Dataset Information

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine.

ABSTRACT: Though many studies have been performed to elucidate molecular mechanism of traditional Chinese medicines (TCMs) by identifying protein-compound interactions, no systematic analysis at herb level was reported. TCMs are prescribed by herbs and all compounds from a certain herb should be considered as a whole, thus studies at herb level may provide comprehensive understanding of TCMs. Here, we proposed a computational strategy to study molecular mechanism of TCM at herb level and used it to analyze a TCM anti-HIV formula. Herb-target network analysis was carried out between 17 HIV-related proteins and SH formula as well as three control groups based on systematic docking. Inhibitory herbs were identified and active compounds enrichment was found to contribute to the therapeutic effectiveness of herbs. Our study demonstrates that computational analysis of TCMs at herb level can catch the rationale of TCM formulation and serve as guidance for novel TCM formula design.

SUBMITTER: Liang H

PROVIDER: S-EPMC5105066 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine.

Liang Hao H Ruan Hao H Ouyang Qi Q Lai Luhua L

Scientific reports 20161111

Though many studies have been performed to elucidate molecular mechanism of traditional Chinese medicines (TCMs) by identifying protein-compound interactions, no systematic analysis at herb level was reported. TCMs are prescribed by herbs and all compounds from a certain herb should be considered as a whole, thus studies at herb level may provide comprehensive understanding of TCMs. Here, we proposed a computational strategy to study molecular mechanism of TCM at herb level and used it to analyz ...[more]

PMID: 27833111

Similar Datasets

Project description:BackgroundYang Xin Tang (YXT) is a traditional Chinese herbal preparation which has been reported to improve cognitive function and memory in patients with dementia. As the underlying mechanism of action of YXT has not been elucidated, we examined the effects of YXT and its major herbal components in regulating gene transcription and molecular targets related to Alzheimer's disease (AD).MethodsAqueous and ethanol extracts of YXT and selected herbal components were prepared and validated by standard methods. A series of biochemical and cellular assays were employed to assess the ability of the herbal extracts to inhibit acetylcholinesterase, reduce β-amyloid aggregation, stimulate the differentiation of neural progenitor cells, suppress cyclooxygenase, and protect neurons against β-amyloid or N-methyl-D-aspartate-induced cytotoxicity. The effects of YXT on multiple molecular targets were further corroborated by a panel of nine reporter gene assays.ResultsExtracts of YXT and two of its constituent herbs, Poria cocos and Poria Sclerotium pararadicis, significantly inhibited β-amyloid aggregation and β-amyloid-induced cytotoxicity. A protective effect of the YXT extract was similarly observed against N-methyl-D-aspartate-induced cytotoxicity in primary neurons, and this activity was shared by extracts of Radix Astragali and Rhizoma Chuanxiong. Although the YXT extract was ineffective, extracts of Poria cocos, Poria Sclerotium pararadicis and Radix Polygalae inhibited acetylcholine esterase, with the latter also capable of upregulating choline acetyltransferase. YXT and its components significantly inhibited the activities of the pro-inflammatory cyclooxygenases. Additionally, extracts of YXT and several of its constituent herbs significantly stimulated the phosphorylation of extracellular signal-regulated kinases and cAMP-responsive element binding protein, two molecular targets involved in learning and memory, as well as in the regulation of neurogenesis.ConclusionsSeveral constituents of YXT possess multiple regulatory effects on known therapeutic targets of AD that range from β-amyloid to acetylcholinesterase. The demonstrated neuroprotective and neurogenic actions of YXT lend credence to its use as an alternative medicine for treating AD.

Project description:Osteoporosis is a widespread bone disease that affects million cases annually. The underlying mechanisms behind the progress of osteoporosis remain enigmatic, which limits detections of biomarkers and therapeutic targets. Hence, this study was aimed at exploring hub molecules to better understand the mechanism of osteoporosis development and discover the traditional Chinese medicine potential drugs for osteoporosis. miRNA and gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Weighted correlation network analysis (WGCNA) was used to identify the key modules for osteoporosis. DIANA Tools was applied to perform pathway enrichment. A miRNA-gene interaction network was constructed, and hub miRNAs and genes were distinguished using Cytoscape software. Receiver operating characteristic (ROC) curves of hub miRNAs and genes were plotted, and correlations with hub genes and osteoporosis-associated factors were evaluated. Potential drugs for osteoporosis in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) were screened, and molecular docking models between these drugs and target genes were showed by AutoDock tools. Two hub modules, 1 miRNA module and 1 gene module, were identified to be the most strongly correlated with osteoporosis by using WGCNA. Then, 3 KEGG pathways including focal adhesion, PI3K-Akt signaling pathway, and gap junction were shared pathways enriched with the miRNAs and genes screened out by WGCNA and differential expression analyses. Finally, after constructing a miRNA-gene interaction network, 6 hub miRNAs (hsa-miR-18b-3p, hsa-miR-361-3p, hsa-miR-484, hsa-miR-519e-5p, hsa-miR-940, and hsa-miR-1275) and 6 hub genes (THBS1, IFNAR2, ARHGAP5, TUBB2B, FLNC, and NTF3) were detected. ROC curves showed good performances of miRNAs and genes for osteoporosis. Correlations with hub genes and osteoporosis-associated factors suggested implicational roles of them for osteoporosis. Based on these hub genes, 3 natural compounds (kainic acid, uridine, and quercetin), which were the active ingredients of 192 herbs, were screened out, and a target-compound-herb network was extracted using TCMSP. Molecular docking models of kainic acid-NTF3, uridine-IFNAR2, and quercetin-THBS1 were exhibited with AutoDock tools. Our study sheds light on the pathogenesis of osteoporosis and provides promising therapeutic targets and traditional Chinese medicine drugs for osteoporosis.

Project description:BackgroundDue to the lack of enough interaction data among compositions, targets and diseases, it is difficult to construct a complete network of Traditional Chinese Medicine (TCM) that comprehensively reflects active compositions and their synergistic network in terms of specific diseases. Therefore, mapping of the full spectrum of interaction between compounds and their targets is of central importance when we use network pharmacology approach to explore the therapeutic potential of the TCM.MethodsTo address this challenge, we developed a large-scale simultaneous interaction prediction approach (SiPA) integrated one interaction network based simple inference model (SIM), focusing on 'logical relevance' between compounds, proteins or diseases, and another compound-target correlation space based interaction prediction model (CTCS-IPM) that was built on the basis of the canonical correlation analysis (CCA) to estimate the position of compounds (or targets) in compound-protein correlated space. Then SiPA was applied to discover reliable multiple interactions for interaction network expansion of a TCM, compound Salvia miltiorrhiza. By means of network analysis, potential active compounds and their related network synergy underlying cardiovascular diseases were evaluated between expanded and original interaction networks. Part of new interactions were validated with existing experimental evidence and molecular docking.ResultsAs evaluated with known test dataset, the established combination approach was proved to make highly accurate prediction, showing a well prediction performance for the SIM and a high recall rate of 85.2% for the CTCS-IPM. Then 710 pairs of new compound-target interactions, 24 pairs of new compound-cardiovascular disease interactions and 294 pairs of new cardiovascular disease-protein interactions were predicted for compound Salvia miltiorrhiza. Results of network analysis suggested the network expansion could dramatically improve the completeness and effectiveness of the network. Validation results of literature and molecular docking manifested that inferred interactions had good reliability.ConclusionsWe provided a practical and efficient way for large-scale inference of multiple interactions of TCM ingredients, which was not limited by the lack of negative samples, sample size and target 3D structures. SiPA could help researchers more accurately prioritize the effective compounds and more completely explore network synergy of TCM for treating specific diseases, indicating a potential way for effectively identifying candidate compound (or target) in drug discovery.

Dataset Information

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine.

Publications

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets