The Generation of Complex Epistasis by Metabolic Networks

生物体的表型由复杂的网络控制，这些网络可以缓冲突变、环境噪声或错误，确保产生正确的表型。相比之下，大多数表型在一个物种的个体之间存在差异，从而允许进化。这包括具有复杂的酶相互作用并受复杂的信号相互作用控制的植物代谢体，但也显示出种间差异。一种鲜为人知的机制，可以使网络显示出广泛的表型变异是多位点上位，同时影响网络的多个节点。上位性经常出现在作物产量和人类疾病易感性等自然变量系统中，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体中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上位网络的机制，该网络限制了代谢组中存在的潜在变异。该项目建立在模式系统拟南芥之前的研究基础上，该模型系统确定了八个在遗传网络中上位性相互作用的自然可变基因，以控制拟南芥的大片初级新陈代谢。其中四个座位上的特定等位基因组合导致植物在TCA循环的一部分中代谢产物的稳态含量增加了800%。具体目标包括克隆这些基因座背后的基因，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，通过分析每个物种中的大量重组近交系群体，将对拟南芥和水稻代谢物中的上位性进行精确的测量。最后，生成的数据将用于使用基于逻辑的算法从头开始开发代谢网络，该算法已经在其他代谢组学数据中识别了新的代谢网络。在未来，这一知识将允许开发整合植物代谢网络中的自然变异的模型，以潜在地预测表型多样性。对大多数生物体的自然变异的分析主要集中在单个基因上，由于相对容易识别和建模，单个基因的影响很大。然而，这只是自然变异和生物进化的一个方面。相反，大多数性状受到复杂的控制，包括显著的上位性效应和较大的表型效应。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特征的见解。了解复杂的上位性交互作用将提供对上位性控制下的其他复杂性状的洞察，如作物产量和人类疾病。该项目将为高中生、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界的职业生涯做好准备。我们将高度鼓励和引导本科生在这个提案的框架内发展和设计他们自己的项目。这项提议可能导致的任何出版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方面发挥了不可或缺的作用。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族裔学生参加暑期实习。此外，首席研究人员将参与大学课堂教学和正在进行的推广工作，以教育社区成员有关植物新陈代谢、数量遗传学、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.gram ene.org)提供。

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).