DMS/NIGMS 1: Modeling Microbial Community Response to Invasion: A Multi-Omics and Multifacton

DMS/NIGMS 1：模拟微生物群落对入侵的反应：多组学和多因素

• 批准号: 10794584
• 负责人: JO E. HANDELSMAN
• 金额: $ 23.91万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-26 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10794584
• 关键词: 16S ribosomal RNA sequencing; Address; Automobile Driving; Behavior; Binding; Biological Assay; Communities; Complex; Computer software; Data; Data Sources; Disease; Engineering; Environmental Health; Event; Exhibits; Experimental Designs; Foundations; Functional disorder; Goals; Human; Internet; Intervention; Invaded; Knock-out; Laboratory Study; Maps; Methods; Modeling; Molecular Profiling; National Institute of General Medical Sciences; Nature; Pathway Analysis; Pathway interactions; Phenotype; Population Dynamics; Proxy; Pseudomonas aeruginosa; Quality Control; Running; Shapes; Statistical Methods; System; Testing; data curation; design; experience; experimental study; improved; innovation; metabolomics; metatranscriptomics; microbial; microbial community; microorganism; multiple omics; response; success; tool

Most microorganisms live in communities containing hundreds or thousands of species, each engaging in a rich web of interactions. The complexity of these interactions makes quantitative predictions about community dynamics difficult. To overcome this, simple proxies for natural communities, designated "model microbial communities," have been designed to support laboratory study. These models are complex enough to exhibit community-specific phenomena but simple enough to reveal governing principles of community interaction. Invasions are among the most destabilizing events that a microbial community can experience, often resulting in community dysfunction or host disease. We propose to use THOR, a model community that we developed, to characterize the response to invasion by Pseudomonas aeruginosa. We will track population dynamics functionally profile molecular interactions. The multi-omics and multifactorial nature of this study present multifaceted opportunities for statistical innovation. A truly integrative analysis cannot simply perform parallel hypothesis tests across assays, and there is a need for a differential testing framework that blends data sources into a unified molecular interaction network. We will draw from advances in selective inference and multi-omics network analysis to develop methods that illuminate the molecular interactions driving community response. This will allow us to tailor interventions that shape dynamics in the THOR model microbial community. We propose: 1. Aim 1: Functionally profile THOR's community response to P. aeruginosa using metabolomics, metatranscriptomics, and 16S rRNA sequencing and establish associated data curation workflows. These are the core data-generating experiments and quality control steps that provide accurate and complementary views of THOR under invasion. 2. Aim 2: Develop differential testing methods that are sensitive to interaction effects and that control module-level false discovery rates. We will introduce methods for selective inference of differential interactome modules, like activated biosynthetic pathways. 3. Aim 3: Consolidate software for differential interactome analysis and experimentally validate knockout targets. These experiments will illustrate a data-driven approach to control invasion dynamics, and our software will make such analysis easily accessible.

大多数微生物都生活在包含数百或数千种的社区中，每种都有参与 在丰富的互动网络中。这些相互作用的复杂性使得有关定量预测 社区动态困难。为了克服这一点，对自然社区的简单代理，指定 “模型微生物群落”旨在支持实验室研究。这些模型是 足够复杂以表现出特定于社区的现象 社区互动的原则。入侵是微生物最不稳定的事件之一 社区可以体验，通常导致社区功能障碍或宿主疾病。我们建议使用 我们开发的模型社区Thor旨在表征假单胞菌对入侵的反应 铜绿。我们将跟踪人群动态功能介绍分子相互作用。 这项研究的多词和多因素性质提出了统计的多方面机会 创新。真正的综合分析不能简单地在分析中进行并行假设检验，并且 需要一个差分测试框架将数据源融合到统一的分子中 交互网络。我们将从选择性推理和多摩斯网络分析的进步中汲取灵感 开发阐明促进社区反应的分子相互作用的方法。这将使我们 量身定制在Thor模型微生物社区中塑造动态的干预措施。我们建议： 1。AIM1：使用代谢组学，在功能上介绍了Thor对铜绿假单胞菌的反应， Metatranscriptomics和16S rRNA测序并建立相关的数据策展工作流。这些 是核心数据生成的实验和质量控制步骤，可提供准确的和 雷神（Thor）入侵的互补观点。 2.目标2：开发对交互作用敏感并控制的差分测试方法 模块级的错误发现率。我们将介绍用于选择性推断差异的方法 相互作用的模块，例如活化的生物合成途径。 3。目标3：合并用于差异相互作用组分析的软件和实验验证敲除 目标。这些实验将说明一种数据驱动的控制入侵动态的方法，我们 软件将使这样的分析容易访问。