Differential gene expression profile of glucocorticoids, testosterone, and dehydroepiandrosterone in human cells.

Glucocorticoids are the major immunomodulating hormones in the human body. Recently, increasing interest in androgens as immunomodulators has emerged. In particular, Dehydroepiandrosterone (DHEA) has been suggested as beneficial in the treatment of some autoimmune disorders. However, the action and role of testicular and adrenal androgens on human immune cells remains unclear. This is the first study to provide large-scale gene expression data on the action of different steroids (DHEA, glucocorticoids, and testosterone) on human peripheral blood mononuclear cells using the recently developed genomic-scale technology of microarrays. Novel computational tools and techniques such as Principal Component Analysis (PCA) were used for analysis, clustering and visualization. We have demonstrated that each steroid has its distinct gene expression profile, although DHEA and testosterone co-regulated most genes in a similar direction while glucocorticoids frequently regulated the same genes in an opposite direction. Our data suggest an important and a complex regulatory role for androgens on human immune cells that should be considered in androgen replacement or treatment strategies.     

Gene expression monitoring for gene discovery in models of peripheral and central nervous system differentiation, regeneration, and trauma

Gene expression monitoring using gene expression microarrays represents an extremely powerful technology for gene discovery in a variety of systems. We describe the results of seven experiments using Incyte GEM technology to compile a proprietary portfolio of data concerning differential gene expression in six different models of neuronal differentiation and regeneration, and recovery from injury or disease. Our first two experiments cataloged genes significantly up- or down-regulated during two phases of the retinoic acid-induced differentiation of the embryonal carcinoma line Ntera-2. To identify genes involved in neuronal regeneration we performed three GEM experiments, which included changes in gene expression in rat dorsal root ganglia during the healing of experimentally injured sciatic nerve, in regenerating neonatal opossum spinal cord, and during lipopolysaccharide stimulation of primary cultures of rat Schwann cells. Finally we have monitored genes involved in the recovery phase of the inflammatory disease of the rat spinal cord, experimental allergic encephalomyelitis, as well as those responsible for protection from oxidative stress in a glutamate-resistant rat hippocampal cell line. Analysis of the results of the approximately 70,000 data points collected is presented.     

Validating nutrient-related gene expression changes from microarrays using RT2 PCR-arrays

Microarray technology allows us to perform high-throughput screening of changes in gene expression. The outcome of microarray experiments largely depends on the applied analysis methods and cut-off values chosen. Results are often required to be verified using a more sensitive detection technique, such as quantitative real-time PCR (qPCR or RT-PCR). Throughout the years, this technique has become a de facto golden standard. Individual qPCRs are time-consuming, but the technology to perform high-throughput qPCR reactions has become available through PCR-arrays that allow up to 384 PCR reactions simultaneously. Our current aim was to investigate the usability of a RT² Profiler? PCR-array as validation in a nutritional intervention study, where the measured changes in gene expression were low. For some differentially expressed genes, the PCR-array confirmed the microarray prediction, though not for all. Furthermore, the PCR-array allowed picking up the expression of genes that were not measurable on the microarray platform but also vice versa. We conclude that both techniques have their own (dis)advantages and specificities, and for less pronounced changes using both technologies may be useful as complementation rather than validation.     

Plant–Pathogen Interactions: What Microarray Tells About It?

Plant defense responses are mediated by elementary regulatory proteins that affect expression of thousands of genes. Over the last decade, microarray technology has played a key role in deciphering the underlying networks of gene regulation in plants that lead to a wide variety of defence responses. Microarray is an important tool to quantify and profile the expression of thousands of genes simultaneously, with two main aims: (1) gene discovery and (2) global expression profiling. Several microarray technologies are currently in use; most include a glass slide platform with spotted cDNA or oligonucleotides. Till date, microarray technology has been used in the identification of regulatory genes, end-point defence genes, to understand the signal transduction processes underlying disease resistance and its intimate links to other physiological pathways. Microarray technology can be used for in-depth, simultaneous profiling of host/pathogen genes as the disease progresses from infection to resistance/susceptibility at different developmental stages of the host, which can be done in different environments, for clearer understanding of the processes involved. A thorough knowledge of plant disease resistance using successful combination of microarray and other high throughput techniques, as well as biochemical, genetic, and cell biological experiments is needed for practical application to secure and stabilize yield of many crop plants. This review starts with a brief introduction to microarray technology, followed by the basics of plant–pathogen interaction, the use of DNA microarrays over the last decade to unravel the mysteries of plant–pathogen interaction, and ends with the future prospects of this technology.     

Detection and identification of Escherichia coli,Shigella, and Salmonella by microarrays using the gyrB gene

Commonly, 16S ribosome RNA (16S rRNA) sequence analysis has been used for identifying enteric bacteria. However, it may not always be applicable for distinguishing closely related bacteria. Therefore, we selected gyrB genes that encode the subunit B protein of DNA gyrase (a topoisomerase type II protein) as target genes. The molecular evolution rate of gyrB genes is higher than that of 16S rRNA, and gyrB genes are distributed universally among bacterial species. Microarray technology includes the methods of arraying cDNA or oligonucleotides on substrates such as glass slides while acquiring a lot of information simultaneously. Thus, it is possible to identify the enteric bacteria easily using microarray technology. We devised a simple method of rapidly identifying bacterial species through the combined use of gyrB genes and microarrays. Closely related bacteria were not identified at the species level using 16S rRNA sequence analysis, whereas they were identified at the species level based on the reaction patterns of oligonucleotides on our microarrays using gyrB genes.     

Current issues for DNA microarrays: platform comparison, double linear amplification, and universal RNA reference

DNA microarray technology has been widely used to simultaneously determine the expression levels of thousands of genes. A variety of approaches have been used, both in the implementation of this technology and in the analysis of the large amount of expression data. However, several practical issues still have not been resolved in a satisfactory manner, and among the most critical is the lack of agreement in the results obtained in different array platforms. In this study, we present a comparison of several microarray platforms [Affymetrix oligonucleotide arrays, custom complementary DNA (cDNA) arrays, and custom oligo arrays printed with oligonucleotides from three different sources] as well as analysis of various methods used for microarray target preparation and the reference design. The results indicate that the pairwise correlations of expression levels between platforms are relative low overall but that the log ratios of the highly expressed genes are strongly correlated, especially between Affymetrix and cDNA arrays. The microarray measurements were compared with quantitative real-time-polymerase chain reaction (QRT-PCR) results for 23 genes, and the varying degrees of agreement for each platform were characterized. We have also developed and tested a double amplification method which allows the use of smaller amounts of starting material. The added round of amplification produced reproducible results as compared to the arrays hybridized with single round amplified targets. Finally, the reliability of using a universal RNA reference for two-channel microarrays was tested and the results suggest that comparisons of multiple experimental conditions using the same control can be accurate.     

Gene expression monitoring for gene discovery in models of peripheral and central nervous system differentiation,regeneration, and trauma

Gene expression monitoring using gene expression microarrays represents an extremely powerful technology for gene discovery in a variety of systems. We describe the results of seven experiments using Incyte GEM technology to compile a proprietary portfolio of data concerning differential gene expression in six different models of neuronal differentiation and regeneration, and recovery from injury or disease. Our first two experiments cataloged genes significantly up‐ or down‐regulated during two phases of the retinoic acid‐induced differentiation of the embryonal carcinoma line Ntera‐2. To identify genes involved in neuronal regeneration we performed three GEM experiments, which included changes in gene expression in rat dorsal root ganglia during the healing of experimentally injured sciatic nerve, in regenerating neonatal opossum spinal cord, and during lipopolysaccharide stimulation of primary cultures of rat Schwann cells. Finally we have monitored genes involved in the recovery phase of the inflammatory disease of the rat spinal cord, experimental allergic encephalomyelitis, as well as those responsible for protection from oxidative stress in a glutamate‐resistant rat hippocampal cell line. Analysis of the results of the approximately 70,000 data points collected is presented. J. Cell. Biochem. 80:171–180, 2000. © 2000 Wiley‐Liss, Inc.     

Validation of rat reference genes for improved quantitative gene expression analysis using low density arrays

Real-time PCR has become increasingly important in gene expression profiling research, and it is widely agreed that normalized data are required for accurate estimates of messenger RNA (mRNA) expression. With increased gene expression profiling in preclinical research and toxicogenomics, a need for reference genes in the rat has emerged, and the studies in this area have not yet been thoroughly evaluated. The purpose of our study was to evaluate a panel of rat reference genes for variation of gene expression in different tissue types. We selected 48 known target genes based on their putative invariability. The gene expression of all targets was examined in 11 types of rat tissues using TaqMan low density array (LDA) technology. The variability of each gene was assessed using a two-step statistical model. The analysis of mean expression using multiple reference genes was shown to provide accurate and reliable normalized expression data. The least five variable genes from each specific tissue were recommended for future tissue-specific studies. Finally, a subset of investigated rat reference genes showing the least variation is recommended for further evaluation using the LDA platform. Our work should considerably enhance a researcher's ability to simply and efficiently identify appropriate reference genes for given experiments.     

Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data

MOTIVATION: An important application of microarray technology is to relate gene expression profiles to various clinical phenotypes of patients. Success has been demonstrated in molecular classification of cancer in which the gene expression data serve as predictors and different types of cancer serve as a categorical outcome variable. However, there has been less research in linking gene expression profiles to the censored survival data such as patients' overall survival time or time to cancer relapse. It would be desirable to have models with good prediction accuracy and parsimony property. RESULTS: We propose to use the L(1) penalized estimation for the Cox model to select genes that are relevant to patients' survival and to build a predictive model for future prediction. The computational difficulty associated with the estimation in the high-dimensional and low-sample size settings can be efficiently solved by using the recently developed least-angle regression (LARS) method. Our simulation studies and application to real datasets on predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed procedure, which we call the LARS-Cox procedure, can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. The LARS-Cox regression gives better predictive performance than the L(2) penalized regression and a few other dimension-reduction based methods. CONCLUSIONS: We conclude that the proposed LARS-Cox procedure can be very useful in identifying genes relevant to survival phenotypes and in building a parsimonious predictive model that can be used for classifying future patients into clinically relevant high- and low-risk groups based on the gene expression profile and survival times of previous patients.     

Construction of replicative and integrative plasmids for setting up the in vivo expression technology in Helicobacter pylori

Some pathogenic factors of Helicobacter pylori, a bacterium involved in peptic ulcer and gastric cancer, have already been identified using either global or particular approach, but there are still some orphan genes and unidentified pathogenic factors. One of the methods used successfully for the identification of virulence genes of many pathogens is the in vivo expression technology. We describe here the construction and sequences of three different plasmids, one integrative and two replicatives, for the identification of virulence genes by using in vivo expression technology in H. pylori, and of potential use in other bacteria such as Campylobacter spp. Moreover, the use of the green fluorescent protein could allow to classify the genes according to the strength of their expression and to identify those which are repressed upon interaction with gastric mucosa.     

Gene-trap mutagenesis: past, present and beyond

Although at least 35,000 human genes have been sequenced and mapped, adequate expression or functional information is available for only approximately 15% of them. Gene-trap mutagenesis is a technique that randomly generates loss-of-function mutations and reports the expression of many mouse genes. At present, several large-scale, gene-trap screens are being carried out with various new vectors, which aim to generate a public resource of mutagenized embryonic stem (ES) cells. This resource now includes more than 8,000 mutagenized ES-cell lines, which are freely available, making it an appropriate time to evaluate the recent advances in this area of genomic technology and the technical hurdles it has yet to overcome.     

Graph-based identification of cancer signaling pathways from published gene expression signatures using PubLiME

Gene expression technology has become a routine application in many laboratories and has provided large amounts of gene expression signatures that have been identified in a variety of cancer types. Interpretation of gene expression signatures would profit from the availability of a procedure capable of assigning differentially regulated genes or entire gene signatures to defined cancer signaling pathways. Here we describe a graph-based approach that identifies cancer signaling pathways from published gene expression signatures. Published gene expression signatures are collected in a database (PubLiME: Published Lists of Microarray Experiments) enabled for cross-platform gene annotation. Significant co-occurrence modules composed of up to 10 genes in different gene expression signatures are identified. Significantly co-occurring genes are linked by an edge in an undirected graph. Edge-betweenness and k-clique clustering combined with graph modularity as a quality measure are used to identify communities in the resulting graph. The identified communities consist of cell cycle, apoptosis, phosphorylation cascade, extra cellular matrix, interferon and immune response regulators as well as communities of unknown function. The genes constituting different communities are characterized by common genomic features and strongly enriched cis-regulatory modules in their upstream regulatory regions that are consistent with pathway assignment of those genes.     

Antibody engineering: the use of Escherichia coli as an expression host.

The hypervariable loops of an antibody molecule are supported on the relatively conserved beta-sheeted frameworks of the heavy- and light-chain variable domains (designated VH and VL domains, respectively). Residues within and flanking these loops interact with antigen and confer the specificity and affinity of antigen binding on the immunoglobulin molecule. Thus, the isolation and expression of VH and VL domain genes are of particular interest both for analysis of the determinants of antibody specificity and for generation of fragments with binding affinities for use in therapy and diagnosis. The PCR can now be used to isolate diverse repertoires of antibody VH and VL domain genes from antibody-producing cells from different species, including humans and mice. The genes can be expressed as either secreted or surface-bound Fv or Fab fragments, using Escherichia coli expression systems, and the desired antigen-binding specificity screened for or, preferably, selected. The use of E. coli as an expression host allows the required antigen-binding specificity to be isolated in clonal form in a matter of days. The VH and VL domain genes can also be hypermutated and higher-affinity variants isolated by screening or selection. Thus, the use of this technology should allow the isolation of novel binding specificities or specificities that are difficult to generate by hybridoma technology. It will also facilitate the isolation of human-derived Fv/Fab fragments that may be less immunogenic in therapy. This approach therefore has almost unlimited potential in the generation of therapeutics with binding specificities to order. The fragments can be used either alone or linked to effector functions in the form of antibody-constant domains or toxins. The new technology could prove to be a method of choice for the rapid and convenient production of designer antibodies.     

Statistical analysis of a small set of time-ordered gene expression data using linear splines

MOTIVATION: Recently, the temporal response of genes to changes in their environment has been investigated using cDNA microarray technology by measuring the gene expression levels at a small number of time points. Conventional techniques for time series analysis are not suitable for such a short series of time-ordered data. The analysis of gene expression data has therefore usually been limited to a fold-change analysis, instead of a systematic statistical approach. METHODS: We use the maximum likelihood method together with Akaike's Information Criterion to fit linear splines to a small set of time-ordered gene expression data in order to infer statistically meaningful information from the measurements. The significance of measured gene expression data is assessed using Student's t-test. RESULTS: Previous gene expression measurements of the cyanobacterium Synechocystis sp. PCC6803 were reanalyzed using linear splines. The temporal response was identified of many genes that had been missed by a fold-change analysis. Based on our statistical analysis, we found that about four gene expression measurements or more are needed at each time point.     

Protein microarrays, biosensors, and cell-based methods for secretome-wide extracellular protein-protein interaction mapping

Approximately one quarter of all human genes encode proteins that function in the extracellular space or serve to bridge the extracellular and intracellular environments. Physical associations between these secretome proteins serve to regulate a wide range of biological activities and consequently represent important therapeutic targets. Moreover, some extracellular proteins are targeted by pathogens to allow host access or immune evasion. Despite the importance of extracellular protein-protein interactions, our knowledge in this area has remained sparse. Weak affinities and low abundance have often hindered efforts to identify these interactions using traditional methods such as biochemical purification and cDNA library expression cloning. Moreover, current large-scale protein-protein interaction mapping techniques largely under represent extracellular protein-protein interactions. This review highlights emerging biosensor and protein microarray technology, along with more traditional cell-based techniques, that are compatible with secretome-wide screens for extracellular protein-protein interaction discovery. A combination of these approaches will serve to rapidly expand our knowledge of the extracellular protein-protein interactome.     

Microarrays in biology and medicine

The remarkable speed with which biotechnology has become critical to the practice of life sciences owes much to a series of technological revolutions. Microarray is the latest invention in this ongoing technological revolution. This technology holds the promise to revolutionize the future of biology and medicine unlike any other technology that preceded it. Development of microarray technology has significantly changed the way questions about diseases and/or biological phenomena are addressed. This is because microarrays facilitate monitoring the expression of thousands of genes or proteins in a single experiment. This enormous power of microarrays has enabled scientists to monitor thousands of genes and their products in a given living organism in one experiment, and to understand how these genes function in an orchestrated manner. Obtaining such a global view of life at the molecular level was impossible using conventional molecular biological techniques. However, despite all the progress made in developing this technology, microarray is yet to reach a point where all data are obtained, analyzed, and shared in a standardized fashion. The present article is a brief overview of microarray technologies and their applications with an emphasis on DNA microarray.     

Allelic imbalance in Drosophila hybrid heads: exons, isoforms, and evolution

Unraveling how regulatory divergence contributes to species differences and adaptation requires identifying functional variants from among millions of genetic differences. Analysis of allelic imbalance (AI) reveals functional genetic differences in cis regulation and has demonstrated differences in cis regulation within and between species. Regulatory mechanisms are often highly conserved, yet differences between species in gene expression are extensive. What evolutionary forces explain widespread divergence in cis regulation? AI was assessed in Drosophila melanogaster-Drosophila simulans hybrid female heads using RNA-seq technology. Mapping bias was virtually eliminated by using genotype-specific references. Allele representation in DNA sequencing was used as a prior in a novel Bayesian model for the estimation of AI in RNA. Cis regulatory divergence was common in the organs and tissues of the head with 41% of genes analyzed showing significant AI. Using existing population genomic data, the relationship between AI and patterns of sequence evolution was examined. Evidence of positive selection was found in 30% of cis regulatory divergent genes. Genes involved in defense, RNAi/RISC complex genes, and those that are sex regulated are enriched among adaptively evolving cis regulatory divergent genes. For genes in these groups, adaptive evolution may play a role in regulatory divergence between species. However, there is no evidence that adaptive evolution drives most of the cis regulatory divergence that is observed. The majority of genes showed patterns consistent with stabilizing selection and neutral evolutionary processes.     

Die Immunsysteme der Bakterien

The immune systems of bacteria and important applications in biotechnology and medicine At the end of the 70s of the last century, a new technique has been developed allowing the synthesis of genes and the inducible expression of their recombinant proteins using restriction enzymes and vectors, mainly plasmids. This era has been designated as genetic engineering and is being replenished by the CRISPR‐Cas9 technology know as genome editing. This technology is about to revolutionize alterations in the genomes of all types of organisms, including bacteria, fungi, plants, animals and even humans. It allows the introduction and elimination of point mutations and even whole genes in all organisms. Important goals are the genetic optimization of crop plants and animals, fighting against cancer in humans and elimination of human viruses and pathogenic multi‐resistant bacteria. Important drawbacks are OFF‐targets which can cause mutations in any gene or influence their expression.