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，其新兴的代谢特性在微生物群落中起着关键作用。 决定人类健康。由宿主相关微生物群实现的代谢过程在以下方面起着决定性作用： 个人健康结果，以及微生物财团的紧急代谢影响环境 从富营养化到气候变化，在全球范围内影响人类健康。因此，我们认为， 人类将受益于定量了解的规则，通过这些规则， 微生物群落及其所处的环境决定了它的紧急代谢。 发现环境变化改变群落结构并决定 如果我们要操纵或设计社区来改善健康状况，代谢功能是必不可少的。 然而，这一任务对于现有方法来说是具有挑战性的。 在初步工作中，我们建立了一个新的定量框架，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 一种将基因含量定量映射到微生物群落代谢动力学的方法。这种洞察力 提供了一种途径，定量连接基因存在于一个社区的代谢动力学。的 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 我们建议通过理解环境梯度、复杂性和 动态影响群落结构和功能。我们通过开发反硝化作用来实现这一目标， 模型代谢过程。拟议工作的成果将有三个方面。第一，微生物研究 已经记录了环境条件和社区组成之间普遍存在的联系，但 我们不了解这些新兴模式的生态学或生理学起源，也不了解它们的代谢 后果使用跨pH梯度的生物群落，我将展示这种模式的出现， 生态互动我将证明这些相互作用一般是由生理性的 微生物性状的权衡，为了解微生物的功能影响提供了一种可推广的途径 环境对群落的影响。其次，我们的初步研究将基因组与社区联系起来， 代谢为一个简单的代谢途径起作用。我将把这种成功扩展到复杂的途径， 通过构建一种方法来预测复杂环境中群落的碳利用率， 营养条件直接来自基因组。我会利用一个强大的基因组代谢模型 和多视角机器学习，从宿主生理学到气候变化。第三，我会用 反硝化群落来测试微生物群落与细胞和生物体一样具有预测性的想法 动态环境中的行为。我建议，社区聚集在环境中， 好氧呼吸和厌氧呼吸（反硝化）的时间表适应，以促进提示 电子受体的利用。我将检验社区层面的学习源于 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透镜，通过它 我们可以看到社区对动态环境的适应。