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.

抽象的 运动与许多健康益处相关，但定义这些影响的分子介质仍然存在 随着组学科学的出现，包括正在进行的研究成为生物医学研究的一个活跃焦点。 体力活动分子传感器联盟 (MoTrPAC) 和多个小规模的努力已经 目前正在进行大量研究，试图绘制出对急性和慢性运动的“完整”分子反应。 专注于此类研究中基因组学、蛋白质组学和代谢组学数据的整合，但另一个 重要的策略是对不同数据集进行荟萃分析，以评估分子生物学的一致性和差异性。 观察到不同运动方式、性别、年龄、物种和其他因素之间的反应。 迄今为止进行的相关荟萃研究，大多数都集中在基因组和转录组上，而很少有 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不一致，以及非目标中未知特征的普遍存在 代谢组学数据。未知代谢物的鉴定和交叉研究整合具有挑战性并且需要。 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——新代谢物的鉴定和反应一致模式的检测已经导致 与基础生物学和人类健康（包括运动）相关的生物学见解。 使用 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 代谢组的已识别和未知运动反应特征的多条件数据库。 整合跨研究的数据，并在可用时跨“组”整合数据，以优先考虑并识别其中的未知数 我们将通过实现两个具体目标来实现这些目标：1）我们将执行全面的数据库。 MotrPAC 数据和研究中与运动相关的小分子特征的调查和对齐 我们将使用我们首创的代谢组学数据清理计算工具， 实验室间数据对齐以及基于网络和相关性的分析，以优先考虑未知特征 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。 使用我们和其他人设计的软件和实验技术来分析代谢组学数据的特征 MS/MS 数据收集用于识别和注释常规库搜索无法处理的特征。 代表了 MoTrPAC 地图构建目标和详细机制研究之间的关键一步 我们将分享具体途径，并通过有针对性的干预措施为人类健康带来潜在益处。 我们的数据库以及通过出版物和上传到公共数据与研究界相关的数据 我们预计我们的努力将有助于加深对锻炼效果的了解。 生化途径水平，并将为未来的研究提供目标，以帮助描述其机制 小分子有助于其对健康的有益作用。