{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T14:30:41Z","timestamp":1788877841746,"version":"build-2803163510"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100023078","name":"State of North Carolina","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100023078","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u00ae 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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.\n                      <jats:italic>ADAMTS1<\/jats:italic>\n                      showed consistent upregulation in EBOV while remaining downregulated or inactive in comparator infections.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgene.2026.1793277","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T22:10:17Z","timestamp":1776377417000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring Ebola virus-associated gene expression through comparative analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Mostafa","family":"Rezapour","sequence":"first","affiliation":[{"name":"Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean V.","family":"Murphy","sequence":"additional","affiliation":[{"name":"Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Ornelles","sequence":"additional","affiliation":[{"name":"Department of Microbiology Immunology, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas D.","family":"Shupe","sequence":"additional","affiliation":[{"name":"Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen J.","family":"Walker","sequence":"additional","affiliation":[{"name":"Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan R.","family":"Jacobson","sequence":"additional","affiliation":[{"name":"Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Metin Nafi","family":"Gurcan","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence Research, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick M.","family":"McNutt","sequence":"additional","affiliation":[{"name":"Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Atala","sequence":"additional","affiliation":[{"name":"Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine","place":["Winston-Salem, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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