Current Metabolomics - Volume 1, Issue 3, 2013

Parkinson's disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergic neurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress are linked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolite changes 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 of PD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This review will 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 will also explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis.

Metabolomics, the comprehensive study of small molecules within a biological sample, has emerged as a powerful tool 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 for rapid advancement in the fields of metabolite separation and identification. Modern mass spectrometry-based techniques rely on robust sampling and extraction methodology, and analytical instrumentation with high sensitivity over a wide mass range (dynamic range), which is unequaled in the metabolomics field. Mass spectrometry-based metabolomics may be performed in a global, untargeted manner, in which fragmentation spectra are compared against databases of known metabolite fragmentation patterns. Contrarily, strategies which utilize recognition of specific functional groups, as well as isotope labeling, enable a targeted approach in mass spectrometry based metabolomics. This review aims to outline the steps taken in a metabolomics experiment, as well as considerations which need to be taken into account in the development of an experimental strategy. In addition, recent literature which utilizes mass spectrometry based metabolomics will be 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 these compounds play in the identification and quantification of metabolites.

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 of both biological (age and body mass index) and technical (season of sample collection, fasting time, handling time, and storage time) covariates on the analysis was assessed. Statistical models including different sets of these covariates were compared and the results illustrate that variation in which covariates were included did not have an appreciable effect on the 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 significantly associated with the different covariates. Thus, the results of this study illustrate that while some of the observed quantitative variance of metabolite features can be explained by biological and technical covariates, the use of non-targeted metabolite profiling of serum by UPLC-MS is valid for studies of biological outcomes in biobanked clinical samples from large-scale studies.

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. 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 of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for 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. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

The presence of elevated glucose concentrations in diabetes is a metabolic change that leads to an increase in the amount of non-enzymatic glycation that occurs for serum proteins. One protein that is affected by this process is the main serum protein, human serum albumin (HSA), which is also an important carrier agent for many drugs and fatty acids in 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 glycation on 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 drugs with normal HSA or glycated HSA and in the presence of the fatty acids. For most of the tested drugs, the addition of physiological 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 linoleic acid. Systems with only direct competition also gave notable changes in the affinities of fatty acids at their sites of drug competition when comparing normal HSA and glycated HSA. This research demonstrated the importance of considering how changes in the concentrations and types of metabolites (e.g., in this case, glucose and fatty acids) can alter the function of a protein such as HSA and its ability to interact with drugs or other agents.

The aim of this contribution is to familiarize the reader with experimental, bioinformatic and statistical strategies currently used in the field of solution NMR based metabolomics. Special emphasis is given to methods that have worked well in our hands. Methods covered include sample preparation, acquisition and processing of NMR spectra, and identification and quantification of metabolites. Further consideration is given to data normalization and scaling, unsupervised and supervised statistical data analysis, the biomedical interpretation of results, and the centralized community-wide storage and retrieval of NMR data.