Analysis of variance for gene expression microarray data.

Application of the S-score algorithm for analysis of oligonucleotide microarrays

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Gene Expression Profiles During Hepatic Stellate Cell Activation in Culture and In Vivo

Reproducibility of microarray studies: concordance of current analysis methods

Resveratrol Delays Age-Related Deterioration and Mimics Transcriptional Aspects of Dietary Restriction without Extending Life Span

BackgroundMicroRNAs (miRNAs) are a class of small RNAs that have been linked to a number of diseases including cancer. The potential application of miRNAs in the diagnostics and therapeutics of ovarian and other cancers is an area of intense interest. A current challenge is the inability to accurately predict the functional consequences of exogenous modulations in the levels of potentially therapeutic miRNAs.MethodsIn an initial effort to systematically address this issue, we conducted miRNA transfection experiments using two miRNAs (miR-7, miR-128). We monitored the consequent changes in global patterns of gene expression by microarray and quantitative (real-time) polymerase chain reaction. Network analysis of the expression data was used to predict the consequence of each transfection on cellular function and these predictions were experimentally tested.ResultsWhile ~20% of the changes in expression patterns of hundreds to thousands of genes could be attributed to direct miRNA-mRNA interactions, the majority of the changes are indirect, involving the downstream consequences of miRNA-mediated changes in regulatory gene expression. The changes in gene expression induced by individual miRNAs are functionally coordinated but distinct between the two miRNAs. MiR-7 transfection into ovarian cancer cells induces changes in cell adhesion and other developmental networks previously associated with epithelial-mesenchymal transitions (EMT) and other processes linked with metastasis. In contrast, miR-128 transfection induces changes in cell cycle control and other processes commonly linked with cellular replication.ConclusionsThe functionally coordinated patterns of gene expression displayed by different families of miRNAs have the potential to provide clinicians with a strategy to treat cancers from a systems rather than a single gene perspective.

In the past decade, DNA microarrays have fundamentally changed the way we study complex biological systems. By measuring the expression levels of thousands of transcripts, the paradigm of studying organisms has shifted from focusing on the local phenomena of a few genes to surveying the whole genome. DNA microarrays are used in a variety of ways, from simple comparisons between two samples to more intricate time-series studies. With the large number of genes being studied, the dimensionality of the problem is inevitably high. The analysis of microarray data thus requires specific approaches. In the case of time-series microarray studies, data analysis is further complicated by the correlation between successive time points in a series.In this review, we survey the methodologies used in the analysis of static and time-series microarray data, covering data pre-processing, identification of differentially expressed genes, profile pattern recognition, pathway analysis, and network reconstruction. When available, examples of their use in mammalian cell cultures are presented.

Protein kinase C (PKC) isozymes are key signal transducers involved in normal physiology and disease and have been widely implicated in cancer progression. Despite our extensive knowledge of the signaling pathways regulated by PKC isozymes and their effectors, there is essentially no information on how individual members of the PKC family regulate gene transcription. Here, we report the first PKC isozyme-specific analysis of global gene expression by microarray using RNAi depletion of diacylglycerol/phorbol ester-regulated PKCs. A thorough analysis of this microarray data revealed unique patterns of gene expression controlled by PKCα, PKCδ, and PKCε, which are remarkably different in cells growing in serum or in response to phorbol ester stimulation. PKCδ is the most relevant isoform in controlling the induction of genes by phorbol ester stimulation, whereas PKCε predominantly regulates gene expression in serum. We also established that two PKCδ-regulated genes, FOSL1 and BCL2A1, mediate the apoptotic effect of phorbol esters or the chemotherapeutic agent etoposide in prostate cancer cells. Our studies offer a unique opportunity for establishing novel transcriptional effectors for PKC isozymes and may have significant functional and therapeutic implications.

Cytoscape is a bioinformatic data analysis and visualization platform that is well-suited to the analysis of gene expression data. To facilitate the analysis of yeast microarray data using Cytoscape, we constructed an interaction network (interactome) using the curated interaction data available from the Saccharomyces Genome Database (www.yeastgenome.org) and the database of yeast transcription factors at YEASTRACT (www.yeastract.com). These data were formatted and imported into Cytoscape using semi-automated methods, including Linux-based scripts, that simplified the process while minimizing the introduction of processing errors. The methods described for the construction of this yeast interactome are generally applicable to the construction of any interactome. Using Cytoscape, we illustrate the use of this interactome through the analysis of expression data from a recent yeast diauxic shift experiment. We also report and briefly describe the complex associations among transcription factors that result in the regulation of thousands of genes through coordinated changes in expression of dozens of transcription factors. These cells are thus able to sensitively regulate cellular metabolism in response to changes in genetic or environmental conditions through relatively small changes in the expression of large numbers of genes, affecting the entire yeast metabolome.

Postmortem and neuroimaging studies show low levels of cortical muscarinic M1 receptors (CHRM1) in patients with schizophrenia which is significant because CHRM signalling has been shown to change levels of gene expression and cortical gene expression is altered in schizophrenia. We decided to identify CHRM1-mediated changes in cortical gene expression by measuring levels of RNA in the cortex of the Chrm1−/− mouse (n = 10), where there would be no signalling by that receptor, and in wild type mouse (n = 10) using the Affymetrix Mouse Exon 1.0 ST Array. We detected RNA for 15,501 annotated genes and noncoding RNA of which 1,467 RNAs were higher and 229 RNAs lower in the cortex of the Chrm1−/− mouse. Pathways and proteins affected by the changes in cortical gene expression in the Chrm1−/− are linked to the molecular pathology of schizophrenia. Our human cortical gene expression data showed 47 genes had altered expression in Chrm1−/− mouse and the frontal pole from patients with schizophrenia with the change in expression of 44 genes being in opposite directions. In addition, genes with altered levels of expression in the Chrm1−/− mouse have been shown to affect amyloid precursor protein processing which is associated with the pathophysiology of Alzheimer’s disease, and 69 genes with altered expression in the Chrm1−/− mouse are risk genes associated with human cognitive ability. Our findings argue CHRM1-mediated changes in gene expression are relevant to the pathophysiologies of schizophrenia and Alzheimer’s disease and the maintenance of cognitive ability in humans.

The use of a constant fold-change to determine significant changes in gene expression has been widely accepted for its intuition and ease of use in microarray data analysis, but this concept has been increasingly criticized because it does not reflect signal intensity and can result in a substantial number of false positives and false negatives. To resolve this dilemma, we have analyzed 65 replicate Affymetrix chip-chip comparisons and determined a series of user adjustable signal-dependent thresholds which do not require replicates and offer a 95% confidence interval. Quantitative RT-PCR shows that such thresholds significantly improve the power to discriminate biological changes in mRNA from noise and reduce false calls compared to the traditional two-fold threshold. The user-friendly nature of this approach means that it can be easily applied by any user of microarray analysis, even those without any specialized knowledge of computational techniques or statistics. Noise is a function of signal intensity not only for Affymetrix data but also for cDNA array data, analysis of which may also be benefited by our methodology.

A new tool for rheumatology: large-scale analysis of gene expression

BackgroundRhodospirillum centenum is a photosynthetic member of the Gram-negative Azospirillum clade members of which exhibit a complex developmental life-cycle featuring morphologically distinct cell types. Under periods of nutrient deprivation, replicative vegetative cells differentiate into metabolically dormant cysts that survive harsh environmental stresses such as desiccation. Encystment involves a multi-stage developmental process that includes the rounding of cells, production of large intracellular storage granules of poly-hydroxybutyrate (PHB) and the excretion of a protective exopolysaccharide coating that envelops dormant cysts.ResultsTo study the process of cyst development, we performed RNA-seq studies on cells that were induced to undergo cyst development. To assay for temporal changes in gene expression, RNA was extracted at 4, 24, 48, 72, 96 hours during development and subjected to deep sequence analysis. These results show that 812 genes exhibit log2 ≥ 1.5-fold changes in expression over a 96 hour cyst induction period demonstrating large global changes in gene expression during cyst development.ConclusionsNotable changes in expression occurred in numerous genes involved in cell wall and lipid biosynthesis, metabolic enzymes, and numerous regulatory genes such as histidine kinases and transcription factors. Many genes involved in protein synthesis and DNA replication were also significantly reduced during late stages of cyst development. Genes previously identified by genetic screens as being critical for cyst development also exhibited changes of expression during cyst induction. This study provides the first transcriptome profile of global changes in gene expression that occur during development of cysts in a Gram-negative species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1250-9) contains supplementary material, which is available to authorized users.

DOI: 10.1111/j.1474-9726.2009.00534.x After publication of Cao et al. (2010) the authors would like to include their microarray data as part of the Supporting Information for the article. The updated list of Supporting Information available for this article is as follows: Supporting Information Additional supporting information may be found in the online version of this article: Fig. S1 Hierarchical clustering heat map of gene expression changes associated with aging and GO biological processes. Fig. S2 Age-associated gene expression changes involved in T-cell signaling transduction pathways as generated by Ingenuity Pathway analysis. a, b, c, d, e, f, g, h: left panel, pathways with age-associated gene expression changes denoted by green, down-, and red, up-regulated genes (detailed gene information, supporting Table S5); right panel, heat maps of expression changes for component genes. I: symbol definitions. Table S1 A gene list of 754 genes (897 genes IDs, viz., some genes have more than two gene IDs) presenting expression changes associated with aged CD8+ T cells. Table S2-1 List of genes with significant monotonic age-associated expression changes from five young, two 65-year and two 80-year-old donor groups, with corresponding functional categories. Table S2-2 Pearson’s correlation analyses of genes within each biological process with monotonic age-associated expression changes. Table S3 A list of gene expression fold-changes as analyzed by microarray and qRT-PCR. Table S4 A list of age-associated gene expression changes involved in gene transcription processes. Table S5 A list of age-associated gene expression changes involved in signal transduction pathways. Table S6 Expression levels of all genes of each donor, as determined using HG-U133A_2 Gene Array chips and normalized by gc-RMA. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer-reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.

Magnocellular neurons (MCNs) in the hypothalamo-neurohypophysial system (HNS) are highly specialized to release large amounts of arginine vasopressin (Avp) or oxytocin (Oxt) into the blood stream and play critical roles in the regulation of body fluid homeostasis. The MCNs are osmosensory neurons and are excited by exposure to hypertonic solutions and inhibited by hypotonic solutions. The MCNs respond to systemic hypertonic and hypotonic stimulation with large changes in the expression of their Avp and Oxt genes, and microarray studies have shown that these osmotic perturbations also cause large changes in global gene expression in the HNS. In this paper, we examine gene expression in the rat supraoptic nucleus (SON) under normosmotic and chronic salt-loading SL) conditions by the first time using “new-generation”, RNA sequencing (RNA-Seq) methods. We reliably detect 9,709 genes as present in the SON by RNA-Seq, and 552 of these genes were changed in expression as a result of chronic SL. These genes reflect diverse functions, and 42 of these are involved in either transcriptional or translational processes. In addition, we compare the SON transcriptomes resolved by RNA-Seq methods with the SON transcriptomes determined by Affymetrix microarray methods in rats under the same osmotic conditions, and find that there are 6,466 genes present in the SON that are represented in both data sets, although 1,040 of the expressed genes were found only in the microarray data, and 2,762 of the expressed genes are selectively found in the RNA-Seq data and not the microarray data. These data provide the research community a comprehensive view of the transcriptome in the SON under normosmotic conditions and the changes in specific gene expression evoked by salt loading.