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<p>The Japanese Encephalitis Virus (JEV), a mosquito-borne zoonotic pathogen, has a high mortality rate in Asia, causing severe neurological effects and fatalities in approximately 68,000 annual cases worldwide. Despite its prevalence, there is currently no specific treatment for JEV. This study leverages bioinformatics to identify potential molecular targets for therapeutic intervention. Data from three GEO profiles—GSE39740, GSE57330, and GSE115167—were retrieved from the NCBI-GEO database and analyzed for differentially expressed genes (DEGs) using the R simpleaffy package. Gene ontology and pathway enrichment analyses of the DEGs were conducted using clusterProfiler. Hub genes and disease networks were identified through Cytoscape, with functionally relevant clusters generated using MCODE and CytoHubba. Heatmaps and Volcano plots were used to visualize DEG regulation patterns. In the analysis, 1,601 DEGs were identified, comprising 1,103 upregulated and 498 downregulated genes. Among these, 37 hub genes were identified, with the top 10 being ACTB, ALB, CASP3, CDK4, CXCL10, CXCL8, CXCR4, DDX58, DICER1, and EGR1. Notably, ACTB emerged as the most connected node with a degree of 243. These hub genes represent promising therapeutic targets and enhance our understanding of JEV-related molecular pathways. The study’s findings contribute significantly to the identification of biomarkers and therapeutic targets for the Japanese Encephalitis Virus, forming a foundation for further research to mitigate its severe impact on global health.</p>

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Deciphering core genetic and pathway signatures of Japanese encephalitis virus using transcriptomics

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Mohd Ali,
  • Mohd Shawaz,
  • Noor Saba Khan,
  • Saumya Choudhary,
  • Anurag Agrawal,
  • Nitesh Kumar Poddar

摘要

The Japanese Encephalitis Virus (JEV), a mosquito-borne zoonotic pathogen, has a high mortality rate in Asia, causing severe neurological effects and fatalities in approximately 68,000 annual cases worldwide. Despite its prevalence, there is currently no specific treatment for JEV. This study leverages bioinformatics to identify potential molecular targets for therapeutic intervention. Data from three GEO profiles—GSE39740, GSE57330, and GSE115167—were retrieved from the NCBI-GEO database and analyzed for differentially expressed genes (DEGs) using the R simpleaffy package. Gene ontology and pathway enrichment analyses of the DEGs were conducted using clusterProfiler. Hub genes and disease networks were identified through Cytoscape, with functionally relevant clusters generated using MCODE and CytoHubba. Heatmaps and Volcano plots were used to visualize DEG regulation patterns. In the analysis, 1,601 DEGs were identified, comprising 1,103 upregulated and 498 downregulated genes. Among these, 37 hub genes were identified, with the top 10 being ACTB, ALB, CASP3, CDK4, CXCL10, CXCL8, CXCR4, DDX58, DICER1, and EGR1. Notably, ACTB emerged as the most connected node with a degree of 243. These hub genes represent promising therapeutic targets and enhance our understanding of JEV-related molecular pathways. The study’s findings contribute significantly to the identification of biomarkers and therapeutic targets for the Japanese Encephalitis Virus, forming a foundation for further research to mitigate its severe impact on global health.