In-Silico Study of Immune System Associated Genes in Case of Type-2 Diabetes With Insulin Action and Resistance, and/or Obesity

Abstract

Type-2 diabetes and obesity are among the leading human diseases and highly complex in terms of diagnostic and therapeutic approaches and are among the most frequent and highly complex and heterogeneous in nature. Based on epidemiological evidence, it is known that the patients suffering from obesity are considered to be at a significantly higher risk of type-2 diabetes. There are several pieces of evidence that support the hypothesis that these diseases interlinked and obesity may aggravate the risk(s) of type-2 diabetes. Multi-level unwanted alterations such as (epi-) genetic alterations, changes at the transcriptional level, and altered signaling pathways (receptor, cytoplasmic, and nuclear level) are the major sources that promote several complex diseases, and such a heterogeneous level of complexity is considered as a major barrier in the development of therapeutics. With so many known challenges, it is critical to understand the relationships and the shared causes between type-2 diabetes and obesity, and these are difficult to unravel and understand. For this purpose, we have selected publicly available datasets of gene expression for obesity and type-2 diabetes, have unraveled the genes and the pathways associated with the immune system, and have also focused on the T-cell signaling pathway and its components. We have applied a simplified computational approach to understanding differential gene expression and patterns and the enriched pathways for obesity and type-2 diabetes. Furthermore, we have also analyzed genes by using network-level understanding. In the analysis, we observe that there are fewer genes that are commonly differentially expressed while a comparatively higher number of pathways are shared between them. There are only 4 pathways that are associated with the immune system in case of obesity and 10 immune-associated pathways in case of type-2 diabetes, and, among them, only 2 pathways are commonly altered. Furthermore, we have presented SPNS1, PTPN6, CD247, FOS, and PIK3R5 as the overexpressed genes, which are the direct components of TCR signaling.

Global Gene Expression and Docking Profiling of COVID-19 Infection

“…MATLAB coding was also used for figure plotting and analysis. The primary idea of the FunCoup network database is that it predicts four different forms of functional coupling or links, such as protein complexes, protein-protein physical interactions, metabolic, and signaling pathways ( Alexeyenko and Sonnhammer 2009 ; Bajrai et al, 2021b ; Bajrai et al, 2021a ; Eldakhakhny et al, 2021 ; Kamal et al, 2020 ; Mobashir et al, 2012 ; Mobashir et al, 2014 ; Warsi et al, 2020 ). Swiss-dock ( http://www.swissdock.ch ) ( Grosdidier et al, 2011 ; Kumar et al, 2020 ) was utilized for docking analysis, with PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ), UniProt ( https://www.uniprot.org/ ), and swiss-model ( https://swissmodel.expasy.org ) as supporting databases ( Kumar et al, 2020 ).…”

A Network-Guided Approach to Discover Phytochemical-Based Anticancer Therapy: Targeting MARK4 for Hepatocellular Carcinoma

“…Still, they are in different directions. To simplify and bring many directional works in one study, we have applied an integrated approach from data acquisition to analysis and network-level understanding (63,69,83,84). From this study, there are many concluding points in terms of the MARK4 interactors, drug-target identifications, mutational profiling, functional aberrations, and clinical relevance, which are quite straightforward from data analysis to presentation, potential drug-target prediction, and conclusion.…”

“…network database to predict four different forms of functional coupling or links, such as protein complexes, protein-protein physical interactions, metabolic, and signaling pathways(51,61,64,65,(67)(68)(69)(70). Swiss-dock was utilized for docking analysis (http://www.swissdock.ch)(18,48), with PubChem (https:// pubchem.ncbi.nlm.nih.gov), UniProt (https://www.uniprot.org/ ), and swiss-model (https://swissmodel.expasy.org) as supporting databases…”

Novel insight into the aetiology of rheumatoid arthritis gained by a cross-tissue transcriptome-wide association study

“…36 37 Notably, SPNS1 has recently been considered as a core component of T cell receptor signalling. 38 A slew of studies have shown that TRIM10 and TRIM27 were involved in several inflammation processes. [39][40][41] TRIM10 exhibited a lower expression in patients with systemic lupus erythematosus than in healthy individuals, which negatively induced the IFN/JAK/STAT signalling pathway through impacting the interaction between IFNAR1 and TYK2.…”