Integrative bioinformatics analysis of transcriptomic data from CD8+ T cells in Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) is a complex, multisystem autoimmune disorder characterized by extensive inflammation that affects nearly all organ systems in the body. It is primarily mediated by auto-antibodies and immune complexes, and it predominantly affects women more than men. This study employs an in-silico approach to identify key genes potentially involved in the pathogenesis of SLE. To identify key genes potentially involved in SLE pathogenesis using in-silico approach. High-throughput sequencing dataset GSE97264, from the Gene Expression Omnibus (GEO) database, which contains RNA transcriptome data from CD8 + T-cells of 18 SLE patients and 14 healthy controls was utilized for the analysis. Differentially expressed genes (DEGs) were identified using the Bioconductor DESeq2 package in R platform. Gene Ontology (GO) and pathway enrichment analyses were performed using the ToppGene suite. Motif analysis of the genes’ promoter regions was conducted using HOMER software. Protein-protein interaction (PPI) and Reactome functional interaction (FI) networks were created using Cytoscape plugins StringApp and ReactomeFIViz, and analysed to identify hub genes. Our analysis identified 931 DEGs, with 577 upregulated and 354 downregulated. GO and pathway enrichment analyses indicated that upregulated genes were associated with immune responses, including cytokine production and receptor activation. Motif analysis identified key regulatory motifs linked to immune regulation in upregulated genes and T-cell activation in downregulated genes. PPI and FI networks analyses revealed 29 cell cycle-associated hub genes, with 10 genes—CDK1, TPX2, BIRC5, CCNA2, BUB1, BUB1B, AURKA, KIF2C, PLK1, and CDCA8—common to both biological networks, suggesting their crucial role in SLE pathogenesis. This study suggests that dysregulation of the identified 10 genes may impact immune responses and contribute to the autoimmune-like conditions observed in SLE. Several of these genes are also implicated in other autoimmune diseases, highlighting their potential as SLE biomarkers. Despite their known roles in other immune-related diseases involving CD8 + T cells, their direct association with SLE had not been previously established. This novel finding underscores the potential of these genes as therapeutic targets and may contribute to the development of diagnostic tools.

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Journal
Journal of King Saud University - Science
Published
2024-08-30
DOI
https://doi.org/10.1016/j.jksus.2024.103417
Citations
1
Primary Topic
Systemic Lupus Erythematosus Research
Type
article
Field-Weighted Citation Impact
0.47

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article

Integrative bioinformatics analysis of transcriptomic data from CD8+ T cells in Systemic Lupus Erythematosus

Sudhriti Bhuiyan, A. Premanand, Shruti Kumari, Sandhanasamy Devanesan et al.
1 citations
Journal of King Saud University - Science
Systemic Lupus Erythematosus Research
0.47
article

Integrative bioinformatics analysis of transcriptomic data from CD8+ T cells in Systemic Lupus Erythematosus

Sudhriti Bhuiyan, A. Premanand, Shruti Kumari, Sandhanasamy Devanesan, Shobana Sampath, Reena Rajkumari Baskaran, Sona Elizabeth Jacob
article en
1 citations

Abstract

Systemic Lupus Erythematosus (SLE) is a complex, multisystem autoimmune disorder characterized by extensive inflammation that affects nearly all organ systems in the body. It is primarily mediated by auto-antibodies and immune complexes, and it predominantly affects women more than men. This study employs an in-silico approach to identify key genes potentially involved in the pathogenesis of SLE. To identify key genes potentially involved in SLE pathogenesis using in-silico approach. High-throughput sequencing dataset GSE97264, from the Gene Expression Omnibus (GEO) database, which contains RNA transcriptome data from CD8 + T-cells of 18 SLE patients and 14 healthy controls was utilized for the analysis. Differentially expressed genes (DEGs) were identified using the Bioconductor DESeq2 package in R platform. Gene Ontology (GO) and pathway enrichment analyses were performed using the ToppGene suite. Motif analysis of the genes’ promoter regions was conducted using HOMER software. Protein-protein interaction (PPI) and Reactome functional interaction (FI) networks were created using Cytoscape plugins StringApp and ReactomeFIViz, and analysed to identify hub genes. Our analysis identified 931 DEGs, with 577 upregulated and 354 downregulated. GO and pathway enrichment analyses indicated that upregulated genes were associated with immune responses, including cytokine production and receptor activation. Motif analysis identified key regulatory motifs linked to immune regulation in upregulated genes and T-cell activation in downregulated genes. PPI and FI networks analyses revealed 29 cell cycle-associated hub genes, with 10 genes—CDK1, TPX2, BIRC5, CCNA2, BUB1, BUB1B, AURKA, KIF2C, PLK1, and CDCA8—common to both biological networks, suggesting their crucial role in SLE pathogenesis. This study suggests that dysregulation of the identified 10 genes may impact immune responses and contribute to the autoimmune-like conditions observed in SLE. Several of these genes are also implicated in other autoimmune diseases, highlighting their potential as SLE biomarkers. Despite their known roles in other immune-related diseases involving CD8 + T cells, their direct association with SLE had not been previously established. This novel finding underscores the potential of these genes as therapeutic targets and may contribute to the development of diagnostic tools.

Journal of King Saud University - ScienceVol. 36(9)
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), King Saud University (SA), Vellore Institute of Technology University (IN)
King Saud University, VIT University
Good health and well-being
Openalex Percentile: Top 32%
Systemic Lupus Erythematosus Research
0.47
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