Whole-Transcriptome RNA Sequencing Reveals Significant Differentially Expressed mRNAs, miRNAs, and lncRNAs and Related Regulating Biological Pathways in the Peripheral Blood of COVID-19 Patients

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initially identified in China and currently worldwide dispersed, resulting in the coronavirus disease 2019 (COVID-19) pandemic. Notably, COVID-19 is characterized by systemic inflammation. However, the potential mechanisms of the "cytokine storm" of COVID-19 are still limited. In this study, fourteen peripheral blood samples from COVID-19 patients (n = 10) and healthy donors (n = 4) were collected to perform the whole-transcriptome sequencing. Lung tissues of COVID-19 patients (70%) presenting with ground-glass opacity. Also, the leukocytes and lymphocytes were significantly decreased in COVID-19 compared with the control group (p < 0.05). In total, 25,482 differentially expressed messenger RNAs (DE mRNA), 23 differentially expressed microRNAs (DE miRNA), and 410 differentially expressed long noncoding RNAs (DE lncRNAs) were identified in the COVID-19 samples compared to the healthy controls. Gene Ontology (GO) analysis showed that the upregulated DE mRNAs were mainly involved in antigen processing and presentation of endogenous antigen, positive regulation of T cell mediated cytotoxicity, and positive regulation of gamma-delta T cell activation. The downregulated DE mRNAs were mainly concentrated in the glycogen biosynthetic process. We also established the protein-protein interaction (PPI) networks of up/downregulated DE mRNAs and identified 4 modules. Functional enrichment analyses indicated that these module targets were associated with positive regulation of cytokine production, cytokine-mediated signaling pathway, leukocyte differentiation, and migration. A total of 6 hub genes were selected in the PPI module networks including AKT1, TNFRSF1B, FCGR2A, CXCL8, STAT3, and TLR2. Moreover, a competing endogenous RNA network showed the interactions between lncRNAs, mRNAs, and miRNAs. Our results highlight the potential pathogenesis of excessive cytokine production such as MSTRG.119845.30/hsa-miR-20a-5p/TNFRSF1B, MSTRG.119845.30/hsa-miR-29b-2-5p/FCGR2A, and MSTRG.106112.2/hsa-miR-6501-5p/STAT3 axis, which may also play an important role in the development of ground-glass opacity in COVID-19 patients. This study gives new insights into inflammation regulatory mechanisms of coding and noncoding RNAs in COVID-19, which may provide novel diagnostic biomarkers and therapeutic avenues for COVID-19 patients.

Figure 1

The workflow of the Whole-transcriptome RNA sequencing analysis of peripheral blood samples of COVID-19 patients.

Figure 2

Volcano plots and heatmaps of differentially expressed genes (DEGs). Volcano plots showing significantly different expressions of mRNAs (a), miRNAs (b), and lncRNAs (c) between the COVID-19 and control groups. Red points: upregulated DEGs; blue points: downregulated DEGs; gray and green points: the genes with no obvious difference in expression levels. Heatmaps of DE mRNAs (d), DE miRNAs (e), and DE lncRNAs (f). Red represents upregulation, and blue represents downregulation.

Figure 3

Go terms and KEGG pathway enrichment of up/downregulated DE mRNAs in peripheral blood samples of COVID-19 patients. GO term functional enrichment with 3 categories (BP, MF, CC) for upregulated DE mRNAs (a) and downregulated DE mRNAs (b). Top 10 pathways enriched by upregulated DE mRNAs (c) and downregulated DE mRNAs (d).

Figure 4

Functional enrichment analyses for target genes of DE miRNA and DE lncRNAs. Bubble plot shows GO (a) and KEGG terms (b) enriched in target genes of DE miRNA. Bubble plot shows GO (c) and KEGG terms (d) enriched in genes regulated by DE lncRNAs.

Figure 5

Two modules obtained from upregulated-PPI network and its functional enrichment analysis. (a) 149 upregulated inflammation-related DE mRNAs in COVID-19 were collected with Venn analysis. (b) PPI network of upregulated inflammation-related DE mRNAs. (c, d) Significant clustered modules from the PPI network. Red rectangles represent upregulated genes. The GO-BP terms and KEGG pathways enrichment analysis for module 1 (e) and module 2 (f), respectively, using the Metascape database.

Figure 6

Two modules obtained from the downregulated PPI network and its functional enrichment analysis. (a) 266 downregulated inflammation-related DE mRNAs in COVID-19 were collected with Venn analysis. (b) PPI network of downregulated inflammation-related DE mRNAs. (c, d) Significant clustered modules from the PPI network. Green rectangles represent downregulated genes. The GO-BP terms and KEGG pathways enrichment analysis for module 1′ (e) and module 2′ (f), respectively, using the Metascape database.

Figure 7

The lncRNA-miRNA-mRNA coexpression network. The downward “v” triangle represents the downregulated DE mRNAs, the upward triangle represents the upregulated DE mRNAs, the red ellipse represents the upregulated DE miRNAs, the green ellipse represents the downregulated DE miRNAs, red diamond indicates upregulated DE lncRNAs, and green diamond indicates downregulated DE lncRNAs.

Figure 8

The competing endogenous RNA (ceRNA) network of COVID-19. Pink octagon indicates the upregulated DE mRNAs, light blue octagon indicates the downregulated DE mRNAs, red rectangle indicates upregulated DE miRNAs, green rectangle indicates downregulated DE miRNAs, red diamond indicates upregulated DE lncRNAs, and green diamond indicates downregulated DE lncRNAs.