Ceramides: a new player in the inflammation-insulin resistance paradigm?

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Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

Plasma sphingosine-1-phosphate is elevated in obesity

Background: Dysfunctional lipid metabolism is a hallmark of obesity and insulin resistance and a risk factor for various cardiovascular and metabolic complications. In addition to the well known increase in plasma triglycerides and free fatty acids, recent work in humans and rodents has shown that obesity is associated with elevations in the bioactive class of sphingolipids known as ceramides. However, in obesity little is known about the plasma concentrations of sphinogsine-1-phosphate (S1P), the breakdown product of ceramide, which is an important signaling molecule in mammalian biology. Therefore, the purpose of this study was to examine the impact of obesity on circulating S1P concentration and its relationship with markers of glucose metabolism and insulin sensitivity. Methodology/Principal Findings: Plasma S1P levels were determined in high-fat diet (HFD)-induced and genetically obese (ob/ob) mice along with obese humans. Circulating S1P was elevated in both obese mouse models and in obese humans compared with lean healthy controls. Furthermore, in humans, plasma S1P positively correlated with total body fat percentage, body mass index (BMI), waist circumference, fasting insulin, HOMA-IR, HbA1c (%), total and LDL cholesterol. In addition, fasting increased plasma S1P levels in lean healthy mice. Conclusion: We show that elevations in plasma S1P are a feature of both human and rodent obesity and correlate with metabolic abnormalities such as adiposity and insulin resistance

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

The D299G/T399I Toll-Like Receptor 4 Variant Associates with Body and Liver Fat: Results from the TULIP and METSIM Studies

BACKGROUND: Toll-like-receptor 4 (TLR) is discussed to provide a molecular link between obesity, inflammation and insulin resistance. Genetic studies with replications in non-diabetic individuals in regard to their fat distribution or insulin resistance according to their carrier status of a common toll-like receptor 4 (TLR4) variant (TLR4(D299G/T399I)) are still lacking. METHODOLOGY/PRINCIPAL FINDINGS: We performed a cross-sectional analysis in individuals phenotyped for prediabetic traits as body fat composition (including magnetic resonance imaging), blood glucose levels and insulin resistance (oral glucose tolerance testing, euglycemic hyperinsulinemic clamp), according to TLR4 genotype determined by candidate SNP analyses (rs4986790). We analyzed N = 1482 non-diabetic individuals from the TÜF/TULIP cohort (South Germany, aged 39±13 y, BMI 28.5±7.9, mean±SD) and N = 5327 non-diabetic participants of the METSIM study (Finland, males aged 58±6 y, BMI 26.8±3.8) for replication purposes. German TLR4(D299G/T399I) carriers had a significantly increased body fat (XG in rs4986790: +6.98%, p = 0.03, dominant model, adjusted for age, gender) and decreased insulin sensitivity (XG: -15.3%, Matsuda model, p = 0.04; XG: -20.6%, p = 0.016, clamp; both dominant models adjusted for age, gender, body fat). In addition, both liver fat (AG: +49.7%; p = 0.002) and visceral adipose tissue (AG: +8.2%; p = 0.047, both adjusted for age, gender, body fat) were significantly increased in rs4986790 minor allele carriers, and the effect on liver fat remained significant also after additional adjustment for visceral fat (p = 0.014). The analysis in METSIM confirmed increased body fat content in association with the rare G allele in rs4986790 (AG: +1.26%, GG: +11.0%; p = 0.010, additive model, adjusted for age) and showed a non-significant trend towards decreased insulin sensitivity (AG: -0.99%, GG: -10.62%). CONCLUSIONS/SIGNIFICANCE: TLR4(D299G/T399I) associates with increased total body fat, visceral fat, liver fat and decreased insulin sensitivity in non-diabetic Caucasians and may contribute to diabetes risk. This finding supports the role of TLR4 as a molecular link between obesity and insulin resistance