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Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach.

ABSTRACT: An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the potential compound-target interactions (CTIs) were predicted using our substructure-drug-target network-based inference (SDTNBI) method. After screening for hepatotoxicity-related genes by assessing the symptoms of HILI, a compound-target interaction network was constructed. A scoring function, namely, Ascore, was developed to estimate the toxicity of chemicals in the liver. We conducted network analysis to determine the possible mechanisms of the biphasic effects using the analysis tools, including BiNGO, pathway enrichment, organ distribution analysis and predictions of interactions with CYP450 enzymes. Among the chemical components of PmT, 54 components with good intestinal absorption were used for analysis, and 2939 CTIs were obtained. After analyzing the mRNA expression data in the BioGPS database, 1599 CTIs and 125 targets related to liver diseases were identified. In the top 15 compounds, seven with Ascore values >3000 (emodin, quercetin, apigenin, resveratrol, gallic acid, kaempferol and luteolin) were obviously associated with hepatotoxicity. The results from the pathway enrichment analysis suggest that multiple interactions between apoptosis and metabolism may underlie PmT-induced liver injury. Many of the pathways have been verified in specific compounds, such as glutathione metabolism, cytochrome P450 metabolism, and the p53 pathway, among others. Hepatitis symptoms, the perturbation of nine bile acids and yellow or tawny urine also had corresponding pathways, justifying our method. In conclusion, this computational systems toxicology method reveals possible toxic components and could be very helpful for understanding the mechanisms of HILI. In this way, the method might also facilitate the identification of novel hepatotoxic herbs.

SUBMITTER: Wang YY

PROVIDER: S-EPMC5457689 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach.

Wang Yin-Yin YY Li Jie J Wu Zeng-Rui ZR Zhang Bo B Yang Hong-Bin HB Wang Qin Q Cai Ying-Chun YC Liu Gui-Xia GX Li Wei-Hua WH Tang Yun Y

Acta pharmacologica Sinica 20170227 5

An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the ...[more]

PMID: 28239160

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Project description:In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the hepatotoxic ingredients in TCMs available until now. In the present study, we initially built a large scale dataset of drug-induced liver injuries (DILIs). Then, 13 types of molecular fingerprints/descriptors and eight machine learning algorithms were utilized to develop single classifiers for DILI, which resulted in 5416 single classifiers. Next, the NaiveBayes algorithm was adopted to integrate the best single classifier of each machine learning algorithm, by which we attempted to build a combined classifier. The accuracy, sensitivity, specificity, and area under the curve of the combined classifier were 72.798, 0.732, 0.724, and 0.793, respectively. Compared to several prior studies, the combined classifier provided better performance both in cross validation and external validation. In our prior study, we developed a herb-hepatotoxic ingredient network and a herb-induced liver injury (HILI) dataset based on pre-clinical evidence published in the scientific literature. Herein, by combining that and the combined classifier developed in this work, we proposed the first instance of a computational toxicology to screen the hepatotoxic ingredients in TCMs. Then Polygonum multiflorum Thunb (PmT) was used as a case to investigate the reliability of the approach proposed. Consequently, a total of 25 ingredients in PmT were identified as hepatotoxicants. The results were highly consistent with records in the literature, indicating that our computational toxicology approach is reliable and effective for the screening of hepatotoxic ingredients in Pmt. The combined classifier developed in this work can be used to assess the hepatotoxic risk of both natural compounds and synthetic drugs. The computational toxicology approach presented in this work will assist with screening the hepatotoxic ingredients in TCMs, which will further lay the foundation for exploring the hepatotoxic mechanisms of TCMs. In addition, the method proposed in this work can be applied to research focused on other adverse effects of TCMs/synthetic drugs.

Project description:BackgroundThe idiosyncratic hepatotoxicity of Polygonum multiflorum (PM) has attracted considerable interest, but the idiosyncratically hepatotoxic components and endogenous metabolite changes resulting from idiosyncratic hepatotoxicity of PM are not well understood. The aim of this study was to identify the idiosyncratically hepatotoxic components and potential endogenous metabolic biomarkers for PM-induced liver injury.MethodsSerum biochemical indicators and hematoxylin and eosin (H&E) staining were evaluated to identify pathological changes. Gas chromatography/mass spectrometry (GC-MS) was performed to identify changes in metabolic biomarkers. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) was applied to determine group clustering trends and differential metabolites.ResultsThe results for the liver index, the liver function index and liver pathology showed that Polygonum multiflorum ethanol extract (PME), 50% ethanol elution fractions and tetrahydroxystilbene glucoside (TSG) from PME can induce idiosyncratic hepatotoxicity. TSG was the main idiosyncratically hepatotoxic component. Forty endogenous metabolites were identified in the rat liver. Six biomarkers, including lower levels of L-valine and higher levels of 3-hydroxybutyric acid, hexadecanoic acid, ribose, phosphoric acid and oxalic acid, were related to PM-induced liver injury. These differential biomarkers led to disruptions in amino acid, fatty acid, oxalate, energy and glucose metabolism. A total of 32 types of endogenous metabolites were identified in rat serum. Ten biomarkers were related to the liver injury induced by TSG, including lower levels of L-valine and L-proline and higher levels of urea, caproic acid, DL-malic acid, D-mannose, 3-hydroxybutyric acid, D-galactose, octadecane and hexadecanoic acid. These differential biomarkers led to disruptions in amino acid, glucose and fat metabolism. The mechanism of idiosyncratic hepatotoxicity in PM involves TSG-induced disruptions in amino acid metabolism, lipid metabolism, energy metabolism and glucose metabolism.ConclusionsThese findings reflect the material basis and metabolic mechanism of idiosyncratic PM hepatotoxicity.

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Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach.

Publications

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach.

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