Transcriptomic analysis of metabolic characteristics of the immune cells in systemic lupus erythematosus patients

doi:10.3969/j.issn.1674-8115.2022.09.006

Abstract

Abstract:

Objective ·To study the metabolic pathway activity level of the immune cell subsets in the patients with systemic lupus erythematosus (SLE) by bioinformatics analysis. Methods ·The matrix expression data of PBMCs collected from SLE patients and healthy controls were downloaded from Gene Expression Omnibus (GEO) Datasets, as well as the transcriptome data of T cell and B cell subsets from SLE patients and healthy controls. The differentially-expressed genes (DEGs) were identified in the standardized sequence data. Pathway enrichment analysis of DEGs was performed by online Enrichr tools, and the common up-regulated pathways were determined by comparative analysis. Gene set enrichment analysis (GSEA) was used to identify pathways that were enriched in the experiment processed with the whole gene expression matrix. RNA-seq data from PBMCs samples of SLE patients and healthy controls were used to characterize the immune cell composition. The targeted pathway was annotated with gene expression. Assay for transposase-accessible chromatin using sequencing (ATAC-seq) technique was performed to detect the chromatin accessibility of glycolysis-related genes in SLE patients and healthy controls. Results ·① Venn diagram depicted 139 common upregulated pathways in GSE169080, GSE144390 and GSE139350 data sets, and GSEA results showed that multiple classical metabolic pathways, including glycolysis, oxidative phosphorylation (OXPHOS) and fatty acid oxidation (FAO), were up-regulated in SLE patients. ② Immune cell composition analysis of PBMCs showed that the proportions of T cells, B cells and NK cells were higher in SLE patients, and the expression of genes encoding multiple enzymes of metabolic pathway in T cells and B cells were higher than those in healthy controls. ③ Compared to healthy controls, the intensity of ATAC-seq signal was significantly enhanced at transcriptional regulatory sites of SLC2A3, PKM and LDHA in peripheral B cells from SLE patients. ④ GSEA results and visualization analysis of metabolic pathways of SLE B cell subsets showed that the memory B cells and plasmablasts displayed a higher metabolic state than na?ve B cells. Conclusion ·Multiple metabolic pathways are altered in SLE patients and the metabolic level of effector B cells is higher than na?ve B cells in SLE patients.

Key words: systemic lupus erythematous (SLE), metabolic pathway, Gene Expression Omnibus (GEO), bioinformatics, B cell

CLC Number:

R593.24⁺1

HAN Xiaxia, JIANG Yang, GU Shuangshuang, DAI Dai, SHEN Nan. Transcriptomic analysis of metabolic characteristics of the immune cells in systemic lupus erythematosus patients[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2022, 42(9): 1197-1207.

Figures/Tables 7

Fig 1 Flow chart of the study

Fig 2 Metabolic pathways analysis of PBMC in SLE patients

Fig 3 Pathway analysis of immune cell subsets in SLE patients

Fig 4 Metabolic pathway activity levels of immune cell subsets in SLE patients

Fig 5 Normalized ATAC-seq signal of SLC2A3, PKM and LDHA, encoding the glycolytic enzyme in peripheral B cells from SLE patients and healthy controls

Fig 6 Metabolic pathway activity levels of B cell subsets in SLE patients

Fig 7 Metabolic pathway activity level of plasmablasts in SLE patients

TrendMD

References 24

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