Metabolomics

Sphingolipids are a diverse class of lipids with various roles in cell functions and subclasses such as ceramides have been associated with cardiovascular diseases (CVD) in previous studies. We aimed to measure molecularly-distinct sphingolipids via a large-scale lipidomic analysis and expand the literature to an Asian population. We performed a lipidomics evaluation of 79 molecularly distinct sphingolipids in the plasma of 2627 ethnically-Chinese Singaporeans. During a mean follow-up of 12.9 years, we documented 152 cases of major CVD (non-fatal myocardial infarction, stroke and cardiovascular death). Total ceramide concentrations were not associated with CVD risk [hazard ratio (HR), 0.99; 95% CI 0.81–1.21], but higher circulating total monohexosylceramides (HR, 1.22; 95% CI 1.03, 1.45), total long-chain sphingolipids (C16–C18) (HR, 1.22; 95% CI 1.02, 1.45) and total 18:1 sphingolipids (HR, 1.21; 95% CI 1.01, 1.46) were associated with higher CVD risk after adjusting for conventional CVD risk factors. Our results do not support the hypothesis that higher ceramide concentrations are linked to higher CVD risk, but suggest that other classes of sphingolipids may affect CVD risk.

According to World Health Organization, an estimated 3 % of the global population is suffering from chronic hepatitis C. Furthermore, 60–70 % of chronically-infected patients develop liver diseases, of which 5–20 % of all cases advance to cirrhosis, and 1–5 % die from hepatitis C related hepatocellular carcinoma. This high incidence might be ascribed to the poor performance of the currently available diagnostic, treatment, and vaccination protocols, together with the lack of knowledge of the underlying disease mechanisms. In this review, we discuss the role that the relatively new research field termed metabolomics, alone or as part of an integrated ‘omics’ approach, has played in the investigation of hepatitis C and associated clinical manifestations. We also consider future research possibilities in this field, and the impact that these results might have on the fight against this global health predicament.

The effects of exercise on the heart and its resistance to disease are well-documented. Recent studies have identified that exercise-induced resistance to arrhythmia is due to the preservation of mitochondrial membrane potential. To identify novel metabolic changes that occur parallel to these mitochondrial alterations, we performed non-targeted metabolomics analysis on hearts from sedentary and exercise-trained rats challenged with isolated heart ischemia–reperfusion injury (I/R). Eight-week old Sprague–Dawley rats were treadmill trained 5 days/week for 6 weeks (exercise duration and intensity progressively increased to 1 h at 30 m/min up a 10.5% incline, 75–80% VO2max). The recovery of pre-ischemic function for sedentary rat hearts was 28.8 ± 5.4% (N = 12) compared to exercise trained hearts, which recovered 51.9% ± 5.7 (N = 14) (p < 0.001). Non-targeted GC–MS metabolomics analysis of (1) sedentary rat hearts; (2) exercise-trained rat hearts; (3) sedentary rat hearts challenged with global ischemia–reperfusion (I/R) injury; and (4) exercise-trained rat hearts challenged with global I/R (10/group) revealed 15 statistically significant metabolites between groups by ANOVA using Metaboanalyst (p < 0.001). Enrichment analysis of these metabolites for pathway-associated metabolic sets indicated a > 10-fold enrichment for ammonia recycling and protein biosynthesis. Subsequent comparison of the sedentary hearts post-I/R and exercise-trained hearts post-I/R further identified significant differences in three metabolites (oleic acid, pantothenic acid, and campesterol) related to pantothenate and CoA biosynthesis (p ≤ 1.24E−05, FDR ≤ 5.07E−4). These studies shed light on novel mechanisms in which exercise-induced cardioprotection occurs in I/R that complement both the mitochondrial stabilization and antioxidant mechanisms recently described. These findings also link protein synthesis and protein degradation (protein quality control mechanisms) with exercise-linked cardioprotection and mitochondrial susceptibility for the first time in cardiac I/R.

Alpha-1-adrenergic receptors (α1-ARs) are G-protein coupled receptors (GPCRs) with three highly homologous subtypes (α1A, α1B, and α1D). Of these three subtypes, only the α1A and α1B are expressed in the heart. Multiple pre-clinical models of heart injury demonstrate cardioprotective roles for the α1A. Non-selective α1-AR activation promotes glycolysis in the heart, but the functional α1-AR subtype and broader metabolic effects have not been studied. Given the high metabolic demands of the heart and previous evidence indicating benefit from α1A activation, we chose to investigate the effects of α1A activation on the cardiac metabolome in vivo. Mice were treated for 1 week with a low, subpressor dose of A61603, a highly selective and potent α1A agonist. Cardiac tissue and serum were analyzed using a non-targeted metabolomics approach. We identified previously unrecognized metabolic responses to α1A activation, most notably broad reduction in the abundance of polyunsaturated fatty acids (PUFAs) and endocannabinoids (ECs). Given the well characterized roles of PUFAs and ECs in inflammatory pathways, these findings suggest a possible role for cardiac α1A-ARs in the regulation of inflammation and may offer novel insight into the mechanisms underlying the cardioprotective benefit of selective pharmacologic α1A activation.

Metabolomic analysis of the urinary organic acids from 39 selected children with defined respiratory chain deficiencies (RCDs) was performed using untargeted gas chromatography–mass spectrometry, revealing the presence of 255 endogenous and 46 exogenous substances. Variable reduction identified 92 variables from the endogenous substances, which could be analysed by univariate and multivariate statistical methods. Using these methods, no characteristic organic acid biomarker profile could be defined of practical value for diagnostic purposes for complex I (CI), complex III (CIII) and multiple complex (CM) deficiencies. The statistical procedures used did, however, disclose 24 metabolites that were practical highly (d > 0.75) and statistically (P < 0.05) significant for the combined and clinically closely related group of RCDs. Several of these metabolites occur in single enzyme inherited metabolic diseases, but most were not previously reported to be linked to the metabolic perturbations that are due to RCDs. Ultimately, we constructed a global metabolic profile of carbohydrate, amino acid and fatty acid catabolism, illuminating the diverse and complex biochemical consequences of these disorders. This metabolomics investigation disclosed a metabolite profile that has the potential to define an extended and characteristic biosignature for RCDs and the development of a non-invasive screening procedure for these disorders.

Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks. The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills. NMRProcFlow was developed to be a graphical and interactive 1D NMR (1H & 13C) spectra processing tool. NMRProcFlow ( http://nmrprocflow.org ), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment. Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

Atmospheric pressure (AP) infrared (IR) matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) was demonstrated for the rapid direct analysis of pharmaceuticals, and excreted human metabolites. More than 50 metabolites and excreted xenobiotics were directly identified in urine samples with high throughput. As the water content of the sample was serving as the matrix, AP IR-MALDI showed no background interference in the low mass range. The structure of targeted ions was elucidated from their fragmentation pattern using collision activated dissociation. The detection limit for pseudoephedrine was found to be in the sub-femtomole range and the semi-quantitative nature of the technique was tentatively demonstrated for a metabolite, fructose, by using a homologous internal standard, sucrose. A potential application of AP IR-MALDI for intestinal permeability studies was also explored using polyethylene glycol.

Correctly measured classification accuracy is an important aspect not only to classify pre-designated classes such as disease versus control properly, but also to ensure that the biological question can be answered competently. We recognised that there has been minimal investigation of pre-treatment methods and its influence on classification accuracy within the metabolomics literature. The standard approach to pre-treatment prior to classification modelling often incorporates the use of methods such as autoscaling, which positions all variables on a comparable scale thus allowing one to achieve separation of two or more groups (target classes). This is often undertaken without any prior investigation into the influence of the pre-treatment method on the data and supervised learning techniques employed. Whilst this is useful for deriving essential information such as predictive ability or visual interpretation in many cases, as shown in this study the standard approach is not always the most suitable option available. Here, a study has been conducted to investigate the influence of six pre-treatment methods—autoscaling, range, level, Pareto and vast scaling, as well as no scaling—on four classification models, including: principal components-discriminant function analysis (PC-DFA), support vector machines (SVM), random forests (RF) and k-nearest neighbours (kNN)—using three publically available metabolomics data sets. We have demonstrated that undertaking different pre-treatment methods can greatly affect the interpretation of the statistical modelling outputs. The results have shown that data pre-treatment is context dependent and that there was no single superior method for all the data sets used. Whilst we did find that vast scaling produced the most robust models in terms of classification rate for PC-DFA of both NMR spectroscopy data sets, in general we conclude that both vast scaling and autoscaling produced similar and superior results in comparison to the other four pre-treatment methods on both NMR and GC–MS data sets. It is therefore our recommendation that vast scaling is the primary pre-treatment method to use as this method appears to be more stable and robust across all the different classifiers that were conducted in this study.

The aim of the study was to investigate the impact of drought stress on the metabolite profiles of barley (Hordeum vulgare L.) grain against the background of natural variability depending on growing location and season. Six barley genotypes were field-grown (i) under normal weather conditions at two different sites and (ii) under induced drought conditions, using a Rain-Out-Shelter. Both trials were performed in three consecutive seasons (2010–2012). Samples were subjected to a gas chromatography-mass spectrometry metabolite profiling procedure, based on the extraction and fractionation of a broad spectrum of low molecular weight metabolites ranging from lipophilic (e.g. triglyceride-derived fatty acids, free fatty acids, fatty alcohols, sterols) to hydrophilic (e.g. sugars, sugar alcohols, acids, amino acids and amines) compounds. The comparative assessment of the profiling data by means of multivariate analyses revealed that differences in lipophilic metabolites were mainly due to seasonal impact. In contrast water deficit was strongly reflected in quantitative changes of polar metabolites, irrespective of natural variability. The impact factor growing location was differently pronounced depending on the growing season. Univariate statistical analysis revealed 17 metabolites, including the monosaccharides fructose and glucose, the trisaccharide raffinose, several organic acids and the biogenic amine γ-aminobutyric acid to be significantly (p-value < 0.01) influenced by drought stress conditions.