Title: Using gene-phenotype studies to identify early disease biomarkers for T1D
Funder: Juvenile Diabetes Research Foundation

Abstract: The main objective of this research project will be to use high-throughput assays and technology to characterize inherited phenotypes predictive of disease risk or activity. The identification of such biomarkers that can be assayed robustly in the clinic is an urgent need to identify high-risk individuals at a stage where they are more likely to respond to therapy or preventive primary care.To do this, I propose to apply a systematic gene to phenotype strategy to elucidate the molecular mechanisms through which genetic risk factors contribute to the pathophysiology of the disease. In particular, I will be using the latest generation of gene expression arrays to comprehensively characterize the expression profile of type 1 diabetes patients during the course of the disease. In addition, I will also develop high-throughput assays to measure the concentration of soluble proteins to characterize their role in the etiology of the disease. In collaboration with the world-class statistics group led by Prof. David Clayton in the JDRF/WT Diabetes and Inflammation Laboratory (DIL), we will apply robust statistical methods to leverage these large genome-wide databases. Importantly, with the concomitant improvement in the genotyping technologies, we will combine these data with a much more comprehensive catalogue of the genetic variants mapping to the genomic regions associated with disease. In this project, I will aim to identify inherited gene expression, cellular or protein traits and monitor their variation during the different stages of the disease.In addition, I will also be interested in characterizing the early disease mechanisms that precede the production of autoantibodies and the onset of clinical symptoms In collaboration with dr Ezio Bonifacio and dr Anette Ziegler from the Diabetes Research Institute in Munich, we will have access to samples and data from 140 children born from T1D mothers, at high risk of developing type 1 diabetes, that were enrolled in the BABYDIET study. As part of this project, I propose to profile the gene expression of these children using the latest generation of gene expression microarrays to characterize biological markers and phenotypes that could differentiate between the genetically predisposed children that develop autoantibodies from those that do not. Type 1 diabetes is one of the most common chronic diseases in children. Currently there are no treatment options for type 1 diabetes and patients are subject to a life-long insulin replacement therapy to prevent the clinical complications associated with this disease. Importantly, the incidence of the disease has been rising rapidly during past decades and it is predicted to double in children under the age of 5 within the next 10 years. To reverse this trend, it is therefore urgent to identify better diagnostic markers, predictive of early disease events, to identify high-risk individuals, at a stage where they more likely to respond to therapy or preventive primary care.The causes for the increase in the incidence of type 1 diabetes are still unknown, largely because we still have an incomplete knowledge of the biological mechanisms that underlie the pathogenesis of the disease. In the last few years, large-scale genome-wide association studies have contributed dramatically to our current understanding of the genes involved in the etiology of type 1 diabetes. The flood of data coming out from these studies has resulted in the identification of hundreds of novel genomic loci containing DNA variants that contribute to disease susceptibility or protection. The major challenge that we now face is to translate this genetic data into a functional setting, to understand the molecular mechanisms that underlie the etiology of type 1 diabetes. Those particular genes that we have identified in the DIL, eg. IFIH1 and INS have provided key information about the etiology of the disease. In recent years, the DIL has been very successful in developing new


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: METABOLIC AND ENDOCRINE