Dataset Information

Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship.

ABSTRACT: The current use of a single chemical component as the representative quality control marker of herbal food supplement is inadequate. In this CD80-Quantitative-Pattern-Activity-Relationship (QPAR) study, we built a bioactivity predictive model that can be applicable for complex mixtures. Through integrating the chemical fingerprinting profiles of the immunomodulating herb Radix Astragali (RA) extracts, and their related biological data of immunological marker CD80 expression on dendritic cells, a chemometric model using the Elastic Net Partial Least Square (EN-PLS) algorithm was established. The EN-PLS algorithm increased the biological predictive capability with lower value of RMSEP (11.66) and higher values of R_p² (0.55) when compared to the standard PLS model. This CD80-QPAR platform provides a useful predictive model for unknown RA extract's bioactivities using the chemical fingerprint inputs. Furthermore, this bioactivity prediction platform facilitates identification of key bioactivity-related chemical components within complex mixtures for future drug discovery and understanding of the batch-to-batch consistency for quality clinical trials.

SUBMITTER: Ng MC

PROVIDER: S-EPMC5350422 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship.

Ng Michelle Chun-Har MC Lau Tsui-Yan TY Fan Kei K Xu Qing-Song QS Poon Josiah J Poon Simon K SK Lam Mary K MK Chau Foo-Tim FT Sze Daniel Man-Yuen DM

BioMed research international 20170228

The current use of a single chemical component as the representative quality control marker of herbal food supplement is inadequate. In this CD80-Quantitative-Pattern-Activity-Relationship (QPAR) study, we built a bioactivity predictive model that can be applicable for complex mixtures. Through integrating the chemical fingerprinting profiles of the immunomodulating herb <i>Radix Astragali</i> (RA) extracts, and their related biological data of immunological marker CD80 expression on dendritic c ...[more]

PMID: 28337449

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Project description:Traumatic brain injury (TBI) is a critical public health and socioeconomic problem worldwide. The herb pair Astragali Radix (AR)-Radix Angelica Sinensis (RAS) is a common prescribed herbal formula or is added to other Chinese medicine prescriptions for traumatic brain injury (TBI) treatment. However, the underlying mechanisms are unclear. In this study, we aimed to explore the active ingredients and action targets of AR-RAS based on the combined methods of network pharmacology prediction and experimental verification. Furthermore, the corresponding potential mechanisms of "multicomponents, multitargets, and multipathways" were disclosed. Methods. A network pharmacology approach including ADME (absorption, distribution, metabolism, and excretion) filter analysis, target prediction, known therapeutic targets collection, Gene Ontology (GO), pathway enrichment analysis, and network construction was used in this study. Further verification experiments were performed to reveal the therapeutic effects of AR-RAS in a rat model of TBI. Results. The comprehensive systematic approach was to successfully identify 14 bioactive ingredients in AR-RAS, while 33 potential targets hit by these ingredients related to TBI. Based on GO annotation analysis, multiple biological processes were significantly regulated by AR-RAS. In addition, 89 novel signaling pathways (P<0.05) underlying the effects of AR-RAS for TBI treatment were identified by DAVID. The neurotrophin signaling pathway was suggested as the major related pathway targeted by AR-RAS to improve axonal growth. The animal experiment confirmed that AR-RAS significantly induced tissue recovery and improved neurological deficits on the 14th day (P<0.01). Treatment with AR-RAS markedly reduced the protein and mRNA expression level of NogoA in the hippocampus of TBI rats. Conclusion. Our work illuminates the "multicompounds, multitargets, and multipathways" curative action of AR-RAS in the treatment of TBI by network pharmacology. The animal experiment verifies the effects of AR-RAS on neurological function improvement and axonal outgrowth via downregulation of NogoA expression, providing a theoretical basis for further research on treatment of TBI.

Project description:BackgroundAcute kidney injury (AKI) is a devastating clinical syndrome with high mortality rate attributed to lack of effective treatment. The herbal pair of Astragali Radix (AR) and Radix Angelica Sinensis (RAS) is a commonly prescribed herbal formula or is added to other traditional Chinese medicine (TCM) prescriptions for the treatment of kidney diseases. AR-RAS has certain protective effects on AKI in experiments, but the relevant mechanisms have yet to be clear. So this study aims to explore the mechanism of action of AR-RAS in AKI by combining network pharmacology and molecular docking methods.MethodsIn Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the major AR-RAS chemical components and associated action targets were found and screened. The DrugBank and GeneCards databases were used to find AKI-related targets. The targets that are in close relationship with AKI were obtained from Therapeutic Target database (TTD), Online Mendelian Inheritance in Man (OMIM), and PharmGKB databases. The "herb-active ingredient-target" network was drawn by Cytoscape 3.8.0 software. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to build the protein-protein interaction network. Bioconductor/R was used to examine Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. AR-RAS components and critical targets were docked using the AutoDock Vina program.ResultsA compound-target network, built by screening and analyzing the results, allowed to identify 19 active components and 101 possible therapeutic targets for AKI. The main ingredients were quercetin, kaempferol, 7-o-methylisocronulatol, formononetin and isorhamnetin. The key targets included AKT serine/threonine kinase 1 (AKT1), nuclear receptor coactivator 1 (NCOA1), JUN, estrogen receptor alpha (ESR1) and mitogen-activated protein kinase 8 (MAPK8). These molecules are targeted by pathways such as the calcium signaling route, the tumor necrosis factor (TNF) signaling pathway and the interleukin-17 (IL-17) signaling pathway, as well as the development of T helper 17 cells. Molecular docking demonstrated that AR-active RAS components exhibited strong binding activities to probable targets of AKI.ConclusionsWe described here the potential active ingredients, possible targets responsible for the efficacy of AR-RAS in AKI treatment, providing a theoretical basis for further research.

Project description:Astragali Radix (AR) has been widely used in traditional Chinese medicine prescriptions for acute and chronic liver injury. However, little is known about the effects of AR on acetaminophen (APAP)-induced liver injury (ALI). In the current study, a network pharmacology-based approach was applied to characterize the action mechanism of AR on ALI. All compounds of AR were obtained from the corresponding databases, and active compounds were selected according to its oral bioavailability and drug-likeness index. The potential genes of AR were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) and PubChem, whereas the potential genes related to ALI were obtained from Online databases (GeneCards and Online Mendelian Inheritance in Man) and Gene Expression Omnibus profiles. The enriched processes, pathways, and target genes of the diseases were analyzed by referring to the Search Tool for the Retrieval of Interacting Genes/Proteins database. A network constructed through Cytoscape software was used to identify the target proteins that connected the compounds in AR with the differential genes of ALI. Subsequently, the potential underlying action mechanisms of AR on ALI predicted by the network pharmacology analyses were experimentally validated in APAP-induced liver injury in mice and HL7702 cells incubated with APAP. The compound-target network included 181 targets, whereas the potential genes related to ALI were 4,621. A total of 49 AR-ALI crossover proteins, corresponding to 49 genes, were filtered into a protein-protein interaction network complex and designated as the potential targets of AR on ALI. Among the genes, the three highest-scoring genes, MYC, MAPK8, and CXCL8 were highly associated with apoptosis in ALI. Then in vitro and in vivo experiments confirmed that AR exhibited its prominent therapeutic effects on ALI mainly via regulating hepatocyte apoptosis related to inhibiting the expressions of MYC (c-Myc), MAPK8 (JNK1), and CXCL8 (IL-8). In conclusion, our study suggested that the combination of network pharmacology prediction with experimental validation might offer a useful tool to characterize the molecular mechanism of AR on ALI.

Dataset Information

Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship.

Publications

Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship.

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