Triglycerides as Potential Biomarkers for Pesticides Pollution in Aquatic Environment Using UPLC-QTOF-MS †

doi:10.7503/cjcu20190615

Abstract

Abstract:

Pesticides exert a long-term negative influence on the aquatic organism and even the aquatic environment. It is necessary to find some potential biomarkers to give insight into the pesticides pollution and provide a rapid, sensitive, early-warning method. In this study, ultra-performance liquid chromatography with quadrupole-time-of-flight mass spectrometry(UPLC-QTOF-MS) was applied to analyze the effect of pesticides on the metabolomics in liver and brain of Crucian carp. Three pesticides, namely trichlorfon, β-cypermethrin and imidacloprid were investigated, respectively. Data was collected by masslynx software in MS ^E mode, analyzed by UNIFI software for automatic detection and data filtering, compared to the online metabolome database. The metabolic difference of liver was more significant than that of the brain. The triglycerides(TGs) species were identified as the different metabolites. Among these three pesticides, imidacloprid had the most significant influence on the metabolism of TGs. The low concentration of acute exposure induced the accumulation of TGs in the liver, and the relationship between the content and the exposure concentration was in line with the growth trend of Michaelis-Menten equation. The results showed that TGs can sensitively reflect the low concentration exposure of imidacloprid pesticides(≤ 20 ng/mL) in a short period of time(≤ 2 h), which can be used as a potential biomarker to investigate the toxicity of imidacloprid to aquatic organisms, and help to establish a new method for estimation the pesticides contamination in aquatic environment.

Key words: Ultra-performance liquid chromatography with quadrupole-time-of-flight mass spectrometry(UPLC-QTOF-MS), Triglycerides, Biomarker, Pesticides pollution

CLC Number:

O657.63

TrendMD:

HE Yu, XIA Qian, WANG Wenli, LUO Fang, CHEN Jinfeng, GUO Yating, WANG Jian, LIN Zhenyu, CHEN Guonan. Triglycerides as Potential Biomarkers for Pesticides Pollution in Aquatic Environment Using UPLC-QTOF-MS ^†[J]. Chem. J. Chinese Universities, 2020, 41(3): 431.

Figures/Tables 8

Fig.1 Total ion chromatograms(TIC) of brain(A) and liver tissues(B) (A1, B1) Blank; (A2, B2) exposure in trichlorfon; (A3, B3) exposure in β-cypermethrin; (A4, B4) exposure in imidacloprid.

Fig.2 Volcano plots of endogenous metabolites(n=12, *P<0.05) (A1) Trichlorfon-treated brain; (A2) trichlorfon-treated liver; (B1) β-cypermethrin-treated brain; (B2) β-cypermethrin-treated liver; (C1) imidacloprid-treated brain; (C2) imidacloprid-treated liver.

Fig.3 Binary contrast spectra of brain(A) and liver(B) tissues (A1) Base peak intensity(BPI) of brain exposed for 0 and 2 h; (A2) chromatogram of difference in brain; (A3) mass spectrum of difference in brain; (B1) BPI of liver exposed for 0 and 2 h; (B2) chromatogram of difference in liver; (B3) mass spectrum of difference in liver.

Sample	Component, m/z	Unknown intensity/ counts	Reference intensity/ counts	Unknown/ reference	Observed retention time/min
In brain	806.5728	3092718	3191399	0.9691	1.37
	896.7709	2516408	2318847	1.0852	1.24
	870.7539	1748903	1984113	0.8815	1.23
	898.7881	1546268	1502813	1.0289	1.54
	832.5871	1376215	1468047	0.9374	1.33
	872.7726	1266039	1342851	0.9428	1.54
In liver	896.7709	4236852	287846	14.7192	1.22
	898.7881	3388133	181398	18.6779	1.52
	872.7726	2467030	158469	15.5679	1.52
	900.8013	2209446	227011	9.7328	1.79
	874.7853	2057944	266732	7.7153	1.79
	806.5728	3340418	3378909	0.9886	1.37
	832.5871	2278588	2178444	1.046	1.33
	146.1175	119687	124507	1.056	2.99

Fig.4 Effect of exposure time on endogenous metabolite(m/z 896.7709) (A) BPI of endogenous metabolite(m/z 896.7709). Exposure time/h: a. 0; b. 2; c. 4; d. 6; e. 8; f. 10; (B) mass spectrum of endogenous metabolite(m/z 896.7709); (C) effect of exposure time on peak area of endogenous metabolite(m/z 896.7709)(n=12, *P<0.05).

Experimental accurate m/z	Theoretical m/z	10⁶δ	Chemical formula	Lipid name	Ion
806.5728	806.5694	4	C₄₆H₈₀NO₈P	PC(38:6)	[M+H]⁺
832.5871	832.5851	2	C₄₈H₈₂NO₈P	PC(40:7)	[M+H]⁺
872.7726	872.7702	3	C₅₅H₉₈O₆	TG(52:4)	[M+NH₄]⁺
874.7867	874.7858	1	C₅₅H₁₀₀O₆	TG(52:3)	[M+NH₄]⁺
896.7709	896.7702	1	C₅₇H₉₈O₆	TG(54:6)	[M+NH₄]⁺
898.7881	898.7858	3	C₅₇H₁₀₀O₆	TG(54:5)	[M+NH₄]⁺
900.8013	900.8015	0	C₅₇H₁₀₂O₆	TG(54:4)	[M+NH₄]⁺

Fig.5 Comparison in detected MS/MS spectra and predicted MS/MS spectra in HMDB (A) Detected MS/MS spectrum of m/z 806.5728; (B) detected MS/MS spectrum of m/z 896.7709; (C) predicted MS/MS spectrum of m/z 806.5728; (D) predicted MS/MS spectrum of m/z 896.7709.

Fig.6 Effect of exposure concentration(n=12, *P<0.05) (A) Heatmap of the metabolites in fish liver exposed to different concentrations(0, 1, 2, 5, 10, 20 ng/mL) of imidacloprid for 2 h. Each concentration is represented in columns, and each metabolites in lines. Blue indicates low content, whereas red indicates high content; (B) Michaelis Menten equation fitting curve of m/z 896.7709 change with exposure concentration.

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