Metabolomics and Clinical Assays Center

代谢组学与临床分析中心 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 制定个性化干预策略，以预防和延迟慢性病的发病 比如肥胖、糖尿病、心血管疾病和癌症。MCAC将a)获得并加工高质量 靶向和非靶向代谢组学数据，b)优先排序、预测和确认未知峰值的身份，c) 提供CLIA认证的临床分析，d)与共同基金数据生态系统合作，e)构建数据 确保公平并实现数据与其他共同基金数据的互操作性的基础设施 和f)与美国国立卫生研究院精准健康营养共同基金(NPH)联盟合作。 MCAC带来了一支来自3所北卡罗来纳大学系统大学的优秀调查团队，这3所大学共同位于 北卡罗来纳州研究校园(NCRC)和杜克大学。苏珊·萨姆纳博士(北卡罗来纳大学教堂山分校， 营养研究所，NCRC，非靶向代谢学)将在专家的支持下担任PI 专门研究营养学和宿主代谢靶向代谢组学的科学家(Christopher Newgard博士， 萨拉·W·斯特德曼营养与代谢中心和杜克分子生理学研究所主任)，膳食 干预措施和有针对性的植物化学分析(科林·凯博士，北卡罗来纳州立大学，NCRC)，CLIA 认证临床分析(Steven Cotten博士，UNCCH)和计算代谢组学(杜秀霞博士，北卡罗来纳大学 夏洛特，NCRC)。我们的团队提供代谢组学方面的长期专业知识的独特组合 技术，再加上对营养、新陈代谢生理学和慢性病机制的深入了解。 我们在靶向和非靶向代谢组学的大规模临床和临床应用方面经验丰富。 流行病学研究，包括在NIH的其他财团。我们使用代谢组学来定义新陈代谢 与饮食摄入量、营养评估、人口统计、生活方式因素、 微生物种群，遗传学，转录学，临床分析，以及健康和健康的临床表型。 我们已经为靶向和非靶向代谢组学开发了全面的信息学能力 暴露的研究。我们已经开发了一个在线质谱库资源，用于区分优先顺序和 通过利用公开的数据预测未知的代谢物。我们生产的高质量MCAC数据集 在具有质量控制和质量保证指标的微调协议下， NPH联盟。MCAC将在勘探过程中提供数据和专家生物学解释 研究对象新陈代谢的异质性，提供了一个路线图，有助于解释为什么 他们对饮食干预的代谢反应不同，以及这预示着未来的疾病风险。这个 MCAC将为人工智能的多模式数据建模和生物信息学提供强大的数据集 用于开发算法以预测个体饮食反应的中心，最终可以 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.