Whole transcriptome analysis of high and low IFN‐α producers reveals differential response patterns following rhinovirus stimulation

Abstract

Objectives Viral respiratory infections cause considerable morbidity and economic loss. While rhinoviruses (RV) typically cause little more than the common cold, they can produce severe infections and disease exacerbations in susceptible individuals, such as those with asthma. Variations in the regulation of key antiviral cytokines, particularly type I interferon (IFN‐α and IFN‐β), may contribute to RV susceptibility. To understand this variability, we compared the transcriptomes of high and low type I IFN producers. Methods Blood mononuclear cells from 238 individuals with or without asthma were cultured in the presence or absence of RV. Those samples demonstrating high or low RV‐stimulated IFN‐α production (N = 75) underwent RNA‐sequencing. Results Gene expression patterns were similar in samples from healthy participants and those with asthma. At baseline, the high IFN‐α producer group showed higher expression of genes associated with plasmacytoid dendritic cells, the innate immune response and vitamin D activation, but lower expression of oxidative stress pathways than the low IFN‐α producer group. After RV stimulation, the high IFN‐α producer group showed higher expression of genes found in immune response biological pathways and lower expression of genes linked to developmental and catabolic processes when compared to the low IFN‐α producer group. Conclusions These differences suggest that the high IFN‐α group has a higher level of immune system readiness, resulting in a more intense and perhaps more focussed pathogen‐specific immune response. These results contribute to a better understanding of the variability in type I IFN production between individuals.

Inference of molecular mechanisms of transcriptional regulation from co-expression data

“…The data set#2 includes 74 RNA-seq experiments for human monocytic cells (Murray et al, 2021). There were two conditions per sample -unstimulated cells and cells stimulated with rhinovirus.…”

Double shrinking (DOSH), a regression-based algorithm for gene regulatory network inference from co-expression data

“…RNA-seq data were taken from [3]. File with gene expression data GSE166292_Ferreira_Normalised_CPM_counts_noFilter.tsv was downloaded from GEO database.…”