Multiple Functions of B Cells in the Pathogenesis of Systemic Lupus Erythematosus

Ma, Kongyang; Du, Wenhan; Wang, Xiaohui; Yuan, Shuguang; Cai, Xiaoyan; Liu, Dongzhou; Li, Jingyi; Lu, Liwei

doi:10.3390/ijms20236021

Cited by 71 publications

(54 citation statements)

References 167 publications

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“…As outlined earlier, the fundamental importance of distributed BCR and TCR signaling pathways in SLE pathogenesis has been well established via both clinical and experimental evidence [51,52]. In this context, we also observed defects in several genes involved in these signaling pathways ( Figures 5B and 6B).…”

Section: Discussionsupporting

confidence: 78%

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

Maleknia

Salehi

Sharifi‐Zarchi

et al. 2020

Preprint

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Background: To perceive a comprehensive illustration of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, draw a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to distinguish key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention. Methods: In the present study, six gene expression microarray datasets included SLE patients (n=220) and healthy controls (n=135) were collected. We integrated the datasets by cross-platform normalization. Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from SLE, TCR and BCR signaling pathways and the parameters of BNs were estimated using integrated datasets. Finally, a meta-analysis was performed to distinguish the signaling pathways alterations in the SLE pathogenesis. Results: We identified the most dysregulated genes involved in the clearance mechanism (most inhibition in MACROH2A2 and H4C9, most activation in SNRPD3, MACROH2A1 and RO60). Augmented autoantigens presentation by MHCII (HLA-DQA1, HLA-DOB and HLA-DPB1) and CD80 and CD86 to CD28 biological functions were also observed. The increased expression of FCGR1A, C8G, C7 and C9 genes and decreasing biological functions in edges from C1R and C1S to C2 and from C1R to C4B, all involved in end-organ damage, were detected. On the other hand, many of subnetworks alterations of TCR and BCR reported in previous research (PI3K/AKT, MAPK, AP-1 transcription factor). Conclusions: Overall, the BNrich as a hybridized network construction method, which integrates intergenic relations inferred from signaling pathways and gene expression profiling, can be employed to study complex diseases. Here we applied BNrich and highlighted significant genes and intergenic relationships and systemic alterations in SLE, TCR and BCR signaling pathways.

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Section: Discussionsupporting

confidence: 78%

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

Maleknia

Salehi

Sharifi‐Zarchi

et al. 2020

Preprint

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“…[52] In a recent study, the FCGR2A polymorphisms have been linked to SLE susceptibility in Mexican patients [46]. As outlined earlier, the fundamental importance of BCR and TCR signaling pathways in SLE pathogenesis has been well established via both clinical and experimental evidence [53][54][55]. In the TCR signaling pathway, we observed lower expression of several genes like CBLB, PPP3CC, NFKB1, CD3E, and ZAP70.…”

Section: Discussionsupporting

confidence: 52%

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

et al. 2020

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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.

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“…Patients with SLE usually present blood circulating antinuclear antibodies, which result in immune complex deposition and complement consumption in multiple organs, leading to cytopenia, glomerulonephritis, rash, and serositis. Accordingly, antinuclear antibodies are crucial for the progression of SLE 39 . Many studies have shown that the imbalance of Treg and Th17 responses is crucial for the pathogenesis of SLE 40‐42 …”

Section: Systemic Lupus Erythematosusmentioning

confidence: 99%

“…Accordingly, antinuclear antibodies are crucial for the progression of SLE. 39 Many studies have shown that the imbalance of Treg and Th17 responses is crucial for the pathogenesis of SLE. [40][41][42] Th17 cells can induce vascular inflammation by increasing the secretion of IL-17A during the pathogenesis of SLE.…”

Section: Sys Temi C Lupus Ery Thematosusmentioning

confidence: 99%

Interleukin‐35 regulates the balance of Th17 and Treg responses during the pathogenesis of connective tissue diseases

Wang

Lei

2020

Int J of Rheum Dis

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Interleukin (IL)-35 belongs to the IL-12 cytokine family and is a heterodimer of the p35 and Epstein-Barr virus-induced gene 3 (EBI3) subunits. Functionally, IL-35 can promote the proliferation and activation of regulatory T cells (Tregs) and suppress the function of T helper 17 (Th17) cells and other inflammatory cells to inhibit immune responses. In recent years, an abnormal IL-35 expression causing a Th17/Treg imbalance has been associated with the development and progression of several connective tissue diseases (CTDs), such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc), dermatomyositis (DM)/polymyositis (PM), and primary Sjögren's syndrome (pSS). Here, we review the role of IL-35 in regulating the balance of Th17/Treg responses in different types of CTDs and provide new insights into the role of IL-35 in these diseases.

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Multiple Functions of B Cells in the Pathogenesis of Systemic Lupus Erythematosus

Cited by 71 publications

References 167 publications

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

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

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

Interleukin‐35 regulates the balance of Th17 and Treg responses during the pathogenesis of connective tissue diseases

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