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Updated: May 5, 2026

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes

Published on: September 27, 2014

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.

Frontiers in Genetics

|May 4, 2026

Summary

This summary is machine-generated.

Identifying Ebola virus (EBOV)-specific host gene signatures is crucial for diagnostics. A novel filtering method identified a 50-gene signature that accurately distinguishes EBOV from other infections.

Keywords:

Ebola virus RNA sequencing differential gene expression analysis gene expression signature host transcriptomic profiling

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Last Updated: May 5, 2026

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

Virology
Genomics
Infectious Diseases

Background:

Ebola virus (EBOV) infection elicits host transcriptional responses that overlap with other pathogens, hindering specific diagnosis.
Distinguishing EBOV-specific gene expression signatures is essential for accurate molecular diagnostics and understanding host responses.

Purpose of the Study:

To develop and validate a host transcriptional signature specific to EBOV infection.
To identify EBOV-specific gene expression markers that can differentiate it from other viral and bacterial infections.

Main Methods:

A multi-step filtering framework was applied to RNA-Seq data from nonhuman primate and human cohorts.
Differential expression analysis and cross-pathogen filtering were used to identify EBOV-specific genes, excluding those from comparator infections and a commercial panel.
A top-50 gene set was optimized and validated in independent test cohorts using principal component analysis and logistic regression.

Main Results:

A signature of 281 EBOV-specific genes was identified after filtering, which was further refined to a top-50 gene set.
The top-50 gene set demonstrated high classification performance (95.0% F1 score) in distinguishing EBOV from non-EBOV infections in test cohorts.
Enrichment analysis revealed that the EBOV-specific genes are associated with vascular, coagulation, secretory, and metabolic pathways, with ADAMTS1 showing specific upregulation.

Conclusions:

Cross-pathogen filtering effectively identifies EBOV-specific transcriptional features beyond general antiviral responses.
The validated 50-gene signature generalizes across independent cohorts and highlights distinct biological pathways.
This EBOV-specific gene signature holds potential for the development of host-based diagnostic tools.