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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)