Comprehensive pulmonary metabolome responses to intratracheal instillation of airborne fine particulate matter in rats

Science of the Total Environment 592 (2017) 41–50

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Comprehensive pulmonary metabolome responses to intratracheal instillation of airborne fine particulate matter in rats Xiaofei Wang a,b,1, Shoufang Jiang c,1, Ying Liu c, Xiaoyan Du b, Weibing Zhang a,⁎, Jie Zhang b,⁎⁎, Heqing Shen b a

Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, China Department of Occupational and Environmental Health, Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan, Hebei, China

b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• PM2.5 induced the significant and comprehensive alteration of pulmonary metabolome. • Fifty potential metabolic biomarkers, mainly lipids and nucleotides, were identified from aqueous and organic extracts. • PM2.5 applied pulmonary toxicity through disturbing pro-oxidant/antioxidant balance.

a r t i c l e

i n f o

Article history: Received 18 January 2017 Received in revised form 5 March 2017 Accepted 7 March 2017 Available online xxxx Editor: Jay Gan Keywords: Fine particulate matter Pulmonary toxicity Lipid metabolism Purine metabolism

a b s t r a c t Airborne fine particulate matter (PM2.5) has been closely related with a variety of lung diseases. Although some modes of action (e.g. oxidative stress, inflammations) have been proposed, but the pulmonary toxicological mechanism remains obscure. In this paper, in order to understand the comprehensive pulmonary response to PM2.5 stress, a non-targeted high-throughput metabolomics strategy was adopted to characterize the overall metabolic changes and relevant toxicological pathways. PM2.5 samples were collected from Tangshan, one of the most polluted cities in China. Adult male rats were treated with PM2.5 suspension once a week at the dose of 1 mg/kg/week through intratracheal instillation in three months. Aqueous and organic metabolite extracts of the lung tissues were subjected to metabolomics analysis using ultra-high performance liquid chromatograph/mass spectrometry. Along with a significant increase of oxidative stress, significant metabolome alterations were observed in the lung tissues of the treated rats. Nineteen metabolites were found decreased and 31 metabolites increased, which are mainly involved in lipid and nucleotide metabolism. Integrated pathway analysis suggests that PM2.5 can induce pulmonary toxicity through disturbing pro-oxidant/antioxidant balance, which may further correlate with metabolism changes of phospholipid, glycerophospholipid, sphingolipid and purine. These findings improve our understanding of the toxicological pathways of PM2.5 exposure. © 2017 Published by Elsevier B.V.

⁎ Correspondence to: W. Zhang, East China University of Science and Technology, Meilong Road 130, Shanghai 200237, China. ⁎⁎ Correspondence to: J. Zhang, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China. E-mail addresses: [email protected] (W. Zhang), [email protected] (J. Zhang). 1 Contribute equally to this article.

http://dx.doi.org/10.1016/j.scitotenv.2017.03.064 0048-9697/© 2017 Published by Elsevier B.V.

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X. Wang et al. / Science of the Total Environment 592 (2017) 41–50

1. Introduction

2. Materials and methods

Along with the rapid industrialization and urbanization, increasing atmospheric pollution characterized by high levels of fine particulate matter (PM2.5) has been reported nationwide in China (Zhang et al., 2015). A lot of epidemiological evidences have indicated that both long-term and short-term exposure to PM2.5 could increase the risk of a variety of diseases, such as respiratory, cardiovascular and ischemic heart diseases (Lim et al., 2012). Recently, the International Agency for Research on Cancer (IARC) concluded that particulate matter (PM) is Group 1 carcinogen to humans (Mara, 2015). Due to its small size, PM2.5 is able to penetrate deeply into the respiratory tract and reach alveolar ducts, thus lung is regarded as a primary target organ. Pulmonary oxidative stress and inflammation are the widely accepted modes of action of PM2.5 (Riva et al., 2011). Following inhalation, PM2.5 is capable to increase oxidative stress, inducing genetic damage (Corsini et al., 2013; Saintgeorges et al., 2009), cell and organelle abnormalities (Deng et al., 2013; Li et al., 2003) and abnormal release of inflammatory mediators involved in the development of lung diseases (Dagher et al., 2005; Potnis et al., 2013). However, the complicated mechanism of PM toxicity remains obscure so far (Pedata et al., 2015). Metabolite is the terminal product of gene expression, and plays a critical role in cellular communication process (Roberts et al., 2012). Furthermore, oxidative stress and inflammation are closely related with downstream metabolic pathways (Galasko and Montine, 2010). Various harmful substances are absorbed or contained in PM2.5, such as heavy metals, polycyclic aromatic hydrocarbons and persistent organic pollutants. Among them, many chemicals have been reported to interfere with metabolic pathways. In individuals exposed to PM2.5, it is likely that more than one metabolic pathway and process are affected, which may contribute to the overall adverse effects. Interpreting how metabolites relate with each other and with upstream biological molecules is significant in assessing their value as potential biomarkers, and will improve our understanding of the global metabolic network as well as the PM2.5 induced pulmonary toxicity. However, characterization of the metabolic disruption effects of PM2.5 is challenging. High-throughput omic technologies provide insights into the systematic molecular response to environmental stress, and are believed to be powerful tools to elucidate PM2.5 toxicity (Thomson et al., 2009;Huang et al., 2014). Compared with traditional techniques, in which present markers are measured singularly, metabolomics strategy has many advantages. Firstly, it is not restricted to known molecular pathways; and secondly it provides plenty of metabolite information which covers all the biological processes of interest. Metabolomics strategy has been widely applied in lung-related clinical studies, and a series of markers and representative pathways have been identified (Deja et al., 2014). It is possible that the early metabolic event of lung diseases could be associated with PM2.5 exposure. However, few studies have examined pulmonary metabolome responses to PM2.5 exposure. Our in-vitro metabolome data demonstrated PM2.5 treatment can elevate oxidative stress and disrupt amino acid biosynthesis and metabolism (Huang et al., 2014). Lipids comprise a group of naturally occurring molecules, and exert important biological functions. Recently, Chen et al. used a targeted analysis strategy to investigate the effects of PM2.5 exposure on pulmonary lipidome, and found that the exposure significantly altered plasmenylcholines and phosphatidylcholine levels in rat (Chen et al., 2014). However, a comprehensive non-targeted metabolomics study is needed to elucidate the influence of PM2.5 exposure on both aqueous and organic metabolic profiles of lung tissue extracts. In this study, for the first time to our knowledge, we investigated the pulmonary metabolome responses and relevant modes of action of PM2.5 exposure in rats, in order to improve our understanding of the toxicological pathways of PM2.5 exposure. A comprehensive nontargeted metabolomics strategy based on liquid chromatography/ high-resolution mass spectrometry platform was adopted in this study.

2.1. PM2.5 collection Tangshan, one of the most PM2.5 polluted cities in China, was chosen as the sampling site. Airborne PM2.5 was collected from November 2014 to May 2015 using a particulate matter sampler (Wuhan Tianhong, China). The sampler was set on the rooftop of the Public Health School of North China University of Technology and Science (located in the central district of Tangshan). After collection, the quartz fibre filter membranes (Whatman, UK) were stored at − 40 °C in darkness for further use. 2.2. PM2.5 suspension PM2.5 suspension was prepared as following: the membranes were cut into small pieces and sonicated in ultrapure water for 30 min. After filtering through sterile gauze, the solution was collected in a new tube. The extraction process was repeated again. The filtered solution was combined and freeze-dried in a Speedvac concentrator (Thermo Fisher Scientific, NC, USA). Prior to the treatment, the PM2.5 suspension was obtained by thoroughly mixing the particulate matter and saline under sonication. 2.3. Characterization of PM2.5 The 28 elements in PM2.5 suspension were measured according to the methods described by Chen et al. (2015). The inorganic ions were determined using an ion chromatography (Dionex, Sunnyvale, CA, USA), and PAHs were measured using a gas chromatography (7890A, Agilent, Santa Clara, CA, USA) coupled to a mass spectrometry (5975C, Agilent, Santa Clara, CA, USA). 2.4. Animal study design The animal study was conducted according to China Animal Welfare Legislation. Twelve healthy adult male Sprague Dawley rats (weighing 180–220 g) were obtained from Shanghai Laboratory Animal Center, China. Animals were housed in stainless steel cages and acclimatized for one week before initiation of PM2.5 suspension treatment. Male rats were maintained at the temperature of 26 ± 2 °C, a relative humidity of 50 ± 5%, and a 12 h light-12 h dark cycle. The rats had ad libitum access to water and a pellet diet. After quarantine and acclimatization, the rats were randomly divided into control and treatment groups. The treatment group (n = 6) was treated by intratracheal instillation of PM2.5 suspension, and the control group (n = 6) was treated with saline. The intratracheal instillation was performed once a week for three consecutive months. For adult rats, the respiratory volume was 0.105 m3/day. For seriously polluted Chinese cities, the reported PM2.5 concentrations were frequently above 500 μg/m3. Therefore, the representative inhaled PM2.5 amount was about 0.05 mg/day (0.105 m3/day × 500 μg/m3) for a rat. Given the interspecies uncertainty factor (10-fold–100-fold) (Walton et al., 2001), the dose used in this study was set as 5 mg/week (0.05 mg/day × 7 day/week × 14.3fold, i.e. 25 mg/kg/week), which was comparable to the ambient levels of PM2.5 in many seriously polluted Chinese cities. 2.5. Pulmonary metabolome analysis The rats were sacrificed by decapitation in 24 h after the last treatment, and the entire lung tissue was harvested and stored at −80 °C. Aqueous and organic metabolites were sequentially extracted according to Nature Protocol by (Want et al., 2012). Briefly, 1.5 mL of prechilled methanol/water (1:1) solvent was added to 100 mg lung tissue, homogenized and then centrifuged at 16,000g, 4 °C for 10 min. The aqueous extract was transferred to a new centrifuge tube and dried using a

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Speedvac concentrator. The remaining precipitates were homogenized again with 1.6 mL of prechilled dichloromethane/methanol (3:1) solvent, then centrifuged at 16,000g, 4 °C for 10 min. The organic extract was transferred to a centrifuge tube and dried in fume hood. The dried extracts were redissolved in 200 μL of methanol/water (1:1) solvent for metabolome acquisition. Quality control (QC) samples were prepared by respectively mixing aliquots of aqueous and organic extracts. Metabolic profile acquisition was conducted using a Waters ACQUITY UPLC System (Waters, Milford, MA, USA) coupled to a QExactive mass spectrometer (Thermo Scientific, USA). For aqueous extracts, chromatographic separation was performed on a HSS T3 column (1.7 μm, 2.1 mm i.d. × 100 mm) at a flow rate of 0.4 mL/min. The mobile phases were (A) water with 0.1% formic acid and (B) methanol with 0.1% formic acid. The gradient program was: 0 min, 0.1% B; 2 min, 0.1% B; 6 min, 25% B; 10 min, 80% B; 12 min, 90% B; 21 min, 99.9% B; and 23 min, 99.9% B. For organic extracts, chromatographic separation was performed on a BEH C8 column (1.7 μm, 2.1 mm i.d. × 100 mm). The mobile phases were (A) water with 0.1% formic acid and (C) methanol/isopropanol at ratio of 85:15 with 0.1% formic acid. The gradient program was: 0 min, 75% C; 1 min, 75% C; 6 min, 85% C; 10 min, 85% C; 15 min, 90% C; 17 min, 90% C; 18 min, 91% C; 21 min, 91% C; 22 min, 92% C; 27 min, 92% C; 29 min, 99.9% C; 32 min, 99.9% C. The Q-Exactive mass spectrometer was operated with a scan range of 70 to 1050 m/z. Spray voltage was set at 3200 V and 2800 V for positive and negative ion modes, respectively. For aqueous extracts, probe heater temperature was set at 350 °C. The flow rates of sheath gas and aux gas were 45 and 15 L min− 1, respectively. For organic extracts, probe heater temperature was set at 320 °C. The flow rates of sheath gas and aux gas were 40 and 10 L min−1, respectively. The samples were injected in a randomized fashion to remove the uncertainties from artefact-related injection order and instrument sensitivity change in batch runs. A QC sample was injected at every six samples throughout the batch. The MS/MS mode was used to identify potential biomarkers with 17,500 resolution. The normalized collision energy (NCE) was set at 30%. UPLC-MS raw data was transformed into mzXML format and processed with XCMS software to obtain a table containing metabolic features (m/z-retention time pairs) and peak intensities. Peak detection, retention time correction and alignment were performed using the following parameters: mass range 70–1050 m/z, peaks finding method centWave, mass tolerance 10 ppm, and retention time (RT) width threshold 40 s. All extracted features were normalized to total intensities to eliminate systematic bias. The features with missing values in N20% of the samples were excluded. The processed tables were Paretoscaled and then imported to SIMCA-P V12 software (Umetrics, Uppsala, Sweden) for multivariate statistical analysis. Partial least-squares discriminant analysis (PLS-DA) was used for group differentiation between the control and treatment groups. To avoid over-fitting of the supervised PLS-DA model, a cross-validation test with 999 permutations was performed. The candidate biomarkers were screened with following criteria: variable importance in projection (VIP) scores N2; pvalues b 0.05; fold change N 1.5. The potential biomarkers were further identified by searching against Human Metabolome Database (HMDB, http://www.hmdb.ca) and LipidSearch™ software (Thermo Scientific ™). The MS/MS information was further used to confirm the identification. The identified biomarkers were subjected to MetaboAnalyst 3.0 (Xia and Wishart, 2016) and Cytoscape 3. 4. 0 software for pathway enrichment analysis and correlation network analysis. 2.6. Biochemical analysis The contents of malondialdehyde (MDA), thiobarbituric acid reactive substances (TBARS), inducible nitric oxide synthase (iNOS) and endothelial nitric oxide synthase (eNOS) were measured using the commercial assay kits (Nanjing Jiancheng Bioengineering Institute, Jiangsu, China). The activities of glutathione peroxidase (GSH-Px),

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catalase (CAT), superoxide dismutase enzyme (SOD) were also evaluated using the assay kits (Nanjing Jiancheng Bioengineering Institute, Jiangsu, China). 2.7. Quantitative PCR Quantitative PCR analysis was performed for GSH-Px, CAT and SOD. Briefly, the total RNAs were extracted using the RNeasy® Mini Kit (Qiagen) from tissue. The total mRNA was converted into cDNA using PrimeScript® RT reagent Kit with gDNA Eraser cDNA synthesis Kits (Takara, Japan). Real-time PCR was carried out using SYBR Green Master Mix reagents (Roche, USA) on a Roche LightCycler® 480 II real-time PCR system (Roche, USA). GAPDH gene was used as an internal PCR control. The fold changes (treated/control) of the tested genes were analyzed using the 2-ΔΔCT method. 2.8. Statistical analysis The statistical analysis was performed using SPSS software (Version 18.0) and data are expressed as mean ± S.D. All the data were analyzed using Mann-Whitney test. In all cases, p b 0.05 was considered as statistically significant. 3. Results 3.1. Characterization of PM2.5 The toxicity of PM2.5 depends on its chemical composition. A summary of elemental and inorganic ion concentrations in PM2.5 suspension was shown in Fig. 1. Eleven high abundant elements (i.e. Na, Ca, K, Al, Fe, Zn, Mg, B, P, Mn, Ba) accounted for N98% of the total elemental mass of PM2.5 suspension. Several important harmful elements were also detected. Among them, the concentration of As, Cr, Pb, Cd and Hg ranged from 0.31 to 68.94 ng/m3. The secondary inorganic ions + (SO42 −, NO− 3 , and NH4 ) were the main inorganic ion components (accounting for 0.332%) of PM2.5 suspension. However, PAHs were not detected in the suspension. 3.2. Pulmonary oxidant and antioxidant imbalance Oxidative stress is the widely accepted mode of action of PM2.5. After intratracheal instillation for three months, the enzyme activities of CAT and SOD were significantly down-regulated, and the activity of GSH-Px was also down-regulated although not significantly. Accordingly, the gene expression of CAT, SOD and GSH-Px also decreased in the treatment group. In addition, the content of MDA increased and TBARS decreased significantly; while the content of eNOS decreased but iNOS did not increase significantly in the treatment group (Fig. 2). The data suggested that PM2.5 exposure disrupted oxidant and antioxidant balance in rat lung. 3.3. Pulmonary metabolome alteration The supervised PLS-DA models provided optimal modeling and predictive abilities for both aqueous (R2Ycum = 0.997, Q2cum = 0.967) and organic extracts (R2Ycum = 0.986, Q2cum = 0.891), achieving a clear distinction between the pulmonary metabolic profiles of the two groups (Fig. 3). This indicated PM2.5 treatment disrupted global pulmonary metabolome in rats. Totally, 15 and 35 differential metabolites were identified as biomarkers from aqueous and organic extracts, respectively (Table 1). The biomarkers were involved in the metabolism of lipid, amino acids and nucleotide. Hierarchical clustering analysis of identified biomarkers was shown in Fig. 4, which consisted of two main clusters in up-regulated biomarkers and a main cluster in down-regulated biomarkers. The up-regulated clusters included triglycerides (TGs), lysophospholipids (LysoPG), amino acid and nucleotide derivatives.

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Fig. 1. Total mass concentration of elements in the particulate matter (ng/m3).

4. Discussion

(87.41) N Cu (35.12) N Cr (17.29) N V (5.22) N Pb (4.98) N Ni (4.88) N Cd (1.98) (ng/m3). Most of the values in present study were lower than the reported concentrations in Beijing (Zhuang et al., 2001; Mori et al., 2003), Islamabad (Shah et al., 2006), Tehran (Sohrabpour et al., 1999) and Seoul (Kim et al., 2004), and comparable to our previous study (Huang et al., 2014), but higher than other Asian cities, such as Kanazawa (Wang et al., 2005) and Uludag (Samura et al., 2003). The heavy metals (i.e. As, Cr, Pb Cd and Hg) were also detected. Especially for As, it is about five times higher than other report (Sohrabpour et al., 1999) (Table S2). − + The secondary inorganic ions (SO2− 4 , NO3 , and NH4 ) are important source of atmospheric particles (Wu et al., 2009), due to their high ratio (38.12 ng/m3), in PM2.5. In present study, the concentrations of SO2− 4 + 3 3 (5.88 ng/m ) and NH (5.88 ng/m ) were much lower than previNO− 3 4 ously reported concentrations in Beijing (Mori et al., 2003) and Uludag (Samura et al., 2003).

4.1. Composition of metal elements and inorganic ions in PM2.5

4.2. Pulmonary metabolome response to PM2.5

The composition of PM2.5 is extremely complicated. Since it is a carrier of metals, the measurement of metal elements in the particles is important in determining their potential adverse health effects. Transition metals such as iron (Fe), vanadium (V), nickel (Ni), chromium (Cr), copper (Cu), and zinc (Zn) can generate reactive oxygen species (ROS) in biological tissues. The concentrations of the metals analyzed followed the order: Na (5576.83) N Ca (4231.89) N K (2139.38) N Al (1793.23) N Fe (1756.59) N Zn (967.35) N Mg (864.22) N Mn

The influence of chronic PM2.5 exposure on the pulmonary metabolic system is not well understood. Due to its high throughput capability, metabolomics has been applied in the toxicity research of environmental pollutant. In our recent studies, PM2.5 extract was found to disturb the metabolome and proteome of human lung epithelial cell (Huang et al., 2014; Huang et al., 2015). However, there is few in-vivo metabolome reports regarding PM2.5 exposure. In this study, we used a comprehensive non-targeted metabolomics approach to

The down-regulated cluster included sphingomyelin (SM), phosphatidylcholine (PC), phosphatidylglycerol (PG) and cholesterol. After Pearson correlation analysis, the correlated biomarkers (p b 0.01, r N 0.6) were imported to Cytoscape 3.4.0, and a visible correlation network was calculated (Fig. 5). The biomarkers were clustered by their class. Three clustering groups were obtained for the biomarkers, including nucleic acid metabolism, phospholipids metabolism and fatty acid metabolism. This indicated that metabolic changes were not a single or accidental incident, instead, PM2.5 caused an alteration in entire metabolic network. Further metabolic pathway analysis showed glycerophospholipid metabolism, purine metabolism and sphingolipid metabolism were the most significant pathways (p b 0.05) (Fig. 6, Table S1). The results were consistent with those from correlation network analysis.

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metabolism, sphingolipid metabolism and purine metabolism) associated with PM2.5 exposure. 4.3. PM2.5 exposure affected pulmonary oxidant and antioxidant balance Many mechanisms are proposed to explain the adverse health effects of PM2.5. Among these, excessive ROS production and subsequent oxidative stress are deemed as a key mechanism for PM2.5 cytotoxicity (Li et al., 2008). The particle itself and absorbed harmful substances are capable to stimulate ROS and reactive nitrogen species (RNS) production (Moura et al., 2012; Tuet et al., 2016). In this study, chronic exposure to PM2.5 increased MDA content, while decreased TBARS contents. Our results also show PM2.5 inhibited endogenous antioxidant enzymes, indicated by a decreased activity and gene expression of SOD, GSH-Px and CAT. Oxidative stress and inflammatory factors generated in lung can disrupt eNOS function; the down-regulation of eNOS content was consistent with a previous study in which a long-term PM2.5 exposure was reported to significantly reduce the eNOS content in pulmonary arteries (Davel et al., 2012). The decrease of eNOS possibly induced a negative feedback of iNOS, in which PM2.5 treatment caused the up-regulation (not significant) of iNOS content in lung. In summary, these data demonstrated that PM2.5 exposure can disrupt oxidant and antioxidant balance in lung tissue. 4.4. PM2.5 exposure disrupted lipid metabolism

Fig. 2. Effect of PM2.5 on pulmonary pro-oxidant/antioxidant balance characterized as fold change of enzyme activities (A) and mRNA expressions (B) of CAT, GSH-Px and SOD, and the contents of MDA, TBARS, iNOS and eNOS (C). Values shown are mean ± SD (n = 6). The data of treatments were calibrated to the control values (control = 1). * p b 0.05.

investigate the pulmonary metabolome responses and relevant modes of action of PM2.5 exposure in rats. A clear distinction was observed between control and treatment groups in the developed PLS-DA model, suggesting PM2.5 could induce significant metabolic perturbation in rat lung tissue. A total of 50 potential exposure effect biomarkers were identified. To identify the relevant metabolic pathways involved in PM2.5 exposure, the biomarkers were further subjected to MetaboAnalyst 3.0 and the relevant pathways were identified. Fig. 7 summarized the major perturbed metabolic patterns and plausible pathways (oxidant and antioxidant balance, glycerophospholipid

Abnormal lipid metabolism has been associated with the activation of oxidative and inflammatory pathways (Zhao et al., 2015). The administration of PM10 has been reported to stimulate neutrophil influx into the respiratory tract, elevate TNF-α and IL-6 levels, and alter the expressions of 242 genes involved inflammation, cholesterol and lipid metabolism (Brocato et al., 2014). In our study, 37 differential lipid chemicals constituted N 70% of total identified biomarkers. Phospholipid, glycerophospholipid and sphingolipid metabolic pathways may be the potential modes of action for PM2.5 pulmonary toxicity. The interaction between PM2.5 and cell membranes is the first step to induce cytotoxicity. Surfactant phospholipids (PC, PG, PE) are the main lipid components of cell membrane, which is the crucial structure to maintain the independent intracellular environment and regulate the exchange of materials inside and outside a cell. In the inflamed lung, phospholipids would be hydrolysed into lysophospholipid (lysoPC, lysoPG and lysoPE), and cause surfactant dysfunction (Hite et al., 1998). The disturbances of phospholipid, lysophospholipid, and glycerophospholipid metabolism were observed in the present study, in accordance with a previous report which also observed pulmonary phosphatidylcholine alteration in the rats exposed to ambient air (Chen et al., 2014). Our data indicated PM2.5 disrupted lipid metabolism on cell membrane through oxidative stress pathways, but the underlying mechanism responsible for the observed changes in different

Fig. 3. Score plots of aqueous (A) and organic (B) metabolome in rat lung tissue with PLS-DA model. ▲ Control group; ■ treatment group.

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Table 1 Metabolic pathway classification, fold change (FC) and p value for identified biomarkers. Super-pathway

Amino acid

Hydrocarbon

Lipid

Sub-pathway

Cysteine metabolism Valine, leucine and isoleucine degradation Methionine, arginine and proline metabolism Riboflavin metabolism Oxidation of fatty acids Sterol hormone metabolism Cholesteryl metabolism Long-chain fatty acid metabolism Arachidonic acid metabolism Lysophospholipid metabolism

Fatty acid metabolism Phosphatidylcholine metabolism

Phosphatidylethanolamine metabolism Phosphatidylglycerol metabolism

Phosphatidylinositol metabolism Sphingomyelin metabolism Sphingolipid metabolism Monoacylglycerol metabolism Diglyceride metabolism Triglycerides metabolism

Nucleotide

Nucleic acid metabolism Purine metabolism

Purine and adenosine metabolism, formation of nucleic acids Pyrimidine metabolism

Metabolite

Carbocisteineb Ketoleucinea Spermidineb 5Am-6-PhUa Acetylcarnitinea Cholesterol sulfateb Cholesteryl acetateb 11CγTb HETEa Prostaglandin A1a LysoPC(14:0)a LysoPE(18:0p)b LysoPG(16:1)b LysoPG(18:2)b LysoPG(22:6)b Oleamidea PC(14:0/14:0)b PC(16:2/14:0)b PC(18:0/16:0)b PC(18:0/18:1)b PE(16:0p/20:5)b PE(16:0p/22:5)b PG(16:0/14:0)b PG(16:0/16:1)b PG(16:0/18:1)b PG(16:0/18:2)b PG(16:1/18:2)b PG(18:0/18:2)b PG(18:1/18:2)b PG(18:2/18:2)b PI(18:0/20:4)b SM(d16:0/22:1)b Sphinganineb MG(20:5)a DG(36:4)a TG(14:0/18:2/20:5)b TG(16:0/16:0/18:3)b TG(16:0/18:2/20:5)b TG(16:1/18:2/20:5)b TG(18:1/18:1/18:1)b TG(18:1/18:1/18:2)b TG(18:1/18:2/18:2)b TG(18:2/18:2/18:2)b TG(20:5/18:2/18:2)b 8-OHGa Adenosinea Deoxyinosinea Guaninea Hypoxanthinea Thymidinea

Chemical formula

C5H9NO4S C6H10O3 C7H19N3 C9H17N4O9P C9H17NO4 C27H46O4S C29H48O2 C25H36O4 C20H32O3 C20H32O4 C22H46NO7P C23H47O6NP C22H42O9P C24H44O9P C28H44O9P C18H35NO C37H73O10NP C39H73O10NP C42H85O8NP C44H87O8NP C41H71O7NP C43H75O7NP C28H44O9P C38H72O10P C40H76O10P C40H74O10P C40H72O10P C42H78O10P C42H76O10P C42H74O10P C47H82O13P C43H88O6N2P C18H39NO2 C23H36O4 C39H68O5 C55H93O6 C53H96O6Na C57H97O6 C57H95O6 C57H104O6Na C57H102O6Na C57H100O6Na C57H99O6 C59H97O6 C10H13N5O6 C10H13N5O4 C10H12N4O4 C5H5N5O C5H4N4O C10H14N2O5

Exposure vs. control FCc

pd

5.53 2.46 0.21 2.97 2.43 0.49 0.4 0.42 1.89 2.09 13.21 0.48 2.29 2.01 2.08 1.87 0.01 0.32 0.49 0.46 7.26 0.27 0.25 0.36 2.54 0.4 0.18 0.32 0.34 0.42 7.2 0.28 0.42 2.39 10.27 14 7.95 5.5 9.72 6.8 5.4 5.47 5.26 5.92 2.7 2.55 2.68 2.41 2.37 2.24

0.0023 0.0002 0.0053 0.0002 b0.0001 0.0002 0.0014 0.0035 0.0111 0.0496 b0.0001 0.0278 0.0002 0.0006 0.0003 0.0153 0.0001 0.0003 0.0014 0.0003 0.0001 0.0001 0.0001 0.0001 0.0001 0.0095 0.0001 0.0053 0.0115 0.0115 0.0001 0.0003 0.0023 0.0179 0.0496 0.0078 0.0064 0.0328 0.0115 0.0452 0.0452 0.0235 0.0138 0.0452 0.0012 0.0003 0.0005 0.0003 0.0003 0.0002

Abbreviation: 5Am-6-PhU: 5-Amino-6-(5′-phosphoribitylamino) uracil; 8-OHG: 8-Hydroxyguanosine; 11CγT: 11′-Carboxy-gamma-tocotrienol. a Metabolites identified in aqueous extract. b Metabolites identified in organic extract. c Fold change of abundance between treatment and control groups. d The statistical significance of abundance change was assessed using ANOVA followed by LSD post hoc test, and p b 0.05 was considered as significant.

lipid metabolites was not completely understood. In this study, the reduction of PC and elevation of lysoPC were possibly due to the activation of phospholipase A2 (PLA-2), induced by the elevated oxidative stress level through a MAPK-dependent and phosphorylation-independent mechanism (van Rossum et al., 2004). Increased levels of PLA-2 and major pro-inflammatory products were also associated with ambient particulate induced atherosclerosis in humans (Brüske et al., 2011). Besides, phospholipid and lipoprotein substance produced in lung reduces the work of breathing by lowering alveolar surface tension during respiration (Hite et al., 2005). PM2.5 exposure could trigger an increase in oxidized phospholipids and then mediate a systemic inflammatory response through TLR4/NADPH oxidase–dependent mechanisms (Kampfrath et al., 2011). The alteration of pulmonary lipid composition has been closely associated with the onset and development of asthma

(Wright et al., 2000; Ho et al., 2013). Taken all these into account, the observed changes in pulmonary phospholipid and glycerophospholipid profile in this study suggested PM2.5 exposure might affect cell membrane integrity, induce pro-inflammatory effects, and result in potential lung diseases. Sphingolipid is another important component of cell membrane, constituting 10–15% of total membrane lipids. Sphingosine and sphingomyelin form primary parts of sphingolipids, and play a pivotal role in cellular responses to oxidative stress. Sphingosine is synthesized from sphinganine. We observed sphinganine and sphingomyelin levels were significantly reduced in treatment group, suggesting the potential association between PM2.5 exposure and sphingolipid pathway. Sphingosine generated from sphinganine is converted to sphingosine-1phosphate (S1P) by sphingosine kinase (Ebenezer et al., 2016). In

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Fig. 4. Hierarchical clustering heatmap analysis of differential metabolites between control (C) and treatment (T) group. The red color of the tile indicates high abundance and blue indicates low abundance. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

present study, S1P concentration may be altered by PM2.5 exposure, indicted by the down-regulation of its precursor – sphinganine. S1P plays a pivotal role in regulating diverse immune regulatory networks that contribute to human respiratory and lung disorders, including asthma, chronic obstructive pulmonary disease, cystic fibrosis, pulmonary hypertension and lung cancer et al. (Ebenezer et al., 2016). Recently, cigarette smoking was reported to induce apoptosis in bronchial epithelial cells via modulation of the S1P system (Barnawi et al., 2016). Interestingly, the treatment of thymoquinone (an antioxidant/antiinflammatory agent) enhanced efferocytic/phagocytic ability and antagonized the effects of cigarette smoking extract (Barnawi et al., 2016). Given its similarity to cigarette smoking, the S1P signal pathway may be a potential target of PM2.5 exposure, but the confirmation of the mechanism needs further study. 4.5. PM2.5 exposure disrupted nucleotide metabolism and induced DNA damage After glycerophospholipid metabolism, the second most influenced pathway was purine metabolism. Purine would be converted to uric acid after serious multiple metabolic pathways. The purine bases and

metabolites are highly active biochemical substances during the processes occurring in living organisms. The significant up-regulation of some purine bases and metabolites (i.e. guanine, adenosine, deoxyinosine and hypoxanthine) indicated PM2.5 stimulated purine degradation process in rat lung. The metabolism of purines produces oxygen free radicals via the xanthine oxidase pathway. This was an important factor of oxidative stress induced by PM2.5. Purine metabolism disorder was reported to have a negative association with lung function (Li et al., 2014). Hypoxanthine and adenosine were also found to associate with neutrophilic inflammation of chronic obstructive pulmonary disease (Jr et al., 2015). Similar to adenosine, the concentration of thymidine was greatly elevated by more than two times in treatment group, further confirmed that PM2.5 exposure may significantly disrupt nucleotide metabolism. PM2.5 is able to induce DNA damage through generating excessive ROS. In this study, many biomarkers were involved in DNA oxidative process. 8-Hydroxyguanosine (8-OHG) was considered as an important indicator of hydroxyl radical damage to RNA (Kasai and Kawai, 2016). Compared to the control group, the 8-OHG concentration increased by approximately three-fold in the treatment group, suggesting PM2.5 exposure induced oxidative stress and DNA oxidative damage in the lung tissue.

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Fig. 5. Correlation network of the identified metabolite biomarkers (p b 0.01, r N 0.6) calculated by Cytoscape 3.4.0. The blue lines between biomarkers indicate positive correlation, while the green ones indicate negative correlation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Besides 8-OHG, another oxidative stress indicator, spermidine was identified as a potential biomarker. Spermidine maintains membrane potential and controls intracellular pH and volume through synchronizing many biological processes. For example, spermidine regulates Ca2+ influx by glutamatergic N-methyl-D-aspartate receptor (NMDA receptor), which has been associated with nitric oxide synthase (NOS) and

cGMP/PKG pathway activation (Hoet and Nemery, 2000). Due to its anti-oxidant activity, spermidine is able to alleviate oxidative damage to DNA components and conduct cytoprotective functions. In this study, the significant down-regulation of spermidine further confirmed that PM2.5 may disrupt pulmonary oxidant-antioxidant balance and induce DNA damage in rats. 5. Conclusion

Fig. 6. Summary of metabolic pathways analyzed with MetaboAnalyst software. Glycerophospholipid metabolism (1), purine metabolism (2) and sphingolipid metabolism (3) were the most significant pathways (p b 0.05).

Many studies have linked PM2.5 exposure with metabolic disorders. However, it is still unclear how PM2.5 affects global metabolic profile in lung tissue. In this study, a significantly altered pulmonary metabolome was observed in the rats exposed to PM2.5 for a long term. A variety of differential metabolites were identified, mainly involved in the metabolism of lipid and nucleotides. Combining the results of oxidative stress analysis, we suggest that PM2.5 may induce pulmonary toxicity through disturbing pro-oxidant/antioxidant balance, which may further relate with the network changes of phospholipid, glycerophospholipid, sphingolipid and purine metabolism and DNA damage in lung tissues. These findings provide the potential exposure effect biomarkers and improve our understanding of the toxicological pathways related to PM2.5 exposure. However, there was a limitation in present study. The collection and handling of PM2.5 particles may change some characteristic of PM2.5 and could not completely simulate the real exposure environment; although intratracheal instillation is the most widely accepted exposure technique in toxicological studies of ambient particulate matter, but it has an intrinsic limitation (Driscoll et al., 2000): the nonphysiological route of administration results in the nonuniform

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Fig. 7. Overview of the metabolic pathways disturbed in rat lung by PM2.5 exposure.

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