Tool For Analysis Of Gene Expression Research Articles

A note on robustness of D-optimal block designs for two-colour microarray experiments

Two-colour microarray experiments form an important tool in gene expression analysis. Due to the high risk of missing observations in microarray experiments, it is fundamental to concentrate not only on optimal designs but also on designs which are robust against missing observations. As an extension of Latif et al. (2009), we define the optimal breakdown number for a collection of designs to describe the robustness, and we calculate the breakdown number for various D-optimal block designs. We show that, for certain values of the numbers of treatments and arrays, the designs which are D-optimal have the highest breakdown number. Our calculations use methods from graph theory.

Exploring Valid Reference Genes for Quantitative Real-time PCR Analysis in Plutella xylostella (Lepidoptera: Plutellidae)

: Quantitative real-time PCR (qRT-PCR), a primary tool in gene expression analysis, requires an appropriate normalization strategy to control for variation among samples. The best option is to compare the mRNA level of a target gene with that of reference gene(s) whose expression level is stable across various experimental conditions. In this study, expression profiles of eight candidate reference genes from the diamondback moth, Plutella xylostella, were evaluated under diverse experimental conditions. RefFinder, a web-based analysis tool, integrates four major computational programs including geNorm, Normfinder, BestKeeper, and the comparative ΔCt method to comprehensively rank the tested candidate genes. Elongation factor 1 (EF1) was the most suited reference gene for the biotic factors (development stage, tissue, and strain). In contrast, although appropriate reference gene(s) do exist for several abiotic factors (temperature, photoperiod, insecticide, and mechanical injury), we were not able to identify a single universal reference gene. Nevertheless, a suite of candidate reference genes were specifically recommended for selected experimental conditions. Our finding is the first step toward establishing a standardized qRT-PCR analysis of this agriculturally important insect pest.

A Resource for Manipulating Gene Expression and Analyzing cis-Regulatory Modules in the Drosophila CNS

Here, we describe the embryonic central nervous system expression of 5,000 GAL4 lines made using molecularly defined cis-regulatory DNA inserted into a single attP genomic location. We document and annotate the patterns in early embryos when neurogenesis is at its peak, and in older embryos where there is maximal neuronal diversity and the first neural circuits are established. We note expression in other tissues, such as the lateral body wall (muscle, sensory neurons, and trachea) and viscera. Companion papers report on the adult brain and larval imaginal discs, and the integrated data setsare available online (http://www.janelia.org/gal4-gen1). This collection of embryonically expressed GAL4 lines will be valuable for determining neuronal morphology and function. The 1,862 lines expressed in small subsets of neurons (<20/segment) will be especially valuable for characterizinginterneuronal diversity and function, because although interneurons comprise the majority of all central nervous system neurons, their gene expression profile and function remain virtually unexplored.

A novel biclustering approach with iterative optimization to analyze gene expression data.

With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions; that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically. We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overlap state to be explicitly considered. In addition, the average of the Pearson's correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented. BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms.

A-Optimal Designs for Two-Color Microarray Experiments for Treatment-Control and All-to-Next Contrasts

Two-color microarray experiments form an important tool in gene expression analysis. Due to the high costs of microarray experiments it is fundamental to plan these experiments carefully. Therefore, we propose a method to construct A-optimal block designs which can be adapted to construct nearly optimal microarray experiments. This method can be applied to derive A-optimal designs for arbitrary contrast settings. In this article, we focus on treatment control comparisons and all-to-next contrasts. We will show that our designs are more efficient than the designs currently in practice.

Abnormal p38 mitogen-activated protein kinase signaling in dilated cardiomyopathy caused by lamin A/C gene mutation

We previously interrogated the transcriptome in heart tissue from Lmna(H222P/H222P) mice, a mouse model of cardiomyopathy caused by lamin A/C gene (LMNA) mutation, and found that the extracellular signal-regulated kinase 1/2 and Jun N-terminal kinase branches of the mitogen-activated protein (MAP) kinase signaling pathway were abnormally hyperactivated prior to the onset of significant cardiac impairment. We have now used an alternative gene expression analysis tool to reanalyze this transcriptome and identify hyperactivation of a third branch of the MAP kinase cascade, p38α signaling. Biochemical analysis of hearts from Lmna(H222P/H222P) mice showed enhanced p38α activation prior to and after the onset of heart disease as well as in hearts from human subjects with cardiomyopathy caused by LMNA mutations. Treatment of Lmna(H222P/H222P) mice with the p38α inhibitor ARRY-371797 prevented left ventricular dilatation and deterioration of fractional shortening compared with placebo-treated mice but did not block the expression of collagen genes involved in cardiac fibrosis. These results demonstrate that three different branches of the MAP kinase signaling pathway with overlapping consequences are involved in the pathogenesis of cardiomyopathy caused by LMNA mutations. They further suggest that pharmacological inhibition of p38α may be useful in the treatment of this disease.

Chemical inducers of heat shock proteins derived from medicinal plants and cytoprotective genes response

Environmental stress induces damage that activates an adaptive response in any organism. The cellular stress response is based on the induction of cytoprotective proteins, the so-called stress or heat shock proteins (HSPs). HSPs are known to function as molecular chaperones which are involved in the therapeutic approach of many diseases. Therefore in the current study we searched nontoxic chaperone inducers in chemical compounds isolated from medicinal plants. Screening of 80 compounds for their Hsp70-inducing activity in human lymphoma U937 cells was performed by western blotting. Five compounds showed significant Hsp70 up-regulation among them shikonin was most potent. Shikonin was able to induce Hsp70 at 0.1 µM after 3 h without activation of heat shock transcription factor 1 (HSF-1). It also induces significant reactive oxygen species generation. The expression level of genes responsive to shikonin was studied using global-scale microarrays and computational gene expression analysis tools. Significant increase in the nuclear factor erythroid 2-related factor 2 (Nrf2, NFEL2L2) -mediated oxidative stress response was observed that leads to the activation of HSP. The results of gene chip analysis were further confirmed by real-time qPCR assay. In short, the detailed mechanisms of Hsp70 induction by shikonin is not fully understood, Nrf2 and its target genes might be involved in the Hsp70 up-regulation in U937 cells.

Power tools for gene expression and clonal analysis in Drosophila

The development of two-component expression systems in Drosophila melanogaster, one of the most powerful genetic models, has allowed the precise manipulation of gene function in specific cell populations. These expression systems, in combination with site-specific recombination approaches, have also led to the development of new methods for clonal lineage analysis. We present a hands-on user guide to the techniques and approaches that have greatly increased resolution of genetic analysis in the fly, with a special focus on their application for lineage analysis. Our intention is to provide guidance and suggestions regarding which genetic tools are most suitable for addressing different developmental questions.

SIGNATURE: A workbench for gene expression signature analysis

BackgroundThe biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise.ResultsWe have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses Bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access.ConclusionsSIGNATURE is available for public use at http://genepattern.genome.duke.edu/signature/.

Development of a genetic system for Marinobacter adhaerens HP15 involved in marine aggregate formation by interacting with diatom cells

Diatom aggregation is substantial for organic carbon flux from the photic zone to deeper waters. Many heterotrophic bacteria ubiquitously found in diverse marine environments interact with marine algae and thus impact organic matter and energy cycling in the ocean. In particular, Marinobacter adhaerens HP15 induces aggregate formation while interacting with the diatom, Thalassiosira weissflogii. To study this effect at the molecular level, a genetic tool system was developed for strain HP15. The antibiotic susceptibility spectrum of this organism was determined and electroporation and conjugation protocols were established. Among various plasmids of different incompatibility groups, only two were shown to replicate in M. adhaerens. 1.4×10−3 transconjugants per recipient were obtained for a broad-host-range vector. Electroporation efficiency corresponded to 1.1×105CFU per μg of DNA. Transposon and gene-specific mutageneses were conducted for flagellum biosynthetic genes. Mutant phenotypes were confirmed by swimming assay and microscopy. Successful expression of two reporter genes in strain HP15 revealed useful tools for gene expression analyses, which will allow studying diverse bacteria–algae interactions at the molecular level and hence to gain a mechanistic understanding of micro-scale processes underlying ocean basin-scale processes. This study is the first report for the genetic manipulation of a Marinobacter species which specifically interacts with marine diatoms and serves as model to additionally analyze various previously reported Marinobacter–algae interactions in depth.

A Nonparametric Mean-Variance Smoothing Method to Assess Arabidopsis Cold Stress Transcriptional Regulator CBF2 Overexpression Microarray Data

Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships. We present a non-parametric mean-variance smoothing method (NPMVS) to analyze differentially expressed genes. In this method, a nonlinear smoothing curve is fitted to estimate the relationship between mean and variance. Inference is then made upon shrinkage estimation of posterior means assuming variances are known. Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed. The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships. A real biological dataset, in which a cold stress transcription factor gene, CBF2, was overexpressed, has also been analyzed with the three methods. Gene ontology and cis-element analysis showed that NPMVS identified more cold and stress responsive genes than the other two methods did. The good performance of NPMVS is mainly due to its shrinkage estimation for both means and variances. In addition, NPMVS exploits a non-parametric regression between mean and variance, instead of assuming a specific parametric relationship between mean and variance. The source code written in R is available from the authors on request.

Gene selection and classification using Taguchi chaotic binary particle swarm optimization

The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and a small sample size. This makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate.In this paper, correlation-based feature selection (CFS) and the Taguchi chaotic binary particle swarm optimization (TCBPSO) were combined into a hybrid method. The K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Experimental results show that this hybrid method effectively simplifies features selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

Cold-modulated expression of genes encoding for key enzymes of the sugar metabolism in spring and autumn cvs. ofBeta vulgarisL.

An integrated approach based on the use of bioinformatics and gene expression analysis tools was carried out to evaluate the organ-specific transcription modulation of nine genes relevant to sugar metabolism ofBeta vulgarisL. plantlets of the autumn cv. Franca and spring cv. Bianca, in response to low-temperature (LT) treatments. Different growth cycles imply different plant capability to adapt to the environment that includes variations in gene expression of key metabolic enzymes. The transcriptional response was evaluated by quantitative PCR analysis before, during and after the LT treatments. The results were correlated with the LT-induced electrolyte leakage measure and the carbohydrate content. Stress-induced transcript level alterations were detected in the two cultivars, suggesting a modulation of sucrose synthesis and carbohydrate partitioning. Cold stress induced deep changes in the autumn cultivar, especially in fructose-1,6-biphosphatase gene expression, irrespective of temperature or exposure time. These differential features of expression profiles constitute first clues on the molecular basis of the differential LT response of sugarbeet autumn and spring cultivars.

Peptide-tags for enhanced DNA microarray performance

DNA microarrays are powerful tools for gene expression analysis and genotyping studies in research and diagnostic applications. A high sensitivity and short time-to-result are prerequisites for their practical application in the clinic. The hybridization efficiency of DNA microarrays depends on the probe density and the probe orientation and thus their accessibility for target molecules. In order to find an optimal probe immobilization procedure a set of different oligonucleotide modifications was tested on epoxy silane functionalized glass slides. It was found that histidine-tagged oligonucleotides resulted in the highest amount of bound probe and by far the best hybridization efficiencies. The detection limit obtained with histidine-tagged probes was up to two orders of magnitude lower compared to commonly used probe modifications. In order to further investigate the binding mechanism of histidine-tags towards functionalized glass substrates a set of different peptide-tags with and without free terminal amino-groups and with different amino acid compositions was tested. The results indicate an impact of the terminal amino group on the covalent surface binding and of aromatic amino acid residues on the enhanced hybridisation efficiency.

HuR's role in gemcitabine efficacy: an exception or opportunity?

Over the next few years, research teams will focus on, and millions of dollars will be spent on, sorting through cancer genomes. Undoubtedly, 'druggable' targets and events will be discovered that will improve our understanding and, hopefully, treatment of cancer. Highlighting an alternative to this 'genome-centric' approach, this review will further explore an underappreciated mechanism of gene expression regulation, posttranscription mRNA:protein interactions. A key molecule involved in this mode of gene regulation is HuR and we have shown that HuR levels and HuR cellular distribution can predict how pancreatic cancer patients responded to the standard of care chemotherapy (i.e., gemcitabine). HuR regulates post-transcriptional processes through: (1) association with specific mRNA cargos with ARE-rich sequences and (2) stress-induced increased cytoplasmic protein levels. Over multiple laboratories and diverse tumor types, it has been shown that HuR supports tumor survival through its regulation of tumor-promoting transcripts such as VEGF. In this article, we will highlight a recent discovery that this potent post-transcriptional gene regulatory mechanism has an impact on a common chemotherapeutic, gemcitabine. Specifically, we have identified the mechanism by which HuR can regulate a key gemcitabine metabolic enzyme, deoxycytidine kinase. We will use this example to explore and hypothesize the functional roles that HuR may have on anticancer drug therapies. We will survey novel high throughput global gene expression analysis tools to discover novel HuR targets. Future multidisciplinary approaches focused on HuR biology will provide critical events and 'druggable' targets in cancer that large-scale genomic sequencing efforts will miss.

PBaSysBioII: an integrative plasmid generating gfp transcriptional fusions for high-throughput analysis of gene expression in Bacillus subtilis

Plasmid pBaSysBioII was constructed for high-throughput analysis of gene expression in Bacillus subtilis. It is an integrative plasmid with a ligation-independent cloning (LIC) site, allowing the generation of transcriptional gfpmut3 fusions with desired promoters. Integration is by a Campbell-type event and is non-mutagenic, placing the fusion at the homologous chromosomal locus. Using phoA, murAA, gapB, ptsG and cggR promoters that are responsive to phosphate availability, growth rate and carbon source, we show that detailed profiles of promoter activity can be established, with responses to changing conditions being measurable within 1 min of the stimulus. This makes pBaSysBioII a highly versatile tool for real-time gene expression analysis in growing cells of B. subtilis.

Using Pre-existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance

Although they have become a widely used experimental technique for identifying differentially expressed (DE) genes, DNA microarrays are notorious for generating noisy data. A common strategy for mitigating the effects of noise is to perform many experimental replicates. This approach is often costly and sometimes impossible given limited resources; thus, analytical methods are needed which increase accuracy at no additional cost. One inexpensive source of microarray replicates comes from prior work: to date, data from hundreds of thousands of microarray experiments are in the public domain. Although these data assay a wide range of conditions, they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods. We present the SVD Augmented Gene expression Analysis Tool (SAGAT), a mathematically principled, data-driven approach for identifying DE genes. SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements. We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2.72 arrays. We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance, resulting in a 50% increase in the number of significant genes detected. We evaluated 11 (58%) of these genes experimentally using qPCR, confirming the directions of expression change for all 11 and statistical significance for three. Use of SAGAT revealed coherent biological changes in three pathways: inflammation, differentiation, and fatty acid synthesis, furthering our molecular understanding of a type 2 diabetes risk factor. We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses, and we provide it as a freely available software package that is immediately applicable to any human microarray study.

New Direct and Frontal Tissue Acquisition Tools for Gene Expression Analysis in Personalized Medicine

As high-throughput genotyping studies are increasingly being complemented with global gene expression/ transcriptomics analyses, tissue sampling and biopsy methods are becoming a prominent bottleneck in personalized medicine. Surprisingly, this topic has seldom been studied in detail. Quantitative molecular biology and high quality tissue sampling methods need to be developed in parallel, in order to understand disease pathophysiology, determine optimal individualized care, and identify molecular targets for novel therapies. This also calls for a critical reexamination of the subject of tissue acquisition and biopsy instruments in pharmacogenomics and personalized medicine. A perfect biopsy should be patient and operator friendly, of high quality, in adequate volume amenable to multiplexed molecular analyses, and affordable. Surgery, although still the gold standard, is seldom chosen because of the costs, risks and constraints of general anesthesia. Tru-cut microbiopsies are largely inappropriate as the samples are too small. Vacuum-assisted large core devices are expensive and principally restricted to the breast tissue. These traditional sampling approaches tend to take more ad random tissue resulting in substantial contamination with blood, and adjacent normal and non-target tissues. Contamination means low sample quality when it comes to quantitative analysis of gene products. Test results most probably become unreliable. The tissue acquisition problem, i.e., “not enough high quality tissue in sufficient quantity”, is addressed by the newer Direct and Frontal (D) harvesting tools which this paper aims to discuss. Recent literature provides evidence that these harvesting tools give tissue samples between 150 and 300 mg of highly specified parts of the diseased area in a way very similar to open surgery. In addition, they open new avenues for future bio-banking, pharmacogenomics and personalized medicine. We suggest that further comparative research and clinical applications of the different tissue sampling and biopsy methods for quantitative molecular biology are timely and essential in the field of personalized medicine. Keywords: Genetic variability and drug dosage, multiple model drug dosage, optimal stochastic adaptive control, population pharmacokinetic modeling, therapeutic drug monitoring

Correlation-based Gene Selection and Classification Using Taguchi-BPSO

Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. In this paper, correlation-based feature selection (CFS) and Taguchi-binary particle swarm optimization (TBPSO) were combined into a hybrid method, and the K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Experimental results show that this hybrid method effectively simplifies feature selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

Gene expression informatics with an automatic histogram-type membership function for non-uniform data

The non-uniformity of gene expression data is one of the factors that make gene expression analysis difficult. Gene expression data often do not follow a normal distribution but rather various distributions within each group. Thus, it is impossible to apply basic statistical techniques such as the t-test. In this study, we have developed an analysis method for gene expression data obtained by microarrays using a fuzzy logic algorithm with original membership functions. The method automatically evaluates the data from a histogram of gene expression information for a patient group. Using this method, we predicted the efficacy of an anti-TNF-α treatment for rheumatoid arthritis. We created a prediction model for the effects of 14 weeks of anti-TNF-α treatment based on the gene expression data from the peripheral blood of rheumatoid arthritis patients before the treatment. The model had a predictive success of 89% in the model-establishing data group, 94% in the training group, and 89% in the validation group. The results suggest that the method presented here could be an extremely effective tool for gene expression analysis.