Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infections and hospital visits during infancy and childhood. Although risk factors for RSV infection have been identified, the role of microbial species in the respiratory tract is only partially known. We aimed to understand the impact of interactions between the nasal microbiome and host transcriptome on the severity and clinical outcomes of RSV infection. We used 16 S rRNA sequencing to characterize the nasal microbiome of infants with RSV infection. We used RNA sequencing to interrogate the transcriptome of CD4+ T cells obtained from the same set of infants. After dimension reduction through principal component (PC) analysis, we performed an integrative analysis to identify significant co-variation between microbial clade and gene expression PCs. We then employed LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) to estimate the clade-gene association patterns for each infant. Our network-based integrative analysis identified several clade-gene associations significantly related to the severity of RSV infection. The microbial taxa with the highest loadings in the implicated clade PCs included Moraxella, Corynebacterium, Streptococcus, Haemophilus influenzae, and Staphylococcus. Interestingly, many of the genes with the highest loadings in the implicated gene PCs are encoded in mitochondrial DNA, while others are involved in the host immune response. This study on microbiome-transcriptome interactions provides insights into how the host immune system mounts a response against RSV and specific infectious agents in nasal microbiota.
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