4.7 Review

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

期刊

出版社

MDPI
DOI: 10.3390/ijms21072533

关键词

metabolomics; lysosomal storage disorders; Niemann-Pick Type C Disease; lipidoses; nuclear magnetic resonance (NMR) analysis; liquid chromatography-mass spectrometric (LC-MS) analysis; multivariate power calculations; validation and cross-validation; biomarkers

资金

  1. National Tay-Sachs and Allied Diseases Foundation
  2. De Montfort University
  3. Royal Society Wolfson Research Merit Award
  4. Beyond Batten Disease Foundation

向作者/读者索取更多资源

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.

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