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Identification of Critical Genes and Pathways for Influenza A Virus Infections via Bioinformatics Analysis
Authors:Gao Chen  Haoyue Li  Mingzhao Hao  Xiaolei Li  Yizhi Dong  Yue Zhang  Xiping Liu  Cheng Lu  Jing Zhao
Affiliation:1.School of Life Science, Hubei University, Wuhan 430062, China;2.Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences (CACMS), Beijing 100700, China; (H.L.); (X.L.); (Y.D.); (Y.Z.); (X.L.); (C.L.);3.Institute of History of Medicine and Medical Literature, China Academy of Chinese Medical Sciences, Beijing 100700, China;
Abstract:
Influenza A virus (IAV) requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system but also provides further targets for antiviral drug development. To uncover critical pathways and potential targets of influenza infection, we assembled a large amount of data from 8 RNA sequencing studies of IAV infection for integrative network analysis. Weighted gene co-expression network analysis (WGCNA) was performed to investigate modules and genes correlated with the time course of infection and/or multiplicity of infection (MOI). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and pathways of the genes in 5 significant modules. Top hub genes were identified using the cytoHubba plugin in the protein interaction network. The correlation between expression levels of 7 top hub genes and time course or MOI was displayed and validated, including BCL2L13, PLSCR1, ARID5A, LMO2, NDRG4, HAP1, and CARD10. Dysregulated expression of these genes potently impacted the development of IAV infection through modulating IAV-related biological processes and pathways. This study provides further insights into the underlying molecular mechanisms and potential targets in IAV infection.
Keywords:influenza A virus   hub gene   bioinformatics   weighted gene co-expression network analysis   protein interaction
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