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，其新兴代谢特性在确定人类健康。由宿主相关的微生物群启用的代谢过程在个人健康结果以及微生物财团的新陈代谢影响环境从富营养化到气候变化的过程，影响全球范围的人类健康。所以，人类将从对规则的定量理解中受益微生物群落及其居住的环境决定了其新陈代谢。发现环境变化改变社区结构并确定的原则如果我们要操纵或设计社区以改善健康结果，代谢功能是必要的。但是，对于现有方法，此任务具有挑战性。在初步工作中，我们建立了一个新的定量框架来预测新陈代谢通过其基因组组成，使用反硝化作为模型代谢过程的细菌群落。结合定量细菌表型，建模和简单的统计方法，我们证明了一个定量将基因含量映射到微生物群落中的代谢产物动力学的方法。这个见解提供了一条定量连接社区中存在的基因与代谢产物动力学的途径。这下一个挑战是利用这种见解来了解社区功能和结构如何取决于环境。我们建议通过了解环境梯度，复杂性和动态影响社区的结构和功能。我们通过将反硝化作为一个模型代谢过程。拟议工作的结果将是三倍。首先，微生物组研究已经记录了环境条件与社区组成之间的普遍关联，但我们不了解这些新兴模式或其代谢的生态或生理起源结果。使用pH梯度跨pH梯度的反硝化社区，我将表明这种模式从生态互动。我将证明这些相互作用通常来自生理的存在在微生物特征上进行权衡，提供了一条可概括的途径来理解社区的环境变化。第二，我们的初步研究将基因组与社区联系起来新陈代谢途径的代谢。我将把这一成功扩展到复杂的途径和环境条件通过构建一种预测社区碳利用的方法直接来自基因组的营养条件。我将利用基因组级代谢建模的强大融合以及多视图机器学习，从宿主生理到气候变化产生影响。第三，我会使用硝化社区以测试以下观点，即像细胞和生物一样，微生物群落表现出预测性动态环境中的行为。我建议社区在具有不同的环境中集会有氧呼吸和厌氧呼吸（反硝化）的时间表适应提示电子受体的利用。我将检验以下假设，即社区级学习来自生态互动和不同的基因调节程序，提供了一种新的概念镜头我们可以查看社区对动态环境的适应。