Environmental modulation of metabolic function in microbial communities

微生物群落代谢功能的环境调节

基本信息

批准号：
10720118
负责人：
Seppe Kuehn
金额：
$ 33.71万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-03 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10720118
关键词：
Acute Affect Automobile Driving Bacteria Basic Science Biological Models Biomass Blood Pressure Carbon Carbon Dioxide Cell Respiration Cells Chemicals Chromosome Mapping Climate Communities Complex Custom Devices Diet Environment Environmental Impact Equilibrium Eutrophication Exhibits Frequencies Genes Genetic Transcription Genome Genomics Genotype Global Warming Goals Growth Health Hematopoietic Neoplasms Human Human body Humanities Individual Knowledge Learning Machine Learning Maps Measurement Mediating Metabolic Metabolic Control Metabolic Pathway Metabolism Methods Microbial Physiology Modeling Molecular Nitrates Nitric Oxide Nitrites Nutrient Oral Organism Outcome Oxidants Oxygen Ozone Pathway interactions Pattern Phenotype Physiological Physiology Play Polysaccharides Predisposition Process Production Property Reaction Regulator Genes Resource Sharing Resources Respiration Role Route Schedule Source Structure System Testing Toxic effect Variant Work bacterial community behavior prediction climate change denitrification design genome-wide host-associated microbial communities improved insight learning community lens microbial microbial community microbiome composition microbiome research microbiota pH gradient pollutant programs success trait

项目摘要

Microbial communities are complex systems whose emergent metabolic properties play a key role in determining human health. Metabolic processes enabled by host-associated microbiota play a defining role in individual health outcomes, and the emergent metabolism of microbial consortia affect environmental processes from eutrophication to climate change, impacting human health on a global scale. Therefore, humanity would benefit from a quantitative understanding of the rules by which the genomic composition of a microbial community, and the environment in which it resides, determines its emergent metabolism. Discovering the principles by which environmental variation alters community structure and determines metabolic function is a necessity if we are to manipulate or design communities to improve health outcomes. However, this task is challenging for existing methods. In preliminary work, we establish a new quantitative framework for predicting the emergent metabolism of a bacterial community from its genomic composition using denitrification as a model metabolic process. Combining quantitative bacterial phenotyping, modeling, and a simple statistical approach we demonstrated a method that quantitatively maps gene content to metabolite dynamics in microbial communities. This insight provides a route to quantitatively connecting the genes present in a community to metabolite dynamics. The next challenge is to use this insight to understand how community function and structure depend on the environment. We propose to extend this success by understanding how environmental gradients, complexity, and dynamics impact community structure and function. We accomplish this by developing denitrification as a model metabolic process. The outcomes of the proposed work will be three-fold. First, microbiome studies have documented ubiquitous associations between environmental conditions and community composition, but we do not understand the ecological or physiological origins of these emergent patterns or their metabolic consequences. Using denitrifying communities across a pH gradient I will show that such patterns emerge from ecological interactions. I will show that these interactions arise generically from the presence of physiological trade-offs on microbial traits, providing a generalizable route to understanding the functional impact of environmental variation on communities. Second, our preliminary study connected genomes to community metabolism for a simple metabolic pathway acting. I will extend this success to complex pathways and environmental conditions by constructing a method for predicting carbon utilization by communities in complex nutrient conditions directly from genomes. I will utilize a powerful blend of genome-scale metabolic modeling and multi-view machine learning, with impacts from host physiology to climate change. Third, I will use denitrifying communities to test the idea that, like cells and organisms, microbial communities exhibit predictive behaviors in dynamic environments. I propose that communities assembled in environments with distinct schedules of aerobic respiration and anaerobic respiration (denitrification) adapt to facilitate the prompt utilization of electron acceptors. I will test the hypothesis that community-level learning emerges from ecological interactions and distinct gene regulatory programs, providing a new conceptual lens through which we can view community adaptation to dynamic environments.

微生物群落是复杂的系统，其新兴的代谢特性在决定人类的健康。宿主相关微生物群启用的代谢过程在以下方面发挥着决定性作用：个人健康结果和微生物群落的新兴代谢影响环境从富营养化到气候变化的过程，在全球范围内影响人类健康。所以，人类将从对基因组组成规则的定量理解中受益。微生物群落及其所处的环境决定了其新兴代谢。发现环境变化改变群落结构并决定的原理如果我们要操纵或设计社区来改善健康结果，代谢功能是必需的。然而，这项任务对于现有方法来说具有挑战性。在前期工作中，我们建立了一个新的定量框架来预测新兴代谢使用反硝化作用作为模型代谢过程，从其基因组组成中分析细菌群落。结合定量细菌表型、建模和简单的统计方法，我们证明了将基因内容定量映射到微生物群落代谢动态的方法。这种洞察力提供了一种定量地将群落中存在的基因与代谢动态联系起来的途径。这下一个挑战是利用这种洞察力来理解社区功能和结构如何依赖于环境。我们建议通过了解环境梯度、复杂性和动态影响群落结构和功能。我们通过发展反硝化技术来实现这一目标模型代谢过程。拟议工作的成果将是三重的。一、微生物组研究已经记录了环境条件和群落组成之间普遍存在的关联，但是我们不了解这些新兴模式的生态或生理起源或其代谢结果。在 pH 梯度上使用反硝化群落，我将证明这种模式是从生态相互作用。我将证明这些相互作用通常是由于生理因素的存在而产生的微生物特性的权衡，提供了一条通用的途径来理解微生物的功能影响社区的环境变化。其次，我们的初步研究将基因组与社区联系起来新陈代谢为简单的代谢途径起作用。我将把这一成功扩展到复杂的途径通过构建一种预测复杂环境中社区碳利用的方法来预测环境条件直接来自基因组的营养条件。我将利用基因组规模代谢模型的强大组合以及多视图机器学习，以及从宿主生理学到气候变化的影响。第三，我会用反硝化群落来测试这样的想法：像细胞和有机体一样，微生物群落表现出预测性动态环境中的行为。我建议社区聚集在具有独特特征的环境中有氧呼吸和无氧呼吸（反硝化）的时间表适应以利于及时电子受体的利用。我将检验社区级学习源自的假设生态相互作用和独特的基因调控程序，提供了一个新的概念镜头我们可以看到社区对动态环境的适应。