IFIH1 polymorphisms are significantly associated with type 1 diabetes and IFIH1 gene expression in peripheral blood mononuclear cells.

Genome-wide association (GWA) studies revealed a number of single nucleotide polymorphisms (SNPs) significantly associated with type 1 diabetes (T1D). In an attempt to confirm some of these candidate associations, we genotyped 2046 Caucasian patients and 2417 normal controls from the United States for SNPs in five genomic regions. While no evidence was obtained for four genomic regions (rs2929366/NM_144715 on chromosome 3, rs9127/Q7Z4C4 on chromosome 5, rs1445898/CAPSL on chromosome 5 and rs2302188/NM_033543 on chromosome 19), we provide strong evidence for association between T1D and multiple SNPs in the IFIH1 linkage disequilibrium (LD) block on chromosome 2q. Among the 10 SNPs genotyped for the 2q region, four SNPs located within the IFIH1 gene or at the 5' region of IFIH1 showed significant association with T1D in the Georgia population [odds ratio (OR) = 1.7-1.9] with the best P-value found at SNP rs1990760 (P = 8 x 10(-8) and OR = 1.9). Several SNPs outside of the IFIH1 gene also showed significant but weaker associations. Furthermore, IFIH1 gene expression levels in peripheral blood mononuclear cells are significantly correlated with IFIH1 genotypes, and higher IFIH1 levels are found in individuals with the susceptible genotypes (P = 0.005). Thus, both genetic association and gene expression data suggest that IFIH1 is the most plausible candidate gene implicated in T1D in this LD block.

APC dysfunction is correlated with defective suppression of T cell proliferation in human type 1 diabetes.

It is widely believed that CD4(+)CD25(+) regulatory T cells (Treg) are defective in type 1 diabetes (T1D) and other autoimmune diseases. However, this conclusion is based on the suboptimal in vitro suppression results from very small numbers of subjects. Furthermore, the cells responsible for the suboptimal suppression have not been defined. Therefore, we carried out extensive in vitro suppression assays using both autologous and heterologous donors of Tregs, effector T cells and antigen-presenting cells (APC) from both T1D patients and normal controls. Our in vitro suppression data indicated that a significantly higher proportion (40.0%) of T1D patients have "very low suppression" activity (defined as<25%) by autologous Treg compared to controls (6.3%) (p=0.002). Meta-analysis of the published results confirmed this observation with 45.7% low suppressors in T1D and 7.8% in controls (p=0.00002). Interestingly, suppression assays using heterologous Tregs, effector T cells and APC suggest that the source of APC is correlated with the suppression activity. The frequencies of CD4(+)CD25(+) and CD4(+)CD25(hi) T cells were found to increase with age in normal controls but not in T1D patients, resulting in significantly higher frequencies of CD4(+)CD25(+) (p=0.001) and CD4(+)CD25(hi) (p=0.009) T cells in young T1D subjects than age-matched controls but slightly lower CD4(+)CD25(+) (p=0.003) and CD4(+)CD25(hi) (p=0.08) T cells in old T1D subjects than age-matched controls.

Type 1 diabetes patients have significantly lower frequency of plasmacytoid dendritic cells in the peripheral blood.

Dendritic cells uniquely orchestrate the delicate balance between T cell immunity and regulation and an imbalance favoring immunogenic rather than tolerogenic DC is believed to contribute to the development of autoimmune diseases such as type 1 diabetes (T1D). In this study, we determined the frequencies of three blood DC subsets (pDC, mDC1 and mDC2) in 72 T1D patients and 75 normal controls using the Miltenyi blood DC enumeration kit. The frequency of blood pDC was found to be negatively correlated with subject age in both normal controls and T1D patients (p=0.0007), while the frequency of mDC1 and mDC2 do not change significantly with subject age. More importantly, the mean frequency of pDC in blood was, after adjusting for age, significantly lower in T1D (mean=0.127%) than controls (mean=0.188%) (p<6.0 x 10(-5)), whereas no difference was observed for mDC1 and mDC2 between T1D and controls. Furthermore, T1D patients have a lower proportion of pDC and higher proportion of mDC1 among the total blood DC population than normal controls. These results indicate that the frequency of blood pDC and the pDC/mDC1 ratio are negatively associated with T1D.

The iPlant Collaborative: Cyberinfrastructure for Plant Biology.

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

Assessing the utility of SELDI-TOF and model averaging for serum proteomic biomarker discovery.

The SELDI-TOF technique was used to profile serum proteins from Type 1 diabetes (T1D) patients and healthy autoantibody-negative (AbN) controls. Univariate and multivariate analyses were performed to identify putative biomarkers for T1D and to assess the reproducibility of the SELDI technique. We found 146 protein/peptide peaks (581 total peaks discovered) in human serum showing statistical differences in expression levels between T1D patients and controls, with 84% of these peaks showing technical replication. Because individual proteins did not offer great power for disease prediction, we used our model averaging approach that combines the information from multiple multivariate models to accurately classify T1D and control subjects (88.9% specificity and 90.0% sensitivity). Analyses of a test subset of the data showed less accuracy (82.8% specificity and 76.2% sensitivity), although the results are still positive. Unfortunately, no multivariate model could be replicated using the same samples. This first attempt of high throughput analyses of the human serum proteome in T1D patients suggests that model averaging is a viable method for developing biomarkers; however, the reproducibility of SELDI-TOF is currently not sufficient to be used for classification of complex diseases like T1D.

Lack of correlation between the levels of soluble cytotoxic T-lymphocyte associated antigen-4 (CTLA-4) and the CT-60 genotypes.

BACKGROUND: Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) plays a critical role in downregulation of antigen-activated immune response and polymorphisms at the CTLA-4 gene have been shown to be associated with several autoimmune diseases including type-1 diabetes (T1D). The etiological mutation was mapped to the CT60-A/G single nucleotide polymorphism (SNP) that is believed to control the processing and production of soluble CTLA-4 (sCTLA-4). METHODS: We therefore determined sCTLA-4 protein levels in the sera from 82 T1D patients and 19 autoantibody positive (AbP) subjects and 117 autoantibody negative (AbN) controls using ELISA. The CT-60 SNP was genotyped for these samples by using PCR and restriction enzyme digestion of a 268 bp DNA segment containing the SNP. Genotyping of CT-60 SNP was confirmed by dye terminating sequencing reaction. RESULTS: Higher levels of sCTLA-4 were observed in T1D (2.24 ng/ml) and AbP (mean = 2.17 ng/ml) subjects compared to AbN controls (mean = 1.69 ng/ml) with the differences between these subjects becoming significant with age (p = 0.02). However, we found no correlation between sCTLA-4 levels and the CTLA-4 CT-60 SNP genotypes. CONCLUSION: Consistent with the higher serum sCTLA-4 levels observed in other autoimmune diseases, our results suggest that sCTLA-4 may be a risk factor for T1D. However, our results do not support the conclusion that the CT-60 SNP controls the expression of sCTLA-4.