CD40LG and GZMB were correlated with adipose tissue macrophage infiltration and involved in obstructive sleep apnea related metabolic dysregulation: Evidence from bioinformatics analysis

Abstract

Both obesity and obstructive sleep apnea (OSA) can lead to metabolic dysregulation and systemic inflammation. Similar to obesity, increasing evidence has revealed that immune infiltration in the visceral adipose tissue (VAT) is associated with obstructive sleep apnea-related morbidity. However, the pathological changes and potential molecular mechanisms in visceral adipose tissue of obstructive sleep apnea patients need to be further studied. Herein, by bioinformatics analysis and clinical validation methods, including the immune-related differentially expressed genes (IRDEGs) analysis, protein-protein interaction network (PPI), functional enrichment analysis, a devolution algorithm (CIBERSORT), spearman's correlation analysis, polymerase chain reaction (PCR), Enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC), we identified and validated 10 hub IRDEGs, the relative mRNA expression of four hub genes (CRP, CD40LG, CCL20, and GZMB), and the protein expression level of two hub genes (CD40LG and GZMB) were consistent with the bioinformatics analysis results. Immune infiltration results further revealed that obstructive sleep apnea patients contained a higher proportion of pro-inflammatory M1 macrophages and a lower proportion of M2 macrophages. Spearman's correlation analysis showed that CD40LG was positively correlated with M1 macrophages and GZMB was negatively correlated with M2 macrophages. CD40LG and GZMB might play a vital role in the visceral adipose tissue homeostasis of obstructive sleep apnea patients. Their interaction with macrophages and involved pathways not only provides new insights for understanding molecular mechanisms but also be of great significance in discovering novel small molecules or other promising candidates as immunotherapies of OSA-associated metabolic complications.

Keywords: CD40L CD-40-ligand; GZMB Granzyme B; adipose tissue–obesity; fat; macrophage infiltration and polarization; metabolic dysfunction; obstructive sleep apena; review.

FIGURE 1

Identification of immune-related differentially expressed genes (IRDEGs). Box plots show the distribution of the relative gene expression before (A) and after (B) normalization of GSE38792. Each box corresponds to one sample. The middle line corresponds to the median. (C) Volcano plot of differentially expressed genes (DEGs). DEGs were screened with the criteria of p-value <0.05. (D) The cluster circular heat map showing the top 10 upregulated and downregulated DEGs. (E) Venn diagram showing the intersection of DEGs and immune-related genes (IRGs). (F) The heatmap of 122 IRDEGs.

FIGURE 2

Protein-protein interaction (PPI) network construction, hub gene identification and functional enrichment analysis. (A) The PPI network based on STRING database and Cytoscape software, red color represents upregulated genes and blue color represent downregulated genes. (B) Hub genes identified by Cytoscape MCODE plug-in, red color represents upregulated genes and blue color represent downregulated genes. (C) Sankey dot of GO enrichment analyses of hub genes. (D) Sankey dot of KEGG pathway enrichment analyses of hub genes. The dot plot showed the hub genes specific to GO terms or KEGG pathways and the total number of genes in each enriched pathway. External validation of the hub genes at mRNA level (E) and protein level (F).

FIGURE 3

The landscape of immune infiltration in VAT between OSA and controls. (A) The relative percentage of 22 subpopulations of immune cells in 11 samples from GSE38792 datasets. (B) Principal components analysis performed on all samples. (C) Violin plot of differences in 22 infiltrating immune cells between OSA and normal controls. The normal group was marked as blue color and OSA group was marked as red color. p values <0.05 were considered as statistical significance.

FIGURE 4

Analysis of the characteristics of macrophage infiltration in collected clinical samples. (A) The relative mRNA expression level of macrophage markers by RT-qPCR methods. (B) The protein expression level of macrophage markers by immunohistochemistry methods. The target protein expression was evaluated by integrated optical density (IOD)/area assay through ImageJ. (C) Representative immunohistochemistry images from the same plane. Magnification, ×200, scale bar = 100 μm. Data are presented as the mean ± SD (n = 10), **p < 0.01.

FIGURE 5

Correlation between hub genes and Immune infiltration cells. Spearman’s correlation analysis between CRP (A), CD40LG (B), CCL20 (C), GZMB (D) and infiltrating immune cells, respectively.The four hub genes were validated by RT-qPCR. The size of the dots represents the strength of the correlation between genes and immune cells; the larger the dots, the stronger the correlation. The color of the dots represents the p-value, the redder the color, the lower the p-value. p < 0.05 was considered statistically significant. CRP, C-Reactive Protein; CCL20, C-C Motif Chemokine Ligand 20; CD40LG, CD40 Ligand; GZMB, Granzyme B.

FIGURE 6

The transcription factors (TFs) regulated network and target drugs in OSA patients. (A) The alluvial plot showing the regulatory network of TFs-genes-immune cells. The left column represents predicted TFs, the middle column represents immune-related hub genes, the right column represents immune cells, and the edge represents the relationship between them. A larger edge width indicates the number of TFs and immune cells (B) Drug-gene network using drug-centric fashions. Yellow circles indicate predictive drug, and blue squares indicate immune-related hub genes.