Machine Learning Analysis of RNA-Seq Data Identifies Key Gene Signatures and Pathways in Mpox Virus-Induced Gastrointestinal Complications Using Colon Organoid Models.

Abstract

Mpox, caused by the Mpox virus (MPXV), emerged globally in 2022 with the Clade IIb strain, presenting a critical public health challenge. While MPXV is primarily characterized by fever and rash, gastrointestinal (GI) complications, such as diarrhea and proctitis, have also been observed. This study is a reanalysis of GSE219036 without own data and focuses on the impact of MPXV infection on the colon, using human-induced pluripotent stem cell-derived colon organoids as a model. We applied a tailored statistical framework for RNA-seq data, Generalized Linear Models with Quasi-Likelihood F-tests and Relaxed Magnitude-Altitude Scoring (GLMQL-RMAS), to identify differentially expressed genes (DEGs) across MPXV clades: MPXV I (Zr-599 Congo Basin), MPXV IIa (Liberia), and MPXV IIb (2022 MPXV). Through a novel methodology called Cross-RMAS, we ranked genes by integrating statistical significance and biological relevance across all clades. Machine learning analysis using the genes identified by Cross-RMAS, demonstrated 100% accuracy in differentiating between the different MPXV strains and mock samples. Furthermore, our findings reveal that MPXV Clade I induces the most extensive alterations in gene expression, with significant upregulation of stress response genes, such as HSPA6 and FOS, and downregulation of genes involved in cytoskeletal organization and vesicular trafficking, such as PSAP and CFL1. In contrast, Clade IIb shows the least impact on gene expression. Through Gene Ontology (GO) analysis, we identified pathways involved in protein folding, immune response, and epithelial integrity that are disrupted in infected cells, suggesting mechanisms by which MPXV may contribute to GI symptoms.