Validation of small-molecule metabolomic profiling for the clinical screening of inborn errors of metabolism. M. J. Miller1, A. D. Kennedy2, A. D. Eckhart2, J. E. Wulff2, M. V. Milburn2, J. A. Ryals2, A. L. Beaudet1, Q. Sun1, V. R. Sutton1, S. H. Elsea1 1) Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX; 2) Metabolon, Research Triangle Park, North Carolina.
Advances in chromatography, mass spectrometry, and informatic technologies have made possible the rapid identification of hundreds of metabolites in a single analysis, but many questions remain about the practical applications of metabolomic profiling in clinical testing. As an initial proof-of-concept, we employed a rapid and scalable metabolomic workflow to analyze 129 plasma samples collected from patients with a confirmed inborn error of metabolism (IEM). In total, 25 different IEMs were represented within our sample set including amino acid, organic acid, fatty acid oxidation, vitamin cofactor, pyrimidine biosynthesis, creatine biosynthesis, and urea cycle disorders. Analysis was completed using a state-of-the-art MS platform, and the resulting spectra were compared against a library of ~2,500 human metabolites. On average, 886 small molecules were detected in a given sample with a core group of 404 analytes found in all specimens. The analytes detected encompass numerous classes of important small molecule biomarkers such as acylcarnitines, amino acids, bile acids, carbohydrates, lipids and nucleotides. For the majority of IEM samples studied, classic pathognomonic compounds were among the most significantly elevated analytes detected. In many cases, metabolomic data afforded a much richer view of a patients metabolic disturbance by identifying: (1) elevated metabolites located far upstream of the genetic defect, (2) treatment related compounds, and (3) spectrally unique analytes that are not yet associated with a biochemical. In total, metabolic profiling was able to correctly diagnose 19 of the 20 disorders in our panel for which plasma analysis is informative. Importantly, to achieve a similar diagnostic outcome in our laboratory, we estimate ten different biochemical tests would be required. As a negative control, we analyzed plasma specimens from 71 patients who had non-diagnostic testing within our laboratory. In a subset of these cases, metabolomic analysis uncovered disturbances that pointed to a genetic disorder (e.g., sarcosinemia and trimethyllysine hydroxylase deficiency) or assisted in the interpretation of concurrent molecular genetic testing. This proof-of-concept study demonstrates that metabolomic profiling is ready for use in the initial detection of a wide range of IEMs and represents an attractive screening option for phenotypically undifferentiated cases with a suspected biochemical genetic etiology.
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