Blood levels of brain-derived neurotrophic factor (BDNF) in systemic lupus erythematous (SLE): a systematic review and meta-analysis

Shobeiri, Parnian; Maleki, Saba; Amanollahi, Mobina; Habibzadeh, Amirhossein; Teixeira, Antônio Lúcio; Rezaei, Nima

doi:10.1186/s42358-023-00291-6

Cited by 3 publications

(3 citation statements)

References 46 publications

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“…Therefore, p75 NTR signaling may exert potential effects on mitochondrial metabolism in B cells to participate in the regulation of SLE pathogenesis ( Figure 5 ). Similar to the upregulation of proBDNF, BDNF levels in B cells and serum increased in patients with SLE compared with healthy controls ( 117 , 118 ). However, the level of serum BDNF and the number of BDNF + B cells were independent of the SLEDAI score ( 117 ).…”

Section: Probdnf and Its Receptors In Slementioning

confidence: 76%

ProBDNF and its receptors in immune-mediated inflammatory diseases: novel insights into the regulation of metabolism and mitochondria

Gao

et al. 2023

Front. Immunol.

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Immune-mediated inflammatory diseases (IMIDs) consist of a common and clinically diverse group of diseases. Despite remarkable progress in the past two decades, no remission is observed in a large number of patients, and no effective treatments have been developed to prevent organ and tissue damage. Brain-derived neurotrophic factor precursor (proBDNF) and receptors, such as p75 neurotrophin receptor (p75NTR) and sortilin, have been proposed to mediate intracellular metabolism and mitochondrial function to regulate the progression of several IMIDs. Here, the regulatory role of proBDNF and its receptors in seven typical IMIDs, including multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, allergic asthma, type I diabetes, vasculitis, and inflammatory bowel diseases, was investigated.

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Section: Probdnf and Its Receptors In Slementioning

confidence: 76%

ProBDNF and its receptors in immune-mediated inflammatory diseases: novel insights into the regulation of metabolism and mitochondria

Gao

et al. 2023

Front. Immunol.

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“…The altered expression of enriched genes including CX3CR1 [599], S100A12 [600], PF4 [601], MPO (myeloperoxidase) [602], RXFP1 [603], S100A8 [604], VEGFD (vascular endothelial growth factor D) [605], CXCL11 [606], IL1A [607], BPI (bactericidal permeability increasing protein) [608], SLC6A4 [609], CXCL10 [610], FCGR3B [611], S100A9 [612], IL1B [613], CXCR2 [614], CTNND2 [615], CD36 [616], PCSK9 [617], FGF2 [618], SHH (sonic hedgehog signaling molecule) [619], KL (klotho) [620], BMPR2 [621], TLR8 [622], GATA3 [623], CCR2 [624], TLR7 [625], CAV1 [626], TFPI (tissue factor pathway inhibitor) [627], and SPAG17 [628] have been proposed as novel biomarkers for systemic sclerosis. A previous study found that enriched genes including CX3CR1 [629], S100A12 [630], MPO (myeloperoxidase) [631], CD5L [632], F11 [633], S100A8 [634], BPI (bactericidal permeability increasing protein) [635], AQP4 [636], MME (membrane metalloendopeptidase) [637], BDNF (brain derived neurotrophic factor) [638], CXCL10 [639], RNASE2…”

Section: Discussionmentioning

confidence: 99%

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Giriyappagoudar,

Vastrad,

Horakeri

et al. 2023

Biomedicines

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Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein–protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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“…The altered expression of CX3CR1 [599], S100A12 [600], PF4 [601], MPO (myeloperoxidase) [602], RXFP1 [603], S100A8 [604], VEGFD (vascular endothelial growth factor D) [605], CXCL11 [606], IL1A [607], BPI (bactericidal permeability increasing protein) [608], SLC6A4 [609], CXCL10 [610], FCGR3B [611], S100A9 [612], IL1B [613], CXCR2 [614], CTNND2 [615], CD36 [616], PCSK9 [617], FGF2 [618], SHH (sonic hedgehog signaling molecule) [619], KL (klotho) [620], BMPR2 [621], TLR8 [622], GATA3 [623], CCR2 [624], TLR7 [625], CAV1 [626], TFPI (tissue factor pathway inhibitor) [627] and SPAG17 [628] have been identified in systemic sclerosis. A previous study found that CX3CR1 [629], S100A12 [630], MPO (myeloperoxidase) [631], CD5L [632], F11 [633], S100A8 [634], BPI (bactericidal permeability increasing protein) [635], AQP4 [636], MME (membrane metalloendopeptidase) [637], BDNF (brain derived neurotrophic factor) [638], CXCL10 [639], RNASE2 [640], FCGR3B [641], S100A9 [642], GPIHBP1 [643], AFF3 [644], APOH (apolipoprotein H) [645], FCN3 [646], PCSK9 [308], LILRA2 [647], APOA1 [648], LRRK2 [649], CD244 [650], TLR3 [651], TLR8 [652], GATA3 [653], CCR2 [654], IFIT3 [655], NEK7 [656], TLR7 [657], CAV1 [658], CR1 [659], TFPI (tissue factor pathway inhibitor) [660], GPER1 [661], SIGLEC14 [662], FOXJ1 [663], GABRP (gamma-aminobutyric acid type A receptor subunit pi) [664] and TSGA10 [665] are positively correlated with the severity of systemic lupus erythematosus, suggesting its potential as a biomarker ...…”

Section: Discussionmentioning

confidence: 99%

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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Blood levels of brain-derived neurotrophic factor (BDNF) in systemic lupus erythematous (SLE): a systematic review and meta-analysis

Cited by 3 publications

References 46 publications

ProBDNF and its receptors in immune-mediated inflammatory diseases: novel insights into the regulation of metabolism and mitochondria

ProBDNF and its receptors in immune-mediated inflammatory diseases: novel insights into the regulation of metabolism and mitochondria

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

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