Current Metabolomics (v.1, #1)

Editorial by Robert Powers (1-1).

Investigation of the metabolic status of the brain is problematic due to its inaccessibility. NMR-based metabolomicsoffers potential diagnostic capabilities via measurement of brain or related biofluid metabolic profiles.

In this review we examine the extent to which NMR and MRS have been employed in studies of neurological disorders,the information obtainable, the strengths and limitations of each technology. The potential for wider application of thesecomplementary technologies in metabolomics research is discussed, both in isolation and in combination. The currentstatus of the statistical framework for dealing with these data sets is also examined and suggestions offered for future directions.

Requirements and Perspectives for Integrating Metabolomics with other Omics Data by Sven Baumann, Stefan Kalkhof, Jorg Hackermuller, Wolfgang Otto, Janina Melanie Tomm, Dirk Klaus Wissenbach, Ulrike Rolle-Kampczyk, Martin von Bergen (15-27).
With the advent of high throughput and high dimensional metabolomics data the question of the appropriate interpretationand integration with other omics data became pivotal. Here we focus on the human model system since it isthe best characterized model system and thereby can be seen as a benchmark for the direction of developments for otherspecies.

In addition the basics on which the selection and the mode how metabolomics data can be combined with other omics dataare reviewed. Genome, transcriptome and proteome data are discussed in respect to their inherent characteristics in termsof measurement, data features, coverage and their relatedness to metabolome data. Due to its recently gained importancewe also review the specific features of serum metabolomics. On the metabolome side we discuss the emerging relevanceof flux and the often neglected tight interconnectedness of small molecules with signaling pathways. The contribution ofmetabolome data to integrated pathway analyses is so far based on either only the combination, intrinsic correlation or detectionof overrepresentation. In order to overcome the shortcomings of these approaches data interpretation should beperformed by making use of a detailed pathway topology. This functional integration is crucial for the intended comprehensivephenotyping and demands for specific precautionary means of data assessment and data evaluation.

Databases and Software for NMR-Based Metabolomics by James J. Ellinger, Roger A. Chylla, Eldon L. Ulrich, John L. Markley (28-40).
New software and increasingly sophisticated NMR metabolite spectral databases are advancing the uniqueabilities of NMR spectroscopy to identify and quantify small molecules in solution for studies of metabolite biomarkersand metabolic flux. Public and commercial databases now contain experimental 1D 1H, 13C and 2D 1H-13C spectra and extractedspectral parameters for over a thousand compounds and theoretical data for thousands more. Public databases containingexperimental NMR data from complex metabolic studies are emerging. These databases are providing informationvital for the construction and testing of new computational algorithms for NMR-based chemometric and quantitative metabolomicsstudies. In this review we focus on database and software tools that support a quantitative NMR approach tothe analysis of 1D and 2D NMR spectra of complex biological mixtures.

Application of Metabolomics in Drug Discovery, Development and Theranostics by Miroslava Cuperlovic-Culf, Adrian S. Culf, Pier Jr Morin, Mohamed Touaibia (41-57).
High throughput analysis of metabolites combined with systems biology is opening new opportunities in medicalresearch and clinical applications. Investigation of metabolic phenotype and metabolic requirements of a disease opensnew avenues for treatments. Analysis of metabolic changes in patients and in disease models following treatments providesnew methods for investigation of therapeutic response. Combined examination of disease metabolic characteristicsand treatment response provides markers for personalization of therapies. Metabolomics finds itself at an increasingly importantjunction in all of these applications. In this review, we will explore the advantages of combining metabolomicswith systems biology to optimize the drug discovery process via in-depth analysis of biological systems and novel metabolism-targeted therapeutics. Assessment of efficacy and specificity of drugs that directly or indirectly target metabolismthrough analyses of metabolic changes will also be presented. Finally, we will highlight examples of metabolic biomarkersexplored as promising theranostics and theranostic imaging tools aimed at therapy optimization and combinedtherapy and diagnostics. The focus of this review will be directed towards cancer, however, the overarching ideas as wellas experimental and analysis approaches are easily transferable to other applications. In cancer research, metabolism isonce again raising interest due to a better appreciation of the significance of altered metabolic phenotype in cancer developmentand progression. Investigation of metabolic changes in cancer following drug applications and identification ofclinically relevant metabolic markers for particular cancer subtypes are becoming increasingly appreciated in treatmentdevelopment and will be explored in this review.

In metabolomics studies, optimal liquid chromatography separations prior to mass spectrometric analysis areimportant with regard to identification of metabolites as distinct from their isomers, and for differentiation of genuine metabolitesfrom fragments and adducts of other molecules and from environmental and system peaks. The role of liquidchromatography in mass spectrometry based metabolomics is reviewed. The basic principles behind liquid chromatographicseparations are discussed with regard to the types of interaction which can occur with stationary phases. Examplesare given in order to illustrate the importance of liquid chromatography in verifying metabolite identity and also to illustratethe different types of separation produced by the various chromatographic phases available. Applications of reversedphase chromatography in metabolomics studies are reviewed for the last three years. Some examples of derivatisation,prior to liquid chromatography, which can be used in order to enhance mass spectrometric detection are covered. Thereview of other chromatographic methods which are less commonly used than reversed phase chromatography covers aperiod of six years since these methods present a wider range of stationary phase chemistries. There is a short review oflipidomics methods using liquid chromatography mass spectrometry drawn from the last two years.

Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a livingsystem. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation,environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimentaland computational technology allow more and more known metabolites to be detected and quantified fromcomplex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profilingdata from a systems perspective, i.e. interpreting the data and performing statistical inference in the context ofpathways andgenome-scale metabolic networks. Recently a number of methods have been developed in this area, andmuch improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysisof metabolic profiling data based on metabolic networks.

Multivariate Analysis in Metabolomics by Bradley Worley, Robert Powers (92-107).
Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids,free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high informationcontent of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusionsfrom any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to findingmeaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis(PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variationor separation are identified for further analysis. However, as with any mathematical treatment, these methods are not apanacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.