Targeted metabolomic analysis of plasma samples for the diagnosis of inherited metabolic disorders.

Metabolomics has become an important tool in clinical research and diagnosis of human diseases. In this work we focused on the diagnosis of inherited metabolic disorders (IMDs) in plasma samples using a targeted metabolomic approach. The plasma samples were analyzed with the flow injection analysis method. All the experiments were performed on a QTRAP 5500 tandem mass spectrometer (AB SCIEX, U.S.A.) with electrospray ionization. The compounds were measured in a multiple reaction monitoring mode. We analyzed 50 control samples and 34 samples with defects in amino acid metabolism (phenylketonuria, maple syrup urine disease, tyrosinemia I, argininemia, homocystinuria, carbamoyl phosphate synthetase deficiency, ornithine transcarbamylase deficiency, nonketotic hyperglycinemia), organic acidurias (methylmalonic aciduria, propionic aciduria, glutaric aciduria I, 3-hydroxy-3-methylglutaric aciduria, isovaleric aciduria), and mitochondrial defects (medium-chain acyl-coenzyme A dehydrogenase deficiency, carnitine palmitoyltransferase II deficiency). The controls were distinguished from the patient samples by principal component analysis and hierarchical clustering. Approximately 80% of patients were clearly detected by absolute metabolite concentrations, the sum of variance for first two principle components was in the range of 44-55%. Other patient samples were assigned due to the characteristic ratio of metabolites (the sum of variance for first two principle components 77 and 83%). This study has revealed that targeted metabolomic tools with automated and unsupervised processing can be applied for the diagnosis of various IMDs.

Evaluation of comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry for the diagnosis of inherited metabolic disorders using an automated data processing strategy.

Organic acidurias are a large group of inherited metabolic disorders (IMDs), commonly diagnosed by GC-MS analysis of organic acids in urine after acidic extraction and trimethylsilylation. In this study, a GC×GC-ToF-MS method has been optimized for the analysis of pathological metabolites in urine. An automated data processing strategy based on the use of mass spectra and GC retention times for the target search and quantification of pathological metabolites has been developed. Using this procedure, each unknown sample is automatically examined for the presence of markers of several diseases at the same time. The method has been applied for the analysis of 6 challenging proficiency testing samples from patients with IMDs (thymidine phosphorylase deficiency, mevalonic aciduria, hawkinsinuria, aromatic l-amino acid decarboxylase deficiency, propionic acidemia and medium-chain acyl-CoA dehydrogenase deficiency). Using the GC×GC-ToF-MS method, we were able to determine complete sets of markers for all the IMDs. The quality of the mass spectral matches for the pathological markers was higher than 800 (out of 1000).