A Meta-Analysis of Multiple Whole Blood Gene Expression Data Unveils a Diagnostic Host-Response Transcript Signature for Respiratory Syncytial Virus

Abstract

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.

Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis

“…In the present study, we conducted a multi-cohort meta-analysis using high-throughput (microarray and RNAseq) data available in public databases ( n = 1209 samples) from blood transcriptomic studies including virus and bacteria-infected patients to find the best minimum gene expression signature that differentiates between both types of infections in all possible scenarios. Meta-analysis of transcriptomic data has proven to be a useful approach to discover gene expression signatures specific to different infectious diseases [ 5 , 18 , 20 ], raising the statistical power compared with individual studies, and finding common trends in transcriptomic response under different conditions, pathogens, and demographic features. Using a gene signature candidate approach following a PReMS algorithm, we obtained a biosignature of 3-gene transcriptomics that accurately distinguishes viral from bacterial infections with high sensitivity and specificity.…”

“…Several host transcriptomic signatures in response to different infections were published in the last decade [ 4 , 12 , 13 , 14 , 15 , 16 , 17 ], but many of them were only focused on the specific pathogen and/or conditions studied, and usually in patients with the same age range or population background. As such, a multi-cohort analysis using publicly available data from different studies can help find common transcriptomic signatures, masking those expression patterns potentially related to specific pathogens, conditions, ages or genetic backgrounds, hence making the translation of these signatures to a generic test and its implementation in the clinical routine more straightforward [ 5 , 18 , 19 , 20 ].…”

Transfer transcriptomic signatures for infectious diseases

“…We used 3 test datasets from three studies: one study on bronchoalveolar lavage in SARS-CoV-2 infection ( 9 ) ( Suppl. Data S1 – study 7), one study on influenza infection ( 10 ) ( Suppl. Data S1 – study 8) and one longitudinal study on TB progression in latently infected individuals ( 11 ) ( Suppl.…”

RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans

“…Since then, RNA analysis has arisen as a powerful screening tool to find diagnostic biomarkers that may be used to develop new tests that overcome the limitations of bacterial culture. Recently, several studies have been exploring host-specific transcriptomic biomarkers that may allow distinguishing between viral and bacterial infections or pathogen-specific signatures [9][10][11][12][13][14][15][16]. Related to transcriptional signatures, there are also several studies relating host genetic susceptibility factors to infectious diseases [17][18][19][20][21].…”