Hana Janeckova1,2, Alzbeta Kalivodova2,3, Lukas Najdekr2, David Friedecky1,2, Karel Hron3, Per Bruheim4, Tomas Adam1,2. 1. Laboratory for Inherited Metabolic Disorders, Department of Clinical Biochemistry, University Hospital Olomouc, Czech Republic. 2. Laboratory of Metabolomics, Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University Olomouc. 3. Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacky University Olomouc. 4. Department of Biotechnology, Norwegian University of Science and Technology, Sem Saelands vei 6/8, NO-7491 Trondheim, Norway.
Abstract
BACKGROUND: Metabolomics is becoming an important tool in clinical research and the diagnosis of human diseases. It has been used in the diagnosis of inherited metabolic disorders with pronounced biochemical abnormalities. The aim of this study was to determine if it could be applied in the diagnosis of inherited metabolic disorders (IMDs) with less clear biochemical profiles from urine samples using an untargeted metabolomic approach. METHODS: A total of 14 control urine samples and 21 samples from infants with cystinuria, maple syrup urine disease, adenylosuccinate lyase deficiency and galactosemia were tested. Samples were analyzed by liquid chromatography on aminopropyl column in aqueous normal phase separation system using gradient elution of acetonitrile/ammonium acetate. Detection was performed by time-of-flight mass spectrometer fitted with electrospray ionisation in positive mode. The data were statistically processed using principal component analysis (PCA), principal component discriminant function analysis (PCA-DFA) and partial least squares (PLS) regression. RESULTS: All patient samples were first distinguished from controls using unsupervised PCA. Discrimination of the patient samples was then unambiguously verified using supervised PCA-DFA. Known markers of the diseases in question were successfully confirmed and a potential new marker emerged from the PLS regression. CONCLUSION: This study showed that untargeted metabolomics can be applied in the diagnosis of mild IMDs with less clear biochemical profiles.
BACKGROUND: Metabolomics is becoming an important tool in clinical research and the diagnosis of human diseases. It has been used in the diagnosis of inherited metabolic disorders with pronounced biochemical abnormalities. The aim of this study was to determine if it could be applied in the diagnosis of inherited metabolic disorders (IMDs) with less clear biochemical profiles from urine samples using an untargeted metabolomic approach. METHODS: A total of 14 control urine samples and 21 samples from infants with cystinuria, maple syrup urine disease, adenylosuccinate lyase deficiency and galactosemia were tested. Samples were analyzed by liquid chromatography on aminopropyl column in aqueous normal phase separation system using gradient elution of acetonitrile/ammonium acetate. Detection was performed by time-of-flight mass spectrometer fitted with electrospray ionisation in positive mode. The data were statistically processed using principal component analysis (PCA), principal component discriminant function analysis (PCA-DFA) and partial least squares (PLS) regression. RESULTS: All patient samples were first distinguished from controls using unsupervised PCA. Discrimination of the patient samples was then unambiguously verified using supervised PCA-DFA. Known markers of the diseases in question were successfully confirmed and a potential new marker emerged from the PLS regression. CONCLUSION: This study showed that untargeted metabolomics can be applied in the diagnosis of mild IMDs with less clear biochemical profiles.
Entities:
Keywords:
inherited metabolic disorders; mass spectrometry; untargeted metabolomics
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