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

• 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
• 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

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）：现在越来越清楚的是，遗传性和获得性人类疾病是由受环境因素影响的多种遗传成分之间的复杂相互作用引起的。因此，扫描基因组以寻找与特定疾病的遗传关联受到以下事实的阻碍：患有相同疾病的不同个体可能具有不同的潜在遗传原因。 当发现关联时，它们往往很弱，或者只与共享特定遗传史的一个个体群体有关。为了缓解这一限制，我们需要更广泛地了解指定特定表型的分子组合， 更好的系统来分析复杂的表型。疾病表型，尤其是 大多数重大疾病，如癌症，往往是由于基本细胞或 发展过程因此，从长远来看，该项目旨在建立一个系统的观点， 驱动这些基本过程的分子机制，并使用这些信息来通知 全基因组关联分析研究。为了开始这个过程，我们将构建一个系统视图 小鼠和人类早期胚胎发生的研究，并将它们与特定的表型结果联系起来 基本的细胞和发育过程。这种方法在以下方面非常成功： 模型C。优雅，其中特定表型所需的基因组被确定， 在全基因组范围内。我们将使用类似的计算和实验方法， 以及使用来自几个模型系统的数据概要来构建早期 小鼠和人类的胚胎发育。具体而言，我们将致力于以下三个方面 相关目标：1：开发一个计算框架来预测全球分子图谱 在小鼠和人类的早期胚胎发育中起重要作用。2：收集高内容 使用~400个小鼠基因的RNAi在早期胚胎发生期间的表型数据;数字编码 它们复杂的表型效应和联合收割机与转录组数据和计算 目标1中的地图。3：开发一个开放访问的网络环境，以浏览和分析这些 复杂的数据，包括早期胚胎表型的延时记录， 用于分析全基因组关联的研究。这项工作将直接影响到 在临床环境中诊断胚胎活力，由于许多 基因，将反过来提供洞察复杂的表型相关的各种 发育和代谢障碍以及其他疾病过程，如癌症， 涉及细胞增殖相对于分化的失调。