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.

超过一千个植物基因组已经被测序，并且这个数字正在迅速增加，虽然基因组测序、组装和基因注释对当今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，例如编码代谢酶的基因，与关键的主要和专门的代谢途径相关，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：代谢物。调解植物代谢物可以防御害虫、病原体和非生物胁迫，吸引互利共生，并在确定生长模式和作物产量方面发挥重要作用，因此，改善代谢基因注释不仅对食品、药物、化妆品和许多其他产品至关重要。了解基础植物生物学，还通过帮助作物育种/工程和合成生物学来产生社会影响。该项目重点关注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存储到公共存储库中。例如 UniProt 和 Gene Ontology 数据库；(2) 开发用于从高质量测序基因组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；来自积累的数据集的新颖的进化和功能见解将与提高本科生参与研究的活动相结合，并举办艺术展览，以展示合成生物学在创造动态的、活的艺术作品方面的力量在大多数植物基因组中，涉及基因。在新陈代谢中属于大基因家族这为剖析产量、果实成熟、胁迫反应和互利相互作用等代谢特征的遗传基础造成了障碍：(1) 数千种酶活性。已发表，其中只有一小部分被记录到蛋白质功能数据库中并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表和工具不是基于底物化学，也不是基于底物化学。考虑到酶的混杂性；(3) 用于快速功能验证计算预测的合成生物学 (SynBio) 工具尚未充分开发，为了应对这些挑战，该项目将 (1) 使用 RNA 载体和技术开发基于 Cas9 的 SynBio 工具。合成转录因子，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在 UniProt 和 GO 数据库中的最大沉积之一，以及开发一个计算工作流程来预测来自 150 个高质量植物基因组的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的体内作用和进化。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化多样性进行采样，并探讨非参考/药用植物的潜在代谢途径。此外，该项目还将与科罗拉多州立大学艺术和艺术史系的教师合作。开发小说SynBio 生成的动态生活艺术作品将使用植物作为“画布”，用 RNA 载体在植物中合成的天然颜色/颜料进行绘制。所有项目成果，包括新的计算工具、生物资源和数据集，都将通过公共访问存储库广泛共享。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Exploring the frontier of rapid prototyping technologies for plant synthetic biology and what could lie beyond

探索植物合成生物学快速原型技术的前沿以及未来的发展

• DOI: 10.1111/nph.19650
• 发表时间: 2024-03
• 期刊: New Phytologist
• 影响因子: 9.4
• 作者: Morey, Kevin;Khakhar, Arjun
• 通讯作者: Khakhar, Arjun