(T1130-03-18) Next-Generation Sequencing Based RNA-Seq Discover the Differentially Expressed Genes and Pathways of Systemic Lupus Erythematosus

Purpose: Identifying disease-related biomarkers is critical during preclinical and clinical stages of drug development. Traditional methods such as quantitative PCR (qPCR) offer limited throughput and are not well-suited for the comprehensive analysis of multiple genes and pathways. In contrast, RNA sequencing (RNA-Seq) is a high-throughput method that enables genome-wide profiling of mRNA expression and the identification of differentially expressed genes and pathways. Systemic lupus erythematosus (SLE) is a complex and heterogeneous autoimmune disease in which the immune system attacks healthy tissues throughout the body [1]. This study aimed to utilize an NGS-based RNA-Seq assay to identify potential gene and pathway-level biomarkers in the peripheral blood of SLE patients.

Methods: RNA Extraction and Library Preparation: Total RNA was extracted from whole blood samples of four SLE patients and four healthy donors. Ribosomal RNA (rRNA) was removed, and the remaining mRNA was reverse transcribed into complementary DNA (cDNA). Strand-specific libraries were prepared using commercial kits, with steps including end repair, adapter ligation, and PCR amplification. Library quality was assessed before sequencing. Sequencing and Data Processing: Libraries were sequenced following quality control procedures. Raw sequencing data were filtered using Trim Galore [2] and assessed for quality using FastQC [3]. The clean data were then aligned to the human transcriptome using STAR [4]. A raw expression matrix was generated with featureCounts [5], and differential expression analysis was performed using DESeq2 [6]. Downstream Analysis: Further analyses were conducted on the differentially expressed genes, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Variation Analysis (GSVA), and the construction of gene interaction networks.

Results: Differentially expressed genes in SLE patients were significantly enriched in immune-related pathways, including Th17 cell differentiation, Th1 and Th2 cell differentiation, and B cell receptor signaling. Gene interaction network analysis identified that hub genes, such as CD28, CD80, CD86, CTLA4, IL-1, IL-2 play key roles in the regulatory network. Compared with healthy donors, the proportion of B cells naive, NK cells resting and Plasma cells in the blood of SLE patients increased significantly. While the proportion of CD8+ T cells and neutrophils significantly decreased.

Conclusion: RNA-seq enables comprehensive analysis of mRNA expression profiles in peripheral blood, revealing differentially expressed genes, signaling pathways, and altered immune cell composition between SLE patients and healthy individuals. This NGS-based RNA-Seq approach provides valuable insights into the molecular state of SLE and holds strong potential for application in biomarker discovery and drug development in a real-world scenario.

References: [1] Kiriakidou M, Ching CL. Systemic Lupus Erythematosus. Ann Intern Med. 2020;172(11):ITC81-ITC96.
[2] Krueger F. Trim galore. Wrapper Tool Cutadapt FastQC Consistently Apply Qual Adapt Trimming FastQ Files, 2015, 516.
[3] Andrews S. FastQC: a quality control tool for high throughput sequence data. Bioinform 2010.
[4] Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England), 29(1), 15–21.
[5] Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics (Oxford, England), 30(7), 923–930.
[6] Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), 550.

Acknowledgements: This study was funded by Accurant Biotech Inc. The authors declare no competing interests. All procedures involving human or animal materials were conducted in accordance with relevant ethical guidelines and approved protocols.


Flowchart of RNA-Seq. The RNA of four SLE and four healthy donors was processed by experimental and bioinformatic pipeline. The differentially expressed genes and pathways were analyzed and identified.


Analysis results based on the differentially expressed genes. (A) KEGG functional enrichment analysis. (B) GO functional enrichment analysis. (C) GSVA analysis. (D) Gene interaction network.


Immune Cell Composition Analysis by CIBERSORT. (A) Proportion of immune cell types in each sample. (B) Comparison of significant immune cell types between SLE patients and healthy donors (HD).