Current Metabolomics (v.1, #2)

Advances in Nutritional Metabolomics by Elizabeth P. Ryan, Adam L. Heuberger, Corey D. Broeckling, Erica C. Borresen, Cadie Tillotson, Jessica E. Prenni (109-120).
Metabolomics is maturing as an experimental approach in nutrition science, and it is a useful analysis for revealing systems biology outcomes associated with changes in diet. A major goal of this review is to present the rapidly evolving body of scientific literature that seeks to reveal connections between an individual's metabolic profile and experimentally manipulated or naturally varied dietary intakes. Metabolite profiles in tissue, serum, urine, or stool reflect changes in metabolic pathways that respond to dietary intervention which makes them accessible samples for revealing metabolic effects of diet. Three broadly defined areas of investigation related to dietary-metabolomic strategies include: (1) describing the metabolite variation within and between dietary exposures or interventions; (2) characterizing the metabolic response to dietary interventions with respect to time; and (3) assessing individual variation in baseline nutritional health and/or disease status. An overview of metabolites that were responsive to dietary interventions as reported from original research in human or animal studies is provided and illustrates the breadth of metabolites affected by dietary intervention. Advantages and drawbacks for assessing metabolic changes are discussed in relation to types of metabolite analysis platforms. A combination of targeted and non-targeted global profiling studies as a component of future dietary intervention trials will increase our understanding of nutrition in a systems context.

Capillary Electrophoresis with Mass Spectrometry: Applications to Metabolomic Analysis by Katie Yan, Jeffrey Brumbaugh, Jeremy Barrett, Sarah Jung, Shen Hu (121-127).
The use of capillary electrophoresis with mass spectrometry (CE-MS) for metabolomic analysis has substantially increased in recent years. This hyphenated technique takes full advantage of the high separation efficiency of CE and the identification capability of MS, providing a powerful tool for rapid analysis and identification of metabolites in complex biological samples. This article focuses on an overview of some recent applications of CE-MS in metabolomics studies. We also discuss the techniques for the coupling of CE with MS and the potential of 2-D CE-MS for metabolomic analysis.

Fluxomics by NMR Spectroscopy from Cells to Organisms Focusing on Liver by Andrey P. Tikunov, Jason H. Winnike, Katherine Tech, Rex E. Jeffries, Charles TA. Semelka, Jesse Martin, Randy McClelland, Lee M Graves, Jeffrey M. Macdonald (128-159).
Metabolism represents interconnected networks of metabolite consumption and creation. The field of metabolomics is focused on metabolite concentrations in metabolic networks. Fluxomics quantifies the flux of substrate through each reaction step or a series of reaction steps and is required for energy balance equations of the system. Quantifying metabolic flux of every pathway is unnecessary because there is at least one regulatory enzyme per pathway. Hence, to predict flux for intermediate metabolic pathways, in silico models typically use the total flux of a pathway merged with literature derived steady-state concentration and kinetic values (Vmax and km) as computational conditions. The assumptions of the model can be false or unreliable in the disease state or with xenobiotic exposure. Therefore, the relatively small amount of experimental flux data used in metabolic model must be robust and reliable. Presently, there is no review that lists the multitude of bioenergetics flux values for multiple species, while also highlighting potential errors associated with experimental procedures or data analysis. In this review, the flux values for multiple metabolic pathways in liver of whole animal, perfused liver, and hepatocyte culture studies will be reviewed. Also, the potential pitfalls of experimental stable isotope resolved metabolomics (SIRM) data are described. For animal studies, the main causes of inter-laboratory variance are the effects of anesthesia, adrenergic response during animal handling, variation in tissue sample acquisition and metabolic quenching, and tissue extraction procedures. Solutions to these issues will be discussed. While cell and organ models hope to reduce the complexity of the whole organism to a controlled system of two or more compartments, they do not fully mimic the whole animal inter-organ response and the dynamic concentrations of the metabolite, gases, soluble or insoluble factors in the central compartment. To demonstrate the effects of these factors, studies of the effect of ethanol on hepatocyte, perfused liver and animal studies will be compared, while SIRM studies of human and rat hepatocyte cultures will demonstrate the problem of cell culture in mimicking the normal metabolism.

Neurodegenerative diseases have become a “hot” topic in recent years. A major factor for this is that as life expectancy of the population in developed countries increases, so does the probability of developing neurodegenerative disorders such as Alzheimer's disease (AD) and Parkinson’s disease (PD), to name the two most well-known. In many cases, however, neurological and mental diseases are poorly understood. In particular, there is a lack of specific biomarkers which would allow early unambiguous identification of a neurodegenerative disease, distinguishing between e.g. AD; PD; PD with dementia; and Dementia with Lewy bodies, or indicating therapeutic effects. Ultimately, this complicates the search for effective treatments. Thus, there is a high demand for preclinical work to elucidate underlying disease mechanisms and pave the way for disease management. In terms of biomarker research, hope has been set on small molecules that participate in metabolism, since they provide a closer link between cellular mechanisms (with genetic as well as environmental inputs) and the disease phenotype. More specifically, it is expected that not one but a combination of several metabolites may serve as an indicator for disease onset and progression, given that neurodegenerative diseases, whilst often described as “idiopathic”, are understood to arise from complex pathologies expressing themselves with a broad spectrum of phenotypes. Therefore, non-targeted metabolic profiling appears to offer great potential for biomarker discovery in this area. One of the major technical platforms for non-targeted metabolic profiling is high resolution nuclear magnetic resonance (NMR) spectroscopy, a technique that is also available for the non-invasive application in vivo. Hence, in theory, biomarker discovery research using NMR spectroscopy based metabolomics provides a promising means for translation from in vitro/ex vivo research to eventual clinical use. This review will therefore discuss the potential for NMR spectroscopy based metabolomics to be applied to biomarker discovery in the field of neurodegenerative disease.

muma, An R Package for Metabolomics Univariate and Multivariate Statistical Analysis by Edoardo Gaude, Francesca Chignola, Dimitrios Spiliotopoulos, Andrea Spitaleri, Michela Ghitti, Jose M Garcia-Manteiga, Silvia Mari, Giovanna Musco (180-189).
Metabolomics, similarly to other high-throughput “-omics” techniques, generates large arrays of data, whose analysis and interpretation can be difficult and not always straightforward. Several software for the detailed metabolomics statistical analysis are available, however there is a lack of simple protocols guiding the user through a standard statistical analysis of the data. Herein we present “muma”, an R package providing a simple step-wise pipeline for metabolomics univariate and multivariate statistical analyses. Based on published statistical algorithms and techniques, muma provides user-friendly tools for the whole process of data analysis, ranging from data imputation and preprocessing, to dataset exploration, to data interpretation through unsupervised/supervised multivariate and/or univariate techniques. Of note, specific tools and graphics aiding the explanation of statistical outcomes have been developed. Finally, a section dedicated to metabolomics data interpretation has been implemented, providing specific techniques for molecular assignments and biochemical interpretation of metabolic patterns. muma is a free, user-friendly and versatile tool suite tailored to assist the user in the interpretation of metabolomics data in the identification of biomarkers and in the analysis of metabolic patterns.