Abnormal polyol metabolism is predominantly associated with diabetes, where excess glucose is converted to sorbitol by aldose reductase (AR). Recently, abnormal polyol metabolism has been implicated in phosphomannomutase 2 congenital disorder of glycosylation (PMM2-CDG) and an AR inhibitor, epalrestat, proposed as a potential therapy. Considering that the PMM2 enzyme is not directly involved in polyol metabolism, the increased polyol production and epalrestat's therapeutic mechanism in PMM2-CDG remained elusive. PMM2-CDG, caused by PMM2 deficiency, presents with depleted GDP-mannose and abnormal glycosylation. Here, we show that, apart from glycosylation abnormalities, PMM2 deficiency affects intracellular glucose flux, resulting in polyol increase. Targeting AR with epalrestat decreases polyols and increases GDP-mannose both in patient-derived fibroblasts and in pmm2 mutant zebrafish. Using tracer studies, we demonstrate that AR inhibition diverts glucose flux away from polyol production toward the synthesis of sugar nucleotides, and ultimately glycosylation. Finally, PMM2-CDG individuals treated with epalrestat show a clinical and biochemical improvement.

Inborn errors of metabolism (IEMs) are rare diseases caused by a defect in a single enzyme, co-factor, or transport protein. For most IEMs, no effective treatment is available and the exact disease mechanism is unknown. The application of metabolomics and, more specifically, tracer metabolomics in IEM research can help to elucidate these disease mechanisms and hence direct novel therapeutic interventions. In this review, we will describe the different approaches to metabolomics in IEM research. We will discuss the strengths and weaknesses of the different sample types that can be used (biofluids, tissues or cells from model organisms; modified cell lines; and patient fibroblasts) and when each of them is appropriate to use.

Tracer metabolomics is a powerful technology for the biomedical community to study and understand disease-inflicted metabolic mechanisms. However, the interpretation of tracer metabolomics results is highly technical, as the metabolites’ abundances, tracer incorporation and positions on the metabolic map all must be jointly interpreted. The field is currently lacking a structured approach to help less experienced researchers start the interpretation of tracer metabolomics datasets. We propose an approach using an intuitive visualization concept aided by a novel open-source tool, and provide guidelines on how researchers can apply the approach and the visualization tool to their own datasets. Using a showcase experiment, we demonstrate that the visualization approach leads to an intuitive interpretation that can ease researchers into understanding their tracer metabolomics data.

Hexose-phosphates play a role in many metabolic pathways, such as glycolysis and glycosylation. To understand the molecular basis of diseases such as congenital disorders of glycosylation (CDG), information about the source and abundance of hexose-phosphates is imperative. Mass spectrometry (MS)-based tracer metabolomics can provide this information, but hexose-phosphates are structural isomers with similar physicochemical properties, which makes them difficult to differentiate using MS. Here, we present and compare two optimized liquid-chromatography-based MS methods for the identification of relevant hexose-phosphates, compatible with tracer metabolomics. A combination of these two methods led to the analysis of eight hexose-monophosphates and two hexose-bisphosphates that can occur in humans. Both methods displayed linearity in 3 to 4 orders of magnitude, with limits of quantification between 0.5 and 50 nM, which is well within the cellular concentration range. The applicability of these methods to biological models was then proven in a study of the effect of galactose treatment in phosphoglucomutase 1 (PGM1)-CDG fibroblasts. Here, we show, for the first time, the hexose-phosphate profiles in CDG and how these change upon treatment.