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: 2310395
• 负责人: Gaurav Moghe
• 金额: $ 117.73万
• 依托单位: Cornell 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=2310395&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.

已经有超过1000个植物基因组被测序，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已经不是瓶颈，但预测和验证基因功能仍然是一个重大挑战。这对于大家族中的基因尤其如此，例如那些编码代谢酶的基因。这些酶与关键的主要和专门的代谢途径有关，目前缺乏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：代谢产物介导对害虫、病原体和非生物胁迫的防御，吸引互利主义者，并在确定生长模式和作物产量方面发挥作用。在社会上，植物代谢产物对食品、药物、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，而且通过帮助作物育种/工程和合成生物学来产生社会影响。该项目侧重于十大植物酶家族，将（1）促进将数百种已发表的酶活性存入公共知识库，如UniProt和Gene Ontology数据库;（2）开发计算管道，用于从高质量测序基因组预测酶功能;（3）开发和应用基于合成生物学的工具，用于快速验证预测的酶功能;（4）开发和应用基于合成生物学的工具，用于快速验证酶功能。以及（4）从积累的数据集中获得新的进化和功能见解。研究工作将与提高包容性的本科生参与研究和艺术展览的活动相结合，以展示合成生物学在创造动态，生活艺术作品中的力量。在大多数植物基因组中，参与代谢的基因属于具有数十个成员的大基因家族，并且注释不足。这就为解剖代谢性状的遗传基础，如产量、果实成熟、应激反应和互利相互作用，造成了障碍。三个关键瓶颈阻碍了这些努力：（1）尽管已经发表了数千种酶活性，但其中只有一小部分被记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表和工具不是基于底物化学，也没有考虑酶的混杂性;（3）合成生物学（SynBio）用于快速功能验证计算预测的工具开发不足。为了应对这些挑战，该项目将（1）使用RNA载体和合成转录因子开发基于Cas9的SynBio工具，从而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10个靶向酶家族的已公布植物酶活性最大的沉积之一沉积到UniProt和GO数据库中，以及开发一个计算工作流程来预测从150个高质量的植物基因组中靶向酶家族成员的底物类别;以及（3）应用这些工作流程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，学生将对植物群的生物化学多样性进行采样，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与艺术史系的教师合作，开发新的SynBio-generated动态活体艺术作品，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。所有的项目成果，包括新的计算工具，生物资源和数据集，将通过公共资源库和国家植物科学会议的培训研讨会广泛分享。该奖项反映了NSF的法定使命，并被认为值得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来支持。