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

An update on multiplexed mass spectrometry-based lysosomal storage disease diagnosis

Lysosomal storage disorders (LSDs) are a type of inherited metabolic disorders in which biomolecules, accumulate as a specific substrate in lysosomes due to specific individual enzyme deficiencies. Despite the fact that LSDs are incurable, various approaches, including enzyme replacement therapy, hematopoietic stem cell transplantation, or gene therapy are now available. Therefore, a timely diagnosis is a critical initial step in patient treatment. The-state-of-the-art in LSD diagnostic uses, in the first stage, enzymatic activity determination by fluorimetry or by mass spectrometry (MS) with the aid of dry blood spots, based on different enzymatic substrate structures. Due to its sensitivity, high precision, and ability to screen for an unprecedented number of diseases in a single assay, multiplexed tandem MS-based enzyme activity assays for the screening of LSDs in newborns have recently received a lot of attention. Here, (i) we review the current approaches used for simultaneous enzymatic activity determination of LSDs in dried blood spots using multiplex-LC-MS/MS; (ii) we explore the need for designing novel enzymatic substrates that generate different enzymatic products with distinct molecular masses in multiplexed-MS studies; and (iii) we give examples of the relevance of affinity-MS technique as a basis for reversing undesirable immune-reactivity in enzyme replacement therapy.

Urinary Metabolic Distinction of Niemann-Pick Class 1 Disease through the Use of Subgroup Discovery

In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved 'normal' (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised 'normal' 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and 'biomarkers' featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with 'normal' levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan-nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.

Proteomic analysis of s-acylated proteins in human retinal pigment epithelial cells and the role of palmitoylation of Niemann-Pick type C1 protein in cholesterol transport

Palmitoylation is a dynamic process that regulates the activity of the modified proteins. Retinal pigment epithelial (RPE) cells play pivotal roles in the visual cycle and maintaining healthy photoreceptor cells. Dysfunctional RPE cells are often associated with degenerative retinal diseases. The aim of the study was to identify potentially palmitoylated proteins in human RPE cells. By using the detergent-resistant membrane, we found 312 potentially palmitoylated peptides which corresponded to 192 proteins in RPE cells, including 55 new candidate proteins which were not reported before. Gene enrichment analysis highlighted significant enrichment of palmitoylated proteins in cell-matrix adhesion, cell-cell recognition, protein cellular localization, and translation, among others. We further studied the effect of 3 potential palmitoylation sites (Cys 799, 900, and 816) of Niemann-Pick type C1 protein (NPC1) on cholesterol accumulation. We found that mutation of any single Cys alone had no significant effect on intracellular cholesterol accumulation while simultaneous mutation of Cys799 and 800 caused significant cholesterol accumulation in the late endosome. No further cholesterol accumulation was observed by adding another mutation at Cys 816. However, the mutation did not alter the cellular localization of the protein. Conclusion: PRE cells have an abundant number of palmitoylated proteins which are involved in cellular processes critical to visual function. The palmitoylation at Cys799 and 800 was needed for cholesterol export, but not the intracellular localization of NPC1.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part I: Emerging Platforms and Perspectives

1H NMR-based metabolomics analysis of human saliva, other oral fluids, and/or tissue biopsies serves as a valuable technique for the exploration of metabolic processes, and when associated with ’state-of-the-art’ multivariate (MV) statistical analysis strategies, provides a powerful means of examining the identification of characteristic metabolite patterns, which may serve to differentiate between patients with oral health conditions (e.g., periodontitis, dental caries, and oral cancers) and age-matched heathy controls. This approach may also be employed to explore such discriminatory signatures in the salivary 1H NMR profiles of patients with systemic diseases, and to date, these have included diabetes, Sjörgen’s syndrome, cancers, neurological conditions such as Alzheimer’s disease, and viral infections. However, such investigations are complicated in view of quite a large number of serious inconsistencies between the different studies performed by independent research groups globally; these include differing protocols and routes for saliva sample collection (e.g., stimulated versus unstimulated samples), their timings (particularly the oral activity abstention period involved, which may range from one to 12 h or more), and methods for sample transport, storage, and preparation for NMR analysis, not to mention a very wide variety of demographic variables that may influence salivary metabolite concentrations, notably the age, gender, ethnic origin, salivary flow-rate, lifestyles, diets, and smoking status of participant donors, together with their exposure to any other possible convoluting environmental factors. In view of the explosive increase in reported salivary metabolomics investigations, in this update, we critically review a wide range of critical considerations for the successful performance of such experiments. These include the nature, composite sources, and biomolecular status of human saliva samples; the merits of these samples as media for the screening of disease biomarkers, notably their facile, unsupervised collection; and the different classes of such metabolomics investigations possible. Also encompassed is an account of the history of NMR-based salivary metabolomics; our recommended regimens for the collection, transport, and storage of saliva samples, along with their preparation for NMR analysis; frequently employed pulse sequences for the NMR analysis of these samples; the supreme resonance assignment benefits offered by homo- and heteronuclear two-dimensional NMR techniques; deliberations regarding salivary biomolecule quantification approaches employed for such studies, including the preprocessing and bucketing of multianalyte salivary NMR spectra, and the normalization, transformation, and scaling of datasets therefrom; salivary phenotype analysis, featuring the segregation of a range of different metabolites into ‘pools’ grouped according to their potential physiological sources; and lastly, future prospects afforded by the applications of LF benchtop NMR spectrometers for direct evaluations of the oral or systemic health status of patients at clinical ‘point-of-contact’ sites, e.g., dental surgeries. This commentary is then concluded with appropriate recommendations for the conduct of future salivary metabolomics studies. Also included are two original case studies featuring investigations of (1) the 1H NMR resonance line-widths of selected biomolecules and their possible dependence on biomacromolecular binding equilibria, and (2) the combined univariate (UV) and MV analysis of saliva specimens collected from a large group of healthy control participants in order to potentially delineate the possible origins of biomolecules therein, particularly host- versus oral microbiome-derived sources. In a follow-up publication, Part II of this series, we conduct censorious reviews of reported observations acquired from a diversity of salivary metabolomics investigations performed to evaluate both localized oral and non-oral diseases. Perplexing problems encountered with these again include those arising from sample collection and preparation protocols, along with 1H NMR spectral misassignments.

Sterol uptake by the NPC system in eukaryotes: a Saccharomyces cerevisiae perspective

Sterols are an essential component of membranes in all eukaryotic cells and the precursor of multiple indispensable cellular metabolites. After endocytotic uptake, sterols are integrated into the lysosomal membrane by the Niemann-Pick type C (NPC) system before redistribution to other membranes. The process is driven by two proteins that, together, compose the NPC system: the lysosomal sterol shuttle protein NPC2 and the membrane protein NPC1 (named NCR1 in fungi), which integrates sterols into the lysosomal membrane. The Saccharomyces cerevisiae NPC system provides a compelling model to study the molecular mechanism of sterol integration into membranes and sterol homeostasis. This review summarizes recent advances in the field, and by interpreting available structural data, we propose a unifying conceptual model for sterol loading, transfer and transport by NPC proteins.

Subcellular metabolomics: Isolation, measurement, and applications

Metabolomics, a technique that profiles global small molecules in biological samples, has been a pivotal tool for disease diagnosis and mechanism research. The sample type in metabolomics covers a wide range, including a variety of body fluids, tissues, and cells. However, little attention was paid to the smaller, relatively independent partition systems in cells, namely the organelles. The organelles are specific compartments/places where diverse metabolic activities are happening in an orderly manner. Metabolic disorders of organelles were found to occur in various pathological conditions such as inherited metabolic diseases, diabetes, cancer, and neurodegenerative diseases. However, at the cellular level, the metabolic outcomes of organelles and cytoplasm are superimposed interactively, making it difficult to describe the changes in subcellular compartments. Therefore, characterizing the metabolic pool in the compartmentalized system is of great significance for understanding the role of organelles in physiological functions and diseases. So far, there are very few research articles or reviews related to subcellular metabolomics. In this review, subcellular fractionation and metabolite analysis methods, as well as the application of subcellular metabolomics in the physiological and pathological studies are systematically reviewed, as a practical reference to promote the continued advancement in subcellular metabolomics.

Who moves the sphinx? An overview of intracellular sphingolipid transport

Lipid bilayers function as boundaries that enclose their content from the surrounding media, and the composition of different membrane types is accurately and dynamically tailored so that they can perform their function. To achieve this balance, lipid biosynthetic machinery and lipid trafficking events are intertwined into an elegant network. In this review, we focus on the intracellular movement of sphingolipids mediated by sphingolipid transfer proteins. Additionally, we will focus on the best characterized and understood mammalian sphingolipid transfer proteins and provide an overview of how they are hypothesized to function. Some are already well understood, while others remain enigmatic. A few are actual lipid transfer proteins, moving lipids from membrane to membrane, while others may have more of a sensor role, possibly reacting to changes in the concentrations of their ligands. Considering the substrates available for cytosolic sphingolipid transfer proteins, one open question that is discussed is whether galactosylceramide is a target. Another question is the exact mechanics by which sphingolipid transfer proteins are targeted to different organelles, such as how four phosphate adapter protein-2, FAPP2 is targeted to the endoplasmic reticulum. The aim of this review is to discuss what is known within the field today and to provide a basic understanding of how these proteins may work.

Analysis of Biomarkers for Congenital Heart Disease Based on Maternal Amniotic Fluid Metabolomics

Congenital heart disease (CHD) is the most common birth defect. The prenatal diagnosis of fetal CHD is completely dependent on ultrasound testing, but only ~40% of CHD can be detected. The purpose of this study is to find good biomarkers in amniotic fluid (AF) to detect CHD in the second trimester, so as to better manage this group of people and reduce the harm of CHD to the fetus. Metabolites analysis were performed in two separate sets. The discovery set consisted of 18 CHD fetal maternal AF samples and 35 control samples, and the validation set consisted of 53 CHD fetal maternal AF samples and 114 control samples. Untargeted metabolite profiles were analyzed by gas chromatography/time-of-flight-mass spectrometry (GC-TOF/MS). Orthogonal partial least square discrimination analysis (OPLS-DA) demonstrated that CHD and control samples had significantly different metabolic profiles. Two metabolites, uric acid and proline, were significantly elevated in CHD and verified in two data sets. Uric acid was associated with CHD [odds ratio (OR): 7.69 (95% CI: 1.18-50.13) in the discovery set and 3.24 (95% CI:1.62-6.48) in the validation set]. Additionally, uric acid showed moderate predictive power; the area under curve (AUC) was 0.890 in the discovery set and 0.741 in the validation set. The sensitivity and specificity of uric acid to detect CHD was, respectively, 94.4 and 74.3% in the discovery set and 67.9 and 71.9% in the validation set. The identification of uric acid as a biomarker for CHD has the potential to stimulate research on the pathological mechanism of CHD and the development of a diagnostic test for clinical applications.

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.

The rapidly evolving view of lysosomal storage diseases

Lysosomal storage diseases are a group of metabolic disorders caused by deficiencies of several components of lysosomal function. Most commonly affected are lysosomal hydrolases, which are involved in the breakdown and recycling of a variety of complex molecules and cellular structures. The understanding of lysosomal biology has progressively improved over time. Lysosomes are no longer viewed as organelles exclusively involved in catabolic pathways, but rather as highly dynamic elements of the autophagic-lysosomal pathway, involved in multiple cellular functions, including signaling, and able to adapt to environmental stimuli. This refined vision of lysosomes has substantially impacted on our understanding of the pathophysiology of lysosomal disorders. It is now clear that substrate accumulation triggers complex pathogenetic cascades that are responsible for disease pathology, such as aberrant vesicle trafficking, impairment of autophagy, dysregulation of signaling pathways, abnormalities of calcium homeostasis, and mitochondrial dysfunction. Novel technologies, in most cases based on high-throughput approaches, have significantly contributed to the characterization of lysosomal biology or lysosomal dysfunction and have the potential to facilitate diagnostic processes, and to enable the identification of new therapeutic targets.

Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

Non-genotoxic hepatocarcinogens (NGHCs) can only be confirmed by 2-year rodent studies. Toxicogenomics (TGx) approaches using gene expression profiles from short-term animal studies could enable early assessment of NGHCs. However, high variance in the modulation of the genes had been noted among exposure styles and datasets. Expanding from our previous strategy in identifying consensus biomarkers in multiple experiments, we aimed to identify time-invariant biomarkers for NGHCs in short-term exposure styles and validate their applicability to long-term exposure styles. In this study, nine time-invariant biomarkers, namely A2m, Akr7a3, Aqp7, Ca3, Cdc2a, Cdkn3, Cyp2c11, Ntf3, and Sds, were identified from four large-scale microarray datasets. Machine learning techniques were subsequently employed to assess the prediction performance of the biomarkers. The biomarker set along with the Random Forest models gave the highest median area under the receiver operating characteristic curve (AUC) of 0.824 and a low interquartile range (IQR) variance of 0.036 based on a leave-one-out cross-validation. The application of the models to the external validation datasets achieved high AUC values of greater than or equal to 0.857. Enrichment analysis of the biomarkers inferred the involvement of chronic inflammatory diseases such as liver cirrhosis, fibrosis, and hepatocellular carcinoma in NGHCs. The time-invariant biomarkers provided a robust alternative for NGHC prediction.