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种的群落中，每种微生物都相互作用