High-risk neuroblastoma, a pediatric tumor originating from the sympathetic nervous system, has a low mutation load but highly recurrent somatic DNA copy number variants. Previously, segmental gains and/or amplifications allowed identification of drivers for neuroblastoma development. Using this approach, combined with gene dosage impact on expression and survival, we identified ribonucleotide reductase subunit M2 (RRM2) as a candidate dependency factor further supported by growth inhibition upon in vitro knockdown and accelerated tumor formation in a neuroblastoma zebrafish model coexpressing human RRM2 with MYCN. Forced RRM2 induction alleviates excessive replicative stress induced by CHK1 inhibition, while high RRM2 expression in human neuroblastomas correlates with high CHK1 activity. MYCN-driven zebrafish tumors with RRM2 co-overexpression exhibit differentially expressed DNA repair genes in keeping with enhanced ATR-CHK1 signaling activity. In vitro, RRM2 inhibition enhances intrinsic replication stress checkpoint addiction. Last, combinatorial RRM2-CHK1 inhibition acts synergistic in high-risk neuroblastoma cell lines and patient-derived xenograft models, illustrating the therapeutic potential.

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.

Objective: To investigate whether ischemia alters donor kidney metabolism and whether these changes are associated with organ function. Background: An unmet need in kidney transplantation is the ability to predict posttransplant organ function before transplantation. Key to such viability testing is a profound understanding of the organ’s complex biochemistry and how ischemia, inevitable during the transplantation process, influences this. Methods: First, metabolic changes in perfusate glucose, lactate, and 20 amino acids, induced by no, 1 hour of warm, or 22 hours of cold ischemia, were investigated during 4-hour perfusion of pig kidneys with autologous whole blood (n = 6/group), simulating the ischemia-reperfusion phase of transplantation. Next, we confirmed similar metabolic changes during normothermic preservation of pigs (n = 3/group; n = 4 for cold ischemia) and discarded human kidneys (n = 6) perfused with a red blood cell-based perfusate. Results: At 2 hours of perfusion with autologous whole blood, abundances of 17/20 amino acids were significantly different between groups, reflecting the type of ischemia. Amino acid changes at 15 minutes and 2 hours of perfusion correlated with future kidney function during perfusion. Similar metabolic patterns were observed during perfusion preservation of pig and discarded human donor kidneys, suggesting an opportunity to assess kidney viability before transplantation. Conclusions: Perfusate metabolite changes during normothermic kidney perfusion represent a unique noninvasive opportunity to assess graft viability. These findings now need validation in transplant studies.

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.