Investigation of secreted protein transcripts as early biomarkers for type 1 diabetes in the mouse model

Type 1 diabetes (T1D) represents a serious health burden in the world, complicated by the fact that disease onset can be preceded by a long time period without evident clinical signs. It would be then of critical importance to detect the disease in its early stages. In this direction, we seek here to identify early preinflammatory markers for autoimmune diabetes, mining our previously reported transcriptome data relevant to distinct early sub-phenotypes in the NOD mouse, associated with early insulin autoantibodies (E-IAA). More specifically we focus on secreted or transmembrane protein transcripts, identifying in this category 71 differentially expressed transcripts which are regulated at the early preinflammatory stages of T1D in the pancreatic lymph nodes (PLN). Following the expression patterns of these 71 transcripts, correspondence analysis (a multivariate analysis method) reveals a clear-cut segregation of the individual samples according to the early subphenotype used. Thus the 71 transcripts coding for secreted proteins constitute a candidate-set of predictive biomarkers for the development of autoimmune damage of the β cells of the pancreas. The majority of these genes have human orthologs and accordingly they represent potential candidate biomarkers for the human disease. In addition, for predictive purposes, the analysis reveals the possibility to reduce significantly the size of the candidate-set in practice, with various genes displaying identical expression profiles.