A genetic ensemble approach for gene-gene interaction identification

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Ho Joshua WK

2010-10-01

Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of

A kernel regression approach to gene-gene interaction detection for case-control studies.

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Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

Inhibitors of Histone Deacetylases Are Weak Activators of the FMR1 Gene in Fragile X Syndrome Cell Lines

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Alexander A. Dolskiy

2017-01-01

Full Text Available Fragile X syndrome is the most common cause of inherited intellectual disability in humans. It is a result of CGG repeat expansion in the 5′ untranslated region (5′ UTR of the FMR1 gene. This gene encodes the FMRP protein that is involved in neuronal development. Repeat expansion leads to heterochromatinization of the promoter, gene silencing, and the subsequent absence of FMRP. To date, there is no specific therapy for the syndrome. All treatments in clinic practice provide symptomatic therapy. The development of drug therapy for Fragile X syndrome treatment is connected with the search for inhibitors of enzymes that are responsible for heterochromatinization. Here, we report a weak transcriptional activity of the FMR1 gene and the absence of FMRP protein after Fragile X syndrome cell lines treatment with two FDA approved inhibitors of histone deacetylases, romidepsin and vorinostat. We demonstrate that romidepsin, an inhibitor of class I histone deacetylases, does not activate FMR1 expression in patient cell cultures, whereas vorinostat, an inhibitor of classes I and II histone deacetylases, activates a low level of FMR1 expression in some patient cell lines.

A gene network simulator to assess reverse engineering algorithms.

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Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

2009-03-01

In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies.

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Schaid, Daniel J; Sinnwell, Jason P; Jenkins, Gregory D; McDonnell, Shannon K; Ingle, James N; Kubo, Michiaki; Goss, Paul E; Costantino, Joseph P; Wickerham, D Lawrence; Weinshilboum, Richard M

2012-01-01

Gene-set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene-set, have plagued current approaches, often leading to ad hoc "fixes." To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene-level and gene-set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene-sets. However, other types of gene-set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene-sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene-sets, we use an efficient computational strategy that accounts for LD and provides accurate step-down adjusted P-values for each gene-set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene-set analyses. © 2011 Wiley Periodicals, Inc.

Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks.

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Wu, Mengmeng; Lin, Zhixiang; Ma, Shining; Chen, Ting; Jiang, Rui; Wong, Wing Hung

2017-12-01

Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.

Polytomy refinement for the correction of dubious duplications in gene trees.

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Lafond, Manuel; Chauve, Cedric; Dondi, Riccardo; El-Mabrouk, Nadia

2014-09-01

Large-scale methods for inferring gene trees are error-prone. Correcting gene trees for weakly supported features often results in non-binary trees, i.e. trees with polytomies, thus raising the natural question of refining such polytomies into binary trees. A feature pointing toward potential errors in gene trees are duplications that are not supported by the presence of multiple gene copies. We introduce the problem of refining polytomies in a gene tree while minimizing the number of created non-apparent duplications in the resulting tree. We show that this problem can be described as a graph-theoretical optimization problem. We provide a bounded heuristic with guaranteed optimality for well-characterized instances. We apply our algorithm to a set of ray-finned fish gene trees from the Ensembl database to illustrate its ability to correct dubious duplications. The C++ source code for the algorithms and simulations described in the article are available at http://www-ens.iro.umontreal.ca/~lafonman/software.php. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.

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Zhao, Yize; Kang, Jian; Yu, Tianwei

2014-06-01

It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.

Effect of the absolute statistic on gene-sampling gene-set analysis methods.

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Nam, Dougu

2017-06-01

Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.

Transcriptional profiling of protein expression related genes of Pichia pastoris under simulated microgravity.

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Feng Qi

Full Text Available The physiological responses and transcription profiling of Pichia pastoris GS115 to simulated microgravity (SMG were substantially changed compared with normal gravity (NG control. We previously reported that the recombinant P. pastoris grew faster under SMG than NG during methanol induction phase and the efficiencies of recombinant enzyme production and secretion were enhanced under SMG, which was considered as the consequence of changed transcriptional levels of some key genes. In this work, transcriptiome profiling of P. pastoris cultured under SMG and NG conditions at exponential and stationary phases were determined using next-generation sequencing (NGS technologies. Four categories of 141 genes function as methanol utilization, protein chaperone, RNA polymerase and protein transportation or secretion classified according to Gene Ontology (GO were chosen to be analyzed on the basis of NGS results. And 80 significantly changed genes were weighted and estimated by Cluster 3.0. It was found that most genes of methanol metabolism (85% of 20 genes and protein transportation or secretion (82.2% of 45 genes were significantly up-regulated under SMG. Furthermore the quantity and fold change of up-regulated genes in exponential phase of each category were higher than those of stationary phase. The results indicate that the up-regulated genes of methanol metabolism and protein transportation or secretion mainly contribute to enhanced production and secretion of the recombinant protein under SMG.

Microarray data and gene expression statistics for Saccharomyces cerevisiae exposed to simulated asbestos mine drainage

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Heather E. Driscoll

2017-08-01

Full Text Available Here we describe microarray expression data (raw and normalized, experimental metadata, and gene-level data with expression statistics from Saccharomyces cerevisiae exposed to simulated asbestos mine drainage from the Vermont Asbestos Group (VAG Mine on Belvidere Mountain in northern Vermont, USA. For nearly 100 years (between the late 1890s and 1993, chrysotile asbestos fibers were extracted from serpentinized ultramafic rock at the VAG Mine for use in construction and manufacturing industries. Studies have shown that water courses and streambeds nearby have become contaminated with asbestos mine tailings runoff, including elevated levels of magnesium, nickel, chromium, and arsenic, elevated pH, and chrysotile asbestos-laden mine tailings, due to leaching and gradual erosion of massive piles of mine waste covering approximately 9 km2. We exposed yeast to simulated VAG Mine tailings leachate to help gain insight on how eukaryotic cells exposed to VAG Mine drainage may respond in the mine environment. Affymetrix GeneChip® Yeast Genome 2.0 Arrays were utilized to assess gene expression after 24-h exposure to simulated VAG Mine tailings runoff. The chemistry of mine-tailings leachate, mine-tailings leachate plus yeast extract peptone dextrose media, and control yeast extract peptone dextrose media is also reported. To our knowledge this is the first dataset to assess global gene expression patterns in a eukaryotic model system simulating asbestos mine tailings runoff exposure. Raw and normalized gene expression data are accessible through the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO Database Series GSE89875 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89875.

TESTING HIGH-DIMENSIONAL COVARIANCE MATRICES, WITH APPLICATION TO DETECTING SCHIZOPHRENIA RISK GENES.

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Zhu, Lingxue; Lei, Jing; Devlin, Bernie; Roeder, Kathryn

2017-09-01

Scientists routinely compare gene expression levels in cases versus controls in part to determine genes associated with a disease. Similarly, detecting case-control differences in co-expression among genes can be critical to understanding complex human diseases; however statistical methods have been limited by the high dimensional nature of this problem. In this paper, we construct a sparse-Leading-Eigenvalue-Driven (sLED) test for comparing two high-dimensional covariance matrices. By focusing on the spectrum of the differential matrix, sLED provides a novel perspective that accommodates what we assume to be common, namely sparse and weak signals in gene expression data, and it is closely related with Sparse Principal Component Analysis. We prove that sLED achieves full power asymptotically under mild assumptions, and simulation studies verify that it outperforms other existing procedures under many biologically plausible scenarios. Applying sLED to the largest gene-expression dataset obtained from post-mortem brain tissue from Schizophrenia patients and controls, we provide a novel list of genes implicated in Schizophrenia and reveal intriguing patterns in gene co-expression change for Schizophrenia subjects. We also illustrate that sLED can be generalized to compare other gene-gene "relationship" matrices that are of practical interest, such as the weighted adjacency matrices.

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.

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Boris P Hejblum

2015-06-01

Full Text Available Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial, and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.

Cytokine genes as potential biomarkers for muscle weakness in OPMD

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Riaz, Muhammad; Raz, Yotam; van der Slujis, Barbara

2016-01-01

is a dominant, late-onset myopathy, caused by an alanine-expansion mutation in the gene encoding for poly(A) binding protein nuclear 1 (expPABPN1). Here, we investigated the hypothesis that cytokines could mark OPMD disease state. We determined cytokines levels the vastus lateralis muscle from genetically...... confirmed expPABPN1 carriers at a symptomatic or a presymptomatic stage. We identified cytokine-related genes candidates from a transcriptome study in a mouse overexpressing exp PABPN1 Six cytokines were found to be consistently down-regulated in OPMD vastus lateralis muscles. Expression levels...

A Nonlinear Model for Gene-Based Gene-Environment Interaction

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Jian Sa

2016-06-01

Full Text Available A vast amount of literature has confirmed the role of gene-environment (G×E interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.

Monte Carlo simulation of a simple gene network yields new evolutionary insights.

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Andrecut, M; Cloud, D; Kauffman, S A

2008-02-07

Monte Carlo simulations of a genetic toggle switch show that its behavior can be more complex than analytic models would suggest. We show here that as a result of the interplay between frequent and infrequent reaction events, such a switch can have more stable states than an analytic model would predict, and that the number and character of these states depend to a large extent on the propensity of transcription factors to bind to and dissociate from promoters. The effects of gene duplications differ even more; in analytic models, these seem to result in the disappearance of bi-stability and thus a loss of the switching function, but a Monte Carlo simulation shows that they can result in the appearance of new stable states without the loss of old ones, and thus in an increase of the complexity of the switch's behavior which may facilitate the evolution of new cellular functions. These differences are of interest with respect to the evolution of gene networks, particularly in clonal lines of cancer cells, where the duplication of active genes is an extremely common event, and often seems to result in the appearance of viable new cellular phenotypes.

The evolutionary process of mammalian sex determination genes focusing on marsupial SRYs.

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Katsura, Yukako; Kondo, Hiroko X; Ryan, Janelle; Harley, Vincent; Satta, Yoko

2018-01-16

Maleness in mammals is genetically determined by the Y chromosome. On the Y chromosome SRY is known as the mammalian male-determining gene. Both placental mammals (Eutheria) and marsupial mammals (Metatheria) have SRY genes. However, only eutherian SRY genes have been empirically examined by functional analyses, and the involvement of marsupial SRY in male gonad development remains speculative. In order to demonstrate that the marsupial SRY gene is similar to the eutherian SRY gene in function, we first examined the sequence differences between marsupial and eutherian SRY genes. Then, using a parsimony method, we identify 7 marsupial-specific ancestral substitutions, 13 eutherian-specific ancestral substitutions, and 4 substitutions that occurred at the stem lineage of therian SRY genes. A literature search and molecular dynamics computational simulations support that the lineage-specific ancestral substitutions might be involved with the functional differentiation between marsupial and eutherian SRY genes. To address the function of the marsupial SRY gene in male determination, we performed luciferase assays on the testis enhancer of Sox9 core (TESCO) using the marsupial SRY. The functional assay shows that marsupial SRY gene can weakly up-regulate the luciferase expression via TESCO. Despite the sequence differences between the marsupial and eutherian SRY genes, our functional assay indicates that the marsupial SRY gene regulates SOX9 as a transcription factor in a similar way to the eutherian SRY gene. Our results suggest that SRY genes obtained the function of male determination in the common ancestor of Theria (placental mammals and marsupials). This suggests that the marsupial SRY gene has a function in male determination, but additional experiments are needed to be conclusive.

ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.

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Brett A McKinney

Full Text Available Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k for each gene to optimize the Relief-F test statistics (importance scores for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to

Gene set analysis using variance component tests.

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Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Simulation of E. coli gene regulation including overlapping cell cycles, growth, division, time delays and noise.

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Ruoyu Luo

Full Text Available Due to the complexity of biological systems, simulation of biological networks is necessary but sometimes complicated. The classic stochastic simulation algorithm (SSA by Gillespie and its modified versions are widely used to simulate the stochastic dynamics of biochemical reaction systems. However, it has remained a challenge to implement accurate and efficient simulation algorithms for general reaction schemes in growing cells. Here, we present a modeling and simulation tool, called 'GeneCircuits', which is specifically developed to simulate gene-regulation in exponentially growing bacterial cells (such as E. coli with overlapping cell cycles. Our tool integrates three specific features of these cells that are not generally included in SSA tools: 1 the time delay between the regulation and synthesis of proteins that is due to transcription and translation processes; 2 cell cycle-dependent periodic changes of gene dosage; and 3 variations in the propensities of chemical reactions that have time-dependent reaction rates as a consequence of volume expansion and cell division. We give three biologically relevant examples to illustrate the use of our simulation tool in quantitative studies of systems biology and synthetic biology.

A powerful score-based test statistic for detecting gene-gene co-association.

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Xu, Jing; Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Li, Hongkai; Wu, Xuesen; Xue, Fuzhong; Liu, Yanxun

2016-01-29

The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

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Paules Richard S

2007-11-01

Full Text Available Abstract Background A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions. Results Through evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratio's, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV or ionizing radiation (IR-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying

Genes and Gene Therapy

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... correctly, a child can have a genetic disorder. Gene therapy is an experimental technique that uses genes to ... or prevent disease. The most common form of gene therapy involves inserting a normal gene to replace an ...

A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design

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Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods. PMID:23620809

Simple Comparative Analyses of Differentially Expressed Gene Lists May Overestimate Gene Overlap.

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Lawhorn, Chelsea M; Schomaker, Rachel; Rowell, Jonathan T; Rueppell, Olav

2018-04-16

Comparing the overlap between sets of differentially expressed genes (DEGs) within or between transcriptome studies is regularly used to infer similarities between biological processes. Significant overlap between two sets of DEGs is usually determined by a simple test. The number of potentially overlapping genes is compared to the number of genes that actually occur in both lists, treating every gene as equal. However, gene expression is controlled by transcription factors that bind to a variable number of transcription factor binding sites, leading to variation among genes in general variability of their expression. Neglecting this variability could therefore lead to inflated estimates of significant overlap between DEG lists. With computer simulations, we demonstrate that such biases arise from variation in the control of gene expression. Significant overlap commonly arises between two lists of DEGs that are randomly generated, assuming that the control of gene expression is variable among genes but consistent between corresponding experiments. More overlap is observed when transcription factors are specific to their binding sites and when the number of genes is considerably higher than the number of different transcription factors. In contrast, overlap between two DEG lists is always lower than expected when the genetic architecture of expression is independent between the two experiments. Thus, the current methods for determining significant overlap between DEGs are potentially confounding biologically meaningful overlap with overlap that arises due to variability in control of expression among genes, and more sophisticated approaches are needed.

Effects of simulated microgravity on gene expression and biological phenotypes of a single generation Caenorhabditis elegans cultured on 2 different media.

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Tee, Ling Fei; Neoh, Hui-Min; Then, Sue Mian; Murad, Nor Azian; Asillam, Mohd Fairos; Hashim, Mohd Helmy; Nathan, Sheila; Jamal, Rahman

2017-11-01

Studies of multigenerational Caenorhabditis elegans exposed to long-term spaceflight have revealed expression changes of genes involved in longevity, DNA repair, and locomotion. However, results from spaceflight experiments are difficult to reproduce as space missions are costly and opportunities are rather limited for researchers. In addition, multigenerational cultures of C. elegans used in previous studies contribute to mixture of gene expression profiles from both larvae and adult worms, which were recently reported to be different. Usage of different culture media during microgravity simulation experiments might also give rise to differences in the gene expression and biological phenotypes of the worms. In this study, we investigated the effects of simulated microgravity on the gene expression and biological phenotype profiles of a single generation of C. elegans worms cultured on 2 different culture media. A desktop Random Positioning Machine (RPM) was used to simulate microgravity on the worms for approximately 52 to 54 h. Gene expression profile was analysed using the Affymetrix GeneChip® C. elegans 1.0 ST Array. Only one gene (R01H2.2) was found to be downregulated in nematode growth medium (NGM)-cultured worms exposed to simulated microgravity. On the other hand, eight genes were differentially expressed for C. elegans Maintenance Medium (CeMM)-cultured worms in microgravity; six were upregulated, while two were downregulated. Five of the upregulated genes (C07E3.15, C34H3.21, C32D5.16, F35H8.9 and C34F11.17) encode non-coding RNAs. In terms of biological phenotype, we observed that microgravity-simulated worms experienced minimal changes in terms of lifespan, locomotion and reproductive capabilities in comparison with the ground controls. Taking it all together, simulated microgravity on a single generation of C. elegans did not confer major changes to their gene expression and biological phenotype. Nevertheless, exposure of the worms to microgravity

Survival of Listeria monocytogenes in simulated gastrointestinal system and transcriptional profiling of stress- and adhesion-related genes

DEFF Research Database (Denmark)

Jiang, Lingli; Olesen, Inger; Andersen, Thomas

2010-01-01

-related genes after exposure to the conditions similar to those encountered in the mouth, stomach, and small intestine. None of the L. monocytogenes strains investigated could survive in the gastric juice at pH 2.5 or 3.0. Their survival increased at higher pH (3.5 and 4.0) in the gastric stress. Relative...... afterpassing through the simulated gastrointestinal tract, whereas that of the adhesion-related gene ami was downregulated. Taken together, this study revealed that L. monocytogenes strains enhanced the expression of stressrelated genes and decreased the transcription of adhesion-related gene in order...

Gene expression and gene therapy imaging

International Nuclear Information System (INIS)

Rome, Claire; Couillaud, Franck; Moonen, Chrit T.W.

2007-01-01

The fast growing field of molecular imaging has achieved major advances in imaging gene expression, an important element of gene therapy. Gene expression imaging is based on specific probes or contrast agents that allow either direct or indirect spatio-temporal evaluation of gene expression. Direct evaluation is possible with, for example, contrast agents that bind directly to a specific target (e.g., receptor). Indirect evaluation may be achieved by using specific substrate probes for a target enzyme. The use of marker genes, also called reporter genes, is an essential element of MI approaches for gene expression in gene therapy. The marker gene may not have a therapeutic role itself, but by coupling the marker gene to a therapeutic gene, expression of the marker gene reports on the expression of the therapeutic gene. Nuclear medicine and optical approaches are highly sensitive (detection of probes in the picomolar range), whereas MRI and ultrasound imaging are less sensitive and require amplification techniques and/or accumulation of contrast agents in enlarged contrast particles. Recently developed MI techniques are particularly relevant for gene therapy. Amongst these are the possibility to track gene therapy vectors such as stem cells, and the techniques that allow spatiotemporal control of gene expression by non-invasive heating (with MRI guided focused ultrasound) and the use of temperature sensitive promoters. (orig.)

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

Science.gov (United States)

Seo, Minseok; Shin, Su-kyung; Kwon, Eun-Young; Kim, Sung-Eun; Bae, Yun-Jung; Lee, Seungyeoun; Sung, Mi-Kyung; Choi, Myung-Sook; Park, Taesung

2016-01-01

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes.

Directory of Open Access Journals (Sweden)

Samuel Sunghwan Cho

Full Text Available Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs. However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods

An efficient parallel stochastic simulation method for analysis of nonviral gene delivery systems

KAUST Repository

Kuwahara, Hiroyuki; Gao, Xin

2011-01-01

DNA molecules into the nucleus of target cells. Several computational and experimental studies have shown that the design process of synthetic gene transfer vectors can be greatly enhanced by computational modeling and simulation. This paper proposes a

Transcription factors and stress response gene alterations in human keratinocytes following Solar Simulated Ultra Violet Radiation.

Science.gov (United States)

Marais, Thomas L Des; Kluz, Thomas; Xu, Dazhong; Zhang, Xiaoru; Gesumaria, Lisa; Matsui, Mary S; Costa, Max; Sun, Hong

2017-10-19

Ultraviolet radiation (UVR) from sunlight is the major effector for skin aging and carcinogenesis. However, genes and pathways altered by solar-simulated UVR (ssUVR), a mixture of UVA and UVB, are not well characterized. Here we report global changes in gene expression as well as associated pathways and upstream transcription factors in human keratinocytes exposed to ssUVR. Human HaCaT keratinocytes were exposed to either a single dose or 5 repetitive doses of ssUVR. Comprehensive analyses of gene expression profiles as well as functional annotation were performed at 24 hours post irradiation. Our results revealed that ssUVR modulated genes with diverse cellular functions changed in a dose-dependent manner. Gene expression in cells exposed to a single dose of ssUVR differed significantly from those that underwent repetitive exposures. While single ssUVR caused a significant inhibition in genes involved in cell cycle progression, especially G2/M checkpoint and mitotic regulation, repetitive ssUVR led to extensive changes in genes related to cell signaling and metabolism. We have also identified a panel of ssUVR target genes that exhibited persistent changes in gene expression even at 1 week after irradiation. These results revealed a complex network of transcriptional regulators and pathways that orchestrate the cellular response to ssUVR.

Imaging gene expression in gene therapy

International Nuclear Information System (INIS)

Wiebe, Leonard I.

1997-01-01

Full text. Gene therapy can be used to introduce new genes, or to supplement the function of indigenous genes. At the present time, however, there is non-invasive test to demonstrate efficacy of the gene transfer and expression processes. It has been postulated that scintigraphic imaging can offer unique information on both the site at which the transferred gene is expressed, and the degree of expression, both of which are critical issue for safety and clinical efficacy. Many current studies are based on 'suicide gene therapy' of cancer. Cells modified to express these genes commit metabolic suicide in the presence of an enzyme encoded by the transferred gene and a specifically-convertible pro drug. Pro drug metabolism can lead to selective metabolic trapping, required for scintigraphy. Herpes simplex virus type-1 thymidine kinase (H S V-1 t k + ) has been use for 'suicide' in vivo tumor gene therapy. It has been proposed that radiolabelled nucleosides can be used as radiopharmaceuticals to detect H S V-1 t k + gene expression where the H S V-1 t k + gene serves a reporter or therapeutic function. Animal gene therapy models have been studied using purine-([ 18 F]F H P G; [ 18 F]-A C V), and pyrimidine- ([ 123 / 131 I]I V R F U; [ 124 / 131I ]) antiviral nucleosides. Principles of gene therapy and gene therapy imaging will be reviewed and experimental data for [ 123 / 131I ]I V R F U imaging with the H S V-1 t k + reporter gene will be presented

A Partial Least Square Approach for Modeling Gene-gene and Gene-environment Interactions When Multiple Markers Are Genotyped

Science.gov (United States)

Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C.

2008-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense SNPs in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches: the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey’s 1-df model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women’s Health Initiative (WHI), this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with BMI. PMID:18615621

A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.

Science.gov (United States)

Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C

2009-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index.

Imaging gene expression in gene therapy

Energy Technology Data Exchange (ETDEWEB)

Wiebe, Leonard I. [Alberta Univ., Edmonton (Canada). Noujaim Institute for Pharmaceutical Oncology Research

1997-12-31

Full text. Gene therapy can be used to introduce new genes, or to supplement the function of indigenous genes. At the present time, however, there is non-invasive test to demonstrate efficacy of the gene transfer and expression processes. It has been postulated that scintigraphic imaging can offer unique information on both the site at which the transferred gene is expressed, and the degree of expression, both of which are critical issue for safety and clinical efficacy. Many current studies are based on `suicide gene therapy` of cancer. Cells modified to express these genes commit metabolic suicide in the presence of an enzyme encoded by the transferred gene and a specifically-convertible pro drug. Pro drug metabolism can lead to selective metabolic trapping, required for scintigraphy. Herpes simplex virus type-1 thymidine kinase (H S V-1 t k{sup +}) has been use for `suicide` in vivo tumor gene therapy. It has been proposed that radiolabelled nucleosides can be used as radiopharmaceuticals to detect H S V-1 t k{sup +} gene expression where the H S V-1 t k{sup +} gene serves a reporter or therapeutic function. Animal gene therapy models have been studied using purine-([{sup 18} F]F H P G; [{sup 18} F]-A C V), and pyrimidine- ([{sup 123}/{sup 131} I]I V R F U; [{sup 124}/{sup 131I}]) antiviral nucleosides. Principles of gene therapy and gene therapy imaging will be reviewed and experimental data for [{sup 123}/{sup 131I}]I V R F U imaging with the H S V-1 t k{sup +} reporter gene will be presented

Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

Science.gov (United States)

Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.

2018-03-01

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

Identification of unique cis-element pattern on simulated microgravity treated Arabidopsis by in silico and gene expression

Science.gov (United States)

Soh, Hyuncheol; Choi, Yongsang; Lee, Taek-Kyun; Yeo, Up-Dong; Han, Kyeongsik; Auh, Chungkyun; Lee, Sukchan

2012-08-01

Arabidopsis gene expression microarray (44 K) was used to detect genes highly induced under simulated microgravity stress (SMS). Ten SMS-inducible genes were selected from the microarray data and these 10 genes were found to be abundantly expressed in 3-week-old plants. Nine out of the 10 SMS-inducible genes were also expressed in response to the three abiotic stresses of drought, touch, and wounding in 3-week-old Arabidopsis plants respectively. However, WRKY46 was elevated only in response to SMS. Six other WRKY genes did not respond to SMS. To clarify the characteristics of the genes expressed at high levels in response to SMS, 20 cis-elements in the promoters of the 40 selected genes including the 10 SMS-inducible genes, the 6 WRKY genes, and abiotic stress-inducible genes were analyzed and their spatial positions on each promoter were determined. Four cis-elements (M/T-G-T-P from MYB1AT or TATABOX5, GT1CONSENSUS, TATABOX5, and POLASIG1) showed a unique spatial arrangement in most SMS-inducible genes including WRKY46. Therefore the M/T-G-T-P cis-element patterns identified in the promoter of WRKY46 may play important roles in regulating gene expression in response to SMS. The presences of the cis-element patterns suggest that the order or spatial positioning of certain groups of cis-elements is more important than the existence or numbers of specific cis-elements. Taken together, our data indicate that WRKY46 is a novel SMS inducible transcription factor and the unique spatial arrangement of cis-elements shown in WRKY46 promoter may play an important role for its response to SMS.

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.

Science.gov (United States)

Kwon, Minseok; Leem, Sangseob; Yoon, Joon; Park, Taesung

2018-03-19

With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants. Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes. The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

Inference of cancer-specific gene regulatory networks using soft computing rules.

Science.gov (United States)

Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Radionuclide reporter gene imaging for cardiac gene therapy

International Nuclear Information System (INIS)

Inubushi, Masayuki; Tamaki, Nagara

2007-01-01

In the field of cardiac gene therapy, angiogenic gene therapy has been most extensively investigated. The first clinical trial of cardiac angiogenic gene therapy was reported in 1998, and at the peak, more than 20 clinical trial protocols were under evaluation. However, most trials have ceased owing to the lack of decisive proof of therapeutic effects and the potential risks of viral vectors. In order to further advance cardiac angiogenic gene therapy, remaining open issues need to be resolved: there needs to be improvement of gene transfer methods, regulation of gene expression, development of much safer vectors and optimisation of therapeutic genes. For these purposes, imaging of gene expression in living organisms is of great importance. In radionuclide reporter gene imaging, ''reporter genes'' transferred into cell nuclei encode for a protein that retains a complementary ''reporter probe'' of a positron or single-photon emitter; thus expression of the reporter genes can be imaged with positron emission tomography or single-photon emission computed tomography. Accordingly, in the setting of gene therapy, the location, magnitude and duration of the therapeutic gene co-expression with the reporter genes can be monitored non-invasively. In the near future, gene therapy may evolve into combination therapy with stem/progenitor cell transplantation, so-called cell-based gene therapy or gene-modified cell therapy. Radionuclide reporter gene imaging is now expected to contribute in providing evidence on the usefulness of this novel therapeutic approach, as well as in investigating the molecular mechanisms underlying neovascularisation and safety issues relevant to further progress in conventional gene therapy. (orig.)

Rapid isolation of gene homologs across taxa: Efficient identification and isolation of gene orthologs from non-model organism genomes, a technical report

Directory of Open Access Journals (Sweden)

Heffer Alison

2011-03-01

Full Text Available Abstract Background Tremendous progress has been made in the field of evo-devo through comparisons of related genes from diverse taxa. While the vast number of species in nature precludes a complete analysis of the molecular evolution of even one single gene family, this would not be necessary to understand fundamental mechanisms underlying gene evolution if experiments could be designed to systematically sample representative points along the path of established phylogenies to trace changes in regulatory and coding gene sequence. This isolation of homologous genes from phylogenetically diverse, representative species can be challenging, especially if the gene is under weak selective pressure and evolving rapidly. Results Here we present an approach - Rapid Isolation of Gene Homologs across Taxa (RIGHT - to efficiently isolate specific members of gene families. RIGHT is based upon modification and a combination of degenerate polymerase chain reaction (PCR and gene-specific amplified fragment length polymorphism (AFLP. It allows targeted isolation of specific gene family members from any organism, only requiring genomic DNA. We describe this approach and how we used it to isolate members of several different gene families from diverse arthropods spanning millions of years of evolution. Conclusions RIGHT facilitates systematic isolation of one gene from large gene families. It allows for efficient gene isolation without whole genome sequencing, RNA extraction, or culturing of non-model organisms. RIGHT will be a generally useful method for isolation of orthologs from both distant and closely related species, increasing sample size and facilitating the tracking of molecular evolution of gene families and regulatory networks across the tree of life.

Gene therapy prospects--intranasal delivery of therapeutic genes.

Science.gov (United States)

Podolska, Karolina; Stachurska, Anna; Hajdukiewicz, Karolina; Małecki, Maciej

2012-01-01

Gene therapy is recognized to be a novel method for the treatment of various disorders. Gene therapy strategies involve gene manipulation on broad biological processes responsible for the spreading of diseases. Cancer, monogenic diseases, vascular and infectious diseases are the main targets of gene therapy. In order to obtain valuable experimental and clinical results, sufficient gene transfer methods are required. Therapeutic genes can be administered into target tissues via gene carriers commonly defined as vectors. The retroviral, adenoviral and adeno-associated virus based vectors are most frequently used in the clinic. So far, gene preparations may be administered directly into target organs or by intravenous, intramuscular, intratumor or intranasal injections. It is common knowledge that the number of gene therapy clinical trials has rapidly increased. However, some limitations such as transfection efficiency and stable and long-term gene expression are still not resolved. Consequently, great effort is focused on the evaluation of new strategies of gene delivery. There are many expectations associated with intranasal delivery of gene preparations for the treatment of diseases. Intranasal delivery of therapeutic genes is regarded as one of the most promising forms of pulmonary gene therapy research. Gene therapy based on inhalation of gene preparations offers an alternative way for the treatment of patients suffering from such lung diseases as cystic fibrosis, alpha-1-antitrypsin defect, or cancer. Experimental and first clinical trials based on plasmid vectors or recombinant viruses have revealed that gene preparations can effectively deliver therapeutic or marker genes to the cells of the respiratory tract. The noninvasive intranasal delivery of gene preparations or conventional drugs seems to be very encouraging, although basic scientific research still has to continue.

Gene-gene interactions and gene polymorphisms of VEGFA and EG-VEGF gene systems in recurrent pregnancy loss.

Science.gov (United States)

Su, Mei-Tsz; Lin, Sheng-Hsiang; Chen, Yi-Chi; Kuo, Pao-Lin

2014-06-01

Both vascular endothelial growth factor A (VEGFA) and endocrine gland-derived vascular endothelial growth factor (EG-VEGF) systems play major roles in angiogenesis. A body of evidence suggests VEGFs regulate critical processes during pregnancy and have been associated with recurrent pregnancy loss (RPL). However, little information is available regarding the interaction of these two major major angiogenesis-related systems in early human pregnancy. This study was conducted to investigate the association of gene polymorphisms and gene-gene interaction among genes in VEGFA and EG-VEGF systems and idiopathic RPL. A total of 98 women with history of idiopathic RPL and 142 controls were included, and 5 functional SNPs selected from VEGFA, KDR, EG-VEGF (PROK1), PROKR1 and PROKR2 were genotyped. We used multifactor dimensionality reduction (MDR) analysis to choose a best model and evaluate gene-gene interactions. Ingenuity pathways analysis (IPA) was introduced to explore possible complex interactions. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL (P<0.01). The MDR test revealed that the KDR (Q472H) polymorphism was the best loci to be associated with RPL (P=0.02). IPA revealed EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3 signaling pathways. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL. EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3.

Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

Directory of Open Access Journals (Sweden)

Xiaosheng Wang

2010-03-01

Full Text Available Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Gene doping: gene delivery for olympic victory

OpenAIRE

Gould, David

2012-01-01

With one recently recommended gene therapy in Europe and a number of other gene therapy treatments now proving effective in clinical trials it is feasible that the same technologies will soon be adopted in the world of sport by unscrupulous athletes and their trainers in so called ‘gene doping’. In this article an overview of the successful gene therapy clinical trials is provided and the potential targets for gene doping are highlighted. Depending on whether a doping gene product is secreted...

FunGene: the functional gene pipeline and repository.

Science.gov (United States)

Fish, Jordan A; Chai, Benli; Wang, Qiong; Sun, Yanni; Brown, C Titus; Tiedje, James M; Cole, James R

2013-01-01

Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer. While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/) offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

FunGene: the Functional Gene Pipeline and Repository

Directory of Open Access Journals (Sweden)

Jordan A. Fish

2013-10-01

Full Text Available Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer.While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/ offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

Science.gov (United States)

Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

2015-01-01

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Screening Reliable Reference Genes for RT-qPCR Analysis of Gene Expression in Moringa oleifera.

Science.gov (United States)

Deng, Li-Ting; Wu, Yu-Ling; Li, Jun-Cheng; OuYang, Kun-Xi; Ding, Mei-Mei; Zhang, Jun-Jie; Li, Shu-Qi; Lin, Meng-Fei; Chen, Han-Bin; Hu, Xin-Sheng; Chen, Xiao-Yang

2016-01-01

Moringa oleifera is a promising plant species for oil and forage, but its genetic improvement is limited. Our current breeding program in this species focuses on exploiting the functional genes associated with important agronomical traits. Here, we screened reliable reference genes for accurately quantifying the expression of target genes using the technique of real-time quantitative polymerase chain reaction (RT-qPCR) in M. oleifera. Eighteen candidate reference genes were selected from a transcriptome database, and their expression stabilities were examined in 90 samples collected from the pods in different developmental stages, various tissues, and the roots and leaves under different conditions (low or high temperature, sodium chloride (NaCl)- or polyethyleneglycol (PEG)- simulated water stress). Analyses with geNorm, NormFinder and BestKeeper algorithms revealed that the reliable reference genes differed across sample designs and that ribosomal protein L1 (RPL1) and acyl carrier protein 2 (ACP2) were the most suitable reference genes in all tested samples. The experiment results demonstrated the significance of using the properly validated reference genes and suggested the use of more than one reference gene to achieve reliable expression profiles. In addition, we applied three isotypes of the superoxide dismutase (SOD) gene that are associated with plant adaptation to abiotic stress to confirm the efficacy of the validated reference genes under NaCl and PEG water stresses. Our results provide a valuable reference for future studies on identifying important functional genes from their transcriptional expressions via RT-qPCR technique in M. oleifera.

Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

Science.gov (United States)

Yang, Cheng-Hong; Chang, Hsueh-Wei

2014-01-01

Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer. PMID:24895547

Weak Organic Acids Decrease Borrelia burgdorferi Cytoplasmic pH, Eliciting an Acid Stress Response and Impacting RpoN- and RpoS-Dependent Gene Expression

Directory of Open Access Journals (Sweden)

Daniel P. Dulebohn

2017-09-01

Full Text Available The spirochete Borrelia burgdorferi survives in its tick vector, Ixodes scapularis, or within various hosts. To transition between and survive in these distinct niches, B. burgdorferi changes its gene expression in response to environmental cues, both biochemical and physiological. Exposure of B. burgdorferi to weak monocarboxylic organic acids, including those detected in the blood meal of fed ticks, decreased the cytoplasmic pH of B. burgdorferi in vitro. A decrease in the cytoplasmic pH induced the expression of genes encoding enzymes that have been shown to restore pH homeostasis in other bacteria. These include putative coupled proton/cation exchangers, a putative Na+/H+ antiporter, a neutralizing buffer transporter, an amino acid deaminase and a proton exporting vacuolar-type VoV1 ATPase. Data presented in this report suggested that the acid stress response triggered the expression of RpoN- and RpoS-dependent genes including important virulence factors such as outer surface protein C (OspC, BBA66, and some BosR (Borreliaoxidative stress regulator-dependent genes. Because the expression of virulence factors, like OspC, are so tightly connected by RpoS to general cellular stress responses and cell physiology, it is difficult to separate transmission-promoting conditions in what is clearly a multifactorial and complex regulatory web.

Genetic evaluation with major genes and polygenic inheritance when some animals are not genotyped using gene content multiple-trait BLUP.

Science.gov (United States)

Legarra, Andrés; Vitezica, Zulma G

2015-11-17

In pedigreed populations with a major gene segregating for a quantitative trait, it is not clear how to use pedigree, genotype and phenotype information when some individuals are not genotyped. We propose to consider gene content at the major gene as a second trait correlated to the quantitative trait, in a gene content multiple-trait best linear unbiased prediction (GCMTBLUP) method. The genetic covariance between the trait and gene content at the major gene is a function of the substitution effect of the gene. This genetic covariance can be written in a multiple-trait form that accommodates any pattern of missing values for either genotype or phenotype data. Effects of major gene alleles and the genetic covariance between genotype at the major gene and the phenotype can be estimated using standard EM-REML or Gibbs sampling. Prediction of breeding values with genotypes at the major gene can use multiple-trait BLUP software. Major genes with more than two alleles can be considered by including negative covariances between gene contents at each different allele. We simulated two scenarios: a selected and an unselected trait with heritabilities of 0.05 and 0.5, respectively. In both cases, the major gene explained half the genetic variation. Competing methods used imputed gene contents derived by the method of Gengler et al. or by iterative peeling. Imputed gene contents, in contrast to GCMTBLUP, do not consider information on the quantitative trait for genotype prediction. GCMTBLUP gave unbiased estimates of the gene effect, in contrast to the other methods, with less bias and better or equal accuracy of prediction. GCMTBLUP improved estimation of genotypes in non-genotyped individuals, in particular if these individuals had own phenotype records and the trait had a high heritability. Ignoring the major gene in genetic evaluation led to serious biases and decreased prediction accuracy. CGMTBLUP is the best linear predictor of additive genetic merit including

Gene-gene, gene-environment, gene-nutrient interactions and single nucleotide polymorphisms of inflammatory cytokines.

Science.gov (United States)

Nadeem, Amina; Mumtaz, Sadaf; Naveed, Abdul Khaliq; Aslam, Muhammad; Siddiqui, Arif; Lodhi, Ghulam Mustafa; Ahmad, Tausif

2015-05-15

Inflammation plays a significant role in the etiology of type 2 diabetes mellitus (T2DM). The rise in the pro-inflammatory cytokines is the essential step in glucotoxicity and lipotoxicity induced mitochondrial injury, oxidative stress and beta cell apoptosis in T2DM. Among the recognized markers are interleukin (IL)-6, IL-1, IL-10, IL-18, tissue necrosis factor-alpha (TNF-α), C-reactive protein, resistin, adiponectin, tissue plasminogen activator, fibrinogen and heptoglobins. Diabetes mellitus has firm genetic and very strong environmental influence; exhibiting a polygenic mode of inheritance. Many single nucleotide polymorphisms (SNPs) in various genes including those of pro and anti-inflammatory cytokines have been reported as a risk for T2DM. Not all the SNPs have been confirmed by unifying results in different studies and wide variations have been reported in various ethnic groups. The inter-ethnic variations can be explained by the fact that gene expression may be regulated by gene-gene, gene-environment and gene-nutrient interactions. This review highlights the impact of these interactions on determining the role of single nucleotide polymorphism of IL-6, TNF-α, resistin and adiponectin in pathogenesis of T2DM.

Genes with minimal phylogenetic information are problematic for coalescent analyses when gene tree estimation is biased.

Science.gov (United States)

Xi, Zhenxiang; Liu, Liang; Davis, Charles C

2015-11-01

The development and application of coalescent methods are undergoing rapid changes. One little explored area that bears on the application of gene-tree-based coalescent methods to species tree estimation is gene informativeness. Here, we investigate the accuracy of these coalescent methods when genes have minimal phylogenetic information, including the implementation of the multilocus bootstrap approach. Using simulated DNA sequences, we demonstrate that genes with minimal phylogenetic information can produce unreliable gene trees (i.e., high error in gene tree estimation), which may in turn reduce the accuracy of species tree estimation using gene-tree-based coalescent methods. We demonstrate that this problem can be alleviated by sampling more genes, as is commonly done in large-scale phylogenomic analyses. This applies even when these genes are minimally informative. If gene tree estimation is biased, however, gene-tree-based coalescent analyses will produce inconsistent results, which cannot be remedied by increasing the number of genes. In this case, it is not the gene-tree-based coalescent methods that are flawed, but rather the input data (i.e., estimated gene trees). Along these lines, the commonly used program PhyML has a tendency to infer one particular bifurcating topology even though it is best represented as a polytomy. We additionally corroborate these findings by analyzing the 183-locus mammal data set assembled by McCormack et al. (2012) using ultra-conserved elements (UCEs) and flanking DNA. Lastly, we demonstrate that when employing the multilocus bootstrap approach on this 183-locus data set, there is no strong conflict between species trees estimated from concatenation and gene-tree-based coalescent analyses, as has been previously suggested by Gatesy and Springer (2014). Copyright © 2015 Elsevier Inc. All rights reserved.

Suicide genes or p53 gene and p53 target genes as targets for cancer gene therapy by ionizing radiation

International Nuclear Information System (INIS)

Liu Bing; Chinese Academy of Sciences, Beijing; Zhang Hong

2005-01-01

Radiotherapy has some disadvantages due to the severe side-effect on the normal tissues at a curative dose of ionizing radiation (IR). Similarly, as a new developing approach, gene therapy also has some disadvantages, such as lack of specificity for tumors, limited expression of therapeutic gene, potential biological risk. To certain extent, above problems would be solved by the suicide genes or p53 gene and its target genes therapies targeted by ionizing radiation. This strategy not only makes up the disadvantage from radiotherapy or gene therapy alone, but also promotes success rate on the base of lower dose. By present, there have been several vectors measuring up to be reaching clinical trials. This review focused on the development of the cancer gene therapy through suicide genes or p53 and its target genes mediated by IR. (authors)

Neighboring Genes Show Correlated Evolution in Gene Expression

Science.gov (United States)

Ghanbarian, Avazeh T.; Hurst, Laurence D.

2015-01-01

When considering the evolution of a gene’s expression profile, we commonly assume that this is unaffected by its genomic neighborhood. This is, however, in contrast to what we know about the lack of autonomy between neighboring genes in gene expression profiles in extant taxa. Indeed, in all eukaryotic genomes genes of similar expression-profile tend to cluster, reflecting chromatin level dynamics. Does it follow that if a gene increases expression in a particular lineage then the genomic neighbors will also increase in their expression or is gene expression evolution autonomous? To address this here we consider evolution of human gene expression since the human-chimp common ancestor, allowing for both variation in estimation of current expression level and error in Bayesian estimation of the ancestral state. We find that in all tissues and both sexes, the change in gene expression of a focal gene on average predicts the change in gene expression of neighbors. The effect is highly pronounced in the immediate vicinity (genes increasing their expression in humans tend to avoid nuclear lamina domains and be enriched for the gene activator 5-hydroxymethylcytosine, we conclude that, most probably owing to chromatin level control of gene expression, a change in gene expression of one gene likely affects the expression evolution of neighbors, what we term expression piggybacking, an analog of hitchhiking. PMID:25743543

Transfection of the IHH gene into rabbit BMSCs in a simulated microgravity environment promotes chondrogenic differentiation and inhibits cartilage aging.

Science.gov (United States)

Liu, Peng-Cheng; Liu, Kuan; Liu, Jun-Feng; Xia, Kuo; Chen, Li-Yang; Wu, Xing

2016-09-27

The effect of overexpressing the Indian hedgehog (IHH) gene on the chondrogenic differentiation of rabbit bone marrow-derived mesenchymal stem cells (BMSCs) was investigated in a simulated microgravity environment. An adenovirus plasmid encoding the rabbit IHH gene was constructed in vitro and transfected into rabbit BMSCs. Two large groups were used: conventional cell culture and induction model group and simulated microgravity environment group. Each large group was further divided into blank control group, GFP transfection group, and IHH transfection group. During differentiation induction, the expression levels of cartilage-related and cartilage hypertrophy-related genes and proteins in each group were determined. In the conventional model, the IHH transfection group expressed high levels of cartilage-related factors (Coll2 and ANCN) at the early stage of differentiation induction and expressed high levels of cartilage hypertrophy-related factors (Coll10, annexin 5, and ALP) at the late stage. Under the simulated microgravity environment, the IHH transfection group expressed high levels of cartilage-related factors and low levels of cartilage hypertrophy-related factors at all stages of differentiation induction. Under the simulated microgravity environment, transfection of the IHH gene into BMSCs effectively promoted the generation of cartilage and inhibited cartilage aging and osteogenesis. Therefore, this technique is suitable for cartilage tissue engineering.

Myopathic mtDNA Depletion Syndrome Due to Mutation in TK2 Gene.

Science.gov (United States)

Martín-Hernández, Elena; García-Silva, María Teresa; Quijada-Fraile, Pilar; Rodríguez-García, María Elena; Rivera, Henry; Hernández-Laín, Aurelio; Coca-Robinot, David; Fernández-Toral, Joaquín; Arenas, Joaquín; Martín, Miguel A; Martínez-Azorín, Francisco

2017-01-01

Whole-exome sequencing was used to identify the disease gene(s) in a Spanish girl with failure to thrive, muscle weakness, mild facial weakness, elevated creatine kinase, deficiency of mitochondrial complex III and depletion of mtDNA. With whole-exome sequencing data, it was possible to get the whole mtDNA sequencing and discard any pathogenic variant in this genome. The analysis of whole exome uncovered a homozygous pathogenic mutation in thymidine kinase 2 gene ( TK2; NM_004614.4:c.323 C>T, p.T108M). TK2 mutations have been identified mainly in patients with the myopathic form of mtDNA depletion syndromes. This patient presents an atypical TK2-related myopathic form of mtDNA depletion syndromes, because despite having a very low content of mtDNA (TK2 gene in mtDNA depletion syndromes and expanded the phenotypic spectrum.

Gene Therapy

Science.gov (United States)

Gene therapy Overview Gene therapy involves altering the genes inside your body's cells in an effort to treat or stop disease. Genes contain your ... that don't work properly can cause disease. Gene therapy replaces a faulty gene or adds a new ...

Imaging reporter gene for monitoring gene therapy

International Nuclear Information System (INIS)

Beco, V. de; Baillet, G.; Tamgac, F.; Tofighi, M.; Weinmann, P.; Vergote, J.; Moretti, J.L.; Tamgac, G.

2002-01-01

Scintigraphic images can be obtained to document gene function at cellular level. This approach is presented here and the use of a reporter gene to monitor gene therapy is described. Two main ways are presented: either the use of a reporter gene coding for an enzyme the action of which will be monitored by radiolabeled pro-drug, or a cellular receptor gene, the action of which is documented by a radio labeled cognate receptor ligand. (author)

Gene-Gene and Gene-Environment Interactions in the Etiology of Breast Cancer

National Research Council Canada - National Science Library

Adegoke, Olufemi

2003-01-01

The objective of this CDA is to evaluate the gene-gene and gene-environment interactions in the etiology of breast cancer in two ongoing case-control studies, the Shanghai Breast Cancer Study (SBCS...

Comparative study on gene set and pathway topology-based enrichment methods.

Science.gov (United States)

Bayerlová, Michaela; Jung, Klaus; Kramer, Frank; Klemm, Florian; Bleckmann, Annalen; Beißbarth, Tim

2015-10-22

Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis. We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods. In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower. We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

Science.gov (United States)

Wang, Yi Kan; Hurley, Daniel G; Schnell, Santiago; Print, Cristin G; Crampin, Edmund J

2013-01-01

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

Gene doping: gene delivery for olympic victory.

Science.gov (United States)

Gould, David

2013-08-01

With one recently recommended gene therapy in Europe and a number of other gene therapy treatments now proving effective in clinical trials it is feasible that the same technologies will soon be adopted in the world of sport by unscrupulous athletes and their trainers in so called 'gene doping'. In this article an overview of the successful gene therapy clinical trials is provided and the potential targets for gene doping are highlighted. Depending on whether a doping gene product is secreted from the engineered cells or is retained locally to, or inside engineered cells will, to some extent, determine the likelihood of detection. It is clear that effective gene delivery technologies now exist and it is important that detection and prevention plans are in place. © 2012 The Author. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.

Genes2FANs: connecting genes through functional association networks

Science.gov (United States)

2012-01-01

Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in

QCD on the BlueGene/L Supercomputer

International Nuclear Information System (INIS)

Bhanot, G.; Chen, D.; Gara, A.; Sexton, J.; Vranas, P.

2005-01-01

In June 2004 QCD was simulated for the first time at sustained speed exceeding 1 TeraFlops in the BlueGene/L supercomputer at the IBM T.J. Watson Research Lab. The implementation and performance of QCD in the BlueGene/L is presented

QCD on the BlueGene/L Supercomputer

Science.gov (United States)

Bhanot, G.; Chen, D.; Gara, A.; Sexton, J.; Vranas, P.

2005-03-01

In June 2004 QCD was simulated for the first time at sustained speed exceeding 1 TeraFlops in the BlueGene/L supercomputer at the IBM T.J. Watson Research Lab. The implementation and performance of QCD in the BlueGene/L is presented.

Translational selection in human: More pronounced in housekeeping genes

KAUST Repository

Ma, Lina

2014-07-10

Background: Translational selection is a ubiquitous and significant mechanism to regulate protein expression in prokaryotes and unicellular eukaryotes. Recent evidence has shown that translational selection is weakly operative in highly expressed genes in human and other vertebrates. However, it remains unclear whether translational selection acts differentially on human genes depending on their expression patterns.Results: Here we report that human housekeeping (HK) genes that are strictly defined as genes that are expressed ubiquitously and consistently in most or all tissues, are under stronger translational selection.Conclusions: These observations clearly show that translational selection is also closely associated with expression pattern. Our results suggest that human HK genes are more efficiently and/or accurately translated into proteins, which will inevitably open up a new understanding of HK genes and the regulation of gene expression.Reviewers: This article was reviewed by Yuan Yuan, Baylor College of Medicine; Han Liang, University of Texas MD Anderson Cancer Center (nominated by Dr Laura Landweber) Eugene Koonin, NCBI, NLM, NIH, United States of America Sandor Pongor, International Centre for Genetic Engineering and biotechnology (ICGEB), Italy. © 2014 Ma et al.; licensee BioMed Central Ltd.

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

Science.gov (United States)

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.

Directory of Open Access Journals (Sweden)

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.

Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning

Directory of Open Access Journals (Sweden)

Tsatsoulis Costas

2010-05-01

Full Text Available Abstract Background There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives us insight into a gene's functionality by informing us how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, we analyzed if we could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. Results We performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the gene's location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours should be included around each gene to provide better classification rules. Our results show that by just incorporating neighbour information from each gene's two-nearest neighbours, the percentage of correctly classified genes to their correct Gene Ontology Slim term for each ontology reaches over 80% with high accuracy (reflected in F-measures over 0.80 of the classification rules produced. Conclusions We confirmed that in classifying genes to their correct Gene Ontology Slim term, the inclusion of neighbour information from those genes is beneficial. Knowing the location of a gene and the Gene Ontology Slim information from neighbouring genes gives us insight into that gene's functionality. This benefit is seen by just including information from a gene's two-nearest neighbouring genes.

Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

Directory of Open Access Journals (Sweden)

Ueki Masao

2012-05-01

Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects

Science.gov (United States)

Marigorta, Urko M.; Gibson, Greg

2014-01-01

The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05–2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits. PMID:25101110

Locating disease genes using Bayesian variable selection with the Haseman-Elston method

Directory of Open Access Journals (Sweden)

He Qimei

2003-12-01

Full Text Available Abstract Background We applied stochastic search variable selection (SSVS, a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors. This allows SSVS to search in the model space more efficiently and avoid the less likely models. Results In applying SSVS, instead of looking at the posterior distribution of each of the candidate models, which is sensitive to the setting of the prior, we ranked the candidate variables (markers according to their marginal posterior probability, which was shown to be more robust to the prior. Compared with traditional methods that consider one marker at a time, our method considers all markers simultaneously and obtains more favorable results. Conclusions We showed that SSVS is a powerful method for identifying linked markers using the Haseman-Elston method, even for weak effects. SSVS is very effective because it does a smart search over the entire model space.

Cell differentiation by interaction of two HMG-box proteins: Mat1-Mc activates M cell-specific genes in S.pombe by recruiting the ubiquitous transcription factor Ste11 to weak binding sites

DEFF Research Database (Denmark)

Kjaerulff, S; Dooijes, D; Clevers, H

1997-01-01

The Schizosaccharomyces pombe mfm1 gene is expressed in an M cell-specific fashion. This regulation requires two HMG-box proteins: the ubiquitous Ste11 transcription factor and the M cell-controlling protein Mat1-Mc. Here we report that the mfm1 promoter contains a single, weak Stell-binding site...... where we could not detect Mat1-Mc in the resulting protein-DNA complex. When we changed a single base in the mfm1 TR-box, such that it resembled those boxes found in ubiquitously expressed genes, Ste11 binding was enhanced, and in vivo the mfm1 gene also became expressed in P cells where Mat1-Mc...

Simulated Microgravity Regulates Gene Transcript Profiles of 2T3 Preosteoblasts: Comparison of the Random Positioning Machine and the Rotating Wall Vessel Bioreactor

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Patel, Mamta J.; Liu, Wenbin; Sykes, Michelle C.; Ward, Nancy E.; Risin, Semyon A.; Risin, Diana; Hanjoong, Jo

2007-01-01

Microgravity of spaceflight induces bone loss due in part to decreased bone formation by osteoblasts. We have previously examined the microgravity-induced changes in gene expression profiles in 2T3 preosteoblasts using the Random Positioning Machine (RPM) to simulate microgravity conditions. Here, we hypothesized that exposure of preosteoblasts to an independent microgravity simulator, the Rotating Wall Vessel (RWV), induces similar changes in differentiation and gene transcript profiles, resulting in a more confined list of gravi-sensitive genes that may play a role in bone formation. In comparison to static 1g controls, exposure of 2T3 cells to RWV for 3 days inhibited alkaline phosphatase activity, a marker of differentiation, and downregulated 61 genes and upregulated 45 genes by more than two-fold as shown by microarray analysis. The microarray results were confirmed with real time PCR for downregulated genes osteomodulin, bone morphogenic protein 4 (BMP4), runx2, and parathyroid hormone receptor 1. Western blot analysis validated the expression of three downregulated genes, BMP4, peroxiredoxin IV, and osteoglycin, and one upregulated gene peroxiredoxin I. Comparison of the microarrays from the RPM and the RWV studies identified 14 gravi-sensitive genes that changed in the same direction in both systems. Further comparison of our results to a published database showing gene transcript profiles of mechanically loaded mouse tibiae revealed 16 genes upregulated by the loading that were shown to be downregulated by RWV and RPM. These mechanosensitive genes identified by the comparative studies may provide novel insights into understanding the mechanisms regulating bone formation and potential targets of countermeasure against decreased bone formation both in astronauts and in general patients with musculoskeletal disorders.

Current approaches to gene regulatory network modelling

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Brazma Alvis

2007-09-01

Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

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Chen Xin

2012-10-01

Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable

Gene Therapy in Fanconi Anemia: A Matter of Time, Safety and Gene Transfer Tool Efficiency.

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Verhoeyen, Els; Roman-Rodriguez, Francisco Jose; Cosset, Francois-Loic; Levy, Camille; Rio, Paula

2017-01-01

Fanconi anemia (FA) is a rare genetic syndrome characterized by progressive marrow failure. Gene therapy by infusion of FA-corrected autologous hematopoietic stem cells (HSCs) may offer a potential cure since it is a monogenetic disease with mutations in the FANC genes, coding for DNA repair enzymes [1]. However, the collection of hCD34+-cells in FA patients implies particular challenges because of the reduced numbers of progenitor cells present in their bone marrow (BM) [2] or mobilized peripheral blood [3-5]. In addition, the FA genetic defect fragilizes the HSCs [6]. These particular features might explain why the first clinical trials using murine leukemia virus derived retroviral vectors conducted for FA failed to show engraftment of corrected cells. The gene therapy field is now moving towards the use of lentiviral vectors (LVs) evidenced by recent succesful clinical trials for the treatment of patients suffering from adrenoleukodystrophy (ALD) [7], β-thalassemia [8], metachromatic leukodystrophy [9] and Wiskott-Aldrich syndrome [10]. LV trials for X-linked severe combined immunodificiency and Fanconi anemia (FA) defects were recently initiated [11, 12]. Fifteen years of preclinical studies using different FA mouse models and in vitro research allowed us to find the weak points in the in vitro culture and transduction conditions, which most probably led to the initial failure of FA HSC gene therapy. In this review, we will focus on the different obstacles, unique to FA gene therapy, and how they have been overcome through the development of optimized protocols for FA HSC culture and transduction and the engineering of new gene transfer tools for FA HSCs. These combined advances in the field hopefully will allow the correction of the FA hematological defect in the near future. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Information dimension analysis of bacterial essential and nonessential genes based on chaos game representation

International Nuclear Information System (INIS)

Zhou, Qian; Yu, Yong-ming

2014-01-01

Essential genes are indispensable for the survival of an organism. Investigating features associated with gene essentiality is fundamental to the prediction and identification of the essential genes. Selecting features associated with gene essentiality is fundamental to predict essential genes with computational techniques. We use fractal theory to make comparative analysis of essential and nonessential genes in bacteria. The information dimensions of essential genes and nonessential genes available in the DEG database for 27 bacteria are calculated based on their gene chaos game representations (CGRs). It is found that weak positive linear correlation exists between information dimension and gene length. Moreover, for genes of similar length, the average information dimension of essential genes is larger than that of nonessential genes. This indicates that essential genes show less regularity and higher complexity than nonessential genes. Our results show that for bacterium with a similar number of essential genes and nonessential genes, the CGR information dimension is helpful for the classification of essential genes and nonessential genes. Therefore, the gene CGR information dimension is very probably a useful gene feature for a genetic algorithm predicting essential genes. (paper)

Differential Gene Expression of Longan Under Simulated Acid Rain Stress.

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Zheng, Shan; Pan, Tengfei; Ma, Cuilan; Qiu, Dongliang

2017-05-01

Differential gene expression profile was studied in Dimocarpus longan Lour. in response to treatments of simulated acid rain with pH 2.5, 3.5, and a control (pH 5.6) using differential display reverse transcription polymerase chain reaction (DDRT-PCR). Results showed that mRNA differential display conditions were optimized to find an expressed sequence tag (EST) related with acid rain stress. The potential encoding products had 80% similarity with a transcription initiation factor IIF of Gossypium raimondii and 81% similarity with a protein product of Theobroma cacao. This fragment is the transcription factor activated by second messenger substances in longan leaves after signal perception of acid rain.

Gene regulatory networks elucidating huanglongbing disease mechanisms.

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Federico Martinelli

Full Text Available Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas, especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation, sucrose metabolism (upregulation, and starch biosynthesis (upregulation. In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70 was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur.

MAGMA: generalized gene-set analysis of GWAS data.

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de Leeuw, Christiaan A; Mooij, Joris M; Heskes, Tom; Posthuma, Danielle

2015-04-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

Selection for the compactness of highly expressed genes in Gallus gallus

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Zhou Ming

2010-05-01

(n = 1105, and compared the first intron length and the average intron length between highly expressed genes (top 5% expressed genes and weakly expressed genes (bottom 5% expressed genes. We found that the first intron length and the average intron length in highly expressed genes are not different from that in weakly expressed genes. We also made a comparison between ubiquitously expressed genes and narrowly expressed somatic genes with similar expression levels. Our data demonstrated that ubiquitously expressed genes are less compact than narrowly expressed genes with the similar expression levels. Obviously, these observations can not be explained by mutational bias hypotheses either. We also found that the significant trend between genes' compactness and expression level could not be affected by local mutational biases. We argued that the selection of economy model is most likely one to explain the relationship between gene expression and gene characteristics in chicken genome. Conclusion Natural selection appears to favor the compactness of highly expressed genes in chicken genome. This observation can be explained by the selection of economy model. Reviewers This article was reviewed by Dr. Gavin Huttley, Dr. Liran Carmel (nominated by Dr. Eugene V. Koonin and Dr. Araxi Urrutia (nominated by Dr. Laurence D. Hurst.

IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering.

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Wu, Lingxiang; Chen, Xiujie; Zhang, Denan; Zhang, Wubing; Liu, Lei; Ma, Hongzhe; Yang, Jingbo; Xie, Hongbo; Liu, Bo; Jin, Qing

2016-01-01

Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample.

Inferring gene networks from discrete expression data

KAUST Repository

Zhang, L.

2013-07-18

The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.

A Generalized Approach for Measuring Relationships Among Genes.

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Wang, Lijun; Ahsan, Md Asif; Chen, Ming

2017-07-21

Several methods for identifying relationships among pairs of genes have been developed. In this article, we present a generalized approach for measuring relationships between any pairs of genes, which is based on statistical prediction. We derive two particular versions of the generalized approach, least squares estimation (LSE) and nearest neighbors prediction (NNP). According to mathematical proof, LSE is equivalent to the methods based on correlation; and NNP is approximate to one popular method called the maximal information coefficient (MIC) according to the performances in simulations and real dataset. Moreover, the approach based on statistical prediction can be extended from two-genes relationships to multi-genes relationships. This application would help to identify relationships among multi-genes.

Sexy gene conversions: locating gene conversions on the X-chromosome.

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Lawson, Mark J; Zhang, Liqing

2009-08-01

Gene conversion can have a profound impact on both the short- and long-term evolution of genes and genomes. Here, we examined the gene families that are located on the X-chromosomes of human (Homo sapiens), chimpanzee (Pan troglodytes), mouse (Mus musculus) and rat (Rattus norvegicus) for evidence of gene conversion. We identified seven gene families (WD repeat protein family, Ferritin Heavy Chain family, RAS-related Protein RAB-40 family, Diphosphoinositol polyphosphate phosphohydrolase family, Transcription Elongation Factor A family, LDOC1-related family, Zinc Finger Protein ZIC, and GLI family) that show evidence of gene conversion. Through phylogenetic analyses and synteny evidence, we show that gene conversion has played an important role in the evolution of these gene families and that gene conversion has occurred independently in both primates and rodents. Comparing the results with those of two gene conversion prediction programs (GENECONV and Partimatrix), we found that both GENECONV and Partimatrix have very high false negative rates (i.e. failed to predict gene conversions), which leads to many undetected gene conversions. The combination of phylogenetic analyses with physical synteny evidence exhibits high resolution in the detection of gene conversions.

EXPRESSION OF SOME ANTIOXIDANT GENES IN SUNFLOWER INFECTED WITH BROOMRAPE

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Tatiana Shestakova

2015-11-01

Full Text Available Expression levels of ROS-scavenging genes (MnSODI, APX3 and AOX1A in leaves (R5 stage; 90 days after sowing of seven sunflower genotypes infected with three Orobanche cumana Wallr. populations were assayed in plants with/without broomrape aerial shoots and control group. Five lines were highly susceptible to all three populations. MS-2161A was resistant and MS-2039A was tolerant to broomrape populations. The expression of studied genes was much more altered in highly susceptible genotypes than in those resistant. Significant differences in number of cases of ROS-scavenging genes with modified transcriptional activity in infected and non-symptomatic plants were not ascertained. The transcriptional activity of MnSODI, APX3 and AOX1A genes was weakly influenced by infection with broomrape (67 % cases or was down-regulated (24 % cases. Some up-regulation cases (9 % for MnSODI (MS-2039 and AOX1A gene (MS-2067 were revealed. AOX1A was the most responsive gene, especially when infection was produced by population from Anenii Noi. 

Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease

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Maria V. Fernández

2018-04-01

Full Text Available Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families (N = 1,235 with late-onset Alzheimer disease (LOAD. After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, and MAS1L as candidate genes for familial LOAD.

PPARγ partial agonist GQ-16 strongly represses a subset of genes in 3T3-L1 adipocytes

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Milton, Flora Aparecida [Faculdade de Ciências da Saúde, Laboratório de Farmacologia Molecular, Universidade de Brasília (Brazil); Genomic Medicine, Houston Methodist Research Institute, Houston, TX (United States); Cvoro, Aleksandra [Genomic Medicine, Houston Methodist Research Institute, Houston, TX (United States); Amato, Angelica A. [Faculdade de Ciências da Saúde, Laboratório de Farmacologia Molecular, Universidade de Brasília (Brazil); Sieglaff, Douglas H.; Filgueira, Carly S.; Arumanayagam, Anithachristy Sigamani [Genomic Medicine, Houston Methodist Research Institute, Houston, TX (United States); Caro Alves de Lima, Maria do; Rocha Pitta, Ivan [Laboratório de Planejamento e Síntese de Fármacos – LPSF, Universidade Federal de Pernambuco (Brazil); Assis Rocha Neves, Francisco de [Faculdade de Ciências da Saúde, Laboratório de Farmacologia Molecular, Universidade de Brasília (Brazil); Webb, Paul, E-mail: pwebb@HoustonMethodist.org [Genomic Medicine, Houston Methodist Research Institute, Houston, TX (United States)

2015-08-28

Thiazolidinediones (TZDs) are peroxisome proliferator-activated receptor gamma (PPARγ) agonists that improve insulin resistance but trigger side effects such as weight gain, edema, congestive heart failure and bone loss. GQ-16 is a PPARγ partial agonist that improves glucose tolerance and insulin sensitivity in mouse models of obesity and diabetes without inducing weight gain or edema. It is not clear whether GQ-16 acts as a partial agonist at all PPARγ target genes, or whether it displays gene-selective actions. To determine how GQ-16 influences PPARγ activity on a gene by gene basis, we compared effects of rosiglitazone (Rosi) and GQ-16 in mature 3T3-L1 adipocytes using microarray and qRT-PCR. Rosi changed expression of 1156 genes in 3T3-L1, but GQ-16 only changed 89 genes. GQ-16 generally showed weak effects upon Rosi induced genes, consistent with partial agonist actions, but a subset of modestly Rosi induced and strongly repressed genes displayed disproportionately strong GQ-16 responses. PPARγ partial agonists MLR24 and SR1664 also exhibit disproportionately strong effects on transcriptional repression. We conclude that GQ-16 displays a continuum of weak partial agonist effects but efficiently represses some negatively regulated PPARγ responsive genes. Strong repressive effects could contribute to physiologic actions of GQ-16. - Highlights: • GQ-16 is an insulin sensitizing PPARγ ligand with reduced harmful side effects. • GQ-16 displays a continuum of weak partial agonist activities at PPARγ-induced genes. • GQ-16 exerts strong repressive effects at a subset of genes. • These inhibitor actions should be evaluated in models of adipose tissue inflammation.

PPARγ partial agonist GQ-16 strongly represses a subset of genes in 3T3-L1 adipocytes

International Nuclear Information System (INIS)

Milton, Flora Aparecida; Cvoro, Aleksandra; Amato, Angelica A.; Sieglaff, Douglas H.; Filgueira, Carly S.; Arumanayagam, Anithachristy Sigamani; Caro Alves de Lima, Maria do; Rocha Pitta, Ivan; Assis Rocha Neves, Francisco de; Webb, Paul

2015-01-01

Thiazolidinediones (TZDs) are peroxisome proliferator-activated receptor gamma (PPARγ) agonists that improve insulin resistance but trigger side effects such as weight gain, edema, congestive heart failure and bone loss. GQ-16 is a PPARγ partial agonist that improves glucose tolerance and insulin sensitivity in mouse models of obesity and diabetes without inducing weight gain or edema. It is not clear whether GQ-16 acts as a partial agonist at all PPARγ target genes, or whether it displays gene-selective actions. To determine how GQ-16 influences PPARγ activity on a gene by gene basis, we compared effects of rosiglitazone (Rosi) and GQ-16 in mature 3T3-L1 adipocytes using microarray and qRT-PCR. Rosi changed expression of 1156 genes in 3T3-L1, but GQ-16 only changed 89 genes. GQ-16 generally showed weak effects upon Rosi induced genes, consistent with partial agonist actions, but a subset of modestly Rosi induced and strongly repressed genes displayed disproportionately strong GQ-16 responses. PPARγ partial agonists MLR24 and SR1664 also exhibit disproportionately strong effects on transcriptional repression. We conclude that GQ-16 displays a continuum of weak partial agonist effects but efficiently represses some negatively regulated PPARγ responsive genes. Strong repressive effects could contribute to physiologic actions of GQ-16. - Highlights: • GQ-16 is an insulin sensitizing PPARγ ligand with reduced harmful side effects. • GQ-16 displays a continuum of weak partial agonist activities at PPARγ-induced genes. • GQ-16 exerts strong repressive effects at a subset of genes. • These inhibitor actions should be evaluated in models of adipose tissue inflammation

Optimal Reference Genes for Gene Expression Normalization in Trichomonas vaginalis

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dos Santos, Odelta; de Vargas Rigo, Graziela; Frasson, Amanda Piccoli; Macedo, Alexandre José; Tasca, Tiana

2015-01-01

Trichomonas vaginalis is the etiologic agent of trichomonosis, the most common non-viral sexually transmitted disease worldwide. This infection is associated with several health consequences, including cervical and prostate cancers and HIV acquisition. Gene expression analysis has been facilitated because of available genome sequences and large-scale transcriptomes in T. vaginalis, particularly using quantitative real-time polymerase chain reaction (qRT-PCR), one of the most used methods for molecular studies. Reference genes for normalization are crucial to ensure the accuracy of this method. However, to the best of our knowledge, a systematic validation of reference genes has not been performed for T. vaginalis. In this study, the transcripts of nine candidate reference genes were quantified using qRT-PCR under different cultivation conditions, and the stability of these genes was compared using the geNorm and NormFinder algorithms. The most stable reference genes were α-tubulin, actin and DNATopII, and, conversely, the widely used T. vaginalis reference genes GAPDH and β-tubulin were less stable. The PFOR gene was used to validate the reliability of the use of these candidate reference genes. As expected, the PFOR gene was upregulated when the trophozoites were cultivated with ferrous ammonium sulfate when the DNATopII, α-tubulin and actin genes were used as normalizing gene. By contrast, the PFOR gene was downregulated when the GAPDH gene was used as an internal control, leading to misinterpretation of the data. These results provide an important starting point for reference gene selection and gene expression analysis with qRT-PCR studies of T. vaginalis. PMID:26393928

Patenting human genes: Chinese academic articles' portrayal of gene patents.

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Du, Li

2018-04-24

The patenting of human genes has been the subject of debate for decades. While China has gradually come to play an important role in the global genomics-based testing and treatment market, little is known about Chinese scholars' perspectives on patent protection for human genes. A content analysis of academic literature was conducted to identify Chinese scholars' concerns regarding gene patents, including benefits and risks of patenting human genes, attitudes that researchers hold towards gene patenting, and any legal and policy recommendations offered for the gene patent regime in China. 57.2% of articles were written by law professors, but scholars from health sciences, liberal arts, and ethics also participated in discussions on gene patent issues. While discussions of benefits and risks were relatively balanced in the articles, 63.5% of the articles favored gene patenting in general and, of the articles (n = 41) that explored gene patents in the Chinese context, 90.2% supported patent protections for human genes in China. The patentability of human genes was discussed in 33 articles, and 75.8% of these articles reached the conclusion that human genes are patentable. Chinese scholars view the patent regime as an important legal tool to protect the interests of inventors and inventions as well as the genetic resources of China. As such, many scholars support a gene patent system in China. These attitudes towards gene patents remain unchanged following the court ruling in the Myriad case in 2013, but arguments have been raised about the scope of gene patents, in particular that the increasing numbers of gene patents may negatively impact public health in China.

Research progress in machine learning methods for gene-gene interaction detection.

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Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

2018-03-20

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

Gene Circuit Analysis of the Terminal Gap Gene huckebein

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Ashyraliyev, Maksat; Siggens, Ken; Janssens, Hilde; Blom, Joke; Akam, Michael; Jaeger, Johannes

2009-01-01

The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb) on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb) domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network. PMID:19876378

Gene expression

International Nuclear Information System (INIS)

Hildebrand, C.E.; Crawford, B.D.; Walters, R.A.; Enger, M.D.

1983-01-01

We prepared probes for isolating functional pieces of the metallothionein locus. The probes enabled a variety of experiments, eventually revealing two mechanisms for metallothionein gene expression, the order of the DNA coding units at the locus, and the location of the gene site in its chromosome. Once the switch regulating metallothionein synthesis was located, it could be joined by recombinant DNA methods to other, unrelated genes, then reintroduced into cells by gene-transfer techniques. The expression of these recombinant genes could then be induced by exposing the cells to Zn 2+ or Cd 2+ . We would thus take advantage of the clearly defined switching properties of the metallothionein gene to manipulate the expression of other, perhaps normally constitutive, genes. Already, despite an incomplete understanding of how the regulatory switch of the metallothionein locus operates, such experiments have been performed successfully

Effect of misspecification of gene frequency on the two-point LOD score.

Science.gov (United States)

Pal, D K; Durner, M; Greenberg, D A

2001-11-01

In this study, we used computer simulation of simple and complex models to ask: (1) What is the penalty in evidence for linkage when the assumed gene frequency is far from the true gene frequency? (2) If the assumed model for gene frequency and inheritance are misspecified in the analysis, can this lead to a higher maximum LOD score than that obtained under the true parameters? Linkage data simulated under simple dominant, recessive, dominant and recessive with reduced penetrance, and additive models, were analysed assuming a single locus with both the correct and incorrect dominance model and assuming a range of different gene frequencies. We found that misspecifying the analysis gene frequency led to little penalty in maximum LOD score in all models examined, especially if the assumed gene frequency was lower than the generating one. Analysing linkage data assuming a gene frequency of the order of 0.01 for a dominant gene, and 0.1 for a recessive gene, appears to be a reasonable tactic in the majority of realistic situations because underestimating the gene frequency, even when the true gene frequency is high, leads to little penalty in the LOD score.

Environmental confounding in gene-environment interaction studies.

Science.gov (United States)

Vanderweele, Tyler J; Ko, Yi-An; Mukherjee, Bhramar

2013-07-01

We show that, in the presence of uncontrolled environmental confounding, joint tests for the presence of a main genetic effect and gene-environment interaction will be biased if the genetic and environmental factors are correlated, even if there is no effect of either the genetic factor or the environmental factor on the disease. When environmental confounding is ignored, such tests will in fact reject the joint null of no genetic effect with a probability that tends to 1 as the sample size increases. This problem with the joint test vanishes under gene-environment independence, but it still persists if estimating the gene-environment interaction parameter itself is of interest. Uncontrolled environmental confounding will bias estimates of gene-environment interaction parameters even under gene-environment independence, but it will not do so if the unmeasured confounding variable itself does not interact with the genetic factor. Under gene-environment independence, if the interaction parameter without controlling for the environmental confounder is nonzero, then there is gene-environment interaction either between the genetic factor and the environmental factor of interest or between the genetic factor and the unmeasured environmental confounder. We evaluate several recently proposed joint tests in a simulation study and discuss the implications of these results for the conduct of gene-environment interaction studies.

Genes from scratch--the evolutionary fate of de novo genes.

Science.gov (United States)

Schlötterer, Christian

2015-04-01

Although considered an extremely unlikely event, many genes emerge from previously noncoding genomic regions. This review covers the entire life cycle of such de novo genes. Two competing hypotheses about the process of de novo gene birth are discussed as well as the high death rate of de novo genes. Despite the high death rate, some de novo genes are retained and remain functional, even in distantly related species, through their integration into gene networks. Further studies combining gene expression with ribosome profiling in multiple populations across different species will be instrumental for an improved understanding of the evolutionary processes operating on de novo genes. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

Gene expression of osteogenic factors following gene therapy in mandibular lengthening.

Science.gov (United States)

Wu, Guoping; Zhou, Bin; Hu, Chunbing; Li, Shaolan

2015-03-01

This study investigated the effect of gene therapy on the expression of osteogenic mediators in mandibular distraction osteogenesis rabbits. Bilateral mandibular osteotomies were performed in 45 New-Zealand rabbits. After a latency of 3 days, the mandibles were elongated using distractors with a rate of 0.8 mm/d for 7 days. After the completion of distraction, the rabbits were randomly divided into 5 groups: 2 μg (0.1 μg/μL) of recombinant plasmid pIRES-hVEGF165-hBMP-2, recombinant plasmid pIRES-hBMP2, recombinant plasmid pIRES-hVEGF165, pIRES, and the same volume of normal saline were injected into the distraction gap of groups A, B, C, D, and E, respectively, followed by electroporation. Three animals were killed at the 7th, 14th, and 28th day after gene transfected in different groups, respectively. The lengthened mandibles were harvested and processed for immunohistochemical examinations; the mean optic densities (MODs) and integral optical density of bone morphogenetic protein (BMP-2) and transforming growth factor β1 (TGF-β1)-positive cells were measured by CMIAS-2001A computerized image analyzer. The data were analyzed with SPSS (SPSS Inc, Chicago, IL). Bone morphogenetic protein 2 and TGF-β1 staining was mainly located in inflammatory cells, monocytes, fibroblasts, osteoblasts, osteocytes, and chondrocytes in the distraction zones. Their strongest expression reached to the peak at the seventh day and decreased at the 14th day of consolidation stage; at the 28th day, they expressed weakly. Image analysis results show that, at the seventh day, the expression of BMP-2 in group B (0.26 ± 0.03, 0.36 ± 0.02) was the strongest; there was significant difference among them (P < 0.01), whereas the expression of TGF-β1 in group C (0.38 ± 0.06, 1.05 ± 0.19) is strongest followed by group A (0.34 ± 0.05, 0.95 ± 0.16) and B (0.33 ± 0.07, 0.90 ± 0.19). At every time point, the level of expression of BMP-2 and TGF-β1 in gene therapy groups (groups A, B, and

Emerging strategies for cell and gene therapy of the muscular dystrophies

OpenAIRE

Muir, Lindsey A.; Chamberlain, Jeffrey S.

2009-01-01

The muscular dystrophies are a heterogeneous group of over 40 disorders that are characterised by muscle weakness and wasting. The most common are Duchenne muscular dystrophy and Becker muscular dystrophy, which result from mutations within the gene encoding dystrophin; myotonic dystrophy type 1, which results from an expanded trinucleotide repeat in the myotonic dystrophy protein kinase gene; and facioscapulohumeral dystrophy, which is associated with contractions in the subtelomeric region ...

Gene Duplicability of Core Genes Is Highly Consistent across All Angiosperms.

Science.gov (United States)

Li, Zhen; Defoort, Jonas; Tasdighian, Setareh; Maere, Steven; Van de Peer, Yves; De Smet, Riet

2016-02-01

Gene duplication is an important mechanism for adding to genomic novelty. Hence, which genes undergo duplication and are preserved following duplication is an important question. It has been observed that gene duplicability, or the ability of genes to be retained following duplication, is a nonrandom process, with certain genes being more amenable to survive duplication events than others. Primarily, gene essentiality and the type of duplication (small-scale versus large-scale) have been shown in different species to influence the (long-term) survival of novel genes. However, an overarching view of "gene duplicability" is lacking, mainly due to the fact that previous studies usually focused on individual species and did not account for the influence of genomic context and the time of duplication. Here, we present a large-scale study in which we investigated duplicate retention for 9178 gene families shared between 37 flowering plant species, referred to as angiosperm core gene families. For most gene families, we observe a strikingly consistent pattern of gene duplicability across species, with gene families being either primarily single-copy or multicopy in all species. An intermediate class contains gene families that are often retained in duplicate for periods extending to tens of millions of years after whole-genome duplication, but ultimately appear to be largely restored to singleton status, suggesting that these genes may be dosage balance sensitive. The distinction between single-copy and multicopy gene families is reflected in their functional annotation, with single-copy genes being mainly involved in the maintenance of genome stability and organelle function and multicopy genes in signaling, transport, and metabolism. The intermediate class was overrepresented in regulatory genes, further suggesting that these represent putative dosage-balance-sensitive genes. © 2016 American Society of Plant Biologists. All rights reserved.

Hybrid stochastic simplifications for multiscale gene networks

Directory of Open Access Journals (Sweden)

Debussche Arnaud

2009-09-01

Full Text Available Abstract Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion 123 which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

DEFF Research Database (Denmark)

Liu, Gang; Lee, Seunggeun; Lee, Alice W

2018-01-01

test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides power gain compared to the standard logistic regression analysis and better control of Type I error when compared to the analysis......There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances the power for testing multiplicative interaction in case......-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated Type I error in the corresponding tests can occur. This paper extends the empirical Bayes (EB) approach previously developed for multiplicative interaction that trades off between bias and efficiency...

Advances in study of reporter gene imaging for monitoring gene therapy

International Nuclear Information System (INIS)

Mu Chuanjie; Zhou Jiwen

2003-01-01

To evaluate the efficiency of gene therapy, it is requisite to monitor localization and expression of the therapeutic gene in vivo. Monitoring expression of reporter gene using radionuclide reporter gene technique is the best method. Adenoviral vectors expressing reporter gene are constructed using gene fusion, bicistronic, double promoter or bidirectional transcriptional recombination techniques, and transferred into target cells and tissues, then injected radiolabeled reporter probes which couple to the reporter genes. The reporter genes can be imaged invasively, repeatedly, quantitatively with γ-camera, PET and SPECT. Recently, several reporter gene and reporter probe systems have been used in studies of gene therapy. The part of them has been used for clinic trials

Construction of the model for the Genetic Analysis Workshop 14 simulated data: genotype-phenotype relationships, gene interaction, linkage, association, disequilibrium, and ascertainment effects for a complex phenotype.

Science.gov (United States)

Greenberg, David A; Zhang, Junying; Shmulewitz, Dvora; Strug, Lisa J; Zimmerman, Regina; Singh, Veena; Marathe, Sudhir

2005-12-30

The Genetic Analysis Workshop 14 simulated dataset was designed 1) To test the ability to find genes related to a complex disease (such as alcoholism). Such a disease may be given a variety of definitions by different investigators, have associated endophenotypes that are common in the general population, and is likely to be not one disease but a heterogeneous collection of clinically similar, but genetically distinct, entities. 2) To observe the effect on genetic analysis and gene discovery of a complex set of gene x gene interactions. 3) To allow comparison of microsatellite vs. large-scale single-nucleotide polymorphism (SNP) data. 4) To allow testing of association to identify the disease gene and the effect of moderate marker x marker linkage disequilibrium. 5) To observe the effect of different ascertainment/disease definition schemes on the analysis. Data was distributed in two forms. Data distributed to participants contained about 1,000 SNPs and 400 microsatellite markers. Internet-obtainable data consisted of a finer 10,000 SNP map, which also contained data on controls. While disease characteristics and parameters were constant, four "studies" used varying ascertainment schemes based on differing beliefs about disease characteristics. One of the studies contained multiplex two- and three-generation pedigrees with at least four affected members. The simulated disease was a psychiatric condition with many associated behaviors (endophenotypes), almost all of which were genetic in origin. The underlying disease model contained four major genes and two modifier genes. The four major genes interacted with each other to produce three different phenotypes, which were themselves heterogeneous. The population parameters were calibrated so that the major genes could be discovered by linkage analysis in most datasets. The association evidence was more difficult to calibrate but was designed to find statistically significant association in 50% of datasets. We also

Down-Regulation of Gene Expression by RNA-Induced Gene Silencing

Science.gov (United States)

Travella, Silvia; Keller, Beat

Down-regulation of endogenous genes via post-transcriptional gene silencing (PTGS) is a key to the characterization of gene function in plants. Many RNA-based silencing mechanisms such as post-transcriptional gene silencing, co-suppression, quelling, and RNA interference (RNAi) have been discovered among species of different kingdoms (plants, fungi, and animals). One of the most interesting discoveries was RNAi, a sequence-specific gene-silencing mechanism initiated by the introduction of double-stranded RNA (dsRNA), homologous in sequence to the silenced gene, which triggers degradation of mRNA. Infection of plants with modified viruses can also induce RNA silencing and is referred to as virus-induced gene silencing (VIGS). In contrast to insertional mutagenesis, these emerging new reverse genetic approaches represent a powerful tool for exploring gene function and for manipulating gene expression experimentally in cereal species such as barley and wheat. We examined how RNAi and VIGS have been used to assess gene function in barley and wheat, including molecular mechanisms involved in the process and available methodological elements, such as vectors, inoculation procedures, and analysis of silenced phenotypes.

Allelic polymorphism of Makoei sheep leptin gene identified by ...

African Journals Online (AJOL)

use

2011-12-05

Dec 5, 2011 ... Lord et al., 1998) have shed light on the influence of leptin on both the .... A weak correlation between leptin serum levels and cow body condition ... Detection of polymorphisms in the ovine leptin (LEP) gene: .... Signals that.

Discovering implicit entity relation with the gene-citation-gene network.

Directory of Open Access Journals (Sweden)

Min Song

Full Text Available In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner.

Detecting Horizontal Gene Transfer between Closely Related Taxa.

Directory of Open Access Journals (Sweden)

Orit Adato

2015-10-01

Full Text Available Horizontal gene transfer (HGT, the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive. We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM. Using CRM, the algorithm assigns a confidence score based on "unusual" sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set. When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.

Reduced rates of gene loss, gene silencing, and gene mutation in Dnmt1-deficient embryonic stem cells

NARCIS (Netherlands)

Chan, M.F.; van Amerongen, R.; Nijjar, T.; Cuppen, E.; Jones, P.A.; Laird, P.W.

2001-01-01

Tumor suppressor gene inactivation is a crucial event in oncogenesis. Gene inactivation mechanisms include events resulting in loss of heterozygosity (LOH), gene mutation, and transcriptional silencing. The contribution of each of these different pathways varies among tumor suppressor genes and by

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

Science.gov (United States)

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.

Inferring Phylogenetic Networks from Gene Order Data

Directory of Open Access Journals (Sweden)

Alexey Anatolievich Morozov

2013-01-01

Full Text Available Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary, sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm. Binary encoding can also be useful, but only when the methods mentioned above cannot be used.

Structural and functional characterization of the exonuclease I (sbcB) gene and gene product from Escherichia coli and a Markov chain analysis of DNA sequences

International Nuclear Information System (INIS)

Phillips, G.J.

1987-01-01

The nucleotide sequence for the structural gene for exonuclease I (sbcB) from Escherichia coli was determined. Two putative promotes for this gene were identified and were predicted to have weak transcription initiation activity. In addition, the sbcB coding region contains many non-optimal codons. These observations are consistent with the suggestions that sbcB is a poorly expressed gene. Several mutant exonuclease I genes were cloned onto pBR322 plasmids. These genes represented both sbcB and xonA mutation. One of the xonA mutation (xonA6) was associated with a 1.2-kb insertion of an IS-30 related mobile genetic element in the 3'-region of the gene. Two of the mutations (xonA2 and xonA6) encode unstable polypeptides. Determination of exonucleolytic activity on single-stranded DNA from cell extracts containing each of the cloned mutant genes revealed no correlation between residual exonucleolytic activity and the pheno-types of sbcB and xonA mutants. A proposal that the exonuclease I protein contains an additional activity besides its ability to degrade single-stranded DNA is presented. Characterization of E. coli strains which overproduce exonuclease I showed increased sensitivity to UV irradiation

Rooted triple consensus and anomalous gene trees

Directory of Open Access Journals (Sweden)

Schmidt Heiko A

2008-04-01

Full Text Available Abstract Background Anomalous gene trees (AGTs are gene trees with a topology different from a species tree that are more probable to observe than congruent gene trees. In this paper we propose a rooted triple approach to finding the correct species tree in the presence of AGTs. Results Based on simulated data we show that our method outperforms the extended majority rule consensus strategy, while still resolving the species tree. Applying both methods to a metazoan data set of 216 genes, we tested whether AGTs substantially interfere with the reconstruction of the metazoan phylogeny. Conclusion Evidence of AGTs was not found in this data set, suggesting that erroneously reconstructed gene trees are the most significant challenge in the reconstruction of phylogenetic relationships among species with current data. The new method does however rule out the erroneous reconstruction of deep or poorly resolved splits in the presence of lineage sorting.

Simulation of weak and strong Langmuir collapse regimes

International Nuclear Information System (INIS)

Hadzievski, L.R.; Skoric, M.M.; Kono, M.; Sato, T.

1998-01-01

In order to check the validity of the self-similar solutions and the existence of weak and strong collapse regimes, direct two dimensional simulation of the time evolution of a Langmuir soliton instability is performed. Simulation is based on the Zakharov model of strong Langmuir turbulence in a weakly magnetized plasma accounting for the full ion dynamics. For parameters considered, agreement with self-similar dynamics of the weak collapse type is found with no evidence of the strong Langmuir collapse. (author)

Norrie disease gene is distinct from the monoamine oxidase genes

OpenAIRE

Sims, Katherine B.; Ozelius, Laurie; Corey, Timothy; Rinehart, William B.; Liberfarb, Ruth; Haines, Jonathan; Chen, Wei Jane; Norio, Reijo; Sankila, Eeva; de la Chapelle, Albert; Murphy, Dennis L.; Gusella, James; Breakefield, Xandra O.

1989-01-01

The genes for MAO-A and MAO-B appear to be very close to the Norrie disease gene, on the basis of loss and /or disruption of the MAO genes and activities in atypical Norrie disease patients deleted for the DXS7 locus; linkage among the MAO genes, the Norrie disease gene, and the DXS7 locus; and mapping of all these loci to the chromosomal region Xp11. The present study provides evidence that the MAO genes are not disrupted in “classic” Norrie disease patients. Genomic DNA from these “nondelet...

Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation.

Science.gov (United States)

Li, Congxin; Cesbron, François; Oehler, Michael; Brunner, Michael; Höfer, Thomas

2018-04-25

Gene regulation is a complex non-equilibrium process. Here, we show that quantitating the temporal regulation of key gene states (transcriptionally inactive, active, and refractory) provides a parsimonious framework for analyzing gene regulation. Our theory makes two non-intuitive predictions. First, for transcription factors (TFs) that regulate transcription burst frequency, as opposed to amplitude or duration, weak TF binding is sufficient to elicit strong transcriptional responses. Second, refractoriness of a gene after a transcription burst enables rapid responses to stimuli. We validate both predictions experimentally by exploiting the natural, optogenetic-like responsiveness of the Neurospora GATA-type TF White Collar Complex (WCC) to blue light. Further, we demonstrate that differential regulation of WCC target genes is caused by different gene activation rates, not different TF occupancy, and that these rates are tuned by both the core promoter and the distance between TF-binding site and core promoter. In total, our work demonstrates the relevance of a kinetic, non-equilibrium framework for understanding transcriptional regulation. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

The GP problem: quantifying gene-to-phenotype relationships.

Science.gov (United States)

Cooper, Mark; Chapman, Scott C; Podlich, Dean W; Hammer, Graeme L

2002-01-01

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

The Mycoplasma hominis vaa gene displays a mosaic gene structure

DEFF Research Database (Denmark)

Boesen, Thomas; Emmersen, Jeppe M. G.; Jensen, Lise T.

1998-01-01

Mycoplasma hominis contains a variable adherence-associated (vaa) gene. To classify variants of the vaa genes, we examined 42 M. hominis isolated by PCR, DNA sequencing and immunoblotting. This uncovered the existence of five gene categories. Comparison of the gene types revealed a modular...

Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma

Science.gov (United States)

2013-01-01

Background Differential gene expression (DGE) analysis is commonly used to reveal the deregulated molecular mechanisms of complex diseases. However, traditional DGE analysis (e.g., the t test or the rank sum test) tests each gene independently without considering interactions between them. Top-ranked differentially regulated genes prioritized by the analysis may not directly relate to the coherent molecular changes underlying complex diseases. Joint analyses of co-expression and DGE have been applied to reveal the deregulated molecular modules underlying complex diseases. Most of these methods consist of separate steps: first to identify gene-gene relationships under the studied phenotype then to integrate them with gene expression changes for prioritizing signature genes, or vice versa. It is warrant a method that can simultaneously consider gene-gene co-expression strength and corresponding expression level changes so that both types of information can be leveraged optimally. Results In this paper, we develop a gene module based method for differential gene expression analysis, named network-based differential gene expression (nDGE) analysis, a one-step integrative process for prioritizing deregulated genes and grouping them into gene modules. We demonstrate that nDGE outperforms existing methods in prioritizing deregulated genes and discovering deregulated gene modules using simulated data sets. When tested on a series of smoker and non-smoker lung adenocarcinoma data sets, we show that top differentially regulated genes identified by the rank sum test in different sets are not consistent while top ranked genes defined by nDGE in different data sets significantly overlap. nDGE results suggest that a differentially regulated gene module, which is enriched for cell cycle related genes and E2F1 targeted genes, plays a role in the molecular differences between smoker and non-smoker lung adenocarcinoma. Conclusions In this paper, we develop nDGE to prioritize

Gene coexpression network analysis of fruit transcriptomes uncovers a possible mechanistically distinct class of sugar/acid ratio-associated genes in sweet orange.

Science.gov (United States)

Qiao, Liang; Cao, Minghao; Zheng, Jian; Zhao, Yihong; Zheng, Zhi-Liang

2017-10-30

The ratio of sugars to organic acids, two of the major metabolites in fleshy fruits, has been considered the most important contributor to fruit sweetness. Although accumulation of sugars and acids have been extensively studied, whether plants evolve a mechanism to maintain, sense or respond to the fruit sugar/acid ratio remains a mystery. In a prior study, we used an integrated systems biology tool to identify a group of 39 acid-associated genes from the fruit transcriptomes in four sweet orange varieties (Citrus sinensis L. Osbeck) with varying fruit acidity, Succari (acidless), Bingtang (low acid), and Newhall and Xinhui (normal acid). We reanalyzed the prior sweet orange fruit transcriptome data, leading to the identification of 72 genes highly correlated with the fruit sugar/acid ratio. The majority of these sugar/acid ratio-related genes are predicted to be involved in regulatory functions such as transport, signaling and transcription or encode enzymes involved in metabolism. Surprisingly, only three of these sugar/acid ratio-correlated genes are weakly correlated with sugar level and none of them overlaps with the acid-associated genes. Weighted Gene Coexpression Network Analysis (WGCNA) has revealed that these genes belong to four modules, Blue, Grey, Brown and Turquoise, with the former two modules being unique to the sugar/acid ratio control. Our results indicate that orange fruits contain a possible mechanistically distinct class of genes that may potentially be involved in maintaining fruit sugar/acid ratios and/or responding to the cellular sugar/acid ratio status. Therefore, our analysis of orange transcriptomes provides an intriguing insight into the potentially novel genetic or molecular mechanisms controlling the sugar/acid ratio in fruits.

Identification of Hematopoietic Stem Cell Engraftment Genes in Gene Therapy Studies.

Science.gov (United States)

Powers, John M; Trobridge, Grant D

2013-09-01

Hematopoietic stem cell (HSC) therapy using replication-incompetent retroviral vectors is a promising approach to provide life-long correction for genetic defects. HSC gene therapy clinical studies have resulted in functional cures for several diseases, but in some studies clonal expansion or leukemia has occurred. This is due to the dyregulation of endogenous host gene expression from vector provirus insertional mutagenesis. Insertional mutagenesis screens using replicating retroviruses have been used extensively to identify genes that influence oncogenesis. However, retroviral mutagenesis screens can also be used to determine the role of genes in biological processes such as stem cell engraftment. The aim of this review is to describe the potential for vector insertion site data from gene therapy studies to provide novel insights into mechanisms of HSC engraftment. In HSC gene therapy studies dysregulation of host genes by replication-incompetent vector proviruses may lead to enrichment of repopulating clones with vector integrants near genes that influence engraftment. Thus, data from HSC gene therapy studies can be used to identify novel candidate engraftment genes. As HSC gene therapy use continues to expand, the vector insertion site data collected will be of great interest to help identify novel engraftment genes and may ultimately lead to new therapies to improve engraftment.

Gene Duplicability of Core Genes Is Highly Consistent across All Angiosperms[OPEN

Science.gov (United States)

Li, Zhen; Van de Peer, Yves; De Smet, Riet

2016-01-01

Gene duplication is an important mechanism for adding to genomic novelty. Hence, which genes undergo duplication and are preserved following duplication is an important question. It has been observed that gene duplicability, or the ability of genes to be retained following duplication, is a nonrandom process, with certain genes being more amenable to survive duplication events than others. Primarily, gene essentiality and the type of duplication (small-scale versus large-scale) have been shown in different species to influence the (long-term) survival of novel genes. However, an overarching view of “gene duplicability” is lacking, mainly due to the fact that previous studies usually focused on individual species and did not account for the influence of genomic context and the time of duplication. Here, we present a large-scale study in which we investigated duplicate retention for 9178 gene families shared between 37 flowering plant species, referred to as angiosperm core gene families. For most gene families, we observe a strikingly consistent pattern of gene duplicability across species, with gene families being either primarily single-copy or multicopy in all species. An intermediate class contains gene families that are often retained in duplicate for periods extending to tens of millions of years after whole-genome duplication, but ultimately appear to be largely restored to singleton status, suggesting that these genes may be dosage balance sensitive. The distinction between single-copy and multicopy gene families is reflected in their functional annotation, with single-copy genes being mainly involved in the maintenance of genome stability and organelle function and multicopy genes in signaling, transport, and metabolism. The intermediate class was overrepresented in regulatory genes, further suggesting that these represent putative dosage-balance-sensitive genes. PMID:26744215

Simulation and estimation of gene number in a biological pathway using almost complete saturation mutagenesis screening of haploid mouse cells.

Science.gov (United States)

Tokunaga, Masahiro; Kokubu, Chikara; Maeda, Yusuke; Sese, Jun; Horie, Kyoji; Sugimoto, Nakaba; Kinoshita, Taroh; Yusa, Kosuke; Takeda, Junji

2014-11-24

Genome-wide saturation mutagenesis and subsequent phenotype-driven screening has been central to a comprehensive understanding of complex biological processes in classical model organisms such as flies, nematodes, and plants. The degree of "saturation" (i.e., the fraction of possible target genes identified) has been shown to be a critical parameter in determining all relevant genes involved in a biological function, without prior knowledge of their products. In mammalian model systems, however, the relatively large scale and labor intensity of experiments have hampered the achievement of actual saturation mutagenesis, especially for recessive traits that require biallelic mutations to manifest detectable phenotypes. By exploiting the recently established haploid mouse embryonic stem cells (ESCs), we present an implementation of almost complete saturation mutagenesis in a mammalian system. The haploid ESCs were mutagenized with the chemical mutagen N-ethyl-N-nitrosourea (ENU) and processed for the screening of mutants defective in various steps of the glycosylphosphatidylinositol-anchor biosynthetic pathway. The resulting 114 independent mutant clones were characterized by a functional complementation assay, and were shown to be defective in any of 20 genes among all 22 known genes essential for this well-characterized pathway. Ten mutants were further validated by whole-exome sequencing. The predominant generation of single-nucleotide substitutions by ENU resulted in a gene mutation rate proportional to the length of the coding sequence, which facilitated the experimental design of saturation mutagenesis screening with the aid of computational simulation. Our study enables mammalian saturation mutagenesis to become a realistic proposition. Computational simulation, combined with a pilot mutagenesis experiment, could serve as a tool for the estimation of the number of genes essential for biological processes such as drug target pathways when a positive selection of

Human Gene Therapy: Genes without Frontiers?

Science.gov (United States)

Simon, Eric J.

2002-01-01

Describes the latest advancements and setbacks in human gene therapy to provide reference material for biology teachers to use in their science classes. Focuses on basic concepts such as recombinant DNA technology, and provides examples of human gene therapy such as severe combined immunodeficiency syndrome, familial hypercholesterolemia, and…

Interplay of bistable kinetics of gene expression during cellular growth

International Nuclear Information System (INIS)

Zhdanov, Vladimir P

2009-01-01

In cells, the bistable kinetics of gene expression can be observed on the level of (i) one gene with positive feedback between protein and mRNA production, (ii) two genes with negative mutual feedback between protein and mRNA production, or (iii) in more complex cases. We analyse the interplay of two genes of type (ii) governed by a gene of type (i) during cellular growth. In particular, using kinetic Monte Carlo simulations, we show that in the case where gene 1, operating in the bistable regime, regulates mutually inhibiting genes 2 and 3, also operating in the bistable regime, the latter genes may eventually be trapped either to the state with high transcriptional activity of gene 2 and low activity of gene 3 or to the state with high transcriptional activity of gene 3 and low activity of gene 2. The probability to get to one of these states depends on the values of the model parameters. If genes 2 and 3 are kinetically equivalent, the probability is equal to 0.5. Thus, our model illustrates how different intracellular states can be chosen at random with predetermined probabilities. This type of kinetics of gene expression may be behind complex processes occurring in cells, e.g., behind the choice of the fate by stem cells

Natural selection on protein-coding genes in the human genome

DEFF Research Database (Denmark)

Bustamente, Carlos D.; Fledel-Alon, Adi; Williamson, Scott

2005-01-01

, showing an excess of deleterious variation within local populations 9, 10 . Here we contrast patterns of coding sequence polymorphism identified by direct sequencing of 39 humans for over 11,000 genes to divergence between humans and chimpanzees, and find strong evidence that natural selection has shaped......Comparisons of DNA polymorphism within species to divergence between species enables the discovery of molecular adaptation in evolutionarily constrained genes as well as the differentiation of weak from strong purifying selection 1, 2, 3, 4 . The extent to which weak negative and positive darwinian...... selection have driven the molecular evolution of different species varies greatly 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 , with some species, such as Drosophila melanogaster, showing strong evidence of pervasive positive selection 6, 7, 8, 9 , and others, such as the selfing weed Arabidopsis thaliana...

Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression.

Science.gov (United States)

Walker, Jeffrey A

2016-01-01

Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori . Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R) methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness) on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set). The original analysis of these data used a linear model (GLS) of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on mean CTRA expression. The standardized effects of Hedonia and Eudaimonia on CTRA gene set expression estimated by GLS were compared to estimates using multivariate (OLS) linear models and generalized estimating equation (GEE) models. The OLS estimates were tested using O'Brien's OLS test, Anderson's permutation [Formula: see text]-test, two permutation F -tests (including GlobalAncova), and a rotation z -test (Roast). The GEE estimates were tested using a Wald test with robust standard errors. The performance (Type I, II, S, and M errors) of all tests was investigated using a Monte Carlo simulation of data explicitly modeled on the re-analyzed dataset. GLS estimates are inconsistent between data sets, and, in each dataset, at least one coefficient is large and highly statistically significant. By contrast, effects estimated by OLS or GEE are very small, especially relative to the standard errors. Bootstrap and permutation GLS distributions suggest that the GLS results in

Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression

Directory of Open Access Journals (Sweden)

Jeffrey A. Walker

2016-10-01

Full Text Available Background Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori. Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set. The original analysis of these data used a linear model (GLS of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on mean CTRA expression. Methods The standardized effects of Hedonia and Eudaimonia on CTRA gene set expression estimated by GLS were compared to estimates using multivariate (OLS linear models and generalized estimating equation (GEE models. The OLS estimates were tested using O’Brien’s OLS test, Anderson’s permutation ${r}_{F}^{2}$ r F 2 -test, two permutation F-tests (including GlobalAncova, and a rotation z-test (Roast. The GEE estimates were tested using a Wald test with robust standard errors. The performance (Type I, II, S, and M errors of all tests was investigated using a Monte Carlo simulation of data explicitly modeled on the re-analyzed dataset. Results GLS estimates are inconsistent between data sets, and, in each dataset, at least one coefficient is large and highly statistically significant. By contrast, effects estimated by OLS or GEE are very small, especially relative to the standard errors. Bootstrap and permutation GLS

Structure, tissue distribution, and chromosomal localization of the prepronociceptin gene.

Science.gov (United States)

Mollereau, C; Simons, M J; Soularue, P; Liners, F; Vassart, G; Meunier, J C; Parmentier, M

1996-08-06

Nociceptin (orphanin FQ), the newly discovered natural agonist of opioid receptor-like (ORL1) receptor, is a neuropeptide that is endowed with pronociceptive activity in vivo. Nociceptin is derived from a larger precursor, prepronociceptin (PPNOC), whose human, mouse, and rat genes we have now isolated. The PPNOC gene is highly conserved in the three species and displays organizational features that are strikingly similar to those of the genes of preproenkephalin, preprodynorphin, and preproopiomelanocortin, the precursors to endogenous opioid peptides, suggesting the four genes belong to the same family-i.e., have a common evolutionary origin. The PPNOC gene encodes a single copy of nociceptin as well as of other peptides whose sequence is strictly conserved across murine and human species; hence it is likely to be neurophysiologically significant. Northern blot analysis shows that the PPNOC gene is predominantly transcribed in the central nervous system (brain and spinal cord) and, albeit weakly, in the ovary, the sole peripheral organ expressing the gene. By using a radiation hybrid cell line panel, the PPNOC gene was mapped to the short arm of human chromosome 8 (8p21), between sequence-tagged site markers WI-5833 and WI-1172, in close proximity of the locus encoding the neurofilament light chain NEFL. Analysis of yeast artificial chromosome clones belonging to the WC8.4 contig covering the 8p21 region did not allow to detect the presence of the gene on these yeast artificial chromosomes, suggesting a gap in the coverage within this contig.

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.

Transcriptional delay stabilizes bistable gene networks.

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Gupta, Chinmaya; López, José Manuel; Ott, William; Josić, Krešimir; Bennett, Matthew R

2013-08-02

Transcriptional delay can significantly impact the dynamics of gene networks. Here we examine how such delay affects bistable systems. We investigate several stochastic models of bistable gene networks and find that increasing delay dramatically increases the mean residence times near stable states. To explain this, we introduce a non-Markovian, analytically tractable reduced model. The model shows that stabilization is the consequence of an increased number of failed transitions between stable states. Each of the bistable systems that we simulate behaves in this manner.

Scuba: scalable kernel-based gene prioritization.

Science.gov (United States)

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Cloning, characterization and targeting of the mouse HEXA gene

Energy Technology Data Exchange (ETDEWEB)

Wakamatsu, N.; Trasler, J.M.; Gravel, R.A. [McGill Univ., Quebec (Canada)] [and others

1994-09-01

The HEXA gene, encoding the {alpha} subunit of {beta}-hexosaminidase A, is essential for the metabolism of ganglioside G{sub M2}, and defects in this gene cause Tay-Sachs disease in humans. To elucidate the role of the gene in the nervous system of the mouse and to establish a mouse model of Tay-Sachs disease, we have cloned and characterized the HEXA gene and targeted a disruption of the gene in mouse ES cells. The mouse HEXA gene spans {approximately}26 kb and consists of 14 exons, similar to the human gene. A heterogeneous transcription initiation site was identified 21-42 bp 5{prime} of the initiator ATG, with two of the sites fitting the consensus CTCA (A = start) as seen for some weak initiator systems. Promoter analysis showed that the first 150 bp 5{prime} of the ATG contained 85% of promoter activity observed in constructs containing up to 1050 bp of 5{prime} sequence. The active region contained a sequence matching that of the adenovirus major late promoter upstream element factor. A survey of mouse tissues showed that the highest mRNA levels were in (max to min): testis (5.5 x brain cortex), adrenal, epididymis, heart, brain, lung, kidney, and liver (0.3 x brain cortex). A 12 kb BstI/SalI fragment containing nine exons was disrupted with the insertion of the bacterial neo{sup r} gene in exon 11 and was targeted into 129/Sv ES cells by homologous recombination. Nine of 153 G418 resistant clones were correctly targeted as confirmed by Southern blotting. The heterozygous ES cells were microinjected into mouse blastocysts and implanted into pseudo-pregnant mice. Nine male chimeric mice, showing that 40-95% chimerism for the 129/Sv agouti coat color marker, are being bred in an effort to generate germline transmission of the disrupted HEXA gene.

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...

Changes in Gene Expression of Arabidopsis Thaliana Cell Cultures Upon Exposure to Real and Simulated Partial- g Forces

Science.gov (United States)

Fengler, Svenja; Spirer, Ina; Neef, Maren; Ecke, Margret; Hauslage, Jens; Hampp, Rüdiger

2016-06-01

Cell cultures of the plant model organism Arabidopsis thaliana were exposed to partial- g forces during parabolic flight and clinostat experiments (0.16 g, 0.38 g and 0.5 g were tested). In order to investigate gravity-dependent alterations in gene expression, samples were metabolically quenched by the fixative RNA later Ⓡ to stabilize nucleic acids and used for whole-genome microarray analysis. An attempt to identify the potential threshold acceleration for the gravity-dependent response showed that the smaller the experienced g-force, the greater was the susceptibility of the cell cultures. Compared to short-term μ g during a parabolic flight, the number of differentially expressed genes under partial- g was lower. In addition, the effect on the alteration of amounts of transcripts decreased during partial- g parabolic flight due to the sequence of the different parabolas (0.38 g, 0.16 g and μ g). A time-dependent analysis under simulated 0.5 g indicates that adaptation occurs within minutes. Differentially expressed genes (at least 2-fold up- or down-regulated in expression) under real flight conditions were to some extent identical with those affected by clinorotation. The highest number of homologuous genes was detected within seconds of exposure to 0.38 g (both flight and clinorotation). To a considerable part, these genes deal with cell wall properties. Additionally, responses specific for clinorotation were observed.

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

Directory of Open Access Journals (Sweden)

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.

Computational fitness landscape for all gene-order permutations of an RNA virus.

Directory of Open Access Journals (Sweden)

Kwang-il Lim

2009-02-01

Full Text Available How does the growth of a virus depend on the linear arrangement of genes in its genome? Answering this question may enhance our basic understanding of virus evolution and advance applications of viruses as live attenuated vaccines, gene-therapy vectors, or anti-tumor therapeutics. We used a mathematical model for vesicular stomatitis virus (VSV, a prototype RNA virus that encodes five genes (N-P-M-G-L, to simulate the intracellular growth of all 120 possible gene-order variants. Simulated yields of virus infection varied by 6,000-fold and were found to be most sensitive to gene-order permutations that increased levels of the L gene transcript or reduced levels of the N gene transcript, the lowest and highest expressed genes of the wild-type virus, respectively. Effects of gene order on virus growth also depended upon the host-cell environment, reflecting different resources for protein synthesis and different cell susceptibilities to infection. Moreover, by computationally deleting intergenic attenuations, which define a key mechanism of transcriptional regulation in VSV, the variation in growth associated with the 120 gene-order variants was drastically narrowed from 6,000- to 20-fold, and many variants produced higher progeny yields than wild-type. These results suggest that regulation by intergenic attenuation preceded or co-evolved with the fixation of the wild type gene order in the evolution of VSV. In summary, our models have begun to reveal how gene functions, gene regulation, and genomic organization of viruses interact with their host environments to define processes of viral growth and evolution.

A gene encoding starch branching enzyme I (SBEI) in apple (Malusxdomestica, Rosaceae) and its phylogenetic relationship to Sbe genes from other angiosperms.

Science.gov (United States)

Han, Yuepeng; Gasic, Ksenija; Sun, Fengjie; Xu, Mingliang; Korban, Schuyler S

2007-06-01

An apple starch-branching enzyme SbeI gene (GenBank Accession No. DQ115404) has been isolated, cloned, and sequenced. The SbeI is a single copy gene in the apple genome, consisting of 14 exons and 13 introns, and covering 6075bp. As detected by RT-PCR, the apple SbeI is expressed at very low levels during early stages of fruit development; while, the highest levels of mRNA transcripts are observed at approximately 44 days post-pollination. Besides fruits, the apple SbeI is also expressed in buds and flowers, and very weakly in leaves. The genomic structure of SbeI in apple is strikingly similar to those reported so far in grasses (Poaceae), with exons 4 through 13 being of identical lengths in both apple and grasses. Moreover, structure similarities in exon lengths have also been detected in SbeII genes of both grasses and eudicots. These findings prompted the investigation of the evolutionary process of the Sbe gene family in angiosperms. A total of 26 Sbe sequences, representing an array of monocots and eudicots, are investigated in this study. Phylogenetic analysis has suggested that Sbe genes have duplicated into SbeI and SbeII prior to the divergence of moncots from eudicots. The SbeII gene is further duplicated into SbeIIa and SbeIIb prior to the radiation of grasses; however, it is not yet clear whether this duplication event has occurred before or after the radiation of the eudicots.

Evolution of homeobox genes.

Science.gov (United States)

Holland, Peter W H

2013-01-01

Many homeobox genes encode transcription factors with regulatory roles in animal and plant development. Homeobox genes are found in almost all eukaryotes, and have diversified into 11 gene classes and over 100 gene families in animal evolution, and 10 to 14 gene classes in plants. The largest group in animals is the ANTP class which includes the well-known Hox genes, plus other genes implicated in development including ParaHox (Cdx, Xlox, Gsx), Evx, Dlx, En, NK4, NK3, Msx, and Nanog. Genomic data suggest that the ANTP class diversified by extensive tandem duplication to generate a large array of genes, including an NK gene cluster and a hypothetical ProtoHox gene cluster that duplicated to generate Hox and ParaHox genes. Expression and functional data suggest that NK, Hox, and ParaHox gene clusters acquired distinct roles in patterning the mesoderm, nervous system, and gut. The PRD class is also diverse and includes Pax2/5/8, Pax3/7, Pax4/6, Gsc, Hesx, Otx, Otp, and Pitx genes. PRD genes are not generally arranged in ancient genomic clusters, although the Dux, Obox, and Rhox gene clusters arose in mammalian evolution as did several non-clustered PRD genes. Tandem duplication and genome duplication expanded the number of homeobox genes, possibly contributing to the evolution of developmental complexity, but homeobox gene loss must not be ignored. Evolutionary changes to homeobox gene expression have also been documented, including Hox gene expression patterns shifting in concert with segmental diversification in vertebrates and crustaceans, and deletion of a Pitx1 gene enhancer in pelvic-reduced sticklebacks. WIREs Dev Biol 2013, 2:31-45. doi: 10.1002/wdev.78 For further resources related to this article, please visit the WIREs website. The author declares that he has no conflicts of interest. Copyright © 2012 Wiley Periodicals, Inc.

Horizontal acquisition of multiple mitochondrial genes from a parasitic plant followed by gene conversion with host mitochondrial genes

Science.gov (United States)

2010-01-01

Background Horizontal gene transfer (HGT) is relatively common in plant mitochondrial genomes but the mechanisms, extent and consequences of transfer remain largely unknown. Previous results indicate that parasitic plants are often involved as either transfer donors or recipients, suggesting that direct contact between parasite and host facilitates genetic transfer among plants. Results In order to uncover the mechanistic details of plant-to-plant HGT, the extent and evolutionary fate of transfer was investigated between two groups: the parasitic genus Cuscuta and a small clade of Plantago species. A broad polymerase chain reaction (PCR) survey of mitochondrial genes revealed that at least three genes (atp1, atp6 and matR) were recently transferred from Cuscuta to Plantago. Quantitative PCR assays show that these three genes have a mitochondrial location in the one species line of Plantago examined. Patterns of sequence evolution suggest that these foreign genes degraded into pseudogenes shortly after transfer and reverse transcription (RT)-PCR analyses demonstrate that none are detectably transcribed. Three cases of gene conversion were detected between native and foreign copies of the atp1 gene. The identical phylogenetic distribution of the three foreign genes within Plantago and the retention of cytidines at ancestral positions of RNA editing indicate that these genes were probably acquired via a single, DNA-mediated transfer event. However, samplings of multiple individuals from two of the three species in the recipient Plantago clade revealed complex and perplexing phylogenetic discrepancies and patterns of sequence divergence for all three of the foreign genes. Conclusions This study reports the best evidence to date that multiple mitochondrial genes can be transferred via a single HGT event and that transfer occurred via a strictly DNA-level intermediate. The discovery of gene conversion between co-resident foreign and native mitochondrial copies suggests

Horizontal acquisition of multiple mitochondrial genes from a parasitic plant followed by gene conversion with host mitochondrial genes

Directory of Open Access Journals (Sweden)

Hao Weilong

2010-12-01

Full Text Available Abstract Background Horizontal gene transfer (HGT is relatively common in plant mitochondrial genomes but the mechanisms, extent and consequences of transfer remain largely unknown. Previous results indicate that parasitic plants are often involved as either transfer donors or recipients, suggesting that direct contact between parasite and host facilitates genetic transfer among plants. Results In order to uncover the mechanistic details of plant-to-plant HGT, the extent and evolutionary fate of transfer was investigated between two groups: the parasitic genus Cuscuta and a small clade of Plantago species. A broad polymerase chain reaction (PCR survey of mitochondrial genes revealed that at least three genes (atp1, atp6 and matR were recently transferred from Cuscuta to Plantago. Quantitative PCR assays show that these three genes have a mitochondrial location in the one species line of Plantago examined. Patterns of sequence evolution suggest that these foreign genes degraded into pseudogenes shortly after transfer and reverse transcription (RT-PCR analyses demonstrate that none are detectably transcribed. Three cases of gene conversion were detected between native and foreign copies of the atp1 gene. The identical phylogenetic distribution of the three foreign genes within Plantago and the retention of cytidines at ancestral positions of RNA editing indicate that these genes were probably acquired via a single, DNA-mediated transfer event. However, samplings of multiple individuals from two of the three species in the recipient Plantago clade revealed complex and perplexing phylogenetic discrepancies and patterns of sequence divergence for all three of the foreign genes. Conclusions This study reports the best evidence to date that multiple mitochondrial genes can be transferred via a single HGT event and that transfer occurred via a strictly DNA-level intermediate. The discovery of gene conversion between co-resident foreign and native

Benchmarking of gene prediction programs for metagenomic data.

Science.gov (United States)

Yok, Non; Rosen, Gail

2010-01-01

This manuscript presents the most rigorous benchmarking of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The comparisons are based on their performances over simulated fragments from one hundred species of diverse lineages. We defined four different types of fragments; two types come from the inter- and intra-coding regions and the other types are from the gene edges. Hoff et al. used only 12 species in their comparison; therefore, their sample is too small to represent an environmental sample. Also, no predecessors has separately examined fragments that contain gene edges as opposed to intra-coding regions. General observations in our results are that performances of all these programs improve as we increase the length of the fragment. On the other hand, intra-coding fragments of our data show low annotation error in all of the programs if compared to the gene edge fragments. Overall, we found an upper-bound performance by combining all the methods.

Fractional populations in multiple gene inheritance.

Science.gov (United States)

Chung, Myung-Hoon; Kim, Chul Koo; Nahm, Kyun

2003-01-22

With complete knowledge of the human genome sequence, one of the most interesting tasks remaining is to understand the functions of individual genes and how they communicate. Using the information about genes (locus, allele, mutation rate, fitness, etc.), we attempt to explain population demographic data. This population evolution study could complement and enhance biologists' understanding about genes. We present a general approach to study population genetics in complex situations. In the present approach, multiple allele inheritance, multiple loci inheritance, natural selection and mutations are allowed simultaneously in order to consider a more realistic situation. A simulation program is presented so that readers can readily carry out studies with their own parameters. It is shown that the multiplicity of the loci greatly affects the demographic results of fractional population ratios. Furthermore, the study indicates that some high infant mortality rates due to congenital anomalies can be attributed to multiple loci inheritance. The simulation program can be downloaded from http://won.hongik.ac.kr/~mhchung/index_files/yapop.htm. In order to run this program, one needs Visual Studio.NET platform, which can be downloaded from http://msdn.microsoft.com/netframework/downloads/default.asp.

Gene coexpression network analysis as a source of functional annotation for rice genes.

Directory of Open Access Journals (Sweden)

Kevin L Childs

Full Text Available With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional

Simulating Results of Experiments on Gene Regulation of the Lactose Operon in Escherichia coli; a Problem-Solving Exercise.

Science.gov (United States)

Hitchen, Trevor; Metcalfe, Judith

1987-01-01

Describes a simulation of the results of real experiments which use different strains of Escherichia coli. Provides an inexpensive practical problem-solving exercise to aid the teaching and understanding of the Jacob and Monod model of gene regulation. (Author/CW)

The drug target genes show higher evolutionary conservation than non-target genes.

Science.gov (United States)

Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

2016-01-26

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Identifying potential maternal genes of Bombyx mori using digital gene expression profiling

Science.gov (United States)

Xu, Pingzhen

2018-01-01

Maternal genes present in mature oocytes play a crucial role in the early development of silkworm. Although maternal genes have been widely studied in many other species, there has been limited research in Bombyx mori. High-throughput next generation sequencing provides a practical method for gene discovery on a genome-wide level. Herein, a transcriptome study was used to identify maternal-related genes from silkworm eggs. Unfertilized eggs from five different stages of early development were used to detect the changing situation of gene expression. The expressed genes showed different patterns over time. Seventy-six maternal genes were annotated according to homology analysis with Drosophila melanogaster. More than half of the differentially expressed maternal genes fell into four expression patterns, while the expression patterns showed a downward trend over time. The functional annotation of these material genes was mainly related to transcription factor activity, growth factor activity, nucleic acid binding, RNA binding, ATP binding, and ion binding. Additionally, twenty-two gene clusters including maternal genes were identified from 18 scaffolds. Altogether, we plotted a profile for the maternal genes of Bombyx mori using a digital gene expression profiling method. This will provide the basis for maternal-specific signature research and improve the understanding of the early development of silkworm. PMID:29462160

Novel gene sets improve set-level classification of prokaryotic gene expression data.

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Holec, Matěj; Kuželka, Ondřej; Železný, Filip

2015-10-28

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

Hessian regularization based non-negative matrix factorization for gene expression data clustering.

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Liu, Xiao; Shi, Jun; Wang, Congzhi

2015-01-01

Since a key step in the analysis of gene expression data is to detect groups of genes that have similar expression patterns, clustering technique is then commonly used to analyze gene expression data. Data representation plays an important role in clustering analysis. The non-negative matrix factorization (NMF) is a widely used data representation method with great success in machine learning. Although the traditional manifold regularization method, Laplacian regularization (LR), can improve the performance of NMF, LR still suffers from the problem of its weak extrapolating power. Hessian regularization (HR) is a newly developed manifold regularization method, whose natural properties make it more extrapolating, especially for small sample data. In this work, we propose the HR-based NMF (HR-NMF) algorithm, and then apply it to represent gene expression data for further clustering task. The clustering experiments are conducted on five commonly used gene datasets, and the results indicate that the proposed HR-NMF outperforms LR-based NMM and original NMF, which suggests the potential application of HR-NMF for gene expression data.

[Gene doping: gene transfer and possible molecular detection].

Science.gov (United States)

Argüelles, Carlos Francisco; Hernández-Zamora, Edgar

2007-01-01

The use of illegal substances in sports to enhance athletic performance during competition has caused international sports organizations such as the COI and WADA to take anti doping measures. A new doping method know as gene doping is defined as "the non-therapeutic use of genes, genetic elements and/or cells that have the capacity to enhance athletic performance". However, gene doping in sports is not easily identified and can cause serious consequences. Molecular biology techniques are needed in order to distinguish the difference between a "normal" and an "altered" genome. Further, we need to develop new analytic methods and biological molecular techniques in anti-doping laboratories, and design programs that avoid the non therapeutic use of genes.

Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets.

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Springer, Mark S; Gatesy, John

2018-02-26

coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset-the 'recombination ratchet'-is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d'etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful

Gene therapy of cancer and development of therapeutic target gene

Energy Technology Data Exchange (ETDEWEB)

Kim, Chang Min; Kwon, Hee Chung

1998-04-01

We applied HSV-tk/GCV strategy to orthotopic rat hepatoma model and showed anticancer effects of hepatoma. The increased expression of Lac Z gene after adenovirus-mediated gene delivery throughout hepatic artery was thought that is increased the possibility of gene therapy for curing hepatoma. With the construction of kGLP-laboratory, it is possible to produce a good quantity and quality of adenovirus in lage-scale production and purification of adenovirus vector. Also, the analysis of hepatoma related genes by PCR-LOH could be used for the diagnosis of patients and the development of therapeutic gene.

Gene therapy of cancer and development of therapeutic target gene

International Nuclear Information System (INIS)

Kim, Chang Min; Kwon, Hee Chung

1998-04-01

We applied HSV-tk/GCV strategy to orthotopic rat hepatoma model and showed anticancer effects of hepatoma. The increased expression of Lac Z gene after adenovirus-mediated gene delivery throughout hepatic artery was thought that is increased the possibility of gene therapy for curing hepatoma. With the construction of kGLP-laboratory, it is possible to produce a good quantity and quality of adenovirus in lage-scale production and purification of adenovirus vector. Also, the analysis of hepatoma related genes by PCR-LOH could be used for the diagnosis of patients and the development of therapeutic gene

REVIEW ARTICLE One gene, many phenotypes

African Journals Online (AJOL)

salah

Phenotype descriptions are valuable information right at the interface of medi- cine and biology. ... the interaction of alleles at different loci. Modifier genes. 5. ... the amount of normal protein is called ..... Institute, using computer simulations,.

Mutual repression enhances the steepness and precision of gene expression boundaries.

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Thomas R Sokolowski

Full Text Available Embryonic development is driven by spatial patterns of gene expression that determine the fate of each cell in the embryo. While gene expression is often highly erratic, embryonic development is usually exceedingly precise. In particular, gene expression boundaries are robust not only against intra-embryonic fluctuations such as noise in gene expression and protein diffusion, but also against embryo-to-embryo variations in the morphogen gradients, which provide positional information to the differentiating cells. How development is robust against intra- and inter-embryonic variations is not understood. A common motif in the gene regulation networks that control embryonic development is mutual repression between pairs of genes. To assess the role of mutual repression in the robust formation of gene expression patterns, we have performed large-scale stochastic simulations of a minimal model of two mutually repressing gap genes in Drosophila, hunchback (hb and knirps (kni. Our model includes not only mutual repression between hb and kni, but also the stochastic and cooperative activation of hb by the anterior morphogen Bicoid (Bcd and of kni by the posterior morphogen Caudal (Cad, as well as the diffusion of Hb and Kni between neighboring nuclei. Our analysis reveals that mutual repression can markedly increase the steepness and precision of the gap gene expression boundaries. In contrast to other mechanisms such as spatial averaging and cooperative gene activation, mutual repression thus allows for gene-expression boundaries that are both steep and precise. Moreover, mutual repression dramatically enhances their robustness against embryo-to-embryo variations in the morphogen levels. Finally, our simulations reveal that diffusion of the gap proteins plays a critical role not only in reducing the width of the gap gene expression boundaries via the mechanism of spatial averaging, but also in repairing patterning errors that could arise because of the

Clustering approaches to identifying gene expression patterns from DNA microarray data.

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Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Differential Retention of Gene Functions in a Secondary Metabolite Cluster.

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Reynolds, Hannah T; Slot, Jason C; Divon, Hege H; Lysøe, Erik; Proctor, Robert H; Brown, Daren W

2017-08-01

In fungi, distribution of secondary metabolite (SM) gene clusters is often associated with host- or environment-specific benefits provided by SMs. In the plant pathogen Alternaria brassicicola (Dothideomycetes), the DEP cluster confers an ability to synthesize the SM depudecin, a histone deacetylase inhibitor that contributes weakly to virulence. The DEP cluster includes genes encoding enzymes, a transporter, and a transcription regulator. We investigated the distribution and evolution of the DEP cluster in 585 fungal genomes and found a wide but sporadic distribution among Dothideomycetes, Sordariomycetes, and Eurotiomycetes. We confirmed DEP gene expression and depudecin production in one fungus, Fusarium langsethiae. Phylogenetic analyses suggested 6-10 horizontal gene transfers (HGTs) of the cluster, including a transfer that led to the presence of closely related cluster homologs in Alternaria and Fusarium. The analyses also indicated that HGTs were frequently followed by loss/pseudogenization of one or more DEP genes. Independent cluster inactivation was inferred in at least four fungal classes. Analyses of transitions among functional, pseudogenized, and absent states of DEP genes among Fusarium species suggest enzyme-encoding genes are lost at higher rates than the transporter (DEP3) and regulatory (DEP6) genes. The phenotype of an experimentally-induced DEP3 mutant of Fusarium did not support the hypothesis that selective retention of DEP3 and DEP6 protects fungi from exogenous depudecin. Together, the results suggest that HGT and gene loss have contributed significantly to DEP cluster distribution, and that some DEP genes provide a greater fitness benefit possibly due to a differential tendency to form network connections. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution 2017. This work is written by US Government employees and is in the public domain in the US.

The normal function of a speciation gene, Odysseus, and its hybrid sterility effect.

Science.gov (United States)

Sun, Sha; Ting, Chau-Ti; Wu, Chung-I

2004-07-02

To understand how postmating isolation is connected to the normal process of species divergence and why hybrid male sterility is often the first sign of speciation, we analyzed the Odysseus (OdsH) gene of hybrid male sterility in Drosophila. We carried out expression analysis, transgenic study, and gene knockout. The combined evidence suggests that the sterility phenotype represents a novel manifestation of the gene function rather than the reduction or loss of the normal one. The gene knockout experiment identified the normal function of OdsH as a modest enhancement of sperm production in young males. The implication of a weak effect of OdsH on the normal phenotype but a strong influence on hybrid male sterility is discussed in light of Haldane's rule of postmating isolation.

DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis

Directory of Open Access Journals (Sweden)

Baseler Michael W

2007-11-01

Full Text Available Abstract Background Due to the complex and distributed nature of biological research, our current biological knowledge is spread over many redundant annotation databases maintained by many independent groups. Analysts usually need to visit many of these bioinformatics databases in order to integrate comprehensive annotation information for their genes, which becomes one of the bottlenecks, particularly for the analytic task associated with a large gene list. Thus, a highly centralized and ready-to-use gene-annotation knowledgebase is in demand for high throughput gene functional analysis. Description The DAVID Knowledgebase is built around the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of gene/protein identifiers from a variety of public genomic resources into DAVID gene clusters. The grouping of such identifiers improves the cross-reference capability, particularly across NCBI and UniProt systems, enabling more than 40 publicly available functional annotation sources to be comprehensively integrated and centralized by the DAVID gene clusters. The simple, pair-wise, text format files which make up the DAVID Knowledgebase are freely downloadable for various data analysis uses. In addition, a well organized web interface allows users to query different types of heterogeneous annotations in a high-throughput manner. Conclusion The DAVID Knowledgebase is designed to facilitate high throughput gene functional analysis. For a given gene list, it not only provides the quick accessibility to a wide range of heterogeneous annotation data in a centralized location, but also enriches the level of biological information for an individual gene. Moreover, the entire DAVID Knowledgebase is freely downloadable or searchable at http://david.abcc.ncifcrf.gov/knowledgebase/.

Characterization of D-myo-inositol 3-phosphate synthase gene expression in two soybean low phytate mutants

International Nuclear Information System (INIS)

Yuan Fengjie; Dong Dekun; Li Baiquan; Yu Xiaomin; Fu Xujun; Zhu Danhua; Zhu Shenlong; Yang Qinghua

2013-01-01

1D-myo-inositol 3-phosphate synthase (MIPS) gene plays a significant role in phytic acid biosynthesis. In this study, we used two low phytic acid mutants Gm-lpa-TW-1, Gm-lpa-ZC-2 and their respective wild type parents Taiwan75 and Zhechun No.3 to analyze the expression pattern and characterization of MIPS1 gene. The results showed that there was a common expression pattern of MIPS1 in soybean developing seeds. Expression was weak as detected by RT-PCR in initial stage, increased in the following stages, and the peak expression was appeared in 22 day after flowering (DAF). The expression of MIPS1 gene of non-seed tissues in mutant Gm-lpa-TW-1 and its wildtype Taiwan75 was very weak. In the developing seeds, the MIPS1 expression by qRT-PCR revealed a significant reduction in 22 DAF in mutant Gm-lpa-TW-1 as compared with the wildtype. Similarly, the expression of MIPS1 gene in non-seed tissue of Zhenchun No.3 and Gm-lpa-ZC-2 was very weak. However, stronger expression in developing seeds of the mutant Gm-lpa-ZC-2 than Zhechun No.3 was found. We concluded that the MIPS1 gene expression in the developing seed exhibited an up-regulation pattern in mutant Gm-lpa-ZC-2, but a down-regulation pattern in the mutant Gm-lpa-TW-1. (authors)

Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data

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Hu Xiaohua

2011-07-01

Full Text Available Abstract Background The wide use of high-throughput DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. Although biomedical researchers typically design microarray experiments to explore specific biological contexts, the relationships between genes are hard to identified because they are complex and noisy high-dimensional data and are often hindered by low statistical power. The main challenge now is to extract valuable biological information from the colossal amount of data to gain insight into biological processes and the mechanisms of human disease. To overcome the challenge requires mathematical and computational methods that are versatile enough to capture the underlying biological features and simple enough to be applied efficiently to large datasets. Methods Unsupervised machine learning approaches provide new and efficient analysis of gene expression profiles. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA and nonnegative matrix factorization (NMF are integrated to identify significant genes and related pathways in microarray gene expression dataset of Alzheimer’s disease. The advantage of these two approaches is they can be performed as a biclustering method by which genes and conditions can be clustered simultaneously. Furthermore, they can group genes into different categories for identifying related diagnostic pathways and regulatory networks. The difference between these two method lies in ICA assume statistical independence of the expression modes, while NMF need positivity constrains to generate localized gene expression profiles. Results In our work, we performed FastICA and non-smooth NMF methods on DNA microarray gene expression data of Alzheimer’s disease respectively. The simulation results shows that both of the methods can clearly classify severe AD samples from control samples, and

Evolutionary dynamics of human autoimmune disease genes and malfunctioned immunological genes

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Podder Soumita

2012-01-01

Full Text Available Abstract Background One of the main issues of molecular evolution is to divulge the principles in dictating the evolutionary rate differences among various gene classes. Immunological genes have received considerable attention in evolutionary biology as candidates for local adaptation and for studying functionally important polymorphisms. The normal structure and function of immunological genes will be distorted when they experience mutations leading to immunological dysfunctions. Results Here, we examined the fundamental differences between the genes which on mutation give rise to autoimmune or other immune system related diseases and the immunological genes that do not cause any disease phenotypes. Although the disease genes examined are analogous to non-disease genes in product, expression, function, and pathway affiliation, a statistically significant decrease in evolutionary rate has been found in autoimmune disease genes relative to all other immune related diseases and non-disease genes. Possible ways of accumulation of mutation in the three steps of the central dogma (DNA-mRNA-Protein have been studied to trace the mutational effects predisposed to disease consequence and acquiring higher selection pressure. Principal Component Analysis and Multivariate Regression Analysis have established the predominant role of single nucleotide polymorphisms in guiding the evolutionary rate of immunological disease and non-disease genes followed by m-RNA abundance, paralogs number, fraction of phosphorylation residue, alternatively spliced exon, protein residue burial and protein disorder. Conclusions Our study provides an empirical insight into the etiology of autoimmune disease genes and other immunological diseases. The immediate utility of our study is to help in disease gene identification and may also help in medicinal improvement of immune related disease.

Carboxylesterase 1 genes

DEFF Research Database (Denmark)

Rasmussen, Henrik Berg; Madsen, Majbritt Busk

2018-01-01

The carboxylesterase 1 gene (CES1) encodes a hydrolase that metabolizes commonly used drugs. The CES1-related pseudogene, carboxylesterase 1 pseudogene 1 (CES1P1), has been implicated in gene exchange with CES1 and in the formation of hybrid genes including the carboxylesterase 1A2 gene (CES1A2...

Bayesian assignment of gene ontology terms to gene expression experiments

Science.gov (United States)

Sykacek, P.

2012-01-01

Motivation: Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment. Results: This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways. Availability: Source code under GPL license is available from the author. Contact: peter.sykacek@boku.ac.at PMID:22962488

Bayesian assignment of gene ontology terms to gene expression experiments.

Science.gov (United States)

Sykacek, P

2012-09-15

Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment. This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways. Source code under GPL license is available from the author. peter.sykacek@boku.ac.at.

Targeting the human lysozyme gene on bovine αs1- casein gene ...

African Journals Online (AJOL)

Targeting an exogenous gene into a favorable gene locus and for expression under endogenous regulators is an ideal method in mammary gland bioreactor research. For this purpose, a gene targeting vector was constructed to targeting the human lysozyme gene on bovine αs1-casein gene locus. In this case, the ...

The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

Science.gov (United States)

Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

2017-01-01

Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

Genome-wide identification of Saccharomyces cerevisiae genes required for tolerance to acetic acid

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Sá-Correia Isabel

2010-10-01

Full Text Available Abstract Background Acetic acid is a byproduct of Saccharomyces cerevisiae alcoholic fermentation. Together with high concentrations of ethanol and other toxic metabolites, acetic acid may contribute to fermentation arrest and reduced ethanol productivity. This weak acid is also a present in lignocellulosic hydrolysates, a highly interesting non-feedstock substrate in industrial biotechnology. Therefore, the better understanding of the molecular mechanisms underlying S. cerevisiae tolerance to acetic acid is essential for the rational selection of optimal fermentation conditions and the engineering of more robust industrial strains to be used in processes in which yeast is explored as cell factory. Results The yeast genes conferring protection against acetic acid were identified in this study at a genome-wide scale, based on the screening of the EUROSCARF haploid mutant collection for susceptibility phenotypes to this weak acid (concentrations in the range 70-110 mM, at pH 4.5. Approximately 650 determinants of tolerance to acetic acid were identified. Clustering of these acetic acid-resistance genes based on their biological function indicated an enrichment of genes involved in transcription, internal pH homeostasis, carbohydrate metabolism, cell wall assembly, biogenesis of mitochondria, ribosome and vacuole, and in the sensing, signalling and uptake of various nutrients in particular iron, potassium, glucose and amino acids. A correlation between increased resistance to acetic acid and the level of potassium in the growth medium was found. The activation of the Snf1p signalling pathway, involved in yeast response to glucose starvation, is demonstrated to occur in response to acetic acid stress but no evidence was obtained supporting the acetic acid-induced inhibition of glucose uptake. Conclusions Approximately 490 of the 650 determinants of tolerance to acetic acid identified in this work are implicated, for the first time, in tolerance to

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

Science.gov (United States)

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.

The Drosophila melanogaster methuselah gene: a novel gene with ancient functions.

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Ana Rita Araújo

Full Text Available The Drosophila melanogaster G protein-coupled receptor gene, methuselah (mth, has been described as a novel gene that is less than 10 million years old. Nevertheless, it shows a highly specific expression pattern in embryos, larvae, and adults, and has been implicated in larval development, stress resistance, and in the setting of adult lifespan, among others. Although mth belongs to a gene subfamily with 16 members in D. melanogaster, there is no evidence for functional redundancy in this subfamily. Therefore, it is surprising that a novel gene influences so many traits. Here, we explore the alternative hypothesis that mth is an old gene. Under this hypothesis, in species distantly related to D. melanogaster, there should be a gene with features similar to those of mth. By performing detailed phylogenetic, synteny, protein structure, and gene expression analyses we show that the D. virilis GJ12490 gene is the orthologous of mth in species distantly related to D. melanogaster. We also show that, in D. americana (a species of the virilis group of Drosophila, a common amino acid polymorphism at the GJ12490 orthologous gene is significantly associated with developmental time, size, and lifespan differences. Our results imply that GJ12490 orthologous genes are candidates for developmental time and lifespan differences in Drosophila in general.

Constructing an integrated gene similarity network for the identification of disease genes.

Science.gov (United States)

Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data

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J. Sunil Rao

2007-01-01

Full Text Available In gene selection for cancer classifi cation using microarray data, we define an eigenvalue-ratio statistic to measure a gene’s contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalueratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.

Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

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Nolan Priedigkeit

2015-02-01

Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life.

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Kannan, Lavanya; Li, Hua; Rubinstein, Boris; Mushegian, Arcady

2013-12-19

The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes and the record of orthologous relationships between their genes to infer the gene content of LUCA, the Last Universal Common Ancestor of all currently living cellular organisms. The crucial parameter, the ratio of gene losses and gene gains, was estimated from the data and was higher in models that take account of the number of in-paralogs in genomes than in models that treat gene presences and absences as a binary trait. While the numbers of genes that are placed confidently into LUCA are similar in the ML methods and in previously published methods that use various parsimony-based approaches, the identities of genes themselves are different. Most of the models of either kind treat the genes found in many existing genomes in a similar way, assigning to them high probabilities of being ancestral ("high ancestrality"). The ML models are more likely than others to assign high ancestrality to the genes that are relatively rare in the present-day genomes.

Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization

Directory of Open Access Journals (Sweden)

McDonald Karen

2011-08-01

Full Text Available Abstract Background Direct gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer. Results The software not only provides general functions for gene analysis and optimization along with an interactive user-friendly interface, but also includes unique features such as programming capability, dedicated mRNA secondary structure prediction, artificial neural network modeling, network & multi-threaded computing, and user-accessible programming modules. The software allows a user to analyze and optimize a sequence using main menu functions or specialized module windows. Alternatively, gene optimization can be initiated by designing a gene construct and configuring an optimization strategy. A user can choose several predefined or user-defined algorithms to design a complicated strategy. The software provides expandable functionality as platform software supporting module development using popular script languages such as VBScript and JScript in the software programming environment. Conclusion Visual Gene Developer is useful for both researchers who want to quickly analyze and optimize genes, and those who are interested in developing and testing new algorithms in bioinformatics. The software is available for free download at http://www.visualgenedeveloper.net.

Gene delivery to the lungs: pulmonary gene therapy for cystic fibrosis.

Science.gov (United States)

Villate-Beitia, Ilia; Zarate, Jon; Puras, Gustavo; Pedraz, José Luis

2017-07-01

Cystic fibrosis (CF) is a monogenic autosomal recessive disorder where the defective gene, the cystic fibrosis transmembrane conductance regulator (CFTR), is well identified. Moreover, the respiratory tract can be targeted through noninvasive aerosolized formulations for inhalation. Therefore, gene therapy is considered a plausible strategy to address this disease. Conventional gene therapy strategies rely on the addition of a correct copy of the CFTR gene into affected cells in order to restore the channel activity. In recent years, genome correction strategies have emerged, such as zinc-finger nucleases, transcription activator-like effector nucleases and clustered regularly interspaced short palindromic repeats associated to Cas9 nucleases. These gene editing tools aim to repair the mutated gene at its original genomic locus with high specificity. Besides, the success of gene therapy critically depends on the nucleic acids carriers. To date, several clinical studies have been carried out to add corrected copies of the CFTR gene into target cells using viral and non-viral vectors, some of them with encouraging results. Regarding genome editing systems, preliminary in vitro studies have been performed in order to repair the CFTR gene. In this review, after briefly introducing the basis of CF, we discuss the up-to-date gene therapy strategies to address the disease. The review focuses on the main factors to take into consideration when developing gene delivery strategies, such as the design of vectors and plasmid DNA, in vitro/in vivo tests, translation to human use, administration methods, manufacturing conditions and regulatory issues.

Iron homeostasis in Arabidopsis thaliana: transcriptomic analyses reveal novel FIT-regulated genes, iron deficiency marker genes and functional gene networks.

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Mai, Hans-Jörg; Pateyron, Stéphanie; Bauer, Petra

2016-10-03

FIT (FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR) is the central regulator of iron uptake in Arabidopsis thaliana roots. We performed transcriptome analyses of six day-old seedlings and roots of six week-old plants using wild type, a fit knock-out mutant and a FIT over-expression line grown under iron-sufficient or iron-deficient conditions. We compared genes regulated in a FIT-dependent manner depending on the developmental stage of the plants. We assembled a high likelihood dataset which we used to perform co-expression and functional analysis of the most stably iron deficiency-induced genes. 448 genes were found FIT-regulated. Out of these, 34 genes were robustly FIT-regulated in root and seedling samples and included 13 novel FIT-dependent genes. Three hundred thirty-one genes showed differential regulation in response to the presence and absence of FIT only in the root samples, while this was the case for 83 genes in the seedling samples. We assembled a virtual dataset of iron-regulated genes based on a total of 14 transcriptomic analyses of iron-deficient and iron-sufficient wild-type plants to pinpoint the best marker genes for iron deficiency and analyzed this dataset in depth. Co-expression analysis of this dataset revealed 13 distinct regulons part of which predominantly contained functionally related genes. We could enlarge the list of FIT-dependent genes and discriminate between genes that are robustly FIT-regulated in roots and seedlings or only in one of those. FIT-regulated genes were mostly induced, few of them were repressed by FIT. With the analysis of a virtual dataset we could filter out and pinpoint new candidates among the most reliable marker genes for iron deficiency. Moreover, co-expression and functional analysis of this virtual dataset revealed iron deficiency-induced and functionally distinct regulons.

The ASK1 gene regulates B function gene expression in cooperation with UFO and LEAFY in Arabidopsis.

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Zhao, D; Yu, Q; Chen, M; Ma, H

2001-07-01

The Arabidopsis floral regulatory genes APETALA3 (AP3) and PISTILLATA (PI) are required for the B function according to the ABC model for floral organ identity. AP3 and PI expression are positively regulated by the LEAFY (LFY) and UNUSUAL FLORAL ORGANS (UFO) genes. UFO encodes an F-box protein, and we have shown previously that UFO genetically interacts with the ASK1 gene encoding a SKP1 homologue; both the F-box containing protein and SKP1 are subunits of ubiquitin ligases. We show here that the ask1-1 mutation can enhance the floral phenotypes of weak lfy and ap3 mutants; therefore, like UFO, ASK1 also interacts with LFY and AP3 genetically. Furthermore, our results from RNA in situ hybridizations indicate that ASK1 regulates early AP3 and PI expression. These results support the idea that UFO and ASK1 together positively regulate AP3 and PI expression. We propose that the UFO and ASK1 proteins are components of a ubiquitin ligase that mediates the proteolysis of a repressor of AP3 and PI expression. Our genetic studies also indicate that ASK1 and UFO play a role in regulating the number of floral organ primordia, and we discuss possible mechanisms for such a regulation.

Cloning and characterization of the major histone H2A genes completes the cloning and sequencing of known histone genes of Tetrahymena thermophila.

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Liu, X; Gorovsky, M A

1996-01-01

A truncated cDNA clone encoding Tetrahymena thermophila histone H2A2 was isolated using synthetic degenerate oligonucleotide probes derived from H2A protein sequences of Tetrahymena pyriformis. The cDNA clone was used as a homologous probe to isolate a truncated genomic clone encoding H2A1. The remaining regions of the genes for H2A1 (HTA1) and H2A2 (HTA2) were then isolated using inverse PCR on circularized genomic DNA fragments. These partial clones were assembled into intact HTA1 and HTA2 clones. Nucleotide sequences of the two genes were highly homologous within the coding region but not in the noncoding regions. Comparison of the deduced amino acid sequences with protein sequences of T. pyriformis H2As showed only two and three differences respectively, in a total of 137 amino acids for H2A1, and 132 amino acids for H2A2, indicating the two genes arose before the divergence of these two species. The HTA2 gene contains a TAA triplet within the coding region, encoding a glutamine residue. In contrast with the T. thermophila HHO and HTA3 genes, no introns were identified within the two genes. The 5'- and 3'-ends of the histone H2A mRNAs; were determined by RNase protection and by PCR mapping using RACE and RLM-RACE methods. Both genes encode polyadenylated mRNAs and are highly expressed in vegetatively growing cells but only weakly expressed in starved cultures. With the inclusion of these two genes, T. thermophila is the first organism whose entire complement of known core and linker histones, including replication-dependent and basal variants, has been cloned and sequenced. PMID:8760889

Intracellular delivery of potential therapeutic genes: prospects in cancer gene therapy.

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Bakhtiar, Athirah; Sayyad, Mustak; Rosli, Rozita; Maruyama, Atsushi; Chowdhury, Ezharul H

2014-01-01

Conventional therapies for malignant cancer such as chemotherapy and radiotherapy are associated with poor survival rates owing to the development of cellular resistance to cancer drugs and the lack of targetability, resulting in unwanted adverse effects on healthy cells and necessitating the lowering of therapeutic dose with consequential lower efficacy of the treatment. Gene therapy employing different types of viral and non-viral carriers to transport gene(s) of interest and facilitating production of the desirable therapeutic protein(s) has tremendous prospects in cancer treatments due to the high-level of specificity in therapeutic action of the expressed protein(s) with diminished off-target effects, although cancer cell-specific delivery of transgene(s) still poses some challenges to be addressed. Depending on the potential therapeutic target genes, cancer gene therapy could be categorized into tumor suppressor gene replacement therapy, immune gene therapy and enzyme- or prodrug-based therapy. This review would shed light on the current progress of delivery of potentially therapeutic genes into various cancer cells in vitro and animal models utilizing a variety of viral and non-viral vectors.

Divergence of gene body DNA methylation and evolution of plant duplicate genes.

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Jun Wang

Full Text Available It has been shown that gene body DNA methylation is associated with gene expression. However, whether and how deviation of gene body DNA methylation between duplicate genes can influence their divergence remains largely unexplored. Here, we aim to elucidate the potential role of gene body DNA methylation in the fate of duplicate genes. We identified paralogous gene pairs from Arabidopsis and rice (Oryza sativa ssp. japonica genomes and reprocessed their single-base resolution methylome data. We show that methylation in paralogous genes nonlinearly correlates with several gene properties including exon number/gene length, expression level and mutation rate. Further, we demonstrated that divergence of methylation level and pattern in paralogs indeed positively correlate with their sequence and expression divergences. This result held even after controlling for other confounding factors known to influence the divergence of paralogs. We observed that methylation level divergence might be more relevant to the expression divergence of paralogs than methylation pattern divergence. Finally, we explored the mechanisms that might give rise to the divergence of gene body methylation in paralogs. We found that exonic methylation divergence more closely correlates with expression divergence than intronic methylation divergence. We show that genomic environments (e.g., flanked by transposable elements and repetitive sequences of paralogs generated by various duplication mechanisms are associated with the methylation divergence of paralogs. Overall, our results suggest that the changes in gene body DNA methylation could provide another avenue for duplicate genes to develop differential expression patterns and undergo different evolutionary fates in plant genomes.

GeneTopics - interpretation of gene sets via literature-driven topic models

Science.gov (United States)

2013-01-01

Background Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set. Methods Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research. Results We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner. Conclusions Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly

Development of a radiation-responsive gene expression system

International Nuclear Information System (INIS)

Ogawa, Ryohei; Morii, Akihiro; Watanabe, Akihiko

2013-01-01

We have obtained a promoter enhancing expression of a gene of our interest connected downstream after activation in response to radiation stimulation and it could be used in radiogenetic therapy, a combination between radiotherapy and gene therapy. The promoter has been chosen out of a library of DNA fragments constructed by connecting the TATA box to randomly combined binding sequences of transcription factors that are activated in response to radiation. Although it was shown that the promoter activation was cell type specific, it turned out that radiation responsive promoters could be obtained for a different type of cells by using another set of transcription factor binding sequences, suggesting that the method would be feasible to obtain promoters functioning in any type of cells. Radiation reactivity of obtained promoters could be improved by techniques such as random introduction of point mutations. The improved promoters significantly enhanced expression of the luciferase gene connected downstream in response to radiation even in vivo, in addition, a gene cassette composed of one such promoter and the fcy::fur gene was confirmed useful for suicide gene therapy as shown in vitro simulation experiment, suggesting possible clinical application. (author)

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life

Science.gov (United States)

2013-01-01

Background The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. Results We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes and the record of orthologous relationships between their genes to infer the gene content of LUCA, the Last Universal Common Ancestor of all currently living cellular organisms. The crucial parameter, the ratio of gene losses and gene gains, was estimated from the data and was higher in models that take account of the number of in-paralogs in genomes than in models that treat gene presences and absences as a binary trait. Conclusion While the numbers of genes that are placed confidently into LUCA are similar in the ML methods and in previously published methods that use various parsimony-based approaches, the identities of genes themselves are different. Most of the models of either kind treat the genes found in many existing genomes in a similar way, assigning to them high probabilities of being ancestral (“high ancestrality”). The ML models are more likely than others to assign high ancestrality to the genes that are relatively rare in the present-day genomes. Reviewers This article was reviewed by Martijn A Huynen, Toni Gabaldón and Fyodor Kondrashov. PMID:24354654

Gene expression variations during Drosophila metamorphosis in real and simulated gravity

Science.gov (United States)

Marco, R.; Leandro-García, L. J.; Benguría, A.; Herranz, R.; Zeballos, A.; Gassert, G.; van Loon, J. J.; Medina, F. J.

Establishing the extent and significance of the effects of the exposure to microgravity of complex living organisms is a critical piece of information if the long-term exploration of near-by planets involving human beings is going to take place in the Future As a first step in this direction we have started to look into the patterns of gene expression during Drosophila development in real and simulated microgravity using microarray analysis of mRNA isolated from samples exposed to different environmental conditions In these experiments we used Affymetrix chips version 1 0 containing probes for more than 14 000 genes almost the complete Drosophila genome 55 of which are tagged with some molecular or functional designation while 45 are still waiting to be identified in functional terms The real microgravity exposure was imposed on the samples during the crew exchanging Soyuz 8 Mission to the ISS in October 2003 when after 11 days in Microgravity the Spanish-born astronaut Pedro Duque returned in the Soyuz 7 capsule carrying the experiments prepared by our Team Due to the constraints in the current ISS experiments in these Missions we limited the stages explored in our experiment to the developmental processes occurring during Drosophila metamorphosis As the experimental conditions at the launch site Baikonour were fairly limited we prepared the experiment in Madrid Toulouse and transp o rted the samples at 15 C in a temperature controlled container to slow down the developmental process a

The identification of functional motifs in temporal gene expression analysis

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Michael G. Surette

2005-01-01

Full Text Available The identification of transcription factor binding sites is essential to the understanding of the regulation of gene expression and the reconstruction of genetic regulatory networks. The in silico identification of cis-regulatory motifs is challenging due to sequence variability and lack of sufficient data to generate consensus motifs that are of quantitative or even qualitative predictive value. To determine functional motifs in gene expression, we propose a strategy to adopt false discovery rate (FDR and estimate motif effects to evaluate combinatorial analysis of motif candidates and temporal gene expression data. The method decreases the number of predicted motifs, which can then be confirmed by genetic analysis. To assess the method we used simulated motif/expression data to evaluate parameters. We applied this approach to experimental data for a group of iron responsive genes in Salmonella typhimurium 14028S. The method identified known and potentially new ferric-uptake regulator (Fur binding sites. In addition, we identified uncharacterized functional motif candidates that correlated with specific patterns of expression. A SAS code for the simulation and analysis gene expression data is available from the first author upon request.

Gene therapy: An overview

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Sudip Indu

2013-01-01

Full Text Available Gene therapy "the use of genes as medicine" involves the transfer of a therapeutic or working copy of a gene into specific cells of an individual in order to repair a faulty gene copy. The technique may be used to replace a faulty gene, or to introduce a new gene whose function is to cure or to favorably modify the clinical course of a condition. The objective of gene therapy is to introduce new genetic material into target cells while causing no damage to the surrounding healthy cells and tissues, hence the treatment related morbidity is decreased. The delivery system includes a vector that delivers a therapeutic gene into the patient′s target cell. Functional proteins are created from the therapeutic gene causing the cell to return to a normal stage. The vectors used in gene therapy can be viral and non-viral. Gene therapy, an emerging field of biomedicine, is still at infancy and much research remains to be done before this approach to the treatment of condition will realize its full potential.

Gene doping in sports.

Science.gov (United States)

Unal, Mehmet; Ozer Unal, Durisehvar

2004-01-01

Gene or cell doping is defined by the World Anti-Doping Agency (WADA) as "the non-therapeutic use of genes, genetic elements and/or cells that have the capacity to enhance athletic performance". New research in genetics and genomics will be used not only to diagnose and treat disease, but also to attempt to enhance human performance. In recent years, gene therapy has shown progress and positive results that have highlighted the potential misuse of this technology and the debate of 'gene doping'. Gene therapies developed for the treatment of diseases such as anaemia (the gene for erythropoietin), muscular dystrophy (the gene for insulin-like growth factor-1) and peripheral vascular diseases (the gene for vascular endothelial growth factor) are potential doping methods. With progress in gene technology, many other genes with this potential will be discovered. For this reason, it is important to develop timely legal regulations and to research the field of gene doping in order to develop methods of detection. To protect the health of athletes and to ensure equal competitive conditions, the International Olympic Committee, WADA and International Sports Federations have accepted performance-enhancing substances and methods as being doping, and have forbidden them. Nevertheless, the desire to win causes athletes to misuse these drugs and methods. This paper reviews the current status of gene doping and candidate performance enhancement genes, and also the use of gene therapy in sports medicine and ethics of genetic enhancement. Copyright 2004 Adis Data Information BV

Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

Science.gov (United States)

Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

2017-08-01

This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Self-focusing therapeutic gene delivery with intelligent gene vector swarms: intra-swarm signalling through receptor transgene expression in targeted cells.

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Tolmachov, Oleg E

2015-01-01

Gene delivery in vivo that is tightly focused on the intended target cells is essential to maximize the benefits of gene therapy and to reduce unwanted side-effects. Cell surface markers are immediately available for probing by therapeutic gene vectors and are often used to direct gene transfer with these vectors to specific target cell populations. However, it is not unusual for the choice of available extra-cellular markers to be too scarce to provide a reliable definition of the desired therapeutically relevant set of target cells. Therefore, interrogation of intra-cellular determinants of cell-specificity, such as tissue-specific transcription factors, can be vital in order to provide detailed cell-guiding information to gene vector particles. An important improvement in cell-specific gene delivery can be achieved through auto-buildup in vector homing efficiency using intelligent 'self-focusing' of swarms of vector particles on target cells. Vector self-focusing was previously suggested to rely on the release of diffusible chemo-attractants after a successful target-specific hit by 'scout' vector particles. I hypothesize that intelligent self-focusing behaviour of swarms of cell-targeted therapeutic gene vectors can be accomplished without the employment of difficult-to-use diffusible chemo-attractants, instead relying on the intra-swarm signalling through cells expressing a non-diffusible extra-cellular receptor for the gene vectors. In the proposed model, cell-guiding information is gathered by the 'scout' gene vector particles, which: (1) attach to a variety of cells via a weakly binding (low affinity) receptor; (2) successfully facilitate gene transfer into these cells; (3) query intra-cellular determinants of cell-specificity with their transgene expression control elements and (4) direct the cell-specific biosynthesis of a vector-encoded strongly binding (high affinity) cell-surface receptor. Free members of the vector swarm loaded with therapeutic cargo

Identification of suitable reference genes for gene expression studies of shoulder instability.

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Mariana Ferreira Leal

Full Text Available Shoulder instability is a common shoulder injury, and patients present with plastic deformation of the glenohumeral capsule. Gene expression analysis may be a useful tool for increasing the general understanding of capsule deformation, and reverse-transcription quantitative polymerase chain reaction (RT-qPCR has become an effective method for such studies. Although RT-qPCR is highly sensitive and specific, it requires the use of suitable reference genes for data normalization to guarantee meaningful and reproducible results. In the present study, we evaluated the suitability of a set of reference genes using samples from the glenohumeral capsules of individuals with and without shoulder instability. We analyzed the expression of six commonly used reference genes (ACTB, B2M, GAPDH, HPRT1, TBP and TFRC in the antero-inferior, antero-superior and posterior portions of the glenohumeral capsules of cases and controls. The stability of the candidate reference gene expression was determined using four software packages: NormFinder, geNorm, BestKeeper and DataAssist. Overall, HPRT1 was the best single reference gene, and HPRT1 and B2M composed the best pair of reference genes from different analysis groups, including simultaneous analysis of all tissue samples. GenEx software was used to identify the optimal number of reference genes to be used for normalization and demonstrated that the accumulated standard deviation resulting from the use of 2 reference genes was similar to that resulting from the use of 3 or more reference genes. To identify the optimal combination of reference genes, we evaluated the expression of COL1A1. Although the use of different reference gene combinations yielded variable normalized quantities, the relative quantities within sample groups were similar and confirmed that no obvious differences were observed when using 2, 3 or 4 reference genes. Consequently, the use of 2 stable reference genes for normalization, especially

The Pathway From Genes to Gene Therapy in Glaucoma: A Review of Possibilities for Using Genes as Glaucoma Drugs.

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Borrás, Teresa

2017-01-01

Treatment of diseases with gene therapy is advancing rapidly. The use of gene therapy has expanded from the original concept of re-placing the mutated gene causing the disease to the use of genes to con-trol nonphysiological levels of expression or to modify pathways known to affect the disease. Genes offer numerous advantages over conventional drugs. They have longer duration of action and are more specific. Genes can be delivered to the target site by naked DNA, cells, nonviral, and viral vectors. The enormous progress of the past decade in molecular bi-ology and delivery systems has provided ways for targeting genes to the intended cell/tissue and safe, long-term vectors. The eye is an ideal organ for gene therapy. It is easily accessible and it is an immune-privileged site. Currently, there are clinical trials for diseases affecting practically every tissue of the eye, including those to restore vision in patients with Leber congenital amaurosis. However, the number of eye trials compared with those for systemic diseases is quite low (1.8%). Nevertheless, judg-ing by the vast amount of ongoing preclinical studies, it is expected that such number will increase considerably in the near future. One area of great need for eye gene therapy is glaucoma, where a long-term gene drug would eliminate daily applications and compliance issues. Here, we review the current state of gene therapy for glaucoma and the possibilities for treating the trabecular meshwork to lower intraocular pressure and the retinal ganglion cells to protect them from neurodegeneration. Copyright© 2017 Asia-Pacific Academy of Ophthalmology.

Integrating Ontological Knowledge and Textual Evidence in Estimating Gene and Gene Product Similarity

Energy Technology Data Exchange (ETDEWEB)

Sanfilippo, Antonio P.; Posse, Christian; Gopalan, Banu; Tratz, Stephen C.; Gregory, Michelle L.

2006-06-08

With the rising influence of the Gene On-tology, new approaches have emerged where the similarity between genes or gene products is obtained by comparing Gene Ontology code annotations associ-ated with them. So far, these approaches have solely relied on the knowledge en-coded in the Gene Ontology and the gene annotations associated with the Gene On-tology database. The goal of this paper is to demonstrate that improvements to these approaches can be obtained by integrating textual evidence extracted from relevant biomedical literature.

UniGene Tabulator: a full parser for the UniGene format.

Science.gov (United States)

Lenzi, Luca; Frabetti, Flavia; Facchin, Federica; Casadei, Raffaella; Vitale, Lorenza; Canaider, Silvia; Carinci, Paolo; Zannotti, Maria; Strippoli, Pierluigi

2006-10-15

UniGene Tabulator 1.0 provides a solution for full parsing of UniGene flat file format; it implements a structured graphical representation of each data field present in UniGene following import into a common database managing system usable in a personal computer. This database includes related tables for sequence, protein similarity, sequence-tagged site (STS) and transcript map interval (TXMAP) data, plus a summary table where each record represents a UniGene cluster. UniGene Tabulator enables full local management of UniGene data, allowing parsing, querying, indexing, retrieving, exporting and analysis of UniGene data in a relational database form, usable on Macintosh (OS X 10.3.9 or later) and Windows (2000, with service pack 4, XP, with service pack 2 or later) operating systems-based computers. The current release, including both the FileMaker runtime applications, is freely available at http://apollo11.isto.unibo.it/software/

Cloning and bioinformatics analysis of CcPILS gene of Hickory (Carya cathayensis)

Science.gov (United States)

Guo, Wenbin; Yuan, Huwei; Gao, Liuxiao; Guo, Haipeng; Qiu, Lingling; Xu, Dongbin; Yan, Daoliang; Zheng, Bingsong

2017-04-01

PILS is a key auxin efflux carrier protein in the auxin signal transduction. A CcPILS gene related to hickory (Carya carthayensis) grafting process was obtained by RACE techniques. The full length of CcPILS gene was1541bp contained a 1263bp length open reading flame (ORF). The CcPILS encoded 294 amino acids with molecular weight of 46 kDa, PI 5.38 and localized at endoplasmic reticulum membrane. The gene contained a central hydrophilic loop separating two hydrophobic domains of about five transmembrane regions each. The gene of CcPILS belonged to Clade III sub-family of PILS and its sequence had high homology with Arabidopsis. Real Time RT-PCR analysis showed that the gene expressions were weakly induced in bud, inflorescence, fruit, leaf and stem, while strongly in root. The expression levels were strongly induced and reached a peak at the third day of grafting in scion and rootstock of hickory, which were 1.45 and 3.45 times higher, respectively, compared to that of control. The results indicated that CcPILS may be involved in regulating the expression of genes related to auxin signal transduction during hickory graft process.

Dichotomous noise models of gene switches

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Potoyan, Davit A., E-mail: potoyan@rice.edu; Wolynes, Peter G., E-mail: pwolynes@rice.edu [Department of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005 (United States)

2015-11-21

Molecular noise in gene regulatory networks has two intrinsic components, one part being due to fluctuations caused by the birth and death of protein or mRNA molecules which are often present in small numbers and the other part arising from gene state switching, a single molecule event. Stochastic dynamics of gene regulatory circuits appears to be largely responsible for bifurcations into a set of multi-attractor states that encode different cell phenotypes. The interplay of dichotomous single molecule gene noise with the nonlinear architecture of genetic networks generates rich and complex phenomena. In this paper, we elaborate on an approximate framework that leads to simple hybrid multi-scale schemes well suited for the quantitative exploration of the steady state properties of large-scale cellular genetic circuits. Through a path sum based analysis of trajectory statistics, we elucidate the connection of these hybrid schemes to the underlying master equation and provide a rigorous justification for using dichotomous noise based models to study genetic networks. Numerical simulations of circuit models reveal that the contribution of the genetic noise of single molecule origin to the total noise is significant for a wide range of kinetic regimes.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

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Vipin Narang

Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

Maximum Gene-Support Tree

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Yunfeng Shan

2008-01-01

Full Text Available Genomes and genes diversify during evolution; however, it is unclear to what extent genes still retain the relationship among species. Model species for molecular phylogenetic studies include yeasts and viruses whose genomes were sequenced as well as plants that have the fossil-supported true phylogenetic trees available. In this study, we generated single gene trees of seven yeast species as well as single gene trees of nine baculovirus species using all the orthologous genes among the species compared. Homologous genes among seven known plants were used for validation of the finding. Four algorithms—maximum parsimony (MP, minimum evolution (ME, maximum likelihood (ML, and neighbor-joining (NJ—were used. Trees were reconstructed before and after weighting the DNA and protein sequence lengths among genes. Rarely a gene can always generate the “true tree” by all the four algorithms. However, the most frequent gene tree, termed “maximum gene-support tree” (MGS tree, or WMGS tree for the weighted one, in yeasts, baculoviruses, or plants was consistently found to be the “true tree” among the species. The results provide insights into the overall degree of divergence of orthologous genes of the genomes analyzed and suggest the following: 1 The true tree relationship among the species studied is still maintained by the largest group of orthologous genes; 2 There are usually more orthologous genes with higher similarities between genetically closer species than between genetically more distant ones; and 3 The maximum gene-support tree reflects the phylogenetic relationship among species in comparison.

A sparse regulatory network of copy-number driven gene expression reveals putative breast cancer oncogenes.

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Yuan, Yinyin; Curtis, Christina; Caldas, Carlos; Markowetz, Florian

2012-01-01

Copy number aberrations are recognized to be important in cancer as they may localize to regions harboring oncogenes or tumor suppressors. Such genomic alterations mediate phenotypic changes through their impact on expression. Both cis- and transacting alterations are important since they may help to elucidate putative cancer genes. However, amidst numerous passenger genes, trans-effects are less well studied due to the computational difficulty in detecting weak and sparse signals in the data, and yet may influence multiple genes on a global scale. We propose an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream transcriptional targets in breast cancer. With respect to goodness of fit on both simulated and real data, the performance of sparse network inference is no worse than other state-of-the-art models but with the advantage of simultaneous feature selection and efficiency. The DNA-RNA interaction network helps to distinguish copy-number driven expression alterations from those that are copy-number independent. Further, our approach yields a quantitative copy-number dependency score, which distinguishes cis- versus trans-effects. When applied to a breast cancer data set, numerous expression profiles were impacted by cis-acting copy-number alterations, including several known oncogenes such as GRB7, ERBB2, and LSM1. Several trans-acting alterations were also identified, impacting genes such as ADAM2 and BAGE, which warrant further investigation. An R package named lol is available from www.markowetzlab.org/software/lol.html.

Interactive visualization of gene regulatory networks with associated gene expression time series data

NARCIS (Netherlands)

Westenberg, M.A.; Hijum, van S.A.F.T.; Lulko, A.T.; Kuipers, O.P.; Roerdink, J.B.T.M.; Linsen, L.; Hagen, H.; Hamann, B.

2008-01-01

We present GENeVis, an application to visualize gene expression time series data in a gene regulatory network context. This is a network of regulator proteins that regulate the expression of their respective target genes. The networks are represented as graphs, in which the nodes represent genes,

Using gene expression noise to understand gene regulation

NARCIS (Netherlands)

Munsky, B.; Neuert, G.; van Oudenaarden, A.

2012-01-01

Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression "noise," has been suggested as a

Gene Conversion in Angiosperm Genomes with an Emphasis on Genes Duplicated by Polyploidization

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Xi-Yin Wang

2011-01-01

Full Text Available Angiosperm genomes differ from those of mammals by extensive and recursive polyploidizations. The resulting gene duplication provides opportunities both for genetic innovation, and for concerted evolution. Though most genes may escape conversion by their homologs, concerted evolution of duplicated genes can last for millions of years or longer after their origin. Indeed, paralogous genes on two rice chromosomes duplicated an estimated 60–70 million years ago have experienced gene conversion in the past 400,000 years. Gene conversion preserves similarity of paralogous genes, but appears to accelerate their divergence from orthologous genes in other species. The mutagenic nature of recombination coupled with the buffering effect provided by gene redundancy, may facilitate the evolution of novel alleles that confer functional innovations while insulating biological fitness of affected plants. A mixed evolutionary model, characterized by a primary birth-and-death process and occasional homoeologous recombination and gene conversion, may best explain the evolution of multigene families.

G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes

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Lemay Danielle G

2012-09-01

Full Text Available Abstract Background In previous studies, gene neighborhoods—spatial clusters of co-expressed genes in the genome—have been defined using arbitrary rules such as requiring adjacency, a minimum number of genes, a fixed window size, or a minimum expression level. In the current study, we developed a Gene Neighborhood Scoring Tool (G-NEST which combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all possible window sizes simultaneously. Results Using G-NEST on atlases of mouse and human tissue expression data, we found that large neighborhoods of ten or more genes are extremely rare in mammalian genomes. When they do occur, neighborhoods are typically composed of families of related genes. Both the highest scoring and the largest neighborhoods in mammalian genomes are formed by tandem gene duplication. Mammalian gene neighborhoods contain highly and variably expressed genes. Co-localized noisy gene pairs exhibit lower evolutionary conservation of their adjacent genome locations, suggesting that their shared transcriptional background may be disadvantageous. Genes that are essential to mammalian survival and reproduction are less likely to occur in neighborhoods, although neighborhoods are enriched with genes that function in mitosis. We also found that gene orientation and protein-protein interactions are partially responsible for maintenance of gene neighborhoods. Conclusions Our experiments using G-NEST confirm that tandem gene duplication is the primary driver of non-random gene order in mammalian genomes. Non-essentiality, co-functionality, gene orientation, and protein-protein interactions are additional forces that maintain gene neighborhoods, especially those formed by tandem duplicates. We expect G-NEST to be useful for other applications such as the identification of core regulatory modules, common transcriptional backgrounds, and chromatin domains. The

The Cumulative Effect of Gene-Gene and Gene-Environment Interactions on the Risk of Prostate Cancer in Chinese Men

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Ming Liu

2016-01-01

Full Text Available Prostate cancer (PCa is a multifactorial disease involving complex genetic and environmental factors interactions. Gene-gene and gene-environment interactions associated with PCa in Chinese men are less studied. We explored the association between 36 SNPs and PCa in 574 subjects from northern China. Body mass index (BMI, smoking, and alcohol consumption were determined through self-administered questionnaires in 134 PCa patients. Then gene-gene and gene-environment interactions among the PCa-associated SNPs were analyzed using the generalized multifactor dimensionality reduction (GMDR and logistic regression methods. Allelic and genotypic association analyses showed that six variants were associated with PCa and the cumulative effect suggested men who carried any combination of 1, 2, or ≥3 risk genotypes had a gradually increased PCa risk (odds ratios (ORs = 1.79–4.41. GMDR analysis identified the best gene-gene interaction model with scores of 10 for both the cross-validation consistency and sign tests. For gene-environment interactions, rs6983561 CC and rs16901966 GG in individuals with a BMI ≥ 28 had ORs of 7.66 (p = 0.032 and 5.33 (p = 0.046, respectively. rs7679673 CC + CA and rs12653946 TT in individuals that smoked had ORs of 2.77 (p = 0.007 and 3.11 (p = 0.024, respectively. rs7679673 CC in individuals that consumed alcohol had an OR of 4.37 (p = 0.041. These results suggest that polymorphisms, either individually or by interacting with other genes or environmental factors, contribute to an increased risk of PCa.

Pulmonary phenotypes associated with genetic variation in telomere-related genes.

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Hoffman, Thijs W; van Moorsel, Coline H M; Borie, Raphael; Crestani, Bruno

2018-05-01

Genomic mutations in telomere-related genes have been recognized as a cause of familial forms of idiopathic pulmonary fibrosis (IPF). However, it has become increasingly clear that telomere syndromes and telomere shortening are associated with various types of pulmonary disease. Additionally, it was found that also single nucleotide polymorphisms (SNPs) in telomere-related genes are risk factors for the development of pulmonary disease. This review focuses on recent updates on pulmonary phenotypes associated with genetic variation in telomere-related genes. Genomic mutations in seven telomere-related genes cause pulmonary disease. Pulmonary phenotypes associated with these mutations range from many forms of pulmonary fibrosis to emphysema and pulmonary vascular disease. Telomere-related mutations account for up to 10% of sporadic IPF, 25% of familial IPF, 10% of connective-tissue disease-associated interstitial lung disease, and 1% of COPD. Mixed disease forms have also been found. Furthermore, SNPs in TERT, TERC, OBFC1, and RTEL1, as well as short telomere length, have been associated with several pulmonary diseases. Treatment of pulmonary disease caused by telomere-related gene variation is currently based on disease diagnosis and not on the underlying cause. Pulmonary phenotypes found in carriers of telomere-related gene mutations and SNPs are primarily pulmonary fibrosis, sometimes emphysema and rarely pulmonary vascular disease. Genotype-phenotype relations are weak, suggesting that environmental factors and genetic background of patients determine disease phenotypes to a large degree. A disease model is presented wherever genomic variation in telomere-related genes cause specific pulmonary disease phenotypes whenever triggered by environmental exposure, comorbidity, or unknown factors.

Primetime for Learning Genes.

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Keifer, Joyce

2017-02-11

Learning genes in mature neurons are uniquely suited to respond rapidly to specific environmental stimuli. Expression of individual learning genes, therefore, requires regulatory mechanisms that have the flexibility to respond with transcriptional activation or repression to select appropriate physiological and behavioral responses. Among the mechanisms that equip genes to respond adaptively are bivalent domains. These are specific histone modifications localized to gene promoters that are characteristic of both gene activation and repression, and have been studied primarily for developmental genes in embryonic stem cells. In this review, studies of the epigenetic regulation of learning genes in neurons, particularly the brain-derived neurotrophic factor gene ( BDNF ), by methylation/demethylation and chromatin modifications in the context of learning and memory will be highlighted. Because of the unique function of learning genes in the mature brain, it is proposed that bivalent domains are a characteristic feature of the chromatin landscape surrounding their promoters. This allows them to be "poised" for rapid response to activate or repress gene expression depending on environmental stimuli.

Learning gene networks under SNP perturbations using eQTL datasets.

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Lingxue Zhang

2014-02-01

Full Text Available The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network

Identification of highly synchronized subnetworks from gene expression data.

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Gao, Shouguo; Wang, Xujing

2013-01-01

There has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously. Here we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search. Application of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization. Inclusion of interaction dynamics is important to the identification of relevant gene networks.

Characterization of Genes for Beef Marbling Based on Applying Gene Coexpression Network

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Dajeong Lim

2014-01-01

Full Text Available Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in M. longissimus with divergent marbling phenotype (marbling scores 2 to 7 using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60 and dihydropyrimidine dehydrogenase (DPYD are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness.

Comparative genome analysis of PHB gene family reveals deep evolutionary origins and diverse gene function.

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Di, Chao; Xu, Wenying; Su, Zhen; Yuan, Joshua S

2010-10-07

PHB (Prohibitin) gene family is involved in a variety of functions important for different biological processes. PHB genes are ubiquitously present in divergent species from prokaryotes to eukaryotes. Human PHB genes have been found to be associated with various diseases. Recent studies by our group and others have shown diverse function of PHB genes in plants for development, senescence, defence, and others. Despite the importance of the PHB gene family, no comprehensive gene family analysis has been carried to evaluate the relatedness of PHB genes across different species. In order to better guide the gene function analysis and understand the evolution of the PHB gene family, we therefore carried out the comparative genome analysis of the PHB genes across different kingdoms. The relatedness, motif distribution, and intron/exon distribution all indicated that PHB genes is a relatively conserved gene family. The PHB genes can be classified into 5 classes and each class have a very deep evolutionary origin. The PHB genes within the class maintained the same motif patterns during the evolution. With Arabidopsis as the model species, we found that PHB gene intron/exon structure and domains are also conserved during the evolution. Despite being a conserved gene family, various gene duplication events led to the expansion of the PHB genes. Both segmental and tandem gene duplication were involved in Arabidopsis PHB gene family expansion. However, segmental duplication is predominant in Arabidopsis. Moreover, most of the duplicated genes experienced neofunctionalization. The results highlighted that PHB genes might be involved in important functions so that the duplicated genes are under the evolutionary pressure to derive new function. PHB gene family is a conserved gene family and accounts for diverse but important biological functions based on the similar molecular mechanisms. The highly diverse biological function indicated that more research needs to be carried out

Genes and Hearing Loss

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... ENTCareers Marketplace Find an ENT Doctor Near You Genes and Hearing Loss Genes and Hearing Loss Patient ... mutation may only have dystopia canthorum. How Do Genes Work? Genes are a road map for the ...

Establishing gene models from the Pinus pinaster genome using gene capture and BAC sequencing.

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Seoane-Zonjic, Pedro; Cañas, Rafael A; Bautista, Rocío; Gómez-Maldonado, Josefa; Arrillaga, Isabel; Fernández-Pozo, Noé; Claros, M Gonzalo; Cánovas, Francisco M; Ávila, Concepción

2016-02-27

In the era of DNA throughput sequencing, assembling and understanding gymnosperm mega-genomes remains a challenge. Although drafts of three conifer genomes have recently been published, this number is too low to understand the full complexity of conifer genomes. Using techniques focused on specific genes, gene models can be established that can aid in the assembly of gene-rich regions, and this information can be used to compare genomes and understand functional evolution. In this study, gene capture technology combined with BAC isolation and sequencing was used as an experimental approach to establish de novo gene structures without a reference genome. Probes were designed for 866 maritime pine transcripts to sequence genes captured from genomic DNA. The gene models were constructed using GeneAssembler, a new bioinformatic pipeline, which reconstructed over 82% of the gene structures, and a high proportion (85%) of the captured gene models contained sequences from the promoter regulatory region. In a parallel experiment, the P. pinaster BAC library was screened to isolate clones containing genes whose cDNA sequence were already available. BAC clones containing the asparagine synthetase, sucrose synthase and xyloglucan endotransglycosylase gene sequences were isolated and used in this study. The gene models derived from the gene capture approach were compared with the genomic sequences derived from the BAC clones. This combined approach is a particularly efficient way to capture the genomic structures of gene families with a small number of members. The experimental approach used in this study is a valuable combined technique to study genomic gene structures in species for which a reference genome is unavailable. It can be used to establish exon/intron boundaries in unknown gene structures, to reconstruct incomplete genes and to obtain promoter sequences that can be used for transcriptional studies. A bioinformatics algorithm (GeneAssembler) is also provided as a

Mining tissue specificity, gene connectivity and disease association to reveal a set of genes that modify the action of disease causing genes

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Reverter Antonio

2008-09-01

Full Text Available Abstract Background The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association. Methods We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported. Results Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes. Conclusion We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing

AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data

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Xia Yuannan

2006-12-01

Full Text Available Abstract Background DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challenge issue becomes how to analyze a large amount of microarray data and make biological sense of them. Affymetrix GeneChips are widely used microarrays, where a variety of statistical algorithms have been explored and used for detecting significant genes in the experiment. These methods rely solely on the quantitative data, i.e., signal intensity; however, qualitative data are also important parameters in detecting differentially expressed genes. Results AffyMiner is a tool developed for detecting differentially expressed genes in Affymetrix GeneChip microarray data and for associating gene annotation and gene ontology information with the genes detected. AffyMiner consists of the functional modules, GeneFinder for detecting significant genes in a treatment versus control experiment and GOTree for mapping genes of interest onto the Gene Ontology (GO space; and interfaces to run Cluster, a program for clustering analysis, and GenMAPP, a program for pathway analysis. AffyMiner has been used for analyzing the GeneChip data and the results were presented in several publications. Conclusion AffyMiner fills an important gap in finding differentially expressed genes in Affymetrix GeneChip microarray data. AffyMiner effectively deals with multiple replicates in the experiment and takes into account both quantitative and qualitative data in identifying significant genes. AffyMiner reduces the time and effort needed to compare data from multiple arrays and to interpret the possible biological implications associated with significant changes in a gene's expression.

Gene therapy in periodontics.

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Chatterjee, Anirban; Singh, Nidhi; Saluja, Mini

2013-03-01

GENES are made of DNA - the code of life. They are made up of two types of base pair from different number of hydrogen bonds AT, GC which can be turned into instruction. Everyone inherits genes from their parents and passes them on in turn to their children. Every person's genes are different, and the changes in sequence determine the inherited differences between each of us. Some changes, usually in a single gene, may cause serious diseases. Gene therapy is 'the use of genes as medicine'. It involves the transfer of a therapeutic or working gene copy into specific cells of an individual in order to repair a faulty gene copy. Thus it may be used to replace a faulty gene, or to introduce a new gene whose function is to cure or to favorably modify the clinical course of a condition. It has a promising era in the field of periodontics. Gene therapy has been used as a mode of tissue engineering in periodontics. The tissue engineering approach reconstructs the natural target tissue by combining four elements namely: Scaffold, signaling molecules, cells and blood supply and thus can help in the reconstruction of damaged periodontium including cementum, gingival, periodontal ligament and bone.

Norrie disease gene is distinct from the monoamine oxidase genes.

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Sims, K B; Ozelius, L; Corey, T; Rinehart, W B; Liberfarb, R; Haines, J; Chen, W J; Norio, R; Sankila, E; de la Chapelle, A

1989-09-01

The genes for MAO-A and MAO-B appear to be very close to the Norrie disease gene, on the basis of loss and/or disruption of the MAO genes and activities in atypical Norrie disease patients deleted for the DXS7 locus; linkage among the MAO genes, the Norrie disease gene, and the DXS7 locus; and mapping of all these loci to the chromosomal region Xp11. The present study provides evidence that the MAO genes are not disrupted in "classic" Norrie disease patients. Genomic DNA from these "nondeletion" Norrie disease patients did not show rearrangements at the MAOA or DXS7 loci. Normal levels of MAO-A activities, as well as normal amounts and size of the MAO-A mRNA, were observed in cultured skin fibroblasts from these patients, and MAO-B activity in their platelets was normal. Catecholamine metabolites evaluated in plasma and urine were in the control range. Thus, although some atypical Norrie disease patients lack both MAO-A and MAO-B activities, MAO does not appear to be an etiologic factor in classic Norrie disease.

Gene profile analysis of osteoblast genes differentially regulated by histone deacetylase inhibitors

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Lamblin Anne-Francoise

2007-10-01

Full Text Available Abstract Background Osteoblast differentiation requires the coordinated stepwise expression of multiple genes. Histone deacetylase inhibitors (HDIs accelerate the osteoblast differentiation process by blocking the activity of histone deacetylases (HDACs, which alter gene expression by modifying chromatin structure. We previously demonstrated that HDIs and HDAC3 shRNAs accelerate matrix mineralization and the expression of osteoblast maturation genes (e.g. alkaline phosphatase, osteocalcin. Identifying other genes that are differentially regulated by HDIs might identify new pathways that contribute to osteoblast differentiation. Results To identify other osteoblast genes that are altered early by HDIs, we incubated MC3T3-E1 preosteoblasts with HDIs (trichostatin A, MS-275, or valproic acid for 18 hours in osteogenic conditions. The promotion of osteoblast differentiation by HDIs in this experiment was confirmed by osteogenic assays. Gene expression profiles relative to vehicle-treated cells were assessed by microarray analysis with Affymetrix GeneChip 430 2.0 arrays. The regulation of several genes by HDIs in MC3T3-E1 cells and primary osteoblasts was verified by quantitative real-time PCR. Nine genes were differentially regulated by at least two-fold after exposure to each of the three HDIs and six were verified by PCR in osteoblasts. Four of the verified genes (solute carrier family 9 isoform 3 regulator 1 (Slc9a3r1, sorbitol dehydrogenase 1, a kinase anchor protein, and glutathione S-transferase alpha 4 were induced. Two genes (proteasome subunit, beta type 10 and adaptor-related protein complex AP-4 sigma 1 were suppressed. We also identified eight growth factors and growth factor receptor genes that are significantly altered by each of the HDIs, including Frizzled related proteins 1 and 4, which modulate the Wnt signaling pathway. Conclusion This study identifies osteoblast genes that are regulated early by HDIs and indicates pathways that

Acute Vhl gene inactivation induces cardiac HIF-dependent erythropoietin gene expression.

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Marta Miró-Murillo

Full Text Available Von Hippel Lindau (Vhl gene inactivation results in embryonic lethality. The consequences of its inactivation in adult mice, and of the ensuing activation of the hypoxia-inducible factors (HIFs, have been explored mainly in a tissue-specific manner. This mid-gestation lethality can be also circumvented by using a floxed Vhl allele in combination with an ubiquitous tamoxifen-inducible recombinase Cre-ER(T2. Here, we characterize a widespread reduction in Vhl gene expression in Vhl(floxed-UBC-Cre-ER(T2 adult mice after dietary tamoxifen administration, a convenient route of administration that has yet to be fully characterized for global gene inactivation. Vhl gene inactivation rapidly resulted in a marked splenomegaly and skin erythema, accompanied by renal and hepatic induction of the erythropoietin (Epo gene, indicative of the in vivo activation of the oxygen sensing HIF pathway. We show that acute Vhl gene inactivation also induced Epo gene expression in the heart, revealing cardiac tissue to be an extra-renal source of EPO. Indeed, primary cardiomyocytes and HL-1 cardiac cells both induce Epo gene expression when exposed to low O(2 tension in a HIF-dependent manner. Thus, as well as demonstrating the potential of dietary tamoxifen administration for gene inactivation studies in UBC-Cre-ER(T2 mouse lines, this data provides evidence of a cardiac oxygen-sensing VHL/HIF/EPO pathway in adult mice.

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data

OpenAIRE

Ezer, Daphne; Moignard, Victoria; G?ttgens, Berthold; Adryan, Boris

2016-01-01

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete ...

Network Security via Biometric Recognition of Patterns of Gene Expression

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Shaw, Harry C.

2016-01-01

Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT (Information Technology) organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time assays of gene expression products.

Network Security via Biometric Recognition of Patterns of Gene Expression

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Shaw, Harry C.

2016-01-01

Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time expression and assay of gene expression products.

Discovering genes underlying QTL

Energy Technology Data Exchange (ETDEWEB)

Vanavichit, Apichart [Kasetsart University, Kamphaengsaen, Nakorn Pathom (Thailand)

2002-02-01

A map-based approach has allowed scientists to discover few genes at a time. In addition, the reproductive barrier between cultivated rice and wild relatives has prevented us from utilizing the germ plasm by a map-based approach. Most genetic traits important to agriculture or human diseases are manifested as observable, quantitative phenotypes called Quantitative Trait Loci (QTL). In many instances, the complexity of the phenotype/genotype interaction and the general lack of clearly identifiable gene products render the direct molecular cloning approach ineffective, thus additional strategies like genome mapping are required to identify the QTL in question. Genome mapping requires no prior knowledge of the gene function, but utilizes statistical methods to identify the most likely gene location. To completely characterize genes of interest, the initially mapped region of a gene location will have to be narrowed down to a size that is suitable for cloning and sequencing. Strategies for gene identification within the critical region have to be applied after the sequencing of a potentially large clone or set of clones that contains this gene(s). Tremendous success of positional cloning has been shown for cloning many genes responsible for human diseases, including cystic fibrosis and muscular dystrophy as well as plant disease resistance genes. Genome and QTL mapping, positional cloning: the pre-genomics era, comparative approaches to gene identification, and positional cloning: the genomics era are discussed in the report. (M. Suetake)

GSEH: A Novel Approach to Select Prostate Cancer-Associated Genes Using Gene Expression Heterogeneity.

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Kim, Hyunjin; Choi, Sang-Min; Park, Sanghyun

2018-01-01

When a gene shows varying levels of expression among normal people but similar levels in disease patients or shows similar levels of expression among normal people but different levels in disease patients, we can assume that the gene is associated with the disease. By utilizing this gene expression heterogeneity, we can obtain additional information that abets discovery of disease-associated genes. In this study, we used collaborative filtering to calculate the degree of gene expression heterogeneity between classes and then scored the genes on the basis of the degree of gene expression heterogeneity to find "differentially predicted" genes. Through the proposed method, we discovered more prostate cancer-associated genes than 10 comparable methods. The genes prioritized by the proposed method are potentially significant to biological processes of a disease and can provide insight into them.

Validation of reference genes for quantifying changes in gene expression in virus-infected tobacco.

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Baek, Eseul; Yoon, Ju-Yeon; Palukaitis, Peter

2017-10-01

To facilitate quantification of gene expression changes in virus-infected tobacco plants, eight housekeeping genes were evaluated for their stability of expression during infection by one of three systemically-infecting viruses (cucumber mosaic virus, potato virus X, potato virus Y) or a hypersensitive-response-inducing virus (tobacco mosaic virus; TMV) limited to the inoculated leaf. Five reference-gene validation programs were used to establish the order of the most stable genes for the systemically-infecting viruses as ribosomal protein L25 > β-Tubulin > Actin, and the least stable genes Ubiquitin-conjugating enzyme (UCE) genes were EF1α > Cysteine protease > Actin, and the least stable genes were GAPDH genes, three defense responsive genes were examined to compare their relative changes in gene expression caused by each virus. Copyright © 2017 Elsevier Inc. All rights reserved.

Human gene therapy: novel approaches to improve the current gene delivery systems.

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Cucchiarini, Magali

2016-06-01

Even though gene therapy made its way through the clinics to treat a number of human pathologies since the early years of experimental research and despite the recent approval of the first gene-based product (Glybera) in Europe, the safe and effective use of gene transfer vectors remains a challenge in human gene therapy due to the existence of barriers in the host organism. While work is under active investigation to improve the gene transfer systems themselves, the use of controlled release approaches may offer alternative, convenient tools of vector delivery to achieve a performant gene transfer in vivo while overcoming the various physiological barriers that preclude its wide use in patients. This article provides an overview of the most significant contributions showing how the principles of controlled release strategies may be adapted for human gene therapy.

From gene engineering to gene modulation and manipulation: can we prevent or detect gene doping in sports?

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Fischetto, Giuseppe; Bermon, Stéphane

2013-10-01

During the last 2 decades, progress in deciphering the human gene map as well as the discovery of specific defective genes encoding particular proteins in some serious human diseases have resulted in attempts to treat sick patients with gene therapy. There has been considerable focus on human recombinant proteins which were gene-engineered and produced in vitro (insulin, growth hormone, insulin-like growth factor-1, erythropoietin). Unfortunately, these substances and methods also became improper tools for unscrupulous athletes. Biomedical research has focused on the possible direct insertion of gene material into the body, in order to replace some defective genes in vivo and/or to promote long-lasting endogenous synthesis of deficient proteins. Theoretically, diabetes, anaemia, muscular dystrophies, immune deficiency, cardiovascular diseases and numerous other illnesses could benefit from such innovative biomedical research, though much work remains to be done. Considering recent findings linking specific genotypes and physical performance, it is tempting to submit the young athletic population to genetic screening or, alternatively, to artificial gene expression modulation. Much research is already being conducted in order to achieve a safe transfer of genetic material to humans. This is of critical importance since uncontrolled production of the specifically coded protein, with serious secondary adverse effects (polycythaemia, acute cardiovascular problems, cancer, etc.), could occur. Other unpredictable reactions (immunogenicity of vectors or DNA-vector complex, autoimmune anaemia, production of wild genetic material) also remain possible at the individual level. Some new substances (myostatin blockers or anti-myostatin antibodies), although not gene material, might represent a useful and well-tolerated treatment to prevent progression of muscular dystrophies. Similarly, other molecules, in the roles of gene or metabolic activators [5-aminoimidazole-4

A model of gene-gene and gene-environment interactions and its implications for targeting environmental interventions by genotype

Directory of Open Access Journals (Sweden)

Wallace Helen M

2006-10-01

Full Text Available Abstract Background The potential public health benefits of targeting environmental interventions by genotype depend on the environmental and genetic contributions to the variance of common diseases, and the magnitude of any gene-environment interaction. In the absence of prior knowledge of all risk factors, twin, family and environmental data may help to define the potential limits of these benefits in a given population. However, a general methodology to analyze twin data is required because of the potential importance of gene-gene interactions (epistasis, gene-environment interactions, and conditions that break the 'equal environments' assumption for monozygotic and dizygotic twins. Method A new model for gene-gene and gene-environment interactions is developed that abandons the assumptions of the classical twin study, including Fisher's (1918 assumption that genes act as risk factors for common traits in a manner necessarily dominated by an additive polygenic term. Provided there are no confounders, the model can be used to implement a top-down approach to quantifying the potential utility of genetic prediction and prevention, using twin, family and environmental data. The results describe a solution space for each disease or trait, which may or may not include the classical twin study result. Each point in the solution space corresponds to a different model of genotypic risk and gene-environment interaction. Conclusion The results show that the potential for reducing the incidence of common diseases using environmental interventions targeted by genotype may be limited, except in special cases. The model also confirms that the importance of an individual's genotype in determining their risk of complex diseases tends to be exaggerated by the classical twin studies method, owing to the 'equal environments' assumption and the assumption of no gene-environment interaction. In addition, if phenotypes are genetically robust, because of epistasis

Characteristics of functional enrichment and gene expression level of human putative transcriptional target genes.

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Osato, Naoki

2018-01-19

Transcriptional target genes show functional enrichment of genes. However, how many and how significantly transcriptional target genes include functional enrichments are still unclear. To address these issues, I predicted human transcriptional target genes using open chromatin regions, ChIP-seq data and DNA binding sequences of transcription factors in databases, and examined functional enrichment and gene expression level of putative transcriptional target genes. Gene Ontology annotations showed four times larger numbers of functional enrichments in putative transcriptional target genes than gene expression information alone, independent of transcriptional target genes. To compare the number of functional enrichments of putative transcriptional target genes between cells or search conditions, I normalized the number of functional enrichment by calculating its ratios in the total number of transcriptional target genes. With this analysis, native putative transcriptional target genes showed the largest normalized number of functional enrichments, compared with target genes including 5-60% of randomly selected genes. The normalized number of functional enrichments was changed according to the criteria of enhancer-promoter interactions such as distance from transcriptional start sites and orientation of CTCF-binding sites. Forward-reverse orientation of CTCF-binding sites showed significantly higher normalized number of functional enrichments than the other orientations. Journal papers showed that the top five frequent functional enrichments were related to the cellular functions in the three cell types. The median expression level of transcriptional target genes changed according to the criteria of enhancer-promoter assignments (i.e. interactions) and was correlated with the changes of the normalized number of functional enrichments of transcriptional target genes. Human putative transcriptional target genes showed significant functional enrichments. Functional

Conditional gene expression in the mouse using a Sleeping Beauty gene-trap transposon

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Hackett Perry B

2006-06-01

Full Text Available Abstract Background Insertional mutagenesis techniques with transposable elements have been popular among geneticists studying model organisms from E. coli to Drosophila and, more recently, the mouse. One such element is the Sleeping Beauty (SB transposon that has been shown in several studies to be an effective insertional mutagen in the mouse germline. SB transposon vector studies have employed different functional elements and reporter molecules to disrupt and report the expression of endogenous mouse genes. We sought to generate a transposon system that would be capable of reporting the expression pattern of a mouse gene while allowing for conditional expression of a gene of interest in a tissue- or temporal-specific pattern. Results Here we report the systematic development and testing of a transposon-based gene-trap system incorporating the doxycycline-repressible Tet-Off (tTA system that is capable of activating the expression of genes under control of a Tet response element (TRE promoter. We demonstrate that the gene trap system is fully functional in vitro by introducing the "gene-trap tTA" vector into human cells by transposition and identifying clones that activate expression of a TRE-luciferase transgene in a doxycycline-dependent manner. In transgenic mice, we mobilize gene-trap tTA vectors, discover parameters that can affect germline mobilization rates, and identify candidate gene insertions to demonstrate the in vivo functionality of the vector system. We further demonstrate that the gene-trap can act as a reporter of endogenous gene expression and it can be coupled with bioluminescent imaging to identify genes with tissue-specific expression patterns. Conclusion Akin to the GAL4/UAS system used in the fly, we have made progress developing a tool for mutating and revealing the expression of mouse genes by generating the tTA transactivator in the presence of a secondary TRE-regulated reporter molecule. A vector like the gene

Microsatellites grant more stable flanking genes

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Joukhadar Reem

2012-10-01

Full Text Available Abstract Background Microsatellites, or simple sequence repeats (SSRs, are DNA sequences that include tandem copies of specific sequences no longer than six bases. SSRs are ubiquitous in all genomes and highly mutable. Presentation of the hypothesis Results from previous studies suggest that flanking regions of SSR are exhibit high stability in a wide range of organisms. We hypothesized that the SSRs ability to discard weak DNA polymerases could be responsible for this unusual stability. . When the weak polymerases are being decayed over SSRs, the flanking sequences would have higher opportunity to be replicated by more stable DNA polymerases. We present evidence of the molecular basis of our hypothesis. Testing the hypothesis The hypothesis could be tested by examining the activity of DNA polymerase during and after a number of PCRs. The PCR reactions should be run with the same SSR locus possessing differences in the SSR length. The hypothesis could also be tested by comparing the mutational rate of a transferred gene between two transformations. The first one has a naked T-DNA (transferred DNA, while the second one has the same T-DNA flanked with two SSRs. Implications of the hypothesis In any transformation experiment, flanking the T-DNA fragment with SSR sequences would result in more stably transferred genes. This process would decrease the unpredictable risks that may occur because of the mutational pressure on this foreign segment.

Oleocanthal Modulates Estradiol-Induced Gene Expression Involving Estrogen Receptor α.

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Keiler, Annekathrin Martina; Djiogue, Sefirin; Ehrhardt, Tino; Zierau, Oliver; Skaltsounis, Leandros; Halabalaki, Maria; Vollmer, Günter

2015-09-01

Oleocanthal is a bioactive compound from olive oil. It has attracted considerable attention as it is anti-inflammatory, antiproliferative, and has been shown to possess neuroprotective properties in vitro and in vivo. Delineated from its polyphenolic structure, the aim of this study was to characterize oleocanthal towards estrogenic properties. This might contribute to partly explain the beneficial effects described for the Mediterranean diet. Estrogenic properties of oleocanthal were assessed by different methods: a) stimulation of reporter gene activity in MVLN or RNDA cells either expressing estrogen receptor α or β, b) stimulation of luciferase reporter gene activity in U2OS osteosarcoma cells expressing estrogen receptor α or β, and c) elucidation of the impact on estradiol-induced gene expression in U2OS cells transduced with both estrogen receptors. Depending on the cell line origin, oleocanthal inhibited luciferase activity (MVLN, U2OS-estrogen receptor β) or weakly induced reporter gene activity at 10 µM in U2OS-estrogen receptor α cells. However, oleocanthal inhibited stimulation of luciferase activity by estradiol from both estrogen receptors. Oleocanthal, if given alone, did not stimulate gene expression in U2OS cells, but it significantly modulated the response of estradiol. Oleocanthal enhanced the effect of estradiol on the regulation of those genes, which are believed to be regulated through heterodimeric estrogen receptors. As the estrogenic response pattern of oleocanthal is rather unique, we compared the results obtained with oleacein. Oleocanthal binds to both estrogen receptors inducing estradiol-agonistic or antiagonistic effects depending on the cell line. Regarding regulation of gene expression in U2OS-estrogen receptor α/β cells, oleocanthal and oleacein enhanced estradiol-mediated regulation of heterodimer-regulated genes. Georg Thieme Verlag KG Stuttgart · New York.

Essential Bacillus subtilis genes

DEFF Research Database (Denmark)

Kobayashi, K.; Ehrlich, S.D.; Albertini, A.

2003-01-01

To estimate the minimal gene set required to sustain bacterial life in nutritious conditions, we carried out a systematic inactivation of Bacillus subtilis genes. Among approximate to4,100 genes of the organism, only 192 were shown to be indispensable by this or previous work. Another 79 genes were...... predicted to be essential. The vast majority of essential genes were categorized in relatively few domains of cell metabolism, with about half involved in information processing, one-fifth involved in the synthesis of cell envelope and the determination of cell shape and division, and one-tenth related...... to cell energetics. Only 4% of essential genes encode unknown functions. Most essential genes are present throughout a wide range of Bacteria, and almost 70% can also be found in Archaea and Eucarya. However, essential genes related to cell envelope, shape, division, and respiration tend to be lost from...

Integrative characterization of germ cell-specific genes from mouse spermatocyte UniGene library

Directory of Open Access Journals (Sweden)

Eddy Edward M

2007-07-01

Full Text Available Abstract Background The primary regulator of spermatogenesis, a highly ordered and tightly regulated developmental process, is an intrinsic genetic program involving male germ cell-specific genes. Results We analyzed the mouse spermatocyte UniGene library containing 2155 gene-oriented transcript clusters. We predict that 11% of these genes are testis-specific and systematically identified 24 authentic genes specifically and abundantly expressed in the testis via in silico and in vitro approaches. Northern blot analysis disclosed various transcript characteristics, such as expression level, size and the presence of isoform. Expression analysis revealed developmentally regulated and stage-specific expression patterns in all of the genes. We further analyzed the genes at the protein and cellular levels. Transfection assays performed using GC-2 cells provided information on the cellular characteristics of the gene products. In addition, antibodies were generated against proteins encoded by some of the genes to facilitate their identification and characterization in spermatogenic cells and sperm. Our data suggest that a number of the gene products are implicated in transcriptional regulation, nuclear integrity, sperm structure and motility, and fertilization. In particular, we found for the first time that Mm.333010, predicted to contain a trypsin-like serine protease domain, is a sperm acrosomal protein. Conclusion We identify 24 authentic genes with spermatogenic cell-specific expression, and provide comprehensive information about the genes. Our findings establish a new basis for future investigation into molecular mechanisms underlying male reproduction.

Sequence-based model of gap gene regulatory network.

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Kozlov, Konstantin; Gursky, Vitaly; Kulakovskiy, Ivan; Samsonova, Maria

2014-01-01

The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded. The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3

Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

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Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

2017-10-03

With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

A new type of gene-disruption cassette with a rescue gene for Pichia pastoris.

Science.gov (United States)

Shibui, Tatsuro; Hara, Hiroyoshi

2017-09-01

Pichia pastoris has been used for the production of many recombinant proteins, and many useful mutant strains have been created. However, the efficiency of mutant isolation by gene-targeting is usually low and the procedure is difficult for those inexperienced in yeast genetics. In order to overcome these issues, we developed a new gene-disruption system with a rescue gene using an inducible Cre/mutant-loxP system. With only short homology regions, the gene-disruption cassette of the system replaces its target-gene locus containing a mutation with a compensatory rescue gene. As the cassette contains the AOX1 promoter-driven Cre gene, when targeted strains are grown on media containing methanol, the DNA fragment, i.e., the marker, rescue and Cre genes, between the mutant-loxP sequences in the cassette is excised, leaving only the remaining mutant-loxP sequence in the genome, and consequently a target gene-disrupted mutant can be isolated. The system was initially validated on ADE2 gene disruption, where the disruption can easily be detected by color-change of the colonies. Then, the system was applied for knocking-out URA3 and OCH1 genes, reported to be difficult to accomplish by conventional gene-targeting methods. All three gene-disruption cassettes with their rescue genes replaced their target genes, and the Cre/mutant-loxP system worked well to successfully isolate their knock-out mutants. This study identified a new gene-disruption system that could be used to effectively and strategically knock out genes of interest, especially whose deletion is detrimental to growth, without using special strains, e.g., deficient in nonhomologous end-joining, in P. pastoris. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1201-1208, 2017. © 2017 American Institute of Chemical Engineers.

Prioritization of candidate disease genes by combining topological similarity and semantic similarity.

Science.gov (United States)

Liu, Bin; Jin, Min; Zeng, Pan

2015-10-01

The identification of gene-phenotype relationships is very important for the treatment of human diseases. Studies have shown that genes causing the same or similar phenotypes tend to interact with each other in a protein-protein interaction (PPI) network. Thus, many identification methods based on the PPI network model have achieved good results. However, in the PPI network, some interactions between the proteins encoded by candidate gene and the proteins encoded by known disease genes are very weak. Therefore, some studies have combined the PPI network with other genomic information and reported good predictive performances. However, we believe that the results could be further improved. In this paper, we propose a new method that uses the semantic similarity between the candidate gene and known disease genes to set the initial probability vector of a random walk with a restart algorithm in a human PPI network. The effectiveness of our method was demonstrated by leave-one-out cross-validation, and the experimental results indicated that our method outperformed other methods. Additionally, our method can predict new causative genes of multifactor diseases, including Parkinson's disease, breast cancer and obesity. The top predictions were good and consistent with the findings in the literature, which further illustrates the effectiveness of our method. Copyright © 2015 Elsevier Inc. All rights reserved.

Discovery of cancer common and specific driver gene sets

Science.gov (United States)

2017-01-01

Abstract Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found. PMID:28168295

A recently transferred cluster of bacterial genes in Trichomonas vaginalis - lateral gene transfer and the fate of acquired genes

Science.gov (United States)

2014-01-01

Background Lateral Gene Transfer (LGT) has recently gained recognition as an important contributor to some eukaryote proteomes, but the mechanisms of acquisition and fixation in eukaryotic genomes are still uncertain. A previously defined norm for LGTs in microbial eukaryotes states that the majority are genes involved in metabolism, the LGTs are typically localized one by one, surrounded by vertically inherited genes on the chromosome, and phylogenetics shows that a broad collection of bacterial lineages have contributed to the transferome. Results A unique 34 kbp long fragment with 27 clustered genes (TvLF) of prokaryote origin was identified in the sequenced genome of the protozoan parasite Trichomonas vaginalis. Using a PCR based approach we confirmed the presence of the orthologous fragment in four additional T. vaginalis strains. Detailed sequence analyses unambiguously suggest that TvLF is the result of one single, recent LGT event. The proposed donor is a close relative to the firmicute bacterium Peptoniphilus harei. High nucleotide sequence similarity between T. vaginalis strains, as well as to P. harei, and the absence of homologs in other Trichomonas species, suggests that the transfer event took place after the radiation of the genus Trichomonas. Some genes have undergone pseudogenization and degradation, indicating that they may not be retained in the future. Functional annotations reveal that genes involved in informational processes are particularly prone to degradation. Conclusions We conclude that, although the majority of eukaryote LGTs are single gene occurrences, they may be acquired in clusters of several genes that are subsequently cleansed of evolutionarily less advantageous genes. PMID:24898731

Gene-based Association Approach Identify Genes Across Stress Traits in Fruit Flies

DEFF Research Database (Denmark)

Rohde, Palle Duun; Edwards, Stefan McKinnon; Sarup, Pernille Merete

Identification of genes explaining variation in quantitative traits or genetic risk factors of human diseases requires both good phenotypic- and genotypic data, but also efficient statistical methods. Genome-wide association studies may reveal association between phenotypic variation and variation...... approach grouping variants accordingly to gene position, thus lowering the number of statistical tests performed and increasing the probability of identifying genes with small to moderate effects. Using this approach we identify numerous genes associated with different types of stresses in Drosophila...... melanogaster, but also identify common genes that affects the stress traits....

Gene doping.

Science.gov (United States)

Haisma, H J; de Hon, O

2006-04-01

Together with the rapidly increasing knowledge on genetic therapies as a promising new branch of regular medicine, the issue has arisen whether these techniques might be abused in the field of sports. Previous experiences have shown that drugs that are still in the experimental phases of research may find their way into the athletic world. Both the World Anti-Doping Agency (WADA) and the International Olympic Committee (IOC) have expressed concerns about this possibility. As a result, the method of gene doping has been included in the list of prohibited classes of substances and prohibited methods. This review addresses the possible ways in which knowledge gained in the field of genetic therapies may be misused in elite sports. Many genes are readily available which may potentially have an effect on athletic performance. The sporting world will eventually be faced with the phenomena of gene doping to improve athletic performance. A combination of developing detection methods based on gene arrays or proteomics and a clear education program on the associated risks seems to be the most promising preventive method to counteract the possible application of gene doping.

Systematic study of association of four GABAergic genes: glutamic acid decarboxylase 1 gene, glutamic acid decarboxylase 2 gene, GABA(B) receptor 1 gene and GABA(A) receptor subunit beta2 gene, with schizophrenia using a universal DNA microarray.

Science.gov (United States)

Zhao, Xu; Qin, Shengying; Shi, Yongyong; Zhang, Aiping; Zhang, Jing; Bian, Li; Wan, Chunling; Feng, Guoyin; Gu, Niufan; Zhang, Guangqi; He, Guang; He, Lin

2007-07-01

Several studies have suggested the dysfunction of the GABAergic system as a risk factor in the pathogenesis of schizophrenia. In the present study, case-control association analysis was conducted in four GABAergic genes: two glutamic acid decarboxylase genes (GAD1 and GAD2), a GABA(A) receptor subunit beta2 gene (GABRB2) and a GABA(B) receptor 1 gene (GABBR1). Using a universal DNA microarray procedure we genotyped a total of 20 SNPs on the above four genes in a study involving 292 patients and 286 controls of Chinese descent. Statistically significant differences were observed in the allelic frequencies of the rs187269C/T polymorphism in the GABRB2 gene (P=0.0450, chi(2)=12.40, OR=1.65) and the -292A/C polymorphism in the GAD1 gene (P=0.0450, chi(2)=14.64 OR=1.77). In addition, using an electrophoretic mobility shift assay (EMSA), we discovered differences in the U251 nuclear protein binding to oligonucleotides representing the -292 SNP on the GAD1 gene, which suggests that the -292C allele has reduced transcription factor binding efficiency compared with the 292A allele. Using the multifactor-dimensionality reduction method (MDR), we found that the interactions among the rs187269C/T polymorphism in the GABRB2 gene, the -243A/G polymorphism in the GAD2 gene and the 27379C/T and 661C/T polymorphisms in the GAD1 gene revealed a significant association with schizophrenia (Pschizophrenia in the Chinese population.

Identification, gene expression and immune function of the novel Bm-STAT gene in virus-infected Bombyx mori.

Science.gov (United States)

Zhang, Xiaoli; Guo, Rui; Kumar, Dhiraj; Ma, Huanyan; Liu, Jiabin; Hu, Xiaolong; Cao, Guangli; Xue, Renyu; Gong, Chengliang

2016-02-10

Genes in the signal transducer and activator of transcription (STAT) family are vital for activities including gene expression and immune response. To investigate the functions of the silkworm Bombyx mori STAT (Bm-STAT) gene in antiviral immunity, two Bm-STAT gene isoforms, Bm-STAT-L for long form and Bm-STAT-S for short form, were cloned. Sequencing showed that the open reading frames were 2313 bp encoding 770 amino acid residues for Bm-STAT-L and 2202 bp encoding 734 amino acid residues for Bm-STAT-S. The C-terminal 42 amino acid residues of Bm-STAT-L were different from the last 7 amino acid residues of Bm-STAT-S. Immunofluorescence showed that Bm-STAT was primarily distributed in the nucleus. Transcription levels of Bm-STAT in different tissues were determined by quantitative PCR, and the results revealed Bm-STAT was mainly expressed in testes. Western blots showed two bands with molecular weights of 70 kDa and 130 kDa in testes, but no bands were detected in ovaries by using anti-Bm-STAT antibody as the primary antibody. Expression of Bm-STAT in hemolymph at 48 h post infection with B. mori macula-like virus (BmMLV) was slightly enhanced compared with controls, suggesting a weak response induced by infection with BmMLV. Hemocyte immunofluorescence showed that Bm-STAT expression was elevated in B. mori nucleopolyhedrovirus (BmNPV)-infected cells. Moreover, resistance of BmN cells to BmNPV was reduced by downregulation of Bm-STAT expression and increased by upregulation. Resistance of BmN cells to BmCPV was not significantly improved by upregulating Bm-STAT expression. Therefore, we concluded that Bm-STAT is a newly identified insect gene of the STAT family. The JAK-STAT pathway has a more specialized role in antiviral defense in silkworms, but JAK-STAT pathway is not triggered in response to all viruses. Copyright © 2015 Elsevier B.V. All rights reserved.

Relationships between protein-encoding gene abundance and corresponding process are commonly assumed yet rarely observed

Science.gov (United States)

Rocca, Jennifer D.; Hall, Edward K.; Lennon, Jay T.; Evans, Sarah E.; Waldrop, Mark P.; Cotner, James B.; Nemergut, Diana R.; Graham, Emily B.; Wallenstein, Matthew D.

2015-01-01

For any enzyme-catalyzed reaction to occur, the corresponding protein-encoding genes and transcripts are necessary prerequisites. Thus, a positive relationship between the abundance of gene or transcripts and corresponding process rates is often assumed. To test this assumption, we conducted a meta-analysis of the relationships between gene and/or transcript abundances and corresponding process rates. We identified 415 studies that quantified the abundance of genes or transcripts for enzymes involved in carbon or nitrogen cycling. However, in only 59 of these manuscripts did the authors report both gene or transcript abundance and rates of the appropriate process. We found that within studies there was a significant but weak positive relationship between gene abundance and the corresponding process. Correlations were not strengthened by accounting for habitat type, differences among genes or reaction products versus reactants, suggesting that other ecological and methodological factors may affect the strength of this relationship. Our findings highlight the need for fundamental research on the factors that control transcription, translation and enzyme function in natural systems to better link genomic and transcriptomic data to ecosystem processes.

Ranking of Prokaryotic Genomes Based on Maximization of Sortedness of Gene Lengths.

Science.gov (United States)

Bolshoy, A; Salih, B; Cohen, I; Tatarinova, T

How variations of gene lengths (some genes become longer than their predecessors, while other genes become shorter and the sizes of these factions are randomly different from organism to organism) depend on organismal evolution and adaptation is still an open question. We propose to rank the genomes according to lengths of their genes, and then find association between the genome rank and variousproperties, such as growth temperature, nucleotide composition, and pathogenicity. This approach reveals evolutionary driving factors. The main purpose of this study is to test effectiveness and robustness of several ranking methods. The selected method of evaluation is measuring of overall sortedness of the data. We have demonstrated that all considered methods give consistent results and Bubble Sort and Simulated Annealing achieve the highest sortedness. Also, Bubble Sort is considerably faster than the Simulated Annealing method.

Exploring the key genes and pathways in enchondromas using a gene expression microarray.

Science.gov (United States)

Shi, Zhongju; Zhou, Hengxing; Pan, Bin; Lu, Lu; Kang, Yi; Liu, Lu; Wei, Zhijian; Feng, Shiqing

2017-07-04

Enchondromas are the most common primary benign osseous neoplasms that occur in the medullary bone; they can undergo malignant transformation into chondrosarcoma. However, enchondromas are always undetected in patients, and the molecular mechanism is unclear. To identify key genes and pathways associated with the occurrence and development of enchondromas, we downloaded the gene expression dataset GSE22855 and obtained the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in enchondromas. In total, 635 genes were identified as DEGs. Of these, 225 genes (35.43%) were up-regulated, and the remaining 410 genes (64.57%) were down-regulated. We identified the predominant gene ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were significantly over-represented in the enchondromas samples compared with the control samples. Subsequently the top 10 core genes were identified from the protein-protein interaction (PPI) network. The enrichment analyses of the genes mainly involved in two significant modules showed that the DEGs were principally related to ribosomes, protein digestion and absorption, ECM-receptor interaction, focal adhesion, amoebiasis and the PI3K-Akt signaling pathway.Together, these data elucidate the molecular mechanisms underlying the occurrence and development of enchondromas and provide promising candidates for therapeutic intervention and prognostic evaluation. However, further experimental studies are needed to confirm these results.

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life

OpenAIRE

Kannan, Lavanya; Li, Hua; Rubinstein, Boris; Mushegian, Arcady

2013-01-01

Background The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. Results We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes...

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks.

Science.gov (United States)

Saik, Olga V; Demenkov, Pavel S; Ivanisenko, Timofey V; Bragina, Elena Yu; Freidin, Maxim B; Goncharova, Irina A; Dosenko, Victor E; Zolotareva, Olga I; Hofestaedt, Ralf; Lavrik, Inna N; Rogaev, Evgeny I; Ivanisenko, Vladimir A

2018-02-13

Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in

Tumor targeted gene therapy

International Nuclear Information System (INIS)

Kang, Joo Hyun

2006-01-01

Knowledge of molecular mechanisms governing malignant transformation brings new opportunities for therapeutic intervention against cancer using novel approaches. One of them is gene therapy based on the transfer of genetic material to an organism with the aim of correcting a disease. The application of gene therapy to the cancer treatment had led to the development of new experimental approaches such as suicidal gene therapy, inhibition of oncogenes and restoration of tumor-suppressor genes. Suicidal gene therapy is based on the expression in tumor cells of a gene encoding an enzyme that converts a prodrug into a toxic product. Representative suicidal genes are Herpes simplex virus type 1 thymidine kinase (HSV1-tk) and cytosine deaminase (CD). Especially, physicians and scientists of nuclear medicine field take an interest in suicidal gene therapy because they can monitor the location and magnitude, and duration of expression of HSV1-tk and CD by PET scanner

Bayesian median regression for temporal gene expression data

Science.gov (United States)

Yu, Keming; Vinciotti, Veronica; Liu, Xiaohui; 't Hoen, Peter A. C.

2007-09-01

Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.

Religion, fertility and genes: a dual inheritance model.

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Rowthorn, Robert

2011-08-22

Religious people nowadays have more children on average than their secular counterparts. This paper uses a simple model to explore the evolutionary implications of this difference. It assumes that fertility is determined entirely by culture, whereas subjective predisposition towards religion is influenced by genetic endowment. People who carry a certain 'religiosity' gene are more likely than average to become or remain religious. The paper considers the effect of religious defections and exogamy on the religious and genetic composition of society. Defections reduce the ultimate share of the population with religious allegiance and slow down the spread of the religiosity gene. However, provided the fertility differential persists, and people with a religious allegiance mate mainly with people like themselves, the religiosity gene will eventually predominate despite a high rate of defection. This is an example of 'cultural hitch-hiking', whereby a gene spreads because it is able to hitch a ride with a high-fitness cultural practice. The theoretical arguments are supported by numerical simulations.

Reconstruction of ribosomal RNA genes from metagenomic data.

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Lu Fan

Full Text Available Direct sequencing of environmental DNA (metagenomics has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes.

PCR-based detection of gene transfer vectors: application to gene doping surveillance.

Science.gov (United States)

Perez, Irene C; Le Guiner, Caroline; Ni, Weiyi; Lyles, Jennifer; Moullier, Philippe; Snyder, Richard O

2013-12-01

Athletes who illicitly use drugs to enhance their athletic performance are at risk of being banned from sports competitions. Consequently, some athletes may seek new doping methods that they expect to be capable of circumventing detection. With advances in gene transfer vector design and therapeutic gene transfer, and demonstrations of safety and therapeutic benefit in humans, there is an increased probability of the pursuit of gene doping by athletes. In anticipation of the potential for gene doping, assays have been established to directly detect complementary DNA of genes that are top candidates for use in doping, as well as vector control elements. The development of molecular assays that are capable of exposing gene doping in sports can serve as a deterrent and may also identify athletes who have illicitly used gene transfer for performance enhancement. PCR-based methods to detect foreign DNA with high reliability, sensitivity, and specificity include TaqMan real-time PCR, nested PCR, and internal threshold control PCR.

Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules.

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Curtis, Ross E; Kim, Seyoung; Woolford, John L; Xu, Wenjie; Xing, Eric P

2013-03-21

Association analysis using genome-wide expression quantitative trait locus (eQTL) data investigates the effect that genetic variation has on cellular pathways and leads to the discovery of candidate regulators. Traditional analysis of eQTL data via pairwise statistical significance tests or linear regression does not leverage the availability of the structural information of the transcriptome, such as presence of gene networks that reveal correlation and potentially regulatory relationships among the study genes. We employ a new eQTL mapping algorithm, GFlasso, which we have previously developed for sparse structured regression, to reanalyze a genome-wide yeast dataset. GFlasso fully takes into account the dependencies among expression traits to suppress false positives and to enhance the signal/noise ratio. Thus, GFlasso leverages the gene-interaction network to discover the pleiotropic effects of genetic loci that perturb the expression level of multiple (rather than individual) genes, which enables us to gain more power in detecting previously neglected signals that are marginally weak but pleiotropically significant. While eQTL hotspots in yeast have been reported previously as genomic regions controlling multiple genes, our analysis reveals additional novel eQTL hotspots and, more interestingly, uncovers groups of multiple contributing eQTL hotspots that affect the expression level of functional gene modules. To our knowledge, our study is the first to report this type of gene regulation stemming from multiple eQTL hotspots. Additionally, we report the results from in-depth bioinformatics analysis for three groups of these eQTL hotspots: ribosome biogenesis, telomere silencing, and retrotransposon biology. We suggest candidate regulators for the functional gene modules that map to each group of hotspots. Not only do we find that many of these candidate regulators contain mutations in the promoter and coding regions of the genes, in the case of the Ribi group

Evolutionary genomics of plant genes encoding N-terminal-TM-C2 domain proteins and the similar FAM62 genes and synaptotagmin genes of metazoans

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Craxton Molly

2007-07-01

Full Text Available Abstract Background Synaptotagmin genes are found in animal genomes and are known to function in the nervous system. Genes with a similar domain architecture as well as sequence similarity to synaptotagmin C2 domains have also been found in plant genomes. The plant genes share an additional region of sequence similarity with a group of animal genes named FAM62. FAM62 genes also have a similar domain architecture. Little is known about the functions of the plant genes and animal FAM62 genes. Indeed, many members of the large and diverse Syt gene family await functional characterization. Understanding the evolutionary relationships among these genes will help to realize the full implications of functional studies and lead to improved genome annotation. Results I collected and compared plant Syt-like sequences from the primary nucleotide sequence databases at NCBI. The collection comprises six groups of plant genes conserved in embryophytes: NTMC2Type1 to NTMC2Type6. I collected and compared metazoan FAM62 sequences and identified some similar sequences from other eukaryotic lineages. I found evidence of RNA editing and alternative splicing. I compared the intron patterns of Syt genes. I also compared Rabphilin and Doc2 genes. Conclusion Genes encoding proteins with N-terminal-transmembrane-C2 domain architectures resembling synaptotagmins, are widespread in eukaryotes. A collection of these genes is presented here. The collection provides a resource for studies of intron evolution. I have classified the collection into homologous gene families according to distinctive patterns of sequence conservation and intron position. The evolutionary histories of these gene families are traceable through the appearance of family members in different eukaryotic lineages. Assuming an intron-rich eukaryotic ancestor, the conserved intron patterns distinctive of individual gene families, indicate independent origins of Syt, FAM62 and NTMC2 genes. Resemblances

Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes.

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Fei Xiao

Full Text Available Combining path consistency (PC algorithms with conditional mutual information (CMI are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference, to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.

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

DEFF Research Database (Denmark)

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

2017-01-01

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

Reference Gene Screening for Analyzing Gene Expression Across Goat Tissue

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Yu Zhang

2013-12-01

Full Text Available Real-time quantitative PCR (qRT-PCR is one of the important methods for investigating the changes in mRNA expression levels in cells and tissues. Selection of the proper reference genes is very important when calibrating the results of real-time quantitative PCR. Studies on the selection of reference genes in goat tissues are limited, despite the economic importance of their meat and dairy products. We used real-time quantitative PCR to detect the expression levels of eight reference gene candidates (18S, TBP, HMBS, YWHAZ, ACTB, HPRT1, GAPDH and EEF1A2 in ten tissues types sourced from Boer goats. The optimal reference gene combination was selected according to the results determined by geNorm, NormFinder and Bestkeeper software packages. The analyses showed that tissue is an important variability factor in genes expression stability. When all tissues were considered, 18S, TBP and HMBS is the optimal reference combination for calibrating quantitative PCR analysis of gene expression from goat tissues. Dividing data set by tissues, ACTB was the most stable in stomach, small intestine and ovary, 18S in heart and spleen, HMBS in uterus and lung, TBP in liver, HPRT1 in kidney and GAPDH in muscle. Overall, this study provided valuable information about the goat reference genes that can be used in order to perform a proper normalisation when relative quantification by qRT-PCR studies is undertaken.

Refining discordant gene trees.

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Górecki, Pawel; Eulenstein, Oliver

2014-01-01

Evolutionary studies are complicated by discordance between gene trees and the species tree in which they evolved. Dealing with discordant trees often relies on comparison costs between gene and species trees, including the well-established Robinson-Foulds, gene duplication, and deep coalescence costs. While these costs have provided credible results for binary rooted gene trees, corresponding cost definitions for non-binary unrooted gene trees, which are frequently occurring in practice, are challenged by biological realism. We propose a natural extension of the well-established costs for comparing unrooted and non-binary gene trees with rooted binary species trees using a binary refinement model. For the duplication cost we describe an efficient algorithm that is based on a linear time reduction and also computes an optimal rooted binary refinement of the given gene tree. Finally, we show that similar reductions lead to solutions for computing the deep coalescence and the Robinson-Foulds costs. Our binary refinement of Robinson-Foulds, gene duplication, and deep coalescence costs for unrooted and non-binary gene trees together with the linear time reductions provided here for computing these costs significantly extends the range of trees that can be incorporated into approaches dealing with discordance.

The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes.

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Marion Ouedraogo

Full Text Available BACKGROUND: There has been a surge in studies linking genome structure and gene expression, with special focus on duplicated genes. Although initially duplicated from the same sequence, duplicated genes can diverge strongly over evolution and take on different functions or regulated expression. However, information on the function and expression of duplicated genes remains sparse. Identifying groups of duplicated genes in different genomes and characterizing their expression and function would therefore be of great interest to the research community. The 'Duplicated Genes Database' (DGD was developed for this purpose. METHODOLOGY: Nine species were included in the DGD. For each species, BLAST analyses were conducted on peptide sequences corresponding to the genes mapped on a same chromosome. Groups of duplicated genes were defined based on these pairwise BLAST comparisons and the genomic location of the genes. For each group, Pearson correlations between gene expression data and semantic similarities between functional GO annotations were also computed when the relevant information was available. CONCLUSIONS: The Duplicated Gene Database provides a list of co-localised and duplicated genes for several species with the available gene co-expression level and semantic similarity value of functional annotation. Adding these data to the groups of duplicated genes provides biological information that can prove useful to gene expression analyses. The Duplicated Gene Database can be freely accessed through the DGD website at http://dgd.genouest.org.

[Key effect genes responding to nerve injury identified by gene ontology and computer pattern recognition].

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Pan, Qian; Peng, Jin; Zhou, Xue; Yang, Hao; Zhang, Wei

2012-07-01

In order to screen out important genes from large gene data of gene microarray after nerve injury, we combine gene ontology (GO) method and computer pattern recognition technology to find key genes responding to nerve injury, and then verify one of these screened-out genes. Data mining and gene ontology analysis of gene chip data GSE26350 was carried out through MATLAB software. Cd44 was selected from screened-out key gene molecular spectrum by comparing genes' different GO terms and positions on score map of principal component. Function interferences were employed to influence the normal binding of Cd44 and one of its ligands, chondroitin sulfate C (CSC), to observe neurite extension. Gene ontology analysis showed that the first genes on score map (marked by red *) mainly distributed in molecular transducer activity, receptor activity, protein binding et al molecular function GO terms. Cd44 is one of six effector protein genes, and attracted us with its function diversity. After adding different reagents into the medium to interfere the normal binding of CSC and Cd44, varying-degree remissions of CSC's inhibition on neurite extension were observed. CSC can inhibit neurite extension through binding Cd44 on the neuron membrane. This verifies that important genes in given physiological processes can be identified by gene ontology analysis of gene chip data.

Radiopharmaceuticals to monitor the expression of transferred genes in gene transfer therapy

International Nuclear Information System (INIS)

Wiebe, L. I.

1997-01-01

The development and application of radiopharmaceuticals has, in many instances, been based on the pharmacological properties of therapeutic agents. The molecular biology-biotechnology revolution has had an important impact on treatment of diseases, in part through the reduced toxicity of 'biologicals', in part because of their specificity for interaction at unique molecular sites and in part because of their selective delivery to the target site. Immunotherapeutic approaches include the use of monoclonal antibodies (MABs), MAB-fragments and chemotactic peptides. Such agents currently form the basis of both diagnostic and immunotherapeutic radiopharmaceuticals. More recently, gene transfer techniques have been advanced to the point that a new molecular approach, gene therapy, has become a reality. Gene therapy offers an opportunity to attack disease at its most fundamental level. The therapeutic mechanism is based on the expression of a specific gene or genes, the product of which will invoke immunological, receptor-based or enzyme-based therapeutic modalities. Several approaches to gene therapy of cancer have been envisioned, the most clinically-advanced concepts involving the introduction of genes that will encode for molecular targets nor normally found in healthy mammalian cells. A number of gene therapy clinical trials are based on the introduction of the Herpes simplex virus type-1 (HSV-1) gene that encodes for viral thymidine kinase (tk+). Once HSV-1 tk+ is expressed in the target (cancer) cell, therapy can be effected by the administration of a highly molecularly-targeted and systemically non-toxic antiviral drug such as ganciclovir. The development of radiodiagnostic imaging in gene therapy will be reviewed, using HSV-1 tk+ and radioiodinated IVFRU as a basis for development of the theme. Molecular targets that could be exploited in gene therapy, other than tk+, will be identified

Radiopharmaceuticals to monitor the expression of transferred genes in gene transfer therapy

Energy Technology Data Exchange (ETDEWEB)

Wiebe, L I [University of Alberta, Edmonton (Canada). Noujaim Institute for Pharmaceutical Oncology Research

1997-10-01

The development and application of radiopharmaceuticals has, in many instances, been based on the pharmacological properties of therapeutic agents. The molecular biology-biotechnology revolution has had an important impact on treatment of diseases, in part through the reduced toxicity of `biologicals`, in part because of their specificity for interaction at unique molecular sites and in part because of their selective delivery to the target site. Immunotherapeutic approaches include the use of monoclonal antibodies (MABs), MAB-fragments and chemotactic peptides. Such agents currently form the basis of both diagnostic and immunotherapeutic radiopharmaceuticals. More recently, gene transfer techniques have been advanced to the point that a new molecular approach, gene therapy, has become a reality. Gene therapy offers an opportunity to attack disease at its most fundamental level. The therapeutic mechanism is based on the expression of a specific gene or genes, the product of which will invoke immunological, receptor-based or enzyme-based therapeutic modalities. Several approaches to gene therapy of cancer have been envisioned, the most clinically-advanced concepts involving the introduction of genes that will encode for molecular targets nor normally found in healthy mammalian cells. A number of gene therapy clinical trials are based on the introduction of the Herpes simplex virus type-1 (HSV-1) gene that encodes for viral thymidine kinase (tk+). Once HSV-1 tk+ is expressed in the target (cancer) cell, therapy can be effected by the administration of a highly molecularly-targeted and systemically non-toxic antiviral drug such as ganciclovir. The development of radiodiagnostic imaging in gene therapy will be reviewed, using HSV-1 tk+ and radioiodinated IVFRU as a basis for development of the theme. Molecular targets that could be exploited in gene therapy, other than tk+, will be identified

Molecular cloning, sequence characterization and expression pattern of Rab18 gene from watermelon (Citrullus lanatus).

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Xinli, Xiao; Lei, Peng

2015-03-04

The complete mRNA sequence of watermelon Rab18 gene was amplified through the rapid amplification of cDNA ends (RACE) method. The full-length mRNA was 1010 bp containing a 645 bp open reading frame, which encodes a protein of 214 amino acids. Sequence analysis revealed that watermelon Rab18 protein shares high homology with the Rab18 of cucumber (99%), muskmelon (98%), Morus notabilis (90%), tomato (89%), wine grape (89%) and potato (88%). Phylogenetic analysis revealed that watermelon Rab18 gene has a closer genetic relationship with Rab18 gene of cucumber and muskmelon. Tissue expression profile analysis indicated that watermelon Rab18 gene was highly expressed in root, stem and leaf, moderately expressed in flower and weakly expressed in fruit.

Next-generation sequencing identifies transportin 3 as the causative gene for LGMD1F.

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Annalaura Torella

Full Text Available Limb-girdle muscular dystrophies (LGMD are genetically and clinically heterogeneous conditions. We investigated a large family with autosomal dominant transmission pattern, previously classified as LGMD1F and mapped to chromosome 7q32. Affected members are characterized by muscle weakness affecting earlier the pelvic girdle and the ileopsoas muscles. We sequenced the whole exome of four family members and identified a shared heterozygous frame-shift variant in the Transportin 3 (TNPO3 gene, encoding a member of the importin-β super-family. The TNPO3 gene is mapped within the LGMD1F critical interval and its 923-amino acid human gene product is also expressed in skeletal muscle. In addition, we identified an isolated case of LGMD with a new missense mutation in the same gene. We localized the mutant TNPO3 around the nucleus, but not inside. The involvement of gene related to the nuclear transport suggests a novel disease mechanism leading to muscular dystrophy.

Identification of a Transcriptionally Forward α Gene and Two υ Genes within the Pigeon (Columba livia) IgH Gene Locus.

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Huang, Tian; Wang, Xifeng; Si, Run; Chi, Hao; Han, Binyue; Han, Haitang; Cao, Gengsheng; Zhao, Yaofeng

2018-06-01

Compared with mammals, the bird Ig genetic system relies on gene conversion to create an Ab repertoire, with inversion of the IgA-encoding gene and very few cases of Ig subclass diversification. Although gene conversion has been studied intensively, class-switch recombination, a mechanism by which the IgH C region is exchanged, has rarely been investigated in birds. In this study, based on the published genome of pigeon ( Columba livia ) and high-throughput transcriptome sequencing of immune-related tissues, we identified a transcriptionally forward α gene and found that the pigeon IgH gene locus is arranged as μ-α-υ1-υ2. In this article, we show that both DNA deletion and inversion may result from IgA and IgY class switching, and similar junction patterns were observed for both types of class-switch recombination. We also identified two subclasses of υ genes in pigeon, which share low sequence identity. Phylogenetic analysis suggests that divergence of the two pigeon υ genes occurred during the early stage of bird evolution. The data obtained in this study provide new insight into class-switch recombination and Ig gene evolution in birds. Copyright © 2018 by The American Association of Immunologists, Inc.

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.

Evaluation of Appropriate Reference Genes for Gene Expression Normalization during Watermelon Fruit Development.

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Qiusheng Kong

Full Text Available Gene expression analysis in watermelon (Citrullus lanatus fruit has drawn considerable attention with the availability of genome sequences to understand the regulatory mechanism of fruit development and to improve its quality. Real-time quantitative reverse-transcription PCR (qRT-PCR is a routine technique for gene expression analysis. However, appropriate reference genes for transcript normalization in watermelon fruits have not been well characterized. The aim of this study was to evaluate the appropriateness of 12 genes for their potential use as reference genes in watermelon fruits. Expression variations of these genes were measured in 48 samples obtained from 12 successive developmental stages of parthenocarpic and fertilized fruits of two watermelon genotypes by using qRT-PCR analysis. Considering the effects of genotype, fruit setting method, and developmental stage, geNorm determined clathrin adaptor complex subunit (ClCAC, β-actin (ClACT, and alpha tubulin 5 (ClTUA5 as the multiple reference genes in watermelon fruit. Furthermore, ClCAC alone or together with SAND family protein (ClSAND was ranked as the single or two best reference genes by NormFinder. By using the top-ranked reference genes to normalize the transcript abundance of phytoene synthase (ClPSY1, a good correlation between lycopene accumulation and ClPSY1 expression pattern was observed in ripening watermelon fruit. These validated reference genes will facilitate the accurate measurement of gene expression in the studies on watermelon fruit biology.

Evaluation of Appropriate Reference Genes for Gene Expression Normalization during Watermelon Fruit Development.

Science.gov (United States)

Kong, Qiusheng; Yuan, Jingxian; Gao, Lingyun; Zhao, Liqiang; Cheng, Fei; Huang, Yuan; Bie, Zhilong

2015-01-01

Gene expression analysis in watermelon (Citrullus lanatus) fruit has drawn considerable attention with the availability of genome sequences to understand the regulatory mechanism of fruit development and to improve its quality. Real-time quantitative reverse-transcription PCR (qRT-PCR) is a routine technique for gene expression analysis. However, appropriate reference genes for transcript normalization in watermelon fruits have not been well characterized. The aim of this study was to evaluate the appropriateness of 12 genes for their potential use as reference genes in watermelon fruits. Expression variations of these genes were measured in 48 samples obtained from 12 successive developmental stages of parthenocarpic and fertilized fruits of two watermelon genotypes by using qRT-PCR analysis. Considering the effects of genotype, fruit setting method, and developmental stage, geNorm determined clathrin adaptor complex subunit (ClCAC), β-actin (ClACT), and alpha tubulin 5 (ClTUA5) as the multiple reference genes in watermelon fruit. Furthermore, ClCAC alone or together with SAND family protein (ClSAND) was ranked as the single or two best reference genes by NormFinder. By using the top-ranked reference genes to normalize the transcript abundance of phytoene synthase (ClPSY1), a good correlation between lycopene accumulation and ClPSY1 expression pattern was observed in ripening watermelon fruit. These validated reference genes will facilitate the accurate measurement of gene expression in the studies on watermelon fruit biology.

Gene composer: database software for protein construct design, codon engineering, and gene synthesis.

Science.gov (United States)

Lorimer, Don; Raymond, Amy; Walchli, John; Mixon, Mark; Barrow, Adrienne; Wallace, Ellen; Grice, Rena; Burgin, Alex; Stewart, Lance

2009-04-21

To improve efficiency in high throughput protein structure determination, we have developed a database software package, Gene Composer, which facilitates the information-rich design of protein constructs and their codon engineered synthetic gene sequences. With its modular workflow design and numerous graphical user interfaces, Gene Composer enables researchers to perform all common bio-informatics steps used in modern structure guided protein engineering and synthetic gene engineering. An interactive Alignment Viewer allows the researcher to simultaneously visualize sequence conservation in the context of known protein secondary structure, ligand contacts, water contacts, crystal contacts, B-factors, solvent accessible area, residue property type and several other useful property views. The Construct Design Module enables the facile design of novel protein constructs with altered N- and C-termini, internal insertions or deletions, point mutations, and desired affinity tags. The modifications can be combined and permuted into multiple protein constructs, and then virtually cloned in silico into defined expression vectors. The Gene Design Module uses a protein-to-gene algorithm that automates the back-translation of a protein amino acid sequence into a codon engineered nucleic acid gene sequence according to a selected codon usage table with minimal codon usage threshold, defined G:C% content, and desired sequence features achieved through synonymous codon selection that is optimized for the intended expression system. The gene-to-oligo algorithm of the Gene Design Module plans out all of the required overlapping oligonucleotides and mutagenic primers needed to synthesize the desired gene constructs by PCR, and for physically cloning them into selected vectors by the most popular subcloning strategies. We present a complete description of Gene Composer functionality, and an efficient PCR-based synthetic gene assembly procedure with mis-match specific endonuclease

Gene Composer: database software for protein construct design, codon engineering, and gene synthesis

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Mixon Mark

2009-04-01

Full Text Available Abstract Background To improve efficiency in high throughput protein structure determination, we have developed a database software package, Gene Composer, which facilitates the information-rich design of protein constructs and their codon engineered synthetic gene sequences. With its modular workflow design and numerous graphical user interfaces, Gene Composer enables researchers to perform all common bio-informatics steps used in modern structure guided protein engineering and synthetic gene engineering. Results An interactive Alignment Viewer allows the researcher to simultaneously visualize sequence conservation in the context of known protein secondary structure, ligand contacts, water contacts, crystal contacts, B-factors, solvent accessible area, residue property type and several other useful property views. The Construct Design Module enables the facile design of novel protein constructs with altered N- and C-termini, internal insertions or deletions, point mutations, and desired affinity tags. The modifications can be combined and permuted into multiple protein constructs, and then virtually cloned in silico into defined expression vectors. The Gene Design Module uses a protein-to-gene algorithm that automates the back-translation of a protein amino acid sequence into a codon engineered nucleic acid gene sequence according to a selected codon usage table with minimal codon usage threshold, defined G:C% content, and desired sequence features achieved through synonymous codon selection that is optimized for the intended expression system. The gene-to-oligo algorithm of the Gene Design Module plans out all of the required overlapping oligonucleotides and mutagenic primers needed to synthesize the desired gene constructs by PCR, and for physically cloning them into selected vectors by the most popular subcloning strategies. Conclusion We present a complete description of Gene Composer functionality, and an efficient PCR-based synthetic gene

Evaluation of suitable reference genes for gene expression studies in bovine muscular tissue

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Dunner Susana

2008-09-01

Full Text Available Abstract Background Real-time reverse transcriptase quantitative polymerase chain reaction (real-time RTqPCR is a technique used to measure mRNA species copy number as a way to determine key genes involved in different biological processes. However, the expression level of these key genes may vary among tissues or cells not only as a consequence of differential expression but also due to different factors, including choice of reference genes to normalize the expression levels of the target genes; thus the selection of reference genes is critical for expression studies. For this purpose, ten candidate reference genes were investigated in bovine muscular tissue. Results The value of stability of ten candidate reference genes included in three groups was estimated: the so called 'classical housekeeping' genes (18S, GAPDH and ACTB, a second set of genes used in expression studies conducted on other tissues (B2M, RPII, UBC and HMBS and a third set of novel genes (SF3A1, EEF1A2 and CASC3. Three different statistical algorithms were used to rank the genes by their stability measures as produced by geNorm, NormFinder and Bestkeeper. The three methods tend to agree on the most stably expressed genes and the least in muscular tissue. EEF1A2 and HMBS followed by SF3A1, ACTB, and CASC3 can be considered as stable reference genes, and B2M, RPII, UBC and GAPDH would not be appropriate. Although the rRNA-18S stability measure seems to be within the range of acceptance, its use is not recommended because its synthesis regulation is not representative of mRNA levels. Conclusion Based on geNorm algorithm, we propose the use of three genes SF3A1, EEF1A2 and HMBS as references for normalization of real-time RTqPCR in muscle expression studies.

Dysregulation of RNA Mediated Gene Expression in Motor Neuron Diseases.

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Gonçalves, Inês do Carmo G; Rehorst, Wiebke A; Kye, Min Jeong

2016-01-01

Recent findings indicate an important role for RNA-mediated gene expression in motor neuron diseases, including ALS (amyotrophic lateral sclerosis) and SMA (spinal muscular atrophy). ALS, also known as Lou Gehrig's disease, is an adult-onset progressive neurodegenerative disorder, whereby SMA or "children's Lou Gehrig's disease" is considered a pediatric neurodevelopmental disorder. Despite the difference in genetic causes, both ALS and SMA share common phenotypes; dysfunction/loss of motor neurons that eventually leads to muscle weakness and atrophy. With advanced techniques in molecular genetics and cell biology, current data suggest that these two distinct motor neuron diseases share more than phenotypes; ALS and SMA have similar cellular pathological mechanisms including mitochondrial dysfunction, oxidative stress and dysregulation in RNA-mediated gene expression. Here, we will discuss the current findings on these two diseases with specific focus on RNA-mediated gene regulation including miRNA expression, pre-mRNA processing and RNA binding proteins.

Ageing genes

DEFF Research Database (Denmark)

Rattan, Suresh

2018-01-01

The idea of gerontogenes is in line with the evolutionary explanation of ageing as being an emergent phenomenon as a result of the imperfect maintenance and repair systems. Although evolutionary processes did not select for any specific ageing genes that restrict and determine the lifespan...... of an individual, the term ‘gerontogenes’ primarily refers to any genes that may seem to influence ageing and longevity, without being specifically selected for that role. Such genes can also be called ‘virtual gerontogenes’ by virtue of their indirect influence on the rate and process of ageing. More than 1000...... virtual gerontogenes have been associated with ageing and longevity in model organisms and humans. The ‘real’ genes, which do influence the essential lifespan of a species, and have been selected for in accordance with the evolutionary life history of the species, are known as the longevity assurance...

Correlating Information Contents of Gene Ontology Terms to Infer Semantic Similarity of Gene Products

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Mingxin Gan

2014-01-01

Full Text Available Successful applications of the gene ontology to the inference of functional relationships between gene products in recent years have raised the need for computational methods to automatically calculate semantic similarity between gene products based on semantic similarity of gene ontology terms. Nevertheless, existing methods, though having been widely used in a variety of applications, may significantly overestimate semantic similarity between genes that are actually not functionally related, thereby yielding misleading results in applications. To overcome this limitation, we propose to represent a gene product as a vector that is composed of information contents of gene ontology terms annotated for the gene product, and we suggest calculating similarity between two gene products as the relatedness of their corresponding vectors using three measures: Pearson’s correlation coefficient, cosine similarity, and the Jaccard index. We focus on the biological process domain of the gene ontology and annotations of yeast proteins to study the effectiveness of the proposed measures. Results show that semantic similarity scores calculated using the proposed measures are more consistent with known biological knowledge than those derived using a list of existing methods, suggesting the effectiveness of our method in characterizing functional relationships between gene products.

Learning gene regulatory networks from gene expression data using weighted consensus

KAUST Repository

Fujii, Chisato; Kuwahara, Hiroyuki; Yu, Ge; Guo, Lili; Gao, Xin

2016-01-01

An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

Learning gene regulatory networks from gene expression data using weighted consensus

KAUST Repository

Fujii, Chisato

2016-08-25

An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

LINE FUSION GENES: a database of LINE expression in human genes

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Park Hong-Seog

2006-06-01

Full Text Available Abstract Background Long Interspersed Nuclear Elements (LINEs are the most abundant retrotransposons in humans. About 79% of human genes are estimated to contain at least one segment of LINE per transcription unit. Recent studies have shown that LINE elements can affect protein sequences, splicing patterns and expression of human genes. Description We have developed a database, LINE FUSION GENES, for elucidating LINE expression throughout the human gene database. We searched the 28,171 genes listed in the NCBI database for LINE elements and analyzed their structures and expression patterns. The results show that the mRNA sequences of 1,329 genes were affected by LINE expression. The LINE expression types were classified on the basis of LINEs in the 5' UTR, exon or 3' UTR sequences of the mRNAs. Our database provides further information, such as the tissue distribution and chromosomal location of the genes, and the domain structure that is changed by LINE integration. We have linked all the accession numbers to the NCBI data bank to provide mRNA sequences for subsequent users. Conclusion We believe that our work will interest genome scientists and might help them to gain insight into the implications of LINE expression for human evolution and disease. Availability http://www.primate.or.kr/line

An intronic microRNA silences genes that are functionally antagonistic to its host gene.

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Barik, Sailen

2008-09-01

MicroRNAs (miRNAs) are short noncoding RNAs that down-regulate gene expression by silencing specific target mRNAs. While many miRNAs are transcribed from their own genes, nearly half map within introns of 'host' genes, the significance of which remains unclear. We report that transcriptional activation of apoptosis-associated tyrosine kinase (AATK), essential for neuronal differentiation, also generates miR-338 from an AATK gene intron that silences a family of mRNAs whose protein products are negative regulators of neuronal differentiation. We conclude that an intronic miRNA, transcribed together with the host gene mRNA, may serve the interest of its host gene by silencing a cohort of genes that are functionally antagonistic to the host gene itself.

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

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Xiaodong Cai

Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

With Reference to Reference Genes: A Systematic Review of Endogenous Controls in Gene Expression Studies.

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Chapman, Joanne R; Waldenström, Jonas

2015-01-01

The choice of reference genes that are stably expressed amongst treatment groups is a crucial step in real-time quantitative PCR gene expression studies. Recent guidelines have specified that a minimum of two validated reference genes should be used for normalisation. However, a quantitative review of the literature showed that the average number of reference genes used across all studies was 1.2. Thus, the vast majority of studies continue to use a single gene, with β-actin (ACTB) and/or glyceraldehyde 3-phosphate dehydrogenase (GAPDH) being commonly selected in studies of vertebrate gene expression. Few studies (15%) tested a panel of potential reference genes for stability of expression before using them to normalise data. Amongst studies specifically testing reference gene stability, few found ACTB or GAPDH to be optimal, whereby these genes were significantly less likely to be chosen when larger panels of potential reference genes were screened. Fewer reference genes were tested for stability in non-model organisms, presumably owing to a dearth of available primers in less well characterised species. Furthermore, the experimental conditions under which real-time quantitative PCR analyses were conducted had a large influence on the choice of reference genes, whereby different studies of rat brain tissue showed different reference genes to be the most stable. These results highlight the importance of validating the choice of normalising reference genes before conducting gene expression studies.

Good genes, complementary genes and human mate preferences.

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Roberts, S Craig; Little, Anthony C

2008-09-01

The past decade has witnessed a rapidly growing interest in the biological basis of human mate choice. Here we review recent studies that demonstrate preferences for traits which might reveal genetic quality to prospective mates, with potential but still largely unknown influence on offspring fitness. These include studies assessing visual, olfactory and auditory preferences for potential good-gene indicator traits, such as dominance or bilateral symmetry. Individual differences in these robust preferences mainly arise through within and between individual variation in condition and reproductive status. Another set of studies have revealed preferences for traits indicating complementary genes, focussing on discrimination of dissimilarity at genes in the major histocompatibility complex (MHC). As in animal studies, we are only just beginning to understand how preferences for specific traits vary and inter-relate, how consideration of good and compatible genes can lead to substantial variability in individual mate choice decisions and how preferences expressed in one sensory modality may reflect those in another. Humans may be an ideal model species in which to explore these interesting complexities.

Gene expression studies of reference genes for quantitative real-time PCR: an overview in insects.

Science.gov (United States)

Shakeel, Muhammad; Rodriguez, Alicia; Tahir, Urfa Bin; Jin, Fengliang

2018-02-01

Whenever gene expression is being examined, it is essential that a normalization process is carried out to eliminate non-biological variations. The use of reference genes, such as glyceraldehyde-3-phosphate dehydrogenase, actin, and ribosomal protein genes, is the usual method of choice for normalizing gene expression. Although reference genes are used to normalize target gene expression, a major problem is that the stability of these genes differs among tissues, developmental stages, species, and responses to abiotic factors. Therefore, the use and validation of multiple reference genes are required. This review discusses the reasons that why RT-qPCR has become the preferred method for validating results of gene expression profiles, the use of specific and non-specific dyes and the importance of use of primers and probes for qPCR as well as to discuss several statistical algorithms developed to help the validation of potential reference genes. The conflicts arising in the use of classical reference genes in gene normalization and their replacement with novel references are also discussed by citing the high stability and low stability of classical and novel reference genes under various biotic and abiotic experimental conditions by employing various methods applied for the reference genes amplification.

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

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Tintle Nathan L

2012-08-01

Full Text Available Abstract Background Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO and Kyoto Encyclopedia of Genes and Genomes (KEGG are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed. Results We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size. Conclusions Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.

Comprehensive analysis of gene expression patterns of hedgehog-related genes

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Baillie David

2006-10-01

Full Text Available Abstract Background The Caenorhabditis elegans genome encodes ten proteins that share sequence similarity with the Hedgehog signaling molecule through their C-terminal autoprocessing Hint/Hog domain. These proteins contain novel N-terminal domains, and C. elegans encodes dozens of additional proteins containing only these N-terminal domains. These gene families are called warthog, groundhog, ground-like and quahog, collectively called hedgehog (hh-related genes. Previously, the expression pattern of seventeen genes was examined, which showed that they are primarily expressed in the ectoderm. Results With the completion of the C. elegans genome sequence in November 2002, we reexamined and identified 61 hh-related ORFs. Further, we identified 49 hh-related ORFs in C. briggsae. ORF analysis revealed that 30% of the genes still had errors in their predictions and we improved these predictions here. We performed a comprehensive expression analysis using GFP fusions of the putative intergenic regulatory sequence with one or two transgenic lines for most genes. The hh-related genes are expressed in one or a few of the following tissues: hypodermis, seam cells, excretory duct and pore cells, vulval epithelial cells, rectal epithelial cells, pharyngeal muscle or marginal cells, arcade cells, support cells of sensory organs, and neuronal cells. Using time-lapse recordings, we discovered that some hh-related genes are expressed in a cyclical fashion in phase with molting during larval development. We also generated several translational GFP fusions, but they did not show any subcellular localization. In addition, we also studied the expression patterns of two genes with similarity to Drosophila frizzled, T23D8.1 and F27E11.3A, and the ortholog of the Drosophila gene dally-like, gpn-1, which is a heparan sulfate proteoglycan. The two frizzled homologs are expressed in a few neurons in the head, and gpn-1 is expressed in the pharynx. Finally, we compare the

Multiscale Embedded Gene Co-expression Network Analysis.

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Won-Min Song

2015-11-01

Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Multiscale Embedded Gene Co-expression Network Analysis.

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Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

The rules of gene expression in plants: Organ identity and gene body methylation are key factors for regulation of gene expression in Arabidopsis thaliana

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Gutiérrez Rodrigo A

2008-09-01

Full Text Available Abstract Background Microarray technology is a widely used approach for monitoring genome-wide gene expression. For Arabidopsis, there are over 1,800 microarray hybridizations representing many different experimental conditions on Affymetrix™ ATH1 gene chips alone. This huge amount of data offers a unique opportunity to infer the principles that govern the regulation of gene expression in plants. Results We used bioinformatics methods to analyze publicly available data obtained using the ATH1 chip from Affymetrix. A total of 1887 ATH1 hybridizations were normalized and filtered to eliminate low-quality hybridizations. We classified and compared control and treatment hybridizations and determined differential gene expression. The largest differences in gene expression were observed when comparing samples obtained from different organs. On average, ten-fold more genes were differentially expressed between organs as compared to any other experimental variable. We defined "gene responsiveness" as the number of comparisons in which a gene changed its expression significantly. We defined genes with the highest and lowest responsiveness levels as hypervariable and housekeeping genes, respectively. Remarkably, housekeeping genes were best distinguished from hypervariable genes by differences in methylation status in their transcribed regions. Moreover, methylation in the transcribed region was inversely correlated (R2 = 0.8 with gene responsiveness on a genome-wide scale. We provide an example of this negative relationship using genes encoding TCA cycle enzymes, by contrasting their regulatory responsiveness to nitrate and methylation status in their transcribed regions. Conclusion Our results indicate that the Arabidopsis transcriptome is largely established during development and is comparatively stable when faced with external perturbations. We suggest a novel functional role for DNA methylation in the transcribed region as a key determinant

Gene Expression Commons: an open platform for absolute gene expression profiling.

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Jun Seita

Full Text Available Gene expression profiling using microarrays has been limited to comparisons of gene expression between small numbers of samples within individual experiments. However, the unknown and variable sensitivities of each probeset have rendered the absolute expression of any given gene nearly impossible to estimate. We have overcome this limitation by using a very large number (>10,000 of varied microarray data as a common reference, so that statistical attributes of each probeset, such as the dynamic range and threshold between low and high expression, can be reliably discovered through meta-analysis. This strategy is implemented in a web-based platform named "Gene Expression Commons" (https://gexc.stanford.edu/ which contains data of 39 distinct highly purified mouse hematopoietic stem/progenitor/differentiated cell populations covering almost the entire hematopoietic system. Since the Gene Expression Commons is designed as an open platform, investigators can explore the expression level of any gene, search by expression patterns of interest, submit their own microarray data, and design their own working models representing biological relationship among samples.

History of gene therapy.

Science.gov (United States)

Wirth, Thomas; Parker, Nigel; Ylä-Herttuala, Seppo

2013-08-10

Two decades after the initial gene therapy trials and more than 1700 approved clinical trials worldwide we not only have gained much new information and knowledge regarding gene therapy in general, but also learned to understand the concern that has persisted in society. Despite the setbacks gene therapy has faced, success stories have increasingly emerged. Examples for these are the positive recommendation for a gene therapy product (Glybera) by the EMA for approval in the European Union and the positive trials for the treatment of ADA deficiency, SCID-X1 and adrenoleukodystrophy. Nevertheless, our knowledge continues to grow and during the course of time more safety data has become available that helps us to develop better gene therapy approaches. Also, with the increased understanding of molecular medicine, we have been able to develop more specific and efficient gene transfer vectors which are now producing clinical results. In this review, we will take a historical view and highlight some of the milestones that had an important impact on the development of gene therapy. We will also discuss briefly the safety and ethical aspects of gene therapy and address some concerns that have been connected with gene therapy as an important therapeutic modality. Copyright © 2013 Elsevier B.V. All rights reserved.

Calcisponges have a ParaHox gene and dynamic expression of dispersed NK homeobox genes.

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Fortunato, Sofia A V; Adamski, Marcin; Ramos, Olivia Mendivil; Leininger, Sven; Liu, Jing; Ferrier, David E K; Adamska, Maja

2014-10-30

Sponges are simple animals with few cell types, but their genomes paradoxically contain a wide variety of developmental transcription factors, including homeobox genes belonging to the Antennapedia (ANTP) class, which in bilaterians encompass Hox, ParaHox and NK genes. In the genome of the demosponge Amphimedon queenslandica, no Hox or ParaHox genes are present, but NK genes are linked in a tight cluster similar to the NK clusters of bilaterians. It has been proposed that Hox and ParaHox genes originated from NK cluster genes after divergence of sponges from the lineage leading to cnidarians and bilaterians. On the other hand, synteny analysis lends support to the notion that the absence of Hox and ParaHox genes in Amphimedon is a result of secondary loss (the ghost locus hypothesis). Here we analysed complete suites of ANTP-class homeoboxes in two calcareous sponges, Sycon ciliatum and Leucosolenia complicata. Our phylogenetic analyses demonstrate that these calcisponges possess orthologues of bilaterian NK genes (Hex, Hmx and Msx), a varying number of additional NK genes and one ParaHox gene, Cdx. Despite the generation of scaffolds spanning multiple genes, we find no evidence of clustering of Sycon NK genes. All Sycon ANTP-class genes are developmentally expressed, with patterns suggesting their involvement in cell type specification in embryos and adults, metamorphosis and body plan patterning. These results demonstrate that ParaHox genes predate the origin of sponges, thus confirming the ghost locus hypothesis, and highlight the need to analyse the genomes of multiple sponge lineages to obtain a complete picture of the ancestral composition of the first animal genome.

Preparation and characterization of magnetic gene vectors for targeting gene delivery

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Zheng, S.W.; Liu, G. [College of Chemistry, Chemical Engineering and Materials Science and Key Laboratory of Organic Synthesis of Jiangsu Province, Soochow University, SIP, Suzhou 215123 (China); Hong, R.Y., E-mail: rhong@suda.edu.cn [College of Chemistry, Chemical Engineering and Materials Science and Key Laboratory of Organic Synthesis of Jiangsu Province, Soochow University, SIP, Suzhou 215123 (China); State Key Laboratory of Multi-phase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080 (China); Li, H.Z. [State Key Laboratory of Multi-phase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080 (China); Li, Y.G., E-mail: ilguoliang@sohu.com [Department of radiology, the First Affiliated Hospital of Soochow University, Suzhou 215007 (China); Wei, D.G., E-mail: dougwei@deas.harvard.edu [Center for Nanoscale Systems, School of Engineering and Applied Science, Harvard University, 11 Oxford Street, Cambridge, MA 02139 (United States)

2012-10-15

Highlights: Black-Right-Pointing-Pointer PEI is ideal candidate polymer for the design of gene delivery systems. Black-Right-Pointing-Pointer PEI-CMD-MNPs exhibited a typical superparamagnetic behavior. Black-Right-Pointing-Pointer PEI-CMD-MNPs were well stable over the entire range of pH and NaCl concentration. Black-Right-Pointing-Pointer DNA-PEI-CMD-MNPs transfected cells by a magnet have higher transfection efficiency and gene expression efficiency. - Abstract: The PEI-CMD-MNPs were successfully prepared by the surface modification of magnetic Fe{sub 3}O{sub 4} nanoparticles with carboxymethyl dextran (CMD) and polyethyleneimine (PEI). The PEI-CMD-MNPs polyplexes exhibited a typical superparamagnetic behavior and were well stable over the entire range of pH and NaCl concentration. These PEI-CMD-MNPs were used as magnetic gene vectors for targeting gene delivery. The prepared MNPs at different surface modification stages were characterized using Fourier transform infrared (FT-IR), thermogravimetric analysis (TGA), field emissions canning electron microscopy (FE-SEM), powder X-ray diffraction (XRD) and dynamic laser light scattering (DLS) analysis. The magnetic properties were studied by vibrating sample magnetometer (VSM). To evaluate the performance of the magnetic nanoparticles as gene transfer vector, the PEI-CMD-MNPs were used to delivery green fluorescent protein (GFP) gene into BHK21 cells. The expression of GFP gene was detected by fluorescence microscope. DNA-PEI-CMD-MNPs polyplexes absorbed by the cells were also monitored by Magnetic resonance imaging (MRI). The transfection efficiency and gene expression efficiency of that transfected with a magnet were much higher than that of standard transfection.

Investigating Gene Function in Cereal Rust Fungi by Plant-Mediated Virus-Induced Gene Silencing.

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Panwar, Vinay; Bakkeren, Guus

2017-01-01

Cereal rust fungi are destructive pathogens, threatening grain production worldwide. Targeted breeding for resistance utilizing host resistance genes has been effective. However, breakdown of resistance occurs frequently and continued efforts are needed to understand how these fungi overcome resistance and to expand the range of available resistance genes. Whole genome sequencing, transcriptomic and proteomic studies followed by genome-wide computational and comparative analyses have identified large repertoire of genes in rust fungi among which are candidates predicted to code for pathogenicity and virulence factors. Some of these genes represent defence triggering avirulence effectors. However, functions of most genes still needs to be assessed to understand the biology of these obligate biotrophic pathogens. Since genetic manipulations such as gene deletion and genetic transformation are not yet feasible in rust fungi, performing functional gene studies is challenging. Recently, Host-induced gene silencing (HIGS) has emerged as a useful tool to characterize gene function in rust fungi while infecting and growing in host plants. We utilized Barley stripe mosaic virus-mediated virus induced gene silencing (BSMV-VIGS) to induce HIGS of candidate rust fungal genes in the wheat host to determine their role in plant-fungal interactions. Here, we describe the methods for using BSMV-VIGS in wheat for functional genomics study in cereal rust fungi.

Non-Maxwellian fast particle effects in gyrokinetic GENE simulations

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Di Siena, A.; Görler, T.; Doerk, H.; Bilato, R.; Citrin, J.; Johnson, T.; Schneider, M.; Poli, E.; JET Contributors

2018-04-01

Fast ions have recently been found to significantly impact and partially suppress plasma turbulence both in experimental and numerical studies in a number of scenarios. Understanding the underlying physics and identifying the range of their beneficial effect is an essential task for future fusion reactors, where highly energetic ions are generated through fusion reactions and external heating schemes. However, in many of the gyrokinetic codes fast ions are, for simplicity, treated as equivalent-Maxwellian-distributed particle species, although it is well known that to rigorously model highly non-thermalised particles, a non-Maxwellian background distribution function is needed. To study the impact of this assumption, the gyrokinetic code GENE has recently been extended to support arbitrary background distribution functions which might be either analytical, e.g., slowing down and bi-Maxwellian, or obtained from numerical fast ion models. A particular JET plasma with strong fast-ion related turbulence suppression is revised with these new code capabilities both with linear and nonlinear gyrokinetic simulations. It appears that the fast ion stabilization tends to be less strong but still substantial with more realistic distributions, and this improves the quantitative power balance agreement with experiments.

Newer Gene Editing Technologies toward HIV Gene Therapy

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Premlata Shankar

2013-11-01

Full Text Available Despite the great success of highly active antiretroviral therapy (HAART in ameliorating the course of HIV infection, alternative therapeutic approaches are being pursued because of practical problems associated with life-long therapy. The eradication of HIV in the so-called “Berlin patient” who received a bone marrow transplant from a CCR5-negative donor has rekindled interest in genome engineering strategies to achieve the same effect. Precise gene editing within the cells is now a realistic possibility with recent advances in understanding the DNA repair mechanisms, DNA interaction with transcription factors and bacterial defense mechanisms. Within the past few years, four novel technologies have emerged that can be engineered for recognition of specific DNA target sequences to enable site-specific gene editing: Homing Endonuclease, ZFN, TALEN, and CRISPR/Cas9 system. The most recent CRISPR/Cas9 system uses a short stretch of complementary RNA bound to Cas9 nuclease to recognize and cleave target DNA, as opposed to the previous technologies that use DNA binding motifs of either zinc finger proteins or transcription activator-like effector molecules fused to an endonuclease to mediate sequence-specific DNA cleavage. Unlike RNA interference, which requires the continued presence of effector moieties to maintain gene silencing, the newer technologies allow permanent disruption of the targeted gene after a single treatment. Here, we review the applications, limitations and future prospects of novel gene-editing strategies for use as HIV therapy.

Validation of suitable reference genes for quantitative gene expression analysis in Panax ginseng

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Meizhen eWang

2016-01-01

Full Text Available Reverse transcription-qPCR (RT-qPCR has become a popular method for gene expression studies. Its results require data normalization by housekeeping genes. No single gene is proved to be stably expressed under all experimental conditions. Therefore, systematic evaluation of reference genes is necessary. With the aim to identify optimum reference genes for RT-qPCR analysis of gene expression in different tissues of Panax ginseng and the seedlings grown under heat stress, we investigated the expression stability of eight candidate reference genes, including elongation factor 1-beta (EF1-β, elongation factor 1-gamma (EF1-γ, eukaryotic translation initiation factor 3G (IF3G, eukaryotic translation initiation factor 3B (IF3B, actin (ACT, actin11 (ACT11, glyceraldehyde-3-phosphate dehydrogenase (GAPDH and cyclophilin ABH-like protein (CYC, using four widely used computational programs: geNorm, Normfinder, BestKeeper, and the comparative ΔCt method. The results were then integrated using the web-based tool RefFinder. As a result, EF1-γ, IF3G and EF1-β were the three most stable genes in different tissues of P. ginseng, while IF3G, ACT11 and GAPDH were the top three-ranked genes in seedlings treated with heat. Using three better reference genes alone or in combination as internal control, we examined the expression profiles of MAR, a multiple function-associated mRNA-like non-coding RNA (mlncRNA in P. ginseng. Taken together, we recommended EF1-γ/IF3G and IF3G/ACT11 as the suitable pair of reference genes for RT-qPCR analysis of gene expression in different tissues of P. ginseng and the seedlings grown under heat stress, respectively. The results serve as a foundation for future studies on P. ginseng functional genomics.

Gene analogue finder: a GRID solution for finding functionally analogous gene products

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Licciulli Flavio

2007-09-01

Full Text Available Abstract Background To date more than 2,1 million gene products from more than 100000 different species have been described specifying their function, the processes they are involved in and their cellular localization using a very well defined and structured vocabulary, the gene ontology (GO. Such vast, well defined knowledge opens the possibility of compare gene products at the level of functionality, finding gene products which have a similar function or are involved in similar biological processes without relying on the conventional sequence similarity approach. Comparisons within such a large space of knowledge are highly data and computing intensive. For this reason this project was based upon the use of the computational GRID, a technology offering large computing and storage resources. Results We have developed a tool, GENe AnaloGue FINdEr (ENGINE that parallelizes the search process and distributes the calculation and data over the computational GRID, splitting the process into many sub-processes and joining the calculation and the data on the same machine and therefore completing the whole search in about 3 days instead of occupying one single machine for more than 5 CPU years. The results of the functional comparison contain potential functional analogues for more than 79000 gene products from the most important species. 46% of the analyzed gene products are well enough described for such an analysis to individuate functional analogues, such as well-known members of the same gene family, or gene products with similar functions which would never have been associated by standard methods. Conclusion ENGINE has produced a list of potential functionally analogous relations between gene products within and between species using, in place of the sequence, the gene description of the GO, thus demonstrating the potential of the GO. However, the current limiting factor is the quality of the associations of many gene products from non

Radiosensitivity and genes

Energy Technology Data Exchange (ETDEWEB)

Qiyue, Hu; Mingyue, Lun [Suzhou Medical Coll., JS (China)

1995-07-01

Reported effects of some oncogenes, tumour suppressor genes and DNA repair genes on sensitivity of cells to ionizing radiation are reviewed. The role of oncogenes in cellular response to irradiation is discussed, especially the extensively studied oncogenes such as the ras gene family. For tumour suppressor genes, mainly the p53, which is increasingly implicated as a gene affecting radiosensitivity, is reviewed. It is considered that there is a cell cycle checkpoint determinant which is postulated to be able to arrest the irradiated cells in G{sub 1} phase to allow them to repair damage before they undergo DNA synthesis. So far there are six DNA repair genes which have been cloned in mammalian cells, but only one, XRCC1, appears to be involved in repair of human X-ray damage. XRCC1 can correct high sisterchromatid exchange levels when transferred into EM{sub 9} cells, but its expression seems to have no correlation with radiosensitivity of human neck and head tumour cells. Radiosensitivity is an intricate issue which may involve many factors. A scheme of cellular reactions after exposure to irradiation is proposed to indicate a possible sequence of events initiated by ionizing radiation.

Radiosensitivity and genes

International Nuclear Information System (INIS)

Hu Qiyue; Lun Mingyue

1995-07-01

Reported effects of some oncogenes, tumour suppressor genes and DNA repair genes on sensitivity of cells to ionizing radiation are reviewed. The role of oncogenes in cellular response to irradiation is discussed, especially the extensively studied oncogenes such as the ras gene family. For tumour suppressor genes, mainly the p53, which is increasingly implicated as a gene affecting radiosensitivity, is reviewed. It is considered that there is a cell cycle checkpoint determinant which is postulated to be able to arrest the irradiated cells in G 1 phase to allow them to repair damage before they undergo DNA synthesis. So far there are six DNA repair genes which have been cloned in mammalian cells, but only one, XRCC1, appears to be involved in repair of human X-ray damage. XRCC1 can correct high sisterchromatid exchange levels when transferred into EM 9 cells, but its expression seems to have no correlation with radiosensitivity of human neck and head tumour cells. Radiosensitivity is an intricate issue which may involve many factors. A scheme of cellular reactions after exposure to irradiation is proposed to indicate a possible sequence of events initiated by ionizing radiation

Length bias correction in gene ontology enrichment analysis using logistic regression.

Science.gov (United States)

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

Stochastic fluctuations and distributed control of gene expression impact cellular memory.

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Guillaume Corre

Full Text Available Despite the stochastic noise that characterizes all cellular processes the cells are able to maintain and transmit to their daughter cells the stable level of gene expression. In order to better understand this phenomenon, we investigated the temporal dynamics of gene expression variation using a double reporter gene model. We compared cell clones with transgenes coding for highly stable mRNA and fluorescent proteins with clones expressing destabilized mRNA-s and proteins. Both types of clones displayed strong heterogeneity of reporter gene expression levels. However, cells expressing stable gene products produced daughter cells with similar level of reporter proteins, while in cell clones with short mRNA and protein half-lives the epigenetic memory of the gene expression level was completely suppressed. Computer simulations also confirmed the role of mRNA and protein stability in the conservation of constant gene expression levels over several cell generations. These data indicate that the conservation of a stable phenotype in a cellular lineage may largely depend on the slow turnover of mRNA-s and proteins.

Stochastic modeling for the expression of a gene regulated by competing transcription factors.

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Hsih-Te Yang

Full Text Available It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data.

Large scale gene expression meta-analysis reveals tissue-specific, sex-biased gene expression in humans

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Benjamin Mayne

2016-10-01

Full Text Available The severity and prevalence of many diseases are known to differ between the sexes. Organ specific sex-biased gene expression may underpin these and other sexually dimorphic traits. To further our understanding of sex differences in transcriptional regulation, we performed meta-analyses of sex biased gene expression in multiple human tissues. We analysed 22 publicly available human gene expression microarray data sets including over 2500 samples from 15 different tissues and 9 different organs. Briefly, by using an inverse-variance method we determined the effect size difference of gene expression between males and females. We found the greatest sex differences in gene expression in the brain, specifically in the anterior cingulate cortex, (1818 genes, followed by the heart (375 genes, kidney (224 genes, colon (218 genes and thyroid (163 genes. More interestingly, we found different parts of the brain with varying numbers and identity of sex-biased genes, indicating that specific cortical regions may influence sexually dimorphic traits. The majority of sex-biased genes in other tissues such as the bladder, liver, lungs and pancreas were on the sex chromosomes or involved in sex hormone production. On average in each tissue, 32% of autosomal genes that were expressed in a sex-biased fashion contained androgen or estrogen hormone response elements. Interestingly, across all tissues, we found approximately two-thirds of autosomal genes that were sex-biased were not under direct influence of sex hormones. To our knowledge this is the largest analysis of sex-biased gene expression in human tissues to date. We identified many sex-biased genes that were not under the direct influence of sex chromosome genes or sex hormones. These may provide targets for future development of sex-specific treatments for diseases.

Distinguishing the rates of gene activation from phenotypic variations.

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Chen, Ye; Lv, Cheng; Li, Fangting; Li, Tiejun

2015-06-18

Stochastic genetic switching driven by intrinsic noise is an important process in gene expression. When the rates of gene activation/inactivation are relatively slow, fast, or medium compared with the synthesis/degradation rates of mRNAs and proteins, the variability of protein and mRNA levels may exhibit very different dynamical patterns. It is desirable to provide a systematic approach to identify their key dynamical features in different regimes, aiming at distinguishing which regime a considered gene regulatory network is in from their phenotypic variations. We studied a gene expression model with positive feedbacks when genetic switching rates vary over a wide range. With the goal of providing a method to distinguish the regime of the switching rates, we first focus on understanding the essential dynamics of gene expression system in different cases. In the regime of slow switching rates, we found that the effective dynamics can be reduced to independent evolutions on two separate layers corresponding to gene activation and inactivation states, and the transitions between two layers are rare events, after which the system goes mainly along deterministic ODE trajectories on a particular layer to reach new steady states. The energy landscape in this regime can be well approximated by using Gaussian mixture model. In the regime of intermediate switching rates, we analyzed the mean switching time to investigate the stability of the system in different parameter ranges. We also discussed the case of fast switching rates from the viewpoint of transition state theory. Based on the obtained results, we made a proposal to distinguish these three regimes in a simulation experiment. We identified the intermediate regime from the fact that the strength of cellular memory is lower than the other two cases, and the fast and slow regimes can be distinguished by their different perturbation-response behavior with respect to the switching rates perturbations. We proposed a

Appendix 1：Upregulated genes in gene expression profile （P<0.05 ...

Indian Academy of Sciences (India)

lazi

Appendix 1: Upregulated genes in gene expression profile«P2）. Probe_s. Gene_Symbol pvalues foldchange. Probe_S. et_ID. Gene_Symbol pvalues foldchange. et_ID. 1370355. 1393751. Scd1. 1.35E-04. 25.77. Loc1009122508.06E-03. 2.55. -at at. 1398250. 1370870. Acot1. 2.43E-02. 12.18. Me1.

Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

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Xiaobo Guo

Full Text Available Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs. It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC curve and the precision-recall (PR curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

Evolution of stress-regulated gene expression in duplicate genes of Arabidopsis thaliana.

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Cheng Zou

2009-07-01

Full Text Available Due to the selection pressure imposed by highly variable environmental conditions, stress sensing and regulatory response mechanisms in plants are expected to evolve rapidly. One potential source of innovation in plant stress response mechanisms is gene duplication. In this study, we examined the evolution of stress-regulated gene expression among duplicated genes in the model plant Arabidopsis thaliana. Key to this analysis was reconstructing the putative ancestral stress regulation pattern. By comparing the expression patterns of duplicated genes with the patterns of their ancestors, duplicated genes likely lost and gained stress responses at a rapid rate initially, but the rate is close to zero when the synonymous substitution rate (a proxy for time is > approximately 0.8. When considering duplicated gene pairs, we found that partitioning of putative ancestral stress responses occurred more frequently compared to cases of parallel retention and loss. Furthermore, the pattern of stress response partitioning was extremely asymmetric. An analysis of putative cis-acting DNA regulatory elements in the promoters of the duplicated stress-regulated genes indicated that the asymmetric partitioning of ancestral stress responses are likely due, at least in part, to differential loss of DNA regulatory elements; the duplicated genes losing most of their stress responses were those that had lost more of the putative cis-acting elements. Finally, duplicate genes that lost most or all of the ancestral responses are more likely to have gained responses to other stresses. Therefore, the retention of duplicates that inherit few or no functions seems to be coupled to neofunctionalization. Taken together, our findings provide new insight into the patterns of evolutionary changes in gene stress responses after duplication and lay the foundation for testing the adaptive significance of stress regulatory changes under highly variable biotic and abiotic environments.

Evolutionary Relationship and Structural Characterization of the EPF/EPFL Gene Family

OpenAIRE

Takata, Naoki; Yokota, Kiyonobu; Ohki, Shinya; Mori, Masashi; Taniguchi, Toru; Kurita, Manabu

2013-01-01

EPF1-EPF2 and EPFL9/Stomagen act antagonistically in regulating leaf stomatal density. The aim of this study was to elucidate the evolutionary functional divergence of EPF/EPFL family genes. Phylogenetic analyses showed that AtEPFL9/Stomagen-like genes are conserved only in vascular plants and are closely related to AtEPF1/EPF2-like genes. Modeling showed that EPF/EPFL peptides share a common 3D structure that is constituted of a scaffold and loop. Molecular dynamics simulation suggested that...

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

International Nuclear Information System (INIS)

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

Energy Technology Data Exchange (ETDEWEB)

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.

CAR gene cluster and transcript levels of carotenogenic genes in Rhodotorula mucilaginosa.

Science.gov (United States)

Landolfo, Sara; Ianiri, Giuseppe; Camiolo, Salvatore; Porceddu, Andrea; Mulas, Giuliana; Chessa, Rossella; Zara, Giacomo; Mannazzu, Ilaria

2018-01-01

A molecular approach was applied to the study of the carotenoid biosynthetic pathway of Rhodotorula mucilaginosa. At first, functional annotation of the genome of R. mucilaginosa C2.5t1 was carried out and gene ontology categories were assigned to 4033 predicted proteins. Then, a set of genes involved in different steps of carotenogenesis was identified and those coding for phytoene desaturase, phytoene synthase/lycopene cyclase and carotenoid dioxygenase (CAR genes) proved to be clustered within a region of ~10 kb. Quantitative PCR of the genes involved in carotenoid biosynthesis showed that genes coding for 3-hydroxy-3-methylglutharyl-CoA reductase and mevalonate kinase are induced during exponential phase while no clear trend of induction was observed for phytoene synthase/lycopene cyclase and phytoene dehydrogenase encoding genes. Thus, in R. mucilaginosa the induction of genes involved in the early steps of carotenoid biosynthesis is transient and accompanies the onset of carotenoid production, while that of CAR genes does not correlate with the amount of carotenoids produced. The transcript levels of genes coding for carotenoid dioxygenase, superoxide dismutase and catalase A increased during the accumulation of carotenoids, thus suggesting the activation of a mechanism aimed at the protection of cell structures from oxidative stress during carotenoid biosynthesis. The data presented herein, besides being suitable for the elucidation of the mechanisms that underlie carotenoid biosynthesis, will contribute to boosting the biotechnological potential of this yeast by improving the outcome of further research efforts aimed at also exploring other features of interest.

Validation of commonly used reference genes for sleep-related gene expression studies

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Castro Rosa MRPS

2009-05-01

Full Text Available Abstract Background Sleep is a restorative process and is essential for maintenance of mental and physical health. In an attempt to understand the complexity of sleep, multidisciplinary strategies, including genetic approaches, have been applied to sleep research. Although quantitative real time PCR has been used in previous sleep-related gene expression studies, proper validation of reference genes is currently lacking. Thus, we examined the effect of total or paradoxical sleep deprivation (TSD or PSD on the expression stability of the following frequently used reference genes in brain and blood: beta-actin (b-actin, beta-2-microglobulin (B2M, glyceraldehyde-3-phosphate dehydrogenase (GAPDH, and hypoxanthine guanine phosphoribosyl transferase (HPRT. Results Neither TSD nor PSD affected the expression stability of all tested genes in both tissues indicating that b-actin, B2M, GAPDH and HPRT are appropriate reference genes for the sleep-related gene expression studies. In order to further verify these results, the relative expression of brain derived neurotrophic factor (BDNF and glycerol-3-phosphate dehydrogenase1 (GPD1 was evaluated in brain and blood, respectively. The normalization with each of four reference genes produced similar pattern of expression in control and sleep deprived rats, but subtle differences in the magnitude of expression fold change were observed which might affect the statistical significance. Conclusion This study demonstrated that sleep deprivation does not alter the expression stability of commonly used reference genes in brain and blood. Nonetheless, the use of multiple reference genes in quantitative RT-PCR is required for the accurate results.

Analysis of essential Arabidopsis nuclear genes encoding plastid-targeted proteins.

Science.gov (United States)

Savage, Linda J; Imre, Kathleen M; Hall, David A; Last, Robert L

2013-01-01

The Chloroplast 2010 Project (http://www.plastid.msu.edu/) identified and phenotypically characterized homozygous mutants in over three thousand genes, the majority of which encode plastid-targeted proteins. Despite extensive screening by the community, no homozygous mutant alleles were available for several hundred genes, suggesting that these might be enriched for genes of essential function. Attempts were made to generate homozygotes in ~1200 of these lines and 521 of the homozygous viable lines obtained were deposited in the Arabidopsis Biological Resource Center (http://abrc.osu.edu/). Lines that did not yield a homozygote in soil were tested as potentially homozygous lethal due to defects either in seed or seedling development. Mutants were characterized at four stages of development: developing seed, mature seed, at germination, and developing seedlings. To distinguish seed development or seed pigment-defective mutants from seedling development mutants, development of seeds was assayed in siliques from heterozygous plants. Segregating seeds from heterozygous parents were sown on supplemented media in an attempt to rescue homozygous seedlings that could not germinate or survive in soil. Growth of segregating seeds in air and air enriched to 0.3% carbon dioxide was compared to discover mutants potentially impaired in photorespiration or otherwise responsive to CO2 supplementation. Chlorophyll fluorescence measurements identified CO2-responsive mutants with altered photosynthetic parameters. Examples of genes with a viable mutant allele and one or more putative homozygous-lethal alleles were documented. RT-PCR of homozygotes for potentially weak alleles revealed that essential genes may remain undiscovered because of the lack of a true null mutant allele. This work revealed 33 genes with two or more lethal alleles and 73 genes whose essentiality was not confirmed with an independent lethal mutation, although in some cases second leaky alleles were identified.

Analysis of essential Arabidopsis nuclear genes encoding plastid-targeted proteins.

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Linda J Savage

Full Text Available The Chloroplast 2010 Project (http://www.plastid.msu.edu/ identified and phenotypically characterized homozygous mutants in over three thousand genes, the majority of which encode plastid-targeted proteins. Despite extensive screening by the community, no homozygous mutant alleles were available for several hundred genes, suggesting that these might be enriched for genes of essential function. Attempts were made to generate homozygotes in ~1200 of these lines and 521 of the homozygous viable lines obtained were deposited in the Arabidopsis Biological Resource Center (http://abrc.osu.edu/. Lines that did not yield a homozygote in soil were tested as potentially homozygous lethal due to defects either in seed or seedling development. Mutants were characterized at four stages of development: developing seed, mature seed, at germination, and developing seedlings. To distinguish seed development or seed pigment-defective mutants from seedling development mutants, development of seeds was assayed in siliques from heterozygous plants. Segregating seeds from heterozygous parents were sown on supplemented media in an attempt to rescue homozygous seedlings that could not germinate or survive in soil. Growth of segregating seeds in air and air enriched to 0.3% carbon dioxide was compared to discover mutants potentially impaired in photorespiration or otherwise responsive to CO2 supplementation. Chlorophyll fluorescence measurements identified CO2-responsive mutants with altered photosynthetic parameters. Examples of genes with a viable mutant allele and one or more putative homozygous-lethal alleles were documented. RT-PCR of homozygotes for potentially weak alleles revealed that essential genes may remain undiscovered because of the lack of a true null mutant allele. This work revealed 33 genes with two or more lethal alleles and 73 genes whose essentiality was not confirmed with an independent lethal mutation, although in some cases second leaky alleles

Integrones: los coleccionistas de genes Integrons: gene collectors

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J. A. Di Conza

2010-02-01

Full Text Available Los integrones son estructuras genéticas que han despertado gran interés, debido a que algunos de ellos vehiculizan genes de resistencia a los antimicrobianos. Están formados por un fragmento que codifica una integrasa (intI y, a continuación, una secuencia attI a la que se unen los genes en casetes que codifican diferentes mecanismos de resistencia. Dentro de intI, en su extremo 3´, hay una secuencia promotora Pc a partir de la cual se transcriben los casetes de resistencia integrados, ya que estos genes carecen de promotor. Sin embargo, estos casetes presentan una secuencia específica denominada attC, la cual es reconocida por la integrasa que se une, por recombinación, a la secuencia attI del integrón en la orientación adecuada para su expresión. Los integrones se han clasificado según la secuencia de su integrasa, pero en la actualidad se prefiere clasificarlos según su localización. Se habla, en general, de "integrones móviles" para referirse a aquellos asociados a secuencias de inserción, transposones y/o plásmidos conjugativos, los que en su mayoría median mecanismos de resistencia, y de "superintegrones", de localización cromosómica y con grandes arreglos de genes en casetes. Los integrones móviles de clase 1 son los más abundantes en aislamientos clínicos y suelen estar asociados a transposones del subgrupo Tn21, seguidos por los de clase 2, derivados principalmente de Tn7. Estos elementos no son móviles por sí mismos, pero su asociación con elementos que sí lo son facilita su transferencia horizontal, lo que explica su amplia difusión entre las bacterias. Esta revisión intenta recopilar la información disponible acerca de los integrones móviles descritos en Argentina hasta la fecha.Integrons gained great interest due to their participation in resistance gene recruitment and expression. Their basic structure includes a fragment that encodes an integrase (intI followed by a recognition sequence (attI into

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

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Ma'ayan Avi

2007-10-01

Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

EXSPRESSION OF MDR-GENES AND MONORESISTANCE GENES IN NON-SMALL-CELL LUNG CANCER

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E. L. Yumov

2014-01-01

Full Text Available We studied the expression of multidrug resistance genes (MDR and monoresistance genes in normal bronchial tissue and tumor tissue of the non-small cell lung cancer (NSCLC after neoadjuvant chemotherapy (NACT (vinorelbine-carboplatine. The study included 30 patients with NSCLC (Т2–4N0–3M0. Normal bronchial tissue, normal lung tissue and tumor tissue collected during surgery following neoadjuvant chemotherapy (NACT served as a material of the study. The expression levels of MDR genes (ABCB1, ABCB2, ABCC1, ABCC2, ABCС5, ABCG1, ABCG2, GSTP and MVP, and monoresistance genes (BRCA1, ERCC1, RRM1, TOP1, TOP2A, TUBB3 and TYMS were estimated by quantitative reverse transcriptase PCR (RT-qPCR. The expression levels of some MDR genes and monoresistance genes (АВСВ1, АВСВ2, ABCG1, ERCC1, GSTP1 and MVP were significantly higher in the bronchi than in tumor tissue. The expression of ABCG1, ABCG2 and ERCC1 genes was higher in patients with T1-2 cancer than in patients with T3-4 cancer. Patients with adenocarcinoma had higher expression of BRCA1, MVP and ABCB1 genes than patients with squamous cell lung cancer. A tendency towards reduction in the expression level of MDR-genes and monoresistance genes was observed in patients with partial tumor regression compared to that observed in patients with stable disease. These findings were consistent with the previous data on reduction in the MDR-gene expression after chemotherapy with a good response in breast cancer patients.

Digital gene expression analysis of gene expression differences within Brassica diploids and allopolyploids.

Science.gov (United States)

Jiang, Jinjin; Wang, Yue; Zhu, Bao; Fang, Tingting; Fang, Yujie; Wang, Youping

2015-01-27

Brassica includes many successfully cultivated crop species of polyploid origin, either by ancestral genome triplication or by hybridization between two diploid progenitors, displaying complex repetitive sequences and transposons. The U's triangle, which consists of three diploids and three amphidiploids, is optimal for the analysis of complicated genomes after polyploidization. Next-generation sequencing enables the transcriptome profiling of polyploids on a global scale. We examined the gene expression patterns of three diploids (Brassica rapa, B. nigra, and B. oleracea) and three amphidiploids (B. napus, B. juncea, and B. carinata) via digital gene expression analysis. In total, the libraries generated between 5.7 and 6.1 million raw reads, and the clean tags of each library were mapped to 18547-21995 genes of B. rapa genome. The unambiguous tag-mapped genes in the libraries were compared. Moreover, the majority of differentially expressed genes (DEGs) were explored among diploids as well as between diploids and amphidiploids. Gene ontological analysis was performed to functionally categorize these DEGs into different classes. The Kyoto Encyclopedia of Genes and Genomes analysis was performed to assign these DEGs into approximately 120 pathways, among which the metabolic pathway, biosynthesis of secondary metabolites, and peroxisomal pathway were enriched. The non-additive genes in Brassica amphidiploids were analyzed, and the results indicated that orthologous genes in polyploids are frequently expressed in a non-additive pattern. Methyltransferase genes showed differential expression pattern in Brassica species. Our results provided an understanding of the transcriptome complexity of natural Brassica species. The gene expression changes in diploids and allopolyploids may help elucidate the morphological and physiological differences among Brassica species.

Evaluation of endogenous control gene(s) for gene expression studies in human blood exposed to 60Co γ-rays ex vivo

International Nuclear Information System (INIS)

Vaiphei, S. Thangminlal; Keppen, Joshua; Nongrum, Saibadaiahun; Sharan, R.N.; Chaubey, R.C.; Kma, L.

2015-01-01

In gene expression studies, it is critical to normalize data using a stably expressed endogenous control gene in order to obtain accurate and reliable results. However, we currently do not have a universally applied endogenous control gene for normalization of data for gene expression studies, particularly those involving 60 Co γ-ray-exposed human blood samples. In this study, a comparative assessment of the gene expression of six widely used housekeeping endogenous control genes, namely 18S, ACTB, B2M, GAPDH, MT-ATP6 and CDKN1A, was undertaken for a range of 60 Co γ-ray doses (0.5, 1.0, 2.0 and 4.0 Gy) at 8.4 Gy min -1 at 0 and 24 h post-irradiation time intervals. Using the NormFinder algorithm, real-time PCR data obtained from six individuals (three males and three females) were analyzed with respect to the threshold cycle (Ct) value and abundance, ΔCt pair-wise comparison, intra- and inter-group variability assessments, etc. GAPDH, either alone or in combination with 18S, was found to be the most suitable endogenous control gene and should be used in gene expression studies, especially those involving qPCR of γ-ray-exposed human blood samples. (author)

Gene Duplication and Gene Expression Changes Play a Role in the Evolution of Candidate Pollen Feeding Genes in Heliconius Butterflies.

Science.gov (United States)

Smith, Gilbert; Macias-Muñoz, Aide; Briscoe, Adriana D

2016-09-02

Heliconius possess a unique ability among butterflies to feed on pollen. Pollen feeding significantly extends their lifespan, and is thought to have been important to the diversification of the genus. We used RNA sequencing to examine feeding-related gene expression in the mouthparts of four species of Heliconius and one nonpollen feeding species, Eueides isabella We hypothesized that genes involved in morphology and protein metabolism might be upregulated in Heliconius because they have longer proboscides than Eueides, and because pollen contains more protein than nectar. Using de novo transcriptome assemblies, we tested these hypotheses by comparing gene expression in mouthparts against antennae and legs. We first looked for genes upregulated in mouthparts across all five species and discovered several hundred genes, many of which had functional annotations involving metabolism of proteins (cocoonase), lipids, and carbohydrates. We then looked specifically within Heliconius where we found eleven common upregulated genes with roles in morphology (CPR cuticle proteins), behavior (takeout-like), and metabolism (luciferase-like). Closer examination of these candidates revealed that cocoonase underwent several duplications along the lineage leading to heliconiine butterflies, including two Heliconius-specific duplications. Luciferase-like genes also underwent duplication within lepidopterans, and upregulation in Heliconius mouthparts. Reverse-transcription PCR confirmed that three cocoonases, a peptidase, and one luciferase-like gene are expressed in the proboscis with little to no expression in labial palps and salivary glands. Our results suggest pollen feeding, like other dietary specializations, was likely facilitated by adaptive expansions of preexisting genes-and that the butterfly proboscis is involved in digestive enzyme production. © The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Identification of nitrogen-fixing genes and gene clusters from metagenomic library of acid mine drainage.

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Zhimin Dai

Full Text Available Biological nitrogen fixation is an essential function of acid mine drainage (AMD microbial communities. However, most acidophiles in AMD environments are uncultured microorganisms and little is known about the diversity of nitrogen-fixing genes and structure of nif gene cluster in AMD microbial communities. In this study, we used metagenomic sequencing to isolate nif genes in the AMD microbial community from Dexing Copper Mine, China. Meanwhile, a metagenome microarray containing 7,776 large-insertion fosmids was constructed to screen novel nif gene clusters. Metagenomic analyses revealed that 742 sequences were identified as nif genes including structural subunit genes nifH, nifD, nifK and various additional genes. The AMD community is massively dominated by the genus Acidithiobacillus. However, the phylogenetic diversity of nitrogen-fixing microorganisms is much higher than previously thought in the AMD community. Furthermore, a 32.5-kb genomic sequence harboring nif, fix and associated genes was screened by metagenome microarray. Comparative genome analysis indicated that most nif genes in this cluster are most similar to those of Herbaspirillum seropedicae, but the organization of the nif gene cluster had significant differences from H. seropedicae. Sequence analysis and reverse transcription PCR also suggested that distinct transcription units of nif genes exist in this gene cluster. nifQ gene falls into the same transcription unit with fixABCX genes, which have not been reported in other diazotrophs before. All of these results indicated that more novel diazotrophs survive in the AMD community.

Identification of nitrogen-fixing genes and gene clusters from metagenomic library of acid mine drainage.

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Dai, Zhimin; Guo, Xue; Yin, Huaqun; Liang, Yili; Cong, Jing; Liu, Xueduan

2014-01-01

Biological nitrogen fixation is an essential function of acid mine drainage (AMD) microbial communities. However, most acidophiles in AMD environments are uncultured microorganisms and little is known about the diversity of nitrogen-fixing genes and structure of nif gene cluster in AMD microbial communities. In this study, we used metagenomic sequencing to isolate nif genes in the AMD microbial community from Dexing Copper Mine, China. Meanwhile, a metagenome microarray containing 7,776 large-insertion fosmids was constructed to screen novel nif gene clusters. Metagenomic analyses revealed that 742 sequences were identified as nif genes including structural subunit genes nifH, nifD, nifK and various additional genes. The AMD community is massively dominated by the genus Acidithiobacillus. However, the phylogenetic diversity of nitrogen-fixing microorganisms is much higher than previously thought in the AMD community. Furthermore, a 32.5-kb genomic sequence harboring nif, fix and associated genes was screened by metagenome microarray. Comparative genome analysis indicated that most nif genes in this cluster are most similar to those of Herbaspirillum seropedicae, but the organization of the nif gene cluster had significant differences from H. seropedicae. Sequence analysis and reverse transcription PCR also suggested that distinct transcription units of nif genes exist in this gene cluster. nifQ gene falls into the same transcription unit with fixABCX genes, which have not been reported in other diazotrophs before. All of these results indicated that more novel diazotrophs survive in the AMD community.

Identification of Nitrogen-Fixing Genes and Gene Clusters from Metagenomic Library of Acid Mine Drainage

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Yin, Huaqun; Liang, Yili; Cong, Jing; Liu, Xueduan

2014-01-01

Biological nitrogen fixation is an essential function of acid mine drainage (AMD) microbial communities. However, most acidophiles in AMD environments are uncultured microorganisms and little is known about the diversity of nitrogen-fixing genes and structure of nif gene cluster in AMD microbial communities. In this study, we used metagenomic sequencing to isolate nif genes in the AMD microbial community from Dexing Copper Mine, China. Meanwhile, a metagenome microarray containing 7,776 large-insertion fosmids was constructed to screen novel nif gene clusters. Metagenomic analyses revealed that 742 sequences were identified as nif genes including structural subunit genes nifH, nifD, nifK and various additional genes. The AMD community is massively dominated by the genus Acidithiobacillus. However, the phylogenetic diversity of nitrogen-fixing microorganisms is much higher than previously thought in the AMD community. Furthermore, a 32.5-kb genomic sequence harboring nif, fix and associated genes was screened by metagenome microarray. Comparative genome analysis indicated that most nif genes in this cluster are most similar to those of Herbaspirillum seropedicae, but the organization of the nif gene cluster had significant differences from H. seropedicae. Sequence analysis and reverse transcription PCR also suggested that distinct transcription units of nif genes exist in this gene cluster. nifQ gene falls into the same transcription unit with fixABCX genes, which have not been reported in other diazotrophs before. All of these results indicated that more novel diazotrophs survive in the AMD community. PMID:24498417

Conditions for the Evolution of Gene Clusters in Bacterial Genomes

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Ballouz, Sara; Francis, Andrew R.; Lan, Ruiting; Tanaka, Mark M.

2010-01-01

Genes encoding proteins in a common pathway are often found near each other along bacterial chromosomes. Several explanations have been proposed to account for the evolution of these structures. For instance, natural selection may directly favour gene clusters through a variety of mechanisms, such as increased efficiency of coregulation. An alternative and controversial hypothesis is the selfish operon model, which asserts that clustered arrangements of genes are more easily transferred to other species, thus improving the prospects for survival of the cluster. According to another hypothesis (the persistence model), genes that are in close proximity are less likely to be disrupted by deletions. Here we develop computational models to study the conditions under which gene clusters can evolve and persist. First, we examine the selfish operon model by re-implementing the simulation and running it under a wide range of conditions. Second, we introduce and study a Moran process in which there is natural selection for gene clustering and rearrangement occurs by genome inversion events. Finally, we develop and study a model that includes selection and inversion, which tracks the occurrence and fixation of rearrangements. Surprisingly, gene clusters fail to evolve under a wide range of conditions. Factors that promote the evolution of gene clusters include a low number of genes in the pathway, a high population size, and in the case of the selfish operon model, a high horizontal transfer rate. The computational analysis here has shown that the evolution of gene clusters can occur under both direct and indirect selection as long as certain conditions hold. Under these conditions the selfish operon model is still viable as an explanation for the evolution of gene clusters. PMID:20168992

Evolutionary history of chordate PAX genes: dynamics of change in a complex gene family.

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Vanessa Rodrigues Paixão-Côrtes

Full Text Available Paired box (PAX genes are transcription factors that play important roles in embryonic development. Although the PAX gene family occurs in animals only, it is widely distributed. Among the vertebrates, its 9 genes appear to be the product of complete duplication of an original set of 4 genes, followed by an additional partial duplication. Although some studies of PAX genes have been conducted, no comprehensive survey of these genes across the entire taxonomic unit has yet been attempted. In this study, we conducted a detailed comparison of PAX sequences from 188 chordates, which revealed restricted variation. The absence of PAX4 and PAX8 among some species of reptiles and birds was notable; however, all 9 genes were present in all 74 mammalian genomes investigated. A search for signatures of selection indicated that all genes are subject to purifying selection, with a possible constraint relaxation in PAX4, PAX7, and PAX8. This result indicates asymmetric evolution of PAX family genes, which can be associated with the emergence of adaptive novelties in the chordate evolutionary trajectory.

The relationship among gene expression, the evolution of gene dosage, and the rate of protein evolution.

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Jean-François Gout

2010-05-01

Full Text Available The understanding of selective constraints affecting genes is a major issue in biology. It is well established that gene expression level is a major determinant of the rate of protein evolution, but the reasons for this relationship remain highly debated. Here we demonstrate that gene expression is also a major determinant of the evolution of gene dosage: the rate of gene losses after whole genome duplications in the Paramecium lineage is negatively correlated to the level of gene expression, and this relationship is not a byproduct of other factors known to affect the fate of gene duplicates. This indicates that changes in gene dosage are generally more deleterious for highly expressed genes. This rule also holds for other taxa: in yeast, we find a clear relationship between gene expression level and the fitness impact of reduction in gene dosage. To explain these observations, we propose a model based on the fact that the optimal expression level of a gene corresponds to a trade-off between the benefit and cost of its expression. This COSTEX model predicts that selective pressure against mutations changing gene expression level or affecting the encoded protein should on average be stronger in highly expressed genes and hence that both the frequency of gene loss and the rate of protein evolution should correlate negatively with gene expression. Thus, the COSTEX model provides a simple and common explanation for the general relationship observed between the level of gene expression and the different facets of gene evolution.

Nonlinear Dynamics in Gene Regulation Promote Robustness and Evolvability of Gene Expression Levels.

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Steinacher, Arno; Bates, Declan G; Akman, Ozgur E; Soyer, Orkun S

2016-01-01

Cellular phenotypes underpinned by regulatory networks need to respond to evolutionary pressures to allow adaptation, but at the same time be robust to perturbations. This creates a conflict in which mutations affecting regulatory networks must both generate variance but also be tolerated at the phenotype level. Here, we perform mathematical analyses and simulations of regulatory networks to better understand the potential trade-off between robustness and evolvability. Examining the phenotypic effects of mutations, we find an inverse correlation between robustness and evolvability that breaks only with nonlinearity in the network dynamics, through the creation of regions presenting sudden changes in phenotype with small changes in genotype. For genotypes embedding low levels of nonlinearity, robustness and evolvability correlate negatively and almost perfectly. By contrast, genotypes embedding nonlinear dynamics allow expression levels to be robust to small perturbations, while generating high diversity (evolvability) under larger perturbations. Thus, nonlinearity breaks the robustness-evolvability trade-off in gene expression levels by allowing disparate responses to different mutations. Using analytical derivations of robustness and system sensitivity, we show that these findings extend to a large class of gene regulatory network architectures and also hold for experimentally observed parameter regimes. Further, the effect of nonlinearity on the robustness-evolvability trade-off is ensured as long as key parameters of the system display specific relations irrespective of their absolute values. We find that within this parameter regime genotypes display low and noisy expression levels. Examining the phenotypic effects of mutations, we find an inverse correlation between robustness and evolvability that breaks only with nonlinearity in the network dynamics. Our results provide a possible solution to the robustness-evolvability trade-off, suggest an explanation for

Nonlinear Dynamics in Gene Regulation Promote Robustness and Evolvability of Gene Expression Levels.

Directory of Open Access Journals (Sweden)

Arno Steinacher

Full Text Available Cellular phenotypes underpinned by regulatory networks need to respond to evolutionary pressures to allow adaptation, but at the same time be robust to perturbations. This creates a conflict in which mutations affecting regulatory networks must both generate variance but also be tolerated at the phenotype level. Here, we perform mathematical analyses and simulations of regulatory networks to better understand the potential trade-off between robustness and evolvability. Examining the phenotypic effects of mutations, we find an inverse correlation between robustness and evolvability that breaks only with nonlinearity in the network dynamics, through the creation of regions presenting sudden changes in phenotype with small changes in genotype. For genotypes embedding low levels of nonlinearity, robustness and evolvability correlate negatively and almost perfectly. By contrast, genotypes embedding nonlinear dynamics allow expression levels to be robust to small perturbations, while generating high diversity (evolvability under larger perturbations. Thus, nonlinearity breaks the robustness-evolvability trade-off in gene expression levels by allowing disparate responses to different mutations. Using analytical derivations of robustness and system sensitivity, we show that these findings extend to a large class of gene regulatory network architectures and also hold for experimentally observed parameter regimes. Further, the effect of nonlinearity on the robustness-evolvability trade-off is ensured as long as key parameters of the system display specific relations irrespective of their absolute values. We find that within this parameter regime genotypes display low and noisy expression levels. Examining the phenotypic effects of mutations, we find an inverse correlation between robustness and evolvability that breaks only with nonlinearity in the network dynamics. Our results provide a possible solution to the robustness-evolvability trade-off, suggest

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach.

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Jing Zhao

Full Text Available Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.

Evidence against the energetic cost hypothesis for the short introns in highly expressed genes

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Niu Deng-Ke

2008-05-01

Full Text Available Abstract Background In animals, the moss Physcomitrella patens and the pollen of Arabidopsis thaliana, highly expressed genes have shorter introns than weakly expressed genes. A popular explanation for this is selection for transcription efficiency, which includes two sub-hypotheses: to minimize the energetic cost or to minimize the time cost. Results In an individual human, different organs may differ up to hundreds of times in cell number (for example, a liver versus a hypothalamus. Considered at the individual level, a gene specifically expressed in a large organ is actually transcribed tens or hundreds of times more than a gene with a similar expression level (a measure of mRNA abundance per cell specifically expressed in a small organ. According to the energetic cost hypothesis, the former should have shorter introns than the latter. However, in humans and mice we have not found significant differences in intron length between large-tissue/organ-specific genes and small-tissue/organ-specific genes with similar expression levels. Qualitative estimation shows that the deleterious effect (that is, the energetic burden of long introns in highly expressed genes is too negligible to be efficiently selected against in mammals. Conclusion The short introns in highly expressed genes should not be attributed to energy constraint. We evaluated evidence for the time cost hypothesis and other alternatives.

Gene coexpression measures in large heterogeneous samples using count statistics.

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Wang, Y X Rachel; Waterman, Michael S; Huang, Haiyan

2014-11-18

With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.

Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules

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Ettore Mosca

2017-09-01

Full Text Available Autism spectrum disorder (ASD is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called “disease modules.” In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists. We defined genome-scale prioritizations of human genes in relation to ASD genes from multiple studies, found significantly connected gene modules associated with ASD and predicted genes functionally related to ASD risk genes. Most of them play a role in synapsis and neuronal development and function; many are related to syndromes that can be in comorbidity with ASD and the remaining are involved in epigenetics, cell cycle, cell adhesion and cancer.

Latitudinal Clines of the Human Vitamin D Receptor and Skin Color Genes

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Dov Tiosano

2016-05-01

Full Text Available The well-documented latitudinal clines of genes affecting human skin color presumably arise from the need for protection from intense ultraviolet radiation (UVR vs. the need to use UVR for vitamin D synthesis. Sampling 751 subjects from a broad range of latitudes and skin colors, we investigated possible multilocus correlated adaptation of skin color genes with the vitamin D receptor gene (VDR, using a vector correlation metric and network method called BlocBuster. We discovered two multilocus networks involving VDR promoter and skin color genes that display strong latitudinal clines as multilocus networks, even though many of their single gene components do not. Considered one by one, the VDR components of these networks show diverse patterns: no cline, a weak declining latitudinal cline outside of Africa, and a strong in- vs. out-of-Africa frequency pattern. We confirmed these results with independent data from HapMap. Standard linkage disequilibrium analyses did not detect these networks. We applied BlocBuster across the entire genome, showing that our networks are significant outliers for interchromosomal disequilibrium that overlap with environmental variation relevant to the genes’ functions. These results suggest that these multilocus correlations most likely arose from a combination of parallel selective responses to a common environmental variable and coadaptation, given the known Mendelian epistasis among VDR and the skin color genes.

No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Noncandidate Genes.

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Johnson, Emma C; Border, Richard; Melroy-Greif, Whitney E; de Leeuw, Christiaan A; Ehringer, Marissa A; Keller, Matthew C

2017-11-15

A recent analysis of 25 historical candidate gene polymorphisms for schizophrenia in the largest genome-wide association study conducted to date suggested that these commonly studied variants were no more associated with the disorder than would be expected by chance. However, the same study identified other variants within those candidate genes that demonstrated genome-wide significant associations with schizophrenia. As such, it is possible that variants within historic schizophrenia candidate genes are associated with schizophrenia at levels above those expected by chance, even if the most-studied specific polymorphisms are not. The present study used association statistics from the largest schizophrenia genome-wide association study conducted to date as input to a gene set analysis to investigate whether variants within schizophrenia candidate genes are enriched for association with schizophrenia. As a group, variants in the most-studied candidate genes were no more associated with schizophrenia than were variants in control sets of noncandidate genes. While a small subset of candidate genes did appear to be significantly associated with schizophrenia, these genes were not particularly noteworthy given the large number of more strongly associated noncandidate genes. The history of schizophrenia research should serve as a cautionary tale to candidate gene investigators examining other phenotypes: our findings indicate that the most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Distinct Trajectories of Massive Recent Gene Gains and Losses in Populations of a Microbial Eukaryotic Pathogen.

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Hartmann, Fanny E; Croll, Daniel

2017-11-01

Differences in gene content are a significant source of variability within species and have an impact on phenotypic traits. However, little is known about the mechanisms responsible for the most recent gene gains and losses. We screened the genomes of 123 worldwide isolates of the major pathogen of wheat Zymoseptoria tritici for robust evidence of gene copy number variation. Based on orthology relationships in three closely related fungi, we identified 599 gene gains and 1,024 gene losses that have not yet reached fixation within the focal species. Our analyses of gene gains and losses segregating in populations showed that gene copy number variation arose preferentially in subtelomeres and in proximity to transposable elements. Recently lost genes were enriched in virulence factors and secondary metabolite gene clusters. In contrast, recently gained genes encoded mostly secreted protein lacking a conserved domain. We analyzed the frequency spectrum at loci segregating a gene presence-absence polymorphism in four worldwide populations. Recent gene losses showed a significant excess in low-frequency variants compared with genome-wide single nucleotide polymorphism, which is indicative of strong negative selection against gene losses. Recent gene gains were either under weak negative selection or neutral. We found evidence for strong divergent selection among populations at individual loci segregating a gene presence-absence polymorphism. Hence, gene gains and losses likely contributed to local adaptation. Our study shows that microbial eukaryotes harbor extensive copy number variation within populations and that functional differences among recently gained and lost genes led to distinct evolutionary trajectories. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Are TMEM genes potential candidate genes for panic disorder?

DEFF Research Database (Denmark)

NO, Gregersen; Buttenschøn, Henriette Nørmølle; Hedemand, Anne

2014-01-01

We analysed single nucleotide polymorphisms in two transmembrane genes (TMEM98 and TMEM132E) in panic disorder (PD) patients and control individuals from the Faroe Islands, Denmark and Germany. The genes encode single-pass membrane proteins and are located within chromosome 17q11.2-q12...