Association study of SREBF-2 gene polymorphisms and the risk of type 2 diabetes in a sample of Iranian population

Galavi, Hamidreza; Noorzehi, Nafiseh; Saravani, Ramin; Sargazi, Saman; Mollashahee-Kohkan, Fatemeh; Shahraki, Hojat

doi:10.1016/j.gene.2018.03.080

Cited by 16 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, we found an association of these two SNPs with not only higher cholesterol and glucose plasma concentrations, but also higher systolic blood pressure (p < 0.05). In contrast to our data, Galavi et al reported that the IVSI2 A1667G (rs2267443) and IVS1 C8407T (rs2267439) SNPs were not associated with plasma lipids levels in T2DM patients [21]. On the other hand, Grarup et al reported that the G allele of the 3’UTR G30009C (rs2297508) SNP was associated with higher cholesterol and glucose plasma levels.…”

Section: Discussioncontrasting

confidence: 99%

“…On the other hand, the SREBF-2 gene presents two relevant SNPs in the intronic region [at positions IVSI C8407T (rs2267439) and IVSI2 A1667G (rs2267443), respectively] and one in the exon 10 [at position G1784C Gly595Ala (rs2228314 has merged into rs4822063)]. These SREBF-2 variants have been associated with and increased risk for T2DM, hypercholesterolemia, premature coronary artery disease, and osteoarthritis [7,10,20,21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SREBF1c and SREBF2 gene polymorphisms are associated with acute coronary syndrome and blood lipid levels in Mexican population

et al. 2019

View full text Add to dashboard Cite

Aim It has recently been reported that the sterol regulatory element-binding transcription factors (SREBF-1c, and -2) contribute to the variation in the plasma lipids levels, which have an important role in the atherosclerotic plaque development. The aim of the present study was to evaluate whether the SREBF1c and SREBF2 gene single nucleotide polymorphisms (SNPs) are associated with plasma lipids levels and ACS susceptibility in a case-control association study. Material and methods A case-control study was carried out in 625 patients with ACS (82% men and 18% women, with a mean age of 57.97 ± 10.5 years) and 700 healthy controls (66% men and 34% women, with a mean age of 54.37 ± 7.65 years). The sample size was calculated for a statistical power of 80%. We genotyped three SREBF1c (rs2297508, rs11656665 and rs11868035) and three SREBF2 (rs2267439, rs2267443, and rs2228314) gene polymorphisms by 5’ exonuclease TaqMan assays. The associations were evaluated by logistic regression under the co-dominant, dominant, recessive, over-dominant and additive inheritance models. The contribution of the genotypes on the plasma lipids levels was evaluated by Student’s t-test. Results Under different models, the SREBF1c rs2297508 (OR = 1.50, pC Res = 0.03), SREBF1c rs11656665 (OR = 1.35, pC Dom = 0.02 and OR = 1.26, pC Add = 0.02) and SREBF2 rs2228314 (OR = 1.78, pC Res = 0.03, OR = 1.27, pC Add = 0.04) SNPs were associated with higher risk of ACS. On the other hand, the SREBF1c rs11868035 SNP was associated with lower risk of ACS (OR = 0.49, pC Co-dom = 0.001, OR = 0.66, pC Dom = 0.003, OR = 0.57, P Res = 0.003 and OR = 0.71, pC Add = 0.001). There was a statistically significant association of both SREBF1c rs11656665 and rs11868035 polymorphisms with plasma triglyceride levels. Conclusions In summary, our data suggest the association of the SREBF1c and SREBF2 SNPs with risk of developing ACS and with triglyceride levels in a Mexican population.

show abstract

Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SREBF1c and SREBF2 gene polymorphisms are associated with acute coronary syndrome and blood lipid levels in Mexican population

et al. 2019

View full text Add to dashboard Cite

show abstract

“…hsa-mir-4326 [566], hsa-mir-1470 [567], hsa-mir-2110 [567], hsa-mir-3615 [568], hsa-mir-1825 [569], STAT3 [570], KLF5 [571], SREBF2 [572], E2F6 [573], GATA3 [574] and USF1 [575] have been shown to be activated in cardiovascular diseases. hsa-mir-3615 [576], hsamir-1825 [577], STAT3 [578], KLF5 [579], SREBF2 [580], GATA3 [Huda et al 2018] and USF1 [Meex et al 2008] were identified to be closely associated in patients with diabetes mellitus. PABPC1, RXRA (retinoid X receptor alpha), hsamir-3668, MAX (myc-associated factor X), POU2F2 and ARID3A were might be the novel biomarkers for COVID-19 infection and its associated complications.…”

Section: Discussionmentioning

confidence: 99%

Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

Vastrad¹,

Vastrad²

2021

Preprint

View full text Add to dashboard Cite

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

show abstract

“…DEGs in GO terms and pathways might be linked with advancement of obesity associated type 2 diabetes mellitus. ERBB2 [36], DACT1 [37], ARAP1 [38], MYH9 [39], INPPL1 [40], SARM1 [41], NOTCH1 [42], ROBO1 [43], MAPK8IP1 [44], ANK1 [45], SARM1 [46], SREBF2 [47], SIK1 [48], PASK (PAS domain containing serine/threonine kinase) [49], NOS2 [50], OAS3 [51], KL (klotho) [52], PECAM1 [53], S100A12 [54], S100P [55], BATF3 [56], PLEK (pleckstrin) [57], ALOX5 [58], ARG1 [59], CXCL8 [60], CXCR1 [61], PTAFR (platelet activating factor receptor) [62], PYGL (glycogen phosphorylase L) [63], TCF4 [64], CAMP (cathelicidin antimicrobial peptide) [65], RUNX2 [66], PLA2G2A [67], GCG (glucagon) [68], RARRES2 [69] and HAP1 [70] were involved in the genesis of type 2 diabetes mellitus. Recent studies have reported that ACHE (acetylcholinesterase) [71], FGFR3 [72], VLDLR (very low density lipoprotein receptor) [73], SHC1 [74], HDAC6 [75], CHRNA2 [76], CASR (calcium sensing receptor) [77], ELK1 [78], TYK2 [79], CIITA (class II major histocompatibility complex transactivator) [80], ZAP70 [81], GPT (glutamic--pyruvic transaminase) [82], CHI3L1 [83], AIF1 [84], MMP9 [85], ITGB2 [86], CFD (complement factor D) [87], C3AR1 [88], LGALS1 [89], CD14 [90], TIMP1 [91], TLR2 [92], LTF (lactotransferrin) [93], BRCA2 [94] and IGFBP3 [95] promotes the development of obesity.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of Key Genes and Pathways for Obesity Associated Type 2 Diabetes Mellitus as a Therapeutic Target

Vastrad¹,

Tengli

Vastrad³

et al. 2020

Preprint

View full text Add to dashboard Cite

Obesity associated type 2 diabetes mellitus is one of the most common metabolic disorder worldwide. The prognosis of obesity associated type 2 diabetes mellitus patients has remained poor, though considerable efforts have been made to improve the treatment of this metabolic disorder. Therefore, identifying significant differentially expressed genes (DEGs) associated in metabolic disorder advancement and exploiting them as new biomarkers or potential therapeutic targets for metabolic disorder is highly valuable. Differentially expressed genes (DEGs) were screened out from gene expression omnibus (GEO) dataset (GSE132831) and subjected to GO and REACTOME pathway enrichment analyses. The protein - protein interactions network, module analysis, target gene - miRNA regulatory network and target gene - TF regulatory network were constructed, and the top ten hub genes were selected. The relative expression of hub genes was detected in RT-PCR. Furthermore, diagnostic value of hub genes in obesity associated type 2 diabetes mellitus patients was investigated using the receiver operating characteristic (ROC) analysis. Small molecules were predicted for obesity associated type 2 diabetes mellitus by using molecular docking studies. A total of 872 DEGs, including 439 up regulated genes and 432 down regulated genes were observed. Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for obesity associated type 2 diabetes mellitus. The hub genes were validated in obesity associated type 2 diabetes mellitus. This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for obesity associated type 2 diabetes mellitus.

show abstract

Association study of SREBF-2 gene polymorphisms and the risk of type 2 diabetes in a sample of Iranian population

Cited by 16 publications

References 23 publications

SREBF1c and SREBF2 gene polymorphisms are associated with acute coronary syndrome and blood lipid levels in Mexican population

SREBF1c and SREBF2 gene polymorphisms are associated with acute coronary syndrome and blood lipid levels in Mexican population

Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

Bioinformatics Analysis of Key Genes and Pathways for Obesity Associated Type 2 Diabetes Mellitus as a Therapeutic Target

Contact Info

Product

Resources

About