PubMed

Related Articles A fully validated GC-TOF-MS method for the quantification of fatty acids revealed alterations in the metabolic profile of fatty acids after smoking cessation. J Chromatogr B Analyt Technol Biomed Life Sci. 2016 Dec 23;1041-1042:141-150 Authors: Goettel M, Niessner R, Pluym N, Scherer G, Scherer M Abstract We developed and validated an efficient and robust method for the simultaneous quantification of 44 fatty acid species in human plasma via GC-TOF-MS. The method is characterized by its robustness, accuracy and precision covering a wide range of fatty acid species with various saturation degrees including short chain fatty acids (beginning with FA 4:0) and long chain fatty acids (up to FA 32:0). The fatty acids were methylated prior to analyses and subsequently detected as fatty acid methyl esters by means of GC-TOF-MS. A highly substituted polar column allowed the separation of geometrical and positional isomers of fatty acid species. The method was applied to plasma samples of a strictly diet controlled clinical smoking cessation study including 39 smokers followed over the course of three months after having quit. Statistical significant alterations within the fatty acid profile were observed when comparing the baseline (subjects still smoking) with one week, one month and three months of smoking cessation. After 3 months of smoking cessation, a partial recovery of alterations in the fatty acid profile evoked by smoking was observed. In conclusion, the developed fatty acid profiling method using GC-TOF-MS has proven as a reliable tool for the quantitative determination of 44 individual fatty acid species within clinical studies. PMID: 28039811 [PubMed - as supplied by publisher]

Related Articles What computational non-targeted mass spectrometry-based metabolomics can gain from shotgun proteomics. Curr Opin Biotechnol. 2016 Dec 28;43:141-146 Authors: Hamzeiy H, Cox J Abstract Computational workflows for mass spectrometry-based shotgun proteomics and untargeted metabolomics share many steps. Despite the similarities, untargeted metabolomics is lagging behind in terms of reliable fully automated quantitative data analysis. We argue that metabolomics will strongly benefit from the adaptation of successful automated proteomics workflows to metabolomics. MaxQuant is a popular platform for proteomics data analysis and is widely considered to be superior in achieving high precursor mass accuracies through advanced nonlinear recalibration, usually leading to five to ten-fold better accuracy in complex LC-MS/MS runs. This translates to a sharp decrease in the number of peptide candidates per measured feature, thereby strongly improving the coverage of identified peptides. We argue that similar strategies can be applied to untargeted metabolomics, leading to equivalent improvements in metabolite identification. PMID: 28039739 [PubMed - as supplied by publisher]

Related Articles Micafungin alters the amino acid, nucleic acid and central carbon metabolism of Candida albicans at subinhibitory concentrations: novel insights into mechanisms of action. J Antimicrob Chemother. 2016 Dec 30;: Authors: Katragkou A, Williams M, Sternberg S, Pantazatos D, Roilides E, Walsh TJ Abstract BACKGROUND: Echinocandins are an important class of antifungal agents in the treatment of invasive candidiasis. However, little is known about the metabolomic effects of echinocandins on Candida We therefore performed LC-high-resolution MS (LC-HRMS)-based metabolomics profiling of the response of Candida albicans cells to increasing concentrations of micafungin to determine the metabolic response of Candida to micafungin subinhibitory injury. METHODS: Isolates of C. albicans were cultured on nitrocellulose filters to mid-logarithmic phase of growth and micafungin (0-0.25 mg/L) was added. At mid-logarithmic phase, replicates were metabolically quenched. Intracellular metabolites were analysed by LC-HRMS. Changes in pool sizes of individual metabolites were analysed by Student's t-test adjusted for multiple hypothesis testing by Benjamini-Hochberg correction. Metabolites were ascribed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways database. RESULTS: Among 3446 detected metabolites, 204 were identified by comparison against pure standard or comparison against a library of mass-retention-time pairs. Fifty had significantly altered abundances in response to increasing micafungin concentrations. Pool sizes of amino acids, nucleic acids and polyamine metabolism were significantly increased at subinhibitory concentrations, while exposure to inhibitory concentrations resulted in a precipitous decrease consistent with fungicidal activity. CONCLUSIONS: Micafungin induces a re-routing of metabolic pathways inhibiting protein synthesis and cell replication. These results shed light on new mechanisms of action of echinocandins. PMID: 28039272 [PubMed - as supplied by publisher]

Related Articles Recent Advances in Lipidomics: Analytical and Clinical Perspectives. Prostaglandins Other Lipid Mediat. 2016 Dec 27;: Authors: Sethi S, Brietzke E Abstract Lipidomics or lipid-profiling is a lipid-targeted metabolomics approach aiming at comprehensive analysis of lipids in biological systems. The extent of information in the genomic and proteomic fields is greater than that in the lipidomics field, because of the complex nature of lipids and the limitations of tools for analysis. Modern technological advances in mass spectrometry and chromatography have greatly improved the developments and applications of metabolic profiling of diverse lipids in complex biological samples. Lipidomics will not only provide insights into the specific functions of lipid species in health and disease, but will also identify potential biomarkers for establishing preventive or therapeutic programs for human diseases. In this review, emphasis is given to the current advances in lipidomics technologies and their applications in disease biomarker discovery, and its clinical application. The application of lipidomics in clinical studies may provide new insights into lipid profiling and pathophysiological mechanisms. We also discuss the lipidomics for the future perspectives and their potential problems. PMID: 28039059 [PubMed - as supplied by publisher]

Related Articles Metabotypes of breast cancer cell lines revealed by non-targeted metabolomics. Metab Eng. 2016 Dec 27;: Authors: Dubuis S, Baenke F, Scherbichler N, Alexander LT, Schulze A, Zamboni N Abstract We present an analysis of intracellular metabolism by non-targeted, high-throughput metabolomics profiling of 18 breast cell lines. We profiled >900 putatively annotated metabolite ions for >100 samples collected under both normoxic and hypoxic conditions and revealed extensive heterogeneity across all metabolic pathways and cell lines. Cell line-specific metabolome profiles dominated over patterns associated with malignancy or with the clinical nomenclature of breast cancer cells. Such characteristic metabolome profiles were reproducible across different laboratories and experiments and exhibited mild to robust changes with change in experimental conditions. To extract a functional overview of cell line heterogeneity, we devised an unsupervised metabotyping procedure that for each pathway automatically recognized metabolic types from metabolome data and assigned cell lines. Our procedure provided a condensed yet global representation of cell line metabolism, revealing the fine structure of metabolic heterogeneity across all tested pathways and cell lines. In follow-up experiments on selected pathways, we confirmed that different metabolic types correlated to differences in the underlying fluxes and difference sensitivity to gene knockdown or pharmacological inhibition. Thus, the identified metabotypes recapitulated functional differences at the pathway level. Metabotyping provides a powerful compression of multi-dimensional data that preserves functional information and serves as a resource for reconciling or understanding heterogeneous metabolic phenotypes or response to inhibition of metabolic pathways. PMID: 28038952 [PubMed - as supplied by publisher]

Proteome alterations associated with transformation of multiple myeloma to secondary plasma cell leukemia. Oncotarget. 2016 Dec 27;: Authors: Zatula A, Dikic A, Mulder C, Sharma A, Vågbø CB, Sousa MM, Waage A, Slupphaug G Abstract Plasma cell leukemia is a rare and aggressive plasma cell neoplasm that may either originate de novo (primary PCL) or by leukemic transformation of multiple myeloma (MM) to secondary PCL (sPCL). The prognosis of sPCL is very poor, and currently no standard treatment is available due to lack of prospective clinical studies. In an attempt to elucidate factors contributing to transformation, we have performed super-SILAC quantitative proteome profiling of malignant plasma cells collected from the same patient at both the MM and sPCL stages of the disease. 795 proteins were found to be differentially expressed in the MM and sPCL samples. Gene ontology analysis indicated a metabolic shift towards aerobic glycolysis in sPCL as well as marked down-regulation of enzymes involved in glycan synthesis, potentially mediating altered glycosylation of surface receptors. There was no significant change in overall genomic 5-methylcytosine or 5-hydroxymethylcytosine at the two stages, indicating that epigenetic dysregulation was not a major driver of transformation to sPCL. The present study constitutes the first attempt to provide a comprehensive map of the altered protein expression profile accompanying transformation of MM to sPCL in a single patient, identifying several candidate proteins that can be targeted by currently available small molecule drugs. Our dataset furthermore constitutes a reference dataset for further proteomic analysis of sPCL transformation. PMID: 28038447 [PubMed - as supplied by publisher]

The integration of GC-MS and LC-MS to assay the metabolomics profiling in Panax ginseng and Panax quinquefolius reveals a tissue- and species-specific connectivity of primary metabolites and ginsenosides accumulation. J Pharm Biomed Anal. 2016 Dec 23;135:176-185 Authors: Liu J, Liu Y, Wang Y, Abozeid A, Zu YG, Tang ZH Abstract The traditional medicine Ginseng mainly including Panax ginseng and Panax quinquefolius is the most widely consumed herbal product in the world. Despite the extensive investigation of biosynthetic pathway of the active compounds ginsenosides, our current understanding of the metabolic interlink between ginsenosides synthesis and primary metabolism at the whole-plant level. In this study, the tissue-specific profiling of primary and the secondary metabolites in two different species of ginseng were investigated by gas chromatography- and liquid chromatography coupled to mass spectrometry. A complex continuous coordination of primary- and secondary-metabolic network was modulated by tissues and species factors during growth. The results showed that altogether 149 primary compounds and 10 ginsenosides were identified from main roots, lateral roots, stems, petioles and leaves in P. ginseng and P. quinquefolius. The partial least squares-discriminate analysis (PLS-DA) revealed obvious compounds distinction among tissue-specific districts relative to species. To survey the dedication of carbon and nitrogen metabolism in different tissues to the accumulation of ginsenosides, we inspected the tissue-specific metabolic changes. Our study testified that the ginsenosides content was dependent on main roots and lateral roots energy metabolism, whereas independent of leaves and petiole photosynthesis during ginsenosides accumulation. When tow species were compared, the results indicated that high rates of C assimilation to C accumulation are closely associated with ginsenosides accumulation in P. ginseng main roots and P. quinquefolius lateral roots, respectively. Taken together, our results suggest that tissue-specific metabolites profiling dynamically changed in process of ginsenosides biosynthesis, which may offer a new train of thoughts to the mechanisms of the ginsenosides biosynthesis at the metabolite level. PMID: 28038384 [PubMed - as supplied by publisher]

Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS. Metabolites. 2016 Dec 29;7(1): Authors: Zarate E, Boyle V, Rupprecht U, Green S, Villas-Boas SG, Baker P, Pinu FR Abstract Gas Chromatography-Mass Spectrometry (GC-MS) has long been used for metabolite profiling of a wide range of biological samples. Many derivatisation protocols are already available and among these, trimethylsilyl (TMS) derivatisation is one of the most widely used in metabolomics. However, most TMS methods rely on off-line derivatisation prior to GC-MS analysis. In the case of manual off-line TMS derivatisation, the derivative created is unstable, so reduction in recoveries occurs over time. Thus, derivatisation is carried out in small batches. Here, we present a fully automated TMS derivatisation protocol using robotic autosamplers and we also evaluate a commercial software, Maestro available from Gerstel GmbH. Because of automation, there was no waiting time of derivatised samples on the autosamplers, thus reducing degradation of unstable metabolites. Moreover, this method allowed us to overlap samples and improved throughputs. We compared data obtained from both manual and automated TMS methods performed on three different matrices, including standard mix, wine, and plasma samples. The automated TMS method showed better reproducibility and higher peak intensity for most of the identified metabolites than the manual derivatisation method. We also validated the automated method using 114 quality control plasma samples. Additionally, we showed that this online method was highly reproducible for most of the metabolites detected and identified (RSD < 20) and specifically achieved excellent results for sugars, sugar alcohols, and some organic acids. To the very best of our knowledge, this is the first time that the automated TMS method has been applied to analyse a large number of complex plasma samples. Furthermore, we found that this method was highly applicable for routine metabolite profiling (both targeted and untargeted) in any metabolomics laboratory. PMID: 28036063 [PubMed]

Targeted metabolomics reveals differences in the extended postprandial plasma metabolome of healthy subjects after intake of whole-grain rye porridges versus refined wheat bread. Mol Nutr Food Res. 2016 Dec 30;: Authors: Shi L, Brunius C, Lindelöf M, Shameh SA, Wu H, Lee I, Landberg R, Moazzami AA Abstract SCOPE: We previously found that whole-grain (WG) rye porridges suppressed appetite and improved glucose metabolism. The present work aimed to investigate potential plasma metabolites that may be related to differences in those appetite and glucose responses. METHODS AND RESULTS: Twenty-one health subjects consumed six isocaloric breakfasts in a randomised cross-over study. Plain WG rye porridges (40 g and 55 g), rye porridge enriched with different inulin: gluten proportions (9:3 g; 6:6 g; 3:9 g), and a 55 g refined wheat bread (control) were served as part of complete breakfast, followed by a standardized lunch. NMR metabolomics assessed 36 plasma metabolites and short chain fatty acids were measured by GC-MS from baseline up to 8 h. Pre-lunch plasma essential amino acids reflected protein composition and post-lunch plasma short chain fatty acids varied with fibre content in breakfasts. No correlations were observed between measured metabolites and glucose, insulin, or appetite responses. CONCLUSIONS: Differences in protein and fibre contents in breakfasts altered postprandial plasma amino acids and short chain fatty acids, respectively, but were unrelated to appetite and glucose responses. Further studies are warrant to identify the underlying mechanisms for the beneficial effects on appetite and second meal glucose responses after rye-based foods. This article is protected by copyright. All rights reserved. PMID: 28035736 [PubMed - as supplied by publisher]

The Association Between Musculoskeletal Pain and Circulating Ornithine: A Population-Based Study. Pain Med. 2016 Dec 29;: Authors: Mäntyselkä P, Ali-Sisto T, Kautiainen H, Niskanen L, Viinamäki H, Velagapudi V, Lehto SM Abstract OBJECTIVE:  Based on several previous clinical studies, we hypothesized that ornithine levels are different among subjects with persistent musculoskeletal pain compared with other subjects in the population. DESIGN:  The study sample consisted of 221 adults with nonpersistent pain, 76 with persistent pain, and 61 with no pain. Concentrations of glutamic acid, ornithine, citrulline, arginine, proline, and spermidine were analyzed using a mass spectrometer. SETTING:  Lapinlahti municipality in Finland. RESULTS:  For the subjects with no pain, nonpersistent pain, and persistent pain, the ornithine concentrations for men were 85.3 µmol/L (SD = 28.9 µmol/L), 98.9 µmol/L (SD = 37.8 µmol/L), and 102.1 µmol/L (SD = 37.1 µmol/L; P = 0.033), respectively. The corresponding concentrations for women were 82.8 µmol/L (SD = 25.2 µmol/L), 83.7 µmol/L (SD = 27.8 µmol/L), and 103.2 µmol/L (SD = 34.9 µmol/L; P = 0.0031). There were no significant differences between the pain groups for any of the other investigated amino acids. Relative sex-specific ornithine concentration adjusted for age, glomerular filtration rate, smoking, body mass index, physical activity, and depressive symptoms was associated with pain (P = 0.025), the ornithine level being higher in the persistent pain group than in the no pain (P = 0.006) and nonpersistent pain (P = 0.032) groups. CONCLUSION:  Ornithine levels are elevated in general population subjects with persistent pain. PMID: 28034972 [PubMed - as supplied by publisher]

Temporal bacterial and metabolic development of the preterm gut reveals specific signatures in health and disease. Microbiome. 2016 Dec 29;4(1):67 Authors: Stewart CJ, Embleton ND, Marrs EC, Smith DP, Nelson A, Abdulkadir B, Skeath T, Petrosino JF, Perry JD, Berrington JE, Cummings SP Abstract BACKGROUND: The preterm microbiome is crucial to gut health and may contribute to necrotising enterocolitis (NEC), which represents the most significant pathology affecting preterm infants. From a cohort of 318 infants, <32 weeks gestation, we selected 7 infants who developed NEC (defined rigorously) and 28 matched controls. We performed detailed temporal bacterial (n = 641) and metabolomic (n = 75) profiling of the gut microbiome throughout the disease. RESULTS: A core community of Klebsiella, Escherichia, Staphyloccocus, and Enterococcus was present in all samples. Gut microbiota profiles grouped into six distinct clusters, termed preterm gut community types (PGCTs). Each PGCT reflected dominance by the core operational taxonomic units (OTUs), except of PGCT 6, which had high diversity and was dominant in bifidobacteria. While PGCTs 1-5 were present in infants prior to NEC diagnosis, PGCT 6 was comprised exclusively of healthy samples. NEC infants had significantly more PGCT transitions prior to diagnosis. Metabolomic profiling identified significant pathways associated with NEC onset, with metabolites involved in linoleate metabolism significantly associated with NEC diagnosis. Notably, metabolites associated with NEC were the lowest in PGCT 6. CONCLUSIONS: This is the first study to integrate sequence and metabolomic stool analysis in preterm neonates, demonstrating that NEC does not have a uniform microbial signature. However, a diverse gut microbiome with a high abundance of bifidobacteria may protect preterm infants from disease. These results may inform biomarker development and improve understanding of gut-mediated mechanisms of NEC. PMID: 28034304 [PubMed - in process]

Related Articles Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease. EBioMedicine. 2016 Mar;5:40-5 Authors: Katsila T, Konstantinou E, Lavda I, Malakis H, Papantoni I, Skondra L, Patrinos GP Abstract Inter-individual variability has been a major hurdle to optimize disease management. Precision medicine holds promise for improving health and healthcare via tailor-made therapeutic strategies. Herein, we outline the paradigm of "pharmacometabolomics-aided pharmacogenomics" in autoimmune diseases. We envisage merging pharmacometabolomic and pharmacogenomic data (to address the interplay of genomic and environmental influences) with information technologies to facilitate data analysis as well as sense- and decision-making on the basis of synergy between artificial and human intelligence. Humans can detect patterns, which computer algorithms may fail to do so, whereas data-intensive and cognitively complex settings and processes limit human ability. We propose that better-informed, rapid and cost-effective omics studies need the implementation of holistic and multidisciplinary approaches. PMID: 27077110 [PubMed - indexed for MEDLINE]

Related Articles Evaluation of carbohydrate-cysteamine thiazolidines as pro-drugs for the treatment of cystinosis. Carbohydr Res. 2016 Dec 18;439:9-15 Authors: Ramazani Y, Levtchenko EN, Van Den Heuvel L, Van Schepdael A, Paul P, Ivanova EA, Pastore A, Hartman TM, Price NP Abstract Cystinosis is a genetic disorder caused by malfunction of cystinosin and is characterized by accumulation of cystine. Cysteamine, the medication used in cystinosis, causes halitosis resulting in poor patient compliance. Halitosis is mainly caused by the formation of dimethylsulfide as the final product in the cysteamine metabolism pathway. We have synthesized carbohydrate-cysteamine thiazolidines, and hypothesized that the hydrolytic breakdown of cysteamine-thiazolidines can result in free cysteamine being released in target organs. To examine our hypothesis, we tested these analogs in vitro in patient-derived fibroblasts. Cystinotic fibroblasts were treated with different concentrations of arabinose-cysteamine, glucose-cysteamine and maltose-cysteamine. We demonstrated that the analogs break down into cysteamine extracellularly and might therefore not be fully taken up by the cells under the form of the pro-drug. Potential modifications of the analogs that enable their intracellular rather than extracellular breakdown, is necessary to pursue the potential of these analogs as pro-drugs. PMID: 28033491 [PubMed - as supplied by publisher]

Related Articles BluePen Biomarkers LLC: integrated biomarker solutions. Future Sci OA. 2016 Jun;2(2):FSO124 Authors: Blair IA, Mesaros C, Lilley P, Nunez M Abstract BluePen Biomarkers provides a unique comprehensive multi-omics biomarker discovery and validation platform. We can quantify, integrate and analyze genomics, proteomics, metabolomics and lipidomics biomarkers, alongside clinical data, demographics and other phenotypic data. A unique bio-inspired signal processing analytic approach is used that has the proven ability to identify biomarkers in a wide variety of diseases. The resulting biomarkers can be used for diagnosis, prognosis, mechanistic studies and predicting treatment response, in contexts from core research through clinical trials. BluePen Biomarkers provides an additional groundbreaking research goal: identifying surrogate biomarkers from different modalities. This not only provides new biological insights, but enables least invasive, least-cost tests that meet or exceed the predictive quality of current tests. PMID: 28031971 [PubMed]

Related Articles Noninvasive metabolic profiling for painless diagnosis of human diseases and disorders. Future Sci OA. 2016 Jun;2(2):FSO106 Authors: Mal M Abstract Metabolic profiling provides a powerful diagnostic tool complementary to genomics and proteomics. The pain, discomfort and probable iatrogenic injury associated with invasive or minimally invasive diagnostic methods, render them unsuitable in terms of patient compliance and participation. Metabolic profiling of biomatrices like urine, breath, saliva, sweat and feces, which can be collected in a painless manner, could be used for noninvasive diagnosis. This review article covers the noninvasive metabolic profiling studies that have exhibited diagnostic potential for diseases and disorders. Their potential applications are evident in different forms of cancer, metabolic disorders, infectious diseases, neurodegenerative disorders, rheumatic diseases and pulmonary diseases. Large scale clinical validation of such diagnostic methods is necessary in future. PMID: 28031956 [PubMed]

Related Articles Identifying biomarkers of dietary patterns by using metabolomics. Am J Clin Nutr. 2016 Dec 28;: Authors: Playdon MC, Moore SC, Derkach A, Reedy J, Subar AF, Sampson JN, Albanes D, Gu F, Kontto J, Lassale C, Liao LM, Männistö S, Mondul AM, Weinstein SJ, Irwin ML, Mayne ST, Stolzenberg-Solomon R Abstract BACKGROUND: Healthy dietary patterns that conform to national dietary guidelines are related to lower chronic disease incidence and longer life span. However, the precise mechanisms involved are unclear. Identifying biomarkers of dietary patterns may provide tools to validate diet quality measurement and determine underlying metabolic pathways influenced by diet quality. OBJECTIVE: The objective of this study was to examine the correlation of 4 diet quality indexes [the Healthy Eating Index (HEI) 2010, the Alternate Mediterranean Diet Score (aMED), the WHO Healthy Diet Indicator (HDI), and the Baltic Sea Diet (BSD)] with serum metabolites. DESIGN: We evaluated dietary patterns and metabolites in male Finnish smokers (n = 1336) from 5 nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985-1988). Metabolites were measured with the use of mass spectrometry. We analyzed cross-sectional partial correlations of 1316 metabolites with 4 diet quality indexes, adjusting for age, body mass index, smoking, energy intake, education, and physical activity. We pooled estimates across studies with the use of fixed-effects meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway analyses. RESULTS: The HEI-2010, aMED, HDI, and BSD were associated with 23, 46, 23, and 33 metabolites, respectively (17, 21, 11, and 10 metabolites, respectively, were chemically identified; r-range: -0.30 to 0.20; P = 6 × 10(-15) to 8 × 10(-6)). Food-based diet indexes (HEI-2010, aMED, and BSD) were associated with metabolites correlated with most components used to score adherence (e.g., fruit, vegetables, whole grains, fish, and unsaturated fat). HDI correlated with metabolites related to polyunsaturated fat and fiber components, but not other macro- or micronutrients (e.g., percentages of protein and cholesterol). The lysolipid and food and plant xenobiotic pathways were most strongly associated with diet quality. CONCLUSIONS: Diet quality, measured by healthy diet indexes, is associated with serum metabolites, with the specific metabolite profile of each diet index related to the diet components used to score adherence. This trial was registered at clinicaltrials.gov as NCT00342992. PMID: 28031192 [PubMed - as supplied by publisher]

Related Articles A novel non-canonical Wnt signature for prostate cancer aggressiveness. Oncotarget. 2016 Dec 24;: Authors: Sandsmark E, Hansen AF, Selnæs KM, Bertilsson H, Bofin AM, Wright AJ, Viset T, Richardsen E, Drabløs F, Bathen TF, Tessem MB, Rye MB Abstract Activation of the Canonical Wnt pathway (CWP) has been linked to advanced and metastatic prostate cancer, whereas the Wnt5a-induced non-canonical Wnt pathway (NCWP) has been associated with both good and poor prognosis. A newly discovered NCWP, Wnt5/Fzd2, has been shown to induce epithelial-to-mesenchymal transition (EMT) in cancers, but has not been investigated in prostate cancer. The aim of this study was to investigate if the CWP and NCWP, in combination with EMT, are associated with metabolic alterations, aggressive disease and biochemical recurrence in prostate cancer. An initial analysis was performed using integrated transcriptomics, ex vivo and in vivo metabolomics, and histopathology of prostatectomy samples (n=129), combined with at least five-year follow-up. This analysis detected increased activation of NCWP through Wnt5a/ Fzd2 as the most common mode of Wnt activation in prostate cancer. This activation was associated with increased expression of EMT markers and higher Gleason score. The transcriptional association between NCWP and EMT was confirmed in five other publicly available patient cohorts (1519 samples in total). A novel gene expression signature of concordant activation of NCWP and EMT (NCWP-EMT) was developed, and this signature was significantly associated with metastasis and shown to be a significant predictor of biochemical recurrence. The NCWP-EMT signature was also associated with decreased concentrations of the metabolites citrate and spermine, which have previously been linked to aggressive prostate cancer. Our results demonstrate the importance of NCWP and EMT in prostate cancer aggressiveness, suggest a novel gene signature for improved risk stratification, and give new molecular insight. PMID: 28030815 [PubMed - as supplied by publisher]

Related Articles The effect of coffee intake on lysophosphatidylcholines: A targeted metabolomic approach. Clin Nutr. 2016 Oct 22;: Authors: Miranda AM, Carioca AA, Steluti J, da Silva ID, Fisberg RM, Marchioni DM Abstract BACKGROUND & AIM: Lysophosphatidylcholines (lysoPC) are known to be a pathological component of oxidized-LDL, and several studies demonstrate its pro-inflammatory properties in vitro. Nevertheless, bioactive compounds found in coffee, such as phenolic acids might inhibit LDL oxidation. The relationship between coffee consumption and lysoPC has not been described previously in humans. The aim of the present study was to assess the association between coffee intake and plasma lysoPC levels in adults. METHODS: Data was from the "Health Survey of Sao Paulo (ISA-Capital)", a cross-sectional population-based survey in Sao Paulo, among 169 individuals aged 20 years or older. This population was categorized into three groups: non-coffee consumers (0 mL/day-G1), low coffee consumers (≤100 mL/day-G2), and high coffee consumers (>100 mL/day-G3). Usual coffee intake was estimated by two 24HR and one FFQ, using Multiple Source Method. Quantification of the metabolites was performed by mass spectrometry (FIA-MS/MS and HPLC-MS/MS) and 14 lysoPC species were identified. The association between coffee intake and lysoPC was analyzed by multiple linear regression adjusted for age, sex, household per capita income, smoking, physical activity, body mass index, total energy intake, use of drugs, vegetables and fruit consumption and caffeine intake. RESULTS: LysoPC levels were significantly lower in G3 than in G1, for the lysoPC a C16:1 (β = -0.56; p = 0.014), lysoPC a C18:1 (β = -2.57; p = 0.018), and lysoPC a C20:4 (β = -1.14; p = 0.037). In opposition, the ratios of C16:0/C16:1 and C18:0/18:1 was higher in G3 (β = 5.04; p = 0.025 and β = 0.28; p = 0.003, respectively). CONCLUSION: LysoPC profile differed according to coffee intake, showing a possible beneficial health effect of this beverage on inflammatory and oxidative processes. PMID: 28029506 [PubMed - as supplied by publisher]

Related Articles Carbon Isotopomer Analysis with Non-Unifom Sampling HSQC NMR for Cell Extract and Live Cell Metabolomics Studies. Anal Chem. 2016 Dec 27;: Authors: Lee S, Wen H, An YJ, Cha JW, Ko YJ, Hyberts SG, Park S Abstract Isotopomer analysis using either (13)C NMR or LC/GC-MS has been an invaluable tool for studying metabolic activities in a variety of systems. Traditional challenges are, however, that (13)C-detected NMR is insensitive despite its high resolution, and that MS-based techniques cannot easily differentiate positional isotopomers. In addition, current (13)C NMR or LC/GC-MS has limitations in detecting metabolites in living cells. Here, we describe a non-uniform sampling-based 2D heteronuclear single quantum coherence (NUS HSQC) approach to measure metabolic isotopomers in both cell lysates and living cells. The method provides a high resolution that can resolve multiplet structures in the (13)C dimension while retaining the sensitivity of the (1)H-indirect detection. The approach was tested in L1210 mouse leukemia cells labeled with (13)C acetate by measuring NUS HSQC with 25% sampling density. The results gave a variety of metabolic information such as (1) higher usage of acetate in acetylation pathway than aspartate synthesis, (2) TCA cycle efficiency changes upon the inhibition of mitochondrial oxidative phosphorylation by pharmacological agents, and (3) position-dependent isotopomer patterns in fatty acids in living cells. In addition, we were able to detect fatty acids along with other hydrophilic molecules in one sample of live cells without extraction. Overall, the high sensitivity and resolution along with the application to live cells should make the NUS HSQC approach attractive in studying carbon flux information in metabolic studies. PMID: 28029042 [PubMed - as supplied by publisher]

Related Articles Comprehensive two-dimensional gas chromatographic profiling and chemometric interpretation of the volatile profiles of sweat in knit fabrics. Anal Bioanal Chem. 2016 Dec 27;: Authors: de la Mata AP, McQueen RH, Nam SL, Harynuk JJ Abstract Human axillary sweat is a poorly explored biofluid within the context of metabolomics when compared to other fluids such as blood and urine. In this paper, we explore the volatile organic compounds emitted from two different types of fabric samples (cotton and polyester) which had been worn repeatedly during exercise by participants. Headspace solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) were employed to profile the (semi)volatile compounds on the fabric. Principal component analysis models were applied to the data to aid in visualizing differences between types of fabrics, wash treatment, and the gender of the subject who had worn the fabric. Statistical tools included with commercial chromatography software (ChromaTOF) and a simple Fisher ratio threshold-based feature selection for model optimization are compared with a custom-written algorithm that uses cluster resolution as an objective function to maximize in a hybrid backward-elimination forward-selection approach for optimizing the chemometric models in an effort to identify some compounds that correlate to differences between fabric types. The custom algorithm is shown to generate better models than the simple Fisher ratio approach. Graphical Abstract A route from samples and questions to data and then answers. PMID: 28028595 [PubMed - as supplied by publisher]