Catálogo de publicaciones - libros

Microarray-based technologies are powerful and widely used genomic techniques for the study of gene expression patterns on a genome-wide scale. The applications of microarrays as research tools in all areas of biology are immense, and modern approaches using these technologies to understand tumor metastasis are described in this chapter. Attention is placed on the steps involved in analysis from start to finish. We also have highlighted our own work, in which gene expression profiles in colorectal metastasis were monitored using cDNA microarrays.

Changes in the general transcription profile have been observed through silencing and activating at the transcriptional level of genes in tumor cells. After the genomic era, molecular biology has changed, and new technologies have allowed assessing transcriptional alterations among different tissue types in a high-throughput manner. Microarray technology is one of the technologies that has contributed to improving our understanding about the defective molecular processes in cancer cells. In this chapter, we report issues about the selection of cDNA clones spotted on the platform, the purity of the tumor samples used in the microarray experiments, and the integrated database with important clinical information of patients that can be associated to a specific molecular portrait. Finally, we focus on the validation of candidate genes selected from microarray experiments through real-time RT-PCR and high-throughput tissue microarray analyses that dramatically facilitate testing of the potential molecular markers.

More and more, array platforms are being used to assess gene expression in a wide range of biological and clinical models. Technologies using arrays have proven to be reliable and affordable for most of the scientific community worldwide. By typing microarrays or proteomics into a search engine such as PubMed, thousands of references can be viewed. Nevertheless, almost everyone in life science research has a story to tell about array experiments that were expensive, did not generate reproducible data, or generated meaningless data. Because considerable resources are required for any experiment using arrays, it is desirable to evaluate the best method and the best design to ask a certain question. Multiple levels of technical problems, such as sample preparation, array spotting, signal acquisition, dye intensity bias, normalization, or sample-contamination, can generate inconsistent results or misleading conclusions. Technical recommendations that offer alternatives and solutions for the most common problems have been discussed extensively in previous work. Less often discussed is the experimental design. A poor design can make array data analysis difficult, even if there are no technical problems. This chapter focuses on experimental design choices in terms of controls such as replicates and comparisons for microarray and proteomics. It also covers data validation and provides examples of studies using diverse experimental designs. The overall emphasis is on design efficiency. Though perhaps obvious, we also emphasize that design choices should be made so that biological questions are answered by clear data analysis.

Understanding the roles and functions of genes and proteins through their interactions with each other and the environment has been reshaped with technological advancements such as gene chips and protein arrays. These techniques simultaneously probe thousands of molecules at any given time. Interrogating the network as opposed to a single entity as in traditional methods necessitates a departure from reductionism and requires developing biological insight in a networks setting. A fundamental challenge is to develop computational methods to analyze this vast amount of data and transform it into meaningful biological knowledge. Because of the nature of the data and the system under investigation, this goal can be accomplished by considering high-throughput data analysis in the context of biological networks. In this chapter, we describe the foundations of this methodology through an overview of the problems encountered along the way and a summary of basic biological and mathematical concepts.

DNA microarrays are most useful for pharmacogenomic discovery when a clear relationship can be made between gene expression in a targeted tissue and drug affect. Unfortunately, the true target of the drug affect is most often a subpopulation of cells within the tissue. Thus, when heterogeneous tissues containing many diverse cell types are profiled, expression changes, especially in low-abundance genes, are often obscured. In this chapter, two examples are presented where a cellular subpopulation is isolated from its complex background, with minimal cellular activation, resulting in increased microarray detection sensitivity. In the first example, erythrocytes (the most abundant cell population in blood) were removed or whole blood was immediately stabilized before RNA isolation. The removal of erythrocytes resulted in a twofold increase in the detectability of leukocyte-specific genes. During the study, protocols for RNA isolation from rat blood were validated. In addition, a list of 91 genes was generated whose expression correlated with the level of erythrocyte contamination in rat blood. In the second example, laser microbeam microdissection (LMM) was used to isolate a specific neuronal population. Our LMM amplification technique was first validated for reproducibility. After validation, data obtained from pooled neurons, cortical tissue slices, and whole brain were compared. Overall, 20% of the transcripts detected in whole brain and 13% of the transcripts detected in tissue slices were not detected in LMM neurons. Many of these transcripts were specific to neuroglial support cells or noncortical neurons, verifying that our LMM technique captured only the neurons of interest. Conversely, 10% of the transcripts detected in LMM neurons were not detected in cortical tissue slices, and 14% were not detected in whole brain. As expected, these transcripts were neuronal specific and were presumably still present in the broader tissue regions. However, in neurons isolated by LMM, the effective concentration of these previously undetectable transcripts was raised because of the elimination of competing signal noise from extraneous cell types, reinforcing the claim that microdissection can be used to increase microarray sensitivity.

Aging of the brain can lead to impairments in cognitive and motor skills and is a major risk factor for several common neurological and psychiatric disorders, such as Alzheimer’s disease and Parkinson disease. A better understanding of the molecular effects of brain aging may help to reveal processes that lead to age-related brain dysfunction. With the need for tissue-specific aging biomarkers, several studies have used DNA microarray analysis to elucidate gene expression changes during aging in rodents and very recently in humans. The use of microarray chips allows the assessment of thousands of genes simultaneously, and the identification of new biomarkers involved in aging that may be potential therapeutic targets for pathological aging. Different quality parameters need to be examined mainly at the level of tissue collection, RNA extraction, and sample preparation and processing. Moreover, the data sets resulting from microarray chips experiments are usually complex and require increasingly powerful and refined computational competences as well as new approaches and tools of analysis. Genomic studies usually follow a pattern of analysis ranging from data extraction and statistical analysis, to gene selection and classification into specific pathways.

The DNA microarray is a powerful method used to detect global expression of genes understand the physical status of cells. Since this technology was established, it has been applied to many fields of medical investigation. Many types of tumors have been analyzed, and correlations have been found between gene expression profiles and biological characteristics such as invasion metastasis, and prognosis. Quantitative analysis of tumor-specific gene expression has revealed that altered gene expression is associated with the pathology and the altered biological function of cancer cells. This chapter describes the clinical application of microarrays (bioarrays) for the identification of potential diagnostic markers for cancer by measuring tumor-specific expression of thousands of genes. Expression profile analysis using a microarray followed by protein expression analysis is useful for the development of molecular biomarkers for cancer diagnosis.

There has been a huge increase in DNA sequence data during the past decade from various biological systems. Most notably, completion of human and several pathogen genomes has enabled us to apply several high-throughput technological innovations to understand the human disease process. This chapter deals with one such technology, i.e., array-based comparative genomic hybridization (aCGH). Genomic alterations have long been implicated in several disease processes, including cancer. Earlier techniques such as conventional karyotyping, G-banding, FISH, and so on, either suffered from a lower resolution or were prohibitively expensive for whole genome coverage. The comparative genomic hybridization technique was the first step towards whole genome profiling of genomic amplifications and deletions; however, it could at best offer a resolution of approx 10–20 Megabases (Mb). The advent of the microarray technology during the later part of the 1990s has enabled the high-resolution mapping of genomic alterations at a high resolution (< 1 Mb). The present chapter discusses the aCGH technology and its use in studying cancer and infection.

The vascular system is locally specialized to accommodate widely varying needs of individual tissues. The regional specialization of vascular structure is closely linked to the topographic differentiation of endothelial cells (ECs). The gene expression programs that characterize specific ECs define their physiological specialization and their role in the development of vascular channels and epithelial organs. Our understanding of EC regional differentiation is very limited. To assess the heterogeneity of ECs on a global scale, we used DNA microarrays to obtain the global gene expression profiles of more than 50 cultured ECs purified from 14 different anatomic locations. We found that ECs from different blood vessels and microvascular ECs from different tissues have distinct and characteristic gene expression profiles. Pervasive differences in gene expression patterns distinguish the ECs of large vessels from microvascular ECs. We identified groups of genes characteristic of arterial and venous endothelium. Hey2, the human homolog of the zebrafish gene , was expressed only in arterial ECs and could trigger arterial-specific gene expression programs when introduced into venous ECs. Several genes critical in the establishment of left-right asymmetry were expressed preferentially in venous ECs, suggesting a surprising link between vascular differentiation and body plan development. Tissue-specific expression patterns in different tissue microvascular ECs suggest they are distinct differentiated cell types that play roles in the local physiology of their respective organs and tissues. Therefore, ECs from different anatomical locations constitute many distinct, differentiated cell types that carry out unique genetic programs to specify the site-specific design and functions of blood vessels to control internal body compartmentalization, regulate the trafficking of circulating cells, and shape the vascular development. In this chapter, we discuss these findings and their implications in different aspects of vascular biology during development and vascular diseases.

Multiple Sclerosis (MS) is the most prevalent chronic inflammatory disease of the CNS, causing severe disability in a significant proportion of patients. Although many findings suggest that MS is caused by an immune response to proteins expressed in the CNS, the target antigens are still unknown. Among the candidates are self- and foreign proteins expressed in the CNS compartment. Here, we describe a new approach to dissect immune responses in the CNS. We applied a protein array based on a human brain cDNA library to decrypt the specificity of the local antibody response in MS. The macroarray, containing 37,000 proteins, enabled us to perform a large-scale screening for disease-associated antigens. Target proteins were further mapped to identify highaffinity ligands and possible mimics. Using this approach, we found MS-specific high-affinity antibody responses to two peptide sequences derived from Epstein-Barr virus (EBV) proteins. Several mimics with lower affinity also were identified. Subsequent analysis revealed an elevated and specific immune response in MS patients against both EBV proteins, suggesting a putative role of EBV in the pathogenesis of MS. The study demonstrates that protein arrays can be successfully applied to identify disease-associated antibody responses in neuroinflammatory diseases.