AN OVERVIEW ON DNA MICROARRAY TECHNOLOGY

About Author:
Krishna Bhatt
Department of Pharmacy, B.N. Girls College of Pharmacy,
(Udaipur, INDIA.)
krishna30.bhatt@yahoo.com

Abstract:
The vast amount of information available through the human genome project is going to have a major impact on medical science. However, the mere sequence information of the whole genome does not answer all our questions. What is required at this stage is a complete understanding of the function of genes and other parts of the genome so as to uncover how sets of genes and their products work together in normal and diseased conditions. One major requirement for these studies is the development of high-throughput technologies. DNA microarrays are some of the most powerful and versatile tools available, and there are several applications of microarray technology e.g. cancer.

REFERENCE ID: PHARMATUTOR-ART-1659

Introduction
One of the amazing things in science following the availability of human genome sequence information is our ability to do experiments on a genome-wide scale. DNA microarrays offer the ability to look at the expression of thousands of genes in a single experiment. This is what is called gene expression profiling, which is very important because it is the set of expressed genes that determines the phenotype of a particular cell. In fact, it is believed that only about one third of the total number of genes is expressed in any given type of cell. This review has two parts: the first part will summarize the principle, the different steps involved in DNA microarray technology and, more importantly, the significance of proper data analysis methodologies; the second part will particularly focus on different applications of microarray technology in cancer biology.

Principle of DNA microarray technology
Microarray techniques provide a platform where one can measure the expression levels of tens of thousands of genes in a sample. Basically, one measures the levels of all mRNA species thereby creating an expression profile or ‘transcriptome’ for the sample under study. Expression profiling is particularly important because it is the set of expressed genes and interplay between the products encoded by them that determine the phenotype of a cell. The microarray experiment involves the preparation of fluorescently labeled cDNA from mRNA, isolated from two different conditions, to be compared with different fluorochromes such as Cy3 (green) and Cy5 (red). The resulting mixture of labeled cDNAs is hybridized to a large number of genes placed as individual spots on a microarray slide. Hybridization results are then analyzed by determining the relative fluorescent intensity at each gene spot with the use of a laser scanner. Spots that fluoresce predominately with one label or the other indicate a gene that is differentially up-regulated or down-regulated in the sample under the conditions of the study.

Steps involved in DNA microarray technology
There are three major steps involved in a typical experiment involving a microarray: preparation of microarrays; preparation of fluorescently labeled cDNA probes and hybridization; and finally scanning, image and data analysis.

Figure: Microarray spotting techniques (Schena 1998)

Preparation of microarrays
Microarrays are available in two different forms:

• oligonucleotide arrays and
• cDNA microarrays.

Oligonucleotide arrays are generated by synthesizing specific oligonucleotides in a predetermined spatial orientation on a solid surface using a technique called photolithography. This technology was pioneered by Affymetrix. Now it is come up with a variety of commercially available arrays representing different organisms.

Oligonucleotide arrays from Affymetrix are made by in situ synthesis of oligonucleotides on the slide, while in all other companies pre-synthesised oligonucleotides are spotted.

Mergen Ltd	Oligonucleotide microarray	Human, mouse and rat arrays, starter arrays
Microarray Centre, University Health Network, Toronto, Canada	cDNA microarray	Human, mouse and yeast cDNA arrays
Genomic Solution	cDNA microarray	GeneMap cancer array, rat cytochrome P450array for drug characterisation

 

MWG Biotech

cDNA microarray

Human, mouse, rat and zebrafish cDNA arrays. Topic arrays for topics like cancer and hepatic metabolism etc

cDNA arrays are generated by printing a double- stranded cDNA onto a solid support, such as glass or nylon, using robotic pins. Two major steps involved in the generation of cDNA microarrays are preparation of PCR amplified cDNA clones and spotting them at a high density on a derivative glass microscopic slide. Some of the commercially available microarrays are given in Table 1.

Although the cDNA arrays are generally made on glass slides, DNA can also be spotted on a solid support like nitrocellulose or charged nylon membranes, and these types of arrays are called paper arrays or microarrays. While the glass microarrays allow test and control samples to be require high cost labeling kits and a scanner.

Table 1

Different types of pre-spotted DNA microarrays available commercially

Source	Type of array	Features
Affymetrix, Inc	Oligonucleotide microarray	rat, mouse, yeast, Arabidopsis, C. elegansand Drosophila genome arrays. Specific arrays for detecting human SNP’s, mutations in p53 and cytochrome P450
Agilent Technologies Inc	cDNA and oligonucleotide	Human (drug target), mouse, rat cDNA arrays. Human, mouse (development) microarrays yeast and Arabidopsis oligonucleotide arrays
Azign Bioscience	cDNA and oligonucleotide microarrays	SpecificTargetArrays for drug targets. DesignArray for specific diseases T
Clontech Laboratories	cDNA and oligonucleotide microarrays	Atlas nylon cDNA arrays, Atlas glass arrays, Atlas plastic arrays for T M T M T M microarrays human, mouse and rat species. Atlas Select arrays and cancer profiling T M arrays for cancer study; BD Clontech™ disease profiling arrays

Preparation of fluorescently labeled cDNA probes and hybridization
In microarray analysis, the differential gene expression is analyzed by co-hybridizing fluorescently labeled cDNA probes prepared from two different RNA sources. The quality of RNA, proper removal of unincorporated fluorescently labeled nucleotides, proper hybridization and post-hybridization washing conditions are some of the important factors that affect the outcome of a microarray experiment.

Isolation of RNA
Good quality RNA is the prerequisite for a microarray experiment. There is no particular preferred way of RNA preparation as long as the quality is not compromised. The Trizol (Gibco BRL) method of RNA preparation is quite frequently used. Impurities in the RNA preparation can affect the efficiency of labeling as well as the stability of the fluorescent label.

Preparation of fluorescently labeled cDNA
This labeling procedure involves the conversion of mRNA to cDNA and labeling the cDNA with fluorescent dyes. The most frequently used fluorescent dyes are Cy3 (green) total or poly (A+) RNA can be used in the reverse transcription reaction. There are two types of labeling methods available:

• direct method or
• indirect method.

Direct labeling involves the incorporation of Cy3 or Cy5-labelled dUTP directly into cDNA during reverse transcription. Although this method is very commonly used, it has the disadvantage that Cy5-labelled nucleotides are less preferentially incorporated compared to Cy3-labelled nucleotides. In the case of indirect labeling, amino-allyl-modified nucleotides are incorporated during the synthesis of first strand cDNA for both samples, and the NHS-ester of the appropriate cyanine dye is subsequently attached by covalent coupling. The following points should be kept in mind regardless of which method is used for labeling. The unincorporated fluorescently labeled nucleotides have to be removed from the cDNA in the case of direct labeling. In the case of indirect labeling, amino-allyl-modified nucleotides and uncoupled dyes have to be removed from the labeled cDNA at appropriate steps. Improper removal of unincorporated nucleotides can result in significant background on the slides. Because Cy3 and Cy5 dyes are photosensitive, exposure to light during various steps of the experiment should be minimized. Information about various commercially available direct and indirect labeling kits is given in Table 2.

Table 2

Different microarray labeling kits

Company	Name of the kit	method required	Labeling Total RNA
Stratagene	Fair Play labeling kit	Indirect	10–20µg
Clontech Atlas	Atlas™ Powerscript fluorescent labeling kit Atlas™ Glass fluorescent labeling kit	Indirect	3µg
Agilent	Direct labeling kit	Indirect	10µg
Perkin	MICROMAX Direct labeling kit	Direct	10µg
Elmer	MICROMAX TSA labeling kit	Direct	20–30µg
Genetix	Hyspot direct labeling kit	Indirect	0.5–1µg
Qiagen	LabelStar Array labeling kit	Direct and indirect	Not available
Genisphere	ARRAY 350RP T M	Not available	0.5–2µg

The product of the labeling reaction can be analyzed either spectrophotometrically or by running an agarose gel. In the spectrophotometric method, one measures the nucleotide/dye ratio by reading absorbance at 260 nm for DNA and either 550 nm for Cy3 or 650 nm for Cy5 (Hegde et al 2000). Alternatively, one can run a small amount of labeled cDNA in an agarose gel, which can be scanned using a laser scanner to verify the average size and quality of labeled cDNA. The LS IV scanner manufactured by Genomic Solutions, USA has this feature.

Hybridization
As with any other hybridization procedure, microarray experiments also require hybridization with high specificity and minimal background. Care should be taken not to allow bubbles to form during hybridization as it leads to problems in the analysis because of excessive background.

Slide scanning, image and data analysis
After hybridization, slides are scanned using a confocal laser scanner capable of interrogating both the Cy3- and Cy5- labeled probes to produce separate TIFF images for each label. These images are subsequently analyzed to calculate the relative levels of expression of each gene. The raw data obtained after image analysis has to be further analyzed before one can identify the list of differentially regulated genes.

Slide scanning
Scanners use red and green lasers operating at appropriate wavelengths to excite Cy3 and Cy5 dyes to create a separate 16-bit TIFF image for each channel. Slides are generally scanned in the Cy5 channel, as it is more susceptible to photo degradation than Cy3. Scanners capable of detecting Cy3 and Cy5 are produced by a number of manufacturers.

Image analysis
A good image will have a low level, uniform background and good signal-to-noise ratio. Image analysis involves spot identification, background determination and calculation of background subtracted fluorescent intensities for both labels. The location of spots has to be identified first because precipitated probe, hybridization artifacts and contaminants such as dust on the surface of the slide can develop spurious signals. Spots are generally made in a particular arrangement and hence it is usually easy to identify the spots and distinguish them from artifacts. Most commercial scanners provide software for spot identification. In addition, there are several public sites that provide free software. After spot identification, the next step is the estimation of background signal. The background is generally calculated locally in the vicinity of each spot. Then the background-subtracted fluorescent intensity for each spot is calculated. The ratios of measured Cy3 to Cy5 intensities are used to identify differentially expressed genes.

Data analysis
Further analysis of crude data obtained after image analysis is required to identify the differentially regulated genes. The crude data should be first subjected to normalization.

Normalization is done to adjust differences in labeling reaction and detection efficiencies for the fluorescent labels and also differences in the quantities of RNA from the samples used in the experiment. There are at least three methods by which one can do normalization.

The most commonly practiced method uses total measured fluorescence intensity. The total integrated intensity across all spots in the microarray should be equal for both channels because the total amount of RNA used for labeling from each sample is equal. Even though the intensity for any one spot may be different in one channel than the other, the average intensity per spot in a microarray experiment should be equal. The average intensity per spot is calculated by dividing the total intensity of all spots for one channel by the total number of spots.

In another method, one can add one or more control RNA samples in equimolar concentrations to both labeling reactions, and the normalization is carried out considering the fact that the sum of the intensities of spots corresponding to the control gene(s) should be equal. A subset of housekeeping genes can also be used for the purpose of normalization.

An appropriate method of normalization should be used taking into consideration the system used and the conditions of the experiment. After normalization is done, differentially expressed genes are identified.  Software packages are available, both commercially and publicly, which can identify spots by overlaying grids on the image, calculate background, normalize data and produce an output data file with absolute expression as well as ratio of expression between test and control sample. A Cy5:Cy3 ratio of one indicates no change, a ratio of less than one indicates down- regulation (greater intensity in Cy3, the control) and a ratio of greater than one indicates up-regulation (greater intensity in Cy5, the experimental condition). A post-normalisation cut-off of twofold up- or down-regulation is normally used to identify the differentially expressed genes.

Although a list of up- and down-regulated genes from microarray data gives a reasonably good amount of information, it does not answer all the questions about the complex process of cancer progression. Generating a pool of similarly behaving genes is called cluster analysis. The widely used methods for clustering microarray data are hierarchical clustering, K-means clustering and self-organizing maps.

Hierarchical clustering takes a bottom-up approach, which starts with each gene in its own cluster. Similar genes are placed close together. This method is useful in its ability to represent varying degrees of similarity and distant relationships among groups of closely related genes (Eisen et al 1998). The output of hierarchical clustering is shown as a dendrogram (tree graph) with the closest branches of the tree representing arrays with similar gene expression patterns. K-means clustering takes a top-down approach, which starts with a specified number of clusters and initial positions for the cluster centers. An advantage of the K- means clustering method is that it is relatively scalable in processing large data sets. Analysis of microarray data by clustering analysis has major implications for cancer clustering analysis has major implications for cancer biology. It is possible to identify groups of genes associated with drug sensitivity and drug resistance, or genes that predict the outcome of a cancer. Tumour grading could also be done more accurately than with the traditional histopathological methods.

Application of DNA microarray technology
DNA microarrays can be used to detect DNA (as in comparative genomic hybridization), or detect RNA (most commonly as cDNA after reverse transcription) that may or may not be translated into proteins. The process of measuring gene expression via cDNA is called expression analysis or expression profiling.

DNA microarrays can be used to measure changes in expression levels, to detect single nucleotide polymorphisms (SNPs), or to genotype or resequence mutant genomes. Microarrays also differ in fabrication, workings, accuracy, efficiency, and cost. Additional factors for microarray experiments are the experimental design and the methods of analyzing the data.

Applications include:

Application  technology	Synopsis
Geneexpression         profiling	In an mRNA or gene expression profiling experiment the expression levels of thousands of genes are simultaneously monitored to study the effects of certain treatments, diseases, and developmental stages on gene expression. For example, microarray-based gene expression profiling can be used to identify genes whose expression is changed in response to pathogens or other organisms by comparing gene expression ininfected to that in uninfected cells or tissues
Comparativegenomic hybridization	Assessing genome content in different cells or closely related organisms.
GeneID	Small microarrays to check IDs of organisms in food and feed (like GMO), mycoplasms in cell culture, or pathogens for disease detection, mostly combining PCR and microarray technology
Chromatin immunoprecipitation on Chip	DNA sequences bound to a particular protein can be isolated by immunoprecipitating that protein (ChIP), these fragments can be then hybridized to a microarray (such as a tiling array) allowing the determination of protein binding site occupancy throughout the genome. Example protein to immunoprecipitate are histone modifications (H3K27me3, H3K4me2, H3K9me3, etc.), Polycomb-group protein (PRC2:Suz12, PRC1:YY1) and trithorax-group protein (Ash1) to study the epigenetic landscape or RNA Polymerase II to study the transcription landscape.
DamID	Analogously to ChIP, genomic regions bound by a protein of interest can be isolated and used to probe a microarray to determine binding site occupancy. Unlike ChIP, DamID does not require antibodies but makes use of adenine methylation near the protein's binding sites to selectively amplify those regions, introduced by expressing minute amounts of protein of interest fused to bacterial DNA adenine methyl transferase.
SNP detection	Identifying single nucleotide polymorphism among alleles within or between populations. Several applications of microarrays make use of SNP detection, including Genotyping, forensic analysis, measuring predisposition to disease, identifying drug-candidates, evaluating germline mutations in individuals or somatic mutations in cancers, assessing loss of heterozygosity, or genetic linkage analysis.
Alternativesplicing detection	An 'exon junction array design uses probes specific to the expected or potential splice sites of predicted exons for a gene. It is of intermediate density, or coverage, to a typical gene expression array (with 1-3 probes per gene) and a genomic tiling array (with hundreds or thousands of probes per gene). It is used to assay the expression of alternative splice forms of a gene. Exon arrays have a different design, employing probes designed to detect each individual exon for known or predicted genes, and can be used for detecting different splicing isoforms.
Fusiongenes microarray	A Fusion gene microarray can detect fusion transcripts, e.g. from cancer specimens. The principle behind this is building on the alternative splicing microarrays. The oligo design strategy enables combined measurements of chimeric transcript junctions with exon-wise measurements of individual fusion partners.
Tiling array	Genome tiling arrays consist of overlapping probes designed to densely represent a genomic region of interest, sometimes as large as an entire human chromosome. The purpose is to empirically detect expression of transcripts or alternatively splice forms which may not have been previously known or predicted.

·         Microarrays in acute lung injury and pulmonary edema

·         Application of Complementary DNA Microarray Technology to Carcinogen Identification, Toxicology, and Drug Safety Evaluation

·         Application of microarray technology in pulmonary diseases

·         Microarrays in Chronic Obstructive Pulmonary Disease

·         Microarrays in sarcoidosis

·         Microarrays in asthma

·         Applications of microarrays in CNS

·         Applications of microarrays in microbial oncology

·         Applications of microarrays in medicine

cDNA microarray technology, which can be used to analyze changes in genome-wide patterns of gene expression, is one new methodological advance that may revolutionize the way some toxicological problems are investigated. The application of a large number of genes or expressed sequence tags in a condensed array on glass slides or nylon filters comprises a cDNA microarray

Alternatively, specific oligonucleotides that are complementary to known genes or expressed sequence tags are deposited on a miniature matrix by a photolithographic process to create an oligonucleotide-based microarray. Either cDNA microarrays or oligonucleotide-based chips may be used for gene expression analysis. Oligonucleotide-based DNA chips are also used for analyzing sequence variations in genomic DNA for screening individuals for DNA mutations and polymorphism variations. This approach has been recently reviewed.

Potential uses of microarrays in toxicology
One example of the use of cDNA microarrays is in the process of drug development. Given that advances in genomics and combinatorial chemistry are leading to the discovery of many new potential drugs, surrogate markers of efficacy and safety are needed to expedite clinical trials. Gene expression profiles can be used as a proof of principle assay to show an effect of a candidate drug in vivo.

Furthermore, cDNA microarrays can be used to detect toxic responses in target and nontarget tissues in rodents and humans. The dose of a drug that maximizes the therapeutic index can potentially be determined from such measurements, which will improve optimization of lead compound development.

By the use of cDNA microarrays, toxic or unanticipated responses in humans may be determined early in a clinical trial prior to overt tissue toxicity, providing a rapid, sensitive surrogate of safety, which is essential for improved clinical trials. Also, microarrays may help identify susceptible individuals who respond to a treatment or who exhibit adverse effects to drugs.

In the area of environmental health sciences, cDNA microarray technology can be used in the identification of potential hazards. It should be relatively easy to establish model systems, both in vitro and in vivo, to examine gene expression changes as indications of chemical effect. In these defined model systems, treatment with known agents, such as polycyclic aromatic hydrocarbons, peroxisome proliferators, oxidant stress, or estrogenic chemicals, agents that lead to activation of signaling pathways will provide a gene expression “signature” on a cDNA microarray, which represents the cellular or tissue response to these agents. It is likely that the molecular response to different agents will induce changes in expression of many genes that are indicative of a general toxic response, but a subset of genes expressed is predicted to be unique for a particular class of compounds, especially at low doses. Once the subsets of prototypic response genes are defined for known agents in established models, treatment of these same systems with unknown, suspect agents may be used to determine whether one or more of these standard signatures is elicited. This approach may flag certain compounds as potential carcinogens/toxicants and will help elucidate the agent’s mechanism of action by identification of the activated signal transduction pathways. Indeed, this approach has already been demonstrated in a recent study investigating the signature response for drug exposure in wild-type yeast compared with yeast that harbor a mutation in genes that are potential targets for compound action. Another important application for cDNA microarrays is in the determination of cross-talk between combinations or mixtures of agents.

Specific cDNA microarray chips may be designed for the purpose of studying toxicant action in humans and in a variety of model organisms, including mouse, rat, and yeast. These cDNA chips will allow the simultaneous monitoring of gene expression changes for receptor-mediated responses, xenobiotic metabolizing enzymes, cell cycle components, oncogenes, tumor suppressor genes, DNA repair genes, estrogen-responsive genes, oxidative stress genes, and genes known to be involved in apoptotic cell death. The advantage of this technology is that expression changes may be easily assessed over a range of doses as well as times of exposure. However, the bioinformatic analysis of these gene expression changes over time and dose is complex and needs to be further developed.

It is possible to use cDNA microarrays to measure biomarkers of exposure or effect in humans. However, these applications will require extensive investigation before they become feasible. Traditional assays measure metabolites of the toxicant, putative tissue damage induced by the toxicant, or DNA adducts present in peripheral blood. One major hurdle in using a gene expression approach for these assays is to obtain tissue samples at a time when it would be most informative as a biomarker. It may be difficult to obtain tissues that exhibit gene expression changes at the mRNA level to assess exposure for the purpose of determining that an exposure occurred prior to the onset of pathological symptoms. This, however, is when exposure should ideally be determined to allow intervention and prevention of disease.

The combined use of chips for measuring DNA sequence and polymorphisms and cDNA based microarrays might also be used to identify susceptible individuals. Currently, polymorphism studies are used to assess individuals that have “susceptible” alleles for gene implicated in disease. Whereas it is not known initially what effect these polymorphisms have on gene function, microarrays might be useful to examine the link between disease susceptibility and individual variability in gene expression. However, large studies on control populations are first needed to understand the intrinsic variability in normal gene expression. Events such as prior exposures, health, and diet of the individual might influence these levels and will need to be taken into account.

In summary, the application of cDNA microarray analysis to the field of toxicology, carcinogen identification, and drug safety provides an opportunity to change and improve the way environmental factors and therapeutics are currently investigated. cDNA microarrays may be used to identify new environmental carcinogens and toxic effects of drugs, to improve the current testing models, and to also understand the mechanism of action of these agents. Defining the mechanisms of action of toxic agents can greatly assist in species extrapolation and risk assessment. This should also lead to the identification of new genes/targets involved in environmentally caused diseases, including cancer and diseases of the immune, nervous, and pulmonary/respiratory systems. 37

* Application of microarray technology in pulmonary diseases
Microarray technology is rapidly becoming a standard technology used in research laboratories all across the world. Since its first application in the mid 1990s microarray technology has been successfully applied to almost every aspect of biomedical research. Research conducted the last ten years has elevated the status of microarray technology from poorly understood and doubtfully applied in the fields of medicine to one that requires attention when the examination of clusters of genes in a single experiment is considered. Far more progress has been made toward an understanding of the pivotal role of microarrays in respiratory research by providing the scientists well-established knowledge concerning numerous genes that can be used as potential drug targets, mediators and inflammatory molecules with important cellular functions, evidence that captured the interest of both clinicians and researchers and caused a consecutive year by year rise of the applications of microarrays in experiments designed to study pulmonary diseases.

1. Microarrays in idiopathic pulmonary fibrosis
Idiopathic pulmonary fibrosis (IPF) is a refractory and lethal interstitial lung disease characterized by fibroblast proliferation, extracellular matrix (ECM) deposition and progressive lung scarring. The incidence of IPF is estimated at 15–40 cases per 100.000 per year, and the mean survival from the time of diagnosis is 3–5 yr regardless of treatment.

These preliminary results suggest that this technology could identify unexpected molecular participants in IPF and might help in the development of novel targets for improved treatment. The method may also allow molecular fingerprinting that could improve the ability to identify subclassifications of pulmonary fibrosis that might be more informative than the current classification based primarily on histologic and radiographic patterns]. Nonetheless these studies characterized as "fishing expeditions" are limited by the inability of microarrays to detect the final expression product (protein), identify genes that are not included in the array and ascribe changes in gene expression in specific cellular types. However our view is that there is nothing wrong with a "fishing expedition" if what you are after is "fish", such as new genes involved in a pathway, potential drug targets or expression markers that can be used in predictive or diagnostic fashion. Hence, these observations are not to diminish their value for understanding basic biological processes and even for understanding, predicting and eventually treating human disease.

2. Microarrays in asthma
Several asthma/atopy associated genes have been identified from linkage and association studies within families and revealed that there are multiple chromosomal regions, containing potential candidate genes, associated with various asthma phenotypes.

Microarray technology offers a new opportunity to gain insight into global gene expression profiles in asthma, leading to the identification of asthma associated genes. Several experimental models have been used for this purpose although no animal disease model is identical to human disease. Zou et al. were the first attempted to produce an allergen-induced gene expression profile in the lung of a non-human primate using genomics tools such as microarrays and real time-(RT)-PCR in an independent way. Microarray data generated from this study and validated by RT-PCR using same lung samples, revealed a differential gene expression pattern between control and challenged animals. Furthermore investigators established that genes identified by microarray technology represented genes truly regulated by inhalation antigen challenge. This was done by determining that the regulated expression levels identified by microarray assay from a single animal were confirmed by RT-PCR studies using multiple similarly treated animals. Potential limitations of this study include the time-limited gene expression profile tested which may not reflect the chronic aspect of asthma and the absence of evidence that the antigens used would produce the same allergic reaction in humans.

Brutsche et al designed an array based composite atopy gene expression (CAGE) score to evaluate the diagnosis of atopy and asthma and assess disease activity in order to guide therapeutic decisions. The CAGE score was determined by using 10 genes dysregulated atopic individuals according to a specific algorithm. The application of this score in a group of asthmatic patients revealed that this approach had a better sensitivity and specificity than total IgE in differentiating atopic from non-atopic subjects. Correlation between CAGE score and total IgE was found, and there was a trend for correlation with asthma severity. It is noteworthy that the CAGE score was able to quantify phenotype-specific alteration in gene expression of atopic individuals. Perspectively the CAGE score can be further improved through a better reproducibility of microarray systems compared with the filter arrays and the possibility of a better selection of genes. Therefore it may be used as a prognostic and diagnostic tool or to monitor the effects and side-effects of asthmatic therapy in the not distant future.

Several morphologic changes in the airways of patients with asthma have been attributed to the Th-2 produced cytokines such as IL-13 and IL-5. However the molecular mechanisms underlying the contributions of these cytokines to asthma remain largely unknown.

Towards this direction Lee et al.applied oligonucleotide microarray technology in primary cultures of three human airway cell types (epithelial, smooth muscle cells and lung fibroblasts) to elucidate the effects of IL-13 in these cell types. Interestingly, the results of this study demonstrated that despite initiation of an identical signaling pathway (STAT6), IL-13 induced highly distinct transcriptional programs in each of the three cell types suggesting a coordinate and distinct contribution to asthma pathogenesis by each of the cell types examined. Although the quality of the genechip analysis was estimated and validated by RT-PCR methods applied in a small number of selective genes, however there are important limitations in this study including the possible differences between transcriptional responses and gene expression profile of a cell type in vivo and in vitro.

One of the greatest disadvantages of microarrays and at the same time challenges for most of the investigators is the objective difficulty dealing with the results of the experiments resulting from the large quantities of information. Currently, the hurdle faced is the routine interpretation of this information to identify among thousands of dysregulated genes, those who are informative, causal and specific to the phenotypic change of interest. Thus, Temple et al.compared the results derived from the application of oligonucleotide microarray technology in eosinophils isolated from human peripheral blood before and after treatment with IL-5 and in an alternative cellular model, TF1.8 cells, whose survival was known to be dependent on IL-5. Comparison of these two models facilitated the identification of the genes that rule the apoptosis and survivability of eosinophils and demonstrated a small group of genes whose regulation was similarly coordinated in both systems. Authors combined different cellular models focused on the same experimental paradigm and looked for common changes. This approach helped the scientists to focus attention on a subset of genes most likely to be causal and relevant to the phenotypic change of interest and filter out non-specific gene expression change. Combination of this method with proteomics approaches and tissue distribution analysis can add another filter for genes of interest and generate data of sufficient scientific rigidity.

Microarrays apart from their remarkable effectiveness in identifying novel gene expression patterns can also be used to clarify physiological mechanisms underlying the actions of numerous drugs, such as those applied in the management of patients with asthma. Several studies have utilized microarray technology to assess the gene expression profile of cells and tissues before and after treatment with commonly applied drugs such as corticosteroids. Two of them are reviewed here.

Microarray analysis performed by Laprise et al. indicated a differential gene expression pattern in bronchial tissues from healthy and asthmatic individuals, a profile that included not only genes previously implicated in the pathogenesis of asthma but also new potential candidates. The remarkable ascertainment of this study, conducted with bronchial tissues which are known as a primary site for airway inflammation and remodeling, was that the expression of one third of the genes was partially or completely corrected by inhaled corticosteroid treatment. The latter evidence further illuminates the true impact of first line therapy offered to asthmatic patients. However application of this technology may be limited by the disease's spatial and temporal heterogeneity due to differences in cellular composition between asthmatic and control tissue. Ultimately the results obtained using microarrays need to be verified firstly with confirmational studies (RT-PCR and in situ hybridization) and secondly with separate experiments.

Mast cells represent key cells in the initiation and progression of asthma, releasing several mediators of inflammation, such as certain cytokines and chemokines. The past few years several studies have been focused on the identification of new mast cell products through the gene expression analysis. In one of them published by Sayama et al. application of cDNA microarrays in only two populations of stimulated human mast cells exhibited among other genes a significant upregulation of the gene encoding IL-11. The latter finding was further confirmed by a separate set of experiments where an increased secretion of IL-11 by activated human mast cells was noted. However further microarray analyses coupled with functional approaches and independent studies examining the potential role of IL-11 in the pathogenetic mechanisms of asthma as well as in the alterations of mast cell proliferation and survival, are required.

Microarrays using nylon membrane radioactive cDNAs have already been applied in the research field of asthma and much good work has been done with this technology.

Therefore more in depth analysis of the microarray results is needed in combination with novel approaches that will help us focus on the specific genes and elucidate their role in the cellular function and the pathogenesis of asthma.

3.         Microarrays in Chronic Obstrustructive pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a chronic disease characterized by progressive airflow obstruction, chronic cough and dyspnea in advanced stages, caused by smoking, environmental, and hereditary factors. It is associated with two clinical entities, chronic bronchitis and emphysema. In nowadays, the invention and application of microarray technology offers scientists the opportunity to gain a better understanding on the pathophysiology of COPD through the identification of novel gene expression patterns, leading to illumination of genes candidates for modern therapeutically approaches.
It is already known that chronic bronchitis can be induced by several types of environmental pollutants such as diesel exhaust particles (DEP). Though recently a microarray study has been published by Koike et al. addressing the effect of such pollutants on the gene expression profiles of alveolar macrophages, however a complete analysis including the transcriptome and proteome, is needed to elucidate the toxic effect of air pollutants on pulmonary cells.
Microarray approach is already being applied in respiratory clinical pharmacology with the identification of genes {Yamanaka et al.} that can serve as potential molecular targets of common drugs applied in the management of patients with chronic bronchitis. However, studies being published in the field of respiratory pharmacogenomics lack of scientific rigidity primarily due to incomplete available arrays that will help scientists to determine much larger numbers of pharmacologically relevant genotypes. Far more progress should be made towards this direction.

One of the major limitations in our attempt to elucidate the exact role of specific cell types in the pathogenesis of COPD is the compact anatomy of the lung which makes unraveling specific cell type gene expression changes difficult, requiring immunoelectron microscopy or laser capture microdissection. The first study to perform quantitative cell type-specific gene expression analysis using the pioneering technology of laser capture microdissection in human tissue samples coupled with RT-PCR and cDNA approach was recently published by Fuke et al. Authors performed individual analyses and revealed a specific cell type upregulation of three inflammatory chemokines reportedly relevant to the pathogenesis of COPD emphasizing the pivotal role of these cells and their products in driving the inflammation. Although data was not fully confirmed by microarray analysis, however discrepancies between methods illustrate more the potential danger of depending solely on array approach rather than limit the scientific consistency of these results. Further research investigating the functional consequences of these changes is required.

Presently, very few studies dealing with the role of HOX genes in the adult respiratory system are available in the literature. Golpon et al. investigated the expression pattern of HOX genes, in fetal and diseased lung specimens (emphysema, primary pulmonary hypertension), by applying two microarray survey techniques and their analysis reflects one of the most detailed and informative studies in this field. They compared the HOX gene expression pattern in human and mouse lungs and found that HOX genes are selectively expressed in the human lung. This study also yielded an altered HOX-gene expression pattern among fetal, adult and lung specimens with emphysema and pulmonary hypertension, by identifying different types of HOX genes over expressed in each of these conditions indicating differential HOX gene expression as a potential factor that contributes to the development of certain pulmonary diseases. Though the overall size of tissue samples studied was small data from this study comprises evidence with high degree of confidence, validated and confirmed both in an independent cohort (degenerate RT-PCR) and by alternative methods (quantitative RT-PCR and in situ hybridization). Possible limitations include small number of tissues studied, incomplete microarray survey techniques and minor discrepancies between the findings generated from validation studies.

Collectively these results suggest that microarray analysis with its ability to highlight gene expression profiles on a large scale and coupled with progressive technologies and independently validated data has led researchers to shed further light into transcriptional programs regulating emphysema and to the identification of common mediators and molecular pathways involved in the pathogenesis of both COPD and pulmonary fibrosis, indicating novel targets for therapeutic interventions and useful genetic markers assessing susceptibility to COPD. Although limitations such as inconsistency between findings derived by microarray approach and independent studies, lack of functional changes assessment and significant data variability can be detectable in these studies, however evidence derived from these analyses is valuable and heavily informative.

4. Microarrays in acute lung injury and pulmonary edema
Acute lung injury (ALI), a severe respiratory syndrome, develops in response to numerous insults. This syndrome that responds poorly in therapeutic interventions and has a poor prognosis has been associated with a myriad of mediators including cytokines, reactive oxygen and nitrogen species, growth factors and proteolytic enzymes. Despite extensive research since the initial description of ALI over 30 yr ago, questions remain about the basic pathophysiologic mechanisms that are critical to the diminished survival and their relationship to therapeutic strategies. McDowell et al. in their attempt to determine the interactions between the great amounts of factors that have been associated with the development of ALI, analyzed 8,374 murine cDNAs for temporal changes and functional relationships throughout the initiation and progression of ALI in mice exposed to particulate NiSO4. Novel interactions between factors (antioxidant genes) previously associated with ALI and factors (surfactant proteins) previously not associated with ALI emerged from the application of functional genomics during nickel-induced ALI. Data derived from this experiment and partially confirmed by Northern blot analysis and nuclease protection assays is valuable and consistent with the ongoing attempts to treat ALI with exogenous surfactant-associated proteins in combination with antioxidant therapy and may determine new therapeutical interventions. This study reveals the great importance of functional genomics not simply to provide a catalogue of all the genes and information about their functions, but to help scientists to understand the possible interplay of components contributing to lung injury.
In summary, these studies exhibit the crucial role of a novel molecular technology in discovering, through global analysis of gene expression, genes previously identified only by their DNA sequence. Although the array analysis provides in some studies a comprehensive overview of gene expression in the lung during ALI and sepsis and after hyperoxia, however there are numerous concerns arising from the large amounts of data variability, the lack of proteomics approaches in most of them and the controversial findings of microarray analysis and confirmational techniques. Unfortunately only three studies used independent methodological criteria to validate a relatively small portion of their results, evidence that highlight the necessity for further more widespread evaluation of these findings. With the use of these approaches, more precise diagnosis and risk assessment of ALI based on expression profiles can be achievable in the next ten years, leading to more accurate determination of prognosis and new therapeutical interventions (Table 5).

6 .Microarrays in sarcoidosis
Sarcoidosis is a chronic systemic disorder characterized by the presence of non-caseating granulomas and accumulation of T-lymphocytes and macrophages in multiple organs. The mechanisms leading to the persistent accumulation of inflammatory cells are not fully understood. Apoptosis, a dynamic process involved in the control of the "tissue load" of immune effecter cells at inflamed sites, limits inflammatory tissue injury and promotes resolution of inflammation.Whether or not reduced apoptosis is involved in the pathogenesis of sarcoidosis is unclear. Rutherford in their attempt to shed further light on apoptosis signals in the peripheral blood of sarcoidosis patients with self limited and progressive disease in comparison with healthy controls used high-density probe arrays containing 12.626 genes. Though this study demonstrated significant differences in the expression of apoptosis-related genes in peripheral blood of patients with acute onset sarcoidosis compared to controls, ultimately did not manage to show a definite profile that was suggestive of survival or apoptosis. Although authors applied functional genomics a potential criticism of their approach is that they cannot give any statement on the activity of the apoptotic pathways. To do so the data should be combined with the proteomics analysis of proteins involved in apoptosis. The latter will contribute to the characterization of protein patterns and will allow for the assessment of overall changes in the protein content associated with apoptosis.  
Collectively these findings not only reveal the importance of the microarray platforms in identifying gene expression patterns that give the scientists the opportunity to elucidate the pathophysiological processes of complex diseases, such as sarcoidosis but also illuminate some of their origin disadvantages.

Applications of microarrays in CNS
Studying gene expression patterns in the CNS using DNA microarrays is challenging because of the existence of many different neuronal subtypes results in intricate anatomical and functional heterogeneity within the CNS.
A goal of modern molecular and cellular neuroscience is to assay gene expression from homogeneous populations of cells within a defined region without potential contamination by expression profiles of adjacent neuronal subtypes and non-neuronal cells. This is a difficult task that requires a combination of approaches and technologies to unravel the complexity of brain nuclei function and dysfunction in the context of neurological pathologies.
Regional genomic analysis is a powerful approach for identification of transcripts that are enriched in a specific region, lamina, or nuclei that differs from adjacent or connected regions. Going one level further, single-cell profiling techniques have the potential to quantify simultaneously expression levels of the entire genome in a given neuron, thereby allowing for the previously unobserved gene interaction(s) to become more evident.

A major drawback of using microarray is the relatively large amount of RNA required. Affymetrix standard protocol recommends starting with 5 μg of total RNA. However, studying brain nuclei function means regional or even single-cell profiling and therefore considerably lower amounts of RNA. Improvement of technologies and development of new ones allow this progression in the understanding of science. One of the most important technical advances for genomic profiling of single-cell or single population is the integration of LCM, RNA amplification, and subsequent cDNA array analysis.
The fidelity of the RNA amplification step is critical to the extraction of meaningful information from microarray experiments. The issue of more than one round of amplification is the loss of linearity. Though, few new inventive amplification methods-in addition to the classical T7-based amplification—have shown promising results; NuGen describes an isothermal mRNA amplification method, which generates micrograms of labeled cDNA from 5 ng of total RNA. Highly reproducible GeneChip array performance (R2 > 0.95) was achieved with independent reactions starting with 5–100 ng Universal Human Reference total RNA. A good correlation was shown between the Affymetrix Standard Protocol (5 μg of total RNA) and NuGen linear amplification method (20 ng starting RNA).

The quality of the RNA extracted from LCM, especially of postmortem brains or paraffin-embedded samples from the archives, is very often problematic: the sample may deteriorate before or during sectioning or during slide staining of formalin fixation, and inadequate extraction and isolation methods. Some strict RNA integrity standards have to be established and strictly respected, i.e., the 260/280 and 260/230 ratios as well as the rRNA 28/18S ratio. Even knowing that RNA integrity is very important, it is not always possible to ensure it. Today, ExpressArt mRNA amplification technology based on TRinucleotide primers allows the complete amplification of all mRNA fragments in severely degraded RNA samples and works very well with extremely low limits of input RNA amounts (picogram range). ExpressArt enables full-length cDNAs to be generated from mRNAs or full-size second DNA strands on single-stranded DNA templates.
An improvement in microarray platform sensitivity is another field of development. Different microarray platforms, i.e., Affymetrix, Agilent, Illumina have worked along this line. Novartis has invented its own high sensitivity chip platform to meet some needs in clinical development. The Novartis Evanescent Resonance platform (NovaChips) approaches an alternative route. Instead of amplifying the relevant biological material before hybridization, NovaChips exploit a physical (optical) amplification scheme to enhance signal intensities. Thanks to a nanostructured surface giving rise to local energy confinement of the incident light, the fluorophore labels attached to the samples are excited much more efficiently leading to increased fluorescence signals and improved limits of detection, thereby lifting low expressed genes above background levels.

On NovaChips, the standard protocol (without amplification!) can be used for samples containing only 10 ng of total RNA. Below this limit, the NuGen protocol provides a linear amplification alternative with no loss in data fidelity. The powerful combination of the (physical) NovaChip fluorescence enhancement by evanescent field excitation and NuGen linear amplification protocol enables the entire genome profiling of minute samples where any other microarray technology fails.
A dilution and correlation study starting with 10 ng of total RNA (rat brain) reduced by a factor 3 down to the picogram level demonstrated that above 1 ng of total RNA input we measure a constant level of present genes with excellent correlation between the individual concentrations. Below 1 ng of RNA, the percentage of present genes starts to diminish. Confidence values between two technical replicates are still above 0.95. Analyzing the data shows that 97% of the genes being detected with only 10 pg (!) of total RNA are also present at 10 ng; see FIGS. 3–6. In other words, performing gene expression analysis with NovaChips from samples with a total RNA input in the picogram range—without a second round of RNA amplification—is possible and will introduce only minor bias to the expression profiles.40
This opened up a new possibility: the full genome profiling with CSF. Working with postmortem brain tissues represent a real challenge. First, because the availability of postmortem material is limited, furthermore postmortem material from unmedicated patients is even rarer. The limitation of the number associated to the extreme diversity among the brains in respect to age, race, postmortem interval, medication history, lifestyle, and other factors represent a real challenge for the interpretation of the data.

 Applications of microarrays in microbial oncology
DNA microarray analysis becomes the technique of the coming decade in molecular biology (Rick et al., 2001). The potential of DNA microarray technology in microbial ecology was first demonstrated using microarrays containing oligonucleotides complementary to16S rRNA sequences of nitrifying bacteria. These bacteria could be detected and identified in environmental samples on the basis of their DNA or RNA hybridizing the probes on the DNA microarray (Guschin et al., 1997).
Thereafter, DNA microarray technology is being optimized to study bacterial diversity in a variety of ecosystems (Small et al., 2001; Loy et al., 2002; El Fantroussi et al., 2003; Peplies et al., 2003). Not only the 16S rRNA gene is used as target for developing diversity microarrays, but also other genes, such as those involved in antibiotic resistance are used as targets (Call et al., 2003; Volokhof et al., 2003).
Besides the expense of the technology, the two of the main problems regarding DNA microarray analysis are the hybridization specificity and quantification of the signals.
El Fantroussi et al. (2003) demonstrated that specific and non-specific hybridization can be discriminated by determining the thermal dissociation curve for each probe target duplex. Another approach to minimize detection of false positives was performed by applying multiple probes for specific targets on the DNA microarray.
Quantification of hybridization signals seems to be a complicated task at present since it has been shown that the signal intensities may vary significantly between targets even those perfectly matching the probes (Loy et al., 2002).
The first attempts to generate microarrays for application to GI tracteco systems have been performed DNA and look promising(Leser et al., 2002b; Wang et al., 2002; Wilson et al., 2002).It is already evident that the application of DNA microarray technology in studying the ecology of the GI tract will be expanded and extended in the near future.48

Applications of microarrays in medicine
DNA microarrays, microscopic arrays of large sets of DNA sequences immobilized on solid substrates, are valuable tools in areas of research that require the identification or quantitation of many specific DNA sequences in complex nucleic acid samples . They are ordered samples of DNA and each sample represents a particular gene. These arrays can then be assayed for changes in the expression patterns of the representative genes after different treatments, different conditions or tissue sources. There are numerous ways to measure gene expression including northern blotting, differential display, serial analysis of gene expression and dot-blot analysis. The problem with all these techniques is that they are unsuitable for the parallel testing of multiple genes' expression. Microarrays, based on Southern's method of nucleotide hybridization, contain multiple DNA sequences (probes) spotted or synthetized on a relatively small surface. This feature of microarrays allows the simultaneous monitoring of the expression of thousands of genes, thus providing a functional aspect to sequence information, in a given sample. Currently, genomic microarrays are used in medicine for the following purposes:
Determination of transcriptional programs of cells for a given cellular function (e.g., cell function, cell differentiation, etc.) or when they are exposed to certain conditions leading to activation, inhibition or apoptosis.
2. Compare and contrast transcriptional programs to aid diagnosis of diseases, predict therapeutic response and provide class discovery and sub-classification of diseases.
3. Identification of genome-wide binding sites for transcriptional factors that regulate the transcription of genes.
4. Prediction of gene function.
5. Identification of new therapeutic targets (target identification, target validation, and drug toxicity).
6. Development of public databases that will help us understand the functioning of complex biological systems.
7. Genetics of gene expression: Although this is a relatively new study field, it is advancing rapidly with major implications in complex clinical traits by the identification of promising candidate genes. Thus, we briefly review the current implementations of this novel approach highlighting its necessity in the research field. Treating mRNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Unlike classical quantitative traits, the genetic linkages associated with transcript abundance permits a more precise look at cellular biochemical processes. Schadt described comprehensive genetic screens of three specific transcriptomes by considering gene expression values as quantitative traits. Authors treated the gene expression levels derived by a microarray analysis in mice liver tissues as quantitative traits in a standard linkage analysis using evenly spaced autosomal markers. Interestingly they found that a substantial portion of these genes had at least one significant gene expression quantitative trait locus (eQTL) depending on the LOD (log odds ratios) scores. Since transcript abundances are increasingly used as surrogates for clinical traits, knowledge about their genetic control can help dissect the genetics of complex traits. In the same study investigators revealed the importance of LOD scores to differentiate whether the expression levels of the genes under study is regulated by variations within the gene itself (cis) or at a separate locus (trans). They found that eQTL with LOD scores are cis acting (gene affects transcription of the gene itself) in most cases, whereas moderately significant eQTL are trans acting (genes acting on the transcription of other genes). Furthermore this study undertook an investigation on how the heritability of gene expression can be studied within and between families and demonstrated that a significant portion of differentially expressed genes derived from reference families had a detectable genetic component. The latter finding suggests that this group of genes may serve as novel therapeutic targets for complex human diseases, given that their degree of genetic control was so readily identifiable in a small number of families.[At the same time, the use of microarrays also in the field of neurobiology had expanded exponentially: studies analyzing heterogeneous large brain areas, even whole brain homogenates were published, pooling of samples was common before the power of individual variation and statistical approaches was understood. Caution in the use of postmortem brain samples was brought by research by the Pritzker Neuropsychiatric Disorders Research Consortium who working on gene expression changes in affective disorders underlined the importance of postmortem interval and agonal period affecting the pH of brain and RNA integrity and thus influencing the data more than actual disease or medication status.

Undoubtedly, microarrays are among the most mature among the molecular profiling tools in the genomics and genetics research. Today, microarrays are being used in combination with other methods and traditional expertise. In neurobiology, on one hand focused approaches are being employed to study detailed questions combining laser capture microdissection (LCM) or capture of single cells11 and microarray technology using different amplification procedures or without, using highly sensitive custom microarrays.12 On the other hand, microarray gene expression profiling is a routine method used among others to, e.g., characterize different phenotypes created by reverse genetics approaches. In drug discovery and development, microarrays are being used preclinically in target identification and validation, in toxicology to profile compounds for toxicity (so called toxicogenomics), to identify biomarkers and clinically, to monitor compound efficacy and/or toxicity. Predictive genomics signatures have become a practice in cancer genomics to monitor disease severity and predict outcome and response to different treatments. Similar efforts are ongoing in different disease areas: in transplantation genomics, signatures of kidney biopsies and blood are soon wished to complement the traditional pathology-based disease classification of acute and chronic rejection.]