PubMed

Related Articles Comparison of blood plasma sample preparation methods for combined LC-MS lipidomics and metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci. 2015 Aug 28;1002:260-266 Authors: Patterson RE, Ducrocq AJ, McDougall DJ, Garrett TJ, Yost RA Abstract The goal of this research was to find the most comprehensive lipid extraction of blood plasma, while also providing adequate aqueous preparation for metabolite analysis. Comparisons have been made previously of the Folch, Bligh-Dyer, and Matyash lipid extractions; furthermore, this paper provides an additional comparison of a phospholipid removal plate for analysis. This plate was used for lipid extraction rather than its intended use in lipid removal for polar analysis, and it proves to be robust for targeted lipid analysis. Folch and Matyash provided reproducible recovery over a range of lipid classes, however the Matyash aqueous layer compared well to a typical methanol preparation for polar metabolite analysis. Thus, the Matyash method is the best choice for an untargeted biphasic extraction for metabolomics and lipidomics in blood plasma. PMID: 26343017 [PubMed - as supplied by publisher]

Related Articles Recent developments in sample preparation and data pre-treatment in metabonomics research. Arch Biochem Biophys. 2015 Sep 2; Authors: Li N, Song YP, Tang H, Wang Y Abstract Metabonomics is a powerful approach for biomarker discovery and an effective tool for pinpointing endpoint metabolic effects of external stimuli, such as pathogens and disease development. Due to its wide applications, metabonomics is required to deal with various biological samples of different properties. Hence sample preparation and corresponding data pre-treatment become important factors in ensuring validity of an investigation. In this review, we summarize some recent developments in metabonomics sample preparation and data-pretreatment procedures. PMID: 26342458 [PubMed - as supplied by publisher]

Related Articles Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring. Anal Bioanal Chem. 2015 Sep 5; Authors: Zhu J, Djukovic D, Deng L, Gu H, Himmati F, Abu Zaid M, Chiorean EG, Raftery D Abstract Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring. PMID: 26342311 [PubMed - as supplied by publisher]

Related Articles Metabolic Characterization of Asthenozoospermia Using Nontargeted Seminal Plasma Metabolomics. Clin Chim Acta. 2015 Sep 2; Authors: Zhang X, Diao R, Zhu X, Li Z, Cai Z Abstract BACKGROUND: Asthenozoospermia (AS) is a common cause of male infertility. Due to the limitation of routine semen analysis, metabolic alterations associated with the disease are unclear. We applied a metabolic profiling strategy as a surrogate method to accurately assess and provide new insights into the etiologies of asthenozoospermia. METHODS: Seminal plasma samples from patients diagnosis with asthenozoospermia (n = 33) and healthy subjects (n = 30) were investigated using a nontargeted metabolomics approach based on proton nuclear magnetic resonance ((1)H NMR) spectroscopy. The spectral data were then subjected to multivariate and univariate analysis to identify metabolites that were correlated with asthenozoospermia. The disturbed metabolic pathways which the biomarkers were involved in were analyzed. RESULTS: Nineteen metabolites including up-regulation or down-regulation of several amino acids, changes in lipids metabolism, phospholipids (choline) metabolism, cholesterol metabolism, nucleoside metabolism, the Krebs cycle and energy metabolism were identified and associated with asthenozoospermia. In particular, the elevation of oxysterols such as 5α-cholesterol and 7-ketocholesterol in seminal plasma of patients with asthenozoospermia was an important finding, indicating the important role of oxidative stress in the mechanism of asthenozoospermia. CONCLUSIONS: The excellent performance of this metabolomics approach offer a highly novel means of etiological diagnosis of asthenozoospermia. PMID: 26342261 [PubMed - as supplied by publisher]

Serum metabolomics research of the anti-hypertensive effects of Tengfu Jiangya tablet on spontaneously hypertensive rats. J Chromatogr B Analyt Technol Biomed Life Sci. 2015 Aug 28;1002:210-217 Authors: Jiang H, Shen Z, Chu Y, Li Y, Li J, Wang X, Yang W, Zhang X, Ju J, Xu J, Yang C Abstract A HPLC/TOF-MS-based metabolomic study was conducted to investigate the holistic therapeutic effects of Tengfu Jiangya Tablet (TJT) on spontaneously hypertensive rats (SHRs). The SHRs were divided into valsartan (VST) group, TJT group and model group, in addition, the Wistar-Kyoto rats (WKY) were taken as normal control. Serum samples were separated and identified by HPLC/TOF-MS, while the obtained data was further processed by partial least-squares discriminant analysis (PLS-DA). A clear cluster among the four groups was observed, and we identified thirteen biomarkers involved involved in sphingolipid metabolism (sphinganine, lysosphingomyelin, ceramide), glycerophospholipid metabolism (phosphatidylcholines, phosphatidylethanolamine, lysophosphatidylcholines), arginine and proline metabolism (l-proline, citrulline), tryptophan metabolism (xanthuiulrenic acid, l-kynurenine, l-tryptophan), arachidonic acid metabolism(leukotriene D4), and linoleic acid metabolism (gamma-linolenic acid). Altered metabolic pathways involved in impaired NO production, inflammation and vascular smooth muscle cells (VSMCs) apoptosis and proliferation, which suggesting the possible pathological state in SHRs. TJT as well as VST altered the metabolic state, suggesting a possible anti-hypertension role by improving NO production, and an extra cardiovascular protection role possibly by amelioration of inflammatory state and vascular remodeling. PMID: 26342163 [PubMed - as supplied by publisher]

Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers. Brief Bioinform. 2015 Sep 4; Authors: Puchades-Carrasco L, Palomino-Schätzlein M, Pérez-Rambla C, Pineda-Lucena A Abstract Metabolomics, a systems biology approach focused on the global study of the metabolome, offers a tremendous potential in the analysis of clinical samples. Among other applications, metabolomics enables mapping of biochemical alterations involved in the pathogenesis of diseases, and offers the opportunity to noninvasively identify diagnostic, prognostic and predictive biomarkers that could translate into early therapeutic interventions. Particularly, metabolomics by Nuclear Magnetic Resonance (NMR) has the ability to simultaneously detect and structurally characterize an abundance of metabolic components, even when their identities are unknown. Analysis of the data generated using this experimental approach requires the application of statistical and bioinformatics tools for the correct interpretation of the results. This review focuses on the different steps involved in the metabolomics characterization of biofluids for clinical applications, ranging from the design of the study to the biological interpretation of the results. Particular emphasis is devoted to the specific procedures required for the processing and interpretation of NMR data with a focus on the identification of clinically relevant biomarkers. PMID: 26342127 [PubMed - as supplied by publisher]

Plasma metabolomics profiling for the prediction of cytomegalovirus DNAemia and analysis of virus-host interaction in allogeneic stem cell transplant recipients. J Gen Virol. 2015 Sep 3; Authors: Monleón D, Giménez E, Muñoz-Cobo B, Morales JM, Solano C, Amat P, Navarro D Abstract Metabolomics analysis of biofluids is being increasingly recognized as a useful tool for the diagnosis and management of a number of infectious diseases. Here we showed that plasma metabolomics profiling by untargeted 1H nuclear magnetic resonance may allow the anticipation of the occurrence of CMV DNAemia in allogeneic stem cell transplant (Allo-SCT). For this purpose, key discriminatory metabolites were total glutathione, taurine, methylamine, trimethylamine N-oxide and lactate, all of which were upregulated in patients eventually developing CMV DNAemia. The overall classification accuracy (predictability) of the projection to latent structure discriminant analysis (PLS-DA) model in cross-validation technical replicates was 73%. Increased levels of alanine, lactate and total fatty acids, and a shift in the fatty acid profile towards unsaturated species were observed in patients with detectable CMV DNA in plasma. The classification accuracy of this PLS-DA model in cross-validation technical replicates was 81%. Plasma metabolomics profiling may prove useful for identifying patients at highest risk for CMV DNAemia thus allowing early inception of antiviral therapy. PMID: 26341195 [PubMed - as supplied by publisher]

[Development of new vaccines]. Enferm Infecc Microbiol Clin. 2015 Sep 1; Authors: González-Romo F, Picazo JJ Abstract Recent and important advances in the fields of immunology, genomics, functional genomics, immunogenetics, immunogenomics, bioinformatics, microbiology, genetic engineering, systems biology, synthetic biochemistry, proteomics, metabolomics and nanotechnology, among others, have led to new approaches in the development of vaccines. The better identification of ideal epitopes, the strengthening of the immune response due to new adjuvants, and the search of new routes of vaccine administration, are good examples of advances that are already a reality and that will favour the development of more vaccines, their use in indicated population groups, or its production at a lower cost. There are currently more than 130 vaccines are under development against the more wished (malaria or HIV), difficult to get (CMV or RSV), severe re-emerging (Dengue or Ebola), increasing importance (Chagas disease or Leishmania), and nosocomial emerging (Clostridium difficile or Staphylococcus aureus) infectious diseases. PMID: 26341041 [PubMed - as supplied by publisher]

16 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2015/09/05PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Metabolic profiling as a tool for prioritizing antimicrobial compounds. J Ind Microbiol Biotechnol. 2015 Sep 3; Authors: Wu C, Choi YH, van Wezel GP Abstract Metabolomics is an analytical technique that allows scientists to globally profile low molecular weight metabolites between samples in a medium- or high-throughput environment. Different biological samples are statistically analyzed and correlated to a bioactivity of interest, highlighting differentially produced compounds as potential biomarkers. Here, we review NMR- and MS-based metabolomics as technologies to facilitate the identification of novel antimicrobial natural products from microbial sources. Approaches to elicit the production of poorly expressed (cryptic) molecules are thereby a key to allow statistical analysis of samples to identify bioactive markers, while connection of compounds to their biosynthetic gene cluster is a determining step in elucidating the biosynthetic pathway and allows downstream process optimization and upscaling. The review focuses on approaches built around NMR-based metabolomics, which enables efficient dereplication and guided fractionation of (antimicrobial) compounds. PMID: 26335567 [PubMed - as supplied by publisher]

Scientific workflow optimization for improved peptide and protein identification. BMC Bioinformatics. 2015;16(1):284 Authors: Holl S, Mohammed Y, Zimmermann O, Palmblad M Abstract BACKGROUND: Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal. RESULTS: We have applied a new optimization framework for the Taverna scientific workflow management system ( http://ms-utils.org/Taverna_Optimization.pdf ) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction. CONCLUSION: Using the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework. PMID: 26335531 [PubMed - as supplied by publisher]

Laboratory Medicine Meets Precision Medicine: the Paradigm of Metabolomics in Perinatology. Proposals from the 4th International Conference on Neonatal and Pediatric Laboratory Medicine. Clin Chim Acta. 2015 Aug 31; Authors: Mussap M, Ferrari M, Fanos V PMID: 26335287 [PubMed - as supplied by publisher]

A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation. BMC Syst Biol. 2015;9(1):51 Authors: Sokolenko S, Aucoin MG Abstract BACKGROUND: The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. RESULTS: Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and systematic errors. To detect systematic errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify systematic errors as small as 2.5 % under a wide range of conditions. CONCLUSION: Both the simulation framework and error correction method represent examples of time-course analysis that can be applied to further developments in (1)H-NMR methodology and the more general application of quantitative metabolomics. PMID: 26335002 [PubMed - as supplied by publisher]

Intracellular Survival of Leishmania major Depends on Uptake and Degradation of Extracellular Matrix Glycosaminoglycans by Macrophages. PLoS Pathog. 2015 Sep;11(9):e1005136 Authors: Naderer T, Heng J, Saunders EC, Kloehn J, Rupasinghe TW, Brown TJ, McConville MJ Abstract Leishmania parasites replicate within the phagolysosome compartment of mammalian macrophages. Although Leishmania depend on sugars as a major carbon source during infections, the nutrient composition of the phagolysosome remains poorly described. To determine the origin of the sugar carbon source in macrophage phagolysosomes, we have generated a N-acetylglucosamine acetyltransferase (GNAT) deficient Leishmania major mutant (∆gnat) that is auxotrophic for the amino sugar, N-acetylglucosamine (GlcNAc). This mutant was unable to grow or survive in ex vivo infected macrophages even when macrophages were cultivated in presence of exogenous GlcNAc. In contrast, the L. major ∆gnat mutant induced normal skin lesions in mice, suggesting that these parasites have access to GlcNAc in tissue macrophages. Intracellular growth of the mutant in ex vivo infected macrophages was restored by supplementation of the macrophage medium with hyaluronan, a GlcNAc-rich extracellular matrix glycosaminoglycan. Hyaluronan is present and constitutively turned-over in Leishmania-induced skin lesions and is efficiently internalized into Leishmania containing phagolysosomes. These findings suggest that the constitutive internalization and degradation of host glycosaminoglycans by macrophages provides Leishmania with essential carbon sources, creating a uniquely favorable niche for these parasites. PMID: 26334531 [PubMed - as supplied by publisher]

The elementome of calcium-based urinary stones and its role in urolithiasis. Nat Rev Urol. 2015 Sep 1; Authors: Ramaswamy K, Killilea DW, Kapahi P, Kahn AJ, Chi T, Stoller ML Abstract Urolithiasis affects around 10% of the US population with an increasing rate of prevalence, recurrence and penetrance. The causes for the formation of most urinary calculi remain poorly understood, but obtaining the chemical composition of these stones might help identify key aspects of this process and new targets for treatment. The majority of urinary stones are composed of calcium that is complexed in a crystalline matrix with organic and inorganic components. Surprisingly, mitigation of urolithiasis risk by altering calcium homeostasis has not been very effective. Thus, studies to identify other therapeutic stone-specific targets, using proteomics, metabolomics and microscopy techniques, have been conducted, revealing a high level of complexity. The data suggest that numerous metals other than calcium and many nonmetals are present within calculi at measurable levels and several have distinct distribution patterns. Manipulation of the levels of some of these elemental components of calcium-based stones has resulted in clinically beneficial changes in stone chemistry and rate of stone formation. The elementome-the full spectrum of elemental content-of calcium-based urinary calculi is emerging as a new concept in stone research that continues to provide important insights for improved understanding and prevention of urinary stone disease. PMID: 26334088 [PubMed - as supplied by publisher]

Related Articles Metabolomics Biomarkers for Breast Cancer. Pathobiology. 2015 Sep;82(3-4):153-65 Authors: Günther UL Abstract Metabolomics represents a more recent addition to the range of omics tools, which are increasingly used in clinical applications. By measuring the composition of small molecules in tissues, blood or urine, it provides a sensitive molecular readout often associated with disease and its states, especially in cancer. Changes in metabolism related to cancer are increasingly well understood and are seen as a major hallmark of cancer. This review covers metabolomics used in human breast cancers, with a focus on its application in clinical diagnostics. There are clear indications that metabolomics could be a useful addition to currently established clinical diagnostic tools for breast cancer, including the possibility to detect cancer and to predict treatment responses and survival rates from blood and tissue samples. PMID: 26330356 [PubMed - in process]

Related Articles Translational metabolomics in cancer research. Biomark Med. 2015 Sep 1; Authors: Snyder NW, Mesaros C, Blair IA Abstract Over the last decade there has been a bottleneck in the introduction of new validated cancer metabolic biomarkers into clinical practice. Unfortunately, there are no biomarkers with adequate sensitivity for the early detection of cancer, and there remain a reliance on cancer antigens for monitoring treatment. The need for new diagnostics has led to the exploration of untargeted metabolomics for discovery of early biomarkers of specific cancers and targeted metabolomics to elucidate mechanistic aspects of tumor progression. The successful translation of such strategies to the treatment of cancer would allow earlier intervention to improve survival. We have reviewed the methodology that is being used to achieve these goals together with recent advances in implementing translational metabolomics in cancer. PMID: 26329905 [PubMed - as supplied by publisher]

Related Articles Metabolic profiling and enzyme analyses indicate a potential role of antioxidant systems in complementing glyphosate resistance in an Amaranthus palmeri biotype. J Agric Food Chem. 2015 Sep 2; Authors: Maroli AS, Nandula V, Dayan FE, Duke S, Gerard P, Tharayil N Abstract Metabolomics and biochemical assays were employed to identify physiological perturbations induced by glyphosate in a susceptible (S) and resistant (R) biotype of Amaranthus palmeri. At 8 h after treatment (HAT), shikimic acid accumulated in both R- and S-biotypes in response to glyphosate application, which was accompanied by an increase in organic acids and aromatic amino acids in the R-biotype and an increase in sugars and branched-chain amino acids in the S-biotype. However, by 80 HAT the metabolite pool of glyphosate-treated R-biotype was similar to that of the water-treated control S and R-biotype, indicating physiological recovery. Furthermore, glyphosate-treated R-biotype had lower reactive oxygen species (ROS) damage, higher ROS scavenging activity, and higher levels of secondary compounds of the shikimate pathway. Thus metabolomics, in conjunction with biochemical assays, indicate that glyphosate induced metabolic perturbations are not limited to shikimate pathway, and the oxidant quenching efficiency could potentially complement the glyphosate resistance in this R-biotype. PMID: 26329798 [PubMed - as supplied by publisher]

Related Articles Investigation on the antidepressant effect of sea buckthorn seed oil through the GC-MS-based metabolomics approach coupled with multivariate analysis. Food Funct. 2015 Sep 2; Authors: Tian JS, Liu CC, Xiang H, Zheng XF, Peng GJ, Zhang X, Du GH, Qin XM Abstract Depression is one of the prevalent and serious mental disorders and the number of depressed patients has been on the rise globally during the recent decades. Sea buckthorn seed oil from traditional Chinese medicine (TCM) is edible and has been widely used for treatment of different diseases for a long time. However, there are few published reports on the antidepressant effect of sea buckthorn seed oil. With the objective of finding potential biomarkers of the therapeutic response of sea buckthorn seed oil in chronic unpredictable mild stress (CUMS) rats, urine metabolomics based on gas chromatography-mass spectrometry (GC-MS) coupled with multivariate analysis was applied. In this study, we discovered a higher level of pimelic acid as well as palmitic acid and a lower level of suberic acid, citrate, phthalic acid, cinnamic acid and Sumiki's acid in urine of rats exposed to CUMS procedures after sea buckthorn seed oil was administered. These changes of metabolites are involved in energy metabolism, fatty acid metabolism and other metabolic pathways as well as in the synthesis of neurotransmitters and it is helpful to facilitate the efficacy evaluation and mechanism elucidating the effect of sea buckthorn seed oil for depression management. PMID: 26328874 [PubMed - as supplied by publisher]

Related Articles DnsID in MyCompoundID for Rapid Identification of Dansylated Amine- and Phenol-Containing Metabolites in LC-MS-Based Metabolomics. Anal Chem. 2015 Sep 1; Authors: Huan T, Wu Y, Tang C, Lin G, Li L Abstract High-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC-MS. The current library consists of 273 unique metabolites, mainly amines and phenols, that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC-MS. PMID: 26327437 [PubMed - as supplied by publisher]