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Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury
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Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury Songyan Gao a,1, Wei Chen b,1, Zhongjiang Peng b, Na Li a, Li Su a, Diya Lv a, Ling Li a, Qishan Lin c, Xin Dong a,n, Zhiyong Guo b,nn, Ziyang Lou a,nnn a
School of Pharmacy, Second Military Medical University, Shanghai 200433, China Changhai Hospital, Second Military Medical University, Shanghai 200433, China c Proteomics/Mass Spec Facility, Center for Functional Genomics, University at Albany, Rensselaer, NY 12144, USA b
art ic l e i nf o
a b s t r a c t
Article history: Received 30 July 2014 Received in revised form 5 February 2015 Accepted 9 March 2015
Ethnopharmacological relevance: Orthosiphon stamineus (OS), a traditional Chinese herb, is often used for promoting urination and treating nephrolithiasis. Aim of the study: Urolithiasis is a major worldwide public health burden due to its high incidence of recurrence and damage to renal function. However, the etiology for urolithiasis is not well understood. Metabonomics, the systematic study of small molecule metabolites present in biological samples, has become a valid and powerful tool for understanding disease phenotypes. In this study, a urinary metabolic profiling analysis was performed in a mouse model of renal calcium oxalate crystal deposition to identify potential biomarkers for crystal-induced renal damage and the anti-crystal mechanism of OS. Materials and methods: Thirty six mice were randomly divided into six groups including Saline, Crystal, Cystone and OS at dosages of 0.5 g/kg, 1 g/kg, and 2 g/kg. A metabonomics approach using ultraperformance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) was developed to perform the urinary metabolic profiling analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were utilized to identify differences between the metabolic profiles of mice in the saline control group and crystal group. Results: Using partial least squares-discriminant analysis, 30 metabolites were identified as potential biomarkers of crystal-induced renal damage. Most of them were primarily involved in amino acid metabolism, taurine and hypotaurine metabolism, purine metabolism, and the citrate cycle (TCA). After the treatment with OS, the levels of 20 biomarkers had returned to the levels of the control samples. Conclusions: Our results suggest that OS has a protective effect for mice with crystal-induced kidney injury via the regulation of multiple metabolic pathways primarily involving amino acid, energy and choline metabolism. & 2015 Published by Elsevier Ireland Ltd.
Keywords: Calcium oxalate crystal Biomarker discovery Metabonomics Orthosiphon stamineus UHPLC-Q-TOF/MS
1. Introduction
Abbreviations: OS, Orthosiphon stamineus; UHPLC-Q-TOF/MS, ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry; PCA, principal component analysis; PLS-DA, partial least squares discriminant analysis; TCA, Tricarboxylic acid cycle; CKD, chronic kidney disease; RSD, relative standard deviation; i.p., intraperitoneally; i.g., intragastrically; Scr, serum creatinine; BUN, blood urea nitrogen; QC, quality control; ESI, electrospray ionization; ANOVA, analysis of variance; TIC, total ion chromatogram; VIP, variable importance value; EIC, extracted ion chromatogram; AAA, aromatic amino acids; ROS, reactive oxygen species n Corresponding author. nn Corresponding author. nnn Corresponding author. Tel./fax: þ86 21 81871335. E-mail addresses: [email protected] (X. Dong), [email protected] (Z. Guo), [email protected] (Z. Lou). 1 These authors contributed equally to this work.
Renal stone disease is one kind of disease characterized by intermittence nephric colic and hematuria. Due to its high incidence of recurrence and harm towards renal function, kidney stones seriously jeopardize overall public health for society and quality of life for affected individuals. The prevalence of urinary stone disease ranges from 1% to 20% (average 10%) worldwide due to various factors, such as geography, climate, race, metabolic dysfunction and diet (Amato et al., 2004; Tiselius, 2003). Several epidemiologic studies have found that the formation of kidney stones increases the risk of chronic kidney disease (CKD) (Rule et al., 2009, 2011), and is associated with cardiovascular diseases, such as myocardial infarction and angina pectoris (Ando et al., 2013; Domingos and Serra, 2011; Rule et al., 2010). The development of
http://dx.doi.org/10.1016/j.jep.2015.03.025 0378-8741/& 2015 Published by Elsevier Ireland Ltd.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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a urinary stone is a multistep process involving crystal nucleation, growth, formation and aggregation. However, the mechanism underlying it is not well understood. Orthosiphon stamineus (Labiatae) has many synonyms, such as, Clerodendranthus spicatus, Orthosiphon spicatus, and Orthosiphon grandiflorum, of which the main active ingredients include flavonoids, terpenes and phenolic acids (Ameer et al., 2012). Previous studies have demonstrated OS could alleviate renal calcium oxalate deposition and urinary calcium excretion in an oxalatestone-forming rat model (Akanae et al., 2010; Zhong et al., 2012); however, no studies have been performed systematically to evaluate the anti-urolithiasis effect and mechanism of OS. Metabonomics, a new emerging approach of systems biology, focuses on the investigation of all endogenous metabolic responses in complex living systems, and has been used for the research of natural and alternative medicine. In the present study, we employed a metabonomics-based approach to explore urinary metabolomic changes in a murine model of crystal induced kidney injury and evaluated the protective effect of OS.
2. Materials and methods 2.1. Chemicals and reagents Chromatographic grade methanol and acetonitrile were purchased from Merck (Darmstadt, Germany). Formic acid was obtained from Fluka (Buchs, Switzerland). Ultrapure water was prepared using a Milli-Q water purification system (Millipore Corp., Billerica, MA, USA). The following compounds were obtained from Sigma-Aldrich (St. Louis, MO, USA): kynurenic acid, L-lysine, L-phenylalanine, L-tyrosine, L-tryptophan, taurine, and citrate. Glyoxylic acid was obtained from TCI (Tokyo, Japan). All other chemicals were of analytical grade. The Cystone (Himalayas, India) powders were dissolved in saline to obtain a concentration of 72 mg/ml. 2.2. Preparation and quality control of OS extract OS was purchased from Anguo (Hebei, China) and authenticated by Prof. Lian-na Sun (School of Pharmacy, Second Military Medical University, Shanghai, China). A voucher specimen was deposited in the Chinese Material Medica specimen room of the Pharmacy School, Second Military Medical University. The whole dried OS plant was ground to powder by a disintegrator. Next, the powder (200 g) was extracted with 1000 ml of 75% ethanol (v/v) for 2 h by an ultrasonic method at room temperature. The extraction solution was filtrated and concentrated using a rotary evaporator at 60 1C until dry. The yield of ethanol extract was 9.6%, as calculated by final weight. The residue was dissolved in saline to obtain the concentration of 50 mg/ml for the following experiments. To test the reproducibility of OS extraction, six OS metabolites, including isosinensetin, eupatorin, salvigenin, caffeic acid, rosmarinic acid and ursolic acid, were measured from the final products by UHPLC-Q-TOF mass spectrometry. The relative standard derivation (RSD) values of the peak areas of each component were less than 5% in six sets of experiments, indicating that the OS extraction is reproducible. Detailed methodology is listed in Supplemental materials section. 2.3. Animal experiment and sample collection All animal studies were performed in accordance with the National Institutes of Health (NIH) guide for the Care and Use of Laboratory Animals. The experimental procedures were approved by the Ethical Committee for the Experimental Use of Animals at Second Military Medical University (Shanghai, China). Thirty six wild-type
67 male C57B/L6 mice at seven to eight weeks old were purchased from 68 the Shanghai SLAC laboratory Animal Co., Ltd. After conditional 69 housing for one week, these mice were randomly divided into Saline, 70 Crystal, Cystone and three doses of OS groups with 6 mice in each 71 group. To establish the crystal renal injury model, all mice excluding 72 the saline control group were i.p. injected with glyoxylate at a dosage 73 of 100 mg/kg once daily for six days. Four hours after each glyoxylate 74 injection, the mice in the OS groups (total 18 mice and 6 mice for 75 each dosage) were i.g. administrated with OS extract at a dosage of 76 0.5 g/kg,1 g/kg, and 2 g/kg. The mice in the Cystone group were i.g. 77 administrated with Cystone at a dosage of 1.2 g/kg, which served as a positive reference control (Patel et al., 2012), while mice from the Q2 78 79 Saline and Crystal groups were given saline. 80 After the last gavages, all mice were placed into metabolic 81 cages for 24 h to collect urine samples. At the end of the study, 82 blood samples were collected by retro-orbital puncture and 83 kidneys were harvested after in situ cardio-perfusion and fixed 84 in formalin for further analysis. After clotting at 4 1C for 2 h, the 85 venous blood was centrifuged at 4000 rpm for 5 min; the sera were harvested for biochemical analysis. All urine and sera 86 samples were immediately stored at 80 1C prior to analysis. 87 88 2.4. Histological and biochemistry analysis 89 90 The kidneys were fixed in 10% buffered formalin and paraffin91 embedded. Sections 3 μm thick were used to conduct von Kossa 92 staining according to the instructions of the commercial kit (Jiemei 93 Gene, Shanghai, China). Five random light microscope images of 94 von Kossa staining were collected. Thirty views were gathered 95 from each group for semi-quantitative examination by image 96 analysis software ipp6.0 (Media Cybernetics, Washington, USA). 97 98 The levels of calcium, phosphate and creatinine in urine, the levels of serum creatinine (Scr), and blood urea nitrogen (BUN) 99 were measured by a BC-2800Vet animal auto biochemistry analy100 zer (Shihai, Guangdong, China). 101 102 2.5. Sample preparation 103 104 Prior to analysis, a 100 μl aliquot of urine sample was thawed at 105 4 1C followed by the addition of 300 μl of acetonitrile to precipitate 106 the proteins. The resulting solution mixture was spun at 13,000 rpm 107 for 15 min at 4 1C. The clear supernatant (150 μl) was transferred to a 108 sampling vial for UHPLC–MS analysis. A QC sample was prepared by 109 pooling aliquots from all urine samples collected in the course of 110 the study. 111 112 2.6. UHPLC-Q-TOF/MS profiling analysis 113 114 115 UHPLC-Q-TOF/MS analysis was performed on an Agilent 1290 116 Infinity LC system equipped with an Agilent 6530 Accurate Mass 117 Quadrupole Time-of-Flight mass spectrometer (Agilent, USA). 118 Chromatographic separations were performed at 40 1C on an 119 ACQUITY UPLC HSS T3 column (2.1 mm 100 mm, 1.8 μm, Waters, 120 Milford, MA). The mobile phase consisted of 0.1% formic acid 121 (A) and ACN modified with 0.1% formic acid (B). The optimized 122 UPLC elution conditions were 0–2 min, 5% B; 2–10 min, 5–15% B; 123 10–14 min, 15–30% B; 14–17 min, 30–95% B; 17–19 min, 95% B, and 124 the posttime was 6 min for equilibrating the system. The flow rate 125 was set to 0.4 ml/min and the injection volume was 2 μl. The auto126 sampler was maintained at 4 1C. 127 An electrospray ionization source (ESI) was operated in both 128 positive and negative modes. The optimized conditions were as 129 follows: capillary voltage, 4 kV in positive mode and 3.5 kV in 130 negative mode; drying gas flow, 11 l/min; gas temperature, 350 1C; 131 nebulizer pressure, 45 psig; fragment voltage, 120 V; skimmer 132 voltage, 60 V. The mass spectrum was collected in profile mode
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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ranging from 50 to 1100 m/z. The biomarker candidates were further analyzed by MS/MS; the collision energy was set from 10 to 40 eV. The QC samples were randomly inserted in the sequence to validate the stability of the system. PCA was employed to assess the clustering of the QC samples in the PCA score plot from all the tested samples. Eight ions, including 4 ions in positive mode and 4 ions in negative mode, were chosen from the LC chromatograms, and RSDs of the retention times and peak areas of those were calculated.
2.7. Data process and statistical analysis The UHPLC–MS raw data were converted into a common data format (.mzdata) files by Agilent MassHunter Qualitative software. The isotope interferences were excluded and the threshold was set to 0.1%. The program XCMS (Smith et al., 2006) (http://metlin.scripps.edu/ download/) was employed for peak extraction, alignment and integration to generate a visual data matrix. After filtering the ions based on 80% rule (Smilde et al., 2005), all of the detected ions in each sample were normalized to the sum of the peak area to obtain the relative intensity of metabolites. Then, the three-dimensional data matrix,
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including the sample names, retention times, m/z pairs, and normalized ion intensities, was imported into the SIMCA-P program (version 11.0, Umetrics, Umea, Sweden) for multivariate statistical analysis after mean-centering and Pareto scaling. The relevant parameters, such as R2X, R2Y, and Q2, were monitored and permutation tests were implemented to evaluate the quality of models. Biochemistry data were expressed by mean 7 SD. The statistical significance of mean values was tested using one-way ANOVA and Tukey's posthoc test through SPSS 17.0 program (IBM, New York, USA). Differences were considered to be significant when p values were less than 0.05. The heat map of different metabolites was performed by MetaboAnalyst platform (http://www.metaboana lyst.ca). Normalized amount of each marker metabolite was plotted in a box plot using the GraphPad Prism 5.0.
2.8. Identification of the potential biomarkers Identification of different metabolites was an important and challenging job in the metabonomics study. Several steps were performed to finally identify the metabolites: (1) ions were confirmed based on the extracted ion chromatogram (EIC); (2) exact
Fig. 1. Representative photomicrographs for von Kossa staining of calcium deposition in the cortex and medulla junction of kidney ( 400), (A) slice in normal Saline group with no crystal deposition; (B) slice in CaOx-induced model group with clearly increased calcium deposition. (C) Slice in the Cystone treated group; (D) slice in the OS treated group at a dosage of 0.5 g/kg; (E) slice in the OS treated group at a dosage of 1 g/kg; (F) slice in the OS treated group at a dosage of 2 g/kg; (G) calcium content was expressed as the area of positive staining in each group, the calcium deposits in mice treated 0.5 g/kg OS were not significantly reduced, those in mice receiving 1 g/kg, 2 g/kg OS and Cystone were significantly decreased compared with which in Crystal group. ***p o0.001 compared with Saline group, ###p o0.001 compared with Crystal group.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 2. Representative total ion chromatograms (TICs) of the urine samples obtained in ESI positive ion mode from Saline group (A), Crystal group (B) and OS group (C) based on UHPLC-Q-TOF/MS.
masses of quasi-molecular ions were put into online databases to get suggestions for the possible metabolites, such as Metlin (http:// metlin.scripps.edu/), Human Metabolome Database (http://www. hmdb.ca/); (3) MS/MS spectra were compared with the MS/MS information from the above databases to verify the structure of the putative metabolites; and (4) the retention times and the fragments of metabolites were compared with those of reference samples (Tan et al., 2012).
3. Results 3.1. Histology and biochemistry Von Kossa staining of calcium deposition in the cortex and medulla junction of kidney showed that calcium deposits were clearly present in kidney sections of mice in the Crystal group. Slimier with slices from mice treated with Cystone, the positive stain in renal sections from 1 g/kg and 2 g/kg OS extract treatment group was much less than that in Crystal group, which did not happened in mice receiving 0.5 g/kg OS extract intervention. Based on the results of dosage selection, 1 g/kg OS extract was defined as the optimal dosage and used in next biochemistry and metabonomics analysis. The representative images of calcium staining for each group are illustrated in Fig. 1A–F. Semi-quantitative analysis confirmed a significant difference in the positive staining area of calcium deposition among different groups (Fig. 1G). In addition, a significant decrease in the ratios of both calcium and phosphate to creatinine was observed in the urine of the Cystone group and the OS treated group as compared to that of the Crystal group (Fig. S1A). Furthermore, the levels of creatinine and BUN in sera were also lower in the Cystone and OS group when compared to those of the Crystal group (Fig. S1B), supporting that OS has a protective effect on CaOx induced renal injury. 3.2. Metabolic profiling analysis of urine PCA score plot including all the test and QC samples shows that the QC sample features are tightly clustered (Fig. S2). In addition, the RSD values of the retention times and peak intensities in QC samples for the stability evaluation were less than 0.65% and
8.65%, respectively (Table S1). The results demonstrated that the stability of the proposed method was satisfying. Urinary metabolic profiling analysis was performed according to the proposed approach. The typical total ion chromatograms (TICs) obtained from the ESI positive and ESI negative are shown in Figs. 2 and S3. We first performed the PCA, an unsupervised multivariate data analysis, to visualize the trends and outliers in the data for both Saline and Crystal groups (Chan et al., 2011; Trygg et al., 2007). The score plots showed that there were no outliers and that the Crystal group was clearly separated from the Saline group (Figs. 3A and S4A). Then, supervised PLS-DA was applied to screen potential metabolite biomarkers (Trygg et al., 2007). As illustrated by the PLS-DA scores plot (Figs. 3B and S4B), the Crystal group was obviously separated from the Saline group. When 2 components were calculated in the positive mode, the cumulative R2X, R2Y and Q2 were 0.402, 0.978 and 0.72, respectively, while the cumulative R2X, R2Y and Q2 in the negative mode were 0.396, 0.989 and 0.8, respectively. No over-fitting was observed in either ESI positive or ESI negative according to the results of the permutation test (Fig. S5). PLS-DA scatter and variable importance plots (Figs. 3C and S4C) can be used to identify characteristic metabolites to differentiate the Crystal group from the Saline group (Huang et al., 2013). In the present research, ions with VIPs greater than 1.0 were considered to be important differential metabolites. One-way ANOVA and Tukey's posthoc test were performed to assess the statistical significance. Finally, 30 metabolites with significant differences between the Saline and Crystal groups were identified as potential biomarkers, which consisted of 24 metabolites selected based on data from the positive mode, and 6 metabolites selected based on data from the negative node. The identification of m/z 190 taken as an example is shown in Fig. 4. Other biomarkers identified using the proposed approach are listed in Table 1. 3.3. Evaluation of protective mechanism of OS on crystal induced kidney injury We have identified 30 urinary metabolites as the potential biomarkers of crystal induced kidney injury in the animal model of glyoxylate induced renal injury (Fig. 5). It would be logical to use them as the monitoring markers to evaluate the protective effects of OS on crystal kidney injured mice. A PCA model was established using these potential marker metabolites as variables. As indicated
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 3. Plots of multivariate statistical analysis based on the metabolites in urine of mice. (A) PCA scores plot of the Saline group and Crystal group in ESI positive ion mode; (B) PLS-DA scores plot of the Saline group and Crystal group in ESI positive ion mode; (C) scatter and variable importance plot of the Saline group and Crystal group in ESI positive ion mode. The variables marked as (□) are the metabolites selected as potential candidates; (D) PCA 3D scores plot derived from the 30 potential metabolic markers in urine from Saline group, Crystal group and OS group.
by R2X¼ 0.72 and Q2 ¼0.534, the model was both reliable and predictive. The OS group was clearly separated in the scores plot from the Crystal group, but was close to the Saline group (Fig. 3D), providing further evidence for the protective effect of OS extract on crystal kidney injury. Detailed changes of the 30 potential metabolite biomarkers in OS treated mice are listed in Table 1. The results indicate that the metabolic pattern in the urine of mice with glyoxylate-induced renal injury was reversed to normal levels after OS treatment. To effectively visualize and characterize remedial effects of OS, the heatmap and the box plots representing the intensity levels of 30 metabolite biomarkers in the different groups are shown in Figs. 6 and 7, respectively. Among the 30 metabolites identified, 20 were reversed to different degrees of normality in the OS treated group, such as N6,N6,N6-Trimethyl-L-lysine, betaine, choline, L-proline, vinylacetylglycine, kynurenic acid, metanephrine, taurine, phenylpyruvate, citric acid, and succinic acid semialdehyde etc. The related pathway of each biomarker is shown in Table 1. These biomarkers identified are mainly involved in amino, energy and choline metabolism, indicating that these metabolism pathways may play key roles in mediating the protective effect of OS on crystal induced kidney injury and that the deeper investigation of these pathways should be helpful in elucidating the therapeutic mechanism of OS.
4. Discussion In this study, we established the glyoxylate-induced crystal related kidney injury model following the method described previously (Okada et al., 2007, Taguchi et al., 2014). Then, we applied this model to investigate the protective effect and mechanism of OS using a metabonomics-based approach. Calcium deposition was
clearly shown in kidney sections from mice in the Crystal group. In addition, the levels of urinary calcium, phosphorus and Scr, BUN were significantly increased in the Crystal group versus Saline group, indicating that the model was successfully established. Previous works based on a genome-wide analysis and cell biology approach have found that the interaction between migrating macrophages and renal tubular cells contributes heavily during crystal formation in hyperoxaluric mice (Okada et al., 2009; Umekawa et al., 2002). According to the results of the present study, 30 important metabolites were identified as potential metabolite biomarkers for crystal renal injury. By searching the KEGG pathway database (http://www.genome.jp/kegg/), we constructed the metabolic network (Fig. 5) clearly showing that glyoxylateinduced crystal renal injury is related to the perturbance of tryptophan tyrosine and phenylalanine metabolism, lysine metabolism, arginine and proline metabolism, glycine, serine and threonine metabolism, taurine and hypotaurine metabolism, purine metabolism and energy metabolism. Thus, metabolomics strategy would be an important complementary technology to the cell wide measurement of RNA, protein, fluxes, and interactions for investigating the mechanism of kidney stone formation. In the Crystal group, several amino acids including lysine, proline, phenylalanine, tryptophan, tyrosine and their relative metabolites were significantly changed. The metabolite imbalance of amino acids may be closely related to the pathogenesis of kidney stone formation. We observed a significant elevation of three important aromatic amino acids (AAA), including phenylalanine, tyrosine and tryptophan. Both phenylalanine and tryptophan are essential amino acids, while tyrosine is a semi-essential amino acid due to partial derivation from phenylalanine by phenylalanine hydroxylase (Matthews, 2007). Tyrosine can also be metabolized to many biologically active catecholamines, such as dopamine,
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 4. The identification of a selected biomarker (m/z 190.05). (A) Extracted ion chromatogram (EIC) of m/z 190.05; (B)the EIC of commercial standard kynurenic acid; (C) MS/MS spectrum of the ion in urine sample and possible MS/MS fragment mechanism; (D) MS/MS spectrum of kynurenic acid standard. The collision energy was 10 V.
norepinephrine and serotonin (Fernstrom and Fernstrom, 2007). In addition, several other metabolites relating to the AAA metabolism, such as phenylpyruvate, phenyllactate, metanephrine and kynurenic acid, were also markedly changed in the Crystal group. The results indicate that the AAA metabolism is clearly disturbed by the crystal renal injury process, which is consistent with previous studies documenting the close association between kidney diseases and the degradation, synthesis, or excretion of AAA (Boirie et al., 2004; Young et al., 2010). Among side metabolites of the AAA metabolism, kynurnic acid is an important metabolite of the kynurenine pathway, which is a major branching pathway of tryptophan metabolism. Kynurenic acid is generally known to be a uremic toxin. The elevated kynurenic acid concentration in serum was reported in patients with chronic renal failure (Uwai, 2012). However, a decreased level of kynurenic acid was observed in the urine of mice of the Crystal group in this study, which is also concordant with a previous study (Zhao et al., 2012). Furthermore,
the kynurenic acid concentration in the OS group was reversed to normal levels, indicating that OS may promote the clearance of the kynurenic acid. Taurine, a sulfur-containing β-amino acid and also a major free intracellular amino acid in mammalian cells (Kerai et al., 1999), is well known as an endogenous anti-oxidant and membrane-stabilizing agent. Many studies have documented the protective effects of taurine against multiple types of kidney damage, including ischemia/reperfusion, hyperglycemia, oxidantstress and xenobiotics (Al-Kahtani, 2010; Guan et al., 2008). In addition, taurine treatment can alleviate the tubular oxidative injury via a mitochondrial-linked pathway in rat models of nephrolithiasis (Li et al., 2009). The increased urine taurine level in mice receiving OS treatment implies that taurine induction might be involved in the protective effect of OS on crystal induced renal damage. Carnitine is an essential cofactor required to transport fatty acids into the inner mitochondrial matrix to provide energy
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Table 1 Potential biomarkers related to crystal induced kidney injury and their metabolic pathways. No. RT (min) m/z
Ion
Formula
Trenda
Identification
Related pathway
Crystal/Saline OS/Crystal ESI þ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 ESI- 25 26 27 28 29 30
0.56 0.58 0.62 0.7 0.71 0.72 0.8 0.98 0.99 1.04 1.08 1.3 1.86 1.87 3.21 3.78 4.99 4.99 6.02 6.29 6.35 7.85 8.15 10.31 0.68 0.7 0.75 0.79 0.97 8.48
147.1122 146.1652 189.1607 118.086 144.1027 126.0861 177.0615 157.0605 139.0498 182.0811 116.0671 184.0603 166.0841 144.0653 195.0765 205.0972 190.05 216.1233 198.1125 232.1183 218.1389 172.0967 174.1124 200.1281 124.0081 163.0333 191.0199 101.0248 167.0219 165.0556
[Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ Na] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [M H] [M H] [M H] [M H] [M H] [M H]
C6H14N2O2 C7H19N3 C9H20N2O2 C5H11NO2 C7H13NO2 C5H13NO C4H8N4O4 C6H8N2O3 C6H6N2O2 C9H11NO3 C5H9NO2 C8H9NO4 C9H11NO2 C6H9NO3 C9H10N2O3 C11H12N2O2 C10H7NO3 C10H17NO4 C10H15NO3 C10H17NO5 C10H19NO4 C8H13NO3 C8H15NO3 C10H17NO3 C2H7NO3S C9H8O3 C6H8O7 C4H6O3 C5H4N4O3 C9H10O3
b L-Lysine
nnn
Spermidinec N6,N6,N6-Trimethyl-L-lysined Betaineb Prolinebetainec Cholined Allantoated 4-Imidazolone-5-propionic acidd Urocanic acidd c L-Tyrosine d L-Proline 4-Pyridoxatec L-Phenylalanineb Vinylacetylglycinec Aminohippuric acidc b L-Tryptophan Kynurenic acidb Propenoyl-L-carnitined Metanephrined Isovalerylglutamicacidd Propionyl-L-carnitined N-butanoyl-lhomoserinelactoned Isovalerylalanined N-hexanoyl-L-Homoserinelactoned Taurineb Phenylpyruvated Citrateb Succinic acid semialdehyded Uric acidb Phenyllactatec
n
↑
↓
nnn
↓ ↑ nn ↓ nnn ↓ n ↓ n ↓ n ↓ n ↑ n ↓ nnn ↓ nn ↑ n ↑ n ↓ nnn ↑ n ↓ nn ↓ n ↓ nn ↓ nn ↓ nn ↑ nnn ↑ nn ↑ n ↓ nnn ↑ nnn ↓ nnn ↑ nn ↓ nn ↓ n
↓ ↓
#
###
↑ ↓ ## ↓ ### ↑ ↓ ↓ # ↓ ↓ ### ↑ ↓ ↓ ### ↓ ↓ ↓ ### ↑ ## ↑ # ↑ # ↓ ↑ ### ↓ ### ↓ ↓ ### ↑ ### ↓ ## ↑ ### ↓ # ↓ ↑ #
Lysine biosynthesis Arginine and proline metabolism Lysine degradation Glycine, serine and threonine metabolism Amino acid metabolism Glycine, serine and threonine metabolism Purine metabolism Histidine metabolism Histidine metabolism Tyrosine metabolism Arginine and proline metabolism Vitamin B6 metabolism Phenylalanine metabolism Fatty acid metabolism Fatty acid metabolism Tryptophan metabolism Tryptophan metabolism Fatty acid metabolism Tyrosine metabolism Fatty acid metabolism Fatty acid metabolism Fatty acid metabolism Fatty acid metabolism Fatty acid metabolism Taurine and hypotaurinemetaboliism Phenylalanine metabolism Citrate cycle (TCA cycle) Alanine, aspartate and glutamate metabolism Purine metabolism phenylalanine metabolism
a
(↑): up-regulated and (↓): down-regulated (*p o 0.05, **p o 0.01, ***p o0.001 versus Saline group). po 0.05 versus Saline group. nn p o0.01 versus Saline group. nnn p o 0.001 versus Saline group. # p o 0.05 versus Crystal group. ## p o 0.01 versus Crystal group. ### p o 0.001 versus Crystal group. b Metabolites validated with standards. c Metabolites analyzed based on MS/MS chromatograms. d Metabolites putatively annotated. n
through β-oxidation (Indiveri et al., 2010). Carnitine deficiency and energy starvation have been reported to play critical role in chemical-induced nephrotoxicity (Sayed-Ahmed, 2010). In contrast, acylcarnitines and particularly propionyl-L-carnitine, which is a product of the enzymatic esterification of carnitine (Murphy et al., 2012), have been described to prevent the development of renal failure by increasing intracellular carnitine levels and energy production (Sayed-Ahmed, 2010). In our study, the levels of short chain acylcarnitines, propionyl-L-carnitine and propenoyl-L-carnitine were down-regulated, which may reflect a decreased level of aerobic oxidation in the mitochondria (Huang et al., 2013). In contrast, the level of the acylcarnitines was elevated in mice urine after OS treatment indicating that OS might exert the protective effect by improving the energy metabolism. How purine metabolism affects kidney diseases has not been clearly demonstrated. Uric acid, the downstream metabolite of purine, has elicited much controversy over its effect in the pathogenesis of stone formation. Uric acid has been reported as a potentially important risk factor for the development and progression of CKD. In contrast, several studies have also showed that soluble uric acid exerts a protective effect as a powerful antioxidizing agent (Johnson et al., 2013; Romero et al., 2009; Shimada et al., 2009). In our study, the absolute concentration of uric acid in the urine was low and down-regulated in the Crystal
group compared to that of the Saline group, supporting the protective effect of uric acid. Allantoin was the oxidation product of the uric acid through urate oxidase in mice; however, uric acid was usually regarded as the end product of the purine metabolite in humans due to the lack of urate oxidase (Romero et al., 2009). Thus, the change of allantoin in mice might be of less clinical significance.
5. Conclusion In summary, a metabonomics approach based on UHPLC/Q-TOF MS has been developed to profile the metabolic alterations of glyoxylate-induced crystal renal injury in mice. As a result of this approach, 30 metabolites, primarily involved in amino acid metabolism, taurine and hypotaurine metabolism, energy metabolism and purine metabolism, have been identified as potential biomarkers of crystal renal injury. The OS treatment significantly decreased the calcium deposition and reversed the metabolic aberrations in mice with crystal renal injury, strongly supporting the therapeutic effect of OS on glyoxylate-induced crystal renal injury; however, further studies are warranted to better understand how specific active compounds of OS are involved in the recovery of those disturbed metabolic pathways.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 5. The metabolic pathway networks resulting from OS modulation to the glyoxylate-induced crystal kidney injury. Identified biomarkers with a changed concentration in the OS treatment group compared to that in the Crystal group are labeled in red (up-regulated) and green (down-regulated), respectively. Metabolites which were not significantly changed in the Crystal group compared with the Saline group are given in black. The words in blue besides the pane indicate the related pathway. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 6. Heatmap based on the relative levels of potential marker metabolites in urine of mice in the Saline group, Crystal group and OS group.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 7. Box plots showing the levels of 30 potential marker metabolites in the Saline group, Crystal group and OS group, *p o 0.05, **p o0.01, ***p o 0.001 for Crystal group versus Saline group; #p o0.05, ##p o 0.01, ###p o 0.001 for OS group versus Crystal group.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Conflict of interest Authors do not have any financial interest regarding the subject matter in the manuscript. Acknowledgments This study was supported in part by Grants from the National Scientific Foundation of China (81270773 and 81403261), TCM Supported Project (13401900105) and Natural Science Founding (13ZR1450100) from Science and Technology Commission of Shanghai Municipality. We also thank Mr. Changcheng Liu for his kind assistance in animal experiments. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.jep.2015.03.025. References Akanae, W., Tsujihata, M., Yoshioka, I., Nonomura, N., Okuyama, A., 2010. Orthosiphon grandiflorum hasaprotective effect in a calcium oxalate stone forming rat model. Urol. Res. 38, 89–96. Al-Kahtani, M.A., 2010. Renal damage mediated by oxidative stress in mice treated with aluminium chloride: protective effects of taurine. J. Biol. Sci. 10, 584–595. Amato, M., Lusini, M., Nelli, F., 2004. Epidemiology of nephrolithiasis today. Urol. Int. 72, 1–5. Ameer, O.Z., Salman, I.M., Asmawi, M.Z., Ibraheem, Z.O., Yam, M.F., 2012. Orthosiphon stamineus: traditional uses, phytochemistry, pharmacology, and toxicology. J. Med. Food 15, 678–690. Ando, R., Nagaya, T., Suzuki, S., Takahashi, H., Kawai, M., Okada, A., Yasui, T., Kubota, Y., Umemoto, Y., Tozawa, K., Kohri, K., 2013. Kidney stone formation is positively associated with conventional risk factors for coronary heart disease in Japanese men. J. Urol. 189, 1340–1346. Boirie, Y., Albright, R., Bigelow, M., Nair, K.S., 2004. Impairment of phenylalanine conversion to tyrosine in end-stage renal disease causing tyrosine deficiency. Kidney Int. 66, 591–596. Chan, E.C., Pasikanti, K.K., Nicholson, J.K., 2011. Global urinary metabolic profiling procedures using gas chromatography–mass spectrometry. Nat. Protoc. 6, 1483–1499. Domingos, F., Serra, A., 2011. Nephrolithiasis is associated with an increased prevalence of cardiovascular disease. Nephrol. Dial. Transplant. 26, 864–868. Fernstrom, J.D., Fernstrom, M.H., 2007. Tyrosine, phenylalanine, and catecholamine synthesis and function in the brain. J. Nutr. 137, 1539S–1547S. Guan, X., Dei-Anane, G., Liang, R., Gross, M.L., Nickkholgh, A., Kern, M., Ludwig, J., Zeier, M., Büchler, M.W., Schmidt, J., 2008. Donor preconditioning with taurine protects kidney grafts from injury after experimental transplantation. J. Surg. Res. 146, 127–134. Huang, Q., Tan, Y., Yin, P., Ye, G., Gao, P., Lu, X., Wang, H., Xu, G., 2013. Metabolic characterization of hepatocellular carcinoma using nontargeted tissue metabolomics. Cancer Res. 73, 4992–5002. Indiveri, C., Pochini, L., Oppedisano, F., Tonazzi, A., 2010. The carnitine transporter network: interactions with drugs. Curr. Chem. Biol. 4, 108–123. Johnson, R.J., Sanchez-Lozada, L.G., Mazzali, M., Feig, D.I., Kanbay, M., Sautin, Y.Y., 2013. What are the key arguments against uric acid as a true risk factor for hypertension? Hypertension 61, 948–951. Kerai, M.D., Waterfield, C.J., Kenyon, S.H., Asker, D.S., Timbrell, J.A., 1999. Reversal of ethanol-induced hepatic steatosis and lipid peroxidation by taurine: a study in rats. Alcohol Alcohol. 34, 529–541. Li, C.Y., Deng, Y.L., Sun, B.H., 2009. Taurine protected kidney from oxidative injury through mitochondrial-linked pathway in a rat model of nephrolithiasis. Urol. Res. 37, 211–220.
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Contents lists available at ScienceDirect
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Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury Songyan Gao a,1, Wei Chen b,1, Zhongjiang Peng b, Na Li a, Li Su a, Diya Lv a, Ling Li a, Qishan Lin c, Xin Dong a,n, Zhiyong Guo b,nn, Ziyang Lou a,nnn a
School of Pharmacy, Second Military Medical University, Shanghai 200433, China Changhai Hospital, Second Military Medical University, Shanghai 200433, China c Proteomics/Mass Spec Facility, Center for Functional Genomics, University at Albany, Rensselaer, NY 12144, USA b
art ic l e i nf o
a b s t r a c t
Article history: Received 30 July 2014 Received in revised form 5 February 2015 Accepted 9 March 2015
Ethnopharmacological relevance: Orthosiphon stamineus (OS), a traditional Chinese herb, is often used for promoting urination and treating nephrolithiasis. Aim of the study: Urolithiasis is a major worldwide public health burden due to its high incidence of recurrence and damage to renal function. However, the etiology for urolithiasis is not well understood. Metabonomics, the systematic study of small molecule metabolites present in biological samples, has become a valid and powerful tool for understanding disease phenotypes. In this study, a urinary metabolic profiling analysis was performed in a mouse model of renal calcium oxalate crystal deposition to identify potential biomarkers for crystal-induced renal damage and the anti-crystal mechanism of OS. Materials and methods: Thirty six mice were randomly divided into six groups including Saline, Crystal, Cystone and OS at dosages of 0.5 g/kg, 1 g/kg, and 2 g/kg. A metabonomics approach using ultraperformance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) was developed to perform the urinary metabolic profiling analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were utilized to identify differences between the metabolic profiles of mice in the saline control group and crystal group. Results: Using partial least squares-discriminant analysis, 30 metabolites were identified as potential biomarkers of crystal-induced renal damage. Most of them were primarily involved in amino acid metabolism, taurine and hypotaurine metabolism, purine metabolism, and the citrate cycle (TCA). After the treatment with OS, the levels of 20 biomarkers had returned to the levels of the control samples. Conclusions: Our results suggest that OS has a protective effect for mice with crystal-induced kidney injury via the regulation of multiple metabolic pathways primarily involving amino acid, energy and choline metabolism. & 2015 Published by Elsevier Ireland Ltd.
Keywords: Calcium oxalate crystal Biomarker discovery Metabonomics Orthosiphon stamineus UHPLC-Q-TOF/MS
1. Introduction
Abbreviations: OS, Orthosiphon stamineus; UHPLC-Q-TOF/MS, ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry; PCA, principal component analysis; PLS-DA, partial least squares discriminant analysis; TCA, Tricarboxylic acid cycle; CKD, chronic kidney disease; RSD, relative standard deviation; i.p., intraperitoneally; i.g., intragastrically; Scr, serum creatinine; BUN, blood urea nitrogen; QC, quality control; ESI, electrospray ionization; ANOVA, analysis of variance; TIC, total ion chromatogram; VIP, variable importance value; EIC, extracted ion chromatogram; AAA, aromatic amino acids; ROS, reactive oxygen species n Corresponding author. nn Corresponding author. nnn Corresponding author. Tel./fax: þ86 21 81871335. E-mail addresses: [email protected] (X. Dong), [email protected] (Z. Guo), [email protected] (Z. Lou). 1 These authors contributed equally to this work.
Renal stone disease is one kind of disease characterized by intermittence nephric colic and hematuria. Due to its high incidence of recurrence and harm towards renal function, kidney stones seriously jeopardize overall public health for society and quality of life for affected individuals. The prevalence of urinary stone disease ranges from 1% to 20% (average 10%) worldwide due to various factors, such as geography, climate, race, metabolic dysfunction and diet (Amato et al., 2004; Tiselius, 2003). Several epidemiologic studies have found that the formation of kidney stones increases the risk of chronic kidney disease (CKD) (Rule et al., 2009, 2011), and is associated with cardiovascular diseases, such as myocardial infarction and angina pectoris (Ando et al., 2013; Domingos and Serra, 2011; Rule et al., 2010). The development of
http://dx.doi.org/10.1016/j.jep.2015.03.025 0378-8741/& 2015 Published by Elsevier Ireland Ltd.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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a urinary stone is a multistep process involving crystal nucleation, growth, formation and aggregation. However, the mechanism underlying it is not well understood. Orthosiphon stamineus (Labiatae) has many synonyms, such as, Clerodendranthus spicatus, Orthosiphon spicatus, and Orthosiphon grandiflorum, of which the main active ingredients include flavonoids, terpenes and phenolic acids (Ameer et al., 2012). Previous studies have demonstrated OS could alleviate renal calcium oxalate deposition and urinary calcium excretion in an oxalatestone-forming rat model (Akanae et al., 2010; Zhong et al., 2012); however, no studies have been performed systematically to evaluate the anti-urolithiasis effect and mechanism of OS. Metabonomics, a new emerging approach of systems biology, focuses on the investigation of all endogenous metabolic responses in complex living systems, and has been used for the research of natural and alternative medicine. In the present study, we employed a metabonomics-based approach to explore urinary metabolomic changes in a murine model of crystal induced kidney injury and evaluated the protective effect of OS.
2. Materials and methods 2.1. Chemicals and reagents Chromatographic grade methanol and acetonitrile were purchased from Merck (Darmstadt, Germany). Formic acid was obtained from Fluka (Buchs, Switzerland). Ultrapure water was prepared using a Milli-Q water purification system (Millipore Corp., Billerica, MA, USA). The following compounds were obtained from Sigma-Aldrich (St. Louis, MO, USA): kynurenic acid, L-lysine, L-phenylalanine, L-tyrosine, L-tryptophan, taurine, and citrate. Glyoxylic acid was obtained from TCI (Tokyo, Japan). All other chemicals were of analytical grade. The Cystone (Himalayas, India) powders were dissolved in saline to obtain a concentration of 72 mg/ml. 2.2. Preparation and quality control of OS extract OS was purchased from Anguo (Hebei, China) and authenticated by Prof. Lian-na Sun (School of Pharmacy, Second Military Medical University, Shanghai, China). A voucher specimen was deposited in the Chinese Material Medica specimen room of the Pharmacy School, Second Military Medical University. The whole dried OS plant was ground to powder by a disintegrator. Next, the powder (200 g) was extracted with 1000 ml of 75% ethanol (v/v) for 2 h by an ultrasonic method at room temperature. The extraction solution was filtrated and concentrated using a rotary evaporator at 60 1C until dry. The yield of ethanol extract was 9.6%, as calculated by final weight. The residue was dissolved in saline to obtain the concentration of 50 mg/ml for the following experiments. To test the reproducibility of OS extraction, six OS metabolites, including isosinensetin, eupatorin, salvigenin, caffeic acid, rosmarinic acid and ursolic acid, were measured from the final products by UHPLC-Q-TOF mass spectrometry. The relative standard derivation (RSD) values of the peak areas of each component were less than 5% in six sets of experiments, indicating that the OS extraction is reproducible. Detailed methodology is listed in Supplemental materials section. 2.3. Animal experiment and sample collection All animal studies were performed in accordance with the National Institutes of Health (NIH) guide for the Care and Use of Laboratory Animals. The experimental procedures were approved by the Ethical Committee for the Experimental Use of Animals at Second Military Medical University (Shanghai, China). Thirty six wild-type
67 male C57B/L6 mice at seven to eight weeks old were purchased from 68 the Shanghai SLAC laboratory Animal Co., Ltd. After conditional 69 housing for one week, these mice were randomly divided into Saline, 70 Crystal, Cystone and three doses of OS groups with 6 mice in each 71 group. To establish the crystal renal injury model, all mice excluding 72 the saline control group were i.p. injected with glyoxylate at a dosage 73 of 100 mg/kg once daily for six days. Four hours after each glyoxylate 74 injection, the mice in the OS groups (total 18 mice and 6 mice for 75 each dosage) were i.g. administrated with OS extract at a dosage of 76 0.5 g/kg,1 g/kg, and 2 g/kg. The mice in the Cystone group were i.g. 77 administrated with Cystone at a dosage of 1.2 g/kg, which served as a positive reference control (Patel et al., 2012), while mice from the Q2 78 79 Saline and Crystal groups were given saline. 80 After the last gavages, all mice were placed into metabolic 81 cages for 24 h to collect urine samples. At the end of the study, 82 blood samples were collected by retro-orbital puncture and 83 kidneys were harvested after in situ cardio-perfusion and fixed 84 in formalin for further analysis. After clotting at 4 1C for 2 h, the 85 venous blood was centrifuged at 4000 rpm for 5 min; the sera were harvested for biochemical analysis. All urine and sera 86 samples were immediately stored at 80 1C prior to analysis. 87 88 2.4. Histological and biochemistry analysis 89 90 The kidneys were fixed in 10% buffered formalin and paraffin91 embedded. Sections 3 μm thick were used to conduct von Kossa 92 staining according to the instructions of the commercial kit (Jiemei 93 Gene, Shanghai, China). Five random light microscope images of 94 von Kossa staining were collected. Thirty views were gathered 95 from each group for semi-quantitative examination by image 96 analysis software ipp6.0 (Media Cybernetics, Washington, USA). 97 98 The levels of calcium, phosphate and creatinine in urine, the levels of serum creatinine (Scr), and blood urea nitrogen (BUN) 99 were measured by a BC-2800Vet animal auto biochemistry analy100 zer (Shihai, Guangdong, China). 101 102 2.5. Sample preparation 103 104 Prior to analysis, a 100 μl aliquot of urine sample was thawed at 105 4 1C followed by the addition of 300 μl of acetonitrile to precipitate 106 the proteins. The resulting solution mixture was spun at 13,000 rpm 107 for 15 min at 4 1C. The clear supernatant (150 μl) was transferred to a 108 sampling vial for UHPLC–MS analysis. A QC sample was prepared by 109 pooling aliquots from all urine samples collected in the course of 110 the study. 111 112 2.6. UHPLC-Q-TOF/MS profiling analysis 113 114 115 UHPLC-Q-TOF/MS analysis was performed on an Agilent 1290 116 Infinity LC system equipped with an Agilent 6530 Accurate Mass 117 Quadrupole Time-of-Flight mass spectrometer (Agilent, USA). 118 Chromatographic separations were performed at 40 1C on an 119 ACQUITY UPLC HSS T3 column (2.1 mm 100 mm, 1.8 μm, Waters, 120 Milford, MA). The mobile phase consisted of 0.1% formic acid 121 (A) and ACN modified with 0.1% formic acid (B). The optimized 122 UPLC elution conditions were 0–2 min, 5% B; 2–10 min, 5–15% B; 123 10–14 min, 15–30% B; 14–17 min, 30–95% B; 17–19 min, 95% B, and 124 the posttime was 6 min for equilibrating the system. The flow rate 125 was set to 0.4 ml/min and the injection volume was 2 μl. The auto126 sampler was maintained at 4 1C. 127 An electrospray ionization source (ESI) was operated in both 128 positive and negative modes. The optimized conditions were as 129 follows: capillary voltage, 4 kV in positive mode and 3.5 kV in 130 negative mode; drying gas flow, 11 l/min; gas temperature, 350 1C; 131 nebulizer pressure, 45 psig; fragment voltage, 120 V; skimmer 132 voltage, 60 V. The mass spectrum was collected in profile mode
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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ranging from 50 to 1100 m/z. The biomarker candidates were further analyzed by MS/MS; the collision energy was set from 10 to 40 eV. The QC samples were randomly inserted in the sequence to validate the stability of the system. PCA was employed to assess the clustering of the QC samples in the PCA score plot from all the tested samples. Eight ions, including 4 ions in positive mode and 4 ions in negative mode, were chosen from the LC chromatograms, and RSDs of the retention times and peak areas of those were calculated.
2.7. Data process and statistical analysis The UHPLC–MS raw data were converted into a common data format (.mzdata) files by Agilent MassHunter Qualitative software. The isotope interferences were excluded and the threshold was set to 0.1%. The program XCMS (Smith et al., 2006) (http://metlin.scripps.edu/ download/) was employed for peak extraction, alignment and integration to generate a visual data matrix. After filtering the ions based on 80% rule (Smilde et al., 2005), all of the detected ions in each sample were normalized to the sum of the peak area to obtain the relative intensity of metabolites. Then, the three-dimensional data matrix,
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including the sample names, retention times, m/z pairs, and normalized ion intensities, was imported into the SIMCA-P program (version 11.0, Umetrics, Umea, Sweden) for multivariate statistical analysis after mean-centering and Pareto scaling. The relevant parameters, such as R2X, R2Y, and Q2, were monitored and permutation tests were implemented to evaluate the quality of models. Biochemistry data were expressed by mean 7 SD. The statistical significance of mean values was tested using one-way ANOVA and Tukey's posthoc test through SPSS 17.0 program (IBM, New York, USA). Differences were considered to be significant when p values were less than 0.05. The heat map of different metabolites was performed by MetaboAnalyst platform (http://www.metaboana lyst.ca). Normalized amount of each marker metabolite was plotted in a box plot using the GraphPad Prism 5.0.
2.8. Identification of the potential biomarkers Identification of different metabolites was an important and challenging job in the metabonomics study. Several steps were performed to finally identify the metabolites: (1) ions were confirmed based on the extracted ion chromatogram (EIC); (2) exact
Fig. 1. Representative photomicrographs for von Kossa staining of calcium deposition in the cortex and medulla junction of kidney ( 400), (A) slice in normal Saline group with no crystal deposition; (B) slice in CaOx-induced model group with clearly increased calcium deposition. (C) Slice in the Cystone treated group; (D) slice in the OS treated group at a dosage of 0.5 g/kg; (E) slice in the OS treated group at a dosage of 1 g/kg; (F) slice in the OS treated group at a dosage of 2 g/kg; (G) calcium content was expressed as the area of positive staining in each group, the calcium deposits in mice treated 0.5 g/kg OS were not significantly reduced, those in mice receiving 1 g/kg, 2 g/kg OS and Cystone were significantly decreased compared with which in Crystal group. ***p o0.001 compared with Saline group, ###p o0.001 compared with Crystal group.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 2. Representative total ion chromatograms (TICs) of the urine samples obtained in ESI positive ion mode from Saline group (A), Crystal group (B) and OS group (C) based on UHPLC-Q-TOF/MS.
masses of quasi-molecular ions were put into online databases to get suggestions for the possible metabolites, such as Metlin (http:// metlin.scripps.edu/), Human Metabolome Database (http://www. hmdb.ca/); (3) MS/MS spectra were compared with the MS/MS information from the above databases to verify the structure of the putative metabolites; and (4) the retention times and the fragments of metabolites were compared with those of reference samples (Tan et al., 2012).
3. Results 3.1. Histology and biochemistry Von Kossa staining of calcium deposition in the cortex and medulla junction of kidney showed that calcium deposits were clearly present in kidney sections of mice in the Crystal group. Slimier with slices from mice treated with Cystone, the positive stain in renal sections from 1 g/kg and 2 g/kg OS extract treatment group was much less than that in Crystal group, which did not happened in mice receiving 0.5 g/kg OS extract intervention. Based on the results of dosage selection, 1 g/kg OS extract was defined as the optimal dosage and used in next biochemistry and metabonomics analysis. The representative images of calcium staining for each group are illustrated in Fig. 1A–F. Semi-quantitative analysis confirmed a significant difference in the positive staining area of calcium deposition among different groups (Fig. 1G). In addition, a significant decrease in the ratios of both calcium and phosphate to creatinine was observed in the urine of the Cystone group and the OS treated group as compared to that of the Crystal group (Fig. S1A). Furthermore, the levels of creatinine and BUN in sera were also lower in the Cystone and OS group when compared to those of the Crystal group (Fig. S1B), supporting that OS has a protective effect on CaOx induced renal injury. 3.2. Metabolic profiling analysis of urine PCA score plot including all the test and QC samples shows that the QC sample features are tightly clustered (Fig. S2). In addition, the RSD values of the retention times and peak intensities in QC samples for the stability evaluation were less than 0.65% and
8.65%, respectively (Table S1). The results demonstrated that the stability of the proposed method was satisfying. Urinary metabolic profiling analysis was performed according to the proposed approach. The typical total ion chromatograms (TICs) obtained from the ESI positive and ESI negative are shown in Figs. 2 and S3. We first performed the PCA, an unsupervised multivariate data analysis, to visualize the trends and outliers in the data for both Saline and Crystal groups (Chan et al., 2011; Trygg et al., 2007). The score plots showed that there were no outliers and that the Crystal group was clearly separated from the Saline group (Figs. 3A and S4A). Then, supervised PLS-DA was applied to screen potential metabolite biomarkers (Trygg et al., 2007). As illustrated by the PLS-DA scores plot (Figs. 3B and S4B), the Crystal group was obviously separated from the Saline group. When 2 components were calculated in the positive mode, the cumulative R2X, R2Y and Q2 were 0.402, 0.978 and 0.72, respectively, while the cumulative R2X, R2Y and Q2 in the negative mode were 0.396, 0.989 and 0.8, respectively. No over-fitting was observed in either ESI positive or ESI negative according to the results of the permutation test (Fig. S5). PLS-DA scatter and variable importance plots (Figs. 3C and S4C) can be used to identify characteristic metabolites to differentiate the Crystal group from the Saline group (Huang et al., 2013). In the present research, ions with VIPs greater than 1.0 were considered to be important differential metabolites. One-way ANOVA and Tukey's posthoc test were performed to assess the statistical significance. Finally, 30 metabolites with significant differences between the Saline and Crystal groups were identified as potential biomarkers, which consisted of 24 metabolites selected based on data from the positive mode, and 6 metabolites selected based on data from the negative node. The identification of m/z 190 taken as an example is shown in Fig. 4. Other biomarkers identified using the proposed approach are listed in Table 1. 3.3. Evaluation of protective mechanism of OS on crystal induced kidney injury We have identified 30 urinary metabolites as the potential biomarkers of crystal induced kidney injury in the animal model of glyoxylate induced renal injury (Fig. 5). It would be logical to use them as the monitoring markers to evaluate the protective effects of OS on crystal kidney injured mice. A PCA model was established using these potential marker metabolites as variables. As indicated
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 3. Plots of multivariate statistical analysis based on the metabolites in urine of mice. (A) PCA scores plot of the Saline group and Crystal group in ESI positive ion mode; (B) PLS-DA scores plot of the Saline group and Crystal group in ESI positive ion mode; (C) scatter and variable importance plot of the Saline group and Crystal group in ESI positive ion mode. The variables marked as (□) are the metabolites selected as potential candidates; (D) PCA 3D scores plot derived from the 30 potential metabolic markers in urine from Saline group, Crystal group and OS group.
by R2X¼ 0.72 and Q2 ¼0.534, the model was both reliable and predictive. The OS group was clearly separated in the scores plot from the Crystal group, but was close to the Saline group (Fig. 3D), providing further evidence for the protective effect of OS extract on crystal kidney injury. Detailed changes of the 30 potential metabolite biomarkers in OS treated mice are listed in Table 1. The results indicate that the metabolic pattern in the urine of mice with glyoxylate-induced renal injury was reversed to normal levels after OS treatment. To effectively visualize and characterize remedial effects of OS, the heatmap and the box plots representing the intensity levels of 30 metabolite biomarkers in the different groups are shown in Figs. 6 and 7, respectively. Among the 30 metabolites identified, 20 were reversed to different degrees of normality in the OS treated group, such as N6,N6,N6-Trimethyl-L-lysine, betaine, choline, L-proline, vinylacetylglycine, kynurenic acid, metanephrine, taurine, phenylpyruvate, citric acid, and succinic acid semialdehyde etc. The related pathway of each biomarker is shown in Table 1. These biomarkers identified are mainly involved in amino, energy and choline metabolism, indicating that these metabolism pathways may play key roles in mediating the protective effect of OS on crystal induced kidney injury and that the deeper investigation of these pathways should be helpful in elucidating the therapeutic mechanism of OS.
4. Discussion In this study, we established the glyoxylate-induced crystal related kidney injury model following the method described previously (Okada et al., 2007, Taguchi et al., 2014). Then, we applied this model to investigate the protective effect and mechanism of OS using a metabonomics-based approach. Calcium deposition was
clearly shown in kidney sections from mice in the Crystal group. In addition, the levels of urinary calcium, phosphorus and Scr, BUN were significantly increased in the Crystal group versus Saline group, indicating that the model was successfully established. Previous works based on a genome-wide analysis and cell biology approach have found that the interaction between migrating macrophages and renal tubular cells contributes heavily during crystal formation in hyperoxaluric mice (Okada et al., 2009; Umekawa et al., 2002). According to the results of the present study, 30 important metabolites were identified as potential metabolite biomarkers for crystal renal injury. By searching the KEGG pathway database (http://www.genome.jp/kegg/), we constructed the metabolic network (Fig. 5) clearly showing that glyoxylateinduced crystal renal injury is related to the perturbance of tryptophan tyrosine and phenylalanine metabolism, lysine metabolism, arginine and proline metabolism, glycine, serine and threonine metabolism, taurine and hypotaurine metabolism, purine metabolism and energy metabolism. Thus, metabolomics strategy would be an important complementary technology to the cell wide measurement of RNA, protein, fluxes, and interactions for investigating the mechanism of kidney stone formation. In the Crystal group, several amino acids including lysine, proline, phenylalanine, tryptophan, tyrosine and their relative metabolites were significantly changed. The metabolite imbalance of amino acids may be closely related to the pathogenesis of kidney stone formation. We observed a significant elevation of three important aromatic amino acids (AAA), including phenylalanine, tyrosine and tryptophan. Both phenylalanine and tryptophan are essential amino acids, while tyrosine is a semi-essential amino acid due to partial derivation from phenylalanine by phenylalanine hydroxylase (Matthews, 2007). Tyrosine can also be metabolized to many biologically active catecholamines, such as dopamine,
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 4. The identification of a selected biomarker (m/z 190.05). (A) Extracted ion chromatogram (EIC) of m/z 190.05; (B)the EIC of commercial standard kynurenic acid; (C) MS/MS spectrum of the ion in urine sample and possible MS/MS fragment mechanism; (D) MS/MS spectrum of kynurenic acid standard. The collision energy was 10 V.
norepinephrine and serotonin (Fernstrom and Fernstrom, 2007). In addition, several other metabolites relating to the AAA metabolism, such as phenylpyruvate, phenyllactate, metanephrine and kynurenic acid, were also markedly changed in the Crystal group. The results indicate that the AAA metabolism is clearly disturbed by the crystal renal injury process, which is consistent with previous studies documenting the close association between kidney diseases and the degradation, synthesis, or excretion of AAA (Boirie et al., 2004; Young et al., 2010). Among side metabolites of the AAA metabolism, kynurnic acid is an important metabolite of the kynurenine pathway, which is a major branching pathway of tryptophan metabolism. Kynurenic acid is generally known to be a uremic toxin. The elevated kynurenic acid concentration in serum was reported in patients with chronic renal failure (Uwai, 2012). However, a decreased level of kynurenic acid was observed in the urine of mice of the Crystal group in this study, which is also concordant with a previous study (Zhao et al., 2012). Furthermore,
the kynurenic acid concentration in the OS group was reversed to normal levels, indicating that OS may promote the clearance of the kynurenic acid. Taurine, a sulfur-containing β-amino acid and also a major free intracellular amino acid in mammalian cells (Kerai et al., 1999), is well known as an endogenous anti-oxidant and membrane-stabilizing agent. Many studies have documented the protective effects of taurine against multiple types of kidney damage, including ischemia/reperfusion, hyperglycemia, oxidantstress and xenobiotics (Al-Kahtani, 2010; Guan et al., 2008). In addition, taurine treatment can alleviate the tubular oxidative injury via a mitochondrial-linked pathway in rat models of nephrolithiasis (Li et al., 2009). The increased urine taurine level in mice receiving OS treatment implies that taurine induction might be involved in the protective effect of OS on crystal induced renal damage. Carnitine is an essential cofactor required to transport fatty acids into the inner mitochondrial matrix to provide energy
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Table 1 Potential biomarkers related to crystal induced kidney injury and their metabolic pathways. No. RT (min) m/z
Ion
Formula
Trenda
Identification
Related pathway
Crystal/Saline OS/Crystal ESI þ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 ESI- 25 26 27 28 29 30
0.56 0.58 0.62 0.7 0.71 0.72 0.8 0.98 0.99 1.04 1.08 1.3 1.86 1.87 3.21 3.78 4.99 4.99 6.02 6.29 6.35 7.85 8.15 10.31 0.68 0.7 0.75 0.79 0.97 8.48
147.1122 146.1652 189.1607 118.086 144.1027 126.0861 177.0615 157.0605 139.0498 182.0811 116.0671 184.0603 166.0841 144.0653 195.0765 205.0972 190.05 216.1233 198.1125 232.1183 218.1389 172.0967 174.1124 200.1281 124.0081 163.0333 191.0199 101.0248 167.0219 165.0556
[Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ Na] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [Mþ H] þ [M H] [M H] [M H] [M H] [M H] [M H]
C6H14N2O2 C7H19N3 C9H20N2O2 C5H11NO2 C7H13NO2 C5H13NO C4H8N4O4 C6H8N2O3 C6H6N2O2 C9H11NO3 C5H9NO2 C8H9NO4 C9H11NO2 C6H9NO3 C9H10N2O3 C11H12N2O2 C10H7NO3 C10H17NO4 C10H15NO3 C10H17NO5 C10H19NO4 C8H13NO3 C8H15NO3 C10H17NO3 C2H7NO3S C9H8O3 C6H8O7 C4H6O3 C5H4N4O3 C9H10O3
b L-Lysine
nnn
Spermidinec N6,N6,N6-Trimethyl-L-lysined Betaineb Prolinebetainec Cholined Allantoated 4-Imidazolone-5-propionic acidd Urocanic acidd c L-Tyrosine d L-Proline 4-Pyridoxatec L-Phenylalanineb Vinylacetylglycinec Aminohippuric acidc b L-Tryptophan Kynurenic acidb Propenoyl-L-carnitined Metanephrined Isovalerylglutamicacidd Propionyl-L-carnitined N-butanoyl-lhomoserinelactoned Isovalerylalanined N-hexanoyl-L-Homoserinelactoned Taurineb Phenylpyruvated Citrateb Succinic acid semialdehyded Uric acidb Phenyllactatec
n
↑
↓
nnn
↓ ↑ nn ↓ nnn ↓ n ↓ n ↓ n ↓ n ↑ n ↓ nnn ↓ nn ↑ n ↑ n ↓ nnn ↑ n ↓ nn ↓ n ↓ nn ↓ nn ↓ nn ↑ nnn ↑ nn ↑ n ↓ nnn ↑ nnn ↓ nnn ↑ nn ↓ nn ↓ n
↓ ↓
#
###
↑ ↓ ## ↓ ### ↑ ↓ ↓ # ↓ ↓ ### ↑ ↓ ↓ ### ↓ ↓ ↓ ### ↑ ## ↑ # ↑ # ↓ ↑ ### ↓ ### ↓ ↓ ### ↑ ### ↓ ## ↑ ### ↓ # ↓ ↑ #
Lysine biosynthesis Arginine and proline metabolism Lysine degradation Glycine, serine and threonine metabolism Amino acid metabolism Glycine, serine and threonine metabolism Purine metabolism Histidine metabolism Histidine metabolism Tyrosine metabolism Arginine and proline metabolism Vitamin B6 metabolism Phenylalanine metabolism Fatty acid metabolism Fatty acid metabolism Tryptophan metabolism Tryptophan metabolism Fatty acid metabolism Tyrosine metabolism Fatty acid metabolism Fatty acid metabolism Fatty acid metabolism Fatty acid metabolism Fatty acid metabolism Taurine and hypotaurinemetaboliism Phenylalanine metabolism Citrate cycle (TCA cycle) Alanine, aspartate and glutamate metabolism Purine metabolism phenylalanine metabolism
a
(↑): up-regulated and (↓): down-regulated (*p o 0.05, **p o 0.01, ***p o0.001 versus Saline group). po 0.05 versus Saline group. nn p o0.01 versus Saline group. nnn p o 0.001 versus Saline group. # p o 0.05 versus Crystal group. ## p o 0.01 versus Crystal group. ### p o 0.001 versus Crystal group. b Metabolites validated with standards. c Metabolites analyzed based on MS/MS chromatograms. d Metabolites putatively annotated. n
through β-oxidation (Indiveri et al., 2010). Carnitine deficiency and energy starvation have been reported to play critical role in chemical-induced nephrotoxicity (Sayed-Ahmed, 2010). In contrast, acylcarnitines and particularly propionyl-L-carnitine, which is a product of the enzymatic esterification of carnitine (Murphy et al., 2012), have been described to prevent the development of renal failure by increasing intracellular carnitine levels and energy production (Sayed-Ahmed, 2010). In our study, the levels of short chain acylcarnitines, propionyl-L-carnitine and propenoyl-L-carnitine were down-regulated, which may reflect a decreased level of aerobic oxidation in the mitochondria (Huang et al., 2013). In contrast, the level of the acylcarnitines was elevated in mice urine after OS treatment indicating that OS might exert the protective effect by improving the energy metabolism. How purine metabolism affects kidney diseases has not been clearly demonstrated. Uric acid, the downstream metabolite of purine, has elicited much controversy over its effect in the pathogenesis of stone formation. Uric acid has been reported as a potentially important risk factor for the development and progression of CKD. In contrast, several studies have also showed that soluble uric acid exerts a protective effect as a powerful antioxidizing agent (Johnson et al., 2013; Romero et al., 2009; Shimada et al., 2009). In our study, the absolute concentration of uric acid in the urine was low and down-regulated in the Crystal
group compared to that of the Saline group, supporting the protective effect of uric acid. Allantoin was the oxidation product of the uric acid through urate oxidase in mice; however, uric acid was usually regarded as the end product of the purine metabolite in humans due to the lack of urate oxidase (Romero et al., 2009). Thus, the change of allantoin in mice might be of less clinical significance.
5. Conclusion In summary, a metabonomics approach based on UHPLC/Q-TOF MS has been developed to profile the metabolic alterations of glyoxylate-induced crystal renal injury in mice. As a result of this approach, 30 metabolites, primarily involved in amino acid metabolism, taurine and hypotaurine metabolism, energy metabolism and purine metabolism, have been identified as potential biomarkers of crystal renal injury. The OS treatment significantly decreased the calcium deposition and reversed the metabolic aberrations in mice with crystal renal injury, strongly supporting the therapeutic effect of OS on glyoxylate-induced crystal renal injury; however, further studies are warranted to better understand how specific active compounds of OS are involved in the recovery of those disturbed metabolic pathways.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 5. The metabolic pathway networks resulting from OS modulation to the glyoxylate-induced crystal kidney injury. Identified biomarkers with a changed concentration in the OS treatment group compared to that in the Crystal group are labeled in red (up-regulated) and green (down-regulated), respectively. Metabolites which were not significantly changed in the Crystal group compared with the Saline group are given in black. The words in blue besides the pane indicate the related pathway. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 6. Heatmap based on the relative levels of potential marker metabolites in urine of mice in the Saline group, Crystal group and OS group.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Fig. 7. Box plots showing the levels of 30 potential marker metabolites in the Saline group, Crystal group and OS group, *p o 0.05, **p o0.01, ***p o 0.001 for Crystal group versus Saline group; #p o0.05, ##p o 0.01, ###p o 0.001 for OS group versus Crystal group.
Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i
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Please cite this article as: Gao, S., et al., Urinary metabonomics elucidate the therapeutic mechanism of Orthosiphon stamineus in mouse crystal-induced kidney injury. Journal of Ethnopharmacology (2015), http://dx.doi.org/10.1016/j.jep.2015.03.025i