Article pubs.acs.org/jpr
Systems Responses of Rats to Mequindox Revealed by Metabolic and Transcriptomic Profiling Xiu-Ju Zhao,† Fuhua Hao,† Chongyang Huang,† Mattias Rantalainen,‡ Hehua Lei,† Huiru Tang,† and Yulan Wang†,* †
Wuhan Center of Magnetic Resonance, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, P. R. China ‡ Department of Statistics, University of Oxford, Oxford OX1 3TG, U.K. S Supporting Information *
ABSTRACT: Mequindox is used as an antibiotic drug in livestock; however, its toxicity remains largely unclear. Previously, we investigated metabolic responses of mice to mequindox exposure. In order to evaluate dependences of animal species in response to mequindox insult, we present the metabolic consequences of mequindox exposure in a rat model, by employing the combination of metabonomics and transcriptomics. Metabolic profiling of urine revealed that metabolic recovery is achieved for rats exposed to a low or moderate dose of mequindox, whereas high levels of mequindox exposure trigger liver dysfunction, causing no such recovery. We found that mequindox exposure causes suppression of the tricarboxylic acid cycle and stimulation of glycolysis, which is in contrast to a mouse model previously investigated. In addition, mequindox dosage induces promotion of β-oxidation of fatty acids, which was confirmed by elevated expressions of acox1, hsd17b2, and cpt1a in liver. Furthermore, altered levels of N-methylnicotinate, 1-methylnicotinamide, and glutathione disulfide highlighted the promotion of vitamin B3 antioxidative cycle in rats exposed to mequindox. Moreover, mequindox exposure altered levels of gut microbiotal related co-metabolites, suggesting a perturbation of the gut microflora of the host. Our work provides a comprehensive view of the toxicological effects of mequindox, which is important in the usage of mequindox in animal and human food safety. KEYWORDS: mequindox, metabonomics, NMR, pattern recognition, rat, transcriptomics
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INTRODUCTION Mequindox (Supplementary Figure 1) belongs to a family of drugs called quinoxalines, and the drug inhibits DNA synthesis of some bacilli.1 Certain types of quinoxalines were banned because of their potential toxicity to animals for food consumption by human. Mequindox is of low toxicity relative to other drugs in the family and hence is widely used in the livestock and poultry industry as an antibiotic drug in China for treating dysentery and other inflammatory conditions.2−4 However, the toxicity of this drug was not fully evaluated. Previous investigations have mainly focused on the study of toxicities of mequindox, pharmacokinetics, and post antibiotic effects.5,6 It was shown that mequindox can produce reactive oxygen species (ROS) and induce cell apoptosis via mitochondria-dependent pathways in porcine adrenocortical cells.7 Gene expression data demonstrated that ROS induced by long-term exposure to mequindox also affected biosynthesis of steroid hormones,8 inhibited both intra- and extra-adrenal rennin-angiotensin-aldosterone systems,9 and activated NADPH oxidase, thus causing activation of the JAKSTAT signaling pathway.10 Long-term exposure to mequindox also caused inflammatory response as reflected by up-regulation of TNFα, IL-6, leading to liver and spleen damage.10 © 2012 American Chemical Society
Furthermore, down-regulated mRNA levels of steroidogenic acute regulatory protein (StAR), cholesterol side-chain cleavage enzyme (P450scc), and 17β-hydroxysteroid dehydrogenase (17β-HSD) in testis were associated with mequindox exposure.11 Recently, we investigated the metabolic responses of mice to acute mequindox insult by employing a 1H nuclear magnetic resonance (NMR)-based metabonomics approach.12 We found that high and moderate levels of mequindox exposure caused suppression of glycolysis and stimulation of fatty acid oxidation accompanied with increased levels of oxidative stress, disruption of amino acid metabolism, and perturbation of gut microbial activity. However, it is not known if different species of animal respond to mequindox insult in the same way or differently. Hence it is important to obtain complementary information on the metabolic effects of mequindox using a different animal model. Utilizing systems biology approaches, for example, by combining metabolic and global gene expression profiling techniques, provides means to determine characteristic end Received: June 14, 2012 Published: July 31, 2012 4712
dx.doi.org/10.1021/pr300533a | J. Proteome Res. 2012, 11, 4712−4721
Journal of Proteome Research
Article
solution for histological assessments. Another section of liver was snap-frozen in liquid nitrogen immediately and stored at −80 °C for NMR and transcriptomic analyses.
point metabolic effects of a toxin as well as providing further understanding of in depth biological processes involved. On the one hand, a drug-related alteration in gene expression levels might induce toxicity-related effects on the mRNA levels. However, such changes in the mRNA level may or may not result in metabolic phenotypes. This is because the biological system might have a certain degree of capacity to resist such perturbation on the whole organism level, and other environmental factors such as diet, lifestyle, and gut microbial activities can also impact on metabolic phenotypes. On the other hand, metabonomics investigation can provide real biological end point changes associated with a given biological process. Metabonomics13,14 is defined as the study of multiparametric metabolic responses of organisms to perturbations (e.g., drug, pathological stressors, or other stimuli) and the successive whole effect of multibiomatrices of system regulation. NMR spectroscopy, combined with multivariate pattern recognition methods, is one of the key techniques and has been demonstrated to successfully investigate perturbations to organisms induced by exogenous factors, such as diseases,15−19 environmental toxins,20,21 and nutritional interventions.22,23 Integration of metabolic and transcriptomic profiles in toxicological studies provides both a global characterization of metabolic disturbances associated with a toxic insult and transcriptomic information that may reveal molecular mechanisms and pathways associated with the perturbation. Combining information from metabonomic and transcriptomic profiling has the potential to further elucidate mechanisms of a toxic effect in more detail and with greater reliability. In order to provide complementary information to our previous investigation of metabolic response of mice to mequindox exposure,12 we evaluated the mechanisms of toxic effects of mequindox using a rat model and employing an integrative approach by combining metabonomic and transcriptomic profiles. Our research highlights the benefit of systems biology strategy in the evaluation of a toxic effect in general, and the information obtained here provides a comprehensive view on the toxic mechanisms of mequindox in particular.
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Clinical Biochemistry and Histopathology Analysis
Sera were analyzed for glucose (Glc), total cholesterol (CHOL), creatinine (CREA), triglyceride (TG), albumin (ALB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and total protein (TP). Fixed liver and kidney were sectioned and stained with hematoxylin and eosin (H&E) and examined under a microscope. NMR Spectroscopy
Urine samples were prepared by mixing 550 μL of urine with 55 μL of phosphate buffer (1.5 M, pH 7.4, NaN3 0.1%, TSP 0.05%, K2HPO4/NaH2PO4 = 4:1).24 Liver tissues (40−50 mg) were extracted twice with 1 mL of 50% aqueous methanol using a tissue-lyser (Qiagen Tissue-Lyser, Retsch GmBH, Germany). After centrifugation at 16,090g for 10 min at 4 °C, the combined supernatants were lyophilized after removal of methanol under vacuum. The extracts were then reconstituted in 600 μL of phosphate buffer for NMR analyses. Plasma samples were prepared by mixing 200 μL of blood plasma with 400 μL of 45 mM saline buffer containing 50% D2O. A total of 550 μL of urine, plasma, and liver extract samples were transferred into 5 mm NMR tube for analyses. Liver tissue samples (10−15 mg) were packed individually into a 4 mm ZrO2 rotor with addition of D2O for high resolution magic-angle-spinning (HRMAS) analyses. 1 H NMR spectra of urine and plasma were acquired at 298 K with a Bruker Avance III 600 MHz NMR spectrometer equipped with a TCI cryoprobe. 1H NMR spectra of liver extracts were acquired at 298 K with a Bruker Avance III 600 MHz NMR spectrometer with a 5 mm TXI probe. 1H HRMAS NMR spectra of intact liver tissues were recorded at 283 K on a Bruker Avance III 600 MHz spectrometer with a 4 mm Bruker MAS II probe at a spinning rate of 5000 Hz. A total of 32 and 128 transients for urine and liver extracts, respectively, were collected using a standard presaturation pulse sequence (90°-3 μs-90°-tm-90°acquire) with irradiation during a 2 s relaxation delay and during the 80 ms mixing time. Additional Carr-Purcell-Meibom-Gill (CPMG) NMR spectra were acquired for plasma and intact liver tissues with a total spin−spin relaxation delay (2nτ) of 70 ms. For assignment purposes, a range of two-dimensional NMR spectra, including J-resolved, homo- and heteronuclear correlation spectroscopy25−27 were also acquired for selected urine, blood plasma, liver extract, and intact tissue samples.
MATERIALS AND METHODS
Animal Experiment and Sample Collection
The animal experiments were carried out according to international guidelines (OECD, 1998). A total of 48 female Wistar rats (6 weeks old) were purchased from the Center for Disease Control (CDC), Hubei, P. R. China and housed in the SPF animal facility at Wuhan Institute of Virology (Hubei, China). All animals had free access to a commercial rodent diet (Huafukang Biotech, Beijing, China) and water throughout the study. After 2 weeks of acclimatization, the rats were randomly grouped into four classes: control group (C, vehicle olive oil, n = 12), low-dose group (L, 10 mg/kg b.w., n = 12), moderate-dose group (M, 50 mg/kg b.w., n = 12) and high-dose group (H, 250 mg/kg b.w., n = 12). The levels of dosage were based on IC50 reported in the literature.6 A single dosage of mequindox was mixed with olive oil and introduced to rats via gavage. Urine samples were collected from each rat at 1 day predose (0 h) and at 8 h, 16 h, then on a daily basis until 7 days postdose. At 8 days postdose, blood was collected into Na-heparin tubes from the eye, and plasma samples were attained after centrifugation. Sera samples for clinical biochemistry assays were also collected when the rats were decapitated after anesthesia with isoflurane. Sections of liver and kidney were taken and stored in formalin
Statistical Analysis of NMR Spectra
The preprocessing of these NMR spectra was performed routinely as previous described.12 Briefly, the NMR spectra were adjusted for phase and baseline distortion and referenced to TSP at δ 0.00 for urine and liver extract spectra, and the methyl resonance of alanine at δ 1.47 for spectra of plasma and liver tissues before being reduced to 4K data points (AMIX, version 3.9.2, Bruker-Biospin, Germany). Aromatic regions containing signals from mequindox in the urine spectra were removed prior to normalization on the total area of the spectrum. Principal component analysis (PCA) was performed using SIMCA-P 11.0 software (Umetrics, Sweden) to overview intrinsic similarity/ dissimilarity within the data set. The PCA trajectory was calculated by averaging respective scores that generated from the same time point of the same group of rats. Differences in metabolic profiles between animals dosed with mequindox and the corresponding controls were revealed by discriminant 4713
dx.doi.org/10.1021/pr300533a | J. Proteome Res. 2012, 11, 4712−4721
Journal of Proteome Research
Article
Figure 1. Typical 1D 1H NMR spectra of urine from control (a) and high-dose (b), of aqueous liver extract from control (c) and high-dose (d), and CPMG spectra of plasma from control (e) and high-dose (f) rats. Key: 1, 2-hydroxyvalerate; 2, isobutyrate; 3, lactate; 4, alanine; 5, 11hydroxymequindox; 6, acetate; 7, 4-cresol glucuronide; 8, succinate; 9, 2-oxoglutarate (2OG); 10, citrate; 11, dimethylamide (DMA); 12, creatine; 13, creatinine; 14, dimethylglycine (DMG); 15, trimethylamine-N-oxide (TMAO); 16, taurine; 17, glycine; 18, hippurate; 19, 1-methylnicotinamide (MND); 20, fumarate; 21, 4-hydroxyphenylpyruvate (HPP); 22, phenylacetylglycine (PAG); 23, formate; 24, leucine; 25, isoleucine; 26, valine; 27, lysine; 28, guanine; 29, glutathione disulfide (GSSG); 30, glumate; 31, aspartate; 32, amino acids and glucose α-CH (Glc, aa); 33, tyrosine; 34, phenylalanine; 35, uracil; 36, uridine; 37, inosine; 38, adenosine; 39, nicotinurate; 40, lipids; 41, D-3-hydroxybutyrate; 42, glutamine; 43, choline; 44, phosphorylcholine (PC); 45, glycerophosphorylcholine (GPC); 46, histidine; 47, N-acetyl-glycoproteins; 48, pyruvate; 49, hypoxanthine; 50, 3hydroxyphenylpropionate (mHPP).
(down-regulated genes) were used as the criteria for selecting significantly changed gene probe sets between control and highdosed group. The CapitalBio Molecule Annotation System (MAS) (version 4.0), KEGG, and GenMAPP were used for pathway analysis (http://bioinfo.capitalbio.com/mas).20 For each pathway, genes with known rat orthologues were compared with sets of significant genes from SAM to define the effects of corresponding pathway.
analysis using orthogonal projection to latent structures discriminant analysis (O-PLS-DA) on the NMR date scaled to unit variance (UV). The quality of the models was assessed by model parameters, such as Q2, denoting predictability of the model, and R2, indicating the goodness of fitting of the model. The prediction performance of the models was evaluated by a 7fold cross validation method,28 CV-ANOVA,29−31 and a permutation test (permutation number = 200). 32 The interpretation of the models was facilitated by the color-coded correlation coefficient plots generated in MATLAB (The Mathworks Inc.; Natwick, USA version 7.1). Metabolites that contributed most to the prediction of the response (class) are shown in red, whereas blue indicated little/no association with the response.
Quantitative Real-Time PCR
qRT-PCR was performed to validate the data obtained in the microarray analysis. cDNA was synthesized using an oligo(dT)15 primer (Invitrogen), according to the manufacturer’s instructions. PCR primers (Supplementary Table 1) were designed using Primer Premier 5.0 software. The housekeeping gene β-actin was used as an internal control. The PCR amplification was conducted at 95 °C for 15 min, followed by 40 cycles of 94 °C for 5 s, 58 °C for 15 s, and 72 °C for 10 s. The relative mRNA levels of selected genes involved in lipid metabolism (especially steroid hormone biosynthesis), signal pathways, and potential diseases were calculated using the 2−ΔΔCt method.37 Values were reported as means ± SD. Statistical differences were determined by the one-way ANOVA multiple range test and the Wilcoxon rank sum test. Statistical significance was set at p < 0.05.
Transcriptomic Analysis
Total RNA was isolated from 11 rat livers, three from the control rats, and eight from high dosed group, with Trizol Reagent (Invitrogen Corp., Carlsbad, CA), according to the manufacturer’s instructions. The concentration and purity of total RNA were determined by spectrophotometer, and the quality assessment was conducted by the integrity of 28S and 18S rRNA. The Affymetrix Rat Genome 230 2.0 array containing 31,099 gene probes composed of 25-mer single-stranded oligonucleotides was used to monitor changes in gene expression (CapitalBio, Beijing, China). The log-transformed signal data obtained for all probes were array-wise normalized using Affymetrix Dchip 2006. The Wilcoxon signed-rank test was utilized for significance analysis of microarrays (SAM, http://www-stat.stanford.edu/ ∼tibs/SAM/).33−36 A permutation test was employed for estimating the false-discovery rate (n = 500). A false discovery rate 2 (up-regulated genes) or
