Project description:Rationale: Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory tract infections and hospitalizations in infants worldwide. Known risk factors, however, incompletely explain the variability of RSV disease severity among children. We postulate that severity of RSV infection is influenced in part by modulation of the host immune response by the local microbial ecosystem at the time of infection. Objectives: To define whether different nasopharyngeal microbiota profiles are associated with distinct host transcriptome profiles and severity in children with RSV infection. Methods: We analyzed the nasopharyngeal microbiota profiles of young children with mild and severe RSV disease and healthy matched controls by 16S-rRNA sequencing. In parallel, we analyzed whole blood gene expression profiles to study the relationship between microbial community composition, the RSV-induced host transcriptional response and clinical disease severity. Measurements and Main results: We identified five nasopharyngeal microbiota profiles characterized by enrichment of H. influenzae, Streptococcus, Corynebacterium, Moraxella or S. aureus. RSV infection and RSV hospitalization were positively associated with H. influenzae and Streptococcus, and negatively associated with S. aureus abundance, independent of age. The host response to RSV was defined by overexpression of interferon-related genes, and this was independent of the microbiota composition. On the other hand, transcriptome profiles of RSV infected children with H. influenzae and Streptococcus-dominated microbiota were characterized by greater overexpression of genes linked to toll-like receptor-signaling and neutrophil activation and were more frequently hospitalized Conclusions: Our data suggest an immunomodulatory role for the resident nasopharyngeal microbial community early in RSV infection, potentially affecting RSV disease severity.
Project description:The objective of this study was to identify gene expression markers of disease severity in a cohort of RSV infected children Respiratory syncytial virus (RSV) is the number one pathogen causing lower respiratory tract infection that leads to hospitalization in young children. Despite growing insights in the disease pathogenesis, the clinical presentation in these children is highly variable and heterogeneous, and reliable markers predictive of disease progression are lacking. We characterized the host response to acute RSV infection to identify biomarkers associated with RSV disease and disease severity. Whole genome transcriptome was analysed early on the disease course in blood samples from otherwise healthy children <2 years of age, who were either hospitalized (n = 110) or evaluated as outpatients (n = 37) due to RSV infection. Age-matched non-RSV-infected healthy children (n = 51) were analysed in parallel. A clustering approach on the transcriptome data revealed biologically meaningful biomarkers associated with progression to severe RSV disease. Overall, the whole blood transcriptome pointed to alterations in frequency of specific immune cell types (neutrophils, T- and B-lymphocytes, NK cells, monocytes) in RSV-infected children. In addition, a cluster enriched for neutrophil degranulation genes, was highly correlated with clinical disease severity. The driver genes of this cluster (OLFM4, ELANE, MMP8, BPI, CEACAM8, LCN2, LTF and MPO) were selected and validated in independent existing transcriptomics datasets. We identified a set of genes involved in neutrophil degranulation as markers for RSV disease severity. Additional prospective studies using these markers are required to further confirm their value as predictive tool in routine clinical care.
Project description:Respiratory viral infections follow an unpredictable clinical course in young children ranging from a common cold to respiratory failure. The transition from mild to severe disease occurs rapidly and is difficult to predict. The pathophysiology underlying disease severity has remained elusive. There is an urgent need to better understand the immune response in this disease to come up with biomarkers that may aid clinical decision making. In a prospective study, flow cytometric and genome-wide gene expression analyses were performed on blood samples of 26 children with a diagnosis of severe, moderate or mild Respiratory Syncytial Virus (RSV) infection. Differentially expressed genes were validated using Q-PCR in a second cohort of 80 children during three consecutive winter seasons. FACS analyses were also performed in the second cohort and on recovery samples of severe cases in the first cohort. Severe RSV infection was associated with a transient but marked decrease in CD4+ T, CD8+ T, and NK cells in peripheral blood. Gene expression analyses in both cohorts identified Olfactomedin4 (OLFM4) as a fully discriminative marker between children with mild and severe RSV infection, giving a PAM cross-validation error of 0%. Patients with an OLFM4 gene expression level above -7.5 were 6 times more likely to develop severe disease, after correction for age at hospitalization and gestational age. In conclusion, by combining genome-wide expression profiling of blood cell subsets with clinically well-annotated samples, OLFM4 was identified as a biomarker for severity of pediatric RSV infection. Samples were taken of 26 patients with acute RSV infections, divided into mild (n=9), moderate (n=9) and severe (n=8) disease. From moderate and severe diseased patients recovery samples were obtained as well.
Project description:This series includes 278 microarrays used to detect respiratory viruses in a set of nasopharyngeal lavage specimens from children with respiratory tract infections Objective: To assess the utility of a pan-viral DNA microarray platform (Virochip) in the detection of viruses associated with pediatric respiratory tract infections. Study Design: The Virochip was compared to conventional clinical direct fluorescent antibody (DFA) and PCR-based testing for the detection of respiratory viruses in 278 consecutive nasopharyngeal aspirate samples from 222 children. Results: The Virochip was superior in performance to DFA, showing a 19% increase in the detection of 7 respiratory viruses included in standard DFA panels, and was similar to virus-specific PCR (sensitivity 85-90%, specificity 99%, PPV 94-96%, NPV 97-98%) in the detection of respiratory syncytial virus, influenza A, and rhino-/enteroviruses. The Virochip also detected viruses not routinely tested for or missed by DFA and PCR, as well as double infections and infections in critically ill patients that DFA failed to detect. Conclusions: Given its favorable sensitivity and specificity profile and greatly expanded spectrum of detection, microarray-based viral testing holds promise for clinical diagnosis of pediatric respiratory tract infections. Keywords: viral detection The series includes 278 clinical specimens
Project description:Diagnosis of acute respiratory viral infection is currently based on clinical symptoms and pathogen detection. Use of host peripheral blood gene expression data to classify individuals with viral respiratory infection represents a novel means of infection diagnosis. We used microarrays to capture peripheral blood gene expression at baseline and time of peak symptoms in healthy volunteers infected intranasally with influenza A H3N2, respiratory syncytial virus or rhinovirus. We determined groups of coexpressed genes that accurately classified symptomatic versus asymptomatic individuals. We experimentally inoculated healthy volunteers with intranasal influenza, respiratory syncytial virus or rhinovirus. Symptoms were documented and peripheral blood samples drawn into PAXgene tubes for RNA isolation.
Project description:In this study we investigated whether there exists a genomic signature that can accurately predict the course of a respiratory syncytial virus (RSV) infection in hospitalized young infants. We used early blood microarray transcriptome profiles from 39 infants that were followed until recovery and of which the level of disease severity was determined retrospectively. Applying support vector machine learning on age by sex standardized transcriptomic data, an 84 gene signature was identified that discriminated hospitalized infants with eventually less severe RSV infection from infants that suffered from most severe RSV disease.
Project description:Respiratory viral infections follow an unpredictable clinical course in young children ranging from a common cold to respiratory failure. The transition from mild to severe disease occurs rapidly and is difficult to predict. The pathophysiology underlying disease severity has remained elusive. There is an urgent need to better understand the immune response in this disease to come up with biomarkers that may aid clinical decision making. In a prospective study, flow cytometric and genome-wide gene expression analyses were performed on blood samples of 26 children with a diagnosis of severe, moderate or mild Respiratory Syncytial Virus (RSV) infection. Differentially expressed genes were validated using Q-PCR in a second cohort of 80 children during three consecutive winter seasons. FACS analyses were also performed in the second cohort and on recovery samples of severe cases in the first cohort. Severe RSV infection was associated with a transient but marked decrease in CD4+ T, CD8+ T, and NK cells in peripheral blood. Gene expression analyses in both cohorts identified Olfactomedin4 (OLFM4) as a fully discriminative marker between children with mild and severe RSV infection, giving a PAM cross-validation error of 0%. Patients with an OLFM4 gene expression level above -7.5 were 6 times more likely to develop severe disease, after correction for age at hospitalization and gestational age. In conclusion, by combining genome-wide expression profiling of blood cell subsets with clinically well-annotated samples, OLFM4 was identified as a biomarker for severity of pediatric RSV infection.