High-throughput metabolomics reveals the perturbed metabolic pathways and biomarkers of Yang Huang syndrome as potential targets for evaluating the therapeutic effects and mechanism of geniposide

Abstract

High-throughput metabolomics can clarify the underlying molecular mechanism of diseases via the qualitative and quantitative analysis of metabolites. This study used the established Yang Huang syndrome (YHS) mouse model to evaluate the efficacy of geniposide (GEN). Urine metabolic data were quantified by ultra-performance liquid chromatography–tandem mass spectrometry. The non-target screening of the massive biological information dataset was performed, and a total of 33 metabolites, including tyramine glucuronide, aurine, and L-cysteine, were identified relating to YHS. These differential metabolites directly participated in the disturbance of phase I reaction and hydrophilic transformation of bilirubin. Interestingly, they were completely reversed by GEN. While, as the auxiliary technical means, we also focused on the molecular prediction and docking results in network pharmacological and integrated analysis part. We used integrated analysis to communicate the multiple results of metabolomics and network pharmacology. This study is the first to report that GEN indirectly regulates the metabolite “tyramine glucuronide” through its direct effect on the target heme oxygenase 1 in vivo. Meanwhile, heme oxygenase-1, a prediction of network pharmacology, was the confirmed metabolic enzyme of phase I reaction in hepatocytes. Our study indicated that the combination of high-throughput metabolomics and network pharmacology is a robust combination for deciphering the pathogenesis of the traditional Chinese medicine (TCM) syndrome.

Keywords

metabolomics / liquid chromatography-mass spectrometry / metabolites / metabolic pathways

Introduction

Metabolomics is a precise and comprehensive method for understanding and anticipating the mechanisms of a complex system [¹,²]. It involves essential information about the dynamic metabolic responses of an organism to comprehensive factors and is applied in the biosciences. Recently, the development of robust analytical techniques has made the global detection of entire biomolecules of an organism possible. Modern tools with high resolution and sensitivity ensure that useful information about a disease can be found. Among them, nuclear magnetic resonance spectroscopy or ultra-performance liquid chromatography–tandem mass spectrometry (UPLC/MS) technology has been widely used for metabolomics given its remarkable performance and advanced data processing capabilities. Nowadays, these methods have been recognized as optimal tools for the analysis of complicated systems, especially for the clarification of traditional Chinese medicine (TCM). TCM has existed for thousands of years as the unique field of life science. However, the precise mechanisms of action of many of its agents remain unclear and severely limit the generalization of TCM. Fortunately, metabolomics, through holistic and systematic studies of metabolic pathways and biomarkers, coincides with the theory of TCM and provides new directions for the basic research, clinical application, and new development of TCM [³-⁹].

Yang Huang syndrome (YHS), as indicated in the authoritative ancient codes and records of TCM “Shang Han Lun,” has been reported for more than a thousand years. YHS is similar to the initial stage of jaundice characteristics associated with yellowing of the skin and pupils. The phenotypic characteristic of YHS differs from that of any other disease due to canary sclera, which is probably the primary distinguishing facet of clinical presentation between YHS and Yin Huang syndrome [¹⁰]. Pharmacological research shows that patients with YHS usually suffer from liver disease [¹¹-²²], which is one of the most threatening diseases in the world. Thus, unambiguous and exhaustive cognition is urgently needed to respond to this common fatal disease. We previously conducted urine metabolomics investigation for the biomarker discovery of patients with YHS [²³]. These biomarkers deciphered the potential pathogenesis and metabolic network of YHS, which is characterized as the bridge for the construction of a YHS mouse model. Our previous work investigated the serum biomarkers of YHS mice, and 22 serum biomarkers were confirmed with the metabolomics method [²⁴].

Collectively, we applied the triplet factors of Rhizoma Zingiberis, ethanol, and a-naphthylisothiocyanate (ANIT) to construct the YHS mouse model on the basis of the theoretical direction of TCM combined with our former investigation. Geniposide (GEN), used for the treatment of YHS for more than 1000 years, exerts synergistic effect with the guidance of TCM practitioners. TCM practitioners believe that the interaction among multiple components plays a synergistic effect. Therefore, the present investigation focused on a metabolomics and network pharmacology technology concerning the mechanism of GEN on the basis of massive biological datasets derived from advanced instrumentation and software (Fig. 1).

Materials and methods

Chemicals and reagents

Analytical formic acid and UPLC-grade acetonitrile were from the Merck Group (Darmstadt, Germany). Deionized water was from Watsons (A.S. Watson Group (Hong Kong) Ltd.). ANIT was from Sigma-Aldrich (Shanghai, China). Rhizoma Zingiberis was from Shi Yi Tang Pharmacy (Harbin, China); GEN (batch number: 110749-200511) was from the China Biological Products Testing Institute. The assay kits of aspartate aminotransferase (AST), alkaline phosphatase (ALP), direct bilirubin (D-Bil), total bilirubin (T-Bil), malondialdehyde (MDA), glutamyl transpeptidase (GGT), glutathioneglutathione peroxidase, total bile acid (TBA), superoxide dismutase (SOD) were from BioSino Bio-Technology and Science Inc. (Beijing, China).

Sample collection and preparation

BALB/c mice (15±5 g) were from Xipuer-Bikai Laboratory Animal Co., Ltd. (Shanghai, China, Qualified number 20180006000662). They were given standardized animal feeding environment [⁹]. Mice were randomly divided into the following three groups after 7 days of adaptation experiment in a metabolic cage in accordance with body weight (n = 6): control, YHS, and GEN. The YHS model was prepared following the established and accepted procedures [⁸]. The YHS and GEN groups were orally administered with Zingiber officinale Rosc. extracted solution (0.013 g/mL) in alcohol (3.125% (v/v)) at 8 a.m. and 2 p.m. at a dose of 0.1 mL/10 g for 14 days. Mice from the control group were orally administered with distilled water of the same dose per day. The YHS group was given different concentrations of ANIT solution (1.5 mg/mL, 1 mg/mL, dissolved in olive oil) from 15 to 16 days once daily at 2 p.m. with a dose of 0.1 mL/10 g. At day 17, the GEN group was treated with 50 mg/kg for 7 days [²⁴]. The control group was orally administered with olive oil of the same dose once a day.

Fresh urine samples were transferred from the metabolism cage and centrifuged immediately (4 °C, 13 000 rpm, 15 min). Then, the supernatants were passed through 0.22 μm filter membrane for metabolomics analysis. All mice were sacrificed for liver tissue and blood samples on day 25 to detect liver histopathology and liver metabolic enzymes.

Chemical ingredient database building and target prediction

GEN maintains high blood concentration and possesses significant pharmacological activity in vivo after oral administration. In recent years, GEN has been used in numerous applications in the life science field. In the present study, we first theoretically predicted the target of GEN from molecular docking point of view by using network pharmacological techniques and methods. We obtained the standard mol file from Chemspider and transformed it into mol2 file by using Bio3D Ultra 14.0 of ChemBioOffice 2014. Disease target prediction was carried out from two aspects: drug target and disease target correlation analyses. The top 10 target proteins with matching values were screened from numerous predictive results. Finally, a ligand–receptor docking process was simulated using Autoduck Tools 1.5.6, and the best software and docking orientation was determined.

Clinical chemistry and histopathology analysis

Histopathological observation was performed by separating the serum samples from fresh blood samples for the characterization of the following clinical chemistry parameters: ALT, AST, ALP, D-Bil, T-Bil, GGT, and TBA. Liver homogenate was used for detecting MDA, GSH-Px, and SOD. Intergroup statistical significance was based on P value of<0.05. The liver sections were also stained with hematoxylin and eosin (H&E) to observe the changes in hepatic tissue and accomplished by the Pathological Center of Heilongjiang University of Chinese Medicine.

Chromatography experiments

Bioinformatics data acquisition section was employed with a Waters ACQUITY UPLC system. The effective separation of components was performed as previously described [⁸].

Mass spectrometry

A G2Si (Waters Q-TOF SYNAPT^™, Waters Corp, Manchester, UK) instrument with electrospray ion source was used in the continuum mode. The detailed parameters were previously described [⁸].

Multivariate data analysis

Data dimension reduction processing in metabolomics is a key procedure for biological information visualization. This multivariate analysis can be also used to visualize inherent clustering behavior for organ-specific substances in accordance with their mechanism of action. An Advanced Progenesis QI platform (Waters Corp, Version: 2.2) was used to acquire the potential and important notes from raw centroid-data files obtained from SYNAPT G2Si-HDMS. Massive amounts of raw data were disposed through automatic data preprocessing with Progenesis QI. Multidimensional bioinformatics data were transformed after dimensionality reduction into a dataset containing a 3D matrix, which was generated as a temporary result for multivariate data analysis with Simca 14.1 (Umetrics, Sweden). The metabolic data completed through the dimensionality reduction of principal component analysis (PCA) had the most substantial influence on health and disease state. Potential differential metabolites were labeled from the Orthogonal Partial Least Squares Discriminant Analysis-Variable Important Projection (OPLS-DA-VIP-plot), whose components with a considerable influence on grouping were screened out by objective numerical values. The Student’s t-test was less than 0.05.

Biomarker identification and metabolic pathway analysis

Identifying YHS biomarkers and the relationship with TCM syndrome (or disease) is the core purpose of metabolomics and directly affects the application and objective knowledge on the bioactivity of TCM. Therefore, we used the advanced metabolomics analysis platform Progenesis QI and IPA (Qiagen, Redwood City) for the structural identification of biomarkers on the basis of the accurate detection of MS and MS/MS signals from a G2Si high-definition MS. The qualitative description of low molecular weight metabolites involved a comparison of their chromatographic retention time and MS/MS fragmentation on the basis of commercial database established from literature. The interpretation of the full scan mass spectra was combined with the results from Human Metabolome Database, Metline, and Kyoto Encyclopedia of Genes and Genomes that match the standard MS/MS structure.

Results

Biochemical analysis and histopathological observations

Massive metabolic data interpretation was first based on macroscopic evaluation index validated assessments, including metabolic indicators of liver function and histopathology. AST, ALP, ALT, D-Bil, T-Bil, MDA, GGT, GSH-Px, TBA, and SOD were used to characterize hepatic function as summarized in the Supplementary Table S1 and Fig. 2. D-Bil and T-Bil are the most common indices for jaundice in clinical practice and directly lead to the exclusive phenomenon of jaundice. The Student’s t-test results between the control and YHS groups show a significant elevation of the biochemical indicators, such as ALT, AST, T-Bil, and TBA. Meanwhile, significant trends show that the function and metabolism of YHS mouse liver has already been damaged and disordered in terms of ALP, D-Bil, MDA, GGT, GSH-Px, and SOD. Furthermore, we established an H&E staining of liver tissue to obtain a straightforward sense of nidus (Fig. 2). Evident changes were observed in the microscopic analyses of H&E-stained liver sections compared with those of the control group. The liver cells of model group mice showed regional laminar necrosis, and the liver lobules showed edema around the central vein and inflammatory cell infiltration. Thus, YHS was successfully established, and the prevention of GEN by recovering the baseline levels of the control group occurred simultaneously.

Urine metabolic profiles

Massive amounts of raw files from the overall quantitative G2Si-HDMS were analyzed using the Masslynx and Progenesis QI software for data pre-processing, such as peak picking, denoising, and normalization for the generation of a bioinformation matrix. This dataset was then exported to Simca 14.1 software for chemometric analysis. Further visual comparison of complex giant system data was impossible due to the multidimensional elements present in the biological samples. Principal components analysis, as a commonly used multivariate statistical method, was performed to reduce the multiple variables to few sets of factors for the investigation of this complex system. Consequently, the principal component 1 versus principal component 2 plots showed that the profiles of each group from the mass spectrum signal present a regular distribution, with significant intragroup aggregation and distinct intergroup segregation (Fig. 3).

Metabolite structural identification

A UPLC-G2Si-HDMS high throughput analyzer coupled with Progenesis QI showed the retention time, precise molecular mass, and MS/MS data for the structural identification of biomarkers. All of the potential endogenous molecules were identified on the basis of the database embedded in the Progenesis QI software. Finally, 33 potential biomarkers (VIP>1.0, P<0.05) were statistically discovered (Table S2), which revealed the maximum difference between the control group and YHS group. A total of 17/33 potential biomarkers were downregulated from the YHS group, and 16/33 were upregulated (Fig. 4). Fig.5 shows an example of the identification process.

Protection and verification of GEN

The box-plot of the potential biomarkers of YHS showed that the biomarkers in the GEN group gravitated toward the control group compared with those of the YHS group. Thus, the significant changes in metabolism occurred as a result of GEN. GEN, as an effective therapeutic medicine, had a verified probe effect on YHS; thus, the specificity of the YHS model was established. These low molecular mass metabolites may respond to the key mechanism of cellular regulatory and metabolic processes in YHS mice. Our results represented the intuitive information from the key enzymes and those reactions, such as pentose and glucuronate interconversions and primary bile acid biosynthesis, which were involved in the metabolic network built with MetaboAnalyst platform (Fig. 6). The relevant metabolic pathways were analyzed, and a clear metabolic network of YHS pathogenesis and GEN protection of all metabolic pathways involved in this experiment were established and summarized (Fig. 7).

Network pharmacological and integrated analysis

Fig. 8 shows the interaction between the active components with GEN and potential target protein in liver disease. A total of 536 nodes were screened for the network structure. GEN (green circle) hits multiple potential targets, which are involved in a variety of physiologic activities and disease directions. Moreover, the 22 predictions of liver disease involved complex pathways and metabolic mechanisms. A common connection exists between the prediction of GEN and liver disease. Heme oxygenase 1, as the potential target of GEN, possesses the core physiologic activities in the phase I reaction of hepatocytes.

Figs. S1 and S2 reveal the connection between metabolomics and network pharmacology, which involves the bilirubin metabolism and activation of key metabolic enzymes by GEN. The molecular docking results showed that the binding energy of GEN and heme oxygenase 1 was greater than 5 eV, indicating a stable binding capacity and molecular docking state. Analysis of numerous network pharmacological predictive results showed that heme oxygenase 1 is a regulatory factor of tyramine glucuronide, which is a urine biomarker of YHS in mice. Tyramine glucuronide should be synthesized with the modification of heme oxygenase 1. Moreover, heme oxygenase 1 is an important metabolic enzyme of bilirubin, that is, hemoglobin should be metabolized into bilirubin under the heme oxygenase 1 modification. Bilirubin is a direct factor in the pathogenesis and the most intuitive manifestation of jaundice. Hence, heme oxygenase 1 metabolism in patients with YHS is abnormal. Through objective urine metabolic signal data from patients with YHS and network pharmacological analysis of GEN, the therapeutic drug has been validated two times. Thus, GEN may play an irreplaceable role in activating the life activity of heme oxygenase 1.

Correlation analysis of biomarkers between clinical samples and the YHS mice

YHS cannot be diagnosed using the traditional biochemical index. The emerging metabolomics methods used in this study shows a comprehensive and dynamic viewpoint, which is a suitable approach for characterizing YHS. Focusing on biomarkers which have potential roles in the regulation of metabolism may reveal the underlying biological mechanism. We have previously reported [²,⁸,²³,²⁴] urine metabolomics analysis for biomarker discovery and detection of jaundice syndromes in patients and relative mouse model. We compared the time-course changes and metabolic pathways of biomarkers and found the bilirubin metabolism and liver injury indicators related to YHS [8, 23].

Discussion

YHS is a unique disease in terms of the combination of its pathology and specific constitution. Therefore, investigating YHS pathogenesis has a considerable role in promoting drug discovery or effective treatments. We used urine metabolomics approach with a platform combined with UPLC-G2Si-HDMS and advanced data processing software to explore the interactions of GEN and YHS. The YHS mouse model was established to test the actual application of GEN to a considerable extent by using Rhizoma Zingiberis, ethanol, and ANIT on the basis of our understanding of TCM. The response to the treatment of GEN and the biomarkers in clinical samples were used to construct the YHS mouse model. The biochemical parameters of clinical liver function and histopathology are shown in Table S1. The concentrations of ALT, AST, T-Bil, and TBA were significantly elevated in YHS mice, and ALP, D-BIL, MDA, g-GT, GSH-Px, and T-SOD exhibited a prominent trend, suggesting that the YHS mouse liver was already damaged. T-Bil and D-Bil are specifically diagnostic indicators of jaundice. All of the biological parameters were significantly regulated to their normal levels after treatment with GEN.

Then, a comprehensive and non-invasive metabolomics study combined with pattern recognition technology was used to explore the pathogenesis of YHS and the protective effect of GEN. PCA was conducted, and a complete separation of control and YHS groups was presented. OPLS-DA analysis was conducted, and a total of 33 different metabolites, which were related to pentose and glucuronate interconversions and primary bile acid biosynthesis, were recognized as major contributors to grouping. Interestingly, most of these biomarkers recovered to normal levels with GEN treatment, thereby proving the effectiveness of GEN and the specialization of the biomarkers.

Tyramine glucuronide is the terminal metabolite of tyramine and is derived from a wealth of sources, including protein degradation or intestinal bacterial action. Its redundant accumulation is excreted via the glucuronidation pathway by UDP-glucuronosyltransferase 1A1, which is also the key enzyme dominating hepatic detoxification [²⁵]. The liver has an irreplaceable function for metabolism and detoxification. Bilirubin shows a certain degree of neurotoxicity due to the terminal metabolite of erythrocyte. The turbulence of bilirubin metabolism increases serum bilirubin concentration, and the clinical manifestation of jaundice finally forms. Modern pharmacological studies demonstrate that the exclusion of bilirubin is solely controlled by UDP-glucuronosyltransferase 1A1 through glucuronidation. The levels of tyramine glucuronide in YHS mice increased markedly compared with that of the control group. This phenomenon may contribute to the decrease of UDP-glucuronosyltransferase 1A1 activities, which influence the metabolism and transport of UDP-glucuronosyltransferase 1A1 activities. Finally, YHS and liver disorders are induced. In recent years, a series of reports on GEN or its active constituents indicates its activation function with UDP-glucuronosyltransferase [²⁶−²⁹]. In our research, the increasing level of tyramine glucuronide in GEN group suggested a better knowledge of the preventive effects of YHS via regulating the disturbed metabolism pathways.

Glutathione metabolism is the fundamental reaction in every cell of an organism. It involves many key metabolites and enzymes, such as glutathione disulfide, glutathione, glucose-6-phosphate dehydrogenase, and glutathione peroxidase. Liver disorder and damage occur when the normal operation of the above compounds is disrupted. The present study found that the intensities of spermidine and L-cysteine were significantly reduced in the YHS group. The correlation between the reactions proved that GSH and GSH-Px concentrations are lower than normal levels. The latter has a broad spectrum of applications as a hepatoprotective, anti-inflammatory, antioxygenation, and detoxication agent and is also used as a common biochemical indicator of liver function. Glucose-6-phosphate dehydrogenase is also a vital enzyme in GSH transformation, and its deficiency is directly related to neonatal jaundice [³⁰]. Combining clinical chemistry with metabolomic data, we found that spermidine and L-cysteine content decreased in YHS mice, thereby verifying the potential pathogenesis of YHS.

Taurine is a key component of bile acid [³¹] and plays an important role in liver metabolism. In addition, there are several putative medicinal effects used for the therapeutic category of liver disease agents. Fig. 7 shows that the content of taurine is significantly lower than in the control group. Thus, the activity of glutamate decarboxylase had been inhibited. A disorder in this reaction may cause cerebral palsy [³²,³³] whose clinical manifestation is hyperbilirubinemia (also known as jaundice) [³⁴].

As one of the definite active ingredients of Yinchenhao Decoction, GEN has a good effect on lung injury and jaundice and plays a protective role through the synergistic mechanism of multiple pathways. The metabolic pathways involved in GEN in the course of treatment are shown in Fig. 7, and a connection exists between them. For example, L-cysteine is the link between glutathione metabolism and the taurine metabolism pathway and is also consistent with the overall view of symptoms. In Fig.7, the different metabolic pathways are linked; thus, GEN may play a role in the treatment of YHS by regulating the above targets or pathways.

Metabolomics and network pharmacology are high-throughput and large-data technologies that have emerged in recent years. With the vigorous development of analytical technology and electronic information industry, promoting these two technologies is possible [³⁵-⁴³]. Importantly, TCM has considerable consistency with its holistic view and the mode of network interaction and synergy [⁴⁴-⁴⁶]. Therefore, it has been widely used to understand the mechanism of action of complex diseases and drugs with complex components [⁴⁷-⁴⁹]. Network pharmacology predicts protein structures that may possibly dock with drugs from the perspective of drug-specific spatial structure and chemical bond stability [⁵⁰-⁵²]. Through the identifies of metabolites, characterizes the changes of metabolites’ content and species in objective signal intensity and category form, and traces the upstream metabolic mechanism through the acknowledged database network. The combination of the two may result in the rapid focusing and targeting of the pathogenesis and drug targets of diseases, especially in the cognitive stage of treating complex diseases by using TCM. In the present study, metabolomics and network pharmacology techniques were used to study the mechanism of GEN as intervention for YHS and provide experimental and theoretical basis for the pathogenesis of YHS and the development of effective monomer drugs.

Conclusions

We established a YHS mouse model on the basis of theoretical direction from TCM combined with a previous investigation and response to GEN. With the successful verification of clinical chemistry and histopathology analyses, a high-throughput, comprehensive, urinary metabolomics platform was established for investigating the metabolic typing of YHS, and 33 differential metabolites were detected and confirmed. After the global pathway analysis of the perturbed metabolism, we constructed a map of the metabolic network of YHS, which was directly involved in pentose and glucuronate interconversions and primary bile acid biosynthesis. The key enzymes and metabolites were significantly associated with YHS to improve our understanding of the precise pathogenesis of YHS. Thus, urine metabolomics is suitable for evaluating the disease/syndrome and provides a promising strategy for the use of TCM in treating YHS.

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