Portal for Open Computational Metabolomics Tools - Yr 4 U2C Supplement

开放计算代谢组学工具门户 - 第四年 U2C 补充材料

• 批准号: 10397265
• 负责人: ARTHUR S EDISON
• 金额: $ 19.5万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10397265
• 关键词: Animal Model; Animals; Area; Biological; CRISPR/Cas technology; Caenorhabditis elegans; Chemicals; DNA sequencing; Data; Development; Genes; Genetic; Genetic Models; Genome; Genomic DNA; Human; Individual; Malignant Neoplasms; Measurement; Measures; Mechanics; Medical; Metabolic Pathway; Modeling; Modernization; Modification; Molecular; Other Genetics; Pathway interactions; Population; Preparation; Sampling; Science; Structure; System; Technology; Testing; base; cost effective; genetic association; genome wide association study; human disease; improved; metabolome; metabolomics; mutant; precision medicine; quantum; quantum chemistry; tool

Overall: Our project combines the significant advantages of a genetic model organism, sophisticated pathway mapping tools, high-throughput and accurate quantum chemistry (QM), and state-of-the-art experimental measurements. The result will be an efficient and cost-effective approach for unknown compound identification in metabolomics, which is one of the major limitations facing this growing field of medical science. Caenorhabditis elegans has several advantages for this study, including over 10,000 available genetic mutants, well-developed CRISPR/Cas9 technology, and a panel of over 500 wild C. elegans isolates with complete genomes. Half of C. elegans genes have homologs to human disease genes, making this model organism an outstanding choice to improve our understanding of metabolic pathways in human disease. We will develop an automated pipeline for sample preparation to reproducibly measure tens of thousands of unknown features by UHPLC-MS/MS. We will use the wild isolates to conduct metabolome-wide genetic association studies (m-GWAS), and SEM-path to locate unknowns in pathways using partial correlations. The relevance of the unknown metabolites to specific pathways will be tested by measuring UHPLC-MS/MS data from genetic mutants of those pathways. Molecular formula and pathway information will be the inputs for automated quantum mechanical calculations of all possible structures, which will be used to accurately calculate NMR chemical shifts that will be matched to experimental data. The correct structures will be validated by comparing them with 2D NMR data of the same compound. The validated computed structures will then be used to improve QM-based MS/MS fragment prediction, using the experimental UHPLC-MS/MS data. This project will enhance many areas of science beyond worms and model organisms. First, C. elegans is the simplest animal model available with significant homology to other animals and humans. The discoveries we make in metabolic pathways will have a direct impact on studies of several human diseases. Second, our approach is highly transferable to other genetic systems and with little modification can be applied to many other applications. Perhaps most important is the relevance to large-scale human precision medicine studies. The wild C. elegans isolates are “individuals” with diverse genomes that are a model for natural populations such as humans. It is true that we are using mutant animals that would not be available in a human precision medicine study, but the mutants are used primarily to validate pathways that are constructed entirely by wild isolate data. Once the approaches are fully developed and validated, the mutants will not be necessary. C. elegans and other genetic model organisms were instrumental in the development of modern genomics and DNA sequencing technologies. Our premise is that the worm will have a comparable impact in metabolomics.

总体而言：我们的项目结合了遗传模式生物、复杂途径的显著优势 测绘工具、高通量和精确的量子化学(QM)和最先进的实验 测量。这一结果将是一种有效且具有成本效益的未知化合物鉴定方法 在代谢组学方面，这是这个不断增长的医学科学领域面临的主要限制之一。 秀丽隐杆线虫对这项研究有几个优势，包括超过10,000个可用的基因 突变株，成熟的CRISPR/Cas9技术，以及500多个野生线虫分离物 完整的基因组。有一半的线虫基因与人类疾病基因同源，形成了这个模型 生物体是提高我们对人类疾病代谢途径的理解的一个突出选择。我们 将开发一条自动化的样品制备管道，以重复测量数万个 我们将利用野生分离株进行代谢全基因组的遗传 关联性研究(m-GWAS)和扫描电子显微镜路径(SEM-PATH)使用部分相关性来定位通路中的未知数。这个 未知代谢物与特定途径的相关性将通过测量UHPLC-MS/MS数据来测试 来自这些途径的基因突变。分子式和途径信息将作为输入 所有可能结构的自动量子力学计算，这将被用来准确地 计算将与实验数据匹配的核磁共振化学位移。正确的结构将是 通过与同一化合物的2D核磁共振数据进行比较，验证了该方法的有效性。经过验证的计算结构 然后将用于改进基于QM的MS/MS片段预测，使用实验性的UHPLC-MS/MS 数据。 该项目将加强除蠕虫和模型生物体以外的许多科学领域。首先，线虫是 最简单的动物模型，与其他动物和人类有显著的同源性。我们的发现 新陈代谢途径的研究将对几种人类疾病的研究产生直接影响。第二，我们的 该方法可高度移植到其他遗传系统，并且只需很少的修改即可应用于许多 其他应用程序。也许最重要的是与大规模人类精确医学研究的相关性。 野生线虫分离株是具有不同基因组的“个体”，是自然种群的模型。 比如人类。诚然，我们使用的是人类无法精确获得的突变动物 医学研究，但突变体主要用于验证完全由野生动物构建的途径 隔离数据。一旦这些方法被充分开发和验证，突变就不再是必要的了。C。 线虫和其他遗传模式生物对现代基因组学的发展和 DNA测序技术。我们的前提是，蠕虫将在代谢组学中产生类似的影响。

项目成果

Culture and Assay of Large-Scale Mixed-Stage Caenorhabditis elegans Populations.

大规模混合阶段秀丽隐杆线虫种群的培养和测定。

• DOI: 10.3791/61453
• 发表时间: 2021
• 期刊: Journal of visualized experiments : JoVE
• 作者: Shaver,AmandaO;Gouveia,GoncaloJ;Kirby,PamelaS;Andersen,ErikC;Edison,ArthurS
• 通讯作者: Edison,ArthurS

Metabolite Structure Assignment Using In Silico NMR Techniques.

• DOI: 10.1021/acs.analchem.0c00768
• 发表时间: 2020-08-04
• 期刊: Analytical chemistry
• 影响因子: 7.4
• 作者: Das S;Edison AS;Merz KM Jr
• 通讯作者: Merz KM Jr

Correlations Between LC-MS/MS-Detected Glycomics and NMR-Detected Metabolomics in Caenorhabditis elegans Development.

秀丽隐杆线虫发育中 LC-MS/MS 检测的糖组学和 NMR 检测的代谢组学之间的相关性。

• DOI: 10.3389/fmolb.2019.00049
• 发表时间: 2019
• 期刊: Frontiers in molecular biosciences
• 影响因子: 5
• 作者: Sheikh,MOsman;Tayyari,Fariba;Zhang,Sicong;Judge,MichaelT;Weatherly,DBrent;Ponce,FrancescaV;Wells,Lance;Edison,ArthurS
• 通讯作者: Edison,ArthurS

Combinatorial Assembly of Modular Glucosides via Carboxylesterases Regulates C. elegans Starvation Survival.

• DOI: 10.1021/jacs.1c05908
• 发表时间: 2021-09-15
• 期刊: Journal of the American Chemical Society
• 影响因子: 15
• 作者: Wrobel CJJ;Yu J;Rodrigues PR;Ludewig AH;Curtis BJ;Cohen SM;Fox BW;O'Donnell MP;Sternberg PW;Schroeder FC
• 通讯作者: Schroeder FC

Long-Term Metabolomics Reference Material.

• DOI: 10.1021/acs.analchem.1c01294
• 发表时间: 2021-07-06
• 期刊: Analytical chemistry
• 影响因子: 7.4
• 作者: Gouveia GJ;Shaver AO;Garcia BM;Morse AM;Andersen EC;Edison AS;McIntyre LM
• 通讯作者: McIntyre LM