Solving the genetic puzzle of systemic lupus erythematosus

The Role of Genetic Risk Factors in Pathogenesis of Childhood-Onset Systemic Lupus Erythematosus

The pathogenesis of childhood-onset systemic lupus erythematosus (cSLE) is complex and not fully understood. It involves three key factors: genetic risk factors, epigenetic mechanisms, and environmental triggers. Genetic factors play a significant role in the development of the disease, particularly in younger individuals. While cSLE has traditionally been considered a polygenic disease, it is now recognized that in rare cases, a single gene mutation can lead to the disease. Although these cases are uncommon, they provide valuable insights into the disease mechanism, enhance our understanding of pathogenesis and immune tolerance, and facilitate the development of targeted treatment strategies. This review aims to provide a comprehensive overview of both monogenic and polygenic SLE, emphasizing the implications of specific genes in disease pathogenesis. By conducting a thorough analysis of the genetic factors involved in SLE, we can improve our understanding of the underlying mechanisms of the disease. Furthermore, this knowledge may contribute to the identification of effective biomarkers and the selection of appropriate therapies for individuals with SLE.

Omics-based integrated analysis identified IKZF2 as a biomarker associated with lupus nephritis

Lupus nephritis (LN) is a crucial complication of systemic lupus erythematosus (SLE). IKZF2 was identified as a lupus susceptibility locus, while its exact molecular function in LN is unknown. We aimed to explore the relationship between IKZF2 and LN based on multi-omics data. In our study, we carried out a meta-analysis of publicly available data, including not only tubulointerstitium, but also glomerulus tissue samples from LN patients and controls. Based on the common differentially expressed genes (co-DEGs) and previous researches, we selected IKZF2 for further analysis. Then, we analyzed potential molecular mechanisms of co-DEGs and IKZF2 in LN. To explore the possible targets of IKZF2, protein-protein interaction network (PPI) network and ceRNA network of IKZF2 were also constructed. Moreover, we performed immune infiltration analysis and evaluated clinical value of IKZF2. A total of 26 co-DEGs were observed in the integration of the above DEGs coming from the four sets of data, of which IKZF2 was selected for further analysis. Functional enrichment analysis from IKZF2 and related PPI network confirmed the tight relationship between IKZF2 and the immune reaction. Moreover, immune filtration analysis revealed the significant correlation between IKZF2 and naïve B cell, NK cell activation, NK cell rest and other immune cells. Receiver operating characteristic (ROC) analysis showed that the areas under the ROC curves were 0.721, 0.80, 0.682, and 0.859 for IKZF2 in four datasets, which demonstrated the clinical value of IKZF2. Our study revealed that IKZF2 may play an essential role in the molecular function and development of LN, and might be a potential biomarker for distinguishing LN patients and healthy ones.

Dysregulation of Signaling Pathways Due to Differentially Expressed Genes From the B-Cell Transcriptomes of Systemic Lupus Erythematosus Patients - A Bioinformatics Approach

Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disorder that is clinically complex and has increased production of autoantibodies. Via emerging technologies, researchers have identified genetic variants, expression profiling of genes, animal models, and epigenetic findings that have paved the way for a better understanding of the molecular and genetic mechanisms of SLE. Our current study aimed to illustrate the essential genes and molecular pathways that are potentially involved in the pathogenesis of SLE. This study incorporates the gene expression profiling data of the microarray dataset GSE30153 from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between the B-cell transcriptomes of SLE patients and healthy controls were screened using the GEO2R web tool. The identified DEGs were subjected to STRING analysis and Cytoscape to explore the protein-protein interaction (PPI) networks between them. The MCODE (Molecular Complex Detection) plugin of Cytoscape was used to screen the cluster subnetworks that are highly interlinked between the DEGs. Subsequently, the clustered DEGs were subjected to functional annotation with ClueGO/CluePedia to identify the significant pathways that were enriched. For integrative analysis, we used GeneGo MetacoreTM, a Cortellis Solution software, to exhibit the Gene Ontology (GO) and enriched pathways between the datasets. Our study identified 4 upregulated and 13 downregulated genes. Analysis of GO and functional enrichment using ClueGO revealed the pathways that were statistically significant, including pathways involving T-cell costimulation, lymphocyte costimulation, negative regulation of vascular permeability, and B-cell receptor signaling. The DEGs were mainly enriched in metabolic networks such as the phosphatidylinositol-3,4,5-triphosphate pathway and the carnitine pathway. Additionally, potentially enriched pathways, such as the signaling pathways induced by oxidative stress and reactive oxygen species (ROS), chemotaxis and lysophosphatidic acid signaling induced via G protein-coupled receptors (GPCRs), and the androgen receptor activation pathway, were identified from the DEGs that were mainly associated with the immune system. Four genes (EGR1, CD38, CAV1, and AKT1) were identified to be strongly associated with SLE. Our integrative analysis using a multitude of bioinformatics tools might promote an understanding of the dysregulated pathways that are associated with SLE development and progression. The four DEGs in SLE patients might shed light on the pathogenesis of SLE and might serve as potential biomarkers in early diagnosis and as therapeutic targets for SLE.

Identification of Regulatory Modules That Stratify Lupus Disease Mechanism through Integrating Multi-Omics Data

Although recent advances in genetic studies have shed light on systemic lupus erythematosus (SLE), its detailed mechanisms remain elusive. In this study, using datasets on SLE transcriptomic profiles, we identified 750 differentially expressed genes (DEGs) in T and B lymphocytes and peripheral blood cells. Using transcription factor (TF) binding data derived from chromatin immunoprecipitation sequencing (ChIP-seq) experiments from the Encyclopedia of DNA Elements (ENCODE) project, we inferred networks of co-regulated genes (NcRGs) based on binding profiles of the upregulated DEGs by significantly enriched TFs. Modularization analysis of NcRGs identified co-regulatory modules among the DEGs and master TFs vital for each module. Remarkably, the co-regulatory modules stratified the common SLE interferon (IFN) signature and revealed SLE pathogenesis pathways, including the complement cascade, cell cycle regulation, NETosis, and epigenetic regulation. By integrative analyses of disease-associated genes (DAGs), DEGs, and enriched TFs, as well as proteins interacting with them, we identified a hierarchical regulatory cascade with TFs regulated by DAGs, which in turn regulates gene expression. Integrative analysis of multi-omics data provided valuable molecular insights into the molecular mechanisms of SLE.

Bioinformatics analysis of differentially expressed gene profiles associated with systemic lupus erythematosus

DNA microarray and high-throughput sequencing have been widely used to identify the differentially expressed genes (DEGs) in systemic lupus erythematosus (SLE). However, the big data from gene microarrays are also challenging to work with in terms of analysis and processing. The presents study combined data from the microarray expression profile (GSE65391) and bioinformatics analysis to identify the key genes and cellular pathways in SLE. Gene ontology (GO) and cellular pathway enrichment analyses of DEGs were performed to investigate significantly enriched pathways. A protein-protein interaction network was constructed to determine the key genes in the occurrence and development of SLE. A total of 310 DEGs were identified in SLE, including 193 upregulated genes and 117 downregulated genes. GO analysis revealed that the most significant biological process of DEGs was immune system process. Kyoto Encyclopedia of Genes and Genome pathway analysis showed that these DEGs were enriched in signaling pathways associated with the immune system, including the RIG-I-like receptor signaling pathway, intestinal immune network for IgA production, antigen processing and presentation and the toll-like receptor signaling pathway. The current study screened the top 10 genes with higher degrees as hub genes, which included 2′-5′-oligoadenylate synthetase 1, MX dynamin like GTPase 2, interferon induced protein with tetratricopeptide repeats 1, interferon regulatory factor 7, interferon induced with helicase C domain 1, signal transducer and activator of transcription 1, ISG15 ubiquitin-like modifier, DExD/H-box helicase 58, interferon induced protein with tetratricopeptide repeats 3 and 2′-5′-oligoadenylate synthetase 2. Module analysis revealed that these hub genes were also involved in the RIG-I-like receptor signaling, cytosolic DNA-sensing, toll-like receptor signaling and ribosome biogenesis pathways. In addition, these hub genes, from different probe sets, exhibited significant co-expressed tendency in multi-experiment microarray datasets (P<0.01). In conclusion, these key genes and cellular pathways may improve the current understanding of the underlying mechanism of development of SLE. These key genes may be potential biomarkers of diagnosis, therapy and prognosis for SLE.

The nephrologist of tomorrow: towards a kidney-omic future

Omics refers to the collective technologies used to explore the roles and relationships of the various types of molecules that make up the phenotype of an organism. Systems biology is a scientific discipline that endeavours to quantify all of the molecular elements of a biological system. Therefore, it reflects the knowledge acquired by omics in a meaningful manner by providing insights into functional pathways and regulatory networks underlying different diseases. The recent advances in biotechnological platforms and statistical tools to analyse such complex data have enabled scientists to connect the experimentally observed correlations to the underlying biochemical and pathological processes. We discuss in this review the current knowledge of different omics technologies in kidney diseases, specifically in the field of pediatric nephrology, including biomarker discovery, defining as yet unrecognized biologic therapeutic targets and linking omics to relevant standard indices and clinical outcomes. We also provide here a unique perspective on the field, taking advantage of the experience gained by the large-scale European research initiative called "Systems Biology towards Novel Chronic Kidney Disease Diagnosis and Treatment" (SysKid). Based on the integrative framework of Systems biology, SysKid demonstrated how omics are powerful yet complex tools to unravel the consequences of diabetes and hypertension on kidney function.