DNA Microarrays: Current Applications Chapter Abstracts

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DNA microarrays are the modern, parallel version of the classic molecular biology techniques of Northern and Southern blotting. While the blotting techniques are capable of detecting the abundance of a specific nucleotide sequence in a biological sample, DNA microarrays allow the exploration of thousands of sequences in a single run. The difference in data throughput has profound implications in the nature of the information that can be derived, as it allows complete analysis of the genetic material and the monitoring of virtually all the expression changes occurring in a biological sample under various conditions. This allows the traditional reductionist view to be replaced by a more systematic view of the molecular events taking place in the cell. As a consequence, analytical methods of investigation based on computational/statistical techniques are required for the interpretation of the high volume of data generated.

Chapter 2
Gene Networks and Application to Drug Discovery
A. Ambesi, M. Bansal, G. Della Gatta and Diego di Bernardo

The drug discovery process is complex, time consuming and expensive, and includes preclinical and clinical phases. The pharmaceutical industry is moving from a symptomatic relief focus towards a more pathology-based approach where a better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying the disease. Computational biology and microarray technology have the potential not only to speed up the drug discovery process, thus reducing the costs, but also to change the way drugs are designed. In this chapter we focus on the different computational and bioinformatics approaches that have been proposed and applied to the different steps involved in the drug development process, with particular emphasis on gene network models. The development of 'gene-network reconstruction' methods is now making it possible to infer a detailed map of the regulatory circuit among genes, proteins and metabolites. It is likely that the development of these technologies will radically change, in the next decades, the drug discovery process, as we know it today.

Chapter 3
Pathway Analysis of Microarray Data
Matteo Pellegrini and Shawn Cokus

During the past decade remarkable new techniques for transcriptional profiling have been developed. These include transcriptional profiling using hybridization microarrays as well as methods to sequence transcribed RNAs. No matter which technology is used, these experiments generate data on thousands of genes across multiple conditions and therefore the analysis of this data is often a daunting task. One of the most promising avenues for interpreting large datasets of expression profiles involves pathway-based analysis. Although pathway analysis of expression data is a relatively new field, many important advances have been made over the past few years. Below we outline some the most significant developments in this area of research.

Chapter 4
Toxicogenomics: Applications of Genomics Technologies for the Study of Toxicity
Uwe Koch

Toxicogenomics is a scientific sub-discipline that combines toxicology with genomics. Toxicogenomics analyses the activity of a toxin or chemical substance on living tissue based upon a profiling of its known effects on genetic material. Toxicogenomics may also be of use as a preventative measure to predict adverse "side", i.e. toxic, effects, of pharmaceutical drugs on susceptible individuals. This involves using genomic techniques such as gene expression level profiling and single-nucleotide polymorphism analysis of the genetic variation of individuals. These studies are then correlated to adverse toxicological effects in clinical trials so that suitable diagnostic markers (measurable signs) for these adverse effects can be developed.

Chapter 5
Microarray Gene Expression Atlases
John C. Castle, Chris J. Roberts, Chun Cheng and Jason M. Johnson

Gene expression atlases are systematically compiled RNA expression measurements intended to show the relative abundance of gene transcripts across diverse collections of samples. Generation of a gene expression atlas is often a large, expensive project. However, once compiled, these atlases are excellent references and empower molecular biologists, bioinformaticians, and statisticians to examine tissue and gene expression to improve our understanding of the molecular processes driving cell function. They can be used to answer many questions, from simple to complex: examples of biological questions include straightforward queries such as where genes are expressed and what genes are expressed in a sample and extend to more complex queries involving gene-gene and tissue-tissue expression correlations and gene functional/pathway assignments. Several public gene expression atlases exist and we present examples from our research.

Chapter 6
Supervised Classification of Genes and Biological Samples
Adrian Tkacz, Leszek Rychlewski, Paolo Uva and Dariusz Plewczynski

Microarray experiments generate large volumes of gene expression data and are currently applied to elucidate a large spectrum of biological problems, in various research contexts. Although the experimental technology has undergone important progresses during the last years and is reaching a consolidated stage, the statistical analysis and the extraction of all the potential information residing in the data, still represents a great challenge for the scientific community. Despite the availability of advanced computational tools, the choice of analytical techniques made by the data analyst has a great impact on the practical interpretation of the results. A basic understanding of these computational methods is therefore needed for experimental design and meaningful data analysis. Here we present a short introduction to supervised computational methods and data analysis tools and we illustrate how these techniques can be used with practical examples taken from the recent literature.

Chapter 7
A Case Study: the Mammary Carcinogenesis in HER2 Transgenic Mice
Federica Cavallo, Guido Forni, Anna Grassi, PierLuigi Lollini and Raffaele Calogero

Microarray transcription profiling was applied on BALB-neuT breast cancer model to understand the molecular mechanisms associated to the halting of tumor growth via HER2 vaccination. The demonstration that vaccines can cure HER2 transplantable tumors was achieved both via vaccination with allogeneic mammary carcinoma cells expressing high levels of both r-HER2 and H-2q class I molecules followed by administration of IL-12 or via conventional intramuscular DNA vaccination followed by boost with H-2q r-HER2 positive tumor cells gene-engineered to release interferon-g (IFN-g). The combination of transcription profiles with protection results indicated that the main effect was a strong polyclonal antibody response, and chronic vaccination is needed to maintain an active IFN-g mediated response in the mammary gland. Furthermore, cross-study comparison of BALB-neuT gene expression array data opens the way to the identification of new tumor associated antigens (TAAs) to be used in conjunction with HER2 to allow a broader coverage of vaccination.

Chapter 8
DNA Microarrays: Beyond mRNA
Armin Lahm

Application of DNA microarrays in the transcriptional profiling of mRNA has reached practical all areas of modern biomedicine. An extensive literature documents the large variety of biological problems this technique has been applied to, examples of which are described in the previous chapters. Here a number of additional applications will be briefly outlined. From a transcription-centered point of view genome-wide detection of mRNA transcript variants and monitoring of non-coding RNA molecules like microRNAs have gained growing attention and can now be approached with the available technology. On the other hand, variations in the primary genetic information like methylation, nucleotide polymorphisms or amplifications/deletions are increasingly used to explain biological and biomedical phenomena. Moreover, combing several of these complementary views of a biological system has allowed to implement integrative approaches linking a macroscopic phenotype with the underlying genetic background and the complex picture present at the transcriptional level. Proteins have encountered DNA microarrays and ChIP-to-chip experiments are revealing details of chromatin dynamics and the transcriptional state of the genome. As technology proceeds and our understanding of the biological phenomena becomes more refined and detailed, each of these -omics based information sources will contribute to obtain a more integrated view of the complexity of biological systems.

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