Circulating GPIHBP1 levels and microvascular complications in patients with type 2 diabetes: A cross-sectional study

Kurooka, Naoko; Eguchi, Jun; Murakami, Kazutoshi; Kamei, Shinji; Kikutsuji, Toru; Sasaki, Shuhei; Seki, A; Yamaguchi, Satoshi; Nojima, Ichiro; Watanabe, Mayu; Higuchi, Chigusa; Katayama, Akihiro; Uchida, Haruhito A.; Nakatsuka, Atsuko; Shikata, Kenichi; Wada, Jun

doi:10.1016/j.jacl.2022.01.006

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…It should be noted that there were some DEGs that were not shared among IPF networks, but have been proven to play indispensable roles in IPF in recent years. Involvement of highly significant DEGs [131], SLC7A11 [132], CLIC5 [133], VIP (vasoactive intestinal peptide) [134], SMAD6 [135], BMPR2 [136], APOA1 [137], IN-SIG1 [138], TLR3 [139], NLRP12 [140], ADRB1 [141], TLR8 [142], GATA3 [143], CCR2 [144], TLR7 [145], CCRL2 [146], BMPER (BMP binding endothelial regulator) [147], CAV1 [148], TFPI (tissue factor pathway inhibitor) [149], FADS1 [150], SUCNR1 [151], CADM2 [152], SLC19A3 [153], SGCG (sarcoglycan gamma) [154] [177], WNT3A [178], APOH (apolipoprotein H) [179], CHRM3 [180], CD36 [181], TRIB3 [182], PCSK9 [183], ACVR1C [184], GPD1 [123], FFAR4 [185], GPX3 [186], FGF2 [187], FASN (fatty acid synthase) [188], DGAT2 [189], DACH1 [190], PNPLA3 [191], FGF9 [192], SLC7A11 [193], VIP (vasoactive intestinal peptide) [194], KL (klotho)…”

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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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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show abstract

“…Serum GPIHBP1 median levels were 849 pg/mL in healthy volunteers and 1,087 pg/mL in patients with a history of cardiovascular or metabolic disease 67 . Serum GPIHBP1 levels were higher in patients with type 2 diabetes compared (952.7 pg/mL) with non‐diabetic individuals (700.6 pg/mL) 68 . Unexpectedly, there were no differences in serum GPIHBP1 levels in type 2 diabetes patients with and without hypertriglyceridemia, whereas serum GPIHBP1 levels were significantly higher in patients with type 2 diabetes with diabetic retinopathy, DN and microvascular complications than in those without these complications.…”

Section: Circulating Levels Of Gpihbp1 and Diabetic Vascular Complica...mentioning

confidence: 99%

Role of glycosylphosphatidylinositol‐anchored high‐density lipoprotein binding protein 1 in hypertriglyceridemia and diabetes

Kurooka

Eguchi

Wada

2023

J of Diabetes Invest

Self Cite

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In diabetes, the impairment of insulin secretion and insulin resistance contribute to hypertriglyceridemia, as the enzymatic activity of lipoprotein lipase (LPL) depends on insulin action. The transport of LPL to endothelial cells and its enzymatic activity are maintained by the formation of lipolytic complex depending on the multiple positive (glycosylphosphatidylinositol‐anchored high‐density lipoprotein binding protein 1 [GPIHBP1], apolipoprotein C‐II [APOC2], APOA5, heparan sulfate proteoglycan [HSPG], lipase maturation factor 1 [LFM1] and sel‐1 suppressor of lin‐12‐like [SEL1L]) and negative regulators (APOC1, APOC3, angiopoietin‐like proteins [ANGPTL]3, ANGPTL4 and ANGPTL8). Among the regulators, GPIHBP1 is a crucial molecule for the translocation of LPL from parenchymal cells to the luminal surface of capillary endothelial cells, and maintenance of lipolytic activity; that is, hydrolyzation of triglyceride into free fatty acids and monoglyceride, and conversion from chylomicron to chylomicron remnant in the exogenous pathway and from very low‐density lipoprotein to low‐density lipoprotein in the endogenous pathway. The null mutation of GPIHBP1 causes severe hypertriglyceridemia and pancreatitis, and GPIGBP1 autoantibody syndrome also causes severe hypertriglyceridemia and recurrent episodes of acute pancreatitis. In patients with type 2 diabetes, the elevated serum triglyceride levels negatively correlate with circulating LPL levels, and positively with circulating APOC1, APOC3, ANGPTL3, ANGPTL4 and ANGPTL8 levels. In contrast, circulating GPIHBP1 levels are not altered in type 2 diabetes patients with higher serum triglyceride levels, whereas they are elevated in type 2 diabetes patients with diabetic retinopathy and nephropathy. The circulating regulators of lipolytic complex might be new biomarkers for lipid and glucose metabolism, and diabetic vascular complications.

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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.

show abstract

Circulating GPIHBP1 levels and microvascular complications in patients with type 2 diabetes: A cross-sectional study

Cited by 4 publications

References 39 publications

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

Role of glycosylphosphatidylinositol‐anchored high‐density lipoprotein binding protein 1 in hypertriglyceridemia and diabetes

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

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