pesticide</td><td>54.8 ± 3.4</td><td>34.2 ± 0.2</td><td>10.9 ± 3.1</td><td>48.2 ± 5.5</td></tr><tr><td>Metolachlor, 2 µg/L</td><td>62.3 ± 4.6</td><td>31.1 ± 1.3</td><td>6.6 ± 3.4</td><td>50.4 ± 4.2</td></tr><tr><td>Metolachlor, 10 µg/L</td><td>66.2 ± 2.9</td><td>29.3 ± 1.9</td><td>4.5 ± 2.7</td><td>53.3 ± 8.6</td></tr><tr><td>ESA-Metolachlor, 2 µg/L</td><td>63.2 ± 8.6</td><td>21.3 ± 42</td><td>15.5 ± 11.6</td><td>55.7 ± 8.1</td></tr><tr><td>ESA-Metolachlor, 10 µg/L</td><td>77.4 ± 5.7</td><td>19.6 ± 3.8</td><td>3 ± 2</td><td>53.2 ± 3.1</td></tr><tr><td>Propiconazole, 2 µg/L</td><td>72.6 ± 5.3</td><td>21 ± 2.3</td><td>6.4 ± 5.3</td><td>54.4 ± 5.6</td></tr><tr><td>Propiconazole, 10 µg/L</td><td>69.8 ± 5.3</td><td>22.5 ± 4.3</td><td>7.5 ± 1.1</td><td>66 ± 7.3</td></tr><tr><td>1,2,4-triazole, 2 µg/L</td><td>72.2 ± 5.5</td><td>22.8 ± 1.2</td><td>5.1 ± 5.3</td><td>55.3 ± 4.9</td></tr><tr><td>1,2,4-triazole, 10 µg/L</td><td>76.6 ± 3.3</td><td>20 ± 1.6</td><td>3.4 ± 3.7</td><td>56.4 ± 1.2</td></tr></tbody></table>

For denitrification activity characterization, rates for nitrate consumption, nitrate production, and N₂O production were calculated with the data obtained during the 0–7-day period, corresponding to the maximal rates. At T0, nitrogen was present as N-NO₃⁻ (100%) in all flasks. The nitrogen balance was also verified at all sampling times.

<figure><img src="image_66.png" /><figcaption>**FIGURE 3** | Acetate consumption rate during denitrification in batch experiments with or without pesticides or metabolites. No acetate consumption took place in abiotic test batch (data not shown). No pesticide: control (absence of pesticides or metabolites); Met: metolachlor; ESA-met: ESA-metolachlor; Propico: propiconazole; Triazol: 1,2,4-triazole. 2 and 10 are pesticide or metabolite concentrations in µg/L. Significant differences between conditions were searched applying the Kruskal-Wallis non-parametric test and are mentioned as a and b letters.</figcaption></figure>

All the results suggest that according to the compound and, in some cases, its concentration, denitrification activity was either not affected or negatively impacted (decrease of nitrate reduction rate and nitrite and N₂O production rates). As mentioned above, the denitrification process in the batch experiments was based on acetate consumption. Acetate was in excess in the solution (Table 2) and its consumption rates in the presence of the tested pesticides and metabolites did not vary significantly from the control (no pesticide), except in the presence of propiconazole at 10 µg/L for which a lower acetate consumption rate was measured (Figure 3). This strongly suggests that there is no direct link between denitrification activity and acetate consumption (in such case, acetate consumption rate would be lowered in conditions such as ESA-metolachlor 10 µg/L).

The PCA analysis of the parameters used to characterize the potential denitrification activity during batch experiments

indicated, according to F1 explaining 65% of the variability (nitrate consumption, nitrite production, and N₂O production rates all contribute to this axis), the following order for the impact of pesticides and metabolites on denitrification (Figure 4): control (without pesticides or metabolites) < S-metolachlor (2 and 10 µg/L) < ESA-metolachlor (2 µg/L) < propiconazole (2 and 10 µg/L) < 1,2,4-triazole (2 and 10 µg/L) < ESA-metolachlor (10 µg/L). ESA-metolachlor at 10 µg/L is thus the condition for which all the denitrification rates (nitrate reduction, nitrite production, and N₂O production) were the most significantly decreased.

Results from batch experiments also suggested that when denitrification inhibition was observed, at least the first step of denitrification was impacted as nitrate reduction rate significantly decreased (Figure 1A). To determine if pesticides also impacted the other steps of the denitrification pathway, the nitrate
<figure><img src="image_67.png" /><figcaption>**FIGURE 4** | Principal component analysis (PCA) of potential denitrification parameters measured during batch experiments in the presence or absence of pesticides at 2 or 10 µg/L. The percentages of explained variations for the first two axes are indicated within the figure. No pest: control (absence of pesticides or metabolites); Met: metolachlor; ESA-met: ESA-metolachlor; Propico: propiconazole; Triazol: 1,2,4-triazole. 2 and 10 are pesticide or metabolite concentrations in µg/L. A, B, and C are replicates.</figcaption></figure>

reduction rate vs. N₂O production rate ratio was calculated. Whatever the compound and its concentration, its presence did not modify significantly this ratio (data not shown). This suggested that the lower N₂O production rates measured in some conditions were directly linked to lower nitrate reduction rates. The impact of the tested pesticides and metabolites thus mainly concerned the first step of denitrification, i.e., nitrate reduction into nitrite.

## Impact of Pesticides and Metabolites on the Bacterial Denitrifying Community

As the first step (nitrate reduction into nitrite) of the denitrification pathway was shown to be mainly impacted, and in order to determine if the proportion of the denitrifying community changed among the microbial community, the relative abundance of *narG* and *napA* genes, both encoding nitrate reductase, was measured using qPCR approaches (Figure 5). Data were normalized by taking into account molecular biomass (total DNA) (Bru et al., 2007; Wakelin et al., 2011; Hernández-del Amo et al., 2018). The presence of pesticides or metabolites did not significantly modify the relative abundance of both genes even in conditions for which nitrate reduction rate was decreased (in particular ESA-metolachlor at 10 µg/L), suggesting an impact (inhibition) of pesticides and metabolites at the protein level rather

than on the nitrate-reducing bacteria abundance within the bacterial community.

## Impact on Microbial Biomass

The impact of pesticides and metabolites on groundwater microbial biomass was evaluated at the end of the batch experiments *via* the measure of molecular biomass (total DNA) and bacterial biomass. Both parameters were the same in the presence or absence of pesticides or metabolites whatever the molecule and its concentration with the exception of propiconazole at 10 µg/L. In this condition, a significant decrease in molecular biomass was observed, and the lowest bacterial biomass was measured (Figure 6). This first strongly suggests that there is no direct correlation between denitrification activity and molecular biomass or bacterial biomass (in this case, a decrease of these parameters should have been observed in the ESA-metolachlor 10 µg/L condition particularly). This also suggests that propiconazole at 10 µg/L can inhibit groundwater microbial growth.

## Impact on Bacterial Diversity

The bacterial community diversity was determined by Illumina 16S rRNA sequencing on batch experiment samples after pesticide or metabolite exposure during 20 days. Results first showed that among all the samples, the two main phyla were
<figure><img src="image_68.png" /><figcaption>**FIGURE 5** | Abundance of *narG* (top) and *napA* (bottom) genes (involved in nitrate reduction) at the end of batch experiments. Results are given as relative abundance, i.e., data were normalized by taking into account molecular biomass. No pest: no pesticide or metabolite (control); Met: metolachlor; ESA-met: ESA-metolachlor; Propico: propiconazole; Triazol: 1,2,4 triazole. 2 and 10 are pesticide or metabolite concentrations in µg/L. Significant differences between conditions were searched applying the Kruskal-Wallis non-parametric test and are mentioned as a letter.</figcaption></figure>

Proteobacteria (as the most dominant phylum) and to a lesser extent Bacteroidetes. As expected, when looking at the genus level, the conditions applied in batch experiments in favor of denitrification led to the selection of bacteria involved in the N cycle. Indeed, the *Aquabacterium* genus, known to reduce nitrate into nitrite (Kalmbach et al., 1999), was the most dominant genus at the end of all batch experiments (Figure 7). In addition, other genera such as *Azospirillum* (Kloos et al., 2001) and *Rhodoferax* (Hougardy and Klemme, 1995), also known to reduce nitrate into nitrite, were detected. No genus was specific to a given batch condition except *Ideonella* that was found only in the condition with ESA-metolachlor at 10 µg/L

and *Rhodoferax* found in the condition with 1,2,4-triazole at 10 µg/L. There was no significant difference between values of Shannon and InvSimpson (1/D) biodiversity indices among most samples (Figure 8), suggesting that pesticides and metabolites at low (environmental) concentrations did not significantly impact groundwater bacterial community diversity. However, results suggested that ESA-metolachlor at 10 µg/L tended to increase Shannon and InvSimpson indices and that propiconazole at 10 µg/L tended to decrease both biodiversity indices. When comparing diversity depending on the pesticide or metabolite type and concentration, bacterial communities were comparable in terms of genus presence and relative abundance tendencies,
<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /><figcaption>**FIGURE 6** | Biomass quantification in flasks at the end of batch experiments. (A) Molecular biomass (measured as total DNA concentration) and (B) bacterial biomass (measured as bacterial 16S rRNA gene copies). No pesticide: control (absence of pesticides or metabolites); Met: metolachlor; ESA-met: ESA-metolachlor; Propico: propiconazole; Triazol: 1,2,4-triazole. 2 and 10 are pesticide or metabolite concentrations in µg/L. Significant differences between conditions were searched applying the Kruskal–Wallis non-parametric test and are mentioned as a, b, and c letters.</figcaption></figure>

except for the conditions with ESA-metolachlor at 10 µg/L and 1,2,4-triazole at 10 µg/L, in which *Ideonella* and *Rhodoferax* were specifically found, respectively, as mentioned above.

DISCUSSION

In this paper, the potential side effects of some pesticides and their
metabolites frequently detected in groundwater on groundwater
microbial ecosystems were investigated. In particular, a focus
was done on the potential impact of these compounds on the
denitrification activity supported by the groundwater bacterial

community. The strategy used in the study was to perform
all the tests (batch experiments at the lab scale) with the
same groundwater sample, which allowed comparing the results.
Indeed, such a strategy enables to circumvent the potential effects
due to hydrogeochemical and seasonal variations in groundwater
and to strong spatial and temporal variations in groundwater
bacterial communities (de Liphay et al., 2004; Imfeld et al., 2018).
In our study, nitrate reduction rate measured in the absence of
pesticides/metabolites (control) was about 5.6 × 10⁻⁵ g N/L/h.
Even if it is difficult to compare acquired data with already
published denitrification rates because of the numerous factors
affecting this process (pH, temperature, nitrate and electron
<figure><img src="image_71.png" /><figcaption>**FIGURE 7** | Relative abundance of bacterial genera at the end of batch experiments, representing at least 1% of obtained sequences in at least one sample. Data represent average values of experimental replicates. No pest: control (absence of pesticides or metabolites); Met: metolachlor; ESA-met: ESA-metolachlor; Propico: propiconazole; Triazol: 1,2,4-triazole. 2 and 10 are pesticide or metabolite concentrations in µg/L.</figcaption></figure>

donor concentrations, etc.), denitrification rates measured in laboratory experiments generally vary between 7.0 × 10⁻⁶ and 1.0 × 10⁻⁴ g N/L/h (Trudell et al., 1986; Starr and Gillham, 1993; Schipper and Vojvodic Vukovic, 1998, 2000; Devlin et al., 2000; André et al., 2011). The values determined in this study for the control (without pesticide) and for the conditions with pesticide/metabolite are thus in the same order of magnitude than other studies.

## Side Effects of Pesticides and Their Metabolites on the Metabolism of Groundwater Microbial Community

Among the compounds tested, S-metolachlor at environmental concentrations was shown to have no or minor impact on denitrification as well as on general microbial parameters (biomass, acetate consumption) of the tested groundwater microbial community. This is in agreement with previous studies such as that of de Liphay et al. (2004) who suggested no major effect of S-metolachlor on *in situ* groundwater bacterial community (using Ecolog and DGGE fingerprinting approaches). Mauffret et al. (2017) showed that

the chloroacetanilide family had no impact on the bacterial community structure but increased the abundance of nitrate reductase *napA* gene. This last point is not in agreement with our results that showed no impact of the tested pesticides on the relative abundance of the nitrate reductase *narG* and *napA* genes involved in the first step of denitrification. One explanation is probably that the data of Mauffret et al. (2017) were obtained from *in situ* groundwater, whereas our results concern batch experiments, and it has been demonstrated that for *in situ* studies particularly, there are difficulties to tease apart the effect of pesticides from the effects of variations of hydrogeological conditions (Imfeld et al., 2018). Moreover, as mentioned above, microbial community diversity as well as the history of pesticide contamination (i.e., communities previously in contact or not with pesticides) can influence the impact of pesticides. Finally, the work of Imfeld et al. (2018) also suggested a little effect of metolachlor on *in situ* bacterial groundwater community (Microtox test and T-RFLP fingerprints). In this same work, the transformation products of S-metolachlor (as ESA and OXA-metolachlor) were shown to have no effect on microbial metabolism. We obtained a similar result in denitrifying condition when considering general growth
<figure><img src="image_72.png" /><figcaption>**FIGURE 8** | Biodiversity index of the bacterial communities at the end of batch experiments (20 days): (Top) Shannon index and (Bottom) InvSimpson index. No pest: control (absence of pesticides or metabolites); Met: metolachlor; ESA-met: ESA-metolachlor; Propico: propiconazole; Triazol: 1,2,4-triazole. 2 and 10 are pesticide or metabolite concentrations in µg/L. Significant differences between conditions were searched applying the Kruskal-Wallis is non-parametric test and are mentioned as a, b, and c letters.</figcaption></figure>

parameters. Indeed, ESA-metolachlor had no impact on biomass and acetate consumption. However, in such anoxic conditions, ESA-metolachlor (at 10 µg/L) had the highest negative impact on denitrification. Taking into account our study and that of Imfeld et al. (2018), it can be suggested that pesticides or metabolites (like ESA-metolachlor) could mainly have side effects on specific communities and/or targeted metabolisms (like denitrification), and not on the whole microbial community.

Concerning the potential side effects of the members of the conazole family, our results suggested that propiconazole and 1,2,4-triazole had a higher adverse effect on the microbial denitrifying community of the studied groundwater than chloroacetanilide [as S-metolachlor and ESA-metolachlor (at 2 µg/L)]. Moreover, propiconazole (at 10 µg/L) was shown to inhibit microbial growth as less microbial biomass was produced in its presence. This is in agreement with Milenkovski et al. (2010) who found (via leucine incorporation experiments) that propiconazole and other fungicides belonging to different chemical classes had an inhibitory effect on microbial metabolism. In their study, these authors did not notice any impact of propiconazole on denitrification but measured an inhibition of the denitrification pathway with other fungicides. Saez et al. (2003), by comparing the impact of various insecticides, herbicides, and fungicides (at 10 µg/L) on the pure denitrifying

strain *Paracoccus denitrificans*, observed the more severe effects on bacterial growth and activity with fungicides. Staley et al. (2015) also concluded, by comparing a large number of studies, that fungicides seemed to be most deadly to microorganisms than herbicides.

At last, this study showed that among the tested compound, metabolites (here, ESA-metolachlor and 1,2,4-triazole) have a more significant impact on denitrification than mother molecules (Figure 4). Bollag and Kurek (1980) already demonstrated that chlordimeform (organochloride insecticide) metabolites (at 25 mg/L and more) negatively impacted soil microbial denitrification (accumulation of nitrite and nitrous oxide), whereas chlordimeform itself had no impact at the same concentrations (and even at 100 mg/L). As mentioned in the *Introduction*, metabolites exhibit high occurrence in groundwater and sometimes with concentrations of the order of micrograms per liter. This was demonstrated by several studies such as Fisher et al. (2021) for ESA-metolachlor and 1,2,4-triazole as well as for other metabolites such as 4-hydroxychlorothalonil (ranging from 0.13 to 368 µg/L). Another recent study in which 116 metabolites on 1,204 sites were analyzed also showed that six metabolites exhibit concentrations higher than 0.1 µg/L [notably 4-hydroxychlorothalonil in 1.6% of the samples with a maximum concentration of 17.540 µg/L, and 22 metabolites with a detection
frequency higher than 1% (Bexfield et al., 2021)]. Our results together with other studies thus suggest that metabolites can greatly impact the microbial ecosystem in groundwater.

## Side Effects of the Tested Pesticides and Metabolites on the Denitrification Pathway

Our study showed that the first step of denitrification (reduction of nitrate into nitrite) supported by nitrate reductase enzymes was the main step impacted/inhibited by the studied pesticides or metabolites. Nitrate reductase is encoded by *narG* or *napA* genes, depending on the bacterial strain. In our study, the abundance of *narG* and *napA* genes was not impacted by the presence of the tested pesticides and metabolites. This suggests that the impact of the tested compounds probably occurs rather on gene expression and/or enzymatic activity itself. Pankaj and Gore (2015) already showed that the activity of enzyme nitrate reductase of plants was inhibited by some phytopharmaceutical products (confidor, omite, and karathane) and their active substances (imidacloprid, propargite, and meptyldinocap). The work of Saez et al. (2003) also suggested that some pesticides (methylparathion, atrazine, and simazine) affected the expression of the nitrate reductase activity of the bacterial strain *P. denitrificans* at a concentration of 10 mg/L. The activity of other enzymes of the denitrification pathway was shown, in some cases, to be also affected by pesticides. As an example, the presence of aldrin, lindane, dimethoate, and methidathion led to the inhibition of nitrite reductase activity in the study conducted by Saez et al. (2003). Su et al. (2019) also observed (in riparian sediments) an inhibition of the activity of nitrate reductase, nitrite reductase, and N₂O reductase (but not NO reductase) enzymes involved in the denitrification pathway by the pesticide chlorothalonil (at 2 mg/kg and more). These authors also demonstrated an impact of this pesticide on *narG* gene relative abundance, but not on the relative abundance of *nirK*, *norB*, and *nosZ* genes involved in the next steps of denitrification. This indicates that there was no direct matching between responses to the presence of pesticides at the protein and genetic levels. In our study, only nitrate reductase activity was impacted and this impact was detected for concentrations as low as 2 or 10 µg/L according to the pesticides or metabolites (Figure 1). However, it has to be mentioned that as we used acetylene in our experiment, the impact of pesticides or metabolites on the last step of denitrification (reduction of N₂O into N₂ by the enzyme nitrous oxide reductase encoded by *nosZ* gene) cannot be evaluated here.

## Diversity of the Groundwater Bacterial Community in the Presence of Pesticides and Metabolites

In our study, the presence and relative abundance of bacterial genera were similar for most of the conditions, suggesting that the composition of the bacterial community was generally weakly affected by the presence of pesticides or metabolites at environmental concentrations. Only two genera were found specific (*Ideonella* for ESA-metolachlor at 10 µg/L and *Rhodoferax* for 1,2,4-triazole at 10 µg/L). This is in agreement

with previous studies that reported minor effects of pesticides on the overall composition of the groundwater microbial community. Indeed, Imfeld et al. (2018) found that OTUs specific to pesticide presence and correlating with metolachlor (even at 5 mg/L) addition in batch experiments ranged only between 0.4 and 3.6% of the total. In the same way, de Liphay et al. (2004) found no DGGE DNA bands that were unique for the herbicide (a mixture of six herbicides at 40 µg/L each)-exposed subsurface sediments (shallow aquifer). Shannon and InvSimpson diversity indices obtained at the end of our batch experiments suggested a potential increase or decrease of bacterial diversity in the presence of ESA-metolachlor or propiconazole at 10 µg/L, respectively. On the opposite, ESA-metolachlor and propiconazole at 2 µg/L, as well as the other tested pesticides and metabolites, did not impact diversity indices suggesting that the diversity may be impacted differently according to the molecule and its concentration. Results obtained with ESA-metolachlor at 10 µg/L are in agreement with several other studies on groundwater impacted by pesticides that, as mentioned above, displayed higher diversity indices [based on carbon source utilization (EcoPlate), colony morphology, DGGE analyses, or Illumina sequencing] in the presence of some pesticides (de Liphay et al., 2004; Janniche et al., 2012; Imfeld et al., 2018). The higher diversity observed in some pesticide-polluted aquifers compared with unpolluted aquifers can be explained by various hypotheses. First, as some microorganisms can metabolize pesticides and use them as a source of C (Munoz et al., 2011; Satapute and Kaliwal, 2016) and even N (Sanchez-Sanchez et al., 2013; Wang et al., 2016) or P (Shushkova et al., 2012) according to pesticide composition, the presence of pesticides can result in an increase in microbial abundance and diversity. This is particularly true in the case of groundwater that is naturally weakly charged in organic matter [as the groundwater we used in this study, whose DOC content was under the limit of detection (Table 1)] and where pesticides can represent a new source of nutrients. Secondly, as pesticides have a lethal effect on some microorganisms but not on all microorganisms, their presence can lead to the cell lysis of non-resistant microorganisms and thus increase nutrient sources for resistant microorganisms and thus impact biodiversity. Thirdly, the presence of a toxic compound, by killing sensitive strains and/or inhibiting (even partially) their activity, limits the number of competitors for available nutrients. Resistant microorganisms can thus thrive in an easier manner, and strains initially in minority, if resistant, can become dominant strains. This could be the case in batch experiments with ESA-metolachlor (10 µg/L) in our study. Indeed, this metabolite partially inhibits and slows down the activity of denitrifying bacteria, and its presence allows the development of microorganisms such as *Ideonella*, *Brevundimonas*, and *Pajaroellobacter* that are in higher abundance or even specific to this condition and that are not known to be linked to denitrification, which leads to a higher diversity. This is in agreement with Staley et al. (2015) who concluded that in a microbial community, even if the direct effects of a pesticide family on some microorganisms are negative, some indirect positive effects can occur for other microorganisms. The direct negative effects can thus tend to be