Evaluation of a UPLC-MS Metabolic Profiling Platform for the Detection and Diagnosis of Inborn Errors of Metabolism

Tue 3:00 PM - Track 3: Newborn Screening

Elizabeth Want Imperial College London

*Elizabeth J. Want, *Vinothini Sivarajah Ivan K. S. Yap, #U Engelke, #L Kluijtmans, #H Blom, #E Morava, *Elaine Holmes, *Jeremy K. Nicholson, *John C. Lindon, #Ron Wevers. *Imperial College London, UK #Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

An ultra performance liquid chromatography - mass spectrometry (UPLC-MS) metabolic profiling platform was assessed for the detection and diagnosis of inborn errors of metabolism (IEM). IEM are genetic diseases involving abnormalities in the synthesis and metabolism of proteins, fats or carbohydrates. IEM can lead to mental and growth retardation, neurological defects and multiple organ failure in early childhood, many of which can be prevented through early diagnosis and treatment e.g. dietary intervention. Rapid, targeted MS/MS assays are often employed for IEM diagnosis, allowing for the screening of multiple disorders in a short time frame. However, there is value in utilizing untargeted metabolic profiling approaches in order to obtain a more global view of metabolic perturbations due to the disorder, which may provide insight into new disease biomarkers. Urine is a valuable biofluid for metabolic profiling studies as the collection is non-invasive and its composition can reflect changes in metabolism due to disease. UPLC provides excellent chromatographic resolution and sensitivity and, when coupled to a Q-ToF mass spectrometer, where accurate mass measurements (<2ppm) can be made on both parent and fragment ions, provides a powerful tool for metabolic profiling. Here, a 12 minute UPLC-QToF-MS assay was developed for the investigation of patients with organic acidurias. In a blind study, urine samples from 25 patients with 5 different IEM were studied in order to identify and characterise the metabolic profiles. Samples were prepared by dilution and centrifugation and chromatographic separations performed on a C18 column. A 12 min gradient was employed starting from 99% A (0.1% formic acid in water) and ending at 99% B (0.1% FA in acetonitrile). Mass spectrometry experiments were performed on a Micromass Q-tof Premier™ in positive and negative electrospray ionisation modes (mass range m/z 50-1000). MSE experiments provided simultaneous fragmentation data and aided in metabolite identification. MS/MS was also used to identify and confirm the elevated organic acids diagnostic for each IEM, through comparison with authentic standards. All five IEM were identified correctly through elevation of specific organic acids and additional metabolic information was obtained for each IEM through this untargeted UPLC-MS approach. Positive and negative mode data offered complementary metabolic information. Further, effects of therapeutic intervention were observed in the metabolic profiles. The different diseases were also differentiated using principal components analysis. This study illustrates the capability of UPLC-MS methodologies for the diagnosis of inborn errors of metabolism, and also the discovery of additional biochemical information. The author would like to acknowledge Waters Corporation for support.

Email: e.want@imperial.ac.uk

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