The association between type 1 diabetes and the ITPR3 gene polymorphism due to linkage disequilibrium with HLA class II

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is the metabolic disorder that appears during pregnancy. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non-GDM samples were analyzed. Functional enrichment analysis were performed using ToppGene. Then we constructed the protein–protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA–hub gene network and TF–hub gene regulatory network. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up-regulated and 430 down-regulated genes. Functional enrichment analysis showed these DEGs were mainly enriched in reproduction, cell adhesion, cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3 and PRKCA were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. This investigation identified hub genes, signal pathways and therapeutic agents, which might help us, enhance our understanding of the mechanisms of GDM and find some novel therapeutic agents for GDM.

PANEV: an R package for a pathway-based network visualization

Background

During the last decade, with the aim to solve the challenge of post-genomic and transcriptomic data mining, a plethora of tools have been developed to create, edit and analyze metabolic pathways. In particular, when a complex phenomenon is considered, the creation of a network of multiple interconnected pathways of interest could be useful to investigate the underlying biology and ultimately identify functional candidate genes affecting the trait under investigation.

Results

PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. The network graph visualization helps to interpret functional profiles of a cluster of genes.

Conclusions

The suite has no species constraints and it is ready to analyze genomic or transcriptomic outcomes. Users need to supply the list of candidate genes, specify the target pathway(s) and the number of interconnected downstream and upstream pathways (levels) required for the investigation. The package is available at https://github.com/vpalombo/PANEV.

Genetics of the HLA region in the prediction of type 1 diabetes

Type 1 diabetes (T1D) is one of the most widely studied complex genetic disorders, and the genes in HLA are reported to account for approximately 40-50% of the familial aggregation of T1D. The major genetic determinants of this disease are polymorphisms of class II HLA genes encoding DQ and DR. The DR-DQ haplotypes conferring the highest risk are DRB1*03:01-DQA1*05:01-DQB1*02:01 (abbreviated "DR3") and DRB1*04:01/02/04/05/08-DQA1*03:01-DQB1*03:02/04 (or DQB1*02; abbreviated "DR4"). The risk is much higher for the heterozygote formed by these two haplotypes (OR = 16.59; 95% CI, 13.7-20.1) than for either of the homozygotes (DR3/DR3, OR = 6.32; 95% CI, 5.12-7.80; DR4/DR4, OR = 5.68; 95% CI, 3.91). In addition, some haplotypes confer strong protection from disease, such as DRB1*15:01-DQA1*01:02-DQB1*06:02 (abbreviated "DR2"; OR = 0.03; 95% CI, 0.01-0.07). After adjusting for the genetic correlation with DR and DQ, significant associations can be seen for HLA class II DPB1 alleles, in particular, DPB1*04:02, DPB1*03:01, and DPB1*02:02. Outside of the class II region, the strongest susceptibility is conferred by class I allele B*39:06 (OR =10.31; 95% CI, 4.21-25.1) and other HLA-B alleles. In addition, several loci in the class III region are reported to be associated with T1D, as are some loci telomeric to class I. Not surprisingly, current approaches for the prediction of T1D in screening studies take advantage of genotyping HLA-DR and HLA-DQ loci, which is then combined with family history and screening for autoantibodies directed against islet-cell antigens. Inclusion of additional moderate HLA risk haplotypes may help identify the majority of children with T1D before the onset of the disease.

Human leukocyte antigen class II and type 1 diabetes in Latin America: a combined meta-analysis of association and family-based studies

Conclusions from association studies could be spurious because of population stratification; therefore we combined association with family studies seeking to confirm which human leukocyte antigen (HLA) class II alleles/haplotypes were associated with type 1 diabetes (T1D) in the admixed Latin America. By calculating the effect summary odds ratios (OR) and their 95% confidence intervals (95% CI), data up to June 2010 showed that risk associations were observed with DRB1*0301-DQA1*0501-DQB1*0201 (odds ratio [OR]: 7.51; 95% confidence interval [CI]: 3.69-15.25) and DQB1*0302 in presence of DRB1*0405 (OR: 11.64; 95% CI: 3.15-43.01) or DRB1*0401 (OR: 5.85; 95% CI: 3.07-11.14). In contrast, DRB1*0404-DQB1*0302 had a nonsignificant TID risk (OR: 2.23; 95% CI: 0.91-5.43). T1D protective associations were observed with DRB1*11-DQA1*0501-DQB1*0301 (OR: 0.24; 95% CI: 0.1-0.56) and DRB1*15-DQA1*0102-DQB1*0602 (OR: 0.35; 95% CI: 0.17-0.73). These results were similar to those observed in Caucasian and other populations, thus highlighting the primary role of class II HLA in T1D regardless of ethnicity. A DRB1*04 risk hierarchy was confirmed with the DRB1*0405 being in the top. A binding prediction analysis disclosed possible receptor-ligand interactions in the HLA-antigenic peptide complex.

Evidence for somatic gene conversion and deletion in bipolar disorder, Crohn's disease, coronary artery disease, hypertension, rheumatoid arthritis, type-1 diabetes, and type-2 diabetes

BACKGROUND

During gene conversion, genetic information is transferred unidirectionally between highly homologous but non-allelic regions of DNA. While germ-line gene conversion has been implicated in the pathogenesis of some diseases, somatic gene conversion has remained technically difficult to investigate on a large scale.

METHODS

A novel analysis technique is proposed for detecting the signature of somatic gene conversion from SNP microarray data. The Wellcome Trust Case Control Consortium has gathered SNP microarray data for two control populations and cohorts for bipolar disorder (BD), cardiovascular disease (CAD), Crohn's disease (CD), hypertension (HT), rheumatoid arthritis (RA), type-1 diabetes (T1D) and type-2 diabetes (T2D). Using the new analysis technique, the seven disease cohorts are analyzed to identify cohort-specific SNPs at which conversion is predicted. The quality of the predictions is assessed by identifying known disease associations for genes in the homologous duplicons, and comparing the frequency of such associations with background rates.

RESULTS

Of 28 disease/locus pairs meeting stringent conditions, 22 show various degrees of disease association, compared with only 8 of 70 in a mock study designed to measure the background association rate (P < 10-9). Additional candidate genes are identified using less stringent filtering conditions. In some cases, somatic deletions appear likely. RA has a distinctive pattern of events relative to other diseases. Similarities in patterns are apparent between BD and HT.

CONCLUSIONS

The associations derived represent the first evidence that somatic gene conversion could be a significant causative factor in each of the seven diseases. The specific genes provide potential insights about disease mechanisms, and are strong candidates for further study.

Association between common variation in genes encoding sweet taste signaling components and human sucrose perception

Variation in taste perception of different chemical substances is a well-known phenomenon in both humans and animals. Recent advances in the understanding of sweet taste signaling have identified a number of proteins involved in this signal transduction. We evaluated the hypothesis that sequence variations occurring in genes encoding taste signaling molecules can influence sweet taste perception in humans. Our population consisted of unrelated individuals (n = 160) of Caucasian, African-American, and Asian descent. Threshold and suprathreshold sensitivities of participants for sucrose were estimated using a sorting test and signal detection analysis that produced cumulative R-index area under the curve (AUC) scores. Genetic association analysis revealed significant correlation of sucrose AUC scores with genetic variation occurring in the GNAT3 gene (single point P = 10(-3) to 10(-4)), which encodes the taste-specific G(alpha) protein subunit gustducin. Subsequent sequencing identified additional GNAT3 variations having significant association with sucrose AUC scores. Collectively, GNAT3 polymorphisms explain 13% of the variation in sucrose perception. Our findings underscore the importance of common genetic variants influencing human taste perception.

Exploring the diabetogenicity of the HLA-B18-DR3 CEH: independent association with T1D genetic risk close to HLA-DOA

The objective of this study was to identify additional diabetes susceptibility markers in the MHC that could be responsible for the differential diabetogenicity of different HLA-DR3 CEHs. High-resolution SNP genotyping of the MHC was carried out in 15 type 1 diabetes (T1D) patients and 39 non-diabetic controls, homozygous for DR3-DQ2 and with one copy of the A(*)30-B(*)18-MICA(*)4-F1C30-DRB1(*)0301-DQB1(*)0201-DPB1(*)0202 HLA haplotype. Significantly associated SNPs were replicated in an independent sample of 554 T1D patients and 841 controls without HLA matching. Electrophoretic mobility shift assay was used to show a functional effect of an associated SNP. Seven SNPs showed evidence of association in the initial discovery experiment. Upon replication, only rs419434 (upstream HLA-DOA gene) remained significant. A functional variant (rs432375) in complete LD with rs419434 was shown to affect USF-1 binding and could be responsible for the association signal in the region. We have identified a new susceptibility locus within the MHC with a modest contribution to T1D (OR=1.93; CI: 1.52-2.44; P=10(-8)) that is independent of HLA-DRB1 locus.

Conditional analyses on the T1DGC MHC dataset: novel associations with type 1 diabetes around HLA-G and confirmation of HLA-B

The major histocompatibility complex (MHC) is known to harbour genetic risk factors for type 1 diabetes (T1D) additional to the class II determinants HLA-DRB1, -DQA1 and -DQB1, but strong linkage disequilibrium (LD) has made efforts to establish their location difficult. This study utilizes a dataset generated by the T1D genetics consortium (T1DGC), with genotypes for 2965 markers across the MHC in 2321 T1D families of multiple (mostly Caucasian) ethnicities. Using a comprehensive approach consisting of complementary conditional methods and LD analyses, we identified three regions with T1D association, independent both of the known class II determinants and of each other. A subset of polymorphisms that could explain most of the association in each region included single nucleotide polymorphisms (SNPs) in the vicinity of HLA-G, particular HLA-B and HLA-DPB1 alleles, and SNPs close to the COL11A2 and RING1 genes. Apart from HLA-B and HLA-DPB1, all of these represent novel associations, and subpopulation analyses did not indicate large population-specific differences among Caucasians for our findings. On account of the unusual genetic complexity of the MHC, further fine mapping is demanded, with the possible exception of HLA-B. However, our results mean that these efforts can be focused on narrow, defined regions of the MHC.