Gene Expression Profiling of Early Acute Febrile Stage of Dengue Infection and Its Comparative Analysis With Streptococcus pneumoniae Infection

Abstract

Infectious diseases are the disorders caused by organisms such as bacteria, viruses, fungi, or parasites. Although many of them are permentantly hazardous, a number of them live in and on our bodies and they are normally harmless or even helpful. Under certain circumstances, some organisms may cause diseases and these infectious diseases may be passed directly from person to person or via intermediate vectors including insects and other animals. Dengue virus and Streptococcus pneumoniae are the critical and common sources of infectious diseases. So, it is critical to understand the gene expression profiling and their inferred functions in comparison to the normal and virus infected conditions. Here, we have analyzed the gene expression profiling for dengue hemorrhagic fever, dengue fever, and normal human dataset. Similar to it, streptococcus pneumoniae infectious data were analyzed and both the outcomes were compared. Our study leads to the conclusion that the dengue hemorrhagic fever arises in result to potential change in the gene expression pattern, and the inferred functions obviously belong to the immune system, but also there are some additional potential pathways which are critical signaling pathways. In the case of pneumoniae infection, 19 pathways were enriched, almost all these pathways are associated with the immune system and 17 of the enriched pathways were common with dengue infection except platelet activation and antigen processing and presentation. In terms of the comparative study between dengue virus and Streptococcus pneumoniae infection, we conclude that cell adhesion molecules (CAMs), MAPK signaling pathway, natural killer cell mediated cytotoxicity, regulation of actin cytoskeleton, and cytokine-cytokine receptor interaction are commonly enriched in all the three cases of dengue infection and Streptococcus pneumoniae infection, focal adhesion was enriched between classical dengue fever — dengue hemorrhagic fever, dengue hemorrhagic fever—normal samples, and SP, and antigen processing and presentation and Leukocyte transendothelial migration were enriched in classical dengue fever —normal samples, dengue hemorrhagic fever—normal samples, and Streptococcus pneumoniae infection.

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 ).…”

Synthesis of Silver Nano Particles Using Myricetin and the In-Vitro Assessment of Anti-Colorectal Cancer Activity: In-Silico Integration

“…Target and normal data both need to have the same number of rows and be normally distributed with comparable variations across classes. We have used the KEGG [ 80 ] database for pathway research and mapped out the pathways and the network analysis [ 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ]. FunCoup2.0 [ 90 ] was used to generate all of the DEGs networks used in this study, and cytoscape [ 91 ] was used to visualize the networks.…”

Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma

“…Now, we proceed for our major goal which is to understand the gene expression patterns ( Lapointe et al, 2004 ; Subramanian et al, 2005 ) and its inferred functions ( Subramanian et al, 2005 ; Mi et al, 2016 ) and also the impact of HCC biomarker genes. We used MATLAB tools (e.g., mattest) for differential gene expression prediction and statistical analysis, and for pathway analysis, we used the KEGG database ( Kanehisa et al, 2007 , 2009 ) and in-house code created for pathway and network research ( Bajrai L. et al, 2021 ; Kamal et al, 2020 ; Khouja et al, 2022a ; Kumar et al, 2020 ; Warsi et al, 2020 ). Furthermore, we took all of the HCC samples from the TCGA database and used cBioPortal to look for mutations, CAN, and SV, as well as prepare a list of genes using a threshold cutoff.…”