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，(a)了解该公司目前在生物乙醇工艺中使用的嗜热细菌Geobacillus thermoglucosidasius的生理和生物化学基本方面，以及(b)利用基因组序列信息和实验测量相结合，开发基于计算机的代谢模型。这将有助于预测如何改变生物体，使其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可以从可再生木质纤维素和可发酵废物成分中生产乙醇的热葡萄糖酸菌菌株，但这是在对生物在复杂发酵条件下的行为知之甚少的情况下完成的。在这一过程中，我们进行了许多观察，这些观察不容易用我们目前有限的生物知识来解释。除了为该项目提供资金外，该公司还将提供它们亲本菌株的基因组序列。这是该物种的第一个（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，从中进行新的发现。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。后者将涉及通过“RNA测序”新开发的“转录组学”方法，以了解生物体如何在不同生理条件下调节其代谢和行为。使用现代高通量测序方法直接分析RNA（严格地说，它必须在测序前转化为DNA）是先前使用微阵列方法的一种进步，因为它不依赖于基因组中哪些是真正的基因序列的初始推断。由于分析基本上不考虑先前的假设，因此它揭示了不同细菌中许多意想不到的调节特征。将该方法应用于细菌的论文从2009年才开始出现，而且大多数都集中在方法开发或致病生物上。该项目将使我们有机会研究与工业相关的生物，解决与生物质工业燃料和化学品生产相关的问题，并最终在工业环境中测试假设和菌株。因此，发现微生物生长和代谢调节的新的和基本的过程是一个很好的机会。这个项目的产出之一将是一组代谢模型。计算机代谢模型可用于预测如何改变代谢通量以达到特定结果（例如促进生长或代谢物过量生产）。因此，作为实验的一部分，我们将在代谢工程程序中使用这些模型来制造一种新的代谢物，而这种代谢物通常不是由这种菌株产生的。利用该模型，我们应该能够预测通过不同途径的通量应该如何变化，以实现快速增长和产品形成的双重要求。除此之外，我们希望将转录组学分析与模型联系起来。代谢模型本质上是静态的图像，没有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”，这些模型可以通过转录组数据的比较分析联系起来。该团队包括一位系统生物学家，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.