Comparative Genomics-driven Discovery of Maize Metabolic Functions

Pi：Andrew D.Hanson(佛罗里达大学盖恩斯维尔分校)Copis：Valérie de CréCy-Lagard，Donald R.McCarty和Jesse F.Gregory(佛罗里达大学盖恩斯维尔分校)，Christopher S.Henry(芝加哥大学)高级合作者：Andrei Osterman(伯纳姆医学研究所)和Svetlana Gerdes(基因组解释联谊会)植物基因组和基因测序的快速进展暴露了关于70%编码蛋白质功能的信息缺乏。此外，这些研究表明，许多这样的未知因素也存在于细菌和古生菌中。比较基因组学是揭示基因功能的一种强有力的方法，在生物体新陈代谢的电子计算机重建中也是先进的。在这个项目中，这两种方法都将被用来提高分配给玉米代谢基因的功能的准确性，并预测未知基因的功能，特别是B类维生素(叶酸、烟酸、硫胺素、吡哆醇、核黄素、泛酸和生物素)。这十个最有希望的功能预测将通过结合细菌中的遗传和代谢图谱方法、重组蛋白的生化分析和玉米的遗传测试来实验验证。该项目的预期总体成果是以维生素B代谢为范式，实施玉米基因功能发现的比较基因组学预测和验证流水线。基因功能(注解)将通过MaizeCyc(http://pathway-dev.gramene.org/gramene/maizecyc.shtml)和SEED(http://theseed.uchicago.edu/FIG/index.cgi)数据库)公开提供，代谢重建可通过模型种子网站(www.theeed.org/Models/)获得。该项目将实施一种跨学科的基因功能发现方法，可扩展到任何代谢网络。在这个过程中，它将通过对数千个代谢基因注释施加一致性，并通过改进数百个未知基因的注释来丰富玉米基因组。这两种影响都将推进未来的功能发现。此外，通过在电子代谢重建中发展基因组规模，这项工作将启动一种系统的方法来理解玉米的代谢。整体而言，这项工作将为学生、博士后助理和教师提供比较基因组学、代谢生物化学和微生物遗传学方面的跨学科培训。此外，该项目将在佛罗里达大学举办为期3天的年度实践讲习班，培训各级研究人员使用SEED和其他比较基因组学数据库预测功能，特别强调培训少数族裔服务机构的教师。最后，该项目将开发一门本科生生物信息学课程，在该课程中，学生将利用比较基因组学参与未知基因的识别和新陈代谢重建，以及研究生课程模块，在该课程模块中，学生将开发对该项目中未知因素的函数预测。

PI: Andrew D. Hanson (University of Florida - Gainesville)CoPIs: Valérie de Crécy-Lagard, Donald R. McCarty, and Jesse F. Gregory (University of Florida - Gainesville), Christopher S. Henry (University of Chicago)Senior collaborators: Andrei Osterman (Burnham Institute for Medical Research) and Svetlana Gerdes (Fellowship for Interpretation of Genomes) The rapid progress in sequencing plant genomes and genes has exposed the lack of information regarding the function of 70% of the proteins encoded. In addition, these studies have shown that many such unknowns also occur in bacteria and archaea. Comparative genomics is a powerful approach to uncover gene function, as is advanced in silico reconstruction of an organism's metabolism. In this project, both approaches will be used to improve the accuracy of functions assigned to maize metabolic genes and to predict functions for unknown genes, with special emphasis on B vitamins (folate, niacin, thiamin, pyridoxine, riboflavin, pantothenate, and biotin). The ten most promising functional predictions will be experimentally validated by combining genetic and metabolic profiling approaches in bacteria with biochemical assays of recombinant proteins and with genetic tests in maize. The expected overall outcome of this project is the implementation of a comparative genomics prediction and validation pipeline for maize gene function discovery, using B vitamin metabolism as a paradigm. Gene functions (annotations) will be publicly available via MaizeCyc (http://pathway-dev.gramene.org/gramene/maizecyc.shtml) and the SEED (http://theseed.uchicago.edu/FIG/index.cgi) databases, with metabolic reconstructions available via the Model SEED website (www.theseed.org/models/).The project will implement an interdisciplinary approach to gene function discovery that is extendable to any metabolic network. In the process, it will enrich the maize genome by imposing consistency on thousands of metabolic gene annotations, and by improving the annotations of hundreds of unknown genes. Both impacts will advance future function discovery. Furthermore, by developing genome-scale in silico metabolic reconstructions, the work will initiate a systems approach to understanding maize metabolism. Integrally, the work will provide cross-disciplinary training in comparative genomics, metabolic biochemistry, and microbial genetics to students, postdoctoral associates, and faculty. In addition the project will provide for an annual 3-day hands-on workshop at the University of Florida to train researchers at all levels to predict functions using the SEED and other comparative genomics databases, with special emphasis on training faculty from minority serving institutions. Finally the project will develop an undergraduate bioinformatics course in which the students participate in unknown gene identification and metabolic reconstructions using comparative genomics and a graduate course module in which students develop functional predictions for unknowns in the project.