Expression of LRP1 and CHOP genes associated with peripheral neuropathy in type 2 diabetes mellitus: Correlations with nerve conduction studies

El‐Horany, Hemat El‐Sayed; Watany, Mona Mohamed; Hagag, Rasha Youssef; El-Attar, Shimaa Hassan; Basiouny, Mohamed A.

doi:10.1016/j.gene.2019.02.105

Cited by 11 publications

(6 citation statements)

References 43 publications

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“…Other studies reported the upregulation of LRP1 in epicardial fat and skin from diabetic patients compared with control individuals, which was consistent with our findings [23,24]. In contrast, another study showed a significant reduction in LRP1 expression in subjects with diabetic peripheral neuropathy compared with the noncomplication group [25]. Highly diverse functions of LRP1 have been reported in different tissues.…”

Section: Discussionsupporting

confidence: 92%

Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis

Liang

Sun

Zhao

et al. 2020

Journal of Diabetes Research

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Objective. To identify susceptibility modules and genes for cardiovascular disease in diabetic patients using weighted gene coexpression network analysis (WGCNA). Methods. The raw data of GSE13760 were downloaded from the Gene Expression Omnibus (GEO) website. Genes with a false discovery rate<0.05 and a log2 fold change≥0.5 were included in the analysis. WGCNA was used to build a gene coexpression network, screen important modules, and filter the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the genes in modules with clinical interest. Genes with a significance over 0.2 and a module membership over 0.8 were used as hub genes. Subsequently, we screened these hub genes in the published genome-wide SNP data of cardiovascular disease. The overlapped genes were defined as key genes. Results. Fourteen gene coexpression modules were constructed via WGCNA analysis. Module greenyellow was mostly significantly correlated with diabetes. The GO analysis showed that genes in the module greenyellow were mainly enriched in extracellular matrix organization, extracellular exosome, and calcium ion binding. The KEGG analysis showed that the genes in the module greenyellow were mainly enriched in antigen processing and presentation, phagosome. Fifteen genes were identified as hub genes. Finally, HLA-DRB1, LRP1, and MMP2 were identified as key genes. Conclusion. This was the first study that used the WGCNA method to construct a coexpression network to explore diabetes-associated susceptibility modules and genes for cardiovascular disease. Our study identified a module and several key genes that acted as essential components in the etiology of diabetes-associated cardiovascular disease, which may enhance our fundamental knowledge of the molecular mechanisms underlying this disease.

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Section: Discussionsupporting

confidence: 92%

Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis

Liang

Sun

Zhao

et al. 2020

Journal of Diabetes Research

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“…Mounting evidence suggests that ER stress is involved in the pathogenesis of diabetes and plays a pivotal role in diabetic complications, such as neuropathy [162], diabetic nephropathy [163], diabetic retinopathy [164], and cardiomyopathy [165,166]. HO-1 and ER stress pathways interact in diabetes (Figure 4).…”

Section: Diabetes Mellitusmentioning

confidence: 99%

Heme, Heme Oxygenase, and Endoplasmic Reticulum Stress—A New Insight into the Pathophysiology of Vascular Diseases

Gáll

Balla

2019

IJMS

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The prevalence of vascular disorders continues to rise worldwide. Parallel with that, new pathophysiological pathways have been discovered, providing possible remedies for prevention and therapy in vascular diseases. Growing evidence suggests that endoplasmic reticulum (ER) stress is involved in a number of vasculopathies, including atherosclerosis, vascular brain events, and diabetes. Heme, which is released from hemoglobin or other heme proteins, triggers various pathophysiological consequence, including heme stress as well as ER stress. The potentially toxic free heme is converted by heme oxygenases (HOs) into carbon monoxide (CO), iron, and biliverdin (BV), the latter of which is reduced to bilirubin (BR). Redox-active iron is oxidized and stored by ferritin, an iron sequestering protein which exhibits ferroxidase activity. In recent years, CO, BV, and BR have been shown to control cellular processes such as inflammation, apoptosis, and antioxidant defense. This review covers our current knowledge about how heme induced endoplasmic reticulum stress (HIERS) participates in the pathogenesis of vascular disorders and highlights recent discoveries in the molecular mechanisms of HO-mediated cytoprotection in heme stress and ER stress, as well as crosstalk between ER stress and HO-1. Furthermore, we focus on the translational potential of HIERS and heme oxygenase-1 (HO-1) in atherosclerosis, diabetes mellitus, and brain hemorrhage.

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“…CXCL5 has been shown to be activated abortion [56]. [85], NOTCH2 [86], LRP1 [87], CLU (clusterin) [88], FCN1 [89], CDKN1A [90], SMAD3 [91], HLA-E [92], PTPRC (protein tyrosine phosphatase receptor type C) [93], MYH9 [94], JAK3 [95], IL6R [96], TIMP1 [97], DOCK8 [98], TNFRSF1B [99], ITGAL (integrin subunit alpha L) [100], CD47 [101], RARA (retinoic acid receptor alpha) [102], DGKD (diacylglycerol kinase delta) [103], PLEK (pleckstrin) [104], PREX1 [105], BSCL2 [106], PANX1 [107], IRF7 [108] [116] were revealed to be expressed in diabetes mellitus, but these genes might be novel targets for GDM. SIX1 [117], GREM1 [118], GHRHR (growth hormone releasing hormone receptor) [119], GPR37L1 [120], CYP2J2 [121], AQP4 [122], ROS1 [123], LBP (lipopolysaccharide binding protein) [124], SGCD (sarcoglycan delta) [133], PTHLH [134], FAP (fibroblast activation protein alpha) [135], THSD7A [136], SHROOM3 [137], ETV1 [138], CYP24A1 [139], SLIT2 [140], GJC1 [141], PPARGC1A [142], TRPM3 [143], IGF1 [144], TRPV6 [145], TLR3 [146], BMP7 [147], DSG2 [148], POSTN (periostin) [149], ENOX1…”

Section: Discussionmentioning

confidence: 99%

“…Pathways include hemostasis [57], neutrophil degranulation [58], immune system [59] and cytokine signaling in immune system [60] are responsible for progression of GDM. LGR5 [61], GREM1 [62], GLRA3 [63], NEUROD4 [64], CYP2J2 [65], KCNH6 [66], LBP (lipopolysaccharide binding protein) [67], CXCL14 [68], RGN (regucalcin) [69], NPY2R [70], SERPINB13 [71], WNT5A [72], EDA (ectodysplasin A) [73], HSD11B2 [74], ACVR1C [75], NEUROD1 [76], SLIT2 [77], PPARGC1A [78], IGF1 [79], OSR1 [80], CYP46A1 [81], TLR3 [82], BMP7 [83], SELP (selectin P) [84], HLA-A [85], NOTCH2 [86], LRP1 [87], CLU (clusterin) [88], FCN1 [89], CDKN1A [90], SMAD3 [91], HLA-E [92], PTPRC (protein tyrosine phosphatase receptor type C) [93], MYH9 [94], JAK3 [95], IL6R [96], TIMP1 [97], DOCK8 [98], TNFRSF1B [99], ITGAL (integrin subunit alpha L) [100], CD47 [101], RARA (retinoic acid receptor alpha) [102], DGKD (diacylglycerol kinase delta) [103], PLEK (pleckstrin) [104], PREX1 [105], BSCL2 [106], PANX1 [107], IRF7 [108], NOTCH1 [109], STIM1 [110], TRIM13 [111], LRBA (LPS responsive beige-like anchor protein) [112], CXCR4 [113], MDM4 [114], MYO9B [115] and PDE5A [116] were revealed to be expressed in diabetes mellitus, but these genes might be novel targets for GDM. SIX1 [117], GREM1 [118], GHRHR (growth hormone releasing hormone receptor) [119], GPR37L1 [120], CYP2J2 [121], AQP4 [122], ROS1 [123], LBP (lipopolysaccharide binding protein) [124], SGCD (sarcoglycan delta) [125], CXCL14 [126], RGN...…”

Section: Discussionmentioning

confidence: 99%

Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

Vastrad¹,

Vastrad²

2021

Preprint

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Gestational diabetes mellitus (GDM) is a metabolic disorder during pregnancy. Numerous biomarkers have been identified that are linked with the occurrence and development of GDM. The aim of this investigation was to identify differentially expressed genes (DEGs) in GDM using a bioinformatics approach to elucidate their molecular pathogenesis. GDM associated expression profiling by high throughput sequencing dataset (GSE154377) was obtained from Gene Expression Omnibus (GEO) database including 28 normal pregnancy samples and 33 GDM samples. DEGs were identified using DESeq2. The gene ontology (GO) and REACTOME pathway enrichments of DEGs were performed by g:Profiler. Protein-protein interaction (PPI) networks were assembled with Cytoscape software and separated into modules using the PEWCC1 algorithm. MiRNA-hub gene regulatory network and TF-hub gene regulatory network were performed with the miRNet database and NetworkAnalyst database. Receiver Operating Characteristic (ROC) analyses was conducted to validate the hub genes. A total of 953 DEGs were identified, of which 478 DEGs were up regulated and 475 DEGs were down regulated. Furthermore, GO and REACTOME pathway enrichment analysis demonstrated that these DEGs were mainly enriched in multicellular organismal process, cell activation, formation of the cornified envelope and hemostasis. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB were identified as key genes in the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB in GDM were validated using ROC analysis. This investigation provides further insights into the molecular pathogenesis of GDM, which might facilitate the diagnosis and treatment of GDM.

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Expression of LRP1 and CHOP genes associated with peripheral neuropathy in type 2 diabetes mellitus: Correlations with nerve conduction studies

Cited by 11 publications

References 43 publications

Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis

Identification of Susceptibility Modules and Genes for Cardiovascular Disease in Diabetic Patients Using WGCNA Analysis

Heme, Heme Oxygenase, and Endoplasmic Reticulum Stress—A New Insight into the Pathophysiology of Vascular Diseases

Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

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