A powerful directed-evolution tool for exploitation of chloroplast engineering biology

用于叶绿体工程生物学开发的强大定向进化工具

• 批准号: BB/Y008162/1
• 负责人: Saul Purton
• 金额: $ 136.3万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY008162%2F1
• 关键词: powerful; directed; evolution; tool; exploitation

Photosynthetic organisms including plants and green algae offer significant potential for sustainable biotechnology in which sunlight is used to power the biosynthesis of industrial products using simple inputs of carbon dioxide, inorganic salts and water. Overlay on this the ability to genetically engineer the plant or algae using modern synthetic biology techniques, and these organisms offer the potential for making a myriad of novel bio-products for a wide range of commercial sectors including pharmaceuticals, nutraceuticals, cosmetics, textiles and food ingredients. Such applied synthetic biology is termed 'engineering biology', and requires reprogramming the cells of the organism with a suite of new genes to make bio-products. An attractive site for housing these genes within the cell is the chloroplast. This sub-cellular compartment contains a minimal chromosome (the 'plastome') harbouring only a hundred-or-so genes, and a simple expression system to decode these genes into enzymes and proteins. Re-design of the plastome using engineering design principles (i.e. standardisation, abstraction and predictable output for a given input) to house new genes is therefore relatively straightforward, and this technology has been developed for several plants and for the single cell alga, Chlamydomonas reinhardtii. However, an initial design is never optimal since numerous parameters need to be tuned to achieve the desired expression of the genes and the optimum design of the proteins encoded by those genes. Such optimisation can involve either multiple iterations where the knowledge gained from the first design informs changes incorporated in the next version of the design, and so-on. Such an approach can be lengthy and costly. Alternatively, millions of different design variants can be tested in parallel, but this requires the generation of millions of test organisms which can be impractical. In this project, we will build on the synthetic biology technology we have developed for the C. reinhardtii chloroplast and create a new and powerful optimisation tool. We will develop a system that allows us to introduce multiple random base changes (i.e. mutations) within the plastome in a controlled manner, and in a focused way so that we don't introduce unwanted mutations into the much larger nuclear genome. Our approach will involve creating a starting strain containing a highly error-prone version of the chloroplast DNA polymerase, which is the enzyme that replicated chloroplast genes. The activity of this polymerase will be tightly regulated, but when induced using a simple vitamin-regulated switch the design landscape of any gene(s) engineered into the plastome can be explored by simply growing the cells to produce millions of daughter cells, each carrying different DNA changes within the plastome. Selection or high-throughput screening of these cells would allow the rapid identification of those variants showing improvements in a desired outcome (higher level of product, more active or stable enzyme, etc.). As a first demonstration of the power of this approach, we will search for more efficient variants of key enzymes within the Calvin-Benson-Bassham cycle. This cyclical biochemical pathway is fundamental to the conversion of CO2 to organic carbon by photosynthesis, and it is known that improvements in the activity of several key enzymes (most notably the enzyme 'Rubisco') would markedly improve the growth of plants and algae. We will use our plastome mutator technology to search in vivo for such improved enzyme variants. This would not only provide new insights into how to improve photosynthetic performance in crop plants, but also produce faster growing C. reinhardtii strains for applications in green industrial biotechnology.

包括植物和绿藻在内的光合作用生物为可持续生物技术提供了巨大的潜力，在可持续生物技术中，阳光被用来通过简单的二氧化碳、无机盐和水的输入来推动工业产品的生物合成。在此基础上，利用现代合成生物学技术对植物或藻类进行基因工程改造，这些生物为包括制药、保健食品、化妆品、纺织品和食品配料在内的广泛商业部门提供了制造无数新型生物产品的潜力。这种应用合成生物学被称为工程生物学，需要用一套新的基因对生物体的细胞进行重新编程，以制造生物产品。在细胞内容纳这些基因的一个有吸引力的位置是叶绿体。这个亚细胞隔间包含一个最小的染色体(‘质体’)，其中只含有100个左右的基因，以及一个简单的表达系统，可以将这些基因解码为酶和蛋白质。因此，使用工程设计原则(即标准化、抽象化和特定输入的可预测输出)重新设计质体组以容纳新基因相对简单，这项技术已被开发用于几种植物和单细胞藻类--莱茵衣藻。然而，最初的设计从来都不是最优的，因为需要调整许多参数来实现所需的基因表达和这些基因编码的蛋白质的最佳设计。这样的优化可以包括多次迭代，其中从第一个设计获得的知识通知合并到下一个设计版本中的更改，以此类推。这样的方法可能会耗时很长，成本也很高。或者，可以并行测试数百万个不同的设计变体，但这需要生成数百万个测试有机体，这可能是不切实际的。在这个项目中，我们将建立在我们为C.reinhardtii叶绿体开发的合成生物技术的基础上，并创建一个新的、强大的优化工具。我们将开发一种系统，允许我们以可控的方式和集中的方式在质体体中引入多个随机碱基变化(即突变)，这样我们就不会在更大的核基因组中引入不想要的突变。我们的方法将包括创建一个包含高度容易出错的叶绿体DNA聚合酶版本的起始菌株，叶绿体DNA聚合酶是复制叶绿体基因的酶。这种聚合酶的活性将受到严格的调控，但当使用简单的维生素调控开关诱导时，通过简单地培养细胞来产生数以百万计的子细胞，就可以探索任何基因(S)在质体中的设计图景，每个子细胞在质体中携带不同的dna变化。这些细胞的选择或高通量筛选将允许快速鉴定那些在预期结果(更高水平的产物、更活跃或更稳定的酶等)中表现出改善的变异。作为这种方法的力量的第一个演示，我们将在Calvin-Benson-Bassham循环中寻找更有效的关键酶变体。这种循环的生化途径是通过光合作用将二氧化碳转化为有机碳的基本途径，众所周知，几种关键酶(最明显的是‘Rubisco’酶)活性的提高将显著促进植物和藻类的生长。我们将使用我们的质构体变异体技术在体内寻找这种改进的酶变体。这不仅将为如何提高作物的光合作用性能提供新的见解，而且还将生产出生长更快的莱茵哈蒂菌菌株，用于绿色工业生物技术。