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

Q3 Immunology and Microbiology Interdisciplinary Perspectives on Infectious Diseases Pub Date : 2022-04-11 DOI:10.1155/2022/3515001
Hilmi Farhan Ramadhani, Annisa Annisa, A. Tedjo, D. Noor, W. Kusuma
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引用次数: 2

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.
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基因本体论和转录组分析的联合富集揭示了干扰素信号对新冠肺炎严重程度的贡献
已知冠状病毒病2019 (COVID-19)的严重程度与过度炎症有关。鉴定与过度炎症相关的重要蛋白质是至关重要的。这些蛋白质可以作为生物标志物或药物发现的潜在靶标。因此,我们结合这些蛋白的富集分析来确定与过度炎症相关的生物学知识。此外,我们进行了转录组学数据分析,以揭示与疾病严重程度有关的基因。方法利用基因本体进行大规模的基因功能分析,以识别与我们的蛋白质相关的显著富集的生物过程、分子功能和细胞成分。模糊k -部聚类是将大规模蛋白质-蛋白质相互作用(PPI)数据功能分组为小规模聚类的一种合适方法。转录组学数据来自GEO数据库(GSE 164805和GPL26963平台)。此外,我们创建了一个数据集,并使用Orange data -mining version 3.30分析基因表达。PPI分析使用STRING数据库进行,置信度评分>0.9。结果4个蛋白与25个分子功能相关,3个与22个细胞组分相关,1个与10个生物过程相关。所有的分子功能、细胞成分和14个生物过程中的9个都与结果集群中存在的与COVID-19细胞因子风暴相关的重要细胞因子相关。表达分析显示干扰素相关基因IFNAR1、IFI6、IFIT1、IFIT3为显著基因,而PPIs显示其相互作用密切相关。结论GOs富集和转录组学分析表明,COVID-19的高炎症和严重程度可能是由干扰素信号引起的。
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来源期刊
CiteScore
4.10
自引率
0.00%
发文量
51
审稿时长
18 weeks
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