Exploring Ebola virus-associated gene expression through comparative analysis
Mostafa Rezapour1, Sean V Murphy1, David A Ornelles2
1Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States.
Introduction:
Ebola virus (EBOV) infection triggers intense host transcriptional responses that overlap extensively with those induced by other viral and bacterial pathogens. This overlap complicates the identification of EBOV-specific gene expression signatures and limits diagnostic specificity. Defining transcriptional markers that distinguish EBOV from other infections is essential for improving molecular diagnostics and advancing understanding of EBOV-specific host responses.
Methods:
We developed a multi-step filtering framework using blood-derived RNA-Seq data from nonhuman primates and human cohorts organized into independent training and test sets. In the training cohort, differential expression analysis was performed using an edgeR-based GLMQL-MAS approach to identify EBOV-associated genes. Candidates were filtered against non-EBOV comparator datasets, including mpox virus, influenza, bacterial pneumonia, acute HIV-1 infection, and multiple SARS-CoV-2 variants, to remove broadly shared host-response genes. Genes included in the NanoString nCounter® Host Response Panel were additionally excluded. The resulting EBOV-specific signature was evaluated in independent EBOV and non-EBOV test cohorts using principal component analysis and logistic regression. Functional enrichment was assessed using KEGG pathways.
Results:
Initial analysis identified numerous interferon-stimulated genes that were similarly upregulated across infections. After cross-infection filtering and NanoString exclusion, 281 EBOV-specific genes were identified. Optimization within the training cohort yielded a top-50 gene set that clearly separated EBOV from Non-EBOV samples. In the independent test cohort, classification performance improved substantially, with the F1 score increasing from 37.5% when all genes were used to 95.0% after applying the top-50 gene set. Enrichment analysis of the top-50 EBOV-specific genes revealed significant association with vascular, coagulation, secretory, and metabolic pathways. ADAMTS1 showed consistent upregulation in EBOV while remaining downregulated or inactive in comparator infections.
Discussion:
Structured cross-pathogen filtering enables identification of EBOV-specific transcriptional features beyond shared antiviral responses. The validated gene signature generalizes across independent cohorts and highlights biologically distinct pathways, which supports its potential utility for host-based diagnostic development.
