Abstract
Gaining insight into the pathophysiology of rare genetic disorders is of great importance for both diagnosis and treatment. A valuable tool in identifying and characterizing these diseases is untargeted metabolomics, a method that allows the analysis of thousands of substances with just a small biological sample. Revealing a metabolic fingerprint
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can provide deeper insights into the complex biochemical processes occurring in cells and tissues. This thesis focuses on understanding the pathophysiology of various rare genetic diseases using untargeted metabolomics and stable isotope tracing in cell models. Specifically, the thesis focuses on disorders in the malate-aspartate shuttle and rare hereditary anemias.
The malate-aspartate shuttle (MAS) is a metabolic process that is particularly important for cells with high energy demands, such as the central nervous system. The MAS is important for intracellular NADH oxidation and regenerates cytosolic NAD+ via the enzyme malate dehydrogenase 1 (MDH1). We reported a deficiency in MDH1 for the first time in two patients, along with the discovery of a biomarker aided by untargeted metabolomics. In a literature review we compared our clinical and biochemical findings to the phenotypes of other MAS disorders, and concluded that there are currently no specific markers to support clinical diagnosis and that disturbed redox homeostasis in MAS disorders affects multiple NAD+-dependent pathways with complex consequences due to compartmental and tissue-specific expression of the components. We further investigated the metabolic consequences of MAS disorders cellular models and found that disruption of the MAS results in impaired serine biosynthesis, a crucial amino acid for the brain. Combined with clinical evidence of improvement of neurodevelopment upon serine supplementation, these findings suggest that patients with MAS disorders may benefit from serine supplementation.
Rare hereditary anemias are conditions characterized by a genetic cause leading to a shortage of red blood cells or hemoglobin in the blood. Since there are still significant gaps in understanding the clinical heterogeneity, genotype-phenotype correlations, and pathophysiology of these disorders, we introduced untargeted metabolomics as a novel investigative tool. In this thesis we studied three types of hereditary anemias: Pyruvate kinase deficiency, Diamond Blackfan anemia and hereditary spherocytosis, and evaluated the diagnostic application of metabolomics in anemia in dried blood spots of patients. Using obtained metabolic fingerprints and machine learning, we were able to make predictions regarding the exclusion or identification of diagnoses for PKD and DBA of anemia. Additionally, we identified pathophysiological leads for future research and explored associations of the metabolic fingerprint with clinical phenotypic heterogeneity.
Overall, the versatile aspects of untargeted metabolomics in clinical and diagnostic applications hold great promise for the field of rare genetic diseases.
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