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

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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.

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

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.

Disease progression and phasic changes in gene expression in a mouse model of osteoarthritis.

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Richard F Loeser

Full Text Available Osteoarthritis (OA is the most common form of arthritis and has multiple risk factors including joint injury. The purpose of this study was to characterize the histologic development of OA in a mouse model where OA is induced by destabilization of the medial meniscus (DMM model and to identify genes regulated during different stages of the disease, using RNA isolated from the joint "organ" and analyzed using microarrays. Histologic changes seen in OA, including articular cartilage lesions and osteophytes, were present in the medial tibial plateaus of the DMM knees beginning at the earliest (2 week time point and became progressively more severe by 16 weeks. 427 probe sets (371 genes from the microarrays passed consistency and significance filters. There was an initial up-regulation at 2 and 4 weeks of genes involved in morphogenesis, differentiation, and development, including growth factor and matrix genes, as well as transcription factors including Atf2, Creb3l1, and Erg. Most genes were off or down-regulated at 8 weeks with the most highly down-regulated genes involved in cell division and the cytoskeleton. Gene expression increased at 16 weeks, in particular extracellular matrix genes including Prelp, Col3a1 and fibromodulin. Immunostaining revealed the presence of these three proteins in cartilage and soft tissues including ligaments as well as in the fibrocartilage covering osteophytes. The results support a phasic development of OA with early matrix remodeling and transcriptional activity followed by a more quiescent period that is not maintained. This implies that the response to an OA intervention will depend on the timing of the intervention. The quiescent period at 8 weeks may be due to the maturation of the osteophytes which are thought to temporarily stabilize the joint.

Integrating high dimensional bi-directional parsing models for gene mention tagging.

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Hsu, Chun-Nan; Chang, Yu-Ming; Kuo, Cheng-Ju; Lin, Yu-Shi; Huang, Han-Shen; Chung, I-Fang

2008-07-01

Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this article, we describe in detail our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2. Our tagger is interesting because it accomplished the highest F-scores among CRF-based methods and second over all. Moreover, we obtained our results by mostly applying open source packages, making it easy to duplicate our results. We first describe in detail how we developed our CRF-based tagger. We designed a very high dimensional feature set that includes most of information that may be relevant. We trained bi-directional CRF models with the same set of features, one applies forward parsing and the other backward, and integrated two models based on the output scores and dictionary filtering. One of the most prominent factors that contributes to the good performance of our tagger is the integration of an additional backward parsing model. However, from the definition of CRF, it appears that a CRF model is symmetric and bi-directional parsing models will produce the same results. We show that due to different feature settings, a CRF model can be asymmetric and the feature setting for our tagger in BioCreative 2 not only produces different results but also gives backward parsing models slight but constant advantage over forward parsing model. To fully explore the potential of integrating bi-directional parsing models, we applied different asymmetric feature settings to generate many bi-directional parsing models and integrate them based on the output scores. Experimental results show that this integrated model can achieve even higher F-score solely based on the training corpus for gene mention tagging. Data sets, programs and an on-line service of our gene

From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality

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Jan-Ulrich Kreft

2017-11-01

Full Text Available Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression, stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s underlying it for the specific microbial system and question investigated is essential for selecting the

Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes.

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Pendar, Hodjat; Platini, Thierry; Kulkarni, Rahul V

2013-04-01

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations; hence, there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic two-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of posttranscriptional and posttranslational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.

Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes

Science.gov (United States)

Pendar, Hodjat; Platini, Thierry; Kulkarni, Rahul V.

2013-04-01

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations; hence, there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic two-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of posttranscriptional and posttranslational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.

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.

Gene Discovery and Functional Analyses in the Model Plant Arabidopsis

DEFF Research Database (Denmark)

Feng, Cai-ping; Mundy, J.

2006-01-01

The present mini-review describes newer methods and strategies, including transposon and T-DNA insertions, TILLING, Deleteagene, and RNA interference, to functionally analyze genes of interest in the model plant Arabidopsis. The relative advantages and disadvantages of the systems are also discus...

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

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Raza, Khalid; Alam, Mansaf

2016-10-01

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Analysis of genetic models and gene effects on main agronomy characters in rapeseed].

Science.gov (United States)

Li, J; Qiu, J; Tang, Z; Shen, L

1992-01-01

According to four different genetic models, the genetic patterns of 8 agronomy traits were analysed by using the data of 24 generations which included positive and negative cross of 81008 x Tower, both of the varieties are of good quality. The results showed that none of 8 characters could fit in with additive-dominance models. Epistasis was found in all of these characters, and it has significant effect on generation means. Seed weight/plant and some other main yield characters are controlled by duplicate interaction genes. The interaction between triple genes or multiple genes needs to be utilized in yield heterosis.

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

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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.

Use of an activated beta-catenin to identify Wnt pathway target genes in caenorhabditis elegans, including a subset of collagen genes expressed in late larval development.

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Jackson, Belinda M; Abete-Luzi, Patricia; Krause, Michael W; Eisenmann, David M

2014-04-16

The Wnt signaling pathway plays a fundamental role during metazoan development, where it regulates diverse processes, including cell fate specification, cell migration, and stem cell renewal. Activation of the beta-catenin-dependent/canonical Wnt pathway up-regulates expression of Wnt target genes to mediate a cellular response. In the nematode Caenorhabditis elegans, a canonical Wnt signaling pathway regulates several processes during larval development; however, few target genes of this pathway have been identified. To address this deficit, we used a novel approach of conditionally activated Wnt signaling during a defined stage of larval life by overexpressing an activated beta-catenin protein, then used microarray analysis to identify genes showing altered expression compared with control animals. We identified 166 differentially expressed genes, of which 104 were up-regulated. A subset of the up-regulated genes was shown to have altered expression in mutants with decreased or increased Wnt signaling; we consider these genes to be bona fide C. elegans Wnt pathway targets. Among these was a group of six genes, including the cuticular collagen genes, bli-1 col-38, col-49, and col-71. These genes show a peak of expression in the mid L4 stage during normal development, suggesting a role in adult cuticle formation. Consistent with this finding, reduction of function for several of the genes causes phenotypes suggestive of defects in cuticle function or integrity. Therefore, this work has identified a large number of putative Wnt pathway target genes during larval life, including a small subset of Wnt-regulated collagen genes that may function in synthesis of the adult cuticle.

Modeling the Activity of Single Genes

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Mjolsness, Eric; Gibson, Michael

1999-01-01

the key questions in gene regulation are: What genes are expressed in a certain cell at a certain time? How does gene expression differ from cell to cell in a multicellular organism? Which proteins act as transcription factors, i.e., are important in regulating gene expression? From questions like these, we hope to understand which genes are important for various macroscopic processes. Nearly all of the cells of a multicellular organism contain the same DNA. Yet this same genetic information yields a large number of different cell types. The fundamental difference between a neuron and a liver cell, for example, is which genes are expressed. Thus understanding gene regulation is an important step in understanding development. Furthermore, understanding the usual genes that are expressed in cells may give important clues about various diseases. Some diseases, such as sickle cell anemia and cystic fibrosis, are caused by defects in single, non-regulatory genes; others, such as certain cancers, are caused when the cellular control circuitry malfunctions - an understanding of these diseases will involve pathways of multiple interacting gene products. There are numerous challenges in the area of understanding and modeling gene regulation. First and foremost, biologists would like to develop a deeper understanding of the processes involved, including which genes and families of genes are important, how they interact, etc. From a computation point of view, there has been embarrassingly little work done. In this chapter there are many areas in which we can phrase meaningful, non-trivial computational questions, but questions that have not been addressed. Some of these are purely computational (what is a good algorithm for dealing with a model of type X) and others are more mathematical (given a system with certain characteristics, what sort of model can one use? How does one find biochemical parameters from system-level behavior using as few experiments as possible?). In

Challenges for modeling global gene regulatory networks during development: insights from Drosophila.

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Wilczynski, Bartek; Furlong, Eileen E M

2010-04-15

Development is regulated by dynamic patterns of gene expression, which are orchestrated through the action of complex gene regulatory networks (GRNs). Substantial progress has been made in modeling transcriptional regulation in recent years, including qualitative "coarse-grain" models operating at the gene level to very "fine-grain" quantitative models operating at the biophysical "transcription factor-DNA level". Recent advances in genome-wide studies have revealed an enormous increase in the size and complexity or GRNs. Even relatively simple developmental processes can involve hundreds of regulatory molecules, with extensive interconnectivity and cooperative regulation. This leads to an explosion in the number of regulatory functions, effectively impeding Boolean-based qualitative modeling approaches. At the same time, the lack of information on the biophysical properties for the majority of transcription factors within a global network restricts quantitative approaches. In this review, we explore the current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks. We suggest to integrate coarse- and find-grain approaches to model gene regulatory networks in cis. We focus on two very well-studied examples from Drosophila, which likely represent typical developmental regulatory modules across metazoans. Copyright (c) 2009 Elsevier Inc. All rights reserved.

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

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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

Cloning of synthetic gene including antigens against Urinary Tract Infections in pET28a+ vector

Directory of Open Access Journals (Sweden)

Zohreh Haghri

2017-12-01

Full Text Available There are many different bacterial infections in the world that patients are suffering from and research teams are trying to find suitable ways to prevent and treat them. Urinary Tract Infections (UTIs are most important infections in the world , and they are more common among women because vaginal cavity is near to urethral opening. The aim of this study is cloning of synthetic gene include antigens against UTIs in pET28a+ vector. Antibiotic resistant has been increasing because of antibiotic overuse recently, so It shows the necessity of developing a vaccine against these infections. There for, it will be imperative to develop a vaccine instead of antibiotics. This infection causes by many organisms, most important of which are Uropathogenic Escherichia coli (UPEC, Proteus mirabilis and Klebsiella pneumoniae Uropathogenic Escherichia .coli is the most important microorganism that causes these infections more than other bacteria, so in developing a vaccine it is the most important one, that have to be considered. The synthetic Gene which was designed against these three bacteria including antigens which are important and common to cause these infections. This gene has involved 1293bp. It was ordered to Gene Ray Biotechnology. Primers were designed by Gene Runner. Gene and pET28a+ vector was checked by SnappGene. Synthetic gene was multiplied by PCR and cloned in pET28a+ vector. Construct was transformed into E. coli TOP10.The clone was confirmed by PCR, Digestion. This data indicates that this gene can be expressed and it might be a vaccine candidate to protect people from these infections in the future.

Synchronous versus asynchronous modeling of gene regulatory networks.

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Garg, Abhishek; Di Cara, Alessandro; Xenarios, Ioannis; Mendoza, Luis; De Micheli, Giovanni

2008-09-01

In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Analysis of functional importance of binding sites in the Drosophila gap gene network model.

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Kozlov, Konstantin; Gursky, Vitaly V; Kulakovskiy, Ivan V; Dymova, Arina; Samsonova, Maria

2015-01-01

The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions. We elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions. The assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.

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

Modeling stochasticity and robustness in gene regulatory networks.

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Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; De Micheli, Giovanni; Xenarios, Ioannis

2009-06-15

Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

Dinucleotide controlled null models for comparative RNA gene prediction.

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Gesell, Tanja; Washietl, Stefan

2008-05-27

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

Dinucleotide controlled null models for comparative RNA gene prediction

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Gesell Tanja

2008-05-01

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

The majority of genes in the pathogenic Neisseria species are present in non-pathogenic Neisseria lactamica, including those designated as 'virulence genes'

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Saunders Nigel J

2006-05-01

Full Text Available Abstract Background Neisseria meningitidis causes the life-threatening diseases meningococcal meningitis and meningococcal septicemia. Neisseria gonorrhoeae is closely related to the meningococcus, but is the cause of the very different infection, gonorrhea. A number of genes have been implicated in the virulence of these related yet distinct pathogens, but the genes that define and differentiate the species and their behaviours have not been established. Further, a related species, Neisseria lactamica is not associated with either type of infection in normally healthy people, and lives as a harmless commensal. We have determined which of the genes so far identified in the genome sequences of the pathogens are also present in this non-pathogenic related species. Results Thirteen unrelated strains of N. lactamica were investigated using comparative genome hybridization to the pan-Neisseria microarray-v2, which contains 2845 unique gene probes. The presence of 127 'virulence genes' was specifically addressed; of these 85 are present in N. lactamica. Of the remaining 42 'virulence genes' only 11 are present in all four of the sequenced pathogenic Neisseria. Conclusion Assessment of the complete dataset revealed that the vast majority of genes present in the pathogens are also present in N. lactamica. Of the 1,473 probes to genes shared by all four pathogenic genome sequences, 1,373 hybridize to N. lactamica. These shared genes cannot include genes that are necessary and sufficient for the virulence of the pathogens, since N. lactamica does not share this behaviour. This provides an essential context for the interpretation of gene complement studies of the pathogens.

Statistical modelling of transcript profiles of differentially regulated genes

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Sergeant Martin J

2008-07-01

Full Text Available Abstract Background The vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression. Results Quantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Split-line" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t = A + (B + CtRt + ε. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data

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

Alteration of gene expression profiling including GPR174 and GNG2 is associated with vasovagal syncope.

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Huang, Yu-Juan; Zhou, Zai-wei; Xu, Miao; Ma, Qing-wen; Yan, Jing-bin; Wang, Jian-yi; Zhang, Quo-qin; Huang, Min; Bao, Liming

2015-03-01

Vasovagal syncope (VVS) causes accidental harm for susceptible patients. However, pathophysiology of this disorder remains largely unknown. In an effort to understanding of molecular mechanism for VVS, genome-wide gene expression profiling analyses were performed on VVS patients at syncope state. A total of 66 Type 1 VVS child patients and the same number healthy controls were enrolled in this study. Peripheral blood RNAs were isolated from all subjects, of which 10 RNA samples were randomly selected from each groups for gene expression profile analysis using Gene ST 1.0 arrays (Affymetrix). The results revealed that 103 genes were differently expressed between the patients and controls. Significantly, two G-proteins related genes, GPR174 and GNG2 that have not been related to VVS were among the differently expressed genes. The microarray results were confirmed by qRT-PCR in all the tested individuals. Ingenuity pathway analysis and gene ontology annotation study showed that the differently expressed genes are associated with stress response and apoptosis, suggesting that the alteration of some gene expression including G-proteins related genes is associated with VVS. This study provides new insight into the molecular mechanism of VVS and would be helpful to further identify new molecular biomarkers for the disease.

Simple mathematical models of gene regulatory dynamics

CERN Document Server

Mackey, Michael C; Tyran-Kamińska, Marta; Zeron, Eduardo S

2016-01-01

This is a short and self-contained introduction to the field of mathematical modeling of gene-networks in bacteria. As an entry point to the field, we focus on the analysis of simple gene-network dynamics. The notes commence with an introduction to the deterministic modeling of gene-networks, with extensive reference to applicable results coming from dynamical systems theory. The second part of the notes treats extensively several approaches to the study of gene-network dynamics in the presence of noise—either arising from low numbers of molecules involved, or due to noise external to the regulatory process. The third and final part of the notes gives a detailed treatment of three well studied and concrete examples of gene-network dynamics by considering the lactose operon, the tryptophan operon, and the lysis-lysogeny switch. The notes contain an index for easy location of particular topics as well as an extensive bibliography of the current literature. The target audience of these notes are mainly graduat...

Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration

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Kelemen Arpad

2008-08-01

Full Text Available Abstract Background This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. Results and conclusion Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.

Sparsity in Model Gene Regulatory Networks

International Nuclear Information System (INIS)

Zagorski, M.

2011-01-01

We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

ADRB3 Gene Trp64Arg Polymorphism and Essential Hypertension: A Meta-Analysis Including 9,555 Subjects.

Science.gov (United States)

Li, Yan-Yan; Lu, Xin-Zheng; Wang, Hui; Zhou, Yan-Hong; Yang, Xin-Xing; Geng, Hong-Yu; Gong, Ge; Kim, Hyun Jun

2018-01-01

Background: Presence of the β 3-Adrenergic receptor (ADRB3) gene Trp64Arg (T64A) polymorphism may be associated with an increased susceptibility for essential hypertension (EH). A clear consensus, however, has yet to be reached. Objective and methods: To further elucidate the relationship between the ADRB3 gene Trp64Arg polymorphism and EH, a meta-analysis of 9,555 subjects aggregated from 16 individual studies was performed. The combined odds ratios (ORs) and their corresponding 95% confidence intervals (CI) were evaluated using either a random or fixed effect model. Results: We found a marginally significant association between ADRB3 gene Trp64Arg polymorphism and EH in the whole population under the additive genetic model (OR: 1.200, 95% CI: 1.00-1.43, P = 0.049). Association within the Chinese subgroup, however, was significant under allelic (OR: 1.150, 95% CI: 1.002-1.320, P = 0.046), dominant (OR: 1.213, 95% CI: 1.005-1.464, P = 0.044), heterozygous (OR: 1.430, 95% CI:1.040-1.970, P = 0.03), and additive genetic models (OR: 1.280, 95% CI: 1.030-1.580, P = 0.02). A significant association was also found in the Caucasian subgroup under allelic (OR: 1.850, 95% CI: 1. 260-2.720, P = 0.002), dominant (OR: 2.004, 95% CI: 1.316-3.052, P = 0.001), heterozygous (OR: 2.220, 95% CI: 1.450-3.400, P = 0.0002), and additive genetic models (OR: 2.000, 95% CI: 1. 330-3.010, P = 0.0009). Conclusions: The presence of the ADRB3 gene Trp64Arg polymorphism is positively associated with EH, especially in the Chinese and Caucasian population. The Arg allele carriers of ADRB3 gene Trp64Arg polymorphism may be at an increased risk for developing EH.

The human genome and sport, including epigenetics, gene doping, and athleticogenomics.

Science.gov (United States)

Sharp, N C Craig

2010-03-01

Hugh Montgomery's discovery of the first of more than 239 fitness genes together with rapid advances in human gene therapy have created a prospect of using genes, genetic elements, and cells that have the capacity to enhance athletic performance (to paraphrase the World Anti-Doping Agency's definition of gene doping). This brief overview covers the main areas of interface between genetics and sport, attempts to provide a context against which gene doping may be viewed, and predicts a futuristic legitimate use of genomic (and possibly epigenetic) information in sport. Copyright 2010 Elsevier Inc. All rights reserved.

Gene expression profiling in the stress control brain region hypothalamic paraventricular nucleus reveals a novel gene network including Amyloid beta Precursor Protein

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Deussing Jan M

2010-10-01

Full Text Available Abstract Background The pivotal role of stress in the precipitation of psychiatric diseases such as depression is generally accepted. This study aims at the identification of genes that are directly or indirectly responding to stress. Inbred mouse strains that had been evidenced to differ in their stress response as well as in their response to antidepressant treatment were chosen for RNA profiling after stress exposure. Gene expression and regulation was determined by microarray analyses and further evaluated by bioinformatics tools including pathway and cluster analyses. Results Forced swimming as acute stressor was applied to C57BL/6J and DBA/2J mice and resulted in sets of regulated genes in the paraventricular nucleus of the hypothalamus (PVN, 4 h or 8 h after stress. Although the expression changes between the mouse strains were quite different, they unfolded in phases over time in both strains. Our search for connections between the regulated genes resulted in potential novel signalling pathways in stress. In particular, Guanine nucleotide binding protein, alpha inhibiting 2 (GNAi2 and Amyloid β (A4 precursor protein (APP were detected as stress-regulated genes, and together with other genes, seem to be integrated into stress-responsive pathways and gene networks in the PVN. Conclusions This search for stress-regulated genes in the PVN revealed its impact on interesting genes (GNAi2 and APP and a novel gene network. In particular the expression of APP in the PVN that is governing stress hormone balance, is of great interest. The reported neuroprotective role of this molecule in the CNS supports the idea that a short acute stress can elicit positive adaptational effects in the brain.

Enteric bacterial metabolites propionic and butyric acid modulate gene expression, including CREB-dependent catecholaminergic neurotransmission, in PC12 cells--possible relevance to autism spectrum disorders.

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Bistra B Nankova

Full Text Available Alterations in gut microbiome composition have an emerging role in health and disease including brain function and behavior. Short chain fatty acids (SCFA like propionic (PPA, and butyric acid (BA, which are present in diet and are fermentation products of many gastrointestinal bacteria, are showing increasing importance in host health, but also may be environmental contributors in neurodevelopmental disorders including autism spectrum disorders (ASD. Further to this we have shown SCFA administration to rodents over a variety of routes (intracerebroventricular, subcutaneous, intraperitoneal or developmental time periods can elicit behavioral, electrophysiological, neuropathological and biochemical effects consistent with findings in ASD patients. SCFA are capable of altering host gene expression, partly due to their histone deacetylase inhibitor activity. We have previously shown BA can regulate tyrosine hydroxylase (TH mRNA levels in a PC12 cell model. Since monoamine concentration is known to be elevated in the brain and blood of ASD patients and in many ASD animal models, we hypothesized that SCFA may directly influence brain monoaminergic pathways. When PC12 cells were transiently transfected with plasmids having a luciferase reporter gene under the control of the TH promoter, PPA was found to induce reporter gene activity over a wide concentration range. CREB transcription factor(s was necessary for the transcriptional activation of TH gene by PPA. At lower concentrations PPA also caused accumulation of TH mRNA and protein, indicative of increased cell capacity to produce catecholamines. PPA and BA induced broad alterations in gene expression including neurotransmitter systems, neuronal cell adhesion molecules, inflammation, oxidative stress, lipid metabolism and mitochondrial function, all of which have been implicated in ASD. In conclusion, our data are consistent with a molecular mechanism through which gut related environmental signals

Multistep Model of Cervical Cancer: Participation of miRNAs and Coding Genes

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Angelica Judith Granados López

2014-09-01

Full Text Available Aberrant miRNA expression is well recognized as an important step in the development of cancer. Close to 70 microRNAs (miRNAs have been implicated in cervical cancer up to now, nevertheless it is unknown if aberrant miRNA expression causes the onset of cervical cancer. One of the best ways to address this issue is through a multistep model of carcinogenesis. In the progression of cervical cancer there are three well-established steps to reach cancer that we used in the model proposed here. The first step of the model comprises the gene changes that occur in normal cells to be transformed into immortal cells (CIN 1, the second comprises immortal cell changes to tumorigenic cells (CIN 2, the third step includes cell changes to increase tumorigenic capacity (CIN 3, and the final step covers tumorigenic changes to carcinogenic cells. Altered miRNAs and their target genes are located in each one of the four steps of the multistep model of carcinogenesis. miRNA expression has shown discrepancies in different works; therefore, in this model we include miRNAs recording similar results in at least two studies. The present model is a useful insight into studying potential prognostic, diagnostic, and therapeutic miRNAs.

Candidate genes for performance in horses, including monocarboxylate transporters

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Inaê Cristina Regatieri

Full Text Available ABSTRACT: Some horse breeds are highly selected for athletic activities. The athletic potential of each animal can be measured by its performance in sports. High athletic performance depends on the animal capacity to produce energy through aerobic and anaerobic metabolic pathways, among other factors. Transmembrane proteins called monocarboxylate transporters, mainly the isoform 1 (MCT1 and its ancillary protein CD147, can help the organism to adapt to physiological stress caused by physical exercise, transporting lactate and H+ ions. Horse breeds are selected for different purposes so we might expect differences in the amount of those proteins and in the genotypic frequencies for genes that play a significant role in the performance of the animals. The study of MCT1 and CD147 gene polymorphisms, which can affect the formation of the proteins and transport of lactate and H+, can provide enough information to be used for selection of athletic horses increasingly resistant to intense exercise. Two other candidate genes, the PDK4 and DMRT3, have been associated with athletic potential and indicated as possible markers for performance in horses. The oxidation of fatty acids is highly effective in generating ATP and is controlled by the expression of PDK4 (pyruvate dehydrogenase kinase, isozyme 4 in skeletal muscle during and after exercise. The doublesex and mab-3 related transcription factor 3 (DMRT3 gene encodes an important transcription factor in the setting of spinal cord circuits controlling movement in vertebrates and may be associated with gait performance in horses. This review describes how the monocarboxylate transporters work during physical exercise in athletic horses and the influence of polymorphisms in candidate genes for athletic performance in horses.

Embryo quality predictive models based on cumulus cells gene expression

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Devjak R

2016-06-01

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

Duchenne Muscular Dystrophy Gene Therapy in the Canine Model

Science.gov (United States)

2015-01-01

Abstract Duchenne muscular dystrophy (DMD) is an X-linked lethal muscle disease caused by dystrophin deficiency. Gene therapy has significantly improved the outcome of dystrophin-deficient mice. Yet, clinical translation has not resulted in the expected benefits in human patients. This translational gap is largely because of the insufficient modeling of DMD in mice. Specifically, mice lacking dystrophin show minimum dystrophic symptoms, and they do not respond to the gene therapy vector in the same way as human patients do. Further, the size of a mouse is hundredfolds smaller than a boy, making it impossible to scale-up gene therapy in a mouse model. None of these limitations exist in the canine DMD (cDMD) model. For this reason, cDMD dogs have been considered a highly valuable platform to test experimental DMD gene therapy. Over the last three decades, a variety of gene therapy approaches have been evaluated in cDMD dogs using a number of nonviral and viral vectors. These studies have provided critical insight for the development of an effective gene therapy protocol in human patients. This review discusses the history, current status, and future directions of the DMD gene therapy in the canine model. PMID:25710459

MOS modeling hierarchy including radiation effects

International Nuclear Information System (INIS)

Alexander, D.R.; Turfler, R.M.

1975-01-01

A hierarchy of modeling procedures has been developed for MOS transistors, circuit blocks, and integrated circuits which include the effects of total dose radiation and photocurrent response. The models were developed for use with the SCEPTRE circuit analysis program, but the techniques are suitable for other modern computer aided analysis programs. The modeling hierarchy permits the designer or analyst to select the level of modeling complexity consistent with circuit size, parametric information, and accuracy requirements. Improvements have been made in the implementation of important second order effects in the transistor MOS model, in the definition of MOS building block models, and in the development of composite terminal models for MOS integrated circuits

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

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

2017-12-28

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

Gene therapy in animal models of autosomal dominant retinitis pigmentosa

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Rossmiller, Brian; Mao, Haoyu

2012-01-01

Gene therapy for dominantly inherited genetic disease is more difficult than gene-based therapy for recessive disorders, which can be treated with gene supplementation. Treatment of dominant disease may require gene supplementation partnered with suppression of the expression of the mutant gene either at the DNA level, by gene repair, or at the RNA level by RNA interference or transcriptional repression. In this review, we examine some of the gene delivery approaches used to treat animal models of autosomal dominant retinitis pigmentosa, focusing on those models associated with mutations in the gene for rhodopsin. We conclude that combinatorial approaches have the greatest promise for success. PMID:23077406

Dichotomous noise models of gene switches

Energy Technology Data Exchange (ETDEWEB)

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.

Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

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Liu Yufeng

2011-01-01

Full Text Available Abstract Background Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR to neoadjuvant chemotherapy were also built using this approach. Results We identified statistically significant prognostic models for relapse-free survival (RFS at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR predictions for the entire population. Conclusions Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA

Microarray analysis of gene expression by skeletal muscle of three mouse models of Kennedy disease/spinal bulbar muscular atrophy.

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Kaiguo Mo

2010-09-01

Full Text Available Emerging evidence implicates altered gene expression within skeletal muscle in the pathogenesis of Kennedy disease/spinal bulbar muscular atrophy (KD/SBMA. We therefore broadly characterized gene expression in skeletal muscle of three independently generated mouse models of this disease. The mouse models included a polyglutamine expanded (polyQ AR knock-in model (AR113Q, a polyQ AR transgenic model (AR97Q, and a transgenic mouse that overexpresses wild type AR solely in skeletal muscle (HSA-AR. HSA-AR mice were included because they substantially reproduce the KD/SBMA phenotype despite the absence of polyQ AR.We performed microarray analysis of lower hindlimb muscles taken from these three models relative to wild type controls using high density oligonucleotide arrays. All microarray comparisons were made with at least 3 animals in each condition, and only those genes having at least 2-fold difference and whose coefficient of variance was less than 100% were considered to be differentially expressed. When considered globally, there was a similar overlap in gene changes between the 3 models: 19% between HSA-AR and AR97Q, 21% between AR97Q and AR113Q, and 17% between HSA-AR and AR113Q, with 8% shared by all models. Several patterns of gene expression relevant to the disease process were observed. Notably, patterns of gene expression typical of loss of AR function were observed in all three models, as were alterations in genes involved in cell adhesion, energy balance, muscle atrophy and myogenesis. We additionally measured changes similar to those observed in skeletal muscle of a mouse model of Huntington's Disease, and to those common to muscle atrophy from diverse causes.By comparing patterns of gene expression in three independent models of KD/SBMA, we have been able to identify candidate genes that might mediate the core myogenic features of KD/SBMA.

Differential cytokine gene expression according to outcome in a hamster model of leptospirosis.

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Frédérique Vernel-Pauillac

Full Text Available BACKGROUND: Parameters predicting the evolution of leptospirosis would be useful for clinicians, as well as to better understand severe leptospirosis, but are scarce and rarely validated. Because severe leptospirosis includes septic shock, similarities with predictors evidenced for sepsis and septic shock were studied in a hamster model. METHODOLOGY/PRINCIPAL FINDINGS: Using an LD50 model of leptospirosis in hamsters, we first determined that 3 days post-infection was a time-point that allowed studying the regulation of immune gene expression and represented the onset of the clinical signs of the disease. In the absence of tools to assess serum concentrations of immune effectors in hamsters, we determined mRNA levels of various immune genes, especially cytokines, together with leptospiraemia at this particular time-point. We found differential expression of both pro- and anti-inflammatory mediators, with significantly higher expression levels of tumor necrosis factor alpha, interleukin 1alpha, cyclo-oxygenase 2 and interleukin 10 genes in nonsurvivors compared to survivors. Higher leptospiraemia was also observed in nonsurvivors. Lastly, we demonstrated the relevance of these results by comparing their respective expression levels using a LD100 model or an isogenic high-passage nonvirulent variant. CONCLUSIONS/SIGNIFICANCE: Up-regulated gene expression of both pro- and anti-inflammatory immune effectors in hamsters with fatal outcome in an LD50 model of leptospirosis, together with a higher Leptospira burden, suggest that these gene expression levels could be predictors of adverse outcome in leptospirosis.

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

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Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

Predictive models for mutations in mismatch repair genes: implication for genetic counseling in developing countries

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Monteiro Santos, Erika Maria [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); International Center of Research and Training (CIPE), AC Camargo Hospital, Sao Paulo (Brazil); Silva Junior, Wilson Araujo da [Sao Paulo University, Department of Genetics, Medical School of Ribeirao Preto, Ribeirao Preto (Brazil); Carraro, Dirce Maria [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); International Center of Research and Training (CIPE), AC Camargo Hospital, Sao Paulo (Brazil); Rossi, Benedito Mauro; Valentin, Mev Dominguez [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); Carneiro, Felipe [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); International Center of Research and Training (CIPE), AC Camargo Hospital, Sao Paulo (Brazil); Oliveira, Ligia Petrolini de [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); Oliveira Ferreira, Fabio de; Junior, Samuel Aguiar [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); Hereditary Colorectal Cancer Registry, AC Camargo Hospital, Sao Paulo (Brazil); Nakagawa, Wilson Toshihiko [Hereditary Colorectal Cancer Registry, AC Camargo Hospital, Sao Paulo (Brazil); Gomy, Israel [Graduation Program, AC Camargo Hospital, Sao Paulo (Brazil); Sao Paulo University, Department of Genetics, Medical School of Ribeirao Preto, Ribeirao Preto (Brazil); Faria Ferraz, Victor Evangelista de [Sao Paulo University, Department of Genetics, Medical School of Ribeirao Preto, Ribeirao Preto (Brazil)

2012-02-09

Lynch syndrome (LS) is the most common form of inherited predisposition to colorectal cancer (CRC), accounting for 2-5% of all CRC. LS is an autosomal dominant disease characterized by mutations in the mismatch repair genes mutL homolog 1 (MLH1), mutS homolog 2 (MSH2), postmeiotic segregation increased 1 (PMS1), post-meiotic segregation increased 2 (PMS2) and mutS homolog 6 (MSH6). Mutation risk prediction models can be incorporated into clinical practice, facilitating the decision-making process and identifying individuals for molecular investigation. This is extremely important in countries with limited economic resources. This study aims to evaluate sensitivity and specificity of five predictive models for germline mutations in repair genes in a sample of individuals with suspected Lynch syndrome. Blood samples from 88 patients were analyzed through sequencing MLH1, MSH2 and MSH6 genes. The probability of detecting a mutation was calculated using the PREMM, Barnetson, MMRpro, Wijnen and Myriad models. To evaluate the sensitivity and specificity of the models, receiver operating characteristic curves were constructed. Of the 88 patients included in this analysis, 31 mutations were identified: 16 were found in the MSH2 gene, 15 in the MLH1 gene and no pathogenic mutations were identified in the MSH6 gene. It was observed that the AUC for the PREMM (0.846), Barnetson (0.850), MMRpro (0.821) and Wijnen (0.807) models did not present significant statistical difference. The Myriad model presented lower AUC (0.704) than the four other models evaluated. Considering thresholds of ≥ 5%, the models sensitivity varied between 1 (Myriad) and 0.87 (Wijnen) and specificity ranged from 0 (Myriad) to 0.38 (Barnetson). The Barnetson, PREMM, MMRpro and Wijnen models present similar AUC. The AUC of the Myriad model is statistically inferior to the four other models.

Predictive models for mutations in mismatch repair genes: implication for genetic counseling in developing countries

International Nuclear Information System (INIS)

Monteiro Santos, Erika Maria; Silva Junior, Wilson Araujo da; Carraro, Dirce Maria; Rossi, Benedito Mauro; Valentin, Mev Dominguez; Carneiro, Felipe; Oliveira, Ligia Petrolini de; Oliveira Ferreira, Fabio de; Junior, Samuel Aguiar; Nakagawa, Wilson Toshihiko; Gomy, Israel; Faria Ferraz, Victor Evangelista de

2012-01-01

Lynch syndrome (LS) is the most common form of inherited predisposition to colorectal cancer (CRC), accounting for 2-5% of all CRC. LS is an autosomal dominant disease characterized by mutations in the mismatch repair genes mutL homolog 1 (MLH1), mutS homolog 2 (MSH2), postmeiotic segregation increased 1 (PMS1), post-meiotic segregation increased 2 (PMS2) and mutS homolog 6 (MSH6). Mutation risk prediction models can be incorporated into clinical practice, facilitating the decision-making process and identifying individuals for molecular investigation. This is extremely important in countries with limited economic resources. This study aims to evaluate sensitivity and specificity of five predictive models for germline mutations in repair genes in a sample of individuals with suspected Lynch syndrome. Blood samples from 88 patients were analyzed through sequencing MLH1, MSH2 and MSH6 genes. The probability of detecting a mutation was calculated using the PREMM, Barnetson, MMRpro, Wijnen and Myriad models. To evaluate the sensitivity and specificity of the models, receiver operating characteristic curves were constructed. Of the 88 patients included in this analysis, 31 mutations were identified: 16 were found in the MSH2 gene, 15 in the MLH1 gene and no pathogenic mutations were identified in the MSH6 gene. It was observed that the AUC for the PREMM (0.846), Barnetson (0.850), MMRpro (0.821) and Wijnen (0.807) models did not present significant statistical difference. The Myriad model presented lower AUC (0.704) than the four other models evaluated. Considering thresholds of ≥ 5%, the models sensitivity varied between 1 (Myriad) and 0.87 (Wijnen) and specificity ranged from 0 (Myriad) to 0.38 (Barnetson). The Barnetson, PREMM, MMRpro and Wijnen models present similar AUC. The AUC of the Myriad model is statistically inferior to the four other models

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

N-Myc regulates expression of pluripotency genes in neuroblastoma including lif, klf2, klf4, and lin28b.

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Rebecca Cotterman

2009-06-01

Full Text Available myc genes are best known for causing tumors when overexpressed, but recent studies suggest endogenous myc regulates pluripotency and self-renewal of stem cells. For example, N-myc is associated with a number of tumors including neuroblastoma, but also plays a central role in the function of normal neural stem and precursor cells (NSC. Both c- and N-myc also enhance the production of induced pluripotent stem cells (iPSC and are linked to neural tumor stem cells. The mechanisms by which myc regulates normal and neoplastic stem-related functions remain largely open questions. Here from a global, unbiased search for N-Myc bound genes using ChIP-chip assays in neuroblastoma, we found lif as a putative N-Myc bound gene with a number of strong N-Myc binding peaks in the promoter region enriched for E-boxes. Amongst putative N-Myc target genes in expression microarray studies in neuroblastoma we also found lif and three additional important embryonic stem cell (ESC-related factors that are linked to production of iPSC: klf2, klf4, and lin28b. To examine the regulation of these genes by N-Myc, we measured their expression using neuroblastoma cells that contain a Tet-regulatable N-myc transgene (TET21N as well as NSC with a nestin-cre driven N-myc knockout. N-myc levels closely correlated with the expression of all of these genes in neuroblastoma and all but lif in NSC. Direct ChIP assays also indicate that N-Myc directly binds the lif promoter. N-Myc regulates trimethylation of lysine 4 of histone H3 in the promoter of lif and possibly in the promoters of several other stem-related genes. Together these findings indicate that N-Myc regulates overlapping stem-related gene expression programs in neuroblastoma and NSC, supporting a novel model by which amplification of the N-myc gene may drive formation of neuroblastoma. They also suggest mechanisms by which Myc proteins more generally contribute to maintenance of pluripotency and self-renewal of ESC as

Innate immune genes including a mucin-like gene, mul-1, induced by ionizing radiation in Caenorhabditis elegans.

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Kimura, Takafumi; Takanami, Takako; Sakashita, Tetsuya; Wada, Seiichi; Kobayashi, Yasuhiko; Higashitani, Atsushi

2012-10-01

The effect of radiation on the intestine has been studied for more than one hundred years. It remains unclear, however, whether this organ uses specific defensive mechanisms against ionizing radiation. The infection with Pseudomonas aeruginosa (PA14) in Caenorhabditis elegans induces up-regulation of innate immune response genes. Here, we found that exposure to ionizing radiation also induces certain innate immune response genes such as F49F1.6 (termed mul-1), clec-4, clec-67, lys-1 and lys-2 in the intestine. Moreover, pre-treatment with ionizing radiation before seeding on PA14 lawn plate significantly increased survival rate in the nematode. We also studied transcription pathway of the mul-1 in response to ionizing radiation. Induction of mul-1 gene was highly dependent on the ELT-2 transcription factor and p38 MAPK. Moreover, the insulin/IGF-1 signal pathway works to enhance induction of this gene. The mul-1 gene showed a different induction pattern from the DNA damage response gene, ced-13, which implies that the expression of this gene might be triggered as an indirect effect of radiation. Silencing of the mul-1 gene led to growth retardation after treatment with ionizing radiation. We describe the cross-tolerance between the response to radiation exposure and the innate immune system.

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

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Gao Shouguo

2011-08-01

Full Text Available Abstract Background Bayesian Network (BN is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable. Results We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information. Conclusion our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.

Modeling gene expression measurement error: a quasi-likelihood approach

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Strimmer Korbinian

2003-03-01

Full Text Available Abstract Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale. Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood. Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic variance structure of the data. As the quasi-likelihood behaves (almost like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also

Bayesian inference based modelling for gene transcriptional dynamics by integrating multiple source of knowledge

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Wang Shu-Qiang

2012-07-01

Full Text Available Abstract Background A key challenge in the post genome era is to identify genome-wide transcriptional regulatory networks, which specify the interactions between transcription factors and their target genes. Numerous methods have been developed for reconstructing gene regulatory networks from expression data. However, most of them are based on coarse grained qualitative models, and cannot provide a quantitative view of regulatory systems. Results A binding affinity based regulatory model is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity and the activity level of transcription factor (TF are incorporated into a general learning model. The sequence features of the promoter and the possible occupancy of nucleosomes are exploited to estimate the binding probability of regulators. Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate. Conclusions We testify the proposed approach on two real-world microarray datasets. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than previous models can do.

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.

Combinatorial explosion in model gene networks

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Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

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...

Animal models of gene-environment interactions in schizophrenia.

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Ayhan, Yavuz; Sawa, Akira; Ross, Christopher A; Pletnikov, Mikhail V

2009-12-07

The pathogenesis of schizophrenia and related mental illnesses likely involves multiple interactions between susceptibility genes of small effects and environmental factors. Gene-environment interactions occur across different stages of neurodevelopment to produce heterogeneous clinical and pathological manifestations of the disease. The main obstacle for mechanistic studies of gene-environment interplay has been the paucity of appropriate experimental systems for elucidating the molecular pathways that mediate gene-environment interactions relevant to schizophrenia. Recent advances in psychiatric genetics and a plethora of experimental data from animal studies allow us to suggest a new approach to gene-environment interactions in schizophrenia. We propose that animal models based on identified genetic mutations and measurable environment factors will help advance studies of the molecular mechanisms of gene-environment interplay.

Predictive models for mutations in mismatch repair genes: implication for genetic counseling in developing countries

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Monteiro Santos Erika

2012-02-01

Full Text Available Abstract Background Lynch syndrome (LS is the most common form of inherited predisposition to colorectal cancer (CRC, accounting for 2-5% of all CRC. LS is an autosomal dominant disease characterized by mutations in the mismatch repair genes mutL homolog 1 (MLH1, mutS homolog 2 (MSH2, postmeiotic segregation increased 1 (PMS1, post-meiotic segregation increased 2 (PMS2 and mutS homolog 6 (MSH6. Mutation risk prediction models can be incorporated into clinical practice, facilitating the decision-making process and identifying individuals for molecular investigation. This is extremely important in countries with limited economic resources. This study aims to evaluate sensitivity and specificity of five predictive models for germline mutations in repair genes in a sample of individuals with suspected Lynch syndrome. Methods Blood samples from 88 patients were analyzed through sequencing MLH1, MSH2 and MSH6 genes. The probability of detecting a mutation was calculated using the PREMM, Barnetson, MMRpro, Wijnen and Myriad models. To evaluate the sensitivity and specificity of the models, receiver operating characteristic curves were constructed. Results Of the 88 patients included in this analysis, 31 mutations were identified: 16 were found in the MSH2 gene, 15 in the MLH1 gene and no pathogenic mutations were identified in the MSH6 gene. It was observed that the AUC for the PREMM (0.846, Barnetson (0.850, MMRpro (0.821 and Wijnen (0.807 models did not present significant statistical difference. The Myriad model presented lower AUC (0.704 than the four other models evaluated. Considering thresholds of ≥ 5%, the models sensitivity varied between 1 (Myriad and 0.87 (Wijnen and specificity ranged from 0 (Myriad to 0.38 (Barnetson. Conclusions The Barnetson, PREMM, MMRpro and Wijnen models present similar AUC. The AUC of the Myriad model is statistically inferior to the four other models.

Full-Length Sequence of Mouse Acupuncture-Induced 1-L (Aig1l Gene Including Its Transcriptional Start Site

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Mika Ohta

2011-01-01

Full Text Available We have been investigating the molecular efficacy of electroacupuncture (EA, which is one type of acupuncture therapy. In our previous molecular biological study of acupuncture, we found an EA-induced gene, named acupuncture-induced 1-L (Aig1l, in mouse skeletal muscle. The aims of this study consisted of identification of the full-length cDNA sequence of Aig1l including the transcriptional start site, determination of the tissue distribution of Aig1l and analysis of the effect of EA on Aig1l gene expression. We determined the complete cDNA sequence including the transcriptional start site via cDNA cloning with the cap site hunting method. We then analyzed the tissue distribution of Aig1l by means of northern blot analysis and real-time quantitative polymerase chain reaction. We used the semiquantitative reverse transcriptase-polymerase chain reaction to examine the effect of EA on Aig1l gene expression. Our results showed that the complete cDNA sequence of Aig1l was 6073 bp long, and the putative protein consisted of 962 amino acids. All seven tissues that we analyzed expressed the Aig1l gene. In skeletal muscle, EA induced expression of the Aig1l gene, with high expression observed after 3 hours of EA. Our findings thus suggest that the Aig1l gene may play a key role in the molecular mechanisms of EA efficacy.

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

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Garzón-Martínez Gina A

2012-04-01

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

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

Science.gov (United States)

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

2012-04-25

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

Normalisation genes for expression analyses in the brown alga model Ectocarpus siliculosus

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Rousvoal Sylvie

2008-08-01

Full Text Available Abstract Background Brown algae are plant multi-cellular organisms occupying most of the world coasts and are essential actors in the constitution of ecological niches at the shoreline. Ectocarpus siliculosus is an emerging model for brown algal research. Its genome has been sequenced, and several tools are being developed to perform analyses at different levels of cell organization, including transcriptomic expression analyses. Several topics, including physiological responses to osmotic stress and to exposure to contaminants and solvents are being studied in order to better understand the adaptive capacity of brown algae to pollution and environmental changes. A series of genes that can be used to normalise expression analyses is required for these studies. Results We monitored the expression of 13 genes under 21 different culture conditions. These included genes encoding proteins and factors involved in protein translation (ribosomal protein 26S, EF1alpha, IF2A, IF4E and protein degradation (ubiquitin, ubiquitin conjugating enzyme or folding (cyclophilin, and proteins involved in both the structure of the cytoskeleton (tubulin alpha, actin, actin-related proteins and its trafficking function (dynein, as well as a protein implicated in carbon metabolism (glucose 6-phosphate dehydrogenase. The stability of their expression level was assessed using the Ct range, and by applying both the geNorm and the Normfinder principles of calculation. Conclusion Comparisons of the data obtained with the three methods of calculation indicated that EF1alpha (EF1a was the best reference gene for normalisation. The normalisation factor should be calculated with at least two genes, alpha tubulin, ubiquitin-conjugating enzyme or actin-related proteins being good partners of EF1a. Our results exclude actin as a good normalisation gene, and, in this, are in agreement with previous studies in other organisms.

Validation of suitable reference genes for expression studies in different pilocarpine-induced models of mesial temporal lobe epilepsy.

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Thalita Ewellyn Batista Sales Marques

Full Text Available It is well recognized that the reference gene in a RT-qPCR should be properly validated to ensure that gene expression is unaffected by the experimental condition. We investigated eight potential reference genes in two different pilocarpine PILO-models of mesial temporal lobe epilepsy (MTLE performing a stability expression analysis using geNorm, NormFinder and BestKepeer softwares. Then, as a validation strategy, we conducted a relative expression analysis of the Gfap gene. Our results indicate that in the systemic PILO-model Actb, Gapdh, Rplp1, Tubb2a and Polr1a mRNAs were highly stable in hippocampus of rats from all experimental and control groups, whereas Gusb revealed to be the most variable one. In fact, we observed that using Gusb for normalization, the relative mRNA levels of the Gfap gene differed from those obtained with stable genes. On the contrary, in the intrahippocampal PILO-model, all softwares included Gusb as a stable gene, whereas B2m was indicated as the worst candidate gene. The results obtained for the other reference genes were comparable to those observed for the systemic Pilo-model. The validation of these data by the analysis of the relative expression of Gfap showed that the upregulation of the Gfap gene in the hippocampus of rats sacrificed 24 hours after status epilepticus (SE was undetected only when B2m was used as the normalizer. These findings emphasize that a gene that is stable in one pathology model may not be stable in a different experimental condition related to the same pathology and therefore, the choice of reference genes depends on study design.

IntPath--an integrated pathway gene relationship database for model organisms and important pathogens.

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Zhou, Hufeng; Jin, Jingjing; Zhang, Haojun; Yi, Bo; Wozniak, Michal; Wong, Limsoon

2012-01-01

Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases--e.g., KEGG, WikiPathways, and BioCyc--are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases. In this paper, we overcome these issues through extraction, normalization and integration of pathway data from several major public databases (KEGG, WikiPathways, BioCyc, etc). We build a database that not only hosts our integrated pathway gene relationship data for public access but also maintains the necessary updates in the long run. This public repository is named IntPath (Integrated Pathway gene relationship database for model organisms and important pathogens). Four organisms--S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens and M. musculus--are included in this version (V2.0) of IntPath. IntPath uses the "full unification" approach to ensure no deletion and no introduced noise in this process. Therefore, IntPath contains much richer pathway-gene and pathway-gene pair relationships and much larger number of non-redundant genes and gene pairs than any of the single-source databases. The gene relationships of each gene (measured by average node degree) per pathway are significantly richer. The gene relationships in each pathway (measured by average number of gene pairs per pathway) are also considerably richer in the integrated pathways. Moderate manual curation are involved to get rid of errors and noises from source data (e.g., the gene ID errors in WikiPathways and

Weighted functional linear regression models for gene-based association analysis.

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Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

Contemporary Animal Models For Human Gene Therapy Applications.

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Gopinath, Chitra; Nathar, Trupti Job; Ghosh, Arkasubhra; Hickstein, Dennis Durand; Nelson, Everette Jacob Remington

2015-01-01

Over the past three decades, gene therapy has been making considerable progress as an alternative strategy in the treatment of many diseases. Since 2009, several studies have been reported in humans on the successful treatment of various diseases. Animal models mimicking human disease conditions are very essential at the preclinical stage before embarking on a clinical trial. In gene therapy, for instance, they are useful in the assessment of variables related to the use of viral vectors such as safety, efficacy, dosage and localization of transgene expression. However, choosing a suitable disease-specific model is of paramount importance for successful clinical translation. This review focuses on the animal models that are most commonly used in gene therapy studies, such as murine, canine, non-human primates, rabbits, porcine, and a more recently developed humanized mice. Though small and large animals both have their own pros and cons as disease-specific models, the choice is made largely based on the type and length of study performed. While small animals with a shorter life span could be well-suited for degenerative/aging studies, large animals with longer life span could suit longitudinal studies and also help with dosage adjustments to maximize therapeutic benefit. Recently, humanized mice or mouse-human chimaeras have gained interest in the study of human tissues or cells, thereby providing a more reliable understanding of therapeutic interventions. Thus, animal models are of great importance with regard to testing new vector technologies in vivo for assessing safety and efficacy prior to a gene therapy clinical trial.

A review of gene-environment correlations and their implications for autism: a conceptual model.

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Meek, Shantel E; Lemery-Chalfant, Kathryn; Jahromi, Laudan B; Valiente, Carlos

2013-07-01

A conceptual model is proposed that explains how gene-environment correlations and the multiplier effect function in the context of social development in individuals with autism. The review discusses the current state of autism genetic research, including its challenges, such as the genetic and phenotypic heterogeneity of the disorder, and its limitations, such as the lack of interdisciplinary work between geneticists and social scientists. We discuss literature on gene-environment correlations in the context of social development and draw implications for individuals with autism. The review expands upon genes, behaviors, types of environmental exposure, and exogenous variables relevant to social development in individuals on the autism spectrum, and explains these factors in the context of the conceptual model to provide a more in-depth understanding of how the effects of certain genetic variants can be multiplied by the environment to cause largely phenotypic individual differences. Using the knowledge gathered from gene-environment correlations and the multiplier effect, we outline novel intervention directions and implications. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Gene expression profiles of human dendritic cells interacting with Aspergillus fumigatus in a bilayer model of the alveolar epithelium/endothelium interface.

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Charles Oliver Morton

Full Text Available The initial stages of the interaction between the host and Aspergillus fumigatus at the alveolar surface of the human lung are critical in the establishment of aspergillosis. Using an in vitro bilayer model of the alveolus, including both the epithelium (human lung adenocarcinoma epithelial cell line, A549 and endothelium (human pulmonary artery epithelial cells, HPAEC on transwell membranes, it was possible to closely replicate the in vivo conditions. Two distinct sub-groups of dendritic cells (DC, monocyte-derived DC (moDC and myeloid DC (mDC, were included in the model to examine immune responses to fungal infection at the alveolar surface. RNA in high quantity and quality was extracted from the cell layers on the transwell membrane to allow gene expression analysis using tailored custom-made microarrays, containing probes for 117 immune-relevant genes. This microarray data indicated minimal induction of immune gene expression in A549 alveolar epithelial cells in response to germ tubes of A. fumigatus. In contrast, the addition of DC to the system greatly increased the number of differentially expressed immune genes. moDC exhibited increased expression of genes including CLEC7A, CD209 and CCL18 in the absence of A. fumigatus compared to mDC. In the presence of A. fumigatus, both DC subgroups exhibited up-regulation of genes identified in previous studies as being associated with the exposure of DC to A. fumigatus and exhibiting chemotactic properties for neutrophils, including CXCL2, CXCL5, CCL20, and IL1B. This model closely approximated the human alveolus allowing for an analysis of the host pathogen interface that complements existing animal models of IA.

Cross-species comparison of biological themes and underlying genes on a global gene expression scale in a mouse model of colorectal liver metastasis and in clinical specimens

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Schirmacher Peter

2008-09-01

-species comparison on a global gene expression scale suggests the validity of an animal model representing the human situation. The actual yield of potential target genes depends on several variables including the animal model, choice of inclusion criteria, inherent species differences and histologic assessment.

Zebrafish homologs of genes within 16p11.2, a genomic region associated with brain disorders, are active during brain development, and include two deletion dosage sensor genes

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Alicia Blaker-Lee

2012-11-01

Deletion or duplication of one copy of the human 16p11.2 interval is tightly associated with impaired brain function, including autism spectrum disorders (ASDs, intellectual disability disorder (IDD and other phenotypes, indicating the importance of gene dosage in this copy number variant region (CNV. The core of this CNV includes 25 genes; however, the number of genes that contribute to these phenotypes is not known. Furthermore, genes whose functional levels change with deletion or duplication (termed ‘dosage sensors’, which can associate the CNV with pathologies, have not been identified in this region. Using the zebrafish as a tool, a set of 16p11.2 homologs was identified, primarily on chromosomes 3 and 12. Use of 11 phenotypic assays, spanning the first 5 days of development, demonstrated that this set of genes is highly active, such that 21 out of the 22 homologs tested showed loss-of-function phenotypes. Most genes in this region were required for nervous system development – impacting brain morphology, eye development, axonal density or organization, and motor response. In general, human genes were able to substitute for the fish homolog, demonstrating orthology and suggesting conserved molecular pathways. In a screen for 16p11.2 genes whose function is sensitive to hemizygosity, the aldolase a (aldoaa and kinesin family member 22 (kif22 genes were identified as giving clear phenotypes when RNA levels were reduced by ∼50%, suggesting that these genes are deletion dosage sensors. This study leads to two major findings. The first is that the 16p11.2 region comprises a highly active set of genes, which could present a large genetic target and might explain why multiple brain function, and other, phenotypes are associated with this interval. The second major finding is that there are (at least two genes with deletion dosage sensor properties among the 16p11.2 set, and these could link this CNV to brain disorders such as ASD and IDD.

Gene expression profiling via LongSAGE in a non-model plant species: a case study in seeds of Brassica napus

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Friedt Wolfgang

2009-07-01

Full Text Available Abstract Background Serial analysis of gene expression (LongSAGE was applied for gene expression profiling in seeds of oilseed rape (Brassica napus ssp. napus. The usefulness of this technique for detailed expression profiling in a non-model organism was demonstrated for the highly complex, neither fully sequenced nor annotated genome of B. napus by applying a tag-to-gene matching strategy based on Brassica ESTs and the annotated proteome of the closely related model crucifer A. thaliana. Results Transcripts from 3,094 genes were detected at two time-points of seed development, 23 days and 35 days after pollination (DAP. Differential expression showed a shift from gene expression involved in diverse developmental processes including cell proliferation and seed coat formation at 23 DAP to more focussed metabolic processes including storage protein accumulation and lipid deposition at 35 DAP. The most abundant transcripts at 23 DAP were coding for diverse protease inhibitor proteins and proteases, including cysteine proteases involved in seed coat formation and a number of lipid transfer proteins involved in embryo pattern formation. At 35 DAP, transcripts encoding napin, cruciferin and oleosin storage proteins were most abundant. Over both time-points, 18.6% of the detected genes were matched by Brassica ESTs identified by LongSAGE tags in antisense orientation. This suggests a strong involvement of antisense transcript expression in regulatory processes during B. napus seed development. Conclusion This study underlines the potential of transcript tagging approaches for gene expression profiling in Brassica crop species via EST matching to annotated A. thaliana genes. Limits of tag detection for low-abundance transcripts can today be overcome by ultra-high throughput sequencing approaches, so that tag-based gene expression profiling may soon become the method of choice for global expression profiling in non-model species.

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

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Li He

2014-01-01

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

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

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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.

Gene-Environment Interplay in Twin Models

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Hatemi, Peter K.

2013-01-01

In this article, we respond to Shultziner’s critique that argues that identical twins are more alike not because of genetic similarity, but because they select into more similar environments and respond to stimuli in comparable ways, and that these effects bias twin model estimates to such an extent that they are invalid. The essay further argues that the theory and methods that undergird twin models, as well as the empirical studies which rely upon them, are unaware of these potential biases. We correct this and other misunderstandings in the essay and find that gene-environment (GE) interplay is a well-articulated concept in behavior genetics and political science, operationalized as gene-environment correlation and gene-environment interaction. Both are incorporated into interpretations of the classical twin design (CTD) and estimated in numerous empirical studies through extensions of the CTD. We then conduct simulations to quantify the influence of GE interplay on estimates from the CTD. Due to the criticism’s mischaracterization of the CTD and GE interplay, combined with the absence of any empirical evidence to counter what is presented in the extant literature and this article, we conclude that the critique does not enhance our understanding of the processes that drive political traits, genetic or otherwise. PMID:24808718

Thermodynamics-based models of transcriptional regulation with gene sequence.

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Wang, Shuqiang; Shen, Yanyan; Hu, Jinxing

2015-12-01

Quantitative models of gene regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled or heuristic approximations of the underlying regulatory mechanisms. In this work, we have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence. The proposed model relies on a continuous time, differential equation description of transcriptional dynamics. The sequence features of the promoter are exploited to derive the binding affinity which is derived based on statistical molecular thermodynamics. Experimental results show that the proposed model can effectively identify the activity levels of transcription factors and the regulatory parameters. Comparing with the previous models, the proposed model can reveal more biological sense.

T-cell transfer and cytokine/TCR gene deletion models in the study of inflammatory bowel disease

DEFF Research Database (Denmark)

Bregenholt, S; Delbro, D; Claesson, Mogens Helweg

1997-01-01

Until recently there existed no appropriate immunological animal models for human inflammatory bowel diseases (IBD). Today a number of models, mostly in the mouse and rat, have proved useful in the study of several aspects of IBD, including the histopathology and the disease-inductive and -protec...... and in gene-deleted mice....

Model-based gene set analysis for Bioconductor.

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Bauer, Sebastian; Robinson, Peter N; Gagneur, Julien

2011-07-01

Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach. The mgsa package has been made available as part of Bioconductor 2.8. It is released under the conditions of the Artistic license 2.0. peter.robinson@charite.de; julien.gagneur@embl.de.

Identification of a novel gene family that includes the interferon-inducible human genes 6–16 and ISG12

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Parker Nadeene

2004-01-01

Full Text Available Abstract Background The human 6–16 and ISG12 genes are transcriptionally upregulated in a variety of cell types in response to type I interferon (IFN. The predicted products of these genes are small (12.9 and 11.5 kDa respectively, hydrophobic proteins that share 36% overall amino acid identity. Gene disruption and over-expression studies have so far failed to reveal any biochemical or cellular roles for these proteins. Results We have used in silico analyses to identify a novel family of genes (the ISG12 gene family related to both the human 6–16 and ISG12 genes. Each ISG12 family member codes for a small hydrophobic protein containing a conserved ~80 amino-acid motif (the ISG12 motif. So far we have detected 46 family members in 25 organisms, ranging from unicellular eukaryotes to humans. Humans have four ISG12 genes: the 6–16 gene at chromosome 1p35 and three genes (ISG12(a, ISG12(b and ISG12(c clustered at chromosome 14q32. Mice have three family members (ISG12(a, ISG12(b1 and ISG12(b2 clustered at chromosome 12F1 (syntenic with human chromosome 14q32. There does not appear to be a murine 6–16 gene. On the basis of phylogenetic analyses, genomic organisation and intron-alignments we suggest that this family has arisen through divergent inter- and intra-chromosomal gene duplication events. The transcripts from human and mouse genes are detectable, all but two (human ISG12(b and ISG12(c being upregulated in response to type I IFN in the cell lines tested. Conclusions Members of the eukaryotic ISG12 gene family encode a small hydrophobic protein with at least one copy of a newly defined motif of ~80 amino-acids (the ISG12 motif. In higher eukaryotes, many of the genes have acquired a responsiveness to type I IFN during evolution suggesting that a role in resisting cellular or environmental stress may be a unifying property of all family members. Analysis of gene-function in higher eukaryotes is complicated by the possibility of

Class B Gene Expression and the Modified ABC Model in Nongrass Monocots

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Akira Kanno

2007-01-01

Full Text Available The discovery of the MADS-box genes and the study of model plants such as Arabidopsis thaliana and Antirrhinum majus have greatly improved our understanding of the molecular mechanisms driving the diversity in floral development. The class B genes, which belong to the MADS-box gene family, are important regulators of the development of petals and stamens in flowering plants. Many nongrass monocot flowers have two whorls of petaloid organs, which are called tepals. To explain this floral morphology, the modified ABC model was proposed. This model was exemplified by the tulip, in which expansion and restriction of class B gene expression is linked to the transition of floral morphologies in whorl 1. The expression patterns of class B genes from many monocot species nicely fit this model; however, those from some species, such as asparagus, do not. In this review, we summarize the relationship between class B gene expression and floral morphology in nongrass monocots, such as Liliales (Liliaceae and Asparagales species, and discuss the applicability of the modified ABC model to monocot flowers.

Use of Maximum Likelihood-Mixed Models to select stable reference genes: a case of heat stress response in sheep

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Salces Judit

2011-08-01

Full Text Available Abstract Background Reference genes with stable expression are required to normalize expression differences of target genes in qPCR experiments. Several procedures and companion software have been proposed to find the most stable genes. Model based procedures are attractive because they provide a solid statistical framework. NormFinder, a widely used software, uses a model based method. The pairwise comparison procedure implemented in GeNorm is a simpler procedure but one of the most extensively used. In the present work a statistical approach based in Maximum Likelihood estimation under mixed models was tested and compared with NormFinder and geNorm softwares. Sixteen candidate genes were tested in whole blood samples from control and heat stressed sheep. Results A model including gene and treatment as fixed effects, sample (animal, gene by treatment, gene by sample and treatment by sample interactions as random effects with heteroskedastic residual variance in gene by treatment levels was selected using goodness of fit and predictive ability criteria among a variety of models. Mean Square Error obtained under the selected model was used as indicator of gene expression stability. Genes top and bottom ranked by the three approaches were similar; however, notable differences for the best pair of genes selected for each method and the remaining genes of the rankings were shown. Differences among the expression values of normalized targets for each statistical approach were also found. Conclusions Optimal statistical properties of Maximum Likelihood estimation joined to mixed model flexibility allow for more accurate estimation of expression stability of genes under many different situations. Accurate selection of reference genes has a direct impact over the normalized expression values of a given target gene. This may be critical when the aim of the study is to compare expression rate differences among samples under different environmental

Progressive IRP Models for Power Resources Including EPP

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Yiping Zhu

2017-01-01

Full Text Available In the view of optimizing regional power supply and demand, the paper makes effective planning scheduling of supply and demand side resources including energy efficiency power plant (EPP, to achieve the target of benefit, cost, and environmental constraints. In order to highlight the characteristics of different supply and demand resources in economic, environmental, and carbon constraints, three planning models with progressive constraints are constructed. Results of three models by the same example show that the best solutions to different models are different. The planning model including EPP has obvious advantages considering pollutant and carbon emission constraints, which confirms the advantages of low cost and emissions of EPP. The construction of progressive IRP models for power resources considering EPP has a certain reference value for guiding the planning and layout of EPP within other power resources and achieving cost and environmental objectives.

The Medicago truncatula lysine motif-receptor-like kinase gene family includes NFP and new nodule-expressed genes

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Arrighi, J.F.; Barre, A.; Amor, Ben B.; Bersoult, A.; Campos Soriano, L.; Mirabella, R.; Carvalho-Niebel, de F.; Journet, E.P.; Ghérardi, M.; Huguet, T.; Geurts, R.; Dénarié, J.; Rougé, P.; Gough, C.

2006-01-01

Rhizobial Nod factors are key symbiotic signals responsible for starting the nodulation process in host legume plants. Of the six Medicago truncatula genes controlling a Nod factor signaling pathway, Nod Factor Perception (NFP) was reported as a candidate Nod factor receptor gene. Here, we provide

Genome mining of Streptomyces scabrisporus NF3 reveals symbiotic features including genes related to plant interactions

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Rodríguez-Luna, Stefany Daniela; Cruz Vázquez, Angélica Patricia; Jiménez Suárez, Verónica; Rodríguez-Sanoja, Romina; Alvarez-Buylla, Elena R.; Sánchez, Sergio

2018-01-01

Endophytic bacteria are wide-spread and associated with plant physiological benefits, yet their genomes and secondary metabolites remain largely unidentified. In this study, we explored the genome of the endophyte Streptomyces scabrisporus NF3 for discovery of potential novel molecules as well as genes and metabolites involved in host interactions. The complete genomes of seven Streptomyces and three other more distantly related bacteria were used to define the functional landscape of this unique microbe. The S. scabrisporus NF3 genome is larger than the average Streptomyces genome and not structured for an obligate endosymbiotic lifestyle; this and the fact that can grow in R2YE media implies that it could include a soil-living stage. The genome displays an enrichment of genes associated with amino acid production, protein secretion, secondary metabolite and antioxidants production and xenobiotic degradation, indicating that S. scabrisporus NF3 could contribute to the metabolic enrichment of soil microbial communities and of its hosts. Importantly, besides its metabolic advantages, the genome showed evidence for differential functional specificity and diversification of plant interaction molecules, including genes for the production of plant hormones, stress resistance molecules, chitinases, antibiotics and siderophores. Given the diversity of S. scabrisporus mechanisms for host upkeep, we propose that these strategies were necessary for its adaptation to plant hosts and to face changes in environmental conditions. PMID:29447216

Genome mining of Streptomyces scabrisporus NF3 reveals symbiotic features including genes related to plant interactions.

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Corina Diana Ceapă

Full Text Available Endophytic bacteria are wide-spread and associated with plant physiological benefits, yet their genomes and secondary metabolites remain largely unidentified. In this study, we explored the genome of the endophyte Streptomyces scabrisporus NF3 for discovery of potential novel molecules as well as genes and metabolites involved in host interactions. The complete genomes of seven Streptomyces and three other more distantly related bacteria were used to define the functional landscape of this unique microbe. The S. scabrisporus NF3 genome is larger than the average Streptomyces genome and not structured for an obligate endosymbiotic lifestyle; this and the fact that can grow in R2YE media implies that it could include a soil-living stage. The genome displays an enrichment of genes associated with amino acid production, protein secretion, secondary metabolite and antioxidants production and xenobiotic degradation, indicating that S. scabrisporus NF3 could contribute to the metabolic enrichment of soil microbial communities and of its hosts. Importantly, besides its metabolic advantages, the genome showed evidence for differential functional specificity and diversification of plant interaction molecules, including genes for the production of plant hormones, stress resistance molecules, chitinases, antibiotics and siderophores. Given the diversity of S. scabrisporus mechanisms for host upkeep, we propose that these strategies were necessary for its adaptation to plant hosts and to face changes in environmental conditions.

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon

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Satoru Koda

2017-11-01

Full Text Available We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX model with a group smoothly clipped absolute deviation (SCAD method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon. To reveal the diurnal changes in the transcriptome in B. distachyon, we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon. On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon, aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

Animal models of Duchenne muscular dystrophy: from basic mechanisms to gene therapy

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McGreevy, Joe W.; Hakim, Chady H.; McIntosh, Mark A.; Duan, Dongsheng

2015-01-01

Duchenne muscular dystrophy (DMD) is a progressive muscle-wasting disorder. It is caused by loss-of-function mutations in the dystrophin gene. Currently, there is no cure. A highly promising therapeutic strategy is to replace or repair the defective dystrophin gene by gene therapy. Numerous animal models of DMD have been developed over the last 30 years, ranging from invertebrate to large mammalian models. mdx mice are the most commonly employed models in DMD research and have been used to lay the groundwork for DMD gene therapy. After ~30 years of development, the field has reached the stage at which the results in mdx mice can be validated and scaled-up in symptomatic large animals. The canine DMD (cDMD) model will be excellent for these studies. In this article, we review the animal models for DMD, the pros and cons of each model system, and the history and progress of preclinical DMD gene therapy research in the animal models. We also discuss the current and emerging challenges in this field and ways to address these challenges using animal models, in particular cDMD dogs. PMID:25740330

Animal models of Duchenne muscular dystrophy: from basic mechanisms to gene therapy.

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McGreevy, Joe W; Hakim, Chady H; McIntosh, Mark A; Duan, Dongsheng

2015-03-01

Duchenne muscular dystrophy (DMD) is a progressive muscle-wasting disorder. It is caused by loss-of-function mutations in the dystrophin gene. Currently, there is no cure. A highly promising therapeutic strategy is to replace or repair the defective dystrophin gene by gene therapy. Numerous animal models of DMD have been developed over the last 30 years, ranging from invertebrate to large mammalian models. mdx mice are the most commonly employed models in DMD research and have been used to lay the groundwork for DMD gene therapy. After ~30 years of development, the field has reached the stage at which the results in mdx mice can be validated and scaled-up in symptomatic large animals. The canine DMD (cDMD) model will be excellent for these studies. In this article, we review the animal models for DMD, the pros and cons of each model system, and the history and progress of preclinical DMD gene therapy research in the animal models. We also discuss the current and emerging challenges in this field and ways to address these challenges using animal models, in particular cDMD dogs. © 2015. Published by The Company of Biologists Ltd.

Real-time PCR expression profiling of genes encoding potential virulence factors in Candida albicans biofilms: identification of model-dependent and -independent gene expression

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Řičicová Markéta

2010-04-01

Full Text Available Abstract Background Candida albicans infections are often associated with biofilm formation. Previous work demonstrated that the expression of HWP1 (hyphal wall protein and of genes belonging to the ALS (agglutinin-like sequence, SAP (secreted aspartyl protease, PLB (phospholipase B and LIP (lipase gene families is associated with biofilm growth on mucosal surfaces. We investigated using real-time PCR whether genes encoding potential virulence factors are also highly expressed in biofilms associated with abiotic surfaces. For this, C. albicans biofilms were grown on silicone in microtiter plates (MTP or in the Centres for Disease Control (CDC reactor, on polyurethane in an in vivo subcutaneous catheter rat (SCR model, and on mucosal surfaces in the reconstituted human epithelium (RHE model. Results HWP1 and genes belonging to the ALS, SAP, PLB and LIP gene families were constitutively expressed in C. albicans biofilms. ALS1-5 were upregulated in all model systems, while ALS9 was mostly downregulated. ALS6 and HWP1 were overexpressed in all models except in the RHE and MTP, respectively. The expression levels of SAP1 were more pronounced in both in vitro models, while those of SAP2, SAP4 and SAP6 were higher in the in vivo model. Furthermore, SAP5 was highly upregulated in the in vivo and RHE models. For SAP9 and SAP10 similar gene expression levels were observed in all model systems. PLB genes were not considerably upregulated in biofilms, while LIP1-3, LIP5-7 and LIP9-10 were highly overexpressed in both in vitro models. Furthermore, an elevated lipase activity was detected in supernatans of biofilms grown in the MTP and RHE model. Conclusions Our findings show that HWP1 and most of the genes belonging to the ALS, SAP and LIP gene families are upregulated in C. albicans biofilms. Comparison of the fold expression between the various model systems revealed similar expression levels for some genes, while for others model-dependent expression

Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.

Science.gov (United States)

Hu, Ming; Zhu, Yu; Taylor, Jeremy M G; Liu, Jun S; Qin, Zhaohui S

2012-01-01

RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. In particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize RNA-Seq data and quantify gene expressions, ineffective. In this study, we propose a Poisson mixed-effects (POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Since the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level. POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html. yuzhu@purdue.edu; zhaohui.qin@emory.edu Supplementary data are available at Bioinformatics online.

On theoretical models of gene expression evolution with random genetic drift and natural selection.

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Osamu Ogasawara

2009-11-01

Full Text Available The relative contributions of natural selection and random genetic drift are a major source of debate in the study of gene expression evolution, which is hypothesized to serve as a bridge from molecular to phenotypic evolution. It has been suggested that the conflict between views is caused by the lack of a definite model of the neutral hypothesis, which can describe the long-run behavior of evolutionary change in mRNA abundance. Therefore previous studies have used inadequate analogies with the neutral prediction of other phenomena, such as amino acid or nucleotide sequence evolution, as the null hypothesis of their statistical inference.In this study, we introduced two novel theoretical models, one based on neutral drift and the other assuming natural selection, by focusing on a common property of the distribution of mRNA abundance among a variety of eukaryotic cells, which reflects the result of long-term evolution. Our results demonstrated that (1 our models can reproduce two independently found phenomena simultaneously: the time development of gene expression divergence and Zipf's law of the transcriptome; (2 cytological constraints can be explicitly formulated to describe long-term evolution; (3 the model assuming that natural selection optimized relative mRNA abundance was more consistent with previously published observations than the model of optimized absolute mRNA abundances.The models introduced in this study give a formulation of evolutionary change in the mRNA abundance of each gene as a stochastic process, on the basis of previously published observations. This model provides a foundation for interpreting observed data in studies of gene expression evolution, including identifying an adequate time scale for discriminating the effect of natural selection from that of random genetic drift of selectively neutral variations.

Designing a Model for the Glogbal Energy System—GENeSYS-MOD: An Application of the Open-Source Energy Modeling System (OSeMOSYS

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Konstantin Löffler

2017-09-01

Full Text Available This paper develops a path for the global energy system up to 2050, presenting a new application of the open-source energy modeling system (OSeMOSYS to the community. It allows quite disaggregate energy and emission analysis: Global Energy System Model (GENeSYS-MOD uses a system of linear equations of the energy system to search for lowest-cost solutions for a secure energy supply, given externally defined constraints, mainly in terms of CO2-emissions. The general algebraic modeling system (GAMS version of OSeMOSYS is updated to the newest version and, in addition, extended and enhanced to include e.g., a modal split for transport, an improved trading system, and changes to storages. The model can be scaled from small-scale applications, e.g., a company, to cover the global energy system. The paper also includes an application of GENeSYS-MOD to analyze decarbonization scenarios at the global level, broken down into 10 regions. Its main focus is on interdependencies between traditionally segregated sectors: electricity, transportation, and heating; which are all included in the model. Model calculations suggests that in order to achieve the 1.5–2 °C target, a combination of renewable energy sources provides the lowest-cost solution, solar photovoltaic being the dominant source. Average costs of electricity generation in 2050 are about 4 €cents/kWh (excluding infrastructure and transportation costs.

Including investment risk in large-scale power market models

DEFF Research Database (Denmark)

Lemming, Jørgen Kjærgaard; Meibom, P.

2003-01-01

Long-term energy market models can be used to examine investments in production technologies, however, with market liberalisation it is crucial that such models include investment risks and investor behaviour. This paper analyses how the effect of investment risk on production technology selection...... can be included in large-scale partial equilibrium models of the power market. The analyses are divided into a part about risk measures appropriate for power market investors and a more technical part about the combination of a risk-adjustment model and a partial-equilibrium model. To illustrate...... the analyses quantitatively, a framework based on an iterative interaction between the equilibrium model and a separate risk-adjustment module was constructed. To illustrate the features of the proposed modelling approach we examined how uncertainty in demand and variable costs affects the optimal choice...

Pairagon+N-SCAN_EST: a model-based gene annotation pipeline

DEFF Research Database (Denmark)

Arumugam, Manimozhiyan; Wei, Chaochun; Brown, Randall H

2006-01-01

This paper describes Pairagon+N-SCAN_EST, a gene annotation pipeline that uses only native alignments. For each expressed sequence it chooses the best genomic alignment. Systems like ENSEMBL and ExoGean rely on trans alignments, in which expressed sequences are aligned to the genomic loci...... with de novo gene prediction by using N-SCAN_EST. N-SCAN_EST is based on a generalized HMM probability model augmented with a phylogenetic conservation model and EST alignments. It can predict complete transcripts by extending or merging EST alignments, but it can also predict genes in regions without EST...

A Mathematical Model Of Ageing In Man Due To Gene Loss | Mbah ...

African Journals Online (AJOL)

Aging is as a result of dysfunction of the body mechanisms due to failure of one organelle, tissue, component or the other. In man there is a pointer towards gene loss as a primary cause of ageing. In this paper we develop a mathematical model describing changes in gene efficiency or gene failure. This model is used to ...

De novo mutations in synaptic transmission genes including DNM1 cause epileptic encephalopathies

DEFF Research Database (Denmark)

2014-01-01

in five individuals and de novo mutations in GABBR2, FASN, and RYR3 in two individuals each. Unlike previous studies, this cohort is sufficiently large to show a significant excess of de novo mutations in epileptic encephalopathy probands compared to the general population using a likelihood analysis (p...... = 8.2 × 10(-4)), supporting a prominent role for de novo mutations in epileptic encephalopathies. We bring statistical evidence that mutations in DNM1 cause epileptic encephalopathy, find suggestive evidence for a role of three additional genes, and show that at least 12% of analyzed individuals have...... analyzed exome-sequencing data of 356 trios with the "classical" epileptic encephalopathies, infantile spasms and Lennox Gastaut syndrome, including 264 trios previously analyzed by the Epi4K/EPGP consortium. In this expanded cohort, we find 429 de novo mutations, including de novo mutations in DNM1...

A framework for scalable parameter estimation of gene circuit models using structural information

KAUST Repository

Kuwahara, Hiroyuki

2013-06-21

Motivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. The Author 2013.

A framework for scalable parameter estimation of gene circuit models using structural information

KAUST Repository

Kuwahara, Hiroyuki; Fan, Ming; Wang, Suojin; Gao, Xin

2013-01-01

Motivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. The Author 2013.

Predictive modelling of gene expression from transcriptional regulatory elements.

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

2015-07-01

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

Protein modelling of triterpene synthase genes from mangrove plants using Phyre2 and Swiss-model

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Basyuni, M.; Wati, R.; Sulistiyono, N.; Hayati, R.; Sumardi; Oku, H.; Baba, S.; Sagami, H.

2018-03-01

Molecular cloning of five oxidosqualene cyclases (OSC) genes from Bruguiera gymnorrhiza, Kandelia candel, and Rhizophora stylosa had previously been cloned, characterized, and encoded mono and -multi triterpene synthases. The present study analyzed protein modelling of triterpene synthase genes from mangrove using Phyre2 and Swiss-model. The diversity was noted within protein modelling of triterpene synthases using Phyre2 from sequence identity (38-43%) and residue (696-703). RsM2 was distinguishable from others for template structure; it used lanosterol synthase as a template (PDB ID: w6j.1.A). By contrast, other genes used human lanosterol synthase (1w6k.1.A). The predicted bind sites were correlated with the product of triterpene synthase, the product of BgbAS was β-amyrin, while RsM1 contained a significant amount of β-amyrin. Similarly BgLUS and KcMS, both main products was lupeol, on the other hand, RsM2 with the outcome of taraxerol. Homology modelling revealed that 696 residues of BgbAS, BgLUS, RsM1, and RsM2 (91-92% of the amino acid sequence) had been modelled with 100% confidence by the single highest scoring template using Phyre2. This coverage was higher than Swiss-model (85-90%). The present study suggested that molecular cloning of triterpene genes provides useful tools for studying the protein modelling related regulation of isoprenoids biosynthesis in mangrove forests.

Gene and MicroRNA transcriptome analysis of Parkinson's related LRRK2 mouse models.

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Véronique Dorval

Full Text Available Mutations in leucine-rich repeat kinase 2 (LRRK2 are the most frequent cause of genetic Parkinson's disease (PD. The biological function of LRRK2 and how mutations lead to disease remain poorly defined. It has been proposed that LRRK2 could function in gene transcription regulation; however, this issue remains controversial. Here, we investigated in parallel gene and microRNA (miRNA transcriptome profiles of three different LRRK2 mouse models. Striatal tissue was isolated from adult LRRK2 knockout (KO mice, as well as mice expressing human LRRK2 wildtype (hLRRK2-WT or the PD-associated R1441G mutation (hLRRK2-R1441G. We identified a total of 761 genes and 24 miRNAs that were misregulated in the absence of LRRK2 when a false discovery rate of 0.2 was applied. Notably, most changes in gene expression were modest (i.e., <2 fold. By real-time quantitative RT-PCR, we confirmed the variations of selected genes (e.g., adra2, syt2, opalin and miRNAs (e.g., miR-16, miR-25. Surprisingly, little or no changes in gene expression were observed in mice expressing hLRRK2-WT or hLRRK2-R1441G when compared to non-transgenic controls. Nevertheless, a number of miRNAs were misexpressed in these models. Bioinformatics analysis identified several miRNA-dependent and independent networks dysregulated in LRRK2-deficient mice, including PD-related pathways. These results suggest that brain LRRK2 plays an overall modest role in gene transcription regulation in mammals; however, these effects seem context and RNA type-dependent. Our data thus set the stage for future investigations regarding LRRK2 function in PD development.

Dynamic gene expression analysis in a H1N1 influenza virus mouse pneumonia model.

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Bao, Yanyan; Gao, Yingjie; Shi, Yujing; Cui, Xiaolan

2017-06-01

H1N1, a major pathogenic subtype of influenza A virus, causes a respiratory infection in humans and livestock that can range from a mild infection to more severe pneumonia associated with acute respiratory distress syndrome. Understanding the dynamic changes in the genome and the related functional changes induced by H1N1 influenza virus infection is essential to elucidating the pathogenesis of this virus and thereby determining strategies to prevent future outbreaks. In this study, we filtered the significantly expressed genes in mouse pneumonia using mRNA microarray analysis. Using STC analysis, seven significant gene clusters were revealed, and using STC-GO analysis, we explored the significant functions of these seven gene clusters. The results revealed GOs related to H1N1 virus-induced inflammatory and immune functions, including innate immune response, inflammatory response, specific immune response, and cellular response to interferon-beta. Furthermore, the dynamic regulation relationships of the key genes in mouse pneumonia were revealed by dynamic gene network analysis, and the most important genes were filtered, including Dhx58, Cxcl10, Cxcl11, Zbp1, Ifit1, Ifih1, Trim25, Mx2, Oas2, Cd274, Irgm1, and Irf7. These results suggested that during mouse pneumonia, changes in the expression of gene clusters and the complex interactions among genes lead to significant changes in function. Dynamic gene expression analysis revealed key genes that performed important functions. These results are a prelude to advancements in mouse H1N1 influenza virus infection biology, as well as the use of mice as a model organism for human H1N1 influenza virus infection studies.

Modeling insertional mutagenesis using gene length and expression in murine embryonic stem cells.

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Alex S Nord

2007-07-01

Full Text Available High-throughput mutagenesis of the mammalian genome is a powerful means to facilitate analysis of gene function. Gene trapping in embryonic stem cells (ESCs is the most widely used form of insertional mutagenesis in mammals. However, the rules governing its efficiency are not fully understood, and the effects of vector design on the likelihood of gene-trapping events have not been tested on a genome-wide scale.In this study, we used public gene-trap data to model gene-trap likelihood. Using the association of gene length and gene expression with gene-trap likelihood, we constructed spline-based regression models that characterize which genes are susceptible and which genes are resistant to gene-trapping techniques. We report results for three classes of gene-trap vectors, showing that both length and expression are significant determinants of trap likelihood for all vectors. Using our models, we also quantitatively identified hotspots of gene-trap activity, which represent loci where the high likelihood of vector insertion is controlled by factors other than length and expression. These formalized statistical models describe a high proportion of the variance in the likelihood of a gene being trapped by expression-dependent vectors and a lower, but still significant, proportion of the variance for vectors that are predicted to be independent of endogenous gene expression.The findings of significant expression and length effects reported here further the understanding of the determinants of vector insertion. Results from this analysis can be applied to help identify other important determinants of this important biological phenomenon and could assist planning of large-scale mutagenesis efforts.

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

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Grigoriev Igor V

2009-02-01

Full Text Available Abstract Background Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR. Results 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6% of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. Conclusion This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger.

Science.gov (United States)

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-02-04

Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.

Subthalamic hGAD65 Gene Therapy and Striatum TH Gene Transfer in a Parkinson’s Disease Rat Model

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Zheng, Deyu; Jiang, Xiaohua; Zhao, Junpeng; Duan, Deyi; Zhao, Huanying; Xu, Qunyuan

2013-01-01

The aim of the present study is to detect a combination method to utilize gene therapy for the treatment of Parkinson’s disease (PD). Here, a PD rat model is used for the in vivo gene therapy of a recombinant adeno-associated virus (AAV2) containing a human glutamic acid decarboxylase 65 (rAAV2-hGAD65) gene delivered to the subthalamic nucleus (STN). This is combined with the ex vivo gene delivery of tyrosine hydroxylase (TH) by fibroblasts injected into the striatum. After the treatment, the rotation behavior was improved with the greatest efficacy in the combination group. The results of immunohistochemistry showed that hGAD65 gene delivery by AAV2 successfully led to phenotypic changes of neurons in STN. And the levels of glutamic acid and GABA in the internal segment of the globus pallidus (GPi) and substantia nigra pars reticulata (SNr) were obviously lower than the control groups. However, hGAD65 gene transfer did not effectively protect surviving dopaminergic neurons in the SNc and VTA. This study suggests that subthalamic hGAD65 gene therapy and combined with TH gene therapy can alleviate symptoms of the PD model rats, independent of the protection the DA neurons from death. PMID:23738148

Gene finding with a hidden Markov model of genome structure and evolution

DEFF Research Database (Denmark)

Pedersen, Jakob Skou; Hein, Jotun

2003-01-01

the model are linear in alignment length and genome number. The model is applied to the problem of gene finding. The benefit of modelling sequence evolution is demonstrated both in a range of simulations and on a set of orthologous human/mouse gene pairs. AVAILABILITY: Free availability over the Internet...

A framework for scalable parameter estimation of gene circuit models using structural information.

Science.gov (United States)

Kuwahara, Hiroyuki; Fan, Ming; Wang, Suojin; Gao, Xin

2013-07-01

Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. http://sfb.kaust.edu.sa/Pages/Software.aspx. Supplementary data are available at Bioinformatics online.

Reference gene selection for quantitative gene expression studies during biological invasions: A test on multiple genes and tissues in a model ascidian Ciona savignyi.

Science.gov (United States)

Huang, Xuena; Gao, Yangchun; Jiang, Bei; Zhou, Zunchun; Zhan, Aibin

2016-01-15

As invasive species have successfully colonized a wide range of dramatically different local environments, they offer a good opportunity to study interactions between species and rapidly changing environments. Gene expression represents one of the primary and crucial mechanisms for rapid adaptation to local environments. Here, we aim to select reference genes for quantitative gene expression analysis based on quantitative Real-Time PCR (qRT-PCR) for a model invasive ascidian, Ciona savignyi. We analyzed the stability of ten candidate reference genes in three tissues (siphon, pharynx and intestine) under two key environmental stresses (temperature and salinity) in the marine realm based on three programs (geNorm, NormFinder and delta Ct method). Our results demonstrated only minor difference for stability rankings among the three methods. The use of different single reference gene might influence the data interpretation, while multiple reference genes could minimize possible errors. Therefore, reference gene combinations were recommended for different tissues - the optimal reference gene combination for siphon was RPS15 and RPL17 under temperature stress, and RPL17, UBQ and TubA under salinity treatment; for pharynx, TubB, TubA and RPL17 were the most stable genes under temperature stress, while TubB, TubA and UBQ were the best under salinity stress; for intestine, UBQ, RPS15 and RPL17 were the most reliable reference genes under both treatments. Our results suggest that the necessity of selection and test of reference genes for different tissues under varying environmental stresses. The results obtained here are expected to reveal mechanisms of gene expression-mediated invasion success using C. savignyi as a model species. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

The Mouse Solitary Odorant Receptor Gene Promoters as Models for the Study of Odorant Receptor Gene Choice.

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Andrea Degl'Innocenti

Full Text Available In vertebrates, several anatomical regions located within the nasal cavity mediate olfaction. Among these, the main olfactory epithelium detects most conventional odorants. Olfactory sensory neurons, provided with cilia exposed to the air, detect volatile chemicals via an extremely large family of seven-transmembrane chemoreceptors named odorant receptors. Their genes are expressed in a monogenic and monoallelic fashion: a single allele of a single odorant receptor gene is transcribed in a given mature neuron, through a still uncharacterized molecular mechanism known as odorant receptor gene choice.Odorant receptor genes are typically arranged in genomic clusters, but a few are isolated (we call them solitary from the others within a region broader than 1 Mb upstream and downstream with respect to their transcript's coordinates. The study of clustered genes is problematic, because of redundancy and ambiguities in their regulatory elements: we propose to use the solitary genes as simplified models to understand odorant receptor gene choice.Here we define number and identity of the solitary genes in the mouse genome (C57BL/6J, and assess the conservation of the solitary status in some mammalian orthologs. Furthermore, we locate their putative promoters, predict their homeodomain binding sites (commonly present in the promoters of odorant receptor genes and compare candidate promoter sequences with those of wild-caught mice. We also provide expression data from histological sections.In the mouse genome there are eight intact solitary genes: Olfr19 (M12, Olfr49, Olfr266, Olfr267, Olfr370, Olfr371, Olfr466, Olfr1402; five are conserved as solitary in rat. These genes are all expressed in the main olfactory epithelium of three-day-old mice. The C57BL/6J candidate promoter of Olfr370 has considerably varied compared to its wild-type counterpart. Within the putative promoter for Olfr266 a homeodomain binding site is predicted. As a whole, our findings

Disease Model Discovery from 3,328 Gene Knockouts by The International Mouse Phenotyping Consortium

Science.gov (United States)

Meehan, Terrence F.; Conte, Nathalie; West, David B.; Jacobsen, Julius O.; Mason, Jeremy; Warren, Jonathan; Chen, Chao-Kung; Tudose, Ilinca; Relac, Mike; Matthews, Peter; Karp, Natasha; Santos, Luis; Fiegel, Tanja; Ring, Natalie; Westerberg, Henrik; Greenaway, Simon; Sneddon, Duncan; Morgan, Hugh; Codner, Gemma F; Stewart, Michelle E; Brown, James; Horner, Neil; Haendel, Melissa; Washington, Nicole; Mungall, Christopher J.; Reynolds, Corey L; Gallegos, Juan; Gailus-Durner, Valerie; Sorg, Tania; Pavlovic, Guillaume; Bower, Lynette R; Moore, Mark; Morse, Iva; Gao, Xiang; Tocchini-Valentini, Glauco P; Obata, Yuichi; Cho, Soo Young; Seong, Je Kyung; Seavitt, John; Beaudet, Arthur L.; Dickinson, Mary E.; Herault, Yann; Wurst, Wolfgang; de Angelis, Martin Hrabe; Lloyd, K.C. Kent; Flenniken, Ann M; Nutter, Lauryl MJ; Newbigging, Susan; McKerlie, Colin; Justice, Monica J.; Murray, Stephen A.; Svenson, Karen L.; Braun, Robert E.; White, Jacqueline K.; Bradley, Allan; Flicek, Paul; Wells, Sara; Skarnes, William C.; Adams, David J.; Parkinson, Helen; Mallon, Ann-Marie; Brown, Steve D.M.; Smedley, Damian

2017-01-01

Although next generation sequencing has revolutionised the ability to associate variants with human diseases, diagnostic rates and development of new therapies are still limited by our lack of knowledge of function and pathobiological mechanism for most genes. To address this challenge, the International Mouse Phenotyping Consortium (IMPC) is creating a genome- and phenome-wide catalogue of gene function by characterizing new knockout mouse strains across diverse biological systems through a broad set of standardised phenotyping tests, with all mice made readily available to the biomedical community. Analysing the first 3328 genes reveals models for 360 diseases including the first for type C Bernard-Soulier, Bardet-Biedl-5 and Gordon Holmes syndromes. 90% of our phenotype annotations are novel, providing the first functional evidence for 1092 genes and candidates in unsolved diseases such as Arrhythmogenic Right Ventricular Dysplasia 3. Finally, we describe our role in variant functional validation with the 100,000 Genomes and other projects. PMID:28650483

Candidate innate immune system gene expression in the ecological model Daphnia.

Science.gov (United States)

Decaestecker, Ellen; Labbé, Pierrick; Ellegaard, Kirsten; Allen, Judith E; Little, Tom J

2011-10-01

The last ten years have witnessed increasing interest in host-pathogen interactions involving invertebrate hosts. The invertebrate innate immune system is now relatively well characterised, but in a limited range of genetic model organisms and under a limited number of conditions. Immune systems have been little studied under real-world scenarios of environmental variation and parasitism. Thus, we have investigated expression of candidate innate immune system genes in the water flea Daphnia, a model organism for ecological genetics, and whose capacity for clonal reproduction facilitates an exceptionally rigorous control of exposure dose or the study of responses at many time points. A unique characteristic of the particular Daphnia clones and pathogen strain combinations used presently is that they have been shown to be involved in specific host-pathogen coevolutionary interactions in the wild. We choose five genes, which are strong candidates to be involved in Daphnia-pathogen interactions, given that they have been shown to code for immune effectors in related organisms. Differential expression of these genes was quantified by qRT-PCR following exposure to the bacterial pathogen Pasteuria ramosa. Constitutive expression levels differed between host genotypes, and some genes appeared to show correlated expression. However, none of the genes appeared to show a major modification of expression level in response to Pasteuria exposure. By applying knowledge from related genetic model organisms (e.g. Drosophila) to models for the study of evolutionary ecology and coevolution (i.e. Daphnia), the candidate gene approach is temptingly efficient. However, our results show that detection of only weak patterns is likely if one chooses target genes for study based on previously identified genome sequences by comparison to homologues from other related organisms. Future work on the Daphnia-Pasteuria system will need to balance a candidate gene approach with more comprehensive

Comparative gene expression analysis of two mouse models of autism:transcriptome profiling of the BTBR and En2-/- hippocampus

Directory of Open Access Journals (Sweden)

Giovanni Provenzano

2016-08-01

Full Text Available Autism spectrum disorders (ASD are characterized by a high degree of genetic heterogeneity. Genomic studies identified common pathological processes underlying the heterogeneous clinical manifestations of ASD, and transcriptome analyses revealed that gene networks involved in synapse development, neuronal activity and immune function are deregulated in ASD. Mouse models provide unique tools to investigate the neurobiological basis of ASD; however, a comprehensive approach to identify transcriptional abnormalities in different ASD models has never been performed. Here we used two well-recognized ASD mouse models, BTBR T+ Itpr3tf/J (BTBR and Engrailed-2 knockout (En2-/-, to identify conserved ASD-related molecular signatures. En2-/- mice bear a mutation within the EN2 transcription factor homeobox, while BTBR is an inbred strain with unknown genetic defects. Hippocampal RNA samples from BTBR, En2-/- and respective control (C57Bl/6J and En2+/+ adult mice were assessed for differential gene expression using microarrays. A total of 153 genes were similarly deregulated in the BTBR and En2-/- hippocampus. Mouse phenotype and gene ontology enrichment analyses were performed on BTBR and En2-/- hippocampal differentially expressed genes (DEGs. Pathways represented in both BTBR and En2-/- hippocampal DEGs included abnormal behavioral response and chemokine/MAP kinase signaling. Genes involved in abnormal function of the immune system and abnormal synaptic transmission/seizures were significantly represented among BTBR and En2-/- DEGs, respectively. Interestingly, both BTBR and En2-/- hippocampal DEGs showed a significant enrichment of ASD and schizophrenia (SCZ-associated genes. Specific gene sets were enriched in the two models: microglial genes were significantly enriched among BTBR DEGs, whereas GABAergic/glutamatergic postsynaptic genes, FMRP-interacting genes and epilepsy-related genes were significantly enriched among En2-/- DEGs. Weighted

Functional validation of candidate genes detected by genomic feature models

DEFF Research Database (Denmark)

Rohde, Palle Duun; Østergaard, Solveig; Kristensen, Torsten Nygaard

2018-01-01

to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) cate- gories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We...... then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated......Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait...

A 1,681-locus consensus genetic map of cultivated cucumber including 67 NB-LRR resistance gene homolog and ten gene loci.

Science.gov (United States)

Yang, Luming; Li, Dawei; Li, Yuhong; Gu, Xingfang; Huang, Sanwen; Garcia-Mas, Jordi; Weng, Yiqun

2013-03-25

Cucumber is an important vegetable crop that is susceptible to many pathogens, but no disease resistance (R) genes have been cloned. The availability of whole genome sequences provides an excellent opportunity for systematic identification and characterization of the nucleotide binding and leucine-rich repeat (NB-LRR) type R gene homolog (RGH) sequences in the genome. Cucumber has a very narrow genetic base making it difficult to construct high-density genetic maps. Development of a consensus map by synthesizing information from multiple segregating populations is a method of choice to increase marker density. As such, the objectives of the present study were to identify and characterize NB-LRR type RGHs, and to develop a high-density, integrated cucumber genetic-physical map anchored with RGH loci. From the Gy14 draft genome, 70 NB-containing RGHs were identified and characterized. Most RGHs were in clusters with uneven distribution across seven chromosomes. In silico analysis indicated that all 70 RGHs had EST support for gene expression. Phylogenetic analysis classified 58 RGHs into two clades: CNL and TNL. Comparative analysis revealed high-degree sequence homology and synteny in chromosomal locations of these RGH members between the cucumber and melon genomes. Fifty-four molecular markers were developed to delimit 67 of the 70 RGHs, which were integrated into a genetic map through linkage analysis. A 1,681-locus cucumber consensus map including 10 gene loci and spanning 730.0 cM in seven linkage groups was developed by integrating three component maps with a bin-mapping strategy. Physically, 308 scaffolds with 193.2 Mbp total DNA sequences were anchored onto this consensus map that covered 52.6% of the 367 Mbp cucumber genome. Cucumber contains relatively few NB-LRR RGHs that are clustered and unevenly distributed in the genome. All RGHs seem to be transcribed and shared significant sequence homology and synteny with the melon genome suggesting conservation of

Unsteady panel method for complex configurations including wake modeling

CSIR Research Space (South Africa)

Van Zyl, Lourens H

2008-01-01

Full Text Available implementations of the DLM are however not very versatile in terms of geometries that can be modeled. The ZONA6 code offers a versatile surface panel body model including a separated wake model, but uses a pressure panel method for lifting surfaces. This paper...

Evolution dynamics of a model for gene duplication under adaptive conflict

Science.gov (United States)

Ancliff, Mark; Park, Jeong-Man

2014-06-01

We present and solve the dynamics of a model for gene duplication showing escape from adaptive conflict. We use a Crow-Kimura quasispecies model of evolution where the fitness landscape is a function of Hamming distances from two reference sequences, which are assumed to optimize two different gene functions, to describe the dynamics of a mixed population of individuals with single and double copies of a pleiotropic gene. The evolution equations are solved through a spin coherent state path integral, and we find two phases: one is an escape from an adaptive conflict phase, where each copy of a duplicated gene evolves toward subfunctionalization, and the other is a duplication loss of function phase, where one copy maintains its pleiotropic form and the other copy undergoes neutral mutation. The phase is determined by a competition between the fitness benefits of subfunctionalization and the greater mutational load associated with maintaining two gene copies. In the escape phase, we find a dynamics of an initial population of single gene sequences only which escape adaptive conflict through gene duplication and find that there are two time regimes: until a time t* single gene sequences dominate, and after t* double gene sequences outgrow single gene sequences. The time t* is identified as the time necessary for subfunctionalization to evolve and spread throughout the double gene sequences, and we show that there is an optimum mutation rate which minimizes this time scale.

Myostatin propeptide gene delivery by gene gun ameliorates muscle atrophy in a rat model of botulinum toxin-induced nerve denervation.

Science.gov (United States)

Tsai, Sen-Wei; Tung, Yu-Tang; Chen, Hsiao-Ling; Yang, Shang-Hsun; Liu, Chia-Yi; Lu, Michelle; Pai, Hui-Jing; Lin, Chi-Chen; Chen, Chuan-Mu

2016-02-01

Muscle atrophy is a common symptom after nerve denervation. Myostatin propeptide, a precursor of myostatin, has been documented to improve muscle growth. However, the mechanism underlying the muscle atrophy attenuation effects of myostatin propeptide in muscles and the changes in gene expression are not well established. We investigated the possible underlying mechanisms associated with myostatin propeptide gene delivery by gene gun in a rat denervation muscle atrophy model, and evaluated gene expression patterns. In a rat botulinum toxin-induced nerve denervation muscle atrophy model, we evaluated the effects of wild-type (MSPP) and mutant-type (MSPPD75A) of myostatin propeptide gene delivery, and observed changes in gene activation associated with the neuromuscular junction, muscle and nerve. Muscle mass and muscle fiber size was moderately increased in myostatin propeptide treated muscles (pmyostatin propeptide gene delivery, especially the mutant-type of MSPPD75A, attenuates muscle atrophy through myogenic regulatory factors and acetylcholine receptor regulation. Our data concluded that myostatin propeptide gene therapy may be a promising treatment for nerve denervation induced muscle atrophy. Copyright © 2016 Elsevier Inc. All rights reserved.

A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network.

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Felipe Leal Valentim

Full Text Available Various environmental signals integrate into a network of floral regulatory genes leading to the final decision on when to flower. Although a wealth of qualitative knowledge is available on how flowering time genes regulate each other, only a few studies incorporated this knowledge into predictive models. Such models are invaluable as they enable to investigate how various types of inputs are combined to give a quantitative readout. To investigate the effect of gene expression disturbances on flowering time, we developed a dynamic model for the regulation of flowering time in Arabidopsis thaliana. Model parameters were estimated based on expression time-courses for relevant genes, and a consistent set of flowering times for plants of various genetic backgrounds. Validation was performed by predicting changes in expression level in mutant backgrounds and comparing these predictions with independent expression data, and by comparison of predicted and experimental flowering times for several double mutants. Remarkably, the model predicts that a disturbance in a particular gene has not necessarily the largest impact on directly connected genes. For example, the model predicts that SUPPRESSOR OF OVEREXPRESSION OF CONSTANS (SOC1 mutation has a larger impact on APETALA1 (AP1, which is not directly regulated by SOC1, compared to its effect on LEAFY (LFY which is under direct control of SOC1. This was confirmed by expression data. Another model prediction involves the importance of cooperativity in the regulation of APETALA1 (AP1 by LFY, a prediction supported by experimental evidence. Concluding, our model for flowering time gene regulation enables to address how different quantitative inputs are combined into one quantitative output, flowering time.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models.

Science.gov (United States)

Moran, Paula; Stokes, Jennifer; Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John; O'Tuathaigh, Colm

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

Science.gov (United States)

Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia. PMID:27725886

Mutual information and the fidelity of response of gene regulatory models

International Nuclear Information System (INIS)

Tabbaa, Omar P; Jayaprakash, C

2014-01-01

We investigate cellular response to extracellular signals by using information theory techniques motivated by recent experiments. We present results for the steady state of the following gene regulatory models found in both prokaryotic and eukaryotic cells: a linear transcription-translation model and a positive or negative auto-regulatory model. We calculate both the information capacity and the mutual information exactly for simple models and approximately for the full model. We find that (1) small changes in mutual information can lead to potentially important changes in cellular response and (2) there are diminishing returns in the fidelity of response as the mutual information increases. We calculate the information capacity using Gillespie simulations of a model for the TNF-α-NF-κ B network and find good agreement with the measured value for an experimental realization of this network. Our results provide a quantitative understanding of the differences in cellular response when comparing experimentally measured mutual information values of different gene regulatory models. Our calculations demonstrate that Gillespie simulations can be used to compute the mutual information of more complex gene regulatory models, providing a potentially useful tool in synthetic biology. (paper)

Metastasis: objections to the same-gene model

NARCIS (Netherlands)

Bernards, R.A.; Weinberg, R.A.

2002-01-01

Sir— The model of cancer metastasis suggested by René Bernards and Robert A. Weinberg in their Concepts essay (Nature 418, 823; 2002) is, in my view, a tautology. The suggestion that the same genes are exclusively responsible both for cancer-cell metastasis and for the emergence

Automated Protocol for Large-Scale Modeling of Gene Expression Data.

Science.gov (United States)

Hall, Michelle Lynn; Calkins, David; Sherman, Woody

2016-11-28

With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In particular, we present predictive models with an average accuracy of greater than 70% across a highly diverse ∼1000 gene expression profile. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

Validating Internal Control Genes for the Accurate Normalization of qPCR Expression Analysis of the Novel Model Plant Setaria viridis.

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Julia Lambret-Frotté

Full Text Available Employing reference genes to normalize the data generated with quantitative PCR (qPCR can increase the accuracy and reliability of this method. Previous results have shown that no single housekeeping gene can be universally applied to all experiments. Thus, the identification of a suitable reference gene represents a critical step of any qPCR analysis. Setaria viridis has recently been proposed as a model system for the study of Panicoid grasses, a crop family of major agronomic importance. Therefore, this paper aims to identify suitable S. viridis reference genes that can enhance the analysis of gene expression in this novel model plant. The first aim of this study was the identification of a suitable RNA extraction method that could retrieve a high quality and yield of RNA. After this, two distinct algorithms were used to assess the gene expression of fifteen different candidate genes in eighteen different samples, which were divided into two major datasets, the developmental and the leaf gradient. The best-ranked pair of reference genes from the developmental dataset included genes that encoded a phosphoglucomutase and a folylpolyglutamate synthase; genes that encoded a cullin and the same phosphoglucomutase as above were the most stable genes in the leaf gradient dataset. Additionally, the expression pattern of two target genes, a SvAP3/PI MADS-box transcription factor and the carbon-fixation enzyme PEPC, were assessed to illustrate the reliability of the chosen reference genes. This study has shown that novel reference genes may perform better than traditional housekeeping genes, a phenomenon which has been previously reported. These results illustrate the importance of carefully validating reference gene candidates for each experimental set before employing them as universal standards. Additionally, the robustness of the expression of the target genes may increase the utility of S. viridis as a model for Panicoid grasses.

Stochastic modelling of two-phase flows including phase change

International Nuclear Information System (INIS)

Hurisse, O.; Minier, J.P.

2011-01-01

Stochastic modelling has already been developed and applied for single-phase flows and incompressible two-phase flows. In this article, we propose an extension of this modelling approach to two-phase flows including phase change (e.g. for steam-water flows). Two aspects are emphasised: a stochastic model accounting for phase transition and a modelling constraint which arises from volume conservation. To illustrate the whole approach, some remarks are eventually proposed for two-fluid models. (authors)

Comparison of evolutionary algorithms in gene regulatory network model inference.

LENUS (Irish Health Repository)

2010-01-01

ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

Religion, fertility and genes: a dual inheritance model.

Science.gov (United States)

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.

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

Directory of Open Access Journals (Sweden)

Paula Moran

2016-01-01

Full Text Available The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia.

Analysis of Gene Expression Variance in Schizophrenia Using Structural Equation Modeling

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Anna A. Igolkina

2018-06-01

Full Text Available Schizophrenia (SCZ is a psychiatric disorder of unknown etiology. There is evidence suggesting that aberrations in neurodevelopment are a significant attribute of schizophrenia pathogenesis and progression. To identify biologically relevant molecular abnormalities affecting neurodevelopment in SCZ we used cultured neural progenitor cells derived from olfactory neuroepithelium (CNON cells. Here, we tested the hypothesis that variance in gene expression differs between individuals from SCZ and control groups. In CNON cells, variance in gene expression was significantly higher in SCZ samples in comparison with control samples. Variance in gene expression was enriched in five molecular pathways: serine biosynthesis, PI3K-Akt, MAPK, neurotrophin and focal adhesion. More than 14% of variance in disease status was explained within the logistic regression model (C-value = 0.70 by predictors accounting for gene expression in 69 genes from these five pathways. Structural equation modeling (SEM was applied to explore how the structure of these five pathways was altered between SCZ patients and controls. Four out of five pathways showed differences in the estimated relationships among genes: between KRAS and NF1, and KRAS and SOS1 in the MAPK pathway; between PSPH and SHMT2 in serine biosynthesis; between AKT3 and TSC2 in the PI3K-Akt signaling pathway; and between CRK and RAPGEF1 in the focal adhesion pathway. Our analysis provides evidence that variance in gene expression is an important characteristic of SCZ, and SEM is a promising method for uncovering altered relationships between specific genes thus suggesting affected gene regulation associated with the disease. We identified altered gene-gene interactions in pathways enriched for genes with increased variance in expression in SCZ. These pathways and loci were previously implicated in SCZ, providing further support for the hypothesis that gene expression variance plays important role in the etiology

Induced Pluripotency and Gene Editing in Disease Modelling: Perspectives and Challenges

Science.gov (United States)

Seah, Yu Fen Samantha; EL Farran, Chadi A.; Warrier, Tushar; Xu, Jian; Loh, Yuin-Han

2015-01-01

Embryonic stem cells (ESCs) are chiefly characterized by their ability to self-renew and to differentiate into any cell type derived from the three main germ layers. It was demonstrated that somatic cells could be reprogrammed to form induced pluripotent stem cells (iPSCs) via various strategies. Gene editing is a technique that can be used to make targeted changes in the genome, and the efficiency of this process has been significantly enhanced by recent advancements. The use of engineered endonucleases, such as homing endonucleases, zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and Cas9 of the CRISPR system, has significantly enhanced the efficiency of gene editing. The combination of somatic cell reprogramming with gene editing enables us to model human diseases in vitro, in a manner considered superior to animal disease models. In this review, we discuss the various strategies of reprogramming and gene targeting with an emphasis on the current advancements and challenges of using these techniques to model human diseases. PMID:26633382

Induced Pluripotency and Gene Editing in Disease Modelling: Perspectives and Challenges

Directory of Open Access Journals (Sweden)

Yu Fen Samantha Seah

2015-12-01

Full Text Available Embryonic stem cells (ESCs are chiefly characterized by their ability to self-renew and to differentiate into any cell type derived from the three main germ layers. It was demonstrated that somatic cells could be reprogrammed to form induced pluripotent stem cells (iPSCs via various strategies. Gene editing is a technique that can be used to make targeted changes in the genome, and the efficiency of this process has been significantly enhanced by recent advancements. The use of engineered endonucleases, such as homing endonucleases, zinc finger nucleases (ZFNs, transcription activator-like effector nucleases (TALENs and Cas9 of the CRISPR system, has significantly enhanced the efficiency of gene editing. The combination of somatic cell reprogramming with gene editing enables us to model human diseases in vitro, in a manner considered superior to animal disease models. In this review, we discuss the various strategies of reprogramming and gene targeting with an emphasis on the current advancements and challenges of using these techniques to model human diseases.

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

Science.gov (United States)

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

Discovery of rare protein-coding genes in model methylotroph Methylobacterium extorquens AM1.

Science.gov (United States)

Kumar, Dhirendra; Mondal, Anupam Kumar; Yadav, Amit Kumar; Dash, Debasis

2014-12-01

Proteogenomics involves the use of MS to refine annotation of protein-coding genes and discover genes in a genome. We carried out comprehensive proteogenomic analysis of Methylobacterium extorquens AM1 (ME-AM1) from publicly available proteomics data with a motive to improve annotation for methylotrophs; organisms capable of surviving in reduced carbon compounds such as methanol. Besides identifying 2482(50%) proteins, 29 new genes were discovered and 66 annotated gene models were revised in ME-AM1 genome. One such novel gene is identified with 75 peptides, lacks homolog in other methylobacteria but has glycosyl transferase and lipopolysaccharide biosynthesis protein domains, indicating its potential role in outer membrane synthesis. Many novel genes are present only in ME-AM1 among methylobacteria. Distant homologs of these genes in unrelated taxonomic classes and low GC-content of few genes suggest lateral gene transfer as a potential mode of their origin. Annotations of methylotrophy related genes were also improved by the discovery of a short gene in methylotrophy gene island and redefining a gene important for pyrroquinoline quinone synthesis, essential for methylotrophy. The combined use of proteogenomics and rigorous bioinformatics analysis greatly enhanced the annotation of protein-coding genes in model methylotroph ME-AM1 genome. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression

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Mao Yu

2009-07-01

Full Text Available Abstract Background The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression. Results In this investigation we studied gene pair co-expression change by using a stochastic process model for approximating the underlying dynamic procedure of the co-expression change during cancer progression. Also, we presented a novel analytical method named 'Stochastic process model for Identifying differentially co-expressed Gene pair' (SIG method. This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous. From these data sets, 428,582 gene pairs and 303,992 gene pairs were identified respectively. Afterwards, we used two different current statistical methods to the same data sets, which were developed to identify gene pair differential co-expression and did not consider cancer progression in algorithm. We then compared these results from three different perspectives: progression analysis, gene pair identification effectiveness analysis, and pathway enrichment analysis. Statistical methods were used to quantify the quality and performance of these different perspectives. They included: Re-identification Scale (RS and Progression Score (PS in progression analysis, True Positive Rate (TPR in gene pair analysis, and Pathway Enrichment Score (PES in pathway analysis. Our results show small values of RS and large values of PS, TPR, and PES; thus, suggesting that gene pairs identified by the SIG method are highly correlated with cancer progression, and highly enriched in disease-specific pathways. From

Computational challenges in modeling gene regulatory events.

Science.gov (United States)

Pataskar, Abhijeet; Tiwari, Vijay K

2016-10-19

Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating "omics" data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology.

Novel gene function revealed by mouse mutagenesis screens for models of age-related disease.

Science.gov (United States)

Potter, Paul K; Bowl, Michael R; Jeyarajan, Prashanthini; Wisby, Laura; Blease, Andrew; Goldsworthy, Michelle E; Simon, Michelle M; Greenaway, Simon; Michel, Vincent; Barnard, Alun; Aguilar, Carlos; Agnew, Thomas; Banks, Gareth; Blake, Andrew; Chessum, Lauren; Dorning, Joanne; Falcone, Sara; Goosey, Laurence; Harris, Shelley; Haynes, Andy; Heise, Ines; Hillier, Rosie; Hough, Tertius; Hoslin, Angela; Hutchison, Marie; King, Ruairidh; Kumar, Saumya; Lad, Heena V; Law, Gemma; MacLaren, Robert E; Morse, Susan; Nicol, Thomas; Parker, Andrew; Pickford, Karen; Sethi, Siddharth; Starbuck, Becky; Stelma, Femke; Cheeseman, Michael; Cross, Sally H; Foster, Russell G; Jackson, Ian J; Peirson, Stuart N; Thakker, Rajesh V; Vincent, Tonia; Scudamore, Cheryl; Wells, Sara; El-Amraoui, Aziz; Petit, Christine; Acevedo-Arozena, Abraham; Nolan, Patrick M; Cox, Roger; Mallon, Anne-Marie; Brown, Steve D M

2016-08-18

Determining the genetic bases of age-related disease remains a major challenge requiring a spectrum of approaches from human and clinical genetics to the utilization of model organism studies. Here we report a large-scale genetic screen in mice employing a phenotype-driven discovery platform to identify mutations resulting in age-related disease, both late-onset and progressive. We have utilized N-ethyl-N-nitrosourea mutagenesis to generate pedigrees of mutagenized mice that were subject to recurrent screens for mutant phenotypes as the mice aged. In total, we identify 105 distinct mutant lines from 157 pedigrees analysed, out of which 27 are late-onset phenotypes across a range of physiological systems. Using whole-genome sequencing we uncover the underlying genes for 44 of these mutant phenotypes, including 12 late-onset phenotypes. These genes reveal a number of novel pathways involved with age-related disease. We illustrate our findings by the recovery and characterization of a novel mouse model of age-related hearing loss.

A comparison of 100 human genes using an alu element-based instability model.

Science.gov (United States)

Cook, George W; Konkel, Miriam K; Walker, Jerilyn A; Bourgeois, Matthew G; Fullerton, Mitchell L; Fussell, John T; Herbold, Heath D; Batzer, Mark A

2013-01-01

The human retrotransposon with the highest copy number is the Alu element. The human genome contains over one million Alu elements that collectively account for over ten percent of our DNA. Full-length Alu elements are randomly distributed throughout the genome in both forward and reverse orientations. However, full-length widely spaced Alu pairs having two Alus in the same (direct) orientation are statistically more prevalent than Alu pairs having two Alus in the opposite (inverted) orientation. The cause of this phenomenon is unknown. It has been hypothesized that this imbalance is the consequence of anomalous inverted Alu pair interactions. One proposed mechanism suggests that inverted Alu pairs can ectopically interact, exposing both ends of each Alu element making up the pair to a potential double-strand break, or "hit". This hypothesized "two-hit" (two double-strand breaks) potential per Alu element was used to develop a model for comparing the relative instabilities of human genes. The model incorporates both 1) the two-hit double-strand break potential of Alu elements and 2) the probability of exon-damaging deletions extending from these double-strand breaks. This model was used to compare the relative instabilities of 50 deletion-prone cancer genes and 50 randomly selected genes from the human genome. The output of the Alu element-based genomic instability model developed here is shown to coincide with the observed instability of deletion-prone cancer genes. The 50 cancer genes are collectively estimated to be 58% more unstable than the randomly chosen genes using this model. Seven of the deletion-prone cancer genes, ATM, BRCA1, FANCA, FANCD2, MSH2, NCOR1 and PBRM1, were among the most unstable 10% of the 100 genes analyzed. This algorithm may lay the foundation for comparing genetic risks posed by structural variations that are unique to specific individuals, families and people groups.

Modeling Electric Double-Layers Including Chemical Reaction Effects

DEFF Research Database (Denmark)

Paz-Garcia, Juan Manuel; Johannesson, Björn; Ottosen, Lisbeth M.

2014-01-01

A physicochemical and numerical model for the transient formation of an electric double-layer between an electrolyte and a chemically-active flat surface is presented, based on a finite elements integration of the nonlinear Nernst-Planck-Poisson model including chemical reactions. The model works...... for symmetric and asymmetric multi-species electrolytes and is not limited to a range of surface potentials. Numerical simulations are presented, for the case of a CaCO3 electrolyte solution in contact with a surface with rate-controlled protonation/deprotonation reactions. The surface charge and potential...... are determined by the surface reactions, and therefore they depends on the bulk solution composition and concentration...

Identification of Region-Specific Myocardial Gene Expression Patterns in a Chronic Swine Model of Repaired Tetralogy of Fallot.

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Sabine Charron

Full Text Available Surgical repair of Tetralogy of Fallot (TOF is highly successful but may be complicated in adulthood by arrhythmias, sudden death, and right ventricular or biventricular dysfunction. To better understand the molecular and cellular mechanisms of these delayed cardiac events, a chronic animal model of postoperative TOF was studied using microarrays to perform cardiac transcriptomic studies. The experimental study included 12 piglets (7 rTOF and 5 controls that underwent surgery at age 2 months and were further studied after 23 (+/- 1 weeks of postoperative recovery. Two distinct regions (endocardium and epicardium from both ventricles were analyzed. Expression levels from each localization were compared in order to decipher mechanisms and signaling pathways leading to ventricular dysfunction and arrhythmias in surgically repaired TOF. Several genes were confirmed to participate in ventricular remodeling and cardiac failure and some new candidate genes were described. In particular, these data pointed out FRZB as a heart failure marker. Moreover, calcium handling and contractile function genes (SLN, ACTC1, PLCD4, PLCZ, potential arrhythmia-related genes (MYO5B, KCNA5, and cytoskeleton and cellular organization-related genes (XIRP2, COL8A1, KCNA6 were among the most deregulated genes in rTOF ventricles. To our knowledge, this is the first comprehensive report on global gene expression profiling in the heart of a long-term swine model of repaired TOF.

Religion, fertility and genes: a dual inheritance model

Science.gov (United States)

Rowthorn, Robert

2011-01-01

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. PMID:21227968

Mining gene expression data of multiple sclerosis.

Directory of Open Access Journals (Sweden)

Pi Guo

Full Text Available Microarray produces a large amount of gene expression data, containing various biological implications. The challenge is to detect a panel of discriminative genes associated with disease. This study proposed a robust classification model for gene selection using gene expression data, and performed an analysis to identify disease-related genes using multiple sclerosis as an example.Gene expression profiles based on the transcriptome of peripheral blood mononuclear cells from a total of 44 samples from 26 multiple sclerosis patients and 18 individuals with other neurological diseases (control were analyzed. Feature selection algorithms including Support Vector Machine based on Recursive Feature Elimination, Receiver Operating Characteristic Curve, and Boruta algorithms were jointly performed to select candidate genes associating with multiple sclerosis. Multiple classification models categorized samples into two different groups based on the identified genes. Models' performance was evaluated using cross-validation methods, and an optimal classifier for gene selection was determined.An overlapping feature set was identified consisting of 8 genes that were differentially expressed between the two phenotype groups. The genes were significantly associated with the pathways of apoptosis and cytokine-cytokine receptor interaction. TNFSF10 was significantly associated with multiple sclerosis. A Support Vector Machine model was established based on the featured genes and gave a practical accuracy of ∼86%. This binary classification model also outperformed the other models in terms of Sensitivity, Specificity and F1 score.The combined analytical framework integrating feature ranking algorithms and Support Vector Machine model could be used for selecting genes for other diseases.

MODEL OF THE TOKAMAK EDGE DENSITY PEDESTAL INCLUDING DIFFUSIVE NEUTRALS

International Nuclear Information System (INIS)

BURRELL, K.H.

2003-01-01

OAK-B135 Several previous analytic models of the tokamak edge density pedestal have been based on diffusive transport of plasma plus free-streaming of neutrals. This latter neutral model includes only the effect of ionization and neglects charge exchange. The present work models the edge density pedestal using diffusive transport for both the plasma and the neutrals. In contrast to the free-streaming model, a diffusion model for the neutrals includes the effect of both charge exchange and ionization and is valid when charge exchange is the dominant interaction. Surprisingly, the functional forms for the electron and neutral density profiles from the present calculation are identical to the results of the previous analytic models. There are some differences in the detailed definition of various parameters in the solution. For experimentally relevant cases where ionization and charge exchange rate are comparable, both models predict approximately the same width for the edge density pedestal

Gene doping detection: evaluation of approach for direct detection of gene transfer using erythropoietin as a model system.

Science.gov (United States)

Baoutina, A; Coldham, T; Bains, G S; Emslie, K R

2010-08-01

As clinical gene therapy has progressed toward realizing its potential, concern over misuse of the technology to enhance performance in athletes is growing. Although 'gene doping' is banned by the World Anti-Doping Agency, its detection remains a major challenge. In this study, we developed a methodology for direct detection of the transferred genetic material and evaluated its feasibility for gene doping detection in blood samples from athletes. Using erythropoietin (EPO) as a model gene and a simple in vitro system, we developed real-time PCR assays that target sequences within the transgene complementary DNA corresponding to exon/exon junctions. As these junctions are absent in the endogenous gene due to their interruption by introns, the approach allows detection of trace amounts of a transgene in a large background of the endogenous gene. Two developed assays and one commercial gene expression assay for EPO were validated. On the basis of ability of these assays to selectively amplify transgenic DNA and analysis of literature on testing of gene transfer in preclinical and clinical gene therapy, it is concluded that the developed approach would potentially be suitable to detect gene doping through gene transfer by analysis of small volumes of blood using regular out-of-competition testing.

Chronic ultraviolet exposure-induced p53 gene alterations in sencar mouse skin carcinogenesis model

International Nuclear Information System (INIS)

Tong, Ying; Smith, M.A.; Tucker, S.B.

1997-01-01

Alterations of the tumor suppressor gene p53 have been found in ultraviolet radiation (UVR) related human skin cancers and in UVR-induced murine skin tumors. However, links between p53 gene alterations and the stages of carcinogenesis induced by UVR have not been clearly defined. We established a chronic UVR exposure-induced Sencar mouse skin carcinogenesis model to determine the frequency of p53 gene alterations in different stages of carcinogenesis, including UV-exposed skin, papillomas, squamous-cell carcinomas (SCCs), and malignant spindle-cell tumors (SCTs). A high incidence of SCCs and SCTs were found in this model. Positive p53 nuclear staining was found in 10137 (27%) of SCCs and 12124 (50%) of SCTs, but was not detected in normal skin or papillomas. DNA was isolated from 40 paraffin-embedded normal skin, UV-exposed skin, and tumor sections. The p53 gene (exons 5 and 6) was amplified from the sections by using nested polymerase chain reaction (PCR). Subsequent single-strand conformation polymorphism (SSCP) assay and sequencing analysis revealed one point mutation in exon 6 (coden 193, C → A transition) from a UV-exposed skin sample, and seven point mutations in exon 5 (codens 146, 158, 150, 165, and 161, three C → T, two C → A, one C → G, and one A → T transition, respectively) from four SCTs, two SCCs and one UV-exposed skin sample. These experimental results demonstrate that alterations in the p53 gene are frequent events in chronic UV exposure-induced SCCs and later stage SCTs in Sencar mouse skin. 40 refs., 5 figs., 1 tab

A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk

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Lewei Duan

2013-01-01

Full Text Available A variety of methods have been proposed for studying the association of multiple genes thought to be involved in a common pathway for a particular disease. Here, we present an extension of a Bayesian hierarchical modeling strategy that allows for multiple SNPs within each gene, with external prior information at either the SNP or gene level. The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. The method is applied to data on 504 SNPs in 38 candidate genes involved in DNA damage response in the WECARE study of second breast cancers in relation to radiotherapy exposure.

BALANCED SCORECARDS EVALUATION MODEL THAT INCLUDES ELEMENTS OF ENVIRONMENTAL MANAGEMENT SYSTEM USING AHP MODEL

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Jelena Jovanović

2010-03-01

Full Text Available The research is oriented on improvement of environmental management system (EMS using BSC (Balanced Scorecard model that presents strategic model of measurem ents and improvement of organisational performance. The research will present approach of objectives and environmental management me trics involvement (proposed by literature review in conventional BSC in "Ad Barska plovi dba" organisation. Further we will test creation of ECO-BSC model based on business activities of non-profit organisations in order to improve envir onmental management system in parallel with other systems of management. Using this approach we may obtain 4 models of BSC that includ es elements of environmen tal management system for AD "Barska plovidba". Taking into acc ount that implementation and evaluation need long period of time in AD "Barska plovidba", the final choice will be based on 14598 (Information technology - Software product evaluation and ISO 9126 (Software engineering - Product quality using AHP method. Those standards are usually used for evaluation of quality software product and computer programs that serve in organisation as support and factors for development. So, AHP model will be bas ed on evolution criteria based on suggestion of ISO 9126 standards and types of evaluation from two evaluation teams. Members of team & will be experts in BSC and environmental management system that are not em ployed in AD "Barska Plovidba" organisation. The members of team 2 will be managers of AD "Barska Plovidba" organisation (including manage rs from environmental department. Merging results based on previously cr eated two AHP models, one can obtain the most appropriate BSC that includes elements of environmental management system. The chosen model will present at the same time suggestion for approach choice including ecological metrics in conventional BSC model for firm that has at least one ECO strategic orientation.

Global gene expression analysis in a mouse model for Norrie disease: late involvement of photoreceptor cells.

Science.gov (United States)

Lenzner, Steffen; Prietz, Sandra; Feil, Silke; Nuber, Ulrike A; Ropers, H-Hilger; Berger, Wolfgang

2002-09-01

Mutations in the NDP gene give rise to a variety of eye diseases, including classic Norrie disease (ND), X-linked exudative vitreoretinopathy (EVRX), retinal telangiectasis (Coats disease), and advanced retinopathy of prematurity (ROP). The gene product is a cystine-knot-containing extracellular signaling molecule of unknown function. In the current study, gene expression was determined in a mouse model of ND, to unravel disease-associated mechanisms at the molecular level. Gene transcription in the eyes of 2-year-old Ndp knockout mice was compared with that in the eyes of age-matched wild-type control animals, by means of cDNA subtraction and microarrays. Clones (n = 3072) from the cDNA subtraction libraries were spotted onto glass slides and hybridized with fluorescently labeled RNA-derived targets. More than 230 differentially expressed clones were sequenced, and their expression patterns were verified by virtual Northern blot analysis. Numerous gene transcripts that are absent or downregulated in the eye of Ndp knockout mice are photoreceptor cell specific. In younger Ndp knockout mice (up to 1 year old), however, all these transcripts were found to be expressed at normal levels. The identification of numerous photoreceptor cell-specific transcripts with a reduced expression in 2-year-old, but not in young, Ndp knockout mice indicates that normal gene expression in these light-sensitive cells of mutant mice is established and maintained over a long period and that rods and cones are affected relatively late in the mouse model of ND. Obviously, the absence of the Ndp gene product is not compatible with long-term survival of photoreceptor cells in the mouse.

Double-gate junctionless transistor model including short-channel effects

International Nuclear Information System (INIS)

Paz, B C; Pavanello, M A; Ávila-Herrera, F; Cerdeira, A

2015-01-01

This work presents a physically based model for double-gate junctionless transistors (JLTs), continuous in all operation regimes. To describe short-channel transistors, short-channel effects (SCEs), such as increase of the channel potential due to drain bias, carrier velocity saturation and mobility degradation due to vertical and longitudinal electric fields, are included in a previous model developed for long-channel double-gate JLTs. To validate the model, an analysis is made by using three-dimensional numerical simulations performed in a Sentaurus Device Simulator from Synopsys. Different doping concentrations, channel widths and channel lengths are considered in this work. Besides that, the series resistance influence is numerically included and validated for a wide range of source and drain extensions. In order to check if the SCEs are appropriately described, besides drain current, transconductance and output conductance characteristics, the following parameters are analyzed to demonstrate the good agreement between model and simulation and the SCEs occurrence in this technology: threshold voltage (V TH ), subthreshold slope (S) and drain induced barrier lowering. (paper)

Model for safety reports including descriptive examples

International Nuclear Information System (INIS)

1995-12-01

Several safety reports will be produced in the process of planning and constructing the system for disposal of high-level radioactive waste in Sweden. The present report gives a model, with detailed examples, of how these reports should be organized and what steps they should include. In the near future safety reports will deal with the encapsulation plant and the repository. Later reports will treat operation of the handling systems and the repository

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

Science.gov (United States)

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.

A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.

Science.gov (United States)

Ye, Meixia; Wang, Zhong; Wang, Yaqun; Wu, Rongling

2015-03-01

Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Molecular diagnostics for congenital hearing loss including 15 deafness genes using a next generation sequencing platform

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De Keulenaer Sarah

2012-05-01

Full Text Available Abstract Background Hereditary hearing loss (HL can originate from mutations in one of many genes involved in the complex process of hearing. Identification of the genetic defects in patients is currently labor intensive and expensive. While screening with Sanger sequencing for GJB2 mutations is common, this is not the case for the other known deafness genes (> 60. Next generation sequencing technology (NGS has the potential to be much more cost efficient. Published methods mainly use hybridization based target enrichment procedures that are time saving and efficient, but lead to loss in sensitivity. In this study we used a semi-automated PCR amplification and NGS in order to combine high sensitivity, speed and cost efficiency. Results In this proof of concept study, we screened 15 autosomal recessive deafness genes in 5 patients with congenital genetic deafness. 646 specific primer pairs for all exons and most of the UTR of the 15 selected genes were designed using primerXL. Using patient specific identifiers, all amplicons were pooled and analyzed using the Roche 454 NGS technology. Three of these patients are members of families in which a region of interest has previously been characterized by linkage studies. In these, we were able to identify two new mutations in CDH23 and OTOF. For another patient, the etiology of deafness was unclear, and no causal mutation was found. In a fifth patient, included as a positive control, we could confirm a known mutation in TMC1. Conclusions We have developed an assay that holds great promise as a tool for screening patients with familial autosomal recessive nonsyndromal hearing loss (ARNSHL. For the first time, an efficient, reliable and cost effective genetic test, based on PCR enrichment, for newborns with undiagnosed deafness is available.

Automatic generation of gene finders for eukaryotic species

DEFF Research Database (Denmark)

Terkelsen, Kasper Munch; Krogh, A.

2006-01-01

and quality of reliable gene annotation grows. Results We present a procedure, Agene, that automatically generates a species-specific gene predictor from a set of reliable mRNA sequences and a genome. We apply a Hidden Markov model (HMM) that implements explicit length distribution modelling for all gene......Background The number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. This calls for procedures to automatically generate species-specific gene finders and to re-train them as the quantity...... structure blocks using acyclic discrete phase type distributions. The state structure of the each HMM is generated dynamically from an array of sub-models to include only gene features represented in the training set. Conclusion Acyclic discrete phase type distributions are well suited to model sequence...

Modelling a linear PM motor including magnetic saturation

NARCIS (Netherlands)

Polinder, H.; Slootweg, J.G.; Compter, J.C.; Hoeijmakers, M.J.

2002-01-01

The use of linear permanent-magnet (PM) actuators increases in a wide variety of applications because of the high force density, robustness and accuracy. The paper describes the modelling of a linear PM motor applied in, for example, wafer steppers, including magnetic saturation. This is important

Cognitive endophenotypes, gene-environment interactions and experience-dependent plasticity in animal models of schizophrenia.

Science.gov (United States)

Burrows, Emma L; Hannan, Anthony J

2016-04-01

Schizophrenia is a devastating brain disorder caused by a complex and heterogeneous combination of genetic and environmental factors. In order to develop effective new strategies to prevent and treat schizophrenia, valid animal models are required which accurately model the disorder, and ideally provide construct, face and predictive validity. The cognitive deficits in schizophrenia represent some of the most debilitating symptoms and are also currently the most poorly treated. Therefore it is crucial that animal models are able to capture the cognitive dysfunction that characterizes schizophrenia, as well as the negative and psychotic symptoms. The genomes of mice have, prior to the recent gene-editing revolution, proven the most easily manipulable of mammalian laboratory species, and hence most genetic targeting has been performed using mouse models. Importantly, when key environmental factors of relevance to schizophrenia are experimentally manipulated, dramatic changes in the phenotypes of these animal models are often observed. We will review recent studies in rodent models which provide insight into gene-environment interactions in schizophrenia. We will focus specifically on environmental factors which modulate levels of experience-dependent plasticity, including environmental enrichment, cognitive stimulation, physical activity and stress. The insights provided by this research will not only help refine the establishment of optimally valid animal models which facilitate development of novel therapeutics, but will also provide insight into the pathogenesis of schizophrenia, thus identifying molecular and cellular targets for future preclinical and clinical investigations. Copyright © 2015 Elsevier B.V. All rights reserved.

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

Energy Technology Data Exchange (ETDEWEB)

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

2012-07-03

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

Comparative GO: a web application for comparative gene ontology and gene ontology-based gene selection in bacteria.

Directory of Open Access Journals (Sweden)

Mario Fruzangohar

Full Text Available The primary means of classifying new functions for genes and proteins relies on Gene Ontology (GO, which defines genes/proteins using a controlled vocabulary in terms of their Molecular Function, Biological Process and Cellular Component. The challenge is to present this information to researchers to compare and discover patterns in multiple datasets using visually comprehensible and user-friendly statistical reports. Importantly, while there are many GO resources available for eukaryotes, there are none suitable for simultaneous, graphical and statistical comparison between multiple datasets. In addition, none of them supports comprehensive resources for bacteria. By using Streptococcus pneumoniae as a model, we identified and collected GO resources including genes, proteins, taxonomy and GO relationships from NCBI, UniProt and GO organisations. Then, we designed database tables in PostgreSQL database server and developed a Java application to extract data from source files and loaded into database automatically. We developed a PHP web application based on Model-View-Control architecture, used a specific data structure as well as current and novel algorithms to estimate GO graphs parameters. We designed different navigation and visualization methods on the graphs and integrated these into graphical reports. This tool is particularly significant when comparing GO groups between multiple samples (including those of pathogenic bacteria from different sources simultaneously. Comparing GO protein distribution among up- or down-regulated genes from different samples can improve understanding of biological pathways, and mechanism(s of infection. It can also aid in the discovery of genes associated with specific function(s for investigation as a novel vaccine or therapeutic targets.http://turing.ersa.edu.au/BacteriaGO.

Rice-arsenate interactions in hydroponics: a three-gene model for tolerance.

Science.gov (United States)

Norton, Gareth J; Nigar, Meher; Williams, Paul N; Dasgupta, Tapash; Meharg, Andrew A; Price, Adam H

2008-01-01

In this study, the genetic mapping of the tolerance of root growth to 13.3 muM arsenate [As(V)] using the BalaxAzucena population is improved, and candidate genes for further study are identified. A remarkable three-gene model of tolerance is advanced, which appears to involve epistatic interaction between three major genes, two on chromosome 6 and one on chromosome 10. Any combination of two of these genes inherited from the tolerant parent leads to the plant having tolerance. Lists of potential positional candidate genes are presented. These are then refined using whole genome transcriptomics data and bioinformatics. Physiological evidence is also provided that genes related to phosphate transport are unlikely to be behind the genetic loci conferring tolerance. These results offer testable hypotheses for genes related to As(V) tolerance that might offer strategies for mitigating arsenic (As) accumulation in consumed rice.

Rice–arsenate interactions in hydroponics: a three-gene model for tolerance

Science.gov (United States)

Norton, Gareth J.; Nigar, Meher; Dasgupta, Tapash; Meharg, Andrew A.; Price, Adam H.

2008-01-01

In this study, the genetic mapping of the tolerance of root growth to 13.3 μM arsenate [As(V)] using the Bala×Azucena population is improved, and candidate genes for further study are identified. A remarkable three-gene model of tolerance is advanced, which appears to involve epistatic interaction between three major genes, two on chromosome 6 and one on chromosome 10. Any combination of two of these genes inherited from the tolerant parent leads to the plant having tolerance. Lists of potential positional candidate genes are presented. These are then refined using whole genome transcriptomics data and bioinformatics. Physiological evidence is also provided that genes related to phosphate transport are unlikely to be behind the genetic loci conferring tolerance. These results offer testable hypotheses for genes related to As(V) tolerance that might offer strategies for mitigating arsenic (As) accumulation in consumed rice. PMID:18453529

Complementation of non-tumorigenicity of HPV18-positive cervical carcinoma cells involves differential mRNA expression of cellular genes including potential tumor suppressor genes on chromosome 11q13.

Science.gov (United States)

Kehrmann, Angela; Truong, Ha; Repenning, Antje; Boger, Regina; Klein-Hitpass, Ludger; Pascheberg, Ulrich; Beckmann, Alf; Opalka, Bertram; Kleine-Lowinski, Kerstin

2013-01-01

The fusion between human tumorigenic cells and normal human diploid fibroblasts results in non-tumorigenic hybrid cells, suggesting a dominant role for tumor suppressor genes in the generated hybrid cells. After long-term cultivation in vitro, tumorigenic segregants may arise. The loss of tumor suppressor genes on chromosome 11q13 has been postulated to be involved in the induction of the tumorigenic phenotype of human papillomavirus (HPV)18-positive cervical carcinoma cells and their derived tumorigenic hybrid cells after subcutaneous injection in immunocompromised mice. The aim of this study was the identification of novel cellular genes that may contribute to the suppression of the tumorigenic phenotype of non-tumorigenic hybrid cells in vivo. We used cDNA microarray technology to identify differentially expressed cellular genes in tumorigenic HPV18-positive hybrid and parental HeLa cells compared to non-tumorigenic HPV18-positive hybrid cells. We detected several as yet unknown cellular genes that play a role in cell differentiation, cell cycle progression, cell-cell communication, metastasis formation, angiogenesis, antigen presentation, and immune response. Apart from the known differentially expressed genes on 11q13 (e.g., phosphofurin acidic cluster sorting protein 1 (PACS1) and FOS ligand 1 (FOSL1 or Fra-1)), we detected novel differentially expressed cellular genes located within the tumor suppressor gene region (e.g., EGF-containing fibulin-like extracellular matrix protein 2 (EFEMP2) and leucine rich repeat containing 32 (LRRC32) (also known as glycoprotein-A repetitions predominant (GARP)) that may have potential tumor suppressor functions in this model system of non-tumorigenic and tumorigenic HeLa x fibroblast hybrid cells. Copyright © 2013 Elsevier Inc. All rights reserved.

Identification of estrogen responsive genes using esophageal squamous cell carcinoma (ESCC) as a model

KAUST Repository

Essack, Magbubah

2012-10-26

Background: Estrogen therapy has positively impact the treatment of several cancers, such as prostate, lung and breast cancers. Moreover, several groups have reported the importance of estrogen induced gene regulation in esophageal cancer (EC). This suggests that there could be a potential for estrogen therapy for EC. The efficient design of estrogen therapies requires as complete as possible list of genes responsive to estrogen. Our study develops a systems biology methodology using esophageal squamous cell carcinoma (ESCC) as a model to identify estrogen responsive genes. These genes, on the other hand, could be affected by estrogen therapy in ESCC.Results: Based on different sources of information we identified 418 genes implicated in ESCC. Putative estrogen responsive elements (EREs) mapped to the promoter region of the ESCC genes were used to initially identify candidate estrogen responsive genes. EREs mapped to the promoter sequence of 30.62% (128/418) of ESCC genes of which 43.75% (56/128) are known to be estrogen responsive, while 56.25% (72/128) are new candidate estrogen responsive genes. EREs did not map to 290 ESCC genes. Of these 290 genes, 50.34% (146/290) are known to be estrogen responsive. By analyzing transcription factor binding sites (TFBSs) in the promoters of the 202 (56+146) known estrogen responsive ESCC genes under study, we found that their regulatory potential may be characterized by 44 significantly over-represented co-localized TFBSs (cTFBSs). We were able to map these cTFBSs to promoters of 32 of the 72 new candidate estrogen responsive ESCC genes, thereby increasing confidence that these 32 ESCC genes are responsive to estrogen since their promoters contain both: a/mapped EREs, and b/at least four cTFBSs characteristic of ESCC genes that are responsive to estrogen. Recent publications confirm that 47% (15/32) of these 32 predicted genes are indeed responsive to estrogen.Conclusion: To the best of our knowledge our study is the first

Identification of estrogen responsive genes using esophageal squamous cell carcinoma (ESCC) as a model

KAUST Repository

Essack, Magbubah; MacPherson, Cameron Ross; Schmeier, Sebastian; Bajic, Vladimir B.

2012-01-01

Background: Estrogen therapy has positively impact the treatment of several cancers, such as prostate, lung and breast cancers. Moreover, several groups have reported the importance of estrogen induced gene regulation in esophageal cancer (EC). This suggests that there could be a potential for estrogen therapy for EC. The efficient design of estrogen therapies requires as complete as possible list of genes responsive to estrogen. Our study develops a systems biology methodology using esophageal squamous cell carcinoma (ESCC) as a model to identify estrogen responsive genes. These genes, on the other hand, could be affected by estrogen therapy in ESCC.Results: Based on different sources of information we identified 418 genes implicated in ESCC. Putative estrogen responsive elements (EREs) mapped to the promoter region of the ESCC genes were used to initially identify candidate estrogen responsive genes. EREs mapped to the promoter sequence of 30.62% (128/418) of ESCC genes of which 43.75% (56/128) are known to be estrogen responsive, while 56.25% (72/128) are new candidate estrogen responsive genes. EREs did not map to 290 ESCC genes. Of these 290 genes, 50.34% (146/290) are known to be estrogen responsive. By analyzing transcription factor binding sites (TFBSs) in the promoters of the 202 (56+146) known estrogen responsive ESCC genes under study, we found that their regulatory potential may be characterized by 44 significantly over-represented co-localized TFBSs (cTFBSs). We were able to map these cTFBSs to promoters of 32 of the 72 new candidate estrogen responsive ESCC genes, thereby increasing confidence that these 32 ESCC genes are responsive to estrogen since their promoters contain both: a/mapped EREs, and b/at least four cTFBSs characteristic of ESCC genes that are responsive to estrogen. Recent publications confirm that 47% (15/32) of these 32 predicted genes are indeed responsive to estrogen.Conclusion: To the best of our knowledge our study is the first

Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data

Directory of Open Access Journals (Sweden)

Keita Mori

2013-01-01

Full Text Available Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided.

Cholesterol-α-glucosyltransferase gene is present in most Helicobacter species including gastric non-Helicobacter pylori helicobacters obtained from Japanese patients.

Science.gov (United States)

Kawakubo, Masatomo; Horiuchi, Kazuki; Matsumoto, Takehisa; Nakayama, Jun; Akamatsu, Taiji; Katsuyama, Tsutomu; Ota, Hiroyoshi; Sagara, Junji

2018-02-01

Non-Helicobacter pylori helicobacters (NHPHs) besides H. pylori infect human stomachs and cause chronic gastritis and mucosa-associated lymphoid tissue lymphoma. Cholesteryl-α-glucosides have been identified as unique glycolipids present in H. pylori and some Helicobacter species. Cholesterol-α-glucosyltransferase (αCgT), a key enzyme for the biosynthesis of cholesteryl-α-glucosides, plays crucial roles in the pathogenicity of H. pylori. Therefore, it is important to examine αCgTs of NHPHs. Six gastric NHPHs were isolated from Japanese patients and maintained in mouse stomachs. The αCgT genes were amplified by PCR and inverse PCR. We retrieved the αCgT genes of other Helicobacter species by BLAST searches in GenBank. αCgT genes were present in most Helicobacter species and in all Japanese isolates examined. However, we could find no candidate gene for αCgT in the whole genome of Helicobacter cinaedi and several enterohepatic species. Phylogenic analysis demonstrated that the αCgT genes of all Japanese isolates show high similarities to that of a zoonotic group of gastric NHPHs including Helicobacter suis, Helicobacter heilmannii, and Helicobacter ailurogastricus. Of 6 Japanese isolates, the αCgT genes of 4 isolates were identical to that of H. suis, and that of another 2 isolates were similar to that of H. heilmannii and H. ailurogastricus. All gastric NHPHs examined showed presence of αCgT genes, indicating that αCgT may be beneficial for these helicobacters to infect human and possibly animal stomachs. Our study indicated that NHPHs could be classified into 2 groups, NHPHs with αCgT genes and NHPHs without αCgT genes. © 2017 John Wiley & Sons Ltd.

A comparison of 100 human genes using an alu element-based instability model.

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George W Cook

Full Text Available The human retrotransposon with the highest copy number is the Alu element. The human genome contains over one million Alu elements that collectively account for over ten percent of our DNA. Full-length Alu elements are randomly distributed throughout the genome in both forward and reverse orientations. However, full-length widely spaced Alu pairs having two Alus in the same (direct orientation are statistically more prevalent than Alu pairs having two Alus in the opposite (inverted orientation. The cause of this phenomenon is unknown. It has been hypothesized that this imbalance is the consequence of anomalous inverted Alu pair interactions. One proposed mechanism suggests that inverted Alu pairs can ectopically interact, exposing both ends of each Alu element making up the pair to a potential double-strand break, or "hit". This hypothesized "two-hit" (two double-strand breaks potential per Alu element was used to develop a model for comparing the relative instabilities of human genes. The model incorporates both 1 the two-hit double-strand break potential of Alu elements and 2 the probability of exon-damaging deletions extending from these double-strand breaks. This model was used to compare the relative instabilities of 50 deletion-prone cancer genes and 50 randomly selected genes from the human genome. The output of the Alu element-based genomic instability model developed here is shown to coincide with the observed instability of deletion-prone cancer genes. The 50 cancer genes are collectively estimated to be 58% more unstable than the randomly chosen genes using this model. Seven of the deletion-prone cancer genes, ATM, BRCA1, FANCA, FANCD2, MSH2, NCOR1 and PBRM1, were among the most unstable 10% of the 100 genes analyzed. This algorithm may lay the foundation for comparing genetic risks posed by structural variations that are unique to specific individuals, families and people groups.

Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis.

Science.gov (United States)

Lu, Tao

2016-01-01

The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.

Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury.

Science.gov (United States)

Sweeney, Timothy E; Lofgren, Shane; Khatri, Purvesh; Rogers, Angela J

2017-08-01

The relevance of animal models to human diseases is an area of intense scientific debate. The degree to which mouse models of lung injury recapitulate human lung injury has never been assessed. Integrating data from both human and animal expression studies allows for increased statistical power and identification of conserved differential gene expression across organisms and conditions. We sought comprehensive integration of gene expression data in experimental acute lung injury (ALI) in rodents compared with humans. We performed two separate gene expression multicohort analyses to determine differential gene expression in experimental animal and human lung injury. We used correlational and pathway analyses combined with external in vitro gene expression data to identify both potential drivers of underlying inflammation and therapeutic drug candidates. We identified 21 animal lung tissue datasets and three human lung injury bronchoalveolar lavage datasets. We show that the metasignatures of animal and human experimental ALI are significantly correlated despite these widely varying experimental conditions. The gene expression changes among mice and rats across diverse injury models (ozone, ventilator-induced lung injury, LPS) are significantly correlated with human models of lung injury (Pearson r = 0.33-0.45, P human lung injury. Predicted therapeutic targets, peptide ligand signatures, and pathway analyses are also all highly overlapping. Gene expression changes are similar in animal and human experimental ALI, and provide several physiologic and therapeutic insights to the disease.

A random variance model for detection of differential gene expression in small microarray experiments.

Science.gov (United States)

Wright, George W; Simon, Richard M

2003-12-12

Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true. We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives. This method is incorporated into BRB-ArrayTools version 3.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html). ftp://linus.nci.nih.gov/pub/techreport/RVM_supplement.pdf

Genotypic and Antimicrobial Susceptibility of Carbapenem-Resistant Acinetobacter baumannii: Analysis of ISAba Elements and blaOXA-23-like Genes Including A New Variant

Directory of Open Access Journals (Sweden)

Abbas eBahador

2015-11-01

Full Text Available Carbapenem-resistant Acinetobacter baumannii (CR-AB causes serious nosocomial infections, especially in ICU wards of hospitals, worldwide. Expression of blaOXA genes is the chief mechanism of conferring carbapenem resistance among CR-AB. Although some blaOXA genes have been studied among CR-AB isolates from Iran, their blaOXA-23-like genes have not been investigated. We used a multiplex-PCR to detect Ambler class A, B, and D carbapenemases of 85 isolates, and determined that 34 harbored blaOXA-23-like genes. Amplified fragment length polymorphism (AFLP genotyping, followed by DNA sequencing of blaOXA-23-like amplicons of CR-AB from each AFLP group was used to characterize their blaOXA-23-like genes. We also assessed the antimicrobial susceptibility pattern of CR-AB isolates, and tested whether they harbored insertion sequences ISAba1 and ISAba4. Sequence comparison with reference strain A. baumannii (NCTC12156 revealed five types of mutations in blaOXA-23-like genes; including one novel variant and four mutants that were already reported from China and the USA. All of the blaOXA-23-like genes mutations were associated with increased minimum inhibitory concentrations (MICs against imipenem. ISAba1 and ISAba4 sequences were detected upstream of blaOXA-23 genes in 19% and 7% of isolates, respectively. The isolation of CR-AB with new blaOXA-23 mutations including some that have been reported from the USA and China highlights CR-AB pervasive distribution, which underscores the importance of concerted national and global efforts to control the spread of CR-AB isolates worldwide.

Identification of a developmental gene expression signature, including HOX genes, for the normal human colonic crypt stem cell niche: overexpression of the signature parallels stem cell overpopulation during colon tumorigenesis.

Science.gov (United States)

Bhatlekar, Seema; Addya, Sankar; Salunek, Moreh; Orr, Christopher R; Surrey, Saul; McKenzie, Steven; Fields, Jeremy Z; Boman, Bruce M

2014-01-15

Our goal was to identify a unique gene expression signature for human colonic stem cells (SCs). Accordingly, we determined the gene expression pattern for a known SC-enriched region--the crypt bottom. Colonic crypts and isolated crypt subsections (top, middle, and bottom) were purified from fresh, normal, human, surgical specimens. We then used an innovative strategy that used two-color microarrays (∼18,500 genes) to compare gene expression in the crypt bottom with expression in the other crypt subsections (middle or top). Array results were validated by PCR and immunostaining. About 25% of genes analyzed were expressed in crypts: 88 preferentially in the bottom, 68 in the middle, and 131 in the top. Among genes upregulated in the bottom, ∼30% were classified as growth and/or developmental genes including several in the PI3 kinase pathway, a six-transmembrane protein STAMP1, and two homeobox (HOXA4, HOXD10) genes. qPCR and immunostaining validated that HOXA4 and HOXD10 are selectively expressed in the normal crypt bottom and are overexpressed in colon carcinomas (CRCs). Immunostaining showed that HOXA4 and HOXD10 are co-expressed with the SC markers CD166 and ALDH1 in cells at the normal crypt bottom, and the number of these co-expressing cells is increased in CRCs. Thus, our findings show that these two HOX genes are selectively expressed in colonic SCs and that HOX overexpression in CRCs parallels the SC overpopulation that occurs during CRC development. Our study suggests that developmental genes play key roles in the maintenance of normal SCs and crypt renewal, and contribute to the SC overpopulation that drives colon tumorigenesis.

Conditions for the Evolution of Gene Clusters in Bacterial Genomes

Science.gov (United States)

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

Overlapping gene expression profiles of model compounds provide opportunities for immunotoxicity screening

International Nuclear Information System (INIS)

Baken, Kirsten A.; Pennings, Jeroen L.A.; Jonker, Martijs J.; Schaap, Mirjam M.; Vries, Annemieke de; Steeg, Harry van; Breit, Timo M.; Loveren, Henk van

2008-01-01

In order to investigate immunotoxic effects of a set of model compounds in mice, a toxicogenomics approach was combined with information on macroscopical and histopathological effects on spleens and on modulation of immune function. Bis(tri-n-butyltin)oxide (TBTO), cyclosporin A (CsA), and benzo[a]pyrene (B[a]P) were administered to C57BL/6 mice at immunosuppressive dose levels. Acetaminophen (APAP) was included in the study since indications of immunomodulating properties of this compound have appeared in the literature. TBTO exposure caused the most pronounced effect on gene expression and also resulted in the most severe reduction of body weight gain and induction of splenic irregularities. All compounds caused inhibition of cell division in the spleen as shown by microarray analysis as well as by suppression of lymphocyte proliferation after application of a contact sensitizer as demonstrated in an immune function assay that was adapted from the local lymph node assay. The immunotoxicogenomics approach applied in this study thus pointed to immunosuppression through cell cycle arrest as a common mechanism of action of immunotoxicants, including APAP. Genes related to cell division such as Ccna2, Brca1, Birc5, Incenp, and Cdkn1a (p21) were identified as candidate genes to indicate anti-proliferative effects of xenobiotics in immune cells for future screening assays. The results of our experiments also show the value of group wise pathway analysis for detection of more subtle transcriptional effects and the potency of evaluation of effects in the spleen to demonstrate immunotoxicity

The null hypothesis of GSEA, and a novel statistical model for competitive gene set analysis

DEFF Research Database (Denmark)

Debrabant, Birgit

2017-01-01

MOTIVATION: Competitive gene set analysis intends to assess whether a specific set of genes is more associated with a trait than the remaining genes. However, the statistical models assumed to date to underly these methods do not enable a clear cut formulation of the competitive null hypothesis....... This is a major handicap to the interpretation of results obtained from a gene set analysis. RESULTS: This work presents a hierarchical statistical model based on the notion of dependence measures, which overcomes this problem. The two levels of the model naturally reflect the modular structure of many gene set...... analysis methods. We apply the model to show that the popular GSEA method, which recently has been claimed to test the self-contained null hypothesis, actually tests the competitive null if the weight parameter is zero. However, for this result to hold strictly, the choice of the dependence measures...

Olkiluoto surface hydrological modelling: Update 2012 including salt transport modelling

International Nuclear Information System (INIS)

Karvonen, T.

2013-11-01

Posiva Oy is responsible for implementing a final disposal program for spent nuclear fuel of its owners Teollisuuden Voima Oyj and Fortum Power and Heat Oy. The spent nuclear fuel is planned to be disposed at a depth of about 400-450 meters in the crystalline bedrock at the Olkiluoto site. Leakages located at or close to spent fuel repository may give rise to the upconing of deep highly saline groundwater and this is a concern with regard to the performance of the tunnel backfill material after the closure of the tunnels. Therefore a salt transport sub-model was added to the Olkiluoto surface hydrological model (SHYD). The other improvements include update of the particle tracking algorithm and possibility to estimate the influence of open drillholes in a case where overpressure in inflatable packers decreases causing a hydraulic short-circuit between hydrogeological zones HZ19 and HZ20 along the drillhole. Four new hydrogeological zones HZ056, HZ146, BFZ100 and HZ039 were added to the model. In addition, zones HZ20A and HZ20B intersect with each other in the new structure model, which influences salinity upconing caused by leakages in shafts. The aim of the modelling of long-term influence of ONKALO, shafts and repository tunnels provide computational results that can be used to suggest limits for allowed leakages. The model input data included all the existing leakages into ONKALO (35-38 l/min) and shafts in the present day conditions. The influence of shafts was computed using eight different values for total shaft leakage: 5, 11, 20, 30, 40, 50, 60 and 70 l/min. The selection of the leakage criteria for shafts was influenced by the fact that upconing of saline water increases TDS-values close to the repository areas although HZ20B does not intersect any deposition tunnels. The total limit for all leakages was suggested to be 120 l/min. The limit for HZ20 zones was proposed to be 40 l/min: about 5 l/min the present day leakages to access tunnel, 25 l/min from

Olkiluoto surface hydrological modelling: Update 2012 including salt transport modelling

Energy Technology Data Exchange (ETDEWEB)

Karvonen, T. [WaterHope, Helsinki (Finland)

2013-11-15

Posiva Oy is responsible for implementing a final disposal program for spent nuclear fuel of its owners Teollisuuden Voima Oyj and Fortum Power and Heat Oy. The spent nuclear fuel is planned to be disposed at a depth of about 400-450 meters in the crystalline bedrock at the Olkiluoto site. Leakages located at or close to spent fuel repository may give rise to the upconing of deep highly saline groundwater and this is a concern with regard to the performance of the tunnel backfill material after the closure of the tunnels. Therefore a salt transport sub-model was added to the Olkiluoto surface hydrological model (SHYD). The other improvements include update of the particle tracking algorithm and possibility to estimate the influence of open drillholes in a case where overpressure in inflatable packers decreases causing a hydraulic short-circuit between hydrogeological zones HZ19 and HZ20 along the drillhole. Four new hydrogeological zones HZ056, HZ146, BFZ100 and HZ039 were added to the model. In addition, zones HZ20A and HZ20B intersect with each other in the new structure model, which influences salinity upconing caused by leakages in shafts. The aim of the modelling of long-term influence of ONKALO, shafts and repository tunnels provide computational results that can be used to suggest limits for allowed leakages. The model input data included all the existing leakages into ONKALO (35-38 l/min) and shafts in the present day conditions. The influence of shafts was computed using eight different values for total shaft leakage: 5, 11, 20, 30, 40, 50, 60 and 70 l/min. The selection of the leakage criteria for shafts was influenced by the fact that upconing of saline water increases TDS-values close to the repository areas although HZ20B does not intersect any deposition tunnels. The total limit for all leakages was suggested to be 120 l/min. The limit for HZ20 zones was proposed to be 40 l/min: about 5 l/min the present day leakages to access tunnel, 25 l/min from

A model of gene expression based on random dynamical systems reveals modularity properties of gene regulatory networks.

Science.gov (United States)

Antoneli, Fernando; Ferreira, Renata C; Briones, Marcelo R S

2016-06-01

Here we propose a new approach to modeling gene expression based on the theory of random dynamical systems (RDS) that provides a general coupling prescription between the nodes of any given regulatory network given the dynamics of each node is modeled by a RDS. The main virtues of this approach are the following: (i) it provides a natural way to obtain arbitrarily large networks by coupling together simple basic pieces, thus revealing the modularity of regulatory networks; (ii) the assumptions about the stochastic processes used in the modeling are fairly general, in the sense that the only requirement is stationarity; (iii) there is a well developed mathematical theory, which is a blend of smooth dynamical systems theory, ergodic theory and stochastic analysis that allows one to extract relevant dynamical and statistical information without solving the system; (iv) one may obtain the classical rate equations form the corresponding stochastic version by averaging the dynamic random variables (small noise limit). It is important to emphasize that unlike the deterministic case, where coupling two equations is a trivial matter, coupling two RDS is non-trivial, specially in our case, where the coupling is performed between a state variable of one gene and the switching stochastic process of another gene and, hence, it is not a priori true that the resulting coupled system will satisfy the definition of a random dynamical system. We shall provide the necessary arguments that ensure that our coupling prescription does indeed furnish a coupled regulatory network of random dynamical systems. Finally, the fact that classical rate equations are the small noise limit of our stochastic model ensures that any validation or prediction made on the basis of the classical theory is also a validation or prediction of our model. We illustrate our framework with some simple examples of single-gene system and network motifs. Copyright © 2016 Elsevier Inc. All rights reserved.

A three-gene expression signature model for risk stratification of patients with neuroblastoma.

Science.gov (United States)

Garcia, Idoia; Mayol, Gemma; Ríos, José; Domenech, Gema; Cheung, Nai-Kong V; Oberthuer, André; Fischer, Matthias; Maris, John M; Brodeur, Garrett M; Hero, Barbara; Rodríguez, Eva; Suñol, Mariona; Galvan, Patricia; de Torres, Carmen; Mora, Jaume; Lavarino, Cinzia

2012-04-01

Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P model was an independent marker for survival (P model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems. ©2012 AACR.

Evaluating bacterial gene-finding HMM structures as probabilistic logic programs

DEFF Research Database (Denmark)

Mørk, Søren; Holmes, Ian

2012-01-01

, a probabilistic dialect of Prolog. Results: We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length...

Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

Directory of Open Access Journals (Sweden)

Liang Jinghang

2012-08-01

Full Text Available Abstract Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs. As a logical model, probabilistic Boolean networks (PBNs consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n or O(nN2n for a sparse matrix. Results This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN. An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n, where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a

Study of a diffusion flamelet model, with preferential diffusion effects included

NARCIS (Netherlands)

Delhaye, S.; Somers, L.M.T.; Bongers, H.; Oijen, van J.A.; Goey, de L.P.H.; Dias, V.

2005-01-01

The non-premixed flamelet model of Peters [1] (model1), which does not include preferential diffusion effects is investigated. Two similar models are presented, but without the assumption of unity Lewis numbers. One of these models was derived by Peters & Pitsch [2] (model2), while the other one was

Gene therapy in nonhuman primate models of human autoimmune disease

NARCIS (Netherlands)

t'Hart, B. A.; Vervoordeldonk, M.; Heeney, J. L.; Tak, P. P.

2003-01-01

Before autoimmune diseases in humans can be treated with gene therapy, the safety and efficacy of the used vectors must be tested in valid experimental models. Monkeys, such as the rhesus macaque or the common marmoset, provide such models. This publication reviews the state of the art in monkey

SEEPAGE MODEL FOR PA INCLUDING DRIFT COLLAPSE

International Nuclear Information System (INIS)

C. Tsang

2004-01-01

The purpose of this report is to document the predictions and analyses performed using the seepage model for performance assessment (SMPA) for both the Topopah Spring middle nonlithophysal (Tptpmn) and lower lithophysal (Tptpll) lithostratigraphic units at Yucca Mountain, Nevada. Look-up tables of seepage flow rates into a drift (and their uncertainty) are generated by performing numerical simulations with the seepage model for many combinations of the three most important seepage-relevant parameters: the fracture permeability, the capillary-strength parameter 1/a, and the percolation flux. The percolation flux values chosen take into account flow focusing effects, which are evaluated based on a flow-focusing model. Moreover, multiple realizations of the underlying stochastic permeability field are conducted. Selected sensitivity studies are performed, including the effects of an alternative drift geometry representing a partially collapsed drift from an independent drift-degradation analysis (BSC 2004 [DIRS 166107]). The intended purpose of the seepage model is to provide results of drift-scale seepage rates under a series of parameters and scenarios in support of the Total System Performance Assessment for License Application (TSPA-LA). The SMPA is intended for the evaluation of drift-scale seepage rates under the full range of parameter values for three parameters found to be key (fracture permeability, the van Genuchten 1/a parameter, and percolation flux) and drift degradation shape scenarios in support of the TSPA-LA during the period of compliance for postclosure performance [Technical Work Plan for: Performance Assessment Unsaturated Zone (BSC 2002 [DIRS 160819], Section I-4-2-1)]. The flow-focusing model in the Topopah Spring welded (TSw) unit is intended to provide an estimate of flow focusing factors (FFFs) that (1) bridge the gap between the mountain-scale and drift-scale models, and (2) account for variability in local percolation flux due to

Animal model for schizophrenia that reflects gene-environment interactions.

Science.gov (United States)

Nagai, Taku; Ibi, Daisuke; Yamada, Kiyofumi

2011-01-01

Schizophrenia is a devastating psychiatric disorder that impairs mental and social functioning and affects approximately 1% of the population worldwide. Genetic susceptibility factors for schizophrenia have recently been reported, some of which are known to play a role in neurodevelopment; these include neuregulin-1, dysbindin, and disrupted-in-schizophrenia 1 (DISC1). Moreover, epidemiologic studies suggest that environmental insults, such as prenatal infection and perinatal complication, are involved in the development of schizophrenia. The possible interaction between environment and genetic susceptibility factors, especially during neurodevelopment, is proposed as a promising disease etiology of schizophrenia. Polyriboinosinic-polyribocytidilic acid (polyI : C) is a synthetic analogue of double-stranded RNA that leads to the pronounced but time-limited production of pro-inflammatory cytokines. Maternal immune activation by polyI : C exposure in rodents is known to precipitate a wide spectrum of behavioral, cognitive, and pharmacological abnormalities in adult offspring. Recently, we have reported that neonatal injection of polyI : C in mice results in schizophrenia-like behavioral alterations in adulthood. In this review, we show how gene-environment interactions during neurodevelopment result in phenotypic changes in adulthood by injecting polyI : C into transgenic mice that express a dominant-negative form of human DISC1 (DN-DISC1). Our findings suggest that polyI : C-treated DN-DISC1 mice are a well-validated animal model for schizophrenia that reflects gene-environment interactions.

Atmosphere-soil-vegetation model including CO2 exchange processes: SOLVEG2

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Nagai, Haruyasu

2004-11-01

A new atmosphere-soil-vegetation model named SOLVEG2 (SOLVEG version 2) was developed to study the heat, water, and CO 2 exchanges between the atmosphere and land-surface. The model consists of one-dimensional multilayer sub-models for the atmosphere, soil, and vegetation. It also includes sophisticated processes for solar and long-wave radiation transmission in vegetation canopy and CO 2 exchanges among the atmosphere, soil, and vegetation. Although the model usually simulates only vertical variation of variables in the surface-layer atmosphere, soil, and vegetation canopy by using meteorological data as top boundary conditions, it can be used by coupling with a three-dimensional atmosphere model. In this paper, details of SOLVEG2, which includes the function of coupling with atmosphere model MM5, are described. (author)

The dynamics of gene expression changes in a mouse model of oral tumorigenesis may help refine prevention and treatment strategies in patients with oral cancer.

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Foy, Jean-Philippe; Tortereau, Antonin; Caulin, Carlos; Le Texier, Vincent; Lavergne, Emilie; Thomas, Emilie; Chabaud, Sylvie; Perol, David; Lachuer, Joël; Lang, Wenhua; Hong, Waun Ki; Goudot, Patrick; Lippman, Scott M; Bertolus, Chloé; Saintigny, Pierre

2016-06-14

A better understanding of the dynamics of molecular changes occurring during the early stages of oral tumorigenesis may help refine prevention and treatment strategies. We generated genome-wide expression profiles of microdissected normal mucosa, hyperplasia, dysplasia and tumors derived from the 4-NQO mouse model of oral tumorigenesis. Genes differentially expressed between tumor and normal mucosa defined the "tumor gene set" (TGS), including 4 non-overlapping gene subsets that characterize the dynamics of gene expression changes through different stages of disease progression. The majority of gene expression changes occurred early or progressively. The relevance of these mouse gene sets to human disease was tested in multiple datasets including the TCGA and the Genomics of Drug Sensitivity in Cancer project. The TGS was able to discriminate oral squamous cell carcinoma (OSCC) from normal oral mucosa in 3 independent datasets. The OSCC samples enriched in the mouse TGS displayed high frequency of CASP8 mutations, 11q13.3 amplifications and low frequency of PIK3CA mutations. Early changes observed in the 4-NQO model were associated with a trend toward a shorter oral cancer-free survival in patients with oral preneoplasia that was not seen in multivariate analysis. Progressive changes observed in the 4-NQO model were associated with an increased sensitivity to 4 different MEK inhibitors in a panel of 51 squamous cell carcinoma cell lines of the areodigestive tract. In conclusion, the dynamics of molecular changes in the 4-NQO model reveal that MEK inhibition may be relevant to prevention and treatment of a specific molecularly-defined subgroup of OSCC.

Conditions for the evolution of gene clusters in bacterial genomes.

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Sara Ballouz

2010-02-01

Full Text Available 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.

Exclusive queueing model including the choice of service windows

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Tanaka, Masahiro; Yanagisawa, Daichi; Nishinari, Katsuhiro

2018-01-01

In a queueing system involving multiple service windows, choice behavior is a significant concern. This paper incorporates the choice of service windows into a queueing model with a floor represented by discrete cells. We contrived a logit-based choice algorithm for agents considering the numbers of agents and the distances to all service windows. Simulations were conducted with various parameters of agent choice preference for these two elements and for different floor configurations, including the floor length and the number of service windows. We investigated the model from the viewpoint of transit times and entrance block rates. The influences of the parameters on these factors were surveyed in detail and we determined that there are optimum floor lengths that minimize the transit times. In addition, we observed that the transit times were determined almost entirely by the entrance block rates. The results of the presented model are relevant to understanding queueing systems including the choice of service windows and can be employed to optimize facility design and floor management.

Dual AAV Gene Therapy for Duchenne Muscular Dystrophy with a 7-kb Mini-Dystrophin Gene in the Canine Model.

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Kodippili, Kasun; Hakim, Chady H; Pan, Xiufang; Yang, Hsiao T; Yue, Yongping; Zhang, Yadong; Shin, Jin-Hong; Yang, N Nora; Duan, Dongsheng

2018-03-01

Dual adeno-associated virus (AAV) technology was developed in 2000 to double the packaging capacity of the AAV vector. The proof of principle has been demonstrated in various mouse models. Yet, pivotal evidence is lacking in large animal models of human diseases. Here we report expression of a 7-kb canine ΔH2-R15 mini-dystrophin gene using a pair of dual AAV vectors in the canine model of Duchenne muscular dystrophy (DMD). The ΔH2-R15 minigene is by far the most potent synthetic dystrophin gene engineered for DMD gene therapy. We packaged minigene dual vectors in Y731F tyrosine-modified AAV-9 and delivered to the extensor carpi ulnaris muscle of a 12-month-old affected dog at the dose of 2 × 10 13 viral genome particles/vector/muscle. Widespread mini-dystrophin expression was observed 2 months after gene transfer. The missing dystrophin-associated glycoprotein complex was restored. Treatment also reduced muscle degeneration and fibrosis and improved myofiber size distribution. Importantly, dual AAV therapy greatly protected the muscle from eccentric contraction-induced force loss. Our data provide the first clear evidence that dual AAV therapy can be translated to a diseased large mammal. Further development of dual AAV technology may lead to effective therapies for DMD and many other diseases in human patients.

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

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Ingkasuwan Papapit

2012-08-01

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

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

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Chiappino-Pepe, Anush; Ataman, Meriç

2017-01-01

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

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

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Anush Chiappino-Pepe

2017-03-01

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

Artificial neural network inference (ANNI: a study on gene-gene interaction for biomarkers in childhood sarcomas.

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Dong Ling Tong

Full Text Available OBJECTIVE: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI. METHOD: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. RESULTS: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS; FCGRT and OLFM1 in Ewing's sarcoma (EWS suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. CONCLUSIONS: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.

An animal model for Norrie disease (ND): gene targeting of the mouse ND gene.

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Berger, W; van de Pol, D; Bächner, D; Oerlemans, F; Winkens, H; Hameister, H; Wieringa, B; Hendriks, W; Ropers, H H

1996-01-01

In order to elucidate the cellular and molecular processes which are involved in Norrie disease (ND), we have used gene targeting technology to generate ND mutant mice. The murine homologue of the ND gene was cloned and shown to encode a polypeptide that shares 94% of the amino acid sequence with its human counterpart. RNA in situ hybridization revealed expression in retina, brain and the olfactory bulb and epithelium of 2 week old mice. Hemizygous mice carrying a replacement mutation in exon 2 of the ND gene developed retrolental structures in the vitreous body and showed an overall disorganization of the retinal ganglion cell layer. The outer plexiform layer disappears occasionally, resulting in a juxtaposed inner and outer nuclear layer. At the same regions, the outer segments of the photoreceptor cell layer are no longer present. These ocular findings are consistent with observations in ND patients and the generated mouse line provides a faithful model for study of early pathogenic events in this severe X-linked recessive neurological disorder.

Gene therapy for ocular diseases.

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Liu, Melissa M; Tuo, Jingsheng; Chan, Chi-Chao

2011-05-01

The eye is an easily accessible, highly compartmentalised and immune-privileged organ that offers unique advantages as a gene therapy target. Significant advancements have been made in understanding the genetic pathogenesis of ocular diseases, and gene replacement and gene silencing have been implicated as potentially efficacious therapies. Recent improvements have been made in the safety and specificity of vector-based ocular gene transfer methods. Proof-of-concept for vector-based gene therapies has also been established in several experimental models of human ocular diseases. After nearly two decades of ocular gene therapy research, preliminary successes are now being reported in phase 1 clinical trials for the treatment of Leber congenital amaurosis. This review describes current developments and future prospects for ocular gene therapy. Novel methods are being developed to enhance the performance and regulation of recombinant adeno-associated virus- and lentivirus-mediated ocular gene transfer. Gene therapy prospects have advanced for a variety of retinal disorders, including retinitis pigmentosa, retinoschisis, Stargardt disease and age-related macular degeneration. Advances have also been made using experimental models for non-retinal diseases, such as uveitis and glaucoma. These methodological advancements are critical for the implementation of additional gene-based therapies for human ocular diseases in the near future.

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

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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

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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

Neural model of gene regulatory network: a survey on supportive meta-heuristics.

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Biswas, Surama; Acharyya, Sriyankar

2016-06-01

Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

Characteristic gene expression profiles in the progression from liver cirrhosis to carcinoma induced by diethylnitrosamine in a rat model

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Zhu Jin

2009-07-01

Full Text Available Abstract Background Liver cancr is a heterogeneous disease in terms of etiology, biologic and clinical behavior. Very little is known about how many genes concur at the molecular level of tumor development, progression and aggressiveness. To explore the key genes involved in the development of liver cancer, we established a rat model induced by diethylnitrosamine to investigate the gene expression profiles of liver tissues during the transition to cirrhosis and carcinoma. Methods A rat model of liver cancer induced by diethylnitrosamine was established. The cirrhotic tissue, the dysplasia nodules, the early cancerous nodules and the cancerous nodules from the rats with lung metastasis were chosen to compare with liver tissue of normal rats to investigate the differential expression genes between them. Affymetrix GeneChip Rat 230 2.0 arrays were used throughout. The real-time quantity PCR was used to verify the expression of some differential expression genes in tissues. Results The pathological changes that occurred in the livers of diethylnitrosamine-treated rats included non-specific injury, fibrosis and cirrhosis, dysplastic nodules, early cancerous nodules and metastasis. There are 349 upregulated and 345 downregulated genes sharing among the above chosen tissues when compared with liver tissue of normal rats. The deregulated genes play various roles in diverse processes such as metabolism, transport, cell proliferation, apoptosis, cell adhesion, angiogenesis and so on. Among which, 41 upregulated and 27 downregulated genes are associated with inflammatory response, immune response and oxidative stress. Twenty-four genes associated with glutathione metabolism majorly participating oxidative stress were deregulated in the development of liver cancer. There were 19 members belong to CYP450 family downregulated, except CYP2C40 upregulated. Conclusion In this study, we provide the global gene expression profiles during the development and

Modeling Gene-Environment Interactions With Quasi-Natural Experiments.

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Schmitz, Lauren; Conley, Dalton

2017-02-01

This overview develops new empirical models that can effectively document Gene × Environment (G×E) interactions in observational data. Current G×E studies are often unable to support causal inference because they use endogenous measures of the environment or fail to adequately address the nonrandom distribution of genes across environments, confounding estimates. Comprehensive measures of genetic variation are incorporated into quasi-natural experimental designs to exploit exogenous environmental shocks or isolate variation in environmental exposure to avoid potential confounders. In addition, we offer insights from population genetics that improve upon extant approaches to address problems from population stratification. Together, these tools offer a powerful way forward for G×E research on the origin and development of social inequality across the life course. © 2015 Wiley Periodicals, Inc.

Validation of housekeeping genes for quantitative real-time PCR in in-vivo and in-vitro models of cerebral ischaemia

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Serena Joaquín

2009-06-01

Full Text Available Abstract Background Studies of gene expression in experimental cerebral ischaemia models can contribute to understanding the pathophysiology of brain ischaemia and to identifying prognostic markers and potential therapeutic targets. The normalization of relative qRT-PCR data using a suitable reference gene is a crucial prerequisite for obtaining reliable conclusions. No validated housekeeping genes have been reported for the relative quantification of the mRNA expression profile activated in in-vitro ischaemic conditions, whereas for the in-vivo model different reference genes have been used. The present study aims to determine the expression stability of ten housekeeping genes (Gapdh, β2m, Hprt, Ppia, Rpl13a, Oaz1, 18S rRNA, Gusb, Ywhaz and Sdha to establish their suitability as control genes for in-vitro and in-vivo cerebral ischaemia models. Results The expression stability of the candidate reference genes was evaluated using the 2-ΔC'T method and ANOVA followed by Dunnett's test. For the in-vitro model using primary cultures of rat astrocytes, all genes analysed except for Rpl13a and Sdha were found to have significantly different levels of mRNA expression. These different levels were also found in the case of the in-vivo model of pMCAO in rats except for Hprt, Sdha and Ywhaz mRNA, where the expression did not vary. Sdha and Ywhaz were identified by geNorm and NormFinder as the two most stable genes. Conclusion We have validated endogenous control genes for qRT-PCR analysis of gene expression in in-vitro and in-vivo cerebral ischaemia models. For normalization purposes, Rpl13a and Sdha are found to be the most suitable genes for the in-vitro model and Sdha and Ywhaz for the in-vivo model. Genes previously used as housekeeping genes for the in-vivo model in the literature were not validated as good control genes in the present study, showing the need for careful evaluation for each new experimental setup.

Essential and non-essential DNA replication genes in the model halophilic Archaeon, Halobacterium sp. NRC-1

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DasSarma Shiladitya

2007-06-01

Full Text Available Abstract Background Information transfer systems in Archaea, including many components of the DNA replication machinery, are similar to those found in eukaryotes. Functional assignments of archaeal DNA replication genes have been primarily based upon sequence homology and biochemical studies of replisome components, but few genetic studies have been conducted thus far. We have developed a tractable genetic system for knockout analysis of genes in the model halophilic archaeon, Halobacterium sp. NRC-1, and used it to determine which DNA replication genes are essential. Results Using a directed in-frame gene knockout method in Halobacterium sp. NRC-1, we examined nineteen genes predicted to be involved in DNA replication. Preliminary bioinformatic analysis of the large haloarchaeal Orc/Cdc6 family, related to eukaryotic Orc1 and Cdc6, showed five distinct clades of Orc/Cdc6 proteins conserved in all sequenced haloarchaea. Of ten orc/cdc6 genes in Halobacterium sp. NRC-1, only two were found to be essential, orc10, on the large chromosome, and orc2, on the minichromosome, pNRC200. Of the three replicative-type DNA polymerase genes, two were essential: the chromosomally encoded B family, polB1, and the chromosomally encoded euryarchaeal-specific D family, polD1/D2 (formerly called polA1/polA2 in the Halobacterium sp. NRC-1 genome sequence. The pNRC200-encoded B family polymerase, polB2, was non-essential. Accessory genes for DNA replication initiation and elongation factors, including the putative replicative helicase, mcm, the eukaryotic-type DNA primase, pri1/pri2, the DNA polymerase sliding clamp, pcn, and the flap endonuclease, rad2, were all essential. Targeted genes were classified as non-essential if knockouts were obtained and essential based on statistical analysis and/or by demonstrating the inability to isolate chromosomal knockouts except in the presence of a complementing plasmid copy of the gene. Conclusion The results showed that ten

Clinical, biochemical, and neuropsychiatric evaluation of a patient with a contiguous gene syndrome due to a microdeletion Xp11.3 including the Norrie disease locus and monoamine oxidase (MAOA and MAOB) genes.

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Collins, F A; Murphy, D L; Reiss, A L; Sims, K B; Lewis, J G; Freund, L; Karoum, F; Zhu, D; Maumenee, I H; Antonarakis, S E

1992-01-01

Norrie disease is a rare X-linked recessive disorder characterized by blindness from infancy. The gene for Norrie disease has been localized to Xp11.3. More recently, the genes for monoamine oxidase (MAOA, MAOB) have been mapped to the same region. This study evaluates the clinical, biochemical, and neuropsychiatric data in an affected male and 2 obligate heterozygote females from a single family with a submicroscopic deletion involving Norrie disease and MAO genes. The propositus was a profoundly retarded, blind male; he also had neurologic abnormalities including myoclonus and stereotopy-habit disorder. Both obligate carrier females had a normal IQ. The propositus' mother met diagnostic criteria for "chronic hypomania and schizotypal features." The propositus' MAO activity was undetectable and the female heterozygotes had reduced levels comparable to patients receiving MAO inhibiting antidepressants. MAO substrate and metabolite abnormalities were found in the propositus' plasma and CSF. This study indicates that subtle biochemical and possibly neuropsychiatric abnormalities may be detected in some heterozygotes with the microdeletion in Xp11.3 due to loss of the gene product for the MAO genes; this deletion can also explain some of the complex phenotype of this contiguous gene syndrome in the propositus.

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

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Lemieux Sébastien

2006-08-01

Full Text Available Abstract Background The identification of differentially expressed genes (DEGs from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. Results On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. Conclusion The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition.

Models of Aire-dependent gene regulation for thymic negative selection

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Dina eDanso-Abeam

2011-05-01

Full Text Available Mutations in the Autoimmune Regulator (AIRE gene lead to Autoimmune Polyendocrinopathy Syndrome type 1 (APS1, characterized by the development of multi-organ autoimmune damage. The mechanism by which defects in AIRE result in autoimmunity has been the subject of intense scrutiny. At the cellular level, the working model explains most of the clinical and immunological characteristics of APS1, with AIRE driving the expression of tissue restricted antigens (TRAs in the epithelial cells of the thymic medulla. This TRA expression results in effective negative selection of TRA-reactive thymocytes, preventing autoimmune disease. At the molecular level, the mechanism by which AIRE initiates TRA expression in the thymic medulla remains unclear. Multiple different models for the molecular mechanism have been proposed, ranging from classical transcriptional activity, to random induction of gene expression, to epigenetic tag recognition effect, to altered cell biology. In this review, we evaluate each of these models and discuss their relative strengths and weaknesses.

Backward-stochastic-differential-equation approach to modeling of gene expression.

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Shamarova, Evelina; Chertovskih, Roman; Ramos, Alexandre F; Aguiar, Paulo

2017-03-01

In this article, we introduce a backward method to model stochastic gene expression and protein-level dynamics. The protein amount is regarded as a diffusion process and is described by a backward stochastic differential equation (BSDE). Unlike many other SDE techniques proposed in the literature, the BSDE method is backward in time; that is, instead of initial conditions it requires the specification of end-point ("final") conditions, in addition to the model parametrization. To validate our approach we employ Gillespie's stochastic simulation algorithm (SSA) to generate (forward) benchmark data, according to predefined gene network models. Numerical simulations show that the BSDE method is able to correctly infer the protein-level distributions that preceded a known final condition, obtained originally from the forward SSA. This makes the BSDE method a powerful systems biology tool for time-reversed simulations, allowing, for example, the assessment of the biological conditions (e.g., protein concentrations) that preceded an experimentally measured event of interest (e.g., mitosis, apoptosis, etc.).

Key Characteristics of Combined Accident including TLOFW accident for PSA Modeling

Energy Technology Data Exchange (ETDEWEB)

Kim, Bo Gyung; Kang, Hyun Gook [KAIST, Daejeon (Korea, Republic of); Yoon, Ho Joon [Khalifa University of Science, Technology and Research, Abu Dhabi (United Arab Emirates)

2015-05-15

The conventional PSA techniques cannot adequately evaluate all events. The conventional PSA models usually focus on single internal events such as DBAs, the external hazards such as fire, seismic. However, the Fukushima accident of Japan in 2011 reveals that very rare event is necessary to be considered in the PSA model to prevent the radioactive release to environment caused by poor treatment based on lack of the information, and to improve the emergency operation procedure. Especially, the results from PSA can be used to decision making for regulators. Moreover, designers can consider the weakness of plant safety based on the quantified results and understand accident sequence based on human actions and system availability. This study is for PSA modeling of combined accidents including total loss of feedwater (TLOFW) accident. The TLOFW accident is a representative accident involving the failure of cooling through secondary side. If the amount of heat transfer is not enough due to the failure of secondary side, the heat will be accumulated to the primary side by continuous core decay heat. Transients with loss of feedwater include total loss of feedwater accident, loss of condenser vacuum accident, and closure of all MSIVs. When residual heat removal by the secondary side is terminated, the safety injection into the RCS with direct primary depressurization would provide alternative heat removal. This operation is called feed and bleed (F and B) operation. Combined accidents including TLOFW accident are very rare event and partially considered in conventional PSA model. Since the necessity of F and B operation is related to plant conditions, the PSA modeling for combined accidents including TLOFW accident is necessary to identify the design and operational vulnerabilities.The PSA is significant to assess the risk of NPPs, and to identify the design and operational vulnerabilities. Even though the combined accident is very rare event, the consequence of combined

Entrainment to periodic initiation and transition rates in a computational model for gene translation.

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Michael Margaliot

Full Text Available Periodic oscillations play an important role in many biomedical systems. Proper functioning of biological systems that respond to periodic signals requires the ability to synchronize with the periodic excitation. For example, the sleep/wake cycle is a manifestation of an internal timing system that synchronizes to the solar day. In the terminology of systems theory, the biological system must entrain or phase-lock to the periodic excitation. Entrainment is also important in synthetic biology. For example, connecting several artificial biological systems that entrain to a common clock may lead to a well-functioning modular system. The cell-cycle is a periodic program that regulates DNA synthesis and cell division. Recent biological studies suggest that cell-cycle related genes entrain to this periodic program at the gene translation level, leading to periodically-varying protein levels of these genes. The ribosome flow model (RFM is a deterministic model obtained via a mean-field approximation of a stochastic model from statistical physics that has been used to model numerous processes including ribosome flow along the mRNA. Here we analyze the RFM under the assumption that the initiation and/or transition rates vary periodically with a common period T. We show that the ribosome distribution profile in the RFM entrains to this periodic excitation. In particular, the protein synthesis pattern converges to a unique periodic solution with period T. To the best of our knowledge, this is the first proof of entrainment in a mathematical model for translation that encapsulates aspects such as initiation and termination rates, ribosomal movement and interactions, and non-homogeneous elongation speeds along the mRNA. Our results support the conjecture that periodic oscillations in tRNA levels and other factors related to the translation process can induce periodic oscillations in protein levels, and may suggest a new approach for re-engineering genetic

EcoGene 3.0.

Science.gov (United States)

Zhou, Jindan; Rudd, Kenneth E

2013-01-01

EcoGene (http://ecogene.org) is a database and website devoted to continuously improving the structural and functional annotation of Escherichia coli K-12, one of the most well understood model organisms, represented by the MG1655(Seq) genome sequence and annotations. Major improvements to EcoGene in the past decade include (i) graphic presentations of genome map features; (ii) ability to design Boolean queries and Venn diagrams from EcoArray, EcoTopics or user-provided GeneSets; (iii) the genome-wide clone and deletion primer design tool, PrimerPairs; (iv) sequence searches using a customized EcoBLAST; (v) a Cross Reference table of synonymous gene and protein identifiers; (vi) proteome-wide indexing with GO terms; (vii) EcoTools access to >2000 complete bacterial genomes in EcoGene-RefSeq; (viii) establishment of a MySql relational database; and (ix) use of web content management systems. The biomedical literature is surveyed daily to provide citation and gene function updates. As of September 2012, the review of 37 397 abstracts and articles led to creation of 98 425 PubMed-Gene links and 5415 PubMed-Topic links. Annotation updates to Genbank U00096 are transmitted from EcoGene to NCBI. Experimental verifications include confirmation of a CTG start codon, pseudogene restoration and quality assurance of the Keio strain collection.

EcoGene 3.0

Science.gov (United States)

Zhou, Jindan; Rudd, Kenneth E.

2013-01-01

EcoGene (http://ecogene.org) is a database and website devoted to continuously improving the structural and functional annotation of Escherichia coli K-12, one of the most well understood model organisms, represented by the MG1655(Seq) genome sequence and annotations. Major improvements to EcoGene in the past decade include (i) graphic presentations of genome map features; (ii) ability to design Boolean queries and Venn diagrams from EcoArray, EcoTopics or user-provided GeneSets; (iii) the genome-wide clone and deletion primer design tool, PrimerPairs; (iv) sequence searches using a customized EcoBLAST; (v) a Cross Reference table of synonymous gene and protein identifiers; (vi) proteome-wide indexing with GO terms; (vii) EcoTools access to >2000 complete bacterial genomes in EcoGene-RefSeq; (viii) establishment of a MySql relational database; and (ix) use of web content management systems. The biomedical literature is surveyed daily to provide citation and gene function updates. As of September 2012, the review of 37 397 abstracts and articles led to creation of 98 425 PubMed-Gene links and 5415 PubMed-Topic links. Annotation updates to Genbank U00096 are transmitted from EcoGene to NCBI. Experimental verifications include confirmation of a CTG start codon, pseudogene restoration and quality assurance of the Keio strain collection. PMID:23197660

The evolution of heart gene delivery vectors

Science.gov (United States)

Wasala, Nalinda B.; Shin, Jin-Hong; Duan, Dongsheng

2012-01-01

Gene therapy holds promise for treating numerous heart diseases. A key premise for the success of cardiac gene therapy is the development of powerful gene transfer vehicles that can achieve highly efficient and persistent gene transfer specifically in the heart. Other features of an ideal vector include negligible toxicity, minimal immunogenicity and easy manufacturing. Rapid progress in the fields of molecular biology and virology has offered great opportunities to engineer various genetic materials for heart gene delivery. Several nonviral vectors (e.g. naked plasmids, plasmid lipid/polymer complexes and oligonucleotides) have been tested. Commonly used viral vectors include lentivirus, adenovirus and adeno-associated virus. Among these, adeno-associated virus has shown many attractive features for pre-clinical experimentation in animal models of heart diseases. We review the history and evolution of these vectors for heart gene transfer. PMID:21837689

Validation of housekeeping genes in the brains of rats submitted to chronic intermittent hypoxia, a sleep apnea model.

Science.gov (United States)

Julian, Guilherme Silva; de Oliveira, Renato Watanabe; Perry, Juliana Cini; Tufik, Sergio; Chagas, Jair Ribeiro

2014-01-01

Obstructive sleep apnea (OSA) is a syndrome characterized by intermittent nocturnal hypoxia, sleep fragmentation, hypercapnia and respiratory effort, and it has been associated with several complications, such as diabetes, hypertension and obesity. Quantitative real-time PCR has been performed in previous OSA-related studies; however, these studies were not validated using proper reference genes. We have examined the effects of chronic intermittent hypoxia (CIH), which is an experimental model mainly of cardiovascular consequences of OSA, on reference genes, including beta-actin, beta-2-microglobulin, glyceraldehyde-3-phosphate dehydrogenase, hypoxanthine guanine phosphoribosyl transferase and eukaryotic 18S rRNA, in different areas of the brain. All stability analyses were performed using the geNorm, Normfinder and BestKeeper software programs. With exception of the 18S rRNA, all of the evaluated genes were shown to be stable following CIH exposure. However, gene stability rankings were dependent on the area of the brain that was analyzed and varied according to the software that was used. This study demonstrated that CIH affects various brain structures differently. With the exception of the 18S rRNA, all of the tested genes are suitable for use as housekeeping genes in expression analyses.

Accurate, model-based tuning of synthetic gene expression using introns in S. cerevisiae.

Directory of Open Access Journals (Sweden)

Ido Yofe

2014-06-01

Full Text Available Introns are key regulators of eukaryotic gene expression and present a potentially powerful tool for the design of synthetic eukaryotic gene expression systems. However, intronic control over gene expression is governed by a multitude of complex, incompletely understood, regulatory mechanisms. Despite this lack of detailed mechanistic understanding, here we show how a relatively simple model enables accurate and predictable tuning of synthetic gene expression system in yeast using several predictive intron features such as transcript folding and sequence motifs. Using only natural Saccharomyces cerevisiae introns as regulators, we demonstrate fine and accurate control over gene expression spanning a 100 fold expression range. These results broaden the engineering toolbox of synthetic gene expression systems and provide a framework in which precise and robust tuning of gene expression is accomplished.

Analysis of two heterologous flowering genes in ¤Brachypodium distachyon¤ demonstrates its potential as a grass model plant

DEFF Research Database (Denmark)

Olsen, P.; Lenk, I.; Jensen, Christian S.

2006-01-01

Despite the great contribution of model organisms, such as Arabidopsis and rice to understand biological processes in plants, these models are less valuable for functional studies of particular genes from temperate grass crop species. Therefore a new model plant is required, displaying features...... including close phylogenetic relationship to the temperate grasses, vernalisation requirement, high transformation efficiency, small genome size and a rapid life cycle. These requirements are all fulfilled by the small annual grass Brachypodium distachyon. As a first step towards implementing this plant...

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

Science.gov (United States)

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

KAUST Repository

Dhavala, Soma S.

2010-09-01

Massively Parallel Signature Sequencing (MPSS) is a high-throughput, counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA type model for this count data using a zero-inflatedPoisson distribution, different from existing methods that assume continuous densities. We adopt two Bayesian hierarchical models-one parametric and the other semiparametric with a Dirichlet process prior that has the ability to "borrow strength" across related signatures, where a signature is a specific arrangement of the nucleotides, usually 16-21 base pairs long. We utilize the discreteness of Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using nonparametric approaches, while controlling the false discovery rate. We identify several differentially expressed genes that have important biological significance and conclude with a summary of the biological discoveries. This article has supplementary materials online. © 2010 American Statistical Association.

Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

KAUST Repository

Dhavala, Soma S.; Datta, Sujay; Mallick, Bani K.; Carroll, Raymond J.; Khare, Sangeeta; Lawhon, Sara D.; Adams, L. Garry

2010-01-01

Massively Parallel Signature Sequencing (MPSS) is a high-throughput, counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA type model for this count data using a zero-inflatedPoisson distribution, different from existing methods that assume continuous densities. We adopt two Bayesian hierarchical models-one parametric and the other semiparametric with a Dirichlet process prior that has the ability to "borrow strength" across related signatures, where a signature is a specific arrangement of the nucleotides, usually 16-21 base pairs long. We utilize the discreteness of Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using nonparametric approaches, while controlling the false discovery rate. We identify several differentially expressed genes that have important biological significance and conclude with a summary of the biological discoveries. This article has supplementary materials online. © 2010 American Statistical Association.

Gene therapy decreases seizures in a model of Incontinentia pigmenti.

Science.gov (United States)

Dogbevia, Godwin K; Töllner, Kathrin; Körbelin, Jakob; Bröer, Sonja; Ridder, Dirk A; Grasshoff, Hanna; Brandt, Claudia; Wenzel, Jan; Straub, Beate K; Trepel, Martin; Löscher, Wolfgang; Schwaninger, Markus

2017-07-01

Incontinentia pigmenti (IP) is a genetic disease leading to severe neurological symptoms, such as epileptic seizures, but no specific treatment is available. IP is caused by pathogenic variants that inactivate the Nemo gene. Replacing Nemo through gene therapy might provide therapeutic benefits. In a mouse model of IP, we administered a single intravenous dose of the adeno-associated virus (AAV) vector, AAV-BR1-CAG-NEMO, delivering the Nemo gene to the brain endothelium. Spontaneous epileptic seizures and the integrity of the blood-brain barrier (BBB) were monitored. The endothelium-targeted gene therapy improved the integrity of the BBB. In parallel, it reduced the incidence of seizures and delayed their occurrence. Neonate mice intravenously injected with the AAV-BR1-CAG-NEMO vector developed no hepatocellular carcinoma or other major adverse effects 11 months after vector injection, demonstrating that the vector has a favorable safety profile. The data show that the BBB is a target of antiepileptic treatment and, more specifically, provide evidence for the therapeutic benefit of a brain endothelial-targeted gene therapy in IP. Ann Neurol 2017;82:93-104. © 2017 American Neurological Association.

A rat model of smoke inhalation injury: Influence of combustion smoke on gene expression in the brain

International Nuclear Information System (INIS)

Lee, Heung M.; Greeley, George H.; Herndon, David N.; Sinha, Mala; Luxon, Bruce A.; Englander, Ella W.

2005-01-01

Acute smoke inhalation causes death and injury in victims of home and industrial fires as well as victims of combat situations. The lethal factors in combustion smoke inhalation are toxic gases and oxygen deficiency, with carbon monoxide (CO) as a primary cause of death. In survivors, inhalation of smoke can result in severe immediate and delayed neuropathologies. To gain insight into the progression of molecular events contributing to smoke inhalation sequelae in the brain, we developed a smoke inhalation rat model and conducted a genome-wide analysis of gene expression. Microarray analysis revealed a modified brain transcriptome with changes peaking at 24 h and subsiding within 7 days post-smoke. Overall, smoke inhalation downregulated genes associated with synaptic function, neurotransmission, and neurotrophic support, and upregulated genes associated with stress responses, including nitric oxide synthesis, antioxidant defenses, proteolysis, inflammatory response, and glial activation. Notably, among the affected genes, many have been previously implicated in other types of brain injury, demonstrating the usefulness of microarrays for analysis of changes in gene expression in complex insults. In accord with previously described modulations of nitric oxide homeostasis in CO poisoning, microarray analysis revealed increased brain expression of nitric oxide synthase (NOS) and NOS ligand after inhalation of smoke. Furthermore, immunostaining showed significant elevations in perivascular NOS and in protein nitration, corroborating the involvement of nitric oxide perturbations in post-smoke sequelae in the brain. Thus, the new rat model, in combination with microarray analyses, affords insight into the complex molecular pathophysiology of smoke inhalation in the brain

batman Interacts with polycomb and trithorax group genes and encodes a BTB/POZ protein that is included in a complex containing GAGA factor.

Science.gov (United States)

Faucheux, M; Roignant, J-Y; Netter, S; Charollais, J; Antoniewski, C; Théodore, L

2003-02-01

Polycomb and trithorax group genes maintain the appropriate repressed or activated state of homeotic gene expression throughout Drosophila melanogaster development. We have previously identified the batman gene as a Polycomb group candidate since its function is necessary for the repression of Sex combs reduced. However, our present genetic analysis indicates functions of batman in both activation and repression of homeotic genes. The 127-amino-acid Batman protein is almost reduced to a BTB/POZ domain, an evolutionary conserved protein-protein interaction domain found in a large protein family. We show that this domain is involved in the interaction between Batman and the DNA binding GAGA factor encoded by the Trithorax-like gene. The GAGA factor and Batman codistribute on polytene chromosomes, coimmunoprecipitate from nuclear embryonic and larval extracts, and interact in the yeast two-hybrid assay. Batman, together with the GAGA factor, binds to MHS-70, a 70-bp fragment of the bithoraxoid Polycomb response element. This binding, like that of the GAGA factor, requires the presence of d(GA)n sequences. Together, our results suggest that batman belongs to a subset of the Polycomb/trithorax group of genes that includes Trithorax-like, whose products are involved in both activation and repression of homeotic genes.

Selection of reference genes in different myocardial regions of an in vivo ischemia/reperfusion rat model for normalization of antioxidant gene expression

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Vesentini Nicoletta

2012-02-01

Full Text Available Abstract Background Changes in cardiac gene expression due to myocardial injury are usually assessed in whole heart tissue. However, as the heart is a heterogeneous system, spatial and temporal heterogeneity is expected in gene expression. Results In an ischemia/reperfusion (I/R rat model we evaluated gene expression of mitochondrial and cytoplasmatic superoxide dismutase (MnSod, Cu-ZnSod and thioredoxin reductase (trxr1 upon short (4 h and long (72 h reperfusion times in the right ventricle (RV, and in the ischemic/reperfused (IRR and the remote region (RR of the left ventricle. Gene expression was assessed by Real-time reverse-transcription quantitative PCR (RT-qPCR. In order to select most stable reference genes suitable for normalization purposes, in each myocardial region we tested nine putative reference genes by geNorm analysis. The genes investigated were: Actin beta (actb, Glyceraldehyde-3-P-dehydrogenase (gapdh, Ribosomal protein L13A (rpl13a, Tyrosine 3-monooxygenase (ywhaz, Beta-glucuronidase (gusb, Hypoxanthine guanine Phosphoribosyltransferase 1 (hprt, TATA binding box protein (tbp, Hydroxymethylbilane synthase (hmbs, Polyadenylate-binding protein 1 (papbn1. According to our findings, most stable reference genes in the RV and RR were hmbs/hprt and hmbs/tbp/hprt respectively. In the IRR, six reference genes were recommended for normalization purposes; however, in view of experimental feasibility limitations, target gene expression could be normalized against the three most stable reference genes (ywhaz/pabp/hmbs without loss of sensitivity. In all cases MnSod and Cu-ZnSod expression decreased upon long reperfusion, the former in all myocardial regions and the latter in IRR alone. trxr1 expression did not vary. Conclusions This study provides a validation of reference genes in the RV and in the anterior and posterior wall of the LV of cardiac ischemia/reperfusion model and shows that gene expression should be assessed separately in

Predicting spatial and temporal gene expression using an integrative model of transcription factor occupancy and chromatin state.

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Bartek Wilczynski

Full Text Available Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal

Introduction: Cancer Gene Networks.

Science.gov (United States)

Clarke, Robert

2017-01-01

Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

Sequential Logic Model Deciphers Dynamic Transcriptional Control of Gene Expressions

Science.gov (United States)

Yeo, Zhen Xuan; Wong, Sum Thai; Arjunan, Satya Nanda Vel; Piras, Vincent; Tomita, Masaru; Selvarajoo, Kumar; Giuliani, Alessandro; Tsuchiya, Masa

2007-01-01

Background Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. Methodology Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. Principal Findings SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. Conclusions/Significance The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological

Sequential logic model deciphers dynamic transcriptional control of gene expressions.

Directory of Open Access Journals (Sweden)

Zhen Xuan Yeo

Full Text Available BACKGROUND: Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. METHODOLOGY: Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. PRINCIPAL FINDINGS: SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. CONCLUSIONS/SIGNIFICANCE: The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet

Diagnosis of ulcerative colitis before onset of inflammation by multivariate modeling of genome-wide gene expression data

DEFF Research Database (Denmark)

Olsen, Jørgen; Gerds, Thomas A; Seidelin, Jakob B

2009-01-01

Background: Endoscopically obtained mucosal biopsies play an important role in the differential diagnosis between ulcerative colitis (UC) and Crohn's disease (CD), but in some cases where neither macroscopic nor microscopic signs of inflammation are present the biopsies provide only inconclusive...... biopsies from 78 patients were included. A diagnostic model was derived with the random forest method based on 71 biopsies from 60 patients. The model-internal out-of-bag performance measure yielded perfect classification. Furthermore, the model was validated in independent 18 noninflamed biopsies from 18...... of random forest modeling of genome-wide gene expression data for distinguishing quiescent and active UC colonic mucosa versus control and CD colonic mucosa.(Inflamm Bowel Dis 2009)....

RELAP5-3D Code Includes ATHENA Features and Models

International Nuclear Information System (INIS)

Riemke, Richard A.; Davis, Cliff B.; Schultz, Richard R.

2006-01-01

Version 2.3 of the RELAP5-3D computer program includes all features and models previously available only in the ATHENA version of the code. These include the addition of new working fluids (i.e., ammonia, blood, carbon dioxide, glycerol, helium, hydrogen, lead-bismuth, lithium, lithium-lead, nitrogen, potassium, sodium, and sodium-potassium) and a magnetohydrodynamic model that expands the capability of the code to model many more thermal-hydraulic systems. In addition to the new working fluids along with the standard working fluid water, one or more noncondensable gases (e.g., air, argon, carbon dioxide, carbon monoxide, helium, hydrogen, krypton, nitrogen, oxygen, SF 6 , xenon) can be specified as part of the vapor/gas phase of the working fluid. These noncondensable gases were in previous versions of RELAP5-3D. Recently four molten salts have been added as working fluids to RELAP5-3D Version 2.4, which has had limited release. These molten salts will be in RELAP5-3D Version 2.5, which will have a general release like RELAP5-3D Version 2.3. Applications that use these new features and models are discussed in this paper. (authors)

Generalized functional linear models for gene-based case-control association studies.

Science.gov (United States)

Fan, Ruzong; Wang, Yifan; Mills, James L; Carter, Tonia C; Lobach, Iryna; Wilson, Alexander F; Bailey-Wilson, Joan E; Weeks, Daniel E; Xiong, Momiao

2014-11-01

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses. © 2014 WILEY PERIODICALS, INC.

No linkage and association of atopy to chromosome 16 including the interleukin-4 receptor gene

DEFF Research Database (Denmark)

Haagerup, A; Bjerke, T; Schiøtz, P O

2001-01-01

BACKGROUND: Several susceptibility genes for atopy have been suggested in recent years. Few have been investigated as intensively as the interleukin-4-receptor alpha (IL4Ralpha) gene on chromosome 16. The results remain in dispute. Therefore, in a robust design, we tested for association of type ...

Good genes, complementary genes and human mate preferences.

Science.gov (United States)

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.

Computational neurogenetic modeling

CERN Document Server

Benuskova, Lubica

2010-01-01

Computational Neurogenetic Modeling is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biol

Sustained phenotypic reversion of junctional epidermolysis bullosa dog keratinocytes: Establishment of an immunocompetent animal model for cutaneous gene therapy

International Nuclear Information System (INIS)

Spirito, Flavia; Capt, Annabelle; Rio, Marcela Del; Larcher, Fernando; Guaguere, Eric; Danos, Olivier; Meneguzzi, Guerrino

2006-01-01

Gene transfer represents the unique therapeutic issue for a number of inherited skin disorders including junctional epidermolysis bullosa (JEB), an untreatable genodermatose caused by mutations in the adhesion ligand laminin 5 (α3β3γ2) that is secreted in the extracellular matrix by the epidermal basal keratinocytes. Because gene therapy protocols require validation in animal models, we have phenotypically reverted by oncoretroviral transfer of the curative gene the keratinocytes isolated from dogs with a spontaneous form of JEB associated with a genetic mutation in the α3 chain of laminin 5. We show that the transduced dog JEB keratinocytes: (1) display a sustained secretion of laminin 5 in the extracellular matrix; (2) recover the adhesion, proliferation, and clonogenic capacity of wild-type keratinocytes; (3) generate fully differentiated stratified epithelia that after grafting on immunocompromised mice produce phenotypically normal skin and sustain permanent expression of the transgene. We validate an animal model that appears particularly suitable to demonstrate feasibility, efficacy, and safety of genetic therapeutic strategies for cutaneous disorders before undertaking human clinical trials

Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion

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Luisier, Raphaëlle; Unterberger, Elif B.; Goodman, Jay I.; Schwarz, Michael; Moggs, Jonathan; Terranova, Rémi; van Nimwegen, Erik

2014-01-01

Gene regulatory interactions underlying the early stages of non-genotoxic carcinogenesis are poorly understood. Here, we have identified key candidate regulators of phenobarbital (PB)-mediated mouse liver tumorigenesis, a well-characterized model of non-genotoxic carcinogenesis, by applying a new computational modeling approach to a comprehensive collection of in vivo gene expression studies. We have combined our previously developed motif activity response analysis (MARA), which models gene expression patterns in terms of computationally predicted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif activities, to disentangle the roles that different transcriptional regulators play in specific biological pathways of tumor promotion. Furthermore, transgenic mouse models enabled us to identify which of these regulatory activities was downstream of constitutive androstane receptor and β-catenin signaling, both crucial components of PB-mediated liver tumorigenesis. We propose novel roles for E2F and ZFP161 in PB-mediated hepatocyte proliferation and suggest that PB-mediated suppression of ESR1 activity contributes to the development of a tumor-prone environment. Our study shows that combining MARA with SVD allows for automated identification of independent transcription regulatory programs within a complex in vivo tissue environment and provides novel mechanistic insights into PB-mediated hepatocarcinogenesis. PMID:24464994

Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes

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Reinagel, Adam; Speth, Elena Bray

2016-01-01

In an introductory biology course, we implemented a learner-centered, model-based pedagogy that frequently engaged students in building conceptual models to explain how genes determine phenotypes. Model-building tasks were incorporated within case studies and aimed at eliciting students' understanding of 1) the origin of variation in a population…

Improving weather predictability by including land-surface model parameter uncertainty

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Orth, Rene; Dutra, Emanuel; Pappenberger, Florian

2016-04-01

The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

Stochastic model for gene transcription on Drosophila melanogaster embryos

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Prata, Guilherme N.; Hornos, José Eduardo M.; Ramos, Alexandre F.

2016-02-01

We examine immunostaining experimental data for the formation of stripe 2 of even-skipped (eve) transcripts on D. melanogaster embryos. An estimate of the factor converting immunofluorescence intensity units into molecular numbers is given. The analysis of the eve dynamics at the region of stripe 2 suggests that the promoter site of the gene has two distinct regimes: an earlier phase when it is predominantly activated until a critical time when it becomes mainly repressed. That suggests proposing a stochastic binary model for gene transcription on D. melanogaster embryos. Our model has two random variables: the transcripts number and the state of the source of mRNAs given as active or repressed. We are able to reproduce available experimental data for the average number of transcripts. An analysis of the random fluctuations on the number of eves and their consequences on the spatial precision of stripe 2 is presented. We show that the position of the anterior or posterior borders fluctuate around their average position by ˜1 % of the embryo length, which is similar to what is found experimentally. The fitting of data by such a simple model suggests that it can be useful to understand the functions of randomness during developmental processes.

Collisional-radiative model including recombination processes for W27+ ion★

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Murakami, Izumi; Sasaki, Akira; Kato, Daiji; Koike, Fumihiro

2017-10-01

We have constructed a collisional-radiative (CR) model for W27+ ions including 226 configurations with n ≤ 9 and ł ≤ 5 for spectroscopic diagnostics. We newly include recombination processes in the model and this is the first result of extreme ultraviolet spectrum calculated for recombining plasma component. Calculated spectra in 40-70 Å range in ionizing and recombining plasma components show similar 3 strong lines and 1 line weak in recombining plasma component at 45-50 Å and many weak lines at 50-65 Å for both components. Recombination processes do not contribute much to the spectrum at around 60 Å for W27+ ion. Dielectronic satellite lines are also minor contribution to the spectrum of recombining plasma component. Dielectronic recombination (DR) rate coefficient from W28+ to W27+ ions is also calculated with the same atomic data in the CR model. We found that larger set of energy levels including many autoionizing states gave larger DR rate coefficients but our rate agree within factor 6 with other works at electron temperature around 1 keV in which W27+ and W28+ ions are usually observed in plasmas. Contribution to the Topical Issue "Atomic and Molecular Data and their Applications", edited by Gordon W.F. Drake, Jung-Sik Yoon, Daiji Kato, and Grzegorz Karwasz.

Gene Therapy in a Large Animal Model of PDE6A-Retinitis Pigmentosa

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Freya M. Mowat

2017-06-01

Full Text Available Despite mutations in the rod phosphodiesterase 6-alpha (PDE6A gene being well-recognized as a cause of human retinitis pigmentosa, no definitive treatments have been developed to treat this blinding disease. We performed a trial of retinal gene augmentation in the Pde6a mutant dog using Pde6a delivery by capsid-mutant adeno-associated virus serotype 8, previously shown to have a rapid onset of transgene expression in the canine retina. Subretinal injections were performed in 10 dogs at 29–44 days of age, and electroretinography and vision testing were performed to assess functional outcome. Retinal structure was assessed using color fundus photography, spectral domain optical coherence tomography, and histology. Immunohistochemistry was performed to examine transgene expression and expression of other retinal genes. Treatment resulted in improvement in dim light vision and evidence of rod function on electroretinographic examination. Photoreceptor layer thickness in the treated area was preserved compared with the contralateral control vector treated or uninjected eye. Improved rod and cone photoreceptor survival, rhodopsin localization, cyclic GMP levels and bipolar cell dendrite distribution was observed in treated areas. Some adverse effects including foci of retinal separation, foci of retinal degeneration and rosette formation were identified in both AAV-Pde6a and control vector injected regions. This is the first description of successful gene augmentation for Pde6a retinitis pigmentosa in a large animal model. Further studies will be necessary to optimize visual outcomes and minimize complications before translation to human studies.

Model-specific selection of molecular targets for heart failure gene therapy

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Katz, Michael G.; Fargnoli, Anthony S.; Tomasulo, Catherine E.; Pritchette, Louella A.; Bridges, Charles R.

2013-01-01

Heart failure (HF) is a complex multifaceted problem of abnormal ventricular function and structure. In recent years, new information has been accumulated allowing for a more detailed understanding of the cellular and molecular alterations that are the underpinnings of diverse causes of HF, including myocardial ischemia, pressure-overload, volume-overload or intrinsic cardiomyopathy. Modern pharmacological approaches to treat HF have had a significant impact on the course of the disease, although they do not reverse the underlying pathological state of the heart. Therefore gene-based therapy holds a great potential as a targeted treatment for cardiovascular diseases. Here, we survey the relative therapeutic efficacy of genetic modulation of β-adrenergic receptor signaling, Ca2+ handling proteins and angiogenesis in the most common extrinsic models of HF. PMID:21954055

Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.

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Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J

2016-01-27

Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes

Identification of Genes Associated with Lemon Floral Transition and Flower Development during Floral Inductive Water Deficits: A Hypothetical Model.

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Li, Jin-Xue; Hou, Xiao-Jin; Zhu, Jiao; Zhou, Jing-Jing; Huang, Hua-Bin; Yue, Jian-Qiang; Gao, Jun-Yan; Du, Yu-Xia; Hu, Cheng-Xiao; Hu, Chun-Gen; Zhang, Jin-Zhi

2017-01-01

Water deficit is a key factor to induce flowering in many woody plants, but reports on the molecular mechanisms of floral induction and flowering by water deficit are scarce. Here, we analyzed the morphology, cytology, and different hormone levels of lemon buds during floral inductive water deficits. Higher levels of ABA were observed, and the initiation of floral bud differentiation was examined by paraffin sections analysis. A total of 1638 differentially expressed genes (DEGs) were identified by RNA sequencing. DEGs were related to flowering, hormone biosynthesis, or metabolism. The expression of some DEGs was associated with floral induction by real-time PCR analysis. However, some DEGs may not have anything to do with flowering induction/flower development; they may be involved in general stress/drought response. Four genes from the phosphatidylethanolamine-binding protein family were further investigated. Ectopic expression of these genes in Arabidopsis changed the flowering time of transgenic plants. Furthermore, the 5' flanking region of these genes was also isolated and sequence analysis revealed the presence of several putative cis -regulatory elements, including basic elements and hormone regulation elements. The spatial and temporal expression patterns of these promoters were investigated under water deficit treatment. Based on these findings, we propose a model for citrus flowering under water deficit conditions, which will enable us to further understand the molecular mechanism of water deficit-regulated flowering in citrus. Based on gene activity during floral inductive water deficits identified by RNA sequencing and genes associated with lemon floral transition, a model for citrus flowering under water deficit conditions is proposed.

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.

Strategies in Gene Therapy for Glioblastoma

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Kwiatkowska, Aneta; Nandhu, Mohan S.; Behera, Prajna; Chiocca, E. Antonio; Viapiano, Mariano S.

2013-01-01

Glioblastoma (GBM) is the most aggressive form of brain cancer, with a dismal prognosis and extremely low percentage of survivors. Novel therapies are in dire need to improve the clinical management of these tumors and extend patient survival. Genetic therapies for GBM have been postulated and attempted for the past twenty years, with variable degrees of success in pre-clinical models and clinical trials. Here we review the most common approaches to treat GBM by gene therapy, including strategies to deliver tumor-suppressor genes, suicide genes, immunomodulatory cytokines to improve immune response, and conditionally-replicating oncolytic viruses. The review focuses on the strategies used for gene delivery, including the most common and widely used vehicles (i.e., replicating and non-replicating viruses) as well as novel therapeutic approaches such as stem cell-mediated therapy and nanotechnologies used for gene delivery. We present an overview of these strategies, their targets, different advantages, and challenges for success. Finally, we discuss the potential of gene therapy-based strategies to effectively attack such a complex genetic target as GBM, alone or in combination with conventional therapy

Strategies in Gene Therapy for Glioblastoma

Energy Technology Data Exchange (ETDEWEB)

Kwiatkowska, Aneta; Nandhu, Mohan S.; Behera, Prajna; Chiocca, E. Antonio; Viapiano, Mariano S., E-mail: mviapiano@partners.org [Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115 (United States)

2013-10-22

Glioblastoma (GBM) is the most aggressive form of brain cancer, with a dismal prognosis and extremely low percentage of survivors. Novel therapies are in dire need to improve the clinical management of these tumors and extend patient survival. Genetic therapies for GBM have been postulated and attempted for the past twenty years, with variable degrees of success in pre-clinical models and clinical trials. Here we review the most common approaches to treat GBM by gene therapy, including strategies to deliver tumor-suppressor genes, suicide genes, immunomodulatory cytokines to improve immune response, and conditionally-replicating oncolytic viruses. The review focuses on the strategies used for gene delivery, including the most common and widely used vehicles (i.e., replicating and non-replicating viruses) as well as novel therapeutic approaches such as stem cell-mediated therapy and nanotechnologies used for gene delivery. We present an overview of these strategies, their targets, different advantages, and challenges for success. Finally, we discuss the potential of gene therapy-based strategies to effectively attack such a complex genetic target as GBM, alone or in combination with conventional therapy.

Genes and Alcohol Consumption: Studies with Mutant Mice

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Mayfield, Jody; Arends, Michael A.; Harris, R. Adron; Blednov, Yuri A.

2017-01-01

In this chapter, we review the effects of global null mutant and overexpressing transgenic mouse lines on voluntary self-administration of alcohol. We examine approximately 200 publications pertaining to the effects of 155 mouse genes on alcohol consumption in different drinking models. The targeted genes vary in function and include neurotransmitter, ion channel, neuroimmune, and neuropeptide signaling systems. The alcohol self-administration models include operant conditioning, two- and four-bottle choice continuous and intermittent access, drinking in the dark limited access, chronic intermittent ethanol, and scheduled high alcohol consumption tests. Comparisons of different drinking models using the same mutant mice are potentially the most informative, and we will highlight those examples. More mutants have been tested for continuous two-bottle choice consumption than any other test; of the 137 mouse genes examined using this model, 97 (72%) altered drinking in at least one sex. Overall, the effects of genetic manipulations on alcohol drinking often depend on the sex of the mice, alcohol concentration and time of access, genetic background, as well as the drinking test. PMID:27055617

Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease.

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Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

2014-01-01

We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. http://rged.wall-eva.net. © The Author(s) 2014. Published by Oxford University Press.

Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease

Science.gov (United States)

Zhang, Qingzhou; Yang, Bo; Chen, Xujiao; Xu, Jing; Mei, Changlin; Mao, Zhiguo

2014-01-01

We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Availability and implementation: Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. Database URL: http://rged.wall-eva.net PMID:25252782

Effects of strain and age on hepatic gene expression profiles in murine models of HFE-associated hereditary hemochromatosis.

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Lee, Seung-Min; Loguinov, Alexandre; Fleming, Robert E; Vulpe, Christopher D

2015-01-01

Hereditary hemochromatosis is an iron overload disorder most commonly caused by a defect in the HFE gene. While the genetic defect is highly prevalent, the majority of individuals do not develop clinically significant iron overload, suggesting the importance of genetic modifiers. Murine hfe knockout models have demonstrated that strain background has a strong effect on the severity of iron loading. We noted that hepatic iron loading in hfe-/- mice occurs primarily over the first postnatal weeks (loading phase) followed by a timeframe of relatively static iron concentrations (plateau phase). We thus evaluated the effects of background strain and of age on hepatic gene expression in Hfe knockout mice (hfe-/-). Hepatic gene expression profiles were examined using cDNA microarrays in 4- and 8-week-old hfe-/- and wild-type mice on two different genetic backgrounds, C57BL/6J (C57) and AKR/J (AKR). Genes differentially regulated in all hfe-/- mice groups, compared with wild-type mice, including those involved in cell survival, stress and damage responses and lipid metabolism. AKR strain-specific changes in lipid metabolism genes and C57 strain-specific changes in cell adhesion and extracellular matrix protein genes were detected in hfe-/- mice. Mouse strain and age are each significantly associated with hepatic gene expression profiles in hfe-/- mice. These affects may underlie or reflect differences in iron loading in these mice.

Evaluation of phenoxybenzamine in the CFA model of pain following gene expression studies and connectivity mapping.

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Chang, Meiping; Smith, Sarah; Thorpe, Andrew; Barratt, Michael J; Karim, Farzana

2010-09-16

We have previously used the rat 4 day Complete Freund's Adjuvant (CFA) model to screen compounds with potential to reduce osteoarthritic pain. The aim of this study was to identify genes altered in this model of osteoarthritic pain and use this information to infer analgesic potential of compounds based on their own gene expression profiles using the Connectivity Map approach. Using microarrays, we identified differentially expressed genes in L4 and L5 dorsal root ganglia (DRG) from rats that had received intraplantar CFA for 4 days compared to matched, untreated control animals. Analysis of these data indicated that the two groups were distinguishable by differences in genes important in immune responses, nerve growth and regeneration. This list of differentially expressed genes defined a "CFA signature". We used the Connectivity Map approach to identify pharmacologic agents in the Broad Institute Build02 database that had gene expression signatures that were inversely related ('negatively connected') with our CFA signature. To test the predictive nature of the Connectivity Map methodology, we tested phenoxybenzamine (an alpha adrenergic receptor antagonist) - one of the most negatively connected compounds identified in this database - for analgesic activity in the CFA model. Our results indicate that at 10 mg/kg, phenoxybenzamine demonstrated analgesia comparable to that of Naproxen in this model. Evaluation of phenoxybenzamine-induced analgesia in the current study lends support to the utility of the Connectivity Map approach for identifying compounds with analgesic properties in the CFA model.

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Science.gov (United States)

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

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Ching-Hsue Cheng

2018-01-01

Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

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

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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.

Modelling time course gene expression data with finite mixtures of linear additive models.

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Grün, Bettina; Scharl, Theresa; Leisch, Friedrich

2012-01-15

A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time. The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).

Discovery of candidate disease genes in ENU-induced mouse mutants by large-scale sequencing, including a splice-site mutation in nucleoredoxin.

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Melissa K Boles

2009-12-01

Full Text Available An accurate and precisely annotated genome assembly is a fundamental requirement for functional genomic analysis. Here, the complete DNA sequence and gene annotation of mouse Chromosome 11 was used to test the efficacy of large-scale sequencing for mutation identification. We re-sequenced the 14,000 annotated exons and boundaries from over 900 genes in 41 recessive mutant mouse lines that were isolated in an N-ethyl-N-nitrosourea (ENU mutation screen targeted to mouse Chromosome 11. Fifty-nine sequence variants were identified in 55 genes from 31 mutant lines. 39% of the lesions lie in coding sequences and create primarily missense mutations. The other 61% lie in noncoding regions, many of them in highly conserved sequences. A lesion in the perinatal lethal line l11Jus13 alters a consensus splice site of nucleoredoxin (Nxn, inserting 10 amino acids into the resulting protein. We conclude that point mutations can be accurately and sensitively recovered by large-scale sequencing, and that conserved noncoding regions should be included for disease mutation identification. Only seven of the candidate genes we report have been previously targeted by mutation in mice or rats, showing that despite ongoing efforts to functionally annotate genes in the mammalian genome, an enormous gap remains between phenotype and function. Our data show that the classical positional mapping approach of disease mutation identification can be extended to large target regions using high-throughput sequencing.

Gene cluster statistics with gene families.

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Raghupathy, Narayanan; Durand, Dannie

2009-05-01

Identifying genomic regions that descended from a common ancestor is important for understanding the function and evolution of genomes. In distantly related genomes, clusters of homologous gene pairs are evidence of candidate homologous regions. Demonstrating the statistical significance of such "gene clusters" is an essential component of comparative genomic analyses. However, currently there are no practical statistical tests for gene clusters that model the influence of the number of homologs in each gene family on cluster significance. In this work, we demonstrate empirically that failure to incorporate gene family size in gene cluster statistics results in overestimation of significance, leading to incorrect conclusions. We further present novel analytical methods for estimating gene cluster significance that take gene family size into account. Our methods do not require complete genome data and are suitable for testing individual clusters found in local regions, such as contigs in an unfinished assembly. We consider pairs of regions drawn from the same genome (paralogous clusters), as well as regions drawn from two different genomes (orthologous clusters). Determining cluster significance under general models of gene family size is computationally intractable. By assuming that all gene families are of equal size, we obtain analytical expressions that allow fast approximation of cluster probabilities. We evaluate the accuracy of this approximation by comparing the resulting gene cluster probabilities with cluster probabilities obtained by simulating a realistic, power-law distributed model of gene family size, with parameters inferred from genomic data. Surprisingly, despite the simplicity of the underlying assumption, our method accurately approximates the true cluster probabilities. It slightly overestimates these probabilities, yielding a conservative test. We present additional simulation results indicating the best choice of parameter values for data

Bifurcation Analysis of Gene Propagation Model Governed by Reaction-Diffusion Equations

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Guichen Lu

2016-01-01

Full Text Available We present a theoretical analysis of the attractor bifurcation for gene propagation model governed by reaction-diffusion equations. We investigate the dynamical transition problems of the model under the homogeneous boundary conditions. By using the dynamical transition theory, we give a complete characterization of the bifurcated objects in terms of the biological parameters of the problem.

Global sensitivity analysis of a dynamic model for gene expression in Drosophila embryos

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McCarthy, Gregory D.; Drewell, Robert A.

2015-01-01

It is well known that gene regulation is a tightly controlled process in early organismal development. However, the roles of key processes involved in this regulation, such as transcription and translation, are less well understood, and mathematical modeling approaches in this field are still in their infancy. In recent studies, biologists have taken precise measurements of protein and mRNA abundance to determine the relative contributions of key factors involved in regulating protein levels in mammalian cells. We now approach this question from a mathematical modeling perspective. In this study, we use a simple dynamic mathematical model that incorporates terms representing transcription, translation, mRNA and protein decay, and diffusion in an early Drosophila embryo. We perform global sensitivity analyses on this model using various different initial conditions and spatial and temporal outputs. Our results indicate that transcription and translation are often the key parameters to determine protein abundance. This observation is in close agreement with the experimental results from mammalian cells for various initial conditions at particular time points, suggesting that a simple dynamic model can capture the qualitative behavior of a gene. Additionally, we find that parameter sensitivites are temporally dynamic, illustrating the importance of conducting a thorough global sensitivity analysis across multiple time points when analyzing mathematical models of gene regulation. PMID:26157608

Genome-wide analysis of Aux/IAA gene family in Solanaceae species using tomato as a model.

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Wu, Jian; Peng, Zhen; Liu, Songyu; He, Yanjun; Cheng, Lin; Kong, Fuling; Wang, Jie; Lu, Gang

2012-04-01

Auxin plays key roles in a wide variety of plant activities, including embryo development, leaf formation, phototropism, fruit development and root initiation and development. Auxin/indoleacetic acid (Aux/IAA) genes, encoding short-lived nuclear proteins, are key regulators in the auxin transduction pathway. But how they work is still unknown. In order to conduct a systematic analysis of this gene family in Solanaceae species, a genome-wide search for the homologues of auxin response genes was carried out. Here, 26 and 27 non redundant AUX/IAAs were identified in tomato and potato, respectively. Using tomato as a model, a comprehensive overview of SlIAA gene family is presented, including the gene structures, phylogeny, chromosome locations, conserved motifs and cis-elements in promoter sequences. A phylogenetic tree generated from alignments of the predicted protein sequences of 31 OsIAAs, 29 AtIAAs, 31 ZmIAAs, and 26 SlIAAs revealed that these IAAs were clustered into three major groups and ten subgroups. Among them, seven subgroups were present in both monocot and dicot species, which indicated that the major functional diversification within the IAA family predated the monocot/dicot divergence. In contrast, group C and some other subgroups seemed to be species-specific. Quantitative real-time PCR (qRT-PCR) analysis showed that 19 of the 26 SlIAA genes could be detected in all tomato organs/tissues, however, seven of them were specifically expressed in some of tomato tissues. The transcript abundance of 17 SlIAA genes were increased within a few hours when the seedlings were treated with exogenous IAA. However, those of other six SlIAAs were decreased. The results of stress treatments showed that most SIIAA family genes responded to at least one of the three stress treatments, however, they exhibited diverse expression levels under different abiotic stress conditions in tomato seedlings. SlIAA20, SlIAA21 and SlIAA22 were not significantly influenced by stress

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

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Cumbie, Jason S; Kimbrel, Jeffrey A; Di, Yanming; Schafer, Daniel W; Wilhelm, Larry J; Fox, Samuel E; Sullivan, Christopher M; Curzon, Aron D; Carrington, James C; Mockler, Todd C; Chang, Jeff H

2011-01-01

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

Directory of Open Access Journals (Sweden)

Jason S Cumbie

Full Text Available GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.

Apple latent spherical virus vectors for reliable and effective virus-induced gene silencing among a broad range of plants including tobacco, tomato, Arabidopsis thaliana, cucurbits, and legumes

International Nuclear Information System (INIS)

Igarashi, Aki; Yamagata, Kousuke; Sugai, Tomokazu; Takahashi, Yukari; Sugawara, Emiko; Tamura, Akihiro; Yaegashi, Hajime; Yamagishi, Noriko; Takahashi, Tsubasa; Isogai, Masamichi; Takahashi, Hideki; Yoshikawa, Nobuyuki

2009-01-01

Apple latent spherical virus (ALSV) vectors were evaluated for virus-induced gene silencing (VIGS) of endogenous genes among a broad range of plant species. ALSV vectors carrying partial sequences of a subunit of magnesium chelatase (SU) and phytoene desaturase (PDS) genes induced highly uniform knockout phenotypes typical of SU and PDS inhibition on model plants such as tobacco and Arabidopsis thaliana, and economically important crops such as tomato, legume, and cucurbit species. The silencing phenotypes persisted throughout plant growth in these plants. In addition, ALSV vectors could be successfully used to silence a meristem gene, proliferating cell nuclear antigen and disease resistant N gene in tobacco and RCY1 gene in A. thaliana. As ALSV infects most host plants symptomlessly and effectively induces stable VIGS for long periods, the ALSV vector is a valuable tool to determine the functions of interested genes among a broad range of plant species.

Trichoderma virens β-glucosidase I (BGLI) gene; expression in Saccharomyces cerevisiae including docking and molecular dynamics studies.

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Wickramasinghe, Gammadde Hewa Ishan Maduka; Rathnayake, Pilimathalawe Panditharathna Attanayake Mudiyanselage Samith Indika; Chandrasekharan, Naduviladath Vishvanath; Weerasinghe, Mahindagoda Siril Samantha; Wijesundera, Ravindra Lakshman Chundananda; Wijesundera, Wijepurage Sandhya Sulochana

2017-06-21

Cellulose, a linear polymer of β 1-4, linked glucose, is the most abundant renewable fraction of plant biomass (lignocellulose). It is synergistically converted to glucose by endoglucanase (EG) cellobiohydrolase (CBH) and β-glucosidase (BGL) of the cellulase complex. BGL plays a major role in the conversion of randomly cleaved cellooligosaccharides into glucose. As it is well known, Saccharomyces cerevisiae can efficiently convert glucose into ethanol under anaerobic conditions. Therefore, S.cerevisiae was genetically modified with the objective of heterologous extracellular expression of the BGLI gene of Trichoderma virens making it capable of utilizing cellobiose to produce ethanol. The cDNA and a genomic sequence of the BGLI gene of Trichoderma virens was cloned in the yeast expression vector pGAPZα and separately transformed to Saccharomyces cerevisiae. The size of the BGLI cDNA clone was 1363 bp and the genomic DNA clone contained an additional 76 bp single intron following the first exon. The gene was 90% similar to the DNA sequence and 99% similar to the deduced amino acid sequence of 1,4-β-D-glucosidase of T. atroviride (AC237343.1). The BGLI activity expressed by the recombinant genomic clone was 3.4 times greater (1.7 x 10 -3  IU ml -1 ) than that observed for the cDNA clone (5 x 10 -4  IU ml -1 ). Furthermore, the activity was similar to the activity of locally isolated Trichoderma virens (1.5 x 10 -3  IU ml -1 ). The estimated size of the protein was 52 kDA. In fermentation studies, the maximum ethanol production by the genomic and the cDNA clones were 0.36 g and 0.06 g /g of cellobiose respectively. Molecular docking results indicated that the bare protein and cellobiose-protein complex behave in a similar manner with considerable stability in aqueous medium. The deduced binding site and the binding affinity of the constructed homology model appeared to be reasonable. Moreover, it was identified that the five hydrogen bonds formed

SiGN-SSM: open source parallel software for estimating gene networks with state space models.

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Tamada, Yoshinori; Yamaguchi, Rui; Imoto, Seiya; Hirose, Osamu; Yoshida, Ryo; Nagasaki, Masao; Miyano, Satoru

2011-04-15

SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. tamada@ims.u-tokyo.ac.jp.

Standardized gene nomenclature for the Brassica genus

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King Graham J

2008-05-01

Full Text Available Abstract The genus Brassica (Brassicaceae, Brassiceae is closely related to the model plant Arabidopsis, and includes several important crop plants. Against the background of ongoing genome sequencing, and in line with efforts to standardize and simplify description of genetic entities, we propose a standard systematic gene nomenclature system for the Brassica genus. This is based upon concatenating abbreviated categories, where these are listed in descending order of significance from left to right (i.e. genus – species – genome – gene name – locus – allele. Indicative examples are provided, and the considerations and recommendations for use are discussed, including outlining the relationship with functionally well-characterized Arabidopsis orthologues. A Brassica Gene Registry has been established under the auspices of the Multinational Brassica Genome Project that will enable management of gene names within the research community, and includes provisional allocation of standard names to genes previously described in the literature or in sequence repositories. The proposed standardization of Brassica gene nomenclature has been distributed to editors of plant and genetics journals and curators of sequence repositories, so that it can be adopted universally.

Roles of Solvent Accessibility and Gene Expression in Modeling Protein Sequence Evolution

OpenAIRE

Kuangyu Wang; Shuhui Yu; Xiang Ji; Clemens Lakner; Alexander Griffing; Jeffrey L. Thorne

2015-01-01

Models of protein evolution tend to ignore functional constraints, although structural constraints are sometimes incorporated. Here we propose a probabilistic framework for codon substitution that evaluates joint effects of relative solvent accessibility (RSA), a structural constraint; and gene expression, a functional constraint. First, we explore the relationship between RSA and codon usage at the genomic scale as well as at the individual gene scale. Motivated by these results, we construc...

The first missense mutation of NHS gene in a Tunisian family with clinical features of NHS syndrome including cardiac anomaly.

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Chograni, Manèl; Rejeb, Imen; Jemaa, Lamia Ben; Châabouni, Myriam; Bouhamed, Habiba Chaabouni

2011-08-01

Nance-Horan Syndrome (NHS) or X-linked cataract-dental syndrome is a disease of unknown gene action mechanism, characterized by congenital cataract, dental anomalies, dysmorphic features and, in some cases, mental retardation. We performed linkage analysis in a Tunisian family with NHS in which affected males and obligate carrier female share a common haplotype in the Xp22.32-p11.21 region that contains the NHS gene. Direct sequencing of NHS coding exons and flanking intronic sequences allowed us to identify the first missense mutation (P551S) and a reported SNP-polymorphism (L1319F) in exon 6, a reported UTR-SNP (c.7422 C>T) and a novel one (c.8239 T>A) in exon 8. Both variations P551S and c.8239 T>A segregate with NHS phenotype in this family. Although truncations, frame-shift and copy number variants have been reported in this gene, no missense mutations have been found to segregate previously. This is the first report of a missense NHS mutation causing NHS phenotype (including cardiac defects). We hypothesize also that the non-reported UTR-SNP of the exon 8 (3'-UTR) is specific to the Tunisian population.

Bayesian model to detect phenotype-specific genes for copy number data

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González Juan R

2012-06-01

Full Text Available Abstract Background An important question in genetic studies is to determine those genetic variants, in particular CNVs, that are specific to different groups of individuals. This could help in elucidating differences in disease predisposition and response to pharmaceutical treatments. We propose a Bayesian model designed to analyze thousands of copy number variants (CNVs where only few of them are expected to be associated with a specific phenotype. Results The model is illustrated by analyzing three major human groups belonging to HapMap data. We also show how the model can be used to determine specific CNVs related to response to treatment in patients diagnosed with ovarian cancer. The model is also extended to address the problem of how to adjust for confounding covariates (e.g., population stratification. Through a simulation study, we show that the proposed model outperforms other approaches that are typically used to analyze this data when analyzing common copy-number polymorphisms (CNPs or complex CNVs. We have developed an R package, called bayesGen, that implements the model and estimating algorithms. Conclusions Our proposed model is useful to discover specific genetic variants when different subgroups of individuals are analyzed. The model can address studies with or without control group. By integrating all data in a unique model we can obtain a list of genes that are associated with a given phenotype as well as a different list of genes that are shared among the different subtypes of cases.

Automated Eukaryotic Gene Structure Annotation Using EVidenceModeler and the Program to Assemble Spliced Alignments

Energy Technology Data Exchange (ETDEWEB)

Haas, B J; Salzberg, S L; Zhu, W; Pertea, M; Allen, J E; Orvis, J; White, O; Buell, C R; Wortman, J R

2007-12-10

EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.

Expression of venom gene homologs in diverse python tissues suggests a new model for the evolution of snake venom.

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Reyes-Velasco, Jacobo; Card, Daren C; Andrew, Audra L; Shaney, Kyle J; Adams, Richard H; Schield, Drew R; Casewell, Nicholas R; Mackessy, Stephen P; Castoe, Todd A

2015-01-01

Snake venom gene evolution has been studied intensively over the past several decades, yet most previous studies have lacked the context of complete snake genomes and the full context of gene expression across diverse snake tissues. We took a novel approach to studying snake venom evolution by leveraging the complete genome of the Burmese python, including information from tissue-specific patterns of gene expression. We identified the orthologs of snake venom genes in the python genome, and conducted detailed analysis of gene expression of these venom homologs to identify patterns that differ between snake venom gene families and all other genes. We found that venom gene homologs in the python are expressed in many different tissues outside of oral glands, which illustrates the pitfalls of using transcriptomic data alone to define "venom toxins." We hypothesize that the python may represent an ancestral state prior to major venom development, which is supported by our finding that the expansion of venom gene families is largely restricted to highly venomous caenophidian snakes. Therefore, the python provides insight into biases in which genes were recruited for snake venom systems. Python venom homologs are generally expressed at lower levels, have higher variance among tissues, and are expressed in fewer organs compared with all other python genes. We propose a model for the evolution of snake venoms in which venom genes are recruited preferentially from genes with particular expression profile characteristics, which facilitate a nearly neutral transition toward specialized venom system expression. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Extending Primitive Spatial Data Models to Include Semantics

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Reitsma, F.; Batcheller, J.

2009-04-01

Our traditional geospatial data model involves associating some measurable quality, such as temperature, or observable feature, such as a tree, with a point or region in space and time. When capturing data we implicitly subscribe to some kind of conceptualisation. If we can make this explicit in an ontology and associate it with the captured data, we can leverage formal semantics to reason with the concepts represented in our spatial data sets. To do so, we extend our fundamental representation of geospatial data in a data model by including a URI in our basic data model that links it to our ontology defining our conceptualisation, We thus extend Goodchild et al's geo-atom [1] with the addition of a URI: (x, Z, z(x), URI) . This provides us with pixel or feature level knowledge and the ability to create layers of data from a set of pixels or features that might be drawn from a database based on their semantics. Using open source tools, we present a prototype that involves simple reasoning as a proof of concept. References [1] M.F. Goodchild, M. Yuan, and T.J. Cova. Towards a general theory of geographic representation in gis. International Journal of Geographical Information Science, 21(3):239-260, 2007.

Long-term effects of ionizing radiation on gene expression in a zebrafish model.

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Lahcen Jaafar

Full Text Available Understanding how initial radiation injury translates into long-term effects is an important problem in radiation biology. Here, we define a set of changes in the transcription profile that are associated with the long-term response to radiation exposure. The study was performed in vivo using zebrafish, an established radiobiological model organism. To study the long-term response, 24 hour post-fertilization embryos were exposed to 0.1 Gy (low dose or 1.0 Gy (moderate dose of whole-body gamma radiation and allowed to develop for 16 weeks. Liver mRNA profiles were then analyzed using the Affymetrix microarray platform, with validation by quantitative PCR. As a basis for comparison, 16-week old adults were exposed at the same doses and analyzed after 4 hours. Statistical analysis was performed in a way to minimize the effects of multiple comparisons. The responses to these two treatment regimes differed greatly: 360 probe sets were associated primarily with the long-term response, whereas a different 2062 probe sets were associated primarily with the response when adults of the same age were irradiated 4 hours before exposure. Surprisingly, a ten-fold difference in radiation dose (0.1 versus 1.0 Gy had little effect. Analysis at the gene and pathway level indicated that the long-term response includes the induction of cytokine and inflammatory regulators and transcription and growth factors. The acute response includes the induction of p53 target genes and modulation of the hypoxia-induced transcription factor-C/EBP axis. Results help define genes and pathways affected in the long-term, low and moderate dose radiation response and differentiate them from those affected in an acute response in the same tissue.

Stress, and pathogen response gene expression in modeled microgravity

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Sundaresan, Alamelu; Pellis, Neal R.

2006-01-01

Purpose: Immune suppression in microgravity has been well documented. With the advent of human exploration and long-term space travel, the immune system of the astronaut must be optimally maintained. It is important to investigate the expression patterns of cytokine genes, because they are directly related to immune response. Heat shock proteins (HSPs), also called stress proteins, are a group of proteins that are present in the cells of every life form. These proteins are induced when a cell responds to stressors such as heat, cold and oxygen deprivation. Microgravity is another stressor that may regulate HSPs. Heat shock proteins trigger immune response through activities that occur both inside the cell (intracellular) and outside the cell (extracellular). Knowledge about these two gene groups could lead to establishment of a blueprint of the immune response and adaptation-related genes in the microgravity environment. Methods: Human peripheral blood cells were cultured in 1g (T flask) and modeled microgravity (MMG, rotating-wall vessel) for 24 and 72 hours. Cell samples were collected and subjected to gene array analysis using the Affymetrix HG_U95 array. Data was collected and subjected to a two-way analysis of variance. The genes related to immune and stress responses were analyzed. Results and Conclusions: HSP70 was up-regulated by more than two fold in microgravity culture, while HSP90 was significantly down-regulated. HSP70 is not typically expressed in all kinds of cells, but it is expressed at high levels in stress conditions. HSP70 participates in translation, protein translocation, proteolysis and protein folding, suppressing aggregation and reactivating denatured proteins. Increased serum HSP70 levels correlate with a better outcome for heat-stroke or severe trauma patients. At the same time, elevated serum levels of HSP70 have been detected in patients with peripheral or renal vascular disease. HSP90 has been identified in the cytosol, nucleus and

Republished review: Gene therapy for ocular diseases.

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Liu, Melissa M; Tuo, Jingsheng; Chan, Chi-Chao

2011-07-01

The eye is an easily accessible, highly compartmentalised and immune-privileged organ that offers unique advantages as a gene therapy target. Significant advancements have been made in understanding the genetic pathogenesis of ocular diseases, and gene replacement and gene silencing have been implicated as potentially efficacious therapies. Recent improvements have been made in the safety and specificity of vector-based ocular gene transfer methods. Proof-of-concept for vector-based gene therapies has also been established in several experimental models of human ocular diseases. After nearly two decades of ocular gene therapy research, preliminary successes are now being reported in phase 1 clinical trials for the treatment of Leber congenital amaurosis. This review describes current developments and future prospects for ocular gene therapy. Novel methods are being developed to enhance the performance and regulation of recombinant adeno-associated virus- and lentivirus-mediated ocular gene transfer. Gene therapy prospects have advanced for a variety of retinal disorders, including retinitis pigmentosa, retinoschisis, Stargardt disease and age-related macular degeneration. Advances have also been made using experimental models for non-retinal diseases, such as uveitis and glaucoma. These methodological advancements are critical for the implementation of additional gene-based therapies for human ocular diseases in the near future.

Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.

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Filippo Trentini

Full Text Available We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients.

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

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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.

Epigenetic profiling of cutaneous T-cell lymphoma: promoter hypermethylation of multiple tumor suppressor genes including BCL7a, PTPRG, and p73.

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van Doorn, Remco; Zoutman, Willem H; Dijkman, Remco; de Menezes, Renee X; Commandeur, Suzan; Mulder, Aat A; van der Velden, Pieter A; Vermeer, Maarten H; Willemze, Rein; Yan, Pearlly S; Huang, Tim H; Tensen, Cornelis P

2005-06-10

To analyze the occurrence of promoter hypermethylation in primary cutaneous T-cell lymphoma (CTCL) on a genome-wide scale, focusing on epigenetic alterations with pathogenetic significance. DNA isolated from biopsy specimens of 28 patients with CTCL, including aggressive CTCL entities (transformed mycosis fungoides and CD30-negative large T-cell lymphoma) and an indolent entity (CD30-positive large T-cell lymphoma), were investigated. For genome-wide DNA methylation screening, differential methylation hybridization using CpG island microarrays was applied, which allows simultaneous detection of the methylation status of 8640 CpG islands. Bisulfite sequence analysis was applied for confirmation and detection of hypermethylation of eight selected tumor suppressor genes. The DNA methylation patterns of CTCLs emerging from differential methylation hybridization analysis included 35 CpG islands hypermethylated in at least four of the 28 studied CTCL samples when compared with benign T-cell samples. Hypermethylation of the putative tumor suppressor genes BCL7a (in 48% of CTCL samples), PTPRG (27%), and thrombospondin 4 (52%) was confirmed and demonstrated to be associated with transcriptional downregulation. BCL7a was hypermethylated at a higher frequency in aggressive (64%) than in indolent (14%) CTCL entities. In addition, the promoters of the selected tumor suppressor genes p73 (48%), p16 (33%), CHFR (19%), p15 (10%), and TMS1 (10%) were hypermethylated in CTCL. Malignant T cells of patients with CTCL display widespread promoter hypermethylation associated with inactivation of several tumor suppressor genes involved in DNA repair, cell cycle, and apoptosis signaling pathways. In view of this, CTCL may be amenable to treatment with demethylating agents.

GoGene: gene annotation in the fast lane.

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Plake, Conrad; Royer, Loic; Winnenburg, Rainer; Hakenberg, Jörg; Schroeder, Michael

2009-07-01

High-throughput screens such as microarrays and RNAi screens produce huge amounts of data. They typically result in hundreds of genes, which are often further explored and clustered via enriched GeneOntology terms. The strength of such analyses is that they build on high-quality manual annotations provided with the GeneOntology. However, the weakness is that annotations are restricted to process, function and location and that they do not cover all known genes in model organisms. GoGene addresses this weakness by complementing high-quality manual annotation with high-throughput text mining extracting co-occurrences of genes and ontology terms from literature. GoGene contains over 4,000,000 associations between genes and gene-related terms for 10 model organisms extracted from more than 18,000,000 PubMed entries. It does not cover only process, function and location of genes, but also biomedical categories such as diseases, compounds, techniques and mutations. By bringing it all together, GoGene provides the most recent and most complete facts about genes and can rank them according to novelty and importance. GoGene accepts keywords, gene lists, gene sequences and protein sequences as input and supports search for genes in PubMed, EntrezGene and via BLAST. Since all associations of genes to terms are supported by evidence in the literature, the results are transparent and can be verified by the user. GoGene is available at http://gopubmed.org/gogene.

Conceptualizing a Dynamic Fall Risk Model Including Intrinsic Risks and Exposures.

Science.gov (United States)

Klenk, Jochen; Becker, Clemens; Palumbo, Pierpaolo; Schwickert, Lars; Rapp, Kilan; Helbostad, Jorunn L; Todd, Chris; Lord, Stephen R; Kerse, Ngaire

2017-11-01

Falls are a major cause of injury and disability in older people, leading to serious health and social consequences including fractures, poor quality of life, loss of independence, and institutionalization. To design and provide adequate prevention measures, accurate understanding and identification of person's individual fall risk is important. However, to date, the performance of fall risk models is weak compared with models estimating, for example, cardiovascular risk. This deficiency may result from 2 factors. First, current models consider risk factors to be stable for each person and not change over time, an assumption that does not reflect real-life experience. Second, current models do not consider the interplay of individual exposure including type of activity (eg, walking, undertaking transfers) and environmental risks (eg, lighting, floor conditions) in which activity is performed. Therefore, we posit a dynamic fall risk model consisting of intrinsic risk factors that vary over time and exposure (activity in context). eHealth sensor technology (eg, smartphones) begins to enable the continuous measurement of both the above factors. We illustrate our model with examples of real-world falls from the FARSEEING database. This dynamic framework for fall risk adds important aspects that may improve understanding of fall mechanisms, fall risk models, and the development of fall prevention interventions. Copyright © 2017 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Quantifying intrinsic and extrinsic variability in stochastic gene expression models.

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Singh, Abhyudai; Soltani, Mohammad

2013-01-01

Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters.

Efficient strategy for detecting gene × gene joint action and its application in schizophrenia.

Science.gov (United States)

Won, Sungho; Kwon, Min-Seok; Mattheisen, Manuel; Park, Suyeon; Park, Changsoon; Kihara, Daisuke; Cichon, Sven; Ophoff, Roel; Nöthen, Markus M; Rietschel, Marcella; Baur, Max; Uitterlinden, Andre G; Hofmann, A; Lange, Christoph

2014-01-01

We propose a new approach to detect gene × gene joint action in genome-wide association studies (GWASs) for case-control designs. This approach offers an exhaustive search for all two-way joint action (including, as a special case, single gene action) that is computationally feasible at the genome-wide level and has reasonable statistical power under most genetic models. We found that the presence of any gene × gene joint action may imply differences in three types of genetic components: the minor allele frequencies and the amounts of Hardy-Weinberg disequilibrium may differ between cases and controls, and between the two genetic loci the degree of linkage disequilibrium may differ between cases and controls. Using Fisher's method, it is possible to combine the different sources of genetic information in an overall test for detecting gene × gene joint action. The proposed statistical analysis is efficient and its simplicity makes it applicable to GWASs. In the current study, we applied the proposed approach to a GWAS on schizophrenia and found several potential gene × gene interactions. Our application illustrates the practical advantage of the proposed method. © 2013 WILEY PERIODICALS, INC.

Long-term transfer and expression of the human beta-globin gene in a mouse transplant model.

Science.gov (United States)

Raftopoulos, H; Ward, M; Leboulch, P; Bank, A

1997-11-01

Somatic gene therapy of hemoglobinopathies depends initially on the demonstration of safe, efficient gene transfer and long-term, high-level expression of the transferred human beta-globin gene in animal models. We have used a beta-globin gene/beta-locus control region retroviral vector containing several modifications to optimize gene transfer and expression in a mouse transplant model. In this report we show that transplantation of beta-globin-transduced hematopoietic cells into lethally irradiated mice leads to the continued presence of the gene up to 8 months posttransplantation. The transferred human beta-globin gene is detected in 3 of 5 mice surviving long term (>4 months) transplanted with bone marrow cells transduced with high-titer virus. Southern blotting confirms the presence of the unrearranged 5.1-kb human beta-globin gene-containing provirus in 2 of these mice. In addition, long-term expression of the transferred gene is seen in 2 mice at levels of 5% and 20% that of endogenous murine beta-globin at 6 and 8 months posttransplantation. We further document stem cell transduction by the successful transfer and high-level expression of the human beta-globin gene from mice transduced 9 months earlier into irradiated secondary recipient mice. These results demonstrate high-level, long-term somatic human beta-globin gene transfer into the hematopoietic stem cells of an animal for the first time, and suggest the potential feasibility of a retroviral gene therapy approach to sickle cell disease and the beta thalassemias.

Altered expression pattern of clock genes in a rat model of depression

DEFF Research Database (Denmark)

Christiansen, Sofie; Bouzinova, Elena; Fahrenkrug, Jan

2016-01-01

BACKGROUND: Abnormalities in circadian rhythms may be causal factors in development of major depressive disorder. The biology underlying a causal relationship between circadian rhythm disturbances and depression is slowly being unraveled. Although there is no direct evidence of dysregulation...... of clock gene expression in depressive patients many studies have reported single-nucleotide polymorphisms in clock genes in these patients. METHODS: In the present study we investigated whether a depression-like state in rats associates with alternations of the diurnal expression of clock genes....... The validated chronic mild stress (CMS) animal model of depression was used to investigate rhythmic expression of three clock genes; Per1, Per2 and Bmal1. Brain and liver tissue was collected from 96 animals after 3.5 weeks of CMS (48 control and 48 depression-like rats) at 4 h sampling interval within 24 h. We...

Large-scale modeling of condition-specific gene regulatory networks by information integration and inference.

Science.gov (United States)

Ellwanger, Daniel Christian; Leonhardt, Jörn Florian; Mewes, Hans-Werner

2014-12-01

Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η(2) (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Female mating preferences determine system-level evolution in a gene network model.

Science.gov (United States)

Fierst, Janna L

2013-06-01

Environmental patterns of directional, stabilizing and fluctuating selection can influence the evolution of system-level properties like evolvability and mutational robustness. Intersexual selection produces strong phenotypic selection and these dynamics may also affect the response to mutation and the potential for future adaptation. In order to to assess the influence of mating preferences on these evolutionary properties, I modeled a male trait and female preference determined by separate gene regulatory networks. I studied three sexual selection scenarios: sexual conflict, a Gaussian model of the Fisher process described in Lande (in Proc Natl Acad Sci 78(6):3721-3725, 1981) and a good genes model in which the male trait signalled his mutational condition. I measured the effects these mating preferences had on the potential for traits and preferences to evolve towards new states, and mutational robustness of both the phenotype and the individual's overall viability. All types of sexual selection increased male phenotypic robustness relative to a randomly mating population. The Fisher model also reduced male evolvability and mutational robustness for viability. Under good genes sexual selection, males evolved an increased mutational robustness for viability. Females choosing their mates is a scenario that is sufficient to create selective forces that impact genetic evolution and shape the evolutionary response to mutation and environmental selection. These dynamics will inevitably develop in any population where sexual selection is operating, and affect the potential for future adaptation.

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.

Prevalence of deleterious germline variants in risk genes including BRCA1/2 in consecutive ovarian cancer patients (AGO-TR-1.

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Philipp Harter

Full Text Available Identification of families at risk for ovarian cancer offers the opportunity to consider prophylactic surgery thus reducing ovarian cancer mortality. So far, identification of potentially affected families in Germany was solely performed via family history and numbers of affected family members with breast or ovarian cancer. However, neither the prevalence of deleterious variants in BRCA1/2 in ovarian cancer in Germany nor the reliability of family history as trigger for genetic counselling has ever been evaluated.Prospective counseling and germline testing of consecutive patients with primary diagnosis or with platinum-sensitive relapse of an invasive epithelial ovarian cancer. Testing included 25 candidate and established risk genes. Among these 25 genes, 16 genes (ATM, BRCA1, BRCA2, CDH1, CHEK2, MLH1, MSH2, MSH6, NBN, PMS2, PTEN, PALB2, RAD51C, RAD51D, STK11, TP53 were defined as established cancer risk genes. A positive family history was defined as at least one relative with breast cancer or ovarian cancer or breast cancer in personal history.In total, we analyzed 523 patients: 281 patients with primary diagnosis of ovarian cancer and 242 patients with relapsed disease. Median age at primary diagnosis was 58 years (range 16-93 and 406 patients (77.6% had a high-grade serous ovarian cancer. In total, 27.9% of the patients showed at least one deleterious variant in all 25 investigated genes and 26.4% in the defined 16 risk genes. Deleterious variants were most prevalent in the BRCA1 (15.5%, BRCA2 (5.5%, RAD51C (2.5% and PALB2 (1.1% genes. The prevalence of deleterious variants did not differ significantly between patients at primary diagnosis and relapse. The prevalence of deleterious variants in BRCA1/2 (and in all 16 risk genes in patients <60 years was 30.2% (33.2% versus 10.6% (18.9% in patients ≥60 years. Family history was positive in 43% of all patients. Patients with a positive family history had a prevalence of deleterious variants

Construction of a mouse model of factor VIII deficiency by gene targeting

Energy Technology Data Exchange (ETDEWEB)

Bi, L.; Lawler, A.; Gearhart, J. [Univ. of Pennsylvania School of Medicine, Philadelphia, PA (United States)] [and others

1994-09-01

To develop a small animal model of hemophilia A for gene therapy experiments, we set out to construct a mouse model for factor VIII deficiency by gene targeting. First, we screened a mouse liver cDNA library using a human FVIII cDNA probe. We cloned a 2.6 Kb partial mouse factor VIII cDNA which extends from 800 base pairs of the 3{prime} end of exon 14 to the 5{prime} end of exon 26. A mouse genomic library made from strain 129 was then screened to obtain genomic fragments covering the exons desired for homologous recombination. Two genomic clones were obtained, and one covering exon 15 through 22 was used for gene targeting. To make gene targeting constructs, a 5.8 Kb genomic DNA fragment covering exons 15 to 19 of the mouse FVIII gene was subcloned, and the neo expression cassette was inserted into exons 16 and 17 separately by different strategies. These two constructs were named MFVIIIC-16 and MFVIIIC-17. The constructs were linearized and transfected into strain 129 mouse ES cells by electroporation. Factor VIII gene-knockout ES cell lines were selected by G-418 and screened by genomic Southern blots. Eight exon 16 targeted cell lines and five exon 17 targeted cell lines were obtained. Three cell lines from each construct were injected into blastocysts and surgically transferred into foster mothers. Multiple chimeric mice with 70-90% hair color derived from the ES-cell genotype were seen with both constructs. Germ line transmission of the ES-cell genotype has been obtained for the MFVIIIC-16 construct, and multiple hemophilia A carrier females have been identified. Factor VIII-deficient males will be conceived soon.

Learning Gene Expression Through Modelling and Argumentation. A Case Study Exploring the Connections Between the Worlds of Knowledge

Science.gov (United States)

Puig, Blanca; Ageitos, Noa; Jiménez-Aleixandre, María Pilar

2017-12-01

There is emerging interest on the interactions between modelling and argumentation in specific contexts, such as genetics learning. It has been suggested that modelling might help students understand and argue on genetics. We propose modelling gene expression as a way to learn molecular genetics and diseases with a genetic component. The study is framed in Tiberghien's (2000) two worlds of knowledge, the world of "theories & models" and the world of "objects & events", adding a third component, the world of representations. We seek to examine how modelling and argumentation interact and connect the three worlds of knowledge while modelling gene expression. It is a case study of 10th graders learning about diseases with a genetic component. The research questions are as follows: (1) What argumentative and modelling operations do students enact in the process of modelling gene expression? Specifically, which operations allow connecting the three worlds of knowledge? (2) What are the interactions between modelling and argumentation in modelling gene expression? To what extent do these interactions help students connect the three worlds of knowledge and modelling gene expression? The argumentative operation of using evidence helps students to relate the three worlds of knowledge, enacted in all the connections. It seems to be a relationship among the number of interactions between modelling and argumentation, the connections between world of knowledge and students' capacity to develop a more sophisticated representation. Despite this is a case study, this approach of analysis reveals potentialities for a deeper understanding of learning genetics though scientific practices.

DISC1 mouse models as a tool to decipher gene-environment interactions in psychiatric disorders

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Tyler eCash-Padgett

2013-09-01

Full Text Available DISC1 was discovered in a Scottish pedigree in which a chromosomal translocation that breaks this gene segregates with psychiatric disorders, mainly depression and schizophrenia. Linkage and association studies in diverse populations support DISC1 as a susceptibility gene to a variety of neuropsychiatric disorders. Many Disc1 mouse models have been generated to study its neuronal functions. These mouse models display variable phenotypes, some of them relevant to schizophrenia, others to depression.The Disc1 mouse models are popular genetic models for studying gene-environment interactions in schizophrenia. Five different Disc1 models have been combined with environmental factors. The environmental stressors employed can be classified as either early immune activation or later social paradigms. These studies cover major time points along the neurodevelopmental trajectory: prenatal, early postnatal, adolescence, and adulthood. Various combinations of molecular, anatomical and behavioral methods have been used to assess the outcomes. Additionally, three of the studies sought to rescue the resulting abnormalities.Here we provide background on the environmental paradigms used, summarize the results of these studies combining Disc1 mouse models with environmental stressors and discuss what we can learn and how to proceed. A major question is how the genetic and environmental factors determine which psychiatric disorder will be clinically manifested. To address this we can take advantage of the many Disc1 models available and expose them to the same environmental stressor. The complementary experiment would be to expose the same model to different environmental stressors. DISC1 is an ideal gene for this approach, since in the Scottish pedigree the same chromosomal translocation results in different psychiatric conditions.

The pathogenomics of McArdle disease--genes, enzymes, models, and therapeutic implications.

Science.gov (United States)

Nogales-Gadea, Gisela; Santalla, Alfredo; Brull, Astrid; de Luna, Noemi; Lucia, Alejandro; Pinós, Tomàs

2015-03-01

Numerous biomedical advances have been made since Carl and Gerty Cori discovered the enzyme phosphorylase in the 1940s and the Scottish physician Brian McArdle reported in 1951 a previously 'undescribed disorder characterized by a gross failure of the breakdown in muscle of glycogen'. Today we know that this disorder, commonly known as 'McArdle disease', is caused by inherited deficiency of the muscle isoform of glycogen phosphorylase (GP). Here we review the main aspects of the 'pathogenomics' of this disease including, among others: the spectrum of mutations in the gene (PYGM) encoding muscle GP; the interplay between the different tissue GP isoforms in cellular cultures and in patients; what can we learn from naturally occurring and recently laboratory-generated animal models of the disease; and potential therapies.

[Effect of topical application of a recombinant adenovirus carrying promyelocytic leukemia gene in a psoriasis-like mouse model].

Science.gov (United States)

Wang, Qiongyu; Zhang, Aijun; Ma, Huiqun; Wang, Shijie; Ma, Yunyun; Zou, Xingwei; Li, Ruilian

2013-03-01

To investigate the effects of topical treatment with adenovirus-mediated promyelocytic leukemia gene (PML) gene in a psoriasis-like mouse model. The effect of adenovirus-mediated PML gene on the granular layer of mouse tail scale epidermis and epithelial mitosis were observed on longitudinal histological sections prepared from the tail skin and vaginal epithelium of the mice. Adenovirus-mediated PML gene significantly inhibited mitosis of mouse vaginal epithelial cells and promoted the formation of granular layer in mouse tail scale epidermis. The therapeutic effect of PML gene in the psoriasis-like mouse model may be associated with increased granular cells and suppressed epidemic cell proliferation.

Grand unified models including extra Z bosons

International Nuclear Information System (INIS)

Li Tiezhong

1989-01-01

The grand unified theories (GUT) of the simple Lie groups including extra Z bosons are discussed. Under authors's hypothesis there are only SU 5+m SO 6+4n and E 6 groups. The general discussion of SU 5+m is given, then the SU 6 and SU 7 are considered. In SU 6 the 15+6 * +6 * fermion representations are used, which are not same as others in fermion content, Yukawa coupling and broken scales. A conception of clans of particles, which are not families, is suggested. These clans consist of extra Z bosons and the corresponding fermions of the scale. The all of fermions in the clans are down quarks except for the standard model which consists of Z bosons and 15 fermions, therefore, the spectrum of the hadrons which are composed of these down quarks are different from hadrons at present

Virtual Genome Walking across the 32 Gb Ambystoma mexicanum genome; assembling gene models and intronic sequence.

Science.gov (United States)

Evans, Teri; Johnson, Andrew D; Loose, Matthew

2018-01-12

Large repeat rich genomes present challenges for assembly using short read technologies. The 32 Gb axolotl genome is estimated to contain ~19 Gb of repetitive DNA making an assembly from short reads alone effectively impossible. Indeed, this model species has been sequenced to 20× coverage but the reads could not be conventionally assembled. Using an alternative strategy, we have assembled subsets of these reads into scaffolds describing over 19,000 gene models. We call this method Virtual Genome Walking as it locally assembles whole genome reads based on a reference transcriptome, identifying exons and iteratively extending them into surrounding genomic sequence. These assemblies are then linked and refined to generate gene models including upstream and downstream genomic, and intronic, sequence. Our assemblies are validated by comparison with previously published axolotl bacterial artificial chromosome (BAC) sequences. Our analyses of axolotl intron length, intron-exon structure, repeat content and synteny provide novel insights into the genic structure of this model species. This resource will enable new experimental approaches in axolotl, such as ChIP-Seq and CRISPR and aid in future whole genome sequencing efforts. The assembled sequences and annotations presented here are freely available for download from https://tinyurl.com/y8gydc6n . The software pipeline is available from https://github.com/LooseLab/iterassemble .

Candidate gene database and transcript map for peach, a model species for fruit trees.

Science.gov (United States)

Horn, Renate; Lecouls, Anne-Claire; Callahan, Ann; Dandekar, Abhaya; Garay, Lilibeth; McCord, Per; Howad, Werner; Chan, Helen; Verde, Ignazio; Main, Doreen; Jung, Sook; Georgi, Laura; Forrest, Sam; Mook, Jennifer; Zhebentyayeva, Tatyana; Yu, Yeisoo; Kim, Hye Ran; Jesudurai, Christopher; Sosinski, Bryon; Arús, Pere; Baird, Vance; Parfitt, Dan; Reighard, Gregory; Scorza, Ralph; Tomkins, Jeffrey; Wing, Rod; Abbott, Albert Glenn

2005-05-01

Peach (Prunus persica) is a model species for the Rosaceae, which includes a number of economically important fruit tree species. To develop an extensive Prunus expressed sequence tag (EST) database for identifying and cloning the genes important to fruit and tree development, we generated 9,984 high-quality ESTs from a peach cDNA library of developing fruit mesocarp. After assembly and annotation, a putative peach unigene set consisting of 3,842 ESTs was defined. Gene ontology (GO) classification was assigned based on the annotation of the single "best hit" match against the Swiss-Prot database. No significant homology could be found in the GenBank nr databases for 24.3% of the sequences. Using core markers from the general Prunus genetic map, we anchored bacterial artificial chromosome (BAC) clones on the genetic map, thereby providing a framework for the construction of a physical and transcript map. A transcript map was developed by hybridizing 1,236 ESTs from the putative peach unigene set and an additional 68 peach cDNA clones against the peach BAC library. Hybridizing ESTs to genetically anchored BACs immediately localized 11.2% of the ESTs on the genetic map. ESTs showed a clustering of expressed genes in defined regions of the linkage groups. [The data were built into a regularly updated Genome Database for Rosaceae (GDR), available at (http://www.genome.clemson.edu/gdr/).].

Modeling of cylindrical surrounding gate MOSFETs including the fringing field effects

International Nuclear Information System (INIS)

Gupta, Santosh K.; Baishya, Srimanta

2013-01-01

A physically based analytical model for surface potential and threshold voltage including the fringing gate capacitances in cylindrical surround gate (CSG) MOSFETs has been developed. Based on this a subthreshold drain current model has also been derived. This model first computes the charge induced in the drain/source region due to the fringing capacitances and considers an effective charge distribution in the cylindrically extended source/drain region for the development of a simple and compact model. The fringing gate capacitances taken into account are outer fringe capacitance, inner fringe capacitance, overlap capacitance, and sidewall capacitance. The model has been verified with the data extracted from 3D TCAD simulations of CSG MOSFETs and was found to be working satisfactorily. (semiconductor devices)

RT-PCR detection of Candida albicans ALS gene expression in the reconstituted human epithelium (RHE) model of oral candidiasis and in model biofilms.

Science.gov (United States)

Green, Clayton B; Cheng, Georgina; Chandra, Jyotsna; Mukherjee, Pranab; Ghannoum, Mahmoud A; Hoyer, Lois L

2004-02-01

An RT-PCR assay was developed to analyse expression patterns of genes in the Candida albicans ALS (agglutinin-like sequence) family. Inoculation of a reconstituted human buccal epithelium (RHE) model of mucocutaneous candidiasis with strain SC5314 showed destruction of the epithelial layer by C. albicans and also formation of an upper fungal layer that had characteristics similar to a biofilm. RT-PCR analysis of total RNA samples extracted from C. albicans-inoculated buccal RHE showed that ALS1, ALS2, ALS3, ALS4, ALS5 and ALS9 were consistently detected over time as destruction of the RHE progressed. Detection of transcripts from ALS7, and particularly from ALS6, was more sporadic, but not associated with a strictly temporal pattern. The expression pattern of ALS genes in C. albicans cultures used to inoculate the RHE was similar to that observed in the RHE model, suggesting that contact of C. albicans with buccal RHE does little to alter ALS gene expression. RT-PCR analysis of RNA samples extracted from model denture and catheter biofilms showed similar gene expression patterns to the buccal RHE specimens. Results from the RT-PCR analysis of biofilm RNA specimens were consistent between various C. albicans strains during biofilm development and were comparable to gene expression patterns in planktonic cells. The RT-PCR assay described here will be useful for analysis of human clinical specimens and samples from other disease models. The method will provide further insight into the role of ALS genes and their encoded proteins in the diverse interactions between C. albicans and its host.

Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction

Directory of Open Access Journals (Sweden)

Dai Hongying

2013-01-01

Full Text Available Abstract Background Multifactor Dimensionality Reduction (MDR has been widely applied to detect gene-gene (GxG interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account. Results We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification of disease susceptibility. We evaluate this new A-MDR approach in a broad range of simulations. Also, we present the results of an application of the A-MDR method to a data set derived from Juvenile Idiopathic Arthritis patients treated with methotrexate (MTX that revealed several GxG interactions in the folate pathway that were associated with treatment response. The epistasis enriched risk score that pooled information from 82 significant GxG interactions distinguished MTX responders from non-responders with 82% accuracy. Conclusions The proposed A-MDR is innovative in the MDR framework to investigate aggregated effects among GxG interactions. New measures (pOR, pRR and pChi are proposed to detect multiple GxG interactions.

Improving transcriptome construction in non-model organisms: integrating manual and automated gene definition in Emiliania huxleyi.

Science.gov (United States)

Feldmesser, Ester; Rosenwasser, Shilo; Vardi, Assaf; Ben-Dor, Shifra

2014-02-22

The advent of Next Generation Sequencing technologies and corresponding bioinformatics tools allows the definition of transcriptomes in non-model organisms. Non-model organisms are of great ecological and biotechnological significance, and consequently the understanding of their unique metabolic pathways is essential. Several methods that integrate de novo assembly with genome-based assembly have been proposed. Yet, there are many open challenges in defining genes, particularly where genomes are not available or incomplete. Despite the large numbers of transcriptome assemblies that have been performed, quality control of the transcript building process, particularly on the protein level, is rarely performed if ever. To test and improve the quality of the automated transcriptome reconstruction, we used manually defined and curated genes, several of them experimentally validated. Several approaches to transcript construction were utilized, based on the available data: a draft genome, high quality RNAseq reads, and ESTs. In order to maximize the contribution of the various data, we integrated methods including de novo and genome based assembly, as well as EST clustering. After each step a set of manually curated genes was used for quality assessment of the transcripts. The interplay between the automated pipeline and the quality control indicated which additional processes were required to improve the transcriptome reconstruction. We discovered that E. huxleyi has a very high percentage of non-canonical splice junctions, and relatively high rates of intron retention, which caused unique issues with the currently available tools. While individual tools missed genes and artificially joined overlapping transcripts, combining the results of several tools improved the completeness and quality considerably. The final collection, created from the integration of several quality control and improvement rounds, was compared to the manually defined set both on the DNA and

Animal models of gene-environment interaction in schizophrenia: a dimensional perspective

Science.gov (United States)

Ayhan, Yavuz; McFarland, Ross; Pletnikov, Mikhail V.

2015-01-01

Schizophrenia has long been considered as a disorder with multifactorial origins. Recent discoveries have advanced our understanding of the genetic architecture of the disease. However, even with the increase of identified risk variants, heritability estimates suggest an important contribution of non-genetic factors. Various environmental risk factors have been proposed to play a role in the etiopathogenesis of schizophrenia. These include season of birth, maternal infections, obstetric complications, adverse events at early childhood, and drug abuse. Despite the progress in identification of genetic and environmental risk factors, we still have a limited understanding of the mechanisms whereby gene-environment interactions (GxE) operate in schizophrenia and psychoses at large. In this review we provide a critical analysis of current animal models of GxE relevant to psychotic disorders and propose that dimensional perspective will advance our understanding of the complex mechanisms of these disorders. PMID:26510407

The consequences of chromosomal aneuploidy on gene expression profiles in a cell line model for prostate carcinogenesis.

Science.gov (United States)

Phillips, J L; Hayward, S W; Wang, Y; Vasselli, J; Pavlovich, C; Padilla-Nash, H; Pezullo, J R; Ghadimi, B M; Grossfeld, G D; Rivera, A; Linehan, W M; Cunha, G R; Ried, T

2001-11-15

Here we report the genetic characterization of immortalized prostate epithelial cells before and after conversion to tumorigenicity using molecular cytogenetics and microarray technology. We were particularly interested to analyze the consequences of acquired chromosomal aneuploidies with respect to modifications of gene expression profiles. Compared with nontumorigenic but immortalized prostate epithelium, prostate tumor cell lines showed high levels of chromosomal rearrangements that led to gains of 1p, 5, 11q, 12p, 16q, and 20q and losses of 1pter, 11p, 17, 20p, 21, 22, and Y. Of 5700 unique targets on a 6.5K cDNA microarray, approximately 3% were subject to modification in expression levels; these included GRO-1, -2, IAP-1,- 2, MMP-9, and cyclin D1, which showed increased expression, and TRAIL, BRCA1, and CTNNA, which showed decreased expression. Thirty % of expression changes occurred in regions the genomic copy number of which remained balanced. Of the remainder, 42% of down-regulated and 51% of up-regulated genes mapped to regions present in decreased or increased genomic copy numbers, respectively. A relative gain or loss of a chromosome or chromosomal arm usually resulted in a statistically significant increase or decrease, respectively, in the average expression level of all of the genes on the chromosome. However, of these genes, very few (e.g., 5 of 101 genes on chromosome 11q), and in some instances only two genes (MMP-9 and PROCR on chromosome 20q), were overexpressed by > or =1.7-fold when scored individually. Cluster analysis by gene function suggests that prostate tumorigenesis in these cell line models involves alterations in gene expression that may favor invasion, prevent apoptosis, and promote growth.

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

Directory of Open Access Journals (Sweden)

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.

Maximum Gene-Support Tree

Directory of Open Access Journals (Sweden)

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 roller chain drive model including contact with guide-bars

DEFF Research Database (Denmark)

Pedersen, Sine Leergaard; Hansen, John Michael; Ambrósio, J. A. C.

2004-01-01

A model of a roller chain drive is developed and applied to the simulation and analysis of roller chain drives of large marine diesel engines. The model includes the impact with guide-bars that are the motion delimiter components on the chain strands between the sprockets. The main components...... and the sprocket centre, i.e. a constraint is added when such distance is less than the pitch radius. The unilateral kinematic constraint is removed when its associated constraint reaction force, applied on the roller, is in the direction of the root of the sprocket teeth. In order to improve the numerical...

A constitutive model for the forces of a magnetic bearing including eddy currents

Science.gov (United States)

Taylor, D. L.; Hebbale, K. V.

1993-01-01

A multiple magnet bearing can be developed from N individual electromagnets. The constitutive relationships for a single magnet in such a bearing is presented. Analytical expressions are developed for a magnet with poles arranged circumferencially. Maxwell's field equations are used so the model easily includes the effects of induced eddy currents due to the rotation of the journal. Eddy currents must be included in any dynamic model because they are the only speed dependent parameter and may lead to a critical speed for the bearing. The model is applicable to bearings using attraction or repulsion.

Heterogeneous stock rat: a unique animal model for mapping genes influencing bone fragility.

Science.gov (United States)

Alam, Imranul; Koller, Daniel L; Sun, Qiwei; Roeder, Ryan K; Cañete, Toni; Blázquez, Gloria; López-Aumatell, Regina; Martínez-Membrives, Esther; Vicens-Costa, Elia; Mont, Carme; Díaz, Sira; Tobeña, Adolf; Fernández-Teruel, Alberto; Whitley, Adam; Strid, Pernilla; Diez, Margarita; Johannesson, Martina; Flint, Jonathan; Econs, Michael J; Turner, Charles H; Foroud, Tatiana

2011-05-01

Previously, we demonstrated that skeletal mass, structure and biomechanical properties vary considerably among 11 different inbred rat strains. Subsequently, we performed quantitative trait loci (QTL) analysis in four inbred rat strains (F344, LEW, COP and DA) for different bone phenotypes and identified several candidate genes influencing various bone traits. The standard approach to narrowing QTL intervals down to a few candidate genes typically employs the generation of congenic lines, which is time consuming and often not successful. A potential alternative approach is to use a highly genetically informative animal model resource capable of delivering very high resolution gene mapping such as Heterogeneous stock (HS) rat. HS rat was derived from eight inbred progenitors: ACI/N, BN/SsN, BUF/N, F344/N, M520/N, MR/N, WKY/N and WN/N. The genetic recombination pattern generated across 50 generations in these rats has been shown to deliver ultra-high even gene-level resolution for complex genetic studies. The purpose of this study is to investigate the usefulness of the HS rat model for fine mapping and identification of genes underlying bone fragility phenotypes. We compared bone geometry, density and strength phenotypes at multiple skeletal sites in HS rats with those obtained from five of the eight progenitor inbred strains. In addition, we estimated the heritability for different bone phenotypes in these rats and employed principal component analysis to explore relationships among bone phenotypes in the HS rats. Our study demonstrates that significant variability exists for different skeletal phenotypes in HS rats compared with their inbred progenitors. In addition, we estimated high heritability for several bone phenotypes and biologically interpretable factors explaining significant overall variability, suggesting that the HS rat model could be a unique genetic resource for rapid and efficient discovery of the genetic determinants of bone fragility. Copyright �

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

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Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

Including policy and management in socio-hydrology models: initial conceptualizations

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Hermans, Leon; Korbee, Dorien

2017-04-01

Socio-hydrology studies the interactions in coupled human-water systems. So far, the use of dynamic models that capture the direct feedback between societal and hydrological systems has been dominant. What has not yet been included with any particular emphasis, is the policy or management layer, which is a central element in for instance integrated water resources management (IWRM) or adaptive delta management (ADM). Studying the direct interactions between human-water systems generates knowledges that eventually helps influence these interactions in ways that may ensure better outcomes - for society and for the health and sustainability of water systems. This influence sometimes occurs through spontaneous emergence, uncoordinated by societal agents - private sector, citizens, consumers, water users. However, the term 'management' in IWRM and ADM also implies an additional coordinated attempt through various public actors. This contribution is a call to include the policy and management dimension more prominently into the research focus of the socio-hydrology field, and offers first conceptual variables that should be considered in attempts to include this policy or management layer in socio-hydrology models. This is done by drawing on existing frameworks to study policy processes throughout both planning and implementation phases. These include frameworks such as the advocacy coalition framework, collective learning and policy arrangements, which all emphasis longer-term dynamics and feedbacks between actor coalitions in strategic planning and implementation processes. A case about longter-term dynamics in the management of the Haringvliet in the Netherlands is used to illustrate the paper.

Dynamic model of gene regulation for the lac operon

International Nuclear Information System (INIS)

Angelova, Maia; Ben-Halim, Asma

2011-01-01

Gene regulatory network is a collection of DNA which interact with each other and with other matter in the cell. The lac operon is an example of a relatively simple genetic network and is one of the best-studied structures in the Escherichia coli bacteria. In this work we consider a deterministic model of the lac operon with a noise term, representing the stochastic nature of the regulation. The model is written in terms of a system of simultaneous first order differential equations with delays. We investigate an analytical and numerical solution and analyse the range of values for the parameters corresponding to a stable solution.

Dynamic model of gene regulation for the lac operon

Energy Technology Data Exchange (ETDEWEB)

Angelova, Maia; Ben-Halim, Asma, E-mail: maia.angelova@northumbria.ac.uk, E-mail: asma.benhalim@northumbria.ac.uk [Intelligent Modelling Lab, School of Computing, Engineering and Information Sciences, Northumbria University, Newcastle upon Tyne NE2 1XE (United Kingdom)

2011-03-01

Gene regulatory network is a collection of DNA which interact with each other and with other matter in the cell. The lac operon is an example of a relatively simple genetic network and is one of the best-studied structures in the Escherichia coli bacteria. In this work we consider a deterministic model of the lac operon with a noise term, representing the stochastic nature of the regulation. The model is written in terms of a system of simultaneous first order differential equations with delays. We investigate an analytical and numerical solution and analyse the range of values for the parameters corresponding to a stable solution.

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max.

Directory of Open Access Journals (Sweden)

Yungang Xu

Full Text Available Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN, a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max, due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs, in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional

Impact of neonatal iron deficiency on hippocampal DNA methylation and gene transcription in a porcine biomedical model of cognitive development.

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Schachtschneider, Kyle M; Liu, Yingkai; Rund, Laurie A; Madsen, Ole; Johnson, Rodney W; Groenen, Martien A M; Schook, Lawrence B

2016-11-03

Iron deficiency is a common childhood micronutrient deficiency that results in altered hippocampal function and cognitive disorders. However, little is known about the mechanisms through which neonatal iron deficiency results in long lasting alterations in hippocampal gene expression and function. DNA methylation is an epigenetic mark involved in gene regulation and altered by environmental factors. In this study, hippocampal DNA methylation and gene expression were assessed via reduced representation bisulfite sequencing and RNA-seq on samples from a previous study reporting reduced hippocampal-based learning and memory in a porcine biomedical model of neonatal iron deficiency. In total 192 differentially expressed genes (DEGs) were identified between the iron deficient and control groups. GO term and pathway enrichment analysis identified DEGs associated with hypoxia, angiogenesis, increased blood brain barrier (BBB) permeability, and altered neurodevelopment and function. Of particular interest are genes previously implicated in cognitive deficits and behavioral disorders in humans and mice, including HTR2A, HTR2C, PAK3, PRSS12, and NETO1. Altered genome-wide DNA methylation was observed across 0.5 million CpG and 2.4 million non-CpG sites. In total 853 differentially methylated (DM) CpG and 99 DM non-CpG sites were identified between groups. Samples clustered by group when comparing DM non-CpG sites, suggesting high conservation of non-CpG methylation in response to neonatal environment. In total 12 DM sites were associated with 9 DEGs, including genes involved in angiogenesis, neurodevelopment, and neuronal function. Neonatal iron deficiency leads to altered hippocampal DNA methylation and gene regulation involved in hypoxia, angiogenesis, increased BBB permeability, and altered neurodevelopment and function. Together, these results provide new insights into the mechanisms through which neonatal iron deficiency results in long lasting reductions in cognitive

Test for positional candidate genes for body composition on pig chromosome 6

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Pérez-Enciso Miguel

2002-07-01

Full Text Available Abstract One QTL affecting backfat thickness (BF, intramuscular fat content (IMF and eye muscle area (MA was previously localized on porcine chromosome 6 in an F2 cross between Iberian and Landrace pigs. This work was done to study the effect of two positional candidate genes on these traits: H-FABP and LEPR genes. The QTL mapping analysis was repeated with a regression method using genotypes for seven microsatellites and two PCR-RFLPs in the H-FABP and LEPR genes. H-FABP and LEPR genes were located at 85.4 and 107 cM respectively, by linkage analysis. The effects of the candidate gene polymorphisms were analyzed in two ways. When an animal model was fitted, both genes showed significant effects on fatness traits, the H-FABP polymorphism showed significant effects on IMF and MA, and the LEPR polymorphism on BF and IMF. But when the candidate gene effect was included in a QTL regression analysis these associations were not observed, suggesting that they must not be the causal mutations responsible for the effects found. Differences in the results of both analyses showed the inadequacy of the animal model approach for the evaluation of positional candidate genes in populations with linkage disequilibrium, when the probabilities of the parental origin of the QTL alleles are not included in the model.

A hydrodynamic model for granular material flows including segregation effects

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Gilberg, Dominik; Klar, Axel; Steiner, Konrad

2017-06-01

The simulation of granular flows including segregation effects in large industrial processes using particle methods is accurate, but very time-consuming. To overcome the long computation times a macroscopic model is a natural choice. Therefore, we couple a mixture theory based segregation model to a hydrodynamic model of Navier-Stokes-type, describing the flow behavior of the granular material. The granular flow model is a hybrid model derived from kinetic theory and a soil mechanical approach to cover the regime of fast dilute flow, as well as slow dense flow, where the density of the granular material is close to the maximum packing density. Originally, the segregation model has been formulated by Thornton and Gray for idealized avalanches. It is modified and adapted to be in the preferred form for the coupling. In the final coupled model the segregation process depends on the local state of the granular system. On the other hand, the granular system changes as differently mixed regions of the granular material differ i.e. in the packing density. For the modeling process the focus lies on dry granular material flows of two particle types differing only in size but can be easily extended to arbitrary granular mixtures of different particle size and density. To solve the coupled system a finite volume approach is used. To test the model the rotational mixing of small and large particles in a tumbler is simulated.

Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.

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Mørk, Søren; Holmes, Ian

2012-03-01

Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. Supplementary data are available at Bioinformatics online.

Clonal Dominance With Retroviral Vector Insertions Near the ANGPT1 and ANGPT2 Genes in a Human Xenotransplant Mouse Model

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Reinhard Haemmerle

2014-01-01

Full Text Available Insertional leukemogenesis represents the major risk factor of hematopoietic stem cell (HSC based gene therapy utilizing integrating viral vectors. To develop a pre-clinical model for the evaluation of vector-related genotoxicity directly in the relevant human target cells, cord blood CD34+ HSCs were transplanted into immunodeficient NOD.SCID.IL2rg−/− (NSG mice after transduction with an LTR-driven gammaretroviral vector (GV. Furthermore, we specifically investigated the effect of prolonged in vitro culture in the presence of cytokines recently described to promote HSC expansion or maintenance. Clonality of human hematopoiesis in NSG mice was assessed by high throughput insertion site analyses and validated by insertion site-specific PCR depicting a GV typical integration profile with insertion sites resembling to 25% those of clinical studies. No overrepresentation of integrations in the vicinity of cancer-related genes was observed, however, several dominant clones were identified including two clones harboring integrations in the ANGPT1 and near the ANGPT2 genes associated with deregulated ANGPT1- and ANGPT2-mRNA levels. While these data underscore the potential value of the NSG model, our studies also identified short-comings such as overall low numbers of engrafted HSCs, limited in vivo observation time, and the challenges of in-depth insertion site analyses by low contribution of gene modified hematopoiesis.

Chromosome structures: reduction of certain problems with unequal gene content and gene paralogs to integer linear programming.

Science.gov (United States)

Lyubetsky, Vassily; Gershgorin, Roman; Gorbunov, Konstantin

2017-12-06

Chromosome structure is a very limited model of the genome including the information about its chromosomes such as their linear or circular organization, the order of genes on them, and the DNA strand encoding a gene. Gene lengths, nucleotide composition, and intergenic regions are ignored. Although highly incomplete, such structure can be used in many cases, e.g., to reconstruct phylogeny and evolutionary events, to identify gene synteny, regulatory elements and promoters (considering highly conserved elements), etc. Three problems are considered; all assume unequal gene content and the presence of gene paralogs. The distance problem is to determine the minimum number of operations required to transform one chromosome structure into another and the corresponding transformation itself including the identification of paralogs in two structures. We use the DCJ model which is one of the most studied combinatorial rearrangement models. Double-, sesqui-, and single-operations as well as deletion and insertion of a chromosome region are considered in the model; the single ones comprise cut and join. In the reconstruction problem, a phylogenetic tree with chromosome structures in the leaves is given. It is necessary to assign the structures to inner nodes of the tree to minimize the sum of distances between terminal structures of each edge and to identify the mutual paralogs in a fairly large set of structures. A linear algorithm is known for the distance problem without paralogs, while the presence of paralogs makes it NP-hard. If paralogs are allowed but the insertion and deletion operations are missing (and special constraints are imposed), the reduction of the distance problem to integer linear programming is known. Apparently, the reconstruction problem is NP-hard even in the absence of paralogs. The problem of contigs is to find the optimal arrangements for each given set of contigs, which also includes the mutual identification of paralogs. We proved that these

Effects of Digested Onion Extracts on Intestinal Gene Expression: An Interspecies Comparison Using Different Intestine Models.

Directory of Open Access Journals (Sweden)

Nicole J W de Wit

Full Text Available Human intestinal tissue samples are barely accessible to study potential health benefits of nutritional compounds. Numbers of animals used in animal trials, however, need to be minimalized. Therefore, we explored the applicability of in vitro (human Caco-2 cells and ex vivo intestine models (rat precision cut intestine slices and the pig in-situ small intestinal segment perfusion (SISP technique to study the effect of food compounds. In vitro digested yellow (YOd and white onion extracts (WOd were used as model food compounds and transcriptomics was applied to obtain more insight into which extent mode of actions depend on the model. The three intestine models shared 9,140 genes which were used to compare the responses to digested onions between the models. Unsupervised clustering analysis showed that genes up- or down-regulated by WOd in human Caco-2 cells and rat intestine slices were similarly regulated by YOd, indicating comparable modes of action for the two onion species. Highly variable responses to onion were found in the pig SISP model. By focussing only on genes with significant differential expression, in combination with a fold change > 1.5, 15 genes showed similar onion-induced expression in human Caco-2 cells and rat intestine slices and 2 overlapping genes were found between the human Caco-2 and pig SISP model. Pathway analyses revealed that mainly processes related to oxidative stress, and especially the Keap1-Nrf2 pathway, were affected by onions in all three models. Our data fit with previous in vivo studies showing that the beneficial effects of onions are mostly linked to their antioxidant properties. Taken together, our data indicate that each of the in vitro and ex vivo intestine models used in this study, taking into account their limitations, can be used to determine modes of action of nutritional compounds and can thereby reduce the number of animals used in conventional nutritional intervention studies.

Genome-wide significant localization for working and spatial memory: Identifying genes for psychosis using models of cognition.

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Knowles, Emma E M; Carless, Melanie A; de Almeida, Marcio A A; Curran, Joanne E; McKay, D Reese; Sprooten, Emma; Dyer, Thomas D; Göring, Harald H; Olvera, Rene; Fox, Peter; Almasy, Laura; Duggirala, Ravi; Kent, Jack W; Blangero, John; Glahn, David C

2014-01-01

It is well established that risk for developing psychosis is largely mediated by the influence of genes, but identifying precisely which genes underlie that risk has been problematic. Focusing on endophenotypes, rather than illness risk, is one solution to this problem. Impaired cognition is a well-established endophenotype of psychosis. Here we aimed to characterize the genetic architecture of cognition using phenotypically detailed models as opposed to relying on general IQ or individual neuropsychological measures. In so doing we hoped to identify genes that mediate cognitive ability, which might also contribute to psychosis risk. Hierarchical factor models of genetically clustered cognitive traits were subjected to linkage analysis followed by QTL region-specific association analyses in a sample of 1,269 Mexican American individuals from extended pedigrees. We identified four genome wide significant QTLs, two for working and two for spatial memory, and a number of plausible and interesting candidate genes. The creation of detailed models of cognition seemingly enhanced the power to detect genetic effects on cognition and provided a number of possible candidate genes for psychosis. © 2013 Wiley Periodicals, Inc.

Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

DEFF Research Database (Denmark)

Kogelman, Lisette; Cirera Salicio, Susanna; Zhernakova, Daria V.

2014-01-01

interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model...... (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. Results WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P ... the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using...

Towards mastering CRISPR-induced gene knock-in in plants: Survey of key features and focus on the model Physcomitrella patens.

Science.gov (United States)

Collonnier, Cécile; Guyon-Debast, Anouchka; Maclot, François; Mara, Kostlend; Charlot, Florence; Nogué, Fabien

2017-05-15

Beyond its predominant role in human and animal therapy, the CRISPR-Cas9 system has also become an essential tool for plant research and plant breeding. Agronomic applications rely on the mastery of gene inactivation and gene modification. However, if the knock-out of genes by non-homologous end-joining (NHEJ)-mediated repair of the targeted double-strand breaks (DSBs) induced by the CRISPR-Cas9 system is rather well mastered, the knock-in of genes by homology-driven repair or end-joining remains difficult to perform efficiently in higher plants. In this review, we describe the different approaches that can be tested to improve the efficiency of CRISPR-induced gene modification in plants, which include the use of optimal transformation and regeneration protocols, the design of appropriate guide RNAs and donor templates and the choice of nucleases and means of delivery. We also present what can be done to orient DNA repair pathways in the target cells, and we show how the moss Physcomitrella patens can be used as a model plant to better understand what DNA repair mechanisms are involved, and how this knowledge could eventually be used to define more performant strategies of CRISPR-induced gene knock-in. Copyright © 2017 Elsevier Inc. All rights reserved.