Article Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX
pubs.acs.org/est
Enantioselective Effects of Metalaxyl Enantiomers in Adolescent Rat Metabolic Profiles Using NMR-Based Metabolomics Jinping Gu,†,§ Chenyang Ji,‡ Siqing Yue,‡ Dan Shu,† Feng Su,† Yinjun Zhang,∥ Yuanyuan Xie,*,†,§ Yi Zhang,‡ Weiping Liu,⊥ and Meirong Zhao*,‡ †
College of Pharmaceutical Sciences, ‡Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, §Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, and ∥College of Biological Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China ⊥ College of Environment & Resource Sciences, Zhejiang University, Hangzhou 310058, China S Supporting Information *
ABSTRACT: More than 30% of the registered pesticides are chiral with one or more chiral centers and exist as two or more enantiomers. The frequency of chiral chemicals and their environmental safety has been considered in their risk assessment in recent decades. Despite the fact that metabolic disturbance is an important sensitive molecular initiating event of toxicology effects, the potential mechanisms of how chiral compounds affect metabolism phenotypes in organisms remain unclear. As a typical chiral pesticide, metalaxyl is an acylalanine fungicide with systemic function. Although the fungicidal activity almost comes from the Renantiomer, the toxicity of both enantiomers in animals and human beings is not yet clear. In this study, a nuclear magnetic resonance (NMR)-based metabolomics approach was adopted to evaluate the enantioselectivity in metabolic perturbations in adolescent rats. On the basis of multivariate statistical results, stable and evident metabolic profiles of the enantiomers were obtained. When rats were exposed to R-metalaxyl, the significantly perturbed metabolic pathways were biosynthesis of valine, leucine, and isoleucine, synthesis and degradation of ketone bodies, and metabolism of glycerolipid. In contrast, more significantly perturbed metabolic pathways were obtained when the rats were exposed to S-metalaxyl, including glycolysis, biosynthesis of valine, leucine, and isoleucine, metabolism of glycine, serine, and threonine, synthesis and degradation of ketone bodies, metabolism of glycerophospholipid and glycerolipid. These abnormal metabolic pathways were closely related to liver metabolism. These results offer more detailed information about the enantioselective metabolic effects of metalaxyl in adolescent development and provide data for the health risk assessment of metalaxyl at molecular level.
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pesticides.9,10 Oral exposure is one of the most common pathways of pesticide exposure, and generally speaking, adolescent individuals are more sensitive to pesticide exposure. All in all, studies about the selective differences of enantiomers on metabolic disturbances from low-dose oral exposure of chiral pesticides in adolescent individuals can provide important knowledge to explore the mechanisms of environmental toxicity of chiral pesticides. Metalaxyl [N-(2,6-dimethylphenyl)-N-(methoxyacetyl)-D,Lalaninemethylester] is a systemic agricultural fungicide, which were widely used to control water mold fungi and oomycetes in many plants.11 Metalaxyl has a chiral carbon atom (Figure S1), and the two enantiomers share similarities in physicochemical functional properties in environments but have different activities in biologic systems,12 and the fungicidal activity
INTRODUCTION The annual consumption of pesticides is increasing yearly, and more than 30% of the pesticides in use are chiral.1 The environmental and health risks of chiral pesticides has been one of the most popular research hotspots for decades.2 Generally, the field has come to a basic consensus that chiral pesticides have enantioselective environmental effects as well as numerous toxicity effects, including endocrine disruption,3 immunotoxicity,4 neurotoxicity,5 and placentotoxicity.6 However, most of the previous studies were confined to the mechanisms of toxicity of insecticides and herbicides under high exposure concentrations.7 Normally, the enantiomeric differences of chiral pesticides are partially related to their differences in biological transformation.8 In the actual environmental exposure process, the exposure concentrations of pesticides are relatively low; thus, their toxicity effect may not be so significant. However, the subsequent transformation of other factors sensitive to the environmental influences of pesticides, including metabolic phenotypes and immunological stress, might be of some relevance with the toxicity of the © XXXX American Chemical Society
Received: December 19, 2017 Revised: April 1, 2018 Accepted: April 13, 2018
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DOI: 10.1021/acs.est.7b06540 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology mostly comes from the R-enantiomer.11 Metalaxyl is known as a low mammalian toxicity fungicide and mainly enriched in water and soil for its mobility and persistence.13 Metalaxyl could inhibit the incorporation of ribonucleotide triphosphates into rRNA by interacting with the RNA polymerase I complex.14 Therefore, the environmental residue of metalaxyl could pose a great threat to human beings and nontarget animals. One in vitro study showed that metalaxyl had adverse effects on chromosomes from humans and some nontarget animals.15 Another study reported that metalaxyl could pose nephrotoxicity in mice.16 Some studies focused on the rat and rabbit liver microsomal degradation of metalaxyl.17,18 Recent research also investigated the enantioselectivity in metabolic disturbance in MCF-7 cells induced by the enantiomers of metalaxyl.19 However, as a type of chiral fungicide, the enantioselective metabolic perturbations of metalaxyl on organisms are not clear, especially in the sensitive stage of adolescence. Metabolic phenotypes are the interactive products of various factors, including dietary, environmental, gut microbial, and genetic.20 Metabolic phenotypes are particularly sensitive to environmental stress, which is an important molecular event in assessing the toxic effects of pollutants.21,22 Metabolomics, a powerful technology that systematically analyzes small-molecule metabolic phenotypes, could profoundly investigate the dynamic metabolic responses to various stimulation.23 Metabolomics analysis is widely used in detecting unsuspected changes and hidden biological events for its untargeted and holistic feature.24 This approach has been extensively used to investigate the toxicity of xenobiotics.25,26 In the present work, we aimed to assess the enantioselective metabolic disturbances caused by different metalaxyl enantiomers in adolescent rats by using nuclear magnetic resonance (NMR)-based metabolomics technique. Multivariate statistical analysis, NMR-based metabolomics profile, metabolite assignment, and pathway analysis were adopted in serum samples from adolescent rats after treatments of R-metalaxyl and S-metalaxyl. These results may help to understand the toxicity and enantioselective metabolic effects of metalaxyl in growth periods of adolescence. Data provided here can also help to demonstrate the underlying mechanism and to evaluate the health risk of metalaxyl.
day. After two-week feed, the rats were sacrificed after 12 h fasting, and the blood samples were collected for following NMR analysis. Blood Biochemical Analysis. Hematologic parameters of blood were measured by using Hematology System ADVIA 2120 (Siemens Healthcare Diagnostics, Eschborn, Germany). Indices of serum including aspartate transaminase (AST), alkaline phosphatase (ALP), alanine aminotransferase (ALT), triglyceride (TG), blood glucose (GLU), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c) were determined by using kits from DiaSys Diagnostic Systems (Shanghai Co. Ltd., Shanghai, China) with an automatic biochemical analyzer (HITACHI 7020, Japan). The results were presented as the mean ± SD. Differences in indices of body composition and indices of blood and serum between the experimental and control groups were examined by using an unpaired Student’s t test (statistical significance: *p < 0.05, **p < 0.01, and ***p < 0.001). Sample Preparation and NMR Measurements. After thawing on the ice, 300 μL serum samples were mixed with 210 μL PBS (pH 7.4, 50 mM). The precipitates were removed by centrifugation (12 000 g, 4 °C, 15 min); then, these supernatants (500 μL) were injected into NMR tubes (5 mm) and analyzed by a BRUKER 600 MHz AVANCE III HD spectrometer with TXI probe at 298 K. The water suppression was used in the acquisition of NMR spectra. The spin−spin relaxation times of 80 ms was set to filter the signals of macromolecules and retain signals of metabolites in the Carr− Purcell−Meiboom−Gill (CPMG) pulse sequence [RD-90°-(τ180°-τ)n-ACQ]. The spectrum width was 12 kHz with 64 k data points. Each spectrum was scanned 64 times with the acquisition time of 2.73 s. The line broadening of 0.3 Hz was used to Fourier transformation. Each spectrum was executed by phase correction and baseline correction carefully. The reference of all spectra from methyl group of lactate was set to δ 1.33. NMR resonances of metabolites were assigned by using the Chenomx NMR Suite (version 7.0; Chenomx Inc., Canada) and another available library.27 The total correlation spectroscopy (TOCSY) of selected samples was recorded to identify the signals of resonances. Multivariate Statistical Analysis. The NMR spectral data had to be preprocessed before the multivariate statistical analysis. Each NMR spectrum was integratedly segmented with a width of 0.001 ppm (bin) by using the MestRova (Mestrelab Research S.L., Espain, Version 9.0). The effective area of spectrum was δ 9.00−0.20, while the area of δ 5.02−4.68 was removed to eliminate aberrant baseline from incomplete water suppression. The remaining integral areas for each NMR spectrum of serum samples were probabilistic quotient normalized.28 At the last, the normalized spectra of serum samples were Pareto-scaled.29 Principal component analysis (PCA) was performed to reveal treats, stress outliers ,as well as display groups among the observations, by using the SIMCA-P+ 12.0 software package (Umetrics AB, Umea, Sweden). A PCA model approximates the transformation into a data set by a low dimensional model level. In PCA model, the results showed that the minimum volume enclosing ellipsoid (MVEE) to separate clusters in the MATLAB (The MathWorks, Inc., USA). Then, both partial least-squares discriminant analysis (PLS-DA)30 and orthogonal signal correction partial least-squares discriminant analysis (OPLS-DA)31 were used to classify the samples and find the relevant variables related to the sample groupings.
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MATERIALS AND METHODS Chemicals and Reagents. R-metalaxyl (purity >95%) and S-metalaxyl (purity >95%) were synthesized in our laboratory. NaH2PO4.2H2O and K2HPO4·3H2O (analytical grade) were purchased from Sinopharm Chemical Reagent CO., Lid. (Shanghai, China). D2O (purity 99.9%) was bought from the Cambridge Isotope Laboratories, Inc. (Tewksbury, USA). Water used in experiment was purified by the Milli-Q system (Millipore, USA). Animal Experiments. Adolescent female Sprague−Dawley rats (age 3 weeks) were purchased from SLRC Laboratory Animal (Shanghai, China) and were bred under specific pathogen-free (SPF)-level laboratory conditions at the Laboratory Animal Research Center of Zhejiang Chinese Medical University (Hangzhou, China). All 24 rats were kept in a stable environment (22−25 °C, relative humidities of 30%−50%, light cycle 12 h light/12 h dark). Control group (n = 8), R group (R-metalaxyl, 100 mg/kg/day, n = 8) and S group (S-metalaxyl, 100 mg/kg/day, n = 8) were randomly set. After one-week habituation, each rat was fed ad libitum and treated with intragastric infusion of 0.5 mL/kg corn oil once a B
DOI: 10.1021/acs.est.7b06540 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology Table 1. Effect of Metalaxyl Enantiomers on Blood Biochemical Indexesa indices
unit
ALP AST ALT GLU CHO1 TG HDL-C LDL-C
IU/L IU/L IU/L mmol/L mmol/L mmol/L mmol/L mmol/L
R-metalaxyl
control 224.01 129.16 28.41 7.19 1.55 0.22 1.00 0.32
± ± ± ± ± ± ± ±
70.36 14.37 3.18 0.39 0.19 0.02 0.12 0.05
181.49 112.03 27.65 6.32 1.79 0.25 1.13 0.35
± ± ± ± ± ± ± ±
45.59 14.99* 3.33 0.49** 0.22* 0.05 0.11* 0.06
S-metalaxyl 167.09 112.98 29.58 6.75 2.00 0.23 1.22 0.38
± ± ± ± ± ± ± ±
47.44 18.19 6.31 0.38* 0.38** 0.04 0.17* 0.08
Each value represents the mean ± SD. Asterisks represent significant difference between two groups as judged by Student’s t test (*p < 0.05; **p < 0.001). a
Figure 1. PCA scores plots of NMR data derived from 1D 1H CPMG spectra of the serum. (A) all rats; (B) control rats vs R-metalaxyl rats; (C) control rats vs S-metalaxyl rats; (D) R-metalaxyl rats vs S-metalaxyl rats.
Metabolites Assignment and Comparison. Before identifying the differential metabolites, the robustness of the PLS-DA model was verified by response permutation testing (RPT). Two criteria were used to identify the differential metabolites: one is the variable importance in the projection (VIP),30 and the other is the correlation coefficients (r) for the variables related with the first predictive component (tp1) in OPLS-DA model.32 The loading plots of OPLS-DA model were refactored in the MATLAB by two criteria (VIP and |r|). In the refactoring loading plots, the peaks were colored in graduated red when |r| > 0.623 and VIP > 1; the peaks were colored in graduated orange when the 0.497 < |r| < 0.623 and VIP > 1; the peaks were colored in graduated blue when |r| < 0.497 or VIP < 1. The relative concentrations of selected metabolites (as percent of control) are reported as the mean values ± s.e.m. Comparisons of the metabolite levels between different groups
were performed by using One-way ANOVA. The univariate analysis was conducted with MATLAB Statistics Toolbox, and the p value 0.05) (Table S1). After exposure to the enantiomers, the changes in the biochemical criteria were basically consistent, except for AST (Table 1). Serum AST levels are associated with liver parenchymal cells.35 The AST activity was decreased when the rats were exposed in R-enantiomer. The R-enantiomer may could deplete pyridoxal phosphate or accumulate some inhibitory substances in the serum. Other fungicides also disturbed liver function. Maneb (MB) can disturb the expression of superoxide dismutase and glutathione peroxidase and induce genotoxicity in liver of adult mice.36 In oral
metabolic changes occurring in significant nodes of metabolic network would potentially affect more important on the pathway than those occurring in relatively isolated or bordered nodes. We performed pathway topology analysis to compute pathway impact values (PIV) with relative-betweenness centrality arithmetic. The significantly disturbed metabolic pathways were defined according to the p values and PIV values.
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RESULTS AND DISCUSSION
Selective Effects of Enantiomers on Physiology. After two-week feeding of the enantiomers, the rats’ weights did not D
DOI: 10.1021/acs.est.7b06540 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology
Figure 4. OPLS-DA loading plots used to identify differential metabolites significantly responsible for distinguishing different groups. (A) Control rats vs R-metalaxyl rats; (B) control rats vs S-metalaxyl rats; (C) R-metalaxyl rats vs S-metalaxyl rats. The graduated red color indicates that the variables are very significant (|r| > 0.623 and VIP > 1); the graduated orange indicates that the variables are significant (0.497 < |r| < 0.623 and VIP > 1); the graduated blue indicates that the variables are insignificant (NS).
results of routine blood indices, we found the effects on liver function from the enantiomers of metalaxyl were selective. Selective Influence of Enantiomers on Metabolic Phenotypes and Metabolites. The PCA could assess the holistic metabolic phenotypes and investigate the essential metabolic profiles. Results of the PCA scores (Figure 1) showed that the metabolic phenotypes of sera from the three groups were distinguishable. Although metalaxyl’s antifungal activity almost comes from the R-enantiomer,11,38 S-metalaxyl
exposure to Carbendazim, liver injury in mice was demonstrated by Oil Red O staining and H&E staining.37 The decrease in GLU and increase in CHO1 and HDL-C in the treated groups suggested that energy metabolism and lipid metabolism were perturbed as a result of metalaxyl exposure. Jin et al. had shown that Carbendazim also induced lipid metabolism disorders by analysis of the changes in mRNA levels of genes related to lipid metabolism.37 According to the E
DOI: 10.1021/acs.est.7b06540 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology
Table 2. Comparison of Metabolite Levels in the Three Groups of the Serum Based on Relative Integrals Calculated from the 1 H NMR Spectra
*** means the difference is significant at the 0.001 level; ** means the difference is significant at the 0.01 level; * means the difference is significant at the 0.05 level; -- means the difference is insignificant at the 0.05 level; Red and blue colors denote that the difference is positive and negative, respectively. bStandard error of the mean. cThe results of Tukey’s multiple comparisons test. a
had changed the rats’ metabolic profiles as well (Figure 1C). The different metabolic profiles between the two enantiomers (R- and S-) are shown in Figure 1D. Representative 1H NMR spectrum of serum of control group is displayed in Figure 2. The main peaks in the spectrum were identified to individual metabolites (Table S2) according to the public NMR databases (Human Metabolome Database, www. hmdb.ca; BMRB Metabolomics, http://www.bmrb.wisc.edu/
metabolomics/) and Chenomx NMR Suite (version 7.0; Chenomx Inc.). The NMR signals of metabolites were verified by TOCSY spectrum (Figure S2). The distinct metabolic phenotypes among the three groups were evaluated by the PLS-DA on the NMR data sets. According to the automatic computation of the predictive principal components, the metabolic phenotypes of the three groups could be clearly distinguished by the scores plots of the F
DOI: 10.1021/acs.est.7b06540 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology
Figure 5. Significantly disturbed metabolic pathways identified from pathway analysis of the serum data using the web service of MetaboAnalyst 3.0. (A) R-metalaxyl rats vs control rats; (B) S-metalaxyl rats vs control rats. Pathway impact values were calculated from the pathway topology analysis, and the p values were computed from the metabolite set enrichment analysis.
rats were exposed to the S-enantiomer, including biosynthesis of glycolysis, valine, leucine, and isoleucine, metabolism of glycine, serine and threonine, the synthesis and degradation of ketone bodies, and metabolism of glycerophospholipid and glycerolipid (Figure 5B). When the rats were exposed to R-metalaxyl, biosynthesis of valine, leucine, and isoleucine was significantly disturbed. The levels of related metabolites (valine, leucine and isoleucine) were also increased in the serum. This phenomenon suggested that the R-enantiomer or its degradation products could enhance the enzyme activities of the synthesis pathways, including threonine dehydrogenase, acetohydroxyacid synthase, keto acid reductoisomerase, dihydroxyacid dehygrogenase and aminotransferase.39 Another disturbed metabolic pathway was the synthesis and degradation of ketone bodies, which are processes related to fatty acids and ketogenic amino acids and could provide energy when there are low glucose levels in the blood.40 The increased levels of LDL/VLD and PUFA, which could be degraded into fatty acids, and the decreased level of glucose in the serum, indicated that the synthesis and degradation of ketone bodies was abnormal. 3-Hydroxybutyrate and acetoacetate are ketone bodies. 3-Hydroxybutyrate is linked with the reduction of acetoacetate in the mitochondria. These two metabolites are energy-rich compounds that transport energy from the liver to other body tissues.41 The results suggested that the R-metalaxyl could disturb the function of mitochondria in the liver. Glycerolipid metabolism was another disturbed metabolic pathway, which is also related to fatty acid metabolism. Glycerol, which is the core metabolite of glycerolipid metabolism, increased when the rats were exposed to R-metalaxyl. The disturbed metabolic pathways and changes in differential metabolites suggested that R-metalaxyl could interfere with the normal workings of mitochondria in the liver. The disruptive effects of fungicide pesticides on liver function have been reported. Ying Zhang et al. found potential liver injury caused by bifenthrin, which could induce oxidative stressrelated genes and disturb the related metabolic profile in mice.42 Takeshi Hano et al. reported changes in hepatic
PLS-DA models (Figure S3). To verify the robustness of the PLS-DA models, we used the response permutation testing (RPT) to verify the corresponding PLS-DA models. The validation plots of the RPTs indicated that all the classified results of the PLS-DA models were reasonable (Figure S4). According to the results of multivariate statistical analysis, both enantiomers of metalaxyl could cause changes in the metabolic profiles of adolescent rats. The different metabolic profiles revealed great differences in metabolites were generated among the three groups. To further confirm differential metabolites used to distinguish these groups, we constructed three pairwise OPLS-DA models based on the NMR data sets (Figure 3). The OPLS-DA scores plot showed distinct separations between pairwise comparisons (Figure 3A−C). The loading plots of OPLS-DA evidently confirmed the differential metabolites contributing to these pairwise separations of the metabolic profiles, which were structured according to the tp1 (Figure 4A−C). In exposure of R-metalaxyl, there are 20 differential metabolites, including 19 increased metabolites and 1 decreased metabolite (Figure 4A). Meanwhile, 21 metabolites increased and 7 metabolites decreased when the rats were exposed to S-metalaxyl (Figure 4B). Comparing the two enantiomers, there were 18 metabolites that increased and 4 metabolites that decreased in the R group (Figure 4C). The detailed differential metabolite information on the OPLS-DA models is shown in Table sS3− S5. We also conducted one-way ANOVA with Tukey’s multiple comparisons among the three groups. The results based on the relative integral of NMR data are shown in Table 2. Selective Influence of Enantiomers on Disturbed Metabolic Pathways. During the process of exposure to enantiomers of metalaxyl, the biological metabolic network is bound to be affected, and the variety, concentrations and relative proportions of endogenic metabolites would change accordingly. When rats were exposed to the R-enantiomer, the major disturbed metabolic pathways were biosynthesis of valine, leucine and isoleucine, the synthesis and degradation of ketone bodies and metabolism of glycerolipid (Figure 5A). Meanwhile, more metabolic pathways were disturbed when the G
DOI: 10.1021/acs.est.7b06540 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology metabolic profiles when marine fish species were exposed to the fungicide polycarbamate.43 Compared with the disturbed metabolic pathways of rats exposed to R-metalaxyl, the major disturbed metabolic pathways were different in rats exposed to S-metalaxyl. The disturbed metabolic pathways were biosynthesis of glycolysis, valine, leucine, and isoleucine, metabolism of glycine, serine and threonine, the synthesis and degradation of ketone bodies, metabolism of glycerophospholipid and glycerolipid. The decreased levels of lactate and pyruvate and the disturbed metabolic pathway of glycolysis suggested that glycolysis was suppressed when the rats were exposed to S-metalaxyl. Yang Y et al. found that Thifluzamide could reduce the activity of lactate dehydrogenase in the liver of zebrafish, which indicated that glycolysis was inhibited.44 In rats exposed to the Senantiomer, there were two disturbed metabolic pathways of amino acid metabolism, including the biosynthesis of valine, leucine and isoleucine as well as the metabolism of glycine, serine, and threonine. The biosynthetic pathway of valine, leucine, and isoleucine was abnormal in rats exposed to either enantiomer. Serine was derived from 3-phospho-D-glycerate, which is an intermediate of glycolysis, and glycine was derived from serine. This abnormal metabolic pathway is strongly correlated with glycolysis. The aberrant metabolic pathways of glycolysis and glycine, serine, and threonine suggested the energy metabolism of rats was transformed by exposure to Smetalaxyl. Wang Y et al. used 1H NMR-based metabolomics to prove that diniconazole could disturb energy metabolism, amino acid metabolism and lipid metabolism in the adult zebrafish.45 Glycerophospholipid metabolism was only abnormal when the rats were exposed to S-enantiomer, and this pathway is linked to glycine, serine and threonine metabolism, and to glycerolipid metabolism. Choline, phosphocholine and glycerophosphocholine, which participate in glycerophospholipid metabolism, were increased when rats were exposed to the S-enantiomer. The metabolites of choline, phosphocholine, and glycerophosphocholine can be converted to each other.46 Enantioselective metabolism studies of the chiral pesticides were conducted on in vitro and in vivo models.47 Considering the metabolism of chiral pesticides, the main situations may be the enantiomers, or the metabolic products of enantiomers could affect different enzymes or the same enzymes at distinct rates.48 In our study, the enantiomers of metalaxyl may have affected different abnormal metabolic pathways due to different degradation products of these enantiomers in the hepatic microsomes.17 Apparent liver microsomal kinetic constants of S-metalaxyl degradation in both rabbit and rat were greater than the apparent kinetic constants of R-metalaxyl degradation. Risk assessment about the hazardous compounds including chiral compounds, persistent organic pollutants (POPs), and pesticides have been widely conducted. This study aimed to analyze the changes in metabolism induced by oral exposure to different enantiomers of metalaxyl in adolescent rats. In spite of the low fungicidal activity of S-metalaxyl of restrain bacteria growth, it affected metabolism in adolescent rats. In actual agricultural production, most the chiral pesticides were used as racemic mixture because of the cost problem. And most invalid enantiomers were still remaining in the environment. And in general, environmental risks of chiral pesticides are based on the EC50, LC50, and LOEC under high concentration exposure. On the other hand, metabolic disturbances under low concentration exposure can also be a supplement to environmental safety evaluation of chiral pesticides. It can provide
plausible explanation for the potential mechanism of selective enantiomers as well. It is suggested that more comprehensive health and ecological assessments are essential to determine whether metalaxyl is a preferable systemic fungicide. Therefore, our research assessed the risk of chiral pesticides, especially agricultural antibiotics, and could provide a more comprehensive understanding of their potential mechanisms. Potential effects on metabolism have generally become a hotspot in the risk assessment of compounds. Many kinds of chiral compounds have been detected in the environment including antibiotics used by humans, veterinary antibiotics and agricultural antibiotics with one or more chiral centers. However, the effects on biological metabolism by the stereoisomers of chiral chemicals in risk assessment are seldom considered. Our research suggested that metabolic phenotype is another major end point of chiral chemicals risk assessment. And metabolic disturbances are also closely related with other adverse end points like turbulence of intestinal microbiota,49 abnormal neural behaviors,50 and so on. Therefore, multiend points of enantioselectivity should be considered in further studies. Meanwhile, the molecular mechanisms of enantioselectivity in metabolism disruption of chiral compounds need further investigation.
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b06540. Figure S1, chemical structure of metalaxyl enantiomers; Figure S2, typical TOCSY spectrum of control serum sample; Figure S3, PLS-DA scores plots of NMR data derived from 1H CPMG spectra of the serum; Figure S4, validation plots of the PLS-DA models, generated from the permutation tests that were randomly permuted 600 times with the corresponding predictive principal component; Table S1, statistical results of SD rats weightings; Table S2, NMR data for the metabolites found in the serum; Table S3, differential metabolites identified from the OPLS-DA analysis of R-metalaxy rats vs control rats; Table S4, differential metabolites identified from the OPLS-DA analysis of S-metalaxy rats vs control rats; Table S5, differential metabolites identified from the OPLS-DA analysis of R-metalaxy rats vs S-metalaxy rats (PDF)
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AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected]. Tel: +86 571 8832 0265. Fax: +86-571-88320265 (M.Z.). *E-mail:
[email protected]. Tel: +86 571 8832 0878. Fax: +86 571 8832 0878 (Y.X.). ORCID
Yinjun Zhang: 0000-0001-8504-1036 Yuanyuan Xie: 0000-0001-8482-9948 Weiping Liu: 0000-0002-1173-892X Meirong Zhao: 0000-0003-3132-9223 Notes
The authors declare no competing financial interest. H
DOI: 10.1021/acs.est.7b06540 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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ACKNOWLEDGMENTS This study was funded by the National Natural Science Foundation of China (21677130, 21337005, and 21577129), the China Postdoctoral Science Foundation (2017M612023) and the Natural Science Foundation of Zhejiang Province, China (LQ18B050003).
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