Mega-scale Identification tools for xenobiotic metabolism

项目摘要 人类进化创造了复杂的新陈代谢系统，以改变和消除潜在的有害物质 我们接触到的化学物质。现有证据表明，这些系统产生了100万或 更多不同的化学代谢物，其中大多数完全没有特征。质量的广泛使用 基于光谱的代谢组学方法表明，许多未识别的质谱学特征是 与人类疾病密切相关。大量流行病学研究表明与环境有关 对许多疾病过程的贡献，我们认为许多不明的质谱学特征 是环境化学物质的代谢物。我们已经建立了一个成功的人类曝光片 研究中心专注于提高对环境对疾病的影响的理解。这个 目前的建议是在此基础上开发功能强大的新化学识别工具，该工具可以 按比例识别人体内数十万种外来化学代谢物。我们有 组建了一个由分析科学家组成的暴露研究团队，他们在质谱学、异物生物方面具有专长 新陈代谢、计算化学和机器人方法，以开发和测试新的化学鉴定 用于识别数十万种外来化学代谢物的工具。我们的方法依赖于 1)预测可能的外源代谢物、各自的加合物形式和离子的计算化学 质谱学中的解离模式，2)酶和细胞异源生物转化的应用 系统，这允许创建包含特定生物转化系统的多孔板以产生 异源生物代谢物，3)离子裂解质谱学和核磁共振波谱方法确认 化学识别和4)机器人系统的专业知识，可用于扩展方法以识别 成千上万的环境化学物质的代谢物。管理核心将维护 实验核心和计算核心之间的组织结构和协调活动， NIH和利益相关者参与和项目协调中心(SEPCC)。实验核心 将开发和提供超高分辨率质谱学的化合物识别能力 支持。计算核心将开发一个预测的外来生物代谢物数据库，以支持 代谢物鉴定。管理核心将与Hercules Exposome保持互动 研究中心，并支持与潜在核心用户的互动。建立里程碑以监测 朝着建立化合物鉴定工具的目标取得进展，这些工具可以扩展到识别数百种 数千种外来化学代谢物。该结果将通过提供新的研究成果促进代谢组学研究 识别未知环境化学品代谢物的方法，并支持更广泛的 药物、食品、微生物组、膳食补充剂和商业产品的代谢物。

Project Summary Human evolution has created complex metabolism systems to transform and eliminate potentially harmful chemicals to which we are exposed. Available evidence indicates that these systems generate a million or more different chemical metabolites, most of which are completely uncharacterized. Widespread use of mass spectrometry-based metabolomics methods shows that many unidentified mass spectral features are significantly associated with human diseases. Substantial epidemiological research implicates environmental contributions to many disease processes, and we believe that many of the unidentified mass spectral features are metabolites of environmental chemicals. We have an established and successful human exposome research center focused on improving the understanding of environmental contributions to disease. The present proposal is to build upon this foundation to develop powerful new chemical identification tools that can be scaled to identify hundreds of thousands of foreign chemical metabolites in the human body. We have assembled an exposome research team of analytical scientists with expertise in mass spectrometry, xenobiotic metabolism, computational chemistry and robotic methods, to develop and test new chemical identification tools to identify hundreds of thousands of foreign chemical metabolites. Our approach relies upon expertise in 1) computational chemistry to predict possible xenobiotic metabolites, respective adduct forms and ion dissociation patterns in mass spectrometry, 2) use of enzymatic and cellular xenobiotic biotransformation systems, which allows creation of multi-well panels containing specific biotransformation systems to generate xenobiotic metabolites, 3) ion fragmentation mass spectrometry and NMR spectroscopy methods to confirm chemical identities and 4) expertise with robotic systems which can be used to scale the approach to identify hundreds of thousands of metabolites of environmental chemicals. An Administrative Core will maintain an organizational structure and coordinate activities between the Experimental Core and the Computational Core, NIH and the Stakeholder Engagement and Program Coordination Center (SEPCC). The Experimental Core will develop and provide compound identification capability with ultra-high-resolution mass spectrometry support. The Computational Core will develop a predicted xenobiotic metabolite database to support metabolite identification. The Administrative Core will maintain interactions with HERCULES Exposome Research Center and support interactions with prospective Core users. Milestones are established to monitor progress toward goals to establish tools for compound identification that can be scaled to identify hundreds of thousands of foreign chemical metabolites. The results will catalyze metabolomics research by providing new ways to identify unknown metabolites of environmental chemicals, and also support identification of a broader range of metabolites of drugs, food, microbiome, dietary supplements and commercial products.