Metabolomics as a tool to unravel the oxidative stress-induced toxicity of ambient air pollutants

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Metabolomics as a tool to unravel the oxidative stress-induced toxicity of ambient air pollutants

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Frank J Kelly, Julia C Fussell NIHR Health Impact of Environmental Hazards HPRU, MRC Centre for Environment and Health, King’s College London, London, United Kingdom

Abstract Environmental metabolomics can sensitively quantitate thousands of chemical signals in a biological sample, providing a broad spectrum of human metabolism measurements to reveal biological effects and associated toxicological mechanisms associated with exposures to the polluting gases and aerosols in the air we breathe. This chapter reviews human, animal, and in vitro metabolic profiling studies characterizing changes in low-molecular-weight metabolites indicative of oxidative stress responses to air pollutants such as those in occupational exposures, industrial aerosols, and urban environments. Findings (e.g., lipid peroxidation, protein breakdown, mitochondrial dysfunction, and reprogramming of energy metabolism) feasibly the result of an oxidative imbalance and a subsequent antioxidant response are discussed. Continued development of this field will strengthen the causal basis for epidemiological findings associating air pollution with ill health. In doing so, it will bring us closer, via evidencebased mitigation strategies, to reduce the public health burden attributable to this complex and insidious environmental pollutant. ­Keywords: Metabolomics, Traffic-related air pollution, Industrial air toxics, Occupational exposures, Ozone, Oxidative stress

­Introduction Investigation into the toxicity of environmental exposures initially adopted what we now refer to as traditional, hypothesis-driven approaches. These are fundamental to epidemiologists since they provide the means to support causal inference by providing associations between physiological end points (e.g., respiratory or cardiovascular symptoms) and underlying molecular events. The latter are generally derived from conventional, a priori defined clinical parameters employing single-end point assays (Bosson et  al., 2013; Chen & Guillemin, 2009; Chuang, Chan, Su, Lee, & Tang, 2007; Jacobs et al., 2011; Ruckerl et al., 2007; Tsai et al., 2012). The limitation of Oxidative Stress. https://doi.org/10.1016/B978-0-12-818606-0.00024-9 © 2020 Elsevier Inc. All rights reserved.

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such an approach however lies in the inability to uncover the multiple molecular targets and novel pathways that are undoubtedly behind the toxicological response to complex environmental exposures such as the polluting gases and aerosols in the air we breathe. Recently, more informative mechanistic studies, employing various “omics” technologies, are focusing more on questions rather than hypotheses (Glass & Hall, 2008; Kell & Oliver, 2004) and as such have greater likelihood of unveiling unexpected relationships and generating novel insights that in turn can lead to hypothesis generation. Transcriptomic and proteomic analyses provide global views of mRNA and protein changes associated with toxicant exposure. For example, proteomic studies of lung toxicity induced by particulate matter <2.5 μm in diameter (PM2.5) have enabled analysis of the global protein profile of cells and found that the expression of an array of proteins involved in oxidative stress, carbohydrate and energy metabolism, signal transduction, and protein synthesis and degradation was altered by PM2.5 (Huang et al., 2014). Further advances in analytical technology, in the form of metabolomics, involve the simultaneous measurement, by mass spectrometry (MS) or nuclear magnetic resonance (NMR), of multiple small metabolites (<1 kDa) arising from specific cellular processes, such as energy production and storage, signal transduction, and apoptosis (Rochfort, 2005). Since metabolites are the terminal products of gene expression or put another way, the final consequence of biological function, their profiles in biological samples report on the actual functional status and may therefore be more representative of atypical physiological status than genomic and proteomic profiles (Johnson, Ivanisevic, & Siuzdak, 2016). Patterns of such large quantities of metabolites are thus able to provide considerable insight into dysregulated metabolic pathways, underlying disease processes, and pathophysiological changes induced by stimuli such as environmental stress, drugs, or nutrition. For example, the identification of a novel pathway linking dietary lipid intake, intestinal microflora, and cardiovascular disease was first predicted from metabolomic profiling studies, providing opportunities for the development of both novel diagnostic tests and therapeutic approaches for atherosclerosis (Wang et al., 2011). Metabolomics can be performed, with (targeted) or without (untargeted) known metabolites being quantified. Targeted metabolomics offers high sensitivity and selectivity in analyzing concentrations of a predefined set of metabolites. This type of analysis is also used to obtain accurate concentrations of metabolites identified by untargeted metabolomics. The aim of untargeted or global metabolomics is to measure the broadest range of metabolites present in a biological sample. Such a high-throughput evaluation minimizes observational biases and offers a greater means to examine the relationship between interconnected metabolites from multiple pathways and reveal novel and unanticipated perturbations. However, since the types of metabolites that are recovered are influenced by the extraction and analytical method, and there are still a large number of unknown metabolites that remain unannotated in metabolite databases, it is not as yet possible to obtain a snapshot of all metabolite classes simultaneously. These analyses and especially untargeted ones result in the generation of large and complicated datasets; therefore, to gain

­Ambient air pollution

b­ iologically relevant conclusions requires computational tools for data processing, statistical analysis, metabolite identification and quantification, and interpretation (Bundy, Davey, & Viant, 2008; Johnson et al., 2016). Subsequently, metabolites can be placed into context with upstream genes and proteins to move closer to the identification of disease mechanisms (Cottret et al., 2010).

­Ambient air pollution Environmental metabolomics is a promising technique to characterize the interactions of living organisms with their environment by identifying metabolic biomarkers that correlate with exposure and predict health end points (Lankadurai, Nagato, & Simpson, 2013). Identifying metabolite perturbations caused by indoor (owing to household exposure to smoke from dirty cook stoves and fuels) and outdoor air pollution, which in 2017 contributed to 5 million deaths globally (HEI, 2019), is a particularly relevant and promising approach in offering further insights into molecular pathways and mechanisms underlying its toxicity. The latter remains an enormous challenge for the reasons outlined in the following points: • Inhalation of particulate and gaseous air pollution causes diverse pathology at sites beyond the lung. The many documented health outcomes are not limited to the numerous, well-established respiratory (asthma, cancer, chronic obstructive pulmonary disease, and infection) and cardiovascular (myocardial infarction, stroke, and atherosclerosis) effects, but extend to adverse birth outcomes, diabetes, neurodevelopment, and cognitive function (Royal College of Physicians, 2016). • Current evidence on the mechanisms linking air pollution with ill health points to the involvement of numerous metabolic pathways and processes through which oxidative stress (an imbalance between oxidants and antioxidants in favor of the oxidants, leading to a disruption of redox signaling and control and/or molecular damage (Sies & Jones, 2007)) has been identified as a unifying feature (Kelly, 2003). Indeed, in the case of the cardiovascular system, accumulating evidence supports the potential for cellular oxidative imbalances to occur in the many functional pathways (endothelial dysfunction, atherosclerosis, coagulation, autonomic nervous system balance, and changes in blood pressure) through which inhaled air pollutants exert adverse effects (Kelly & Fussell, 2017). • In urban areas especially, air pollution constitutes a complex heterogeneous mixture of reactive gases (nitrogen dioxide and ozone) combined with primary and secondary PM upon which harmful substances such as heavy metals and polycyclic aromatic hydrocarbons (PAHs) are absorbed or contained. Moreover, particulate air pollution differs in not only chemical composition, mass, size, number, shape, and surface area but also source, solubility, and reactivity (Kelly & Fussell, 2012). Added to this, PM varies in space and time as a consequence

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of atmospheric chemistry and weather conditions, as well as complex interactions with ozone and nitrogen dioxide that share biologically plausible associations with various health end points (Kelly, 2003). A meaningful metabolic signature in biological samples in response to air pollution exposure is plausible since while some ambient air pollutants have potential to enter the bloodstream directly from the lungs, others can induce oxidative stress and inflammation in the lungs, triggering a systemic response that can be observed in the peripheral blood. Furthermore, oxidative stress as a consequence of normal metabolism, mechanisms to limit the latter, and responses to repair any oxidative damage are all closely related with downstream metabolic pathways (Galasko & Montine, 2010). Summarized in Table 1 are the studies that have used metabolic profiling technologies to characterize changes in low-molecular-weight metabolites in response to gaseous and particulate air pollution exposure and, in doing so, are shedding more light into the role of oxidative stress in eliciting the health effects of this environmental threat. Studies included cover human, animal, and in vitro research that have detected metabolites indicative of oxidative stress responses to traffic-related air pollution (TRAP), industrial air toxics, occupational exposures, and ozone.

­Industrial air toxics Studies have examined metabolic changes linked to a number of redox-active ambient pollutants including PAHs, metal, and benzene that are prevalent in industrial areas (Chen et  al., 2017; Wang et  al., 2019, 2015). Wang et  al. (2019, 2015) adopted a global metabolomic approach to investigate the urinary metabolic changes in a large (n = 566) susceptible Chinese population, constituting children and elderly people, living in either an area polluted by the coking industry and as such with chronic exposure to relatively low concentrations of PAH, metals, and benzene, or a ­nonpolluted control area (Wang et al., 2019, 2015). The total concentrations of 16 priority PAHs in ambient air were 65 times higher than those in the nonpolluted area. In ­addition, concentrations of 9 PAH metabolites, 17 metals, and 1 benzene metabolite (S-phenylmercapturic acid [S-PMA]) were measured in the urine of the same population to evaluate personal exposure. The metabolic consequences of chronic ambient air pollutant exposure relating to amino acid, purine, lipid, and glucuronic acid metabolism varied significantly between individuals in the exposed and control groups. As components of ambient air PM, PAHs and ­multiple metals (including cadmium, cobalt, chromium, copper, iron, mercury, lithium, molybdenum, nickel, lead, selenium, and zinc) were found to have a close association with these biological effects. In individuals exposed to coking-related pollutants, 2 amino acid derivatives (3-­methylhistidine and pyroglutamic acid), 3 organic acids (azelaic acid, decenedioic acid, and hydroxytetradecanedioic acid), 1 glucuronide conjugate ­ (decenedioylglucuronide), and 11 acylcarnitines (heptenedioylcarnitine, octenedioylcarnitine, nonenedioylcarnitine, 3-hydroxydecanoylcarnitine, ­dodecanedioylcarnitine, ­nonanoylcarnitine,

Table 1  Metabolomic studies characterizing changes in low-molecular-weight metabolites, indicative of oxidative stress, in response to gaseous and particulate air pollution exposure

Study

Pollutant exposure

Population Sample for analysis

Notable metabolite signal

Mainly affected metabolic pathways

Acylcarnitines, azelaic acid, decenedioic acid, decenedioylglucuronide, hydroxytetradecanedioic acid, 3-methylhistidine, pyroglutamic acid, uric acid

Amino acid metabolism Glucuronic acid metabolism Lipid metabolism Purine metabolism

Benzenoids, hydrocarbons, lipids/lipid-like molecules, organic acids and derivatives, organoheterocyclic compounds, organonitrogen compounds, organooxygen compounds, organosulfur compound, phenylpropanoids and polyketides, homogeneous nonmetal compound Inosine, 3′-adenylic acid, inosinic, phosphocreatine, glutathione, l-malic acid, citric acid, l-lactate, fructose 1,6-bisphosphate, l-tryptophan l-glutamine, oxidized glutathione, hypoxanthine, adenosine, guanosine, guanosine monophosphate, l-proline, N-acetylglutamic acid, 5-aminopentanoic acid, l-glutamic acid, lcarnitine, phenylacetylglycine, spermidine, l-phenylalanine

Alanine aspartate and glutamate metabolism Glycine, serine, and threonine metabolism Tryptophan metabolism Phenylalanine metabolism Glutathione metabolism Arginine and proline metabolism Purine metabolism Tricarboxylic acid (TCA) cycle and glycolysis

Industrial air toxics Chronic environmental exposure to benzene, metals, and polycyclic aromatic hydrocarbons (PAHs)

Chen, Yuan, Shie, Wu, and Chan (2017)

Chronic environmental exposure to benzene, metals, and PAHs

Song et al. (2019)

Taiyuan winter total PM2.5 water-soluble and organic-soluble fractions 25, 50, and 100 mg/L for 24 h

566 children and elderly people living in an area polluted (n = 369) by the coking industry and a nonpolluted control area (n = 197) in China Children (aged ~7) and elderly (aged >50) Urine 252 children and elderly people living different distances from largest petrochemical complex in Taiwan n = 111 high exposure; n = 141 low exposure Children (aged 9–15) and elderly (aged >55) Urine BEAS-2B cells

­Industrial air toxics

Wang et al. (2019, 2015)

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Continued

Pollutant exposure

Wang, Jiang, et al. (2017), Wang, Xue, et al. (2017)

Tangshan PM2.5: 1 mg/kg/week IT or saline for 3 consecutive months

Notable metabolite signal

Mainly affected metabolic pathways

Male Sprague-Dawley rats (n = 6 per group) Lung

Adenosine, deoxyinosine, guanine, hypoxanthine, 8-hydroxyguanosine, sphinganine, sphingomyelin, surfactant phospholipids, for example, phosphatidylcholine (PC), lysophospholipid (lyso) PC, spermidine

Lipid metabolism Purine metabolism

Arachidonic acid metabolism Leukotriene metabolism Tryptophan metabolism Vitamin E metabolism Predominantly associated with xenobioticmediated oxidative stress and acute inflammatory response: for example, leukotriene metabolism, vitamin E metabolism and cytochrome P450

Traffic-related air pollution Ladva et al. (2018)

Acute (2 h) invehicle air pollution on highways or nonhighway/clinic exposure, ACE Study

49 adults; mean age 26 y Plasma

Particulate Al, Fe, Pb exposures: 110 features

Liang et al. (2018)

Residential distance (20 m versus 1.4 km) from a highway; DRIVE study

54 college students; mean age 19 y Plasma and saliva

1291 unique metabolic features were significantly associated with ≥1 traffic indicator, including BC, CO, NOx, PM2.5 10 identified metabolites: adenosine 5-monophosphate, arginine, bis(2-ethylhexyl) phthalate, cytosine, histidine, 3-Hydroxykynurenine, hypoxanthine, (s)-lactate, glyceraldehyde Gamma linolenic acid, proline

CHAPTER 24  Metabolomics as a tool to unravel the oxidative stress

Study

Population Sample for analysis

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Table 1  Metabolomic studies characterizing changes in low-molecular-weight metabolites, indicative of oxidative stress, in response to gaseous and particulate air pollution exposure—cont’d

Walker et al. (2019)

Low (mean 16,000 particles/cm3) versus high (mean 24,000 particles/cm3) UFP exposures over 1 year; CAFEH study

59 adults; n = 25 ≤ 50 y; n = 34 > 50 y Plasma

Brower et al. (2016)

Mixed vehicle exhaust: 100 or 300 μg/m3 or filtered air for 6 h

Male, 10-week-old C57/BL6 mice; n = 6 per group

Huang et al. (2015)

PM2.5 sampled from Xiamen City, China

A549 cells

38 pathways identified related to Inflammation Endothelial function mitochondrial bioenergetics Amino acid metabolism Energy production Oxidative stress Lipid metabolism

Nitrogen metabolism, citrate cycle Aminoacyl-tRNA biosynthesis Phenylalanine, tyrosine, and tryptophan biosynthesis Glutathione, glyoxylate, and dicarboxylate metabolism

Occupation air pollutants Kuo et al. (2012)

Welding fumes

Acetone, betaine/ trimethylamine N-oxide, creatine, creatinine, glycine, gluconate, serine, S-sulfocysteine, taurine, hippurate

Amino acid metabolism Carbohydrate metabolism Oxidation reduction pathways Urea metabolism Continued

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35 male welders and 16 male office workers Age: 45–64 Urine

­Industrial air toxics

318 metabolic features associated with lipid peroxidation, endogenous inhibitors of nitric oxide, and vehicle exhaust exposure biomarkers including Glutamate and linolenic acid Notable changes 2-Methylbutyrylglycine, 2-hydroxy-3-methylvalerate, 3-methyl-2-oxobutyrate, alphahydroxyisocaproate, creatine, taurine, cysteine, lactate, mannose, glycerate, branched chain amino acid catabolites, butyrylcarnitine, fatty acids Oxidized glutathione, cysteine-glutathione disulfide, 13-HODE, 9-HODE, 12,13-diHOME 16 metabolites including: cis-Aconitate, malate, pantothenate, adenosine diphosphate Glutamate, NAcGlu, phenylalanine, tryptophan, glutathione

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Study

Pollutant exposure

Population Sample for analysis

Notable metabolite signal

Mainly affected metabolic pathways

Ozone Mathews, Kasahara, et al. (2017)

Ozone: 2 ppm for 3 h

Lean wild-type and obese db/db female mice Lung

Cheng et al. (2018)

Ozone: 0.3 ppm for 2 h while undergoing intermittent exercise

23 adults Bronchoalveolar lavage fluid

Items in bold reflect changes in metabolic outcomes associated with oxidative stress.

Bacterial/mammalian cometabolites, citrulline, heme, hypotaurine (db/ db), monoglycerides (WT), PC lysolipids (WT), branched chain amino acid metabolites, lysolipids, glutathione Allantoin, alphaketoglutamate, alanine, arginine, aspartate, cyanoamino acid, glutamate, glutamine, glycerophospholipid, glycine, proline, serine, sphingolipid, threonine, threonate

Amino acid metabolism Lipid metabolism Microbiome

Aldarate metabolism Amino acid metabolism Aminoacyl-tRNA biosynthesis Ascorbate metabolism Ether lipid metabolism Lipid metabolism

CHAPTER 24  Metabolomics as a tool to unravel the oxidative stress

Table 1  Metabolomic studies characterizing changes in low-molecular-weight metabolites, indicative of oxidative stress, in response to gaseous and particulate air pollution exposure—cont’d

­Industrial air toxics

decadienylcarnitine, hydroxydodecenoylcarnitine, dodecadienylcarnitine, and dodecenoylcarnitine) were significantly increased. Uric acid was the only metabolite that was significantly decreased. Several of these observations are indicative of an increased PAH and metal exposure causing various oxidative stress-related effects in that the following metabolites, other than uric acid, were all significantly increased in the exposed populations: • Pyroglutamic acid—an oxidative product of proline and a metabolite of the γ-glutamyl cycle through which glutathione is synthesized and degraded (Pederzolli et al., 2007). Human urinary pyroglutamic acid has also been identified as a potential biomarker of oxidative stress induced by chronic cadmium (Gao et al., 2014). • 3-Methylhistidine—a key constituent of muscle proteins and a marker of muscle protein breakdown (Aranibar et al., 2011), which in turn can be promoted by oxidative stress (Powers, Smuder, & Criswell, 2011). • Azelaic acid, decenedioic acid, and hydroxytetradecanedioic acid—by-products of lipid peroxidation and indicators of oxidative stress (Maes, Mihaylova, & Leunis, 2006). • Decenedioylglucuronide—catalyzed by UDP-glucuronosyltransferases (UGTs), which catalyze the conjugation of xenobiotics or their reactive metabolites with glucuronic acid and as such act as indirect antioxidants (Kalthoff, Ehmer, Freiberg, Manns, & Strassburg, 2010). The expression of UGTs can be induced by oxidative stress, metals, and xenobiotic exposure (Jennings, Limonciel, Felice, & Leonard, 2013; Kalthoff et al., 2010). • Several unusual medium-chain acylcarnitines—synthesized with l-carnitine and the corresponding medium-chain fatty acids (Mentlein, Reuter, & Heymann, 1985) and in the main stem from incomplete mitochondrial β-oxidation of longchain fatty acids and/or lipid peroxidation (Chen et al., 2009). • Uric acid (significantly decreased in the exposed children and elderly nonsmokers)—a powerful antioxidant and end product of purine metabolism. Taken together, these results prompted the authors to surmise that the perturbation of metabolites reflected the oxidative stress-related biological effects induced by ambient PAH and metal exposure, including the depletion of antioxidants, accelerated muscle proteolysis, elevated UGT activity, increased lipid peroxidation, and mitochondrial lipid metabolism dysfunction. Despite increases in benzene being higher than those for PAHs and metals, significant associations between benzene exposure and the metabolic biomarkers were not observed (Wang et al., 2019). Two possible explanations relate to a weaker oxidation capacity of benzene compared with that of PAHs and metals and a much lower exposure level to benzene such that its involvement in oxidative stress was great enough to be detected. Another study to identify potential metabolites linking industrial air toxic exposures to oxidative stress through plausible exposure-related pathways focused on 252 children and elderly subjects living at varying distances from oil refineries and coal-fired power plants within a petrochemical complex in Taiwan

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(Chen et al., 2017). The pollution-affected area was characterized by elevated ambient concentrations of vanadium and PAHs, and its residents had increased urine concentrations of 1-OHP, vanadium, nickel, copper, arsenic, strontium, cadmium, mercury, thallium, and biomarkers of oxidative stress (8-hydroxy-2′-deoxyguanosine [8-OHdG], 4-­hydroxy-2-nonenal-mercapturic acid [HNE-MA], 8-isoprostaglandin F2α [8-isoPF2α], and 8-nitroguanine [8-NO2Gua]) compared with low-exposure subjects. Untargeted urine metabolomics identified age-dependent potential metabolites responsible for the separation between high- and low-exposure groups, while pathway analysis revealed four biological pathways (tryptophan metabolism; phenylalanine metabolism; glycine, serine, and threonine metabolism; and alanine, aspartate, and glutamate metabolism) that could associate multiple exposures with increased oxidative stress. Specifically, tryptophan, the metabolism of which has been shown to be involved with increased oxidative stress and cancer, neurodegenerative diseases, rhinitis, and asthma (Chen & Guillemin, 2009; Ciprandi, De Amici, Tosca, & Fuchs, 2010; Gostner, Becker, Kofler, Strasser, & Fuchs, 2016; Stoy et al., 2005) was downregulated in the high-exposure compared with low-­exposure group in children and correlated with 8-OHdG, HNE-MA, and 8-isoPGF2α. A downstream metabolite of tryptophan metabolism, 1H-indole-3-acetamide, was also identified and associated with 8-NO2Gua. Altered phenylalanine metabolism was also identified in children. Phenylalanine, which has been used as a biomarker of oxidative damage (Orhan, Vermeulen, Tump, Zappey, & Meerman, 2004), was significantly correlated with exposures as well as 8-OHdG and HNE-MA, while its downstream metabolites, hippuric acid, 4-hydroxy benzoic acid, and succinic acid, correlated with HNE-MA, 8-isoPGF2α, and 8-NO2Gua. In elderly subjects, glycine, serine, and threonine metabolism, a pathway closely related to oncogenic transformation and the biosynthesis of the antioxidant glutathione (Amelio, Cutruzzola, Antonov, Agostini, & Melino, 2014), was identified, with threonine correlating with 8-NO2Gua; serine with 8-OHdG, HNE-MA, and 8-NO2Gua; and glyceric acid with HNE-MA and 8-NO2Gua. Altered alanine, aspartate, and glutamate metabolism was identified in both children and elderly subjects. Aspartic acid was downregulated in high-­exposure subjects and associated with 8-NO2Gua and HNE-MA in children and elderly participants, respectively. The relevance here is that studies have shown that aspartic acid could increase glutathione levels and decrease lipid peroxidation in animal models (Sivakumar, Babu, & Shyamaladevi, 2011). Finally, threonate was upregulated in both age groups, indicative of deregulation in its antioxidant precursor, ascorbic acid (Gao et al., 2012), associating multiple exposures with HNE-MA and 8-NO2Gua in children and all four oxidative stress biomarkers in elderly participants. The investigators drew from these results, a complicated web illustrating the association between exposures and different oxidative stress-induced health effects through age-dependent diverse biological pathways (Fig. 1). The industrial city of Taiyuan in China suffers from particularly serious PM2.5 pollution owing to a large number of coal combustion and active industrial activities (Cao et al., 2014). In the winter of 2016, the average concentration of PM2.5 was 175 μg/m3—significantly higher than concentrations of approximately 11–18 μg/m3

­Industrial air toxics

FIG. 1 Exposure pathways of petrochemical of air pollution and the effects on urine metabolic profile changes and increased oxidative stress. Reproduced from Chen, C. S., Yuan, T. H., Shie, R. H., Wu, K. Y., & Chan, C. C. (2017). Linking sources to early effects by profiling urine metabolome of residents living near oil refineries and coal-fired power plants. Environment International, 102, 87–96.

measured in European cities during the cold period (Jedynska et  al., 2015). Furthermore, the large-scale combustion of coal in Taiyuan generates high concentrations of particularly toxic organic chemicals (Li, Kou, Geng, Dong, & Cai, 2014). Nontargeted and targeted metabolomics combined with multiple cytotoxicity assays (indices of oxidative stress, inflammation, mitochondrial respiration, and glycolysis and the expression levels of key genes) has been used to investigate the overall metabolic changes and relevant toxicological pathways caused by Taiyuan winter total PM2.5 and its water-soluble and organic-soluble fractions in human lung ­bronchial epithelial cells (BEAS-2B) (Song et  al., 2019). The exposure concentrations were chosen according to the Taiyuan winter actual PM2.5 concentration. Significant metabolome alterations were observed after exposure to total PM2.5 or its organic-­soluble fraction, and moreover, the higher the exposure concentrations, the more number of components changed, indicating that PM2.5 affected the bronchial epithelial cells in a dose-dependent manner. The most influenced pathways were glutathione metabolism, purine metabolism (upregulated cluster of metabolites), and arginine and proline metabolism (downregulated cluster of amino acids).

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In ­addition, the ­tricarboxylic acid (TCA) cycle (decreased) and glycolysis (increased) were affected in cells exposed to total PM2.5 samples and its organic-soluble fraction, indicating that the energy metabolism was affected. The specific perturbations in metabolites indicative of antioxidant-oxidant balance disruption included the oxidation of GSH to GSSG, and this was reinforced by gene expression data showing a large increase in the expression of nuclear factor erythroid 2-related factor 2 and its downstream genes heme oxygenase 1 and quinone oxidoreductase. While purine metabolism, which produces reactive oxygen species (ROS) via the xanthine oxidase (XO) pathway, was one of the most significantly affected pathways following total PM2.5 exposure, gene expression data revealed increased concentrations of XO. Exposure also disrupted arginine metabolism. One well-described pathway of arginine metabolism involves its conversion to nitric oxide, the accumulation of which could generate reactive nitrogen species. A speculative scenario as a result of the interplay between increased oxidative stress and inflammation, mitochondrial dysfunction, energy metabolic reprogramming, oxidative stress, and inflammation and energy metabolic reprogramming from oxidative phosphorylation to glycolysis is illustrated in Fig. 2. Another group (Wang, Jiang, et al., 2017) have investigated the pulmonary metabolome responses to PM2.5 sampled from Tangshan, China’s top steelmaking city and one of the country’s most polluted urban areas (Luo, Hanb, & Liu, 2017). Adult male rats (n = 6) were treated by intratracheal instillation with PM2.5 suspension once a week at the dose of 1 mg/kg/week for 3 consecutive months. The control group (n = 6) was treated with saline. In an attempt to understand the comprehensive pulmonary response to PM2.5, a nontargeted metabolomics strategy was adopted together with biochemical analyses of oxidant (malondialdehyde and thiobarbituric acid reactive substances) and antioxidant (catalase, glutathione peroxidase, and superoxide dismutase) imbalance to characterize the overall metabolic changes and relevant toxicological pathways. Along with the biochemical findings of a significant increase in oxidative stress, significant metabolome alterations were observed in the lung tissues of the treated rats. A variety of identified metabolites are mainly involved in the metabolism of lipid and nucleotides. Abnormal lipid metabolism has been associated with the activation of oxidative and inflammatory pathways (Zhao et al., 2015), and in this study, the reduction in the integral surfactant phospholipids (e.g., phosphatidylcholine [PC]) and elevation in lysophospholipid PC suggests increased hydrolysis, possibly owing to oxidative stress-induced activation of phospholipase A2 (PLA-2). Sphingolipid is another important component of cell membrane. Sphingosine, which is synthesized from sphinganine, and sphingomyelin form primary parts of sphingolipids and play a pivotal role in the cellular responses to oxidative stress. Wang, Jiang, et  al. (2017) observed significantly reduced concentrations of sphinganine and sphingomyelin levels in treatment group. Oxidative stress indicators identified in the pulmonary metabolome of treated rats included significant up- and downregulations in 8-hydroxyguanosine and spermidine, respectively. Spermidine maintains membrane potential and controls intracellular pH and volume through synchronizing many biological processes and possesses antioxidant

­Traffic-related air pollution

FIG. 2 Schematic overview of the disturbed metabolic pathways in BEAS-2B cells upon PM2.5 exposure. Molecules marked in red represented the upregulated metabolites and in green represented the downregulated metabolites. Reproduced from Song, Y., Li, R., Zhang, Y., Wei, J., Chen, W., Chung, C. K. A., & Cai, Z. (2019). Mass spectrometry-based metabolomics reveals the mechanism of ambient fine particulate matter and its components on energy metabolic reprogramming in BEAS-2B cells. The Science of the Total Environment, 651, 3139–3150.

­activity (Kim, 2017). Combining the results may suggest that PM2.5-induced pulmonary ­toxicity through disturbing pro-oxidant/antioxidant balance, causing changes of phospholipid, sphingolipid, and purine metabolism and DNA damage in lung tissues.

­Traffic-related air pollution Traffic-related air pollution has been linked to numerous adverse health effects, but the specific constituents responsible for these effects and how they contribute to corresponding biological responses are poorly understood (HEI, 2010). This uncertainty is, in part, due to the chemical and physical heterogeneity of this pollutant source and the numerous biological factors and pathways that may mediate responses (Bates et al., 2015; Brown et al., 2012; Zanobetti, Austin, Coull, Schwartz, & Koutrakis, 2014; Zora et al., 2013). It is not surprising therefore that to address these research gaps and uncertainties, workers have adopted a m ­ etabolomic ­approach to

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gain further insights into the toxicological mechanisms u­ nderlying TRAP exposurerelated diseases in human panel-based protocols (Ladva et al., 2018; Liang et al., 2018; Walker et al., 2019), animal models (Brower et al., 2016), and in vitro studies (Huang et al., 2015). Ladva et al. (2018) used measurements collected as part of a longitudinal panel (Atlanta Commuters Exposure [ACE] study) of car commuters, to examine in-vehicle air pollution concentrations, targeted inflammatory and vascular injury biomarkers, and metabolomic profiles associated with on-road traffic exposures during morning rush hour commutes in Atlanta (Ladva et  al., 2018). Sixty adults participated in a crossover study, in which each participant conducted a highway commute and was randomized to either a (a) side-street commute or (b) clinic exposure session. The metabolomics analyses detected 10-h perturbations in features associated with in-vehicle, particulate metal exposures (aluminum, lead, and iron), which reflected changes in arachidonic acid, leukotriene, and tryptophan metabolism. Other TRP parameters including PM2.5 mass and particle number concentration had no association. The association of tryptophan metabolism with lead exposure is consistent with the evidence of this metal, despite being redox inactive, participating in the depletion of antioxidants (Matovic, Buha, Ethukic-Cosic, & Bulat, 2015; Valko, Jomova, Rhodes, Kuca, & Musilek, 2016). Furthermore, while aluminum is also redox inactive, it has demonstrated ability to shift biological systems into a state of oxidative stress (Verstraeten, Aimo, & Oteiza, 2008). In the Dorm Room Inhalation to Vehicle Emission (DRIVE) study, the ability of metabolomic profiling to reflect internal exposures to complex traffic-related air pollution mixtures was assessed (Liang et  al., 2018). Here, untargeted metabolomics was applied to a 12-week field protocol incorporating repeated biomonitoring (plasma and saliva) in a panel of 54 college students living in dormitories either near (20 m) or far (1.4 km) from a major highway. In parallel, a suite of TRAP (black carbon, carbon monoxide, nitrogen oxides, and PM2.5) were measured at multiple ambient and indoor sites at varying distances from the highway artery. Workers observed 1291 unique metabolic features significantly associated with at least one ­traffic indicator and confirmed the chemical identities of 10 metabolites indicative of endogenous metabolic signals, including arginine, histidine, gamma linolenic acid, and hypoxanthine. Pathway analysis indicated elicitation of inflammatory and oxidative stress-related pathways, and among these, those involved in leukotriene and vitamin E metabolism consistently showed the strongest associations with TRAPs in both saliva and plasma samples. Tentatively matched metabolites in the latter pathway included dehydrogenation precursors of tocopherols and tocotrienols. Of note, 11 metabolic features were matched to vitamin E metabolites, and the intensities of these antioxidants decreased with higher TRAP exposures. Other identified metabolites worthy of discussion include arginine and histidine and hypoxanthine. In a study examining the serum amino acid profiles in obese and nonobese women, both histidine and arginine were found to be negatively associated with inflammation and oxidative stress (Niu et  al., 2012). Hypoxanthine, a purine derivative that protects against cellular oxidative injury by inhibiting the activation of nuclear

­Traffic-related air pollution

poly(ADP-ribose) polymerase (Durkacz, Omidiji, Gray, & Shall, 1980; Szabo, 1998; Szabo & Dawson, 1998), was positively associated with both indoor and outdoor CO levels. Vlaanderen et al. (2017) has also identified hypoxanthine as associated with a 5-h exposure to real-world ambient air pollution (Vlaanderen et al., 2017) as did Song et al. (2019) and Wang, Jiang, et al. (2017) in their in vitro and rat metabolomic profiling studies with PM2.5. Further efforts to delineate biological response mechanisms associated with a year-long averaged personal ultrafine particle (UFP) exposure have incorporated untargeted metabolomics to profile plasma from 59 participants enrolled in the Community Assessment of Freeway Exposure and Health (CAFEH) study. Metabolic variations associated with UFP exposure were assessed using a cross-sectional study design based upon low (mean 16,000 particles/cm3) and high (mean 24,000 particles/ cm3) annual average UFP exposures. Prior to this, nonmetabolomic characterization of amino acids, lipid/fatty acid metabolites, cofactors, and cellular respiration identified five metabolites that were differentially expressed between low and high exposures, including arginine, aspartic acid, glutamine, cystine, and methionine sulfoxide. Elevated methionine sulfoxide and cystine are consistent with increased oxidative stress. In humans, the major extracellular thiol/disulfide redox couple is cysteine and its disulfide form, cystine (Go & Jones, 2011). Increased oxidation of the couple and thus a shift toward a more positive redox potential leads to an activation of proinflammatory cytokines (Iyer et al., 2009), regulates initial atherosclerotic events (Go & Jones, 2005), and is associated with cardiovascular disease and endothelial function (Ashfaq et al., 2008). Methionine sulfoxide is the oxidation product of methionine with reactive oxygen species and a recognized marker of oxidative stress (Mashima, Nakanishi-Ueda, & Yamamoto, 2003; Moskovitz, Berlett, Poston, & Stadtman, 1997). Analysis of the metabolome identified 316 features associated with UFP exposure, consistent with increased lipid peroxidation, endogenous inhibitors of nitric oxide, and vehicle exhaust exposure biomarkers. Identified decreases in linolenic acid and glutamate provide additional evidence of oxidative stress in that glutamate is a precursor for glutathione and linoleic acid is a polyunsaturated fatty acid susceptible to lipid peroxidation (Yin, Xu, & Porter, 2011). Network correlation analysis and metabolic pathway enrichment identified 38 pathways and included variations related to inflammation, endothelial function, and mitochondrial bioenergetics. Experimental studies aimed at unraveling the toxicity of urban combustion emissions have also highlighted complex metabolomic alterations (Brower et  al., 2016; Huang et  al., 2015). For example, investigating the temporal (immediately and 18 h post) effects of acute exposure (100 or 300 μg/m3 for 6 h) to mixed (gasoline and diesel) vehicle emissions (MVE) on metabolites in the serum of C57Bl/6 mice highlighted both concentration-dependent and concentration-independent changes in numerous serum biochemicals (Brower et  al., 2016). Among the more profoundly altered metabolites immediately after the MVE exposure were elevations in oxidized glutathione and cysteine-glutathione disulfide. Additional evidence for oxidative stress included an increase in taurine that can effectively scavenge ROS at

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FIG. 3 Schematic overview of the disturbed metabolic pathways in mitochondria of A549 cells on PM2.5 exposure. Molecules marked in red represent the differential metabolites detected by metabolomics. Reproduced from Huang, Q., Zhang, J., Luo, L., Wang, X., Wang, X., Alamdar, A., Peng, S., Liu, L., Tian, M., & Shen, H. (2015). Metabolomics reveals disturbed metabolic pathways in human lung epithelial cells exposed to airborne fine particulate matter. Toxicology Research, 4, 939–947 with permission from The Royal Society of Chemistry.

p­ hysiological concentrations (Oliveira et al., 2010); in cysteine, an intermediate in ­taurine and glutathione synthesis; and in 13-HODE, 9-HODE, and 12,13-DiHOME. By 18 h post exposure, serum metabolite differences between animals exposed to MVE versus those exposed to FA were less pronounced. Another group investigated the pulmonary metabolome responses to PM2.5 in human lung epithelial cells (A549) and again demonstrated that oxidative stress was the most important factor to cause metabolism dysfunction after exposure (Huang et al., 2015). The PM2.5 extract was sampled from a suburban region with rapid urbanization in Xiamen City, China. PM2.5 significantly changed the abundance of 16 metabolites in a dose-dependent manner, and the citrate cycle, amino acid biosynthesis, and glutathione metabolism were the three major metabolic pathways disturbed. In addition, changes in the expression of several key genes involved in these pathways further validated the metabolic alterations. Notably, GPX1 and SOD2 (a specific antioxidant enzyme in mitochondria) were both elevated in cells following PM2.5 exposure (Fig. 3).

­Occupational exposures The increased susceptibility to acute disorders and chronic diseases in several working groups following prolonged exposure to poor air quality has prompted a small number of studies investigating associations between occupational air ­pollutants and

­Ozone

metabolite disturbances (Kuo et al., 2012; Pradhan, Das, Meena, Nanda, & Rajamani, 2016; Wei et al., 2013). The impact of air pollutants from welding processes is a major concern in occupational medicine and public health (Hobson, Seixas, Sterling, & Racette, 2011). Welding fumes contain large amounts of various gases and ultrafine and fine particles containing metals and their oxides (Antonini et  al., 2005; Kim et al., 2003; Tessier & Pascal, 2006). They can be viewed as oxidative pollutants (Liu, Wu, & Chen, 2007), producing adverse health effects on welders through increased oxidative stress (Han et al., 2005). Kuo et al. (2012) characterized a series of urinary metabolomic changes associated with the long-term, complex effects of welding fume exposure in workers at a Taiwanese shipyard (Kuo et al., 2012). Compared with 16 male office workers, the metabolomic patterns of 35 male welders exhibited higher concentrations of glycine, taurine, betaine/trimethylamine N-oxide, serine, S-sulfocysteine, hippurate, gluconate, creatinine, and acetone and lower levels of creatine. Among these are metabolites that play a role in modulating oxidation/reduction pathways. In addition to increases in taurine, in agreement with Brower et al. (2016), glycine is able to inhibit ROS production in human neutrophils (Giambelluca & Gende, 2009), and betaine has been shown to restore glutathione concentrations and quench free radicals to lessen liver injury (Varatharajalu, Garige, Leckey, Gong, & Lakshman, 2010). The increased concentrations of these metabolites may be indicative of a self-protective mechanism against increased oxidative stress in welders. Similarly, the significant decrease in creatine concentrations among the welders may be explained by ROS and free radical induced decreases in creatine kinase activity (Genet, Kale, & Baquer, 2000; Koufen et al., 1999).

­Ozone Exposure to ozone can result in mild pulmonary irritation and infection, exacerbation of lung disease, and increased risk of mortality in people with underlying cardiorespiratory disease, effects that have set the WHO guideline value for ozone at 100 μg/m3 (~0.05 ppm) for an 8-h daily average (WHO, 2005). Controlled human studies have shown that ozone initially reacts with lipid-rich components in the lung lining fluid and the membranes of epithelial cells lining the respiratory airways forming stable lipid peroxides. This results in oxidative stress, depletion of antioxidant reserves, damage to cells, and release of proinflammatory mediators followed by cellular repair (Bromberg, 2016; Devlin, Raub, & Folinsbee, 1997; Leikauf et al., 1995). To gain a greater understanding of this series of mechanistic events in the airways, Cheng et  al. (2018) undertook a targeted metabolomics evaluation of bronchoalveolar lavage fluid (BALF) following controlled human exposure to ozone in two-arm crossover study (Cheng et  al., 2018). Healthy adult volunteers were randomly exposed to filtered air and to 0.3-ppm ozone for 2 h while undergoing intermittent exercise with a minimum of 4 weeks between exposures. Although these concentrations are higher than those found on average in ambient air in the United States (0.059 ppm), the United Kingdom (0.044 ppm), and China (0.068 ppm)

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(HEI, 2019), a large number of Chinese cities experience high-ozone events, with hourly maximum concentrations frequently exceeding 0.15 ppm (Wang, Xue, et al., 2017; Li et  al., 2017). Bronchoscopy was performed and BALF obtained at 1 or 24 h post exposure. At 1-h post exposure, metabolite changes were associated with increased glycolysis and antioxidant responses, suggesting rapid increased energy utilization as part of the cellular response to oxidative stress. Changes in the metabolic signals at 24 h indicated a broader set of responses consistent with tissue repair. Changes associated with increased lipid membrane turnover were also observed. At 1-h but not 24-h post ozone exposure, significant increases in allantoin and threonate were observed. These are metabolites of the antioxidants urate and ascorbate, respectively, and the early-phase increases are consistent ozone-induced oxidative stress. The lack of elevation at 24 h suggests a transition from active oxidative stress to repair of damage. Glutathione was not significantly altered, possibly owing to its relatively high concentrations (1–10 μM) compared with those of urate (200 μM) and ascorbate (50 μM) (van der Vliet et al., 1999). Although sample numbers were small, GSTM1 null but not GSTM1 positive subjects had statistically significant elevations of glucose and glutamine (as well threonate) levels in BALF samples after ozone exposure, which is consistent with increased sensitivity to ozone-induced oxidative stress (Holloway, Savarimuthu Francis, Fong, & Yang, 2012; Peng et al., 2016; Zhang et  al., 2014). The altered energy-related metabolism (increases in α-ketoglutarate, glutamate, and glutamine) led the authors to suggest that mitochondrial function may be impaired during ozone-induced oxidative stress and that elevated TCA cycle replenishment from an alternative source is used to support cellular repair. Global metabolomic profiling of the lungs of female obese db/db and lean wildtype (WT; C57BL/6J) mice 24 h following a 3-h exposure to ozone (2 ppm) was performed to identify metabolites that could contribute to the augmented pulmonary responses to ozone observed in obese mice (Mathews, Kasahara, et al., 2017; Mathews, Krishnamoorthy, et al., 2017; Johnston et al., 2008). Ozone differentially affected the lung metabolome in obese versus lean mice. These included biochemicals related to carbohydrate and lipid metabolism, which were each increased in the lungs of obese versus lean mice. Of relevance to the present review, the osmolyte hypotaurine that acts as an antioxidant in the mammalian reproductive tract (Yancey, 2005) was significantly increased after ozone in db/db but not WT mice. Since reductions in blood hypotaurine in db/db versus WT mice have been reported by others (Yun et al., 2013), the increase in pulmonary hypotaurine in db/db mice exposed to ozone may have occurred to protect the lungs from oxidative damage.

­Discussion Environmental metabolomics has emerged as a means for sensitively quantitating thousands of chemical signals in a biological sample, providing a broad spectrum of measurements of human metabolism that may reveal biological effects and associated toxicological mechanisms associated with an exposure (Ladva et al., 2017).

­Discussion

Within the environmental science arena, this approach is proving to be useful in exploring the association between exposure to poor air quality and global metabolic disruptions and, as such, is beginning to contribute to our understanding of the molecular events in the exposure-disease continuum. Indeed, the studies reviewed in this chapter demonstrate the utility of metabolomics in combination with various statistical models to identify complex changes in several key biological pathways associated with real-world exposures of complicated air pollutants such as those present in occupational exposures, industrial aerosols, and urban environments (Table 1). Moreover, the metabolomic observations sometimes backed up with additional cytotoxicity assays from these studies are providing additional support for a role of oxidative stress in ill health elicited by poor air quality. Notwithstanding the interpretative challenges associated with the complicated web of metabolic outcomes that have been observed in studies utilizing different air pollutants, methods, and models, many findings, feasibly the result of an oxidative imbalance and a subsequent antioxidant response, have been discussed. For example, lipid peroxidation, protein breakdown, mitochondrial dysfunction, antioxidants depletion, purine metabolism interruption, and citric acid cycle perturbation were observed in human populations exposed to ambient PAHs and metals (Wang et al., 2019, 2015). Similarly, mitochondrial disturbance was found in humans after ozone exposure (Cheng et al., 2018) and in in vitro PM2.5 models (Huang et al., 2015; Song et al., 2019). The latter three studies have also all observed reprogramming of energy metabolism, possibly as a possible means to support cellular repair. In addition, rat and mice studies revealed changes in lipid metabolism, particularly in phospholipids and sphingolipids, that occurred after PM2.5 or vehicle emissions exposure, possibly through disturbing pro-oxidant/antioxidant balance (Wang, Jiang, et al., 2017; Zhao et al., 2019; Brower et al., 2016). Studies of metabolomics and human health have mainly considered human blood and urine however noninvasive sampling of exhaled breath condensate (EBC), and saliva may serve as less-intensive alternatives. Indeed, Ladva et  al. (2017) demonstrated largely comparable metabolic profiles in plasma, EBC, and saliva when metabolomics is used to identify features associated with exposure to complex road traffic-related air pollution mixtures. The analysis by Liang et  al. (2018) reviewed in this chapter extracted 20,766 metabolic features from plasma samples and 29,013 from saliva samples. Roughly 45% were detected in both types of samples. Moreover, 12 significant metabolic pathways were shared by both saliva and plasma. The advantages of saliva as a matrix have recently been reviewed by Bessonneau, Pawliszyn, and Rappaport (2017), focusing on not only the simplicity of collection to both improve sample repeatability and participation rates in cohort studies and thus enlarge (repeated) sets of biospecimens but also the rich and dynamic molecular source of this media (Bessonneau et al., 2017). The ultimate goal of such research is to identify molecular events in an exposureeffect continuum to provide valuable information to intervene and change the outcome. Detecting and monitoring markers of adverse outcomes following exposure to different air pollutants in large human cohorts could divulge differential toxicities

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and thereby help to target regulatory efforts to those pollutants that pose the greatest risk to public health. Metabolomic phenotyping of large cohorts in epidemiological studies is limited at present to the work reporting strong associations between shortterm exposure to NO2 and changes in long-chain fatty acid concentration (WardCaviness et al., 2016) and another study where findings in at-risk individuals have potential to uncover and clarify air pollution-metabolomics associations in a population particularly susceptible to the health effects of air pollution (Breitner et al., 2016). Continued development of this field, in combination with complementary “omics” technologies such as genomics and proteomics, and traditional hypothesisled research will be crucial to help strengthen the causal basis for the epidemiological findings that associate air pollution with an ever-growing number of diseases. Achieving this will bring us significantly closer, via evidence-based mitigations strategies, to reduce the public health burden attributable to this complex and insidious environmental pollutant.

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