Abstract
Background
In recent years, studies have shown that exposure to environmental pollutants (e.g., radiation, heavy metal substances, air pollutants, organic pollutants) is a leading cause of human non-communicable diseases. The key to disease prevention is to clarify the harmful mechanisms and toxic effects of environmental pollutants on the body. Metabolomics is a high-sensitivity, high-throughput omics technology that can obtain detailed metabolite information of an organism. It is a crucial tool for gaining a comprehensive understanding of the pathway network regulation mechanism of the organism. Its application is widespread in many research fields such as environmental exposure assessment, medicine, systems biology, and biomarker discovery.
Aim of review
Recent findings show that metabolomics can be used to obtain molecular snapshots of organisms after environmental exposure, to help understand the interaction between environmental exposure and organisms, and to identify potential biomarkers and biological mechanisms.
Key scientific concepts of review
This review focuses on the application of metabolomics to understand the biological effects of radiation, heavy metals, air pollution, and persistent organic pollutants exposure, and examines some potential biomarkers and toxicity mechanisms.
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Environmental exposure is a complicated and insidious threat, and there is extensive scientific research to prevent, diagnose, and treat its negative effects. As a tool to detect and analyze metabolites produced by cells and discover markers, metabolomics can rapidly promote the development of environmental exposure research. The review combined metabolomics and environmental pollutant exposure keywords to conduct comprehensive searches in CNKI, CBM, NCBI PubMed, and Google Scholar web scientific databases. Based on the research direction of the research group, many high-level reviews, experimental papers, and theoretical articles were accessed. Scientific research published from 2012 to 2022 on radiation, heavy metals, PM2.5, and persistent organic pollutants exposure factors was considered.
1 Metabolomics is viewed as a functional readout of biological samples
Metabolomics is an emerging experimental tool that has received attention in examining the biological effects of environmental contaminants on animals, plants, and microorganisms (Alseekh et al., 2021). Depending on the specific research objective and/or purposes, the proper metabolomics analysis methods can deal with the associated environmental exposure with less effort than traditional methods. The tools for predicting and evaluating radiation damage are mainly classical cytogenetic experiments, such as micronucleus test and two-point chromosome test (Blumenthal et al., 2014; Dainiak et al., 2019). Metabolomics strategies can be mainly divided into complementary untargeted metabolomics and targeted metabolomics, which are commonly used simultaneously or sequentially (Fig. 1) (González-Peña et al., 2019; Di Minno et al., 2021). Untargeted metabolomics is an exploratory analysis aimed at conducting a comprehensive qualitative and quantitative assessment of the dynamic alterations of the metabolome to get diverse metabolic characteristics of the control and experimental groups, and it is generally used to identify or screen new metabolic biomarkers. In contrast, the goal of targeted metabolomics is to conduct quantitative analysis or verification of specific metabolites (such as potential biomarkers) (Lopes et al., 2017). Generally, targeted metabolomics methods require higher analytical accuracy to accomplish complete quantitative accuracy. The development of metabolomics requires analytical technical support, and thus nuclear magnetic resonance (NMR) and mass spectrometry (MS) have become the main methods for metabolomics research (González-Peña et al., 2019). The former is a rapid, non-destructive separation technique that can measure the chemical and physical properties of molecules and detect metabolites that are difficult to ionize or derivate (Belhaj et al., 2021; Jacob et al., 2019; Nagana et al., 2017). The latter combines diverse chromatographic separation techniques, including gas chromatography-mass spectrometry (GC–MS), liquid chromatography-mass spectrometry (LC–MS), and capillary electrophoresis-mass spectrometry (CE-MS) with higher sensitivity and greater selectivity. GC–MS can specifically distinguish and detect volatile and hydrophilic low-molecular-weight metabolites (Alseekh et al., 2021; Misra, 2021). LC–MS is a practical metabolomic tool. It can detect metabolites with higher molecular weights and more complex chemical structures with suitable repeatability and high specificity (Beale et al., 2018; Pautova et al., 2021). The emerging ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF–MS) has present advantages in capturing metabolite information accurately and rapidly (Zhou et al., 2018). The CE-MS separation technique that relies on the charge-to-size ratio is a powerful tool for analyzing charged metabolites and polar samples (Mever et al., 2019). Although the efficiency and sensitivity of CE-MS are higher than that of GC–MS and LC–MS, this method cannot be applied universally because of its poor reproducibility and technical challenges (Zhang et al., 2021b). Low molecular weight metabolites such as amino acids, nucleic acids, organic acids, and lipids in biological samples can be captured and quantified by this series of technology platforms, which can enhance the utilization of biological samples and obtain the most comprehensive cellular metabolism information (Antcliffe & Gordon, 2016; Au, 2018; Samczuk et al., 2018).
Representative workflow of metabolomics
1 Identify the research subjects impacted by environmental exposure; 2 Collect readily accessible samples including plasma, serum, urine, saliva, tissue fluid, etc.; 3 Comprehensively identify metabolites by untargeted metabolomics; 4 Screen meaningful biomarkers; 5 Targeted metabolomics for qualitative and quantitative validation of biomarkers.
2 Metabolomics contributes to an improved understanding of the mechanisms of action of different environmental stimuli
Presently, metabolomics is an advanced and promising omic technology. It can be used to obtain abundant amounts of information on the metabolic state of cells and provide vital data for studying biological effects, which has significant advantages in environmental monitoring, disease detection, diagnosis, treatment, and drug development (Alonso et al., 2015; Klupczyńska et al., 2015; Lee et al., 2019). Similarly, metabolomics has become a sensitive tool for measuring environmental exposures and biological responses, helping to comprehend the mechanism of action of specific environmental factors on the body, as well as diseases (Belhaj et al., 2021; Pernot et al., 2012). Metabolomics research aims to describe the effects of endogenous and exogenous factors on the metabolic characteristics of the body and provides a new methodological framework for environmental exposure research (Pham et al., 2021; Tang et al., 2021). Numerous studies using metabolomics approaches have found that environmental exposures (e.g., physical radiation, air pollutants, heavy metals, and organic pollutants) stimulate metabolic disturbances and induce chronic disease. (Fig. 2). As human exposure data, including data on pollutants, lifestyle factors, and behavior, are often limited or inadequate, the estimation of chronic disease risk is severely hampered. Metabolomics can characterize and quantify small molecules in complex biological samples such as blood, urine, or tissue, describe the dynamic metabolic responses of living systems after environmental exposure stimuli, and trace related biomarkers and metabolic mechanisms (Kyrtopoulos et al., 2013; Rinschen et al., 2019).
Adverse effects of environmental exposure on the organism
3 Metabolomics plays an important role in radiation exposure research
Radiation exposure is a typical environmental exposure. Long-term radiation exposure will produce complex biological effects, causing severe damage to the body (Pan et al., 2021; Singh et al., 2021). Numerous studies have shown that radiation exposure can damage the genetic material of cells and impact the metabolic activities within cells. It is necessary to extract markers of radiation damage from biological samples to help identify exposed individuals and detect both early and delayed systemic and tissue-specific damage (Menon et al., 2016; Reisz et al., 2014). Radiation metabolomics provides meaningful interpretations of changes in radiation stress response metabolites and elucidates the effects of environmental radiation on human physiological states. It determines potential biomarkers and metabolic pathways and helps identify treatments for radiation exposure injury (Fig. 3; Table 1) (Hu et al., 2012; Pannkuk et al., 2017; Satyamitra et al., 2020).
Radiation induced metabolic disorder and apoptosis pathway
Radiation exposure induces ionization of water molecules in cells, up-regulation of ROS levels in cells, enhanced oxidative stress response, disturbance of metabolic functions, and DNA damage, which ultimately lead to apoptosis.
Receiving different types and doses of radiation can result in various adverse biological effects. Exposure to high doses of radiation will generate heavier unfavorable effects and pose a severe threat to organisms (Chen et al., 2021c). Jelonek and Pernot found that radiation damage is dependent on the dose and time of radiation exposure, and metabolomics can be used to screen the characteristics of metabolic molecules in different periods to obtain time-dose-related biomarkers (Jelonek et al., 2017; Pernot et al., 2012). When plasma lipid metabolism in mice was studied via γ-ray radiation, it was found that radiation can induce manifest changes in lipid metabolism and affect the process of lipid-mediated signal transduction (Ghosh et al., 2013). Epidemiological studies have shown that exposure of the chest or the whole body to ionizing radiation can induce morbidity and mortality of cardiovascular diseases (Azimzadeh et al., 2017; Jones et al., 2019). Potential biomarkers calcium citrate and citrine were identified in the urine of mice exposed to different dose rates of cesium and strontium radiation in vitro and in vivo, suggesting that both the source of exposure and the radiation dose rate had a significant effect on the metabolic activity of the mice (Goudarzi et al., 2016). Radiation can cause significant changes in DNA damage metabolic pathways in the body's tissues, interfere with the body's normal metabolic activities, and cause potential threats to the body (Ghaleb et al., 2022; Kumar et al., 2021). As blood cell metabolites, 2′ -deoxycytidine and choline can be used as effective markers of radiation exposure to evaluate exposure dose during accidental radiation events (Goudarzi et al., 2014). Although numerous meaningful biomarkers have been verified in recent studies, many challenges remain in applying them to medicine. For instance, it is difficult to find a single accurate biomarker with massive metabolite information. Additionally, verification analyses are required under different research conditions and should be executed in the future to follow up on radiation exposure damage research.
4 The potential and usefulness of metabolomics approaches have been widely demonstrated in the heavy metal exposure field
Heavy metals can affect cell activity and lead to cell apoptosis or death. Long-term exposure can lead to acute or chronic tissue, organ damage, and even carcinogenesis (Kosakivska et al., 2021; Wang et al., 2021). Metabolomics can be used to evaluate the toxicity of heavy metals and develop new toxicity biomarkers through models constructed in the laboratory, which creates a new vision and approach for heavy metal exposure research (Table 2) (García-Sevillano et al., 2015; Rodríguez-Moro et al., 2021). For non-occupationally exposed populations, diet is the main source of heavy metal exposure. (Hu et al., 2021; Suomi et al., 2021). In humans, cadmium exposure can result in a variety of adverse effects, such as renal and hepatic dysfunction, damage to the adrenals and hemopoietic system, and it can also cause neurotoxicity (Balali-Mood et al., 2021; Satarug, 2018; Tinkov et al., 2018). For example, using UPLC-Q-TOF–MS to discover the dose-dependent toxicity of CdCl2 to PC-12 cells from a metabolomic perspective, and to identify key metabolic pathways and potential biomarkers of cadmium exposure (Zong et al., 2018). Similarly, mouse urine studies based on UPLC-MS technology have also identified dozens of potential biomarkers of cadmium exposure, such as guanidinosuccinic acid and phenylacetylglycine, which induce renal oxidative stress and interfere with amino acid metabolism, fatty acid metabolism, and energy metabolism (Chen et al., 2018; Zhang et al., 2019). Arsenic is a toxic heavy metal pollutant, and exposure can trigger metabolic disorders, immune dysfunction, oxidative stress, and even cancer (Chen et al., 2019; Ramsey et al., 2013; Xu et al., 2019; Yu et al., 2016). There are shreds of evidence that arsenic exposure has toxic effects on various organ tissues (liver, kidney, and the male reproductive system) (Guvvala et al., 2017; Li et al., 2017; Liu et al., 2020; Silva et al., 2017). LC–MS-based metabolomic studies found that GC-1spg and PC12 cells exposed to arsenic trioxide (ATO) induced metabolic disturbances, enhanced oxidative stress, cell membrane disruption, mitochondrial dysfunction, and autophagy (Chen et al., 2020; Qi et al., 2021). A study on arsenic exposure and male infertility obtained carnitine, estrone, and LysoPC (10:0) a series of potential biomarkers involved in oxidative stress, and lipid metabolism, and may serve as an assessment marker for arsenic-induced male dysfunction (Wu et al., 2021b).
Heavy metal mercury is ubiquitous in the environment. Mercury exposure may increase the expression of heat shock proteins and oxidative phosphorylation genes, promoting detoxification and energy metabolism (Jiang et al., 2021b). In earthworms, low-dose mercury exposure can cause energy metabolism disorders, amino acid metabolism disorders, and osmotic pressure changes, resulting in toxic effects (Tang et al., 2018). Chromium is a metal that is highly toxic to organisms. Accumulation of hexavalent chromium in the human body will inhibit growth, affect components of the antioxidant system, and lead to DNA breakage and irreversible changes in chromosomes (Gutiérrez-Corona et al., 2016; Ventura et al., 2021; Zhao et al., 2019b). The heavy metal manganese is a neurotoxic heavy metal substance. Baker (2017) used metabolomics technology to explore the biomarkers in urine under manganese exposure, helping to better understand the diagnosis and prognosis of manganese exposure (Baker et al., 2017; Zhong et al., 2021). These experimental studies have found that metabolomics methods can provide convincible evidence for understanding the toxic effects of heavy metal pollutants on organisms.
5 Metabolomics has been increasingly applied to the study of PM2.5 exposure damage to the body
The main harmful component of air pollution, particulate matter 2.5 (PM2.5), is considered by the World Health Organization to be a key indicator in evaluating the impact of air pollution on human health (Vo et al., 2020). Human epidemiological studies and controlled animal studies indicated that PM exposure may increase oxidative stress and metabolite changes, leading to various adverse health consequences (e.g., cardiovascular disease, respiratory disease, cancer, central nervous system disease, and adverse pregnancy outcomes) (Malley et al., 2017; Peixoto et al., 2017; Schraufnagel et al., 2019a, 2019b; Turner et al., 2016). Moreover, the severe metabolic disorder caused by PM2.5 exposure can reduce the immune capacity of the body and result in other poisonous substances having toxic effects on the body (Bernatsky et al., 2016; Geng et al., 2021). Human epidemiological studies and untargeted metabolomics assessments have found that the mature mechanism of PM2.5 exposure is the differential response of metabolic pathways associated with oxidative stress and inflammation (Table 3) (Costa et al., 2017; Jin et al., 2021).
Metabolomics is applied to investigate the negative effects of PM2.5 exposure on the metabolic activities of organisms (Liu et al., 2021a). Different human blood metabolomics studies have found that the metabolic pathways of glycerophospholipids, sphingolipids and glutathione are severely disrupted after long-term and short-term exposure to PM2.5, and phospholipid catabolic metabolism is an important pathway of exposure injury. LysoPC (p-20:0) and LysoPC (p-18:1 (9z)) are the potentially effective biomarkers (Chu et al., 2021; Nassan et al., 2021). Further studies in human lung fibroblasts (HEL 299) found that PM2.5 could lead to the reduction of mitochondria-related metabolites, trigger the production of intracellular reactive oxygen species and mitochondrial dysfunction, and induce a high level of apoptosis (Shon et al., 2020). Additionally, GC and LC- MS were used to evaluate the changes in an acute exposure mouse model, and significant changes were found in amino acid metabolism, lipid metabolism, and glucose metabolism in urine. Similar results were obtained using NMR (Du et al., 2020; Zhang et al., 2018). Wang et al. established the time-response relationship between PM2.5 and liver toxicity in mice using LC–MS metabolomics, using different biomarkers to distinguish different stages of liver damage. 4-Pyridine acid and succinate are markers of mild oxidative stress in the liver, proline can indicate severe oxidative stress and inflammation in the liver, and KYNA is a biomarker of significant oxidative damage and inflammation in the liver (Wang et al., 2021c). Exposure to environmental PM2.5 during pregnancy or newborn period can also produce strong neurotoxic effects. For mice in early stages of pregnancy, exposure to PM2.5 can stimulate the dopamine pathway in the brain, inhibit the glutamate pathway, and increase the risk of diseases in the offspring (Church et al., 2018; Cui et al., 2019). Additionally, studies have shown that when mice are sub-chronically exposed to high concentrations of PM2.5, the richness and composition of intestinal and pulmonary microbiota are significantly reduced, leading to glucose metabolism disorder, and five reliable differential metabolites (glutamate, glutamine, formic acid, pyruvate, and lactic acid) are obtained (Ran et al., 2021; Wang et al., 2018). Consequently, metabolomics can be a novel and promising method for assessing PM2.5 damage and elucidating its mechanism of toxicity.
6 Metabolomics analysis can provide scientific tools for exploring toxicity and risk assessment of POPs
Persistent organic pollutants (POPs) are the most common endogenous and exogenous exposures. Because they are highly resistant to metabolic degradation and readily accumulate in adipose tissue, long-term exposure can lead to neurodegenerative diseases, inflammation, hepatotoxicity, nephrotoxicity, insulin resistance, allergy, metabolic diseases, and carcinogenesis (Ranjbar et al., 2020; Żwierełło et al., 2020). The compounds 2,3,7,8-tetrachlorodibenzo-p-dioxins (TCDD), 2,3,7,8-tetrachlorodibenzo-furan (TCDF), and polychlorinated biphenyls (PCBs) are typical environmental pollutants as well as aryl hydrocarbon receptor (AHR) agonists (AHR activation regulates intestinal microbial community structure and function) (Chen et al., 2021b; Stedtfeld et al., 2017; Zhang et al., 2015a). Presently, many laboratories use metabolomics studies to reveal and explain exposure-related metabolic disturbances and their risks to chronic diseases, providing a scientific basis for elucidating the toxicity mechanisms of environmental pollutant exposure (Table 4) (You et al., 2022). The environmental pollutant TCDD can cause cancer, reproductive and developmental issues, damage to the immune system, and interfere with the endocrine system (Gaspari et al., 2021; Maqbool et al., 2016; Patrizi B et al., 2018). Untargeted metabolomics was used to analyze inflammatory lipid metabolites in mouse liver, serum, and urine to elucidate the association between TCDD exposure and hepatotoxicity. The results suggest that TCDD can activate the AHR and induce the release of pro-inflammatory factors, which may lead to the development of steatohepatitis (Doskey et al., 2020). A study on acute exposure to TCDD in mice showed that acute exposure to TCDD caused dysregulation of extracellular signal-regulated protein kinases, metabolic disturbances of biologically active lipids, amino acids, etc., resulting in various organ damage and dysfunction (Dopkins et al., 2021). TCDF is the most typical environmental pollutant of the dioxins. When mice were exposed to TCDF, 1H-NMR analysis of their metabolism revealed that gluconeogenesis and glycogenolysis were inhibited, which stimulated the synthesis of liver fat and induced inflammatory reaction (Yuan et al., 2020). Exposure to TCDF caused intestinal microflora disorder, with increased production of lipopolysaccharide and glutamate resulting in intestinal inflammation (Nichols et al., 2019; Zhang et al., 2015b). Of PCBs, 3,3′,4,4′,5-pentachlorobiphenyl (PCB126) is the most toxic. Long-term exposure to low-dose PCB126 can cause an accumulation of fatty acids (e.g., palmitic acid, palmitoleic acid, and linoleic acid) in heart tissue, leading to cardiac hypertrophy. The increase in collagen synthase and extracellular matrix protein induced by glycine and threonine up-regulation indicates cardiac fibrosis (Wang et al., 2021a). Polycyclic aromatic hydrocarbons (PAHs) are recognized as carcinogens. Recent epidemiological studies have demonstrated links between cardiovascular disease, diabetes, and neurodegenerative diseases (Lu et al., 2021; Wu et al., 2021a). In human and animal experiments, PAHs have been found to cause oxidative stress and injury, protein biosynthesis disorders, organ dysfunction, and metabolic disorders (Gao et al., 2018). LC–MS-based untargeted metabolomics was used to analyze urine samples from children and adolescents from industrially polluted areas to explore the metabolic pathways of pollutants associated with liver dysfunction and chronic kidney disease. The study obtained a series of biomarkers of lysophosphatidylcholine (LPCs), phosphatidylcholine (PCs), and sphingomyelin to clarify their biological mechanism (Chen et al., 2021a). The organic pollutant 2,2,4,4′ -tetrabromodiphenyl ether (BDE-47) can cause obesity disease. When mice are exposed to BDE-47 through a high-fat diet, lipid metabolism disorders occur, and the accumulation of saturated fatty acids and triglycerides in fat induces inflammation and obesity (Yang et al., 2021a). In a separate study, a mouse model of BDE-47 exposure showed that BDE-47 mainly affected glycerophospholipid metabolism in the brain, and potential biomarkers were obtained, including phosphatidylcholine, lysophosphatidylcholine, sphingomyelin, biogenic amines, and various amino acids (Li et al., 2020a). Additionally, POPs can produce adverse biological effects. Triphenyl phosphate (TPP) can affect glycolysis, citric acid cycling, oxidative phosphorylation, and lipid and protein metabolism pathways; induce apoptosis of normal liver cells (L02); damage cell ultrastructure; and increase reactive oxygen species (Wang et al., 2020). Triclocarban (TCC) is an endocrine disruptor widely present in nursing products, which can inhibit the activity of human soluble cyclooxyhydrolase involved in the regulation of blood pressure and inflammation and is a potential cause of colon diseases (Zhang et al., 2020). Zhang et al. used untargeted metabolomics to study the effects of Triclocarban exposure on the liver and gut microbiota in mice. This study found that Triclocarban exposure caused metabolic disturbances in the liver microenvironment and enhanced oxidative stress in mice. At the same time, Triclocarban derivatives interfered with the composition of gut microbes and induced chronic diseases such as colitis. (Li et al., 2018; Zhang et al., 2021a). These studies revealed the mechanism of POPs toxicity to the body and screened a series of meaningful biomarkers. Existing research and data prove that metabolomics is an effective means to find environmental exposure disease-related biomarkers to elucidate the mechanism of action.
7 The potential for efficient and sensitive analysis of metabolomics is a boon for scientific research
Metabolomics aims to comprehensively monitor the level of metabolite changes induced by endogenous and exogenous factors in living systems, screen significantly differential metabolites, and obtain potential biomarkers (Wang et al., 2021b). It has been widely used in disease diagnosis, nutrition, new drug research and development, drug toxicity assessment, environmental toxicology, systems biology, and other research fields (Fu et al., 2022; Steuer et al., 2019; Tran et al., 2020). Metabolomics-based toxicity studies of environmental pollutants can provide new insights into the impact of environmental exposures on human health and enhance the study of pathogenic mechanisms of environmental pollutant exposures. We reviewed biomarkers and pathogenesis associated with exposure to various environmental pollutants from a metabolomic perspective. The findings enhanced our understanding of the biological effects of exposure to environmental pollutants. However, there are still many gaps in the research on metabolic disorders and signaling disorders caused by environmental pollutants exposure, which cannot fully meet our needs for disease prevention and treatment. Most metabolomics studies are still in the stage of biomarker discovery using a large amount of sample data, lacking targeted validation of potential biomarkers and exploration of metabolic pathways. Metabolomics can be combined with advanced sequencing technologies to screen and validate biomarkers associated with specific environmental exposures and diseases, providing new ideas and insights for exploring pathogenic mechanisms.
Data availability
The data and materials in this article are online and publicly available without request.
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Funding
This work was supported by Key Scientific Research Project of Hunan Health Commission (No.202102051816), Hunan Natural Science Foundation (2019JJ40238) and (2019JJ50509), Hunan Health Commission (C2019096), Hunan Provincial Department of Education (10C0479), the project of Hengyang Science and Technology Bureau (2020jh042).
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SW and YW contributed equally to the writing of this article and were major contributors in draft manuscripts. YG, YC, JC, LL, HY, YY, XL, and GL were involved in literature search. LY was involved in conception and instruction of the study and critically revised this manuscript.
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Wei, S., Wei, Y., Gong, Y. et al. Metabolomics as a valid analytical technique in environmental exposure research: application and progress. Metabolomics 18, 35 (2022). https://doi.org/10.1007/s11306-022-01895-7
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DOI: https://doi.org/10.1007/s11306-022-01895-7