Collaborative Research: TRTech-PGR: PlantSynBio: FuncZyme: Building a pipeline for rapid prediction and functional validation of plant enzyme activities

超过一千个植物基因组已经被测序，而且这个数字还在迅速增加。虽然基因组测序、组装和基因注释对今天的研究人员来说不再是瓶颈，但预测和验证基因功能仍然是一个重大挑战。大家族中的基因尤其如此，比如那些编码代谢酶的基因。这类酶与关键的初级和专门化代谢途径有关，目前缺乏有意义的注释是发现途径的主要障碍。化学是植物界的语言：代谢物调节对害虫、病原体和非生物胁迫的防御，吸引互惠共生，并在确定生长模式和作物产量方面发挥作用。在社会上，植物代谢物对食品、药品、化妆品和许多其他产品都很重要。因此，改进代谢基因注释不仅对理解基础植物生物学至关重要，而且对帮助作物育种/工程和合成生物学产生社会影响也是至关重要的。该项目侧重于十个最大的植物酶家族，将(1)促进将数百种已发表的酶活性储存到公共储存库，如UniProt和基因本体论数据库；(2)开发用于从高质量测序的基因组中预测酶功能的计算管道；(3)开发和应用基于合成生物学的工具来快速验证预测的酶功能；以及(4)从积累的数据集获得新的进化和功能见解。研究工作将与提高本科生参与研究的包容性的活动相结合，并举办艺术展，展示合成生物学在创作动态、生动的艺术作品方面的力量。在大多数植物基因组中，参与新陈代谢的基因属于大的基因家族，有几十个成员，注释很差。这为剖析新陈代谢性状的遗传基础创造了障碍，如产量、果实成熟、胁迫反应和互利互动。三个关键的瓶颈阻碍了这些努力：(1)虽然已经发表了数千种酶活性，但其中只有一小部分被登录到蛋白质功能数据库中，并可用于强大的功能预测程序和机器学习方法；(2)现有的功能转移词汇和工具不是基于底物化学，没有考虑酶的混杂；以及(3)合成生物学(SynBio)工具用于快速功能验证计算预测还不够开发。为了应对这些挑战，该项目将(1)开发一个使用RNA载体和合成转录因子的基于Cas9的SynBio工具，从而能够在三个被子植物物种中进行高通量基因功能验证；(2)促进已发表的10个目标酶家族的植物酶活性到UniProt和GO数据库的最大沉积之一，以及开发一个计算工作流来从150个高质量植物基因组中预测目标酶家族成员的底物类；以及(3)应用这些工作流来研究这些酶家族的体内作用和进化。在培训和推广方面，该项目将让本科生参与途径发现研究，学生将采样植物区系中的生化多样性，并探索非参考/药用植物的潜在代谢途径。此外，该项目将与科罗拉多州立大学艺术和艺术史系的教职员工合作，开发新的SynBio生成的动态生活艺术作品，其中植物将被用作使用RNA载体在植物中合成的自然颜色/颜料绘制的“画布”。包括新的计算工具、生物资源和数据集在内的所有项目成果将通过公共访问储存库和国家植物科学会议的培训研讨会广泛共享。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Over a thousand plant genomes have already been sequenced and this number is rapidly increasing. While genome sequencing, assembly and gene annotation are less of a bottleneck for researchers today, predicting and validating gene functions is still a major challenge. This is especially the case for genes in large families, such as those encoding metabolic enzymes. Such enzymes are associated with critical primary and specialized metabolic pathways, and the current lack of their meaningful annotation is a major barrier to pathway discovery. Chemistry is the language of the plant world: metabolites mediate defenses against pests, pathogens and abiotic stresses, attract mutualists and play a role in defining growth patterns and crop yield. Societally, plant metabolites are important for foods, drugs, cosmetics and numerous other products. Improving metabolic gene annotation is therefore crucial not just for understanding fundamental plant biology, but also for societal impacts by aiding crop breeding/engineering and synthetic biology. This project, focusing on ten of the largest plant enzyme families, will (1) facilitate deposition of hundreds of published enzyme activities into public repositories such as the UniProt and Gene Ontology databases; (2) develop computational pipelines for predicting enzyme function from high-quality sequenced genomes; (3) develop and apply synthetic biology-based tools for rapid validation of predicted enzyme function; and (4) derive novel evolutionary and functional insights from the accumulated datasets. Research efforts will be coupled with activities that improve inclusive undergraduate participation in research and an art exhibition to demonstrate the power of synthetic biology in creating dynamic, living art pieces. In most plant genomes, genes involved in metabolism belong to large gene families with dozens of members and are poorly annotated. This creates a barrier for dissecting the genetic basis of metabolic traits such as yield, fruit ripening, stress response, and mutualistic interactions. Three critical bottlenecks stymie these efforts: (1) although thousands of enzyme activities have been published, only a miniscule fraction of these are logged into protein function databases and available for use by powerful function prediction programs and machine learning approaches; (2) existing vocabularies and tools for function transfer are not based on substrate chemistry and do not take into account enzyme promiscuity; and, (3) synthetic biology (SynBio) tools for rapid functional validation of computational predictions are insufficiently developed. To address these challenges, this project will (1) develop a Cas9-based SynBio tool using RNA vectors and synthetic transcription factors, enabling high-throughput gene function validation in three angiosperm species; (2) facilitate one of the largest depositions of published plant enzyme activities of 10 targeted enzyme families into the UniProt and GO databases, as well as develop a computational workflow to predict substrate classes of the targeted enzyme family members from 150 high-quality plant genomes; and, (3) apply these workflows to investigate in vivo roles and evolution of these enzyme families. With respect to training and outreach, the project will engage undergraduate students in pathway discovery studies where students will sample biochemical diversity in flora and probe underlying metabolic pathways of non-reference/medicinal plants. In addition, the project will work with faculty in the Colorado State University’s Department of Art and Art History to develop novel SynBio-generated dynamic living art pieces where plants will be used as “canvases” painted with natural colors/pigments synthesized in planta using RNA vectors. All project outcomes that include new computational tools, biological resources and datasets will be shared broadly through public access repositories and through training workshops at national plant science conferences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.