Hamiltonian energy as an efficient approach to identify the significant key regulators in biological networks

Integrating protein interaction and pathway crosstalk network reveals a promising therapeutic approach for psoriasis through apoptosis induction

Psoriasis is a complex inflammatory skin disease manifested by altered proliferation and differentiation of keratinocytes with dysfunctional apoptosis. This study aimed to identify regulatory factors and comprehend the underlying mechanisms of inefficient apoptosis to open up promising therapeutic approaches. Incorporating human protein interactions, apoptosis proteins, and physical relationships of psoriasis-apoptosis proteins helped us to generate a psoriasis-apoptosis interaction (SAI) network. Subsequently, topological and functional analyses of the SAI network revealed effective proteins, functional modules, hub motifs, dysregulated pathways and transcriptional gene regulatory factors. Network pharmacology, molecular docking and molecular dynamics simulation methods identified the potential drug-target interactions. RELA, MAPK1, MAPK3, MMP9, IL1B, AKT1 and STAT1 were revealed as effective proteins. The MAPK1-MAPK3-RELA motif was identified as a hub regulator in the crosstalk between 41 pathways. Among all pathways, "lipid and atherosclerosis" was found to be the predominant pathway. Acetylcysteine, arsenic-trioxide, β-elemene, bortezomib and curcumin were identified as potential drugs to inhibit pathway crosstalk. Experimental verifications were performed using the literature search, GSE13355 and GSE14905 microarray datasets. Drug-protein-pathway interactions associated with apoptosis were deciphered. These findings highlight the role of hub motif-mediated pathway-pathway crosstalk associated with apoptosis in the complexity of psoriasis and suggest crosstalk inhibition as an effective therapeutic approach.

Identification of key regulators in Sarcoidosis through multidimensional systems biological approach

Sarcoidosis is a multi-organ disorder where immunology, genetic and environmental factors play a key role in causing Sarcoidosis, but its molecular mechanism remains unclear. Identification of its genetics profiling that regulates the Sarcoidosis network will be one of the main challenges to understand its aetiology. We have identified differentially expressed genes (DEGs) by analyzing the gene expression profiling of Sarcoidosis and compared it with healthy control. Gene set enrichment analysis showed that these DEGs were mainly enriched in the inflammatory response, immune system, and pathways in cancer. Sarcoidosis protein interaction network was constructed by a total of 877 DEGs (up-down) and calculated its network topological properties, which follow hierarchical scale-free fractal nature up to six levels of the organization. We identified a large number of leading hubs that contain six key regulators (KRs) including ICOS, CTLA4, FLT3LG, CD33, GPR29 and ITGA4 are deeply rooted in the network from top to bottom, considering a backbone of the network. We identified the transcriptional factors (TFs) which are closely interacted with KRs. These genes and their TFs regulating the Sarcoidosis network are expected to be the main target for the therapeutic approaches and potential biomarkers. However, experimental validations of KRs needed to confirm their efficacy.

Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.