AI Article Synopsis

  • Humans and animals are at risk of avian influenza, which could lead to a pandemic from a new virus that spreads effectively between humans.
  • The study conducted a meta-analysis on three microarray datasets to identify gene expression related to avian influenza, discovering 1,284 common differentially expressed genes.
  • Key findings included important biological pathways related to immune response and specific hub genes, which could provide insights for developing better treatments for the disease.

Article Abstract

Humans and other animals are both susceptible to avian influenza virus. The avian influenza (AI) pandemic could be brought on by the appearance of a new, radical AI virus capable of spreading disease and maintaining prolonged human-to-human transmissions. The possibility of an AI pandemic makes it important for public health. Despite efforts to identify a linkage between them, the hierarchical relationship between all the factors that influence the pathophysiology of this disease, the shared biological pathways, and the exact identities of its important triggers are yet unknown. To find shared gene expression profiles and overlapping biological processes, an integrated gene expression meta-analysis was carried out for three independent microarray data of the avian influenza virus. This study found 1284 common differentially expressed genes (DEGs), of which 73 were overexpressed and 119 were under-expressed, analyzed using various packages in the R tool. The extensive biological, functional enrichment and pathway analysis was performed using the EnrichR tool and identified the defence response to the symbiont (GO:0140546), Interferon Alpha/Beta Signaling (R-HSA-909733), and spliceosome as the most enriched terms of biological process and pathways respectively. In a network meta-analysis, ISG15 and RELA were pinpointed as the top hub genes for over and under-expression, respectively. This meta-analysis technique for avian influenza infection highlights important gene profiles and their linked pathways. These findings highlight the value of using meta-analysis to detect novel gene markers that may offer key insight into disease pathogenesis and perhaps pave the way for creating more effective therapeutic approaches.

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Source
http://dx.doi.org/10.1080/07391102.2024.2431662DOI Listing

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