Interleukin-10 as Covid-19 biomarker targeting KSK and its analogues: Integrated network pharmacology.

COVID-19 caused by the SARS-CoV-2 virus is widespread in all regions, and it disturbs host immune system functioning leading to extreme inflammatory reaction and hyperactivation of the immune response. Kabasura Kudineer (KSK) is preventive medicine against viral infections and a potent immune booster for inflammation-related diseases. We hypothesize that KSK and KSK similar plant compounds, might prevent or control the COVID-19 infection in the human body. 1,207 KSK and KSK similar compounds were listed and screened via the Swiss ADME tool and PAINS Remover; 303 compounds were filtered including active and similar drug compounds. The targets were retrieved from similar drugs of the active compounds of KSK. Finally, 573 genes were listed after several screening steps. Next, network analysis was performed to finalize the potential target gene: construction of protein-protein interaction of 573 genes using STRING, identifying top hub genes in Cytoscape plug-ins (MCODE and cytoHubba). These ten hub genes play a crucial role in the inflammatory response. Target-miRNA interaction was also constructed using the miRNet tool to interpret miRNAs of the target genes and their functions. Functional annotation was done via DAVID to gain a complete insight into the mechanism of the enriched pathways and other diseases related to the given target genes. In Molecular Docking analysis, IL10 attained top rank in Target-miRNA interaction and also the gene formed prominent exchanges with an excellent binding score (> = -8.0) against 19 compounds. Among them, Guggulsterone has an acute affinity score of -8.8 for IL10 and exhibits anti-inflammatory and immunomodulatory properties. Molecular Dynamics simulation study also performed for IL10 and the interacting ligand compounds using GROMACS. Finally, Guggulsterone will be recommended to enhance immunity against several inflammatory diseases, including COVID19.

Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin.

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.