PROJECT SUMMARY To date, efforts to define and apply precision endotyping has been limited to studies of adults. However, immune development in early life (IDEAL) is dynamic and varies between individuals suggesting that endotypes corresponding to distinct pathophysiological mechanisms will be age-dependent. We propose therefore a novel approach in which we will study well-defined longitudinal childhood cohorts and use in silico integrative analyses of existing and prospectively collected data coupled with age-specific human in vitro model systems to identify agents that redirect IDEAL away from disease endotypes towards those associated with health. We have selected three clinical endpoints to correlate with systems biology data to identify IDEAL endotypes: a) vaccine responsiveness, as vaccines are the most important biomedical intervention to reduce childhood disease; b) respiratory infection which represents the greatest burden of childhood infectious disease; and c) asthma, an immune-mediated respiratory disease which manifests in childhood and results in substantial health burden. Each of these endpoints demonstrates substantial inter-individual variability enabling powerful systems biology tools to extract meaningful correlations. We will harmonize and study an IDEAL Meta-Cohort (IMC) comprised of longitudinal childhood cohorts enrolled in North America, Africa and Australasia. Our Clinical Core in Rochester, NY, is nationally prominent in the study of childhood immune ontogeny. Project (PR) 1 will employ cutting edge, cross- platform integrative bioinformatics tools to identify endotypes associated with clinical endpoints. PR2, will apply epigenetic analysis tools to the same samples and translate to host immune parameters the in silico-derived signatures. In PR3, key endotype-associated biomarkers and pathways will be dissected in vitro to establish cause and effect and identify agents (e.g., proteins, metabolites, adjuvants, vaccines) that may redirect IDEAL away from unfavorable endotypes and towards favorable ones. We have optimized sample-sparing assays to enable systems biology in infants and our published preliminary data demonstrate feasibility, robust IDEAL, and suggest distinct signatures by clinical status. Our cross- platform validation and correlation with endotypes correlating with clinical phenotypes will identify predictive/actionable biomarkers by i) characterizing IDEAL and microbiome in systemic/mucosal compartments (Overall Aim 1), ii) identifying endotype-specific biomarkers (Overall Aim 2), identifying in vitro interventions that re-direct IDEAL endotypes towards health (Overall Aim 3). Overall, we will enhance and accelerate discovery of new approaches to predict and prevent childhood disease.