The integration and mining of TIGER data will allow us to identify, at an unprecedented level of complexity, the molecular signatures that are directly linked to the mechanisms of progressive b cell failure that leads to IGT and T2D. Data fusion aims to put these different types of data into a single coherent inference and analysis framework for integrative analysis. This has the advantage of avoiding biases by accounting for co-linearity in the data. It also allows us to generate novel data-driven hypotheses that are hard to detect when narrowing on individual aspects of a complex disease phenotype. Novel data analysis tools will be developed by T2DSystems' partners to integrate molecular level data, such as genetic variation, methylation and gene expression, with quantitative (or as required, qualitative) and predictive network models and clinical data. T2DSystems will thus define genetic and environmental determinants of epigenetic states and transcriptional activity in human islets. This will be validated by establishing novel pancreatic beta cell models, using CRISPR and induced pluripotent stem cells (iPSCs). These models of high and low risk molecular and genetic factors will constitute a valuable resource for biomarker discovery, pharmacologic research and drug development. The biomarkers and clinically relevant variables will enable the identification of stratified pancreatic beta cell phenotypes. This will provide a rationale for the development of new diagnostic, preventive and therapeutic strategies for T2D.
To translate these findings into stratified pancreatic beta cell phenotypes in human cohorts. This will facilitate understanding of pancreatic beta cell pathophysiology in vivo and enable stratified prevention and therapy