Proteomic Stable Isotope Probing as a Novel Approach for Linking Prebiotics with Active Gut Microbiota

蛋白质组稳定同位素探测作为连接益生元与活性肠道微生物群的新方法

• 批准号: 10276744
• 负责人: Chongle Pan
• 金额: $ 38.09万
• 依托单位: UNIVERSITY OF OKLAHOMA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10276744
• 关键词: Acceleration; Address; Algorithms; Animals; Assimilations; Bioinformatics; Carbon Isotopes; Cloud Computing; Coculture Techniques; Communities; Complement; Complex; Computer Analysis; Data; Data Analyses; Databases; Detection; Diabetes Mellitus; Diet; Dietary Fiber; Disease; Environment; Equilibrium; Foundations; Genotype; Goals; Health; Human; In Situ; In Vitro; Inulin; Knowledge; Label; Link; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Metabolic; Metabolism; Metagenomics; Methodology; Methods; Microbe; Modeling; Mus; Nutrition Disorders; Obesity; Organism; Outcome; Pathway interactions; Pattern; Peptides; Performance; Play; Population; Probiotics; Proteins; Proteomics; Public Health; Reproducibility; Research; Research Personnel; Resources; Ribosomal RNA; Role; Running; Shotguns; Specificity; Techniques; Therapeutic; Time; Work; base; carbon fiber; complex data; computerized data processing; cost; data acquisition; data standards; deep learning algorithm; dysbiosis; experimental study; gut bacteria; gut microbes; gut microbiome; gut microbiota; hemicellulose; high throughput technology; human microbiota; improved; in vivo; innovation; large datasets; maltodextrin; metabolic abnormality assessment; metaproteomics; metatranscriptomics; microbial; microbial community; microbiome; microbiota; microorganism; novel; novel strategies; nutrition; parallel computer; prebiotics; prevent; protein biomarkers; searchable database; stable isotope; tool; two-dimensional

PROJECT SUMMARY/ABSTRACT Characterization of the metabolic interactions between organisms is key to understanding the mechanisms of disease and symbioses between microbes and their animal hosts. Our long-term goal is to advance the applicability and accessibility of proteomic stable isotope probing (SIP) in ways that make it a valuable tool for microbiome researchers looking to measure in situ metabolic interactions of human microbiota. The objective of this proposal is to improve the performance and reproducibility of experimental measurements and accelerate the computational analysis of proteomic SIP experiments and to demonstrate the value of this method for studying the in vivo and in vitro metabolism of prebiotics by gut microbiota. Expected outcomes will represent a significant advance, because optimizing the use of prebiotics as therapeutics requires identification of the specific microorganisms capable of metabolizing prebiotics. By identifying proteins of specific taxa that are synthesized as a direct result of prebiotic assimilation, proteomic SIP will provide unambiguous links between prebiotic metabolism and the specific microorganisms responsible for this activity. We will accomplish this objective by pursuing three specific aims: 1) to increase the performance and reproducibility of mass spectrometry measurements by optimizing data-independent acquisition (DIA) methods for proteomic SIP; 2) to significantly accelerate the computing-intensive database search step by adapting the Sipros algorithm to use graphic processing units (GPUs) and cloud computing; and 3) to track in vivo and in vitro prebiotic assimilation patterns by microbial populations within simple consortia and complex natural communities. Our proposed work includes several innovations, such as the application of deep learning algorithms to improve the analysis mass spectrometry data, leveraging GPU-based parallel computing and cloud computing to accelerate the computational steps in the data analysis workflow, and using proteomic SIP for the first time to track prebiotic metabolism by gut microbes. The expected outcomes of the project include (a) a new DIA- based workflow for proteomic SIP that can identify significantly more labeled peptides at higher accuracy of enrichment estimation, (b) a new computational workflow that is faster to run, more scalable to large datasets, and more accessible to researchers, and (c) establish novel foundational knowledge on the specificity of prebiotic metabolism by microbes in the gut. These outcomes will establish proteomic SIP as a valuable -omics tool that will complement existing approaches to study the metabolism of gut microbiota, and specifically highlight its ability to investigate metabolism of prebiotics and probiotics as they relate to treating microbial dysbiosis and nutrition-related diseases.

项目概要/摘要 生物体之间代谢相互作用的表征是理解其机制的关键 疾病以及微生物与其动物宿主之间的共生关系。我们的长期目标是推动 蛋白质组稳定同位素探测 (SIP) 的适用性和可及性使其成为有价值的工具 微生物组研究人员希望测量人类微生物群的原位代谢相互作用。目标 该提案的目的是提高实验测量的性能和可重复性 加速蛋白质组 SIP 实验的计算分析并证明其价值 研究肠道微生物群对益生元的体内和体外代谢的方法。预期成果将 代表了一项重大进步，因为优化益生元作为治疗剂的使用需要确定 能够代谢益生元的特定微生物。通过识别特定分类群的蛋白质 作为前生元同化的直接结果而合成，蛋白质组 SIP 将提供明确的链接 益生元代谢和负责此活动的特定微生物之间的关系。我们将完成 通过追求三个具体目标来实现这一目标：1）提高质量的性能和再现性 通过优化蛋白质组 SIP 的数据独立采集 (DIA) 方法进行光谱测量； 2） 通过采用 Sipros 算法来显着加速计算密集型数据库搜索步骤 使用图形处理单元 (GPU) 和云计算； 3) 追踪体内和体外益生元 简单群落和复杂自然群落中微生物种群的同化模式。我们的 提出的工作包括多项创新，例如应用深度学习算法来改进 分析质谱数据，利用基于 GPU 的并行计算和云计算 加速数据分析工作流程中的计算步骤，并首次使用蛋白质组 SIP 追踪肠道微生物的益生元代谢。该项目的预期成果包括 (a) 一个新的 DIA- 基于蛋白质组 SIP 的工作流程，可以以更高的准确度识别更多标记的肽 富集估计，(b) 一种新的计算工作流程，运行速度更快，更适合大型数据集， 并且更容易为研究人员所利用，并且（c）建立关于其特殊性的新颖的基础知识 肠道微生物的益生元代谢。这些结果将使蛋白质组 SIP 成为有价值的组学 工具将补充现有的研究肠道微生物群代谢的方法，特别是 强调其研究益生元和益生菌代谢的能力，因为它们与治疗微生物有关 生态失调和营养相关疾病。