Bioinformatics identification of key candidate genes and pathways associated with systemic lupus erythematosus

Biochemical study of ZNF76 rs10947540 and SCUBE3 rs1888822 single nucleotide polymorphisms in the Egyptian patients with systemic lupus Erythematosus

Systemic lupus erythematosus (SLE) is a common autoimmune disease marked by the formation of apoptotic debris and the presence of autoantibodies that target nuclear components. At this moment, the actual cause of SLE is uncertain. Genetic variables have been well proven to have a significant role in the propensity of SLE. This study aimed to investigate the effect of (ZNF76) rs (10947540) and (SCUBE) rs (1888822) gene polymorphism in patients with systemic lupus erythematosus. A case control study has been carried out at Medical Biochemistry & Molecular biology and Rheumatology unit of Internal Medicine Departments, Faculty of Medicine, Menoufia University, Egypt, for 1-year duration between 1 June 2022 and 1 June 2023. Sixty patients were females (75%) and twenty patients were males (25%). Their ages ranged from 19 to 53 years. Their disease durations ranged from 7 months to 20 years. The findings indicated that the TC genotype of the ZNF76 rs10947540 gene increases the risk of SLE by 2.274-fold, while the dominant TC + CC increases the risk by 2.472-fold, and the C allele increases the risk by 2.115-fold. Additionally, the results showed that the TT genotype of the SCUBE3 rs1888822 gene increases the risk of SLE by 3.702-fold, the dominant GT + TT increases the risk by 2.304-fold, and the T allele increases the risk by 2.089-fold, while the GT genotype increases the risk by 1.918-fold. The study revealed significant associations between the genotypes of these polymorphisms and certain clinical parameters in SLE patients. These findings highlight the potential genetic contributions to SLE susceptibility and its clinical manifestations, providing valuable insights for future research and potential personalized approaches to the management of this complex autoimmune disease.

Dynamic RNA profiles in the small intestinal epithelia of cats after Toxoplasma gondii infection

BACKGROUND

Felids are the only definitive hosts of Toxoplasma gondii. However, the biological features of the feline small intestine following T. gondii infection are poorly understood. We investigated the changes in the expression of RNAs (including mRNAs, long non-coding RNAs and circular RNAs) in the small intestinal epithelia of cats following T. gondii infection to improve our understanding of the life cycle of T. gondii and cat responses to T. gondii infection.

METHODS

Fifteen cats were randomly assigned to five groups, and the infection groups were inoculated with 600 tissue cysts of the T. gondii Pru strain by gavage. The small intestinal epithelia of cats were collected at 6, 10, 14, and 30 days post infection (DPI). Using high-throughput RNA sequencing (RNA-seq), we investigated the changes in RNA expression. The expression levels of differentially expressed (DE) genes and non-coding RNAs (ncRNAs) identified by RNA-seq were validated by quantitative reverse transcription PCR (qRT-PCR). Differential expression was determined using the DESeq R package.

RESULTS

In total, 207 annotated lncRNAs, 20,552 novel lncRNAs, 3342 novel circRNAs and 19,409 mRNAs were identified. Among these, 70 to 344 DE mRNAs, lncRNAs and circRNAs were detected, and the post-cleavage binding sites between 725 ncRNAs and 2082 miRNAs were predicted. Using the co-location method, we predicted that a total of 235 lncRNAs target 1044 protein-coding genes, while the results of co-expression analysis revealed that 174 lncRNAs target 2097 mRNAs. Pathway enrichment analyses of the genes targeted by ncRNAs suggested that most ncRNAs were significantly enriched in immune or diseases-related pathways. NcRNA regulatory networks revealed that a single ncRNA could be directly or indirectly regulated by multiple genes or ncRNAs that could influence the immune response of cats. Co-expression analysis showed that 242 circRNAs, mainly involved in immune responses, were significantly associated with T. gondii infection. In contrast, 1352 protein coding RNAs, mainly involved in nucleic acid process/repair pathways or oocyte development pathways, were negatively associated with T. gondii infection.

CONCLUSIONS

This study is the first to reveal the expression profiles of circRNAs, lncRNAs and mRNAs in the cat small intestine following T. gondii infection and will facilitate the elucidation of the role of ncRNAs in the pathogenesis of T. gondii infection in its definitive host, thereby facilitating the development of novel intervention strategies against T. gondii infection in humans and animals.

Integrative Bioinformatics Analysis Identifies DDX60 as a Potential Biomarker for Systemic Lupus Erythematosus

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease with strong heterogeneity, leading to variable clinical symptoms, which makes diagnosis and activity evaluation difficult.

Methods

The original dataset of GSE88884 was analyzed to screen differentially expressed genes (DEGs) of SLE and the correlation between DEGs and clinical parameters (SLEDAI, anti-dsDNA, C3, and C4). The result was validated by microarray GSE121239 and SLE patients with RT-qPCR. Next, receiver operator characteristic (ROC) analysis, correlation analysis, and ordinal logistic regression were applied, respectively, to evaluate the capability of diagnosis and prediction of the candidate biomarker. Subsequently, the biological functions of the candidate biomarker were investigated through KEGG and GO enrichment, protein-protein interaction network, and the correlation matrix.

Results

A total of 283 DEGs were screened, and seven of them were overlapped with SLE-related genes. DDX60 was identified as the candidate biomarker. Analyses of GSE88884, GSE121239, and SLE patients with RT-qPCR indicated that DDX60 expression level is significantly higher in patients with high disease activity. ROC analysis and the area under the ROC curve (AUC = 0.8818) suggested that DDX60 has good diagnostic performance. DDX60 expression level was positively correlated with SLEDAI scores (r = 0.24). For every 1-unit increase in DDX60 expression value, the odds of a higher stage of activity of SLE disease are multiplied by 1.47. The function of DDX60 mainly focuses on IFN-I-induced antiviral activities, RIG-I signaling, and innate immune. Moreover, DDX60 plays a synergistic role with DDX58, IFIH1, OASL, IFIT1, and other related genes in the SLE pathogenesis. Conclusions. DDX60 is differently expressed in SLE, and it is significantly related to both serological indicators and the disease activity of SLE. We suggested that DDX60 might be a potential biomarker for SLE diagnosis and management.

Predicted Immune-Related Genes and Subtypes in Systemic Lupus Erythematosus Based on Immune Infiltration Analysis

Objective

The present investigation is aimed at identifying key immune-related genes linked with SLE and their roles using integrative analysis of publically available gene expression datasets.

Methods

Four gene expression datasets pertaining to SLE, 2 from whole blood and 2 experimental PMBC, were sourced from GEO. Shared differentially expressed genes (DEG) were determined as SLE-related genes. Immune cell infiltration analysis was performed using CIBERSORT, and case samples were subjected to k-means cluster analysis using high-abundance immune cells. Key immune-related SLE genes were identified using correlation analysis with high-abundance immune cells and subjected to functional enrichment analysis for enriched Gene Ontology Biological Processes and KEGG pathways. A PPI network of genes interacting with the key immune-related SLE genes was constructed. LASSO regression analysis was performed to identify the most significant key immune-related SLE genes, and correlation with clinicopathological features was examined.

Results

309 SLE-related genes were identified and found functionally enriched in several pathways related to regulation of viral defenses and T cell functions. k-means cluster analysis identified 2 sample clusters which significantly differed in monocytes, dendritic cell resting, and neutrophil abundance. 65 immune-related SLE genes were identified, functionally enriched in immune response-related signaling, antigen receptor-mediated signaling, and T cell receptor signaling, along with Th17, Th1, and Th2 cell differentiation, IL-17, NF-kappa B, and VEGF signaling pathways. LASSO regression identified 9 key immune-related genes: DUSP7, DYSF, KCNA3, P2RY10, S100A12, SLC38A1, TLR2, TSR2, and TXN. Imputed neutrophil percentage was consistent with their expression pattern, whereas anti-Ro showed the inverse pattern as gene expression.

Conclusions

Comprehensive bioinformatics analyses revealed 9 key immune-related genes and their associated functions highly pertinent to SLE pathogenesis, subtypes, and identified valuable candidates for experimental research.

Clinical Values of the Identified Hub Genes in Systemic Lupus Erythematosus

Objective

This study was conducted to identify the biomarkers and mechanisms associated with systemic lupus erythematosus(SLE) at a transcriptome level.

Methods

Microarray datasets were downloaded, and differentially expressed genes (DEGs) were identified. Enrichment and protein-protein interaction networks were analyzed, and hub genes were discovered. The levels of top 10 hub genes were validated by another dataset. The diagnostic accuracy of the hub genes was evaluated with the area under the curve of the receiver operating characteristic curve (ROC-AUC). The odds ratios (OR) and 95% confidence intervals (CI) of the relationship between clinical manifestations and hub genes were estimated with multivariable logistic regression. The relationships between the expression levels of the 10 identified hub genes and SLEDAI scores were subjected to linear correlation analysis. Changes in the expression levels of the hub genes during patient follow-up were examined through one-way repeated measures ANOVA.

Results

A total of 136 DEGs were identified. Enrichment analysis indicated that DEGs were primarily enriched in type I interferon-associated pathways. The identified hub genes were verified by the GSE65391 dataset. The 10 hub genes had good diagnostic performances. Seven (except IFI6, OAS1 and IFIT3) of the 10 hub genes were positively associated with SLEDAI. The combination models of IFIT3, ISG15, MX2, and IFIH1 were effective in diagnosing mucosal ulcers among patients with SLE. The expression levels of IRF7, IFI35, IFIT3, and ISG15 decreased compared with the baseline expression (not significantly).

Conclusions

In this work, the clinical values of the identified hub genes in SLE were demonstrated.

Strategies of Targeting Inflammasome in the Treatment of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by multiple organ dysfunction resulting from the production of multiple autoantibodies and adaptive immune system abnormalities involving T and B lymphocytes. In recent years, inflammasomes have been recognized as an important component of innate immunity and have attracted increasing attention because of their pathogenic role in SLE. In short, inflammasomes regulate the abnormal differentiation of immune cells, modulate pathogenic autoantibodies, and participate in organ damage. However, due to the clinical heterogeneity of SLE, the pathogenic roles of inflammasomes are variable, and thus, the efficacy of inflammasome-targeting therapies is uncertain. To provide a foundation for the development of such therapeutic strategies, in this paper, we review the role of different inflammasomes in the pathogenesis of SLE and their correlation with clinical phenotypes and propose some corresponding treatment strategies.

Identification of Novel Immune Cell-Relevant Therapeutic Targets and Validation of Roles of TK1 in BMSCs of Systemic Lupus Erythematosus

Objective: Systemic lupus erythematosus (SLE) displays the characteristics of abnormal activity of the immune system, contributing to diverse clinical symptoms. Herein, this study was conducted for discovering novel immune cell-relevant therapeutic targets.

Methods: The abundance of diverse immune cells was estimated in PBMCs of SLE and healthy controls from the GSE50772 dataset with CIBERSORT approach. Immune cell-relevant co-expression modules were screened with WGCNA and relevant characteristic genes were determined with LASSO algorithm. Inflammatory chemokines were measured in serum of twenty SLE patients and twenty controls through ELISA. Bone marrow mesenchymal stem cells (BMSCs) were isolated and TK1 expression was measured in BMSCs through RT-qPCR and western blotting. TK1-overexpressed and TK-1-silenced BMSCs of SLE were conducted and apoptosis and cell cycle were measured with flow cytometry. Apoptosis-, cell cycle- and senescence-relevant proteins were tested with western blotting.

Results: We determined three co-expression modules strongly linked to immune cells. Five characteristic genes (CXCL1, CXCL2, CXCL8, CXCR1 and TK1) were screened and ROC curves proved the excellent diagnostic performance of this LASSO model. Inflammatory chemokines presented widespread up-regulations in serum of Systemic lupus erythematosus patients, demonstrating the activation of inflammatory response. TK1 expression was remarkably elevated in SLE BMSCs than controls. TK1 overexpression enhanced IL-1β expression, apoptosis, cell cycle arrest, and senescent phenotypes of SLE BMSCs and the opposite results were proved in TK1-silenced SLE BMSCs.

Conclusion: Collectively, our findings demonstrate that silencing TK1 alleviates inflammation, growth arrest and senescence in BMSCs of SLE, which highlights TK1 as a promising therapeutic target against SLE.

Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach

Using correlation analysis to study the potential connection between brain genetics and imaging has become an effective method to understand neurodegenerative diseases. Sparse canonical correlation analysis (SCCA) makes it possible to study high-dimensional genetic information. The traditional SCCA methods can only process single-modal genetic and image data, which to some extent weaken the close connection of the brain's biological network. In some recently proposed multimodal SCCA methods, due to the limitations of penalty items, the pre-processed data needs to be further filtered to make the dimensions uniform, which may destroy the potential association of data in the same modal. In this research, in order to combine data between different modalities and to ensure that the chain relationship or graph network relationship within the same modality will not be destroyed, the original generalized fused lasso penalty was replaced with the fused pairwise group lasso (FGL) and the graph-guided pairwise group lasso (GGL) based on the method of joint sparse canonical correlation analysis (JSCCA). We used prior knowledge to construct a supervised bivariate learning model and use linear regression to select quantitative traits (QTs) of images that are strongly correlated with the Mini-mental State Examination (MMSE) scores. Compared with FGL-SCCA, the model we constructed obtained a higher gene-ROI correlation coefficient and identified more significant biomarkers, providing a theoretical basis for further understanding the complex pathology of neurodegenerative diseases.

Investigation of Genes Associated With Atherosclerosis in Patients With Systemic Lupus Erythematosus

Objectives

In this study, we aimed to identify patients with systemic lupus erythematosus (SLE) who are genetically at risk for developing atherosclerosis.

Patients and methods

Between November 2014 and May 2016, a total of 38 patients with SLE (36 females, 2 males; mean age: 37.6 years; range, 18 to 71 years) and 32 healthy females (mean age: 31.5 years; range, 19 to 54 years) were included in the study. Carotid intima-media thickness (CIMT) was measured using high-resolution B-mode ultrasonography. SurePrint G3 Human Gene Expression 8x60K Microarray kit was used in our study. Genes showing differences in expression between the groups were identified by using GeneSpring GX 10.0 program. Pathway analyses of gene expressions were performed using Ingenuity Pathways Analysis (IPA). Gene ontology analyses were performed using the Protein Analysis Through Evolutionary Relationships (PANTHER).

Results

Clinical findings of SLE patients were mainly photosensitivity (71.1%), arthritis (63.2%), lupus nephritis (55.3%), thrombocytopenia (26.3%), and autoimmune hemolytic anemia (21.1%). A total of 155 genes showing expression level difference were detected between SLE patients and healthy controls. In molecular network analysis, 28.2% of all genes were found to be directly or indirectly associated with atherosclerosis and cardiovascular disease.

Conclusion

In SLE patients, many genes are expressed differently from healthy individuals. Expression of these genes is important in the pathogenesis of SLE. Genes identified differently in gene expression analysis can help us to identify SLE patients at risk for atherosclerosis in the Turkish population.

Identification of hub genes, pathways, and related transcription factors in systemic lupus erythematosus: A preliminary bioinformatics analysis

BACKGROUND

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by multiple organ damage and the production of a variety of autoantibodies. The pathogenesis of SLE has not been fully defined, and it is difficult to treat. Our study aimed to identify candidate genes that may be used as biomarkers for the screening, diagnosis, and treatment of SLE.

METHODS

We used the GEO2R tool to identify the differentially expressed genes (DEGs) in SLE-related datasets retrieved from the Gene Expression Omnibus (GEO). In addition, we also identified the biological functions of the DEGs by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Additionally, we constructed protein-protein interaction (PPI) networks to identify hub genes, as well as the regulatory network of transcription factors related to DEGs.

RESULTS

Two datasets were identified for use from the GEO (GSE50772, GSE4588), and 34 up-regulated genes and 4 down-regulated genes were identified by GEO2R. Pathway analysis of the DEGs revealed enrichment of the interferon alpha/beta signaling pathway; GO analysis was mainly enriched in response to interferon alpha, regulation of ribonuclease activity. PPIs were constructed through the STRING database and 14 hub genes were selected and 1 significant module (score = 12.923) was obtained from the PPI network. Additionally, 11 key transcription factors that interacted closely with the 14 hub DEGs were identified from the gene transcription factor network.

CONCLUSIONS

Bioinformatic analysis is an effective tool for screening the original genomic data in the GEO database, and a large number of SLE-related DEGs were identified. The identified hub DEGs may be potential biomarkers of SLE.

Expression and function of Smad7 in autoimmune and inflammatory diseases

Transforming growth factor-β (TGF-β) plays a critical role in the pathological processes of various diseases. However, the signaling mechanism of TGF-β in the pathological response remains largely unclear. In this review, we discuss advances in research of Smad7, a member of the I-Smads family and a negative regulator of TGF-β signaling, and mainly review the expression and its function in diseases. Smad7 inhibits the activation of the NF-κB and TGF-β signaling pathways and plays a pivotal role in the prevention and treatment of various diseases. Specifically, Smad7 can not only attenuate growth inhibition, fibrosis, apoptosis, inflammation, and inflammatory T cell differentiation, but also promotes epithelial cells migration or disease development. In this review, we aim to summarize the various biological functions of Smad7 in autoimmune diseases, inflammatory diseases, cancers, and kidney diseases, focusing on the molecular mechanisms of the transcriptional and posttranscriptional regulation of Smad7.

The ZNF76 rs10947540 polymorphism associated with systemic lupus erythematosus risk in Chinese populations

Systemic lupus erythematosus (SLE) is a typical autoimmune disease with a strong genetic disposition. Genetic studies have revealed that single-nucleotide polymorphisms (SNPs) in zinc finger protein (ZNF)-coding genes are associated with susceptibility to autoimmune diseases, including SLE. The objective of the current study was to evaluate the correlation between ZNF76 gene polymorphisms and SLE risk in Chinese populations. A total of 2801 individuals (1493 cases and 1308 controls) of Chinese Han origin were included in this two-stage genetic association study. The expression of ZNF76 was evaluated, and integrated bioinformatic analysis was also conducted. The results showed that 28 SNPs were associated with SLE susceptibility in the GWAS cohort, and the association of rs10947540 was successfully replicated in the independent replication cohort (P_replication = 1.60 × 10^-2, OR 1.19, 95% CI 1.03-1.37). After meta-analysis, the association between rs10947540 and SLE was pronounced (P_meta = 9.62 × 10^-6, OR 1.29, 95% CI 1.15-1.44). Stratified analysis suggested that ZNF76 rs10947540 C carriers were more likely to develop relatively high levels of serum creatinine (Scr) than noncarriers (CC + CT vs. TT, p = 9.94 × 10^-4). The bioinformatic analysis revealed that ZNF76 rs10947540 was annotated as an eQTL and that rs10947540 was correlated with decreased expression of ZNF76. Remarkably, significantly reduced expression of ZNF76 was confirmed by expression data from both our laboratory and an array-based expression database. Taken together, these results suggest that ZNF76 rs10947540 is a possible susceptibility factor associated with SLE susceptibility. The mechanism underlying the relationship between ZNF76 and SLE pathogenesis still requires further investigation.

Forkhead box O1-mediated ubiquitination suppresses RIG-I-mediated antiviral immune responses

RNA virus infection activates the RIG-I-like Receptor (RLR) signaling pathway to produce type I interferons (IFNs), the key components of the antiviral immune response. Forkhead box O1 (FoxO1) is a host transcription factor that participates in multiple biological processes. In this study, FoxO1 was identified as a critical negative regulator of RIG-I-triggered signaling. FoxO1 promoted Sendai virus (SeV) replication and downregulated type I IFN production. Upon SeV infection, FoxO1 suppressed K63-linked ubiquitination of TRAF3 and the interaction between TRAF3 and TBK1, after which the production of type I IFNs via the interferon regulatory transcription factor 3 (IRF3) pathways was reduced. In addition, FoxO1 destabilized IRF3 by facilitating E3 ligase TRIM22- or TRIM21-mediated K48-linked ubiquitination of IRF3. Moreover, the inhibitory effect of FoxO1 was found to depend on its DNA binding domain (DBD). Thus, our findings highlight novel important roles of FoxO1 in controlling RLR-mediated antiviral innate immunity.

IFN-γ Mediates the Development of Systemic Lupus Erythematosus

Objective

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can affect all organs in the body. It is characterized by overexpression of antibodies against autoantigen. Although previous bioinformatics analyses have identified several genetic factors underlying SLE, they did not discriminate between naive and individuals exposed to anti-SLE drugs. Here, we evaluated specific genes and pathways in active and recently diagnosed SLE population.

Methods

GSE46907 matrix downloaded from Gene Expression Omnibus (GEO) was analyzed using R, Metascape, STRING, and Cytoscape to identify differentially expressed genes (DEGs), enrichment pathways, protein-protein interaction (PPI), and hub genes between naive SLE individuals and healthy controls.

Results

A total of 134 DEGs were identified, in which 29 were downregulated, whereas 105 were upregulated in active and newly diagnosed SLE cases. GO term analysis revealed that transcriptional induction of the DEGs was particularly enhanced in response to secretion of interferon-γ and interferon-α and regulation of cytokine production innate immune responses among others. KEGG pathway analysis showed that the expression of DEGs was particularly enhanced in interferon signaling, IFN antiviral responses by activated genes, class I major histocompatibility complex (MHC-I) mediated antigen processing and presentation, and amyloid fiber formation. STAT1, IRF7, MX1, OASL, ISG15, IFIT3, IFIH1, IFIT1, OAS2, and GBP1 were the top 10 DEGs.

Conclusions

Our findings suggest that interferon-related gene expression and pathways are common features for SLE pathogenesis, and IFN-γ and IFN-γ-inducible GBP1 gene in naive SLE were emphasized. Together, the identified genes and cellular pathways have expanded our understanding on the mechanism underlying development of SLE. They have also opened a new frontier on potential biomarkers for diagnosis, biotherapy, and prognosis for SLE.

An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus

BACKGROUND

A comprehensive intuition of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, is a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to underscore key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention.

METHODS

In the present study, the human peripheral blood mononuclear cell (PBMC) microarray datasets (n = 6), generated by three platforms, which included SLE patients (n = 220) and healthy control samples (n = 135) were collected. Across each platform, we integrated the datasets by cross-platform normalization (CPN). Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from KEGG-indexed SLE, TCR, and BCR signaling pathways; the values of the node (gene) and edge (intergenic relationships) parameters were estimated within each integrated datasets. Parameters with the FDR < 0.05 were considered significant. Finally, a mixture model was performed to decipher the signaling pathway alterations in the SLE patients compared to healthy controls.

RESULTS

In the SLE signaling pathway, we identified the dysregulation of several nodes involved in the (1) clearance mechanism (SSB, MACROH2A2, TRIM21, H2AX, and C1Q gene family), (2) autoantigen presentation by MHCII (HLA gene family, CD80, IL10, TNF, and CD86), and (3) end-organ damage (FCGR1A, ELANE, and FCGR2A). As a remarkable finding, we demonstrated significant perturbation in CD80 and CD86 to CD28, CD40LG to CD40, C1QA and C1R to C2, and C1S to C4A edges. Moreover, we not only replicated previous studies regarding alterations of subnetworks involved in TCR and BCR signaling pathways (PI3K/AKT, MAPK, VAV gene family, AP-1 transcription factor) but also distinguished several significant edges between genes (PPP3 to NFATC gene families). Our findings unprecedentedly showed that different parameter values assign to the same node based on the pathway topology (the PIK3CB parameter values were 1.7 in TCR vs - 0.5 in BCR signaling pathway).

CONCLUSIONS

Applying the BNrich as a hybridized network construction method, we highlight under-appreciated systemic alterations of SLE, TCR, and BCR signaling pathways in SLE. Consequently, having such a systems biology approach opens new insights into the context of multifactorial disorders.