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

Inclusive production of ρ0(770),f0(980)\rho^{0}(770), f_0(980) and f2(1270)f_2(1270) mesons in νμ\nu_{\mu} charged current interactions

The inclusive production of the meson resonances $\rho^{0}(770)$, $f_0(980)$ and $f_2(1270)$ in neutrino-nucleus charged current interactions has been studied with the NOMAD detector exposed to the wide band neutrino beam generated by 450 GeV protons at the CERN SPS. For the first time the $f_{0}(980)$ meson is observed in neutrino interactions. The statistical significance of its observation is 6 standard deviations. The presence of $f_{2}(1270)$ in neutrino interactions is reliably established. The average multiplicity of these three resonances is measured as a function of several kinematic variables. The experimental results are compared to the multiplicities obtained from a simulation based on the Lund model. In addition, the average multiplicity of $\rho^{0}(770)$ in antineutrino - nucleus interactions is measured.Comment: 23 pages, 14 figures, 8 tables. To appear in Nucl. Phys.

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c

Plasma lysophosphatidylcholine levels are reduced in obesity and type 2 diabetes

BACKGROUND: Obesity and type 2 diabetes (T2DM) are associated with increased circulating free fatty acids and triacylglycerols. However, very little is known about specific molecular lipid species associated with these diseases. In order to gain further insight into this, we performed plasma lipidomic analysis in a rodent model of obesity and insulin resistance as well as in lean, obese and obese individuals with T2DM. METHODOLOGY/PRINCIPAL FINDINGS: Lipidomic analysis using liquid chromatography coupled to mass spectrometry revealed marked changes in the plasma of 12 week high fat fed mice. Although a number of triacylglycerol and diacylglycerol species were elevated along with of a number of sphingolipids, a particularly interesting finding was the high fat diet (HFD)-induced reduction in lysophosphatidylcholine (LPC) levels. As liver, skeletal muscle and adipose tissue play an important role in metabolism, we next determined whether the HFD altered LPCs in these tissues. In contrast to our findings in plasma, only very modest changes in tissue LPCs were noted. To determine when the change in plasma LPCs occurred in response to the HFD, mice were studied after 1, 3 and 6 weeks of HFD. The HFD caused rapid alterations in plasma LPCs with most changes occurring within the first week. Consistent with our rodent model, data from our small human cohort showed a reduction in a number of LPC species in obese and obese individuals with T2DM. Interestingly, no differences were found between the obese otherwise healthy individuals and the obese T2DM patients. CONCLUSION: Irrespective of species, our lipidomic profiling revealed a generalized decrease in circulating LPC species in states of obesity. Moreover, our data indicate that diet and adiposity, rather than insulin resistance or diabetes per se, play an important role in altering the plasma LPC profile

LipidXplorer: A Software for Consensual Cross-Platform Lipidomics

LipidXplorer is the open source software that supports the quantitative characterization of complex lipidomes by interpreting large datasets of shotgun mass spectra. LipidXplorer processes spectra acquired on any type of tandem mass spectrometers; it identifies and quantifies molecular species of any ionizable lipid class by considering any known or assumed molecular fragmentation pathway independently of any resource of reference mass spectra. It also supports any shotgun profiling routine, from high throughput top-down screening for molecular diagnostic and biomarker discovery to the targeted absolute quantification of low abundant lipid species. Full documentation on installation and operation of LipidXplorer, including tutorial, collection of spectra interpretation scripts, FAQ and user forum are available through the wiki site at: https://wiki.mpi-cbg.de/wiki/lipidx/index.php/Main_Page

Flexibility of a Eukaryotic Lipidome – Insights from Yeast Lipidomics

Mass spectrometry-based shotgun lipidomics has enabled the quantitative and comprehensive assessment of cellular lipid compositions. The yeast Saccharomyces cerevisiae has proven to be a particularly valuable experimental system for studying lipid-related cellular processes. Here, by applying our shotgun lipidomics platform, we investigated the influence of a variety of commonly used growth conditions on the yeast lipidome, including glycerophospholipids, triglycerides, ergosterol as well as complex sphingolipids. This extensive dataset allowed for a quantitative description of the intrinsic flexibility of a eukaryotic lipidome, thereby providing new insights into the adjustments of lipid biosynthetic pathways. In addition, we established a baseline for future lipidomic experiments in yeast. Finally, flexibility of lipidomic features is proposed as a new parameter for the description of the physiological state of an organism

Plasma and Liver Lipidomics Response to an Intervention of Rimonabant in ApoE*3Leiden.CETP Transgenic Mice

Background: Lipids are known to play crucial roles in the development of life-style related risk factors such as obesity, dyslipoproteinemia, hypertension and diabetes. The first selective cannabinoid-1 receptor blocker rimonabant, an anorectic anti-obesity drug, was frequently used in conjunction with diet and exercise for patients with a body mass index greater than 30 kg/m2 with associated risk factors such as type II diabetes and dyslipidaemia in the past. Less is known about the impact of this drug on the regulation of lipid metabolism in plasma and liver in the early stage of obesity. Methodology/Principal Findings: We designed a four-week parallel controlled intervention on apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE&z.ast;3Leiden.CETP) transgenic mice with mild overweight and hypercholesterolemia. A liquid chromatography-linear ion trap-Fourier transform ion cyclotron resonance-mass spectrometric approach was employed to investigate plasma and liver lipid responses to the rimonabant intervention. Rimonabant was found to induce a significant body weight loss (9.4%, p<0.05) and a significant plasma total cholesterol reduction (24%, p<0.05). Six plasma and three liver lipids in ApoE&z.ast;3Leiden.CETP transgenic mice were detected to most significantly respond to rimonabant treatment. Distinct lipid patterns between the mice were observed for both plasma and liver samples in rimonabant treatment vs. non-treated controls. This study successfully applied, for the first time, systems biology based lipidomics approaches to evaluate treatment effects of rimonabant in the early stage of obesity. Conclusion: The effects of rimonabant on lipid metabolism and body weight reduction in the early stage obesity were shown to be moderate in ApoE&z.ast;3Leiden.CETP mice on high-fat diet. © 2011 Hu et al

Detailed Physiologic Characterization Reveals Diverse Mechanisms for Novel Genetic Loci Regulating Glucose and Insulin Metabolism in Humans

OBJECTIVERecent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action.RESEARCH DESIGN AND METHODSWe investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084).RESULTSThe glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 × 10−71). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction.CONCLUSIONSGenetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes

Gout. Epidemiology of gout

Gout is the most prevalent form of inflammatory arthropathy. Several studies suggest that its prevalence and incidence have risen in recent decades. Numerous risk factors for the development of gout have been established, including hyperuricaemia, genetic factors, dietary factors, alcohol consumption, metabolic syndrome, hypertension, obesity, diuretic use and chronic renal disease. Osteoarthritis predisposes to local crystal deposition. Gout appears to be an independent risk factor for all-cause mortality and cardiovascular mortality and morbidity, additional to the risk conferred by its association with traditional cardiovascular risk factors

Impact of Type 2 Diabetes Susceptibility Variants on Quantitative Glycemic Traits Reveals Mechanistic Heterogeneity

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition