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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，伦敦帝国理工学院和巴斯大学的研究人员将与 TMO Renewables Ltd 公司合作，（a）了解该公司在当前生物乙醇工艺中使用的嗜热细菌热葡糖苷酶地芽孢杆菌（Geobacillus thermoglucosidasius）的生理学和生物化学的基本方面，以及（b）结合基因组序列信息和实验，开发基于计算机的代谢模型。测量，这将有助于预测如何对生物体进行改变，以便它可以从各种不同的底物中产生特定的最终产品。尽管该公司已成功培育出一种热葡糖苷酸地芽孢杆菌菌株，该菌株可以利用可再生木质纤维素和废物的可发酵成分生产乙醇，但该公司对该生物体在复杂发酵条件下的行为知之甚少。在此过程中，出现了许多观察结果，但根据我们目前对生物体有限的了解，这些观察结果并不容易解释。除了对该项目提供财务贡献外，该公司还将提供其亲本菌株的基因组序列。这是该嗜热物种的第一个（可用的）完整基因组序列，为学术研究人员提供了一个重要的平台来进行新的发现。在此平台上，研究团队将应用最新开发的方法进行模型构建、模型验证和生理研究。后者将涉及新开发的“转录组学”方法，通过“RNA测序”来了解生物体在不同生理条件下如何调节其新陈代谢和行为。使用现代高通量测序方法直接分析RNA（严格来说，在测序之前必须将其转换为DNA）是之前使用微阵列的方法的进步，因为它不依赖于对基因组中真实基因序列的初步推论。由于该分析本质上不考虑先前的假设，因此它揭示了不同细菌的许多意想不到的调节特征。将这种方法应用于细菌的论文直到 2009 年才开始出现，其中大多数要么关注方法开发，要么关注病原生物。该项目将使我们有机会研究与工业相关的生物体，解决与工业燃料和生物质化学生产相关的问题，并最终在工业背景下测试假设和菌株。因此，很有可能发现微生物生长和代谢调节的新的基本过程。该项目的产出之一将是一组代谢模型。计算机模拟代谢模型可用于预测如何改变代谢通量以实现特定结果（例如促进生长或代谢物过量产生）。因此，作为本次练习的一部分，我们将使用代谢工程程序中的模型来制造一种新的代谢物，这种代谢物通常不会由该菌株产生。使用该模型，我们应该能够预测如何改变通过不同途径的通量，以实现快速生长和产物形成的双重要求。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，没有充分考虑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”，可以通过转录组数据的比较分析将其联系起来。该团队包括一位擅长整合不同类型数据的系统生物学家，他将探索以有意义的方式将两种类型的分析联系起来的可能性。