Neural and metabolic dysregulation in PMM2-deficient human in vitro neural models.

Phosphomannomutase 2-congenital disorder of glycosylation (PMM2-CDG) is a rare inborn error of metabolism caused by deficiency of the PMM2 enzyme, which leads to impaired protein glycosylation. While the disorder presents with primarily neurological symptoms, there is limited knowledge about the specific brain-related changes caused by PMM2 deficiency. Here, we demonstrate aberrant neural activity in 2D neuronal networks from PMM2-CDG individuals. Utilizing multi-omics datasets from 3D human cortical organoids (hCOs) derived from PMM2-CDG individuals, we identify widespread decreases in protein glycosylation, highlighting impaired glycosylation as a key pathological feature of PMM2-CDG, as well as impaired mitochondrial structure and abnormal glucose metabolism in PMM2-deficient hCOs, indicating disturbances in energy metabolism. Correlation between PMM2 enzymatic activity in hCOs and symptom severity suggests that the level of PMM2 enzyme function directly influences neurological manifestations. These findings enhance our understanding of specific brain-related perturbations associated with PMM2-CDG, offering insights into the underlying mechanisms and potential directions for therapeutic interventions.

Tracer metabolomics reveals the role of aldose reductase in glycosylation.

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.

TraVis Pies: A Guide for Stable Isotope Metabolomics Interpretation Using an Intuitive Visualization.

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.

Smartwatch-Based Blood Pressure Measurement Demonstrates Insufficient Accuracy.

Background: Novel smartwatch-based cuffless blood pressure (BP) measuring devices are coming to market and receive FDA and CE labels. These devices are often insufficiently validated for clinical use. This study aims to investigate a recently CE-cleared smartwatch using cuffless BP measurement in a population with normotensive and hypertensive individuals scheduled for 24-h BP measurement. Methods: Patients that were scheduled for 24-h ambulatory blood pressure monitoring (ABPM) were recruited and received an additional Samsung Galaxy Watch Active 2 smartwatch for simultaneous BP measurement on their opposite arm. After calibration, patients were asked to measure as much as possible in a 24-h period. Manual activation of the smartwatch is necessary to measure the BP. Accuracy was calculated using sensitivity, specificity, positive and negative predictive values and ROC curves. Bland-Altman method and Taffé methods were used for bias and precision assessment. BP variability was calculated using average real variability, standard deviation and coefficient of variation. Results: Forty patients were included. Bland-Altman and Taffé methods demonstrated a proportional bias, in which low systolic BPs are overestimated, and high BPs are underestimated. Diastolic BPs were all overestimated, with increasing bias toward lower BPs. Sensitivity and specificity for detecting systolic and/or diastolic hypertension were 83 and 41%, respectively. ROC curves demonstrate an area under the curve (AUC) of 0.78 for systolic hypertension and of 0.93 for diastolic hypertension. BP variability was systematically higher in the ABPM measurements compared to the smartwatch measurements. Conclusion: This study demonstrates that the BP measurements by the Samsung Galaxy Watch Active 2 show a systematic bias toward a calibration point, overestimating low BPs and underestimating high BPs, when investigated in both normotensive and hypertensive patients. Standards for traditional non-invasive sphygmomanometers are not met, but these standards are not fully applicable to cuffless devices, emphasizing the urgent need for new standards for cuffless devices. The smartwatch-based BP measurement is not yet ready for clinical usage. Future studies are needed to further validate wearable devices, and also to demonstrate new possibilities of non-invasive, high-frequency BP monitoring.

Understanding Inborn Errors of Metabolism through Metabolomics.

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.