Meta-Analysis of Metabolic Determinants of Exercise Response in Common Funds Data

共同基金数据中运动反应代谢决定因素的荟萃分析

• 批准号: 10772237
• 负责人: CHARLES ROBERT EVANS
• 金额: $ 29.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10772237
• 关键词: Acute; Age; Animal Model; Animals; Archives; Biochemical; Biochemical Pathway; Biological; Biology; Biomedical Research; Catalogs; Chemical Structure; Chronic; Communities; Computer software; Custom; Data; Data Collection; Data Reporting; Data Set; Databases; Detection; Disease; Disparity; Exercise; Funding; Future; Gene Expression; Genes; Genome; Genomics; Goals; Health; Health Benefit; Human; Inbreeding; Intervention; Knowledge; Laboratories; Libraries; Maps; Measurable; Mediator; Meta-Analysis; Metabolic; Metadata; Methods; Modeling; Molecular; Names; Organism; Outcome; Pathway Analysis; Pathway interactions; Pattern; Physical activity; Plasma; Population; Prevalence; Probability; Productivity; Proteins; Proteomics; Publications; Rattus; Research; Research Design; Resolution; Resources; Role; Science; Software Tools; Source; Structure; Surveys; Techniques; Tissues; Transducers; United States National Institutes of Health; Validation; Work; adduct; analytical method; comparative; computerized tools; data archive; data cleaning; data hub; data integration; experience; experimental study; follow-up; improved; in silico; innovation; insight; instrument; liquid chromatography mass spectrometry; metabolome; metabolomics; mode of exercise; multiple omics; novel; response; sample archive; sex; small molecule; software development; tool; transcriptome; transcriptomics

Abstract Exercise is associated numerous health benefits, but defining the molecular mediators of these effects remains an active focus of biomedical research. With the advent of the ‘omics sciences, studies including the ongoing Molecular Transducers of Physical Activity Consortium (MoTrPAC) and multiple smaller-scale efforts have sought to map the “complete” molecular response to acute and chronic exercise. Much research is currently focused on integration of genomics, proteomics and metabolomics data within such studies, but another important strategy is meta-analysis of distinct data sets to evaluate consistencies and differences in molecular responses observed between different modes of exercise, sex, age, species, and other factors. Of the exercise- related meta-studies performed to date, most have focused on the genome and transcriptome whereas few have included metabolomics data. Reasons for this shortcoming include differences in analytical methods, inconsistency in compound naming and data reporting, and prevalence of unknown features in untargeted metabolomics data. Unknown metabolite identification and cross-study integration is challenging and requires application of computational and experimental strategies in a coordinated manner. Yet, the potential benefits are substantial – identification of novel metabolites and detection of consistent patterns of response have led to biological insights relevant to fundamental biology and human health, including exercise. Using data from NIH Common Fund data archives, we propose to develop a multi-study, multi-organism and multi-condition database of identified and unknown exercise responsive features of the metabolome. We will integrate data across studies and, when available, across ‘omes, to prioritize and identify unknowns within this database. We will achieve these goals by carrying out two specific aims: 1) We will perform a comprehensive survey and alignment of exercise-related small molecule features in MotrPAC data and from studies in the Metabolomics Workbench. We will use computational tools we have pioneered for metabolomics data cleaning, inter-laboratory data alignment, and network- and correlation-based analysis to prioritize unknown features for follow-up. 2) We will systematically track, annotate and identify high-priority exercise-responsive unknown features in metabolomics data using software and experimental techniques we and others have devised for MS/MS data collection to identify and annotate features not tractable by routine library search. Our study represents a crucial step between the map-building aims of MoTrPAC and detailed mechanistic studies of specific pathways and that hold potential for human health benefits through targeted interventions. We will share our database and associated data with the research community through publications and uploads to public data archives. We anticipate our efforts will contribute to improved understanding of the effects of exercise at the biochemical pathway level and will offer targets for future studies to help delineate the mechanisms by which small molecules contribute to its salutary effects on health.

摘要 运动与许多健康益处相关，但定义这些效应的分子介质仍然存在 生物医学研究的一个活跃焦点。随着“组学科学”的出现，包括正在进行的 身体活动联盟的分子传感器（MoTrPAC）和多个小规模的努力， 试图绘制急性和慢性运动的“完整”分子反应。目前，许多研究 专注于在这些研究中整合基因组学，蛋白质组学和代谢组学数据，但另一个 重要策略是对不同数据集进行荟萃分析， 不同运动方式、性别、年龄、物种等因素之间观察到的反应。关于演习- 到目前为止，相关的元研究大多集中在基因组和转录组上， 包括代谢组学数据。造成这一缺陷的原因包括分析方法的差异， 化合物命名和数据报告的不一致性，以及非靶向患者中未知特征的普遍性 代谢组学数据。未知代谢物鉴定和交叉研究整合具有挑战性，需要 以协调的方式应用计算和实验策略。然而，潜在的好处是 实质性-新代谢物的鉴定和一致反应模式的检测已经导致 与基础生物学和人类健康相关的生物学见解，包括运动。 利用NIH共同基金数据档案中的数据，我们建议开发一个多研究、多生物体和 代谢组的已鉴定和未知运动响应特征的多条件数据库。我们将 整合各研究的数据，并在可用时整合各研究组的数据，以确定其中未知因素的优先顺序， 数据库我们将通过以下两个具体目标来实现这些目标：1）我们将执行全面的 调查和对齐运动相关的小分子特征在MotrPAC数据和研究中， 代谢组学我们将使用我们在代谢组学数据清理方面开创的计算工具， 实验室间数据对齐，以及基于网络和相关性的分析，以确定未知特征的优先级， 随访2)我们将系统地跟踪，注释和识别高优先级的运动响应未知 利用我们和其他人设计的软件和实验技术， MS/MS数据收集，以识别和注释常规库检索无法处理的特征。我们的研究 代表了MoTrPAC的地图构建目标和详细的机械研究之间的关键一步， 这是一个具有特定途径和通过有针对性的干预措施对人类健康有益潜力的领域。我们将分享 我们的数据库和相关数据与研究界通过出版物和上传到公共数据 档案我们预计，我们的努力将有助于更好地了解运动对人体的影响。 生物化学途径水平，并将为未来的研究提供目标，以帮助描绘的机制， 小分子有助于其对健康的有益作用。