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).

生物体表型由复杂的网络控制，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，大多数表型在物种个体之间存在差异，从而允许进化。这包括具有复杂酶相互作用并受复杂信号相互作用控制的植物代谢组，但也显示出种间变异。一种可以让网络表现出广泛表型变异的鲜为人知的机制是多位点上位性，它同时影响网络的多个节点。上位性经常出现在自然变量系统中，例如作物产量和人类疾病易感性，但其分子机制很少被识别。该项目将使用水稻和拟南芥代谢组的自然变异作为模型系统，以确定代谢组数量性状基因座形成限制代谢组内存在的潜在变异的上位网络的机制。该项目建立在先前对拟南芥模型系统的研究基础上，该系统确定了八个自然可变基因座，这些基因座在遗传网络中上位相互作用以控制拟南芥初级代谢的范围。其中四个位点的特定等位基因组合导致植物 TCA 循环部分代谢物的稳态含量增加 800%。 具体目标包括克隆这些位点背后的基因，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，将通过分析每个物种的大量重组自交系群体来精确测量拟南芥和水稻代谢组中的上位性。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。未来，这些知识将有助于开发将自然变异整合到植物代谢网络中的模型，以潜在地预测表型多样性。由于识别和建模相对容易，大多数生物体的自然变异分析都集中在具有大影响的单个基因上。然而，这只是自然变异和生物进化的一方面。相比之下，大多数性状都受到复杂的控制，包括具有重大表型后果的显着上位性。该提案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。了解复杂的上位相互作用将有助于深入了解其他复杂性状，例如受上位控制的作物产量和人类疾病。拟议的项目将为高中生、本科生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方面的培训，为他们未来在工业或学术领域的职业生涯做好准备。将大力鼓励和指导本科生在该提案的框架内开发和设计自己的项目。该提案可能产生的任何出版物都可能包括至少一名本科生作为合著者，他在设计和解释实验中发挥了不可或缺的作用。既定的外展计划将用于招募来自美国各地高中和大学的少数族裔学生进行暑期实习。此外，首席研究员将参与大学课堂教学和持续的外展工作，以教育社区成员有关植物代谢、定量遗传学、生物化学、分子生物学及其在析因实验中的整合。所有数据将通过项目网站提供，并长期通过拟南芥信息资源（TAIR：www.arabidopsis.org）和 Gramene（www.gramene.org）提供。