Environmental modulation of metabolic function in microbial communities

微生物群落代谢功能的环境调节

• 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
• 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

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，其新陈代谢特性在 决定着人类的健康。寄主相关微生物区系启动的新陈代谢过程在 个人健康结果，以及微生物群体的紧急新陈代谢影响环境 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。因此， 人类将受益于对规则的定量理解，根据这些规则， 微生物群落及其所处的环境决定了它的新陈代谢。 发现环境变化改变群落结构并决定它们的原理 如果我们要操纵或设计社区来改善健康结果，代谢功能是必要的。 然而，这项任务对现有方法来说是具有挑战性的。 在前期工作中，我们建立了一个新的定量框架来预测紧急代谢 利用反硝化作用作为模型代谢过程，从细菌群落的基因组组成来分析细菌群落。 结合定量细菌表型、建模和一种简单的统计方法，我们演示了 将基因含量定量映射到微生物群落中的代谢物动态的方法。这种洞察力 提供了一条将群落中存在的基因与代谢动力学定量联系起来的途径。这个 下一个挑战是利用这种洞察力来理解社区的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 动态影响社区结构和功能。我们通过将反硝化作为一种 对代谢过程进行建模。拟议工作的结果将是三方面的。第一，微生物组研究 都记录了环境条件和群落组成之间无处不在的联系，但 我们不了解这些新出现的模式的生态或生理起源或它们的代谢。 后果。使用跨pH梯度的反硝化群落，我将展示这样的模式来自 生态互动。我将证明，这些相互作用一般是由生理因素的存在引起的 微生物特性的权衡，提供了一种普遍的途径来理解 环境对社区的影响。第二，我们的初步研究将基因组与群落联系起来 代谢为一条简单的代谢途径作用。我将把这一成功扩展到复杂的道路和 构建复合体群落碳利用预测方法的环境条件 直接来自基因组的营养条件。我将利用基因组规模新陈代谢模型的强大混合 以及多视角机器学习，从宿主生理到气候变化的影响。第三，我将使用 反硝化群落，以测试这样一个想法，即像细胞和有机体一样，微生物群落表现出可预测的 动态环境中的行为。我建议社区聚集在不同的环境中 调整好氧呼吸和无氧呼吸(反硝化)的时间表，以便于提示 电子受体的利用。我将检验这样一个假设，即社区水平的学习产生于 生态相互作用和独特的基因调控计划，提供了一个新的概念镜头，通过 我们可以看到社区对动态环境的适应。