Exploring microbial communication through small molecules in multiple species domains for biotechnological use

酿酒酵母是一种被广泛研究的生物。它在学术和工业上的重要性是广泛的，作为研究真核生物、代谢工程或工业发酵过程中细胞过程的模型，在这些过程中，它在风味和香气的生物合成中的贡献不仅取决于培养条件，而且还取决于该物种是唯一的贡献者还是它所参与的微生物联盟的构象。共存可以建立有利于该群体的共生关系。在这些条件下，有机体共享营养物质和空间资源。例如，代谢相互作用可导致互补代谢活动的激活和/或任务在不同贡献物种之间的分配。与单一培养相比，微生物群落在一系列特性上经常表现出更强的能力，包括但不限于它们降解化合物、产生不同代谢物、刺激群落生长以及提高它们面对恶劣环境条件的适应能力。这些优越的特性对生物技术应用也很有用；特别是微生物群体被用来满足对天然香料和芳香日益增长的需求。然而，尽管一些互动可能有利于感兴趣的过程，但其他互动可能会阻碍它。因此，了解这些机制有助于发展良好的控制过程。通过与其他酵母和细菌共存培养酿酒酵母，观察到了一系列新的性质，导致代谢能力增强；一些细菌可以调节酵母代谢，减少有毒乙醇的产量，酿酒酵母可以抑制或促进其他微生物的生长，或者可以使其他微生物表现出特定的表型。这对于非酵母菌是一种具有大量在标准实验室条件下不表达的隐蔽次生代谢物途径的共培养尤其相关，例如链霉菌，作为一个属，它是生物技术应用中生物活性次生代谢物的主要生产者。本项目旨在系统地研究酿酒酵母与其自然栖息地共生的物种之间的相互作用，特别是在葡萄皮和葡萄地上，这有助于提高产品的质量，并通过提高对真菌侵染的抵抗力为宿主植物提供自然保护。在相互作用的物种之间建立联系的小分子，将使用两个不同的模型系统进行经验性鉴定：微生物联盟和具有生物技术意义的成对相互作用模型。在这个项目中，学生将接受一系列高通量细胞分析工具的培训，并对“组学”数据进行统计评估。学生将被介绍各种数据框架和蜂窝测量数据标准化的概念。数字孪生概念将通过代谢网络模型的线性和非线性规划在亚细胞领域进行探索。该项目具体属于EPSRC汇款中的生活与环境变化和制造未来主题和制造技术、工艺系统：组件和集成、资源效率和合成生物学研究领域。克兰菲尔德大学的索菲亚·库尔姆佩里博士将参与该项目，并将担任该学生的第三级导师。

Saccharomyces cerevisiae is a broadly studied organism. Its academic and industrial importance is extensive, as model to study cellular process in eukaryotes, metabolic engineering or in industrial fermentation processes in which its contribution in the biosynthesis of flavours and aromas depends not only on culture conditions but also on whether the species is the sole contributor or on the conformation of the microbial consortium in which it takes part.The coexistence can allow the establishment of symbiotic relations that favour the group. Under these conditions, the organisms share resources as nutrients and space. The metabolic interactions can lead to, for example, the activation of complementary metabolic activities and/or the distribution of tasks across different contributing species. The microbial communities frequently show enhanced capacity in comparison to their monocultures in a range of properties including but not limited to their capacity to degrade compounds, produce different metabolites, stimulate the community growth, as well as to increase their resilience to face hostile environmental conditions. These superior characteristics are also useful for biotechnological applications; in particular, microbial consortia are used to respond to an increasing demand of natural flavours and aromas. However, while some interactions can favour the processes of interest, others may hinder it. Therefore, understanding these mechanisms could contribute to developing well-controlled processes.By culturing S. cerevisiae in coexistence with other yeasts and bacteria, a novel range of properties leading to enhanced metabolic capacity has been observed; some bacteria can modulate the yeast metabolism and reduce the yield of the toxic ethanol, S. cerevisiae can inhibit or enhance the growth of other microorganisms or can cause other microorganisms to manifest specific phenotypes. This is particularly relevant for cocultures where the non-Saccharomyces species is one with a large variety of cryptic secondary metabolites pathways that are not expressed under standard laboratory conditions such as Streptomyces, which, as a genus, is a major producer of bioactive secondary metabolites of biotechnological application.This project aims to systematically investigate the interactions S. cerevisiae establishes with species co-occurring in its natural habitat, particularly on the grape skin and on the vine-floor, which contributes to the enhancement of the quality of the produce as well as providing natural protection to its host plant via improving resistance against fungal infestations. The small molecules, which establish the communication between the interacting species, will be identified empirically, using two different model systems: a microbial consortium and pairwise interaction models of biotechnological significance.In this project, the student will be trained in a range of high-throughput cellular analytics tools and statistical evaluation of 'omics' data. The student will be introduced to various data frameworks and concepts of data standardisation for cellular measurements. Digital twin concepts will be explored within the sub-cellular domain through linear and non-linear programming of metabolic network models.The project falls specifically within the Living with Environmental Change and Manufacturing the Future Themes and Manufacturing Technologies, Process systems: components and integration, Resource efficiency and Synthetic Biology Research Areas within the EPSRC remit.Dr Sofia Kourmpetli from Cranfield University will be involved in the project and will act as a Tertiary Supervisor for the student.