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Sample records for metabolism genes predicted

  1. Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.

    Hyun-Seob Song

    Full Text Available Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.

  2. ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

    Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Mendiola, Marta; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B.; Machado, Isidro; Ramos, David; Gironella, Meritxell; Espinosa-Salinas, Isabel; Ramos, Ricardo; Martín-Hernández, Roberto; Risueño, Alberto; De Las Rivas, Javier; Reglero, Guillermo; Yaya, Ricardo; Fernández-Martos, Carlos; Aparicio, Jorge; Maurel, Joan; Feliu, Jaime; de Molina, Ana Ramírez

    2015-01-01

    Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group. PMID:25749516

  3. Common variants in SOCS7 gene predict obesity, disturbances in lipid metabolism and insulin resistance.

    Tellechea, M L; Steinhardt, A Penas; Rodriguez, G; Taverna, M J; Poskus, E; Frechtel, G

    2013-05-01

    Specific Suppressor of Cytokine Signaling (SOCS) members, such as SOCS7, may play a role in the development of insulin resistance (IR) owing to their ability to inhibit insulin signaling pathways. The objective was to explore the association between common variants and related haplotypes in SOCS7 gene and metabolic traits related to obesity, lipid metabolism and IR. 780 unrelated men were included in a cross-sectional study. We selected three tagged SNPs that capture 100% of SNPs with minor allele frequency ≥ 0.10. Analyses were done separately for each SNP and followed up by haplotype analysis. rs8074124C was associated with both obesity (p = 0.005) and abdominal obesity (p = 0.002) and allele C carriers showed, in comparison with TT carriers, lower BMI (p = 0.001) and waist circumference (p = 0.001). rs8074124CC- carriers showed lower fasting insulin (p = 0.017) and HOMA-IR (p = 0.018) than allele T carriers. rs12051836C was associated with hypertriglyceridemia (p = 0.009) and hypertriglyceridemic waist (p = 0.006). rs12051836CC- carriers showed lower fasting insulin (p = 0.043) and HOMA-IR (p = 0.042). Haplotype-based association analysis (rs8074124 and rs12051836 in that order) showed associations with lipid and obesity -related phenotypes, consistent with single locus analysis. Haplotype analysis also revealed association between haplotype CT and both decreased HDL-C (p = 0.026) and HDL-C (p = 0.014) as a continuous variable. We found, for the first time, significant associations between SOCS7 common variants and related haplotypes and obesity, IR and lipid metabolism disorders. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

  4. Pleiotropic genes for metabolic syndrome and inflammation

    Kraja, Aldi T; Chasman, Daniel I; North, Kari E

    2014-01-01

    Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factor...

  5. Novel genes in LDL metabolism

    Christoffersen, Mette; Tybjærg-Hansen, Anne

    2015-01-01

    PURPOSE OF REVIEW: To summarize recent findings from genome-wide association studies (GWAS), whole-exome sequencing of patients with familial hypercholesterolemia and 'exome chip' studies pointing to novel genes in LDL metabolism. RECENT FINDINGS: The genetic loci for ATP-binding cassette......-exome sequencing and 'exome chip' studies have additionally suggested several novel genes in LDL metabolism including insulin-induced gene 2, signal transducing adaptor family member 1, lysosomal acid lipase A, patatin-like phospholipase domain-containing protein 5 and transmembrane 6 superfamily member 2. Most...... of these findings still require independent replications and/or functional studies to confirm the exact role in LDL metabolism and the clinical implications for human health. SUMMARY: GWAS, exome sequencing studies, and recently 'exome chip' studies have suggested several novel genes with effects on LDL cholesterol...

  6. Comparative transcriptome analyses of three medicinal Forsythia species and prediction of candidate genes involved in secondary metabolisms.

    Sun, Luchao; Rai, Amit; Rai, Megha; Nakamura, Michimi; Kawano, Noriaki; Yoshimatsu, Kayo; Suzuki, Hideyuki; Kawahara, Nobuo; Saito, Kazuki; Yamazaki, Mami

    2018-05-07

    The three Forsythia species, F. suspensa, F. viridissima and F. koreana, have been used as herbal medicines in China, Japan and Korea for centuries and they are known to be rich sources of numerous pharmaceutical metabolites, forsythin, forsythoside A, arctigenin, rutin and other phenolic compounds. In this study, de novo transcriptome sequencing and assembly was performed on these species. Using leaf and flower tissues of F. suspensa, F. viridissima and F. koreana, 1.28-2.45-Gbp sequences of Illumina based pair-end reads were obtained and assembled into 81,913, 88,491 and 69,458 unigenes, respectively. Classification of the annotated unigenes in gene ontology terms and KEGG pathways was used to compare the transcriptome of three Forsythia species. The expression analysis of orthologous genes across all three species showed the expression in leaf tissues being highly correlated. The candidate genes presumably involved in the biosynthetic pathway of lignans and phenylethanoid glycosides were screened as co-expressed genes. They express highly in the leaves of F. viridissima and F. koreana. Furthermore, the three unigenes annotated as acyltransferase were predicted to be associated with the biosynthesis of acteoside and forsythoside A from the expression pattern and phylogenetic analysis. This study is the first report on comparative transcriptome analyses of medicinally important Forsythia genus and will serve as an important resource to facilitate further studies on biosynthesis and regulation of therapeutic compounds in Forsythia species.

  7. Metabolic network prediction through pairwise rational kernels.

    Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

    2014-09-26

    Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

  8. Population FBA predicts metabolic phenotypes in yeast.

    Piyush Labhsetwar

    2017-09-01

    Full Text Available Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA successfully described metabolic heterogeneity in a population of independent Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen, while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells are predicted to perform significant amount of respiration, use serine-glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We use a genetic algorithm to determine the proteomics constraints necessary to reproduce the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but

  9. Motif-independent prediction of a secondary metabolism gene cluster using comparative genomics: application to sequenced genomes of Aspergillus and ten other filamentous fungal species.

    Takeda, Itaru; Umemura, Myco; Koike, Hideaki; Asai, Kiyoshi; Machida, Masayuki

    2014-08-01

    Despite their biological importance, a significant number of genes for secondary metabolite biosynthesis (SMB) remain undetected due largely to the fact that they are highly diverse and are not expressed under a variety of cultivation conditions. Several software tools including SMURF and antiSMASH have been developed to predict fungal SMB gene clusters by finding core genes encoding polyketide synthase, nonribosomal peptide synthetase and dimethylallyltryptophan synthase as well as several others typically present in the cluster. In this work, we have devised a novel comparative genomics method to identify SMB gene clusters that is independent of motif information of the known SMB genes. The method detects SMB gene clusters by searching for a similar order of genes and their presence in nonsyntenic blocks. With this method, we were able to identify many known SMB gene clusters with the core genes in the genomic sequences of 10 filamentous fungi. Furthermore, we have also detected SMB gene clusters without core genes, including the kojic acid biosynthesis gene cluster of Aspergillus oryzae. By varying the detection parameters of the method, a significant difference in the sequence characteristics was detected between the genes residing inside the clusters and those outside the clusters. © The Author 2014. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

  10. Gene polymorphisms as risk factors for predicting the cardiovascular manifestations in Marfan syndrome. Role of folic acid metabolism enzyme gene polymorphisms in Marfan syndrome.

    Benke, Kálmán; Ágg, Bence; Mátyás, Gábor; Szokolai, Viola; Harsányi, Gergely; Szilveszter, Bálint; Odler, Balázs; Pólos, Miklós; Maurovich-Horvat, Pál; Radovits, Tamás; Merkely, Béla; Nagy, Zsolt B; Szabolcs, Zoltán

    2015-10-01

    Folic acid metabolism enzyme polymorphisms are believed to be responsible for the elevation of homocysteine (HCY) concentration in the blood plasma, correlating with the pathogenesis of aortic aneurysms and aortic dissection. We studied 71 Marfan patients divided into groups based on the severity of cardiovascular involvement: no intervention required (n=27, Group A); mild involvement requiring intervention (n=17, Group B); severe involvement (n=27, Group C) subdivided into aortic dilatation (n=14, Group C1) and aortic dissection (n=13, Group C2), as well as 117 control subjects. We evaluated HCY, folate, vitamin B12 and the polymorphisms of methylenetetrahydrofolate reductase (MTHFR;c.665C>T and c.1286A>C), methionine synthase (MTR;c.2756A>G) and methionine synthase reductase (MTRR;c.66A>G). Multiple comparisons showed significantly higher levels of HCY in Group C2 compared to Groups A, B, C1 and control group (pMarfan patients, and especially aortic dissection, is associated with higher HCY plasma levels and prevalence of homozygous genotypes of folic acid metabolism enzymes than mild or no cardiovascular involvement. These results suggest that impaired folic acid metabolism has an important role in the development and remodelling of the extracellular matrix of the aorta.

  11. Predicting metabolic pathways by sub-network extraction.

    Faust, Karoline; van Helden, Jacques

    2012-01-01

    Various methods result in groups of functionally related genes obtained from genomes (operons, regulons, syntheny groups, and phylogenetic profiles), transcriptomes (co-expression groups) and proteomes (modules of interacting proteins). When such groups contain two or more enzyme-coding genes, graph analysis methods can be applied to extract a metabolic pathway that interconnects them. We describe here the way to use the Pathway extraction tool available on the NeAT Web server ( http://rsat.ulb.ac.be/neat/ ) to piece together the metabolic pathway from a group of associated, enzyme-coding genes. The tool identifies the reactions that can be catalyzed by the products of the query genes (seed reactions), and applies sub-graph extraction algorithms to extract from a metabolic network a sub-network that connects the seed reactions. This sub-network represents the predicted metabolic pathway. We describe here the pathway prediction process in a step-by-step way, give hints about the main parametric choices, and illustrate how this tool can be used to extract metabolic pathways from bacterial genomes, on the basis of two study cases: the isoleucine-valine operon in Escherichia coli and a predicted operon in Cupriavidus (Ralstonia) metallidurans.

  12. Lipoprotein metabolism indicators improve cardiovascular risk prediction.

    Daniël B van Schalkwijk

    Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

  13. Apolipoprotein gene involved in lipid metabolism

    Rubin, Edward; Pennacchio, Len A.

    2007-07-03

    Methods and materials for studying the effects of a newly identified human gene, APOAV, and the corresponding mouse gene apoAV. The sequences of the genes are given, and transgenic animals which either contain the gene or have the endogenous gene knocked out are described. In addition, single nucleotide polymorphisms (SNPs) in the gene are described and characterized. It is demonstrated that certain SNPs are associated with diseases involving lipids and triglycerides and other metabolic diseases. These SNPs may be used alone or with SNPs from other genes to study individual risk factors. Methods for intervention in lipid diseases, including the screening of drugs to treat lipid-related or diabetic diseases are also disclosed.

  14. Metabolic gene polymorphism frequencies in control populations

    Garte, Seymour; Gaspari, Laura; Alexandrie, Anna-Karin

    2001-01-01

    Using the International Project on Genetic Susceptibility to Environmental Carcinogens (GSEC) database containing information on over 15,000 control (noncancer) subjects, the allele and genotype frequencies for many of the more commonly studied metabolic genes (CYP1A1, CYP2E1, CYP2D6, GSTM1, GSTT1...

  15. Machine learning methods for metabolic pathway prediction

    Karp Peter D

    2010-01-01

    Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

  16. Machine learning methods for metabolic pathway prediction

    2010-01-01

    Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

  17. Gene prediction validation and functional analysis of redundant pathways

    Sønderkær, Mads

    2011-01-01

    have employed a large mRNA-seq data set to improve and validate ab initio predicted gene models. This direct experimental evidence also provides reliable determinations of UTR regions and polyadenylation sites, which are not easily predicted in plants. Furthermore, once an annotated genome sequence...... is available, gene expression by mRNA-Seq enables acquisition of a more complete overview of gene isoform usage in complex enzymatic pathways enabling the identification of key genes. Metabolism in potatoes This information is useful e.g. for crop improvement based on manipulation of agronomically important...

  18. The SMARTCyp cytochrome P450 metabolism prediction server

    Rydberg, Patrik; Gloriam, David Erik Immanuel; Olsen, Lars

    2010-01-01

    The SMARTCyp server is the first web application for site of metabolism prediction of cytochrome P450-mediated drug metabolism.......The SMARTCyp server is the first web application for site of metabolism prediction of cytochrome P450-mediated drug metabolism....

  19. Comparative gene expression of intestinal metabolizing enzymes.

    Shin, Ho-Chul; Kim, Hye-Ryoung; Cho, Hee-Jung; Yi, Hee; Cho, Soo-Min; Lee, Dong-Goo; Abd El-Aty, A M; Kim, Jin-Suk; Sun, Duxin; Amidon, Gordon L

    2009-11-01

    The purpose of this study was to compare the expression profiles of drug-metabolizing enzymes in the intestine of mouse, rat and human. Total RNA was isolated from the duodenum and the mRNA expression was measured using Affymetrix GeneChip oligonucleotide arrays. Detected genes from the intestine of mouse, rat and human were ca. 60% of 22690 sequences, 40% of 8739 and 47% of 12559, respectively. Total genes of metabolizing enzymes subjected in this study were 95, 33 and 68 genes in mouse, rat and human, respectively. Of phase I enzymes, the mouse exhibited abundant gene expressions for Cyp3a25, Cyp4v3, Cyp2d26, followed by Cyp2b20, Cyp2c65 and Cyp4f14, whereas, the rat showed higher expression profiles of Cyp3a9, Cyp2b19, Cyp4f1, Cyp17a1, Cyp2d18, Cyp27a1 and Cyp4f6. However, the highly expressed P450 enzymes were CYP3A4, CYP3A5, CYP4F3, CYP2C18, CYP2C9, CYP2D6, CYP3A7, CYP11B1 and CYP2B6 in the human. For phase II enzymes, glucuronosyltransferase Ugt1a6, glutathione S-transferases Gstp1, Gstm3 and Gsta2, sulfotransferase Sult1b1 and acyltransferase Dgat1 were highly expressed in the mouse. The rat revealed predominant expression of glucuronosyltransferases Ugt1a1 and Ugt1a7, sulfotransferase Sult1b1, acetyltransferase Dlat and acyltransferase Dgat1. On the other hand, in human, glucuronosyltransferases UGT2B15 and UGT2B17, glutathione S-transferases MGST3, GSTP1, GSTA2 and GSTM4, sulfotransferases ST1A3 and SULT1A2, acetyltransferases SAT1 and CRAT, and acyltransferase AGPAT2 were dominantly detected. Therefore, current data indicated substantial interspecies differences in the pattern of intestinal gene expression both for P450 enzymes and phase II drug-metabolizing enzymes. This genomic database is expected to improve our understanding of interspecies variations in estimating intestinal prehepatic clearance of oral drugs.

  20. Vertebrate gene predictions and the problem of large genes

    Wang, Jun; Li, ShengTing; Zhang, Yong

    2003-01-01

    To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistent...

  1. Differential retention of metabolic genes following whole-genome duplication.

    Gout, Jean-François; Duret, Laurent; Kahn, Daniel

    2009-05-01

    Classical studies in Metabolic Control Theory have shown that metabolic fluxes usually exhibit little sensitivity to changes in individual enzyme activity, yet remain sensitive to global changes of all enzymes in a pathway. Therefore, little selective pressure is expected on the dosage or expression of individual metabolic genes, yet entire pathways should still be constrained. However, a direct estimate of this selective pressure had not been evaluated. Whole-genome duplications (WGDs) offer a good opportunity to address this question by analyzing the fates of metabolic genes during the massive gene losses that follow. Here, we take advantage of the successive rounds of WGD that occurred in the Paramecium lineage. We show that metabolic genes exhibit different gene retention patterns than nonmetabolic genes. Contrary to what was expected for individual genes, metabolic genes appeared more retained than other genes after the recent WGD, which was best explained by selection for gene expression operating on entire pathways. Metabolic genes also tend to be less retained when present at high copy number before WGD, contrary to other genes that show a positive correlation between gene retention and preduplication copy number. This is rationalized on the basis of the classical concave relationship relating metabolic fluxes with enzyme expression.

  2. Prediction of cytochrome P450 mediated metabolism

    Olsen, Lars; Oostenbrink, Chris; Jørgensen, Flemming Steen

    2015-01-01

    Cytochrome P450 enzymes (CYPs) form one of the most important enzyme families involved in the metabolism of xenobiotics. CYPs comprise many isoforms, which catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be formed. However, it is often hard...... to rationalize what metabolites these enzymes generate. In recent years, many different in silico approaches have been developed to predict binding or regioselective product formation for the different CYP isoforms. These comprise ligand-based methods that are trained on experimental CYP data and structure...

  3. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

    Caroline Colijn

    2009-08-01

    Full Text Available Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression, extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB. Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.

  4. Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

    Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

    2018-02-23

    Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

  5. Predicting effects of structural stress in a genome-reduced model bacterial metabolism

    Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles

    2012-08-01

    Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment.

  6. Inferring metabolic states in uncharacterized environments using gene-expression measurements.

    Sergio Rossell

    Full Text Available The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of

  7. Drug Metabolizing Enzyme and Transporter Gene Variation, Nicotine Metabolism, Prospective Abstinence, and Cigarette Consumption.

    Andrew W Bergen

    Full Text Available The Nicotine Metabolite Ratio (NMR, ratio of trans-3'-hydroxycotinine and cotinine, has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3. Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.

  8. Global Metabolic Reconstruction and Metabolic Gene Evolution in the Cattle Genome

    Kim, Woonsu; Park, Hyesun; Seo, Seongwon

    2016-01-01

    The sequence of cattle genome provided a valuable opportunity to systematically link genetic and metabolic traits of cattle. The objectives of this study were 1) to reconstruct genome-scale cattle-specific metabolic pathways based on the most recent and updated cattle genome build and 2) to identify duplicated metabolic genes in the cattle genome for better understanding of metabolic adaptations in cattle. A bioinformatic pipeline of an organism for amalgamating genomic annotations from multiple sources was updated. Using this, an amalgamated cattle genome database based on UMD_3.1, was created. The amalgamated cattle genome database is composed of a total of 33,292 genes: 19,123 consensus genes between NCBI and Ensembl databases, 8,410 and 5,493 genes only found in NCBI or Ensembl, respectively, and 266 genes from NCBI scaffolds. A metabolic reconstruction of the cattle genome and cattle pathway genome database (PGDB) was also developed using Pathway Tools, followed by an intensive manual curation. The manual curation filled or revised 68 pathway holes, deleted 36 metabolic pathways, and added 23 metabolic pathways. Consequently, the curated cattle PGDB contains 304 metabolic pathways, 2,460 reactions including 2,371 enzymatic reactions, and 4,012 enzymes. Furthermore, this study identified eight duplicated genes in 12 metabolic pathways in the cattle genome compared to human and mouse. Some of these duplicated genes are related with specific hormone biosynthesis and detoxifications. The updated genome-scale metabolic reconstruction is a useful tool for understanding biology and metabolic characteristics in cattle. There has been significant improvements in the quality of cattle genome annotations and the MetaCyc database. The duplicated metabolic genes in the cattle genome compared to human and mouse implies evolutionary changes in the cattle genome and provides a useful information for further research on understanding metabolic adaptations of cattle. PMID

  9. Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

    Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.; Du, Niu; Mih, Nathan; Diamond, Spencer; Lee, Jenny J.; Golden, Susan S.; Palsson, Bernhard O.

    2016-01-01

    The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology. PMID:27911809

  10. Sugar Lego: gene composition of bacterial carbohydrate metabolism genomic loci.

    Kaznadzey, Anna; Shelyakin, Pavel; Gelfand, Mikhail S

    2017-11-25

    Bacterial carbohydrate metabolism is extremely diverse, since carbohydrates serve as a major energy source and are involved in a variety of cellular processes. Bacterial genes belonging to same metabolic pathway are often co-localized in the chromosome, but it is not a strict rule. Gene co-localization in linked to co-evolution and co-regulation. This study focuses on a large-scale analysis of bacterial genomic loci related to the carbohydrate metabolism. We demonstrate that only 53% of 148,000 studied genes from over six hundred bacterial genomes are co-localized in bacterial genomes with other carbohydrate metabolism genes, which points to a significant role of singleton genes. Co-localized genes form cassettes, ranging in size from two to fifteen genes. Two major factors influencing the cassette-forming tendency are gene function and bacterial phylogeny. We have obtained a comprehensive picture of co-localization preferences of genes for nineteen major carbohydrate metabolism functional classes, over two hundred gene orthologous clusters, and thirty bacterial classes, and characterized the cassette variety in size and content among different species, highlighting a significant role of short cassettes. The preference towards co-localization of carbohydrate metabolism genes varies between 40 and 76% for bacterial taxa. Analysis of frequently co-localized genes yielded forty-five significant pairwise links between genes belonging to different functional classes. The number of such links per class range from zero to eight, demonstrating varying preferences of respective genes towards a specific chromosomal neighborhood. Genes from eleven functional classes tend to co-localize with genes from the same class, indicating an important role of clustering of genes with similar functions. At that, in most cases such co-localization does not originate from local duplication events. Overall, we describe a complex web formed by evolutionary relationships of bacterial

  11. Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics.

    Maldonado, Elaina M; Leoncikas, Vytautas; Fisher, Ciarán P; Moore, J Bernadette; Plant, Nick J; Kierzek, Andrzej M

    2017-11-01

    The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

  12. Lipoprotein metabolism indicators improve cardiovascular risk prediction

    Schalkwijk, D.B. van; Graaf, A.A. de; Tsivtsivadze, E.; Parnell, L.D.; Werff-van der Vat, B.J.C. van der; Ommen, B. van; Greef, J. van der; Ordovás, J.M.

    2014-01-01

    Background: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to

  13. Combining gene prediction methods to improve metagenomic gene annotation

    Rosen Gail L

    2011-01-01

    Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

  14. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Matthew B Biggs

    2017-03-01

    Full Text Available Genome-scale metabolic network reconstructions (GENREs are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA. We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  15. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    Huthmacher Carola

    2010-08-01

    Full Text Available Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte. Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. Conclusions The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.

  16. A role for gene duplication and natural variation of gene expression in the evolution of metabolism.

    Daniel J Kliebenstein

    Full Text Available BACKGROUND: Most eukaryotic genomes have undergone whole genome duplications during their evolutionary history. Recent studies have shown that the function of these duplicated genes can diverge from the ancestral gene via neo- or sub-functionalization within single genotypes. An additional possibility is that gene duplicates may also undergo partitioning of function among different genotypes of a species leading to genetic differentiation. Finally, the ability of gene duplicates to diverge may be limited by their biological function. METHODOLOGY/PRINCIPAL FINDINGS: To test these hypotheses, I estimated the impact of gene duplication and metabolic function upon intraspecific gene expression variation of segmental and tandem duplicated genes within Arabidopsis thaliana. In all instances, the younger tandem duplicated genes showed higher intraspecific gene expression variation than the average Arabidopsis gene. Surprisingly, the older segmental duplicates also showed evidence of elevated intraspecific gene expression variation albeit typically lower than for the tandem duplicates. The specific biological function of the gene as defined by metabolic pathway also modulated the level of intraspecific gene expression variation. The major energy metabolism and biosynthetic pathways showed decreased variation, suggesting that they are constrained in their ability to accumulate gene expression variation. In contrast, a major herbivory defense pathway showed significantly elevated intraspecific variation suggesting that it may be under pressure to maintain and/or generate diversity in response to fluctuating insect herbivory pressures. CONCLUSION: These data show that intraspecific variation in gene expression is facilitated by an interaction of gene duplication and biological activity. Further, this plays a role in controlling diversity of plant metabolism.

  17. Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

    Richard Danger

    2018-01-01

    Full Text Available Bronchiolitis obliterans syndrome (BOS, the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group, and 26 samples at or after BOS diagnosis (diagnosis group. An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group. We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1, T-cell leukemia/lymphoma protein 1A (TCL1A, and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p < 0.01 and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS.

  18. Fast prediction of cytochrome P450 mediated drug metabolism

    Rydberg, Patrik Åke Anders; Poongavanam, Vasanthanathan; Oostenbrink, Chris

    2009-01-01

    Cytochrome P450 mediated metabolism of drugs is one of the major determinants of their kinetic profile, and prediction of this metabolism is therefore highly relevant during the drug discovery and development process. A new rule-based method, based on results from density functional theory...... calculations, for predicting activation energies for aliphatic and aromatic oxidations by cytochromes P450 is developed and compared with several other methods. Although the applicability of the method is currently limited to a subset of P450 reactions, these reactions describe more than 90...

  19. Predicting Hydrologic Function With Aquatic Gene Fragments

    Good, S. P.; URycki, D. R.; Crump, B. C.

    2018-03-01

    Recent advances in microbiology techniques, such as genetic sequencing, allow for rapid and cost-effective collection of large quantities of genetic information carried within water samples. Here we posit that the unique composition of aquatic DNA material within a water sample contains relevant information about hydrologic function at multiple temporal scales. In this study, machine learning was used to develop discharge prediction models trained on the relative abundance of bacterial taxa classified into operational taxonomic units (OTUs) based on 16S rRNA gene sequences from six large arctic rivers. We term this approach "genohydrology," and show that OTU relative abundances can be used to predict river discharge at monthly and longer timescales. Based on a single DNA sample from each river, the average Nash-Sutcliffe efficiency (NSE) for predicted mean monthly discharge values throughout the year was 0.84, while the NSE for predicted discharge values across different return intervals was 0.67. These are considerable improvements over predictions based only on the area-scaled mean specific discharge of five similar rivers, which had average NSE values of 0.64 and -0.32 for seasonal and recurrence interval discharge values, respectively. The genohydrology approach demonstrates that genetic diversity within the aquatic microbiome is a large and underutilized data resource with benefits for prediction of hydrologic function.

  20. Predicting cellular growth from gene expression signatures.

    Edoardo M Airoldi

    2009-01-01

    Full Text Available Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.

  1. Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model

    Ingkasuwan Papapit

    2012-08-01

    Full Text Available Abstract Background Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM. Results Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF. A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090, which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene. The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070 and constans-like (COL: At2g21320, were identified as positive regulators of starch synthase 4 (SS4: At4g18240. The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines. Conclusions In this study, we utilized a systematic approach of microarray

  2. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  3. Genomic Prediction of Gene Bank Wheat Landraces

    José Crossa

    2016-07-01

    Full Text Available This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H for the highly heritable traits, days to heading (DTH, and days to maturity (DTM. Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E. Two alternative prediction strategies were studied: (1 random cross-validation of the data in 20% training (TRN and 80% testing (TST (TRN20-TST80 sets, and (2 two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm

  4. Coordinated and interactive expression of genes of lipid metabolism and inflammation in adipose tissue and liver during metabolic overload.

    Wen Liang

    Full Text Available BACKGROUND: Chronic metabolic overload results in lipid accumulation and subsequent inflammation in white adipose tissue (WAT, often accompanied by non-alcoholic fatty liver disease (NAFLD. In response to metabolic overload, the expression of genes involved in lipid metabolism and inflammatory processes is adapted. However, it still remains unknown how these adaptations in gene expression in expanding WAT and liver are orchestrated and whether they are interrelated. METHODOLOGY/PRINCIPAL FINDINGS: ApoE*3Leiden mice were fed HFD or chow for different periods up to 12 weeks. Gene expression in WAT and liver over time was evaluated by micro-array analysis. WAT hypertrophy and inflammation were analyzed histologically. Bayesian hierarchical cluster analysis of dynamic WAT gene expression identified groups of genes ('clusters' with comparable expression patterns over time. HFD evoked an immediate response of five clusters of 'lipid metabolism' genes in WAT, which did not further change thereafter. At a later time point (>6 weeks, inflammatory clusters were induced. Promoter analysis of clustered genes resulted in specific key regulators which may orchestrate the metabolic and inflammatory responses in WAT. Some master regulators played a dual role in control of metabolism and inflammation. When WAT inflammation developed (>6 weeks, genes of lipid metabolism and inflammation were also affected in corresponding livers. These hepatic gene expression changes and the underlying transcriptional responses in particular, were remarkably similar to those detected in WAT. CONCLUSION: In WAT, metabolic overload induced an immediate, stable response on clusters of lipid metabolism genes and induced inflammatory genes later in time. Both processes may be controlled and interlinked by specific transcriptional regulators. When WAT inflammation began, the hepatic response to HFD resembled that in WAT. In all, WAT and liver respond to metabolic overload by

  5. Ecological transition predictably associated with gene degeneration.

    Wessinger, Carolyn A; Rausher, Mark D

    2015-02-01

    Gene degeneration or loss can significantly contribute to phenotypic diversification, but may generate genetic constraints on future evolutionary trajectories, potentially restricting phenotypic reversal. Such constraints may manifest as directional evolutionary trends when parallel phenotypic shifts consistently involve gene degeneration or loss. Here, we demonstrate that widespread parallel evolution in Penstemon from blue to red flowers predictably involves the functional inactivation and degeneration of the enzyme flavonoid 3',5'-hydroxylase (F3'5'H), an anthocyanin pathway enzyme required for the production of blue floral pigments. Other types of genetic mutations do not consistently accompany this phenotypic shift. This pattern may be driven by the relatively large mutational target size of degenerative mutations to this locus and the apparent lack of associated pleiotropic effects. The consistent degeneration of F3'5'H may provide a mechanistic explanation for the observed asymmetry in the direction of flower color evolution in Penstemon: Blue to red transitions are common, but reverse transitions have not been observed. Although phenotypic shifts in this system are likely driven by natural selection, internal constraints may generate predictable genetic outcomes and may restrict future evolutionary trajectories. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  6. A comparative analysis of soft computing techniques for gene prediction.

    Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

    2013-07-01

    The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    Hao, Ke; Zhong, Hua; Greenawalt, Danielle; Ferguson, Mark D; Ng, Irene O; Sham, Pak C; Poon, Ronnie T; Molony, Cliona; Schadt, Eric E; Dai, Hongyue; Luk, John M; Lamb, John; Zhang, Chunsheng; Xie, Tao; Wang, Kai; Zhang, Bin; Chudin, Eugene; Lee, Nikki P; Mao, Mao

    2011-01-01

    The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome

  8. Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks

    Chiappino-Pepe, Anush; Ataman, Meriç

    2017-01-01

    Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention. PMID:28333921

  9. Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks.

    Anush Chiappino-Pepe

    2017-03-01

    Full Text Available Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA. Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention.

  10. The WWOX Gene Modulates HDL and Lipid Metabolism

    Iatan, Iulia; Choi, Hong Y.; Ruel, Isabelle; Linga Reddy, M.V. Prasad; Kil, Hyunsuk; Lee, Jaeho; Abu Odeh, Mohammad; Salah, Zaidoun; Abu-Remaileh, Muhannad; Weissglas-Volkov, Daphna; Nikkola, Elina; Civelek, Mete; Awan, Zuhier; Croce, Carlo M.; Aqeilan, Rami I.; Pajukanta, Päivi; Aldaz, C. Marcelo; Genest, Jacques

    2014-01-01

    Background Low high-density lipoprotein-cholesterol (HDL-C) constitutes a major risk factor for atherosclerosis. Recent studies from our group reported a genetic association between the WW domain-containing oxidoreductase (WWOX) gene and HDL-C levels. Here, through next-generation resequencing, in vivo functional studies and gene microarray analyses, we investigated the role of WWOX in HDL and lipid metabolism. Methods and Results Using next-generation resequencing of the WWOX region, we first identified 8 variants significantly associated and perfectly segregating with the low-HDL trait in two multi-generational French Canadian dyslipidemic families. To understand in vivo functions of WWOX, we used liver-specific Wwoxhep−/− and total Wwox−/− mice models, where we found decreased ApoA-I and ABCA1 levels in hepatic tissues. Analyses of lipoprotein profiles in Wwox−/−, but not Wwox hep−/− littermates, also showed marked reductions in serum HDL-C concentrations, concordant with the low-HDL findings observed in families. We next obtained evidence of a gender-specific effect in female Wwoxhep−/− mice, where an increase in plasma triglycerides and altered lipid metabolic pathways by microarray analyses were observed. We further identified a significant reduction in ApoA-I and LPL, and upregulation in Fas, Angptl4 and Lipg, suggesting that the effects of Wwox involve multiple pathways, including cholesterol homeostasis, ApoA-I/ABCA1 pathway, and fatty acid biosynthesis/triglyceride metabolism. Conclusions Our data indicate that WWOX disruption alters HDL and lipoprotein metabolism through several mechanisms and may account for the low-HDL phenotype observed in families expressing the WWOX variants. These findings thus describe a novel gene involved in cellular lipid homeostasis, which effects may impact atherosclerotic disease development. PMID:24871327

  11. PROXIMAL: a method for Prediction of Xenobiotic Metabolism.

    Yousofshahi, Mona; Manteiga, Sara; Wu, Charmian; Lee, Kyongbum; Hassoun, Soha

    2015-12-22

    Contamination of the environment with bioactive chemicals has emerged as a potential public health risk. These substances that may cause distress or disease in humans can be found in air, water and food supplies. An open question is whether these chemicals transform into potentially more active or toxic derivatives via xenobiotic metabolizing enzymes expressed in the body. We present a new prediction tool, which we call PROXIMAL (Prediction of Xenobiotic Metabolism) for identifying possible transformation products of xenobiotic chemicals in the liver. Using reaction data from DrugBank and KEGG, PROXIMAL builds look-up tables that catalog the sites and types of structural modifications performed by Phase I and Phase II enzymes. Given a compound of interest, PROXIMAL searches for substructures that match the sites cataloged in the look-up tables, applies the corresponding modifications to generate a panel of possible transformation products, and ranks the products based on the activity and abundance of the enzymes involved. PROXIMAL generates transformations that are specific for the chemical of interest by analyzing the chemical's substructures. We evaluate the accuracy of PROXIMAL's predictions through case studies on two environmental chemicals with suspected endocrine disrupting activity, bisphenol A (BPA) and 4-chlorobiphenyl (PCB3). Comparisons with published reports confirm 5 out of 7 and 17 out of 26 of the predicted derivatives for BPA and PCB3, respectively. We also compare biotransformation predictions generated by PROXIMAL with those generated by METEOR and Metaprint2D-react, two other prediction tools. PROXIMAL can predict transformations of chemicals that contain substructures recognizable by human liver enzymes. It also has the ability to rank the predicted metabolites based on the activity and abundance of enzymes involved in xenobiotic transformation.

  12. Isoeugenol monooxygenase and its putative regulatory gene are located in the eugenol metabolic gene cluster in Pseudomonas nitroreducens Jin1.

    Ryu, Ji-Young; Seo, Jiyoung; Unno, Tatsuya; Ahn, Joong-Hoon; Yan, Tao; Sadowsky, Michael J; Hur, Hor-Gil

    2010-03-01

    The plant-derived phenylpropanoids eugenol and isoeugenol have been proposed as useful precursors for the production of natural vanillin. Genes involved in the metabolism of eugenol and isoeugenol were clustered in region of about a 30 kb of Pseudomonas nitroreducens Jin1. Two of the 23 ORFs in this region, ORFs 26 (iemR) and 27 (iem), were predicted to be involved in the conversion of isoeugenol to vanillin. The deduced amino acid sequence of isoeugenol monooxygenase (Iem) of strain Jin1 had 81.4% identity to isoeugenol monooxygenase from Pseudomonas putida IE27, which also transforms isoeugenol to vanillin. Iem was expressed in E. coli BL21(DE3) and was found to lead to isoeugenol to vanillin transformation. Deletion and cloning analyses indicated that the gene iemR, located upstream of iem, is required for expression of iem in the presence of isoeugenol, suggesting it to be the iem regulatory gene. Reverse transcription, real-time PCR analyses indicated that the genes involved in the metabolism of eugenol and isoeugenol were differently induced by isoeugenol, eugenol, and vanillin.

  13. An algorithm to discover gene signatures with predictive potential

    Hallett Robin M

    2010-09-01

    Full Text Available Abstract Background The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. Results Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. Conclusions We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

  14. Predicting basal metabolic rates in Malaysian adult elite athletes.

    Wong, Jyh Eiin; Poh, Bee Koon; Nik Shanita, Safii; Izham, Mohd Mohamad; Chan, Kai Quin; Tai, Meng De; Ng, Wei Wei; Ismail, Mohd Noor

    2012-11-01

    This study aimed to measure the basal metabolic rate (BMR) of elite athletes and develop a gender specific predictive equation to estimate their energy requirements. 92 men and 33 women (aged 18-31 years) from 15 sports, who had been training six hours daily for at least one year, were included in the study. Body composition was measured using the bioimpedance technique, and BMR by indirect calorimetry. The differences between measured and estimated BMR using various predictive equations were calculated. The novel equation derived from stepwise multiple regression was evaluated using Bland and Altman analysis. The predictive equations of Cunningham and the Food and Agriculture Organization/World Health Organization/United Nations University either over- or underestimated the measured BMR by up to ± 6%, while the equations of Ismail et al, developed from the local non-athletic population, underestimated the measured BMR by 14%. The novel predictive equation for the BMR of athletes was BMR (kcal/day) = 669 + 13 (weight in kg) + 192 (gender: 1 for men and 0 for women) (R2 0.548; standard error of estimates 163 kcal). Predicted BMRs of elite athletes by this equation were within 1.2% ± 9.5% of the measured BMR values. The novel predictive equation presented in this study can be used to calculate BMR for adult Malaysian elite athletes. Further studies may be required to validate its predictive capabilities for other sports, nationalities and age groups.

  15. Importance of metabolism in pharmacological studies: possible in vitro predictability

    Delaforge, M.

    1998-01-01

    Metabolic transformation of drug leads to the formation of a large number of secondary compounds. These metabolites may (a) participate to the elimination of the patent drug, (b) have similar or different therapeutic effects compared to the parent drug (c) exert toxic effects. Cytochromes P450 are the main enzymes involved in the biotransformation of exogenous drugs, leading to oxidized, reduced or peroxidized metabolites. Different isozymes of P450 are present in already all the organs and differ by their affinity for substrate families. P450 3A is the most abundant P450 protein in the adult human liver and is able to transform hundreds of substrates into either drugs or endogenous compounds such as testosterone. Its catalytic activities are regulated either by induction or by inhibition. Attempts to predict metabolic transformation of a given drug are based on the amount of P450 expressed in heterologous systems, induction, and inhibition experiments and by comparison to classical P450 substrates. Erythromycin metabolism and its P450 effects are used to illustrate the complexity and the consequences of metabolic transformation of a given drug

  16. Metabolic syndrome and atypical antipsychotics: Possibility of prediction and control.

    Franch Pato, Clara M; Molina Rodríguez, Vicente; Franch Valverde, Juan I

    Schizophrenia and other psychotic disorders are associated with high morbidity and mortality, due to inherent health factors, genetic factors, and factors related to psychopharmacological treatment. Antipsychotics, like other drugs, have side-effects that can substantially affect the physical health of patients, with substantive differences in the side-effect profile and in the patients in which these side-effects occur. To understand and identify these risk groups could help to prevent the occurrence of the undesired effects. A prospective study, with 24 months follow-up, was conducted in order to analyse the physical health of severe mental patients under maintenance treatment with atypical antipsychotics, as well as to determine any predictive parameters at anthropometric and/or analytical level for good/bad outcome of metabolic syndrome in these patients. There were no significant changes in the physical and biochemical parameters individually analysed throughout the different visits. The baseline abdominal circumference (lambda Wilks P=.013) and baseline HDL-cholesterol levels (lambda Wilks P=.000) were the parameters that seem to be more relevant above the rest of the metabolic syndrome constituents diagnosis criteria as predictors in the long-term. In the search for predictive factors of metabolic syndrome, HDL-cholesterol and abdominal circumference at the time of inclusion were selected, as such that the worst the baseline results were, the higher probability of long-term improvement. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

  17. Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

    Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

    2012-07-15

    Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of EOperon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

  18. Subchronic effects of valproic acid on gene expression profiles for lipid metabolism in mouse liver

    Lee, Min-Ho; Kim, Mingoo; Lee, Byung-Hoon; Kim, Ju-Han; Kang, Kyung-Sun; Kim, Hyung-Lae; Yoon, Byung-Il; Chung, Heekyoung; Kong, Gu; Lee, Mi-Ock

    2008-01-01

    Valproic acid (VPA) is used clinically to treat epilepsy, however it induces hepatotoxicity such as microvesicular steatosis. Acute hepatotoxicity of VPA has been well documented by biochemical studies and microarray analysis, but little is known about the chronic effects of VPA in the liver. In the present investigation, we profiled gene expression patterns in the mouse liver after subchronic treatment with VPA. VPA was administered orally at a dose of 100 mg/kg/day or 500 mg/kg/day to ICR mice, and the livers were obtained after 1, 2, or 4 weeks. The activities of serum liver enzymes did not change, whereas triglyceride concentration increased significantly. Microarray analysis revealed that 1325 genes of a set of 32,996 individual genes were VPA responsive when examined by two-way ANOVA (P 1.5). Consistent with our previous results obtained using an acute VPA exposure model (Lee et al., Toxicol Appl Pharmacol. 220:45-59, 2007), the most significantly over-represented biological terms for these genes included lipid, fatty acid, and steroid metabolism. Biological pathway analysis suggests that the genes responsible for increased biosynthesis of cholesterol and triglyceride, and for decreased fatty acid β-oxidation contribute to the abnormalities in lipid metabolism induced by subchronic VPA treatment. A comparison of the VPA-responsive genes in the acute and subchronic models extracted 15 commonly altered genes, such as Cyp4a14 and Adpn, which may have predictive power to distinguish the mode of action of hepatotoxicants. Our data provide a better understanding of the molecular mechanisms of VPA-induced hepatotoxicity and useful information to predict steatogenic hepatotoxicity

  19. Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

    Zulfiqar, Asma, E-mail: asmazulfiqar08@yahoo.com [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Paulose, Bibin, E-mail: bpaulose@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Chhikara, Sudesh, E-mail: sudesh@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States); Dhankher, Om Parkash, E-mail: parkash@psis.umass.edu [Department of Plant, Soil, and Insect Sciences, 270 Stockbridge Road, University of Massachusetts Amherst, MA 01003 (United States)

    2011-10-15

    Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: > Molecular mechanism of Cr uptake and detoxification in plants is not well known. > We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. > 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. > Pathways linked to stress, ion transport, and sulfur assimilation were affected. > This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

  20. Plant interactions alter the predictions of metabolic scaling theory.

    Yue Lin

    Full Text Available Metabolic scaling theory (MST is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning. Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric, and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.

  1. Prediction of residual metabolic activity after treatment in NSCLC patients

    Rios Velazquez, Emmanuel; Aerts, Hugo J.W.L.; Oberije, Cary; Ruysscher, Dirk De; Lambin, Philippe

    2010-01-01

    Purpose. Metabolic response assessment is often used as a surrogate of local failure and survival. Early identification of patients with residual metabolic activity is essential as this enables selection of patients who could potentially benefit from additional therapy. We report on the development of a pre-treatment prediction model for metabolic response using patient, tumor and treatment factors. Methods. One hundred and one patients with inoperable NSCLC (stage I-IV), treated with 3D conformal radical (chemo)-radiotherapy were retrospectively included in this study. All patients received a pre and post-radiotherapy fluorodeoxyglucose positron emission tomography-computed tomography FDG-PET-CT scan. The electronic medical record system and the medical patient charts were reviewed to obtain demographic, clinical, tumor and treatment data. Primary outcome measure was examined using a metabolic response assessment on a post-radiotherapy FDG-PET-CT scan. Radiotherapy was delivered in fractions of 1.8 Gy, twice a day, with a median prescribed dose of 60 Gy. Results. Overall survival was worse in patients with residual metabolic active areas compared with the patients with a complete metabolic response (p=0.0001). In univariate analysis, three variables were significantly associated with residual disease: larger primary gross tumor volume (GTVprimary, p=0.002), higher pre-treatment maximum standardized uptake value (SUV max , p=0.0005) in the primary tumor and shorter overall treatment time (OTT, p=0.046). A multivariate model including GTVprimary, SUV max , equivalent radiation dose at 2 Gy corrected for time (EQD2, T) and OTT yielded an area under the curve assessed by the leave-one-out cross validation of 0.71 (95% CI, 0.65-0.76). Conclusion. Our results confirmed the validity of metabolic response assessment as a surrogate of survival. We developed a multivariate model that is able to identify patients at risk of residual disease. These patients may benefit from

  2. Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.

    Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E; Turnbaugh, Peter J; Bright, Roselie A; Altman, Russ B

    2018-01-01

    Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

  3. Exploring the Optimal Strategy to Predict Essential Genes in Microbes

    Yao Lu

    2011-12-01

    Full Text Available Accurately predicting essential genes is important in many aspects of biology, medicine and bioengineering. In previous research, we have developed a machine learning based integrative algorithm to predict essential genes in bacterial species. This algorithm lends itself to two approaches for predicting essential genes: learning the traits from known essential genes in the target organism, or transferring essential gene annotations from a closely related model organism. However, for an understudied microbe, each approach has its potential limitations. The first is constricted by the often small number of known essential genes. The second is limited by the availability of model organisms and by evolutionary distance. In this study, we aim to determine the optimal strategy for predicting essential genes by examining four microbes with well-characterized essential genes. Our results suggest that, unless the known essential genes are few, learning from the known essential genes in the target organism usually outperforms transferring essential gene annotations from a related model organism. In fact, the required number of known essential genes is surprisingly small to make accurate predictions. In prokaryotes, when the number of known essential genes is greater than 2% of total genes, this approach already comes close to its optimal performance. In eukaryotes, achieving the same best performance requires over 4% of total genes, reflecting the increased complexity of eukaryotic organisms. Combining the two approaches resulted in an increased performance when the known essential genes are few. Our investigation thus provides key information on accurately predicting essential genes and will greatly facilitate annotations of microbial genomes.

  4. Delivery Mode and the Transition of Pioneering Gut-Microbiota Structure, Composition and Predicted Metabolic Function

    Noel T. Mueller

    2017-12-01

    Full Text Available Cesarean (C-section delivery, recently shown to cause excess weight gain in mice, perturbs human neonatal gut microbiota development due to the lack of natural mother-to-newborn transfer of microbes. Neonates excrete first the in-utero intestinal content (referred to as meconium hours after birth, followed by intestinal contents reflective of extra-uterine exposure (referred to as transition stool 2 to 3 days after birth. It is not clear when the effect of C-section on the neonatal gut microbiota emerges. We examined bacterial DNA in carefully-collected meconium, and the subsequent transitional stool, from 59 neonates [13 born by scheduled C-section and 46 born by vaginal delivery] in a private hospital in Brazil. Bacterial DNA was extracted, and the V4 region of the 16S rRNA gene was sequenced using the Illumina MiSeq (San Diego, CA, USA platform. We found evidence of bacterial DNA in the majority of meconium samples in our study. The bacterial DNA structure (i.e., beta diversity of meconium differed significantly from that of the transitional stool microbiota. There was a significant reduction in bacterial alpha diversity (e.g., number of observed bacterial species and change in bacterial composition (e.g., reduced Proteobacteria in the transition from meconium to stool. However, changes in predicted microbiota metabolic function from meconium to transitional stool were only observed in vaginally-delivered neonates. Within sample comparisons showed that delivery mode was significantly associated with bacterial structure, composition and predicted microbiota metabolic function in transitional-stool samples, but not in meconium samples. Specifically, compared to vaginally delivered neonates, the transitional stool of C-section delivered neonates had lower proportions of the genera Bacteroides, Parabacteroides and Clostridium. These differences led to C-section neonates having lower predicted abundance of microbial genes related to metabolism of

  5. Predicting Metabolic Syndrome Using the Random Forest Method

    Apilak Worachartcheewan

    2015-01-01

    Full Text Available Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III criteria. The Random Forest (RF method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females. RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.

  6. Vitamin D metabolic pathway genes and pancreatic cancer risk.

    Hannah Arem

    Full Text Available Evidence on the association between vitamin D status and pancreatic cancer risk is inconsistent. This inconsistency may be partially attributable to variation in vitamin D regulating genes. We selected 11 vitamin D-related genes (GC, DHCR7, CYP2R1, VDR, CYP27B1, CYP24A1, CYP27A1, RXRA, CRP2, CASR and CUBN totaling 213 single nucleotide polymorphisms (SNPs, and examined associations with pancreatic adenocarcinoma. Our study included 3,583 pancreatic cancer cases and 7,053 controls from the genome-wide association studies of pancreatic cancer PanScans-I-III. We used the Adaptive Joint Test and the Adaptive Rank Truncated Product statistic for pathway and gene analyses, and unconditional logistic regression for SNP analyses, adjusting for age, sex, study and population stratification. We examined effect modification by circulating vitamin D concentration (≤50, >50 nmol/L for the most significant SNPs using a subset of cohort cases (n = 713 and controls (n = 878. The vitamin D metabolic pathway was not associated with pancreatic cancer risk (p = 0.830. Of the individual genes, none were associated with pancreatic cancer risk at a significance level of p<0.05. SNPs near the VDR (rs2239186, LRP2 (rs4668123, CYP24A1 (rs2762932, GC (rs2282679, and CUBN (rs1810205 genes were the top SNPs associated with pancreatic cancer (p-values 0.008-0.037, but none were statistically significant after adjusting for multiple comparisons. Associations between these SNPs and pancreatic cancer were not modified by circulating concentrations of vitamin D. These findings do not support an association between vitamin D-related genes and pancreatic cancer risk. Future research should explore other pathways through which vitamin D status might be associated with pancreatic cancer risk.

  7. Gene expression in plant lipid metabolism in Arabidopsis seedlings.

    An-Shan Hsiao

    Full Text Available Events in plant lipid metabolism are important during seedling establishment. As it has not been experimentally verified whether lipid metabolism in 2- and 5-day-old Arabidopsis thaliana seedlings is diurnally-controlled, quantitative real-time PCR analysis was used to investigate the expression of target genes in acyl-lipid transfer, β-oxidation and triacylglycerol (TAG synthesis and hydrolysis in wild-type Arabidopsis WS and Col-0. In both WS and Col-0, ACYL-COA-BINDING PROTEIN3 (ACBP3, DIACYLGLYCEROL ACYLTRANSFERASE1 (DGAT1 and DGAT3 showed diurnal control in 2- and 5-day-old seedlings. Also, COMATOSE (CTS was diurnally regulated in 2-day-old seedlings and LONG-CHAIN ACYL-COA SYNTHETASE6 (LACS6 in 5-day-old seedlings in both WS and Col-0. Subsequently, the effect of CIRCADIAN CLOCK ASSOCIATED1 (CCA1 and LATE ELONGATED HYPOCOTYL (LHY from the core clock system was examined using the cca1lhy mutant and CCA1-overexpressing (CCA1-OX lines versus wild-type WS and Col-0, respectively. Results revealed differential gene expression in lipid metabolism between 2- and 5-day-old mutant and wild-type WS seedlings, as well as between CCA1-OX and wild-type Col-0. Of the ACBPs, ACBP3 displayed the most significant changes between cca1lhy and WS and between CCA1-OX and Col-0, consistent with previous reports that ACBP3 is greatly affected by light/dark cycling. Evidence of oil body retention in 4- and 5-day-old seedlings of the cca1lhy mutant in comparison to WS indicated the effect of cca1lhy on storage lipid reserve mobilization. Lipid profiling revealed differences in primary lipid metabolism, namely in TAG, fatty acid methyl ester and acyl-CoA contents amongst cca1lhy, CCA1-OX, and wild-type seedlings. Taken together, this study demonstrates that lipid metabolism is subject to diurnal regulation in the early stages of seedling development in Arabidopsis.

  8. Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

    Saidi, Rabie

    2017-08-28

    It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

  9. Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

    Saidi, Rabie; Boudellioua, Imene; Martin, Maria J.; Solovyev, Victor

    2017-01-01

    It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

  10. Plant interactions alter the predictions of metabolic scaling theory

    Lin, Yue; Berger, Uta; Grimm, Volker

    2013-01-01

    Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of 24/3 between mean individual biomass and density during densitydependent mortality (self-thinning). Empirical tests have...... processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive....... of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories...

  11. Anthropometric Indicators Predict Metabolic Syndrome Diagnosis in Maintenance Hemodialysis Patients.

    Vogt, Barbara Perez; Ponce, Daniela; Caramori, Jacqueline Costa Teixeira

    2016-06-01

    Obesity has been considered the key in metabolic syndrome (MetS) development, and fat accumulation may be responsible for the occurrence of metabolic abnormalities in hemodialysis patients. The use of gold-standard methods to evaluate obesity is limited, and anthropometric measures may be the simplest methods. However, no study has investigated the association between anthropometric indexes and MetS in these patients. Therefore, the aim was to determine which anthropometric indexes had the best association and prediction for MetS in patients undergoing hemodialysis. Cross-sectional study that included patients older than 18 years, undergoing hemodialysis for at least 3 months. Patients with liver disease and cancer or those receiving corticosteroids or antiretroviral therapy were excluded. Diagnostic criteria from Harmonizing Metabolic Syndrome were used for the diagnosis of MetS. Anthropometric indexes evaluated were body mass index (BMI); percent standard of triceps skinfold thickness and of middle arm muscle circumference; waist circumference (WC); sagittal abdominal diameter; neck circumference; waist-to-hip, waist-to-thigh, and waist-to-height ratios; sagittal index; conicity index; and body fat percentage. Ninety-eight patients were included, 54.1% male, and mean age was 57.8 ± 12.9 years. The prevalence of MetS was 74.5%. Individuals with MetS had increased accumulation of abdominal fat and general obesity. Waist-to-height ratio was the variable independently associated with MetS diagnosis (odds ratio, 1.21; 95% confidence interval, 1.09-1.34; P < .01) and that better predicts MetS, followed by WC and BMI (area under the curve of 0.840, 0.836, and 0.798, respectively, P < .01). Waist-to-height ratio was the best anthropometric predictor of MetS in maintenance hemodialysis patients. © 2015 American Society for Parenteral and Enteral Nutrition.

  12. Gene-Gene Interactions in the Folate Metabolic Pathway and the Risk of Conotruncal Heart Defects

    Philip J. Lupo

    2010-01-01

    Full Text Available Conotruncal and related heart defects (CTRD are common, complex malformations. Although there are few established risk factors, there is evidence that genetic variation in the folate metabolic pathway influences CTRD risk. This study was undertaken to assess the association between inherited (i.e., case and maternal gene-gene interactions in this pathway and the risk of CTRD. Case-parent triads (n=727, ascertained from the Children's Hospital of Philadelphia, were genotyped for ten functional variants of nine folate metabolic genes. Analyses of inherited genotypes were consistent with the previously reported association between MTHFR A1298C and CTRD (adjusted P=.02, but provided no evidence that CTRD was associated with inherited gene-gene interactions. Analyses of the maternal genotypes provided evidence of a MTHFR C677T/CBS 844ins68 interaction and CTRD risk (unadjusted P=.02. This association is consistent with the effects of this genotype combination on folate-homocysteine biochemistry but remains to be confirmed in independent study populations.

  13. Thermodynamic principles governing metabolic operation : inference, analysis, and prediction

    Niebel, Bastian

    2015-01-01

    The principles governing metabolic flux are poorly understood. Because diverse organisms show similar metabolic flux patterns, we hypothesized that fundamental thermodynamic constraints might shape cellular metabolism. We developed a constraint-based model for Saccharomyces cerevisiae that included

  14. Identifying genes and gene networks involved in chromium metabolism and detoxification in Crambe abyssinica

    Zulfiqar, Asma; Paulose, Bibin; Chhikara, Sudesh; Dhankher, Om Parkash

    2011-01-01

    Chromium pollution is a serious environmental problem with few cost-effective remediation strategies available. Crambe abyssinica (a member of Brassicaseae), a non-food, fast growing high biomass crop, is an ideal candidate for phytoremediation of heavy metals contaminated soils. The present study used a PCR-Select Suppression Subtraction Hybridization approach in C. abyssinica to isolate differentially expressed genes in response to Cr exposure. A total of 72 differentially expressed subtracted cDNAs were sequenced and found to represent 43 genes. The subtracted cDNAs suggest that Cr stress significantly affects pathways related to stress/defense, ion transporters, sulfur assimilation, cell signaling, protein degradation, photosynthesis and cell metabolism. The regulation of these genes in response to Cr exposure was further confirmed by semi-quantitative RT-PCR. Characterization of these differentially expressed genes may enable the engineering of non-food, high-biomass plants, including C. abyssinica, for phytoremediation of Cr-contaminated soils and sediments. - Highlights: → Molecular mechanism of Cr uptake and detoxification in plants is not well known. → We identified differentially regulated genes upon Cr exposure in Crambe abyssinica. → 72 Cr-induced subtracted cDNAs were sequenced and found to represent 43 genes. → Pathways linked to stress, ion transport, and sulfur assimilation were affected. → This is the first Cr transcriptome study in a crop with phytoremediation potential. - This study describes the identification and isolation of differentially expressed genes involved in chromium metabolism and detoxification in a non-food industrial oil crop Crambe abyssinica.

  15. A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

    Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

    2015-01-01

    Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

  16. Transcriptome analysis reveals candidate genes involved in luciferin metabolism in Luciola aquatilis (Coleoptera: Lampyridae

    Wanwipa Vongsangnak

    2016-10-01

    Full Text Available Bioluminescence, which living organisms such as fireflies emit light, has been studied extensively for over half a century. This intriguing reaction, having its origins in nature where glowing insects can signal things such as attraction or defense, is now widely used in biotechnology with applications of bioluminescence and chemiluminescence. Luciferase, a key enzyme in this reaction, has been well characterized; however, the enzymes involved in the biosynthetic pathway of its substrate, luciferin, remains unsolved at present. To elucidate the luciferin metabolism, we performed a de novo transcriptome analysis using larvae of the firefly species, Luciola aquatilis. Here, a comparative analysis is performed with the model coleopteran insect Tribolium casteneum to elucidate the metabolic pathways in L. aquatilis. Based on a template luciferin biosynthetic pathway, combined with a range of protein and pathway databases, and various prediction tools for functional annotation, the candidate genes, enzymes, and biochemical reactions involved in luciferin metabolism are proposed for L. aquatilis. The candidate gene expression is validated in the adult L. aquatilis using reverse transcription PCR (RT-PCR. This study provides useful information on the bio-production of luciferin in the firefly and will benefit to future applications of the valuable firefly bioluminescence system.

  17. Photorespiratory metabolism: genes, mutants, energetics, and redox signaling.

    Foyer, Christine H; Bloom, Arnold J; Queval, Guillaume; Noctor, Graham

    2009-01-01

    Photorespiration is a high-flux pathway that operates alongside carbon assimilation in C(3) plants. Because most higher plant species photosynthesize using only the C(3) pathway, photorespiration has a major impact on cellular metabolism, particularly under high light, high temperatures, and CO(2) or water deficits. Although the functions of photorespiration remain controversial, it is widely accepted that this pathway influences a wide range of processes from bioenergetics, photosystem II function, and carbon metabolism to nitrogen assimilation and respiration. Crucially, the photorespiratory pathway is a major source of H(2)O(2) in photosynthetic cells. Through H(2)O(2) production and pyridine nucleotide interactions, photorespiration makes a key contribution to cellular redox homeostasis. In so doing, it influences multiple signaling pathways, particularly those that govern plant hormonal responses controlling growth, environmental and defense responses, and programmed cell death. The potential influence of photorespiration on cell physiology and fate is thus complex and wide ranging. The genes, pathways, and signaling functions of photorespiration are considered here in the context of whole plant biology, with reference to future challenges and human interventions to diminish photorespiratory flux.

  18. Citric Acid Metabolism in Resistant Hypertension: Underlying Mechanisms and Metabolic Prediction of Treatment Response.

    Martin-Lorenzo, Marta; Martinez, Paula J; Baldan-Martin, Montserrat; Ruiz-Hurtado, Gema; Prado, Jose Carlos; Segura, Julian; de la Cuesta, Fernando; Barderas, Maria G; Vivanco, Fernando; Ruilope, Luis Miguel; Alvarez-Llamas, Gloria

    2017-11-01

    Resistant hypertension (RH) affects 9% to 12% of hypertensive adults. Prolonged exposure to suboptimal blood pressure control results in end-organ damage and cardiovascular risk. Spironolactone is the most effective drug for treatment, but not all patients respond and side effects are not negligible. Little is known on the mechanisms responsible for RH. We aimed to identify metabolic alterations in urine. In addition, a potential capacity of metabolites to predict response to spironolactone was investigated. Urine was collected from 29 patients with RH and from a group of 13 subjects with pseudo-RH. For patients, samples were collected before and after spironolactone administration and were classified in responders (n=19) and nonresponders (n=10). Nuclear magnetic resonance was applied to identify altered metabolites and pathways. Metabolites were confirmed by liquid chromatography-mass spectrometry. Citric acid cycle was the pathway most significantly altered ( P citric acid cycle and deregulation of reactive oxygen species homeostasis control continue its activation after hypertension was developed. A metabolic panel showing alteration before spironolactone treatment and predicting future response of patients is shown. These molecular indicators will contribute optimizing the rate of control of RH patients with spironolactone. © 2017 American Heart Association, Inc.

  19. Variability of Creatine Metabolism Genes in Children with Autism Spectrum Disorder

    Jessie M. Cameron

    2017-07-01

    Full Text Available Creatine deficiency syndrome (CDS comprises three separate enzyme deficiencies with overlapping clinical presentations: arginine:glycine amidinotransferase (GATM gene, glycine amidinotransferase, guanidinoacetate methyltransferase (GAMT gene, and creatine transporter deficiency (SLC6A8 gene, solute carrier family 6 member 8. CDS presents with developmental delays/regression, intellectual disability, speech and language impairment, autistic behaviour, epileptic seizures, treatment-refractory epilepsy, and extrapyramidal movement disorders; symptoms that are also evident in children with autism. The objective of the study was to test the hypothesis that genetic variability in creatine metabolism genes is associated with autism. We sequenced GATM, GAMT and SLC6A8 genes in 166 patients with autism (coding sequence, introns and adjacent untranslated regions. A total of 29, 16 and 25 variants were identified in each gene, respectively. Four variants were novel in GATM, and 5 in SLC6A8 (not present in the 1000 Genomes, Exome Sequencing Project (ESP or Exome Aggregation Consortium (ExAC databases. A single variant in each gene was identified as non-synonymous, and computationally predicted to be potentially damaging. Nine variants in GATM were shown to have a lower minor allele frequency (MAF in the autism population than in the 1000 Genomes database, specifically in the East Asian population (Fisher’s exact test. Two variants also had lower MAFs in the European population. In summary, there were no apparent associations of variants in GAMT and SLC6A8 genes with autism. The data implying there could be a lower association of some specific GATM gene variants with autism is an observation that would need to be corroborated in a larger group of autism patients, and with sub-populations of Asian ethnicities. Overall, our findings suggest that the genetic variability of creatine synthesis/transport is unlikely to play a part in the pathogenesis of autism

  20. Variability of Creatine Metabolism Genes in Children with Autism Spectrum Disorder.

    Cameron, Jessie M; Levandovskiy, Valeriy; Roberts, Wendy; Anagnostou, Evdokia; Scherer, Stephen; Loh, Alvin; Schulze, Andreas

    2017-07-31

    Creatine deficiency syndrome (CDS) comprises three separate enzyme deficiencies with overlapping clinical presentations: arginine:glycine amidinotransferase ( GATM gene, glycine amidinotransferase), guanidinoacetate methyltransferase ( GAMT gene), and creatine transporter deficiency ( SLC6A8 gene, solute carrier family 6 member 8). CDS presents with developmental delays/regression, intellectual disability, speech and language impairment, autistic behaviour, epileptic seizures, treatment-refractory epilepsy, and extrapyramidal movement disorders; symptoms that are also evident in children with autism. The objective of the study was to test the hypothesis that genetic variability in creatine metabolism genes is associated with autism. We sequenced GATM , GAMT and SLC6A8 genes in 166 patients with autism (coding sequence, introns and adjacent untranslated regions). A total of 29, 16 and 25 variants were identified in each gene, respectively. Four variants were novel in GATM , and 5 in SLC6A8 (not present in the 1000 Genomes, Exome Sequencing Project (ESP) or Exome Aggregation Consortium (ExAC) databases). A single variant in each gene was identified as non-synonymous, and computationally predicted to be potentially damaging. Nine variants in GATM were shown to have a lower minor allele frequency (MAF) in the autism population than in the 1000 Genomes database, specifically in the East Asian population (Fisher's exact test). Two variants also had lower MAFs in the European population. In summary, there were no apparent associations of variants in GAMT and SLC6A8 genes with autism. The data implying there could be a lower association of some specific GATM gene variants with autism is an observation that would need to be corroborated in a larger group of autism patients, and with sub-populations of Asian ethnicities. Overall, our findings suggest that the genetic variability of creatine synthesis/transport is unlikely to play a part in the pathogenesis of autism spectrum

  1. An Approximation to the Temporal Order in Endogenous Circadian Rhythms of Genes Implicated in Human Adipose Tissue Metabolism

    GARAULET, MARTA; ORDOVÁS, JOSÉ M.; GÓMEZ-ABELLÁN, PURIFICACIÓN; MARTÍNEZ, JOSE A.; MADRID, JUAN A.

    2015-01-01

    Although it is well established that human adipose tissue (AT) shows circadian rhythmicity, published studies have been discussed as if tissues or systems showed only one or few circadian rhythms at a time. To provide an overall view of the internal temporal order of circadian rhythms in human AT including genes implicated in metabolic processes such as energy intake and expenditure, insulin resistance, adipocyte differentiation, dyslipidemia, and body fat distribution. Visceral and subcutaneous abdominal AT biopsies (n = 6) were obtained from morbid obese women (BMI ≥ 40 kg/m2). To investigate rhythmic expression pattern, AT explants were cultured during 24-h and gene expression was analyzed at the following times: 08:00, 14:00, 20:00, 02:00 h using quantitative real-time PCR. Clock genes, glucocorticoid metabolism-related genes, leptin, adiponectin and their receptors were studied. Significant differences were found both in achrophases and relative-amplitude among genes (P 30%). When interpreting the phase map of gene expression in both depots, data indicated that circadian rhythmicity of the genes studied followed a predictable physiological pattern, particularly for subcutaneous AT. Interesting are the relationships between adiponectin, leptin, and glucocorticoid metabolism-related genes circadian profiles. Their metabolic significance is discussed. Visceral AT behaved in a different way than subcutaneous for most of the genes studied. For every gene, protein mRNA levels fluctuated during the day in synchrony with its receptors. We have provided an overall view of the internal temporal order of circadian rhythms in human adipose tissue. PMID:21520059

  2. Analysis of gene evolution and metabolic pathways using the Candida Gene Order Browser

    Fitzpatrick, David A

    2010-05-10

    Abstract Background Candida species are the most common cause of opportunistic fungal infection worldwide. Recent sequencing efforts have provided a wealth of Candida genomic data. We have developed the Candida Gene Order Browser (CGOB), an online tool that aids comparative syntenic analyses of Candida species. CGOB incorporates all available Candida clade genome sequences including two Candida albicans isolates (SC5314 and WO-1) and 8 closely related species (Candida dubliniensis, Candida tropicalis, Candida parapsilosis, Lodderomyces elongisporus, Debaryomyces hansenii, Pichia stipitis, Candida guilliermondii and Candida lusitaniae). Saccharomyces cerevisiae is also included as a reference genome. Results CGOB assignments of homology were manually curated based on sequence similarity and synteny. In total CGOB includes 65617 genes arranged into 13625 homology columns. We have also generated improved Candida gene sets by merging\\/removing partial genes in each genome. Interrogation of CGOB revealed that the majority of tandemly duplicated genes are under strong purifying selection in all Candida species. We identified clusters of adjacent genes involved in the same metabolic pathways (such as catabolism of biotin, galactose and N-acetyl glucosamine) and we showed that some clusters are species or lineage-specific. We also identified one example of intron gain in C. albicans. Conclusions Our analysis provides an important resource that is now available for the Candida community. CGOB is available at http:\\/\\/cgob.ucd.ie.

  3. Chronic obstructive pulmonary disease candidate gene prioritization based on metabolic networks and functional information.

    Xinyan Wang

    Full Text Available Chronic obstructive pulmonary disease (COPD is a multi-factor disease, in which metabolic disturbances played important roles. In this paper, functional information was integrated into a COPD-related metabolic network to assess similarity between genes. Then a gene prioritization method was applied to the COPD-related metabolic network to prioritize COPD candidate genes. The gene prioritization method was superior to ToppGene and ToppNet in both literature validation and functional enrichment analysis. Top-ranked genes prioritized from the metabolic perspective with functional information could promote the better understanding about the molecular mechanism of this disease. Top 100 genes might be potential markers for diagnostic and effective therapies.

  4. Effects of Castration on Expression of Lipid Metabolism Genes in the Liver of Korean Cattle

    Baik, Myunggi; Nguyen, Trang Hoa; Jeong, Jin Young; Piao, Min Yu; Kang, Hyeok Joong

    2015-01-01

    Castration induces the accumulation of body fat and deposition of intramuscular fat in Korean cattle, resulting in improved beef quality. However, little is known about the metabolic adaptations in the liver following castration. To understand changes in lipid metabolism following castration, hepatic expression levels of lipid metabolism genes were compared between Korean bulls and steers. Steers had higher (p

  5. A network approach to predict pathogenic genes for Fusarium graminearum.

    Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

    2010-10-04

    Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which

  6. Reranking candidate gene models with cross-species comparison for improved gene prediction

    Pereira Fernando CN

    2008-10-01

    Full Text Available Abstract Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc. Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.

  7. Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    Hou, Siqing

    2018-05-21

    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.

  8. Semi-supervised prediction of gene regulatory networks using ...

    2015-09-28

    Sep 28, 2015 ... Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging ... two types of methods differ primarily based on whether ..... negligible, allowing us to draw the qualitative conclusions .... research will be conducted to develop additional biologically.

  9. Elucidation of primary metabolic pathways in Aspergillus species: orphaned research in characterizing orphan genes.

    Andersen, Mikael Rørdam

    2014-11-01

    Primary metabolism affects all phenotypical traits of filamentous fungi. Particular examples include reacting to extracellular stimuli, producing precursor molecules required for cell division and morphological changes as well as providing monomer building blocks for production of secondary metabolites and extracellular enzymes. In this review, all annotated genes from four Aspergillus species have been examined. In this process, it becomes evident that 80-96% of the genes (depending on the species) are still without verified function. A significant proportion of the genes with verified metabolic functions are assigned to secondary or extracellular metabolism, leaving only 2-4% of the annotated genes within primary metabolism. It is clear that primary metabolism has not received the same attention in the post-genomic area as many other research areas--despite its role at the very centre of cellular function. However, several methods can be employed to use the metabolic networks in tandem with comparative genomics to accelerate functional assignment of genes in primary metabolism. In particular, gaps in metabolic pathways can be used to assign functions to orphan genes. In this review, applications of this from the Aspergillus genes will be examined, and it is proposed that, where feasible, this should be a standard part of functional annotation of fungal genomes. © The Author 2014. Published by Oxford University Press.

  10. Embryo quality predictive models based on cumulus cells gene expression

    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  11. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning.

    He, Zhili; Zhang, Ping; Wu, Linwei; Rocha, Andrea M; Tu, Qichao; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D; Wu, Liyou; Yang, Yunfeng; Elias, Dwayne A; Watson, David B; Adams, Michael W W; Fields, Matthew W; Alm, Eric J; Hazen, Terry C; Adams, Paul D; Arkin, Adam P; Zhou, Jizhong

    2018-02-20

    Contamination from anthropogenic activities has significantly impacted Earth's biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly ( P contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. IMPORTANCE Disentangling the relationships between biodiversity and ecosystem functioning is an important but poorly understood topic in ecology. Predicting ecosystem functioning on the basis of biodiversity is even more difficult, particularly with microbial biomarkers. As an exploratory effort, this study used key microbial functional genes as biomarkers to provide predictive understanding of environmental contamination and ecosystem functioning. The results indicated that the overall functional gene richness/diversity decreased as uranium increased in groundwater, while specific key microbial guilds increased significantly as

  12. Coordinations between gene modules control the operation of plant amino acid metabolic networks

    Galili Gad

    2009-01-01

    Full Text Available Abstract Background Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results. Results Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes. Conclusion Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.

  13. Adult Attachment Interview Discourse Patterns Predict Metabolic Syndrome in Midlife

    Davis, Cynthia R.; Usher, Nicole; Dearing, Eric; Barkai, Ayelet R.; Crowell-Doom, Cindy; Mantzoros, Christos S.; Crowell, Judith A.

    2017-01-01

    Objective Adult attachment discourse patterns and current family relationship quality were examined as predictors of health behaviors and number of Metabolic Syndrome (MetS) criteria met. Methods A sample of 215 White/European American and Black/African American adults, aged 35 to 55, were examined cross-sectionally. Discourse was assessed with the Adult Attachment Interview (AAI), specifically: 1) coherence, a marker of attachment security, 2) unresolved trauma/loss, a marker of disorganized and distorted cognition related to trauma, and 3) idealization, the tendency to minimize the impact of stressful experiences. Health behaviors of diet, exercise, smoking and alcohol use were also assessed, as were adverse childhood experiences, current depressive symptoms and relationship functioning. MetS includes hypertension, hyperglycemia, high triglycerides, low HDL cholesterol, and obesity. Results Using path analysis and accounting for childhood adversity and depressive symptoms, AAI coherence and unresolved trauma or loss were directly linked to number of MetS criteria met (β = −.22 and .21 respectively). Idealization was indirectly linked to MetS through poor diet (β = −.26 and −.36 respectively), predicting 21% of the variance in number of MetS criteria met. Conclusions Attachment representations related to stress appraisal and care-seeking behaviors appear to serve as cognitive mechanisms increasing risk of MetS. PMID:25264975

  14. Computational prediction and experimental validation of Ciona intestinalis microRNA genes

    Pasquinelli Amy E

    2007-11-01

    Full Text Available Abstract Background This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development. Results In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling. Conclusion The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.

  15. Evolutionary Rate Heterogeneity of Primary and Secondary Metabolic Pathway Genes in Arabidopsis thaliana.

    Mukherjee, Dola; Mukherjee, Ashutosh; Ghosh, Tapash Chandra

    2015-11-10

    Primary metabolism is essential to plants for growth and development, and secondary metabolism helps plants to interact with the environment. Many plant metabolites are industrially important. These metabolites are produced by plants through complex metabolic pathways. Lack of knowledge about these pathways is hindering the successful breeding practices for these metabolites. For a better knowledge of the metabolism in plants as a whole, evolutionary rate variation of primary and secondary metabolic pathway genes is a prerequisite. In this study, evolutionary rate variation of primary and secondary metabolic pathway genes has been analyzed in the model plant Arabidopsis thaliana. Primary metabolic pathway genes were found to be more conserved than secondary metabolic pathway genes. Several factors such as gene structure, expression level, tissue specificity, multifunctionality, and domain number are the key factors behind this evolutionary rate variation. This study will help to better understand the evolutionary dynamics of plant metabolism. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  16. Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

    Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

    2013-03-28

    Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. Copyright © 2013 Elsevier Inc. All rights reserved.

  17. Prediction of lithium-ion battery capacity with metabolic grey model

    Chen, Lin; Lin, Weilong; Li, Junzi; Tian, Binbin; Pan, Haihong

    2016-01-01

    Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures. - Highlights: • The metabolic mechanism is introduced in a grey system for capacity prediction. • Three metabolic grey models are presented and studied. • The universality of these models under different conditions is assessed. • A few data points are required for predicting the capacity with these models.

  18. Predictability of Genetic Interactions from Functional Gene Modules

    Jonathan H. Young

    2017-02-01

    Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

  19. Validation of resting metabolic rate prediction equations for teenagers

    Paulo Henrique Santos da Fonseca

    2007-09-01

    Full Text Available The resting metabolic rate (RMR can be defi ned as the minimum rate of energy spent and represents the main component of the energetic outlay. The purpose of this study is to validate equations to predict the resting metabolic rate in teenagers (103 individuals, being 51 girls and 52 boys, with age between 10 and 17 years from Florianópolis – SC – Brazil. It was measured: the body weight, body height, skinfolds and obtained the lean and body fat mass through bioimpedance. The nonproteic RMR was measured by Weir’s equation (1949, utilizing AeroSport TEEM-100 gas analyzer. The studied equations were: Harry and Benedict (1919, Schofi eld (1985, WHO/FAO/UNU (1985, Henry and Rees (1991, Molnár et al. (1998, Tverskaya et al. (1998 and Müller et al. (2004. In order to study the cross-validation of the RMR prediction equations and its standard measure (Weir 1949, the following statistics procedure were calculated: Pearson’s correlation (r ≥ 0.70, the “t” test with the signifi cance level of p0.05 in relation to the standard measure, with exception of the equations suggested for Tverskaya et al. (1998, and the two models of Müller et al (2004. Even though there was not a signifi cant difference, only the models considered for Henry and Rees (1991, and Molnár et al. (1995 had gotten constant error variation under 5%. All the equations analyzed in the study in girls had not reached criterion of correlation values of 0.70 with the indirect calorimetry. Analyzing the prediction equations of RMR in boys, all of them had moderate correlation coeffi cients with the indirect calorimetry, however below 0.70. Only the equation developed for Tverskaya et al. (1998 presented differences (p ABSTRACT0,05 em relação à medida padrão (Weir 1949, com exceção das equações sugeridas por Tverskaya et al. (1998 e os dois modelos de Müller et al (2004. Mesmo não havendo diferença signifi cativa, somente os modelos propostos por Henry e Rees (1991

  20. Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

    Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

    2014-01-01

    Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

  1. A global evolutionary and metabolic analysis of human obesity gene risk variants.

    Castillo, Joseph J; Hazlett, Zachary S; Orlando, Robert A; Garver, William S

    2017-09-05

    It is generally accepted that the selection of gene variants during human evolution optimized energy metabolism that now interacts with our obesogenic environment to increase the prevalence of obesity. The purpose of this study was to perform a global evolutionary and metabolic analysis of human obesity gene risk variants (110 human obesity genes with 127 nearest gene risk variants) identified using genome-wide association studies (GWAS) to enhance our knowledge of early and late genotypes. As a result of determining the mean frequency of these obesity gene risk variants in 13 available populations from around the world our results provide evidence for the early selection of ancestral risk variants (defined as selection before migration from Africa) and late selection of derived risk variants (defined as selection after migration from Africa). Our results also provide novel information for association of these obesity genes or encoded proteins with diverse metabolic pathways and other human diseases. The overall results indicate a significant differential evolutionary pattern for the selection of obesity gene ancestral and derived risk variants proposed to optimize energy metabolism in varying global environments and complex association with metabolic pathways and other human diseases. These results are consistent with obesity genes that encode proteins possessing a fundamental role in maintaining energy metabolism and survival during the course of human evolution. Copyright © 2017. Published by Elsevier B.V.

  2. Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification.

    Cankorur-Cetinkaya, Ayca; Dikicioglu, Duygu; Oliver, Stephen G

    2017-11-01

    Genome-scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene-reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome-scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation-reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development

  3. Global discriminative learning for higher-accuracy computational gene prediction.

    Axel Bernal

    2007-03-01

    Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

  4. Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

    Liying Yang

    2016-01-01

    Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

  5. A deep auto-encoder model for gene expression prediction.

    Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

    2017-11-17

    Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

  6. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

    Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

    2004-03-05

    Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

  7. Altered Clock and Lipid Metabolism-Related Genes in Atherosclerotic Mice Kept with Abnormal Lighting Condition

    Zhu Zhu

    2016-01-01

    Full Text Available Background. The risk of atherosclerosis is elevated in abnormal lipid metabolism and circadian rhythm disorder. We investigated whether abnormal lighting condition would have influenced the circadian expression of clock genes and clock-controlled lipid metabolism-related genes in ApoE-KO mice. Methods. A mouse model of atherosclerosis with circadian clock genes expression disorder was established using ApoE-KO mice (ApoE-KO LD/DL mice by altering exposure to light. C57 BL/6J mice (C57 mice and ApoE-KO mice (ApoE-KO mice exposed to normal day and night and normal diet served as control mice. According to zeitgeber time samples were acquired, to test atheromatous plaque formation, serum lipids levels and rhythmicity, clock genes, and lipid metabolism-related genes along with Sirtuin 1 (Sirt1 levels and rhythmicity. Results. Atherosclerosis plaques were formed in the aortic arch of ApoE-KO LD/DL mice. The serum lipids levels and oscillations in ApoE-KO LD/DL mice were altered, along with the levels and diurnal oscillations of circadian genes, lipid metabolism-associated genes, and Sirt1 compared with the control mice. Conclusions. Abnormal exposure to light aggravated plaque formation and exacerbated disorders of serum lipids and clock genes, lipid metabolism genes and Sirt1 levels, and circadian oscillation.

  8. Growth hormone regulation of metabolic gene expression in muscle: a microarray study in hypopituitary men.

    Sjögren, Klara; Leung, Kin-Chuen; Kaplan, Warren; Gardiner-Garden, Margaret; Gibney, James; Ho, Ken K Y

    2007-07-01

    Muscle is a target of growth hormone (GH) action and a major contributor to whole body metabolism. Little is known about how GH regulates metabolic processes in muscle or the extent to which muscle contributes to changes in whole body substrate metabolism during GH treatment. To identify GH-responsive genes that regulate substrate metabolism in muscle, we studied six hypopituitary men who underwent whole body metabolic measurement and skeletal muscle biopsies before and after 2 wk of GH treatment (0.5 mg/day). Transcript profiles of four subjects were analyzed using Affymetrix GeneChips. Serum insulin-like growth factor I (IGF-I) and procollagens I and III were measured by RIA. GH increased serum IGF-I and procollagens I and III, enhanced whole body lipid oxidation, reduced carbohydrate oxidation, and stimulated protein synthesis. It induced gene expression of IGF-I and collagens in muscle. GH reduced expression of several enzymes regulating lipid oxidation and energy production. It reduced calpain 3, increased ribosomal protein L38 expression, and displayed mixed effects on genes encoding myofibrillar proteins. It increased expression of circadian gene CLOCK, and reduced that of PERIOD. In summary, GH exerted concordant effects on muscle expression and blood levels of IGF-I and collagens. It induced changes in genes regulating protein metabolism in parallel with a whole body anabolic effect. The discordance between muscle gene expression profiles and metabolic responses suggests that muscle is unlikely to contribute to GH-induced stimulation of whole body energy and lipid metabolism. GH may regulate circadian function in skeletal muscle by modulating circadian gene expression with possible metabolic consequences.

  9. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  10. Combining gene signatures improves prediction of breast cancer survival.

    Xi Zhao

    Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

  11. Bioinformatic prediction and functional characterization of human KIAA0100 gene

    He Cui

    2017-02-01

    Full Text Available Our previous study demonstrated that human KIAA0100 gene was a novel acute monocytic leukemia-associated antigen (MLAA gene. But the functional characterization of human KIAA0100 gene has remained unknown to date. Here, firstly, bioinformatic prediction of human KIAA0100 gene was carried out using online softwares; Secondly, Human KIAA0100 gene expression was downregulated by the clustered regularly interspaced short palindromic repeats (CRISPR/CRISPR-associated (Cas 9 system in U937 cells. Cell proliferation and apoptosis were next evaluated in KIAA0100-knockdown U937 cells. The bioinformatic prediction showed that human KIAA0100 gene was located on 17q11.2, and human KIAA0100 protein was located in the secretory pathway. Besides, human KIAA0100 protein contained a signalpeptide, a transmembrane region, three types of secondary structures (alpha helix, extended strand, and random coil , and four domains from mitochondrial protein 27 (FMP27. The observation on functional characterization of human KIAA0100 gene revealed that its downregulation inhibited cell proliferation, and promoted cell apoptosis in U937 cells. To summarize, these results suggest human KIAA0100 gene possibly comes within mitochondrial genome; moreover, it is a novel anti-apoptotic factor related to carcinogenesis or progression in acute monocytic leukemia, and may be a potential target for immunotherapy against acute monocytic leukemia.

  12. A network approach to predict pathogenic genes for Fusarium graminearum.

    Xiaoping Liu

    Full Text Available Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB, which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other

  13. Co-regulation of metabolic genes is better explained by flux coupling than by network distance.

    Richard A Notebaart

    2008-01-01

    Full Text Available To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

  14. Plasma apolipoprotein M is reduced in metabolic syndrome but does not predict intima media thickness

    Dullaart, Robin P F; Plomgaard, Peter; de Vries, Rindert

    2009-01-01

    BACKGROUND: Apolipoprotein (apo) M may exert anti-atherogenic properties in experimental studies. Its hepatic gene expression may be linked to glucose and lipid metabolism. Plasma apoM is decreased in obese mouse models. We hypothesized that plasma apoM is lower in metabolic syndrome (Met...

  15. Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links

    Nicholls, Andrew W.; Salek, Reza M.; Marques-Vidal, Pedro; Morya, Edgard; Sameshima, Koichi; Montoliu, Ivan; Da Silva, Laeticia; Collino, Sebastiano; Martin, François-Pierre; Rezzi, Serge; Steinbeck, Christoph; Waterworth, Dawn M.; Waeber, Gérard; Vollenweider, Peter; Beckmann, Jacques S.; Le Coutre, Johannes; Mooser, Vincent; Bergmann, Sven; Genick, Ulrich K.; Kutalik, Zoltán

    2014-01-01

    Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10−8) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10−44) and lysine (rs8101881, P = 1.2×10−33), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers. PMID:24586186

  16. Nur77 coordinately regulates expression of genes linked to glucose metabolism in skeletal muscle.

    Chao, Lily C; Zhang, Zidong; Pei, Liming; Saito, Tsugumichi; Tontonoz, Peter; Pilch, Paul F

    2007-09-01

    Innervation is important for normal metabolism in skeletal muscle, including insulin-sensitive glucose uptake. However, the transcription factors that transduce signals from the neuromuscular junction to the nucleus and affect changes in metabolic gene expression are not well defined. We demonstrate here that the orphan nuclear receptor Nur77 is a regulator of gene expression linked to glucose utilization in muscle. In vivo, Nur77 is preferentially expressed in glycolytic compared with oxidative muscle and is responsive to beta-adrenergic stimulation. Denervation of rat muscle compromises expression of Nur77 in parallel with that of numerous genes linked to glucose metabolism, including glucose transporter 4 and genes involved in glycolysis, glycogenolysis, and the glycerophosphate shuttle. Ectopic expression of Nur77, either in rat muscle or in C2C12 muscle cells, induces expression of a highly overlapping set of genes, including glucose transporter 4, muscle phosphofructokinase, and glycogen phosphorylase. Furthermore, selective knockdown of Nur77 in rat muscle by small hairpin RNA or genetic deletion of Nur77 in mice reduces the expression of a battery of genes involved in skeletal muscle glucose utilization in vivo. Finally, we show that Nur77 binds the promoter regions of multiple genes involved in glucose metabolism in muscle. These results identify Nur77 as a potential mediator of neuromuscular signaling in the control of metabolic gene expression.

  17. Gene Prediction in Metagenomic Fragments with Deep Learning

    Shao-Wu Zhang

    2017-01-01

    Full Text Available Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features and using deep stacking networks learning model, we present a novel method (called Meta-MFDL to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

  18. Gene-specific function prediction for non-synonymous mutations in monogenic diabetes genes.

    Quan Li

    Full Text Available The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.

  19. Multiple Suboptimal Solutions for Prediction Rules in Gene Expression Data

    Osamu Komori

    2013-01-01

    Full Text Available This paper discusses mathematical and statistical aspects in analysis methods applied to microarray gene expressions. We focus on pattern recognition to extract informative features embedded in the data for prediction of phenotypes. It has been pointed out that there are severely difficult problems due to the unbalance in the number of observed genes compared with the number of observed subjects. We make a reanalysis of microarray gene expression published data to detect many other gene sets with almost the same performance. We conclude in the current stage that it is not possible to extract only informative genes with high performance in the all observed genes. We investigate the reason why this difficulty still exists even though there are actively proposed analysis methods and learning algorithms in statistical machine learning approaches. We focus on the mutual coherence or the absolute value of the Pearson correlations between two genes and describe the distributions of the correlation for the selected set of genes and the total set. We show that the problem of finding informative genes in high dimensional data is ill-posed and that the difficulty is closely related with the mutual coherence.

  20. Nur77 coordinately regulates expression of genes linked to glucose metabolism in skeletal muscle

    Chao, Lily C.; Zhang, Zidong; Pei, Liming; Saito, Tsugumichi; Tontonoz, Peter; Pilch, Paul F.

    2007-01-01

    Innervation is important for normal metabolism in skeletal muscle, including insulin-sensitive glucose uptake. However, the transcription factors that transduce signals from the neuromuscular junction to the nucleus and affect changes in metabolic gene expression are not well defined. We demonstrate here that the orphan nuclear receptor Nur77 is a regulator of gene expression linked to glucose utilization in muscle. In vivo, Nur77 is preferentially expressed in glycolytic compared to oxidativ...

  1. Increased fat oxidation and regulation of metabolic genes with ultraendurance exercise

    Helge, Jørn Wulff; Rehrer, N J; Pilegaard, H

    2007-01-01

    AIM: Regular endurance exercise stimulates muscle metabolic capacity, but effects of very prolonged endurance exercise are largely unknown. This study examined muscle substrate availability and utilization during prolonged endurance exercise, and associated metabolic genes. METHODS: Data were...... exercise markedly increases plasma fatty acid availability and fat utilization during exercise. Exercise-induced regulation of genes encoding proteins involved in fatty acid recruitment and oxidation may contribute to these changes....

  2. Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study

    Stroeve, Johanna H M; Saccenti, Edoardo; Bouwman, Jildau

    2016-01-01

    predictive for weight loss were acetoacetate, triacylglycerols, phosphatidylcholines, specific amino acids, and creatine and creatinine. This metabolic profile suggests that high energy metabolism activity results in higher amounts of weight loss. CONCLUSIONS: Possible predictive (pre-diet) markers were...

  3. The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

    Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

    2014-06-01

    With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

  4. Validated Predictions of Metabolic Energy Consumption for Submaximal Effort Movement

    Tsianos, George A.; MacFadden, Lisa N.

    2016-01-01

    Author Summary Muscles consume metabolic energy to generate movement. Performing a movement over a long period of time or at a high intensity strains the respiratory and cardiovascular systems that need to replenish the energy reserves in muscle. Furthermore, consuming and replenishing metabolic energy involves biochemical reactions with byproducts that cause muscle fatigue. These biochemical reactions also produce heat that increases body temperature, potentially causing central fatigue. A m...

  5. Inductive matrix completion for predicting gene-disease associations.

    Natarajan, Nagarajan; Dhillon, Inderjit S

    2014-06-15

    Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has bigdata.ices.utexas.edu/project/gene-disease. © The Author 2014. Published by Oxford University Press.

  6. Towards prediction of metabolic products of polyketide synthases: an in silico analysis.

    Gitanjali Yadav

    2009-04-01

    Full Text Available Sequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase (PKS family of genes not only in microbes and fungi but also in plants and other eukaryotes. PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties. The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis. We have carried out a comprehensive in silico analysis of modular and iterative gene clusters to test whether chemical structures of the secondary metabolites can be predicted from PKS protein sequences. Here, we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes. Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts. For iterative PKS proteins, structural models of iterative ketosynthase (KS domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket. Furthermore, we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs. For modular PKS proteins, we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities. Taken together, our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters. These results have promising potential for discovery of

  7. Diet-gene interactions between dietary fat intake and common polymorphisms in determining lipid metabolism

    Corella, D.

    2009-07-01

    Current dietary guidelines for fat intake have not taken into consideration the possible genetic differences underlying the individual variability in responsiveness to dietary components. Genetic variability has been identified in humans for all the known lipid metabolism-related genes resulting in a plethora of candidate genes and genetic variants to examine in diet-gene interaction studies focused on fat consumption. Some examples of fat-gene interaction are reviewed. These include: the interaction between total intake and the 14C/T in the hepatic lipase gene promoter in determining high-density lipoprotein cholesterol (HDL-C) metabolism; the interaction between polyunsaturated fatty acids (PUFA) and the 5G/A polymorphism in the APOA1 gene plasma HDL-C concentrations; the interaction between PUFA and the L162V polymorphism in the PPARA gene in determining triglycerides and APOC3 concentrations; and the interaction between PUFA intake and the -1131T>C in the APOA5 gene in determining triglyceride metabolism. Although hundreds of diet-gene interaction studies in lipid metabolism have been published, the level of evidence to make specific nutritional recommendations to the population is still low and more research in nutrigenetics has to be undertaken. (Author) 31 refs.

  8. Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

    Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

    2000-01-01

    A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society

  9. Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

    Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

    2000-09-18

    A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society.

  10. Prediction of epigenetically regulated genes in breast cancer cell lines

    Loss, Leandro A; Sadanandam, Anguraj; Durinck, Steffen; Nautiyal, Shivani; Flaucher, Diane; Carlton, Victoria EH; Moorhead, Martin; Lu, Yontao; Gray, Joe W; Faham, Malek; Spellman, Paul; Parvin, Bahram

    2010-05-04

    panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

  11. Coregulation of terpenoid pathway genes and prediction of isoprene production in Bacillus subtilis using transcriptomics

    Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C.; Wiley, H. S.; Ahring, Birgitte K.; Linggi, Bryan E.

    2013-06-19

    The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding the system level regulation and control of the pathway. To address this limitation, we examined Bacillus subtilis grown under multiple conditions and then determined the relationship between altered isoprene production and the pattern of gene expression. We found that terpenoid genes appeared to fall into two distinct subsets with opposing correlations with respect to the amount of isoprene produced. The group whose expression levels positively correlated with isoprene production included dxs, the gene responsible for the commitment step in the pathway, as well as ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. This analysis showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model which accurately predicts production of this secondary metabolite across many simulated environmental conditions.

  12. Gene expression variability in human hepatic drug metabolizing enzymes and transporters.

    Lun Yang

    Full Text Available Interindividual variability in the expression of drug-metabolizing enzymes and transporters (DMETs in human liver may contribute to interindividual differences in drug efficacy and adverse reactions. Published studies that analyzed variability in the expression of DMET genes were limited by sample sizes and the number of genes profiled. We systematically analyzed the expression of 374 DMETs from a microarray data set consisting of gene expression profiles derived from 427 human liver samples. The standard deviation of interindividual expression for DMET genes was much higher than that for non-DMET genes. The 20 DMET genes with the largest variability in the expression provided examples of the interindividual variation. Gene expression data were also analyzed using network analysis methods, which delineates the similarities of biological functionalities and regulation mechanisms for these highly variable DMET genes. Expression variability of human hepatic DMET genes may affect drug-gene interactions and disease susceptibility, with concomitant clinical implications.

  13. Prediction of human protein function according to Gene Ontology categories

    Jensen, Lars Juhl; Gupta, Ramneek; Stærfeldt, Hans Henrik

    2003-01-01

    developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors...

  14. Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

    Noar, Roslyn D; Daub, Margaret E

    2016-01-01

    Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana, is a Dothideomycete fungus closely related to fungi that produce polyketides important for plant pathogenicity. We utilized the M. fijiensis genome sequence to predict PKS genes and their gene clusters and make bioinformatics predictions about the types of compounds produced by these clusters. Eight PKS gene clusters were identified in the M. fijiensis genome, placing M. fijiensis into the 23rd percentile for the number of PKS genes compared to other Dothideomycetes. Analysis of the PKS domains identified three of the PKS enzymes as non-reducing and two as highly reducing. Gene clusters contained types of genes frequently found in PKS clusters including genes encoding transporters, oxidoreductases, methyltransferases, and non-ribosomal peptide synthases. Phylogenetic analysis identified a putative PKS cluster encoding melanin biosynthesis. None of the other clusters were closely aligned with genes encoding known polyketides, however three of the PKS genes fell into clades with clusters encoding alternapyrone, fumonisin, and solanapyrone produced by Alternaria and Fusarium species. A search for homologs among available genomic sequences from 103 Dothideomycetes identified close homologs (>80% similarity) for six of the PKS sequences. One of the PKS sequences was not similar (< 60% similarity) to sequences in any of the 103 genomes, suggesting that it encodes a unique compound. Comparison of the M. fijiensis PKS sequences with those of two other banana pathogens, M. musicola and M. eumusae, showed that these two species have close homologs to five of the M. fijiensis PKS sequences, but three others were not found in either species. RT-PCR and RNA-Seq analysis showed that the melanin PKS cluster was down-regulated in infected banana as compared to growth in culture. Three other clusters, however were strongly upregulated during disease development in banana, suggesting that they may encode

  15. Bioinformatics Prediction of Polyketide Synthase Gene Clusters from Mycosphaerella fijiensis.

    Roslyn D Noar

    Full Text Available Mycosphaerella fijiensis, causal agent of black Sigatoka disease of banana, is a Dothideomycete fungus closely related to fungi that produce polyketides important for plant pathogenicity. We utilized the M. fijiensis genome sequence to predict PKS genes and their gene clusters and make bioinformatics predictions about the types of compounds produced by these clusters. Eight PKS gene clusters were identified in the M. fijiensis genome, placing M. fijiensis into the 23rd percentile for the number of PKS genes compared to other Dothideomycetes. Analysis of the PKS domains identified three of the PKS enzymes as non-reducing and two as highly reducing. Gene clusters contained types of genes frequently found in PKS clusters including genes encoding transporters, oxidoreductases, methyltransferases, and non-ribosomal peptide synthases. Phylogenetic analysis identified a putative PKS cluster encoding melanin biosynthesis. None of the other clusters were closely aligned with genes encoding known polyketides, however three of the PKS genes fell into clades with clusters encoding alternapyrone, fumonisin, and solanapyrone produced by Alternaria and Fusarium species. A search for homologs among available genomic sequences from 103 Dothideomycetes identified close homologs (>80% similarity for six of the PKS sequences. One of the PKS sequences was not similar (< 60% similarity to sequences in any of the 103 genomes, suggesting that it encodes a unique compound. Comparison of the M. fijiensis PKS sequences with those of two other banana pathogens, M. musicola and M. eumusae, showed that these two species have close homologs to five of the M. fijiensis PKS sequences, but three others were not found in either species. RT-PCR and RNA-Seq analysis showed that the melanin PKS cluster was down-regulated in infected banana as compared to growth in culture. Three other clusters, however were strongly upregulated during disease development in banana, suggesting that

  16. Opposing Fat Metabolism Pathways Triggered by a Single Gene

    Gilst, Marc R. Van; Hadjivassiliou, Haralambos; Jolly, Amber; Yamamoto, Keith R

    2005-01-01

    Mammalian nuclear hormone receptors (NHRs), such as liver X receptor, farnesoid X receptor, and peroxisome proliferator-activated receptors (PPARs), precisely control energy metabolism. Consequently, these receptors are important targets for the treatment of metabolic diseases, including diabetes and obesity. A thorough understanding of NHR fat regulatory networks has been limited, however, by a lack of genetically tractable experimental systems. Here we show that deletion of the Caenorhabdit...

  17. Combining many interaction networks to predict gene function and analyze gene lists.

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. The role of gene-gene interaction in the prediction of criminal behavior.

    Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

    2014-04-01

    A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. GOPET: A tool for automated predictions of Gene Ontology terms

    Glatting Karl-Heinz

    2006-03-01

    Full Text Available Abstract Background Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. Description We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO. Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool. It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar Conclusion Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.

  20. Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates

    Glazier, Douglas S.; Hirst, Andrew G.; Atkinson, D.

    2016-01-01

    in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR

  1. Comparative metabolomics in primates reveals the effects of diet and gene regulatory variation on metabolic divergence.

    Blekhman, Ran; Perry, George H; Shahbaz, Sevini; Fiehn, Oliver; Clark, Andrew G; Gilad, Yoav

    2014-07-28

    Human diets differ from those of non-human primates. Among few obvious differences, humans consume more meat than most non-human primates and regularly cook their food. It is hypothesized that a dietary shift during human evolution has been accompanied by molecular adaptations in metabolic pathways. Consistent with this notion, comparative studies of gene expression levels in primates have found that the regulation of genes with metabolic functions tend to evolve rapidly in the human lineage. The metabolic consequences of these regulatory differences, however, remained unknown. To address this gap, we performed a comparative study using a combination of gene expression and metabolomic profiling in livers from humans, chimpanzees, and rhesus macaques. We show that dietary differences between species have a strong effect on metabolic concentrations. In addition, we found that differences in metabolic concentration across species are correlated with inter-species differences in the expression of the corresponding enzymes, which control the same metabolic reaction. We identified a number of metabolic compounds with lineage-specific profiles, including examples of human-species metabolic differences that may be directly related to dietary differences.

  2. Identification of genes specifically required for the anaerobic metabolism of benzene in Geobacter metallireducens

    Zhang, Tian; Tremblay, Pier-Luc; Chaurasia, Akhilesh Kumar

    2014-01-01

    Although the biochemical pathways for the anaerobic degradation of many of the hydrocarbon constituents in petroleum reservoirs have been elucidated, the mechanisms for anaerobic activation of benzene, a very stable molecule, are not known. Previous studies have demonstrated that Geobacter...... metallireducens can anaerobically oxidize benzene to carbon dioxide with Fe(III) as the sole electron acceptor and that phenol is an intermediate in benzene oxidation. In an attempt to identify enzymes that might be involved in the conversion of benzene to phenol, whole-genome gene transcript abundance...... was compared in cells metabolizing benzene and cells metabolizing phenol. Eleven genes had significantly higher transcript abundance in benzene-metabolizing cells. Five of these genes had annotations suggesting that they did not encode proteins that could be involved in benzene metabolism and were not further...

  3. In silico prediction of xenobiotic metabolism in humans

    Mu, Fangping [Los Alamos National Laboratory

    2009-01-01

    Xenobiotic metabolism in humans is catalyzed by a few enzymes with broad substrate specificities, which provide the overall broad chemical specificity for nearly all xenobiotics that humans encounter. Xenobiotic metabolism are classified into functional group biotransformations. Based on bona fide reactions and negative examples for each reaction class, support vector machine (SVM) classifiers are built. The input to SVM is a set of atomic and molecular features to define the electrostatic, steric, energetic, geometrical and topological environment of the atoms in the reaction center under the molecule. Results show that the overall sensitivity and specificity of classifiers is around 87%.

  4. Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

    Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

    2017-10-03

    Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

  5. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases

    Karp Peter D

    2004-06-01

    Full Text Available Abstract Background The PathoLogic program constructs Pathway/Genome databases by using a genome's annotation to predict the set of metabolic pathways present in an organism. PathoLogic determines the set of reactions composing those pathways from the enzymes annotated in the organism's genome. Most annotation efforts fail to assign function to 40–60% of sequences. In addition, large numbers of sequences may have non-specific annotations (e.g., thiolase family protein. Pathway holes occur when a genome appears to lack the enzymes needed to catalyze reactions in a pathway. If a protein has not been assigned a specific function during the annotation process, any reaction catalyzed by that protein will appear as a missing enzyme or pathway hole in a Pathway/Genome database. Results We have developed a method that efficiently combines homology and pathway-based evidence to identify candidates for filling pathway holes in Pathway/Genome databases. Our program not only identifies potential candidate sequences for pathway holes, but combines data from multiple, heterogeneous sources to assess the likelihood that a candidate has the required function. Our algorithm emulates the manual sequence annotation process, considering not only evidence from homology searches, but also considering evidence from genomic context (i.e., is the gene part of an operon? and functional context (e.g., are there functionally-related genes nearby in the genome? to determine the posterior belief that a candidate has the required function. The method can be applied across an entire metabolic pathway network and is generally applicable to any pathway database. The program uses a set of sequences encoding the required activity in other genomes to identify candidate proteins in the genome of interest, and then evaluates each candidate by using a simple Bayes classifier to determine the probability that the candidate has the desired function. We achieved 71% precision at a

  6. Oxidative Metabolism Genes Are Not Responsive to Oxidative Stress in Rodent Beta Cell Lines

    Faer Morrison

    2012-01-01

    Full Text Available Altered expression of oxidative metabolism genes has been described in the skeletal muscle of individuals with type 2 diabetes. Pancreatic beta cells contain low levels of antioxidant enzymes and are particularly susceptible to oxidative stress. In this study, we explored the effect of hyperglycemia-induced oxidative stress on a panel of oxidative metabolism genes in a rodent beta cell line. We exposed INS-1 rodent beta cells to low (5.6 mmol/L, ambient (11 mmol/L, and high (28 mmol/L glucose conditions for 48 hours. Increases in oxidative stress were measured using the fluorescent probe dihydrorhodamine 123. We then measured the expression levels of a panel of 90 oxidative metabolism genes by real-time PCR. Elevated reactive oxygen species (ROS production was evident in INS-1 cells after 48 hours (P<0.05. TLDA analysis revealed a significant (P<0.05 upregulation of 16 of the 90 genes under hyperglycemic conditions, although these expression differences did not reflect differences in ROS. We conclude that although altered glycemia may influence the expression of some oxidative metabolism genes, this effect is probably not mediated by increased ROS production. The alterations to the expression of oxidative metabolism genes previously observed in human diabetic skeletal muscle do not appear to be mirrored in rodent pancreatic beta cells.

  7. Radiation Exposure Alters Expression of Metabolic Enzyme Genes in Mice

    Wotring, V. E.; Mangala, L. S.; Zhang, Y.; Wu, H.

    2011-01-01

    Most administered pharmaceuticals are metabolized by the liver. The health of the liver, especially the rate of its metabolic enzymes, determines the concentration of circulating drugs as well as the duration of their efficacy. Most pharmaceuticals are metabolized by the liver, and clinically-used medication doses are given with normal liver function in mind. A drug overdose can result in the case of a liver that is damaged and removing pharmaceuticals from the circulation at a rate slower than normal. Alternatively, if liver function is elevated and removing drugs from the system more quickly than usual, it would be as if too little drug had been given for effective treatment. Because of the importance of the liver in drug metabolism, we want to understand the effects of spaceflight on the enzymes of the liver and exposure to cosmic radiation is one aspect of spaceflight that can be modeled in ground experiments. Additionally, it has been previous noted that pre-exposure to small radiation doses seems to confer protection against later and larger radiation doses. This protective power of pre-exposure has been called a priming effect or radioadaptation. This study is an effort to examine the drug metabolizing effects of radioadaptation mechanisms that may be triggered by early exposure to low radiation doses.

  8. Maternal obesity disrupts circadian rhythms of clock and metabolic genes in the offspring heart and liver.

    Wang, Danfeng; Chen, Siyu; Liu, Mei; Liu, Chang

    2015-06-01

    Early life nutritional adversity is tightly associated with the development of long-term metabolic disorders. Particularly, maternal obesity and high-fat diets cause high risk of obesity in the offspring. Those offspring are also prone to develop hyperinsulinemia, hepatic steatosis and cardiovascular diseases. However, the precise underlying mechanisms leading to these metabolic dysregulation in the offspring remain unclear. On the other hand, disruptions of diurnal circadian rhythms are known to impair metabolic homeostasis in various tissues including the heart and liver. Therefore, we investigated that whether maternal obesity perturbs the circadian expression rhythms of clock, metabolic and inflammatory genes in offspring heart and liver by using RT-qPCR and Western blotting analysis. Offspring from lean and obese dams were examined on postnatal day 17 and 35, when pups were nursed by their mothers or took food independently. On P17, genes examined in the heart either showed anti-phase oscillations (Cpt1b, Pparα, Per2) or had greater oscillation amplitudes (Bmal1, Tnf-α, Il-6). Such phase abnormalities of these genes were improved on P35, while defects in amplitudes still existed. In the liver of 17-day-old pups exposed to maternal obesity, the oscillation amplitudes of most rhythmic genes examined (except Bmal1) were strongly suppressed. On P35, the oscillations of circadian and inflammatory genes became more robust in the liver, while metabolic genes were still kept non-rhythmic. Maternal obesity also had a profound influence in the protein expression levels of examined genes in offspring heart and liver. Our observations indicate that the circadian clock undergoes nutritional programing, which may contribute to the alternations in energy metabolism associated with the development of metabolic disorders in early life and adulthood.

  9. Gene Coexpression Analysis Reveals Complex Metabolism of the Monoterpene Alcohol Linalool in Arabidopsis Flowers[W][OPEN

    Ginglinger, Jean-François; Boachon, Benoit; Höfer, René; Paetz, Christian; Köllner, Tobias G.; Miesch, Laurence; Lugan, Raphael; Baltenweck, Raymonde; Mutterer, Jérôme; Ullmann, Pascaline; Beran, Franziska; Claudel, Patricia; Verstappen, Francel; Fischer, Marc J.C.; Karst, Francis; Bouwmeester, Harro; Miesch, Michel; Schneider, Bernd; Gershenzon, Jonathan; Ehlting, Jürgen; Werck-Reichhart, Danièle

    2013-01-01

    The cytochrome P450 family encompasses the largest family of enzymes in plant metabolism, and the functions of many of its members in Arabidopsis thaliana are still unknown. Gene coexpression analysis pointed to two P450s that were coexpressed with two monoterpene synthases in flowers and were thus predicted to be involved in monoterpenoid metabolism. We show that all four selected genes, the two terpene synthases (TPS10 and TPS14) and the two cytochrome P450s (CYP71B31 and CYP76C3), are simultaneously expressed at anthesis, mainly in upper anther filaments and in petals. Upon transient expression in Nicotiana benthamiana, the TPS enzymes colocalize in vesicular structures associated with the plastid surface, whereas the P450 proteins were detected in the endoplasmic reticulum. Whether they were expressed in Saccharomyces cerevisiae or in N. benthamiana, the TPS enzymes formed two different enantiomers of linalool: (−)-(R)-linalool for TPS10 and (+)-(S)-linalool for TPS14. Both P450 enzymes metabolize the two linalool enantiomers to form different but overlapping sets of hydroxylated or epoxidized products. These oxygenated products are not emitted into the floral headspace, but accumulate in floral tissues as further converted or conjugated metabolites. This work reveals complex linalool metabolism in Arabidopsis flowers, the ecological role of which remains to be determined. PMID:24285789

  10. Effects of Metabolic Programming on Juvenile Play Behavior and Gene Expression in the Prefrontal Cortex of Rats.

    Hehar, Harleen; Ma, Irene; Mychasiuk, Richelle

    2016-01-01

    Early developmental processes, such as metabolic programming, can provide cues to an organism, which allow it to make modifications that are predicted to be beneficial for survival. Similarly, social play has a multifaceted role in promoting survival and fitness of animals. Play is a complex behavior that is greatly influenced by motivational and reward circuits, as well as the energy reserves and metabolism of an organism. This study examined the association between metabolic programming and juvenile play behavior in an effort to further elucidate insight into the consequences that early adaptions have on developmental trajectories. The study also examined changes in expression of four genes (Drd2, IGF1, Opa1, and OxyR) in the prefrontal cortex known to play significant roles in reward, bioenergetics, and social-emotional functioning. Using four distinct variations in developmental programming (high-fat diet, caloric restriction, exercise, or high-fat diet combined with exercise), we found that dietary programming (high-fat diet vs. caloric restriction) had the greatest impact on play behavior and gene expression. However, exercise also induced changes in both measures. This study demonstrates that metabolic programming can alter neural circuits and bioenergetics involved in play behavior, thus providing new insights into mechanisms that allow programming to influence the evolutionary success of an organism. © 2016 S. Karger AG, Basel.

  11. Phylogenomic analysis of secondary metabolism genes sheds light on their evolution in Aspergilli

    Theobald, Sebastian; Vesth, Tammi Camilla; Rasmussen, Jane Lind Nybo

    .Natural products are encoded by genes located in close proximity, called secondary metabolic gene clusters, which makes them interesting targets for genomic analysis. We use a modified version of the Secondary Metabolite Unique Regions Finder (SMURF) algorithm, combined with InterPro annotations to create...... approximate maximum likelihood trees of conserved domains from secondary metabolic genes across 56 species, giving insights into the secondary metabolism gene diversity and evolution.In this study we can describe the evolution of non ribosomal peptide synthetases (NRPS), polyketide synthases (PKS) and hybrids.......In the aspMine project, we are sequencing and analyzing over 300 species of Aspergilli, agroup of filamentous fungi rich in natural compounds. The vast amount of data obtained from these species challenges the way we were mining for products and requires new pipelines for secondary metabolite analysis...

  12. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

    Zhili He

    2018-02-01

    Full Text Available Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN, representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5 increased significantly (P < 0.05 as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.

  13. Characterization and detection of a widely distributed gene cluster that predicts anaerobic choline utilization by human gut bacteria.

    Martínez-del Campo, Ana; Bodea, Smaranda; Hamer, Hilary A; Marks, Jonathan A; Haiser, Henry J; Turnbaugh, Peter J; Balskus, Emily P

    2015-04-14

    Elucidation of the molecular mechanisms underlying the human gut microbiota's effects on health and disease has been complicated by difficulties in linking metabolic functions associated with the gut community as a whole to individual microorganisms and activities. Anaerobic microbial choline metabolism, a disease-associated metabolic pathway, exemplifies this challenge, as the specific human gut microorganisms responsible for this transformation have not yet been clearly identified. In this study, we established the link between a bacterial gene cluster, the choline utilization (cut) cluster, and anaerobic choline metabolism in human gut isolates by combining transcriptional, biochemical, bioinformatic, and cultivation-based approaches. Quantitative reverse transcription-PCR analysis and in vitro biochemical characterization of two cut gene products linked the entire cluster to growth on choline and supported a model for this pathway. Analyses of sequenced bacterial genomes revealed that the cut cluster is present in many human gut bacteria, is predictive of choline utilization in sequenced isolates, and is widely but discontinuously distributed across multiple bacterial phyla. Given that bacterial phylogeny is a poor marker for choline utilization, we were prompted to develop a degenerate PCR-based method for detecting the key functional gene choline TMA-lyase (cutC) in genomic and metagenomic DNA. Using this tool, we found that new choline-metabolizing gut isolates universally possessed cutC. We also demonstrated that this gene is widespread in stool metagenomic data sets. Overall, this work represents a crucial step toward understanding anaerobic choline metabolism in the human gut microbiota and underscores the importance of examining this microbial community from a function-oriented perspective. Anaerobic choline utilization is a bacterial metabolic activity that occurs in the human gut and is linked to multiple diseases. While bacterial genes responsible for

  14. Beneficial effect of CETP gene polymorphism in combination with a Mediterranean diet influencing lipid metabolism in metabolic syndrome patients: CORDIOPREV study

    The cholesteryl ester transfer protein (CETP) gene has been implicated in high-density lipoprotein (HDL-C) metabolism. However, little is known about the impact of this gene on metabolic syndrome (MetS) patients and its interaction with diet. Here, we evaluate whether the consumption of a Mediterran...

  15. Methods for the Isolation of Genes Encoding Novel PHA Metabolism Enzymes from Complex Microbial Communities.

    Cheng, Jiujun; Nordeste, Ricardo; Trainer, Maria A; Charles, Trevor C

    2017-01-01

    Development of different PHAs as alternatives to petrochemically derived plastics can be facilitated by mining metagenomic libraries for diverse PHA cycle genes that might be useful for synthesis of bio-plastics. The specific phenotypes associated with mutations of the PHA synthesis pathway genes in Sinorhizobium meliloti and Pseudomonas putida, allows the use of powerful selection and screening tools to identify complementing novel PHA synthesis genes. Identification of novel genes through their function rather than sequence facilitates the functional proteins that may otherwise have been excluded through sequence-only screening methodology. We present here methods that we have developed for the isolation of clones expressing novel PHA metabolism genes from metagenomic libraries.

  16. Dendrobium nobile Lindl. alkaloids regulate metabolism gene expression in livers of mice.

    Xu, Yun-Yan; Xu, Ya-Sha; Wang, Yuan; Wu, Qin; Lu, Yuan-Fu; Liu, Jie; Shi, Jing-Shan

    2017-10-01

    In our previous studies, Dendrobium nobile Lindl. alkaloids (DNLA) has been shown to have glucose-lowering and antihyperlipidaemia effects in diabetic rats, in rats fed with high-fat diets, and in mice challenged with adrenaline. This study aimed to examine the effects of DNLA on the expression of glucose and lipid metabolism genes in livers of mice. Mice were given DNLA at doses of 10-80 mg/kg, po for 8 days, and livers were removed for total RNA and protein isolation to perform real-time RT-PCR and Western blot analysis. Dendrobium nobile Lindl. alkaloids increased PGC1α at mRNA and protein levels and increased glucose metabolism gene Glut2 and FoxO1 expression. DNLA also increased the expression of fatty acid β-oxidation genes Acox1 and Cpt1a. The lipid synthesis regulator Srebp1 (sterol regulatory element-binding protein-1) was decreased, while the lipolysis gene ATGL was increased. Interestingly, DNLA increased the expression of antioxidant gene metallothionein-1 and NADPH quinone oxidoreductase-1 (Nqo1) in livers of mice. Western blot on selected proteins confirmed these changes including the increased expression of GLUT4 and PPARα. DNLA has beneficial effects on liver glucose and lipid metabolism gene expressions, and enhances the Nrf2-antioxidant pathway gene expressions, which could play integrated roles in regulating metabolic disorders. © 2017 Royal Pharmaceutical Society.

  17. Hepatic xenobiotic metabolizing enzyme and transporter gene expression through the life stages of the mouse.

    Janice S Lee

    Full Text Available BACKGROUND: Differences in responses to environmental chemicals and drugs between life stages are likely due in part to differences in the expression of xenobiotic metabolizing enzymes and transporters (XMETs. No comprehensive analysis of the mRNA expression of XMETs has been carried out through life stages in any species. RESULTS: Using full-genome arrays, the mRNA expression of all XMETs and their regulatory proteins was examined during fetal (gestation day (GD 19, neonatal (postnatal day (PND 7, prepubescent (PND32, middle age (12 months, and old age (18 and 24 months in the C57BL/6J (C57 mouse liver and compared to adults. Fetal and neonatal life stages exhibited dramatic differences in XMET mRNA expression compared to the relatively minor effects of old age. The total number of XMET probe sets that differed from adults was 636, 500, 84, 5, 43, and 102 for GD19, PND7, PND32, 12 months, 18 months and 24 months, respectively. At all life stages except PND32, under-expressed genes outnumbered over-expressed genes. The altered XMETs included those in all of the major metabolic and transport phases including introduction of reactive or polar groups (Phase I, conjugation (Phase II and excretion (Phase III. In the fetus and neonate, parallel increases in expression were noted in the dioxin receptor, Nrf2 components and their regulated genes while nuclear receptors and regulated genes were generally down-regulated. Suppression of male-specific XMETs was observed at early (GD19, PND7 and to a lesser extent, later life stages (18 and 24 months. A number of female-specific XMETs exhibited a spike in expression centered at PND7. CONCLUSIONS: The analysis revealed dramatic differences in the expression of the XMETs, especially in the fetus and neonate that are partially dependent on gender-dependent factors. XMET expression can be used to predict life stage-specific responses to environmental chemicals and drugs.

  18. Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

    Megchelenbrink, Wout; Katzir, Rotem; Lu, Xiaowen; Ruppin, Eytan; Notebaart, Richard A

    2015-09-29

    Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.

  19. ABDOMINAL OBESITY, AN ANTHROPOMETRIC PARAMETER PREDICTING METABOLIC DISORDERS

    Maricel Castellanos González

    2011-08-01

    Full Text Available Background: Waist circumference perimeter, as an indirect indicator of abdominal obesity, is commonly presented as an essential element in the clinical assessment of obesity. The link between abdominal obesity and insulin resistance is proposed as the core of metabolic syndrome’s pathophysiology and complications. Objective: To determine whether individuals with abdominal obesity present characteristics related to metabolic syndrome’s factors that differ from those observed in individuals with no abdominal obesity. Methods: A comparative analytical study was performed including cases control and design in two different groups. The sample was composed of 98 individuals of both sexes randomly selected out of a universe of 510 workers population at the Medical University of Cienfuegos from September to December 2005. They were all tested as to blood pressure, cholesterol, HDL cholesterol, fasting glucose and triglycerides. Results: Abdominal obesity was found in 30.6% of individuals. It was predominant in females (83.3% older than 40 years. The number of cases of obesity linked to hypertension was similar to the number of cases with low HDL cholesterol (53.3%. Impaired glucose was found in 16.7% of cases. Conclusions: Abdominal obesity is a health problem in the population included in this study and it increases as age does. Individuals with abdominal obesity are exposed to a higher risk of metabolic disorders, such as low levels of HDL cholesterol, high levels of triglycerides and total cholesterol, glucose alterations and hypertension.

  20. Molecular effect of fenofibrate on PBMC gene transcription related to lipid metabolism in patients with metabolic syndrome.

    Moreno-Indias, I; Tinahones, F J; Clemente-Postigo, M; Castellano-Castillo, D; Fernández-García, J C; Macias-Gonzalez, M; Queipo-Ortuño, M I; Cardona, F

    2017-06-01

    Both fasting and postprandial hypertriglyceridaemia are considered independent risk factors for atherosclerosis. Treatment of hypertriglyceridaemia is based on fibrates, which activate the peroxisome proliferator-activated receptor alpha (PPARα). However, the metabolic pathways that activate or inhibit fibrates, and how the postprandial triglyceride levels are modified, have not yet been fully described. Accordingly, the aim of this study was to determine the feasibility of peripheral blood mononuclear cells (PBMC) to study the effects of fenofibrate in patients with the metabolic syndrome. A fat overload was given to 50 patients before and after treatment with fenofibrate for 3 months. Anthropometric and biochemical variables as well as gene expression in PBMC were analysed. After treatment with fenofibrate, we observed a decrease in both baseline and postprandial (3 h after the fat overload) levels of serum triglycerides, cholesterol and uric acid and an increase in HDL cholesterol and apolipoprotein AI levels. After treatment, there was also a rise in PPARα and RXRα expression and changes in genes regulated by PPARα, both baseline and postprandial. Furthermore, in vitro experiments showed that a PPARα agonist changed the expression of genes related with lipid metabolism. Treatment with fenofibrate reduced fasting and postprandial serum triglyceride levels, possibly through a mechanism related with an increase in the expression of RXRα and PPARα, by activating the pathways involved in the uptake and degradation of triglycerides and increasing the synthesis of apolipoprotein. These results suggest that PBMC may be useful for the easy study of fenofibrate actions. © 2017 John Wiley & Sons Ltd.

  1. Waist-to-height: cutoff matters in predicting metabolic syndrome in Mexican children.

    Elizondo-Montemayor, Leticia; Serrano-González, Mónica; Ugalde-Casas, Patricia A; Bustamante-Careaga, Humberto; Cuello-García, Carlos

    2011-06-01

    Body-mass index (BMI), waist circumference (WC), and, recently, waist-to-height ratio (WHtR) have been proposed as clinical indexes to identify children at cardiometabolic risk. The aim was to identify the usefulness of WHtR cutoffs, WC, and BMI as predictors of metabolic syndrome in Mexican children, according to BMI z-scores, and the severity of obesity to cardiometabolic risk factors and metabolic syndrome. This was a cross-sectional study of 214 overweight/obese and 47 normal-weight Mexican children 6-12 years old. Children were divided in groups according to BMI z-scores. Anthropometric and biochemical measurements were determined. Receiver-operating characteristic (ROC) curves and areas under the curves were calculated to compare the abilities of the anthropometric measurements to predict metabolic syndrome. The overall prevalence of metabolic syndrome was 23.3%, ranging from 11.0% in the overweight group to 73.9% in the severely obese one. Children with metabolic syndrome had significantly higher WHtR, WC, BMI, percentage of body fat, triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), systolic and diastolic blood pressure, and lower high-density lipoprotein cholesterol (HDL-C). A WHtR cutoff point of 0.59 from the ROC curve was identified as strong predictor of metabolic syndrome in our population, whereas a cutoff of 0.5 showed very poor specificity (22.7%). WC predicted metabolic syndrome as well. Cutoff values for WHtR make a difference in predicting metabolic syndrome. A cutoff of 0.59 for WHtR strongly predicted metabolic syndrome; it might be a simpler to use screening tools and counters for short people. Further studies are required to determine the cutoff points for an accurate prediction, because there are few in children and none in Mexico.

  2. Can Thrifty Gene(s or Predictive Fetal Programming for Thriftiness Lead to Obesity?

    Ulfat Baig

    2011-01-01

    Full Text Available Obesity and related disorders are thought to have their roots in metabolic “thriftiness” that evolved to combat periodic starvation. The association of low birth weight with obesity in later life caused a shift in the concept from thrifty gene to thrifty phenotype or anticipatory fetal programming. The assumption of thriftiness is implicit in obesity research. We examine here, with the help of a mathematical model, the conditions for evolution of thrifty genes or fetal programming for thriftiness. The model suggests that a thrifty gene cannot exist in a stable polymorphic state in a population. The conditions for evolution of thrifty fetal programming are restricted if the correlation between intrauterine and lifetime conditions is poor. Such a correlation is not observed in natural courses of famine. If there is fetal programming for thriftiness, it could have evolved in anticipation of social factors affecting nutrition that can result in a positive correlation.

  3. Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning

    Zhang, Ping; Wu, Linwei; Rocha, Andrea M.; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D.; Wu, Liyou; Watson, David B.; Adams, Michael W. W.; Alm, Eric J.; Adams, Paul D.; Arkin, Adam P.

    2018-01-01

    ABSTRACT Contamination from anthropogenic activities has significantly impacted Earth’s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning. PMID:29463661

  4. Motif-Independent De Novo Detection of Secondary Metabolite Gene Clusters – Towards Identification of Novel Secondary Metabolisms from Filamentous Fungi -

    Myco eUmemura

    2015-05-01

    Full Text Available Secondary metabolites are produced mostly by clustered genes that are essential to their biosynthesis. The transcriptional expression of these genes is often cooperatively regulated by a transcription factor located inside or close to a cluster. Most of the secondary metabolism biosynthesis (SMB gene clusters identified to date contain so-called core genes with distinctive sequence features, such as polyketide synthase (PKS and non-ribosomal peptide synthetase (NRPS. Recent efforts in sequencing fungal genomes have revealed far more SMB gene clusters than expected based on the number of core genes in the genomes. Several bioinformatics tools have been developed to survey SMB gene clusters using the sequence motif information of the core genes, including SMURF and antiSMASH.More recently, accompanied by the development of sequencing techniques allowing to obtain large-scale genomic and transcriptomic data, motif-independent prediction methods of SMB gene clusters, including MIDDAS-M, have been developed. Most these methods detect the clusters in which the genes are cooperatively regulated at transcriptional levels, thus allowing the identification of novel SMB gene clusters regardless of the presence of the core genes. Another type of the method, MIPS-CG, uses the characteristics of SMB genes, which are highly enriched in non-syntenic blocks (NSBs, enabling the prediction even without transcriptome data although the results have not been evaluated in detail. Considering that large portion of SMB gene clusters might be sufficiently expressed only in limited uncommon conditions, it seems that prediction of SMB gene clusters by bioinformatics and successive experimental validation is an only way to efficiently uncover hidden SMB gene clusters. Here, we describe and discuss possible novel approaches for the determination of SMB gene clusters that have not been identified using conventional methods.

  5. Regulation of metabolic products and gene expression in Fusarium asiaticum by agmatine addition.

    Suzuki, Tadahiro; Kim, Young-Kyung; Yoshioka, Hifumi; Iwahashi, Yumiko

    2013-05-01

    The metabolic products resulting from the cultivation of F. asiaticum in agmatine were identified using capillary electrophoresis-time of flight mass spectrometry. Glyoxylic acid was detected from fungal cultures grown in agmatine, while it was absent in control cells. The abundance of other metabolic products of the glycolytic pathway also increased because of agmatine; however, there was no increase in the amounts of pyruvic acid or metabolites from the tricarboxylic acid cycle. Moreover, gene expression levels within Fusarium asiaticum exposed to agmatine were analyzed by DNA microarray. Changes in gene expression levels directed the changes in metabolic products. Our results suggest that acetyl coenzyme A, which is a starting substrate for the biosynthesis of deoxynivalenol (DON), was simultaneously produced by activated β-oxidation. Furthermore, the content of 4-aminobutyrate (GABA) was increased in the agmatine addition culture medium. GABA can be synthesized from agmatine through putrescine and might influence the regulation of DON-related genes.

  6. Polymorphisms in fatty acid metabolism-related genes are associated with colorectal cancer risk

    Hoeft, B.; Linseisen, J.; Beckmann, L.

    2010-01-01

    as contributing factor to colon carcinogenesis. We examined the association between genetic variability in 43 fatty acid metabolism-related genes and colorectal risk in 1225 CRC cases and 2032 controls participating in the European Prospective Investigation into Cancer and Nutrition study. Three hundred......Colorectal cancer (CRC) is the third most common malignant tumor and the fourth leading cause of cancer death worldwide. The crucial role of fatty acids for a number of important biological processes suggests a more in-depth analysis of inter-individual differences in fatty acid metabolizing genes...... variants with CRC risk. Our results support the key role of prostanoid signaling in colon carcinogenesis and suggest a relevance of genetic variation in fatty acid metabolism-related genes and CRC risk....

  7. Effect of AGTR1 and BDKRB2 gene polymorphisms on atorvastatin metabolism in a Mexican population

    Herrera-González, Sarahí; Martínez-Treviño, Denisse Aideé; Aguirre-Garza, Marcelino; Gómez-Silva, Magdalena; Barrera-Saldaña, Hugo Alberto; León-Cachón, Rafael Baltazar Reyes

    2017-01-01

    Discrepancies in the response to drugs are partially due to polymorphisms in genes involved in drug metabolism and transport. The frequency, pattern and impact of these polymorphisms vary among populations. In the present study, the pharmacokinetics and pharmacogenetics of atorvastatin (ATV) in a Mexican population were investigated. The study cohort exhibited differing ATV metabolizing phenotypes, and in subsequent allelic discrimination assays, single nucleotide polymorphisms in the angiote...

  8. No association between type 1 diabetes and genetic variation in vitamin D metabolism genes

    Thorsen, Steffen U; Mortensen, Henrik B; Carstensen, Bendix

    2014-01-01

    BACKGROUND: Vitamin D, certain single nucleotide polymorphisms (SNPs) in the vitamin D-receptor (VDR) gene and vitamin D metabolism genes have been associated with type 1 diabetes (T1D). OBJECTIVE: We wanted to examine if the most widely studied SNPs in genes important for production, transport......, and action of vitamin D were associated with T1D or to circulating levels of vitamin D 25-hydroxyvitamin D [25(OH)D] in a juvenile Danish population. METHODS: We genotyped eight SNPs in five vitamin D metabolism genes in 1467 trios. 25(OH)D status were analyzed in 1803 children (907 patients and 896 siblings......). RESULTS: We did not demonstrate association with T1D for SNPs in the following genes: CYP27B1, VDR, GC, CYP2R1, DHCR7, and CYP24A1. Though, variants in the GC gene were significantly associated with 25(OH)D levels in the joint model. CONCLUSION: Some of the most examined SNPs in vitamin D metabolism genes...

  9. Predictive modelling of gene expression from transcriptional regulatory elements.

    Budden, David M; Hurley, Daniel G; Crampin, Edmund J

    2015-07-01

    Predictive modelling of gene expression provides a powerful framework for exploring the regulatory logic underpinning transcriptional regulation. Recent studies have demonstrated the utility of such models in identifying dysregulation of gene and miRNA expression associated with abnormal patterns of transcription factor (TF) binding or nucleosomal histone modifications (HMs). Despite the growing popularity of such approaches, a comparative review of the various modelling algorithms and feature extraction methods is lacking. We define and compare three methods of quantifying pairwise gene-TF/HM interactions and discuss their suitability for integrating the heterogeneous chromatin immunoprecipitation (ChIP)-seq binding patterns exhibited by TFs and HMs. We then construct log-linear and ϵ-support vector regression models from various mouse embryonic stem cell (mESC) and human lymphoblastoid (GM12878) data sets, considering both ChIP-seq- and position weight matrix- (PWM)-derived in silico TF-binding. The two algorithms are evaluated both in terms of their modelling prediction accuracy and ability to identify the established regulatory roles of individual TFs and HMs. Our results demonstrate that TF-binding and HMs are highly predictive of gene expression as measured by mRNA transcript abundance, irrespective of algorithm or cell type selection and considering both ChIP-seq and PWM-derived TF-binding. As we encourage other researchers to explore and develop these results, our framework is implemented using open-source software and made available as a preconfigured bootable virtual environment. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  10. Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions

    Edwards Jeremy S

    2000-07-01

    Full Text Available Abstract Background Genome sequencing and bioinformatics are producing detailed lists of the molecular components contained in many prokaryotic organisms. From this 'parts catalogue' of a microbial cell, in silico representations of integrated metabolic functions can be constructed and analyzed using flux balance analysis (FBA. FBA is particularly well-suited to study metabolic networks based on genomic, biochemical, and strain specific information. Results Herein, we have utilized FBA to interpret and analyze the metabolic capabilities of Escherichia coli. We have computationally mapped the metabolic capabilities of E. coli using FBA and examined the optimal utilization of the E. coli metabolic pathways as a function of environmental variables. We have used an in silico analysis to identify seven gene products of central metabolism (glycolysis, pentose phosphate pathway, TCA cycle, electron transport system essential for aerobic growth of E. coli on glucose minimal media, and 15 gene products essential for anaerobic growth on glucose minimal media. The in silico tpi-, zwf, and pta- mutant strains were examined in more detail by mapping the capabilities of these in silico isogenic strains. Conclusions We found that computational models of E. coli metabolism based on physicochemical constraints can be used to interpret mutant behavior. These in silica results lead to a further understanding of the complex genotype-phenotype relation. Supplementary information: http://gcrg.ucsd.edu/supplementary_data/DeletionAnalysis/main.htm

  11. Identification of rat genes by TWINSCAN gene prediction, RT-PCR, and direct sequencing

    Wu, Jia Qian; Shteynberg, David; Arumugam, Manimozhiyan

    2004-01-01

    an alternative approach: reverse transcription-polymerase chain reaction (RT-PCR) and direct sequencing based on dual-genome de novo predictions from TWINSCAN. We tested 444 TWINSCAN-predicted rat genes that showed significant homology to known human genes implicated in disease but that were partially...... in the single-intron experiment. Spliced sequences were amplified in 46 cases (34%). We conclude that this procedure for elucidating gene structures with native cDNA sequences is cost-effective and will become even more so as it is further optimized.......The publication of a draft sequence of a third mammalian genome--that of the rat--suggests a need to rethink genome annotation. New mammalian sequences will not receive the kind of labor-intensive annotation efforts that are currently being devoted to human. In this paper, we demonstrate...

  12. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

    Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

    2013-12-23

    Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

  13. Hematopoietic Gene Therapies for Metabolic and Neurologic Diseases.

    Biffi, Alessandra

    2017-10-01

    Increasingly, patients affected by metabolic diseases affecting the central nervous system and neuroinflammatory disorders receive hematopoietic cell transplantation (HCT) in the attempt to slow the course of their disease, delay or attenuate symptoms, and improve pathologic findings. The possible replacement of brain-resident myeloid cells by the transplanted cell progeny contributes to clinical benefit. Genetic engineering of the cells to be transplanted (hematopoietic stem cell) may endow the brain myeloid progeny of these cells with enhanced or novel functions, contributing to therapeutic effects. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Comparison of multiple gene assembly methods for metabolic engineering

    Chenfeng Lu; Karen Mansoorabadi; Thomas Jeffries

    2007-01-01

    A universal, rapid DNA assembly method for efficient multigene plasmid construction is important for biological research and for optimizing gene expression in industrial microbes. Three different approaches to achieve this goal were evaluated. These included creating long complementary extensions using a uracil-DNA glycosylase technique, overlap extension polymerase...

  15. Dinucleotide controlled null models for comparative RNA gene prediction

    Gesell Tanja

    2008-05-01

    Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

  16. Dinucleotide controlled null models for comparative RNA gene prediction.

    Gesell, Tanja; Washietl, Stefan

    2008-05-27

    Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

  17. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

    Hojung Nam

    2014-09-01

    Full Text Available Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH, succinate dehydrogenase (SDH, and fumarate hydratase (FH that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes, expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.

  18. Adaptive evolution of mitochondrial energy metabolism genes associated with increased energy demand in flying insects.

    Yang, Yunxia; Xu, Shixia; Xu, Junxiao; Guo, Yan; Yang, Guang

    2014-01-01

    Insects are unique among invertebrates for their ability to fly, which raises intriguing questions about how energy metabolism in insects evolved and changed along with flight. Although physiological studies indicated that energy consumption differs between flying and non-flying insects, the evolution of molecular energy metabolism mechanisms in insects remains largely unexplored. Considering that about 95% of adenosine triphosphate (ATP) is supplied by mitochondria via oxidative phosphorylation, we examined 13 mitochondrial protein-encoding genes to test whether adaptive evolution of energy metabolism-related genes occurred in insects. The analyses demonstrated that mitochondrial DNA protein-encoding genes are subject to positive selection from the last common ancestor of Pterygota, which evolved primitive flight ability. Positive selection was also found in insects with flight ability, whereas no significant sign of selection was found in flightless insects where the wings had degenerated. In addition, significant positive selection was also identified in the last common ancestor of Neoptera, which changed its flight mode from direct to indirect. Interestingly, detection of more positively selected genes in indirect flight rather than direct flight insects suggested a stronger selective pressure in insects having higher energy consumption. In conclusion, mitochondrial protein-encoding genes involved in energy metabolism were targets of adaptive evolution in response to increased energy demands that arose during the evolution of flight ability in insects.

  19. Horizontal gene transfer of an entire metabolic pathway between a eukaryotic alga and its DNA virus

    Monier, Adam; Pagarete, António; de Vargas, Colomban; Allen, Michael J.; Read, Betsy; Claverie, Jean-Michel; Ogata, Hiroyuki

    2009-01-01

    Interactions between viruses and phytoplankton, the main primary producers in the oceans, affect global biogeochemical cycles and climate. Recent studies are increasingly revealing possible cases of gene transfers between cyanobacteria and phages, which might have played significant roles in the evolution of cyanobacteria/phage systems. However, little has been documented about the occurrence of horizontal gene transfer in eukaryotic phytoplankton/virus systems. Here we report phylogenetic evidence for the transfer of seven genes involved in the sphingolipid biosynthesis pathway between the cosmopolitan eukaryotic microalga Emiliania huxleyi and its large DNA virus EhV. PCR assays indicate that these genes are prevalent in E. huxleyi and EhV strains isolated from different geographic locations. Patterns of protein and gene sequence conservation support that these genes are functional in both E. huxleyi and EhV. This is the first clear case of horizontal gene transfer of multiple functionally linked enzymes in a eukaryotic phytoplankton–virus system. We examine arguments for the possible direction of the gene transfer. The virus-to-host direction suggests the existence of ancient viruses that controlled the complex metabolic pathway in order to infect primitive eukaryotic cells. In contrast, the host-to-virus direction suggests that the serial acquisition of genes involved in the same metabolic pathway might have been a strategy for the ancestor of EhVs to stay ahead of their closest relatives in the great evolutionary race for survival. PMID:19451591

  20. Metagenomic analysis revealed highly diverse microbial arsenic metabolism genes in paddy soils with low-arsenic contents

    Xiao, Ke-Qing; Li, Li-Guan; Ma, Li-Ping; Zhang, Si-Yu; Bao, Peng; Zhang, Tong; Zhu, Yong-Guan

    2016-01-01

    Microbe-mediated arsenic (As) metabolism plays a critical role in global As cycle, and As metabolism involves different types of genes encoding proteins facilitating its biotransformation and transportation processes. Here, we used metagenomic analysis based on high-throughput sequencing and constructed As metabolism protein databases to analyze As metabolism genes in five paddy soils with low-As contents. The results showed that highly diverse As metabolism genes were present in these paddy soils, with varied abundances and distribution for different types and subtypes of these genes. Arsenate reduction genes (ars) dominated in all soil samples, and significant correlation existed between the abundance of arr (arsenate respiration), aio (arsenite oxidation), and arsM (arsenite methylation) genes, indicating the co-existence and close-relation of different As resistance systems of microbes in wetland environments similar to these paddy soils after long-term evolution. Among all soil parameters, pH was an important factor controlling the distribution of As metabolism gene in five paddy soils (p = 0.018). To the best of our knowledge, this is the first study using high-throughput sequencing and metagenomics approach in characterizing As metabolism genes in the five paddy soil, showing their great potential in As biotransformation, and therefore in mitigating arsenic risk to humans. - Highlights: • Use metagenomics to analyze As metabolism genes in paddy soils with low-As content. • These genes were ubiquitous, abundant, and associated with diverse microbes. • pH as an important factor controlling their distribution in paddy soil. • Imply combinational effect of evolution and selection on As metabolism genes. - Metagenomics was used to analyze As metabolism genes in paddy soils with low-As contents. These genes were ubiquitous, abundant, and associated with diverse microbes.

  1. Differential expression of metabolic genes in tumor and stromal components of primary and metastatic loci in pancreatic adenocarcinoma.

    Nina V Chaika

    Full Text Available Pancreatic cancer is the fourth leading cause of cancer related deaths in the United States with a five-year survival rate of 6%. It is characterized by extremely aggressive tumor growth rate and high incidence of metastasis. One of the most common and profound biochemical phenotypes of animal and human cancer cells is their ability to metabolize glucose at high rates, even under aerobic conditions. However, the contribution of metabolic interrelationships between tumor cells and cells of the surrounding microenvironment to the progression of cancer is not well understood. We evaluated differential expression of metabolic genes and, hence, metabolic pathways in primary tumor and metastases of patients with pancreatic adenocarcinoma.We analyzed the metabolic gene (those involved in glycolysis, tri-carboxylic acid pathway, pentose-phosphate pathway and fatty acid metabolism expression profiles of primary and metastatic lesions from pancreatic cancer patients by gene expression arrays. We observed two principal results: genes that were upregulated in primary and most of the metastatic lesions; and genes that were upregulated only in specific metastatic lesions in a site-specific manner. Immunohistochemical (IHC analyses of several metabolic gene products confirmed the gene expression patterns at the protein level. The IHC analyses also revealed differential tumor and stromal expression patterns of metabolic enzymes that were correlated with the metastasis sites.Here, we present the first comprehensive studies that establish differential metabolic status of tumor and stromal components and elevation of aerobic glycolysis gene expression in pancreatic cancer.

  2. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    Huthmacher, Carola; Hoppe, Andreas; Bulik, Sascha; Holzh?tter, Hermann-Georg

    2010-01-01

    Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicte...

  3. Whole genome transcript profiling of drug induced steatosis in rats reveals a gene signature predictive of outcome.

    Nishika Sahini

    Full Text Available Drug induced steatosis (DIS is characterised by excess triglyceride accumulation in the form of lipid droplets (LD in liver cells. To explore mechanisms underlying DIS we interrogated the publically available microarray data from the Japanese Toxicogenomics Project (TGP to study comprehensively whole genome gene expression changes in the liver of treated rats. For this purpose a total of 17 and 12 drugs which are diverse in molecular structure and mode of action were considered based on their ability to cause either steatosis or phospholipidosis, respectively, while 7 drugs served as negative controls. In our efforts we focused on 200 genes which are considered to be mechanistically relevant in the process of lipid droplet biogenesis in hepatocytes as recently published (Sahini and Borlak, 2014. Based on mechanistic considerations we identified 19 genes which displayed dose dependent responses while 10 genes showed time dependency. Importantly, the present study defined 9 genes (ANGPTL4, FABP7, FADS1, FGF21, GOT1, LDLR, GK, STAT3, and PKLR as signature genes to predict DIS. Moreover, cross tabulation revealed 9 genes to be regulated ≥10 times amongst the various conditions and included genes linked to glucose metabolism, lipid transport and lipogenesis as well as signalling events. Additionally, a comparison between drugs causing phospholipidosis and/or steatosis revealed 26 genes to be regulated in common including 4 signature genes to predict DIS (PKLR, GK, FABP7 and FADS1. Furthermore, a comparison between in vivo single dose (3, 6, 9 and 24 h and findings from rat hepatocyte studies (2 h, 8 h, 24 h identified 10 genes which are regulated in common and contained 2 DIS signature genes (FABP7, FGF21. Altogether, our studies provide comprehensive information on mechanistically linked gene expression changes of a range of drugs causing steatosis and phospholipidosis and encourage the screening of DIS signature genes at the preclinical stage.

  4. Ontogeny of hepatic energy metabolism genes in mice as revealed by RNA-sequencing.

    Helen J Renaud

    Full Text Available The liver plays a central role in metabolic homeostasis by coordinating synthesis, storage, breakdown, and redistribution of nutrients. Hepatic energy metabolism is dynamically regulated throughout different life stages due to different demands for energy during growth and development. However, changes in gene expression patterns throughout ontogeny for factors important in hepatic energy metabolism are not well understood. We performed detailed transcript analysis of energy metabolism genes during various stages of liver development in mice. Livers from male C57BL/6J mice were collected at twelve ages, including perinatal and postnatal time points (n = 3/age. The mRNA was quantified by RNA-Sequencing, with transcript abundance estimated by Cufflinks. One thousand sixty energy metabolism genes were examined; 794 were above detection, of which 627 were significantly changed during at least one developmental age compared to adult liver. Two-way hierarchical clustering revealed three major clusters dependent on age: GD17.5-Day 5 (perinatal-enriched, Day 10-Day 20 (pre-weaning-enriched, and Day 25-Day 60 (adolescence/adulthood-enriched. Clustering analysis of cumulative mRNA expression values for individual pathways of energy metabolism revealed three patterns of enrichment: glycolysis, ketogenesis, and glycogenesis were all perinatally-enriched; glycogenolysis was the only pathway enriched during pre-weaning ages; whereas lipid droplet metabolism, cholesterol and bile acid metabolism, gluconeogenesis, and lipid metabolism were all enriched in adolescence/adulthood. This study reveals novel findings such as the divergent expression of the fatty acid β-oxidation enzymes Acyl-CoA oxidase 1 and Carnitine palmitoyltransferase 1a, indicating a switch from mitochondrial to peroxisomal β-oxidation after weaning; as well as the dynamic ontogeny of genes implicated in obesity such as Stearoyl-CoA desaturase 1 and Elongation of very long chain fatty

  5. Ghrelin Gene Variants Influence on Metabolic Syndrome Components in Aged Spanish Population

    Mora, Mireia; Adam, Victoria; Palomera, Elisabet; Blesa, Sebastian; Díaz, Gonzalo; Buquet, Xavier; Serra-Prat, Mateu; Martín-Escudero, Juan Carlos; Palanca, Ana; Chaves, Javier Felipe; Puig-Domingo, Manuel

    2015-01-01

    BACKGROUND: The role of genetic variations within the ghrelin gene on cardiometabolic profile and nutritional status is still not clear in humans, particularly in elderly people. OBJECTIVES: We investigated six SNPs of the ghrelin gene and their relationship with metabolic syndrome (MS) components. SUBJECTS AND METHODS: 824 subjects (413 men/411 women, age 77.31±5.04) participating in the Mataró aging study (n = 310) and the Hortega study (n = 514) were analyzed. Anthropometric variables, ghr...

  6. Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain.

    Gavai, Anand K.; Supandi, Farahaniza; Hettling, Hannes; Murell, Paul; Leunissen, Jack A.M.; van Beek, Johannes H.G.M.

    2015-01-01

    Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the

  7. Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain

    Gavai, A.K.; Supandi, F.; Hettling, H.; Murrell, P.; Leunissen, J.A.M.; Beek, van J.H.G.M.

    2015-01-01

    Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the

  8. Coordinated Expression of Phosphoinositide Metabolic Genes during Development and Aging of Human Dorsolateral Prefrontal Cortex.

    Stanley I Rapoport

    Full Text Available Phosphoinositides, lipid-signaling molecules, participate in diverse brain processes within a wide metabolic cascade.Gene transcriptional networks coordinately regulate the phosphoinositide cascade during human brain Development and Aging.We used the public BrainCloud database for human dorsolateral prefrontal cortex to examine age-related expression levels of 49 phosphoinositide metabolic genes during Development (0 to 20+ years and Aging (21+ years.We identified three groups of partially overlapping genes in each of the two intervals, with similar intergroup correlations despite marked phenotypic differences between Aging and Development. In each interval, ITPKB, PLCD1, PIK3R3, ISYNA1, IMPA2, INPPL1, PI4KB, and AKT1 are in Group 1, PIK3CB, PTEN, PIK3CA, and IMPA1 in Group 2, and SACM1L, PI3KR4, INPP5A, SYNJ1, and PLCB1 in Group 3. Ten of the genes change expression nonlinearly during Development, suggesting involvement in rapidly changing neuronal, glial and myelination events. Correlated transcription for some gene pairs likely is facilitated by colocalization on the same chromosome band.Stable coordinated gene transcriptional networks regulate brain phosphoinositide metabolic pathways during human Development and Aging.

  9. Lactococcus lactis Metabolism and Gene Expression during Growth on Plant Tissues

    Golomb, Benjamin L.

    2014-01-01

    Lactic acid bacteria have been isolated from living, harvested, and fermented plant materials; however, the adaptations these bacteria possess for growth on plant tissues are largely unknown. In this study, we investigated plant habitat-specific traits of Lactococcus lactis during growth in an Arabidopsis thaliana leaf tissue lysate (ATL). L. lactis KF147, a strain originally isolated from plants, exhibited a higher growth rate and reached 7.9-fold-greater cell densities during growth in ATL than the dairy-associated strain L. lactis IL1403. Transcriptome profiling (RNA-seq) of KF147 identified 853 induced and 264 repressed genes during growth in ATL compared to that in GM17 laboratory culture medium. Genes induced in ATL included those involved in the arginine deiminase pathway and a total of 140 carbohydrate transport and metabolism genes, many of which are involved in xylose, arabinose, cellobiose, and hemicellulose metabolism. The induction of those genes corresponded with L. lactis KF147 nutrient consumption and production of metabolic end products in ATL as measured by gas chromatography-time of flight mass spectrometry (GC-TOF/MS) untargeted metabolomic profiling. To assess the importance of specific plant-inducible genes for L. lactis growth in ATL, xylose metabolism was targeted for gene knockout mutagenesis. Wild-type L. lactis strain KF147 but not an xylA deletion mutant was able to grow using xylose as the sole carbon source. However, both strains grew to similarly high levels in ATL, indicating redundancy in L. lactis carbohydrate metabolism on plant tissues. These findings show that certain strains of L. lactis are well adapted for growth on plants and possess specific traits relevant for plant-based food, fuel, and feed fermentations. PMID:25384484

  10. Fatty acid CoA ligase-4 gene polymorphism influences fatty acid metabolism in metabolic syndrome, but not in depression.

    Zeman, Miroslav; Vecka, Marek; Jáchymová, Marie; Jirák, Roman; Tvrzická, Eva; Stanková, Barbora; Zák, Ales

    2009-04-01

    The composition of polyunsaturated fatty acids (PUFAs) in cell membranes and body tissues is altered in metabolic syndrome (MetS) and depressive disorder (DD). Within the cell, fatty acid coenzyme A (CoA) ligases (FACLs) activate PUFAs by esterifying with CoA. The FACL4 isoform prefers PUFAs (arachidonic and eicosapentaenoic acid) as substrates, and the FACL4 gene is mapped to Xq23. We have analyzed the association between the common single nucleotide polymorphism (SNP) (rs1324805, C to T substitution) in the first intron of the FACL4 gene and MetS or DD. The study included 113 healthy subjects (54 Males/59 Females), 56 MetS patients (34M/22F) and 41 DD patients (7M/34F). In MetS group, T-carriers and patients with CC or C0 (CC/C0) genotype did not differ in the values of metabolic indices of MetS and M/F ratio. Nevertheless, in comparison with CC/C0, the T-allele carriers were characterized by enhanced unfavorable changes in fatty acid metabolism typical for MetS: higher content of dihomogammalinolenic acid (P phosphatidylcholine (PC) (P = 0.052), lower index of Delta5 desaturation (P insulin, conjugated dienes and index of insulin resistance, but showed no significant association with the studied SNP. The present study shows that the common SNP (C to T substitution) in the first intron of the FACL4 gene is associated with altered FA composition of plasma phosphatidylcholines in patients with MetS.

  11. Functional Gene Diversity and Metabolic Potential of the Microbial Community in an Estuary-Shelf Environment

    Yu Wang

    2017-06-01

    Full Text Available Microbes play crucial roles in various biogeochemical processes in the ocean, including carbon (C, nitrogen (N, and phosphorus (P cycling. Functional gene diversity and the structure of the microbial community determines its metabolic potential and therefore its ecological function in the marine ecosystem. However, little is known about the functional gene composition and metabolic potential of bacterioplankton in estuary areas. The East China Sea (ECS is a dynamic marginal ecosystem in the western Pacific Ocean that is mainly affected by input from the Changjiang River and the Kuroshio Current. Here, using a high-throughput functional gene microarray (GeoChip, we analyzed the functional gene diversity, composition, structure, and metabolic potential of microbial assemblages in different ECS water masses. Four water masses determined by temperature and salinity relationship showed different patterns of functional gene diversity and composition. Generally, functional gene diversity [Shannon–Weaner’s H and reciprocal of Simpson’s 1/(1-D] in the surface water masses was higher than that in the bottom water masses. The different presence and proportion of functional genes involved in C, N, and P cycling among the bacteria of the different water masses showed different metabolic preferences of the microbial populations in the ECS. Genes involved in starch metabolism (amyA and nplT showed higher proportion in microbial communities of the surface water masses than of the bottom water masses. In contrast, a higher proportion of genes involved in chitin degradation was observed in microorganisms of the bottom water masses. Moreover, we found a higher proportion of nitrogen fixation (nifH, transformation of hydroxylamine to nitrite (hao and ammonification (gdh genes in the microbial communities of the bottom water masses compared with those of the surface water masses. The spatial variation of microbial functional genes was significantly correlated

  12. Effects of castration on expression of lipid metabolism genes in the liver of korean cattle.

    Baik, Myunggi; Nguyen, Trang Hoa; Jeong, Jin Young; Piao, Min Yu; Kang, Hyeok Joong

    2015-01-01

    Castration induces the accumulation of body fat and deposition of intramuscular fat in Korean cattle, resulting in improved beef quality. However, little is known about the metabolic adaptations in the liver following castration. To understand changes in lipid metabolism following castration, hepatic expression levels of lipid metabolism genes were compared between Korean bulls and steers. Steers had higher (pcastration of bulls. However, we found no differences in the hepatic expression levels of genes related to triglyceride synthesis (mitochondrial glycerol-3-phosphate acyltransferase, diacylglycerol O-acyltransferase 1 and 2) and fatty acid (FA) oxidation (carnitine palmitoyltransferase 1A, C-4 to C-12 straight chain acyl-CoA dehydrogenase, very long chain acyl-CoA dehydrogenase) between bulls and steers. No differences in gene expression for very-low-density lipoprotein (VLDL) secretion, including apolipoprotein B mRNA and microsomal triglyceride transfer protein (MTTP) protein, were observed in the liver although MTTP mRNA levels were higher in steers compared to bulls. In conclusion, FA synthesis may contribute to increased hepatic lipid deposition in steers following castration. However, hepatic lipid metabolism, including triglyceride synthesis, FA oxidation, and VLDL secretion, was not significantly altered by castration. Our results suggest that hepatic lipid metabolism does not significantly contribute to increased body fat deposition in steers following castration.

  13. Hemodynamic and metabolic factors in the prediction of diastolic dysfunction

    Nielsen, M.; Pareek, M.; Olesen, T. B.

    2015-01-01

    , and later detection of grade 2 or 3 diastolic dysfunction (DD) in 243 men and 22 women aged 28 to 57 years at the time of inclusion, using binary logistic regression analysis. Study subjects came from a random population based sample and were included 1974-1992, whilst the echocardiography was performed...... between HOMA-2B and other variables in the prediction of diastolic dysfunction. Conclusions: In a binary logistic regression model adjusted for age, sex, and time, HOMA-2B was significantly associated with the development of grade 2 or 3 diastolic dysfunction. It is suggested that subjects with increased...

  14. The Impact of Drug Metabolism Gene Polymorphisms on Therapeutic Response and Survival in Diffuse Large B-Cell Lymphoma Patients.

    Pál, Ildikó; Illés, Árpád; Gergely, Lajos; Pál, Tibor; Radnay, Zita; Szekanecz, Zoltán; Zilahi, Erika; Váróczy, László

    2018-04-01

    Diffuse large B-cell lymphoma (DLBCL) accounts for 30% of all non-Hodgkin lymphomas (NHL) and 80% of agressive lymphomas. Besides the traditional International Prognostic Index (IPI), some other factors may also influence the prognosis of DLBCL patients. To study how the genetic polymorphisms in the metabolic pathway influence the event-free and overall survivals and therapeutic responses in DLBCL. The study was comprised of 51 patients (32 men, 19 women). The average age was 53.1 years. DLBCL was diagnosed between 2011 and 2016 and the average follow-up time was 3.78 years. These patients received 1-8 cycles (an average of 6.2 cycles) of rituximab, cyclophosphamide, doxorubicin, vincristin, prednisolon (R-CHOP) immunochemotherapy. Real-time polymerase chain reaction was used to determine the genetic polymorphisms of CYP2E1, GSTP1, NAT1, and NAT2 genes. Our results showed that the polymorphisms of CYP2E1, GSTP1, and NAT1 genes did not influence the prognosis of DLBCL patients significantly. In terms of the NAT2 gene, GG homozygous patients showed slightly better therapeutic response and survival results compared to those bearing an A allele; however, the differences were not statistically significant. Our results could not confirm that genetic polymorphism in metabolic pathways has any predictive role in DLBCL.

  15. Gene Coexpression Analysis Reveals Complex Metabolism of the Monoterpene Alcohol Linalool in Arabidopsis FlowersW

    Ginglinger, J.F.; Boachon, B.; Hofer, R.; Paetz, C.; Kollner, T.G.; Miesch, L.; Lugan, R.; Baltenweck, R.; Mutterer, J.; Ullman, P.; Verstappen, F.W.A.; Bouwmeester, H.J.

    2013-01-01

    The cytochrome P450 family encompasses the largest family of enzymes in plant metabolism, and the functions of many of its members in Arabidopsis thaliana are still unknown. Gene coexpression analysis pointed to two P450s that were coexpressed with two monoterpene synthases in flowers and were thus

  16. Polymorphisms in Renal Ammonia Metabolism Genes Correlate With 24-Hour Urine pH

    Benjamin K. Canales

    2017-11-01

    Discussion: Overall, these findings suggest that variants in common genes involved in ammonia metabolism may substantively contribute to basal urine pH regulation. These variations might influence the likelihood of developing disease conditions associated with altered urine pH, such as uric acid or calcium phosphate kidney stones.

  17. Oral facial clefts and gene polymorphisms in metabolism of folate/one-carbon and vitamin A

    Boyles, Abee L; Wilcox, Allen J; Taylor, Jack A

    2009-01-01

    An increased risk of facial clefts has been observed among mothers with lower intake of folic acid or vitamin A around conception. We hypothesized that the risk of clefts may be further moderated by genes involved in metabolizing folate or vitamin A. We included 425 case-parent triads in which th...

  18. Variation in genes related to hepatic lipid metabolism and changes in waist circumference and body weight

    Meidtner, Karina; Fisher, Eva; Angquist, Lars

    2014-01-01

    We analysed single nucleotide polymorphisms (SNPs) tagging the genetic variability of six candidate genes (ATF6, FABP1, LPIN2, LPIN3, MLXIPL and MTTP) involved in the regulation of hepatic lipid metabolism, an important regulatory site of energy balance for associations with body mass index (BMI...

  19. Does basal metabolic rate predict weight gain?12

    Anthanont, Pimjai; Jensen, Michael D

    2016-01-01

    Background: Some previous studies have indicated that a low basal metabolic rate (BMR) is an independent predictor of future weight gain, but low rates of follow-up and highly select populations may limit the ability to generalize the results. Objective: We assessed whether adults with a low BMR gain more weight than do adults with a high BMR who are living in a typical Western environment. Design: We extracted BMR, body-composition, demographic, and laboratory data from electronic databases of 757 volunteers who were participating in our research protocols at the Mayo Clinic between 1995 and 2012. Research study volunteers were always weight stable, had no acute illnesses and no confounding medication use, and were nonsmokers. The top and bottom 15th percentiles of BMR, adjusted for fat-free mass (FFM), fat mass, age, and sex, were identified. Follow-up electronic medical record system data were available for 163 subjects, which allowed us to determine their subsequent weight changes for ≥3 y (mean: ∼9.7 y). Results: By definition, the BMR was different in the high-BMR group (2001 ± 317 kcal/d; n = 86) than in the low-BMR group (1510 ± 222 kcal/d; n = 77), but they were comparable with respect to age, body mass index, FFM, and fat mass. Rates of weight gain were not greater in the bottom BMR group (0.3 ± 1.0 kg/y) than in the top BMR group (0.5 ± 1.5 kg/y) (P = 0.17). Conclusion: Adults with low BMRs did not gain more weight than did adults with high BMRs, implying that habitual differences in food intake or activity counterbalance variations in BMR as a risk factor for weight gain in a typical Western population. PMID:27581474

  20. Glucose Metabolism Gene Expression Patterns and Tumor Uptake of 18F-Fluorodeoxyglucose After Radiation Treatment

    Wilson, George D.; Thibodeau, Bryan J.; Fortier, Laura E.; Pruetz, Barbara L.; Galoforo, Sandra; Baschnagel, Andrew M.; Chunta, John; Oliver Wong, Ching Yee; Yan, Di; Marples, Brian; Huang, Jiayi

    2014-01-01

    Purpose: To investigate whether radiation treatment influences the expression of glucose metabolism genes and compromises the potential use of 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) as a tool to monitor the early response of head and neck cancer xenografts to radiation therapy (RT). Methods and Materials: Low passage head and neck squamous cancer cells (UT14) were injected to the flanks of female nu/nu mice to generate xenografts. After tumors reached a size of 500 mm 3 they were treated with either sham RT or 15 Gy in 1 fraction. At different time points, days 3, 9, and 16 for controls and days 4, 7, 12, 21, 30, and 40 after irradiation, 2 to 3 mice were assessed with dynamic FDG-PET acquisition over 2 hours. Immediately after the FDG-PET the tumors were harvested for global gene expression analysis and immunohistochemical evaluation of GLUT1 and HK2. Different analytic parameters were used to process the dynamic PET data. Results: Radiation had no effect on key genes involved in FDG uptake and metabolism but did alter other genes in the HIF1α and glucose transport–related pathways. In contrast to the lack of effect on gene expression, changes in the protein expression patterns of the key genes GLUT1/SLC2A1 and HK2 were observed after radiation treatment. The changes in GLUT1 protein expression showed some correlation with dynamic FDG-PET parameters, such as the kinetic index. Conclusion: 18 F-fluorodeoxyglucose positron emission tomography changes after RT would seem to represent an altered metabolic state and not a direct effect on the key genes regulating FDG uptake and metabolism

  1. Genetic variation in eleven phase I drug metabolism genes in an ethnically diverse population.

    Solus, Joseph F; Arietta, Brenda J; Harris, James R; Sexton, David P; Steward, John Q; McMunn, Chara; Ihrie, Patrick; Mehall, Janelle M; Edwards, Todd L; Dawson, Elliott P

    2004-10-01

    The extent of genetic variation found in drug metabolism genes and its contribution to interindividual variation in response to medication remains incompletely understood. To better determine the identity and frequency of variation in 11 phase I drug metabolism genes, the exons and flanking intronic regions of the cytochrome P450 (CYP) isoenzyme genes CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4 and CYP3A5 were amplified from genomic DNA and sequenced. A total of 60 kb of bi-directional sequence was generated from each of 93 human DNAs, which included Caucasian, African-American and Asian samples. There were 388 different polymorphisms identified. These included 269 non-coding, 45 synonymous and 74 non-synonymous polymorphisms. Of these, 54% were novel and included 176 non-coding, 14 synonymous and 21 non-synonymous polymorphisms. Of the novel variants observed, 85 were represented by single occurrences of the minor allele in the sample set. Much of the variation observed was from low-frequency alleles. Comparatively, these genes are variation-rich. Calculations measuring genetic diversity revealed that while the values for the individual genes are widely variable, the overall nucleotide diversity of 7.7 x 10(-4) and polymorphism parameter of 11.5 x 10(-4) are higher than those previously reported for other gene sets. Several independent measurements indicate that these genes are under selective pressure, particularly for polymorphisms corresponding to non-synonymous amino acid changes. There is relatively little difference in measurements of diversity among the ethnic groups, but there are large differences among the genes and gene subfamilies themselves. Of the three CYP subfamilies involved in phase I drug metabolism (1, 2, and 3), subfamily 2 displays the highest levels of genetic diversity.

  2. Study on the correlation between KCNJ11 gene polymorphism and metabolic syndrome in the elderly.

    Jiang, Fan; Liu, Ning; Chen, Xiao Zhuang; Han, Kun Yuan; Zhu, Cai Zhong

    2017-09-01

    The aim of the study was to examine the correlation between KCNJ11 gene polymorphism and metabolic syndrome in elderly patients. From January 2014 to January 2015, 54 elderly patients with metabolic syndrome were enrolled in this study as the observation group. During the same period, 46 healthy elderly individuals were enrolled in this study as the control group. KCNJ11 gene polymorphism (rs28502) was analyzed using polymerase chain reaction-restriction fragment length polymorphism. The expression levels of mRNA in different genotypes were detected using FQ-PCR. ELISA was used to evaluate the KCNJ11 protein expression in different genotypes. KCNJ11 gene polymorphism and metabolic syndrome was studied by measuring the blood pressure levels in patients with different genotypes. Three genotypes of KCNJ11 gene in rs28502 were CC, CT and TT. The CC, CT and TT genotype frequencies in healthy population were 8.5, 9.2 and 82.2%, respectively, while the genotype frequencies in patients with metabolic syndrome were 42.4, 49.8 and 7.8%, respectively. There were significant differences between groups (P≤0.05). However, the genotype frequencies of C/T in healthy individuals and metabolic syndrome patients were 35.3 and 38.3%, respectively. There were no significant differences between groups (P>0.05). FQ-PCR results showed that the KCNJ11 mRNA expression levels in the control and observation groups had no significant differences (P>0.05). However, the results obtained from ELISA analysis revealed that KCNJ11 protein expression level in the observation group was significantly higher than that in the control group (Pmetabolic syndrome in the elderly. Elderly patients with the CC and TT genotypes are more likely to develop metabolic syndrome.

  3. Predictive Genomic Analyses Inform the Basis for Vitamin Metabolism and Provisioning in Bacteria-Arthropod Endosymbioses.

    Serbus, Laura R; Rodriguez, Brian Garcia; Sharmin, Zinat; Momtaz, A J M Zehadee; Christensen, Steen

    2017-06-07

    The requirement of vitamins for core metabolic processes creates a unique set of pressures for arthropods subsisting on nutrient-limited diets. While endosymbiotic bacteria carried by arthropods have been widely implicated in vitamin provisioning, the underlying molecular mechanisms are not well understood. To address this issue, standardized predictive assessment of vitamin metabolism was performed in 50 endosymbionts of insects and arachnids. The results predicted that arthropod endosymbionts overall have little capacity for complete de novo biosynthesis of conventional or active vitamin forms. Partial biosynthesis pathways were commonly predicted, suggesting a substantial role in vitamin provisioning. Neither taxonomic relationships between host and symbiont, nor the mode of host-symbiont interaction were clear predictors of endosymbiont vitamin pathway capacity. Endosymbiont genome size and the synthetic capacity of nonsymbiont taxonomic relatives were more reliable predictors. We developed a new software application that also predicted that last-step conversion of intermediates into active vitamin forms may contribute further to vitamin biosynthesis by endosymbionts. Most instances of predicted vitamin conversion were paralleled by predictions of vitamin use. This is consistent with achievement of provisioning in some cases through upregulation of pathways that were retained for endosymbiont benefit. The predicted absence of other enzyme classes further suggests a baseline of vitamin requirement by the majority of endosymbionts, as well as some instances of putative mutualism. Adaptation of this workflow to analysis of other organisms and metabolic pathways will provide new routes for considering the molecular basis for symbiosis on a comprehensive scale. Copyright © 2017 Serbus et al.

  4. Predictive Genomic Analyses Inform the Basis for Vitamin Metabolism and Provisioning in Bacteria-Arthropod Endosymbioses

    Laura R. Serbus

    2017-06-01

    Full Text Available The requirement of vitamins for core metabolic processes creates a unique set of pressures for arthropods subsisting on nutrient-limited diets. While endosymbiotic bacteria carried by arthropods have been widely implicated in vitamin provisioning, the underlying molecular mechanisms are not well understood. To address this issue, standardized predictive assessment of vitamin metabolism was performed in 50 endosymbionts of insects and arachnids. The results predicted that arthropod endosymbionts overall have little capacity for complete de novo biosynthesis of conventional or active vitamin forms. Partial biosynthesis pathways were commonly predicted, suggesting a substantial role in vitamin provisioning. Neither taxonomic relationships between host and symbiont, nor the mode of host-symbiont interaction were clear predictors of endosymbiont vitamin pathway capacity. Endosymbiont genome size and the synthetic capacity of nonsymbiont taxonomic relatives were more reliable predictors. We developed a new software application that also predicted that last-step conversion of intermediates into active vitamin forms may contribute further to vitamin biosynthesis by endosymbionts. Most instances of predicted vitamin conversion were paralleled by predictions of vitamin use. This is consistent with achievement of provisioning in some cases through upregulation of pathways that were retained for endosymbiont benefit. The predicted absence of other enzyme classes further suggests a baseline of vitamin requirement by the majority of endosymbionts, as well as some instances of putative mutualism. Adaptation of this workflow to analysis of other organisms and metabolic pathways will provide new routes for considering the molecular basis for symbiosis on a comprehensive scale.

  5. In silico method for modelling metabolism and gene product expression at genome scale

    Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem; Portnoy, Vasiliy A.; Lewis, Nathan E.; Orth, Jeffrey D.; Rutledge, Alexandra C.; Smith, Richard D.; Adkins, Joshua N.; Zengler, Karsten; Palsson, Bernard O.

    2012-07-03

    Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

  6. Predicting growth of the healthy infant using a genome scale metabolic model.

    Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

    2017-01-01

    An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.

  7. Icariin Is A PPARα Activator Inducing Lipid Metabolic Gene Expression in Mice

    Yuan-Fu Lu

    2014-11-01

    Full Text Available Icariin is effective in the treatment of hyperlipidemia. To understand the effect of icariin on lipid metabolism, effects of icariin on PPARα and its target genes were investigated. Mice were treated orally with icariin at doses of 0, 100, 200, and 400 mg/kg, or clofibrate (500 mg/kg for five days. Liver total RNA was isolated and the expressions of PPARα and lipid metabolism genes were examined. PPARα and its marker genes Cyp4a10 and Cyp4a14 were induced 2-4 fold by icariin, and 4-8 fold by clofibrate. The fatty acid (FA binding and co-activator proteins Fabp1, Fabp4 and Acsl1 were increased 2-fold. The mRNAs of mitochondrial FA β-oxidation enzymes (Cpt1a, Acat1, Acad1 and Hmgcs2 were increased 2-3 fold. The mRNAs of proximal β-oxidation enzymes (Acox1, Ech1, and Ehhadh were also increased by icariin and clofibrate. The expression of mRNAs for sterol regulatory element-binding factor-1 (Srebf1 and FA synthetase (Fasn were unaltered by icariin. The lipid lysis genes Lipe and Pnpla2 were increased by icariin and clofibrate. These results indicate that icariin is a novel PPARα agonist, activates lipid metabolism gene expressions in liver, which could be a basis for its lipid-lowering effects and its beneficial effects against diabetes.

  8. Metabolism of oxycodone in human hepatocytes from different age groups and prediction of hepatic plasma clearance

    Timo eKorjamo

    2012-01-01

    Full Text Available Oxycodone is commonly used to treat severe pain in adults and children. It is extensively metabolized in the liver in adults, but the maturation of metabolism is not well understood. Our aim was to study the metabolism of oxycodone in cryopreserved human hepatocytes from different age groups (3 days, 2 and 5 months, 4 years, adult pool and predict hepatic plasma clearance of oxycodone using these data. Oxycodone (0.1, 1 and 10 µM was incubated with hepatocytes for 4 hours, and 1 µM oxycodone also with CYP3A inhibitor ketoconazole (1 µM. Oxycodone and noroxycodone concentrations were determined at several time points with liquid chromatography-mass spectrometry. In vitro clearance of oxycodone was used to predict hepatic plasma clearance, using the well-stirred model and published physiological parameters. Noroxycodone was the major metabolite in all batches and ketoconazole inhibited the metabolism markedly in most cases. A clear correlation between in vitro oxycodone clearance and CYP3A4 activity was observed. The predicted hepatic plasma clearances were typically much lower than the published median total plasma clearance from pharmacokinetic studies. In general, this in vitro to in vivo extrapolation method provides valuable information on the maturation of oxycodone metabolism that can be utilized in the design of clinical pharmacokinetic studies in infants and young children.

  9. Altered Levels of Aroma and Volatiles by Metabolic Engineering of Shikimate Pathway Genes in Tomato Fruits

    Vered Tzin

    2015-06-01

    Full Text Available The tomato (Solanum lycopersicum fruit is an excellent source of antioxidants, dietary fibers, minerals and vitamins and therefore has been referred to as a “functional food”. Ripe tomato fruits produce a large number of specialized metabolites including volatile organic compounds. These volatiles serve as key components of the tomato fruit flavor, participate in plant pathogen and herbivore defense, and are used to attract seed dispersers. A major class of specialized metabolites is derived from the shikimate pathway followed by aromatic amino acid biosynthesis of phenylalanine, tyrosine and tryptophan. We attempted to modify tomato fruit flavor by overexpressing key regulatory genes in the shikimate pathway. Bacterial genes encoding feedback-insensitive variants of 3-Deoxy-D-Arabino-Heptulosonate 7-Phosphate Synthase (DAHPS; AroG209-9 and bi-functional Chorismate Mutase/Prephenate Dehydratase (CM/PDT; PheA12 were expressed under the control of a fruit-specific promoter. We crossed these transgenes to generate tomato plants expressing both the AroG209 and PheA12 genes. Overexpression of the AroG209-9 gene had a dramatic effect on the overall metabolic profile of the fruit, including enhanced levels of multiple volatile and non-volatile metabolites. In contrast, the PheA12 overexpression line exhibited minor metabolic effects compared to the wild type fruit. Co-expression of both the AroG209-9 and PheA12 genes in tomato resulted overall in a similar metabolic effect to that of expressing only the AroG209-9 gene. However, the aroma ranking attributes of the tomato fruits from PheA12//AroG209-9 were unique and different from those of the lines expressing a single gene, suggesting a contribution of the PheA12 gene to the overall metabolic profile. We suggest that expression of bacterial genes encoding feedback-insensitive enzymes of the shikimate pathway in tomato fruits provides a useful metabolic engineering tool for the modification of

  10. Fat oxidation at rest predicts peak fat oxidation during exercise and metabolic phenotype in overweight men

    Rosenkilde, M; Nordby, P; Nielsen, L B

    2010-01-01

    OBJECTIVE: To elucidate if fat oxidation at rest predicts peak fat oxidation during exercise and/or metabolic phenotype in moderately overweight, sedentary men. DESIGN: Cross-sectional study.Subjects:We measured respiratory exchange ratio (RER) at rest in 44 moderately overweight, normotensive...... the International Diabetes Federation criteria, we found that there was a lower accumulation of metabolic risk factors in L-RER than in H-RER (1.6 vs 3.5, P=0.028), and no subjects in L-RER and four of eight subjects in H-RER had the metabolic syndrome. Resting RER was positively correlated with plasma...... triglycerides (Pexercise was positively correlated with plasma free fatty acid concentration at rest (Pexercise and a healthy metabolic...

  11. Aspergillus flavus Blast2GO gene ontology database: elevated growth temperature alters amino acid metabolism

    The availability of a representative gene ontology (GO) database is a prerequisite for a successful functional genomics study. Using online Blast2GO resources we constructed a GO database of Aspergillus flavus. Of the predicted total 13,485 A. flavus genes 8,987 were annotated with GO terms. The mea...

  12. Analysis of the Genome and Chromium Metabolism-Related Genes of Serratia sp. S2.

    Dong, Lanlan; Zhou, Simin; He, Yuan; Jia, Yan; Bai, Qunhua; Deng, Peng; Gao, Jieying; Li, Yingli; Xiao, Hong

    2018-05-01

    This study is to investigate the genome sequence of Serratia sp. S2. The genomic DNA of Serratia sp. S2 was extracted and the sequencing library was constructed. The sequencing was carried out by Illumina 2000 and complete genomic sequences were obtained. Gene function annotation and bioinformatics analysis were performed by comparing with the known databases. The genome size of Serratia sp. S2 was 5,604,115 bp and the G+C content was 57.61%. There were 5373 protein coding genes, and 3732, 3614, and 3942 genes were respectively annotated into the GO, KEGG, and COG databases. There were 12 genes related to chromium metabolism in the Serratia sp. S2 genome. The whole genome sequence of Serratia sp. S2 is submitted to the GenBank database with gene accession number of LNRP00000000. Our findings may provide theoretical basis for the subsequent development of new biotechnology to repair environmental chromium pollution.

  13. Dataset of the human homologues and orthologues of lipid-metabolic genes identified as DAF-16 targets their roles in lipid and energy metabolism

    Lavender Yuen-Nam Fan

    2017-04-01

    Full Text Available The data presented in this article are related to the review article entitled ‘Unravelling the role of fatty acid metabolism in cancer through the FOXO3-FOXM1 axis’ (Saavedra-Garcia et al., 2017 [24]. Here, we have matched the DAF-16/FOXO3 downstream genes with their respective human orthologues and reviewed the roles of these targeted genes in FA metabolism. The list of genes listed in this article are precisely selected from literature reviews based on their functions in mammalian FA metabolism. The nematode Caenorhabditis elegans gene orthologues of the genes are obtained from WormBase, the online biological database of C. elegans. This dataset has not been uploaded to a public repository yet.

  14. Dataset of the human homologues and orthologues of lipid-metabolic genes identified as DAF-16 targets their roles in lipid and energy metabolism.

    Fan, Lavender Yuen-Nam; Saavedra-García, Paula; Lam, Eric Wing-Fai

    2017-04-01

    The data presented in this article are related to the review article entitled 'Unravelling the role of fatty acid metabolism in cancer through the FOXO3-FOXM1 axis' (Saavedra-Garcia et al., 2017) [24]. Here, we have matched the DAF-16/FOXO3 downstream genes with their respective human orthologues and reviewed the roles of these targeted genes in FA metabolism. The list of genes listed in this article are precisely selected from literature reviews based on their functions in mammalian FA metabolism. The nematode Caenorhabditis elegans gene orthologues of the genes are obtained from WormBase, the online biological database of C. elegans. This dataset has not been uploaded to a public repository yet.

  15. Computational modeling to predict nitrogen balance during acute metabolic decompensation in patients with urea cycle disorders.

    MacLeod, Erin L; Hall, Kevin D; McGuire, Peter J

    2016-01-01

    Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. A validated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection.

  16. The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization.

    Elad Noor

    2016-11-01

    Full Text Available Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants, but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM, a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or

  17. False positive reduction in protein-protein interaction predictions using gene ontology annotations

    Lin Yen-Han

    2007-07-01

    Full Text Available Abstract Background Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results Gene Ontology (GO annotations were used to reduce false positive protein-protein interactions (PPI pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Conclusion Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially

  18. Predicting growth of the healthy infant using a genome scale metabolic model

    Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

    2017-01-01

    to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body...

  19. Early Posttransplant Tryptophan Metabolism Predicts Long-term Outcome of Human Kidney Transplantation

    Vavrincova-Yaghi, Diana; Seelen, Marc A.; Kema, Ido P.; Deelman, Leo E.; Heuvel, van den Marius; Breukelman, Henk; Van den Eynde, Benoit J.; Henning, Rob H.; van Goor, Harry; Sandovici, Maria

    Background. Chronic transplant dysfunction (CTD) is the leading cause of long-term loss of the renal allograft. So far, no single test is available to reliably predict the risk for CTD. Monitoring of tryptophan (trp) metabolism through indoleamine 2.3-dioxygenase (IDO) has been previously proposed

  20. C282Y-HFE gene variant affects cholesterol metabolism in human neuroblastoma cells.

    Ali-Rahmani, Fatima; Huang, Michael A; Schengrund, C-L; Connor, James R; Lee, Sang Y

    2014-01-01

    Although disruptions in the maintenance of iron and cholesterol metabolism have been implicated in several cancers, the association between variants in the HFE gene that is associated with cellular iron uptake and cholesterol metabolism has not been studied. The C282Y-HFE variant is a risk factor for different cancers, is known to affect sphingolipid metabolism, and to result in increased cellular iron uptake. The effect of this variant on cholesterol metabolism and its possible relevance to cancer phenotype was investigated using wild type (WT) and C282Y-HFE transfected human neuroblastoma SH-SY5Y cells. Expression of C282Y-HFE in SH-SY5Y cells resulted in a significant increase in total cholesterol as well as increased transcription of a number of genes involved in its metabolism compared to cells expressing WT-HFE. The marked increase in expression of NPC1L1 relative to that of most other genes, was accompanied by a significant increase in expression of NPC1, a protein that functions in cholesterol uptake by cells. Because inhibitors of cholesterol metabolism have been proposed to be beneficial for treating certain cancers, their effect on the viability of C282Y-HFE neuroblastoma cells was ascertained. C282Y-HFE cells were significantly more sensitive than WT-HFE cells to U18666A, an inhibitor of desmosterol Δ24-reductase the enzyme catalyzing the last step in cholesterol biosynthesis. This was not seen for simvastatin, ezetimibe, or a sphingosine kinase inhibitor. These studies indicate that cancers presenting in carriers of the C282Y-HFE allele might be responsive to treatment designed to selectively reduce cholesterol content in their tumor cells.

  1. Functional genomics tools applied to plant metabolism: a survey on plant respiration, its connections and the annotation of complex gene functions

    Wagner L. Araújo

    2012-09-01

    Full Text Available The application of post-genomic techniques in plant respiration studies has greatly improved our ability to assign functions to gene products. In addition it has also revealed previously unappreciated interactions between distal elements of metabolism. Such results have reinforced the need to consider plant respiratory metabolism as part of a complex network and making sense of such interactions will ultimately require the construction of predictive and mechanistic models. Transcriptomics, proteomics, metabolomics and the quantification of metabolic flux will be of great value in creating such models both by facilitating the annotation of complex gene function, determining their structure and by furnishing the quantitative data required to test them. In this review we highlight how these experimental approaches have contributed to our current understanding of plant respiratory metabolism and its interplay with associated process (e.g. photosynthesis, photorespiration and nitrogen metabolism. We also discuss how data from these techniques may be integrated, with the ultimate aim of identifying mechanisms that control and regulate plant respiration and discovering novel gene functions with potential biotechnological implications.

  2. Effects of Castration on Expression of Lipid Metabolism Genes in the Liver of Korean Cattle

    Myunggi Baik

    2015-01-01

    Full Text Available Castration induces the accumulation of body fat and deposition of intramuscular fat in Korean cattle, resulting in improved beef quality. However, little is known about the metabolic adaptations in the liver following castration. To understand changes in lipid metabolism following castration, hepatic expression levels of lipid metabolism genes were compared between Korean bulls and steers. Steers had higher (p<0.001 hepatic lipids contents and higher (p<0.01 mRNA levels of lipogenic acetyl-CoA carboxylase. This differential gene expression may, in part, contribute to increased hepatic lipid content following the castration of bulls. However, we found no differences in the hepatic expression levels of genes related to triglyceride synthesis (mitochondrial glycerol-3-phosphate acyltransferase, diacylglycerol O-acyltransferase 1 and 2 and fatty acid (FA oxidation (carnitine palmitoyltransferase 1A, C-4 to C-12 straight chain acyl-CoA dehydrogenase, very long chain acyl-CoA dehydrogenase between bulls and steers. No differences in gene expression for very-low-density lipoprotein (VLDL secretion, including apolipoprotein B mRNA and microsomal triglyceride transfer protein (MTTP protein, were observed in the liver although MTTP mRNA levels were higher in steers compared to bulls. In conclusion, FA synthesis may contribute to increased hepatic lipid deposition in steers following castration. However, hepatic lipid metabolism, including triglyceride synthesis, FA oxidation, and VLDL secretion, was not significantly altered by castration. Our results suggest that hepatic lipid metabolism does not significantly contribute to increased body fat deposition in steers following castration.

  3. Microbial Communities and Their Predicted Metabolic Functions in Growth Laminae of a Unique Large Conical Mat from Lake Untersee, East Antarctica

    Hyunmin Koo

    2017-08-01

    Full Text Available In this study, we report the distribution of microbial taxa and their predicted metabolic functions observed in the top (U1, middle (U2, and inner (U3 decadal growth laminae of a unique large conical microbial mat from perennially ice-covered Lake Untersee of East Antarctica, using NextGen sequencing of the 16S rRNA gene and bioinformatics tools. The results showed that the U1 lamina was dominated by cyanobacteria, specifically Phormidium sp., Leptolyngbya sp., and Pseudanabaena sp. The U2 and U3 laminae had high abundances of Actinobacteria, Verrucomicrobia, Proteobacteria, and Bacteroidetes. Closely related taxa within each abundant bacterial taxon found in each lamina were further differentiated at the highest taxonomic resolution using the oligotyping method. PICRUSt analysis, which determines predicted KEGG functional categories from the gene contents and abundances among microbial communities, revealed a high number of sequences belonging to carbon fixation, energy metabolism, cyanophycin, chlorophyll, and photosynthesis proteins in the U1 lamina. The functional predictions of the microbial communities in U2 and U3 represented signal transduction, membrane transport, zinc transport and amino acid-, carbohydrate-, and arsenic- metabolisms. The Nearest Sequenced Taxon Index (NSTI values processed through PICRUSt were 0.10, 0.13, and 0.11 for U1, U2, and U3 laminae, respectively. These values indicated a close correspondence with the reference microbial genome database, implying high confidence in the predicted metabolic functions of the microbial communities in each lamina. The distribution of microbial taxa observed in each lamina and their predicted metabolic functions provides additional insight into the complex microbial ecosystem at Lake Untersee, and lays the foundation for studies that will enhance our understanding of the mechanisms responsible for the formation of these unique mat structures and their evolutionary significance.

  4. Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

    Korkola, James E; Waldman, Frederic M; Blaveri, Ekaterina; DeVries, Sandy; Moore, Dan H II; Hwang, E Shelley; Chen, Yunn-Yi; Estep, Anne LH; Chew, Karen L; Jensen, Ronald H

    2007-01-01

    Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients

  5. Predicting metabolic syndrome using decision tree and support vector machine methods

    Farzaneh Karimi-Alavijeh

    2016-06-01

    Full Text Available BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS: This study aims to employ decision tree and support vector machine (SVM to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP, diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs, total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS: SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758, 0.74 (0.72 and 0.757 (0.739 in SVM (decision tree method. CONCLUSION: The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most

  6. Predicting metabolic syndrome using decision tree and support vector machine methods.

    Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

    2016-05-01

    Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According

  7. Variants of Insulin-Signaling Inhibitor Genes in Type 2 Diabetes and Related Metabolic Abnormalities

    Carlo de Lorenzo

    2013-01-01

    Full Text Available Insulin resistance has a central role in the pathogenesis of several metabolic diseases, including type 2 diabetes, obesity, glucose intolerance, metabolic syndrome, atherosclerosis, and cardiovascular diseases. Insulin resistance and related traits are likely to be caused by abnormalities in the genes encoding for proteins involved in the composite network of insulin-signaling; in this review we have focused our attention on genetic variants of insulin-signaling inhibitor molecules. These proteins interfere with different steps in insulin-signaling: ENPP1/PC-1 and the phosphatases PTP1B and PTPRF/LAR inhibit the insulin receptor activation; INPPL1/SHIP-2 hydrolyzes PI3-kinase products, hampering the phosphoinositide-mediated downstream signaling; and TRIB3 binds the serine-threonine kinase Akt, reducing its phosphorylation levels. While several variants have been described over the years for all these genes, solid evidence of an association with type 2 diabetes and related diseases seems to exist only for rs1044498 of the ENPP1 gene and for rs2295490 of the TRIB3 gene. However, overall the data recapitulated in this Review article may supply useful elements to interpret the results of novel, more technically advanced genetic studies; indeed it is becoming increasingly evident that genetic information on metabolic diseases should be interpreted taking into account the complex biological pathways underlying their pathogenesis.

  8. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

    Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

    2016-09-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. © 2016 American Society of Plant Biologists. All rights reserved.

  9. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions1

    Zuñiga, Cristal; Li, Chien-Ting; Zielinski, Daniel C.; Guarnieri, Michael T.; Antoniewicz, Maciek R.; Zengler, Karsten

    2016-01-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

  10. Comprehensive evaluation of one-carbon metabolism pathway gene variants and renal cell cancer risk.

    Todd M Gibson

    Full Text Available Folate and one-carbon metabolism are linked to cancer risk through their integral role in DNA synthesis and methylation. Variation in one-carbon metabolism genes, particularly MTHFR, has been associated with risk of a number of cancers in epidemiologic studies, but little is known regarding renal cancer.Tag single nucleotide polymorphisms (SNPs selected to produce high genomic coverage of 13 gene regions of one-carbon metabolism (ALDH1L1, BHMT, CBS, FOLR1, MTHFR, MTR, MTRR, SHMT1, SLC19A1, TYMS and the closely associated glutathione synthesis pathway (CTH, GGH, GSS were genotyped for 777 renal cell carcinoma (RCC cases and 1,035 controls in the Central and Eastern European Renal Cancer case-control study. Associations of individual SNPs (n = 163 with RCC risk were calculated using unconditional logistic regression adjusted for age, sex and study center. Minimum p-value permutation (Min-P tests were used to identify gene regions associated with risk, and haplotypes were evaluated within these genes.The strongest associations with RCC risk were observed for SLC19A1 (P(min-P = 0.03 and MTHFR (P(min-P = 0.13. A haplotype consisting of four SNPs in SLC19A1 (rs12483553, rs2838950, rs2838951, and rs17004785 was associated with a 37% increased risk (p = 0.02, and exploratory stratified analysis suggested the association was only significant among those in the lowest tertile of vegetable intake.To our knowledge, this is the first study to comprehensively examine variation in one-carbon metabolism genes in relation to RCC risk. We identified a novel association with SLC19A1, which is important for transport of folate into cells. Replication in other populations is required to confirm these findings.

  11. Homeobox gene Dlx-2 is implicated in metabolic stress-induced necrosis

    Lim Sung-Chul

    2011-09-01

    Full Text Available Abstract Background In contrast to tumor-suppressive apoptosis and autophagic cell death, necrosis promotes tumor progression by releasing the pro-inflammatory and tumor-promoting cytokine high mobility group box 1 (HMGB1, and its presence in tumor patients is associated with poor prognosis. Thus, necrosis has important clinical implications in tumor development; however, its molecular mechanism remains poorly understood. Results In the present study, we show that Distal-less 2 (Dlx-2, a homeobox gene of the Dlx family that is involved in embryonic development, is induced in cancer cell lines dependently of reactive oxygen species (ROS in response to glucose deprivation (GD, one of the metabolic stresses occurring in solid tumors. Increased Dlx-2 expression was also detected in the inner regions, which experience metabolic stress, of human tumors and of a multicellular tumor spheroid, an in vitro model of solid tumors. Dlx-2 short hairpin RNA (shRNA inhibited metabolic stress-induced increase in propidium iodide-positive cell population and HMGB1 and lactate dehydrogenase (LDH release, indicating the important role(s of Dlx-2 in metabolic stress-induced necrosis. Dlx-2 shRNA appeared to exert its anti-necrotic effects by preventing metabolic stress-induced increases in mitochondrial ROS, which are responsible for triggering necrosis. Conclusions These results suggest that Dlx-2 may be involved in tumor progression via the regulation of metabolic stress-induced necrosis.

  12. Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates.

    Glazier, Douglas S; Hirst, Andrew G; Atkinson, David

    2015-03-07

    Metabolism fuels all biological activities, and thus understanding its variation is fundamentally important. Much of this variation is related to body size, which is commonly believed to follow a 3/4-power scaling law. However, during ontogeny, many kinds of animals and plants show marked shifts in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR < 1), or the reverse, are associated with significant changes in body shape (indexed by bL = the scaling exponent of the relationship between body mass and body length). The observed inverse correlations between bR and bL are predicted by metabolic scaling theory that emphasizes resource/waste fluxes across external body surfaces, but contradict theory that emphasizes resource transport through internal networks. Geometric estimates of the scaling of surface area (SA) with body mass (bA) further show that ontogenetic shifts in bR and bA are positively correlated. These results support new metabolic scaling theory based on SA influences that may be applied to ontogenetic shifts in bR shown by many kinds of animals and plants. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  13. The metabolic syndrome: validity and utility of clinical definitions for cardiovascular disease and diabetes risk prediction.

    Cameron, Adrian

    2010-02-01

    The purpose of clinical definitions of the metabolic syndrome is frequently misunderstood. While the metabolic syndrome as a physiological process describes a clustering of numerous age-related metabolic abnormalities that together increase the risk for cardiovascular disease and type 2 diabetes, clinical definitions include obesity which is thought to be a cause rather than a consequence of metabolic disturbance, and several elements that are routinely measured in clinical practice, including high blood pressure, high blood glucose and dyslipidaemia. Obesity is frequently a central player in the development of the metabolic syndrome and should be considered a key component of clinical definitions. Previous clinical definitions have differed in the priority given to obesity. Perhaps more importantly than its role in a clinical definition, however, is obesity in isolation before the hallmarks of metabolic dysfunction that typify the syndrome have developed. This should be treated seriously as an opportunity to prevent the consequences of the global diabetes epidemic now apparent. Clinical definitions were designed to identify a population at high lifetime CVD and type 2 diabetes risk, but in the absence of several major risk factors for each condition, are not optimal risk prediction devices for either. Despite this, the metabolic syndrome has several properties that make it a useful construct, in conjunction with short-term risk prediction algorithms and sound clinical judgement, for the identification of those at high lifetime risk of CVD and diabetes. A recently published consensus definition provides some much needed clarity about what a clinical definition entails. Even this, however, remains a work in progress until more evidence becomes available, particularly in the area of ethnicity-specific waist cut-points. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

  14. Diverse and Abundant Secondary Metabolism Biosynthetic Gene Clusters in the Genomes of Marine Sponge Derived Streptomyces spp. Isolates

    Stephen A. Jackson

    2018-02-01

    Full Text Available The genus Streptomyces produces secondary metabolic compounds that are rich in biological activity. Many of these compounds are genetically encoded by large secondary metabolism biosynthetic gene clusters (smBGCs such as polyketide synthases (PKS and non-ribosomal peptide synthetases (NRPS which are modular and can be highly repetitive. Due to the repeats, these gene clusters can be difficult to resolve using short read next generation datasets and are often quite poorly predicted using standard approaches. We have sequenced the genomes of 13 Streptomyces spp. strains isolated from shallow water and deep-sea sponges that display antimicrobial activities against a number of clinically relevant bacterial and yeast species. Draft genomes have been assembled and smBGCs have been identified using the antiSMASH (antibiotics and Secondary Metabolite Analysis Shell web platform. We have compared the smBGCs amongst strains in the search for novel sequences conferring the potential to produce novel bioactive secondary metabolites. The strains in this study recruit to four distinct clades within the genus Streptomyces. The marine strains host abundant smBGCs which encode polyketides, NRPS, siderophores, bacteriocins and lantipeptides. The deep-sea strains appear to be enriched with gene clusters encoding NRPS. Marine adaptations are evident in the sponge-derived strains which are enriched for genes involved in the biosynthesis and transport of compatible solutes and for heat-shock proteins. Streptomyces spp. from marine environments are a promising source of novel bioactive secondary metabolites as the abundance and diversity of smBGCs show high degrees of novelty. Sponge derived Streptomyces spp. isolates appear to display genomic adaptations to marine living when compared to terrestrial strains.

  15. Analysis of Polymorphism of Angiotensin System Genes (ACE, AGTR1, and AGT) and Gene ITGB3 in Patients with Arterial Hypertension in Combination with Metabolic Syndrome.

    Zotova, T Yu; Kubanova, A P; Azova, M M; Aissa, A Ait; Gigani, O O; Frolov, V A

    2016-07-01

    Changes in the frequencies of genotypes and mutant alleles of ACE, AGTR1, AGT, and ITGB3 genes were analyzed in patients with arterial hypertension coupled with metabolic syndrome (N=15) and compared with population data and corresponding parameters in patients with isolated hypertension (N=15). Increased frequency of genotype ID of ACE gene (hypertension predictor) was confirmed for both groups. In case of isolated hypertension, M235M genotype (gene AGT) was more frequent, in case of hypertension combined with metabolic syndrome, the frequency of genotypes A1166C and C1166C of the gene AGTR1 was higher in comparison with population data. Comparison of mutant allele frequencies in the two groups showed that at the 90% significance level allele T of the AGT gene was more frequent in hypertension coupled with metabolic syndrome (OR=1.26) and genotype A1166A of the AGTR1 gene was more frequent in the group with isolated hypertension.

  16. Co-ordinate regulation of lactate metabolism genes in yeast: the role of the lactate permease gene JEN1.

    Lodi, T; Fontanesi, F; Guiard, B

    2002-01-01

    In the yeast Saccharomyces cerevisiae, the first step in lactate metabolism is its transport across the plasma membrane, a proton symport process mediated by the product of the gene JEN1. Under aerobic conditions, the expression of JEN1 is regulated by the carbon source: the gene is repressed by glucose and induced by non-fermentable substrates. JEN1 expression is also controlled by oxygen availability, but is unaffected by the absence of haem biosynthesis. JEN1 is negatively regulated by the repressors Mig1p and Mig2p, and requires Cat8p for full derepression. In this report we demonstrate that, in addition to these regulators, the Hap2/3/4/5 complex interacts specifically with a CAAT-box element in the JEN1 promoter, and acts to derepress JEN1 expression. We also provide evidence for transcriptional stimulation of JEN1 by the protein kinase Snf1p. Data are presented which provide a better understanding of the molecular mechanisms implicated in the co-regulation of genes involved in the metabolism of lactate.

  17. Effects of Radiation and Dietary Iron on Expression of Genes and Proteins Involved in Drug Metabolism

    Faust, K. M.; Wotring, V. E.

    2014-01-01

    Liver function, especially the rate of metabolic enzyme activities, determines the concentration of circulating drugs and the duration of their efficacy. Most pharmaceuticals are metabolized by the liver, and clinically-used medication doses are given with normal liver function in mind. A drug overdose can result in the case of a liver that is damaged and removing pharmaceuticals from the circulation at a rate slower than normal. Alternatively, if liver function is elevated and removing drugs from the system more quickly than usual, it would be as if too little drug had been given for effective treatment. Because of the importance of the liver in drug metabolism, we want to understand any effects of spaceflight on the enzymes of the liver. Dietary factors and exposure to radiation are aspects of spaceflight that are potential oxidative stressors and both can be modeled in ground experiments. In this experiment, we examined the effects of high dietary iron and low dose gamma radiation (individually and combined) on the gene expression of enzymes involved in drug metabolism, redox homeostasis, and DNA repair. METHODS All procedures were approved by the JSC Animal Care and Use Committee. Male Sprague-Dawley rats were divided into 4 groups (n=8); control, high Fe diet (650 mg iron/kg), radiation (fractionated 3 Gy exposure from a Cs- 137 source) and combined high Fe diet + radiation exposure. Animals were euthanized 24h after the last treatment of radiation; livers were removed immediately and flash -frozen in liquid nitrogen. Expression of genes thought to be involved in redox homeostasis, drug metabolism and DNA damage repair was measured by RT-qPCR. Where possible, protein expression of the same genes was measured by western blotting. All data are expressed as % change in expression normalized to reference gene expression; comparisons were then made of each treatment group to the sham exposed/ normal diet control group. Data was considered significant at phigh Fe

  18. Uncovering transcriptional regulation of glycerol metabolism in Aspergilli through genome-wide gene expression data anlysis

    Salazar, Margarita Pena; Vongsangnak, Wanwipa; Panagiotou, Gianni

    2009-01-01

    Glycerol is catabolized by a wide range of microorganisms including Aspergillus species. To identify the transcriptional regulation of glycerol metabolism in Aspergillus, we analyzed data from triplicate batch fermentations of three different Aspergilli (Aspergillus nidulans, Aspergillus oryzae...... and Aspergillus niger) with glucose and glycerol as carbon sources. Protein comparisons and cross-analysis with gene expression data of all three species resulted in the identification of 88 genes having a conserved response across the three Aspergilli. A promoter analysis of the up-regulated genes led...... to the identification of a conserved binding site for a putative regulator to be 5′-TGCGGGGA-3′, a binding site that is similar to the binding site for Adr1 in yeast and humans. We show that this Adr1 consensus binding sequence was over-represented on promoter regions of several genes in A. nidulans, A. oryzae and A...

  19. Increased missense mutation burden of Fatty Acid metabolism related genes in nunavik inuit population.

    Zhou, Sirui; Xiong, Lan; Xie, Pingxing; Ambalavanan, Amirthagowri; Bourassa, Cynthia V; Dionne-Laporte, Alexandre; Spiegelman, Dan; Turcotte Gauthier, Maude; Henrion, Edouard; Diallo, Ousmane; Dion, Patrick A; Rouleau, Guy A

    2015-01-01

    Nunavik Inuit (northern Quebec, Canada) reside along the arctic coastline where for generations their daily energy intake has mainly been derived from animal fat. Given this particular diet it has been hypothesized that natural selection would lead to population specific allele frequency differences and unique variants in genes related to fatty acid metabolism. A group of genes, namely CPT1A, CPT1B, CPT1C, CPT2, CRAT and CROT, encode for three carnitine acyltransferases that are important for the oxidation of fatty acids, a critical step in their metabolism. Exome sequencing and SNP array genotyping were used to examine the genetic variations in the six genes encoding for the carnitine acyltransferases in 113 Nunavik Inuit individuals. Altogether ten missense variants were found in genes CPT1A, CPT1B, CPT1C, CPT2 and CRAT, including three novel variants and one Inuit specific variant CPT1A p.P479L (rs80356779). The latter has the highest frequency (0.955) compared to other Inuit populations. We found that by comparison to Asians or Europeans, the Nunavik Inuit have an increased mutation burden in CPT1A, CPT2 and CRAT; there is also a high level of population differentiation based on carnitine acyltransferase gene variations between Nunavik Inuit and Asians. The increased number and frequency of deleterious variants in these fatty acid metabolism genes in Nunavik Inuit may be the result of genetic adaptation to their diet and/or the extremely cold climate. In addition, the identification of these variants may help to understand some of the specific health risks of Nunavik Inuit.

  20. Metabolism of very long-chain Fatty acids: genes and pathophysiology.

    Sassa, Takayuki; Kihara, Akio

    2014-02-01

    Fatty acids (FAs) are highly diverse in terms of carbon (C) chain-length and number of double bonds. FAs with C>20 are called very long-chain fatty acids (VLCFAs). VLCFAs are found not only as constituents of cellular lipids such as sphingolipids and glycerophospholipids but also as precursors of lipid mediators. Our understanding on the function of VLCFAs is growing in parallel with the identification of enzymes involved in VLCFA synthesis or degradation. A variety of inherited diseases, such as ichthyosis, macular degeneration, myopathy, mental retardation, and demyelination, are caused by mutations in the genes encoding VLCFA metabolizing enzymes. In this review, we describe mammalian VLCFAs by highlighting their tissue distribution and metabolic pathways, and we discuss responsible genes and enzymes with reference to their roles in pathophysiology.

  1. Substrate availability and transcriptional regulation of metabolic genes in human skeletal muscle during recovery from exercise

    Pilegaard, Henriette; Osada, Takuya; Andersen, Lisbeth Tingsted

    2005-01-01

    before exercise and 2, 5, 8, and 24 hours after exercise. Muscle glycogen was restored to near resting levels within 5 hours in the HC trial, but remained depressed through 24 hours in the LC trial. During the 2- to 8-hour recovery period, leg glucose uptake was 5- to 15-fold higher with HC ingestion......In skeletal muscle of humans, transcription of several metabolic genes is transiently induced during recovery from exercise when no food is consumed. To determine the potential influence of substrate availability on the transcriptional regulation of metabolic genes during recovery from exercise, 9...... male subjects (aged 22-27) completed 75 minutes of cycling exercise at 75% V¿o2max on 2 occasions, consuming either a high-carbohydrate (HC) or low-carbohydrate (LC) diet during the subsequent 24 hours of recovery. Nuclei were isolated and tissue frozen from vastus lateralis muscle biopsies obtained...

  2. ALK7 Gene Polymorphism is Associated with Metabolic Syndrome Risk and Cardiovascular Remodeling

    Zhang, Wenchao; Wang, Hui; Zhang, Wei [Key Laboratory of Cardiovascular Remodeling and Function Research Chinese Ministry of Education and Chinese Ministry of Public Health, Qilu Hospital of Shandong University, Jinan (China); Lv, Ruijuan [Department of Emergency, Qilu Hospital of Shandong University, Jinan (China); Wang, Zhihao [Key Laboratory of Cardiovascular Remodeling and Function Research Chinese Ministry of Education and Chinese Ministry of Public Health, Qilu Hospital of Shandong University, Jinan (China); Department of Geriatrics, Qilu Hospital of Shandong University, Jinan (China); Shang, Yuanyuan; Zhang, Yun; Zhong, Ming [Key Laboratory of Cardiovascular Remodeling and Function Research Chinese Ministry of Education and Chinese Ministry of Public Health, Qilu Hospital of Shandong University, Jinan (China); Chen, Yuguo; Tang, Mengxiong, E-mail: tangmengxiongsdu8@163.com [Key Laboratory of Cardiovascular Remodeling and Function Research Chinese Ministry of Education and Chinese Ministry of Public Health, Qilu Hospital of Shandong University, Jinan (China); Department of Emergency, Qilu Hospital of Shandong University, Jinan (China)

    2013-08-15

    Activin receptor-like kinase 7 (ALK7) is a type I receptor for the TGF-β superfamily and has recently been demonstrated to play an important role in the maintenance of metabolic homeostasis. To investigate the association of the ALK7 gene polymorphism with metabolic syndrome (MetS) and cardiovascular remodeling in MetS patients. The single nucleotide polymorphism rs13010956 in the ALK7 gene was genotyped in 351 Chinese subjects undergoing carotid and cardiac ultrasonography. The associations of the ALK7 gene polymorphism with the MetS phenotype, MetS parameters, and cardiovascular ultrasonic features were analyzed. The rs13010956 polymorphism in the ALK7 gene was found to be significantly associated with the MetS phenotype in females (p < 0.05) and was also significantly associated with blood pressure in the total (p < 0.05) and female populations (p < 0.01). Further analysis revealed that rs13010956 was associated with mean intima-media thickness of the carotid arteries in females (p < 0.05). After control for body mass index, blood pressure, fasting blood glucose, and triglycerides, rs13010956 was also found to be significantly associated with left ventricular mass index in the total (p < 0.05) and female populations (p < 0.05). Our findings suggested that the ALK7 gene polymorphism rs13010956 was significantly associated with MetS risk in females and may be involved in cardiovascular remodeling in MetS patients.

  3. Assembly and multiple gene expression of thermophilic enzymes in Escherichia coli for in vitro metabolic engineering.

    Ninh, Pham Huynh; Honda, Kohsuke; Sakai, Takaaki; Okano, Kenji; Ohtake, Hisao

    2015-01-01

    In vitro reconstitution of an artificial metabolic pathway is an emerging approach for the biocatalytic production of industrial chemicals. However, several enzymes have to be separately prepared (and purified) for the construction of an in vitro metabolic pathway, thereby limiting the practical applicability of this approach. In this study, genes encoding the nine thermophilic enzymes involved in a non-ATP-forming chimeric glycolytic pathway were assembled in an artificial operon and co-expressed in a single recombinant Escherichia coli strain. Gene expression levels of the thermophilic enzymes were controlled by their sequential order in the artificial operon. The specific activities of the recombinant enzymes in the cell-free extract of the multiple-gene-expression E. coli were 5.0-1,370 times higher than those in an enzyme cocktail prepared from a mixture of single-gene-expression strains, in each of which a single one of the nine thermophilic enzymes was overproduced. Heat treatment of a crude extract of the multiple-gene-expression cells led to the denaturation of indigenous proteins and one-step preparation of an in vitro synthetic pathway comprising only a limited number of thermotolerant enzymes. Coupling this in vitro pathway with other thermophilic enzymes including the H2 O-forming NADH oxidase or the malate/lactate dehydrogenase facilitated one-pot conversion of glucose to pyruvate or lactate, respectively. © 2014 Wiley Periodicals, Inc.

  4. ALK7 Gene Polymorphism is Associated with Metabolic Syndrome Risk and Cardiovascular Remodeling

    Zhang, Wenchao; Wang, Hui; Zhang, Wei; Lv, Ruijuan; Wang, Zhihao; Shang, Yuanyuan; Zhang, Yun; Zhong, Ming; Chen, Yuguo; Tang, Mengxiong

    2013-01-01

    Activin receptor-like kinase 7 (ALK7) is a type I receptor for the TGF-β superfamily and has recently been demonstrated to play an important role in the maintenance of metabolic homeostasis. To investigate the association of the ALK7 gene polymorphism with metabolic syndrome (MetS) and cardiovascular remodeling in MetS patients. The single nucleotide polymorphism rs13010956 in the ALK7 gene was genotyped in 351 Chinese subjects undergoing carotid and cardiac ultrasonography. The associations of the ALK7 gene polymorphism with the MetS phenotype, MetS parameters, and cardiovascular ultrasonic features were analyzed. The rs13010956 polymorphism in the ALK7 gene was found to be significantly associated with the MetS phenotype in females (p < 0.05) and was also significantly associated with blood pressure in the total (p < 0.05) and female populations (p < 0.01). Further analysis revealed that rs13010956 was associated with mean intima-media thickness of the carotid arteries in females (p < 0.05). After control for body mass index, blood pressure, fasting blood glucose, and triglycerides, rs13010956 was also found to be significantly associated with left ventricular mass index in the total (p < 0.05) and female populations (p < 0.05). Our findings suggested that the ALK7 gene polymorphism rs13010956 was significantly associated with MetS risk in females and may be involved in cardiovascular remodeling in MetS patients

  5. GABA metabolism pathway genes, UGA1 and GAD1, regulate replicative lifespan in Saccharomycescerevisiae

    Kamei, Yuka; Tamura, Takayuki [Department of Bioscience, Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, 1266 Tamura, Nagahama, Shiga 526-0829 (Japan); Yoshida, Ryo [Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871 (Japan); Ohta, Shinji [Department of Bioscience, Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, 1266 Tamura, Nagahama, Shiga 526-0829 (Japan); Fukusaki, Eiichiro [Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871 (Japan); Mukai, Yukio, E-mail: y_mukai@nagahama-i-bio.ac.jp [Department of Bioscience, Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, 1266 Tamura, Nagahama, Shiga 526-0829 (Japan)

    2011-04-01

    Highlights: {yields}We demonstrate that two genes in the yeast GABA metabolism pathway affect aging. {yields} Deletion of the UGA1 or GAD1 genes extends replicative lifespan. {yields} Addition of GABA to wild-type cultures has no effect on lifespan. {yields} Intracellular GABA levels do not differ in longevity mutants and wild-type cells. {yields} Levels of tricarboxylic acid cycle intermediates positively correlate with lifespan. -- Abstract: Many of the genes involved in aging have been identified in organisms ranging from yeast to human. Our previous study showed that deletion of the UGA3 gene-which encodes a zinc-finger transcription factor necessary for {gamma}-aminobutyric acid (GABA)-dependent induction of the UGA1 (GABA aminotransferase), UGA2 (succinate semialdehyde dehydrogenase), and UGA4 (GABA permease) genes-extends replicative lifespan in the budding yeast Saccharomycescerevisiae. Here, we found that deletion of UGA1 lengthened the lifespan, as did deletion of UGA3; in contrast, strains with UGA2 or UGA4 deletions exhibited no lifespan extension. The {Delta}uga1 strain cannot deaminate GABA to succinate semialdehyde. Deletion of GAD1, which encodes the glutamate decarboxylase that converts glutamate into GABA, also increased lifespan. Therefore, two genes in the GABA metabolism pathway, UGA1 and GAD1, were identified as aging genes. Unexpectedly, intracellular GABA levels in mutant cells (except for {Delta}uga2 cells) did not differ from those in wild-type cells. Addition of GABA to culture media, which induces transcription of the UGA structural genes, had no effect on replicative lifespan of wild-type cells. Multivariate analysis of {sup 1}H nuclear magnetic resonance spectra for the whole-cell metabolite levels demonstrated a separation between long-lived and normal-lived strains. Gas chromatography-mass spectrometry analysis of identified metabolites showed that levels of tricarboxylic acid cycle intermediates positively correlated with lifespan

  6. GABA metabolism pathway genes, UGA1 and GAD1, regulate replicative lifespan in Saccharomycescerevisiae

    Kamei, Yuka; Tamura, Takayuki; Yoshida, Ryo; Ohta, Shinji; Fukusaki, Eiichiro; Mukai, Yukio

    2011-01-01

    Highlights: →We demonstrate that two genes in the yeast GABA metabolism pathway affect aging. → Deletion of the UGA1 or GAD1 genes extends replicative lifespan. → Addition of GABA to wild-type cultures has no effect on lifespan. → Intracellular GABA levels do not differ in longevity mutants and wild-type cells. → Levels of tricarboxylic acid cycle intermediates positively correlate with lifespan. -- Abstract: Many of the genes involved in aging have been identified in organisms ranging from yeast to human. Our previous study showed that deletion of the UGA3 gene-which encodes a zinc-finger transcription factor necessary for γ-aminobutyric acid (GABA)-dependent induction of the UGA1 (GABA aminotransferase), UGA2 (succinate semialdehyde dehydrogenase), and UGA4 (GABA permease) genes-extends replicative lifespan in the budding yeast Saccharomycescerevisiae. Here, we found that deletion of UGA1 lengthened the lifespan, as did deletion of UGA3; in contrast, strains with UGA2 or UGA4 deletions exhibited no lifespan extension. The Δuga1 strain cannot deaminate GABA to succinate semialdehyde. Deletion of GAD1, which encodes the glutamate decarboxylase that converts glutamate into GABA, also increased lifespan. Therefore, two genes in the GABA metabolism pathway, UGA1 and GAD1, were identified as aging genes. Unexpectedly, intracellular GABA levels in mutant cells (except for Δuga2 cells) did not differ from those in wild-type cells. Addition of GABA to culture media, which induces transcription of the UGA structural genes, had no effect on replicative lifespan of wild-type cells. Multivariate analysis of 1 H nuclear magnetic resonance spectra for the whole-cell metabolite levels demonstrated a separation between long-lived and normal-lived strains. Gas chromatography-mass spectrometry analysis of identified metabolites showed that levels of tricarboxylic acid cycle intermediates positively correlated with lifespan extension. These results strongly suggest

  7. Predictive gene testing for Huntington disease and other neurodegenerative disorders.

    Wedderburn, S; Panegyres, P K; Andrew, S; Goldblatt, J; Liebeck, T; McGrath, F; Wiltshire, M; Pestell, C; Lee, J; Beilby, J

    2013-12-01

    Controversies exist around predictive testing (PT) programmes in neurodegenerative disorders. This study sets out to answer the following questions relating to Huntington disease (HD) and other neurodegenerative disorders: differences between these patients in their PT journeys, why and when individuals withdraw from PT, and decision-making processes regarding reproductive genetic testing. A case series analysis of patients having PT from the multidisciplinary Western Australian centre for PT over the past 20 years was performed using internationally recognised guidelines for predictive gene testing in neurodegenerative disorders. Of 740 at-risk patients, 518 applied for PT: 466 at risk of HD, 52 at risk of other neurodegenerative disorders - spinocerebellar ataxias, hereditary prion disease and familial Alzheimer disease. Thirteen percent withdrew from PT - 80.32% of withdrawals occurred during counselling stages. Major withdrawal reasons related to timing in the patients' lives or unknown as the patient did not disclose the reason. Thirty-eight HD individuals had reproductive genetic testing: 34 initiated prenatal testing (of which eight withdrew from the process) and four initiated pre-implantation genetic diagnosis. There was no recorded or other evidence of major psychological reactions or suicides during PT. People withdrew from PT in relation to life stages and reasons that are unknown. Our findings emphasise the importance of: (i) adherence to internationally recommended guidelines for PT; (ii) the role of the multidisciplinary team in risk minimisation; and (iii) patient selection. © 2013 The Authors; Internal Medicine Journal © 2013 Royal Australasian College of Physicians.

  8. Characterization of Genes Encoding Key Enzymes Involved in Anthocyanin Metabolism of Kiwifruit during Storage Period

    Li, Boqiang; Xia, Yongxiu; Wang, Yuying; Qin, Guozheng; Tian, Shiping

    2017-01-01

    ‘Hongyang’ is a red fleshed kiwifruit with high anthocyanin content. In this study, we mainly investigated effects of different temperatures (25 and 0°C) on anthocyanin biosynthesis in harvested kiwifruit, and characterized the genes encoding key enzymes involved in anthocyanin metabolism, as well as evaluated the mode of the action, by which low temperature regulates anthocyanin accumulation in ‘Hongyang’ kiwifruit during storage period. The results showed that low temperature could effectiv...

  9. Association Study between Ghrelin Gene Polymorphism and Metabolic Syndrome in a Han Chinese Population.

    You, Yueyue; Yu, Yaqin; Wu, Yanhua; Rao, Wenwang; Zhang, Yangyu; Liu, Yingyu; Yang, Guang; Fu, Yingli; Shi, Jieping; Kou, Changgui

    2017-01-01

    Ghrelin, in humans, is a hormone secreted from the stomach with an orexigenic effect, which is good for digestion and absorption, as well as regulating physical growth, metabolism, and energy balance. It is also involved in the development of metabolic syndrome (MetS) and type 2 diabetes mellitus (T2DM). This study assessed the association between single nucleotide variants of the GHRL gene and the risk of metabolic syndrome in a Han Chinese population. A case-control study was performed on 3780 Han Chinese comprising 1813 MetS cases and 1967 controls. Three missense polymorphisms in GHRL (rs26802, rs10490816, and rs696217) were selected, and the association between these polymorphisms and the risk of MetS was investigated. Metabolic syndrome was defined according to the criteria of the International Diabetes Federation (IDF). Using Pearson's 2 test, we found that there were no significant differences in genotype distributions and allele frequencies between cases and controls (all p > 0.05). There were also no significant differences in haplotype distributions between MetS cases and healthy controls. Furthermore, we confirmed that rs26802 of the GHRL gene is associated with body mass index (BMI), waist circumference, systolic blood pressure (SBP), and fasting glucose; rs10490816 is associated with triglycerides (TG) and total cholesterol (TC); while rs696217 is associated with hip circumference and fasting glucose. We concluded that mutations in the GHRL gene did not confer risk for MetS in our study population. Therefore, functional analysis and replication studies in other populations are needed to further investigate the exact role of the GHRL gene in MetS.

  10. Conservation of lipid metabolic gene transcriptional regulatory networks in fish and mammals.

    Carmona-Antoñanzas, Greta; Tocher, Douglas R; Martinez-Rubio, Laura; Leaver, Michael J

    2014-01-15

    Lipid content and composition in aquafeeds have changed rapidly as a result of the recent drive to replace ecologically limited marine ingredients, fishmeal and fish oil (FO). Terrestrial plant products are the most economic and sustainable alternative; however, plant meals and oils are devoid of physiologically important cholesterol and long-chain polyunsaturated fatty acids (LC-PUFA), eicosapentaenoic (EPA), docosahexaenoic (DHA) and arachidonic (ARA) acids. Although replacement of dietary FO with vegetable oil (VO) has little effect on growth in Atlantic salmon (Salmo salar), several studies have shown major effects on the activity and expression of genes involved in lipid homeostasis. In vertebrates, sterols and LC-PUFA play crucial roles in lipid metabolism by direct interaction with lipid-sensing transcription factors (TFs) and consequent regulation of target genes. The primary aim of the present study was to elucidate the role of key TFs in the transcriptional regulation of lipid metabolism in fish by transfection and overexpression of TFs. The results show that the expression of genes of LC-PUFA biosynthesis (elovl and fads2) and cholesterol metabolism (abca1) are regulated by Lxr and Srebp TFs in salmon, indicating highly conserved regulatory mechanism across vertebrates. In addition, srebp1 and srebp2 mRNA respond to replacement of dietary FO with VO. Thus, Atlantic salmon adjust lipid metabolism in response to dietary lipid composition through the transcriptional regulation of gene expression. It may be possible to further increase efficient and effective use of sustainable alternatives to marine products in aquaculture by considering these important molecular interactions when formulating diets. © 2013.

  11. Transcriptional expression changes of glucose metabolism genes after exercise in thoroughbred horses.

    Gim, Jeong-An; Ayarpadikannan, Selvam; Eo, Jungwoo; Kwon, Yun-Jeong; Choi, Yuri; Lee, Hak-Kyo; Park, Kyung-Do; Yang, Young Mok; Cho, Byung-Wook; Kim, Heui-Soo

    2014-08-15

    Physical exercise induces gene expression changes that trigger glucose metabolism pathways in organisms. In the present study, we monitored the expression levels of LDHA (lactate dehydrogenase) and GYS1 (glycogen synthase 1) in the blood, to confirm the roles of these genes in exercise physiology. LDHA and GYS1 are related to glucose metabolism and fatigue recovery, and these processes could elicit economically important traits in racehorses. We collected blood samples from three retired thoroughbred racehorses, pre-exercise and immediately after 30 min of exercise. We extracted total RNA and small RNA (≤ 200 nucleotide-long) from the blood, and assessed the expression levels of LDHA, GYS1, and microRNAs (miRNAs), by using qRT-PCR. We showed that LDHA and GYS1 were down-regulated, whereas eca-miR-33a and miR-17 were up-regulated, after exercise. We used sequences from the 3' UTR of LDHA and GYS1, containing eca-miR-33a and miR-17 binding sites, to observe the down-regulation activity of each gene expression. We observed that the two miRNAs, namely, eca-miR-33a and miR-17, inhibited LDHA and GYS1 expression via binding to the 3' UTR sequences of each gene. Our results indicate that eca-miR-33a and miR-17 play important roles in the glucose metabolism pathway. In addition, our findings provide a basis for further investigation of the exercise metabolism of racehorses. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Network-based prediction and knowledge mining of disease genes.

    Carson, Matthew B; Lu, Hui

    2015-01-01

    In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident. We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship. Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network

  13. Association of gene polymorphism of the fat mass and obesity associated gene with metabolic syndrome: a retrospective cohort study in Japanese workers.

    Kawajiri, Tomoka; Osaki, Yoneatsu; Kishimoto, Takuji

    2012-06-01

    To investigate whether gene polymorphism of the fat mass and obesity associated gene (FTO) is associated with metabolic syndrome (MS), we used two MS criteria, the National Cholesterol Education Program-Adult Treatment panel III (NCEP-ATPIII) definition in 2003 and the Japanese definition in 2005. Subjects were respectively 859 and 865 Japanese workers at a company in Shimane Prefecture, Japan. They were non-MS individuals in 1998 and had regular health checkups between 1998 and 2006. The Cox proportional hazard regression was used to predict MS. Three SNPs in the FTO, rs9939609, rs1121980 and rs1558902, were genotyped by the TaqMan PCR assay and a retrospective study was performed. The three SNPs in the FTO were significantly associated with body mass index, and rs1121980 and rs1558902 were associated with fasting plasma glucose. MS defined by the NCEP-ATPIII definition was significantly associated with additive and dominant models of rs9939609 and rs1121980, and the dominant model of rs1558902, even after adjusting for confounding factors such as age, sex and lifestyle. MS defined by the Japanese definition was significantly associated with the additive model of rs1121980 and additive and dominant models of rs1558902 in multivariate analysis. These results suggested that FTO gene polymorphisms, rs9939609, rs1121980 and rs1558902, were associated with an increased risk of MS among Japanese workers.

  14. Prediction of the metabolic cost of walking with and without loads.

    Duggan, A; Haisman, M F

    1992-04-01

    Measurement of the metabolic cost of walking inconveniences subjects, and requires skilled technical support and expensive equipment. These factors have stimulated interest in predictive equations. The present study assessed existing equations. Under each of 17 combinations of gradient (0-6%) and carried load (4.1-37.4 kg), 7-12 men undertook treadmill walking at 1.67 m/s. Measured oxygen consumption and respiratory exchange ratio were used to calculate metabolic rate (MRobserved). Metabolic rate was also predicted from the equation of Pandolf et al. (1977) (MRpandolf) and, where appropriate, from another five equations relating to walking without loads. MRobserved and MRpandolf did not differ significantly (p greater than 0.05) under any combination of gradient and load. The overall mean MRobserved and MRpandolf of 609 W and 602 W, respectively, also did not differ significantly (p greater than 0.05). These variables were highly correlated (r = 0.94) with a standard deviation about the prediction error of 47 W. For level walking without loads, the mean predictions from the equations of Pandolf et al. (1977) and Cotes and Meade (1960) did not differ significantly (p greater than 0.05) from the mean MRobserved of 428 Watts, but four other equations overestimated by 17-74 W. In conclusion, the Pandolf et al. (1977) equation has given good results across the range of combinations of load and gradient tested, and the errors are considered acceptable for most practical purposes.

  15. Metabolism

    ... Are More Common in People With Type 1 Diabetes Metabolic Syndrome Your Child's Weight Healthy Eating Endocrine System Blood Test: Basic Metabolic Panel (BMP) Activity: Endocrine System Growth Disorders Diabetes Center Thyroid Disorders Your Endocrine System Movie: Endocrine ...

  16. Rescue of Metabolic Alterations in AR113Q Skeletal Muscle by Peripheral Androgen Receptor Gene Silencing

    Elisa Giorgetti

    2016-09-01

    Full Text Available Spinal and bulbar muscular atrophy (SBMA, a progressive degenerative disorder, is caused by a CAG/glutamine expansion in the androgen receptor (polyQ AR. Recent studies demonstrate that skeletal muscle is an important site of toxicity that contributes to the SBMA phenotype. Here, we sought to identify critical pathways altered in muscle that underlie disease manifestations in AR113Q mice. This led to the unanticipated identification of gene expression changes affecting regulators of carbohydrate metabolism, similar to those triggered by denervation. AR113Q muscle exhibits diminished glycolysis, altered mitochondria, and an impaired response to exercise. Strikingly, the expression of genes regulating muscle energy metabolism is rescued following peripheral polyQ AR gene silencing by antisense oligonucleotides (ASO, a therapeutic strategy that alleviates disease. Our data establish the occurrence of a metabolic imbalance in SBMA muscle triggered by peripheral expression of the polyQ AR and indicate that alterations in energy utilization contribute to non-neuronal disease manifestations.

  17. The Association of Polymorphisms in Leptin/Leptin Receptor Genes and Ghrelin/Ghrelin Receptor Genes With Overweight/Obesity and the Related Metabolic Disturbances: A Review

    Ghalandari; Hosseini-Esfahani; Mirmiran

    2015-01-01

    Context Leptin and ghrelin are two important appetite and energy balance-regulating peptides. Common polymorphisms in the genes coding these peptides and their related receptors are shown to be associated with body weight, different markers of obesity and metabolic abnormalities. This review article aims to investigate the association of common polymorphisms of these genes with overweight/obesity and the metabolic disturbances related to it. E...

  18. Blood lead levels, iron metabolism gene polymorphisms and homocysteine: a gene-environment interaction study.

    Kim, Kyoung-Nam; Lee, Mee-Ri; Lim, Youn-Hee; Hong, Yun-Chul

    2017-12-01

    Homocysteine has been causally associated with various adverse health outcomes. Evidence supporting the relationship between lead and homocysteine levels has been accumulating, but most prior studies have not focused on the interaction with genetic polymorphisms. From a community-based prospective cohort, we analysed 386 participants (aged 41-71 years) with information regarding blood lead and plasma homocysteine levels. Blood lead levels were measured between 2001 and 2003, and plasma homocysteine levels were measured in 2007. Interactions of lead levels with 42 genotyped single-nucleotide polymorphisms (SNPs) in five genes ( TF , HFE , CBS , BHMT and MTR ) were assessed via a 2-degree of freedom (df) joint test and a 1-df interaction test. In secondary analyses using imputation, we further assessed 58 imputed SNPs in the TF and MTHFR genes. Blood lead concentrations were positively associated with plasma homocysteine levels (p=0.0276). Six SNPs in the TF and MTR genes were screened using the 2-df joint test, and among them, three SNPs in the TF gene showed interactions with lead with respect to homocysteine levels through the 1-df interaction test (plead levels. Blood lead levels were positively associated with plasma homocysteine levels measured 4-6 years later, and three SNPs in the TF gene modified the association. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  19. Expression profiling of Crambe abyssinica under arsenate stress identifies genes and gene networks involved in arsenic metabolism and detoxification

    Kandasamy Suganthi

    2010-06-01

    Full Text Available Abstract Background Arsenic contamination is widespread throughout the world and this toxic metalloid is known to cause cancers of organs such as liver, kidney, skin, and lung in human. In spite of a recent surge in arsenic related studies, we are still far from a comprehensive understanding of arsenic uptake, detoxification, and sequestration in plants. Crambe abyssinica, commonly known as 'abyssinian mustard', is a non-food, high biomass oil seed crop that is naturally tolerant to heavy metals. Moreover, it accumulates significantly higher levels of arsenic as compared to other species of the Brassicaceae family. Thus, C. abyssinica has great potential to be utilized as an ideal inedible crop for phytoremediation of heavy metals and metalloids. However, the mechanism of arsenic metabolism in higher plants, including C. abyssinica, remains elusive. Results To identify the differentially expressed transcripts and the pathways involved in arsenic metabolism and detoxification, C. abyssinica plants were subjected to arsenate stress and a PCR-Select Suppression Subtraction Hybridization (SSH approach was employed. A total of 105 differentially expressed subtracted cDNAs were sequenced which were found to represent 38 genes. Those genes encode proteins functioning as antioxidants, metal transporters, reductases, enzymes involved in the protein degradation pathway, and several novel uncharacterized proteins. The transcripts corresponding to the subtracted cDNAs showed strong upregulation by arsenate stress as confirmed by the semi-quantitative RT-PCR. Conclusions Our study revealed novel insights into the plant defense mechanisms and the regulation of genes and gene networks in response to arsenate toxicity. The differential expression of transcripts encoding glutathione-S-transferases, antioxidants, sulfur metabolism, heat-shock proteins, metal transporters, and enzymes in the ubiquitination pathway of protein degradation as well as several unknown

  20. Expression profiling of Crambe abyssinica under arsenate stress identifies genes and gene networks involved in arsenic metabolism and detoxification

    2010-01-01

    Background Arsenic contamination is widespread throughout the world and this toxic metalloid is known to cause cancers of organs such as liver, kidney, skin, and lung in human. In spite of a recent surge in arsenic related studies, we are still far from a comprehensive understanding of arsenic uptake, detoxification, and sequestration in plants. Crambe abyssinica, commonly known as 'abyssinian mustard', is a non-food, high biomass oil seed crop that is naturally tolerant to heavy metals. Moreover, it accumulates significantly higher levels of arsenic as compared to other species of the Brassicaceae family. Thus, C. abyssinica has great potential to be utilized as an ideal inedible crop for phytoremediation of heavy metals and metalloids. However, the mechanism of arsenic metabolism in higher plants, including C. abyssinica, remains elusive. Results To identify the differentially expressed transcripts and the pathways involved in arsenic metabolism and detoxification, C. abyssinica plants were subjected to arsenate stress and a PCR-Select Suppression Subtraction Hybridization (SSH) approach was employed. A total of 105 differentially expressed subtracted cDNAs were sequenced which were found to represent 38 genes. Those genes encode proteins functioning as antioxidants, metal transporters, reductases, enzymes involved in the protein degradation pathway, and several novel uncharacterized proteins. The transcripts corresponding to the subtracted cDNAs showed strong upregulation by arsenate stress as confirmed by the semi-quantitative RT-PCR. Conclusions Our study revealed novel insights into the plant defense mechanisms and the regulation of genes and gene networks in response to arsenate toxicity. The differential expression of transcripts encoding glutathione-S-transferases, antioxidants, sulfur metabolism, heat-shock proteins, metal transporters, and enzymes in the ubiquitination pathway of protein degradation as well as several unknown novel proteins serve as

  1. PDHA1 gene knockout in prostate cancer cells results in metabolic reprogramming towards greater glutamine dependence

    Li, Yaqing; Li, Xiaoran; Li, Xiaoli; Zhong, Yali; Ji, Yasai; Yu, Dandan; Zhang, Mingzhi; Wen, Jian-Guo; Zhang, Hongquan; Goscinski, Mariusz Adam; Nesland, Jahn M.; Suo, Zhenhe

    2016-01-01

    Alternative pathways of metabolism endowed cancer cells with metabolic stress. Inhibiting the related compensatory pathways might achieve synergistic anticancer results. This study demonstrated that pyruvate dehydrogenase E1α gene knockout (PDHA1 KO) resulted in alterations in tumor cell metabolism by rendering the cells with increased expression of glutaminase1 (GLS1) and glutamate dehydrogenase1 (GLUD1), leading to an increase in glutamine-dependent cell survival. Deprivation of glutamine induced cell growth inhibition, increased reactive oxygen species and decreased ATP production. Pharmacological blockade of the glutaminolysis pathway resulted in massive tumor cells apoptosis and dysfunction of ROS scavenge in the LNCaP PDHA1 KO cells. Further examination of the key glutaminolysis enzymes in human prostate cancer samples also revealed that higher levels of GLS1 and GLUD1 expression were significantly associated with aggressive clinicopathological features and poor clinical outcome. These insights supply evidence that glutaminolysis plays a compensatory role for cell survival upon alternative energy metabolism and targeting the glutamine anaplerosis of energy metabolism via GLS1 and GLUD1 in cancer cells may offer a potential novel therapeutic strategy. PMID:27462778

  2. PPARγ regulates the expression of cholesterol metabolism genes in alveolar macrophages

    Baker, Anna D.; Malur, Anagha; Barna, Barbara P.; Kavuru, Mani S.; Malur, Achut G.; Thomassen, Mary Jane

    2010-01-01

    Peroxisome proliferator-activated receptor-gamma (PPARγ) is a nuclear transcription factor involved in lipid metabolism that is constitutively expressed in the alveolar macrophages of healthy individuals. PPARγ has recently been implicated in the catabolism of surfactant by alveolar macrophages, specifically the cholesterol component of surfactant while the mechanism remains unclear. Studies from other tissue macrophages have shown that PPARγ regulates cholesterol influx, efflux, and metabolism. PPARγ promotes cholesterol efflux through the liver X receptor-alpha (LXRα) and ATP-binding cassette G1 (ABCG1). We have recently shown that macrophage-specific PPARγ knockout (PPARγ KO) mice accumulate cholesterol-laden alveolar macrophages that exhibit decreased expression of LXRα and ABCG1 and reduced cholesterol efflux. We hypothesized that in addition to the dysregulation of these cholesterol efflux genes, the expression of genes involved in cholesterol synthesis and influx was also dysregulated and that replacement of PPARγ would restore regulation of these genes. To investigate this hypothesis, we have utilized a Lentivirus expression system (Lenti-PPARγ) to restore PPARγ expression in the alveolar macrophages of PPARγ KO mice. Our results show that the alveolar macrophages of PPARγ KO mice have decreased expression of key cholesterol synthesis genes and increased expression of cholesterol receptors CD36 and scavenger receptor A-I (SRA-I). The replacement of PPARγ (1) induced transcription of LXRα and ABCG1; (2) corrected suppressed expression of cholesterol synthesis genes; and (3) enhanced the expression of scavenger receptors CD36. These results suggest that PPARγ regulates cholesterol metabolism in alveolar macrophages.

  3. PPAR{gamma} regulates the expression of cholesterol metabolism genes in alveolar macrophages

    Baker, Anna D.; Malur, Anagha; Barna, Barbara P.; Kavuru, Mani S. [Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, East Carolina University (United States); Malur, Achut G. [Department of Microbiology and Immunology, East Carolina University (United States); Thomassen, Mary Jane, E-mail: thomassenm@ecu.edu [Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, East Carolina University (United States); Department of Microbiology and Immunology, East Carolina University (United States)

    2010-03-19

    Peroxisome proliferator-activated receptor-gamma (PPAR{gamma}) is a nuclear transcription factor involved in lipid metabolism that is constitutively expressed in the alveolar macrophages of healthy individuals. PPAR{gamma} has recently been implicated in the catabolism of surfactant by alveolar macrophages, specifically the cholesterol component of surfactant while the mechanism remains unclear. Studies from other tissue macrophages have shown that PPAR{gamma} regulates cholesterol influx, efflux, and metabolism. PPAR{gamma} promotes cholesterol efflux through the liver X receptor-alpha (LXR{alpha}) and ATP-binding cassette G1 (ABCG1). We have recently shown that macrophage-specific PPAR{gamma} knockout (PPAR{gamma} KO) mice accumulate cholesterol-laden alveolar macrophages that exhibit decreased expression of LXR{alpha} and ABCG1 and reduced cholesterol efflux. We hypothesized that in addition to the dysregulation of these cholesterol efflux genes, the expression of genes involved in cholesterol synthesis and influx was also dysregulated and that replacement of PPAR{gamma} would restore regulation of these genes. To investigate this hypothesis, we have utilized a Lentivirus expression system (Lenti-PPAR{gamma}) to restore PPAR{gamma} expression in the alveolar macrophages of PPAR{gamma} KO mice. Our results show that the alveolar macrophages of PPAR{gamma} KO mice have decreased expression of key cholesterol synthesis genes and increased expression of cholesterol receptors CD36 and scavenger receptor A-I (SRA-I). The replacement of PPAR{gamma} (1) induced transcription of LXR{alpha} and ABCG1; (2) corrected suppressed expression of cholesterol synthesis genes; and (3) enhanced the expression of scavenger receptors CD36. These results suggest that PPAR{gamma} regulates cholesterol metabolism in alveolar macrophages.

  4. A comprehensive association analysis of homocysteine metabolic pathway genes in Singaporean Chinese with ischemic stroke.

    Hui-Qi Low

    Full Text Available BACKGROUND: The effect of genetic factors, apart from 5,10-methylenetetrahydrofolate reductase (MTHFR polymorphisms, on elevated plasma homocysteine levels and increasing ischemic stroke risk have not been fully elucidated. We conducted a comprehensive analysis of 25 genes involved in homocysteine metabolism to investigate association of common variants within these genes with ischemic stroke risk. METHODOLOGY/PRINCIPAL FINDINGS: The study was done in two stages. In the initial study, SNP and haplotype-based association analyses were performed using 147 tagging Single Nucleotide Polymorphisms (SNPs in 360 stroke patients and 354 non-stroke controls of Singaporean Chinese ethnicity. Joint association analysis of significant SNPs was then performed to assess the cumulative effect of these variants on ischemic stroke risk. In the replication study, 8 SNPs were selected for validation in an independent set of 420 matched case-control pairs of Singaporean Chinese ethnicity. SNP analysis from the initial study suggested 3 risk variants in the MTRR, SHMT1 and TCN2 genes which were moderately associated with ischemic stroke risk, independent of known stroke risk factors. Although the replication study failed to support single-SNP associations observed in the initial study, joint association analysis of the 3 variants in combined initial and replication samples revealed a trend of elevated risk with an increased number of risk alleles (Joint P(trend = 1.2×10(-6. CONCLUSIONS: Our study did not find direct evidence of associations between any single polymorphisms of homocysteine metabolic pathway genes and ischemic stroke, but suggests that the cumulative effect of several small to moderate risk variants from genes involved in homocysteine metabolism may jointly confer a significant impact on ischemic stroke risk.

  5. The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction

    Garzón-Martínez Gina A

    2012-04-01

    Full Text Available Abstract Background Physalis peruviana commonly known as Cape gooseberry is a member of the Solanaceae family that has an increasing popularity due to its nutritional and medicinal values. A broad range of genomic tools is available for other Solanaceae, including tomato and potato. However, limited genomic resources are currently available for Cape gooseberry. Results We report the generation of a total of 652,614 P. peruviana Expressed Sequence Tags (ESTs, using 454 GS FLX Titanium technology. ESTs, with an average length of 371 bp, were obtained from a normalized leaf cDNA library prepared using a Colombian commercial variety. De novo assembling was performed to generate a collection of 24,014 isotigs and 110,921 singletons, with an average length of 1,638 bp and 354 bp, respectively. Functional annotation was performed using NCBI’s BLAST tools and Blast2GO, which identified putative functions for 21,191 assembled sequences, including gene families involved in all the major biological processes and molecular functions as well as defense response and amino acid metabolism pathways. Gene model predictions in P. peruviana were obtained by using the genomes of Solanum lycopersicum (tomato and Solanum tuberosum (potato. We predict 9,436 P. peruviana sequences with multiple-exon models and conserved intron positions with respect to the potato and tomato genomes. Additionally, to study species diversity we developed 5,971 SSR markers from assembled ESTs. Conclusions We present the first comprehensive analysis of the Physalis peruviana leaf transcriptome, which will provide valuable resources for development of genetic tools in the species. Assembled transcripts with gene models could serve as potential candidates for marker discovery with a variety of applications including: functional diversity, conservation and improvement to increase productivity and fruit quality. P. peruviana was estimated to be phylogenetically branched out before the

  6. The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction.

    Garzón-Martínez, Gina A; Zhu, Z Iris; Landsman, David; Barrero, Luz S; Mariño-Ramírez, Leonardo

    2012-04-25

    Physalis peruviana commonly known as Cape gooseberry is a member of the Solanaceae family that has an increasing popularity due to its nutritional and medicinal values. A broad range of genomic tools is available for other Solanaceae, including tomato and potato. However, limited genomic resources are currently available for Cape gooseberry. We report the generation of a total of 652,614 P. peruviana Expressed Sequence Tags (ESTs), using 454 GS FLX Titanium technology. ESTs, with an average length of 371 bp, were obtained from a normalized leaf cDNA library prepared using a Colombian commercial variety. De novo assembling was performed to generate a collection of 24,014 isotigs and 110,921 singletons, with an average length of 1,638 bp and 354 bp, respectively. Functional annotation was performed using NCBI's BLAST tools and Blast2GO, which identified putative functions for 21,191 assembled sequences, including gene families involved in all the major biological processes and molecular functions as well as defense response and amino acid metabolism pathways. Gene model predictions in P. peruviana were obtained by using the genomes of Solanum lycopersicum (tomato) and Solanum tuberosum (potato). We predict 9,436 P. peruviana sequences with multiple-exon models and conserved intron positions with respect to the potato and tomato genomes. Additionally, to study species diversity we developed 5,971 SSR markers from assembled ESTs. We present the first comprehensive analysis of the Physalis peruviana leaf transcriptome, which will provide valuable resources for development of genetic tools in the species. Assembled transcripts with gene models could serve as potential candidates for marker discovery with a variety of applications including: functional diversity, conservation and improvement to increase productivity and fruit quality. P. peruviana was estimated to be phylogenetically branched out before the divergence of five other Solanaceae family members, S

  7. Functional analysis of lipid metabolism genes in wine yeasts during alcoholic fermentation at low temperature.

    López-Malo, María; García-Ríos, Estéfani; Chiva, Rosana; Guillamon, José M

    2014-10-29

    Wine produced by low-temperature fermentation is mostly considered to have improved sensory qualities. However few commercial wine strains available on the market are well-adapted to ferment at low temperature (10 - 15°C). The lipid metabolism of Saccharomyces cerevisiae plays a central role in low temperature adaptation. One strategy to modify lipid composition is to alter transcriptional activity by deleting or overexpressing the key genes of lipid metabolism. In a previous study, we identified the genes of the phospholipid, sterol and sphingolipid pathways, which impacted on growth capacity at low temperature. In the present study, we aimed to determine the influence of these genes on fermentation performance and growth during low-temperature wine fermentations. We analyzed the phenotype during fermentation at the low and optimal temperature of the lipid mutant and overexpressing strains in the background of a derivative commercial wine strain. The increase in the gene dosage of some of these lipid genes, e.g., PSD1 , LCB3, DPL1 and OLE1, improved fermentation activity during low-temperature fermentations, thus confirming their positive role during wine yeast adaptation to cold. Genes whose overexpression improved fermentation activity at 12°C were overexpressed by chromosomal integration into commercial wine yeast QA23. Fermentations in synthetic and natural grape must were carried out by this new set of overexpressing strains. The strains overexpressing OLE1 and DPL1 were able to finish fermentation before commercial wine yeast QA23. Only the OLE1 gene overexpression produced a specific aroma profile in the wines produced with natural grape must.

  8. Functional analysis of lipid metabolism genes in wine yeasts during alcoholic fermentation at low temperature

    María López-Malo

    2014-10-01

    Full Text Available Wine produced by low-temperature fermentation is mostly considered to have improved sensory qualities. However few commercial wine strains available on the market are well-adapted to ferment at low temperature (10 – 15°C. The lipid metabolism of Saccharomyces cerevisiae plays a central role in low temperature adaptation. One strategy to modify lipid composition is to alter transcriptional activity by deleting or overexpressing the key genes of lipid metabolism. In a previous study, we identified the genes of the phospholipid, sterol and sphingolipid pathways, which impacted on growth capacity at low temperature. In the present study, we aimed to determine the influence of these genes on fermentation performance and growth during low-temperature wine fermentations. We analyzed the phenotype during fermentation at the low and optimal temperature of the lipid mutant and overexpressing strains in the background of a derivative commercial wine strain. The increase in the gene dosage of some of these lipid genes, e.g., PSD1, LCB3, DPL1 and OLE1, improved fermentation activity during low-temperature fermentations, thus confirming their positive role during wine yeast adaptation to cold. Genes whose overexpression improved fermentation activity at 12°C were overexpressed by chromosomal integration into commercial wine yeast QA23. Fermentations in synthetic and natural grape must were carried out by this new set of overexpressing strains. The strains overexpressing OLE1 and DPL1 were able to finish fermentation before commercial wine yeast QA23. Only the OLE1 gene overexpression produced a specific aroma profile in the wines produced with natural grape must.

  9. Genes related to antioxidant metabolism are involved in Methylobacterium mesophilicum-soybean interaction.

    Araújo, Welington Luiz; Santos, Daiene Souza; Dini-Andreote, Francisco; Salgueiro-Londoño, Jennifer Katherine; Camargo-Neves, Aline Aparecida; Andreote, Fernando Dini; Dourado, Manuella Nóbrega

    2015-10-01

    The genus Methylobacterium is composed of pink-pigmented methylotrophic bacterial species that are widespread in natural environments, such as soils, stream water and plants. When in association with plants, this genus colonizes the host plant epiphytically and/or endophytically. This association is known to promote plant growth, induce plant systemic resistance and inhibit plant infection by phytopathogens. In the present study, we focused on evaluating the colonization of soybean seedling-roots by Methylobacterium mesophilicum strain SR1.6/6. We focused on the identification of the key genes involved in the initial step of soybean colonization by methylotrophic bacteria, which includes the plant exudate recognition and adaptation by planktonic bacteria. Visualization by scanning electron microscopy revealed that M. mesophilicum SR1.6/6 colonizes soybean roots surface effectively at 48 h after inoculation, suggesting a mechanism for root recognition and adaptation before this period. The colonization proceeds by the development of a mature biofilm on roots at 96 h after inoculation. Transcriptomic analysis of the planktonic bacteria (with plant) revealed the expression of several genes involved in membrane transport, thus confirming an initial metabolic activation of bacterial responses when in the presence of plant root exudates. Moreover, antioxidant genes were mostly expressed during the interaction with the plant exudates. Further evaluation of stress- and methylotrophic-related genes expression by qPCR showed that glutathione peroxidase and glutathione synthetase genes were up-regulated during the Methylobacterium-soybean interaction. These findings support that glutathione (GSH) is potentially a key molecule involved in cellular detoxification during plant root colonization. In addition to methylotrophic metabolism, antioxidant genes, mainly glutathione-related genes, play a key role during soybean exudate recognition and adaptation, the first step in

  10. Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism

    2016-03-15

    RESEARCH ARTICLE Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism Francisco G...jaques.reifman.civ@mail.mil Abstract A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm -based infections that are difficult to...eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic

  11. Leptin receptor and ghrelin genes polymorphisms in relation to the metabolism of lipids

    Anna Trakovická

    2015-10-01

    Full Text Available The aim of this work was to analyse genetic polymorphisms in genes encoding leptin receptor (LEPR and ghrelin (GHR as genetic markers of metabolic disorders in human nutrition. Genomic DNA was obtained from in total 84 human blood samples. Effect of analysed genetic markers was evaluated for three biochemical parameters: total cholesterol, HDL and LDL cholesterol. The PCR-RFLP method was used for identification of SNPs in LEPR (Gln223Arg and GHR (171T/C genes. In analysed population prevalence of heterozygous LEPRAG (47.62% and GHRCT (40.48% genotypes was observed. Frequency of LEPRA and LEPRB alleles were 0.55 and 0.45, respectively. Similar the GHRC allele had only slight predominance than GHRT allele (0.54/0.46. In population was found higher level of observed heterozygosity across loci (0.44. For both SNPs was found high effective allele number (1.98 which was also transferred to the median level of polymorphic information content (0.37. Association analysis of LEPR and GHR genotypes effect on selected biochemical parameters was performed using GLM procedure. Significant association was found only for levels of LDL cholesterol (P<0.01. Our study shows that both genes are involved in nutritional status and therefore can be considered as candidate genes of lipids metabolism disorders and obesity.

  12. Mining predicted essential genes of Brugia malayi for nematode drug targets.

    Sanjay Kumar

    Full Text Available We report results from the first genome-wide application of a rational drug target selection methodology to a metazoan pathogen genome, the completed draft sequence of Brugia malayi, a parasitic nematode responsible for human lymphatic filariasis. More than 1.5 billion people worldwide are at risk of contracting lymphatic filariasis and onchocerciasis, a related filarial disease. Drug treatments for filariasis have not changed significantly in over 20 years, and with the risk of resistance rising, there is an urgent need for the development of new anti-filarial drug therapies. The recent publication of the draft genomic sequence for B. malayi enables a genome-wide search for new drug targets. However, there is no functional genomics data in B. malayi to guide the selection of potential drug targets. To circumvent this problem, we have utilized the free-living model nematode Caenorhabditis elegans as a surrogate for B. malayi. Sequence comparisons between the two genomes allow us to map C. elegans orthologs to B. malayi genes. Using these orthology mappings and by incorporating the extensive genomic and functional genomic data, including genome-wide RNAi screens, that already exist for C. elegans, we identify potentially essential genes in B. malayi. Further incorporation of human host genome sequence data and a custom algorithm for prioritization enables us to collect and rank nearly 600 drug target candidates. Previously identified potential drug targets cluster near the top of our prioritized list, lending credibility to our methodology. Over-represented Gene Ontology terms, predicted InterPro domains, and RNAi phenotypes of C. elegans orthologs associated with the potential target pool are identified. By virtue of the selection procedure, the potential B. malayi drug targets highlight components of key processes in nematode biology such as central metabolism, molting and regulation of gene expression.

  13. The association of polymorphisms in 5-fluorouracil metabolism genes with outcome in adjuvant treatment of colorectal cancer

    Shoaib, Afzal; Gusella, Milena; Jensen, Søren Astrup

    2011-01-01

    The purpose of this study was to investigate whether specific combinations of polymorphisms in 5-fluorouracil (5-FU) metabolism-related genes were associated with outcome in 5-FU-based adjuvant treatment of colorectal cancer....

  14. Genetic polymorphisms and possible gene-gene interactions in metabolic and DNA repair genes: Effects on DNA damage

    Naccarati, Alessio; Souček, P.; Štětina, R.; Haufroid, V.; Kumar, R.; Vodičková, Ludmila; Trtková, K.; Dušinská, M.; Hemminki, K.; Vodička, Pavel

    2006-01-01

    Roč. 593, 1-2 (2006), s. 22-31 ISSN 0027-5107 R&D Projects: GA ČR GA310/03/0437 Institutional research plan: CEZ:AV0Z5039906 Keywords : Single-strand breaks * Genetic polymorphisms * Metabolism Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 4.111, year: 2006

  15. In Silico Prediction of Horizontal Gene Transfer Events in Lactobacillus bulgaricus and Streptococcus thermophilus Reveals Protocooperation in Yogurt Manufacturing▿ †

    Liu, Mengjin; Siezen, Roland J.; Nauta, Arjen

    2009-01-01

    Lactobacillus bulgaricus and Streptococcus thermophilus, used in yogurt starter cultures, are well known for their stability and protocooperation during their coexistence in milk. In this study, we show that a close interaction between the two species also takes place at the genetic level. We performed an in silico analysis, combining gene composition and gene transfer mechanism-associated features, and predicted horizontally transferred genes in both L. bulgaricus and S. thermophilus. Putative horizontal gene transfer (HGT) events that have occurred between the two bacterial species include the transfer of exopolysaccharide (EPS) biosynthesis genes, transferred from S. thermophilus to L. bulgaricus, and the gene cluster cbs-cblB(cglB)-cysE for the metabolism of sulfur-containing amino acids, transferred from L. bulgaricus or Lactobacillus helveticus to S. thermophilus. The HGT event for the cbs-cblB(cglB)-cysE gene cluster was analyzed in detail, with respect to both evolutionary and functional aspects. It can be concluded that during the coexistence of both yogurt starter species in a milk environment, agonistic coevolution at the genetic level has probably been involved in the optimization of their combined growth and interactions. PMID:19395564

  16. In silico prediction of horizontal gene transfer events in Lactobacillus bulgaricus and Streptococcus thermophilus reveals protocooperation in yogurt manufacturing.

    Liu, Mengjin; Siezen, Roland J; Nauta, Arjen

    2009-06-01

    Lactobacillus bulgaricus and Streptococcus thermophilus, used in yogurt starter cultures, are well known for their stability and protocooperation during their coexistence in milk. In this study, we show that a close interaction between the two species also takes place at the genetic level. We performed an in silico analysis, combining gene composition and gene transfer mechanism-associated features, and predicted horizontally transferred genes in both L. bulgaricus and S. thermophilus. Putative horizontal gene transfer (HGT) events that have occurred between the two bacterial species include the transfer of exopolysaccharide (EPS) biosynthesis genes, transferred from S. thermophilus to L. bulgaricus, and the gene cluster cbs-cblB(cglB)-cysE for the metabolism of sulfur-containing amino acids, transferred from L. bulgaricus or Lactobacillus helveticus to S. thermophilus. The HGT event for the cbs-cblB(cglB)-cysE gene cluster was analyzed in detail, with respect to both evolutionary and functional aspects. It can be concluded that during the coexistence of both yogurt starter species in a milk environment, agonistic coevolution at the genetic level has probably been involved in the optimization of their combined growth and interactions.

  17. Global loss of bmal1 expression alters adipose tissue hormones, gene expression and glucose metabolism.

    David John Kennaway

    Full Text Available The close relationship between circadian rhythm disruption and poor metabolic status is becoming increasingly evident, but role of adipokines is poorly understood. Here we investigated adipocyte function and the metabolic status of mice with a global loss of the core clock gene Bmal1 fed either a normal or a high fat diet (22% by weight. Bmal1 null mice aged 2 months were killed across 24 hours and plasma adiponectin and leptin, and adipose tissue expression of Adipoq, Lep, Retn and Nampt mRNA measured. Glucose, insulin and pyruvate tolerance tests were conducted and the expression of liver glycolytic and gluconeogenic enzyme mRNA determined. Bmal1 null mice displayed a pattern of increased plasma adiponectin and plasma leptin concentrations on both control and high fat diets. Bmal1 null male and female mice displayed increased adiposity (1.8 fold and 2.3 fold respectively on the normal diet, but the high fat diet did not exaggerate these differences. Despite normal glucose and insulin tolerance, Bmal1 null mice had increased production of glucose from pyruvate, implying increased liver gluconeogenesis. The Bmal1 null mice had arrhythmic clock gene expression in epigonadal fat and liver, and loss of rhythmic transcription of a range of metabolic genes. Furthermore, the expression of epigonadal fat Adipoq, Retn, Nampt, AdipoR1 and AdipoR2 and liver Pfkfb3 mRNA were down-regulated. These results show for the first time that global loss of Bmal1, and the consequent arrhythmicity, results in compensatory changes in adipokines involved in the cellular control of glucose metabolism.

  18. Introduction and expression of genes for metabolic engineering applications in Saccharomyces cerevisiae.

    Da Silva, Nancy A; Srikrishnan, Sneha

    2012-03-01

    Metabolic pathway engineering in the yeast Saccharomyces cerevisiae leads to improved production of a wide range of compounds, ranging from ethanol (from biomass) to natural products such as sesquiterpenes. The introduction of multienzyme pathways requires precise control over the level and timing of expression of the associated genes. Gene number and promoter strength/regulation are two critical control points, and multiple studies have focused on modulating these in yeast. This MiniReview focuses on methods for introducing genes and controlling their copy number and on the many promoters (both constitutive and inducible) that have been successfully employed. The advantages and disadvantages of the methods will be presented, and applications to pathway engineering will be highlighted. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

  19. Epistasis Analysis for Estrogen Metabolic and Signaling Pathway Genes on Young Ischemic Stroke Patients

    Hsieh, Yi-Chen; Jeng, Jiann-Shing; Lin, Huey-Juan; Hu, Chaur-Jong; Yu, Chia-Chen; Lien, Li-Ming; Peng, Giia-Sheun; Chen, Chin-I; Tang, Sung-Chun; Chi, Nai-Fang; Tseng, Hung-Pin; Chern, Chang-Ming; Hsieh, Fang-I; Bai, Chyi-Huey; Chen, Yi-Rhu; Chiou, Hung-Yi; Jeng, Jiann-Shing; Tang, Sung-Chun; Yeh, Shin-Joe; Tsai, Li-Kai; Kong, Shin; Lien, Li-Ming; Chiu, Hou-Chang; Chen, Wei-Hung; Bai, Chyi-Huey; Huang, Tzu-Hsuan; Chi-Ieong, Lau; Wu, Ya-Ying; Yuan, Rey-Yue; Hu, Chaur-Jong; Sheu, Jau- Jiuan; Yu, Jia-Ming; Ho, Chun-Sum; Chen, Chin-I; Sung, Jia-Ying; Weng, Hsing-Yu; Han, Yu-Hsuan; Huang, Chun-Ping; Chung, Wen-Ting; Ke, Der-Shin; Lin, Huey-Juan; Chang, Chia-Yu; Yeh, Poh-Shiow; Lin, Kao-Chang; Cheng, Tain-Junn; Chou, Chih-Ho; Yang, Chun-Ming; Peng, Giia-Sheun; Lin, Jiann-Chyun; Hsu, Yaw-Don; Denq, Jong-Chyou; Lee, Jiunn-Tay; Hsu, Chang-Hung; Lin, Chun-Chieh; Yen, Che-Hung; Cheng, Chun-An; Sung, Yueh-Feng; Chen, Yuan-Liang; Lien, Ming-Tung; Chou, Chung-Hsing; Liu, Chia-Chen; Yang, Fu-Chi; Wu, Yi-Chung; Tso, An-Chen; Lai, Yu- Hua; Chiang, Chun-I; Tsai, Chia-Kuang; Liu, Meng-Ta; Lin, Ying-Che; Hsu, Yu-Chuan; Chen, Chih-Hung; Sung, Pi-Shan; Chern, Chang-Ming; Hu, Han-Hwa; Wong, Wen-Jang; Luk, Yun-On; Hsu, Li-Chi; Chung, Chih-Ping; Tseng, Hung-Pin; Liu, Chin-Hsiung; Lin, Chun-Liang; Lin, Hung-Chih; Hu, Chaur-Jong

    2012-01-01

    Background Endogenous estrogens play an important role in the overall cardiocirculatory system. However, there are no studies exploring the hormone metabolism and signaling pathway genes together on ischemic stroke, including sulfotransferase family 1E (SULT1E1), catechol-O-methyl-transferase (COMT), and estrogen receptor α (ESR1). Methods A case-control study was conducted on 305 young ischemic stroke subjects aged ≦ 50 years and 309 age-matched healthy controls. SULT1E1 -64G/A, COMT Val158Met, ESR1 c.454−397 T/C and c.454−351 A/G genes were genotyped and compared between cases and controls to identify single nucleotide polymorphisms associated with ischemic stroke susceptibility. Gene-gene interaction effects were analyzed using entropy-based multifactor dimensionality reduction (MDR), classification and regression tree (CART), and traditional multiple regression models. Results COMT Val158Met polymorphism showed a significant association with susceptibility of young ischemic stroke among females. There was a two-way interaction between SULT1E1 -64G/A and COMT Val158Met in both MDR and CART analysis. The logistic regression model also showed there was a significant interaction effect between SULT1E1 -64G/A and COMT Val158Met on ischemic stroke of the young (P for interaction = 0.0171). We further found that lower estradiol level could increase the risk of young ischemic stroke for those who carry either SULT1E1 or COMT risk genotypes, showing a significant interaction effect (P for interaction = 0.0174). Conclusions Our findings support that a significant epistasis effect exists among estrogen metabolic and signaling pathway genes and gene-environment interactions on young ischemic stroke subjects. PMID:23112845

  20. Effects of achilline on lipid metabolism gene expression in cell culture

    A. V. Ratkin

    2016-01-01

    Full Text Available Objective. Evaluation in vitro of the mechanisms of the hypolipidemic effect of sesquiterpene γ-lactone achilline in the hepatoma tissue culture (HTC.Materials and methods.The influence of sesquiterpene γ-lactone achilline and gemfibrozil (comparison drug on the viability, lipid content and expression of key genes of lipid metabolism in the hepatoma tissue culture. The lipid content was assessed by fluorescent method with the vital dye Nile Red, the cell viability was assessed using MTT assay.Results. Cultivation of of cell cultures of rat’s hepatoma cell line HTC for 48 h with achilline in a concentration of from 0.25 to 1.0 mm and gemfibrozil from 0,25 to 0,5 mm did not change cell viability compared to control. In these same concentrations of the test substance reduced the lipid content in the cells, assessed by fluorescent method with the vital dye Nile Red. To study the mechanism of hypolipidemicaction of achillinedetermined the expression of key genes of lipid metabolism in cell culture lines HTC. The possible mechanism of hypolipidemic action of achilline can be attributed to the increased transport and oxidation of long-chain fatty acids in mitochondria, as evidenced by the increase in the gene expression of carnitine-palmitoyltransferase 2 (Cpt2. The decrease in cholesterol level may be due to increased synthesis of bile acids from cholesterol, due to increased gene expression of 7-alphahydroxylase (Cyp7a1. Conclusion. In cell cultures of rat’s hepatoma cell line HTC sesquiterpene γ-lactone achilline reduces the accumulation of lipids in cells, as evidenced by the decrease in the fluorescence of Nile Red, increased gene expression of the carnitine-palmitoyltransferase 2 (Cpt2 gene and 7-alpha-hydroxylase (Cyp7a1.

  1. Characterization of Genes Encoding Key Enzymes Involved in Anthocyanin Metabolism of Kiwifruit during Storage Period.

    Li, Boqiang; Xia, Yongxiu; Wang, Yuying; Qin, Guozheng; Tian, Shiping

    2017-01-01

    'Hongyang' is a red fleshed kiwifruit with high anthocyanin content. In this study, we mainly investigated effects of different temperatures (25 and 0°C) on anthocyanin biosynthesis in harvested kiwifruit, and characterized the genes encoding key enzymes involved in anthocyanin metabolism, as well as evaluated the mode of the action, by which low temperature regulates anthocyanin accumulation in 'Hongyang' kiwifruit during storage period. The results showed that low temperature could effectively enhance the anthocyanin accumulation of kiwifruit in the end of storage period (90 days), which related to the increase in mRNA levels of ANS1, ANS2, DRF1, DRF2 , and UGFT2 . Moreover, the transcript abundance of MYBA1-1 and MYB5-1 , the genes encoding an important component of MYB-bHLH-WD40 (MBW) complex, was up-regulated, possibly contributing to the induction of specific anthocyanin biosynthesis genes under the low temperature. To further investigate the roles of AcMYB5-1/5-2/A1-1 in regulation of anthocyanin biosynthesis, genes encoding the three transcription factors were transiently transformed in Nicotiana benthamiana leaves. Overexpression of AcMYB5-1/5-2/A1-1 activated the gene expression of NtANS and NtDFR in tobacco. Our results suggested that low temperature storage could stimulate the anthocyanin accumulation in harvested kiwifruit via regulating several structural and regulatory genes involved in anthocyanin biosynthesis.

  2. Polyunsaturated fatty acid regulation of gene transcription: a molecular mechanism to improve the metabolic syndrome.

    Clarke, S D

    2001-04-01

    This review addresses the hypothesis that polyunsaturated fatty acids (PUFA), particularly those of the (n-3) family, play pivotal roles as "fuel partitioners" in that they direct fatty acids away from triglyceride storage and toward oxidation, and that they enhance glucose flux to glycogen. In doing this, PUFA may protect against the adverse symptoms of the metabolic syndrome and reduce the risk of heart disease. PUFA exert their beneficial effects by up-regulating the expression of genes encoding proteins involved in fatty acid oxidation while simultaneously down-regulating genes encoding proteins of lipid synthesis. PUFA govern oxidative gene expression by activating the transcription factor peroxisome proliferator-activated receptor alpha. PUFA suppress lipogenic gene expression by reducing the nuclear abundance and DNA-binding affinity of transcription factors responsible for imparting insulin and carbohydrate control to lipogenic and glycolytic genes. In particular, PUFA suppress the nuclear abundance and expression of sterol regulatory element binding protein-1 and reduce the DNA-binding activities of nuclear factor Y, Sp1 and possibly hepatic nuclear factor-4. Collectively, the studies discussed suggest that the fuel "repartitioning" and gene expression actions of PUFA should be considered among criteria used in defining the dietary needs of (n-6) and (n-3) and in establishing the dietary ratio of (n-6) to (n-3) needed for optimum health benefit.

  3. Inherited Variation in Cytokine, Acute Phase Response, and Calcium Metabolism Genes Affects Susceptibility to Infective Endocarditis

    Anastasia V. Ponasenko

    2017-01-01

    Full Text Available Infective endocarditis (IE is a septic inflammation of the endocardium. Recognition of microbial patterns, cytokine and acute phase responses, hemostasis features, and alterations in plasma lipid and calcium profile all have been reported to affect pathogenesis and clinical course of IE. Having recruited 123 patients with IE and 300 age-, sex-, and ethnicity-matched healthy blood donors, we profiled their genomic DNA for 35 functionally significant polymorphisms within the 22 selected genes involved in the abovementioned pathways, with the further genetic association analysis. We found that the G/A genotype of the rs1143634 polymorphism within the IL1B gene, the G/T genotype of the rs3212227 polymorphism within the IL12B gene, the A/G genotype of the rs1130864 polymorphism within the CRP gene, and the G allele of the rs1801197 polymorphism within the CALCR gene were associated with a decreased risk of IE whereas the T/T genotype of the rs1205 polymorphism within the CRP gene was associated with a higher risk of IE. Furthermore, heterozygous genotypes of the rs1143634 and rs3212227 polymorphisms were associated with the higher plasma levels of IL-1β and IL-12, respectively. Our results indicate that inherited variation in the cytokine, acute phase response, and calcium metabolism pathways may be linked to IE.

  4. Inherited Variation in Cytokine, Acute Phase Response, and Calcium Metabolism Genes Affects Susceptibility to Infective Endocarditis

    Rutkovskaya, Natalia V.; Kondyukova, Natalia V.; Odarenko, Yuri N.; Kazachek, Yana V.; Tsepokina, Anna V.; Barbarash, Leonid S.

    2017-01-01

    Infective endocarditis (IE) is a septic inflammation of the endocardium. Recognition of microbial patterns, cytokine and acute phase responses, hemostasis features, and alterations in plasma lipid and calcium profile all have been reported to affect pathogenesis and clinical course of IE. Having recruited 123 patients with IE and 300 age-, sex-, and ethnicity-matched healthy blood donors, we profiled their genomic DNA for 35 functionally significant polymorphisms within the 22 selected genes involved in the abovementioned pathways, with the further genetic association analysis. We found that the G/A genotype of the rs1143634 polymorphism within the IL1B gene, the G/T genotype of the rs3212227 polymorphism within the IL12B gene, the A/G genotype of the rs1130864 polymorphism within the CRP gene, and the G allele of the rs1801197 polymorphism within the CALCR gene were associated with a decreased risk of IE whereas the T/T genotype of the rs1205 polymorphism within the CRP gene was associated with a higher risk of IE. Furthermore, heterozygous genotypes of the rs1143634 and rs3212227 polymorphisms were associated with the higher plasma levels of IL-1β and IL-12, respectively. Our results indicate that inherited variation in the cytokine, acute phase response, and calcium metabolism pathways may be linked to IE. PMID:28659664

  5. MOCAT: a metagenomics assembly and gene prediction toolkit.

    Kultima, Jens Roat; Sunagawa, Shinichi; Li, Junhua; Chen, Weineng; Chen, Hua; Mende, Daniel R; Arumugam, Manimozhiyan; Pan, Qi; Liu, Binghang; Qin, Junjie; Wang, Jun; Bork, Peer

    2012-01-01

    MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.

  6. MOCAT: a metagenomics assembly and gene prediction toolkit.

    Jens Roat Kultima

    Full Text Available MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.

  7. Analysis of clock-regulated genes in Neurospora reveals widespread posttranscriptional control of metabolic potential

    Hurley, Jennifer M.; Dasgupta, Arko; Emerson, Jillian M.; Zhou, Xiaoying; Ringelberg, Carol S.; Knabe, Nicole; Lipzen, Anna M.; Lindquist, Erika A.; Daum, Christopher G.; Barry, Kerrie W.; Grigoriev, Igor V.; Smith, Kristina M.; Galagan, James E.; Bell-Pedersen, Deborah; Freitag, Michael; Cheng, Chao; Loros, Jennifer J.; Dunlap, Jay C.

    2014-01-01

    Neurospora crassa has been for decades a principal model for filamentous fungal genetics and physiology as well as for understanding the mechanism of circadian clocks. Eukaryotic fungal and animal clocks comprise transcription-translation–based feedback loops that control rhythmic transcription of a substantial fraction of these transcriptomes, yielding the changes in protein abundance that mediate circadian regulation of physiology and metabolism: Understanding circadian control of gene expression is key to understanding eukaryotic, including fungal, physiology. Indeed, the isolation of clock-controlled genes (ccgs) was pioneered in Neurospora where circadian output begins with binding of the core circadian transcription factor WCC to a subset of ccg promoters, including those of many transcription factors. High temporal resolution (2-h) sampling over 48 h using RNA sequencing (RNA-Seq) identified circadianly expressed genes in Neurospora, revealing that from ∼10% to as much 40% of the transcriptome can be expressed under circadian control. Functional classifications of these genes revealed strong enrichment in pathways involving metabolism, protein synthesis, and stress responses; in broad terms, daytime metabolic potential favors catabolism, energy production, and precursor assembly, whereas night activities favor biosynthesis of cellular components and growth. Discriminative regular expression motif elicitation (DREME) identified key promoter motifs highly correlated with the temporal regulation of ccgs. Correlations between ccg abundance from RNA-Seq, the degree of ccg-promoter activation as reported by ccg-promoter–luciferase fusions, and binding of WCC as measured by ChIP-Seq, are not strong. Therefore, although circadian activation is critical to ccg rhythmicity, posttranscriptional regulation plays a major role in determining rhythmicity at the mRNA level. PMID:25362047

  8. Expression Profile of Genes Related to Drug Metabolism in Human Brain Tumors.

    Pantelis Stavrinou

    Full Text Available Endogenous and exogenous compounds as well as carcinogens are metabolized and detoxified by phase I and II enzymes, the activity of which could be crucial to the inactivation and hence susceptibility to carcinogenic factors. The expression of these enzymes in human brain tumor tissue has not been investigated sufficiently. We studied the association between tumor pathology and the expression profile of seven phase I and II drug metabolizing genes (CYP1A1, CYP1B1, ALDH3A1, AOX1, GSTP1, GSTT1 and GSTM3 and some of their proteins.Using qRT-PCR and western blotting analysis the gene and protein expression in a cohort of 77 tumors were investigated. The major tumor subtypes were meningioma, astrocytoma and brain metastases, -the later all adenocarcinomas from a lung primary.Meningeal tumors showed higher expression levels for AOX1, CYP1B1, GSTM3 and GSTP1. For AOX1, GSTM and GSTP1 this could be verified on a protein level as well. A negative correlation between the WHO degree of malignancy and the strength of expression was identified on both transcriptional and translational level for AOX1, GSTM3 and GSTP1, although the results could have been biased by the prevalence of meningiomas and glioblastomas in the inevitably bipolar distribution of the WHO grades. A correlation between the gene expression and the protein product was observed for AOX1, GSTP1 and GSTM3 in astrocytomas.The various CNS tumors show different patterns of drug metabolizing gene expression. Our results suggest that the most important factor governing the expression of these enzymes is the histological subtype and to a far lesser extent the degree of malignancy itself.

  9. Significantly increased risk of carotid atherosclerosis with arsenic exposure and polymorphisms in arsenic metabolism genes

    Hsieh, Yi-Chen; Lien, Li-Ming; Chung, Wen-Ting; Hsieh, Fang-I; Hsieh, Pei-Fan; Wu, Meei-Maan; Tseng, Hung-Pin; Chiou, Hung-Yi; Chen, Chien-Jen

    2011-01-01

    Individual susceptibility to arsenic-induced carotid atherosclerosis might be associated with genetic variations in arsenic metabolism. The purpose of this study is to explore the interaction effect on risk of carotid atherosclerosis between arsenic exposure and risk genotypes of purine nucleoside phosphorylase (PNP), arsenic (+3) methyltransferase (As3MT), and glutathione S-transferase omega 1 (GSTO1) and omega 2 (GSTO2). A community-based case-control study was conducted in northeastern Taiwan to investigate the arsenic metabolic-related genetic susceptibility to carotid atherosclerosis. In total, 863 subjects, who had been genotyped and for whom the severity of carotid atherosclerosis had been determined, were included in the present study. Individual well water was collected and arsenic concentration determined using hydride generation combined with flame atomic absorption spectrometry. The result showed that a significant dose-response trend (P=0.04) of carotid atherosclerosis risk associated with increasing arsenic concentration. Non-significant association between genetic polymorphisms of PNP Gly51Ser, Pro57Pro, As3MT Met287Thr, GSTO1 Ala140Asp, and GSTO2 A-183G and the risk for development of carotid atherosclerosis were observed. However, the significant interaction effect on carotid atherosclerosis risk was found for arsenic exposure (>50 μg/l) and the haplotypes of PNP (p=0.0115). A marked elevated risk of carotid atherosclerosis was observed in subjects with arsenic exposure of >50 μg/l in drinking water and those who carried the PNP A-T haplotype and at least either of the As3MT risk polymorphism or GSTO risk haplotypes (OR, 6.43; 95% CI, 1.79-23.19). In conclusion, arsenic metabolic genes, PNP, As3MT, and GSTO, may exacerbate the formation of atherosclerosis in individuals with high levels of arsenic concentration in well water (>50 μg/l). - Highlights: →Arsenic metabolic genes might be associated with carotid atherosclerosis. → A case

  10. Significantly increased risk of carotid atherosclerosis with arsenic exposure and polymorphisms in arsenic metabolism genes

    Hsieh, Yi-Chen [School of Public Health, College of Public Health and Nutrition, Taipei Medical University, 250 Wusing St., Taipei 11031, Taiwan (China); Lien, Li-Ming [Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan (China); School of Medicine, Taipei Medical University, Taipei, Taiwan (China); Department of Neurology, Shin Kong WHS Memorial Hospital, Taipei, Taiwan (China); Chung, Wen-Ting [Department of Neurology, Wanfang Hospital, Taipei Medical University, Taipei, Taiwan (China); Graduate Institute of Clinical Medicine, Taipei Medical University, Taipei, Taiwan (China); Hsieh, Fang-I; Hsieh, Pei-Fan [School of Public Health, College of Public Health and Nutrition, Taipei Medical University, 250 Wusing St., Taipei 11031, Taiwan (China); Wu, Meei-Maan [School of Public Health, College of Public Health and Nutrition, Taipei Medical University, 250 Wusing St., Taipei 11031, Taiwan (China); Graduate Institute of Basic Medicine, College of Medicine, Fu-Jen Catholic University, Taipei, Taiwan (China); Tseng, Hung-Pin [Department of Neurology, Lotung Poh-Ai Hospital, I-Lan, Taiwan (China); Chiou, Hung-Yi, E-mail: hychiou@tmu.edu.tw [School of Public Health, College of Public Health and Nutrition, Taipei Medical University, 250 Wusing St., Taipei 11031, Taiwan (China); Chen, Chien-Jen [Genomics Research Center, Academia Sinica, Taipei, Taiwan (China)

    2011-08-15

    Individual susceptibility to arsenic-induced carotid atherosclerosis might be associated with genetic variations in arsenic metabolism. The purpose of this study is to explore the interaction effect on risk of carotid atherosclerosis between arsenic exposure and risk genotypes of purine nucleoside phosphorylase (PNP), arsenic (+3) methyltransferase (As3MT), and glutathione S-transferase omega 1 (GSTO1) and omega 2 (GSTO2). A community-based case-control study was conducted in northeastern Taiwan to investigate the arsenic metabolic-related genetic susceptibility to carotid atherosclerosis. In total, 863 subjects, who had been genotyped and for whom the severity of carotid atherosclerosis had been determined, were included in the present study. Individual well water was collected and arsenic concentration determined using hydride generation combined with flame atomic absorption spectrometry. The result showed that a significant dose-response trend (P=0.04) of carotid atherosclerosis risk associated with increasing arsenic concentration. Non-significant association between genetic polymorphisms of PNP Gly51Ser, Pro57Pro, As3MT Met287Thr, GSTO1 Ala140Asp, and GSTO2 A-183G and the risk for development of carotid atherosclerosis were observed. However, the significant interaction effect on carotid atherosclerosis risk was found for arsenic exposure (>50 {mu}g/l) and the haplotypes of PNP (p=0.0115). A marked elevated risk of carotid atherosclerosis was observed in subjects with arsenic exposure of >50 {mu}g/l in drinking water and those who carried the PNP A-T haplotype and at least either of the As3MT risk polymorphism or GSTO risk haplotypes (OR, 6.43; 95% CI, 1.79-23.19). In conclusion, arsenic metabolic genes, PNP, As3MT, and GSTO, may exacerbate the formation of atherosclerosis in individuals with high levels of arsenic concentration in well water (>50 {mu}g/l). - Highlights: {yields}Arsenic metabolic genes might be associated with carotid atherosclerosis. {yields

  11. Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.

    Gupta, Rishi R; Gifford, Eric M; Liston, Ted; Waller, Chris L; Hohman, Moses; Bunin, Barry A; Ekins, Sean

    2010-11-01

    Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

  12. Genetic alterations in fatty acid transport and metabolism genes are associated with metastatic progression and poor prognosis of human cancers.

    Nath, Aritro; Chan, Christina

    2016-01-04

    Reprogramming of cellular metabolism is a hallmark feature of cancer cells. While a distinct set of processes drive metastasis when compared to tumorigenesis, it is yet unclear if genetic alterations in metabolic pathways are associated with metastatic progression of human cancers. Here, we analyzed the mutation, copy number variation and gene expression patterns of a literature-derived model of metabolic genes associated with glycolysis (Warburg effect), fatty acid metabolism (lipogenesis, oxidation, lipolysis, esterification) and fatty acid uptake in >9000 primary or metastatic tumor samples from the multi-cancer TCGA datasets. Our association analysis revealed a uniform pattern of Warburg effect mutations influencing prognosis across all tumor types, while copy number alterations in the electron transport chain gene SCO2, fatty acid uptake (CAV1, CD36) and lipogenesis (PPARA, PPARD, MLXIPL) genes were enriched in metastatic tumors. Using gene expression profiles, we established a gene-signature (CAV1, CD36, MLXIPL, CPT1C, CYP2E1) that strongly associated with epithelial-mesenchymal program across multiple cancers. Moreover, stratification of samples based on the copy number or expression profiles of the genes identified in our analysis revealed a significant effect on patient survival rates, thus confirming prominent roles of fatty acid uptake and metabolism in metastatic progression and poor prognosis of human cancers.

  13. Metabolism and gene polymorphisms of the folate pathway in Brazilian women with history of recurrent abortion.

    Boas, Wendell Vilas; Gonçalves, Rozana Oliveira; Costa, Olívia Lúcia Nunes; Goncalves, Marilda Souza

    2015-02-01

    To investigate the association between polymorphisms in genes that encode enzymes involved in folate- and vitamin B12-dependent homocysteine metabolism and recurrent spontaneous abortion (RSA). We investigated the C677T and A1298C polymorphisms of the methylenetetrahydrofalate reductase gene (MTHFR), the A2756G polymorphism of the methionine synthase gene (MS) and the 844ins68 insertion of the cystathionine beta synthetase gene (CBS). The PCR technique followed by RFLP was used to assess the polymorphisms; the serum levels of homocysteine, vitamin B12 and folate were investigated by chemiluminescence. The EPI Info Software version 6.04 was used for statistical analysis. Parametric variables were compared by Student's t-test and nonparametric variables by the Wilcoxon rank sum test. The frequencies of gene polymorphisms in 89 women with a history of idiopathic recurrent miscarriage and 150 controls were 19.1 and 19.6% for the C677T, insertion, 20.8 and 26% for the A1298C insertion, 14.2 and 21.9% for the A2756G insertion, and 16.4 and 18% for the 844ins68 insertion, respectively. There were no significant differences between case and control groups in any of the gene polymorphisms investigated. However, the frequency of the 844ins68 insertion in the CBS gene was higher among women with a history of loss during the third trimester of pregnancy (p=0.003). Serum homocysteine, vitamin B12 and folate levels id not differ between the polymorphisms studied in the case and control groups. However, linear regression analysis showed a dependence of serum folate levels on the maintenance of tHcy levels. The investigated gene polymorphisms and serum homocysteine, vitamin B12 and folate levels were not associated with idiopathic recurrent miscarriage in the present study. Further investigations are needed in order to confirm the role of the CBS 844ins68 insertion in recurrent miscarriage.

  14. Effects of long-term football training on the expression profile of genes involved in muscle oxidative metabolism

    Alfieri, A; Martone, D; Randers, Morten Bredsgaard

    2015-01-01

    and a muscle biopsy from the vastus lateralis were collected at T0 (pre intervention) and at T1 (post intervention). Gene expression was measured by RTqPCR on RNA extracted from muscle biopsies. The expression levels of the genes principally involved in energy metabolism (PPARγ, adiponectin, AMPKα1/α2, TFAM...... to improve the expression of muscle molecular biomarkers that are correlated to oxidative metabolism in healthy males....... are directly or indirectly involved in the glucose and lipid oxidative metabolism. Multiple linear regression analysis revealed that fat percentage was independently associated with NAMPT, PPARγ and adiponectin expression. In conclusion, long-term recreational football training could be a useful tool...

  15. Quercetin Impacts Expression of Metabolism- and Obesity-Associated Genes in SGBS Adipocytes

    Andreas Leiherer

    2016-05-01

    Full Text Available Obesity is characterized by the rapid expansion of visceral adipose tissue, resulting in a hypoxic environment in adipose tissue which leads to a profound change of gene expression in adipocytes. As a consequence, there is a dysregulation of metabolism and adipokine secretion in adipose tissue leading to the development of systemic inflammation and finally resulting in the onset of metabolic diseases. The flavonoid quercetin as well as other secondary plant metabolites also referred to as phytochemicals have anti-oxidant, anti-inflammatory, and anti-diabetic effects known to be protective in view of obesity-related-diseases. Nevertheless, its underlying molecular mechanism is still obscure and thus the focus of this study was to explore the influence of quercetin on human SGBS (Simpson Golabi Behmel Syndrome adipocytes’ gene expression. We revealed for the first time that quercetin significantly changed expression of adipokine (Angptl4, adipsin, irisin and PAI-1 and glycolysis-involved (ENO2, PFKP and PFKFB4 genes, and that this effect not only antagonized but in part even overcompensated the effect mediated by hypoxia in adipocytes. Thus, these results are explained by the recently proposed hypothesis that the protective effect of quercetin is not solely due to its free radical-scavenging activity but also to a direct effect on mitochondrial processes, and they demonstrate that quercetin might have the potential to counteract the development of obesity-associated complications.

  16. Effects of abhydrolase domain containing 5 gene (ABHD5) expression and variations on chicken fat metabolism.

    Ouyang, Hongjia; Liu, Qing; Xu, Jiguo; Zeng, Fang; Pang, Xiaolin; Jebessa, Endashaw; Liang, Shaodong; Nie, Qinghua; Zhang, Xiquan

    2016-01-01

    Abhydrolase domain containing 5 gene (ABHD5), also known as comparative gene identification 58 (CGI-58), is a member of the α/β-hydrolase family as a protein cofactor of ATGL stimulating its triacylglycerol hydrolase activity. In this study, we aim to characterize the expression and variations of ABHD5 and to study their functions in chicken fat metabolism. We compared the ABHD5 expression level in various tissues and under different nutrition conditions, identified the variations of ABHD5, and associated them with production traits in an F2 resource population of chickens. Overexpression analysis with two different genotypes and siRNA interfering analysis of ABHD5 were performed in chicken preadipocytes. Chicken ABDH5 was expressed widely and most predominantly in adipose tissue. Five SNPs of the ABHD5 gene were identified and genotyped in the F2 resource population. The c.490C > T SNP was associated with subcutaneous fat thickness (P  C SNP was also associated with chicken body weight (P chicken preadipocytes, overexpression of wild type ABDH5 did not affect the mRNA level of ATGL (adipose triglyceride lipase) but markedly decreased (P chickens with a high fat diet. These results suggest that expression and variations of ABHD5 may affect fat metabolism through regulating the activity of ATGL in chickens. © 2015 Poultry Science Association Inc.

  17. Metabolic alterations, HFE gene mutations and atherogenic lipoprotein modifications in patients with primary iron overload.

    Meroño, Tomás; Brites, Fernando; Dauteuille, Carolane; Lhomme, Marie; Menafra, Martín; Arteaga, Alejandra; Castro, Marcelo; Saez, María Soledad; Ballerga, Esteban González; Sorroche, Patricia; Rey, Jorge; Lesnik, Philippe; Sordá, Juan Andrés; Chapman, M John; Kontush, Anatol; Daruich, Jorge

    2015-05-01

    Iron overload (IO) has been associated with glucose metabolism alterations and increased risk of cardiovascular disease (CVD). Primary IO is associated with mutations in the HFE gene. To which extent HFE gene mutations and metabolic alterations contribute to the presence of atherogenic lipoprotein modifications in primary IO remains undetermined. The present study aimed to assess small, dense low-density lipoprotein (LDL) levels, chemical composition of LDL and high-density lipoprotein (HDL) particles, and HDL functionality in IO patients. Eighteen male patients with primary IO and 16 sex- and age-matched controls were recruited. HFE mutations (C282Y, H63D and S65C), measures of insulin sensitivity and secretion (calculated from the oral glucose tolerance test), chemical composition and distribution profile of LDL and HDL subfractions (isolated by gradient density ultracentrifugation) and HDL functionality (as cholesterol efflux and antioxidative activity) were studied. IO patients compared with controls exhibited insulin resistance (HOMA-IR (homoeostasis model assessment-estimated insulin resistance): +93%, PHFE genotypes. C282Y homozygotes (n=7) presented a reduced β-cell function and insulin secretion compared with non-C282Y patients (n=11) (-58% and -73%, respectively, PHFE gene mutations are involved in the presence of atherogenic lipoprotein modifications in primary IO. To what extent such alterations could account for an increase in CVD risk remains to be determined.

  18. Predicting Metabolic Syndrome in Obese Children and Adolescents: Look, Measure and Ask

    Nicola Santoro

    2013-02-01

    Full Text Available Objective: To verify in obese children whether or not the presence of i high waist-to-height ratio (WHtR, ii family history for type 2 diabetes (T2D and iii acanthosis nigricans (AN, singularly or together, might predict the occurrence of metabolic syndrome or prediabetes. Methods. 1,080 Italian obese children (567 females were enrolled. Blood pressure, fasting plasma glucose, insulin, and lipids were measured, and oral glucose tolerance test (OGTT was performed. The WHtR was calculated, family history for T2D was assessed, and the presence of AN was noticed. The odds ratios for showing metabolic syndrome and/or prediabetes according to the presence of these features were calculated. Results: The prevalence of metabolic syndrome was 29.2%. AN (OR1.81; p = 0.002 and WHtR higher than 0.60 (OR 2.24; p Conclusions: Three simple actions, i.e., looking at the patient, asking about T2D family history, and measuring WHtR, may represent a powerful tool in the hands of pediatricians to identify obese children with high cardiovascular and metabolic risk.

  19. The genes and enzymes of sucrose metabolism in moderately thermophilic methanotroph Methylocaldum szegediense O12.

    But, Sergey Y; Solntseva, Natalia P; Egorova, Svetlana V; Mustakhimov, Ildar I; Khmelenina, Valentina N; Reshetnikov, Alexander; Trotsenko, Yuri A

    2018-05-01

    Four enzymes involved in sucrose metabolism: sucrose phosphate synthase (Sps), sucrose phosphate phosphatase (Spp), sucrose synthase (Sus) and fructokinase (FruK), were obtained as his-tagged proteins from the moderately thermophilic methanotroph Methylocaldum szegediense O12. Sps, Spp, FruK and Sus demonstrated biochemical properties similar to those of other bacterial counterparts, but the translated amino acid sequences of Sps and Spp displayed high divergence from the respective microbial enzymes. The Sus of M. szegediense O12 catalyzed the reversible reaction of sucrose cleavage in the presence of ADP or UDP and preferred ADP as a substrate, thus implying a connection between sucrose and glycogen metabolism. Sus-like genes were found only in a few methanotrophs, whereas amylosucrase was generally used in sucrose cleavage in this group of bacteria. Like other microbial fructokinases, FruK of M. szegediense O12 showed a high specificity to fructose.

  20. Transcriptome Analysis of Syringa oblata Lindl. Inflorescence Identifies Genes Associated with Pigment Biosynthesis and Scent Metabolism.

    Jian Zheng

    Full Text Available Syringa oblata Lindl. is a woody ornamental plant with high economic value and characteristics that include early flowering, multiple flower colors, and strong fragrance. Despite a long history of cultivation, the genetics and molecular biology of S. oblata are poorly understood. Transcriptome and expression profiling data are needed to identify genes and to better understand the biological mechanisms of floral pigments and scents in this species. Nine cDNA libraries were obtained from three replicates of three developmental stages: inflorescence with enlarged flower buds not protruded, inflorescence with corolla lobes not displayed, and inflorescence with flowers fully opened and emitting strong fragrance. Using the Illumina RNA-Seq technique, 319,425,972 clean reads were obtained and were assembled into 104,691 final unigenes (average length of 853 bp, 41.75% of which were annotated in the NCBI non-redundant protein database. Among the annotated unigenes, 36,967 were assigned to gene ontology categories and 19,956 were assigned to eukaryoticorthologous groups. Using the Kyoto Encyclopedia of Genes and Genomes pathway database, 12,388 unigenes were sorted into 286 pathways. Based on these transcriptomic data, we obtained a large number of candidate genes that were differentially expressed at different flower stages and that were related to floral pigment biosynthesis and fragrance metabolism. This comprehensive transcriptomic analysis provides fundamental information on the genes and pathways involved in flower secondary metabolism and development in S. oblata, providing a useful database for further research on S. oblata and other plants of genus Syringa.

  1. Differential selection on carotenoid biosynthesis genes as a function of gene position in the metabolic pathway: a study on the carrot and dicots.

    Jérémy Clotault

    Full Text Available Selection of genes involved in metabolic pathways could target them differently depending on the position of genes in the pathway and on their role in controlling metabolic fluxes. This hypothesis was tested in the carotenoid biosynthesis pathway using population genetics and phylogenetics.Evolutionary rates of seven genes distributed along the carotenoid biosynthesis pathway, IPI, PDS, CRTISO, LCYB, LCYE, CHXE and ZEP, were compared in seven dicot taxa. A survey of deviations from neutrality expectations at these genes was also undertaken in cultivated carrot (Daucus carota subsp. sativus, a species that has been intensely bred for carotenoid pattern diversification in its root during its cultivation history. Parts of sequences of these genes were obtained from 46 individuals representing a wide diversity of cultivated carrots. Downstream genes exhibited higher deviations from neutral expectations than upstream genes. Comparisons of synonymous and nonsynonymous substitution rates between genes among dicots revealed greater constraints on upstream genes than on downstream genes. An excess of intermediate frequency polymorphisms, high nucleotide diversity and/or high differentiation of CRTISO, LCYB1 and LCYE in cultivated carrot suggest that balancing selection may have targeted genes acting centrally in the pathway.Our results are consistent with relaxed constraints on downstream genes and selection targeting the central enzymes of the carotenoid biosynthesis pathway during carrot breeding history.

  2. Gene and genome-centric analyses of koala and wombat fecal microbiomes point to metabolic specialization for Eucalyptus digestion

    Miriam E. Shiffman

    2017-11-01

    Full Text Available The koala has evolved to become a specialist Eucalyptus herbivore since diverging from its closest relative, the wombat, a generalist herbivore. This niche adaptation involves, in part, changes in the gut microbiota. The goal of this study was to compare koala and wombat fecal microbiomes using metagenomics to identify potential differences attributable to dietary specialization. Several populations discriminated between the koala and wombat fecal communities, most notably S24-7 and Synergistaceae in the koala, and Christensenellaceae and RF39 in the wombat. As expected for herbivores, both communities contained the genes necessary for lignocellulose degradation and urea recycling partitioned and redundantly encoded across multiple populations. Secondary metabolism was overrepresented in the koala fecal samples, consistent with the need to process Eucalyptus secondary metabolites. The Synergistaceae population encodes multiple pathways potentially relevant to Eucalyptus compound metabolism, and is predicted to be a key player in detoxification of the koala’s diet. Notably, characterized microbial isolates from the koala gut appear to be minor constituents of this habitat, and the metagenomes provide the opportunity for genome-directed isolation of more representative populations. Metagenomic analysis of other obligate and facultative Eucalyptus folivores will reveal whether putatively detoxifying bacteria identified in the koala are shared across these marsupials.

  3. Hyperandrogenemia predicts metabolic phenotype in polycystic ovary syndrome: the utility of serum androstenedione.

    O'Reilly, Michael W; Taylor, Angela E; Crabtree, Nicola J; Hughes, Beverly A; Capper, Farfia; Crowley, Rachel K; Stewart, Paul M; Tomlinson, Jeremy W; Arlt, Wiebke

    2014-03-01

    Polycystic ovary syndrome (PCOS) is a triad of anovulation, insulin resistance, and hyperandrogenism. Androgen excess may correlate with metabolic risk and PCOS consensus criteria define androgen excess on the basis of serum T. Here we studied the utility of the androgen precursor serum androstenedione (A) in conjunction with serum T for predicting metabolic dysfunction in PCOS. Eighty-six PCOS patients fulfilling Rotterdam diagnostic consensus criteria and 43 age- and body mass index-matched controls underwent measurement of serum androgens by tandem mass spectrometry and an oral glucose tolerance test with homeostatic model assessment of insulin resistance and insulin sensitivity index calculation. We analyzed 24-hour urine androgen excretion by gas chromatography/mass spectrometry. PCOS patients had higher levels of serum androgens and urinary androgen metabolites than controls (all P PCOS cohort, both serum A and T were positively correlated with the free androgen index (T × 100/SHBG) and total androgen metabolite excretion (all P androgen excretion than NA/NT (P androgen phenotype (NA/NT, 0%; HA/NT, 14%; HA/HT, 25%, P = .03). Simultaneous measurement of serum T and A represents a useful tool for predicting metabolic risk in PCOS women. HA levels are a sensitive indicator of PCOS-related androgen excess.

  4. Metabolism

    ... lin), which signals cells to increase their anabolic activities. Metabolism is a complicated chemical process, so it's not ... how those enzymes or hormones work. When the metabolism of body chemicals is ... Hyperthyroidism (pronounced: hi-per-THIGH-roy-dih-zum). Hyperthyroidism ...

  5. JAZF1 can regulate the expression of lipid metabolic genes and inhibit lipid accumulation in adipocytes

    Ming, Guang-feng [Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha 410078, Hunan (China); Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha 410008, Hunan (China); Xiao, Di; Gong, Wei-jing [Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha 410078, Hunan (China); Liu, Hui-xia; Liu, Jun [Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, Hunan (China); Zhou, Hong-hao [Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha 410078, Hunan (China); Liu, Zhao-qian, E-mail: liuzhaoqian63@126.com [Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha 410078, Hunan (China)

    2014-03-14

    Highlights: • JAZF1 was significantly upregulated during the differentiation of 3T3-L1 preadipocytes. • JAZF1 overexpression inhibited lipid accumulation in differentiated mature 3T3-L1 adipocytes. • JAZF1 overexpression inhibited the expression of SREBP1, ACC, and FAS. • JAZF1 overexpression upregulated the expression of HSL and ATGL. • SREBP1 and JAZF1 could regulate each other in adipocytes. - Abstract: JAZF1 is a newly identified gene with unknown functions. A recent genome-wide association study showed that JAZF1 is associated with type 2 diabetes and is highly expressed in liver and adipose tissue. Studies have demonstrated that JAZF1 is the co-repressor for nuclear orphan receptor TAK1, whereas most nuclear orphan receptor family members are involved in the regulation of lipid metabolism. Therefore, JAZF1 could be closely related to glycolipid metabolism. In this study, JAZF1 was significantly upregulated during the induced differentiation process of 3T3-L1 preadipocytes. The overexpression of JAZF1 inhibited lipid accumulation in differentiated mature 3T3-L1 adipocytes and significantly inhibited the expression of SREBPl, ACC, and FAS, which were important in lipid synthesis, while upregulating the expression of key enzyme hormone-sensitive lipase in lipoclasis. Moreover, SREBPl exhibited an inhibitory function on the expression of JAZF1. SREBP1 reversed the inhibitory action on lipid accumulation of JAZF1. SREBP1 and JAZF1 were observed to regulate each other in adipocytes. Therefore, JAZF1 could regulate the expression of particular genes related to lipid metabolism and inhibit lipid accumulation in adipocytes. This result suggests that JAZF1 may be a potential target for the treatment of diseases, such as obesity and lipid metabolism disorders.

  6. JAZF1 can regulate the expression of lipid metabolic genes and inhibit lipid accumulation in adipocytes

    Ming, Guang-feng; Xiao, Di; Gong, Wei-jing; Liu, Hui-xia; Liu, Jun; Zhou, Hong-hao; Liu, Zhao-qian

    2014-01-01

    Highlights: • JAZF1 was significantly upregulated during the differentiation of 3T3-L1 preadipocytes. • JAZF1 overexpression inhibited lipid accumulation in differentiated mature 3T3-L1 adipocytes. • JAZF1 overexpression inhibited the expression of SREBP1, ACC, and FAS. • JAZF1 overexpression upregulated the expression of HSL and ATGL. • SREBP1 and JAZF1 could regulate each other in adipocytes. - Abstract: JAZF1 is a newly identified gene with unknown functions. A recent genome-wide association study showed that JAZF1 is associated with type 2 diabetes and is highly expressed in liver and adipose tissue. Studies have demonstrated that JAZF1 is the co-repressor for nuclear orphan receptor TAK1, whereas most nuclear orphan receptor family members are involved in the regulation of lipid metabolism. Therefore, JAZF1 could be closely related to glycolipid metabolism. In this study, JAZF1 was significantly upregulated during the induced differentiation process of 3T3-L1 preadipocytes. The overexpression of JAZF1 inhibited lipid accumulation in differentiated mature 3T3-L1 adipocytes and significantly inhibited the expression of SREBPl, ACC, and FAS, which were important in lipid synthesis, while upregulating the expression of key enzyme hormone-sensitive lipase in lipoclasis. Moreover, SREBPl exhibited an inhibitory function on the expression of JAZF1. SREBP1 reversed the inhibitory action on lipid accumulation of JAZF1. SREBP1 and JAZF1 were observed to regulate each other in adipocytes. Therefore, JAZF1 could regulate the expression of particular genes related to lipid metabolism and inhibit lipid accumulation in adipocytes. This result suggests that JAZF1 may be a potential target for the treatment of diseases, such as obesity and lipid metabolism disorders

  7. Folate metabolism gene 5,10-methylenetetrahydrofolate reductase (MTHFR is associated with ADHD in myelomeningocele patients.

    Catherine J Spellicy

    Full Text Available The objective of this study was to examine the relation between the 5, 10-methylenetetrahydrofolate reductase (MTHFR gene and behaviors related to attention- deficit/hyperactivity disorder (ADHD in individuals with myelomeningocele. The rationale for the study was twofold: folate metabolizing genes, (e.g. MTHFR, are important not only in the etiology of neural tube defects but are also critical to cognitive function; and individuals with myelomeningocele have an elevated incidence of ADHD. Here, we tested 478 individuals with myelomeningocele for attention-deficit hyperactivity disorder behavior using the Swanson Nolan Achenbach Pelham-IV ADHD rating scale. Myelomeningocele participants in this group for whom DNAs were available were genotyped for seven single nucleotide polymorphisms (SNPs in the MTHFR gene. The SNPs were evaluated for an association with manifestation of the ADHD phenotype in children with myelomeningocele. The data show that 28.7% of myelomeningocele participants exhibit rating scale elevations consistent with ADHD; of these 70.1% had scores consistent with the predominantly inattentive subtype. In addition, we also show a positive association between the SNP rs4846049 in the 3'-untranslated region of the MTHFR gene and the attention-deficit hyperactivity disorder phenotype in myelomeningocele participants. These results lend further support to the finding that behavior related to ADHD is more prevalent in patients with myelomeningocele than in the general population. These data also indicate the potential importance of the MTHFR gene in the etiology of the ADHD phenotype.

  8. mRNA expression of genes regulating lipid metabolism in ringed seals (Pusa hispida) from differently polluted areas

    Castelli, Martina Galatea; Rusten, Marte; Goksøyr, Anders; Routti, Heli

    2014-01-01

    Highlights: •Genes regulating lipid metabolism were studied in ringed seals. •We compared highly contaminated Baltic seals and less contaminated Svalbard seals. •mRNA expression of hepatic PPARγ was higher in the Baltic seals. •mRNA expression of adipose PPARγ target genes was higher in the Baltic seals. •Contaminant exposure may affect lipid metabolism in the Baltic ringed seals. -- Abstract: There is a growing concern about the ability of persistent organic pollutants (POPs) to influence lipid metabolism. Although POPs are found at high concentrations in some populations of marine mammals, for example in the ringed seal (Pusa hispida) from the Baltic Sea, little is known about the effects of POPs on their lipid metabolism. An optimal regulation of lipid metabolism is crucial for ringed seals during the fasting/molting season. This is a physiologically stressful period, during which they rely on the energy stored in their fat reserves. The mRNA expression levels for seven genes involved in lipid metabolism were analyzed in liver and/or blubber tissue from molting ringed seals from the polluted Baltic Sea and a less polluted reference location, Svalbard (Norway). mRNA expression of genes encoding peroxisome proliferator-activated receptors (PPAR) α and γ and their target genes acyl-coenzyme A oxidase 1 (ACOX1) and cluster of differentiation 36 (CD36) were analyzed in liver. mRNA expression level of genes encoding PPARβ, PPARγ and their target genes encoding fatty acid binding protein 4 (FABP4) and adiponectin (ADIPOQ) were measured in inner and middle blubber layers. In addition, we evaluated the influence of molting status on hepatic mRNA expression of genes encoding PPARs and their target genes in ringed seals from Svalbard. Our results show higher mRNA expression of genes encoding hepatic PPARγ and adipose PPARβ, FABP4, and ADIPOQ in the Baltic seals compared to the Svalbard seals. A positive relationship between mRNA expressions of genes

  9. mRNA expression of genes regulating lipid metabolism in ringed seals (Pusa hispida) from differently polluted areas

    Castelli, Martina Galatea [Norwegian Polar Institute, Fram Centre, 9296 Tromsø (Norway); University of Bergen, Department of Biology, 5020 Bergen (Norway); Rusten, Marte; Goksøyr, Anders [University of Bergen, Department of Biology, 5020 Bergen (Norway); Routti, Heli, E-mail: heli.routti@npolar.no [Norwegian Polar Institute, Fram Centre, 9296 Tromsø (Norway)

    2014-01-15

    Highlights: •Genes regulating lipid metabolism were studied in ringed seals. •We compared highly contaminated Baltic seals and less contaminated Svalbard seals. •mRNA expression of hepatic PPARγ was higher in the Baltic seals. •mRNA expression of adipose PPARγ target genes was higher in the Baltic seals. •Contaminant exposure may affect lipid metabolism in the Baltic ringed seals. -- Abstract: There is a growing concern about the ability of persistent organic pollutants (POPs) to influence lipid metabolism. Although POPs are found at high concentrations in some populations of marine mammals, for example in the ringed seal (Pusa hispida) from the Baltic Sea, little is known about the effects of POPs on their lipid metabolism. An optimal regulation of lipid metabolism is crucial for ringed seals during the fasting/molting season. This is a physiologically stressful period, during which they rely on the energy stored in their fat reserves. The mRNA expression levels for seven genes involved in lipid metabolism were analyzed in liver and/or blubber tissue from molting ringed seals from the polluted Baltic Sea and a less polluted reference location, Svalbard (Norway). mRNA expression of genes encoding peroxisome proliferator-activated receptors (PPAR) α and γ and their target genes acyl-coenzyme A oxidase 1 (ACOX1) and cluster of differentiation 36 (CD36) were analyzed in liver. mRNA expression level of genes encoding PPARβ, PPARγ and their target genes encoding fatty acid binding protein 4 (FABP4) and adiponectin (ADIPOQ) were measured in inner and middle blubber layers. In addition, we evaluated the influence of molting status on hepatic mRNA expression of genes encoding PPARs and their target genes in ringed seals from Svalbard. Our results show higher mRNA expression of genes encoding hepatic PPARγ and adipose PPARβ, FABP4, and ADIPOQ in the Baltic seals compared to the Svalbard seals. A positive relationship between mRNA expressions of genes

  10. Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study

    Stroeve, J.H.M.; Saccenti, E.; Bouwman, J.; Dane, A.; Strassburg, K.; Vervoort, J.; Hankemeier, T.; Astrup, A.; Smilde, A.K.; Ommen, B. van; Saris, W.H.M.

    2016-01-01

    Objective: Aim is to predict successful weight loss by metabolic signatures at baseline and to identify which differences in metabolic status may underlie variations in weight loss success. Methods: In DiOGenes, a randomized, controlled trial, weight loss was induced using a low calorie diet (800

  11. A genome-wide gene function prediction resource for Drosophila melanogaster.

    Han Yan

    2010-08-01

    Full Text Available Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

  12. Evolution of hepatic glucose metabolism: liver-specific glucokinase deficiency explained by parallel loss of the gene for glucokinase regulatory protein (GCKR.

    Zhao Yang Wang

    Full Text Available Glucokinase (GCK plays an important role in the regulation of carbohydrate metabolism. In the liver, phosphorylation of glucose to glucose-6-phosphate by GCK is the first step for both glycolysis and glycogen synthesis. However, some vertebrate species are deficient in GCK activity in the liver, despite containing GCK genes that appear to be compatible with function in their genomes. Glucokinase regulatory protein (GCKR is the most important post-transcriptional regulator of GCK in the liver; it participates in the modulation of GCK activity and location depending upon changes in glucose levels. In experimental models, loss of GCKR has been shown to associate with reduced hepatic GCK protein levels and activity.GCKR genes and GCKR-like sequences were identified in the genomes of all vertebrate species with available genome sequences. The coding sequences of GCKR and GCKR-like genes were identified and aligned; base changes likely to disrupt coding potential or splicing were also identified.GCKR genes could not be found in the genomes of 9 vertebrate species, including all birds. In addition, in multiple mammalian genomes, whereas GCKR-like gene sequences could be identified, these genes could not predict a functional protein. Vertebrate species that were previously reported to be deficient in hepatic GCK activity were found to have deleted (birds and lizard or mutated (mammals GCKR genes. Our results suggest that mutation of the GCKR gene leads to hepatic GCK deficiency due to the loss of the stabilizing effect of GCKR.

  13. Metabolic regulation in meagre, Argyrosomus regius (Asso, 1801: Study of gene-diet interactions on lipid metabolism

    Francisca Silva-Brito

    2014-06-01

    stimulates the expression of the fads2. In contrast, Elovl5 is very efficient to convert the desaturated products of ∆6/∆8 and so Elovl5 expression is not enhanced, since the activity of this enzyme is already higher. These findings may explain the differences in the expression of two genes, between hepatic fads2 and elovl5. Furthermore, increased hepatic fads2 expression between FO-S and VO-S treatments was 114 fold. Such induction was much greater than observed in Salmo salad (Zheng et al., 2005. However, Salmo salad has separate and distinct genes for ∆6 and ∆5 desaturases (Zheng et al., 2005 and, due to genome duplication, it has two genes with ∆6 activity (Monroig et al., 2010. Moreover, lipid peroxidation in liver increases with the number of fatty acid (FA double bonds (Haggag, Elsanhoty & Ramadan, 2014. D'Aquino et al. (1991 observed that rats fed diets with fish oil had increased lipid peroxidation. Our results indicate that, in FO-S, selenium may have protected FA from peroxidation, thus dietary HUFA seemed to have been sufficient to maintain the phospholipid turnover and induction of FA metabolism genes did not occur. In FO-NS diet membranes were not protected efficiently from lipid peroxidation, and therefore a higher expression of FA metabolism genes was necessary to offset the damage, consequently, biosynthesis of HUFA was more stimulated. ROS-induced oxidative stress has been associated with expression and protein levels of transcription factors (Okuno et al., 2012. A reduction of ROS (Reactive Oxygen Species has been observed in fish fed VO with selenium, when compared to VO without selenium (data not presented. It is plausible to infer that a stimulation of expression and level of protein SREBP-1 by a reduction of ROS. SREBP-1 play a role on the regulation of genes involved in biosynthesis of HUFA, as fads2 and elovl5 (Jump, Tripathy & Depner, 2013. In conclusion, our results showed that vegetable oils have an effect on expression level of genes

  14. Can we rely on predicted basal metabolic rate in chronic pancreatitis outpatients?

    Olesen, Søren Schou; Holst, Mette; Køhler, Marianne; Drewes, Asbjørn Mohr; Rasmussen, Henrik Højgaard

    2015-04-01

    Malnutrition is a common complication to chronic pancreatitis (CP) and many patients need nutritional support. An accurate estimation of the basal metabolic rate (BMR) is essential when appropriate nutritional support is to be initiated, but in the clinical settings BMR is cumbersome to measure. We therefore investigated whether BMR can be reliable predicted from a standard formula (the Harris-Benedict equation) in CP outpatients. Twenty-eight patients with clinical stable CP and no current alcohol abuse were enrolled. Patients were stratified according to nutritional risk using the Nutrition Risk Screening 2002 system. Body composition was estimated using bioelectrical impedance. BMR was measured using indirect calorimetry and predicted using the Harris-Benedict equation based on anthropometric data. The average predicted BMR was 1371 ± 216 kcal/day compared to an average measured BMR of 1399 ± 231 kcal/day (P = 0.4). The corresponding limits of agreement were -347 to 290 kcal/day. Twenty-two patients (79%) had a measured BMR between 85 and 115% of the predicted BMR. When analysing patients stratified according to nutritional risk profiles, no differences between predicted and measured BMR were evident for any of the risk profile subgroups (all P > 0.2). The BMR was correlated to fat free mass determined by bioelectrical impedance (rho = 0.55; P = 0.003), while no effect modification was seen from nutritional risk stratification in a linear regression analysis (P = 0.4). The Harris-Benedict equation reliable predicts the measured BMR in four out of five clinical stable CP outpatients with no current alcohol abuse. Copyright © 2015 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.

  15. Modulation of Xenobiotic Metabolizing Enzyme and Transporter Gene Expression in Primary Cultures of Human Hepatocytes by ToxCast Chemicals

    ToxCast chemicals were assessed for induction or suppression of xenobiotic metabolizing enzyme and transporter gene expression using primary human hepatocytes. The mRNA levels of 14 target and 2 control genes were measured: ABCB1, ABCB11, ABCG2, SLCO1B1, CYP1A1, CYP1A2, CYP2B6, C...

  16. Carboxylesterase 1A2 encoding gene with increased transcription and potential rapid drug metabolism in Asian populations

    Rasmussen, Henrik Berg; Madsen, Majbritt Busk; Lyauk, Yassine Kamal

    2017-01-01

    The carboxylesterase 1 gene (CES1) encodes a hydrolase implicated in the metabolism of commonly used drugs. CES1A2, a hybrid of CES1 and a CES1-like pseudogene, has a promoter that is weak in most individuals. However, some individuals harbor a promoter haplotype of this gene with two overlapping...

  17. Effect of high-intensity training on exercise-induced gene expression specific to ion homeostasis and metabolism

    Nordsborg, Nikolai; Bangsbo, Jens; Pilegaard, Henriette

    2003-01-01

    Changes in gene expression during recovery from high-intensity, intermittent, one-legged exercise were studied before and after 5.5 wk of training. Genes related to metabolism, as well as Na+, K+, and pH homeostasis, were selected for analyses. After the same work was performed before and after...

  18. Accurate prediction of the functional significance of single nucleotide polymorphisms and mutations in the ABCA1 gene.

    Liam R Brunham

    2005-12-01

    Full Text Available The human genome contains an estimated 100,000 to 300,000 DNA variants that alter an amino acid in an encoded protein. However, our ability to predict which of these variants are functionally significant is limited. We used a bioinformatics approach to define the functional significance of genetic variation in the ABCA1 gene, a cholesterol transporter crucial for the metabolism of high density lipoprotein cholesterol. To predict the functional consequence of each coding single nucleotide polymorphism and mutation in this gene, we calculated a substitution position-specific evolutionary conservation score for each variant, which considers site-specific variation among evolutionarily related proteins. To test the bioinformatics predictions experimentally, we evaluated the biochemical consequence of these sequence variants by examining the ability of cell lines stably transfected with the ABCA1 alleles to elicit cholesterol efflux. Our bioinformatics approach correctly predicted the functional impact of greater than 94% of the naturally occurring variants we assessed. The bioinformatics predictions were significantly correlated with the degree of functional impairment of ABCA1 mutations (r2 = 0.62, p = 0.0008. These results have allowed us to define the impact of genetic variation on ABCA1 function and to suggest that the in silico evolutionary approach we used may be a useful tool in general for predicting the effects of DNA variation on gene function. In addition, our data suggest that considering patterns of positive selection, along with patterns of negative selection such as evolutionary conservation, may improve our ability to predict the functional effects of amino acid variation.

  19. Two different secondary metabolism gene clusters occupied the same ancestral locus in fungal dermatophytes of the arthrodermataceae.

    Zhang, Han; Rokas, Antonis; Slot, Jason C

    2012-01-01

    Dermatophyte fungi of the family Arthrodermataceae (Eurotiomycetes) colonize keratinized tissue, such as skin, frequently causing superficial mycoses in humans and other mammals, reptiles, and birds. Competition with native microflora likely underlies the propensity of these dermatophytes to produce a diversity of antibiotics and compounds for scavenging iron, which is extremely scarce, as well as the presence of an unusually large number of putative secondary metabolism gene clusters, most of which contain non-ribosomal peptide synthetases (NRPS), in their genomes. To better understand the historical origins and diversification of NRPS-containing gene clusters we examined the evolution of a variable locus (VL) that exists in one of three alternative conformations among the genomes of seven dermatophyte species. The first conformation of the VL (termed VLA) contains only 539 base pairs of sequence and lacks protein-coding genes, whereas the other two conformations (termed VLB and VLC) span 36 Kb and 27 Kb and contain 12 and 10 genes, respectively. Interestingly, both VLB and VLC appear to contain distinct secondary metabolism gene clusters; VLB contains a NRPS gene as well as four porphyrin metabolism genes never found to be physically linked in the genomes of 128 other fungal species, whereas VLC also contains a NRPS gene as well as several others typically found associated with secondary metabolism gene clusters. Phylogenetic evidence suggests that the VL locus was present in the ancestor of all seven species achieving its present distribution through subsequent differential losses or retentions of specific conformations. We propose that the existence of variable loci, similar to the one we studied, in fungal genomes could potentially explain the dramatic differences in secondary metabolic diversity between closely related species of filamentous fungi, and contribute to host adaptation and the generation of metabolic diversity.

  20. Diet-gene interactions between dietary fat intake and common polymorphisms in determining lipid metabolism

    Corella, Dolores

    2009-03-01

    Full Text Available Current dietary guidelines for fat intake have not taken into consideration the possible genetic differences underlying the individual variability in responsiveness to dietary components. Genetic variability has been identified in humans for all the known lipid metabolim-related genes resulting in a plethora of candidate genes and genetic variants to examine in diet-gene interaction studies focused on fat consumption. Some examples of fat-gene interaction are reviewed. These include: the interaction between total intake and the 514C/T in the hepatic lipase gene promoter in determining high-density lipoprotein cholesterol (HDL-C metabolism; the interaction between polyunsaturated fatty acids (PUFA and the 75G/A polymorphism in the APOA1 gene plasma HDL-C concentrations; the interaction between PUFA and the L162V polymorphism in the PPARA gene in determining triglycerides and APOC3 concentrations; and the interaction between PUFA intake and the 1131TC in the APOA5 gene in determining triglyceride metabolism. Although hundreds of diet-gene interaction studies in lipid metabolism have been published, the level of evidence to make specific nutritional recommendations to the population is still low and more research in nutrigenetics has to be undertaken.Las recomendaciones dietéticas actuales referentes al consumo de grasas en la dieta han sido realizadas sin tener en cuenta las posibles diferencias genéticas de las personas que podrían ser las responsables de las diferentes respuestas interindividuales que frecuentemente se observan ante la misma dieta. La presencia de variabilidad genética ha sido puesta de manifiesto para todos los genes relacionados con el metabolismo lipídico, por lo que existe un ingente número de genes y de variantes genéticas para ser incluidas en los estudios sobre interacciones dieta-genotipo en el ámbito específico del consumo de grasas y aceites. Se revisarán algunos ejemplos sobre interacciones grasa

  1. Studies of metabolic phenotypic correlates of 15 obesity associated gene variants

    Sandholt, Camilla Helene; Vestmar, Marie Aare; Bille, Dorthe Sadowa

    2011-01-01

    associate with type 2 diabetes and to elucidate potential underlying metabolic mechanisms. Methods: 15 gene variants in 14 loci including TMEM18 (rs7561317), SH2B1 (rs7498665), KCTD15 (rs29941), NEGR1 (rs2568958), ETV5 (rs7647305), BDNF (rs4923461, rs925946), SEC16B (rs10913469), FAIM2 (rs7138803), GNPDA2......, which could suggest neuronal and peripheral distinctive ways of actions for the protein. SH2B1 rs7498665 associated with type 2 diabetes independently of BMI....

  2. Expression of genes associated with carbohydrate metabolism in cotton stems and roots

    Scheffler Jodi

    2009-01-01

    Full Text Available Abstract Background Cotton (Gossypium hirsutum L is an important crop worldwide that provides fiber for the textile industry. Cotton is a perennial plant that stores starch in stems and roots to provide carbohydrates for growth in subsequent seasons. Domesticated cotton makes these reserves available to developing seeds which impacts seed yield. The goals of these analyses were to identify genes and physiological pathways that establish cotton stems and roots as physiological sinks and investigate the role these pathways play in cotton development during seed set. Results Analysis of field-grown cotton plants indicated that starch levels peaked about the time of first anthesis and then declined similar to reports in greenhouse-grown cotton plants. Starch accumulated along the length of the stem and the shape and size of the starch grains from stems were easily distinguished from transient starch. Microarray analyses compared gene expression in tissues containing low levels of starch with tissues rapidly accumulating starch. Statistical analysis of differentially expressed genes indicated increased expression among genes associated with starch synthesis, starch degradation, hexose metabolism, raffinose synthesis and trehalose synthesis. The anticipated changes in these sugars were largely confirmed by measuring soluble sugars in selected tissues. Conclusion In domesticated cotton starch stored prior to flowering was available to support seed production. Starch accumulation observed in young field-grown plants was not observed in greenhouse grown plants. A suite of genes associated with starch biosynthesis was identified. The pathway for starch utilization after flowering was associated with an increase in expression of a glucan water dikinase gene as has been implicated in utilization of transient starch. Changes in raffinose levels and levels of expression of genes controlling trehalose and raffinose biosynthesis were also observed in vegetative

  3. Influence of Coding Variability in APP-Aβ Metabolism Genes in Sporadic Alzheimer's Disease.

    Celeste Sassi

    Full Text Available The cerebral deposition of Aβ42, a neurotoxic proteolytic derivate of amyloid precursor protein (APP, is a central event in Alzheimer's disease (AD(Amyloid hypothesis. Given the key role of APP-Aβ metabolism in AD pathogenesis, we selected 29 genes involved in APP processing, Aβ degradation and clearance. We then used exome and genome sequencing to investigate the single independent (single-variant association test and cumulative (gene-based association test effect of coding variants in these genes as potential susceptibility factors for AD, in a cohort composed of 332 sporadic and mainly late-onset AD cases and 676 elderly controls from North America and the UK. Our study shows that common coding variability in these genes does not play a major role for the disease development. In the single-variant association analysis, the main hits, none of which statistically significant after multiple testing correction (1.9e-4genes mainly involved in Aβ extracellular degradation (TTR, ACE, clearance (LRP1 and APP trafficking and recycling (SORL1. These results were partially replicated in the gene-based analysis (c-alpha and SKAT tests, that reports ECE1, LYZ and TTR as nominally associated to AD (1.7e-3 genes is not a critical factor for AD development and 2 Aβ degradation and clearance, rather than Aβ production, may play a key role in the etiology of sporadic AD.

  4. Higher schizotypy predicts better metabolic profile in unaffected siblings of patients with schizophrenia.

    Atbasoglu, E Cem; Gumus-Akay, Guvem; Guloksuz, Sinan; Saka, Meram Can; Ucok, Alp; Alptekin, Koksal; Gullu, Sevim; van Os, Jim

    2018-04-01

    Type 2 diabetes (T2D) is more frequent in schizophrenia (Sz) than in the general population. This association is partly accounted for by shared susceptibility genetic variants. We tested the hypotheses that a genetic predisposition to Sz would be associated with higher likelihood of insulin resistance (IR), and that IR would be predicted by subthreshold psychosis phenotypes. Unaffected siblings of Sz patients (n = 101) were compared with a nonclinical sample (n = 305) in terms of IR, schizotypy (SzTy), and a behavioural experiment of "jumping to conclusions". The measures, respectively, were the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Structured Interview for Schizotypy-Revised (SIS-R), and the Beads Task (BT). The likelihood of IR was examined in multiple regression models that included sociodemographic, metabolic, and cognitive parameters alongside group status, SIS-R scores, and BT performance. Insulin resistance was less frequent in siblings (31.7%) compared to controls (43.3%) (p model that examined all relevant parameters included the tSzTy tertiles, TG and HDL-C levels, and BMI, as significant predictors of IR. Lack of IR was predicted by the highest as compared to the lowest SzTy tertile [OR (95%CI): 0.43 (0.21-0.85), p = 0.015]. Higher dopaminergic activity may contribute to both schizotypal features and a favourable metabolic profile in the same individual. This is compatible with dopamine's regulatory role in glucose metabolism via indirect central actions and a direct action on pancreatic insulin secretion. The relationship between dopaminergic activity and metabolic profile in Sz must be examined in longitudinal studies with younger unaffected siblings.

  5. Learning-Induced Gene Expression in the Hippocampus Reveals a Role of Neuron -Astrocyte Metabolic Coupling in Long Term Memory

    Tadi, Monika; Allaman, Igor; Lengacher, Sylvain; Grenningloh, Gabriele; Magistretti, Pierre J.

    2015-01-01

    We examined the expression of genes related to brain energy metabolism and particularly those encoding glia (astrocyte)-specific functions in the dorsal hippocampus subsequent to learning. Context-dependent avoidance behavior was tested in mice using the step-through Inhibitory Avoidance (IA) paradigm. Animals were sacrificed 3, 9, 24, or 72 hours after training or 3 hours after retention testing. The quantitative determination of mRNA levels revealed learning-induced changes in the expression of genes thought to be involved in astrocyte-neuron metabolic coupling in a time dependent manner. Twenty four hours following IA training, an enhanced gene expression was seen, particularly for genes encoding monocarboxylate transporters 1 and 4 (MCT1, MCT4), alpha2 subunit of the Na/K-ATPase and glucose transporter type 1. To assess the functional role for one of these genes in learning, we studied MCT1 deficient mice and found that they exhibit impaired memory in the inhibitory avoidance task. Together, these observations indicate that neuron-glia metabolic coupling undergoes metabolic adaptations following learning as indicated by the change in expression of key metabolic genes.

  6. Learning-Induced Gene Expression in the Hippocampus Reveals a Role of Neuron -Astrocyte Metabolic Coupling in Long Term Memory.

    Monika Tadi

    Full Text Available We examined the expression of genes related to brain energy metabolism and particularly those encoding glia (astrocyte-specific functions in the dorsal hippocampus subsequent to learning. Context-dependent avoidance behavior was tested in mice using the step-through Inhibitory Avoidance (IA paradigm. Animals were sacrificed 3, 9, 24, or 72 hours after training or 3 hours after retention testing. The quantitative determination of mRNA levels revealed learning-induced changes in the expression of genes thought to be involved in astrocyte-neuron metabolic coupling in a time dependent manner. Twenty four hours following IA training, an enhanced gene expression was seen, particularly for genes encoding monocarboxylate transporters 1 and 4 (MCT1, MCT4, alpha2 subunit of the Na/K-ATPase and glucose transporter type 1. To assess the functional role for one of these genes in learning, we studied MCT1 deficient mice and found that they exhibit impaired memory in the inhibitory avoidance task. Together, these observations indicate that neuron-glia metabolic coupling undergoes metabolic adaptations following learning as indicated by the change in expression of key metabolic genes.

  7. Learning-Induced Gene Expression in the Hippocampus Reveals a Role of Neuron -Astrocyte Metabolic Coupling in Long Term Memory

    Tadi, Monika

    2015-10-29

    We examined the expression of genes related to brain energy metabolism and particularly those encoding glia (astrocyte)-specific functions in the dorsal hippocampus subsequent to learning. Context-dependent avoidance behavior was tested in mice using the step-through Inhibitory Avoidance (IA) paradigm. Animals were sacrificed 3, 9, 24, or 72 hours after training or 3 hours after retention testing. The quantitative determination of mRNA levels revealed learning-induced changes in the expression of genes thought to be involved in astrocyte-neuron metabolic coupling in a time dependent manner. Twenty four hours following IA training, an enhanced gene expression was seen, particularly for genes encoding monocarboxylate transporters 1 and 4 (MCT1, MCT4), alpha2 subunit of the Na/K-ATPase and glucose transporter type 1. To assess the functional role for one of these genes in learning, we studied MCT1 deficient mice and found that they exhibit impaired memory in the inhibitory avoidance task. Together, these observations indicate that neuron-glia metabolic coupling undergoes metabolic adaptations following learning as indicated by the change in expression of key metabolic genes.

  8. Metabolic flexibility of mitochondrial respiratory chain disorders predicted by computer modelling.

    Zieliński, Łukasz P; Smith, Anthony C; Smith, Alexander G; Robinson, Alan J

    2016-11-01

    Mitochondrial respiratory chain dysfunction causes a variety of life-threatening diseases affecting about 1 in 4300 adults. These diseases are genetically heterogeneous, but have the same outcome; reduced activity of mitochondrial respiratory chain complexes causing decreased ATP production and potentially toxic accumulation of metabolites. Severity and tissue specificity of these effects varies between patients by unknown mechanisms and treatment options are limited. So far most research has focused on the complexes themselves, and the impact on overall cellular metabolism is largely unclear. To illustrate how computer modelling can be used to better understand the potential impact of these disorders and inspire new research directions and treatments, we simulated them using a computer model of human cardiomyocyte mitochondrial metabolism containing over 300 characterised reactions and transport steps with experimental parameters taken from the literature. Overall, simulations were consistent with patient symptoms, supporting their biological and medical significance. These simulations predicted: complex I deficiencies could be compensated using multiple pathways; complex II deficiencies had less metabolic flexibility due to impacting both the TCA cycle and the respiratory chain; and complex III and IV deficiencies caused greatest decreases in ATP production with metabolic consequences that parallel hypoxia. Our study demonstrates how results from computer models can be compared to a clinical phenotype and used as a tool for hypothesis generation for subsequent experimental testing. These simulations can enhance understanding of dysfunctional mitochondrial metabolism and suggest new avenues for research into treatment of mitochondrial disease and other areas of mitochondrial dysfunction. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  9. Predicting Basal Metabolic Rate in Men with Motor Complete Spinal Cord Injury.

    Nightingale, Tom E; Gorgey, Ashraf S

    2018-01-08

    To assess the accuracy of existing basal metabolic rate (BMR) prediction equations in men with chronic (>1 year) spinal cord injury (SCI). The primary aim is to develop new SCI population-specific BMR prediction models, based on anthropometric, body composition and/or demographic variables that are strongly associated with BMR. Thirty men with chronic SCI (Paraplegic; n = 21, Tetraplegic; n = 9), aged 35 ± 11 years (mean ± SD) participated in this cross-sectional study. Criterion BMR values were measured by indirect calorimetry. Body composition (dual energy X-ray absorptiometry; DXA) and anthropometric measurements (circumferences and diameters) were also taken. Multiple linear regression analysis was performed to develop new SCI-specific BMR prediction models. Criterion BMR values were compared to values estimated from six existing and four developed prediction equations RESULTS: Existing equations that use information on stature, weight and/or age, significantly (P BMR by a mean of 14-17% (187-234 kcal/day). Equations that utilised fat-free mass (FFM) accurately predicted BMR. The development of new SCI-specific prediction models demonstrated that the addition of anthropometric variables (weight, height and calf circumference) to FFM (Model 3; r = 0.77), explained 8% more of the variance in BMR than FFM alone (Model 1; r = 0.69). Using anthropometric variables, without FFM, explained less of the variance in BMR (Model 4; r = 0.57). However, all the developed prediction models demonstrated acceptable mean absolute error ≤ 6%. BMR can be more accurately estimated when DXA derived FFM is incorporated into prediction equations. Utilising anthropometric measurements provides a promising alternative to improve the prediction of BMR, beyond that achieved by existing equations in persons with SCI.

  10. Validity of predictive equations for basal metabolic rate in Japanese adults.

    Miyake, Rieko; Tanaka, Shigeho; Ohkawara, Kazunori; Ishikawa-Takata, Kazuko; Hikihara, Yuki; Taguri, Emiko; Kayashita, Jun; Tabata, Izumi

    2011-01-01

    Many predictive equations for basal metabolic rate (BMR) based on anthropometric measurements, age, and sex have been developed, mainly for healthy Caucasians. However, it has been reported that many of these equations, used widely, overestimate BMR not only for Asians, but also for Caucasians. The present study examined the accuracy of several predictive equations for BMR in Japanese subjects. In 365 healthy Japanese male and female subjects, aged 18 to 79 y, BMR was measured in the post-absorptive state using a mask and Douglas bag. Six predictive equations were examined. Total error was used as an index of the accuracy of each equation's prediction. Predicted BMR values by Dietary Reference Intakes for Japanese (Japan-DRI), Adjusted Dietary Reference Intakes for Japanese (Adjusted-DRI), and Ganpule equations were not significantly different from the measured BMR in either sex. On the other hand, Harris-Benedict, Schofield, and Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University equations were significantly higher than the measured BMR in both sexes. The prediction error by Japan-DRI, Adjusted-DRI, and Harris-Benedict equations was significantly correlated with body weight in both sexes. Total error using the Ganpule equation was low in both males and females (125 and 99 kcal/d, respectively). In addition, total error using the Adjusted-DRI equation was low in females (95 kcal/d). Thus, the Ganpule equation was the most accurate in predicting BMR in our healthy Japanese subjects, because the difference between the predicted and measured BMR was relatively small, and body weight had no effect on the prediction error.

  11. Prediction of work metabolism from heart rate measurements in forest work: some practical methodological issues.

    Dubé, Philippe-Antoine; Imbeau, Daniel; Dubeau, Denise; Auger, Isabelle; Leone, Mario

    2015-01-01

    Individual heart rate (HR) to workload relationships were determined using 93 submaximal step-tests administered to 26 healthy participants attending physical activities in a university training centre (laboratory study) and 41 experienced forest workers (field study). Predicted maximum aerobic capacity (MAC) was compared to measured MAC from a maximal treadmill test (laboratory study) to test the effect of two age-predicted maximum HR Equations (220-age and 207-0.7 × age) and two clothing insulation levels (0.4 and 0.91 clo) during the step-test. Work metabolism (WM) estimated from forest work HR was compared against concurrent work V̇O2 measurements while taking into account the HR thermal component. Results show that MAC and WM can be accurately predicted from work HR measurements and simple regression models developed in this study (1% group mean prediction bias and up to 25% expected prediction bias for a single individual). Clothing insulation had no impact on predicted MAC nor age-predicted maximum HR equations. Practitioner summary: This study sheds light on four practical methodological issues faced by practitioners regarding the use of HR methodology to assess WM in actual work environments. More specifically, the effect of wearing work clothes and the use of two different maximum HR prediction equations on the ability of a submaximal step-test to assess MAC are examined, as well as the accuracy of using an individual's step-test HR to workload relationship to predict WM from HR data collected during actual work in the presence of thermal stress.

  12. Identifying the Gene Signatures from Gene-Pathway Bipartite Network Guarantees the Robust Model Performance on Predicting the Cancer Prognosis

    Li He

    2014-01-01

    Full Text Available For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.

  13. Taxonomic and predicted metabolic profiles of the human gut microbiome in pre-Columbian mummies.

    Santiago-Rodriguez, Tasha M; Fornaciari, Gino; Luciani, Stefania; Dowd, Scot E; Toranzos, Gary A; Marota, Isolina; Cano, Raul J

    2016-11-01

    Characterization of naturally mummified human gut remains could potentially provide insights into the preservation and evolution of commensal and pathogenic microorganisms, and metabolic profiles. We characterized the gut microbiome of two pre-Columbian Andean mummies dating to the 10-15th centuries using 16S rRNA gene high-throughput sequencing and metagenomics, and compared them to a previously characterized gut microbiome of an 11th century AD pre-Columbian Andean mummy. Our previous study showed that the Clostridiales represented the majority of the bacterial communities in the mummified gut remains, but that other microbial communities were also preserved during the process of natural mummification, as shown with the metagenomics analyses. The gut microbiome of the other two mummies were mainly comprised by Clostridiales or Bacillales, as demonstrated with 16S rRNA gene amplicon sequencing, many of which are facultative anaerobes, possibly consistent with the process of natural mummification requiring low oxygen levels. Metagenome analyses showed the presence of other microbial groups that were positively or negatively correlated with specific metabolic profiles. The presence of sequences similar to both Trypanosoma cruzi and Leishmania donovani could suggest that these pathogens were prevalent in pre-Columbian individuals. Taxonomic and functional profiling of mummified human gut remains will aid in the understanding of the microbial ecology of the process of natural mummification. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  14. Dynamic gene expression for metabolic engineering of mammalian cells in culture.

    Le, Huong; Vishwanathan, Nandita; Kantardjieff, Anne; Doo, Inseok; Srienc, Michael; Zheng, Xiaolu; Somia, Nikunj; Hu, Wei-Shou

    2013-11-01

    Recombinant mammalian cells are the major hosts for the production of protein therapeutics. In addition to high expression of the product gene, a hyper-producer must also harbor superior phenotypic traits related to metabolism, protein secretion, and growth control. Introduction of genes endowing the relevant hyper-productivity traits is a strategy frequently used to enhance the productivity. Most of such cell engineering efforts have been performed using constitutive expression systems. However, cells respond to various environmental cues and cellular events dynamically according to cellular needs. The use of inducible systems allows for time dependent expression, but requires external manipulation. Ideally, a transgene's expression should be synchronous to the host cell's own rhythm, and at levels appropriate for the objective. To that end, we identified genes with different expression dynamics and intensity ranges using pooled transcriptome data. Their promoters may be used to drive the expression of the transgenes following the desired dynamics. We isolated the promoter of the Thioredoxin-interacting protein (Txnip) gene and demonstrated its capability to drive transgene expression in concert with cell growth. We further employed this Chinese hamster promoter to engineer dynamic expression of the mouse GLUT5 fructose transporter in Chinese hamster ovary (CHO) cells, enabling them to utilize sugar according to cellular needs rather than in excess as typically seen in culture. Thus, less lactate was produced, resulting in a better growth rate, prolonged culture duration, and higher product titer. This approach illustrates a novel concept in metabolic engineering which can potentially be used to achieve dynamic control of cellular behaviors for enhanced process characteristics. © 2013 Published by Elsevier Inc.

  15. Metabolic activity in the insular cortex and hypothalamus predicts hot flashes: an FDG-PET study.

    Joffe, Hadine; Deckersbach, Thilo; Lin, Nancy U; Makris, Nikos; Skaar, Todd C; Rauch, Scott L; Dougherty, Darin D; Hall, Janet E

    2012-09-01

    Hot flashes are a common side effect of adjuvant endocrine therapies (AET; leuprolide, tamoxifen, aromatase inhibitors) that reduce quality of life and treatment adherence in breast cancer patients. Because hot flashes affect only some women, preexisting neurobiological traits might predispose to their development. Previous studies have implicated the insula during the perception of hot flashes and the hypothalamus in thermoregulatory dysfunction. The aim of the study was to understand whether neurobiological factors predict hot flashes. [18F]-Fluorodeoxyglucose (FDG) positron emission tomography (PET) brain scans coregistered with structural magnetic resonance imaging were used to determine whether metabolic activity in the insula and hypothalamic thermoregulatory and estrogen-feedback regions measured before and in response to AET predict hot flashes. Findings were correlated with CYP2D6 genotype because of CYP2D6 polymorphism associations with tamoxifen-induced hot flashes. We measured regional cerebral metabolic rate of glucose uptake (rCMRglu) in the insula and hypothalamus on FDG-PET. Of 18 women without hot flashes who began AET, new-onset hot flashes were reported by 10 (55.6%) and were detected objectively in nine (50%) participants. Prior to the use of all AET, rCMRglu in the insula (P ≤ 0.01) and hypothalamic thermoregulatory (P = 0.045) and estrogen-feedback (P = 0.007) regions was lower in women who reported developing hot flashes. In response to AET, rCMRglu was further reduced in the insula in women developing hot flashes (P ≤ 0.02). Insular and hypothalamic rCMRglu levels were lower in intermediate than extensive CYP2D6 metabolizers. Trait neurobiological characteristics predict hot flashes. Genetic variability in CYP2D6 may underlie the neurobiological predisposition to hot flashes induced by AET.

  16. Identifying essential genes in bacterial metabolic networks with machine learning methods

    2010-01-01

    Background Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective. Results We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway. Conclusions Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism. PMID:20438628

  17. GeneChip expression profiling reveals the alterations of energy metabolism related genes in osteocytes under large gradient high magnetic fields.

    Wang, Yang; Chen, Zhi-Hao; Yin, Chun; Ma, Jian-Hua; Li, Di-Jie; Zhao, Fan; Sun, Yu-Long; Hu, Li-Fang; Shang, Peng; Qian, Ai-Rong

    2015-01-01

    The diamagnetic levitation as a novel ground-based model for simulating a reduced gravity environment has recently been applied in life science research. In this study a specially designed superconducting magnet with a large gradient high magnetic field (LG-HMF), which can provide three apparent gravity levels (μ-g, 1-g, and 2-g), was used to simulate a space-like gravity environment. Osteocyte, as the most important mechanosensor in bone, takes a pivotal position in mediating the mechano-induced bone remodeling. In this study, the effects of LG-HMF on gene expression profiling of osteocyte-like cell line MLO-Y4 were investigated by Affymetrix DNA microarray. LG-HMF affected osteocyte gene expression profiling. Differentially expressed genes (DEGs) and data mining were further analyzed by using bioinfomatic tools, such as DAVID, iReport. 12 energy metabolism related genes (PFKL, AK4, ALDOC, COX7A1, STC1, ADM, CA9, CA12, P4HA1, APLN, GPR35 and GPR84) were further confirmed by real-time PCR. An integrated gene interaction network of 12 DEGs was constructed. Bio-data mining showed that genes involved in glucose metabolic process and apoptosis changed notablly. Our results demostrated that LG-HMF affected the expression of energy metabolism related genes in osteocyte. The identification of sensitive genes to special environments may provide some potential targets for preventing and treating bone loss or osteoporosis.

  18. GeneChip expression profiling reveals the alterations of energy metabolism related genes in osteocytes under large gradient high magnetic fields.

    Yang Wang

    Full Text Available The diamagnetic levitation as a novel ground-based model for simulating a reduced gravity environment has recently been applied in life science research. In this study a specially designed superconducting magnet with a large gradient high magnetic field (LG-HMF, which can provide three apparent gravity levels (μ-g, 1-g, and 2-g, was used to simulate a space-like gravity environment. Osteocyte, as the most important mechanosensor in bone, takes a pivotal position in mediating the mechano-induced bone remodeling. In this study, the effects of LG-HMF on gene expression profiling of osteocyte-like cell line MLO-Y4 were investigated by Affymetrix DNA microarray. LG-HMF affected osteocyte gene expression profiling. Differentially expressed genes (DEGs and data mining were further analyzed by using bioinfomatic tools, such as DAVID, iReport. 12 energy metabolism related genes (PFKL, AK4, ALDOC, COX7A1, STC1, ADM, CA9, CA12, P4HA1, APLN, GPR35 and GPR84 were further confirmed by real-time PCR. An integrated gene interaction network of 12 DEGs was constructed. Bio-data mining showed that genes involved in glucose metabolic process and apoptosis changed notablly. Our results demostrated that LG-HMF affected the expression of energy metabolism related genes in osteocyte. The identification of sensitive genes to special environments may provide some potential targets for preventing and treating bone loss or osteoporosis.

  19. Clinical utility of polymorphisms in one-carbon metabolism for breast cancer risk prediction

    Shaik Mohammad Naushad

    2011-01-01

    Full Text Available This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1 G80A, thymidylate synthase (TYMS 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR C677T and catecholamine-O-methyl transferase (COMT genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII C1561T and cytosolic serine hydroxymethyl transferase (cSHMT C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC curves were plotted and the area under ROC curve (C was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93 while the predictability was lost in subjects with one protective allele (C=0.60. However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles. The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.

  20. An in silico assessment of gene function and organization of the phenylpropanoid pathway metabolic networks in Arabidopsis thaliana and limitations thereof

    Costa, Michael A.; Collins, R. Eric; Anterola, Aldwin M.; Cochrane, Fiona C.; Davin, Laurence B.; Lewis, Norman G.

    2003-01-01

    The Arabidopsis genome sequencing in 2000 gave to science the first blueprint of a vascular plant. Its successful completion also prompted the US National Science Foundation to launch the Arabidopsis 2010 initiative, the goal of which is to identify the function of each gene by 2010. In this study, an exhaustive analysis of The Institute for Genomic Research (TIGR) and The Arabidopsis Information Resource (TAIR) databases, together with all currently compiled EST sequence data, was carried out in order to determine to what extent the various metabolic networks from phenylalanine ammonia lyase (PAL) to the monolignols were organized and/or could be predicted. In these databases, there are some 65 genes which have been annotated as encoding putative enzymatic steps in monolignol biosynthesis, although many of them have only very low homology to monolignol pathway genes of known function in other plant systems. Our detailed analysis revealed that presently only 13 genes (two PALs, a cinnamate-4-hydroxylase, a p-coumarate-3-hydroxylase, a ferulate-5-hydroxylase, three 4-coumarate-CoA ligases, a cinnamic acid O-methyl transferase, two cinnamoyl-CoA reductases) and two cinnamyl alcohol dehydrogenases can be classified as having a bona fide (definitive) function; the remaining 52 genes currently have undetermined physiological roles. The EST database entries for this particular set of genes also provided little new insight into how the monolignol pathway was organized in the different tissues and organs, this being perhaps a consequence of both limitations in how tissue samples were collected and in the incomplete nature of the EST collections. This analysis thus underscores the fact that even with genomic sequencing, presumed to provide the entire suite of putative genes in the monolignol-forming pathway, a very large effort needs to be conducted to establish actual catalytic roles (including enzyme versatility), as well as the physiological function(s) for each member

  1. Novel inborn error of folate metabolism: identification by exome capture and sequencing of mutations in the MTHFD1 gene in a single proband.

    Watkins, David; Schwartzentruber, Jeremy A; Ganesh, Jaya; Orange, Jordan S; Kaplan, Bernard S; Nunez, Laura Dempsey; Majewski, Jacek; Rosenblatt, David S

    2011-09-01

    An infant was investigated because of megaloblastic anaemia, atypical hemolytic uraemic syndrome, severe combined immune deficiency, elevated blood levels of homocysteine and methylmalonic acid, and a selective decreased synthesis of methylcobalamin in cultured fibroblasts. Exome sequencing was performed on patient genomic DNA. Two mutations were identified in the MTHFD1 gene, which encodes a protein that catalyses three reactions involved in cellular folate metabolism. This protein is essential for the generation of formyltetrahydrofolate and methylenetetrahydrofolate and important for nucleotide and homocysteine metabolism. One mutation (c.727+1G>A) affects the splice acceptor site of intron 8. The second mutation, c.517C>T (p.R173C), changes a critical arginine residue in the NADP-binding site of the protein. Mutations affecting this arginine have previously been shown to affect enzyme activity. Both parents carry a single mutation and an unaffected sibling carries neither mutation. The combination of two mutations in the MTHFRD1 gene, predicted to have severe consequences, in the patient and their absence in the unaffected sibling, supports causality. This patient represents the first case of an inborn error of folate metabolism affecting the trifunctional MTHFD1 protein. This report reinforces the power of exome capture and sequencing for the discovery of novel genes, even when only a single proband is available for study.

  2. Gene-diet-interactions in folate-mediated one-carbon metabolism modify colon cancer risk.

    Liu, Amy Y; Scherer, Dominique; Poole, Elizabeth; Potter, John D; Curtin, Karen; Makar, Karen; Slattery, Martha L; Caan, Bette J; Ulrich, Cornelia M

    2013-04-01

    The importance of folate-mediated one-carbon metabolism (FOCM) in colorectal carcinogenesis is emphasized by observations that high dietary folate intake is associated with decreased risk of colon cancer (CC) and its precursors. Additionally, polymorphisms in FOCM-related genes have been repeatedly associated with risk, supporting a causal relationship between folate and colorectal carcinogenesis. We investigated ten candidate polymorphisms with defined or probable functional impact in eight FOCM-related genes (SHMT1, DHFR, DNMT1, MTHFD1, MTHFR, MTRR, TCN2, and TDG) in 1609 CC cases and 1974 controls for association with CC risk and for interaction with dietary factors. No polymorphism was statistically significantly associated with overall risk of CC. However, statistically significant interactions modifying CC risk were observed for DNMT1 I311V with dietary folate, methionine, vitamin B2 , and vitamin B12 intake and for MTRR I22M with dietary folate, a predefined one-carbon dietary pattern, and vitamin B6 intake. We observed statistically significant gene-diet interactions with five additional polymorphisms. Our results provide evidence that FOCM-related dietary intakes modify the association between CC risk and FOCM allelic variants. These findings add to observations showing that folate-related gene-nutrient interactions play an important role in modifying the risk of CC. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Diet-Gene Interactions and PUFA Metabolism: A Potential Contributor to Health Disparities and Human Diseases

    Floyd H. Chilton

    2014-05-01

    Full Text Available The “modern western” diet (MWD has increased the onset and progression of chronic human diseases as qualitatively and quantitatively maladaptive dietary components give rise to obesity and destructive gene-diet interactions. There has been a three-fold increase in dietary levels of the omega-6 (n-6 18 carbon (C18, polyunsaturated fatty acid (PUFA linoleic acid (LA; 18:2n-6, with the addition of cooking oils and processed foods to the MWD. Intense debate has emerged regarding the impact of this increase on human health. Recent studies have uncovered population-related genetic variation in the LCPUFA biosynthetic pathway (especially within the fatty acid desaturase gene (FADS cluster that is associated with levels of circulating and tissue PUFAs and several biomarkers and clinical endpoints of cardiovascular disease (CVD. Importantly, populations of African descent have higher frequencies of variants associated with elevated levels of arachidonic acid (ARA, CVD biomarkers and disease endpoints. Additionally, nutrigenomic interactions between dietary n-6 PUFAs and variants in genes that encode for enzymes that mobilize and metabolize ARA to eicosanoids have been identified. These observations raise important questions of whether gene-PUFA interactions are differentially driving the risk of cardiovascular and other diseases in diverse populations, and contributing to health disparities, especially in African American populations.

  4. Trehalose metabolism genes render rice white tip nematode Aphelenchoides besseyi (Nematoda: Aphelenchoididae) resistant to an anaerobic environment

    Chen, Qiaoli; Zhang, Ruizhi; Ling, Yaming

    2018-01-01

    ABSTRACT After experiencing anaerobic environments, Aphelenchoides besseyi will enter a state of suspended animation known as anoxybiosis, during which it may use trehalose as an energy supply to survive. To explore the function of trehalose metabolism, two trehalose-6-phosphate synthase (TPS) genes (Ab-tps1 and Ab-tps2) encoding enzymes catalysing trehalose synthesis, and three trehalase (TRE) genes (Ab-ntre1, Ab-ntre2 and Ab-atre) encoding enzymes catalysing the hydrolysis of trehalose, were identified and investigated. Ab-tps1 and Ab-tps2 were active during certain periods of anoxybiosis for A. besseyi, and Ab-tps2, Ab-ntre1, Ab-ntre2 and Ab-atre were active during certain periods of recovery. The results of RNA interference experiments suggested that TRE genes regulated each other and both TPS genes, while a single TPS gene only regulated the other TPS gene. However, two TPS genes together could regulate TRE genes, which indicated a feedback mechanism between these genes. All these genes also positively regulated the survival and resumption of active metabolism of the nematode. Genes functioning at re-aeration have a greater impact on nematode survival, suggesting that these genes could play roles in anoxybiosis regulation, but may function within restricted time frames. Changes in trehalose levels matched changes in TRE activity during the anoxybiosis–re-aeration process, suggesting that trehalose may act as an energy supply source. The observation of up-regulation of TPS genes during anoxybiosis suggested a possible signal role of trehalose. Trehalose metabolism genes could also work together to control trehalose levels at a certain level when the nematode is under anaerobic conditions. PMID:29158222

  5. Fatty Liver Index and Lipid Accumulation Product Can Predict Metabolic Syndrome in Subjects without Fatty Liver Disease

    Yuan-Lung Cheng

    2017-01-01

    Full Text Available Background. Fatty liver index (FLI and lipid accumulation product (LAP are indexes originally designed to assess the risk of fatty liver and cardiovascular disease, respectively. Both indexes have been proven to be reliable markers of subsequent metabolic syndrome; however, their ability to predict metabolic syndrome in subjects without fatty liver disease has not been clarified. Methods. We enrolled consecutive subjects who received health check-up services at Taipei Veterans General Hospital from 2002 to 2009. Fatty liver disease was diagnosed by abdominal ultrasonography. The ability of the FLI and LAP to predict metabolic syndrome was assessed by analyzing the area under the receiver operating characteristic (AUROC curve. Results. Male sex was strongly associated with metabolic syndrome, and the LAP and FLI were better than other variables to predict metabolic syndrome among the 29,797 subjects. Both indexes were also better than other variables to detect metabolic syndrome in subjects without fatty liver disease (AUROC: 0.871 and 0.879, resp., and the predictive power was greater among women. Conclusion. Metabolic syndrome increases the cardiovascular disease risk. The FLI and LAP could be used to recognize the syndrome in both subjects with and without fatty liver disease who require lifestyle modifications and counseling.

  6. Salmonella Modulates Metabolism During Growth under Conditions that Induce Expression of Virulence Genes

    Kim, Young-Mo; Schmidt, Brian; Kidwai, Afshan S.; Jones, Marcus B.; Deatherage, Brooke L.; Brewer, Heather M.; Mitchell, Hugh D.; Palsson, Bernhard O.; McDermott, Jason E.; Heffron, Fred; Smith, Richard D.; Peterson, Scott N.; Ansong, Charles; Hyduke, Daniel R.; Metz, Thomas O.; Adkins, Joshua N.

    2013-04-05

    Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Excitingly, we observed possible sequestration of metabolites recently suggested to have immune modulating roles. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Model-guided analysis suggested that alterations in metabolism prioritized other activities necessary for pathogenesis instead, such as lipopolysaccharide biosynthesis.

  7. Phase I metabolic genes and risk of lung cancer: multiple polymorphisms and mRNA expression.

    Melissa Rotunno

    2009-05-01

    Full Text Available Polymorphisms in genes coding for enzymes that activate tobacco lung carcinogens may generate inter-individual differences in lung cancer risk. Previous studies had limited sample sizes, poor exposure characterization, and a few single nucleotide polymorphisms (SNPs tested in candidate genes. We analyzed 25 SNPs (some previously untested in 2101 primary lung cancer cases and 2120 population controls from the Environment And Genetics in Lung cancer Etiology (EAGLE study from six phase I metabolic genes, including cytochrome P450s, microsomal epoxide hydrolase, and myeloperoxidase. We evaluated the main genotype effects and genotype-smoking interactions in lung cancer risk overall and in the major histology subtypes. We tested the combined effect of multiple SNPs on lung cancer risk and on gene expression. Findings were prioritized based on significance thresholds and consistency across different analyses, and accounted for multiple testing and prior knowledge. Two haplotypes in EPHX1 were significantly associated with lung cancer risk in the overall population. In addition, CYP1B1 and CYP2A6 polymorphisms were inversely associated with adenocarcinoma and squamous cell carcinoma risk, respectively. Moreover, the association between CYP1A1 rs2606345 genotype and lung cancer was significantly modified by intensity of cigarette smoking, suggesting an underlying dose-response mechanism. Finally, increasing number of variants at CYP1A1/A2 genes revealed significant protection in never smokers and risk in ever smokers. Results were supported by differential gene expression in non-tumor lung tissue samples with down-regulation of CYP1A1 in never smokers and up-regulation in smokers from CYP1A1/A2 SNPs. The significant haplotype associations emphasize that the effect of multiple SNPs may be important despite null single SNP-associations, and warrants consideration in genome-wide association studies (GWAS. Our findings emphasize the necessity of post

  8. The predictive ability of triglycerides and waist (hypertriglyceridemic waist) in assessing metabolic triad change in obese children and adolescents.

    Hobkirk, James P; King, Roderick F; Gately, Paul; Pemberton, Philip; Smith, Alexander; Barth, Julian H; Harman, Nicola; Davies, Ian; Carroll, Sean

    2013-10-01

    The metabolic triad [fasting insulin, apolipoprotein B, and low-density lipoporotein (LDL) peak particle density] is characteristic of increased intra-abdominal adipose tissue and insulin resistance and can be predicted by the simple and adoptable screening tool, the hypertriglyceridemic waist. The associations between hypertriglyceridemic waist components [fasting triglycerides (TG) and waist circumference cut-points derived from a child-specific metabolic syndrome definition] with the metabolic triad were examined in obese youth before and after weight loss. A continuous metabolic triad score (MTS) was calculated as a cumulative and standardized residual score of fasting insulin, apolipoprotein B, and LDL peak particle density (z-scores of the metabolic triad variables regressed onto age and sex). The predictive ability of TG and waist in assessing metabolic triad change was undertaken in 75 clinically obese boys and girls, aged 8-18, body mass index (BMI) 34.2±6.4 kg/m(2) before and after weight loss. Fasting TG concentrations (r(2)=0.216, PFasting TG change was the only significant predictor of the MTS change (r(2)=0.177, Pfasting TG concentration (but not waist circumference) was the only significant predictor of MTS change. Fasting TG may be the most important metabolic syndrome component to best characterize the metabolic heterogeneity in obese cohorts and the changes in metabolic risk in clinically obese youth.

  9. Xenobiotic Metabolizing Gene Variants and Renal Cell Cancer: A Multicenter Study

    Heck, Julia E. [International Agency for Research on Cancer, Lyon (France); Department of Epidemiology, School of Public Health, University of California Los Angeles, Los Angeles, CA (United States); Moore, Lee E. [Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD (United States); Lee, Yuan-Chin A. [International Agency for Research on Cancer, Lyon (France); Department of Epidemiology, School of Public Health, University of California Los Angeles, Los Angeles, CA (United States); McKay, James D. [International Agency for Research on Cancer, Lyon (France); Hung, Rayjean J. [Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Toronto, ON (Canada); Karami, Sara [Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD (United States); Gaborieau, Valérie [International Agency for Research on Cancer, Lyon (France); Szeszenia-Dabrowska, Neonila [Department of Epidemiology, Institute of Occupational Medicine, Lodz (Poland); Zaridze, David G. [Cancer Research Centre, Institute of Carcinogenesis, Moscow (Russian Federation); Mukeriya, Anush [Cancer Research Centre, Department of Epidemiology, Moscow (Russian Federation); Mates, Dana [Institute of Public Health, Bucharest (Romania); Foretova, Lenka [Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno (Czech Republic); Janout, Vladimir; Kollárová, Helena [Department of Preventive Medicine, Faculty of Medicine, Palacky University, Olomouc (Czech Republic); Bencko, Vladimir [First Faculty of Medicine, Institute of Hygiene and Epidemiology, Charles University in Prague, Prague, Czech Republic (Czech Republic); Rothman, Nathaniel [Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD (United States); Brennan, Paul [International Agency for Research on Cancer, Lyon (France); Chow, Wong-Ho [Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD (United States); Boffetta, Paolo, E-mail: paolo.boffetta@mssm.edu [International Prevention Research Institute, Lyon (France); Tisch Cancer Institute, Mt. Sinai School of Medicine, New York, NY (United States)

    2012-02-20

    Background: The countries of Central and Eastern Europe have among the highest worldwide rates of renal cell cancer (RCC). Few studies have examined whether genetic variation in xenobiotic metabolic pathway genes may modify risk for this cancer. Methods: The Central and Eastern Europe Renal Cell Cancer study was a hospital-based case–control study conducted between 1998 and 2003 across seven centers in Central and Eastern Europe. Detailed data were collected from 874 cases and 2053 controls on demographics, work history, and occupational exposure to chemical agents. Genes [cytochrome P-450 family, N-acetyltransferases, NAD(P)H:quinone oxidoreductase I (NQO1), microsomal epoxide hydrolase (mEH), catechol-O-methyltransferase (COMT), uridine diphosphate-glucuronosyltransferase (UGT)] were selected for the present analysis based on their putative role in xenobiotic metabolism. Haplotypes were calculated using fastPhase. Odds ratios and 95% confidence intervals were estimated by unconditional logistic regression adjusted for country of residence, age, sex, smoking, alcohol intake, obesity, and hypertension. Results: We observed an increased risk of RCC with one SNP. After adjustment for multiple comparisons it did not remain significant. Neither NAT1 nor NAT2 slow acetylation was associated with disease. Conclusion: We observed no association between this pathway and renal cell cancer.

  10. Xenobiotic Metabolizing Gene Variants and Renal Cell Cancer: A Multicenter Study

    Heck, Julia E.; Moore, Lee E.; Lee, Yuan-Chin A.; McKay, James D.; Hung, Rayjean J.; Karami, Sara; Gaborieau, Valérie; Szeszenia-Dabrowska, Neonila; Zaridze, David G.; Mukeriya, Anush; Mates, Dana; Foretova, Lenka; Janout, Vladimir; Kollárová, Helena; Bencko, Vladimir; Rothman, Nathaniel; Brennan, Paul; Chow, Wong-Ho; Boffetta, Paolo

    2012-01-01

    Background: The countries of Central and Eastern Europe have among the highest worldwide rates of renal cell cancer (RCC). Few studies have examined whether genetic variation in xenobiotic metabolic pathway genes may modify risk for this cancer. Methods: The Central and Eastern Europe Renal Cell Cancer study was a hospital-based case–control study conducted between 1998 and 2003 across seven centers in Central and Eastern Europe. Detailed data were collected from 874 cases and 2053 controls on demographics, work history, and occupational exposure to chemical agents. Genes [cytochrome P-450 family, N-acetyltransferases, NAD(P)H:quinone oxidoreductase I (NQO1), microsomal epoxide hydrolase (mEH), catechol-O-methyltransferase (COMT), uridine diphosphate-glucuronosyltransferase (UGT)] were selected for the present analysis based on their putative role in xenobiotic metabolism. Haplotypes were calculated using fastPhase. Odds ratios and 95% confidence intervals were estimated by unconditional logistic regression adjusted for country of residence, age, sex, smoking, alcohol intake, obesity, and hypertension. Results: We observed an increased risk of RCC with one SNP. After adjustment for multiple comparisons it did not remain significant. Neither NAT1 nor NAT2 slow acetylation was associated with disease. Conclusion: We observed no association between this pathway and renal cell cancer.

  11. Nonsense mutations in the human β-globin gene affect mRNA metabolism

    Baserga, S.J.; Benz, E.J. Jr.

    1988-01-01

    A number of premature translation termination mutations (nonsense mutations) have been described in the human α- and β-globin genes. Studies on mRNA isolated from patients with β 0 -thalassemia have shown that for both the β-17 and the β-39 mutations less than normal levels of β-globin mRNA accumulate in peripheral blood cells. (The codon at which the mutation occurs designates the name of the mutation; there are 146 codons in human β-globin mRNA). In vitro studies using the cloned β-39 gene have reproduced this effect in a heterologous transfection system and have suggested that the defect resides in intranuclear metabolism. The authors have asked if this phenomenon of decreased mRNA accumulation is a general property of nonsense mutations and if the effect depends on the location or the type of mutation. Toward this end, they have studied the effect of five nonsense mutations and two missense mutations on the expression of human β-globin mRNA in a heterologous transfection system. In all cases studied, the presence of a translation termination codon correlates with a decrease in the steady-state level of mRNA. The data suggest that the metabolism of a mammalian mRNA is affected by the presence of a mutation that affects translation

  12. Incremental Predictive Value of Serum AST-to-ALT Ratio for Incident Metabolic Syndrome: The ARIRANG Study

    Ahn, Song Vogue; Baik, Soon Koo; Cho, Youn zoo; Koh, Sang Baek; Huh, Ji Hye; Chang, Yoosoo; Sung, Ki-Chul; Kim, Jang Young

    2016-01-01

    Aims The ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) is of great interest as a possible novel marker of metabolic syndrome. However, longitudinal studies emphasizing the incremental predictive value of the AST-to-ALT ratio in diagnosing individuals at higher risk of developing metabolic syndrome are very scarce. Therefore, our study aimed to evaluate the AST-to-ALT ratio as an incremental predictor of new onset metabolic syndrome in a population-based cohort study. Material and Methods The population-based cohort study included 2276 adults (903 men and 1373 women) aged 40–70 years, who participated from 2005–2008 (baseline) without metabolic syndrome and were followed up from 2008–2011. Metabolic syndrome was defined according to the harmonized definition of metabolic syndrome. Serum concentrations of AST and ALT were determined by enzymatic methods. Results During an average follow-up period of 2.6-years, 395 individuals (17.4%) developed metabolic syndrome. In a multivariable adjusted model, the odds ratio (95% confidence interval) for new onset of metabolic syndrome, comparing the fourth quartile to the first quartile of the AST-to-ALT ratio, was 0.598 (0.422–0.853). The AST-to-ALT ratio also improved the area under the receiver operating characteristic curve (AUC) for predicting new cases of metabolic syndrome (0.715 vs. 0.732, P = 0.004). The net reclassification improvement of prediction models including the AST-to-ALT ratio was 0.23 (95% CI: 0.124–0.337, Pmetabolic syndrome and had incremental predictive value for incident metabolic syndrome. PMID:27560931

  13. Determinants of human adipose tissue gene expression: impact of diet, sex, metabolic status, and cis genetic regulation.

    Nathalie Viguerie

    2012-09-01

    Full Text Available Weight control diets favorably affect parameters of the metabolic syndrome and delay the onset of diabetic complications. The adaptations occurring in adipose tissue (AT are likely to have a profound impact on the whole body response as AT is a key target of dietary intervention. Identification of environmental and individual factors controlling AT adaptation is therefore essential. Here, expression of 271 transcripts, selected for regulation according to obesity and weight changes, was determined in 515 individuals before, after 8-week low-calorie diet-induced weight loss, and after 26-week ad libitum weight maintenance diets. For 175 genes, opposite regulation was observed during calorie restriction and weight maintenance phases, independently of variations in body weight. Metabolism and immunity genes showed inverse profiles. During the dietary intervention, network-based analyses revealed strong interconnection between expression of genes involved in de novo lipogenesis and components of the metabolic syndrome. Sex had a marked influence on AT expression of 88 transcripts, which persisted during the entire dietary intervention and after control for fat mass. In women, the influence of body mass index on expression of a subset of genes persisted during the dietary intervention. Twenty-two genes revealed a metabolic syndrome signature common to men and women. Genetic control of AT gene expression by cis signals was observed for 46 genes. Dietary intervention, sex, and cis genetic variants independently controlled AT gene expression. These analyses help understanding the relative importance of environmental and individual factors that control the expression of human AT genes and therefore may foster strategies aimed at improving AT function in metabolic diseases.

  14. Shift work or food intake during the rest phase promotes metabolic disruption and desynchrony of liver genes in male rats.

    Salgado-Delgado, Roberto C; Saderi, Nadia; Basualdo, María del Carmen; Guerrero-Vargas, Natali N; Escobar, Carolina; Buijs, Ruud M

    2013-01-01

    In the liver, clock genes are proposed to drive metabolic rhythms. These gene rhythms are driven by the suprachiasmatic nucleus (SCN) mainly by food intake and via autonomic and hormonal pathways. Forced activity during the normal rest phase, induces also food intake, thus neglecting the signals of the SCN, leading to conflicting time signals to target tissues of the SCN. The present study explored in a rodent model of night-work the influence of food during the normal sleep period on the synchrony of gene expression between clock genes and metabolic genes in the liver. Male Wistar rats were exposed to forced activity for 8 h either during the rest phase (day) or during the active phase (night) by using a slow rotating wheel. In this shift work model food intake shifts spontaneously to the forced activity period, therefore the influence of food alone without induced activity was tested in other groups of animals that were fed ad libitum, or fed during their rest or active phase. Rats forced to be active and/or eating during their rest phase, inverted their daily peak of Per1, Bmal1 and Clock and lost the rhythm of Per2 in the liver, moreover NAMPT and metabolic genes such as Pparα lost their rhythm and thus their synchrony with clock genes. We conclude that shift work or food intake in the rest phase leads to desynchronization within the liver, characterized by misaligned temporal patterns of clock genes and metabolic genes. This may be the cause of the development of the metabolic syndrome and obesity in individuals engaged in shift work.

  15. Metabolic syndrome, diabetes and atherosclerosis: Influence of gene-environment interaction

    Andreassi, Maria Grazia

    2009-01-01

    Despite remarkable progress in diagnosis and understanding of risk factors, cardiovascular disease (CVD) remains still the leading cause of morbidity and mortality in the world's developed countries. The metabolic syndrome, a cluster of risk factors (visceral obesity, insulin resistance, dyslipidaemia, and hypertension), is increasingly being recognized as a new risk factor for type 2 diabetes and atherosclerotic cardiovascular disease. Nevertheless, there is wide variation in both the occurrence of disease and age of onset, even in individuals who display very similar risk profiles. There is now compelling evidence that a complex interplay between genetic determinants and environmental factors (still largely unknown) is the reason for this large inter-individual variation in disease susceptibility. The purpose of the present review is to describe the current status of our knowledge concerning the gene-environment interactions potentially implicated in the pathogenesis of metabolic syndrome, diabetes and cardiovascular disease. It focuses predominantly on studies of genes (peroxisome proliferator-activated receptor-gamma, alcohol dehydrogenase type 1C, apolipoprotein E, glutathione S-transferases T1 and M1) that are known to be modified by dietary and lifestyle habits (fat diet, intake of alcohol and smoking habit). It also describes the limited current understanding of the role of genetic variants of xenobiotic metabolizing enzymes and their interactions with environmental toxicants. Additional studies are needed in order to clarify whether inter-individual differences in detoxification of environmental toxicants may have an essential role in the development of CVD and contribute to the emerging field of 'environmental cardiology'. Such knowledge may be particularly relevant for improving cardiovascular risk stratification and conceiving the development of 'personalized intervention program'.

  16. Metabolic syndrome, diabetes and atherosclerosis: Influence of gene-environment interaction

    Andreassi, Maria Grazia, E-mail: andreas@ifc.cnr.it [CNR Institute of Clinical Physiology, G. Pasquinucci Hospital, Via Aurelia Sud, Massa (Italy)

    2009-07-10

    Despite remarkable progress in diagnosis and understanding of risk factors, cardiovascular disease (CVD) remains still the leading cause of morbidity and mortality in the world's developed countries. The metabolic syndrome, a cluster of risk factors (visceral obesity, insulin resistance, dyslipidaemia, and hypertension), is increasingly being recognized as a new risk factor for type 2 diabetes and atherosclerotic cardiovascular disease. Nevertheless, there is wide variation in both the occurrence of disease and age of onset, even in individuals who display very similar risk profiles. There is now compelling evidence that a complex interplay between genetic determinants and environmental factors (still largely unknown) is the reason for this large inter-individual variation in disease susceptibility. The purpose of the present review is to describe the current status of our knowledge concerning the gene-environment interactions potentially implicated in the pathogenesis of metabolic syndrome, diabetes and cardiovascular disease. It focuses predominantly on studies of genes (peroxisome proliferator-activated receptor-gamma, alcohol dehydrogenase type 1C, apolipoprotein E, glutathione S-transferases T1 and M1) that are known to be modified by dietary and lifestyle habits (fat diet, intake of alcohol and smoking habit). It also describes the limited current understanding of the role of genetic variants of xenobiotic metabolizing enzymes and their interactions with environmental toxicants. Additional studies are needed in order to clarify whether inter-individual differences in detoxification of environmental toxicants may have an essential role in the development of CVD and contribute to the emerging field of 'environmental cardiology'. Such knowledge may be particularly relevant for improving cardiovascular risk stratification and conceiving the development of 'personalized intervention program'.

  17. The Severity of Fatty Liver Disease Relating to Metabolic Abnormalities Independently Predicts Coronary Calcification

    Lee, Ying-Hsiang; Wu, Yih-Jer; Liu, Chuan-Chuan; Hou, Charles Jia-Yin; Yeh, Hung-I.; Tsai, Cheng-Ho; Shih, Shou-Chuan; Hung, Chung-Lieh

    2011-01-01

    Background. Nonalcoholic fatty liver disease (NAFLD) is one of the metabolic disorders presented in liver. The relationship between severity of NAFLD and coronary atherosclerotic burden remains largely unknown. Methods and Materials. We analyzed subjects undergoing coronary calcium score evaluation by computed tomography (MDCT) and fatty liver assessment using abdominal ultrasonography. Framingham risk score (FRS) and metabolic risk score (MRS) were obtained in all subjects. A graded, semiquantitative score was established to quantify the severity of NAFLD. Multivariate logistic regression analysis was used to depict the association between NAFLD and calcium score. Results. Of all, 342 participants (female: 22.5%, mean age: 48.7 ± 7.0 years) met the sufficient information rendering detailed analysis. The severity of NAFLD was positively associated with MRS (X 2 = 6.12, trend P < 0.001) and FRS (X 2 = 5.88, trend P < 0.001). After multivariable adjustment for clinical variables and life styles, the existence of moderate to severe NAFLD was independently associated with abnormal calcium score (P < 0.05). Conclusion. The severity of NAFLD correlated well with metabolic abnormality and was independently predict coronary calcification beyond clinical factors. Our data suggests that NAFLD based on ultrasonogram could positively reflect the burden of coronary calcification

  18. Quality of relationships with parents and friends in adolescence predicts metabolic risk in young adulthood.

    Ehrlich, Katherine B; Hoyt, Lindsay Till; Sumner, Jennifer A; McDade, Thomas W; Adam, Emma K

    2015-09-01

    This study was designed to examine whether family and peer relationships in adolescence predict the emergence of metabolic risk factors in young adulthood. Participants from a large, nationally representative cohort study (N = 11,617 for these analyses) reported on their relationship experiences with parents and close friends during adolescence. Fourteen years later, interviewers collected blood samples, as well as anthropometric and blood pressure measurements. Blood samples were analyzed for HbA1c. Ordered logistic regressions revealed that for females, supportive parent-child relationships and close male friendships in adolescence were associated with reduced odds of having elevated metabolic risk markers in young adulthood. These effects remained significant even after controlling for baseline measures of body mass index (BMI) and health and demographic covariates. The protective effects of close relationships were not significant for males, however. Exploratory analyses with 2-parent families revealed that supportive father-child relationships were especially protective for females. These findings suggest that, for females, close and supportive relationships with parents and male friends in adolescence may reduce the risk of metabolic dysregulation in adulthood. (c) 2015 APA, all rights reserved).

  19. Transcriptome Analysis of Three Sheep Intestinal Regions reveals Key Pathways and Hub Regulatory Genes of Large Intestinal Lipid Metabolism.

    Chao, Tianle; Wang, Guizhi; Ji, Zhibin; Liu, Zhaohua; Hou, Lei; Wang, Jin; Wang, Jianmin

    2017-07-13

    The large intestine, also known as the hindgut, is an important part of the animal digestive system. Recent studies on digestive system development in ruminants have focused on the rumen and the small intestine, but the molecular mechanisms underlying sheep large intestine metabolism remain poorly understood. To identify genes related to intestinal metabolism and to reveal molecular regulation mechanisms, we sequenced and compared the transcriptomes of mucosal epithelial tissues among the cecum, proximal colon and duodenum. A total of 4,221 transcripts from 3,254 genes were identified as differentially expressed transcripts. Between the large intestine and duodenum, differentially expressed transcripts were found to be significantly enriched in 6 metabolism-related pathways, among which PPAR signaling was identified as a key pathway. Three genes, CPT1A, LPL and PCK1, were identified as higher expression hub genes in the large intestine. Between the cecum and colon, differentially expressed transcripts were significantly enriched in 5 lipid metabolism related pathways, and CEPT1 and MBOAT1 were identified as hub genes. This study provides important information regarding the molecular mechanisms of intestinal metabolism in sheep and may provide a basis for further study.

  20. Test accuracy of metabolic indicators in predicting decreased fertility in dairy cows

    Lomander, H; Gustafsson, H; Svensson, C

    2012-01-01

    Negative energy balance is a known risk factor for decreased fertility in dairy cows. This study evaluated the accuracy of plasma concentrations of nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and insulin-like growth factor 1 (IGF-1)—factors related to negative energy balance...... was low when metabolic indicators measured as single values in early lactation were used to predict fertility in dairy cows, but accuracy was influenced by cow-level factors such as parity. The prevalence of the target condition (in this case, decreased fertility) also influences test usefulness......—in predicting decreased fertility. One plasma sample per cow was collected from 480 cows in 12 herds during the period from d 4 to 21 in milk and analyzed for NEFA, BHBA, and IGF-1. For each cow, data on breed, parity, calving date, gynecological examinations, and insemination dates were obtained. Milk samples...

  1. Hierarchical Status Predicts Behavioral Vulnerability and Nucleus Accumbens Metabolic Profile Following Chronic Social Defeat Stress.

    Larrieu, Thomas; Cherix, Antoine; Duque, Aranzazu; Rodrigues, João; Lei, Hongxia; Gruetter, Rolf; Sandi, Carmen

    2017-07-24

    Extensive data highlight the existence of major differences in individuals' susceptibility to stress [1-4]. While genetic factors [5, 6] and exposure to early life stress [7, 8] are key components for such neurobehavioral diversity, intriguing observations revealed individual differences in response to stress in inbred mice [9-12]. This raised the possibility that other factors might be critical in stress vulnerability. A key challenge in the field is to identify non-invasively risk factors for vulnerability to stress. Here, we investigated whether behavioral factors, emerging from preexisting dominance hierarchies, could predict vulnerability to chronic stress [9, 13-16]. We applied a chronic social defeat stress (CSDS) model of depression in C57BL/6J mice to investigate the predictive power of hierarchical status to pinpoint which individuals will exhibit susceptibility to CSDS. Given that the high social status of dominant mice would be the one particularly challenged by CSDS, we predicted and found that dominant individuals were the ones showing a strong susceptibility profile as indicated by strong social avoidance following CSDS, while subordinate mice were not affected. Data from 1 H-NMR spectroscopy revealed that the metabolic profile in the nucleus accumbens (NAc) relates to social status and vulnerability to stress. Under basal conditions, subordinates show lower levels of energy-related metabolites compared to dominants. In subordinates, but not dominants, levels of these metabolites were increased after exposure to CSDS. To the best of our knowledge, this is the first study that identifies non-invasively the origin of behavioral risk factors predictive of stress-induced depression-like behaviors associated with metabolic changes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Polymorphisms in the LPL and CETP Genes and Haplotype in the ESR1 Gene Are Associated with Metabolic Syndrome in Women from Southwestern Mexico

    José Ángel Cahua-Pablo

    2015-09-01

    Full Text Available Metabolic syndrome (MetS is a combination of metabolic disorders associated with an increased risk for cardiovascular disease (CVD. Studies in women reported associations between polymorphisms in ESR1, LPL and CETP genes and MetS. Our aim was to evaluate the association between variants in ESR1, LPL and CETP genes with MetS and its components. Four hundred and eighty women were analyzed, anthropometric features and biochemical profiles were evaluated, and genotyping was performed by real-time PCR. We found an association with elevated glucose levels (odds ratio (OR = 2.9; p = 0.013 in carrying the AA genotype of rs1884051 in the ESR1 gene compared with the GG genotype, and the CC genotype of rs328 in the LPL gene was associated with MetS compared to the CG or GG genotype (OR = 2.8; p = 0.04. Moreover, the GA genotype of rs708272 in the CETP gene is associated with MetS compared to the GG or AA genotype (OR = 1.8; p = 0.006. In addition the ACTCCG haplotype in the ESR1 gene is associated with a decrease in the risk of MetS (OR = 0.02; p < 0.001. In conclusion, our results show the involvement of the variants of ESR1, LPL and CETP genes in metabolic events related to MetS or some of its features.

  3. Benchmarking of gene prediction programs for metagenomic data.

    Yok, Non; Rosen, Gail

    2010-01-01

    This manuscript presents the most rigorous benchmarking of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The comparisons are based on their performances over simulated fragments from one hundred species of diverse lineages. We defined four different types of fragments; two types come from the inter- and intra-coding regions and the other types are from the gene edges. Hoff et al. used only 12 species in their comparison; therefore, their sample is too small to represent an environmental sample. Also, no predecessors has separately examined fragments that contain gene edges as opposed to intra-coding regions. General observations in our results are that performances of all these programs improve as we increase the length of the fragment. On the other hand, intra-coding fragments of our data show low annotation error in all of the programs if compared to the gene edge fragments. Overall, we found an upper-bound performance by combining all the methods.

  4. Genetic variation in hormone metabolizing genes and risk of testicular germ cell tumors.

    Figueroa, Jonine D; Sakoda, Lori C; Graubard, Barry I; Chanock, Stephen; Rubertone, Mark V; Erickson, R Loren; McGlynn, Katherine A

    2008-11-01

    Testicular germ cell tumors (TGCT) that arise in young men are composed of two histologic types, seminomas and nonseminomas. Risk patterns for the two types appear to be similar and may be related to either endogenous or exogenous hormonal exposures in utero. Why similar risk patterns would result in different histologic types is unclear, but could be related to varying genetic susceptibility profiles. Genetic variation in hormone metabolizing genes could potentially modify hormonal exposures, and thereby affect which histologic type a man develops. To examine this hypothesis, 33 single nucleotide polymorphisms (SNPs) in four hormone metabolism candidate genes (CYP1A1, CYP17A1, HSD17B1, HSD17B4) and the androgen receptor gene (AR) were genotyped. Associations with TGCT were evaluated among 577 TGCT cases (254 seminoma, 323 nonseminoma) and 707 controls from the US Servicemen's Testicular Tumor Environmental and Endocrine Determinants (STEED) study. There were no significant associations with TGCT overall based on a test using an additive model. However, compared to homozygotes of the most common allele, two nonredundant SNPs in CYP1A1 were inversely associated with nonseminoma: CYP1A1 promoter SNP rs4886605 OR = 0.75 (95% CI = 0.54-1.04) among the heterozygotes and OR = 0.37, 95% CI = 0.12-1.11 among the homozygotes with a p-value for trend = 0.02; rs2606345 intron 1 SNP, OR = 0.69 (95% CI = 0.51-0.93) among heterozygotes and OR = 0.70 (95% CI = 0.42-1.17) among homozygotes, with a p-value for trend = 0.02. Caution in interpretation is warranted until findings are replicated in other studies; however, the results suggest that genetic variation in CYP1A1 may be associated with nonseminoma.

  5. A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors

    2017-02-01

    affecting the function of Fanconi Anemia (FA) genes ( FANCA /B/C/D2/E/F/G/I/J/L/M, PALB2) or DNA damage response genes involved in HR 5 (ATM, ATR...Award Number: W81XWH-10-1-0585 TITLE: A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors...To) 15 July 2010 – 2 Nov.2016 4. TITLE AND SUBTITLE A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP

  6. Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

    Teng Shaolei

    2013-01-01

    Full Text Available Abstract Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs and Support Vector Machines (SVMs were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.

  7. Predictive value of MSH2 gene expression in colorectal cancer treated with capecitabine

    Jensen, Lars H; Danenberg, Kathleen D; Danenberg, Peter V

    2007-01-01

    was associated with a hazard ratio of 0.5 (95% confidence interval, 0.23-1.11; P = 0.083) in survival analysis. CONCLUSION: The higher gene expression of MSH2 in responders and the trend for predicting overall survival indicates a predictive value of this marker in the treatment of advanced CRC with capecitabine.......PURPOSE: The objective of the present study was to evaluate the gene expression of the DNA mismatch repair gene MSH2 as a predictive marker in advanced colorectal cancer (CRC) treated with first-line capecitabine. PATIENTS AND METHODS: Microdissection of paraffin-embedded tumor tissue, RNA...

  8. The Association of Polymorphisms in Leptin/Leptin Receptor Genes and Ghrelin/Ghrelin Receptor Genes With Overweight/Obesity and the Related Metabolic Disturbances: A Review.

    Ghalandari, Hamid; Hosseini-Esfahani, Firoozeh; Mirmiran, Parvin

    2015-07-01

    Leptin and ghrelin are two important appetite and energy balance-regulating peptides. Common polymorphisms in the genes coding these peptides and their related receptors are shown to be associated with body weight, different markers of obesity and metabolic abnormalities. This review article aims to investigate the association of common polymorphisms of these genes with overweight/obesity and the metabolic disturbances related to it. The keywords leptin, ghrelin, polymorphism, single-nucleotide polymorphism (SNP), obesity, overweight, Body Mass Index, metabolic syndrome, and type 2 diabetes mellitus (T2DM) (MeSH headings) were used to search in the following databases: Pubmed, Sciencedirect (Elsevier), and Google scholar. Overall, 24 case-control studies, relevant to our topic, met the criteria and were included in the review. The most prevalent leptin/leptin receptor genes (LEP/LEPR) and ghrelin/ghrelin receptor genes (GHRL/GHSR) single nucleotide polymorphisms studied were LEP G-2548A, LEPR Q223R, and Leu72Met, respectively. Nine studies of the 17 studies on LEP/LEPR, and three studies of the seven studies on GHRL/GHSR showed significant relationships. In general, our study suggests that the association between LEP/LEPR and GHRL/GHSR with overweight/obesity and the related metabolic disturbances is inconclusive. These results may be due to unidentified gene-environment interactions. More investigations are needed to further clarify this association.

  9. Polymorphism of antimalaria drug metabolizing, nuclear receptor, and drug transport genes among malaria patients in Zanzibar, East Africa.

    Ferreira, Pedro Eduardo; Veiga, Maria Isabel; Cavaco, Isa; Martins, J Paulo; Andersson, Björn; Mushin, Shaliya; Ali, Abullah S; Bhattarai, Achuyt; Ribeiro, Vera; Björkman, Anders; Gil, José Pedro

    2008-02-01

    Artemisinin-based combination therapy is a main strategy for malaria control in Africa. Zanzibar introduced this new treatment policy in 2003. The authors have studied the prevalence of a number of functional single nucleotide polymorphisms (SNPs) in genes associated with the elimination of the artemisinin-based combination therapy compounds in use in Zanzibar to investigate the frequencies of subgroups potentially at higher drug exposure and therefore possible higher risk of toxicity. One hundred three unrelated children with uncomplicated malaria from the Unguja and Pemba islands of Zanzibar were enrolled. With use of polymerase chain reaction (PCR)-restriction fragment length polymorphism and real-time PCR-based allele discrimination methods, the CYP2B6 (G15631T), CYP3A4 (A-392G), CYP3A5 (A6986G, G14690A, 27131-132 insT, C3699T) SNPs and MDR1 SNPs C3435T, G2677T/A, and T-129C were analyzed. PCR product sequencing was applied to regulatory regions of MDR1, the CYP3A4 proximal promoter, and to exons 2 and 5 of PXR, a gene coding for a nuclear factor activated by artemisinin antimalarials and associated with the transcription induction of most of the studied genes. Homozygous subjects for alleles coding for low activity proteins were found at the following frequencies: 1) MDR1: 2.9%; 2) CYP2B6: 9.7%; 3) CYP3A5: 14.1%; and 4) CYP3A4: 49.5%. No functionally relevant allele was found in the analyzed regions of PXR. A new MDR1 SNP was found (T-158C), located in a putative antigen recognition element. Ten (10.1%) subjects were predicted to be low metabolizers simultaneously for CYP3A4 and CYP3A5. This fraction of the population is suggested to be under higher exposure to certain antimalarials, including lumefantrine and quinine.

  10. Genomic Organization and Expression of Iron Metabolism Genes in the Emerging Pathogenic Mold Scedosporium apiospermum

    Yohann Le Govic

    2018-04-01

    Full Text Available The ubiquitous mold Scedosporium apiospermum is increasingly recognized as an emerging pathogen, especially among patients with underlying disorders such as immunodeficiency or cystic fibrosis (CF. Indeed, it ranks the second among the filamentous fungi colonizing the respiratory tract of CF patients. However, our knowledge about virulence factors of this fungus is still limited. The role of iron-uptake systems may be critical for establishment of Scedosporium infections, notably in the iron-rich environment of the CF lung. Two main strategies are employed by fungi to efficiently acquire iron from their host or from their ecological niche: siderophore production and reductive iron assimilation (RIA systems. The aim of this study was to assess the existence of orthologous genes involved in iron metabolism in the recently sequenced genome of S. apiospermum. At first, a tBLASTn analysis using A. fumigatus iron-related proteins as query revealed orthologs of almost all relevant loci in the S. apiospermum genome. Whereas the genes putatively involved in RIA were randomly distributed, siderophore biosynthesis and transport genes were organized in two clusters, each containing a non-ribosomal peptide synthetase (NRPS whose orthologs in A. fumigatus have been described to catalyze hydroxamate siderophore synthesis. Nevertheless, comparative genomic analysis of siderophore-related clusters showed greater similarity between S. apiospermum and phylogenetically close molds than with Aspergillus species. The expression level of these genes was then evaluated by exposing conidia to iron starvation and iron excess. The expression of several orthologs of A. fumigatus genes involved in siderophore-based iron uptake or RIA was significantly induced during iron starvation, and conversely repressed in iron excess conditions. Altogether, these results indicate that S. apiospermum possesses the genetic information required for efficient and competitive iron uptake

  11. Adiponectin gene polymorphism is selectively associated with the concomitant presence of metabolic syndrome and essential hypertension.

    Hsin-Bang Leu

    Full Text Available OBJECTIVE: Cardiovascular risk increases with the presence of both metabolic syndrome (MetS and hypertension (HTN. Although the adiponectin (ADIPOQ gene has been reported to be involved in MetS, its association with HTN remained undetermined. This study aimed to investigate the association of ADIPOQ gene with the phenotypes of HTN and MetS. METHODS: A total of 962 participants from 302 families from the Taiwan young-onset hypertension genetic study were enrolled. Plasma adiponectin were measured, and association analysis was conducted by using GEE regression-based method. Another study, of 1448 unrelated participants, was conducted to replicate the association between ADIPOQ gene and variable phenotypes of MetS with or without HTN. RESULTS: Among 962 subjects from family samples, the lowest plasma adiponectin value was observed in MetS with HTN component (9.3±0.47 µg/ml compared with hypertensives (13.4±0.74 µg /ml or MetS without HTN (11.9±0.60 µg/ml, P<0.05. The SNP rs1501299 (G276T in ADIPOQ gene was found associated with the presence of HTN in MetS (odds ratio for GG+GT vs. TT = 2.46; 95% CI: 1.14-5.3, p = 0.02, but not rs2241766 (T45G. No association of ADIPOQ gene with HTN alone or MetS without HTN was observed. The significant association of the SNP rs1501299 (G276T with the phenotype of presence of HTN in MetS was confirmed (odds ratio for GG+GT vs. TT = 2.15; 95% CI: 1.1-4.3 in the replication study. CONCLUSIONS: ADIPOQ genetic variants were selectively and specifically associated with the concomitant presence of MetS and HTN, suggesting potential genetic linkage between MetS and HTN.

  12. FCERI AND HISTAMINE METABOLISM GENE VARIABILITY IN SELECTIVE RESPONDERS TO NSAIDS

    Gemma Amo

    2016-09-01

    Full Text Available The high-affinity IgE receptor (Fcε RI is a heterotetramer of three subunits: Fcε RIα, Fcε RIβ and Fcε RIγ (αβγ2 encoded by three genes designated as FCER1A, FCER1B (MS4A2 and FCER1G, respectively. Recent evidence points to FCERI gene variability as a relevant factor in the risk of developing allergic diseases. Because Fcε RI plays a key role in the events downstream of the triggering factors in immunological response, we hypothesized that FCERI gene variants might be related with the risk of, or with the clinical response to, selective (IgE mediated non-steroidal anti-inflammatory (NSAID hypersensitivity.From a cohort of 314 patients suffering from selective hypersensitivity to metamizole, ibuprofen, diclofenac, paracetamol, acetylsalicylic acid (ASA, propifenazone, naproxen, ketoprofen, dexketoprofen, etofenamate, aceclofenac, etoricoxib, dexibuprofen, indomethacin, oxyphenylbutazone or piroxicam, and 585 unrelated healthy controls that tolerated these NSAIDs, we analyzed the putative effects of the FCERI SNPs FCER1A rs2494262, rs2427837 and rs2251746; FCER1B rs1441586, rs569108 and rs512555; FCER1G rs11587213, rs2070901 and rs11421. Furthermore, in order to identify additional genetic markers which might be associated with the risk of developing selective NSAID hypersensitivity, or which may modify the putative association of FCERI gene variations with risk, we analyzed polymorphisms known to affect histamine synthesis or metabolism, such as rs17740607, rs2073440, rs1801105, rs2052129, rs10156191, rs1049742 and rs1049793 in the HDC, HNMT and DAO genes.No major genetic associations with risk or with clinical presentation, and no gene-gene interactions, or gene-phenotype interactions (including age, gender, IgE concentration, antecedents of atopy, culprit drug or clinical presentation were identified in patients. However, logistic regression analyses indicated that the presence of antecedents of atopy and the DAO SNP rs2052129 (GG

  13. Anthropometry and physical activity level in the prediction of metabolic syndrome in children.

    Andaki, Alynne Christian Ribeiro; Tinôco, Adelson Luiz Araújo; Mendes, Edmar Lacerda; Andaki Júnior, Roberto; Hills, Andrew P; Amorim, Paulo Roberto S

    2014-10-01

    To evaluate the effectiveness of anthropometric measures and physical activity level in the prediction of metabolic syndrome (MetS) in children. Cross-sectional study with children from public and private schools. Children underwent an anthropometric assessment, blood pressure measurement and biochemical evaluation of serum for determination of TAG, HDL-cholesterol and glucose. Physical activity level was calculated and number of steps per day obtained using a pedometer for seven consecutive days. Viçosa, south-eastern Brazil. Boys and girls (n 187), mean age 9·90 (SD 0·7) years. Conicity index, sum of four skinfolds, physical activity level and number of steps per day were accurate in predicting MetS in boys. Anthropometric indicators were accurate in predicting MetS for girls, specifically BMI, waist circumference measured at the narrowest point and at the level of the umbilicus, four skinfold thickness measures evaluated separately, the sum of subscapular and triceps skinfold thickness, the sum of four skinfolds and body fat percentage. The sum of four skinfolds was the most accurate method in predicting MetS in both genders.

  14. Comprehensive transcriptome analysis reveals novel genes involved in cardiac glycoside biosynthesis and mlncRNAs associated with secondary metabolism and stress response in Digitalis purpurea

    Wu Bin

    2012-01-01

    Full Text Available Abstract Background Digitalis purpurea is an important ornamental and medicinal plant. There is considerable interest in exploring its transcriptome. Results Through high-throughput 454 sequencing and subsequent assembly, we obtained 23532 genes, of which 15626 encode conserved proteins. We determined 140 unigenes to be candidates involved in cardiac glycoside biosynthesis. It could be grouped into 30 families, of which 29 were identified for the first time in D. purpurea. We identified 2660 mRNA-like npcRNA (mlncRNA candidates, an emerging class of regulators, using a computational mlncRNA identification pipeline and 13 microRNA-producing unigenes based on sequence conservation and hairpin structure-forming capability. Twenty five protein-coding unigenes were predicted to be targets of these microRNAs. Among the mlncRNA candidates, only 320 could be grouped into 140 families with at least two members in a family. The majority of D. purpurea mlncRNAs were species-specific and many of them showed tissue-specific expression and responded to cold and dehydration stresses. We identified 417 protein-coding genes with regions significantly homologous or complementary to 375 mlncRNAs. It includes five genes involved in secondary metabolism. A positive correlation was found in gene expression between protein-coding genes and the homologous mlncRNAs in response to cold and dehydration stresses, while the correlation was negative when protein-coding genes and mlncRNAs were complementary to each other. Conclusions Through comprehensive transcriptome analysis, we not only identified 29 novel gene families potentially involved in the biosynthesis of cardiac glycosides but also characterized a large number of mlncRNAs. Our results suggest the importance of mlncRNAs in secondary metabolism and stress response in D. purpurea.

  15. The mRNA expression profile of metabolic genes relative to MHC isoform pattern in human skeletal muscles

    Plomgaard, Peter; Penkowa, Milena; Leick, Lotte

    2006-01-01

    The metabolic profile of rodent muscle is generally reflected in the myosin heavy chain (MHC) fiber-type composition. The present study was conducted to test the hypothesis that metabolic gene expression is not tightly coupled with MHC fiber-type composition for all genes in human skeletal muscle....... Triceps brachii, vastus lateralis quadriceps, and soleus muscle biopsies were obtained from normally physically active, healthy, young male volunteers, because these muscles are characterized by different fiber-type compositions. As expected, citrate synthase and 3-hydroxyacyl dehydrogenase activity...... of a broad range of metabolic genes. The triceps muscle had two- to fivefold higher MHC IIa, phosphofructokinase, and LDH A mRNA content and two- to fourfold lower MHC I, lipoprotein lipase, CD36, hormone-sensitive lipase, and LDH B and hexokinase II mRNA than vastus lateralis or soleus. Interestingly...

  16. RNA-Seq Analysis of Developing Pecan (Carya illinoinensis) Embryos Reveals Parallel Expression Patterns among Allergen and Lipid Metabolism Genes.

    Mattison, Christopher P; Rai, Ruhi; Settlage, Robert E; Hinchliffe, Doug J; Madison, Crista; Bland, John M; Brashear, Suzanne; Graham, Charles J; Tarver, Matthew R; Florane, Christopher; Bechtel, Peter J

    2017-02-22

    The pecan nut is a nutrient-rich part of a healthy diet full of beneficial fatty acids and antioxidants, but can also cause allergic reactions in people suffering from food allergy to the nuts. The transcriptome of a developing pecan nut was characterized to identify the gene expression occurring during the process of nut development and to highlight those genes involved in fatty acid metabolism and those that commonly act as food allergens. Pecan samples were collected at several time points during the embryo development process including the water, gel, dough, and mature nut stages. Library preparation and sequencing were performed using Illumina-based mRNA HiSeq with RNA from four time points during the growing season during August and September 2012. Sequence analysis with Trinotate software following the Trinity protocol identified 133,000 unigenes with 52,267 named transcripts and 45,882 annotated genes. A total of 27,312 genes were defined by GO annotation. Gene expression clustering analysis identified 12 different gene expression profiles, each containing a number of genes. Three pecan seed storage proteins that commonly act as allergens, Car i 1, Car i 2, and Car i 4, were significantly up-regulated during the time course. Up-regulated fatty acid metabolism genes that were identified included acyl-[ACP] desaturase and omega-6 desaturase genes involved in oleic and linoleic acid metabolism. Notably, a few of the up-regulated acyl-[ACP] desaturase and omega-6 desaturase genes that were identified have expression patterns similar to the allergen genes based upon gene expression clustering and qPCR analysis. These findings suggest the possibility of coordinated accumulation of lipids and allergens during pecan nut embryogenesis.

  17. Altered gene regulation and potential association with metabolic resistance development to imidacloprid in the tarnished plant bug, Lygus lineolaris.

    Zhu, Yu Cheng; Luttrell, Randall

    2015-01-01

    Chemical spray on cotton is almost an exclusive method for controlling tarnished plant bug (TPB), Lygus lineolaris. Frequent use of imidacloprid is a concern for neonicotinoid resistance in this key pest. Information of how and why TPB becomes less susceptible to imidacloprid is essential for effective monitoring and managing resistance. Microarray analysis of 6688 genes in imidacloprid-selected TPB (Im1500FF) revealed 955 upregulated and 1277 downregulated (≥twofold) genes in Im1500FF, with 369 and 485 of them annotated. Five P450 and nine esterase genes were significantly upregulated, and only one esterase gene and no P450 genes were downregulated. Other upregulated genes include helicases, phosphodiesterases, ATPases and kinases. Pathway analyses identified 65 upregulated cDNAs that encode 51 different enzymes involved in 62 different pathways, including P450 and esterase genes for drug and xenobiotic metabolisms. Sixty-four downregulated cDNAs code only 17 enzymes that are associated with only 23 pathways mostly related to food digestion. This study demonstrated a significant change in gene expression related to metabolic processes in imidacloprid-selected TPB, resulting in overexpression of P450 and esterase genes for potential excess detoxification and cross/multiple resistance development. The identification of these and other enzyme genes establishes a foundation to explore the complicity of potential imidacloprid resistance in TPB. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

  18. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression.

    Guilloux, Jean-Philippe; Bassi, Sabrina; Ding, Ying; Walsh, Chris; Turecki, Gustavo; Tseng, George; Cyranowski, Jill M; Sibille, Etienne

    2015-02-01

    Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).

  19. Methyl jasmonate affects phenolic metabolism and gene expression in blueberry (Vaccinium corymbosum).

    Cocetta, Giacomo; Rossoni, Mara; Gardana, Claudio; Mignani, Ilaria; Ferrante, Antonio; Spinardi, Anna

    2015-02-01

    Blueberry (Vaccinium corymbosum) is a fruit very much appreciated by consumers for its antioxidant potential and health-promoting traits. Its beneficial potential properties are mainly due to a high content of anthocyanins and their amount can change after elicitation with methyl jasmonate. The aim of this work is to evaluate the changes in expression of several genes, accumulation of phenolic compounds and alterations in antioxidant potential in two different blueberry cultivars ('Duke' and 'Blueray') in response to methyl jasmonate (0.1 mM). Results showed that 9 h after treatment, the expression of phenylalanine ammonium lyase, chalcone synthase and anthocyanidin synthase genes was stimulated more in the 'Blueray' variety. Among the phenols measured an increase was recorded also for epicatechin and anthocyanin concentrations. 'Duke' is a richer sourche of anthocyanins compared to 'Blueray', treatment with methyl jasmonate promoted in 'Blueray' an increase in pigments as well as in the antioxidant potential, especially in fully ripe berries, but treated 'Duke' berries had greater levels, which were not induced by methyl jasmonate treatment. In conclusion, methyl jasmonate was, in some cases, an effective elicitor of phenolic metabolism and gene expression in blueberry, though with different intensity between cultivars. © 2014 Scandinavian Plant Physiology Society.

  20. Hypolipidemic effect of dietary pea proteins: Impact on genes regulating hepatic lipid metabolism.

    Rigamonti, Elena; Parolini, Cinzia; Marchesi, Marta; Diani, Erika; Brambilla, Stefano; Sirtori, Cesare R; Chiesa, Giulia

    2010-05-01

    Controversial data on the lipid-lowering effect of dietary pea proteins have been provided and the mechanisms behind this effect are not completely understood. The aim of the study was to evaluate a possible hypolipidemic activity of a pea protein isolate and to determine whether pea proteins could affect the hepatic lipid metabolism through regulation of genes involved in cholesterol and fatty acid homeostasis. Rats were fed Nath's hypercholesterolemic diets for 28 days, the protein sources being casein or a pea protein isolate from Pisum sativum. After 14 and 28 days of dietary treatment, rats fed pea proteins had markedly lower plasma cholesterol and triglyceride levels than rats fed casein (pPea protein-fed rats displayed higher hepatic mRNA levels of LDL receptor versus those fed casein (ppea protein-fed rats than in rats fed casein (ppea proteins in rats. Moreover, pea proteins appear to affect cellular lipid homeostasis by upregulating genes involved in hepatic cholesterol uptake and by downregulating fatty acid synthesis genes.

  1. Identification of genes encoding granule-bound starch synthase involved in amylose metabolism in banana fruit.

    Hongxia Miao

    Full Text Available Granule-bound starch synthase (GBSS is responsible for amylose synthesis, but the role of GBSS genes and their encoded proteins remains poorly understood in banana. In this study, amylose content and GBSS activity gradually increased during development of the banana fruit, and decreased during storage of the mature fruit. GBSS protein in banana starch granules was approximately 55.0 kDa. The protein was up-regulated expression during development while it was down-regulated expression during storage. Six genes, designated as MaGBSSI-1, MaGBSSI-2, MaGBSSI-3, MaGBSSI-4, MaGBSSII-1, and MaGBSSII-2, were cloned and characterized from banana fruit. Among the six genes, the expression pattern of MaGBSSI-3 was the most consistent with the changes in amylose content, GBSS enzyme activity, GBSS protein levels, and the quantity or size of starch granules in banana fruit. These results suggest that MaGBSSI-3 might regulate amylose metabolism by affecting the variation of GBSS levels and the quantity or size of starch granules in banana fruit during development or storage.

  2. Interleukin 6 regulates metallothionein gene expression and zinc metabolism in hepatocyte monolayer cultures

    Schroeder, J.J.; Cousins, R.J.

    1990-01-01

    Attention has focused on the cytokine interleukin 6 (IL-6) as a major mediator of acute-phase protein synthesis in hepatocytes in response to infection and tissue injury. The authors have evaluated the effects of IL-6 and IL-1α as well as extracellular zinc and glucocorticoid hormone on metal-lothionein gene expression and cellular zinc accumulation in rat hepatocyte monolayer cultures. Further, they have evaluated the teleological basis for cytokine mediation by examining cyto-protection from CCl 4 -induced damage. Incubation of hepatocytes with IL-6 led to concentration-dependent and time-dependent increases in metallothionein-1 and -2 mRNA and metallothionein protein. The level of each was increased within 3 hr after the addition of IL-6 at 10 ng/ml. Maximal increases the metallothionein mRNA and metallothionein protein were achieved after 12 hr and 36 hr, respectively. Concomitant with the up-regulation of metallothionein gene expression, IL-6 also increased cellular zinc. Responses to IL-6 required the synthetic glucocorticoid hormone dexamethasone and were optimized by increased extracellular zinc. Thus, IL-6 is a major cytokine mediator of metallothionein gene expression and zinc metabolism in hepatocytes and provides cytoprotection from CCl 4 -induced hepatotoxicity via a mode consistent with dependence upon increased cellular metallothionein synthesis and zinc accumulation

  3. Identification of Genes Encoding Granule-Bound Starch Synthase Involved in Amylose Metabolism in Banana Fruit

    Liu, Weixin; Xu, Biyu; Jin, Zhiqiang

    2014-01-01

    Granule-bound starch synthase (GBSS) is responsible for amylose synthesis, but the role of GBSS genes and their encoded proteins remains poorly understood in banana. In this study, amylose content and GBSS activity gradually increased during development of the banana fruit, and decreased during storage of the mature fruit. GBSS protein in banana starch granules was approximately 55.0 kDa. The protein was up-regulated expression during development while it was down-regulated expression during storage. Six genes, designated as MaGBSSI-1, MaGBSSI-2, MaGBSSI-3, MaGBSSI-4, MaGBSSII-1, and MaGBSSII-2, were cloned and characterized from banana fruit. Among the six genes, the expression pattern of MaGBSSI-3 was the most consistent with the changes in amylose content, GBSS enzyme activity, GBSS protein levels, and the quantity or size of starch granules in banana fruit. These results suggest that MaGBSSI-3 might regulate amylose metabolism by affecting the variation of GBSS levels and the quantity or size of starch granules in banana fruit during development or storage. PMID:24505384

  4. Gene expression profiling in Entamoeba histolytica identifies key components in iron uptake and metabolism.

    Nora Adriana Hernández-Cuevas

    Full Text Available Entamoeba histolytica is an ameboid parasite that causes colonic dysentery and liver abscesses in humans. The parasite encounters dramatic changes in iron concentration during its invasion of the host, with relatively low levels in the intestinal lumen and then relatively high levels in the blood and liver. The liver notably contains sources of iron; therefore, the parasite's ability to use these sources might be relevant to its survival in the liver and thus the pathogenesis of liver abscesses. The objective of the present study was to identify factors involved in iron uptake, use and storage in E. histolytica. We compared the respective transcriptomes of E. histolytica trophozoites grown in normal medium (containing around 169 µM iron, low-iron medium (around 123 µM iron, iron-deficient medium (around 91 µM iron, and iron-deficient medium replenished with hemoglobin. The differentially expressed genes included those coding for the ATP-binding cassette transporters and major facilitator transporters (which share homology with bacterial siderophores and heme transporters and genes involved in heme biosynthesis and degradation. Iron deficiency was associated with increased transcription of genes encoding a subset of cell signaling molecules, some of which have previously been linked to adaptation to the intestinal environment and virulence. The present study is the first to have assessed the transcriptome of E. histolytica grown under various iron concentrations. Our results provide insights into the pathways involved in iron uptake and metabolism in this parasite.

  5. Gene expression profiling in Entamoeba histolytica identifies key components in iron uptake and metabolism.

    Hernández-Cuevas, Nora Adriana; Weber, Christian; Hon, Chung-Chau; Guillen, Nancy

    2014-01-01

    Entamoeba histolytica is an ameboid parasite that causes colonic dysentery and liver abscesses in humans. The parasite encounters dramatic changes in iron concentration during its invasion of the host, with relatively low levels in the intestinal lumen and then relatively high levels in the blood and liver. The liver notably contains sources of iron; therefore, the parasite's ability to use these sources might be relevant to its survival in the liver and thus the pathogenesis of liver abscesses. The objective of the present study was to identify factors involved in iron uptake, use and storage in E. histolytica. We compared the respective transcriptomes of E. histolytica trophozoites grown in normal medium (containing around 169 µM iron), low-iron medium (around 123 µM iron), iron-deficient medium (around 91 µM iron), and iron-deficient medium replenished with hemoglobin. The differentially expressed genes included those coding for the ATP-binding cassette transporters and major facilitator transporters (which share homology with bacterial siderophores and heme transporters) and genes involved in heme biosynthesis and degradation. Iron deficiency was associated with increased transcription of genes encoding a subset of cell signaling molecules, some of which have previously been linked to adaptation to the intestinal environment and virulence. The present study is the first to have assessed the transcriptome of E. histolytica grown under various iron concentrations. Our results provide insights into the pathways involved in iron uptake and metabolism in this parasite.

  6. Mood stabilizing drugs regulate transcription of immune, neuronal and metabolic pathway genes in Drosophila.

    Herteleer, L; Zwarts, L; Hens, K; Forero, D; Del-Favero, J; Callaerts, P

    2016-05-01

    Lithium and valproate (VPA) are drugs used in the management of bipolar disorder. Even though they reportedly act on various pathways, the transcriptional targets relevant for disease mechanism and therapeutic effect remain unclear. Furthermore, multiple studies used lymphoblasts of bipolar patients as a cellular proxy, but it remains unclear whether peripheral cells provide a good readout for the effects of these drugs in the brain. We used Drosophila culture cells and adult flies to analyze the transcriptional effects of lithium and VPA and define mechanistic pathways. Transcriptional profiles were determined for Drosophila S2-cells and adult fly heads following lithium or VPA treatment. Gene ontology categories were identified using the DAVID functional annotation tool with a cut-off of p neuronal development, neuronal function, and metabolism. (i) Transcriptional effects of lithium and VPA in Drosophila S2 cells and heads show significant overlap. (ii) The overlap between transcriptional alterations in peripheral versus neuronal cells at the single gene level is negligible, but at the gene ontology and pathway level considerable overlap can be found. (iii) Lithium and VPA act on evolutionarily conserved pathways in Drosophila and mammalian models.

  7. A Natural Light/Dark Cycle Regulation of Carbon-Nitrogen Metabolism and Gene Expression in Rice Shoots.

    Li, Haixing; Liang, Zhijun; Ding, Guangda; Shi, Lei; Xu, Fangsen; Cai, Hongmei

    2016-01-01

    Light and temperature are two particularly important environmental cues for plant survival. Carbon and nitrogen are two essential macronutrients required for plant growth and development, and cellular carbon and nitrogen metabolism must be tightly coordinated. In order to understand how the natural light/dark cycle regulates carbon and nitrogen metabolism in rice plants, we analyzed the photosynthesis, key carbon-nitrogen metabolites, and enzyme activities, and differentially expressed genes and miRNAs involved in the carbon and nitrogen metabolic pathway in rice shoots at the following times: 2:00, 6:00, 10:00, 14:00, 18:00, and 22:00. Our results indicated that more CO2 was fixed into carbohydrates by a high net photosynthetic rate, respiratory rate, and stomatal conductance in the daytime. Although high levels of the nitrate reductase activity, free ammonium and carbohydrates were exhibited in the daytime, the protein synthesis was not significantly facilitated by the light and temperature. In mRNA sequencing, the carbon and nitrogen metabolism-related differentially expressed genes were obtained, which could be divided into eight groups: photosynthesis, TCA cycle, sugar transport, sugar metabolism, nitrogen transport, nitrogen reduction, amino acid metabolism, and nitrogen regulation. Additionally, a total of 78,306 alternative splicing events have been identified, which primarily belong to alternative 5' donor sites, alternative 3' acceptor sites, intron retention, and exon skipping. In sRNA sequencing, four carbon and nitrogen metabolism-related miRNAs (osa-miR1440b, osa-miR2876-5p, osa-miR1877 and osa-miR5799) were determined to be regulated by natural light/dark cycle. The expression level analysis showed that the four carbon and nitrogen metabolism-related miRNAs negatively regulated their target genes. These results may provide a good strategy to study how natural light/dark cycle regulates carbon and nitrogen metabolism to ensure plant growth and

  8. Exploring gene expression signatures for predicting disease free survival after resection of colorectal cancer liver metastases.

    Nikol Snoeren

    Full Text Available BACKGROUND AND OBJECTIVES: This study was designed to identify and validate gene signatures that can predict disease free survival (DFS in patients undergoing a radical resection for their colorectal liver metastases (CRLM. METHODS: Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France and the University Medical Center Utrecht (The Netherlands. Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort. RESULT: No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort. CONCLUSIONS: No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.

  9. Cholesterol Transporters ABCA1 and ABCG1 Gene Expression in Peripheral Blood Mononuclear Cells in Patients with Metabolic Syndrome

    Zahra Tavoosi

    2015-01-01

    Full Text Available ABCA1 and ABCG1 genes encode the cholesterol transporter proteins that play a key role in cholesterol and phospholipids homeostasis. This study was aimed at evaluating and comparing ABCA1 and ABCG1 genes expression in metabolic syndrome patients and healthy individuals. This case-control study was performed on 36 patients with metabolic syndrome and the same number of healthy individuals in Hamadan (west of Iran during 2013-2014. Total RNA was extracted from mononuclear cells and purified using RNeasy Mini Kit column. The expression of ABCA1 and ABCG1 genes was performed by qRT-PCR. Lipid profile and fasting blood glucose were measured using colorimetric procedures. ABCG1 expression in metabolic syndrome patients was significantly lower (about 75% compared to that of control group, while for ABCA1 expression, there was no significant difference between the two studied groups. Comparison of other parameters such as HDL-C, FBS, BMI, waist circumference, and systolic and diastolic blood pressure between metabolic syndrome patients and healthy individuals showed significant differences (P<0.05. Decrease in ABCG1 expression in metabolic syndrome patients compared to healthy individuals suggests that hyperglycemia, related metabolites, and hyperlipidemia over the transporter capacity resulted in decreased expression of ABCG1. Absence of a significant change in ABCA1 gene expression between two groups can indicate a different regulation mechanism for ABCA1 expression.

  10. More to NAD+ than meets the eye: A regulator of metabolic pools and gene expression in Arabidopsis.

    Gakière, Bertrand; Fernie, Alisdair R; Pétriacq, Pierre

    2018-01-05

    Since its discovery more than a century ago, nicotinamide adenine dinucleotide (NAD + ) is recognised as a fascinating cornerstone of cellular metabolism. This ubiquitous energy cofactor plays vital roles in metabolic pathways and regulatory processes, a fact emphasised by the essentiality of a balanced NAD + metabolism for normal plant growth and development. Research on the role of NAD in plants has been predominantly carried out in the model plant Arabidopsis thaliana (Arabidopsis) with emphasis on the redox properties and cellular signalling functions of the metabolite. This review examines the current state of knowledge concerning how NAD can regulate both metabolic pools and gene expression in Arabidopsis. Particular focus is placed on recent studies highlighting the complexity of metabolic regulations involving NAD, more particularly in the mitochondrial compartment, and of signalling roles with respect to interactions with environmental fluctuations most specifically those involving plant immunity. Copyright © 2018 Elsevier Inc. All rights reserved.

  11. Composition and Predicted Metabolic Capacity of Upper and Lower Airway Microbiota of Healthy Dogs in Relation to the Fecal Microbiota.

    Aaron C Ericsson

    Full Text Available The upper and lower airways of healthy humans are reported to harbor stable and consistent bacterial populations, and the composition of these communities is altered in individuals affected with several respiratory diseases. Data regarding the presence of airway microbiota in other animals are scant and a better understanding of the composition and metabolic function of such bacterial populations is essential for the development of novel therapeutic and diagnostic modalities for use in both veterinary and human medicine. Based on targeted next-generation sequencing of feces and samples collected at multiple levels of the airways from 16 healthy female dogs, we demonstrate that canine airways harbor a topographically continuous microbiota with increasing relative abundance of proteobacterial species from the upper to lower airways. The lung-associated microbiota, as assessed via bronchoalveolar lavage fluid (BALF, was the most consistent between dogs and was dominated by three distinct taxa, two of which were resolved to the species level and one to the level of family. The gene content of the nasal, oropharyngeal, and lung-associated microbiota, predicted using the Phylogenetic Investigations into Communities by Reconstruction of Unobserved States (PICRUSt software, provided information regarding the glyoxylate and citrate cycle metabolic pathways utilized by these bacterial populations to colonize such nutrient-poor, low-throughput environments. These data generated in healthy subjects provide context for future analysis of diseased canine airways. Moreover, as dogs have similar respiratory anatomy, physiology, and immune systems as humans, are exposed to many of the same environmental stimuli, and spontaneously develop similar respiratory diseases, these data support the use of dogs as a model species for prospective studies of the airway microbiota, with findings translatable to the human condition.

  12. Composition and Predicted Metabolic Capacity of Upper and Lower Airway Microbiota of Healthy Dogs in Relation to the Fecal Microbiota.

    Ericsson, Aaron C; Personett, Alexa R; Grobman, Megan E; Rindt, Hansjorg; Reinero, Carol R

    2016-01-01

    The upper and lower airways of healthy humans are reported to harbor stable and consistent bacterial populations, and the composition of these communities is altered in individuals affected with several respiratory diseases. Data regarding the presence of airway microbiota in other animals are scant and a better understanding of the composition and metabolic function of such bacterial populations is essential for the development of novel therapeutic and diagnostic modalities for use in both veterinary and human medicine. Based on targeted next-generation sequencing of feces and samples collected at multiple levels of the airways from 16 healthy female dogs, we demonstrate that canine airways harbor a topographically continuous microbiota with increasing relative abundance of proteobacterial species from the upper to lower airways. The lung-associated microbiota, as assessed via bronchoalveolar lavage fluid (BALF), was the most consistent between dogs and was dominated by three distinct taxa, two of which were resolved to the species level and one to the level of family. The gene content of the nasal, oropharyngeal, and lung-associated microbiota, predicted using the Phylogenetic Investigations into Communities by Reconstruction of Unobserved States (PICRUSt) software, provided information regarding the glyoxylate and citrate cycle metabolic pathways utilized by these bacterial populations to colonize such nutrient-poor, low-throughput environments. These data generated in healthy subjects provide context for future analysis of diseased canine airways. Moreover, as dogs have similar respiratory anatomy, physiology, and immune systems as humans, are exposed to many of the same environmental stimuli, and spontaneously develop similar respiratory diseases, these data support the use of dogs as a model species for prospective studies of the airway microbiota, with findings translatable to the human condition.

  13. Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites.

    Razmilic, Valeria; Castro, Jean F; Andrews, Barbara; Asenjo, Juan A

    2018-07-01

    The first genome scale model (GSM) for Streptomyces leeuwenhoekii C34 was developed to study the biosynthesis pathways of specialized metabolites and to find metabolic engineering targets for enhancing their production. The model, iVR1007, consists of 1,722 reactions, 1,463 metabolites, and 1,007 genes, it includes the biosynthesis pathways of chaxamycins, chaxalactins, desferrioxamines, ectoine, and other specialized metabolites. iVR1007 was validated using experimental information of growth on 166 different sources of carbon, nitrogen and phosphorous, showing an 83.7% accuracy. The model was used to predict metabolic engineering targets for enhancing the biosynthesis of chaxamycins and chaxalactins. Gene knockouts, such as sle03600 (L-homoserine O-acetyltransferase), and sle39090 (trehalose-phosphate synthase), that enhance the production of the specialized metabolites by increasing the pool of precursors were identified. Using the algorithm of flux scanning based on enforced objective flux (FSEOF) implemented in python, 35 and 25 over-expression targets for increasing the production of chaxamycin A and chaxalactin A, respectively, that were not directly associated with their biosynthesis routes were identified. Nineteen over-expression targets that were common to the two specialized metabolites studied, like the over-expression of the acetyl carboxylase complex (sle47660 (accA) and any of the following genes: sle44630 (accA_1) or sle39830 (accA_2) or sle27560 (bccA) or sle59710) were identified. The predicted knockouts and over-expression targets will be used to perform metabolic engineering of S. leeuwenhoekii C34 and obtain overproducer strains. © 2018 Wiley Periodicals, Inc.

  14. Dietary fat influences the expression of contractile and metabolic genes in rat skeletal muscle.

    Wataru Mizunoya

    Full Text Available Dietary fat plays a major role in obesity, lipid metabolism, and cardiovascular diseases. To determine whether the intake of different types of dietary fats affect the muscle fiber types that govern the metabolic and contractile properties of the skeletal muscle, we fed male Wistar rats with a 15% fat diet derived from different fat sources. Diets composed of soybean oil (n-6 polyunsaturated fatty acids (PUFA-rich, fish oil (n-3 PUFA-rich, or lard (low in PUFAs were administered to the rats for 4 weeks. Myosin heavy chain (MyHC isoforms were used as biomarkers to delineate the skeletal muscle fiber types. Compared with soybean oil intake, fish oil intake showed significantly lower levels of the fast-type MyHC2B and higher levels of the intermediate-type MyHC2X composition in the extensor digitorum longus (EDL muscle, which is a fast-type dominant muscle. Concomitantly, MyHC2X mRNA levels in fish oil-fed rats were significantly higher than those observed in the soybean oil-fed rats. The MyHC isoform composition in the lard-fed rats was an intermediate between that of the fish oil and soybean oil-fed rats. Mitochondrial uncoupling protein 3, pyruvate dehydrogenase kinase 4, and porin mRNA showed significantly upregulated levels in the EDL of fish oil-fed rats compared to those observed in soybean oil-fed and lard-fed rats, implying an activation of oxidative metabolism. In contrast, no changes in the composition of MyHC isoforms was observed in the soleus muscle, which is a slow-type dominant muscle. Fatty acid composition in the serum and the muscle was significantly influenced by the type of dietary fat consumed. In conclusion, dietary fat affects the expression of genes related to the contractile and metabolic properties in the fast-type dominant skeletal muscle, where the activation of oxidative metabolism is more pronounced after fish oil intake than that after soybean oil intake.

  15. Effects of using coding potential, sequence conservation and mRNA structure conservation for predicting pyrroly-sine containing genes

    Have, Christian Theil; Zambach, Sine; Christiansen, Henning

    2013-01-01

    for prediction of pyrrolysine incorporating genes in genomes of bacteria and archaea leading to insights about the factors driving pyrrolysine translation and identification of new gene candidates. The method predicts known conserved genes with high recall and predicts several other promising candidates...... for experimental verification. The method is implemented as a computational pipeline which is available on request....

  16. Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

    Kim Seon-Young

    2009-05-01

    Full Text Available Abstract Background Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature. Results A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+ patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures Conclusion Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.

  17. Bioinformatics analysis of the predicted polyprenol reductase genes in higher plants

    Basyuni, M.; Wati, R.

    2018-03-01

    The present study evaluates the bioinformatics methods to analyze twenty-four predicted polyprenol reductase genes from higher plants on GenBank as well as predicted the structure, composition, similarity, subcellular localization, and phylogenetic. The physicochemical properties of plant polyprenol showed diversity among the observed genes. The percentage of the secondary structure of plant polyprenol genes followed the ratio order of α helix > random coil > extended chain structure. The values of chloroplast but not signal peptide were too low, indicated that few chloroplast transit peptide in plant polyprenol reductase genes. The possibility of the potential transit peptide showed variation among the plant polyprenol reductase, suggested the importance of understanding the variety of peptide components of plant polyprenol genes. To clarify this finding, a phylogenetic tree was drawn. The phylogenetic tree shows several branches in the tree, suggested that plant polyprenol reductase genes grouped into divergent clusters in the tree.

  18. Genetic disorders of vitamin B12 metabolism: eight complementation groups – eight genes

    Froese, D. Sean; Gravel, Roy A.

    2010-01-01

    Vitamin B12 (cobalamin, Cbl) is an essential nutrient in human metabolism. Genetic diseases of vitamin B12 utilisation constitute an important fraction of inherited newborn disease. Functionally, B12 is the cofactor for methionine synthase and methylmalonyl CoA mutase. To function as a cofactor, B12 must be metabolised through a complex pathway that modifies its structure and takes it through subcellular compartments of the cell. Through the study of inherited disorders of vitamin B12 utilisation, the genes for eight complementation groups have been identified, leading to the determination of the general structure of vitamin B12 processing and providing methods for carrier testing, prenatal diagnosis and approaches to treatment. PMID:21114891

  19. Emiliania Huxleyi (Prymnesiophyceae): Nitrogen-metabolism genes and their expression in response to external nitrogen souces

    Bruhn, Annette; LaRoche, Julie; Richardson, Katherine

    2010-01-01

    The availability and composition of dissolved nitrogen in ocean waters are factors that influence species composition in natural phytoplankton communities. The same factors affect the ratio of organic to inorganic carbon incorporation in calcifying species, such as the coccolithophore Emiliania...... huxleyi (Lohman) W. W. Hay et H. Mohler. E. huxleyi has been shown to thrive on various nitrogen sources, including dissolved organic nitrogen. Nevertheless, assimilation of dissolved nitrogen under nitrogen-replete and -limited conditions is not well understood in this ecologically important species....... In this study, the complete amino acid sequences for three functional genes involved in nitrogen metabolism in E. huxleyi were identified: a putative formamidase, a glutamine synthetase (GSII family), and assimilatory nitrate reductase. Expression patterns of the three enzymes in cells grown on inorganic...

  20. Maternal intake of methyl-group donors affects DNA methylation of metabolic genes in infants.

    Pauwels, Sara; Ghosh, Manosij; Duca, Radu Corneliu; Bekaert, Bram; Freson, Kathleen; Huybrechts, Inge; Langie, Sabine A S; Koppen, Gudrun; Devlieger, Roland; Godderis, Lode

    2017-01-01

    Maternal nutrition during pregnancy and infant nutrition in the early postnatal period (lactation) are critically involved in the development and health of the newborn infant. The Maternal Nutrition and Offspring's Epigenome (MANOE) study was set up to assess the effect of maternal methyl-group donor intake (choline, betaine, folate, methionine) on infant DNA methylation. Maternal intake of dietary methyl-group donors was assessed using a food-frequency questionnaire (FFQ). Before and during pregnancy, we evaluated maternal methyl-group donor intake through diet and supplementation (folic acid) in relation to gene-specific ( IGF2 DMR, DNMT1 , LEP , RXRA ) buccal epithelial cell DNA methylation in 6 months old infants ( n  = 114) via pyrosequencing. In the early postnatal period, we determined the effect of maternal choline intake during lactation (in mothers who breast-fed for at least 3 months) on gene-specific buccal DNA methylation ( n  = 65). Maternal dietary and supplemental intake of methyl-group donors (folate, betaine, folic acid), only in the periconception period, was associated with buccal cell DNA methylation in genes related to growth ( IGF2 DMR), metabolism ( RXRA ), and appetite control ( LEP ). A negative association was found between maternal folate and folic acid intake before pregnancy and infant LEP (slope = -1.233, 95% CI -2.342; -0.125, p  = 0.0298) and IGF2 DMR methylation (slope = -0.706, 95% CI -1.242; -0.107, p  = 0.0101), respectively. Positive associations were observed for maternal betaine (slope = 0.875, 95% CI 0.118; 1.633, p  = 0.0241) and folate (slope = 0.685, 95% CI 0.245; 1.125, p  = 0.0027) intake before pregnancy and RXRA methylation. Buccal DNMT1 methylation in the infant was negatively associated with maternal methyl-group donor intake in the first and second trimester of pregnancy and negatively in the third trimester. We found no clear association between maternal choline intake

  1. A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

    Glusman, Gustavo; Qin, Shizhen; El-Gewely, M. Raafat; Siegel, Andrew F; Roach, Jared C; Hood, Leroy; Smit, Arian F. A

    2006-01-01

    The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.” PMID:16543943

  2. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

    Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

    2017-11-24

    Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

  3. Prediction of highly expressed genes in microbes based on chromatin accessibility

    Willenbrock, Hanni; Ussery, David

    2007-01-01

    BACKGROUND: It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed...

  4. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

    Assaf Gottlieb

    2017-11-01

    Full Text Available Abstract Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort

  5. Maternal obesity programs mitochondrial and lipid metabolism gene expression in infant umbilical vein endothelial cells.

    Costa, S M R; Isganaitis, E; Matthews, T J; Hughes, K; Daher, G; Dreyfuss, J M; da Silva, G A P; Patti, M-E

    2016-11-01

    Maternal obesity increases risk for childhood obesity, but molecular mechanisms are not well understood. We hypothesized that primary umbilical vein endothelial cells (HUVEC) from infants of overweight and obese mothers would harbor transcriptional patterns reflecting offspring obesity risk. In this observational cohort study, we recruited 13 lean (pre-pregnancy body mass index (BMI) obese ('ov-ob', BMI⩾25.0 kg m -2 ) women. We isolated primary HUVEC, and analyzed both gene expression (Primeview, Affymetrix) and cord blood levels of hormones and adipokines. A total of 142 transcripts were differentially expressed in HUVEC from infants of overweight-obese mothers (false discovery rate, FDRmaternal BMI (FDRmaternal obesity, we analyzed the cord blood lipidome and noted significant increases in the levels of total free fatty acids (lean: 95.5±37.1 μg ml -1 , ov-ob: 124.1±46.0 μg ml -1 , P=0.049), palmitate (lean: 34.5±12.7 μg ml -1 , ov-ob: 46.3±18.4 μg ml -1 , P=0.03) and stearate (lean: 20.8±8.2 μg ml -1 , ov-ob: 29.7±17.2 μg ml -1 , P=0.04), in infants of overweight-obese mothers. Prenatal exposure to maternal obesity alters HUVEC expression of genes involved in mitochondrial and lipid metabolism, potentially reflecting developmentally programmed differences in oxidative and lipid metabolism.

  6. Association of the vitamin D metabolism gene CYP24A1 with coronary artery calcification

    Shen, Haiqing; Bielak, Lawrence F.; Ferguson, Jane F.; Streeten, Elizabeth A.; Yerges-Armstrong, Laura M.; Liu, Jie; Post, Wendy; O'Connell, Jeffery R.; Hixson, James E.; Kardia, Sharon L.R.; Sun, Yan V.; Jhun, Mina A.; Wang, Xuexia; Mehta, Nehal N.; Li, Mingyao; Koller, Daniel L.; Hakonarson, Hakan; Keating, Brendan J.; Rader, Daniel J.; Shuldiner, Alan R.; Peyser, Patricia A.; Reilly, Muredach P.; Mitchell, Braxton D.

    2010-01-01

    Objective The Vitamin D endocrine system is essential for calcium homeostasis, and low levels of vitamin D metabolites have been associated with cardiovascular disease risk. We hypothesized that DNA sequence variation in genes regulating vitamin D metabolism and signaling pathways might influence variation in coronary artery calcification (CAC). Methods and Results We genotyped single nucleotide polymorphisms (SNPs) in GC, CYP27B1, CYP24A1, and VDR and tested their association with CAC quantity, as measured by electron beam computed tomography. Initial association studies were carried out in a discovery sample comprised of 697 Amish subjects and SNPs nominally associated with CAC quantity (4 SNPs in CYP24A1, P = 0.008-0.00003) were then tested for association with CAC quantity in two independent cohorts of subjects of European Caucasian ancestry (Genetic Epidemiology Network of Arteriopathy (GENOA) Study (n = 916) and The Penn Coronary Artery Calcification (PennCAC) sample (n = 2,061)). One of the four SNPs, rs2762939, was associated with CAC quantity in both GENOA (P = 0.007) and PennCAC (P = 0.01). In all three populations the rs2762939 C allele was associated with lower CAC quantity. Meta-analysis for the association of this SNP with CAC quantity across all three studies yielded a P value of 2.9 × 10-6. Conclusion A common SNP in the CYP24A1 gene was associated with CAC quantity in three independent populations. This result suggests a role for vitamin D metabolism in the development of CAC quantity. PMID:20847308

  7. Endophytic actinobacteria: Diversity, secondary metabolism and mechanisms to unsilence biosynthetic gene clusters.

    Dinesh, Raghavan; Srinivasan, Veeraraghavan; T E, Sheeja; Anandaraj, Muthuswamy; Srambikkal, Hamza

    2017-09-01

    Endophytic actinobacteria, which reside in the inner tissues of host plants, are gaining serious attention due to their capacity to produce a plethora of secondary metabolites (e.g. antibiotics) possessing a wide variety of biological activity with diverse functions. This review encompasses the recent reports on endophytic actinobacterial species diversity, in planta habitats and mechanisms underlying their mode of entry into plants. Besides, their metabolic potential, novel bioactive compounds they produce and mechanisms to unravel their hidden metabolic repertoire by activation of cryptic or silent biosynthetic gene clusters (BGCs) for eliciting novel secondary metabolite production are discussed. The study also reviews the classical conservative techniques (chemical/biological/physical elicitation, co-culturing) as well as modern microbiology tools (e.g. next generation sequencing) that are being gainfully employed to uncover the vast hidden scaffolds for novel secondary metabolites produced by these endophytes, which would subsequently herald a revolution in drug engineering. The potential role of these endophytes in the agro-environment as promising biological candidates for inhibition of phytopathogens and the way forward to thoroughly exploit this unique microbial community by inducing expression of cryptic BGCs for encoding unseen products with novel therapeutic properties are also discussed.

  8. Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

    Boudellioua, Imene; Saidi, Rabie; Hoehndorf, Robert; Martin, Maria J.; Solovyev, Victor

    2016-01-01

    The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.

  9. Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

    Boudellioua, Imene

    2016-07-08

    The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.

  10. Two Gene Clusters Coordinate Galactose and Lactose Metabolism in Streptococcus gordonii

    Zeng, Lin; Martino, Nicole C.

    2012-01-01

    Streptococcus gordonii is an early colonizer of the human oral cavity and an abundant constituent of oral biofilms. Two tandemly arranged gene clusters, designated lac and gal, were identified in the S. gordonii DL1 genome, which encode genes of the tagatose pathway (lacABCD) and sugar phosphotransferase system (PTS) enzyme II permeases. Genes encoding a predicted phospho-β-galactosidase (LacG), a DeoR family transcriptional regulator (LacR), and a transcriptional antiterminator (LacT) were also present in the clusters. Growth and PTS assays supported that the permease designated EIILac transports lactose and galactose, whereas EIIGal transports galactose. The expression of the gene for EIIGal was markedly upregulated in cells growing on galactose. Using promoter-cat fusions, a role for LacR in the regulation of the expressions of both gene clusters was demonstrated, and the gal cluster was also shown to be sensitive to repression by CcpA. The deletion of lacT caused an inability to grow on lactose, apparently because of its role in the regulation of the expression of the genes for EIILac, but had little effect on galactose utilization. S. gordonii maintained a selective advantage over Streptococcus mutans in a mixed-species competition assay, associated with its possession of a high-affinity galactose PTS, although S. mutans could persist better at low pHs. Collectively, these results support the concept that the galactose and lactose systems of S. gordonii are subject to complex regulation and that a high-affinity galactose PTS may be advantageous when S. gordonii is competing against the caries pathogen S. mutans in oral biofilms. PMID:22660715

  11. Association of MEP1A gene variants with insulin metabolism in central European women with polycystic ovary syndrome.

    Lam, Uyen D P; Lerchbaum, Elisabeth; Schweighofer, Natascha; Trummer, Olivia; Eberhard, Katharina; Genser, Bernd; Pieber, Thomas R; Obermayer-Pietsch, Barbara

    2014-03-10

    Polycystic ovary syndrome (PCOS) shows not only hyperandrogenemia, hirsutism and fertility problems, but also metabolic disturbances including obesity, cardiovascular events and type-2 diabetes. Accumulating evidence suggests some degree of inflammation associated with prominent aspects of PCOS. We aimed to investigate the association of genetic variants 3'UTR rs17468190 (G/T) of the inflammation-associated gene MEP1A (GenBank ID: NM_005588.2) with metabolic disturbances in PCOS and healthy control women. Genetic variants rs17468190 (G/T) of MEP1A gene were analyzed in 576 PCOS women and 206 controls by using the Taqman fluorogenic 5'-exonuclease assay. This polymorphism was tested for association with anthropometric, metabolic, hormonal, and functional parameters of PCOS. There was a borderline significant difference in genotype distribution between PCOS and control women (p=0.046). In overweight/obese PCOS patients, the variants rs17468190 (G/T) in the MEP1A gene are associated with glucose and insulin metabolism. In a dominant model, the GG genotype of the MEP1A gene was more strongly associated with insulin metabolism in overweight/obese PCOS women (body mass index, BMI>25 kg/m(2)), than in GT+TT genotypes. The MEP1A GG-carriers showed a significantly increased homeostatic model assessment - insulin resistance (HOMA-IR) (p=0.003), elevation of fasting insulin (p=0.004) and stimulated insulin (30 min, pdisease modification in PCOS. It might contribute to the abnormalities of glucose metabolism and insulin sensitivity and serve as a diagnostic or therapeutic target gene for PCOS. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. DNA Hypermethylation of the Serotonin Receptor Type-2A Gene Is Associated with a Worse Response to a Weight Loss Intervention in Subjects with Metabolic Syndrome

    Aurora Perez-Cornago

    2014-06-01

    Full Text Available Understanding the regulation of gene activities depending on DNA methylation has been the subject of much recent study. However, although polymorphisms of the HTR2A gene have been associated with both obesity and psychiatric disorders, the role of HTR2A gene methylation in these illnesses remains uncertain. The aim of this study was to evaluate the association of HTR2A gene promoter methylation levels in white blood cells (WBC with obesity traits and depressive symptoms in individuals with metabolic syndrome (MetS enrolled in a behavioural weight loss programme. Analyses were based on 41 volunteers (mean age 49 ± 1 year recruited within the RESMENA study. Depressive symptoms (as determined using the Beck Depression Inventory, anthropometric and biochemical measurements were analysed at the beginning and after six months of weight loss treatment. At baseline, DNA from WBC was isolated and cytosine methylation in the HTR2A gene promoter was quantified by a microarray approach. In the whole-study sample, a positive association of HTR2A gene methylation with waist circumference and insulin levels was detected at baseline. Obesity measures significantly improved after six months of dietary treatment, where a lower mean HTR2A gene methylation at baseline was associated with major reductions in body weight, BMI and fat mass after the treatment. Moreover, mean HTR2A gene methylation at baseline significantly predicted the decrease in depressive symptoms after the weight loss treatment. In conclusion, this study provides newer evidence that hypermethylation of the HTR2A gene in WBC at baseline is significantly associated with a worse response to a weight-loss intervention and with a lower decrease in depressive symptoms after the dietary treatment in subjects with MetS.

  13. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

    Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

    Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

  14. The metabolic regulator CodY links L. monocytogenes metabolism to virulence by directly activating the virulence regulatory gene, prfA

    Lobel, Lior; Sigal, Nadejda; Borovok, Ilya; Belitsky, Boris R.; Sonenshein, Abraham L.; Herskovits, Anat A.

    2015-01-01

    Summary Metabolic adaptations are critical to the ability of bacterial pathogens to grow within host cells and are normally preceded by sensing of host-specific metabolic signals, which in turn can influence the pathogen's virulence state. Previously, we reported that the intracellular bacterial pathogen Listeria monocytogenes responds to low availability of branched-chain amino acids (BCAA) within mammalian cells by up-regulating both BCAA biosynthesis and virulence genes. The induction of virulence genes required the BCAA-responsive transcription regulator, CodY, but the molecular mechanism governing this mode of regulation was unclear. In this report, we demonstrate that CodY directly binds the coding sequence of the L. monocytogenes master virulence activator gene, prfA, 15 nt downstream of its start codon, and that this binding results in up-regulation of prfA transcription specifically under low concentrations of BCAA. Mutating this site abolished CodY binding and reduced prfA transcription in macrophages, and attenuated bacterial virulence in mice. Notably, the mutated binding site did not alter prfA transcription or PrfA activity under other conditions that are known to activate PrfA, such as during growth in the presence of glucose-1-phosphate. This study highlights the tight crosstalk between L. monocytogenes metabolism and virulence' while revealing novel features of CodY-mediated regulation. PMID:25430920

  15. Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways.

    Li, Chunquan; Han, Junwei; Yao, Qianlan; Zou, Chendan; Xu, Yanjun; Zhang, Chunlong; Shang, Desi; Zhou, Lingyun; Zou, Chaoxia; Sun, Zeguo; Li, Jing; Zhang, Yunpeng; Yang, Haixiu; Gao, Xu; Li, Xia

    2013-05-01

    Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

  16. Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

    Lenka, Sangram K; Lohia, Bikash; Kumar, Abhay; Chinnusamy, Viswanathan; Bansal, Kailash C

    2009-02-01

    Abscisic acid (ABA), the popular plant stress hormone, plays a key role in regulation of sub-set of stress responsive genes. These genes respond to ABA through specific transcription factors which bind to cis-regulatory elements present in their promoters. We discovered the ABA Responsive Element (ABRE) core (ACGT) containing CGMCACGTGB motif as over-represented motif among the promoters of ABA responsive co-expressed genes in rice. Targeted gene prediction strategy using this motif led to the identification of 402 protein coding genes potentially regulated by ABA-dependent molecular genetic network. RT-PCR analysis of arbitrarily chosen 45 genes from the predicted 402 genes confirmed 80% accuracy of our prediction. Plant Gene Ontology (GO) analysis of ABA responsive genes showed enrichment of signal transduction and stress related genes among diverse functional categories.

  17. A validated disease specific prediction equation for resting metabolic rate in underweight patients with COPD

    Anita Nordenson

    2010-09-01

    Full Text Available Anita Nordenson2, Anne Marie Grönberg1,2, Lena Hulthén1, Sven Larsson2, Frode Slinde11Department of Clinical Nutrition, Sahlgrenska Academy at University of Gothenburg, Göteborg, Sweden; 2Department of Internal Medicine/Respiratory Medicine and Allergology, Sahlgrenska Academy at University of Gothenburg, SwedenAbstract: Malnutrition is a serious condition in chronic obstructive pulmonary disease (COPD. Successful dietary intervention calls for calculations of resting metabolic rate (RMR. One disease-specific prediction equation for RMR exists based on mainly male patients. To construct a disease-specific equation for RMR based on measurements in underweight or weight-losing women and men with COPD, RMR was measured by indirect calorimetry in 30 women and 11 men with a diagnosis of COPD and body mass index <21 kg/m2. The following variables, possibly influencing RMR were measured: length, weight, middle upper arm circumference, triceps skinfold, body composition by dual energy x-ray absorptiometry and bioelectrical impedance, lung function, and markers of inflammation. Relations between RMR and measured variables were studied using univariate analysis according to Pearson. Gender and variables that were associated with RMR with a P value <0.15 were included in a forward multiple regression analysis. The best-fit multiple regression equation included only fat-free mass (FFM: RMR (kJ/day = 1856 + 76.0 FFM (kg. To conclude, FFM is the dominating factor influencing RMR. The developed equation can be used for prediction of RMR in underweight COPD patients.Keywords: pulmonary disease, chronic obstructive, basal metabolic rate, malnutrition, body composition

  18. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

    Hyun-Seob Song

    2015-03-01

    Full Text Available Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

  19. A seven-gene CpG-island methylation panel predicts breast cancer progression

    Li, Yan; Melnikov, Anatoliy A.; Levenson, Victor; Guerra, Emanuela; Simeone, Pasquale; Alberti, Saverio; Deng, Youping

    2015-01-01

    DNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors. CpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes. Seven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63–79 %, 53–84 % specificities, positive predictive values of 59–83 % and negative predictive values of 63–80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression. Our findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings. The online version of this article (doi:10.1186/s12885-015-1412-9) contains supplementary

  20. A metabolomics-based method for studying the effect of yfcC gene in Escherichia coli on metabolism.

    Wang, Xiyue; Xie, Yuping; Gao, Peng; Zhang, Sufang; Tan, Haidong; Yang, Fengxu; Lian, Rongwei; Tian, Jing; Xu, Guowang

    2014-04-15

    Metabolomics is a potent tool to assist in identifying the function of unknown genes through analysis of metabolite changes in the context of varied genetic backgrounds. However, the availability of a universal unbiased profiling analysis is still a big challenge. In this study, we report an optimized metabolic profiling method based on gas chromatography-mass spectrometry for Escherichia coli. It was found that physiological saline at -80°C could ensure satisfied metabolic quenching with less metabolite leakage. A solution of methanol/water (21:79, v/v) was proved to be efficient for intracellular metabolite extraction. This method was applied to investigate the metabolome difference among wild-type E. coli, its yfcC deletion, and overexpression mutants. Statistical and bioinformatic analysis of the metabolic profiling data indicated that the expression of yfcC potentially affected the metabolism of glyoxylate shunt. This finding was further validated by real-time quantitative polymerase chain reactions showing that expression of aceA and aceB, the key genes in glyoxylate shunt, was upregulated by yfcC. This study exemplifies the robustness of the proposed metabolic profiling analysis strategy and its potential roles in investigating unknown gene functions in view of metabolome difference. Copyright © 2014 Elsevier Inc. All rights reserved.

  1. A Nonsense Variant in the ACADVL Gene in German Hunting Terriers with Exercise Induced Metabolic Myopathy

    Vincent Lepori

    2018-05-01

    Full Text Available Several enzymes are involved in fatty acid oxidation, which is a key process in mitochondrial energy production. Inherited defects affecting any step of fatty acid oxidation can result in clinical disease. We present here an extended family of German Hunting Terriers with 10 dogs affected by clinical signs of exercise induced weakness, muscle pain, and suspected rhabdomyolysis. The combination of clinical signs, muscle histopathology and acylcarnitine analysis with an elevated tetradecenoylcarnitine (C14:1 peak suggested a possible diagnosis of acyl-CoA dehydrogenase very long chain deficiency (ACADVLD. Whole genome sequence analysis of one affected dog and 191 controls revealed a nonsense variant in the ACADVL gene encoding acyl-CoA dehydrogenase very long chain, c.1728C>A or p.(Tyr576*. The variant showed perfect association with the phenotype in the 10 affected and more than 500 control dogs of various breeds. Pathogenic variants in the ACADVL gene have been reported in humans with similar myopathic phenotypes. We therefore considered the detected variant to be the most likely candidate causative variant for the observed exercise induced myopathy. To our knowledge, this is the first description of this disease in dogs, which we propose to name exercise induced metabolic myopathy (EIMM, and the identification of the first canine pathogenic ACADVL variant. Our findings provide a large animal model for a known human disease and will enable genetic testing to avoid the unintentional breeding of affected offspring.

  2. A Nonsense Variant in the ACADVL Gene in German Hunting Terriers with Exercise Induced Metabolic Myopathy.

    Lepori, Vincent; Mühlhause, Franziska; Sewell, Adrian C; Jagannathan, Vidhya; Janzen, Nils; Rosati, Marco; Alves de Sousa, Filipe Miguel Maximiano; Tschopp, Aurélie; Schüpbach, Gertraud; Matiasek, Kaspar; Tipold, Andrea; Leeb, Tosso; Kornberg, Marion

    2018-05-04

    Several enzymes are involved in fatty acid oxidation, which is a key process in mitochondrial energy production. Inherited defects affecting any step of fatty acid oxidation can result in clinical disease. We present here an extended family of German Hunting Terriers with 10 dogs affected by clinical signs of exercise induced weakness, muscle pain, and suspected rhabdomyolysis. The combination of clinical signs, muscle histopathology and acylcarnitine analysis with an elevated tetradecenoylcarnitine (C14:1) peak suggested a possible diagnosis of acyl-CoA dehydrogenase very long chain deficiency (ACADVLD). Whole genome sequence analysis of one affected dog and 191 controls revealed a nonsense variant in the ACADVL gene encoding acyl-CoA dehydrogenase very long chain, c.1728C>A or p.(Tyr576*). The variant showed perfect association with the phenotype in the 10 affected and more than 500 control dogs of various breeds. Pathogenic variants in the ACADVL gene have been reported in humans with similar myopathic phenotypes. We therefore considered the detected variant to be the most likely candidate causative variant for the observed exercise induced myopathy. To our knowledge, this is the first description of this disease in dogs, which we propose to name exercise induced metabolic myopathy (EIMM), and the identification of the first canine pathogenic ACADVL variant. Our findings provide a large animal model for a known human disease and will enable genetic testing to avoid the unintentional breeding of affected offspring. Copyright © 2018 Lepori et al.

  3. Characterizing bacterial gene expression in nitrogen cycle metabolism with RT-qPCR.

    Graham, James E; Wantland, Nicholas B; Campbell, Mark; Klotz, Martin G

    2011-01-01

    Recent advances in DNA sequencing have greatly accelerated our ability to obtain the raw information needed to recognize both known and potential novel modular microbial genomic capacity for nitrogen metabolism. With PCR-based approaches to quantifying microbial mRNA expression now mainstream in most laboratories, researchers can now more efficiently propose and test hypotheses on the contributions of individual microbes to the biological accessibility of nitrogen upon which all other life depends. We review known microbial roles in these key nitrogen transformations, and describe the necessary steps in carrying out relevant gene expression studies. An example experimental design is then provided characterizing Nitrosococcus oceani mRNA expression in cultures responding to ammonia. The approach described, that of assessing microbial genome inventory and testing putative modular gene expression by mRNA quantification, is likely to remain an important tool in understanding individual microbial contributions within microbial community activities that maintain the Earth's nitrogen balance. Copyright © 2011 Elsevier Inc. All rights reserved.

  4. Genetic basis of growth adaptation of Escherichia coli after deletion of pgi, a major metabolic gene.

    Pep Charusanti

    2010-11-01

    Full Text Available Bacterial survival requires adaptation to different environmental perturbations such as exposure to antibiotics, changes in temperature or oxygen levels, DNA damage, and alternative nutrient sources. During adaptation, bacteria often develop beneficial mutations that confer increased fitness in the new environment. Adaptation to the loss of a major non-essential gene product that cripples growth, however, has not been studied at the whole-genome level. We investigated the ability of Escherichia coli K-12 MG1655 to overcome the loss of phosphoglucose isomerase (pgi by adaptively evolving ten replicates of E. coli lacking pgi for 50 days in glucose M9 minimal medium and by characterizing endpoint clones through whole-genome re-sequencing and phenotype profiling. We found that 1 the growth rates for all ten endpoint clones increased approximately 3-fold over the 50-day period; 2 two to five mutations arose during adaptation, most frequently in the NADH/NADPH transhydrogenases udhA and pntAB and in the stress-associated sigma factor rpoS; and 3 despite similar growth rates, at least three distinct endpoint phenotypes developed as defined by different rates of acetate and formate secretion. These results demonstrate that E. coli can adapt to the loss of a major metabolic gene product with only a handful of mutations and that adaptation can result in multiple, alternative phenotypes.

  5. Concerted down-regulation of immune-system related genes predicts metastasis in colorectal carcinoma

    Fehlker, Marion; Huska, Matthew R; Jöns, Thomas; Andrade-Navarro, Miguel A; Kemmner, Wolfgang

    2014-01-01

    This study aimed at the identification of prognostic gene expression markers in early primary colorectal carcinomas without metastasis at the time point of surgery by analyzing genome-wide gene expression profiles using oligonucleotide microarrays. Cryo-conserved tumor specimens from 45 patients with early colorectal cancers were examined, with the majority of them being UICC stage II or earlier and with a follow-up time of 41–115 months. Gene expression profiling was performed using Whole Human Genome 4x44K Oligonucleotide Microarrays. Validation of microarray data was performed on five of the genes in a smaller cohort. Using a novel algorithm based on the recursive application of support vector machines (SVMs), we selected a signature of 44 probes that discriminated between patients developing later metastasis and patients with a good prognosis. Interestingly, almost half of the genes was related to the patients’ immune response and showed reduced expression in the metastatic cases. Whereas up to now gene signatures containing genes with various biological functions have been described for prediction of metastasis in CRC, in this study metastasis could be well predicted by a set of gene expression markers consisting exclusively of genes related to the MHC class II complex involved in immune response. Thus, our data emphasize that the proper function of a comprehensive network of immune response genes is of vital importance for the survival of colorectal cancer patients

  6. Genomic Features That Predict Allelic Imbalance in Humans Suggest Patterns of Constraint on Gene Expression Variation

    Fédrigo, Olivier; Haygood, Ralph; Mukherjee, Sayan; Wray, Gregory A.

    2009-01-01

    Variation in gene expression is an important contributor to phenotypic diversity within and between species. Although this variation often has a genetic component, identification of the genetic variants driving this relationship remains challenging. In particular, measurements of gene expression usually do not reveal whether the genetic basis for any observed variation lies in cis or in trans to the gene, a distinction that has direct relevance to the physical location of the underlying genetic variant, and which may also impact its evolutionary trajectory. Allelic imbalance measurements identify cis-acting genetic effects by assaying the relative contribution of the two alleles of a cis-regulatory region to gene expression within individuals. Identification of patterns that predict commonly imbalanced genes could therefore serve as a useful tool and also shed light on the evolution of cis-regulatory variation itself. Here, we show that sequence motifs, polymorphism levels, and divergence levels around a gene can be used to predict commonly imbalanced genes in a human data set. Reduction of this feature set to four factors revealed that only one factor significantly differentiated between commonly imbalanced and nonimbalanced genes. We demonstrate that these results are consistent between the original data set and a second published data set in humans obtained using different technical and statistical methods. Finally, we show that variation in the single allelic imbalance-associated factor is partially explained by the density of genes in the region of a target gene (allelic imbalance is less probable for genes in gene-dense regions), and, to a lesser extent, the evenness of expression of the gene across tissues and the magnitude of negative selection on putative regulatory regions of the gene. These results suggest that the genomic distribution of functional cis-regulatory variants in the human genome is nonrandom, perhaps due to local differences in evolutionary

  7. Integrative microRNA and proteomic approaches identify novel osteoarthritis genes and their collaborative metabolic and inflammatory networks.

    Dimitrios Iliopoulos

    Full Text Available BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103 and proteins (PPARA, BMP7, IL1B to be highly correlated with Body Mass Index (BMI. Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic

  8. Prediction of highly expressed genes in microbes based on chromatin accessibility

    Ussery David W

    2007-02-01

    Full Text Available Abstract Background It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes. Results We find that absolute gene expression levels are correlated with the position preference in many microbial genomes. It is postulated that in these regions, the DNA may be more accessible to the transcriptional machinery. Moreover, ribosomal proteins and ribosomal RNA are encoded by DNA having significantly lower position preference values than other genes in fast-replicating microbes. Conclusion This insight into DNA structure-dependent gene expression in microbes may be exploited for predicting the expression of non-translated genes such as non-coding RNAs that may not be predicted by any of the conventional codon usage bias approaches.

  9. Accurate prediction of secondary metabolite gene clusters in filamentous fungi

    Andersen, Mikael Rørdam; Nielsen, Jakob Blæsbjerg; Klitgaard, Andreas

    2013-01-01

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify...... used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom....

  10. Gene prediction and RFX transcriptional regulation analysis using comparative genomics

    Chu, Jeffrey Shih Chieh

    2011-01-01

    Regulatory Factor X (RFX) is a family of transcription factors (TF) that is conserved in all metazoans, in some fungi, and in only a few single-cellular organisms. Seven members are found in mammals, nine in fishes, three in fruit flies, and a single member in nematodes and fungi. RFX is involved in many different roles in humans, but a particular function that is conserved in many metazoans is its regulation of ciliogenesis. Probing over 150 genomes for the presence of RFX and ciliary genes ...

  11. Association mapping of starch chain length distribution and amylose content in pea (Pisum sativum L.) using carbohydrate metabolism candidate genes.

    Carpenter, Margaret A; Shaw, Martin; Cooper, Rebecca D; Frew, Tonya J; Butler, Ruth C; Murray, Sarah R; Moya, Leire; Coyne, Clarice J; Timmerman-Vaughan, Gail M

    2017-08-01

    Although starch consists of large macromolecules composed of glucose units linked by α-1,4-glycosidic linkages with α-1,6-glycosidic branchpoints, variation in starch structural and functional properties is found both within and between species. Interest in starch genetics is based on the importance of starch in food and industrial processes, with the potential of genetics to provide novel starches. The starch metabolic pathway is complex but has been characterized in diverse plant species, including pea. To understand how allelic variation in the pea starch metabolic pathway affects starch structure and percent amylose, partial sequences of 25 candidate genes were characterized for polymorphisms using a panel of 92 diverse pea lines. Variation in the percent amylose composition of extracted seed starch and (amylopectin) chain length distribution, one measure of starch structure, were characterized for these lines. Association mapping was undertaken to identify polymorphisms associated with the variation in starch chain length distribution and percent amylose, using a mixed linear model that incorporated population structure and kinship. Associations were found for polymorphisms in seven candidate genes plus Mendel's r locus (which conditions the round versus wrinkled seed phenotype). The genes with associated polymorphisms are involved in the substrate supply, chain elongation and branching stages of the pea carbohydrate and starch metabolic pathways. The association of polymorphisms in carbohydrate and starch metabolic genes with variation in amylopectin chain length distribution and percent amylose may help to guide manipulation of pea seed starch structural and functional properties through plant breeding.

  12. Cpt1a gene expression in peripheral blood mononuclear cells as an early biomarker of diet-related metabolic alterations

    Diaz-Rua, Ruben

    2016-11-23

    Background: Research on biomarkers that provide early information about the development of future metabolic alterations is an emerging discipline. Gene expression analysis in peripheral blood mononuclear cells (PBMC) is a promising tool to identify subjects at risk of developing diet-related diseases.

  13. The Effect of Selenium Supplementation on Glucose Homeostasis and the Expression of Genes Related to Glucose Metabolism

    Ewa Jablonska

    2016-12-01

    Full Text Available The aim of the study was to evaluate the effect of selenium supplementation on the expression of genes associated with glucose metabolism in humans, in order to explain the unclear relationship between selenium and the risk of diabetes. For gene expression analysis we used archival samples of cDNA from 76 non-diabetic subjects supplemented with selenium in the previous study. The supplementation period was six weeks and the daily dose of selenium was 200 µg (as selenium yeast. Blood for mRNA isolation was collected at four time points: before supplementation, after two and four weeks of supplementation, and after four weeks of washout. The analysis included 15 genes encoding selected proteins involved in insulin signaling and glucose metabolism. In addition, HbA1c and fasting plasma glucose were measured at three and four time points, respectively. Selenium supplementation was associated with a significantly decreased level of HbA1c but not fasting plasma glucose (FPG and significant down-regulation of seven genes: INSR, ADIPOR1, LDHA, PDHA, PDHB, MYC, and HIF1AN. These results suggest that selenium may affect glycemic control at different levels of regulation, linked to insulin signaling, glycolysis, and pyruvate metabolism. Further research is needed to investigate mechanisms of such transcriptional regulation and its potential implication in direct metabolic effects.

  14. Transcriptome analysis of temporal regulation of carbon metabolism by CcpA in Bacillus subtilis reveals additional target genes

    Lulko, Andrzej T.; Buist, Girbe; Kok, Jan; Kuipers, Oscar P.

    2007-01-01

    The pleiotropic regulator of carbon metabolism in Grampositive bacteria, CcpA, regulates gene expression by binding to so-called cre elements, which are located either upstream or in promoter regions, or in open-reading frames. In this study we compared the transcriptomes of Bacillus subtilis 168

  15. Consortium analysis of gene and gene–folate interactions in purine and pyrimidine metabolism pathways with ovarian carcinoma risk

    Kelemen, Linda E; Terry, Kathryn L; Goodman, Marc T

    2014-01-01

    SCOPE: We reevaluated previously reported associations between variants in pathways of one-carbon (1-C) (folate) transfer genes and ovarian carcinoma (OC) risk, and in related pathways of purine and pyrimidine metabolism, and assessed interactions with folate intake. METHODS AND RESULTS: Odds rat...

  16. Clustering gene expression data based on predicted differential effects of GV interaction.

    Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

    2005-02-01

    Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

  17. GGDonto ontology as a knowledge-base for genetic diseases and disorders of glycan metabolism and their causative genes.

    Solovieva, Elena; Shikanai, Toshihide; Fujita, Noriaki; Narimatsu, Hisashi

    2018-04-18

    Inherited mutations in glyco-related genes can affect the biosynthesis and degradation of glycans and result in severe genetic diseases and disorders. The Glyco-Disease Genes Database (GDGDB), which provides information about these diseases and disorders as well as their causative genes, has been developed by the Research Center for Medical Glycoscience (RCMG) and released in April 2010. GDGDB currently provides information on about 80 genetic diseases and disorders caused by single-gene mutations in glyco-related genes. Many biomedical resources provide information about genetic disorders and genes involved in their pathogenesis, but resources focused on genetic disorders known to be related to glycan metabolism are lacking. With the aim of providing more comprehensive knowledge on genetic diseases and disorders of glycan biosynthesis and degradation, we enriched the content of the GDGDB database and improved the methods for data representation. We developed the Genetic Glyco-Diseases Ontology (GGDonto) and a RDF/SPARQL-based user interface using Semantic Web technologies. In particular, we represented the GGDonto content using Semantic Web languages, such as RDF, RDFS, SKOS, and OWL, and created an interactive user interface based on SPARQL queries. This user interface provides features to browse the hierarchy of the ontology, view detailed information on diseases and related genes, and find relevant background information. Moreover, it provides the ability to filter and search information by faceted and keyword searches. Focused on the molecular etiology, pathogenesis, and clinical manifestations of genetic diseases and disorders of glycan metabolism and developed as a knowledge-base for this scientific field, GGDonto provides comprehensive information on various topics, including links to aid the integration with other scientific resources. The availability and accessibility of this knowledge will help users better understand how genetic defects impact the

  18. Glucose Metabolism Gene Expression Patterns and Tumor Uptake of {sup 18}F-Fluorodeoxyglucose After Radiation Treatment

    Wilson, George D., E-mail: george.wilson@beaumont.edu [Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, Michigan (United States); Beaumont BioBank, William Beaumont Hospital, Royal Oak, Michigan (United States); Thibodeau, Bryan J.; Fortier, Laura E.; Pruetz, Barbara L. [Beaumont BioBank, William Beaumont Hospital, Royal Oak, Michigan (United States); Galoforo, Sandra; Baschnagel, Andrew M.; Chunta, John [Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, Michigan (United States); Oliver Wong, Ching Yee [Department of Diagnostic Radiology and Molecular Imaging Medicine, William Beaumont Hospital, Royal Oak, Michigan (United States); Yan, Di; Marples, Brian [Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, Michigan (United States); Huang, Jiayi [Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, Michigan (United States); Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri (United States)

    2014-11-01

    Purpose: To investigate whether radiation treatment influences the expression of glucose metabolism genes and compromises the potential use of {sup 18}F-fluorodeoxyglucose positron emission tomography (FDG-PET) as a tool to monitor the early response of head and neck cancer xenografts to radiation therapy (RT). Methods and Materials: Low passage head and neck squamous cancer cells (UT14) were injected to the flanks of female nu/nu mice to generate xenografts. After tumors reached a size of 500 mm{sup 3} they were treated with either sham RT or 15 Gy in 1 fraction. At different time points, days 3, 9, and 16 for controls and days 4, 7, 12, 21, 30, and 40 after irradiation, 2 to 3 mice were assessed with dynamic FDG-PET acquisition over 2 hours. Immediately after the FDG-PET the tumors were harvested for global gene expression analysis and immunohistochemical evaluation of GLUT1 and HK2. Different analytic parameters were used to process the dynamic PET data. Results: Radiation had no effect on key genes involved in FDG uptake and metabolism but did alter other genes in the HIF1α and glucose transport–related pathways. In contrast to the lack of effect on gene expression, changes in the protein expression patterns of the key genes GLUT1/SLC2A1 and HK2 were observed after radiation treatment. The changes in GLUT1 protein expression showed some correlation with dynamic FDG-PET parameters, such as the kinetic index. Conclusion: {sup 18}F-fluorodeoxyglucose positron emission tomography changes after RT would seem to represent an altered metabolic state and not a direct effect on the key genes regulating FDG uptake and metabolism.

  19. Genes responsible for vaginal extracellular matrix metabolism are modulated by women's reproductive cycle and menopause

    Oksana Shynlova

    2013-04-01

    Full Text Available Objectives To analyze the expression of genes involved in extracellular matrix (ECM biogenesis and remodeling in vaginal tissue of women with clinically normal pelvic floor support (defined as controls according to the phase of menstrual cycle and postmenopausal women with and without pelvic organ prolapse (POP. Materials and Methods This study examined the expression of matrix metalloproteinases (MMPs, their tissue inhibitors (TIMPs, and the Lysyl oxidase (LOX family genes in the anterior vaginal wall of Caucasian women by real-time RT-PCR. Initially, mRNA expression was assessed in premenopausal controls in the secretory (group 1, n = 10 vs. proliferative (group 2, n = 8 phase of menstrual cycle. In addition, we compared premenopausal controls in the proliferative phase (group 2 vs. postmenopausal controls (group 3, n = 5. Finally, we analyzed postmenopausal controls (group 3 vs. postmenopausal women with advanced POP (group 4, n = 13. Results According to the phase of menstrual cycle, MMP1 was significantly reduced (p = 0.003, whereas the expression of TIMP1 and LOXL4 was significantly up-regulated during proliferative phase (both p < 0.01 when compared to the secretory phase in premenopausal control women. Regarding menopausal status/ageing, all MMPs were down-regulated, while TIMP3, TIMP4 and LOXL2 were significantly up-regulated in postmenopausal control women when compared to premenopausal controls (p = 0.005, p = 0.01 and p < 0.001, correspondingly. TIMP4 and LOXL2 mRNA levels were significantly decreased in postmenopausal POP patients compared to asymptomatic postmenopausal controls (p < 0.01 for both. Conclusions Our results indicate that ovarian cycle and age-related changes influence the expression of genes encoding proteins responsible for ECM metabolism in human vagina. Moreover