Biclique extension as an effective approach to identify missing links in metabolic compound-protein interaction networks

Network-based drug repurposing for HPV-associated cervical cancer

In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction

Computational approaches to the characterization and prediction of compound-protein interactions have a long research history and are well established, driven primarily by the needs of drug development. While, in principle, many of the computational methods developed in the context of drug development can also be applied directly to the investigation of metabolite-protein interactions, the interactions of metabolites with proteins (enzymes) are characterized by a number of particularities that result from their natural evolutionary origin and their biological and biochemical roles, as well as from a different problem setting when investigating them. In this review, these special aspects will be highlighted and recent research on them and developed computational approaches presented, along with available resources. They concern, among others, binding promiscuity, allostery, the role of posttranslational modifications, molecular steering and crowding effects, and metabolic conversion rate predictions. Recent breakthroughs in the field of protein structure prediction and newly developed machine learning techniques are being discussed as a tremendous opportunity for developing a more detailed molecular understanding of metabolism.

Network Pharmacology Analysis and Experimental Validation to Investigate the Mechanism of Total Flavonoids of Rhizoma Drynariae in Treating Rheumatoid Arthritis

Objective

The study aimed to explore the mechanism of total flavonoids of Rhizoma Drynariae (TFRD) in the treatment of rheumatoid arthritis (RA) based on network pharmacology and experimental validation.

Methods

The active components of TFRD were identified from TCMSP and TCMID databases. Relevant targets of the active compounds of TFRD and RA-related targets were predicted by public databases online. A component-target (C-T) regulatory network was constructed by Cytoscape. The genes of TFRD regulating RA were imported into STRING database to construct a protein-protein interaction (PPI) network in order to predict the key targets. KEGG enrichment analysis was performed to predict the crucial mechanism of TFRD against RA. The active components of TFRD underwent molecular docking with the key proteins. Collagen-induced arthritis (CIA) model of rats and inflammatory factors-stimulated fibroblast-like synoviocytes were used in vivo and in vitro to validate the efficacy and predicted critical mechanisms of TFRD.

Results

Network Pharmacology analysis revealed that TFRD had 14 active compounds, corresponding to 213 targets, and RA related to 2814 genes. There were 137 intersection genes between TFRD and RA. KEGG indicated that therapeutic effects of TFRD on RA involves T cell receptor signaling pathway, Th17 cell differentiation, IL-17 signaling pathway, TNF signaling pathway, MAPK signaling pathway and PI3K/AKT signaling pathway. In vivo experiments suggested TFRD can alleviate the inflammatory response, joint swelling and synovial abnormality of CIA rats. TFRD contributed to the decrease of Th17 cells and the down-regulated secretion of IL-17A and TNF-α of activated lymphocyte in CIA model. In vitro experiments confirmed TFRD can effectively inhibit the inflammatory response of fibroblast-like synoviocytes and suppress the abnormal activation of MAPK, PI3K/AKT and NFκB signaling pathways.

Conclusion

The treatment of RA with TFRD is closely related to inhibiting Th17 differentiation and inflammatory response of synoviocytes.