Multiple Processes May Involve in the IgG4-RD Pathogenesis: An Integrative Study via Proteomic and Transcriptomic Analysis

The spectrum of B cells in the pathogenesis, diagnosis and therapeutic applications of immunoglobulin G4-related disease

Immunoglobulin G4 (IgG4)-related disease is a chronic fibroinflammatory disease mediated by immune disorders. Given the challenging clinical diagnosis and treatment, knowledge of the pathogenesis of IgG4-related disease is important. The typical elevation of serum IgG4 concentrations and infiltration of IgG4-positive plasma cells in the involved tissues indicate the involvement of B lymphocytes in the pathogenesis of IgG4-related disease. Mass production of autoantibodies reflects abnormal activation of B cells, which causes tissue damage. Circulating plasmablasts are recently discovered markers that correlate with serum IgG4 concentration, the extent of organ involvement and disease activity. B-cell depletion therapy is an emerging curative strategy that can significantly alleviate clinical manifestations and achieve remission in patients with IgG4-related disease. These findings highlight the potential role of B cells in IgG4-related disease. In this review, we discuss the pathogenic impact of B lymphocytes on IgG4-related disease and describe novel therapies targeting B cells.

Single-cell transcriptome analysis profiles cellular and molecular alterations in submandibular gland and blood in IgG4-related disease

OBJECTIVES

The aim of this study is to profile the transcriptional landscapes of affected tissues and peripheral blood mononuclear cells (PBMCs) at the single-cell level in IgG4-related disease (IgG4-RD). Identifying the cell populations and crosstalk between immune cells and non-immune cells will assist us in understanding the aetiology of IgG4-RD.

METHODS

We performed single-cell RNA sequencing analysis on submandibular glands (SMGs) and PBMCs from patients with IgG4-RD and matched controls. Additionally, bulk RNA sequencing of PBMCs was used to construct the immune repertoire. Furthermore, multiplex immunofluorescence staining was performed to validate the transcriptomic results.

RESULTS

We identified three novel subsets of tissue-resident immune cells in the SMGs of patients with IgG4-RD. TOP2A_B cells and TOP2A_T cells had stemness signatures, and trajectory analysis showed that TOP2A_B cells may differentiate into IgG4+plasma cells and that TOP2A_T cells may differentiate into T follicular helper (Tfh) cells. ICOS_PD-1_B cells with Tfh-like characteristics appeared to be an intermediate state in the differentiation from B cells to IgG4+plasma cells. The cellular communication patterns within immune cells and between immune cells and non-immune cells were altered in IgG4-RD compared with controls. Consistently, infection-related pathways were shared in B cells and T cells from SMGs and PBMCs. Furthermore, immune clonotype analysis of PBMC samples showed the complementary determining region 3 amino acid CQQSYSTPYTF was expanded in patients with IgG4-RD.

CONCLUSION

Our data revealed the cellular and molecular changes at the single-cell resolution of IgG4-RD and provide valuable insights into the aetiology and novel therapeutic targets of the autoimmune disease.

A Comprehensive Weighted Gene Co-expression Network Analysis Uncovers Potential Targets in Diabetic Kidney Disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman's correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Proteomic characteristics of saliva in patients with different subgroups of IgG4-RD

Background

Immunoglobulin G4-related disease (IgG4-RD) is a newly defined disease entity, with great heterogeneity among IgG4-RD subgroups with different organ involvement patterns. Identification of the proteomic characteristics of IgG4-RD subgroups will be critical for the understanding of the pathogenic mechanisms of IgG4-RD.

Method

In this study, we performed proteomic analysis using Tandem Mass Tags (TMT) technology with "high field" mass analyzer with improved resolution and sequencing speed to investigate the proteomic profile of saliva and plasma samples from ten untreated IgG4-RD patients and five healthy controls (HCs). Differentially expressed proteins (DEPs) were identified by "t test" function in R package. Functional enrichment analysis was used to investigate pathways enriched in IgG4-RD samples.

Results

Most salivary DEPs identified in IgG4-RD patients compared with HCs were mainly enriched in neutrophil mediated GO bioprocess. Within the comparisons between four IgG4-RD subgroups, more DEPs were identified in the comparison of Mikulicz group and Head and neck group. Among four subgroups of IgG4-RD, Head and neck group showed the most distinctive proteomic expression pattern when compared with HCs. Moreover, "Neutrophil mediated process" related GO bioprocess was commonly identified between comparisons of Mikulicz group and Head and neck group, Head and neck group and Retroperitoneal aorta group, Head and neck group and HCs, IgG4-RD patients with saliva gland involvement and those without saliva gland involvement. Key DEPs that involved in this GO bioprocess were identified. Besides, we performed proteomic analysis for plasma samples between ten IgG4-RD and five HCs and there were several DEPs identified overlapped in saliva and plasma.

Conclusion

We identified multiple processes/factors and several signaling pathways in saliva that may be involved in the IgG4-RD pathogenesis.

A comprehensive weighted gene co-expression network analysis uncovers potential targets in diabetic kidney disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman’s correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Identification of the Potential Molecular Mechanism of TGFBI Gene in Persistent Atrial Fibrillation

Background

Transforming growth factor beta-induced protein (TGFBI, encoded by TGFBI gene), is an extracellular matrix protein, widely expressed in variety of tissues. It binds to collagens type I, II, and IV and plays important roles in the interactions of cell with cell, collagen, and matrix. It has been reported to be associated with myocardial fibrosis, and the latter is an important pathophysiologyical basis of atrial fibrillation (AF). However, the mechanism of TGFBI in AF remains unclear. We aimed to detect the potential mechanism of TGFBI in AF via bioinformatics analysis.

Methods

The microarray dataset of GSE115574 was examined to detect the genes coexpressed with TGFBI from 14 left atrial tissue samples of AF patients. TGFBI coexpression genes were then screened using the R package. Using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of TGFBI and its coexpression genes. The modules and hub genes of the PPI-network were then identified. Another dataset, GSE79768 was examined to verify the hub genes. DrugBank was used to detect the potential target drugs.

Results

In GSE115574 dataset, a total of 1818 coexpression genes (769 positive and 1049 negative) were identified, enriched in 120 biological processes (BP), 38 cellular components (CC), 36 molecular functions (MF), and 39 KEGG pathways. A PPI-network with average 12.2-degree nodes was constructed. The genes clustered in the top module constructed from this network mainly play a role in PI3K-Akt signaling pathway, viral myocarditis, inflammatory bowel disease, and platelet activation. CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 were identified and finally verified as the hub genes, mainly enriched in pathways like leukocyte transendothelial migration, PI3K-Akt signaling pathway, viral myocarditis, rheumatoid arthritis, and platelet activation. Pegcetacoplan, ocriplasmin, and carvedilol were the potential target drugs.

Conclusions

We used microdataset to identify the potential functions and mechanisms of the TGFBI and its coexpression genes in AF patients. Our findings suggest that CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 may be the hub genes.