Computational Core

总体而言：我们的项目结合了遗传模式生物的显著优势，复杂的途径 映射工具，高通量和准确的量子化学（QM），以及最先进的实验 测量.这将为未知化合物的鉴定提供一种高效、经济的方法 代谢组学，这是这个不断发展的医学科学领域面临的主要限制之一。 秀丽隐杆线虫在这项研究中有几个优势，包括超过10，000个可用的遗传基因， 突变体，成熟的CRISPR/Cas9技术，以及一组超过500种野生C.秀丽线虫分离株， 完整的基因组C的一半。线虫基因与人类疾病基因有同源性， 微生物是一个杰出的选择，以提高我们对人类疾病代谢途径的理解。我们 将开发一个自动化的样品制备管道，以重复测量成千上万的 我们将使用野生分离株进行代谢组范围的遗传学研究， 关联研究（m-GWAS）和SEM路径，以使用偏相关来定位路径中的未知数。的 将通过测量UHPLC-MS/MS数据检测未知代谢物与特定途径的相关性 基因突变体的基因。分子式和途径信息将作为输入， 自动化的量子力学计算所有可能的结构，这将用于准确地 计算将与实验数据匹配的NMR化学位移。正确的结构将是 通过将它们与相同化合物的2D NMR数据进行比较来验证。经验证的计算结构 然后将用于改进基于QM的MS/MS片段预测，使用实验UHPLC-MS/MS 数据 计算核心（CC）将有两个主要组成部分，代谢途径映射和量子 NMR和MS/MS数据的化学计算。通路映射与实验核心接口 在从野生分离株和LC-MS/MS分析产生m-GWAS结果中的作用。这些基因关联 将已知代谢物与已知基因联系起来。通过定位未知特征， 通过偏相关，这将大大减少未知数可用的化学空间。QM 计算将使用该途径信息来限制给定分子的可能结构的数量 公式，将由实验核心获得。QM计算的输出将是准确的 NMR化学位移来自与化合物的LC-MS/MS相同的色谱保留时间的数据。 未知，使我们能够找到最好的计算结构。我们还将改进计算MS/MS 预测。所有的实验和计算数据将被添加到一个关系数据库， 允许我们搜索任何字段（例如保留时间窗口、m/z值等）。CC将提供强大的 两个地点的计算基础设施、用于分析的共享笔记本以及将数据存放到存储库。

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. The Computational Core (CC) will have two primary components, metabolite pathway mapping and quantum chemical calculations of NMR and MS/MS data. The pathway mapping interfaces with the Experimental Core in the generation of m-GWAS results from wild isolates and LC-MS/MS analysis. These genetic associations will relate known metabolites to known genes. These pathways will be expanded by locating unknown features through partial correlations, which will significantly reduce the chemical space available to the unknowns. QM calculations will use this pathway information to limit the number of possible structures for a given molecular formula, which will be obtained by the Experimental Core. The output of the QM calculations will be accurate NMR chemical shifts on data from the same chromatographic retention times as the LC-MS/MS of the unknown, allowing us to find the best computed structure. We also will improve computational MS/MS predictions. All of the experimental and computational data will be added to a relational database, which will allow us to search any field (e.g. retention time windows, m/z values, etc.). The CC will provide robust computing infrastructure at two sites, shared notebooks for analysis, and deposition of data to repositories.