PubMed

Emerging new strategies for successful metabolite identification in metabolomics. Bioanalysis. 2016 Feb 26; Authors: Bingol K, Bruschweiler-Li L, Li D, Zhang B, Xie M, Brüschweiler R Abstract This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties. PMID: 26915807 [PubMed - as supplied by publisher]

New nodes and edges in the glucosinolate molecular network revealed by proteomics and metabolomics of Arabidopsis myb28/29 and cyp79B2/B3 glucosinolate mutants. J Proteomics. 2016 Feb 22; Authors: Mostafa I, Zhu N, Yoo MJ, Balmant KM, Misra BB, Dufresne C, Abou-Hashem M, Chen S, El-Domiaty M Abstract Glucosinolates present in Brassicales are important for human health and plant defense against insects and pathogens. Here we investigate the proteomes and metabolomes of Arabidopsis myb28/29 and cyp79B2/B3 mutants deficient in aliphatic glucosinolates and indolic glucosinolates, respectively. Quantitative proteomics of the myb28/29 and cyp79B2/B3 mutants led to the identification of 2785 proteins, of which 142 proteins showed significant changes in the two mutants compared to wild type (WT). By mapping the differential proteins using STRING, we detected 59 new edges in the glucosinolate metabolic network. These connections can be classified as primary with direct roles in glucosinolate metabolism, secondary related to plant stress responses, and tertiary involved in other biological processes. Gene Ontology analysis of the differential proteins showed high level of enrichment in the nodes belonging to metabolic process including glucosinolate biosynthesis and response to stimulus. Using metabolomics, we quantified 292 metabolites covering a broad spectrum of metabolic pathways, and 89 exhibited differential accumulation patterns between the mutants and WT. The changing metabolites (e.g., γ-glutamyl amino acids, auxins and glucosinolate hydrolysis products) complement our proteomics findings. This study contributes toward engineering and breeding of glucosinolate profiles in plants in efforts to improve human health, crop quality and productivity. BIOLOGICAL SIGNIFICANCE: Glucosinolates in Brassicales constitute an important group of natural metabolites important for plant defense and human health. Its biosynthetic pathways and transcriptional regulation have been well-studied. Using Arabidopsis mutants of important genes in glucosinolate biosynthesis, quantitative proteomics and metabolomics led to identification of many proteins and metabolites that are potentially related to glucosinolate metabolism. This study provides a comprehensive insight into the molecular networks of glucosinolate metabolism, and will facilitate efforts toward engineering and breeding of glucosinolate profiles for enhanced crop defense, and nutritional value. PMID: 26915584 [PubMed - as supplied by publisher]

Serum metabolomic profiling in patients with systemic lupus erythematosus by GC/MS. Mod Rheumatol. 2016 Feb 26;:1-25 Authors: Yan B, Huang J, Zhang C, Hu X, Gao M, Shi A, Zha W, Shi L, Huang C, Yang L Abstract OBJECTIVES: The aim of this study is to characterize the serum metabolic profiles of patients with systemic lupus erythematosus (SLE) using metabolomics. METHODS: Serum samples were collected from patients with SLE (n=80) and gender- and age-matched healthy controls (n=57). Metabolite profiles were performed with gas chromatography-mass spectrometry (GC/MS) in conjunction with multivariate statistical analysis, and possible biomarker metabolites were identified. RESULTS: SLE and disease severity-related metabolic phenotypes were identified in sera. Parameters of the metabolomic model were correlated with SLEDAI (SLE disease activity index) scores in SLE. The metabolic signature of SLE patients comprised metabolite changes associated with amino acid turnover or protein biosynthesis, saccharometabolism, lipid metabolism and gut microbial metabolism. Disease activity-related alterations included glutamate, 2-hydroxyisobutyrate, citrate, glycerol, linoleic acid and propylparaben metabolites. Parts of endogenous metabolites related to SLE had the relationship with serum immunological parameters and organ manifestations. Moreover, receiver operating characteristic (ROC) curve analysis revealed a higher diagnosis accuracy of endogenous metabolites. CONCLUSIONS: Our study distinguished serum metabotypes associated with SLE and disease activities. The implementation of this metabolomic strategy may help to develop biochemical insight into the metabolic alterations in SLE. PMID: 26915395 [PubMed - as supplied by publisher]

Selection of Taste Markers Related to Lactic Acid Bacteria Microflora Metabolism for Chinese Traditional Paocai: A GC-MS-based Metabolomics Approach. J Agric Food Chem. 2016 Feb 26; Authors: Zhao N, Zhang C, Yang Q, Guo Z, Yang B, Lu W, Li D, Tian F, Liu X, Zhang H, Chen W Abstract Traditional paocai brine (PB) is continuously propagated by back slopping and contains numerous lactic acid bacteria (LAB) strains. Although PB is important for the quality of paocai (Chinese sauerkraut), the taste features, taste-related compounds of PB-paocai and the effects of LAB communities from PB on the taste compounds remain unclear. An electronic tongue was used to evaluate the taste features of 13 PB-paocai samples. Umami, saltiness, bitterness, sweetness and aftertaste astringency were the main taste features of PB-paocai. Fourteen compounds were identified as discriminant taste markers for PB-paocai via GC-MS-based multi-marker profiling. A LAB co-culture (Lactobacillus plantarum, Lactobacillus buchneri and Pediococcus ethanoliduran) from PB could significantly increase glutamic acid (umami), sucrose (sweetness), glycine (sweetness), lactic acid (sourness) and γ-amino-butyric acid in PB-paocai, which would endow it with important flavor features. Such features could then facilitate starter screening and fermentation optimization to produce paocai-related foods with better nutritional and sensory qualities. PMID: 26915389 [PubMed - as supplied by publisher]

Related Articles Comparative Metabolomic Profiling of Hepatocellular Carcinoma Cells Treated with Sorafenib Monotherapy vs. Sorafenib-Everolimus Combination Therapy. Med Sci Monit. 2015;21:1781-91 Authors: Zheng JF, Lu J, Wang XZ, Guo WH, Zhang JX Abstract BACKGROUND: Sorafenib-everolimus combination therapy may be more effective than sorafenib monotherapy for hepatocellular carcinoma (HCC). To better understand this effect, we comparatively profiled the metabolite composition of HepG2 cells treated with sorafenib, everolimus, and sorafenib-everolimus combination therapy. MATERIAL AND METHODS: A 2D HRMAS 1H-NMR metabolomic approach was applied to identify the key differential metabolites in 3 experimental groups: sorafenib (5 µM), everolimus (5 µM), and combination therapy (5 µM sorafenib +5 µM everolimus). MetaboAnalyst 3.0 was used to perform pathway analysis. RESULTS: All OPLS-DA models displayed good separation between experimental groups, high-quality goodness of fit (R2), and high-quality goodness of predication (Q2). Sorafenib and everolimus have differential effects with respect to amino acid, methane, pyruvate, pyrimidine, aminoacyl-tRNA biosynthesis, and glycerophospholipid metabolism. The addition of everolimus to sorafenib resulted in differential effects with respect to pyruvate, amino acid, methane, glyoxylate and dicarboxylate, glycolysis or gluconeogenesis, glycerophospholipid, and purine metabolism. CONCLUSIONS: Sorafenib and everolimus have differential effects on HepG2 cells. Sorafenib preferentially affects glycerophospholipid and purine metabolism, while the addition of everolimus preferentially affects pyruvate, amino acid, and glucose metabolism. This phenomenon may explain (in part) the synergistic effects of sorafenib-everolimus combination therapy observed in vivo. PMID: 26092946 [PubMed - indexed for MEDLINE]

Related Articles Quantitative proteomics of bronchoalveolar lavage fluid in idiopathic pulmonary fibrosis. J Proteome Res. 2015 Feb 6;14(2):1238-49 Authors: Foster MW, Morrison LD, Todd JL, Snyder LD, Thompson JW, Soderblom EJ, Plonk K, Weinhold KJ, Townsend R, Minnich A, Moseley MA Abstract The proteomic analysis of bronchoalveolar lavage fluid (BALF) can give insight into pulmonary disease pathology and response to therapy. Here, we describe the first gel-free quantitative analysis of BALF in idiopathic pulmonary fibrosis (IPF), a chronic and fatal scarring lung disease. We utilized two-dimensional reversed-phase liquid chromatography and ion-mobility-assisted data-independent acquisition (HDMSE) for quantitation of >1000 proteins in immunodepleted BALF from the right middle and lower lobes of normal controls and patients with IPF. Among the analytes that were increased in IPF were well-described mediators of pulmonary fibrosis (osteopontin, MMP7, CXCL7, CCL18), eosinophil- and neutrophil-derived proteins, and proteins associated with fibroblast foci. For additional discovery and targeted validation, BALF was also screened by multiple reaction monitoring (MRM), using the JPT Cytokine SpikeMix library of >400 stable isotope-labeled peptides. A refined MRM assay confirmed the robust expression of osteopontin, and demonstrated, for the first time, upregulation of the pro-fibrotic cytokine, CCL24, in BALF in IPF. These results show the utility of BALF proteomics for the molecular profiling of fibrotic lung diseases and the targeted quantitation of soluble markers of IPF. More generally, this study addresses critical quality control measures that should be widely applicable to BALF profiling in pulmonary disease. PMID: 25541672 [PubMed - indexed for MEDLINE]

25 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 2016/02/26PubMed 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.

24 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 2016/02/24PubMed 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.

Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices. Brief Bioinform. 2016 Feb 19; Authors: Taylor SL, Ruhaak LR, Kelly K, Weiss RH, Kim K Abstract With expanded access to, and decreased costs of, mass spectrometry, investigators are collecting and analyzing multiple biological matrices from the same subject such as serum, plasma, tissue and urine to enhance biomarker discoveries, understanding of disease processes and identification of therapeutic targets. Commonly, each biological matrix is analyzed separately, but multivariate methods such as MANOVAs that combine information from multiple biological matrices are potentially more powerful. However, mass spectrometric data typically contain large amounts of missing values, and imputation is often used to create complete data sets for analysis. The effects of imputation on multiple biological matrix analyses have not been studied. We investigated the effects of seven imputation methods (half minimum substitution, mean substitution, k-nearest neighbors, local least squares regression, Bayesian principal components analysis, singular value decomposition and random forest), on the within-subject correlation of compounds between biological matrices and its consequences on MANOVA results. Through analysis of three real omics data sets and simulation studies, we found the amount of missing data and imputation method to substantially change the between-matrix correlation structure. The magnitude of the correlations was generally reduced in imputed data sets, and this effect increased with the amount of missing data. Significant results from MANOVA testing also were substantially affected. In particular, the number of false positives increased with the level of missing data for all imputation methods. No one imputation method was universally the best, but the simple substitution methods (Half Minimum and Mean) consistently performed poorly. PMID: 26896791 [PubMed - as supplied by publisher]

Metabolomics approach to explore the effects of Kai-Xin-San on Alzheimer's disease using UPLC/ESI-Q-TOF mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2016 Feb 8;1015-1016:50-61 Authors: Chu H, Zhang A, Han Y, Lu S, Kong L, Han J, Liu Z, Sun H, Wang X Abstract Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that influences elderly populations, with no effective method for its treatment so far. To improve its diagnosis and treatment, changes of small molecule metabolite during AD should be elucidated. Kai-Xin-San (KXS) is an herbal formulae that has been widely used to treat mental disorders, especially amnesia and depression in China. Experimental AD was induced in rats by an intraperitoneal injection of d-galactose (d-gal) and administered intragastrically with aluminum chloride (AlCl3) simultaneously for 105 days. Morris water maze task as a behavior test was used for testing the effects of KXS on AD model and pathological changes to the brain were assessed by hematoxylin-eosin staining and immunohistochemistry. The levels of Bcl-2 and ChAT in hippocampus were evaluated by western-blot. Furthermore, metabolite profiling of AD was performed through ultra-performance liquid chromatography/electrospray ionization quadruple time-of- flight-high-definition mass spectrometry (UPLC/ESI-Q-TOF/HDMS) combined with pattern recognition approaches and pathway analysis. d-gal and AlCl3-treated caused a decline in spatial learning and memory, hippocampal histopathological abnormalities and increased Aβ1-40 levels in the brain cortex and hippocampus along with decreased Bcl-2 and ChAT expression in the hippocampus. KXS significantly improved the cognitive impairment induced by d-gal and AlCl3, attenuated hippocampal histopathological abnormalities, reduced Aβ1-40 levels and increased Bcl-2 and ChAT expression in the hippocampus. A total of 48 metabolites were considered as potential biomarkers of AD, and 36 metabolites may correlate with the regulation of KXS treatment on AD. Changes in AD metabolic profiling were close to normal states through regulating multiple perturbed pathways after KXS treatment. This study has revealed the potential biomarkers and metabolic networks of AD, illuminated the biochemistry mechanism of AD and the metabolic pathways influenced by KXS. PMID: 26896572 [PubMed - as supplied by publisher]

Identifying new Risk Markers and Potential Targets for Coronary Artery Disease: The Value of the Lipidome and Metabolome. Cardiovasc Drugs Ther. 2016 Feb 20; Authors: Laaksonen R Abstract PURPOSE: This systematic review was performed to summarize published data on lipidomic and metabolomic risk markers of coronary artery disease. METHODS: Studies were identified from a literature search of PubMed. RESULTS: Published data shows that analysis of metabolites and lipids offers an opportunity to increase our knowledge of the biological processes related to development and progression of atherosclerotic coronary disease. It is evident that advanced analytical technologies are able to detect and identify a large number of molecules that may have important structural and functional roles over and above currently used biomarkers in the cardiovascular field. It is suggested in a number of reports that the novel biomarkers can be used to improve risk stratification and patient selection for different treatments. Also, monitoring treatment efficacy and safety as well as lifestyle changes should be facilitated by such novel markers. CONCLUSION: Until now a plethora of biomarker candidates associated with cardiovascular event risk have been identified, but very few have passed through clinical and analytical validation and found their way into clinical use. Consequently, the appetite of physicians to use these novel tests in daily clinical routine has not yet been truly tested. PMID: 26896184 [PubMed - as supplied by publisher]

The One-carbon Carrier Methylofuran from Methylobacterium extorquens AM1 Contains a Large Number of Alpha- and Gamma-linked Glutamic Acid Residues. J Biol Chem. 2016 Feb 19; Authors: Hemmann J, Saurel O, Ochsner AM, Stodden BK, Kiefer P, Milon A, Vorholt JA Abstract Methylobacterium extorquens AM1 uses dedicated cofactors for one-carbon unit conversion. Based on the sequence identities of enzymes and activity determinations, a methanofuran (MFR) analog was proposed to be involved in formaldehyde oxidation in the Alphaproteo-bacterium. Here, we report the structure of the cofactor, which we termed methylofuran. Using an in vitro enzyme assay and LC-MS, methylofuran was identified in cell extracts and further purified. From the exact mass and MS-MS fragmentation pattern, the structure of the cofactor was determined to consist of a polyglutamic acid side chain linked to a core structure similar to the one present in archaeal methanofuran variants. NMR analyses showed that the core structure contains a furan ring. However, instead of the tyramine moiety that is present in MFR cofactors, a tyrosine residue is present in methylofuran, which was further confirmed by MS through the incorporation of a 13C-labeled precursor. Methylofuran was present as a mixture of different species with varying numbers of glutamic acid residues in the side chain, ranging from 12 to 24. Notably, the glutamic acid residues were not solely γ-linked, as is the case for all known methanofurans, but were identified by NMR as a mixture of α- and γ-linked amino acids. Considering the unusual peptide chain, the elucidation of the structure presented here sets the basis for further research on this cofactor, which is probably the largest cofactor known so far. PMID: 26895963 [PubMed - as supplied by publisher]

Branched-Chain Amino Acids and Insulin Metabolism: The Insulin Resistance Atherosclerosis Study (IRAS). Diabetes Care. 2016 Feb 19; Authors: Lee CC, Watkins SM, Lorenzo C, Wagenknecht LE, Il'yasova D, Chen YI, Haffner SM, Hanley AJ Abstract OBJECTIVE: Recent studies using untargeted metabolomics approaches have suggested that plasma branched-chain amino acids (BCAAs) are associated with incident diabetes. However, little is known about the role of plasma BCAAs in metabolic abnormalities underlying diabetes and whether these relationships are consistent across ethnic populations at high risk for diabetes. We investigated the associations of BCAAs with insulin sensitivity (SI), acute insulin response (AIR), and metabolic clearance of insulin (MCRI) in a multiethnic cohort. RESEARCH DESIGN AND METHODS: In 685 participants without diabetes of the Insulin Resistance Atherosclerosis Study (290 Caucasians, 165 African Americans, and 230 Hispanics), we measured plasma BCAAs (sum of valine, leucine, and isoleucine) by mass spectrometry and SI, AIR, and MCRI by frequently sampled intravenous glucose tolerance tests. RESULTS: Elevated plasma BCAAs were inversely associated with SI and MCRI and positively associated with fasting insulin in regression models adjusted for potential confounders (β = -0.0012 [95% CI -0.0018, -0.00059], P < 0.001 for SI; β = -0.0013 [95% CI -0.0018, -0.00082], P < 0.001 for MCRI; and β = 0.0015 [95% CI 0.0008, 0.0023], P < 0.001 for fasting insulin). The association of BCAA with SI was significantly modified by ethnicity, with the association only being significant in Caucasians and Hispanics. Elevated plasma BCAAs were associated with incident diabetes in Caucasians and Hispanics (multivariable-adjusted odds ratio per 1-SD increase in plasma BCAAs: 1.67 [95% CI 1.21, 2.29], P = 0.002) but not in African Americans. Plasma BCAAs were not associated with SI-adjusted AIR. CONCLUSIONS: Plasma BCAAs are associated with incident diabetes and underlying metabolic abnormalities, although the associations were generally stronger in Caucasians and Hispanics. PMID: 26895884 [PubMed - as supplied by publisher]

Related Articles Metabolic profiling during HIV-1 and HIV-2 infection of primary human monocyte-derived macrophages. Virology. 2016 Feb 16;491:106-114 Authors: Hollenbaugh JA, Montero C, Schinazi RF, Munger J, Kim B Abstract We evaluated cellular metabolism profiles of HIV-1 and HIV-2 infected primary human monocyte-derived macrophages (MDMs). First, HIV-2 GL-AN displays faster production kinetics and greater amounts of virus as compared to HIV-1s: YU-2, 89.6 and JR-CSF. Second, quantitative LC-MS/MS metabolomics analysis demonstrates very similar metabolic profiles in glycolysis and TCA cycle metabolic intermediates between HIV-1 and HIV-2 infected macrophages, with a few notable exceptions. The most striking metabolic change in MDMs infected with HIV-2 relative to HIV-1-infected MDMs was the increased levels of quinolinate, a metabolite in the tryptophan catabolism pathway that has been linked to HIV/AIDS pathogenesis. Third, both HIV-1 and HIV-2 infected MDMs showed elevated levels of ribose-5-phosphate, a key metabolic component in nucleotide biosynthesis. Finally, HIV-2 infected MDMs display increased dNTP concentrations as predicted by Vpx-mediated SAMHD1 degradation. Collectively, these data show differential metabolic changes during HIV-1 and HIV-2 infection of macrophages. PMID: 26895248 [PubMed - as supplied by publisher]

Related Articles Complex coordinated extracellular metabolism: Acid phosphatases activate diluted human leukocyte proteins to generate energy flow as NADPH from purine nucleotide ribose. Redox Biol. 2016 Feb 2;8:271-284 Authors: Hibbs JB, Vavrin Z, Cox JE Abstract Complex metabolism is thought to occur exclusively in the crowded intracellular environment. Here we report that diluted enzymes from lysed human leukocytes produce extracellular energy. Our findings involve two pathways: the purine nucleotide catabolic pathway and the pentose phosphate pathway, which function together to generate energy as NADPH. Glucose6P fuel for NADPH production is generated from structural ribose of purine ribonucleoside monophosphates, ADP, and ADP-ribose. NADPH drives glutathione reductase to reduce an oxidized glutathione disulfide-glutathione redox couple. Acid phosphatases initiate ribose5P salvage from purine ribonucleoside monophosphates, and transaldolase controls the direction of carbon chain flow through the nonoxidative branch of the pentose phosphate pathway. These metabolic control points are regulated by pH. Biologically, this energy conserving metabolism could function in perturbed extracellular spaces. PMID: 26895212 [PubMed - as supplied by publisher]

Related Articles Effects of heat-treatment on the stability and composition of metabolomic extracts from the earthworm Eisenia fetida. Metabolomics. 2016;12:47 Authors: Schock TB, Strickland S, Steele EJ, Bearden DW Abstract Environmental metabolomics studies employing earthworms as sentinels for soil contamination are numerous, but the instability of the metabolite extracts from these organisms has been minimally addressed. This study evaluated the efficacy of adding a heat-treatment step in two commonly used extraction protocols (Bligh and Dyer and D2O phosphate buffer) as a pre-analytical stabilization method. The resulting metabolic profiles of Eisenia fetida were assessed using principal component analysis and NMR spectral evaluations. The heated Bligh and Dyer extractions produced stabilized profiles with minimal variation of the extracted metabolomic profiles over time, providing a more suitable method for metabolomic analysis of earthworm extracts. PMID: 26893595 [PubMed - as supplied by publisher]

Related Articles Toward automated chromatographic fingerprinting: A non-alignment approach to gas chromatography mass spectrometry data. Anal Chim Acta. 2016 Mar 10;911:42-58 Authors: Vestner J, de Revel G, Krieger-Weber S, Rauhut D, du Toit M, de Villiers A Abstract In contrast to targeted analysis of volatile compounds, non-targeted approaches take information of known and unknown compounds into account, are inherently more comprehensive and give a more holistic representation of the sample composition. Although several non-targeted approaches have been developed, there's still a demand for automated data processing tools, especially for complex multi-way data such as chromatographic data obtained from multichannel detectors. This work was therefore aimed at developing a data processing procedure for gas chromatography mass spectrometry (GC-MS) data obtained from non-targeted analysis of volatile compounds. The developed approach uses basic matrix manipulation of segmented GC-MS chromatograms and PARAFAC multi-way modelling. The approach takes retention time shifts and peak shape deformations between samples into account and can be done with the freely available N-way toolbox for MATLAB. A demonstration of the new fingerprinting approach is presented using an artificial GC-MS data set and an experimental full-scan GC-MS data set obtained for a set of experimental wines. PMID: 26893085 [PubMed - in process]

Related Articles Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery. Anal Chim Acta. 2016 Mar 10;911:27-34 Authors: Yun YH, Deng BC, Cao DS, Wang WT, Liang YZ Abstract Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable. PMID: 26893083 [PubMed - in process]

Related Articles Soft Corals Biodiversity in the Egyptian Red Sea: a Comparative MS and NMR Metabolomics Approach of Wild and Aquarium Grown Species. J Proteome Res. 2016 Feb 19; Authors: Farag MA, Porzel A, Al-Hammady MA, Hegazy MF, Meyer A, Mohamed TA, Westphal H, Wessjohann LA Abstract Marine life has developed unique metabolic and physiologic capabilities and advanced symbiotic relationships to survive in the varied and complex marine ecosystems. Herein, metabolite composition of the soft coral genus Sarcophyton was profiled with respect to its species, and different habitats along the coastal Egyptian Red Sea via 1H-NMR and UPLC-MS large-scale metabolomics analyses. Current study extends the application of comparative secondary metabolite profiling from plants to corals to reveal for metabolite compositional differences among its species, using a novel comparative MS and NMR approach (COMET). This was applied for the first time to investigate the metabolism of 16 Sarcophyton species in the context of their genetic diversity and growth habitat. Under optimized conditions, we were able to simultaneously identify 120 metabolites including sixty five diterpenes (65), eight sesquiterpenes (8), eighteen sterols (18) and fifteen oxylipids (15). Principal component analysis (PCA), hierarchical clustering analysis (HCA) and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) were used to define both similarities and differences among samples. For corals classification based on species type, UPLC-MS was found to be more effective than NMR. The main differentiations emanate from cembranoids and oxylipids. The specific markers that contribute to discrimination between soft corals of S. ehrenbergi from 3 different growing habitats also belonged to cembrane type diterpenes, with aquarium S. ehrenbergi corals being less enriched in cembranoids compared to sea corals. PCA analysis using either NMR or UPLC-MS datasets was found equally effective in predicting the species origin of unknown Sarcophyton species. Cyclopropane containing sterols observed in abundance in the corals may act as cellular membrane protectant against the action of coral toxins, i.e. cembranoids. PMID: 26892921 [PubMed - as supplied by publisher]

Related Articles Abdominal obesity and circulating metabolites: A twin study approach. Metabolism. 2016 Mar;65(3):111-21 Authors: Bogl LH, Kaye SM, Rämö JT, Kangas AJ, Soininen P, Hakkarainen A, Lundbom J, Lundbom N, Ortega-Alonso A, Rissanen A, Ala-Korpela M, Kaprio J, Pietiläinen KH Abstract OBJECTIVE: To investigate how obesity, insulin resistance and low-grade inflammation link to circulating metabolites, and whether the connections are due to genetic or environmental factors. SUBJECTS AND METHODS: Circulating serum metabolites were determined by proton NMR spectroscopy. Data from 1368 (531 monozygotic (MZ) and 837 dizygotic (DZ)) twins were used for bivariate twin modeling to derive the genetic (rg) and environmental (re) correlations between waist circumference (WC) and serum metabolites. Detailed examination of the associations between fat distribution (DEXA) and metabolic health (HOMA-IR, CRP) was performed among 286 twins including 33 BMI-discordant MZ pairs (intrapair BMI difference ≥3kg/m(2)). RESULTS: Fat, especially in the abdominal area (i.e. WC, android fat % and android to gynoid fat ratio), together with HOMA-IR and CRP correlated significantly with an atherogenic lipoprotein profile, higher levels of branched-chain (BCAA) and aromatic amino acids, higher levels of glycoprotein, and a more saturated fatty acid profile. In contrast, a higher proportion of gynoid to total fat associated with a favorable metabolite profile. There was a significant genetic overlap between WC and several metabolites, most strongly with phenylalanine (rg=0.40), glycoprotein (rg=0.37), serum triglycerides (rg=0.36), BCAAs (rg=0.30-0.40), HDL particle diameter (rg=-0.33) and HDL cholesterol (rg=-0.30). The effect of acquired obesity within the discordant MZ pairs was particularly strong for atherogenic lipoproteins. CONCLUSIONS: A wide range of unfavorable alterations in the serum metabolome was associated with abdominal obesity, insulin resistance and low-grade inflammation. Twin modeling and obesity-discordant twin analysis suggest that these associations are partly explained by shared genes but also reflect mechanisms independent of genetic liability. PMID: 26892522 [PubMed - in process]