Combination of Enrichment Using Gene Ontology and Transcriptomic Analysis Revealed Contribution of Interferon Signaling to Severity of COVID-19

Abstract

Introduction. The severity of coronavirus disease 2019 (COVID-19) was known to be affected by hyperinflammation. Identification of important proteins associated with hyperinflammation is critical. These proteins can be a potential target either as biomarkers or targets in drug discovery. Therefore, we combined enrichment analysis of these proteins to identify biological knowledge related to hyperinflammation. Moreover, we conducted transcriptomic data analysis to reveal genes contributing to disease severity. Methods. We performed large-scale gene function analyses using gene ontology to identify significantly enriched biological processes, molecular functions, and cellular components associated with our proteins. One of the appropriate methods to functionally group large-scale protein-protein interaction (PPI) data into small-scale clusters is fuzzy K-partite clustering. We collected the transcriptomics data from GEO Database (GSE 164805 and GPL26963 platform). Moreover, we created a data set and analyzed gene expression using Orange Data-mining version 3.30. PPI analysis was performed using the STRING database with a confidence score >0.9. Results. This study indicated that four proteins were associated with 25 molecular functions, three were associated with 22 cellular components, and one was associated with ten biological processes. All GOs of molecular function, cellular components, and 9 of 14 biological processes were associated with important cytokines related to the COVID-19 cytokine storm present in the resulting cluster. The expression analysis showed the interferon-related genes IFNAR1, IFI6, IFIT1, and IFIT3 were significant genes, whereas PPIs showed their interactions were closely related. Conclusion. A combination of enrichment using GOs and transcriptomic analysis showed that hyperinflammation and severity of COVID-19 may be caused by interferon signaling.

Identification of autophagy-related genes in neuropathic pain through bioinformatic analysis

“…The STRING database is a database for searching interactions between known and predicted proteins [ 24 ]. In this study, we used the STRING database to take the minimum required interaction score greater than 0.900 as the criteria for ensuring accuracy, and took the top10 genes with the minimum required interaction score as the co-expression of ARDEGs [ 25 , 26 ]. The ten co-expressed genes and autophagy-related differentially expressed genes were used to construct a PPI network, which was visualized using Cytoscape software [ 27 ].…”

Integrated identification of key immune related genes and patterns of immune infiltration in calcified aortic valvular disease: A network based meta-analysis

“…The STRING software ( Szklarczyk et al, 2019 ) was adopted to construct PPI network of DEIRGs, which was visualized by Cytoscape software 3.8.1 ( Shannon et al, 2003 ). According to previously published studies, we chose the confidence value of 0.9 and the maximum number of connections of 3 to screen out relatively reliable protein interaction relationships on the basis of preserving protein correlations as much as possible ( Wang et al, 2021 ; Ramadhani et al, 2022 ; Yadalam et al, 2022 ). Cytohubba is a plug-in of Cytoscape software for identifying hub gene nodes ( Chin et al, 2014 ).…”