Metabolic alterations precede cardiometabolic disease onset. Here we present ceramide- and dihydroceramide-profiling data from a nested case-cohort (type 2 diabetes T2D, n = 775; cardiovascular ...disease CVD, n = 551; random subcohort n = 1137) in the prospective EPIC-Potsdam study. We apply the novel NetCoupler-algorithm to link a data-driven (dihydro)ceramide network to T2D and CVD risk. Controlling for confounding by other (dihydro)ceramides, ceramides C18:0 and C22:0 and dihydroceramides C20:0 and C22:2 are associated with higher and ceramide C20:0 and dihydroceramide C26:1 with lower T2D risk. Ceramide C16:0 and dihydroceramide C22:2 are associated with higher CVD risk. Genome-wide association studies and Mendelian randomization analyses support a role of ceramide C22:0 in T2D etiology. Our results also suggest that (dh)ceramides partly mediate the putative adverse effect of high red meat consumption and benefits of coffee consumption on T2D risk. Thus, (dihydro)ceramides may play a critical role in linking genetic predisposition and dietary habits to cardiometabolic disease risk.
It is not yet resolved how lifestyle factors and intermediate phenotypes interrelate with metabolic pathways. We aimed to investigate the associations between diet, physical activity, ...cardiorespiratory fitness and obesity with serum metabolite networks in a population-based study.
The present study included 2380 participants of a randomly drawn subcohort of the European Prospective Investigation into Cancer and Nutrition-Potsdam. Targeted metabolomics was used to measure 127 serum metabolites. Additional data were available including anthropometric measurements, dietary assessment including intake of whole-grain bread, coffee and cake and cookies by food frequency questionnaire, and objectively measured physical activity energy expenditure and cardiorespiratory fitness in a subsample of 100 participants. In a data-driven approach, Gaussian graphical modeling was used to draw metabolite networks and depict relevant associations between exposures and serum metabolites. In addition, the relationship of different exposure metabolite networks was estimated.
In the serum metabolite network, the different metabolite classes could be separated. There was a big group of phospholipids and acylcarnitines, a group of amino acids and C6-sugar. Amino acids were particularly positively associated with cardiorespiratory fitness and physical activity. C6-sugar and acylcarnitines were positively associated with obesity and inversely with intake of whole-grain bread. Phospholipids showed opposite associations with obesity and coffee intake. Metabolite networks of coffee intake and obesity were strongly inversely correlated (body mass index (BMI): r = -0.57 and waist circumference: r = -0.59). A strong positive correlation was observed between metabolite networks of BMI and waist circumference (r = 0.99), as well as the metabolite networks of cake and cookie intake with cardiorespiratory fitness and intake of whole-grain bread (r = 0.52 and r = 0.50; respectively).
Lifestyle factors and phenotypes seem to interrelate in various metabolic pathways. A possible protective effect of coffee could be mediated via counterbalance of pathways of obesity involving hepatic phospholipids. Experimental studies should validate the biological mechanisms.
Genome-wide association studies have identified gene regions associated with type 1 diabetes. The aim of this study was to determine how the combined allele frequency of multiple susceptibility genes ...can stratify islet autoimmunity and/or type 1 diabetes risk. Children of parents with type 1 diabetes and prospectively followed from birth for the development of islet autoantibodies and diabetes were genotyped for single-nucleotide polymorphisms at 12 type 1 diabetes susceptibility genes (ERBB3, PTPN2, IFIH1, PTPN22, KIAA0350, CD25, CTLA4, SH2B3, IL2, IL18RAP, IL10 and COBL). Non-human leukocyte antigen (HLA) risk score was defined by the total number of risk alleles at these genes. Receiver operator curve analysis showed that the non-HLA gene combinations were highly effective in discriminating diabetes and most effective in children with a high-risk HLA genotype. The greatest diabetes discrimination was obtained by the sum of risk alleles for eight genes (IFIH1, CTLA4, PTPN22, IL18RAP, SH2B3, KIAA0350, COBL and ERBB3) in the HLA-risk children. Non-HLA-risk allele scores stratified risk for developing islet autoantibodies and diabetes, and progression from islet autoimmunity to diabetes. Genotyping at multiple susceptibility loci in children from affected families can identify neonates with sufficient genetic risk of type 1 diabetes to be considered for early intervention.
Background
Genome‐wide association studies (GWAS) have identified many risk loci for asthma, but effect sizes are small, and in most cases, the biological mechanisms are unclear. Targeted metabolite ...quantification that provides information about a whole range of pathways of intermediary metabolism can help to identify biomarkers and investigate disease mechanisms. Combining genetic and metabolic information can aid in characterizing genetic association signals with high resolution. This work aimed to investigate the interrelation of current asthma, candidate asthma risk alleles and a panel of metabolites.
Methods
We investigated 151 metabolites, quantified by targeted mass spectrometry, in fasting serum of asthmatic and nonasthmatic individuals from the population‐based KORA F4 study (N = 2925). In addition, we analysed effects of single‐nucleotide polymorphisms (SNPs) at 24 asthma risk loci on these metabolites.
Results
Increased levels of various phosphatidylcholines and decreased levels of various lyso‐phosphatidylcholines were associated with asthma. Likewise, asthma risk alleles from the PDED3 and MED24 genes at the asthma susceptibility locus 17q21 were associated with increased concentrations of various phosphatidylcholines with consistent effect directions.
Conclusions
Our study demonstrated the potential of metabolomics to infer asthma‐related biomarkers by the identification of potentially deregulated phospholipids that associate with asthma and asthma risk alleles.
Birth by Cesarean section increases the risk of developing type 1 diabetes later in life. We aimed to elucidate common regulatory processes observed after Cesarean section and the development of ...islet autoimmunity, which precedes type 1 diabetes, by investigating the transcriptome of blood cells in the developing immune system. To investigate Cesarean section effects, we analyzed longitudinal gene expression profiles from peripheral blood mononuclear cells taken at several time points from children with increased familial and genetic risk for type 1 diabetes. For islet autoimmunity, we compared gene expression differences between children after initiation of islet autoimmunity and age-matched children who did not develop islet autoantibodies. Finally, we compared both results to identify common regulatory patterns. We identified the pentose phosphate pathway and pyrimidine metabolism - both involved in nucleotide synthesis and cell proliferation - to be differentially expressed in children born by Cesarean section and after islet autoimmunity. Comparison of global gene expression signatures showed that transcriptomic changes were systematically and significantly correlated between Cesarean section and islet autoimmunity. Moreover, signatures of both Cesarean section and islet autoimmunity correlated with transcriptional changes observed during activation of isolated CD4+ T lymphocytes. In conclusion, we identified shared molecular changes relating to immune cell activation in children born by Cesarean section and children who developed autoimmunity. Our results serve as a starting point for further investigations on how a type 1 diabetes risk factor impacts the young immune system at a molecular level.
Protein sequences are the most important source of evolutionary and functional information for new proteins. In order to facilitate the computationally intensive tasks of sequence analysis, the ...Similarity Matrix of Proteins (SIMAP) database aims to provide a comprehensive and up-to-date dataset of the pre-calculated sequence similarity matrix and sequence-based features like InterPro domains for all proteins contained in the major public sequence databases. As of September 2007, SIMAP covers ∼17 million proteins and more than 6 million non-redundant sequences and provides a complete annotation based on InterPro 16. Novel features of SIMAP include a new, portlet-based web portal providing multiple, structured views on retrieved proteins and integration of protein clusters and a unique search method for similar domain architectures. Access to SIMAP is freely provided for academic use through the web portal for individuals at http://mips.gsf.de/simap/and through Web Services for programmatic access at http://mips.gsf.de/webservices/services/SimapService2.0?wsdl.
Abstract
BACKGROUND
Glioblastoma (GBM) remains a poorly treatable disease with high mortality. Tumor metabolism in GBM is a critical mechanism responsible for accelerated growth because of ...upregulation of glucose, amino acid, and fatty acid utilization. However, therapies targeting GBM metabolism, whether through the use of small-molecule compounds or dietary interventions to limit nutrient sources, have failed in clinical trials. Metabolic bypass is an important mechanism that is often overlooked in GBM trials, since many trials have focused instead on combining anti-metabolic therapy with cytotoxic treatments. The goal of this research is to use a multi-pronged treatment approach with targeted drug and dietary therapy to leverage metabolic susceptibilities in GBM.
MATERIALS AND METHODS
We first interrogated the TCGA database and a cancer metabolite database for alterations in glucose and amino acid signatures in GBM relative to other human cancers and relative to low-grade glioma. We identified the amino acid cysteine as contributing to a novel metabolic susceptibility pathway in GBM. To study the role of cysteine in GBM pathogenesis, we treated patient-derived GBM cells with a variety of FDA-approved cysteine-promoting compounds in vitro, including N-acetylcysteine (NAC). We measured cell proliferation, energy production, mitochondrial metabolism, and reactive oxygen species to study mechanisms of oxidoreductive stress. Results: From our TCGA and cancer metabolite database analyses, we found that GBM exhibits the highest levels of cysteine and methionine pathway gene expression of 32 human cancers and that GBM exhibits high levels of cysteine-related metabolites compared to low-grade gliomas. Cysteine compounds, including NAC, reduce growth of GBM cells, which is exacerbated by glucose deprivation. This growth inhibition is associated with reduced mitochondrial metabolism, manifest by reduction in ATP generation, NADPH/NADP+ ratio, mitochondrial membrane potential, and oxygen consumption rate. Through measurement of mitochondrial hydrogen peroxide, we found that NAC-treated cells exhibit a paradoxical increase in mitochondrial hydrogen peroxide levels, likely due to inhibition of thioreductase and glutathione reductase systems. Through genetic and pharmacological studies, we found that induction of thioredoxin-2 rescues NAC-mediated cytotoxicity and that inhibition of thioreductase and glutathione reductase exacerbates mitochondrial toxicity and reductive stress.
CONCLUSIONS
We show that cysteine compounds reduce cell growth and induce mitochondrial toxicity in GBM through reductive stress. This metabolic phenotype is exacerbated by glucose deprivation. This pathway is targetable with FDA-approved cysteine-promoting compounds and could synergize with glucose-lowering treatments, including the ketogenic diet, for GBM.
Chronic kidney disease (CKD), impairment of kidney function, is a serious public health problem, and the assessment of genetic factors influencing kidney function has substantial clinical relevance. ...Here, we report a meta-analysis of genome-wide association studies for kidney function-related traits, including 71,149 east Asian individuals from 18 studies in 11 population-, hospital- or family-based cohorts, conducted as part of the Asian Genetic Epidemiology Network (AGEN). Our meta-analysis identified 17 loci newly associated with kidney function-related traits, including the concentrations of blood urea nitrogen, uric acid and serum creatinine and estimated glomerular filtration rate based on serum creatinine levels (eGFRcrea) (P < 5.0 × 10(-8)). We further examined these loci with in silico replication in individuals of European ancestry from the KidneyGen, CKDGen and GUGC consortia, including a combined total of ∼110,347 individuals. We identify pleiotropic associations among these loci with kidney function-related traits and risk of CKD. These findings provide new insights into the genetics of kidney function.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
•Identification of unknown metabolites based on their biochemical and genetic links.•Network-based inference of pathways and reactions of unknown metabolites.•In silico metabolite identification ...without mass spectra.•Automated characterization of unknown metabolites leads to concrete candidates.•Two metabolites, identified by a systems biology approach, could be verified.
Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules.
Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC–MS and GC–MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC–MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively.
Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites.
It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such ...considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.
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