Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression...

Area of Science:

• Virology
• Immunology
• Bioinformatics

Background:

• Zoonotic virus spillover, including Hantavirus and SARS-CoV-2, significantly impacts public health.
• Patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) may be at increased risk for SARS-CoV-2 infection due to similar clinical presentations.
• No validated treatments currently exist for co-infection or managing shared clinical features.

Purpose of the Study:

• To identify common genes and biological pathways dysregulated in both HFRS and COVID-19.
• To explore the potential of identified genes as biomarkers for disease classification.
• To lay the groundwork for developing novel therapeutic strategies for linked viral infections.

Main Methods:

• Differential gene expression analysis of transcriptomic data from Hantavirus and SARS-CoV-2 infected peripheral blood mononuclear cells (PBMCs).
• Functional annotation and enrichment analysis to identify common biological processes.
• Construction of protein-protein interaction (PPI) networks to identify hub genes.
• Machine learning models (Random Forest, PLDA, voomNSC, SVM) for evaluating biomarker potential.

Main Results:

• Identification of common differentially expressed genes (DEGs) enriched in immune and inflammatory responses.
• Six hub genes (RAD51, ALDH1A1, UBA52, CUL3, GADD45B, CDKN1A) were identified as commonly dysregulated in HFRS and COVID-19.
• Machine learning classifiers achieved >70% accuracy, indicating the biomarker potential of these hub genes.

Conclusions:

• This study is the first to reveal commonly dysregulated biological processes and pathways in HFRS and COVID-19.
• The identified hub genes show promise as potential biomarkers for distinguishing between HFRS and COVID-19.
• These findings could facilitate the development of personalized treatments to prevent or manage co-infections.