Mayo Clinic Undiagnosed Disease Network Metabolomics Core

作为未诊断疾病网络的第二阶段代谢组学核心，我们的主要目标是应用 最先进的、非靶向和靶向/定量代谢组学方法、生物信息学和专家 对UDN临床流程中登记的患者的生物样本的临床解释。到目前为止，我们 活动的中心是“N=1”方法，即分析单个先证者的样本，并 与适当的对照组或家族三人组进行比较。虽然这种方法在一个 在个体患者水平上，一个缺点是内在的生物变异性，这是技术复制没有解决的。 尽管如此，UDN生物资料库中储存的大量I期样本以及广泛的临床 信息和元数据提供了一个独特的机会，通过询问 在仔细定义的病例集群中的代谢组谱，以及来自 参考人口。本行政副刊的目标是继续一项始于 第二阶段的开始，目标是分析大量要执行的银行UDN生物样本 代谢组学分析在按表型、症状或相关HPO术语和 将这些结果与参考队列进行比较。 这项提议的主要目标是： 1.来自UDN生物储存库的血清样本和来自临床站点的样本的CDG检测 但仍被送往生物库。 2.未经历这种类型的群集性表型的扩展队列中的广泛代谢组谱 之前的代谢组学分析。 3.分析对照样本的参考队列，使我们能够产生适当的参考 当前参考范围通常不包括儿童的特定研究平台的范围。 4.通过应用额外的信息学和模式检测工具来实施我们的生物信息学方法。 我们相信，作为补充的结果产生的数据将导致对未诊断的CDG的识别 UDN患者和对代谢组学如何帮助治疗罕见疾病的新的基本理解 诊断，也可作为未来的参考数据集。

As the Phase II Metabolomics Core for the Undiagnosed Disease Network (UDN), our main goal is to apply state-of-the-art, untargeted and targeted/quantitative metabolomics approaches, bioinformatics, and expert clinical interpretation to biological samples from patients enrolled in the UDN clinical pipeline. To date, our activities have centered around an “N=1” approach, where a single proband’s samples are analyzed and compared with appropriate controls or familial trio. While this approach generates biochemical insights at an individual patient level, a drawback is inherent biological variability that is unaddressed by technical replicates. Nevertheless, the abundance of banked Phase I samples in the UDN biorepository alongside extensive clinical information, and metadata, provides a unique opportunity to move beyond an “N=1” status quo by interrogating metabolomic profiles in carefully defined clusters of cases alongside a large number of specimens from a reference population. The objective of this administrative supplement is to continue an initiative that began at the start of Phase II with the goal of analyzing a large number of banked UDN biospecimens to perform metabolomics analyses in cohorts logically grouped by phenotype, symptom, or related HPO terms and compare these results against a reference cohort. The main goals of this proposal will be: 1. CDG testing of serum samples from the UDN biorepository and samples from clinical sites that have not yet been sent to biorepository. 2. Broad metabolomic profiling in expanded cohorts of clustered phenotypes that did not undergo this type of metabolomics analysis previously. 3. Analysis of a reference cohort of control samples that will allow us to generate appropriate reference ranges for specific research platforms where current reference ranges do not typically include children. 4. Implement our bioinformatics approach by applying additional tools for informatics and pattern detection. We believe that the data generated as a result of this supplement will lead to identification of CDG in undiagnosed UDN individuals and new fundamental understanding of how metabolomics may help aid in rare disease diagnosis, and also serve as a reference dataset for the future.