Healthy first-degree relatives of patients with type 1 diabetes exhibit significant differences in basal gene expression pattern of immunocompetent cells compared to controls: expression pattern as predeterminant of autoimmune diabetes

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

BACKGROUND

There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.

METHODS

Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.

RESULTS

The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.

CONCLUSION

This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

Prevalence of Inflammatory Pathways Over Immuno-Tolerance in Peripheral Blood Mononuclear Cells of Recent-Onset Type 1 Diabetes

Background

Changes in innate and adaptive immunity occurring in/around pancreatic islets had been observed in peripheral blood mononuclear cells (PBMC) of Caucasian T1D patients by some, but not all researchers. The aim of our study was to investigate whether gene expression patterns of PBMC of the highly admixed Brazilian population could add knowledge about T1D pathogenic mechanisms.

Methods

We assessed global gene expression in PBMC from two groups matched for age, sex and BMI: 20 patients with recent-onset T1D (≤ 6 months from diagnosis, in a time when the autoimmune process is still highly active), testing positive for one or more islet autoantibodies and 20 islet autoantibody-negative healthy controls.

Results

We identified 474 differentially expressed genes between groups. The most expressed genes in T1D group favored host defense, inflammatory and anti-bacterial/antiviral effects (LFT, DEFA4, DEFA1, CTSG, KCNMA1) and cell cycle progression. Several of the downregulated genes in T1D target cellular repair, control of inflammation and immune tolerance. They were related to T helper 2 pathway, induction of FOXP3 expression (AREG) and immune tolerance (SMAD6). SMAD6 expression correlated negatively with islet ZnT8 antibody. The expression of PDE12, that offers resistance to viral pathogens was decreased and negatively related to ZnT8A and GADA levels. The increased expression of long non coding RNAs MALAT1 and NEAT1, related to inflammatory mediators, autoimmune diseases and innate immune response against viral infections reinforced these data.

Conclusions

Our analysis suggested the activation of cell development, anti-infectious and inflammatory pathways, indicating immune activation, whereas immune-regulatory pathways were downregulated in PBMC from recent-onset T1D patients with a differential genetic profile.

Gene Expression Analysis of the Pre-Diabetic Pancreas to Identify Pathogenic Mechanisms and Biomarkers of Type 1 Diabetes

Type 1 Diabetes (T1D) occurs as a result of the autoimmune destruction of pancreatic β-cells by self-reactive T cells. The etiology of this disease is complex and difficult to study due to a lack of disease-relevant tissues from pre-diabetic individuals. In this study, we performed gene expression analysis on human pancreas tissues obtained from the Network of Pancreatic Organ Donors with Diabetes (nPOD), and showed that 155 genes were differentially expressed by ≥2-fold in the pancreata of autoantibody-positive (AA+) at-risk individuals compared to healthy controls. Only 48 of these genes remained changed by ≥2-fold in the pancreata of established T1D patients. Pathway analysis of these genes showed a significant association with various immune pathways. We were able to validate the differential expression of eight disease-relevant genes by QPCR analysis: A significant upregulation of CADM2, and downregulation of TRPM5, CRH, PDK4, ANGPL4, CLEC4D, RSG16, and FCGR2B was confirmed in the pancreata of AA+ individuals versus controls. Studies have already implicated FCGR2B in the pathogenesis of disease in non-obese diabetic (NOD) mice. Here we showed that CADM2, TRPM5, PDK4, and ANGPL4 were similarly changed in the pancreata of pre-diabetic 12-week-old NOD mice compared to NOD.B10 controls, suggesting a possible role for these genes in the pathogenesis of both T1D and NOD disease. The loss of the leukocyte-specific gene, FCGR2B, in the pancreata of AA+ individuals, is particularly interesting, as it may serve as a potential whole blood biomarker of disease progression. To test this, we quantified FCGR2B expression in peripheral blood samples of T1D patients, and AA+ and AA- first-degree relatives of T1D patients enrolled in the TrialNet Pathway to Prevention study. We showed that FCGR2B was significantly reduced in the peripheral blood of AA+ individuals compared to AA- controls. Together, these findings demonstrate that gene expression analysis of pancreatic tissue and peripheral blood samples can be used to identify disease-relevant genes and pathways and potential biomarkers of disease progression in T1D.

Comparative analysis of mRNA expression profiles in Type 1 and Type 2 diabetes mellitus

Aim: We aimed to understand the individual and shared features of Type 1 diabetes (T1D) and Type 2 diabetes (T2D) by analyzing the gene expression profile. Materials & methods: An integrated analysis was performed with microarray datasets for T1D and T2D. Compared with normal control, shared and specific differentially expressed genes (DEGs) in T1D and T2D were obtained. Functional annotation, further validation and receiver operating characteristic curve analysis were performed. Results: Five and three datasets for T1D and T2D were downloaded, respectively. In total, 141 (85 T1D vs 56 normal controls) and 70 (29 T2D vs 41 normal controls) peripheral blood samples were included in T1D and T2D group, respectively. Compared with normal controls, 119 and 146 DEGs were found in T1D and T2D, respectively. PNP and CCR1 have great diagnostic value for both T1D and T2D. MGAM and NAMPT had great diagnostic value for T2D. Conclusion: Our finding provided clues for developing biomarkers for diabetes.

Recent Advances on Microbiota Involvement in the Pathogenesis of Autoimmunity

Autoimmune disorders derive from genetic, stochastic, and environmental factors that all together interact in genetically predisposed individuals. The impact of an imbalanced gut microbiome in the pathogenesis of autoimmunity has been suggested by an increasing amount of experimental evidence, both in animal models and humans. Several physiological mechanisms, including the establishment of immune homeostasis, are influenced by commensal microbiota in the gut. An altered microbiota composition produces effects in the gut immune system, including defective tolerance to food antigens, intestinal inflammation, and enhanced gut permeability. In particular, early findings reported differences in the intestinal microbiome of subjects affected by several autoimmune conditions, including prediabetes or overt disease compared to healthy individuals. The present review focuses on microbiota-host homeostasis, its alterations, factors that influence its composition, and putative involvement in the development of autoimmune disorders. In the light of the existing literature, future studies are necessary to clarify the role played by microbiota modifications in the processes that cause enhanced gut permeability and molecular mechanisms responsible for autoimmunity onset.

On the pathogenesis of insulin-dependent diabetes mellitus: the role of microbiota

Type 1 diabetes (T1D) is an autoimmune disorder characterized by the selective destruction of insulin-producing β cells as result of a complex interplay between genetic, stochastic and environmental factors in genetically susceptible individuals. An increasing amount of experimental data from animal models and humans has supported the role played by imbalanced gut microbiome in T1D pathogenesis. The commensal intestinal microbiota is fundamental for several physiologic mechanisms, including the establishment of immune homeostasis. Alterations in its composition have been correlated to changes in the gut immune system, including defective tolerance to food antigens, intestinal inflammation and enhanced gut permeability. Early findings reported differences in the intestinal microbiome of subjects affected by prediabetes or overt disease compared to healthy individuals. The present review focuses on microbiota-host homeostasis, its alterations, factors that influence microbiome composition and discusses their putative correlation with T1D development. Further studies are necessary to clarify the role played by microbiota modifications in the processes that cause enhanced permeability and the autoimmune mechanisms responsible for T1D onset.

Longitudinal monitoring of gene expression in ultra-low-volume blood samples self-collected at home

Blood transcriptional profiles could serve as biomarkers of clinical changes in subjects at-risk for or diagnosed with diabetes. However, transcriptional variation over time is poorly understood due to the impracticality of frequent longitudinal phlebotomy in large patient cohorts. We have developed a novel transcriptome assessment method that could be applied to fingerstick blood samples self-collected by study volunteers. Fifteen μL of blood from a fingerstick yielded sufficient RNA to analyse > 176 transcripts by high-throughput quantitative polymerase chain reaction (PCR). We enrolled 13 subjects with type 1 diabetes and 14 controls to perform weekly collections at home for a period of 6 months. Subjects returned an average of 24 of 26 total weekly samples, and transcript data were obtained successfully for > 99% of samples returned. A high degree of correlation between fingerstick data and data from a standard 3 mL venipuncture sample was observed. Increases in interferon-stimulated gene expression were associated with self-reported respiratory infections, indicating that real-world transcriptional changes can be detected using this assay. In summary, we show that longitudinal monitoring of gene expression is feasible using ultra-low-volume blood samples self-collected by study participants at home, and can be used to monitor changes in gene expression frequently over extended periods.

Integrated analysis of different microarray studies to identify candidate genes in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D), an autoimmune disease, occurs most commonly in children. Identifying altered gene expression in peripheral blood mononuclear cells (PBMCs) of T1D may lead to new strategies for preserving or improving β-ell function in patients with T1D.

METHODS

The Gene Expression Omnibus database was searched for microarray studies in PBMCs of T1D. Subsequently, gene expression datasets from multiple microarray studies were integrated to obtain differentially expressed genes (DEGs) between T1D and normal controls (NC). Gene function analysis was performed to determine the functions of the DEGs identified.

RESULTS

Four microarray studies were available for analysis, including 199 T1D samples and 74 NC samples. Analysis revealed 695 genes that were significantly differentially expressed in PBMCs from T1D compared with NC samples, with 450 upregulated and 245 downregulated. Signal transduction (gene ontology [GO]: 0007165; false discovery rate [FDR] = 1.54 × 10^-7 ) and protein binding (GO: 0005515; FDR = 2.93 × 10^-24 ) were significantly enriched for the GO categories of biological processes and molecular functions, respectively. The most significant pathway in the Kyoto Encyclopedia of Genes and Genomes analysis was arachidonic acid metabolism (FDR = 1.44 × 10^-3 ). Protein-protein interaction network analysis showed that the significant hub proteins contained immature colon carcinoma transcript 1 (ICT1; degree = 214; clustering coefficient [C] = 4.39 × 10^-5 ), zinc finger and BTB domain containing 16 (ZBTB16; degree = 112; C = 8.04 × 10^-4 ), and SERTA domain containing 1 (SERTAD1; degree = 38; C = 0.0014).

CONCLUSIONS

This integrated analysis will help develop improved therapies and interventions for T1D by identifying novel drug targets.

Investigation of coordination and order in transcription regulation of innate and adaptive immunity genes in type 1 diabetes

Background

Type 1 diabetes (T1D) is an autoimmune disease and extensive evidence has indicated a critical role of both the innate and the adaptive arms of immune system in disease development. To date most clinical trials of immunomodulation therapies failed to show efficacy. A number of gene expression studies of T1D have been carried out. However, a systems analysis of the expression variations of the innate and adaptive immunity gene sets, or their co-expression network structures in cohorts at different disease states or of different disease risks, is not available till now.

Methods

We utilized data from a large gene expression study that included transcription profiles of control peripheral blood mononuclear cells (PBMC) exposed to plasma of 148 human subjects from four cohorts that included unrelated healthy controls (uHC), recent onset T1D patients (RO-T1D), and healthy siblings of probands that possess high (HRS, High Risk Sibling) or low (LRS, Low Risk Sibling) risk HLA haplotypes. Both weighted and non-weighted co-expression networks were constructed in each cohort separately, and edge weight distribution and the activation of known protein complexes were examined. The co-expression networks of the innate and adaptive immunity genes were further examined in more detail through a number of network measures that included network density, Shannon entropy, h-index, and the scaling exponent γ of degree distribution. Pathway analysis was carried out using CoGA, a tool for detecting significant network structural changes of a gene set.

Results

Weighted network edge distribution revealed a globally weakened co-expression network induced by the RO-T1D cohort as compared to that by the uHC, suggesting a broad spectrum loss of transcriptional coordination. The two healthy T1D family cohorts (HRS and LRS) induced more active but heterogeneous transcription coordination globally, and among both the innate and the adaptive immunity genes, than the uHC. This finding is consistent with our previous report of these cohorts sharing a heightened innate inflammatory state. The spike-in of IL-1RA to RO-T1D sera improved co-expression network strength of both the innate and the adaptive immunity genes, and enabled a global order recovery in transcription regulation that resulted in significantly increased number of activated protein complexes. Many of the top pathways that showed significant difference in co-expression network structures and order between RO-T1D and uHC have strong links to T1D.

Conclusions

Network level analysis of the innate and adaptive immunity genes, and the whole genome, revealed striking cohort-dependent differences in co-expression network structural measures, suggesting their potential in cohort classification and disease-relevant pathway identification. The results demonstrated the advantages of systems analysis in defining molecular signatures as well as in predicting targets in future research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0243-8) contains supplementary material, which is available to authorized users.

Blood-based signatures in type 1 diabetes

Type 1 diabetes mellitus is one of the most common chronic diseases in childhood. It develops through autoimmune destruction of the pancreatic beta cells and results in lifelong dependence on exogenous insulin. The pathogenesis of type 1 diabetes involves a complex interplay of genetic and environmental factors and has historically been attributed to aberrant adaptive immunity; however, there is increasing evidence for a role of innate inflammation. Over the past decade new methodologies for the analysis of nucleic acid and protein signals have been applied to type 1 diabetes. These studies are providing a new understanding of type 1 diabetes pathogenesis and have the potential to inform the development of new biomarkers for predicting diabetes onset and monitoring therapeutic interventions. In this review we will focus on blood-based signatures in type 1 diabetes, with special attention to both direct transcriptomic analyses of whole blood and immunocyte subsets, as well as plasma/serum-induced transcriptional signatures. Attention will also be given to proteomics, microRNA assays and markers of beta cell death. We will also discuss the results of blood-based profiling in type 1 diabetes within the context of the genetic and environmental factors implicated in the natural history of autoimmune diabetes.

Identification of novel risk genes associated with type 1 diabetes mellitus using a genome-wide gene-based association analysis

Aims/Introduction

Type 1 diabetes mellitus is a serious disorder characterized by destruction of pancreatic β-cells, culminating in absolute insulin deficiency. Genetic factors contribute to the susceptibility of type 1 diabetes mellitus. The aim of the present study was to identify more susceptibility genes of type 1 diabetes mellitus.

Materials and Methods

We carried out an initial gene-based genome-wide association study in a total of 4,075 type 1 diabetes mellitus cases and 2,604 controls by using the Gene-based Association Test using Extended Simes procedure. Furthermore, we carried out replication studies, differential expression analysis and functional annotation clustering analysis to support the significance of the identified susceptibility genes.

Results

We identified 452 genes associated with type 1 diabetes mellitus, even after adapting the genome-wide threshold for significance (P < 9.05E-04). Among these genes, 171 were newly identified for type 1 diabetes mellitus, which were ignored in single-nucleotide polymorphism-based association analysis and were not previously reported. We found that 53 genes have supportive evidence from replication studies and/or differential expression studies. In particular, seven genes including four non-human leukocyte antigen (HLA) genes (RASIP1, STRN4, BCAR1 and MYL2) are replicated in at least one independent population and also differentially expressed in peripheral blood mononuclear cells or monocytes. Furthermore, the associated genes tend to enrich in immune-related pathways or Gene Ontology project terms.

Conclusions

The present results suggest the high power of gene-based association analysis in detecting disease-susceptibility genes. Our findings provide more insights into the genetic basis of type 1 diabetes mellitus.

The effect of diabetes-associated autoantigens on cell processes in human PBMCs and their relevance to autoimmune diabetes development

Type 1 Diabetes (T1D) is considered to be a T-helper- (Th-) 1 autoimmune disease; however, T1D pathogenesis likely involves many factors, and sufficient tools for autoreactive T cell detection for the study of this disease are currently lacking. In this study, using gene expression microarrays, we analysed the effect of diabetes-associated autoantigens on peripheral blood mononuclear cells (PBMCs) with the purpose of identifying (pre)diabetes-associated cell processes. Twelve patients with recent onset T1D, 18 first-degree relatives of the TD1 patients (DRL; 9/18 autoantibody positive), and 13 healthy controls (DV) were tested. PBMCs from these individuals were stimulated with a cocktail of diabetes-associated autoantigens (proinsulin, IA-2, and GAD65-derived peptides). After 72 hours, gene expression was evaluated by high-density gene microarray. The greatest number of functional differences was observed between relatives and controls (69 pathways), from which 15% of the pathways belonged to "immune response-related" processes. In the T1D versus controls comparison, more pathways (24%) were classified as "immune response-related." Important pathways that were identified using data from the T1D versus controls comparison were pathways involving antigen presentation by MHCII, the activation of Th17 and Th22 responses, and cytoskeleton rearrangement-related processes. Genes involved in Th17 and TGF-beta cascades may represent novel, promising (pre)diabetes biomarkers.

Novel biomarkers in type 1 diabetes

Biomarkers are useful tools for research into type 1 diabetes (T1D) for a number of purposes, including elucidation of disease pathogenesis, risk prediction, and therapeutic monitoring. Susceptibility genes and islet autoantibodies are currently the most useful biomarkers for T1D risk prediction. However, these markers do not fully meet the needs of scientists and physicians for several reasons. First, improvement of the specificity and sensitivity is still desirable to achieve better positive predictive values. Second, autoantibodies appear relatively late in the disease process, thus limiting their value in early disease prediction. Third, the currently available biomarkers are not useful for assessing therapeutic outcomes because some are not involved in the disease process (autoantibodies) and others do not change during disease progression (susceptibility genes). Therefore, considerable effort has been devoted to the discovery of novel T1D biomarkers in the last three decades. The advent of high-throughput technologies for genetic, transcriptomic, and proteomic studies has allowed genome-wide examinations of genetic polymorphisms, global gene changes, and protein expression changes in T1D patients and prediabetic subjects. These large-scale studies resulted in the discovery of a large number of susceptibility genes and changes in gene and protein expression. While these studies have provided a number of novel biomarker candidates, their clinical benefits remain to be evaluated in prospective studies, and no new "star biomarker" has been identified until now. Previous studies suggest that significant improvements in study design and analytical methodologies have to be made to identify clinically relevant biomarkers. In this review, we discuss progress, opportunities, challenges, and future directions in the development of T1D biomarkers, mainly by focusing on the genetic, transcriptomic, and proteomic aspects.

From biomarkers to a clue of biology: a computation-aided perspective of immune gene expression profiles in human type 1 diabetes

Dysregulated expression of key immune genes may cause breakdown of immunological tolerance and development of autoimmune disorders such as type 1 diabetes (T1D). General immune insufficiencies have also been implicated as a trigger of autoimmunity, due to their potential impact on immune homeostasis. Recent studies have detected evidence of systemic reduction in immune gene expression in long-term diabetic patients but the changes were not present before or at T1D onset. The changes could not be merely correlated with alteration in metabolic parameters. The studies also identified a dynamic expression pattern of several well-known as well as little-studied, immune-related genes during the course of T1D. An intriguing “ratio profile” of immune regulatory genes, such as CTLA4 and members of the S100 family, versus “baseline” immune genes, such as CD3G, prompted us to further examine immune gene expression relationships for a set of molecules representing T cells, B cells, and myeloid cells. No evidence was found to suggest an overall breach of tolerance equilibrium in T1D. Perplexingly, patients with long-term T1D presented a gene expression profile that was surprisingly more coordinated in analyses of “networking” relationship. Computational analyses of the “ratio profiles” or “relationship profiles” of immune gene expression might provide a clue for further studies of immunobiology in human T1D and other autoimmune diseases, as to how the profiles may be related to the pathogenic cause of the disease, to the effect of the diseases on immune homeostasis, or to an immunological process associated with the course of the diseases but is neither a direct cause nor a direct effect of the diseases.

Improving power of genome-wide association studies with weighted false discovery rate control and prioritized subset analysis

The issue of large-scale testing has caught much attention with the advent of high-throughput technologies. In genomic studies, researchers are often confronted with a large number of tests. To make simultaneous inference for the many tests, the false discovery rate (FDR) control provides a practical balance between the number of true positives and the number of false positives. However, when few hypotheses are truly non-null, controlling the FDR may not provide additional advantages over controlling the family-wise error rate (e.g., the Bonferroni correction). To facilitate discoveries from a study, weighting tests according to prior information is a promising strategy. A 'weighted FDR control' (WEI) and a 'prioritized subset analysis' (PSA) have caught much attention. In this work, we compare the two weighting schemes with systematic simulation studies and demonstrate their use with a genome-wide association study (GWAS) on type 1 diabetes provided by the Wellcome Trust Case Control Consortium. The PSA and the WEI both can increase power when the prior is informative. With accurate and precise prioritization, the PSA can especially create substantial power improvements over the commonly-used whole-genome single-step FDR adjustment (i.e., the traditional un-weighted FDR control). When the prior is uninformative (true disease susceptibility regions are not prioritized), the power loss of the PSA and the WEI is almost negligible. However, a caution is that the overall FDR of the PSA can be slightly inflated if the prioritization is not accurate and precise. Our study highlights the merits of using information from mounting genetic studies, and provides insights to choose an appropriate weighting scheme to FDR control on GWAS.

Case report: type 1 diabetes in monozygotic quadruplets

Type 1 diabetes (T1D) is an autoimmune disease characterized by the lack of insulin due to an autoimmune destruction of pancreatic beta cells. Here, we report a unique case of a family with naturally conceived quadruplets in which T1D was diagnosed in two quadruplets simultaneously. At the same time, the third quadruplet was diagnosed with the pre-diabetic stage. Remarkably, all four quadruplets were positive for anti-islet cell antibodies, GAD65 and IA-A2. Monozygotic status of the quadruplets was confirmed by testing 14 different short tandem repeat polymorphisms. Serological examination confirmed that all quadruplets and their father suffered from a recent enteroviral infection of EV68-71 serotype. To assess the nature of the molecular pathological processes contributing to the development of diabetes, immunocompetent cells isolated from all family members were characterized by gene expression arrays, immune-cell enumerations and cytokine-production assays. The microarray data provided evidence that viral infection, and IL-27 and IL-9 cytokine signalling contributed to the onset of T1D in two of the quadruplets. The propensity of stimulated immunocompetent cells from non-diabetic members of the family to secrete high level of IFN-α further corroborates this conclusion. The number of T regulatory cells as well as plasmacytoid and/or myeloid dendritic cells was found diminished in all family members. Thus, this unique family is a prime example for the support of the so-called 'fertile-field' hypothesis proposing that genetic predisposition to anti-islet autoimmunity is 'fertilized' and precipitated by a viral infection leading to a fully blown T1D.