Type 1 Diabetes (T1D) is an autoimmune disorder characterized by the loss of function of insulin producing pancreatic cells leading to myriad manifestations of the disease in affected individuals. When left untreated this can lead to death. Many studies have shown that development of the disease is the result of interactions between immunological, genetic, and environmental factors. Although, many environmental factors have been implicated, the mechanisms of involvement or major determinants are yet to be clearly identified. Published studies have suggested that T1D results from environmental triggers acting on genetically susceptible individuals and that microbial infection and their immunological consequences are suspected to take part in the pathogenesis. Several mechanisms have been proposed for the causation of beta-cell damage by microbes. Data from retrospective and prospective epidemiological studies strongly suggest the involvement of enteroviruses in the development of T1D. Altered microbial diversity in the gut microbiota has been shown to trigger an abnormal mucosal immune response to further the progression of T1D. Genetic susceptibility traits for T1D are becoming more numerous, including loci for HLA, insulin, protein tyrosine phosphatase-22, cytotoxic T-lymphocyte-associated protein 4, the interleukin-2 receptor and C- type lectin. This complexity renders them less desirable as predictive tests. Although genetic susceptibility genes are known to play a substantial role in the development of T1D, cellular changes as well as environmental triggers may serve as excellent, potentially more universal biomarkers for risk assessment. For this study, we propose to analyze blood, stool and urine samples from children and adolescent patient populations who have been recently diagnosed with T1D, their siblings, and at-risk cohorts from the TrialNet network (www.diabetestrialnet.org). We propose to apply high-throughput genomics, proteomics and metabolomics techniques to identify molecular signatures discriminating these cohorts. By deconvolution of high-resolution molecular data, we expect to identify viral-microbial specific correlated patterns of proteins and metabolites.
Our aim i s to discover and verify candidate biomarkers from a large number of biological constituents;from genomic, proteomic and metabolomic datasets through correlation of host microbiome data and genotype. Our purpose is to advance non-invasive clinical tests by applying novel molecular methods to correlate disease status for early diagnosis and prediction of T1D onset, setting the stage for potential therapies or intervention strategies.
The incidence of Type I Diabetes (T1D) has been rising rapidly in children and adolescents over the last 20 years. T1D is an autoimmune disease that occurs when a child's body cannot produce insulin, and genetics as well as environmental factors play a role in the development of this disease. We propose to study the complex interplay of environmental, immunological and genetic factors to discover molecular signatures with a potential to advance clinical diagnosis and therapy at or before the onset of T1D.
