Association of CD247 (CD3ζ) gene polymorphisms with T1D and AITD in the population of northern Sweden

Risk of celiac disease autoimmunity is modified by interactions between CD247 and environmental exposures

Season of birth, viral infections, HLA haplogenotypes and non-HLA variants are implicated in the development of celiac disease and celiac disease autoimmunity, suggesting a combined role of genes and environmental exposures. The aim of the study was to further decipher the biological pathways conveying the season of birth effect in celiac disease autoimmunity to gain novel insights into the early pathogenesis of celiac disease. Interactions between season of birth, genetics, and early-life environmental factors on the risk of celiac autoimmunity were investigated in the multicenter TEDDY birth cohort study. Altogether 6523 genetically predisposed children were enrolled to long-term follow-up with prospective sampling and data collection at six research centers in the USA, Germany, Sweden and Finland. Celiac disease autoimmunity was defined as positive tissue transglutaminase antibodies in two consecutive serum samples. There was a significant season of birth effect on the risk of celiac autoimmunity. The effect was dependent on polymorphisms in CD247 gene encoding for CD3ζ chain of TCR-CD3 complex. In particular, children with major alleles for SNP rs864537A > G, in CD247 (AA genotype) had an excess risk of celiac autoimmunity when born March-August as compared to other months. The interaction of CD247 with season of birth on autoimmunity risk was accompanied by interactions with febrile infections between the ages of 3-6 months. Considering the important role of TCR-CD3 complex in the adaptive immune response and our findings here, CD247 variants and their possible effect of subgroups in autoimmunity development could be of interest in the design of future gene-environment studies of celiac disease. ClinicalTrials.gov Identifier: NCT00279318.

A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions

MOTIVATION

Linear or nonlinear interactions of multiple single-nucleotide polymorphisms (SNPs) play an important role in understanding the genetic basis of complex human diseases. However, combinatorial analytics in high-dimensional space makes it extremely challenging to detect multiorder SNP interactions. Most classic approaches can only perform one task (for detecting k-order SNP interactions) in each run. Since prior knowledge of a complex disease is usually not available, it is difficult to determine the value of k for detecting k-order SNP interactions.

METHODS

A novel multitasking ant colony optimization algorithm (named MTACO-DMSI) is proposed to detect multiorder SNP interactions, and it is divided into two stages: searching and testing. In the searching stage, multiple multiorder SNP interaction detection tasks (from 2nd-order to kth-order) are executed in parallel, and two subpopulations that separately adopt the Bayesian network-based K2-score and Jensen-Shannon divergence (JS-score) as evaluation criteria are generated for each task to improve the global search capability and the discrimination ability for various disease models. In the testing stage, the G test statistical test is adopted to further verify the authenticity of candidate solutions to reduce the error rate.

RESULT

Three multiorder simulated disease models with different interaction effects and three real age-related macular degeneration (AMD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) datasets were used to investigate the performance of the proposed MTACO-DMSI. The experimental results show that the MTACO-DMSI has a faster search speed and higher discriminatory power for diverse simulation disease models than traditional single-task algorithms. The results on real AMD data and RA and T1D datasets indicate that MTACO-DMSI has the ability to detect multiorder SNP interactions at a genome-wide scale. Availability and implementation: https://github.com/shouhengtuo/MTACO-DMSI/.

Advances in CD247

CD247, which is also known as CD3ζ, CD3H, CD3Q, CD3Z, IMD25, T3Z, and TCRZ, encodes CD3ζ protein, which is expressed primarily in natural killer (NK) and T cells. Since the discovery of the ζ peptide in 1986, it has been continuously investigated. In this paper, we review the composition, molecular mechanisms and regulatory factors of CD247 expression in T cells; and review the autoimmune diseases, tumors and inflammatory diseases associated with CD247, providing a detailed and comprehensive reference for further research on the mechanism of CD247 and related diseases.

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

Objectives:

The underlying molecular mechanisms of diabetic nephropathy have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of diabetic nephropathy.

Methods:

We downloaded next-generation sequencing data set GSE142025 from Gene Expression Omnibus database having 28 diabetic nephropathy samples and nine normal control samples. The differentially expressed genes between diabetic nephropathy and normal control samples were analyzed. Biological function analysis of the differentially expressed genes was enriched by Gene Ontology and REACTOME pathways. Then, we established the protein–protein interaction network, modules, miRNA-differentially expressed gene regulatory network and transcription factor-differentially expressed gene regulatory network. Hub genes were validated by using receiver operating characteristic curve analysis.

Results:

A total of 549 differentially expressed genes were detected including 275 upregulated and 274 downregulated genes. The biological process analysis of functional enrichment showed that these differentially expressed genes were mainly enriched in cell activation, integral component of plasma membrane, lipid binding, and biological oxidations. Analyzing the protein–protein interaction network, miRNA-differentially expressed gene regulatory network and transcription factor-differentially expressed gene regulatory network, we screened hub genes MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB, and NR4A1 by the Cytoscape software. The receiver operating characteristic curve analysis confirmed that hub genes were of diagnostic value.

Conclusions:

Taken above, using integrated bioinformatics analysis, we have identified key genes and pathways in diabetic nephropathy, which could improve our understanding of the cause and underlying molecular events, and these key genes and pathways might be therapeutic targets for diabetic nephropathy.

A simple genotyping method for CD247 3'-untranslated region polymorphism rs1052231 and characterization of a reference cell panel

CD247 (or CD3‐ζ chain) is an essential adaptor and signal‐transducing molecule of the T‐cell antigen receptor (TCR) complex, and it also couples to NK‐cell activating receptors such as NKp46, NKp30 and CD16A (FcγRIII). Noncoding sequence polymorphisms and variations in CD247 expression, a tightly regulated process, have been related with an altered immune response in multiple health conditions. A single nucleotide polymorphism (T > A) at nucleotide 844 of the CD247 3′‐untranslated region, rs1052231, has been related with lower CD247 gene expression and it has been investigated as a potential biomarker of autoimmune disease. We present here a simple, accurate, reliable, time‐efficient, and cost‐effective method for CD247‐rs1052231 genotyping. Using this method, based on polymerase chain reaction with confronting two‐pair primers (PCR‐CTPP), we have also characterized the CD247‐rs1052231 genotypes in a panel of worldwide available cell lines, which should facilitate study of the role of this polymorphism in immunity and human health.

Assessment of differentially methylated loci in individuals with end-stage kidney disease attributed to diabetic kidney disease: an exploratory study

BACKGROUND

A subset of individuals with type 1 diabetes mellitus (T1DM) are predisposed to developing diabetic kidney disease (DKD), the most common cause globally of end-stage kidney disease (ESKD). Emerging evidence suggests epigenetic changes in DNA methylation may have a causal role in both T1DM and DKD. The aim of this exploratory investigation was to assess differences in blood-derived DNA methylation patterns between individuals with T1DM-ESKD and individuals with long-duration T1DM but no evidence of kidney disease upon repeated testing to identify potential blood-based biomarkers. Blood-derived DNA from individuals (107 cases, 253 controls and 14 experimental controls) were bisulphite treated before DNA methylation patterns from both groups were generated and analysed using Illumina's Infinium MethylationEPIC BeadChip arrays (n = 862,927 sites). Differentially methylated CpG sites (dmCpGs) were identified (false discovery rate adjusted p ≤ × 10-8 and fold change ± 2) by comparing methylation levels between ESKD cases and T1DM controls at single site resolution. Gene annotation and functionality was investigated to enrich and rank methylated regions associated with ESKD in T1DM.

RESULTS

Top-ranked genes within which several dmCpGs were located and supported by functional data with methylation look-ups in other cohorts include: AFF3, ARID5B, CUX1, ELMO1, FKBP5, HDAC4, ITGAL, LY9, PIM1, RUNX3, SEPTIN9 and UPF3A. Top-ranked enrichment pathways included pathways in cancer, TGF-β signalling and Th17 cell differentiation.

CONCLUSIONS

Epigenetic alterations provide a dynamic link between an individual's genetic background and their environmental exposures. This robust evaluation of DNA methylation in carefully phenotyped individuals has identified biomarkers associated with ESKD, revealing several genes and implicated key pathways associated with ESKD in individuals with T1DM.

Type 1 Diabetes and Autoimmune Thyroid Disease-The Genetic Link

Type 1 diabetes (T1D) and autoimmune thyroid disease (AITD) are the most frequent chronic autoimmune diseases worldwide. Several autoimmune endocrine and non-endocrine disorders tend to occur together. T1D and AITD often cluster in individuals and families, seen in the formation of autoimmune polyendocrinopathy (AP). The close relationship between these two diseases is largely explained by sharing a common genetic background. The HLA antigens DQ2 (DQA1*0501-DQB1*0201) and DQ8 (DQA1*0301-DQB1*0302), tightly linked with DR3 and DR4, are the major common genetic predisposition. Moreover, functional single nucleotide polymorphisms (or rare variants) of various genes, such as the cytotoxic T-lymphocyte- associated antigen (CTLA4), the protein tyrosine phosphatase non-receptor type 22 (PTPN22), the interleukin-2 Receptor (IL2Ra), the Vitamin D receptor (VDR), and the tumor-necrosis-factor-α (TNF) that are involved in immune regulation have been identified to confer susceptibility to both T1D and AITD. Other genes including cluster of differentiation 40 (CD40), the forkhead box P3 (FOXP3), the MHC Class I Polypeptide-Related Sequence A (MICA), insulin variable number of tandem repeats (INS-VNTR), the C-Type Lectin Domain Containing 16A (CLEC16A), the Erb-B2 Receptor Tyrosine Kinase 3 (ERBB3) gene, the interferon-induced helicase C domain-containing protein 1 (IFIH1), and various cytokine genes are also under suspicion to increase susceptibility to T1D and AITD. Further, BTB domain and CNC homolog 2 (BACH2), C-C motif chemokine receptor 5 (CCR5), SH2B adaptor protein 3 (SH2B3), and Rac family small GTPase 2 (RAC2) are found to be associated with T1D and AITD by various independent genome wide association studies and overlap in our list, indicating a strong common genetic link for T1D and AITD. As several susceptibility genes and environmental factors contribute to the disease aetiology of both T1D and AITD and/or AP subtype III variant (T1D+AITD) simultaneously, all patients with T1D should be screened for AITD, and vice versa.

Microarray profile of B cells from Graves' disease patients reveals biomarkers of proliferation

B lymphocytes are the source of autoantibodies against the thyroid-stimulating hormone receptor (TSHR) in Graves' disease (GD). Characterization of autoimmune B-cell expression profiles might enable a better understanding of GD pathogenesis. To reveal this, the expression levels of long noncoding RNAs (lncRNAs) and mRNAs (genes) in purified B cells from patients with newly diagnosed GD and healthy individuals were compared using microarrays, which elucidated 604 differentially expressed lncRNAs (DE-lncRNAs) and 410 differentially expressed genes (DEGs). GO and pathway analyses revealed that the DEGs are mainly involved in immune response. A protein-protein interaction network presented experimentally validated interactions among the DEGs. Two independent algorithms were used to identify the DE-lncRNAs that regulate the DEGs. Functional annotation of the deregulated lncRNA-mRNA pairs identified 14 pairs with mRNAs involved in cell proliferation. The lncRNAs TCONS_00022357-XLOC_010919 and n335641 were predicted to regulate TCL1 family AKT coactivator A (TCL1A), and the lncRNA n337845 was predicted to regulate SH2 domain containing 1A (SH2D1A). TCL1A and SH2D1A are highly involved in B-cell proliferation. The differential expression of both genes was validated by qRT-PCR. In conclusion, lncRNA and mRNA expression profiles of B cells from patients with GD indicated that the lncRNA-mRNA pairs n335641-TCL1A, TCONS_00022357-XLOC_010919-TCL1A, and n337845-SH2D1A may participate in GD pathogenesis by modulating B-cell proliferation and survival. Therefore, the identified lncRNA and mRNA may represent novel biomarkers and therapeutic targets for GD.