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  • 1 Wenzhou Medical University, Wenzhou 325000, China
  • | 2 The First People's Hospital of Wenling, Wenling 317500, China
  • | 3 Laboratory Animal Centre of Wenzhou Medical University, Wenzhou 325035, China
  • | 4 Analytical and Testing Centre of Wenzhou Medical University, Wenzhou 325035, China
  • | 5 People's Hospital of Lishui City, Lishui 323000, China
Open access

In this study, we developed a urine metabolomic method by gas chromatography–mass spectrometry (GC–MS) combination with biomedical results to evaluate the effect of activated carbon on methomyl poisoning rats. The rats were divided into four groups, methomyl group, two activated carbon treatment group, and control group. According to the biochemical results, it indicated that activated carbon treated rats could cause liver and kidney function changes. According to the urine metabolomics results, activated carbon treatment group (10 min) and activated carbon treatment group (30 min) could be distinguished from methomyl group, and activated carbon treatment group (10 min) could be separated from activated carbon treatment group (30 min) rats, which indicated that the treatment of rats by activated carbon in different time had a different effect. The results indicate that metabolomic method by GC–MS may be useful to elucidate activated carbon treated on methomyl poisoning rats.

Abstract

In this study, we developed a urine metabolomic method by gas chromatography–mass spectrometry (GC–MS) combination with biomedical results to evaluate the effect of activated carbon on methomyl poisoning rats. The rats were divided into four groups, methomyl group, two activated carbon treatment group, and control group. According to the biochemical results, it indicated that activated carbon treated rats could cause liver and kidney function changes. According to the urine metabolomics results, activated carbon treatment group (10 min) and activated carbon treatment group (30 min) could be distinguished from methomyl group, and activated carbon treatment group (10 min) could be separated from activated carbon treatment group (30 min) rats, which indicated that the treatment of rats by activated carbon in different time had a different effect. The results indicate that metabolomic method by GC–MS may be useful to elucidate activated carbon treated on methomyl poisoning rats.

Introduction

Methomyl is an insecticide belonging to the family of carbamate pesticides. Because of its broad spectrum of activity and efficacy, it is widely used. Generally, the mortality rate for cases of carbamate poisoning is low, but fatalities secondary to methomyl poisoning have been reported [13]. All patients who died with methomyl poisoning experienced cardiac arrest and died from multiple organ dysfunction syndrome (MODS) [4]. Due to lack of effective clinical treatment, the mortality rate of methomyl poisoning was comparable to that of World Health Organization Hazard Class I organophosphate compounds.

Several previous studies had indicated that antioxidant, such as vitamin E [5], vitamin C and selenium [6], afforded protection but not treatment in methomyl-induced toxicity in the animal. Our present study was designed to investigate the effect of administering activated charcoal on rats induced by acute methomyl treatment. Metabolomics is an important science for the understanding of biological systems and the prediction of their behavior, through the profiling of metabolites [711]. In this present study, we adopted a metabolomics approach with gas chromatography–mass spectrometry (GC–MS) to evaluate effect of activated carbon on methomyl poisoning rats.

Experimental

Instrumentation and Conditions

Agilent 6890N-5975B GC/MS with HP-5MS (0.25 mm × 30 m × 0.25 mm) was purchased from Agilent Company (Santa Clara, California, USA). The metabolomics GC–MS conditions were set according to our previous work [12].

Metabolomics Study

Thirty-two male Sprague-Dawley rats (200–220 g) were obtained from Laboratory Animal Center of Wenzhou Medical University (Wenzhou, China). Rats were hosed at Laboratory Animal Center of Wenzhou Medical University at room temperature (25 °C), humidity of 60%–80%, 12–12 hours of light and dark cycles, given free access to conventional food and water during 7 days, and fasted for feed 12 hours before the experiment began. The rats were randomly divided in four groups: control group, methomyl group, activated carbon treatment group (10 min), and activated carbon treatment group (30 min), 8 rats for each group. The methomyl group rats were given methomyl by intragastric administration of 10 mg kg−1; activated carbon treatment group (10 min) was given activated carbon 1000 mg kg−1 at 10 min after intragastric administration of 10 mg kg−1 methomyl; activated carbon treatment group (30 min) was given activated carbon 1000 mg kg−1 at 30 min after intragastric administration of 10 mg kg−1 methomyl; control group rats were given saline by intragastric administration. All experimental procedures were approved ethically by the Administration Committee of Experimental Animals of Wenzhou Medical University.

Urine samples were collected from the rats from the methomyl group, activated carbon treatment group (10 min), and activated carbon treatment group (30 min) at 8:00 am after 2 days, respectively. The urine was stored at −80 °C until measurement. The sample preparation for GC–MS analysis was according to our previous work [13].

Biochemical Tests

After metabolomics study, the blood was collected from the tail vein for biochemical tests of urine glutamic-pyruvic transaminase, total protein, albumin, globulin, glutamic oxalacetic transaminase, alkaline phosphatase, urea, creatinine, and uric acid (these were used to evaluate the liver and kidney function).

Data Analysis

The resulting data were processed through partial least squares discriminant analysis (PLS-DA) and principal component analysis (PCA) using SIMCA-P 11.5 software (Umetrics, Umea, Sweden) [13].

Statistical Analysis

Statistical analysis was carried out using SPSS software (version 18.0, SPSS). P < 0.05 was considered statistically significant with independent samples T-test.

Results and Discussion

Metabolomics Study

Figure 1 provides the typical metabolic profiles of urine acquired through GC–MS technique. The metabolites in the urine were identified using the NIST 2005.

Figure 1.
Figure 1.

Typical GC–MS total ion chromatogram of rat urine of methomyl group and two activated carbon treatment group

Citation: Acta Chromatographica Acta Chromatographica 30, 1; 10.1556/1326.2017.00146

We compared the GC–MS urine spectrum of PCA of the activated carbon treatment group (10 min) and activated carbon treatment group (30 min) with the rats in the methomyl group (Figure 2A) (the corresponding load diagram was shown in Figure 2B). However, the results of the PCA were not good enough to distinguish between methomyl group, activated carbon treatment group (10 min), and activated carbon treatment group (30 min). Then, we used PLS-DA to compare activated carbon treatment group (10 min) and activated carbon treatment group (30 min) with the rats in the methomyl group (Figure 3A) (the corresponding load diagram was shown in Figure 3B, PLS-3D was shown in Figure 3C). As could be seen in Figure 3B and C, activated carbon treatment group (10 min) and activated carbon treatment group (30 min) could be distinguished from methomyl group, and activated carbon treatment group (10 min) could be separated from activated carbon treatment group (30 min) rats, which indicated that the treatment of rats by activated carbon in different time had a different effect.

Figure 2.
Figure 2.

PCA score results of rat urine samples (A) of methomyl group and two activated carbon treatment group. Methomyl group (class 1), activated carbon treatment group (10 min, 1000 mg kg−1) (class 2), activated carbon treatment group (30 min, 1000 mg kg−1) (class 3); the corresponding load diagram (B)

Citation: Acta Chromatographica Acta Chromatographica 30, 1; 10.1556/1326.2017.00146

Figure 3.
Figure 3.

PLS-DA score results of rat urine samples (A) of methomyl group and two activated carbon treatment group. Methomyl group (class 1), activated carbon treatment group (10 min, 1000 mg kg−1) (class 2), activated carbon treatment group (30 min, 1000 mg kg−1) (class 3); the corresponding load diagram (B); PLS-3D score result (C)

Citation: Acta Chromatographica Acta Chromatographica 30, 1; 10.1556/1326.2017.00146

Biochemical Tests

There is no significant difference for glutamic-pyruvic transaminase, total protein, globulin, uric acid among methomyl group, activated carbon treatment group (10 min), activated carbon treatment group (30 min), and control group for biochemical results (Table 1). Albumin decreased in two activated carbon treatment group compared to methomyl group, while there was no significant difference compared to control group.

Table 1.

Biochemical results in rat serum of methomyl group and two activated carbon treatment group

Biochemical indexControlMethomylActivated carbon (10 min, 1000 mg kg−1)Activated carbon (30 min, 1000 mg kg−1)
Glutamic-pyruvic transaminase38.8 ± 11.933.1 ± 4.532.4 ± 5.246.0 ± 19.2
Total protein49.0 ± 6.053.5 ± 1.452.2 ± 1.852.0 ± 1.8
Albumin22.9 ± 3.123.3 ± 1.021.3 ± 2.3*22.3 ± 0.8*
Globulin26.1 ± 3.030.2 ± 1.2Ұ30.9 ± 2.3Ұ29.7 ± 1.4Ұ
Glutamic oxalacetic transaminase146.0 ± 46.8142.3 ± 38.0158.8 ± 42.3147.5 ± 73.6
Alkaline phosphatase440.4 ± 75.7352.0 ± 106.9266.2 ± 22.7ҰҰ239.3 ± 31.9*,ҰҰ
Urea11.7 ± 2.38.2 ± 0.6Ұ6.3 ± 0.7**,ҰҰ13.1 ± 7.0
Creatinine17.6 ± 1.719.7 ± 1.0Ұ23.3 ± 1.8**,ҰҰ33.5 ± 10.2*,Ұ
Uric acid51.0 ± 20.662.3 ± 18.366.7 ± 28.351.4 ± 25.6

Compared with control group, ҰP < 0.05 and ҰҰP < 0.01; compared with methomyl group, *P < 0.05 and **P < 0.01; compared with activated carbon treatment group (10 min, 1000 mg kg−1), #P < 0.05, as indicated by the statistical analysis T-test.

Globulin increased in methomyl group and two activated carbon treatment group, and all had significant difference compared to control group, which indicated that methomyl poisoning may stimulate the synthesis of globulin. Alkaline phosphatase was decreased in two activated carbon treatment groups compared to control group, while there was no significant difference between control group and methomyl group. Creatinine increased in methomyl group and two activated carbon treatment group compared to control group, and there was significant difference between the two activated carbon treatment groups and methomyl group. According to the biochemical results, it indicated that activated carbon treated rats could cause liver and kidney function changes.

Changes in Metabolite

Activated charcoal is the most frequently employed method of gastrointestinal decontamination to treat many kinds of poisoning in the developed world. Its tremendous surface area permits the binding of many toxins and drugs in the gastrointestinal lumen, reducing their systemic absorption [14]. In the current study, intragastric administration of activated carbon may also relieve the absorption of methomyl. Metabolomics is a newly emerging omics approach to the investigation of metabolic phenotype changes induced by environmental or endogenous factors [1518].

The identification of endogenous compounds that can be used as metabolic biomarkers would represent an alternative approach of significant importance to detect hidden effects. In this study, the changes of metabolites in methomyl group, activated carbon treatment group (10 min), and activated carbon treatment group (30 min) were shown in Table 2. Compared to the methomyl group, retinoic acid, 2(3H)-furanone, alloxanoic acid, hexadecanoic acid, and citric acid decreased in activated carbon treatment group (10 min), and benzeneacetic acid, d-ribose, and d-xylose decreased in activated carbon treatment group (30 min). Compared to the activated carbon treatment group (10 min), arabitol, retinoic acid, l-leucine, benzeneacetic acid, and alloxanoic acid decreased in activated carbon treatment group (30 min).

Table 2.

Summary of the changes in relative levels of metabolites in rat urine of methomyl group and two activated carbon treatment group

No.Retention time/minMetaboliteVIPMethomylActivated carbon (10 min, 1000 mg kg−1)Activated carbon (30 min, 1000 mg kg−1)
113.660Arabitol1.5870.3600.3590.230#
213.913Retinoic acid1.0490.2280.304*0.245#
314.971l-Leucine1.0370.0310.0270.000#
415.084Benzeneacetic acid1.7220.2050.1810.090**,##
515.192Glycerol1.1700.1300.1940.108
615.412d-Ribose1.2180.0780.0810.018**
715.518d-Xylose1.1540.2840.2720.156*
815.647Quinoline1.0160.0190.0260.033#
916.0892(3H)-Furanone1.3470.3690.280*0.260
1017.970Alloxanoic acid4.2651.4492.379*1.249#
1118.087Ribitol1.3930.1840.2350.231
1218.839d-Gluconic acid1.2310.3940.4200.557
1319.145Galactonic acid1.1970.1880.1660.091
1419.263Hexadecanoic acid1.1650.5330.441*0.470
1519.990Inositol2.3820.1270.6080.270
1622.685Citric acid1.0290.0220.054*0.017

Note: Variable importance in the projection (VIP) was acquired from the PLS-DA model with a threshold of 1.0. Compared with methomyl group, *P < 0.05 and **P < 0.01; compared with activated carbon treatment group (10 min, 1000 mg kg−1), #P < 0.05 and ##P < 0.01, as indicated by the statistical analysis T-test.

Conclusion

We demonstrated that metabolomic methods based on GC–MS could provide a useful tool to evaluate the effect of activated carbon treatment in methomyl poisoning rats, combined with the biochemical results, which indicated that activated carbon treatment could cause liver and kidney function changes.

Disclosure of Conflict of Interest

The authors declare no conflict of interest.

Acknowledgments

This study was supported by grants from WenZhou Science and Technology Bureau (Y20140493 and Y20140688) and Wenling City Science and Technology Project (2015C311029 and 2015C312060).

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  • 1.

    Meng, S. L.; Chen, J. Z.; Xu, P.; Qu, J. H.; Fan, L. M.; Song, C.; Qiu, L. P. Bull. Environ. Contam. Toxicol. 2014 , 92 , 388 392 .

  • 2.

    Meng, S. L.; Chen, J. Z.; Hu, G. D.; Song, C.; Fan, L. M.; Qiu, L. P.; Xu, P. Ecotoxicol. Environ. Saf. 2014 , 101 , 1 6 .

  • 3.

    Lin, C. M. Neurol. Int. 2014 , 6 , 5307 .

  • 4.

    Lee, B. K.; Jeung, K. W.; Lee, H. Y.; Jung, Y. H. Clin. Toxicol. (Phila) 2011 , 49 , 828 833 .

  • 5.

    Garg, D. P.; Kiran, R.; Bansal, A. K.; Malhotra, A.; Dhawan, D. K. Drug Chem. Toxicol. 2008 , 31 , 487 499 .

  • 6.

    Djeffal, A.; Messarah, M.; Boumendjel, A.; Kadeche, L.; Feki, A. E. Toxicol. Ind. Health 2015 , 31 , 31 43 .

  • 7.

    Zaitsu, K.; Hayashi, Y.; Kusano, M.; Tsuchihashi, H.; Ishii, A. Drug Metab. Pharmacokinet. 2016 , 31 , 21 26 .

  • 8.

    Yi, L.; Dong, N.; Yun, Y.; Deng, B.; Ren, D.; Liu, S.; Liang, Y. Anal. Chim. Acta 2016 , 914 , 17 34 .

  • 9.

    Pallares-Mendez, R.; Aguilar-Salinas, C. A.; Cruz-Bautista, I.; Del Bosque-Plata, L. Ann. Med. 2016 , 48 , 89 102 .

  • 10.

    Li, S.; Dunlop, A. L.; Jones, D. P.; Corwin, E. J. Biol. Res. Nurs. 2016 , 18 , 12 22 .

  • 11.

    Guasch-Ferre, M.; Hruby, A.; Toledo, E.; Clish, C. B.; Martinez-Gonzalez, M. A.; Salas-Salvado, J.; Hu, F. B. Diabetes Care 2016 , 39 , 833 846 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    Zhang, M.; Deng, M.; Ma, J.; Wang X. Chem. Pharm. Bull. (Tokyo) 2014 , 62 , 505 507 .

  • 13.

    Zhang, Q.; Wu, H.; Wen, C.; Sun, F.; Yang, X.; Hu, L. Int. J. Clin. Exp. Pathol. 2015 , 8 , 9320 9325 .

  • 14.

    Juurlink, D. N. Br. J. Clin. Pharmacol. 2016 , 81 , 482 487 .

  • 15.

    Patti, G. J.; Yanes, O.; Siuzdak, G. Nat. Rev. Mol. Cell Biol. 2012 , 13 , 263 269 .

  • 16.

    Wang, Z.; Ma, J.; Zhang, M.; Wen, C.; Huang, X.; Sun, F.; Wang, S.; Hu, L.; Lin, G.; Wang, X. Biol. Pharm. Bull. 2015 , 38 , 1049 1053 .

  • 17.

    Wen, C.; Zhang, M.; Zhang, Y.; Sun, F.; Ma, J.; Hu, L.; Lin, G.; Wang, X. Biomed. Chromatogr. 2016 , 30 , 81 84 .

  • 18.

    Wen, C.; Wang, Z.; Zhang, M.; Wang, S.; Geng, P.; Sun, F.; Chen, M.; Lin, G.; Hu, L.; Ma, J.; Wang, X. Biomed. Chromatogr. 2016 , 30 , 75 80 .

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Senior editors

Editor(s)-in-Chief: Kowalska, Teresa

Editor(s)-in-Chief: Sajewicz, Mieczyslaw

Editors(s) Danica Agbaba (University of Belgrade, Belgrade, Serbia);
Ivana Stanimirova-Daszykowska (University of Silesia, Katowice, Poland),
Monika Waksmundzka-Hajnos (Medical University of Lublin, Lublin, Poland)

Editorial Board

R. Bhushan (The Indian Institute of Technology, Roorkee, India)
J. Bojarski (Jagiellonian University, Kraków, Poland)
B. Chankvetadze (State University of Tbilisi, Tbilisi, Georgia)
M. Daszykowski (University of Silesia, Katowice, Poland)
T.H. Dzido (Medical University of Lublin, Lublin, Poland)
A. Felinger (University of Pécs, Pécs, Hungary)
K. Glowniak (Medical University of Lublin, Lublin, Poland)
B. Glód (Siedlce University of Natural Sciences and Humanities, Siedlce, Poland)
K. Kaczmarski (Rzeszow University of Technology, Rzeszów, Poland)
H. Kalász (Semmelweis University, Budapest, Hungary)
I. Klebovich (Semmelweis University, Budapest, Hungary)
A. Koch (Private Pharmacy, Hamburg, Germany)
Ł. Komsta (Medical University of Lublin, Lublin, Poland)
P. Kus (Univerity of Silesia, Katowice, Poland)
D. Mangelings (Free University of Brussels, Brussels, Belgium)
E. Mincsovics (Corvinus University of Budapest, Budapest, Hungary)
G. Morlock (Giessen University, Giessen, Germany)
J. Sherma (Lafayette College, Easton, PA, USA)
R. Skibiński (Medical University of Lublin, Lublin, Poland)
B. Spangenberg (Offenburg University of Applied Sciences, Germany)
T. Tuzimski (Medical University of Lublin, Lublin, Poland)
Y. Vander Heyden (Free University of Brussels, Brussels, Belgium)
A. Voelkel (Poznań University of Technology, Poznań, Poland)
B. Walczak (University of Silesia, Katowice, Poland)
W. Wasiak (Adam Mickiewicz University, Poznań, Poland)

KOWALSKA, TERESA
E-mail: kowalska@us.edu.pl

SAJEWICZ, MIECZYSLAW
E-mail:msajewic@us.edu.pl

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Acta Chromatographica
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