Current Metabolomics最新文献

Polycystic ovary syndrome (PCOS), a common female endocrinopathy, is a complex metabolic syndrome of enhanced weight gain. The goal of this pilot study was to evaluate metabolic differences between normal (n=10) and PCOS (n=10) women via breath carbon isotope ratio, urinary nitrogen and nuclear magnetic resonance (NMR)-determined serum metabolites. Breath carbon stable isotopes measured by cavity ring down spectroscopy (CRDS) indicated diminished (p<0.030) lipid use as a metabolic substrate during overnight fasting in PCOS compared to normal women. Accompanying urinary analyses showed a trending correlation (p<0.057) between overnight total nitrogen and circulating testosterone in PCOS women, alone. Serum analyzed by NMR spectroscopy following overnight, fast and at 2 h following an oral glucose tolerance test showed that a transient elevation in blood glucose levels decreased circulating levels of lipid, glucose and amino acid metabolic intermediates (acetone, 2-oxocaporate, 2-aminobutyrate, pyruvate, formate, and sarcosine) in PCOS women, whereas the 2 h glucose challenge led to increases in the same intermediates in normal women. These pilot data suggest that PCOS-related inflexibility in fasting-related switching between lipid and carbohydrate/protein utilization for carbon metabolism may contribute to enhanced weight gain.

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. Sulfonylureas 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.

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.

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.

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.

New software and increasingly sophisticated NMR metabolite spectral databases are advancing the unique abilities of NMR spectroscopy to identify and quantify small molecules in solution for studies of metabolite biomarkers and metabolic flux. Public and commercial databases now contain experimental 1D ¹H, ¹³C and 2D ¹H-¹³C spectra and extracted spectral parameters for over a thousand compounds and theoretical data for thousands more. Public databases containing experimental NMR data from complex metabolic studies are emerging. These databases are providing information vital for the construction and testing of new computational algorithms for NMR-based chemometric and quantitative metabolomics studies. In this review we focus on database and software tools that support a quantitative NMR approach to the analysis of 1D and 2D NMR spectra of complex biological mixtures.

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 information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning 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 variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. 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 experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.