Common Marker Genes Identified from Various Sample Types for Systemic Lupus Erythematosus

B cell subsets-related biomarkers and molecular pathways for systemic lupus erythematosus by transcriptomics analyses

BACKGROUND

Systemic lupus erythematosus (SLE), an autoimmune disease, is characterised by B-cell abnormalities and a loss of tolerance that can produce autoantibody. However, the imperative genes and molecular pathways involved in the change of B cell populations remain unclear.

METHODS

The expression of B cell subsets between SLE and healthy controls (HCs) was detected based on micro-array transcriptome data. The Weighted Gene Co-Expression Network Analysis (WGCNA) further revealed the co-expression modules of naïve and memory B cells. Whereafter, we performed the functional enrichment analysis, Protein-protein interaction (PPI) networks construction and feature selection to screen hub genes. Ultimately, we recruited SLE patients and HCs from the Second Hospital of Shanxi Medical University and further verified these genes in transcriptome sequencing samples.

RESULTS

Total of 1087 SLE patients and 86 HCs constituted in the study. Compared to HCs, the levels of peripheral naïve B cells of SLE patients decreased, while memory B cells increased. WGCNA identified two modules with the highest correlation for the subsequent analysis. The purple module was primarily in connection with naïve B cells, and the GO analysis indicated that these genes were mainly abundant in B cell activation. The blue module relevant to memory B cells was most significantly enriched in the "defence response to virus" correlation pathway. Then we screened six hub genes by PPI and feature selection. Finally, four biomarkers (IFI27, IFITM1, MX2, IRF7) were identified by transcriptome sequencing verification.

CONCLUSION

Our study identified hub genes and key pathways associated with the naïve and memory B cells respectively, which may offer novel insights into the behaviours of B cells and the pathogenesis of SLE.

Shared and Distinctive Transcriptomic and Proteomic Pathways in Adult and Juvenile Dermatomyositis

OBJECTIVE

Transcript and protein expression were interrogated to examine gene locus and pathway regulation in the peripheral blood of active adult dermatomyositis (DM) and juvenile DM patients receiving immunosuppressive therapies.

METHODS

Expression data from 14 DM and 12 juvenile DM patients were compared to matched healthy controls. Regulatory effects at the transcript and protein level were analyzed by multi-enrichment analysis for assessment of affected pathways within DM and juvenile DM.

RESULTS

Expression of 1,124 gene loci were significantly altered at the transcript or protein levels across DM or juvenile DM, with 70 genes shared. A subset of interferon-stimulated genes was elevated, including CXCL10, ISG15, OAS1, CLEC4A, and STAT1. Innate immune markers specific to neutrophil granules and neutrophil extracellular traps were up-regulated in both DM and juvenile DM, including BPI, CTSG, ELANE, LTF, MPO, and MMP8. Pathway analysis revealed up-regulation of PI3K/AKT, ERK, and p38 MAPK signaling, whose central components were broadly up-regulated in DM, while peripheral upstream and downstream components were differentially regulated in both DM and juvenile DM. Up-regulated components shared by DM and juvenile DM included cytokine:receptor pairs LGALS9:HAVCR2, LTF/NAMPT/S100A8/HSPA1A:TLR4, CSF2:CSF2RA, EPO:EPOR, FGF2/FGF8:FGFR, several Bcl-2 components, and numerous glycolytic enzymes. Pathways unique to DM included sirtuin signaling, aryl hydrocarbon receptor signaling, protein ubiquitination, and granzyme B signaling.

CONCLUSION

The combination of proteomics and transcript expression by multi-enrichment analysis broadened the identification of up- and down-regulated pathways among active DM and juvenile DM patients. These pathways, particularly those which feed into PI3K/AKT and MAPK signaling and neutrophil degranulation, may be potential therapeutic targets.

Multi-ancestry meta-analysis identifies 5 novel loci for ischemic stroke and reveals heterogeneity of effects between sexes and ancestries

Stroke is the second leading cause of death and disability worldwide. Stroke prevalence varies by sex and ancestry, possibly due to genetic heterogeneity between subgroups. We performed a genome-wide meta-analysis of 16 biobanks across multiple ancestries to study the genetics of ischemic stroke (60,176 cases, 1,310,725 controls) as part of the Global Biobank Meta-analysis Initiative (GBMI) and further combined the results with previously published MegaStroke. Five novel loci for ischemic stroke (LAMC1, CALCRL, PLSCR1, CDKN1A, and SWAP70) were identified after replication in four additional datasets. One previously reported locus showed significant ancestry heterogeneity (ABO), and one showed significant sex heterogeneity (ALDH2). The ALDH2 association was male specific (males p = 1.67e-24, females p = 0.126) and was additionally observed only in the East Asian ancestry (male) samples. These findings emphasize the need for more diverse datasets with large sample sizes to further understand the genetic predisposition of stroke in different ancestry and sex groups.

Cuproptosis-related gene identification and immune infiltration analysis in systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to self-antigen, autoantibody production, and abnormal immune response. Cuproptosis is a recently reported cell death form correlated with the initiation and development of multiple diseases. This study intended to probe cuproptosis-related molecular clusters in SLE and constructed a predictive model.

Methods

We analyzed the expression profile and immune features of cuproptosis-related genes (CRGs) in SLE based on GSE61635 and GSE50772 datasets and identified core module genes associated with SLE occurrence using the weighted correlation network analysis (WGCNA). We selected the optimal machine-learning model by comparing the random forest (RF) model, support vector machine (SVM) model, generalized linear model (GLM), and the extreme gradient boosting (XGB) model. The predictive performance of the model was validated by nomogram, calibration curve, decision curve analysis (DCA), and external dataset GSE72326. Subsequently, a CeRNA network based on 5 core diagnostic markers was established. Drugs targeting core diagnostic markers were acquired using the CTD database, and Autodock vina software was employed to perform molecular docking.

Results

Blue module genes identified using WGCNA were highly related to SLE initiation. Among the four machine-learning models, the SVM model presented the best discriminative performance with relatively low residual and root-mean-square error (RMSE) and high area under the curve (AUC = 0.998). An SVM model was constructed based on 5 genes and performed favorably in the GSE72326 dataset for validation (AUC = 0.943). The nomogram, calibration curve, and DCA validated the predictive accuracy of the model for SLE as well. The CeRNA regulatory network includes 166 nodes (5 core diagnostic markers, 61 miRNAs, and 100 lncRNAs) and 175 lines. Drug detection showed that D00156 (Benzo (a) pyrene), D016604 (Aflatoxin B1), D014212 (Tretinoin), and D009532 (Nickel) could simultaneously act on the 5 core diagnostic markers.

Conclusion

We revealed the correlation between CRGs and immune cell infiltration in SLE patients. The SVM model using 5 genes was selected as the optimal machine learning model to accurately evaluate SLE patients. A CeRNA network based on 5 core diagnostic markers was constructed. Drugs targeting core diagnostic markers were retrieved with molecular docking performed.

Identification of Key Biomarkers in Systemic Lupus Erythematosus by a Multi-Cohort Analysis

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple body systems with heterogeneous clinical manifestations. Since gene expression analyses have been accomplished on diverse types of samples to specify SLE-related genes, single-cohort transcriptomics have not produced reliable results. Using an integrated multi-cohort analysis framework, we analyzed whole blood cells from SLE patients from three transcriptomics cohorts (n=1222) and identified a five-gene signature that distinguished SLE patients from controls. We validated the diagnostic performance of this five-gene signature in six independent validation cohorts (n= 469), with an area under the receiver operating characteristic curve of 0.88 [95% CI 0.7 − 0.96]. This five-gene signature may be associated with the proportion of SLE immune cells, and generalizable across ages and sample types with real diagnostic value for clinical application.

Screening Biomarkers for Systemic Lupus Erythematosus Based on Machine Learning and Exploring Their Expression Correlations With the Ratios of Various Immune Cells

Background

Systemic lupus erythematosus (SLE) is an autoimmune illness caused by a malfunctioning immunomodulatory system. China has the second highest prevalence of SLE in the world, from 0.03% to 0.07%. SLE is diagnosed using a combination of immunological markers, clinical symptoms, and even invasive biopsy. As a result, genetic diagnostic biomarkers for SLE diagnosis are desperately needed.

Method

From the Gene Expression Omnibus (GEO) database, we downloaded three array data sets of SLE patients' and healthy people's peripheral blood mononuclear cells (PBMC) (GSE65391, GSE121239 and GSE61635) as the discovery metadata (n_SLE = 1315, n_normal = 122), and pooled four data sets (GSE4588, GSE50772, GSE99967, and GSE24706) as the validate data set (n_SLE = 146, n_normal = 76). We screened the differentially expressed genes (DEGs) between the SLE and control samples, and employed the least absolute shrinkage and selection operator (LASSO) regression, and support vector machine recursive feature elimination (SVM-RFE) analyze to discover possible diagnostic biomarkers. The candidate markers' diagnostic efficacy was assessed using the receiver operating characteristic (ROC) curve. The reverse transcription quantitative polymerase chain reaction (RT-qPCR) was utilized to confirm the expression of the putative biomarkers using our own Chinese cohort (n_SLE = 13, n_normal = 10). Finally, the proportion of 22 immune cells in SLE patients was determined using the CIBERSORT algorithm, and the correlations between the biomarkers' expression and immune cell ratios were also investigated.

Results

We obtained a total of 284 DEGs and uncovered that they were largely involved in several immune relevant pathways, such as type І interferon signaling pathway, defense response to virus, and inflammatory response. Following that, six candidate diagnostic biomarkers for SLE were selected, namely ABCB1, EIF2AK2, HERC6, ID3, IFI27, and PLSCR1, whose expression levels were validated by the discovery and validation cohort data sets. As a signature, the area under curve (AUC) values of these six genes reached to 0.96 and 0.913, respectively, in the discovery and validation data sets. After that, we checked to see if the expression of ABCB1, IFI27, and PLSCR1 in our own Chinese cohort matched that of the discovery and validation sets. Subsequently, we revealed the potentially disturbed immune cell types in SLE patients using the CIBERSORT analysis, and uncovered the most relevant immune cells with the expression of ABCB1, IFI27, and PLSCR1.

Conclusion

Our study identified ABCB1, IFI27, and PLSCR1 as potential diagnostic genes for Chinese SLE patients, and uncovered their most relevant immune cells. The findings in this paper provide possible biomarkers for diagnosing Chinese SLE patients.

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Background

Dermatomyositis is an autoimmune disease characterized by damage to the skin and muscles. CD4⁺ T cells are of crucial importance in the occurrence and development of dermatomyositis (DM). However, there are few bioinformatics studies on potential pathogenic genes and immune cell infiltration of DM. Therefore, this study intended to explore CD4⁺ T-cell infiltration–associated key genes in DM and construct a new model to predict the level of CD4⁺ T-cell infiltration in DM.

Methods

GSE46239, GSE142807, GSE1551, and GSE193276 datasets were downloaded. The WGCNA and CIBERSORT algorithms were performed to identify the most correlated gene module with CD4⁺ T cells. Matascape was used for GO enrichment and KEGG pathway analysis of the key gene module. LASSO regression analysis was used to identify the key genes and construct the prediction model. The correlation between the key genes and CD4⁺ T-cell infiltration was investigated. GSEA was performed to research the underlying signaling pathways of the key genes. The key gene-correlated transcription factors were identified through the RcisTarget and Gene-motif rankings databases. The miRcode and DIANA-LncBase databases were used to build the lncRNA-miRNA-mRNA network.

Results

In the brown module, 5 key genes (chromosome 1 open reading frame 106 (C1orf106), component of oligomeric Golgi complex 8 (COG8), envoplakin (EVPL), GTPases of immunity-associated protein family member 6 (GIMAP6), and interferon-alpha inducible protein 6 (IFI6)) highly associated with CD4⁺ T-cell infiltration were identified. The prediction model was constructed and showed better predictive performance in the training set, and this satisfactory model performance was validated in another skin biopsy dataset and a muscle biopsy dataset. The expression levels of the key genes promoted the CD4⁺ T-cell infiltration. GSEA results revealed that the key genes were remarkably enriched in many immunity-associated pathways, such as JAK/STAT signaling pathway. The cisbp_M2205, transcription factor-binding site, was enriched in C1orf106, EVPL, and IF16. Finally, 3,835 lncRNAs and 52 miRNAs significantly correlated with key genes were used to build a ceRNA network.

Conclusion

The C1orf106, COG8, EVPL, GIMAP6, and IFI6 genes are associated with CD4⁺ T-cell infiltration. The prediction model constructed based on the 5 key genes may better predict the level of CD4⁺ T-cell infiltration in damaged muscle and lesional skin of DM. These key genes could be recognized as potential biomarkers and immunotherapeutic targets of DM.

RNASE2 Mediates Age-Associated B Cell Expansion Through Monocyte Derived IL-10 in Patients With Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is characterized by the production of pathogenic autoantibodies. Ribonuclease A family member 2 (RNase2) is known to have antiviral activity and immunomodulatory function. Although RNASE2 level has been reported to be elevated in SLE patients based on mRNA microarray detection, its pathologic mechanism remains unclear. Here, we confirmed that RNASE2 was highly expressed in PBMCs from SLE patients and associated with the proportion of CD11c⁺T-bet⁺ B cells, a class of autoreactive B cells also known as age-associated B cells (ABCs). We showed that reduction of RNASE2 expression by small interfering RNA led to the decrease of ABCs in vitro, accompanied by total IgG and IL-10 reduction. In addition, we demonstrated that both RNASE2 and IL-10 in peripheral blood of lupus patients were mainly derived from monocytes. RNASE2 silencing in monocytes down-regulated IL-10 production and consequently reduced ABCs numbers in monocyte-B cell co-cultures, which could be restored by the addition of recombinant IL-10. Based on above findings, we concluded that RNASE2 might induce the production of ABCs via IL-10 secreted from monocytes, thus contributing to the pathogenesis of SLE.

Exploring the Differences in Molecular Mechanisms and Key Biomarkers Between Membranous Nephropathy and Lupus Nephritis Using Integrated Bioinformatics Analysis

Background: Both membranous nephropathy (MN) and lupus nephritis (LN) are autoimmune kidney disease. In recent years, with the deepening of research, some similarities have been found in the pathogenesis of these two diseases. However, the mechanism of their interrelationship is not clear. The purpose of this study was to investigate the differences in molecular mechanisms and key biomarkers between MN and LN. Method: The expression profiles of GSE99325, GSE99339, GSE104948 and GSE104954 were downloaded from GEO database, and the differentially expressed genes (DEGs) of MN and LN samples were obtained. We used Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for enrichment analysis of DEGs. A protein-protein interaction (PPI) network of DEGs was constructed using Metascape. We filtered DEGs with NetworkAnalyst. Finally, we used receiver operating characteristic (ROC) analysis to identify the most significant DEGs for MN and LN. Result: Compared with LN in the glomerulus, 14 DEGs were up-regulated and 77 DEGs were down-regulated in MN. Compared with LN in renal tubules, 21 DEGs were down-regulated, but no up-regulated genes were found in MN. According to the result of GO and KEGG enrichment, PPI network and Networkanalyst, we screened out six genes (IFI6, MX1, XAF1, HERC6, IFI44L, IFI44). Interestingly, among PLA2R, THSD7A and NELL1, which are the target antigens of podocyte in MN, the expression level of NELL1 in MN glomerulus is significantly higher than that of LN, while there is no significant difference in the expression level of PLA2R and THSD7A. Conclusion: Our study provides new insights into the pathogenesis of MN and LN by analyzing the differences in gene expression levels between MN and LN kidney samples, and is expected to be used to prepare an animal model of MN that is more similar to human.

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.

Abnormalities of the type I interferon signaling pathway in lupus autoimmunity

Type I interferons (IFNs), mostly IFNα and IFNβ, and the type I IFN Signature are important in the pathogenesis of Systemic Lupus Erythematosus (SLE), an autoimmune chronic condition linked to inflammation. Both IFNα and IFNβ trigger a signaling cascade that, through the activation of JAK1, TYK2, STAT1 and STAT2, initiates gene transcription of IFN stimulated genes (ISGs). Noteworthy, other STAT family members and IFN Responsive Factors (IRFs) can also contribute to the activation of the IFN response. Aberrant type I IFN signaling, therefore, can exacerbate SLE by deregulated homeostasis leading to unnecessary persistence of the biological effects of type I IFNs. The etiopathogenesis of SLE is partially known and considered multifactorial. Family-based and genome wide association studies (GWAS) have identified genetic and transcriptional abnormalities in key molecules directly involved in the type I IFN signaling pathway, namely TYK2, STAT1 and STAT4, and IRF5. Gain-of-function mutations that heighten IFNα/β production, which in turn maintains type I IFN signaling, are found in other pathologies like the interferonopathies. However, the distinctive characteristics have yet to be determined. Signaling molecules activated in response to type I IFNs are upregulated in immune cell subsets and affected tissues of SLE patients. Moreover, Type I IFNs induce chromatin remodeling leading to a state permissive to transcription, and SLE patients have increased global and gene-specific epigenetic modifications, such as hypomethylation of DNA and histone acetylation. Epigenome wide association studies (EWAS) highlight important differences between SLE patients and healthy controls in Interferon Stimulated Genes (ISGs). The combination of environmental and genetic factors may stimulate type I IFN signaling transiently and produce long-lasting detrimental effects through epigenetic alterations. Substantial evidence for the pathogenic role of type I IFNs in SLE advocates the clinical use of neutralizing anti-type I IFN receptor antibodies as a therapeutic strategy, with clinical studies already showing promising results. Current and future clinical trials will determine whether drugs targeting molecules of the type I IFN signaling pathway, like non-selective JAK inhibitors or specific TYK2 inhibitors, may benefit people living with lupus.

Gene expression profiling in skin reveals strong similarities between subacute and chronic cutaneous lupus that are distinct from lupus nephritis

Subacute cutaneous lupus erythematosus (SCLE) and chronic cutaneous lupus erythematosus (CCLE) are represented in the majority of cutaneous lupus subtypes, each of which has variable implications for systemic manifestations such as lupus nephritis. On dermatologic exam, SCLE and CCLE are distinct. However, it is often difficult to diagnose the subtype from histology alone. Our study utilized whole-genome microarray expression analysis on human skin samples of SCLE, CCLE, and healthy controls, along with human samples of lupus nephritis and normal kidney tissue to compare cutaneous lupus subtypes to each other, as well as lupus nephritis. The data revealed that cutaneous lupus subtypes were distinct from healthy control skin, with gene expression predominantly characterized by upregulation of type 1 interferon and T-cell chemotactic genes. However, the cutaneous lupus subtypes were very similar to one another; comparative analyses revealed few statistically significant differences in gene expression. There were also distinct differences between the gene signatures of cutaneous lupus and lupus nephritis. Cutaneous lupus samples revealed gene signatures demonstrating a prominent inflammatory component that may suggest the skin as an early site of initiation of lupus pathogenesis, while lupus nephritis reflected recruitment and activation of M2 macrophages and a wound healing signature.

Bioinformatics analysis of epigenetic and SNP-related molecular markers in systemic lupus erythematosus

We analyzed gene expression in peripheral blood mononuclear cells (PBMCs) from patients with systemic lupus erythematosus (SLE) using public databases. The goal was to identify lupus biomarkers by determining whether differentially expressed genes are mediated by methylation, miRNA, or SNP. Two cDNA microarrays were subjected to integration analysis, and we calculated the mutually differentially expressed genes (|log2fold change (FC)| > 1, P < 0.05). These genes were analyzed using gene otology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. The differences in methylation sites for two methylation chips were calculated and the differentially methylated sites were annotated. These genes were compared to the differentially expressed genes. We obtained 135 differentially expressed microRNAs from the microRNA-chip results using PBMCs from SLE and healthy individuals. Predictive microRNA target genes were identified using GO, KEGG pathways, and PPI networks. The target genes identified were compared to the differentially expressed genes. We downloaded Chinese SLE genome-wide association study data from SLE-related literature, analyzed the loci with a P value < 0.05, and used annotated SLE-associated SNPs. We selected the genes corresponding to an SNP located on an exon and determined the intersection with the differentially expressed genes. We found 18 differentially expressed genes in both cDNA microarrays. The methylation chips had 50 corresponding methylation sites. On the basis of these results, we identified two genes, IFI44 and IFI44L. We further identified 135 differentially expressed microRNAs predicted to affect 5766 target genes. Two identified genes were in common with the differentially expressed genes. Finally, SNP annotated genes and cDNA chip genes overlap with identified MX1. Therefore, we used existing data to analyze the causes of differential gene expression in SLE, introducing new methods for determining biomarkers and therapeutic targets.

Comparative Analysis on Abnormal Methylome of Differentially Expressed Genes and Disease Pathways in the Immune Cells of RA and SLE

We identified abnormally methylated, differentially expressed genes (DEGs) and pathogenic mechanisms in different immune cells of RA and SLE by comprehensive bioinformatics analysis. Six microarray data sets of each immune cell (CD19⁺ B cells, CD4⁺ T cells and CD14⁺ monocytes) were integrated to screen DEGs and differentially methylated genes by using R package “limma.” Gene ontology annotations and KEGG analysis of aberrant methylome of DEGs were done using DAVID online database. Protein-protein interaction (PPI) network was generated to detect the hub genes and their methylation levels were compared using DiseaseMeth 2.0 database. Aberrantly methylated DEGs in CD19⁺ B cells (173 and 180), CD4⁺ T cells (184 and 417) and CD14⁺ monocytes (193 and 392) of RA and SLE patients were identified. We detected 30 hub genes in different immune cells of RA and SLE and confirmed their expression using FACS sorted immune cells by qPCR. Among them, 12 genes (BPTF, PHC2, JUN, KRAS, PTEN, FGFR2, ALB, SERB-1, SKP2, TUBA1A, IMP3, and SMAD4) of RA and 12 genes (OAS1, RSAD2, OASL, IFIT3, OAS2, IFIH1, CENPE, TOP2A, PBK, KIF11, IFIT1, and ISG15) of SLE are proposed as potential biomarker genes based on receiver operating curve analysis. Our study suggests that MAPK signaling pathway could potentially differentiate the mechanisms affecting T- and B- cells in RA, whereas PI3K pathway may be used for exploring common disease pathways between RA and SLE. Compared to individual data analyses, more dependable and precise filtering of results can be achieved by integrating several relevant data sets.

Characterization of Type I Interferon-Associated Chemokines and Cytokines in Lacrimal Glands of Nonobese Diabetic Mice

Type I interferons (IFNs) are required for spontaneous lacrimal gland inflammation in the nonobese diabetic (NOD) mouse model of Sjögren’s disease, but the consequences of type I IFN signaling are not well-defined. Here, we use RNA sequencing to define cytokine and chemokine genes upregulated in lacrimal glands of NOD mice in a type I IFN-dependent manner. Interleukin (IL)-21 was the highest differentially expressed cytokine gene, and Il21 knockout NOD mice were relatively protected from lacrimal gland inflammation. We defined a set of chemokines upregulated early in disease including Cxcl9 and Cxcl10, which share a receptor, CXCR3. CXCR3⁺ T cells were enriched in lacrimal glands with a dominant proportion of CXCR3⁺ regulatory T cells. Together these data define the early cytokine and chemokine signals associated with type I IFN-signaling in the development of lacrimal gland inflammation in NOD mice providing insight into the role of type I IFN in autoimmunity development.

EIF2AK2 selectively regulates the gene transcription in immune response and histones associated with systemic lupus erythematosus

PKR, also known as EIF2AK2, is an IFN-stimulated gene (ISG) and shows a higher expression in probands with systemic lupus erythematosus (SLE), which is likely responsible for the impaired translational and proliferative responses to mitogens in T cells from SLE patients. In this study, we overexpressed EIF2AK2 in HeLa cells to study EIF2AK2-regulated genes using RNA-seq technology, followed by bioinformatic analysis of target genes of EIF2AK2-regulated transcriptional factors (TFs). Overexpression of EIF2AK2 promotes HeLa cell apoptosis. EIF2AK2 selectively represses the transcription of histone protein genes associated with SLE, immune response genes and TF genes, which was validated by RT-qPCR experiments. Analysis of motifs overrepresented in the promoter regions of EIF2AK2-regulated genes revealed eighteen EIF2AK2-regulated TFs involved in establishing the EIF2AK2 network. Eight out of these predicted EIF2AK2-regulated TFs were further verified by RT-qPCR selectively in both HeLa and Jurkat cells, and most such as HEY2, TFEC, BATF2, GATA3 and ATF3 and FOXO6 are known to regulate immune response. Our results suggest that the dsRNA-dependent kinase EIF2AK2 selectively regulates the transcription of immune response and SLE-associated histone protein genes, and such a selectivity is likely to be operated by EIF2AK2-targeted TFs. The EIF2AK2-TFs axis potentially offers new therapeutic targets for counteracting immunological disease in the future.

Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis

Background

Systemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmune, inflammatory processes, and tissue destruction. Some seriously-ill patients could develop into lupus nephritis. However, the cause and underlying molecular events of SLE needs to be further resolved.

Methods

The expression profiles of GSE144390, GSE4588, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between SLE and healthy samples. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by metascape etc. online analyses. The protein–protein interaction (PPI) networks of the DEGs were constructed by GENEMANIA software. We performed Gene Set Enrichment Analysis (GSEA) to further understand the functions of the hub gene, Weighted gene co‐expression network analysis (WGCNA) would be utilized to build a gene co‐expression network, and the most significant module and hub genes was identified. CIBERSORT tools have facilitated the analysis of immune cell infiltration patterns of diseases. The receiver operating characteristic (ROC) analyses were conducted to explore the value of DEGs for SLE diagnosis.

Results

In total, 6 DEGs (IFI27, IFI44, IFI44L, IFI6, EPSTI1 and OAS1) were screened, Biological functions analysis identified key related pathways, gene modules and co‐expression networks in SLE. IFI27 may be closely correlated with the occurrence of SLE. We found that an increased infiltration of moncytes, while NK cells resting infiltrated less may be related to the occurrence of SLE.

Conclusion

IFI27 may be closely related pathogenesis of SLE, and represents a new candidate molecular marker of the occurrence and progression of SLE. Moreover immune cell infiltration plays important role in the progession of SLE.

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.

Bioinformatic analysis reveals that the OAS family may play an important role in lupus nephritis

BACKGROUND

Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. However, the molecular mechanisms of LN remain unclear. The lack of understanding hinders the development of specific targeted therapy for this progressive disease.

OBJECTIVES

In the present study, we used bioinformatics analysis of gene expression profiles from the Gene Expression Omnibus to identify novel targets and potential biomarkers for LN.

MATERIAL AND METHODS

A GSE32591 dataset, which included 31 LN glomerular biopsy tissues and 14 living donors' glomerular tissues, was downloaded for further analysis. Differentially expressed genes in LN were analyzed by the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the differentially expressed genes by using the Disease Ontology Semantic and Enrichment and the clusterProfiler software. The protein-protein interaction (PPI) network was then formed using STRING online tool.

RESULTS

440 genes, including 310 upregulated genes and 130 downregulated genes, were found as differentially expressed genes. GO and KEGG analyses revealed that immune response is significantly enriched in such genes. The PPI network showed that ISG15, MX1, OAS1, OAS2, and OAS3 were the hub genes enriched in LN. Along with literature review, the OAS family genes were revealed to be closely associated with LN progression.

CONCLUSIONS

our studies provided new insight into the molecular pathogenesis of LN. The OAS family may play an important role in LN and act as a novel molecular candidate for the further study of LN.

STAT1 transcriptionally regulates the expression of S1PR1 by binding its promoter region

Sphingosine 1-phosphate receptor 1 (S1PR1) plays a pivotal role in mediating trafficking and migration of immune cells. Previous reports also identify S1PR1 as an important susceptibility gene of asthma and other autoimmune disorders. However, little has been known about the regulatory mechanism of S1PR1 expression. Thus we systematically investigated the transcriptional regulation of S1PR1 in this study. Promoter activity of S1PR1 gene was carefully screened using series of pGL3-Basic reporter vectors, containing full length (range from transcription start site to upstream -1 kb region) or several truncated fragments of S1PR1 promoter. We identified an area (from -29 to -12 bp) of the S1PR1 promoter as the minimal promoter region. Bioinformatics prediction results showed that several transcription factors were recruited to these sites. EMSA and ChIP assays demonstrated the transcriptional factor STAT1 could bind to the region. We also found that the level of S1PR1 level was significantly reduced when STAT1 was knocked-down. Consistent with the reduction of S1PR1 caused by depletion of STAT1, overexpression of STAT1 resulted in up-regulation of S1PR1. In addition, both mRNA and protein levels of S1PR1 were increased when STAT1 was activated by IFN-γ, and decreased when STAT1 was inhibited by fludarabine. Besides, the levels of STAT1 and S1PR1 expression were positively correlated in peripheral blood leukocytes derived from 41 healthy individuals. Our study showed that transcription factor STAT1 could bind to upstream region of -29 bp to -12 bp of the S1PR1 promoter and stimulate the expression of S1PR1.

Population-Specific Patterns of Epigenetic Defects in the B Cell Lineage in Patients With Systemic Lupus Erythematosus

OBJECTIVE

To determine the stage of B cell development at which a systemic lupus erythematosus (SLE)-associated DNA methylation signature originates in African American (AA) and European American (EA) subjects, and to assess whether epigenetic defects in B cell development patterns could be predictive of SLE status in individual and mixed immune cell populations.

METHODS

B cells from AA patients (n = 31) and EA patients (n = 49) with or without SLE were sorted using fluorescence-activated cell sorting into 5 B cell subsets. DNA methylation, measured at ~460,000 CpG sites, was interrogated in each subset. Enrichment analysis of transcription factor interaction at SLE-associated methylation sites was performed. A random forests algorithm was used to identify an epigenetic signature of SLE in the B cell subsets, which was then validated in an independent cohort of AA and EA patients and healthy controls.

RESULTS

Regression analysis across all B cell stages resulted in identification of 60 CpGs that reached genome-wide significance for SLE-associated methylation differences (P ≤ 1.07 × 10^-7 ). Interrogation of ethnicity-specific CpGs associated with SLE revealed a hypomethylated pattern that was enriched for interferon (IFN)-regulated genes and binding of EBF1 in AA patients (each P < 0.001). AA patients with SLE could be distinguished from healthy controls when the predictive model developed with the transitional B cell subset was applied to other B cell subsets (mean receiver operating characteristic [ROC] area under the curve [AUC] 0.98), and when applied to CD19+ pan-B cells (mean ROC AUC 0.95) and CD4+ pan-T cells (mean ROC AUC 0.97) from the independent validation cohort.

CONCLUSION

These results indicate that SLE-specific methylation patterns are ethnicity dependent. A pattern of epigenetic changes near IFN-regulated genes early in B cell development is a hallmark of SLE in AA female subjects. EBF1 binding sites are highly enriched for significant methylation changes, implying that this may be a potential regulator of SLE-associated epigenetic changes.

Docosahexaenoic Acid Consumption Impedes Early Interferon- and Chemokine-Related Gene Expression While Suppressing Silica-Triggered Flaring of Murine Lupus

Exposure of lupus-prone female NZBWF1 mice to respirable crystalline silica (cSiO2), a known human autoimmune trigger, initiates loss of tolerance, rapid progression of autoimmunity, and early onset of glomerulonephritis. We have previously demonstrated that dietary supplementation with the ω-3 polyunsaturated fatty acid docosahexaenoic acid (DHA) suppresses autoimmune pathogenesis and nephritis in this unique model of lupus flaring. In this report, we utilized tissues from prior studies to test the hypothesis that DHA consumption interferes with upregulation of critical genes associated with cSiO2-triggered murine lupus. A NanoString nCounter platform targeting 770 immune-related genes was used to assess the effects cSiO2 on mRNA signatures over time in female NZBWF1 mice consuming control (CON) diets compared to mice fed diets containing DHA at an amount calorically equivalent to human consumption of 2 g per day (DHA low) or 5 g per day (DHA high). Experimental groups of mice were sacrificed: (1) 1 d after a single intranasal instillation of 1 mg cSiO2 or vehicle, (2) 1 d after four weekly single instillations of vehicle or 1 mg cSiO2, and (3) 1, 5, 9, and 13 weeks after four weekly single instillations of vehicle or 1 mg cSiO2. Genes associated with inflammation as well as innate and adaptive immunity were markedly upregulated in lungs of CON-fed mice 1 d after four weekly cSiO2 doses but were significantly suppressed in mice fed DHA high diets. Importantly, mRNA signatures in lungs of cSiO2-treated CON-fed mice over 13 weeks reflected progressive amplification of interferon (IFN)- and chemokine-related gene pathways. While these responses in the DHA low group were suppressed primarily at week 5, significant downregulation was observed at weeks 1, 5, 9, and 13 in mice fed the DHA high diet. At week 13, cSiO2 treatment of CON-fed mice affected 214 genes in kidney tissue associated with inflammation, innate/adaptive immunity, IFN, chemokines, and antigen processing, mostly by upregulation; however, feeding DHA dose-dependently suppressed these responses. Taken together, dietary DHA intake in lupus-prone mice impeded cSiO2-triggered mRNA signatures known to be involved in ectopic lymphoid tissue neogenesis, systemic autoimmunity, and glomerulonephritis.

Uncovering potential lncRNAs and nearby mRNAs in systemic lupus erythematosus from the Gene Expression Omnibus dataset

Aim: The aim of this study was to find potential differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in systemic lupus erythematosus. Materials & methods: Differentially expressed lncRNAs and mRNAs were obtained in the Gene Expression Omnibus dataset. Functional annotation of differentially expressed mRNAs was performed, followed by protein-protein interaction network analysis. Then, the interaction network of lncRNA-nearby targeted mRNA was built. Results: Several interaction pairs of lncRNA-nearby targeted mRNA including NRIR-RSAD2, RP11-153M7.5-TLR2, RP4-758J18.2-CCNL2, RP11-69E11.4-PABPC4 and RP11-496I9.1-IRF7/HRAS/PHRF1 were identified. Measles and MAPK were significantly enriched signaling pathways of differentially expressed mRNAs. Conclusion: Our study identified several differentially expressed lncRNAs and mRNAs. And their interactions may play a crucial role in the process of systemic lupus erythematosus.

Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

BACKGROUND

Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. In the present study, we used CIBERSORT and gene set enrichment analysis (GSEA) of gene expression profiles to identify immune cell infiltration characteristics and related core genes in LN.

RESULTS

Datasets from the Gene Expression Omnibus, GSE32591 and GSE113342, were downloaded for further analysis. The GSE32591 dataset, which included 32 LN glomerular biopsy tissues and 14 glomerular tissues from living donors, was analyzed by CIBERSORT. Different immune cell types in LN were analyzed by the Limma software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on GSEA were performed by clusterProfiler software. Lists of core genes were derived from Spearman correlation between the most significant GO term and differentially expressed immune cell gene from CIBERSORT. GSE113342 was employed to validate the association between selected core genes and clinical manifestation. Five types of immune cells revealed important associations with LN, and monocytes emerged as having the most prominent differences. GO and KEGG analyses indicated that immune response pathways are significantly enriched in LN. The Spearman correlation indicated that 15 genes, including FCER1G, CLEC7A, MARCO, CLEC7A, PSMB9, and PSMB8, were closely related to clinical features.

CONCLUSIONS

This study is the first to identify immune cell infiltration with microarray data of glomeruli in LN by using CIBERSORT analysis and provides novel evidence and clues for further research of the molecular mechanisms of LN.

Placenta Transcriptome Profiling in Intrauterine Growth Restriction (IUGR)

Intrauterine growth restriction (IUGR) is a serious pathological complication associated with compromised fetal development during pregnancy. The aim of the study was to broaden knowledge about the transcriptomic complexity of the human placenta by identifying genes potentially involved in IUGR pathophysiology. RNA-Seq data were used to profile protein-coding genes, detect alternative splicing events (AS), single nucleotide variant (SNV) calling, and RNA editing sites prediction in IUGR-affected placental transcriptome. The applied methodology enabled detection of 37,501 transcriptionally active regions and the selection of 28 differentially-expressed genes (DEGs), among them 10 were upregulated and 18 downregulated in IUGR-affected placentas. Functional enrichment annotation indicated that most of the DEGs were implicated in the processes of inflammation and immune disorders related to IUGR and preeclampsia. Additionally, we revealed that some genes (S100A13, GPR126, CTRP1, and TFPI) involved in the alternation of splicing events were mainly implicated in angiogenic-related processes. Significant SNVs were overlapped with 6533 transcripts and assigned to 2386 coding sequence (CDS), 1528 introns, 345 5’ untranslated region (UTR), 1260 3’UTR, 918 non-coding RNA (ncRNA), and 10 intergenic regions. Within CDS regions, 543 missense substitutions with functional effects were recognized. Two known mutations (rs4575, synonymous; rs3817, on the downstream region) were detected within the range of AS and DEG candidates: PA28β and PINLYP, respectively. Novel genes that are dysregulated in IUGR were detected in the current research. Investigating genes underlying the IUGR is crucial for identification of mechanisms regulating placental development during a complicated pregnancy.

Hepatic expression profiles in retroviral infection: relevance to drug hypersensitivity risk

HIV‐infected patients show a markedly increased risk of delayed hypersensitivity (HS) reactions to potentiated sulfonamide antibiotics (trimethoprim/sulfamethoxazole or TMP/SMX). Some studies have suggested altered SMX biotransformation in HIV infection, but hepatic biotransformation pathways have not been evaluated directly. Systemic lupus erythematosus (SLE) is another chronic inflammatory disease with a higher incidence of sulfonamide HS, but it is unclear whether retroviral infection and SLE share risk factors for drug HS. We hypothesized that retroviral infection would lead to dysregulation of hepatic pathways of SMX biotransformation, as well as pathway alterations in common with SLE that could contribute to drug HS risk. We characterized hepatic expression profiles and enzymatic activities in an SIV‐infected macaque model of retroviral infection, and found no evidence for dysregulation of sulfonamide drug biotransformation pathways. Specifically, NAT1,NAT2,CYP2C8,CYP2C9,CYB5R3,MARC1/2, and glutathione‐related genes (GCLC,GCLM,GSS,GSTM1, and GSTP1) were not differentially expressed in drug naïve SIVmac239‐infected male macaques compared to age‐matched controls, and activities for SMX N‐acetylation and SMX hydroxylamine reduction were not different. However, multiple genes that are reportedly over‐expressed in SLE patients were also up‐regulated in retroviral infection, to include enhanced immunoproteasomal processing and presentation of antigens as well as up‐regulation of gene clusters that may be permissive to autoimmunity. These findings support the hypothesis that pathways downstream from drug biotransformation may be primarily important in drug HS risk in HIV infection.