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.

大多数微生物生活在包含数百或数千个物种的群落中，每个物种都参与其中 在丰富的互动网络中。这些相互作用的复杂性使得定量预测 社区动态困难。为了克服这个问题，自然群落的简单代理，指定 “模型微生物群落”旨在支持实验室研究。这些型号是 复杂到足以展示特定社区的现象，但又简单到足以揭示治理 社区互动的原则。入侵是微生物造成的最不稳定的事件之一 社区可以经历，常常导致社区功能障碍或宿主疾病。我们建议使用 THOR，我们开发的一个模型群落，用于描述对假单胞菌入侵的反应 铜绿假单胞菌。我们将跟踪种群动态，从功能上分析分子相互作用。 这项研究的多组学和多因素性质为统计提供了多方面的机会 创新。真正的综合分析不能简单地在测定中执行并行假设检验，并且 需要一个差异测试框架，将数据源混合到统一的分子中 互动网络。我们将借鉴选择性推理和多组学网络分析的进展 开发阐明驱动社区反应的分子相互作用的方法。这将使我们 定制干预措施，塑造 THOR 模型微生物群落的动态。我们建议： 1. 目标 1：使用代谢组学功能分析 THOR 社区对铜绿假单胞菌的反应， 宏转录组学和 16S rRNA 测序，并建立相关的数据管理工作流程。这些 是核心数据生成实验和质量控制步骤，可提供准确和 雷神受到入侵的互补观点。 2. 目标 2：开发对交互效应敏感且能够控制的差异测试方法 模块级错误发现率。我们将介绍微分选择性推理的方法 相互作用组模块，如激活的生物合成途径。 3. 目标 3：整合差异相互作用组分析软件并通过实验验证基因敲除 目标。这些实验将说明控制入侵动态的数据驱动方法，以及我们的 软件将使此类分析变得容易。