IIBR Informatics: Comprehensive Metabolism Phenotype Characterization and Interpretation

IIBR 信息学：综合代谢表型表征和解释

• 批准号: 2020026
• 负责人: Hunter Moseley
• 金额: $ 116.39万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020026&HistoricalAwards=false
• 关键词: IIBR; Informatics; Comprehensive; Metabolism; Phenotype

Metabolism is the combination of chemical processes associated with organisms. Comprehensive molecular characterization of metabolism, i.e. detecting, identifying, and measuring the amounts of metabolites which include feed molecules, intermediates, and product molecules of metabolism, can provide the most information-rich phenotypic description of the active state of an organism or an ecosystem. In particular, new high-end analytical instrumentation applied to experiments that enrich metabolites with stable isotopes (non-radioactive types of atoms like carbon-13) can generate detailed submolecular features representing isotopic flux through cellular and systemic metabolism for thousands of metabolites extracted from cells, tissue, biofluids, and environmental samples. The challenge now is in analyzing this complex data to derive new biological knowledge and in making this data and knowledge available in a highly reusable format in public scientific repositories. However, data analysis methods for these state-of-the-art technologies are lacking. This project will address this technical gap by developing new data analysis tools that enable effective analysis, integration, interpretation, and public deposition of large metabolomics analytical datasets collected from new high-end instruments. This project will provide research exposure and training for high school students, undergraduate students, graduate students, and postdoctoral fellows. Ultra-accurate-mass and high-resolution Fourier transform mass spectrometry (FTMS) applied in stable isotope-resolved metabolomics (SIRM) experiments can generate detailed isotopologue features representing isotopic flux through cellular and systemic metabolism for thousands of metabolites from cells, tissue, biofluids, and environmental samples. The challenge now is in analyzing this complex, submolecular data to derive new biological knowledge, since appropriate data analysis methods are lacking. This project will address this technical gap by developing new data analysis tools that enable effective analysis, integration, interpretation, and public deposition of untargeted SIRM and non-SIRM analytical data collected from high-end instrumentation. Objective 1 will develop a novel multi-scan peak characterization method that properly handles multiple data quality issues present in FTMS spectra, minimizing intra-scan variance while removing spectral artifacts that are dangerous to downstream data analyses. Objective 1 will also develop a singularly unique tool implementing Small Molecule Isotope Resolved Formula Enumeration, which will provide a truly untargeted isotope-specific molecular formula (IMF) assignment of FTMS peaks without using a database of known/expected metabolites. Objective 2 will develop a novel metabolic network placement method that utilizes SIRM isotopologue data for robust metabolite placement. An interoperable set of omics integration tools centered on a comprehensive atom-resolved interaction network will allow cell/tissue-specific and subcellular-specific metabolite network placement, modeling, and interpretation. Objective 3 will develop libraries and tools that create conformant depositions to an evolving mwTab format and standard of metadata quality, with isotope-resolved IUPAC International Chemical Identifiers (InChI) that greatly enhance reuse. The comprehensive characterization and interpretation of metabolism afforded by this proposal will have broad impact in the following specific ways: 1) Provide new metabolism research infrastructure that will be impactful for non-model organisms. 2) Broadly disseminate, via open-source code repositories (e.g. GitHub, Python Package Index, Bioconductor), highly-reusable, fully-documented, production-level software tools that enable novel metabolomics data analyses and public deposition. 3) Provide research training in data science, metabolomics, and systems biology to high school, undergraduate, and graduate students as well as postdoctoral fellows.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

新陈代谢是与生物体有关的化学过程的组合。代谢的综合分子表征，即检测、鉴定和测量代谢产物（包括代谢的饲料分子、中间体和产物分子）的数量，可以提供生物体或生态系统活性状态的信息最丰富的表型描述。特别是，新的高端分析仪器应用于用稳定同位素（非放射性类型的原子，如碳-13）丰富代谢物的实验，可以生成详细的亚分子特征，代表从细胞、组织、生物流体和环境样品中提取的数千种代谢物通过细胞和系统代谢的同位素通量。现在的挑战是分析这些复杂的数据，以获得新的生物学知识，并在公共科学知识库中以高度可重用的格式提供这些数据和知识。然而，缺乏针对这些最先进技术的数据分析方法。该项目将通过开发新的数据分析工具来解决这一技术差距，这些工具能够有效地分析、整合、解释和公开存储从新型高端仪器收集的大型代谢组学分析数据集。该项目将为高中生、本科生、研究生和博士后提供研究机会和培训。用于稳定同位素分解代谢组学（SIRM）实验的超精确质量和高分辨率傅立叶变换质谱（FTMS）可以生成详细的同位素特征，代表来自细胞、组织、生物流体和环境样品的数千种代谢物通过细胞和系统代谢的同位素通量。由于缺乏适当的数据分析方法，现在的挑战是分析这些复杂的亚分子数据以获得新的生物学知识。该项目将通过开发新的数据分析工具来解决这一技术差距，这些工具能够有效地分析、集成、解释和公开存储从高端仪器收集的非目标SIRM和非SIRM分析数据。目标1将开发一种新的多扫描峰表征方法，该方法可以正确处理FTMS光谱中存在的多个数据质量问题，最大限度地减少扫描内方差，同时去除对下游数据分析有危险的光谱伪影。目标1还将开发一种独特的工具，实现小分子同位素解析公式枚举，该工具将在不使用已知/预期代谢物数据库的情况下，为FTMS峰提供真正的非靶向同位素特异性分子式（IMF）分配。目标2将开发一种新的代谢网络放置方法，该方法利用SIRM同位素数据进行稳健的代谢物放置。一套可互操作的组学集成工具集中在一个全面的原子解析相互作用网络上，将允许细胞/组织特异性和亚细胞特异性代谢物网络的放置、建模和解释。目标3将开发库和工具，以创建符合不断发展的mwTab格式和元数据质量标准的一致性沉积，并使用同位素解析的IUPAC国际化学标识符（InChI），大大提高重用性。本提案所提供的代谢的综合表征和解释将在以下具体方面产生广泛影响：1)提供新的代谢研究基础设施，将对非模式生物产生影响。2)通过开源代码存储库（例如GitHub、Python Package Index、Bioconductor）广泛传播高度可重用、文档完整的生产级软件工具，使新的代谢组学数据分析和公开存储成为可能。3)为高中生、本科生、研究生和博士后提供数据科学、代谢组学和系统生物学方面的研究培训。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Identifying and sharing per-and polyfluoroalkyl substances hot-spot areas and exposures in drinking water.

• DOI: 10.1038/s41597-023-02277-x
• 发表时间: 2023-06-16
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Ojha, Sweta;Thompson, P. Travis;Powell, Christian D.;Moseley, Hunter N. B.;Pennell, Kelly G.
• 通讯作者: Pennell, Kelly G.

Academic Tracker: Software for tracking and reporting publications associated with authors and grants.

• DOI: 10.1371/journal.pone.0277834
• 发表时间: 2022
• 期刊: PloS one
• 影响因子: 3.7

The mwtab Python Library for RESTful Access and Enhanced Quality Control, Deposition, and Curation of the Metabolomics Workbench Data Repository.

mwtab Python 库，用于 RESTful 访问和增强代谢组学工作台数据存储库的质量控制、沉积和管理。

• DOI: 10.3390/metabo11030163
• 发表时间: 2021-03-12
• 期刊: Metabolites
• 影响因子: 4.1
• 作者: Powell CD;Moseley HNB
• 通讯作者: Moseley HNB

A proposed FAIR approach for disseminating geospatial information system maps.

• DOI: 10.1038/s41597-023-02281-1
• 发表时间: 2023-06-16
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Thompson, P. Travis;Ojha, Sweta;Powell, Christian D.;Pennell, Kelly G.;Moseley, Hunter N. B.
• 通讯作者: Moseley, Hunter N. B.