Multimodal Assessment of Estrogen Receptor mRNA Profiles to Quantify Estrogen Pathway Activity in Breast Tumors

Investigation on the mechanism of 2,3,4',5-Tetrahydroxystilbene 2-o-D-glucoside in the treatment of inflammation based on network pharmacology

BACKGROUND

Inflammation is the pathogenesis of various chronic diseases plaguing clinic for years.Fallopia multiflora (Thunb.) is a traditional Chinese herbal medicine with a long history of application in detoxification and anti-inflammation. 2,3,4',5-Tetrahydroxystilbene 2-o-D-glucoside (TSG) is a main active compound of F. multiflora. However, the mechanism of TSG in the treatment of inflammation remains unknown.

METHODS

Network pharmacology and molecular docking were employed to explore the mechanism of anti-inflammatory effect of TSG. Potential targets of TSG and inflammation were obtained from Swiss Target Prediction, Pharm Mapper, and GeneCards database. Protein-protein interaction (PPI) networks, GO and KEGG pathway enrichment analysis were performed to elucidate the interaction of targets. Moreover, the anti-inflammatory effect of TSG was validated by in vitro experiments using flow cytometry, RT-qPCR, Western blot, and immunocytochemistry assays.

RESULTS

PPI network and gene enrichment analysis showed that TSG may exert a protein kinase binding activity, and IKBKB, MAPK1, NFKBIA, and RELA were predicted as the targets of anti-inflammation. Verified by molecular docking and Western blot, TSG may target NF-κB and ERK2 related signals to alleviate inflammatory damage. Furthermore, TSG effectively downregulated the expression of inflammatory cytokine, the nuclear translocation of NF-κB p65, and the production of reactive oxygen species (ROS).

CONCLUSION

TSG possesses significant anti-inflammatory effect. TSG may display a protein kinase binding activity and target NF-κB and ERK2 related signals to treat the inflammation. This work may enlighten the potential application of TSG in anti-inflammation and indicate network pharmacology was an effective tool for the further study of TCM.

TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions

Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF-target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF-target interaction database for humans-TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)-which was constructed using sentence-based text mining, followed by manual curation. Here, we present TRRUST v2 (www.grnpedia.org/trrust) with a significant improvement from the previous version, including a significantly increased size of the database consisting of 8444 regulatory interactions for 800 TFs in humans. More importantly, TRRUST v2 also contains a database for TF-target interactions in mice, including 6552 TF-target interactions for 828 mouse TFs. TRRUST v2 is also substantially more comprehensive and less biased than other TF-target interaction databases. We also improved the web interface, which now enables prioritization of key TFs for a physiological condition depicted by a set of user-input transcriptional responsive genes. With the significant expansion in the database size and inclusion of the new web tool for TF prioritization, we believe that TRRUST v2 will be a versatile database for the study of the transcriptional regulation involved in human diseases.