Type 1 Diabetes (T1D) is a chronic inflammatory disease resulting from complex interactions between susceptibility genes, the environment, and the immune system, ultimately leading to the destruction o f pancreatic islet cells and insulin deficiency. Previous studies have examined the series o f molecular, cellular, and protein changes occurring within subsets of individuals and how these are associated with particular disease states. Genome wide association studies have revealed a large number o f genetic susceptibility intervals including those implicated in disease pathogenesis, the identification o f various markers for risk assessment, the classification o f disease or complications, and finally markers for monitoring therapies for disease. However, none of these studies to date is without seriously limitations. First, although microarray based gene expression profiling is a powerful tool in discovery; results must be validated by alternate techniques. Second, due to the inherent heterogeneity of the human population large sample sizes in each group must be used in order to handle the expected large expression variations among individual subject. Third, for accurate normalization of Real-Time PCR expression data appropriate reference genes must be selected. We proposed a large scale gene expression validation study to address the limitations of previous studies. Validation studies were performed using high throughput Real-Time RT-PCR on peripheral blood mononuclear cells (PBMCs) o f 928 individuals with T1D and 922 individuals as antibody negative (AbN) controls, recruited through the Prospective Assessment in Newborns of Diabetes Autoimmunity (PANDA) study. This dissertation work validated the gene expression changes among 28 genes shown to have differential expression in T1D patients as compared to controls. These genes were selected based on their function, role in inflammatory or the immune response, and any previously documented reference to a role in T1D. Our aims were to 1) identify gene expression changes which may be occurring specifically in diabetic complications, and 2) identify gene expression changes which may result in an increased state o f oxidative stress in the diabetic state. For validation studies, we divided the 28 genes into two subsets based on related function to ask whether any gene expression signatures could be associated with diabetes, diabetic complications, or oxidative stress in the diabetic state. Our studies revealed genes that are involved in inflammation, immune regulation, and antigen processing and presentation are significantly altered in the PBMCs o f T1D patients. Eight genes (S100A8, S100A9, MNDA, SELL, TGFB1, PSMB3, CD74, and IL12A) were shown to have higher expression, with three genes (GNLY, PSMA4, and SMAD7) having lower expression, in T1D when compared to controls. The data also suggested that inflammatory mediators secreted mainly by myeloid cells are implicated in T1D and its complications (Odds ratios OR = 1.3-2.6, adjusted P value= 0.005- 1.08 x 10 8), and particularly in those patients with nephropathy (OR=4.8-7.9, adjusted P value < 0.005). Validation studies also revealed nine genes (LAT2, MAPK1, APOBEC3B, SOD2, NDUFB3, STK40, PRKD2, ITGB2, and COX7B) with higher expression in T1D. These genes are involved in general pathways of inflammation and immune response; however SOD2, NDUFB3, and COX7B (OR=l.l-1.27, adjusted P value= 0.007-0.47) are functionally involved in the mechanisms o f the mitochondria and may play a role in the increased state of oxidative stress seen in T1D. In these studies we have validated and confirmed the gene expression differences between T1D and control subjects initially suggested by microarray. Our experimental design has addressed each of the limitations posed by earlier studies in the largest scale study to date on gene expression profiles in human T1D. We have demonstrated that gene expression is significantly different between autoantibody negative (AbN) controls and T1D patients without any complications. Genes implicated in immune function (S100A8, S100A9, MNDA, IL12A), immune regulation and promotion (TGFB1, SELL), antigen processing and presentation (CD74, PSMB3), and mitochondrial function (SOD2, NDUFB3, COX7B) have higher expression in T1D and support the notion that chronic inflammation and cellular oxidative stress contribute to the development of T1D and associated complications. The understanding gained from our results implies a translational potential for the use o f gene expression profiles in the classification o f at risk individuals for both T1D and complication. Further, our understanding into the role that the immune system plays in cellular oxidative stress leading to the diabetic state may serve to provide prevention therapies however there remains much to be learned before this is attainable.

Type 1 diabetes (T1D) is an autoimmune disease resulting from the complex interaction between multiple susceptibility genes, environmental factors and the immune system. Over 40 T1D susceptibility regions have been suggested by recent genome-wide association studies; however, the specific genes and their role in the disease remain elusive. The objective of this study is to identify the susceptibility gene(s) in the 12q13 region and investigate the functional link to the disease pathogenesis. A total of 19 SNPs in the 12q13 region were analyzed by the TaqMan assay for 1,434 T1D patients and 1,865 controls. Thirteen of the SNPs are associated with T1D (best p = 4x10(-11)), thus providing confirmatory evidence for at least one susceptibility gene in this region. To identify candidate genes, expression of six genes in the region was analyzed by real-time RT-PCR for PBMCs from 192 T1D patients and 192 controls. SNP genotypes in the 12q13 region are the main factors that determine ERBB3 mRNA levels in PBMCs. The protective genotypes for T1D are associated with higher ERBB3 mRNA level (p<10(-10)). Furthermore, ERBB3 protein is expressed on the surface of CD11c(+) cells (dendritic cells and monocytes) in peripheral blood after stimulation with LPS, polyI:C or CpG. Subjects with protective genotypes have significantly higher percentages of ERBB3(+) monocytes and dendritic cells (p = 1.1x10(-9)); and the percentages of ERBB3(+) cells positively correlate with the ability of APC to stimulate T cell proliferation (R(2) = 0.90, p<0.0001). Our results indicate that ERBB3 plays a critical role in determining APC function and potentially T1D pathogenesis.

BACKGROUND: Infection with high-risk type human papilloma viruses (HPVs) is associated with cervical carcinomas and with a subset of head and neck squamous cell carcinomas. Viral E6 and E7 oncogenes cooperate to achieve cell immortalization by a mechanism that is not yet fully understood. Here, human keratinocytes were immortalized by long-term expression of HPV type 16 E6 or E7 oncoproteins, or both. Proteomic profiling was used to compare expression levels for 741 discrete protein features. RESULTS: Six replicate measurements were performed for each group using two-dimensional difference gel electrophoresis (2D-DIGE). The median within-group coefficient of variation was 19-21%. Significance of between-group differences was tested based on Significance Analysis of Microarray and fold change. Expression of 170 (23%) of the protein features changed significantly in immortalized cells compared to primary keratinocytes. Most of these changes were qualitatively similar in cells immortalized by E6, E7, or E6/7 expression, indicating convergence on a common phenotype, but fifteen proteins (~2%) were outliers in this regulatory pattern. Ten demonstrated opposite regulation in E6- and E7-expressing cells, including the cell cycle regulator p16INK4a; the carbohydrate binding protein Galectin-7; two differentially migrating forms of the intermediate filament protein Cytokeratin-7; HSPA1A (Hsp70-1); and five unidentified proteins. Five others had a pattern of expression that suggested cooperativity between the co-expressed oncoproteins. Two of these were identified as forms of the small heat shock protein HSPB1 (Hsp27). CONCLUSION: This large-scale analysis provides a framework for understanding the cooperation between E6 and E7 oncoproteins in HPV-driven carcinogenesis.

Background: Chemokine (C-C motif) ligand 2 (CCL2), commonly known as monocyte chemoattractant protein-1 (MCP-1), has been implicated in the pathogenesis of many diseases characterized by monocytic infiltration. However, limited data have been reported on MCP-1 in type 1 diabetes (T1D) and the findings are inconclusive and inconsistent.

BACKGROUND: Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis. RESULTS: Before considering data integration, it is necessary to evaluate the genome-wide concordance so that misleading results can be avoided. Based on the test results, different subsequent actions are suggested. The evaluation of genome-wide concordance and the data integration can be achieved based on the normal distribution based mixture models. CONCLUSION: The results from our simulation study suggest that misleading results can be generated if the genome-wide concordance issue is not appropriately considered. Our method provides a rigorous parametric solution. The results also show that our method is robust to certain model misspecification and is practically useful for the integrative analysis of differential expression.

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.

Electronic supplementary material: The online version of this article (doi:10.1007/s10654-011-9583-4) contains supplementary material, which is available to authorized users.

A systematic study has been conducted of all available reports in PubMed and OMIM (Online Mendelian Inheritance in Man) to examine the genetic and molecular basis of quantitative genetic loci (QTL) of diabetes with the main focus on genes and polymorphisms. The major question is, What can the QTL tell us? Specifically, we want to know whether those genome regions differ from other regions in terms of genes relevant to diabetes. Which genes are within those QTL regions, and, among them, which genes have already been linked to diabetes? whether more polymorphisms have been associated with diabetes in the QTL regions than in the non-QTL regions.Our search revealed a total of 9038 genes from 26 type 1 diabetes QTL, which cover 667,096,006 bp of the mouse genomic sequence. On one hand, a large number of candidate genes are in each of these QTL; on the other hand, we found that some obvious candidate genes of QTL have not yet been investigated. Thus, the comprehensive search of candidate genes for known QTL may provide unexpected benefit for identifying QTL genes for diabetes.

INTRODUCTION: Recently, we reported the development of the C57BL/6.NOD-Aec1Aec2 mouse that carries two genetic intervals derived from the non-obese diabetic (NOD) mouse capable of conferring Sj??gren's syndrome (SjS)-like disease in SjS-non-susceptible C57BL/6 mice. In an attempt to define the molecular bases underlying the onset of stomatitis sicca (xerostomia) in this C57BL/6.NOD-Aec1Aec2 mouse model, we have carried out a study using genomic microarray technology. METHODS: By means of oligonucleotide microarrays, gene expression profiles of salivary glands at 4, 8, 12, 16, and 20 weeks of age were generated for C57BL/6.NOD-Aec1Aec2 male mice. Using Linear Models for Microarray Analysis and B-statistics software, 480 genes were identified as being differentially expressed (P < 0.01 and Q < 0.0001) during the development of SjS-like disease in the salivary glands. RESULTS: The 480 genes could be arranged into four clusters, with each cluster defining a unique pattern of temporal expression, while the individual genes within each cluster could be grouped according to related biological functions. By means of pair-wise analysis, temporal changes in transcript expressions provided profiles indicating that many additional genes are differentially expressed at specific time points during the development of disease. Multiple genes reportedly showing an association with autoimmunity and/or SjS, in either humans or mouse models, were found to exhibit differential expressions, both quantitatively and temporally. Selecting various families of genes associated with specific functions (for example, antibody production, complement, and chemokines), we noted that only a limited number of family members showed differential expressions and these correlated with specific phases of disease. CONCLUSIONS: Taking advantage of known functions of these genes, investigators can construct interactive gene pathways, leading to modeling of possible underlying events inducing salivary gland dysfunction. Thus, these different approaches to analyzing microarray data permit the identification of multiple sets of genes of interest whose expressions and expression profiles may correlate with molecular mechanisms, signaling pathways, and/or immunological processes involved in the development and onset of SjS.

MicroRNAs (miRNAs) are a newly discovered type of small non-protein coding RNA that function in the inhibition of effective mRNA translation, and may serve as susceptibility genes for various disease developments. The SNP rs12416605, located in human type 1 diabetes IDDM10 locus, changes the seeding sequence (UGU[G/A]CCC) of miRNA miR-938 and potentially alters miR-938 targets, including IL-16 and IL-17A. In an attempt to test whether miR-938 may be a susceptibility gene for IDDM10, we assessed the possible association of the miR-938 SNP with T1D in an American Caucasian cohort of 622 patients and 723 healthy controls by TaqMan assay. Our current data do not support the association between the SNP in miR-938 and type 1 diabetes.