Meta-Analysis of Whole Blood Transcriptome Datasets Characterizes the Immune Response of Respiratory Syncytial Virus Infection in Children.

Respiratory syncytial virus (RSV) is the most common and critical viral pathogen causing acute lower respiratory tract infection in infants and young children and has a huge disease burden worldwide. At present, there are many studies on RSV transcriptomics exploring the mechanism of disease, but different studies show different gene expression patterns and results due to different sample collection platforms and data analysis strategies. A meta-analysis was performed on eight whole blood transcriptome datasets containing 436 children with acute RSV infection and 241 healthy children. A total of 319 differentially expressed genes (DEGs) (P value <0.0001) were identified in a meta-analysis using a random effect model. Functional enrichment analysis showed that several pathways related to immunity were significantly altered, including the "chemokine signaling pathway", "natural killer cell mediated cytotoxicity" and "cytokine-cytokine receptor interaction". Immune cell type analysis showed that the proportion of neutrophils in most RSV-infected children was higher than that in healthy children. These immune characteristics may help to provide new insights into RSV infection in children.

Feng Q ，Lin S ，Liu H ，Yang B ，Han L ，Han X ，Xu L ，Xie Z ... -  《Frontiers in Cellular and Infection Microbiology》

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

Barral-Arca R ，Gómez-Carballa A ，Cebey-López M ，Bello X ，Martinón-Torres F ，Salas A ... -  《INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES》

Identification of potential immune/diagnosis related gene-immunocyte subtype networks in extracellular immune response to respiratory syncytial virus infection.

Respiratory syncytial virus (RSV) is one of the important pathogenic agents of pediatric respiratory tract infection. Weighted gene co-expression network analysis (WGCNA) is used to study autoimmune diseases, which can find potential hub genes. The diagnostic model based on hub genes and machine learning makes it possible to diagnose the extracellular immune response to RSV infection early. The aim of the present study was to identify potential immune, diagnose and treatment related genes expressed in RSV-infected cells. Firstly, gene expression data were downloaded from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Secondly, WGCNA was performed based on DEGs to obtain hub genes related to immunity score. Thirdly, protein-protein interaction (PPI) and the immune infiltration analysis of hub immune related genes were performed. Finally, diagnostic and immune related genes were identified by machine learning, followed by functional analysis. Totally, 2063 DEGs were identified in the extracellular immune response to RSV infection. Among which, 10 key immune and diagnosis related genes were identified, including ITGA2B, GP9, ITGB3, SELP, PPBP, MPL, CXCL8, NFE2, PTGS1 and LY6G6F. Several immune/diagnosis related gene-immunocyte subtype networks were identified, such as CXCL8-Type 17 T helper cell, LY6G6F-CD56 bright natural killer cell, PPBP-activated CD4 T cell/T follicular helper cell, NFE2/PTGS1/SELP-activated dendritic cell, GP9/ITGA2B/MPL-activated CD8 T cell. ITGB3, MPL and PTGS1 could be considered as therapeutic targets. Some significantly enriched signaling pathways were identified, including hematopoietic cell lineage (involving GP9 and ITGA2B), cytokine-cytokine receptor interaction (involving MPL), chemokine signaling pathway (involving PPBP) and arachidonic acid metabolism (involving PTGS1). The 10-immune related gene signature may be used as potential diagnostic markers for the extracellular immune response to RSV infection, which may provide a new field in searching for diagnostic and therapeutic molecules in the extracellular immune response to RSV infection.

Wang B ，Liu H 《-》

[Study on transcriptome characteristics of respiratory syncytial virus bronchiolitis in children by RNA sequencing].

Wang LY ，Le YS ，Li HY ，Liu ZW ，Weng TT ，Chen XF ，Liu PN ，Dong L ... -  《-》

Human respiratory syncytial virus in children with lower respiratory tract infections or influenza-like illness and its co-infection characteristics with viruses and atypical bacteria in Hangzhou, China.

Human respiratory syncytial virus (RSV) is the most important viral pathogen in children. However, its epidemic patterns and co-infection characteristics are not fully understood. We attempted to determine the level of genetic variation of RSV, and describe the prevalence and co-infection characteristics of RSV in Hangzhou during two epidemic seasons. Single respiratory samples from 1820 pediatric patients were screened for RSV and genotyped by RT-PCR and sequencing. In all RSV positive specimens, we screened for viruses and atypical bacteria. Demographic and clinical information was recorded and analyzed. A total of 34.5% and 3.8% of samples from acute lower respiratory tract infections (ALRI) and influenza-like illness (ILI) were positive for RSV, respectively. Phylogenetic analysis revealed that 61.1% of the selected 167 RSV strains were NA1, 31.1% were BA, 3.6% were ON1, 2.4% were CB1, and 1.8% were NA3. A new genotype, BA11 was identified, which comprised 98.1% of BA strains in this study, while the rest were BA10. A total of 36.4% and 9.1% of RSV-positive children with ALRI and ILI respectively were found to be co-infected. Rhinovirus was the most common additional respiratory virus, followed by human metapneumovirus. Except for fever, no significant differences in other clinical presentation between the RSV mono-infection and co-infection groups were observed. The circulating RSV strains had high genetic variability with RSV-B showing a more local pattern. In ALRI cases, co-infection of RSV with other viruses or atypical bacteria has no significant effect on the clinical presentation except fever.

Yu X ，Kou Y ，Xia D ，Li J ，Yang X ，Zhou Y ，He X ... -  《-》