Gene expression profiles of homogentisate-treated Fah−/− Hpd−/−mice using DNA microarrays

2006 ◽  
Vol 89 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Yasuhiko Tanaka ◽  
Kimitoshi Nakamura ◽  
Shirou Matsumoto ◽  
Yoshiko Kimoto ◽  
Akito Tanoue ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Gene Expression Profiles

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

Monitoring expression of genes involved in drug metabolism and toxicology using DNA microarrays

2001 ◽  
Vol 5 (4) ◽  
pp. 161-170 ◽  
Author(s):  
DAVID GERHOLD ◽  
MEIQING LU ◽  
JIAN XU ◽  
CHRISTOPHER AUSTIN ◽  
C. THOMAS CASKEY ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Metabolism ◽  
Stress Responses ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Metabolic Effects ◽  
Microarray Technology ◽  
Drug Candidates ◽  
Expression Of Genes

Oligonucleotide DNA microarrays were investigated for utility in measuring global expression profiles of drug metabolism genes. This study was performed to investigate the feasibility of using microarray technology to minimize the long, expensive process of testing drug candidates for safety in animals. In an evaluation of hybridization specificity, microarray technology from Affymetrix distinguished genes up to a threshold of ∼90% DNA identity. Oligonucleotides representing human cytochrome P-450 gene CYP3A5 showed heterologous hybridization to CYP3A4 and CYP3A7 RNAs. These genes could be clearly distinguished by selecting a subset of oligonucleotides that hybridized selectively to CYP3A5. Further validation of the technology was performed by measuring gene expression profiles in livers of rats treated with vehicle, 3-methylcholanthrene (3MC), phenobarbital, dexamethasone, or clofibrate and by confirming data for six genes using quantitative RT-PCR. Responses of drug metabolism genes, including CYPs, epoxide hydrolases ( EHs), UDP-glucuronosyl transferases ( UGTs), glutathione sulfotransferases ( GSTs), sulfotransferases ( STs), drug transporter genes, and peroxisomal genes, to these well-studied compounds agreed well with, and extended, published observations. Additional gene regulatory responses were noted that characterize metabolic effects or stress responses to these compounds. Thus microarray technology can provide a facile overview of gene expression responses relevant to drug metabolism and toxicology.

scholarly journals Environment and vascular bed origin influence differences in endothelial transcriptional profiles of coronary and iliac arteries

2010 ◽  
Vol 299 (3) ◽  
pp. H837-H846 ◽  
Author(s):  
Kelley A. Burridge ◽  
Morton H. Friedman
Keyword(s):  
Gene Expression ◽  
Cell Biology ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Local Environment ◽  
Epigenetic Mechanism ◽  
Transcriptional Profiles ◽  
Vascular Bed

Atherosclerotic plaques tend to form in the major arteries at certain predictable locations. As these arteries vary in atherosusceptibility, interarterial differences in endothelial cell biology are of considerable interest. To explore the origin of differences observed between typical atheroprone and atheroresistant arteries, we used DNA microarrays to compare gene expression profiles of harvested porcine coronary (CECs) and iliac artery endothelial cells (IECs) grown in static culture out to passage 4. Fewer differences were observed between the transcriptional profiles of CECs and IECs in culture compared with in vivo, suggesting that most differences observed in vivo were due to distinct environmental cues in the two arteries. One-class significance of microarrays revealed that most in vivo interarterial differences disappeared in culture, as fold differences after passaging were not significant for 85% of genes identified as differentially expressed in vivo at 5% false discovery rate. However, the three homeobox genes, HOXA9, HOXA10, and HOXD3, remained underexpressed in coronary endothelium for all passages by at least nine-, eight-, and twofold, respectively. Continued differential expression, despite removal from the in vivo environment, suggests that primarily heritable or epigenetic mechanism(s) influences transcription of these three genes. Quantitative real-time polymerase chain reaction confirmed expression ratios for seven genes associated with atherogenesis and over- or underexpressed by threefold in CECs relative to IECs. The present study provides evidence that both local environment and vascular bed origin modulate gene expression in arterial endothelium. The transcriptional differences observed here may provide new insights into pathways responsible for coronary artery susceptibility.

Metabolic and transcriptional patterns accompanying glutamine depletion and repletion in mouse hepatoma cells: a model for physiological regulatory networks

2004 ◽  
Vol 16 (2) ◽  
pp. 247-255 ◽  
Author(s):  
Matthew S. Wong ◽  
R. Michael Raab ◽  
Isidore Rigoutsos ◽  
Gregory N. Stephanopoulos ◽  
Joanne K. Kelleher
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Dna Microarrays ◽  
Cell Function ◽  
De Novo ◽  
Expression Profiles ◽  
Mammalian Cell Culture ◽  
Gene Expression Profiles ◽  
Tca Cycle ◽  
Glycolytic Flux

An important objective in postgenomic biology is to link gene expression to function by developing physiological networks that include data from the genomic and functional levels. Here, we develop a model for the analysis of time-dependent changes in metabolites, fluxes, and gene expression in a hepatic model system. The experimental framework chosen was modulation of extracellular glutamine in confluent cultures of mouse Hepa1-6 cells. The importance of glutamine has been demonstrated previously in mammalian cell culture by precipitating metabolic shifts with glutamine depletion and repletion. Our protocol removed glutamine from the medium for 24 h and returned it for a second 24 h. Flux assays of glycolysis, the tricarboxylic acid (TCA) cycle, and lipogenesis were used at specified intervals. All of these fluxes declined in the absence of glutamine and were restored when glutamine was repleted. Isotopomer spectral analysis identified glucose and glutamine as equal sources of lipogenic carbon. Metabolite measurements of organic acids and amino acids indicated that most metabolites changed in parallel with the fluxes. Experiments with actinomycin D indicated that de novo mRNA synthesis was required for observed flux changes during the depletion/repletion of glutamine. Analysis of gene expression data from DNA microarrays revealed that many more genes were anticorrelated with the glycolytic flux and glutamine level than were correlated with these indicators. In conclusion, this model may be useful as a prototype physiological regulatory network where gene expression profiles are analyzed in concert with changes in cell function.

COMPARING PEARSON, SPEARMAN AND HOEFFDING'S D MEASURE FOR GENE EXPRESSION ASSOCIATION ANALYSIS

2009 ◽  
Vol 07 (04) ◽  
pp. 663-684 ◽  
Author(s):  
ANDRÉ FUJITA ◽  
JOÃO RICARDO SATO ◽  
MARCOS ANGELO ALMEIDA DEMASI ◽  
MARI CLEIDE SOGAYAR ◽  
CARLOS EDUARDO FERREIRA ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
General Sense ◽  
Nonlinear Associations ◽  
Definition Of ◽  
Spearman’S Correlation

DNA microarrays have become a powerful tool to describe gene expression profiles associated with different cellular states, various phenotypes and responses to drugs and other extra- or intra-cellular perturbations. In order to cluster co-expressed genes and/or to construct regulatory networks, definition of distance or similarity between measured gene expression data is usually required, the most common choices being Pearson's and Spearman's correlations. Here, we evaluate these two methods and also compare them with a third one, namely Hoeffding's D measure, which is used to infer nonlinear and non-monotonic associations, i.e. independence in a general sense. By comparing three different variable association approaches, namely Pearson's correlation, Spearman's correlation and Hoeffding's D measure, we aimed at assessing the most approppriate one for each purpose. Using simulations, we demonstrate that the Hoeffding's D measure outperforms Pearson's and Spearman's approaches in identifying nonlinear associations. Our results demonstrate that Hoeffding's D measure is less sensitive to outliers and is a more powerful tool to identify nonlinear and non-monotonic associations. We have also applied Hoeffding's D measure in order to identify new putative genes associated with tp53. Therefore, we propose the Hoeffding's D measure to identify nonlinear associations between gene expression profiles.

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