Experimental 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。 实验核心(EC)将负责准备和收集以下几项光谱数据 不同类型的线虫代谢组样本。这包括：(I)一份大型参考样本 普通实验室菌株“n2”，(Ii)一组超过100个野生线虫分离物，代表了一组遗传上的 不同但纯合的“个体”，这将被用来绘制保守的生化途径使用 全基因组关联(m-Gwas)方法，以及(Iii)将用于验证的一组缺失突变 基因功能预测和表征已知遗传途径中的未知特征。这些样品将 利用LC-MS/MS(快速和广泛)的互补优势 代谢物覆盖)、高分辨率FTMS(直接确定实验分子式)以及 核磁共振(原子级结构数据)。当使用《计算核心》中描述的方法进行分析时， 生成的光谱数据将用于开发一条未知化合物鉴定的自动管道 这将普遍适用于各种不同的模型系统，包括高等动物和人类 样本。

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 Experimental Core (EC) will be responsible for the preparation and spectral data collection for several different types of C. elegans metabolome samples. This includes (i) a large-scale reference sample of the common laboratory strain “N2”, (ii) a set of over 100 wild C. elegans isolates, representing a set of genetically diverse but homozygous “individuals”, which will be used for mapping conserved biochemical pathways using a genome-wide association (m-GWAS) approach, and (iii) a set of deletion mutants that will be used to validate gene function predictions and characterize unknown features in known genetic pathways. These samples will be characterized by taking advantage of the complementary strengths of LC-MS/MS (speed and broad metabolite coverage), high-resolution FTMS (direct determination of experimental molecular formulas), and NMR (atomic-level structural data). When analyzed with approaches described in the Computational Core, the generated spectral data will be used to develop an automatic pipeline of unknown compound identification that will be generally applicable to a wide range of diverse model systems, including higher animals and human samples.