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Reduced expression of the insulin receptor in mouse insulinoma (MIN6) cells reveals multiple roles of insulin signaling in gene expression, proliferation, insulin content, and secretion

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JOURNAL OF BIOLOGICAL CHEMISTRY 2005年第6期280卷 4992-5003页

作者： Ohsugi, M Cras-Méneur, C Zhou, YY Bernal-Mizrachi, E Johnson, JD Luciani, DS Polonsky, KS Permutt, MA Washington Univ Sch Med Div Endocrinol Metab & Lipid Res St Louis MO 63110 USA

The role of insulin signaling in pancreatic beta cells has become increasingly apparent. Stably transformed insulinoma cell lines (MIN6) were created with small interfering RNA resulting in the reduction of insulin receptor (IR) expression up to 80% (insulin receptor knockdown, IRKDDelta80). Functionally perturbed IR signaling was confirmed with the absence of insulin-stimulated insulin receptor substrate 1 tyrosine phosphorylation. Additionally, Akt phosphorylation was reduced and responded poorly to glucose stimulation. Gene expression profiling revealed that reduced IR expression was associated with alterations in expression of > 1,500 genes with diverse functions. IRKD cells exhibited low rate of proliferation due to delay in transition from G(0)/G(1) to S phase, whereas susceptibility to apoptosis did not differ from that of control cells. Insulin content was reduced in proportion to the reduction of IR. IRKD cells maintained glucose responsiveness as measured by NAD(P)H generation, whereas Ca2+ responses and insulin secretion were enhanced. IRKDDelta80 and control cells were treated with glucose (25 mm) or insulin (100 nM) for 45 min, and gene expression profiles were assessed. Transcriptional activation of several hundred early response genes common to both glucose and insulin stimulation was observed in control cells. In IRKDDelta80 cells' insulin failed to activate any genes as anticipated. Importantly, glucose stimulation of gene expression in IRKDDelta80 cells showed that most genes previously activated by glucose were no longer activated, suggesting a major autocrine/paracrine effect of insulin on glucose-regulated gene expression. On the other hand, there were a number of glucose-regulated genes in the IRKDDelta80 cells that were not previously observed in control cells, suggesting a feedback regulation of insulin signaling on glucose-regulated gene expression. These results demonstrate important roles of the insulin receptor in islet beta cell gene exp

关键词： PANCREATIC BETA-CELLS microarray data CYTOSOLIC CA2+ GLUCOSE MICE TRANSCRIPTION ACTIVATION FEEDBACK KINASE GROWTH

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Consistent across-tissue signatures of differential gene expression in Crohn's disease

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IMMUNOGENETICS 2005年第10期57卷 709-716页

作者： Hughes, AL Univ S Carolina Dept Biol Sci Columbia SC 29208 USA

An approach based on analysis of variance was applied to raw expression data on 44,760 transcripts in order to identify those with significant differential expression across ileum and colon in Crohn's disease (CD) and ulcerative colitis (UC). The design treated tissue as a block effect, thereby removing this effect statistically and increasing the power to test for effects of disease states (control, CD, and UC). A significant F-statistic for the disease effect was not correlated with the ratios CD/control or UC/control, evidently because many transcripts with high-expression ratios to the control showed inconsistent patterns across tissues. Of 1,053 transcripts showing a significant effect of disease state at the 1% level by the bootstrap test, 508 showed significant difference at the 1% level in a post hoc test for difference between the mean scores for CD and control. These included a number of genes relevant to the mechanism of pathogenesis of CD and a number of genes mapping to genomic regions that have previously shown linkage to CD in association studies.

关键词： Crohn's disease gene expression inflammatory bowel disease microarray data

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microarray expression analysis using seed-based clustering method

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ON THE CONVERGENCE OF BIO-INFORMATION-, ENVIRONMENTAL-, ENERGY-, SPACE- AND NANO-TECHNOLOGIES, PTS 1 AND 2 2005年第I期277-279卷 343-348页

作者： Shin, M Park, SH Elect & Telecommun Res Inst Bioinformat Team Taejon 305350 South Korea

Clustering methods have been often used to find biologically relevant groups of genes or conditions based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying groups of genes with similar expression profiles, the functionalities of unknown genes can be inferred from those of known genes in the same group. In this paper we address a novel clustering approach, called seed-based clustering, where seed genes are first systematically chosen by computational analysis of their expression profiles, and then the clusters are generated by using the seed genes as initial values for k-means clustering. The seed-based clustering method has strong mathematical foundations and requires only a few matrix computations for seed extraction. As a result, it provides stability of clustering results by eliminating randomness in the selection of initial values for cluster generation. Our empirical results reported here indicate that the entire, clustering process can be systematically pursued using seedbased clustering, and that its performance is favorable compared to current approaches.

关键词： microarray data gene expression data analysis clustering algorithm seed extraction

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The ArrayExpress gene expression database: a software engineering and implementation perspective

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BIOINFORMATICS 2005年第8期21卷 1495-1501页

作者： Sarkans, U Parkinson, H Lara, GG Oezcimen, A Sharma, A Abeygunawardena, N Contrino, S Holloway, E Rocca-Serra, P Mukherjee, G Shojatalab, M Kapushesky, M Sansone, SA Farne, A Rayner, T Brazma, A European Bioinformat Inst EMBL Outstn Hinxton Cambridge CB10 1SD England

Motivation: The lack of microarray data management systems and databases is still one of the major problems faced by many life sciences laboratories. While developing the public repository for microarray data ArrayExpress we had to find novel solutions to many non-trivial software engineering problems. Our experience will be both relevant and useful for most bioinformaticians involved in developing information systems for a wide range of high-throughput technologies. Results: ArrayExpress has been online since February 2002, growing exponentially to well over 10 000 hybridizations (as of September 2004). It has been demonstrated that our chosen design and implementation works for databases aimed at storage, access and sharing of high-throughput data.

关键词： microarray data data ACCESS INFORMATION REPOSITORY SYSTEM

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Differential and trajectory methods for time course gene expression data

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BIOINFORMATICS 2005年第13期21卷 3009-3016页

作者： Liang, YL Tayo, B Cai, XY Kelemen, A Univ Buffalo Dept Biostat Buffalo NY 14226 USA Univ Buffalo Dept Social & Prevent Med Buffalo NY 14226 USA Niagara Univ Dept Comp & Informat Sci Lewiston NY 14109 USA

Motivation: The issue of high dimensionality in microarray data has been, and remains, a hot topic in statistical and computational analysis. Efficient gene filtering and differentiation approaches can reduce the dimensions of data, help to remove redundant genes and noises, and highlight the most relevant genes that are major players in the development of certain diseases or the effect of drug treatment. The purpose of this study is to investigate the efficiency of parametric (including Bayesian and non-Bayesian, linear and non-linear), nonparametric and semi-parametric gene filtering methods through the application of time course microarray data from multiple sclerosis patients being treated with interferon-beta-1a. The analysis of variance with bootstrapping (parametric), class dispersion (semi-parametric) and Pareto (non-parametric) with permutation methods are presented and compared for filtering and finding differentially expressed genes. The Bayesian linear correlated model, the Bayesian non-linear model the and non-Bayesian mixed effects model with bootstrap were also developed to characterize the differential expression patterns. Furthermore, trajectory-clustering approaches were developed in order to investigate the dynamic patterns and inter-dependency of drug treatment effects on gene expression. Results: Results show that the presented methods performed significant differently but all were adequate in capturing a small number of the potentially relevant genes to the disease. The parametric method, such as the mixed model and two Bayesian approaches proved to be more conservative. This may because these methods are based on overall variation in expression across all time points. The semiparametric (class dispersion) and non-parametric (Pareto) methods were appropriate in capturing variation in expression from time point to time point, thereby making them more suitable for investigating significant monotonic changes and trajectories of changes in gene exp

关键词： SINGULAR-VALUE DECOMPOSITION MULTIPLE-SCLEROSIS microarray data STATISTICAL-METHODS CLASSIFICATION MODELS

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Significance analysis of functional categories in gene expression studies: a structured permutation approach

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BIOINFORMATICS 2005年第9期21卷 1943-1949页

作者： Barry, WT Nobel, AB Wright, FA Univ N Carolina Dept Biostat Chapel Hill NC 27599 USA Univ N Carolina Dept Stat Chapel Hill NC 27599 USA

Motivation: In high-throughput genomic and proteomic experiments, investigators monitor expression across a set of experimental conditions. To gain an understanding of broader biological phenomena, researchers have until recently been limited to post hoc analyses of significant gene lists. Method: We describe a general framework, significance analysis of function and expression (SAFE), for conducting valid tests of gene categories ab initio. SAFE is a two-stage, permutation-based method that can be applied to various experimental designs, accounts for the unknown correlation among genes and enables permutation-based estimation of error rates. Results: The utility and flexibility of SAFE is illustrated with a microarray dataset of human lung carcinomas and gene categories based on Gene Ontology and the Protein Family database. Significant gene categories were observed in comparisons of (1) tumor versus normal tissue, (2) multiple tumor subtypes and (3) survival times.

关键词： FALSE DISCOVERY RATE HUMAN LUNG-CANCER PANCREATIC-CANCER microarray data ADENOCARCINOMA PATTERNS ONTOLOGY dataBASE TARGET CELLS

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Mixture model on the variance for the differential analysis of gene expression data

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2005年第1期54卷 31-50页

作者： Delmar, P Robin, S Tronik-Le Roux, D Daudin, JJ Lab Fournier SA F-21121 Daix France Ecole Cent Paris Chatenay Malabry France INRA Paris France Commisariat Energie Atom Evry France

In microarray experiments, accurate estimation of the gene variance is a key step in the identification of differentially expressed genes. Variance models go from the too stringent homoscedastic assumption to the overparameterized model assuming a specific variance for each gene. Between these two extremes there is some room for intermediate models. We propose a method that identifies clusters of genes with equal variance. We use a mixture model on the gene variance distribution. A test statistic for ranking and detecting differentially expressed genes is proposed. The method is illustrated with publicly available complementary deoxyribonucleic acid microarray experiments, an unpublished data set and further simulation studies.

关键词： differential analysis gene expression microarray data mixture model simulated microarray data variance modelling

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Computational cluster validation in post-genomic data analysis

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BIOINFORMATICS 2005年第15期21卷 3201-3212页

作者： Handl, J Knowles, J Kell, DB Univ Manchester Sch Chem Manchester M60 1QD Lancs England

Motivation: The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge-whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. Results: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. Availability: The software used in the experiments is available at http://***/handl/clustervalidation/ Contact: ***@*** Supplementary information: Enlarged colour plots are provided in the Supplementary Material, which is available at http://***/handl/clustervalidation/

关键词： GENE-EXPRESSION data microarray data MOLECULAR CLASSIFICATION PATTERNS VALIDITY PREDICTION CRITERIA NETWORK INDEXES NUMBER

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Spatial analysis of cDNA microarray experiments

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CROP SCIENCE 2005年第2期45卷 748-757页

作者： Burgueño, J Crossa, J Grimanelli, D Leblanc, O Autran, D CIMMYT Biometr & Stat Unit Int Maize & Wheat Improvement Ctr Mexico City 06600 DF Mexico IRD Mexico City 06501 DF Mexico

microarray experiments allow RNA level measurements for many genes in multiple samples. However, mining the biological information from the large sets of data generated by microarrays requires the use of appropriate statistical methods to adjust the observed values for experimentally introduced variability (normalization process) before testing differences among samples. Normalization of microarray experiments is a critical step for reducing false positives and false negatives. This paper explores the normalization of cDNA microarray experiments by a method that uses the blank spot intensity values to make spatial adjustment (SA) of both foreground and background DNA spot intensity values, by fitting an autoregressive mixed linear model through the residual maximum likelihood (REML) methodology in the direction of the rows and the columns of the microarray. Application of this spatial normalization to three cDNA array experiments serves as a case study to validate the SA. Results show that the spatial analysis allows selection of candidate genes with lesser numbers of false positive and false negative genes.

关键词： .microarrays spatial analysis normalization candidate genes REML autoregressive Mixed linear microarray data positives foreground false positive false negative intensity values

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A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data

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BIOINFORMATICS 2005年第13期21卷 3025-3033页

作者： Teschendorff, AE Wang, YZ Barbosa-Morais, NL Brenton, JD Caldas, C Univ Cambridge Hutchinson MRC Res Ctr Canc Genom Program Dept Oncol Cambridge CB2 2XZ England

Motivation: Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem. Results: We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion. Using simulated data, we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using freely available tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturing biologically relevant structure.

关键词： microarray data CLASS DISCOVERY BREAST-CANCER CLASSIFICATION PATTERNS

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