A concise guide to cDNA microarray analysis

Options available--from start to finish--for obtaining data from DNA microarrays II

Microarray technology has undergone a rapid evolution. With widespread interest in large-scale genomic research, an abundance of equipment and reagents have now become available and affordable to a large cross section of the scientific community. As protocols become more refined, careful investigators are able to obtain good quality microarray data quickly. In most recent times, however, perhaps one of the biggest obstacles researchers face is not the manufacture and use of microarrays at the bench, but storage and analysis of the array data. This review discusses the most recent equipment, reagents and protocols available to the researcher, as well as describing data analysis and storage options available from the evolving field of microarray informatics.

Novel genes regulated by Sonic Hedgehog in pluripotent mesenchymal cells

Sonic Hedgehog is a secreted morphogen involved in patterning a wide range of structures in the developing embryo. Disruption of the Hedgehog signalling cascade leads to a number of developmental disorders and plays a key role in the formation of a range of human cancers. The identification of genes regulated by Hedgehog is crucial to understanding how disruption of this pathway leads to neoplastic transformation. We have used a Sonic Hedgehog (Shh) responsive mouse cell line, C3H/10T1/2, to provide a model system for hedgehog target gene discovery. Following activation of cell cultures with Shh, RNA was used to interrogate microarrays to investigate downstream transcriptional consequences of hedgehog stimulation. As a result 11 target genes have been identified, seven of which are induced (Thrombomodulin, GILZ, BF-2, Nr4a1, IGF2, PMP22, LASP1) and four of which are repressed (SFRP-1, SFRP-2, Mip1-gamma, Amh) by Shh. These targets have a diverse range of putative functions and include transcriptional regulators and molecules known to be involved in regulating cell growth or apoptosis. The corroboration of genes previously implicated in hedgehog signalling, along with the finding of novel targets, demonstrates both the validity and power of the C3H/10T1/2 system for Shh target gene discovery.

Identification of genes involved in resistance to interferon-alpha in cutaneous T-cell lymphoma

Interferon-alpha therapy has been shown to be active in the treatment of mycosis fungoides although the individual response to this therapy is unpredictable and dependent on essentially unknown factors. In an effort to better understand the molecular mechanisms of interferon-alpha resistance we have developed an interferon-alpha resistant variant from a sensitive cutaneous T-cell lymphoma cell line. We have performed expression analysis to detect genes differentially expressed between both variants using a cDNA microarray including 6386 cancer-implicated genes. The experiments showed that resistance to interferon-alpha is consistently associated with changes in the expression of a set of 39 genes, involved in signal transduction, apoptosis, transcription regulation, and cell growth. Additional studies performed confirm that STAT1 and STAT3 expression and interferon-alpha induction and activation are not altered between both variants. The gene MAL, highly overexpressed by resistant cells, was also found to be expressed by tumoral cells in a series of cutaneous T-cell lymphoma patients treated with interferon-alpha and/or photochemotherapy. MAL expression was associated with longer time to complete remission. Time-course experiments of the sensitive and resistant cells showed a differential expression of a subset of genes involved in interferon-response (1 to 4 hours), cell growth and apoptosis (24 to 48 hours.), and signal transduction.

Loss of a FYN-regulated differentiation and growth arrest pathway in advanced stage neuroblastoma

Tumor stage, age of patient, and amplification of MYCN predict disease outcome in neuroblastoma. To gain insight into the underlying molecular pathways, we have obtained expression profiles from 94 primary neuroblastoma specimens. Advanced tumor stages show a characteristic expression profile that includes downregulation of multiple genes involved in signal transduction through Fyn and the actin cytoskeleton. High expression of Fyn and high Fyn kinase activity are restricted to low-stage tumors. In culture, expression of active Fyn kinase induces differentiation and growth arrest of neuroblastoma cells. Expression of Fyn predicts long-term survival independently of MYCN amplification. Amplification of MYCN correlates with deregulation of a distinct set of genes, many of which are target genes of Myc. Our data demonstrate a causal role for Fyn kinase in the genesis of neuroblastoma.

Competitive hybridization on spotted microarrays as a tool to conduct comparative genomic analyses of Xylella fastidiosa strains

Xylella fastidiosa strains are responsible for several plant diseases and since such isolates display a broad host range and complex biological behavior, genomic comparisons employing microarray hybridizations may provide an effective method to compare them. Thus, we performed a thorough validation of this type of approach using two recently sequenced strains of this phytopathogen. By matching microarray hybridization results to direct sequence comparisons, we were able to establish precise cutoff ratios for common and exclusive sequences, allowing the identification of exclusive genes involved in important biological traits. This validation will enable the use of microarray-based comparisons across a wide variety of microorganisms

Quantitative examination of mechanophysical tumor cell enrichment in fine-needle aspiration specimens

BACKGROUND

The advent of cDNA microarrays and other molecular technologies necessitates the acquisition of tumor cell-enriched material because nonmalignant cells often decrease the sensitivity of the assays. Fine-needle aspiration (FNA) specimens from carcinoma have long been noted to be enriched in malignant cells. The current study quantitated the relative representation of tumor versus nontumor cells in FNA specimens compared with tissue sections using breast carcinoma as a model.

METHODS

Five patients who had undergone both a diagnostic FNA and a surgical excision for breast carcinoma between January and July of 1996 were selected. Five random cellular fields from representative slides of the FNA (using the ThinPrep preparation of the wash) and surgical excision specimens were photographed digitally at x20 power. The cells were judged as tumor or nontumor cells and then were counted manually in each field.

RESULTS

The calculated percentage of malignant cells in the FNA specimen (as represented on the ThinPrep slide) ranged from 66-93% compared with the calculated percentage of 37-78% noted in histologic sections. The average of all 5 fields from all 5 cases showed that 83.1% of the total cells were malignant in the ThinPrep preparation compared with 62.3% in the histologic sections. This difference was highly statistically significant when analyzed using the chi-square test (P = 0.0009).

CONCLUSIONS

The percentage of malignant cells on FNA specimens from breast carcinoma, as assessed by ThinPrep, was found to be significantly higher than that obtained by surgical excision. The results of the current study quantitatively confirm the impression of practicing cytopathologists, but also suggest that FNA will provide a good substrate for cDNA microarray and other molecular analyses.

Complete genome sequence and comparative genomic analysis of an emerging human pathogen, serotype V Streptococcus agalactiae

The 2,160,267 bp genome sequence of Streptococcus agalactiae, the leading cause of bacterial sepsis, pneumonia, and meningitis in neonates in the U.S. and Europe, is predicted to encode 2,175 genes. Genome comparisons among S. agalactiae, Streptococcus pneumoniae, Streptococcus pyogenes, and the other completely sequenced genomes identified genes specific to the streptococci and to S. agalactiae. These in silico analyses, combined with comparative genome hybridization experiments between the sequenced serotype V strain 2603 V/R and 19 S. agalactiae strains from several serotypes using whole-genome microarrays, revealed the genetic heterogeneity among S. agalactiae strains, even of the same serotype, and provided insights into the evolution of virulence mechanisms.

Comparison of different labeling methods for two-channel high-density microarray experiments

In this report we evaluate three methods for labeling nucleic acids to be hybridized to a cDNA microarray: direct labeling, indirect amino-allyl labeling, and the dendrimer labeling method (Genisphere). The dendrimer method requires the smallest quantity of sample, 2.5 microg of total RNA compared with 20 microg with the direct or indirect methods. Therefore, we wanted to know whether the performance of the dendrimer method is comparable to the other methods, or whether significant information is lost. Performance can be considered in terms of sensitivity, dynamic range, and reproducibility of the quantitative signals for gene intensity. We compared the three labeling methods by generating three sets of eight self-to-self hybridizations using the same total RNA sample in all cases ("replicate study"). In our analysis, we controlled for the effects of print-tip and background subtraction biases. We also performed a smaller study, namely, a dilution series study with five dilution points per labeling method, to evaluate one aspect of predictive ability. From the replicate study, the dendrimer method appeared to perform as well, and often better, with respect to reproducibility and ability to detect expression. However, in the dilution series study, this method was outperformed by the other two in terms of predictive ability and did not perform very well. These findings are helping to guide our decisions on what labeling method to use for subsequent studies, based on the purpose of a specific study and its limitations in terms of available material.

Evaluation of cadmium-induced transcriptome alterations by three color cDNA labeling microarray analysis on a T-cell line

Beside heavy metals, cadmium (Cd(2+)) is a ubiquitous toxic metal with a well established apoptotic and genotoxic effect, chronic exposure of which has been involved in a variety of pathological conditions. In the present study, we investigated by 1455 genes cDNA microarrays the toxic and apoptotic effect of Cd(2+), on the T-cell line CCRF-CEM, applying a three laser differential analysis, on the same microarray slide. The cells were cultured for 6 and 24 h in the absence (control) or presence of Cd(2+) (10 or 20 microM), RNAs were extracted and the produced cDNAs were labeled with rhodamine derivatives fluorescent dyes. A microarray slide was simultaneously hybridized by the labeled cDNAs and analyzed. We found that, in relation to control, treatment of the cells for 6 h with 10 and 20 microM Cd(2+), induces up-regulation in 20 and 34 genes, respectively. Treatment for 24 h with 10 and 20 microM Cd(2+) induces up-regulation in 22 and 84 genes, respectively. Twenty-eight genes were found down-regulated only after treatment for 24 h with Cd(2+) 10 microM. These data suggest that Cd(2+) produces a time- and dose-dependent molecular cascade, induces disturbances in different subcellular compartments, influencing thereafter the normal cellular functions, the differentiation process, the malignant transformation and the cell death.

Microarray results: how accurate are they?

BACKGROUND

DNA microarray technology is a powerful technique that was recently developed in order to analyze thousands of genes in a short time. Presently, microarrays, or chips, of the cDNA type and oligonucleotide type are available from several sources. The number of publications in this area is increasing exponentially.

RESULTS

In this study, microarray data obtained from two different commercially available systems were critically evaluated. Our analysis revealed several inconsistencies in the data obtained from the two different microarrays. Problems encountered included inconsistent sequence fidelity of the spotted microarrays, variability of differential expression, low specificity of cDNA microarray probes, discrepancy in fold-change calculation and lack of probe specificity for different isoforms of a gene.

CONCLUSIONS

In view of these pitfalls, data from microarray analysis need to be interpreted cautiously.

Manufacturing DNA microarrays from unpurified PCR products

For the production of DNA microarrays from PCR products, purification of the the DNA fragments prior to spotting is a major expense in cost and time. Also, a considerable amount of material is lost during this process and contamination might occur. Here, a protocol is presented that permits the manufacture of microarrays from unpurified PCR products on aminated surfaces such as glass slides coated with the widely used poly(L-lysine) or aminosilane. The presence of primer molecules in the PCR sample does not increase the non-specific signal upon hybridisation. Overall, signal intensity on arrays made of unpurified PCR products is 94% of the intensity obtained with the respective purified molecules. This slight loss in signal, however, is offset by a reduced variation in the amount of DNA present at the individual spot positions across an array, apart from the considerable savings in time and cost. In addition, a larger number of arrays can be made from one batch of amplification products.

Gene and protein expression profiles of Shewanella oneidensis during anaerobic growth with different electron acceptors

Changes in mRNA and protein expression profiles of Shewanella oneidenesis MR-1 during switch from aerobic to fumarate-, Fe(III)-, or nitrate-reducing conditions were examined using DNA microarrays and two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). In response to changes in growth conditions, 121 of the 691 arrayed genes displayed at least a two-fold difference in transcript abundance as determined by microarray analysis. Genes involved in aerobic respiration encoding cytochrome c and d oxidases and TCA cycle enzymes were repressed under anaerobic conditions. Genes induced during anaerobic respiration included those involved in cofactor biosynthesis and assembly (moaACE, ccmHF, nosD, cysG), substrate transport (cysUP, cysTWA, dcuB), and anaerobic energy metabolism (dmsAB, psrC, pshA, hyaABC, hydA). Transcription of genes encoding a periplasmic nitrate reductase (napBHGA), cytochrome c552, and prismane was elevated 8- to 56-fold in response to the presence of nitrate, while cymA, ifcA, and frdA were specifically induced three- to eightfold under fumarate-reducing conditions. The mRNA levels for two oxidoreductase-like genes of unknown function and several cell envelope genes involved in multidrug resistance increased two- to fivefold specifically under Fe(III)-reducing conditions. Analysis of protein expression profiles under aerobic and anaerobic conditions revealed 14 protein spots that showed significant differences in abundance on 2-D gels. Protein identification by mass spectrometry indicated that the expression of prismane, dihydrolipoamide succinyltransferase, and alcaligin siderophore biosynthesis protein correlated with the microarray data.

New approaches to lymphoma diagnosis

Recent years have brought an explosion of new diagnostic tools to the pathology of lymphomas, which have permitted more precise disease definition and recognition of factors that can predict prognosis and response to treatment. These new methods exploit both the biological features of normal lymphocytes as they progress through differentiation pathways and the genetic abnormalities that characterize malignant transformation. These features can be assessed in individual tumors with techniques that detect proteins (immunophenotyping), messenger RNA (in-situ hybridization), or changes in DNA [Southern blot, PCR, fluorescence in-situ hybridization (FISH), and gene sequencing]. Recently, the novel technology of "gene chips" or DNA microarrays has greatly enhanced the efficiency of analyzing expression of many genes simultaneously at the RNA level. Understanding the relationship of lymphoid neoplasms to their normal counterparts and the genetic events that lead to malignant transformation in lymphoid cells are essential for physicians caring for patients with lymphoma, since these are the basis of modern classification, diagnosis, and prognosis prediction. Although microarray technology is not ready for prime time in the daily diagnosis of lymphoma, practitioners should understand its potential and limitations. The vast majority of lymphoid neoplasms worldwide are derived from B lymphocytes at various stages of differentiation. The review by Harald Stein and colleagues present the events of normal B-cell differentiation that are relevant to understanding the biology of B-cell neoplasia. These include antigen receptor [immunoglobulin (Ig)] gene rearrangement, somatic mutations of the Ig variable region genes, receptor editing, Ig heavy chain class switch, and differential expression of a variety of adhesion molecules and receptor proteins as the cell progresses from a precursor B cell to a mature plasma cell. Most lymphoid neoplasms have genetic abnormalities, many of which appear to occur during the gene rearrangements and mutations that characterize normal B-cell differentiation. Dr. Riccardo Dalla Favera reviews the mechanisms of these translocations and other abnormalities, and their consequences for lymphocyte biology. The association of specific abnormalities with individual lymphomas is reviewed. Dr. Wing C. Chan reviews the technology and applications of DNA microarray analysis, its promises and pitfalls, and what it has already told us about the biology of lymphomas. Finally, what does this all mean? The applications, both current and future, of these discoveries to the diagnosis and treatment of patients with lymphoma are discussed by Dr. Nancy Lee Harris.

Calorie restriction extends Saccharomyces cerevisiae lifespan by increasing respiration

Calorie restriction (CR) extends lifespan in a wide spectrum of organisms and is the only regimen known to lengthen the lifespan of mammals. We established a model of CR in budding yeast Saccharomyces cerevisiae. In this system, lifespan can be extended by limiting glucose or by reducing the activity of the glucose-sensing cyclic-AMP-dependent kinase (PKA). Lifespan extension in a mutant with reduced PKA activity requires Sir2 and NAD (nicotinamide adenine dinucleotide). In this study we explore how CR activates Sir2 to extend lifespan. Here we show that the shunting of carbon metabolism toward the mitochondrial tricarboxylic acid cycle and the concomitant increase in respiration play a central part in this process. We discuss how this metabolic strategy may apply to CR in animals.

The use of DNA microarrays for the developmental expression analysis of cDNAs from the oesophageal gland cell region of Heterodera glycines

Summary A microarray was printed containing cDNAs from a library made from cytoplasm microaspirated from the oesophageal gland cell region of parasitic stages of the soybean cyst nematode, Heterodera glycines. The array contained both previously described clones (Wang et al. Mol. Plant-Microbe Interact. 2001, 14, 536-544) and uncharacterized cDNAs. Fluorescent probes for array hybridization were prepared using RNA polymerase amplification of nematode mRNA. Developmental expression profiles of the arrayed cDNAs were determined by hybridizing the microarray with probes from parasitic and non-parasitic H. glycines life stages. Distinct patterns of developmental expression were ascertained for the previously described gland expressed genes. In addition, four H. glycines cDNAs (SCN1018, SCN1020, SCN1028 and SCN1167) were identified that showed up-regulation in one or more parasitic life stages. Clone SCN1018 encodes a C-type lectin domain and is expressed in the hypodermis of females. Clone SCN1020 encodes a probable S-adenosylmethionine synthetase. Clone SCN1028 encodes a piwi protein with high similarity to the germ-line-specific protein R06C7.1 of Caenorhabditis elegans. The sequence of SCN1167 had no similarity to known genes. This paper describes the first use of cDNA microarrays to analyse genes of a plant-parasitic nematode and establishes a functional method to mine nematode cDNA libraries.

Microarray Gene Expression Analysis of Murine Tumor Heterogeneity Defined by Dynamic Contrast-Enhanced MRI

Current methods of studying angiogenesis are limited in their ability to serially evaluate in vivo function throughout a target tissue. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and pharmacokinetic modeling provide a useful method for evaluating tissue vasculature based on contrast accumulation and washout. While it is often assumed that areas of high contrast enhancement and washout comprise areas of increased angiogenesis and tumor activity, the actual molecular pathways that are active in such areas are poorly understood. Using DCE-MRI in a murine subcutaneous tumor model, we were able to perform pharmacokinetic functional analysis of a tumor, coregistration of MRI images with histological cross-sections, immunohistochemistry, laser capture microdissection, and genetic profiling of tumor heterogeneity based on pharmacokinetic parameters. Using imaging as a template for biologic investigation, we have not found evidence of increased expression of proangiogenic modulators at the transcriptional level in either distinct pharmacokinetic region. Furthermore, these regions show no difference on histology and CD31 immunohistochemistry. However, the expression of ribosomal proteins was greatly increased in high enhancement and washout regions, implying increased protein translation and consequent increased cellular activity. Together, these findings point to the potential importance of posttranscriptional regulation in angiogenesis and the need for the development of angiogenesis-specific contrast agents to evaluate in vivo angiogenesis at a molecular level.

Functional genomics of the endocrine pancreas: the pancreas clone set and PancChip, new resources for diabetes research

Over the past 5 years, microarrays have greatly facilitated large-scale analysis of gene expression levels. Although these arrays were not specifically geared to represent tissues and pathways known to be affected by diabetes, they have been used in both type 1 and type 2 diabetes research. To prepare a tool that is particularly useful in the study of type 1 diabetes, we have assembled a nonredundant set of 3,400 clones representing genes expressed in the mouse pancreas or pathways known to be affected by diabetes. We have demonstrated the usefulness of this clone set by preparing a cDNA glass microarray, the PancChip, and using it to analyze pancreatic gene expression from embryonic day 14.5 through adulthood in mice. The clone set and corresponding array are useful resources for diabetes research.

Microarray analysis of the Mycobacterium tuberculosis transcriptional response to the acidic conditions found in phagosomes

We used microarrays and real-time reverse transcription-PCR to analyze the global transcriptional response of Mycobacterium tuberculosis to low pH in vitro, which may mimic an environmental signal encountered by phagocytosed mycobacteria. Eighty-one genes were differentially expressed >1.5-fold, including many involved in fatty acid metabolism. The most highly induced genes showed homology with nonribosomal peptide synthetases/polyketide synthases.

Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals

In this paper, fluorescent microarray images and various analysis techniques are described to improve the microarray data acquisition processes. Signal intensities produced by rarely expressed genes are initially correctly detected, but they are often lost in corrections for background, log or ratio. Our analyses indicate that a simple correlation between the mean and median signal intensities may be the best way to eliminate inaccurate microarray signals. Unlike traditional quality control methods, the low intensity signals are retained and inaccurate signals are eliminated in this mean and median correlation. With larger amounts of microarray data being generated, it becomes increasingly more difficult to analyze data on a visual basis. Our method allows for the automatic quantitative determination of accurate and reliable signals, which can then be used for normalization. We found that a mean to median correlation of 85% or higher not only retains more data than current methods, but the retained data is more accurate than traditional thresholds or common spot flagging algorithms. We have also found that by using pin microtapping and microvibrations, we can control spot quality independent from initial PCR volume.

Role of gene expression microarray analysis in finding complex disease genes

The promise of gene expression studies using microarray technology has inspired much new hope for finding complex diseases genes. It has become clear that complex diseases result from collective actions of many genetic and nongenetic factors. Therefore, genetic dissection of complex diseases should be carried out in a global context. The technology of gene expression microarray analysis (GEMA) can provide such global information on transcription activities of essentially all genes simultaneously. It is hoped that this promising technology can be applied to samples drawn from large-scale, well-defined genetic epidemiological studies and help us untangle the web of pathways leading to complex diseases. However, extremely noisy GEMA data pose serious challenges in terms of the statistical methodologies needed. Extensive work is needed in order to respond to the challenges before one can fully utilize the potential power provided by GEMA. We begin in this paper by identifying several statistical problems related to the application of GEMA to genetic epidemiological analysis, and consider study designs that might benefit from this promising new technology. While it is still too early to tell how much of the enormous potential of GEMA will be realized ultimately, its success will probably depend most critically on the ability of statistical genetics to rise to the challenge of mining information from a sea of noise.

A custom-built insulin resistance gene chip

OBJECTIVES/AIM

Microarray (gene chip) technology offers a powerful new tool for analyzing the expression of large numbers of genes in many experimental samples. The aim of this study was to design, construct, and use a gene chip to measure the expression levels of key genes in metabolic pathways related to insulin resistance.

METHODS

We selected genes that were implicated in the development of insulin resistance, including genes involved in insulin signaling; glucose uptake, oxidation, and storage; fat uptake, oxidation, and storage; cytoskeletal components; and transcription factors. The key regulatory genes in the pathways were identified, along with other recently identified candidate genes such as calpain-10. A total of 242 selected genes (including 32 internal control elements) were sequence-verified, purified, and arrayed on aldehyde-coated slides.

RESULTS

Where more than 1 clone containing the gene of interest was available, we chose those containing the genes in the 5' orientation and an insert size of around 1.5 kb. Of the 262 clones purchased, 56 (21%) were found to contain sequences other than those expected. In addition, 2 (1%) did not grow under standard conditions and were assumed to be nonviable. In these cases, alternate clones containing the gene of interest were chosen as described above. The current version of the Insulin Resistance Gene Chip contains 210 genes of interest, plus 48 control elements. A full list of the genes is available at http://www.hbs.deakin.edu.au/mru/research/gene_chip_tech/genechip_three.htm/.

CONCLUSIONS

The human Insulin Resistance Gene Chip that we have constructed will be a very useful tool for investigating variation in the expression of genes relevant to insulin resistance under various experimental conditions. Initially, the gene chip will be used in studies such as exercise interventions, fasting, euglycemic-hyperinsulinemic clamps, and administration of antidiabetic agents.

DNA microarray analysis of the expression profiles of luteinized granulosa cells as a function of ovarian reserve

OBJECTIVE

To examine the expression profiles of luteinized granulosa cells isolated from women with normal or diminished ovarian reserve undergoing in vitro fertilization.

DESIGN

Expression profiling by complementary DNA microarray analysis.

SETTING

Women undergoing in vitro fertilization-embryo transfer in a university-based fertility clinic.

PATIENT(S)

Eighteen women with normal or decreased ovarian reserve.

INTERVENTION(S)

All patients were given gonadotropin stimulation in preparation for IVF with granulosa cells isolated at the time of follicular aspiration.

MAIN OUTCOME MEASURE(S)

Expression profiles of luteinized granulosa cells isolated from each woman were determined by using DNA microarray analysis.

RESULT(S)

Changes in patterns of gene expression in granulosa cells were observed between women with normal and diminished ovarian reserve. These genes included several anonymous expressed sequence tags and also expressed sequence tags that correspond to known genes.

CONCLUSION(S)

Expression profiling of granulosa cells by DNA microarray may yield signature patterns that reflect the status of ovarian reserve and the competence of granulosa cells.

Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range

Gene expression ratios derived from spotted-glass microarray experiments have become invaluable to researchers by providing sensitive and comprehensive indicators of the molecular underpinnings of cell behaviors and states. However, several drawbacks to this form of data have been noted, including the inability to relate ratios to absolute expression levels or to compare experimental conditions not measured with the same control. In this study we demonstrate a method for overcoming these obstacles. First, instead of cohybridizing labeled experimental and control samples, we hybridize each sample against labeled oligos complementary to every microarray feature. Ratios between sample intensities and intensities of the oligo reference measure sample RNA levels on a scale that relates to their absolute abundance, instead of to the variable and unknown abundances of a cDNA reference. We demonstrate that results from this type of hybridization are accurate and retain absolute abundance information far better than conventional microarray ratios. Next, to ensure the accurate measurement of sample and oligo reference intensities, which may differ by several orders of magnitude, we use a linear regression algorithm, implemented in a freely available PERL script, to combine the linear ranges of multiple scans taken at different scanner sensitivity settings onto an extended linear scale. We discuss future applications of our method to measure RNA expression on the absolute scale of number of transcripts per cell from any organism for which oligo-based spotted-glass microarrays are available.

Initiating oncogenic event determines gene-expression patterns of human breast cancer models

Molecular expression profiling of tumors initiated by transgenic overexpression of c-myc, c-neu, c-ha-ras, polyoma middle T antigen (PyMT) or simian virus 40 T/t antigen (T-ag) targeted to the mouse mammary gland have identified both common and oncogene-specific events associated with tumor formation and progression. The tumors shared great similarities in their gene-expression profiles as compared with the normal mammary gland with an induction of cell-cycle regulators, metabolic regulators, zinc finger proteins, and protein tyrosine phosphatases, along with the suppression of some protein tyrosine kinases. Selection and hierarchical clustering of the most variant genes, however, resulted in separating the mouse models into three groups with distinct oncogene-specific patterns of gene expression. Such an identification of targets specified by particular oncogenes may facilitate development of lesion-specific therapeutics and preclinical testing. Moreover, similarities in gene expression between human breast cancers and the mouse models have been identified, thus providing an important component for the validation of transgenic mammary cancer models.

Knowledge-based assessment of gene expression data from chemiluminescence detection

The first problem in gene expression profiling to be solved is choosing the appropriate gene array, detection procedure, image analysis and data generation depending on the organism of interest, equipment and budget. The next one is how to deduce biologically meaningful data. We assessed gene expression data from chemiluminescent detection and empirically found criteria for the reliable identification of biologically meaningful expression ratios. Current statistical assessments are often applied unreflectedly concerning problems occurring in practice. So interesting results are considered to be irrelevant. This requires a laborious data check. We suggest automation. Our empirically found criteria were transformed into and validated by a knowledge-based system. This system is adaptable to all other methods of expression profiling. We compared the experience-based and new knowledge-based assessment of the expression data from our chemiluminescent and additionally radioactive detection of several experiments with published data to evaluate our entire procedure. With our adaptation of chemiluminescence detection to commercially available Escherichia coli gene arrays we present a useful alternative to common procedures in gene expression monitoring. Moreover, with our consideration of plasmid-harbouring E. coli strains we provide the opportunity to monitor gene expression during processes requiring any plasmids (e.g. recombinant protein expression).

Identifying differentially expressed genes in cDNA microarray experiments

A major goal of microarray experiments is to determine which genes are differentially expressed between samples. Differential expression has been assessed by taking ratios of expression levels of different samples at a spot on the array and flagging spots (genes) where the magnitude of the fold difference exceeds some threshold. More recent work has attempted to incorporate the fact that the variability of these ratios is not constant. Most methods are variants of Student's t-test. These variants standardize the ratios by dividing by an estimate of the standard deviation of that ratio; spots with large standardized values are flagged. Estimating these standard deviations requires replication of the measurements, either within a slide or between slides, or the use of a model describing what the standard deviation should be. Starting from considerations of the kinetics driving microarray hybridization, we derive models for the intensity of a replicated spot, when replication is performed within and between arrays. Replication within slides leads to a beta-binomial model, and replication between slides leads to a gamma-Poisson model. These models predict how the variance of a log ratio changes with the total intensity of the signal at the spot, independent of the identity of the gene. Ratios for genes with a small amount of total signal are highly variable, whereas ratios for genes with a large amount of total signal are fairly stable. Log ratios are scaled by the standard deviations given by these functions, giving model-based versions of Studentization. An example is given.

Iterative normalization of cDNA microarray data

This paper describes a new approach to normalizing microarray expression data. The novel feature is to unify the tasks of estimating normalization coefficients and identifying control gene set. Unification is realized by constructing a window function over the scatter plot defining the subset of constantly expressed genes and by affecting optimization using an iterative procedure. The structure of window function gates contributions to the control gene set used to estimate normalization coefficients. This window measures the consistency of the matched neighborhoods in the scatter plot and provides a means of rejecting control gene outliers. The recovery of normalizational regression and control gene selection are interleaved and are realized by applying coupled operations to the mean square error function. In this way, the two processes bootstrap one another. We evaluate the technique on real microarray data from breast cancer cell lines and complement the experiment with a data cluster visualization study.

DNA microarrays in prostate cancer

DNA microarray technology provides a means to examine large numbers of molecular changes related to a biological process in a high throughput manner. This review discusses plausible utilities of this technology in prostate cancer research, including definition of prostate cancer predisposition, global profiling of gene expression patterns associated with cancer initiation and progression, identification of new diagnostic and prognostic markers, and discovery of novel patient classification schemes. The technology, at present, has only been explored in a limited fashion in prostate cancer research. Some hurdles to be overcome are the high cost of the technology, insufficient sample size and repeated experiments, and the inadequate use of bioinformatics. With the completion of the Human Genome Project and the advance of several highly complementary technologies, such as laser capture microdissection, unbiased RNA amplification, customized functional arrays (eg, single-nucleotide polymorphism chips), and amenable bioinformatics software, this technology will become widely used by investigators in the field. The large amount of novel, unbiased hypotheses and insights generated by this technology is expected to have a significant impact on the diagnosis, treatment, and prevention of prostate cancer. Finally, this review emphasizes existing, but currently underutilized, data-mining tools, such as multivariate statistical analyses, neural networking, and machine learning techniques, to stimulate wider usage.

Mouse eye gene microarrays for investigating ocular development and disease

Microarray technology can facilitate simultaneous expression analysis of thousands of genes and assist in delineating cellular pathways involved in development or disease pathogenesis. Since public databases and commercial cDNA microarrays have an under-representation of eye-expressed genes, we generated over 3000 expressed sequence tags from three unamplified mouse eye/retina cDNA libraries. These eye-expressed genes were used to produce cDNA microarrays. Methodology for printing of slides, hybridization, scanning and data analysis has been optimized. The I-gene microarrays will be useful for establishing expression profiles of the mouse eye/retina and provide a resource for defining molecular pathways involved in development, aging and disease.

Transcriptional and proteomic analysis of a ferric uptake regulator (fur) mutant of Shewanella oneidensis: possible involvement of fur in energy metabolism, transcriptional regulation, and oxidative stress

The iron-directed, coordinate regulation of genes depends on the fur (ferric uptake regulator) gene product, which acts as an iron-responsive, transcriptional repressor protein. To investigate the biological function of a fur homolog in the dissimilatory metal-reducing bacterium Shewanella oneidensis MR-1, a fur knockout strain (FUR1) was generated by suicide plasmid integration into this gene and characterized using phenotype assays, DNA microarrays containing 691 arrayed genes, and two-dimensional polyacrylamide gel electrophoresis. Physiological studies indicated that FUR1 was similar to the wild-type strain when they were compared for anaerobic growth and reduction of various electron acceptors. Transcription profiling, however, revealed that genes with predicted functions in electron transport, energy metabolism, transcriptional regulation, and oxidative stress protection were either repressed (ccoNQ, etrA, cytochrome b and c maturation-encoding genes, qor, yiaY, sodB, rpoH, phoB, and chvI) or induced (yggW, pdhC, prpC, aceE, fdhD, and ppc) in the fur mutant. Disruption of fur also resulted in derepression of genes (hxuC, alcC, fhuA, hemR, irgA, and ompW) putatively involved in iron uptake. This agreed with the finding that the fur mutant produced threefold-higher levels of siderophore than the wild-type strain under conditions of sufficient iron. Analysis of a subset of the FUR1 proteome (i.e., primarily soluble cytoplasmic and periplasmic proteins) indicated that 11 major protein species reproducibly showed significant (P < 0.05) differences in abundance relative to the wild type. Protein identification using mass spectrometry indicated that the expression of two of these proteins (SodB and AlcC) correlated with the microarray data. These results suggest a possible regulatory role of S. oneidensis MR-1 Fur in energy metabolism that extends the traditional model of Fur as a negative regulator of iron acquisition systems.

Transcript array analysis in rheumatology

Transcript array analysis is a novel technique that examines the expression of thousands of genes simultaneously. Transcript array analyses are being used to clarify the diagnosis and prognosis of malignancies, and to understand the underlying pathogenesis of complex human disorders such as the rheumatic diseases. In this review, the authors will outline the use of transcript arrays to simultaneously assess gene activation of hundreds or thousands of genes, and their potential use in understanding and managing rheumatic disorders. The authors focus on the use of transcript arrays to confirm and refine disease diagnoses, to generate new hypotheses regarding pathophysiology of rheumatic diseases, and to the possible profiling of patients with respect to their likely response to therapies.

Toxicogenomics, drug discovery, and the pathologist

The field of toxicogenomics, which currently focuses on the application of large-scale differential gene expression (DGE) data to toxicology, is starting to influence drug discovery and development in the pharmaceutical industry. Toxicological pathologists, who play key roles in the development of therapeutic agents, have much to contribute to DGE studies, especially in the experimental design and interpretation phases. The intelligent application of DGE to drug discovery can reveal the potential for both desired (therapeutic) and undesired (toxic) responses. The pathologist's understanding of anatomic, physiologic, biochemical, immune, and other underlying factors that drive mechanisms of tissue responses to noxious agents turns a bewildering array of gene expression data into focused research programs. The latter process is critical for the successful application of DGE to toxicology. Pattern recognition is a useful first step, but mechanistically based DGE interpretation is where the long-term future of these new technologies lies. Pathologists trained to carry out such interpretations will become important members of the research teams needed to successfully apply these technologies to drug discovery and safety assessment. As a pathologist using DGE, you will need to learn to read DGE data in the same way you learned to read glass slides, patiently and with a desire to learn and, later, to teach. In return, you will gain a greater depth of understanding of cell and tissue function, both in health and disease.

Functional genomics approaches to understanding brain disorders

The completed draft of the human genome sequence has facilitated a revolution in neuroscience research. This sequence information and the development of new technologies used to analyze gene expression on a genomic scale provides a new and powerful means to investigate brain disorders of unknown etiology and to isolate novel drug targets for these disorders. The term functional genomics broadly describes a set of technologies and strategies directed at the problem of determining the function of genes, and understanding how the genome works together to generate whole patterns of biological function. The most powerful of these functional genomics approaches, expression profiling or DNA microarrays, can be used to analyze the expression of thousands of genes simultaneously. The results to date from the application of DNA microarray methods to postmortem diseased human brain tissue, animal models and cell culture models of brain disorders provide an exciting glimpse into the future of this field.

Microarrays in hematology

Microarrays are fast becoming routine tools for the high-throughput analysis of gene expression in a wide range of biologic systems, including hematology. Although a number of approaches can be taken when implementing microarray-based studies, all are capable of providing important insights into biologic function. Although some technical issues have not been resolved, microarrays will continue to make a significant impact on hematologically important research.

PRESTA: associating promoter sequences with information on gene expression

BACKGROUND

Large sets of well-characterized promoter sequences are required to facilitate the understanding of promoter architecture. The major sequence databases are a prospective source of upstream regulatory regions, but suffer from inaccurate annotation. The software tool PRESTA (PRomoter EST Association) presented in this study is designed for efficient recovery of characterized and partially verified promoters from GenBank and EMBL libraries.

RESULTS

The PRESTA algorithm examines the putative GenBank/EMBL promoters and automatically removes most of the poorly annotated entries. The remaining records are connected to expressed sequence tags (ESTs) through a high-stringency BLAST search. The frequency and source of recovered ESTs provide an estimate of the activity and expression pattern of the promoter, and the ESTs' 5' ends assist in transcription start-site verification. The PRESTA database provides easy access to non-redundant upstream regulatory regions recently extracted by the PRESTA algorithm. The current size of this resource is 552 human and 241 mouse promoters. Surprisingly, no overlap between the PRESTA database and the Eukaryotic Promoter Database (EPD) was detected by sequence comparison.

CONCLUSIONS

The PRESTA algorithm demonstrates the principle of promoter verification by mapping EST 5' ends. The publicly available PRESTA database collects hundreds of characterized and partially verified promoter sequences and is complementary to other promoter databases.

Within the fold: assessing differential expression measures and reproducibility in microarray assays

BACKGROUND

'Fold-change' cutoffs have been widely used in microarray assays to identify genes that are differentially expressed between query and reference samples. More accurate measures of differential expression and effective data-normalization strategies are required to identify high-confidence sets of genes with biologically meaningful changes in transcription. Further, the analysis of a large number of expression profiles is facilitated by a common reference sample, the construction of which must be carefully addressed.

RESULTS

We carried out a series of 'self-self' hybridizations in which aliquots of the same RNA sample were labeled separately with Cy3 and Cy5 fluorescent dyes and co-hybridized to the same microarray. From this, we can analyze the intensity-dependent behavior of microarray data, define a statistically significant measure of differential expression that exploits the structure of the fluorescent signals, and measure the inherent reproducibility of the technique. We also devised a simple procedure for identifying and eliminating low-quality data for replicates within and between slides. We examine the properties required of a universal reference RNA sample and show how pooling a small number of samples with a diverse representation of expressed genes can outperform more complex mixtures as a reference sample.

CONCLUSION

Analysis of cell-line samples can identify systematic structure in measured gene-expression levels. A general procedure for analyzing cDNA microarray data is proposed and validated. We show that pooled reference samples should be based not only on the expression of individual genes in each cell line but also on the expression levels of genes within cell lines.

Have microarrays failed to deliver for developmental biology?

Comprehensive microarrays covering large numbers of the predicted expressed transcripts for some invertebrates and vertebrates have been available for some time. Despite predictions that this technology will transform biology, to date there have been few published studies using microarrays to generate novel insights in developmental biology.

StressDB: A Locally Installable Web-Based Relational Microarray Database Designed for Small User Communities

We have built a microarray database, StressDB, for management of microarray data from our studies on stress-modulated genes in Arabidopsis. StressDB provides small user groups with a locally installable web-based relational microarray database. It has a simple and intuitive architecture and has been designed for cDNA microarray technology users. StressDB uses Windows^™ 2000 as the centralized database server with Oracle^™ 8i as the relational database management system. It allows users to manage microarray data and data-related biological information over the Internet using a web browser. The source-code is currently available on request from the authors and will soon be made freely available for downloading from our website athttp://arastressdb.cac.psu.edu.

Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions

The complexity of biological systems provides for a great diversity of relationships between genes. The current analysis of whole-genome expression data focuses on relationships based on global correlation over a whole time-course, identifying clusters of genes whose expression levels simultaneously rise and fall. There are, of course, other potential relationships between genes, which are missed by such global clustering. These include activation, where one expects a time-delay between related expression profiles, and inhibition, where one expects an inverted relationship. Here, we propose a new method, which we call local clustering, for identifying these time-delayed and inverted relationships. It is related to conventional gene-expression clustering in a fashion analogous to the way local sequence alignment (the Smith-Waterman algorithm) is derived from global alignment (Needleman-Wunsch). An integral part of our method is the use of random score distributions to assess the statistical significance of each cluster. We applied our method to the yeast cell-cycle expression dataset and were able to detect a considerable number of additional biological relationships between genes, beyond those resulting from conventional correlation. We related these new relationships between genes to their similarity in function (as determined from the MIPS scheme) or their having known protein-protein interactions (as determined from the large-scale two-hybrid experiment); we found that genes strongly related by local clustering were considerably more likely than random to have a known interaction or a similar cellular role. This suggests that local clustering may be useful in functional annotation of uncharacterized genes. We examined many of the new relationships in detail. Some of them were already well-documented examples of inhibition or activation, which provide corroboration for our results. For instance, we found an inverted expression profile relationship between genes YME1 and YNT20, where the latter has been experimentally documented as a bypass suppressor of the former. We also found new relationships involving uncharacterized yeast genes and were able to suggest functions for many of them. In particular, we found a time-delayed expression relationship between J0544 (which has not yet been functionally characterized) and four genes associated with the mitochondria. This suggests that J0544 may be involved in the control or activation of mitochondrial genes. We have also looked at other, less extensive datasets than the yeast cell-cycle and found further interesting relationships. Our clustering program and a detailed website of clustering results is available at http://www.bioinfo.mbb.yale.edu/expression/cluster (or http://www.genecensus.org/expression/cluster).

Applications of DNA microarrays in microbial systems

DNA microarray technology allows a parallel analysis of RNA abundance and DNA homology for thousands of genes in a single experiment. Over the past few years, this powerful technology has been used to explore transcriptional profiles and genome differences for a variety of microorganisms, greatly facilitating our understanding of microbial metabolism. With the increasing availability of complete microbial genomes, DNA microarrays are becoming a common tool in many areas of microbial research, including microbial physiology, pathogenesis, epidemiology, ecology, phylogeny, pathway engineering and fermentation optimization.

Regulatory sequence analysis: application to the interpretation of gene expression

Microarray technologies for measuring mRNA abundances in cells allow monitoring of gene expression levels for tens of thousands of genes in parallel. By measuring expression responses across hundreds of different conditions or timepoints a relatively detailed gene expression map starts to emerge. Using cluster analysis techniques, it is possible to identify genes that are consistently coexpressed under several different conditions or treatments. These sets of coexpressed genes can then be compared to existing knowledge about biochemical or signalling pathways, the function of unknown genes can be hypothesised by comparing them to other genes with characterised function, or from trends in expression profiles in general - why cell needs to transcribe or silence the genes during particular treatment. The regulation of genes on the DNA level is largely guided by particular sequence features, the transcription factor binding sites, and other signals encaptured in DNA. By analyzing the regulatory regions of the DNA of the genes consistently coexpressed, we can discover the potential signals hidden in DNA by computational analysis methods. The prerequisite for this kind of analysis is the existence of genomic DNA sequence, knowledge about gene locations, and experimental gene expression measurements for a variety of conditions. This article surveys some of the analysis methods and studies for such a computational discovery approach for yeast Saccharomyces cerevisiae.

Gene expression microarray analysis in cancer biology, pharmacology, and drug development: progress and potential

With the imminent completion of the Human Genome Project, biomedical research is being revolutionised by the ability to carry out investigations on a genome wide scale. This is particularly important in cancer, a disease that is caused by accumulating abnormalities in the sequence and expression of a number of critical genes. Gene expression microarray technology is gaining increasingly widespread use as a means to determine the expression of potentially all human genes at the level of messenger RNA. In this commentary, we review developments in gene expression microarray technology and illustrate the progress and potential of the methodology in cancer biology, pharmacology, and drug development. Important applications include: (a) development of a more global understanding of the gene expression abnormalities that contribute to malignant progression; (b) discovery of new diagnostic and prognostic indicators and biomarkers of therapeutic response; (c) identification and validation of new molecular targets for drug development; (d) provision of an improved understanding of the molecular mode of action during lead identification and optimisation, including structure-activity relationships for on-target versus off-target effects; (e) prediction of potential side-effects during preclinical development and toxicology studies; (f) confirmation of a molecular mode of action during hypothesis-testing clinical trials; (g) identification of genes involved in conferring drug sensitivity and resistance; and (h) prediction of patients most likely to benefit from the drug and use in general pharmacogenomic studies. As a result of further technological improvements and decreasing costs, the use of microarrays will become an essential and potentially routine tool for cancer and biomedical research.

Microarrays: handling the deluge of data and extracting reliable information

Application of powerful, high-throughput genomics technologies is becoming more common and these technologies are evolving at a rapid pace. Genomics facilities are being established in major research institutions to produce inexpensive, customized cDNA microarrays that are accessible to researchers in a broad range of fields. These high-throughput platforms have generated a massive onslaught of data, which threatens to overwhelm researchers. Although microarrays show great promise, the technology has not matured to the point of consistently generating robust and reliable data when used in the average laboratory. This article addresses several aspects related to the handling of the deluge of microarray data and extracting reliable information from these data. We review the essential elements of data acquisition, data processing and data analysis, and briefly discuss issues related to the quality, validation and storage of data. Our goal is to point out some of the problems that must be overcome before this promising technology can achieve its full potential.

Robust and efficient synthetic method for forming DNA microarrays

The field of DNA microarray technology has necessitated the cooperative efforts of interdisciplinary scientific teams to achieve its primary goal of rapidly measuring global gene expression patterns. A collaborative effort was established to produce a chemically reactive surface on glass slide substrates to which unmodified DNA will covalently bind for improvement of cDNA microarray technology. Using the p-aminophenyl trimethoxysilane (ATMS)/diazotization chemistry that was developed, microarrays were fabricated and analyzed. This immobilization method produced uniform spots containing equivalent or greater amounts of DNA than commercially available immobilization techniques. In addition, hybridization analyses of microarrays made with ATMS/diazotization chemistry showed very sensitive detection of the target sequence, two to three orders of magnitude more sensitive than the commercial chemistries. Repeated stripping and re-hybridization of these slides showed that DNA loss was minimal, allowing multiple rounds of hybridization. Thus, the ATMS/diazotization chemistry facilitated covalent binding of unmodified DNA, and the reusable microarrays that were produced showed enhanced levels of hybridization and very low background fluorescence.

DNA exhibits multi-stranded binding recognition on glass microarrays

In the course of exploring the hybridization properties of glass DNA microarrays, multi-stranded DNA structures were observed that could not be accounted for by classical Watson-Crick base pairing. Non-denatured double-stranded DNA array elements were shown to hybridize to single-stranded (ss)DNA probes. Similarly, ssDNA array elements were shown to bind duplex DNA probes. This led to a series of experiments demonstrating the formation of multi-stranded DNA structures on the surface of microarrays. These structures were observed with a number of heterogeneous sequences, including both purine and pyrimidine bases, with shared sequence identity between the ssDNA and one of the duplex strands. Furthermore, we observed a strong binding preference near the ends of duplexes containing a 3'-homologous strand. We suggest that such binding interactions on cationic solid surfaces could serve as a model for a number of biological processes mediated through multi-stranded DNA.