Investigation of Dioscorea bulbifera Rhizome-Induced Hepatotoxicity

Sep 13, 2017 - In total, 55 metabolites distributed in 33 metabolic pathways were identified as being significantly altered in DBR-treated rats. Corre...
1 downloads 6 Views 5MB Size
Download PDF
Article pubs.acs.org/crt

Investigation of Dioscorea bulbifera Rhizome-Induced Hepatotoxicity in Rats by a Multisample Integrated Metabolomics Approach Dong-Sheng Zhao,† Li-Long Jiang,† Ya-Xi Fan, Ling-Li Wang, Zhuo-Qing Li, Wei Shi, Ping Li,* and Hui-Jun Li* State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing 210009, China S Supporting Information *

ABSTRACT: The use of herbal medicines continues to expand globally, meanwhile, herb-associated hepatotoxicity is becoming a safety issue. As a conventional Chinese medicinal herb, Dioscorea bulbifera rhizome (DBR) has been documented to cause hepatic toxicity. However, the exact underlying mechanism remains largely unexplored. In the present study, we aimed to profile entire endogenous metabolites in a biological system using a multisample integrated metabolomics strategy. Our findings offered additional insights into the molecular mechanism of the DBRinduced hepatotoxicity. We identified different metabolites from rat plasma, urine, and feces by employing gas chromatography-mass spectrometry in combination with multivariate analysis. In total, 55 metabolites distributed in 33 metabolic pathways were identified as being significantly altered in DBR-treated rats. Correlation network analysis revealed that the hub metabolites of hepatotoxicity were mainly associated with amino acid, bile acid, purine, pyrimidine, lipid, and energy metabolism. As such, DBR affected the physiological and biological functions of liver via the regulation of multiple metabolic pathways to an abnormal state. Notably, our findings also demonstrated that the multisample integrated metabolomics strategy has a great potential to identify more biomarkers and pathways in order to elucidate the mechanistic complexity of toxicity of traditional Chinese medicine.

■

and mechanistic insights into biochemical effects of drugs.19,20 As a proof of concept, metabolomics can further be employed as a perfect approach to link the gap between TCM and molecular pharmacology and/or toxicology.21−23 Several publications have demonstrated the application of metabolomics, laying emphasis on understanding the pathophysiology in the TCM-involved liver damage.24−26 Like most TCM, DBR may exert a complicated hepatotoxicity through the currently accepted “low content, multicomponent, and multitarget” model.27,28 Some metabolomics research on DBR-induced liver injury has been conducted by analyzing a single type of biosample from DBR-administrated rats;11,29,30 nevertheless, it is often not sufficient to characterize the pathophysiology from the perspective of systems biology. A multisample integrated metabolomics approach is thus highly desired for precisely describing the status of DBR-induced hepatotoxicity along with elucidating the underlying molecular mechanism. This work specially integrated plasma, urine, and fecal metabolomics profiles to analyze and identify important differences in toxicity in rats over a 12-week period of administration of DBR using gas chromatography-mass spectrometry (GC-MS) techniques in combination with multivariate

INTRODUCTION Dioscorea bulbifera rhizome (DBR), termed as “Huang Yao Zi” and “Huang Du” in Chinese, is originally derived from the dry tubers of Dioscorea bulbifera L. (Fam. Dioscoreaceae).1 In traditional Chinese medicine (TCM), such medicine has been employed to treat various disorders with a long history, such as hemoptysis, nosebleeds, laryngitis, goiter, pyogenic skin infections, scrofula, trauma, testicle inflammation, and tumors.2 Moreover, it is used globally as a folk medicine to cure boils and wound infections in some African countries3 as well as inflammation, pain, piles, and ulcers in India.4,5 However, experimental studies and clinical reports have revealed that DBR could lead to toxicity, particularly in the liver.5−10 Limited mechanistic studies have reported that DBR-associated hepatotoxicity might be ascribed to liver oxidative stress injury and oxidative damage to hepatocellular mitochondria.10−13 This makes it necessary to continually unravel the detailed mechanism underlying DBR-induced liver injury. As a formidable tool, metabolomics is usually used to systematically assess the molecular responses of living systems to all external stimuli, based on global metabolite profiles in biological specimens, such as blood, urine, or tissue, or in medicinal plants.14−16 Metabolite profiling can be used to characterize pathological states in humans and animals with great potential.17,18 Moreover, it can also offer diagnostic information © 2017 American Chemical Society

Received: June 26, 2017 Published: September 13, 2017 1865

DOI: 10.1021/acs.chemrestox.7b00176 Chem. Res. Toxicol. 2017, 30, 1865−1873

Article

Chemical Research in Toxicology

Figure 1. Biochemical parameters (A) and liver histopathology (control group (B), low-dose group (C) and high-dose group (D)) of rats in different groups. Results were presented as the mean ± SD (%) (n = 6). Significant differences from the value of control group with the administration of DBR were noted (*p < 0.05, **p < 0.01). Green arrow represents cell swelling, and blue arrow represents cell necrosis. intragastrically administrated with 1.8 and 18 g/kg (dose calculated as grams of crude material used to create the extract per kilogram of rat body weight) of the above-mentioned DBR extract once a day, respectively. Equivalent volumes of 0.5% CMC-Na solution were administrated to the control group via the same method. Food and water were given ad libitum and body weight was measured weekly for adjustment of the drug dosage. After 12 weeks of administration of DBR extract or CMC-Na solution, 24 h urine and feces were collected from all rats in individual metabolic cages. All urine samples were immediately centrifuged at 3000 rpm for 10 min, and the supernatants were stored at −80 °C until GCMS analysis. All fecal samples were lyophilized and stored at −80 °C before further analysis. Blood was collected from the carotid artery and centrifuged at 3000 rpm for 10 min at 4 °C. An appropriate quantity of supernatant was used for biochemical analysis, and the remainder was stored at −80 °C prior to GC-MS analysis. At the end of the experiment, rats were sacrificed by decapitation, and the liver tissues were immediately removed and fixed in 10% formalin for histopathological examination. Biochemical Analysis and Histopathological Examination. Biochemical parameters of serum, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), the ratio of aspartate aminotransferase to alanine aminotransferase (AAR), alkaline phosphatase (ALP), γ-glutamyl transferase (GGT), total bile acid (TBA), and total bilirubin (TBIL) were analyzed on a Beckman CX3 automatic biochemistry analyzer (Beckman-Coulter, Brea, CA, USA). Liver specimens from the same lobe in each animal were fixed in 10% neutral formalin, embedded in paraffin, sectioned to a thickness of approximately 5 μm, stained with hematoxylin and eosin, and examined for histopathological changes under the microscope (Olympus DX45, Tokyo, Japan). Metabolic Profiling analysis. Sample preparation was done by following previously published protocols with minor modifications.31 An aliquot of 50 μL of plasma or 50 mg of fecal sample was placed in a 1.5 mL Eppendorf (EP) tube and mixed with 30 μL of IS (0.5 mg/mL). Protein precipitation was then carried out using 500 μL precooled methanol followed by vortex mixing for 5 min before centrifuging at 13,000 rpm for 10 min at 4 °C. A 400 μL aliquot of supernatant was transferred to a clean EP tube and dried under a gentle stream of nitrogen gas. The residue was derivatized by the addition of 40 μL of methoxyamine hydrochloride (15 mg/mL in pyridine) at 60 °C for 2 h. To each sample we added 60 μL of BSTFA (1% TMCS) and heated the mixture at 70 °C (13 000 rpm) for 60 min. The derivation was cooled

statistical data analysis. In addition, the relationships among identified metabolites were assessed using correlation network technology. The robust integrated metabolomics strategy was constructed to recognize more hepatotoxic biomarkers in order to comprehensively explore underlying hepatotoxic mechanisms of DBR.

■

MATERIALS AND METHODS

Plant Material and Chemical Reagents. The DBR sample was purchased from the Bozhou herb market (Anhui, China) and was originally authenticated by Prof. Hui-Jun Li, China Pharmaceutical University. A representative specimen (DBR20150820YN) was deposited in the laboratory. The material was cut into small pieces, refluxed with 80% (v/v) ethanol at a solid−liquid ratio of 1:10 for 2 h, and filtered. The extraction was repeated three times. The combined filtrates were concentrated under reduced pressure to remove ethanol and then lyophilized. Before use, the dried powder was redissolved and dispersed in 0.5% carboxymethyl cellulose sodium salt (CMC-Na) aqueous solution for intragastric administration to rats. The highperformance liquid chromatography (HPLC) method was applied to determine the main toxic components in DBR extract (Figure S1), revealing the contents of diosbulbin B (2.15%, w/w) and 8epidiosbulbin E (0.057%, w/w). Chromatographic grade methanol was provided by Tedia (Fairfield, USA). Urease (≥45 units/mg) was purchased from Aladdin (Shanghai, China). N,O-bis(trimethylsilyl) trifluoroacetamide (BSTFA) with 1% trimethylchlorosilane (TMCS) was purchased from Macklin (Shanghai, China). Methoxyamine hydrochloride, pyridine, heptadecanoic acid (internal standard, IS), and all standard compounds were obtained from Sigma-Aldrich (Shanghai, China). Ultrapure water was prepared using a MUL-9000 Milli-Q purification system (Massachusetts, USA). Animal Protocols. All experiments and animal care were conducted in accordance with the Provision and General Recommendation of Chinese Experimental Animals Administration Legislation and were approved by the Science and Technology Department of Jiangsu Province (license number: SYXK (SU) 2016-0011). Male Sprague− Dawley rats, weighing 200−220 g, were provided by Sino-British Sippr/ BK Lab Animal Ltd. (Shanghai, China). The animals were acclimatized to environmentally controlled conditions (temperature 22 ± 2 °C, relative humidity 50 ± 10%, and 12 h light/dark cycle) with free access to standard diet and water. After 1 week, the rats were randomly assigned to three groups (n = 6): low-dose and high-dose groups were 1866

DOI: 10.1021/acs.chemrestox.7b00176 Chem. Res. Toxicol. 2017, 30, 1865−1873

Article

Chemical Research in Toxicology and centrifuged at 13,000 rpm for 10 min prior to GC-MS analysis. Similarly, an aliquot of 50 μL of urine sample was mixed with 20 μL of urease (50 mg/mL, 37 °C, 60 min) to remove the high urea content. The protein precipitation and derivatization procedures were performed as described above for plasma or feces. Quality control (QC) samples were prepared by pooling aliquots of all the plasma, urine, or fecal samples and were processed with the same procedure as that followed for the experiment samples. Each 2 μL aliquot of derivatized sample was injected in a 30:1 split ratio into an Agilent 7890B/5977A GC-MS equipped with an HP-5MS capillary column (30 m × 0.25 mm × 0.25 μm, Agilent J & W Scientific, USA). Helium was used as the carrier gas with a constant flow rate of 1 mL/min. The temperature program was as follows: the initial temperature was 80 °C, followed by holding for 2 min, then elevated to 300 °C at a rate of 10 °C/min, and maintained for 6 min. The temperatures of the injector, transfer line, and ion source were set to 250, 280, and 230 °C, respectively. The mass range (50−600 m/z) in a fullscan mode for electron impact ionization (70 eV) was applied. The solvent delay time was set to 5 min. Data Processing and Statistical analysis. Raw GC-MS data was exported to mzData format by Mass Hunter Workstation Software (Version B.06.00, Agilent Technologies) and subsequently sent to Mass Profiler Professional (MPP 12.0, Agilent Technologies). The pretreatment process included novel nonlinear retention time alignment, baseline filtration, peak identification, matching, integration, and IS (m/ z = 117, retention time (tR) = 18.00 min) normalization. The resulting data matrix consisting of variables, sample code, and peak area was further processed using Microsoft Excel 2010 (Microsoft, Redmond, WA, USA). Creatinine normalization was applied to reduce the deviation originating from discrepant urine concentrations between each sample. Finally, the normalized data set was imported into SIMCA 14.1 (Umetrics, Sweden) for multivariate statistical analysis and SPSS 22.0 (SPSS Inc., Chicago, IL, USA) for univariate data analysis. On the basis of variable importance in the projection (VIP) values >1.0 obtained from the orthogonal partial least-squares discriminant analysis (OPLSDA) model and p values 0.5 is considered as good, and a Q2 > 0.9 is regarded as excellent, but these parameters are highly dependent on application. The difference between R2Y and Q2 larger than 0.2−0.3 reflects the presence of many irrelevant model terms or a few outlying data points.33 As listed in Table 1, the values of R2X,

the right) were higher than the permuted R2-values (green color in the left), indicating that the identified groups were well separated. Metabolic Pathway Analysis. We further performed metabolic pathway analysis based on the 55 integrated metabolites. As a web-based server supporting pathway analysis, MetaboAnalyst 3.0 (www.metaboanalyst.ca) integrates enrichment analysis and pathway topology analysis to discover the significantly relevant pathways affected by DBR administration. Figure S4 shows that elevated D-glutamine and D-glutamate metabolism participated in the most relevant pathways affected by DBR, with the impact value of 1.0. Moreover, several other important pathways, including valine, leucine, and isoleucine biosynthesis, galactose metabolism, alanine, aspartate and glutamate metabolism, cysteine and methionine metabolism, as well as glycine, serine, and threonine metabolism, were activated in response to DBR administration compared with the normal control group. Furthermore, DBRinduced pathways were related to purine metabolism, pyrimidine metabolism, and primary bile acid biosynthesis. In addition, we constructed a metabolic network through the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp) pathway database. Our data suggested that the comprehensive metabolic profile changes in rats associated with DBR-induced hepatotoxicity were mainly related to the amino acid, bile acid, purine, pyrimidine, lipid, and energy metabolism (Figure 7).

Table 1. Summary of OPLS-DA

a

biosample

Aa

R2X

R2Y

Q2

differenceb

plasma urine feces plasma + urine + feces

5 4 2 5

0.809 0.750 0.605 0.699

0.889 0.923 0.722 0.993

0.782 0.747 0.238 0.771

0.107 0.167 0.484 0.222

The number of latent variables. bthe difference between R2Y and Q2.

R2Y, and Q2 were also acceptable for single plasma and urine, but not feces data sets, whereas the integration of three data sets provided better results (R2Y = 0.993 and Q2 = 0.771), indicating an excellent prediction. The 2D-score plots showed compared performances of OPLS-DA (Figure 6). In Figure 6A−C, the metabolomics data from single source, only urine could make a good separation among the three groups. Moreover, a clearer distribution pattern among three groups was observed from the integrated data sets (Figure 6D), indicating that the integrated metabolomics data could offer greater discriminant information. Furthermore, cluster analysis model was used to assess the discrimination power based on the data of integrated differential metabolites. Figure S3A shows that there was a satisfactory classification among the clustering of control, low-, and highdose groups. To further validate the overall separation quality for the identified groups according to the integrated differential metabolites, consisting of random permutation, class membership and the performance of 20 iterations were conducted in Figure S3B. The Q2 regression line (blue color) had a negative intercept, and all original points of the R2-value (green color in

■

DISCUSSION

The medical plant DBR is a typically hepatotoxic herb, however, its mechanism of toxicity has not been completely explored to date. In the present study, we integrated plasma, urine, and feces metabolomics profiles to analyze and identify important differences in DBR-induced liver injury in rats using GC-MS technique combined with multivariate analysis. Furthermore, we discussed the mechanism of DBR-induced hepatotoxicity based on correlation networks of many identified metabolites. These 1869

DOI: 10.1021/acs.chemrestox.7b00176 Chem. Res. Toxicol. 2017, 30, 1865−1873

Article

Chemical Research in Toxicology

Figure 6. Score plots of OPLS-DA for plasma (A), urine (B), feces (C), and plasma + urine + feces (D). The green circle represents control group, the blue square represents low dose group, and the red triangle represents high dose group.

Figure 7. Metabolic pathways interrupted by DBR. Metabolite names appear in blue fonts are differential metabolites identified in the present study.

analytes.35 Consequently, we adopted a two-step derivatization procedure, in which the residue was subjected to oximation using methoxyamine hydrochloride, followed by trimethylsilyl derivatization using BSTFA with 1% TMCS. With long-term administration of DBR, liver injury was observed with the elevation of biochemical indices in serum, such as AST, AAR, GGT, TBA, and TBIL, and confirmed by hepatic cell swelling and necrosis in liver histopathology. Currently, serum liver enzymes are used as conventional biomarkers for the clinical diagnosis and assessment of hepatic diseases. The serum level of ALT is regarded as the gold standard, particularly in acute hepatocellular damage.36 However, the level of ALT was not increased in rats with long-term administration of DBR in our

changed metabolites indicated that multiple metabolic pathways were interrupted and regulated in response to DBR administration, including amino acid, bile acid, purine, pyrimidine, lipid, and energy metabolism. GC-MS, an excellent analytical platform within the “toolbox” for metabolomic studies, is a well-established bioanalytical platform ideal for detecting myriad molecules including volatile small metabolites, isomeric compounds, derivatized organic acids, and carbohydrates.34 However, we could not detect some compounds by GC-MS due to their nature of nonvolatility or polarity in the complex matrix of biological samples. Chemical derivatization carries out the functions of decreasing the polarity and enhancing the thermal stability and volatility of the 1870

DOI: 10.1021/acs.chemrestox.7b00176 Chem. Res. Toxicol. 2017, 30, 1865−1873

Article

Chemical Research in Toxicology

metabolomics studies, such as different analytical platforms or different omics.

study. Remarkably, we found that AST and AAR were more sensitive biomarkers than other biochemical parameters indicating the mechanism of DBR-induced liver injury. Regular change of AST activities or AAR during the DBR treatment, which showed a dose-dependent increase, correlated well with the histological observations. The DBR-induced hepatotoxicity had pronounced impacts on amino acids metabolism (L-glutamic acid, glycine, L-valine, Lcysteine, L-isoleucine, and L-alanine). In particular, the most notable changes were observed in D-glutamine and D-glutamate metabolism. We showed that L-glutamic acid was predominant in 55 altered metabolites in response to DBR, while its content was increased in plasma and its excretion was decreased in feces compared with the control group. In addition, the most relevant pathway affected by DBR was found to be the glutamatecentered metabolism (D-glutamine and D-glutamate metabolism; glutathione metabolism; alanine, aspartate, and glutamate metabolism). As a significant biological metabolic pathway in the body, the tricarboxylic acid (TCA) cycle is involved in not only aerobic glucose oxidation but also the major pathways for lipid and amino acid metabolism, suggesting that suppression of such a cycle can result in organ degeneration. Some altered saccharide metabolites, including fructose, glucose, and galactose, which were responsible for galactose metabolism as well as the TCA cycle pathway, presented a strong link to the energy metabolism in DBR-treated groups. In epidemiological and clinical studies, stearic acid has been associated with reduced low-density lipoprotein cholesterol to a greater extent compared with other saturated fatty acids.37 A significant increase in stearic acid was found in the plasma, and the excretion of stearic acid was obviously decreased in feces, implying that DBR probably induced liver injury resulting from elevated stearic acid. In humans and higher primates, as the final oxidation product of purine metabolism, uric acid (UA) is finally excreted in urine. The serum UA level is associated with elevated serum liver enzymes and may be an important cause and risk factor of chronic liver damage.38 Intriguingly, a remarkable alteration of UA occurred in plasma from the low-dose group (1.32-fold enrichment) and high-dose group (1.45-fold enrichment) was observed, which was mainly regulated by the purine metabolism pathway. Therefore, the incidence of DBR-induced liver injury might be predicted by plasma UA level as a risk factor, and more investigations should focus on the clinical utility in the prediction of DBR-induced liver damage. If this is confirmed, we might be able to prevent DBR poisoning by reducing the plasma UA levels. As such, purine metabolism might be a potential metabolic pathway for investigation of disturbance, and UA could serve as a promising biomarker to facilitate the clinical monitoring of DBRinduced hepatotoxicity. The metabolic correlation network, a complex biological network, has integrative advantages in interpreting the results of metabolomics. Here, we outlined a multisample integrated metabolomics strategy coupled with the metabolic correlation network to investigate toxicity using DBR as a case study. The findings indicated that the multisample integrated metabolomics technology produced a more distinct metabolic pattern between DBR-treated and untreated rats than a single sample did. In addition, the integrated technology could identify more biomarkers and pathways to elucidate the mechanistic complexity of DBR-induced hepatotoxicity. Meanwhile, the integrated metabolomics strategy might be extended to other multisource

■

SUMMARY In conclusion, our present work demonstrated research on DBRinduced liver damage with a multisample integrated metabolomics method. Interestingly, a more distinct metabolic pattern existed between DBR-treated and untreated rats, which could be elucidated by PCA and OPLS-DA with a panel of the integrated characteristic metabolites. These newly identified pathways could help us further analyze the toxic mechanism and explore new potential therapeutic targets. It is noteworthy that several primary metabolites found in rats with DBR-induced liver injury, namely L-glutamic acid, UA, ureidopropionic acid, and ursodeoxycholic acid, were found as the specific metabolites in response to DBR challenge. Certainly, the main limitation of this work is the quantitative and dynamic analysis of the specific metabolites in DBR-treated rats, which should be carried out in a further experimental validation.

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.chemrestox.7b00176. Typical chromatograms of mixed standards and DBR samples; Venn diagram showing the number of identified metabolites in plasma, urine, and feces; clustering analysis, validation of separation quality, and pathway mapping based on the integrated metabolites; the differential metabolites in plasma, urine, and feces (PDF)

■

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Dong-Sheng Zhao: 0000-0003-3265-5442 Author Contributions †

These authors contributed equally to this work and should be considered co-first authors. Funding

This work was partially supported by the National Natural Science Foundation of China (grant nos. 81573562 and 81773993) and the Natural Science Foundation of Jiangsu Province (grant no. BK20151442), and the project was funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions. Notes

The authors declare no competing financial interest.

■

ABBREVIATIONS AAR, the ratio of aspartate aminotransferase to alanine aminotransferase; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BSTFA, N,O-bis(trimethylsilyl) trifluoroacetamide; CMC-Na, carboxymethyl cellulose sodium salt; DBR, Dioscoreae bulbiferae Rhizoma; GC-MS, gas chromatography-mass spectrometry; GGT, γ-glutamyl transferase; IS, internal standard; OPLS-DA, orthogonal partial least-squares discriminant analysis; PCA, principal component analysis; QC, quality control; TBA, total 1871

DOI: 10.1021/acs.chemrestox.7b00176 Chem. Res. Toxicol. 2017, 30, 1865−1873

Article

Chemical Research in Toxicology

(18) Li, M., Li, H., Jiang, P., Liu, X. Q., Xu, D., and Wang, F. (2014) Investigating the pathological processes of rhegmatogenous retinal detachment and proliferative vitreoretinopathy with metabolomics analysis. Mol. BioSyst. 10, 1055−1062. (19) Antti, H., Ebbels, T. M. D., Keun, H. C., Bollard, M. E., Beckonert, O., Lindon, J. C., Nicholson, J. K., and Holmes, E. (2004) Statistical experimental design and partial least squares regression analysis of biofluid metabonomic NMR and clinical chemistry data for screening of adverse drug effects. Chemom. Intell. Lab. Syst. 73, 139−149. (20) Zhao, X. J., Zhang, Y., Meng, X. L., Yin, P. Y., Deng, C., Chen, J., Wang, Z., and Xu, G. W. (2008) Effect of a traditional Chinese medicine preparation Xindi soft capsule on rat model of acute blood stasis: a urinary metabonomics study based on liquid chromatography-mass spectrometry. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 873, 151−158. (21) Beyoğlu, D., and Idle, J. R. (2013) The metabolomic window into hepatobiliary disease. J. Hepatol. 59, 842−858. (22) Qiu, J. (2007) ’Back to the future’ for Chinese herbal medicines. Nat. Rev. Drug Discovery 6, 506−507. (23) Qiu, J. (2007) Traditional medicine: a culture in the balance. Nature 448, 126−128. (24) Gou, X. J., Tao, Q., Feng, Q., Peng, J. H., Sun, S. J., Cao, H. J., Zheng, N. N., Zhang, Y. Y., Hu, Y. Y., and Liu, P. (2013) Urinary metabonomics characterization of liver fibrosis induced by CCl4 in rats and intervention effects of Xia Yu Xue Decoction. J. Pharm. Biomed. Anal. 74, 62−65. (25) Wang, M., Lamers, R. J., Korthout, H. J., et al. (2005) Metabolomics in the context of systems biology: bridging traditional Chinese medicine and molecular pharmacology. Phytother. Res. 19, 173−182. (26) Wang, J. S., Van, D. H. R., Spruit, S., Hankermeier, T., Chan, K., Van, D. G. J., Xu, G. W., and Wang, M. (2009) Quality and safety of Chinese herbal medicines guided by a systems biology perspective. J. Ethnopharmacol. 126, 31−41. (27) Wang, X. J., Sun, H., Zhang, A. H., Sun, W. J., Wang, P., and Wang, Z. G. (2011) Potential role of metabolomics approaches in the area of traditional Chinese medicine: as pillars of the bridge between Chinese and Western medicine. J. Pharm. Biomed. Anal. 55, 859−868. (28) Zhang, A. H., Sun, H., Wang, Z. G., Sun, W. J., Wang, P., and Wang, X. J. (2010) Metabolomics: towards understanding traditional Chinese medicine. Planta Med. 76, 2026−2035. (29) Feng, J. J., Hao, G., Min, C. Y., Xie, L., Liu, L., Lv, T., and Xie, H. P. (2014) Metabonomics of liver toxicity from traditional Chinese medicine Huang-Yao-Zi studied by mass spectrum-based orthogonal projection method. Chemom. Intell. Lab. Syst. 135, 201−207. (30) Xu, Y., Chen, C. C., Yang, L., Wang, J. M., Ji, L. L., Wang, Z. T., and Hu, Z. B. (2011) Evaluation on hepatotoxicity caused by Dioscorea bulbifera based on analysis of bile acids. Acta Pharm. Sin. 46, 39−44. (31) Hua, Y. L., Ji, P., Xue, Z. Y., and Wei, Y. M. (2015) Construction and analysis of correlation networks based on gas chromatography-mass spectrometry metabonomics data for lipopolysaccharide-induced inflammation and intervention with volatile oil from Angelica sinensis in rats. Mol. BioSyst. 11, 3174−3187. (32) Gika, H. G., Theodoridis, G. A., Wingate, J. E., and Wilson, I. D. (2007) Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine. J. Proteome Res. 6, 3291−3303. (33) Eriksson, L., Johansson, E., Kettaneh-World, N., Trygg, J., Wikstrom, C., and Wold, S. (2006) Multi- and megavariate data analysis, part I - basic principles and applications, p 97, Umetrics Academy, Sweden. (34) Koek, M. M., Jellema, R. H., van der Greef, J., Tas, A. C., and Hankemeier, T. (2011) Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives. Metabolomics 7, 307−328. (35) Pasikanti, K. K., Ho, P. C., and Chan, E. C. (2008) Gas chromatography/mass spectrometry in metabolic profiling of biological fluids. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 871, 202−211.

bile acid; TBIL, total bilirubin; TCA, tricarboxylic acid; TCM, traditional Chinese medicine; TIC, total ion current; TMCS, trimethylchlorosilane; UA, uric acid; VIP, variable importance in the projection

■

REFERENCES

(1) Jiangsu New Medical College. (1978) Dictionary of Chinese crude drugs, p 2059, Shanghai Scientific Technological Publisher, Shanghai. (2) Editorial Board of Chinese Materia Medica. (1999) Zhong Hua Ben Cao, Vol 8, pp 7278−7280, Shanghai Science and Technology Press, Shanghai. (3) Mbiantcha, M., Kamanyi, A., Teponno, R. B., Tapondjou, A. L., Watcho, P., and Nguelefack, T. B. (2011) Analgesic and antiinflammatory properties of extracts from the bulbils of Dioscorea bulbifera L. var. sativa (Dioscoreaceae) in mice and rats. Evid. Based Complement Alternat. Med. 2011, 1−9. (4) Gupta, D., and Singh, J. (1989) p-Hydroxyacetophenone derivatives from Dioscorea bulbifera. Phytochemistry 28, 947−949. (5) Lather, A., Gupta, V., Garg, S., Singh, A., and Sachdeva, K. (2011) Pharmacological potential of the plants used in treatment of piles-A review. J. Natura Conscintia 2, 255−265. (6) Chen, Y., Xia, Q., Song, C., and Ming, D. P. (2006) Study on the hepatoxicity mechanisms of Huangyaozi (Rhizoma Dioscoreae bulbiferae) on mouse liver by cDNA microarray. J. Mol. Cell Biol. 39, 568−572. (7) Huang, Z. F., Hua, B. C., Chen, X. F., Shi, D. H., Cheng, X. L., Wang, Y. H., and Liao, H. J. (2013) Analysis of liver injury in 78 cases caused by Rhizoma Dioscoreae Bulbiferae and related preparation. Chin. J. Exp. Tradit. Med. Formulae 19, 295−297. (8) Lu, X. H., and Wu, Q. H. (2014) Literature analysis of 33 cases of liver injury induced by the compound preparation of Dioscorea bulbifera L. J. Sichuan Tradit. Chin. Med. 39 (2), 162−164. (9) Su, Y. W., Chen, X., Jiang, Z. Z., Wang, T., Wang, C., Zhang, Y., Wen, J., Xue, M., Zhu, D., Zhang, Y., Su, Y. J., Xing, T. Y., Zhang, C. Y., and Zhang, L. Y. (2012) A panel of serum microRNAs as specific biomarkers for diagnosis of compound- and herb-induced liver injury in rats. PLoS One 7, e37395. (10) Wang, J. M., Liang, Q. N., Ji, L. L., Liu, H., Wang, C. H., and Wang, Z. T. (2011) Gender-related difference in liver injury induced by Dioscorea bulbifera L. rhizome in mice. Hum. Exp. Toxicol. 30, 1333− 1341. (11) Liu, Y. R., Huang, R. Q., Liu, L. J., Peng, J. N., Xiao, B. K., Yang, J. Y., Miao, Z. C., and Huang, H. L. (2010) Metabonomics study of urine from Sprague−Dawley rats exposed to Huang-yao-zi using 1H NMR spectroscopy. J. Pharm. Biomed. Anal. 52, 136−141. (12) Wang, J. Z., Liu, S. M., Zhao, Y., and Tang, Q. (2009) Experimental studies on liver cell injury induced by diterpence lactones extracted from Dioscorea bulbifera. J. Adv. Drug React. 11, 13−16. (13) Wang, J. M., Ji, L. L., Liu, H., and Wang, Z. T. (2010) Study of the hepatotoxicity induced by Dioscorea bulbifera L rhizome in mice. Biosci. Trends 4, 79−85. (14) Griffin, J. L., Walker, L. A., Garrod, S., Holmes, E., Shore, R. F., and Nicholson, J. K. (2000) NMR spectroscopy based metabonomic studies on the comparative biochemistry of the kidney and urine of the bank vole (Clethrionomys glareolus), wood mouse (Apodemus sylvaticus), white toothed shrew (Crocidura suaveolens) and the laboratory rat. Comp. Biochem. Physiol., Part B: Biochem. Mol. Biol. 127, 357−367. (15) de Falco, B., Incerti, G., Pepe, R., Amato, M., and Lanzotti, V. (2016) Metabolomic fingerprinting of Romaneschi Globe Artichokes by NMR spectroscopy and multivariate data analysis. Phytochem. Anal. 27, 304−314. (16) Kwon, H. N., Phan, H. D., Xu, W. J., Ko, Y. J., and Park, S. (2016) Application of a smartphone metabolomics platform to the authentication of Schisandra sinensis. Phytochem. Anal. 27, 199−205. (17) Irie, M., Fujimura, Y., Yamato, M., Miura, D., and Wariishi, H. (2014) Integrated MALDI-MS imaging and LC-MS techniques for visualizing spatiotemporal metabolomic dynamics in a rat stroke model. Metabolomics 10, 473−483. 1872

DOI: 10.1021/acs.chemrestox.7b00176 Chem. Res. Toxicol. 2017, 30, 1865−1873

Article

Chemical Research in Toxicology (36) Watkins, P. B. (2009) Biomarkers for the diagnosis and management of drug-induced liver injury. Semin. Liver Dis. 29, 393−399. (37) Hunter, J. E., Zhang, J., and Kris-Etherton, P. M. (2010) Cardiovascular disease risk of dietary stearic acid compared with trans, other saturated, and unsaturated fatty acids: a systematic review. Am. J. Clin. Nutr. 91, 46−63. (38) Afzali, A., Weiss, N. S., Boyko, E. J., and Ioannou, G. N. (2010) Association between serum uric acid level and chronic liver disease in the United States. Hepatology 52, 578−589.

1873

DOI: 10.1021/acs.chemrestox.7b00176 Chem. Res. Toxicol. 2017, 30, 1865−1873