Metabolomics and Clinical Assays Center

摘要（代谢组学和临床分析中心，MCAC） 确定个体在新陈代谢方面的差异以及对饮食摄入的反应，对于 制定个性化的干预策略，预防和延迟慢性疾病的发作， 肥胖症、糖尿病、心血管疾病和癌症。MCAC将a）获得和处理高质量的 靶向和非靶向代谢组学数据，B）对未知峰进行优先级排序、预测和确认，c） 提供CLIA认证的临床检测，d）与共同基金数据生态系统合作，e）构建数据 基础设施，确保公平性，并实现数据与其他共同基金数据的互操作性 f）与NIH共同基金营养精准健康（NPH）联盟合作。 MCAC带来了来自3所大学的优秀调查团队，这些大学位于 北卡罗来纳州研究校园（NCRC）和杜克大学。苏珊·萨姆纳博士（查佩尔山， 营养研究所，NCRC，非靶向代谢组学）将在专家的支持下担任PI 专门研究营养和宿主代谢的靶向代谢组学的科学家（Christopher Newgard博士， 导演：Sarah W.斯特德曼营养和代谢中心和杜克分子生理学研究所），饮食 干预措施和有针对性的植物化学分析（科林凯博士，北卡罗来纳州州立大学，NCRC），CLIA 经认证的临床分析（Steven Cotten博士，UNCCH）和计算代谢组学（Xiuxiang Du博士， 夏洛特，NCRC）。我们的团队提供了一个独特的组合，长期的专业知识，在代谢组学 技术，再加上对营养，代谢生理学和慢性疾病机制的深入了解。 我们在靶向和非靶向代谢组学在大规模临床研究中的应用方面经验丰富， 流行病学研究，包括其他NIH联盟。我们用代谢组学来定义代谢 与饮食摄入、营养评估、人口统计学、生活方式因素 微生物种群、遗传学、转录组学、临床测定和健康和保健的临床表型。 我们开发了针对靶向和非靶向代谢组学的全面信息学能力， 很麻烦的研究。我们已经开发了一个在线质谱知识库资源，用于优先排序， 通过利用公开数据预测未知代谢物。我们的高质量MCAC数据集 在经过微调的协议与质量控制和质量保证指标，将是至关重要的成功， NPH财团。MCAC将提供数据和专家生物学解释， 研究对象之间代谢的异质性，提供了一个路线图，将有助于解释为什么个人 他们对饮食干预的代谢反应不同，这预示着未来的疾病风险。的 MCAC将为多模态数据建模和生物信息学的人工智能提供强大的数据集 中心用于开发预测个人饮食反应的算法， 翻译为设计有针对性的饮食干预措施，以改善健康和生活质量。

Abstract (Metabolomics and Clinical Assay Center, MCAC) Determining how individuals differ in their metabolism, and in their response to dietary intake, is critical to developing personalized intervention strategies for preventing and delaying the onset of chronic diseases such as obesity, diabetes, cardiovascular disease, and cancer. The MCAC will a) acquire and process high quality targeted and untargeted metabolomics data, b) prioritize, predict, and confirm the identity of unknown peaks, c) provide CLIA certified clinical assays, d) collaborate with the Common Fund Data Ecosystem, e) construct a data infrastructure which ensures FAIRness and enables interoperability of the data with other Common Fund data sets, and f) collaboratively work with the NIH Common Fund Nutrition for Precision Health (NPH) Consortium. The MCAC brings an outstanding team of investigators from 3 UNC Systems Universities that are co-located on the North Carolina Research Campus (NCRC) and Duke University. Dr. Susan Sumner (UNC Chapel Hill, Nutrition Research Institute, NCRC, Untargeted Metabolomics) will serve as the PI with support from expert scientists who specialize in nutrition and targeted metabolomics of host metabolism (Dr. Christopher Newgard, Director, Sarah W. Stedman Nutrition and Metabolism Center and Duke Molecular Physiology Institute), dietary interventions and targeted phytochemical analysis (Dr. Colin Kay, North Carolina State University, NCRC), CLIA certified clinical assays (Dr. Steven Cotten, UNCCH), and Computational Metabolomics (Dr. Xiuxia Du, UNC Charlotte, NCRC). Our team provides a unique combination of long-standing expertise in metabolomics technologies, coupled with deep knowledge of nutrition, metabolic physiology, and chronic disease mechanisms. We are experienced with the application of targeted and untargeted metabolomics in large-scale clinical and epidemiology studies, including in other NIH Consortia. We have used metabolomics to define metabolic signatures and pathways associated with dietary intake, nutrition assessments, demographics, lifestyle factors, microbial populations, genetics, transcriptomics, clinical assays, and clinical phenotypes of health and wellness. We have developed comprehensive informatics capabilities for targeted and untargeted metabolomics and exposome research. We have developed an online mass spectral knowledge base resource for prioritizing and predicting unknown metabolites by leveraging publicly available data. Our high quality MCAC datasets produced under fine-tuned protocols with quality control and quality assurance metrics, will be essential for success of the NPH Consortium. The MCAC will provide data and expert biological interpretation in exploration of the heterogeneity in metabolism among study subjects, providing a roadmap that will help explain why individuals differ in their metabolic responses to dietary interventions, and what this portends for future disease risk. The MCAC will provide a robust data set to the Artificial Intelligence for Multimodal Data Modeling and Bioinformatics Center for use in development of algorithms to predict individual dietary responses that can ultimately be translated for design of targeted dietary interventions to improve health and quality of life.