The study of mammalian metabolism through NMR-based metabolomics

High-resolution NMR spectroscopy has been widely used to monitor metabolism almost since the technique's development. It is now one of the principle technologies used in metabolomics, to profile the metabolite compliment of a cell, tissue, organism, or biofluid. This chapter describes how tissue extracts are prepared for NMR spectroscopy and, in particular, focuses on two approaches based on perchloric acid and methanol/chloroform extractions. This is followed by a description of key NMR experiments that can be used to profile tissue extracts, biofluids, or intact tissues. While these NMR techniques should be optimized for a particular sample set, we provide some tried and tested starting parameters for these experiments which should allow the user to acquire good quality spectra

High protein feeding induces de novo lipogenesis in healthy humans: a randomised 3-way crossover study

BACKGROUND. Dietary changes have led to a growing prevalence of Type 2 diabetes and non-alcoholic fatty liver disease. A hallmark of both disorders is hepatic lipid accumulation, derived in part from increased de novo lipogenesis. Despite high protein diets being popular for weight loss to tackle these metabolic disorders, the effect of dietary protein on de novo lipogenesis is poorly studied. We aimed to characterise the effect of dietary protein on de novo lipid synthesis. METHODS. Herein, we use a 3-way crossover interventional study in healthy males to determine the effect of high protein feeding on de novo lipogenesis as well as in vitro models to determine the effects of specific amino acids on fatty acid synthesis. The primary outcome was change in de novo lipogenesis-associated triglycerides in response to protein feeding. RESULTS. We demonstrate that high protein feeding, rich in glutamate, increases de novo lipogenesis-associated triglycerides in plasma (1.5-fold compared to Control; p < 0.0001) and liver-derived very low-density lipoprotein particles (1.8 fold; p < 0.0001) in samples from human subjects (n = 9 per group). In hepatocytes, we show that glutamate derived carbon is incorporated into palmitate and subsequently into triglycerides. In addition, supplementation with glutamate, glutamine and leucine, but not lysine increases synthesised triglyceride content in cells and decreases glucose uptake. Glutamate, glutamine and leucine increase activation of protein kinase B, suggesting that these amino acids induce de novo lipogenesis via the insulin signalling cascade. CONCLUSION. These findings provide mechanistic insight into how select amino acids may induce de novo lipogenesis and insulin resistance, suggesting that high protein feeding to tackle diabetes and obesity requires greater consideration.Medical Research Council funded. grants MR/P011705/1, MC_UP_A090_1006 and MR/P01836X/

Out of Johnson's Shadow: James Boswell as Travel Writer

James Boswell has generally been regarded as a key figure in the evolution of the biography via his work on Samuel Johnson. Ranging over his public, published writing, his private-public unpublished journal writing (read by his friend John Johnston), and his private-private unpublished writing (his personal journals) this thesis sets out to address how he should also be seen as a travelogue writer of note. The most important contention is that the rise of Boswell as a travel writer is key to understanding his prowess as an auto/biographical writer – with the topography of the man-monument central. The principal aim is to stress that he was ‘Corsica Boswell’ long before he was ‘Johnson Boswell’

IsotopicLabelling: an R package for the analysis of MS isotopic patterns of labelled analytes.

Abstract Motivation Labelling experiments in biology usually make use of isotopically enriched substrates, with the two most commonly employed isotopes for metabolism being 2H and 13C. At the end of the experiment some metabolites will have incorporated the labelling isotope, to a degree that depends on the metabolic turnover. In order to propose a meaningful biological interpretation, it is necessary to estimate the amount of labelling, and one possible route is to exploit the fact that MS isotopic patterns reflect the isotopic distributions. Results We developed the IsotopicLabelling R package, a tool able to extract and analyze isotopic patterns from liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-MS (GC-MS) data relative to labelling experiments. This package estimates the isotopic abundance of the employed stable isotope (either 2H or 13C) within a specified list of analytes. Availability and Implementation The IsotopicLabelling R package is freely available at https://github.com/RuggeroFerrazza/IsotopicLabelling. Supplementary information Supplementary data are available at Bioinformatics online

So what have data standards ever done for us? The view from metabolomics

The standardization of reporting of data promises to revolutionize biology by allowing community access to data generated in laboratories across the globe. This approach has already influenced genomics and transcriptomics. Projects that have previously been viewed as being too big to implement can now be distributed across multiple sites. There are now public databases for gene sequences, transcriptomic profiling and proteomic experiments. However, progress in the metabolomic community has seemed to falter recently, and whereas there are ontologies to describe the metadata for metabolomics there are still no central repositories for the datasets themselves. Here, we examine some of the challenges and potential benefits of further efforts towards data standardization in metabolomics and metabonomics

Applications of metabolomics and proteomics to the mdx mouse model of Duchenne muscular dystrophy: lessons from downstream of the transcriptome

Functional genomic studies are dominated by transcriptomic approaches, in part reflecting the vast amount of information that can be obtained, the ability to amplify mRNA and the availability of commercially standardized functional genomic DNA microarrays and related techniques. This can be contrasted with proteomics, metabolomics and metabolic flux analysis (fluxomics), which have all been much slower in development, despite these techniques each providing a unique viewpoint of what is happening in the overall biological system. Here, we give an overview of developments in these fields 'downstream' of the transcriptome by considering the characterization of one particular, but widely used, mouse model of human disease. The mdx mouse is a model of Duchenne muscular dystrophy (DMD) and has been widely used to understand the progressive skeletal muscle wasting that accompanies DMD, and more recently the associated cardiomyopathy, as well as to unravel the roles of the other isoforms of dystrophin, such as those found in the brain. Studies using proteomics, metabolomics and fluxomics have characterized perturbations in calcium homeostasis in dystrophic skeletal muscle, provided an understanding of the role of dystrophin in skeletal muscle regeneration, and defined the changes in substrate energy metabolism in the working heart. More importantly, they all point to perturbations in proteins, metabolites and metabolic fluxes reflecting mitochondrial energetic alterations, even in the early stage of the dystrophic pathology. Philosophically, these studies also illustrate an important lesson relevant to both functional genomics and the mouse phenotyping in that the knowledge generated has advanced our understanding of cell biology and physiological organization as much as it has advanced our understanding of the disease

Metabolic Profiling of the Diabetic Heart: Towards a Richer Picture

The increasing global prevalence of diabetes has been accompanied by a rise in diabetes-related conditions. This includes diabetic cardiomyopathy, a progressive form of heart disease that occurs with both insulin-dependent (type-1) and insulin-independent (type-2) diabetes and arises in the absence of hypertension or coronary artery disease. Over time, diabetic cardiomyopathy can develop into overt heart failure. Like other forms of cardiomyopathy, diabetic cardiomyopathy is accompanied by alterations in metabolism which could lead to further progression of the pathology, with metabolic derangement postulated to precede functional changes in the diabetic heart. Moreover in the case of type-2 diabetes, underlying insulin resistance is likely to prevent the canonical substrate switch of the failing heart away from fatty acid oxidation towards increased use of glycolysis. Analytical chemistry techniques, collectively known as metabolomics, are useful tools for investigating the condition. In this article, we provide a comprehensive review of those studies that have employed metabolomic techniques, namely chromatography, mass spectrometry and nuclear magnetic resonance spectroscopy, to profile metabolic remodelling in the diabetic heart of human patients and animal models. These studies collectively demonstrate that glycolysis and glucose oxidation are suppressed in the diabetic myocardium and highlight a complex picture regarding lipid metabolism. The diabetic heart typically shows an increased reliance on fatty acid oxidation, yet triacylglycerols and other lipids accumulate in the diabetic myocardium indicating probable lipotoxicity. The application of lipidomic techniques to the diabetic heart has identified specific lipid species that become enriched and which may in turn act as plasma-borne biomarkers for the condition. Metabolomics is proving to be a powerful approach, allowing a much richer analysis of the metabolic alterations that occur in the diabetic heart. Careful physiological interpretation of metabolomic results will now be key in order to establish which aspects of the metabolic derangement are causal to the progression of diabetic cardiomyopathy and might form the basis for novel therapeutic intervention.RCUK and BH

Metabolomic study of the LDL receptor null mouse fed a high-fat diet reveals profound perturbations in choline metabolism that are shared with ApoE null mice

Failure to express or expression of dysfunctional low-density lipoprotein receptors (LDLR) causes familial hypercholesterolemia in humans, a disease characterized by elevated blood cholesterol concentrations, xanthomas, and coronary heart disease, providing compelling evidence that high blood cholesterol concentrations cause atherosclerosis. In this study, we used 1H nuclear magnetic resonance spectroscopy to examine the metabolic profiles of plasma and urine from the LDLR knockout mice. Consistent with previous studies, these mice developed hypercholesterolemia and atherosclerosis when fed a high-fat/cholesterol/cholate-containing diet. In addition, multivariate statistical analysis of the metabolomic data highlighted significant differences in tricarboxylic acid cycle and fatty acid metabolism, as a result of high-fat/cholesterol diet feeding. Our metabolomic study also demonstrates that the effect of high-fat/cholesterol/cholate diet, LDLR gene deficiency, and the diet-genotype interaction caused a significant perturbation in choline metabolism, notably the choline oxidation pathway. Specifically, the loss in the LDLR caused a marked reduction in the urinary excretion of betaine and dimethylglycine, especially when the mice are fed a high-fat/cholesterol/cholate diet. Furthermore, as we demonstrate that these metabolic changes are comparable with those detected in ApoE knockout mice fed the same high-fat/cholesterol/cholate diet they may be useful for monitoring the onset of atherosclerosis across animal models

A Metadata description of the data in "A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human.".

BACKGROUND: Metabolomics is a rapidly developing functional genomic tool that has a wide range of applications in diverse fields in biology and medicine. However, unlike transcriptomics and proteomics there is currently no central repository for the depositing of data despite efforts by the Metabolomics Standard Initiative (MSI) to develop a standardised description of a metabolomic experiment. FINDINGS: In this manuscript we describe how the MSI description has been applied to a published dataset involving the identification of cross-species metabolic biomarkers associated with type II diabetes. The study describes sample collection of urine from mice, rats and human volunteers, and the subsequent acquisition of data by high resolution 1H NMR spectroscopy. The metadata is described to demonstrate how the MSI descriptions could be applied in a manuscript and the spectra have also been made available for the mouse and rat studies to allow others to process the data. CONCLUSIONS: The intention of this manuscript is to stimulate discussion as to whether the MSI description is sufficient to describe the metadata associated with metabolomic experiments and encourage others to make their data available to other researchers

Integration of metabolomics, lipidomics and clinical data using a machine learning method.

BACKGROUND: The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, -γ, and -δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry. RESULTS: In order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content. CONCLUSIONS: We found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transaminase (AST), a hepatic leakage enzyme to assess organ damage, and albumin, indicative of altered liver synthetic function, were established. Furthermore, by establishing correlations and network connections between eicosanoids, phospholipids and triacylglycerols, we provide evidence that these lipids function as a key link between inflammatory processes and intermediary metabolism