DNA microarrays--perspective of application for drug effectivity and safety evaluation

Microarray technology provides a unique tool for the determination of gene expression at the level of messenger RNA (mRNA). Microarray has been successfully applied to the high throughput simultaneous expression of many thousands of genes in a single experiment. One important application of DNA microarray technology, within the context of drugs effectiveness and safety evaluation studies, is its use as a screening tool for the identification of biochemical pathways, potential targets for novel molecular therapeutics, for the identification of molecular mechanisms of toxicity and to understand and predict individual drug sensitivity and resistance. The purpose of this review is presentation of the utility of DNA microarray technology in all phases of the drug discovery process.     

Microarrays in biology and medicine

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

Microarray probe expression measures, data normalization and statistical validation

DNA microarray technology is a high-throughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation of differential expression.     

Using formal concept analysis for microarray data comparison

Microarray technologies, which can measure tens of thousands of gene expression values simultaneously in a single experiment, have become a common research method for biomedical researchers. Computational tools to analyze microarray data for biological discovery are needed. In this paper, we investigate the feasibility of using formal concept analysis (FCA) as a tool for microarray data analysis. The method of FCA builds a (concept) lattice from the experimental data together with additional biological information. For microarray data, each vertex of the lattice corresponds to a subset of genes that are grouped together according to their expression values and some biological information related to gene function. The lattice structure of these gene sets might reflect biological relationships in the dataset. Similarities and differences between experiments can then be investigated by comparing their corresponding lattices according to various graph measures. We apply our method to microarray data derived from influenza-infected mouse lung tissue and healthy controls. Our preliminary results show the promise of our method as a tool for microarray data analysis.     

Plant–Pathogen Interactions: What Microarray Tells About It?

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

Development of public toxicogenomics software for microarray data management and analysis

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.     

Microarray expression profiling of tissues from mice with uniparental duplications of Chromosomes 7 and 11 to identify imprinted genes

Microarray analysis allows the screening of thousands of identifiable genes in a single experiment. The challenge of this approach is to combine the new technology with established genetic tools to associate genes with specific biological function. In this study we have designed a screen to identify imprinted genes from mice with uniparental duplications of proximal Chromosomes (Chrs) 7 and 11, using microarray analysis. By comparing the expression patterns in embryonic and newborn tissues of maternally versus paternally inherited proximal Chrs 7 and 11, we have correctly identified four out of five known imprinted genes represented on a microarray. We have additionally identified two novel imprinted candidate genes as well as a differentially expressed clone that is a potential downstream target. Interpretation of the microarray data requires careful preparation of age- and strain-matched samples and attention to detail in tissue dissection technique. Received: 15 March 2001 / Accepted: 13 June 2001     

机械点样DNA微点阵技术及其在基因表达分析上的应用(I)

从芯片制作、芯片杂交、芯片扫读与图像分析、基因表达数据分析等方面,详细介绍了机械点样DNA微点阵技术及其应用于多基因表达分析的基本步骤与原理。     

MAPS: a microarray project system for gene expression experiment information and data validation

SUMMARY: MAPS is a MicroArray Project System for management and interpretation of microarray gene expression experiment information and data. Microarray project information is organized to track experiments and results that are: (1) validated by performing analysis on stored replicate gene expression data; and (2) queried according to the biological classifications of genes deposited on microarray chips.     

机械点样DNA微点阵技术及其在基因表达分析上的应用(II)

从芯片制作、芯片杂交、芯片扫读与图像分析、基因表达数据分析等方面,详细介绍了机械点样DNA微点阵技术及其应用于多基因表达分析的基本步骤与原理。     

Coupled analysis of gene expression and chromosomal location

Microarray technology can be used to assess simultaneously global changes in expression of mRNA or genomic DNA copy number among thousands of genes in different biological states. In many cases, it is desirable to determine if altered patterns of gene expression correlate with chromosomal abnormalities or assess expression of genes that are contiguous in the genome. We describe a method, differential gene locus mapping (DIGMAP), which aligns the known chromosomal location of a gene to its expression value deduced by microarray analysis. The method partitions microarray data into subsets by chromosomal location for each gene interrogated by an array. Microarray data in an individual subset can then be clustered by physical location of genes at a subchromosomal level based upon ordered alignment in genome sequence. A graphical display is generated by representing each genomic locus with a colored cell that quantitatively reflects its differential expression value. The clustered patterns can be viewed and compared based on their expression signatures as defined by differential values between control and experimental samples. In this study, DIGMAP was tested using previously published studies of breast cancer analyzed by comparative genomic hybridization (CGH) and prostate cancer gene expression profiles assessed by cDNA microarray experiments. Analysis of the breast cancer CGH data demonstrated the ability of DIGMAP to deduce gene amplifications and deletions. Application of the DIGMAP method to the prostate data revealed several carcinoma-related loci, including one at 16q13 with marked differential expression encompassing 19 known genes including 9 encoding metallothionein proteins. We conclude that DIGMAP is a powerful computational tool enabling the coupled analysis of microarray data with genome location.