Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

Abstract Background A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions. Results Through evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratio's, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV) or ionizing radiation (IR)-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying biological processes affected by IR- and/or UV- induced DNA damage. Conclusion EPIG competed with CLICK and performed better than CAST in extracting patterns from simulated data. EPIG extracted more biological informative patterns and co-expressed genes from both C. elegans and IR/UV-treated human fibroblasts. Using Gene Ontology analysis of the genes in the patterns extracted by EPIG, several key biological categories related to p53-dependent cell cycle control were revealed from the IR/UV data. Among them were mitotic cell cycle, DNA replication, DNA repair, cell cycle checkpoint, and G0-like status transition. EPIG can be applied to data sets from a variety of experimental designs

Transcript Profiling Identifies Dynamic Gene Expression Patterns and an Important Role for Nrf2/Keap1 Pathway in the Developing Mouse Esophagus

Morphological changes during human and mouse esophageal development have been well characterized. However, changes at the molecular level in the course of esophageal morphogenesis remain unclear. This study aims to globally profile critical genes and signaling pathways during the development of mouse esophagus. By using microarray analysis this study also aims to determine how the Nrf2/Keap1 pathway regulates the morphogenesis of the esophageal epithelium.Gene expression microarrays were used to survey gene expression in the esophagus at three critical phases: specification, metaplasia and maturation. The esophagi were isolated from wild-type, Nrf2(-/-), Keap1(-/-), or Nrf2(-/-)Keap1(-/-) embryos or young adult mice. Array data were statistically analyzed for differentially expressed genes and pathways. Histochemical and immunohistochemical staining were used to verify potential involvement of the Wnt pathway, Pparβ/δ and the PI3K/Akt pathway in the development of esophageal epithelium.Dynamic gene expression patterns accompanied the morphological changes of the developing esophagus at critical phases. Particularly, the Nrf2/Keap1 pathway had a baseline activity in the metaplasia phase and was further activated in the maturation phase. The Wnt pathway was active early and became inactive later in the metaplasia phase. In addition, Keap1(-/-) mice showed increased expression of Nrf2 downstream targets and genes involved in keratinization. Microarray and immunostaining data also suggested that esophageal hyperkeratosis in the Keap1(-/-) mice was due to activation of Pparβ/δ and the PI3K/Akt pathway.Morphological changes of the esophageal epithelium are associated with dynamic changes in gene expression. Nrf2/Keap1 pathway activity is required for maturation of mouse esophageal epithelium

A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples

The TempO-SeqTM platform allows for targeted transcriptomic analysis and is currently used by many groups to perform high-throughput gene expression analysis. Herein we performed a comparison of gene expression characteristics measured using 45 purified RNA samples from the livers of rats exposed to chemicals that fall into one of five modes of action (MOAs). These samples have been previously evaluated using AffymetrixTM rat genome 230 2.0 microarrays and Illumina® whole transcriptome RNA-Seq. Comparison of these data with TempO-Seq analysis using the rat S1500+ beta gene set identified clear differences in the platforms related to signal to noise, root mean squared error, and/or sources of variability. Microarray and TempO-Seq captured the most variability in terms of MOA and chemical treatment whereas RNA-Seq had higher noise and larger differences between samples within a MOA. However, analysis of the data by hierarchical clustering, gene subnetwork connectivity and biological process representation of MOA-varying genes revealed that the samples clearly grouped by treatment as opposed to gene expression platform. Overall these findings demonstrate that the results from the TempO-Seq platform are consistent with findings on other more established approaches for measuring the genome-wide transcriptome

Altered gene expression and DNA damage in peripheral blood cells from Friedreich's ataxia patients: Cellular model of pathology

The neurodegenerative disease Friedreich's ataxia (FRDA) is the most common autosomal-recessively inherited ataxia and is caused by a GAA triplet repeat expansion in the first intron of the frataxin gene. In this disease, transcription of frataxin, a mitochondrial protein involved in iron homeostasis, is impaired, resulting in a significant reduction in mRNA and protein levels. Global gene expression analysis was performed in peripheral blood samples from FRDA patients as compared to controls, which suggested altered expression patterns pertaining to genotoxic stress. We then confirmed the presence of genotoxic DNA damage by using a gene-specific quantitative PCR assay and discovered an increase in both mitochondrial and nuclear DNA damage in the blood of these patients (p<0.0001, respectively). Additionally, frataxin mRNA levels correlated with age of onset of disease and displayed unique sets of gene alterations involved in immune response, oxidative phosphorylation, and protein synthesis. Many of the key pathways observed by transcription profiling were downregulated, and we believe these data suggest that patients with prolonged frataxin deficiency undergo a systemic survival response to chronic genotoxic stress and consequent DNA damage detectable in blood. In conclusion, our results yield insight into the nature and progression of FRDA, as well as possible therapeutic approaches. Furthermore, the identification of potential biomarkers, including the DNA damage found in peripheral blood, may have predictive value in future clinical trials

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes-5

<p><b>Copyright information:</b></p><p>Taken from "Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes"</p><p>http://www.biomedcentral.com/1471-2105/8/427</p><p>BMC Bioinformatics 2007;8():427-427.</p><p>Published online 2 Nov 2007</p><p>PMCID:PMC2194742.</p><p></p>ures from (A) to (F), there are four data points marked as crosses. The four data points from left to right correspond to inter-group 1 to 4, respectively. The labels of the vertical axis indicate the mean values of the data points. The vertical bars are the standard deviation of 0.4 to each of the mean values

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes-2

<p><b>Copyright information:</b></p><p>Taken from "Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes"</p><p>http://www.biomedcentral.com/1471-2105/8/427</p><p>BMC Bioinformatics 2007;8():427-427.</p><p>Published online 2 Nov 2007</p><p>PMCID:PMC2194742.</p><p></p> in Table 1. A) The patterns extracted by EPIG are labelled from 1 to 5 correspond to the distributions A to E, respectively. All profiles were categorized to their respective pattern. B) The pattern extracted by CLICK from Cluster 1 with 32 profiles assigned to it appears to have emerged from both distributions C and D in Table 1. The patterns for Clusters 2 and 3 correspond to distributions A and B in Table 1. The two clusters have 16 and 15 profiles assigned respectively

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes-3

<p><b>Copyright information:</b></p><p>Taken from "Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes"</p><p>http://www.biomedcentral.com/1471-2105/8/427</p><p>BMC Bioinformatics 2007;8():427-427.</p><p>Published online 2 Nov 2007</p><p>PMCID:PMC2194742.</p><p></p>eated. For each treatment, there were three individual cell lines, F1-HTERT, F3-HTERT and F10-HTERT, positioned from left to right. Each cell line consisted of eight data points with four different treatment conditions, i.e., sham-treatment and 2, 6, and 24 h post-treatment colored red, green, blue and magenta, respectively. The vertical axes with zero at the middle are the changes in gene expression (log2 intensity) relative to the sham-treated controls

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes-7

<p><b>Copyright information:</b></p><p>Taken from "Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes"</p><p>http://www.biomedcentral.com/1471-2105/8/427</p><p>BMC Bioinformatics 2007;8():427-427.</p><p>Published online 2 Nov 2007</p><p>PMCID:PMC2194742.</p><p></p> in Table 1. A) The patterns extracted by EPIG are labelled from 1 to 5 correspond to the distributions A to E, respectively. All profiles were categorized to their respective pattern. B) The pattern extracted by CLICK from Cluster 1 with 32 profiles assigned to it appears to have emerged from both distributions C and D in Table 1. The patterns for Clusters 2 and 3 correspond to distributions A and B in Table 1. The two clusters have 16 and 15 profiles assigned respectively

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes-0

<p><b>Copyright information:</b></p><p>Taken from "Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes"</p><p>http://www.biomedcentral.com/1471-2105/8/427</p><p>BMC Bioinformatics 2007;8():427-427.</p><p>Published online 2 Nov 2007</p><p>PMCID:PMC2194742.</p><p></p>ures from (A) to (F), there are four data points marked as crosses. The four data points from left to right correspond to inter-group 1 to 4, respectively. The labels of the vertical axis indicate the mean values of the data points. The vertical bars are the standard deviation of 0.4 to each of the mean values