Development of Geobacillus thermoglucosidasius as a robust platform for production of chemicals from renewables through modelling and experimentation

通过建模和实验开发热葡萄糖苷土芽孢杆菌作为利用可再生能源生产化学品的强大平台

基本信息

批准号：
BB/J002410/1
负责人：
Michael Danson
金额：
$ 35.07万
依托单位：
University of Bath
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FJ002410%2F1
关键词：
Development Geobacillus thermoglucosidasius robust platform

项目摘要

In this project, researchers from Imperial College London and the University of Bath will work together with the company TMO Renewables Ltd to (a) understand fundamental aspects of the physiology and biochemistry of the thermophilic bacterium Geobacillus thermoglucosidasius, which the company uses in its current bio-ethanol process, and (b) develop computer based metabolic models, using a combination of genome sequence information and experimental measurements, which will be useful for predicting how to make changes to the organism so that it can produce a specific end-product from a variety of different substrates. While the company has been successful in creating a strain of Geobacillus thermoglucosidasius that can produce ethanol from renewable lignocellulose and fermentable components of waste, this was done with little understanding of how the organism behaves under complex fermentation conditions. During this process, many observations have been made that are not easy to explain from our limited current knowledge of the organism. As well as a financial contribution to the project, the company will provide the genome sequence for their parent strain. This is the first (available) complete genome sequence for this species of thermophile and provides the academic researchers with a significant platform from which to make new discoveries. Building on this platform, the research team will apply recently-developed methods for model building, model validation and physiological investigation. The latter will involve the newly-developed approach of 'transcriptomics' by 'RNA -sequencing' to understand how the organism regulates its metabolism and behaviour under different physiological conditions. Direct analysis of RNA (strictly speaking, it has to be converted to DNA before sequencing) using modern methods of high-throughput sequencing is an advance on the previous approach using microarrays, because it does not rely on initial deduction of which are bona-fide gene sequences in a genome. Because the analysis is essentially blind to prior assumptions, it has revealed many unexpected features of regulation in different bacteria. Papers on the application of this method to bacteria only started appearing in 2009, and most of these either focus on methods development or pathogenic organisms. This project will give us the opportunity to look at an industrially relevant organism, addressing questions that are pertinent to industrial fuel and chemical production from biomass and ultimately testing hypotheses and strains in an industrial context. Therefore, there is a strong chance for discovering new and fundamental processes underlying the regulation of microbial growth and metabolism. One of the outputs from this project will be a set of metabolic models. In silico metabolic models can be useful for predicting how metabolic flux should be altered to achieve a specific outcome (eg enhanced growth or metabolite overproduction). So, as part of this exercise, we will use the models in a metabolic engineering programme to make a new metabolite, not normally produced by this strain. Using the model, we should be able to predict how flux through different pathways should be changed to accomplish the dual requirements of rapid growth and product formation. In addition to this, we hope to link the transcriptomic analysis to the models. Metabolic models are essentially static pictures that do not adequately incorporate the dynamic aspects of physiological regulation. By studying cells under different growth conditions, we can generate a set of 'condition-specific models' which can be linked through comparative analysis of the transcriptomic data. The team involves a systems biologist who is expert at integrating different types of data, who will explore the possibility of linking the two types of analysis in a meaningful manner.

在该项目中，来自伦敦帝国学院和巴斯大学的研究人员将与该公司TMO Renewables Ltd合作，以了解（a）了解嗜热细菌的生理学和生物化学的基本方面 - 乙醇过程，以及（b）使用基因组序列信息和实验测量的组合开发基于计算机的代谢模型，这对于预测如何对生物进行更改很有用，以便它可以从A中产生特定的终产物多种不同的基板。尽管该公司已经成功地产生了热葡萄糖糖菌的菌株，该菌株可以从可再生的木质纤维素和可发酵的废物组成部分产生乙醇，但对生物体在复杂发酵条件下的行为几乎没有理解。在此过程中，从我们当前对生物体的有限了解中解释的许多观察结果并不容易解释。除了对该项目的财务贡献外，该公司还将为其母公司压力提供基因组序列。这是该物种的第一个（可用的）完整基因组序列，并为学术研究人员提供了一个重要的平台，可以从中获得新的发现。在这个平台上，研究团队将应用最近开发的方法来建立模型验证和生理研究。后者将涉及“转录组学”新开发的方法，即“ RNA” - 测序”，以了解有机体如何调节其在不同生理条件下的代谢和行为。直接对RNA进行直接分析（严格地说，必须使用现代的高通量测序方法在测序之前转换为DNA），这是使用微阵列的先前方法的进步基因组中的基因序列。由于该分析本质上是对先前假设的视而不见的，因此它揭示了不同细菌中调节的许多意外特征。有关该方法在细菌中应用的论文仅在2009年才开始出现，其中大多数要么着重于方法开发或致病生物。该项目将使我们有机会查看与工业相关的生物，解决与生物质工业燃料和化学生产有关的问题，并最终在工业环境中检验假设和菌株。因此，发现微生物生长和新陈代谢的调节基础的新的和基本的过程很有可能。该项目的输出之一将是一组代谢模型。在计算机中，代谢模型可用于预测应如何改变代谢通量以实现特定结果（例如增强生长或代谢产物生产过多）。因此，作为本练习的一部分，我们将使用代谢工程计划中的模型来制造一种新的代谢物，通常不是由这种菌株产生。使用该模型，我们应该能够预测应如何更改通过不同途径的通量来完成快速生长和产品形成的双重要求。除此之外，我们希望将转录组分析与模型联系起来。代谢模型本质上是静态图片，无法充分纳入生理调节的动态方面。通过研究不同生长条件下的细胞，我们可以生成一组“条件特异性模型”，这些模型可以通过对转录组数据的比较分析来链接。该团队涉及一名系统生物学家，他是整合不同类型数据的专家，他们将探索以有意义的方式链接两种类型的分析的可能性。