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Functional genome analysis of plants has entered the high-throughput stage. The complete genome information from key species such as Arabidopsis thaliana and rice is now available and will further boost the application of a range of new technologies to functional plant gene analysis. To broadly assign functions to unknown genes, different fast and multiparallel approaches are currently used and developed. These new technologies are based on known methods but are adapted and improved to accommodate for comprehensive, large-scale gene analysis, i.e. such techniques are novel in the sense that their design allows researchers to analyse many genes at the same time and at an unprecedented pace. Such methods allow analysis of the different constituents of the cell that help to deduce gene function, namely the transcripts, proteins and metabolites. Similarly the phenotypic variations of entire mutant collections can now be analysed in a much faster and more efficient way than before. The different methodologies have developed to form their own fields within the functional genomics technological platform and are termed transcriptomics, proteomics, metabolomics and phenomics. Gene function, however, cannot solely be inferred by using only one such approach. Rather, it is only by bringing together all the information collected by different functional genomic tools that one will be able to unequivocally assign functions to unknown plant genes. This review focuses on current technical developments and their impact on the field of plant functional genomics. The lower plant Physcomitrella is introduced as a new model system for gene function analysis, owing to its high rate of homologous recombination.
Functional genomics is facing the challenge of functionally identifying thousands of genes generated by the various genome projects during the past decade. Success will require novel high-capacity technologies that can cope with very large numbers of molecules. Here, Carl Borrebaeck reviews recent developments in molecular libraries and their role in rational gene identification.
Functional genomics is inundating the pharmaceutical industry with large numbers of potential gene targets from several sources such as gene expression profiling experiments (DNA microchips, proteomics) or database mining. Oligonucleotide-based RNA-knock down technologies such as antisense or RNA interference can aid in the filtering and prioritization of target candidates in the drug discovery process.
The Phytophthora Functional Genomics Database (PFGD; http://www.pfgd.org), developed by the National Center for Genome Resources in collaboration with The Ohio State University-Ohio Agricultural Research and Development Center (OSU-OARDC), is a publicly accessible information resource for Phytophthora-plant interaction research. PFGD contains transcript, genomic, gene expression and functional assay data for Phytophthora infestans, which causes late blight of potato, and Phytophthora sojae, which affects soybeans. Automated analyses are performed on all sequence data, including consensus sequences derived from clustered and assembled expressed sequence tags. The PFGD search filter interface allows intuitive navigation of transcript and genomic data organized by library and derived queries using modifiers, annotation keywords or sequence names. BLAST services are provided for libraries built from the transcript and genomic sequences. Transcript data visualization tools include Quality Screening, Multiple Sequence Alignment and Features and Annotations viewers. A genomic browser that supports comparative analysis via novel dynamic functional annotation comparisons is also provided. PFGD is integrated with the Solanaceae Genomics Database (SolGD; http://www.solgd.org) to help provide insight into the mechanisms of infection and resistance, specifically as they relate to the genus Phytophthora pathogens and their plant hosts.
The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology-a spatially addressable, large-scale technology for measurement of specific immunological response-the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models.
Genomes operate as sophisticated information storage systems. Generic repeated signals in the DNA format expression of coding sequence files and organize additional functions essential for genome replication and accurate transmission to progeny cells. Retroelements comprise a major fraction of many genomes and contain a surprising diversity of functional signals. In this article, we summarize some features of the taxonomic distribution of retroelements, especially mammalian SINEs, tabulate functional roles documented for different classes of retroelements, and discuss their potential roles as genome organizers. In particular, the fact that certain retroelements serve as boundaries for heterochromatin domains and provide a significant fraction of scaffolding/matrix attachment regions (S/MARs) suggests that the reversed transcribed component of the genome plays a major architectonic role in higher order physical structuring. Employing an information science model, the "functionalist" perspective on repetitive DNA leads to new ways of thinking about the systemic organization of cellular genomes and provides several novel possibilities involving retroelements in evolutionarily significant genome reorganization.
The human genome project was successful in sequencing the entire human genome and ended earlier than expected. The vast genetic information now available will have far-reaching consequences for medicine in the twenty-first century. The knowledge gained from the mapping and sequencing of human genes on a genome-wide scale--commonly referred to as structural genomics--is prerequisite for studies that focus on the functional aspects of genes. A recently invented technique, known as gene chip, or DNA microarray, technology, allows the study of the function of thousands of genes at once, thereby opening the door to the new field of functional genomics. At its core, the DNA microarray utilizes a unique feature of DNA known as complementary hybridization. As such, it is not different from Southern (DNA) blot or northern (RNA) blot hybridizations, or the polymerase chain reaction, with the exception that it allows expression profiling of the entire human genome in a single hybridization experiment. The article highlights the principles, technology, and applications of DNA microarrays as they pertain to the field of dermatology and dermatopathology. The most important applications are the gene expression profiling of skin cancer, especially of melanoma. Other potential applications include gene expression profiling of inflammatory skin diseases, the mutational analysis of genodermatoses, and polymorphism screening, as well as drug development and chemosensitivity prediction. cDNA microarrays will shape the diagnostic approach of the dermatology and the dermatopathology of the future and may lead to new therapeutic options.
Psychiatric genetics, while promising to unravel the mechanisms of psychiatric disorders, has proven to be a challenging field. Psychiatric disorders, like other common genetic traits, are complex and heterogeneous. Psychiatric genetics has also suffered from a lack of quantifiable, biology-based phenotypes. However, the field is currently at an opportune moment. The work of various investigators is on the verge of paying rich dividends. Efforts at positional cloning are being greatly accelerated by the fruits of the Human Genome Project. New tools of functional genomics, such as expression profiling and proteomics, are being applied to animal models. These two methods can complement each other in an approach we have termed convergent functional genomics. Lastly, improvements in the measurement of biologically distinct endophenotypes--or phenomic--will lead to a better understanding of the mapping of genes to phenotypes in both animal and human systems.
Functional modeling of human genes and diseases requires suitable mammalian model organisms. For its genetic malleability, the mouse is likely to continue to play a major role in defining basic genetic traits and complex pathological disorders. Recently, gene targeting techniques have been extended towards developing new engineering strategies for generating extensive lesions and rearrangements in mouse chromosomes. While these advances create new opportunities to address similar aberrations observed in human diseases, they also open new ways of scaling-up mutagenesis projects that try to catalogue and annotate cellular functions of mammalian genes.
Topoisomerase IIA (Topo IIA) is an essential ubiquitous enzyme involved in controlling DNA topology during multiple processes of genome function, and has been implicated in the generation of double-stranded breaks (DSB) in genomic DNA prior to DNA integration in plant genomes. Despite extensive characterization of type II topoisomerases from bacteria, viruses and animals, no studies on the specificity of plant Topo IIA-mediated DNA cleavage have been reported. We mapped and characterized Arabidopsis thaliana Topo IIA (AtTopoIIA) cleavage sites and demonstrated that they were cleaved in vivo. The consensus for the AtTopoIIA cleavage sites (ANNNRN downward arrowGTACNTNNNY) was significantly different from recognition sequences reported for Topo IIA from other organisms. The mapped cleavage sites were abundant in the Arabidopsis genome, exhibited a weak consensus, and were cleaved with relatively low efficiency. Use of the systematic evolution of ligands by exponential enrichment (SELEX) protocol identified a single, efficiently cleaved sequence TATATATATGTATATATATA that was over-represented in the genome. The mapped AtTopoIIA cleavage sites and the SELEX sites differed in their genomic distribution and associations with gene regulatory elements, matrix attachment regions, stress-induced DNA duplex destabilization sequences and T-DNA loci, suggesting different genome functions. Mapped AtTopoIIA sites but not SELEX sites were strongly associated with T-DNA integration sites, providing evidence for the involvement of AtTopoIIA-mediated DSB formation in T-DNA integration.
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In addition to their value for wood products, members of the genus Populus (poplars) provide a range of ecological services, including carbon sequestration, bioremediation, nutrient cycling, biofiltration and diverse habitats. They are also widely used model organisms for tree molecular biology and biotechnology. The sequencing of the poplar genome to an approximately 6x depth adds to a long list of important attributes for research. These include facile transformation, vegetative propagation, rapid growth, modest genome size and extensive expressed sequence tags. Here, we discuss how the genome sequence and transformability of poplar, together with its high levels of genetic and ecological diversity, are enabling new insights into the genetic programs controlling ontogeny, ecological adaptation and environmental physiology of trees.
Developments in high-throughput measurement technologies for biological molecules have created a paradigm shift in modern life science research. The field of systems biology attempts to provide a systems-level understanding by systematically organising the genomic, functional genomic and proteomic data obtained from genetic and environmental perturbations of interest and using the data to build a descriptive and mechanistic model of the biological phenomena. The goal is to build a mathematical framework with some predictive abilities. This review highlights the need for system-level understanding, lists some of the high-throughput measurement tools of importance in systems biology, reviews various types of experimental and computational approaches being used in systems biology research and attempts to address some of the challenges facing this research community.
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Considerable advances in structural genomics have been witnessed in the last year. Several pilot studies have begun to report their initial results, and new centers have been funded to join the endeavor. The legacies of the genome sequencing efforts, namely high-throughput molecular biology and whole-organism genome sequences, have been integrated as front-end modules for structural genomics pipelines. Impressive advances have been made in NMR spectroscopy and X-ray crystallography. New methods in structural bioinformatics and computational chemistry have been published that provide the means to exploit the wealth of new information in drug discovery. Not surprisingly, the biopharmaceutical industry has been quick to recognize the benefits of these new developments and has begun to adopt them. This article reviews recent results from structural genomics initiatives and the potential applications of new information and technologies in the drug discovery process.
Classically, biologists try to understand their complex systems by simplifying them to a level where the problem is tractable, typically moving from whole animal and organ-level biology to the immensely powerful "cellular" and "molecular" approaches. However, the limitations of this reductionist approach are becoming apparent, leading to calls for a new, "integrative" physiology. Rather than use the term as a rallying cry for classical organismal physiology, we have defined it as the study of how gene products integrate into the function of whole tissues and intact organisms. From this viewpoint, the convergence between integrative physiology and functional genomics becomes clear; both seek to understand gene function in an organismal context, and both draw heavily on transgenics and genetics in genetic models to achieve their goal. This convergence between historically divergent fields provides powerful leverage to those physiologists who can phrase their research questions in a particular way. In particular, the use of appropriate genetic model organisms provides a wealth of technologies (of which microarrays and knock-outs are but two) that allow a new precision in physiological analysis. We illustrate this approach with an epithelial model system, the Malpighian (renal) tubule of Drosophila melanogaster. With the use of the beautiful genetic tools and extensive genomic resources characteristic of this genetic model, it has been possible to gain unique insights into the structure, function, and control of epithelia.
Functional genomics is a systematic and high-throughput effort to analyze the functions of genes and gene products. Functional genomics is divided into gene- and phenotype-driven approaches. Gene-driven approaches to the functional genomics of sleep have demonstrated that transcripts of many genes change as a function of behavioral state. A phenotype-driven approach includes identification and characterization of gene function through the analyses of natural polygenic traits, creation of transgenic animals or high-throughput mutagenesis. Identification of a gene for narcolepsy through QTL analyses and concomitantly using a transgenic approach is one example of the phenotype-driven approach to the functional genomics of sleep. Though the majority of functional genomics is currently performed in mice, the rat is emerging as an important model for genomic research. Since rest in Drosophila shares many features with mammalian sleep, this allows a comparative functional genomics approach to the study of rest and sleep. The concepts outlined here for the functional genomics of sleep are applicable to respiration research.