A Computational Framework for Designing Microbiome Manipulation

描述（由申请人提供） 翻译后摘要：人类微生物组-复杂的合奏微生物，填充人体-对我们的健康有着巨大的影响。现在，全球范围的研究计划为以前未知的微生物组组成提供了初步见解，并揭示了与各种疾病和宿主表型相关的显著组成变化。特别是肠道微生物组在许多基本过程中起着关键作用，并积极促进各种疾病状态，包括肥胖，糖尿病，炎症性肠病，心血管疾病和神经系统疾病。具体而言，最近的研究已经证明，将供体微生物群转移到受体中可以诱导各种供体表型或促进患病受体的恢复。这些研究结果表明，通过直接操纵肠道微生物组是一种有希望的治疗途径。例如，这种临床干预可以针对其组成代表某种疾病状态（如糖尿病）的微生物组，并促进组成转变为健康配置。或者，个体可以被一种新的“设计者”微生物组殖民，这种微生物组具有一些优选的代谢能力，例如，允许营养不良的儿童群体从有限的饮食中收获更多的能量。然而，为了让基于微生物组的治疗向前发展并实现其全部潜力，需要一个全面的计算工具包来指导这种操作并提出有希望的干预路线。在这个项目中，我们将相应地开发一个计算框架，用于设计针对一组特定的预期代谢目标的微生物组操作。该框架将包括两个主要组成部分：微生物组代谢的系统级计算机模拟模型，能够成功预测肠道环境中给定微生物聚生体的代谢活性，以及优化模块，用于搜索可能的微生物组组成空间，以获得最接近所需代谢目标的微生物组。我们将使用多种代谢建模和分析方法，并提出新的计算方法来研究复杂的多物种群落。这种系统级的计算方法在研究和设计单物种代谢方面被证明是非常强大和有效的，但尚未应用于研究整个群落的代谢。还将开发计算技术，以计算不同物种的丰度，并评估所设计的微生物组的复原力。这项拟议的跨学科研究将计算系统生物学、计算机模型和复杂网络分析与基因组和宏基因组数据相结合，在构建用于指导和告知基于微生物组的临床干预措施的计算框架方面迈出了关键的第一步。该项目代表了人类微生物组研究的一个重大飞跃，并为利用这一新获得的知识直接促进人类健康提供了计算工具。 公共卫生相关性：人体微生物组对我们的健康有着巨大的影响，并与各种疾病状态有关，从肥胖和糖尿病到心血管疾病和神经系统疾病。通过靶向特异性干预或通过整个微生物组移植的微生物组操纵是一个令人兴奋的临床前沿，具有许多有前途的医学应用。在这个项目中，我们将开发一种新的计算框架，将微生物组的预测模型及其对宿主的影响与优化技术相结合，用于设计此类操作并为临床干预工作提供信息。

DESCRIPTION (Provided by the applicant) Abstract: The human microbiome - the complex ensemble of microorganisms that populate the human body - has a tremendous impact on our health. World-wide research initiatives now provide preliminary insights into the previously uncharted composition of the microbiome, and reveal marked compositional changes associated with a wide range of diseases and host phenotypes. The gut microbiome, especially, plays a key role in many essential processes and actively contributes to various disease states, including obesity, diabetes, inflammatory bowel disease, cardiovascular diseases, and neurological disorders. Specifically, recent studies have demonstrated that transferring a donor microbiota into a recipient can induce various donor phenotypes or prompt the recovery of a sick recipient. These findings suggest a promising therapeutic avenue via directed manipulation of the gut microbiome. Such clinical interventions could target, for example, a microbiome whose composition typifies a certain disease state, such as diabetes, and promote a compositional shift into a healthy configuration. Alternatively, individuals could be colonized with a new ""designer"" microbiome with some preferred metabolic capacities, allowing, for example, populations of undernourished children to harvest more energy from a limited diet. However, to allow microbiome-based therapy to move forward and to realize its full potential, a comprehensive computational toolkit is required for directing such manipulations and proposing promising intervention routes. In this project, we will accordingly develop a computational framework for designing microbiome manipulation targeted at a specific set of desired metabolic goals. This framework will encompass two main components: A system-level in-silico model of microbiome metabolism, capable of successfully predicting the metabolic activity of a given microbial consortia in the gut environment, and an optimization module which will be used to search the space of possible microbiome compositions for those that most closely match the required metabolic goals. We will use multiple metabolic modeling and analysis approaches, and present novel computational methods for studying complex multi-species communities. Such system-level computational methods proved extremely powerful and effective in studying and engineering single species metabolism, but have not yet been applied to study community-wide metabolism. Computational techniques will also be developed to account for varying species abundances and to assess the resilience of designed microbiomes. This proposed cross-disciplinary research integrates computational systems biology, in-silico models, and complex networks analysis, with genomic and metagenomic data, taking a first crucial step in the construction of a computational framework for guiding and informing microbiome-based clinical interventions. This project represents a major leap forward in the study of the human microbiome and in providing computational tools for harnessing this newly gained knowledge directly for promoting human health. Public Health Relevance: The human microbiome has a tremendous impact on our health and has been associated with various disease states ranging from obesity and diabetes to cardiovascular diseases and neurological disorders. Microbiome manipulation, either via targeted specific intervention or via whole microbiome transplantation, is an exciting clinical frontier with numerous promising medical applications. In this project, we will develop a novel computational framework, integrating a predictive model of the microbiome and its impact on the host with optimization techniques, for designing such manipulations and for informing clinical intervention efforts.