A novel biomarker of COVI-19: MMP8 emerged by integrated bulk RNAseq and single-cell sequencing

Abstract

COVID-19 has been emerging as the most influential illness which has caused great costs to the heath of population and social economy. Sivelestat sodium (SS) is indicated as an effective cure for lung dysfunction, a characteristic symptom of COVID-19 infection, but its pharmacological target is still unclear. Therefore, a deep understanding of the pathological progression and molecular alteration is an urgent issue for settling the diagnosis and therapy problems of COVID-19. In this study, the bulk ribonucleic acid sequencing (RNA-seq) data of healthy donors and non-severe and severe COVID-19 patients were collected. Then, target differentially expressed genes (DEGs) were screened through integrating sequencing data and the pharmacological database. Besides, with the help of functional and molecular interaction analyses, the potential effect of target gene alteration on COVID-19 progression was investigated. Single-cell sequencing was performed to evaluate the cell distribution of target genes, and the possible interaction of gene-positive cells with other cells was explored by intercellular ligand-receptor pattern analysis. The results showed that matrix metalloproteinase 8 (MMP8) was upregulated in severe COVID-19 patients, which was also identified as a targeting site to SS. Additionally, MMP8 took a core part in the regulatory interaction network of the screened DEGs in COVID-19 and was dramatically correlated with the inflammatory signaling pathway. The further investigations indicated that MMP8 was mainly expressed in myelocytes with a high degree of heterogeneity. MMP8-positive myelocytes interacted with other cell types through RETN-TLR4 and RETN-CAP1 ligand-receptor patterns. These findings emphasize the important role of MMP8 in COVID-19 progression and provide a potential therapeutic target for COVID-19 patients.

Keywords: COVID-19; Cell-to-cell communication; MMP8; RNA sequencing; Single-cell sequencing.

Fig. 1

Analysis of DEGs during COVID-19 progression. (A, B) Volcano diagram showing the profile of DEGs between healthy group and severe COVID-19 group (A), as well as healthy group and non-severe COVID-19 group (B), and the expression profile was extracted from the GSE213313 database. DEGs were defined as log₂FC > 1 and the adjust P value < 0.05. (C) The hierarchical bi-clustering analysis indicated significant DEGs. (D) 3D structure of SS from PubChem. (E) The normalization fit score of pharmacological sites from Pharmmapper. (F) Venn diagram showing the DEG components from different datasets. (G) Expression of MMP8 in the blood samples of severe COVID-19 patients at different pathological phases. (H) Expression of MMP8 in the blood samples of non-severe COVID-19 patients at different pathological phases. (I) Western blotting examined the MMP8 protein level in critical/non-critical COVID-19 patients and healthy donors (n = 3). (J) qRT-PCR examined the MMP8 mRNA expression in critical/non-critical COVID-19 patients and healthy donors (n = 10). * represents P < 0.05, ** represents P < 0.01, ***represents P < 0.001, ****represents P < 0.001, ns represents no significance.

Fig. 2

Structure and SS binding site of MMP8. A–C The binding energy is – 7.828, – 7.811 and –  7.658 kcal/mol, respectively.

Fig. 3

Regulatory network of MMP8 in COVID-19 datasets. (A, B) WGCNA assessing the soft-threshold power. (C–E) Heatmaps presenting eigengene adjacency and correlation network of the 55 target DEGs, as well as module trait of MMP8. (F) Correlation analysis of module membership and MMP8 expression significance. (G, H) PPI network indicating the interactions between target genes.

Fig. 4

Functional analysis of MMP8. (A, B) Enriched GO and KEGG terms for biological processes of MMP8. (C, D) GESA enrichment analysis illustrating MMP8 expression related functional processes.

Fig. 5

ScRNA-seq presenting the landscape of MMP8-positive cells in COVID-19. (A, C, D and F) t-SNE plots showing cell distribution in the blood sample of COVID-19. (B) Heatmap presenting cell-situation related genes. (E and G) Relative proportions of different cell types for each dataset.

Fig. 6

Cell-to-cell communication analysis of MMP-8-positive myelocytes and other cells in COVID-19. (A, B) Circle chart presenting the number of interactions (A) or the total interaction intensity (B) between myelocytes and other types of cells. (C) Chord diagram indicating all significant interactions from MMP-8-positive myelocytes to others. (D) Chord diagram indicating all significant interactions from other cells to MMP-8-positive myelocytes. (E) Heatmap identifying the efferent or afferent contribution of all signals to various cells. (F) Non negative matrix factorization analysis of ligand-receptor data to infer the number of patterns based on two metrics implemented in the NMF R package, which include Cophenetic and Silhouette. An appropriate number of patterns for a pattern number range is the one where the Cophenetic and Silhouette values begin to drop abruptly. (G) Pattern clusters of ligand-receptor data obtained from the Cophenetic and Silhouette values between MMP-8-positive myelocytes and other types of cells. (H) Circle chart presenting significant interactions from MMP-8-positive myelocytes to others in RETN-TLR4 pathway and RETN-CAP1 pathway. (I) Violin plots showing the member gene expression in RETN pathway in various cell subtypes.