Dataset Information

Microarray analysis of genes from animals treated with a traditional formulation ChandraprabhaVati reveals its therapeutic targets.

ABSTRACT: Background:Traditional medicinal preparations have not received global acceptance, and their therapeutic benefits remain disputed due to lack of scientific evidence on their mechanism of action. Microarray analysis has emerged as a powerful technique that can aid in understanding the complex signaling networks activated by these formulations and thereby assess their beneficial as well as adverse effects. Aim:The present work aims to investigate the differential influence of ChandraprabhaVati, Ayurvedic formulation used in the treatment of diabetes, anemia, urinary, respiratory, skin and liver disorders. Materials and methods:The RNA from the liver of rats treated with different doses of ChandraprabhaVati for 28 days was isolated and studied for the genome-wide changes in the expression. Results:The results revealed several molecular targets that could contribute to the therapeutic effects of ChandraprabhaVati. Several genes have been differentially expressed, among those miRNAs miR-434, miR877, and miRlet7e contribute to the anti-diabetic, anti-fibrotic and anti-inflammatory of CPV. The rejuvenative activity of CPV may be due to the MeOX1 and Upf3b genes. Up-regulation of Hbaa2 gene facilitates the anti-anemic effect. Interestingly gender-specific differential expressions of genes were also observed. Rab3d were found to be altered in female when compared to male animals. Conclusion:Thus the microarray data for the CPV treated animals has revealed molecular targets that may be responsible for the various known therapeutic effects and also identified new beneficial effects of CPV.

SUBMITTER: Rajagopal P

PROVIDER: S-EPMC6957807 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Microarray analysis of genes from animals treated with a traditional formulation <i>ChandraprabhaVati</i> reveals its therapeutic targets.

Rajagopal Pratheppa P Chellappan David Raj DR Sridharan Sriram S Pemiah Brindha B Krishnaswamy Sridharan S Sethuraman Swaminathan S Sekar KalpoondiRajan K Krishnan Uma Maheswari UM

Journal of traditional and complementary medicine 20190801 1

<h4>Background</h4>Traditional medicinal preparations have not received global acceptance, and their therapeutic benefits remain disputed due to lack of scientific evidence on their mechanism of action. Microarray analysis has emerged as a powerful technique that can aid in understanding the complex signaling networks activated by these formulations and thereby assess their beneficial as well as adverse effects.<h4>Aim</h4>The present work aims to investigate the differential influence of <i>Cha ...[more]

PMID: 31956556

Similar Datasets

Project description:BackgroundTo analyze the key prognostic genes and potential traditional Chinese medicine targets in glioblastoma (GBM) by bioinformatics and network pharmacology.MethodsGBM datasets were obtained from the Gene Expression Omnibus (GEO) database to clarify the differentially-expressed genes (DEGs) in the carcinoma and paracancerous tissues. The molecular functions (MF) and signaling pathways of enriched DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The STRING database and Cytoscape software were used to construct the protein-protein interaction (PPI) network and screen hub genes to focus on genes with greater clinical significance. The transcription expression and prognosis of hub genes were analyzed using the Gene Expression Profiling Interactive Analysis 2 (GEPIA 2) database. The important compounds and target molecules were obtained via the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) database. We identified the active ingredients by setting the property values of pharmacokinetic attribute values. We constructed the network of "Chinese medicine ingredients-DEGs target" and screened out the target genes and active ingredients with high correlation scores. Finally, molecular docking verification was carried out using AutoDock Tools and PyMOL.ResultsWe obtained 271 DEGs, including 212 up-regulated genes and 59 down-regulated genes and screened ten hub genes. GO and KEGG analyses suggested that the hub genes were mainly involved in the following biological processes: the cell cycle, cell division, and cell adhesion, as well as extracellular matrix adhesion-related pathways, the p53 signaling pathways, and cadherin binding involved in cell-cell adhesion. We established the interaction network between the components and DEGs to screen out the traditional Chinese medicine active component (luteolin) and target genes (BIRC5 and CCNB1) for the treatment of GBM. The molecular docking results showed that the bindings of protein receptors, BIRC5 and CCNB1, with the compound ligand, luteolin, were stable and formed by hydrogen bonding interaction.ConclusionsIn this study, we determined that luteolin potentially inhibits glioblastoma proliferation and migration through key target genes, BIRC5 and CCNB1, via bioinformatics and network pharmacology analysis, and affects the prognosis of GBM patients, providing new ideas for clinical targeted therapy and new drug development.

Project description:BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Dataset Information

Microarray analysis of genes from animals treated with a traditional formulation ChandraprabhaVati reveals its therapeutic targets.

Publications

Microarray analysis of genes from animals treated with a traditional formulation <i>ChandraprabhaVati</i> reveals its therapeutic targets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets