PubMed

Related Articles Aneuploid Cell Survival Relies upon Sphingolipid Homeostasis. Cancer Res. 2017 Oct 01;77(19):5272-5286 Authors: Tang YC, Yuwen H, Wang K, Bruno PM, Bullock K, Deik A, Santaguida S, Trakala M, Pfau SJ, Zhong N, Huang T, Wang L, Clish CB, Hemann MT, Amon A Abstract Aneuploidy, a hallmark of cancer cells, poses an appealing opportunity for cancer treatment and prevention strategies. Using a cell-based screen to identify small molecules that could selectively kill aneuploid cells, we identified the compound N-[2-hydroxy-1-(4-morpholinylmethyl)-2-phenylethyl]-decanamide monohydrochloride (DL-PDMP), an antagonist of UDP-glucose ceramide glucosyltransferase. DL-PDMP selectively inhibited proliferation of aneuploid primary mouse embryonic fibroblasts and aneuploid colorectal cancer cells. Its selective cytotoxic effects were based on further accentuating the elevated levels of ceramide, which characterize aneuploid cells, leading to increased apoptosis. We observed that DL-PDMP could also enhance the cytotoxic effects of paclitaxel, a standard-of-care chemotherapeutic agent that causes aneuploidy, in human colon cancer and mouse lymphoma cells. Our results offer pharmacologic evidence that the aneuploid state in cancer cells can be targeted selectively for therapeutic purposes, or for reducing the toxicity of taxane-based drug regimens. Cancer Res; 77(19); 5272-86. ©2017 AACR. PMID: 28775166 [PubMed - indexed for MEDLINE]

Related Articles A distinct plasma lipid signature associated with poor prognosis in castration-resistant prostate cancer. Int J Cancer. 2017 Nov 15;141(10):2112-2120 Authors: Lin HM, Mahon KL, Weir JM, Mundra PA, Spielman C, Briscoe K, Gurney H, Mallesara G, Marx G, Stockler MR, PRIMe Consortium, Parton RG, Hoy AJ, Daly RJ, Meikle PJ, Horvath LG Abstract Lipids are known to influence tumour growth, inflammation and chemoresistance. However, the association of circulating lipids with the clinical outcome of metastatic castration-resistant prostate cancer (CRPC) is unknown. We investigated associations between the plasma lipidome and clinical outcome in CRPC. Lipidomic profiling by liquid chromatography-tandem mass spectrometry was performed on plasma samples from a Phase 1 discovery cohort of 96 CRPC patients. Results were validated in an independent Phase 2 cohort of 63 CRPC patients. Unsupervised analysis of lipidomic profiles (323 lipid species) classified the Phase 1 cohort into two patient subgroups with significant survival differences (HR 2.31, 95% CI 1.44-3.68, p = 0.0005). The levels of 46 lipids were individually prognostic and were predominantly sphingolipids with higher levels associated with poor prognosis. A prognostic three-lipid signature was derived (ceramide d18:1/24:1, sphingomyelin d18:2/16:0, phosphatidylcholine 16:0/16:0) and was also associated with shorter survival in the Phase 2 cohort (HR 4.8, 95% CI 2.06-11.1, p = 0.0003). The signature was an independent prognostic factor when modelled with clinicopathological factors or metabolic characteristics. The association of plasma lipids with CRPC prognosis suggests a possible role of these lipids in disease progression. Further research is required to determine if therapeutic modulation of the levels of these lipids by targeting their metabolic pathways may improve patient outcome. PMID: 28741687 [PubMed - indexed for MEDLINE]

26 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 2017/10/27PubMed comprises more than millions of 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.

Related Articles Cellular bioenergetics is impaired in patients with chronic fatigue syndrome. PLoS One. 2017;12(10):e0186802 Authors: Tomas C, Brown A, Strassheim V, Elson J, Newton J, Manning P Abstract Chronic fatigue syndrome (CFS) is a highly debilitating disease of unknown aetiology. Abnormalities in bioenergetic function have been cited as one possible cause for CFS. Preliminary studies were performed to investigate cellular bioenergetic abnormalities in CFS patients. A series of assays were conducted using peripheral blood mononuclear cells (PBMCs) from CFS patients and healthy controls. These experiments investigated cellular patterns in oxidative phosphorylation (OXPHOS) and glycolysis. Results showed consistently lower measures of OXPHOS parameters in PBMCs taken from CFS patients compared with healthy controls. Seven key parameters of OXPHOS were calculated: basal respiration, ATP production, proton leak, maximal respiration, reserve capacity, non-mitochondrial respiration, and coupling efficiency. While many of the parameters differed between the CFS and control cohorts, maximal respiration was determined to be the key parameter in mitochondrial function to differ between CFS and control PBMCs due to the consistency of its impairment in CFS patients found throughout the study (p≤0.003). The lower maximal respiration in CFS PBMCs suggests that when the cells experience physiological stress they are less able to elevate their respiration rate to compensate for the increase in stress and are unable to fulfil cellular energy demands. The metabolic differences discovered highlight the inability of CFS patient PBMCs to fulfil cellular energetic demands both under basal conditions and when mitochondria are stressed during periods of high metabolic demand. PMID: 29065167 [PubMed - in process]

Related Articles How to model temporal changes in non-targeted metabolomics study? A Bayesian multilevel perspective. J Sep Sci. 2017 Oct 24;: Authors: Wiczling P, Daghir-Wojtkowiak E, Yumba Mpanga A, Szczesny D, Kaliszan R, Markuszewski MJ Abstract Analysis of time series data addresses the question on mechanisms underlying normal physiology and its alteration under pathological conditions. However, adding time variable to high-dimension, collinear, noisy data is a challenge in terms of mining and analysis. Here, we used Bayesian multilevel modeling for time series metabolomics in vivo study to model different levels of random effects occurring as a consequence of hierarchical data structure. A multilevel linear model assuming different treatment effects with double exponential prior, considering major sources of variability and robustness to outliers was proposed and tested in terms of performance. The treatment effect for each metabolite was close to zero suggesting small if any effect of cancer on metabolomics profile change. The average difference in 964 signals for all metabolites varied by a factor ranging from 0.8 to 1.25. The inter-rat variability (expressed as a coefficient of variation) ranged from 3-30% across all metabolites with median around 10%, whereas the inter-occasion variability ranged from 0-30% with a median around 5%. Approximately 36% of metabolites contained outlying data points. The complex correlation structure between metabolite signals was revealed. We conclude that kinetics of metabolites can be modeled using tools accepted in pharmacokinetics type of studies. This article is protected by copyright. All rights reserved. PMID: 29064638 [PubMed - as supplied by publisher]

Related Articles Molecular Affinity of Mabolo Extracts to an Octopamine Receptor of a Fruit Fly. Molecules. 2017 Oct 24;22(10): Authors: Dacanay FND, Ladra MCJA, Junio HA, Nellas RB Abstract Essential oils extracted from plants are composed of volatile organic compounds that can affect insect behavior. Identifying the active components of the essential oils to their biochemical target is necessary to design novel biopesticides. In this study, essential oils extracted from Diospyros discolor (Willd.) were analyzed using gas chromatography mass spectroscopy (GC-MS) to create an untargeted metabolite profile. Subsequently, a conformational ensemble of the Drosophila melanogaster octopamine receptor in mushroom bodies (OAMB) was created from a molecular dynamics simulation to resemble a flexible receptor for docking studies. GC-MS analysis revealed the presence of several metabolites, i.e. mostly aromatic esters. Interestingly, these aromatic esters were found to exhibit relatively higher binding affinities to OAMB than the receptor's natural agonist, octopamine. The molecular origin of this observed enhanced affinity is the π -stacking interaction between the aromatic moieties of the residues and ligands. This strategy, computational inspection in tandem with untargeted metabolomics, may provide insights in screening the essential oils as potential OAMB inhibitors. PMID: 29064449 [PubMed - in process]

Related Articles Modelling of Hydrophilic Interaction Liquid Chromatography Stationary Phases Using Chemometric Approaches. Metabolites. 2017 Oct 24;7(4): Authors: Navarro-Reig M, Ortiz-Villanueva E, Tauler R, Jaumot J Abstract Metabolomics is a powerful and widely used approach that aims to screen endogenous small molecules (metabolites) of different families present in biological samples. The large variety of compounds to be determined and their wide diversity of physical and chemical properties have promoted the development of different types of hydrophilic interaction liquid chromatography (HILIC) stationary phases. However, the selection of the most suitable HILIC stationary phase is not straightforward. In this work, four different HILIC stationary phases have been compared to evaluate their potential application for the analysis of a complex mixture of metabolites, a situation similar to that found in non-targeted metabolomics studies. The obtained chromatographic data were analyzed by different chemometric methods to explore the behavior of the considered stationary phases. ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA) and partial least squares regression (PLS) were used to explore the experimental factors affecting the stationary phase performance, the main similarities and differences among chromatographic conditions used (stationary phase and pH) and the molecular descriptors most useful to understand the behavior of each stationary phase. PMID: 29064436 [PubMed]

Related Articles Long-term stability of human plasma metabolites during storage at -80 °C. J Proteome Res. 2017 Oct 24;: Authors: Haid M, Muschet C, Wahl S, Römisch-Margl W, Prehn C, Möller G, Adamski J Abstract Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at 80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ® p180 targeted-metabolomics assay at 193 time points. Modelling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% and -14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: +15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: acylcarnitines: 12.1%, lysophosphatidylcholines: -15.1%, diacyl-phosphatidylcholines: -17.0%, acyl-alkyl-phosphatidylcholines: -13.3%, sphingomyelins: -14.8%. We conclude that storage of plasma samples at -80 °C for up to five years can lead to altered concentration levels of amino acids, acylcarnitines, glycerophospholipids, sphingomyelins and the sum of hexoses. These alterations have to be considered when analyzing metabolomics data from long-term epidemiological studies. PMID: 29064256 [PubMed - as supplied by publisher]

Related Articles Validating quantitative untargeted lipidomics across nine liquid chromatography-high-resolution mass spectrometry platforms. Anal Chem. 2017 Oct 24;: Authors: Cajka T, Smilowitz JT, Fiehn O Abstract Liquid chromatography-mass spectrometry (LC-MS) methods are most often used for untargeted metabolomics and lipidomics. However, methods have not been standardized as accepted 'best practice' documents, and reports lack harmonization with respect to quantitative data that enable inter-study comparisons. Researchers use a wide variety of high-resolution mass spectrometers under different operating conditions, and it is unclear if results would yield different biological conclusions depending on the instrument performance. To this end, we used 126 identical human plasma samples and 29 quality control samples from a nutritional intervention study. We investigated lipidomic data acquisitions across nine different MS instruments (1 single TOF, 1 Q/orbital ion trap and 7 QTOF instruments). Sample preparations, chromatography conditions, and data processing methods were kept identical. Single-point internal standard calibrations were used to estimated absolute concentrations for 307 unique lipids identified by accurate mass, MS/MS spectral match and retention times. Quantitative results were highly comparable between the LC-MS platforms tested. Using partial least squares discriminant analysis (PLS-DA) to compare results between platforms, a 92% overlap for the most discriminating lipids based on variable importance in projection (VIP) scores was achieved for all lipids that were detected by at least two instrument platforms. Importantly, even the relative positions of individual samples on the PLS-DA projections were identical. The key for success in harmonizing results was to avoid ion saturation by carefully evaluating linear dynamic ranges using serial dilutions and adjusting the resuspension volume and/or injection volume before running actual study samples. PMID: 29064229 [PubMed - as supplied by publisher]

Related Articles Exhaled breath condensate methods adapted from human studies using longitudinal metabolomics for predicting early health alterations in dolphins. Anal Bioanal Chem. 2017 Oct 23;: Authors: Borras E, Aksenov AA, Baird M, Novick B, Schivo M, Zamuruyev KO, Pasamontes A, Parry C, Foutouhi S, Venn-Watson S, Weimer BC, Davis CE Abstract Monitoring health conditions is essential to detect early asymptomatic stages of a disease. To achieve this, blood, urine and breath samples are commonly used as a routine clinical diagnostic. These samples offer the opportunity to detect specific metabolites related to diseases and provide a better understanding of their development. Although blood samples are commonly used routinely to monitor health, the implementation of a relatively noninvasive technique, such as exhaled breath condensate (EBC) analysis, may further benefit the well-being of both humans and other animals. EBC analysis can be used to track possible physical or biochemical alterations caused by common diseases of the bottlenose dolphin (Tursiops truncatus), such as infections or inflammatory-mediated processes. We have used an untargeted metabolomic method with liquid chromatography-mass spectrometry analysis of EBC samples to determine biomarkers related to disease development. In this study, five dolphins under human care were followed up for 1 year. We collected paired blood, physical examination information, and EBC samples. We then statistically correlated this information to predict specific health alterations. Three dolphins provided promising case study information about biomarkers related to cutaneous infections, respiratory infections, dental disease, or hormonal changes (pregnancy). The use of complementary liquid chromatography platforms, with hydrophilic interaction chromatography and reverse-phased columns, allowed us to detect a wide spectrum of EBC biomarker compounds that could be related to these health alterations. Moreover, these two analytical techniques not only provided complementary metabolite information but in both cases they also provided promising diagnostic information for these health conditions. Graphical abstract Collection of the exhaled condensed breath from a bottlenose dolphin from U.S. Navy Marine Mammal Program (MMP). PMID: 29063162 [PubMed - as supplied by publisher]

Related Articles Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism. Synth Syst Biotechnol. 2017 Mar;2(1):5-12 Authors: Nielsen JC, Nielsen J Abstract The genomic era has revolutionized research on secondary metabolites and bioinformatics methods have in recent years revived the antibiotic discovery process after decades with only few new active molecules being identified. New computational tools are driven by genomics and metabolomics analysis, and enables rapid identification of novel secondary metabolites. To translate this increased discovery rate into industrial exploitation, it is necessary to integrate secondary metabolite pathways in the metabolic engineering process. In this review, we will describe the novel advances in discovery of secondary metabolites produced by filamentous fungi, highlight the utilization of genome-scale metabolic models (GEMs) in the design of fungal cell factories for the production of secondary metabolites and review strategies for optimizing secondary metabolite production through the construction of high yielding platform cell factories. PMID: 29062956 [PubMed]

Related Articles The Challenge of Human Spermatozoa Proteome: A Systematic Review. J Reprod Infertil. 2017 Jul-Sep;18(3):267-279 Authors: Gilany K, Minai-Tehrani A, Amini M, Agharezaee N, Arjmand B Abstract Currently, there are 20,197 human protein-coding genes in the most expertly curated database (UniProtKB/Swiss-Pro). Big efforts have been made by the international consortium, the Chromosome-Centric Human Proteome Project (C-HPP) and independent researchers, to map human proteome. In brief, anno 2017 the human proteome was outlined. The male factor contributes to 50% of infertility in couples. However, there are limited human spermatozoa proteomic studies. Firstly, the development of the mapping of the human spermatozoa was analyzed. The human spermatozoa have been used as a model for missing proteins. It has been shown that human spermatozoa are excellent sources for finding missing proteins. Y chromosome proteome mapping is led by Iran. However, it seems that it is extremely challenging to map the human spermatozoa Y chromosome proteins based on current mass spectrometry-based proteomics technology. Post-translation modifications (PTMs) of human spermatozoa proteome are the most unexplored area and currently the exact role of PTMs in male infertility is unknown. Additionally, the clinical human spermatozoa proteomic analysis, anno 2017 was done in this study. PMID: 29062791 [PubMed]

Related Articles Metabolic Comparison of Dorsal versus Ventral Cells Directly in the Live 8-cell Frog Embryo by Microprobe Single-cell CE-ESI-MS. Anal Methods. 2017 Sep 14;9(34):4964-4970 Authors: Onjiko RM, Plotnick DO, Moody SA, Nemes P Abstract Single-cell mass spectrometry (MS) empowers the characterization of metabolomic changes as cells differentiate to different tissues during early embryogenesis. Using whole-cell dissection and capillary electrophoresis electrospray ionization (CE-ESI) MS, we recently uncovered metabolic cell-to-cell differences in the 8- and 16-cell embryo of the South African clawed frog (Xenopus laevis), raising the question whether metabolic cell heterogeneity is also detectable across the dorsal-ventral axis of the 8-cell embryo. Here, we tested this hypothesis directly in the live embryo by quantifying single-cell metabolism between the left dorsal-animal (D1L) and left ventral-animal (V1L) cell pairs in the same embryo using microprobe single-cell CE-ESI-MS in the positive ion mode. After quantifying ~70 molecular features, including 52 identified metabolites, that were reproducibly detected in both cells among n = 5 different embryos, we employed supervised multivariate data analysis based on partial least squares discriminant analysis (PLSDA) to compare metabolism between the cell types. Statistical analysis revealed that asparagine, glycine betaine, and a yet-unidentified molecule were statistically significantly enriched in the D1L cell compared to V1L (p < 0.05 and fold change ≥ 1.5). These results demonstrate that cells derived from the same hemisphere (animal pole) harbor different metabolic activity along the dorsal-ventral axis as early as the 8-cell stage. Apart from providing new evidence of metabolic cell heterogeneity during early embryogenesis, this study demonstrates that microprobe single-cell CE-ESI-MS enables the analysis of multiple single cells in the same live vertebrate embryo. PMID: 29062391 [PubMed]

Related Articles Modelling diabetic nephropathy in mice. Nat Rev Nephrol. 2017 Oct 24;: Authors: Azushima K, Gurley SB, Coffman TM Abstract Diabetic nephropathy (DN) is a leading cause of end-stage renal disease in the developed world. Accordingly, an urgent need exists for new, curative treatments as well as for biomarkers to stratify risk of DN among individuals with diabetes mellitus. A barrier to progress in these areas has been a lack of animal models that faithfully replicate the main features of human DN. Such models could be used to define the pathogenesis, identify drug targets and test new therapies. Owing to their tractability for genetic manipulation, mice are widely used to model human diseases, including DN. Questions have been raised, however, about the general utility of mouse models in human drug discovery. Standard mouse models of diabetes typically manifest only modest kidney abnormalities, whereas accelerated models, induced by superimposing genetic stressors, recapitulate key features of human DN. Incorporation of systems biology approaches and emerging data from genomics and metabolomics studies should enable further model refinement. Here, we discuss the current status of mouse models for DN, their limitations and opportunities for improvement. We emphasize that future efforts should focus on generating robust models that reproduce the major clinical and molecular phenotypes of human DN. PMID: 29062142 [PubMed - as supplied by publisher]

Related Articles Early-onset and classical forms of type 2 diabetes show impaired expression of genes involved in muscle branched-chain amino acids metabolism. Sci Rep. 2017 Oct 23;7(1):13850 Authors: Hernández-Alvarez MI, Díaz-Ramos A, Berdasco M, Cobb J, Planet E, Cooper D, Pazderska A, Wanic K, O'Hanlon D, Gomez A, de la Ballina LR, Esteller M, Palacin M, O'Gorman DJ, Nolan JJ, Zorzano A Abstract The molecular mechanisms responsible for the pathophysiological traits of type 2 diabetes are incompletely understood. Here we have performed transcriptomic analysis in skeletal muscle, and plasma metabolomics from subjects with classical and early-onset forms of type 2 diabetes (T2D). Focused studies were also performed in tissues from ob/ob and db/db mice. We document that T2D, both early and late onset, are characterized by reduced muscle expression of genes involved in branched-chain amino acids (BCAA) metabolism. Weighted Co-expression Networks Analysis provided support to idea that the BCAA genes are relevant in the pathophysiology of type 2 diabetes, and that mitochondrial BCAA management is impaired in skeletal muscle from T2D patients. In diabetic mice model we detected alterations in skeletal muscle proteins involved in BCAA metabolism but not in obese mice. Metabolomic analysis revealed increased levels of branched-chain keto acids (BCKA), and BCAA in plasma of T2D patients, which may result from the disruption of muscle BCAA management. Our data support the view that inhibition of genes involved in BCAA handling in skeletal muscle takes place as part of the pathophysiology of type 2 diabetes, and this occurs both in early-onset and in classical type 2 diabetes. PMID: 29062026 [PubMed - in process]

Related Articles AML-specific cytotoxic antibodies in patients with durable graft versus leukemia responses. Blood. 2017 Oct 23;: Authors: Gillissen MA, Kedde M, de Jong G, Moiset G, Yasuda E, Levie SE, Bakker AQ, Claassen YB, Wagner K, Böhne M, Hensbergen PJ, Speijer D, van Helden PM, Beaumont T, Spits H, Hazenberg MD Abstract Most acute myeloid leukemia (AML) patients can only be cured when an allogeneic hematopoietic stem cell transplantation (HSCT) induces a graft versus leukemia immune response (GvL). While the role of T cells and NK cells in tumor immunology has been established, less is known about the contribution of B cells. From B cells of high-risk AML patients with potent and lasting GvL responses we isolated monoclonal antibodies directed against antigens expressed on the cell surface of AML cells but not on normal hematopoietic and non-hematopoietic cells. A number of these donor-derived antibodies recognized the U5 snRNP200 complex, a component of the spliceosome that in normal cells is found in the cell. In AML however, the U5 snRNP200 complex is exposed on the cell membrane of leukemic blasts. U5 snRNP200 complex-specific antibodies induced death of AML cells in a FcR dependent way in the absence of cytotoxic leukocytes or complement. In an AML mouse model, treatment with U5 snRNP200 complex-specific antibodies led to significant tumor growth inhibition. Thus, donor derived U5 snRNP200 complex-recognizing AML-specific antibodies may contribute to anti-tumor responses. PMID: 29061569 [PubMed - as supplied by publisher]

Related Articles Using MutPred derived mtDNA load scores to evaluate mtDNA variation in hypertension and diabetes in a two-population cohort: The SABPA study. J Genet Genomics. 2017 Mar 20;44(3):139-149 Authors: Venter M, Malan L, van Dyk E, Elson JL, van der Westhuizen FH Abstract Mitochondrial DNA (mtDNA) variation has been implicated in many common complex diseases, but inconsistent and contradicting results are common. Here we introduce a novel mutational load hypothesis, which also considers the collective effect of mainly rare variants, utilising the MutPred Program. We apply this new methodology to investigate the possible role of mtDNA in two cardiovascular disease (CVD) phenotypes (hypertension and hyperglycaemia), within a two-population cohort (n = 363; mean age 45 ± 9 yrs). Very few studies have looked at African mtDNA variation in the context of complex disease, and none using complete sequence data in a well-phenotyped cohort. As such, our study will also extend our knowledge of African mtDNA variation, with complete sequences of Southern Africans being especially under-represented. The cohort showed prevalence rates for hypertension (58.6%) and prediabetes (44.8%). We could not identify a statistically significant role for mtDNA variation in association with hypertension or hyperglycaemia in our cohort. However, we are of the opinion that the method described will find wide application in the field, being especially useful for cohorts from multiple locations or with a variety of mtDNA lineages, where the traditional haplogroup association method has been particularly likely to generate spurious results in the context of association with common complex disease. PMID: 28298255 [PubMed - indexed for MEDLINE]

Related Articles Early Prediction of Developing Type 2 Diabetes by Plasma Acylcarnitines: A Population-Based Study. Diabetes Care. 2016 Sep;39(9):1563-70 Authors: Sun L, Liang L, Gao X, Zhang H, Yao P, Hu Y, Ma Y, Wang F, Jin Q, Li H, Li R, Liu Y, Hu FB, Zeng R, Lin X, Wu J Abstract OBJECTIVE: Acylcarnitines were suggested as early biomarkers even prior to insulin resistance in animal studies, but their roles in predicting type 2 diabetes were unknown. Therefore, we aimed to determine whether acylcarnitines could independently predict type 2 diabetes by using a targeted metabolic profiling approach. RESEARCH DESIGN AND METHODS: A population-based prospective study was conducted among 2,103 community-living Chinese individuals aged 50-70 years from Beijing and Shanghai with a mean follow-up duration of 6 years. Fasting glucose, glycohemoglobin, and insulin were determined at baseline and in a follow-up survey. Baseline plasma acylcarnitines were profiled by liquid chromatography-tandem mass spectrometry. RESULTS: Over the 6-year period, 507 participants developed diabetes. A panel of acylcanitines, especially with long chain, was significantly associated with increased risk of type 2 diabetes. The relative risks of type 2 diabetes per SD increase of the predictive model score were 2.48 (95% CI 2.20-2.78) for the conventional and 9.41 (95% CI 7.62-11.62) for the full model including acylcarnitines, respectively. Moreover, adding selected acylcarnitines substantially improved predictive ability for incident diabetes, as area under the receiver operator characteristic curve improved to 0.89 in the full model compared with 0.73 in the conventional model. Similar associations were obtained when the predictive models were established separately among Beijing or Shanghai residents. CONCLUSIONS: A panel of acylcarnitines, mainly involving mitochondrial lipid dysregulation, significantly improved predictive ability for type 2 diabetes beyond conventional risk factors. These findings need to be replicated in other populations, and the underlying mechanisms should be elucidated. PMID: 27388475 [PubMed - indexed for MEDLINE]

18 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 2017/10/24PubMed comprises more than millions of 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.

IgG glycosylation and DNA methylation are interconnected with smoking. Biochim Biophys Acta. 2017 Oct 18;: Authors: Wahl A, Kasela S, Monotoro EC, van Iterson M, Štambuk J, Sharma S, van den Akker E, Klaric L, Benedetti E, Razdorov G, Trbojević-Akmačić I, Vučković F, Ugrina I, Beekman M, Deelen J, van Heemst D, Heijmans BT, Consortium BIOS, Wuhrer M, Plomp R, Keser T, Šimurina M, Pavić T, Gudelj I, Krištić J, Grallert H, Kunze S, Peters A, Bell JT, Spector TD, Milani L, Eline Slagboom P, Lauc G, Gieger C Abstract BACKGROUND: Glycosylation is one of the most common post-translation modifications with large influences on protein structure and function. The effector function of immunoglobulin G (IgG) alters between pro- and anti-inflammatory, based on its glycosylation. IgG glycan synthesis is highly complex and dynamic. METHODS: With the use of two different analytical methods for assessing IgG glycosylation, we aim to elucidate the link between DNA methylation and glycosylation of IgG by means of epigenome-wide association studies. In total, 3000 individuals from 4 cohorts were analyzed. RESULTS: The overlap of the results from the two glycan measurement panels yielded DNA methylation of 7 CpG-sites on 5 genomic locations to be associated with IgG glycosylation: cg25189904 (chr.1, GNG12); cg05951221, cg21566642 and cg01940273 (chr.2, ALPPL2); cg05575921 (chr.5, AHRR); cg06126421 (6p21.33); and cg03636183 (chr.19, F2RL3). Mediation analyses with respect to smoking revealed that the effect of smoking on IgG glycosylation may be at least partially mediated via DNA methylation levels at these 7 CpG-sites. CONCLUSION: Our results suggest the presence of an indirect link between DNA methylation and IgG glycosylation that may in part capture environmental exposures. GENERAL SIGNIFICANCE: An epigenome-wide analysis conducted in four population-based cohorts revealed an association between DNA methylation and IgG glycosylation patterns. Presumably, DNA methylation mediates the effect of smoking on IgG glycosylation. PMID: 29055820 [PubMed - as supplied by publisher]