Urinary excretion and metabolism of miglustat and valproate in patients with Niemann-Pick type C1 disease: One- and two-dimensional solution-state (1)H NMR studies

A New Sensitive Fluorescence Sensor and Photocatalyst for Determination and Degradation of Sodium Valproate Using g-C3N4@Fe3O4@CuWO4 Nanocomposite and FCCD Optimization

In this research, carbon nitride nanocomposite coupled with Fe3O4 and CuWO4 was thermally synthesized and characterized by different techniques, including SEM, TEM, XRD, EDX, and FTIR. Due to sodium valproate's luminescence quenching of this nanocomposite, a reliable, accurate, sensitive, selective, and fast-acting sodium valproate assay was proposed. Optimization of this fluorescent sensor was carried out by the FCCD approach. In the optimum conditions, the plot of sodium valproate concentration versus nanocomposite fluorescence emission showed a linear response (R2 = 0.9918), with a range of 0-0.55 µM, the limit of detection (S/N = 3) equal to 0.85 nM and limit of qualification equal to 2.82 nM. Photocatalytic activity of g-C3N4@Fe3O4@CuWO4 (40%) nanocomposite exhibited a good potency to sodium valproate degradation. Active species of degradation including superoxide radicals, holes, and hydroxyl radicals were investigated using ammonium oxalate, benzoquinone, and 2-propanol to identify the mechanism of photodegradation action. The activity of benzoquinone in the photocatalytic process led to a reduction in the rate of analyte degradation, which indicates the prominent role of superoxide radicals compared to other species in the degradation process. The degradation rate of the analyte using the Fenton reagent was found to be around two times more than in the Fenton reagent-free process. The possible mechanism for the fluorescence sensor and photocatalytic degradation reaction was also discussed.

Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased 1H NMR-Linked Metabolomics Strategy

Biomarkers currently available for the diagnosis, prognosis, and therapeutic monitoring of GM1 gangliosidosis type 2 (GM1T2) disease are mainly limited to those discovered in targeted proteomic-based studies. In order to identify and establish new, predominantly low-molecular-mass biomarkers for this disorder, we employed an untargeted, multi-analyte approach involving high-resolution 1H NMR analysis coupled to a range of multivariate analysis and computational intelligence technique (CIT) strategies to explore biomolecular distinctions between blood plasma samples collected from GM1T2 and healthy control (HC) participants (n = 10 and 28, respectively). The relationship of these differences to metabolic mechanisms underlying the pathogenesis of GM1T2 disorder was also investigated. 1H NMR-linked metabolomics analyses revealed significant GM1T2-mediated dysregulations in ≥13 blood plasma metabolites (corrected p < 0.04), and these included significant upregulations in 7 amino acids, and downregulations in lipoprotein-associated triacylglycerols and alanine. Indeed, results acquired demonstrated a profound distinctiveness between the GM1T2 and HC profiles. Additionally, employment of a genome-scale network model of human metabolism provided evidence that perturbations to propanoate, ethanol, amino-sugar, aspartate, seleno-amino acid, glutathione and alanine metabolism, fatty acid biosynthesis, and most especially branched-chain amino acid degradation (p = 10-12-10-5) were the most important topologically-highlighted dysregulated pathways contributing towards GM1T2 disease pathology. Quantitative metabolite set enrichment analysis revealed that pathological locations associated with these dysfunctions were in the order fibroblasts > Golgi apparatus > mitochondria > spleen ≈ skeletal muscle ≈ muscle in general. In conclusion, results acquired demonstrated marked metabolic imbalances and alterations to energy demand, which are consistent with GM1T2 disease pathogenesis mechanisms.

Metabolomic Studies of Lipid Storage Disorders, with Special Reference to Niemann-Pick Type C Disease: A Critical Review with Future Perspectives

Lysosomal storage disorders (LSDs) are predominantly very rare recessive autosomal neurodegenerative diseases.Sphingolipidoses, a sub-group of LSDs, result from defects in lysosomal enzymes involved in sphingolipid catabolism, and feature disrupted storage systems which trigger complex pathogenic cascades with other organelles collaterally affected. This process leads to cell dysfunction and death, particularly in the central nervous system. One valuable approach to gaining insights into the global impact of lysosomal dysfunction is through metabolomics, which represents a discovery tool for investigating disease-induced modifications in the patterns of large numbers of simultaneously-analysed metabolites, which also features the identification of biomarkers Here, the scope and applications of metabolomics strategies to the investigation of sphingolipidoses is explored in order to facilitate our understanding of the biomolecular basis of these conditions. This review therefore surveys the benefits of applying ’state-of-the-art’ metabolomics strategies, both univariate and multivariate, to sphingolipidoses, particularly Niemann-Pick type C disease. Relevant limitations of these techniques are also discussed, along with the latest advances and developments. We conclude that metabolomics strategies are highly valuable, distinctive bioanalytical techniques for probing LSDs, most especially for the detection and validation of potential biomarkers. They also show much promise for monitoring disease progression and the evaluation of therapeutic strategies and targets.

Loss of Mevalonate/Cholesterol Homeostasis in the Brain: A Focus on Autism Spectrum Disorder and Rett Syndrome

The mevalonate (MVA)/cholesterol pathway is crucial for central nervous system (CNS) development and function and consequently, any dysfunction of this fundamental metabolic pathway is likely to provoke pathologic changes in the brain. Mutations in genes directly involved in MVA/cholesterol metabolism cause a range of diseases, many of which present neurologic and psychiatric symptoms. This raises the question whether other diseases presenting similar symptoms are related albeit indirectly to the MVA/cholesterol pathway. Here, we summarized the current literature suggesting links between MVA/cholesterol dysregulation and specific diseases, namely autism spectrum disorder and Rett syndrome.