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Contains fulltext : 290862.pdf (Publisher’s version ) (Open Access) Congenital disorders of glycosylation (CDG) are a clinically and biochemically heterogeneous subgroup of inherited metabolic disorders. Most CDG with abnormal N-glycosylation can be detected by transferrin screening, however, MOGS-CDG escapes this routine screening. Combined with the clinical heterogeneity of reported cases, diagnosing MOGS-CDG can be challenging. Here, we clinically characterize ten MOGS-CDG cases including six previously unreported individuals, showing a phenotype characterized by dysmorphic features, global developmental delay, muscular hypotonia, and seizures in all patients and in a minority vision problems and hypogammaglobulinemia. Glycomics confirmed accumulation of a Glc(3) Man(7) GlcNAc(2) glycan in plasma. For quantification of the diagnostic Glcα1-3Glcα1-3Glcα1-2Man tetrasaccharide in urine, we developed and validated a liquid chromatography-mass spectrometry method of 2-aminobenzoic acid (2AA) labeled urinary glycans. As an internal standard, isotopically labeled (13) C(6) -2AA Glc(3) Man was used, while labeling efficiency was controlled by use of (12) C(6) -2AA and (13) C(6) -2AA labeled laminaritetraose. Recovery, linearity, intra- and interassay coefficients of variability of these labeled compounds were determined. Furthermore, Glc(3) Man was specifically identified by retention time matching against authentic MOGS-CDG urine and compared with Pompe urine. Glc(3) Man was increased in all six analyzed cases, ranging from 34.1 to 618.0 μmol/mmol creatinine (reference

Infrared ion spectroscopy (IRIS) continues to see increasing use as an analytical tool for small-molecule identification in conjunction with mass spectrometry (MS). The IR spectrum of an m/z selected population of ions constitutes a unique fingerprint that is specific to the molecular structure. However, direct translation of an IR spectrum to a molecular structure remains challenging, as reference libraries of IR spectra of molecular ions largely do not exist. Quantum-chemically computed spectra can reliably be used as reference, but the challenge of selecting the candidate structures remains. Here we introduce an in silico library of vibrational spectra of common MS adducts of over 4500 compounds found in the human metabolome database (HMDB). In total, the library currently contains more than 75 000 spectra computed at the DFT level that can be queried with an experimental IR spectrum. Moreover, we introduce a database of 189 experimental IRIS spectra, which is employed to validate the automated spectral matching routines. This demonstrates that 75% of metabolites in the experimental dataset is correctly identified, based solely on their exact m/z and IRIS spectrum. Additionally, we demonstrate an approach for specifically identifying substructures by performing a search without m/z constraints to find structural analogues. Such an unsupervised search paves the way towards the de novo identification of unknowns that are absent in spectral libraries. We apply the in silico spectral library to identify an unknown in a plasma sample as 3-hydroyxhexanoic acid, highlighting the potential of the method.

Hyperprolinemia type II (HPII) is an inborn error of metabolism due to genetic variants in ALDH4A1, leading to a deficiency in Δ-1-pyrroline-5-carboxylate (P5C) dehydrogenase. This leads to an accumulation of toxic levels of P5C, an intermediate in proline catabolism. The accumulating P5C spontaneously reacts with, and inactivates, pyridoxal 5’-phosphate, a crucial cofactor for many enzymatic processes, which is thought to be the pathophysiological mechanism for HPII. Here, we describe the use of a combination of LC-QTOF untargeted metabolomics, NMR spectroscopy and infrared ion spectroscopy (IRIS) to identify and characterize biomarkers for HPII that result of the spontaneous reaction of P5C with malonic acid and acetoacetic acid. We show that these biomarkers can differentiate between HPI, caused by a deficiency of proline oxidase activity, and HPII. The elucidation of their molecular structures yields insights into the disease pathophysiology of HPII.

We used next-generation metabolic screening to identify new biomarkers for improved diagnosis and pathophysiological understanding of glucose transporter type 1 deficiency syndrome (GLUT1DS), comparing metabolic cerebrospinal fluid (CSF) profiles from 12 patients to those of 116 controls. This confirmed decreased CSF glucose and lactate levels in patients with GLUT1DS and increased glutamine at group level. We identified three novel biomarkers significantly decreased in patients, namely gluconic + galactonic acid, xylose-α1-3- glucose, and xylose-α1-3-xylose-α1-3- glucose, of which the latter two have not previously been identified in body fluids. CSF concentrations of gluconic + galactonic acid may be reduced as these metabolites could serve as alternative substrates for the pentose phosphate pathway. Xylose-α1-3-glucose and xylose-α1-3- xylose-α1-3-glucose may originate from glycosylated proteins ; their decreased levels are hypothetically the consequence of insufficient glucose, one of two substrates for O- glucosylation. Since many proteins are O- glucosylated, this deficiency may affect cellular processes and thus contribute to GLUT1DS pathophysiology. The novel CSF biomarkers have the potential to improve the biochemical diagnosis of GLUT1DS. Our findings imply that brain glucose deficiency in GLUT1DS may cause disruptions at the cellular level that go beyond energy metabolism, underlining the importance of developing treatment strategies that directly target cerebral glucose uptake.

Contains fulltext : 290351.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 237508.pdf (Publisher’s version ) (Open Access) The current diagnostic work-up of inborn errors of metabolism (IEM) is rapidly moving toward integrative analytical approaches. We aimed to develop an innovative, targeted urine metabolomics (TUM) screening procedure to accelerate the diagnosis of patients with IEM. Urinary samples, spiked with three stable isotope-labeled internal standards, were analyzed for 258 diagnostic metabolites with an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) configuration run in positive and negative ESI modes. The software automatically annotated peaks, corrected for peak overloading, and reported peak quality and shifting. Robustness and reproducibility were satisfactory for most metabolites. Z-scores were calculated against four age-group-matched control cohorts. Disease phenotypes were scored based on database metabolite matching. Graphical reports comprised a needle plot, annotating abnormal metabolites, and a heatmap showing the prioritized disease phenotypes. In the clinical validation, we analyzed samples of 289 patients covering 78 OMIM phenotypes from 12 of the 15 society for the study of inborn errors of metabolism (SSIEM) disease groups. The disease groups include disorders in the metabolism of amino acids, fatty acids, ketones, purines and pyrimidines, carbohydrates, porphyrias, neurotransmitters, vitamins, cofactors, and creatine. The reporting tool easily and correctly diagnosed most samples. Even subtle aberrant metabolite patterns as seen in mild multiple acyl-CoA dehydrogenase deficiency (GAII) and maple syrup urine disease (MSUD) were correctly called without difficulty. Others, like creatine transporter deficiency, are illustrative of IEM that remain difficult to diagnose. We present TUM as a powerful diagnostic screening tool that merges most urinary diagnostic assays expediting the diagnostics for patients suspected of an IEM.

Untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics strategies are being increasingly applied in metabolite screening for a wide variety of medical conditions. The long-standing "grand challenge" in the utilization of this approach is metabolite identification-confidently determining the chemical structures of m/z-detected unknowns. Here, we use a novel workflow based on the detection of molecular features of interest by high-throughput untargeted LC-MS analysis of patient body fluids combined with targeted molecular identification of those features using infrared ion spectroscopy (IRIS), effectively providing diagnostic IR fingerprints for mass-isolated targets. A significant advantage of this approach is that in silico-predicted IR spectra of candidate chemical structures can be used to suggest the molecular structure of unknown features, thus mitigating the need for the synthesis of a broad range of physical reference standards. Pyridoxine-dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine metabolism, resulting from a mutation in the ALDH7A1 gene that leads to an accumulation of toxic levels of alpha-aminoadipic semialdehyde (alpha-AASA), piperideine-6-carboxylate (P6C), and pipecolic acid in body fluids. While alpha-AASA and P6C are known biomarkers for PDE in urine, their instability makes them poor candidates for diagnostic analysis from blood, which would be required for application in newborn screening protocols. Here, we use combined untargeted metabolomics-IRIS to identify several new biomarkers for PDE-ALDH7A1 that can be used for diagnostic analysis in urine, plasma, and cerebrospinal fluids and that are compatible with analysis in dried blood spots for newborn screening. The identification of these novel metabolites has directly provided novel insights into the pathophysiology of PDE-ALDH7A1.

Contains fulltext : 283153.pdf (Publisher’s version ) (Closed access)

Contains fulltext : 244271.pdf (Publisher’s version ) (Open Access) BACKGROUND: Treatment of animal models with ataxia telangiectasia (A-T) with nicotinamide riboside (NR) improved their neurological outcome and survival. OBJECTIVE: The aim of this study is to investigate the effects of NR in patients with A-T. METHODS: In this open-label, proof-of-concept study, 24 patients with A-T were treated with NR during four consecutive months. The effects of NR on ataxia, dysarthria, quality of life, and laboratory parameters were analyzed. RESULTS: During treatment, ataxia scores improved; mean total Scale for the Assessment and Rating of Ataxia and International Cooperative Ataxia Rating Scale scores decreased to 2.4 and 10.1 points, respectively. After NR withdrawal, ataxia scores worsened. In immunodeficient patients, the mean serum IgG concentration increased substantially until the end of the study period with 0.52 g/L. Untargeted metabolomics analysis revealed increased plasma levels of NR metabolites and purine nucleosides during treatment. Adverse effects did not occur. CONCLUSIONS: Treatment with NR is tolerated well and associated with improvement in ataxia and serum immunoglobulin concentrations in patients with A-T. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Liquid chromatography-mass spectrometry (LC-MS) is, due to its high sensitivity and selectivity, currently the method of choice in (bio)analytical studies involving the (comprehensive) profiling of metabolites in body fluids. However, as closely related isomers are often hard to distinguish on the basis of LC-MS(MS) and identification is often dependent on the availability of reference standards, the identification of the chemical structures of detected mass spectral features remains the primary limitation. Infrared ion spectroscopy (IRIS) aids identification of MS-detected ions by providing an infrared (IR) spectrum containing structural information for a detected MS-feature. Moreover, IR spectra can be routinely and reliably predicted for many types of molecular structures using quantum-chemical calculations, potentially avoiding the need for reference standards. In this work, we demonstrate a workflow for reference-free metabolite identification that combines experiments based on high-pressure liquid chromatography (HPLC), MS and IRIS with quantum-chemical calculations that efficiently generate IR spectra and give the potential to enable reference-standard free metabolite identification. Additionally, a scoring procedure is employed which shows the potential for automated structure assignment of unknowns. Via a simple, illustrative example where we identify lysine in the plasma of a hyperlysinemia patient, we show that this approach allows the efficient assignment of a database-derived molecular structure to an unknown.

Untargeted LC-MS based metabolomics strategies are being increasingly applied in metabolite screening for a wide variety of medical conditions. The long-standing “grand challenge” in the utilization of this approach is metabolite identification – confidently determining the chemical structures of m/z-detected unknowns. Here, we use a novel workflow based on the detection of molecular features of interest by high-throughput untargeted LC-MS analysis of patient body fluids combined with targeted molecular identification of those features using infrared ion spectroscopy (IRIS), effectively providing diagnostic IR fingerprints for mass-isolated targets. A significant advantage of this approach is that in silico predicted IR spectra of candidate chemical structures can be used to suggest the molecular structure of unknown features, thus mitigating the need for the synthesis of a broad range of physical reference standards. Pyridoxine dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine metabolism, resulting from a mutation in the ALDH7A1 gene that leads to an accumulation of toxic levels of α-aminoadipic semialdehyde (α-AASA), piperideine-6-carboxylate (P6C), and pipecolic acid in body fluids. While α-AASA and P6C are known biomarkers for PDE in urine, their instability makes them poor candidates for diagnostic analysis from blood, which would be required for application in newborn screening protocols. Here, we use combined untargeted metabolomics-IRIS to identify several new biomarkers for PDE-ALDH7A1 that can be used for diagnostic analysis in urine, plasma, and cerebrospinal fluids, and are compatible with analysis in dried blood spots for newborn screening. The identification of these novel metabolites has directly rendered novel insights in the pathophysiology of PDE-ALDH7A1.

Pyridoxine-dependent epilepsy (PDE-ALDH7A1), also known as antiquitin deficiency, is an inborn error of lysine metabolism that presents with refractory epilepsy in newborns. Bi-allelic ALDH7A1 variants lead to deficiency of α-aminoadipic semialdehyde dehydrogenase, resulting in accumulation of piperideine-6-carboxylate (P6C), and secondary deficiency of the important co-factor pyridoxal-5’-phosphate (PLP, active vitamin B6) through its complexation with P6C. Vitamin B6 supplementation resolves epilepsy in patients, but despite this treatment, intellectual disability may occur. Early diagnosis and treatment, preferably based on newborn screening, potentially optimize long-term clinical outcome. However, the currently known diagnostic PDE-ALDH7A1 biomarkers are incompatible with newborn screening procedures. Using a combination of the innovative analytical methods untargeted metabolomics and infrared ion spectroscopy, we have been able to discover novel biomarkers for PDE-ALDH7A1: 2S,6S-and 2S,6R-oxopropylpiperidine-2-carboxylic acid (2-OPP) and 6-oxopiperidine-2-carboxylic acid (6-oxoPIP). We demonstrate the applicability of 2-OPP as a PDE-ALDH7A1 biomarker in newborn screening. Additionally, we show that 2-OPP accumulates in brain tissue of patients and Aldh7a1 knock-out mice, and induces epilepsy-like behavior in a zebrafish model system. We speculate that 2-OPP may contribute to ongoing neurotoxicity, also in treated PDE-ALDH7A1 patients. As 2-OPP formation appears to increase upon ketosis, we emphasize the importance of avoiding catabolism in PDE-ALDH7A1 patients.

Purpose Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors. Design A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics. Methods Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods. Results Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected. Conclusions Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.

The identification of disease biomarkers plays a crucial role in developing diagnostic strategies for inborn errors of metabolism and understanding their pathophysiology. A primary metabolite that accumulates in the inborn error phenylketonuria is phenylalanine, however its levels do not always directly correlate with clinical outcomes. Here we combine infrared ion spectroscopy and NMR spectroscopy to identify the Phe-glucose Amadori rearrangement product as a biomarker for phenylketonuria. Additionally, we find analogous amino acid-glucose metabolites formed in the body fluids of patients accumulating methionine, lysine, proline and citrulline. Amadori rearrangement products are well-known intermediates in the formation of advanced glycation end-products and have been associated with the pathophysiology of diabetes mellitus and ageing, but are now shown to also form under conditions of aminoacidemia. They represent a general class of metabolites for inborn errors of amino acid metabolism that show potential as biomarkers and may provide further insight in disease pathophysiology., Rianne van Outersterp et al. combine mass spectrometry, NMR, and infrared ion spectroscopy to identify amino acid-hexose conjugates in the blood plasma from patients with metabolic disorders such as phenylketonuria (PKU). These conjugates, or Amadori rearrangement products, are generally not detectable in blood samples from unaffected individuals, and may therefore represent disease biomarkers.

Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis is imperative. Whole-exome sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is typically used to identify disease-causing variants; however, it can be challenging to identify the causal candidate gene when a large number of rare and potentially pathogenic variants are detected. Here, we present a network-based approach, metPropagate, that uses untargeted metabolomics (UM) data from a single patient and a group of controls to prioritize candidate genes in patients with suspected IEMs. We validate metPropagate on 107 patients with IEMs diagnosed in Miller et al. (2015) and 11 patients with both CNS and metabolic abnormalities. The metPropagate method ranks candidate genes by label propagation, a graph-smoothing algorithm that considers each gene’s metabolic perturbation in addition to the network of interactions between neighbors. metPropagate was able to prioritize at least one causative gene in the top 20th percentile of candidate genes for 92% of patients with known IEMs. Applied to patients with suspected neurometabolic disease, metPropagate placed at least one causative gene in the top 20th percentile in 9/11 patients, and ranked the causative gene more highly than Exomiser’s phenotype-based ranking in 6/11 patients. Interestingly, ranking by a weighted combination of metPropagate and Exomiser scores resulted in improved prioritization. The results of this study indicate that network-based analysis of UM data can provide an additional mode of evidence to prioritize causal genes in patients with suspected IEMs.

The membrane protein ANKH was known to prevent pathological mineralization of joints and was thought to export pyrophosphate (PPi) from cells. This did not explain, however, the presence of ANKH in tissues, such as brain, blood vessels and muscle. We now report that in cultured cells ANKH exports ATP, rather than PPi, and, unexpectedly, also citrate as a prominent metabolite. The extracellular ATP is rapidly converted into PPi, explaining the role of ANKH in preventing ankylosis. Mice lacking functional Ank (Ankank/ank mice) had plasma citrate concentrations that were 65% lower than those detected in wild type control animals. Consequently, citrate excretion via the urine was substantially reduced in Ankank/ank mice. Citrate was even undetectable in the urine of a human patient lacking functional ANKH. The hydroxyapatite of Ankank/ank mice contained dramatically reduced levels of both, citrate and PPi and displayed diminished strength. Our results show that ANKH is a critical contributor to extracellular citrate and PPi homeostasis and profoundly affects bone matrix composition and, consequently, bone quality., Author summary Biallelic inactivating mutations in the progressive ankylosis gene (ANKH/Ank, humans/mice) cause a severe form of arthritis, primarily affecting joints of hands, feet and the spine. Low extracellular concentrations of the mineralization inhibitor pyrophosphate underlie the joint calcification in mice and humans that are devoid of functional Ank or ANKH, respectively. It was previously thought that ANKH mediates the release of pyrophosphate from the cytosol to the extracellular environment. We here show, however, that PPi is formed in the extracellular environment by Ectonucleotide Pyrophosphatase Phosphodiesterase 1 (ENPP1), from ATP released via ANKH. Untargeted metabolomics further revealed that ANKH provides a pathway for the release of nucleoside triphosphates in general and, unexpectedly, the Krebs-cycle intermediate citrate. Mice lacking Ank activity (Ankank/ank mice), have substantially reduced concentrations of citrate in their plasma and urine. Citrate was even undetectable in urine of a human patient lacking functional ANKH. Bones of Ankank/ank mice have reduced mechanical strength and their bone matrix contains dramatically reduced levels of PPi and citrate. Our data demonstrate that ANKH/Ank crucially contributes to the disposition of citrate and PPi in vivo and profoundly affects bone quality. Extracellular citrate is ubiquitous, and additional phenotypes are therefore expected to be associated with absence of Ank activity.

Following publication of the original article, the authors would like to correct a sentence in the paragraph “1H-NMR spectra were recorded at 298 K…” under the heading “NMR experiments”. The sentence currently reads: “The LED pulse sequence had the form -RD-901-G1- 1801-G1-901-G2-T-901-G1-1801-G1-901-G2-t-901-acquire FID, where RD is a relaxation delay, 901 is a 901 RF pulse, G1 is the pulsed-field gradient that is applied to allow editing, 1801 is a 1801 RF pulse, G2 is a spoil gradient applied to remove unwanted magnetization components. The diffusion delay Δ is the time during which the molecules are allowed to diffuse—this is the period (901-G1-1801-G1- 901-G2-T-); and t is a delay to allow the longitudinal eddy currents caused within the sample to decay (Beckonert et al. 2007).” The sentence should read: “The LED pulse sequence had the form -RD-90°-G1- 180°-G1-90°-G2-T-90°-G1-180°-G1-90°-G2-t-90°-acquire FID, where RD is a relaxation delay, 90° is a 90° RF pulse, G1 is the pulsed-field gradient that is applied to allow editing, 180° is a 180° RF pulse, G2 is a spoil gradient applied to remove unwanted magnetization components. The diffusion delay ? is the time during which the molecules are allowed to diffuse—this is the period (90°-G1-180°-G1-90°-G2 T-); and t is a delay to allow the longitudinal eddy currents caused within the sample to decay (Beckonert et al. 2007).” This has been corrected with this erratum.

The identification of molecular biomarkers is critical for diagnosing and treating patients and for establishing a fundamental understanding of the pathophysiology and underlying biochemistry of inborn errors of metabolism. Currently, liquid chromatography/high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy are the principle methods used for biomarker research and for structural elucidation of small molecules in patient body fluids. While both are powerful techniques, several limitations exist that often make the identification of unknown compounds challenging. Here, we describe how infrared ion spectroscopy has the potential to be a valuable orthogonal technique that provides highly-specific molecular structure information while maintaining ultra-high sensitivity. Here, we characterize and distinguish two well-known biomarkers of inborn errors of metabolism, glutaric acid for glutaric aciduria and ethylmalonic acid for short-chain acyl-CoA dehydrogenase deficiency, using infrared ion spectroscopy. In contrast to tandem mass spectra, in which ion fragments can hardly be predicted, we show that the prediction of an IR spectrum allows reference-free identification in the case that standard compounds are either commercially or synthetically unavailable. Finally, we illustrate how functional group information can be obtained from an IR spectrum for an unknown and how this is valuable information to, for example, narrow down a list of candidate structures resulting from a database query. Early diagnosis in inborn errors of metabolism is crucial for enabling treatment and depends on the identification of biomarkers specific for the disorder. Infrared ion spectroscopy has the potential to play a pivotal role in the identification of challenging biomarkers.

Lithium, given to bipolar disorder patients, causes nephrogenic diabetes insipidus (Li-NDI), a urinary-concentrating defect. Li-NDI occurs due to downregulation of principal cell AQP2 expression, which coincides with principal cell proliferation. The metabolic effect of lithium on principal cells, however, is unknown and investigated here. In earlier studies, we showed that the carbonic anhydrase (CA) inhibitor acetazolamide attenuated Li-induced downregulation in mouse-collecting duct (mpkCCD) cells. Of the eight CAs present in mpkCCD cells, siRNA and drug treatments showed that downregulation of CA9 and to some extent CA12 attenuated Li-induced AQP2 downregulation. Moreover, lithium induced cell proliferation and increased the secretion of lactate. Lithium also increased urinary lactate levels in wild-type mice that developed Li-NDI but not in lithium-treated mice lacking ENaC, the principal cell entry site for lithium. Inhibition of aerobic glycolysis with 2-deoxyglucose (2DG) attenuated lithium-induced AQP2 downregulation in mpkCCD cells but did not attenuate Li-NDI in mice. Interestingly, NMR analysis demonstrated that lithium also increased the urinary succinate, fumarate, citrate, and NH4+levels, which were, in contrast to lactate, not decreased by 2DG. Together, our data reveal that lithium induces aerobic glycolysis and glutaminolysis in principal cells and that inhibition of aerobic glycolysis, but not the glutaminolysis, does not attenuate Li-NDI.

Specific diagnostic markers are the key to effective diagnosis and treatment of inborn errors of metabolism (IEM). Untargeted metabolomics allows for the identification of potential novel diagnostic biomarkers. Current separation techniques coupled to high-resolution mass spectrometry provide a powerful tool for structural elucidation of unknown compounds in complex biological matrices. This is a proof-of-concept study testing this methodology to determine the molecular structure of as yet uncharacterized m/z signals that were significantly increased in plasma samples from patients with phenylketonuria and 3-hydroxy-3-methylglutaryl-CoA lyase deficiency. A hybrid linear ion trap-orbitrap high resolution mass spectrometer, capable of multistage fragmentation, was used to acquire accurate masses and product ion spectra of the uncharacterized m/z signals. In order to determine the molecular structures, spectral databases were searched and fragmentation prediction software was used. This approach enabled structural elucidation of novel compounds potentially useful as biomarkers in diagnostics and follow-up of IEM patients. Two new conjugates, glutamyl-glutamyl-phenylalanine and phenylalanine-hexose, were identified in plasma of phenylketonuria patients. These novel markers showed high inter-patient variation and did not correlate to phenylalanine levels, illustrating their potential added value for follow-up. As novel biomarkers for 3-hydroxy-3-methylglutaryl-CoA lyase deficiency, three positional isomers of 3-methylglutaconyl carnitine could be detected in patient plasma. Our results highlight the applicability of current accurate mass multistage fragmentation techniques for structural elucidation of unknown metabolites in human biofluids, offering an unprecedented opportunity to gain further biochemical insights in known inborn errors of metabolism by enabling high confidence identification of novel biomarkers.

Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis before irreversible damage occurs is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, Whole Exome Sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is currently the primary method used to identify novel disease-causing variants; however, it can be challenging to identify the causal candidate gene given the large number of plausible variants. Using untargeted metabolomics (UM) to prioritize metabolically relevant candidate genes is a promising approach to diagnosing known or novel IEMs in a single patient. Here, we present a network-based bioinformatics approach, metPropagate, that uses UM data from a single patient and a group of controls to prioritize candidate genes. We validate metProp on 107 patients with diagnosed IEMs and 11 patients with novel IEMs diagnosed through the TIDE gene discovery project at BC Children’s Hospital. The metPropagate method ranks candidate genes by considering the network of interactions between them. This is done by using a graph smoothing algorithm called label propagation to quantify the metabolic disruption in genes’ local neighbourhood. metPropagate was able to prioritize the causative gene in the top 20th percentile of candidate genes for 91% of patients with known IEM disorders. For novel IEMs, metPropagate placed the causative gene in the top 20th percentile in 9/11 patients. Using metPropagate, the causative gene was ranked higher than Exomiser’s phenotype-based ranking in 6/11 patients. The results of this study indicate that for diagnostic and gene discovery purposes, network-based analysis of metabolomics data can lend support to WES gene-discovery methods by providing an additional mode of evidence to help identify causal genes.

Timely diagnosis is essential for patients with neurometabolic disorders to enable targeted treatment. Next‐Generation Metabolic Screening (NGMS) allows for simultaneous screening of multiple diseases and yields a holistic view of disturbed metabolic pathways. We applied this technique to define a cerebrospinal fluid (CSF) reference metabolome and validated our approach with patients with known neurometabolic disorders. Samples were measured using ultra‐high‐performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry followed by (un)targeted analysis. For the reference metabolome, CSF samples from patients with normal general chemistry results and no neurometabolic diagnosis were selected and grouped based on sex and age (0‐2/2‐5/5‐10/10‐15 years). We checked the levels of known biomarkers in CSF from seven patients with five different neurometabolic disorders to confirm the suitability of our method for diagnosis. Untargeted analysis of 87 control CSF samples yielded 8036 features for semiquantitative analysis. No sex differences were found, but 1782 features (22%) were different between age groups (q

Introduction When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr–Purcell–Meiboom–Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. Objectives Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. Methods We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares–Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. Results We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation ( Conclusion Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.

The membrane protein Ankylosis homologue (ANKH, mouse orthologue: ANK) prevents mineralization of joint-space and articular cartilage. The accepted view is that ANKH mediates cellular release of inorganic pyrophosphate (PPi), a strong physiological inhibitor of mineralization. Using global metabolite profiling, we identified citrate as the most prominent metabolite leaving HEK293 cells in an ANKH-dependent manner. Although PPi levels were increased in culture medium of HEK293-ANKH cells, PPi was formed extracellularly after release of ATP and other nucleoside triphosphates.Ankank/ankmice, which lack functional ANK, had substantially reduced concentrations of citrate in plasma and urine, while citrate was undetectable in urine of a human patient lacking functional ANKH. Bone hydroxyapatite ofAnkank/ankmice also contained markedly reduced levels of citrate and PPi and displayed diminished strength. Together, our data show that ANKH is a crucial factor in extracellular citrate and PPi homeostasis that is essential for normal bone development.

Combining the individual analytical strengths of mass spectrometry and infrared spectroscopy, infrared ion spectroscopy is increasingly recognized as a powerful tool for small-molecule identification in a wide range of analytical applications. Mass spectrometry is itself a leading analytical technique for small-molecule identification on the merit of its outstanding sensitivity, selectivity and versatility. The foremost shortcoming of the technique, however, is its limited ability to directly probe molecular structure, especially when contrasted against spectroscopic techniques. In infrared ion spectroscopy, infrared vibrational spectra are recorded for mass-isolated ions and provide a signature that can be matched to reference spectra, either measured from standards or predicted using quantum-chemical calculations. Here we present an overview of the potential for this technique to develop into a versatile analytical method for identifying molecular structures in mass spectrometry-based analytical workflows. In this tutorial perspective, we introduce the reader to the technique of infrared ion spectroscopy and highlight a selection of recent experimental advances and applications in current analytical challenges, in particular in the field of untargeted metabolomics. We report on the coupling of infrared ion spectroscopy with liquid chromatography and present experiments that serve as proof-of-principle ex- amples of strategies to address outstanding challenges.

Exploratory metabolomics studies of cerebrospinal fluid (CSF), using proton nuclear magnetic resonance (1H NMR) spectroscopy, hold major potential application in neurodiagnostics. Such studies, however, rely upon established databases of known metabolites. Here we address the 'unknowns' in the 1H NMR spectra of CSF from treated pediatric meningitis cases. Through knowledge of the clinical information given by the pediatrician and analytical application of 1H NMR spectroscopy on pure reference compounds of the medication used, we identified four of the previously unknown compounds in the 1H NMR CSF spectra - the drugs pyrazinamide, isoniazid, acyclovir, and sulfamethoxazole. We report on the one- and two-dimensional 1H NMR spectral data and chemical information of these four compounds. By expanding our knowledge of 1H NMR CSF spectra from treated meningitis cases, we are able to bring 1H NMR closer to the forefront of neurodiagnostics.

The -omics technologies are becoming increasingly important in health care and are expected to contribute to personalized health care. In a typical experiment, cases and controls are compared as a two-class classification problem. This approach is often unsuitable, for example, because the classes are not well defined due to associated populations being biologically too heterogeneous. Recently, statistical health monitoring (SHM) was introduced as a complementary approach to allow for predictions at the individual level. This approach could be of use in all sorts of applications such as diagnosis of rare diseases, analysis of individual patterns in disease manifestation, disease monitoring, or personalized therapy.SHM uses the framework of statistical process monitoring (SPM) in a clinical setting. The method essentially combines estimation of Mahalanobis distances (MD) with principal component analysis (PCA) to evaluate the difference in the -omics data of an individual subject to a normal reference range (normal operating conditions). It is well known from SPM, however, that reliable identification of the variables primarily responsible for this difference is hampered by the smearing effect, which is a result of the PCA step. To avoid this problem, we propose to combine estimation of the MD with variable selection via an 11-norm penalty instead of using dimension reduction. This way a sparse MD metric is obtained.The effectiveness of this method is illustrated by several simulation studies and its application to urine H-1-NMR metabolomics data for diagnosis of multiple inborn errors of metabolism.

The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia&ndash, telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables.

Genetic defects in the pyrimidine nucleoside transporters of the CNT transporter family have not yet been reported. Metabolic investigations in a patient with infantile afebrile tonic-clonic seizures revealed increased urinary uridine and cytidine excretion. Segregation of this metabolic trait in the family showed the same biochemical phenotype in a healthy older brother of the index. Whole exome sequencing revealed biallelic mutations in SLC28A1 encoding the pyrimidine nucleoside transporter CNT1 in the index and his brother. Both parents and unaffected sibs showed the variant in heterozygous state. The transporter is expressed in the kidneys. Compelling evidence is available for the disrupting effect of the mutation on the transport function thus explaining the increased excretion of the pyrimidine nucleosides. The exome analysis also revealed a pathogenic mutation in PRRT2 in the index, explaining the epilepsy phenotype in infancy. At present, both the index (10 years) and his older brother are asymptomatic. Mutations in SLC28A1 cause a novel inborn error of metabolism that can be explained by the disrupted activity of the pyrimidine nucleoside transporter CNT1. This is the first report describing a defect in the family of CNT concentrative pyrimidine nucleoside transporter proteins encoded by the SLC28 gene family. In all likelihood, the epilepsy phenotype in the index is unrelated to the SLC28A1 defect, as this can be fully explained by the pathogenic PRRT2 variant. Clinical data on more patients are required to prove whether pathogenic mutations in SLC28A1 have any clinical consequences or are to be considered a benign metabolic phenotype.

The implementation of whole-exome sequencing in clinical diagnostics has generated a need for functional evaluation of genetic variants. In the field of inborn errors of metabolism (IEM), a diverse spectrum of targeted biochemical assays is employed to analyze a limited amount of metabolites. We now present a single-platform, high-resolution liquid chromatography quadrupole time of flight (LC-QTOF) method that can be applied for holistic metabolic profiling in plasma of individual IEM-suspected patients. This method, which we termed “next-generation metabolic screening” (NGMS), can detect >10,000 features in each sample. In the NGMS workflow, features identified in patient and control samples are aligned using the “various forms of chromatography mass spectrometry (XCMS)” software package. Subsequently, all features are annotated using the Human Metabolome Database, and statistical testing is performed to identify significantly perturbed metabolite concentrations in a patient sample compared with controls. We propose three main modalities to analyze complex, untargeted metabolomics data. First, a targeted evaluation can be done based on identified genetic variants of uncertain significance in metabolic pathways. Second, we developed a panel of IEM-related metabolites to filter untargeted metabolomics data. Based on this IEM-panel approach, we provided the correct diagnosis for 42 of 46 IEMs. As a last modality, metabolomics data can be analyzed in an untargeted setting, which we term “open the metabolome” analysis. This approach identifies potential novel biomarkers in known IEMs and leads to identification of biomarkers for as yet unknown IEMs. We are convinced that NGMS is the way forward in laboratory diagnostics of IEMs. Electronic supplementary material The online version of this article (10.1007/s10545-017-0131-6) contains supplementary material, which is available to authorized users.

Contains fulltext : 152310.pdf (Publisher’s version ) (Closed access) A unique, unsolved O-mannosyl glycan on alpha-dystroglycan is essential for its interaction with protein ligands in the extracellular matrix. Defective O-mannosylation leads to a group of muscular dystrophies, called dystroglycanopathies. Mutations in isoprenoid synthase domain containing (ISPD) represent the second most common cause of these disorders, however, its molecular function remains uncharacterized. The human ISPD (hISPD) crystal structure showed a canonical N-terminal cytidyltransferase domain linked to a C-terminal domain that is absent in cytidyltransferase homologs. Functional studies demonstrated cytosolic localization of hISPD, and cytidyltransferase activity toward pentose phosphates, including ribulose 5-phosphate, ribose 5-phosphate, and ribitol 5-phosphate. Identity of the CDP sugars was confirmed by liquid chromatography quadrupole time-of-flight mass spectrometry and two-dimensional nuclear magnetic resonance spectroscopy. Our combined results indicate that hISPD is a cytidyltransferase, suggesting the presence of a novel human nucleotide sugar essential for functional alpha-dystroglycan O-mannosylation in muscle and brain. Thereby, ISPD deficiency can be added to the growing list of tertiary dystroglycanopathies.

Sialic acids are important components of glycoproteins and glycolipids essential for cellular communication, infection, and metastasis. The importance of sialic acid biosynthesis in human physiology is well illustrated by the severe metabolic disorders in this pathway. However, the biological role of sialic acid catabolism in humans remains unclear. Here, we present evidence that sialic acid catabolism is important for heart and skeletal muscle function and development in humans and zebrafish. In two siblings, presenting with sialuria, exercise intolerance/muscle wasting, and cardiac symptoms in the brother, compound heterozygous mutations [chr1:182775324C>T (c.187C>T; p.Arg63Cys) and chr1:182772897A>G (c.133A>G; p.Asn45Asp)] were found in the N-acetylneuraminate pyruvate lyase gene (NPL). In vitro, NPL activity and sialic acid catabolism were affected, with a cell-type-specific reduction of N-acetyl mannosamine (ManNAc). A knockdown of NPL in zebrafish resulted in severe skeletal myopathy and cardiac edema, mimicking the human phenotype. The phenotype was rescued by expression of wild-type human NPL but not by the p.Arg63Cys or p.Asn45Asp mutants. Importantly, the myopathy phenotype in zebrafish embryos was rescued by treatment with the catabolic products of NPL: N-acetyl glucosamine (GlcNAc) and ManNAc; the latter also rescuing the cardiac phenotype. In conclusion, we provide the first report to our knowledge of a human defect in sialic acid catabolism, which implicates an important role of the sialic acid catabolic pathway in mammalian muscle physiology, and suggests opportunities for monosaccharide replacement therapy in human patients.

Mendelian disorders of cholesterol biosynthesis typically result in multi-system clinical phenotypes, underlining the importance of cholesterol in embryogenesis and development. FDFT1 encodes for an evolutionarily conserved enzyme, squalene synthase (SS, farnesyl-pyrophosphate farnesyl-transferase 1), which catalyzes the first committed step in cholesterol biosynthesis. We report three individuals with profound developmental delay, brain abnormalities, 2-3 syndactyly of the toes, and facial dysmorphisms, resembling Smith-Lemli-Opitz syndrome, the most common cholesterol biogenesis defect. The metabolite profile in plasma and urine suggested that their defect was at the level of squalene synthase. Whole-exome sequencing was used to identify recessive disease-causing variants in FDFT1. Functional characterization of one variant demonstrated a partial splicing defect and altered promoter and/or enhancer activity, reflecting essential mechanisms for regulating cholesterol biosynthesis/uptake in steady state.

Selenium-binding protein 1 (SELENBP1) has been associated with several cancers, although its exact role is unknown. We show that SELENBP1 is a methanethiol oxidase (MTO), related to the MTO in methylotrophic bacteria, that converts methanethiol to H2O2, formaldehyde, and H2S, an activity not previously known to exist in humans. We identified mutations in SELENBP1 in five patients with cabbage-like breath odor. The malodor was attributable to high levels of methanethiol and dimethylsulfide, the main odorous compounds in their breath. Elevated urinary excretion of dimethylsulfoxide was associated with MTO deficiency. Patient fibroblasts had low SELENBP1 protein levels and were deficient in MTO enzymatic activity; these effects were reversed by lentivirus-mediated expression of wild-type SELENBP1. Selenbp1-knockout mice showed biochemical characteristics similar to those in humans. Our data reveal a potentially frequent inborn error of metabolism that results from MTO deficiency and leads to a malodor syndrome. info:eu-repo/semantics/publishedVersion

Item does not contain fulltext CONTEXT AND OBJECTIVE: Pheochromocytomas and paragangliomas (PGLs) are neuroendocrine tumors of sympathetic or parasympathetic paraganglia. Nearly 40% of PGLs are caused by germline mutations. The present study investigated the effect of genetic alterations on metabolic networks in PGLs. DESIGN: Homogenates of 32 sporadic PGLs and 48 PGLs from patients with mutations in SDHB, SDHD, SDHAF-2, VHL, RET, and NF-1 were subjected to proton ((1)H) nuclear magnetic resonance (NMR) spectroscopy at 500 MHz for untargeted and HPLC tandem mass spectrometry for targeted metabolite profiling. RESULTS: (1)H NMR spectroscopy identified 28 metabolites in PGLs of which 12 showed genotype-specific differences. Part of these results published earlier reported low complex II activity (P < .0001) and low ATP/ADP/AMP content (P < .001) in SDH-related PGLs compared with sporadics and PGLs of other genotypes. Extending these results, low levels of N-acetylaspartic acid (NAA; P < .05) in SDH tumors and creatine (P < .05) in VHL tumors were observed compared with sporadics and other genotypes. Positive correlation was observed between NAA and ATP/ADP/AMP content (P < .001) and NAA and complex II activity (P < .0001) of PGLs. Targeted purine analysis in PGLs showed low adenine in cluster 1 compared with cluster 2 tumors (SDH P < .0001; VHL P < .05) whereas lower levels (P < .05) of guanosine and hypoxanthine were observed in RET tumors compared with SDH tumors. Principal component analysis (PCA) of metabolites could distinguish PGLs of different genotypes. CONCLUSIONS: The present study gives a comprehensive picture of alterations in energy metabolism in SDH- and VHL-related PGLs and establishes the interrelationship of energy metabolism and amino acid and purine metabolism in PGLs.

Contains fulltext : 153789.pdf (Publisher’s version ) (Open Access) Serum free light chain (sFLC) assays are well established in the diagnosis and monitoring of plasma cell disorders. However, current FLC immunoassays are subject to several analytical issues, which results in a lack of harmonized results. To facilitate sFLC standardization, we investigated the strengths and limitations of mass spectrometry as a novel technological platform for sFLC quantification. Stable isotope labeled reference peptides are added to serum samples for quantitation by selected reaction monitoring (SRM). The use of redundant peptide sets allows for quality control measures during data analysis. Measurements on serum provide information on intact immunoglobulins, but depletion of these intact molecules from the sera during sample processing permits the quantitation of sFLC. sFLC concentrations measured with SRM were comparable to those obtained by nephelometry and showed excellent linearity (r(2) > 0.99). In samples with high levels of sFLC, SRM data was more consistent with serum protein electrophoresis than nephelometric data and SRM is unaffected by antigen excess. The lower limits of quantitation were 3.8 and 2.7 mg/L for kappa and lambda sFLC. Errors due to polymorphic sequences were prevented by comparison of redundant peptide pairs. The application of stable isotope labeling combined with SRM can overcome many of the current potential analytical issues of sFLC analysis. We describe which hurdles still need to be taken to make SRM a robust and more accurate method for sFLC measurements.

Corrigendum: NANS-mediated synthesis of sialic acid is required for brain and skeletal development

Purpose: Pheochromocytomas and paragangliomas (PGL) are neuroendocrine tumors of sympathetic and parasympathetic paraganglia. This study investigated the relationships between genotype-specific differences in mitochondrial function and catecholamine content in PGL tumors. Experimental Design: Respiratory chain enzyme assays and 1H-nuclear magnetic resonance (NMR) spectroscopy at 500 MHz were conducted on homogenates of 35 sporadic PGLs and 59 PGLs from patients with hereditary mutations in succinate dehydrogenase subunits B and D (SDHB, SDHD), succinate dehydrogenase assembly factor 2, von Hippel-Lindau (VHL), rearranged during transfection (RET), neurofibromatosis type 1 (NF1), and myc-associated factor X. Results: In SDHx-related PGLs, a significant decrease in complex II activity (P < 0.0001) and a significant increase in complex I, III, and IV enzyme activities were observed when compared to sporadic, RET, and NF1 tumors. Also, a significant increase in citrate synthase (P < 0.0001) enzyme activity was observed in SDHx-related PGLs when compared to sporadic-, VHL-, RET-, and NF1-related tumors. An increase in succinate accumulation (P < 0.001) and decrease in ATP/ADP/AMP accumulation (P < 0.001) was observed when compared to sporadic PGLs and PGLs of other genotypes. Positive correlations (P < 0.01) were observed between respiratory chain complex II activity and total catecholamine content and ATP/ADP/AMP and total catecholamine contents in tumor tissues. Conclusions: This study for the first time establishes a relationship between determinants of energy metabolism, like activity of respiratory chain enzyme complex II, ATP/ADP/AMP content, and catecholamine content in PGL tumors. Also, this study for the first time successfully uses NMR spectroscopy to detect catecholamines in PGL tumors and provides ex vivo evidence for the accumulation of succinate in PGL tumors with an SDHx mutation. Clin Cancer Res; 19(14); 3787–95. ©2013 AACR.

Contains fulltext : 174068.pdf (Publisher’s version ) (Open Access) Small molecule identification is a continually expanding field of research and represents the core challenge in various areas of (bio)analytical science, including metabolomics. Here, we unequivocally differentiate enantiomeric N-acetylhexosamines in body fluids using infrared ion spectroscopy, providing orthogonal identification of molecular structure unavailable by standard liquid chromatography/high-resolution tandem mass spectrometry. These results illustrate the potential of infrared ion spectroscopy for the identification of small molecules from complex mixtures.

The diagnosis of respiratory chain deficiencies (RCDs) is complicated and the need for a diagnostic biomarker or biosignature has been widely expressed. In this study, the metabolic profile of a selected group of 29 RCD patients, with a predominantly muscle disease phenotype, and 22 controls were investigated using targeted and untargeted analyses of three sub-sections of the human metabolome, including urinary organic acids and amino acids [measured by gas chromatography–mass spectrometry (GC–MS)], as well as acylcarnitines (measured by electrospray ionization tandem MS). Although MS technologies are highly sensitive and selective, they are restrictive by being applied only to sub-sections of the metabolome; an untargeted nuclear magnetic resonance (NMR) spectroscopy approach was therefore also included. After data reduction and pre-treatment, a biosignature comprising six organic acids (lactic, succinic, 2-hydroxyglutaric, 3-hydroxyisobutyric, 3-hydroxyisovaleric and 3-hydroxy-3-methylglutaric acids), six amino acids (alanine, glycine, glutamic acid, serine, tyrosine and α-aminoadipic acid) and creatine, was constructed from uni- and multivariate statistical analyses and verified by cross-validation. The results presented here provide the first proof-of-concept that the metabolomics approach is capable of defining a biosignature for RCDs. We postulate that the composite of organic acids ≈ amino acids > creatine > betaine > carnitines represents the basic biosignature for RCDs. Validated through a prospective study, this could offer an improved ability to assign individual patients to a group with defined RCD characteristics and improve case selection for biopsy procedures, especially in infants and children.