Computational Core

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。 计算核心(CC)将有两个主要组成部分，代谢物路径图和量子 核磁共振和MS/MS数据的化学计算。路径图与实验核心的接口 通过对野生菌株的分离和LC-MS/MS分析，得到了m-GWAs的生成结果。这些遗传关联 会将已知的代谢物与已知的基因联系起来。这些路径将通过定位未知要素进行扩展 通过部分关联，这将显著减少未知的化学空间。QM 计算将使用这一途径信息来限制给定分子的可能结构的数量 公式，该公式将由实验核心获得。QM计算的输出将是准确的 核磁共振化学位移来自与LC-MS/MS相同保留时间的数据 未知，让我们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中，该数据库将 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 两个站点的计算基础设施，用于分析的共享笔记本，以及将数据存储到存储库。

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.