Project Summary/Abstract Mechanisms of inter-organ signaling have been established as hallmarks of nearly every pathophysiologic condition, where many exist as related and complex diseases. While significant work has been focused on understanding how individual cell types contribute and respond to specific perturbations related to common, complex disease, an equally-important but relatively less-explored question involves how relationships between organs are altered in the context of an integrated living organism. Current technical advances, such as proteomic analysis of plasma or conditioned media, have allowed for a more unbiased visualization and discovery of additional inter-tissue signaling molecules. However, one important feature which is lacking from these approaches is the ability to gain insight as to the function, mechanisms of action and target tissue(s) of relevant molecules. To begin to address these constraints, we initially developed a correlation-based bioinformatics framework which uses multi-tissue gene expression and/or proteomic data, as well as publicly available resources to statistically rank and functionally annotate endocrine proteins involved in tissue cross-talk. Using this approach, we identified many known and experimentally validated several novel inter-tissue circuits. This was this first study to directly link an endocrine-focused bioinformatics pipeline from population data directly to experimentally-validated mechanisms of inter-tissue communication. While these validations provide strong support for exploiting natural variation to discover new modes of communication, these serve as simple proof-of-principle studies and, thus has promising potential for expansion. As a result, we have developed a series of in silico tools to guide discovery of endocrine interactions. Specifically, pathway-targeted enrichments, Bayesian network interrogation and scalable machine learning. The goal of this proposal is to closely bridge these computational tools with experimental methods to systematically dissect mechanisms by which tissues communicate and how these interactions are perturbed in metabolic disease settings. Given that we survey genetic variation to guide prediction of new modes of endocrine communication, these findings are likely to be robust across diverse backgrounds. We will implement high- throughput screening of specific tissue communication circuits which operate under disease-specific conditions of metabolism (ex. Obesity and Type 2 Diabetes), define which are conserved from mice to humans and mechanistically dissect pathophysiologic impacts of endocrine communication through in vivo experimentation. The success of these aims relies heavily on bridging computational and experimental approaches, justified by the training and focus of the PI. Collectively, these objectives will begin with unbiased computational approaches, validate using high-throughput in vitro assays and evaluate therapeutic potential of ne...