ARTICLE pubs.acs.org/est
Evaluating the Transcriptomic and Metabolic Profile of Mice Exposed to Source Drinking Water Yan Zhang, Xuxiang Zhang, Bing Wu, and Shupei Cheng* State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210046, China
bS Supporting Information ABSTRACT: Transcriptomic and metabonomic methods were used to investigate mice’s responses to drinking source water (DSW) exposure. After mice were fed with DSW for 90 days, hepatic transcriptome was characterized by microarray and serum metabonome were determined by 1H nuclear magnetic resonance (NMR) spectroscopy. A total of 243 differentially expressed genes (DEGs) were identified, among which 141 genes were up-regulated and 102 genes were down-regulated. Metabonomics revealed significant changes in concentrations of creatine, pyruvate, glutamine, lysine, choline, acetate, lipids, taurine, and trimethylamine oxide. Four biological pathways were identified by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis where both gene expression and metabolite concentrations were altered in response to DSW exposure. These results highlight the significance of combined use of transcriptomic and metabonomic approaches in evaluating potential health risk induced by DSW contaminated with various hazardous materials.
’ INTRODUCTION The Yangtze River Basin is the most economically developed region in China, and the river is being used as drinking water source for most of people living in the basin. In recent years, Yangtze River has received numerous contaminants from both natural and anthropogenic sources. Many contaminants have been detected in this drinking source water (DSW), such as polycyclic aromatic hydrocarbons (PAHs),1 phthalates (PAEs),2 organochlorine pesticides (OCPs),3 and heavy metals (especially arsenic and cadmium).4,5 These pollutants are well-known to induce various toxicities even at trace concentrations,6,7 so the river water pollution may deserve more public health concern. As a promising toxicological method, omics analysis offers an unprecedented potential to unravel the complex effect pathways of chemical contaminants and to significantly enhance our understanding of their effects on the health of organisms.8 Transcriptomic profiling discovers the genetic interplay by simultaneously monitoring thousands of genes to provide an in-depth investigation of toxicities of environmental contaminants.9,10 Metabonomic profiling is a practical approach to capture the metabolic systems response to perturbations, by measuring variations in metabolites.11 Many studies have indicated that environmental contaminants can induce aberrant regulation of transcriptome.12 14 Correspondingly, metabonome of many organisms, such as fish,15 earthworm,16 and shellfish,17 have proved to be affected by environmental contaminants. However, little is known about the application of these omics methods to assess the health risks induced by natural water, although humans and wildlife are generally exposed to mixed toxicants in the real environment. At the same time, traditional toxicological assay r 2011 American Chemical Society
usually is not sensitive enough for health risk assessment of DSW due to the presence of trace-level pollutants in the water. This study aimed to evaluate the potential toxicity of DSW on male mice using transcriptomic and metabonomic profiling, and provide scientific information for early warning of environmental health risk of DSW.
’ EXPERIMENTAL SECTION DSW Sample. To investigate the temporal variations of pollutants in DSW of Yangtze River (Nanjing China), water samples were collected from the river in March, April, and May 2010. Water samples (10 L) were collected in brown glass bottles previously washed with detergent, followed by deionized water, 2 M nitric acid, then deionized water again, and finally raw water, and then placed in an ice bath. The samples were filtered through 0.45-μm micropore membrane filters and kept at 20 °C until analysis. The organic components in the DSW were measured by DSQ II Single Quadruple GC/MS (ThermoQuest, San Jose, CA, USA) according to EPA Method 525.2. Metal ions were detected by ICP-J-A1100 (Jarell-Ash Inc., USA), except for arsenic, which was measured by atomic fluorescence spectrophotometry (AFS, Special Issue: Ecogenomics: Environmental Received: April 22, 2011 Accepted: July 27, 2011 Revised: July 26, 2011 Published: July 27, 2011 78
dx.doi.org/10.1021/es201369x | Environ. Sci. Technol. 2012, 46, 78–83
Environmental Science & Technology
ARTICLE
AF-610A). The details about the chemical analysis were described in our previous study.2 Animal Treatment. The mouse (Mus musculus) was chosen as a model species because of its metabolic and physiological features similar to humans, good sensitivity to toxic effects of environmental pollutants, and the availability of genome and metabonome database. A total of 20 male mice (Mus musculus) (17 31 g) were purchased from Animal Center of Academy of MMS Laboratory. Prior to the study, all the mice were treated with distilled water for over 2 weeks. After the acclimation, the mice were randomly divided into two groups: the control group (n = 10, treated with distilled water) and the DSW group (n = 10, treated with DSW sampled in March 2010). The mice were maintained in a 12-h light/12-h dark cycle at 25 °C and 50% humidity, with free access to food and water. At the end of feeding trials (90 days), all mice were anesthetized with diethyl ether. Blood was collected by eyeball removal with a heparinized syringe and put into ice-cold tubes. Serum was separated by centrifugation at 3000 rpm for 15 min at 4 °C and stored at 80 °C. Livers were removed and placed in RNAlater (Takara Bio) at 4 °C. After 24 h at 4 °C, RNAlater was aspirated from liver tissue and livers were placed at 80 °C for subsequent processing. Hepatic Histopathology and Clinical Biochemistry. The liver tissue was removed, washed in normal saline, fixed in 10% formalin for at least 12 h, and sectioned at 5-μm thickness. Tissue slides were subsequently stained with hematoxylin and eosin (H&E), and observed under an optical microscope.18 Biochemical indices of serum were analyzed with Olympus 2700 analyzer (Olympus Co., Japan).19 PPARR and PPARγ expression in liver tissue were determined using ELISA methods.20 RNA Isolation. Nine mice in each group were selected and the livers of every three mice were homogenized together to obtain a total RNA sample (1 g liver for each mouse) using TRIzol (Invitrogen, USA) according to the manufacturer protocol. RNA samples were cleaned using Qiagen RNeasy MiniRNA Cleanup Kit (Qiagen, USA) according to the Affymetrix recommended protocol. RNA samples with integrity number over 7 (corresponding to 28S:18S rRNA ratio g1.8) were used for gene expression analysis. Transcriptomic Analysis. The Affymetrix Mouse Genome 430A 2.0 array was used for gene expression profiling as previously described.21 Three RNA samples in the treated or control group were hybridized separately onto three arrays to compare the genomic expression between the two groups. Data sets were captured using the GeneChip Scanner 3000 7G and GeneChip Operating Software (Affymetrix, California, USA). The data were analyzed by Partek Discovery Suite (Partek Inc., St. Louis, MO, USA) with a false discovery rate (FDR) cutoff of 0.1.22
The ratio of average expression values between the treated and control groups was used to represent the fold changes in gene expression. Differentially expressed genes (DEGs) were identified as the ones with a fold-change of more than (1.5 (t-test, p < 0.05).21 For the functional classification, DEGs obtained were mapped onto Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (http://www.genome.jp/kegg/ pathway) using Molecule Annotation System 3.0 (MAS 3.0) (http://bioinfo.capitalbio.com/mas3) to interpret the biological meaning of the altered expressions. All data are publicly available at NCBI’s Gene Expression Omnibus (http://www.ncbi.nlm. nih.gov/geo/query/acc.cgi?acc=GSE30940). Metabonomics Analysis. 1H nuclear magnetic resonance (NMR) spectroscopy was performed on the serum sample mixed with 75 μL of buffer solution (0.2 M Na2HPO4/0.2 M NaH2PO4) and 75 μL of D2O at a final volume of 500 μL. NMR spectra were measured at 600.13 MHz using a Bruker AV600 spectrometer (Bruker, Germany), with 32 free induction decays (FIDs) collected into 64 000 data points. Water resonances were suppressed by Carr Purcell Merbom Gill (CPMG) spin echo pulse sequence (RD-90°-(τ-180°-τ)-ACQ) with a total spin echo delay (2nτ) of 40 ms. Exponential line-broadenings of 0.3 Hz were applied before Fourier transformation, and spectra were phase and baseline corrected by using MestRec 4.9 software (www.mestrec. com). All the spectra were referenced to the CH3 resonance of creatine at δ3.04. Then, water resonances (5.0 4.5 ppm) were removed, and each spectrum was segmented into 0.005 ppm bins. The total area of each binned spectrum was normalized to unity to facilitate comparison among the samples. The metabolite resonances were identified according to previous studies.23 Statistical Analysis. Partial least-squares discriminant analysis (PLS-DA) was used to explore the main effects in the microarray and NMR data sets by using SIMCA-P software (Umetric, Umea, Sweden). Cross-validation of the model was conducted by the default leave-one-out procedure. R2X and R2Y represent the percentages of original X and Y data sets used to construct PLSDA model. Higher R2X and R2Y indicate that more original data are represented. Q2 reflects the predictive capacity of the model, and higher Q2 means better predictability of the model. OneWay ANOVA was used to analyze differences of biochemical parameters between groups.
’ RESULTS AND DISCUSSION Toxicities of DSW on Mice. Seventeen types of organic pollutants were detected in the DSW, including three PAEs and eleven PAHs (Table 1). Among these pollutants, PAEs
Table 1. Concentrations of Organic Pollutants in DSW (ng/L)a pollutant isophorone dimethyl phthalate
11 ( 2 79 ( 8
pollutant
concentrations
benzo(a)anthracene bis(2-ethylhexyl)phthalate
15 ( 2 1820 ( 281
fluorine
28 ( 9
benzo(b)fluoranthene
anthracene
96 ( 4
benzo(k)fluoranthene
phenanthrene
19 ( 1
benzo(a)pyrene
di-n-butyl phthalate pyrene bis(2-ethylhexyl)adipate chrysene a
concentrations
3391 ( 1265
benzo(g,h,i)perylene
14 ( 6
indeno(1,2,3-cd)pyrene
206 ( 84
dibenz(a,h)anthracene
161 ( 17 99 ( 12 195 ( 14 2(1 13 ( 12 5 (2
31 ( 4
Values are presented as means ( standard deviations (n = 3). 79
dx.doi.org/10.1021/es201369x |Environ. Sci. Technol. 2012, 46, 78–83
Environmental Science & Technology
ARTICLE
Figure 1. Partial least-squares discriminant analysis (PLS-DA) of liver transcriptional signature (each symbol is an individual microarray) (O DSW group, b control).
Figure 2. Partial least-squares discriminant analysis (PLS-DA) of sera 1 H NMR spectra (O DSW group, b control).
microarray data sets showed that the treatment and control groups were readily separated (R2X = 0.772, R2Y = 1.000 and Q2 = 0.996) (Figure 1), suggesting that DSW exposure obviously affected hepatic transcriptome. A total of 243 genes were determined to be DEGs, among which 141 genes were upregulated and 102 genes were down-regulated. Most of the expression alterations were in the range of 1.5 2.0 fold (75.3%) and 2.0 3.0 fold (19.0%) (Figure S4). In this study, we also investigated serum metabonome using 1 H NMR. Compared with control, ten metabolites were significantly altered in the DSW treatment group, including decreased creatine, pyruvate, glutamine, lysine, choline, citrate, lipids, taurine, and trimethylamine oxide (TMAO) and increased acetate (Table 2). PLS-DA indicated that the samples of the two groups were clearly separated (R2X = 0.592, R2Y = 0.793 and Q2 = 0.516) (Figure 2), suggesting that the metabolic profiles of mice serum were significantly changed due to DSW exposure. Many traditional biological approaches have been used to evaluate health risk of drinking water, such as Ames test29 and comet assay.30 However, these studies focused on the effects of extracts with much higher concentrations of pollutants than natural water. Furthermore, those assays usually identify a single toxic effect, such as mutagenicity or genotoxicity. Comparatively, omics techniques are more sensitive and they are able to provide comprehensive toxicological information of the chemical mixture. Recently, transcriptomic and metabonomic methods have been used to investigate health impacts of copper exposure on a fish model31 and to select sensitive markers characterizing environmentally relevant low-level dibenzanthracene exposure.22 Biochemical Pathways Affected by DSW Exposure. The DEGs were further classified by using the KEGG pathway analyses with the criteria that pathways must have four or more DEGs and the DEGs in that pathway are overrepresented based on a hypergeometric test with p e 0.05.22 Table S4 (Supporting Information) shows the changes in the biological pathways affected by DSW exposure. Transcriptional and metabonomic data sets indicated that four primary pathways were significantly affected, including xenobiotics and drug metabolism, bile acid biosynthesis, and PPAR signaling pathway (Table 3). DSW exposure induced significant changes in xenobiotics and drug metabolism (Figure 3A). Seven genes were abnormally expressed, such as up-regulation of Adh4 (1.5 fold), Cyp1a1 (1.6 fold), Cyp2b10 (2.9 fold), Cyp2c55 (5.7 fold), Cyp2c65 (3.9 fold), and Gstm4 (1.8 fold) and down-regulation of Fmo2 ( 2.2 fold). Cyp1a1, Cyp2b10, Cyp2c55, and Cyp2c65, and Gstm4 are
Table 2. Different Metabolites between Control and Treatment Groupsa metabolite
NMR resonance (ppm)
fold change
p value
creatine
3.04 (singlet)
0.82
0.040
pyruvate glutamine
2.38 (singlet) 2.46 (multiplet)
0.49 0.42
0.002 0.002
lysine
1.72 (multiplet)
0.70
0.002
choline
3.21 (singlet)
0.52
0.012
acetate
1.91 (singlet)
1.19
0.009
citrate
2.55 (doublet)
0.74
0.011
lipids
2.77 (multiplet)
0.63
0.002
taurine
3.40 (triplet)
0.82
0.015
TMAO
3.27 (singlet)
0.64
0.001
a
The NMR resonance that was integrated, the fold change in intensity between the DSW group and control, and the p value (ANOVA) are listed for each metabolite.
(5290 ng/L) and PAHs (679 ng/L) contributed 97% to the total concentrations. For inorganic chemicals in DSW (Table S1), arsenic was 13 μg/L higher than the MCLs of U.S. EPA (
