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An integrated metabolomics and proteomics approach to identify metabolic abnormalities in rats with Dioscorea bulbifera rhizome-induced hepatotoxicity Dong-Sheng Zhao, Li-Long Jiang, Ling-Li Wang, Zi-Tian Wu, Zhuo-Qing Li, Wei Shi, Ping Li, Yan Jiang, and Hui-Jun Li Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.8b00066 • Publication Date (Web): 27 Jul 2018 Downloaded from http://pubs.acs.org on July 28, 2018

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Chemical Research in Toxicology

An integrated metabolomics and proteomics approach to identify metabolic abnormalities in rats with Dioscorea bulbifera rhizome-induced hepatotoxicity

Dong-Sheng Zhao a, Li-Long Jiang a, Ling-Li Wang a, Zi-Tian Wu a, Zhuo-Qing Li a, Wei Shi a, Ping Li a, Yan Jiang b, *, Hui-Jun Li a, *

a

State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia

Lane, Nanjing 210009, China b

Nanjing Forestry University, Nanjing 210037, China

*Corresponding authors: Assoc. Prof. Yan Jiang, Tel.:+86-25-85427544; Prof. Hui-Jun Li, Tel./Fax: +86-25-83271379. E-mail addresses: [email protected] (Yan Jiang); [email protected] (Hui-Jun Li)

Key words: Dioscorea bulbifera rhizome; Hepatotoxicity; Proteomics; Metabolomics; Integrated analysis

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Chemical Research in Toxicology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Table of Contents (TOC) graphic


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ABSTRACT Previous study has shown that Dioscorea bulbifera rhizome (DBR) can induce hepatotoxicity in clinical practice. However, its underlying mechanisms remain largely unexplored. In the present study, we investigated the global effect of DBR exposure on the proteomic and metabolomic profiles in rats over a 12-week administration using an integrated proteomics and metabolomics approach. The abundance of 1,366 proteins and 58 metabolites in the liver of rats after subchronic exposure to DBR was dose-dependently altered. The results indicated that DBR mainly damaged hepatic cells through the aberrant regulation of multiple systems mainly including purine metabolism, pyrimidine metabolism, taurine and hypotaurine metabolism, and bile acid metabolism. Notably, the deregulated proteins including Pnp, Dpyd, Upp1, and Tymp and the differential metabolites including uridine, uracil, cytidine, thymine, adenine, adenosine, adenosine 3’-monophosphate, and deoxycytidine were well correlated to purine and pyrimidine metabolism, which might be novel pathways involved in metabolic abnormalities in rats with DBR-induced liver damage. Collectively, these findings not only contributed to understanding the mechanisms underlying the hepatotoxicity of DBR, but also illustrated the power of integrated proteomics and metabolomics approaches to improve the identification of metabolic pathways and biomarkers indicative of herb-induced liver injury.



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INTRODUCTION Among the known pharmacopoeia in the world, the earliest recorded of Dioscorea bulbifera rhizome (DBR) (Fam. Dioscoreaceae) was found in “Tang Bencao” (657-659 A.D.). As one type of traditional Chinese medicines (TCMs), DBR has various pharmacological activities, including antitumor 1, anti-feedant 2, and anti-inflammatory activities 3, as well as goiter inhibitory effects 4. Despite the popularity of DBR as a folk remedy, its adverse effects have become an important safety concern in clinical practice. Many cases of liver injury have been linked to consumption of DBR and related remedies models

7-11

5, 6

. The hepatotoxicity of DBR has also been reported in animal

. Although a few studies have attributed the DBR-induced hepatotoxicity to different

causes, including oxidative damage to hepatic mitochondria 8, oxidative stress injury 7, or bile acid (BA) metabolic disorders

9-11

, a consensus is still far from being reached. Therefore, it is

urgently necessary to reveal the underlying mechanism of DBR-induced hepatic damage. Like most TCMs, DBR may exert its complicated hepatotoxic effects via the generally accepted “low content, multi-component, and multi-target” mode 12. Systems biology approaches, such as global protein expression proﬁling and metabolomics, play an important role in toxicological studies and such approaches have been widely used to comprehensively understand the activities of different toxicants 13, 14. Moreover, the integration of proteomic and metabolomic profiles may offer a greater reliability in expounding metabolic alterations and allow the further elucidation of the toxic effects and mechanisms 15, 16. To date, only few reports have studied the toxic effects of DBR exposure on rats using metabolomics analysis

8-10

. The integrated

proteomics and metabolomics approaches are still rarely applied to characterize DBR-induced 4 ACS Paragon Plus Environment

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hepatic toxicity. In a previous work, we reported a multi-sample integrated metabolomics approach investigating DBR-induced hepatotoxicity in rats

17

. DBR has a potential hepatotoxic effect on

the physiological and biological functions of organisms via regulating multiple metabolic pathways to an abnormal state. To validate such results and further understanding the molecular mechanism of DBR-induced liver injury, we aimed to investigate the alterations of proteomic and metabolomic profiles in rat liver after a 12-week DBR exposure with isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics approach and ultra-high performance liquid chromatography/electrospray ionization-quadrupole time-of-flight mass spectrometry (UHPLC/ESI-Q-TOF-MS)-based metabolomics approach. Furthermore, the signaling pathways related to the toxic effects of DBR were summarized by correlation network analysis and validated by immunohistochemistry (IHC) and Western blot analyses. The obtained results of our study provided a comprehensive insight into the mechanisms underlying DBR-induced hepatotoxicity. This, in turn, may help to identify novel therapeutic targets, as well as potential biomarkers that aid in the classification of patients for optimal therapy.

EXPERIMENTAL PROCEDURES Herb material and reagents. Samples of DBR were obtained from the Bozhou Herb Market (Anhui Province, China) and originally validated by Prof. Hui-Jun Li, China Pharmaceutical University. A representative specimen (DBR20150820YN) was deposited in the laboratory. DBR was sectioned into small pieces, refluxed with 80% (v/v) ethanol at a solid-liquid 5 ACS Paragon Plus Environment


ratio of 1:10 for 2 h and then filtered. Such procedure was conducted in triplicate. Ethanol was removed by concentrating the pooled filtrates under reduced pressure, and then the residuals were lyophilized. The dried powder was re-dissolved and dispersed in aqueous solution of 0.5% carboxymethyl cellulose sodium salt (CMC-Na) at various concentrations (0, 22.5, and 225 g/L) prior to intragastric administration to rats. The main toxic components in DBR extract were determined by high performance liquid chromatography (HPLC) (Supplemental Figure S2), showing that the contents of diosbulbin B and 8-epidiosbulbin E acetate were 2.15% ( w/w) and 0.057% (w/w), respectively. Methanol of HPLC grade was supplied by Fisher (Hudson, USA). Acetonitrile (ACN) of HPLC grade was provided by Merk (Darmstadt, Germany). Ammonium acetate of HPLC grade was purchased from Sigma-Aldrich (St. Louis, MO, USA). Ultrapure water was prepared using a Milli-Q purification system (MUL-9000, Millipore Corp., Billerica, MA, USA). Animal experiments.

All experiments and animal care were conducted in accordance

with the Provision and General Recommendation of Chinese Experimental Animals Administration Legislation. Animal protocols were approved by the Science and Technology Department of Jiangsu Province (license number: SYXK (SU) 2016-0011). Sprague-Dawley (SD) rats (male, 6 weeks old, weighing 200–220 g) were provided by Sino-British Sippr/BK Lab Animal Ltd. (Shanghai, China). The animals were maintained in an air-controlled facility with a 12 h-light/12 h-dark photoperiod at a temperature of 22 ± 2°C and a relative humidity of 50 ± 10%. Every six rats were housed in one cage, and unlimited access to


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food and water was permitted. After 1 week of acclimatization, the rats were randomly divided into three groups (n = 6 per group). In the following 12 weeks, rats were administered with DBR (0, 1.8, and 18 g/kg body weight) via gastric gavage once a day. Body weight of each rat was monitored once per week, and the drug dosage was adjusted accordingly. At the end of the experiment, blood was drawn from the carotid artery, followed by centrifugation at 4,500 × g for 10 min at 4°C. The supernatant was collected for biochemical analysis. After rats were sacrificed by cervical dislocation, and livers were dissected, weighed and sectioned into small pieces. One portion was fixed in 10% formalin for histopathological examination, and the others were immediately frozen in liquid nitrogen and stored at −80°C prior to further analysis. Clinical biochemistry and histopathology.

Biochemistry analysis of serum samples was

performed with a Beckman CX3 automatic biochemistry analyzer (Beckman-Coulter, Brea, CA, USA) using liver functional diagnostic kits (Beckman-Coulter). The following parameters of liver injury were tested, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total BA (TBA), and total bilirubin (TBILI). Liver tissues were sectioned into 5-µm sections using the tissue slicer (Leica RM2016, Shanghai, China) and stained with hematoxylin and eosin, followed by histological observation under the microscope (Olympus DX45, Tokyo, Japan). Metabolomics study.

Six samples from each group were respectively analyzed.

Metabolites were extracted as previously described 18. To ensure quality of data, quality control (QC) samples were pooled by mixing plasma (10 µL) from subjects. Detailed sample preparation, 7 ACS Paragon Plus Environment


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QC samples, chromatographic conditions, and MS parameters were provided in Supplemental Material. The raw MS data (wiff.scan files) were transferred MzXML files using ProteoWizard’s msConvert and processed using XCMS for further analysis. The metabolites were identified according to accurate mass (< 5 ppm) and MS/MS data, which were matched with the standard database (Shanghai Applied Protein Technology Co., Ltd.). The MetaboAnalyst (www.metaboanalyst.ca) web-based system was used for the multivariate statistical analysis. Principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were performed after the Pareto scaling. The combination of statistically significant thresholds of variable importance in the projection (VIP) values obtained from OPLS-DA model and two-tailed Student’s t test (p value) on the raw data was used to determine the significantly different metabolites, and VIP values larger than 1.0 and p values less than 0.05 were considered as statistically significant. Proteomics study.

Equal amounts of liver samples from all animals in each group were

pooled and prepared. Two biological replicates were prepared for control group, and three biological replicates were prepared for low-dose and high-dose groups, which were used for proteomics study. Detailed protein extraction was provided in Supplemental Material. Protein was digested according to the FASP procedure as previously described

19

, and the

8-plex iTRAQ reagent was used to label the resulting peptide mixture following the manufacturer’s protocols (Applied Biosystems, Foster City, CA, USA). Detailed protein


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digestion was presented in Supplemental Material. For labeling, 70 µL of ethanol containing each iTRAQ reagent was added to the respective peptide mixture. The samples were labeled as (Control-1)-113, (Control-2)-114, (Low-dose-1)-115, (Low-dose-2)-116, (Low-dose-3)-117, (High-dose-1)-118, (High-dose-2)-119, and (High-dose-3)-121, then multiplexed and vacuum dried. Strong cation exchange (SCX) chromatography was performed to fraction the iTRAQ-labeled peptides using the AKTA Purifier system (GE Healthcare, Chicago, IL, USA), and a Q Exactive mass spectrometer coupled to an Easy nLC 1000 HPLC system (Proxeon Biosystems, Thermo Fisher Scientific, Waltham, MA, USA) was used to perform LC-ESI-MS/MS experiments. Detailed peptide fractionation and LC-ESI-MS/MS analysis were presented in Supplemental Material. MS/MS spectra were searched using Mascot engine (Matrix Science, London, UK, version 2.2, Thermo Scientific) embedded into Proteome Discoverer 1.3 (Thermo Electron, San Jose, CA) against Uniprot rat database (35,838 sequences, downloaded at January 5th, 2017) and the decoy database. For protein identification, several options were used as follows: peptide mass tolerance = 20 ppm, MS/MS tolerance = 0.1 Da, enzyme = trypsin, missed cleavage = 2, fixed modification: carbamidomethyl (C), iTRAQ 8-plex (K), iTRAQ 8-plex (N-term), variable modification: oxidation (M), false discovery rate (FDR) ≤ 0.01. Integrative analysis.

To gain further insight into the DBR-caused toxicity and determine

the relationships between different biological functional levels, the MetaboAnalyst 3.0 software (http://www.metaboanalyst.ca) was used to conduct pathway enrichment analysis. As a 9 ACS Paragon Plus Environment


multi-omics data analysis tool (http://www.omicsbean.com), OmicsBean was employed to further assay integrated pathway analysis of the proteomics and metabolomics data, where distributions in biological functions, subcellular locations and molecular functions were assigned to metabolites and proteins based on Gene Ontology (GO) categories. IHC and Western blot analysis.

Liver tissues were fixed with 4% paraformaldehyde.

Detailed staining method was provided in Supplemental Material. Spot 1 camera was employed to acquire images of stained sections using the Spot 2.1 software (Diagnostic Instruments, Sterling Heights, MI, USA). The gain was set for capturing all images after the determination of the highest possible gain setting without the introduction of noise. Immunoreactive signals of Pnp, Dpyd, Upp1, Tymp, Abcc2, Abcc3, Bsep, or Baat in the DBR treatment group were quantified by Image-pro plus 6.0 software (Media Cybernetics Inc., Rockville, MD, USA). Detailed quantitative evaluation of the density of proteins was presented in Supplemental Material. Western blot analysis was performed using a previously described method with minor modifications

20

. The detailed information was provided in Supplemental Material. The

immunoreactive bands were visualized with an enhanced chemiluminescence kit (Servicebio Bioscience Inc., Wuhan, China). β-Actin was employed as a loading control. Statistical analysis.

The data were all presented as mean ± standard deviation (SD), and

the statistical analysis was carried out with SPSS software 22.0 (SPSS Inc., Chicago, IL, USA). Proteomics and metabolomics data were examined using two-tailed Student’s t test, and p < 0.05 was regarded as statistically significant. 10 ACS Paragon Plus Environment

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RESULTS Characterization of DBR-triggered liver injury. DBR-treated rats were monitored to determine the characteristics of DBR-induced hepatotoxicity. After a 12-week administration of DBR, no fatality was observed in any groups, and locomotion behaviors were significantly decreased in low-dose and high-dose groups. However, compared with control group, a huge body weight loss was observed in the DBR-exposured rats (Supplemental Figure S3A). Moreover, the liver weight index (liver weight/body weight ratio) was increased in a dosage-dependent manner after DBR treatment (Supplemental Figure S3B), suggesting the development of liver injury after DBR administration. In addition, the serum AST activity and the ratio of AST to ALT (AAR) were dramatically increased in all DBR-treated groups (Supplemental Figure S3C). The levels of GGT, TBA, and TBIL in the high-dose group were clearly higher compared with the control group. Obvious liver damages, evidenced by the dose-dependent hepatic cell swelling and necrosis, were observed in DBR-treated rats (Supplemental Figure S3D). Alteration of metabolites in liver. To analyze metabolic alterations in the DBR-treated groups, we assessed liver metabolites by UHPLC-Q-TOF-MS/MS. Supplemental Figure S4 illustrates a representative total ion chromatogram (TIC). After PCA, the metabolomic profiles of DBR-treated groups were clearly separated from control group for both positive and negative ion modes (Figures 1A and 1C). The differences among groups were identified by the supervised OPLS-DA model (Figures 1B and 1D), and the models for both positive and negative ion modes were also successfully established. These findings displayed that DBR administration caused 11 ACS Paragon Plus Environment


significant metabolic alterations in rat livers. The extracted variables mostly contributing to group distinction were screened as the potential biomarkers for DBR administration. Detailed screening criteria was provided in Supplemental Material. Following these criteria, 58 altered metabolites were considered as potential biomarkers, among which 21 metabolites were increased, while 37 were decreased by the DBR treatment (Figure 1E). We analyzed the metabolic pathways involved in the differential metabolites using the MetaboAnalyst 3.0 software (http://www.metaboanalyst.ca). The software mainly generated four metabolic pathways with a p value < 0.05, and such pathways were significantly related to the metabolic alterations caused by DBR (Supplemental Figure S5A). Those four pathways were identified as purine metabolism, pyrimidine metabolism, primary BA biosynthesis, and taurine and hypotaurine metabolism. Accordingly, the purine and pyrimidine metabolism might be the specific metabolic pathways affected by DBR in rat liver. Notably, we also found six and fifteen target metabolites that were up-regulated and down-regulated, respectively, which seem to be highly specific to the hepatotoxicity of DBR (Supplemental Table S1). Expressions of DBR-regulated proteins in liver.

To characterize the expressions of

proteins in response to DBR exposure, the iTRAQ labeling quantitative proteomics technique was used to analyze the liver proteome of the control and DBR-treated groups. A total of 28,494 unique peptides encoding 4,659 proteins (≥ 1 peptide) were identified in all eight samples. Fold changes (FCs) of proteins were determined by the ratios of the iTRAQ reporter ions between the control and DBR-treated groups. A total of 1,366 proteins showed significant differential 12 ACS Paragon Plus Environment

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expressions (FC > ± 1.2 and p < 0.05) with an FDR of less than 0.01 at the peptide and protein level in a dose-dependent manner, including 627 up-regulated proteins and 739 down-regulated ones. To obtain an overview of the effects of DBR exposure on rat livers, the differentially expressed proteins (DEPs) were categorized according to their biological processes based on the Gene Ontology database (http://geneontology.org/), and these proteins were classified into three groups as follows: biological process, molecular function, and cellular component (Supplemental Figure S5B). The main biological process included lipid metabolic process, fatty acid metabolic process, small molecule catabolic process and so on. Based on the different molecular functions, these proteins were divided into following groups: oxidoreductase activity, catalytic activity and so forth. The cellular components classification of these proteins mapped them to various subcellular structures, locations and macromolecular complexes, including mitochondrial part, mitochondrion, mitochondrial matrix and so on. These results confirmed previous reports that the toxic effects of DBR are related to oxidative stress-induced injury and damage to mitochondria 7, 8, 10

. The differentially accumulated proteins were further classified according to the pathways

using the KEGG data-base. The major pathways, which the DEPs were involved, included carbon metabolism, purine metabolism, pyrimidine metabolism, peroxisome, primary BA biosynthesis, biosynthesis of unsaturated fatty acids, fatty acid metabolism and so on. Notably, we also found six up-regulated and 11 down-regulated target proteins that were highly specific to the hepatotoxicity of DBR (Supplemental Table S2). 13 ACS Paragon Plus Environment


Integrative proteomics and metabolomics analysis.

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Many significantly regulated

proteins and metabolites, related to the hepatotoxic effect of DBR, were identified. For further holistic analysis, the differential abundant proteins and metabolites were integrated to provide a systems biological interpretation. We first used the MetaboAnalyst 3.0 software to build the gene-metabolite functional enrichment based on the modulated 1,366 proteins and 58 metabolites data from the control and DBR-treated rats. Figure 2A reveals that these proteins and metabolites were primarily involved in taurine and hypotaurine metabolism, pyrimidine metabolism, purine metabolism, pantothenate and CoA biosynthesis, pentose phosphate pathway, and primary BA biosynthesis. Additionally, the signaling pathways affected by DBR in rat liver were identified by network diagrams generated using IPA software. IPA mapped the top-ranked network based on part of the DEPs and differential metabolites, which were eligible for network generation with a score of ≥ 2 and a p value < 0.05 (Figure 2B). The specifically differential 9 proteins including Pnp, Dpyd, Tymp, Upp1, Bsep, Abcc2, Abcc3, Baat, and cysteine-sulfinate decarboxylase (Csad) and the altered 14 metabolites including adenine, adenosine, adenosine 3’-monophosphate, uridine, uracil, cytidine, deoxycytidine, thymine, taurine, hypotaurine, taurodeoxycholic acid, taurochenodeoxycholic acid, tauroursodeoxycholic acid, and taurolithocholic acid, which were identified as being mainly involved in purine metabolism, pyrimidine metabolism, primary BA biosynthesis, bile secretion, and taurine and hypotaurine metabolism, were closely associated with the hepatotoxicity of DBR. In addition, these results suggested that Pnp, Dpyd, Tymp, Upp1, Bsep, Abcc2, Abcc3, and Baat were the key regulators of this subnetwork, indicating their critical roles in the long-term hepatotoxic effect of DBR. 14 ACS Paragon Plus Environment

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Verification of altered protein expressions.

Using an integrated proteomics and

metabolomics approach, were found that the proteins, Abcc2 and Abcc3 with up-regulated expressions as well as Pnp, Dpyd, Upp1, Tymp, Bsep, and Baat with down-regulated expressions in the rat liver following DBR exposure, were tightly related to the liver injury. To further establish a link between DBR-induced hepatotoxicity and the purine, pyrimidine, and BA metabolic pathways, the expression changes of the proteins involved in these pathways were further verified by IHC analysis. The results showed that in the DBR-treated groups, the expressions of Abcc2 and Abcc3 were increased, and the expressions of Pnp, Tymp, Bsep, Dpyd, Upp1, and Baat were decreased in the liver cells (Figure 3A). In addition, the expressions of these proteins at the protein level were further analyzed by Western blot analysis. Figure 3B shows that the expressions of Pnp, Upp1, Dpyd, Baat, and Tymp were all significantly down-regulated in the rat liver exposed to DBR, suggesting that the purine, pyrimidine, and BA metabolic pathways were inhibited by the DBR treatment. Therfore, these results implied that DBR might induce liver toxicity through the dysregulation of purine, pyrimidine, and BA metabolism.

DISCUSSION The DBR-associated hepatotoxicity has been well recognized. However, its underlying mechanism of toxicity remains largely unexplored to date. The critical proteins and metabolites as well as their involved biological systems that are affected by environmental stresses can be identified by global profiling technologies, toxicoproteomics and toxicometabolomics, which 15 ACS Paragon Plus Environment


greatly improves our understanding of DBR-associated toxic mechanisms. Accordingly, we investigated the alterations of protein and metabolic profiles in rat liver after DBR administration using the integrated proteomics and metabolomics approach. The metabolic pathways, which are thought to be associated with the hepatotoxicity of DBR, were screened by the integration analysis and network correlation using the deregulated metabolites and proteins. The present results confirmed, more importantly, and extended previous opinions that the toxic effects of DBR are associated with oxidative stress injury, damage to mitochondria, and bile acid metabolic disorders

7-11

, from a more comprehensive view. Nine differentially abundant proteins and 14

metabolites were closely related to purine metabolism, pyrimidine metabolism, taurine and hypotaurine metabolism, and BA metabolism (primary BA biosynthesis and bile secretion) pathways. Among these identified pathways, purine and pyrimidine metabolisms were novel pathways involved in metabolic abnormalities in the liver from DBR-treated rats. We also conducted subchronic toxicological experiments to characterize the liver injury induced by the long-term, low-level and high-level exposures to DBR in rats. The doses of 1.8 g/kg and 18 g/kg were set at a high level to uncover any potential toxicity of DBR, which were approximately 1 and 10 times the upper of clinically equivalent doses. These doses could be helpful in understanding the mechanisms underlying the DBR-induced hepatotoxicity. Our observations revealed that a dose-dependent hepatotoxicity was induced after a 12-week exposure to DBR, evidenced by the increase of liver weight index and serum AST level, as well as the inhibition of body growth. Moreover, histopathological study suggested that DBR treatment triggered obvious hepatic cell swelling and necrosis. 16 ACS Paragon Plus Environment

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BAs are normally present at micromolar concentrations in the peripheral circulation, and there is a balance between its free and conjugated forms 21. Defective canalicular export, due to impaired canalicular transport or a physical obstruction to bile flow, results in intrahepatic BA accumulation, greatly contributing to liver disease via triggering apoptosis and necrosis of hepatocytes

22

. Baat is the sole enzyme, which promotes the conjugation of primary and

secondary BA to taurine and glycine

23

. Bsep, Abcc2, and Abcc3 are the ATP-binding cassette

transporters essential for the bile secretion 24. We found that Bsep and Baat were down-regulated, while Abcc 2 and Abcc 3 were up-regulated in the liver, revealing that the BA metabolism was impaired by DBR exposure

9-11

. Taurine-conjugated BAs are the major bile salts in bile 25. Bile

salts play a key physiological role due to their inherently cytotoxic detergent nature, and impaired bile secretion can cause severe liver disease

24

. Apart from taurocholic acid, the

contents of taurodeoxycholic acid, taurochenodeoxycholic acid, tauroursodeoxycholic acid, and taurolithocholic acid were significantly decreased in DBR-treated rats, probably corresponding to the mechanism of DBR-induced liver damage. These findings were in agreement with previous reports that taurine-conjugated BAs are more prominent than other BAs 9, 10, indicating that these metabolites might function as the potentially early biomarkers for DBR-induced liver toxicity. The activity of Csad, an enzyme greatly contributing to hypotaurine biosynthesis, is abundantly present in tissue

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. Taurine pretreatment can protect hepatocytes from necrosis and

atrophy 27. Once the liver is damaged, the taurine level will be compensatorily increased in the liver to protect the liver from damage. The evidence suggests that rat liver can rapidly convert 17 ACS Paragon Plus Environment


hypotaurine to taurine, and such mechanism is obstructed after hepatocyte damage

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26

. In the

present study, the expression of Csad was dramatically down-regulated by DBR, subsequently leading to reduced level of hypotaurine. This finding suggested that the inhibition of protein synthesis by DBR decreased the conversion of hypotaurine and increased the intracellular pool of taurine to protect the liver from damage. In the liver, purinergic signaling is a shifting balance that greatly contributes to tissue homeostasis

28

. Under physiological conditions, intercellular purinergic signaling plays a

fundamental role within hepatic lobules in several key regulatory processes, including cell survival, proliferation and cell death, ion homeostasis, bile formation, glucose metabolism and blood flow

29

. However, the purine-mediated signaling alteration shifts the balance towards

pathological states and may influence drug-induced liver injury as a result of direct interference with drug metabolism or with cell death pathways

30

. In our present study, the DEPs and

differential metabolites related to the hepatotoxicity were involved in the purine metabolic pathway. The protein expression of Pnp was dramatically inhibited in a dose-dependent manner, suggesting that the liver purine metabolism was disturbed during DBR-induced liver damage. As one of the two purine nucleobases, adenine is used in forming nucleotides of the nucleic acids. Its derivatives, such as adenosine and adenosine 3’-monophosphate, play a critical role in biochemical processes. Previous study has shown that adenosine administration produced a drastic antilipolytic effect, leading to delay fatty liver and cell necrosis induced by carbon tetrachloride

31

. Moreover, the levels of adenine, adenosine, and adenosine 3’-monophosphate

were decreased in the liver, especially adenine in the high-dose DBR group. Accordingly, the 18 ACS Paragon Plus Environment

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disturbances in purinergic discharge and signaling pathway might play key roles in the DBR-induced hepatotoxicity, representing a potentially specific metabolic pathway for the mechanism. It has been proposed that Pnp may be a simple and reliable biomarker for hepatic endothelial cell injury 32. These findings raised a rational hypothesis that Pnp was a specific and sensitive biomarker for the DBR-induced hepatotoxicity. Pyrimidine derivatives may also play regulatory roles in cellular signaling and energy metabolism in order to maintain cellular homeostasis. However, the roles of pyrimidine nucleotide derivatives in cellular function remain largely unexplored compared with those purine derivatives

33

. In this study, we also found that the DEPs and differential metabolites related to

the hepatotoxicity were involved in the pyrimidine metabolic pathway. The expressions of Pnp, Dpyd, Upp1, and Tymp at the protein level were down-regulated in the pyrimidine metabolic pathway, especially Pnp and Tymp, which were significantly dysregulated. We also evaluated the levels of pyrimidine derivatives, which have potentially useful pharmacology, the elevated uridine, uracil, cytidine, and thymine and the decreased deoxycytidine were observed in the liver. As a common and naturally occurring pyrimidine derivative, uracil is one of the four nucleobases in the nucleic acid of RNA. Uridine, a pyrimidine nucleoside containing uracil and ribose, can exert protective effects against drug-induced hepatotoxicity and psychiatric disorders

34

. In

addition, administration of uridine or cytidine suppresses liver hexosamine and collagen and reduces ornithine carbamyl transferase activity in liver, thus preventing the liver cell necrosis and fibrosis 35. Therefore, the increased uridine was likely to prevent the DBR-induced liver damage. Our results revealed that pyrimidine metabolism was critical for the hepatotoxicity process, and 19 ACS Paragon Plus Environment


it might represent a potentially novel metabolic pathway involved in the mechanism of DBR-induced liver injury. Moreover, Pnp and Tymp might be potential protein targets and specific and sensitive biomarkers for the DBR-triggered hepatotoxicity.

CONCLUSIONS In summary, we elucidated the molecular mechanisms of DBR-induced liver injury in rats and characterized the primary metabolic pathways involved in this process through a systematic approach integrating metabolomics and proteomics data. Our study identified the changes of protein expression profile and metabolite production in the liver of rats after DBR administration. The important pathways identified as mediators of DBR-induced liver damage included the purine, pyrimidine, BA, amino acid, lipids and energy metabolism, among which purine and pyrimidine metabolism might be the potentially specific pathways mediating the response to DBR. Meanwhile, the potential biomarkers, including Pnp, Dpyd, Upp1, Tymp, adenine, adenosine, adenosine 3'-monophosphate, uridine, uracil, cytidine, thymine, and deoxycytidine, were closely related to the hepatotoxicity. Collectively, these findings provided valuable insights into the mechanisms underlying the hepatotoxicity of DBR and illustrated the power of integrated proteomics and metabolomics approaches to identify specific metabolic pathways and potential biomarkers indicative of herb-induced liver injury.

NOTES The authors declare no conflict of interests. The mass spectrometry proteomics data have been public available on iProX database (www.iprox.org) with id IPX0000984000 20 ACS Paragon Plus Environment

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(http://www.iprox.org/page/PDV014.html?projectId=IPX0000984000).

FUNDING This work was supported in part by the National Natural Science Foundation of China (Grants No. 81573562, 81773993, and 81130068), the Natural Science Foundation of Jiangsu Province (BK20151442) and the Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.

ABBREVIATIONS

AAR, the ratio of aspartate aminotransferase to alanine aminotransferase; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BA, bile acid; CMC-Na, carboxymethyl cellulose sodium salt; DBR, Dioscoreae Bulbiferae Rhizoma; ESI, electrospray ionization; GO, gene ontology; GGT, gamma-glutamyl transferase; IHC, immunohistochemistry; iTRAQ, isobaric tags for relative and absolute quantitation; OPLS-DA, orthogonal partial least squares discriminant analysis; PCA, principal component analysis; QC, Quality control; TBIL, total bilirubin; TCM, traditional Chinese medicine; TIC, total ion current; LC, liquid chromatography; VIP, variable importance in the projection.

Supporting Information [Sample preparation, chromatographic conditions, MS parameters, and QC samples for metabolomics analysis; Protein extraction and digestion, peptide fractionation, and LC- ESIMS/MS analysis for proteomics analysis; Staining and quantitative evaluation for IHC; Western 21 ACS Paragon Plus Environment


blot analysis; Typical chromatograms of standards solution and DBR sample; Characterization of DBR-induced liver injury in rats; Total ion current chromatograms of liver; Analysis of the differential metabolites and proteins related to the hepatotoxicity of DBR; Differential metabolites in rat liver responded to exposure to DBR; Differentially expressed proteins associated with DBR-caused hepatotoxicity in rat liver.] This material is available free of charge via the Internet at http://pubs.acs.org.

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Figure Legends Figure 1. Metabolomic multivariate analysis. (A) and (B) The score plots of PCA and OPLS-DA in the positive ion mode, respectively (n = 6). (C) and (D) The score plots of the PCA and OPLS-DA in the negative ion mode, respectively (n = 6). (E) Heatmaps visualization of the 58 differentially abundant metabolites (The columns represent the samples and the rows indicate differentiating metabolites. C1-C6, L1-L6, and H1-H6 belong to the control group, low-dose group, and high-dose group, respectively. The colors from blue to red indicate the increasing production levels of metabolites). Figure 2. Integrated analysis of the differentially abundant metabolites and proteins. (A) Integrated metabolic pathway enrichment analysis and visual exploration of the differentially abundant proteins and metabolites. The stacked bars show a summary of the joint evidence from 27 ACS Paragon Plus Environment


enrichment analysis and topology analysis, respectively. (B) Network analysis of the differentially abundant proteins and metabolites. The network with the highest score that identifies molecules involved in the DBR-induced liver injury and abnormalities is presented. Figure 3. Validation of immunohistochemistry (IHC) and Western blot analysis results. (A) Representative images and quantitative evaluation of liver IHC analysis after DBR treatment in rats (Original magnification: × 400, the scale bar is 50 µm). (B) Average relative intensity values for the blots. The values are all expressed as mean ± SD (n = 3), *p or #p < 0.05 vs. the control group; **p or ##p < 0.01 vs. the control group.


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Figure 1 29 ACS Paragon Plus Environment


Figure 2


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Figure 3


Integrated Metabolomics and Proteomics Approach To Identify

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