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.

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.

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.

MDH1 deficiency is a metabolic disorder of the malate-aspartate shuttle associated with early onset severe encephalopathy.

The reversible oxidation of L-malate to oxaloacetate is catalyzed by NAD(H)-dependent malate dehydrogenase (MDH). MDH plays essential roles in the malate-aspartate shuttle and the tricarboxylic acid cycle. These metabolic processes are important in mitochondrial NADH supply for oxidative phosphorylation. Recently, bi-allelic mutations in mitochondrial MDH2 were identified in patients with global developmental delay, epilepsy and lactic acidosis. We now report two patients from an extended consanguineous family with a deleterious variant in the cytosolic isoenzyme of MDH (MDH1). The homozygous missense variant in the NAD+-binding domain of MDH1 led to severely diminished MDH protein expression. The patients presented with global developmental delay, epilepsy and progressive microcephaly. Both patients had normal concentrations of plasma amino acids, acylcarnitines, lactate, and urine organic acids. To identify the metabolic consequences of MDH1 deficiency, untargeted metabolomics was performed on dried blood spots (DBS) from the patients and in MDH1 knockout HEK293 cells that were generated by Crispr/Cas9. Increased levels of glutamate and glycerol-3-phosphate were found in DBS of both patients. In MDH1 KO HEK293 cells, increased levels of glycerol-3-phosphate were also observed, as well as increased levels of aspartate and decreased levels of fumarate. The consistent finding of increased concentrations of glycerol-3-phosphate may represent a compensatory mechanism to enhance cytosolic oxidation of NADH by the glycerol-P-shuttle. In conclusion, MDH1 deficiency is a new metabolic defect in the malate-aspartate shuttle characterized by a severe neurodevelopmental phenotype with elevated concentrations of glycerol-3-phosphate as a potential biomarker.