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Computing hepatic intercellular communications in response to toxicant exposures

计算肝脏细胞间通讯以响应有毒物质暴露

基本信息

批准号：
9115599
负责人：
Allison N Tegge
金额：
$ 2.1万
依托单位：
VIRGINIA POLYTECHNIC INST AND ST UNIV
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-15 至 2016-12-24
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9115599
关键词：
Affect Algorithms Animal Model Automobile Driving Behavior Benchmarking Bile fluid Biological Assay Biomedical Engineering Cell physiology Chemicals Cholesterol Homeostasis Coagulation Process Communication Computing Methodologies Coupling Data Databases Endothelial Cells Environment Environmental Exposure Exposure to Future Genes Genomics Goals Graph Health Heart Diseases Hepatic Hepatocyte Human In Vitro Intervention Kupffer Cells Ligands Light Literature Liver Lung diseases Malignant Neoplasms Methodology Methods Modeling Network-based Organ Parkinson Disease Pathway Analysis Pathway interactions Pattern Phenotype Physiological Primary carcinoma of the liver cells Process Production Proteins Proteomics Publishing Rattus Research Research Proposals Role Signal Pathway Signal Transduction Stimulus Stream System Techniques Testing Tissues Toxic Environmental Substances Toxicant exposure Tweens base career cell type data integration environmental chemical experience genetic regulatory protein human disease improved in vivo insight intercellular communication liver function liver-specific protein nervous system disorder novel population based receptor receptor binding research study response skills toxicant transcriptomics

项目摘要

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.

描述(申请人提供)：该人群每天都暴露在环境化学品中。这种接触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。肝脏是研究环境毒物如何影响身体的基本器官，因为它有能力解毒这些外来化学物质，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，研究这些细胞类型的通讯方式对于了解肝脏的生理功能是至关重要的。然而，对肝脏细胞间通讯的活体研究对人类和模型生物都是困难的。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被开发出来，它代表了最重要的细胞类型(肝细胞、肝窦内皮细胞和库普弗细胞)，并保持了细胞类型特异性的表型和行为，作为昂贵实验的替代。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情况下和暴露于环境毒物后肝脏模型内发生的细胞信号的理解，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地识别肝细胞类型之间的正常细胞间通信模式，并利用转录组和蛋白质组数据来预测环境化学物质如何扰乱正常的肝脏细胞间通信模式。这项研究提案将开发与肝脏细胞间信号和通信相关的基于网络的新计算方法。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。这些方法将做出具有生物学信息性和相关性的预测和优先顺序，从而推动未来的实验。为此，将创建一个与大鼠肝脏特异蛋白相关的蛋白-蛋白质和调控相互作用以及信号通路的数据库，该数据库来自已发表的文献，作为计算肝脏特异信号网络的起始框架。这个网络将被用来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的相互作用，并整合来自3D生物工程肝脏模型的转录和蛋白质组数据。这种数据集成将导致识别特定于生物工程肝脏系统。此外，还将计算由于肝脏模型暴露于各种环境化学物质而产生的毒物特定信号网络。从这些毒物特定的网络中，将进行差异网络分析，以确定化学特定的信号模式，这将随后确定未来实验的优先顺序。综上所述，这项提案将描述并提出毒物改变自然细胞间通讯模式的机制。