The Generation of Complex Epistasis by Metabolic Networks

代谢网络产生复杂的上位性

• 批准号: 0820580
• 负责人: Daniel Kliebenstein
• 金额: $ 131.41万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0820580&HistoricalAwards=false
• 关键词: Generation; Complex; Epistasis; Metabolic; Networks

Organismal phenotypes are controlled by complex networks that buffer against mutation, environmental noise or error ensuring the proper phenotype is produced. In contrast, most phenotypes vary between individuals of a species allowing for evolution. This includes the plant metabolome that has complex enzymatic interactions and is controlled by intricate signaling interactions but also shows inter-specific variation. One poorly understood mechanism that can allow networks to show extensive phenotypic variation is multi-locus epistasis that simultaneously impacts multiple nodes of a network. Epistasis is frequently found in naturally variable systems such as crop yield and human disease susceptibility but the molecular mechanism is rarely identified. This project will use natural variation in the rice and Arabidopsis metabolomes as model systems to identify mechanisms by which metabolomic Quantitative Trait Loci form an epistatic network that constrains potential variation present within the metabolome. The project builds on previous studies in the model system Arabidopsis thaliana that identified eight naturally variable loci that epistatically interact in a genetic network to control swaths of Arabidopsis primary metabolism. Specific allele combinations at four of these loci lead to plants with 800% increases in steady state content of metabolites within part of the TCA cycle. Specific objectives include cloning the genes underlying these loci and manipulating the homologous genes in rice to test their ability to control primary metabolism in a monocot crop. In addition, precise measures of epistasis in the Arabidopsis and rice metabolomes will be made by analyzing a large Recombinant Inbred Line population in each species. Finally the data generated will be used to develop a metabolic network de novo using a logic based algorithm that has identified novel metabolic networks in other metabolomics data. In the future, this knowledge will allow for the development of models that integrate natural variation in plant metabolic networks to potentially predict phenotypic diversity. Analysis of natural variation in most organisms focuses on single genes of large effect due to relative ease of identification and modeling. However, this is only one aspect of natural variation and organismal evolution. In contrast, most traits are under complex control including significant epistasis with large phenotypic consequences. This proposal will begin to provide insights into how epistasis and biological networks may control complex traits. Understanding complex epistatic interactions will provide insights into other complex traits such as crop yield and human disease that are under epistatic control. The proposed project will provide research opportunities for high school, undergraduate, and graduate students. Students will be trained in modern metabolic biochemistry and molecular genetics to prepare them for future careers in industry or academics. The undergraduate students will be highly encouraged and guided to develop and devise their own projects within the frame of this proposal. Any publication likely to result from this proposal will likely include at least one undergraduate student as a co-author who was integral in designing and interpreting the experiments. Established outreach programs will be used to recruit minority students from local high schools and colleges throughout the USA for summer internships. In addition, the principal investigator will be involved in teaching, both in a university classroom setting and in ongoing outreach efforts to educate community members about plant metabolism, quantitative genetics, biochemistry, molecular biology and their integration in factorial experiments. All data will be available through the project website and long-term through The Arabidopsis Information Resource (TAIR: www.arabidopsis.org) and Gramene (www.gramene.org).

生物表型由复杂的网络控制，这些网络可以缓冲突变，环境噪音或错误，确保产生正确的表型。相比之下，大多数表型在允许进化的物种个体之间变化。这包括植物代谢组，其具有复杂的酶相互作用，并由复杂的信号相互作用控制，但也显示出种间变异。一个知之甚少的机制，可以让网络显示出广泛的表型变异是多位点上位性，同时影响网络的多个节点。上位性常存在于作物产量和人类疾病易感性等自然变异系统中，但其分子机制尚不清楚。本项目将利用水稻和拟南芥代谢组中的自然变异作为模型系统，以确定代谢组学数量性状基因座形成上位性网络的机制，该网络约束代谢组中存在的潜在变异。该项目建立在先前在模型系统拟南芥中的研究的基础上，该研究确定了8个自然可变的基因座，这些基因座在遗传网络中相互作用，以控制拟南芥初级代谢的条带。这些基因座中的四个的特定等位基因组合导致植物在TCA循环的一部分内代谢物的稳态含量增加800%。 具体目标包括克隆这些位点的基因，并操纵水稻中的同源基因，以测试它们控制单子叶作物初级代谢的能力。 此外，拟南芥和水稻代谢组中上位性的精确测量将通过分析每个物种中的大型重组近交系群体来进行。最后，生成的数据将用于使用基于逻辑的算法从头开发代谢网络，该算法已在其他代谢组学数据中识别出新的代谢网络。在未来，这些知识将允许开发模型，将自然变异整合到植物代谢网络中，以预测表型多样性。大多数生物的自然变异分析集中在单个基因的大影响，由于相对容易识别和建模。然而，这只是自然变异和生物进化的一个方面。相比之下，大多数性状是在复杂的控制下，包括显着的上位性与大的表型后果。这项提议将开始提供关于上位性和生物网络如何控制复杂性状的见解。了解复杂的上位性相互作用将提供洞察其他复杂性状，如作物产量和人类疾病的上位性控制。拟议的项目将为高中，本科和研究生提供研究机会。学生将接受现代代谢生物化学和分子遗传学的培训，为他们未来在工业或学术界的职业生涯做好准备。本科生将受到高度鼓励和指导，在本提案的框架内开发和设计自己的项目。任何出版物可能会导致从这个建议将可能包括至少一个本科生作为共同作者谁是不可分割的设计和解释实验。已建立的外展计划将用于从美国各地的当地高中和大学招募少数民族学生进行暑期实习。此外，首席研究员将参与教学，无论是在大学课堂设置和正在进行的外联工作，以教育社区成员有关植物代谢，数量遗传学，生物化学，分子生物学及其在因子实验中的整合。所有数据将通过项目网站和拟南芥信息资源（TAIR：www.arabidopsis.org）和Gramene（www.gramene.org）长期提供。