scholarly journals Study on the Differentially Expressed Genes and Signaling Pathways in Systemic Lupus Erythematosus Using Integrated Bioinformatics Method

2020 ◽  
Author(s):  
Jing Liang ◽  
Xin Zhang ◽  
Wenjia Zhao
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Scientific Research ◽  
Differentially Expressed ◽  
Statistical Hypothesis ◽  
Systemic Lupus ◽  
Ppi Networks ◽  
Geo Database

Abstract Background: Systemic lupus erythematosus (SLE) is a chronic immune connective tissue disease, which is common in women of childbearing age and easy to cause multiple organ inflammatory injury. The occurrence of prostate cancer is the result of multiple factors and genes, but we have little understanding of the mechanism involved. In this study, we deeply explored and analyzed the existing gene data in GEO database in order to find the key genes and new therapeutic targets of SLE.Results: The expression profile dataset of GDS4185, GDS4888, GDS4889 and GDS4890 containing 99 specimens, 42 cases of SLE patients and 57 cases of normal volunteers, were downloaded from the Gene Expression Omnibus (GEO) website. The differentially expressed genes (DEGs) in different tissues was analyzed by statistical hypothesis T test. The gene ontology (GO) enrichment analysis was carried out by the DAVID online tool. KEGG pathway annotation of DEGs was carried out by the KOBAS online computing database. The protein–protein interaction (PPI) networks of the DEGs were built from the STRING website and Cytoscape software. A total of 839 DEGs were calculated from the four GEO datasets. The GO and KEGG analysis indicated that the functions of DEGs mostly participated in the Osteoclast differentiation, HTLV-I infection, Measles, FoxO signaling pathway, Herpes simplex infection, Primary immunodeficiency, Jak-STAT signaling pathway. The following 14 closely related genes, HERC5, TP53, CDC20, GNB2, GNB4, PPP2R1A, GNAI2, PMCH, SOCS3, HERC6, STAT1, SOCS1, ISG15, IFIT3, were key nodes from the PPI network. These genes may have synergistic or indirect interactions with each other in the process of biological metabolism inducing the pathogenesis of SLE.Conclusion: Mining geo database has great scientific research value. In the future, scientific research must fully excavate a variety of database analysis methods. In this study, the screened candidate genes provide effective theoretical basis for the diagnosis, treatment, expected evaluation and related laboratory research of SLE, which are worthy of further experimental verification.

scholarly journals Integrative Analysis for Elucidating Transcriptomics Landscapes of Systemic Lupus Erythematosus

2021 ◽  
Vol 12 ◽  
Author(s):  
Haihong Zhang ◽  
Yanli Wang ◽  
Jinghui Feng ◽  
Shuya Wang ◽  
Yan Wang ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Potential Candidate ◽  
Systemic Lupus

Systemic lupus erythematosus (SLE) is a complex and heterogeneous autoimmune disease that the immune system attacks healthy cells and tissues. SLE is difficult to get a correct and timely diagnosis, which makes its morbidity and mortality rate very high. The pathogenesis of SLE remains to be elucidated. To clarify the potential pathogenic mechanism of SLE, we performed an integrated analysis of two RNA-seq datasets of SLE. Differential expression analysis revealed that there were 4,713 and 2,473 differentially expressed genes, respectively, most of which were up-regulated. After integrating differentially expressed genes, we identified 790 common differentially expressed genes (DEGs). Gene functional enrichment analysis was performed and found that common differentially expressed genes were significantly enriched in some important immune-related biological processes and pathways. Our analysis provides new insights into a better understanding of the pathogenic mechanisms and potential candidate markers for systemic lupus erythematosus.

scholarly journals Novel expression signatures identified by transcriptional analysis of separated leucocyte subsets in systemic lupus erythematosus and vasculitis

2009 ◽  
Vol 69 (6) ◽  
pp. 1208-1213 ◽  
Author(s):  
Paul A Lyons ◽  
Eoin F McKinney ◽  
Tim F Rayner ◽  
Alexander Hatton ◽  
Hayley B Woffendin ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Mononuclear Cells ◽  
Differentially Expressed ◽  
Transcriptional Analysis ◽  
Expression Data ◽  
Systemic Lupus ◽  
Disease Pathogenesis

ObjectiveTo optimise a strategy for identifying gene expression signatures differentiating systemic lupus erythematosus (SLE) and antineutrophil cytoplasmic antibody-associated vasculitis that provide insight into disease pathogenesis and identify biomarkers.Methods44 vasculitis patients, 13 SLE patients and 25 age and sex-matched controls were enrolled. CD4 and CD8 T cells, B cells, monocytes and neutrophils were isolated from each patient and, together with unseparated peripheral blood mononuclear cells (PBMC), were hybridised to spotted oligonucleotide microarrays.ResultsUsing expression data obtained from purified cells a substantial number of differentially expressed genes were identified that were not detectable in the analysis of PBMC. Analysis of purified T cells identified a SLE-associated, CD4 T-cell signature consistent with type 1 interferon signalling driving the generation and survival of tissue homing T cells and thereby contributing to disease pathogenesis. Moreover, hierarchical clustering using expression data from purified monocytes provided significantly improved discrimination between the patient groups than that obtained using PBMC data, presumably because the differentially expressed genes reflect genuine differences in processes underlying disease pathogenesis.ConclusionAnalysis of leucocyte subsets enabled the identification of gene signatures of both pathogenic relevance and with better disease discrimination than those identified in PBMC. This approach thus provides substantial advantages in the search for diagnostic and prognostic biomarkers in autoimmune disease.

scholarly journals Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

2020 ◽  
Author(s):  
Gaia Andreoletti ◽  
Cristina M. Lanata ◽  
Ishan Paranjpe ◽  
Tia S. Jain ◽  
Joanne Nititham ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
B Cells ◽  
Disease Activity ◽  
Nk Cells ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Differentially Expressed ◽  
Cell Type ◽  
Systemic Lupus

AbstractSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. The aim of this study is to leverage large-scale transcriptomic data from diverse populations to better sub-classify SLE patients into more clinically actionable groups. We leverage cell sorted RNA-seq data (CD14+ monocytes, B cells, CD4+T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four tier analytical approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on the individual cell type data resulted in three clusters for CD4 and CD14, and two clusters for B cells and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters of each immune cell and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across the four cell types were involved in SLE or other autoimmune related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity defined by SLEDAI score >=6) and White-low, Asians-low (SLEDAI < 6). Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable precision medicine for SLE.

scholarly journals Expression of Human Endogenous Retroviruses in Systemic Lupus Erythematosus: Multiomic Integration With Gene Expression

2021 ◽  
Vol 12 ◽  
Author(s):  
Nathaniel Stearrett ◽  
Tyson Dawson ◽  
Ali Rahnavard ◽  
Prathyusha Bachali ◽  
Matthew L. Bendall ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Nuclear Material ◽  
Endogenous Retroviruses ◽  
Differentially Expressed ◽  
Potential Contribution ◽  
Systemic Lupus ◽  
Disease Heterogeneity ◽  
Human Endogenous Retroviruses

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies predominantly to nuclear material. Many aspects of disease pathology are mediated by the deposition of nucleic acid containing immune complexes, which also induce the type 1interferon response, a characteristic feature of SLE. Notably, SLE is remarkably heterogeneous, with a variety of organs involved in different individuals, who also show variation in disease severity related to their ancestries. Here, we probed one potential contribution to disease heterogeneity as well as a possible source of immunoreactive nucleic acids by exploring the expression of human endogenous retroviruses (HERVs). We investigated the expression of HERVs in SLE and their potential relationship to SLE features and the expression of biochemical pathways, including the interferon gene signature (IGS). Towards this goal, we analyzed available and new RNA-Seq data from two independent whole blood studies using Telescope. We identified 481 locus specific HERV encoding regions that are differentially expressed between case and control individuals with only 14% overlap of differentially expressed HERVs between these two datasets. We identified significant differences between differentially expressed HERVs and non-differentially expressed HERVs between the two datasets. We also characterized the host differentially expressed genes and tested their association with the differentially expressed HERVs. We found that differentially expressed HERVs were significantly more physically proximal to host differentially expressed genes than non-differentially expressed HERVs. Finally, we capitalized on locus specific resolution of HERV mapping to identify key molecular pathways impacted by differential HERV expression in people with SLE.

scholarly journals Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

2020 ◽  
Author(s):  
Gaia Andreoletti ◽  
Cristina Lanata ◽  
Ishan Paranjpe ◽  
Tia Jain ◽  
Joanne Nititham ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
Disease Activity ◽  
Nk Cells ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Cd4 T Cells ◽  
Differentially Expressed ◽  
Cell Type ◽  
Systemic Lupus

Abstract Systemic lupus erythematosus (SLE) is an autoimmune disease in which outcomes vary among different racial groups. We leverage cell-sorted RNA-seq data (CD14 + monocytes, B cells, CD4 + T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four-tier approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on each cell-type resulted in three clusters for CD4 and CD14, and two for B and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across all cells were involved in SLE or other autoimmune-related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into groups base on their disease activity and ethnicity. Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4 + T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable SLE precision medicine.

scholarly journals Transcriptomic analysis of immune cells in a multi-ethnic cohort of systemic lupus erythematosus patients identifies ethnicity- and disease-specific expression signatures

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaia Andreoletti ◽  
Cristina M. Lanata ◽  
Laura Trupin ◽  
Ishan Paranjpe ◽  
Tia S. Jain ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
Disease Activity ◽  
Nk Cells ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Cd4 T Cells ◽  
Differentially Expressed ◽  
Cell Type ◽  
Systemic Lupus

AbstractSystemic lupus erythematosus (SLE) is an autoimmune disease in which outcomes vary among different racial groups. We leverage cell-sorted RNA-seq data (CD14+ monocytes, B cells, CD4+ T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four-tier approach including unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning to identify SLE subgroups within this multiethnic cohort. K-means clustering on each cell-type resulted in three clusters for CD4 and CD14, and two for B and NK cells. To understand the identified clusters, correlation analysis revealed significant positive associations between the clusters and clinical parameters including disease activity as well as ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across cells were involved in SLE or other autoimmune-related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Finally, random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White individuals. The results from these analyses will help stratify patients based on their gene expression signatures to enable SLE precision medicine.

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

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xingwang Zhao ◽  
Longlong Zhang ◽  
Juan Wang ◽  
Min Zhang ◽  
Zhiqiang Song ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Tissue Destruction ◽  
Systemic Lupus ◽  
Ppi Networks

Abstract 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.

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

Epigenomics ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 1795-1809 ◽  
Author(s):  
Haiyu Cao ◽  
Dong Li ◽  
Huixiu Lu ◽  
Jing Sun ◽  
Haibin Li
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Noncoding Rnas ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Systemic Lupus ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

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.

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