No abstract

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.

M.vanderHam-3@umcutrecht.nl (M.v.d.H.); B.Prinsen@umcutrecht.nl (H.C.M.T.P.); M.H.Broeks-3@umcutrecht.nl (M.H.B.); M.G.deSain@umcutrecht.nl (M.G.M.d.S.-v.d.V.); N.Verhoeven@umcutrecht.nl (N.M.V.-D.)Abstract: 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.

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.