Impaired Cognitive Functioning in Patients with Tyrosinemia Type I Receiving Nitisinone

ObjectiveTo examine cognitive functioning in patients with tyrosinemia type I treated with nitisinone and a protein-restricted diet.Study designWe performed a cross-sectional study to establish cognitive functioning in children with tyrosinemia type I compared with their unaffected siblings. Intelligence was measured using age-appropriate Wechsler Scales. To assess cognitive development over time, we retrieved sequential IQ scores in a single-center subset of patients. We also evaluated whether plasma phenylalanine and tyrosine levels during treatment was correlated with cognitive development.ResultsAverage total IQ score in 10 patients with tyrosinemia type I receiving nitisinone was significantly lower compared with their unaffected siblings (71 ± 13 vs 91 ± 13; P = .008). Both verbal and performance IQ subscores differed (77 ± 14 vs 95 ± 11; P < .05 and 70 ± 11 vs 87 ± 15; P < .05, respectively). Repeated IQ measurements in a single-center subset of 5 patients revealed a decline in average IQ score over time, from 96 ± 15 to 69 ± 11 (P < .001). No significant association was found between IQ score and either plasma tyrosine or phenylalanine concentration.ConclusionPatients with tyrosinemia type I treated with nitisinone are at risk for impaired cognitive function despite a protein-restricted diet

Accurate discrimination of Hartnup disorder from other aminoacidurias using a diagnostic ratio

Introduction: Hartnup disorder is caused by a deficiency of the sodium dependent B 0 AT1 neutral amino acid transporter in the proximal kidney tubules and jejunum. Biochemically, Hartnup disorder is diagnosed via amino acid excretion patterns. However, these patterns can closely resemble amino acid excretion patterns of generalized aminoaciduria, which may induce a risk for misdiagnosis and preclusion from treatment. Here we explore whether calculating a diagnostic ratio could facilitate correct discrimination of Hartnup disorder from other aminoacidurias. Methods: 27 amino acid excretion patterns from 11 patients with genetically confirmed Hartnup disorder were compared to 68 samples of 16 patients with other aminoacidurias. Amino acid fold changes were calculated by dividing the quantified excretion values over the upper limit of the age-adjusted reference value. Results: Increased excretion of amino acids is not restricted to amino acids classically related to Hartnup disorder ("Hartnup amino acids", HAA), but also includes many other amino acids, not classically related to Hartnup disorder ("other amino acids", OAA). The fold change ratio of HAA over OAA was 6.1 (range: 2.4-9.6) in the Hartnup cohort, versus 0.2 (range: 0.0-1.6) in the aminoaciduria cohort ( p < .0001), without any overlap observed between the cohorts. Discussion: Excretion values of amino acids not classically related to Hartnup disorder are frequently elevated in patients with Hartnup disorder, which may cause misdiagnosis as generalized aminoaciduria and preclusion from vitamin B3 treatment. Calculation of the HAA/OAA ratio improves diagnostic differentiation of Hartnup disorder from other aminoacidurias

A second case of glutaminase hyperactivity: Expanding the phenotype with epilepsy

Glutaminase (GLS) hyperactivity was first described in 2019 in a patient with profound developmental delay and infantile cataract. Here, we describe a 4-year-old boy with GLS hyperactivity due to a de novo heterozygous missense variant in GLS, detected by trio whole exome sequencing. This boy also exhibits developmental delay without dysmorphic features, but does not have cataract. Additionally, he suffers from epilepsy with tonic clonic seizures. In line with the findings in the previously described patient with GLS hyperactivity, in vivo 3 T magnetic resonance spectroscopy (MRS) of the brain revealed an increased glutamate/glutamine ratio. This increased ratio was also found in urine with UPLC-MS/MS, however, inconsistently. This case indicates that the phenotypic spectrum evoked by GLS hyperactivity may include epilepsy. Clarifying this phenotypic spectrum is of importance for the prognosis and identification of these patients. The combination of phenotyping, genetic testing, and metabolic diagnostics with brain MRS and in urine is essential to identify new patients with GLS hyperactivity and to further extend the phenotypic spectrum of this disease

Monitoring phenylalanine concentrations in the follow-up of phenylketonuria patients:An inventory of pre-analytical and analytical variation

Background: Reliable measurement of phenylalanine (Phe) is a prerequisite for adequate follow-up of phenylketonuria (PKU) patients. However, previous studies have raised concerns on the intercomparability of plasma and dried blood spot (DBS) Phe results. In this study, we made an inventory of differences in (pre-)analytical methodology used for Phe determination across Dutch laboratories, and compared DBS and plasma results. Methods: Through an online questionnaire, we assessed (pre-)analytical Phe measurement procedures of seven Dutch metabolic laboratories. To investigate the difference between plasma and DBS Phe, participating laboratories received simultaneously collected plasma-DBS sets from 23 PKU patients. In parallel, 40 sample sets of DBS spotted from either venous blood or capillary fingerprick were analyzed. Results: Our data show that there is no consistency on standard operating procedures for Phe measurement. The association of DBS to plasma Phe concentration exhibits substantial inter-laboratory variation, ranging from a mean difference of −15.5% to +30.6% between plasma and DBS Phe concentrations. In addition, we found a mean difference of +5.8% in Phe concentration between capillary DBS and DBS prepared from venous blood. Conclusions: The results of our study point to substantial (pre-)analytical variation in Phe measurements, implicating that bloodspot Phe results should be interpreted with caution, especially when no correction factor is applied. To minimize variation, we advocate pre-analytical standardization and analytical harmonization of Phe measurements, including consensus on application of a correction factor to adjust DBS Phe to plasma concentrations

Cross-omics: Integrating genomics with metabolomics in clinical diagnostics

Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: A method that uses untargeted metabolomics results of patient’s dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction

Untargeted metabolomics for metabolic diagnostic screening with automated data interpretation using a knowledge-based algorithm

Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. To facilitate its implementation in metabolic diagnostic screening, we developed a method for automated data interpretation that preselects the most likely inborn errors of metabolism (IEM). The input parameters of the knowledge-based algorithm were (1) weight scores assigned to 268 unique metabolites for 119 different IEM based on literature and expert opinion, and (2) metabolite Z-scores and ranks based on direct-infusion high resolution mass spectrometry. The output was a ranked list of differential diagnoses (DD) per sample. The algorithm was first optimized using a training set of 110 dried blood spots (DBS) comprising 23 different IEM and 86 plasma samples comprising 21 different IEM. Further optimization was performed using a set of 96 DBS consisting of 53 different IEM. The diagnostic value was validated in a set of 115 plasma samples, which included 58 different IEM and resulted in the correct diagnosis being included in the DD of 72% of the samples, comprising 44 different IEM. The median length of the DD was 10 IEM, and the correct diagnosis ranked first in 37% of the samples. Here, we demonstrate the accuracy of the diagnostic algorithm in preselecting the most likely IEM, based on the untargeted metabolomics of a single sample. We show, as a proof of principle, that automated data interpretation has the potential to facilitate the implementation of untargeted metabolomics for metabolic diagnostic screening, and we provide suggestions for further optimization of the algorithm to improve diagnostic accuracy

Direct infusion based metabolomics identifies metabolic disease in patients’ dried blood spots and plasma

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

Farnesoid X Receptor Activation Promotes Hepatic Amino Acid Catabolism and Ammonium Clearance in Mice

Background & Aims The nuclear receptor subfamily 1 group H member 4 (NR1H4 or farnesoid X receptor [FXR]) regulates bile acid synthesis, transport, and catabolism. FXR also regulates postprandial lipid and glucose metabolism. We performed quantitative proteomic analyses of liver tissues from mice to evaluate these functions and investigate whether FXR regulates amino acid metabolism. Methods To study the role of FXR in mouse liver, we used mice with a disruption of Nr1h4 (FXR-knockout mice) and compared them with floxed control mice. Mice were gavaged with the FXR agonist obeticholic acid or vehicle for 11 days. Proteome analyses, as well as targeted metabolomics and chromatin immunoprecipitation, were performed on the livers of these mice. Primary rat hepatocytes were used to validate the role of FXR in amino acid catabolism by gene expression and metabolomics studies. Finally, control mice and mice with liver-specific disruption of Nr1h4 (liver FXR-knockout mice) were re-fed with a high-protein diet after 6 hours fasting and gavaged a 15NH4Cl tracer. Gene expression and the metabolome were studied in the livers and plasma from these mice. Results In livers of control mice and primary rat hepatocytes, activation of FXR with obeticholic acid increased expression of proteins that regulate amino acid degradation, ureagenesis, and glutamine synthesis. We found FXR to bind to regulatory sites of genes encoding these proteins in control livers. Liver tissues from FXR-knockout mice had reduced expression of urea cycle proteins, and accumulated precursors of ureagenesis, compared with control mice. In liver FXR-knockout mice on a high-protein diet, the plasma concentration of newly formed urea was significantly decreased compared with controls. In addition, liver FXR-knockout mice had reduced hepatic expression of enzymes that regulate ammonium detoxification compared with controls. In contrast, obeticholic acid increased expression of genes encoding enzymes involved in ureagenesis compared with vehicle in C57Bl/6 mice. Conclusions 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

Identification of a Loss-of-Function Mutation in the Context of Glutaminase Deficiency and Neonatal Epileptic Encephalopathy

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.(Asp232Glufs2) 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.