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

合作研究：TRTech-PGR：PlantSynBio：FuncZyme：建立植物酶活性快速预测和功能验证的管道

• 批准号: 2310396
• 负责人: Arjun Khakhar
• 金额: $ 62.27万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310396&HistoricalAwards=false
• 关键词: Collaborative; Research; TRTech; PGR; PlantSynBio

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.

超过一千种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、组装和基因注释现在已经不是研究人员的瓶颈，但预测和验证基因功能仍然是一个主要挑战。对于大家族的基因来说尤其如此，比如那些编码代谢酶的基因。这些酶与关键的初级和专门的代谢途径有关，目前缺乏它们有意义的注释是途径发现的主要障碍。化学是植物世界的语言：代谢物介导对害虫、病原体和非生物胁迫的防御，吸引共生菌，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，而且对作物育种/工程和合成生物学的社会影响也至关重要。该项目重点关注10个最大的植物酶家族，将(1)促进将数百个已发表的酶活性存入公共存储库，如UniProt和Gene Ontology数据库；(2)建立高质量基因组测序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；(4)从累积的数据集中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活动和艺术展览相结合，以展示合成生物学在创造动态、生活艺术作品方面的力量。在大多数植物基因组中，与代谢有关的基因属于有数十个成员的大基因家族，并且注释很少。这为剖析代谢性状的遗传基础创造了障碍，如产量、果实成熟、应激反应和相互作用。三个关键的瓶颈阻碍了这些努力：(1)尽管已经发表了数千种酶的活性，但其中只有极小的一部分被记录到蛋白质功能数据库中，可供强大的功能预测程序和机器学习方法使用；(2)现有的功能转移词汇和工具不是基于底物化学，也没有考虑到酶的混杂性；(3)用于计算预测的快速功能验证的合成生物学（SynBio）工具开发不足。为了应对这些挑战，该项目将(1)利用RNA载体和合成转录因子开发基于cas9的SynBio工具，在三种被子植物物种中实现高通量基因功能验证；(2)促进将已发表的10个目标酶家族的植物酶活性最大的一个存储到UniProt和GO数据库中，并开发一个计算工作流来预测150个高质量植物基因组中目标酶家族成员的底物类别；(3)应用这些工作流程来研究这些酶家族在体内的作用和进化。在培训和推广方面，该项目将让本科生参与途径发现研究，学生将在植物群中取样生化多样性，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师合作，开发新的synbio生成的动态生活艺术作品，其中植物将被用作“画布”，用RNA载体在植物中合成的天然颜色/颜料绘制。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存储库和国家植物科学会议的培训讲习班广泛共享。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。