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

基本信息

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

项目摘要

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.

已经对一千多个植物基因组进行了测序，并且该数量正在迅速增加。虽然基因组测序，组装和基因注释对于当今的研究人员来说却不是瓶颈，但预测和验证基因功能仍然是一个主要挑战。对于大家庭的基因，例如编码代谢酶的基因尤其如此。这种酶与关键的原发性和专业代谢途径有关，目前缺乏有意义的注释是发现途径的主要障碍。化学是植物世界的语言：代谢物培养基防御害虫，病原体和非生物压力，吸引共同主义者并在定义生长模式和作物产量中发挥作用。在社会上，植物代谢物对于食品，药物，化妆品和许多其他产品很重要。因此，改善代谢基因注释不仅对于了解基本的植物生物学至关重要，而且对于通过协助作物育种/工程和合成生物学的社会影响也至关重要。该项目着重于十个最大的植物酶家族，（1）将促进数百个已发表的酶活动的沉积到公共储存库中，例如Uniprot和Gene Ontology数据库；（2）开发用于预测高质量测序基因组酶功能的计算管道；（3）开发并应用基于合成生物学的工具来快速验证预测的酶功能；（4）从累积的数据集中得出新颖的进化和功能见解。研究工作将加上改善包容性本科参与研究和艺术展览的活动，以展示合成生物学在创造动态活力艺术作品方面的力量。在大多数植物基因组中，涉及代谢的基因属于具有数十个成员的大基因家族，注释不良。这为解剖代谢特征的遗传基础（例如产量，成熟，压力反应和相互互动）的遗传基础创造了障碍。三个关键的瓶颈困扰了这些努力：（1）尽管已经发布了数千种酶活性，但仅将其中的一小部分登录到蛋白质功能数据库中，并可以通过强大的功能预测程序和机器学习方法使用；（2）现有的词汇和功能传递工具不是基于底物化学的，也不考虑酶滥交；（3）用于快速功能验证计算预测的合成生物学（SYNBIO）工具的开发不足。为了应对这些挑战，该项目将（1）使用RNA矢量和合成转录因子开发基于CAS9的合成工具，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10个有针对性酶家族已发表的植物酶活性的最大沉积之一，以及数据库中，以及开发计算工作流程，以预测来自150个高质量植物基因组的靶向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角色和演变。关于培训和外展，该项目将吸引本科生参与途径发现研究，其中学生将在植物区系进行生化多样性，并在非参考/药用植物的代谢途径中进行探测。此外，该项目将与科罗拉多州立大学艺术与艺术历史系的教师合作，以开发新型Synbio生成的动态生活艺术品，其中将使用RNA载体在Planta中合成的自然色/色素绘制的植物将植物用作“画布”。包括新计算工具，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上的培训讲习班进行广泛共享。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来获得的支持。