INSPIRE: Unraveling Factors that Determine Fast Growth of a Photoautotroph

INSPIRE奖部分由生物科学理事会分子和细胞生物科学部的系统和合成生物学项目资助；工程理事会化学、生物工程、环境和运输系统部门的生物技术和生化工程项目和环境工程项目；综合活动办公室和国际科学与工程办公室。该奖项旨在对影响模式光合生物生长的过程和动力学产生系统级的见解。因此，这个项目对于理解复杂和基本的生物过程具有重要意义，并且对生物技术的应用具有重要意义。光合作用是地球上生命的引擎，而依靠光合作用生存的生物，如蓝藻、藻类和植物，主要负责生物圈中的碳封存。在今天的地球上，这些产生氧气的光合生物每年吸收100兆吨（1000亿吨）的碳。相比之下，需要化学营养物质作为能量来源的生物体每年仅吸收0.3亿吨（0.3亿吨）。这个INSPIRE项目旨在揭示控制蓝藻（一种模式光合生物）生长速度的最重要因素。它汇集了两个不同的领域，蓝藻生理学和数学建模，以抽象生长参数和动态的基本规则。它将帮助设计蓝藻和其他光合微生物的工程工具包，以增加生物质生产，这是生物技术应用的基本要求，用于阳光驱动的碳中和生产食品、饲料和燃料。该项目还将为国际本科生提供培训，并在iGEM培训计划下吸引学生。研究小组最近发现了一种单细胞蓝藻，长聚球菌菌株，UTEX 2973，在迄今为止研究的所有蓝藻菌株中生长速度最快。令人惊讶的是，这种菌株是长聚球菌PCC 7942的近亲，PCC 7942是一种研究得很好的蓝藻菌株。两者的基因组序列同源性大于99%。然而，2973的生长潜力是7942的近3倍。研究人员将通过实验分子方法比较聚球菌7942和聚球菌2973，利用适应性进化和开发代谢和表达（ME）模型，确定导致蓝藻快速生长的关键因素。他们将比较这两种菌株在不同光照强度、二氧化碳水平和生长阶段下的转录组。将聚囊球菌2973和聚囊球菌7942的ME模型与转录组学数据相结合，有望揭示导致聚囊球菌2973生物量增加的代谢和表达差异。还将测量光合作用和碳固定效率，以更好地阐明这些菌株之间的机制差异。此外，通过基因组比较鉴定的突变子集将被分析，以努力概括快速生长表型。

This INSPIRE award is partially funded by the Systems and Synthetic Biology program in the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences; the Biotechnology and Biochemical Engineering program and the Environmental Engineering program in the Division of Chemical, Bioengineering, Environmental, and Transport Systems in the Directorate for Engineering; the Office of Integrated Activities, and the Office of International Science & Engineering. This award is aimed at generating systems level insights into the processes and dynamics that influence growth in model photosynthetic organisms. As such, this project has important implications for fundamental understanding of a complex and essential biological process, and has significant implications for biotechnology applications. Photosynthesis is the engine of life on our planet, and organisms that live off of photosynthesis, such as cyanobacteria, algae, and plants, are primarily responsible for carbon sequestration in the biosphere. On the planet today, such oxygen-producing photosynthetic organisms assimilate 100 petagrams (100 gigatons) of carbon per year. In contrast, organisms that need chemical nutrients as sources of energy assimilate only 0.3 petagrams (0.3 gigatons) per year. This INSPIRE project is aimed at unraveling the most important factors that control growth rates of cyanobacteria, a model photosynthetic organism. It brings together two different fields, cyanobacteria physiology and mathematical modeling, to abstract fundamental rules of growth parameters and dynamics. It will help design tool kits for engineering cyanobacteria and other photosynthetic microorganisms to enable increased biomass production, an essential requirement for biotechnology applications for sunlight driven carbon-neutral production of food, feed and fuel. The project will also provide training for international undergraduates and engage students under the iGEM training program. The team of researchers identified recently a unicellular cyanobacterium, Synechococcus elongatus strain, UTEX 2973, with the fastest growth rate among all cyanobacterial strains studied to date. Surprisingly, this strain is a close relative of Synechococcus elongatus PCC 7942, a well-studied model cyanobacterial strain. The two share greater than 99% genome sequence identity. However, the growth potential of Synechococcus 2973 is nearly three-fold better than Synechococcus 7942. The investigators will determine the key factors that are responsible for fast growth of cyanobacteria by applying experimental molecular approaches to compare Synechococcus 7942 with Synechococcus 2973, using adaptive evolution, and developing metabolic and expression (ME) models. They will compare the transcriptome of the two strains under different light intensities, CO2 levels and growth phases. The integrating of ME models for Synechococcus 2973 and Synechococcus 7942 with the transcriptomic data generated are expected to uncover metabolic and expression differences that contribute to the increased biomass production of Synechococcus 2973. The photosynthetic and carbon fixation efficiencies will also be measured to better elucidate mechanistic differences between these strains. In addition, a subset of mutations identified by genome comparison will be analyzed in an effort to recapitulate the fast-growth phenotype.