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: 10463696
• 负责人: Chongle Pan
• 金额: $ 34.75万
• 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/10463696
• 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; machine learning model; 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.

项目摘要/摘要 生物之间代谢相互作用的特征是理解其机制的关键 微生物与其动物宿主之间的疾病和共生。我们的长期目标是推进 蛋白质组稳定同位素探测(SIP)的适用性和可及性使其成为一种有价值的工具 微生物组研究人员希望测量人类微生物区系的原位代谢相互作用。目标是 这项建议的目的是改善实验测量的性能和重复性，并 加速蛋白质组SIP实验的计算分析并展示其价值 利用肠道微生物区系研究益生元的体内外代谢方法。预期结果将 代表着一项重大进步，因为优化益生元作为治疗药物的使用需要识别 能够代谢益生元的特定微生物。通过鉴定特定类群的蛋白质 都是作为益生菌同化的直接结果合成的，蛋白质组SIP将提供明确的联系 在益生代谢和负责这一活动的特定微生物之间。我们将完成 这一目标通过追求三个具体目标：1)提高质量的性能和重现性 通过优化蛋白质组SIP的数据独立获取(DIA)方法进行光谱测量；2) 通过采用SIPOS算法来显著加快计算密集型数据库搜索步骤 使用图形处理器(GPU)和云计算；以及3)跟踪体内和体外的益生元 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 提出的工作包括几个创新，如应用深度学习算法来改进 分析质谱学数据，利用基于GPU的并行计算和云计算 加快数据分析工作流程中的计算步骤，并首次使用蛋白质组SIP来 通过肠道微生物跟踪益生菌代谢。该项目的预期成果包括：(A)新的DIA-- 基于蛋白质组SIP的工作流，可以以更高的准确率识别更多的标记多肽 丰富估计，(B)运行速度更快、对大型数据集更具伸缩性的新计算工作流， 并且更容易为研究人员所获得，以及(C)建立关于 肠道微生物的益生新陈代谢。这些结果将使蛋白质组蛋白成为一种有价值的组学 该工具将补充现有研究肠道微生物区系新陈代谢的方法，特别是 突出其研究益生菌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。