Current Metabolomics (v.1, #3)

NMR Metabolomics Analysis of Parkinson's Disease by Shulei Lei, Robert Powers (191-209).
Parkinson's disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergicneurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress arelinked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolitechanges in biofluids, cell lysates, tissues and tumors in order to correlate these metabolomic changes to a disease state.Thus, the application of metabolomics to investigate PD provides a systematic approach to understand the pathology ofPD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This reviewwill examine current research into PD mechanisms with a focus on mitochondrial dysfunction and oxidative stress. Neurotoxin-based PD animal models and the rationale for metabolomics studies in PD will also be discussed. The review willalso explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis.

Mass Spectrometry-Based Metabolic Profiling by Ryan A. Grove, Jiri Adamec (210-219).
Metabolomics, the comprehensive study of small molecules within a biological sample, has emerged as a powerfultool in the field of systems biology. Concomitantly, the development of mass spectrometry-based metabolomic platforms,in particular high performance liquid chromatography and gas chromatography coupled methods, has allowed forrapid advancement in the fields of metabolite separation and identification. Modern mass spectrometry-based techniquesrely on robust sampling and extraction methodology, and analytical instrumentation with high sensitivity over a widemass range (dynamic range), which is unequaled in the metabolomics field. Mass spectrometry-based metabolomics maybe performed in a global, untargeted manner, in which fragmentation spectra are compared against databases of knownmetabolite fragmentation patterns. Contrarily, strategies which utilize recognition of specific functional groups, as well asisotope labeling, enable a targeted approach in mass spectrometry based metabolomics. This review aims to outline thesteps taken in a metabolomics experiment, as well as considerations which need to be taken into account in the developmentof an experimental strategy. In addition, recent literature which utilizes mass spectrometry based metabolomics willbe presented, along with the methodology by which these studies elucidate biological functions and metabolic pathways.The availability of isotope coded standards and derivatization agents will also be addressed, along with the role thesecompounds play in the identification and quantification of metabolites.

Influence of Biological and Technical Covariates on Non-targeted Metabolite Profiling in a Large-scale Epidemiological Study by Jitender Kumar, Corey D. Broeckling, Fredrik Wiklund, Erik Ingelsson, Jessica E. Prenni (220-226).
Non-targeted metabolite profiling using ultra performance liquid chromatography-mass spectrometry (UPLCMS)was performed as part of a large-scale epidemiological study involving biobanked serum samples. The influence ofboth biological (age and body mass index) and technical (season of sample collection, fasting time, handling time, andstorage time) covariates on the analysis was assessed. Statistical models including different sets of these covariates werecompared and the results illustrate that variation in which covariates were included did not have an appreciable effect onthe number or composition of biologically significant metabolite features associated with body mass index or age.Furthermore, when all covariates were included in the model, there was little overlap of metabolite features significantlyassociated with the different covariates. Thus, the results of this study illustrate that while some of the observed quantitativevariance of metabolite features can be explained by biological and technical covariates, the use of non-targetedmetabolite profiling of serum by UPLC-MS is valid for studies of biological outcomes in biobanked clinical samples fromlarge-scale studies.

Biomarker Discovery and Translation in Metabolomics by G. A. Nagana Gowda, Daniel Raftery (227-240).
The multifaceted field of metabolomics has witnessed exponential growth in both methods development andapplications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding,diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary ofmetabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers andprovides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been testedfor translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis,monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Currentadvances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens usingnon-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applicationsin clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority ofpromising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasison biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Effects of Fatty Acids and Glycation on Drug Interactions with Human Serum Albumin by Jeanethe A. Anguizola, Sara B.G. Basiaga, David S. Hage (241-252).
The presence of elevated glucose concentrations in diabetes is a metabolic change that leads to an increase inthe amount of non-enzymatic glycation that occurs for serum proteins. One protein that is affected by this process is themain serum protein, human serum albumin (HSA), which is also an important carrier agent for many drugs and fatty acidsin the circulatory system. Sulfonylurea drugs, used to treat type 2 diabetes, are known to have significant binding to HSA.This study employed ultrafiltration and high-performance affinity chromatography to examine the effects of HSA glycationon the interactions of several sulfonylurea drugs (i.e., acetohexamide, tolbutamide and gliclazide) with fatty acids,whose concentrations in serum are also affected by diabetes. Similar overall changes in binding were noted for these drugswith normal HSA or glycated HSA and in the presence of the fatty acids. For most of the tested drugs, the addition ofphysiological levels of the fatty acids to normal HSA and glycated HSA produced weaker binding. At low fatty acid concentrations,many of these systems followed a direct competition model while others involved a mixed-mode interaction.In some cases, there was a change in the interaction mechanism between normal HSA and glycated HSA, as seen with linoleicacid. Systems with only direct competition also gave notable changes in the affinities of fatty acids at their sites ofdrug competition when comparing normal HSA and glycated HSA. This research demonstrated the importance of consideringhow changes in the concentrations and types of metabolites (e.g., in this case, glucose and fatty acids) can alter thefunction of a protein such as HSA and its ability to interact with drugs or other agents.

Current Experimental, Bioinformatic and Statistical Methods used in NMR Based Metabolomics by Helena U. Zacharias, Jochen Hochrein, Matthias S. Klein, Claudia Samol, Peter J. Oefner, Wolfram Gronwald (253-268).
The aim of this contribution is to familiarize the reader with experimental, bioinformatic and statistical strategiescurrently used in the field of solution NMR based metabolomics. Special emphasis is given to methods that haveworked well in our hands. Methods covered include sample preparation, acquisition and processing of NMR spectra, andidentification and quantification of metabolites. Further consideration is given to data normalization and scaling, unsupervisedand supervised statistical data analysis, the biomedical interpretation of results, and the centralized community-widestorage and retrieval of NMR data.