ABSTRACT Our proposed multidisciplinary investigations have the long-term objective to identify and validate specific microbiota and metabolomic profiles that can predict loss of tolerance in infants genetically at risk of autoimmunity in order to implement early preventive interventions to re-establish tolerance and ultimately prevent autoimmunity. We have focused our research effort on celiac disease (CD), a unique model of autoimmunity for which the triggering environmental factor (ingestion of gluten containing grains), a close genetic association with HLA genes (DQ2 or DQ8) and a highly specific humoral autoimmune response (autoantibodies to tissue transglutaminase) are known. Our recent studies have subverted the previous notion that loss of gluten tolerance occurs at the time of its introduction in the child's diet; rather it can occur at any time in life as a consequence of other environmental stimuli. Our preliminary data generated during the previous funding period also suggest that gut microbiome composition and consequent changes in specific metabolic pathways precede the onset of the disease and may contribute to switching from tolerance to immune response to gluten. To achieve our objective, we will capitalize on our unique birth prospective cohort of infants at-risk of CD to compare microbiome, metabolome, and immune profiles of children who will develop CD with age- and sex-matched controls (both HLA DQ2/DQ8 negative and positive infants who did not develop the disease) in order to address three specific aims. With Aim 1, we propose to maintain the infrastructure and maximize surveillance of the existing prospective cohort of infants at-risk for CD with the goal of studying genome, metagenomic, metabolomic, and immune profiles of CD in at-risk infants to define the multi-omics makeup associated with the development of CD autoimmunity. With Aim 2, we will investigate the molecular and functional effects of specific gut microbes and metabolites found altered in our preliminary studies on gluten-induced mucosal innate immune response by using co-cultures of gut organoids and macrophages from children who developed CD. With Aim 3, we will use multi-omics statistical analysis and machine learning to identify microbiome biomarkers of CD and to construct an inclusive model that integrates omics and meta’omics data from the host and microbiota as well as clinical metadata in order to predict the chance of CD development in at-risk children. Overall, the outcome of our studies may have far-reaching impact not only on CD, but also on other autoimmune diseases in which the diet-genome- microbiome interaction in the pathogenesis of the disease has been hypothesized. Since in the U.S. almost 3 million people are affected by CD and approximately 17 million people suffers of other autoimmune diseases and that currently there are no effective strategies to prevent these conditions, this project can potentially have a tremendous impact on public h...