Experimental 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 实验核心（EC）将负责准备和光谱数据收集的几个 不同类型的C。elegans代谢组样本。这包括（i）大规模的参考样品， 普通实验室菌株“N2”，（ii）一组超过100个野生C. elegans分离株，代表了一组基因 多样但纯合的“个体”，将用于使用一种 全基因组关联（m-GWAS）方法，和（iii）一组缺失突变体，将用于验证 基因功能预测和表征已知遗传途径中的未知特征。这些样本将 其特征在于利用LC-MS/MS的互补优势（速度和宽 代谢物覆盖率），高分辨率FTMS（直接测定实验分子式），以及 NMR（原子级结构数据）。当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未知化合物识别的自动流水线 这将普遍适用于各种各样的模型系统，包括高等动物和人类 样品

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.