The SMARTCyp cytochrome P450 metabolism prediction server

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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....

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

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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.

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

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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.

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

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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

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

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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

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

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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

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

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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.

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

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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

Apolipoprotein gene involved in lipid metabolism

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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.

Prediction of lithium-ion battery capacity with metabolic grey model

International Nuclear Information System (INIS)

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.

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

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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.

Computational prediction and experimental validation of Ciona intestinalis microRNA genes

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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.

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

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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

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

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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.

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

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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.

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

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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.

Vertebrate gene predictions and the problem of large genes

DEFF Research Database (Denmark)

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...

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

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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

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

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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.

Temporal expression-based analysis of metabolism.

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Sara B Collins

Full Text Available Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM. We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such "history-dependent" sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

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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.

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

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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.

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

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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

Machine learning methods for metabolic pathway prediction

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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.

Machine learning methods for metabolic pathway prediction

Science.gov (United States)

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

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

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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.

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

Science.gov (United States)

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.

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

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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.

Combining gene prediction methods to improve metagenomic gene annotation

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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.

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

International Nuclear Information System (INIS)

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.

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

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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.

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

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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.

Comparative gene expression of intestinal metabolizing enzymes.

Science.gov (United States)

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.

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

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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.

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

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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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Lactococcus lactis Metabolism and Gene Expression during Growth on Plant Tissues

Science.gov (United States)

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

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

Science.gov (United States)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

Increased fat oxidation and regulation of metabolic genes with ultraendurance exercise

DEFF Research Database (Denmark)

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....

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

Science.gov (United States)

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.

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

Science.gov (United States)

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...

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

Metabolic gene polymorphism frequencies in control populations

DEFF Research Database (Denmark)

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...

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

Science.gov (United States)

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

Clinicopathologic and gene expression parameters predict liver cancer prognosis

International Nuclear Information System (INIS)

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

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

Directory of Open Access Journals (Sweden)

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.

The WWOX Gene Modulates HDL and Lipid Metabolism

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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

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

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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.

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

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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...

Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions

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Orth Jeffrey D

2012-05-01

Full Text Available Abstract Background The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network. Results A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions. Conclusions Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.

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

International Nuclear Information System (INIS)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

Fast prediction of cytochrome P450 mediated drug metabolism

DEFF Research Database (Denmark)

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...

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

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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.

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

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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.

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

DEFF Research Database (Denmark)

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...

An algorithm to discover gene signatures with predictive potential

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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.

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

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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.

Predicting metabolic syndrome using decision tree and support vector machine methods

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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

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

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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

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

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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

Predictability of Genetic Interactions from Functional Gene Modules

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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.

Variability of Creatine Metabolism Genes in Children with Autism Spectrum Disorder

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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

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

Science.gov (United States)

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

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

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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.

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

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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.

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

DEFF Research Database (Denmark)

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....

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

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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.

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

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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...

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

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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.

A comparative analysis of soft computing techniques for gene prediction.

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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.

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

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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

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

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Kumari Sonal Choudhary

2016-06-01

Full Text Available Epithelial to mesenchymal transition (EMT is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR, are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E and mesenchymal (EGFR_M networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

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Choudhary, Kumari Sonal; Rohatgi, Neha; Halldorsson, Skarphedinn; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-06-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

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

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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.

Exploring the Optimal Strategy to Predict Essential Genes in Microbes

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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.

Identification of the Consistently Altered Metabolic Targets in Human Hepatocellular Carcinoma.

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Nwosu, Zeribe Chike; Megger, Dominik Andre; Hammad, Seddik; Sitek, Barbara; Roessler, Stephanie; Ebert, Matthias Philip; Meyer, Christoph; Dooley, Steven

2017-09-01

Cancer cells rely on metabolic alterations to enhance proliferation and survival. Metabolic gene alterations that repeatedly occur in liver cancer are largely unknown. We aimed to identify metabolic genes that are consistently deregulated, and are of potential clinical significance in human hepatocellular carcinoma (HCC). We studied the expression of 2,761 metabolic genes in 8 microarray datasets comprising 521 human HCC tissues. Genes exclusively up-regulated or down-regulated in 6 or more datasets were defined as consistently deregulated. The consistent genes that correlated with tumor progression markers ( ECM2 and MMP9) (Pearson correlation P < .05) were used for Kaplan-Meier overall survival analysis in a patient cohort. We further compared proteomic expression of metabolic genes in 19 tumors vs adjacent normal liver tissues. We identified 634 consistent metabolic genes, ∼60% of which are not yet described in HCC. The down-regulated genes (n = 350) are mostly involved in physiologic hepatocyte metabolic functions (eg, xenobiotic, fatty acid, and amino acid metabolism). In contrast, among consistently up-regulated metabolic genes (n = 284) are those involved in glycolysis, pentose phosphate pathway, nucleotide biosynthesis, tricarboxylic acid cycle, oxidative phosphorylation, proton transport, membrane lipid, and glycan metabolism. Several metabolic genes (n = 434) correlated with progression markers, and of these, 201 predicted overall survival outcome in the patient cohort analyzed. Over 90% of the metabolic targets significantly altered at the protein level were similarly up- or down-regulated as in genomic profile. We provide the first exposition of the consistently altered metabolic genes in HCC and show that these genes are potentially relevant targets for onward studies in preclinical and clinical contexts.

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

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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.

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

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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.

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

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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.

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

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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.

GeneChip expression profiling reveals the alterations of energy metabolism related genes in osteocytes under large gradient high magnetic fields.

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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.

GeneChip expression profiling reveals the alterations of energy metabolism related genes in osteocytes under large gradient high magnetic fields.

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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.

Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth

Science.gov (United States)

2012-01-01

Background Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes. Results Here we employed a systematic and iterative process, designated as Reconciling In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype). Conclusion This study presents validation and refinement of a newly reconstructed metabolic model of the yeast S. pombe, through improving the metabolic model’s predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data. PMID:22631437

PPARγ regulates the expression of cholesterol metabolism genes in alveolar macrophages

International Nuclear Information System (INIS)

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.

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

International Nuclear Information System (INIS)

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

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

Energy Technology Data Exchange (ETDEWEB)

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

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

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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.

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

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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

Combining gene signatures improves prediction of breast cancer survival.

Directory of Open Access Journals (Sweden)

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

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

PROXIMAL: a method for Prediction of Xenobiotic Metabolism.

Science.gov (United States)

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.

Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation

Energy Technology Data Exchange (ETDEWEB)

Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M.; Young, Vincent B.; Jansson, Janet K.; Fredricks, David N.; Borenstein, Elhanan; Sanchez, Laura M.

2015-12-22

Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in

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

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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

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

Science.gov (United States)

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

Transcriptome data modeling for targeted plant metabolic engineering.

Science.gov (United States)

Yonekura-Sakakibara, Keiko; Fukushima, Atsushi; Saito, Kazuki

2013-04-01

The massive data generated by omics technologies require the power of bioinformatics, especially network analysis, for data mining and doing data-driven biology. Gene coexpression analysis, a network approach based on comprehensive gene expression data using microarrays, is becoming a standard tool for predicting gene function and elucidating the relationship between metabolic pathways. Differential and comparative gene coexpression analyses suggest a change in coexpression relationships and regulators controlling common and/or specific biological processes. In conjunction with the newly emerging genome editing technology, network analysis integrated with other omics data should pave the way for robust and practical plant metabolic engineering. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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.

An in silico assessment of gene function and organization of the phenylpropanoid pathway metabolic networks in Arabidopsis thaliana and limitations thereof

Science.gov (United States)

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

Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02.

Science.gov (United States)

Guo, Jing; Zhang, Hong; Wang, Cheng; Chang, Ji-Wei; Chen, Ling-Ling

2016-05-01

We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02. Copyright © 2016 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

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

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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.

Identification of the Consistently Altered Metabolic Targets in Human Hepatocellular CarcinomaSummary

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Zeribe Chike Nwosu

2017-09-01

Full Text Available Background & Aims: Cancer cells rely on metabolic alterations to enhance proliferation and survival. Metabolic gene alterations that repeatedly occur in liver cancer are largely unknown. We aimed to identify metabolic genes that are consistently deregulated, and are of potential clinical significance in human hepatocellular carcinoma (HCC. Methods: We studied the expression of 2,761 metabolic genes in 8 microarray datasets comprising 521 human HCC tissues. Genes exclusively up-regulated or down-regulated in 6 or more datasets were defined as consistently deregulated. The consistent genes that correlated with tumor progression markers (ECM2 and MMP9 (Pearson correlation P < .05 were used for Kaplan-Meier overall survival analysis in a patient cohort. We further compared proteomic expression of metabolic genes in 19 tumors vs adjacent normal liver tissues. Results: We identified 634 consistent metabolic genes, ∼60% of which are not yet described in HCC. The down-regulated genes (n = 350 are mostly involved in physiologic hepatocyte metabolic functions (eg, xenobiotic, fatty acid, and amino acid metabolism. In contrast, among consistently up-regulated metabolic genes (n = 284 are those involved in glycolysis, pentose phosphate pathway, nucleotide biosynthesis, tricarboxylic acid cycle, oxidative phosphorylation, proton transport, membrane lipid, and glycan metabolism. Several metabolic genes (n = 434 correlated with progression markers, and of these, 201 predicted overall survival outcome in the patient cohort analyzed. Over 90% of the metabolic targets significantly altered at the protein level were similarly up- or down-regulated as in genomic profile. Conclusions: We provide the first exposition of the consistently altered metabolic genes in HCC and show that these genes are potentially relevant targets for onward studies in preclinical and clinical contexts. Keywords: Liver Cancer, HCC, Tumor Metabolism

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

Science.gov (United States)

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.

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

DEFF Research Database (Denmark)

Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim

2008-01-01

Background: Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number...... to a genome scale metabolic model of A. oryzae. Results: Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted...... model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. Conclusion: A much enhanced annotation of the A. oryzae genome was performed and a genomescale metabolic model of A. oryzae was reconstructed. The model accurately predicted...

Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

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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.

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

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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.

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

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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

Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

KAUST Repository

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.

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

Science.gov (United States)

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.

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

OpenAIRE

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...

The search for putative unifying genetic factors for components of the metabolic syndrome

DEFF Research Database (Denmark)

Sjögren, M; Lyssenko, V; Jonsson, Anna Elisabet

2008-01-01

The metabolic syndrome is a cluster of factors contributing to increased risk of cardiovascular disease and type 2 diabetes but unifying mechanisms have not been identified. Our aim was to study whether common variations in 17 genes previously associated with type 2 diabetes or components...... of the metabolic syndrome and variants in nine genes with inconsistent association with at least two components of the metabolic syndrome would also predict future development of components of the metabolic syndrome, individually or in combination....

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

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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

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

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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

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

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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.

Embryo quality predictive models based on cumulus cells gene expression

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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.

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

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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.

Determinants of human adipose tissue gene expression: impact of diet, sex, metabolic status, and cis genetic regulation.

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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.

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

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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.

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

KAUST Repository

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.

Gene expression of transporters and phase I/II metabolic enzymes in murine small intestine during fasting

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van der Meijde Jolanda

2007-08-01

Full Text Available Abstract Background Fasting has dramatic effects on small intestinal transport function. However, little is known on expression of intestinal transport and phase I/II metabolism genes during fasting and the role the fatty acid-activated transcription factor PPARα may play herein. We therefore investigated the effects of fasting on expression of these genes using Affymetrix GeneChip MOE430A arrays and quantitative RT-PCR. Results After 24 hours of fasting, expression levels of 33 of the 253 analyzed transporter and phase I/II metabolism genes were changed. Upregulated genes were involved in transport of energy-yielding molecules in processes such as glycogenolysis (G6pt1 and mitochondrial and peroxisomal oxidation of fatty acids (Cact, Mrs3/4, Fatp2, Cyp4a10, Cyp4b1. Other induced genes were responsible for the inactivation of the neurotransmitter serotonin (Sert, Sult1d1, Dtd, Papst2, formation of eicosanoids (Cyp2j6, Cyp4a10, Cyp4b1, or for secretion of cholesterol (Abca1 and Abcg8. Cyp3a11, typically known because of its drug metabolizing capacity, was also increased. Fasting had no pronounced effect on expression of phase II metabolic enzymes, except for glutathione S-transferases which were down-regulated. Time course studies revealed that some genes were acutely regulated, whereas expression of other genes was only affected after prolonged fasting. Finally, we identified 8 genes that were PPARα-dependently upregulated upon fasting. Conclusion We have characterized the response to fasting on expression of transporters and phase I/II metabolic enzymes in murine small intestine. Differentially expressed genes are involved in a variety of processes, which functionally can be summarized as a increased oxidation of fat and xenobiotics, b increased cholesterol secretion, c increased susceptibility to electrophilic stressors, and d reduced intestinal motility. This knowledge increases our understanding of gut physiology, and may be of relevance

Application of meta-omics techniques to understand greenhouse gas emissions originating from ruminal metabolism.

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Wallace, Robert J; Snelling, Timothy J; McCartney, Christine A; Tapio, Ilma; Strozzi, Francesco

2017-01-16

Methane emissions from ruminal fermentation contribute significantly to total anthropological greenhouse gas (GHG) emissions. New meta-omics technologies are beginning to revolutionise our understanding of the rumen microbial community structure, metabolic potential and metabolic activity. Here we explore these developments in relation to GHG emissions. Microbial rumen community analyses based on small subunit ribosomal RNA sequence analysis are not yet predictive of methane emissions from individual animals or treatments. Few metagenomics studies have been directly related to GHG emissions. In these studies, the main genes that differed in abundance between high and low methane emitters included archaeal genes involved in methanogenesis, with others that were not apparently related to methane metabolism. Unlike the taxonomic analysis up to now, the gene sets from metagenomes may have predictive value. Furthermore, metagenomic analysis predicts metabolic function better than only a taxonomic description, because different taxa share genes with the same function. Metatranscriptomics, the study of mRNA transcript abundance, should help to understand the dynamic of microbial activity rather than the gene abundance; to date, only one study has related the expression levels of methanogenic genes to methane emissions, where gene abundance failed to do so. Metaproteomics describes the proteins present in the ecosystem, and is therefore arguably a better indication of microbial metabolism. Both two-dimensional polyacrylamide gel electrophoresis and shotgun peptide sequencing methods have been used for ruminal analysis. In our unpublished studies, both methods showed an abundance of archaeal methanogenic enzymes, but neither was able to discriminate high and low emitters. Metabolomics can take several forms that appear to have predictive value for methane emissions; ruminal metabolites, milk fatty acid profiles, faecal long-chain alcohols and urinary metabolites have all

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

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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.

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

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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.

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

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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.

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

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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.

Zea mays iRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

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Saha, Rajib; Suthers, Patrick F.; Maranas, Costas D.

2011-01-01

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species. PMID:21755001

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

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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

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

International Nuclear Information System (INIS)

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

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

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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

Gene expression in plant lipid metabolism in Arabidopsis seedlings.

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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.

Inductive matrix completion for predicting gene-disease associations.

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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.

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

DEFF Research Database (Denmark)

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

A deep auto-encoder model for gene expression prediction.

Science.gov (United States)

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.

Model-driven discovery of underground metabolic functions in Escherichia coli

DEFF Research Database (Denmark)

Guzmán, Gabriela I.; Utrilla, José; Nurk, Sergey

2015-01-01

-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence......E, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations....

Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.

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Valadez-Cano, Cecilio; Olivares-Hernández, Roberto; Resendis-Antonio, Osbaldo; DeLuna, Alexander; Delaye, Luis

2017-04-14

Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore. We reconstructed the metabolic networks of the chromatophore of P. chromatophora CCAC 0185 and a close free-living relative, the cyanobacterium Synechococcus sp. WH 5701. We found that the evolution of free-living to host-restricted lifestyle rendered a fragile metabolic network where >80% of genes in the chromatophore are essential for metabolic functionality. Despite the lack of experimental information, the metabolic reconstruction of the chromatophore suggests that the host provides several metabolites to the endosymbiont. By using these metabolites as intracellular conditions, in silico simulations of genome evolution by gene lose recover with 77% accuracy the actual metabolic gene content of the chromatophore. Also, the metabolic model of the chromatophore allowed us to predict by flux balance analysis a maximum rate of reduced-carbon released by the endosymbiont to the host. By inspecting the central metabolism of the chromatophore and the free-living cyanobacteria we found that by

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Energy Technology Data Exchange (ETDEWEB)

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.

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

OpenAIRE

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...

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

Energy Technology Data Exchange (ETDEWEB)

Seaver, Samuel M. D.; Bradbury, Louis M. T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

2015-03-10

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

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

International Nuclear Information System (INIS)

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

Incremental Predictive Value of Serum AST-to-ALT Ratio for Incident Metabolic Syndrome: The ARIRANG Study

Science.gov (United States)

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

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

Science.gov (United States)

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.

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

OpenAIRE

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

The gut microbiota modulates host amino acid and glutathione metabolism in mice

DEFF Research Database (Denmark)

Mardinoglu, Adil; Shoaie, Saeed; Bergentall, Mattias

2015-01-01

, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism...... conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon....... To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences...

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

Directory of Open Access Journals (Sweden)

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.

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

LENUS (Irish Health Repository)

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.

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

Science.gov (United States)

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.

Transcriptome Analysis of Three Sheep Intestinal Regions reveals Key Pathways and Hub Regulatory Genes of Large Intestinal Lipid Metabolism.

Science.gov (United States)

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.

Global discriminative learning for higher-accuracy computational gene prediction.

Directory of Open Access Journals (Sweden)

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.

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.

Science.gov (United States)

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.

Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

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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.

Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning.

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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

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

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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.

Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

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Gu, Deqing; Jian, Xingxing; Zhang, Cheng; Hua, Qiang

2017-01-01

Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

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

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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.

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

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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.

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

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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.

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

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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.

Evolutionary programming as a platform for in silico metabolic engineering

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Förster Jochen

2005-12-01

Full Text Available Abstract Background Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. Results In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. Conclusion We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span

Polymorphisms in the LPL and CETP Genes and Haplotype in the ESR1 Gene Are Associated with Metabolic Syndrome in Women from Southwestern Mexico

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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.

Novel inborn error of folate metabolism: identification by exome capture and sequencing of mutations in the MTHFD1 gene in a single proband.

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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.

Probability-based collaborative filtering model for predicting gene-disease associations.

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Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-12-28

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

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

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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.

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

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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.

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

DEFF Research Database (Denmark)

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...

The metabolic regulator CodY links L. monocytogenes metabolism to virulence by directly activating the virulence regulatory gene, prfA

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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

Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1

Energy Technology Data Exchange (ETDEWEB)

Henry, Christopher S.; Rotman, Ella; Lathem, Wyndham W.; Tyo, Keith E. J.; Hauser, Alan R.; Mandel, Mark J.

2017-02-15

Klebsiella pneumoniae has a reputation for causing a wide range of infectious conditions, with numerous highly virulent and antibiotic-resistant strains. Metabolic models have the potential to provide insights into the growth behavior, nutrient requirements, essential genes, and candidate drug targets in these strains. Here we develop a metabolic model for KPPR1, a highly virulent strain of K. pneumoniae. We apply a combination of Biolog phenotype data and fitness data to validate and refine our KPPR1 model. The final model displays a predictive accuracy of 75% in identifying potential carbon and nitrogen sources for K. pneumoniae and of 99% in predicting nonessential genes in rich media. We demonstrate how this model is useful in studying the differences in the metabolic capabilities of the low-virulence MGH 78578 strain and the highly virulent KPPR1 strain. For example, we demonstrate that these strains differ in carbohydrate metabolism, including the ability to metabolize dulcitol as a primary carbon source. Our model makes numerous other predictions for follow-up verification and analysis.

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

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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.

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

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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.

A Natural Light/Dark Cycle Regulation of Carbon-Nitrogen Metabolism and Gene Expression in Rice Shoots.

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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

A natural light/dark cycle regulation of carbon-nitrogen metabolism and gene expression in rice shoots

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Haixing Li

2016-08-01

Full Text Available 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

Protein-DNA binding dynamics predict transcriptional response to nutrients in archaea.

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Todor, Horia; Sharma, Kriti; Pittman, Adrianne M C; Schmid, Amy K

2013-10-01

Organisms across all three domains of life use gene regulatory networks (GRNs) to integrate varied stimuli into coherent transcriptional responses to environmental pressures. However, inferring GRN topology and regulatory causality remains a central challenge in systems biology. Previous work characterized TrmB as a global metabolic transcription factor in archaeal extremophiles. However, it remains unclear how TrmB dynamically regulates its ∼100 metabolic enzyme-coding gene targets. Using a dynamic perturbation approach, we elucidate the topology of the TrmB metabolic GRN in the model archaeon Halobacterium salinarum. Clustering of dynamic gene expression patterns reveals that TrmB functions alone to regulate central metabolic enzyme-coding genes but cooperates with various regulators to control peripheral metabolic pathways. Using a dynamical model, we predict gene expression patterns for some TrmB-dependent promoters and infer secondary regulators for others. Our data suggest feed-forward gene regulatory topology for cobalamin biosynthesis. In contrast, purine biosynthesis appears to require TrmB-independent regulators. We conclude that TrmB is an important component for mediating metabolic modularity, integrating nutrient status and regulating gene expression dynamics alone and in concert with secondary regulators.

Fatty Liver Index and Lipid Accumulation Product Can Predict Metabolic Syndrome in Subjects without Fatty Liver Disease

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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.

Glucose Metabolism Gene Expression Patterns and Tumor Uptake of {sup 18}F-Fluorodeoxyglucose After Radiation Treatment

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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.

Context-specific metabolic networks are consistent with experiments.

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Scott A Becker

2008-05-01

Full Text Available Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

Trehalose metabolism genes render rice white tip nematode Aphelenchoides besseyi (Nematoda: Aphelenchoididae) resistant to an anaerobic environment

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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

Prediction of highly expressed genes in microbes based on chromatin accessibility

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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.

Functional Potential of Bacterial Communities using Gene Context Information

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Anwesha Mohapatra

2017-12-01

Full Text Available Estimation of the functional potential of a bacterial genome can be determined by accurate annotation of its metabolic pathways. Existing homology based methods for pathway annotation fail to account for homologous genes that participate in multiple pathways, causing overestimation of gene copy number. Mere presence of constituent genes of a candidate pathway which are dispersed on a genome often results in incorrect annotation, thereby leading to erroneous gene abundance and pathway estimation. Clusters of evolutionarily conserved coregulated genes are characteristic features in bacterial genomes and their spatial arrangement in the genome is constrained by the pathway encoded by them. Thus, in order to improve the accuracy of pathway prediction, it is important to augment homology based annotation with gene organization information. In this communication, we present a methodology considering prioritization of gene context for improved pathway annotation. Extensive literature mining was performed to confirm conserved juxtaposed arrangement of gene components of various pathways. Our method was utilized to identify and analyse the functional potential of all available completely sequenced bacterial genomes. The accuracy of the predicted gene clusters and their importance in metabolic pathways will be demonstrated using a few case studies. One of such case study corresponds to butyrate production pathways in gut bacteria where it was observed that gut pathogens and commensals possess a distinct set of pathway components. In another example, we will demonstrate how our methodology improves the prediction accuracy of carbohydrate metabolic potential in human microbial communities. Applicability of our method for estimation of functional potential in bacterial communities present in diverse environments will also be illustrated.

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

Indian Academy of Sciences (India)

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.

Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

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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.

A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4

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Zi-Ru Dai

2015-06-01

Full Text Available Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s and the site(s of modification. The newly established model was applied to predict the metabolic site(s of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s of CYP3A4 on steroids with high predictive accuracy.

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

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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.

Highlighting the Need for Systems-level Experimental Characterization of Plant Metabolic Enzymes

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Martin Karl Magnus Engqvist

2016-07-01

Full Text Available The biology of living organisms is determined by the action and interaction of a large number of individual gene products, each with specific functions. Discovering and annotating the function of gene products is key to our understanding of these organisms. Controlled experiments and bioinformatic predictions both contribute to functional gene annotation. For most species it is difficult to gain an overview of what portion of gene annotations are based on experiments and what portion represent predictions. Here, I survey the current state of experimental knowledge of enzymes and metabolism in Arabidopsis thaliana as well as eleven economically important crops and forestry trees – with a particular focus on reactions involving organic acids in central metabolism. I illustrate the limited availability of experimental data for functional annotation of enzymes in most of these species. Many enzymes involved in metabolism of citrate, malate, fumarate, lactate, and glycolate in crops and forestry trees have not been characterized. Furthermore, enzymes involved in key biosynthetic pathways which shape important traits in crops and forestry trees have not been characterized. I argue for the development of novel high-throughput platforms with which limited functional characterization of gene products can be performed quickly and relatively cheaply. I refer to this approach as systems-level experimental characterization. The data collected from such platforms would form a layer intermediate between bioinformatic gene function predictions and in-depth experimental studies of these functions. Such a data layer would greatly aid in the pursuit of understanding a multiplicity of biological processes in living organisms.

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

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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.

Effects of achilline on lipid metabolism gene expression in cell culture

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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.

Prediction of cytochrome P450 mediated metabolism

DEFF Research Database (Denmark)

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...

Association of MEP1A gene variants with insulin metabolism in central European women with polycystic ovary syndrome.

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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.

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

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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.

Plant interactions alter the predictions of metabolic scaling theory.

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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.

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

DEFF Research Database (Denmark)

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...

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

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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.

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

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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.

Phylogenetic analysis of vitamin B12-related metabolism in Mycobacterium tuberculosis

OpenAIRE

Young, Douglas B.; Comas, I?aki; de Carvalho, Luiz P. S.

2015-01-01

Comparison of genome sequences from clinical isolates of Mycobacterium tuberculosis with phylogenetically-related pathogens Mycobacterium marinum, Mycobacterium kansasii, and Mycobacterium leprae reveals diversity amongst genes associated with vitamin B12-related metabolism. Diversity is generated by gene deletion events, differential acquisition of genes by horizontal transfer, and single nucleotide polymorphisms (SNPs) with predicted impact on protein function and transcriptional regulation...

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

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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.

Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

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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.

Shift work or food intake during the rest phase promotes metabolic disruption and desynchrony of liver genes in male rats.

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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.

Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.

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Xin Fang

Full Text Available The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB, to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to

The mRNA expression profile of metabolic genes relative to MHC isoform pattern in human skeletal muscles

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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...

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

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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.

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

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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.

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

OpenAIRE

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...

The CanOE strategy: integrating genomic and metabolic contexts across multiple prokaryote genomes to find candidate genes for orphan enzymes.

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Adam Alexander Thil Smith

2012-05-01

Full Text Available Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes, a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short. The first step locates "genomic metabolons", i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12.

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

DEFF Research Database (Denmark)

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...

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

International Nuclear Information System (INIS)

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

A metabolomics-based method for studying the effect of yfcC gene in Escherichia coli on metabolism.

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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.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software.

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Peach, Megan L; Zakharov, Alexey V; Liu, Ruifeng; Pugliese, Angelo; Tawa, Gregory; Wallqvist, Anders; Nicklaus, Marc C

2012-10-01

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

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

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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

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

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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.

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

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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

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

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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.

Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis

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Reifman Jaques

2010-11-01

Full Text Available Abstract Background During infection, Mycobacterium tuberculosis confronts a generally hostile and nutrient-poor in vivo host environment. Existing models and analyses of M. tuberculosis metabolic networks are able to reproduce experimentally measured cellular growth rates and identify genes required for growth in a range of different in vitro media. However, these models, under in vitro conditions, do not provide an adequate description of the metabolic processes required by the pathogen to infect and persist in a host. Results To better account for the metabolic activity of M. tuberculosis in the host environment, we developed a set of procedures to systematically modify an existing in vitro metabolic network by enhancing the agreement between calculated and in vivo-measured gene essentiality data. After our modifications, the new in vivo network contained 663 genes, 838 metabolites, and 1,049 reactions and had a significantly increased sensitivity (0.81 in predicted gene essentiality than the in vitro network (0.31. We verified the modifications generated from the purely computational analysis through a review of the literature and found, for example, that, as the analysis suggested, lipids are used as the main source for carbon metabolism and oxygen must be available for the pathogen under in vivo conditions. Moreover, we used the developed in vivo network to predict the effects of double-gene deletions on M. tuberculosis growth in the host environment, explore metabolic adaptations to life in an acidic environment, highlight the importance of different enzymes in the tricarboxylic acid-cycle under different limiting nutrient conditions, investigate the effects of inhibiting multiple reactions, and look at the importance of both aerobic and anaerobic cellular respiration during infection. Conclusions The network modifications we implemented suggest a distinctive set of metabolic conditions and requirements faced by M. tuberculosis during

Genomic Evidence of Chemotrophic Metabolisms in Deep-Dwelling Chloroflexi Conferred by Ancient Horizontal Gene Transfer Events

Science.gov (United States)

Momper, L. M.; Magnabosco, C.; Amend, J.; Osburn, M. R.; Fournier, G. P.

2017-12-01

The marine and terrestrial subsurface biospheres represent quite likely the largest reservoirs for life on Earth, directly impacting surface processes and global cycles throughout Earth's history. In the deep subsurface biosphere (DSB) organic carbon and energy are often extremely scarce. However, archaea and bacteria are able to persist in the DSB to at least 3.5 km below surface [1]. Understanding how they persist, and by what metabolisms they subsist, are key questions in this biosphere. To address these questions we investigated 5 global DSB environments: one legacy mine in South Dakota, USA, 3 mines in South Africa and marine fluids circulating beneath the Juan de Fuca Ridge. Boreholes within these mines provided access to fluids buried beneath the earth's surface and sampled depths down to 3.1 km. Geochemical data were collected concomitantly with DNA for metagenomic sequencing. We examined genomes of the ancient and deeply branching Chloroflexi for metabolic capabilities and interrogated the geochemical drivers behind those metabolisms with in situ thermodynamic modeling of reaction energetics. In total, 23 Chloroflexi genomes were identified and analyzed from the 5 subsurface sites. Genes for nitrate reduction (nar) and sulfite reduction (dsr) were found in many of the South Africa Chloroflexi but were absent from genomes collected in South Dakota. Indeed, nitrate reduction was among the most energetically favorable reactions in South African fluids (10-14 kJ cell-1 sec -1 per mol of reactant) and sulfur reduction with Fe2+ or H2 was also exergonic [2]. Conversely, genes for nitrite and nitrous oxide reduction (nrf, nir and nos) were found in genomes collected in South Dakota and Juan de Fuca, but not South Africa. We examined the origin of genes conferring these metabolisms in the Chloroflexi genomes. We discovered evidence for horizontal gene transfer (HGT) for all of these putative metabolisms. Retention of these genes in Chloroflexi lineages indicates

Mediterranean dietary pattern and VEGF +405 G/C gene polymorphisms in patients with metabolic syndrome: An aspect of gene-nutrient interaction.

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Hajiluian, Ghazaleh; Abbasalizad Farhangi, Mahdieh; Jahangiry, Leila

2017-01-01

To evaluate the relationship between Mediterranean dietary pattern, anthropometric and metabolic biomarkers and vascular endothelial growth factor (VEGF) +405 G/C gene polymorphism in patient with metabolic syndrome (Mets). In this study 150 patients with Mets and 50 healthy subjects were enrolled. Dietary intakes were evaluated with a semi-quantitative food-frequency questionnaire (FFQ) and Mediterranean dietary quality index (Med-DQI) was assessed. Anthropometric assessments and blood pressure measurement were performed. Biochemical assays including fasting serum glucose (FSG), matrix metalloproteinase-3 (MMP-3), liver enzymes and lipid profiles were also assessed. Polymorphism of +405 G/C VEGF gene was determined utilizing polymerase chain reaction-restriction fragments length polymorphism (PCR-RFLP) method. Serum high density lipoprotein-cholesterol (HDL-C) was significantly lower and low density lipoprotein cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC) concentrations and FSG were significantly higher in metabolic syndrome patients compared with control group (P consumption of "cholesterol" had significantly upper serum TG; also high consumption of "fish" and "vegetables-fruits" was associated with a significantly lower serum LDL concentrations. In metabolic syndrome patients with CC genotype, mean score of "saturated fatty acid" subgroup was significantly higher compared with other genotypes; whereas, in healthy individuals, mean score of "fruit-vegetable" subgroup in individuals of CC and GG genotype was significantly higher (P<0.05). Our findings indicated a significant relationship between Mediterranean dietary quality index and both anthropometric and metabolic risk factors. We also indicated a higher "saturated fatty acid" intake in CC genotype among metabolic syndrome patients.

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

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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.

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

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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.

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

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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

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

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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

Comprehensive transcriptome analysis unravels the existence of crucial genes regulating primary metabolism during adventitious root formation in Petunia hybrida.

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Ahkami, Amirhossein; Scholz, Uwe; Steuernagel, Burkhard; Strickert, Marc; Haensch, Klaus-Thomas; Druege, Uwe; Reinhardt, Didier; Nouri, Eva; von Wirén, Nicolaus; Franken, Philipp; Hajirezaei, Mohammad-Reza

2014-01-01

To identify specific genes determining the initiation and formation of adventitious roots (AR), a microarray-based transcriptome analysis in the stem base of the cuttings of Petunia hybrida (line W115) was conducted. A microarray carrying 24,816 unique, non-redundant annotated sequences was hybridized to probes derived from different stages of AR formation. After exclusion of wound-responsive and root-regulated genes, 1,354 of them were identified which were significantly and specifically induced during various phases of AR formation. Based on a recent physiological model distinguishing three metabolic phases in AR formation, the present paper focuses on the response of genes related to particular metabolic pathways. Key genes involved in primary carbohydrate metabolism such as those mediating apoplastic sucrose unloading were induced at the early sink establishment phase of AR formation. Transcriptome changes also pointed to a possible role of trehalose metabolism and SnRK1 (sucrose non-fermenting 1- related protein kinase) in sugar sensing during this early step of AR formation. Symplastic sucrose unloading and nucleotide biosynthesis were the major processes induced during the later recovery and maintenance phases. Moreover, transcripts involved in peroxisomal beta-oxidation were up-regulated during different phases of AR formation. In addition to metabolic pathways, the analysis revealed the activation of cell division at the two later phases and in particular the induction of G1-specific genes in the maintenance phase. Furthermore, results point towards a specific demand for certain mineral nutrients starting in the recovery phase.

The Effect of Selenium Supplementation on Glucose Homeostasis and the Expression of Genes Related to Glucose Metabolism

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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.

Predicting cellular growth from gene expression signatures.

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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.

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

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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

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

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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.

Predicting Metabolic Syndrome Using the Random Forest Method

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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.

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

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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

A network approach to predict pathogenic genes for Fusarium graminearum.

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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

A network approach to predict pathogenic genes for Fusarium graminearum.

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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

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

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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.

A real-time control system of gene expression using ligand-bound nucleic acid aptamer for metabolic engineering.

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Wang, Jing; Cui, Xun; Yang, Le; Zhang, Zhe; Lv, Liping; Wang, Haoyuan; Zhao, Zhenmin; Guan, Ningzi; Dong, Lichun; Chen, Rachel

2017-07-01

Artificial control of bio-functions through regulating gene expression is one of the most important and attractive technologies to build novel living systems that are useful in the areas of chemical synthesis, nanotechnology, pharmacology, cell biology. Here, we present a novel real-time control system of gene regulation that includes an enhancement element by introducing duplex DNA aptamers upstream promoter and a repression element by introducing a RNA aptamer upstream ribosome binding site. With the presence of ligands corresponding to the DNA aptamers, the expression of the target gene can be potentially enhanced at the transcriptional level by strengthening the recognition capability of RNAP to the recognition region and speeding up the separation efficiency of the unwinding region due to the induced DNA bubble around the thrombin-bound aptamers; while with the presence of RNA aptamer ligand, the gene expression can be repressed at the translational level by weakening the recognition capability of ribosome to RBS due to the shielding of RBS by the formed aptamer-ligand complex upstream RBS. The effectiveness and potential utility of the developed gene regulation system were demonstrated by regulating the expression of ecaA gene in the cell-free systems. The realistic metabolic engineering application of the system has also tested by regulating the expression of mgtC gene and thrombin cDNA in Escherichia coli JD1021 for controlling metabolic flux and improving thrombin production, verifying that the real-time control system of gene regulation is able to realize the dynamic regulation of gene expression with potential applications in bacterial physiology studies and metabolic engineering. Copyright © 2017. Published by Elsevier Inc.

Metabolic syndrome, diabetes and atherosclerosis: Influence of gene-environment interaction

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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'.

Metabolic syndrome, diabetes and atherosclerosis: Influence of gene-environment interaction

International Nuclear Information System (INIS)

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'.

A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

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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

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

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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.

Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways.

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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.

Cholesterol Transporters ABCA1 and ABCG1 Gene Expression in Peripheral Blood Mononuclear Cells in Patients with Metabolic Syndrome

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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.

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

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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.

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

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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

MicrobesFlux: a web platform for drafting metabolic models from the KEGG database

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Feng Xueyang

2012-08-01

Full Text Available Abstract Background Concurrent with the efforts currently underway in mapping microbial genomes using high-throughput sequencing methods, systems biologists are building metabolic models to characterize and predict cell metabolisms. One of the key steps in building a metabolic model is using multiple databases to collect and assemble essential information about genome-annotations and the architecture of the metabolic network for a specific organism. To speed up metabolic model development for a large number of microorganisms, we need a user-friendly platform to construct metabolic networks and to perform constraint-based flux balance analysis based on genome databases and experimental results. Results We have developed a semi-automatic, web-based platform (MicrobesFlux for generating and reconstructing metabolic models for annotated microorganisms. MicrobesFlux is able to automatically download the metabolic network (including enzymatic reactions and metabolites of ~1,200 species from the KEGG database (Kyoto Encyclopedia of Genes and Genomes and then convert it to a metabolic model draft. The platform also provides diverse customized tools, such as gene knockouts and the introduction of heterologous pathways, for users to reconstruct the model network. The reconstructed metabolic network can be formulated to a constraint-based flux model to predict and analyze the carbon fluxes in microbial metabolisms. The simulation results can be exported in the SBML format (The Systems Biology Markup Language. Furthermore, we also demonstrated the platform functionalities by developing an FBA model (including 229 reactions for a recent annotated bioethanol producer, Thermoanaerobacter sp. strain X514, to predict its biomass growth and ethanol production. Conclusion MicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG

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

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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.

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

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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

Identifying essential genes in bacterial metabolic networks with machine learning methods

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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

Comprehensive Transcriptome Analysis Unravels the Existence of Crucial Genes Regulating Primary Metabolism during Adventitious Root Formation in Petunia hybrida

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Ahkami, Amirhossein; Scholz, Uwe; Steuernagel, Burkhard; Strickert, Marc; Haensch, Klaus-Thomas; Druege, Uwe; Reinhardt, Didier; Nouri, Eva; von Wirén, Nicolaus; Franken, Philipp; Hajirezaei, Mohammad-Reza

2014-01-01

To identify specific genes determining the initiation and formation of adventitious roots (AR), a microarray-based transcriptome analysis in the stem base of the cuttings of Petunia hybrida (line W115) was conducted. A microarray carrying 24,816 unique, non-redundant annotated sequences was hybridized to probes derived from different stages of AR formation. After exclusion of wound-responsive and root-regulated genes, 1,354 of them were identified which were significantly and specifically induced during various phases of AR formation. Based on a recent physiological model distinguishing three metabolic phases in AR formation, the present paper focuses on the response of genes related to particular metabolic pathways. Key genes involved in primary carbohydrate metabolism such as those mediating apoplastic sucrose unloading were induced at the early sink establishment phase of AR formation. Transcriptome changes also pointed to a possible role of trehalose metabolism and SnRK1 (sucrose non-fermenting 1- related protein kinase) in sugar sensing during this early step of AR formation. Symplastic sucrose unloading and nucleotide biosynthesis were the major processes induced during the later recovery and maintenance phases. Moreover, transcripts involved in peroxisomal beta-oxidation were up-regulated during different phases of AR formation. In addition to metabolic pathways, the analysis revealed the activation of cell division at the two later phases and in particular the induction of G1-specific genes in the maintenance phase. Furthermore, results point towards a specific demand for certain mineral nutrients starting in the recovery phase. PMID:24978694

Comprehensive transcriptome analysis unravels the existence of crucial genes regulating primary metabolism during adventitious root formation in Petunia hybrida.

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Amirhossein Ahkami

Full Text Available To identify specific genes determining the initiation and formation of adventitious roots (AR, a microarray-based transcriptome analysis in the stem base of the cuttings of Petunia hybrida (line W115 was conducted. A microarray carrying 24,816 unique, non-redundant annotated sequences was hybridized to probes derived from different stages of AR formation. After exclusion of wound-responsive and root-regulated genes, 1,354 of them were identified which were significantly and specifically induced during various phases of AR formation. Based on a recent physiological model distinguishing three metabolic phases in AR formation, the present paper focuses on the response of genes related to particular metabolic pathways. Key genes involved in primary carbohydrate metabolism such as those mediating apoplastic sucrose unloading were induced at the early sink establishment phase of AR formation. Transcriptome changes also pointed to a possible role of trehalose metabolism and SnRK1 (sucrose non-fermenting 1- related protein kinase in sugar sensing during this early step of AR formation. Symplastic sucrose unloading and nucleotide biosynthesis were the major processes induced during the later recovery and maintenance phases. Moreover, transcripts involved in peroxisomal beta-oxidation were up-regulated during different phases of AR formation. In addition to metabolic pathways, the analysis revealed the activation of cell division at the two later phases and in particular the induction of G1-specific genes in the maintenance phase. Furthermore, results point towards a specific demand for certain mineral nutrients starting in the recovery phase.

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

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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.

Importance of metabolism in pharmacological studies: possible in vitro predictability

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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

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

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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.

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

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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.

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

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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...

Cpt1a gene expression in peripheral blood mononuclear cells as an early biomarker of diet-related metabolic alterations

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Rubén Díaz-Rúa

2016-11-01

Full Text Available 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. Objective: We analysed PBMC expression of key energy homeostasis-related genes in a time-course analysis in order to find out early markers of metabolic alterations due to sustained intake of high-fat (HF and high-protein (HP diets. Design: We administered HF and HP diets (4 months to adult Wistar rats in isocaloric conditions to a control diet, mainly to avoid overweight associated with the intake of hyperlipidic diets and, thus, to be able to characterise markers of metabolically obese normal-weight (MONW syndrome. PBMC samples were collected at different time points of dietary treatment and expression of relevant energy homeostatic genes analysed by real-time reverse transcription-polymerase chain reaction. Serum parameters related with metabolic syndrome, as well as fat deposition in liver, were also analysed. Results: The most outstanding results were those obtained for the expression of the lipolytic gene carnitine palmitoyltransferase 1a (Cpt1a. Cpt1a expression in PBMC increased after only 1 month of exposure to both unbalanced diets, and this increased expression was maintained thereafter. Interestingly, in the case of the HF diet, Cpt1a expression was altered even in the absence of increased body weight but correlated with alterations such as higher insulin resistance, alteration of serum lipid profile and, particularly, increased fat deposition in liver, a feature characteristic of metabolic syndrome, which was even observed in animals fed with HP diet. Conclusions: We propose Cpt1a gene expression analysis in PBMC as an early biomarker of metabolic alterations associated with MONW phenotype due to the intake of isocaloric HF diets, as

Improved Triacylglycerol Production in Acinetobacter baylyi ADP1 by Metabolic Engineering

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Karp Matti

2011-05-01

Full Text Available Abstract Background Triacylglycerols are used in various purposes including food applications, cosmetics, oleochemicals and biofuels. Currently the main sources for triacylglycerol are vegetable oils, and microbial triacylglycerol has been suggested as an alternative for these. Due to the low production rates and yields of microbial processes, the role of metabolic engineering has become more significant. As a robust model organism for genetic and metabolic studies, and for the natural capability to produce triacylglycerol, Acinetobacter baylyi ADP1 serves as an excellent organism for modelling the effects of metabolic engineering for energy molecule biosynthesis. Results Beneficial gene deletions regarding triacylglycerol production were screened by computational means exploiting the metabolic model of ADP1. Four deletions, acr1, poxB, dgkA, and a triacylglycerol lipase were chosen to be studied experimentally both separately and concurrently by constructing a knock-out strain (MT with three of the deletions. Improvements in triacylglycerol production were observed: the strain MT produced 5.6 fold more triacylglycerol (mg/g cell dry weight compared to the wild type strain, and the proportion of triacylglycerol in total lipids was increased by 8-fold. Conclusions In silico predictions of beneficial gene deletions were verified experimentally. The chosen single and multiple gene deletions affected beneficially the natural triacylglycerol metabolism of A. baylyi ADP1. This study demonstrates the importance of single gene deletions in triacylglycerol metabolism, and proposes Acinetobacter sp. ADP1 as a model system for bioenergetic studies regarding metabolic engineering.

An integrated analysis of genes and pathways exhibiting metabolic differences between estrogen receptor positive breast cancer cells

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Mandal, Soma; Davie, James R

2007-01-01

The sex hormone estrogen (E2) is pivotal to normal mammary gland growth and differentiation and in breast carcinogenesis. In this in silico study, we examined metabolic differences between ER(+)ve breast cancer cells during E2 deprivation. Public repositories of SAGE and MA gene expression data generated from E2 deprived ER(+)ve breast cancer cell lines, MCF-7 and ZR75-1 were compared with normal breast tissue. We analyzed gene ontology (GO), enrichment, clustering, chromosome localization, and pathway profiles and performed multiple comparisons with cell lines and tumors with different ER status. In all GO terms, biological process (BP), molecular function (MF), and cellular component (CC), MCF-7 had higher gene utilization than ZR75-1. Various analyses showed a down-regulated immune function, an up-regulated protein (ZR75-1) and glucose metabolism (MCF-7). A greater percentage of 77 common genes localized to the q arm of all chromosomes, but in ZR75-1 chromosomes 11, 16, and 19 harbored more overexpressed genes. Despite differences in gene utilization (electron transport, proteasome, glycolysis/gluconeogenesis) and expression (ribosome) in both cells, there was an overall similarity of ZR75-1 with ER(-)ve cell lines and ER(+)ve/ER(-)ve breast tumors. This study demonstrates integral metabolic differences may exist within the same cell subtype (luminal A) in representative ER(+)ve cell line models. Selectivity of gene and pathway usage for strategies such as energy requirement minimization, sugar utilization by ZR75-1 contrasted with MCF-7 cells, expressing genes whose protein products require ATP utilization. Such characteristics may impart aggressiveness to ZR75-1 and may be prognostic determinants of ER(+)ve breast tumors

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

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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

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

Science.gov (United States)

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

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

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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.

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

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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.

Genetic polymorphisms in CYP1A1, GSTM1, GSTP1 and GSTT1 metabolic genes and risk of lung cancer in Asturias

International Nuclear Information System (INIS)

López-Cima, M Felicitas; Álvarez-Avellón, Sara M; Pascual, Teresa; Fernández-Somoano, Ana; Tardón, Adonina

2012-01-01

Metabolic genes have been associated with the function of metabolizing and detoxifying environmental carcinogens. Polymorphisms present in these genes could lead to changes in their metabolizing and detoxifying ability and thus may contribute to individual susceptibility to different types of cancer. We investigated if the individual and/or combined modifying effects of the CYP1A1 MspI T6235C, GSTM1 present/null, GSTT1 present/null and GSTP1 Ile105Val polymorphisms are related to the risk of developing lung cancer in relation to tobacco consumption and occupation in Asturias, Northern Spain. A hospital-based case–control study (CAPUA Study) was designed including 789 lung cancer patients and 789 control subjects matched in ethnicity, age, sex, and hospital. Genotypes were determined by PCR or PCR-RFLP. Individual and combination effects were analysed using an unconditional logistic regression adjusting for age, pack-years, family history of any cancer and occupation. No statistically significant main effects were observed for the carcinogen metabolism genes in relation to lung cancer risk. In addition, the analysis did not reveal any significant gene-gene, gene-tobacco smoking or gene-occupational exposure interactions relative to lung cancer susceptibility. Lastly, no significant gene-gene combination effects were observed. These results suggest that genetic polymorphisms in the CYP1A1, GSTM1, GSTT1 and GSTP1 metabolic genes were not significantly associated with lung cancer risk in the current study. The results of the analysis of gene-gene interactions of CYP1A1 MspI T6235C, GSTM1 present/null, GSTT1 present/null and GSTP1 Ile105Val polymorphisms in lung cancer risk indicate that these genes do not interact in lung cancer development

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

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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...

Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

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Brooks J Paul

2010-03-01

Full Text Available Abstract Background Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405 is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous. Results Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production. Conclusions By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum

Analysis of pan-genome to identify the core genes and essential genes of Brucella spp.

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Yang, Xiaowen; Li, Yajie; Zang, Juan; Li, Yexia; Bie, Pengfei; Lu, Yanli; Wu, Qingmin

2016-04-01

Brucella spp. are facultative intracellular pathogens, that cause a contagious zoonotic disease, that can result in such outcomes as abortion or sterility in susceptible animal hosts and grave, debilitating illness in humans. For deciphering the survival mechanism of Brucella spp. in vivo, 42 Brucella complete genomes from NCBI were analyzed for the pan-genome and core genome by identification of their composition and function of Brucella genomes. The results showed that the total 132,143 protein-coding genes in these genomes were divided into 5369 clusters. Among these, 1710 clusters were associated with the core genome, 1182 clusters with strain-specific genes and 2477 clusters with dispensable genomes. COG analysis indicated that 44 % of the core genes were devoted to metabolism, which were mainly responsible for energy production and conversion (COG category C), and amino acid transport and metabolism (COG category E). Meanwhile, approximately 35 % of the core genes were in positive selection. In addition, 1252 potential essential genes were predicted in the core genome by comparison with a prokaryote database of essential genes. The results suggested that the core genes in Brucella genomes are relatively conservation, and the energy and amino acid metabolism play a more important role in the process of growth and reproduction in Brucella spp. This study might help us to better understand the mechanisms of Brucella persistent infection and provide some clues for further exploring the gene modules of the intracellular survival in Brucella spp.

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

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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

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

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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.

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

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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.

A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification.

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Ponasenko, Anastasia V; Khutornaya, Maria V; Kutikhin, Anton G; Rutkovskaya, Natalia V; Tsepokina, Anna V; Kondyukova, Natalia V; Yuzhalin, Arseniy E; Barbarash, Leonid S

2016-08-31

Severe bioprosthetic mitral valve calcification is a significant problem in cardiovascular surgery. Unfortunately, clinical markers did not demonstrate efficacy in prediction of severe bioprosthetic mitral valve calcification. Here, we examined whether a genomics-based approach is efficient in predicting the risk of severe bioprosthetic mitral valve calcification. A total of 124 consecutive Russian patients who underwent mitral valve replacement surgery were recruited. We investigated the associations of the inherited variation in innate immunity, lipid metabolism and calcium metabolism genes with severe bioprosthetic mitral valve calcification. Genotyping was conducted utilizing the TaqMan assay. Eight gene polymorphisms were significantly associated with severe bioprosthetic mitral valve calcification and were therefore included into stepwise logistic regression which identified male gender, the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene as independent predictors of severe bioprosthetic mitral valve calcification. The developed genomics-based model had fair predictive value with area under the receiver operating characteristic (ROC) curve of 0.73. In conclusion, our genomics-based approach is efficient for the prediction of severe bioprosthetic mitral valve calcification.

RNA-Seq Analysis of Developing Pecan (Carya illinoinensis) Embryos Reveals Parallel Expression Patterns among Allergen and Lipid Metabolism Genes.

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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.

Pathway discovery in metabolic networks by subgraph extraction.

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Faust, Karoline; Dupont, Pierre; Callut, Jérôme; van Helden, Jacques

2010-05-01

Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well. We comparatively evaluated seven sub-network extraction approaches on 71 known metabolic pathways from Saccharomyces cerevisiae and a metabolic network obtained from MetaCyc. The best performing approach is a novel hybrid strategy, which combines a random walk-based reduction of the graph with a shortest paths-based algorithm, and which recovers the reference pathways with an accuracy of approximately 77%. Most of the presented algorithms are available as part of the network analysis tool set (NeAT). The kWalks method is released under the GPL3 license.

Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling

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Mahadevan Radhakrishnan

2010-05-01

Full Text Available Abstract Background Geobacter sulfurreducens is a member of the Geobacter species, which are capable of oxidation of organic waste coupled to the reduction of heavy metals and electrode with applications in bioremediation and bioenergy generation. While the metabolism of this organism has been studied through the development of a stoichiometry based genome-scale metabolic model, the associated regulatory network has not yet been well studied. In this manuscript, we report on the implementation of a thermodynamics based metabolic flux model for Geobacter sulfurreducens. We use this updated model to identify reactions that are subject to regulatory control in the metabolic network of G. sulfurreducens using thermodynamic variability analysis. Findings As a first step, we have validated the regulatory sites and bottleneck reactions predicted by the thermodynamic flux analysis in E. coli by evaluating the expression ranges of the corresponding genes. We then identified ten reactions in the metabolic network of G. sulfurreducens that are predicted to be candidates for regulation. We then compared the free energy ranges for these reactions with the corresponding gene expression fold changes under conditions of different environmental and genetic perturbations and show that the model predictions of regulation are consistent with data. In addition, we also identify reactions that operate close to equilibrium and show that the experimentally determined exchange coefficient (a measure of reversibility is significant for these reactions. Conclusions Application of the thermodynamic constraints resulted in identification of potential bottleneck reactions not only from the central metabolism but also from the nucleotide and amino acid subsystems, thereby showing the highly coupled nature of the thermodynamic constraints. In addition, thermodynamic variability analysis serves as a valuable tool in estimating the ranges of ΔrG' of every reaction in the model

Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

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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.

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

OpenAIRE

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...

Association between Two Resistin Gene Polymorphisms and Metabolic Syndrome in Jilin, Northeast China: A Case-Control Study

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Yingli Fu

2017-01-01

Full Text Available Metabolic syndrome (MetS is a significant health care problem worldwide and is characterized by increased fasting glucose and obesity. Resistin is a protein hormone produced both by adipocytes and immunocompetent cells, including those residing in adipose tissue, and is believed to modulate glucose tolerance and insulin action. This study examined the association of resistin gene polymorphisms, rs1862513 and rs3745368, and related haplotypes with the development of metabolic syndrome in a Han Chinese population. This case-control study was performed on 3792 subjects, including 1771 MetS cases and 2021 healthy controls from the Jilin province of China. Metabolic syndrome was defined according to the criteria of the International Diabetes Federation (IDF. Logistic regression analysis was used to estimate the relationship between gene polymorphism and MetS. Our results showed that there were no significant associations between MetS and the genotype distributions in four kinds of inheritance models, allele frequencies, and related haplotypes of resistin gene polymorphisms rs1862513 and rs3745368 (all p values > 0.05. Based on our study findings, we concluded that mutations in resistin genes are not associated with the presence of MetS in a Han Chinese population from Jilin province in China.

Plant interactions alter the predictions of metabolic scaling theory

DEFF Research Database (Denmark)

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...

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

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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.

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

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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.

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

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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.

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

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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.

Mitochondrial Gene Expression Profiles and Metabolic Pathways in the Amygdala Associated with Exaggerated Fear in an Animal Model of PTSD.

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Li, He; Li, Xin; Smerin, Stanley E; Zhang, Lei; Jia, Min; Xing, Guoqiang; Su, Yan A; Wen, Jillian; Benedek, David; Ursano, Robert

2014-01-01

The metabolic mechanisms underlying the development of exaggerated fear in post-traumatic stress disorder (PTSD) are not well defined. In the present study, alteration in the expression of genes associated with mitochondrial function in the amygdala of an animal model of PTSD was determined. Amygdala tissue samples were excised from 10 non-stressed control rats and 10 stressed rats, 14 days post-stress treatment. Total RNA was isolated, cDNA was synthesized, and gene expression levels were determined using a cDNA microarray. During the development of the exaggerated fear associated with PTSD, 48 genes were found to be significantly upregulated and 37 were significantly downregulated in the amygdala complex based on stringent criteria (p metabolism, one with transcriptional factors, and one with chromatin remodeling. Thus, informatics of a neuronal gene array allowed us to determine the expression profile of mitochondrial genes in the amygdala complex of an animal model of PTSD. The result is a further understanding of the metabolic and neuronal signaling mechanisms associated with delayed and exaggerated fear.

A seven-gene CpG-island methylation panel predicts breast cancer progression

International Nuclear Information System (INIS)

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

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

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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

Gene Prediction in Metagenomic Fragments with Deep Learning

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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.

Sasang constitutional types for the risk prediction of metabolic syndrome: a 14-year longitudinal prospective cohort study.

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Lee, Sunghee; Lee, Seung Ku; Kim, Jong Yeol; Cho, Namhan; Shin, Chol

2017-09-02

To examine whether the use of Sasang constitutional (SC) types, such as Tae-yang (TY), Tae-eum (TE), So-yang (SY), and So-eum (SE) types, increases the accuracy of risk prediction for metabolic syndrome. From 2001 to 2014, 3529 individuals aged 40 to 69 years participated in a longitudinal prospective cohort. The Cox proportional hazard model was utilized to predict the risk of developing metabolic syndrome. During the 14 year follow-up, 1591 incident events of metabolic syndrome were observed. Individuals with TE type had higher body mass indexes and waist circumferences than individuals with SY and SE types. The risk of developing metabolic syndrome was the highest among individuals with the TE type, followed by the SY type and the SE type. When the prediction risk models for incident metabolic syndrome were compared, the area under the curve for the model using SC types was significantly increased to 0.8173. Significant predictors for incident metabolic syndrome were different according to the SC types. For individuals with the TE type, the significant predictors were age, sex, body mass index (BMI), education, smoking, drinking, fasting glucose level, high-density lipoprotein (HDL) cholesterol level, systolic and diastolic blood pressure, and triglyceride level. For Individuals with the SE type, the predictors were sex, smoking, fasting glucose, HDL cholesterol level, systolic and diastolic blood pressure, and triglyceride level, while the predictors in individuals with the SY type were age, sex, BMI, smoking, drinking, total cholesterol level, fasting glucose level, HDL cholesterol level, systolic and diastolic blood pressure, and triglyceride level. In this prospective cohort study among 3529 individuals, we observed that utilizing the SC types significantly increased the accuracy of the risk prediction for the development of metabolic syndrome.

Bioinformatics analysis of the predicted polyprenol reductase genes in higher plants

Science.gov (United States)

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.

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

NARCIS (Netherlands)

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

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

NARCIS (Netherlands)

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

Applications of computational modeling in metabolic engineering of yeast.

Science.gov (United States)

Kerkhoven, Eduard J; Lahtvee, Petri-Jaan; Nielsen, Jens

2015-02-01

Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.

Gene prediction in metagenomic fragments: A large scale machine learning approach

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Morgenstern Burkhard

2008-04-01

Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

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

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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.

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

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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.

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

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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

Identification of circular RNAs from the parental genes involved in multiple aspects of cellular metabolism in barley

DEFF Research Database (Denmark)

Shirvanehdeh, Behrooz Darbani; Noeparvar, Shahin; Borg, Søren

2016-01-01

circular RNAs as novel interactors in the regulation of gene expression in plants and imply the comprehensiveness of this regulatory pathway by identifying circular RNAs for a diverse set of genes. These genes are involved in several aspects of cellular metabolism as hormonal signaling, intracellular...... protein sorting, carbohydrate metabolism and cell-wall biogenesis, respiration, amino acid biosynthesis, transcription and translation, and protein ubiquitination. Additionally, these parental loci of circular RNAs, from both nuclear and mitochondrial genomes, encode for different transcript classes...... and elucidate their cellular-level alterations across tissues and in response to micronutrients iron and zinc. In further support of circular RNAs’ functional roles in plants, we report several cases where fluctuations of circRNAs do not correlate with the levels of their parental-loci encoded linear...

Analysis and prediction of gene splice sites in four Aspergillus genomes

DEFF Research Database (Denmark)

Wang, Kai; Ussery, David; Brunak, Søren

2009-01-01

Several Aspergillus fungal genomic sequences have been published, with many more in progress. Obviously, it is essential to have high-quality, consistently annotated sets of proteins from each of the genomes, in order to make meaningful comparisons. We have developed a dedicated, publicly available......, splice site prediction program called NetAspGene, for the genus Aspergillus. Gene sequences from Aspergillus fumigatus, the most common mould pathogen, were used to build and test our model. Compared to many animals and plants, Aspergillus contains smaller introns; thus we have applied a larger window...... better splice site prediction than other available tools. NetAspGene will be very helpful for the study in Aspergillus splice sites and especially in alternative splicing. A webpage for NetAspGene is publicly available at http://www.cbs.dtu.dk/services/NetAspGene....

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

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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.

Xenobiotic Metabolizing Gene Variants and Renal Cell Cancer: A Multicenter Study

International Nuclear Information System (INIS)

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.

Xenobiotic Metabolizing Gene Variants and Renal Cell Cancer: A Multicenter Study

Energy Technology Data Exchange (ETDEWEB)

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.

GGDonto ontology as a knowledge-base for genetic diseases and disorders of glycan metabolism and their causative genes.

Science.gov (United States)

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

Metabolic Genetic Screens Reveal Multidimensional Regulation of Virulence Gene Expression in Listeria monocytogenes and an Aminopeptidase That Is Critical for PrfA Protein Activation.

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Friedman, Sivan; Linsky, Marika; Lobel, Lior; Rabinovich, Lev; Sigal, Nadejda; Herskovits, Anat A

2017-06-01

Listeria monocytogenes is an environmental saprophyte and intracellular bacterial pathogen. Upon invading mammalian cells, the bacterium senses abrupt changes in its metabolic environment, which are rapidly transduced to regulation of virulence gene expression. To explore the relationship between L. monocytogenes metabolism and virulence, we monitored virulence gene expression dynamics across a library of genetic mutants grown under two metabolic conditions known to activate the virulent state: charcoal-treated rich medium containing glucose-1-phosphate and minimal defined medium containing limiting concentrations of branched-chain amino acids (BCAAs). We identified over 100 distinct mutants that exhibit aberrant virulence gene expression profiles, the majority of which mapped to nonessential metabolic genes. Mutants displayed enhanced, decreased, and early and late virulence gene expression profiles, as well as persistent levels, demonstrating a high plasticity in virulence gene regulation. Among the mutants, one was noteworthy for its particularly low virulence gene expression level and mapped to an X-prolyl aminopeptidase (PepP). We show that this peptidase plays a role in posttranslational activation of the major virulence regulator, PrfA. Specifically, PepP mediates recruitment of PrfA to the cytoplasmic membrane, a step identified as critical for PrfA protein activation. This study establishes a novel step in the complex mechanism of PrfA activation and further highlights the cross regulation of metabolism and virulence. Copyright © 2017 American Society for Microbiology.

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

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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.

Comprehensive transcriptome analysis reveals novel genes involved in cardiac glycoside biosynthesis and mlncRNAs associated with secondary metabolism and stress response in Digitalis purpurea

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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.

Conserved and divergent rhythms of crassulacean acid metabolism-related and core clock gene expression in the cactus Opuntia ficus-indica.

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Mallona, Izaskun; Egea-Cortines, Marcos; Weiss, Julia

2011-08-01

The cactus Opuntia ficus-indica is a constitutive Crassulacean acid metabolism (CAM) species. Current knowledge of CAM metabolism suggests that the enzyme phosphoenolpyruvate carboxylase kinase (PPCK) is circadian regulated at the transcriptional level, whereas phosphoenolpyruvate carboxylase (PEPC), malate dehydrogenase (MDH), NADP-malic enzyme (NADP-ME), and pyruvate phosphate dikinase (PPDK) are posttranslationally controlled. As little transcriptomic data are available from obligate CAM plants, we created an expressed sequence tag database derived from different organs and developmental stages. Sequences were assembled, compared with sequences in the National Center for Biotechnology Information nonredundant database for identification of putative orthologs, and mapped using Kyoto Encyclopedia of Genes and Genomes Orthology and Gene Ontology. We identified genes involved in circadian regulation and CAM metabolism for transcriptomic analysis in plants grown in long days. We identified stable reference genes for quantitative polymerase chain reaction and found that OfiSAND, like its counterpart in Arabidopsis (Arabidopsis thaliana), and OfiTUB are generally appropriate standards for use in the quantification of gene expression in O. ficus-indica. Three kinds of expression profiles were found: transcripts of OfiPPCK oscillated with a 24-h periodicity; transcripts of the light-active OfiNADP-ME and OfiPPDK genes adapted to 12-h cycles, while transcript accumulation patterns of OfiPEPC and OfiMDH were arrhythmic. Expression of the circadian clock gene OfiTOC1, similar to Arabidopsis, oscillated with a 24-h periodicity, peaking at night. Expression of OfiCCA1 and OfiPRR9, unlike in Arabidopsis, adapted best to a 12-h rhythm, suggesting that circadian clock gene interactions differ from those of Arabidopsis. Our results indicate that the evolution of CAM metabolism could be the result of modified circadian regulation at both the transcriptional and posttranscriptional

Cpt1a gene expression in peripheral blood mononuclear cells as an early biomarker of diet-related metabolic alterations

KAUST Repository

Diaz Rua, Ruben; Palou, Andreu; Oliver, Paula

2016-01-01

subjects at risk of developing diet-related diseases.Objective: We analysed PBMC expression of key energy homeostasis-related genes in a time-course analysis in order to find out early markers of metabolic alterations due to sustained intake of high-fat (HF) and highprotein (HP) diets.Design: We administered HF and HP diets (4 months) to adult Wistar rats in isocaloric conditions to a control diet, mainly to avoid overweight associated with the intake of hyperlipidic diets and, thus, to be able to characterise markers of metabolically obese normal-weight (MONW) syndrome. PBMC samples were collected at different time points of dietary treatment and expression of relevant energy homeostatic genes analysed by real-time reverse transcription-polymerase chain reaction. Serum parameters related with metabolic syndrome, as well as fat deposition in liver, were also analysed.Results: The most outstanding results were those obtained for the expression of the lipolytic gene carnitine palmitoyltransferase 1a (Cpt1a). Cpt1a expression in PBMC increased after only 1 month of exposure to both unbalanced diets, and this increased expression was maintained thereafter. Interestingly, in the case of the HF diet, Cpt1a expression was altered even in the absence of increased body weight but correlated with alterations such as higher insulin resistance, alteration of serum lipid profile and, particularly, increased fat deposition in liver, a feature characteristic of metabolic syndrome, which was even observed in animals fed with HP diet.Conclusions: We propose Cpt1a gene expression analysis in PBMC as an early biomarker of metabolic alterations associated with MONW phenotype due to the intake of isocaloric HF diets, as well as a marker of increased risk of metabolic diseases

Evolutionary rate patterns of the Gibberellin pathway genes

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Zhang Fu-min

2009-08-01

Full Text Available Abstract Background Analysis of molecular evolutionary patterns of different genes within metabolic pathways allows us to determine whether these genes are subject to equivalent evolutionary forces and how natural selection shapes the evolution of proteins in an interacting system. Although previous studies found that upstream genes in the pathway evolved more slowly than downstream genes, the correlation between evolutionary rate and position of the genes in metabolic pathways as well as its implications in molecular evolution are still less understood. Results We sequenced and characterized 7 core structural genes of the gibberellin biosynthetic pathway from 8 representative species of the rice tribe (Oryzeae to address alternative hypotheses regarding evolutionary rates and patterns of metabolic pathway genes. We have detected significant rate heterogeneity among 7 GA pathway genes for both synonymous and nonsynonymous sites. Such rate variation is mostly likely attributed to differences of selection intensity rather than differential mutation pressures on the genes. Unlike previous argument that downstream genes in metabolic pathways would evolve more slowly than upstream genes, the downstream genes in the GA pathway did not exhibited the elevated substitution rate and instead, the genes that encode either the enzyme at the branch point (GA20ox or enzymes catalyzing multiple steps (KO, KAO and GA3ox in the pathway had the lowest evolutionary rates due to strong purifying selection. Our branch and codon models failed to detect signature of positive selection for any lineage and codon of the GA pathway genes. Conclusion This study suggests that significant heterogeneity of evolutionary rate of the GA pathway genes is mainly ascribed to differential constraint relaxation rather than the positive selection and supports the pathway flux theory that predicts that natural selection primarily targets enzymes that have the greatest control on fluxes.

Bioinformatic prediction and functional characterization of human KIAA0100 gene

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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.

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

OpenAIRE

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...

Vitamin D metabolic pathway genes and pancreatic cancer risk.

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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.

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

Science.gov (United States)

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.

Systemic responses to inhaled ozone in mice: cachexia and down-regulation of liver xenobiotic metabolizing genes

Energy Technology Data Exchange (ETDEWEB)

Last, Jerold A [Pulmonary and Critical Care Medicine, School of Medicine, Toxic Substances Program, 1131 Surge I, University of California, Davis, CA 95616-8723 (United States); Gohil, Kishorchandra [Pulmonary and Critical Care Medicine, School of Medicine, Toxic Substances Program, 1131 Surge I, University of California, Davis, CA 95616-8723 (United States); Mathrani, Vivek C [Pulmonary and Critical Care Medicine, School of Medicine, Toxic Substances Program, 1131 Surge I, University of California, Davis, CA 95616-8723 (United States); Kenyon, Nicholas J [Pulmonary and Critical Care Medicine, School of Medicine, Toxic Substances Program, 1131 Surge I, University of California, Davis, CA 95616-8723 (United States)

2005-10-15

Rats or mice acutely exposed to high concentrations of ozone show an immediate and significant weight loss, even when allowed free access to food and water. The mechanisms underlying this systemic response to ozone have not been previously elucidated. We have applied the technique of global gene expression analysis to the livers of C57BL mice acutely exposed to ozone. Mice lost up to 14% of their original body weight, with a 42% decrease in total food consumption. We previously had found significant up-regulation of genes encoding proliferative enzymes, proteins related to acute phase reactions and cytoskeletal functions, and other biomarkers of a cachexia-like inflammatory state in lungs of mice exposed to ozone. These results are consistent with a general up-regulation of different gene families responsive to NF-{kappa}B in the lungs of the exposed mice. In the present study, we observed significant down-regulation of different families of mRNAs in the livers of the exposed mice, including genes related to lipid and fatty acid metabolism, and to carbohydrate metabolism in this tissue, consistent with a systemic cachexic response. Several interferon-dependent genes were down-regulated in the liver, suggesting a possible role for interferon as a signaling molecule between lung and liver. In addition, transcription of several mRNAs encoding enzymes of xenobiotic metabolism in the livers of mice exposed to ozone was decreased, suggesting cytokine-mediated suppression of cytochrome P450 expression. This finding may explain a previously controversial report from other investigators more than 20 years ago of prolongation of pentobarbital sleeping time in mice exposed to ozone.

Systemic responses to inhaled ozone in mice: cachexia and down-regulation of liver xenobiotic metabolizing genes

International Nuclear Information System (INIS)

Last, Jerold A.; Gohil, Kishorchandra; Mathrani, Vivek C.; Kenyon, Nicholas J.

2005-01-01

Rats or mice acutely exposed to high concentrations of ozone show an immediate and significant weight loss, even when allowed free access to food and water. The mechanisms underlying this systemic response to ozone have not been previously elucidated. We have applied the technique of global gene expression analysis to the livers of C57BL mice acutely exposed to ozone. Mice lost up to 14% of their original body weight, with a 42% decrease in total food consumption. We previously had found significant up-regulation of genes encoding proliferative enzymes, proteins related to acute phase reactions and cytoskeletal functions, and other biomarkers of a cachexia-like inflammatory state in lungs of mice exposed to ozone. These results are consistent with a general up-regulation of different gene families responsive to NF-κB in the lungs of the exposed mice. In the present study, we observed significant down-regulation of different families of mRNAs in the livers of the exposed mice, including genes related to lipid and fatty acid metabolism, and to carbohydrate metabolism in this tissue, consistent with a systemic cachexic response. Several interferon-dependent genes were down-regulated in the liver, suggesting a possible role for interferon as a signaling molecule between lung and liver. In addition, transcription of several mRNAs encoding enzymes of xenobiotic metabolism in the livers of mice exposed to ozone was decreased, suggesting cytokine-mediated suppression of cytochrome P450 expression. This finding may explain a previously controversial report from other investigators more than 20 years ago of prolongation of pentobarbital sleeping time in mice exposed to ozone

Predicting basal metabolic rates in Malaysian adult elite athletes.

Science.gov (United States)

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.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

KAUST Repository

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.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

KAUST Repository

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.

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

International Nuclear Information System (INIS)

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

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

Energy Technology Data Exchange (ETDEWEB)

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

Reveal genes functionally associated with ACADS by a network study.

Science.gov (United States)

Chen, Yulong; Su, Zhiguang