scholarly journals Multi–cell type gene coexpression network analysis reveals coordinated interferon response and cross–cell type correlations in systemic lupus erythematosus

2021 ◽  
Vol 31 (4) ◽  
pp. 659-676
Author(s):  
Bharat Panwar ◽  
Benjamin J. Schmiedel ◽  
Shu Liang ◽  
Brandie White ◽  
Enrique Rodriguez ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Network Analysis ◽  
Lupus Erythematosus ◽  
Interferon Response ◽  
Gene Coexpression Network ◽  
Cell Type ◽  
Systemic Lupus ◽  
Gene Coexpression ◽  
Type Gene ◽  
Coexpression Network Analysis

scholarly journals The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chuxiang Lei ◽  
Dan Yang ◽  
Wenlin Chen ◽  
Haoxuan Kan ◽  
Fang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Clinical Significance ◽  
Immune Cells ◽  
Complement System ◽  
Aortic Wall ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
The Complement System ◽  
Coexpression Network Analysis

Abstract Background Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks. Methods We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm. Results We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells. Conclusion Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

scholarly journals Weighted gene coexpression network analysis reveals hub genes involved in cholangiocarcinoma progression and prognosis

2019 ◽  
Vol 49 (10) ◽  
pp. 1195-1206 ◽  
Author(s):  
Aiping Tian ◽  
Ke Pu ◽  
Boxuan Li ◽  
Min Li ◽  
Xiaoguang Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

scholarly journals Correction to: Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Drought Sensitivity ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Weighted Gene Coexpression Network Analysis Identified MicroRNA Coexpression Modules and Related Pathways in Type 2 Diabetes Mellitus

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tianyu Feng ◽  
Kexin Li ◽  
Pingping Zheng ◽  
Yanjun Wang ◽  
Yaogai Lv ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Network Analysis ◽  
Rna Sequencing ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Objective. Type 2 diabetes mellitus (T2DM) is a metabolic disease with high incidence, which has seriously affected human life and health. MicroRNA, a short-chain noncoding RNA, plays an important role in T2DM. Identification of meaningful microRNA modules and the role of microRNAs provide a basis for searching potential biomarkers of T2DM. Materials and Methods. In this study, three newly diagnosed patients with T2DM and three controls were selected for Whole Peripheral Blood RNA Sequencing to establish a microRNA library. Weighted gene coexpression network analysis (WGCNA) was applied to construct coexpression modules and to detect the trait-related microRNA modules; then, KEGG enrichment analysis was performed to predict the biological function of the interest modules, and candidate hub microRNAs were screened out by the value of module membership (MM) and protein-protein interaction (PPI) network. Result. Four microRNA modules (blue, brown, magenta, and turquoise) were highly associated with the T2DM; the number of miRNAs in these modules ranged from 41 to 469. The Fc gamma R-mediated phagocytosis pathway, Rap1 signaling pathway, MAPK signaling pathway, and Lysosome pathway were common pathways in three of the four modules. RPS27A, UBC, and RAC1 were the top three proteins in our study; their corresponding RNAs were miR-1271-5p, miR-130a-3p, miR-130b-3p, and miR-574-3p. Conclusion. In summary, this study identified blood miRNAs in human T2DM using RNA sequencing. The findings may be the foundation for understanding the potential role of miRNAs in T2DM.

Cell-type specificity of anti-CD45 autoantibodies in systemic lupus erythematosus

1996 ◽  
Vol 39 (4) ◽  
pp. 592-599 ◽  
Author(s):  
Jan Czyzyk ◽  
Philip Fernsten ◽  
Melody Shaw ◽  
John B. Winfield
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Systemic Lupus

scholarly journals Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Li ◽  
Xiao_nan He ◽  
Chao Li ◽  
Ling Gong ◽  
Min Liu
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods. Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results. A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions. FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

scholarly journals FcγRIIA expression accelerates nephritis and increases platelet activation in systemic lupus erythematosus

Blood ◽  
2020 ◽  
Vol 136 (25) ◽  
pp. 2933-2945
Author(s):  
Imene Melki ◽  
Isabelle Allaeys ◽  
Nicolas Tessandier ◽  
Benoit Mailhot ◽  
Nathalie Cloutier ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Mouse Model ◽  
Platelet Activation ◽  
Lupus Erythematosus ◽  
Bone Marrow Cells ◽  
Human Platelets ◽  
Interferon Response ◽  
Type I ◽  
Systemic Lupus ◽  
Autoimmune Inflammatory Disease

Abstract Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disease characterized by deposits of immune complexes (ICs) in organs and tissues. The expression of FcγRIIA by human platelets, which is their unique receptor for immunoglobulin G antibodies, positions them to ideally respond to circulating ICs. Whereas chronic platelet activation and thrombosis are well-recognized features of human SLE, the exact mechanisms underlying platelet activation in SLE remain unknown. Here, we evaluated the involvement of FcγRIIA in the course of SLE and platelet activation. In patients with SLE, levels of ICs are associated with platelet activation. Because FcγRIIA is absent in mice, and murine platelets do not respond to ICs in any existing mouse model of SLE, we introduced the FcγRIIA (FCGR2A) transgene into the NZB/NZWF1 mouse model of SLE. In mice, FcγRIIA expression by bone marrow cells severely aggravated lupus nephritis and accelerated death. Lupus onset initiated major changes to the platelet transcriptome, both in FcγRIIA-expressing and nonexpressing mice, but enrichment for type I interferon response gene changes was specifically observed in the FcγRIIA mice. Moreover, circulating platelets were degranulated and were found to interact with neutrophils in FcγRIIA-expressing lupus mice. FcγRIIA expression in lupus mice also led to thrombosis in lungs and kidneys. The model recapitulates hallmarks of human SLE and can be used to identify contributions of different cellular lineages in the manifestations of SLE. The study further reveals a role for FcγRIIA in nephritis and in platelet activation in SLE.

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