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. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: Environ Toxicol Chem. 2022 Jan;41(1):122–133. doi: 10.1002/etc.5245

Induced Hepatic Glutathione and Metabolomic Alterations Following Mixed Pesticide and Fertilizer Exposures in Juvenile Leopard Frogs (Lithobates sphenocephala)

Robin J Van Meter 1, Donna A Glinski 2, S Thomas Purucker 3, W Matthew Henderson 4
PMCID: PMC8935487  NIHMSID: NIHMS1771513  PMID: 34967044

Abstract

The increasing use of agrochemicals, alone and in combination, has been implicated as a potential causative factor in the decline of amphibians worldwide. Fertilizers and pesticides are frequently combined into single-use tank mixtures for agricultural applications to decrease costs while meeting the food demands of a growing human population. Limited data are available on the effects of increased nitrogen levels in nontarget species, such as amphibians, and therefore investigating alterations in the nitrogen cycle and its impacts on amphibians needs to be considered in best management practices going forward. The objective of the present study was to elucidate the impact of fertilizer (urea) and herbicide (atrazine and/or alachlor) tank mixtures on the hepatic metabolome of juvenile leopard frogs as well as to investigate alterations in oxidative stress by relating these changes to glutathione (GSH) levels. Herbicide exposure only moderately increased this parameter in amphibians, however, urea alone and in combination with either atrazine or alachlor statistically elevated GSH levels. Interestingly, urea also inhibited pesticide uptake: calculated bioconcentration factors were greatly decreased for atrazine and alachlor when urea was present in the exposure mixture. Metabolomic profiling identified fluxes in hepatic metabolites that are involved in GSH and carbohydrate metabolic processes as well as altered intermediates in the urea cycle. Ultimately, understanding the biological impacts of nitrogenous fertilizers alone and in combination with pesticide exposure will inform best management practices to conserve declining amphibian populations worldwide.

Keywords: urea, atrazine, alachlor, GSH, biomarkers, amphibian

Introduction

Agrochemical production and usage are essential to food security for an ever-increasing human population, despite the well-studied contributions of agrochemicals to significant and widespread amphibian declines around the globe (Baker et al., 2013; Mann et al., 2009; Marco & Ortiz-Santaliestra, 2009). Among common agricultural fertilizers, urea was 5% of the total worldwide usage in the 1960s and has continually increased each decade as a viable nitrogen source for forests and row crops (Gilbert et al., 2006). In 2018, total nitrogen consumption worldwide was 103,704,900 tonne and of that, urea constituted 49,856,800 tonne or approximately 49% of total agricultural usage, with regional estimates ranging approximately from 24% to 87% (International Fertilizer Association, 2020). Urea is desirable due to its chemical stability, decreased explosivity, and use in either solid or liquid form. It is also more cost effective, both because urea is easier to transport, and because its higher nitrogen content allows farmers to apply a smaller quantity to achieve the same positive outcomes for crop yield (Gilbert et al., 2006). Furthermore, because it is a suitable carrier for herbicides in liquid form, urea is highly desirable as a tank mixture although its usage is not without concern. Soil leaching and volatilization as ammonia are among the drawbacks of urea application in agricultural settings (reviewed in Gould et al., 1986). Impacts on nontarget species have also emerged as a concern of urea applications in agricultural settings by contributing to elevated nitrogen exposures (Hatch et al., 2001).

Although the impact of nitrogenous fertilizers on amphibians as nontarget species has been adequately documented in the literature, the vast majority of published research details adverse impacts of nitrate, nitrite, and/or ammonia exposure (reviewed in Mann et al., 2009; Marco & Ortiz-Santaliestra, 2009). Thus, even though urea constitutes nearly 50% of total worldwide fertilizer usage, its impact on amphibians has received relatively little research focus. Interestingly, natural urea production can play an important role in amphibian hydration status. Amphibians use their skin to drink water, and this water conservation adaptation is critical for staying hydrated, even in environments that are quite dry (Grundy & Storey, 1994; Jørgensen, 1997). When estivating amphibians reach a certain level of dehydration, their metabolic system switches from carbohydrate catabolism to a more protein-based catabolism, by increasing urea levels in the blood (Grundy & Storey, 1994). The higher the rates of urea in the plasma, the greater the rate of urea excretion at equilibrium, thus affording the amphibian the ability to tolerate very dry conditions (Jørgensen, 1997). Furthermore, when under osmotic stress, such as during estivation, amphibians can increase urea concentrations in the body to prevent desiccation (Grundy & Storey, 1994). Because urea is becoming the most common fertilizer encountered among amphibians and other nontarget organisms, there is a great need for additional research on urea exposure and associated effects in these species.

Given that urea is often applied to crops as a tank mixture with one or multiple herbicides, co-exposure of agrochemicals to nontarget species is a growing concern. Adverse impacts of pesticides on amphibians are well documented (Agostini et al., 2020; Baker et al., 2013; Brühl et al., 2011; Mann et al., 2009), but published data on tank mixtures containing pesticides plus a fertilizer co-applicant and resultant effects on amphibians following exposure are scant. A meta-analysis of the effects of 16 chemical classes on amphibian survival and growth indicated that agrochemical mixtures may be particularly deleterious to amphibians, given that different chemical classes can exert varying lethal and sublethal impacts (Baker et al., 2013). Van Meter et al. (2019) reported a 20%–37% decrease in acetylcholinesterase activity in brain tissues following urea exposure in juvenile southern leopard frogs (Lithobates (synonym Rana) sphenocephala) relative to individual pesticide treatment groups, as well as an increased bioconcentration of pesticides when in a mixture containing urea. Co-exposure to carbaryl and nitrates in green frog (Rana clamitans) tadpoles resulted in decreased mass and survivorship (Boone et al., 2005). Given the likelihood of simultaneous exposure to both fertilizers (such as urea) and pesticides in migrating amphibians and the lack of data to support conservation and risk assessment, continued research on co-exposures emerges as a research priority.

Elucidating biological indices of chemical exposure in amphibians is challenging but important for risk assessment. Among these indices, biomarkers of xenobiotic exposure in amphibians have been well documented in the literature, particularly for pesticides (Venturino et al., 2003; Venturino & D'Angelo, 2005). Glutathione S-transferase (GST) activity and reduced glutathione (GSH) have been commonly reported as reliable biomarkers of pesticide exposure in amphibians. Following exposure, pesticide metabolism often results in oxidative stress through the production of reactive oxygen species (ROS). As a ROS scavenger, GSH plays a critical role in intracellular response (Venturino et al., 2003). The GST enzymatic response to oxidative stress (ROS production) induced by pesticide exposure among amphibians has been successfully studied in blood (see Attademo et al., 2007; Lajmanovich et al., 2008) and homogenized tissues (see Ezemonye & Tongo, 2010; Greulich & Pflugmacher, 2004), suggesting that this is a viable method for both nondestructive and destructive biomarker sampling techniques.

Metabolomics is another novel approach for elucidating biomarkers of pesticide exposure among amphibians. Following exposure to single, double, or triple mixtures of a fungicide, herbicide, and insecticide treatment in southern leopard frog juveniles, Van Meter et al. (2018) identified 50 hepatic metabolites that were altered by a pesticide mixture. Likewise, Glinski et al. (2019) reported both up- and down-regulation of amino acids, carbohydrates, and nucleic acids essential to key biological pathways, such as GSH metabolism, following mixed pesticide exposure in southern leopard frogs. Elucidating how agrochemicals (even if present at low levels) alter typical biological function in amphibians is essential to informing management efforts.

Because of the widespread and increasing use of registered pesticides and fertilizer products around the world, in combination with well-documented amphibian declines, we aimed to evaluate the impacts of a common tank mixture on terrestrial frogs through biomarker analysis. This is a greatly understudied yet highly desirable area of research that is needed to support amphibian risk assessments during agrochemical registration processes (Johnson et al., 2017). Given that urea is an excellent carrier solvent for pesticides, and little is known about its effect on amphibian physiology, the impact of an agrochemical mixture was simulated by pairing urea with two of the most heavily used herbicides worldwide: atrazine and alachlor. Following terrestrial amphibian exposures, pesticide bioconcentration factors (BCFs) as well as hepatic GSH activity and metabolomic profiling for all single, double, and triple agrochemical treatments were analyzed. Based on previous hepatic metabolomic profiling for amphibians following mixed pesticide exposure (Glinski et al., 2019; Van Meter et al., 2018), and corticosterone and acetylcholinesterase biomarker analysis for a urea tank mixture (Van Meter et al., 2019), we expected that BCFs, GSH activity levels, and perturbations to the hepatic metabolome would increase in the complete tank mixture containing urea, atrazine, and alachlor relative to individual and double herbicide treatments.

Materials and Methods

Chemicals & Soils

All solvents and urea (more than 99% purity) were obtained from Fisher Scientific. Atrazine and alachlor (more than 98% purity) were purchased from Chem Service. Soils were collected from a grassland plantation that is managed through infrequent, controlled burns at the River and Field Campus of Washington College, Chestertown, MD, USA and stored in a walk-in cold room at 4 °C. Prior to experimental use, soils were passed through a 2-mm sieve to remove large pieces of debris. The soil was classified as a Unicorn-Sassafras loam and is typical of soils in agricultural fields with low organic matter.

Amphibian Rearing and Care

Three southern leopard frogs (L. sphenocephala) were collected as egg masses from vernal pools in Queen Anne's County (MD, USA) and transported to Washington College. Eggs were hatched indoors in 10-gallon aquaria, and tadpoles were transferred to outdoor 110-gallon polyethylene tanks filled with aged tap water after reaching Gosner stage 25 (the free-feeding stage; Gosner, 1960). Tadpoles were fed TetraMin® tropical fish flakes ad libitum through metamorphosis. At metamorphosis, all juveniles were transferred to outdoor 110-gallon polyethylene tanks lined with moist sphagnum moss to simulate a terrestrial habitat and fed purchased crickets for 30–90 days until experimentation. Our protocol (SU16-003) was approved by the Washington College Institutional Animal Care & Use Committee.

Agrochemical Exposures

Juvenile leopard frogs were divided randomly across eight chemical treatments: control, atrazine, alachlor, urea, atrazine + alachlor, atrazine + urea, alachlor + urea, and the full chemical combination of atrazine + alachlor + urea. Six frogs were randomly assigned to each chemical treatment for a total sample size of n = 48 frog exposures. After exposure, all frogs were immediately euthanized for analysis of active ingredient body burden and GSH, as detailed in the sections GSH and Soil and amphibian GC–MS analysis.

Chemical exposures followed the methods outlined in Van Meter et al. (2016, 2019). Briefly, 0.94-L Pyrex® glass bowls were lined with 150 g soil. Pesticide active ingredients were applied at the labeled application rate: atrazine = 23.6 μg/cm2; alachlor = 30.9 μg/cm2. Urea was applied within the range of application rates suggested for atrazine and alachlor tank mixtures: 2.2 mg/cm2. All chemicals were dissolved in 75 ml methanol (MeOH), either individually or in combination, prior to application to the soil surface using hand-held Preval® aerosol spray guns. Methanol was allowed to volatilize off the soil surfaces overnight in a fume hood. Frogs were dehydrated for approximately 12 h by placing them in dry, unlined glass aquariums the night before experimentation. The following morning, soils were rehydrated with 50 ml of spring water, and frogs were immediately placed onto the contaminated soil surface for an 8-h exposure. At the termination of the experiment, frogs were euthanized and stored in a −80 °C freezer, along with soil samples from each experimental chamber, until extraction and GSH analysis.

Glutathione (GSH)

Methods for GSH analysis from frog livers (n = 48) followed Tipple & Rogers (2012). Briefly, approximately 45 mg of liver tissue was homogenized in a sodium phosphate–ethylenediaminetetraacetic acid assay buffer. The homogenized sample was centrifuged at 12,000 rpm for 20 min, and the supernatant was removed for final GSH analysis. Reaction mixtures contained 5,5′-dithiobis-(2-nitrobenzoic acid), GSH reductase, and ß-nicotinamide adenine dinucleotide phosphate. All samples were stored on ice and analyzed colorimetrically using 96-well plates on a plate reader set at 405 nm. All samples were run in duplicate.

Soil & Amphibian Agrochemical Extraction

Pesticide extraction methods for amphibians and soils followed those outlined in Van Meter et al. (2014). Briefly, amphibians were weighed, and tissues were homogenized followed by freeze drying. One composite soil sample (~1 g) from each experimental chamber was weighed and placed in a 15-ml centrifuge tube. Tetraconazole internal standard was added to both amphibian and soil samples prior to extraction. Soil and amphibian samples were then extracted two times in 5 ml MeOH, blown down to approximately 1 ml under nitrogen, and reconstituted with 10 ml Milli-Q water. Three milliliters of methyl-tert butyl ether (MTBE) was then added, and the samples were allowed to gravimetrically separate before the addition of sodium sulfate. The MTBE layer (1 ml) was then pipetted off the surface, transferred to a 2-ml centrifuge tube, and centrifuged at 13 500 rpm for 15 min. The final sample was transferred to a gas chromatography (GC) vial for analysis via GC–mass spectrometry (MS). Atrazine metabolites, desethyl atrazine (DEA), and deisopropyl atrazine (DIA) were summed in total with atrazine when detected. From concentration data obtained for the frogs, BCFs were calculated for each frog as:

BCF=Cf∕Cs

where Cf is the frog whole-body tissue concentration and Cs is the average composite soil concentration within each treatment, both at the end of the 8-hour exposure. Although BCFs typically refer to accumulation of contaminants from an aquatic medium at steady state, they also describe dietary and dermal accumulation in terrestrial environments, as presented in our study (Henson-Ramsey et al., 2008; Kenaga, 1980).

Amphibian Metabolomics Sample Preparation

Metabolomic extraction methods followed those detailed in Viant (2007). Briefly, liver samples (~20 mg) were homogenized using a tissue lyser and extracted using MeOH and chloroform to separate the polar and nonpolar metabolites. Following phase separation, samples were placed in a Savant SpeedVac Plus evaporator overnight. The polar fraction of each sample was derivatized with 50 μl of methoxyamine hydrochloride at 20 mg/ml in pyridine and placed in a 60 °C oven for 2.5 h. During the incubation process, samples were vortexed every 30 min. After cooling, 100 μl of N,O-bistrifluoroacetamide with 10% methyltrichlorosilane was added to each sample. The samples were then placed in the oven again for 1.5 h and vortexed every 30 min. The final derivatized samples were then cooled to room temperature and transferred to GC vials with inserts for GC–MS analysis as described in the next section.

Soil and Amphibian GC/MS Analysis

Soil and frog extracts were analyzed on an Agilent 7890A gas chromatograph coupled to an Agilent 5975C mass spectrometer (Agilent Technologies). All data were collected and processed using ChemStation software. All injections (2 μl) were made in splitless mode, and the carrier gas was helium maintained at a constant flow of 1.0 ml/min. The inlet and transfer line were held constant at 275 °C, and the MS source and MS quad temperatures were 230 and 150 °C, respectively. For chromatographic separation, the initial oven temperature was held at 70 °C for 1 min, ramped 50 °C/min to 150 °C, ramped 6 °C/min to 200 °C, and finally ramped 16 °C/min to 280 °C and held for 4.0 min (total runtime 19.9 min). Atrazine, alachlor, and two metabolites of atrazine, DEA and DIA, were analyzed in selected ion monitoring mode using electron ionization. Atrazine was monitored at 200 and 174 m/z, alachlor at 160 and 188 m/z, DEA at 172 and 174 m/z, and DIA at 173 and 158 m/z. The internal standard, tetraconazole, was monitored at 336 and 338 m/z. Blanks were run at the beginning and intermittently throughout the run to minimize carryover, standards were analyzed at the start and end of each run, and quality assurance/quality control samples were analyzed throughout.

GSH and BCF statistical analyses

The GSH statistical analyses were performed in R Ver 4.0.2 (R Core Team, 2020). A three-way analysis of variance (ANOVA) with interaction terms was conducted to determine the effects of exposure on GSH (nM/ml) by comparing treatment means. The rstatix package (Kassambara, 2021) was used to identify outliers within each treatment using boxplot methods, to assess normality, and to confirm homogeneity of variance. Data were natural log-transformed for normality purposes. Four individual amphibians were identified as outliers across the seven treatments and not used for the ANOVA. Normality within each treatment was then confirmed with the Shapiro test and visual inspection of the quantile–quantile plot. Homogeneity of variance across the groups was verified using the Levene test. The aov function from the R core stats package was used to perform the ANOVA. The rstatix package was also used to perform posthoc tests. We followed up significant interactions with simple main effects tests using main effects posthoc tests. Posthoc analyses were performed using the Tukey honestly significant difference function. Summary statistics were calculated for atrazine and alachlor BCFs individually, and were in addition compared within a two-way ANOVA factorial design approach similar to the GSH approach.

GC/MS-based metabolomics

Polar metabolomic samples were analyzed on an Agilent 6890 gas chromatograph coupled to an Agilent 5973 mass spectrometer (Agilent Technologies). All injections (2 μl) were made in splitless mode, and the carrier gas was helium maintained at a constant flow of 0.8 ml/min. The inlet and transfer line were held constant at 250 and 280 °C (respectively), and the MS source and MS quad temperatures were 230 and 150 °C, respectively. For chromatographic separation, the initial oven temperature was held at 60 °C for 2 min, ramped 8 °C/min to 300 °C, and held for 5.0 min. Mass spectra were acquired from 50–650 m/z, with blanks and quality assurance/quality control samples analyzed every 10 samples.

Chromatograms were exported as netcdf files and uploaded into XCMS Online (Ver 2.7.2; Tautenhahn et al., 2012) for spectral preprocessing. Feature detection was performed with the centWave method, and retention times were corrected with Obiwarp using vendor-recommended parameters for conventional GC–MS data. Statistical comparisons (i.e., control vs. treated amphibians) were performed using an unpaired Welch's t-test following locally estimated scatterplot smoothing (LOESS) normalization and represented in cloud plots. These visualizations were used to assess the directional change in metabolites of each treatment group compared with control samples. Furthermore, metabolites identified by XCMS were subjected to metabolite set enrichment analysis (Xia & Wishart, 2010) in MetaboAnalyst and searched against the Small Molecule Pathway Database (The Metabolomics Information Centre, n.d.) to identify relevant metabolite sets (i.e., impacted pathways). For each enriched pathway identified, two identified metabolites had to be identified in that particular pathway with a p value <0.05 with at least two hits.

Initially identified peaks were then further refined using ANOVA methods similar to what was performed for the GSH analyses on the raw chromatogram data to investigate main effects. The final set of high-priority metabolites was determined by filtering significant peaks for those with a significant main effect (single treatment of atrazine, alachlor, or urea) with p < 0.01.

Results

Glutathione (GSH)

All treatments, except for the combined atrazine–alachlor treatment, resulted in higher GSH levels relative to the control. The GSH concentrations were highest in frogs exposed to both urea and atrazine in the atrazine + urea treatment (Figure 1A). This atrazine + urea treatment increased GSH levels by 87% in frogs relative to the single atrazine treatment. Atrazine and urea had a significant main effect on liver GSH levels (nM/ml; F1,36 = 4.78, p = 0.035 and F1,36 = 22.99, p < 0.001, respectively). The GSH levels in the urea treatment were, on average, 85% and 76% higher than in the control and atrazine-treated frogs, respectively (Figure 1A) and were consistently elevated in any combined treatment containing urea. The GSH levels for treatments containing alachlor were the most variable, having one of the highest GSH concentrations among the main effect treatments, but also contributing to the lowest GSH concentrations in the doublet ZL treatment (atrazine + alachlor); the overall effect of alachlor on GSH production was not significant as a main effect. When atrazine was paired with alachlor in the double pesticide treatment, there was a significant interactive effect on GSH levels (F1,36 = 11.15, p = 0.002) whereby atrazine depressed the effect of alachlor on GSH (Figure 1A). The addition of atrazine alongside alachlor in the atrazine + alachlor treatment decreased GSH by 883% relative to frogs in the single alachlor treatment (Tukey's test, p = 0.002). Although atrazine + alachlor was the only significant dual treatment, the other two dual stressor combinations did yield identical, nearly significant values of F1,36 = 3.23 and p = 0.08 for atrazine + urea and F1,36 = 3.23, p = 0.08 for alachlor + urea.

Fig 1.

Fig 1.

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(A) Glutathione (GSH; nM/ml) analyzed from southern leopard frog (Lithobates sphenocephala) livers following agrochemical exposure. (B) Atrazine (C) and alachlor bioconcentration factors (BCFs) in southern leopard frogs across agrochemical treatments. C = control; L = alachlor; N = urea; Z = atrazine.

Bioconcentration Factor (BCF)

Soil and frog pesticide concentrations are presented in Table 1. Atrazine BCFs ranged from 0.03 to 2.48 with a mean of 0.48 and a standard deviation of 0.64 (Figure 1B). Alachlor BCFs ranged from 0.02 to 1.68 with a mean of 0.32 and a standard deviation of 0.48 (Figure 1C). We also performed two-way ANOVAs on each set of BCF observations to determine whether the presence of urea and the other pesticide affected the uptake of the pesticide of interest but found no significant effects or interactions.

Table 1.

Average agrochemical concentrations in soils and frog tissues (ppm) ± SE

Soil Frog tissues
Treatment Atrazine Alachlor Urea-N NO3-N
and
NO2-N		 NH4-N Atrazine Alachlor
C 0.0 ± 0.0 0.0 ± 0.0 0.6 ± 0.1 15.0 ± 4.1 4.4 ± 0.5 0.0 ± 0.0 0.0 ± 0.0
Z 10.1 ± 0.7 X X X X 3.9 ± 1.6 X
L X 14.8 ± 1.9 X X X X 2.3 ± 1.0
N X X 828.9 ± 66.6 10.9 ± 1.5 18.3 ± 1.8 X X
ZL 7.9 ± 1.2 14.2 ± 2.9 X X X 3.5 ± 1.3 4.6 ± 1.6
ZN 13.9 ± 1.5 X 713.8 ± 117.9 20.51 ± 2.6 21.4 ± 2.9 1.3 ± 0.5 X
LN X 14.9 ± 1.3 646.2 ± 118.9 19.7 ± 5.1 14.5 ± 2.2 X 2.2 ± 0.6
ZLN 6.4 ± 0.2 10.1 ± 0.5 905.0 ± 331.1 15.9 ± 6.0 40.0 ± 15.9 3.7 ± 1.4 4.4 ± 2.0
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C = control; L = alachlor; N = urea; X = chemical analysis not performed; Z = atrazine.

Metabolomics

We used GC–MS-based metabolomic profiling to investigate perturbations in the hepatic metabolome of amphibians exposed to agrochemicals. Following spectral preprocessing and alignment, XCMS was used to construct cloud plots of each treatment compared with controls (Figure 2). Cloud plot analysis allowed visualization of the spectral features (retention time:m/z pairs) that were statistically different in the chromatograms from treated and control amphibians (Figure 2). Although limited biological information can be garnered from these visualizations, patterns in metabolite changes and their respective abundances in the various exposures can readily be seen. Based on these plots, the number of spectral features and thus, perturbed metabolites, were higher in alachlor, alachlor + urea, and atrazine + urea exposures compared with atrazine and atrazine + alachlor + urea (Figure 2). Interestingly, atrazine + alachlor and atrazine + alachlor + urea appeared to result in more spectral features below the x-axis, suggesting that more metabolites were higher in control samples and were down-regulated in these exposures. Comparatively, these exposures appeared to elicit similar responses in the metabolome of amphibians based on these visual classifications.

Fig 2.

Fig 2.

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Cloud plot analysis of the spectral features (retention time:m/z pairs) that were statistically different between gas chromatography–mass spectrometry chromatograms from treated (Z = atrazine; L = alachlor; N = urea) and control amphibians. Metabolites that are up-regulated (higher in treated samples) appear above the x-axis as green shaded circles, and down-regulated metabolites (those that had higher abundances in control samples) appear below the x-axis in red shaded circles. The p value is represented by how dark or light the color is, with darker colors indicating greater significance. Fold change is represented by the radius of each feature.

Metabolites that were significantly altered based on Welch's t-test following LOESS normalization were putatively identified using commercially available databases. Exposure to alachlor resulted in the greatest number of identified metabolites perturbed followed by alachlor + urea and urea (Table 2). The atrazine + alachlor and atrazine + alachlor + urea exposures had the least amount of statistically relevant metabolites to be identified (Table 2). Across all treatments, glucose, talose, mannobiose, tyrosine, malic acid, maltose, phosphoric acid, and stearic acid were the most identified metabolites perturbed regardless of exposure. Metabolites identified that appeared to be unique to each exposure treatment included beta-alanine (alachlor), gluconic acid (alachlor), phenylalanine (alachlor), and valine (urea). Using these metabolites, pathway enrichment analysis identified 21 metabolite sets impacted by at least one agrochemical exposure (Table 3). Of these, gluconeogenesis, glucose–alanine cycle, GSH metabolism, and lactose synthesis were common among all single and double exposures. Interestingly, the metabolites identified as significant in the atrazine + alachlor + urea exposures were not able to robustly identify any perturbed pathways in this treatment.

Table 2.

Metabolites significantly altered across treatment as identified from Welch's t-test analysis following LOESS normalization of the raw spectra from amphibians exposed to agrochemicals and their mixtures

Pesticide treatment
Metabolite Z L N ZL ZN LN ZLN Metabolite Total
Acetic acid ↓ ↓ 2
Alanine ↓ ↓ ↓ 3
Arabinose ↓ ↓ 2
Beta-alanine ↓ 1
Galactose ↑ ↓ ↑ 3
Gluconic acid ↓ 1
Glucose ↑ ↑ ↓ ↓ ↓ ↓ ↓ 7
Glutamic acid ↓ ↓ ↓ 3
Glycerol-3-phosphate ↑ ↑ ↑ ↑ 4
Glyceryl-glycoside ↓ ↓ 2
Glycine ↑ ↓ 2
Inositol ↑ ↑ ↑ 3
Lysine ↑ ↓ 2
Malic acid ↑ ↓ ↓ ↓ ↓ 5
Maltose ↑ ↑ ↓ ↓ ↓ 5
Mannobiose ↓ ↑ ↓ ↓ ↓ ↓ 6
Methyl phosphoric acid ↓ ↑ ↑ ↑ 4
Myo-inositol ↓ ↓ ↓ ↑ 4
Phenylalanine ↓ 1
Phosphoric acid ↓ ↓ ↓ ↑ ↓ 5
Stearic acid ↓ ↓ ↓ ↓ ↓ 5
Talose ↑ ↑ ↓ ↓ ↓ ↓ ↓ 7
Threonine ↑ ↓ ↑ ↓ 4
Tyrosine ↓ ↓ ↓ ↓ ↓ ↓ 6
Uridine ↑ ↓ ↓ 3
Valine ↓ 1
Xylose ↓ ↓ 2
Treatment total 11 19 16 8 13 18 8
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LOESS = locally estimated scatterplot smoothing; L = alachor; N = urea; Z = atrazine.

Table 3.

Metabolites that were statistically altered in singlet and doublet exposures and for which their effects were sustained or ameliorated in the triplet exposure

Metabolite C-Z-
ZLN		 C-L-ZLN C-N-ZLN C-ZL-ZLN C-ZN-ZLN C-LN-ZLN
Acetic acid – –
Alanine + – +
Aspartic acid +
Caproic acid – –
Carbamic acid +
Fructose –
Fumaric acid + + +
Galactose + +
Gluconic acid +
Glucose + + + +
Glucose-6- phosphate +
Glutamic acid –
Glyceric acid –
Glycerol +
Glycine + + – – – –
Hydroxypyruvic acid +
Inositol –
Keto gluconic acid –
Lactic acid +
Lactose – – – –
Lysine
Maltose – + – – – –
Mannobiose – – – – – –
Mannose + + + +
Monopalmitin +
Oleic acid – – –
Oxalic acid + +
Palmitic acid +
Phosphoric acid – – – –
Proline +
Pyroglutamic acid + +
Ribitol – +
Talose + + + + +
Ribose-5-phosphate +
Stearic acid – + – – +
Threonine – – –
Urea –
Valine + +
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+ = up-regulated; – = down-regulated; C = control; L = alachor; N = urea; Z = atrazine.

In the comparison control–atrazine–atrazine + alachlor + urea, glycerol, glycine, and mannose all increased with “increasing” exposure, whereas the opposite was observed for lactose, maltose, phosphate, ribitol, threonine, and urea (Table 4). Glycine was also up-regulated in the control–alachlor–atrazine + alachlor + urea and control–atrazine + alachlor–atrazine + alachlor + urea comparisons. Interestingly though, when urea was included in the mixture among the control–urea–atrazine + alachlor + urea, control–atrazine + urea–atrazine + alachlor + urea, and control–alachlor + urea–atrazine + alachlor + urea comparisons, glycine was down-regulated. Similar comparisons were identified with disruptions in mannose: it was up-regulated in atrazine, urea, atrazine + urea, and alachlor + urea comparisons whereas threonine was down-regulated in atrazine, alachlor, and atrazine + alachlor exposures. In contrast, fumaric acid was only higher in alachlor and atrazine + alachlor comparisons (not in alachlor + urea) suggesting that including urea in the mixture alters the biological impact of this metabolite (Table 4).

Table 4.

Biological pathways identified as significant with metabolite set enrichment from the agrochemical exposures in amphibians

Z L N ZL ZN LN
Metabolite set Total Hits p
value		 Hits p
value		 Hits p
value		 Hits p
value		 Hits p
value		 Hits p
value
Alanine metabolism 17 3 0.003 2 0.021
Ammonia recycling 32 2 0.044 3 0.016
Cysteine metabolism 26 2 0.030
Galactose metabolism 38 3 0.006 3 0.026 3 0.013
Gluconeogenesis 35 3 0.005 3 0.021 3 0.001 3 0.005 3 0.013
Glucose–alanine cycle 13 2 0.008 3 0.001 2 0.012 2 0.003 2 0.014
Glutamate metabolism 49 4 0.009 2 0.040
Glutathione metabolism 21 2 0.020 4 0.000 2 0.031 2 0.008 2 0.036
Glycerol phosphate shuttle 11 2 0.015 2 0.005 2 0.010
Glycine and serine metabolism 59 4 0.017 3 0.042
Glycolysis 25 2 0.028 2 0.011 2 0.028 2 0.050
Inositol metabolism 33 2 0.046 2 0.046
Inositol phosphate metabolism 26 2 0.030 2 0.030
Lactose degradation 9 2 0.004 2 0.010 2 0.006
Lactose synthesis 20 2 0.018 2 0.046 2 0.007 2 0.018 2 0.033
Malate–aspartate shuttle 10 2 0.005 2 0.012
Phenylalanine and tyrosine metabolism 28 2 0.034 3 0.011
Propanoate metabolism 42 3 0.034
Selenoamino acid metabolism 28 2 0.014
Urea cycle 29 2 0.036 3 0.013 2 0.015
Warburg effect 58 3 0.021
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Note: atrazine + alachlor + urea did not produce any hits or significant p values and is therefore not shown.

L = alachor; N = urea; Total = total metabolites within each metabolite set; Z = atrazine.

Finally, highly differentiated metabolites compared with the control are depicted in the Figure 3 heat map. These are identified peaks with a main effect (single treatments of atrazine, alachlor, or urea) from the factorial design having p < 0.01. The atrazine treatment significantly impacted six metabolites relative to the control group, alachlor impacted five, and the urea treatment had the largest deviation from the control group with 23 perturbed metabolites. Interestingly, there is only minimal overlap in metabolites that were perturbed by exposure to individual pesticide or fertilizer treatments relative to the control group. Mixture effects on the hepatic metabolome were also seen for all doublet and triplet chemical exposures (Figure 3). The atrazine + urea exposure resulted in a significant effect on three unique metabolites whereas the atrazine + alachlor + urea impaired eight metabolites relative to the control. Most notably, the double pesticide atrazine + alachlor treatment impacted 26 identified metabolites.

Fig 3.

Fig 3.

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Heat map of metabolites with a significant main effect (p < 0.01) from atrazine, alachlor, or urea across the seven treatments. Color scale is based on the negative log of the f value (−log(0.01) = 4.61)

When we used perturbations from individual exposures to best predict their combinatorial effects, both similarities and differences were observed (Figure 3). Intriguingly, for the atrazine + alachlor treatment, of the 26 metabolites identified as being perturbed relative to the control group, only maltose was similarly perturbed by the atrazine treatment. Similarly, only glycerophosphoric acid was perturbed in both the urea and alachlor + urea treatments. Stearic acid and inositol perturbation was conserved in the atrazine + alachlor + urea triple treatment as predicted by urea singlet and atrazine + alachlor doublet treatments. The atrazine + urea treatment shared no perturbed metabolites with either the atrazine or urea singlet treatments.

Discussion

Glutathione (GSH)

Both GSH and its enzymatic catalyst, GST, are viable indicators of exposure and induction of oxidative stress (Ezemonye & Tongo, 2010; Ferrari et al., 2011; Venturino et al., 2003). In the present study, GSH levels were markedly increased following exposure to urea, but pesticide exposure only slightly to moderately increased this parameter. Generally, following pesticide exposure an increase in GST activity subsequently results in a decrease in GSH due to its conjugation with xenobiotics to facilitate water solubility and excretion via urine rather than lipid adherence (Anguiano et al., 2001; Ezemonye & Tongo, 2010). However, when investigating pesticide tolerance in amphibians, Ferrai et al. (2011) noted that larval toads (Rhinella arenarum) were able to sustain normal levels of GSH, representing the robust antioxidant response in this species. Furthermore, an ability to compensate after dichlorvos exposure was observed: GSH levels were increased in livers of goldfish following exposure, suggesting an adaptive mechanism to combat increased GST activity (Liu et al., 2015). Aquatic cadmium exposure in Rana ridibunda adults for 96 or 240 h resulted in significant changes in GSH. However, not all tissues analyzed showed the same time-related trends in GSH increase or decrease, which highlights that production of GSH varies in response to xenobiotic exposure by organ system and detoxification potential (Sura et al., 2006).

Although xenobiotic exposure triggers GST production in an effort to detoxify, GST activity may vary by exposure dose, exposure duration time, and tissue type. Greulich & Pflugmacher (2004) exposed two amphibian species to cypermethrin as free-feeding stage tadpoles (Gosner stage 25) for up to 24 h to quantify GST detoxification enzymes. Their study showed that GST increased significantly at all doses and across all time points, excluding the highest dose. Similarly, the systemic herbicide isoproturon resulted in increases in GST activity at lower levels of exposure in Bombina tadpoles, but at higher levels of exposure beyond those considered environmentally relevant, GST activity did not increase (Greulich et al., 2002). Following a 28-day exposure to endosulfan and diazinon in water at varying concentrations, GST levels increased in the African common toad (Bufo regularis; Ezemonye & Tongo, 2010). Likewise, in a field study of rococo toads (Chaunus schneideri) in Argentina, amphibians collected from farm fields had significantly higher levels of plasma GST relative to those collected from a reference site (Attademo et al., 2007). Interestingly, amphibian larvae exposed to one of four glyphosate formulations for short-term static toxicity tests (up to 48 h) all experienced decreased GST activity, which may have been indicative of oxidative stress and GST depletion (Lajmanovich et al., 2011). It is apparent that in amphibians, timing of exposure, exposure level, and even organismal age, including pre and post metamorphosis, can affect the consequences of xenobiotic exposure at GST and GSH levels.

Under the hypothesis that coupling metabolomic profiling with changes in GSH can add biological plausibility to this “adaptive response” to agrochemical exposure, we also investigated the fluxes in the biochemical profiles. When we used the perturbed metabolites statistically identified across all exposure scenarios, GSH metabolism was found to be among the top important biological pathways impacted by exposure. Specifically, this pathway was enriched in the atrazine, urea, atrazine + alachlor, and alachlor + urea exposures, but was identified as the most impacted biological pathway perturbed by alachlor. Due to their important role in the metabolic pathway of GSH and their apparent fluctuations as seen in the present exposure study, glycine, glutamic acid, and alanine are worth noting. Specifically, glycine was significantly perturbed in the urea exposure. Glycine and glutamate are known precursors of GSH, and these compounds have been used as pharmacological supplementation to correct depleting levels of GSH (Nguyen et al., 2014). Together, these data suggest that at extremely high levels of exposure, xenobiotics may not be tolerated, potentially saturating the system, and complete detoxification cannot occur.

Interestingly, in the present study, when urea was used concurrently with atrazine or alachlor, GSH levels were increased in all double and triple co-exposures. In a metabolomic study, it must be noted that these intermediates (i.e., urea and ammonium) are also part of the normal biochemical constituents in amphibians. The role of urea is best documented in the urea cycle, which is designed to facilitate the excretion of ammonia. Ammonia is toxic to amphibians so growth, survival, and metamorphosis were all suppressed in three species of anurans exposed to ecologically relevant concentrations of ammonia (Jofre & Karasov, 1999). Furthermore, larval stages of five amphibian species exposed to nitrate and nitrite at regulatory permissible levels showed swimming abnormalities and increased mortality (Marco et al., 1999). Marco et al. (2001) found that terrestrial salamanders actively avoided urea-treated substrates and that higher urea doses increased mortality. Because urea is a denaturing solute, when present in high concentrations it can potentially impair proteins and enzymes that are critical for normal metabolic regulatory processes (Grundy & Storey, 1994).

Although limited information is available, it can be hypothesized that ammonia will induce oxidative stress in amphibians as it does in numerous other species. Thus, the increased levels of GSH following urea and pesticide exposure are potentially indicative of the biological response of amphibians trying to counteract exposure stress (i.e., dealing with increased formation of nonenzymatic ROS scavengers including reduced GSH; Cappello et al., 2016). Grundy & Storey (1994) suggested that urea exposure may counterbalance the adverse effects of other highly concentrated plasma ions in altering protein function. This accumulation of urea may serve to limit damage to cells and macromolecules that might otherwise be observed under high osmotic stress, as was seen among spadefoot toads (Scaphiopus couchii) or northern leopard frogs (Lithobates pipiens). Likewise, among wood frogs (Lithobates sylvaticus) injected with either a saline or urea solution and then subjected to a freeze/thaw cycle, urea-exposed frogs had higher survival rates and lower levels of extracellular proteins in their blood, which indicated a lower amount of cellular damage (Costanzo & Lee, 2008). Therefore, urea appears to offer some protective capabilities at lower concentrations but can also result in increased mortality when present in high concentrations.

The increased levels of GSH following exposures of urea and urea in mixtures can also best help explain the modification of BCFs quantified in the present study. In both instances, when urea was combined with either atrazine or alachlor, the BCF of each constituent pesticide was greatly reduced compared with its exposure alone. In a study with Xenopus laevis tadpoles, GSH levels were increased following exposure to atrazine, and it was postulated that this up-regulation facilitated the efficient removal of atrazine and its metabolites (Zaya et al., 2011). Similar trends in GST and GSH were observed in carp exposed to alachlor, suggesting that GSH levels readily aid in renal excretion of this pesticide as well (Yi et al., 2007).

Bioconcentration Factor (BCF)

Van Meter et al. (2018) found that co-exposure of atrazine in double and triple pesticide mixtures facilitated increased bioaccumulation of the pesticides of concurrent exposure among juvenile green frogs, although not linearly. These results are similar to ours: atrazine and alachlor co-exposures increased the pesticide BCF, but co-exposures with urea decreased the BCF or caused no additional pesticide bioaccumulation. Interestingly, it is important to note that when juvenile leopard frogs were exposed simultaneously to alachlor, atrazine, and urea followed by a 1-h depuration period, alachlor and atrazine BCFs were greatly increased (Van Meter et al., 2019). This finding suggests that the role of urea in facilitating xenobiotic excretion or retention may largely be due to amphibian hydration status: amphibians with ample access to water may actively excrete urea to maintain plasma homeostasis at the cost of pesticide retention (see Glinski et al., 2021).

Metabolomics

Metabolites statistically identified in the present study that share in the urea cycle include glutamic acid, alanine, and phosphoric acid. Glutamic acid was significantly altered in the single treatments, and alanine was altered in the alachlor, atrazine + alachlor, and alachlor + urea treatments. Furthermore, glutamic acid was negatively correlated in the control–alachlor + urea–atrazine + alachlor + urea comparison, whereas alanine was positively correlated with alachlor and alachlor + urea exposures, and this effect appears to be linearly sustained in the triplet mixture. Liu et al. (2015) also observed similar trends in glutamate levels in goldfish exposed to dichlorvos: these fluxes were associated with increased oxidative stress and subsequent modifications in GSH levels. Furthermore, in the present study, glycine was positively correlated in all single pesticide exposures as well as the double mixture of atrazine + alachlor, and it is an important metabolite in GSH metabolism. Van Meter et al. (2018) reported both up- and down-regulation of glycine in juvenile green frogs (R. clamitans) following treatment with single and double exposures of the insecticide malathion and the fungicide propiconazole, which suggests varying modes of action by pesticides on glycine production.

Increasing urea levels in the blood of amphibians can result in energy conversion from carbohydrate catabolism to a more protein-based catabolism (Grundy & Storey, 1994). Thus, the increasing levels of amino acids in the present study could be the result of protein degradation and subsequent increases in the constituent amino acids due to ammonia recycling following urea exposure (Spinelli et al., 2017). Pathways relevant to energy metabolism impacted in the present study include both glucogenesis (altered in atrazine, alachlor, atrazine + alachlor, atrazine + urea, and alachlor + urea exposures) and glycolysis (altered in atrazine, atrazine + alachlor, atrazine + urea, and alachlor + urea exposures). Overall, the presence of metabolites significantly perturbed by exposure to agrochemicals and their mixtures highlights the importance of glucogenesis/glycolysis, the glucose–alanine cycle, GSH metabolism, and the urea cycle.

Conclusion

Co-exposure to pesticides and fertilizing compounds, such as urea, present a realistic exposure scenario for amphibians in agricultural landscapes given the desirability of tank mixture applications to farm fields. As evidenced in our study and supported by the limited research on agrochemical mixture impacts on amphibians, urea plays a significant role in pesticide detoxification by eliciting GSH activity. Despite this stress response in an effort to detoxify, amphibian pesticide BCFs were not significantly decreased in the presence of urea, and metabolomic profiling indicated perturbations to several biological pathways of significance. Given the frequency of urea usage and the lack of published data elucidating the effects on amphibians as nontarget species when urea is applied as a tank mixture, the effectiveness and reach of current conservation and risk assessment measures are limited. Although it is challenging to keep pace with new product registrations and various application mixtures as applied in real-time agricultural scenarios, a continued research focus on the impacts of agrochemicals on terrestrial amphibians is essential to most constructively impact and inform global risk assessment, xenotiobic policies, and use practices.

The present study investigated the impact of herbicide exposure alone and when combined with urea on GSH homeostasis and the hepatic metabolome of juvenile leopard frogs (L. sphenocephala). Together, these data aid in explaining the increase in oxidative stress resulting from co-exposure, the modification of pesticide uptake as GSH levels are increased, and the correlation of metabolites perturbed with disruptions in cellular energetic processes, which suggest the potential for significant impairments to amphibian health. Developing exposure indices for pesticides and other agrochemicals as well as their mixtures is necessary to inform proactive attempts to protect these and other nontarget species.

Acknowledgements:

The authors are appreciative of the efforts of J. Portmann with live amphibian care, R. Adelizzi for assistance with GSH analysis, and R. F. Seim for metabolomics contributions. B. Acrey and C. Lavelle provided valuable feedback during the manuscript revision process.

Footnotes

Disclaimer:

This article has been reviewed in accordance with US Environmental Protection Agency (USEPA) policy and approved for publication. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the USEPA. Any mention of trade names, products, or services does not imply an endorsement by the USEPA.

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