Integrated analysis of mRNA and long noncoding RNA profiles in peripheral blood mononuclear cells of patients with bronchial asthma

Abstract

Background: Bronchial asthma is a heterogeneous disease with distinct disease phenotypes and underlying pathophysiological mechanisms. Long non-coding RNAs (lncRNAs) are involved in numerous functionally different biological and physiological processes. The aim of this study was to identify differentially expressed lncRNAs and mRNAs in patients with asthma and further explore the functions and interactions between lncRNAs and mRNAs.

Methods: Ten patients with asthma and 9 healthy controls were enrolled in this study. RNA was isolated from peripheral blood mononuclear cells. We performed microarray analysis to evaluate lncRNA and mRNA expression. The functions of the differentially expressed mRNAs were analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. A global signal transduction network was constructed to identify the core mRNAs. An lncRNA-mRNA network was constructed. Five mRNAs showing the greatest differences in expression levels or high degrees in the gene-gene functional interaction network, with their correlated lncRNAs, were validated by real-time quantitative polymerase chain reaction.

Results: We identified 2229 differentially expressed mRNAs and 1397 lncRNAs between the asthma and control groups. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified many pathways associated with inflammation and cell survival. The gene-gene functional interaction network suggested that some core mRNAs are involved in the pathogenesis of bronchial asthma. The lncRNA-mRNA co-expression network revealed correlated lncRNAs. CXCL8, FOXO3, JUN, PIK3CA, and G0S2 and their related lncRNAs NONHSAT115963, AC019050.1, MTCYBP3, KB-67B5.12, and HNRNPA1P12 were identified according to their differential expression levels and high degrees in the gene-gene network.

Conclusions: We identified the core mRNAs and their related lncRNAs and predicted the biological processes and signaling pathways involved in asthma.

Keywords: Bronchial asthma; Long non-coding RNA; Microarray analysis; Peripheral blood mononuclear cell; mRNA.

Fig. 1

Differential expression of lncRNAs and mRNAs in patients with asthma. Volcano plots are used to distinguish between differentially expressed lncRNAs (A) and mRNAs (B). Red and blue indicate up- and down-regulation, respectively. Hierarchical clustering analysis of dysregulated lncRNAs (C) and mRNAs (D). Relative lncRNA or mRNA expression is depicted according to the color scale. Red indicates elevated expression and blue indicates reduced expression

Fig. 2

GO and KEGG pathway analyses. GO analysis of significantly upregulated (A) and downregulated (B) mRNAs clustered in the BP. KEGG pathways analysis of differentially upregulated (C) and downregulated mRNAs (D). The abscissa shows ‐LogP, and the ordinate shows GO terms or KEGG pathways. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process

Fig. 3

Global signal transduction network analysis of asthma mRNAs. In the constructed network, nodes represent mRNAs, the size of the node’s area represents the value of the degree, and red indicates up-regulation and blue indicates down-regulation. The nodes are connected by an edge. The acronyms a, b, c, p, inh, and ind (e) indicate activation, binding, compound, phosphorylation, inhibition, and indirect effect, respectively

Fig. 4

RT‐qPCR validation of selected mRNAs and lncRNAs. A Relative expression levels of the mRNAs and lncRNAs between the patients with asthma and healthy control assessed by RT-qPCR. The y-axis represents the relative expression (2^−ΔΔCT). Data are presented as the median (interquartile range). All data were normalized to GAPDH expression. B Comparison of mean fold-changes between microarray data and RT-qPCR results. **P < 0.01; ***P < 0.001; **** P < 0.0001; ns, no significance. RT-qPCR, real-time quantitative polymerase chain reaction