Integrative approaches to dissection of endocrine communication

剖析内分泌通讯的综合方法

• 批准号: 10680567
• 负责人: Marcus Michael Seldin
• 金额: $ 61.8万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680567
• 关键词: Address; Bayesian Network; Bioinformatics; Cardiovascular system; Communication; Complex; Data; Disease; Disease model; Dissection; Endocrine; Gene Expression; Genetic Variation; Goals; Human; Individual; Link; Machine Learning; Metabolic Diseases; Metabolism; Methods; Mus; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Organ; Organism; Pathway interactions; Plasma; Population; Proteins; Proteomics; Resources; Series; Signal Transduction; Signaling Molecule; Surveys; Therapeutic; Tissues; Training; Validation; Variant; Visualization; Work; bioinformatics pipeline; cell type; computerized tools; guided inquiry; high throughput screening; in silico; in vitro Assay; in vivo; insight; mouse model; novel; population survey; success; tool

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.

项目概要/摘要 器官间信号传导机制已被确立为几乎所有疾病的标志 病理生理状况，其中许多作为相关且复杂的疾病而存在。虽然重要的工作已经 专注于了解单个细胞类型如何对特定扰动做出贡献和响应 与常见、复杂的疾病相关，一个同样重要但相对较少探索的问题涉及如何 在一个完整的生物体的背景下，器官之间的关系发生了改变。目前技术 血浆或条件培养基的蛋白质组学分析等进步使得我们能够更加公正地进行研究 其他组织间信号分子的可视化和发现。然而，有一个重要的特点 这些方法缺乏的是深入了解功能和作用机制的能力 以及相关分子的靶组织。为了开始解决这些限制，我们最初开发了一个 基于相关性的生物信息学框架，使用多组织基因表达和/或蛋白质组数据，如 以及公开可用的资源，对参与的内分泌蛋白进行统计排名和功能注释 组织串扰。使用这种方法，我们确定了许多已知的并经过实验验证的几种新颖的 组织间电路。这是第一项直接将内分泌生物信息学管道与 人口数据直接传输到经过实验验证的组织间通讯机制。虽然这些 验证为利用自然变异发现新的通信模式提供了强有力的支持， 这些作为简单的原理验证研究，因此具有广阔的扩展潜力。因此， 我们开发了一系列计算机工具来指导内分泌相互作用的发现。具体来说， 以路径为目标的丰富、贝叶斯网络询问和可扩展的机器学习。目标是 该建议是将这些计算工具与实验方法紧密结合起来，系统地剖析 组织通讯的机制以及这些相互作用在代谢疾病中如何受到干扰 设置。鉴于我们调查遗传变异来指导新内分泌模式的预测 沟通，这些发现在不同背景下可能都是有效的。我们将实施高 在疾病特定条件下运行的特定组织通讯回路的通量筛选 新陈代谢（例如肥胖和 2 型糖尿病），定义哪些从小鼠到人类是保守的， 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，其理由是 PI 的培训和重点。总的来说，这些目标将从公正的计算开始 方法，使用高通量体外测定进行验证并评估新内分泌的治疗潜力 使用小鼠疾病模型进行相互作用。