BRC-BIO: Model-driven discovery and characterization of the metabolism of pigment-producing microalgae at genome-scale

BRC-BIO：模型驱动的基因组规模色素产生微藻代谢的发现和表征

• 批准号: 2313313
• 负责人: Cristal Zuniga
• 金额: $ 50.3万
• 依托单位: San Diego State University Foundation
• 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313313&HistoricalAwards=false
• 关键词: BRC; BIO; Model; driven; discovery

Photosynthetic organisms, including microalgae, can produce high-value pigments by consuming light and carbon dioxide, the most abundant greenhouse gas in the atmosphere. Microalgae have a complex and very active metabolism; they are responsible for approximately 20% of the total photosynthesis on earth. However, the lack of understanding of microalgal metabolism has limited the ability to use these organisms to their full potential to address environmental, food, and health challenges. This research project focuses on developing computational tools to understand the light metabolism of the marine microalga Dunaliella salina and the freshwater microalga Haematococcus lacustris. The genome sequences of these microalgae will be used to build two knowledgebases that will be converted into metabolic models to further understand and control the necessary metabolic requirements to promote accumulation of pigments, specifically carotenoids. By increasing the fundamental understanding of the biology of these microalgae, the research will enable new light-driven solutions to sustainability challenges. For this dual experimental and computational project, a diverse research team will be formed that includes graduate, undergraduate and K-12 researchers. Students will engage in learning, teaching, and training in computational biology, nurturing the next generation of computational biologists who will develop future tools for biosustainability and the bioeconomy. Systems biology approaches have resulted in genome-scale computational models that accurately represent the metabolism of microorganisms. When applied to photosynthetic microorganisms, metabolic models offer the potential to exploit natural metabolic robustness and diversity to develop biotechnological processes. Of special interest in microalgae-based biotechnology is the production of pigments. Microalgal species such as Dunaliella salina and Haematococcus lacustris contain extraordinary genetic capabilities to produce carotenoids for a wide range of biotechnological applications. In this project, two genome-scale metabolic models will be built, one for the halophilic microalga Dunaliella salina and another for the freshwater microalga Haematococcus lacustris. The models will serve as knowledgebases that allow the evaluation and prediction of carbon, nitrogen, and phosphorus pathways under different conditions (e.g., nutrient inputs, uptake capabilities of new metabolites, and different light sources). Available multi-omics data and genomic information will be used to validate the content of the metabolic models. These models will be applied to study microalgal pigment production under stress conditions, providing insights into innovative culture conditions to maximize productivity. Overall, this research will guide the development of new culture strategies to improve pigment production in these biotechnologically important organisms. This hybrid experimental and computational project will further the establishment of a research program in computational biology of microalgae at San Diego State University to develop robust light-driven bioprocesses for improved growth and pigment production.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.

包括微藻在内的光合生物可以通过消耗光和二氧化碳（大气中最丰富的温室气体）来生产高价值的色素。微藻具有复杂而活跃的新陈代谢；它们承担了地球上大约20%的光合作用。然而，缺乏对微藻代谢的了解限制了利用这些生物充分发挥其潜力来解决环境、食物和健康挑战的能力。本研究项目的重点是开发计算工具来了解海洋微藻杜氏盐藻和淡水微藻湖红球菌的光代谢。这些微藻的基因组序列将用于建立两个知识库，这些知识库将转化为代谢模型，以进一步了解和控制促进色素，特别是类胡萝卜素积累的必要代谢需求。通过增加对这些微藻生物学的基本了解，这项研究将为可持续发展的挑战提供新的光驱动解决方案。对于这个双重实验和计算项目，将形成一个多元化的研究团队，包括研究生，本科生和K-12研究人员。学生将参与计算生物学的学习、教学和培训，培养下一代计算生物学家，他们将为生物可持续性和生物经济开发未来的工具。系统生物学方法已经产生了基因组规模的计算模型，可以准确地代表微生物的代谢。当应用于光合微生物时，代谢模型提供了利用自然代谢稳健性和多样性来开发生物技术过程的潜力。在以微藻为基础的生物技术中，特别令人感兴趣的是色素的生产。盐渍杜氏藻和湖红球菌等微藻具有非凡的遗传能力，可生产广泛应用于生物技术的类胡萝卜素。在本项目中，将建立两个基因组尺度的代谢模型，一个是嗜盐微藻杜氏盐藻，另一个是淡水微藻湖红球菌。这些模型将作为知识库，允许评估和预测不同条件下的碳、氮和磷途径（例如，营养输入、新代谢物的吸收能力和不同的光源）。可用的多组学数据和基因组信息将用于验证代谢模型的内容。这些模型将应用于研究应激条件下微藻色素的生产，为创新培养条件提供见解，以最大限度地提高生产力。总的来说，这项研究将指导新的培养策略的发展，以提高这些生物技术上重要的生物的色素生产。这个混合实验和计算项目将进一步在圣地亚哥州立大学建立一个微藻计算生物学研究项目，以开发强大的光驱动生物过程，以改善生长和色素生产。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。