Association of BANK1 and cytokine gene polymorphisms with type 1 diabetes in Tunisia

Zouidi, Ferjani; Stayoussef, Mouna; Bouzid, Dorra; Fourati, H.; Abida, Olfa; Costa, João; Ayed, M. Ben; Fakhfakh, R.; Kammoun, T.; Hachicha, Monjia; Penha‐Gonçalves, Carlos; Masmoudi, Hatem

doi:10.1016/j.gene.2013.12.008

Cited by 14 publications

(10 citation statements)

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“…Increased levels of these cytokines were detected in patients with diabetes and obesity [9][10][11][12][13]. IL4 and IL15 gene polymorphisms were correlated with diabetes [20][21][22]. The results of our study suggest that IL4 and IL15 gene polymorphisms are not the significant risk factors of GDM.…”

Section: Discussionmentioning

confidence: 52%

“…Several polymorphisms in the CCL2, CCL5, IL4 and IL15 genes have been found that may alter gene expression and production of proteins [14][15][16][17][18][19]. These polymorphisms were correlated with diabetes susceptibility and severity [20][21][22][23][24]. The aim of this study is to examine the association between CCL2, CCL5, IL4 and IL15 gene polymorphisms and the development of gestational diabetes mellitus.…”

Section: Introductionmentioning

confidence: 99%

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CCL2, CCL5, IL4 and IL15 Gene Polymorphisms in Women with Gestational Diabetes Mellitus

et al. 2016

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Gestational diabetes mellitus (GDM) is a glucose intolerance that occurs during pregnancy. Several studies suggest that inflammation contributes to pregnancy-induced insulin resistance and the development of glucose intolerance. The aim of this study is to examine the association between the ,, and gene polymorphisms and the development of GDM. This study included 411 pregnant women who underwent a 75 g oral glucose tolerance test at 24-28 weeks of gestation. Participants were categorised into 2 groups according to results of the oral glucose tolerance test (OGTT). The GDM group included 204 pregnant women who were diagnosed with GDM. The normal glucose tolerance group included 207 pregnant women with normal values in the OGTT. To discriminate the rs1024611 and rs4586, rs2107538, rs2243250, rs2857261 and rs2254514 alleles, TaqMan Pre-Designed SNP Genotyping Assays were used. GDM was significantly associated with genotypes and alleles of the rs1024611 and rs4586 polymorphisms, while there was no statistically significant association between the rs2107538, rs2243250, rs2857261, and rs2254514 gene polymorphisms and GDM. In a multivariate regression analysis, age and BMI before pregnancy were independent significant predictors of a higher risk of GDM, while a lower number of G alleles rs1024611 was protective against GDM. Moreover, women with the GG rs1024611 and CC rs4586 genotype tended to have lower body mass and BMI increases during pregnancy, as well as lower newborn body mass. The results of our study suggest an association between gene polymorphisms and GDM.

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Section: Discussionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

CCL2, CCL5, IL4 and IL15 Gene Polymorphisms in Women with Gestational Diabetes Mellitus

et al. 2016

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“…Hou et al [44] and Olesen et al [45] demonstrated that DACT2 and KCND3 were found to be substantially related to atrial fibrillation. Ge and Concannon [46], Ferjeni et al [47], Anquetil et al [48], Glawe et al [49], Kawabata et al [50], Li et al [51], Buraczynska et al [52], Amini et al [53], Yang et al [54], Du Toit et al [55], Hirose et al [56], Zhang et al [57], Griffin et al [58], Zouidi et al [59], Trombetta et al [60], Alharbi et al [61], Ikarashi et al [62], Dharmadhikari et al [63], Sutton et al [64] and Deng et al [65] reported that UBASH3A, ZAP70, IDO1, ITGAL (integrin subunit alpha L). ITGB7, RASGRP1, CNR1, SLC2A1, SLC11A1, GPR84, SSTR5, KCNB1, GLUL (glutamate-ammonia ligase), BANK1, CACNA1E, LGR5, AQP3, SIGLEC7, SSTR2 and DNER (delta/notch like EGF repeat containing) could be an index for diabetes, but these genes might be responsible for progression of HF.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Vastrad¹,

Tengli

Vastrad³

2021

Preprint

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Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene - miRNA regulatory network and target gene - TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

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“…The observed association between the wGRS for SLE and T1D-related phenotypes in BioVU and eMERGE, along with the nominal association in the IVWR, suggest shared genetic risk between these phenotypes. Genetic studies have not only shown that SLE and T1D share risk loci (IRF7, 38 SOCS1, 39 IKZF1, 40 TNFAIP3, 41 IL10, 24 TCF7, 49 and BANK1 50 ), but they also have common risk alleles (e.g. : rs2476601 in PTPN22, rs2304256 in TYK2, 36 rs2111485 in IFIH1, 51 and rs1801274 in FCGR2A 52 ) or risk alleles in close LD (e.g.…”

Section: Discussionmentioning

confidence: 99%

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis

Kawai

Shi

Liu

et al. 2021

Lupus

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Objectives To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. Methods Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. Results The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10−5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. Conclusion A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

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Association of BANK1 and cytokine gene polymorphisms with type 1 diabetes in Tunisia

Cited by 14 publications

References 33 publications

CCL2, CCL5, IL4 and IL15 Gene Polymorphisms in Women with Gestational Diabetes Mellitus

CCL2, CCL5, IL4 and IL15 Gene Polymorphisms in Women with Gestational Diabetes Mellitus

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis

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