A Meta-Analysis of Multiple Whole Blood Gene Expression Data Unveils a Diagnostic Host-Response Transcript Signature for Respiratory Syncytial Virus
Identificadores
Identificadores
URI: http://hdl.handle.net/20.500.11940/16595
PMID: 32155831
DOI: 10.3390/ijms21051831
ISSN: 1661-6596
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Fecha de publicación
2020Título de revista
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Tipo de contenido
Journal Article
DeCS
ARN | estudios de casos y controles | infecciones del tracto respiratorio | infecciones por virus sincitial respiratorio | humanos | estudios de cohortes | perfiles de expresión génica | transducción de señales | interacciones huésped-patógenoMeSH
Host-Pathogen Interactions | Respiratory Syncytial Virus Infections | Humans | RNA | Signal Transduction | Gene Expression Profiling | Case-Control Studies | Respiratory Tract Infections | Cohort StudiesResumen
Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infection worldwide. The absence of a commercial vaccine and the limited success of current therapeutic strategies against RSV make further research necessary. We used a multi-cohort analysis approach to investigate host transcriptomic biomarkers and shed further light on the molecular mechanism underlying RSV-host interactions. We meta-analyzed seven transcriptome microarray studies from the public Gene Expression Omnibus (GEO) repository containing a total of 922 samples, including RSV, healthy controls, coronaviruses, enteroviruses, influenzas, rhinoviruses, and coinfections, from both adult and pediatric patients. We identified > 1500 genes differentially expressed when comparing the transcriptomes of RSV-infected patients against healthy controls. Functional enrichment analysis showed several pathways significantly altered, including immunologic response mediated by RSV infection, pattern recognition receptors, cell cycle, and olfactory signaling. In addition, we identified a minimal 17-transcript host signature specific for RSV infection by comparing transcriptomic profiles against other respiratory viruses. These multi-genic signatures might help to investigate future drug targets against RSV infection.