Flux an important, but neglected, component of functional genomics

Genomics approaches aimed at understanding metabolism currently tend to involve mainly expression profiling, although proteomics and steady-state metabolite profiling are increasingly being carried out as alternative strategies. These approaches provide rich information on the inventory of the cell. It is, however, of growing importance that such approaches are augmented by sophisticated integrative analyses and a higher-level understanding of cellular dynamics to provide insights into mechanisms that underlie biological processes. We argue the need for, and discuss theoretical and practical aspects of, the determination of metabolic flux as a component of functional genomics.     

Current and future approaches using genetically modified mice in endocrine research

Genetically modified mouse models have been used widely to advance our knowledge in the field of endocrinology and metabolism. A number of different approaches to generate genetically modified mice are now available, which provide the power to analyze the role of individual proteins in vivo. However, there are a number of points to be considered in the use and interpretation of these models. This review discusses the advantages and disadvantages involved in the generation and use of different genetically modified mouse models in endocrine research, including conventional techniques (e.g., overexpression, knockout, and knock-in models), tissue- and/or time-specific deletion of target genes [e.g., Cre-loxP and short interfering (si)RNA transgenic approaches], and gene-trap approaches to undertake functional genomics. This review also highlights the many factors that should be considered when assessing the phenotype of these mouse models, many of which are relevant to all murine physiological studies. These approaches are a powerful means by which to dissect the function of genes and are revolutionizing our understanding of endocrine physiology and metabolism.     

Metabolite profiling for plant functional genomics

Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of "metabolic phenotypes" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.     

Defining the Predicted Protein Secretome of the Fungal Wheat Leaf Pathogen Mycosphaerella graminicola

The Dothideomycete fungus Mycosphaerella graminicola is the causal agent of Septoria tritici blotch, a devastating disease of wheat leaves that causes dramatic decreases in yield. Infection involves an initial extended period of symptomless intercellular colonisation prior to the development of visible necrotic disease lesions. Previous functional genomics and gene expression profiling studies have implicated the production of secreted virulence effector proteins as key facilitators of the initial symptomless growth phase. In order to identify additional candidate virulence effectors, we re-analysed and catalogued the predicted protein secretome of M. graminicola isolate IPO323, which is currently regarded as the reference strain for this species. We combined several bioinformatic approaches in order to increase the probability of identifying truly secreted proteins with either a predicted enzymatic function or an as yet unknown function. An initial secretome of 970 proteins was predicted, whilst further stringent selection criteria predicted 492 proteins. Of these, 321 possess some functional annotation, the composition of which may reflect the strictly intercellular growth habit of this pathogen, leaving 171 with no functional annotation. This analysis identified a protein family encoding secreted peroxidases/chloroperoxidases (PF01328) which is expanded within all members of the family Mycosphaerellaceae. Further analyses were done on the non-annotated proteins for size and cysteine content (effector protein hallmarks), and then by studying the distribution of homologues in 17 other sequenced Dothideomycete fungi within an overall total of 91 predicted proteomes from fungal, oomycete and nematode species. This detailed M. graminicola secretome analysis provides the basis for further functional and comparative genomics studies.     

From microarrays to mechanisms of brain development and function

Microarray technology provides a powerful approach to understand complex biological systems. The most common application of microarray technology is to document gene expression profiles of all genes within a genome in response to specific conditions such as disease, drug application, or genotype. One result of this technology is the ability to ascribe activities to genes with unknown functions - such rationale is the basis behind ‘functional genomics’. This approach is particularly well-suited to studies of the brain because roughly one third to one half of all genes in vertebrate genomes are expressed in the brain. However, less than half of such genes have any defined function. While a large number of studies have applied microarray technology to the brain, few studies have followed up the expression profiling approach with functional characterization of the genes identified. In this review, I highlight recent research that reflects the initial promise of functional genomics in the brain. I focus on neural differentiation with particular emphasis on synapse development.     

植物功能基因组学研究进展

植物功能基因组学是从整体水平研究基因的功能及表达规律的科学。对植物功能基因组学的研究将助于我们对基因功能的理解和对植物性状的定性改造和利用。本文简要介绍了植物功能基因组学的概念、研究方法和最新研究进展。     

Functional genomics as an emerging strategy for the investigation of central mechanisms in experimental hypertension

Centrally mediated increases in sympathetic nerve activity and attenuated arterial baroreflexes contribute to the pathogenesis of hypertension. Despite the characterization of cellular and physiological mechanisms that regulate blood pressure and alterations that contribute to hypertension, the genetic and molecular basis of this pathophysiology remains poorly understood. Strategies to identify genes that contribute to central pathophysiologic mechanisms in hypertension include integrative biochemistry and physiology as well as functional genomics. This article summarizes recent progress in applying functional genomics to elucidate the genetic basis of altered central blood pressure regulatory mechanisms in hypertension. We describe approaches others and we have undertaken to investigate gene expression profiles in hypertensive models in order to identify genes that contribute to the pathogenesis of hypertension. Finally, we provide the readers a roadmap for negotiating the route from experimental findings of gene expression profiling to translating their therapeutic potential. The combination of gene expression profiling and the phenotypic characterization of in vitro and in vivo loss or gain of function experiments for candidate genes have the potential to identify genes involved in the pathogenesis of hypertension and may present novel targets for therapy.     

Exploiting naturally occurring DNA variation and molecular profiling data to dissect disease and drug response traits

Identifying the key drivers of common human diseases and associated signaling pathways remains one of the primary objectives in the biomedical and life sciences. In this respect, common inbred strains of mice have played a crucial role, and recent advances in the development of genomics and bioinformatics tools have significantly enhanced their utility for this purpose. These advances have enabled a more holistic, network-oriented view of biological systems that facilitates elucidation of the underlying causes of disease and the best ways to target them. Success in reconstructing gene networks underlying disease traits (or other complex traits like drug response) and identifying the key drivers of these traits now largely rests on integrative approaches that combine data from multiple different sources. Such integrative genomics approaches that take into account genotypic, molecular profiling and clinical data in segregating mouse populations have recently been developed. Key to this integration has been the development and application of sophisticated algorithms to mine the diversity of data.     

Mouse Models of Polyglutamine Diseases in Therapeutic Approaches: Review and Data Table. Part II

Mouse models of human diseases are created both to understand the pathogenesis of the disorders and to find successful therapies for them. This work is the second part in a series of reviews of mouse models of polyglutamine (polyQ) hereditary disorders and focuses on in vivo experimental therapeutic approaches. Like part I of the polyQ mouse model review, this work is supplemented with a table that contains data from experimental studies of therapeutic approaches in polyQ mouse models. The aim of this review was to characterize the benefits and outcomes of various therapeutic strategies in mouse models. We examine whether the therapeutic strategies are specific to a single disease or are applicable to more than one polyQ disorder in mouse models. In addition, we discuss the suitability of mouse models in therapeutic approaches. Although the majority of therapeutic studies were performed in mouse models of Huntington disease, similar strategies were also used in other disease models.     

Physiological variation in metabolic phenotyping and functional genomic studies: use of orthogonal signal correction and PLS-DA

Metabolic phenotyping, or metabotyping, is increasingly being used as a probe in functional genomics studies. However, such profiling is subject to intrinsic physiological variation found in all animal populations. Using a nuclear magnetic resonance-based metabonomic approach, we show that diurnal variations in metabolism can obscure the interpretation of strain-related metabolic differences in two phenotypically normal mouse strains (C57BL10J and Alpk:ApfCD). To overcome this problem, diurnal-related metabolic variation was removed from these spectral data by application of orthogonal signal correction (OSC), a data filtering method. Interpretation of the removed orthogonal variation indicated that diurnal-related variation had been removed and that the AM samples contained higher levels of creatine, hippurate, trimethylamine, succinate, citrate and 2-oxo-glutarate and lower levels of taurine, trimethylamine-N-oxide, spermine and 3-hydroxy-iso-valerate relative to the PM samples. We propose OSC will have great potential removing confounding variation obscuring subtle changes in metabolism in functional genomic studies and will be of benefit to optimising interpretation of proteomic and genomic datasets.     

A functional analysis of mouse models of cardiac disease through metabolic profiling

Since the completion of the human and mouse genomes, the focus in mammalian biology has been on assessing gene function. Tools are needed for assessing the phenotypes of the many mouse models that are now being generated, where genes have been "knocked out," "knocked in," or mutated, so that gene expression can be understood in its biological context. Metabolic profiling of cardiac tissue through high resolution NMR spectroscopy in conjunction with multivariate statistics has been used to classify mouse models of cardiac disease. The data sets included metabolic profiles from mouse models of Duchenne muscular dystrophy, two models of cardiac arrhythmia, and one of cardiac hypertrophy. The metabolic profiles demonstrate that the strain background is an important component of the global metabolic phenotype of a mouse, providing insight into how a given gene deletion may result in very different responses in diverse populations. Despite these differences associated with strain, multivariate statistics were capable of separating each mouse model from its control strain, demonstrating that metabolic profiles could be generated for each disease. Thus, this approach is a rapid method of phenotyping mouse models of disease.