Monkeypox Virus Crosstalk with HIV: An Integrated Skin Transcriptome and Machine Learning Study.

Area of Science:

• Virology
• Immunology
• Genomics

Background:

• The monkeypox virus (MPXV) outbreak poses a significant public health threat.
• Individuals with human immunodeficiency virus (HIV) experience disproportionately severe MPXV outcomes and higher mortality.
• Shared molecular mechanisms between MPXV and HIV are not well understood.

Purpose of the Study:

• To identify common differentially expressed genes (DEGs) between MPXV and HIV infections.
• To elucidate shared molecular pathways and potential therapeutic targets.
• To validate key biomarkers using machine learning approaches.

Main Methods:

• Analysis of two public gene expression datasets (GSE219036, GSE184320) to find common DEGs.
• Functional enrichment analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).
• Construction of protein-protein interaction (PPI) networks and machine learning (ML) validation of hub genes.

Main Results:

• Identified 59 common DEGs between MPXV and HIV.
• Highlighted shared pathways such as ERK cascade, NF-κB signaling, and immune responses.
• Identified and validated five key genes (SPRED1, SPHK1, ATF3, AKT3, AKT1S1) with significant immune correlations.

Conclusions:

• MPXV and HIV share common pathogenic mechanisms.
• Key genes and pathways identified provide novel insights into co-infection pathogenesis.
• Findings offer opportunities for developing targeted management strategies for HIV patients co-infected with MPXV.