Mayo Clinic Undiagnosed Disease Network Metabolomics Core

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.