NSF Postdoctoral Fellowship in Biology: Exploring Cell-type Regulatory Dynamics of CAM and C4 Photosynthesis in Portulaca

NSF 生物学博士后奖学金：探索马齿苋中 CAM 和 C4 光合作用的细胞类型调节动力学

• 批准号: 2208915
• 负责人: Ian Gilman
• 金额: $ 21.6万
• 依托单位: Gilman, Ian S
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208915&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; Exploring

This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2022. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Dr. Ian S. Gilman is “Exploring Cell-type Regulatory Dynamics of CAM and C4 Photosynthesis in Portulaca”. The host institution for the fellowship is Michigan State University and the sponsoring scientist is Dr. Robert VanBuren.C4 photosynthesis and Crassulacean Acid Metabolism (CAM) are plant adaptations that increase the efficiency of photosynthesis. Many of the world’s most important crops use C4 photosynthesis, including maize, sugarcane, and millet, which allows them to quickly grow in low nutrient and high light environments. CAM greatly increases the efficiency of plants’ water use and is therefore commonly found in plants in water-scarce environments, such as the cacti of North American deserts. It was once thought that plants could use either C4 photosynthesis or CAM, but not both because they would compete for use of the same necessary enzymes and metabolites. However, the Purslanes (Portulaca)—common weeds across the globe—were discovered to combine C4 photosynthesis and CAM, which allows them to grow extremely fast in low nutrient and low water habitats like sidewalk cracks. Understanding how C4 photosynthesis and CAM can be combined will provide new ways to improve the drought tolerance of crops with C4 photosynthesis and shed light on fundamental questions of how genes are regulated for multiple roles. Broader impacts from this project will enhance engagement with the local community, both on and off campus, to highlight connections between botany and computer science, demonstrate how common weeds could revolutionize agriculture, and discuss the benefits of genetic engineering. Training objectives include obtaining expertise in horticulture, systems biology, molecular and computational methods development, and data integration.C4 photosynthesis (C4) and Crassulacean Acid Metabolism (CAM) are carbon concentrating mechanisms (CCMs) that have evolved as plant responses to the low CO2 world of the past 30 million years. Both CCMs have co-opted the same set of ancient metabolic modules to boost the concentration of CO2 needed for photosynthesis, but have deployed these modules in contrasting ways. C4 concentrates CO2 spatially through a two-cell CO2 pump, while CAM accomplishes CO2 concentration with temporally coordinated carbon storage and re-release. These adaptations confer C4 species with the highest rates of plant photosynthesis, characterized by maize and sugarcane, and CAM plants with extremely high water use efficiencies, emblematic of cacti, aloes, and agaves. Although C4 and CAM have evolved independently in hundreds of lineages and share many biochemical components, only two land plant lineages are known to use both C4 and CAM (C4+CAM): Portulaca and Trianthema, C4 plants that facultatively exhibit CAM in response to abiotic stress. Portulaca, with multiple independent origins of C4+CAM, offers unique insights into how multiple CCMs can be integrated to increase the drought tolerance of highly productive C4 crops. This project will leverage systems and computational biology to identify the genetic elements controlling the temporal and spatial coordination of CAM and C4 in Portulaca at the cell-type level. The first goal of the project is to capture expression dynamics of individual cells using single cell RNAseq and identify CCM-related cis-regulatory elements using assay for transposase-accessible chromatin using sequencing (ATACseq). Machine learning based methods will use these data to construct gene regulatory networks that distinguish cis-elements and regulatory dynamics governing C4 and CAM. Finally, regulatory networks will be compared between species to identify shared and unique elements underlying the evolution of CCMs in Portulaca. Data generated for this project will be made available to the public though NCBI's Short Read Archive (SRA) and DataDryad (https://datadryad.org), and step-by-step walkthroughs of analyses will be hosted on GitHub (https://github.com).Keywords: gene regulatory networks, single-cell sequencing, C4 photosynthesis, Crassulacean Acid Metabolism, ATACseq, transcriptomicsThis 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.

这项行动为2022财年的NSF植物基因组博士后生物学研究奖学金提供资金。该研究金支持研究员在东道实验室的研究和培训计划，研究员还提出了扩大生物学参与的计划。本奖学金的研究和培训计划的标题是由伊恩·S. Gilman是“探索马齿苋CAM和C4光合作用的细胞类型调节动力学”。该奖学金的主办机构是密歇根州立大学，赞助科学家是罗伯特·范布伦博士。C4光合作用和景天科酸代谢（CAM）是植物适应，提高光合作用的效率。世界上许多最重要的作物都使用C4光合作用，包括玉米、甘蔗和小米，这使它们能够在低营养和高光照环境中快速生长。CAM极大地提高了植物的水分利用效率，因此通常存在于缺水环境中的植物中，例如北美沙漠的仙人掌。人们曾经认为，植物可以使用C4光合作用或CAM，但不能同时使用两者，因为它们会竞争使用相同的必需酶和代谢物。然而，马齿苋（Portulaca）-全球常见的杂草-被发现结合联合收割机C4光合作用和CAM，这使得它们能够在低营养和低水分的栖息地（如人行道裂缝）中生长非常快。了解C4光合作用和CAM如何结合将提供新的方法来提高C4光合作用作物的耐旱性，并阐明基因如何调控多种作用的基本问题。从这个项目的更广泛的影响将加强与当地社区的参与，无论是在校园内外，突出植物学和计算机科学之间的联系，展示如何常见的杂草可以彻底改变农业，并讨论基因工程的好处。培训目标包括获得园艺，系统生物学，分子和计算方法开发以及数据集成方面的专业知识。C4光合作用（C4）和景天科酸代谢（CAM）是碳浓缩机制（CCM），在过去的3000万年中，植物对低CO2世界的反应已经进化。这两种CCM都采用了相同的古代代谢模块来提高光合作用所需的CO2浓度，但以截然不同的方式部署了这些模块。C4通过双电池CO2泵在空间上浓缩CO2，而CAM通过时间协调的碳储存和再释放来实现CO2浓缩。这些适应赋予C4物种以最高的植物光合作用速率，以玉米和甘蔗为特征，CAM植物具有极高的水分利用效率，象征仙人掌，芦荟和龙舌兰。尽管C4和CAM在数百个谱系中独立进化，并共享许多生化成分，但已知只有两种陆地植物谱系同时使用C4和CAM（C4+CAM）：马齿苋和三叶草，这两种C4植物在响应非生物胁迫时会兼性地表现出CAM。马齿苋具有多个独立的C4+CAM来源，为如何整合多种CCM以提高高产C4作物的耐旱性提供了独特的见解。该项目将利用系统和计算生物学来确定在细胞类型水平上控制马齿苋中CAM和C4的时空协调的遗传因素。该项目的第一个目标是使用单细胞RNAseq捕获单个细胞的表达动力学，并使用转座酶可接近染色质测序（ATACseq）鉴定CCM相关的顺式调控元件。基于机器学习的方法将使用这些数据构建基因调控网络，区分顺式元件和控制C4和CAM的调控动力学。最后，监管网络将物种之间进行比较，以确定共同的和独特的元素，在马齿苋CCM的演变。该项目生成的数据将通过NCBI的Short Read Archive（SRA）和DataDryad（https：//www.example.com）向公众提供datadryad.org，逐步分析的流程将托管在GitHub（https：github.com）上。关键词：基因调控网络、单细胞测序、C4光合作用、景天科酸代谢、ATACseq、转录组学该奖项反映了NSF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。