A New Prospect for the Treatment of Nephrotic Syndrome Based on Network Pharmacology Analysis

Network pharmacology analysis of Chandraprabha Vati: A new hope for the treatment of Metabolic Syndrome

BACKGROUND

Drug research is increasingly using Network Pharmacology (NP) to tackle complex conditions like Metabolic Syndrome (MetS), which is characterized by obesity, hyperglycemia, and dyslipidemia. Single-action drugs are inadequate to treat MetS, which is marked by a range of complications including glucose intolerance, hyperlipidemia, mitochondrial dysfunction, and inflammation.

OBJECTIVES

To analyze Chandraprabha vati using Network Pharmacology to assess its potential in alleviating MetS-related complications.

MATERIAL AND METHODS

The genes related to MetS, inflammation, and the target genes of the CPV components were identified using network pharmacology tools like DisgNET and BindingDB. Followed by mapping of the CPV target genes with the genes implicated in MetS and inflammation to identify putative potential targets. Gene ontology, pathway enrichment analysis, and STRING database were employed for further exploration. Furthermore, drug-target-protein interactions network were visualized using Cytoscape 3.9.1.

RESULTS

The results showed that out of the 225 target genes of the CPV components, 33 overlapping and 19 non-overlapping genes could be potential targets for MetS. Similarly, 14 overlapping and 7 non-overlapping genes could be potential targets for inflammation. The CPV bioactives target genes were found to be involved in lipid and insulin homeostasis via several pathways revealed by the pathway analysis. The importance of CPV in treating MetS was supported by GO enrichment data; this could be due to its potential to influence pathways linked to metabolism, ER stress, mitochondrial dysfunction, oxidative stress, and inflammation.

CONCLUSIONS

These results offer a promising approach to developing treatment and repurposing CPV for complex conditions such as MetS.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

Decreased GM3 correlates with proteinuria in minimal change nephrotic syndrome and focal segmental glomerulosclerosis

BACKGROUND

Glycolipids on cell membrane rafts play various roles by interacting with glycoproteins. Recently, it was reported that the glycolipid GM3 is expressed in podocytes and may play a role in podocyte protection. In this report, we describe the correlation between changes in GM3 expression in glomeruli and proteinuria in minimal change nephrotic syndrome (MCNS) and focal segmental glomerulosclerosis (FSGS) patients.

METHODS

We performed a case-control study of the correlation between nephrin/GM3 expression levels and proteinuria in MCNS and FSGS patients who underwent renal biopsy at our institution between 2009 and 2014. Normal renal tissue sites were used from patients who had undergone nephrectomy at our institution and gave informed consent.

RESULTS

Both MCNS and FSGS had decreased GM3 and Nephrin expression compared with the normal (normal vs. MCNS, FSGS; all p < 0.01). Furthermore, in both MCNS and FSGS, GM3 expression was negatively correlated with proteinuria (MCNS: r = - 0.61, p < 0.01, FSGS: r = - 0.56, p < 0.05). However, nephrin expression had a trend to correlate with proteinuria in FSGS (MCNS: r = 0.19, p = 0.58, FSGS: r = - 0.48, p = 0.06). Furthermore, in a simple linear regression analysis, GM3 expression also correlated with proteinuric change after 12 months of treatment (MCNS: r = 0.40, p = 0.38, FSGS: r = 0. 68, p < 0.05).

CONCLUSION

We showed for the first time that decreased GM3 expression correlates with proteinuria in MCNS and FSGS patients. Further studies are needed on the podocyte-protective effects of GM3.

Network Pharmacology and In Vivo Experimental Validation to Uncover the Renoprotective Mechanisms of Fangji Huangqi Decoction on Nephrotic Syndrome

Background

Fangji Huangqi decoction (FHD) is a traditional Chinese medicine formula that has the potential efficacy for nephrotic syndrome (NS) treatment. This study aims to explore the effects and underlying mechanisms of FHD against NS via network pharmacology and in vivo experiments.

Methods

The bioactive compounds and targets of FHD were retrieved from the TCMSP database. NS-related targets were collected from GeneCards and DisGeNET databases. The compound-target and protein-protein interaction networks were constructed by Cytoscape 3.8 and BisoGenet, respectively. GO and KEGG analyses were performed by the DAVID online tool. The interactions between active compounds and hub genes were revealed by molecular docking. An NS rat model was established to validate the renoprotective effects and molecular mechanisms of FHD against NS in vivo.

Results

A total of 32 hub genes were predicted to play essential roles in FHD treating NS. Eight main bioactive compounds of FHD had the good affinity with 9 hub targets (CCL2, IL-10, PTGS2, TNF, MAPK1, IL-6, CXCL8, TP53, and VEGFA). The therapeutic effect of FHD on NS was closely involved in the regulation of inflammation and PI3K-Akt pathway. In vivo experiments confirmed the renoprotective effect of FHD on NS, evidenced by reducing the levels of proteinuria, serum creatinine, blood urea nitrogen, and inflammatory factors in NS rats. The PI3K activator 740Y-P weakened the effects of FHD against NS. Furthermore, FHD downregulated the levels of PTGS2, MAPK1, IL-6, and p-Akt in NS rats.

Conclusions

FHD alleviates kidney injury and inflammation in NS by targeting PTGS2, MAPK1, IL-6, and PI3K-Akt pathway.

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.