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

Abstract

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.

The rapidly evolving view of lysosomal storage diseases

“…This approach has allowed the identification of markers that may serve to facilitate early‐stage diagnosis and monitoring of response to therapy, for example, galabiosyl ceramide analogs in Fabry disease (Boutin & Auray‐Blais, 2015), oxysterols in Niemann‐Pick disease type C2 (Reunert et al , 2015), and various sphingolipids in some sphingolipidoses (Reunert et al , 2015). However, some issues need to be addressed in these studies, such as those related to the heterogeneity of samples used for the analyses and the number of patients required to be recruited in order to satisfy statistical significance (Percival et al , 2020).…”

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

“…However, no significant difference in the [Cr/PCr]:[Cn] signal intensity ratio was found between the HC and GM1T2 classifications. Although Cr is a highly significant contributor to the Cr/PCr signal, it is also likely to contain contributions from PCr in view of very similar chemical shift values for these biomolecules’ -CH 2 function resonances (δ = 3.93 (this study) and 3.936 [ 21 ], respectively at pH 7.0). Although there is only a sparse level of scientific literature reports available on the presence and detectability of PCr in human plasma, it has been found to be present in extracts of whole blood [ 107 ].…”

“…The observation of a MV-significant variable that is not significant if subjected to one or more UV tests is a not unusual occurrence in metabolomics analyses. Possible explanations for such differences are consistency effects, specifically no univariate differences are observed because of high variance contributions of biological and/or bioanalytical sources, the escalation of false negatives (type 2 errors) arising from post-hoc ANOVA-type corrections applied to counter FDRs, and also the MV complementation (and correlation) of explanatory biomolecule variables when considered together as a whole [ 17 , 21 ]. These explanations are also applicable to differential ranking orders of metabolites found between UV and MV methods of data analysis.…”

“…This preliminary pilot study found that for FDR and overall power levels of 0.05 and 0.80, respectively, a minimum sample size of ca. n = 30 per group was required [ 21 ]. Although our figure of n = 28 for the HC group was almost satisfactory for this purpose, the value of n = 10 patients for the GM1T2 cohort ( n = 9 following outlier removal as described in Section 3.3.1 below) was insufficient.…”

“…Therefore, here we have elected to focus on both univariate (UV) and MV metabolomics comparisons of the plasma profiles of GM1T2 patients with those arising from a healthy human control population, since resonances of, or those putatively arising from these xenobiotics and/or their metabolites were not visible in the 700 MHz 1 H NMR profiles acquired at all. A short account of an experimental pilot study focused on this LSD, which includes a MV power calculation for future studies, has been reported in [ 21 ].…”