- Email: [email protected]
Q-TOF MS
Toxicology Letters 229 (2014) 474–481
Contents lists available at ScienceDirect
Toxicology Letters journal homepage: www.elsevier.com/locate/toxlet
The metabolomic profiling of serum in rats exposed to arsenic using UPLC/Q-TOF MS Cheng Wang a,1, Rennan Feng b,1, Yuanyuan Li c, Yunbo Zhang a , Zhen Kang d, Wei Zhang c , Dian-Jun Sun c, * a
Department of Environmental Hygiene, Public Health College, Harbin Medical University, Harbin 150081, Heilongjiang Province, China Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin 150081, Heilongjiang Province, China c The Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, 194 Xuefu Road, Nangang District, Harbin 150081, Heilongjiang Province, China d Department of Environmental Hygiene, Harbin Center for Disease Control and Prevention, Harbin 150056, Heilongjiang Province, China b
H I G H L I G H T S
Significant impact of chronic arsenic exposure on the metabolism of organism was found. Endogenous metabolite profile of serum was investigated by UPLC/Q-TOF MS. Nine principal metabolites reflecting the changes of metabolism were identified.
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 April 2014 Received in revised form 28 May 2014 Accepted 1 June 2014 Available online 21 June 2014
Chronic arsenicosis induced by excessive arsenic intake can cause damages to multi-organ systems, skin cancer and various internal cancers. However, the key metabolic changes and biomarkers which can reflect these changes remain unclear resulting in a lack of effective prevention and treatments. The aim of this study is to determine the impact of chronic arsenic exposure on the metabolism of organism, and find the metabolites changes by using metabolomic techniques. Thirty male Wistar rats were randomly divided into three groups. The arsenite was administered in water, and the doses were 0, 10, and 50 mg/L, respectively. The exposure lasted for 6 months. The endogenous metabolite profile of serum was investigated by ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. Partial least squares discriminant analysis (PLS-DA) enabled clusters to be visualized. Nine serum principal metabolites contributing to the clusters were identified, which were CPA (18:2(9Z,12Z)/0:0), LysoPC (14:0), LysoPC (18:4 (6Z,9Z,12Z,15Z)), LysoPC (P-18:0), L-palmitoylcarnitine, LysoPC (20:2(11Z,14Z)) in positive ESI mode and deoxygcholylglycine, LysoPE (0:0/20:2(11Z,14Z)), 15(S)hydroxyeicosatrienoic acid in negative ESI. These changes of metabolites in rats suggested the changed metabolism in rats exposed to arsenic. These findings may further aid diagnose and serve as targets for therapeutic intervention of arsenicosis. ã 2014 Published by Elsevier Ireland Ltd.
Keywords: Chronic arsenicosis Biomarker Mechanism Metabonomics
1. Introduction
* Corresponding author. Tel.: +86 451 86612695; fax: +86 451 86676184. E-mail addresses: [email protected] (C. Wang), [email protected] (R. Feng), [email protected] (Y. Li), [email protected] (Y. Zhang), [email protected] (Z. Kang), [email protected] (W. Zhang), [email protected] (D.-J. Sun). 1 Cheng Wang and Rennan Feng contributed equally to this work. http://dx.doi.org/10.1016/j.toxlet.2014.06.001 0378-4274/ ã 2014 Published by Elsevier Ireland Ltd.
Arsenic is a metalloid material, which is widely distributed throughout the environment in water, food and air. Elevated levels of arsenic in the body can lead to the arsenicosis. The dominant basis of arsenicosis is from ground water that naturally contains high concentrations of arsenic. A study in 2007 found that over 137 million people in more than 70 countries are probably affected by arsenicosis from drinking water (USA Today, 2007). As early as 1981, arsenic has been considered as a human carcinogen that causes skin cancer and various internal cancers by WHO
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
(WHO, 1981). Researchers have explored a lot on the mechanism of arsenic toxicology (Thomas et al., 2001; Watanabe and Hirano, 2013). However, the exact mechanism of chronic arsenicosis currently remains unclear resulting in a lack of effective treatments. In addition, skin lesions, which are common and that which often appeared in the later stages of chronic arsenicosis, are often used to evaluate the levels of arsenicosis in China (WS/T211-2001). However, it cannot reflect the damages to multi-organ systems occurring in chronic arsenicosis as a result of excessive arsenic intake. Such damages resulted in series of physiological and pathological changes to liver (Bashir et al., 2006; Liu et al., 2000), kidney (Majhi et al., 2011; Huang et al., 2009) and brain (Namgung and Xia, 2001; Wang et al., 2012). Therefore, it is of great significance to study the impact of chronic arsenic exposure on an organism, which could be found through metabolites changes of organism by using metabonomics technique. Metabonomics is a sensitive and unbiased analytical method that assesses all metabolites in biological samples (Dettmer et al., 2007). The technique can generate substantial amounts of metabolic data that can give surprisingly detailed insights into the changes in metabolic processes in whole organisms (Nicholson et al., 1999; Nicholson and Wilson, 2003). Metabonomics can determine the relationships between phenotype and metabolism, and can be used to identify the key metabolites associated with a particular phenotype and investigate the biological function and metabolic changes in the organism. This approach has been used to identify serum principal metabolites and investigate the mechanism of exogenous material on organism (Nicholls et al., 2001; Nicholson and Wilson,1989; Griffin et al., 2001). Recently, ultra-performance liquid chromatography/mass spectrometry (UPLC/MS) has been applied widely in metabolomics studies owing to its high sensitivity and reproducibility (Lenz and Wilson, 2007; Yang et al., 2004). In this field, ultra-performance liquid chromatography and Q-TOF mass spectrometry (UPLC/Q-TOF MS) adds a new dimension to metabolism studies, enabling attainment of better detection limits, better throughput, and increased chromatographic resolution, which in turn will improve data quality from the mass spectrometer. We used metabolomics analyses based on UPLC-Q-TOF MS to gain a broader understanding of metabolic changes in rats exposed to arsenic, and to find the biomarkers that could reflect these changes. These metabolic changes and biomarkers might be important for future diagnosis and serve as targets for therapeutic intervention of arsenicosis. 2. Materials and methods
475
and the total arsenic intake of each animal was calculated. The animals were sacrificed under chloral hydrate anesthesia after 6 months. Blood samples were collected and stored at 80 C for the detection of serum arsenic and metabolomics analysis. Tissues of brain, liver and kidney samples were rapidly frozen in liquid nitrogen and stored at 80 C until use. Specimens of liver and kidney were fixed in 10% neutral formaldehyde for histopathology analysis. All experiments were approved by the Animal Ethics Committee of Harbin Medical University and were carried out in accordance with the Guide for the Care and Use of Laboratory Animals (National Research Council, 1996, USA). 2.2. Light microscope analysis of liver and kidney histology The liver and kidney were fixed in 10% neutral formaldehyde, paraffin-embedded, sectioned, and stained with hematoxylineosin. A microimaging system (Olympus, Tokyo, Japan) was used to observe the pathological changes at 400 magnification. 2.3. Chemicals and reagents The products used in this study were analytical pure sodium arsenic (Secondary Factory of Shanghai Chemical Reagent, Shanghai, China), high-performance liquid chromatography (HPLC)-grade acetonitrile and methanol (Honeywell Burdick & Jackson, Muskegon, MI, USA), HPLC-grade formic acid (Beijing Reagent Company, Beijing, China), and leucine enkephalin (Sigma– Aldrich, St Louis, MO, USA). Deionized water was purified by the Milli-Q system (Millipore, Billerica, MA). Leucine enkephalin was purchased from Sigma–Aldrich (St. Louis, MO, USA). Standards were purchased from Sigma (Sigma–Aldrich, St Louis, MO, USA, 99% purity). 2.4. Arsenic level assay The levels of total arsenic in serum, brain, liver and kidney were determined using hydride generation atomic fluorescence spectrometry according to a national standardized method in China (GB/T 5009.11-2003). Briefly, the proper amount of samples was digested by heating them with concentrated HNO3 and HCLO4. After digestion, 100 g/L sulfocarbamide and ascorbic acid was added to the samples. The standard samples were obtained with standard reference materials (GBW08611). The arsenic levels in diluted samples and standard samples were determined by an atomic fluorescence spectrometer (AFS-930, Beijing Titan Instruments, China).
2.1. Animals and treatment 2.5. UPLC/Q-TOF MS analysis Thirty healthy male Wistar rats aged 4–6 weeks and weighing 100–120 g were purchased from Vital River Laboratory Animal Technology Co. Ltd. All rats were allowed to acclimatize in communal plastic cages for 7 days before treatment. Temperature and humidity were regulated at 20 2 C and 50 15%, respectively. A cycle of 12 h light/12 h dark was established. The animals were randomly divided into three groups including control, low arsenic exposure group (LAE), and high arsenic exposure group (HAE). Arsenic was provided to animals via drinking water. Based on median lethal dose (LD50) of sodium arsenitethrough oral administering on rats (41 mg/kg), about 1/5 of the LD50 was chosen as the high arsenic exposure dose (approximately 50 mg/L sodium arsenite in drinking water), the correspondent low arsenic dose is 1/5 of the high arsenic dose (10 mg/L sodium arsenite in drinking water), and the control group had no sodium arsenite (distilled water) in the drinking water. Conventional diet (AIN93G) and drinking water were available at all times throughout the study. Water consumption of each animal was recorded per day
UPLC/Q-TOF MS analysis was performed using an ACQUITY UPLC system (Waters Corporation, Milford, MA, USA) coupled to a Micromass Q-tof (Quatropde-Time of Flight) Mass Spectrometer (Waters Corp., Manchester, UK) with electrospray ionization (ESI) in positive and negative modes. After deproteinization with methanol (1:5), the supernatant were dried with nitrogen at 37 C, resolved with acetonitrile and water (3:1), filtrated with 0.22 mm filter membrance, and transferred into an autosampler vial. A 2 mL aliquot of supernatant was injected into an ACQUITY UPLC BEH-C18 column (50 mm 4.6 mm i.d., 1.7 mm; Waters Corporation, Milford, MA, USA). The flow rate of the mobile phase was 400 mL/min. Analytes were eluted from the column with a gradient, where A was 0.1% formic acid in water and B was 0.1% formic acid in acetonitrile. The initial composition of B was 2% and increased to 20% from 0 to 2 min, 20–70% from 2 to 5 min, and 70–98% from 5 to 11 min, followed by re-equilibration to the initial conditions in 7 min. Each run time was 18 min. For MS analysis, the
476
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
Table 1 Arsenic levels in serum, liver and kidney, and the correlations among them. Control Total arsenic intake (mg) Arsenic levels in serum (mg/L) Arsenic levels in brain (mg/g) Arsenic levels in liver (mg/g) Arsenic levels in kidney (mg/g) a b
0 5.6 1.4 0.1 0.1 0.4 0.1 0.5 0.1
LAE
HAE a
88.4 6.3 28.9 11.4a 1.9 0.3a 7.5 2.4a 15.3 3.2a
Correlation with total arsenic intake (r) a,b
367.8 17.6 42.5 13.1a 2.7 0.9a,b 10.9 2.3a,b 46.1 16.4a,b
0.773a 0.821a 0.824a 0.886a
Significant difference compared to that of the control group or significant correlations between total arsenic intake and arsenic levels in serum, liver and kidney, p < 0.01. Significant difference compared to that of the LAE group, p < 0.05.
source temperature was set at 100 C with a cone gas flow of 50 L/h. A desolvation gas temperature of 300 C and a desolvation gas flow of 600 L/h were used. The capillary voltage was set at 3000 V in positive ESI mode and 2600 V in negative ESI mode, and the cone voltage to 35 V. All analyses were performed using the lock spray to ensure accuracy and reproducibility. A lock-mass of leucine enkephalin for positive ESI mode (m/z = 556.2771) and negative ESI mode (m/z = 554.2615) was used via a LockSprayTM interface. The MS data were collected in centroid mode from m/z 50 to 1000, and the lock spray frequency set at 0.40 s and averaged over 10 scans for correction. Order effects in the statistical analysis were avoided due to the randomized crossover design used. In addition, the repeatability of the present method was evaluated using a representative pooled quality control (QC) sample. The QC sample was prepared by mixing equal volumes of the serum samples from three control rats, three LAE rat and three HAE rats. One QC sample was injected at the start of the analytical batch, followed by analysis of one QC sample at every sixth sample injection throughout the analytical workflow. The overlapped performance of each spectral peak was evaluated, and six single ions with different m/z and retention time were extracted. The reproducibility of the QC sample was examined by analyzing the differences in the retention time and peak intensity of the six ions. 2.6. Data analysis Statistical analysis was performed using SPSS (version 13.01S; Beijing Stats Data Mining Co. Ltd. Beijing, China). Data were presented as mean SD. Differences between groups were analyzed using one-way ANOVA, followed by two-group
comparisons with LSD or Dunnett T3. All P values were 2-tailed and a P value < 0.05 was considered significant for all statistical analyses in this study. The UPLC/Q-TOF MS data were analyzed using the MarkerLynx Application Manager 4.1 SCN 714 (Waters Corporation, Milford, MA, USA). Mass window was set at 0.02 Da, noise elimination level at 10.00, RT tolerance at 0.01 min and RT window at 0.2 min. The resulting 3D matrix contained arbitrarily assigned peak indexes (retention time-m/z pairs), sample names (observations), and normalized ion intensities for each peak area was exported to EZINFO 2.0 (an component of MarkerLynx) for visualizing the score plot and for obtaining the greatest variable importance in projection (VIP) values using partial least squares discriminant analysis (PLS-DA). The metabolites with VIP values > 1.0 in the model were regarded as potential biomarkers (Eriksson et al., 2001). The goodness of the fit was quantified by R2Y, while the predictive ability was indicated by Q2. A crossvalidation procedure and testing with 100 random permutations were performed to avoid the over-fitting of supervised PLS-DA models, using SIMCA-P software (version 11.5; Umetrics AB, Umeå, Sweden). The probable empirical formulas of the potential biomarkers were first identified based on accurate mass measurement (mass error of <20 ppm) and by considering the relative intensities of the isotope peaks through the high-resolution MS spectra. Then, the MassFragmentTM application manager (MassLynx v4.1, Waters Corp., USA) was used to facilitate the MS/MS fragment ion-analysis process by way of chemically intelligent peak-matching algorithms. Briefly, the UPLC/MS/MS product ion spectrum of metabolites was matched with the structure message of metabolites (obtained from the Human Metabolome Databases
Fig. 1. Light microscopy features of the liver and kidney in rats after arsenic exposure for 6 months. The arrows indicate the changes of liver or kidney. Histopathology of kidney (A–D): (A) control rats; (B) LAE rats; (C and D) high arsenic exposed rats. Histopathology of liver (E–H): (E) control rats; (F) LAE rats (lipid droplets – normal arrows, cellular swelling – long and thin arrow); (G and H) high arsenic exposed rats.
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
477
Fig. 2. Serum metabolic profiling of control and arsenic exposed rat in positive (A) and negative (B) ESI mode.
(HMDB)) according to some parameters, such as deviation from calculated mass (ppm), double bond equivalent (DBE), and i-fit value (the isotopic pattern of the selected ion). Finally, the biomarkers were further confirmed by standard compounds based on both retention times and MS/MS spectra. Sequentially, the implicated pathways of biomarkers were interpreted using databases, including HMDB (http://www.hmdb.ca) and KEGG (http://www.genome.jp/kegg/). 3. Results 3.1. Arsenic-exposed animal model established The total arsenic intakes (per animal in entire study) in the low arsenic exposure group and high arsenic exposure group were 88.4 6.3 mg and 367.8 17.6 mg, respectively. The levels of arsenic in serum, brain, liver and kidney were significantly higher in arsenic-exposed groups compared with the control group, and the levels of arsenic in brain, liver and kidney were significantly higher in HAE groups compared with the LAE group. In addition, the correlations between the total water arsenic intakes and
arsenic levels in serum, brain, liver and kidney were analyzed. There were significant correlations between total arsenic intakes and arsenic levels in serum, brain, liver and kidney (Table 1). 3.2. Histopathology Light microscope analysis was used to examine the histopathological changes of liver and kidney in rats after arsenic exposure. The results showed that, in the kidney of a rat at 6 months, the structures of glomerular and renal tubule were normal in control group (Fig. 1A); in renal tubule, the epithelial cells showed a mild cellular swelling in low-arsenic exposed group compared with the control group (Fig. 1B), and severe cellular swelling was found in high-arsenic exposed group (Fig. 1C). In addition, calcareous infarct could be found at area of medullary collecting duct (Fig. 1D). In liver of rats, the structure of hepatocytes was normal in control group (Fig. 1E). There were a small amount of deposits of round lipid droplets at hepatocytes of liver and the cytoplasm of hepatocytes showed cellular swelling in the low-arsenic exposed group (Fig. 1F). Also there were a number of deposits of round lipid droplets in hepatocytes around central venous, and cytoplasm
Fig. 3. Score plot with PLS-DA of serum metabolite in control (diamond, green), Low dose arsenic (diamond, yellow) and High dose arsenic (diamond, blue) exposed rat in positive (A) and negative (B) ESI mode. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
478
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
showed more severe cellular swelling in high-arsenic exposed group (Fig. 1G). Some neutrophils were found at the portal area of the liver (Fig. 1H). 3.3. Quality control of the sample in UPLC/Q-TOF MS analysis Quality control of sample analysis was conducted by evaluating the repeatability of the present method using a mixture of 9 serum samples injected interval of 6 samples (n = 6). The overlapped performance of the spectral peak was evaluated (see Additional file 1). The relative standard deviation (RSD) of retention time ranged from 0.00% to 0.14% for the positive ESI mode, and from 0.00% to 0.09% for the negative ESI mode. The RSD of peak intensity ranged from 2.63% to 8.08% for the positive ESI mode, and from 1.94% to 7.63% for the negative ESI mode (see Additional file 1). The results indicated that method had excellent repeatability.
the projection. Analysis of these data using PLS-DA revealed that dosing with sodium arsenite was associated with significant increases or decreases in several compounds in positive and negative ESI modes. The principal ions that were seen to increase in positive ESI mode was m/z = 417.2455, and in negative ESI were those for 448.3034 and 504.3018, which eluted at 3.56, 3.01 and 4.41 min and were identified as CPA (18:2(9Z,12Z)/0:0), deoxygcholylglycine and LysoPE (0:0/20:2 (11Z,14Z)) respectively. The principal ions that were seen to decrease in positive ESI mode were those for m/z = 468.3070, 516.3081, 508.3767, 400.3445 and 548.3729 and in negative ESI was 321.2377, which eluted at 5.27, 5.46, 5.86, 6.08, 6.61, and 5.24 min and were identified as LysoPC (14:0), LysoPC (18:4 (6Z,9Z,12Z,15Z)), LysoPC (P-18:0), L-palmitoylcarnitine, LysoPC (20:2(11Z,14Z)) and 15(S)-hydroxyeicosatrienoic acid respectively (Table 2 and Fig. 4). 4. Discussion
3.4. UPLC/Q-TOF MS fingerprinting and multivariate analysis All the serum samples collected in this study were analyzed by UPLC/QTOF-MS with positive and negative ESI (Fig. 2). Pattern recognition via PLS-DA was performed on positive and negative ESI data. PLS-DA is a multivariate classification method based on PLS. PLS-DA explains maximum separation between defined class samples in the data set. As shown by the PLS-DA scores plot (for the first two components, R2Y = 0.884 and Q2 = 0.526 in positive ESI mode, and for the first three components, R2Y = 0.892 and Q2 = 0.439 in negative ESI mode, Fig. 3), the control and arsenic exposed rat could be separated into distinct clusters, for distinct metabolic alterations between the three groups. To assess the risk that the current PLS-DA model was spurious, the permutation test for PLS-DA was applied. All R2Y and Q2 values to the left were lower than the original points to the right (see Additional file 2), showing that the PLS-DA model was valid. The results suggested that the arsenic exposure had led to the serum metabolite alterations in our rat model. 3.5. Potential biomarkers Components with important roles in the separation were picked out according to the parameter variable importance in
In this study, an animal model of arsenic exposure was successfully established by administering sodium arsenite through drinking water. The results showed that arsenic was significantly increased in serum, brain, liver, and kidney in rats after arsenic exposure for 6 months, and there were significant correlations between total arsenic intake and arsenic levels in serum, brain, liver, and kidney. Excessive arsenic intake can cause the damage to multiorgan systems. Skin lesions are common and often appeared in the late stage of chronic arsenicosis. Therefore, it is of great significance to study the early impact of chronic arsenic exposure on other organs, such as liver, kidney. Arsenic is mainly accumulated in the liver (Bashir et al., 2006; Liu et al., 2000), and kidney (Majhi et al., 2011; Huang et al., 2009) is the main excretion organ for arsenic and its metabolites, which would be inevitably damaged during chronic arsenic exposure. The results in the present study suggested that there were significant pathological changes and lesions in a dose– response relationship at 6 months in liver and kidney of rats. Therefore, the metabolic changes of early damage due to arsenic toxicity must be elucidated in order to find effective targets for the prevention and intervention. Now most of the researches focused on the mechanism of chronic arsenicosis and several hypotheses on this mechanism have been proposed, including the theory of arsenic metabolism, lipid
Table 2 Potential biomarkers in UPLC/Q-TOF MS positive and negative ion modes. Retention time (min)
Measured mass (Da)
Calculated mass (Da)
Mass error Elemental (PPM) composition
Postulated identity
VIP values
Positive ion mode 3.56 417.2455 5.27 468.3070 5.46 516.3081 5.86 508.3767 6.08 400.3445 6.61 548.3729
417.2515 468.3090 516.309 508.3767 400.3427 548.3716
14 4 2 0 4 2
C21H37O6P C22H46NO7P C26H46NO7P C26H54NO6P C23H45NO4 C28H54NO7P
CPA(18:2(9Z,12Z)/0:0)b LysoPC(14:0)a LysoPC(18:4(6Z,9Z,12Z,15Z))a LysoPC(P-18:0)a b L-Palmitoylcarnitine LysoPC(20:2(11 Z,14Z))a
Negative ion mode 3.01 448.3034 4.41 504.3018 5.24 321.2377
448.3063 504.309 321.243
17 4 10
C26H43NO5 C25H48NO7P C20H34O3
Deoxycholylglycineb 1.98 Lysophosphatidylethanolamineb 3.64 15(S)-Hydroxyeicosatrienoic 5.67 acidb
Values are mean SD. a Biomarkers identified by exact mass data and MS fragmentation and confirmed using standard samples. b Biomarkers identified by exact mass data and MS fragmentation. * P < 0.05 compared with control group. ** P < 0.01 compared with control group. *** P < 0.05 compared with low dose group.
6.28 7.63 3.75 4.36 3.94 6.75
Normalized peak intensity (relative contents) Control group
Low dose group
0.15 0.17 62.64 7.69 23.13 2.08 17.67 1.69 40.71 8.36 56.17 9.88
4.94 6.37 48.38 8.21 18.48 3.63 12.97 2.29 34.35 6.39 48.22 11.10
High dose group
12.96 8.86**,*** 44.23 12.64** 17.99 5.92* 12.18 4.05** 32.50 4.40* 38.32 12.69**
2.45 2.29 3.12 2.13 5.31 3.36* 86.10 8.20 85.59 11.07 101.153 19.09*,*** 41.86 16.28 30.08 6.88 25.66 14.07*
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
479
Fig. 4. Mass fragment information of principal metabolites with different collision energy (ev). (A) CPA (18:2(9Z,12Z)/0:0) (23 ev); (B) LysoPC (14:0) (23 ev); (C) LysoPC (18:4 (6Z,9Z,12Z,15Z)) (23 ev); (D) LysoPC (P-18:0) (30 ev); (E) L-palmitoylcarnitine (15 ev); (F) LysoPC (20:2(11Z,14Z)) (23 ev); (G) deoxycholylglycine (25 ev); (H) lysophosphatidylethanolamine (23 ev); and (I) 15(S)-hydroxyeicosatrienoic acid (23 ev).
peroxidation, and cancer. However, the exact mechanism of chronic arsenicosis is not clear yet, and the key metabolic changes and potential biomarkers after arsenic exposure remain to be determined. Metabolomics technology can achieve high-throughput and rapid detection of metabolites and analysis of large datasets, and could be used to identify, among tens of thousands of metabolites, those with greatest phenotypic contribution. Through investigating the metabolic pathways of these principal metabolites, the important metabolic changes and relations between these changes with arsenic exposure could be found. In the present study, 9 potential biomarkers including CPA (18:2 (9Z,12Z)/0:0), LysoPC (14:0), LysoPC (18:4 (6Z,9Z,12Z,15Z)), LysoPC (P-18:0), L-palmitoylcarnitine, LysoPC (20:2(11Z,14Z)) in positive ESI mode and deoxygcholylglycine, LysoPE (0:0/20:2(11Z,14Z)), 15 (S)-hydroxyeicosatrienoic acid in negative ESI were identified. Biological functions and metabolic pathways of these metabolites
were investigated by using databases such as HMDB and KEGG, which are available electronically, and could be used for querying metabolic pathways and some metabonomic information. CPA (18:2(9Z,12Z)/0:0) is a cyclic phosphatidic acid. It is a glycerophospholipid in which a cyclic phosphate moiety occupies two glycerol substitution sites. CPA analogs with various fatty acyl chains were detected in human serum and some other organisms (Kobayashi et al.,1999). CPA's have a cyclic phosphate at the sn-2 and sn-3 positions of the glycerol carbons, and this structure is absolutely necessary for their activities. Cyclic phosphatidic acid is known to be a specific inhibitor of DNA polymerase alpha, which can interfere with DNA synthesis and repair (Murakami-Murofushi et al., 1995), induces actin stress fiber formation (Fischer et al., 1998). Increased CPA in high dose arsenic exposed rats suggested the metabolic changes, which might cause cancer or damage organs (such as kidney or liver) induced by arsenic exposure.
480
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
LysoPC (14:0), LysoPC (18:4(6Z,9Z,12Z,15Z)), LysoPC (P-18:0) and LysoPC (20:2(11Z,14Z)) are the lysophosphatidylcholines. They are a monoglycerophospholipid in which a phosphorylcholine moiety occupies a glycerol substitution site. These lysophosphatidylcholines have different combinations of fatty acids of varying lengths and saturation attached at the C-1 position. The different fatty acids are myristic acid, stearidonic acid, plasmalogen 18:0 and eicosadienoic acid, respectively, which mostly derived from liver. In blood plasma lysophosphatidylcholine are formed by a specific enzyme system, lecithin–cholesterol acyltransferase (LCAT), which is also secreted from the liver. The enzyme catalyzes the transfer of the fatty acids at position sn-2 of phosphatidylcholine to the free cholesterol in plasma, with formation of cholesterol esters and lysophosphatidylcholine. Lysophosphatidylcholines have important roles in lipid signaling by acting on lysophospholipid receptors (LPL-R). LPL-R group are members of the G protein-coupled receptor family of integral membrane proteins that are important for lipid signaling (Chun et al., 2002). The activated receptor can have a range of effects on the cell. These include primary effects of inhibition of adenylyl cyclase and release of calcium from the endoplasmic reticulum, as well as secondary effects of preventing apoptosis and increasing cell proliferation (Meyer zu Heringdorf and Jakobs, 2007). These biomarkers were all significantly decreased after arsenic exposure, which suggested the metabolic changes of liver, which might cause the whole metabolic changes in individuals. L-Palmitoylcarnitine is a long-chain acyl fatty acid derivative ester of carnitine which facilitates the transfer of longchain fatty acids from cytoplasm into mitochondria during the oxidation of fatty acids. L-Palmitoylcarnitine is a surface-active molecule, which can change activity of several enzymes and transporters localized in the membrane by changing the membrane fluidity and surface charge (Watanabe et al., 1989; Haruna et al., 2000). L-Palmitoylcarnitine was also reported to diminish the complete binding of phorbol esters, the protein kinase C activators and to decrease the autophosphorylation of the enzyme (Nałecz et al., 2004), and therefore influence the cell growth, differentiation, metabolism, and regulation of gene expression, transcriptional activation. L-Palmitoylcarnitine is significantly lower in arsenic exposed rats than that in normal rats. It suggested that arsenic exposure might induce the metabolic disruption of fatty acids, which might be one of the reasons for damages to heart, brain and neural (Watanabe et al., 1989; Haruna et al., 2000; Nałecz et al., 2004; Szczepankowska and Nałecz (2003); Goñi et al., 1996) and kidney (Rosca et al., 2012) in pathogenesis of arsenicosis. Deoxycholylglycine is a bile salt formed in the liver by conjugation of deoxycholate with glycine. It can act as a detergent to solubilize fats for absorption and is itself absorbed. A study (Demers and Hepner, 1976) indicated that some patients with hepatobiliary disease had the higher deoxycholylglycine in serum, which could suggest the hepatic dysfunction. Khurana' study (Khurana et al., 2012) about effects of deoxycholylglycine on myogenic tone and agonist-induced contraction in rat resistance arteries showed that elevated serum deoxycholylglycine could alter vasomotor responses, and was likely pertinent to vascular dysfunction in cirrhosis. Lysophosphatidylethanolamine (LPE) is a hydrolysis product of PE by phospholipase A2, which plays a role in cellmediated cell signaling and activation of other enzymes. LPE can stimulate calcium signaling via phospholipase C activation, and stimulate a membrane bound receptor, different from well known LPA receptors, stimulate chemotactic migration and cellular invasion in SK-OV3 ovarian cancer cells (Park et al., 2007). 15(S)Hydroxyeicosatrienoic acid (15S-HETrE) is a polyunsaturated fatty acid that has been reported to modulate arachidonic acid (AA) metabolism and tumorigenesis. 15S-HETrE can suppress cyclooxygenase-2 (COX-2) over expression and/or prostaglandin E2 (PGE2) biosynthesis (Pham et al., 2004), and inhibit [(3)H] thymidine
uptake in parallel with the upregulation of peroxisome proliferator-activated receptor-gamma expression (a growth modulating nuclear receptor, PPAR gamma) (Pham et al., 2003), and, thus, alleviate tumor growth and progression. Deoxycholylglycine and LPE were all significantly higher, and 15(S)-hydroxyeicosatrienoic acid was significantly lower after arsenic exposure, which might partly explain the cause of cancer or metabolic disruptions of liver induced by arsenic exposure. To the best of our knowledge, this is the first report to evaluate the metabolic changes in rats after arsenic exposure using UPLC/QTOF MS. In the present study, some other principal metabolites were detected, but remain unidentified at present. The unidentified metabolites and their metabolic pathways might include the other metabolic changes after arsenic exposure. This is also a possible limitation of this study, unlike GC–MS or NMR, for which large databases existing, the databases of endogenous biomolecules based LC–MS techniques for metabolomics research have not yet been constructed. This may limit the identification and analysis of metabolic pathway on the detected principal metabolites. In addition, there are significant differences in arsenic metabolism between rats and humans, which maybe another limitation of this study. 5. Conclusions In summary, metabolite profiling using UPLC-TOF MS integrated with modern multivariate statistical techniques was successfully applied to evaluate the metabolic changes of rats exposed to the arsenic, and we found some biomarkers which could reflect these changes. These potential biomarkers of chronic arsenic exposure, however, at high doses in the rats and as such results must be validated in human studies before they can serve for diagnosis or therapeutic intervention of arsenicosis. Conflict of interest None of the authors has any conflicting interests. Transparency document The Transparency document associated with this article can be found in the online version. Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant No. 81273013), Specialized Research Fund for the Doctoral Program of Higher Education (No. 20102307120025), and China Postdoctoral Science Foundation funded project (No. 20100471017). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.toxlet.2014.06.001. References Bashir, S., Sharma, Y., Irshad, M., Nag, T.C., Tiwari, M., Kabra, M., Dogra, T.D., 2006. Arsenic induced apoptosis in rat liver following repeated 60 days exposure. Toxicology 217 (1), 63–70. Chun, J., Goetzl, E.J., Hla, T., Igarashi, Y., Lynch, K.R., Moolenaar, W., Pyne, S., Tigyi, G., 2002. International Union of Pharmacology. XXXIV. Lysophospholipid receptor nomenclature. Pharmacol. Rev. 54 (2), 265–269. Demers, L.M., Hepner, G.W., 1976. Levels of immunoreactive glycine-conjugated bile acids in health and hepatobiliary disease. Am. J. Clin. Pathol. 66 (5), 831–839. Dettmer, K., Aronov, P.A., Hammock, B.D., 2007. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26 (1), 51–78.
C. Wang et al. / Toxicology Letters 229 (2014) 474–481 Eriksson, L., Johansson, E., Kettaneh-Wold, N., Wold, S., 2001. Multi- and Megavariate Data Analysis: Principles and Applications. Umetrics, Umeå. Fischer, D.J., Liliom, K., Guo, Z., Nusser, N., Virág, T., Murakami-Murofushi, K., Kobayashi, S., Erickson, J.R., Sun, G., Miller, D.D., Tigyi, G., 1998. Naturally occurring analogs of lysophosphatidic acid elicit different cellular responses through selective activation of multiple receptor subtypes. Mol. Pharmacol. 54 (6), 979–988. Goñi, F.M., Requero, M.A., Alonso, A., 1996. Palmitoylcarnitine, a surface-active metabolite. FEBS Lett. 390 (1), 1–5. Griffin, J.L., Walker, L.A., Shore, R.F., Shore, J.K., Nicholson, J.K., 2001. Metabolic profiling of chronic cadmium exposure in the rat. Chem. Res. Toxicol. 14 (10), 1428–1434. Haruna, T., Horie, M., Takano, M., Kono, Y., Yoshida, H., Otani, H., Kubota, T., Ninomiya, T., Akao, M., Sasayama, S., 2000. Alteration of the membrane lipid environment by L-palmitoylcarnitine modulates K(ATP) channels in guinea-pig ventricular myocytes. Pflugers Arch. 441 (2–3), 200–207. Huang, M., Choi, S.J., Kim, D.W., Kim, N.Y., Park, C.H., Yu, S.D., Kim, D.S., Park, K.S., Song, J.S., Kim, H., Choi, B.S., Yu, I.J., Park, J.D., 2009. Risk assessment of low level cadmium and arsenic on the kidney. J. Toxicol. Environ. Health A 72 (21–22), 1493–1498. Khurana, S., Raina, H., Pappas, V., Raufman, J.P., Pallone, T.L., 2012. Effects of deoxycholylglycine, a conjugated secondary bile acid, on myogenic tone and agonist-induced contraction in rat resistance arteries. PLoS One 7 (2), e32006. Kobayashi, T., Tanaka-Ishii, R., Taguchi, R., Ikezawa, H., Murakami-Murofushi, K., 1999. Existence of a bioactive lipid, cyclic phosphatidic acid, bound to human serum albumin. Life Sci. 65 (21), 2185–2191. Lenz, E.M., Wilson, I.D., 2007. Analytical strategies in metabonomics. J. Proteome. Rev. 6 (2), 443–458. Liu, J., Liu, Y., Goyer, R.A., Achanzar, W., Waalkes, M.P., 2000. Metallothionein-I/II null mice are more sensitive than wild-type mice to the hepatotoxic and nephrotoxic effects of chronic oral or injected inorganic arsenicals. Toxicol. Sci. 55 (2), 460–467. Majhi, C.R., Khan, S., Leo, M.D., Manimaran, A., Sankar, P., Sarkar, S.N., 2011. Effects of acetaminophen on reactive oxygen species and nitric oxide redox signaling in kidney of arsenic-exposed rats. Food Chem. Toxicol. 49 (4), 974–982. Meyer zu Heringdorf, D., Jakobs, K.H., 2007. Lysophospholipid receptors: signalling, pharmacology and regulation by lysophospholipid metabolism. Biochim. Biophys. Acta 1768 (4), 923–940. Murakami-Murofushi, K., Kobayashi, S., Onimura, K., Matsumoto, M., Shioda, M., Yoshida, S., Shoji, M., Murofushi, H.,1995. Selective inhibition of DNA polymerasealpha family with chemically synthesized derivatives of PHYLPA, a unique Physarum lysophosphatidic acid. Biochim. Biophys. Acta 1258 (1), 57–60. Nałecz, K.A., Miecz, D., Berezowski, V., Cecchelli, R., 2004. Carnitine: transport and physiological functions in the brain. Mol. Aspects Med. 25 (5–6), 551–567. Namgung, U., Xia, Z., 2001. Arsenic induces apoptosis in rat cerebellar neurons via activation of JNK3 and p38 MAP kinases. Toxicol. Appl. Pharmacol.174 (2),130–138. Nicholls, A.W., Holmes, E., Lindon, J.C., Shockcor, J.P., Farrant, R.D., Haselden, J.N., Damment, S.J., Waterfield, C.J., Nicholson, J.K., 2001. Metabonomic investigations into hydrazine toxicity in the rat. Chem. Res. Toxicol. 14 (8), 975–987.
481
Nicholson, J.K., Lindon, J.C., Holmes, E., 1999. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29 (11), 1181–1189. Nicholson, J.K., Wilson, I.D., 1989. High resolution proton magnetic resonance spectroscopy of biological fluids. Prog. Nucl. Magn. Reson. Spectrosc. 21 (4), 449–501. Nicholson, J.K., Wilson, I.D., 2003. Opinion: understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2 (8), 668–676. Park, K.S., Lee, H.Y., Lee, S.Y., Kim, M.K., Kim, S.D., Kim, J.M., Yun, J., Im, D.S., Bae, Y.S., 2007. Lysophosphatidylethanolamine stimulates chemotactic migration and cellular invasion in SK-OV3 human ovarian cancer cells: Involvement of pertussis toxin-sensitive G-protein coupled receptor. FEBS Lett. 581 (23), 4411– 4416. Pham, H., Banerjee, T., Nalbandian, G.M., Ziboh, V.A., 2003. Activation of peroxisome proliferator-activated receptor (PPAR)-gamma by 15S-hydroxyeicosatrienoic acid parallels growth suppression of androgen-dependent prostatic adenocarcinoma cells. Cancer Lett. 189 (1), 17–25. Pham, H., Banerjee, T., Ziboh, V.A., 2004. Suppression of cyclooxygenase-2 overexpression by 15S-hydroxyeicosatrienoic acid in androgen-dependent prostatic adenocarcinoma cells. Int. J. Cancer 111 (2), 192–197. Rosca, M.G., Vazquez, E.J., Chen, Q., Kerner, J., Kern, T.S., Hoppel, C.L., 2012. Oxidation of fatty acids is the source of increased mitochondrial reactive oxygen species production in kidney cortical tubules in early diabetes. Diabetes 61 (8), 2074– 2083. Szczepankowska, D., Nałecz, K.A., 2003. Palmitoylcarnitine modulates palmitoylation of proteins: implication for differentiation of neural cells. Neurochem. Res. 28 (3–4), 645–651. Thomas, D.J., Styblo, M., Lin, S., 2001. The cellular metabolism and systemic toxicity of arsenic. Toxicol. Appl. Pharmacol. 176 (2), 127–144. USA Today, 2007. Arsenic in drinking water seen as threat. http://usatoday30. usatoday.com/news/world/2007-08-30-553404631_x.htm (accessed 04.03.13). Wang, J., Zhang, W., Sun, D.J., Song, L., Li, Y.Y., Xu, C.B., 2012. Analysis of neuroglobin mRNA expression in rat Brain due to arsenite-induced oxidative stress. Environ. Toxicol. 27 (9), 503–509. Watanabe, H., Kobayashi, A., Hayashi, H., Yamazaki, N., 1989. Effects of long-chain acyl carnitine on membrane fluidity of human erythrocytes. Biochim. Biophys. Acta 980 (3), 315–318. Watanabe, T., Hirano, S., 2013. Metabolism of arsenic and its toxicological relevance. Arch. Toxicol. 87 (6), 969–979. WHO, 1981. Arsenic. Environmental Health Criteria 18. Geneva: lnternational Programme on Chemical Safety. http://www.inchem.org/documents/ehc/ehc/ ehc018.htm (accessed 04.03.13). Yang, J., Xu, G., Hong, Q., Liebich, H.M., Lutz, K., Schmülling, R.M., Wahl, H.G., 2004. Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 813 (1), 53–58.
Contents lists available at ScienceDirect
Toxicology Letters journal homepage: www.elsevier.com/locate/toxlet
The metabolomic profiling of serum in rats exposed to arsenic using UPLC/Q-TOF MS Cheng Wang a,1, Rennan Feng b,1, Yuanyuan Li c, Yunbo Zhang a , Zhen Kang d, Wei Zhang c , Dian-Jun Sun c, * a
Department of Environmental Hygiene, Public Health College, Harbin Medical University, Harbin 150081, Heilongjiang Province, China Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin 150081, Heilongjiang Province, China c The Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, 194 Xuefu Road, Nangang District, Harbin 150081, Heilongjiang Province, China d Department of Environmental Hygiene, Harbin Center for Disease Control and Prevention, Harbin 150056, Heilongjiang Province, China b
H I G H L I G H T S
Significant impact of chronic arsenic exposure on the metabolism of organism was found. Endogenous metabolite profile of serum was investigated by UPLC/Q-TOF MS. Nine principal metabolites reflecting the changes of metabolism were identified.
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 April 2014 Received in revised form 28 May 2014 Accepted 1 June 2014 Available online 21 June 2014
Chronic arsenicosis induced by excessive arsenic intake can cause damages to multi-organ systems, skin cancer and various internal cancers. However, the key metabolic changes and biomarkers which can reflect these changes remain unclear resulting in a lack of effective prevention and treatments. The aim of this study is to determine the impact of chronic arsenic exposure on the metabolism of organism, and find the metabolites changes by using metabolomic techniques. Thirty male Wistar rats were randomly divided into three groups. The arsenite was administered in water, and the doses were 0, 10, and 50 mg/L, respectively. The exposure lasted for 6 months. The endogenous metabolite profile of serum was investigated by ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. Partial least squares discriminant analysis (PLS-DA) enabled clusters to be visualized. Nine serum principal metabolites contributing to the clusters were identified, which were CPA (18:2(9Z,12Z)/0:0), LysoPC (14:0), LysoPC (18:4 (6Z,9Z,12Z,15Z)), LysoPC (P-18:0), L-palmitoylcarnitine, LysoPC (20:2(11Z,14Z)) in positive ESI mode and deoxygcholylglycine, LysoPE (0:0/20:2(11Z,14Z)), 15(S)hydroxyeicosatrienoic acid in negative ESI. These changes of metabolites in rats suggested the changed metabolism in rats exposed to arsenic. These findings may further aid diagnose and serve as targets for therapeutic intervention of arsenicosis. ã 2014 Published by Elsevier Ireland Ltd.
Keywords: Chronic arsenicosis Biomarker Mechanism Metabonomics
1. Introduction
* Corresponding author. Tel.: +86 451 86612695; fax: +86 451 86676184. E-mail addresses: [email protected] (C. Wang), [email protected] (R. Feng), [email protected] (Y. Li), [email protected] (Y. Zhang), [email protected] (Z. Kang), [email protected] (W. Zhang), [email protected] (D.-J. Sun). 1 Cheng Wang and Rennan Feng contributed equally to this work. http://dx.doi.org/10.1016/j.toxlet.2014.06.001 0378-4274/ ã 2014 Published by Elsevier Ireland Ltd.
Arsenic is a metalloid material, which is widely distributed throughout the environment in water, food and air. Elevated levels of arsenic in the body can lead to the arsenicosis. The dominant basis of arsenicosis is from ground water that naturally contains high concentrations of arsenic. A study in 2007 found that over 137 million people in more than 70 countries are probably affected by arsenicosis from drinking water (USA Today, 2007). As early as 1981, arsenic has been considered as a human carcinogen that causes skin cancer and various internal cancers by WHO
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
(WHO, 1981). Researchers have explored a lot on the mechanism of arsenic toxicology (Thomas et al., 2001; Watanabe and Hirano, 2013). However, the exact mechanism of chronic arsenicosis currently remains unclear resulting in a lack of effective treatments. In addition, skin lesions, which are common and that which often appeared in the later stages of chronic arsenicosis, are often used to evaluate the levels of arsenicosis in China (WS/T211-2001). However, it cannot reflect the damages to multi-organ systems occurring in chronic arsenicosis as a result of excessive arsenic intake. Such damages resulted in series of physiological and pathological changes to liver (Bashir et al., 2006; Liu et al., 2000), kidney (Majhi et al., 2011; Huang et al., 2009) and brain (Namgung and Xia, 2001; Wang et al., 2012). Therefore, it is of great significance to study the impact of chronic arsenic exposure on an organism, which could be found through metabolites changes of organism by using metabonomics technique. Metabonomics is a sensitive and unbiased analytical method that assesses all metabolites in biological samples (Dettmer et al., 2007). The technique can generate substantial amounts of metabolic data that can give surprisingly detailed insights into the changes in metabolic processes in whole organisms (Nicholson et al., 1999; Nicholson and Wilson, 2003). Metabonomics can determine the relationships between phenotype and metabolism, and can be used to identify the key metabolites associated with a particular phenotype and investigate the biological function and metabolic changes in the organism. This approach has been used to identify serum principal metabolites and investigate the mechanism of exogenous material on organism (Nicholls et al., 2001; Nicholson and Wilson,1989; Griffin et al., 2001). Recently, ultra-performance liquid chromatography/mass spectrometry (UPLC/MS) has been applied widely in metabolomics studies owing to its high sensitivity and reproducibility (Lenz and Wilson, 2007; Yang et al., 2004). In this field, ultra-performance liquid chromatography and Q-TOF mass spectrometry (UPLC/Q-TOF MS) adds a new dimension to metabolism studies, enabling attainment of better detection limits, better throughput, and increased chromatographic resolution, which in turn will improve data quality from the mass spectrometer. We used metabolomics analyses based on UPLC-Q-TOF MS to gain a broader understanding of metabolic changes in rats exposed to arsenic, and to find the biomarkers that could reflect these changes. These metabolic changes and biomarkers might be important for future diagnosis and serve as targets for therapeutic intervention of arsenicosis. 2. Materials and methods
475
and the total arsenic intake of each animal was calculated. The animals were sacrificed under chloral hydrate anesthesia after 6 months. Blood samples were collected and stored at 80 C for the detection of serum arsenic and metabolomics analysis. Tissues of brain, liver and kidney samples were rapidly frozen in liquid nitrogen and stored at 80 C until use. Specimens of liver and kidney were fixed in 10% neutral formaldehyde for histopathology analysis. All experiments were approved by the Animal Ethics Committee of Harbin Medical University and were carried out in accordance with the Guide for the Care and Use of Laboratory Animals (National Research Council, 1996, USA). 2.2. Light microscope analysis of liver and kidney histology The liver and kidney were fixed in 10% neutral formaldehyde, paraffin-embedded, sectioned, and stained with hematoxylineosin. A microimaging system (Olympus, Tokyo, Japan) was used to observe the pathological changes at 400 magnification. 2.3. Chemicals and reagents The products used in this study were analytical pure sodium arsenic (Secondary Factory of Shanghai Chemical Reagent, Shanghai, China), high-performance liquid chromatography (HPLC)-grade acetonitrile and methanol (Honeywell Burdick & Jackson, Muskegon, MI, USA), HPLC-grade formic acid (Beijing Reagent Company, Beijing, China), and leucine enkephalin (Sigma– Aldrich, St Louis, MO, USA). Deionized water was purified by the Milli-Q system (Millipore, Billerica, MA). Leucine enkephalin was purchased from Sigma–Aldrich (St. Louis, MO, USA). Standards were purchased from Sigma (Sigma–Aldrich, St Louis, MO, USA, 99% purity). 2.4. Arsenic level assay The levels of total arsenic in serum, brain, liver and kidney were determined using hydride generation atomic fluorescence spectrometry according to a national standardized method in China (GB/T 5009.11-2003). Briefly, the proper amount of samples was digested by heating them with concentrated HNO3 and HCLO4. After digestion, 100 g/L sulfocarbamide and ascorbic acid was added to the samples. The standard samples were obtained with standard reference materials (GBW08611). The arsenic levels in diluted samples and standard samples were determined by an atomic fluorescence spectrometer (AFS-930, Beijing Titan Instruments, China).
2.1. Animals and treatment 2.5. UPLC/Q-TOF MS analysis Thirty healthy male Wistar rats aged 4–6 weeks and weighing 100–120 g were purchased from Vital River Laboratory Animal Technology Co. Ltd. All rats were allowed to acclimatize in communal plastic cages for 7 days before treatment. Temperature and humidity were regulated at 20 2 C and 50 15%, respectively. A cycle of 12 h light/12 h dark was established. The animals were randomly divided into three groups including control, low arsenic exposure group (LAE), and high arsenic exposure group (HAE). Arsenic was provided to animals via drinking water. Based on median lethal dose (LD50) of sodium arsenitethrough oral administering on rats (41 mg/kg), about 1/5 of the LD50 was chosen as the high arsenic exposure dose (approximately 50 mg/L sodium arsenite in drinking water), the correspondent low arsenic dose is 1/5 of the high arsenic dose (10 mg/L sodium arsenite in drinking water), and the control group had no sodium arsenite (distilled water) in the drinking water. Conventional diet (AIN93G) and drinking water were available at all times throughout the study. Water consumption of each animal was recorded per day
UPLC/Q-TOF MS analysis was performed using an ACQUITY UPLC system (Waters Corporation, Milford, MA, USA) coupled to a Micromass Q-tof (Quatropde-Time of Flight) Mass Spectrometer (Waters Corp., Manchester, UK) with electrospray ionization (ESI) in positive and negative modes. After deproteinization with methanol (1:5), the supernatant were dried with nitrogen at 37 C, resolved with acetonitrile and water (3:1), filtrated with 0.22 mm filter membrance, and transferred into an autosampler vial. A 2 mL aliquot of supernatant was injected into an ACQUITY UPLC BEH-C18 column (50 mm 4.6 mm i.d., 1.7 mm; Waters Corporation, Milford, MA, USA). The flow rate of the mobile phase was 400 mL/min. Analytes were eluted from the column with a gradient, where A was 0.1% formic acid in water and B was 0.1% formic acid in acetonitrile. The initial composition of B was 2% and increased to 20% from 0 to 2 min, 20–70% from 2 to 5 min, and 70–98% from 5 to 11 min, followed by re-equilibration to the initial conditions in 7 min. Each run time was 18 min. For MS analysis, the
476
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
Table 1 Arsenic levels in serum, liver and kidney, and the correlations among them. Control Total arsenic intake (mg) Arsenic levels in serum (mg/L) Arsenic levels in brain (mg/g) Arsenic levels in liver (mg/g) Arsenic levels in kidney (mg/g) a b
0 5.6 1.4 0.1 0.1 0.4 0.1 0.5 0.1
LAE
HAE a
88.4 6.3 28.9 11.4a 1.9 0.3a 7.5 2.4a 15.3 3.2a
Correlation with total arsenic intake (r) a,b
367.8 17.6 42.5 13.1a 2.7 0.9a,b 10.9 2.3a,b 46.1 16.4a,b
0.773a 0.821a 0.824a 0.886a
Significant difference compared to that of the control group or significant correlations between total arsenic intake and arsenic levels in serum, liver and kidney, p < 0.01. Significant difference compared to that of the LAE group, p < 0.05.
source temperature was set at 100 C with a cone gas flow of 50 L/h. A desolvation gas temperature of 300 C and a desolvation gas flow of 600 L/h were used. The capillary voltage was set at 3000 V in positive ESI mode and 2600 V in negative ESI mode, and the cone voltage to 35 V. All analyses were performed using the lock spray to ensure accuracy and reproducibility. A lock-mass of leucine enkephalin for positive ESI mode (m/z = 556.2771) and negative ESI mode (m/z = 554.2615) was used via a LockSprayTM interface. The MS data were collected in centroid mode from m/z 50 to 1000, and the lock spray frequency set at 0.40 s and averaged over 10 scans for correction. Order effects in the statistical analysis were avoided due to the randomized crossover design used. In addition, the repeatability of the present method was evaluated using a representative pooled quality control (QC) sample. The QC sample was prepared by mixing equal volumes of the serum samples from three control rats, three LAE rat and three HAE rats. One QC sample was injected at the start of the analytical batch, followed by analysis of one QC sample at every sixth sample injection throughout the analytical workflow. The overlapped performance of each spectral peak was evaluated, and six single ions with different m/z and retention time were extracted. The reproducibility of the QC sample was examined by analyzing the differences in the retention time and peak intensity of the six ions. 2.6. Data analysis Statistical analysis was performed using SPSS (version 13.01S; Beijing Stats Data Mining Co. Ltd. Beijing, China). Data were presented as mean SD. Differences between groups were analyzed using one-way ANOVA, followed by two-group
comparisons with LSD or Dunnett T3. All P values were 2-tailed and a P value < 0.05 was considered significant for all statistical analyses in this study. The UPLC/Q-TOF MS data were analyzed using the MarkerLynx Application Manager 4.1 SCN 714 (Waters Corporation, Milford, MA, USA). Mass window was set at 0.02 Da, noise elimination level at 10.00, RT tolerance at 0.01 min and RT window at 0.2 min. The resulting 3D matrix contained arbitrarily assigned peak indexes (retention time-m/z pairs), sample names (observations), and normalized ion intensities for each peak area was exported to EZINFO 2.0 (an component of MarkerLynx) for visualizing the score plot and for obtaining the greatest variable importance in projection (VIP) values using partial least squares discriminant analysis (PLS-DA). The metabolites with VIP values > 1.0 in the model were regarded as potential biomarkers (Eriksson et al., 2001). The goodness of the fit was quantified by R2Y, while the predictive ability was indicated by Q2. A crossvalidation procedure and testing with 100 random permutations were performed to avoid the over-fitting of supervised PLS-DA models, using SIMCA-P software (version 11.5; Umetrics AB, Umeå, Sweden). The probable empirical formulas of the potential biomarkers were first identified based on accurate mass measurement (mass error of <20 ppm) and by considering the relative intensities of the isotope peaks through the high-resolution MS spectra. Then, the MassFragmentTM application manager (MassLynx v4.1, Waters Corp., USA) was used to facilitate the MS/MS fragment ion-analysis process by way of chemically intelligent peak-matching algorithms. Briefly, the UPLC/MS/MS product ion spectrum of metabolites was matched with the structure message of metabolites (obtained from the Human Metabolome Databases
Fig. 1. Light microscopy features of the liver and kidney in rats after arsenic exposure for 6 months. The arrows indicate the changes of liver or kidney. Histopathology of kidney (A–D): (A) control rats; (B) LAE rats; (C and D) high arsenic exposed rats. Histopathology of liver (E–H): (E) control rats; (F) LAE rats (lipid droplets – normal arrows, cellular swelling – long and thin arrow); (G and H) high arsenic exposed rats.
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
477
Fig. 2. Serum metabolic profiling of control and arsenic exposed rat in positive (A) and negative (B) ESI mode.
(HMDB)) according to some parameters, such as deviation from calculated mass (ppm), double bond equivalent (DBE), and i-fit value (the isotopic pattern of the selected ion). Finally, the biomarkers were further confirmed by standard compounds based on both retention times and MS/MS spectra. Sequentially, the implicated pathways of biomarkers were interpreted using databases, including HMDB (http://www.hmdb.ca) and KEGG (http://www.genome.jp/kegg/). 3. Results 3.1. Arsenic-exposed animal model established The total arsenic intakes (per animal in entire study) in the low arsenic exposure group and high arsenic exposure group were 88.4 6.3 mg and 367.8 17.6 mg, respectively. The levels of arsenic in serum, brain, liver and kidney were significantly higher in arsenic-exposed groups compared with the control group, and the levels of arsenic in brain, liver and kidney were significantly higher in HAE groups compared with the LAE group. In addition, the correlations between the total water arsenic intakes and
arsenic levels in serum, brain, liver and kidney were analyzed. There were significant correlations between total arsenic intakes and arsenic levels in serum, brain, liver and kidney (Table 1). 3.2. Histopathology Light microscope analysis was used to examine the histopathological changes of liver and kidney in rats after arsenic exposure. The results showed that, in the kidney of a rat at 6 months, the structures of glomerular and renal tubule were normal in control group (Fig. 1A); in renal tubule, the epithelial cells showed a mild cellular swelling in low-arsenic exposed group compared with the control group (Fig. 1B), and severe cellular swelling was found in high-arsenic exposed group (Fig. 1C). In addition, calcareous infarct could be found at area of medullary collecting duct (Fig. 1D). In liver of rats, the structure of hepatocytes was normal in control group (Fig. 1E). There were a small amount of deposits of round lipid droplets at hepatocytes of liver and the cytoplasm of hepatocytes showed cellular swelling in the low-arsenic exposed group (Fig. 1F). Also there were a number of deposits of round lipid droplets in hepatocytes around central venous, and cytoplasm
Fig. 3. Score plot with PLS-DA of serum metabolite in control (diamond, green), Low dose arsenic (diamond, yellow) and High dose arsenic (diamond, blue) exposed rat in positive (A) and negative (B) ESI mode. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
478
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
showed more severe cellular swelling in high-arsenic exposed group (Fig. 1G). Some neutrophils were found at the portal area of the liver (Fig. 1H). 3.3. Quality control of the sample in UPLC/Q-TOF MS analysis Quality control of sample analysis was conducted by evaluating the repeatability of the present method using a mixture of 9 serum samples injected interval of 6 samples (n = 6). The overlapped performance of the spectral peak was evaluated (see Additional file 1). The relative standard deviation (RSD) of retention time ranged from 0.00% to 0.14% for the positive ESI mode, and from 0.00% to 0.09% for the negative ESI mode. The RSD of peak intensity ranged from 2.63% to 8.08% for the positive ESI mode, and from 1.94% to 7.63% for the negative ESI mode (see Additional file 1). The results indicated that method had excellent repeatability.
the projection. Analysis of these data using PLS-DA revealed that dosing with sodium arsenite was associated with significant increases or decreases in several compounds in positive and negative ESI modes. The principal ions that were seen to increase in positive ESI mode was m/z = 417.2455, and in negative ESI were those for 448.3034 and 504.3018, which eluted at 3.56, 3.01 and 4.41 min and were identified as CPA (18:2(9Z,12Z)/0:0), deoxygcholylglycine and LysoPE (0:0/20:2 (11Z,14Z)) respectively. The principal ions that were seen to decrease in positive ESI mode were those for m/z = 468.3070, 516.3081, 508.3767, 400.3445 and 548.3729 and in negative ESI was 321.2377, which eluted at 5.27, 5.46, 5.86, 6.08, 6.61, and 5.24 min and were identified as LysoPC (14:0), LysoPC (18:4 (6Z,9Z,12Z,15Z)), LysoPC (P-18:0), L-palmitoylcarnitine, LysoPC (20:2(11Z,14Z)) and 15(S)-hydroxyeicosatrienoic acid respectively (Table 2 and Fig. 4). 4. Discussion
3.4. UPLC/Q-TOF MS fingerprinting and multivariate analysis All the serum samples collected in this study were analyzed by UPLC/QTOF-MS with positive and negative ESI (Fig. 2). Pattern recognition via PLS-DA was performed on positive and negative ESI data. PLS-DA is a multivariate classification method based on PLS. PLS-DA explains maximum separation between defined class samples in the data set. As shown by the PLS-DA scores plot (for the first two components, R2Y = 0.884 and Q2 = 0.526 in positive ESI mode, and for the first three components, R2Y = 0.892 and Q2 = 0.439 in negative ESI mode, Fig. 3), the control and arsenic exposed rat could be separated into distinct clusters, for distinct metabolic alterations between the three groups. To assess the risk that the current PLS-DA model was spurious, the permutation test for PLS-DA was applied. All R2Y and Q2 values to the left were lower than the original points to the right (see Additional file 2), showing that the PLS-DA model was valid. The results suggested that the arsenic exposure had led to the serum metabolite alterations in our rat model. 3.5. Potential biomarkers Components with important roles in the separation were picked out according to the parameter variable importance in
In this study, an animal model of arsenic exposure was successfully established by administering sodium arsenite through drinking water. The results showed that arsenic was significantly increased in serum, brain, liver, and kidney in rats after arsenic exposure for 6 months, and there were significant correlations between total arsenic intake and arsenic levels in serum, brain, liver, and kidney. Excessive arsenic intake can cause the damage to multiorgan systems. Skin lesions are common and often appeared in the late stage of chronic arsenicosis. Therefore, it is of great significance to study the early impact of chronic arsenic exposure on other organs, such as liver, kidney. Arsenic is mainly accumulated in the liver (Bashir et al., 2006; Liu et al., 2000), and kidney (Majhi et al., 2011; Huang et al., 2009) is the main excretion organ for arsenic and its metabolites, which would be inevitably damaged during chronic arsenic exposure. The results in the present study suggested that there were significant pathological changes and lesions in a dose– response relationship at 6 months in liver and kidney of rats. Therefore, the metabolic changes of early damage due to arsenic toxicity must be elucidated in order to find effective targets for the prevention and intervention. Now most of the researches focused on the mechanism of chronic arsenicosis and several hypotheses on this mechanism have been proposed, including the theory of arsenic metabolism, lipid
Table 2 Potential biomarkers in UPLC/Q-TOF MS positive and negative ion modes. Retention time (min)
Measured mass (Da)
Calculated mass (Da)
Mass error Elemental (PPM) composition
Postulated identity
VIP values
Positive ion mode 3.56 417.2455 5.27 468.3070 5.46 516.3081 5.86 508.3767 6.08 400.3445 6.61 548.3729
417.2515 468.3090 516.309 508.3767 400.3427 548.3716
14 4 2 0 4 2
C21H37O6P C22H46NO7P C26H46NO7P C26H54NO6P C23H45NO4 C28H54NO7P
CPA(18:2(9Z,12Z)/0:0)b LysoPC(14:0)a LysoPC(18:4(6Z,9Z,12Z,15Z))a LysoPC(P-18:0)a b L-Palmitoylcarnitine LysoPC(20:2(11 Z,14Z))a
Negative ion mode 3.01 448.3034 4.41 504.3018 5.24 321.2377
448.3063 504.309 321.243
17 4 10
C26H43NO5 C25H48NO7P C20H34O3
Deoxycholylglycineb 1.98 Lysophosphatidylethanolamineb 3.64 15(S)-Hydroxyeicosatrienoic 5.67 acidb
Values are mean SD. a Biomarkers identified by exact mass data and MS fragmentation and confirmed using standard samples. b Biomarkers identified by exact mass data and MS fragmentation. * P < 0.05 compared with control group. ** P < 0.01 compared with control group. *** P < 0.05 compared with low dose group.
6.28 7.63 3.75 4.36 3.94 6.75
Normalized peak intensity (relative contents) Control group
Low dose group
0.15 0.17 62.64 7.69 23.13 2.08 17.67 1.69 40.71 8.36 56.17 9.88
4.94 6.37 48.38 8.21 18.48 3.63 12.97 2.29 34.35 6.39 48.22 11.10
High dose group
12.96 8.86**,*** 44.23 12.64** 17.99 5.92* 12.18 4.05** 32.50 4.40* 38.32 12.69**
2.45 2.29 3.12 2.13 5.31 3.36* 86.10 8.20 85.59 11.07 101.153 19.09*,*** 41.86 16.28 30.08 6.88 25.66 14.07*
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
479
Fig. 4. Mass fragment information of principal metabolites with different collision energy (ev). (A) CPA (18:2(9Z,12Z)/0:0) (23 ev); (B) LysoPC (14:0) (23 ev); (C) LysoPC (18:4 (6Z,9Z,12Z,15Z)) (23 ev); (D) LysoPC (P-18:0) (30 ev); (E) L-palmitoylcarnitine (15 ev); (F) LysoPC (20:2(11Z,14Z)) (23 ev); (G) deoxycholylglycine (25 ev); (H) lysophosphatidylethanolamine (23 ev); and (I) 15(S)-hydroxyeicosatrienoic acid (23 ev).
peroxidation, and cancer. However, the exact mechanism of chronic arsenicosis is not clear yet, and the key metabolic changes and potential biomarkers after arsenic exposure remain to be determined. Metabolomics technology can achieve high-throughput and rapid detection of metabolites and analysis of large datasets, and could be used to identify, among tens of thousands of metabolites, those with greatest phenotypic contribution. Through investigating the metabolic pathways of these principal metabolites, the important metabolic changes and relations between these changes with arsenic exposure could be found. In the present study, 9 potential biomarkers including CPA (18:2 (9Z,12Z)/0:0), LysoPC (14:0), LysoPC (18:4 (6Z,9Z,12Z,15Z)), LysoPC (P-18:0), L-palmitoylcarnitine, LysoPC (20:2(11Z,14Z)) in positive ESI mode and deoxygcholylglycine, LysoPE (0:0/20:2(11Z,14Z)), 15 (S)-hydroxyeicosatrienoic acid in negative ESI were identified. Biological functions and metabolic pathways of these metabolites
were investigated by using databases such as HMDB and KEGG, which are available electronically, and could be used for querying metabolic pathways and some metabonomic information. CPA (18:2(9Z,12Z)/0:0) is a cyclic phosphatidic acid. It is a glycerophospholipid in which a cyclic phosphate moiety occupies two glycerol substitution sites. CPA analogs with various fatty acyl chains were detected in human serum and some other organisms (Kobayashi et al.,1999). CPA's have a cyclic phosphate at the sn-2 and sn-3 positions of the glycerol carbons, and this structure is absolutely necessary for their activities. Cyclic phosphatidic acid is known to be a specific inhibitor of DNA polymerase alpha, which can interfere with DNA synthesis and repair (Murakami-Murofushi et al., 1995), induces actin stress fiber formation (Fischer et al., 1998). Increased CPA in high dose arsenic exposed rats suggested the metabolic changes, which might cause cancer or damage organs (such as kidney or liver) induced by arsenic exposure.
480
C. Wang et al. / Toxicology Letters 229 (2014) 474–481
LysoPC (14:0), LysoPC (18:4(6Z,9Z,12Z,15Z)), LysoPC (P-18:0) and LysoPC (20:2(11Z,14Z)) are the lysophosphatidylcholines. They are a monoglycerophospholipid in which a phosphorylcholine moiety occupies a glycerol substitution site. These lysophosphatidylcholines have different combinations of fatty acids of varying lengths and saturation attached at the C-1 position. The different fatty acids are myristic acid, stearidonic acid, plasmalogen 18:0 and eicosadienoic acid, respectively, which mostly derived from liver. In blood plasma lysophosphatidylcholine are formed by a specific enzyme system, lecithin–cholesterol acyltransferase (LCAT), which is also secreted from the liver. The enzyme catalyzes the transfer of the fatty acids at position sn-2 of phosphatidylcholine to the free cholesterol in plasma, with formation of cholesterol esters and lysophosphatidylcholine. Lysophosphatidylcholines have important roles in lipid signaling by acting on lysophospholipid receptors (LPL-R). LPL-R group are members of the G protein-coupled receptor family of integral membrane proteins that are important for lipid signaling (Chun et al., 2002). The activated receptor can have a range of effects on the cell. These include primary effects of inhibition of adenylyl cyclase and release of calcium from the endoplasmic reticulum, as well as secondary effects of preventing apoptosis and increasing cell proliferation (Meyer zu Heringdorf and Jakobs, 2007). These biomarkers were all significantly decreased after arsenic exposure, which suggested the metabolic changes of liver, which might cause the whole metabolic changes in individuals. L-Palmitoylcarnitine is a long-chain acyl fatty acid derivative ester of carnitine which facilitates the transfer of longchain fatty acids from cytoplasm into mitochondria during the oxidation of fatty acids. L-Palmitoylcarnitine is a surface-active molecule, which can change activity of several enzymes and transporters localized in the membrane by changing the membrane fluidity and surface charge (Watanabe et al., 1989; Haruna et al., 2000). L-Palmitoylcarnitine was also reported to diminish the complete binding of phorbol esters, the protein kinase C activators and to decrease the autophosphorylation of the enzyme (Nałecz et al., 2004), and therefore influence the cell growth, differentiation, metabolism, and regulation of gene expression, transcriptional activation. L-Palmitoylcarnitine is significantly lower in arsenic exposed rats than that in normal rats. It suggested that arsenic exposure might induce the metabolic disruption of fatty acids, which might be one of the reasons for damages to heart, brain and neural (Watanabe et al., 1989; Haruna et al., 2000; Nałecz et al., 2004; Szczepankowska and Nałecz (2003); Goñi et al., 1996) and kidney (Rosca et al., 2012) in pathogenesis of arsenicosis. Deoxycholylglycine is a bile salt formed in the liver by conjugation of deoxycholate with glycine. It can act as a detergent to solubilize fats for absorption and is itself absorbed. A study (Demers and Hepner, 1976) indicated that some patients with hepatobiliary disease had the higher deoxycholylglycine in serum, which could suggest the hepatic dysfunction. Khurana' study (Khurana et al., 2012) about effects of deoxycholylglycine on myogenic tone and agonist-induced contraction in rat resistance arteries showed that elevated serum deoxycholylglycine could alter vasomotor responses, and was likely pertinent to vascular dysfunction in cirrhosis. Lysophosphatidylethanolamine (LPE) is a hydrolysis product of PE by phospholipase A2, which plays a role in cellmediated cell signaling and activation of other enzymes. LPE can stimulate calcium signaling via phospholipase C activation, and stimulate a membrane bound receptor, different from well known LPA receptors, stimulate chemotactic migration and cellular invasion in SK-OV3 ovarian cancer cells (Park et al., 2007). 15(S)Hydroxyeicosatrienoic acid (15S-HETrE) is a polyunsaturated fatty acid that has been reported to modulate arachidonic acid (AA) metabolism and tumorigenesis. 15S-HETrE can suppress cyclooxygenase-2 (COX-2) over expression and/or prostaglandin E2 (PGE2) biosynthesis (Pham et al., 2004), and inhibit [(3)H] thymidine
uptake in parallel with the upregulation of peroxisome proliferator-activated receptor-gamma expression (a growth modulating nuclear receptor, PPAR gamma) (Pham et al., 2003), and, thus, alleviate tumor growth and progression. Deoxycholylglycine and LPE were all significantly higher, and 15(S)-hydroxyeicosatrienoic acid was significantly lower after arsenic exposure, which might partly explain the cause of cancer or metabolic disruptions of liver induced by arsenic exposure. To the best of our knowledge, this is the first report to evaluate the metabolic changes in rats after arsenic exposure using UPLC/QTOF MS. In the present study, some other principal metabolites were detected, but remain unidentified at present. The unidentified metabolites and their metabolic pathways might include the other metabolic changes after arsenic exposure. This is also a possible limitation of this study, unlike GC–MS or NMR, for which large databases existing, the databases of endogenous biomolecules based LC–MS techniques for metabolomics research have not yet been constructed. This may limit the identification and analysis of metabolic pathway on the detected principal metabolites. In addition, there are significant differences in arsenic metabolism between rats and humans, which maybe another limitation of this study. 5. Conclusions In summary, metabolite profiling using UPLC-TOF MS integrated with modern multivariate statistical techniques was successfully applied to evaluate the metabolic changes of rats exposed to the arsenic, and we found some biomarkers which could reflect these changes. These potential biomarkers of chronic arsenic exposure, however, at high doses in the rats and as such results must be validated in human studies before they can serve for diagnosis or therapeutic intervention of arsenicosis. Conflict of interest None of the authors has any conflicting interests. Transparency document The Transparency document associated with this article can be found in the online version. Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant No. 81273013), Specialized Research Fund for the Doctoral Program of Higher Education (No. 20102307120025), and China Postdoctoral Science Foundation funded project (No. 20100471017). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.toxlet.2014.06.001. References Bashir, S., Sharma, Y., Irshad, M., Nag, T.C., Tiwari, M., Kabra, M., Dogra, T.D., 2006. Arsenic induced apoptosis in rat liver following repeated 60 days exposure. Toxicology 217 (1), 63–70. Chun, J., Goetzl, E.J., Hla, T., Igarashi, Y., Lynch, K.R., Moolenaar, W., Pyne, S., Tigyi, G., 2002. International Union of Pharmacology. XXXIV. Lysophospholipid receptor nomenclature. Pharmacol. Rev. 54 (2), 265–269. Demers, L.M., Hepner, G.W., 1976. Levels of immunoreactive glycine-conjugated bile acids in health and hepatobiliary disease. Am. J. Clin. Pathol. 66 (5), 831–839. Dettmer, K., Aronov, P.A., Hammock, B.D., 2007. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26 (1), 51–78.
C. Wang et al. / Toxicology Letters 229 (2014) 474–481 Eriksson, L., Johansson, E., Kettaneh-Wold, N., Wold, S., 2001. Multi- and Megavariate Data Analysis: Principles and Applications. Umetrics, Umeå. Fischer, D.J., Liliom, K., Guo, Z., Nusser, N., Virág, T., Murakami-Murofushi, K., Kobayashi, S., Erickson, J.R., Sun, G., Miller, D.D., Tigyi, G., 1998. Naturally occurring analogs of lysophosphatidic acid elicit different cellular responses through selective activation of multiple receptor subtypes. Mol. Pharmacol. 54 (6), 979–988. Goñi, F.M., Requero, M.A., Alonso, A., 1996. Palmitoylcarnitine, a surface-active metabolite. FEBS Lett. 390 (1), 1–5. Griffin, J.L., Walker, L.A., Shore, R.F., Shore, J.K., Nicholson, J.K., 2001. Metabolic profiling of chronic cadmium exposure in the rat. Chem. Res. Toxicol. 14 (10), 1428–1434. Haruna, T., Horie, M., Takano, M., Kono, Y., Yoshida, H., Otani, H., Kubota, T., Ninomiya, T., Akao, M., Sasayama, S., 2000. Alteration of the membrane lipid environment by L-palmitoylcarnitine modulates K(ATP) channels in guinea-pig ventricular myocytes. Pflugers Arch. 441 (2–3), 200–207. Huang, M., Choi, S.J., Kim, D.W., Kim, N.Y., Park, C.H., Yu, S.D., Kim, D.S., Park, K.S., Song, J.S., Kim, H., Choi, B.S., Yu, I.J., Park, J.D., 2009. Risk assessment of low level cadmium and arsenic on the kidney. J. Toxicol. Environ. Health A 72 (21–22), 1493–1498. Khurana, S., Raina, H., Pappas, V., Raufman, J.P., Pallone, T.L., 2012. Effects of deoxycholylglycine, a conjugated secondary bile acid, on myogenic tone and agonist-induced contraction in rat resistance arteries. PLoS One 7 (2), e32006. Kobayashi, T., Tanaka-Ishii, R., Taguchi, R., Ikezawa, H., Murakami-Murofushi, K., 1999. Existence of a bioactive lipid, cyclic phosphatidic acid, bound to human serum albumin. Life Sci. 65 (21), 2185–2191. Lenz, E.M., Wilson, I.D., 2007. Analytical strategies in metabonomics. J. Proteome. Rev. 6 (2), 443–458. Liu, J., Liu, Y., Goyer, R.A., Achanzar, W., Waalkes, M.P., 2000. Metallothionein-I/II null mice are more sensitive than wild-type mice to the hepatotoxic and nephrotoxic effects of chronic oral or injected inorganic arsenicals. Toxicol. Sci. 55 (2), 460–467. Majhi, C.R., Khan, S., Leo, M.D., Manimaran, A., Sankar, P., Sarkar, S.N., 2011. Effects of acetaminophen on reactive oxygen species and nitric oxide redox signaling in kidney of arsenic-exposed rats. Food Chem. Toxicol. 49 (4), 974–982. Meyer zu Heringdorf, D., Jakobs, K.H., 2007. Lysophospholipid receptors: signalling, pharmacology and regulation by lysophospholipid metabolism. Biochim. Biophys. Acta 1768 (4), 923–940. Murakami-Murofushi, K., Kobayashi, S., Onimura, K., Matsumoto, M., Shioda, M., Yoshida, S., Shoji, M., Murofushi, H.,1995. Selective inhibition of DNA polymerasealpha family with chemically synthesized derivatives of PHYLPA, a unique Physarum lysophosphatidic acid. Biochim. Biophys. Acta 1258 (1), 57–60. Nałecz, K.A., Miecz, D., Berezowski, V., Cecchelli, R., 2004. Carnitine: transport and physiological functions in the brain. Mol. Aspects Med. 25 (5–6), 551–567. Namgung, U., Xia, Z., 2001. Arsenic induces apoptosis in rat cerebellar neurons via activation of JNK3 and p38 MAP kinases. Toxicol. Appl. Pharmacol.174 (2),130–138. Nicholls, A.W., Holmes, E., Lindon, J.C., Shockcor, J.P., Farrant, R.D., Haselden, J.N., Damment, S.J., Waterfield, C.J., Nicholson, J.K., 2001. Metabonomic investigations into hydrazine toxicity in the rat. Chem. Res. Toxicol. 14 (8), 975–987.
481
Nicholson, J.K., Lindon, J.C., Holmes, E., 1999. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29 (11), 1181–1189. Nicholson, J.K., Wilson, I.D., 1989. High resolution proton magnetic resonance spectroscopy of biological fluids. Prog. Nucl. Magn. Reson. Spectrosc. 21 (4), 449–501. Nicholson, J.K., Wilson, I.D., 2003. Opinion: understanding ‘global’ systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2 (8), 668–676. Park, K.S., Lee, H.Y., Lee, S.Y., Kim, M.K., Kim, S.D., Kim, J.M., Yun, J., Im, D.S., Bae, Y.S., 2007. Lysophosphatidylethanolamine stimulates chemotactic migration and cellular invasion in SK-OV3 human ovarian cancer cells: Involvement of pertussis toxin-sensitive G-protein coupled receptor. FEBS Lett. 581 (23), 4411– 4416. Pham, H., Banerjee, T., Nalbandian, G.M., Ziboh, V.A., 2003. Activation of peroxisome proliferator-activated receptor (PPAR)-gamma by 15S-hydroxyeicosatrienoic acid parallels growth suppression of androgen-dependent prostatic adenocarcinoma cells. Cancer Lett. 189 (1), 17–25. Pham, H., Banerjee, T., Ziboh, V.A., 2004. Suppression of cyclooxygenase-2 overexpression by 15S-hydroxyeicosatrienoic acid in androgen-dependent prostatic adenocarcinoma cells. Int. J. Cancer 111 (2), 192–197. Rosca, M.G., Vazquez, E.J., Chen, Q., Kerner, J., Kern, T.S., Hoppel, C.L., 2012. Oxidation of fatty acids is the source of increased mitochondrial reactive oxygen species production in kidney cortical tubules in early diabetes. Diabetes 61 (8), 2074– 2083. Szczepankowska, D., Nałecz, K.A., 2003. Palmitoylcarnitine modulates palmitoylation of proteins: implication for differentiation of neural cells. Neurochem. Res. 28 (3–4), 645–651. Thomas, D.J., Styblo, M., Lin, S., 2001. The cellular metabolism and systemic toxicity of arsenic. Toxicol. Appl. Pharmacol. 176 (2), 127–144. USA Today, 2007. Arsenic in drinking water seen as threat. http://usatoday30. usatoday.com/news/world/2007-08-30-553404631_x.htm (accessed 04.03.13). Wang, J., Zhang, W., Sun, D.J., Song, L., Li, Y.Y., Xu, C.B., 2012. Analysis of neuroglobin mRNA expression in rat Brain due to arsenite-induced oxidative stress. Environ. Toxicol. 27 (9), 503–509. Watanabe, H., Kobayashi, A., Hayashi, H., Yamazaki, N., 1989. Effects of long-chain acyl carnitine on membrane fluidity of human erythrocytes. Biochim. Biophys. Acta 980 (3), 315–318. Watanabe, T., Hirano, S., 2013. Metabolism of arsenic and its toxicological relevance. Arch. Toxicol. 87 (6), 969–979. WHO, 1981. Arsenic. Environmental Health Criteria 18. Geneva: lnternational Programme on Chemical Safety. http://www.inchem.org/documents/ehc/ehc/ ehc018.htm (accessed 04.03.13). Yang, J., Xu, G., Hong, Q., Liebich, H.M., Lutz, K., Schmülling, R.M., Wahl, H.G., 2004. Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 813 (1), 53–58.