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Sample records for based gene expression

  1. Classification of genes based on gene expression analysis

    NASA Astrophysics Data System (ADS)

    Angelova, M.; Myers, C.; Faith, J.

    2008-05-01

    Systems biology and bioinformatics are now major fields for productive research. DNA microarrays and other array technologies and genome sequencing have advanced to the point that it is now possible to monitor gene expression on a genomic scale. Gene expression analysis is discussed and some important clustering techniques are considered. The patterns identified in the data suggest similarities in the gene behavior, which provides useful information for the gene functionalities. We discuss measures for investigating the homogeneity of gene expression data in order to optimize the clustering process. We contribute to the knowledge of functional roles and regulation of E. coli genes by proposing a classification of these genes based on consistently correlated genes in expression data and similarities of gene expression patterns. A new visualization tool for targeted projection pursuit and dimensionality reduction of gene expression data is demonstrated.

  2. Classification of genes based on gene expression analysis

    SciTech Connect

    Angelova, M. Myers, C. Faith, J.

    2008-05-15

    Systems biology and bioinformatics are now major fields for productive research. DNA microarrays and other array technologies and genome sequencing have advanced to the point that it is now possible to monitor gene expression on a genomic scale. Gene expression analysis is discussed and some important clustering techniques are considered. The patterns identified in the data suggest similarities in the gene behavior, which provides useful information for the gene functionalities. We discuss measures for investigating the homogeneity of gene expression data in order to optimize the clustering process. We contribute to the knowledge of functional roles and regulation of E. coli genes by proposing a classification of these genes based on consistently correlated genes in expression data and similarities of gene expression patterns. A new visualization tool for targeted projection pursuit and dimensionality reduction of gene expression data is demonstrated.

  3. Tri-mean-based statistical differential gene expression detection.

    PubMed

    Ji, Zhaohua; Wu, Chunguo; Wang, Yao; Guan, Renchu; Tu, Huawei; Wu, Xiaozhou; Liang, Yanchun

    2012-01-01

    Based on the assumption that only a subset of disease group has differential gene expression, traditional detection of differentially expressed genes is under the constraint that cancer genes are up- or down-regulated in all disease samples compared with normal samples. However, in 2005, Tomlins assumed and discussed the situation that only a subset of disease samples would be activated, which are often referred to as outliers. PMID:23155761

  4. GECC: Gene Expression Based Ensemble Classification of Colon Samples.

    PubMed

    Rathore, Saima; Hussain, Mutawarra; Khan, Asifullah

    2014-01-01

    Gene expression deviates from its normal composition in case a patient has cancer. This variation can be used as an effective tool to find cancer. In this study, we propose a novel gene expressions based colon classification scheme (GECC) that exploits the variations in gene expressions for classifying colon gene samples into normal and malignant classes. Novelty of GECC is in two complementary ways. First, to cater overwhelmingly larger size of gene based data sets, various feature extraction strategies, like, chi-square, F-Score, principal component analysis (PCA) and minimum redundancy and maximum relevancy (mRMR) have been employed, which select discriminative genes amongst a set of genes. Second, a majority voting based ensemble of support vector machine (SVM) has been proposed to classify the given gene based samples. Previously, individual SVM models have been used for colon classification, however, their performance is limited. In this research study, we propose an SVM-ensemble based new approach for gene based classification of colon, wherein the individual SVM models are constructed through the learning of different SVM kernels, like, linear, polynomial, radial basis function (RBF), and sigmoid. The predicted results of individual models are combined through majority voting. In this way, the combined decision space becomes more discriminative. The proposed technique has been tested on four colon, and several other binary-class gene expression data sets, and improved performance has been achieved compared to previously reported gene based colon cancer detection techniques. The computational time required for the training and testing of 208 × 5,851 data set has been 591.01 and 0.019 s, respectively. PMID:26357050

  5. Control of alphavirus-based gene expression using engineered riboswitches.

    PubMed

    Bell, Christie L; Yu, Dong; Smolke, Christina D; Geall, Andrew J; Beard, Clayton W; Mason, Peter W

    2015-09-01

    Alphavirus-based replicons are a promising nucleic acid vaccine platform characterized by robust gene expression and immune responses. To further explore their use in vaccination, replicons were engineered to allow conditional control over their gene expression. Riboswitches, comprising a ribozyme actuator and RNA aptamer sensor, were engineered into the replicon 3' UTR. Binding of ligand to aptamer modulates ribozyme activity and, therefore, gene expression. Expression from DNA-launched and VRP-packaged replicons containing riboswitches was successfully regulated, achieving a 47-fold change in expression and modulation of the resulting type I interferon response. Moreover, we developed a novel control architecture where riboswitches were integrated into the 3' and 5' UTR of the subgenomic RNA region of the TC-83 virus, leading to an 1160-fold regulation of viral replication. Our studies demonstrate that the use of riboswitches for control of RNA replicon expression and viral replication holds promise for development of novel and safer vaccination strategies. PMID:26005949

  6. A Resampling Based Clustering Algorithm for Replicated Gene Expression Data.

    PubMed

    Li, Han; Li, Chun; Hu, Jie; Fan, Xiaodan

    2015-01-01

    In gene expression data analysis, clustering is a fruitful exploratory technique to reveal the underlying molecular mechanism by identifying groups of co-expressed genes. To reduce the noise, usually multiple experimental replicates are performed. An integrative analysis of the full replicate data, instead of reducing the data to the mean profile, carries the promise of yielding more precise and robust clusters. In this paper, we propose a novel resampling based clustering algorithm for genes with replicated expression measurements. Assuming those replicates are exchangeable, we formulate the problem in the bootstrap framework, and aim to infer the consensus clustering based on the bootstrap samples of replicates. In our approach, we adopt the mixed effect model to accommodate the heterogeneous variances and implement a quasi-MCMC algorithm to conduct statistical inference. Experiments demonstrate that by taking advantage of the full replicate data, our algorithm produces more reliable clusters and has robust performance in diverse scenarios, especially when the data is subject to multiple sources of variance. PMID:26671802

  7. Web-based interrogation of gene expression signatures using EXALT

    PubMed Central

    2009-01-01

    Background Widespread use of high-throughput techniques such as microarrays to monitor gene expression levels has resulted in an explosive growth of data sets in public domains. Integration and exploration of these complex and heterogeneous data have become a major challenge. Results The EXALT (EXpression signature AnaLysis Tool) online program enables meta-analysis of gene expression profiles derived from publically accessible sources. Searches can be executed online against two large databases currently containing more than 28,000 gene expression signatures derived from GEO (Gene Expression Omnibus) and published expression profiles of human cancer. Comparisons among gene expression signatures can be performed with homology analysis and co-expression analysis. Results can be visualized instantly in a plot or a heat map. Three typical use cases are illustrated. Conclusions The EXALT online program is uniquely suited for discovering relationships among transcriptional profiles and searching gene expression patterns derived from diverse physiological and pathological settings. The EXALT online program is freely available for non-commercial users from http://seq.mc.vanderbilt.edu/exalt/. PMID:20003458

  8. Regulated Expression of Adenoviral Vectors-Based Gene Therapies

    PubMed Central

    Curtin, James F.; Candolfi, Marianela; Puntel, Mariana; Xiong, Weidong; Muhammad, A. K. M.; Kroeger, Kurt; Mondkar, Sonali; Liu, Chunyan; Bondale, Niyati; Lowenstein, Pedro R.; Castro, Maria G.

    2008-01-01

    Summary Regulatable promoter systems allow gene expression to be tightly controlled in vivo. This is highly desirable for the development of safe, efficacious adenoviral vectors that can be used to treat human diseases in the clinic. Ideally, regulatable cassettes should have minimal gene expression in the “OFF” state, and expression should quickly reach therapeutic levels in the “ON” state. In addition, the components of regulatable cassettes should be non-toxic at physiological concentrations and should not be immunogenic, especially when treating chronic illness that requires long-lasting gene expression. In this chapter, we will describe in detail protocols to develop and validate first generation (Ad) and high-capacity adenoviral (HC-Ad) vectors that express therapeutic genes under the control of the TetON regulatable system. Our laboratory has successfully used these protocols to regulate the expression of marker genes, immune stimulatory genes, and toxins for cancer gene therapeutics, i.e., glioma that is a deadly form of brain cancer. We have shown that this third generation TetON regulatable system, incorporating a doxycycline (DOX)-sensitive rtTA2S-M2 inducer and tTSKid silencer, is non-toxic, relatively non-immunogenic, and can tightly regulate reporter transgene expression downstream of a TRE promoter from adenoviral vectors in vitro and also in vivo. PMID:18470649

  9. Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

    PubMed Central

    Huttenhower, Curtis; Flamholz, Avi I; Landis, Jessica N; Sahi, Sauhard; Myers, Chad L; Olszewski, Kellen L; Hibbs, Matthew A; Siemers, Nathan O; Troyanskaya, Olga G; Coller, Hilary A

    2007-01-01

    Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes). Results We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets

  10. Surface-based mapping of gene expression and probabilistic expression maps in the mouse cortex.

    PubMed

    Ng, Lydia; Lau, Chris; Sunkin, Susan M; Bernard, Amy; Chakravarty, M Mallar; Lein, Ed S; Jones, Allan R; Hawrylycz, Michael

    2010-02-01

    The Allen Brain Atlas (ABA, www.brain-map.org) is a genome wide, spatially registered collection of cellular resolution in situ hybridization gene expression image data of the C57Bl/6J mouse brain. Derived from the ABA, the Anatomic Gene Expression Atlas (AGEA, http://mouse.brain-map.org/agea) has demonstrated both laminar and areal spatial gene expression correlations in the mouse cortex. While the mouse cortex is lissencephalic, its curvature and substantial bending in boundary areas renders it difficult to visualize and analyze laminar versus areal effects in a rectilinear coordinate framework. In context of human and non-human primate cortex, surface-based representation has proven useful for understanding relative locations of laminar, columnar, and areal features. In this paper, we describe a methodology for constructing surface-based flatmaps of the mouse cortex that enables mapping of gene expression data from individual genes in the ABA, or probabilistic expression maps from the AGEA, to identify and visualize genetic relationships between layers and areas. PMID:19818854

  11. DigiNorthern, digital expression analysis of query genes based on ESTs.

    PubMed

    Wang, Jianxin; Liang, Ping

    2003-03-22

    DigiNorthern (DN) is a web-based tool for virtually displaying expression profiles of query genes based on EST sequences. Two utilities are available: DN1 takes one query gene and quantitatively display its expression levels in tissues/organs that express the gene with comparison between normal and neoplastic status of each tissue; DN2 takes two sequences as query genes and compares their expression profiles side by side. PMID:12651725

  12. Gene function analysis in osteosarcoma based on microarray gene expression profiling

    PubMed Central

    Zhao, Liang; Zhang, Jinghua; Tan, Hongyu; Wang, Weidong; Liu, Yilin; Song, Ruipeng; Wang, Limin

    2015-01-01

    Osteosa rcoma is an aggressive malignant neoplasm that exhibits osteoblastic differentiation and produces malignant osteoid. The aim of this study was to find feature genes associated with osteosarcoma and correlative gene functions which can distinguish cancer tissues from non-tumor tissues. Gene expression profile GSE14359 was downloaded from Gene Expression Omnibus (GEO) database, including 10 osteosarcoma samples and 2 normal samples. The differentially expressed genes (DEGs) between osteosarcoma and normal specimens were identified using limma package of R. DAVID was applied to mine osteosarcoma associated genes and analyze the GO enrichment on gene functions and KEGG pathways. Then, corresponding protein-protein interaction (PPI) network of DEGs was constructed based on the data collected from STRING datasets. Principal component of top10 DEGs and PPI network of top 20 DEGs were further analyzed. Finally, transcription factors were predicted by uploading the two groups of DEGs to TfactS database. A total of 437 genes, including 114 up-regulated genes and 323 down-regulated genes, were filtered as DEGs, of which 46 were associated with osteosarcoma by Disease Module. GO and KEGG pathway enrichment analysis showed that genes mainly affected the process of immune response and the development of skeletal and vascular system. The PPI network analysis elucidated that hemoglobin and histocompatibility proteins and enzymes, which were associated with immune response, were closely associated with osteosarcoma. Transcription factors MYC and SP1 were predicted to be significantly related to osteosarcoma. The discovery of gene functions and transcription factors has the potential to use in clinic for diagnosis of osteosarcoma in future. In addition, it will pave the way to studying mechanism and effective therapies for osteosarcoma. PMID:26379830

  13. GEDA: new knowledge base of gene expression in drug addiction.

    PubMed

    Suh, Young Ju; Yang, Moon Hee; Yoon, Suk Joon; Park, Jong Hoon

    2006-07-31

    Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and nonredundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain. PMID:16889689

  14. Network-Based Inference Framework for Identifying Cancer Genes from Gene Expression Data

    PubMed Central

    Yang, Bo; Zhang, Junying; Yin, Yaling; Zhang, Yuanyuan

    2013-01-01

    Great efforts have been devoted to alleviate uncertainty of detected cancer genes as accurate identification of oncogenes is of tremendous significance and helps unravel the biological behavior of tumors. In this paper, we present a differential network-based framework to detect biologically meaningful cancer-related genes. Firstly, a gene regulatory network construction algorithm is proposed, in which a boosting regression based on likelihood score and informative prior is employed for improving accuracy of identification. Secondly, with the algorithm, two gene regulatory networks are constructed from case and control samples independently. Thirdly, by subtracting the two networks, a differential-network model is obtained and then used to rank differentially expressed hub genes for identification of cancer biomarkers. Compared with two existing gene-based methods (t-test and lasso), the method has a significant improvement in accuracy both on synthetic datasets and two real breast cancer datasets. Furthermore, identified six genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1) susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Among these oncogenes, TSPYL5 and CCNE2 have been already known as prognostic biomarkers in breast cancer, CD55 has been suspected of playing an important role in breast cancer prognosis from literature evidence, and other three genes are newly discovered breast cancer biomarkers. More generally, the differential-network schema can be extended to other complex diseases for detection of disease associated-genes. PMID:24073403

  15. Modeling Gene Networks in Saccharomyces cerevisiae Based on Gene Expression Profiles

    PubMed Central

    Zhang, Yulin; Lv, Kebo; Wang, Shudong; Su, Jionglong; Meng, Dazhi

    2015-01-01

    Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network in Saccharomyces cerevisiae can differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function. PMID:26839582

  16. Liver lobe and strain difference in gene expression after hydrodynamics-based gene delivery in mice.

    PubMed

    Nakamura, Shingo; Maehara, Tadaaki; Watanabe, Satoshi; Ishihara, Masayuki; Sato, Masahiro

    2015-01-01

    Hydrodynamics-based gene delivery (HGD) is a widely recognized technique for delivering exogenous DNA with high efficiency to murine hepatocytes. In this study, we investigated stimulation of exogenous DNA uptake and expression using a commercially available reagent for HGD. We also examined which mouse strain and mouse liver lobe would achieve the best gene delivery performance. Mice were injected with a solution containing reporter plasmid DNA or DNA and a gene delivery reagent. One day after the HGD procedure, liver samples were isolated and subjected to biochemical and histochemical analyses. The reporter plasmid DNA showed the strongest signal when the DNA was dissolved in TransIT-EE Hydrodynamic Delivery Solution (Takara Bio Inc., Shiga, Japan). Evaluation of transgene expression in each hepatic lobe in ICR, C57BL/6N, Balb/cA, and B6C3F1 mice showed that ICR mice exhibited the best gene transfer and that the right median lobe had the highest level of transgene expression. These findings suggest the importance of choice in mouse strains and liver lobes when performing gene-based manipulations of the liver. PMID:25153456

  17. Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

    PubMed

    Kibinge, Nelson; Ono, Naoaki; Horie, Masafumi; Sato, Tetsuo; Sugiura, Tadao; Altaf-Ul-Amin, Md; Saito, Akira; Kanaya, Shigehiko

    2016-06-01

    Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer. PMID:27064123

  18. Web-based digital gene expression atlases for the mouse.

    PubMed

    Geffers, Lars; Herrmann, Bernhard; Eichele, Gregor

    2012-10-01

    Over the past 15 years the publicly available mouse gene expression data determined by in situ hybridization have dramatically increased in scope and spatiotemporal resolution. As a consequence of resources and tools available in the post-genomic era, full transcriptomes in the mouse brain and in the mouse embryo can be studied. Here we introduce and discuss seven current databases (MAMEP, EMBRYS, GenePaint, EURExpress, EuReGene, BGEM, and GENSAT) that grant access to large collections of expression data in mouse. We review the experimental focus, coverage, data assessment, and annotation for each of these databases and the implementation of analytic tools and links to other relevant databases. We provide a user-oriented summary of how to interrogate each database. PMID:22936000

  19. Gene-Expression-Based Predictors for Breast Cancer.

    PubMed

    Gupta, Arjun; Mutebi, Miriam; Bardia, Aditya

    2015-10-01

    An important and often complicated management decision in early stage hormone receptor (HR)-positive breast cancer relates to the use of adjuvant systemic chemotherapy. Although traditional clinicopathologic markers exist, tremendous progress has been achieved in the field of predictive biomarkers and genomics with both prognostic and predictive capabilities to identify patients who will potentially benefit from additional therapy. The use of these genomic tests in the neoadjuvant setting is also being studied and may lead to these tests providing clinical benefit even earlier in the disease course. Landmark articles published in the last few years have expanded our knowledge of breast cancer genomics to an unprecedented level, and mutational analysis via next-generation sequencing methods allows the identification of molecular targets for novel targeted therapeutic agents and clinical trials testing efficacy of targeted therapies, such as PI3K inhibitors, in addition to endocrine therapy for HR-positive breast cancer, are ongoing. We provide an in-depth review on the role of gene expression-based predictors in early stage breast cancer and an overview of future directions, including next-generation sequencing. Over the coming years, we anticipate a significant increase in utilization of genomic-based predictors for individualized selection and duration of endocrine therapy with and without genotype-driven targeted therapy, and a major decrease in the use of chemotherapy, possibly even leading to a chemotherapy-free road for early stage HR-positive breast cancer. PMID:26215189

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

    PubMed Central

    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

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

    PubMed

    Cho, Samuel Sunghwan; Kim, Yongkang; Yoon, Joon; 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

  2. Persistent Gene Expression in Mouse Nasal Epithelia following Feline Immunodeficiency Virus-Based Vector Gene Transfer

    PubMed Central

    Sinn, Patrick L.; Burnight, Erin R.; Hickey, Melissa A.; Blissard, Gary W.; McCray, Paul B.

    2005-01-01

    Gene transfer development for treatment or prevention of cystic fibrosis lung disease has been limited by the inability of vectors to efficiently and persistently transduce airway epithelia. Influenza A is an enveloped virus with natural lung tropism; however, pseudotyping feline immunodeficiency virus (FIV)-based lentiviral vector with the hemagglutinin envelope protein proved unsuccessful. Conversely, pseudotyping FIV with the envelope protein from influenza D (Thogoto virus GP75) resulted in titers of 106 transducing units (TU)/ml and conferred apical entry into well-differentiated human airway epithelial cells. Baculovirus GP64 envelope glycoproteins share sequence identity with influenza D GP75 envelope glycoproteins. Pseudotyping FIV with GP64 from three species of baculovirus resulted in titers of 107 to 109 TU/ml. Of note, GP64 from Autographa californica multicapsid nucleopolyhedrovirus resulted in high-titer FIV preparations (∼109 TU/ml) and conferred apical entry into polarized primary cultures of human airway epithelia. Using a luciferase reporter gene and bioluminescence imaging, we observed persistent gene expression from in vivo gene transfer in the mouse nose with A. californica GP64-pseudotyped FIV (AcGP64-FIV). Longitudinal bioluminescence analysis documented persistent expression in nasal epithelia for ∼1 year without significant decline. According to histological analysis using a LacZ reporter gene, olfactory and respiratory epithelial cells were transduced. In addition, methylcellulose-formulated AcGP64-FIV transduced mouse nasal epithelia with much greater efficiency than similarly formulated vesicular stomatitis virus glycoprotein-pseudotyped FIV. These data suggest that AcGP64-FIV efficiently transduces and persistently expresses a transgene in nasal epithelia in the absence of agents that disrupt the cellular tight junction integrity. PMID:16188984

  3. Gene expression-based biomarkers for Anopheles gambiae age grading.

    PubMed

    Wang, Mei-Hui; Marinotti, Osvaldo; Zhong, Daibin; James, Anthony A; Walker, Edward; Guda, Tom; Kweka, Eliningaya J; Githure, John; Yan, Guiyun

    2013-01-01

    Information on population age structure of mosquitoes under natural conditions is fundamental to the understanding of vectorial capacity and crucial for assessing the impact of vector control measures on malaria transmission. Transcriptional profiling has been proposed as a method for predicting mosquito age for Aedes and Anopheles mosquitoes, however, whether this new method is adequate for natural conditions is unknown. This study tests the applicability of transcriptional profiling for age-grading of Anopheles gambiae, the most important malaria vector in Africa. The transcript abundance of two An. gambiae genes, AGAP009551 and AGAP011615, was measured during aging under laboratory and field conditions in three mosquito strains. Age-dependent monotonic changes in transcript levels were observed in all strains evaluated. These genes were validated as age-grading biomarkers using the mark, release and recapture (MRR) method. The MRR method determined a good correspondence between actual and predicted age, and thus demonstrated the value of age classifications derived from the transcriptional profiling of these two genes. The technique was used to establish the age structure of mosquito populations from two malaria-endemic areas in western Kenya. The population age structure determined by the transcriptional profiling method was consistent with that based on mosquito parity. This study demonstrates that the transcription profiling method based on two genes is valuable for age determination of natural mosquitoes, providing a new approach for determining a key life history trait of malaria vectors. PMID:23936017

  4. Single base resolution analysis of 5-hydroxymethylcytosine in 188 human genes: implications for hepatic gene expression.

    PubMed

    Ivanov, Maxim; Kals, Mart; Lauschke, Volker; Barragan, Isabel; Ewels, Philip; Käller, Max; Axelsson, Tomas; Lehtiö, Janne; Milani, Lili; Ingelman-Sundberg, Magnus

    2016-08-19

    To improve the epigenomic analysis of tissues rich in 5-hydroxymethylcytosine (hmC), we developed a novel protocol called TAB-Methyl-SEQ, which allows for single base resolution profiling of both hmC and 5-methylcytosine by targeted next-generation sequencing. TAB-Methyl-SEQ data were extensively validated by a set of five methodologically different protocols. Importantly, these extensive cross-comparisons revealed that protocols based on Tet1-assisted bisulfite conversion provided more precise hmC values than TrueMethyl-based methods. A total of 109 454 CpG sites were analyzed by TAB-Methyl-SEQ for mC and hmC in 188 genes from 20 different adult human livers. We describe three types of variability of hepatic hmC profiles: (i) sample-specific variability at 40.8% of CpG sites analyzed, where the local hmC values correlate to the global hmC content of livers (measured by LC-MS), (ii) gene-specific variability, where hmC levels in the coding regions positively correlate to expression of the respective gene and (iii) site-specific variability, where prominent hmC peaks span only 1 to 3 neighboring CpG sites. Our data suggest that both the gene- and site-specific components of hmC variability might contribute to the epigenetic control of hepatic genes. The protocol described here should be useful for targeted DNA analysis in a variety of applications. PMID:27131363

  5. Design-Based Learning for Biology: Genetic Engineering Experience Improves Understanding of Gene Expression

    ERIC Educational Resources Information Center

    Ellefson, Michelle R.; Brinker, Rebecca A.; Vernacchio, Vincent J.; Schunn, Christian D.

    2008-01-01

    Gene expression is a difficult topic for students to learn and comprehend, at least partially because it involves various biochemical structures and processes occurring at the microscopic level. Designer Bacteria, a design-based learning (DBL) unit for high-school students, applies principles of DBL to the teaching of gene expression. Throughout…

  6. Selecting key genes associated with osteosarcoma based on a differential expression network.

    PubMed

    Wang, Y B; Jia, N; Xu, C M; Zhao, L; Zhao, Y; Wang, X; Jia, T H

    2015-01-01

    Despite recent advances in osteosarcoma diagnosis and therapy, much remains unclear about the molecular mechanisms involved in the disorder, and the discovery of novel drug-targeted genes is essential. We explored the potential molecular mechanisms and target genes involved in the development and progression of osteosarcoma. First, we identified the differentially expressed genes in osteosarcoma patients and matching normal controls. We then constructed a differential expression network based on differential and non-differential interactions. Pathway-enrichment analysis was performed based on the nodes contained in the main differential expression network. Centrality analysis was used to select hub genes that may play vital roles in the progression of human osteosarcoma. Our research revealed a total of 176 differentially expressed genes including 82 upregulated and 94 downregulated genes. A differential expression network was constructed that included 992 gene pairs (1043 nodes). Pathway-enrichment analysis indicated that the nodes in the differential expression network were mainly enriched in several pathways such as those involved in cancer, cell cycle, ubiquitin-mediated proteolysis, DNA replication, ribosomes, T-cell receptor signaling, spliceosomes, neurotrophin signaling, oxidative phosphorylation, and tight junctions. Six hub genes (APP, UBC, CAND1, RPA, YWHAG, and NEDD8) were discovered; of these, two genes (UBC and RPA) were also found to be disease genes. Our study predicted that UBC and RPA had potential as target genes for the diagnosis and treatment of osteosarcoma. PMID:26782416

  7. GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms

    PubMed Central

    2010-01-01

    Background An important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases. Results Here we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina. Conclusions GeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions

  8. Identification of hub genes and pathways associated with retinoblastoma based on co-expression network analysis.

    PubMed

    Wang, Q L; Chen, X; Zhang, M H; Shen, Q H; Qin, Z M

    2015-01-01

    The objective of this paper was to identify hub genes and pathways associated with retinoblastoma using centrality analysis of the co-expression network and pathway-enrichment analysis. The co-expression network of retinoblastoma was constructed by weighted gene co-expression network analysis (WGCNA) based on differentially expressed (DE) genes, and clusters were obtained through the molecular complex detection (MCODE) algorithm. Degree centrality analysis of the co-expression network was performed to explore hub genes present in retinoblastoma. Pathway-enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Validation of hub gene expression in retinoblastoma was performed by reverse transcription-polymerase chain reaction (RT-PCR) analysis. The co-expression network based on 221 DE genes between retinoblastoma and normal controls consisted of 210 nodes and 3965 edges, and 5 clusters of the network were evaluated. By assessing the centrality analysis of the co-expression network, 21 hub genes were identified, such as SNORD115-41, RASSF2, and SNORD115-44. According to RT-PCR analysis, 16 of the 21 hub genes were differently expressed, including RASSF2 and CDCA7, and 5 were not differently expressed in retinoblastoma compared to normal controls. Pathway analysis showed that genes in 2 clusters were enriched in 3 pathways: purine metabolism, p53 signaling pathway, and melanogenesis. In this study, we successfully identified 16 hub genes and 3 pathways associated with retinoblastoma, which may be potential biomarkers for early detection and therapy for retinoblastoma. PMID:26662407

  9. Differential co-expression analysis of venous thromboembolism based on gene expression profile data

    PubMed Central

    MING, ZHIBING; DING, WENBIN; YUAN, RUIFAN; JIN, JIE; LI, XIAOQIANG

    2016-01-01

    The aim of the present study was to screen differentially co-expressed genes and the involved transcription factors (TFs) and microRNAs (miRNAs) in venous thromboembolism (VTE). Microarray data of GSE19151 were downloaded from Gene Expression Omnibus, including 70 patients with VTE and 63 healthy controls. Principal component analysis (PCA) was performed using R software. Differential co-expression analysis was performed using R, followed by screening of modules using Cytoscape. Functional annotation was performed using Database for Annotation, Visualization, and Integrated Discovery. Moreover, Fisher test was used to screen key TFs and miRNAs for the modules. PCA revealed the disease and healthy samples could not be distinguished at the gene expression level. A total of 4,796 upregulated differentially co-expressed genes (e.g. zinc finger protein 264, electron-transfer-flavoprotein, beta polypeptide and Janus kinase 2) and 3,629 downregulated differentially co-expressed genes (e.g. adenylate cyclase 7 and single-stranded DNA binding protein 2) were identified, which were further mined to obtain 17 and eight modules separately. Functional annotation revealed that the largest upregulated module was primarily associated with acetylation and the largest downregulated module was mainly involved in mitochondrion. Moreover, 48 TFs and 62 miRNA families were screened for the 17 upregulated modules, such as E2F transcription factor 4, miR-30 and miR-135 regulating the largest module. Conversely, 35 TFs and 18 miRNA families were identified for the 8 downregulated modules, including mitochondrial ribosomal protein S12 and miR-23 regulating the largest module. Differentially co-expressed genes regulated by TFs and miRNAs may jointly contribute to the abnormal acetylation and mitochondrion presentation in the progression of VTE. PMID:27284300

  10. Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile.

    PubMed

    Long, Jin; Liu, Zhe; Wu, Xingda; Xu, Yuanhong; Ge, Chunlin

    2016-05-01

    The present study aimed to screen for potential genes and subnetworks associated with pancreatic cancer (PC) using the gene expression profile. The expression profile GSE 16515 was downloaded from the Gene Expression Omnibus database, which included 36 PC tissue samples and 16 normal samples. Limma package in R language was used to screen differentially expressed genes (DEGs), which were grouped as up‑ and downregulated genes. Then, PFSNet was applied to perform subnetwork analysis for all the DEGs. Moreover, Gene Ontology (GO) and REACTOME pathway enrichment analysis of up‑ and downregulated genes was performed, followed by protein‑protein interaction (PPI) network construction using Search Tool for the Retrieval of Interacting Genes Search Tool for the Retrieval of Interacting Genes. In total, 1,989 DEGs including 1,461 up‑ and 528 downregulated genes were screened out. Subnetworks including pancreatic cancer in PC tissue samples and intercellular adhesion in normal samples were identified, respectively. A total of 8 significant REACTOME pathways for upregulated DEGs, such as hemostasis and cell cycle, mitotic were identified. Moreover, 4 significant REACTOME pathways for downregulated DEGs, including regulation of β‑cell development and transmembrane transport of small molecules were screened out. Additionally, DEGs with high connectivity degrees, such as CCNA2 (cyclin A2) and PBK (PDZ binding kinase), of the module in the protein‑protein interaction network were mainly enriched with cell‑division cycle. CCNA2 and PBK of the module and their relative pathway cell‑division cycle, and two subnetworks (pancreatic cancer and intercellular adhesion subnetworks) may be pivotal for further understanding of the molecular mechanism of PC. PMID:27035224

  11. Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile

    PubMed Central

    LONG, JIN; LIU, ZHE; WU, XINGDA; XU, YUANHONG; GE, CHUNLIN

    2016-01-01

    The present study aimed to screen for potential genes and subnetworks associated with pancreatic cancer (PC) using the gene expression profile. The expression profile GSE 16515 was downloaded from the Gene Expression Omnibus database, which included 36 PC tissue samples and 16 normal samples. Limma package in R language was used to screen differentially expressed genes (DEGs), which were grouped as up- and downregulated genes. Then, PFSNet was applied to perform subnetwork analysis for all the DEGs. Moreover, Gene Ontology (GO) and REACTOME pathway enrichment analysis of up- and downregulated genes was performed, followed by protein-protein interaction (PPI) network construction using Search Tool for the Retrieval of Interacting Genes Search Tool for the Retrieval of Interacting Genes. In total, 1,989 DEGs including 1,461 up- and 528 downregulated genes were screened out. Subnetworks including pancreatic cancer in PC tissue samples and intercellular adhesion in normal samples were identified, respectively. A total of 8 significant REACTOME pathways for upregulated DEGs, such as hemostasis and cell cycle, mitotic were identified. Moreover, 4 significant REACTOME pathways for downregulated DEGs, including regulation of β-cell development and transmembrane transport of small molecules were screened out. Additionally, DEGs with high connectivity degrees, such as CCNA2 (cyclin A2) and PBK (PDZ binding kinase), of the module in the protein-protein interaction network were mainly enriched with cell-division cycle. CCNA2 and PBK of the module and their relative pathway cell-division cycle, and two subnetworks (pancreatic cancer and intercellular adhesion subnetworks) may be pivotal for further understanding of the molecular mechanism of PC. PMID:27035224

  12. Neighborhood Rough Set Reduction-Based Gene Selection and Prioritization for Gene Expression Profile Analysis and Molecular Cancer Classification

    PubMed Central

    Hou, Mei-Ling; Wang, Shu-Lin; Li, Xue-Ling; Lei, Ying-Ke

    2010-01-01

    Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers. PMID:20625410

  13. A co-expression modules based gene selection for cancer recognition.

    PubMed

    Lu, Xinguo; Deng, Yong; Huang, Lei; Feng, Bingtao; Liao, Bo

    2014-12-01

    Gene expression profiles are used to recognize patient samples for cancer diagnosis and therapy. Gene selection is crucial to high recognition performance. In usual gene selection methods the genes are considered as independent individuals and the correlation among genes is not used efficiently. In this description, a co-expression modules based gene selection method for cancer recognition is proposed. First, in the cancer dataset a weighted correlation network is constructed according to the correlation between each pair of genes, different modules from this network are identified and the significant modules are selected for following exploration. Second, based on these informative modules information gain is applied to selecting the feature genes for cancer recognition. Then using LOOCV, the experiments with different classification algorithms are conducted and the results show that the proposed method makes better classification accuracy than traditional gene selection methods. At last, via gene ontology enrichment analysis the biological significance of the co-expressed genes in specific modules was verified. PMID:24440175

  14. Microarray-Based Analysis of Cell-Cycle Gene Expression During Spermatogenesis in the Mouse1

    PubMed Central

    Roy Choudhury, Dipanwita; Small, Chris; Wang, Yufeng; Mueller, Paul R.; Rebel, Vivienne I.; Griswold, Michael D.; McCarrey, John R.

    2010-01-01

    Mammalian spermatogenesis is a continuum of cellular differentiation in a lineage that features three principal stages: 1) a mitotically active stage in spermatogonia, 2) a meiotic stage in spermatocytes, and 3) a postreplicative stage in spermatids. We used a microarray-based approach to identify changes in expression of cell-cycle genes that distinguish 1) mitotic type A spermatogonia from meiotic pachytene spermatocytes and 2) pachytene spermatocytes from postreplicative round spermatids. We detected expression of 550 genes related to cell-cycle function in one or more of these cell types. Although a majority of these genes were expressed during all three stages of spermatogenesis, we observed dramatic changes in levels of individual transcripts between mitotic spermatogonia and meiotic spermatocytes and between meiotic spermatocytes and postreplicative spermatids. Our results suggest that distinct cell-cycle gene regulatory networks or subnetworks are associated with each phase of the cell cycle in each spermatogenic cell type. In addition, we observed expression of different members of certain cell-cycle gene families in each of the three spermatogenic cell types investigated. Finally, we report expression of 221 cell-cycle genes that have not previously been annotated as part of the cell cycle network expressed during spermatogenesis, including eight novel genes that appear to be testis-specific. PMID:20631398

  15. An adaptable system for improving transposon-based gene expression in vivo via transient transgene repression

    PubMed Central

    Doherty, Joseph E.; Woodard, Lauren E.; Bear, Adham S.; Foster, Aaron E.; Wilson, Matthew H.

    2013-01-01

    Transposons permit permanent cellular genome engineering in vivo. However, transgene expression falls rapidly postdelivery due to a variety of mechanisms, including immune responses. We hypothesized that delaying initial transgene expression would improve long-term transgene expression by using an engineered piggyBac transposon system that can regulate expression. We found that a 2-part nonviral Tet-KRAB inducible expression system repressed expression of a luciferase reporter in vitro. However, we also observed nonspecific promoter-independent repression. Thus, to achieve temporary transgene repression after gene delivery in vivo, we utilized a nonintegrating version of the repressor plasmid while the gene of interest was delivered in an integrating piggyBac transposon vector. When we delivered the luciferase transposon and repressor to immunocompetent mice by hydrodynamic injection, initial luciferase expression was repressed by 2 orders of magnitude. When luciferase expression was followed long term in vivo, we found that expression was increased >200-fold compared to mice that received only the luciferase transposon and piggyBac transposase. We found that repression of early transgene expression could prevent the priming of luciferase-specific T cells in vivo. Therefore, transient transgene repression postgene delivery is an effective strategy for inhibiting the antitransgene immune response and improving long-term expression in vivo without using immunosuppression.—Doherty, J. E., Woodard, L. E., Bear, A. S., Foster, A. E., Wilson, M. H. An adaptable system for improving transposon-based gene expression in vivo via transient transgene repression. PMID:23752206

  16. Optimal consistency in microRNA expression analysis using reference-gene-based normalization.

    PubMed

    Wang, Xi; Gardiner, Erin J; Cairns, Murray J

    2015-05-01

    Normalization of high-throughput molecular expression profiles secures differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches. While the same general principles apply to microRNA (miRNA) normalization, there is mounting evidence that global shifts in their expression patterns occur in specific circumstances, which pose a challenge for normalizing miRNA expression data. As an alternative to global normalization, which has the propensity to flatten large trends, normalization against constitutively expressed reference genes presents an advantage through their relative independence. Here we investigated the performance of reference-gene-based (RGB) normalization for differential miRNA expression analysis of microarray expression data, and compared the results with other normalization methods, including: quantile, variance stabilization, robust spline, simple scaling, rank invariant, and Loess regression. The comparative analyses were executed using miRNA expression in tissue samples derived from subjects with schizophrenia and non-psychiatric controls. We proposed a consistency criterion for evaluating methods by examining the overlapping of differentially expressed miRNAs detected using different partitions of the whole data. Based on this criterion, we found that RGB normalization generally outperformed global normalization methods. Thus we recommend the application of RGB normalization for miRNA expression data sets, and believe that this will yield a more consistent and useful readout of differentially expressed miRNAs, particularly in biological conditions characterized by large shifts in miRNA expression. PMID:25797570

  17. Learning-based Segmentation Framework for Tissue Images Containing Gene Expression Data

    SciTech Connect

    Bello, Musodiq; Ju, Tao; Carson, James P.; Warren, Joe; Chiu, Wah; Kakadiaris, Ioannis

    2007-05-01

    Abstract Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the over 20,000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our contribution is a novel hybrid atlas that utilizes a statistical shape model based on a subdivision mesh, texture differentiation at region boundaries, and features of anatomical landmarks to delineate boundaries of anatomical regions in gene expression images. This atlas, which provides a common coordinate system for internal brain data, was trained on 36 images manually annotated by neuroanatomists and tested on 64 images. Our framework has achieved a mean overlap ratio of up to 91 § 7% in this challenging dataset. This tool for large-scale annotation will help scientists interpret gene expression patterns more efficiently.

  18. Interval based fuzzy systems for identification of important genes from microarray gene expression data: Application to carcinogenic development.

    PubMed

    De, Rajat K; Ghosh, Anupam

    2009-12-01

    In the present article, we develop two interval based fuzzy systems for identification of some possible genes mediating the carcinogenic development in various tissues. The methodology involves dimensionality reduction, classifying the genes through incorporation of the notion of linguistic fuzzy sets low, medium and high, and finally selection of some possible genes mediating a particular disease, obtained by a rule generation/grouping technique. The effectiveness of the proposed methodology, is demonstrated using five microarray gene expression datasets dealing with human lung, colon, sarcoma, breast cancer and leukemia. Moreover, the superior capability of the methodology in selecting important genes, over five other existing gene selection methods, viz., Significance Analysis of Microarrays (SAM), Signal-to-Noise Ratio (SNR), Neighborhood analysis (NA), Bayesian Regularization (BR) and Data-adaptive (DA) is demonstrated, in terms of the enrichment of each GO category of the important genes based on P-values. The results are appropriately validated by earlier investigations, gene expression profiles and t-test. The proposed methodology has been able to select genes that are more biologically significant in mediating the development of a disease than those obtained by the others. PMID:19591962

  19. Knowledge-based analysis of microarray gene expression data by using support vector machines

    SciTech Connect

    William Grundy; Manuel Ares, Jr.; David Haussler

    2001-06-18

    The authors introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. They test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, they use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

  20. Network-based biomarkers enhance classical approaches to prognostic gene expression signatures

    PubMed Central

    2014-01-01

    Background Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene expression and network connectivity information combined, the latter drawn from prior knowledge of protein-protein interactions. The second approach focuses on identification of informative sub-networks (small networks of interacting proteins, again from prior knowledge networks). For all methods we performed 100 rounds of 5-fold cross-validation under 3 different classifiers. For network-based approaches, we considered two different protein-protein interaction networks. We quantified resulting patterns of misclassification and discussed the relative value of each relative to ongoing development of prognostic biomarkers. Results We found that single-gene, gene-set and network methods yielded similar error rates in melanoma and ovarian cancer data. Crucially, however, our novel and detailed patient-level analyses revealed that the different methods were correctly classifying alternate subsets of patients in each cohort. We also found that the network-based NetRank feature selection method was the most stable. Conclusions Next-generation methods of gene expression signature modelling harness data from external networks and are foreshadowed as a standard mode of analysis. But what do they add

  1. An artificial cell based on gene expression in vesicle

    NASA Astrophysics Data System (ADS)

    Noireaux, Vincent

    2006-03-01

    A new experimental approach is presented to build an artificial cell using the translation machinery of a cell-free expression system as the hardware and a DNA synthetic program as the software. Cytoplasmic extracts, encapsulated in phospholipid vesicles, are used to assemble custom-made genetic circuits to develop the functions of a minimal cell. The objective is to understand how a DNA algorithm can be designed to build an operating system that has some of the properties of life. We show how a long-lived bioreactor is built to carry out in vitro transcription and translation in cell-sized vesicles. To develop the synthetic membrane into an active interface, a few amphipathic peptides and an insertion mechanism of integral membrane proteins have been tested. With vesicles composed of different phospholipids, the fusion protein alpha-hemolysin-eGFP can be expressed to reveal patterns on the membrane. Finally, specific degradation mechanisms are introduced to create a sink for the synthesized messengers and proteins. Perspectives and limitations of this approach will be discussed.

  2. Accurate Gene Expression-Based Biodosimetry Using a Minimal Set of Human Gene Transcripts

    SciTech Connect

    Tucker, James D.; Joiner, Michael C.; Thomas, Robert A.; Grever, William E.; Bakhmutsky, Marina V.; Chinkhota, Chantelle N.; Smolinski, Joseph M.; Divine, George W.; Auner, Gregory W.

    2014-03-15

    Purpose: Rapid and reliable methods for conducting biological dosimetry are a necessity in the event of a large-scale nuclear event. Conventional biodosimetry methods lack the speed, portability, ease of use, and low cost required for triaging numerous victims. Here we address this need by showing that polymerase chain reaction (PCR) on a small number of gene transcripts can provide accurate and rapid dosimetry. The low cost and relative ease of PCR compared with existing dosimetry methods suggest that this approach may be useful in mass-casualty triage situations. Methods and Materials: Human peripheral blood from 60 adult donors was acutely exposed to cobalt-60 gamma rays at doses of 0 (control) to 10 Gy. mRNA expression levels of 121 selected genes were obtained 0.5, 1, and 2 days after exposure by reverse-transcriptase real-time PCR. Optimal dosimetry at each time point was obtained by stepwise regression of dose received against individual gene transcript expression levels. Results: Only 3 to 4 different gene transcripts, ASTN2, CDKN1A, GDF15, and ATM, are needed to explain ≥0.87 of the variance (R{sup 2}). Receiver-operator characteristics, a measure of sensitivity and specificity, of 0.98 for these statistical models were achieved at each time point. Conclusions: The actual and predicted radiation doses agree very closely up to 6 Gy. Dosimetry at 8 and 10 Gy shows some effect of saturation, thereby slightly diminishing the ability to quantify higher exposures. Analyses of these gene transcripts may be advantageous for use in a field-portable device designed to assess exposures in mass casualty situations or in clinical radiation emergencies.

  3. Allen Brain Atlas-Driven Visualizations: a web-based gene expression energy visualization tool.

    PubMed

    Zaldivar, Andrew; Krichmar, Jeffrey L

    2014-01-01

    The Allen Brain Atlas-Driven Visualizations (ABADV) is a publicly accessible web-based tool created to retrieve and visualize expression energy data from the Allen Brain Atlas (ABA) across multiple genes and brain structures. Though the ABA offers their own search engine and software for researchers to view their growing collection of online public data sets, including extensive gene expression and neuroanatomical data from human and mouse brain, many of their tools limit the amount of genes and brain structures researchers can view at once. To complement their work, ABADV generates multiple pie charts, bar charts and heat maps of expression energy values for any given set of genes and brain structures. Such a suite of free and easy-to-understand visualizations allows for easy comparison of gene expression across multiple brain areas. In addition, each visualization links back to the ABA so researchers may view a summary of the experimental detail. ABADV is currently supported on modern web browsers and is compatible with expression energy data from the Allen Mouse Brain Atlas in situ hybridization data. By creating this web application, researchers can immediately obtain and survey numerous amounts of expression energy data from the ABA, which they can then use to supplement their work or perform meta-analysis. In the future, we hope to enable ABADV across multiple data resources. PMID:24904397

  4. Bootstrap-based differential gene expression analysis for RNA-Seq data with and without replicates

    PubMed Central

    2014-01-01

    A major application of RNA-Seq is to perform differential gene expression analysis. Many tools exist to analyze differentially expressed genes in the presence of biological replicates. Frequently, however, RNA-Seq experiments have no or very few biological replicates and development of methods for detecting differentially expressed genes in these scenarios is still an active research area. In this paper we introduce a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. We compared IsoDE against four existing methods (Fisher's exact test, GFOLD, edgeR and Cuffdiff) on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. Experiments on MAQC RNA-Seq datasets without replicates show that IsoDE has consistently high accuracy as defined by the qPCR ground truth, frequently higher than that of the compared methods, particularly for low coverage data and at lower fold change thresholds. In experiments on RNA-Seq datasets with up to 7 replicates, IsoDE has also achieved high accuracy. Furthermore, unlike GFOLD and edgeR, IsoDE accuracy varies smoothly with the number of replicates, and is relatively uniform across the entire range of gene expression levels. The proposed non-parametric method based on bootstrapping has practical running time, and achieves robust performance over a broad range of technologies, number of replicates, sequencing depths, and minimum fold change thresholds. PMID:25435284

  5. WF-MSB: a weighted fuzzy-based biclustering method for gene expression data.

    PubMed

    Chen, Lien-Chin; Yu, Philip S; Tseng, Vincent S

    2011-01-01

    Biclustering is an important analysis method on gene expression data for finding a subset of genes sharing compatible expression patterns. Although some biclustering algorithms have been proposed, few provided a query-driven approach for biologists to search the biclusters, which contain a certain gene of interest. In this paper, we proposed a generalised fuzzy-based approach, namely Weighted Fuzzy-based Maximum Similarity Biclustering (WF-MSB), for extracting a query-driven bicluster based on the user-defined reference gene. A fuzzy-based similarity measurement and condition weighting approach are used to extract significant biclusters in expression levels. Both of the most similar bicluster and the most dissimilar bicluster to the reference gene are discovered by WF-MSB. The proposed WF-MSB method was evaluated in comparison with MSBE on a real yeast microarray data and synthetic data sets. The experimental results show that WF-MSB can effectively find the biclusters with significant GO-based functional meanings. PMID:21491846

  6. Integrating biological knowledge based on functional annotations for biclustering of gene expression data.

    PubMed

    Nepomuceno, Juan A; Troncoso, Alicia; Nepomuceno-Chamorro, Isabel A; Aguilar-Ruiz, Jesús S

    2015-05-01

    Gene expression data analysis is based on the assumption that co-expressed genes imply co-regulated genes. This assumption is being reformulated because the co-expression of a group of genes may be the result of an independent activation with respect to the same experimental condition and not due to the same regulatory regime. For this reason, traditional techniques are recently being improved with the use of prior biological knowledge from open-access repositories together with gene expression data. Biclustering is an unsupervised machine learning technique that searches patterns in gene expression data matrices. A scatter search-based biclustering algorithm that integrates biological information is proposed in this paper. In addition to the gene expression data matrix, the input of the algorithm is only a direct annotation file that relates each gene to a set of terms from a biological repository where genes are annotated. Two different biological measures, FracGO and SimNTO, are proposed to integrate this information by means of its addition to-be-optimized fitness function in the scatter search scheme. The measure FracGO is based on the biological enrichment and SimNTO is based on the overlapping among GO annotations of pairs of genes. Experimental results evaluate the proposed algorithm for two datasets and show the algorithm performs better when biological knowledge is integrated. Moreover, the analysis and comparison between the two different biological measures is presented and it is concluded that the differences depend on both the data source and how the annotation file has been built in the case GO is used. It is also shown that the proposed algorithm obtains a greater number of enriched biclusters than other classical biclustering algorithms typically used as benchmark and an analysis of the overlapping among biclusters reveals that the biclusters obtained present a low overlapping. The proposed methodology is a general-purpose algorithm which allows

  7. A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer’s Disease Diagnosis

    PubMed Central

    Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard JB.

    2015-01-01

    Background: Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer’s disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. Objectives: This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Methods: Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Results: Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Conclusions: Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach. PMID:26484910

  8. A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression

    PubMed Central

    Nguyen, Nha; Vo, An; Choi, Inchan

    2015-01-01

    Abstract Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation. PMID:25383910

  9. Genomic DNA-based absolute quantification of gene expression in Vitis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Many studies in which gene expression is quantified by polymerase chain reaction represent the expression of a gene of interest (GOI) relative to that of a reference gene (RG). Relative expression is founded on the assumptions that RG expression is stable across samples, treatments, organs, etc., an...

  10. Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data.

    PubMed

    Kaneko, Shuhei; Hirakawa, Akihiro; Hamada, Chikuma

    2015-01-01

    In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso) has been widely used to select genes that truly correlated with a patient's survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV). However, this method can pass over (or fail to identify) true positive genes (i.e., it identifies false negatives) in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number of true positive (TP) genes for a series of values of a tuning parameter that assumes a mixture distribution for the lasso estimates. Using our developed method, we performed a simulation study to examine its precision in estimating the number of TP genes. Additionally, we applied our method to a real gene expression dataset and found that it was able to identify genes correlated with survival that a CV method was unable to detect. PMID:26146513

  11. Molecular mechanisms associated with breast cancer based on integrated gene expression profiling by bioinformatics analysis.

    PubMed

    Wu, Di; Han, Bing; Guo, Liang; Fan, Zhimin

    2016-07-01

    In this study, we aimed to gain more insights into the underlying molecular mechanisms responsible for breast cancer (BC) progression. Three gene expression profiles of human BC were integrated and used to screen the differentially expressed genes (DEGs) between healthy breast samples and BC samples. Protein-protein interaction (PPI) network of DEGs was constructed by mapping DEGs into the Search Tool for the Retrieval of Interacting Genes (STRING) database; then the subnetworks of PPI were constructed with plug-in, MCODE and DEGs in Subnetwork 1 were analysed based on Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway database ( http://www.genome.jp/kegg /). In addition, co-expression network of DEGs was established using the Cytoscape. Totalally 931 DEGs were selected, including 340 up-regulated genes and 591 down-regulated genes. KEGG pathway analysis for DEGs in Subnetwork 1 showed that the pathogenesis of BC was associated with cell cycle, oocyte meiosis, progesterone-mediated oocyte maturation and p53 signalling pathways. Meanwhile, the most significant-related DEGs were found by co-expression network analysis of DEGs. In conclusion, CCNG1 might be involved in the progression of BC via inhibiting cell proliferation, and ADAMTS1 might play a crucial role in BC development through the regulation of angiogenesis. PMID:26804550

  12. A method for multiplex gene synthesis employing error correction based on expression.

    PubMed

    Hsiau, Timothy H-C; Sukovich, David; Elms, Phillip; Prince, Robin N; Strittmatter, Tobias; Stritmatter, Tobias; Ruan, Paul; Curry, Bo; Anderson, Paige; Sampson, Jeff; Anderson, J Christopher

    2015-01-01

    Our ability to engineer organisms with new biosynthetic pathways and genetic circuits is limited by the availability of protein characterization data and the cost of synthetic DNA. With new tools for reading and writing DNA, there are opportunities for scalable assays that more efficiently and cost effectively mine for biochemical protein characteristics. To that end, we have developed the Multiplex Library Synthesis and Expression Correction (MuLSEC) method for rapid assembly, error correction, and expression characterization of many genes as a pooled library. This methodology enables gene synthesis from microarray-synthesized oligonucleotide pools with a one-pot technique, eliminating the need for robotic liquid handling. Post assembly, the gene library is subjected to an ampicillin based quality control selection, which serves as both an error correction step and a selection for proteins that are properly expressed and folded in E. coli. Next generation sequencing of post selection DNA enables quantitative analysis of gene expression characteristics. We demonstrate the feasibility of this approach by building and testing over 90 genes for empirical evidence of soluble expression. This technique reduces the problem of part characterization to multiplex oligonucleotide synthesis and deep sequencing, two technologies under extensive development with projected cost reduction. PMID:25790188

  13. A Method for Multiplex Gene Synthesis Employing Error Correction Based on Expression

    PubMed Central

    Hsiau, Timothy H.-C.; Sukovich, David; Elms, Phillip; Prince, Robin N.; Stritmatter, Tobias; Ruan, Paul; Curry, Bo; Anderson, Paige; Sampson, Jeff; Anderson, J. Christopher

    2015-01-01

    Our ability to engineer organisms with new biosynthetic pathways and genetic circuits is limited by the availability of protein characterization data and the cost of synthetic DNA. With new tools for reading and writing DNA, there are opportunities for scalable assays that more efficiently and cost effectively mine for biochemical protein characteristics. To that end, we have developed the Multiplex Library Synthesis and Expression Correction (MuLSEC) method for rapid assembly, error correction, and expression characterization of many genes as a pooled library. This methodology enables gene synthesis from microarray-synthesized oligonucleotide pools with a one-pot technique, eliminating the need for robotic liquid handling. Post assembly, the gene library is subjected to an ampicillin based quality control selection, which serves as both an error correction step and a selection for proteins that are properly expressed and folded in E. coli. Next generation sequencing of post selection DNA enables quantitative analysis of gene expression characteristics. We demonstrate the feasibility of this approach by building and testing over 90 genes for empirical evidence of soluble expression. This technique reduces the problem of part characterization to multiplex oligonucleotide synthesis and deep sequencing, two technologies under extensive development with projected cost reduction. PMID:25790188

  14. Transcriptome based identification and tissue expression profiles of chemosensory genes in Blattella germanica (Blattaria: Blattidae).

    PubMed

    Niu, Dong-Juan; Liu, Yan; Dong, Xiao-Tong; Dong, Shuang-Lin

    2016-06-01

    Blattalla germanica is one of the most notorious household insect pests, and evolutionally more primitive than those well studied moths and flies, regarding the molecular mechanisms of chemosensation. In this study, we sequenced, for the first time, the antennal transcriptome of B. germanica using the Illumina HiSeq™ 2000 platform and then conducted the bioinformatic analysis of the data. In total, we identified 73 putative chemosensory genes, with 62 genes being novel in this species. These chemosensory genes included 48 odorant binding proteins (OBPs), 9 chemosensory proteins (CSPs), 6 sensory neuron membrane proteins (SNMPs), 5 odorant receptors (ORs) and 5 ionotropic receptors (IRs). Notably, Plus-C OBPs account for an exceptionally high proportion (39.58%) of the total 48 OBPs in this primitive insect. To predict the chemosensory functions of the genes, a detailed global tissue expression profiling was investigated by reverse transcription polymerase chain reaction (RT-PCR). Most OBP genes showed a chemosensory tissue biased profile, while CSP transcripts were widely and evenly expressed in different tissues. Furthermore, we found that more than half the chemosensory genes were expressed in the cerci, implying the important chemosensory functions of the organ in B. germanica. Taken together, our study provides important bases for elucidation of the molecular mechanisms and evolution of insect chemosensation, and for development of the chemosensation based techniques to control B. germanica. PMID:26994445

  15. Analysis of ripening-related gene expression in papaya using an Arabidopsis-based microarray

    PubMed Central

    2012-01-01

    Background Papaya (Carica papaya L.) is a commercially important crop that produces climacteric fruits with a soft and sweet pulp that contain a wide range of health promoting phytochemicals. Despite its importance, little is known about transcriptional modifications during papaya fruit ripening and their control. In this study we report the analysis of ripe papaya transcriptome by using a cross-species (XSpecies) microarray technique based on the phylogenetic proximity between papaya and Arabidopsis thaliana. Results Papaya transcriptome analyses resulted in the identification of 414 ripening-related genes with some having their expression validated by qPCR. The transcription profile was compared with that from ripening tomato and grape. There were many similarities between papaya and tomato especially with respect to the expression of genes encoding proteins involved in primary metabolism, regulation of transcription, biotic and abiotic stress and cell wall metabolism. XSpecies microarray data indicated that transcription factors (TFs) of the MADS-box, NAC and AP2/ERF gene families were involved in the control of papaya ripening and revealed that cell wall-related gene expression in papaya had similarities to the expression profiles seen in Arabidopsis during hypocotyl development. Conclusion The cross-species array experiment identified a ripening-related set of genes in papaya allowing the comparison of transcription control between papaya and other fruit bearing taxa during the ripening process. PMID:23256600

  16. Network-based gene expression analysis of intracranial aneurysm tissue reveals role of antigen presenting cells.

    PubMed

    Krischek, B; Kasuya, H; Tajima, A; Akagawa, H; Sasaki, T; Yoneyama, T; Ujiie, H; Kubo, O; Bonin, M; Takakura, K; Hori, T; Inoue, I

    2008-07-17

    Little is known about the pathology and pathogenesis of the rupture of intracranial aneurysms. For a better understanding of the molecular processes involved in intracranial aneurysm (IA) formation we performed a gene expression analysis comparing ruptured and unruptured aneurysm tissue to a control artery. Tissue samples of six ruptured and four unruptured aneurysms, and four cerebral arteries serving as controls, were profiled using oligonucleotide microarrays. Gene ontology classification of the differentially expressed genes was analyzed and regulatory functional networks and canonical pathways were identified with a network-based computational pathway analysis tool. Real time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemical staining were performed as confirmation. Analysis of aneurysmal and control tissue revealed 521 differentially expressed genes. The most significantly associated gene ontology term was antigen processing (P=1.64E-16). Further network-based analysis showed the top scoring regulatory functional network to be built around overexpressed major histocompatibility class (MHC) I and II complex related genes and confirmed the canonical pathway "Antigen Presentation" to have the highest upregulation in IA tissue (P=7.3E-10). Real time RT-PCR showed significant overexpression of MHC class II genes. Immunohistochemical staining showed strong positivity for MHC II molecule specific antibody (HLA II), for CD68 (macrophages, monocytes), for CD45RO (T-cells) and HLA I antibody. Our results offer strong evidence for MHC class II gene overexpression in human IA tissue and that antigen presenting cells (macrophages, monocytes) play a key role in IA formation. PMID:18538937

  17. A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network.

    PubMed

    Ruan, Xiyun; Li, Hongyun; Liu, Bo; Chen, Jie; Zhang, Shibao; Sun, Zeqiang; Liu, Shuangqing; Sun, Fahai; Liu, Qingyong

    2015-08-01

    The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson's correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson's correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis. PMID:26058425

  18. Straightforward and sensitive RT-qPCR based gene expression analysis of FFPE samples

    PubMed Central

    Zeka, Fjoralba; Vanderheyden, Katrien; De Smet, Els; Cuvelier, Claude A.; Mestdagh, Pieter; Vandesompele, Jo

    2016-01-01

    Fragmented RNA from formalin-fixed paraffin-embedded (FFPE) tissue is a known obstacle to gene expression analysis. In this study, the impact of RNA integrity, gene-specific reverse transcription and targeted cDNA preamplification was quantified in terms of reverse transcription polymerase chain reaction (RT-qPCR) sensitivity by measuring 48 protein coding genes on eight duplicate cultured cancer cell pellet FFPE samples and twenty cancer tissue FFPE samples. More intact RNA modestly increased gene detection sensitivity by 1.6 fold (earlier detection by 0.7 PCR cycles, 95% CI = 0.593–0.850). Application of gene-specific priming instead of whole transcriptome priming during reverse transcription further improved RT-qPCR sensitivity by a considerable 4.0 fold increase (earlier detection by 2.0 PCR cycles, 95% CI = 1.73–2.32). Targeted cDNA preamplification resulted in the strongest increase of RT-qPCR sensitivity and enabled earlier detection by an average of 172.4 fold (7.43 PCR cycles, 95% CI = 6.83–7.05). We conclude that gene-specific reverse transcription and targeted cDNA preamplification are adequate methods for accurate and sensitive RT-qPCR based gene expression analysis of FFPE material. The presented methods do not involve expensive or complex procedures and can be easily implemented in any routine RT-qPCR practice. PMID:26898768

  19. Microarray Based Gene Expression Analysis of Murine Brown and Subcutaneous Adipose Tissue: Significance with Human

    PubMed Central

    Boparai, Ravneet K.; Kondepudi, Kanthi Kiran; Mantri, Shrikant; Bishnoi, Mahendra

    2015-01-01

    Background Two types of adipose tissues, white (WAT) and brown (BAT) are found in mammals. Increasingly novel strategies are being proposed for the treatment of obesity and its associated complications by altering amount and/or activity of BAT using mouse models. Methodology/Principle Findings The present study was designed to: (a) investigate the differential expression of genes in LACA mice subcutaneous WAT (sWAT) and BAT using mouse DNA microarray, (b) to compare mouse differential gene expression with previously published human data; to understand any inter- species differences between the two and (c) to make a comparative assessment with C57BL/6 mouse strain. In mouse microarray studies, over 7003, 1176 and 401 probe sets showed more than two-fold, five-fold and ten-fold change respectively in differential expression between murine BAT and WAT. Microarray data was validated using quantitative RT-PCR of key genes showing high expression in BAT (Fabp3, Ucp1, Slc27a1) and sWAT (Ms4a1, H2-Ob, Bank1) or showing relatively low expression in BAT (Pgk1, Cox6b1) and sWAT (Slc20a1, Cd74). Multi-omic pathway analysis was employed to understand possible links between the organisms. When murine two fold data was compared with published human BAT and sWAT data, 90 genes showed parallel differential expression in both mouse and human. Out of these 90 genes, 46 showed same pattern of differential expression whereas the pattern was opposite for the remaining 44 genes. Based on our microarray results and its comparison with human data, we were able to identify genes (targets) (a) which can be studied in mouse model systems to extrapolate results to human (b) where caution should be exercised before extrapolation of murine data to human. Conclusion Our study provides evidence for inter species (mouse vs human) differences in differential gene expression between sWAT and BAT. Critical understanding of this data may help in development of novel ways to engineer one form of adipose

  20. Integrated microRNA-gene analysis of coronary artery disease based on miRNA and gene expression profiles

    PubMed Central

    XU, XIANGDONG; LI, HONGSONG

    2016-01-01

    The present study aimed to investigate the key genes and microRNAs (miRNA/miRs) associated with coronary artery disease (CAD) progression. The gene expression profile of GSE20680 and GSE12288, and the miRNA expression profile of GSE28858 were downloaded from the gene expression omnibus database. The differentially expressed genes (DEGs) in GSE20680 and GSE12288, and the differentially expressed miRNAs in GSE28858 were screened using the limma package in R software. Common DEGs between GSE20680 and GSE12288 were selected. Functions and pathways of DEGs and miRNAs were enriched using the DAVID tool from the GO and KEGG databases. The regulatory network of miRNA and selected CAD-associated DEGs was constructed. A total of 270 DEGs (167 upregulated and 103 downregulated) based on the GSE20680 dataset, and 2,268 DEGs (534 upregulated and 1,734 downregulated) based on the GSE12288 dataset, were screened. For the differentially expressed miRNAs, 214 were identified (102 upregulated and 112 downregulated) in CAD samples and were screened. Interferon regulatory factor 2 (IRF2) and cell death-inducing DFFA-like effector b (CIDEB), which are regulated by signal transducer and activator of transcription 3 and myc-associated factor X, were identified as common DEGs for CAD. miR-455-5p, miR-455-3p and miR-1257, which are involved in the major histocompatibility complex (MHC)protein assembly pathway and peptide antigen assembly with MHC class I protein complex pathway, may regulate various miRNAs and target genes, including pro-opiomelancortin (POMC), toll-like receptor 4 (TLR4), interleukin 10 (IL10), activating transcription factor 6 (ATF6) and calreticulin (CALR). The current study identified IRF2 and CIDEB as crucial genes, and miRNA-455-5p, miRNA-455-3p and miR-1257 along with their target genes POMC, TLR4 and CALR, as miRNAs involved in CAD progression. Thus, the present study may provide a basis for future research into the progression mechanism of CAD. PMID:26936111

  1. A novel sparse coding algorithm for classification of tumors based on gene expression data.

    PubMed

    Kolali Khormuji, Morteza; Bazrafkan, Mehrnoosh

    2016-06-01

    High-dimensional genomic and proteomic data play an important role in many applications in medicine such as prognosis of diseases, diagnosis, prevention and molecular biology, to name a few. Classifying such data is a challenging task due to the various issues such as curse of dimensionality, noise and redundancy. Recently, some researchers have used the sparse representation (SR) techniques to analyze high-dimensional biological data in various applications in classification of cancer patients based on gene expression datasets. A common problem with all SR-based biological data classification methods is that they cannot utilize the topological (geometrical) structure of data. More precisely, these methods transfer the data into sparse feature space without preserving the local structure of data points. In this paper, we proposed a novel SR-based cancer classification algorithm based on gene expression data that takes into account the geometrical information of all data. Precisely speaking, we incorporate the local linear embedding algorithm into the sparse coding framework, by which we can preserve the geometrical structure of all data. For performance comparison, we applied our algorithm on six tumor gene expression datasets, by which we demonstrate that the proposed method achieves higher classification accuracy than state-of-the-art SR-based tumor classification algorithms. PMID:26337064

  2. Gene expression-based classifications of fibroadenomas and phyllodes tumours of the breast.

    PubMed

    Vidal, Maria; Peg, Vicente; Galván, Patricia; Tres, Alejandro; Cortés, Javier; Ramón y Cajal, Santiago; Rubio, Isabel T; Prat, Aleix

    2015-06-01

    using gene expression-based data is feasible and might provide clinically useful biological and prognostic information. PMID:25687451

  3. Gene Expression-Based Screen for Parkinson's Disease Identifies GW8510 as a Neuroprotective Agent.

    PubMed

    Wimalasena, Nivanthika K; Le, Viet Q; Wimalasena, Kandatege; Schreiber, Stuart L; Karmacharya, Rakesh

    2016-07-20

    We carried out a gene expression-based in silico screen in order to identify small molecules with gene-expression profiles that are anticorrelated with a gene-expression profile for Parkinson's disease (PD). We identified the cyclin-dependent kinase 2/5 (CDK2/5) inhibitor GW8510 as our most significant hit and characterized its effects in rodent MN9D cells and in human neuronal cells derived from induced pluripotent stem cells. GW8510 demonstrated neuroprotective ability in MN9D cells in the presence of 1-methyl-4-phenylpyridium (MPP(+)), a widely used neurotoxin model for Parkinson's disease. In order to delineate the nature and extent of GW8510's neuroprotective properties, we studied GW8510 in human neuronal cells in the context of various mechanisms of cellular stress. We found that GW8510 was protective against small-molecule mitochondrial and endoplasmic reticulum stressors. Our findings illustrate an approach to using small-molecule gene expression libraries to identify compounds with therapeutic potential in human diseases. PMID:27270122

  4. Microarray-based gene expression profiles in rabbit retina due to negative pressure suction.

    PubMed

    Zhao, H X; Niu, C M; Guan, W Y

    2012-01-01

    We investigated a possible molecular pathogenesis involving retinal ganglion cell apoptosis following transient high intraocular pressure. Changes in the gene expression profiles of the retina were detected via gene chip methodology. Twelve New Zealand white rabbits were randomly assigned to control and 3-min negative pressure suction groups. The control group was treated only with a laser, and the experimental group was also treated with suction for 3 min, using a negative pressure generator. Total RNA was then extracted from the retinal tissue at different recovery stages to analyze gene expression profiles using the Agilent rabbit one-way gene chip. The groups were then compared. Immediately after negative pressure suction induction, 704 genes were differentially expressed. Among these, 485 genes were upregulated, and 219 were downregulated. Expression of the genes encoding CRYAA, CRYAB, and TLR3 genes, which are involved in apoptosis, was elevated. The KRT18 gene, which is involved in apoptosis, had reduced expression. Seven days after negative pressure suction, 482 genes were differentially expressed. Among these, 178 genes were upregulated, and 304 were downregulated. Expression of the genes encoding CRYAB, IL1-BETA and IL1R1, which are involved in apoptosis, was upregulated. Ten days after negative pressure suction, 402 genes were differentially expressed. Of these, 213 genes were upregulated, and 189 were downregulated. Apoptosis genes CRYAB, CRYBA3, CRYBB2, IL1- BETA, and IL1R1 showed higher expression levels. We concluded that negative pressure suction for long periods of time (for example, 3 min) results in changes in gene expression. Genes with higher fold changes help protect retinal ganglion cells from apoptosis. We suggest that promoting the expression of these genes should be considered as a new means for treating ischemic-hypoxic retinopathy. PMID:22653643

  5. Recent developments in StemBase: a tool to study gene expression in human and murine stem cells

    PubMed Central

    Sandie, Reatha; Palidwor, Gareth A; Huska, Matthew R; Porter, Christopher J; Krzyzanowski, Paul M; Muro, Enrique M; Perez-Iratxeta, Carolina; Andrade-Navarro, Miguel A

    2009-01-01

    Background Currently one of the largest online repositories for human and mouse stem cell gene expression data, StemBase was first designed as a simple web-interface to DNA microarray data generated by the Canadian Stem Cell Network to facilitate the discovery of gene functions relevant to stem cell control and differentiation. Findings Since its creation, StemBase has grown in both size and scope into a system with analysis tools that examine either the whole database at once, or slices of data, based on tissue type, cell type or gene of interest. As of September 1, 2008, StemBase contains gene expression data (microarray and Serial Analysis of Gene Expression) from 210 stem cell samples in 60 different experiments. Conclusion StemBase can be used to study gene expression in human and murine stem cells and is available at . PMID:19284540

  6. Ontology based molecular signatures for immune cell types via gene expression analysis

    PubMed Central

    2013-01-01

    Background New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity. Results We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types. Conclusions This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights. PMID:24004649

  7. An EST-based analysis identifies new genes and reveals distinctive gene expression features of Coffea arabica and Coffea canephora

    PubMed Central

    2011-01-01

    Background Coffee is one of the world's most important crops; it is consumed worldwide and plays a significant role in the economy of producing countries. Coffea arabica and C. canephora are responsible for 70 and 30% of commercial production, respectively. C. arabica is an allotetraploid from a recent hybridization of the diploid species, C. canephora and C. eugenioides. C. arabica has lower genetic diversity and results in a higher quality beverage than C. canephora. Research initiatives have been launched to produce genomic and transcriptomic data about Coffea spp. as a strategy to improve breeding efficiency. Results Assembling the expressed sequence tags (ESTs) of C. arabica and C. canephora produced by the Brazilian Coffee Genome Project and the Nestlé-Cornell Consortium revealed 32,007 clusters of C. arabica and 16,665 clusters of C. canephora. We detected different GC3 profiles between these species that are related to their genome structure and mating system. BLAST analysis revealed similarities between coffee and grape (Vitis vinifera) genes. Using KA/KS analysis, we identified coffee genes under purifying and positive selection. Protein domain and gene ontology analyses suggested differences between Coffea spp. data, mainly in relation to complex sugar synthases and nucleotide binding proteins. OrthoMCL was used to identify specific and prevalent coffee protein families when compared to five other plant species. Among the interesting families annotated are new cystatins, glycine-rich proteins and RALF-like peptides. Hierarchical clustering was used to independently group C. arabica and C. canephora expression clusters according to expression data extracted from EST libraries, resulting in the identification of differentially expressed genes. Based on these results, we emphasize gene annotation and discuss plant defenses, abiotic stress and cup quality-related functional categories. Conclusion We present the first comprehensive genome-wide transcript

  8. A contribution to the study of plant development evolution based on gene co-expression networks

    PubMed Central

    Romero-Campero, Francisco J.; Lucas-Reina, Eva; Said, Fatima E.; Romero, José M.; Valverde, Federico

    2013-01-01

    Phototrophic eukaryotes are among the most successful organisms on Earth due to their unparalleled efficiency at capturing light energy and fixing carbon dioxide to produce organic molecules. A conserved and efficient network of light-dependent regulatory modules could be at the bases of this success. This regulatory system conferred early advantages to phototrophic eukaryotes that allowed for specialization, complex developmental processes and modern plant characteristics. We have studied light-dependent gene regulatory modules from algae to plants employing integrative-omics approaches based on gene co-expression networks. Our study reveals some remarkably conserved ways in which eukaryotic phototrophs deal with day length and light signaling. Here we describe how a family of Arabidopsis transcription factors involved in photoperiod response has evolved from a single algal gene according to the innovation, amplification and divergence theory of gene evolution by duplication. These modifications of the gene co-expression networks from the ancient unicellular green algae Chlamydomonas reinhardtii to the modern brassica Arabidopsis thaliana may hint on the evolution and specialization of plants and other organisms. PMID:23935602

  9. A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression

    PubMed Central

    2014-01-01

    Background Cancer subtype information is critically important for understanding tumor heterogeneity. Existing methods to identify cancer subtypes have primarily focused on utilizing generic clustering algorithms (such as hierarchical clustering) to identify subtypes based on gene expression data. The network-level interaction among genes, which is key to understanding the molecular perturbations in cancer, has been rarely considered during the clustering process. The motivation of our work is to develop a method that effectively incorporates molecular interaction networks into the clustering process to improve cancer subtype identification. Results We have developed a new clustering algorithm for cancer subtype identification, called “network-assisted co-clustering for the identification of cancer subtypes” (NCIS). NCIS combines gene network information to simultaneously group samples and genes into biologically meaningful clusters. Prior to clustering, we assign weights to genes based on their impact in the network. Then a new weighted co-clustering algorithm based on a semi-nonnegative matrix tri-factorization is applied. We evaluated the effectiveness of NCIS on simulated datasets as well as large-scale Breast Cancer and Glioblastoma Multiforme patient samples from The Cancer Genome Atlas (TCGA) project. NCIS was shown to better separate the patient samples into clinically distinct subtypes and achieve higher accuracy on the simulated datasets to tolerate noise, as compared to consensus hierarchical clustering. Conclusions The weighted co-clustering approach in NCIS provides a unique solution to incorporate gene network information into the clustering process. Our tool will be useful to comprehensively identify cancer subtypes that would otherwise be obscured by cancer heterogeneity, using high-throughput and high-dimensional gene expression data. PMID:24491042

  10. ‘LungGENS’: a web-based tool for mapping single-cell gene expression in the developing lung

    PubMed Central

    Du, Yina; Guo, Minzhe; Whitsett, Jeffrey A; Xu, Yan

    2015-01-01

    We developed LungGENS (Lung Gene Expression iN Single-cell), a web-based bioinformatics resource for querying single-cell gene expression databases by entering a gene symbol or a list of genes or selecting a cell type of their interest. Gene query provides quantitative RNA expression of the gene of interest in each lung cell type. Cell type query returns associated selective gene signatures and genes encoding cell surface markers and transcription factors in interactive heatmap and tables. LungGENS will be broadly applicable in respiratory research, providing a cell-specific RNA expression resource at single-cell resolution. LungGENS is freely available for non-commercial use at https://research.cchmc.org/pbge/lunggens/default.html. PMID:26130332

  11. 'LungGENS': a web-based tool for mapping single-cell gene expression in the developing lung.

    PubMed

    Du, Yina; Guo, Minzhe; Whitsett, Jeffrey A; Xu, Yan

    2015-11-01

    We developed LungGENS (Lung Gene Expression iN Single-cell), a web-based bioinformatics resource for querying single-cell gene expression databases by entering a gene symbol or a list of genes or selecting a cell type of their interest. Gene query provides quantitative RNA expression of the gene of interest in each lung cell type. Cell type query returns associated selective gene signatures and genes encoding cell surface markers and transcription factors in interactive heatmap and tables. LungGENS will be broadly applicable in respiratory research, providing a cell-specific RNA expression resource at single-cell resolution. LungGENS is freely available for non-commercial use at https://research.cchmc.org/pbge/lunggens/default.html. PMID:26130332

  12. An siRNA-based method for efficient silencing of gene expression in mature brown adipocytes.

    PubMed

    Isidor, Marie S; Winther, Sally; Basse, Astrid L; Petersen, M Christine H; Cannon, Barbara; Nedergaard, Jan; Hansen, Jacob B

    2016-01-01

    Brown adipose tissue is a promising therapeutic target for opposing obesity, glucose intolerance and insulin resistance. The ability to modulate gene expression in mature brown adipocytes is important to understand brown adipocyte function and delineate novel regulatory mechanisms of non-shivering thermogenesis. The aim of this study was to optimize a lipofection-based small interfering RNA (siRNA) transfection protocol for efficient silencing of gene expression in mature brown adipocytes. We determined that a critical parameter was to deliver the siRNA to mature adipocytes by reverse transfection, i.e. transfection of non-adherent cells. Using this protocol, we effectively knocked down both high- and low-abundance transcripts in a model of mature brown adipocytes (WT-1) as well as in primary mature mouse brown adipocytes. A functional consequence of the knockdown was confirmed by an attenuated increase in uncoupled respiration (thermogenesis) in response to β-adrenergic stimulation of mature WT-1 brown adipocytes transfected with uncoupling protein 1 siRNA. Efficient gene silencing was also obtained in various mouse and human white adipocyte models (3T3-L1, primary mouse white adipocytes, hMADS) with the ability to undergo "browning." In summary, we report an easy and versatile reverse siRNA transfection protocol to achieve specific silencing of gene expression in various models of mature brown and browning-competent white adipocytes, including primary cells. PMID:27386153

  13. RNA-based, transient modulation of gene expression in human haematopoietic stem and progenitor cells

    PubMed Central

    Diener, Yvonne; Jurk, Marion; Kandil, Britta; Choi, Yeong-Hoon; Wild, Stefan; Bissels, Ute; Bosio, Andreas

    2015-01-01

    Modulation of gene expression is a useful tool to study the biology of haematopoietic stem and progenitor cells (HSPCs) and might also be instrumental to expand these cells for therapeutic approaches. Most of the studies so far have employed stable gene modification by viral vectors that are burdensome when translating protocols into clinical settings. Our study aimed at exploring new ways to transiently modify HSPC gene expression using non-integrating, RNA-based molecules. First, we tested different methods to deliver these molecules into HSPCs. The delivery of siRNAs with chemical transfection methods such as lipofection or cationic polymers did not lead to target knockdown, although we observed more than 90% fluorescent cells using a fluorochrome-coupled siRNA. Confocal microscopic analysis revealed that despite extensive washing, siRNA stuck to or in the cell surface, thereby mimicking a transfection event. In contrast, electroporation resulted in efficient, siRNA-mediated protein knockdown. For transient overexpression of proteins, we used optimised mRNA molecules with modified 5′- and 3′-UTRs. Electroporation of mRNA encoding GFP resulted in fast, efficient and persistent protein expression for at least seven days. Our data provide a broad-ranging comparison of transfection methods for hard-to-transfect cells and offer new opportunities for DNA-free, non-integrating gene modulation in HSPCs. PMID:26599627

  14. Gene expression-based risk score in diffuse large B-cell lymphoma.

    PubMed

    Bret, Caroline; Klein, Bernard; Moreaux, Jérôme

    2012-12-01

    Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma and displays heterogeneous clinical and molecular characteristics. In this study, high throughput gene expression profiling of DLBCL tumor samples was used to design a 12-gene expression-based risk score (GERS) predictive for patient's overall survival. GERS allowed identifying a high-risk group comprising 46,4% of the DLBCL patients in two independent cohorts (n=414 and n=69). GERS was shown to be an independent predictor of survival when compared to the previously published prognostic factors, including the International Prognostic Index (IPI). GERS displayed a prognostic value in germinal-center B-cell-like subgroup (GCB) and activated B cell-like (ABC) molecular subgroups of patients as well as in DLBCL patients treated with cyclophosphamide, doxorubicin, vincristine and prednisone (CHOP) or rituximab-CHOP (R-CHOP) regimens. Combination of GERS and IPI lead to a potent prognostic classification of DLBCL patients. Finally, a genomic instability gene signature was highlighted in gene expression profiles of patients belonging to the high-risk GERS-defined group. PMID:23482333

  15. Prognostic prediction through biclustering-based classification of clinical gene expression time series.

    PubMed

    Carreiro, André V; Anunciação, Orlando; Carriço, João A; Madeira, Sara C

    2011-01-01

    The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perpective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient's response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series. PMID:21926438

  16. Graph-based identification of cancer signaling pathways from published gene expression signatures using PubLiME.

    PubMed

    Finocchiaro, Giacomo; Mancuso, Francesco Mattia; Cittaro, Davide; Muller, Heiko

    2007-01-01

    Gene expression technology has become a routine application in many laboratories and has provided large amounts of gene expression signatures that have been identified in a variety of cancer types. Interpretation of gene expression signatures would profit from the availability of a procedure capable of assigning differentially regulated genes or entire gene signatures to defined cancer signaling pathways. Here we describe a graph-based approach that identifies cancer signaling pathways from published gene expression signatures. Published gene expression signatures are collected in a database (PubLiME: Published Lists of Microarray Experiments) enabled for cross-platform gene annotation. Significant co-occurrence modules composed of up to 10 genes in different gene expression signatures are identified. Significantly co-occurring genes are linked by an edge in an undirected graph. Edge-betweenness and k-clique clustering combined with graph modularity as a quality measure are used to identify communities in the resulting graph. The identified communities consist of cell cycle, apoptosis, phosphorylation cascade, extra cellular matrix, interferon and immune response regulators as well as communities of unknown function. The genes constituting different communities are characterized by common genomic features and strongly enriched cis-regulatory modules in their upstream regulatory regions that are consistent with pathway assignment of those genes. PMID:17389643

  17. Distributed Function Mining for Gene Expression Programming Based on Fast Reduction.

    PubMed

    Deng, Song; Yue, Dong; Yang, Le-chan; Fu, Xiong; Feng, Ya-zhou

    2016-01-01

    For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining. PMID:26751200

  18. A cell-based in vitro alternative to identify skin sensitizers by gene expression

    SciTech Connect

    Hooyberghs, Jef Schoeters, Elke; Lambrechts, Nathalie; Nelissen, Inge; Witters, Hilda; Schoeters, Greet; Heuvel, Rosette van den

    2008-08-15

    The ethical and economic burden associated with animal testing for assessment of skin sensitization has triggered intensive research effort towards development and validation of alternative methods. In addition, new legislation on the registration and use of cosmetics and chemicals promote the use of suitable alternatives for hazard assessment. Our previous studies demonstrated that human CD34{sup +} progenitor-derived dendritic cells from cord blood express specific gene profiles upon exposure to low molecular weight sensitizing chemicals. This paper presents a classification model based on this cell type which is successful in discriminating sensitizing chemicals from non-sensitizing chemicals based on transcriptome analysis of 13 genes. Expression profiles of a set of 10 sensitizers and 11 non-sensitizers were analyzed by RT-PCR using 9 different exposure conditions and a total of 73 donor samples. Based on these data a predictive dichotomous classifier for skin sensitizers has been constructed, which is referred to as . In a first step the dimensionality of the input data was reduced by selectively rejecting a number of exposure conditions and genes. Next, the generalization of a linear classifier was evaluated by a cross-validation which resulted in a prediction performance with a concordance of 89%, a specificity of 97% and a sensitivity of 82%. These results show that the present model may be a useful human in vitro alternative for further use in a test strategy towards the reduction of animal use for skin sensitization.

  19. RANWAR: rank-based weighted association rule mining from gene expression and methylation data.

    PubMed

    Mallik, Saurav; Mukhopadhyay, Anirban; Maulik, Ujjwal

    2015-01-01

    Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported. PMID:25265613

  20. Practical implications of gene-expression-based assays for breast oncologists

    PubMed Central

    Prat, Aleix; Ellis, Matthew J.; Perou, Charles M.

    2013-01-01

    Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Two statistical approaches underlie these advancements. Supervised analyses have led to the development of gene-expression signatures designed to predict survival and/or treatment response, which has resulted in the development of new clinical assays. Unsupervised analyses have identified numerous biological signatures including signatures of cell type of origin, signaling pathways, and of cellular proliferation. Included within these biological signatures are the molecular subtypes known as the ‘intrinsic’ subtypes of breast cancer. This classification has expanded our appreciation of the heterogeneity of breast cancer and has provided a way to sub-classify the disease in a manner that might have clinical utility. In this Review, we discuss the clinical utility of gene-expression-based assays and their technical potential as clinical tools vis-a-vis the performance of breast cancer biomarkers that are the current standard of care. PMID:22143140

  1. A Digital Gene Expression-Based Bovine Gene Atlas Evaluating 92 Adult, Juvenile and Fetal Cattle Tissues

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A comprehensive transcriptome survey, or “Gene Atlas,” provides information essential for a complete understanding of the genomic biology of an organism. Using a digital gene expression approach, we developed a Gene Atlas of RNA abundance in 92 adult, juvenile and fetal cattle tissues. The samples...

  2. Algorithmic Approach for Removing the Redundancy in Diabetic Gene Categories Based on Semantic Similarity and Gene Expression Data.

    PubMed

    Kumar, Atul; Sharmila, D Jeya Sundara

    2016-06-01

    Even after so much advancement in gene expression microarray technology, the main hindrance in analyzing microarray data is its limited number of samples as compared to a number of factors, which is a major impediment in revealing actual gene functionality and valuable information from the data. Analyzing gene expression data can indicate the factors which are differentially expressed in the diseased tissue. As most of these genes have no part to play in causing the disease of interest, thus, identification of disease-causing genes can reveal not just the case of the disease, but also its pathogenic mechanism. There are a lot of gene selection methods available which have the capacity to remove irrelevant genes, but most of them are not sufficient enough in removing redundancy in genes from microarray data, which increases the computational cost and decreases the classification accuracy. Combining the gene expression data with the gene ontology information can be helpful in determining the redundancy which can then be removed using the algorithm mentioned in the work. The gene list obtained after these sequential steps of the algorithm can be analyzed further to obtain the most deterministic genes responsible for type 2 diabetes. PMID:26289404

  3. In vivo imaging of inducible tyrosinase gene expression with an ultrasound array-based photoacoustic system

    NASA Astrophysics Data System (ADS)

    Harrison, Tyler; Paproski, Robert J.; Zemp, Roger J.

    2012-02-01

    Tyrosinase, a key enzyme in the production of melanin, has shown promise as a reporter of genetic activity. While green fluorescent protein has been used extensively in this capacity, it is limited in its ability to provide information deep in tissue at a reasonable resolution. As melanin is a strong absorber of light, it is possible to image gene expression using tyrosinase with photoacoustic imaging technologies, resulting in excellent resolutions at multiple-centimeter depths. While our previous work has focused on creating and imaging MCF-7 cells with doxycycline-controlled tyrosinase expression, we have now established the viability of these cells in a murine model. Using an array-based photoacoustic imaging system with 5 MHz center frequency, we capture interleaved ultrasound and photoacoustic images of tyrosinase-expressing MCF-7 tumors both in a tissue mimicking phantom, and in vivo. Images of both the tyrosinase-expressing tumor and a control tumor are presented as both coregistered ultrasound-photoacoustic B-scan images and 3-dimensional photoacoustic volumes created by mechanically scanning the transducer. We find that the tyrosinase-expressing tumor is visible with a signal level 12dB greater than that of the control tumor in vivo. Phantom studies with excised tumors show that the tyrosinase-expressing tumor is visible at depths in excess of 2cm, and have suggested that our imaging system is sensitive to a transfection rate of less than 1%.

  4. Sex-based differences in gene expression in hippocampus following postnatal lead exposure

    SciTech Connect

    Schneider, J.S. Anderson, D.W.; Sonnenahalli, H.; Vadigepalli, R.

    2011-10-15

    The influence of sex as an effect modifier of childhood lead poisoning has received little systematic attention. Considering the paucity of information available concerning the interactive effects of lead and sex on the brain, the current study examined the interactive effects of lead and sex on gene expression patterns in the hippocampus, a structure involved in learning and memory. Male or female rats were fed either 1500 ppm lead-containing chow or control chow for 30 days beginning at weaning.Blood lead levels were 26.7 {+-} 2.1 {mu}g/dl and 27.1 {+-} 1.7 {mu}g/dl for females and males, respectively. The expression of 175 unique genes was differentially regulated between control male and female rats. A total of 167 unique genes were differentially expressed in response to lead in either males or females. Lead exposure had a significant effect without a significant difference between male and female responses in 77 of these genes. In another set of 71 genes, there were significant differences in male vs. female response. A third set of 30 genes was differentially expressed in opposite directions in males vs. females, with the majority of genes expressed at a lower level in females than in males. Highly differentially expressed genes in males and females following lead exposure were associated with diverse biological pathways and functions. These results show that a brief exposure to lead produced significant changes in expression of a variety of genes in the hippocampus and that the response of the brain to a given lead exposure may vary depending on sex. - Highlights: > Postnatal lead exposure has a significant effect on hippocampal gene expression patterns. > At least one set of genes was affected in opposite directions in males and females. > Differentially expressed genes were associated with diverse biological pathways.

  5. A microarray-based analysis of gene expression profiles of maize kernel during late development stages

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Maize oligonuleotide array was used to analyze the temporal patterns of gene expression in maize kernels during late developmental stages. There is a total of 57,452 70-mer oligonucleotides on the array. In this study, we analyzed gene expression profiles in the process of kernel development of inbr...

  6. A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network

    PubMed Central

    RUAN, XIYUN; LI, HONGYUN; LIU, BO; CHEN, JIE; ZHANG, SHIBAO; SUN, ZEQIANG; LIU, SHUANGQING; SUN, FAHAI; LIU, QINGYONG

    2015-01-01

    The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson’s correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson’s correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis. PMID:26058425

  7. Ribozyme-based aminoglycoside switches of gene expression engineered by genetic selection in S. cerevisiae.

    PubMed

    Klauser, Benedikt; Atanasov, Janina; Siewert, Lena K; Hartig, Jörg S

    2015-05-15

    Systems for conditional gene expression are powerful tools in basic research as well as in biotechnology. For future applications, it is of great importance to engineer orthogonal genetic switches that function reliably in diverse contexts. RNA-based switches have the advantage that effector molecules interact immediately with regulatory modules inserted into the target RNAs, getting rid of the need of transcription factors usually mediating genetic control. Artificial riboswitches are characterized by their simplicity and small size accompanied by a high degree of modularity. We have recently reported a series of hammerhead ribozyme-based artificial riboswitches that allow for post-transcriptional regulation of gene expression via switching mRNA, tRNA, or rRNA functions. A more widespread application was so far hampered by moderate switching performances and a limited set of effector molecules available. Here, we report the re-engineering of hammerhead ribozymes in order to respond efficiently to aminoglycoside antibiotics. We first established an in vivo selection protocol in Saccharomyces cerevisiae that enabled us to search large sequence spaces for optimized switches. We then envisioned and characterized a novel strategy of attaching the aptamer to the ribozyme catalytic core, increasing the design options for rendering the ribozyme ligand-dependent. These innovations enabled the development of neomycin-dependent RNA modules that switch gene expression up to 25-fold. The presented aminoglycoside-responsive riboswitches belong to the best-performing RNA-based genetic regulators reported so far. The developed in vivo selection protocol should allow for sampling of large sequence spaces for engineering of further optimized riboswitches. PMID:24871672

  8. Problem-Based Test: The Effect of Fibroblast Growth Factor on Gene Expression

    ERIC Educational Resources Information Center

    Szeberenyi, Jozsef

    2011-01-01

    This paper shows the results of an experiment in which the effects of fibroblast growth factor (FGF), actinomycin D (Act D; an inhibitor of transcription), and cycloheximide (CHX; an inhibitor of translation) were studied on the expression of two genes: a gene called "Fnk" and the gene coding for glyceraldehyde-3-phosphate dehydrogenase (GAPDH).…

  9. Blood-based gene expression signatures of medication-free outpatients with major depressive disorder: integrative genome-wide and candidate gene analyses

    PubMed Central

    Hori, Hiroaki; Sasayama, Daimei; Teraishi, Toshiya; Yamamoto, Noriko; Nakamura, Seiji; Ota, Miho; Hattori, Kotaro; Kim, Yoshiharu; Higuchi, Teruhiko; Kunugi, Hiroshi

    2016-01-01

    Several microarray-based studies have investigated gene expression profiles in major depressive disorder (MDD), yet with highly variable findings. We examined blood-based genome-wide expression signatures of MDD, focusing on molecular pathways and networks underlying differentially expressed genes (DEGs) and behaviours of hypothesis-driven, evidence-based candidate genes for depression. Agilent human whole-genome arrays were used to measure gene expression in 14 medication-free outpatients with MDD who were at least moderately ill and 14 healthy controls matched pairwise for age and sex. After filtering, we compared expression of entire probes between patients and controls and identified DEGs. The DEGs were evaluated by pathway and network analyses. For the candidate gene analysis, we utilized 169 previously prioritized genes and examined their case-control separation efficiency and correlational co-expression network in patients relative to controls. The 317 screened DEGs mapped to a significantly over-represented pathway, the “synaptic transmission” pathway. The protein-protein interaction network was also significantly enriched, in which a number of key molecules for depression were included. The co-expression network of candidate genes was markedly disrupted in patients. This study provided evidence for an altered molecular network along with several key molecules in MDD and confirmed that the candidate genes are worthwhile targets for depression research. PMID:26728011

  10. GENE EXPRESSION NETWORKS

    EPA Science Inventory

    "Gene expression network" is the term used to describe the interplay, simple or complex, between two or more gene products in performing a specific cellular function. Although the delineation of such networks is complicated by the existence of multiple and subtle types of intera...

  11. Blood-Based Gene-Expression Biomarkers of Post-Traumatic Stress Disorder among Deployed Marines: A Pilot Study

    PubMed Central

    Tylee, Daniel S.; Chandler, Sharon D.; Nievergelt, Caroline M.; Liu, Xiaohua; Pazol, Joel; Woelk, Christopher H.; Lohr, James B.; Kremen, William S.; Baker, Dewleen G.; Glatt, Stephen J.; Tsuang, Ming T.

    2014-01-01

    The etiology of post-traumatic stress disorder (PTSD) likely involves the interaction of numerous genes and environmental factors. Similarly, gene-expression levels in peripheral blood are influenced by both genes and environment, and expression levels of many genes show good correspondence between peripheral blood and brain tissues. In that context, this pilot study sought to test the following hypotheses: 1) post-trauma expression levels of a gene subset in peripheral blood would differ between Marines with and without PTSD; 2) a diagnostic biomarker panel of PTSD among high-risk individuals could be developed based on gene expression in readily assessable peripheral blood cells; and 3) a diagnostic panel based on expression of individual exons would surpass the accuracy of a model based on expression of full-length gene transcripts. Gene-expression levels in peripheral blood samples from 50 U.S. Marines (25 PTSD cases and 25 non-PTSD comparison subjects) were determined by microarray following their return from deployment to war-zones in Iraq or Afghanistan. The original sample was carved into training and test subsets for construction of support vector machine classifiers. The panel of peripheral blood biomarkers achieved 80% prediction accuracy in the test subset based on the expression of just two full-length transcripts (GSTM1 and GSTM2). A biomarker panel based on 20 exons attained an improved 90% accuracy in the test subset. Though further refinement and replication of these biomarker profiles are required, these preliminary results provide proof-of-principle for the diagnostic utility of blood-based mRNA-expression in PTSD among trauma-exposed individuals. PMID:25311155

  12. A T7 RNA polymerase-based toolkit for the concerted expression of clustered genes.

    PubMed

    Arvani, Solmaz; Markert, Annette; Loeschcke, Anita; Jaeger, Karl-Erich; Drepper, Thomas

    2012-06-15

    Bacterial genes whose enzymes are either assembled into complex multi-domain proteins or form biosynthetic pathways are frequently organized within large chromosomal clusters. The functional expression of clustered genes, however, remains challenging since it generally requires an expression system that facilitates the coordinated transcription of numerous genes irrespective of their natural promoters and terminators. Here, we report on the development of a novel expression system that is particularly suitable for the homologous expression of multiple genes organized in a contiguous cluster. The new expression toolkit consists of an Ω interposon cassette carrying a T7 RNA polymerase specific promoter which is designed for promoter tagging of clustered genes and a small set of broad-host-range plasmids providing the respective polymerase in different bacteria. The uptake hydrogenase gene locus of the photosynthetic non-sulfur purple bacterium Rhodobacter capsulatus which consists of 16 genes was used as an example to demonstrate functional expression only by T7 RNA polymerase but not by bacterial RNA polymerase. Our findings clearly indicate that due to its unique properties T7 RNA polymerase can be applied for overexpression of large and complex bacterial gene regions. PMID:22285639

  13. Parkinson's disease candidate gene prioritization based on expression profile of midbrain dopaminergic neurons

    PubMed Central

    2010-01-01

    Background Parkinson's disease is the second most common neurodegenerative disorder. The pathological hallmark of the disease is degeneration of midbrain dopaminergic neurons. Genetic association studies have linked 13 human chromosomal loci to Parkinson's disease. Identification of gene(s), as part of the etiology of Parkinson's disease, within the large number of genes residing in these loci can be achieved through several approaches, including screening methods, and considering appropriate criteria. Since several of the indentified Parkinson's disease genes are expressed in substantia nigra pars compact of the midbrain, expression within the neurons of this area could be a suitable criterion to limit the number of candidates and identify PD genes. Methods In this work we have used the combination of findings from six rodent transcriptome analysis studies on the gene expression profile of midbrain dopaminergic neurons and the PARK loci in OMIM (Online Mendelian Inheritance in Man) database, to identify new candidate genes for Parkinson's disease. Results Merging the two datasets, we identified 20 genes within PARK loci, 7 of which are located in an orphan Parkinson's disease locus and one, which had been identified as a disease gene. In addition to identifying a set of candidates for further genetic association studies, these results show that the criteria of expression in midbrain dopaminergic neurons may be used to narrow down the number of genes in PARK loci for such studies. PMID:20716345

  14. Robust patterning of gene expression based on internal coordinate system of cells.

    PubMed

    Ogawa, Ken-ichiro; Miyake, Yoshihiro

    2015-06-01

    Cell-to-cell communication in multicellular organisms is established through the transmission of various kinds of chemical substances such as proteins. It is well known that gene expression triggered by a chemical substance in individuals has stable spatial patterns despite the individual differences in concentration patterns of the chemical substance. This fact reveals an important property of multicellular organisms called "robustness", which allows the organisms to generate their forms while maintaining proportion. Robustness has been conventionally accounted for by the stability of solutions of dynamical equations that represent a specific interaction network of chemical substances. However, any biological system is composed of autonomous elements. In general, an autonomous element does not merely accept information on the chemical substance from the environment; instead, it accepts the information based on its own criteria for reaction. Therefore, this phenomenon needs to be considered from the viewpoint of cells. Such a viewpoint is expected to allow the consideration of the autonomy of cells in multicellular organisms. This study aims to explain theoretically the robust patterning of gene expression from the viewpoint of cells. For this purpose, we introduced a new operator for transforming a state variable of a chemical substance from an external coordinate system to an internal coordinate system of each cell, which describes the observation of the chemical substance by cells. We then applied this operator to the simplest reaction-diffusion model of the chemical substance to investigate observation effects by cells. Our mathematical analysis of this extended model indicates that the robust patterning of gene expression against individual differences in concentration pattern of the chemical substance can be explained from the viewpoint of cells if there is a regulation field that compensates for the difference between cells seen in the observation results

  15. Gene expression technology

    SciTech Connect

    Goeddel, D.V. )

    1990-01-01

    The articles in this volume were assemble to enable the reader to design effective strategies for the expression of cloned genes and cDNAs. More than a compilation of papers describing the multitude of techniques now available for expressing cloned genes, this volume provides a manual that should prove useful for solving the majority of expression problems one likely to encounter. The four major expression systems commonly available to most investigators are stressed: Escherichia coli, Bacillus subtilis, yeast, and mammalian cells. Each of these system has its advantages and disadvantages, details of which are found in Chapter 1 and the strategic overviews for the four major sections of the volume. The papers in each of these sections provide many suggestions on how to proceed if initial expression levels are not sufficient.

  16. Autonomous bacterial localization and gene expression based on nearby cell receptor density

    PubMed Central

    Wu, Hsuan-Chen; Tsao, Chen-Yu; Quan, David N; Cheng, Yi; Servinsky, Matthew D; Carter, Karen K; Jee, Kathleen J; Terrell, Jessica L; Zargar, Amin; Rubloff, Gary W; Payne, Gregory F; Valdes, James J; Bentley, William E

    2013-01-01

    Escherichia coli were genetically modified to enable programmed motility, sensing, and actuation based on the density of features on nearby surfaces. Then, based on calculated feature density, these cells expressed marker proteins to indicate phenotypic response. Specifically, site-specific synthesis of bacterial quorum sensing autoinducer-2 (AI-2) is used to initiate and recruit motile cells. In our model system, we rewired E. coli's AI-2 signaling pathway to direct bacteria to a squamous cancer cell line of head and neck (SCCHN), where they initiate synthesis of a reporter (drug surrogate) based on a threshold density of epidermal growth factor receptor (EGFR). This represents a new type of controller for targeted drug delivery as actuation (synthesis and delivery) depends on a receptor density marking the diseased cell. The ability to survey local surfaces and initiate gene expression based on feature density represents a new area-based switch in synthetic biology that will find use beyond the proposed cancer model here. PMID:23340842

  17. Spheroid-based 3-dimensional culture models: Gene expression and functionality in head and neck cancer.

    PubMed

    Schmidt, Marianne; Scholz, Claus-Juergen; Polednik, Christine; Roller, Jeanette

    2016-04-01

    In the present study a panel of 12 head and neck cancer (HNSCC) cell lines were tested for spheroid formation. Since the size and morphology of spheroids is dependent on both cell adhesion and proliferation in the 3-dimensional (3D) context, morphology of HNSCC spheroids was related to expression of E-cadherin and the proliferation marker Ki67. In HNSCC cell lines the formation of tight regular spheroids was dependent on distinct E-cadherin expression levels in monolayer cultures, usually resulting in upregulation following aggregation into 3D structures. Cell lines expressing only low levels of E-cadherin in monolayers produced only loose cell clusters, frequently decreasing E-cadherin expression further upon aggregation. In these cell lines no epidermal growth factor receptor (EGFR) upregulation occurred and proliferation generally decreased in spheroids/aggregates independent of E-cadherin expression. In a second approach a global gene expression analysis of the larynx carcinoma cell line HLaC78 monolayer and the corresponding spheroids was performed. A global upregulation of gene expression in HLaC78 spheroids was related to genes involved in cell adhesion, cell junctions and cytochrome P450-mediated metabolism of xenobiotics. Downregulation was associated with genes controlling cell cycle, DNA-replication and DNA mismatch repair. Analyzing the expression of selected genes of each functional group in monolayer and spheroid cultures of all 12 cell lines revealed evidence for common gene expression shifts in genes controlling cell junctions, cell adhesion, cell cycle and DNA replication as well as genes involved in the cytochrome P450-mediated metabolism of xenobiotics. PMID:26797047

  18. A simple plasmid-based transient gene expression method using High Five cells.

    PubMed

    Shen, Xiao; Pitol, Ana K; Bachmann, Virginie; Hacker, David L; Baldi, Lucia; Wurm, Florian M

    2015-12-20

    The High Five (H5) cell line, derived from the lepidopteran Trichoplusia ni, is one of the major insect cell hosts for the production of recombinant proteins using the baculovirus expression vector system (BEVS). Here, we describe a simple polyethylenimine (PEI)-based transient gene expression (TGE) process for the rapid production of recombinant proteins from suspension-adapted H5 cells. The method was optimized using two model proteins, enhanced green fluorescent protein (EGFP) and human tumor necrosis factor receptor-Fc fusion protein (TNFR-Fc). After screening several promoter and enhancer combinations for high levels of TNFR:Fc production, an expression vector containing the Autographa californica multicapsid nucleopolyhedrovirus immediate early 1 (ie1) promoter and homologous region 5 (hr5) enhancer was selected. Cells were transfected at a density of 2×10(6) cells/mL by direct addition of DNA and PEI. Under optimized conditions, a 90% transfection efficiency (percentage of EGFP-positive cells) was obtained. In addition, we observed volumetric TNFR-Fc yields over 150μg/mL within 4 days of transfection. The method was found to be reproducible and scalable to 300mL. This plasmid-based transient transfection process is a simple and efficient alternative to the BEVS for recombinant protein production in H5 cells. PMID:26476358

  19. A Microarray-Based Gene Expression Analysis to Identify Diagnostic Biomarkers for Unknown Primary Cancer

    PubMed Central

    Kurahashi, Issei; Fujita, Yoshihiko; Arao, Tokuzo; Kurata, Takayasu; Koh, Yasuhiro; Sakai, Kazuko; Matsumoto, Koji; Tanioka, Maki; Takeda, Koji; Takiguchi, Yuichi; Yamamoto, Nobuyuki; Tsuya, Asuka; Matsubara, Nobuaki; Mukai, Hirofumi; Minami, Hironobu; Chayahara, Naoko; Yamanaka, Yasuhiro; Miwa, Keisuke; Takahashi, Shin; Takahashi, Shunji; Nakagawa, Kazuhiko; Nishio, Kazuto

    2013-01-01

    Background The biological basis for cancer of unknown primary (CUP) at the molecular level remains largely unknown, with no evidence of whether a common biological entity exists. Here, we assessed the possibility of identifying a common diagnostic biomarker for CUP using a microarray gene expression analysis. Methods Tumor mRNA samples from 60 patients with CUP were analyzed using the Affymetrix U133A Plus 2.0 GeneChip and were normalized by asinh (hyperbolic arc sine) transformation to construct a mean gene-expression profile specific to CUP. A gene-expression profile specific to non-CUP group was constructed using publicly available raw microarray datasets. The t-tests were performed to compare the CUP with non-CUP groups and the top 59 CUP specific genes with the highest fold change were selected (p-value<0.001). Results Among the 44 genes that were up-regulated in the CUP group, 6 genes for ribosomal proteins were identified. Two of these genes (RPS7 and RPL11) are known to be involved in the Mdm2–p53 pathway. We also identified several genes related to metastasis and apoptosis, suggesting a biological attribute of CUP. Conclusions The protein products of the up-regulated and down-regulated genes identified in this study may be clinically useful as unique biomarkers for CUP. PMID:23671674

  20. Gene expression networks.

    PubMed

    Thomas, Reuben; Portier, Christopher J

    2013-01-01

    With the advent of microarrays and next-generation biotechnologies, the use of gene expression data has become ubiquitous in biological research. One potential drawback of these data is that they are very rich in features or genes though cost considerations allow for the use of only relatively small sample sizes. A useful way of getting at biologically meaningful interpretations of the environmental or toxicological condition of interest would be to make inferences at the level of a priori defined biochemical pathways or networks of interacting genes or proteins that are known to perform certain biological functions. This chapter describes approaches taken in the literature to make such inferences at the biochemical pathway level. In addition this chapter describes approaches to create hypotheses on genes playing important roles in response to a treatment, using organism level gene coexpression or protein-protein interaction networks. Also, approaches to reverse engineer gene networks or methods that seek to identify novel interactions between genes are described. Given the relatively small sample numbers typically available, these reverse engineering approaches are generally useful in inferring interactions only among a relatively small or an order 10 number of genes. Finally, given the vast amounts of publicly available gene expression data from different sources, this chapter summarizes the important sources of these data and characteristics of these sources or databases. In line with the overall aims of this book of providing practical knowledge to a researcher interested in analyzing gene expression data from a network perspective, the chapter provides convenient publicly accessible tools for performing analyses described, and in addition describe three motivating examples taken from the published literature that illustrate some of the relevant analyses. PMID:23086841

  1. Sex-Based Differences in Gene Expression in Hippocampus Following Postnatal Lead Exposure

    PubMed Central

    Schneider, J.S.; Anderson, D.W.; Sonnenahalli, H.; Vadigepalli, R.

    2011-01-01

    The influence of sex as an effect modifier of childhood lead poisoning has received little systematic attention. Considering the paucity of information available concerning the interactive effects of lead and sex on the brain, the current study examined the interactive effects of lead and sex on gene expression patterns in the hippocampus, a structure involved in learning and memory. Male or female rats were fed either 1500 ppm lead-containing chow or control chow for 30 days beginning at weaning. Blood lead levels were 26.7 ± 2.1 μg/dl and 27.1 ± 1.7 μg/dl for females and males, respectively. The expression of 175 unique genes was differentially regulated between control male and female rats. A total of 167 unique genes were differentially expressed in response to lead in either males or females. Lead exposure had a significant effect without a significant difference between male and female responses in 77 of these genes. In another set of 71 genes, there were significant differences in male vs. female response. A third set of 30 genes was differentially expressed in opposite directions in males vs. females, with the majority of genes expressed at a lower level in females than in males. Highly differentially expressed genes in males and females following lead exposure were associated with diverse biological pathways and functions. These results show that a brief exposure to lead produced significant changes in expression a variety of genes in the hippocampus and that the response of the brain to a given lead exposure may vary depending on sex. PMID:21864555

  2. An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous

    PubMed Central

    Matsumoto, Tomotaka; Mineta, Katsuhiko; Osada, Naoki; Araki, Hitoshi

    2015-01-01

    Recent studies suggest the existence of a stochasticity in gene expression (SGE) in many organisms, and its non-negligible effect on their phenotype and fitness. To date, however, how SGE affects the key parameters of population genetics are not well understood. SGE can increase the phenotypic variation and act as a load for individuals, if they are at the adaptive optimum in a stable environment. On the other hand, part of the phenotypic variation caused by SGE might become advantageous if individuals at the adaptive optimum become genetically less-adaptive, for example due to an environmental change. Furthermore, SGE of unimportant genes might have little or no fitness consequences. Thus, SGE can be advantageous, disadvantageous, or selectively neutral depending on its context. In addition, there might be a genetic basis that regulates magnitude of SGE, which is often referred to as “modifier genes,” but little is known about the conditions under which such an SGE-modifier gene evolves. In the present study, we conducted individual-based computer simulations to examine these conditions in a diploid model. In the simulations, we considered a single locus that determines organismal fitness for simplicity, and that SGE on the locus creates fitness variation in a stochastic manner. We also considered another locus that modifies the magnitude of SGE. Our results suggested that SGE was always deleterious in stable environments and increased the fixation probability of deleterious mutations in this model. Even under frequently changing environmental conditions, only very strong natural selection made SGE adaptive. These results suggest that the evolution of SGE-modifier genes requires strict balance among the strength of natural selection, magnitude of SGE, and frequency of environmental changes. However, the degree of dominance affected the condition under which SGE becomes advantageous, indicating a better opportunity for the evolution of SGE in different genetic

  3. In Silico Analysis of Microarray-Based Gene Expression Profiles Predicts Tumor Cell Response to Withanolides

    PubMed Central

    Efferth, Thomas; Greten, Henry Johannes

    2012-01-01

    Withania somnifera (L.) Dunal (Indian ginseng, winter cherry, Solanaceae) is widely used in traditional medicine. Roots are either chewed or used to prepare beverages (aqueous decocts). The major secondary metabolites of Withania somnifera are the withanolides, which are C-28-steroidal lactone triterpenoids. Withania somnifera extracts exert chemopreventive and anticancer activities in vitro and in vivo. The aims of the present in silico study were, firstly, to investigate whether tumor cells develop cross-resistance between standard anticancer drugs and withanolides and, secondly, to elucidate the molecular determinants of sensitivity and resistance of tumor cells towards withanolides. Using IC50 concentrations of eight different withanolides (withaferin A, withaferin A diacetate, 3-azerininylwithaferin A, withafastuosin D diacetate, 4-B-hydroxy-withanolide E, isowithanololide E, withafastuosin E, and withaperuvin) and 19 established anticancer drugs, we analyzed the cross-resistance profile of 60 tumor cell lines. The cell lines revealed cross-resistance between the eight withanolides. Consistent cross-resistance between withanolides and nitrosoureas (carmustin, lomustin, and semimustin) was also observed. Then, we performed transcriptomic microarray-based COMPARE and hierarchical cluster analyses of mRNA expression to identify mRNA expression profiles predicting sensitivity or resistance towards withanolides. Genes from diverse functional groups were significantly associated with response of tumor cells to withaferin A diacetate, e.g. genes functioning in DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others.

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

    PubMed

    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. PMID:27036626

  5. Caste-based differences in gene expression in the polyembryonic wasp Copidosoma floridanum.

    PubMed

    Donnell, David M; Strand, Michael R

    2006-02-01

    The polyembryonic parasitoid Copidosoma floridanum produces two larval castes, soldiers and reproductives, during development within its host. Soldier larvae defend the brood against competitors while reproductive larvae develop into adult wasps. As with other caste-forming insects, the distinct morphological and behavioral features of soldier and reproductive larvae likely involve differential gene expression. In this study we used a bi-directional suppression subtractive hybridization (SSH) approach to isolate differentially expressed genes from C. floridanum soldier and reproductive larvae. We isolated 230 novel expressed sequence tags (ESTs) from the two subtractions (114 soldier/116 reproductive ESTs). Among these ESTs were sequences with significant similarity to genes coding for serine proteinases, proteinase inhibitors, odorant-binding and chemosensory proteins, and cuticular proteins. Also, three novel genes were isolated that resemble one another in conceptual translation and share the cysteine spacing pattern of short scorpion toxins and insect defensins. Reverse transcription-polymerase chain reaction (RT-PCR) analysis of 20 ESTs from the two libraries indicated that 85% were differentially expressed in one caste or the other. We conclude that our SSH strategy was effective in identifying a number of genes differentially expressed in soldier and reproductive larvae and that several of these genes will be useful in characterizing caste-specific gene networks in C. floridanum. PMID:16431281

  6. Integrated Analysis of Expression Profile Based on Differentially Expressed Genes in Middle Cerebral Artery Occlusion Animal Models.

    PubMed

    Zhou, Huaqiang; Qiu, Zeting; Gao, Shaowei; Chen, Qinchang; Li, Si; Tan, Wulin; Liu, Xiaochen; Wang, Zhongxing

    2016-01-01

    Stroke is one of the most common causes of death, only second to heart disease. Molecular investigations about stroke are in acute shortage nowadays. This study is intended to explore a gene expression profile after brain ischemia reperfusion. Meta-analysis, differential expression analysis, and integrated analysis were employed on an eight microarray series. We explored the functions and pathways of target genes in gene ontology (GO) enrichment analysis and constructed a protein-protein interaction network. Meta-analysis identified 360 differentially expressed genes (DEGs) for Mus musculus and 255 for Rattus norvegicus. Differential expression analysis identified 44 DEGs for Mus musculus and 21 for Rattus norvegicus. Timp1 and Lcn2 were overexpressed in both species. The cytokine-cytokine receptor interaction and chemokine signaling pathway were highly enriched for the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. We have exhibited a global view of the potential molecular differences between middle cerebral artery occlusion (MCAO) animal model and sham for Mus musculus or Rattus norvegicus, including the biological process and enriched pathways in DEGs. This research helps contribute to a clearer understanding of the inflammation process and accurate identification of ischemic infarction stages, which might be transformed into a therapeutic approach. PMID:27213359

  7. Integrated Analysis of Expression Profile Based on Differentially Expressed Genes in Middle Cerebral Artery Occlusion Animal Models

    PubMed Central

    Zhou, Huaqiang; Qiu, Zeting; Gao, Shaowei; Chen, Qinchang; Li, Si; Tan, Wulin; Liu, Xiaochen; Wang, Zhongxing

    2016-01-01

    Stroke is one of the most common causes of death, only second to heart disease. Molecular investigations about stroke are in acute shortage nowadays. This study is intended to explore a gene expression profile after brain ischemia reperfusion. Meta-analysis, differential expression analysis, and integrated analysis were employed on an eight microarray series. We explored the functions and pathways of target genes in gene ontology (GO) enrichment analysis and constructed a protein-protein interaction network. Meta-analysis identified 360 differentially expressed genes (DEGs) for Mus musculus and 255 for Rattus norvegicus. Differential expression analysis identified 44 DEGs for Mus musculus and 21 for Rattus norvegicus. Timp1 and Lcn2 were overexpressed in both species. The cytokine-cytokine receptor interaction and chemokine signaling pathway were highly enriched for the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. We have exhibited a global view of the potential molecular differences between middle cerebral artery occlusion (MCAO) animal model and sham for Mus musculus or Rattus norvegicus, including the biological process and enriched pathways in DEGs. This research helps contribute to a clearer understanding of the inflammation process and accurate identification of ischemic infarction stages, which might be transformed into a therapeutic approach. PMID:27213359

  8. Model-Based Characterization of Inflammatory Gene Expression Patterns of Activated Macrophages

    PubMed Central

    Ehlting, Christian; Thomas, Maria; Zanger, Ulrich M.; Sawodny, Oliver; Häussinger, Dieter; Bode, Johannes G.

    2016-01-01

    Macrophages are cells with remarkable plasticity. They integrate signals from their microenvironment leading to context-dependent polarization into classically (M1) or alternatively (M2) activated macrophages, representing two extremes of a broad spectrum of divergent phenotypes. Thereby, macrophages deliver protective and pro-regenerative signals towards injured tissue but, depending on the eliciting damage, may also be responsible for the generation and aggravation of tissue injury. Although incompletely understood, there is emerging evidence that macrophage polarization is critical for these antagonistic roles. To identify activation-specific expression patterns of chemokines and cytokines that may confer these distinct effects a systems biology approach was applied. A comprehensive literature-based Boolean model was developed to describe the M1 (LPS-activated) and M2 (IL-4/13-activated) polarization types. The model was validated using high-throughput transcript expression data from murine bone marrow derived macrophages. By dynamic modeling of gene expression, the chronology of pathway activation and autocrine signaling was estimated. Our results provide a deepened understanding of the physiological balance leading to M1/M2 activation, indicating the relevance of co-regulatory signals at the level of Akt1 or Akt2 that may be important for directing macrophage polarization. PMID:27464342

  9. Model-Based Characterization of Inflammatory Gene Expression Patterns of Activated Macrophages.

    PubMed

    Rex, Julia; Albrecht, Ute; Ehlting, Christian; Thomas, Maria; Zanger, Ulrich M; Sawodny, Oliver; Häussinger, Dieter; Ederer, Michael; Feuer, Ronny; Bode, Johannes G

    2016-07-01

    Macrophages are cells with remarkable plasticity. They integrate signals from their microenvironment leading to context-dependent polarization into classically (M1) or alternatively (M2) activated macrophages, representing two extremes of a broad spectrum of divergent phenotypes. Thereby, macrophages deliver protective and pro-regenerative signals towards injured tissue but, depending on the eliciting damage, may also be responsible for the generation and aggravation of tissue injury. Although incompletely understood, there is emerging evidence that macrophage polarization is critical for these antagonistic roles. To identify activation-specific expression patterns of chemokines and cytokines that may confer these distinct effects a systems biology approach was applied. A comprehensive literature-based Boolean model was developed to describe the M1 (LPS-activated) and M2 (IL-4/13-activated) polarization types. The model was validated using high-throughput transcript expression data from murine bone marrow derived macrophages. By dynamic modeling of gene expression, the chronology of pathway activation and autocrine signaling was estimated. Our results provide a deepened understanding of the physiological balance leading to M1/M2 activation, indicating the relevance of co-regulatory signals at the level of Akt1 or Akt2 that may be important for directing macrophage polarization. PMID:27464342

  10. eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes.

    PubMed

    Chang, Zheng; Wang, Zhenjia; Ashby, Cody; Zhou, Chuan; Li, Guojun; Zhang, Shuzhong; Huang, Xiuzhen

    2014-01-01

    Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice. PMID:25374455

  11. Cluster analysis of gene expression data based on self-splitting and merging competitive learning.

    PubMed

    Wu, Shuanhu; Liew, Alan Wee-Chung; Yan, Hong; Yang, Mengsu

    2004-03-01

    Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene expression changes during yeast cell cycle, for which the fundamental patterns of gene expression and assignment of genes to clusters are well understood from numerous previous studies. Comparative studies with several clustering algorithms illustrate the effectiveness of our method. PMID:15055797

  12. A PSO-Based Approach for Pathway Marker Identification From Gene Expression Data.

    PubMed

    Mandal, Monalisa; Mondal, Jyotirmay; Mukhopadhyay, Anirban

    2015-09-01

    In this article, a new and robust pathway activity inference scheme is proposed from gene expression data using Particle Swarm Optimization (PSO). From microarray gene expression data, the corresponding pathway information of the genes are collected from a public database. For identifying the pathway markers, the expression values of each pathway consisting of genes, termed as pathway activity, are summarized. To measure the goodness of a pathway activity vector, t-score is widely used in the existing literature. The weakness of existing techniques for inferring pathway activity is that they intend to consider all the member genes of a pathway. But in reality, all the member genes may not be significant to the corresponding pathway. Therefore, those genes, which are responsible in the corresponding pathway, should be included only. Motivated by this, in the proposed method, using PSO, important genes with respect to each pathway are identified. The objective is to maximize the average t-score. For the pathway activities inferred from different percentage of significant pathways, the average absolute t -scores are plotted. In addition, the top 50% pathway markers are evaluated using 10-fold cross validation and its performance is compared with that of other existing techniques. Biological relevance of the results is also studied. PMID:25935045

  13. Prediction on the Inhibition Ratio of Pyrrolidine Derivatives on Matrix Metalloproteinase Based on Gene Expression Programming

    PubMed Central

    Li, Yuqin; You, Guirong; Jia, Baoxiu; Si, Hongzong; Yao, Xiaojun

    2014-01-01

    Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R2) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs. PMID:24971318

  14. Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming.

    PubMed

    Li, Yuqin; You, Guirong; Jia, Baoxiu; Si, Hongzong; Yao, Xiaojun

    2014-01-01

    Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R (2)) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs. PMID:24971318

  15. [Homologous expression of Burkholderia cepacia G63 lipase gene based on T7 RNA polymerase expression system].

    PubMed

    Jia, Bin; Yang, Jiangke; Yan, Yunjun

    2009-02-01

    In order to realize over-expression of Burkholderia cepacia (B. cepacia) lipase, we introduced the widely used T7 RAN polymerase expression system into B. cepacia G63 to over-express the lipase gene. By using PCR technique, we amplified the T7 RNA polymerase gene (T7 RNAP) from the BL21 (DE3) and cloned it into the suicide plasmid pJQ200SK. After that, we flanked T7 RNAP with two 500 bp homologous fragments and integrated it into the genomes of B. cepacia by tri-parental mating, so that T7 RNAP was under-controlled by lipase gene (lipA) promoter. Then, we cloned the lipA and its partner gene lipB into the vector pUCPCM and pBBR22b both or separately. Therefore, we got 7 expression plasmids pBBR22blipAB, pBBR22blipA, pUCPCMlipAB, pUCPCMlipA, pUCPCMdeltalipAlipB, pUCPCMdeltalipA, pUCPCMdeltalipB, and then electroporated them into B. cepacia containing T7 RNA. After shake flask culture, we found B. cepacia containing pUCPCMlipAB produced the most quantity of lipase, and lipase activity was up to 607.2 U/mg, 2.8-folds higher than that of the wild strain. Moreover, lipase activities of all engineering strains except the one containing pUCPCMdeltalipB were enhanced to some extent. The specific activities of wild type B. cepacia and B. cepacia containing pUCPCMlipAB were respectively 29 984 U/mg and 30 875 U/mg after ammonium sulfate precipitation and gel filtration chromatography. The T7 RNA polymerase expression system could effectively enhanced lipase expression in B. cepacia, and secretion signal PelB and ribosome-binding site may promote lipase expression in engineering strain. PMID:19459326

  16. Do count-based differential expression methods perform poorly when genes are expressed in only one condition?

    PubMed

    Zhou, Xiaobei; Robinson, Mark D

    2015-01-01

    A response to 'Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data' by Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND and Betel D in Genome Biology, 2013, 14:R95. PMID:26450178

  17. Tailored chemotherapy based on tumour gene expression analysis: breast cancer patients' misinterpretations and positive attitudes.

    PubMed

    Pellegrini, I; Rapti, M; Extra, J-M; Petri-Cal, A; Apostolidis, T; Ferrero, J-M; Bachelot, T; Viens, P; Julian-Reynier, C; Bertucci, F

    2012-03-01

    The aim of this study was to document how breast cancer patients perceive their prognosis and a tailored treatment based on tumour gene expression analysis, and to identify the features of this approach that may impact its clinical application. In-depth interviews were conducted at three French cancer centres with 37 women (35-69 years of age) with node-positive breast cancer undergoing an adjuvant chemotherapy regimen defined on the basis of the genomic signature predicting the outcome after chemotherapy. Several concerns were identified. First, some misconceptions about these methods were identified due to semantic confusions between the terms 'genomic' and 'genetic', which generated anxiety and uncertainty about the future. Second, the 'not done' and 'not interpretable' signatures were misinterpreted by the women and associated with highly negative connotations. However, the use of tumour genomic analysis to adapt the treatment to each patient received most of the patients' approval because it was perceived as an approach facilitating personalised medicine. In conclusion, improving the quality of provider/patient communications should enable patients to play a more active part in the decision making about their treatment. This will ensure that those who agree to have tumour gene analysis have realistic expectations and sound deductions about the final result disclosure process. PMID:22070677

  18. Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes

    SciTech Connect

    Schena, M.; Heller, R.; Chai, A.; Davis, R.W.

    1996-10-01

    Microarrays containing 1046 human cDNAs of unknown sequence were printed on glass with high-speed robotics. These 1.0-cm{sup 2} DNA {open_quotes}chips{close_quotes} were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. Array elements that displayed differential expression patterns under given experimental conditions were characterized by sequencing. The identification of known and novel heat shock and phorbol ester-regulated genes in human T cells demonstrates the sensitivity of the assay. Parallel gene analysis with microarrays provides a rapid and efficient method for large-scale human gene discovery. 33 refs., 3 figs., 2 tabs.

  19. Microfluidic droplet-based PCR instrumentation for high-throughput gene expression profiling and biomarker discovery

    PubMed Central

    Hayes, Christopher J.; Dalton, Tara M.

    2015-01-01

    PCR is a common and often indispensable technique used in medical and biological research labs for a variety of applications. Real-time quantitative PCR (RT-qPCR) has become a definitive technique for quantitating differences in gene expression levels between samples. Yet, in spite of this importance, reliable methods to quantitate nucleic acid amounts in a higher throughput remain elusive. In the following paper, a unique design to quantify gene expression levels at the nanoscale in a continuous flow system is presented. Fully automated, high-throughput, low volume amplification of deoxynucleotides (DNA) in a droplet based microfluidic system is described. Unlike some conventional qPCR instrumentation that use integrated fluidic circuits or plate arrays, the instrument performs qPCR in a continuous, micro-droplet flowing process with droplet generation, distinctive reagent mixing, thermal cycling and optical detection platforms all combined on one complete instrument. Detailed experimental profiling of reactions of less than 300 nl total volume is achieved using the platform demonstrating the dynamic range to be 4 order logs and consistent instrument sensitivity. Furthermore, reduced pipetting steps by as much as 90% and a unique degree of hands-free automation makes the analytical possibilities for this instrumentation far reaching. In conclusion, a discussion of the first demonstrations of this approach to perform novel, continuous high-throughput biological screens is presented. The results generated from the instrument, when compared with commercial instrumentation, demonstrate the instrument reliability and robustness to carry out further studies of clinical significance with added throughput and economic benefits. PMID:27077035

  20. Microfluidic droplet-based PCR instrumentation for high-throughput gene expression profiling and biomarker discovery.

    PubMed

    Hayes, Christopher J; Dalton, Tara M

    2015-06-01

    PCR is a common and often indispensable technique used in medical and biological research labs for a variety of applications. Real-time quantitative PCR (RT-qPCR) has become a definitive technique for quantitating differences in gene expression levels between samples. Yet, in spite of this importance, reliable methods to quantitate nucleic acid amounts in a higher throughput remain elusive. In the following paper, a unique design to quantify gene expression levels at the nanoscale in a continuous flow system is presented. Fully automated, high-throughput, low volume amplification of deoxynucleotides (DNA) in a droplet based microfluidic system is described. Unlike some conventional qPCR instrumentation that use integrated fluidic circuits or plate arrays, the instrument performs qPCR in a continuous, micro-droplet flowing process with droplet generation, distinctive reagent mixing, thermal cycling and optical detection platforms all combined on one complete instrument. Detailed experimental profiling of reactions of less than 300 nl total volume is achieved using the platform demonstrating the dynamic range to be 4 order logs and consistent instrument sensitivity. Furthermore, reduced pipetting steps by as much as 90% and a unique degree of hands-free automation makes the analytical possibilities for this instrumentation far reaching. In conclusion, a discussion of the first demonstrations of this approach to perform novel, continuous high-throughput biological screens is presented. The results generated from the instrument, when compared with commercial instrumentation, demonstrate the instrument reliability and robustness to carry out further studies of clinical significance with added throughput and economic benefits. PMID:27077035

  1. Crosstalk analysis of pathways in breast cancer using a network model based on overlapping differentially expressed genes

    PubMed Central

    SUN, YONG; YUAN, KAI; ZHANG, PENG; MA, RONG; ZHANG, QI-WEN; TIAN, XING-SONG

    2015-01-01

    Multiple signal transduction pathways can affect each other considerably through crosstalk. However, the presence and extent of this phenomenon have not been rigorously studied. The aim of the present study was to identify strong and normal interactions between pathways in breast cancer and determine the main pathway. Five sets of breast cancer data were downloaded from the high-throughput Gene Expression Omnibus (GEO) and analyzed to identify differentially expressed (DE) genes using the Rank Product (RankProd) method. A list of pathways with differential expression was obtained by gene set enrichment analysis (GSEA) of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The DE genes that overlapped between pathways were identified and a crosstalk network diagram based on the overlap of DE genes was constructed. A total of 1,464 DE genes and 26 pathways were identified. In addition, the number of DE genes that overlapped between specific pathways were determined, and the greatest degree of overlap was between the extracellular matrix (ECM)-receptor interaction and Focal adhesion pathways, which had 22 overlapping DE genes. Weighted pathway analysis of the crosstalk between pathways identified that Pathways in cancer was the main pathway in breast cancer. PMID:26622386

  2. Microarray-based gene expression profiling of peripheral blood mononuclear cells in dairy cows with experimental hypocalcemia and milk fever.

    PubMed

    Sasaki, K; Yamagishi, N; Kizaki, K; Sasaki, K; Devkota, B; Hashizume, K

    2014-01-01

    Although a molecular diagnostic assay using clinically accessible tissue, such as blood, would facilitate evaluation of disease conditions in humans and animals, little information exists on microarray-based gene expression profiling of circulating leukocytes from clinically hypocalcemic cows. Therefore, peripheral blood mononuclear cells from dairy cows with experimentally induced hypocalcemia or spontaneous milk fever were subjected to oligo-microarray analysis to identify specific biomarker genes. In experimental hypocalcemia induced by a 4-h infusion of 10% disodium EDTA (n=4), 32 genes were significantly up- or downregulated compared with control treatment (4-h infusion of 11% calcium EDTA; n=4). In cows with milk fever (n=8), 98 genes were expressed differentially (either up- or downregulated) compared with healthy parturient cows (n=5). From these data, the following 5 genes were selected as being strongly related to both experimental hypocalcemia and milk fever: protein kinase (cAMP-dependent, catalytic) inhibitor β (PKIB); DNA-damage-inducible transcript 4 (DDIT4); period homolog 1 (PER1); NUAK family, SNF1-like kinase, 1 (NUAK1); and expressed sequence tag (BI537947). Another gene (neuroendocrine secretory protein 55, NESP55) was also determined to be specific for milk fever, independently of hypocalcemia. The mRNA expression of these 6 genes in milk fever cases was verified by quantitative real-time reverse-transcription PCR and was significantly different compared with their expression in healthy parturient cows. In the present study, the selected genes appeared to be candidate biomarkers of milk fever because the continuous interactions between blood cells and the entire body suggest that subtle intracellular changes occur in association with disease. However, before any genomic biomarkers are incorporated into clinical evaluation of the disease, the effect of hypocalcemia on the mRNA expression of these genes in the tissues that regulate calcium

  3. Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications

    PubMed Central

    Barat, Ana; Ruskin, Heather J.; Byrne, Annette T.; Prehn, Jochen H. M.

    2015-01-01

    Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype.

  4. Digital Gene Expression Analysis Based on De Novo Transcriptome Assembly Reveals New Genes Associated with Floral Organ Differentiation of the Orchid Plant Cymbidium ensifolium.

    PubMed

    Yang, Fengxi; Zhu, Genfa

    2015-01-01

    Cymbidium ensifolium belongs to the genus Cymbidium of the orchid family. Owing to its spectacular flower morphology, C. ensifolium has considerable ecological and cultural value. However, limited genetic data is available for this non-model plant, and the molecular mechanism underlying floral organ identity is still poorly understood. In this study, we characterize the floral transcriptome of C. ensifolium and present, for the first time, extensive sequence and transcript abundance data of individual floral organs. After sequencing, over 10 Gb clean sequence data were generated and assembled into 111,892 unigenes with an average length of 932.03 base pairs, including 1,227 clusters and 110,665 singletons. Assembled sequences were annotated with gene descriptions, gene ontology, clusters of orthologous group terms, the Kyoto Encyclopedia of Genes and Genomes, and the plant transcription factor database. From these annotations, 131 flowering-associated unigenes, 61 CONSTANS-LIKE (COL) unigenes and 90 floral homeotic genes were identified. In addition, four digital gene expression libraries were constructed for the sepal, petal, labellum and gynostemium, and 1,058 genes corresponding to individual floral organ development were identified. Among them, eight MADS-box genes were further investigated by full-length cDNA sequence analysis and expression validation, which revealed two APETALA1/AGL9-like MADS-box genes preferentially expressed in the sepal and petal, two AGAMOUS-like genes particularly restricted to the gynostemium, and four DEF-like genes distinctively expressed in different floral organs. The spatial expression of these genes varied distinctly in different floral mutant corresponding to different floral morphogenesis, which validated the specialized roles of them in floral patterning and further supported the effectiveness of our in silico analysis. This dataset generated in our study provides new insights into the molecular mechanisms underlying floral

  5. Digital Gene Expression Analysis Based on De Novo Transcriptome Assembly Reveals New Genes Associated with Floral Organ Differentiation of the Orchid Plant Cymbidium ensifolium

    PubMed Central

    Yang, Fengxi; Zhu, Genfa

    2015-01-01

    Cymbidium ensifolium belongs to the genus Cymbidium of the orchid family. Owing to its spectacular flower morphology, C. ensifolium has considerable ecological and cultural value. However, limited genetic data is available for this non-model plant, and the molecular mechanism underlying floral organ identity is still poorly understood. In this study, we characterize the floral transcriptome of C. ensifolium and present, for the first time, extensive sequence and transcript abundance data of individual floral organs. After sequencing, over 10 Gb clean sequence data were generated and assembled into 111,892 unigenes with an average length of 932.03 base pairs, including 1,227 clusters and 110,665 singletons. Assembled sequences were annotated with gene descriptions, gene ontology, clusters of orthologous group terms, the Kyoto Encyclopedia of Genes and Genomes, and the plant transcription factor database. From these annotations, 131 flowering-associated unigenes, 61 CONSTANS-LIKE (COL) unigenes and 90 floral homeotic genes were identified. In addition, four digital gene expression libraries were constructed for the sepal, petal, labellum and gynostemium, and 1,058 genes corresponding to individual floral organ development were identified. Among them, eight MADS-box genes were further investigated by full-length cDNA sequence analysis and expression validation, which revealed two APETALA1/AGL9-like MADS-box genes preferentially expressed in the sepal and petal, two AGAMOUS-like genes particularly restricted to the gynostemium, and four DEF-like genes distinctively expressed in different floral organs. The spatial expression of these genes varied distinctly in different floral mutant corresponding to different floral morphogenesis, which validated the specialized roles of them in floral patterning and further supported the effectiveness of our in silico analysis. This dataset generated in our study provides new insights into the molecular mechanisms underlying floral

  6. Peripheral Blood Mononuclear Cell Gene Expression Remains Broadly Altered Years after Successful Interferon-Based Hepatitis C Virus Treatment

    PubMed Central

    Waldron, Paul Ravi; Holodniy, Mark

    2015-01-01

    Background. Inflammatory gene expression in peripheral blood mononuclear cells (PBMCs) is altered in chronic Hepatitis C Virus (HCV) infection. Duration of changes after pegylated interferon- (peg-IFN-) based HCV treatment is unclear. Methods. PBMC mRNA expression of 184 inflammatory response genes was analyzed (nCounter GX Human Inflammation Kit, Nanostring) from peg-IFN treatment nonresponders (NR, n = 18), sustained virologic responders (SVR, n = 22), and spontaneous clearers (SC, n = 15). Logistic regression was used for comparison. Results. Median time from last treatment was 2 and 2.7 years in SVR and NR, respectively (p = NS). Mean mRNA counts were significantly different for 42 and 29 genes comparing SVR to SC patients and NR to SC, respectively, and no genes comparing SVR to NR. Differential expression of 24 genes was significantly different in both SVR and NR groups compared to SC. Among these 24 acute and chronic inflammatory cascade genes, significant upregulation was noted for proinflammatory transcription regulators Fos, CEBPB, and MyD88 in SVR and NR compared to SC. HDAC4 was significantly downregulated in SVR and NR compared to the SC group. Conclusions. PBMC inflammatory gene expression patterns in SVR resemble NR more than SC patients. A generalized inflammatory response persists in PBMCs long after successful peg-IFN treatment for HCV infection. PMID:26568966

  7. MRI of Transgene Expression: Correlation to Therapeutic Gene Expression

    PubMed Central

    Ichikawa, Tomotsugu; Högemanny, Dagmar; Saeki, Yoshinaga; Tyminski, Edyta; Terada, Kinya; Weissleder, Ralph; Chiocca, E Antonio; Basilion, James P

    2002-01-01

    Abstract Magnetic resonance imaging (MRI) can provide highresolution 3D maps of structural and functional information, yet its use of mapping in vivo gene expression has only recently been explored. A potential application for this technology is to noninvasively image transgene expression. The current study explores the latter using a nonregulatable internalizing engineered transferrin receptor (ETR) whose expression can be probed for with a superparamagnetic Tf-CLIO probe. Using an HSV-based amplicon vector system for transgene delivery, we demonstrate that: 1) ETR is a sensitive MR marker gene; 2) several transgenes can be efficiently expressed from a single amplicon; 3) expression of each transgene results in functional gene product; and 4) ETR gene expression correlates with expression of therapeutic genes when the latter are contained within the same amplicon. These data, taken together, suggest that MRI of ETR expression can serve as a surrogate for measuring therapeutic transgene expression. PMID:12407446

  8. Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature

    PubMed Central

    2016-01-01

    Gene Expression Music Algorithm (GEMusicA) is a method for the transformation of DNA microarray data into melodies that can be used for the characterization of differentially expressed genes. Using this method we compared gene expression profiles from endothelial cells (EC), hematopoietic stem cells, neuronal stem cells, embryonic stem cells (ESC), and mesenchymal stem cells (MSC) and defined a set of genes that can discriminate between the different stem cell types. We analyzed the behavior of public microarray data sets from Ewing sarcoma (“Ewing family tumors,” EFT) cell lines and biopsies in GEMusicA after prefiltering DNA microarray data for the probe sets from the stem cell signature. Our results demonstrate that individual Ewing sarcoma cell lines have a high similarity to ESC or EC. Ewing sarcoma cell lines with inhibited Ewing sarcoma breakpoint region 1-Friend leukemia virus integration 1 (EWSR1-FLI1) oncogene retained the similarity to ESC and EC. However, correlation coefficients between GEMusicA-processed expression data between EFT and ESC decreased whereas correlation coefficients between EFT and EC as well as between EFT and MSC increased after knockdown of EWSR1-FLI1. Our data support the concept of EFT being derived from cells with features of embryonic and endothelial cells. PMID:27446218

  9. Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers

    PubMed Central

    Ashworth, Justin; Bernard, Brady; Reynolds, Sheila; Plaisier, Christopher L.; Shmulevich, Ilya; Baliga, Nitin S.

    2014-01-01

    Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein–DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases. PMID:25378323

  10. Effective identification of essential proteins based on priori knowledge, network topology and gene expressions.

    PubMed

    Li, Min; Zheng, Ruiqing; Zhang, Hanhui; Wang, Jianxin; Pan, Yi

    2014-06-01

    Identification of essential proteins is very important for understanding the minimal requirements for cellular life and also necessary for a series of practical applications, such as drug design. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which makes it possible to detect proteins' essentialities from the network level. Considering that most species already have a number of known essential proteins, we proposed a new priori knowledge-based scheme to discover new essential proteins from protein interaction networks. Based on the new scheme, two essential protein discovery algorithms, CPPK and CEPPK, were developed. CPPK predicts new essential proteins based on network topology and CEPPK detects new essential proteins by integrating network topology and gene expressions. The performances of CPPK and CEPPK were validated based on the protein interaction network of Saccharomyces cerevisiae. The experimental results showed that the priori knowledge of known essential proteins was effective for improving the predicted precision. The predicted precisions of CPPK and CEPPK clearly exceeded that of the other 10 previously proposed essential protein discovery methods: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), and Network Centrality (NC). Especially, CPPK achieved 40% improvement in precision over BC, CC, SC, EC, and BN, and CEPPK performed even better. CEPPK was also compared to four other methods (EPC, ORFL, PeC, and CoEWC) which were not node centralities and CEPPK was showed to achieve the best results. PMID:24565748

  11. Gene expression analysis of biopsy samples reveals critical limitations of transcriptome-based molecular classifications of hepatocellular carcinoma.

    PubMed

    Makowska, Zuzanna; Boldanova, Tujana; Adametz, David; Quagliata, Luca; Vogt, Julia E; Dill, Michael T; Matter, Mathias S; Roth, Volker; Terracciano, Luigi; Heim, Markus H

    2016-04-01

    Molecular classification of hepatocellular carcinomas (HCC) could guide patient stratification for personalized therapies targeting subclass-specific cancer 'driver pathways'. Currently, there are several transcriptome-based molecular classifications of HCC with different subclass numbers, ranging from two to six. They were established using resected tumours that introduce a selection bias towards patients without liver cirrhosis and with early stage HCCs. We generated and analyzed gene expression data from paired HCC and non-cancerous liver tissue biopsies from 60 patients as well as five normal liver samples. Unbiased consensus clustering of HCC biopsy profiles identified 3 robust classes. Class membership correlated with survival, tumour size and with Edmondson and Barcelona Clinical Liver Cancer (BCLC) stage. When focusing only on the gene expression of the HCC biopsies, we could validate previously reported classifications of HCC based on expression patterns of signature genes. However, the subclass-specific gene expression patterns were no longer preserved when the fold-change relative to the normal tissue was used. The majority of genes believed to be subclass-specific turned out to be cancer-related genes differentially regulated in all HCC patients, with quantitative rather than qualitative differences between the molecular subclasses. With the exception of a subset of samples with a definitive β-catenin gene signature, biological pathway analysis could not identify class-specific pathways reflecting the activation of distinct oncogenic programs. In conclusion, we have found that gene expression profiling of HCC biopsies has limited potential to direct therapies that target specific driver pathways, but can identify subgroups of patients with different prognosis. PMID:27499918

  12. Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women

    PubMed Central

    Niu, Tianhua; Zhou, Yu; Zhang, Lan; Zeng, Yong; Zhu, Wei; Wang, Yu-ping; Deng, Hong-wen

    2016-01-01

    Background Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women. Methods Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher’s method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test’s p-values < 0.05 or I2 values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher’s combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein–protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test. Results Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment

  13. Histogenetic Compartments of the Mouse Centromedial and Extended Amygdala Based on Gene Expression Patterns during Development

    PubMed Central

    García-López, Margarita; Abellán, Antonio; Legaz, Isabel; Rubenstein, John L.R.; Puelles, Luis; Medina, Loreta

    2016-01-01

    The amygdala controls emotional and social behavior and regulates instinctive reflexes such as defense and reproduction by way of descending projections to the hypothalamus and brainstem. The descending amygdalar projections are suggested to show a cortico-striato-pallidal organization similar to that of the basal ganglia (Swanson [2000] Brain Res 886:113–164). To test this model we investigated the embryological origin and molecular properties of the mouse centromedial and extended amygdalar subdivisions, which constitute major sources of descending projections. We analyzed the distribution of key regulatory genes that show restricted expression patterns within the subpallium (Dlx5, Nkx2.1, Lhx6, Lhx7/8, Lhx9, Shh, and Gbx1), as well as genes considered markers for specific subpallial neuronal subpopulations. Our results indicate that most of the centromedial and extended amygdala is formed by cells derived from multiple subpallial subdivisions. Contrary to a previous suggestion, only the central—but not the medial—amygdala derives from the lateral ganglionic eminence and has striatal-like features. The medial amygdala and a large part of the extended amygdala (including the bed nucleus of the stria terminalis) consist of subdivisions or cell groups that derive from subpallial, pallial (ventral pallium), or extratelencephalic progenitor domains. The subpallial part includes derivatives from the medial ganglionic eminence, the anterior peduncular area, and possibly a novel subdivision, called here commissural preoptic area, located at the base of the septum and related to the anterior commissure. Our study provides a molecular and morphological foundation for understanding the complex embryonic origins and adult organization of the centromedial and extended amygdala. PMID:17990271

  14. 2A self-cleaving peptide-based multi-gene expression system in the silkworm Bombyx mori

    PubMed Central

    Wang, Yuancheng; Wang, Feng; Wang, Riyuan; Zhao, Ping; Xia, Qingyou

    2015-01-01

    Fundamental and applied studies of silkworms have entered the functional genomics era. Here, we report a multi-gene expression system (MGES) based on 2A self-cleaving peptide (2A), which regulates the simultaneous expression and cleavage of multiple gene targets in the silk gland of transgenic silkworms. First, a glycine-serine-glycine spacer (GSG) was found to significantly improve the cleavage efficiency of 2A. Then, the cleavage efficiency of six types of 2As with GSG was analyzed. The shortest porcine teschovirus-1 2A (P2A-GSG) exhibited the highest cleavage efficiency in all insect cell lines that we tested. Next, P2A-GSG successfully cleaved the artificial human serum albumin (66 kDa) linked with human acidic fibroblast growth factor (20.2 kDa) fusion genes and vitellogenin receptor fragment (196 kD) of silkworm linked with EGFP fusion genes, importantly, vitellogenin receptor protein was secreted to the outside of cells. Furthermore, P2A-GSG successfully mediated the simultaneous expression and cleavage of a DsRed and EGFP fusion gene in silk glands and caused secretion into the cocoon of transgenic silkworms using our sericin1 expression system. We predicted that the MGES would be an efficient tool for gene function research and innovative research on various functional silk materials in medicine, cosmetics, and other biomedical areas. PMID:26537835

  15. Gene ontology based characterization of expressed sequence tags (ESTs) of Brassica rapa cv. Osome.

    PubMed

    Arasan, Senthil Kumar Thamil; Park, Jong-In; Ahmed, Nasar Uddin; Jung, Hee-Jeong; Lee, In-Ho; Cho, Yong-Gu; Lim, Yong-Pyo; Kang, Kwon-Kyoo; Nou, Ill-Sup

    2013-07-01

    Chinese cabbage (Brassica rapa) is widely recognized for its economic importance and contribution to human nutrition but abiotic and biotic stresses are main obstacle for its quality, nutritional status and production. In this study, 3,429 Express Sequence Tag (EST) sequences were generated from B. rapa cv. Osome cDNA library and the unique transcripts were classified functionally using a gene ontology (GO) hierarchy, Kyoto encyclopedia of genes and genomes (KEGG). KEGG orthology and the structural domain data were obtained from the biological database for stress related genes (SRG). EST datasets provided a wide outlook of functional characterization of B. rapa cv. Osome. In silico analysis revealed % 83 of ESTs to be well annotated towards reeds one dimensional concept. Clustering of ESTs returned 333 contigs and 2,446 singlets, giving a total of 3,284 putative unigene sequences. This dataset contained 1,017 EST sequences functionally annotated to stress responses and from which expression of randomly selected SRGs were analyzed against cold, salt, drought, ABA, water and PEG stresses. Most of the SRGs showed differentially expression against these stresses. Thus, the EST dataset is very important for discovering the potential genes related to stress resistance in Chinese cabbage, and can be of useful resources for genetic engineering of Brassica sp. PMID:23898551

  16. Prediction of Metabolic Flux Distribution from Gene Expression Data Based on the Flux Minimization Principle

    PubMed Central

    Song, Hyun-Seob; Reifman, Jaques; Wallqvist, Anders

    2014-01-01

    Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts. PMID:25397773

  17. RNase One Gene Isolation, Expression, and Affinity Purification Models Research Experimental Progression and Culminates with Guided Inquiry-Based Experiments

    ERIC Educational Resources Information Center

    Bailey, Cheryl P.

    2009-01-01

    This new biochemistry laboratory course moves through a progression of experiments that generates a platform for guided inquiry-based experiments. RNase One gene is isolated from prokaryotic genomic DNA, expressed as a tagged protein, affinity purified, and tested for activity and substrate specificity. Student pairs present detailed explanations…

  18. Multi-Parametric Profiling Network Based on Gene Expression and Phenotype Data: A Novel Approach to Developmental Neurotoxicity Testing

    PubMed Central

    Nagano, Reiko; Akanuma, Hiromi; Qin, Xian-Yang; Imanishi, Satoshi; Toyoshiba, Hiroyoshi; Yoshinaga, Jun; Ohsako, Seiichiroh; Sone, Hideko

    2012-01-01

    The establishment of more efficient approaches for developmental neurotoxicity testing (DNT) has been an emerging issue for children’s environmental health. Here we describe a systematic approach for DNT using the neuronal differentiation of mouse embryonic stem cells (mESCs) as a model of fetal programming. During embryoid body (EB) formation, mESCs were exposed to 12 chemicals for 24 h and then global gene expression profiling was performed using whole genome microarray analysis. Gene expression signatures for seven kinds of gene sets related to neuronal development and neuronal diseases were selected for further analysis. At the later stages of neuronal cell differentiation from EBs, neuronal phenotypic parameters were determined using a high-content image analyzer. Bayesian network analysis was then performed based on global gene expression and neuronal phenotypic data to generate comprehensive networks with a linkage between early events and later effects. Furthermore, the probability distribution values for the strength of the linkage between parameters in each network was calculated and then used in principal component analysis. The characterization of chemicals according to their neurotoxic potential reveals that the multi-parametric analysis based on phenotype and gene expression profiling during neuronal differentiation of mESCs can provide a useful tool to monitor fetal programming and to predict developmentally neurotoxic compounds. PMID:22312247

  19. Multi-parametric profiling network based on gene expression and phenotype data: a novel approach to developmental neurotoxicity testing.

    PubMed

    Nagano, Reiko; Akanuma, Hiromi; Qin, Xian-Yang; Imanishi, Satoshi; Toyoshiba, Hiroyoshi; Yoshinaga, Jun; Ohsako, Seiichiroh; Sone, Hideko

    2012-01-01

    The establishment of more efficient approaches for developmental neurotoxicity testing (DNT) has been an emerging issue for children's environmental health. Here we describe a systematic approach for DNT using the neuronal differentiation of mouse embryonic stem cells (mESCs) as a model of fetal programming. During embryoid body (EB) formation, mESCs were exposed to 12 chemicals for 24 h and then global gene expression profiling was performed using whole genome microarray analysis. Gene expression signatures for seven kinds of gene sets related to neuronal development and neuronal diseases were selected for further analysis. At the later stages of neuronal cell differentiation from EBs, neuronal phenotypic parameters were determined using a high-content image analyzer. Bayesian network analysis was then performed based on global gene expression and neuronal phenotypic data to generate comprehensive networks with a linkage between early events and later effects. Furthermore, the probability distribution values for the strength of the linkage between parameters in each network was calculated and then used in principal component analysis. The characterization of chemicals according to their neurotoxic potential reveals that the multi-parametric analysis based on phenotype and gene expression profiling during neuronal differentiation of mESCs can provide a useful tool to monitor fetal programming and to predict developmentally neurotoxic compounds. PMID:22312247

  20. A genome-wide cis-regulatory element discovery method based on promoter sequences and gene co-expression networks

    PubMed Central

    2013-01-01

    Background Deciphering cis-regulatory networks has become an attractive yet challenging task. This paper presents a simple method for cis-regulatory network discovery which aims to avoid some of the common problems of previous approaches. Results Using promoter sequences and gene expression profiles as input, rather than clustering the genes by the expression data, our method utilizes co-expression neighborhood information for each individual gene, thereby overcoming the disadvantages of current clustering based models which may miss specific information for individual genes. In addition, rather than using a motif database as an input, it implements a simple motif count table for each enumerated k-mer for each gene promoter sequence. Thus, it can be used for species where previous knowledge of cis-regulatory motifs is unknown and has the potential to discover new transcription factor binding sites. Applications on Saccharomyces cerevisiae and Arabidopsis have shown that our method has a good prediction accuracy and outperforms a phylogenetic footprinting approach. Furthermore, the top ranked gene-motif regulatory clusters are evidently functionally co-regulated, and the regulatory relationships between the motifs and the enriched biological functions can often be confirmed by literature. Conclusions Since this method is simple and gene-specific, it can be readily utilized for insufficiently studied species or flexibly used as an additional step or data source for previous transcription regulatory networks discovery models. PMID:23368633

  1. Antiphospholipid antibodies in a large population-based cohort: genome-wide associations and effects on monocyte gene expression.

    PubMed

    Müller-Calleja, Nadine; Rossmann, Heidi; Müller, Christian; Wild, Philipp; Blankenberg, Stefan; Pfeiffer, Norbert; Binder, Harald; Beutel, Manfred E; Manukyan, Davit; Zeller, Tanja; Lackner, Karl J

    2016-07-01

    The antiphospholipid syndrome (APS) is characterised by venous and/or arterial thrombosis and pregnancy morbidity in women combined with the persistent presence of antiphospholipid antibodies (aPL). We aimed to identify genetic factors associated with the presence of aPL in a population based cohort. Furthermore, we wanted to clarify if the presence of aPL affects gene expression in circulating monocytes. Titres of IgG and IgM against cardiolipin, β2glycoprotein 1 (anti-β2GPI), and IgG against domain 1 of β2GPI (anti-domain 1) were determined in approx. 5,000 individuals from the Gutenberg Health Study (GHS) a population based cohort of German descent. Genotyping was conducted on Affymetrix Genome-Wide Human SNP 6.0 arrays. Monocyte gene expression was determined in a subgroup of 1,279 individuals by using the Illumina HT-12 v3 BeadChip. Gene expression data were confirmed in vitro and ex vivo by qRT-PCR. Genome wide analysis revealed significant associations of anti-β2GPI IgG and APOH on chromosome 17, which had been previously identified by candidate gene approaches, and of anti-domain1 and MACROD2 on chromosome 20 which has been listed in a previous GWAS as a suggestive locus associated with the occurrence of anti-β2GPI antibodies. Expression analysis confirmed increased expression of TNFα in monocytes and identified and confirmed neuron navigator 3 (NAV3) as a novel gene induced by aPL. In conclusion, MACROD2 represents a novel genetic locus associated with aPL. Furthermore, we show that aPL induce the expression of NAV3 in monocytes and endothelial cells. This will stimulate further research into the role of these genes in the APS. PMID:27098658

  2. A green fluorescent protein (GFP)-based plasmid system to study post-transcriptional control of gene expression in vivo.

    PubMed

    Urban, Johannes H; Vogel, Jörg

    2009-01-01

    Small non-coding RNAs (sRNAs) are an emerging class of regulators of bacterial gene expression, which mainly modulate the translation of trans-encoded mRNAs. Typically, these molecules are 50-200 nucleotides in size and do not contain expressed open reading frames (ORFs). In Escherichia coli, about 70 members of this group have been identified to date and further estimates assume hundreds of sRNAs per bacterial genome. Regulation of gene expression by sRNAs is predominantly mediated by physical sRNA/target mRNA interactions that are based on short and imperfect complementarity. Although the contribution of sRNAs to overall bacterial gene regulation is now being appreciated, the function of many sRNAs is still unknown and their targets await to be uncovered. We recently developed a modular two-plasmid system, based on the green fluorescent protein (GFP) as non-invasive reporter of gene expression, to rapidly monitor the regulatory potential of sRNA/target mRNA pairs under investigation in vivo. The specialized reporter plasmid series also provides a suitable platform to study the function of cis-encoded riboregulators such as natural riboswitches, thermosensors, or engineered aptamer-based regulatory switches. PMID:19381569

  3. UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data

    PubMed Central

    Wang, Zhenjia; Li, Guojun; Robinson, Robert W.; Huang, Xiuzhen

    2016-01-01

    Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general trend-preserving biclusters which are the most meaningful substructures hidden in gene expression data remains a highly challenging problem. We found an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified. We tested it on synthetic and real datasets and compared its performance with currently competitive biclustering tools. We found that the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters. The latter was somewhat better at finding narrow biclusters, the task for which it was specifically designed. PMID:27001340

  4. COPD subtypes identified by network-based clustering of blood gene expression.

    PubMed

    Chang, Yale; Glass, Kimberly; Liu, Yang-Yu; Silverman, Edwin K; Crapo, James D; Tal-Singer, Ruth; Bowler, Russ; Dy, Jennifer; Cho, Michael; Castaldi, Peter

    2016-03-01

    One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naïve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. PMID:26773458

  5. Towards predicting metastatic progression of melanoma based on gene expression data

    PubMed Central

    Li, Yuanyuan; Krahn, Juno M.; Flake, Gordon P.; Umbach, David M.; Li, Leping

    2015-01-01

    Summary Primary and metastatic melanoma tumors share the same cell origin, making it challenging to identify genomic biomarkers that can differentiate them. Primary tumors themselves can be heterogeneous, reflecting ongoing genomic changes as they progress toward metastasizing. We developed a computational method to explore this heterogeneity and to predict metastatic progression of the primary tumors. We applied our method separately to gene expression and to microRNA (miRNA) expression data from ~450 primary and metastatic skin cutaneous melanoma (SKCM) samples from the Cancer Genome Atlas (TCGA). Metastatic progression scores from RNA-seq data were significantly associated with clinical staging of patients’ lymph nodes whereas scores from miRNA-seq data were significantly associated with Clark’s level. The loss of expression of many characteristic epithelial lineage genes in primary SKCM tumor samples was highly correlated with predicted progression scores. We suggest that those genes/miRNAs might serve as putative biomarkers for SKCM metastatic progression. PMID:25847062

  6. UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data.

    PubMed

    Wang, Zhenjia; Li, Guojun; Robinson, Robert W; Huang, Xiuzhen

    2016-01-01

    Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general trend-preserving biclusters which are the most meaningful substructures hidden in gene expression data remains a highly challenging problem. We found an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified. We tested it on synthetic and real datasets and compared its performance with currently competitive biclustering tools. We found that the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters. The latter was somewhat better at finding narrow biclusters, the task for which it was specifically designed. PMID:27001340

  7. Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining

    PubMed Central

    Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

    2015-01-01

    Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data

  8. Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

    PubMed

    Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

    2015-01-01

    Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data

  9. Correlation of gene expression and contaminat concentrations in wild largescale suckers: a field-based study

    USGS Publications Warehouse

    Christiansen, Helena E.; Mehinto, Alvina C.; Yu, Fahong; Perry, Russell W.; Denslow, Nancy D.; Maule, Alec G.; Mesa, Matthew G.

    2014-01-01

    Toxic compounds such as organochlorine pesticides (OCs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ether flame retardants (PBDEs) have been detected in fish, birds, and aquatic mammals that live in the Columbia River or use food resources from within the river. We developed a custom microarray for largescale suckers (Catostomus macrocheilus) and used it to investigate the molecular effects of contaminant exposure on wild fish in the Columbia River. Using Significance Analysis of Microarrays (SAM) we identified 72 probes representing 69 unique genes with expression patterns that correlated with hepatic tissue levels of OCs, PCBs, or PBDEs. These genes were involved in many biological processes previously shown to respond to contaminant exposure, including drug and lipid metabolism, apoptosis, cellular transport, oxidative stress, and cellular chaperone function. The relation between gene expression and contaminant concentration suggests that these genes may respond to environmental contaminant exposure and are promising candidates for further field and laboratory studies to develop biomarkers for monitoring exposure of wild fish to contaminant mixtures found in the Columbia River Basin. The array developed in this study could also be a useful tool for studies involving endangered sucker species and other sucker species used in contaminant research.

  10. Correlation of gene expression and contaminant concentrations in wild largescale suckers: a field-based study.

    PubMed

    Christiansen, Helena E; Mehinto, Alvine C; Yu, Fahong; Perry, Russell W; Denslow, Nancy D; Maule, Alec G; Mesa, Matthew G

    2014-06-15

    Toxic compounds such as organochlorine pesticides (OCs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ether flame retardants (PBDEs) have been detected in fish, birds, and aquatic mammals that live in the Columbia River or use food resources from within the river. We developed a custom microarray for largescale suckers (Catostomus macrocheilus) and used it to investigate the molecular effects of contaminant exposure on wild fish in the Columbia River. Using Significance Analysis of Microarrays (SAM) we identified 72 probes representing 69 unique genes with expression patterns that correlated with hepatic tissue levels of OCs, PCBs, or PBDEs. These genes were involved in many biological processes previously shown to respond to contaminant exposure, including drug and lipid metabolism, apoptosis, cellular transport, oxidative stress, and cellular chaperone function. The relation between gene expression and contaminant concentration suggests that these genes may respond to environmental contaminant exposure and are promising candidates for further field and laboratory studies to develop biomarkers for monitoring exposure of wild fish to contaminant mixtures found in the Columbia River Basin. The array developed in this study could also be a useful tool for studies involving endangered sucker species and other sucker species used in contaminant research. PMID:24050789

  11. Analysis of differentially expressed genes based on microarray data of glioma

    PubMed Central

    Jiang, Chun-Ming; Wang, Xiao-Hua; Shu, Jin; Yang, Wei-Xia; Fu, Ping; Zhuang, Li-Li; Zhou, Guo-Ping

    2015-01-01

    Glioma represents one of the main causes of cancer-related death worldwide. Unfortunately, its exact molecular mechanisms remain poorly understood, which limits the prognosis and therapy. This study aimed to identify the critical genes, transcription factors and the possible biochemical pathways that may affect glioma progression at transcription level. After downloading micro-array data from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between glioma and normal samples were screened. We predicted novel glioma-related genes and carried on online software DAVID to conduct GO enrichment and transcription factor analysis of these selected genes. String software was applied to construct a PPI protein interaction network, as well as to find the key genes and transcription factors in the regulation of glioma. A total of 97 DEGs were identified associated with cancer, the GO enrichment analysis indicated these DEGs were mainly relevant to immune responses as well as regulation of cell growth. In addition, the transcription factor analysis showed these DEGs were regulated by the binding sites of transcription factors GLI2, SP1, SMAD7, SMAD3, RELA, STAT5B, CTNNB1, STAT5A, TFAP2A and SP3. PPI protein interaction network analysis demonstrated the hub nodes in the interaction network were EGFR, TGFB1, FN1 and MYC. The hub DEGs may be the most critical in glioma and could be considered as drug targets for glioma therapy after further exploration. Besides, with the identification of regulating transcription factors, the pathogenesis of glioma at transcription level might be brought to light. PMID:26770324

  12. Gene Expression Profiling of Development and Anthocyanin Accumulation in Kiwifruit (Actinidia chinensis) Based on Transcriptome Sequencing

    PubMed Central

    Zeng, Shaohua; Xiao, Gong; Wang, Gan; Wang, Ying; Peng, Ming; Huang, Hongwen

    2015-01-01

    Red-fleshed kiwifruit (Actinidia chinensis Planch. ‘Hongyang’) is a promising commercial cultivar due to its nutritious value and unique flesh color, derived from vitamin C and anthocyanins. In this study, we obtained transcriptome data of ‘Hongyang’ from seven developmental stages using Illumina sequencing. We mapped 39–54 million reads to the recently sequenced kiwifruit genome and other databases to define gene structure, to analyze alternative splicing, and to quantify gene transcript abundance at different developmental stages. The transcript profiles throughout red kiwifruit development were constructed and analyzed, with a focus on the biosynthesis and metabolism of compounds such as phytohormones, sugars, starch and L-ascorbic acid, which are indispensable for the development and formation of quality fruit. Candidate genes for these pathways were identified through MapMan and phylogenetic analysis. The transcript levels of genes involved in sucrose and starch metabolism were consistent with the change in soluble sugar and starch content throughout kiwifruit development. The metabolism of L-ascorbic acid was very active, primarily through the L-galactose pathway. The genes responsible for the accumulation of anthocyanin in red kiwifruit were identified, and their expression levels were investigated during kiwifruit development. This survey of gene expression during kiwifruit development paves the way for further investigation of the development of this uniquely colored and nutritious fruit and reveals which factors are needed for high quality fruit formation. This transcriptome data and its analysis will be useful for improving kiwifruit genome annotation, for basic fruit molecular biology research, and for kiwifruit breeding and improvement. PMID:26301713

  13. [Study on action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis based on techniques of gene expression profile and molecular fingerprint].

    PubMed

    Zhou, Wei; Song, Xiang-gang; Chen, Chao; Wang, Shu-mei; Liang, Sheng-wang

    2015-08-01

    Action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis were discussed based on gene expression profile and molecular fingerprint in this paper. First, gene expression profiles of atherosclerotic carotid artery tissues and histologically normal tissues in human body were collected, and were screened using significance analysis of microarray (SAM) to screen out differential gene expressions; then differential genes were analyzed by Gene Ontology (GO) analysis and KEGG pathway analysis; to avoid some genes with non-outstanding differential expression but biologically importance, Gene Set Enrichment Analysis (GSEA) were performed, and 7 chemical ingredients with higher negative enrichment score were obtained by Cmap method, implying that they could reversely regulate the gene expression profiles of pathological tissues; and last, based on the hypotheses that similar structures have similar activities, 336 ingredients of compound Danshen dripping pills were compared with 7 drug molecules in 2D molecular fingerprints method. The results showed that 147 differential genes including 60 up-regulated genes and 87 down regulated genes were screened out by SAM. And in GO analysis, Biological Process ( BP) is mainly concerned with biological adhesion, response to wounding and inflammatory response; Cellular Component (CC) is mainly concerned with extracellular region, extracellular space and plasma membrane; while Molecular Function (MF) is mainly concerned with antigen binding, metalloendopeptidase activity and peptide binding. KEGG pathway analysis is mainly concerned with JAK-STAT, RIG-I like receptor and PPAR signaling pathway. There were 10 compounds, such as hexadecane, with Tanimoto coefficients greater than 0.85, which implied that they may be the active ingredients (AIs) of compound Danshen dripping pills in treatment of carotid atherosclerosis (CAs). The present method can be applied to the research on material

  14. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data.

    PubMed

    Xia, Jianguo; Gill, Erin E; Hancock, Robert E W

    2015-06-01

    Meta-analysis of gene expression data sets is increasingly performed to help identify robust molecular signatures and to gain insights into underlying biological processes. The complicated nature of such analyses requires both advanced statistics and innovative visualization strategies to support efficient data comparison, interpretation and hypothesis generation. NetworkAnalyst (http://www.networkanalyst.ca) is a comprehensive web-based tool designed to allow bench researchers to perform various common and complex meta-analyses of gene expression data via an intuitive web interface. By coupling well-established statistical procedures with state-of-the-art data visualization techniques, NetworkAnalyst allows researchers to easily navigate large complex gene expression data sets to determine important features, patterns, functions and connections, thus leading to the generation of new biological hypotheses. This protocol provides a step-wise description of how to effectively use NetworkAnalyst to perform network analysis and visualization from gene lists; to perform meta-analysis on gene expression data while taking into account multiple metadata parameters; and, finally, to perform a meta-analysis of multiple gene expression data sets. NetworkAnalyst is designed to be accessible to biologists rather than to specialist bioinformaticians. The complete protocol can be executed in ∼1.5 h. Compared with other similar web-based tools, NetworkAnalyst offers a unique visual analytics experience that enables data analysis within the context of protein-protein interaction networks, heatmaps or chord diagrams. All of these analysis methods provide the user with supporting statistical and functional evidence. PMID:25950236

  15. DeepSAGE Based Differential Gene Expression Analysis under Cold and Freeze Stress in Seabuckthorn (Hippophae rhamnoides L.)

    PubMed Central

    Chaudhary, Saurabh; Sharma, Prakash C.

    2015-01-01

    Seabuckthorn (Hippophae rhamnoides L.), an important plant species of Indian Himalayas, is well known for its immense medicinal and nutritional value. The plant has the ability to sustain growth in harsh environments of extreme temperatures, drought and salinity. We employed DeepSAGE, a tag based approach, to identify differentially expressed genes under cold and freeze stress in seabuckthorn. In total 36.2 million raw tags including 13.9 million distinct tags were generated using Illumina sequencing platform for three leaf tissue libraries including control (CON), cold stress (CS) and freeze stress (FS). After discarding low quality tags, 35.5 million clean tags including 7 million distinct clean tags were obtained. In all, 11922 differentially expressed genes (DEGs) including 6539 up regulated and 5383 down regulated genes were identified in three comparative setups i.e. CON vs CS, CON vs FS and CS vs FS. Gene ontology and KEGG pathway analysis were performed to assign gene ontology term to DEGs and ascertain their biological functions. DEGs were mapped back to our existing seabuckthorn transcriptome assembly comprising of 88,297 putative unigenes leading to the identification of 428 cold and freeze stress responsive genes. Expression of randomly selected 22 DEGs was validated using qRT-PCR that further supported our DeepSAGE results. The present study provided a comprehensive view of global gene expression profile of seabuckthorn under cold and freeze stresses. The DeepSAGE data could also serve as a valuable resource for further functional genomics studies aiming selection of candidate genes for development of abiotic stress tolerant transgenic plants. PMID:25803684

  16. DeepSAGE based differential gene expression analysis under cold and freeze stress in seabuckthorn (Hippophae rhamnoides L.).

    PubMed

    Chaudhary, Saurabh; Sharma, Prakash C

    2015-01-01

    Seabuckthorn (Hippophae rhamnoides L.), an important plant species of Indian Himalayas, is well known for its immense medicinal and nutritional value. The plant has the ability to sustain growth in harsh environments of extreme temperatures, drought and salinity. We employed DeepSAGE, a tag based approach, to identify differentially expressed genes under cold and freeze stress in seabuckthorn. In total 36.2 million raw tags including 13.9 million distinct tags were generated using Illumina sequencing platform for three leaf tissue libraries including control (CON), cold stress (CS) and freeze stress (FS). After discarding low quality tags, 35.5 million clean tags including 7 million distinct clean tags were obtained. In all, 11922 differentially expressed genes (DEGs) including 6539 up regulated and 5383 down regulated genes were identified in three comparative setups i.e. CON vs CS, CON vs FS and CS vs FS. Gene ontology and KEGG pathway analysis were performed to assign gene ontology term to DEGs and ascertain their biological functions. DEGs were mapped back to our existing seabuckthorn transcriptome assembly comprising of 88,297 putative unigenes leading to the identification of 428 cold and freeze stress responsive genes. Expression of randomly selected 22 DEGs was validated using qRT-PCR that further supported our DeepSAGE results. The present study provided a comprehensive view of global gene expression profile of seabuckthorn under cold and freeze stresses. The DeepSAGE data could also serve as a valuable resource for further functional genomics studies aiming selection of candidate genes for development of abiotic stress tolerant transgenic plants. PMID:25803684

  17. Gene Express Inc.

    PubMed

    Saccomanno, Colette F

    2006-07-01

    Gene Express, Inc. is a technology-licensing company and provider of Standardized Reverse Transcription Polymerase Chain Reaction (StaRT-PCR) services. Designed by and for clinical researchers involved in pharmaceutical, biomarker and molecular diagnostic product development, StaRT-PCR is a unique quantitative and standardized multigene expression measurement platform. StaRT-PCR meets all of the performance characteristics defined by the US FDA as required to support regulatory submissions [101,102] , and by the Clinical Laboratory Improvement Act of 1988 (CLIA) as necessary to support diagnostic testing [1] . A standardized mixture of internal standards (SMIS), manufactured in bulk, provides integrated quality control wherein each native template target gene is measured relative to a competitive template internal standard. Bulk production enables the compilation of a comprehensive standardized database from across multiple experiments, across collaborating laboratories and across the entire clinical development lifecycle of a given compound or diagnostic product. For the first time, all these data are able to be directly compared. Access to such a database can dramatically shorten the time from investigational new drug (IND) to new drug application (NDA), or save time and money by hastening a substantiated 'no-go' decision. High-throughput StaRT-PCR is conducted at the company's automated Standardized Expression Measurement (SEM) Center. Currently optimized for detection on a microcapillary electrophoretic platform, StaRT-PCR products also may be analyzed on microarray, high-performance liquid chromatography (HPLC), or matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) platforms. SEM Center services deliver standardized genomic data--data that will accelerate the application of pharmacogenomic technology to new drug and diagnostic test development and facilitate personalized medicine. PMID:16886903

  18. Gene expression during memory formation.

    PubMed

    Igaz, Lionel Muller; Bekinschtein, Pedro; Vianna, Monica M R; Izquierdo, Ivan; Medina, Jorge H

    2004-01-01

    For several decades, neuroscientists have provided many clues that point out the involvement of de novo gene expression during the formation of long-lasting forms of memory. However, information regarding the transcriptional response networks involved in memory formation has been scarce and fragmented. With the advent of genome-based technologies, combined with more classical approaches (i.e., pharmacology and biochemistry), it is now feasible to address those relevant questions--which gene products are modulated, and when that processes are necessary for the proper storage of memories--with unprecedented resolution and scale. Using one-trial inhibitory (passive) avoidance training of rats, one of the most studied tasks so far, we found two time windows of sensitivity to transcriptional and translational inhibitors infused into the hippocampus: around the time of training and 3-6 h after training. Remarkably, these periods perfectly overlap with the involvement of hippocampal cAMP/PKA (protein kinase A) signaling pathways in memory consolidation. Given the complexity of transcriptional responses in the brain, particularly those related to processing of behavioral information, it was clearly necessary to address this issue with a multi-variable, parallel-oriented approach. We used cDNA arrays to screen for candidate inhibitory avoidance learning-related genes and analyze the dynamic pattern of gene expression that emerges during memory consolidation. These include genes involved in intracellular kinase networks, synaptic function, DNA-binding and chromatin modification, transcriptional activation and repression, translation, membrane receptors, and oncogenes, among others. Our findings suggest that differential and orchestrated hippocampal gene expression is necessary in both early and late periods of long-term memory consolidation. Additionally, this kind of studies may lead to the identification and characterization of genes that are relevant for the pathogenesis

  19. Insights in to the pathogenesis of axial spondyloarthropathy based on gene expression profiles

    PubMed Central

    2009-01-01

    Introduction Axial spondyloarthropathy (SpA) is a group of inflammatory diseases, with ankylosing spondylitis as the prototype. SpA affects the axial skeleton, entheses, joints and, at times, the eyes. This study tested the hypothesis that SpA is characterized by a distinct pattern of gene expression in peripheral blood of affected individuals compared with healthy controls. Methods High-density, human GeneChip® probe arrays were used to profile mRNA of peripheral blood cells from 18 subjects with SpA and 25 normal individuals. Samples were processed as two separate sets at different times (11 SpA + 12 control subjects in primary set (Set 1); 7 SpA+ 13 control subjects in the validation set (Set 2)). Blood samples were taken at a time when patients were not receiving systemic immunomodulatory therapy. Differential expression was defined as a 1.5-fold change with a q value < 5%. Gene ontology and pathway information were also studied. Results Signals from 134 probe sets (representing 95 known and 12 unknown gene transcripts) were consistently different from controls in both Sets 1 and 2. Included among these were transcripts for a group of 20 genes, such as interleukin-1 (IL-1) receptors 1 and 2, Nod-like receptor family, pyrin domain containing 2 (NLRP2), secretory leukocyte peptidase inhibitor (SLPI), secreted protein acidic and rich in cysteine (SPARC), and triggering receptor expressed on myeloid cells 1 (TREM-1) that are clearly related to the immune or inflammatory response and a group of 4 transcripts that have a strong role in bone remodeling. Conclusions Our observations are the first to implicate SPARC, SLPI, and NLRP2, a component of the innate immune system, in the pathogenesis of SpA. Our results also indicate a possible role for IL-1 and its receptors in SpA. In accord with the bone pathology component of SpA, we also found that expression levels of transcripts reflecting bone remodeling factors are also distinguishable in peripheral blood from

  20. Effects of a hydroxyapatite-based biomaterial on gene expression in osteoblast-like cells.

    PubMed

    Sibilla, P; Sereni, A; Aguiari, G; Banzi, M; Manzati, E; Mischiati, C; Trombelli, L; del Senno, L

    2006-04-01

    Biostite is a hydroxyapatite-derived biomaterial that is used in periodontal and bone reconstructive procedures due to its osteoconductive properties. Since the molecular effects of this biomaterial on osteoblasts are still unknown, we decided to assess whether it may specifically modulate osteoblast functions in vitro. We found that a brief exposure to Biostite significantly reduced the proliferation of MG-63 and SaOS-2 osteoblast-like cells to approximately 50% of the plateau value. Furthermore, gene array analysis of MG-63 cells showed that Biostite caused a differential expression of 37 genes which are involved in cell proliferation and interaction, and related to osteoblast differentiation and tissue regeneration. Results were confirmed by RT-PCR, Western blot, and by an increase in alkaline phosphatase (ALP) specific activity. Biostite also increased levels of polycystin-2, a mechano-sensitive Ca(2+) channel, a promising new marker of bone cell differentiation. Biostite, therefore, may directly affect osteoblasts by enhancing chondro/osteogenic gene expression and cytoskeleton-related signaling pathways, which may contribute to its clinical efficacy. PMID:16567558

  1. Gene expression-based dosimetry by dose and time in mice following acute radiation exposure.

    PubMed

    Tucker, James D; Divine, George W; Grever, William E; Thomas, Robert A; Joiner, Michael C; Smolinski, Joseph M; Auner, Gregory W

    2013-01-01

    Rapid and reliable methods for performing biological dosimetry are of paramount importance in the event of a large-scale nuclear event. Traditional dosimetry approaches lack the requisite rapid assessment capability, ease of use, portability and low cost, which are factors needed for triaging a large number of victims. Here we describe the results of experiments in which mice were acutely exposed to (60)Co gamma rays at doses of 0 (control) to 10 Gy. Blood was obtained from irradiated mice 0.5, 1, 2, 3, 5, and 7 days after exposure. mRNA expression levels of 106 selected genes were obtained by reverse-transcription real time PCR. Stepwise regression of dose received against individual gene transcript expression levels provided optimal dosimetry at each time point. The results indicate that only 4-7 different gene transcripts are needed to explain ≥ 0.69 of the variance (R(2)), and that receiver-operator characteristics, a measure of sensitivity and specificity, of ≥ 0.93 for these statistical models were achieved at each time point. These models provide an excellent description of the relationship between the actual and predicted doses up to 6 Gy. At doses of 8 and 10 Gy there appears to be saturation of the radiation-response signals with a corresponding diminution of accuracy. These results suggest that similar analyses in humans may be advantageous for use in a field-portable device designed to assess exposures in mass casualty situations. PMID:24358280

  2. Microarray-based characterization of differential gene expression during vocal fold wound healing in rats

    PubMed Central

    Welham, Nathan V.; Ling, Changying; Dawson, John A.; Kendziorski, Christina; Thibeault, Susan L.; Yamashita, Masaru

    2015-01-01

    The vocal fold (VF) mucosa confers elegant biomechanical function for voice production but is susceptible to scar formation following injury. Current understanding of VF wound healing is hindered by a paucity of data and is therefore often generalized from research conducted in skin and other mucosal systems. Here, using a previously validated rat injury model, expression microarray technology and an empirical Bayes analysis approach, we generated a VF-specific transcriptome dataset to better capture the system-level complexity of wound healing in this specialized tissue. We measured differential gene expression at 3, 14 and 60 days post-injury compared to experimentally naïve controls, pursued functional enrichment analyses to refine and add greater biological definition to the previously proposed temporal phases of VF wound healing, and validated the expression and localization of a subset of previously unidentified repair- and regeneration-related genes at the protein level. Our microarray dataset is a resource for the wider research community and has the potential to stimulate new hypotheses and avenues of investigation, improve biological and mechanistic insight, and accelerate the identification of novel therapeutic targets. PMID:25592437

  3. An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data.

    PubMed

    Dragomir, A; Maraziotis, I; Bezerianos, A

    2006-01-01

    We focus on developing a pattern recognition method suitable for performing supervised analysis tasks on molecular data resulting from microarray experiments. Molecular characterization of tissue samples using microarray gene expression profiling is expected to uncover fundamental aspects related to cancer diagnosis and drug discovery. There is therefore a need for reliable, accurate classification methods. With this study we propose a framework for constructing an ensemble of individually trained SVM classifiers, each of them specialized on subsets of the input space. The fuzzy approach used for partitioning the data produces overlapping subsets of the input space that facilitates subsequent classification tasks. PMID:17946338

  4. Evolution of gene expression after gene amplification.

    PubMed

    Garcia, Nelson; Zhang, Wei; Wu, Yongrui; Messing, Joachim

    2015-05-01

    We took a rather unique approach to investigate the conservation of gene expression of prolamin storage protein genes across two different subfamilies of the Poaceae. We took advantage of oat plants carrying single maize chromosomes in different cultivars, called oat-maize addition (OMA) lines, which permitted us to determine whether regulation of gene expression was conserved between the two species. We found that γ-zeins are expressed in OMA7.06, which carries maize chromosome 7 even in the absence of the trans-acting maize prolamin-box-binding factor (PBF), which regulates their expression. This is likely because oat PBF can substitute for the function of maize PBF as shown in our transient expression data, using a γ-zein promoter fused to green fluorescent protein (GFP). Despite this conservation, the younger, recently amplified prolamin genes in maize, absent in oat, are not expressed in the corresponding OMAs. However, maize can express the oldest prolamin gene, the wheat high-molecular weight glutenin Dx5 gene, even when maize Pbf is knocked down (through PbfRNAi), and/or another maize transcription factor, Opaque-2 (O2) is knocked out (in maize o2 mutant). Therefore, older genes are conserved in their regulation, whereas younger ones diverged during evolution and eventually acquired a new repertoire of suitable transcriptional activators. PMID:25912045

  5. Evolution of Gene Expression after Gene Amplification

    PubMed Central

    Garcia, Nelson; Zhang, Wei; Wu, Yongrui; Messing, Joachim

    2015-01-01

    We took a rather unique approach to investigate the conservation of gene expression of prolamin storage protein genes across two different subfamilies of the Poaceae. We took advantage of oat plants carrying single maize chromosomes in different cultivars, called oat–maize addition (OMA) lines, which permitted us to determine whether regulation of gene expression was conserved between the two species. We found that γ-zeins are expressed in OMA7.06, which carries maize chromosome 7 even in the absence of the trans-acting maize prolamin-box-binding factor (PBF), which regulates their expression. This is likely because oat PBF can substitute for the function of maize PBF as shown in our transient expression data, using a γ-zein promoter fused to green fluorescent protein (GFP). Despite this conservation, the younger, recently amplified prolamin genes in maize, absent in oat, are not expressed in the corresponding OMAs. However, maize can express the oldest prolamin gene, the wheat high-molecular weight glutenin Dx5 gene, even when maize Pbf is knocked down (through PbfRNAi), and/or another maize transcription factor, Opaque-2 (O2) is knocked out (in maize o2 mutant). Therefore, older genes are conserved in their regulation, whereas younger ones diverged during evolution and eventually acquired a new repertoire of suitable transcriptional activators. PMID:25912045

  6. Gene Expression Studies in Mosquitoes

    PubMed Central

    Chen, Xlao-Guang; Mathur, Geetika; James, Anthony A.

    2009-01-01

    Research on gene expression in mosquitoes is motivated by both basic and applied interests. Studies of genes involved in hematophagy, reproduction, olfaction, and immune responses reveal an exquisite confluence of biological adaptations that result in these highly-successful life forms. The requirement of female mosquitoes for a bloodmeal for propagation has been exploited by a wide diversity of viral, protozoan and metazoan pathogens as part of their life cycles. Identifying genes involved in host-seeking, blood feeding and digestion, reproduction, insecticide resistance and susceptibility/refractoriness to pathogen development is expected to provide the bases for the development of novel methods to control mosquito-borne diseases. Advances in mosquito transgenesis technologies, the availability of whole genome sequence information, mass sequencing and analyses of transcriptomes and RNAi techniques will assist development of these tools as well as deepen the understanding of the underlying genetic components for biological phenomena characteristic of these insect species. PMID:19161831

  7. A gene expression based predictor for high risk myeloma treated with intensive therapy and autologous stem cell rescue

    PubMed Central

    Wu, Ping; Walker, Brian A.; Broyl, Annemiek; Kaiser, Martin; Johnson, David C.; Kuiper, Rowan; van Duin, Mark; Gregory, Walter M.; Davies, Faith E.; Brewer, Daniel; Hose, Dirk; Sonneveld, Pieter

    2015-01-01

    Myeloma is characterized by a highly variable clinical outcome. Despite the effectiveness of high-dose therapy, 15% of patients relapse within 1 year. We show that these cases also have a significantly shorter post-relapse survival compared to the others (median 14.9 months vs. 40 months, p = 8.03 × 10− 14). There are no effective approaches to define this potentially distinct biological group such that treatment could be altered. In this work a series of uniformly treated patients with myeloma were used to develop a gene expression profiling (GEP)-based signature to identify this high risk clinical behavior. Gene enrichment analyses applied to the top differentially expressed genes showed a significant enrichment of epigenetic regulators as well as “stem cell” myeloma genes. A derived 17-gene signature effectively identifies patients at high risk of early relapse as well as impaired overall survival. Integrative genomic analyses showed that epigenetic mechanisms may play an important role on transcription of these genes. PMID:24913504

  8. Serial analysis of gene expression.

    PubMed

    Velculescu, V E; Zhang, L; Vogelstein, B; Kinzler, K W

    1995-10-20

    The characteristics of an organism are determined by the genes expressed within it. A method was developed, called serial analysis of gene expression (SAGE), that allows the quantitative and simultaneous analysis of a large number of transcripts. To demonstrate this strategy, short diagnostic sequence tags were isolated from pancreas, concatenated, and cloned. Manual sequencing of 1000 tags revealed a gene expression pattern characteristic of pancreatic function. New pancreatic transcripts corresponding to novel tags were identified. SAGE should provide a broadly applicable means for the quantitative cataloging and comparison of expressed genes in a variety of normal, developmental, and disease states. PMID:7570003

  9. An Efficient LCM-Based Method for Tissue Specific Expression Analysis of Genes and miRNAs

    PubMed Central

    Gautam, Vibhav; Singh, Archita; Singh, Sharmila; Sarkar, Ananda K.

    2016-01-01

    Laser Capture Microdissection (LCM) is a powerful tool to isolate and study gene expression pattern of desired and less accessible cells or tissues from a heterogeneous population. Existing LCM-based methods fail to obtain high quality RNA including small RNAs from small microdissected plant tissue and therefore, are not suitable for miRNA expression studies. Here, we describe an efficient and cost-effective method to obtain both high quality RNA and miRNAs from LCM-derived embryonic root apical meristematic tissue, which is difficult to access. We have significantly modified and improved the tissue fixation, processing, sectioning and RNA isolation steps and minimized the use of kits. Isolated RNA was checked for quality with bioanalyzer and used for gene expression studies. We have confirmed the presence of 19-24 nucleotide long mature miRNAs using modified stem-loop RT-PCR. This modified LCM-based method is suitable for tissue specific expression analysis of both genes and small RNAs (miRNAs). PMID:26861910

  10. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators.

    PubMed

    Hieronymus, Haley; Lamb, Justin; Ross, Kenneth N; Peng, Xiao P; Clement, Cristina; Rodina, Anna; Nieto, Maria; Du, Jinyan; Stegmaier, Kimberly; Raj, Srilakshmi M; Maloney, Katherine N; Clardy, Jon; Hahn, William C; Chiosis, Gabriela; Golub, Todd R

    2006-10-01

    Although androgen receptor (AR)-mediated signaling is central to prostate cancer, the ability to modulate AR signaling states is limited. Here we establish a chemical genomic approach for discovery and target prediction of modulators of cancer phenotypes, as exemplified by AR signaling. We first identify AR activation inhibitors, including a group of structurally related compounds comprising celastrol, gedunin, and derivatives. To develop an in silico approach for target pathway identification, we apply a gene expression-based analysis that classifies HSP90 inhibitors as having similar activity to celastrol and gedunin. Validating this prediction, we demonstrate that celastrol and gedunin inhibit HSP90 activity and HSP90 clients, including AR. Broadly, this work identifies new modes of HSP90 modulation through a gene expression-based strategy. PMID:17010675

  11. Elaboration of gene expression-based clinical decision aids for kidney transplantation: where do we stand?

    PubMed

    Brouard, Sophie; Giral, Magali; Soulillou, Jean-Paul; Ashton-Chess, Joanna

    2011-04-15

    Successful kidney transplant management throughout the graft lifespan depends on adequate diagnosis (i.e., recognition of a particular type of graft rejection or injury) and prognosis (i.e., predicting future events or outcome). The currently used methods (mainly graft histology, immunosuppressive drug level monitoring, measurement of renal function, and DSA) have proven highly useful on a population level by indicating good or bad outcome, but are difficult to translate into meaningful tests for individual patients. There is thus a need for diagnostic and predictive tests that add value by being more informative to each patient, more powerful, addressing more specific questions or providing less invasive interventions. Gene expression profiling using microarrays or quantitative PCR has become a benchmark in research into novel and informative monitoring assays for transplantation. A wealth of gene expression studies are reported in the literature spanning two decades. There is now a need for clinical validation so that such tests can become standardized and approved for widespread integration into the standard of care to improve outcome for kidney transplant recipients. PMID:21283062

  12. Statistical detection of differentially expressed genes based on RNA-seq: from biological to phylogenetic replicates.

    PubMed

    Gu, Xun

    2016-03-01

    RNA-seq has been an increasingly popular high-throughput platform to identify differentially expressed (DE) genes, which is much more reproducible and accurate than the previous microarray technology. Yet, a number of statistical issues remain to be resolved in data analysis, largely due to the high-throughput data volume and over-dispersion of read counts. These problems become more challenging for those biologists who use RNA-seq to measure genome-wide expression profiles in different combinations of sampling resources (species or genotypes) or treatments. In this paper, the author first reviews the statistical methods available for detecting DE genes, which have implemented negative binomial (NB) models and/or quasi-likelihood (QL) approaches to account for the over-dispersion problem in RNA-seq samples. The author then studies how to carry out the DE test in the context of phylogeny, i.e., RNA-seq samples are from a range of species as phylogenetic replicates. The author proposes a computational framework to solve this phylo-DE problem: While an NB model is used to account for data over-dispersion within biological replicates, over-dispersion among phylogenetic replicates is taken into account by QL, plus some special treatments for phylogenetic bias. This work helps to design cost-effective RNA-seq experiments in the field of biodiversity or phenotype plasticity that may involve hundreds of species under a phylogenetic framework. PMID:26108230

  13. The Gene Expression Omnibus database

    PubMed Central

    Clough, Emily; Barrett, Tanya

    2016-01-01

    The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome–protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/. PMID:27008011

  14. The Gene Expression Omnibus Database.

    PubMed

    Clough, Emily; Barrett, Tanya

    2016-01-01

    The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome-protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/. PMID:27008011

  15. Prediction of lung cancer based on serum biomarkers by gene expression programming methods.

    PubMed

    Yu, Zhuang; Chen, Xiao-Zheng; Cui, Lian-Hua; Si, Hong-Zong; Lu, Hai-Jiao; Liu, Shi-Hai

    2014-01-01

    In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are frequently- used lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer. PMID:25422226

  16. Aberrant Gene Expression in Humans

    PubMed Central

    Yang, Ence; Ji, Guoli; Brinkmeyer-Langford, Candice L.; Cai, James J.

    2015-01-01

    Gene expression as an intermediate molecular phenotype has been a focus of research interest. In particular, studies of expression quantitative trait loci (eQTL) have offered promise for understanding gene regulation through the discovery of genetic variants that explain variation in gene expression levels. Existing eQTL methods are designed for assessing the effects of common variants, but not rare variants. Here, we address the problem by establishing a novel analytical framework for evaluating the effects of rare or private variants on gene expression. Our method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers. Using population-scale mRNA sequencing data, we identify outlier individuals using a multivariate approach. We find that outlier individuals are more readily detected with respect to gene sets that include genes involved in cellular regulation and signal transduction, and less likely to be detected with respect to the gene sets with genes involved in metabolic pathways and other fundamental molecular functions. Analysis of polymorphic data suggests that private SNPs of outlier individuals are enriched in the enhancer and promoter regions of corresponding aberrantly-expressed genes, suggesting a specific regulatory role of private SNPs, while the commonly-occurring regulatory genetic variants (i.e., eQTL SNPs) show little evidence of involvement. Additional data suggest that non-genetic factors may also underlie aberrant gene expression. Taken together, our findings advance a novel viewpoint relevant to situations wherein common eQTLs fail to predict gene expression when heritable, rare inter-individual variation exists. The analytical framework we describe, taking into consideration the reality of differential phenotypic robustness, may be valuable for investigating

  17. Method of controlling gene expression

    DOEpatents

    Peters, Norman K.; Frost, John W.; Long, Sharon R.

    1991-12-03

    A method of controlling expression of a DNA segment under the control of a nod gene promoter which comprises administering to a host containing a nod gene promoter an amount sufficient to control expression of the DNA segment of a compound of the formula: ##STR1## in which each R is independently H or OH, is described.

  18. Widespread ectopic expression of olfactory receptor genes

    PubMed Central

    Feldmesser, Ester; Olender, Tsviya; Khen, Miriam; Yanai, Itai; Ophir, Ron; Lancet, Doron

    2006-01-01

    Background Olfactory receptors (ORs) are the largest gene family in the human genome. Although they are expected to be expressed specifically in olfactory tissues, some ectopic expression has been reported, with special emphasis on sperm and testis. The present study systematically explores the expression patterns of OR genes in a large number of tissues and assesses the potential functional implication of such ectopic expression. Results We analyzed the expression of hundreds of human and mouse OR transcripts, via EST and microarray data, in several dozens of human and mouse tissues. Different tissues had specific, relatively small OR gene subsets which had particularly high expression levels. In testis, average expression was not particularly high, and very few highly expressed genes were found, none corresponding to ORs previously implicated in sperm chemotaxis. Higher expression levels were more common for genes with a non-OR genomic neighbor. Importantly, no correlation in expression levels was detected for human-mouse orthologous pairs. Also, no significant difference in expression levels was seen between intact and pseudogenized ORs, except for the pseudogenes of subfamily 7E which has undergone a human-specific expansion. Conclusion The OR superfamily as a whole, show widespread, locus-dependent and heterogeneous expression, in agreement with a neutral or near neutral evolutionary model for transcription control. These results cannot reject the possibility that small OR subsets might play functional roles in different tissues, however considerable care should be exerted when offering a functional interpretation for ectopic OR expression based only on transcription information. PMID:16716209

  19. De novo sequencing-based transcriptome and digital gene expression analysis reveals insecticide resistance-relevant genes in Propylaea japonica (Thunberg) (Coleoptea: Coccinellidae).

    PubMed

    Tang, Liang-De; Wang, Xing-Min; Jin, Feng-Liang; Qiu, Bao-Li; Wu, Jian-Hui; Ren, Shun-Xiang

    2014-01-01

    The ladybird Propylaea japonica (Thunberg) is one of most important natural enemies of aphids in China. This species is threatened by the extensive use of insecticides but genomics-based information on the molecular mechanisms underlying insecticide resistance is limited. Hence, we analyzed the transcriptome and expression profile data of P. japonica in order to gain a deeper understanding of insecticide resistance in ladybirds. We performed de novo assembly of a transcriptome using Illumina's Solexa sequencing technology and short reads. A total of 27,243,552 reads were generated. These were assembled into 81,458 contigs and 33,647 unigenes (6,862 clusters and 26,785 singletons). Of the unigenes, 23,965 (71.22%) have putative homologues in the non-redundant (nr) protein database from NCBI, using BLASTX, with a cut-off E-value of 10(-5). We examined COG, GO and KEGG annotations to better understand the functions of these unigenes. Digital gene expression (DGE) libraries showed differences in gene expression profiles between two insecticide resistant strains. When compared with an insecticide susceptible profile, a total of 4,692 genes were significantly up- or down- regulated in a moderately resistant strain. Among these genes, 125 putative insecticide resistance genes were identified. To confirm the DGE results, 16 selected genes were validated using quantitative real time PCR (qRT-PCR). This study is the first to report genetic information on P. japonica and has greatly enriched the sequence data for ladybirds. The large number of gene sequences produced from the transcriptome and DGE sequencing will greatly improve our understanding of this important insect, at the molecular level, and could contribute to the in-depth research into insecticide resistance mechanisms. PMID:24959827

  20. Poisson-based self-organizing feature maps and hierarchical clustering for serial analysis of gene expression data.

    PubMed

    Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

    2007-01-01

    Serial analysis of gene expression (SAGE) is a powerful technique for global gene expression profiling, allowing simultaneous analysis of thousands of transcripts without prior structural and functional knowledge. Pattern discovery and visualization have become fundamental approaches to analyzing such large-scale gene expression data. From the pattern discovery perspective, clustering techniques have received great attention. However, due to the statistical nature of SAGE data (i.e., underlying distribution), traditional clustering techniques may not be suitable for SAGE data analysis. Based on the adaptation and improvement of Self-Organizing Maps and hierarchical clustering techniques, this paper presents two new clustering algorithms, namely, PoissonS and PoissonHC, for SAGE data analysis. Tested on synthetic and experimental SAGE data, these algorithms demonstrate several advantages over traditional pattern discovery techniques. The results indicate that, by incorporating statistical properties of SAGE data, PoissonS and PoissonHC, as well as a hybrid approach (neuro-hierarchical approach) based on the combination of PoissonS and PoissonHC, offer significant improvements in pattern discovery and visualization for SAGE data. Moreover, a user-friendly platform, which may improve and accelerate SAGE data mining, was implemented. The system is freely available on request from the authors for nonprofit use. PMID:17473311

  1. Effect of medium/ω-6 long chain triglyceride-based emulsion on leucocyte death and inflammatory gene expression

    PubMed Central

    Cury-Boaventura, M F; Gorjão, R; Martins de Lima, T; Fiamoncini, J; Godoy, A B P; Deschamphs, F C; Soriano, F G; Curi, R

    2011-01-01

    Lipid emulsion (LE) containing medium/ω-6 long chain triglyceride-based emulsion (MCT/ω-6 LCT LE) has been recommended in the place of ω-6 LCT-based emulsion to prevent impairment of immune function. The impact of MCT/ω-6 LCT LE on lymphocyte and neutrophil death and expression of genes related to inflammation was investigated. Seven volunteers were recruited and infusion of MCT/ω-6 LCT LE was performed for 6 h. Four volunteers received saline and no change was found. Blood samples were collected before, immediately afterwards and 18 h after LE infusion. Lymphocytes and neutrophils were studied immediately after isolation and after 24 and 48 h in culture. The following determinations were carried out: plasma-free fatty acids, triacylglycerol and cholesterol concentrations, plasma fatty acid composition, neutral lipid accumulation in lymphocytes and neutrophils, signs of lymphocyte and neutrophil death and lymphocyte expression of genes related to inflammation. MCT/ω-6 LCT LE induced lymphocyte and neutrophil death. The mechanism for MCT/ω-6 LCT LE-dependent induction of leucocyte death may involve changes in neutral lipid content and modulation of expression of genes related to cell death, proteolysis, cell signalling, inflammatory response, oxidative stress and transcription. PMID:21682721

  2. ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling

    PubMed Central

    Conde, Lucía; Montaner, David; Burguet-Castell, Jordi; Tárraga, Joaquín; Medina, Ignacio; Al-Shahrour, Fátima; Dopazo, Joaquín

    2007-01-01

    We present the ISACGH, a web-based system that allows for the combination of genomic data with gene expression values and provides different options for functional profiling of the regions found. Several visualization options offer a convenient representation of the results. Different efficient methods for accurate estimation of genomic copy number from array-CGH hybridization data have been included in the program. Moreover, the connection to the gene expression analysis package GEPAS allows the use of different facilities for data pre-processing and analysis. A DAS server allows exporting the results to the Ensembl viewer where contextual genomic information can be obtained. The program is freely available at: http://isacgh.bioinfo.cipf.es or within http://www.gepas.org. PMID:17468499

  3. Quantitative multiplex quantum dot in-situ hybridisation based gene expression profiling in tissue microarrays identifies prognostic genes in acute myeloid leukaemia

    SciTech Connect

    Tholouli, Eleni; MacDermott, Sarah; Hoyland, Judith; Yin, John Liu; Byers, Richard

    2012-08-24

    Highlights: Black-Right-Pointing-Pointer Development of a quantitative high throughput in situ expression profiling method. Black-Right-Pointing-Pointer Application to a tissue microarray of 242 AML bone marrow samples. Black-Right-Pointing-Pointer Identification of HOXA4, HOXA9, Meis1 and DNMT3A as prognostic markers in AML. -- Abstract: Measurement and validation of microarray gene signatures in routine clinical samples is problematic and a rate limiting step in translational research. In order to facilitate measurement of microarray identified gene signatures in routine clinical tissue a novel method combining quantum dot based oligonucleotide in situ hybridisation (QD-ISH) and post-hybridisation spectral image analysis was used for multiplex in-situ transcript detection in archival bone marrow trephine samples from patients with acute myeloid leukaemia (AML). Tissue-microarrays were prepared into which white cell pellets were spiked as a standard. Tissue microarrays were made using routinely processed bone marrow trephines from 242 patients with AML. QD-ISH was performed for six candidate prognostic genes using triplex QD-ISH for DNMT1, DNMT3A, DNMT3B, and for HOXA4, HOXA9, Meis1. Scrambled oligonucleotides were used to correct for background staining followed by normalisation of expression against the expression values for the white cell pellet standard. Survival analysis demonstrated that low expression of HOXA4 was associated with poorer overall survival (p = 0.009), whilst high expression of HOXA9 (p < 0.0001), Meis1 (p = 0.005) and DNMT3A (p = 0.04) were associated with early treatment failure. These results demonstrate application of a standardised, quantitative multiplex QD-ISH method for identification of prognostic markers in formalin-fixed paraffin-embedded clinical samples, facilitating measurement of gene expression signatures in routine clinical samples.

  4. Microarray-based analysis of the differential expression of melanin synthesis genes in dark and light-muzzle Korean cattle.

    PubMed

    Kim, Sang Hwan; Hwang, Sue Yun; Yoon, Jong Taek

    2014-01-01

    The coat color of mammals is determined by the melanogenesis pathway, which is responsible for maintaining the balance between black-brown eumelanin and yellow-reddish pheomelanin. It is also believed that the color of the bovine muzzle is regulated in a similar manner; however, the molecular mechanism underlying pigment deposition in the dark-muzzle has yet to be elucidated. The aim of the present study was to identify melanogenesis-associated genes that are differentially expressed in the dark vs. light muzzle of native Korean cows. Using microarray clustering and real-time polymerase chain reaction techniques, we observed that the expression of genes involved in the mitogen-activated protein kinase (MAPK) and Wnt signaling pathways is distinctively regulated in the dark and light muzzle tissues. Differential expression of tyrosinase was also noticed, although the difference was not as distinct as those of MAPK and Wnt. We hypothesize that emphasis on the MAPK pathway in the dark-muzzle induces eumelanin synthesis through the activation of cAMP response element-binding protein and tyrosinase, while activation of Wnt signaling counteracts this process and raises the amount of pheomelanin in the light-muzzle. We also found 2 novel genes (GenBank No. NM-001076026 and XM-588439) with increase expression in the black nose, which may provide additional information about the mechanism of nose pigmentation. Regarding the increasing interest in the genetic diversity of cattle stocks, genes we identified for differential expression in the dark vs. light muzzle may serve as novel markers for genetic diversity among cows based on the muzzle color phenotype. PMID:24811126

  5. Limits of Peripheral Blood Mononuclear Cells for Gene Expression-Based Biomarkers in Juvenile Idiopathic Arthritis

    PubMed Central

    Wong, Laiping; Jiang, Kaiyu; Chen, Yanmin; Hennon, Teresa; Holmes, Lucy; Wallace, Carol A.; Jarvis, James N.

    2016-01-01

    Juvenile Idiopathic Arthritis (JIA) is one of the most common chronic disease conditions affecting children in the USA. As with many rheumatic diseases, there is growing interest in using genomic technologies to develop biomarkers for either diagnosis or to guide treatment (“personalized medicine”). Here, we explore the use of gene expression patterns in peripheral blood mononuclear cells (PBMC) as a first step approach to developing such biomarkers. Although PBMC carry many theoretical advantages for translational research, we have found that sample heterogeneity makes RNASeq on PBMC unsuitable as a first-step method for screening biomarker candidates in JIA. RNASeq studies of homogeneous cell populations are more likely to be useful and informative. PMID:27385437

  6. Quality Measures for Gene Expression Biclusters

    PubMed Central

    Pontes, Beatriz; Girldez, Ral; Aguilar-Ruiz, Jess S.

    2015-01-01

    An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to the problem complexity, heuristic searches are usually used instead of exhaustive algorithms. Furthermore, most of biclustering approaches use a measure or cost function that determines the quality of biclusters. Having a suitable quality metric for bicluster is a critical aspect, not only for guiding the search, but also for establishing a comparison criteria among the results obtained by different biclustering techniques. In this paper, we analyse a large number of existing approaches to quality measures for gene expression biclusters, as well as we present a comparative study of them based on their capability to recognize different expression patterns in biclusters. PMID:25763839

  7. Quality measures for gene expression biclusters.

    PubMed

    Pontes, Beatriz; Girldez, Ral; Aguilar-Ruiz, Jess S

    2015-01-01

    An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to the problem complexity, heuristic searches are usually used instead of exhaustive algorithms. Furthermore, most of biclustering approaches use a measure or cost function that determines the quality of biclusters. Having a suitable quality metric for bicluster is a critical aspect, not only for guiding the search, but also for establishing a comparison criteria among the results obtained by different biclustering techniques. In this paper, we analyse a large number of existing approaches to quality measures for gene expression biclusters, as well as we present a comparative study of them based on their capability to recognize different expression patterns in biclusters. PMID:25763839

  8. Gene Expression in Oligodendroglial Tumors

    PubMed Central

    Shaw, Elisabeth J.; Haylock, Brian; Husband, David; du Plessis, Daniel; Sibson, D. Ross; Warnke, Peter C.; Walker, Carol

    2010-01-01

    Background: Oligodendroglial tumors with 1p/19q loss are more likely to be chemosensitive and have longer survival than those with intact 1p/19q, but not all respond to chemotherapy, warranting investigation of the biological basis of chemosensitivity. Methods: Gene expression profiling was performed using amplified antisense RNA from 28 oligodendroglial tumors treated with chemotherapy (26 serial stereotactic biopsy, 2 resection). Expression of differentially expressed genes was validated by real-time PCR. Results: Unsupervised hierarchical clustering showed clustering of multiple samples from the same case in 14/17 cases and identified subgroups associated with tumor grade and 1p/19q status. 176 genes were differentially expressed, 164 being associated with 1p/19q loss (86% not on 1p or 19q). 94 genes differed between responders and non-responders to chemotherapy; 12 were not associated with 1p/19q loss. Significant differential expression was confirmed in 11/13 selected genes. Novel genes associated with response to therapy included SSBP2, GFRA1, FAP and RASD1. IQGAP1, INA, TGIF1, NR2F2 and MYCBP were differentially expressed in oligodendroglial tumors with 1p/19q loss. Conclusion: Gene expression profiling using serial stereotactic biopsies indicated greater homogeneity within tumors than between tumors. Genes associated with 1p/19q status or response were identified warranting further elucidation of their role in oligodendroglial tumors. PMID:20966545

  9. Gene Expression Patterns in Ovarian Carcinomas

    PubMed Central

    Schaner, Marci E.; Ross, Douglas T.; Ciaravino, Giuseppe; Sørlie, Therese; Troyanskaya, Olga; Diehn, Maximilian; Wang, Yan C.; Duran, George E.; Sikic, Thomas L.; Caldeira, Sandra; Skomedal, Hanne; Tu, I-Ping; Hernandez-Boussard, Tina; Johnson, Steven W.; O'Dwyer, Peter J.; Fero, Michael J.; Kristensen, Gunnar B.; Børresen-Dale, Anne-Lise; Hastie, Trevor; Tibshirani, Robert; van de Rijn, Matt; Teng, Nelson N.; Longacre, Teri A.; Botstein, David; Brown, Patrick O.; Sikic, Branimir I.

    2003-01-01

    We used DNA microarrays to characterize the global gene expression patterns in surface epithelial cancers of the ovary. We identified groups of genes that distinguished the clear cell subtype from other ovarian carcinomas, grade I and II from grade III serous papillary carcinomas, and ovarian from breast carcinomas. Six clear cell carcinomas were distinguished from 36 other ovarian carcinomas (predominantly serous papillary) based on their gene expression patterns. The differences may yield insights into the worse prognosis and therapeutic resistance associated with clear cell carcinomas. A comparison of the gene expression patterns in the ovarian cancers to published data of gene expression in breast cancers revealed a large number of differentially expressed genes. We identified a group of 62 genes that correctly classified all 125 breast and ovarian cancer specimens. Among the best discriminators more highly expressed in the ovarian carcinomas were PAX8 (paired box gene 8), mesothelin, and ephrin-B1 (EFNB1). Although estrogen receptor was expressed in both the ovarian and breast cancers, genes that are coregulated with the estrogen receptor in breast cancers, including GATA-3, LIV-1, and X-box binding protein 1, did not show a similar pattern of coexpression in the ovarian cancers. PMID:12960427

  10. Computational Analysis of HIV-1 Resistance Based on Gene Expression Profiles and the Virus-Host Interaction Network

    PubMed Central

    Huang, Tao; Xu, Zhongping; Chen, Lei; Cai, Yu-Dong; Kong, Xiangyin

    2011-01-01

    A very small proportion of people remain negative for HIV infection after repeated HIV-1 viral exposure, which is called HIV-1 resistance. Understanding the mechanism of HIV-1 resistance is important for the development of HIV-1 vaccines and Acquired Immune Deficiency Syndrome (AIDS) therapies. In this study, we analyzed the gene expression profiles of CD4+ T cells from HIV-1-resistant individuals and HIV-susceptible individuals. One hundred eighty-five discriminative HIV-1 resistance genes were identified using the Minimum Redundancy-Maximum Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The virus protein target enrichment analysis of the 185 HIV-1 resistance genes suggested that the HIV-1 protein nef might play an important role in HIV-1 infection. Moreover, we identified 29 infection information exchanger genes from the 185 HIV-1 resistance genes based on a virus-host interaction network analysis. The infection information exchanger genes are located on the shortest paths between virus-targeted proteins and are important for the coordination of virus infection. These proteins may be useful targets for AIDS prevention or therapy, as intervention in these pathways could disrupt communication with virus-targeted proteins and HIV-1 infection. PMID:21394196

  11. Development of tobacco ringspot virus-based vectors for foreign gene expression and virus-induced gene silencing in a variety of plants.

    PubMed

    Zhao, Fumei; Lim, Seungmo; Igori, Davaajargal; Yoo, Ran Hee; Kwon, Suk-Yoon; Moon, Jae Sun

    2016-05-01

    We report here the development of tobacco ringspot virus (TRSV)-based vectors for the transient expression of foreign genes and for the analysis of endogenous gene function in plants using virus-induced gene silencing. The jellyfish green fluorescent protein (GFP) gene was inserted between the TRSV movement protein (MP) and coat protein (CP) regions, resulting in high in-frame expression of the RNA2-encoded viral polyprotein. GFP was released from the polyprotein via an N-terminal homologous MP-CP cleavage site and a C-terminal foot-and-mouth disease virus (FMDV) 2 A catalytic peptide in Nicotiana benthamiana. The VIGS target gene was introduced in the sense and antisense orientations into a SnaBI site, which was created by mutating the sequence following the CP stop codon. VIGS of phytoene desaturase (PDS) in N. benthamiana, Arabidopsis ecotype Col-0, cucurbits and legumes led to obvious photo-bleaching phenotypes. A significant reduction in PDS mRNA levels in silenced plants was confirmed by semi-quantitative RT-PCR. PMID:26950504

  12. A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data.

    PubMed

    Lin, Zhixiang; Li, Mingfeng; Sestan, Nenad; Zhao, Hongyu

    2016-04-01

    The statistical methodology developed in this study was motivated by our interest in studying neurodevelopment using the mouse brain RNA-Seq data set, where gene expression levels were measured in multiple layers in the somatosensory cortex across time in both female and male samples. We aim to identify differentially expressed genes between adjacent time points, which may provide insights on the dynamics of brain development. Because of the extremely small sample size (one male and female at each time point), simple marginal analysis may be underpowered. We propose a Markov random field (MRF)-based approach to capitalizing on the between layers similarity, temporal dependency and the similarity between sex. The model parameters are estimated by an efficient EM algorithm with mean field-like approximation. Simulation results and real data analysis suggest that the proposed model improves the power to detect differentially expressed genes than simple marginal analysis. Our method also reveals biologically interesting results in the mouse brain RNA-Seq data set. PMID:26926866

  13. Differential gene expression analysis between anagen and telogen of Capra hircus skin based on the de novo assembled transcriptome sequence.

    PubMed

    Xu, Teng; Guo, Xudong; Wang, Hui; Hao, Fei; Du, Xiaoyuan; Gao, Xiaoyu; Liu, Dongjun

    2013-05-10

    Capra hircus, an economically important livestock, plays an indispensable role in the world animal fiber industry. In the present study, using Illumina/Solexa high throughput sequencing technology, we sequenced and de novo assembled the goat skin transcriptome corresponding to the anagen and telogen of the hair growth cycle. Approximately 53Mb of transcriptome sequences consisting of 57,040 high quality contigs was obtained. More than 8300 contigs were predicted to contain a full length coding sequence. Approximately 43% of the total contigs were identified as harboring homologs of sequences from other organisms in the public database. Based on the assembled transcript-derived contigs, we identified about 7000 transcripts that were differentially expressed between the anagen and telogen libraries. These differentially expressed genes were mainly enriched in signal transduction mechanisms, extracellular structures and cytoskeleton from the KOG database and in ECM receptor interaction, focal adhesion and gap junction from the KEGG pathway database, indicating the essential roles of these genes may play in cell-to-cell and cell-to-matrix communications during the active hair growth phase. In addition, many signaling pathway associated ligands and/or receptors were also identified as up-regulated genes during the anagen phase compared with the telogen stage, suggesting that enhanced cross-talk among signaling transduction pathways may be required for anagen of the hair cycle. These differentially expressed genes, especially those that were over-represented in each of the functional clusters and biochemical pathways, provide valuable resources and opportunities for characterizing the gene functions associated with hair fiber growth as well as for breeding elite Cashmere goat species. PMID:23466980

  14. Methodological Limitations in Determining Astrocytic Gene Expression

    PubMed Central

    Peng, Liang; Guo, Chuang; Wang, Tao; Li, Baoman; Gu, Li; Wang, Zhanyou

    2013-01-01

    Traditionally, astrocytic mRNA and protein expression are studied by in situ hybridization (ISH) and immunohistochemically. This led to the concept that astrocytes lack aralar, a component of the malate-aspartate-shuttle. At least similar aralar mRNA and protein expression in astrocytes and neurons isolated by fluorescence-assisted cell sorting (FACS) reversed this opinion. Demonstration of expression of other astrocytic genes may also be erroneous. Literature data based on morphological methods were therefore compared with mRNA expression in cells obtained by recently developed methods for determination of cell-specific gene expression. All Na,K-ATPase-α subunits were demonstrated by immunohistochemistry (IHC), but there are problems with the cotransporter NKCC1. Glutamate and GABA transporter gene expression was well determined immunohistochemically. The same applies to expression of many genes of glucose metabolism, whereas a single study based on findings in bacterial artificial chromosome (BAC) transgenic animals showed very low astrocytic expression of hexokinase. Gene expression of the equilibrative nucleoside transporters ENT1 and ENT2 was recognized by ISH, but ENT3 was not. The same applies to the concentrative transporters CNT2 and CNT3. All were clearly expressed in FACS-isolated cells, followed by biochemical analysis. ENT3 was enriched in astrocytes. Expression of many nucleoside transporter genes were shown by microarray analysis, whereas other important genes were not. Results in cultured astrocytes resembled those obtained by FACS. These findings call for reappraisal of cellular nucleoside transporter expression. FACS cell yield is small. Further development of cell separation methods to render methods more easily available and less animal and cost consuming and parallel studies of astrocytic mRNA and protein expression by ISH/IHC and other methods are necessary, but new methods also need to be thoroughly checked. PMID:24324456

  15. Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles

    PubMed Central

    2014-01-01

    Background High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action. Results In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. Conclusions Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical. PMID:24678894

  16. Identification of tissue of origin in carcinoma of unknown primary with a microarray-based gene expression test

    PubMed Central

    2010-01-01

    Background Carcinomas of unknown primary (CUP) represent approximately 3%-5% of malignant neoplasms. Identifying the tissue of origin (TOO) in these tumors allows for more specific treatment and improves outcomes. However, primary classification remains a challenge in many cases. We evaluated the ability of a microarray-based gene expression test to identify the TOO in tumor specimens from 21 patients with a diagnosis of CUP. Methods The Pathwork® TOO Test was used to measure gene expression patterns for 1550 genes; these were compared for similarity to patterns from 15 known tissue types. Results The TOO Test yielded a clear single positive call for the primary site in 16 of 21 (76%) specimens and was indeterminate in 5 (24%). The positive results were consistent with clinicopathologic suggestions in 10 of the 16 cases (62%). In the remaining six cases the positive results were considered plausible based on clinical information. Positive calls included colorectal (5), breast (4), ovarian (3), lung (2), and pancreas (2). The TOO Test ruled out an average of 11 primary tissues in each CUP specimen. Conclusion The Pathwork TOO Test reduced diagnostic uncertainty in all CUP cases and could be a valuable addition or alternative to current diagnostic methods for classifying uncertain primary cancers. PMID:20205775

  17. Susceptibility to Acute Rheumatic Fever Based on Differential Expression of Genes Involved in Cytotoxicity, Chemotaxis, and Apoptosis

    PubMed Central

    Smyth, Gordon K.; Gooding, Travis; Oshlack, Alicia; Harrington, Zinta; Currie, Bart; Carapetis, Jonathan R.; Robins-Browne, Roy; Curtis, Nigel

    2014-01-01

    It is unknown why only some individuals are susceptible to acute rheumatic fever (ARF). We investigated whether there are differences in the immune response, detectable by gene expression, between individuals who are susceptible to ARF and those who are not. Peripheral blood mononuclear cells (PBMCs) from 15 ARF-susceptible and 10 nonsusceptible (control) adults were stimulated with rheumatogenic (Rh+) group A streptococci (GAS) or nonrheumatogenic (Rh−) GAS. RNA from stimulated PBMCs from each subject was cohybridized with RNA from unstimulated PBMCs on oligonucleotide arrays to compare gene expression. Thirty-four genes were significantly differentially expressed between ARF-susceptible and control groups after stimulation with Rh+ GAS. A total of 982 genes were differentially expressed between Rh+ GAS- and Rh− GAS-stimulated samples from ARF-susceptible individuals. Thirteen genes were differentially expressed in the same direction (predominantly decreased) between the two study groups and between the two stimulation conditions, giving a strong indication of their involvement. Seven of these were immune response genes involved in cytotoxicity, chemotaxis, and apoptosis. There was variability in the degree of expression change between individuals. The high proportion of differentially expressed apoptotic and immune response genes supports the current model of autoimmune and cytokine dysregulation in ARF. This study also raises the possibility that a “failed” immune response, involving decreased expression of cytotoxic and apoptotic genes, contributes to the immunopathogenesis of ARF. PMID:24478089

  18. Susceptibility to acute rheumatic fever based on differential expression of genes involved in cytotoxicity, chemotaxis, and apoptosis.

    PubMed

    Bryant, Penelope A; Smyth, Gordon K; Gooding, Travis; Oshlack, Alicia; Harrington, Zinta; Currie, Bart; Carapetis, Jonathan R; Robins-Browne, Roy; Curtis, Nigel

    2014-02-01

    It is unknown why only some individuals are susceptible to acute rheumatic fever (ARF). We investigated whether there are differences in the immune response, detectable by gene expression, between individuals who are susceptible to ARF and those who are not. Peripheral blood mononuclear cells (PBMCs) from 15 ARF-susceptible and 10 nonsusceptible (control) adults were stimulated with rheumatogenic (Rh+) group A streptococci (GAS) or nonrheumatogenic (Rh-) GAS. RNA from stimulated PBMCs from each subject was cohybridized with RNA from unstimulated PBMCs on oligonucleotide arrays to compare gene expression. Thirty-four genes were significantly differentially expressed between ARF-susceptible and control groups after stimulation with Rh+ GAS. A total of 982 genes were differentially expressed between Rh+ GAS- and Rh- GAS-stimulated samples from ARF-susceptible individuals. Thirteen genes were differentially expressed in the same direction (predominantly decreased) between the two study groups and between the two stimulation conditions, giving a strong indication of their involvement. Seven of these were immune response genes involved in cytotoxicity, chemotaxis, and apoptosis. There was variability in the degree of expression change between individuals. The high proportion of differentially expressed apoptotic and immune response genes supports the current model of autoimmune and cytokine dysregulation in ARF. This study also raises the possibility that a "failed" immune response, involving decreased expression of cytotoxic and apoptotic genes, contributes to the immunopathogenesis of ARF. PMID:24478089

  19. Inferring robust gene networks from expression data by a sensitivity-based incremental evolution method

    PubMed Central

    2012-01-01

    Background Reconstructing gene regulatory networks (GRNs) from expression data is one of the most important challenges in systems biology research. Many computational models and methods have been proposed to automate the process of network reconstruction. Inferring robust networks with desired behaviours remains challenging, however. This problem is related to network dynamics but has yet to be investigated using network modeling. Results We propose an incremental evolution approach for inferring GRNs that takes network robustness into consideration and can deal with a large number of network parameters. Our approach includes a sensitivity analysis procedure to iteratively select the most influential network parameters, and it uses a swarm intelligence procedure to perform parameter optimization. We have conducted a series of experiments to evaluate the external behaviors and internal robustness of the networks inferred by the proposed approach. The results and analyses have verified the effectiveness of our approach. Conclusions Sensitivity analysis is crucial to identifying the most sensitive parameters that govern the network dynamics. It can further be used to derive constraints for network parameters in the network reconstruction process. The experimental results show that the proposed approach can successfully infer robust GRNs with desired system behaviors. PMID:22595005

  20. Maxi-circles, glycosomes, gene transposition, expression sites, transsplicing, transferrin receptors and base J.

    PubMed

    Borst, Piet

    2016-01-01

    This is a personal story of the author of his research on trypanosomatids, covering a period of 1970-2015. Some of the highlights include the discovery of new aspects of kDNA, the mini-circle heterogeneity and the maxi-circle; the glycosome; the discovery of gene transposition as a major mechanism for antigenic variation; trans-splicing as an essential step in the synthesis of all trypanosome mRNAs; Pulsed Field Gradient gels to size-fractionate chromosome-sized DNA molecules of protozoa; the sequence of trypanosome telomeres and their growth and contraction; the first ABC-transporter of trypanosomatids, LtpgpA; the variable transferrin receptor of T. brucei and its role in Fe uptake; and base J, its structure, biosynthesis and function. PMID:27021571

  1. A comparative gene expression database for invertebrates

    PubMed Central

    2011-01-01

    Background As whole genome and transcriptome sequencing gets cheaper and faster, a great number of 'exotic' animal models are emerging, rapidly adding valuable data to the ever-expanding Evo-Devo field. All these new organisms serve as a fantastic resource for the research community, but the sheer amount of data, some published, some not, makes detailed comparison of gene expression patterns very difficult to summarize - a problem sometimes even noticeable within a single lab. The need to merge existing data with new information in an organized manner that is publicly available to the research community is now more necessary than ever. Description In order to offer a homogenous way of storing and handling gene expression patterns from a variety of organisms, we have developed the first web-based comparative gene expression database for invertebrates that allows species-specific as well as cross-species gene expression comparisons. The database can be queried by gene name, developmental stage and/or expression domains. Conclusions This database provides a unique tool for the Evo-Devo research community that allows the retrieval, analysis and comparison of gene expression patterns within or among species. In addition, this database enables a quick identification of putative syn-expression groups that can be used to initiate, among other things, gene regulatory network (GRN) projects. PMID:21861937

  2. Risk stratification of T-cell Acute Lymphoblastic Leukemia patients based on gene expression, mutations and copy number variation.

    PubMed

    Mirji, Gauri; Bhat, Jaydeep; Kode, Jyoti; Banavali, Shripad; Sengar, Manju; Khadke, Prashant; Sait, Osama; Chiplunkar, Shubhada

    2016-06-01

    Gene expression, copy number variations (CNV), mutations and survival were studied to delineate TCRγδ+T-cell acute lymphoblastic leukemia (T-ALL) as a distinct subgroup from TCRαβ+T-ALL. Gene Ontology analysis showed that differential regulation of genes involved in pathways for leukemogenesis, apoptosis, cytokine-cytokine receptor interaction and antigen processing/presentation may offer a survival benefit to TCRγδ+T-ALL patients. Genes involved in disease biology and having equal expression in both the subgroups, were further analysed for mutations and CNV using droplet digital PCR. TCRγδ+T-ALL patients exhibited differential level of mutations for NOTCH1 and IKZF3; however BRAF mutations were detected at equal levels in both the subgroups. Although TCRγδ+T-ALL patients with these mutations demonstrated improved disease-free survival (DFS) as compared TCRαβ+T-ALL patients, it was not statistically significant. Patients with homozygous deletion of CDKN2A/CDKN2B showed poor DFS in each subgroup. TCRγδ+T-ALL patients with wild type/heterozygous deletion of CDKN2A/CDKN2B possess significantly better DFS over TCRαβ+T-ALL patients (p=0.017 and 0.045, respectively). Thus, the present study has for the first time demonstrated TCRγδ clonality and CDKN2A/CDKN2B CNV together as potential prognostic markers in management of T-ALL. Further understanding the functional significance of differentially regulated genes in T-ALL patients would aid in designing risk based treatment strategies in subset specific manner. PMID:27070758

  3. Machine learning-based classification of diffuse large B-cell lymphoma patients by eight gene expression profiles.

    PubMed

    Zhao, Shuangtao; Dong, Xiaoli; Shen, Wenzhi; Ye, Zhen; Xiang, Rong

    2016-05-01

    Gene expression profiling (GEP) had divided the diffuse large B-cell lymphoma (DLBCL) into molecular subgroups: germinal center B-cell like (GCB), activated B-cell like (ABC), and unclassified (UC) subtype. However, this classification with prognostic significance was not applied into clinical practice since there were more than 1000 genes to detect and interpreting was difficult. To classify cancer samples validly, eight significant genes (MYBL1, LMO2, BCL6, MME, IRF4, NFKBIZ, PDE4B, and SLA) were selected in 414 patients treated with CHOP/R-CHOP chemotherapy from Gene Expression Omnibus (GEO) data sets. Cutoffs for each gene were obtained using receiver-operating characteristic curves (ROC) new model based on the support vector machine (SVM) estimated the probability of membership into one of two subgroups: GCB and Non-GCB (ABC and UC). Furtherly, multivariate analysis validated the model in another two cohorts including 855 cases in all. As a result, patients in the training and validated cohorts were stratified into two subgroups with 94.0%, 91.0%, and 94.4% concordance with GEP, respectively. Patients with Non-GCB subtype had significantly poorer outcomes than that with GCB subtype, which agreed with the prognostic power of GEP classification. Moreover, the similar prognosis received in the low (0-2) and high (3-5) IPI scores group demonstrated that the new model was independent of IPI as well as GEP method. In conclusion, our new model could stratify DLBCL patients with CHOP/R-CHOP regimen matching GEP subtypes effectively. PMID:26869285

  4. DNA microarray-based experimental strategy for trustworthy expression profiling of the hippocampal genes by astaxanthin supplementation in adult mouse.

    PubMed

    Yook, Jang Soo; Shibato, Junko; Rakwal, Randeep; Soya, Hideaki

    2016-03-01

    Naturally occurring astaxantin (ASX) is one of the noticeable carotenoid and dietary supplement, which has strong antioxidant and anti-inflammatory properties, and neuroprotective effects in the brain through crossing the blood-brain barrier. Specially, we are interested in the role of ASX as a brain food. Although ASX has been suggested to have potential benefit to the brain function, the underlying molecular mechanisms and events mediating such effect remain unknown. Here we examined molecular factors in the hippocampus of adult mouse fed ASX diets (0.1% and 0.5% doses) using DNA microarray (Agilent 4 × 44 K whole mouse genome chip) analysis. In this study, we described in detail our experimental workflow and protocol, and validated quality controls with the housekeeping gene expression (Gapdh and Beta-actin) on the dye-swap based approach to advocate our microarray data, which have been uploaded to Gene Expression Omnibus (accession number GSE62197) as a gene resource for the scientific community. This data will also form an important basis for further detailed experiments and bioinformatics analysis with an aim to unravel the potential molecular pathways or mechanisms underlying the positive effects of ASX supplementation on the brain, in particular the hippocampus. PMID:26981356

  5. DNA microarray-based experimental strategy for trustworthy expression profiling of the hippocampal genes by astaxanthin supplementation in adult mouse

    PubMed Central

    Yook, Jang Soo; Shibato, Junko; Rakwal, Randeep; Soya, Hideaki

    2015-01-01

    Naturally occurring astaxantin (ASX) is one of the noticeable carotenoid and dietary supplement, which has strong antioxidant and anti-inflammatory properties, and neuroprotective effects in the brain through crossing the blood–brain barrier. Specially, we are interested in the role of ASX as a brain food. Although ASX has been suggested to have potential benefit to the brain function, the underlying molecular mechanisms and events mediating such effect remain unknown. Here we examined molecular factors in the hippocampus of adult mouse fed ASX diets (0.1% and 0.5% doses) using DNA microarray (Agilent 4 × 44 K whole mouse genome chip) analysis. In this study, we described in detail our experimental workflow and protocol, and validated quality controls with the housekeeping gene expression (Gapdh and Beta-actin) on the dye-swap based approach to advocate our microarray data, which have been uploaded to Gene Expression Omnibus (accession number GSE62197) as a gene resource for the scientific community. This data will also form an important basis for further detailed experiments and bioinformatics analysis with an aim to unravel the potential molecular pathways or mechanisms underlying the positive effects of ASX supplementation on the brain, in particular the hippocampus. PMID:26981356

  6. Relationships among CFTR expression, HCO3- secretion, and host defense may inform gene- and cell-based cystic fibrosis therapies.

    PubMed

    Shah, Viral S; Ernst, Sarah; Tang, Xiao Xiao; Karp, Philip H; Parker, Connor P; Ostedgaard, Lynda S; Welsh, Michael J

    2016-05-10

    Cystic fibrosis (CF) is caused by mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel. Airway disease is the major source of morbidity and mortality. Successful implementation of gene- and cell-based therapies for CF airway disease requires knowledge of relationships among percentages of targeted cells, levels of CFTR expression, correction of electrolyte transport, and rescue of host defense defects. Previous studies suggested that, when ∼10-50% of airway epithelial cells expressed CFTR, they generated nearly wild-type levels of Cl(-) secretion; overexpressing CFTR offered no advantage compared with endogenous expression levels. However, recent discoveries focused attention on CFTR-mediated HCO3 (-) secretion and airway surface liquid (ASL) pH as critical for host defense and CF pathogenesis. Therefore, we generated porcine airway epithelia with varying ratios of CF and wild-type cells. Epithelia with a 50:50 mix secreted HCO3 (-) at half the rate of wild-type epithelia. Likewise, heterozygous epithelia (CFTR(+/-) or CFTR(+/∆F508)) expressed CFTR and secreted HCO3 (-) at ∼50% of wild-type values. ASL pH, antimicrobial activity, and viscosity showed similar relationships to the amount of CFTR. Overexpressing CFTR increased HCO3 (-) secretion to rates greater than wild type, but ASL pH did not exceed wild-type values. Thus, in contrast to Cl(-) secretion, the amount of CFTR is rate-limiting for HCO3 (-) secretion and for correcting host defense abnormalities. In addition, overexpressing CFTR might produce a greater benefit than expressing CFTR at wild-type levels when targeting small fractions of cells. These findings may also explain the risk of airway disease in CF carriers. PMID:27114540

  7. Selection of Reference Genes for qPCR- and ddPCR-Based Analyses of Gene Expression in Senescing Barley Leaves

    PubMed Central

    Zmienko, Agnieszka; Samelak-Czajka, Anna; Goralski, Michal; Sobieszczuk-Nowicka, Ewa; Kozlowski, Piotr; Figlerowicz, Marek

    2015-01-01

    Leaf senescence is a tightly regulated developmental or stress-induced process. It is accompanied by dramatic changes in cell metabolism and structure, eventually leading to the disintegration of chloroplasts, the breakdown of leaf proteins, internucleosomal fragmentation of nuclear DNA and ultimately cell death. In light of the global and intense reorganization of the senescing leaf transcriptome, measuring time-course gene expression patterns in this model is challenging due to the evident problems associated with selecting stable reference genes. We have used oligonucleotide microarray data to identify 181 genes with stable expression in the course of dark-induced senescence of barley leaf. From those genes, we selected 5 candidates and confirmed their invariant expression by both reverse transcription quantitative PCR and droplet digital PCR (ddPCR). We used the selected reference genes to normalize the level of the expression of the following senescence-responsive genes in ddPCR assays: SAG12, ICL, AGXT, CS and RbcS. We were thereby able to achieve a substantial reduction in the data variability. Although the use of reference genes is not considered mandatory in ddPCR assays, our results show that it is advisable in special cases, specifically those that involve the following conditions: i) a low number of repeats, ii) the detection of low-fold changes in gene expression or iii) series data comparisons (such as time-course experiments) in which large sample variation greatly affects the overall gene expression profile and biological interpretation of the data. PMID:25723393

  8. Discovering Hidden Connections among Diseases, Genes and Drugs Based on Microarray Expression Profiles with Negative-Term Filtering

    PubMed Central

    2014-01-01

    Microarrays based on gene expression profiles (GEPs) can be tailored specifically for a variety of topics to provide a precise and efficient means with which to discover hidden information. This study proposes a novel means of employing existing GEPs to reveal hidden relationships among diseases, genes, and drugs within a rich biomedical database, PubMed. Unlike the co-occurrence method, which considers only the appearance of keywords, the proposed method also takes into account negative relationships and non-relationships among keywords, the importance of which has been demonstrated in previous studies. Three scenarios were conducted to verify the efficacy of the proposed method. In Scenario 1, disease and drug GEPs (disease: lymphoma cancer, lymph node cancer, and drug: cyclophosphamide) were used to obtain lists of disease- and drug-related genes. Fifteen hidden connections were identified between the diseases and the drug. In Scenario 2, we adopted different diseases and drug GEPs (disease: AML-ALL dataset and drug: Gefitinib) to obtain lists of important diseases and drug-related genes. In this case, ten hidden connections were identified. In Scenario 3, we obtained a list of disease-related genes from the disease-related GEP (liver cancer) and the drug (Capecitabine) on the PharmGKB website, resulting in twenty-two hidden connections. Experimental results demonstrate the efficacy of the proposed method in uncovering hidden connections among diseases, genes, and drugs. Following implementation of the weight function in the proposed method, a large number of the documents obtained in each of the scenarios were judged to be related: 834 of 4028 documents, 789 of 1216 documents, and 1928 of 3791 documents in Scenarios 1, 2, and 3, respectively. The negative-term filtering scheme also uncovered a large number of negative relationships as well as non-relationships among these connections: 97 of 834, 38 of 789, and 202 of 1928 in Scenarios 1, 2, and 3, respectively

  9. A roadmap for zinc trafficking in the developing barley grain based on laser capture microdissection and gene expression profiling

    PubMed Central

    Tauris, Birgitte; Borg, Søren; Gregersen, Per L.; Holm, Preben B.

    2009-01-01

    Nutrients destined for the developing cereal grain encounter several restricting barriers on their path towards their final storage sites in the grain. In order to identify transporters and chelating agents that may be involved in transport and deposition of zinc in the barley grain, expression profiles have been generated of four different tissue types: the transfer cells, the aleurone layer, the endosperm, and the embryo. Cells from these tissues were isolated with the ‘laser capture microdissection’ technology and the extracted RNA was subjected to three rounds of T7-based amplification. The amplified RNA was subsequently hybridized to Affymetrix 22K Barley GeneChips. Due to the short average length of the amplified transcripts and the positioning of numerous probe sets at locations more than 400 base pairs (bp) from the poly(A)-tail, a normalization approach was used where the probe positions were taken into account. On the basis of the expression levels of a number of metal homeostasis genes, a working model is proposed for the translocation of zinc from the phloem to the storage sites in the developing grain. PMID:19297552

  10. A Class-Information-Based Sparse Component Analysis Method to Identify Differentially Expressed Genes on RNA-Seq Data.

    PubMed

    Liu, Jin-Xing; Xu, Yong; Gao, Ying-Lian; Zheng, Chun-Hou; Wang, Dong; Zhu, Qi

    2016-01-01

    With the development of deep sequencing technologies, many RNA-Seq data have been generated. Researchers have proposed many methods based on the sparse theory to identify the differentially expressed genes from these data. In order to improve the performance of sparse principal component analysis, in this paper, we propose a novel class-information-based sparse component analysis (CISCA) method which introduces the class information via a total scatter matrix. First, CISCA normalizes the RNA-Seq data by using a Poisson model to obtain their differential sections. Second, the total scatter matrix is gotten by combining the between-class and within-class scatter matrices. Third, we decompose the total scatter matrix by using singular value decomposition and construct a new data matrix by using singular values and left singular vectors. Then, aiming at obtaining sparse components, CISCA decomposes the constructed data matrix by solving an optimization problem with sparse constraints on loading vectors. Finally, the differentially expressed genes are identified by using the sparse loading vectors. The results on simulation and real RNA-Seq data demonstrate that our method is effective and suitable for analyzing these data. PMID:27045835

  11. Dynamic modeling of gene expression data

    NASA Technical Reports Server (NTRS)

    Holter, N. S.; Maritan, A.; Cieplak, M.; Fedoroff, N. V.; Banavar, J. R.

    2001-01-01

    We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small.

  12. Dynamic modeling of gene expression data

    PubMed Central

    Holter, Neal S.; Maritan, Amos; Cieplak, Marek; Fedoroff, Nina V.; Banavar, Jayanth R.

    2001-01-01

    We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small. PMID:11172013

  13. Transcriptional regulation of secretin gene expression.

    PubMed

    Nishitani, J; Rindi, G; Lopez, M J; Upchurch, B H; Leiter, A B

    1995-01-01

    Expression of the gene encoding the hormone secretin is restricted to a specific enteroendocrine cell type and to beta-cells in developing pancreatic islets. To characterize regulatory elements in the secretin gene responsible for its expression in secretin-producing cells, we used a series of reporter genes for transient expression assays in transfection studies carried out in secretin-producing islet cell lines. Analysis of the transcriptional activity of deletion mutants identified a positive cis regulatory domain between 174 and 53 base pairs upstream from the transcriptional initiation site which was required for secretin gene expression in secretin-producing HIT insulinoma cells. Within this enhancer were sequences resembling two binding sites for the transcription factor Sp1, as well as a consensus sequence for binding to helix-loop-helix proteins. Analysis of these three elements by site-directed mutagenesis suggests that each is important for full transcriptional activity. The role of proximal enhancer sequences in directing secretin gene expression to appropriate tissues is further supported by studies in transgenic mice revealing that 1.6 kilobases of the secretin gene 5' flanking sequence were sufficient to direct the expression of either human growth hormone or simian virus 40 large T-antigen reporter genes to all major secretin-producing tissues. PMID:8774991

  14. A Modified ABCDE Model of Flowering in Orchids Based on Gene Expression Profiling Studies of the Moth Orchid Phalaenopsis aphrodite

    PubMed Central

    Lee, Ann-Ying; Chen, Chun-Yi; Chang, Yao-Chien Alex; Chao, Ya-Ting; Shih, Ming-Che

    2013-01-01

    Previously we developed genomic resources for orchids, including transcriptomic analyses using next-generation sequencing techniques and construction of a web-based orchid genomic database. Here, we report a modified molecular model of flower development in the Orchidaceae based on functional analysis of gene expression profiles in Phalaenopsis aphrodite (a moth orchid) that revealed novel roles for the transcription factors involved in floral organ pattern formation. Phalaenopsis orchid floral organ-specific genes were identified by microarray analysis. Several critical transcription factors including AP3, PI, AP1 and AGL6, displayed distinct spatial distribution patterns. Phylogenetic analysis of orchid MADS box genes was conducted to infer the evolutionary relationship among floral organ-specific genes. The results suggest that gene duplication MADS box genes in orchid may have resulted in their gaining novel functions during evolution. Based on these analyses, a modified model of orchid flowering was proposed. Comparison of the expression profiles of flowers of a peloric mutant and wild-type Phalaenopsis orchid further identified genes associated with lip morphology and peloric effects. Large scale investigation of gene expression profiles revealed that homeotic genes from the ABCDE model of flower development classes A and B in the Phalaenopsis orchid have novel functions due to evolutionary diversification, and display differential expression patterns. PMID:24265826

  15. A modified procedure for replica plating of mammalian cells allowing selection of clones based on gene expression.

    PubMed

    Hornsby, P J; Yang, L; Lala, D S; Cheng, C Y; Salmons, B

    1992-02-01

    The polyester cloth replica-plating technique for selection of mammalian cell clones was modified by growing cells in colonies on a flexible polytetrafluoroethylene membrane and then transferring them completely to polyester cloth (27-microns mesh), from which a replica was made by allowing cells to transfer to a cloth of smaller pore size (17-microns mesh). Using this technique, two phenotype selection methods are demonstrated here: in situ hybridization for detection of a specific mRNA and a photographic film assay for detection of luciferase expression. Cells were transfected with pSV2AL-A delta 5' in which firefly luciferase cDNA is under the control of the simian virus 40 promoter. The luciferase assay was adapted for colonies on polyester cloth; cells were permeabilized with digitonin to allow access of ATP and luciferin to the cell without disruption of colonies. Clones selected for expression or nonexpression of luciferase by the photographic film assay were positive or negative for expression after isolation from the cloth replica and subsequent growth under conventional culture conditions. The replica-plating procedure described here should be generally applicable to most mammalian cell types. The ability to produce replicas of colonies, combined with in situ hybridization or assays that can be adapted to in situ detection, provides phenotype selection for clones based on gene expression independent of growth characteristics. PMID:1616718

  16. Microarray-based gene expression analysis of strong seed dormancy in rice cv. N22 and less dormant mutant derivatives.

    PubMed

    Wu, Tao; Yang, Chunyan; Ding, Baoxu; Feng, Zhiming; Wang, Qian; He, Jun; Tong, Jianhua; Xiao, Langtao; Jiang, Ling; Wan, Jianmin

    2016-02-01

    Seed dormancy in rice is an important trait related to the pre-harvest sprouting resistance. In order to understand the molecular mechanisms of seed dormancy, gene expression was investigated by transcriptome analysis using seeds of the strongly dormant cultivar N22 and its less dormant mutants Q4359 and Q4646 at 24 days after heading (DAH). Microarray data revealed more differentially expressed genes in Q4359 than in Q4646 compared to N22. Most genes differing between Q4646 and N22 also differed between Q4359 and N22. GO analysis of genes differentially expressed in both Q4359 and Q4646 revealed that some genes such as those for starch biosynthesis were repressed, whereas metabolic genes such as those for carbohydrate metabolism were enhanced in Q4359 and Q4646 seeds relative to N22. Expression of some genes involved in cell redox homeostasis and chromatin remodeling differed significantly only between Q4359 and N22. The results suggested a close correlation between cell redox homeostasis, chromatin remodeling and seed dormancy. In addition, some genes involved in ABA signaling were down-regulated, and several genes involved in GA biosynthesis and signaling were up-regulated. These observations suggest that reduced seed dormancy in Q4359 was regulated by ABA-GA antagonism. A few differentially expressed genes were located in the regions containing qSdn-1 and qSdn-5 suggesting that they could be candidate genes underlying seed dormancy. Our work provides useful leads to further determine the underling mechanisms of seed dormancy and for cloning seed dormancy genes from N22. PMID:26713549

  17. A Study on Effect of Electroacupuncture on Gene Expression in Hypothalamus of Rats with Stress-Induced Prehypertension Based on Gene Chip Technology

    PubMed Central

    Xie, Xiaojia; Guo, Yan; Liu, Qingguo; Wang, Zhaoyang; Guo, Changqing

    2015-01-01

    Objective. To explore the effect of electroacupuncture (EA) on gene expression in the hypothalamus of rats with stress-induced prehypertension and try to reveal its biological mechanism with gene chip technology. Methods. The stress-induced hypertensive rat model was prepared by combining electric foot-shocks with generated noise. Molding cycle lasted for 14 days and EA intervention was applied on model + EA group during model preparation. Rat Gene 2.0 Array technology was used for the determination of gene expression profiles and the screened key genes were verified by real-time fluorescence quantitative PCR method. Results. Compared with the blank group, 234 genes were upregulated and 73 were downregulated in the model group. Compared with the model group, 110 genes were upregulated and 273 genes were downregulated in model + EA group. The PCR results of the key genes including HSPB1, P2RX4, PPP1R14A, and TH are consistent with that of gene chip test. Conclusion. EA could significantly lower blood pressure of stress-induced prehypertension rats and affect its gene expression profile in hypothalamus. Genes and their signal transduction pathway that related to the contraction of vascular smooth muscle, concentration of Ca2+, and excitability of sympathetic nerve may be involved in EA's antihypertensive mechanism. PMID:26229544

  18. Mining Temporal Protein Complex Based on the Dynamic PIN Weighted with Connected Affinity and Gene Co-Expression

    PubMed Central

    Shen, Xianjun; Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Yang, Jincai

    2016-01-01

    The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN (the original static PIN) to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization. PMID:27100396

  19. Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

    PubMed Central

    2013-01-01

    Background Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. Results A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. Conclusions The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease. PMID:24188919

  20. Mixed-model reanalysis of primate data suggests tissue and species biases in oligonucleotide-based gene expression profiles.

    PubMed

    Hsieh, Wen-Ping; Chu, Tzu-Ming; Wolfinger, Russell D; Gibson, Greg

    2003-10-01

    An emerging issue in evolutionary genetics is whether it is possible to use gene expression profiling to identify genes that are associated with morphological, physiological, or behavioral divergence between species and whether these genes have undergone positive selection. Some of these questions were addressed in a recent study (Enard et al. 2002) of the difference in gene expression among human, chimp, and orangutan, which suggested an accelerated rate of divergence in gene expression in the human brain relative to liver. Reanalysis of the Affymetrix data set using analysis of variance methods to quantify the contributions of individuals and species to variation in expression of 12,600 genes indicates that as much as one-quarter of the genome shows divergent expression between primate species at the 5% level. The magnitude of fold change ranges from 1.2-fold up to 8-fold. Similar conclusions apply to reanalysis of Enard et al. 2002 parallel murine data set. However, biases inherent to short oligonucleotide microarray technology may account for some of the tissue and species effects. At high significance levels, more differences were observed in the liver than in the brain in each of the pairwise species comparisons, so it is not clear that expression divergence is accelerated in the human brain. Further, there is an apparent bias toward upregulation of gene expression in the brain in both primates and mice, whereas genes are equally likely to be up- or downregulated in the liver when these species diverge. A small subset of genes that are candidates for adaptive divergence may be identified on the basis of a high ratio of interspecific to intraspecific divergence. PMID:14573485

  1. Differential hippocampal gene expression and pathway analysis in an etiology-based mouse model of major depressive disorder.

    PubMed

    Zubenko, George S; Hughes, Hugh B; Jordan, Rick M; Lyons-Weiler, James; Cohen, Bruce M

    2014-09-01

    We have recently reported the creation and initial characterization of an etiology-based recombinant mouse model of a severe and inherited form of Major Depressive Disorder (MDD). This was achieved by replacing the corresponding mouse DNA sequence with a 6-base DNA sequence from the human CREB1 promoter that is associated with MDD in individuals from families with recurrent, early-onset MDD (RE-MDD). In the current study, we explored the effect of the pathogenic Creb1 allele on gene expression in the mouse hippocampus, a brain region that is altered in structure and function in MDD. Mouse whole-genome profiling was performed using the Illumina MouseWG-6 v2.0 Expression BeadChip microarray. Univariate analysis identified 269 differentially-expressed genes in the hippocampus of the mutant mouse. Pathway analyses highlighted 11 KEGG pathways: the phosphatidylinositol signaling system, which has been widely implicated in MDD, Bipolar Disorder, and the action of mood stabilizers; gap junction and long-term potentiation, which mediate cognition and memory functions often impaired in MDD; cardiac muscle contraction, insulin signaling pathway, and three neurodegenerative brain disorders (Alzheimer's, Parkinson's, and Huntington's Diseases) that are associated with MDD; ribosome and proteasome pathways affecting protein synthesis/degradation; and the oxidative phosphorylation pathway that is key to energy production. These findings illustrate the merit of this congenic C57BL/6 recombinant mouse as a model of RE-MDD, and demonstrate its potential for highlighting molecular and cellular pathways that contribute to the biology of MDD. The results also inform our understanding of the mechanisms that underlie the comorbidity of MDD with other disorders. PMID:25059218

  2. Linking Α to Ω: diverse and dynamic RNA-based mechanisms to regulate gene expression by 5'-to-3' communication.

    PubMed

    Filbin, Megan E; Kieft, Jeffrey S

    2016-01-01

    Communication between the 5' and 3' ends of a eukaryotic messenger RNA (mRNA) or viral genomic RNA is a ubiquitous and important strategy used to regulate gene expression. Although the canonical interaction between initiation factor proteins at the 5' end of an mRNA and proteins bound to the polyadenylate tail at the 3' end is well known, in fact there are many other strategies used in diverse ways. These strategies can involve "non-canonical" proteins, RNA structures, and direct RNA-RNA base-pairing between distal elements to achieve 5'-to-3' communication. Likewise, the communication induced by these interactions influences a variety of processes linked to the use and fate of the RNA that contains them. Recent studies are revealing how dynamic these interactions are, possibly changing in response to cellular conditions or to link various phases of the mRNA's life, from translation to decay. Thus, 5'-to-3' communication is about more than just making a closed circle; the RNA elements and associated proteins are key players in controlling gene expression at the post-transcriptional level. PMID:27610229

  3. Evolution of insect metamorphosis: a microarray-based study of larval and adult gene expression in the ant Camponotus festinatus.

    PubMed

    Goodisman, Michael A D; Isoe, Jun; Wheeler, Diana E; Wells, Michael A

    2005-04-01

    Holometabolous insects inhabit almost every terrestrial ecosystem. The evolutionary success of holometabolous insects stems partly from their developmental program, which includes discrete larval and adult stages. To gain an understanding of how development differs among holometabolous insect taxa, we used cDNA microarray technology to examine differences in gene expression between larval and adult Camponotus festinatus ants. We then compared expression patterns obtained from our study to those observed in the fruitfly Drosophila melanogaster. We found that many genes showed distinct patterns of expression between the larval and adult ant life stages, a result that was confirmed through quantitative reverse-transcriptase polymerase chain reaction. Genes involved in protein metabolism and possessing structural activity tended to be more highly expressed in larval than adult ants. In contrast, genes relatively upregulated in adults possessed a greater diversity of functions and activities. We also discovered that patterns of expression observed for homologous genes in D. melanogaster differed substantially from those observed in C. festinatus. Our results suggest that the specific molecular mechanisms involved in metamorphosis will differ substantially between insect taxa. Systematic investigation of gene expression during development of other taxa will provide additional information on how developmental pathways evolve. PMID:15926695

  4. Covariance Structure Models for Gene Expression Microarray Data

    ERIC Educational Resources Information Center

    Xie, Jun; Bentler, Peter M.

    2003-01-01

    Covariance structure models are applied to gene expression data using a factor model, a path model, and their combination. The factor model is based on a few factors that capture most of the expression information. A common factor of a group of genes may represent a common protein factor for the transcript of the co-expressed genes, and hence, it…

  5. Nuclear Neighborhoods and Gene Expression

    PubMed Central

    Zhao, Rui; Bodnar, Megan S.; Spector, David L.

    2009-01-01

    Summary The eukaryotic nucleus is a highly compartmentalized and dynamic environment. Chromosome territories are arranged non-randomly within the nucleus and numerous studies have indicated that a gene’s position in the nucleus can impact its transcriptional activity. Here, we focus on recent advances in our understanding of the influence of specific nuclear neighborhoods on gene expression or repression. Nuclear neighborhoods associated with transcriptional repression include the inner nuclear membrane/nuclear lamina and peri-nucleolar chromatin, whereas neighborhoods surrounding the nuclear pore complex, PML nuclear bodies, and nuclear speckles seem to be transcriptionally permissive. While nuclear position appears to play an important role in gene expression, it is likely to be only one piece of a flexible puzzle that incorporates numerous parameters. We are still at a very early, yet exciting stage in our journey toward deciphering the mechanism(s) that govern the permissiveness of gene expression/repression within different nuclear neighborhoods. PMID:19339170

  6. Differential Gene Expression in Glaucoma

    PubMed Central

    Jakobs, Tatjana C.

    2014-01-01

    In glaucoma, regardless of its etiology, retinal ganglion cells degenerate and eventually die. Although age and elevated intraocular pressure (IOP) are the main risk factors, there are still many mysteries in the pathogenesis of glaucoma. The advent of genome-wide microarray expression screening together with the availability of animal models of the disease has allowed analysis of differential gene expression in all parts of the eye in glaucoma. This review will outline the findings of recent genome-wide expression studies and discuss their commonalities and differences. A common finding was the differential regulation of genes involved in inflammation and immunity, including the complement system and the cytokines transforming growth factor β (TGFβ) and tumor necrosis factor α (TNFα). Other genes of interest have roles in the extracellular matrix, cell–matrix interactions and adhesion, the cell cycle, and the endothelin system. PMID:24985133

  7. Pathway-Based Factor Analysis of Gene Expression Data Produces Highly Heritable Phenotypes That Associate with Age

    PubMed Central

    Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

    2015-01-01

    Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 “pathway phenotypes” that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold (P<5.38×10−5). These phenotypes are more heritable (h2=0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. PMID:25758824

  8. Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.

    PubMed

    Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

    2015-05-01

    Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold ([Formula: see text]). These phenotypes are more heritable ([Formula: see text]) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. PMID:25758824

  9. Transgenic Arabidopsis Gene Expression System

    NASA Technical Reports Server (NTRS)

    Ferl, Robert; Paul, Anna-Lisa

    2009-01-01

    The Transgenic Arabidopsis Gene Expression System (TAGES) investigation is one in a pair of investigations that use the Advanced Biological Research System (ABRS) facility. TAGES uses Arabidopsis thaliana, thale cress, with sensor promoter-reporter gene constructs that render the plants as biomonitors (an organism used to determine the quality of the surrounding environment) of their environment using real-time nondestructive Green Fluorescent Protein (GFP) imagery and traditional postflight analyses.

  10. Engineering of ribozyme-based aminoglycoside switches of gene expression by in vivo genetic selection in Saccharomyces cerevisiae.

    PubMed

    Klauser, Benedikt; Rehm, Charlotte; Summerer, Daniel; Hartig, Jörg S

    2015-01-01

    Synthetic RNA-based switches are a growing class of genetic controllers applied in synthetic biology to engineer cellular functions. In this chapter, we detail a protocol for the selection of posttranscriptional controllers of gene expression in yeast using the Schistosoma mansoni hammerhead ribozyme as a central catalytic unit. Incorporation of a small molecule-sensing aptamer domain into the ribozyme renders its activity ligand-dependent. Aptazymes display numerous advantages over conventional protein-based transcriptional controllers, namely, the use of little genomic space for encryption, their modular architecture allowing for easy reprogramming to new inputs, the physical linkage to the message to be controlled, and the ability to function without protein cofactors. Herein, we describe the method to select ribozyme-based switches of gene expression in Saccharomyces cerevisiae that we successfully implemented to engineer neomycin- and theophylline-responsive switches. We also highlight how to adapt the protocol to screen for switches responsive to other ligands. Reprogramming of the sensor unit and incorporation into any RNA of interest enables the fulfillment of a variety of regulatory functions. However, proper functioning of the aptazyme is largely dependent on optimal connection between the aptamer and the catalytic core. We obtained functional switches from a pool of variants carrying randomized connection sequences by an in vivo selection in MaV203 yeast cells that allows screening of a large sequence space of up to 1×10(9) variants. The protocol given explains how to construct aptazyme libraries, carry out the in vivo selection and characterize novel ON- and OFF-switches. PMID:25605392

  11. Serum-based culture conditions provoke gene expression variability in mouse embryonic stem cells as revealed by single cell analysis

    PubMed Central

    Guo, Guoji; Pinello, Luca; Han, Xiaoping; Lai, Shujing; Shen, Li; Lin, Ta-Wei; Zou, Keyong; Yuan, Guo-Cheng; Orkin, Stuart H.

    2015-01-01

    Summary Variation in gene expression is an important feature of mouse embryonic stem cells (ESCs). However, the mechanisms responsible for global gene expression variation in ESCs are not fully understood. We performed single cell mRNA-seq analysis of mouse ESCs and uncovered significant heterogeneity in ESCs cultured in serum. We define highly variable gene clusters with distinct chromatin states; and show that bivalent genes are prone to expression variation. At the same time, we identify an ESC priming pathway that initiates the exit from the naïve ESC state. Finally, we provide evidence that a large proportion of intracellular network variability is due to the extracellular culture environment. Serum free culture reduces cellular heterogeneity and transcriptome variation in ESCs. PMID:26804902

  12. ESTROGENIC STATUS MODULATES DMBA-MEDIATED HEPATIC GENE EXPRESSION: MICROARRAY-BASED ANALYSIS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Estrogenic status in women influences the metabolism and toxicity of polycyclic aromatic hydrocarbons (PAH). The objective of this study was to examine the influence of estradiol (E2) on 7,12 dimethylbenz(a)anthracene (DMBA), a ligand for aryl hydrocarbon receptor, mediated changes on gene expressio...

  13. Plant Omics Data Center: An Integrated Web Repository for Interspecies Gene Expression Networks with NLP-Based Curation

    PubMed Central

    Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro

    2015-01-01

    Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. PMID:25505034

  14. Neighboring Genes Show Correlated Evolution in Gene Expression

    PubMed Central

    Ghanbarian, Avazeh T.; Hurst, Laurence D.

    2015-01-01

    When considering the evolution of a gene’s expression profile, we commonly assume that this is unaffected by its genomic neighborhood. This is, however, in contrast to what we know about the lack of autonomy between neighboring genes in gene expression profiles in extant taxa. Indeed, in all eukaryotic genomes genes of similar expression-profile tend to cluster, reflecting chromatin level dynamics. Does it follow that if a gene increases expression in a particular lineage then the genomic neighbors will also increase in their expression or is gene expression evolution autonomous? To address this here we consider evolution of human gene expression since the human-chimp common ancestor, allowing for both variation in estimation of current expression level and error in Bayesian estimation of the ancestral state. We find that in all tissues and both sexes, the change in gene expression of a focal gene on average predicts the change in gene expression of neighbors. The effect is highly pronounced in the immediate vicinity (<100 kb) but extends much further. Sex-specific expression change is also genomically clustered. As genes increasing their expression in humans tend to avoid nuclear lamina domains and be enriched for the gene activator 5-hydroxymethylcytosine, we conclude that, most probably owing to chromatin level control of gene expression, a change in gene expression of one gene likely affects the expression evolution of neighbors, what we term expression piggybacking, an analog of hitchhiking. PMID:25743543

  15. Blood-Based Gene Expression Signatures of Infants and Toddlers with Autism

    ERIC Educational Resources Information Center

    Glatt, Stephen J.; Tsuang, Ming T.; Winn, Mary; Chandler, Sharon D.; Collins, Melanie; Lopez, Linda; Weinfeld, Melanie; Carter, Cindy; Schork, Nicholas; Pierce, Karen; Courchesne, Eric

    2012-01-01

    Objective: Autism spectrum disorders (ASDs) are highly heritable neurodevelopmental disorders that onset clinically during the first years of life. ASD risk biomarkers expressed early in life could significantly impact diagnosis and treatment, but no transcriptome-wide biomarker classifiers derived from fresh blood samples from children with…

  16. Using PCR to Target Misconceptions about Gene Expression

    PubMed Central

    Wright, Leslie K.; Newman, Dina L.

    2013-01-01

    We present a PCR-based laboratory exercise that can be used with first- or second-year biology students to help overcome common misconceptions about gene expression. Biology students typically do not have a clear understanding of the difference between genes (DNA) and gene expression (mRNA/protein) and often believe that genes exist in an organism or cell only when they are expressed. This laboratory exercise allows students to carry out a PCR-based experiment designed to challenge their misunderstanding of the difference between genes and gene expression. Students first transform E. coli with an inducible GFP gene containing plasmid and observe induced and un-induced colonies. The following exercise creates cognitive dissonance when actual PCR results contradict their initial (incorrect) predictions of the presence of the GFP gene in transformed cells. Field testing of this laboratory exercise resulted in learning gains on both knowledge and application questions on concepts related to genes and gene expression. PMID:23858358

  17. Introduction to the Gene Expression Analysis.

    PubMed

    Segundo-Val, Ignacio San; Sanz-Lozano, Catalina S

    2016-01-01

    In 1941, Beadle and Tatum published experiments that would explain the basis of the central dogma of molecular biology, whereby the DNA through an intermediate molecule, called RNA, results proteins that perform the functions in cells. Currently, biomedical research attempts to explain the mechanisms by which develops a particular disease, for this reason, gene expression studies have proven to be a great resource. Strictly, the term "gene expression" comprises from the gene activation until the mature protein is located in its corresponding compartment to perform its function and contribute to the expression of the phenotype of cell.The expression studies are directed to detect and quantify messenger RNA (mRNA) levels of a specific gene. The development of the RNA-based gene expression studies began with the Northern Blot by Alwine et al. in 1977. In 1969, Gall and Pardue and John et al. independently developed the in situ hybridization, but this technique was not employed to detect mRNA until 1986 by Coghlan. Today, many of the techniques for quantification of RNA are deprecated because other new techniques provide more information. Currently the most widely used techniques are qPCR, expression microarrays, and RNAseq for the transcriptome analysis. In this chapter, these techniques will be reviewed. PMID:27300529

  18. MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation.

    PubMed

    Wang, Luman; Mo, Qiaochu; Wang, Jianxin

    2015-01-01

    Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches. PMID:26881263

  19. MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation

    PubMed Central

    Wang, Luman; Mo, Qiaochu; Wang, Jianxin

    2015-01-01

    Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches. PMID:26881263

  20. A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose–Response Curve

    PubMed Central

    Goswami, C Pankaj; Cheng, L; Alexander, PS; Singal, A; Li, L

    2015-01-01

    Gene expression data before and after treatment with an individual drug and the IC20 of dose–response data were utilized to predict two drugs' interaction effects on a diffuse large B-cell lymphoma (DLBCL) cancer cell. A novel drug interaction scoring algorithm was developed to account for either synergistic or antagonistic effects between drug combinations. Different core gene selection schemes were investigated, which included the whole gene set, the drug-sensitive gene set, the drug-sensitive minus drug-resistant gene set, and the known drug target gene set. The prediction scores were compared with the observed drug interaction data at 6, 12, and 24 hours with a probability concordance (PC) index. The test result shows the concordance between observed and predicted drug interaction ranking reaches a PC index of 0.605. The scoring reliability and efficiency was further confirmed in five drug interaction studies published in the GEO database. PMID:26225234

  1. Time-course investigation of the gene expression profile during Fasciola hepatica infection: A microarray-based study.

    PubMed

    Rojas-Caraballo, Jose; López-Abán, Julio; Fernández-Soto, Pedro; Vicente, Belén; Collía, Francisco; Muro, Antonio

    2015-12-01

    Fasciolosis is listed as one of the most important neglected tropical diseases according with the World Health Organization and is also considered as a reemerging disease in the human beings. Despite there are several studies describing the immune response induced by Fasciola hepatica in the mammalian host, investigations aimed at identifying the expression profile of genes involved in inducing hepatic injury are currently scarce. Data presented here belong to a time-course investigation of the gene expression profile in the liver of BALB/c mice infected with F. hepatica metacercariae at 7 and 21 days after experimental infection. The data published here have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE69588, previously published by Rojas-Caraballo et al. (2015) in PLoS One [1]. PMID:26697343

  2. A new high-performance heterologous fungal expression system based on regulatory elements from the Aspergillus terreus terrein gene cluster.

    PubMed

    Gressler, Markus; Hortschansky, Peter; Geib, Elena; Brock, Matthias

    2015-01-01

    Recently, the Aspergillus terreus terrein gene cluster was identified and selected for development of a new heterologous expression system. The cluster encodes the specific transcription factor TerR that is indispensable for terrein cluster induction. To identify TerR binding sites, different recombinant versions of the TerR DNA-binding domain were analyzed for specific motif recognition. The high affinity consensus motif TCGGHHWYHCGGH was identified from genes required for terrein production and binding site mutations confirmed their essential contribution to gene expression in A. terreus. A combination of TerR with its terA target promoter was tested as recombinant expression system in the heterologous host Aspergillus niger. TerR mediated target promoter activation was directly dependent on its transcription level. Therefore, terR was expressed under control of the regulatable amylase promoter PamyB and the resulting activation of the terA target promoter was compared with activation levels obtained from direct expression of reporters from the strong gpdA control promoter. Here, the coupled system outcompeted the direct expression system. When the coupled system was used for heterologous polyketide synthase expression high metabolite levels were produced. Additionally, expression of the Aspergillus nidulans polyketide synthase gene orsA revealed lecanoric acid rather than orsellinic acid as major polyketide synthase product. Domain swapping experiments assigned this depside formation from orsellinic acid to the OrsA thioesterase domain. These experiments confirm the suitability of the expression system especially for high-level metabolite production in heterologous hosts. PMID:25852654

  3. A new high-performance heterologous fungal expression system based on regulatory elements from the Aspergillus terreus terrein gene cluster

    PubMed Central

    Gressler, Markus; Hortschansky, Peter; Geib, Elena; Brock, Matthias

    2015-01-01

    Recently, the Aspergillus terreus terrein gene cluster was identified and selected for development of a new heterologous expression system. The cluster encodes the specific transcription factor TerR that is indispensable for terrein cluster induction. To identify TerR binding sites, different recombinant versions of the TerR DNA-binding domain were analyzed for specific motif recognition. The high affinity consensus motif TCGGHHWYHCGGH was identified from genes required for terrein production and binding site mutations confirmed their essential contribution to gene expression in A. terreus. A combination of TerR with its terA target promoter was tested as recombinant expression system in the heterologous host Aspergillus niger. TerR mediated target promoter activation was directly dependent on its transcription level. Therefore, terR was expressed under control of the regulatable amylase promoter PamyB and the resulting activation of the terA target promoter was compared with activation levels obtained from direct expression of reporters from the strong gpdA control promoter. Here, the coupled system outcompeted the direct expression system. When the coupled system was used for heterologous polyketide synthase expression high metabolite levels were produced. Additionally, expression of the Aspergillus nidulans polyketide synthase gene orsA revealed lecanoric acid rather than orsellinic acid as major polyketide synthase product. Domain swapping experiments assigned this depside formation from orsellinic acid to the OrsA thioesterase domain. These experiments confirm the suitability of the expression system especially for high-level metabolite production in heterologous hosts. PMID:25852654

  4. Microarray-based compendium of hepatic gene expression profiles for prototypical ADME gene-inducing compounds in rats and mice in vivo.

    PubMed

    Slatter, J G; Cheng, O; Cornwell, P D; de Souza, A; Rockett, J; Rushmore, T; Hartley, D; Evers, R; He, Y; Dai, X; Hu, R; Caguyong, M; Roberts, C J; Castle, J; Ulrich, R G

    2006-01-01

    To examine species-specific aspects of the induction of absorption, distribution, metabolism and excretion (ADME)-related genes, we used 25 000 gene oligonucleotide microarrays to construct a rodent gene-response compendium that compared hepatic gene expression profiles and developed consensus aryl hydrocarbon receptor (AhR), constitutive androstane receptor (CAR) and pregnane X-receptor (PXR) ligand signatures relevant to drug clearance. Twenty-six inducer compounds were chosen from the literature. Rats and mice received one of six dose levels (log2 dose escalation, 32-fold dose range) of each compound daily for 3 days. Animals were necropsied 6-9 h after the last dose, and tissues were collected for RNA analysis. Hepatic gene expression profiles were obtained using Rosetta Resolver expression analysis system, and ADME-related genes were extracted. Cross-talk among nuclear receptors or hepatoxicity at high dose levels resulted in large signatures (usually >1000 genes at p < 0.01) for most compounds. After ADME gene transcript enrichment, agglomerative clustering separated AhR ligands from CAR/PXR ligands, but it was difficult to distinguish CAR from PXR ligands. Consensus signatures were derived from groups of AhR, CAR and PXR ligands; and cross-talk among responding genes was determined. Many compounds had distinct log dose-response profiles, and relative potencies for ligands were established. Robust responses by CYP1A1, CYP2B10 (CAR responsive in mice) and CYP2B15 (CAR responsive in rats) and CYP3A1 (PXR responsive in rats) were used to benchmark the relative potency of different ligands and to determine the relative selectivity for AhR, CAR or PXR. By using a compendium of gene expression profiles, we defined species-specific induction patterns across the ADME transcriptome. PMID:17118914

  5. PERSPECTIVE: Toward an artificial cell based on gene expression in vesicles

    NASA Astrophysics Data System (ADS)

    Noireaux, Vincent; Bar-Ziv, Roy; Godefroy, Jeremy; Salman, Hanna; Libchaber, Albert

    2005-09-01

    We present a new experimental approach to build an artificial cell using the translation machinery of a cell-free expression system as the hardware and a DNA synthetic genome as the software. This approach, inspired by the self-replicating automata of von Neumann, uses cytoplasmic extracts, encapsulated in phospholipid vesicles, to assemble custom-made genetic circuits to develop the functions of a minimal cell. Although this approach can find applications, especially in biotechnology, the primary goal is to understand how a DNA algorithm can be designed to build an operating system that has some of the properties of life. We provide insights on this cell-free approach as well as new results to transform step by step a long-lived vesicle bioreactor into an artificial cell. We show how the green fluorescent protein can be anchored to the membrane and we give indications of a possible insertion mechanism of integral membrane proteins. With vesicles composed of different phospholipids, the fusion protein alpha-hemolysin-eGFP can be expressed to reveal patterns on the membrane. The specific degradation complex ClpXP from E. coli is introduced to create a sink for the synthesized proteins. Perspectives and subsequent limitations of this approach are discussed.

  6. Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo.

    PubMed

    Sayal, Rupinder; Dresch, Jacqueline M; Pushel, Irina; Taylor, Benjamin R; Arnosti, David N

    2016-01-01

    Enhancers constitute one of the major components of regulatory machinery of metazoans. Although several genome-wide studies have focused on finding and locating enhancers in the genomes, the fundamental principles governing their internal architecture and cis-regulatory grammar remain elusive. Here, we describe an extensive, quantitative perturbation analysis targeting the dorsal-ventral patterning gene regulatory network (GRN) controlled by Drosophila NF-κB homolog Dorsal. To understand transcription factor interactions on enhancers, we employed an ensemble of mathematical models, testing effects of cooperativity, repression, and factor potency. Models trained on the dataset correctly predict activity of evolutionarily divergent regulatory regions, providing insights into spatial relationships between repressor and activator binding sites. Importantly, the collective predictions of sets of models were effective at novel enhancer identification and characterization. Our study demonstrates how experimental dataset and modeling can be effectively combined to provide quantitative insights into cis-regulatory information on a genome-wide scale. PMID:27152947

  7. Gene expression profile of pulpitis.

    PubMed

    Galicia, J C; Henson, B R; Parker, J S; Khan, A A

    2016-06-01

    The cost, prevalence and pain associated with endodontic disease necessitate an understanding of the fundamental molecular aspects of its pathogenesis. This study was aimed to identify the genetic contributors to pulpal pain and inflammation. Inflamed pulps were collected from patients diagnosed with irreversible pulpitis (n=20). Normal pulps from teeth extracted for various reasons served as controls (n=20). Pain level was assessed using a visual analog scale (VAS). Genome-wide microarray analysis was performed using Affymetrix GeneTitan Multichannel Instrument. The difference in gene expression levels were determined by the significance analysis of microarray program using a false discovery rate (q-value) of 5%. Genes involved in immune response, cytokine-cytokine receptor interaction and signaling, integrin cell surface interactions, and others were expressed at relatively higher levels in the pulpitis group. Moreover, several genes known to modulate pain and inflammation showed differential expression in asymptomatic and mild pain patients (⩾30 mm on VAS) compared with those with moderate to severe pain. This exploratory study provides a molecular basis for the clinical diagnosis of pulpitis. With an enhanced understanding of pulpal inflammation, future studies on treatment and management of pulpitis and on pain associated with it can have a biological reference to bridge treatment strategies with pulpal biology. PMID:27052691

  8. Investigation of Gene Regulatory Networks Associated with Autism Spectrum Disorder Based on MiRNA Expression in China

    PubMed Central

    Chen, Zhao; Li, Jiada; Hu, Zhengmao; Qiu, Rong; Zhuang, Wei; Tang, Beisha; Xia, Kun; Jiang, Hong

    2015-01-01

    Autism spectrum disorder (ASD) comprise a group of neurodevelopmental disorders characterized by deficits in social and communication capacities and repetitive behaviors. Increasing neuroscientific evidence indicates that the neuropathology of ASD is widespread and involves epigenetic regulation in the brain. Differentially expressed miRNAs in the peripheral blood from autism patients were identified by high-throughput miRNA microarray analyses. Five of these miRNAs were confirmed through quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis. A search for candidate target genes of the five confirmed miRNAs was performed through a Kyoto encyclopedia of genes and genomes (KEGG) biological pathways and Gene Ontology enrichment analysis of gene function to identify gene regulatory networks. To the best of our knowledge, this study provides the first global miRNA expression profile of ASD in China. The differentially expressed miR-34b may potentially explain the higher percentage of male ASD patients, and the aberrantly expressed miR-103a-3p may contribute to the abnormal ubiquitin-mediated proteolysis observed in ASD. PMID:26061495

  9. Systems Biophysics of Gene Expression

    PubMed Central

    Vilar, Jose M.G.; Saiz, Leonor

    2013-01-01

    Gene expression is a process central to any form of life. It involves multiple temporal and functional scales that extend from specific protein-DNA interactions to the coordinated regulation of multiple genes in response to intracellular and extracellular changes. This diversity in scales poses fundamental challenges to the use of traditional approaches to fully understand even the simplest gene expression systems. Recent advances in computational systems biophysics have provided promising avenues to reliably integrate the molecular detail of biophysical process into the system behavior. Here, we review recent advances in the description of gene regulation as a system of biophysical processes that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. There is now basic mechanistic understanding on how promoters controlled by multiple, local and distal, DNA binding sites for transcription factors can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including precision and flexibility of the transcriptional responses. PMID:23790365

  10. Control of Renin Gene Expression

    PubMed Central

    Glenn, Sean T.; Jones, Craig A.; Gross, Kenneth W.; Pan, Li

    2015-01-01

    Renin, as part of the renin-angiotensin system, plays a critical role in the regulation of blood pressure, electrolyte homeostasis, mammalian renal development and progression of fibrotic/hypertrophic diseases. Renin gene transcription is subject to complex developmental and tissue-specific regulation. Initial studies using the mouse As4.1 cell line, which has many characteristics of the renin-expressing juxtaglomerular cells of the kidney, have identified a proximal promoter region (−197 to −50 bp) and an enhancer (−2866 to −2625 bp) upstream of the Ren-1c gene, which are critical for renin gene expression. The proximal promoter region contains several transcription factor-binding sites including a binding site for the products of the developmental control genes Hox. The enhancer consists of at least 11 transcription factor-binding sites and is responsive to various signal transduction pathways including cAMP, retinoic acid, endothelin-1, and cytokines, all of which are known to alter renin mRNA levels. Furthermore, in vivo models have validated several of these key components found within the proximal promoter region and the enhancer as well as other key sites necessary for renin gene transcription. PMID:22576577