Integrative approaches to dissection of endocrine communication

项目总结/摘要 器官间信号传导机制已被确立为几乎每一种疾病的标志。 病理生理状况，其中许多作为相关和复杂的疾病存在。虽然重要的工作 一直专注于了解单个细胞类型如何对特定的扰动做出贡献和反应 与常见的复杂疾病相关，一个同样重要但相对较少探索的问题涉及如何 器官之间的关系在一个完整的生物体中发生了变化。目前的技术 例如血浆或条件培养基的蛋白质组学分析的进步，已经允许更无偏见的 可视化和发现额外的组织间信号分子。然而，一个重要的特征 这些方法所缺乏的是深入了解其功能、作用机制的能力， 和相关分子的靶组织。为了开始解决这些约束，我们最初开发了一个 基于相关性的生物信息学框架，其使用多组织基因表达和/或蛋白质组学数据， 以及公开可用的资源，以统计排名和功能注释内分泌蛋白参与 组织串扰。使用这种方法，我们确定了许多已知的和实验验证的几个新的 组织间回路这是第一项直接将内分泌为重点的生物信息学管道从 群体数据直接用于实验验证的组织间通信机制。虽然这些 验证为利用自然变化来发现新的通信模式提供了强有力的支持， 这些都是简单的原理验证研究，因此具有很好的推广潜力。因此，在本发明中， 我们已经开发了一系列计算机工具来指导内分泌相互作用的发现。具体地说， 路径目标丰富，贝叶斯网络询问和可扩展的机器学习。的目标 这项建议是将这些计算工具与实验方法紧密联系起来，系统地剖析 组织之间的交流机制以及这些相互作用在代谢疾病中是如何被扰乱的 设置.鉴于我们调查遗传变异，以指导预测新的内分泌模式， 这些研究结果可能在不同背景下都很有说服力。我们将实施高- 在疾病特异性条件下操作的特异性组织通信回路的通量筛选 代谢（例如，肥胖和2型糖尿病），定义了从小鼠到人类的保守性， 通过体内实验机械地剖析内分泌通讯的病理生理影响。 这些目标的成功在很大程度上依赖于桥接计算和实验方法，理由如下： PI的培训和重点。总的来说，这些目标将开始与公正的计算 方法，使用高通量体外试验进行验证，并评估新的内分泌治疗潜力。 使用小鼠疾病模型的相互作用。

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 new endocrine interactions using mouse models of disease.