Metabolic Alterations in NADSYN1-Deficient Cells.

NAD synthetase 1 (encoded by the gene NADSYN1) is a cytosolic enzyme that catalyzes the final step in the biosynthesis of nicotinamide adenine dinucleotide (NAD+) from tryptophan and nicotinic acid. NADSYN1 deficiency has recently been added to the spectrum of congenital NAD+ deficiency disorders. To gain insight into the metabolic consequences of NADSYN1 deficiency, the encoding gene was disrupted in A549 and HEK293T cells, and the metabolome was profiled in the presence of different NAD+ precursors, including tryptophan, nicotinamide and nicotinic acid. We demonstrate that when precursors of the NAD+ salvage pathway in the form of nicotinamide become limiting, NADSYN1 deficiency results in a decline in intracellular NAD+ levels even in the presence of other potential NAD+ sources such as tryptophan and nicotinic acid. As a consequence, alterations in 122 and 69 metabolites are observed in NADSYN1-deficient A549 and HEK293T cells compared to the wild-type cell line (FC > 2 and p < 0.05). We thus show that NADSYN1 deficiency results in a metabolic phenotype characterized by alterations in glycolysis, the TCA cycle, the pentose phosphate pathway, and the polyol pathway.

A one-year pilot study comparing direct-infusion high resolution mass spectrometry based untargeted metabolomics to targeted diagnostic screening for inherited metabolic diseases.

Background: Early diagnosis of inherited metabolic diseases (IMDs) is important because treatment may lead to reduced mortality and improved prognosis. Due to their diversity, it is a challenge to diagnose IMDs in time, effecting an emerging need for a comprehensive test to acquire an overview of metabolite status. Untargeted metabolomics has proven its clinical potential in diagnosing IMDs, but is not yet widely used in genetic metabolic laboratories. Methods: We assessed the potential role of plasma untargeted metabolomics in a clinical diagnostic setting by using direct infusion high resolution mass spectrometry (DI-HRMS) in parallel with traditional targeted metabolite assays. We compared quantitative data and qualitative performance of targeted versus untargeted metabolomics in patients suspected of an IMD (n = 793 samples) referred to our laboratory for 1 year. To compare results of both approaches, the untargeted data was limited to polar metabolites that were analyzed in targeted plasma assays. These include amino acid, (acyl)carnitine and creatine metabolites and are suitable for diagnosing IMDs across many of the disease groups described in the international classification of inherited metabolic disorders (ICIMD). Results: For the majority of metabolites, the concentrations as measured in targeted assays correlated strongly with the semi quantitative Z-scores determined with DI-HRMS. For 64/793 patients, targeted assays showed an abnormal metabolite profile possibly indicative of an IMD. In 55 of these patients, similar aberrations were found with DI-HRMS. The remaining 9 patients showed only marginally increased or decreased metabolite concentrations that, in retrospect, were most likely to be clinically irrelevant. Illustrating its potential, DI-HRMS detected additional patients with aberrant metabolites that were indicative of an IMD not detected by targeted plasma analysis, such as purine and pyrimidine disorders and a carnitine synthesis disorder. Conclusion: This one-year pilot study showed that DI-HRMS untargeted metabolomics can be used as a first-tier approach replacing targeted assays of amino acid, acylcarnitine and creatine metabolites with ample opportunities to expand. Using DI-HRMS untargeted metabolomics as a first-tier will open up possibilities to look for new biomarkers.

Parasitic, bacterial, viral, immune-mediated, metabolic and nutritional factors associated with nodding syndrome.

Nodding syndrome is a neglected, disabling and potentially fatal epileptic disorder of unknown aetiology affecting thousands of individuals mostly confined to Eastern sub-Saharan Africa. Previous studies have identified multiple associations-including Onchocerca volvulus, antileiomodin-1 antibodies, vitamin B6 deficiency and measles virus infection-yet, none is proven causal. We conducted a case-control study of children with early-stage nodding syndrome (symptom onset <1 year). Cases and controls were identified through a household survey in the Greater Mundri area in South Sudan. A wide range of parasitic, bacterial, viral, immune-mediated, metabolic and nutritional risk factors was investigated using conventional and state-of-the-art untargeted assays. Associations were examined by multiple logistic regression analysis, and a hypothetical causal model was constructed using structural equation modelling. Of 607 children with nodding syndrome, 72 with early-stage disease were included as cases and matched to 65 household- and 44 community controls. Mansonella perstans infection (odds ratio 7.04, 95% confidence interval 2.28-21.7), Necator americanus infection (odds ratio 2.33, 95% confidence interval 1.02-5.3), higher antimalarial seroreactivity (odds ratio 1.75, 95% confidence interval 1.20-2.57), higher vitamin E concentration (odds ratio 1.53 per standard deviation increase, 95% confidence interval 1.07-2.19) and lower vitamin B12 concentration (odds ratio 0.56 per standard deviation increase, 95% confidence interval 0.36-0.87) were associated with higher odds of nodding syndrome. In a structural equation model, we hypothesized that Mansonella perstans infection, higher vitamin E concentration and fewer viral exposures increased the risk of nodding syndrome while lower vitamin B12 concentration, Necator americanus and malaria infections resulted from having nodding syndrome. We found no evidence that Onchocerca volvulus, antileiomodin-1 antibodies, vitamin B6 and other factors were associated with nodding syndrome. Our results argue against several previous causal hypotheses including Onchocerca volvulus. Instead, nodding syndrome may be caused by a complex interplay between multiple pathogens and nutrient levels. Further studies need to confirm these associations and determine the direction of effect.

The malate-aspartate shuttle is important for de novo serine biosynthesis.

The malate-aspartate shuttle (MAS) is a redox shuttle that transports reducing equivalents across the inner mitochondrial membrane while recycling cytosolic NADH to NAD+. We genetically disrupted each MAS component to generate a panel of MAS-deficient HEK293 cell lines in which we performed [U-13C]-glucose tracing. MAS-deficient cells have reduced serine biosynthesis, which strongly correlates with the lactate M+3/pyruvate M+3 ratio (reflective of the cytosolic NAD+/NADH ratio), consistent with the NAD+ dependency of phosphoglycerate dehydrogenase in the serine synthesis pathway. Among the MAS-deficient cells, those lacking malate dehydrogenase 1 (MDH1) show the most severe metabolic disruptions, whereas oxoglutarate-malate carrier (OGC)- and MDH2-deficient cells are less affected. Increasing the NAD+-regenerating capacity using pyruvate supplementation resolves most of the metabolic disturbances. Overall, we show that the MAS is important for de novo serine biosynthesis, implying that serine supplementation could be used as a therapeutic strategy for MAS defects and possibly other redox disorders.

Maintenance of cellular vitamin B6 levels and mitochondrial oxidative function depend on pyridoxal 5'-phosphate homeostasis protein.

Recently, biallelic variants in PLPBP coding for pyridoxal 5'-phosphate homeostasis protein (PLPHP) were identified as a novel cause of early-onset vitamin B6-dependent epilepsy. The molecular function and precise role of PLPHP in vitamin B6 metabolism are not well understood. To address these questions, we used PLPHP-deficient patient skin fibroblasts and HEK293 cells and YBL036C (PLPHP ortholog)-deficient yeast. We showed that independent of extracellular B6 vitamer type (pyridoxine, pyridoxamine, or pyridoxal), intracellular pyridoxal 5'-phosphate (PLP) was lower in PLPHP-deficient fibroblasts and HEK293 cells than controls. Culturing cells with pyridoxine or pyridoxamine led to the concentration-dependent accumulation of pyridoxine 5'-phosphate and pyridoxamine 5'-phosphate (PMP), respectively, suggesting insufficient pyridox(am)ine 5'-phosphate oxidase activity. Experiments utilizing 13C4-pyridoxine confirmed lower pyridox(am)ine 5'-phosphate oxidase activity and revealed increased fractional turnovers of PLP and pyridoxal, indicating increased PLP hydrolysis to pyridoxal in PLPHP-deficient cells. This effect could be partly counteracted by inactivation of pyridoxal phosphatase. PLPHP deficiency had a distinct effect on mitochondrial PLP and PMP, suggesting impaired activity of mitochondrial transaminases. Moreover, in YBL036C-deficient yeast, PLP was depleted and PMP accumulated only with carbon sources requiring mitochondrial metabolism. Lactate and pyruvate accumulation along with the decrease of tricarboxylic acid cycle intermediates downstream of α-ketoglutarate suggested impaired mitochondrial oxidative metabolism in PLPHP-deficient HEK293 cells. We hypothesize that impaired activity of mitochondrial transaminases may contribute to this depletion. Taken together, our study provides new insights into the pathomechanisms of PLPBP deficiency and reinforces the link between PLPHP function, vitamin B6 metabolism, and mitochondrial oxidative metabolism.

Optimising urinary catecholamine metabolite diagnostics for neuroblastoma.

INTRODUCTION: The analysis of urinary catecholamine metabolites is a cornerstone of neuroblastoma diagnostics. Currently, there is no consensus regarding the sampling method, and variable combinations of catecholamine metabolites are being used. We investigated if spot urine samples can be reliably used for analysis of a panel of catecholamine metabolites for the diagnosis of neuroblastoma. METHODS: Twenty-four-hour urine or spot urine samples were collected from patients with and without neuroblastoma at diagnosis. Homovanillic acid (HVA), vanillylmandelic acid (VMA), dopamine, 3-methoxytyramine, norepinephrine, normetanephrine, epinephrine and metanephrine were measured by high-performance liquid chromatography coupled with fluorescence detection (HPLC-FD) and/or ultra-performance liquid chromatography coupled with electrospray tandem mass spectrometry (UPLC-MS/MS). RESULTS: Catecholamine metabolite levels were measured in urine samples of 400 neuroblastoma patients (24-hour urine, n = 234; spot urine, n = 166) and 571 controls (all spot urine). Excretion levels of catecholamine metabolites and the diagnostic sensitivity for each metabolite were similar in 24-hour urine and spot urine samples (p > .08 and >.27 for all metabolites). The area under the receiver-operating-characteristic curve (AUC) of the panel containing all eight catecholamine metabolites was significantly higher compared to that of only HVA and VMA (AUC = 0.952 vs. 0.920, p = .02). No differences were observed in metabolite levels between the two analysis methods. CONCLUSION: Catecholamine metabolites in spot urine and 24-hour urine resulted in similar diagnostic sensitivities. The Catecholamine Working Group recommends the implementation of spot urine as standard of care. The panel of eight catecholamine metabolites has superior diagnostic accuracy over VMA and HVA.

Untargeted metabolic analysis in dried blood spots reveals metabolic signature in 22q11.2 deletion syndrome.

The 22q11.2 deletion syndrome (22q11.2DS) is characterized by a well-defined microdeletion and is associated with increased risk of neurodevelopmental phenotypes including autism spectrum disorders (ASD) and intellectual impairment. The typically deleted region in 22q11.2DS contains multiple genes with the potential of altering metabolism. Deficits in metabolic processes during early brain development may help explain the increased prevalence of neurodevelopmental phenotypes seen in 22q11.2DS. However, relatively little is known about the metabolic impact of the 22q11.2 deletion, while such insight may lead to increased understanding of the etiology. We performed untargeted metabolic analysis in a large sample of dried blood spots derived from 49 22q11.2DS patients and 87 controls, to identify a metabolic signature for 22q11.2DS. We also examined trait-specific metabolomic patterns within 22q11.2DS patients, focusing on intelligence (intelligence quotient, IQ) and ASD. We used the Boruta algorithm to select metabolites distinguishing patients from controls, patients with ASD from patients without, and patients with an IQ score in the lowest range from patients with an IQ score in the highest range. The relevance of the selected metabolites was visualized with principal component score plots, after which random forest analysis and logistic regression were used to measure predictive performance of the selected metabolites. Analysis yielded a distinct metabolic signature for 22q11.2DS as compared to controls, and trait-specific (IQ and ASD) metabolomic patterns within 22q11.2DS patients. The metabolic characteristics of 22q11.2DS provide insights in biological mechanisms underlying the neurodevelopmental phenotype and may ultimately aid in identifying novel therapeutic targets for patients with developmental disorders.

Clinical relevance of testing for metabolic vitamin B12 deficiency in patients with polyneuropathy.

OBJECTIVE: Determine vitamin B12 threshold levels below which additional testing of methylmalonic acid (MMA) and/or homocysteine (Hcy) is useful to diagnose metabolic vitamin B12 deficiency in patients with polyneuropathy, and how vitamin B12, MMA and Hcy levels relate to the effect of supplementation therapy. METHODS: In a retrospective cohort study of 331 patients with polyneuropathy, vitamin B12, MMA and Hcy were measured. Linear regression models with vitamin B12 as dependent and Hcy or MMA as covariate were compared, to assess which was best related to vitamin B12. Threshold vitamin B12 levels for metabolic deficiency (defined as elevatede metabolites) were determined using logistic regression with elevated metabolites as dependent and vitamin B12 as covariate. A structured interview was conducted in 42 patients to evaluate response to vitamin B12 supplementation. RESULTS: MMA was best related to vitamin B12. Using elevated MMA for metabolic deficiency, we found 90% sensitivity at a vitamin B12 threshold level <264 pmol/L (358 pg/mL) and 95% sensitivity at <304 pmol/L (412 pg/mL). Improvement after supplementation was reported by 19% patients and stabilization by 24%. 88% of patients with improvement and 90% with stabilization either had absolute deficiency (Vitamin B12 < 148 pmol/L) or metabolic deficiency (elevated MMA and vitamin B12 ≥ 148 pmol/L). There were no additional patients with improvement or stabilization with isolated elevated Hcy. CONCLUSION: Testing of MMA has additional value in identifying patients with clinically relevant metabolic deficiency when vitamin B12 is below 304 pmol/L (412 pg/mL). Supplementation can be effective in patients with absolute and metabolic deficiency.

The potential and limitations of intrahepatic cholangiocyte organoids to study inborn errors of metabolism.

Inborn errors of metabolism (IEMs) comprise a diverse group of individually rare monogenic disorders that affect metabolic pathways. Mutations lead to enzymatic deficiency or dysfunction, which results in intermediate metabolite accumulation or deficit leading to disease phenotypes. Currently, treatment options for many IEMs are insufficient. Rarity of individual IEMs hampers therapy development and phenotypic and genetic heterogeneity suggest beneficial effects of personalized approaches. Recently, cultures of patient-own liver-derived intrahepatic cholangiocyte organoids (ICOs) have been established. Since most metabolic genes are expressed in the liver, patient-derived ICOs represent exciting possibilities for in vitro modeling and personalized drug testing for IEMs. However, the exact application range of ICOs remains unclear. To address this, we examined which metabolic pathways can be studied with ICOs and what the potential and limitations of patient-derived ICOs are to model metabolic functions. We present functional assays in patient ICOs with defects in branched-chain amino acid metabolism (methylmalonic acidemia), copper metabolism (Wilson disease), and transporter defects (cystic fibrosis). We discuss the broad range of functional assays that can be applied to ICOs, but also address the limitations of these patient-specific cell models. In doing so, we aim to guide the selection of the appropriate cell model for studies of a specific disease or metabolic process.

Dried blood spot metabolomics reveals a metabolic fingerprint with diagnostic potential for Diamond Blackfan Anaemia.

The diagnostic evaluation of Diamond Blackfan Anaemia (DBA), an inherited bone marrow failure syndrome characterised by erythroid hypoplasia, is challenging because of a broad phenotypic variability and the lack of functional screening tests. In this study, we explored the potential of untargeted metabolomics to diagnose DBA. In dried blood spot samples from 18 DBA patients and 40 healthy controls, a total of 1752 unique metabolite features were identified. This metabolic fingerprint was incorporated into a machine-learning algorithm, and a binary classification model was constructed using a training set. The model showed high performance characteristics (average accuracy 91·9%), and correct prediction of class was observed for all controls (n = 12) and all but one patient (n = 4/5) from the validation or 'test' set (accuracy 94%). Importantly, in patients with congenital dyserythropoietic anaemia (CDA) - an erythroid disorder with overlapping features - we observed a distinct metabolic profile, indicating the disease specificity of the DBA fingerprint and underlining its diagnostic potential. Furthermore, when exploring phenotypic heterogeneity, DBA treatment subgroups yielded discrete differences in metabolic profiles, which could hold future potential in understanding therapy responses. Our data demonstrate that untargeted metabolomics in dried blood spots is a promising new diagnostic tool for DBA.

Inborn disorders of the malate aspartate shuttle.

Over the last few years, various inborn disorders have been reported in the malate aspartate shuttle (MAS). The MAS consists of four metabolic enzymes and two transporters, one of them having two isoforms that are expressed in different tissues. Together they form a biochemical pathway that shuttles electrons from the cytosol into mitochondria, as the inner mitochondrial membrane is impermeable to the electron carrier NADH. By shuttling NADH across the mitochondrial membrane in the form of a reduced metabolite (malate), the MAS plays an important role in mitochondrial respiration. In addition, the MAS maintains the cytosolic NAD+ /NADH redox balance, by using redox reactions for the transfer of electrons. This explains why the MAS is also important in sustaining cytosolic redox-dependent metabolic pathways, such as glycolysis and serine biosynthesis. The current review provides insights into the clinical and biochemical characteristics of MAS deficiencies. To date, five out of seven potential MAS deficiencies have been reported. Most of them present with a clinical phenotype of infantile epileptic encephalopathy. Although not specific, biochemical characteristics include high lactate, high glycerol 3-phosphate, a disturbed redox balance, TCA abnormalities, high ammonia, and low serine, which may be helpful in reaching a diagnosis in patients with an infantile epileptic encephalopathy. Current implications for treatment include a ketogenic diet, as well as serine and vitamin B6 supplementation.

Untargeted metabolic profiling in dried blood spots identifies disease fingerprint for pyruvate kinase deficiency.

The diagnostic evaluation and clinical characterization of rare hereditary anemia (RHA) is to date still challenging. In particular, there is little knowledge on the broad metabolic impact of many of the molecular defects underlying RHA. In this study we explored the potential of untargeted metabolomics to diagnose a relatively common type of RHA: Pyruvate Kinase Deficiency (PKD). In total, 1903 unique metabolite features were identified in dried blood spot samples from 16 PKD patients and 32 healthy controls. A metabolic fingerprint was identified using a machine learning algorithm, and subsequently a binary classification model was designed. The model showed high performance characteristics (AUC 0.990, 95%CI 0.981-0.999) and an accurate class assignment was achieved for all newly added control (13) and patient samples (6), with the exception of one patient (accuracy 94%). Important metabolites in the metabolic fingerprint included glycolytic intermediates, polyamines and several acyl carnitines. In general, the application of untargeted metabolomics in dried blood spots is a novel functional tool that holds promise for diagnostic stratification and studies on disease pathophysiology in RHA.

Biallelic variants in HPDL, encoding 4-hydroxyphenylpyruvate dioxygenase-like protein, lead to an infantile neurodegenerative condition.

PURPOSE: Dioxygenases are oxidoreductase enzymes with roles in metabolic pathways necessary for aerobic life. 4-hydroxyphenylpyruvate dioxygenase-like protein (HPDL), encoded by HPDL, is an orphan paralogue of 4-hydroxyphenylpyruvate dioxygenase (HPD), an iron-dependent dioxygenase involved in tyrosine catabolism. The function and association of HPDL with human diseases remain unknown. METHODS: We applied exome sequencing in a cohort of over 10,000 individuals with neurodevelopmental diseases. Effects of HPDL loss were investigated in vitro and in vivo, and through mass spectrometry analysis. Evolutionary analysis was performed to investigate the potential functional separation of HPDL from HPD. RESULTS: We identified biallelic variants in HPDL in eight families displaying recessive inheritance. Knockout mice closely phenocopied humans and showed evidence of apoptosis in multiple cellular lineages within the cerebral cortex. HPDL is a single-exonic gene that likely arose from a retrotransposition event at the base of the tetrapod lineage, and unlike HPD, HPDL is mitochondria-localized. Metabolic profiling of HPDL mutant cells and mice showed no evidence of altered tyrosine metabolites, but rather notable accumulations in other metabolic pathways. CONCLUSION: The mitochondrial localization, along with its disrupted metabolic profile, suggests HPDL loss in humans links to a unique neurometabolic mitochondrial infantile neurodegenerative condition.

Consensus guidelines for the diagnosis and management of pyridoxine-dependent epilepsy due to α-aminoadipic semialdehyde dehydrogenase deficiency.

Pyridoxine-dependent epilepsy (PDE-ALDH7A1) is an autosomal recessive condition due to a deficiency of α-aminoadipic semialdehyde dehydrogenase, which is a key enzyme in lysine oxidation. PDE-ALDH7A1 is a developmental and epileptic encephalopathy that was historically and empirically treated with pharmacologic doses of pyridoxine. Despite adequate seizure control, most patients with PDE-ALDH7A1 were reported to have developmental delay and intellectual disability. To improve outcome, a lysine-restricted diet and competitive inhibition of lysine transport through the use of pharmacologic doses of arginine have been recommended as an adjunct therapy. These lysine-reduction therapies have resulted in improved biochemical parameters and cognitive development in many but not all patients. The goal of these consensus guidelines is to re-evaluate and update the two previously published recommendations for diagnosis, treatment, and follow-up of patients with PDE-ALDH7A1. Members of the International PDE Consortium initiated evidence and consensus-based process to review previous recommendations, new research findings, and relevant clinical aspects of PDE-ALDH7A1. The guideline development group included pediatric neurologists, biochemical geneticists, clinical geneticists, laboratory scientists, and metabolic dieticians representing 29 institutions from 16 countries. Consensus guidelines for the diagnosis and management of patients with PDE-ALDH7A1 are provided.

A novel mouse model for pyridoxine-dependent epilepsy due to antiquitin deficiency.

Pyridoxine-dependent epilepsy (PDE) is a rare autosomal recessive disease caused by mutations in the ALDH7A1 gene leading to blockade of the lysine catabolism pathway. PDE is characterized by recurrent seizures that are resistant to conventional anticonvulsant treatment but are well-controlled by pyridoxine (PN). Most PDE patients also suffer from neurodevelopmental deficits despite adequate seizure control with PN. To investigate potential pathophysiological mechanisms associated with ALDH7A1 deficiency, we generated a transgenic mouse strain with constitutive genetic ablation of Aldh7a1. We undertook extensive biochemical characterization of Aldh7a1-KO mice consuming a low lysine/high PN diet. Results showed that KO mice accumulated high concentrations of upstream lysine metabolites including ∆1-piperideine-6-carboxylic acid (P6C), α-aminoadipic semialdehyde (α-AASA) and pipecolic acid both in brain and liver tissues, similar to the biochemical picture in ALDH7A1-deficient patients. We also observed preliminary evidence of a widely deranged amino acid profile and increased levels of methionine sulfoxide, an oxidative stress biomarker, in the brains of KO mice, suggesting that increased oxidative stress may be a novel pathobiochemical mechanism in ALDH7A1 deficiency. KO mice lacked epileptic seizures when fed a low lysine/high PN diet. Switching mice to a high lysine/low PN diet led to vigorous seizures and a quick death in KO mice. Treatment with PN controlled seizures and improved survival of high-lysine/low PN fed KO mice. This study expands the spectrum of biochemical abnormalities that may be associated with ALDH7A1 deficiency and provides a proof-of-concept for the utility of the model to study PDE pathophysiology and to test new therapeutics.

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.

NaV1.1 and NaV1.6 selective compounds reduce the behavior phenotype and epileptiform activity in a novel zebrafish model for Dravet Syndrome.

Dravet syndrome is caused by dominant loss-of-function mutations in SCN1A which cause reduced activity of Nav1.1 leading to lack of neuronal inhibition. On the other hand, gain-of-function mutations in SCN8A can lead to a severe epileptic encephalopathy subtype by over activating NaV1.6 channels. These observations suggest that Nav1.1 and Nav1.6 represent two opposing sides of the neuronal balance between inhibition and activation. Here, we hypothesize that Dravet syndrome may be treated by either enhancing Nav1.1 or reducing Nav1.6 activity. To test this hypothesis we generated and characterized a novel DS zebrafish model and tested new compounds that selectively activate or inhibit the human NaV1.1 or NaV1.6 channel respectively. We used CRISPR/Cas9 to generate two separate Scn1Lab knockout lines as an alternative to previous zebrafish models generated by random mutagenesis or morpholino oligomers. Using an optimized locomotor assay, spontaneous burst movements were detected that were unique to Scn1Lab knockouts and disappear when introducing human SCN1A mRNA. Besides the behavioral phenotype, Scn1Lab knockouts show sudden, electrical discharges in the brain that indicate epileptic seizures in zebrafish. Scn1Lab knockouts showed increased sensitivity to the GABA antagonist pentylenetetrazole and a reduction in whole organism GABA levels. Drug screenings further validated a Dravet syndrome phenotype. We tested the NaV1.1 activator AA43279 and two novel NaV1.6 inhibitors MV1369 and MV1312 in the Scn1Lab knockouts. Both type of compounds significantly reduced the number of spontaneous burst movements and seizure activity. Our results show that selective inhibition of NaV1.6 could be just as efficient as selective activation of NaV1.1 and these approaches could prove to be novel potential treatment strategies for Dravet syndrome and other (genetic) epilepsies. Compounds tested in zebrafish however, should always be further validated in other model systems for efficacy in mammals and to screen for potential side effects.

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.