Microarrays in infection and immunity

Over the past decade, microarrays have revolutionized the scientific world as dramatically as the internet has changed everyday life. From the initial applications of DNA microarrays to uncover gene expression patterns that are diagnostic and prognostic of cancer, understanding the interplay between immune responses and disease has been a prime application of this technology. More recent efforts have moved beyond genetic analysis to functional analysis of the molecules involved, including identification of immunodominant antigens and peptides as well as the role of post-translational glycosylation. Here, we focus on recent applications of microarray technology in understanding the detailed chemical biology of immune responses to disease in an effort to guide development of vaccines and other protective therapies.     

Applications of DNA and protein microarrays in comparative physiology

DNA microarrays have revolutionized gene expression studies and made large-scale parallel measurement of whole genome expression a feasible technique in model species where genomes are well characterized. Such studies are perfectly suited to unraveling the complex regulation and/or interaction of both genes and proteins likely involved in most physiological processes. Gene expression profiles are currently being used to identify genes underlying a range of physiological responses. Characterization of these genes will help to elucidate the pathways and processes regulating physiological processes. Expanding the use of DNA microarrays to non-model species that have been critical in elucidating certain physiological pathways will be valuable in determining the genes associated with these processes. Approaches that do not require complete genome information have recently been applied to "non-model" organisms. As whole genomes are sequenced for non-model organisms, the application of DNA microarrays to comparative physiology will expand even further. The recent development of protein microarrays will be critical in understanding the regulation of physiological processes not accounted for at the genomic level. Together, DNA and protein microarrays provide the most thorough and efficient method of understanding the molecular basis of physiological processes to date. In turn, classical physiological approaches will be vital in characterizing and verifying the function of the novel genes identified by microarray experiments. Ultimately, DNA and protein microarray expression profiles may be used to predict physiological responses.     

Clustering Algorithms: On Learning,Validation, Performance,and Applications to Genomics

The development of microarray technology has enabled scientists to measure the expression of thousands of genes simultaneously, resulting in a surge of interest in several disciplines throughout biology and medicine. While data clustering has been used for decades in image processing and pattern recognition, in recent years it has joined this wave of activity as a popular technique to analyze microarrays. To illustrate its application to genomics, clustering applied to genes from a set of microarray data groups together those genes whose expression levels exhibit similar behavior throughout the samples, and when applied to samples it offers the potential to discriminate pathologies based on their differential patterns of gene expression. Although clustering has now been used for many years in the context of gene expression microarrays, it has remained highly problematic. The choice of a clustering algorithm and validation index is not a trivial one, more so when applying them to high throughput biological or medical data. Factors to consider when choosing an algorithm include the nature of the application, the characteristics of the objects to be analyzed, the expected number and shape of the clusters, and the complexity of the problem versus computational power available. In some cases a very simple algorithm may be appropriate to tackle a problem, but many situations may require a more complex and powerful algorithm better suited for the job at hand. In this paper, we will cover the theoretical aspects of clustering, including error and learning, followed by an overview of popular clustering algorithms and classical validation indices. We also discuss the relative performance of these algorithms and indices and conclude with examples of the application of clustering to computational biology.Key Words: Clustering, genomics, profiling, microarray, validation index.     

The application of DNA microarrays in gene expression analysis

DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed. These comprise array manufacturing and design, array hybridisation, scanning, and data handling. Furthermore, it is discussed how DNA microarrays can be applied in the working fields of: safety, functionality and health of food and gene discovery and pathway engineering in plants.     

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.     

A general framework for designing and validating oligomer-based DNA microarrays and its application to Clostridium acetobutylicum

While DNA microarray analysis is widely accepted as an essential tool for modern biology, its use still eludes many researchers for several reasons, especially when microarrays are not commercially available. In that case, the design, construction, and use of microarrays for a sequenced organism constitute substantial, time-consuming, and expensive tasks. Recently, it has become possible to construct custom microarrays using industrial manufacturing processes, which offer several advantages, including speed of manufacturing, quality control, no up-front setup costs, and need-based microarray ordering. Here, we describe a strategy for designing and validating DNA microarrays manufactured using a commercial process. The 22K microarrays for the solvent producer Clostridium acetobutylicum ATCC 824 are based on in situ-synthesized 60-mers employing the Agilent technology. The strategy involves designing a large library of possible oligomer probes for each target (i.e., gene or DNA sequence) and experimentally testing and selecting the best probes for each target. The degenerate C. acetobutylicum strain M5 lacking the pSOL1 megaplasmid (with 178 annotated open reading frames [genes]) was used to estimate the level of probe cross-hybridization in the new microarrays and to establish the minimum intensity for a gene to be considered expressed. Results obtained using this microarray design were consistent with previously reported results from spotted cDNA-based microarrays. The proposed strategy is applicable to any sequenced organism.     

Application of the S-score algorithm for analysis of oligonucleotide microarrays

In the past several years, oligonucleotide microarrays have emerged as a widely used tool for the simultaneous, non-biased measurement of expression levels for thousands of genes. Several challenges exist in successfully utilizing this biotechnology; principal among these is analysis of microarray data. An experiment to measure differential gene expression can consist of a dozen microarrays, each consisting of over a hundred thousand data points. Previously, we have described the use of a novel algorithm for analyzing oligonucleotide microarrays and assessing changes in gene expression. This algorithm describes changes in expression in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. Software is available that simplifies the use of the application of this algorithm so that it may be applied to improving the analysis of oligonucleotide microarray data. The application of this method to problems of the central nervous system is discussed.     

Do DNA microarrays have their future behind them?

The first report on DNA microarray technology appeared in Science in 1995. Starting from gene expression profiling, its application fields have considerably evolved and extend from microbiology to cancer study. DNA microarrays are now routinely used for the detection of single nucleotide polymorphisms, microRNAs analysis, study of copy number variation, CpG methylations detection.... Furthermore, the approval of DNA microarray technology by US Food and Drug Administration has opened the door to new applications in clinical diagnostics. At the same time, DNA arrays have to face the concurrence of the latest generation of very high throughput sequencing devices which are predicted to make the microarray technology obsolete. This review will discuss on this paradoxical situation.     

A General Framework for Designing and Validating Oligomer-Based DNA Microarrays and Its Application to Clostridium acetobutylicum

While DNA microarray analysis is widely accepted as an essential tool for modern biology, its use still eludes many researchers for several reasons, especially when microarrays are not commercially available. In that case, the design, construction, and use of microarrays for a sequenced organism constitute substantial, time-consuming, and expensive tasks. Recently, it has become possible to construct custom microarrays using industrial manufacturing processes, which offer several advantages, including speed of manufacturing, quality control, no up-front setup costs, and need-based microarray ordering. Here, we describe a strategy for designing and validating DNA microarrays manufactured using a commercial process. The 22K microarrays for the solvent producer Clostridium acetobutylicum ATCC 824 are based on in situ-synthesized 60-mers employing the Agilent technology. The strategy involves designing a large library of possible oligomer probes for each target (i.e., gene or DNA sequence) and experimentally testing and selecting the best probes for each target. The degenerate C. acetobutylicum strain M5 lacking the pSOL1 megaplasmid (with 178 annotated open reading frames [genes]) was used to estimate the level of probe cross-hybridization in the new microarrays and to establish the minimum intensity for a gene to be considered expressed. Results obtained using this microarray design were consistent with previously reported results from spotted cDNA-based microarrays. The proposed strategy is applicable to any sequenced organism.     

Genomics and microarray for detection and diagnostics

Genomics provided biomedical scientists an inventory of all genes and sequences present in a living being. This provides an unique opportunity to the scientists to predict and study biological functions of these genes. The changes in the gene expression regulated by genomic sequences therefore reflect changes in the molecular processes working in a cell or tissue in response to external factors including exposure to toxic compounds and pathogens. Microarray offers a biotechnological revolution with the help of DNA chemistry, silicon chip technology and optics to be used to monitor gene expression for thousands of genes in one single experiment. Briefly, 20,000 to 100,000 unique DNA molecules get applied by a robot to the surface of silicon wafers (approximately the size of a microscope slide). Using a single microarray experiment, the expression level of 20,000 to 100,000 genes will be examined in one single experiment. Genomics and microarray have a significant role and impact on the design and development of modern detection and diagnostic tools in several different ways. Microarray tools are now used on regular basis for monitoring gene expression of large number of genes and also frequently applied to DNA sequence analysis, immunology, genotyping, and molecular diagnosing. For diagnostics, these tools can be used to distinguish and differentiate between different DNA fragments that differ by as little as a single nucleotide polymorphism (SNP). These microarrays can be divided based on the gene density spots that will be high density (>10,000 spots) per slide, medium (< 1000 > 100) and low density (< 100). High-density arrays have proven to be very useful in disease diagnosis especially in diagnosis and classification of different types of cancers. These microarray tools hold tremendous potential for pathogen detection, which will be comprised, of unique sets of genes (also referred to as "signatures") able to unambiguously identify the species and strain of pathogens of interest.     

Interactome Mapping: Using Protein Microarray Technology to Reconstruct Diverse Protein Networks

A major focus of systems biology is to characterize interactions between cellular components, in order to develop an accurate picture of the intricate networks within biological systems. Over the past decade, protein microarrays have greatly contributed to advances in proteomics and are becoming an important platform for systems biology. Protein microarrays are highly flexible, ranging from large-scale proteome microarrays to smaller customizable microarrays, making the technology amenable for detection of a broad spectrum of biochemical properties of proteins. In this article, we will focus on the numerous studies that have utilized protein microarrays to reconstruct biological networks including protein-DNA interactions, posttranslational protein modifications (PTMs), lectin-glycan recognition, pathogen-host interactions and hierarchical signaling cascades. The diversity in applications allows for integration of interaction data from numerous molecular classes and cellular states, providing insight into the structure of complex biological systems. We will also discuss emerging applications and future directions of protein microarray technology in the global frontier.