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

在这个项目中，来自伦敦帝国理工学院和巴斯大学的研究人员将与TMO可再生能源有限公司合作，以(A)了解该公司在当前的生物乙醇工艺中使用的嗜热菌热葡萄糖苷杆菌的生理和生物化学的基本方面，以及(B)利用基因组序列信息和实验测量的组合，开发基于计算机的新陈代谢模型，这将有助于预测如何对生物体进行改变，以便它能够从各种不同的底物中产生特定的最终产品。虽然该公司已经成功地创造了一种热葡萄糖苷地杆菌，可以从可再生的木质纤维素和废物的可发酵成分中生产乙醇，但人们对这种生物在复杂的发酵条件下的行为知之甚少。在这一过程中，人们观察到了许多不容易解释的现象，因为我们目前对该生物的了解有限。除了为该项目提供资金外，该公司还将提供其母株的基因组序列。这是这个嗜热物种的第一个(可获得的)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，从这个平台上进行新的发现。在这个平台上，研究团队将应用最近开发的方法进行建模、模型验证和生理调查。后者将涉及新开发的通过‘RNA测序’进行‘转录’的方法，以了解生物体如何在不同的生理条件下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(严格说来，它必须在测序前转换为DNA)是对以前使用微阵列的方法的进步，因为它不依赖于最初的推断，即基因组中真实的基因序列。由于这项分析基本上忽视了先前的假设，它揭示了不同细菌中许多意想不到的调控特征。关于将这种方法应用于细菌的论文直到2009年才开始出现，其中大多数要么集中在方法开发上，要么集中在病原体上。这个项目将使我们有机会研究与工业相关的有机体，解决与从生物质中生产工业燃料和化学品相关的问题，并最终在工业背景下测试假设和菌株。因此，有很大机会发现微生物生长和代谢调节的新的和基本的过程。该项目的成果之一将是一套代谢模型。在电子计算机中，代谢模型可用于预测代谢流量应如何改变以实现特定结果(例如，促进生长或代谢产物过量生产)。因此，作为这次练习的一部分，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，这种代谢物通常不是由这种菌株产生的。使用该模型，我们应该能够预测通过不同途径的通量应该如何改变，以实现快速增长和产品形成的双重要求。除此之外，我们希望将转录分析与模型联系起来。代谢模型本质上是静态的，没有充分结合生理调节的动态方面。通过研究不同生长条件下的细胞，我们可以产生一套条件特定的模型，这些模型可以通过对转录数据的比较分析而联系在一起。该团队包括一名系统生物学家，他擅长整合不同类型的数据，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。

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.