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).

有机表型受缓冲，环境噪声或误差缓冲的复杂网络控制，以确保产生适当的表型。相反，大多数表型在允许进化的物种个体之间有所不同。这包括具有复杂酶促相互作用的植物代谢组，并由复杂的信号相互作用控制，但也显示出特定的变化。一个鲜为人知的机制，可以使网络显示出广泛的表型变化是多级别的上述性，同时会影响网络的多个节点。上毒经常在自然可变系统（例如作物产量和人类疾病敏感性）中发现，但很少鉴定出分子机制。该项目将使用大米和拟南芥代谢组中的自然变异作为模型系统，以识别代谢组定量性状基因座形成的上皮网络，该网络构成了代谢组中存在的潜在变化。该项目基于模型系统拟南芥的先前研究，该研究鉴定出八个自然变化的基因座，这些基因座在遗传网络中与遗传网络相互作用，以控制拟南芥的主要代谢。这些基因座的四个基因座的特定等位基因组合导致植物在TCA周期的一部分内代谢物的稳态含量增加了800％。 具体目标包括克隆这些基因座的基因并操纵大米中的同源基因，以测试其控制单子蛋白作物中原代代谢的能力。 此外，通过分析每个物种中的大型重组近交系数，将对拟南芥和水稻代谢组中的上毒的精确度量进行。最后，生成的数据将用于使用基于逻辑的算法来开发从头开始的代谢网络，该算法已经确定了其他代谢组学数据中的新代谢网络。将来，这些知识将允许开发模型，这些模型可以整合植物代谢网络中的自然变异，以预测表型多样性。大多数生物体自然变异的分析集中在相对易于识别和建模的情况下，大多数效应的单个基因。但是，这只是自然变异和生物进化的一个方面。相比之下，大多数特征受到复杂的控制，包括具有较大表型后果的明显上毒。该提案将开始提供有关上毒和生物网络如何控制复杂特征的见解。了解复杂的上皮相互作用将为其他复杂的特征（例如作物产量和受到上毒控制的人类疾病）提供见解。拟议的项目将为高中，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的培训，以准备为行业或学术界的未来职业做好准备。本科生将受到强烈的鼓励和指导，以在本提案的框架内开发和设计自己的项目。任何可能由该提案产生的出版物都可能包括至少一名本科生作为合着者，他们在设计和解释实验中是不可或缺的。既定的外展计划将用于招募来自美国当地高中和大学的少数民族学生进行暑期实习。此外，首席研究人员将参与教学，包括大学课堂环境，以及正在进行的外展工作，以教育社区成员有关植物代谢，定量遗传学，生物化学，分子生物学及其在循序分子实验中的整合。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）提供。