Systems modelling of microbial communities using in vitro and computational approaches

使用体外和计算方法对微生物群落进行系统建模

• 批准号: RGPIN-2020-03922
• 负责人: Raymond, Frédéric
• 金额: $ 2.7万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739566
• 关键词: Systems; modelling; microbial; communities; using

Understanding and predicting how microbial communities react to changes in their environment is critical for the development of microbiome-based applications. There are many fields where being able to control the composition and activity of microbiomes would be beneficial, including biotechnology, bioremediation and animal/human nutrition. Previous studies suggest that by knowing the composition of a microbiome, we could predict its response to specific interventions. In vitro models of microbial communities provide useful systems for the high-throughput study of the impact of molecules on microbiomes. It has been shown previously that in vitro microbiomes allowed discoveries that were replicated in animal models. These approaches are also able to cost-effectively provide a large quantity of information that is necessary to model microbial communities with machine learning. To study the microbiome, scientists sequence the genomes of bacterial communities. This provides large quantities of DNA sequences that must be carefully interpreted. In most studies, researchers quantify the abundance of the taxonomical origin of bacteria and determine the metabolic functions of the genes encoded in bacterial genomes. However, only 35% to 45% of genes from the gut microbiome can be associated with actual functions. This often limits the analysis and interpretation of microbiome-related studies and overlooks the extensive functional and ecological possibilities of microbial communities. In this research program, we aim to create the basis of a framework for the targeted modulation of microbial communities. To do so, we will address two critical aspects that need to be resolved before we can deliberately modulate microbiomes to get specific effects. First, we will devise new ways to represent microbiomes in a manner that allows to efficiently consider all the genes and bacterial species, including those with unknown functions. We will use machine learning to discover meaningful patterns in the data that may be overlooked using current methods based on bacteria quantification and gene function profiling. Second, we will use machine learning to predict the response of the microbiome to specific conditions and to determine the best sequence of interventions to obtain a desired microbiome effect. We will use in vitro culture of stool samples in presence of three trace minerals as a model to develop new methods to make possible targeted microbiome modulation. The use of machine learning to model the response of microbiomes to their chemical environment will permit a deeper understanding of the interplay between microorganisms and their environment. Overall, our research program will provide methods to modulate microbiomes that will be applicable to many fields, including biotechnology and personalized nutrition for both humans and animals. This project will also provide specific guidelines to design studies to optimize microbial communities using machine learning.

了解和预测微生物群落如何对其环境变化做出反应对于开发基于微生物组的应用程序至关重要。能够控制微生物组的组成和活性在许多领域都是有益的，包括生物技术、生物修复和动物/人类营养。先前的研究表明，通过了解微生物组的组成，我们可以预测其对特定干预措施的反应。微生物群落的体外模型为分子对微生物组影响的高通量研究提供了有用的系统。先前已经表明，体外微生物组允许在动物模型中复制的发现。这些方法还能够经济高效地提供通过机器学习建模微生物群落所需的大量信息。为了研究微生物组，科学家对细菌群落的基因组进行了测序。这提供了大量必须仔细解释的 DNA 序列。在大多数研究中，研究人员量化细菌分类学起源的丰度，并确定细菌基因组中编码的基因的代谢功能。然而，肠道微生物组中只有 35% 至 45% 的基因与实际功能相关。这通常限制了微生物组相关研究的分析和解释，并忽视了微生物群落广泛的功能和生态可能性。在这个研究项目中，我们的目标是为微生物群落的有针对性的调节奠定框架的基础。为此，我们将解决两个关键问题，然后我们才能有意识地调节微生物组以获得特定效果。首先，我们将设计新的方法来表示微生物组，以便有效地考虑所有基因和细菌物种，包括那些具有未知功能的基因和细菌物种。我们将使用机器学习来发现数据中有意义的模式，而使用基于细菌定量和基因功能分析的当前方法可能会忽略这些模式。其次，我们将使用机器学习来预测微生物组对特定条件的反应，并确定最佳干预顺序以获得所需的微生物组效果。我们将使用存在三种微量矿物质的粪便样本的体外培养作为模型来开发新方法，以实现有针对性的微生物组调节。使用机器学习来模拟微生物组对其化学环境的反应将有助于更深入地了解微生物与其环境之间的相互作用。总体而言，我们的研究计划将提供适用于许多领域的调节微生物组的方法，包括生物技术和人类和动物的个性化营养。该项目还将提供具体的指导方针来设计研究，以利用机器学习优化微生物群落。