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BackgroundAmino acidopathies are a class of inborn errors of metabolism (IEM) that can be diagnosed by analysis of amino acids (AA) in plasma. Current strategies for AA analysis include cation exchange HPLC with post-column ninhydrin derivatization, GC-MS, and LC-MS/MS-related methods. Major drawbacks of the current methods are time-consuming procedures, derivative problems, problems with retention, and MS-sensitivity. The use of hydrophilic interaction liquid chromatography (HILIC) columns is an ideal separation mode for hydrophilic compounds like AA. Here we report a HILIC-method for analysis of 36 underivatized AA in plasma to detect defects in AA metabolism that overcomes the major drawbacks of other methods.MethodsA rapid, sensitive, and specific method was developed for the analysis of AA in plasma without derivatization using HILIC coupled with tandem mass-spectrometry (Xevo TQ, Waters).ResultsExcellent separation of 36 AA (24 quantitative/12 qualitative) in plasma was achieved on an Acquity BEH Amide column (2.1×100 mm, 1.7 μm) in a single MS run of 18 min. Plasma of patients with a known IEM in AA metabolism was analyzed and all patients were correctly identified.ConclusionThe reported method analyzes 36 AA in plasma within 18 min and provides baseline separation of isomeric AA such as leucine and isoleucine. No separation was obtained for isoleucine and allo-isoleucine. The method is applicable to study defects in AA metabolism in plasma.Electronic supplementary materialThe online version of this article (doi:10.1007/s10545-016-9935-z) contains supplementary material, which is available to authorized users.

In metabolic diagnostics, there is an emerging need for a comprehensive test to acquire a complete view of metabolite status. Here, we describe a non-quantitative direct-infusion high-resolution mass spectrometry (DI-HRMS) based metabolomics method and evaluate the method for both dried blood spots (DBS) and plasma. 110 DBS of 42 patients harboring 23 different inborn errors of metabolism (IEM) and 86 plasma samples of 38 patients harboring 21 different IEM were analyzed using DI-HRMS. A peak calling pipeline developed in R programming language provided Z-scores for ~1875 mass peaks corresponding to ~3835 metabolite annotations (including isomers) per sample. Based on metabolite Z-scores, patients were assigned a ‘most probable diagnosis’ by an investigator blinded for the known diagnoses of the patients. Based on DBS sample analysis, 37/42 of the patients, corresponding to 22/23 IEM, could be correctly assigned a ‘most probable diagnosis’. Plasma sample analysis, resulted in a correct ‘most probable diagnosis’ in 32/38 of the patients, corresponding to 19/21 IEM. The added clinical value of the method was illustrated by a case wherein DI-HRMS metabolomics aided interpretation of a variant of unknown significance (VUS) identified by whole-exome sequencing. In summary, non-quantitative DI-HRMS metabolomics in DBS and plasma is a very consistent, high-throughput and nonselective method for investigating the metabolome in genetic disease.

Patients with tyrosinemia type I treated with nitisinone are at risk for impaired cognitive function despite a protein-restricted diet.

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IMPORTANCE The identification and understanding of the monogenic causes of neurodevelopmental disorders are of high importance for personalized treatment and genetic counseling. OBJECTIVE To identify and characterize novel genes for a specific neurodevelopmental disorder characterized by refractory seizures, respiratory failure, brain abnormalities, and death in the neonatal period; describe the outcome of glutaminase deficiency in humans; and understand the underlying pathological mechanisms. DESIGN, SETTING, AND PARTICIPANTS We performed exome sequencing of cases of neurodevelopmental disorders without a clear genetic diagnosis, followed by genetic and bioinformatic evaluation of candidate variants and genes. Establishing pathogenicity of the variants was achieved by measuring metabolites in dried blood spots by a hydrophilic interaction liquid chromatography method coupled with tandem mass spectrometry. The participants are 2 families with a total of 4 children who each had lethal, therapy-refractory early neonatal seizures with status epilepticus and suppression bursts, respiratory insufficiency, simplified gyral structures, diffuse volume loss of the brain, and cerebral edema. Data analysis occurred from October 2017 to June 2018. MAIN OUTCOMES AND MEASURES Early neonatal epileptic encephalopathy with glutaminase deficiency and lethal outcome. RESULTS A total of 4 infants from 2 unrelated families, each of whom died less than 40 days after birth, were included. We identified a homozygous frameshift variant p.(Asp232Glufs*2) in GLS in the first family, as well as compound heterozygous variants p.(Gln81*) and p.(Arg272Lys) in GLS in the second family. The GLS gene encodes glutaminase (Enzyme Commission 3.5.1.2), which plays a major role in the conversion of glutamine into glutamate, the main excitatory neurotransmitter of the central nervous system. All 3 variants probably lead to a loss of function and thus glutaminase deficiency. Indeed, glutamine was increased in affected children (available z scores, 3.2 and 11.7). We theorize that the potential reduction of glutamate and the excess of glutamine were a probable cause of the described physiological and structural abnormalities of the central nervous system. CONCLUSIONS AND RELEVANCE We identified a novel autosomal recessive neurometabolic disorder of loss of function of glutaminase that leads to lethal early neonatal encephalopathy. This inborn error of metabolism underlines the importance of GLS for appropriate glutamine homeostasis and respiratory regulation, signal transduction, and survival.

In livers of mice, FXR regulates amino acid catabolism and detoxification of ammonium via ureagenesis and glutamine synthesis. Failure of the urea cycle and hyperammonemia are common in patients with acute and chronic liver diseases; compounds that activate FXR might promote ammonium clearance in these patients.

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