Changing Expression Profiles of Messenger RNA, MicroRNA, Long Non-coding RNA, and Circular RNA Reveal the Key Regulators and Interaction Networks of Competing Endogenous RNA in Pulmonary Fibrosis

Abstract

Pulmonary fibrosis is a kind of interstitial lung disease with architectural remodeling of tissues and excessive matrix deposition. Apart from messenger RNA (mRNA), microRNA (miRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA) could also play important roles in the regulatory processes of occurrence and progression of pulmonary fibrosis. In the present study, the pulmonary fibrosis model was administered with bleomycin. Whole transcriptome sequencing analysis was applied to investigate the expression profiles of mRNAs, lncRNAs, circRNAs, and miRNAs. After comparing bleomycin-induced pulmonary fibrosis model lung samples and controls, 286 lncRNAs, 192 mRNAs, 605 circRNAs, and 32 miRNAs were found to be differentially expressed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential functions of these differentially expressed (DE) mRNAs and non-coding RNAs (ncRNAs). The terms related to inflammatory response and tumor necrosis factor (TNF) signaling pathway were enriched, implying potential roles in regulatory process. In addition, two co-expression networks were also constructed to understand the internal regulating relationships of these mRNAs and ncRNAs. Our study provides a systematic perspective on the potential functions of these DE mRNAs and ncRNAs during PF process and could help pave the way for effective therapeutics for this devastating and complex disease.

Keywords: ceRNA; co-expression network; pulmonary fibrosis; regulation; whole transcriptome sequencing.

FIGURE 1

The Masson sections of Sprague–Dawley (SD) rat pulmonary. (A–C) The control group treated with saline. (D–F) The model group treated with bleomycin. Blue area represents fibrous nodules.

FIGURE 2

The expression profiles of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in PF models. (A) Volcano plot of differentially expressed (DE) mRNAs between control group and model group. (B) Volcano plot of DE lncRNAs between control group and model group. Red dots represent the mRNAs or lncRNAs up-regulated in PF model samples, blue dots the down-regulated mRNAs or lncRNAs, and the gray dots the ones that showed no differences. (C) Heatmap of DE mRNAs between control group and model group. (D) Heatmap of DE lncRNAs between control group and model group.

FIGURE 3

The expression profiles of circular RNAs (circRNAs) and microRNAs (miRNAs) in PF models. (A) Volcano plot of differentially expressed (DE) circRNAs between control group and model group. (B) Volcano plot of DE miRNAs between control group and model group. Red dots represent the circRNAs or miRNAs up-regulated in PF model samples, blue dots down-regulated circRNAs or miRNAs, and the gray dots the ones that showed no differences. (C) Heatmap of DE miRNAs between control group and model group. (D) Heatmap of DE circRNAs between control group and model group.

FIGURE 4

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis enrichment of differentially expressed (DE) competing endogenous RNAs (ceRNAs). (A) KEGG pathway analysis enrichment of DE mRNAs. (B) KEGG pathway analysis enrichment of DE long non-coding RNAs (lncRNAs). (C) KEGG pathway analysis enrichment of DE circRNAs. (D) KEGG pathway analysis enrichment of DE miRNAs.

FIGURE 5

The network of differentially expressed (DE) competing endogenous RNAs (ceRNAs). (A) The interaction of microRNAs (mRNAs) and long non-coding RNAs (lncRNAs). (B) The interaction of cirRNAs and miRNA.

FIGURE 6

Verification of the expression patterns in both qRT-PCR and RNA-seq. The data are shown as mean ± SD (n = 3). Groups with different asterisks are significantly different (p < 0.05). (A) The expression pattern of mRNA in both qRT-PCR and RNA-seq. (B) The expression pattern of miRNA in both qRT-PCR and RNA-seq. (C) The expression pattern of lncRNA in both qRT-PCR and RNA-seq.