Computing hepatic intercellular communications in response to toxicant exposures

描述（由申请人提供）：每天，人们都会接触到环境中的化学物质。这种暴露与许多人类疾病有关，从心脏病到帕金森氏症，甚至几种癌症。肝脏是研究环境毒素如何影响身体的重要器官，因为它具有排毒这些外来化学物质的能力，以及其他关键职责。肝脏包含多种细胞类型，每种细胞类型都有不同的功能，研究这些细胞类型如何交流对于理解肝脏的生理功能是必不可少的。然而，肝脏细胞间通讯的体内研究对人类和模式生物都是困难的。作为一种有吸引力的替代方案，体外生物工程肝脏模型已经开发出来，它代表了最重要的细胞类型（肝细胞、肝正弦内皮细胞（LSECs）和Kupffer细胞），并维持细胞类型特异性的表型和行为，作为昂贵实验的替代品。使用这些肝脏模型，结合基因组和计算方法，有望提高我们对正常条件下和暴露于环境毒物后肝脏模型内发生的细胞信号传导的理解，从而产生更有影响力和生物学意义的见解。开发新的计算方法寻求定量识别肝细胞类型之间正常的细胞间通讯模式，并利用转录组学和蛋白质组学数据来预测环境化学物质如何干扰正常的肝脏细胞间通讯模式。本研究计划将发展与肝脏细胞间信号和通讯相关的基于网络的新型计算方法。计算方法将使用毒物反应分析和生物工程肝脏模型的实验数据进行测试，基准和验证。这些方法将做出预测和优先排序，这些预测和优先排序具有生物学信息和相关性，从而推动未来的实验。为此，将创建一个基于已发表文献的大鼠相关肝脏特异性蛋白质-蛋白质和调节相互作用以及信号通路的数据库，作为计算肝脏特异性信号网络的起始框架。该网络将用于从高通量响应分析中预测失调的信号模式。肝脏特异性背景网络将扩展到包括细胞间相互作用，并整合来自3D生物工程肝脏模型的转录组学和蛋白质组学数据。这种数据集成将导致识别特定的蜂窝间通信模式

DESCRIPTION (provided by applicant): On a daily basis, the population is exposed to environmental chemicals. This exposure has been associated with many human diseases ranging from heart disease to Parkinson's disease, and even several cancers. The liver is an essential organ to study for understanding how environmental toxicants affect the body due to its capability of detoxifying these foreign chemicals, among other key responsibilities. The liver contains multiple cell types, each having distinct functionality, and investigating how these cell types communicate is imperative to understanding physiological functions of the liver. However, in vivo studies of inter-cellular communications in the liver are difficult for both humans and model organisms. Emerging as an attractive alternative, in vitro bioengineered liver models have been developed that represent the most important cell types (hepatocytes, liver sinusoidal endothelial cells (LSECs), and Kupffer cells) and maintain cell-type-specific phenotypes and behaviors as a surrogate for costly experiments. Using these liver models, in combination with genomic and computational methods, promises to improve our understanding of the cellular signaling that occurs within the liver models under both normal conditions and after an exposure to environmental toxicants, thereby resulting in more impactful and biologically meaningful insights. Developing novel computational methods seek to quantitatively identify the normal inter-cellular communication patterns between the liver cell types and utilize transcriptomic and proteomic data for predicting how environmental chemicals perturb the normal hepatic inter-cellular communication patterns. This research proposal will develop novel network based computational methods related to inter-cellular signaling and communication in the liver. Computational methods will be tested, benchmarked and validated using experimental data from both toxicant response assays and bioengineered liver models. These approaches will make predictions and prioritizations that are biologically informative and relevant, thus driving future experiments. To this end, a database of rat-related liver-specific protein-protein and regulatory interactions and signaling pathways derived from published literature as a starting framework for computing liver-specific signaling networks will be created. This network will be used to predict dysregulated signaling patterns from high-throughput response assays. The liver-specific background network will be extended to include inter-cellular interactions and integrate transcriptomic and proteomic data from 3D bioengineered liver models. This data integration will result in the identification of inter-cellular communication patterns specific for bioengineered liver systems. Additionally, toxicant-specific signaling network as a result of the liver models' exposure to various environmental chemicals will be computed. From these toxicant-specific networks, differential network analysis will be performed to identify chemical specific signaling patterns, which subsequently will prioritize future experiments. Taken together, this proposal will characterize and propose mechanisms in which toxicants alter the natural inter-cellular communication patterns.