A systems biology approach to mammalian early embryogenesis

哺乳动物早期胚胎发生的系统生物学方法

基本信息

批准号：
7681616
负责人：
KRISTIN C GUNSALUS
金额：
$ 37.09万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-01 至 2012-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7681616
关键词：
Automobile Driving Basic Science Biological Models Biological Process Caenorhabditis elegans Cell Proliferation Cell physiology Cells Classification Clinical Clinical Research Comparative Study Competence Complex Couples Data Data Set Development Developmental Process Diagnosis Disease Embryo Embryonic Development Environment Environmental Risk Factor Event Foundations Gene Components Gene Expression Gene Expression Profile Genes Genetic Genome Genome Scan Goals Hereditary Disease Human Image Individual Infertility Internet Invertebrates Life Link Malignant Neoplasms Mammals Maps Metabolic Diseases MicroRNAs Microscopy Modeling Molecular Molecular Analysis Molecular Profiling Mus Outcome Phenotype Population Post-Transcriptional Regulation Process Property Proteins Protocols documentation RNA Interference Recording of previous events Resolution Resources Role Specific qualifier value Staging System Systems Analysis Systems Biology Time Tissues Translating Work blastocyst clinical Diagnosis computer framework disease phenotype experience genetic association genome wide association study genome-wide human DICER1 protein insight molecular phenotype phenomics predictive modeling protein protein interaction spatiotemporal time use tool

项目摘要

DESCRIPTION (provided by applicant): It is now becoming clear that heritable and acquired human disorders arise from a complex interplay between multiple genetic components influenced by environmental factors. Therefore, scanning the genome for genetic associations to a specific disease is hampered by the fact that different individuals with the same disease could have different underlying genetic causes. When associations are found, they are often weak or relate to only one population of individuals sharing a particular genetic history. To alleviate this limitation we need a broader view of the molecular assemblages that specify particular phenotypes and need to develop better systems for the analysis of complex phenotypes. Disease phenotypes, especially for the most significant diseases, like cancer, are often the result of a breakdown in a basic cellular or developmental process. Thus, in the long term, this project aims to build a systems view of the molecular mechanisms driving these basic processes and use this information to inform the analysis of genome wide association studies. To begin this process we will build a systems view of early embryogenesis in mouse and human and connect them to specific phenotypic outcomes underlying basic cell and developmental processes. This approach has been very successful in the model C. elegant, where groups of genes required for specific phenotypes were identified on a genome-wide scale. We will use similar computational and experimental approaches as well as use a compendium of data from several model systems to build a systems view of early embryonic development in mouse and human. Specifically we will work on the following three related aims: 1: Develop a computational framework to predict the global molecular map underlying early embryonic development in mouse and human. 2: Gather high-content phenotypic data during early embryogenesis using RNAi of ~400 mouse genes; digitally encode their complex phenotypic effect and combine with transcriptome data and the computational map built in Aim 1. 3: Develop an open-access Web-environment to navigate and analyze these complex data, including time-lapse recordings of early embryonic phenotypes so that they can be used to analyze genome-wide associations' studies. This work will have direct implications for diagnosis of embryonic viability in a clinical setting and, due to the pleiotropic roles of many genes, will in turn provide insights into complex phenotypes associated with a variety of developmental and metabolic disorders as well as other disease processes like cancer, which involves deregulation of cell proliferation vs. differentiation.

描述（由申请人提供）：现在很清楚，可遗传和获得的人类疾病是由受环境因素影响的多个遗传成分之间的复杂相互作用引起的。因此，扫描与特定疾病的遗传关联的基因组受到了以下事实，即患有相同疾病的个体可能具有不同的潜在遗传原因。当发现关联时，它们通常很弱或仅与一个共享特定遗传史的个体人群有关。为了减轻这一限制更好地分析复杂表型的系统。疾病表型，尤其是癌症（例如癌症）最重要的疾病通常是基本细胞或发展过程。因此，从长远来看，该项目旨在建立一个系统的视图分子机制推动了这些基本过程，并使用此信息来告知基因组广泛关联研究的分析。为了开始此过程，我们将构建一个系统视图小鼠和人类的早期胚胎发生，并将其连接到特定的表型结果基本的基本细胞和发育过程。这种方法非常成功型号C。优雅，其中确定了特定表型所需的基因组在全基因组范围内。我们将使用类似的计算和实验方法与以及使用来自多个模型系统的数据汇编来构建早期的系统视图小鼠和人类的胚胎发育。具体而言，我们将在以下三个相关目的：1：开发一个计算框架来预测全局分子图小鼠和人类的早期胚胎发育的基础。 2：收集高意见早期胚胎发生期间的表型数据使用〜400小鼠基因的RNAi；数字编码它们复杂的表型效应，并与转录组数据和计算相结合内置的目标1。3：开发开放式网络环境以导航和分析这些环境复杂的数据，包括早期胚胎表型的延时记录，以便它们可以用于分析全基因组关联的研究。这项工作将有直接的影响为了诊断临床环境中的胚胎生存能力，并且由于许多多作用基因，反过来将提供与各种相关的复杂表型的见解发育和代谢疾病以及其他疾病过程，例如癌症，涉及细胞增殖与分化的管制。