Untargeted metabolomic analysis of pregnant women exposure to perfluorooctanoic acid at different degrees

doi:10.1007/s11783-023-1628-x

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (3) : 28 https://doi.org/10.1007/s11783-023-1628-x

RESEARCH ARTICLE

Untargeted metabolomic analysis of pregnant women exposure to perfluorooctanoic acid at different degrees

Kaige Yang¹, Zhouyi Zhang¹, Kangdie Hu¹, Bo Peng¹, Weiwei Wang¹, Hong Liang³, Chao Yan¹, Mingyuan Wu^1,²(

), Yan Wang¹(

)

¹. School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
². Engineering Research Center of Cell and Therapeutic Antibody (Ministry of Education), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
³. NHC Key Laboratory of Reproduction Regulation, Shanghai Institute of Planned Parenthood, Shanghai 200237, China

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Abstract

● Metabolome can distinguish pregnant women exposure to PFOA at different degrees.

● Metabolome can reveal the metabolic changes of pregnant women exposure to PFOA.

● PFOA exposure degrees could affect the GSH metabolism of pregnant women.

● PFOA exposure degrees could change the microbiota metabolism of pregnant women.

Perfluorooctanoic acid (PFOA) is a novel type of persistent synthetic organic pollutant, and its exposure on pregnant women can cause some adverse effects, such as pregnancy-induced hypertension, gestational diabetes mellitus, and preeclampsia. Therefore, understanding the metabolic changes caused by PFOA exposure is of great significance to protect pregnant women from its adverse effects. In this study, the metabolomes from the urine samples of pregnant women exposure to PFOA at different degrees were analyzed by GC-MS and LC-MS. The samples in different groups were distinguished and the differential metabolites were screened based on the VIP value, FC, and P-value of each comparison group through multivariate statistical analysis. The pathways related to differential metabolites were searched to reveal the effects of PFOA exposure on metabolic changes in pregnant women at different degrees. Finally, the ROC of differential metabolites was performed, and the differential metabolites with large area under the curve (AUC) values were selected and compared to identify the mutually differential metabolites. Meanwhile, these metabolites were fitted with a multivariable to explore if they could be used to distinguish different groups. The quantitative comparison of mutually differential metabolites revealed that the levels of L-cysteine, glycine, and 5-aminovaleric acid were positively correlated with the degree of PFOA exposure, indicating that different degrees of PFOA exposure could affect the synthesis or degradation of GSH and change the metabolism of oral or intestinal microbiota. Additionally, they may cause oxidative stress and abnormal fat metabolism in pregnant women.

Keywords Perfluorooctanoic acid Exposure Pregnant women Metabolomic GSH Microbiota metabolism

Corresponding Author(s): Mingyuan Wu,Yan Wang

Issue Date: 18 November 2022

Cite this article:

Kaige Yang,Zhouyi Zhang,Kangdie Hu, et al. Untargeted metabolomic analysis of pregnant women exposure to perfluorooctanoic acid at different degrees[J]. Front. Environ. Sci. Eng., 2023, 17(3): 28.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1628-x
https://academic.hep.com.cn/fese/EN/Y2023/V17/I3/28

Fig.1 Multivariate statistical analysis of the metabolomic data of pregnant women’s urine samples in the QC, Exp-L, Exp-M, and Exp-H groups acquired by GC-MS and LC-MS. The PCA score plots of metabolomic data of pregnant women’s urine samples in the QC, Exp-L, Exp-M, and Exp-H groups acquired by (a) GC-MS and (b) LC-MS. The OPLS-DA score plots of metabolomic data of (c) Exp-H vs. Exp-M, (d) Exp-M vs. Exp-L, and (e) Exp-H vs. Exp-L groups acquired by GC-MS. The OPLS-DA score plots of metabolomic data of (f) Exp-H vs. Exp-M, (g) Exp-M vs. Exp-L, and (h) Exp-H vs. Exp-L groups acquired by LC-MS.

Fig.2 Volcano plots of metabolites in the urine samples of pregnant women in the Exp-H, Exp-M, and Exp-L groups acquired by GC-MS and LC-MS. The volcano plots of metabolites in the urine samples of (a) Exp-H vs. Exp-M, (b) Exp-M vs. Exp-L, and (c) Exp-H vs. Exp-L groups acquired by GC-MS. The volcano plots of metabolites in the urine samples in (d) Exp-H vs. Exp-M, (e) Exp-M vs. Exp-L, and (f) Exp-H vs. Exp-L groups acquired by LC-MS.

Fig.3 Bubble diagrams of the KEGG pathways of differential metabolites in Exp-H, Exp-M, and Exp-L groups acquired by GC-MS and LC-MS. Bubble diagrams of the KEGG pathways of differential metabolites observed in (a) Exp-H vs. Exp-M, (b) Exp-M vs. Exp-L, and (c) Exp-H vs. Exp-L by GC-MS. Bubble diagrams of the KEGG pathways of differential metabolites detected in (d) Exp-H vs. Exp-M, (e) Exp-M vs. Exp-L, and (f) Exp-H vs. Exp-L by LC-MS.

Fig.4 Box plots of L-cysteine, dehydroabietic acid, glycine, and 5-aminovaleric acid contents in pregnant women’s urine samples in the Exp-L, Exp-M, and Exp-H groups: (a) L-cysteine and (b) dehydroabietic acid analyzed by GC-MS; and (c) glycine and (d) 5-aminovaleric acid analyzed by LC-MS. *p < 0.05, ** p < 0.01, *** p < 0.001.

Fig.5 ROC of fitted multivariable from the comparison pairs Exp-H vs. Exp-M, Exp-M vs. Exp-L, and Exp-H vs. Exp-L. ROC of the multivariable was fitted with the data on L-cysteine and dehydroabietic acid acquired by GC-MS from the comparison pairs (a) Exp-H vs. Exp-M, (b) Exp-M vs. Exp-L, and (c) Exp-H vs. Exp-L. ROC of multivariable fitted using the data on glycine and 5-aminovaleric acid acquired by LC-MS from the comparison pairs (d) Exp-H vs. Exp-M, (e) Exp-M vs. Exp-L, and (f) Exp-H vs. Exp-L.

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