GEPS: the Gene Expression Pattern Scanner

Gene Expression Pattern Scanner (GEPS) is a web-based server to provide interactive pattern analysis of user-submitted microarray data for facilitating their further interpretation. Putative gene expression patterns such as correlated expression, similar expression and specific expression are determined globally and systematically using geometric comparison and correlation analysis methods. These patterns can be visualized via linear plot with quantitative measures. User-defined threshold value is allowed to customize the format of the pattern search results. For better understanding of gene expression, patterns derived from 329 205 non-redundant gene expression records from the GNF SymAltas and the Gene Expression Omnibus are also provided. These profiles cover 24 277 human genes in 79 tissues, 32 905 mouse genes in 61 tissues and 4201 rat genes in 44 tissues. GEPS is available at

Metabolic syndrome influences cardiac gene expression pattern at the transcript level in male ZDF rats

Background: Metabolic syndrome (coexisting visceral obesity, dyslipidemia, hyperglycemia, and hypertension) is a prominent risk factor for cardiovascular morbidity and mortality, however, its effect on cardiac gene expression pattern is unclear. Therefore, we examined the possible alterations in cardiac gene expression pattern in male Zucker Diabetic Fatty (ZDF) rats, a model of metabolic syndrome. Methods: Fasting blood glucose, serum insulin, cholesterol and triglyceride levels were measured at 6, 16, and 25 wk of age in male ZDF and lean control rats. Oral glucose tolerance test was performed at 16 and 25 wk of age. At week 25, total RNA was isolated from the myocardium and assayed by rat oligonucleotide microarray for 14921 genes. Expression of selected genes was confirmed by qRT-PCR. Results: Fasting blood glucose, serum insulin, cholesterol and triglyceride levels were significantly increased, glucose tolerance and insulin sensitivity were impaired in ZDF rats compared to leans. In hearts of ZDF rats, 36 genes showed significant up-regulation and 49 genes showed down-regulation as compared to lean controls. Genes with significantly altered expression in the heart due to metabolic syndrome includes functional clusters of metabolism (e.g. 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 2; argininosuccinate synthetase; 2-amino-3ketobutyrate-coenzyme A ligase), structural proteins (e.g. myosin IXA; aggrecan1), signal transduction (e. g. activating transcription factor 3; phospholipase A2; insulin responsive sequence DNA binding protein-1) stress response (e.g. heat shock 70kD protein 1A; heat shock protein 60; glutathione S-transferase Yc2 subunit), ion channels and receptors (e.g. ATPase, (Na+)/K+ transporting, beta 4 polypeptide; ATPase, H+/K+ transporting, nongastric, alpha polypeptide). Moreover some other genes with no definite functional clusters were also changed such as e. g. S100 calcium binding protein A3; ubiquitin carboxy-terminal hydrolase L1; interleukin 18. Gene ontology analysis revealed several significantly enriched functional inter-relationships between genes influenced by metabolic syndrome. Conclusions: Metabolic syndrome significantly alters cardiac gene expression profile which may be involved in development of cardiac pathologies in the presence of metabolic syndrome

Gene expression pattern analysis

Microarray technology provides approach to measure the expression levels of large number of genes simultaneously and to look insight into the transcriptional state of the cell. It can be used for searching for co-expressed genes under certain conditions. As such, it has become a powerful tool in genetic network research and functional genomics. Meanwhile, the technology produces large amounts of data and the data interpretation becomes a major bottleneck. In this study, public yeast gene expression data is analyzed by Principal Components Analysis (PCA), Hierarchical Clustering, Self Organizing Mapping (SOM) and Adaptive Resonance Theory 2 (ART-2). The four statistical methods are also applied to maize chloroplast protein expression data in greening process. PCA can reduce the dimensionality of data set. The first few components contain most variance in the data and represent meaningful expression patterns. ART-2, a neural network method is for the first time applied to gene expression analysis in our study. ART-2 provides very good clustering quality. Compared with Hierarchical Clustering and SOM, ART-2 is not limited by the rigid structure of Hierarchical Clustering and is not required to determine the clustering number in the beginning such as SOM. ART-2 has ability to deal with noise in the data and is easy to implement and interpret the result. The algorithm is also fast and scalable

Associative memory in gene regulation networks

The pattern of gene expression in the phenotype of an organism is determined in part by the dynamical attractors of the organism’s gene regulation network. Changes to the connections in this network over evolutionary time alter the adult gene expression pattern and hence the fitness of the organism. However, the evolution of structure in gene expression networks (potentially reflecting past selective environments) and its affordances and limitations with respect to enhancing evolvability is poorly understood in general. In this paper we model the evolution of a gene regulation network in a controlled scenario. We show that selected changes to connections in the regulation network make the currently selected gene expression pattern more robust to environmental variation. Moreover, such changes to connections are necessarily ‘Hebbian’ – ‘genes that fire together wire together’ – i.e. genes whose expression is selected for in the same selective environments become co-regulated. Accordingly, in a manner formally equivalent to well-understood learning behaviour in artificial neural networks, a gene expression network will therefore develop a generalised associative memory of past selected phenotypes. This theoretical framework helps us to better understand the relationship between homeostasis and evolvability (i.e. selection to reduce variability facilitates structured variability), and shows that, in principle, a gene regulation network has the potential to develop ‘recall’ capabilities normally reserved for cognitive systems

A Field Approach to 3D Gene Expression Pattern Characterization

We present a vector field method for obtaining the spatial organization of 3D patterns of gene expression based on gradients and lines of force obtained by numerical integration. The convergence of these lines of force in local maxima are centers of gene expression, providing a natural and powerful framework to characterize the organization and dynamics of biological structures. We apply this novel methodology to analyze the expression pattern of the Enhanced Green Fluorescent Protein (EGFP) driven by the promoter of light chain myosin II during zebrafish heart formation.Comment: 9 pages, 2 figures, The following article has been submitted to Applied Physics Letters. If it is published, it will be found online at http://apl.aip.or

The PRMT1 gene expression pattern in colon cancer

The methylation of arginine has been implicated in many cellular processes, such as regulation of transcription, mRNA splicing, RNA metabolism and transport. The enzymes responsible for this modification are the protein arginine methyltransferases. The most abundant methyltransferase in human cells is protein arginine methyltransferase 1. Methylation processes appear to interfere in the emergence of several diseases, including cancer. During our study, we examined the expression pattern of protein arginine methyltransferase 1 gene in colon cancer patients. The emerging results showed that the expression of one of the gene variants is associated with statistical significant probability to clinical and histological parameters, such as nodal status and stage. This is a first attempt to acquire an insight on the possible relation of the expression pattern of protein arginine methyltransferase 1 and colon cancer progression

Hypoxia-induced gene expression pattern in doxorubicin resistant MCF7 cells

Purpose: To investigate hypoxia-induced gene expression pattern in doxorubicin-resistant human breast cancer cells (MCF7). Methods: Human breast cancer cells (MCF7) were exposed to 60 episodes of 8 h hypoxia thrice a week for three months. Chemo-resistance to doxorubicin was assessed using 3-(4,5-dimethylthiazol-2- yl)-2, 5-diphenyl tetrazolium bromide (MTT) cell proliferation assay. Real-time quantitative polymerase chain reaction (qRT-PCR) assay was performed to assess gene expression pattern in doxorubicinresistant cells on exposure to hypoxia. Results: Hypoxia significantly increased the resistance of MCF7 cells to doxorubicin, with a maximum of 16.42-fold enhancement after 25 episodes of 8-h hypoxia, while the resistance thereafter significantly decreased with prolonged episodes of hypoxia (p < 0.05). Gene expression analysis revealed significant changes in 42 genes. The expressions of 10 of these genes were significantly upregulated, while those of 32 genes were significantly down-regulated (p < 0.05). Cytochrome P450 family 1, subfamily A, member1 (CYP1A1) was the most conspicuous upregulated gene (13.32-fold), while breast cancer gene 1 (BRCA1) was the most down-regulated (8.23-fold). Gene expression analysis after 60 episodes of 8-h hypoxia revealed the upregulation of CYP1A1 (5.77-fold). Similarly, 27 genes were significantly down-regulated, with BRCA2 as the most down-regulated gene (8.11-fold). Topoisomerase (DNA) II alpha (TOP2A) was the most down-regulated among genes involved in drug metabolism and resistance (6.37-fold), while cyclin-dependent kinase 2 (CDK2) was the most profoundly downregulated among genes involved in cell cycle regulation (3.56-fold). Conclusion: These results indicate that development of resistance to doxorubicin by MCF7 cells after short-term hypoxia results from the upregulation of genes responsible for the metabolism of doxorubicin and for shifting the cells to alternative pathway driven principally by EGF and ESR2. The observed down-regulation is an adaptation of the MCF7 cells to survive under long-term hypoxia

Dissecting Sources of Quantitative Gene Expression Pattern Divergence Between Drosophila Species

The function of a transcriptional circuit is compared in three closely related species of Drosophila. Using quantitative imaging of gene expression, targeted transgenic reporter fly lines, and a computational framework, the sources of their differing expression outputs are identified

Human Cardiac-Specific cDNA Array for Idiopathic Dilated Cardiomyopathy: Sex-Related Differences

Idiopathic dilated cardiomyopathy (IDCM) constitutes a large portion of patients with heart failure of unknown etiology. Up to 50% of all transplant recipients carry this clinical diagnosis. Female-specific gene expression in IDCM has not been explored. We report sex-related differences in the gene expression profile of ventricular myocardium from patients undergoing cardiac transplantation. We produced and sequenced subtractive cDNA libraries, using human left ventricular myocardium obtained from male transplant recipients with IDCM and nonfailing human heart donors. With the resulting sequence data, we generated a custom human heart failure microarray for IDCM containing 1,145 cardiac-specific oligonucleotide probes. This array was used to characterize RNA samples from female IDCM transplant recipients. We identified a female gene expression pattern that consists of 37 upregulated genes and 18 downregulated genes associated with IDCM. Upon functional analysis of the gene expression pattern, deregulated genes unique to female IDCM were those that are involved in energy metabolism and regulation of transcription and translation. For male patients we found deregulation of genes related to muscular contraction. These data suggest that 1) the gene expression pattern we have detected for IDCM may be specific for this disease and 2) there is a sex-specific profile to IDCM. Our observations further suggest for the first time ever novel targets for treatment of IDCM in women and men