Meet-in-metabolite analysis: A novel strategy to identify connections between arsenic exposure and male infertility.

Affiliation

Wu Y(1), Ding R(2), Zhang X(3), Zhang J(4), Huang Q(3), Liu L(5), Shen H(6).
Author information:
(1)Department of Health Inspection and Quarantine, The School of Public Health, Fujian Medical University, Fuzhou, Fujian 350122, PR China.
(2)College of Environmental Science and Engineering, Fujian Normal University, Fuzhou, Fujian Province 350007, PR China.
(3)Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, PR China.
(4)State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, PR China; Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, PR China.
(5)Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, PR China; School of Public Health, Shanxi Medical University, Taiyuan 030001, PR China.
(6)State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, PR China; Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, PR China. Electronic address: [Email]

Abstract

BACKGROUND: Despite a trend in the use of systems epidemiology to fill the knowledge gap between risk-factor exposure and adverse outcomes in the OMICS data, such as the metabolome, seriously hindrances need to be overcome for identifying molecular connections. OBJECTIVES: Using male infertility phenotypes and arsenic exposure, we aimed to identify intermediate biomarkers that reflect both arsenic exposure and male infertility with a meet-in-metabolite analysis (MIMA). METHODS: Urinary arsenic levels and metabolome were measured by using inductively coupled plasma-mass spectrometry (ICP-MS) and HPLC-quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS), respectively. To identify arsenic-related metabolic markers (A-MIMA), the intermediate markers were profiled by orthogonal projections to latent structures (OPLS-DA). To detect infertility-related metabolic markers (I-MIMA), the intermediate markers were investigated by weighted gene co-expression network analysis. The key node markers, related to both A-MIMA and I-MIMA, were determined by O2PLS and defined as MIMA markers. Finally, network analysis was used to construct the MIMA-related metabolic network. RESULTS: Twelve markers each were defined through the significant associations with arsenic exposure (A-MIMA) and/or infertility (I-MIMA), respectively. Seven of them, including acetyl-N-formyl-5-methoxykynurenamine, carnitine, estrone, 2-oxo-4-methylthiobutanoic acid, malonic acid, valine, and LysoPC (10:0), were defined through the associations with both arsenic exposure and male infertility (MIMA markers). These intermediate markers were involved majorly in oxidative stress, one-carbon metabolism, steroid hormone homeostasis, and lipid metabolism pathways. The core correlation network analysis further highlighted that testosterone is a vital link between the effect of arsenic and male infertility. CONCLUSIONS: From arsenic exposure to male infertility, the arsenic methylation that coupled one-carbon metabolism disruption with oxidation stress may have extended its effect to fatty acid oxidation and steroidogenesis dysfunction. Testosterone is at the hub between arsenic exposure and male infertility modules and, along with the related metabolic pathways, may service as a potential surrogate marker in risk assessment for male dysfunction due to arsenic exposure.