Genome Organisation for Optimising Synthetic Secondary Metabolism

用于优化合成次级代谢的基因组组织

• 批准号: BB/K006290/1
• 负责人: Thomas Ellis
• 金额: $ 43.45万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK006290%2F1
• 关键词: Genome; Organisation; Optimising; Synthetic; Secondary

This project will research genome organisation and in particular how changing the location and arrangement of metabolic enzyme genes within a yeast genome can alter the amount of the metabolite they produce, in this case the antibiotic penicillin. Antibiotics are just one class of complex chemicals that the diverse array of organisms on Earth has naturally evolved to produce. The production of chemicals by life is known as metabolism and the more complex high-value chemicals that specialist cells produce (e.g. in plants) are called secondary metabolites and these include most therapeutic molecules known today. Production of secondary metabolites in cells requires specific enzymes which are encoded by genes usually under strict control (regulation). As our understanding of biology improves through many fundamental research breakthroughs, scientists are now looking to re-engineer secondary metabolism to produce valuable compounds in cellular systems that are easy to work with. Microbes like brewer's yeast are perfect as they are easy to culture and so could cheaply produce high yields of valuable compounds from renewable resources like sugar.The most promising way to perform this 'metabolic engineering' is to use what is known as a synthetic biology approach, where genes and their controllers are treated as modular components with well-defined behaviours and then combined in a rational design-based manner. So far the synthetic biology approach to metabolic engineering has been successful in producing compounds useful as anti-malarials, cosmetics and biofuels by taking genes for enzymes found in plants and exotic microbes and combining these inside industrially-used microbes such as yeast. Crucial for achieving high yields of production in these microbes is fine-control over the precise levels of enzymes in each cell.One method of tuning enzyme levels that is currently unexplored by scientists is how the genes for these enzymes are physically arranged within a cell's genome. It is already known that gene location and orientation within a genome plays an important role in gene expression in all forms of life. Recently, it has also been established that in cells that naturally perform secondary metabolism, such as plant cells, the location of genes that make up a pathway is often tightly conserved, usually in occurring in gene 'clusters' in areas known as 'sub-telomeric regions'. Clearly, if nature and evolution are correct, then the location of where pathway genes are added to a genome must affect the enzyme levels and therefore the pathway output.This project seeks to test the hypothesis that modifying pathway gene location within a genome can result in improved yields of a high-value secondary metabolite, in this case penicillin. The genes encoding the penicillin pathway will be added to the genome of a lab yeast strain and cells will be selected that produce the greatest amounts of penicillin. The amount produced will then be monitored in a series of experiments where the pathway genes are systematically rearranged around five different places in the genome. This will give valuable information on how the arrangement of genes in the genome affects the pathway. Finally, the pathway genes will be placed in an engineered lab strain specifically designed to shuffle parts of its genome when under an evolutionary pressure. The strain will be grown to compete against bacteria and in doing will automatically rearrange pathway genes to produce the most penicillin. This project will therefore provide an important new synthetic biology approach to metabolic engineering, and also uncover valuable new information on the fundamental science of genomes and genome evolution.

该项目将研究基因组组织，特别是改变酵母基因组内代谢酶基因的位置和排列如何改变它们产生的代谢物的数量，在本例中是抗生素青霉素。抗生素只是地球上各种生物体自然进化产生的一类复杂化学物质。生命产生的化学物质被称为新陈代谢，而特殊细胞（例如在植物中）产生的更复杂的高价值化学物质被称为次级代谢物，其中包括当今已知的大多数治疗分子。细胞中次生代谢物的产生需要特定的酶，这些酶通常由受到严格控制（调节）的基因编码。随着我们对生物学的理解通过许多基础研究突破得到提高，科学家们现在正在寻求重新设计次级代谢，以在细胞系统中产生易于使用的有价值的化合物。像啤酒酵母这样的微生物是完美的，因为它们很容易培养，因此可以从糖等可再生资源中廉价地生产出高产量的有价值的化合物。执行这种“代谢工程”最有希望的方法是使用所谓的合成生物学方法，其中基因及其控制器被视为具有明确定义行为的模块化组件，然后以基于合理设计的方式组合。到目前为止，代谢工程的合成生物学方法已经成功地通过提取植物和外来微生物中的酶基因，并将这些基因与工业使用的微生物（如酵母）中的这些基因相结合，成功地生产出可用作抗疟疾药物、化妆品和生物燃料的化合物。在这些微生物中实现高产量的关键是对每个细胞中酶的精确水平进行精细控制。科学家目前尚未探索的一种调节酶水平的方法是这些酶的基因在细胞基因组中的物理排列方式。众所周知，基因组内的基因定位和方向在所有生命形式的基因表达中起着重要作用。最近，还确定了在自然进行次级代谢的细胞中，例如植物细胞，组成途径的基因的位置通常是严格保守的，通常出现在被称为“子-”区域的基因“簇”中。端粒区域'。显然，如果自然和进化是正确的，那么途径基因添加到基因组中的位置必定会影响酶水平，从而影响途径输出。该项目旨在检验这样的假设：修改基因组内的途径基因位置可以导致提高高价值次生代谢产物（本例中为青霉素）的产量。编码青霉素途径的基因将被添加到实验室酵母菌株的基因组中，并且将选择产生最大量青霉素的细胞。然后，将在一系列实验中监测产生的量，其中途径基因在基因组中的五个不同位置系统地重新排列。这将为基因组中基因的排列如何影响该途径提供有价值的信息。最后，这些途径基因将被放置在一个工程化的实验室菌株中，该菌株专门设计用于在进化压力下对其部分基因组进行重组。该菌株将在与细菌竞争的过程中自动重新排列途径基因以产生最多的青霉素。因此，该项目将为代谢工程提供一种重要的新合成生物学方法，并揭示有关基因组和基因组进化基础科学的有价值的新信息。

项目成果

Biosynthesis of therapeutic natural products using synthetic biology.

利用合成生物学生物合成治疗性天然产物。

• DOI: 10.1016/j.addr.2016.04.010
• 发表时间: 2016-10-01
• 期刊: Advanced drug delivery reviews
• 影响因子: 16.1
• 作者: A. Awan;William M Shaw;T. Ellis
• 通讯作者: T. Ellis

The Synthetic Genome Summer Course.

合成基因组暑期课程。

• DOI: http://dx.10.1093/synbio/ysy020
• 发表时间: 2018
• 期刊: Synthetic biology (Oxford, England)
• 作者: Blount BA

Off-Colony Screening of Biosynthetic Libraries by Rapid Laser-Enabled Mass Spectrometry.

通过快速激光质谱法对生物合成文库进行菌落外筛选。

• DOI: http://dx.10.1021/acssynbio.9b00243
• 发表时间: 2019
• 期刊: ACS synthetic biology
• 影响因子: 4.7
• 作者: Gowers GF

Improved betulinic acid biosynthesis using synthetic yeast chromosome recombination and semi-automated rapid LC-MS screening.

使用合成酵母染色体重组和半自动快速 LC-MS 筛选改进桦木酸生物合成。

• DOI: http://dx.10.1038/s41467-020-14708-z
• 发表时间: 2020
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Gowers GF

Burden-driven feedback control of gene expression.

基因表达的负荷驱动反馈控制。

• DOI: http://dx.10.1038/nmeth.4635
• 发表时间: 2018
• 期刊: Nature methods
• 影响因子: 48
• 作者: Ceroni F